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/Tensorflow/Scratch2/Untitled10.ipynb | 372ba1e1437b2b7dccf2f0ea067be5ec6c2aa571 | [] | no_license | JunyongKang/AI-study | https://github.com/JunyongKang/AI-study | 1 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 24,741 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# +
import numpy as np
import matplotlib.pyplot as plt
# %matplotlib inline
X = np.linspace(-np.pi, np.pi, 256,endpoint=True)
C,S = np.cos(X), np.sin(X)
plt.plot(X,C)
plt.plot(X,S)
# -
22021"
acq_name = "TileScan_Tp1-5"
exp_folder = os.path.join("D:", "Tolga", "Colony Images", f"{exp_name}")
base_folder = os.path.join(exp_folder, f"{exp_name}_{acq_name}")
base_folder
# +
def collect_metadata(base_folder, exp_name, acq_name):
exp_folder = f"D:/Tolga/Colony Images/{exp_name}/"
base_folder = os.path.join(exp_folder, f"{exp_name}_{acq_name}/")
metadata_folder = os.path.join(base_folder, "MetaData/")
xml_path = metadata_folder + f"{exp_name}_{acq_name}_Properties.xml"
# Read xml and prepare for the soup
with open(xml_path) as fp:
soup = BeautifulSoup(fp, "xml")
# Check for merged?
# Collect info about the tilescan
tilescan_info = soup.find_all("Tile")
# Variables to keep the information
xix_lst = np.zeros(len(tilescan_info), dtype=np.int32)
yix_lst = np.zeros_like(xix_lst)
xpos_lst = np.zeros_like(xix_lst, dtype=np.double)
ypos_lst = np.zeros_like(yix_lst, dtype=np.double)
# Run through each tile and save the position
for tile_idx in range(len(tilescan_info)):
tile = tilescan_info[tile_idx]
xix_lst[tile_idx] = tile.get("FieldX")
yix_lst[tile_idx] = tile.get("FieldY")
xpos_lst[tile_idx] = tile.get("PosX")
ypos_lst[tile_idx] = tile.get("PosY")
xpos_lst[tile_idx] *= 1e6
ypos_lst[tile_idx] *= 1e6
xix_unique_ar = np.unique(xix_lst)
yix_unique_ar = np.unique(yix_lst)
tiles = {"xix_lst": xix_lst,
"yix_lst": yix_lst,
"xix_unique_ar": xix_unique_ar,
"yix_unique_ar": yix_unique_ar,
"xpos_lst": xpos_lst,
"ypos_lst": ypos_lst,
"tile_xcnt": len(xix_unique_ar),
"tile_ycnt": len(yix_unique_ar)}
# Collect dimenions: x,y,z,t,stage (stage gives info about tilescan)
dimension_desc = soup.find_all("DimensionDescription")
dimensions = {}
for desc in dimension_desc:
dimid = desc.get("DimID")
unit = desc.get("Unit").replace("\xb5", "u")[-2:] # Replace greek letter of \mu with letter u
length = desc.get("Length")
counts = np.int32(desc.get("NumberOfElements"))
voxel = desc.get("Voxel")
# convert all length values to um
if len(unit) > 0: # implies a numerical value
length = np.double(length)
voxel = np.double(voxel)
if unit == "mm":
length *= 1000
voxel *= 1000
unit = "um"
dimensions[dimid] = {"Length": length,
"NumberOfElements": counts,
"Unit": unit,
"Voxel": voxel
}
return {"dimensions": dimensions,
"tiles": tiles,
"base_folder": base_folder,
"exp_name": exp_name,
"acq_name": acq_name,
"xml_path": xml_path}
# Add base_folder, exp_name, acq_name
# -
metadata = collect_metadata(base_folder, exp_name, acq_name)
metadata
# Block is defined by the x and y values of each edge
class Block:
# __init__(self, left_top, right_bottom, width, height, xvoxel, yvoxel):
def __init__(self, left_top, right_bottom, width, height, xvoxel, yvoxel):
"""
@param left_top: The location of the left_top point
@param right_bottom: The location of the right bottom
@param width: Number of pixels in x
@param height: Number of pixels in y
@param xvoxel: width of each pixel in physical length
@param yvoxel: height of each pixel in physical length
left_top and right_bottom are calculated from the xml.properties file inside the Metadata folder
The edges self.left, self.right, self.top and self.bottom defines the corresponding edges
self.xidx and self.yidx are the indices that would correspond to the image
Purpose of this class:
The tilescan images have overlapping grids, so to merge all the grid-based tilescan images,
we need to redefine the block in the positions of each grid block
How to use:
First, we initialize each block with the overlapping coordinates. Then, consequentially, fix the overlapping
coordinates by using the function overlap_fix(block). The parameter block here is an object of Block, which is near.
The overlap_fix first checks where the overlap occurs with the block, then redefines the overlapping edge
Note: The overlap_fix function consequentially assume overlapping blocks have the same non-overlapping position.
Make sure to check this with the plot below.
"""
# Edges
self.left = left_top[0]
self.right = right_bottom[0]
self.top = left_top[1]
self.bottom = right_bottom[1]
# x,y pixel count
self.width = width
self.height = height
# x and y pixel size in physical length
self.xvoxel = xvoxel
self.yvoxel = yvoxel
# x,y pixel indices
self.xidx = np.arange(self.width)
self.yidx = np.arange(self.height)
# def is_inside_h(self, y):
# # Checks whether the y-coordinate is inside the horizontal range defined by the block
# if self.bottom < y and self.top > y:
# return True
# else:
# return False
# def is_inside_v(self, x):
# # Checks whether the x-coordinate is inside the vertical range defined by the block
# if self.left < x and self.right > x:
# return True
# else:
# return False
# def is_inside(self, x, y):
# """
# There must be a check for the microscope parameters flipx and swap-xy
# The function returns true if the given location is inside the block
# """
# print("Comparing x- (%g,%g) -- %g" % (left,right, x))
# print("Comparing y- (%g,%g) -- %g" % (bottom, top, y))
# if self.left < x and self.right > x and self.bottom < y and self.top > y: # inside the block
# return True
# else:
# return False
def remove_left(self, right, xsz): # Remove the overlapping pixels up to right
# The length of the overlap
length = right - self.left
# print("x - Length = ", length)
# self.left = right
# Overlapping is removed
self.xidx = np.arange(int(length/self.xvoxel), xsz)
# print("First x-index = ", self.xidx[0])
def remove_bottom(self, top, ysz): # Remove the overlapping pixels up to top
# The length of the overlap
length = top - self.bottom
# print("y - Length = ", length)
# selt.
# Overlapping is removed
self.yidx = np.arange(int(length/self.yvoxel), ysz)
# print("First y-index = ", self.yidx[0])
# def overlap_fix(self, block):
# # Overlap on the right --> Right of the self block is the left of the param block
# if self.is_inside_v(block.left):
# block.remove_left(self.right)
# if self.is_inside_h(block.bottom):
# block.remove_bottom(self.top)
def merge(metadata): # For now, assumes all the acquire is xyzt
###############################
#### Construct the filepaths for each tilescan tif image
###############################
# Construct the filepaths to collect the tilescan images based on the provided metadata
znum = metadata["dimensions"]["Z"]["NumberOfElements"] # Number of z planes
# Find the number of digits
znum_digit = len(str(znum))
# zstr for file path
zstr_holder = f"z%0{znum_digit}d"
snum = metadata["dimensions"]["Stage"]["NumberOfElements"] # Number of tilescan images
# Find the number of digits
snum_digit = len(str(snum))
# sstr for file path
sstr_holder = f"s%0{snum_digit}d"
tnum = metadata["dimensions"]["T"]["NumberOfElements"] # Number of tilescan images
# Find the number of digits
tnum_digit = len(str(tnum))
# sstr for file path
tstr_holder = f"t%0{tnum_digit}d"
fname_list = []
for tix in range(tnum):
for six in range(snum):
for zix in range(znum):
tstr = tstr_holder % (tix)
sstr = sstr_holder % (six)
zstr = zstr_holder % (zix)
# Combine all params
fname = f"{base_folder}TileScan/{exp_name}_{acq_name}_{tstr}_{sstr}_{zstr}.tif"
fname_list.append(fname)
###########################################
###### Calculating the positions of each tile
###########################################
xix_unique_ar = metadata["tiles"]["xix_unique_ar"]
yix_unique_ar = metadata["tiles"]["yix_unique_ar"]
xpos_lst = metadata["tiles"]["xpos_lst"]
ypos_lst = metadata["tiles"]["ypos_lst"]
xix_lst = metadata["tiles"]["xix_lst"]
yix_lst = metadata["tiles"]["yix_lst"]
xvoxel = metadata["dimensions"]["X"]["Voxel"]
yvoxel = metadata["dimensions"]["Y"]["Voxel"]
xsz = metadata["dimensions"]["X"]["NumberOfElements"]
ysz = metadata["dimensions"]["Y"]["NumberOfElements"]
# Define all the blocks and plot the edges
blocks_2d = np.empty((len(xix_unique_ar), len(yix_unique_ar)), dtype=Block)
for six in range(snum):
(x,y) = (xpos_lst[six], ypos_lst[six]) # convert to um
# Define the square
left = x-xvoxel*xsz/2
right = x+xvoxel*xsz/2
top = y+yvoxel*ysz/2
bottom = y-yvoxel*ysz/2
xix = xix_lst[six]
yix = yix_lst[six]
blocks_2d[xix, yix] = Block((left, top), (right, bottom), xsz, ysz, xvoxel, yvoxel)
overlap_blocks = blocks_2d.copy()
for xix in xix_unique_ar[1:]:
for yix in yix_unique_ar:
# remove vertical overlap
blocks_2d[xix, yix].remove_left(blocks_2d[xix-1,yix].right, xsz)
for yix in yix_unique_ar[1:]:
for xix in xix_unique_ar:
# remove horizontal overlap
blocks_2d[xix, yix].remove_bottom(blocks_2d[xix,yix-1].top, ysz)
return (overlap_blocks, blocks_2d)
(overlap_blocks, blocks_2d) = merge(metadata)
# +
# Before removing overlap
colors = ["r", "b", "g", "y", "c"]
fig, ax = plt.subplots(figsize=(4,4))
cix = 0
for block in overlap_blocks.flatten():
left = block.left
right = block.right
top = block.top
bottom = block.bottom
color = colors[cix%len(colors)]
cix = cix+1
ax.plot([left, right], [top, top], color) # Top edge
ax.plot([right, right], [top ,bottom], color) # Right edge
ax.plot([right, left], [bottom, bottom], color) # Bottom edge
ax.plot([left, left], [bottom, top], color) # Left edge
ax.axis("equal")
# +
# After removing overlap
colors = ["r", "b", "g", "y", "c"]
fig, ax = plt.subplots(figsize=(4,4))
cix = 0
for block in overlap_blocks.flatten():
if True: #cix%2 == 0:
left = block.left + block.xidx[0]*block.xvoxel
right = block.left + block.xidx[-1]*block.xvoxel
top = block.bottom + block.yidx[-1]*block.yvoxel
bottom = block.bottom + block.yidx[0]*block.yvoxel
color = colors[cix%len(colors)]
ax.plot([left, right], [top, top], color) # Top edge
ax.plot([right, right], [top ,bottom], color) # Right edge
ax.plot([right, left], [bottom, bottom], color) # Bottom edge
ax.plot([left, left], [bottom, top], color) # Left edge
cix = cix+1
ax.axis("equal")
# ax.set_xlim(79000,87000)
# ax.set_ylim(36000,42000)
# -
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%0A
# + id="jjKuaohAuQaj" colab_type="code" colab={}
def cifar_generator(image_array, batch_size=32):
while True:
for indexs in range(0, len(image_array), batch_size):
images = x_train[indexs: indexs+batch_size]
labels = x_test[indexs: indexs+batch_size]
yield images, labels
# + id="hLgYhf31u2NV" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 269} outputId="a8ca5dfa-1bb0-4ea9-c137-8cd1b67273cc"
cifar_gen = cifar_generator(x_train)
images, labels = next(cifar_gen)
img_combine(images)
# + id="UN1hDWtAu2P-" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 269} outputId="3953972d-199f-47d7-eda0-a9b069e26d3a"
images, labels = next(cifar_gen)
img_combine(images)
# + [markdown] id="SXmqDvfSvAmZ" colab_type="text"
#
# + [markdown] id="W0NG_arSvArS" colab_type="text"
# 可以看到兩次的圖片並不一樣，這樣就可以開始訓練囉！
# + [markdown] id="jCKMXqY8vApG" colab_type="text"
# 作業
#
# 請參考昨天的程式碼，將訓練資料讀取方式改寫成 Generator，並將原本的 model.fit 改為
#
# model.fit_generator 來進行訓練。請參考 Keras 官方文件中 fit_generator 的說明
# + id="67QaDaHUvAJN" colab_type="code" colab={}
from tensorflow import keras
batch_size = 128 # batch 的大小，如果出現 OOM error，請降低這個值
num_classes = 10 # 類別的數量，Cifar 10 共有 10 個類別
epochs = 10 # 訓練的 epochs 數量
# + id="tro0Q34RvAOM" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 69} outputId="a5e1ca8b-fd53-4f17-98d4-9f1dd0f59810"
from tensorflow.keras.datasets import cifar10
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
# Convert class vectors to binary class matrices.
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
# + id="sIfopihNvAL1" colab_type="code" colab={}
def cifar_generator(image_array, batch_size=32):
while True:
for indexs in range(0, len(image_array), batch_size):
images = x_train[indexs: indexs+batch_size]
labels = y_train[indexs: indexs+batch_size]
yield images, labels
cifar_gen = cifar_generator(x_train, batch_size)
images, labels = next(cifar_gen)
# + id="Bg2dgdQIvKNv" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 869} outputId="d39323f5-f7b5-452c-d849-429b8032fd2e"
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten
from tensorflow.keras.layers import Conv2D, MaxPooling2D
model = Sequential()
model.add(Conv2D(32, (3, 3), padding='same',
input_shape=x_train.shape[1:]))
model.add(Activation('relu'))
model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes))
model.add(Activation('softmax'))
model.summary()
# + id="d2Hr7efmvQlc" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 87} outputId="fdba2bb9-805a-45a5-ec81-bd9146e3e435"
model.compile(loss='categorical_crossentropy',
optimizer=keras.optimizers.RMSprop(),
metrics=['accuracy'])
history = model.fit_generator(cifar_gen,
steps_per_epoch=50000//batch_size,
epochs=epochs,
verbose=1,
validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
| 138,249 |
/Monte Carlo.ipynb | 43dddbbee824bc3520cc297812aa34251ab89750 | [
"MIT"
] | permissive | fabigr8/OR_PA3_Simulationen | https://github.com/fabigr8/OR_PA3_Simulationen | 0 | 0 | null | 2021-03-02T09:13:53 | 2021-03-02T09:11:39 | Jupyter Notebook | Jupyter Notebook | false | false | .py | 8,273 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# name: python3
# ---
# + [markdown] id="jWTOs0VpTYkW" colab_type="text"
# **Objective:** To Train DNN of MNIST data to detect digits in image by hiting validation accuracy of 99.4 and parmeters less then 20000.
#
# **Architecture** :
#
# Convoultion with Relu(10,3,3)--->**BN-**->Convoultion with Relu(10,3,3)-->**BN-**->Convoultion with Relu(20,3,3)-->**BN**
#
# |
# |
# MAXPool(2,2)
# |
# |
# |
# Convoultion with Relu(10,1,1)--->**BN-**->Convoultion with Relu(10,3,3)-->**BN-**->Convoultion with Relu(20,1,1)-->**BN-**->Convoultion(10,1,1)-->**BN**->Convoultion(10,7,7)
#
# |
# |
# Flatten
# |
# |
# Softmax
# **OUTCOME**: 11 K parameters at Validation aaccuracy of 98.84
#
# **Architecture Variation** :
#
# Convoultion with Relu(10,3,3)--->**BN-**->Convoultion with Relu(10,3,3)-->**BN-**->Convoultion with Relu(20,3,3)-->**BN**
#
# |
# |
# MAXPool(2,2)
# |
# |
# |
# **BN**->Convoultion with Relu(10,1,1)--->**BN-**->Convoultion with Relu(10,3,3)-->**BN-**->Convoultion with Relu(20,1,1)-->**BN-**->Convoultion with relu(10,1,1)-->Convoultion(10,7,7)
#
# |
# |
# Flatten
# |
# |
# Softmax
#
#
# **OUTCOME**: with batch size 66 and epoch 365 gave 11 K parameters at Validation aaccuracy of 99.26
#
#
# **INFERENCE**: BN after relu or before relu doesn not make impact, but increasing batch size and Epoc pushed val acc to 0.9926
#
# + [markdown] id="aNyZv-Ec52ot" colab_type="text"
# # **Import Libraries and modules**
# + id="3m3w1Cw49Zkt" colab_type="code" colab={}
# https://keras.io/
# !pip install -q keras
import keras
# + id="Eso6UHE080D4" colab_type="code" colab={}
import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten, Add, BatchNormalization
from keras.layers import Convolution2D, MaxPooling2D
from keras.utils import np_utils
from keras.datasets import mnist
# + [markdown] id="zByEi95J86RD" colab_type="text"
# ### Load pre-shuffled MNIST data into train and test sets
# + id="7eRM0QWN83PV" colab_type="code" colab={}
(X_train, y_train), (X_test, y_test) = mnist.load_data()
# + id="4a4Be72j8-ZC" colab_type="code" outputId="484146d9-f331-4440-8a49-6b73a18f00b3" colab={"base_uri": "https://localhost:8080/", "height": 304}
print (X_train.shape)
from matplotlib import pyplot as plt
# %matplotlib inline
plt.imshow(X_train[0])
# + id="dkmprriw9AnZ" colab_type="code" colab={}
X_train = X_train.reshape(X_train.shape[0], 28, 28,1)
X_test = X_test.reshape(X_test.shape[0], 28, 28,1)
# + id="X2m4YS4E9CRh" colab_type="code" colab={}
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 255
# + id="0Mn0vAYD9DvB" colab_type="code" outputId="797fb171-c50a-4e01-8536-84626138c7cb" colab={"base_uri": "https://localhost:8080/", "height": 34}
y_train[:10]
# + id="ZG8JiXR39FHC" colab_type="code" colab={}
# Convert 1-dimensional class arrays to 10-dimensional class matrices
Y_train = np_utils.to_categorical(y_train, 10)
Y_test = np_utils.to_categorical(y_test, 10)
# + id="fYlFRvKS9HMB" colab_type="code" outputId="402a3140-26d8-4aa5-bd73-19b443999f98" colab={"base_uri": "https://localhost:8080/", "height": 194}
Y_train[:10]
# + id="osKqT73Q9JJB" colab_type="code" outputId="ff82918f-4b39-4ee8-d967-b7354d073e87" colab={"base_uri": "https://localhost:8080/", "height": 214}
from keras.layers import Activation
model = Sequential()
model.add(Convolution2D(10, 3, 3, activation='relu', input_shape=(28,28,1))) # 26
model.add(BatchNormalization())
model.add(Convolution2D(10, 3, 3, activation='relu')) # 24
model.add(BatchNormalization())
model.add(Convolution2D(20, 3, 3, activation='relu')) # 22
model.add(MaxPooling2D(pool_size=(2, 2))) # 11
model.add(BatchNormalization())
model.add(Convolution2D(10, 1, 1, activation='relu')) # 11
model.add(BatchNormalization())
model.add(Convolution2D(10, 3, 3, activation='relu')) # 9
model.add(BatchNormalization())
model.add(Convolution2D(20, 3, 3, activation='relu')) # 7
model.add(BatchNormalization())
model.add(Convolution2D(10, 1, activation='relu')) #7
model.add(Convolution2D(10, 7))
model.add(Flatten())
model.add(Activation('softmax'))
# + id="TzdAYg1k9K7Z" colab_type="code" outputId="42c66517-1946-4430-f73d-71d7e5f4adb5" colab={"base_uri": "https://localhost:8080/", "height": 745}
model.summary()
# + id="Zp6SuGrL9M3h" colab_type="code" colab={}
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
# + id="cDcGBRW9XqXW" colab_type="code" colab={}
model.fit(X_train, Y_train, batch_size=66, nb_epoch=365, validation_data=(X_test, Y_test))
model.save_weights('Assignment4_Second.h5')
# + id="4xWoKhPY9Of5" colab_type="code" colab={}
#count=0
#testscore=0
#while ( testscore< 0.994 ) :
#model.fit(X_train, Y_train, batch_size=60, nb_epoch=10000, verbose = 1)
#score = model.evaluate(X_test, Y_test, verbose=0)
#testscore=score[1]
#model.save_weights('Assignment4_Second.h5')
#count= count +10000
#print(score)
#print(count)
# + id="AtsH-lLk-eLb" colab_type="code" colab={}
score = model.evaluate(X_test, Y_test, verbose=0)
model.save_weights('Assignment4_Second.h5')
# + id="mkX8JMv79q9r" colab_type="code" colab={}
print(score)
# + id="XWoQoGV4dBJC" colab_type="code" colab={}
# + id="OCWoJkwE9suh" colab_type="code" colab={}
y_pred = model.predict(X_test)
# + id="Ym7iCFBm9uBs" colab_type="code" colab={}
print(y_pred[:9])
print(y_test[:9])
# + id="CT--y98_dr2T" colab_type="code" colab={}
layer_dict = dict([(layer.name, layer) for layer in model.layers])
# + id="2GY4Upv4dsUR" colab_type="code" colab={}
import numpy as np
from matplotlib import pyplot as plt
from keras import backend as K
# %matplotlib inline
# util function to convert a tensor into a valid image
def deprocess_image(x):
# normalize tensor: center on 0., ensure std is 0.1
x -= x.mean()
x /= (x.std() + 1e-5)
x *= 0.1
# clip to [0, 1]
x += 0.5
x = np.clip(x, 0, 1)
# convert to RGB array
x *= 255
#x = x.transpose((1, 2, 0))
x = np.clip(x, 0, 255).astype('uint8')
return x
def vis_img_in_filter(img = np.array(X_train[2]).reshape((1, 28, 28, 1)).astype(np.float64),
layer_name = 'conv2d_14'):
layer_output = layer_dict[layer_name].output
img_ascs = list()
for filter_index in range(layer_output.shape[3]):
# build a loss function that maximizes the activation
# of the nth filter of the layer considered
loss = K.mean(layer_output[:, :, :, filter_index])
# compute the gradient of the input picture wrt this loss
grads = K.gradients(loss, model.input)[0]
# normalization trick: we normalize the gradient
grads /= (K.sqrt(K.mean(K.square(grads))) + 1e-5)
# this function returns the loss and grads given the input picture
iterate = K.function([model.input], [loss, grads])
# step size for gradient ascent
step = 5.
img_asc = np.array(img)
# run gradient ascent for 20 steps
for i in range(20):
loss_value, grads_value = iterate([img_asc])
img_asc += grads_value * step
img_asc = img_asc[0]
img_ascs.append(deprocess_image(img_asc).reshape((28, 28)))
if layer_output.shape[3] >= 35:
plot_x, plot_y = 6, 6
elif layer_output.shape[3] >= 23:
plot_x, plot_y = 4, 6
elif layer_output.shape[3] >= 11:
plot_x, plot_y = 2, 6
else:
plot_x, plot_y = 1, 2
fig, ax = plt.subplots(plot_x, plot_y, figsize = (12, 12))
ax[0, 0].imshow(img.reshape((28, 28)), cmap = 'gray')
ax[0, 0].set_title('Input image')
fig.suptitle('Input image and %s filters' % (layer_name,))
fig.tight_layout(pad = 0.3, rect = [0, 0, 0.9, 0.9])
for (x, y) in [(i, j) for i in range(plot_x) for j in range(plot_y)]:
if x == 0 and y == 0:
continue
ax[x, y].imshow(img_ascs[x * plot_y + y - 1], cmap = 'gray')
ax[x, y].set_title('filter %d' % (x * plot_y + y - 1))
vis_img_in_filter()
# + id="9tvptcn8dxvp" colab_type="code" colab={}
| 9,606 |
/DL0321EN_1_1_Loading_Data_py_v1_0.ipynb | 26008321cf5497ff0ecea26112139fabd368dbd0 | [] | no_license | BenjLian/Coursera_Capstone | https://github.com/BenjLian/Coursera_Capstone | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 274,059 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python
# language: python
# name: conda-env-python-py
# ---
# + [markdown] id="view-in-github" colab_type="text"
# <a href="https://colab.research.google.com/github/BenjLian/Coursera_Capstone/blob/main/DL0321EN_1_1_Loading_Data_py_v1_0.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# + [markdown] button=false deletable=true new_sheet=false run_control={"read_only": false} id="39hd8wjpBKOn"
# <a href="https://cognitiveclass.ai"><img src = "https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/Logos/organization_logo/organization_logo.png" width = 400> </a>
#
# <h1 align=center><font size = 5>Loading Data</font></h1>
#
# + [markdown] id="MN-H2CS4BKOs"
# <h2>Objective</h2><ul><li> How to download and visualize the image dataset.</li></ul>
#
# + [markdown] button=false deletable=true new_sheet=false run_control={"read_only": false} id="hw5jNU1dBKOs"
# ## Introduction
#
# + [markdown] button=false deletable=true new_sheet=false run_control={"read_only": false} id="vXnas9IiBKOs"
# Crack detection has vital importance for structural health monitoring and inspection. In this series of labs, you learn everything you need to efficiently build a classifier using a pre-trained model that would detect cracks in images of concrete. For problem formulation, we will denote images of cracked concrete as the positive class and images of concrete with no cracks as the negative class.
#
# In this lab, I will walk you through the process of loading and visualizing the image dataset.
#
# **Please note**: You will encounter questions that you will need to answer in order to complete the quiz for this module.
#
# + [markdown] button=false deletable=true new_sheet=false run_control={"read_only": false} id="SsjYW9S8BKOt"
# ## Table of Contents
#
# <div class="alert alert-block alert-info" style="margin-top: 20px">
#
# <font size = 3>
#
# 1. <a href="#item11">Download Data</a>
# 2. <a href="#item12">Import Libraries and Packages</a>
# 3. <a href="#item13">Load Images</a>
# </font>
#
#
# </div>
#
# + [markdown] button=false deletable=true new_sheet=false run_control={"read_only": false} id="CvhUmR5kBKOt"
#
#
# + [markdown] button=false deletable=true new_sheet=false run_control={"read_only": false} id="g_Mb07AkBKOt"
# <a id='item11'></a>
#
# + [markdown] button=false deletable=true new_sheet=false run_control={"read_only": false} id="oYi0pYsmBKOt"
# ## Download Data
#
# + [markdown] button=false deletable=true new_sheet=false run_control={"read_only": false} id="8_ncaKkjBKOu"
# For your convenience, I have placed the data on a server which you can retrieve easily using the **wget** command. So let's run the following line of code to get the data. Given the large size of the image dataset, it might take some time depending on your internet speed.
#
# + button=false deletable=true jupyter={"outputs_hidden": true} new_sheet=false run_control={"read_only": false} id="sm4-4-UaBKOu"
# get the data
# !wget https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DL0321EN/data/images/concrete_crack_images_for_classification.zip
# + [markdown] button=false deletable=true new_sheet=false run_control={"read_only": false} id="e5Ge0QhtBKOv"
# And now if you check the left directory pane, you should see the zipped file _concrete_crack_images_for_classification.zip_ appear. So, let's go ahead and unzip the file to access the images. Given the large number of images in the dataset, this might take a couple of minutes, so please be patient, and wait until the code finishes running.
#
# + button=false deletable=true new_sheet=false run_control={"read_only": false} id="YO6rtqcsBKOw"
# !unzip concrete_crack_images_for_classification.zip
# + [markdown] button=false deletable=true new_sheet=false run_control={"read_only": false} id="PQYCAep-BKOx"
# Now, you should see two folders appear in the left pane: _Positive_ and _Negative_. _Negative_ is the negative class like we defined it earlier and it represents the concrete images with no cracks. _Positive_ on the other hand is the positive class and represents the concrete images with cracks.
#
# + [markdown] button=false deletable=true new_sheet=false run_control={"read_only": false} id="jQt6dQKMBKOx"
# **Important Note**: There are thousands and thousands of images in each folder, so please don't attempt to double click on the folders. This may consume all of your memory and you may end up with a **50\*** error. So please **DO NOT DO IT**.
#
# + [markdown] button=false deletable=true new_sheet=false run_control={"read_only": false} id="tx2_gnULBKOy"
#
#
# + [markdown] button=false deletable=true new_sheet=false run_control={"read_only": false} id="W2kSL58iBKOy"
# <a id='item12'></a>
#
# + [markdown] button=false deletable=true new_sheet=false run_control={"read_only": false} id="JYvA7wUZBKOy"
# ## Import Libraries and Packages
#
# + [markdown] button=false deletable=true new_sheet=false run_control={"read_only": false} id="IdoQmbQVBKOy"
# Before we proceed, let's import the libraries and packages that we will need to complete the rest of this lab.
#
# + button=false deletable=true new_sheet=false run_control={"read_only": false} id="qlUrCAIxBKOy"
import os
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
# + [markdown] button=false deletable=true new_sheet=false run_control={"read_only": false} id="G436tCKQBKOz"
#
#
# + [markdown] button=false deletable=true new_sheet=false run_control={"read_only": false} id="vTk-WrYMBKOz"
# <a id='item13'></a>
#
# + [markdown] button=false deletable=true new_sheet=false run_control={"read_only": false} id="KIGNupI_BKOz"
# ## Load Images
#
# + [markdown] button=false deletable=true new_sheet=false run_control={"read_only": false} id="0OzTfX50BKOz"
# Next, we will use the standard approach of loading all images into memory and demonstrate how this approach is not efficient at all when it comes to building deep learning models for classifying images.
#
# + [markdown] button=false deletable=true new_sheet=false run_control={"read_only": false} id="mvfwQcXjBKOz"
# Let's start by reading in the negative images. First, we will use **os.scandir** to build an iterator to iterate through _./Negative_ directory that contains all the images with no cracks.
#
# + button=false deletable=true new_sheet=false run_control={"read_only": false} colab={"base_uri": "https://localhost:8080/"} id="C5A_JcBNBKOz" outputId="68e8fcba-b90c-4596-fa76-2db4a204493e"
negative_files = os.scandir('./Negative')
negative_files
# + [markdown] button=false deletable=true new_sheet=false run_control={"read_only": false} id="GQNCWotXBKOz"
# Then, we will grab the first file in the directory.
#
# + button=false deletable=true new_sheet=false run_control={"read_only": false} colab={"base_uri": "https://localhost:8080/"} id="JE7d_QxkBKO0" outputId="1c1cda43-8b1a-46d5-dc78-1ecaeb1fb94b"
file_name = next(negative_files)
file_name
# + [markdown] button=false deletable=true new_sheet=false run_control={"read_only": false} id="3nEl2QU9BKO0"
# Since the directory can contain elements that are not files, we will only read the element if it is a file.
#
# + button=false deletable=true new_sheet=false run_control={"read_only": false} colab={"base_uri": "https://localhost:8080/"} id="m1xbpeSaBKO0" outputId="c52aaf6b-91cc-455a-c353-70541fb2d1e4"
os.path.isfile(file_name)
# + [markdown] button=false deletable=true new_sheet=false run_control={"read_only": false} id="8xUHQy_hBKO0"
# Get the image name.
#
# + button=false deletable=true new_sheet=false run_control={"read_only": false} colab={"base_uri": "https://localhost:8080/", "height": 35} id="KQJ_LREvBKO0" outputId="e8d82f4b-1755-468d-838d-c12872743abd"
image_name = str(file_name).split("'")[1]
image_name
# + [markdown] button=false deletable=true new_sheet=false run_control={"read_only": false} id="v7r2m1_kBKO0"
# Read in the image data.
#
# + button=false deletable=true new_sheet=false run_control={"read_only": false} id="vr4vc12cBKO0"
image_data = plt.imread('./Negative/{}'.format(image_name))
image_data
# + [markdown] button=false deletable=true new_sheet=false run_control={"read_only": false} id="B5oVxEFxBKO1"
# ### **Question**: What is the dimension of a single image according to **image_data**?
#
# + button=false deletable=true new_sheet=false run_control={"read_only": false} colab={"base_uri": "https://localhost:8080/"} id="rS-1w7jvBKO1" outputId="f669c3ca-b7b6-4f09-e7da-9e69cc30c29a"
## You can use this cell to type your code to answer the above question
image_data.shape
# + [markdown] button=false deletable=true new_sheet=false run_control={"read_only": false} id="TnrPd7-zBKO1"
# Let's view the image.
#
# + button=false deletable=true new_sheet=false run_control={"read_only": false} colab={"base_uri": "https://localhost:8080/", "height": 286} id="bCXDcomBBKO1" outputId="c6e7a543-b349-4385-c98f-1cd99f6f5d5a"
plt.imshow(image_data)
# + [markdown] button=false deletable=true new_sheet=false run_control={"read_only": false} id="9sK6RMh9BKO1"
# Now that we are familiar with the process of reading in an image data, let's loop through all the image in the _./Negative_ directory and read them all in and save them in the list **negative_images**. We will also time it to see how long it takes to read in all the images.
#
# + button=false deletable=true new_sheet=false run_control={"read_only": false} colab={"base_uri": "https://localhost:8080/"} id="b2_VaJURBKO1" outputId="a5034abc-03a2-4cdd-e9c6-a8ad535b432a"
# %%time
negative_images = []
for file_name in negative_files:
if os.path.isfile(file_name):
image_name = str(file_name).split("'")[1]
image_data = plt.imread('./Negative/{}'.format(image_name))
negative_images.append(image_data)
negative_images = np.array(negative_images)
# + [markdown] button=false deletable=true new_sheet=false run_control={"read_only": false} id="e9qP7yaiBKO2"
# Oops! The kernel died due to an out-of-memory error. Since the kernel died, you may have to run the above cell to load the libraries and packages again.
#
# Loading images into memory is definitely not the right approach when working with images as you can hit your limit on memory and other resources fairly quickly. Therefore, let's repeat the previous process but let's save the paths to the images in a variable instead of loading and saving the images themselves.
#
# + [markdown] button=false deletable=true new_sheet=false run_control={"read_only": false} id="U_5N8uFtBKO2"
# So instead of using **os.scandir**, we will use **os.listdir**.
#
# + button=false deletable=true new_sheet=false run_control={"read_only": false} id="6IzpEr5VBKO2"
negative_images = os.listdir('./Negative')
negative_images
# + [markdown] button=false deletable=true new_sheet=false run_control={"read_only": false} id="EbsgkSR0BKO2"
# Notice how the images are not sorted, so let's call the <code>sort</code> method to sort the images.
#
# + button=false deletable=true new_sheet=false run_control={"read_only": false} id="aqpuuuJhBKO2"
negative_images.sort()
negative_images
# + [markdown] button=false deletable=true new_sheet=false run_control={"read_only": false} id="a0iGaKLRBKO2"
# Before we can show an image, we need to open it, which we can do using the **Image** module in the **PIL** library. So to open the first image, we run the following:
#
# + button=false deletable=true new_sheet=false run_control={"read_only": false} id="Jn7aS5aBBKO2"
image_data = Image.open('./Negative/{}'.format(negative_images[0]))
# + [markdown] button=false deletable=true new_sheet=false run_control={"read_only": false} id="5MISMEC-BKO3"
# Then to view the image, you can simply run:
#
# + button=false deletable=true new_sheet=false run_control={"read_only": false} colab={"base_uri": "https://localhost:8080/", "height": 244} id="F29rPN-QBKO3" outputId="f0085dd5-3796-453a-bba5-d63e851499f2"
image_data
# + [markdown] button=false deletable=true new_sheet=false run_control={"read_only": false} id="vW4N2dUsBKO3"
# or use the <code>imshow</code> method as follows:
#
# + button=false deletable=true new_sheet=false run_control={"read_only": false} colab={"base_uri": "https://localhost:8080/", "height": 286} id="ZbXxJQS1BKO3" outputId="708578f5-0b2a-492e-9eb3-b4a677035232"
plt.imshow(image_data)
# + [markdown] button=false deletable=true new_sheet=false run_control={"read_only": false} id="O3zgAEK-BKO3"
# Let's loop through all the images in the <code>./Negative</code> directory and add save their paths.
#
# + button=false deletable=true new_sheet=false run_control={"read_only": false} id="NdB6wlJFBKO3"
negative_images_dir = ['./Negative/{}'.format(image) for image in negative_images]
negative_images_dir
# + [markdown] button=false deletable=true new_sheet=false run_control={"read_only": false} id="ti82ktRBBKO3"
# Let's check how many images with no cracks exist in the dataset.
#
# + button=false deletable=true new_sheet=false run_control={"read_only": false} colab={"base_uri": "https://localhost:8080/"} id="jfe88pB_BKO4" outputId="98277d3e-3b49-4c50-d2b6-6655e15d4f9f"
len(negative_images_dir)
# + [markdown] button=false deletable=true new_sheet=false run_control={"read_only": false} id="1EXUX_6CBKO4"
# ### Question: Show the next four images.
#
# + button=false deletable=true new_sheet=false run_control={"read_only": false} colab={"base_uri": "https://localhost:8080/", "height": 244} id="a4Sga8SMBKO4" outputId="6402efbd-cfe5-47f8-c184-2e7ff1749408"
## You can use this cell to type your code to answer the above question
Image.open('./Negative/{}'.format(negative_images[1]))
#Image.open('./Negative/{}'.format(negative_images[2]))
#Image.open('./Negative/{}'.format(negative_images[3]))
#Image.open('./Negative/{}'.format(negative_images[4]))
# + [markdown] button=false deletable=true new_sheet=false run_control={"read_only": false} id="QPz3n-8YBKO4"
# **Your turn**: Save the paths to all the images in the _./Positive_ directory in a list called **positive_images_dir**. Make sure to sort the paths.
#
# + button=false deletable=true new_sheet=false run_control={"read_only": false} id="OveR-FQQBKO4"
## Type your answer here
positive_images = os.listdir('./Positive')
positive_images.sort()
positive_images_dir = ['./Positive/{}'.format(image) for image in positive_images]
positive_images_dir
# + [markdown] button=false deletable=true new_sheet=false run_control={"read_only": false} id="Ipko56h6BKO4"
# ### Question: How many images of cracked concrete exist in the _./Positive_ directory?
#
# + button=false deletable=true new_sheet=false run_control={"read_only": false} colab={"base_uri": "https://localhost:8080/"} id="9Hsj37lHBKO4" outputId="b374b950-e4c2-4874-980d-52ece9bc9471"
## You can use this cell to type your code to answer the above question
len(positive_images_dir)
# + [markdown] button=false deletable=true new_sheet=false run_control={"read_only": false} id="VNZFhWOOBKO5"
# ### Question: Show the first four images with cracked concrete.
#
# + button=false deletable=true new_sheet=false run_control={"read_only": false} colab={"base_uri": "https://localhost:8080/", "height": 244} id="4fwh7keDBKO5" outputId="a2b7b22c-99ef-4a4c-a351-b1c24c7ab9b6"
## You can use this cell to type your code to answer the above question
Image.open('./Positive/{}'.format(positive_images[1]))
#Image.open('./Negative/{}'.format(negative_images[2]))
#Image.open('./Negative/{}'.format(negative_images[3]))
#Image.open('./Negative/{}'.format(negative_images[4]))
# + [markdown] button=false deletable=true new_sheet=false run_control={"read_only": false} id="ZL4t6NBWBKO5"
#
#
# + [markdown] button=false deletable=true new_sheet=false run_control={"read_only": false} id="O1EmvucmBKO5"
# ### Thank you for completing this lab!
#
# This notebook was created by Alex Aklson. I hope you found this lab interesting and educational.
#
# + [markdown] button=false deletable=true new_sheet=false run_control={"read_only": false} id="ShSKgKLMBKO5"
# This notebook is part of a course on **Coursera** called _AI Capstone Project with Deep Learning_. If you accessed this notebook outside the course, you can take this course online by clicking [here](https://cocl.us/DL0321EN_Coursera_Week1_LAB1).
#
# + [markdown] id="slLui9eBBKO5"
# <h2>About the Authors:</h2>
#
# <a href="https://www.linkedin.com/in/joseph-s-50398b136/">Joseph Santarcangelo</a> has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.
#
# + [markdown] id="5-r_KStpBKO5"
# [Alex Aklson](https://www.linkedin.com/in/aklson?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DL0321EN-SkillsNetwork-20647850&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DL0321EN-SkillsNetwork-20647850&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ). Ph.D., is a data scientist in the Digital Business Group at IBM Canada. Alex has been intensively involved in many exciting data science projects such as designing a smart system that could detect the onset of dementia in older adults using longitudinal trajectories of walking speed and home activity. Before joining IBM, Alex worked as a data scientist at Datascope Analytics, a data science consulting firm in Chicago, IL, where he designed solutions and products using a human-centred, data-driven approach. Alex received his Ph.D. in Biomedical Engineering from the University of Toronto.
#
# + [markdown] id="aFQeGN0UBKO6"
# ## Change Log
#
# | Date (YYYY-MM-DD) | Version | Changed By | Change Description |
# | ----------------- | ------- | ---------- | ----------------------------------------------------------- |
# | 2020-09-18 | 2.0 | Shubham | Migrated Lab to Markdown and added to course repo in GitLab |
#
# + [markdown] button=false deletable=true new_sheet=false run_control={"read_only": false} id="ts31hMtKBKO6"
# <hr>
#
# Copyright © 2020 [IBM Developer Skills Network](https://cognitiveclass.ai?utm_source=bducopyrightlink&utm_medium=dswb&utm_campaign=bdu&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DL0321EN-SkillsNetwork-20647850&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DL0321EN-SkillsNetwork-20647850&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DL0321EN-SkillsNetwork-20647850&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DL0321EN-SkillsNetwork-20647850&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ). This notebook and its source code are released under the terms of the [MIT License](https://bigdatauniversity.com/mit-license?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DL0321EN-SkillsNetwork-20647850&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-DL0321EN-SkillsNetwork-20647850&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ).
#
],'p':[1,2]}
classifier = GridSearchCV(KNeighborsClassifier(), neighbor_para, cv= 5 )
#classifier = KNeighborsClassifier(n_neighbors=5)
classifier.fit(X_train, y_train)
print('')
print("Best parameters set found on development set:")
print('')
print(classifier.best_params_)
print('')
means = classifier.cv_results_['mean_test_score']
stds = classifier.cv_results_['std_test_score']
for mean, std, params in zip(means, stds, classifier.cv_results_['params']):
print("%0.3f (+/-%0.03f) for %r"
% (mean, std * 2, params))
print('')
y_pred = classifier.predict(X_test)
#Checking performance of classification model
print('')
print('Confusion Matrix:')
print(confusion_matrix(y_test, y_pred))
print('')
print('Classification Report:')
print(classification_report(y_test, y_pred))
print('Accuracy Score:')
print accuracy_score(y_test, y_pred)
print('Error Rate:')
print(str(1 - accuracy_score(y_test, y_pred)))
def main():
KNNclassifier('1 -> 80% data for training and 20% data for testing', 0.20, x_data, y_target)
KNNclassifier('2 -> 60% data for training and 40% data for testing', 0.40, x_data, y_target)
KNNclassifier('3 -> 50% data for training and 50% data for testing', 0.50, x_data, y_target)
main()
UmN8158wK3HI/huvgNLh37J47tvIM971/F1tUuFC8px/pl1dhR5MfBLQ04s6cFVSc/Q/2VByJii1Ysoyg7sxzc3QibJsvEgjcERFS5yLTF2wdartwyNZeNPbXqrZhXh+YMU/BGDUP19aBNLHlOEEdF4CiI04GIj0COtOjVqOj+dS1R2Rh68lJUjA5r9LpM28Z3AthKiBMR8CYRwBlAC80XJ4DWgLGEuFqFiiNwhty+j1HbyPdhb0f4M2uCNcAaYA2wBlgDw9HABAQ4BGssp1uEhpuRIxrAcXcg5H6KZhet5vQjas/cw9EPPNiQdxzLMrZj8axNWDBjHTLfK0TGe6uRMa0AGdNWYe70VZg7w7C0fMxNz8ccw2alLUf69KWYMW0Jpk9bhOnTF2HG9BzMmLEI6TOWYFbaMsxJW4GMtHzMSyvA/PRCLEgrwvy0YmQZlknDnqYXYy5BpPc2IPPdLcj6RwkWzTiINfMvYOtyFw6su43Sva1wlH4iAQ4NoXJ8YgGcXRXYUHAI2XPWIiu9QACczBnLkDNnDQoW7cSm/NPYs6EWZ/c0w3H6c9yqfIQmpfqCcQAAIABJREFUAXBeIURvrM35PGjuG4I35ORYJvaJiS6NSUBVhI5Kh9CBtYCAsUy1OYSIykoBDZlKoNGNsEsNL9KP0aCH5miKZ3HT8TSEIhrmyOdU17CeVTyvpiELCFrQRgKc+CCLnslemezPqSKc5JwcxtwZJsCh57WW7Y4kADgiosY1gLBhIfcAlFlz4Ojz99gdPfVZOf2JUyuixHpm+zOOxmcqEwlwXiLofYZmz+9o9PyKm44fUH66DYd2eLEu/yTyckqwIGMtZs7IxXvvzsf093IwY1ou0qfnYeb0lZg1fRVmzViD2TMKMTutCHPSizA3vRhzyNKKxD7aPzuNvl9j2GrMTisQNiutADNnFCB9egHSKZ2xGjNnrEHmnI3IW7Qf6/JLBcQ5sbsRFSc/FvCgoYbmYqE5cWiOk25EvAQVbQDHTXmu57MBTdw9YvnssLsHpsUdYqUgTDKIY4GY6GFWic5JdHz0/lioY0Tp6PVN1DUFc5IBHNmeEGwIefpBEKfZ1YdmdzeaadJi73Pccv4Kx4UvUHo4jAPbfdheXIO1eZewfN5p5C84h/V5lfigyItD227jwqF/wn3+W9x2/I5m9wtRBmHKfzEBr8xvWp48CoKINi0a3kTBHDpXWC8i3miz4I4CPRLeWNE7MoKHVq2SkTtyBaqIWIHKWPbc14kWw+R+0g21v8oIMFuQWZal0o6quwlSdz9CYigowdvXMgLHGw/gUCSfAjgEcQjgWBCHQJHMQ8onq+5TWxx29SBClgjgG/8HctJo9V8R2zaORrvB1+B8ZQ2wBlgDrAHWAGuANZCaBiYowJEQJxre0AORs25E4AiA8wwB1yM0uh7CVfovnN5zDdsLL6BwyUHkZ+/B0swPsGj2ZuTM3oScOe8jZ85G5MzdiJyMDcjJpKEaZGuRnVWMBVlFyMwowJzZy5GetgQz0hYhLX0R0mbmIH3WIsyetRQZc5Yji4ZeZRRhceZ6LJ33PpbN24Kl87Ygd94WLMncgkUZW5Azdwuy52xG9qztWDhzFxal78eyOcdRnHMF2/O9OLi+Eef33YWj9FPcct1HAwGc2k/gqmzDFYrA2VWB9QUHkT27GFnpqzAvfQUy05YiZ85qrFpUgvfzT2H3+hqc2d2EmlOf4WbFIzQSwHFR6D45GuSsUIednCwK14+FN62+AUjrR6tXmjXUKjXxxFYyBVMscCOjVOSSv9Jp6EbI2SUgDpUnvfEWpmBPnMlXae4LZVHRAp4euZSwh67fb82JYc6NQcPv1BA8GeFlRd7ozkzstv5sysEhZ8dyeqKfVUXj6NDKOi9eBI4ENyFnP4S5+hFyDRgmnTdy4KT1IUTgQMAD6fApR1VGQSinMHE6vgCnHSHvCwS8T9HseYxGzy+44byP8tMtOLjDjbX5x5C36AMxPHFm+mK8N20eZsxYiPS0XMyeuQJzZxUgc04xsuZuwIKMTcietxk5WVuxaP425GRtw8KsbcieR7YV2ZlbsCBzMxZkbsL8jI3IytiAeXPXI2NOMebOLsTsWasxa+YqpKfnIy1tBWbNLED2vC3Iy9mPwrwz2LnRjdN7g6g+8wX8ZQ9wq/oxAu6XiPi7EfEZAMGEgcrZ1vNZAhuCCyF3t2bxQI6MJKHjzIiSuJAnGrxIx1+Bn3jfDX2fgFOinVAQx4iA0+qarHMEcbT2wGXUIyPaI+KV0WFB9wAC7n40e7rR7G1Hs/8Zbrl+Qs35/+D0wQbs2lSJjQXnkZ9zHNnp+5Az6xDys89i/fJylKzz4eTuEKpOf47rVb/ituMpmmnyYl8fWvwDiPhkxFnInDhZgRkJb1QkULxUAhzjeF8vIso0oBMLc1SkjopYInAsoQxF1YhtmszY14VWf5dIaYLjSFwjENgtlqpXMC4klliXMCoaBhq6cveK+h5yUb0ngGtAHAY40VrUdcnbnDesAdYAa4A1wBpgDbwlGphAAIecBOkcxKbKgVAAhxz6ToTczxFw/44m18/wXPwU5w82Yt+mamwuOIt1ecexetFBLJ+/G8sX7MLybLKdWL6wBMsX7cDyxR8gb8k2LFuyBUuXbELuko1YmF2EzHn5mDU7V0Cb9NmLMHNODmbNXYSMzGWYPy8fOfMLkZu9AStytqBgcQnWLNmFgsW7sWrxbuQv2o0VObuQl70LuQt2YmnWHiybdxB5mcewKuscNuRWYUdBPQ6ub8b5fffgLP0Mt5z3ccsGcA7uqsC6ggNYMLsI89LzMS99OTLTlyJn7mrkL9qBjStPYtf6apze3YjqU5/hhglwOkBvrVv8Wvi8ADgEcSTIobezBG7a/K/RVvcabf4BtAmYM1oQR63kIlfAkvNJSEhDwCbs7EawtlOk5Bi2eftx19cv5p0gCCLn21Bv/lXajRaXYcY8HmJJZLF6DM1XQef3o01MbEqTmxoTnNrgjdKVACsumpfGMgFmaL6QOG+j1T46hu4/PsQxnlG8tZZ6VefR/Df2IVQUdUPgJkjm6JOp+ixgDsEbCW7IcZUmnT4BbwSgUw6sDhRoW4EGK1XAx7ynMWrgaMgbDSWRAOcPNHl/Q6PnJ9xwfofyM2EcLHFi7aojyFu8DQuyVmPmrIWYlpaBtPSFmDVrKebOyUdWZhGyszZicfY25OaUIG/xbqzI3YuVy/ZjRe5+LF+yH3mL92HZ4r1YungPli7ajdycXVi8sASLsrdj4fytWJD1PrIy1yEjoxCz5+QjfWYepqctxvS0XMxKX43MWRuxcO5OrF15Hvu21OP8obuoPfcVrpb/hCb3M7TUdyHitwCOHNZi5afueAt44+pG0LCQywI5AtQoSGMCni4BeuR3qUbVJAM49vKP/UyTDZOJ+yZ44euT5u1FmEzcowaWBMiRUWQmwHH1ooUAjoA4FNn3Gi1eig55jZDnNQIEcTw9CPheIVD3FLdcP6K69EMc31eHbevOojDvCBZn7Masv23G7L9vR87sg1iZfQYb8qtwcFsDLh39N/xXHuJmzRM0uToQ8Q+gte5/EfG/RthLkT59wsJGZA2lSuviO7O+yKggUWY0VInM1289s68PYXp+DeJY2xJoyeFqRgQNTXCtG0Ecmg+H5sbx9wijbRm1lTgN09LqYoU2WqWNjMAOaYx+Uyszo74HXb0IKohDUTheNRdO7PxZcggVR+CYWh2j9o2vr/pinLIWWAOsAdYAa4A18KY0MMEADgnBDnF0cRgOvQj5JyfoBQLuJ2hy/ypWdCo/0YKTu69i35Ya7NxQjq1FF7Bx1RlsLCA7jY2rT2PjmpPYWHgcG4qOYX3RYawtOoCiwj0oXFOCvOXvY2HOGszJWIqZcxYhfc5CpM9dgFkZ2cjMWoaF2QVYumgdVi3diuKVe7Bp9RFsLTyBLcZy5JsKT4nrry84ibWrTmDtytNYt+I81i+/jE0rq1FSWIcDG+7g5LY2lB3+DzwXv0KD+wc0eL4VQ6goAqestAEHd5ULgDN/diEy0/ORmZ6HzPRcLMwoEABnQ/5J7CSAs6cJ1ac/x82qx2hyvhQr6dAb5lb/AFp8EtrQ21sJb1RoPQEcAjcWwBGROFFROHqep7ZtCkmt5kJgRplPbtMxFMUScnSJtNXdJ4/xDYhUwB51voiqkZE78trRq+K0Efzx9OGupx/3vHS+tDbvgAA5Eub0o9WtrA/0e2SqslkAJ85QAhURJKCSjNAhcBMigKMgjgF8xPXE8TTMwIg+MkCQGfFjn8RYDZsyoE1UFI4aPkFzYYjIGwI4RkSHWinJcGJNMGPOA2JFW1HklQIP5n2Yc/+kVq4qr1JNJcB5hZD3OQLeJxLgeB/ihvMbXDkTwsGdNSguOIRlSzZj/vx8pM+Zj2nps5E+ewFmz81FZuYq5CzcgLzcD1Cw4gCKCo5iXeFJbFx7BpvWleL9taXYWFyK9UVk57Cu8CzWFZ7B2sJTKFp9AoUFx7B61WHkr9iLvLwdWJK7CdkLizB33nKkzc7GtLQFmDFjqRhaNXv6BqxYdARbi6pwZMdtXDr6EdwXv0WD4zHCdR0I+8jBpnlLrOFCBAzseU5QREEcHd6YkTZiWBnBN1sEjrl/sAgaBW/iwR7N8dchgAFsFLgJCVDTBwE9BMDoR8RPYIP2EdzpkWZAJoJLag4nqWnVLhv1iSCp9zVaCSp45SpJIc+fCHp7EfS/QrD+CW65v0dVaQuO7XVi89qjKFi6BwvnbsWMvxQj7a8bkZW+C0uzjqF4+WXs3XId5498BG/Zj7hR8wSNBEz8f6K1/v8i4v9fhL0DJsAhWCOgkwBPdO8S7BDwDLppOJcEOCGKDqJ2kIwiecjq5HNH/AbEESDLiMwRQMcAKmIInZyDy5xAXUEcmpeLhlbR3Dh+am97xbaMalLn66mKwlHghiZiJ+uSkTkUoaMgH6UimqsXIRcZRd5RPbYBHPqsrWbX4n4th0/xECqzfU+1zeLjxua/gPOV85U1wBpgDbAGWANjowHT73b3goIa3gk6uhB29iPioslW5Rs9imiQc6lYzu+bKRAVkUFvLAngELSgcPtHuF71HVwX/oMrJ0MoPdSAU/uu4dguPw7tcONQiWE73Ti404EDu2pwYHcl9u0uw+6d51Gy4zS2bz+K4rUlWLZ8HTLn52Hm3IVImzsfM+ZkIm3uPGTOz8XixauxMu99rFu9G9vWH8O+rRdweEc5DpdU4vDOahzeVSMc1P07arB3Rw32bXdi3zYv9m+tx6Ftt3CiJIRzez7E5QOfwXHma9RX3Eej9yFu++6j3vEpXJV3BcA5sKscawsOQACcmSuROXMZMtKXYGHGKuQv/gAEcHZtqMaZvc2oPfslGmr/EEMOQuR0+PvQUmcAHK9c2YjekAvzvkaL7zVaCd4YAIdgT6uPInbUHAdDK2NLQEbEjUfCmnu+AdiNgI4AKC4SGwEYOjb2ODpPAhkDAimoQ8BHs3sGvPnQ9xpk97yvcdc0BXIoMkeZXCpYzOmgvaGNiUpRcwFpKR2jAI6AOMZwKisaJ9EwLH0OFVmnLMdLm6RYrUJlltUAaHiKmMNCRNRYTr45HEo4nGo4iYy6InAnTJwro64ExBFDMdQcQPJex6IOC4DjJYDzDAHv72jy/oJG7wPcdP0XV84GcGBnFYoK9mPpkg3IWrAcaXMz8N6sdKTPycKczFxkzV+FZUu3oGj1AWzZeA4lW8uwZ0cV9u924tBeDw7u8eDAbg/27/JgnzA39u1yYe8uJ/bsqsWenTXYVVKJ7dvOY9Om41i3fj9WFmxGTu4qzM5agOmzMjA9bSGmT1+GtGmrsThrF4pXnMOO9W6c2N2MilOf4GrlTwh4XyDolRFtshxkXiqYI+cWUZFuBHVUFIsaOmVEswhIowOYRNuyfGmeGjMaJu656ntK48MbE9ooiEORKhSBIgAGQYwBhP0DCFPq60fI24egp0cYRYUo8ET3Iud4kkMUZT2ievUabQa8afH+iRYa2uMlyPJ/BBAK1RHA+R23PN+i6nwQx/ZVYfP6fSjI+wA5mRuR/vc1mPmPdZg/aweWLjiMohUXsXfrNZw/+hG85Q9wo/YPNNKqU3X/i5b6/4eI//8g5HuNIM21I2CNBpzEMxLAoe/IBkDDuSgiKOx7jYj/T0Tq/kQLWT3Za/H8lAcEsKT1iXYz4u8FWYsyX48AM3p0jZpImmBNi4DlfWihoV40NCsKAEUDnLCXYE0XQt5OhLwd0gTEURMdq7lyDJhD7Y0wijIigBgH4Ki2guCNADgEcSgqSsF6OVkyz4EzNp2nsWg/+ZpcVqwB1gBrgDXAGmANDKYBy/+e8ADHgjc0zIbmZwi7X4llZ5vdv6Oh9idcq/wW3rLP4LzwL9SUfoiKM20oOxWRdjqCMmFBXD7ThEtnGnDhzHWcOenFiWM1OHy4DNu2H0XBmm1YsHAVZmUsRvoccvgyMWN2JrIW5GFp7joU5pcIeHPggzKcOeDFpaM3cPl4A8pO3sGV0024fKoJF0824QLZiSAuHovg0tG7KDv6ESqOf4qak/+F6/T3qL/8ALdqf0Jz3a+44/8BV52fawCnQgCcLIrAmZmPjJl5yJi5BNkZq7ByyQfYsOoEdm2swtkDTXCe/wJ3XE8Q9L0UUQOR+i601BPI6ULYJ9/4kuMgwvt93WjxG2H/dT1oraPhVnJ5XfG9iGySQwisCU9jwYQEHnQczUekrAut3i60+bpx19+De3W9NusR++n7NjrO2y2OtY7vwYd1vfiwvk+k4nx/D+76yHpx10vAR0XdUOSNjN655+vHh/4B3BMmh2Pd9fWhTawi0yN/x9slVpBp9XWi1UdLAEuzVo+hJaTJ2hEWK8oYk5GKCUvl3BY0txBNEB1ydSLopLTLMDkpM03MTDBHAB0RfWNELKj5eFQq5lOxwwDbHB1UJv5utNRReXUi4qdokFcI0+o8wgiQ0Od2hH30HR3TZZzTg5Y6mjtEDpGRw0ykoy/mEDEcQx1aJdserAGxfx8FcHyJAM4+LF2yUQCc9LmZmDZrFmbOzUbGvOVYsKAYBfl7sPX9szi424kTh67i7LEGXDjdjMtnw7h8JoxLpyO4eCqCi5SeDhsWwsUzQVw4E8D50404eawehw/WYM/ui3h/00GsXLURWQtzkTZ7Hmak5WD6tFxM/0c+5s/eiuU5h7FuxSXs3VSPC4fuwnv5O9xxERSl1ZBoTikDpImoJ8NRp/lxaIgVzZXj70LE1ynLQjjnNISMykiVl0pVuclUHkPbdDxpj4CRNBmhQcNEZaSGGG4TFbGjwxty8o0yFpE0NFRHDtcJEjDwdSFCdb2+F61X+9BS34dIfS/Cdb0I+bsR9HUi4O1AwNOOgJeMVhCjezK0Re2GWImpF3f9fWjz96FVQAulWwIg/QYY6ULo6jMEr/2KW96vUXWhCcf2l2Pz+j1YvWIbFs1fj1nTCjBrWjEWzNmOpQsPoGjlOezdXocLx+/CV/k9bjgeo5H0Xd+Plqt/CtAU8vXJ6B4BmroR9HQh6KGUTMKnoKcXyuh4AbPr+9BCz3y1D63XetFyrQct17rRcrUb1FZG6jul1XUgUteBlvpOtArrkmldJ1rrOmU9pHrm7USY2gVakYoAj4i+oQgeHdiobYrgInBD52ia8L1AiMz7AiEPGelMa4O0FbgkrCWAQxqUIF5AGgI1ygTAoZcshvEkxhyFo72gsLfR/JkdA9YAa4A1wBpgDUxuDUxygNOBoBhG9RRNrse44/wFDc4HuFn7Pa7XfIerVd+gruJr1FV8hbpKsq/hr/oCvqpP4Kn+SEwaXHWlGWUXb6D0nBd7957HunV7kLOoUAyjoiFU02ZmCogzf8FKrFi2CetW78fuLRdwap8Hlaea4bpwD57LH8FX/h/4Kz+Fr+JTeCo+hbv8U7ivfA5P2VfwXP4Gvkv3UXf5Aa6W/YIbZY/RUPMYTZ7HCF19jKa6B7jm/BLuinsoO9eAA7sI4BxE1uwiZMzMx9yZeZibvgQLMgngbMeGguNiUtCzB+/AefEzsUxz0P8UQf8zhPzPEPQ9RcBDDugTNDlpjqAnaHb9gYD7KYKeZwh5niPsfSGAQJgcJQUvhOMg3wjLFZ7k2+AomGMuGy6HF0TEOTR58kuEPS8Q8b5Ei+8VWv02M/e1o83fjlYy3ytxrDy+HW117bhb1xH1fauPjuuQcIigjwA5FEljROf4+nHP3497dX24W9eDu3XdaPPT5KId4toR8ZzPEPY8RcjzBEHP7wi6aaLax2h2PUKTsN/Q6PrN2H4s8q3ZTfn1DEEPrUpEq+JQtNcrBF1k7Qi6OgwjoNMlhlZZkTkEvQjgyBXAKHrNimIzJlam6B5atUqAAIIANGSkAyH/K4T8LxHyPzfK8g80e0krv6HR/as016+4Q6uv0b27fxdLddOEwUFyCv3tCNfRtQjgdSPk60HIazi47m4EzdWSLABgRvRErVITDe6SN/QGrKLhWWJuD4ISzxEggOP7FY2+B7hhRODs31mFwlX7xZxTWQtWIH3uPEybPUfMMzUvaxVysjdi7Zoj2PNBJc4ebcCVsxFUX/wnXOWfwFf1BbyVX8Jb8RU85V/BQ2nFl3If7afvq76Ap+pTVF1uw6XS2wLQ7tldinXrdmLRknzMmpuN9PTFmP4eLT9OK8ptwKKMXVi58Di2FdbgJK3sVvolbtXS6nZU/jRZLUU10RwqvXJYla8LYVFelNeyvII+GjJGkzb/IcqEyobKSDfSmDA3lRvZY8OeiPMCnmdi6fUgRf+Q3jyvEKT5hIzhNjTMyRpqo8pPzQNDn2kYFIGbTnFewPMSzR66rxcI+l4hVNeOcH0HQnUdCPo7EPC3i/lqmn0vxapRTd5naPI+NYx0R1FU8nyCUQJuqlWXCFgp4EQQkSCjvwPBumcI1P2GprofcN31CcpLb+Lwvkt4f10JClZsxqIFazF7Rr5YXWxBxmbkLtyNwpUnsWebC6XHw/BUfI1rtQ/R4H6CZh9djwBTNwLeLjR7OoUFPJ2Q1oUAgRxvHPN1IkRlVE9Gz01A6AVC9c/FPQb9fyDge2KZ93eh16DvD4T8TxGueyYsUvcckboX0vwvJUj10QTLFsQR8IbmwLHNgyPhDUXbUH2gMn2GgOcJAh5qY4y2x/EIjY5HaHb+jgCBePdTMTSY2mS5shUNvyJoSNE0FIUj5x2iuYcsgMOrUCVvnyZ3J42fjcuPNcAaYA2wBlgDrAFdA5MA4ERH3qhJbsWKRmIiY4rCoblwnhqOEzlNv6DB8TNu1j7EjZoH0mopNP8Brju+x3XHNwKY1Dk+hrOyDZWXG3GxtB77917C+nX7kZNTjDlzl2Hm7BxMn5mFGbOysGDBKqxctg0bVh/G3q3lOHfgOhzn7qGu7HNcrfgK16u/wU3HfVyvvY9rNfdRT1b9Pa5W/YirlQ9xveIX3Kx8jNtVT9FY/QLN5Bz6niJ89Qma/A9xzfElXOX3cPlsA/bvrEDxKgI4xZg7cxXmpOdhdvoSzM/Ix4oldA9HsWtTOU4fuInq0o9wo/ZH3HQ8wPXqH1Bf8S38Zf+F5+KXcJV+itoz/4Hj7CdwnfsM7nNfwHv+K9Rd+gZXy+7jRsUDNFT/gkbH7xJWuOltMAGdDmP5W1tYvzlkgxxJerPcId4eNzuf4k7tI9yq/hk3Kx+K616/8iPIbpQ/wM2Kh7hV9RMaa39FwPUYYXKSyJEh+OB8hCbHb7hT8wtuV/+MW5U/4VbFQ9ykoRTlD9BQ+Quaan5H0PkcYXe7WEqclugWq055jUgbfw/a/N1oE2/L29FCAIQAlvMR7tT8hJtVP+B6+beoK/sKvkufw3PhU7hKP4Hz3H/gEPZv1J77NxylH8N5/lO4zn8Bz8Wv4S/7DlcrfsSNqp/RUPsIjc4/EBAwhzTXjqCbIE4nglHRODT0QQ6bkhOLWhDHHIYo4E2PeIsf8XUg5CNH+ynuuB7hluNn3KghzXwHf/nX8F7+Au6Ln8B5/l+oPfeRsJpz/0LN2X+jlu639FM4L3wOz6X/oq78e1wX90owk+rDSxFZQVEYwsk1IhbkfCcKAESnJswx5/CxQI7ecFjbFryh+asI/IU87QJECIDj/RV3vBLglJ0NYH9JNMBJm5OFabPmYtbcJciatwaLs7dg/ZqT2P+BC+ePBVF17iO4Ln+GuspvcL32e1yr+QHXqn/EtaoHMqVt2lf7A66TOX7ANcd9eKs+g+PKXVRcbMTxIzXYuuUI8lasxbysXMyZswzpM/Iw4x8EcNYjJ6MEy7MPY/PqchwraUDV6Y9xreIBbtdQvSDYOCAjmkTETRdCPopSeY4mzx+445Zldr36R9SVfwdf2X/hvvgFHKWfoPbcf1BzVtm/xTbtI6PvXRc+h/vil/Bd/i/qy+/jRtUDNNT+gjtUJwggimFc5PxTPaNoOmOeGrMeKnhjrNKkwZsm1zM01D7GjSqKTHwo7GrFQ9RXPBRDxK5V/YLr1bS8+29oINjkfSysgZZ8d/+MG86HuF77AFerHqC+/EdcLX+IaxU/4UbFr7hV9Qi3ax9LMOz5A42u33Hb8Rtu1vyMa1X34a/8HJ7yf4rom5OHa1Cy7RgKCzZiWW4xFsxbhZkzlmHWjHxkzV2PxdnbkZ93ENs2XsHRfddQduYuai58Cvflb1BX/hOuVT3Cjerfcav2D9x2PEej6yWa3O0S4HgpcsiIHhKRQzJ6qNn7Ek2eZyIyscH5CDdrf8Y1o230XfkK3stfwn3xMzjPU72S5jr/CdwXPoXn0ufwXf4KdVf+i6sV3+FG1Q+4VfsQd5y/giI9g75nCBPI8bf/f/be+62tM9/XPn/AO3sS26BeQYUqgUASCCTRe+9FNFGFJEB0MB1s7MSJ48RxYidx7Njpvc0kmWRmzj92n+t5BLYzM7uc9z1z3uy99cNzCbhyOYu1vlroudenSOgqoc0ZvJGZSSI36ckSdikB4b6SQH13/gM2Zx6wOnGXpdE7LIRuExt8jdjAayyF3mRt/G22px+yO/cB+9HPOIp/fXYvFhBHAN+n8CaZP/RM7s0zsPjq0pmdUkKfVI3403tV6sNf6lykZiA1A6kZSM1AagZSM/CffwZ+wwDnH4ObJMARm8pkDo4ImJQbRvnEWnxQPn8KLjYWn7Eb//TXa+EjdpYes5N4yNbiPRLzN4lMnhAe2aSteQpfSRuWTC8aTS5KlY00pYl0lZkMcxHOnBrKXD20Vs4z1rHP0uhttmffZWf+PfbiHyRVEosfsbvwEZfFin8sA1Evz3/GzvwX7Ee/4Sj2fbJCWDy9Xv6ak7XP2U88Zm3+DWKTL0prV3PtMwBHUYBakYsy3Y5R5yQvq4LSok7qA2H6WlaZGrzK8sybJKbuEBt9hZn+60x0nTDSts9go8jdWaO/bpOB+m2GGnYZaT4k3HGV2Z4bxAZfZXnsHlsz77EfE4qUzzla+Eqez+NFsWk8C9r8O+uGsAaIJ/DfIlQDO/Mfsj79gMQ3CijKAAAgAElEQVT4m8RDrzM/cIuZnpeZ6X2ZSP8rRAdfZSH0Oqvht9iOPOBg8QP24o/leduee8D61DusjN9lMfS6PKbowC3m+18h0vcKi8NvsB5+wO7cxxzGvuJYVKWLJiphSRIKFhEmuiwsDt9xdeUbrq58xcmyaLF5n625d1kNv8nC6C0igy8w2XPMWMceodbLDDZv0te4/sxao79pk8HmHYZa9hltP2Gy90UiQ6+yOH6XtekHXI5+KFUWUiEhNmbnEGfhHOL8IINHkwDnLEPq2Y2VCGZe+kkuWTuc+E7OwOHS5+zExCbzPompN4iNvcLs0AuEe48Z7dxjuG2L/qZVehuW6W1YoadhlZ76NfoaN+lv2mawZZeRjhOm+l4iOvI6ifB91mcfsx0VIcLfylpnYZFJKhV+kIqcc4jzpJnoCRQQob1n69+FOL+GN38HcM4ycJIA5y7hwUOaa2cpcrZgt5VhMDlIAhwdam0OJqMXe2Y1ZcUh2mpWmOi5nvx9Jt9mM/KInfhH7MQ+Zif6CTvzAhqI9Qk7sU/YFQq8BVFZ/il7Sx+zFX+XtegdluZuMDq4RmPdMC5XkMxMB0a9A43CgeKCA72iBIu2ihxTM2XOMdqr1gj3vEhi4h22Zj/iIP6NbGo7Fba0lR+kzeZo+Sv5/9kW1yzyLkuTd4mOvsr0wItM9Fwl1H7AQNM2vQ0b9NSvy9Vdl3wV34ufi+s21LrHaMcR4Z5T5gZfZmHsdVan32Jz7j12Yx/JWRNw70goUYTK5BmAk7xuyXyU86Bqqb4R13nxa3bmP2Z9+iFL43eJjbxOZOg1ZvpvMd33CrMDrxEZvkN09C6JqXdYn3/I5aX3uZx4zMbCA1aj77A0d5f45B0JU6YHX2F28FXmBl+XmVsLI/dYmXyXrcgjCcs3Iw9YmXqLxbHXiYRuMDVwzFjvJn3tURqqB/F5asnNcWHJdKDX5pB+yYYyPQe9tohMcxl52XX43AM0VM7R27rFcOcR4z0vMNX/KpHhuyyMvSthzsacuJ9+ISFOEtwIeJOENodL33Cw9LVUDQkV0eXYR2zMvSePS8CauaGXmOy9yljnAaG2XQabt+lr2KCvcYP+xk0GmjYZat4m1LrLaPuBvIfO9L9ANHSTxQkB6UW49WMOlj7lRN5jBMQRFrOkVVUq3oSV7pklrI2yjU2qsj5mY+Y+iyOvMdNzjfG2Q0aa9hms22Gg5jLjLSdEem6QCL3O+sTbXJ55zEH0M5nzJqvLpVrvzzKAXoTQyyWBjrBNnav6zmBxCuCkLFQpC1VqBlIzkJqB1AykZiA1A/9lZ+A3CnD+bXhzTg6T1h5h8xFKkaeSfvGEPBkUmcyWSGZMfC83QsKecrT6BUfCurT6iPX4HeIzLzA1tktH8yxlJZ1YMkrRqPNRquykKTNQqDLJNHsoyK6n3DVAR9Uik12nrE+8w0H0E44WPuck8RVXpUxfWBVEje43yTpdCTmEWkPkJwilyM+8uPIXros8itXvuLL2JXtLj1mLvEks/CJjA5dprpnB7WzHoPaiURSgUuSiSLeh1zrIywpSWtxBXXCc7uYlxvsOiE/cJBJ6gfHuA/qb1umoitNUPkOtZ4xgwQCVhSGqXWPUFodpKonQGUwwULfJWOshkf6XWJm4y+78++zHPuZA2qxEZsfZOXxi3xDKCqEA+AkRlizPr1SOfMrG7EMWx+8wO3CDia6rDLcc0Fsr4NFlBhv3CLUcMNF5hfmhl1mdEpk977I9/zbrs3dZmbwjActc/4uEO68Qaj4g1LTPcOOeBE7hjmvEh+6wMfWIvfnPOIh9LRUvyfrgH8/yKMTT8K85WRa2B5EH9CHb8/dZmbxNNPQikz2HUjHVXRuntWKWhvIwNSWjVLiHk8szTIVniKqSUWpKw9SVzdJcsUBPwyahjiOmB26wMP4GG5H3OEh8htjAi8whaXGREOcPHC2cV0MLBc7PzzTEnG+ukg1VAuBck+GoIrdGWKW+YH/xQ2n5W5x8jdmRa4z37TPQtk5HXZzmyhnqyseo9A4SkG1n/QSKBwgUDVHhHqHKO0FN6RTNwTg9jVuMdl1lduiWVDCsz73P7qKwxXzDgVCNJP7AUULYTX7gSNqqktXS56G35zkq4vVfgzjn772/b4tLWsZ+pcA5Azg7Sw/ZiN9lYvCA5pqnAEdvcpCmNnFRaUCtzcNs9JGd2UDAPUFn7RZTvaKp7W2p/NiNfvo3VrbvOF44W4tCpSLygL6TgOXK6nccrXzK/vJ7XF6+y9zEIZ2t05R668myF5NpcqFXF6C66MSg9JKpDZJlrKMkf5Dm4CKj7VeIj7zBuqgUj33NtZVfOF39KVktvvoth8ufc3nhfdYi77Aw9TqzoRuM9RzT37JFR22CpmCE6pIJgsUjBIpCBIqG8buSS3wtfiaum8ixaqmI0123ynDbHlP9p8TGbrEyfY/t6CM5a8crIi9FKJqELeo8aPhp81IS3pwrcERIbhKqbs09Zmn8TWYHXmKi+5RQ6zF9Dfv01O0x0HxEqP2U8e4XmRsR1rS7bC09YCvxLqvxuyxGbhOZepnJkeuM9F6hr+WAgZYjhlqvEGo9Jdx1g2jotbPjvM/K1B2iIy8x1XuFUPs2vSJsunaK2kA/PncDjrwSMjLs6PVWVCoRIG0mPd2GVpOPyejGbgtIqOcvGZIQp7Vmic56AVgPCLVfZ6pPqGSEIucRm3MfsxMT1q7vOUyI9R3CAnaQ+IqDxBfsJwS0/ISNyAOWJt8gEnqZcM8JQ63bdNUu0Rycpd43SbW4N7qGqSgaprI4RJV7hFrvGPW+KZr9c7RVLtDXsM5Ixz7T/afEx26xPvs2e4sfcrzyBfK6CLvjWQZV0lInrIvPLgHevuRAgMX4+yxP3GG65yoDdet0+GM0e+eoK5yixjlFR9kSofod5rpPWRp+lY3w2+xFPuQoLixV38m/HcLKdw5vZAi9ADki70YCHHF/+UcA5yynSLanPVXTSQVr/B+07z37Ye8sxF2Gvj8JdP/P/9Tq6T0s9bukzkVqBlIzkJqB1AykZiA1A//5ZuA3CHD+Y/BGDJsMX32SyXIWvrskAiTPg0aTQa5XZFXsWYWuyAYRoGX9Sw5XP2Qz/haLMy8xM3ZAV8s85SU92DLK0KqdqFTZKJSZKFVWLOYSCrOb8BcO01G5ylTnDTYmHnE4L1QhybyCU/mE/gdORDCoDAcVG+Vk0KaoGRbS9muiMWn5r1xf+ROnq99zZfUr9pfEZvBuEuD07zwDcErQKAtRKfNRKkXFspPcrCAlRa1U+0O01MzS27zMSNcuA60bdNTEqPdNUFE0QJmjE292M0XWWty2Brz2FkqzOwjk91NdOEKDZ4p2f5zB+m2muk6lnH81/A5bc++zH/+Co2eCTI9Fe4q0Z5wDnD9yJDaVQomw8BEr028RGX6J0c59qS5oDS5S45mVq9EXpTWwSHfNKiPte8wOnpKYvEV87Ib8OtxzJJ+I99Vv0FGxRLMvRrMv+mT11WwR7rzO4shdNqffZycirFHC4vUzJ0si3+XcgvQZu7H32RLWNbGhDL1IuPuAgeY1OmujNAUmqCoZwF/URWlBG+68Jlw59bhy6nDl1lGYW0dRXhPu/Ha8jh7KXcNUl05LkNNVv8lI54kEI4kpAZ4esT3/sbQpCYXGobBSCYAjK4CFfUqob5I1v09tVGfqm8SPMrxZ5AUJYLYVecjy5BvMhV5gtGeXnpYErbVz1AXGCHr7KCtqx+tsojivBldOJYXZ1RRm11KYLY69ieJc8bt0UlY4eHa8S3Q37DDSeY2ZQZHB9IDVmQ/Zmv+M3QWhUvhe5okcngEcEfp6DnCSqo6nlqp/BHGe3uT/Vn2TBFUS4CwlLVQHS5+xt/QBAuCsx5IAp6lmFpezBZu1HL1RABwzFwTA0TgwG8rJzmgmWDxNd+0eM72vkxgTFrxPOIh9wxURxi2CtsXGMyYq6ZO19KJ2WSryzpqBRDC3yBA6XP6QveX7RMNX6WmNUOZtIsfuwWIqwqASACcfvcJNhqYMu74KT14vTf4oobZDosO3WZt8j73ol1yT1dMCfH3N/uKn0oq5OPkG08M3CPUc0NuyQWvNArXlUwS9I/gK+3DnteHKbpL3jMKsRgrOlvi6MCt53UqcPfiLhiTsaQrO01W3wmCrCEQ/JTZ6mzVh3xH2wrho2hPn9EcZ5CsqwY+XzivixesvCKApbFZHAtYtfsba9H1pCxQAord+g7aKZepLY9R4ojSWL9FWKVr6LhPqPGYq9ALxmVtEZ24yNX7KyNA+vT1btLes0lSzRHV5lNryBRr8CZoDa9JmNtZ1hejoy6zO3mZ+5DoTPXv0Ny7TXj1LvT9E0NuJt7AWZ64PmzUfg9GMVmdCqTKSlm4gXZGBWpOFweAgM8NNTlYFhfnNlBT14nePUOmdptYXo7Vinf4moVK6SWxU2AQfyVmWcyxyfM7gzd7SZ2zHPpBWqcTUPSKhm0z0nDDQvEVnzSJNgWlqSkIEXN34nB1481opzmqUy53diCeniZK8VnyOTsoL+iTYqfOFaakQ12VZArbp/hdYGL/D2oxQHwmrm8guEjbFM5Ajc4CeApzjxLfsxz/lckTYz94iMvACQ40bNPumqS4YJpA7gM/WS6mlhxrnGB2+KEO160x3HLM4dIutqXfZn/+Eo/hX8iFFEuAIiPOLbBEUr6dCbSPgisjcOocsv1LgpADO03vWf74PaKljT12z1AykZiA1A6kZSM1Aagb+dgZ+2wBHNBwJ8PFk/foCiqDYJ0tWrT5t65B12Ym/cLr8V64m/oqou70ilggjXfkjV9bOqm7j91maucXs2DHdLXECJX3YMwLo1IWoVbkolOKpsU22pxRmteB3jtIR3GCq4yYb4x9yGPmO45jYWP7EqWhiSfycrOaVrSliU/Unjhf+JFUZoiXk2uJfuL7012STi2g3Wf1aVi2vRe4RC99grH9XqhTczg4MagFwXKhVDlTqHETIa05WAI+rmQpfPzX+ERqCk7RURagrCxMo6seT00JBZg355gC5hhKytUXk6kvIN5TjNFbiyqjDbWmmxNaBP3eAes8MXRUrhJoOmem9SWL8bWlLOc/fEFXKyerbZxpfhBog8Q2Hy1+ws/A+i+HXCfed0NO0SlMgQsA1isvWLVdJ7gD+ghA13knaq+MMtW0wNXDIaOdl+ppWaa+K01A2Q1XxBOX5w3iz+ijNHsCXM0hZ7hANJfP01+8mj23sPhvTH7EXFQ1MP3MsQ3q/k7kUO7EPWJ16i9jITSZ7jxlsXqetap7qEqGC6KHE0YIrpwaHLUiupYysjBJsJg9Wk1sui6kYm7kEu9lPdkYVedYGXDmdlDiF8iVMY3CR3sZdxruvMzd0m6WJ+2zOJYNuD0S4sVSACeXK2Qwu/IWrYi3+8iSvJ6m+SYYsH8Q+Y2P6XeIjrxHuvUJ/yzrNVTMESwcoLeqgKL+BfHsFudYysjO9ZGUUYTcXYjMVYRPHbPRgNZZiM5ZjMwXJzayjIKsDT/4Q/uJpGgIrdDccMtp1k9mhuyxNPmIr+jl7i9+yv/g9B4siGPaZ1p5nQM6/pcR5egN5FuCcqYyE8kiAvr8FOItJgDM+cIAEOI4WrNZydEYHl1RmLiiMqNVOzPoAOeZWKorm6Kk9ZK73HitjH3J5+kvZ/CU2q6cLP3Ma/4Wr0T9zNfoLV8SK/Zxc8adqJ9HYdZT4hP3lh8TD1+htjVLubSbH7sViLEKvcqK8kIcu3YVZXYpVF8Sd20lD+RxDLXtEhm6xEn6X3fnPOV0W5+k7dmOfsjH3iIWJN5jou0ZP8yYNlfNU+CYodQ1QmNtGvr2BXEu1nCOb0cf5shp9PLuyxPszsxqHrQFXdhsljl6pzKkumaS9ZoVQ+xFzQ6+yFH6HjdkP2Y19zaFUTgl4I2C0AJgC3Ij15zOAI7KOvuZg8ROWw28y2XOFnrplGsvnqCgax5M9iMvWT2neKMGiKWp9Udpqlulr32J08IDhgV26OlZoaIgQrJzAVzpCsWsQZ04vhTmDuPNGKXWEqfZG6a5fJ9x/RDz8gmzg62tapNE/QYW7lxJnE84sEVBcjMWch9FoQaPXodLoUKh1pCt1KJRGVBoLWn0WBmM+ZjHTGeVkZ1aTZ22iwN5JcfYA/sIpmgKrDDQdM9UnwO87bMx9lFSULX/HwfLXHMj70EeszLxDdFS8n64x0LJDW9WiVNQFXIMSzLhzGii0V+G0BMnP8JNrKiPX5Huy8sx+HJmVOC21FNoacee0U+roI+AKUV82R2ftGqG2I6b7bhIfFRk577Mb+5J9kcEmII4AOCtPl4Bpl+c/ZHXybeIjrzDWtkt7IEIwvwdPZhNFxgYKdHUUaOoptXRQnT9Ei2eKwepVZjpPWR17U+aCHUQ/53hBhLL/LOGNBDhSjfPzMwDnp2cAjsjLSS5ZIS7a01IKnP+yMuqn9+Rffz5K/Tx1PlIzkJqB1AykZiA1A/91Z+ApxPmR/3EU/SMnYkMUP8vwkBvQM5m2fNL3zz4RzwCb/5cAR4Q9nib+wrVlAUr+J6eJv8olIU7iZ66u/JGr699yvPYp2wv3SczeYm78hJ5WAXD6ycoMoFcXolHlojwDOBZTKQX2FsqdI7QH15k8AzgHke84iv4BqQJIJFU+YoN1JNYZvJEAJ54Mtj1d+DPXFv6SzEERFdES4HzIugQ4L/0dwNGqXGjUTjTaXEymAnKy/RQXNlLu7abc3UuZq58y5wDFWZ3km+qxKP0YL7oxXnBhuODA8HwupotOzGkuMtM9WETmh6IUi6KMLHUlRZYOAs5RGkpjDDTuExl8VYaRJhtoztprRNCuzOBIthgJWCEaj6SVJP6I6NgrhDp3aamaJ1Acwmltx5RWIZdNW0u+uQl3TieVnhAtlbMMtIgn9VFqS8OUOQYptnXhMLWSpW7AklaLXVFPtrqJPG0rZbljtJavMNJ6yvzgG6yEH7EzL4JERQWyaNMRSotP2YzclzaO0Y5dumri1JVOUOboxZlRT66xCrvOT6a6BLOyGKOiCH1aIdpLBWgvOdFccqK+5EBzqRBNWhHaNA8GhQ+zOohFV0O2qQlP3iBV3lnaKtcYaj2WmSArUw8k7BKBsWKDn2yhOYM4QoEja36TWT2nCVHfLiqJv+N05Rt2Io9ZGL3NeNcx3XUr1JZN4nV2kZNZhdXox6TxoksvRJvmRJcuslqEWkSE7jrkz8Sxi+NVX3ShvlCE9lIJhvQAZlUtdn0rxTkhKjxRWiq3GGq7xtzwG6zNfcDOwpfsLYqQV5Ef8hOHz1Yvn0Gc/28AR6i0hHXkK84VOJcXH7IWexMBcES7VKGjGaulDJ0hn0sqIxfSDajV+Zj1frLNLVQUz9JTe8Bc311Wxj/g8swXsr79WuJPCDvHVfFeiv6Jk+hPnER/5Fis2A/JJQKlRSaRaIRKfMj+yrvEJoUCZ44ybyPZdjeZxkJ0ynwUz+egTSvEqBI5OAGKsjukfW6geYe5wZssT7wjs3ZEVbi4xhuzj1kK32Ny4EW6Gzep8k1TlN9Dnq0Fq6EGg7IcfXopujQvuktudJeKnq408bVLLu2lQvRpRRjSPZiUPjLVfmy6SrKNdeSZmygvGKE5uMRQ6xEzA7ckLNye/1QG9opGJtEsdiLvNecQ5wzgyJwjEa78kQy+DrXt0OifprxggEJrO1ZVLaa0KrJ0TRRYuvDmDVHhnaChMkJnyxKtjTEqgxO43b3k5LZgtTRi0tehTa/CoKwnU91Clq6T4qwQDeVRhtovMxe6wlDbOs2BWcqdfbhsTWQbA5hU4r2UhVqRiUplQKlWo1RrUaj0KFRGlCozKrVVqnA0mjzUSieqNJesF9de8mNMqyYjvYl8Yz/+gjlaK7YJtb3A3NCbrE6/z34iaVM9WPmS/eVP2Yq/R2z8NqM9J3TUrVJXNoevYBinpZUcYy1Z+kps2nIs6hIyVV4ylB7MCndypbsxy+UhQ1FChsJHptKPVV2JXVtDtr4Bl62HQGGYxrIlaUOb6HqZhdF32I58mlTiiTpw2QD3FOAICL4585iFEWGdukZP9QrVrlEKjfVkpfuxX/Jje74c2/N+HMoa3MYWym3dtHhmGW3YZVHci6cesidC3OPfnQGcpxBHqm8EqDn/u/xEgZMCOKkPrP/sz2mpfz81Y6kZSM1AagZSM5Cagf8/Z+DvAM5x7GdOngE4QkWQ9NeLp+7/7Iv1fxbgXF/5nxLknC79lauLf5Efgv8W4CzP3iIiAc4CQQlwgug15wDHJhU4AuA47c2UOUZoC6wRbn+Z9bEP2J/7lsPoHzheSNa9ngh7w7Ob4oUzBY7YdAqFQPwXTuN/5nTxR64uCwuVUOA8C3D2pEpBKHD06hK0ahdajROtLhejqYAsu49CRy1eVytuRxtF2W1yc5ZnbMCqqsB4oQTt713onitE/7wzCXEuOjFeLMB4qRDDxUK0zxeg+b0T7fMurOoKnBltlOQO0+xfYrTjKomwCOz8UIZAH4iNicwUEoHGPyCsYEdygy4Azmdsxx8SGX2JgfZN6oNTlDh7ZZ6I8l88KH5XjP5SKZmaALnmWjx57VR4BmmqmKTKG6I0vxtnRjNZmloy06owPBdA+zs/ht9XYL5Qg+VSA8WWQeo8cQbqhULoFkvj97k8L4JE/8BhQsAIERb9kKXwq4x379FRM0+VZ4iSvHYc5loyFWWY00sxpXkxXHLLpb/kRq40N/qzpUsTG24P2kseNBfdaC6Kr70SjOjTfWQZGyjKFioJYatYJtR2zHzoNmszD7k8/xEHC1+etdIIC5944n3WECM2WMvJoGURWn28JJqsPmZl8q4MVe2uXaWmZBJvfg85GQICeNCmF6G+WIDiOQeq5wVYKkCfXoRRwqdiDAq3XOKYNReLUT9fhOr5YlTPeVE950N7IYhd34IraxB/0TStVRuMdF4nNnZPQpztqGhPEuGvP0lLztHZ67mV6n8P4DxV3wi7mMilSgIcESj7GbtLHyIBTvRNxs4BTr4AOL4zgGPgQroOtToPk66MbHMTwaJpemr3mesTjWnvc3lWhGuLNqizAGhhn4z9xEnsR05khfsfOI5/n1zCzrb4LYcifyfxiN3lt5gPH9PdOo3PW0eW3UWG0YlWmUf689loJMAROTgC4HQ+ATizgy+TEGGykY9lrolodFoKv83M4E0GWveo889TnN+LxViNUR1Am1aK4rlilM+J6+FGe8GD/qIHQ5oXY7oXk0K8iu/F7BXJpbtYjO5CMdoLydnTSwjnw2FpJeAal3OWhIWvsSoyoOJfcSAbvkRY7k9Ia6hoGjpX4AiAI3Jglj4gNnqTgZYNakpHcOe2k22oRXfBh/J3HoyXAth0teRntuDO78bvGaYmOEFFuVDcdJNlb8BgCKJR+VFcLOfi70pR/D6A9vlqjJcayDf1UOWeprdpnamBI6mkayybwZfXT0FmEzatH12aE8XzVhQy70aHQqVEodJIgKNUmVCqM1GpbajV2fLaK9OdpF8oJO1filH8iw/17yvRPVePXd1NSe40jWXrDDRdZWbgdWmj2k8I5c1Xsjlre/Exy7P3CA9eo6txjUqfgKED5FtbyFBXYlSUY1T4MClK5HUwKwXAKSFDWUqG0pdcCp8EN+Z0H6a0MoyXyjBcKsdwsRz9BT82dQOFmb2U5U9SX7pMf8MJM32vszopwtJFiPYXT6vapQpHVJ5/LdvMokOvMd5xQkdgkUDeILmqSjKe85LxL17Mv/OS8bsSsi4EcCirKdY1UusYpb9qnfmeGzJnbWfmIw5j3z4DcATEeUZ9kwI4/xc+l/yzP/ek/v1//mfL1DlOnePUDKRmIDUDqRn4rzgDZwqc4/hTgHPlCbwRG7XfFsCRrTdPbFRiI5Nc0ray9GeunalwhAJHqG8EwJF1rEKBs/YNx6tnCpyZW0TGTuhpiT8FOP9AgSMAjs8RojWwxkT7y6w9AThi8yjClJP5IecbYfkq7VN/4uQM4Aj7xzUJcH7iauJ7rq4IgPMR65G3iIVfZqxfAJw5fg1wHGh0wkKVjyXTQ25WgILcOgqyGyiwN1NoacVhbiLHUItdU4FNXY5dW062rpwcQxl2XQlWjYcMVRFGRSG6NAfqS7moL+VjUpVg01eTb24lWDRJV+2mbDNaHH+L9ZnHsopXZDk8qTJeONugJ5LKl+34AyJjNxjo2KC+IkyJq5vsjBrUF9won3ehT/eQoSnFbgrgtNfiyW/BX9SDN7+dAmsTOfoarGJDk1aB8fkght8HMT1fTealemzpzbgtw9S6Y/TW7jHZ/RKLY2+xPf8RJyvfyravjbn7LE2+ymTfId31caq8/RRn1+PIrCJL75dP1DOVPiyqMixC6aAJyqfxOcYa8sx15GeI/7YRh6WJvIx6sk012AyVZOr8mNSl6BUeqcrJ0Ap7TwMuexfB4jDtVSuy+Wl++FVWJu+zE/2YKysiTPmPXEmIdqxf5JLQYfWPMuD6cPFTLkdFpfNbzAxclw04At548npwWBuxGvzolUXolcXolQLSeDCrfViltaiaPEsdDmsdDls9TnsDeZZass1V2AxBMjR+TEIBcqkM3SWhNqoh29iM0yaOd4q26g1GO68TCb3J8pRoqBLX7yeOEsl1nBDWnKf5N+ez/Lc5OE9v/OcWqv8IwHmPtejdpwDH0SQBjt6QR5pKzwWFFrU6F5OulCxTA4GiSbprdpntu8Py+CO2Zz7lMP4NVxM/JFvHRNbNosi8+oEr5wHmi6IR6HuuCCgqQsSXP2Yv8YDtpTvMju/R0TKB11OF3ebEbMxHK+yRF7LRpgsFjqgSr6Aop4v68jkGW3eZG7pJQrSmzX8olRWi1Wtu6DUG245orlzEVzRCjqURnaoUnaIUXXoJurQSjIoyMtUB7LoqcgxixmpxCHubtQFHZi35GdXkmirI0gewanyYlY7DqcIAACAASURBVCUY091JsJNWLBU7dkM1RVldBFwTtARXGG67yvzwm9JKdXn+c4RlT1RVizm7cgZwhEVGWtcSn8tMrdjYSwy2rVJTNizfc7nmKgmTVL93YUgrwaIJyFl32Bspzm+jtLgbd2EneTmNZJor0WnL0SjLUaeVo3y+HM3FCgxpNZgVjTjM3VS6p+iuX2WiV+RMrdNWIfJ1wvjye+XvatP7MKgcaFV2VCojCpUKhUqLUqhxztU36myE+karKUCndstzqVcEMClqyFA2YVG24zANUe6M0OwXTVFnAGf6EfsJAW8+YyP6SIYwz4zeoK99m9rgDEXObvKzWrCbazFpApjU5fL9YdUFsBuC5JiqyMuowWGpx2lpwGlposDaLF/zzQ3kGOuw62qwqCoxpQfQXSgjU1FDrqGDIuswwYII7cHLjLbdIDb8Divhx1KJI5qwhApHWqmWzwHOI2LDt2WIe0fFIsH8QfI0VWQ+7yXzOS+Zv/di+X0J2ZeECqeKIm0dVXlD9AQSTHeckgi9ydbk+xxEv/41wDnPuTlX3YgHK+dfn9mnhI0qZaH6r/hBLfU7Pf07lDoXqXORmoHUDKRmIDUD/91nQAAc8UT7DOCcyDDWZ8DIbxrgJC1KAuJIxZCwUS39RUIc8SrhzeKfkwBn+RmAE79PYuYWc2PHZwCn71cWKpGBI0KMM40lOGxNlJ4BnPH2l1gd+4C9uW85iH7P0b8CcESldBLeCOtHMr/jKcD5w68BzoQAOPt/B3A02nw0umx0BmGjKsRqLpHHmJtZhSOznoJMkXvTTEFmo7QMFWTW4bLVU5xVjyengQJbFbkZ5VgNHszaQvTqfNTKLJTpWbLlSq8swaSqwJXVS23pPH2Ne0z1voyAONuRjxBVxgLgJNu+hMJIPOn/isOVT9iO3ycy9gIDnWvUV41TWtxFjrUabXoxaqEcUbowaYuxiIpocxm5liAOWzW5GQKwVGBV+clM95ORFsB8MYjpQiWZl2qxpjdiV7bitoaoKY7SVX2Z8c4XiI++wdb8+xyvfsVW9CGL4VdlbXFf0xK1viGKsqux6YqxatxkqoRFwoNF7cOuDcoNtSOjEZetDW9uFz5nH+UFg7IhKFg0QlnhAJ78LlzZzeRZa7AayzGoi1Cn5aFXFGNSl2HVVuGyd1LpnqSjepXRjhOiodtszj3iyuo3XBG5RssisFqEjf7M6cqPXFv/A1fXvmY3/j4r028yP/Iig21b1JfPUJzTRZaxBovOj1HtQacsxKh2k6EtxWoISFWOgHSevC58BX2UFw0QcA9Q4R2irKgXr7MDV04z+dY67MZKzKpy9GmlGNLLMCn9mFVBCmydVLinaa/eZKTjBeZH3mYj8hlHK8KGdraEJefMAngOb8Trfxzg/EJSgSMgkLBQCQXO51KBs734HquxXwMcm6UUvTGPNLWeixLg5GDUlmA31uF3hemq2WG69zaJsYdsznzMQfwrriT+wFW5/oiwpF1N/FF+f0WCm++4uvodp+tifSMDyncS77CxcIup0S1am0K43QGstjxMxly06hwUl7LRKgoxaUqx6Cspzu2m3h9hqG2XudBNEpNvsRX9gKPlr1mevC/zj1qrVvEXT+DIaseoLefS8w5Uwh6l8GJWl2PTCzjQQKGtFU92p1SZ+Qt6CRb34y/spszZIcNyi7IayM8Q1r4yMlQeaevTpRWgviigailZhlqcmR34CwQs3GSi6yUWx++zPiPCc7+U9dXnAOfq2X1NtHAdLX8ulUex8RcZbF+m1j+At6CJPEsFJqUb9QWnVHOZ1V6s+jKyTAFyLFXkZ9WSY6vBYg5i0JahVor8LR9aRTm69AAGRRVmZS0WlbjHdFNRHKazdpnRrj0GWzbprlumLRinxjuBz9mF01YjM6bMhnx02gwUahVKtU6GGCtVGajUdjSaXLRaJ3pdMSZ9GZmGSmzGBrKMwvbURZ6xn2LbBBWuOC3B7TOAc5uV6fc4WP6SywsfkZh5i9nRlxnq3qOxJorX3Y/VUkWmuQKzwY9RU4ZZ68dqrJDvJYfItspuwZPXQamzh7LCfspdQqk2THnhECX5vRRnd1JgbSXbUEemqgLdhVKMaQKMN5Gr78ZjH6fOs0xv7QlT3a8TH7nPxsyHHCx+9QTgHC//gcPFb9iYeUQ89Drhrqt0Vi4RdA6Rr63CerEkuS6UYLtQSk6aADgVuDTVVOT001kWZ7z5kHj/a6yPv8de5KukelRYgM/hzbl9KqXASSlw/unK6P/uH45Tv39qg5iagdQMpGYgNQO/1Rn4O4Aj6pDPlwAkvz0Fzt+pcGSQahLinJ5BnCTASbYCSQXOsggP/objMwCRmHnlXwc4CqvMwREAJ9/WREn+MC2BVQTAWRl7n925b9ifFwBHVJn/+ESF82QT/O8CnG/OFDhvE5u4+TcAx4tGKIEkwMlCq89Gr8/HqHORofNiNwbIy6il0NJCsb0NT3Y7JbntMrDTX9hFRXEPVd5efIUtuHKrybH6sJiLMBry0Ghtsh5dkZ5F+sV80p8rwmaop8QRki01omp4bvA1uQEReTeyplm2AP0kq7uPJcD5mK2Fd4iMX2ega5X66jFKPR3kZlWhVxehUTrQqZ0YdU5MhgLMehdmbRFmjRuz2kOGsDKIzIl0H5mKABZFJVZFLTZVI1nqNnK0nXjso9S4Y3RWbUl7V3TktmwBOl77jLW5e8wOnzLYukpDYAyvowGrrgD182Z0F7MwpDkwK4qlGinPVCPPkzenG3/BENWeCRmeLEKXRZVze9WSDGCu8Y0TcPfjcbaQZwuSoXehSrehupSD6qKwNLmwaispzu6i2jtFV+0aU73XWZ16m5OVL6UKR1imJLxZ/plraz9wbeN7rqx/wWb0PtGxlxjt3qGlOiLbimz6SrSX3GhF9k56ATqlE7O4tuaghDJF4noWDlDpnaDOP0tjxRwt1fO010VpqpyipnyUoLefkoJ2nPZ6bIYARoWwfglLVQFpv8sjQx3EZe+hwj1LZ80O4d7XWJ35KAlwJMT5EyfL/xjgPIE4Z61vT2/e4l7wa/WNyHcSzXAnIiNJtCH9WwDHWorBmEu6WsclhRq1Oguj1oPNUEN54Tgd1dtM9bzK4ui7bEx/wF7sC4QS7EriO66KLCEBYcX7WKieVr6VCqfT9W+5tvktp1tfc7D6mK3FN1iOvsjY8AqNDf24inxkWrMxGLPQqLNQpGWjVYqZ9GE1VOPO76UhOM9Qxx6R0Zskpu+xFX3M4fKXLEzcY7D1iOrSCK6cXqzGGhRpLn73/1hRXHTIwHEB0ISSypPbib9gkCr3KPWlEzQHpmirnKE5GKahbJTa0iGCrm48uY04Miuw6Uswq13o0vNRXrDLTCZ9mhdTegXOTDFn8/Q3HTI3+DqJiQdsRz6R9dXJzCWRd/JnGVp7IpRyy8n69PjEdQY7l6gN9lHiqiff7sescaG5lIs+3YFRVSC/z9AWk6HzkCGOQVeCQeNFq/KgUXjQqXzSHpahrcKircWmayBL10yhtYdKd5iOmgQjHbsMt11msHmb/sZNGUpeUzpKSUEbzuwK7JYijEYbSo3IwNEj7VMqYZ/KkjXiep0Lk6EUW0YluSK8OFsAVKGiC+HJmqDcEaGuZIX2yl0GW06ZHXydlZmHHKx8wWb8MdHwbUb7jmlvTOD3jZCbW49S40KtLUar9aDXlpBh8JOVWYMzS7TMdeBz9VHhDVFbHqbeP01jcI7miiiNgQg1pZOy/l2EFxfYWrHrazBc8knbWYaiDpuqFadpkGBBlLbALiMtLzM3cJfVycfsL3zJkagWX/6eJwBnNpmBM9VzSnf1MpWFwzgNNdjTfXJlpfnITvORp/DjFO9TbRXB7B7aSiKE6neJ9NxkZfRddue+SAKcfwRvFs8+UKQUOCmQkwI5qRlIzUBqBlIzkJqB1Az8N5sBCXB+SCpwFn6RzUknv2GA86SB6omN6kyFIzaWMnw5qcI5lXkkZwBHtHH8DcBZnnmFyLMKnIynIcaKM4CT8QzAaQ6sMtb+Estj77MjAI5U4AiA8zcWFLGZFccm7VO/VuBcE61VQk2w8rcARzT1RHA7O9GrvWhUBag1uai0NjQ6uwQ4ZoNoIiqVShZPbiv+wj6q3EPUlo7Q6BfZGWHaqqforJuhu3GW5upRqgM9lJc2UeQKkp1bjNGShdJgQqG2kpaWLZUEmbpKXDk9VHrERn+bcPeLLIffYX/hMw4XvpYbcynLT/yB4+UvOVwRAOdt5sZP6e9apq4mRImnTTZlaTUOVMpsNOpsdLpsDIYcTIZ8MgyFWAzF2E0l5JjLyc+opMBSi8vaRJG9HXdWD97sAUpyQpTmjlPtnqetYo3B5gMm+0SN72025u+zv/w+i5OvMNp9mdbqaYLuDgqzysnQ2lFf1KJPt2BW5WPTeqTSwZ3dRrlzkGpPmCb/PJ01CfqbNhlquUyobZ/RjiO5Ee1rXqazIUJj1Qjl3haceWVkmHMx6vJk8K36ooMMdSkOS4NsqBHtNEMtu8RGXmU3/gEHi59LtZJoJDtd+RNXRR7G6pfsJz5gaeo1xvv26WiIUekbxpXbTIbWJ3NYhPLGoC6UkCvb4qcwt4HSwm4qSkZpCM7RUbtCb/MWA21bDHduM9JzmcHONXpbF+lsjNBYOUbA001Rbj3ZZj+ZWrfcqCues2NWecnLbMKbP0xd2RKDrdeIjr7LdkzY0L7nYOmHJMAREEc0HIk5lrP79FWAmfOVtFI+C3B+4XRBrH8EcD5ie+kRq7F7jA0e0lgzg8vZhO1XAEeFSmXHoC3GaqjCVzBCW+UGE103iQ6/xfLEQ7ZEE1P0M/Zin7Mf+4KD2JccxL9gP/4Ze/FP2Vv4WFba7y1+yM7iI1Yid4iGrxEObdPVNkUw0EyuoxBDRgY6vQW12oYyPRuduogMfTlZ5jq8zn6aKmOEuvaZH3+F5Zl7bMUec7jyBYnptxjvO6W9fplg6SiF+S1kmEpQKnMwGzzkWCspymum3NVLTekYLcEI3TWL9DUsM9QqlFrrDLctM9iySH9TjI6aSerLRcNZG8V51eRaSjHrHCgvZaK+lIMuTVidRENSCwFXmI6qTcY6XyQaepONmfclIDtJ/JErS8I6I+x6AsB9y/HKp+yvPCAePmWwM05NRSfeoiry7CWYtHmoL9nQKbIwqHMxa/PJ1BdiNbplA1tWZhlZlgDZ1gpybNXkZzVQmNNCcb5oN+vG6+ij1DFAhXuc1so4gy1bTPYdM9l3wlTfFab7Txnt3KWncZGmqnGCvk6KCyvIsjvRGvSotUaUajMKGWychU5bgMnoxWapwJnbQomrn8rSSWp8olVvgcbyFdoqtumtP2S47RqTva8QH7/HxvxjDle/YCP2HnNjNxno2qGhNkKJtxdbdpA0TQ5KrRONrgiDoQRrpvj3mygp6qHSN0J9xTRtdXF6W1bpb9tgsH2boY5dBtsu09u8Rmf9koSk1b4xfIW9FNqFxaqVQmsnLmsfJTkT1Jcs01N7xETnLTmjopFqb+FLDhPfciSVUN8jLFXbkQ9ZmXyLWOgmodZtmsun8eW0UWiupsBQgUMbIF/tx6WvxGOqwWepp65gkO5AjPHmfaJ9r7A69oA9AXCehTfPqm9SAOe/2Qe13+oTwNRxPX3AkToXqXORmoHUDKRmIDUD/3dm4MqCADhxEVT7M8I+JeBNciXVN78lBc75ZlJCnDNAcg5KhGroKcARYbJ/5qrcXIqQ1Z/+OQBn4QdEgPGvII7c9CYBjmjOedZC9e8BnGIZYuxFrSqQFeIKtUVCHIMhn0yTm2xLgOK8ZoLufhr9oqI7Qk9DnMHWBCOda4z1bBDu32Jq6DKh3gS9HbO0NoeorGqj2BvAmpeHJsOMUmchXZHFxedzMWr85Fvb8DnHaPInCLVdIT7yJjsipFfYNha/55qwBS3/kZOVrzha+YithbeYG79Kf1eCupphSjwtZGeVo1HnoVDaZVCpUPvo9XZMxjwsJhf2DC/5tgBFOXWUOgSAEpaMIaq9Y9SVTlNfGqHBF6PBtyQ3rgLeTPScEgndZGnyddbn32J74V3mRq7R17xEVWk/xXk15GQWYVSbUaepMKosWHROckw+irMbCbj6qfdN01G1xEDTJmNdh8wMXGNu6AbRkKgFfpXoyA1mQydMDe8w3LNIS/0w5aV15OW6sFkKMOkcaNLyZJiw3RjEaWuStguhRJjsPWVt5h1ZG3y4+LWEN8I+dSxqjhMfsxW7nzze1mWq/cN4CtrItVZiUBWjSXNKeJOhL8KW4aEwt5ay4i5q/eO01sbobdlgtPuIyYHrzIRekJa1aFi8XmF65IDJ4csMdi3RUjtBsKSLorxqcgQQ0Iqso0yMqkKZ6+OwdFBeOEVHzR7hvjusTIv2rq/YWxDNOeJ9ISDA2Qz/bwCcc3gjAI64Wf9agfMRl5cesRa7x/jgIU21IqOkGbvNh8GUi0Kt5aJCiUplRa9xkakPyur25ooEox3XmR24TXzkLiuT77Ix8x6bs4/ZnH2frdkP2JJfv8fG3EPWZ++zNvsOK7P3WJq+w+zIKSO9m3S1zlFT2YvbXYHFnoPGYECtzUClFAAnB4NGVLJXkJvZhM81TGv1IqM9R8QmXmVl9m224+9LgLMWeZe50ZsMd+/RWh8jUDpAfm4NGWY3edmVuAtaCXoHaAxO0VW3SKhNANBDpvuPiQyfEBu9SnTkmEjogLnhPSZ61uhvidBaPUKVrwO3oxJ7RiGqdBPqNLucCRGqbdfXSdVfQ9ki/Y2HTPeJevOHSbvO0vdckXlASYBzRag/Vj/hQACcyVMGu2JUV7TjKaog1+7GqM1GdSkTrcKKQZ2FWZeLxViIPcNDrrUcR3YVrvwGPIXJTBy/d0ACj1p/mPrAFA2BWZqCEdqrFxlo2WKi54hI6AVioy+zKM7X1B3iYy8xNbjPcPcyHU1hKvytFDhLMJkFOBPWqQzS0jJQKrIx6IqwZJSTl11PaXEftcFpOhsFqNxmoPVAZv8IaCXAzezQbWJjd1mdeSCtU0drX7IRe8D0yA2629apqghTVNxGhq2UC2orCm0uGoMLo6mE7KwaPK5OKstHaamdp7d1hZHeHaZDV5gdvc7c6IvMj71MZPQGM8PXmBw8Zqxvj77WFVqr56j2jVDhGSFYNI6/MExVsYDKWww1n8og44XRpLVNApylbzgUACchAM637MY+YXPuIatTbzDdd0Jv3RK1nmHK89rx2pooNtdRaKjGm9lAub2FqrwOWr0TDFSvMNV+zMLAa6xPPGRv7ssneTbnFeFPMm9SACcFcFJPW1MzkJqB1AykZiA1A6kZ+G85A6JA5n8cx388gzdnAEfm4fwpqST5DVmongCc+E+cPFnneTNJ29c5xJEZEQLgiGYtAXCE8uUZC9X/EQXOOcB5FuKcqxYkvDkHOD9zTQQZL/7IqQgxftJCdR5inMzAOQc4KlUBSlUO6SoLSo0dg6EAa4aPfHsNZa4eGvxT9DaIQN1tJvsOiISuEp94gaWpG6zM3mQtcouF6VNmxncYG07Q2TFOsLKFfJcXg9WOxpCFQpnDxefz0Kt8ZJkaKcrqp9oToa9hj8jAq2zNPGZv/jNOFr7nheVfuLbyA1ckwPmY7YW3iIxfZeBXAMcvAY4yXQSYiqwLO3pdNhnGAglvcq1BinMbKXf1UFs6SrN/hvbKGN21K/Q1bNLXsEN/wz4DDUeMtJ0y2fsi86FXWAjfZnnmDdYid1mP3iXcf0BL9Sye/GZyMn1k6vPRKo2o0zWYNHbspmIc1gp8BaJdKExXrWiP2mW67yrxsVdYnXmDjbl7bM3f53LsIduxt9mM3WE9fpP49BHDfTEaanvxuIPk55ZK+5k2PV8G35rUJVh1QZl1UuebZqh1l4Wx26zPPGQ//rnMvjld/YHDxBfsLDxmdfaOVN+0VM/gKRD2rEosBhF+W4AmzYFJI1RVXvJsfkpdrdT6R+hsiDHUucXkwBWi47dYnnmT1chdNmJ32Vq8y8bC66zFXmUl+jKRiUOGupek2qrc3UphbhCbuRBteqasHzerS7Fqa3HZB6krW2Go9QbRkfdYmf6Yy7GvOV75kSsrP0mAcyyapERY9YK4F5yvf12B828BnL2lM4ATPwc4sxQVNGO3JwFOuiYJcEQuik7jwKwvxe3ooiEosmj2CfddZ3boZWJjr7EUvsNS+A0S4TdZDt8lEX5D/mwxfJuFiVeJjd8kMvoi08NXGexcp61+lkp/H57iOnJyPBhMVpQaYePJRKXIQp3uwKwRwcm1OK0dBIon6KgVwbynLIbvsDb3LjtxYTX7mu34B1KRExl7mVDPLs01EXzuXvJz6ihxdUnQ0Vw1R2/TGuPdB8yHbpAIv8bq9G02InfYir3BVvR1NqOvshm9xdLUKTPD2wx3xWmvHyVQ0kSe3YNaIQCODc2lfNkwZtFUUWTvldXqbZVbjHW8yOKYUMV9wfHid5ws/pBsCBSBtcvfcrL6KYerDyXAGeiKUx3swO2qJMfuwaDJQXXJglZhx6DOwazLx2YqlvDGmV2N29FCubuPqvIxGivnaK2N09W4TF/LBgOtWwy2Xma4bZfRzkOm+k6ZDwlwI5rY7nE5+i4HS++zs/AOq3O3iIVPGB9cobVhmFJvFRZrFgajaJ2ycOlipoRnRr2bLGslLmcbVf5xOpuXmRg4YXr4BpGR14iP3WVx4j7Lk++yOv2Q9blHXI5/yH7iU07Wv2I9+i6TQ9fpaFwm4BvB6WzClOnleWWmBDg6QxEZGWU48hrxlw7SXDvHQOc64aFDopMvsha7w0b8Lpvxt9lauM9W/B02YvdYm3+D5dlXmRu9xmjvLj3Ny3TULtFSuSTtpS2BNfrrhfrmJtGht0hMCLD4UVKBs/iNBDdHS98h1sHCF+zFP2In9h4Lo68w1r5LR8U8de5Rgo4BSu1deDLaKM/qptoxSFPxqFTfjDTsMNv1AksivDr8WGbgJO2MouHurCb83DKVAjj/LT+wpZ5s/t95spk6z6nznJqB1AykZiA1A7/lGUgCHLlhE/DmbP2nAjgC5iTrus9VOM+qb0Q1urCG/ArgxN4hMf0Kc6PH9DTHCXr7yDqzUKmVuSgUFkSQ8bmFSthQ/qGF6hzgPGujegJwfiKpwPkTp3EBcH4+AzgiePUrWSO+FrlHNPzSWQvVLMUFHeg0XlTKAhTKXNKUVlSabEzGYrKsFRTmtlBZMkp7zSKjHftMD5wSHXmJxOSrrM3dYXP+Ltvxt9hZfIf16GsszV0nMrXP0ECc+oY+XN4A5qw89KY8VOo8Ll3IR6csxWaow2npIuiaortmm5nem6xPPWR37hOO49/yQuJnrota7JWvOF79mMsLb0uA0y/yNqqGKHG3kGP3o1U7UCmy0aiy0WmT9il7ZgmOrCrc+S0E3QM0+qfpqkkw2LTFSNu+bGqZ6nmBqZ4bTPW8zHTPLeYGbxMfvUNi6i4rs2KJzdXrLM3cYrhrkzr/GE57DRaDG6MmB43SiEapJ9OQR561jOK8BqpKhmmvjhFq35XtTwtjt1ibvcdO/AF7C++xv/g+h0sfcrD8iP2V++yt3mVt4QZTYxt0d4SprGjFXVxNtq0UncqJNt2JXlGESeUlx1xD0B2SjTwzgy+yNHFX1ooLm96Vle/ZX/yYreh9afca7tygzj+CM7sKu1koZIrRpjnQpTux6D3kWf248+uo8vXTXjdHqGuT6aETCSaEsmQr9oDtxQfsJB6wt/KA3ZV32Fm+x+XlN0hEXmRyeJve1gh1wX5Ki+rItbnRqzLQK3MwKIsxKUSdewfB4nm66o4I971JfOw9NiOfcyRUVcs/cpIQLWN/D3CeAFNJ93/dQPWPAM7xWQbOOcBZj99jYuiQ5rpZigtbyLKXYTTnIgDOBaUChcqERpODUV+MM6eegHeIluoY3Y2r9LduMtyxy2j3PmPdB1I9NdZ9xFjXAaNd+4x07RLqvMxg+yb9bWt0Ny3RWDVJsLSX4oIGcrPLycwsQKPLRKEySvWNRpknG79E41iBrY3SvCHqfVH6G3eYGXiJ5am3ZDD1bvxTjpeF1exTNiLvkZh+k9nQiwx2XKa5ep5K3yiNlbN01C3R37rFeI9Q2bzM8uSbbEYEGLzP7uID9hNi3Wcv8TZ7iXtsRF/hf7H3nu1t3Vfe7vkEMxNLIojK3sEGsPfee++9k+gsIsVOsClusmXHcixLsqxerWa5yUmecp5vdZ9r/QFSlEsmOZPMk5ngxbogUZYMbiyAe9/7V+xju4z1naWraYrywhYsibkYdeEKsCiA85aVCG0xKVEt5KWMKktRX/0B9v7fs2F7oOrSJVh8X4LbXaKg8gGcrYXr2MYO6W6xU1okNqZS4mOyMOnNBJ+OxqCJJ1SfSERICglRuVgSKlSGUlF2H9VFkzRXuekSWNOyyVD7LmPdh0x0/5bJnneY6nmX2b4LOAY/UiBtceIy52a/YNN5m935B2y7b3DO/inzM+8yNbRGW+MohXnVxMUlEh6egEEfy5lT0WiDEgkzZRMfU6Y+6yqLJ+hsWmZq4G1sIx/hGr/M4uSXnJ2+zcrMHc7N3WHVfpcN13225x+yu/SYpdkrjPUc0lTtpiCnn5TkGsIiMvmNJhKN3ozRlEZ4eB4pibUU5fbRWG2jv32VycF9XFMfcM51mTX3VdY919nw3FCz7r7OuvsLVp1XmJ/+mLnhdxjt3mWwbZvexi06azbprvEy3PIuM92f4Br6gqXxW6qFatPxFdvOZwrc7Eilu+s5206pfr/PpuM2C+OfMt39NgMNa7SXe2gqsFGTOUmFZZTazEmacudoL3LSX3WOiaYD7N0XWRi6wsr4HTZnxcJ6FCr+/evGKQVx/CdWR0An0EIVADqBu7CBHQjsQGAHAjsQ2IHADvxT7IACOCr7wvmKXeePr21U9v8qCpwjIU3YAwAAIABJREFUgOPLwpFWHJ/65pVS3+zaxBL2Laq1Zv4JOxJifAxwdmivs1GY1UlsRCFGyZ4JTiAoKEpBnPAQUUfUqByRuoIFBn+WgfO6RvzYRvVnAc43HHgE4Dxm03VLqUrmRt9hsHuN2vJJ0lObfgJwYtAZzERG5JAYV0lmajtVhVN01q0w0f029sGLuMc+YWn6MufmrrFmv86G8yZb7tusCsSxf4R77m1Gh5ZpaBgiK7eCaLOFMLmw1acQdCpZAZwoYzlJEU3kpwzTXLLEeOvbLIxcYVVCb+eect71PefdLzmY/4rdhbusOS4zO7RPV7OT8pJustPrSIgtVOHF+mAzRp2ELovtK43kuGKyUhsoyeqlrniazuolhlu8THa+w2zvBRwDv8M9/Htcw5dxD13BPXSV+bFrLE1dU41Ty7YrLEx/gnNcMosOaa93UZTdrYBbqF7ateLRB4dj1IcTG2HFYi4lL62F2uJxuuvPKngjioGlqc/U8dn23GbHcxev5x678/fZXbiDd+lLvMtXWZ3/CNvUNgO9Dhrq+yjMbyQlsYgQvQV9ULIvcFhjJcqUT05qO/Ulcwy27jDb/yFLU9fZkeYuzxPW7DdZnP4U2/Bv6WpwUZLToexNkSYroQKDToslK5X48FzSzBUUZjRRXzqiGoQm+704Rt9jYfoS5+xfsOG6zYbnFpvzN9hcuMHm4nU2F6+xuXiFs46LzI55GewS68oIJXkNpJqzCTUIwInFJAHJp7OJMdaSnTxKbeEKvQ3vMdXzexYmbrPleo7X/RKvSyyUJwGOLwfnbwVw6iunybA2EBf3GuD8JjhI1Ylr9TGYjMnERuVjSawhL72D4uw+yvKGqCwYpbpIbDwTVBdO+qZggqqCMfVnFfnDyupSktNLYVYXmZYGUszlxEXlEhFmxWRKUPad4OBwBRVNWouqaE+KrCEnsZuyjEmaSxYZbNxlru8jFickLPg2m3YJTxYQ95hV2x0FcVzjl5jqe4eBti06G87S17LBULuX8e7fMjfwIZ5xUXVdZ91xW8FZtWeLt9lZvMn24pdKIXNOApan32Z2ZJOBTge15V1kpBYSaozEpI3FcCYR3VsWwjQFJIY3kp04SEWWU8GD2d5PWJu7x5b9CV7H16jmPT/A8S7cZ2v+OrbRQ7qaHZQUtpBmKSUuOguj2BpPxWAITiDMkExUqJXkuBKyLU2U5vZTVzpLe91ZBtq8jPe8y3T/B9iGPsYlaqex3+MZ+4z5scssjF9haeIay1PXWZ25oRQmO6777M9LiO8d1p2iZPkQ29gOXS3TlBQ2YE6wEBWZjMlgRnM6Dp3Gp7aKjy4nPaWVquIpupvXmB26gGv89yxOXWdl9j5rtkesOx6x6XzElusR255H7Mw/wrv4iMWZzxnrOaC52k1hTj+pfoDzlgAcnRmDMY2w0FySzQJw+mmqcTDQucH00Hk8Mx+z5rnGhkBbz002PQJw77A1f0c1mG0t3GLVeY3F2c9wTQjI+ZDpvguMd77HeMcHqtbdNXyNpYnbnJu+x/rcI9U6tSMAx/lcNfWptj7nE7bluTvvsTL9BZ6RS8z1Cpw+YLhxh77qdbrKV+itXGOgZoPhui0mW84reDM/eJmV8ZusTT1ga+6ZL5dKQZwAwPlHvhMWeG6BO7WBHQjsQGAHAjsQ2IHADvxn7YAP4KgTxB/Z+28AcPYE4Kgq9FeI+sbrBzi7kh0hAMdzj5UTAKdNAZwOYsMLFMDRagTgRBKkiUIBnOgashJ7UQCn8c0Q423VQvVLGTjfsWcXBY5PhXNg/55DGedLDjzSniMA5yaLM5eYG32bwe5VFfT6GuBY0AQnciY4Fr0xmeioAlIT68nL6KehzMVAqxf70CcsTFxheeY6a7ZbbDrvse16gNfzSGXVbM/fZt1zlRXP75iZ2KGtdZr8wnrik7KIjE5HmmA0p1MwarKJNJSSEFpHTmI/jYUeRpoP8Qx9xrnxW2zPfMV5x3c+gOP5ir2Fe6wfAZwmB2VFXWSl1WKOLVSgwxicTIghlfAQC6K+SU+qoTirh/qSabpqzzLavo994GMWRqWGVy4Gb7E+JxdD91mffcD67COlNNh03mfLc5d195csqOP0DiM9mzRUTpNjbVENOgZNEvrgWHTaCEKM0STEZpGZWkNpbg8t1Q5GOrYRq83y7FU2nDdUVsjB2UccLD32zeJXHJx9wMG5uxys3WR75TMWHG8zNXaOnq4Zqiq6SbdWEmZKQ6dJUtXi2tNmQnXpWM31lOeN0Vm3zHjX23jGPmdLXXA+VODJM/ER0wO7tNbMkJ/eSFRIKqE6MyaNGcPpBMK1FpKjC1VbWGVBDx11c4x1r+OeeI/luUusOa+pi0vvwkN2Fu+zs3iX7cU77CzdYmf5FjsrN1mb/wzPzDtMDa/R2z5HdVk7GZY8wk3hmLRRPiDwGyvh2lKscT2UZDlpqfAy3HYR58h1NhxfqdyOHedLvA4fxPHZp15bpwTi+D6M/jIFzrbroYJOa67riAJHrCv1VdNkpDUQH59PaEQiZwwGfhN8htM6A8H6CPTGOEJDU4gKz0TCnM0xJcoumBJfSWpCNanx1aTGVZMSW0NKbDXJsZUkx1aQKNX0USXERxYRG55PZEgmoQYLeq0ZbXAswRrJegonWBOJQZtEmD5TWeDS41ooSRuhsdBNb80WE+3v4hr8PStTN1mbve+DJK6XSu2y5XzMhuMe5+bEEvc5ztGPmRu6gG34Io4RsXcJeLjGublbbLoe4lWQ8zF7S4/ZX37E3vIDdpfv4l2+zca8D7q5pg8YH1iiqXaAnIxSIkJjCNXHYQwSgJOi6uDjQ+tIj+ulOH2WjspNprs/YnXmDpu2r9ixv/Bbar5jV2XgSIjxdRXg3NnsoLigFWtqGbHR2Ri1SWhOxWEITiTMaCEmPIO0pCqKsnuoK5uhs2GZ4Y59Zgc/wjNxVUGU5dmbrNnusm6XuecLjLY/UOHREiC9ZX/IjvMxe27JfXrGruc+W67rrDov4Zo6pK/dTkVJK8lJmcRGWwk1JRN8xoxeYyHClEd8dCUZKe1UF8/S17KFfURapq6yMiuWJLEjvVB76Z1/zu6CKIye4F14jHf+IYtTlxnr9gEcATSW5BrCIzI5pYlAo01Ar7cSaswhKb6GotwBmmtdDHVtMzvyDguzl9iY/xIBNQrazN9jZ/4+3qWH7C0/Yn/lMd6l+2wu3GHNfZMV23UWp6/hHruCa+QKC+NfKtvU2uxDNiRQ2/FE7YjY2gTceJ1fq0cBOqLCEYizYbvL6uxNlqevqdY619An2HovMtN5gdnOC8x1XcDW9QGuvk9YHL7CuYlbrE8/YHP2K7ZtL/yWRlHhBADOf9ZJUeD/c/SZH3gM7EJgBwI7ENiBwA4EduAfcQcUwFEZMc5fBzhv3on/e7yQ37Lv8I/kOxz9Wj2+/v8dP4/j/JujLJw3c3AE4kj2jahvfg5wxN5yGdf4+0wN7NBWa6Mw802Ac+YI4JiySJSK4MReakWB8wsAR6wnx+obf5OPep5+gHNg/44jgHPgkirkp+wvPGLTdYPFmU+QYNrB7nPUlE+cUOAIwEnijDQ6GVOJlcyI5BaKskdpqTrLaOc7zI9fY2X2Nmu2++pOtdR+73qkXvlrDhZeqhak7cXbbCxexTHzW7o6nBSVtJKYkkd0TBYhpnSCT6cqgBOhLyY+pJrshB7qC1wMN+7jHviUlbEbbE8/5rz9W86r5/4V+wv32HB+7lPgCMAp7CTLWoc5ppBQnRWTNpUwg5XIkHQSovKUTaMyf4T26nmGW3ex9X3E2Ykv2ZiTC8HHeF3PVMD0nvsl0rCzq9qRpMXpKXtnH7E5f4uFmU+YHjqgr/0s1SWjpCfXE6JLR3smAa0mBp02kpCQWBLjc8lOa6C8YJD2Wg9jXft4xj/l3NyXSFOR1C3vLz5mb+ERewK63I/Ym7/P3tId9lZusnX2MouO95iZ2GSgz0lNdT8ZadWEhQjASST4TDxBp2IxBCeTFFtBYWYvjWJna/XiGP6EdYccm3sszVzGOfoeYz3rNFaMkW2pJNwQh0kTjTEoFuPpeCJ1VlJjSyhIa6K2eJCeBheTvdssTH2ocnnWHNfZct1l2/OQLc89Nt132HCLEucWmws32Vq6yar7Mp7Z95gZ3WKw20FdZSdZaXmEh4Ri1IajPxNH8L8lExJUQFJUG/nWaWqLVulregfb4Oes2UUlIOoBuVD85o0WKvWZ4FeT+T64/hKA84Q3Ac6nxwAn8w2AY+Tfgs9wSqdDYwhBZ4xAb4jGYIjDqE/ApE/EZEgm1JBCqCGVEH2qCnwWBY3sl1jaBBQagpPQB5nRnokn+HQsQaeiOXMqitNvRXDmVARBZyJU+5FeG0eYIY2YkAKSIqrJT+mlLs9Gd+U6Y82/xdbzMUuj11ibvsfm3GN27JIzI+9rUSeJSkmggg/krNpusTx3neXZL1meucHZ6ZucmxXQ8Ug1EO14nuOdf4pXoMPiY3YWfQBue+ku655rLNk/VtbGiaFlmuuGyMsqIyo8hjD53jVmtL9JxnQmhzhTNdaYLgotU7SVrzPZ9SEr07fZtMnzE4DznbJQCcARCKEAzsh5OpocFOW3YUkpJzYqB4M2Gc2pePUYbkojNiKbzFTJWxqhrXZB2aVmBj5iYfK62geBetKk5PU8Z9fzQo2yBnq+Ztf9gl3Xc3blPeuSavcXHM5/zZ6007lvsu66zPz0Owx2uqkq7SA1OZu4mAzCTBa0Z5IwaKT9q4CEqCoyUjqoKbHT3+rFOfapyrpZsz1k2yW2vu/Zm/+W/cVv2F/8mr1FgThf4Z1/oNSGEz0HtNXOU5o/qLKVIiOzOK2JVO1gep2FEEM25phq8jP7aKhw0Ne6wUT/b3FOfMw5hwQif8ma4ybr9jvq/SrvMXmt9pafsHv2K7xLj9lZeszW/EPWnfc5N3eXlZm7rKlGNAE3T322qWNwI/DmCOAIxHmuwI4Em/tAzmPVVCfNaauzt1mevMHS2BcsjX7B4sg1FoevsTz2JauTd9mcfcS27Snbtufs2AWsSh6Vz0b1OsBYYI7/5+IR2AlYqP4pJNP/iCeRgef0+hw1cCwCxyKwA4EdCOxAYAf+M3bAB3AkDPOXAI5kyxxZgk48/u2f2N8e4OxKjs//VYDzrVLgCMA59I8AnH3PU/YWHrIpLT2q9vg8g90r1JSPk5ba6LNQaQXgJHMmOAGDyUpcjFgOOijJmaKtZp3x7g9ZnLzF6txDNuxP2JILBpfPCiOtWHvu71WGx/b8fTYWruOcfo+ejnlKSjtJthQRE5uLhIkeAZwoQzEJodXkJPbQWOhipGkfz+CnrIx+yfbUIw5t33Le+TWHnq84WPQBnDmxUB0DnFrM0YWEaK0YNaI0sRJhTCchsoDc1FZqCqforj2ncm5cg5dZm37Atl2ggVQif8u++3vVtrLrlDpraUWSxqtnKvNCrA6e6Y+YHNilu8VDRfEg1uQa1V4kjULSLKTVRWIyxREfm0tGaj3FOf0KrPQ1bTPd+yHOoc/wjF5RuT5y8bQ88SXLcjd97EvOTlzj7NRllmYv4Zn+gOmRXQZ7z9LWOkNpSRepKeWYDBYVwKoJiiXoTDQGnRlzTDG5ae1UF03R27jO7MCHrNpus2q/xfzkJ8wOHTLUcZa60n4yk0sI1Udi1ERgCoomJCieKL2V1Ohi8lIaqMzto7VyjqGWVWYHfotz+CLukU+ZH73Kwth1Bevmx6/iGf8cz8RlPJOX8Ux9hmP8Q6YG9xjuWaareZqK4mbSUjIJM+kxakPQB0WjfUvsWnkkRTWTaxmnuuCsqhSf7f895+busel4wrZDLj59dkAfzPXnbvxHAI7zC5ZsRwBnyqfAScgnNDKRM0Yj/6Y9zSl9MEEGPVqTCa0xFJ0xDL0xHL0hAoMhEoM+Cr2MLhq9NhpdcAy6YHmMRqd+H4VWLtw14WiCwtAEyaOMNE7J34vFaEwgPNRCfGQhlrgacpI6qMqeoqN8mbGmQ2xdH6nMkdXJ22wLvLGJbcWXMSN13bKfe+5vFLyQC3IJEl63yYW42GPusTR5l7OT91meesjK1GNWpmUesjx9j7MzYr+6xdLsDZZmr+OZ+hT72HtMDe0w0DVPXUUvWWnFRIZFEWqQqu8EdG8lERKUQ1xINWmxXRSlTdFWsc6UAJyZW6pOXQCTD+BIftFTBXA2PNeZVQDHSVF+O5aUCmKi8jBoU9GcNqvHcFMGsZG5ZFmaVe6NZN6Mdb2Dffgyy9OS2fKSbec37Ljk3/WFW++55fNEjoP/WKg9ecm+6yUH7m8475H3r4C728pG5Zl6l4F2D5XFnaQkZhMXlUGY0Yr2tFgQ01XjmDmqhsyUbmpLnAy07uMau6xyb9ZEXSQQ3P3KX3H/Dbvz0pb2TKkKdxcesjx7hZkBySM6S03pODnpTardLCg4Cq3WjEFnJUSfTWxEBenJYscbp77UTWfdGkOq1U6a536HffBTnIOf4x4WZYwvd0feD6vy2joesOZ8yJrjIWv2hwhYElvXhv0rH7xxvGmZEuWNtLDJvFbhvFC2qm2nZOI8Y8v5VL3XRLmzNvOQ1akHrE7dV7M2eZ/16Yd+1c0zBei8Ct6IKs5vZ/TbqI6VOEfKuADACYCbo10IPAZ2IbADgR0I7EBgBwI78E+xA68BjusXFDiSg2MXO8W3b4Ccf1SAI9Bmzz//9wGOz0L1UwXOXwZwrGi0yQRpJZQznfjYSjIsXZTmzdBRu8Vkz8ecnb6HXPBs2kWu/zU7zm98QbQCQBzfs+N6xpbnvrIMOKffp7dzgbKyLlKtxb4sEgE4pySYN4toYzGJ4dXkJfXQVORktHkPz+Alzh0DnG+OAc7h4n02XVewDR/Q/QbAKSIk2KoalgTkSFhvfEQBuZZ26opm6WvYZKrrAgsjX7A599TXDCYXH+rO8Q8q00Oet1fUVU65C/+U3cVHbLhv4J78kLG+LaUuKCvsw5JU5QM4mniCFcCJwmiKVZYRqUTOz+iiqnCatqqzDDbvM9b+LhOdF5jq+oCZ7ovMdImF4SIzHReZ7rrAtAS1qirhbaXyaWuyU1czQn5Oi6qLNmhT0JxJIFgULaKg0SWSEFVEZkozpTmjdNQuM9X7PiuzN5TaxzV2kck+L73NbqqKuklLLMCkDcOgCcOkiSJEE0+UwUJyVAFZiTUUZ3RQVzRGR42b4dZNxjr2GO94m4mO95no+FDVSE90XWCi633Gu99lvPsdxnreZrhrl/62FQXSmmpGKM6tI8WcTojBoJq59EGx6E4lE6bNJymqiZzUUSrzFuiq22O69xOWZ+6wYX/MtsOnONlTINfXePOXKXBe8bpG/AVel1zIP/BZqBTAucRo7xb1VVMoBY4f4AQZjfxGe4bTei1BBgNak6hwfPDGIPBGH4leF6mUVQJitJootEHRBJ+RIFyZGHSivNJEo5UJkj+P8n0tOA59sKh4kgkzWYkMyyQ+uhCruY48SxflWWO0li4w3LCLvfsjFofEtnKTrdmH7DqesecUdYu0PMlufse+W8K7X6pwaq9H6qJFPfeAVdtdlqclpPYG86M38IzcwD18A+fQlzgGr2Eb+JzZgd8zO3CJ2UEBeh8zNfAuo72SWbRMZ9MsFcVtpKfmExEaQag+GqMm3gdwNNnEh1arZrji9GnaK9eZkma4nwAcgSpKHeS5p0J4Z4bP097kBziplcRG52PQWdCcTsQo+T8hmcRG5pGT1kZt6Sx9zVtM9X2IZ+wLVue+Ysf1PV7/7Lq+R2bPJWHJvvHthPwskOPzLQeu7zjvkWMkipQ7rNklHP5d+ls9VBRJW1c2MRFSZW5Fe0qCwDOINBVjjqolK6WHuhIXg20HuMc/5+z0HdbnxJIk/99X/uchUPoFu24BOE/YW5BMous4hj9gqGOT5mqpdu/CHFdMsDYenTYZgzYNky6bqJASEqPrSTd3kp82TEXeLI1lC3TVbtBb71VNd8NN7zDWJp8LH6l8G8fQZVxjV5mfvM7SjCit7iigs+54qPKQtpSi5qlS15zMvPFBm69VNpHkE3lFuaU+k8WG97X6fJbP6C3nC7YcL9iyP/eN7blS2vjUNi/w2uXfeMmuUsP5lDe+fLqjIOPXj8c/g0WJc/w56vs8Ve/j4/fySTvkt+zav2VP5g04e+JumR8IHTi/R8an+jnx54ETw3+KE8Pj/Qq83oHXO7ADgR0I7EBgBwI78A+3AwrgyAn63i8CnCOL0r9z0vcffmH/NgocaaNS4Mavvvm/q8D5eQvVgesl+55nysLz2kL1SwocK8HaFB/AEStSXBVZ1h7K8+forN9muu8TVmYfqLwIuSDYdsqFwjfsOL5lx/4tO7Zv2XY8Zct1TwEc1/T79HUuUFHejTWthPi4PMIE4LyVTIgmkxhjkd9a0kNzsZOx5j3mBeCMXWdr6iEHcy85dL7g0POEw6UHbLqv+gGO02ehstSSEFWIKdiK/kwKpmCLqsiOCy8gz9JBQ4mNwaYd5no/4uz4TXWhc+h5xYH7FQdyceiUCngfLPTaJEz3BV6xhC08ZN11XQUYj3Rv0Fo/R0l+D6mJlccAR6v1KXAMpliiozJINpeTZW2hJGeQ2uI5WiuX6arZUNNds0lPzZZvqrfokalZp7t2ha66JdprXTRWzVBTNkJpUTcZ1lriogpU9bTmVIKybOk0CcrSEx9RTFpCIwVpA7RWLDLR9Q7L09LudA378AVGuzbpqLdRltdOSkIuhuAQ9JpQjMFRhGrjiTRaMEfmYo0vJye1gVLJJCmeoL3aTWfNEp3VK3RWr9NZtUlnzYaarrp1OmvX6Kg9R3vtMq018+oitqFynOqSHnIzKkmMlQvYEAyS+xKUgOG0lQhdIclRzeQkj1CRI/++l8luqT+/qYJYtxzP2FVKqKMLdYE4ry/8Xp/Mi43qBzUHjh84aqKSP98VC8kxwJEwWFHgXGLkBMBJSMgnLDIJjdHEW9ogzuh1BBtMfgtVFAZDLCZ9PEatBFPHoxNgdjqO4FPxBL8lk4D2lBndabPKI9KfSeTkGDUpSgUWqhdQkEtseBEJ0WVYEurJs/ZQkTNBU4mb/vptZrousDRyhbWpW2zNPWDX+YQDzwv23V8rdcm+HA/PS/bnX7C/+Jy9xScKKEoe0dbCHVYdN1iakXyUyziGfs9c/yWme37HROdFRtvfZ6j1HQaaDxlsOWCwdZ/B9j36WzfpaV6io9FGY/UwRbn1pCZlER4STog+CqPUnJ9KIlSTTUJYNRkJ3ZRkzNBRtfEXApzDnwEco06yrpIw6tKICMkiNjKfvPROVRcubVO2wd+xOHlDgeB9z4/sifrF9YNPCeeUPfC9P9V7VH6vLD0+gHOoAI5AnmdsOQR2fI5r7B0FLssLOkiMyyY6PEMp8gQWG874AU50nQ/glLoYajvAMy5ZXndYtz1hR+3SK7zO79gRRZD9ubKxSdvWweIzNhy38YxfYrL3kK6GJcqkkc5cjV6bikEUgFppYcsmVJ9PpLFENeyZI+pJT+gg3zJEefY01Xl26gs9NBefpb18jZ7abQaa9hlpf5vJngvMDv4O59hnLExfY8V2iw3XA7aVTUwyo56w43qqALk0TglgOrZPOcTy5B8F1L9Rn8uiajoar0sURt8rleS+5wcOTsy+W95/PjWiKG2OFYlKffNLqjiBNwGA8/rzKQCaAscisAOBHQjsQGAHAjsQ2IH//jvgV+CIAuLHn7dQ/ZdT4JwAOL9qofq1EONfaaGSEGOpES9YYEi1UN1ibfoZm3Mv2Lb/uRaq7/014j/4a8RfcSB39iWnRoUYH2XgCMDxZeCkHbVQaf8cwNlhuu8SKxKk6ZALJ5Hqyx1ff5OQ/VsEghwBnM35L3FNvUdfxwIVZV1YrcXKahRmTEPzm0RMQelEGyUbpJL85E6aiu2MNnvxDH7Cyth1tqcecmjzK3Dm/QDH5QM4XU0CcLrIstQpRYpR4wc4GgthujR8AKeTxhI7Qy2Sf/M7lsdvq4ucQ/ePHLjkmMgFolwYShX8d+za5M6z3HX/SlUUr7u+wDn+PiPd67TWzVKS30WquQKjTmxNcUqBE6yLRG+MITzcQlx0PskJlWQkN5Nn7aUka5zy7BkqcmaozJml6miy56iSyZmmMneCirwxynIHKcrqJi+jlUxLPUlxpUSF5qAPSkF7KhHdaR8wEIVRfHgJ6fFNFFoF4Cww0fk2Z6evsTx9FfvQe4x2bqhg4tK8NlISctBrQ9BpQjAERxKiiyPcmExMWAZmCaiOLycruYGCtE5KcwYpzxmlPGeCCnne2bNU5MxSkTtDRZ7MFOV5k5TnTVCaO0JJTh9F2R3kZTRgMRcRG2HBFByBIShGtV0ZTlkJDy5ULWPSalSe5aCjapvJrossTd5gfe4hW/a/BuD4IM5rgCOh4ZIX8ysAp2eL+kqfAichPp+wiEQFcE5pNWj0BnSGcEymWMLDkomJTMccnUdiTIGy5JmjCjFHFZEQWURCRDEJ4cUkRJRgjiwhMapUTVJ0GUeTElNJalwNlvh60pOaybF0UpDRR1nuKHXFdtoqz9JXv82khBYPfMrqxE02Z++zY3/MnuupD+AcQRyVxyTZN9KGdJdV25csTkt21kfMDr/LRO95htv36G3cprtuk47qDdoq12kpW6WpdJmG4kXqijzUFbupK3ZRV+KktniGqqIRygt6KMppIj21iPiYFEKMJpVZZFCKKbG8nQQ4078AcHwhxj9X4PgBTkE7VqXAKcAHcJL9ACeb2MgCBXAayx3q+QvAWZLjYJcsqlcK4IjqRsDN0ew7pdlPwJ28TyWg/VsFDhTAUfbHZ2w77rBmu4Jr7F36WjyUF3SSFJdDTHgmofo0tKdTMQRlEmUSZUwdWak9yto01H6IZ+KKqg3fsD1R4GbPJY2I37Pj+IYdUaWIKsr9QsG0Tcdsjw2EAAAgAElEQVR9lqauYh/+kOHOHZqq7ORndhEfU0JMRBGRIQWE6nMJ1eURbigg0lhMbGgZSVF1pMW3kJPcTX5qP0WWYUqsY5RnTFGVM0dtgYvGkgVaK1forNukt9nLSOfbTPdfxDn6GYtTX6jMsXXHA9U2t+N+htct+Uiy9z7VjQAn9ZzleQtMFwil5nulLNpx/oBX4JjLf5zdr9h3/+gb+drR+I/9a4AjqsrXQPVYPaPgTQDgBE5U//ufqAZe48BrHNiBwA4EdiCwA4EdeL0Dry1UqoHKXyMu8EPBG/8J+09OHv/2B/DvoMBR34MvB+e4RlyCRX9WI25XNeJxUiOus/hqxDVRaIKj1B3rxJhaH8Ap9AOc4Vusz/gAjpysS+bMz0OMBUYIwPmBffsrDuyvOLT/yIFT7uofAZyjFqrXIcY+gJON9lcBjo3O+l8DOP4aaPt3CuDsqDaXe2x6ruNWAGeeitIurBa5aJSqaQtB/xqP6YyVaEMeSeFl5Cd30FRkY6RpB/fA71gZvc729EN/iPFLDuefcLD4gE3XVeaGD+lqclFW1K1CjMVSJABHp5qtLIQKwAkrIN/SSWOpg+GWfex9n7A8fodd+zccuuR4+AGOH94oaf8bAOc+G85rOMfeY6R7jda6GUryukhJKFOV5ZJJowmORgCOzhCFyZRIeGg60eF5xEXKBX4NKTHNWGLbsMa2kRbXTvqJyYjrID2ujfT4FtLim7DE15ESW6XajeKjiogKkQvBDPRnUtGdTkbvn9DgdBLCSkmPb6bQMkhruQCc33J26ipnpz/HNvQuI51rtNfNUprXSvIRwAkOwaCNwKSLJdRgJsKUSlRYJrERuZijikmKqSA1rhZLXCPWuBasce2kxXWQFt/um4R20hJaSTPLNGM1N2JJqCE1voLk2GLiIrKIMEqzWDSGM/EYTiWj/006YUGFJIY1kpXQT2n6HO0Vm8qWtTTxJesqTPr/L8DxqXD+IoBjrSchPo/wiESlujmt1RCsD8FgjCYsJJG4qCxSE0rJThW1TLOqgs9PayM/rZ18azv5lg6l5pJ9KrB2UpjWRWF6t5qi9G6K0nsozuynJHOQ0qwRKvImqS220Vjhob1mmd6GLYZbD5jsfB/nwKecHbvO5uwDvPavlPpm3/1MvTfl/Xk00iK0brvN8sxVXGMfM9l3nv62ddrqPCoct7pohtLsCYozxylKH6PQOkZ+6gh5KUPkJA2QndhLdpJMD9nJ3WQltZGRVE9aorRrFRAfZVUNVAadDoMmFP2ZGPSnEgkLzsYcfqTAEYDzUwvVCx/w/AULVYdYqAo6sKZWERstACcNzelkjNp0wk05xEQUkpfepTKihqURbvATH8CxPWNfgMIJiOADN/IeFYAjoE6Ucv6GPcd3HDq/57xbbDvP2XbcVQDHPf4efS3zVBR0kRSfS2xEFmGGdHSnLRjFrum3NmWn9lJf5ma4/ZB5P8DZtD1Rn6VHAEcCfCXIV1kqXS+RoPNtx2PV6rQweZnZwffoaV6lpmSKrLQ2Us11xEeVE27MJ1SXS5g+h3B9DpHGXGJDikgILydZPhOi6kiNrMcS2UhadAvyOZBl7iE3eYCCtFFKsiapyJ+joWxRwZyhtvNM913EPfY5K3O32fZ8xY7nKOj5a7xusXr54Y3dD28coiAScPMDO06xhP2I1/UHNbuuP6DG+Qd2nX9g73j8NmanACxRQfmVUKJO9GfhqJ9lytLsg2g+BY4EWh/ZUf0KnoCF6h9O7vy3P296fSIX+LcDxyKwA4EdCOxAYAcCO/DPswPHAGf3uEJc6rf9AEfdcX0T4vx9luPvBXB8VeLKDiLWiIWnfoBzBffEBaYHvbTX2ynK7kQAjkkBHDMaTRTBwdFEhGaTFFNLtihwChcZan6PeT/A2bJ9re62/hzgHKlJBOIcAZwfObT/gQO5Y+p5wd7CVydqxH8J4KT9goWql/J8AThepvs+ZUXqth3P2XKcyL8RJYRfxXIEcLb8AKe/3UNFSSeW1ELiorMI0aUQ9C/SiJRCtD6bpPBS8pPbaCqaZaRpC/fAR6yMfuEDOI7v+K1bAM5TBXA2jgBOsw/gZFrrEeBhCPIDnCALodo0YgXgWLtoKnUx2nqAo/8SK+N3VQ7DoUuOh++icF/BLrGcSfDzN+w5nrPnfszuvAQm+wFO1yqtddMU53aQnFCqWnWCzsSi0fgATrA+Eq0+Fr3OF9hqCE7DqMnEeCYX05k8Qs7kExKUT2hQgZowTQHhavIJC84jLDhXKR/EUmYKSsN4xoLhaE5bMIiC4JRvQoMySAgtQ+qoFcApW2Ci4zxnJ6+oQGTb4DsMd6zSVivAqUUBHJ0uBK0EC+sk4yVaWYVMejMhBqldt2BSDV7pmDQZmDTZhGjyCNUUEKYpIiy4kDCtTAFhunzCZfR56uI0TJ+l6rFF8RSiTcWkScSo4E0i+t9Y0P9rBqGnCzGb6smI7aXEOk1b+RoTHRd4DXCe/hUWqpMKHAE4osw4UuBIg5Fk4JywUB0pcATgxPkAjtZg5Iw2GJ0+jBBTPJGhFlLii8m1NlKe109NoShmxqkvmaShdJqGkhkaimeoL5LHWRpK5mgsmaOp1OabMjvNZQ6ay120VHhoqViks3aV3qZtFVw73vUu070XsQ9cwj38OcsTN9mYfaAybwQ+7LtfQ5v9+Wf4RkJn7/rgzfjHTPQe0NW4RHXJBHkZnWQkt5ASW098WBWxIVXEmqqINVYRY6giWl9BlK6MSG0pkboSInXF/sknQpdJuF4Ap9lX9R5sQqfRoAsyoTsdhf6U+QTA6aIk488BnO/xup+x7bl/nIHT0eSiWACORQCOQGkBOClvApy0bhrLXYycBDhzzxS8EYBzEtz4IKsPtir7nATbS7PeEcARcCBBvQ7J5LqGe/x9+lsWqCjoITk+n7iIHMINmRjOpGEKziY6pJSkmHqyLb00nAA451RFug/g7Cs77w8qy8trF0We7Ne3KptIKrqlynzVdpP5yd8z3ntIR/0SFUVj5KZ3kRJfR6RJGvGyCdVlEqLeFxbCBGDpMonQZRMRnE14UDbhp3MIP51LRFABkcHF6jWT8GhzRAPJMa1kpw5QnjtHU/lZ+pr2mO77SNWsHwMcqTmfl7yub/C6BNx8w7b9W7bt37Ft/55th8CbH/EKqHH9iV3X/1CPXuefUOP4E17/7Dr+iG/+wK5D1LAnIM6vApwTECcAcALA5j9sZf/nOfH9+5zHBo5f4LgGdiCwA4EdCOzAf84O+ACOCr59xa5DRuDNmwDn7/9i/K0Aju+5CzjZlRpx//wM4Niv4Jm8wMzQzwGOXisAJ/pnAKf+rwY4Eqj8in1R3tj/wKH9j75QSPfX7C08YdN1i8WZS2/UiL9W4PwU4FSTZf0FgCNhmCcAjrrQsflsSF6H5DTcY9vzJZ6p9xCAU1nSgTVF7vxnEqpLJuhfYjCeTiZal0VSWBF5SS00Fk4z3LiJq/8iK6PXjgHOeQVwnrC/eJ915xWfAqfZTVlRDz6AU6wAjva0hJVaCNGmExtWSL61m+YyN6Nthzj6f8/K+D12bXL3/o8qAPdAQS5f4LMAnAO71Mj7AM6eAjiifniXka5ztNZOUZzbTnJ8CXqpWT8dQ5DANl0kWkMUemMcRmMiIaZUQoxphBoyCNNnE6bLOb4bL3fkIwxHk3vi19lEGGSyiNDLZBJpyCbKmEO0KZdoo0yeUivFhRRhjaknN6mLsowxOivPMtX1NstTV1ie+gzb4NsMd5yjrW5aARwJc9XpTARrTeh04RgM0RiNcZhMZkJDkgkNsRBqshKqnnM6oYZM3/PW5xKul+eYS4RRJocIk0w2EaYsImVCsojyT3RIFtGmTN/I89bnE6UrJt5YjTW6jbykISoy5+is3GCq8wPOTvoUONv2p+yKvU9lYf1aiLF8IB1ViX+vwI3PRvULAMcpAOcai3OXGOnZpL5iUlnSBOBEhCei1RsJ0gZj0EcQZkokJjyLjKQaynJ6aS6fo7PGQ0/9En2NKww0rdLfuEp/wyp9DWv0yzSuM9C4wUDTBoNNmww2bzHUvM1Qi1cpvYZbDxnrfJep3ovMDVzCMfw5ntFrLI7fYHnyDuszD9mxP/U3Kb1kX+w5btm5p+xJ9pL7MV7PI1ZmruEYvshY1y6dDYtUlYyRnd6COb6E2KhCIkPyCNFlEaqVHcslXJdHhC6fSMlf0ecdT5Q+jyiDTBaRhjQiDMmEGeIJEZgXbESrCUIXZER3Jgr9aTPh2iMFjgCcqT+jwPlLAE46mtNSvZ5BuCmXmPAi8o4BzgGOwUucnZAcoNcA5zW0EXBzYhzyeSZQWl7/730KHJVh5QM467ZreMYv0N+ySGVhL8nxBcRFyP5mYTiTTkhwDjFiZzoBcEY6DlmYuMK52Tts2Z6o7KUD148KIu2pn0W+UHZlsZSQc6c0ZT1h0/mAlbnr2Ec+YqRrj/aGRWpKpyjI6sFirsMcXUJcZB7RYRlEhqQSabIQabT6jr/WSph8Rp1KxfSWBdNbVoyn0jGezlTgNFxXRKSxjMSoRjKT+yjJmqKhdImB5gNsQ5c4N3cHsVJtuUTR+bUCODuub9h2nIA39ldsO35kx/lHvE4BN//r9Tj/F17H0fxPvHaZ/+GHOQJyfgJxFMA5UuFIALFvJEj6tQLHp8I5DjAOKHACQCcAdAI7ENiBwA4EdiCwA4Ed+G+5A8I2/h/ltXe8wisAR8GP/5oA56iByvf4SgEUgTh/D4Cz+WcVOEdtWHLB8wcO7H/k0P4nX96L+yV7C0/ZdN1mceZT5kbfZrB7lZrySX4Z4GSQEHcEcOwnFDiP2bBLq4lPgaPgjVLffMfenNy1forXdZ+d+S+Zn3qPgQ4fwElTACeDMF0Smn+JxnQ6iWhdBolhBeQlNtJYMMlQwzrOvg9ZHrnK1tQDDh3fcii1wZ6v2Fu4x5rjCrNDB3Q1uykv7iXL2kB81GuAI1aqUF06cWGFFFh7FMAZaz2Po+/3nBu/z57te847/8ShQ47NKw5s33Ng+44D27cc2F5y4JCLyUfsee6y4ZBcjXcY6VyhpXaS4pw2kuKK0QWZOX0qygdwpLXIFE1ouJmoaAtx8dmYE/JIMheRklhKalLZiZHfl2I5msRSLGrKsJplykkzV5BurvRPNenmatITakhPqCU9vpYscyMFlm7KM0eoy5+lr26Nud53OTd9hZXpz7APvc1I5znaFcBpJsmcTbDOhEZrRKuXtqUoTKFxhEckExWdRmxcFvEJuZjN+SQlFpKcWEyyWaaEFHOpmlT1fZSQmlhMaqJ8X4Xq0ZJUgjWpjPSkCtKTKklPrCLdLFNLRkIDGfHN5CR2UmQReDNFQ4GL/rodZnsusjJ1g405qXT3AZw/30L15wCOZEFJoKsocMT2dgRwPmGk2w9wUutIiM0lIsyMTm9EExyMUR+lLq7jI/LJs7RQVzRJX8MKo207THbtM9P7W+b632Wu711me99ltkfmPTVzve8z13sBW5/MB9j7P8Q+8LECEs7Bz3ANX8Ezep2FiVuq6nt56gHnph+xPvOVghXSFiRBxQcuAVdfqyDeXZe0ID1kyyn16rdxjf6OkQ4vLVUuyguGyE5vJjGhiPAIK2FhFgXeQgyphJnSiArNJDY8h4TIfMzRBSTFFJESV0xqXAmW+BKsCaVYEopIjc8jOS4Tc0wqMeFxhBlD0Ws16DUmDEFRGM4kEq7LJjFCLFR/DuBI8PxfA3AyCTflERNeTF5aD43lbkbaD3EMfuoHOM8RcPIGsHH+yKGaP3Do+FGFVr8JcH7gvAI4L9h23Gfd9gWe8Q8YaFmisqCXlPhC4iLyfgHgNBwrcOQ5CMBZnfk1gOP/LHW8Yk8pU75TmTg7riesO+6xOH0F++hFJvoP6W09R1P1HOWFA+RntZBprSQ1KZ/E+HQSY9Mxx0jGUjrx4WnEhliI1qcQqU0hPDiFUE0qIRoLIcHphGizVI5OdKgP4qQliFVvkqbyFYZa38YxfJmFyRus2h6w43mO1/OSI4CzpdQ3P7Bt/5Ft+x/YEYWN83+y6/p/feP8P+yeHMf/YVfG/r/9IOdPeO1+iOMUS5Uvi0ga+nw2qp8CHB/EUT/jJOxYwM3RqBDqk9k5gRaqv//NqP+cO2+B7yNwnAM7ENiBwA4EdiCwA/+8O+AHON8peOO1v8L7Xx3gqJwGOdn/JYDjz8CRutv/oAJHQox9+Qw/zcA5slD9igLH41PgiM1kYeYSs6N/zkKViDHkLwM4XqcvA0dqYiUIWBQ4XrcAnBvMT19goGOBypIurClFxEdlEaZLQfMvsZhOJxOly8QcWkhOYiP1BZMMNqzj6PuAsyNX2Jy6r1QxB86vla3JO3+XVcdlZoYO6BQLVXGPryI6uhi9xqrCSo8BTnjRMcAZbTv/KwqcV+wfAxw/xHE8Z9/1mD3PPTYcV3GNCsA5R2vNJEXZbSTGFqlGqNNvRSm7m1YfiTE0lugYC0mJeaSnVZCdWUdBdisled2U5vdSmt9zPPI133SpTJ2S3C6OpjS3m7K8HsryeinL7VWqkNLsPtRk9VOa1U951hA1eZM0FtlpLZ1nuMmLo/9D1ma+4Nz05ziG32G0a432+hlK8ltOABwTOn0EBmMsYWFJxMRkkJiYj9VSTkZ6DTlZDRTktlKY205hTgeF2Z0UZndRlNNFUW4Xxbny2EFhThuF2a0UZrdRnNOhvofSvB5Kc3sozemlJKeX0uwByrKldWeMqtwpagvsNBZ5aCtfYbj5UNmJzk3fYtP2iG37MxWCLc03xxeCv9hCJR+UPhWOKDBeK3BOApwHPoDjEAWOH+CUT5KZWk9CbB7hYUloBWYF6zEZYokKTcMcWUxRRjetlU4mOvewD76Pe/QjFic/ZXn6MstTlzk7+TlnJ/wjVrXJKyzLTF1lZeoa56a/4NzMl6zO3mJt7i5rc/dZs0mGzVes256yYXvGps1X37zj+NqnOBJLo+sb9qRJSJqFnI+UbWrNdpNzs9eY6n2b9upFijIHsJrriY8uJiw0DZ00ZRmSUPDGmE5UWC7xkcUkx1aquvLM5EZyLM3kp7dSkNFGYWY7xVkdFGa1kJ9RT256JRmphSTFW4kKj8ag02LQhmLQxGAISiJCn+MDOOZOSjImaa9aY6r7A1UjviGvlyjU5LU6kYGz5v6CmeFDJAOnuPDIQiUZOD4FjuFYgeMDOE0nAM7yxG22556rXCpfNtVrkHMEcBTY8StwVL6XvP7O7zlUgccnLVSiwFl6Q4ET7lfgmIJzECjyVytw7JK94x/nd+xKaLBHrGOPWHPdZtlxjfm5T5kdfYfhnk06mzw0VE1SUdRHflYjmZYKMlIqSE8uJz2xTAG1lOhCEsPziQ/JIcaYrRR3odoMQoIzMAVnYAjOIESbT4S+jGhjHanR3ZRkSqvdBqPtF7ANXmZp6jZbLsnC+Zodl+TzfMOWslB9z7b9B7Ydr9hx/MFnl1L2KQE5PiXOnut/sef0za7zf7PrEBXOn9gR6GP/Ea/fRrXnlBsQAnFOWpn9ChxR4ihr2et6cd9evG6yevPvSaukNEq+hjo/O/kL1Ij/t7xT97PXOXBHNvA6B3YgsAOBHQjsQGAH/svuwAmA84OCNycBzpFs/e//w/9vY6FSyptfAzjSLnMUYuwHONMnLVSRRSoD5xctVMl91EuI8YkMHF8LlbQ/ST7DCYjjl7vLRY48H1+IsdiopHXpKMT4KzZcN48BzsCJFiqDXkKMjyxUfw7gPGLjJxYqCf2UzAhVxe18yo77ATsLN5mf/oCBjiUqS3qwppQQHy0Bn1Y0/xqP8XQqkbosEkILyTY3UZc/xUDDOva+D1TN8sbUffbtL9l3SjPUI3bm73DO8RnTQ/t0NjspKxGAU09cdJEf4KRgUAqcDOLCi8hP8ylwfBaqT30WKrtYqP7gt1D5bRkK4viUOPtKHfEVe577bDqu4RqVVqdVn4Uqu52k2GK0pxM481YkmqBIdPpIQsPiiI/PJN1aTmFei1IG1VWM0Vxjo63OSVutk9Zah29qHLTW2GmtttOixkZLtU1VcrfU2Hx/VmtXjy1Vdloq7bRUOHxT7qS13E1H5SLd1efoq91gquNtPMOXWJ+7werMVZwj7zHevUFnwxyl+W0kmXP8CpwQ9VzFPhURnqpCfa2pFeRmN1Fc0Ell6SD1VZM0Vs/QWDVLQ6VN1T03VtppqrKp8X19mvqKKRoqpmiqmqG5eo6WGrt6/s3y31XaaK500lLpprVqgfaqs3RWr9Jds0FfvZfJzgsqC2Z19i6b9sc+gCOB3P49PoY4/vDyN9//fwXAmb3ESPcW9eVTZKY2kBCTT3hoMlptKEEaIyZDPNGhWSRFlVOWPUhX3VnmBt5jceJTzs1eZdNxgx33HbZdd9h23lVBuRKWezzOe+w47ymlmdf9gF3PY/bmn7A3/5y9+RfsenwBsztuX0aJ5JRIW5svT+VIueCDTzvOr9hyCvS5qXJvFiYvMdS2TXXhNKmxDUQaCzDp0tBqEgg6E4l8TojlLSo0D3NUOZaEBrJTOilI76cke5iK/DFqiiepK5mivmyahvIZ6svGqSkdoKK4k+K8etJT84mLScRg0GPQhWEIjlUAJ1yfjTlSVFQdFGdItfyqqrdembnJhu2hAjjyWgnA2XE/ZctzjzX3NWaGD+hodlBc2I7VUklszFGIcQr/LsCxvUDlUv2KCkcAzr5SwfgycATkCcA5cEvdtVg5X2fg9LUsUlHoy8CJlQwcYyb6IMnAEYDzkwycjkPmJ8VCdZdNmyjBvldKIAUulIXquzcbmOT1c3/N7vxzvItP2Fl8yNbSPTaWbrHkuIxj8gMmB/cZkAyqeie1ZaOU5fdQkttNsYDQrA7y01rJSWki01yPNa6a5OhyEsKlcrxA2RVDtJnoNWnoz2ShP52H4ZTk49STmTBMVe48XTV7jHdeVAqvDedjdtzP2Xb51JBbdj/EcXzH9nGIsS/AeNf1R3bdf2TP/Sf23P+DPZd/xGIlViuHwJtX7Nh/wCvfu8AbCZU+AjgnAKvkTskoO9UbEOenCpyT4EcAji+E+rjJ6qcnbwGA81/2ZO7Nz+l/3ruSgeMQeO0DOxDYgcAOBHbgv/sOKIAjd+l8+Tevc3B8dzx9apK//0H4KwGOai2SCwnfHDdmnbxTqy42RIUjdy8lPPhr9hef4l24xzn7FdyTH5zIwOkiTgCO3qouzCTAOFgbTWRoNsmxtWQLwClaYPgY4Dxl0/YCqY09uuhVF1RHJ9gCcfzPzQdx/G09crff84zdhcdsuG+wMPuJUuAIwKmumFAWqiOAo9GmEqQVgJP5Kxaq1yHG286X7Dh9IZo70oJik18/Ydt9n+35m3gUwDlLZUkf1pRy4qPzCdOno/lXM8bTViJ1OSSEFpOd2ExdwTQDDRvY+z5kaeQqAnD2JJfGJRfEj9lZ8AOc4X06WpyUlvSQke4DODqNhWDJwBGAo0/3A5xumsrcjLQdYu8XgHNXQSZfiPFJW8YP7Nt+UHaqfcdL9l1P2PM8YNMhtowLTPT4gEh5Xhep8WUYNEloTkWjDYpCp4skNDQOc0IO2ek1lBV2U185QUejh8GODUa7vYx2Hc0Oo107jHb6ZqRzh5HObYY7t3zTtcVwt8wmI/Jr+XrHNiPtMjuMtO0w2rbLePsBkx2/ZbrzXVxDv2Nl8qqqUt6wXWd+/EOm+nboaXZSUdRJSlIeWl0ImmAJMo5Er4sjLMSCOa6QTGsdxXld1JSP0lbvoL9jhcHOdQY7thjs2GGwfZehdi9DHTsMdWz7vt6+wUD7OoPt6wx1bDLctcVIl+97GFJ/b5Ohdgnw9aqq6JH2A0Y7zjPW8Q4TXe9jH/yUxYnr/gaqJ+w4nuP9iwGO/FDwZaC8qcARFYs/xNh5mzXnFyzNfcpo9zb1FdNkpjaSEFNIeGgKwdpwgjQhmPSJqqY9KaqK0uxhumqXme27wML4Z5ybvc6W8w5ezwO87ofsuB4qe5NYnI7G63yI1/mIXbHbiWLLLTsjddgv2PO8ZFfAjftbdlzf4pURldqRUk2CcVU4rkDYZ+y4BODcY2X2C+YnLmEbfp/uhmVKc4YxR1YSqsvCGJyKThOHNjiaUKOF2Ih8kuOqyExpoyhziKqCWRpKPbRULdFRt0JP0xq9LWv0ta4z0LZOb8synU1OWuunqKvsIz+niiRzGiajCYMuHL0mBu1pMwIQ4sMrSDO3q4artupzTPa8z/LMDdbtD9h2PFMQatf1LTvuJ2x57rLmvsrMiAAcO8WFbVgtFcTG5GMUUHs6GYM2nbDjDJwemircjHYc4hz6FKXAOQY4Ei7+WoFz8te+Fir5bPMpOFT+iqiAJFjYcZdV+1VcE+/T17pAeWE3SQl5xEZmE2bMQB9k9YUYh5aS6M/AUS1UJwGOsvJJm5LPOiS2oeMQY9lPtaNfsyvV3fPP8C4IwHnM9tJDts7e45z7SxZsn+GYvMj08NuM9nrpaztHZ+M8HQ0e2utctNU4aa6UhqlpaorGqcwfpiS7j4KMTrJTm0mNryY2oohQfSbG4CyMZwTgFBEeXENqVC9F1jnqC1cZaHwb28BnrNrusyWZPA4JV37OprK1Sk7PN6pC3CvHR6xurh/wun9g1/1KzZ77R3bl+/SPlAiIhVnAjVd+fvitUypU+rjW/bVCTuXfHAOcf0+B4/s5rixYxwDn6Ge7ANkTJ3oBgPPm8Th5bAK/DhybwA4EdiCwA4EdCOxAYAf+AXbAB3BcbwKc17Jtka3/5ATv7/Kk/zKAIyeaR6qg40d/3bkPmJyQ2h8rcX5g33US4NxnVUKMpz5gZniX9gaHsqhIi1KIABxdIsHBMWi1MUSG5ZAcW0dOcj/1RYsMN7/rb6F6ytYRwJEacdeJ7AG/1P3kyfLRBa/K2vA8ZXfh0WuAM3aeX5/CZwYAACAASURBVAc4Sf8uwNl0vOA1wHmpbF07NpHzP2HLdZ8tAThTH9LfsUxlyQBpqVXERxep5iLNvyVjPJNOpD6XhLASshNbqCucYbBxE0f/RZZGr7I+/YA9xzfsuV+wOy8A5y7nnJeZOgI4pd1kpNcRG1OIAjhnUjAEWwl5A+C4GGk7wN5/SdWIe+3fHOdtyF19USkdj02aqQQYyYW45JF8ycKEAJFtepudVBX2kZ5YQYg2Be3pGHSaKHTaCEJDYkky55GX1Uh16RDt9S4FNmzDF3CN/+6NcY/9DvfYJ/6RP/sY1/hHOGUmLuL8/9h76/a2rnVf+wOcd69CjJKZmZmZmZlRtlhmZjttSmmTtmmatGFmbru6P9l9rmdMKXYKa+39nrP23j2X/hiXbKdN5KlnSnPc8weTZ7U1cRbL+Jfq59axr7COnlO5KLaRr7GPfoNj9AJzo9+xMvUjm8ab7Nvk+d5gYeo8M0MHDLQ7qCrtISWxAF99EN7eQfh4h+HjHUWQfyrx0SXkpDdTUTREa51ZgZuZ0Y8xj3+Beewc5rFvsIxdwDL6LebRrzGPnsc8eg7z6FeYR7/EPOZ8bhNfYZ04p56n/Mw4dlblgphGv8I0cg7TyHnMI99gHrmAZeQicxNXkNafDdNDtiTs2vzcqUzRlGRvFTjKRuU6N09s8v4A4OxbBOA8Vi1UmwrgXGFx9gKjPTs0VBjITG4mNrKIkKAUfHxC8fQKJkCfSHhgHgnhNZRmjdJZs8pM71nmxi6xYrjOtuUu+45H7EmwsATGWp6wa5GmouO1b3nMvlUCiZ84g4ifq7rpfdsr1aylNtAWCb8V0CBWUVlOpZqo1cwvNIBje8im9TaLhu8xj3zBeO8+LVUW8tO6iQzU8p0EGup8pOkskvCQTBJjKshKaaM0Z4S6UisdtRv0txwy0vExE72fYhj6gpmhL5gd/oLZkbNMD33MWP82Qz2LdLYYKC9pJSU5l4DAYNVO5usVideHMfh7SwB4OamxAobGaKteZrLnU5YMV1k33VWvmVISSfOR7RFb9lus2S5hGDnQAE5RK6mp5URF5uGnTzkBcHK0EOP0kwDnAksTt9j+pwDHVSN+IotFqYAEkD1hw3xLgXHLxKf0tjooL+wiITaXyLAsBXB0Xqn4+2QTLgAnqp6sFKkRtzPsBDjLSg2mKXCOAY68Vq/YNb1k1/SCXZPMqWQtPVU13jsOATiP2Fl8xPbSAzbmBeJcZ1EsVTPfYZ06j3FM3ufPMD34MVMDp5noO1RAV8Bnf+sa3Y0LtNVaaaqaoaZ0lIKsDpLjqggLylFNVgHeAnAKCfKsJC6oncyYUUrTrHRU7jLVfZ4lw03WTaKGfMSGUcC+qJGes2MR9ZcAxJPrtQKKe7Y3qCWfuyeXzKmaTzkPRUkjn11S7S6PP3PghCtvg4udCjmlppHXQq0/UuC4Ac47kOpfcg1z8v3R/bX7eLtnwD0D7hlwz4B7Btwz8K+bAQVw5OJQ1Yir0EStAUSrknVeNP7LL3hcm0SxNUgL0Ynvf/dvaxaOtwDHqXb5Y4CjAR0FcBwvOBQFzvxdVs3HAKfjLcApJtAvFT8BOL6R+MomTQBOdB05SScBznXWDccAR2wM2jqZOyCyf2dg5InfRbI2xOKxN3+fddtVHDPnMSiAs6wUOKnJTbyrwHEBnBpnC9XJEGNR4MidbxfAEWjzkm3jS6TifMssVeV32bRfxzb1Jf3ty1SUDJGaXENMZAnBfjl4v5+Mv2emas2JDS4jO6GNusIZBpu2MA98xfzYD6wb7qn63rcAZ/42K9aLTI3s095qoaS0m/T0OiIjC/H1ScbbMxG9T4oCOFHKQiUKnBMAZ+KW2pC5AlMPzGLN+Ltq65LGrgOjfP8aqXfetz9gy3KN+cmvmBnYY7DNTl3JIFmJ1aqKWe8VjV4Ajk8IQQFRJMUVUJTTSkPlFP1tq8wOfcqS8Uc2rLfYsN5WG/RN6x1kbTmX+t52m037LTbsN1m3X2fdcY01x1X1uGG/zob9Bpu2m2ptWW+xZbl9vMy32bXe48DxkCOZL9ttlg0XMI9+xHDXIrXl/aQmF6nKbC/vIGW/8fKIIlCXSkJ0OfkZHdSVTdLbuszM8Mcsm79n3XadDdttNmz32bQ9ZMN6n3XrPdYtd1i33GZd/T632LDdZMN+i02HttbtN1mzXWfVeo0VWZbrLJuvs2ySdYNl401WjLdYM91lwyzH9gnblmfsWN61Ah4DnOOsDe2cdL0R/V6B8w7Asd5k3XqFJeN3jPXs0lgxS1ZKC3GRJYQEpeHjE46nZwj+umTCAwtJCK+nNHOCzup1DD1f4Rj9QYXablseciB2KFHUyLkjOUwWeTxeYu0TddihVR4lmFjg32t1TsomeNfys7acyoYdsbWY3rBjfM32rNQ+y2b7CTv2h2zabjE39S3TAx8x0LZKbekkmYnNqq7d91QCOq8Y9D6RBPhFExuRR2ZSAyU5gzSUWelu2Gas6wtmBy9iHbvMvOGqdszNN7TXwHKdeeP3WA1nmZ04ZLh/kfqafjIzigkMCkWvC8PbM4JT70eh80xVVqOU2FYFcFqrl5jo+ZTFmStOgCMwS6xEL9kW8OS4yar9EtMj+3S0GCkubiU1rZyoqFz8FcBJwM83jeCA3wKc01iGLqhmrm3jC6eF6o8VOPJ5oFqhnABMUy+9VpBsWwUK33QCnE/obbVTVtRJfGwOkWGZBPuno/NKwd83612AU64BHPvkJQTgbJgkeP0nlDrFIjXiToAjQe2mZ+yYnrKj2vXEHvqEHccTduafsLP4hN2VJ+wsPWZr8SGbC/fZnLvLuuM2a/abrFqvs2K5orJyFk2XmJu5gG3yPOaxs8wMn2Gif5/h7nV6WueU5SonvYmosHxC/HMI9BGAU0DAqVIidY0kBveSFT1JQ+EqI62fMz95lVXJW5q9z/qsBnG2zKLoEsWiVIxLXo/AnOfOJV+LikgUYs5lFdjoXMqSq82vAji2EwBHVbZLbbss7Vx0WaGOz9l/BHBOKENFxXqiVe7tRZZbgeO+s/i7ay7X+7778e154j5G7vPEPQPuGXDPgHsG/htnQAM4NrEZiWxdW3IHVN35s/zPBTiianFBnD+2UB2rcWRDdyAAZ+EJe78BOJoCp1u1KAX6pf0DgCMWqk9wjLgAzjNloToGOE4VjsoRcdozTK809YooSkTFYn2mbEi7srmwXcExcw7D2BEDPQJwxvnPAZz7ToDzjG2LNFE5AY5AHJMAHKkqv4sACOvUl/S1r1JRMkJqch0xEWWqZcXr/RT8PKUyu4CY4DKy4tuoLZxhoHEL4wmAIxXTe3axLTxge/4Wy9bvmBzZp63VTHFpF2kZtURGFuDzBwDnbWVx2yHG/q9ZnLjJjkk22r+oqmAFCt8CnF85MP6qLDpqw25/yLb1OkvTX2MeOWK8Z5nmyjHy0xqIDJTA0Vj8fSLQ+wQT6BdBQlQeeWnNVBeN09O4ykz/FyzPStCoWHseK7uJbPx2bU/Zs4nC55nK0tibe+rM03jI1tx9Nhx3WLXfZMl0jXnDFezjl3FM/MjilGTc3GJj9i5bpnsq9FYqp/ftjzh0POZoTv7uO6zMXsQ28SkT/es01oyQlV5OQJBm9fLxjsTbI4YAn3SVnZKd1E5F/jhdDctMD3zKsukKG7a7bNkesy3hqPbX7NhfsW17wbZkbdhc6gOBDo/YFvBgf6AAz5LxJo7pK1jGLmEdv4x96gpz09dYnLnJklEsLndZs9xn3fKQTctjtixP2RbljLy+LhWZywao7ub/GcB5o2qkjy1UEqaqKXDE6iSQa8MqAOM7xnv3aKwwkpXcSlxkKSGB6Xh7R+LhGYa/LpXwgGLiwxopVXXZm0x3n8cxcoXVacm3ecKBQ2q+nVBGNq2qOtkJei2vOFTrpWqTkoypQ1HbCQRQFpRf3oJpCYV9a1Ex/cSOUYM4Mos71qdqtjftN3FMfc1k/wE9LfNUFY+QnlBPkC4Dn1Ox+HpGKWAY6BdFfFQBuamtVBZM0lazwnDbJxgHLzM3cYdFwz1WTPKaPGLd9ogN+yM2HA9Ztt7AMXsB4+QnjPSvUlczREZ6KQGBocoG+A7ACZbWqhYKM0dprV5koucTFmd+ZM10R712u9aXCgSo199xg1X790yPCsCZpbi4hdTUMgVw/PTJeHnE4yfV2QHZRIQUkpfefcJC5VLgvODQ+qu2/sBCpQEcTX2jFExiRZNMIdsrVae9brnJsul7zBNn6FEAp4P42GwFcCQr6C3ACXYqcFJ7qS+3M9RxGvvkZZZn7rAhYdpiF7L9yp66qSAA5yW7ohAzP1XB7MoaannEpuWRmuMV8z0WjXeYn73FoukOy5Z7rNoesG5/yIbjEZuOR2zNyeMDNuz3WLeJgvAGS6YrLMxexjH9LZaJs8rKOj6wRWv9rAoKj48uJDwol2CdAJx8Ak6VEKFrICG4m8yoceoLlhlu+QTH+I+sSGi28R7rxodsmh4rhdS2hGLbniqQvm68z4rhDstTtxQsk/fAxfGbrEzdUZX2m8ZHziDx5+xZX7Jve82+7Q0Htp84sDnVN+/Am2NV7LsAR+beDXDcm2w3aHHPgHsG3DPgngH3DLhn4P/lGVAA51Dd5RNo86vKHxCAc6g22AJwfv4vIGwnFDf/QQXO/1+Ac1KBM/3WQiUAp4g/AjhJbxU4fwZwXHdLRcIu2QVO9Y3KbHjJvllbB5aXWlWx/aHK4ZHWGBfA6f9TgJPotFAdK3C66neZ7vuW5Zl3Ac62tKCcgDgql8F6VylKLJMugDNKarK06ZQTpM/D871U9B7ZhOgLiQ4qJzO+jZqCGfoat5jt/xLH6GXWDHfZs8id5OfsOB6wNX+LJQVw9mhrNVFc2qkAToQT4Hh5JqLzSSFAn66yJDSAY2G47cAJcG4o1YNmCZCATq0eeN/0q6pcPzD+uwbmRGVhE/Byk5WZCzgmPmVmcIvOOgMlWa3EheYSok8k0Fc21cH468KICcsmI6Gekswh2quXmer5giWDqGWesm19pu6Ky8ZX7DUH9tccOX7icO4Nh/OvOVh8xd7Cc7XZW7ffY8lyE9vkjxj6LzDS9iUTHecx9n2PY/gqyxO3WZ+5x475EQeOpxyeWFLdvma8zPz0WQzDO7Q1TJGXU0toWBwB/tHofWPx8YjH3zuD6OAy0mJaKEobprVykYlu2aRfZV2qrOX52n9iz/Ere46/s+f4mT2HfP+G/bnX7M+9YtfxXAGdDesjVoz3sI1fZbr/O4bazjLWdV59bR75AfvEdRYMt1kx3WfDKmDvCZuWp2xZnFYPpViR7BHZLLpyNpwNN6KGeuec1D4Q3m2h0gCOquF+C3CusWy6qKxITZUmspLbiIssIzgwA2/vKDw8IvD3TSMsoIT40CZKMqbpqNxiuutrHCPXWJ2S4/uMQ/sbFZTrmhft/Uh7Tzo0v+HQIoHYriXBuj9zaBE4KErCX9m3/Ltae+Zf2ZNmH8kZkZBYF8QRQGCTPBVRstzAPnWO8f5d1bBWUTRAakI1Qbo0fE5F4+sZgc47lEB9BInRhSo3pa7USE/jDlO95xW8WTM+Z938gg3rC7bsL9lyvGB7/gU7Cy9Ys99jzngJ4+RnDPetUVs9THpaKX4BoSobycszklMfRKPzSiNCAE5cM4VZI7TWLDLee0YDOObbCl4ogGN/wbbjAZtzLoCzR7sCOM2kppUSFZWNnz4JL4849L4pBPtnaQAnTQCO1ZmB4wQ4s6JiEvXNHylw5HhqTUgCb3bMku/ySlVnS0C0ANI1gSIqRPgMPW02Sos6iIvNIiI8g6CAVHy9kvH3zSQ8uIT46HqlKBSAM9x5hH3qEsuzt9lQzXlOBY4AHJVbJBa35+xZJPdHat4fsWl+wLrpvoI+9olrzA5fZqLvO2aGf8Aqsz5zlxXzQ9ZtT9iee8Hu/Ct25uV1eMrW3GM2HQ+PYY71Govm73HMnsM0+TG97Q4qivtIji8lKlRCjfPx93ABnHoSg7vJih6nrmCJoeYz2McuszJzkzWjqNoeqjycLRWILV8/QILCFyauqtBwU/8FZnu+wdD1NdOd5zH3X2Ru5AorUwKFJZz6Mfu2FxzaJezeCXCc56R2XjoBjdMuJRcmboDjvkD9f/kC1f27uefbPQPuGXDPgHsG3DPw+xk4AXAE3vz78V3Ykyqcf7lE6ATAOWE5ete2cfLJv2ujeqvAUaoc2dwdq29E9q8pcJ5zuCD2pTtOC9XnGIZ3ObZQCcDRLFSSf+OyULkAToMKMZYMnOtsKAuVBL8KlHECHGeDj8q+UQGpztBNUd7IUiqHZ+yqIOC7rFldAOePM3B8fJPx9k0g4DchxscA56SF6gUCcNSmSoVnanfFN20CcK6pPJe+9hUqSoZJTa4lJqKUIH0Onu8lo/fIJERXQFRQmQI41QUGehs2mek/qwDOquEuu2KxsT9zApybLFkvMDlyEuDUOBU4Sb+zULkUOMPtGsBZmNAAjtgDXCoJrfLdaaMy/l3blDgBzo71NuumyywazmEbP01/s53K/F6SIksID0gjSBeL3lsCYENUJXViVDmiamkoszDaeZr5abFQ3WPT9oBt2yN27I+V6uZg/gVHC684vfiKo+UXHK0852D5CdsL91iz32DB9AOGoXMMtHxEc+kGHRW7DDd9wkz318wN/8Dq1E12zA84mn/G0dwzDh0CG0RBcJ8N0xWWZr/BMnGa7hYLJQWtREelqwDfAF0iOs9E/LzSCPcvJD6smsz4DupLTAx37DM3fZFV8002rA/Zcbxgb/4Ne/Ov2Zt/xf7CS/YXXrC/+JyDRa2FZ8t+jzXLLRYMVzAMnKe/+TRNZet01O4x2PoJU72iXrrM3NRNVk0P2LI/Y8v2nC1pzVEA56VS38idfg3kyh18gZH/GOBouU4Szi3n27sAZ1vsamLlMl9iovcAATjZCuCUEhyYjpd3BB4eoUoVEuZfTHxoIyXpU3RUbDLd+bWCZL8FOIdWDcwoQGOWf1dsXG84kmV5w2kFcX7iSAGc30IcATkyVxrAOQlxlPpIlFjzj9hy3FIAZ6x3l/YGC2UF/aTGawDHVwCORwQ6Lw3gJMUUUZzdRXOlWYVFm4a/Yc10n925l+zMvWBn/jk788/YXXzG3vJzDlaeszl/RzUlmSbPMNS3TG3VAGmpxej9QvD1EQtVJB4fii0wjcgQF8AZprVmgfHej1mc+YE1F8ARG44otASqzt1gzXERw9ge7a0zFBc3kZpaQlRkNn66RDxPxSpbowI4wQXIOSk14mMdTguVZOAIwPmN8kaDYC5LrQvgvPkdwNm0PmbNInXeosD5xKnA6VSh4hFhmRrA8U7C31cq14uIj64mO62T+gozw117OKYusGy8zoblAbu2l+zb3yjIqurCLc/Ysz5lz/qEfVHRWSRv5i4rszeZE8A68A2DbZ/SXrfHQNsnTPV/jXXiBxZmb7Jmva+gzf7SS/aXnrO39Iy9pSfsLT1mb/khu0sP2Fm6w+bCVdbnv2fR+iWjooyqHCU9uYrYiBLC/YsI8CxUFqoIZaHqISt6grr8ZQabzmAbvaRycESBI6ogUd4IwNk0y/vAHRYnf8Qy+A1TnZ8x0vwRg/WH9Fbv0VO1x1jzJxh7zjM/eonV6Rtsme4rVeDR3CsOHa85sP0mW+1tzs0JcOOCOK78m98pcOSz0JmB48yL09oaf2uh0j5TXTk7qmHsZObOv/wa4OTnu/tr98WqewbcM+CeAfcMuGfAPQPuGfizGXgLcA4VvHECHJtI6U+ocP5LLt7+COL82Qv3LsBxtVG5LlQF4JyEOG8BzvxjpX5ZNX+PfUoAzg4dDSaKcjqVAifoBMDROTNwBODkJvXTUDTPSPMnzI1cZ2NGy8D5LcCRg6kBHJcKR7NSaVWvEnb8nF37Y3bm7rFm1TJwZiQDp2eVGmcLlb8+G52P5IQk4e0TT0BABnFR1WSn9FKRb0IAjkEpcLQWqrcZOBJoKsvyWoEc2Uhs2u44Ac4X9LVLiPEgacnVxEQUE6jPwuO9JPQe6comEBVUSkZ8KxrA2WCm7yx22VgY7mgAx/aUbcd9tuaPAU57q4mS0k7SM2pVZbFk4Pj8JgNHAZwyTYEz23+ehXHZqEimiQvgOGHbiSDjfZMGBGTDJoqWTfNVVo3fsWA4q+rEG0rHyEyoJTYsj9CAJPQ+Yfh6BRPsn0R0aAFJ0bWU54/R3bTK7PCXzBkusTh7hRXTddYtt5S1bMf+gL25x+zNPWJ3/gG78/dUlsiy+Qfshm+YHf2cgbY9msqWKEk1Uplpo7VkncG6IwydX+IYvsS64RaHjicK3BzYnqoQ3V2r3Hm/wZr5Eg7DFwx2LlFTNkBKUgnRkbmEBmbg55WM3jOVYF2OCshNiqilLHdU2ahmhj7DPvWdUuKsmu+xYXvMpl3gwkO25x6wPXefrbm7bM1JFs51lmYvY5/8VoXlDrTI812gOH2KqjwbLRXr9DUeMd71FeZhgWB32bQ+Y1PBmxda+LXK3pC7/T9zaP/Fadn4jwEcOccUwFGwR+wfT9ixCii7w5bthgI4k70HNFeayElpI15ZqNLw9g7HwyMEf99kwgOKiA9toCR9ko4KqWQ/h2PoKquTd5UC58j+hiPbTxy9BTi/qH9TAzg/KYAj8EZbAnBcKhxRD7qUOMf2UIGFWuOes6rZ+po9x3P25x+zrQDOecYVwLFSXjBAanwNQbp0fE/FoPOIQC8ARxdBUnQBRVltNJYbGGjdwDj8FavmW+xKJouqt37E9vwDdhZk3Wdn8T6rtitYp88yObRDd5uFipJOkpPy0fsFKwWOj1ck3qdi8fMRgFNCcmwT+RmDNFU6GOmSuu2LLM9eV+oTyVjZtT/Xzsm566zNCcDZpcMFcNKOAY6X+jtTCPbLIjK4UAGc5ncAzk22TgCcd8GNHEOtylplCpkF4Mh7jNbuJQocDeDcYtl8CfPkp/S0OSgt7CIuJoeIsAzV2OXrJTk8qYQF5RIXVUZmSjM1pZP0t61hHvuCuanvWJ65xobpPttKaSOwRs5/bYm6S1rGtkx3WZ6+hn30ewx9XzLQfEhzxQrleWaaKlboadpnvOczjCPf4pj+kVXzbbaU9dP5Wiw+UK/FzuI9dhbvsrVwk/W5H1m1X2DO9AXDPSvUVoySnlRNbHgpAhgDPIoIOFVOpL6ZpNB+cuKmVRPVYPMnWEeOAc6WsmlKLtkD1oyShXUN69A3THScoa92k/ayJZoL56nPtVOXbaejbI2hhn0MXZ9hH7rAytQ1BagOHc85sDutVEoR967yRj5n3ipvBOCcBDvvABwNummNkq732Z+1ZkbnTQ7ts9IJbyQTR0EbAaCyXN/Ln//ZZ7H75+5j454B9wy4Z8A9A+4ZcM+Aewb+K2fgBMARaPOuAuetjeq/9OJNQM4/G4LfAJwTeThKceMEOC6IowCO/TkH84/YlRYl00Vsk58pgNPeYNQATlghgfoU/HXx6Hwj0euiiAjJJdkJcBqL5xlt+ZT5kRtszjxRrS2/Bzia/eQY4riAjvMOqFUCLZ+wMyehtNdwSI342McM9qxRWzFJenIzAToBOKn4eCfi7RVLgF8acZFVvwM4KzMP2ZDaWvPxRlxT4LgAzmMnwLmKdfJz+joWqSwdID2liphI+V0z8fhbAjqPNIJ1uRwDnGl6GzYw9H+hAI5kN0ijiuQ5aADnxlsFTkeridKyLjIzaomOKkDvm4yvl7RQpagacQkxzkvtoqHUzGDLPjN955gXgGP8A4Ajr5npJ7Wktnxf3XmXzds9ZaPasPzIiukC0/17dNSaKcnuIi2+kuiwLAJ0Ufh4io0qjtCATKJCi8lJ7aCmdIaelnXGek8zPfSZanZyTAkIuszy7BXWTDdYMV5VEGTe8J1qcpK2muGuHbqalqgrMVOUNk5aeC/Z0cOUpczSmDdPb+U2U22fMzd8iW3TPXbND5W9Q3J7NBuRBCVfZcn0DZMD27TWz1CU10paUhXRYQUq/0bvkUKgTwah+hyig4rJSW6npmSanuZVRruPmB6UBqyLzE1fYX5GGp1+ZMn0I4vGyyzMfs/87AWsE19hGDzDSOcu3Q3L1BWblB0rJbKN7PgBStIN1OTP01G1x2j7V1iGr7Ey+5g1oyv8+pWq195zZm38ZxU42uZPQIkAtxcK4OwKwFEB0TdU25sAnJYqM7mpbSRElxIWnKZghWqh0iUTEVBIggvglG8w1X4O++AVBXB2zc94C3BkQ2nWauePzD9zZBZ4o63T5jfIUmocsVU5W7Jcz09tYBWEcFr2nF8rkCMtco4XToBzG8f01yrUtqvJQWXREOkJdYToM9CJisUzCn/vMIL1ESRE5pCTUk9l/hAdtQ4muo+Ym/yeNfNN1i03WLfdUOHXAmpXLPLaXVJZK2N923Q126itGCIvq474mEz8/ILR+4aphitfrzgCdOlEBheTGFWvzvvaEjN9zTvMDp3DMfEDy4bbbEqYr2QiOe6xNX+ddQE4406AU9JEmgI4WUqB4wI4If7ZRAUXqmat3wOcZ28VOP8Q4FjeOCGxC+C8VpY8CddetlzGMvUZPW1zCuDERucSHppBoJ+A3Tj03omEBGQQHZ5PSkIVZfn9qgFqrHuHmYFPsAyfVw1kS5PXWDPcZNMo7z1SG/8AAaN71kesGm5gHfqW8Y4zdNduUl9spzhjgvS4bgozxqkuNNNStUxf8y5j3Z9gHP4ax9QlFmZ+ZNl09Tjk23KFZam6N17EbjiHaeITpob26Gi0UioKv9gKooKLCdEX43+qmECPCqL920iNHKYgyURjyQZDLZ9iHREwKhaqeyoLSDKq1o13WZq+imPsOyY7z9BTs059vomK9AlKkscojB+mMG6YynQDTfl2uitWGGs5wjrwNWvTN9ixPFTNaGJdVZk27+RS/RbYaN+LdVcDOS7YYbrCTgAAIABJREFU43p8V42qqW+Of+Y6R5Q1UWXeabZpDeDI1ycgjnz9Tz+b/9lnt/vP3cfQPQPuGXDPgHsG3DPgngH3DPyfzMAJgCPw5n8CwPmPvqDHIcYu5Y3r8e1Fqesuo9ic7M/YnxNbitTdfodt8lOmh7Zpl9DK7A5iwgqcACdOAzj6KCJDckmJricveYAmJ8BZGJW71U/ZMUkugwQTS2OSBKdKwKrzolmascxvUNYu9bV2wSxNI7tis5mTfIbrzM18y+z4GYZ616mtnCLDBXC8U/HxSsDLM5oAfSpxEZVkJ/dQmW+iu34PQ98FVgwSmPmcLQVwnJkUSn2jZVSoDBylwLmCdfIz+jsWqCrrJyO1gtjIAgVYPP4Wh+6UKEEE4JSQHt9KVcE0PY0awLGNfs/y9B0VdKsBHNksCsD5lqnRPTraTJSXd5GVWUdMdCF+uhR0yiqRSpA+g6iQQnJTuqgvMTPQvIeh9xxzY9dVS5aElGoWKjk2Tjm/6Q0Hpjfsm2TjItYJqY8WW8Udduw32bRewTLyKQOty9SVjpGf0ayySIL8Y/HyDELnHYW/byJBfpnER1eSk9FFZckUzbUOelrXGO3dZ2b4U4yjZ1WtuGPyAtax80jVuDQPjQi4aVykoXyW0rwhcpK6SQprJsK7mlhdI2kh3RTEjFCbYaGvahtj11esTV9nc+YuO6ZHWjuSTYKqH6g5W7dexjT2Mf3tC8qWUZDdRlJMBcH6LHSnkvHzTCHAW45/BolRleSld1FdPEVrzRy9LVuM9nzM9NBZZke+xCQV55NfYRr/AuPYJ8yMfMRY7w49TYsqJLg8d4ScxA4Sw2oI9S4g2r+a5LAOsmNHqch00Fl1xGTX98xNPGB5+gnrRmkvkyBasatoYamHKjBVg5D/EQvV2/PsLcB5yq7tITtSX28VgHOZqd5DWqvM5KW1kxRTRnhImhbY6x1MoD6JyMACEsPqKU2foKN8nan2r7AP/sjq5B12Tc84LQocq2aNOjL/nSPzLyiAY9IgzmnzTwreKIBjesOR6Q2HzjmSWRIoKPOlnqvaoJ6EOH9Xv/u+AjhP2HbcZm76W6YGjuhpWaC6eJSMxAZC9ZnoPeLw94omwCecEH0EcWGZpMdVUJDWTkPpNAMt6xiHvmB++iKLMxdVoO+K5RILs99in/oK09hnjPXs0Nlgo7pkiPzMRlISiokKT8HfLwQ/fQR+vjH4+SSocyciuJi4iBrSEzqoyDfQUbvKePcnmIa+ZX7iumo82hK7jv2uBnDmLzIzvktn2wwlCuAUExV1DHD8fVII9c8mOriIgrQeXADHKi1UEzfZntUAzjG80axTKqNK5d9IU6FWx74jEMd6AuDYnrBuvcOK9UesU1/Q2zZPaUE3MZG5hAWnq7p4Ce729YpVMCc8OAMJCc7PaKG2dIyexjmGWjcY7zxkpuczrINfszD2PauGa2yZb7NjERvnffasD1mc+FHZkTor16jKmSUvaYCkiCbC/EpUm1lmfBdFGaPUFJppq15hoHWfyd7PmB36EuvEN8zPXGTO8B32qW+xTnyNcfQLJgdOM9y1RXfLAjWlE+SmtTvVN0UE+RTh96EAnCpiAzvJiBmnJN1GS9k2w22fYxl1AhzTfbYkc8f2hJWZWzjGvsfYf5aBhi0aC8wUJPSSEd5CakgTyYGNJAU0khXeSWHcABVpE3SULjDR+hHzo9+zMXubLeNDdszyGSOV4u+udxU37/7Z8X/rAjgy/y5gI3ljJ5emVP0zeONS4byjxHFDHDfEckM89wy4Z8A9A+4ZcM+Aewb+W2dAAzgqsPjfObT8lQCOC/RoahzJ5Hh3nbBRCWixPWPf8VCBgGWj1Mh+wvTQFu2ijshuJzo0n0BdslJxiALHT+cEODH15AvAKVlgrPUzFsZcAOcFe9Iu9RbgSAisdrdSNox7siRzwCx1uGLb+LsWyumQ7BJpp7mhVBTG8U8Y7t2grnJaAZxAUeB4peDjGY/UTUvVcmx4hQZw8kx017kAziMnwJHwYpd1SuDNa7bNr1SY5qb1Nht22VR9ykDHPNVlvWSmlRMblUeQPpVTf4vB91QyQWLlUQCnxQlw1pUCRwM4txXAkY2JdrffBXBks2iioryL7Kw6YqML8deloPdOIsD3JMDpVMqQ/qZdpnu+UgBn0yhqDdkQuiT+miXgwPSaA1HfmF6wb37GnuWJuusuQGRv7p5ST81Nfc1E7y6d9RYqC/vISKokLDgJb69gvL3C8faKxscrnpCgbGKjy0lLaaYwb4DaCgMdTXMMdm0y0rPLeO8R0/1nGO85ZLB9k57mJVqqzVQWjJCb2k5iVBVRAaUEeRTg+7+yCfi3AiI9qkjSN5MfNURL/jzjTR+zOPoDa1MyEw/ZN7/gwPaCA8cT9ufvsz13Hcf0V0z079DVbKWqZIjM5EbCA/PQe6ag80hE5xGP3jOO8MAcBXEk7Lckd5i6chMdDSv0t+8w1LXLaO8e4wP7jPZtM9S9zkDnMh0NFmpKRilI7yAluobowEKCPNPx+l8x+L2XQahnGdH6FpXZUV+wwVDzeazDd1mYeMTajAQYv0HUN/sCblzrPxhirJ1rrg2g5EC90EK6TwIcoxPgVJvJT28nObaMiNA09PpQfHyCCPRLdAKcOkrTx+koX2Oq7UvsAz+wOiEA56kCOKddAMf0d45Mv3Ak8EatnzhtkvVGrSPTa2QdqjmSWXqNsuPJ98oeInlYmiVIgtpVLo78/o6X7M/LfN9h3nABw9BH9LevUFc2QXZyE+H+2fh7JhDoHUOQb4QCONHBqSRFFJIRV0N5Tj/t1VbGOncxDn2KZexz7JNnmZv+EvPYGaYH9hnp2qCz3kZ18TC5aY0kRRcREy6hvvEE+ocS4BdJgD6WAF0SwX6ZhAcVEhNaSXJ0M0WZozSUzdHXtM9k95dYR35gxXBfZa5s2u+wNX+NjYWLzEzIOWmgpKSRtPRioqOy8NclKFuWBnByiA4pVgCnpdzOeMdp3gU4cmxcmTfacXIBHDlXNYDzk5qbHatAnDds216zJaoT2x1WrVewTX9Jb9siZQW96n1GARxdIt4eUfjI+5lvLCEBiUSHZ5CeWEFJTgcNpeO0VZnpqV1ksHGTyfbTmAfOsjhxkU3TdbbMtzSIY33A3Oj3DDUdUJtnJjuuh4SQOoJ98vD8WzwBnllE+JeQEFZPVkIPZdmTNJY66KrfYKhtj4nejzGOfMHs8GdMD3zCZN/HjHTu0dO8RmuNndrSaQoz+0iNayAisJBgXQGBXsX4fVhKkFct8SE95CRMU5G9QJtStZ3FOvoDi4ZbKv/IBXCWDNexDH/DZNdHdFQsUJY2THJQDVFehUR4FBD2YQFhHxQQ411Okn8tGaHNVKVP0le9jrnvK1Ymr7JuuMu2UYKb/wzQ/Ed+fhLeCMRxBsafgDgHZuf5YPlFhX+7lDdvH8W6qOyLJ5Q47ou2/9aLtv+TO3bu/9d13eh+dM+CewbcM+CeAfcM/HVn4ATA+VW1tkhziyvMUsuQkE3aX+UXPAlxfgtwnrInWQj2G7gAjkEATt0MhZmtRAbn4OedgE4BgDB8vcMJD8omOaqWvKR+pcARgLM4pt2tlvrhtwBH1Rv/xOFbgPMzeybncsEbacGxSYuQBNM+YcN+S8n3TROfMdK3Sb0AnKRm/H2y8PVIUpsuzw/C8fNJJDasjOykbirzjArgzPSKAkcAjtSFvwtwtk8CHJsGcGwCcDodVJd1k5lWQmxUDkH6ZE79LRrfU0kE+WYREVhMWlwzlQVTdDesM933OVZR4Bg0gLNj/w3AGRG7hpHy0g4yM2qIicxH75OEr2cCslmU3JDoP1Tg3OBdgCPwRuqqBdy8ZN/4gn3TM/bMsnkRgPOYPdsj9hwP2XXcZ2n2EqbRTxnt2aCtboaS/DaS4vMJCY7D3z8WX99oPDwj0fslERKaTXRMMcnJteRktVNSOEhN+SR1FbM0VlpoqXLQUG6mpniaivwRirK6yUpqJDGqjMigHEJ12QSeykT/b+mEfJBPnK6SjJAWShKGaSuaZ6L5NAsj37M6cYOtGQE4LzmwvmLf/pS9uYcqU2Vx5gLG0TOM9GzQWmeiNK+XlNhqIoPyCPFPJ8A3Hr13FMH+KUSG5BEfVU5aYhN5mT2UFoxTXTpDbfkM9ZUzNNbMUF81RW3FGNVlw0rlkJveSGpsObGhuUT4pRHkFY/+vXCCPdOI9isjObSNgsRpmoq3GGr+GvPgXebHH7M288JZ2fyLAjgK4ihV1J8ocFR9t6jMNKWZKytDU7ZI2OoL9qWeXcKXRYFjucHq7CUmew5oqTSRl9pKYnQxYcHJ+PgG4unlR4AujojAXBLDailNG6OjbJWp1rMawBm/za7xKUfW1yrXRlPdCLzRAM5p089OeHMMcATknIQ4Gsh5pUKW5T1MC2YVW4iocDSAs2f9SYUB7809Yctxh8XZ7zGPf87EwA5tdWZKcrpJkDBbv3RCdPEE+UQQ6B1CmD6emKBMEsOKyElopCJ7kOYyI1218/Q0LNDfvMhA6xLdDXZaq400lE5Smd+v3msyk6pIji0kPiqLmIhkIsJiCA2KJcgvDn+fBAJ8tHwkyV+JDq4hI66HksxpGooX6a0/ZLr7G+bHb7FmFCvmLTbnrjoBzg4dbdMUF9eTmlpIVGQGfr7xeH8Yg793CmF+OcQEuwCOhBgfYR38lqXxm2zNPHNmBh1v6I+zbzTYuqsUOMcAZ9vmBDj2Z1qrk/0ac7PnGepeo6Z8hLSkCgVxQgIESEfh7RGG3juCAH0koYFxxEVkkx5fTkFaM6WZPVRlj1CXN017mYORph3M/Z+zNHmRtZkrCuLs2R6wOHGJiY7TtJbbKc0YJCOunuigXPSe0QT5ar9jdFAJSRF1ZCV0UpQ+SmXeLPUlVlorF+iqX6WzboX2mmXaqhdpKrdTUzhLWc4Y+Wl9pMW1EBtWRYi+gGDfQoJ8BN5UEaFvJjVqiMJUM7WFa3TXnWai+zz2iassz95h3fxAgSyBOIvTVzEPnWOi85D2ChtlaX0kB5cR5ZNNpGc24aeyCf8whxjvQhL9ykkLrqU8dYiuigUMXZ+wINljU7fYmn385wBHFJ6y/hTwSCPiSYDjUt5oYd4uJc7vAY6AHFcGjhPeuAHOX+ga6K9yreZ+nn+d62r3a+V+rdwz4J4B9wz8T5sBBXDUhbr5V/bNv3JgljuwrqX54f+nPek/fz4nAY587YQ4SoHzVFlbtm03WJ69gE1qqYe3aa+bpSCjjYiAXHQeCaou2PPDMLw8wgnzz1YbgdzEfpqKNAXO4tgtto3P2FUAR6w+UrOsbWw1qbnTLvUW4EjzjSwXwHnJ3sJTNmWzaJTQz7OM9u+oDXpGYgv+Xln4fJiM1/uxeLwXjp9XIrEh5WQndFOZa6S7do+Z3u9YNTx+C3C04GKX+uY12yZNgbNlu8Om4wr26c8Y7JynpqyHzNRS4iJzlQLHQxQ4H6YQ5JNDREAJqbEtVOZPOwHOF06Ac4dt6wt27E+1AF2xUFkuMCUNXs0myoo7yUytITq8AJ1nEj4fSsOSbEAz1WYxL7WbhhILgy0HzPSeZ37sJMBxwgIFcDR4s2d8zp7xGXump8cQx/aYPdUe9Yh18zUWDN9iEeVS9wpN1WPkZ9cTHyt5G2n46eM5dSoSb+9YBXECg9IJC88jJrqUpPha0pKayUhuIyu5i5zkXrXJk02bZB3Fh0vuRSFhflkE+iQT4JlKwIdp+L+XQYRnIcmBdeRGdVKVOkFn6RITzR8xN3SRlfHrbBoE4GiKLFF77dkfsW2/w4rxB9VsNDv6MQMdKzRUTlOQ2UlKbBUx4fmEBKTi5xNNgFi/9GlITklkSDFxkdUkxzWRnthORlIbmSmtZKW2kpnSREZyPWlJNSTHlRMXIQ1iWYTpUwnxTSRYbCqnoojUZZEUUkNWTC9l6SZaS3cZbv4GU/8d5scE4Lxkz/YL+3axEf3iDJV22S4kT+NEC5WqEnfNuetOvPO9QQUFi4VQQlfF9vaIbYsET99gZeYSk10HNJebyE1uJT6ymJCAJLy8Ajl1So+fTxwR/nkkhta9C3D6L7Mydoud2SccWV45s21+4sj4C0fGn53rJ04bZb1xrtecdipwNIjzikPziWV5/acAR5qPdh1P2LZL/fSPzBm+xjR+hv62JWpLxshKrCcutIAI/1SCvKPwOxVMkFc0ob7JRPplkxBSSkZ0A/lJXZSkDSiwUJ49REXOECUZveSndJAV36xgQ3pcNWlx5aTEFZMcm09ibBYJMWlEhaUQ4p+kznedRwp+npkEeOUR7FtGbHAz6dH9FCQbaCxaY6j5c6xDV5B8Ksnc2XAIwPleZeC0txgoLGwgJbmIyPAs/CQM/YN4ArzTCfPLIza4jMLUPlrKHYy3f4R18MKfApx31Tc/88cA5w1b9uds2h+w4bjJgukCY/07NNfOkJ/dQkpCOREhWeg8Y/H+MBxfj3B0XmH4+0YQ6pdIdFAWiaHFpIRXkRHRQHZkG2XJw7SVOhhv3cc+fJ6lyUtsGm+yb3/IiuEKs32f0tewQl3RJIVizYuSrJoEQvSpyu4Wpssl0r+EuOAaksNbSI/uJiehn4LUYUoyJyjJGKc4fZyitDHyU4bJiu8lNbqdxPAmYoJqCPMrJdA7nyDvYkJ8KwnX1xMX3EV2/CTl2XM0l+0y0PwZhv7vmJ8Sq+A9NqSByv6ULdtjFqeuYhr8ivGOfdrKrZSm9ZAcUkq0AByvLCJEKeSRTYxPAQl+paQEVlKS1EdbiZ2J1iNsCqpJWP6jPwY0Tniz+w8hzj8DOAJyfuGPAc4JiONW4LjhzV/mBp57g/Hn18buY+M+Nu4ZcM+Aewb+X5oBJ8CRZoq/a8us3aFTF3YKgAgI+au86L8FOBrE0Sq8BeA8VIG4y7MXsU98wczQPu21ZvLTOgnTF+DzfjKe78Vx6m+ReLwfTYguj8SwBnLjB2kqWmS85XME4OwYn78DcOQgHisTfgtwnPYpAWS2n9l3vOJgQdpj7qlQWuvkOcb7D2ioMJOR0IHfqVx83kvF628JePx/0ehPpRATVEl2fA+V2SZ6avaY6RGA84gt0wu2zVIf/ppdyb+RdhiTBnC2LZLHcJcth9h4zjLUsURN6QBZKVXERxQRrMvC89+S0H2QSZC3bE4rSIvpUHeru+s3me79EtvoZVYMd9lRG9xn7EgL0vwtli0XmRo6oKPJSmlhDxnJDUSFFOP7YSo+76fg55FBsE8OscHl5Kf001RqZ7jliNk+UQ7cZMsk1dWaJUOOnVLgmF6xJ9aqWSfAEYijVDii6njKvu0J+xIAbZMmpWusWUQpIZtsya0YIjutnvjoEoL8Mzn1fiynPojD81QCnp6JeHsno/NJx983m0BdHkG+BQT7FBPiLXfXiwjwyEP/YRa699PxfT8Fn/cS8f5bPLr3UvB/P5OgD/KI8a0kI7SN4vhh6rPM9JSvM9n8CfaB71kevcHm9EMkfFksdRJmvGt7xLbtHuuW6yybLjE/+zXTg0f0NC9SUzJBfnonqbE1RAXnEeCThJ9nssrF8fkgBd2HGfh55hDoXUCQb5FSAoidI1gnG/ocpZiSAORA7zQFmfw9kvE/lUSgRzIhnimEeacSH1hKRlQbhUlj1OTM01FxyHDzt8z23mZuVCxUUtf8CwcOF8CRDZ8rCNX1KMooZzvcW1B53PJ06LRdaBlQGsDZdQEc002Wpy8pFUJTmVnl88SFlSlI5fFhKB+8F4TeK4lwvyISQxopTZ2ko3SdSVHg9F1mWUDpzGMOzS81ECPZNsaf3q4j4xu09Zoj42tOG19xZHIus0AfgTfy/zqX6/m/zV76hT3L3zUoYX2pAUr7A9atN1gxX2bB+A0TfXu01VgozughNbqamKBcpXDy+Vso+vej8P8ggaBTaYSIqsK7kChdOTH6KmL9q4kLrCY+qIZovwrCfUoI9igg3LeI2KBSkiIrSIurJC1e1FalpCcVEx+ZT1hAFnoPOYdS8XkvE5/3ctC9X0iwZzVR+lYSg/opTbXRVXWEsfcSi1O3WDHeYN1+lY35yxjGDmhvNlGQ30pyYgWRoQX4eafj/X4qAV45hOtLiA2qpjB1mJayBcbbzmAduMjS2C22Zp47N/MueK9ZqSSnSrNPnQQ4P7Fj1da27Se27S/YcjxW7WjLlh+YHv6YruZ5KoqHyEptJja8WDWveX0Qg/cHUQrk+HwYju7DGPw+SCTgg1SCPsgm5IMCwj8oITmgmcq0KXqr1zH2fMHc6EXWZ26x75DspltYh79mrP2Ajuo5KvNGyEpoJCool3D/XEJ8cgj0yCHgVB6BHkUEe5QR4lVJuK+8FvXEBjapFSePAU1E+zcQoasl1LuSIM8yAjxK8D8luTdinyojXFdPTEA7KRFDFKaYqC1Yo7PmNKPt5zANXWZpRtQ3D1UT17b9mQoyXpi6inHgK0bbDmgrt1Ga1k9ySAXRvnlEeecR6ZVHpGc+MT7FxOvKSfSvojC+l6YCK8ON+5gEdI9cYX1awtH/QGVjfsOu+bVz/SMlzkkFzkkLlQZv3ADnr3Jd436ef51rUPdr5X6t3DPgngH3DLhn4F8/A78BOKLC+SsDHDlgv4c4GsB5pm2qrbdZmb2MY/I8ppEzdDcsUJo9TFxoNcE++QR4ZaHzSFUbqciAMlIj2ilIHKOleJWJ1rMsjd0+AXCkLUmUCk5bibJQ/QbgmI4Bjgrutb9if+45W/YHLBuvqjDNqYGPaa2eJy91gHBdBUFeBQR4ZKP/MJVg71wSQ+vJSxikOttKT80BM93fsTr9kE0JojW9ZMf8SgEcuah3QZwdy1N2bFqY7vz0eUa7NmismKAgo0UDB4El+H+YS7B3MRF+VcQFN5IV309NgZVeVVV+DvuoZG3ccwKc5+yoKus7qi54ZvhjeloWqSkdIy+9k8TIWoK88wnwkE1UIRESKhpaR1HaCM2lC4y0fMRs37csjN9iy6gBHC3E2KXCecW+/Hz2BccqHIE4z1Sg8b71mQqi3rVLTfVtNm1Xkd9rsm+f7kaHeh4FmV0kx9QS6l9AkF8uAbos9L4Z6H3S0Xtn4ueVhb+X5JnkKGgT4JGvjoH+g2x072cqgOP3QQb+pzII9MwkzKdAbcrj/WvJDO+gJHGU+mwLXaWrjNQfYew8x8LwFdYm7rA980SBzkMJBbZqjUxSqb1puc2a5RrL5stYxr9kvFee7yINZQZKsvvJSGggNrRYZcEIMAzwzMbfI5sAeY6e8r2sXPVzyfgIEGWGZzoBnmkEeoplKoMQ7yzCfXOI8isgLrCEpJAKsmLaKEkdpSbXRlvZFgMNnzPVeRnr4D0WJ56wPisKnJ8VxJFKd7Fb/B7guMDksfpG2ZCUpUJsFlpuhsp+EuuYTWvh2rbcY9N0ixXDD0x1f0xb5TxFaQOkxDSoGmux2nl7xBEkzzmgkuTQVsrSDHSWbTLddg77wI9KgbM984gD8wsnwJFsm59USLEKKjZq4EbgzZHx1R8AnJccWV5yaHnBoXp8xaFAHJXxI7/vSYAj4eLP2LY/YtN2V9Wzr1h+wDjyGYNt6zRJqHVWH1nxosTIJ8Q7iRCvVEK80gnxzCLEM4dQzwLCPIsJ9yolwruMSN9yonSVRPiWE+5dRqhXKVF+lSSFi7WnmYL0dorE1pfbQVlhB7npTaTEVBMZWEqIbzHB3iUEepaq9qMwnwZi/DpIDBygJMVGZ8UBhq7vmB+/rtWK266zOX+F2fGP6Wqdo7RIwsqbiIuqJkRfhL9nPqG6MqIDakkMbaYkbZK2slXG2z7DOnCJpbE7bBleaOrLEwrMdxU4EmCsrR2LC+D8zI4COK/Ycjxla+4BK9ZrmMe/ZLh7h9Z6KxWFI2SltBITWkqoXw7BunQCfZII8Eog0DOZII90gk5lEfJhHmEfFhHxYRkp/i2Up0zRVb7GdIdUbF9kzXCLA4eEtt9ncfIHTINfMtK+R3u1g8r8UbKTm0mLaSAhrJoo/3LCfcsI9RF4U0awZzmhXgLSqojU1ThXLVH6WiJ1tYT7VBPqXaVAjzyG+VYRrq8mJrCJpLBu0qOHKUiepTpvibbKAwabzzLdexH72HVWTQ8UvJFA6W3bMzYtj1mcvo51+Fume87QXbNMVc44mVGNJASWE+dXRrRvCVHeJcTpKkj0ryElqIHixCGaC+0MNRxg7DnPnLyv/AHAEdXNMbwRiPPPAM5JiOOyUR0/uhU4//qLLPeFrPsYu2fAPQPuGXDPgHsG3DPwf3MGnABHLug0C5UAHM1GJVkIYkH6KylwZDj+EcB5zLb1LqvGqyxMX8A6fpbB1g1qi2bJjOskPrSOqMAKwvyKCNUXqXrjrJg+ilMMtJVsMtl2juWxO38IcKTuVdscygWzK8RYcnB+0ZbYqGSTbHvFnmxErA9YMV7HMXURw9DndNWvUZY9RVJYC7GBtUT6lRPmW0i0fzlpke1KSVGbY6e35pDZ7u9YmXzA5swzto0v2DG9evdurPkndp0qkF2xahkuMNkraiMLFXn95CZ3kBwhd56lGreOhJAWUiK6KEgep6Fogf7GI2b6vsY+eoXVmfvs2mSD+5wd+yO2HHdZMf2IaeRzhjo2VfBvee4omfEdRAdUE6GvUI9xQbWkRrZSmjFJa9kyIy1nMPZdYEHu9kvOjSgh1GZaLDnyujkDjI0vtRBjsahJ05fpuWp3OrA+58AJCHasUtl7i+WZS1hHv2Sy95D+llWaKs2U5g6TkdBGcky9Cn8ODy4ixD+fYH0eQbpcgnxyCfIWcJVHsFceQbI8c9Vde3kM9S0g0q+Y2KByksLqlYolN66P8rRJVfnbU7HBaONHzHZ+hWPwMmuTt9kyPGTX+FxZdKQxSXJw9tTxl3m7j4RJr1uvszDzPZaxL5nqP63AQGu1RWXv5Ka0kR7bQEImkqF+AAAgAElEQVR4tQpODtcXE6YrJNSngBBvAQZ5hMhz9somxFtWFiHemYTpshW0iQ0sITGkkrTIerJjW8lP7KIic5yGQjudlZsMNp5hsvNbzAM3WBh/zMr0MzaMEnwtc3IMb34LcFzWwONHUd/8kQJH+501gPNE1T5vme+wNnOV2b7P6albozJvmtzkHpKi6wgLkhY0se2VERfcQGp4N5UZZrord5np+IY5qREfFyj22AlwREXz2lkbruXcHIra5oTq5rTpFWqJ+saiwZsjBW8E4GgQxxU4fqwqEQXOz6pKfdfmtAnaH2oQx3aDualvMQycYahtg7ZqC1XSTJbYSGJYMXFBRUT7FxKpKyDCp5Bw7yLCvZwAx6ecKN9KogUC+NUS619HXEADKRGt5CR2U5o1SHXRGPXlk7TUGOhoNFJXPq6AXmZCO0mRzcSHNhEd0ECUXyNxgR0kBfeRFjZCeZqDzopDpju+xTFyFQnMFdXQ5vx1zJNf0N+xRnX5FPnZfaQmtBEdUkeYvprowEYSQtpIjeimPMNIR/kWE21nsfb/wNLYPbYMLzlwWWidEOcY4Gh2nHcBzs/sWAXg/KwFGYv6xPGINestHNMXmRn5jOGeXdrr56gqmiAnRWyDdSRElBMdUkBEQDaR/nlE+RUSrS8hRldOnK6KeN86MkM6qUybobNsjYnWT5VKaHX6NgeOl+xKYLLpNovTP2AZOcdY1wFd9YvUlRoozxkhP6WXtOhWEsMaiQuqI8qvmggBaTqBN7JEzSRqnFpi/OrUivaT/67eqdBpJiG0jeSITjJiB8lPmqQkw0JN/jJtFfsMNH3OVPdFLMNXWZy6w6ZSOory5qlaG+ZHLBtuMjdxCfPwVwy1bNNUbKIwqYf0iEaSg+uI96tWir5E/zpSg5vIDG+nLGWc1uIFhhuPmO35mrnhqwrgiLLy7XqrunGpb/6zAMelwnEBHPl8P848cgFZ7dGZg+O2UP2FFMjui+P/mxfH7r/LPU/uGXDPgHsG3DPwP3UGFMA5lGaWtxk4EmbsktH/1TJwXIP2LsRRChzLc3blLqnlPuvGGywZRIXzDWOdhyrcUnIRsuJ7SI1qIz6sntiQOtKiushPGKU8zUxn2Q7TbV+zPHbXCXBeqhBjlwLnzwHOSYgjG+ZX7Nqes2l5yMrsTeanLmMcPoe0NNUV2siJHyQ9qpPksGZlwUgKbSQ7to/SlGnqc+fprzlitus7Vibus2F4wtbsc3bEkmR+rUIt5XkoZYv1JXv2J+w57qsskpmBj+lrXKK+ZIrSzGGy47tJCG4hOayT9Kg+suNGKE030lK6xlDTGWb7LuAYvcbqjNR4C8B5wY7tMVu2e6yarqr6bWlw6mlcpr7ERGHaMCnhbSSGNJMU2kJKWCtZMT1UZs3SXr7GaMsnGHu/Y3HstoJOLoCjheGehDhOkCO/k1iqVCPVCw4sEhAsyp2n7FoeIioPeR2XDT8wN3kB0/BZRjr2aK9ZoKpgiqLMAbKS2kiMqiEmVEKJiwn3KyRUl6dWmG8e2sonzCdfwZIInYCbCpLD68iIaSEvsVdls1Rnz6rNVX/NDhMtn2Lu+Ya5IWlKusX27CP2TM84ML/itPUXTtt+5tAm1dyicHmmjtm2TQJO77JukQDt/83emz/ZdWR3fv4vbM1YGqn3bolks8km2dyaJMAF4IZ93wuo9e1Vha1Q66u3ACRBskk22dwAEgABgthR+wqwu9VqjaYnJLVGbjtCskIeexyW7YmJsB1hS/F1fDPz3Mx73321Yasq5g8Zed99d8mbee5993ze95z8BPuaPkS65ieo2ZDHuhdasfyJOjz14DYVZvSDb72Iu772HL73B0vx7X/9BL71e4/hmyz/zSO6/N4j+NbvPYxv/6uH8b3f/zHu+dozBtyswY/v3YKnH6jB8480YfWSfdj8fB67Vh1VSW8z209iX+0FHGoaVOqbrtQwmPA6r/7Rt2Ea2n4kz42uNbSJwpsYBY5SHpnxSZ9DR/IzpLf/FNtX9uLlJc1Y8qM9eOj7G3DXt5/Hd772LP7k6y/h+99chwe+vQ3LHmrGlmUlJDd8gH07T+FQ7TlQgVNMD6gwqMOZEXA2qiOZURxOU40zjHJqCIdVEXgzhCMZXQhvDmcGUM5K0QmmGe5FcMiwoF4VQjWO3tyotXGG6TF/UcsFZed7Gz5ApuZN7NmYx/rnc3jusR14hHmUvvci7v3GMvzxHzytkkV/5/eovHkK3/qvl+Dbv/cMvvuvnlPw5u4/XKESSd//7Q149J7tWPqjWrz4VBJrljdj88r92LmhA3Xb89iy5iBWPpfB04/U4vH7duBHd23Ffd/eiO9/fQPu++ZWPPDtXfjRd+vx3AP7sOGZMurWvofcjk+xv/4UOrJn0L33c2Tr38POTQWseL4ZSx+vx8P37cC939mAP/mjNbjnGxtx37e24sHv7sRzD7VgwzMF1K15F9mtJ3Gw5gJ6GpnsmTnQdB40rcSkoy+Az1XgGHijAM4EmMy4OzeIrmZOJ87Q0FNoafwY6bqfomZTCetfOoDlTzbiyYe24eF71+K+P34e93zzadzzjWfx/a8vw71fex4/+NqLCmbc/0er8eh3tmL5AymsX9KBmhWvIb3lI7TVf4FSywhKrQTgfehpvoj29Gdorn1fzSa3fU031i7fq2D8Ez/k820T7v/OGtz9tZfwvT9Yju/+/jJ8978lvHlO1d/7/eX44z94AX/yb17EXX+4Qj0L7/naWtz/7U146E924LF792DJA0kse7QVLz/ZgXXPlbCd8GbDh8jsOIW9dV+gPXkZeYLtlkGlvunO9KEzdRmHEmdxoPEk9tZ/hLqNr2D9sv145qHdePSuDXjgW6vx/T98GX/yr1/AvX+4Eg98cx0e+d5mPPtAI9YsOYidLx9RAIdwjgqcqeHNjQAcrRTVScg1xPEAR94ffD1fX1h9u7xtehvwNuBtwNuAtwGd6uK/EoCjXt4zGuBwJiqlwFEqnIVsLAQawyhkOOtOH3oyl9CZYhjVKexvPK6my966Io+Xn2rFMw83qeSWj9y7DT+6ZwueuK8WzzyYxguP7MeWZUeQWP8hDu25gHxyEL0KmtgQKuX4GkWJmkJcTSNuZqJKTaCoQqnoBA0jnx1AV/oyDiWpjjmF3O6PsHv9Uaxf1onnHk5j6QP1+PG9O/HIXZvx2N1bsfT+ejz/oxxWP9GOnS8eRXoTZz66gK7Gq+hJDKCXoUcqETAHVEO3osyG1HIJ7cmTyOx8G7vX9apZcl5+Mo2nH2rAo3fvwuP31CqF0dIfpvHCYwew4bkidq9+C6ltx7B39xm0N1020IkA5yq6sxfQnjqNltqPkNj+JmrWF7F+eRuefzyNH99bg0fv2oFH796Bx+7Zgafu24OXHm/FpmW9qF3zE6S3HsMBKiuUAkcrlghwLCAYRZmzBblqnCSVAcMoZXQITDEziALDw9KX0Z2+iK7UOTWeB5tOIr3zXexZfwQbXzyEFU9n8Oxju/H4DzfioXtW4v4/fhHf//ZzuOsbT+Oury/FXV9fgru+zjCvpbj768/gnq8/h3u/9QIepILl3o146oEdWPZoI1Y8SRXLIWx/qYT6tW8is/VD7N91CoeovFEzTw2otlF5cyQ3iSO5CZRzvLYRFAg0cgPobe5DvvmKcjq7m79AV/MZHEweR7rmLdRsKGDDiwew4uk0nnt0D564bzMeuXstHvzey7jvW8tw79efxve/xrIE9/zRElXf+/Ul+ME3luKH31mOR+5ahSd+sAnPPFiDFx5NYNVTLVj/bDu2vVTEnrVvILH5feR2nsC+2i9wqPEqutIj6EqNoJuJrglxUjrsTqtvdBJjHRKlx4czq9kiUwrLLDVuCJWeSlwBtuxl5DMXVBhVc80HqF3/Gja+0ImXlzbj6Ufr8cj9W/HAPRyXbXjknho1S9bLj7dhx4uvIcPcLjWn0V53Hj2JKxrgKBhDQDaKIxkqcTS8EYBzJD2EIwRorNW2gzicHVClnO0HSyk7qJRROkeRC3Am0Jsd0xCnWYPK3pZ+ldSY4VTt6VM4mDiG7O63sHt9D9Ytz2H54zVY+uBWPPb99Xjwuytx/7dewr1ffwH3/NFy3P2Hy/H9P+Lnlbjvm2vw4Hc3qfuBz5JnH05ixZIWNQvS9jU9qN1cRqLmKJob30LD9iPYtqYbq5ftwwtPZlXC8ifvr8dj36/Fj+9twJM/SGLJD7J4+bEObFn+ChrW/cwAnM/QkTmLnr3nFDSp2/Y6NqzqwkvP7cMzj2Xw2H31eOiuPXjk7no8/v0mPPmDFF5+nMd4DY3rPkTzttNo230ZPY0Ep9dnDnAyhDgTyOcmwTw43blhdOX60ZmjCucc2jJnsD91AqndP0XNxsPY8FIbVjyTwbIf1+LJh7bg0R+sxaP3rlN9+Pg9G/Hjuzfjx3dvxRN3bcMzP6DarRWbnu3B7pVvILPlGNrqz6PYPIbDe8dR3jeC8n7O5ndFJXFurf8YyZ1vo2Z9GRtfbMdLT7H/6vHkD3fgkXs24oHvrcb931mJH3xrBe79xsu495srcN+3VuL+b6/CD7/DMdqAh/54Cx6+i3luarH0QYKbFrz8VBvWPpvH5hdewc5Vb6FhI+ENQeh5tDVdRle6H4UWAqVh5AmwDMDpSJ3HoeQZtCVPILXjLaXCWbkki2d/VIsn7t2BH31vE+7/xjr86Ltb8PjdO7H0vlq89FgzNi3rxp7VryO15QPsrfkM7VT2iQLHhMeGw6dmCnDcMCoNbgjmWDzAWcjvNr7t/iXe24C3AW8D3ga8DXwVbSCkwFmcAIdQY0QDnEw/8tkr6M5cRCdDPFKn0bz7IzRs/gm2rSxhw/NdWPNcG1Ys3YuXnmrGqqUHse6ZLmx6roQ9q95Gbuun6KhjuAyVIZUKHAkJsgCHoVRats6wNEKeApMNZwbBJMOd6YtoT32BA42nkNlJR/d1bHmxFxuWdWDN0/ux8qksVj7VjLVLD2LTs93Y/vxhNK79KVq3n0RXw2XkE/22LSkmnGVCYFHg6JwkxWbOVvU59tV9hPSON1G7gf8k92DDsnaseHI/Vj55EKue6sDqpV3YuKyEXSvfROOmD9DMEIHaL9CRuKIBDhO9Mkwgcwkdqs0n0LLnQ6R2vI0961/Blhd7sGrJfrz8RCtWsDzZgtVL9mPL83nsXnUUiU0/QwtnzqFjToCjEuZapYdAnMMEOgrijKKUolM5ooAO16nvVM4h9v0getP9yKf70JO+go7EBeyrPYHMjp+hYdNR7FyTx6aXDmD1c0m8uKQWy368E08/shlPPrgOTz64Bk88sApP/HAFnnxgNZ56cL2axvjpH23Hssf24MUnmrByaRYblh3Etpfz2LPmVTRtfEeFcuyjY1V3Hl2Nl5FP9quZp8pUhWTGcSQ7qVQ45ew4qPQoqFAqhn0MmhA0znR0Cd3N59GePq3+oU/vYgjYEexc04PNLzHHRgYrnmrAC4/vwnMPb8bTD67H0w+sw9IfrsWSH65R9dIH1uLpB9dh2cNb8OLju7HqqSTWPduKzcs7sP2lImpWvYb69e8gteVDNCt4cxZtDZfQkehHd3okKAJvelWi1DEd0qZCwAxcmwG84b/2GvgQ4AyBuYo49Xtv5iIYRrW//lOkd7yH2o1HsW11EetfbMfLz7bi+SVZvPBUq5oae8WTh7Dl+cOoX/dTtGw/gbY9Z9HZcAF5BXCobmJ4GuEMw6OYoJghVYMopwbVsgY4or6x8Oawgjd9KGf7oELwVPvYzjGVRFspcLIEOFThEOKMGCXOkBqvntxldGb4nDiFffUfIrnjdexen8eWl/dj3bIsVixpwvLH9uDZh3di6YPb8NQPt+DJ+zdjyQ+3Y+mDNXjmoVos4xTWP85hxZJ9WLesHVtW5LFr/WE0bH0dqZq30dzwMxxIf4xs3bto3P4Gdq0rq23Us+jZNqxccgCrlhzC6iUdWLOkG1ufP4La1W8jveVj7K05pcJ1OjPn1Axx+5MnkdrzPvZsfQNb1x7B+hd7seLpDrzwRBteepL3eydWPdmNLc+/htrVP0Nmy0ns23Ue7bX9yDeNoZT6EqWUVuHEKXAYAqpCzghvMhPIZyd1yYkKZ0hBnI7sZRzKnMfB9Odorj+GxM6fYs+mV7B9bY9S7K1ZnsXKZ5pUbqGVSxJY+VQSK59MYdWTaax+IoP1S/dh23LCm9fQtOE9tDCcqPGySgJfap1Aae8YSvtGUGgdQJcKRT2DvQ2fILXrPdRtPortq3ux8aU2rFnWjJeXNuH5J/Zg2WM1eOaRnVj60HZV1CxhP9qJZx/ejeceqcfyRxN4/vEMXn5qH1Y/w0TFeWx56Qh2rHoDe9a9i8bNH2vlzZ4v0NZwBR3JfnQTJreMBACnR4F5/Vzv4O9LmqD7AzRuOYodq7uxYfkBrFzSjOWPJPH0Aw1Y9nASLz7OZ/xebFrehZrVryKx+T207PoEB+o+R0fTZZXfjDnO3BKGODPJgTMdwCH01zDWqnB8CNVX8YXQX7N3hLwNeBvwNuBtwNvA/LeBrxDAGUJBhVH1KYjTk72k1CRtCb5kH0Nqx3to3PIW6jbRSXsFu9Yexu61r2HP2jdRt/YdpDd/jP27zqCr4apWvKSG9exJwcw9xgEmpAly4OgXZ4akUdGkc28wp8Eg8pl+9DCkK3sZXXR2Gj9D655jSG9/D4ktP0H9Bp67hN1rSqhd8yrq176BpnXvoHnbMeXg5hN9OtkvQRLbQoCjikCcYTUbEmdvyqtwo1M40PAxWva8h/T2t9Gw8Q3lMDC0qWbV66hZ9SZq176DxKYPkd3+qcpV01Z/EZ0JhsQwSTJnuyLA4T/PDEM7i0OJUzjQ+AlyNe+jafNb2L3mFexadRi7VpVVqVl9BPXr30RyM9UCx7Gfip6GS+hRiiG2U0vARIVDQEMQoiGOATmcfcjAGw1wRnUyWk4Nn+GYEuQwmTMT855HW+Nn2Fd/DLmanyKx/TXUbe7FrnXt2LpyHza8mMPaZUmsfq4Rq56tx8pnalW9ZlkS65ZnsOGFFmx68QC2vtyO7at6ULv+CBJb3kJu5wfYu/tTHKw7g46GC+hOXFXwhhCppKanNgAnM4HDyhEimDLgkBCHeY+aNRTIN2uI05U9pwDigaZPsLf+Q2Rr3kFi22uo31hEzdpO7Fh5AFtezGHj8hQ2LEti/XMJrH2mCeuebVLLXLfp+Ry2vXwAu1Z1o3ZtGQ0bjiKx+R2kt36A3M7j2Lv7Mxyop/LmIjoSV9W08z2ZERU6pcOndC4Npb5R4IbgiWVuAEdNJZ5lIuM+cDYqqnA4a9CBhhNo3vMRUrveRf22N1Gz6RXsWF/GjrVHsGP1q9jJMK8N7yK77Tj215wxgOwS8smrSoGjAY5JRiyzSqWHFLwhzNHKGw1wVOhUViAO1TcEOFTgDOipztUsYRrgFLKT6BWAQxVOlqFUBuLkhoziTCei1nmzPkRu99tIbn8VdZuK2LW2C1tfPohNL+zD+uUtWPtsBmueyWDtcy1Y/9w+bFjehs0vcJte7Fh1GLvXHUXDlreQ2vkumms/xL7GY2hLn0Bn82kcTJ1Aa8PHyNT8DInt76Bhy09Qu+F17F73GnavPYrda97AntVvonHDe0hv/Ritu07iYN3naE+cQ1fmAnpaLuFQ5iz2JU4hS2hS8wHqtr6LmvU/wY7Vb2DHqjexc9VPsGvlW6hf/wHSW05g786zaNtzGZ31g+htGtfwhgAnxTxoOnxK5fNyZkLSeXAYfsbcQey/SeRzVOKMoSc3rEOpsn0gxGnPXMCB5GkFV3K1Hyhg1bT9KOq2lLBnY49SNO1e14OaNT3YvTqPPSyrelG3poym9W8gvfk9NG8/jgN7Pkdn01UF3ThzWrFlDMXWURRbOXtYP7pzl9HBZ2jiM1CNk6l5F4ntb6B+82HUrO/B9jWHsHXFQWx6UY8Tx0qXVmxYvg8bn+cYdmDLSz3YvqKMXauPYs+6t1C/8T0ktn6EzI5P0VJzGvtqz6Gt8TI6k0ymTKDN+3oEBSq3aC/ZQfRkmQ+Hz/QLKoH5wcQJtNR9gPTOn6B+8yvYtaaAzS90Yt2zbdi4vEOB7+0rCqhdxxxh76Kl5jgO1J/Bocbz6ExcVbMMqpkGHYgzN4BjfotEdePWSrXpAhxR2BnFHfPgMMeblAUzK+X8fwHzL8l+jLwNeBvwNuBtwNuAt4HZ2oAGOPwXXfIfLLoQKu1Ia2efzmU/Crk+FFv6UGxlvgs6aOfQnjyDgyqs6lOVl6a1jrDjOFpqPkHzzk81fKi/iJ6mfhSSwxqaMGzJATgVSYyZUDnDl98JHM5OKueYCpze9BB6MwMoNA+oGamKexmyoaed5qxFHUn+s/4J9tdxphnK6T9C665j2LvzE7TVnlFJc4spzhozpBQgnL5aF0KcUV3SBBwDKGb7UMhdRm+Os0mdRXf2tAqpOlD/KVp3H0PzrmPK2c9uP65CMlprPsP+PWdVyAL/Ae5O9uuwLwKcdL9Su3SnOR07Z4O6gHzzeXSkzignvXXPx2iu+RDNuz5Abtf7qm6tOYZ9u09g/57TOFR/Hp1NVxTAoRNSUIohfdPSQdAqFipZIhAnze9soeKFsz2pcBgVHqdD5DiuhebLKu9Pd/YM2tOf4EDifbTWv41MzVE0bSujblMeezZ0o2Z9pwI7u9d3oXZjHvWbS2jc8gqatr6Gpq1vILntLTTXvA/2EyEEZ79h2FYhw1mxqCAaCuCNtF3DG+3s0C441gWGUuW0k1dophKnH70mzwpn9eptuYB8C6dGP4321Cc40PQh9tb+FLldbyC17QiaNhfRuKkX9RvyqFvfg7oNPWq5YWMvmjaXkd52FLmdb6O1hlO0f4z9tZ/iYP1pHGo8q1RJXakr6FYqJUJDJi4eVdPOq9nKVFJU3h8ENszdox02F+JMFz6lFThaccTkzXoGLt5nVzXEyV5UoFQBq/QZHEieQGvTMTQ3fIRc7cfI7T6G7K5jaN19Egf2nMGhuvPobLiE7qYr6E32KYBTJChzwI1MDU54Q0UOlTm6mOTFCuAMOvlvCG8YQkWFEBU4zIMzjkJ2QhUVQpUZ1eEqzFGl8lRROSU5jC6gO/eFGqPOzAm0p49hf+P7aN7zDtI7Xkdi66to2HwYdRuKqN1QRN2Gw2r2rYZNr6Npy1tIbnsX6R0fIFfzMfbWfaIUd4dSp9GZPYuelvMo7LuIntbz6MqdVSFBTG5+oOkk9tV/gr21x9G65xO07P4ELbs+xd7dp7C/9ozKCdPeeAGdyYsKqvZQadd8CZ3MDZM7j7b0WexvOoOWOoZonkBu1wlkd36KLGHErtPYv/scDtVdQmdDn0pg3JsYQzE1qUI9Ge6pQ2zo9BsQqVSMOldSAHGoXBIAlhtDPjeCntwQupnjK3sVndnL6MxeQGf2C3RkP1fhaG2p4ziQILx6D/vq38Pe2nfRuuddtO5+F3trWN7D3homOz+G/btP4GDtabQTmib79SxpuTEUm0dRbBlBsXUYpb3DKO0bQkmFVF1FvoVjdRYdnPktcQx7G95Hc+07SO96E4ntR9Gw+QjqN7FwjA6r5YbNR9G4+U0ktr6D1LafIbPjI+R2fYqW3aewr+5zHGw4j0NNl9CZvIru9IACN71Zgj7e16YwT1iONjOAfI4zAPJ5rhOYd2ZOoz19AvubPka25l00bXkTteteRf3G19G0+U0kt76tZq3aV/sJDjWeQWfyArqSl9Gd6kN3alDPNJiyKpxZARzmRKMq0xQVMuXCGy6bsFutvomBNx7g+ETGHtp5G/A24G3A24C3AW8D88YGFMChfFpmodIzUDlJjBfcLFSVFE8nGtZqDc4OpCBOc7+COHzRViFVKUIcqkrOoK2JyXE/w4GG0+pf7gO1Z9Fer3PO5DnNdYoAR0KW6OBoJ1gSwKp/rc30rnSE1IsxE0WrBMMCcDjV9CCKzYMothDm0OFlcl7mDqHz/RkONZ5AW8OnaKs7ibbaU2irPY3OhvMmtIROLROPDqMUAjhGiUO4Q9DA6bezV1DIXdIQJ/uFCqlqbzqDg/Wf4UDdaQVX9u1m/TkO1p3DofqL6Gi8jK5EH3pSgyYPAwHOAHpMyBKVQ/nsJTVVeVf6HNoTZ3Cg/iT2153A/rpPFUhgfaDuFNrqP8eh+nPoaLyE7kSfSrrsAhz2X6DCUfBGQxAJpSqndZ4crcCRnDl2imsdIqdnfhKIk8+eQ1easIoO80fYW/cz5Ha/o/4JT+14A8ntR5XiJbn9dbUus+sd5Vzlan6GXM0HKrSO13Ko6Qy6UufRk76M3vRVpeIivGFeJVHf6PAvQif+i02HiGFs2i5CEMc4eAQDvbmr6G2+ohLmEiJ2Z8+hM30G7YmTONh4XME7OrXNu95GbudbyO74CTLb30Rm+0/UMj8373oHrbvfx77aj3Gg7lO01Z/CoYYzaG88h86EOIH8F39Ahe1pJZWZ1cbMaCMzT2nVjVHfyD/tFbV19rTTJzPYcD2hiA6jUrl/sv0qaThDFrXa7SI6MpzB7HMcSHJGrpPY18jQpM+wr/Y0DtafxaGG8+hsvIzuJiqc+tDLGcjSg6rEARwNckxYlYRXBVOHm9mnlPJGq28ImHQOHJPIWEEcghyGBkUATnZIzeTGvE/KGc9eQDfvncwZdKZP4VDiE+xv+Aite95Hc827yO56B5kdbyG9/S1kdryD7I73kN35AZp3fazgy949vDf4XPkchxJfoCN9Hl3ZCyqkrnfvFRDmMb8Uw6EYotie/BxtvEcbT+NgA+/VMyqshsl82xvOq3upK0En/4oKx+zJ9aE7dxVduSs6Dw2TpCcv4EDjOeyvP4t9dWexr/Ys9ql7/DwO1V/SSdCbBpFPDKOQZAgVc3WZvCjK4dfwhrau84gxFJWFIEfnESooiEOQM6pm86IqpSdQonBmJqpRCGvvaJcAACAASURBVMnPo5NwOvUZ2pMn0JY4jram4zjYcAwH63VpqzsOXT7FobpTaK+n4u0cuhhKxBnplI2Nocgk4c2jOqmxSWxMkMOQqt4WTud9SUF5guW2BGfqOo69CsgTor2H3C6Wd5HbRVDK2fY4hh+hZTdhGfNFncb+urMa3Cj1GlU3fQre9GRoFyOqKDBLiEOFnSomXNKAv3zuEnpy5w38O4P2FKEc1YEfILXtXRXumdvJc38Iwpu2xtPoTJ5Ht8rvdRXdqf4ZABwNwgnDVXHUUvretvCGv0uVszTae5rPrlAhuJHiPgv8C9y8eYGb7T92fvvK90PfJ75PvA14G/A24G1godmAATjM03INxRSnEl/o04hHjZCghUoZG3KjIc4AinSkGcaUoUrhErpSF9GZuqBKR1L/u92ZuIROKlEIHpJMGGzgDZUuRkEizroGOHq9eplW2xgVDkNTDMAppAmTOKMSk9wOophjzdwhRrWQuYie9Dl0J8+iK/E5uhJfoKvpHLoamdj1slIm0KENAI4COTpfTBBKpRQ4VIoYFQ4hTvYSeuk0puncX0Bn03l0NF1AR+MFtDdcVEkzea1diavqH+884Y1qK502TlU+aFQ4fchnriKfpXNrcgqlzqMjwamjz6K96fOgdDQZmEBnM3kVPexDM+25KHBCAEc5EQbSmFw4GuQwpMoU5UwIwJFQLI4xFRYDSmGlYRghzudqNqRDTSdxsIGz9hzHvrpj2FdHNQTBzsfYV39c5WphqM+BBoaafaZyi7Q3faH+DSe84fX2ZqgCEPVNBOCoto2jrP7ddgEOc+FoJY5SdigwwITaA0rhwWmRCQkIxKjyYVJmKn7amz5DW8MJHKynooY5MY5Hyic4WH8ChxpOob3xDDoSZ9HJcBr17/0l7dinmOy5X40bQ+AIcLQDbhxxUUEZAEn7nFmxTp/8e6/vAX2fKdtmaJvKOcWQRV7fFTXzWkfmAg6lCXK+wKHkORxKUN1wAR1Nl9CVuGLvMzrsCt5QZUZbt/ZeZhJjt6gE10xyzTLkFIa4mZKVWahEgcOQRw1v6OgGAId2rlQ4dNQ5TgKiLmvVGaewz3yBzhRhm4astKsD9VTMHcN+NU6fKJh5oP4zBV/aGhjGdh7tTRfRkWQSdYYhEmxdUWOvklwrG+C9xO8uoSttnkXqOXQBnQmGM7JcRleSKjbCVeaAIpyjKoQzIQ2oohUw/ehMX0VH8graE5d1abqscskwHKkr0a9yUeWD5xkBDuGNOPm8r+S5qQFOJcQRkMOQNA1w2Hd5lS+Leb4G0JNh4ng+Yy+jR+VF4j15VvVfZ5JqkzPoTJhCWKrKWfW8625iuCJD6fqUDQfqKTXL2yhKhDgK5BDmUJXDkKZBrZxSuc7Yj1QIaiB2KHFahVi2NZ5CW+NJtBGQN57EoUYq1j7HoSY+v/gcY76oKwradBGiEFyrsFdzD5n7OVDXOQCHz3OqcPR9bQGtVgYxnxLVikyETHuh3ZzAwYaTChR3Js+pfmKuMRVimx6YEcDRihwDcAhyHIijfpNCChwZX6cOFDgOwBFwo2r9PA6eDR7geIDjbcDbgLcBbwPeBrwNeBu4YzagAI6Syyv5/KQCOBriMKxKXuijUGQhfB5BibMZhZwQKie0MoWOOEEOnUzlaKb50uwU5bhRrcPt6PgO66m6U6MoBGFKdHL4D6eocAxM4Et0aiRQ6fDFl6EoSqWgnGYm4TUQR0EHggeqZQhbqJjpU4lgC1mG7LBcQYHqjxRDSpjUVatvrBNrr5VJf3U4lXZ8dbjPAIqZPhQyVJDo0ksgwWtWiYD71T/cAmxUiFea16uvWSlclGJjyEAcOo1MCM1iHPQMlQAsdNQuoyd9SYUc5dNXVKJhJhwmAJJjxylwVD+5s1KFAI7MUGVBjnU26YyIWkDGl7l7mEz3EvIEYpkLCpB0U/mgChUvdCb5+YJymLXjTKePMOUKetLMH8TjENxQAaDhDR1Zzoql7YvtkjZZgMO2iV0INNEKGJ1PSAGVAOYMqpnJtP3xnDy37sOe9EX0pC6iO6awj3VhslP2s24vx4YheipMT83aRVszNqwUFHTMpTi2a1RDFki697nj8CknX5QaejabAF6q4/NcWvGm8j1lCRiY84mKjKsqvKZLAZ2r4NTL3So0bwB5Yx/q3iCAVBBSw4MwrDTKMxfiiBqNoEeFt0ltpqDneGVHHAWOVuHohMZ0ejXUCo+RAW3qWcC8PoSWRnlmbKonTXUWVRPnnEJbYxJn2hEVUAw9HAwKk5irkjXjrvpH95G6p4J7S99jCiKZ8WR/suj+pS3pQuWLLiMqlCmfHTGfGTbHwhxWuliQZ8fe3kscc/M8Mcob/cyUZ6fAHG0/Liyo7EO2je2V56y+J/MZAlHOsGRK+hJ6Q+UyCmk+8/pQ4ExPhNAcUzV+DJ3k2OnxK3HGt9w4Ss0sRp0TqGJEEWNhqVK+5a6gV4WVUpXI8NKrCvqqGePUPUmIRyBF+BpXTGhkAHKo/qMKZ8gUQmSq7PrUlOeF1j4U9/ah0EqgQ3UQ/yTg7HnnFLAltKUKlGq1QnO/hlC0jczgLABO/IxU6l5WfzRoJY4dZ+d+FoATgjaO+obJzF2461/Y7tgL20L7h9C31/0N9cveHrwNeBvwNuBt4ObYQABwCqlJsKg8CCqBpc2DsDA724EagSMizoc4I9rJ1DMaidqEyhi+kOt/6rWDoJO6Bv9mpvTLsCtJD9Q46mVZEgpr9Q/BDfO2WIAjcETaY2qlEqCjwnwdDPtgMQlYM5yuWqsJJHTEAhwqEni9OpyKIVXa4TXKBQUejBpHOUNctjAi1plX12GBlIY4VOEQ4vCfaOuEKjUJYZgqVCxoKMZ8MQI+xOEkFJIcDlEFjgtw1L+9xlGwoVQuLCE0EWAiMMGAnADa6WvWbdBAQzmTgcNMp1kKnWkWJiPVRZxjgR9axeXCG/Y5b8RqAEcrccJOrv6HXCs+RPlhwnfUlMHWKdd9ZkHf3D5HbCwANwbgRMd5SufMcfpMHg2dK4UQx1UdGYWPgQvsR+nTnuyQCbFhwlfT18F2sp/AJamlz8WuZ1gr0EaQY/bPCMARFY4GAQIeZJxtLX2voYlAMYG+vSpZswY7hDtBCQCaVmzosWaYlhQZdxPOFly/M/bqGcScQlLYZreYthMwKAWRniGKIU2S20cv87MUO9MYwZUKm3PVbGrszXMk6DsNRPXzwn1m0K5kfKTWQChs7xxTgYfmeatgKKHMdEU/pzSQI4ATCEeIo/M2lbITKGcnUc5dQzk3iVKoTKCY00UAD5U6JYZdtQyj3DKkCpfV+uYxA4K4jz5WsH9uHEXm32FR8MgdCxkj1kZZqRR2kudsEKW9uhQ4RT0TmVN9pWAgYdZlZTuEPiqktpnH0LCtJzWkc+BUTWKsE5G7z1S3/y3A0c/yyt9yq7oLwqVCIEf/+eABzs154arsf39c3yfeBrwNeBvwNuBtwNvA7GxAA5zUuII3AcAJklhqx2zhdWoY3lgHThw5WzNMoMBCxYySn9MRHUcxy+mRpTAnEPN9GLVBVUm6AA8TtqVUBCMqVwrzpbCosAQChqCYATOAp5wdgS7DKGclJMQ6sGFoEwklMYqEamoFtZ6OmYI9YijilAsEEdWVrNfbaUCl+0o5Cwo20AG1Tqf+Z1+cXrcWB870teRrMLUoVRS0Cf7plfaJ8kbqMCwppydQTk+aIqox9xrMmAR5O5i7Qzu7MiVy9VpvKw6RclhFneD0YaUCx55fwIY+hhxvHIUMywQK6ckbKBPmODwWjy1KEt3fApzc2iqH5B6x/Tz9fS42wVrbiwU4BuJwBragLQIXNLDQCZRH0ZPRRT4LzJJ+llo5nwIJ2N8BiLH378zXCWSorKsDHLHb6WrX1rnM7Xnt7ItxM77XUEhLkTF3x0/GUPefgsXZMHAhdAmXcf2sYg4zNcsdZ7q7hmLmui7Z6yhmr5kyiWKWZcI828yMYwHAcfvXVS8RGhMeW4BsFU7yXIrakrYP2oZVcbq2E7U52d9tQ+UYq+ehej4yiTkVIgbeZK/jcPbnKFeUX6CUNSX3c5RzX6Kcu45y8yTKzRMoN4+bwmWuIwS6jpIqP0cp9yVK2WsoqQT0/C0Yhwb6ogISiFMJcHT4HUNjh1RoV2nvCFiKLUMmzMuoggJ1F5PrD6DYPIQiEyILwEnfaoCj848peE6lTbUSPJc5jtHx8599n3gb8DbgbcDbgLcBbwPeBm6XDSiAU0iNBQCnoBJZ6mSWOjxoob6wOU5ByOkOrxeQwrAorQiJAzhOsta0QBzrpIss3apwBAqZMBDlfBrH0QE3sr0a7ADg6BmWApCjpqk2jk0kZGQqmCNhEFqZE75ma1yuUyXOVvS69M0YABwTakKn14U39l92cXbFqZXPVl0hDroGZhaw0BipUrLts8tWhWOVNzrfDKGNyduU5nIlxNEQRcCCrauDGz1NsnbAub0NNaG9hAEY2xiGSq7TWhXgpOnYs9woxCEMECCk2ypAolIhEXW4eS22j6dfrrSXMMCJQhy3r8MQJwpvCHECuzDLIYCjQE5UTRTzObi/XEhaCW2kb6SvbC32eiO1XIuMMceI8EbADWuOO4uMXXWAo9UmRjGTjdbMR0SAw2cUQfM1lDLXTeEyAY7AGw1wNJiOATghSBaFOCaXUBCeFrUlsSNtI1G7cO8J/byU7Vnre8oFjXZZ2qEVOPq5yGckYYMFOOXsl7DFwpxS9udQhTBGgRmt1CnnJvSsa5x5TS1TcXPNbEOIQ3hzPQA4Gug7ACfnQhwDcFTYoH7uiUKRIKbYPIJS66gphDjDCtQwXw+hjZ4ZkWFXGvgwIbLKJaRm/mPy+GozUIXVN70Cxp17Sd1Djsou9h43cKYqvPEhVLN8Trq27ZdjbW5Wvzu+D30fehvwNuBtwNuAtwHXBmIBjoY41hlzd1g4y+Jox9cEG4QCdB449bbKayPKGpUTQKCNW7uQIww6NOyyChyl6pE8HkEuD3Eq9Xa2L10Zu/svqA690uFX9jp02+PVNxbq8Pp0qQQOvO5KZ1xUFfY7e7NolYx2hLWzO7WDax0wbhcDb1wnfToHw7SVShfdNhkHATYW4BRV+J9ANhvqxvGxQGYmy1GHWsbW9okeP9smDZSkbeLI2lw4QQiesjNpI2uGK05VbG4q5qeSGeN0zc823FFDDwKnCLQQ8BQosMSeotcz1We5Vtb6OqOOuv1c2cc6hEjCh8J1FN5YaOZeRwywCYVHutu6yzH9Ee2f0OfpzhP9Pnou2grHXcaY4+OOoYy1fM/a2KpKdE6IrEslvNHggHlgbBiRhjgW4Ghwo2EOl82sgoGSxKhJTGJ1BU0VOI0qYASeRGtHHaPsSmxG7EOuK3yd1SCOQHT7zLAhrir3jYJG8SFUKnwqew3l7HVblNJGA51SliDGwhsbYjWhc+cwf06Oyyb8SiluCG40wNEKJlEuCcBh3h0X4HD85XnIsDkdZqpyOjEEjgCnZQyl1jEdqmVCuLTaRiCO5FvT4VMCx114kzczx+lwqVEQ2NgyXQ6c6G+OjJlX4NjfYadPPGDw0MrbgLcBbwPeBrwNeBuYpzZgAM64zn3DWahUHhzHIVRO80J9sREnVcMaq+KQcBx9XQQ4uogTxdAEF9xwWRxzt3addOOoq39BJVRI57qxyVjF0XNhgHF61L+gZhYQNwdBIF2XMdDXZEFNNZAj8Eb2Yy0Ollu718Nl9zu7r3KyAidXO7CVqpuoY8vP+po1xBFlgq2n/ofYbYssu+0VBzHqHIsD6TjGxkEOQxxpRyVscLfT4MUdM+kXaRNrt122HwOwJyoecdRDDr7b3rhlXqdcI2eKY7luip45Lh7iOODitgMcfR06VCzcvxIyFae6iYIcsR9dx9mXu07uL1M7YFADSEdJFdhyZJ9gvXtcWXbBgqyT/c2xg/F1x1HsVGr3Oy7Lc4fQRtqok55rUONCG1HoSc0EszJ7EKexn0Q5c80pBt4oiCMhoQbgKFAt8Jg2bZ+X4dA0AThRcCPbu/eDXJvYbPiawxBH7in2ofSn28d2WYVtBTlwmMuIfUIVkahwCHEckEO1jVLQSM1QKDccygE4JqxMK5Uk5MyEn6nQNCqcGFbLohVQOg8O8xDp8bfPQlHg6HxheqYqTns+ZsooirkRFSbFPDdMki+5lVRifTVbnMmd5ChvKuBNahS9UhyQI6pGuY+mfr6acfMKHP+COk9fUD1ck2err70teBvwNuBtwNuAtQEFcPQ/xXYacesMaudiIXdYHLSx68ZQTnH63LEwwOE/52ZmjnhwI866duCtA6YdEq2+IcSJJis2zl7gWDoAwJyvnKETFi56qmZuKwMnjtNUyVxlG9lHziVtr1bLdrIfa30sDXHE0ZKaTpYsu7U4tlILLAnXUzsY0ha31u3WNivOoQ1NsUoWcSSjDjJhQrgN4c8WNgTjKg52CG5VtikMcfT3AcBxxjx8XLed1ZbFGSbEEXjzZQTg8DtuJ9drnGMCARNWpMbxtilwwtfiArFwf081FvxO7Ie1a19xy7KtQJAqtYyFgiWyT7SOHt/ChIoE4Op4hDDha57+swE3gX3ZMQtUMQpUCKxhLTmyTK0SpGuIczgzgcOZSVU0yCHMsUXn8RKII1PBuwDH3utyz4dBjuTPkmcLa3lOiL2zD1x7FfAotinPHQu8LZyTPtbJi93EyRbgxCcy1nlqJjXEUQmNBdhUhpBJHiANZHS+MwJ7m0dIcgkxn9CkylWlAA4hDpVRTGLs2E6cIlEraKg8lFmsONW6nm6dUEd/T1AzAD1DH5OsE+ZYBU+vATiSoDikvCG8IbgRiGM+e4Aj9uhre2/6vvB94W3A24C3AW8D3gYWkw2EAY5yDu0Lt/23dOEOuoU1VnUjSWcPM3eJATga4ohDZR2xsFMeN+uR7KP/PVcv0IHUfaYAR5wp+QfddbpMAmWl8KCTxLEQB2o6gCPjpp0rmbVpqtoqcGRfe75KB1ocrqiz6zrD4uzEO+rVAY5us20PP1fCm3BeEZNbRCUHNvlHVOJpM0YqLMW2Rzth1T474xo42NXbJG2ztd1Wch3ZOv7YYbAj24gtyn0pyhsCnCjEESdZ9jVAwLWZ2wJw2A4p0n7Wul2zBTgSgldpf2G7k+3Cx5e+kLAk2ydhVY5rs1x2jy127oIFruM2DIvkMXl9cs1Su9cuy057HLuytqHt0QU45WwcuBnB4QzLKA5nxnA4M27KBI4YiEOYYwGOhsIBxHEBtVJguPe7vefts0aeOVJHt9f3p+4HsVVRh8ln9ovuB97Xtv+lv9mnbh/L8pCa3UnPQhUFOHY2Kp3bx5mJyoSMWWCjEzjrz6Ko0bBeJRTnc0MBGwOEDbzRKjIJqXSfY25oqIRQuSGjsq3MPjZmQp5GkE8zt80g8qkB5FP9pgyodcFMf86MfbHwRpQ3AnF8DhwHKEbt039eTC+u/lq8PXsb8DbgbcDbwFfZBioATpAQliFEIWiw8AylEt7oZLMEN4dTLOMoq6JVOBbiiLMl/xbr2gUftm+MQyYz75gQKv3CbQGOltjTUTHAwPxrHzj8JglpNARC57Fgjhe2gU6SjAMdqZkAHA0SdNs5Y1O0aChlr03Ag3uecG4M7biKUyu1cXiDJLLxjnLYuXb7Qm9vr0/aIbULbzg+kgR2Er0Zt9AJc2b3CZLERhx4UXZUa2+gqtDja0FS2CaC8TNwyX6WdrOO9EXoczWH3l3P66UT7Kpvfo5imiUe4tj2xoxjAABpQ/L9TGr3mqLjocfEQoxKp10777wu0x9yL0ypiKrmJLsOsywbxUNwPFFTsW1ufzptkLYEdmDyVE0JcDj9tQE4an8eT8bIvW4LLCrPL+2ptI0A3lBdE4U3hDbZYRzOsFiIcyQzhiOZcVM0xDmiFDlU5VhFHwGOVvjpsKtA3VclefjM7EPbhe4DXrP0Ae3VhTgWbPE+UX0SKFn4/NAAp2CmGdchRQbiMBlwxZTqDGGSmblMUmbOHKhy2milkVXZOMCGoVBKTeMkkE6Po1eVCfSmWSZVHSSaVs8RsScBM6zj7NO1Q+7D88lzSSCODrHKpwTiUIkzCA1v+B2Lk6Q4NYJ8UEaRT+niFTgzeW75bWZ2H/t+8v3kbcDbgLcBbwPeBhaCDYQATil9DapIvgT+SxuCBgtnUOPgjQI3Ct6MK3hDgKMhTgTgMKQq+KfYOuwWcsg6cVw0HFBwogLgmFAqleTSBTjagQsc/gDgSP6K6ygHs8kQ4ESBWhiqxIc6cLzYRoE04Sm3LcyR71mLky5jbf5xr5jCWfJiSB0/K03grNPRDRxr6wApoBU40XJOabe0pRIWyCw+YXhDkDOhSgjiqCm7DcAx7WBYUaW6wD2/LEsbdD9qx5POZ3wJxjPoR+4nx5pLLbYoTjEVOIQ20wGcuHt3ptdcrZ1uX+j+CPeDOOjSVtayzr1n9PED2zAgJwr39Oc4B1mAjVvLdmJb4mxbJz0KUSrHxemfkL3TxmUWJplSm+sIUHkttAVeZxywiBuHav1r1qvZ2Hh/R8OlXHijIc6RzAiOZEahAY5AHAIcW1yAEw7PFOWfCaOas526tsB+4LhLX7gAR+xB7mfek3wuWAWOhTcDmBnAsRBHT/PNRMOmqJw1Om9NCOS48CYAN+PoTcUU832BSdCDROhiY1KHc56pEFr1rOP18fyTJv8OQ7IIcngswhkNcXpTFt4I6FdgKM3jMlTKwJvkCPJBGUU+GQE5XoFzg8/aae7LOd8f/riVz1rfJ75PvA14G/A24G3A28BcbcABOIQEBDiS9NIk7lUQZ+F1cBzAIZyIQhxXgVNUuXCYD8cW65Brp0MgjlU4mH/SBU7EAhztXGpHxf3HXRxiO4tMVQVOhSLKcTZdGCGOp1rHcbPnsMBGVDguuImDN9yf53GSl8rxg9o4ssqZlTZZewmcdAEn0k+O+sLdxhqytFtqcfo0ONHTL+t/tQXaSG1DHsR5N2MkYRvqRVza6ta23dO1I2oX4c/SZqnjjjvTdTyGAAJxjAXiRNU32kHWbYk7t3utXJ5pG2Q7OabU4TGxMEegjdR6O2uL+njuuGtHXhziaC1wZia17BsGOFF4w3PHX7/TR4GN0/4FVArIkXwwPA7tS8AFxyAMKvR1VztfzHoFcDg7kJmFzs17oxQ4VN7EKXAqAY7OiyOqG7eOKnA4pjFtmdE6bQ96/N1+cOFNpS2o8Y8AnMoQKqPwUwmMJXyKSYwlkbHMxiWhVDMDOBqiUA0zht7UGPIsyUhJ6e/4PbfTAEfsy601wNFARitndC4aDXCYn6espihnPh6dEFnDfkIczlillTeEN+o3wuTYEaApAKcnOYyeBMuILgJzUiM6F44HODdgw3O1fb/f3J8bvu9833kb8DbgbcDbgLeBudiAATh0QAzAcfIm6HwJN/Jif+cGpRLgMAeOE0JlwqiYA6ciD44AnAB+iMMaX2vn0ChM5CVa/Xsq/8zyH2ZTHLUJ99POnfwTTmgmAM2tXfWNjIfjaE4JcDgG3EcAFOFNFNzoz3o7OT73M+cIObJVYI5sq2o77iEn3YE44rBHv7dGLH3j1mHVi4Y4AmmcsCmluNHrtVMpAEfDM3sOtw9tm+330nduG+ayXO3YM1nP88l1CxygYyy5cKJOcjws0dfkXi+XZ3L+6DZx1z8VyJH2sJZ99TGjYx/YhAv3AgffuYfkXqqoTUie7F8RNiV2wDp6XfI50kch2xeII+ob6UMej2Mk40NYocfMwjQ5/gzqKMBRIGcEZSpynOKGUOk8OG4uHMmJIzNUucBGnjdS3wwFjmunLsSxMIt9YvtDj4Gd3c6EYKowKgnLlHpYhU9paFMN3liAU3YUOFTluGFWVOLIzGga3hgVC9UsoWKgjoI4VMHo0CeBKuHaBTjyzCfgYXgXgdIkys3XVGGeHqpyLMDRKhxR3gSzWzmguxLgWJCjFDke4MzxWTaDe7Hqc8LvO7ffD99vvt+8DXgb8DbgbcDbwI3agAU4nD7cqG+UCoT/GqqwHr50L7yOngrguEqcqgDHOH/W6RTns7IOHFE6jjEAJ4A35vtg+8Ch1c6PzkchahyBOaxdgCNjEXE0XYASqGFkW2mzOFDRWr5nLfuwNucIObExAEe2i8AbOZa9XqM+quiHKFiRNrjt0ssalgnQMM6Y5LmRmmELyoF2HfY4p136UM5Xra5sx0zsQm9T7ZizWS+AwHWMXXBjnWTXQY4/v0CH2Zzf3XaqvrB2FQYYXO/up49XYRdxgE9gjKqngzgRgEMHugrEEdusXottTBGqGNg7r402Z1UmUVhR/Txu3zrLDsQJlDixIMeodNR3BDhUGTqANphiXGCNW8sMVDI2zvlDz4Hp1sv+0g9uX2iYVc0uKwGOgJxwzXCysOpGplbX03qX1PTe4yhnxzElwFG5yrSaRmZxknwyOjzJhiVxfZBjxiQMjs7yZEGOgBtTSwiVAjgTBuAwlIqJlA3AYf4cBfolFHBEz3BVocBhm0agFDhU4ajCzyakigCnSvsUFHX+NIi1Qz+NeOR3bzp799/H2tGsnhm+D30fehvwNuBtwNuAt4G52oACOIXUKIop7RxSAVImvFmUAMeqcESNI3XYwbSOX3R93OdYR9R5aVbfx0CLymO5zpVZjvxrXTnQjqMpICUAOK6zLk6WONnyOVq7N5M5tgAcOX7V2t3XLs+of6q+/IXbF++QVwM2dhy12sm2if1IyFfZn9XWhdtROXZx31c71mzX62MLFAmH62gHuRIYcJ/Znmem28dda3TdVHZmzzNT2wjuoRDQiQCb6D1XBd7wWDPrmyr3VnB/yXHC46NhRTy0mtl5zXEjEEcnN9a5cVR+HKXGMQBH2bNWGMozTeqQrRpnveTWCq7Jtcy1dsdf7kexAbcvwseXKe4t5A6DG1mvrt1MqV5S0SvanwAAIABJREFUtQtuZHkagCPwRuWgsdNwa4BjYUgI3AjEqQJILMCh4saFN64ChwCHKpwowDH7mETICrYQ3jAxc0iBowFOPjkMW9z2eoAzq/tqxvd/2Fb9OXx/eBvwNuBtwNuAtwFvA/PBBgzAGUEhpf81DQEczuixaHLguNOIx4OckKMTUgy4zom7bI14Vo5o6AXSPd5Mlu05rQFFHM3AwXQBDveb7vjRYzsAZ5bQxrZNH3O6/oluX/nZtj0e4oRhTXSbyuNZe4j7rvo6247q/Rntxxv97J5TO8UCc+JhAbe/0XPOZH+3XTNZjj/mdLZxY9+7dlFN6RXfrnAfRu6xiv6d6vpncvybuc1UbZHvbub5eCw5brU6ej7bnwHEic4Exs9U3gjIUjNyjaGcrSxU4Cj1jVHgKBUO1S8SQkU1lgI4oyoxsE4OrBU2wexOVLRIOBLBjYE2bl1dgUMYU1l0CJUBODlOca5DumK3JZyUWbWkrRkBTZLIWCCOnZWqVylwNMSJtk9BIQdshm3ajIlX4Nym52X0HvCfY+2x4tnq+8n3k7cBbwPeBrwNeBtwbUABnN7kMArJcaPC0SE7QfgUX+4W4A9qfAiVddqj38+/a3QdoemM1jpDKuwpBHD43XT7x33vHLPieHM9Ztx5bmxdNed+umue/+Mf1y+uTcQtx+3j101nCzfve3dMvur9Ln0R7QfnuVIBhaPbms8K4IyhnKkEN2GYYxU4FQBHqVqqQZx4GOKCGy5H4UgchHHXERhpgMOwrglVFFBy4Ex4eyrKdBsD2CQAR2aiSg0704kLcCK80SXaRg9wqtjUnH4T/bFu3rPS96XvS28D3ga8DXgb8DYwVxsIAE5vcsxR4RjlzQKeRpwdEnXSp/o81w6cP/vNwTGa8iX2Zh9vft2kUVuYP+M4v/rJ94sfj5tnA84zpQIKT9HPohCJBTgG2lB5IyVn8uC4ChxRthiQo8CJgTIKfijljYAQq9JRQGTW8MZCGBfglHITWhFUBeBomCOzpzlqHqUE4kxTFjRppZBeZxU4DOFy9pNwQ6/AmeOfGFPY5JS/nX6/m/fM8H3p+9LbgLcBbwPeBrwNRG1AAZx8Yhi9iTGlwtGhVJK7oNq/qAunI6OOetznaKcs3M+OgxT8u30jYxV3PK67kWPOn33FFhbL9fjrmD+25ccibiyc50kFwJniuVIBcBxYI9DGrZXaRYdPBSFULsCJhTgMVXLDkASkzL7WKhqGSunCsCkFb1T41LgN5YoJuXIVOcFyEMrlABvJzaNCvSLgyTnuzVfgxI2rX+fvd28D3ga8DXgb8DbgbcDbwO2yAQtwqMBJjmsVjsqHY3NH3K7G+PPcDMN3nKSbAlrc403hZN2Uc92M6/fH8PeRt4H5aQPOsyQW4FR5vlSEUEUAjsy05UIco74JA5xwTpxKQKLzzRTSs4c2nDJcT09OQGOKAjhU3UxogBPMQMVZsETlM6KTH6tExmH1TNA+ApkA4sTn5pFwr1uvwPH31vy8t/y4+HHxNuBtwNuAtwFvA18VGwiHUBmAU0gxnGoMRZabMkOJN6jba1BVHKE5Q5abfTxvD7fXHnx/+/6eDzYwE4Aj2zjtDQGcKvCGEMcBOAQ3Gt7opMEqlClYx9w0BC4GmETgyFQAJ4AzGU4FXr0UM5wuXJdKgCMQRpQzMnsVYY6FOJIPR8MhAT5SO1OhO+2/NQBHz3QWJJOe8++IM6b+GItGyeqfrd6uvQ14G/A24G3A28DttYEA4Ij6pqCUOFTjjEJNL+5ftPyLlrcBbwPeBrwN3LANCJzhzFLDUAnXg1BP57tgnfkxrAZwRHlj6lJG55gReOMCHLVOhTIJ2KkGcDQY0dDEVeJElDVV4c0ECoQ3FQDHTCGu9pNwLQE4w+hND6OQFjUOkxmbhMZBuJfbFrNMpVB6TM+WZWbNuvkAZxRlzv6lxmBUzwh2w3Zwe19y/Eul729vA94GvA14G/A24G1gMdmABTgpHT5FgMOExr0CcFJ+wBfTgPtr8fbsbcDbwJ2xAQfSEODMBOIE4EBmoTJKmxC8mYCeNXF2ACeY6SmkYBGAU6mu0eobo6zJaFCjVTgTKKRNMeuLmUkUs7qUspw+3AE46nxheBMLcAhvpKgQLRfimPbdUoBj4I0HOB7eemjnbcDbgLcBbwPeBrwNzBsb8ADHG+O8McY741R6Z973u7eB22MDMQBHQI5bBwocKnVYqPyoBnA0vLEAJwxxdCLhMRVOFahwVG4amVI8rGbR4VOV8IagJh7gCLyZRCE9iUJGl5kBHK286U0PGQXOsMmHYxQ4Am+UCkefvyJsSwGcMfTeEgVONYDDcfT3jO8DbwPeBrwNeBvwNuBtwNvAnbABD3D8i6h/Gfc24G3A28BtsIE7B3C0ksWGT8UqW0zy4gDURMKkgvXMbRMocGYDcCYVBNJhT64CRwBOJITKBTiETgFEEvXNuAmhuhUAx4E3FQocD3DuxMuaP6d3ErwNeBvwNuBtwNuAtwHagAE4I2r2KU4h7kOovGH4h4O3AW8D3gZuvg3caYDjhCS5cMQJTwogiYI0YdXLjQGca2AoFY8R5K1R05aLCicmB07QRjslubRPKXHScqwbBThG4ZQdQ1mVCLzxAOc2wE3/vLn5zxvfp75PvQ14G/A24G1gcdpAJcBRuXD8LFTe4Benwftx9ePqbeBO2cBCADgMraoEJgJOguTEs1LgXEMpJwCHih0BLq4Kp3ImKjULlUCcqPpGgaCbAHCCHEOENgQ4MfDGAxwPcLxC0duAtwFvA94GvA14G5g3NqAATiGlFTjF1ASKqXEU01L8NOLe2btTzp4/r7c9bwOLywYWIMAJhS45s0vNGOAQ3FQDODIbFVU4AnDsNOKcCUumEtd1JDfPbBQ4nNXKzS0kyyGAUwXeZEcis1D5EKrFdV/656wfT28D3ga8DXgb8DawkGygEuCkJ6CmY+WUrJlxlTxyIV2Qb6u/Ab0NeBvwNjAfbWC+AxwzdberwFEJj6nIMbNPST0jgHMNRcKbCoAz7kz9LSqcOIAThjl2avNZ5sBRU5KPVAE4NlF0VfWNBzjz5h83/1ybj8813yZvl94GvA14G/A2cHttIAxwCG8MwNGzeniA4w3y9hqk72/f394GFqsNzF+Ao6cUN4oXCVsK4I2EVDkQZ04A5xo4O1UhTYAzpiGOM6V4Ie0Cm+iyO4U4lxk+NcMQqswIiiyiunFrNcuXhjjxAGcEZQ9wPMDxsnlvA94GvA14G/A24G1g3thAAHBU+JSCN5MoZyZRznJ6VgKc8XnTWO/YLVbHzl+Xt21vA4vfBuYpwCGwkXClAN4w4bGAG6lvBOBcV0ocBXAyE+g18EVBG4IbKZkouDGfOUNWpPAYGgRJTh1zHOcY/IFX8EYAjjtdu1oWBY4BNYQ1ccUJtSopAOTv18V/v/ox9mPsbcDbgLcBbwPeBuajDRiAw/h4qm8mUVLwhgCHIMeEUXni5iGWtwFvA94GvA3ckA3cKYAjAKZaHTc7Vdy2UwEcJieeRIEKmwxnm5pU4VPhECqGVPF7bsswKFdVUwXcCIxRSh3mzKFyJ1pG0Zvid3EAx6hvqgKcYZSMCicW3AjMcZQ6ix/gjKGUdsvtfHl1zxu3fDvb4s81H1/afZu8XXob8DbgbcDbQBjgEN64ACfrAY6/SfxN4m3A24C3gZthA7cL4EyglB1XpVJFEwdmZrLOgTfMg1MRQkUoMxEBOBri2Bw4GuDofSUhsQtxqiwHwMaCGsIaVQhuDLypBDgOvJkS4GiIMzuAw7G8GTYx344RB024bqp2OnbthqdVXa52rOi5qX6W4n5XbX+/fupx8v3j+8fbgLcBbwPeBhaHDYQBjihwTAiVT2K8OAbZ36x+HL0NeBu48zbgOLoVoTyECKa4jq9SfoyqZPrlDKe5HtclM65CfKkSlaKS7/NPB1VuJsCJwJsQwCGIIbgRgKMhjlbgEOBMOkmMbxLAEWgjdSiPzggKGVuC8CkPcKaBMHw+uJAkCk6izw/Hll17nfFy9Hju+WUWUNYTalZQDXKkfXH7+nV3/vnmx8CPgbcBbwPeBrwN3B4bqAQ4Jomxhjd8ieFLw+1pjD/PTPvZfXmc6T5+O29f3ga8DdxJG3CeWwJr4mrXCZ4zwNEg58YVODHwpgLgOBDHwJwwwJlEKcfZqDTQCStwoqFUMSqcqAJHwE2gvInOZDUXgDOMcnbqIqFWOoSKY3knbWmm53ZszrWr0LIcSwCJBSjx4CTmmHF2HLcu9rw8f+W5NbyZjIE40t6YdoSOv1DGSK7H1wvjnvLj5MfJ24C3AW8Dd9oGQgCnmI57WfAA504PUvj8cS9t/kYK95HvD98f3gbmnw04z64451bWuU7obAEO87YFKpwq8IUA5kaLqG44G1RF0SFWVN9oBQ4BDpU4/CzhV3H7VVlXDeA4ypvetIY4hbSFN1TihBQ40r9V6ukBjsmXE4zPfL/HHHsL2hy3Tq6D7zoCb7TypahCmASuyHbmGFX6MVCSTfV90B45ppzDPT/fx6SIEsd9H4u7lrh1cg5fz79noh8TPybeBrwNeBvwNjB7G4gAHL4kyIvLGIppXXzHzr5jb02fxb2cybr50kbfjlsz9r5ffb8udBuQZxVnPnJCpqLLgXMrMyTNIoRKJd63EGdWoCYTA3a4zi0CfhTAqQJcZHu1rQY3YYBTZb9gZirOOOVsEwdwKuDNMAhx4gHOMIoZXabq95kBnGEsDAWOY2uuPcUuy30VBTgCTVwlsjlu1GbV5yGUMtOVaJige26+b0nYlICbawbiSFsE4Mzl+uRcvva/Jd4GvA14G/A24G1gIdtAJcBJjaOgyhiKKQ9w5tfgOi9tfGGseBn1N+P8Gi8/Hn48vA1YG4g8v2Kd4MhzbS4KHKPCmRG8Edii6nELa4xSRsMbrq/8rlJ5Y6GL2s/AHipvpgU4MiV4ilOC61mmAogj3xHaxIRN9aYJb3QppIdRyOgi0MatKwEOgYOGCjMGOMFvz3y27SlsLU0bk8Lt5Do0wCnJjJzMCaiWowBH9jX1tNDGhTruvnHn5rnMjKDpayilr6OkarctbC/3dY4VvZf8u4EzrjK+vra27vvC94W3AW8D3gYWsg1UABwFb5JjKCRHUUhxenE/wPNngCMvpXN6SeNL6q0sN2ov07XtRo+/UPav1g8Lpf2+nfPnuTFfxiLy/Io6nfLZfa7dJoAjMCYANQq+aHAT/s7MWKWAjgU2sg3r8DGoBpoJwNHQJp8aBQunCi+wMDwrqsCJVd/MBeC4YGEI5WxcsXlxBPSoWo3RfLGraDti7MyFHaFlbsv9+bwlPGEhLJFCmDINwEkPoTTjItBF2ihtjzt/NYDDbbmfHMOtHWVb7PdyvtnW1X6PpC2zPZ7f3v8+eBvwNuBtwNuAt4G52oAHOAsKUDkvasG/oM66aV+qp3oJu5nfzfaGnOu5Z3uehbL9dP2xUK7jq9TO6e5DGdOvUp9Er9XpI4E1cbXreN4qgBNR3lQAmKwLb2xiYQVnqnxXyBjgIhDHVeCo2agkB04M+CGkMfBGAZzUqAU4GQ13gqnDTb4byXsj6hvWor5h7SpvZNlCmDC8KcXCGwE6DhSQ8Zr2tyY69rfzs7EzaSvrELRxP3Nbto33Z1T9IhBHwI5cg7s/l2cKcNz95F6QY0bPz3PPBeDwuNEwLTnXTGtpk9Ty7Jqulu19PdcXcr+ftx1vA94GvA14G5iJDVQAnKIJn6L6xitw5psROS9g6qXU+ew6PbEv19O9fM30e3mZjavdY8y079x95rI80/Pc6e2mGit+J+1z+8DtY3c9l2X7uNo9Xtz3ft3U/Teb/qk2ru4xphu7r8p4OX3lOtfRZfdZdisAzhTwRkAOQY1eNkqY9CgKGZYxFE3RwMbAHVHMKNWMBjka9ui8OkECYxWaJcdmbffXAGcE+RQTEsu5zDZKhUNlDouddcouW3gjsCau1gAnAm8YAlQF4HC9zevigJzY3xjX5u/ksrEz167mBHAIUAhSoiocF8TMFOBE95F7QfqJzwg+7yPhUymGULG4MEme/3KMaO2Mk9sHM1kO7j1pF+vo86vaZ3cfWY62baafZX9f37zfKt+Xvi+9DXgb8DawWGxgCoDjc+DMv0F2Xn7kZSx44XK+i325rvbSNdP1LkyYalmON9OHhGw/13qm57mT20XHJu6ztE/6QWYj0bWV8cv3sn20jh47+r3/fHPva9Pfcj+yDu5J6WsZM6llvdQyZvJ5sdZynbcniXHVHDgG4AissbWBKQJVWCtgosEJlzXEialD28lxTDiVUeLoGagE3sg2AnB0fhvCm3iAUx3e2MTFRnWTHUaRJVDhDKGY0cXCGAEzgyhlBqsDnIr8Lq66Yz7aaZyN8VqjAGUY5TQLt+d18N7k79r0yhe9n+zPemia4m6rz8nz2nO7548CnC+r5sEpp0fNMZxrVtdzAwAneH65YyvPrelqdx8uR9s128/R4/nPN/e3y/en709vA94GvA0sVBuoBDichUqSGPtZqKZROtxuw3degMRhnPIlyW2fvHxNBV9m890Eymm3jKOcZhkzhS+X0xXZ1q31cfTLdFx75Drc2r3O+bbsjNmMxkpfl52NRGaFk76Q6467zrhzxW3n192cB7bpb7kX3VqNtfQzx4zjFzd20TGTfRZj7Vyr21fRZfc+uWUKHAEpbh0DVSKKF07NrUsE4jgAR6lnokqcQPUj54s5lwqhigAcFa7FbWcCcDhtuIE3AcCx8IYQJwxwDLxRAGcQ5SyLhE3pOry9ASEcr5B9zydbjbsnzXU7KhwLYQhS+DvF3yD+nk2inL6Gcuq6LlxW6+S3TUMTu/90ACcKb1yA4/4+8vw8R7QNX0bawe/dtuj2VMCS6D01m8/B/SfjKs8v+Q2Kq6PPNudeD443l3XSBl/fnN8s34++H70NeBvwNrAYbEABHJ2oWP/z46cRn8+G7bwAVXshC70sybXIy1VY1aEhwczWWaBCOxFww5ddFnmp1PBl5hDHBTfyAivHiHtJlHVyPVLLdc632hmv0LhUW8/285pkTAhvOJ2sC3GqXXO1Y3L9fOuXxdIe0+fBvSgOctTB1WMaD3Hixm2x9E/0OpxrDfosRi3g3itzBTgBMImZBlx9JyCFtYRJSeJgDUy0+kbClcw03QHAiYCcUFiT5K/hcZykxqHzugCH22tAo9Q3KU4HbkKosjpkSyl/IjBJwqe0AsfMPCXqGwVwwvAmDHAceBMCOGGIs/ABjsAbyVMTBSoCcAw8SU2inCLAMeCEy3MGONFzufBGzisQR37/JlAO2kCI5LSD64PfWm4v+/I+c+4tLsfeX/J8mqqOKqz0781U7wozf65F2hhtc9XP0eeI/+x/070NeBvwNuBt4KttAxbgZCZRYnGcxaJX4MxD53c2L0Fi3OL0CxiYfT09wHEhjoAZecGUz9VqC230v4v6sz2ngBup5Xqkluucb/Vsxorbsv28Jhkfghsp0WuPu9Zq54vb1q+78R8/09+BsySOUSXAoQM0c0dnsY6NY59Bn91qgKNBjZ4OXOejkanBg9Apk78mmPlJ1DMxwMQqcATgmHomACeU/0YAjlHyyLlU/hsNcFSunQjAIawRcCP1zQU4UylwXOdenlfzzVar3JMq0bAOo7LqGR3GpEGIC3Ai4KQC4MSBGa6TUKq47+VcUstvo9T8bXTbICqgKMAhxOF23F72dcfAuceqgpyYe869HxVIkWO6v0fyuxSuZ/5ci7StKrCJbidt8fWN/2b5PvR96G3A24C3gcVgA5UAx4E48ZJ/P/DzY+Bn+pIjkCP80jX9P2ricFarRYUjtYYuFsBUgzWyPrp9+PPM4Q2vbz7bZHScpvos16HHTMbI9oWMpdSyfbSWc0TX+883z1akj2P+7Q4cE/Y3x8ree9bZkbFwjhNynOT7xVQ71+o6jNHloP/YtyyjKGXGUGbJjuuSGUdZlQmUM7qUMhNQJatrm4hYVDCcApyKnMgMU8E03Xq2p/D03eHQpUqAIwDGBStVFDjTAhyeSx+HipsA4CiIE3ces22aEClOgcM8OGEVTlhR46hwIqFTOpSqiqMfjM98tU1jZ65dhcKnBKKwJgQReCLqm7kCHBfcuOeQ81jg4sKXcBuqKXDYJlG8ym+lhTg377nGMZXfl/CzS36PpI7/XRKbcO71wF7msk6O5+ubO8a+P31/ehvwNuBtYCHbQDzAUS/C4rjzR9wP8sLrA3kJ0+MoL12uosOuizqYMvZS6xc5+8Im629mrUOz7IwfcceWa3LrhWCbM3lxjV6He43TLUf39Z9v/f0aGdOQs8jvZAw4dtH7S8ZTtpFjyefFWst1xkCviv4z284W4BDeKIAThjQCbYrMKWOmAdeAxyhhAogzjkLahFelx0FVjjt9dyXAoVrGhTdcnivAscfRAGcURcKbLGGOATgM4XLOp9U3AnDicuCEIU4Y4IhiLDoL1TDKWV0qQnECZ3w+22jEzgJ4o9dLAmELUWYDcAhjXFDD5dnCmmogh2CGf4jEgSQCHAnnErWr/BkizxOpOTayLLX+PY37zZ9+nVWByrbhdwE5R9QmnHEI7GY266LH85/t74rvC98X3ga8DXgb+CrbQAjglCWMSgBOhj/6/HH2RjJ/+0BenqJ13AubvIhJbR3LqV/M3GPHgZWZrHMBzUyWo8d02+AuLxTbnO7FdSbX4V53teWZHMdvM/f7ebpx5PfSvxwjucfsPaedH34n27n7yLrFWDt95wKb6LLr7N0wwNGAphrA0RBHA5teghtVJlBIsxDghCGOzk1jw6ZcmCLqmZsDcAhjCHBMUQDHnNdAHAtvXIAzgmKWxZ2Fyi5bIOPAG9P/Am10PYKy6nujgnLHRC3Pd/t0bC3SdgtuXPVNNXAi0IRwhcAkCnAE3rhQxoIVmw8uus7dnsdw23KjAEc/dyqfPcynNpcizy7W+nlmAY77OxRnE9XHoSJvT2ic4o7l19nfDN8Xvi+8DXgb8DbwVbaBCoBDiENJupajjyvp+le5g+bXtbsvS9HlOGDDly335StuWRzMqV7MoueKfo7CFvnsghrmV5ppkf3kOKyj5+Tnxfjwinvhda8zrh+mW+fu75fnbjdxYxO3jn0sYyL3l9x7YtPy/VdpPJy+ikIb97PryN00gMNkwEZ9IyqckBInCm8mFcSxAEcrcTTAocImrISx8OZmKXCmATgVyZQZQsV9BOAYNU4wlbiGOBbgVIZHBeAmO4IySyzAWUj26thbBDpZYEKwYpQv1ZIHBzlwCFkIW5jrRlQ4Al9cACMhTnG1gJzZAJxoHhxR4NjjW6Ciny8atPCZI8DmGorp6zMs3JZF9mVtn188toXQ8hxjvZBsw7fVj5e3AW8D3ga8DSxcG6gCcATieIBzJ4ybs4JVFvdFSZbdl7WooygvXO5LnLyQud9xWfaVFzPWco6Z1u6+AmBmCmyi2+n22ZdStw0L92aL2lJ4jEdQDDmujoMVrJdrZ3+4/e0uu30VXZb9fR0di+k/R53BuM9uv+oxCjtSct/JeHEbd5/Fvuz0mQtsosuBvc8hB44KoeLvloRQiQKHoUgxAMdAHJ3QeAKFDMGNWyYCFY7OjcPjyaxRNuTp1gMcR31TAW/0dyF4o1Q4Bug4EKc6wDHQRuBNdhRllXuI+YcWm10KRHHgjQpbilO+uAqcKMBx1TdyLAODgpkaCVvcwu/l/AJxBALJMaLtiAIcSWQcB3D0b6/Al1L6GkoBuPkSxfTPZ1C4nYY9en/5fZZju8+vr9ozbLHdC/56vlq/wX68/Xh7G1gsNhAPcLKTKGeZGFK/CC+Wi10o1xF27AXmuM64foGy4EUcQ9YCaaar3X2iAMc910xe0GR7ebHji5689EnNF8lqxbQ1NYliKtyu8D99i+fBUznGGuAoiBM4tBLmILMa0QFmH7C/7ZhZO7DrLOCRsZnJOC6e/r3597oDH1zAoJaj/RYdH/e+5LLcJzI20f0X62enDwMbd0ClrHP7d04KHAE4Jr9NNCFwdgyl7LgqGurIVOOTBuBcQyEtRStxgrw4nHI8ADg26XAU4LhhVDp8S2bAMvl11HHMsYLjWSCk1DTZEZRyEkYlMEZAjk5aLDl5XHhTyo6CRYdS2f10Qmj2t4AxW1Nto1Q3BDdSFh3AkftNQIkAkGj4VDSJsSheCFyovBEFThS8mOMoxY4kHI7Wciy2wQU4PJa0ayYAR46jr0HnjrO/u3wP0L+3X6KU/jlK6V+gyJL6JYqpP52i/FJvp0AP972OUuaanqFUhdjzHF/V59difS7767r57wu+T32fehvwNnBrbSACcK6hrOCNAByGUvHH+tY2wh8/3L+Vzj0hjn35nNp5DwOQOOdepofXtQAinSiTBqGcAZHjz6QOFENjjprHdVoFJok0m//uybIj1U5NokCAoyCOhRG2vU5bg3PadQvJjirH2AE4dGDFmZU6BAo0ILAv0vJCHa1dm+Fy2M7859n0hwMfAsAQ3V/627VduR/lHvAAR+W+ELuOq4P+FcAwi1moDJjRChwLSFzAofLD5IZRdEt2BIUsEwUL9BHQIrU9lgY4cSFUMmOV1GOQGa3U7Fexs1CZ46rExy7A0eCmlBtDkSWUC4ftFJBjAI35XsGb3KgCPxrkjKBEEBRAm0qAY+ENIc5iBji8X/U9amEJAYgLXmT6bkKc6zqhsPpegMuNABwXusjxCG4kETKBjtMeJi1OOeqbtKiBCIXssfTvgFbHhP844R8mX6KUIcARiEOAM0VRoIcqHYE311HO8L3wGnSORAE48qyLPgP9Z/+76m3A24C3AW8D3gZutQ1UBTiHjQrHA5zba4SuY19IjaCQsoDCggwXlLjOoizzexZ3X/tPbNGR1EenmS1mBlGtFDKDKGSGYorOv6DyQ6jzSjuizqsLbdzojHGbAAAgAElEQVRl7dwS3hRS46qEwVPc9bjXVrl8q2+cGz1+MDap0cgY097iYUGwj+rjqW1AHJVwfXtt+Ub7aP7t746L7kuOifRx2GbF9qO1vjdkH16jPsZXYWyc/osDN7LOtf9ZK3C0sobTjvPHTYOOYRSY1Dc7hGJuEKXmAZRaBlBu7Ud5bz/KrYMotw6h3DqMUsswis0juuRGUJSiAI+FJqEcOKkR9KZGY0sAcCT/TsYFQgKLCHokLEvDH4IkzkBVyo2romej0usUQFLXpmemErgTgjdRiBMDcMLg5qugwJF7zIU4onoJQ5xS6jpKwaxPhCoCXKYCONxOFDc8XrTwe/dYhDcCcHhcAhxpD49zDbYdU8EbgnsBOKwd1WuGAMZCnCCEKvVzFONKmuFTOoSKCh6BN4cJcLKTOjeiUuB4gDP/fp/Evn3tx8bbgLcBbwOL3QYiAIfKGyk+hOp2Dr51zgVGEN7oYr8TkBGu5eWf2/eqMox8cgj55CB6EgOq5JP9yCcHkE/1m9KHfErKVeRTMy2yj6571PF43EHkU0PIp4a1I5OmU+ImBtV5JazyxsTYZ7REu5ghxLEAh/tah1iuV/pmZvXtHL+ZnMuOo2k/4VwswLEP3op9lMMv/eHUKWc5ADx6nYYF9pgzaavfxvSXG0ailjWEsOPCPhYoo52nIOFnahxFt1S1aVFfabtYvH1/awGOCvsVBQ6n31aQg3BZwA1BzQCOHhrG2z3jeP/wdXz82i9x/I0/w6dv/luc+MlvcOz1f4sPj/wa7xV/hbe6f46jhyZxZO8oSs1asUMQVMiY4iYynhLgmATKJi9P0ah8XCCvQI96ZtopyBneVcpNoJgL5+6xKiEDgNRU4wJ8CH1YIiocD3CqKBAJIkS5GIEfCoRwHb8nWBHYIiFUGrro+1WOw+2jRY6vj8PtNayJHo/3B5873E7aImHHAmVYS5vc4+plefYoiEPlTABwqKgxcCb1JQrJ6+hNXEM+MalKb2ISLIUkw5j1OV14czhn3gv9DKVV7Mj/vi7e3y0/tn5svQ14G5h/NlAJcDgLlau+8SFUt/wH2zqD4tiPoGjgDaGM/T7spIfBTRjadDf1o6vxKjobLuvSeBldTZfRnbjklIvoTrBcqFLke1t3JS7BlsvoSrBcRXeiD92JfnQTGiWHkVcgaQy9KRaCHE7LS4gjyhuCm+soZa+jnDUQh98bBY4FOOaazT/qNNiqJaQ4mn/OsB1HDW4E3iiVlVJaucBF/8NplVRjCvZwH130Z+7rluA7B+J4gDPHB28Ab8Z0IlfzmXmK9FhyDATgMNxU551QeagcNVlg09w2BNpoowJvpJ5jWxdEeNztAThlleNGh4IW0kPoTQ+imNOKm1f29+Od3nEcf+OXOPfRv8PVk7/F4Oe/w+j5v8fExX/E4Jm/w6VPfofP3/8tjr/xF3i3+Au81jaGUssQSs1DSsWjVYjDKKRZGEql70GlwknKM4+1gTEEM+r5RfUNYU74OS4gJ4A4CvAQ2kyglJtEkRCH4VfZCRSyWsHTmxkHi4I5HuBM8Rvt2FzVe0TgiwtOBJhwHb/nfcljMR/ZkKklNxm/k2MIvKmEKwKCwseS48kxo8eSdkhdefxKSGzgj5pRlMoZqnDM7236OgopgpsJ9DSOo7th1JbGUeQTVMDqZxnDpqi8OZxjmURIla3yI+rfKP9iv5if2f7avH17G/A24G1gPtpAPMAJphL3s1DdjkFzHXtR3bi1+716+TdOAB0HV3FDtY0LbtrrLqCt9hzaar/Aofov0NFwDh2NLF+YchYdjSyfo9Mpep18J9uylv1Zn1elnXXTRXQ0XUZn0xV0NfWhKyEQZxT51BjyqXEFcXrp1ArEEXiT+xLl3JcoZa+BKpzA2VXqHcfRUdc8BbwRsBOBOLdj/GZ6jmAcI9ClEHLswy/+Wt2hHX8X1NjlERSSI+hNWsUWvwvOVdVp8Q/kacctQ3AzhrKp1Ww8tDOxsdSYCfdjmBTDC66bGV90Lqfe5Dh0GUNvcgwFlhiAU0gNo5hm0RBn2nYt2DF1nGkJl4qr5xhCJQqcMsOOmBMmM6zgTW+6H6XmfhzZ14fX2vrw4avXcP7jf4fxi7/Dr0b/Eb+5/r/ht7/6L/jbP/9/8Jtr/xm/GPhPGD3/Dzh/7D/g2Ou/xptd4yjvZdgVQRDDSCWUlFNzOwCH8EaKAtcmB44BOAJvVO0AVlFw2UTJGt4Us4Q3pmQnUVAAhzNlTSh4owHOuII6OhmzqG+8AkffQ469uTYVLLvPQAEwLsQRpctsAE74+W3VPS7skHYZeJMZdKAQv2O7pD1RcCNt0udRtqNUfvyNsKrVklLK8M84Qhidx0YlI2Zy7iThzSi66ofRUTuIjrpBdNYNqdLTyN8TPvcmFLAhuDliilbhOMpsPhcX7LPIHXu/7MfR24C3AW8D3gYWlg2EAA7/QWaiunKGP9ImgbH/kb7lLymBQ8h/cx3ljVLfuAoc+fdW8h+kGTJFtQtDl7RTUcoN43DLiCpHWodxZO8QXtk3hFf3D+HVA0N47eAgXjs44JR+vHaQpc8U+cxatuM+LEN4VZVhvHpwGK8c0OXI/hEc3juCcusISi2jKOZGUciOoTczhnyaAGcM+aQGOfnUBHqZrJhKnADiUIljAE4QdmX+XQ6Siup8D9H8D+7nQJkjDvY8e7kMxjmkohFIZV781X0neVN0CA6d/kJyFL2qjKA3MYx8UIaQT+jSmxxW9hOcx+mHuHX+YT3NwzoANxrkyHTKui+Nw6ScJ4b/UVl2HcXUdRWG0JuYQL5pDPmmUVNGkE8Y0BaCbQJvNMARSBSq55kdz91uxHGNSdLtgpzAwZbkuzNLYmwBDmeZIljhc7Efvek+HN47gNfbB/FO7xBO/+xXGD3/H/CbL/8jfveb/xN/99f/N/6n/+6f8b/8D/+Mv/ur/w9/8+v/C39x7f/A+MV/wNkP/xLvlq7hlYODOLxXK3kIhKjqoQJHJUdWChwH3hgVjrpvVViUk+umQn1jnW464xriUGkzCQ1wrqGYu4ZCloUQZxK9GSka5lCV4wFO9F52bM21p9hl2VegiUAc1i6kELs1swMqNU4UuJjneBCWxf2lyHm4D5NJ8ziDKGUGTC0qHDmmtKNSdUMwJOCPqj5la/JHgAE5hDDqfS5IQKxhUCE5jp6mEXTVD6GjbkCVzroBdNYPoKdxSP3WMPfh4eyEVt4EAGcC5ey4A7T52+UA7UXznJJx8vXcn/W+73zfeRvwNuBt4FbagAU46h9k/ovsAc6t7PC4Y9uXoCkAjlKYGPm9AThq6lrCm6SGN0dax/B62zW80/ULvNf7p/iw/Gc49uqf4/hRll/j+NE/w/HXf4VPVPlTfPI6yy/xyRssvzDll3qd+o7fc/s/U+X469z/16ocO/prfHz01/jotV/j/SN/pnJGvJP/U7zZyZCD6zi8dxLFZsr8CW5G0Z2QMoaexDjyyQn0cspzTlGau6adFSpwgiSfswc4hDlRiBPX33dqnR1ngTaszQu/ATd6pg+dD4izcVGRpP7VT1DePoKepiH0NA6iu3EA3Q0Dum7ki/egAjmEflSNqCSukZdre3770n2n+mLBnNeFOKH+1GOnck6o2dOuoaDgjQlPaBpDdyPDE0bQ3TCM7oYhdDcOqfEjcOtN0lEaglXfyOxAhBVSBByxXgw/hI5T7QKb6LLrZM8iibFyLulgMgdMljM6DaAn2Yd8+ipePUB4M4qPX5/E1c/+PX49/vf4u9/+Z/yv/+P/i//9H/8F//l//hf8l//0L/inf/gX/Mf//p/x97/9Z/zFtX/CwJnf4dgbv8QbnYTVBDj96EkyX1i/AkQuwKFyQVRX4lDbsCgB0O69LzZknXEVasrnoAI4hDfXUQgAjgY5vVGIY0KrdM4cUeF81XPgzNbW5P4S2CIgRgCOwBsBL6yjIVTO89zkzYmFN4G9xwCcyDEDSOMCG3dZhRxrwC8KP9oeoY5S/xDiZJ0/5AgJU4TKfCYNorO+H11SGvqRbxpUzySlPMyO43COhSCHipxxBXCm+n1ZMM91D5tu+R+T3hbkmeJrbwveBrwN3HwbCAAOHRGVw4F5HLwC57b+uFnHuhLgMMeC+j5Q38i/uZythOqbQfSk+lDI9uNo2xje7f05jr/2a5x++zc4/+Ffo+/E79B/6nfoO/m36Dv5NyrnQ9/Jv0bfKZa/MuUv0XdKl/5Tf4n+U3+F/lN/rUrfqd+i7yTL36DvlCkn/wZXTvwWlz/9a1z65K/wxYf/Hp+9+xf49Cd/jg9f+VO803sdR9vHUN47jELzIHrS/ehMXEVnUz86mwbQ1TSE7sSIAjsENpLfQWoLcey1ukqbasvzGd7w4aXH2Tpw+gWbYYr6BVsrCLhMh47/qlJ1Q+XGEPJNA+hp6kN3wxV01V9ER915VTrrL6Cz/hK6Gi4jn+hHITWkpwJWEMDkVaGKKyjSDl37h+p0D1Vx6Oy4hfPY6FnT6DRRISWQrauB/2j3obP+CjrrL6Oj/lIwTt2NV9CT6ENvkuM1gCJzagRO3QjKmVFTXIAjjuR07Z3P38/WqZ6tAkc7mAQ4BCu9qQF0J3Ry9jfah/HhK9dw5me/wuSV3+Fv/+Kf8P+z9x5ebWRb2vf3V35rfe87a2bu3LluGxBCIhiTJKFITk4YkzE5KSckRM6YbDAGjG1ssAkOOGHD8619Th2phHG3u+/tO913xFp7VUlCqWrXUZ1fPfvZRzunOHlzhq9vz3B6zOPk9RnevzrFm+eneDR3ggHHUzTfnsZ1fQBFmWRkTN5iVr7/EqljH3X3o2OJl8jJS+VYmaPUXUo+Zon/F943cjWFADgEtk2qAZjUgxziqAZgZBGCIUkEAW9eWsUVOGR6LCufIiNjaiEebiMuQQeWa5RnovPU+aXUSjwMEv/IOXXRZ/vlPAt3Ygx7UIlx8VuoxveTeJz2tyh3FPdd9BwB5WTjRvg3QHoN1g3SI3V9pCUpuih+5HWl15fUN1ydSb8XXAnGIA4pf6gEVCXlAXttAjTU0MABXbwV2iu90MVFQp9gZ2MSjUe8pXxfGOLQ7xPrSspUZOIzRry7Iucx4rHIMvY7c1Gexu6L5UUsB2I5EMuBWA78thygc4X/h//wRozvaELJI+aB889ILPmJz/kSKvK5YSd0dNIkmVUy48okAjgeUBconcIKU7IdFdoAaotH0XHnPpz3HiDYs4Vx7wtM9u1iwr+Dcd9TjPm2WYz7HmPcvyXFJsb9Ivh9E356nGIbE74nGBfh588f9W5h2LOJIdcjBK1r8HaswNG8hM6aWTTdGEdV4SCuGwIo0fiQf80NY4oDepUDOqUDmgQKJ3SkHiIPCVUfCz4JIbPOAEzCoJOVUImr1xcv+faRtpN0kvzP2G+/7T0EEKBjS4R0lZSueLKrnHTiTTCPSqLcMClpku+EOckBU5IVJmUPjMouKXpgUtJ9dpiTnLCoPchPpgkYv2JM3ir8dQQcjJxUU979tu/wv+l5Yn/xq9qsXEEhrTMfE9qefF8ZEj0wJLoZOKAyG6PSAaPSBmOSFcakXimsMCXZYGL70gmTvHyCTZpoMi0m0HL/nRjAIS+isMomiUo5KCT4KZQGkgKHJsL6RILFNtZhr1zjR13pMHrqptmYt73yDgRrzr4iEl+As0/A6XsOdUiJM963ywD1TVOQda8igJNzxQpdvIPtawI4pI7ieeHnPkcKmbF4GOCIFuTycYpP7kmlwZUWvHSSoEwUwFENwqQa5AAnaQDGpIEwxPl5gCPBG2ks4JAwAgstKpqknw8BczhEDKvB/o6xIvL7JocZP7oeGa9+fKz6PsCJQBIBSwimSBdJwvuB4IisjJUpXsTnjXweAeAiKhnxHFoKgENL8Vxait/z8xBH/nnoPeh/5a9x8TpTeEnwmEMcUcpHOcjfg5VqqVywqJ2wJDtgSbbDkmyDRS1Fsg154XAgL9mF/BQv8pN9yFP3waLmv09mCeDIgRY38RYQR2xH+TaKXv/xffi/6Tcm9l1jeRHLgVgOxHIglgO/Lge+BThMiSOutJCM+F9h4vDrNso/O4kiJ7j8ZCfcXUo6+f8ewNEzgGOHVtENc4oVt0wBtNyagLtlGcPOx5gbeoWNuQ/YXvqErcUP2Fx4h42FN9hYeI2N+SNszB9iY/6Ax8I+NinmD7A5fyjFETbnX2Nz/o20jDxv/f4+Hs6+xOrMHpYnd7Ew+gKzg88x3vcEIccGvJ0PYG1cQFvVDOorxnEnfwjluUEUZHihp6vYiXbolE7kKt3QJ3lgkEAOV+FEPB0YtGLqI3HiyyFOFLQRpVN/eHhDeRgBAkyBQ1J7CeTwCSpNnCT4kuSGReVG0VUfyrP6cEsXRKU+gDvGAO6a+nDXFECVMYg7hn5U6kO4kdOP0owAClP97OSbJmek7hClOtzcWEwmeK79s3P9z/V+Yl9J7cDJhDihD4YEqWSB+VPRxEu6iq5yIS/FjeIMP65rgrhtGEClKYQ75hCqLP24Y+rHbUMQN3VBVOQEUJLhR2EqKaZcbD9bVLROE2oOcfjkWahw/thj2I/t1+9PrCMKJFGWIv3vryqhkjw6JAWOXiEAjg3lWj/qy4bR2zCDib4tbD94y9Q3DOAQuKE4Ac4+noUBzrOHXzHmf462O6TA8TP/L6PKEQY4VLoa7khGxuQEbkQwo3Ly4ZGCxjcp2NgljVU0GY4GOFxVQ/5gBG3M6iGYVBQEcAZhTKIggMMhDgc45JfDwXe0AkcAHKG8IXgTiW/hjYA5EsT5Byhwzv+2RcMMMRZ9bxk9+afX+lV5Fla18e//LbyhY5cgh3gfAU0IwFAZq1TKymDOxQCFQxYBbn7uOdJ3FL9VrAxa5AS1pRfr9PsmKU+ZJxIZ/1Ocf3/+egLgMAUgAznCOJ0aHHgZTCaQTOCmIM2B4gwHyrJduKHzopJ+R8z9uGvpRzWLEO4Y+3FTG0R5dhAl14IoTOPlU6YkOZQic27qsEbB81sYsPPtKUExtm1l62GTdtk4IC+XDK//6H6O/d+PHQ+x7RTbTrEciOVALAf+1XKAzl3CChzWSUUOcMJ13LEd/3vv+G9PdMUJEoELmfqG1CpJZBDsh17phi7RCo2iA+a0blQVBNgV5kHHOhZG9vBk5SPePj/Fh1e8LOD45Sne7Z3iLcXuKd68+Mri7e4XRMdXvH1xincXhHje6+efcbjzEQdP3+PVkw94+fgj9jY/4enaezyaf4OVyX3MDLzAkOsxvB2r6K6bZyDnujEI81UndElW6JII4pAqx8khDnWOUQeiI9wi1w8T6ywTATlhiBOeDP0Z8lRM2MSkhU7MaZ18Tzi44VdM3TCrXChIdeOGJoAayxBayifQfnMS3XcmYa2egq16Gr1VM+iunEXHzVnUF47jdu4gStL7UJBCkzDyWHHDQB5J1OlI1qqcTxzl20t+Ui2//3/zOu0r7oVBHV7I/NNAxsRxfhgSqFsLtZF2w0QAJtmJvBQqY/Sg0hhCQ+kYOipn0VU1i57qWfTWzqL77gzab02huWICdUWjqDQMoCzTjzxqca12M/VUnvoigPOvsg9kOXZucv27AZw4UuDYUKH1o6F8GLbGGUwGN/Fk9Q1O3koKHAFvPgOnH3gp1Ze3Z3i69hUj3qdouT2Bch154LhgSLIj57IV2jgH9Aku6bjiJYrUDY4DHKF2EwCHJugixIRWQAPat3T80zgggQMqn2IAZwhm9TBMKoohmJKGJIDDIY6RWkInhVgZ1bcAxwezmgCOl0cY3JBpLgXBwotC5B8vsfp7FThRv2th83auWCLV0veD/keKXzW+X5Rj8vIkoXQRSwngMIhzbj+EIY4AM2IpBzzivouWcujBx3mhnpWX1HF4I9SlomyYt4vn8IbMqoXBNb+fxu+wv5Jkfm2Quj3qFeQx52feeHrm19SDvBQrijNsuK51oMriRUNJCO23xtBzd5pFL/2WVM+g7cYEGopGccc4hOuaARRfCyEvmXfV4x0ixfuK85MIoPxZeCNgThjSyPbTd+/7Vxn3Yt/j9z5/jr1+LMdiORDLgf9tOfANwOEnkXIDP5rExBLj994GUSe6dMIqu1L3PYCTq3RBm9iDHEUrzFc7UVsWhLvtPmYGHuPx8lsc752y8gBc9HcK4O+I8BXrz8CZLKj84NPhGd68OMXe1lc8mn+L+0N7GHBtobdxHtWlQ8jPciJX3QOdqhdapRXaRBvouxhIMcIAThBmagUcDgJYEsA5D3F+4eTeovSD4vfefz/++ucAjuRhwK9g0pVi3pXEonIiT+1Acbobd00htF2fhLthCX2tyxjofoAxxxrGHOsYtj7CYPcG+lrX0V05j7r8MZRlBlCYRvJ3AjguGBQupsKhCREHEvwzRH/m8yfUf6Rt9j/1WQTACYIZSpP5dlwfcq9wE1AOxpwwqxzIS3WgMN2O6zovGstGYK2dQ7BrHaHeRxiyP8KIcwMD1nX0ta/C1biMrqr7qC8aZVe78wj+qF3IU7vBAQ6fRLPJ8x8qd//e/SDLsX8iwMklgKPzo7FiGPZ7M5jq38TTtSMQpAGVUJHyhsYwob45PmOPPV07wbBnG/dujaJU40LeVW6InC0BnFwGcOgYI3BzLlg3QQJ8UkgAh6tAJIgjjV0XAxwqmxqCWTUiAZxhCeDIII4EcExUcvWNAkcOcDwwq0h5I+ANX14McGQqsLD66bfvd/G7xvyAmCpJVl72g7fFa9Ayesy66LaUY1H5RQDnYogTgScETiRIoqQuibxTInVL/PUhnk9Lrp751tdNgBoObsjsnwcZ/4ugdvHUcUzqNhY2+BfPlZYypY4hMcgaBBDAofzUJfRCp+hAXlo3ynKsqDQ70FTeh57qMfjaFjDYu4ZB6xqGrA9ZeJuX0H1nFg3F46g0DKMsaxB5Kf3gUCgAA6l86DdLKHDCyiGhIIpefgN1fliFI8aKi/Zx7L5fPg5i2yi2jWI5EMuBWA78K+dADOD80Enh73gQyLvcsFIh0WkqsmRX7SSfGKG+IQUOmQNrE7uQo7gH89VW1JZ54WqfxPTgOraW95nShkBL1N8ZAIpT7v3AII6479csGfwhQPQFZ19lXhKfgK/HZ/h4dIY3e6fY3fqMreV3WJk6xIjvCRxtK2i8OYFb+SGU6vywpLuQq7JBr3JBr/KG/XDIF0d4/kTBmzDAiZ4EiZN8+cEq4I1Yyh/73dfD+/V87pwDOHT1na5EsxNbD/O7Ia8bs8oGi8qKkmtONBQNwVE7hxHbBmZ821ge3MHG5B42pl7h4dgrrAztY9a3C9+9NdwrnUBphg8FKVR+RQoBJwc4ifTa9FkkOCv/fJLhsTnqSuj5z/3nui3y4eeWv5wDEYBjTuyHSdEPY3wQBgrWEpz8buwwq23IT7OiKIMmSF503Z1EqHcdC0MvsTy6j7XJAzyaPcSD8ZeYDe5gxPEYvtZVtN2Ywq1cmjT2wqS0cQ8jFe03r1RG9/Pb/Hvf7Ze/18+/7u/3fDEpE0ozUdpzbinPwzBE4H5AP+aBQx5aZJLuhjbewTxwSnM8qC4KoP3uEELOBazOPsHh8zf4/OaEKXG+vDvDybszvD84w+vdU7x6eoqlyZdwdyygssAH89UuGAg6K6zQXLFDF++EPkEo2ySTcIUPJhbCd4rgjYdHkgfGKIgQGb9oe3MVnlB2kAJnEGbVMMyqUQnijMCUJIM4ykEYKZha5yKA44VZTeqb8/BGtKzm5ZlUoinCrCLVX8QjhysCf2OusDFFKv9jCiMa487H+bIgoUY8/3/idkS1dD73IznLc4y8v6h8lAeVuknrUfuAl1BxNYwAOARc5PBmAEzpRLDsopA8iZhiilRTLGj/cWUUK3FjAEZS1IQhjIA0vFNjBNrQbQI3At5wgMNvR16DPOLYhR22pHVRatUPQ2I/9Il9yFU4oU3ohjahBQXpnbhpsKK+1IXe2kGEehcwH3qC9akDPJJiY/oAs33PEOx4iO47c6grnMBN3QjyU0NS50gCOEIJRNuL4FM0sLno9kUQJ/q3RjYuyI/98PpvzMH/6XO72Pv/AHCN7dvI2BXbFrFtEcuBWA78eA7Qb2tUCVVMgfPjG+/vTrTwJFr4XPAlPzE7B3CkungCNyJYd6fETmQrGmFKv4eaMhdcHWOYHnyAzeUXeL37kV1dZgBHgjbsijMz7jzD2ZdT4OsZcHrGoQ794y9BHEGDzogA0aXrT9xAgl6D7hJGoB/O2MTo+OAMRy9O8XL7FOvz7zA1sAt/7yN01c+jpmwUJTo/DCk2GNRO6FXkh+NjfjjkicN8cdgJIqlvfJFg6iRhnBh9Ui/fJwLciKX8sd91/Zv9KnJKKF/EhISWYtJC34cmGmRsSya3vTCrulCaaUdLxQiC7Q+wNLCLzelD7D44xtvtE7zb/orDR1+x9+ArtqY/YMS6jfYb0yhOdyEv2cFMj42J1O1IKvUQAEd47pDhK/usUvlW+IRZnFCLz/3nW56f4H3v9s/ngQA4/SCAY04MwaTgQaUytF0NiVaY1b0ouNqDkuweVBf64G6Zw9zAczxfPcHLja843D5lpYyvNr/g8fw7LI/sY9S5jZ7q+7htCEAX1wF9QjeMiQRxyA9HTKJ/frt/7zvR/T//vf4nHhc5JS2jFBL/eIBD6iUylc5NcDKAU5Thwi2TB3XlPrjaxzE9uIInD3dw8PwIr/fe43j/C4M3B88+49mjY6wvHmLE/wjtNaMo1vYiJ/EeNIp2aBJ6oY1zMnUDtYLnoEDW5Y0ADitXJIgjwRuCOD8EcGgskAyMfwjg8BIqocAxq0i1SO3DSX0TgTemsPKG4I2Ic+AmbKZN6hyRf7RfuEnzr8onObxh40vE50v4fTGQzMYh0TBB7rsnFMBiKcbN6LFenv+Rz8fzSwAcKiElFSI3gz+vxOEQLQrgMBAiFDSkvpGDGwmaKUjaRAwAACAASURBVIdglIJAW1SQ4XQUyCFPo/MKGgIxvCwqGtzI4c3FAIf+n9Q8ousYN/wPgnKAwSJJMUSgRadwQBPfiZz4JhRldqAq3462Sh/6uqYwP7iF5w+OcbxzivdSfHh+iifz7zHb9wLe5jW0Xr+PO6ZxFKQNQEvKQ+pwlcgbDHCAdB7giDLB6OVF3kNRJZPnx4J/od+hSF7+T4y5sfeMbf9YDsRyIJYD/6o5QBf+/7QAR34C96Pr0TuSTgz/h5JbdpLLJ9HREMcsV6AIU0MZvOEKHAc0AuBc5QDH2TGKqaEH2Fh+jsMX71lpAEGZsFnnySnOPos44wzmq1ROJSDP90qrzsOdMMw5B34kdQ+DOaI04QO/sv1k7TMWJ48w4H6Mzvr7uJUfhPGqFYYUOzM3JlNjZmys9IBMmg3h0gNuyBlVWiYrnxL+LtEmweLEXyzP7+tzk8pvThzF4+ef9zN58739GmVeLMCN/Ao0Xa13wai0S92KumBMakdplhWdlWMYc23iydw7HG5+wZeXZzj9eMZKPr6+OcOXV2c42jzF/cBLdFfNofAqV/CYkuwwEMBhExh6fXrffpiT+hFuWc4gzkUA56LvLAdP31v//iQr+hj93vMvvv/n9+u3+zf8XqyUReZJwoyHI5/x549/el2aRPIOfRZlCBblAAvKNwNrx9sDs7obhdd6UKbpQUN5HwZsq9iae4eTgzOcks8K7auvwOm7M7x9dopny5+xMPAKrqZl3DEGkXWpBZorBHF6YUqikiyaQPOJs1y1cP6zhr8j64IV3QHm/P9+73bkNSLbhO773v//9vvFsSQtz0/a5Lflx6EACNSBioXoPiWWF3WhCsCsIo8OL8jIWJ/oRP5VB8q0DtyyONBBCgTXHJant7D98CV2t97iYOcLXr84xbP1D1i9f4DpwadwdVDJZwD6lFak/XctMn5qQc6VHgZvyMCYAI0wbzVTXoVDKHC+BTjkhXNRGRXfzzQe8Mk498AhE2NS4UhGxgwWcJVHuKyHdaySDIxVooW4ADhemFQELajduYhvFThMeSOVbgp/nCiIw/bHz+UEP/7Yd4iC17QfeIjxhnUyYt2MOLjh99N4xMekSAdMGdCRgLP8WGAlPMzTi/JWjBkih+lY4ACNyhwpB6g7HKmhIjAh+oJAxJNGUuKwciq5GkeAHII4EsBJ4r5E5E3Ey92o5I0bTrMW8EIdxeCKgDjSUhWEgSIpAP13Iwh9EoVcrROAkbo00jZU036nIAUWgT+COKT8GYBByQFOTnw7suMaUJLdhbpSN6wNAxj3ruDJ4hG+HPCLLkyFS7/5ZwBBnI2ZYwzbCDAvoSZ/EoXp/dDEeaBL8MJAv7ns/ekzfE+F8xsBjvy4D6+fz7uf+e39Xcat8+8fu/3bfwNi2y627WI5EMuBWA78o3LgTwlwvjfp+LH7xWTvouU/ObGkk90LAQ49RhBHKquKnGDKFTgEcDokBU4Tasqd4ABnBRvLO1EAhylvvpzh9OQrvnw8wefjz/j45jM+HH3C8cEnvHtF8VmKExy/+sLj5VccU7DbJ3i//xnvDz7hw+FHfHz9EZ/efMTJu8/4+v4rn6gSsKGyLemEUJRr0W0yDSU1zrNHJ1icPEDAvo6WqglUmHwoyvHAfNXFTY0TqTsVhYsZNRsk2buZVDhybyABcJiXgihFov0qrtzKl2J/i31ME+RIKQe/Ykuy+2+Dy73F88TriKXs/nPwhtoc8yvO4n/5kqtuBLzhRpfcK8MJg9IKg7ILhqR2GJKaUZLdja67o5j0beL5gw84fnbKoECYnVHb4zdnOH56ivnQPrqr7jPDSoOyl6lDmIklm8DQhIZf4ecApx95apo0BWChiR99dsmfgE9KxWRILMUkSSzF5xe+Ed8xtxRdeMKeCZGcFrlNE7CIISdfj0zKxPtFL38J6LCxgJmgRsMb1lr3HMQR4wYNqmKdL2l/RQCOOQxwQmyyTnAslzrAJXeh8FoXyrTdaLrehxHXGp6tvGfAhgRq7BigxQnw6eUZ9h5+xfLIAdz3lnHb0If0/2pC1qU2aOOoPTwpsDjAoe0TViyEyyt5vvHPJ6nQJEjFAILsO4R/JFheCpNsnvcCPHx/eX5b/Mht6fOwPJKOiwveW37cXXglPjx5E8coffYfBzh55J1FY6fSF27rbklxoCDDhhJNL+rK/bDeG8WAex7Tgw+xMP4ED2Zf4uHcEWaHdzDoeQRXxwKabg+jNNeBrIQmJP+lFul/4wCHAQEFed+IsSIa3jAFjvC+oSVBG1YeKk1s5Y+FASMBJ8p9Op5okk+T8hDrPiVggDAsphIfgzLIJuncW0U6plR+0BgpV+CEO6SFAQ4HOef9cOh2BPJElCps/0SNC9GgUBwn4eNVnrMSvAlDG4IO34SAN3xJjwvYc37JnkvqE0m1SJ2XOJSWjw0iT+m49zID928BjgzeCG816Xcl/DvLxitpf8jAiJEZSQ/DmMSDl7Vxo2lW8hYGbuRhRLCNyqnkKpwgjCoeVBaVmxSATtmHXAoCOQLsfLMMwKAieMOhjVndD3NyP2hJ8MYYjhBrN69XBqBV2JEd14asuHqU5HShvtwD+70hTAfX8GLtHRufxO+IWH7dP8P2/AemEOytWUB1/hjyr/qR9ZMdmngXcinnpYtLtK0MdIxJwTpSMcWsyPMIwGQlbHQc0O+rHNaKdfkxH14Xv61iGf07Gh7fYuDmdwDuYpvHlrE8i+VALAdiOfBHzAE6x/3TKXCiJ1nSCZt8Yn9+PTypiZzofX8C+EuJKq4k/9L//cDjEsChycn3QkxywyeWrO6cQxzugXMRwFnGxsoODnfff1NCdfblCz4df8Lbg2PsP3+Nnc1X2HzwAg/uP8Xy9DaWp59ghcVTrExLMUXLJ3gwQxOdbazNbePR4hNsPXjGyhBebL3Ewc4R3r48xsfXJ6yTS9h7R1L1MAXChzN8fHOGN69O8fTRJ9wf24XfuorWu5OoKh5EscYHXZKNmRrrEu3IVZJpqAsGKi1iV65pQkeTOT6hY9uGrr4KI2DW1USc4EtLBi1one97dhCy50fgDV1tDnskMK8EKmUSvgn8pDMCcc6fREq3z+1DgjcXAZwoeEOtWaUggGNg37cXemUHcpUtyE1qRHF2OzrvDmHCt47nD97h3c5XDnAkNRSpO76+PsOb7VPMBnfRXjkFo6oLOkUndAk90CXYoKMyEgW1lKXJIfdnsKhCyFOHkMcmTASa6DiST8CliVrYaLQPYsLEJ5nSRFMy6TQoSV7vhz6RvEe8UnjY0pAoTvJJck9XbWnCSaUAfD28vADkfDtBixzD4X2a2AczCzF5k3WwCU+QIyCHg46LJqKy57MxQ3ovyqHEIC+jUoZgJo8M1qLXjlxFJ8zJnSjM6ES5rhNNN3wYdq3gyfIbBtZo/zCYSQDnM/Bh7xQv1j4xfxxH4wJu5PqQ+h/1uPZXggPdTNUTyXXaL3IlAh8nouCNpL6JgKnI94rkujhmBBAhECrgg2SyK0GFcA5Q15hwPpzfLudvR7YtbRf2WcQkLHy8Rt77dwM4SUGWzzynKcekSTwp0FR2mFN7kZfehQqDHTVlXrRW96Pn3jDsrZNwd87B07WI7sZp3KscQVVJEKV6JwypnUi/3IiUv9Tj2t/akHOll3X3ofIprsCh7RgpmxLwRkxUmf8HgWc1hwYEcwykLlREIjeB1kktRMcOKbv6JDhDE32pNIY8UZQB0HFG/ib0f3SsiWOLvEf4e0ldp8gQnqlvqHzLzYLGNNY1LQrWyMGNWBcAhytWCOJEckG+r8U65YN0rAhozeCNUIjIy26kMS8K5PTDrIoEV+P0w6LmkJlAswh6jEAOjaORcUisE3CRd0eiMY9CgATueRNVjkv7Rh6S4pP2kYHBNxqzaKyicZNauxOsGYUpaQRGFuRLJADOCCzqEcm7iNQ5opyKQxwGciS1jFHdD1LW6BL90CR4oVH4kEtGxuogjCkhGKnzU3I/DMlBGOk+WZgYvAnBkhyCWR2CSU3QhiAOLUMwJIWgV/ZBq7AxgJMZV4/inE7UlblhbxrEZOABdh68Zr8bAtyI5aeXp9icfYNB2yN0Vs2g0jIAU4oL6f/dhawrNmipHFfFy5v1Si9yFRTSOM+2F431PMdJ9US/3bQthRcUBznyEj2pfFKMF+Hl+fOn7/zuxuBNDN7EciCWA7EciOXA/8IcoPOyPxXA+QbenIc137stn5CdK2eJhjnnTxzktwW8EUv5Y79xXYIRcoATpTKRfZ/zEIcAh5YUOInkgcMVOI6OEUwOcYBztPeeq2HE2RlNIr9+wfGb93i5c4jHD19gaWYTY/3L8Ntm4OqcYOHumoSnawruTprYTMLdQcsJeLom4O0ZR59tAgPuGYwG5jA1uISlqXVsLD/FzuZLHL54i0+vP/PSLXrfqPKtSMcX8sbZfvge90d30Wd9iLa707hh7odOZYUmsRfaRCsI4uiVpEpxSQag0QCHthkDIgQmvokgjImREFdtKX+iJ5B0MimAjYuZCJuUYilBHCqVSPRKA+QFJ5IyeMPLPEgtIAc4pOKg58nhkoA3kvSdyj2UduQmdkOX2Aadsgm6pDoUZbWg424IE/5V7Ky+xtudz1EAh9oefz06w+HjU0z1PUPLzTHolO3IiWtDTlwXNHG9zMiV5O/UScSYyEswLKoBDnAkFc43ACcMPvy8NbKkcuIQR0yYuFKA1ADcNNOPXJqIKLzQKdzQJbjZyT3dZpNNdqVWKlGgq8mSnwMBHAaACAKdC6ZKiAJ0P9h6WLQgDn8PMdmMLM8rVqLGFlYOI9odE8DhEMdEZsYsr2jSYkWuogPm5HbmMVGe24GmGx4MORexvXTAJkjUlU3ATII5x7un2HlwzDxybPX3Ua5xQ/VvtUj7yz1k/dSFXIVDynVSU5CShE9YhfqA5zxNmAmwiFKdyHdi8ERSGEUDHDlA4fBGTKqilxLYiQI4coXJt+v0ntRS3cBCKFNEqZR4X3HcidvnfG/ElXhahidx4n85sP1eCVVeEi+joiXBGzmUJFUATSTp2NIre5Cb1AFTWgfyszpQktuJclM3Ksw2XLc4cd3iRrHOBnNGF7TJbchSNCP9chNS/9qI1P9qQsalDmiuWKMADlPhSJ43wvuGwxsOVKiUi3xpLMm8vIlAiz6RylHc0MVTuKCNc/H1BA87dsIgh4AmK5+h4yICR2nCzCKByoM8DFCwSTKBaAZuuOKHrUveO8yDh02kOcQhkMMiDHO42TFX7FwEcKScYYBO7GsfDAoeNC4IxRgr7yFAI5X4iONc+LeQ/wsv/YksowAOjUkEl5MpBpBPkcKDbhPY4aa9YhwS4z9X5ERDHGpzTYoQAbgEsIn4qkVUS/QYB14G6XeHQxwqFyKlC6lqhmFWj8kgjqTEUY3ArCaAMyore/sW4LDXUfeDAE6uMsDATVacG9kJXugIaJOqJm0AptQQC2NKPyhMyZEwJ4dgSRlgYU4eiAI4BG8ocpV+aBQ2ZMW1IuNKHYpzOlBX5oKtaQATfct4snyAL4cy7zvpHOH9i1OsTx+gv+cBWm+P4aahD/qkXqT8pQUZl7qgibeDFLFU3qxTeKCNJ6NwN1snoEPbmbYZGYjz8kVRviaVsElm0lEleueP+W9OxC/4zY06h/uN517fvE/sdf6IV5ljnymWl7EciOVALAe+zYE/lImxMJul5fd2VtQkS4Ib58HG+dsciFxw5Z6VR3x7ciA+R/RnENDm/PL7nzX6+T/2f1HfL6wc4m3Fz38vvRzgXGtCdbkDHOAs4dGDZzjaO/62jfjXL3hz+BbPtvawMreFkeAi7O2jqL/lxZ0SB6pKnbhb5kJ1mRvVZS7cLXXhbokT1aUO1JQ7UFthR8MtJ1qrfehuCsLRMYR+1xQmBxaxPPMI22vcGJTKqpgHDpkaU5xIZVXSiSLd9/rlKR6vvsfsyC5cHQ9QUzYMQ6oVWmUPdMpe6GiCLEEcduVYKUw1+1jpDysvkaAIBzgRYCPgDbVVpWCPh7cnTQzptSR4kySADS2dsqDbbpgEwEn0caVH1MmjlD/hEg8ObyKlL1IZlVTWxUFSBN5QOROTwCcRwLFBl9gJbWIztMp6aJOqUZjdiI7qAMb9K9hZO8Sb5x++ATgnh2d4tXmKcd82mq4PIyehGZmXm5F1uQPZV3qQc8UObTwZugZgUHAvB3MSARw+GaIyKg5wIlfa2aScwQ8OcAzMtJe3kCUlD9uurIyDOp4EmcGlTuFjPgnaBA+0CW5oqY2tQoI4pMpRkuQ+GuAwE08J2tDjXFlAKgQRpPyJbj3M/I6Y2uo8zJErb8R6NNyIfC9+//cgDgch/DUYtGIAJwCTgucSGRjrFdSitw2mlDYUZbWhQt+GezddGHLN4fHiS5wcneH0mPtMUdqTb9Hb55/wdOU1ZkNP0VM7jZJsBxL/TzVS/qMJmZdINeXgV6xpbGMqhZAM4tB+onGMAI6AN2IZ/T2FYoKr1QQI4dCEq28ItJCPi3xCT1fKz3u0SEqcnwFhHN54oE/wwJDgYTCHPh8Hpfy9LUl+qbNW9Gf5e0uoGLQhcCMiCuDQtiKAQ8DDzjryZJMnSEIzshSNyFLUIZNFPTISGnAtvgGpP9VB9V81UPx7FRL/vQZJ/1GP5L804epfW5F5qQvaODsry2GqDlJ2SGNDGN6IsioCBlTORAAn2Q9LSh9MalJOkmKBJr0uaOKc0FwRIQM5BGjY8RIxdKfJMU2aaWKcq3AxHx4yaKZ1uo8rHUhpEzFMjlpnihLqmiYFU0bIIA75LpFiR1LtyEEOVyjS2MC/Lwd1XugJCrOgbcxLwLi5Lfdoofylkh/m80LKIXaccz8XUUYkBzkEcQjOCHjDoE3qIPJZDCE/dQh5yYPscVKz8HGId0aKrHMloIA44YshUqkU2x+s1IyOLw5xwiVnagJfLhiSqHOZDXryDyPQxVSvBJu4AsesHodJNQZj0igMymEYlaTCIeXNqARwuAonosCh51E5nFQWR4oZdYiVTuUkeJFx2YXMODe0ND4KgJNGAIfgDSlyKLgix5TSD3OKADiDEADHoKJuVaTq6UduUj90iT7kJFiReaUF167UoDC7HbVlTtjuhTDet4TtpVf4vM+9ucLlzgDePT/F6uQe+roWce/GICpyXdAktkP17w24+t9tyI7rYaXN5FOnVbihiXdDE0ewXlLhEKAUecryk9SfEYgjlK3fBzjnz5Pk52eiJFp+H62ff07sdmybxHIglgOxHIjlwL92DtD5zR9CgSOgiXx5UfJFAY4fBDh0AhYutxFSb2nizz0uIicEFmUfvv0M56GN/PbvmyDy7ytOSgXIiQCcBpgkgGPvGMbE0CIerTzF4d67bwDO2dcTHL16ja2HO7g/sY4+xwxaavpRbrYiP6cDhdouFOt6UKrvRam+B6W5Uui7UWboQpmxExXmTtwq7EFVqRV1NxxorfGgtyUAT+8ARoNTWJ59gGebT/H65QHev36Hk/efuAqBfHGkPwI471+f4dWzr9hYPsawbxvtNbMo0flgSXfCmEwlVFbWtjdXMuJlV7bp6jzBEma+y40weec00YK3HybqxJHYD4MiiNwEigD0CX5eosBUAqIziYA1ojOLC2YVD2rBbRGdWZQemGlCygCOn0Ec1vqbQQRePhBWcZEaJ5xjAhryE0+CN7wkhhsJ86vOfIJOVzV1ib3QJLRBo2iCRlkLraoKhZoGdNT6MRFcwvP1A7zZPcbpMblOc3UTKXA+H57h5eYXjHk30VAxiKz4Jlz76R4yfmpD5k9dyLpsQ84VN+skoosPQa+g7iohmJMo6LOQMogrOsTEP5x3kvqFIAYr7UgMMCVPriIACp0iCG1CANqEPmgTfNAy9Q0pcOj7kCE1TTx90DMD7j4YSG1DXg9Kij7kJvqhU8iDFDwc5ND7yRU4BJGo+5PY9rxsSpRP8SV/TMAbaRkGd7SvfjzC4EjRByOLAIykYlL0Qa9wQpdAV6SbYUxuZkqp68Y2NN92YdhzH9vLu1yB80GmwPlwijfP37Or3zP9j9FTO8kAjvL/1jCAk/G3Tmjj7UyhQYoLUi7QRJUH+YdEAxx+PESgW/S+k5dA8ck3K6GRuvJwACUmV7xMUZi8kiLBQqoR5o3E/ZHYdpMgjpjAh5cSuCF4w0uLuIrGoqLXEOH7hwOcvKRABNzIAE6+gDjsWCSwQICBYKIdmoQe5Ci6kKMg5WIbshNbkaVoRWZCGzLi23D1citS/9YC9V+bkfLXFqT9rR3XLtExRCDUBl08dXVzS99TlF7S9o0E97OiEhxehkPQhoImtqScIbDJ4E0UwHGy+wjs0OMEeej/mYm75J9jIsDAIIMoiyJlIg9SKdJ3JMNmah9NwRUQHO5wRYQEfxh8dIfBD4EeUuuYpZDDG1pnAEcqMyVgJdRWBG9y46VI8Enjq4CwHNiw45sd4z4Q4KXb5PdCxz8rCZNUeAxWErwRqpuUQRSkDqEwbZgt81MGYUmm1uo0dknAON4PXYIfuQl9LPSsSxIfp9ixQ78R6iDykoOwsAjAkkz3UbcuyfCZwRxque6BWU2whuAN/e50Q5fQyxRxuaQmoZI1ZT/0ykEYGLgZgT5xOBxG5QhT5VjUY6ztO/nhMHPjc2VUrMwpDHACyEnwIeOKCxmkwlF4oVH6oFP5pfBBq/JCp/IhV+WHXhXgJVakQEoegDl5ECb1AAyqEAc3yiB0ieR9Q6/rQeaVbqT/1IS0S3dRkNWK2jIHbM0hTASX8WRlHwT+hT+X9LOM4xcEcF7A3zWHxuv9KNXakBV/D4n/dhcp/9WEzMud3KdO6YIu0SON97RfCdzwUllSmBFYJBhNQbnIjfQ5MKRxiAMcWSlVWHUnP5+KnJd962snf4zW5c+Lrce2RywHYjkQy4FYDvxr58AfBuBQosnBCa1/L/nCk7C/A+BEnxDwkwEOb6IBjvhMUbL+8MmGADnf/6zf+w4/en/4u4Zr+6Ua/yQ6oRQlVALg2GHvGML44ALWV558F+Ds7x3i4coTjA+uwNY+hqoyN3LTmnEtoQ7ZykZo1c3ITW2FXgpDaisMaS0wpN2D4WoTjOmNMGc0wZLVhPycJpQa7+FmQQuqylrRXm9HwDmI+2Nz2FrdwMtnuzg+eoezL5IXCHm3sFIu4OTDGT68PsP+zikWJvaZCudO4QBKtD7kpTuQm0RAg/u40ISZlSvIAQ4pFJhHiFhyMMIULQQIFH5o471MfcImRvEO6OLt0CVYkZtAXhbkOdLDW3arrbAk21jkJdtAYVHbYVFRRycnzEkEe1ww0QSYOtCQB4bkgyE+V2QCTWoIrhoJe0MQMBRtc5mygkoMuAkllRrok1zQJnYhO/4eshV1yEm6C636Nop0deis92EytIgXm/t4u3eM0/fkEs3/SNXx6fAMe5sfMeJdR31FABlxtbh6qQ7pl5pw7VIbMgji/GRD9mU3NFf80MUTgODlB+yKOYMa5wGOAAAceBA8pKvsVApFJVFa8m2I9yCHhRsampQq6Ds4oZNMqPU0GaIr2jQxUntgUHugV3mQS7BK6YE2ka7iuvhz6fkJ9Dp0n0yBIL0vVwBRqY4M4BAQS5SHgDgygPMrgI38WBPrQrEjSkX40stySBNPV6TrYVA3oCjrHm6YWtFa5cSo7z6ePNjF17dStzCJt51++ILXO2/xePklpoIb6K4dQ3GOFYn/VoXk/6zHtUutyIknVY8DbNLIJkTcJ4gBaJVkVkvjXtifRihaCFKLfcb3JS+N4rCSDJe5oTXlvpVNTvkEtZdNWA1KGwwqO0xqB8zJLuSleFhY2MSWK9VIBccgAcEB8lWiK+zxvKW2PoEUSS4GNoT6Jo9BIAFw/nkKnAjAkYAXKSiUNAl3MFWfNrEHWmUXNMoO5CjbkZ3YjixFOzIT2pER145rVzqQfrkD1650ISuuBzkJ5P1B4wZ9VycMCfQ9RReqCLjhEIdUTPReBGGcbF8SOCI/Ek2CFPFW5MTx0MTZEAkrNHFWaOIJINiQq7TDoHLAlOyEOcWFvDQP8tLcyEt1wZLihIXuVztgUlHnOgJ/fL9q4rqhieuBNp5K/Oh+mkTzybSWxkC5MoIpTEh5IpkfMz8YUmEJPxxauniwLnm8JTcBQNr3ujgntHEO1lqd2qtr453M7JZBKnY88+M7h+5PcEpjBJnVS90GqRyHVENU+pXsgyXFj/zUAArSgihMD6EofRAFaSHkURkRAQwlQRsvNFc8yL7sYkttnBe6eB9yGaTnZZg0rpGaJz8lxNpgF6SGkJ9Kt4PISyGQI+/YRQDLBZOKFDcEbQiktkET3w5tfDe0tP+ZmpAgVBA6xaAUA9AlDCBXMciUOFyFczHACRtQk1cNlVAlB6FL8iNH4UFGnAPX4uzIiLcjM96OrARZKGzIoXbgiS5oCQDS7z6Vn5L3TTLBG1LdBJmaR5voY3462QRv4mxIv9yG1L/VIuVvt5CXRSXWNthbQpjsX8LT1Vf48lr6MRY/JgCOd79gdeoZfJ3TaKjwoVjTyRQ8cf/fDaj+swbXfmqBNrGXQRxS4YQBPUF6Buu9bOyiHCMYTUHHTLQKRwZuosqnxHnUeTgTuQDCLoJcqJwWz40tf/TcMvZ/sVyJ5UAsB2I58OfNgT8swPmlpBITLLaUQE5YLn3+NpvECTWE/ORAeJMQtJHHtzDJovSB4mKQ8/slgPierMMDXQEXIEcGcHISG2BKb0R1uR329kGMD8xjffkJDnbffqPAOf16gpfPD7Ayv4VB/wLa6kIoNnQh+dIdXPk/FVD8xy2o/lqF1Es1SPupFld/qkX65TqkX67F1cvVuHr5LtJ+qkTaT7eR+tNNpP50Helx5chILEO2qhRF+rtoutMDrzWEmZF5bD7YxtHeG15GxdqVn+Hs9JQZu4rW5l/fn2HrwQcMerbQdHscqQZYmgAAIABJREFUt/KCKMp2IVfVjZx4OpnuRS6Vlih4296wAod1UIq0qmUKBXZllVQepAKhyQQZOfYiJ64bOXEd3BsmvgWa+HvQJjQhN/EeDKpWmFPaYUntQF6aLFI7YUnphFndBZOqG0YlBz5UOqNXkBeGTWrTTVcYaXJLfhTCOJN8ImSlBQLgkIqCXQEOwEyTCBakULFCo2hl8CUz4Q5ykm5Bm3IDxfpqdDV6MDW4gN3H+3j36h0IBIg/8lX5eHiKF5vHGPauoLbcjfS420j9WyXSLlXj6qUGpF9qxrVL7ci41MO6ieTEkXqEPi+ZTUoeEVSSI1MRcCNdqWMIKQCSSE1AXUicbDJL8nzarnSVNyuuG9kJ3UzZQBNjXVIXctVd0Cd3wZDSA2NKL4ypNhhSrMhV90JHYE5JSoge9jx6flZ8D7Lie5FNk9sEO3SkJpAUCPQ5mckrtbFVcDhGYCwawkrHslBFhVsM/7zi5nvjDD/u6L3oPUlZQqoLUjgQqHBAm9CB7LhGZF6ugl5djaLsety03EN7tQNj/lk8W33Bu1BJLcRpf51+OMHRzhtsLb3AZHANnbWDKNJ0IvHfb0D9lyq2r7Jo4qjoljygBMjhSgxe8sHLKcVYx0ukIibFDOKQESsrkSH/KGliT6+Z0AmCTiwSWpGT0AKNogU6ZStyVW0wqNthSumEJa0HeVetyEuzwpLaC3NKL0zqXhhVPdxHRkFQtQe6ePJX6oWOQEGCnW0XAjyk2rCofMhLDiBP3SepeaQOSazzG0EnXs514VI+zv5CG/Fw6ZRMgSP3wREd1sjIl5XGJFmRm9QNnaoDWlUrNKoW5ChbkJXYgkxFC7IU5H1DUKeT5SiZqhvUThjVVFojqVyiAI7cL4bW6bgiBQxN+nugSSAo24Es1g2olS2pM1B2HPlU0XjUAU087Re+nkP3EzhQdCA3qRMGdTfb/nlXbSjMcKAow4HCa3YUXrOh4Gov8lJ7YE7uZsbluYn0mq3I/OkeMn9qRvaVdmgTyNC8F9qEXmjjepmHjzaOjM0J6vDyK94li7x6qPMhlRUJgEP7krqiOWBKsrMwKm2gYOWDCT3QEiy60oWcK13IJuDFgo8JBL/o2M5k0YWs+C5pnOiGJrEbWmU3dEnkS9QDvboXphQbLGl25Kc7UXDNjaIMH4oz+1Bw1QdzCh0DXNVBJaFZP1mR8bdeZF2yI/syqZc80JEaiMZcUkCSCislhMK0QRRfG2YgqPDqAArS+hkgykvpgyWZvrMHJrUTJpWN5XeusgNaRTNy4hpY++3suGZks/1Ev0V25FDJULwfmvg+aOIDLHQJVEY6yDpTmcngWEVqHK7AoZbeYXiTJHWgUgdgTKauUx7kJDqQGd+La3FdSL/SibTL7Uj7iZRgPNKvtCEjrhNZTDlmh5bGYPKZofI0dYjBHJ3Sx2B4ToITWfE2ZMb1Iv1yO1Iv1UP919tQ/bUUlsw61JT3wN4axNTAIp49fMUAs/gdEcv3eyd4ML0Nb+c46iocKMy+h7SfbuKn/7cQyn+/yS4M5CjaWYmznoCX2gOjmj4PqczIG4ervAgUasj0OM7OVGthgEMlh6RmDR/j8nMn+bmZWOfwhsZ6Xn4sbovHxVL+OrH17/2uxe6P5UYsB2I5EMuBf40c+EMBnN+SVAJw/NKSv7b4sY9e/jK8EUCHQxwBc6KX4n+il7/lO8mfI76XHOCw+1gLT7qi3IFvAA4pcATAicz12Tna2devePXiCA/mn2DQv4S2uiGUGHqRcqkacf/3FpT/eRcp/03ApgnXrtxDxpVmZMa1IDOuGRlxTci40oBrV+pw7Uot0q9UI/1yFa7F3ca1hBvIUJTDlFmFW0WtaK11wmcdxuzYMrYfvcD715/w6f1nfPl8grNTMsXhJUDM5PgT8HzzBNNDz2FrXkD99RFU6H3Qq7uRdaUdmvgeBnCE0oUmrTQxY5MzKvMgZYI08TCSCSWpPlSk/rBDr7LCmGwFTYCKsuwo1ThQkevATaMDlRYn7ha4UVvsQ0NZHxorArLoQ0N5APWlQdSX9KOmMIgqSwC3DH24rvOhLNuLogwP8tPcsKS4YVLJShUSJT8A1n2Jrv7zdrSkomDlfHS1O9mHvFQ/CtL72CSlONuDEo0DpbpelBu7cCO/A7eL29BU1YuAcxQrs+s4fPEaH15/wNmnyE6lNtWf3pxi/+l7zA4/Qk/TEEr0bSjStKNY24MSrR2lWjdKNT6U5gRQktWPwmsB9t5UjsF8MiR4Q9tXlH/QJJwmvVSiQd2xDElkXkmKAJrQ2mBOtSOPJlqZLpRoPKgw+HHLEkBlQRBVRf2oLulHbVkIdeUDqK8YQsP1EdSVj6CmdAh3iwdRWTCAm5YQrhv7UZYbQLGmDwVZVD7nhTnNA2OKmyl3DDQhJpBDcEwhOhwRsOAm0cIs2qIMgIJDHdnxLTOYJvD3Y8EHd1K5UMkITchpO1iSPSi46kVxhg+l2S6UaWyoyO3GnfxeNN5woLPWjb6eISyOr+Ll1j6YgfEnMg7n0yMCOG923+LZw5dYGN+Er3sadde9MF1rRn5WJ9tXFQYfrhuCKM/tR5k2hJKcEAozgshL8zOVAk22ySiUjQdCOSUMWlmnNmm/MRNWG/TKXuiTumFUd8OU2g3L1W4UZvailD673ombZjcq8724W+xDbWkf6sqDaKgIoenGEBorBtFQNoC60hBqi/pxt6AfdyxB3DYGUaH1oyTTi/w02i40ASb1ApWfkAmzm5XjWKLgze8DcCwSuBFLAW+EkTEfI/hEnZRFlLcFGQ4Ua5woN7hw3eRGhdGDCqMXFUYfyg0UfpTr+1Ci9aMw04u8q26Ykqn7Dgc43ygKWAkS95ChHMlP86LwmhfFWV6UZHtQqnGjVOtCmc6FMlpSaFxsPMpPJ0DWBaO6AwZVO3KTWmFQt6HgWjcbq24avajKD6CuZAD3ro+g+cYomm/QchjN14fRVD6EhtIB1BWHcMfSh+u5bhRn2ZB/tReW1B6Y1D1s/+cmcugmlDm6BOqgRgCHFEPk10OKFCrTo7FUAByCNwRurDCrrShIc6Akw4MKjR/XNX24ru3DDR0t/SjX+FGa42PfOf+aC8ZUO3TqXmiTupGj7GTlapqkdugpBzOsKNI4Uab34KalD1WFNF4MoLZ0EPXllHcjaKoYQ9P1Cdy7PoW64nFU5Y3gpn4QZdlBBnRoX2gTHFzxE+dEzhUqSyMvlgjEoe9DCpzCNK7iIQUOU98weEOKIw87pvOvEixys2O6NMfBjutybS8qdDZU6Fyo0PlQoQ2iNKcfxZkDKEgfhCV1gBsRM/8vKq0iUDPEzY3DAIegDt0faftuUPXBqPbDmOKFKc3Lxru8TA/yszwoyPaiMMeLQo0XRRoPirQeFGvpfg/yMjwwsXHRC2OyHwY1tRWnNuR+pmbSkMpK5YQpzY38TC+Kcjwo0thQRCXRuW2s1NnaEsKQdworM4/wcvuI+XMJcCOWH/ZPsLm4g2HPHHob+1FbbkeRpgU6dS0s11pRrLHhhrEP1/VBlGmDKM4KoiA9AEsqQRw3U18SHCTljSbOztRZpMbREwBnnlGkDqTxVTZGR62LMTx6KeBNTIHzrzHxkJ/jxtZj+zSWA7EciOXAr8+BPz3A+XU7/duThmh4w8un8pR+iBAlVNHL8yBH+n8CC1GlYNH/9+s+K9+ZYYAj+U+I2wQDDElO6KgMQKbAIVPhCTnAIfNg+d/pGfZ332KNTtICq+hsHEO5yYmrVxqR+O/VSP5rA9IvtyA7oZMrKqgrkrIHWrpimtgFTWIHNIo25ChakaNoQY6CSn7qkaWoRqaiErmp1SjSNeFWUQeaq10IOCewOPMIezuHeH3wFh/ff8Dp6WeJ3vAPRn44r56dYmXmACHnBjrrZlCZH4AhpQuZl1tAJQGkwAkbzorWxJIxJW8DS1cAXdAnkXdOLwx0RTe1F5Z0K4pzHLhp8qKmJIB7twbRcXcYvQ1jcLZMwNsxjb7u++i3ziNkX0TItoB+2zz6rXMI9i4g0LOIQPcSvG0LsDfOoqt6inV6qisZQqW5H+VaP4oyvAziUMcs5j3BPBNIsUJXgyWAw7weuIkpQQlTshsF6Ryq3DD0o6oghPqKQbRUDaGzfhA990Kwt/cj4BzB3Pgynjx6juOj9zg5/oSzEwI45F9whrPPZzg5PsXrvU9Ym3+GAc88WmsCaKnqR+vdIbRXj6H97iRaK6fRcmsW967P4m7+GCq0IeSlkacGTfg5uOGKHF4CIsANGSvT5C9X2QODugemlB7kXbOyCektSx+qS0NoujWC9ppJWJvuw9m2AE/nInzdS+izLiNoW0W/4yFCzkcI2h+xjmPe7lW4OlZga15Cd8MC2qrn0HhzCndLRnHTPIBSXRAFmT7WvpbKsJjygXXQoYmlzAOJtUDnHYhoAs/jHNgh0EOw7xy8YR2NJAgoYCCb7Iv/Y3CEgBFN2u0wq50ozvDiho5A3iDqSwfRcmsIXTXDcDSPoq97AkPuKdwfXsL2ylO8efEWpI6i1uGU3zg9w9nHExzvv8fe9hEeLT7HRP8DODsmUH/Tj6bKAbTeHUNn3Sw6aubQUnkfjTdmUVMyhZvGERRl0RV3UpEIoEXqLh4MujGwQ/uSPFGcDLZR95jcpC4Y1J3IS+9GscaKCqMDVUU+NNzoR3vNCHqbJuBonYan6z76rPMI2pYQcqxg0LmKkH0Fwd5l+LsX4e1YgKtlHvamefTW3kfLzUnUFg3jpj6I0mwfCq7yVt25iTZQBx8Cg3LDWL4uyr3+cQqcyH6XOlEJ/5uwOo+XyuSleJF/1YPCDBeuG/yoLg6h+fYI2qsn0FEzhc7aGXTWzqKjehbtd2fRemcW9eXjuGUeQDGBxVQnU2rQd+MAR/L1oLJKBqzcHPCl+VCWQ5BrADVFI6gvG0HTjVG0VI6i9c4oWmlZOYrmWyOoLQnilsmN0pxeFGZ2Iv9aOyxXW1CY0Y6bRjvqSwNovzMCW8M0G39C1jUM2h9iKBxrGLCuob9nFYGuB3A2zaHz7jgaywdwN9+P67kuFGZYYSIlXFI363DHSoKoRCsK4JCqi5SMZLRLpZ7UsYlKpwjGWWFK6kZeSg/Kst24YwqisXQUzRXjaLs5ic470+i4M43W21NovjmB+vJRVOb3o0zvhSXTBuPVbuSmtkNDZblprSjQ9OC6hUzyA7h3ZxjdDdNwtS/C17WMvp4VBKwrCFofIGhdQ7BnHcGeDbhb1mCtW0J75RwaSqdQaR5GaXYQllSC9dQByYXsyw7kkCF0nIt1RaIyTwLnBHFIicM9cHguMB8hpryhUjQ3SrJ8uKnvw938ftSV9OPe9RDabofQfmcIHXfG0VE1jfbK+2iqmEVt0TQqzVOo0I6hMGMI1NJbT2buDNQMhVuLE8wxqjjAMZB3DnmHkYkzqWfIxyjVjcIsPyoM/agsHEJ16SjqKsbReGsSzZXTaK6cQvOdKbTcmUJd+Shu5w9K42IfLFdJwRMAtR1npahKJzSJVpivulCeG8Dd4mE03hxHy51RtNeOoKdpGH7rNMaCi1iaeojttefcS+3jtyVUn45OsLP+EvOjaxhwzcDROoyWKh9qypxovBlEW9UYeurm0FY5i7rSCdw2j6BME2IXIkilxsrN4m28fIoADiuxFN3SIupJOZD5tevfQHoGgH79ye9vOR+LPSe2nWM5EMuBWA7EcuCPkAN/KoBDcOTv32gRiPNL8EZAHPnyPKDJU/rCsCc/yY88CgZxouGNUOtw6fCPfw8BbIQXB7sttR6niRpJvjWJDTCnN6Km3M66Qk0OSQocmkQSK5H/nQEHe8d4uLSL0f51dN+bwo08LzITWqH+SyOuXmpjMvdc8lRQ8dIBUzLJpMnHxMl8MrgKw8ogCU3qdUntyElsQpaiFjlJNchNrYMpsx7lljZ0NgUw0j+PjbUd7D7fx9s3b3F6+im6JdVX3pFq68E7zAy/gLtjGTWlIRhTO5kZL7XDZgCHmQOLEhLRFpa3zKUTeYI3usRuaBTt0Cd3wnKtG0U5vbhlcaHxRj+668fg7bqPAecixgOrmBt+hKWJx1idfor1uefYmH+BR3PPsT63g4ezz7A2s4PVmRdYnd7FwsgOJgNbGHKuwdexiJ66aTRdH0GlhUOc/HR6fwe0zL9ElP9QFxmCNty4l07emcRcyX0tijK97Erm3cJBtFROwN58H0H7Mob9KxgPrWB6ZBlLs2vYXn+Gw70jfPlwgtPPX1greCbrOP2Ks5NTfP14ivdHJ3i2sY+FyU2EPHMIuRcw6F7BkGcdQ64NhOybCPZuwtexgY4787ibP8JUAlw9JJmtspIqKlUg5Y0TemYi3Q1NAknm22GgbZrexUBAZYEXTbcG0dM4BU/XAgZcDzEZ2sbc6A4WJ55jZXoXa/df4uH8AR4tHmFz+S0eLb7B6twRlqcPMD/+CtODuxjt28GAaxvuzofoql9Aw81JNqEp0wVguUrlAjZW+kKqDgZvmCluH/LUVJ4TRJ66H3kqeYg20kFmdJ0nTeSZ6bUEcjiwIbBzUUgqHQZwCGY5mbdIXqqDqU6qCwbRcnMCtob76OtawqhnDTOhdSyNbeDh7BaerD7D4dNDfDr6yOHNCQEc6vZyygDOx6MPONp9h52NAzy4/xQTA6vw22YRdCxhwL3OzLwH3U/g79mEs3UdXbUPUFs2hfLcEAxqF+8AQwovhWQay0r2aP/xEjfW/piOXQne5KraYUhtQ1FOD25YqJOcF201Q3C2TyPkWsZ4/zpmR7awNPUEq3PP8XB+F48WXmJzaR/r8y+xOruL5ckdLIw+xf3Bp5gKPsWYZxv+jlV018yivnSEKXJKsjwwJVMXtV7WspvnkGjZLDoy/c4ARwJ6lBdif5OihAyZqQynONOLMo0HNUUD6KiehLdjCSH7Qww6NzDkfowRzxMMurYRsj9GsHcL1sZlNFRMoFzXh7w0DnDId4yMjGk8IsNgDqtczACXfIOKrvlx2zSIxvIJdNfMwd60AE/7EgK9K+i3EZyQoFjXInrrx9F0PYDbFhuu67tQpm1DUXYzKnLbUV/uQW/9KII9CxjzrmN+8CkeTu9j4/4RNueOsDV/hK25I2zMHrK2z2uT+5jtf4ohx0N42gjkjKKuJIAKnQOWtE6Y1J3QU3kjlVQxc96IAod3hiOPmQEGcug44yCQvHV6YEzqQH5qF24bvLhXPgJb3Tw8zcsIdK5h0LbBItjzEL7OVThaltBaNYG7Jf0o0TuQl9MNY0YrdGmNMGXeQ0VeL2pv+tHZOApPzxxG/OuYH9vB8tQuHkzvYnV2D2uze1idfoUHk/tYHj/EbP8uRt1PEezehL3xAVpuzqIqjyBOgEFovdLFjNqzLtuRfYWDHG08lfKQUo2XU5HCiCnXZMpCo8qJvDRS2PShpnAArbfGmLG4u2UGwZ45DNiWMWhfw5B9EwPWx/C0PkJP7Sqaby6humAGFbpR5F0NsU5bZE5sSCIvnBFWSsUADt1OCjHAk6sgw3YqlfXAkOyCKc2Jcn0A1SUjaLs7g96mBTjbHsDXvY6gbRNB2wYP+yPYm5cY1LlTMIyy3H4UZgZhSiF442MlVRqlDTmJ3SjMdqO6ZAhddXPwdq0i5HiI0b5HmBnewvL0E2wsPcezjT0cPH+Nj68/f3tuAODk7Qn2nx5iY+kJFsYfYjy4iH7HDLxdkwhaFzHoWseY7xkC3RvoqVtGQ9k0bhmHUJwVYCq1nLge5uGkZRCH/JbIC0qoJ8kXjkrZ5Q0HqOkABd0nj0jJ1HnAEwM4P37++PefL8feK7YNYzkQy4FYDvwRc+BPAXCiocl5lcvfczvasFgOan58nQOcfKUfBHDyk+g2DwFtzi9/DcQRAIfUJ6y1MUEsSSVwEcBxdgyBAM4jKqH6DsA53PuA9aWXGOvfRO+9WdzKDyBb0YmUv7Yi4zIpbai2nZtKmklunhKEhRk/UktcKv/xwpzsgTnZzcw1DSpq/d2BbFLjJDYgW1mHbFUtTJmNqL3lgsc2jvnpdWxtPMPBwT6+fn0PQKorIbh0BhwfneHF1ieszR0h5FzHvdvDDMBkXrkHBnAUTt59iClaJN8W0TJX5QadjFNpD3lG6NUdKMzuxQ2zEzVlfrRVD8LVPo1B9zJmhzawPLWNjcUdPFvfw+7WPl49OcLhzlscPX+Hw+dvcfDsDfafvsb+kzd4tf0Or7bfY2f9DTYX97Ey9RwzA9sYdD6Eu20RndUzqC8bw03jAAqu+WFK8cKo9sKgInNOL/R0xZVBHLoC60OuklRTVhhTbCjJ8eC2hUpWhmG7N4ch9zqDH2s0iV5+gccPd7DzeBcHe4f48PY9zk6+4uzLF4CVoJ2w5dmXrzj9fIrPx1+Y59H2+h5W7j/GyuxTrN5/gYdzr7A6c4DliX3Mj+xjOrgHd+sqGsrHUZRJ+8/JrmIbyIBS4WWdaUhJROVSYQiQ1AFzWicDNzfNTtSW9aGjdgTurvsY9q1iZugxVmZeYHPlEM8evcWLrXfYe3KM/Z0POHz+GUe7X/Hm5SmOdk+xv/MVe09OsLP5CVur7/Fw4R2Wp19jcmAXIdc2XB1r6KidR23ZGK4b+lFAZWpXSeFEXXJ4W2YqzWH+KskEcAjYfAtweCnNeUDDlTg/B3D4sSUZNzNfI/KQ6Ub+VTtumwJovjEGR9M8U0IQ0NiY28eTlQO8WD/Aq8f7eL1zhA8H73F6fIIzgjcn1Eb8FLSfSIHz+e0nHB9+wOGLd3i2uY/1pR3MT21haWYHD+7v4+HCW6zMvMHM0D6GvM/h6dxEc+UcrhsHQH4szEyXDGQZwKHOarTPuPcEhzekWLLBlEJlg1YUZPaiTG/D3RIvmqtCsLaMIeCYx3joIZamn+LR8i62H77Ci8eHeLXzBgd0DLx4j9d7H3H4/D1ePXmH3a032Fl/jScPXmNr8TXWZ48wO/Acg/ZNOO8to71yBrVFI6jQBVBwjYx2vTDTcaDyseAKOdp3/wCAQ9AtqnxOKK8I2khBAId8plh5pZ95nRQytZsPN/Q+NN8cZVBlMrCNxbE9rEwcYHX6DR7OHmNl8g0WR49wf/AAQesW2qtmccPQx7xZTGo7g1MM4ChkAIc61yW7mSKEPFtqikbRVT2PQPc6hl1bmOp/gkWCFJM7WBonGPYMswOP0W9bQG/DEOrLHagu7sXdwm7cKehEfYUNtqZBDDkXsDD8GOuze9h58AaHjz/jzbOveEuxQ/EFr59+weH2CQ62TvBk+Q1WJ3cx07+FkHUZtoZJNFWEcMvoRlmOA/lpVuiV3cglD69E8iyiEiry7KISzxCo7bWRfFqYmotUVFYYk7pgVLWhML0TNQV96KmZwkDvOsY9j3G/fwcrY6+wMv4KCyN7uD/4AuN9T+DpWkJL9QhuFrpRbrGixNSFQkMrKgq60Fjlg61jDAO+JcyMbmJtYRc7G2+xt32Ml0+O8YrGjSfHePn4A/Y2P+HFo894vPgea1NvsDB0gDHPc/jbH6G7ehH1pZO4ZRxmJaG5ZPJLJslklkzdveLIL4dKWbkCkpSQvBsYqdh4q3C6EJF/1YnbpiArTbM33kewZwnjvodYHNnCg4kdrE68xOrkIZZHX2PCt4tA12P01q2hqWIOt4xjyE8PwqCi1+ZlVHrlMAykvpEUOAzgJJLxMXXa8yKXFHTJDpjSbLhlCaLlziTcHSsYcG5iPPAM94dfYmniEEsTB1gc38fi+CsMeR4ztWJ9xRhumEIoyg7AmOLnpu+JDmiUvdAkdbCStObKcfh7VjHZ/xSLE7t4tHjAxuTdx2+xv/MOr1++w/ujD/jy/gsbn+SlzLR++v4E7169xd7jV9he3cH6whMsT21ifmyD5e/D2QNszH/A/cF9BHq20PH/s/ceTJFta3rmn9J0S6ORRppRTKi7J0LTrdHVSLe77zHl8d57T1GOKqAK77333kPhC0/hvYfEZyYmeSa+tTOBqlPnnnO7x/TtS0WsyIQCktx77cX+nvV+75swSuLzbtVOFepcpTyQFMQRn6Ub9Y3MMYE3YpxvgTdtKi1SEiM1gGMBOb8Mc+4Bzn0x9U+xmLr/ne7n5f0cuJ8D/1/OgT9xgHMLf34CbGwbiPilIdDGrh4Fb+4AHPmcBnHqCLe7O25VOb/2JP8cwJEWkG8DnPZbgCMmxj9poRIFjp6Z8V0+Ni9SmjXM+8hW/GxLcX1QiJdE5UrSknghiCeCk0S7tql413B5Lp+7G8OqDAxFGVCGn20Rvrb5+IgpqF0mQW7ZfHhdSXFOOx0tw0yOzbK5ucn55TFwRxp0DYbja/Y2LlmePqW3eZnClF7Vby/GvhLpKilUliQizShYUxwIbFBtNg4Vqsh67lXOK/9ykl4JZOimrnhYQYZPXQtMD6+zMr3D5uIee2s6jnYOONk7Qn9wgvHojPNjvXo0Hp1iODxF1BJ6nRG97pzjbSP7q2dsLhyzPHXA9NAuw53b9DSsU184R0HiOElR/bwN6CTKS2Je69VxDLCrI9BOEsMaCbSTaG1JFCkh2LlUedMkRDWRE99FY+kEw91rrM2dsrN6xt7GCfvbhxzuH3B6dMK5Xo/p4gKJgUda0ERaZZKPLxUcuDJecnZkQLd9zNaKju2VI+3nrJ2zu3zBxpyRlUk9M0PHtFXMkxPXQ5RXBSGOZaoVTyCAtB2IL0agbQWBtlobWrhrKc89S4kJqSbtbSvFab3Ul4zS1TDN8MdlZse2WZ7ZZ3PpmP0NPUe7Rk72zzk7OMdwdIHx5Irz02suzq7Vo/7oWsG6oz0T+5smtlevWF+4ZGHSwOTQCZ+6dQpcVOfPkhc/TNKLj8QGt/PSS+adxP1qEdeaAkeLCVaFu6WFShXx3wY3FvApj1+0UFnapuxld1gSrDRYGqzimCUVqoDn7mUkRbVQkjJAa/k0Ix3rLI8fcrR+pZL0UZ1oAAAgAElEQVRbznaM6HfPMOpOuTw2KqPp63MT2rjiWpRTxgsuzy44Pznn7NDAwc4pO+uHrC3tsynF6/oluk0TW0smZscM6li0Vq+SnzzCu9AWBVnFGFbSfCRqXQpvSQVT58729joIcSwnyquamJB6kl+3kJPQRWX+IC01oupaYHxolbmpLdYXder1ddtHHOtOODs6RX+sx3BsUL+j4ciI/kDP6f4ZxztnHG4a0Ml8WrpgZUrPzMARI+17dFWvUZs7q4q51FdSVHfxyruVUMeG26HaHTXVXJhqg/sHmhhbVFM3EOdnAI6YhZu9sQQ6v/CoV+2O78MaKErup6Nqls8De6x91rM5e8HOgom9JRObs1csT1wwN6znY/26aheR4yitgwKqpaVQMwCuRNYeWYOVCa746zhXq3YrUd/U5E7R37TBRM8uC2MHbM6fsL10yub8MeszRyyN7zHUPkNjSQ95CdXkxldRkFRLcVo9tWJE3zDK54FlNmcP0K2ccbZ1yeXBNVdH5nF4rWLqL3XXnO9fY9y95nD1gu35U1YmdCoKur9xgeaSCcrSBpVXTmyQwPdSBWY0c2sx5BYAKNHbrQTbdRAs7UBiGi4QX9om7QsIccjmpWcBmdHN1OWOMNS8ylTPDosjR2zOGNieM7IxY2B1Ss/syCE9zYtUFAySEtdAUmwtKe9rSIuvIT+zmYbqAQY+zjA7ucXqgkDDE/N6cYFe1oxDI4YDI/r9C053TZxsmzhYM7G7cMXG9BULnwyMdR7SU7tNQ8EyhQmTJD3v54VnK+EuTQoYBtjU4G8tChx5H5qpsTJrFzBlgf0O4udVxnP3KpKet1Kc1E9LyRQDTQtM92+wMa1jd+GE3QUjewsmNmeumOo9VgCpOmeGnLhh3od2Ee6qGQgHqKj0FgJtOgiS4+gg7VXSRtVGoF0z/pKIaCPpe1UEOZYR4lqivH9K0ofoqJ3nU9cmn4cOWJ4ysDl/xcbcJeuz56zNGhjp3qKhZJqs9z3EhrUQ5dNAkKPEhFfiY1OGn7THORUQG1FPScYA3Q0LfB4SAHPEzoqBkz0TZ7or9IcXGI6NXJydc22UzQBzMqTJLIiVNk+jQGY9p3vHHGweqr+T27I+LeyrjQzdqomjDRNLY+f0NW5RkTlF2ptepSQVfymvx/n4PJUUNGnTk3VKUwuK8uZLeNNOqJ0MATk/B3O+rci5Bzj3RdKvvX++/7r7uXI/B+7nwD/XOfBHAnDuJkTdfX4LYH5OpfNLJ+5b3/cTmHPHE+db//clwNGgjgCcr5U3dz/+pd/L8v//IIDTrilwdFsn3wY4W3pmxnbovgNw/O3KcHsoKSICcMRbwAxwHAXatN4ZZohzp0ASvwuBKIH2osQpVhDHyyYbf6cctQObmdRAdVkXA73jrCyvYDg/ErH2bWPXNarAP94zsbNyycjHTSpyPvEupJogx3ylBBGAE2RdT5DsqtpIT720tghokLhdDTYIkHgbVE18VD3FaT20VU0pRcvS5AFbi6ccbl6gl4Ln6JrLM/EkkQJbVBJmo9kr7VGlY8lz8VpWKgqUn8nV6TXGw2tO9645WDexvWhibfqK0e5DWstXKUqZJOXVAG+D2glzrcNP4oNtqwmQgsjO7FdgW6FShgTgvAmsJfl1G4UpfXTXL7Iwfqx+P/ndLvXXXBquETBzdX7J1cWFBnAuBdoIvNEADldXXF9dq99VPnVl0GCJJHuZ9FqMten0GsP+NUcCB+av6GvaoCBxgEjPMgIdxN9G2inES6JWtYD5S2qNTSGhLiW88CnjXXAl6e9aqModorthnsn+bRYnD9le0nO8Y0J/cM35ifZ6N8dTjp1KHbtjdySWCybzcT1HvUfj8bX6flHo7K9r8GJu7JzBtj2aSpcoThkn/U0f7wJbNcWXY40COdIWo4aoLFQ8u0RGfz1+pWmxapeyqG4kqrxeGSYHWUsLQAn+1nlEeZaTEd1Bbd44gy3rLI2dcLxhQkyk1RyRR4McA1HaXGkF0rk8Xt7AG/VcCifjNSajdo7Pz64xnmjP1bG7hIuTa3ZXTcyOntPbsktJxoQCOOJN5fFY0orEl0grTCWpS5LBAmzLVZJZiErzKSE6sEZ5KpVmDNBcMcVAxzLTo7tsLgscNHK0d4H+yIS8vswzyzVwc87kPFmuAblGjJKida2MTyUe3bB7zdG6ib1FE0uj54x06OioXKMqc1r5LMUGd6jkIFHqic+SGsocV0s2u5tyJs+/SKO6SaixKHY01aEG3cxw7vcCHAF7lkQlzTD8pad4wjQQH9lIRdYnBltX2Zw1cLptwrivwRCT+X0drpnYnr9ipHOPyqwxYkPFt6SQIElMshWAI2uPqNREvWYBONIWWYGYkYsfTFv5AtMDR6xPGzlcN3EpkfJyrI+uMequOd68YG5kne4GMYutpyK7ifqiTtqr+hlqm2RhZIP95TMFaeT6VfPMfD3dnBdZm+TcqHl3jfz+AnQMonZbvmB14oTPvXv0N6xSnTVK2qt2otzLlSFxiH25OV5c1lJRcrUQZNNOkE2TWgskRUuUZxrAyeGVTxF57zvoqJhmdkDH+pSeg5UrjHvXCizJe9LLurhh4vOnHVprJ8hJbSEvvYWSvA6qSnpoaxxV8HBj+QQBuQJ1pZtWrbN3115ZO+6+rxPt/FzorjndMLEzd8Xy6BUT3We0la1TlDjJh9Bu3vi2Eukm7aqaL46CBwpGNWhpe2LyLa3AotYUM3aHUpXslfa6g+rsEfoaFpkZ2GZn7oTzfZM6nnJMZVzsXbPx+YqRjn0ai+bJTxjmfVgHojrxsSrX0qmsG/G3lpapNuV/E+bUoSVF2TXhL9Hn1qK+LCfQsYQQ10KS37RSUzDGp851tfZvL14pYHVxeM35gaQLasd0YeyYztoF8pP6eB/RTJR3HQH2lbg/KlXpff7ic+VSSOLLJvXzRj9usT5rUOdC/tZ98XfMogqUtk5zaydXJuTvCNKSK0pBWbcMJkx6k5qzMv/UUH8zNV+vozUTUz0nNBYukBM7SFxoG2EulXg8zMX7SaECOJpXlJjAm1vZlPpGFDcCbH4NwLmrxrltqboHOPcFmeX++P7xfi7cz4H7OfCnOgf+iQOcb/vV/HyCgXz9P24yfwvoWD73LXgjn7ttn2pUbVSWr7sLbL58/ut/x18GOIVfeOBU5XfQpwDOKt8COHLzv7d5xvToNt1N85RmfuJDZCsB9mW4PyrCRxWHkqok3gGyiy1tCS3aUDBHnktaiRTLjcgEEsm9Mld1qFQ/x9e2UHnq+NrnEBVYRMLbSvIyGuhoGWB+dpEzvQCc2yQlJd02aiqc410pAPaVCe6H5/WEuUqCTpkqmAKf1RIo3gYSgatMOEuVMWegXRGhzkVEB1SR9LKR3IQOWiommezbUuoTKS5UItCdl7ylR/+AZ1LcihBGf62iWAWKjH08prlshcLkcRJfdPPcqw5fBUIkKaxWqXBE2SRQx9da4rVLVdJWWnQnZRnDDLZvKiCkDG+/9StdXWO6uMSkAI68uNZChTmSXcDI7/snP1duwqVgHenapyjlE+HupfjbFarIan8baUGowd+mHD+bImVUHeFRzLuQSlJe11OW2cvHhjnmRw/QrZnQ72vARknwf98L/5r/s0AdMyQ43jaxMnXJSJeO1nKBOKMkRnYQ4VJFmFMV4dK65ygmuea2KqWckRt8UdDcjl+zFnx9fUnSlQwBhYFWYsIpu8m5vPCqUObVrWVzfO47UODCdPJTE9BbQCVFtxngGC+4NornhLQtiB/OlxZQ3zpEUrytz14x3HVEec5n3oU24/wgA9cHOXg/lXkvhsUNBEm0sqhCbKV1sJhQ1yIiPIqIf16n1FLtNdOM922xMnPM0bYZWCpT5W+96h/wuTvXgACD7dkrpvtP6anbpDJzitRXH1UUdISbGMVWE+qopThp5rjfhjg3IOcfBXAE3jSpSGxJppO1SVo+X3rVEyPmxVFN1BWMMdm3w8mWCZPAqzvrgoADUbnI/30eOKY2f1IZoIvPV4DEXgvAkdYjOy1WPFQSuKQlxqmcEKdSXvpUq7bK3oY11qcvON7UiuAvjqwIHwzXbC8eMNL9mbqiVprLu+htGmG8d47lyW0O140qhv6L7/ulDyyAVGpxUbztXnOwbGJlTE9PzTJFCf03rVRhjpJQJuBWVBKivGsmyKaNQOtG5Vvib11GoE0RQfb5hDjm8MavhOLkj/Q3LLHx2aDg3dXhnWNneW3DNRuLpwx0zVNe0El1aQ+tdSP0dkwzNbLBzpqBK/03rptfem/m/1frroC2bRN78wJxTmguWiLr3SDx4Z3KQDrMuYZAu0qkrUquEw12ivpGfJpqCHWsJETUVPbF6rrOiumkqWiC8a4NNj4fKzXT1+upXLMCLWYGRMG4SFHyMB/C25SaxuuJbHxU4PusDj+rZgJtWxBPnHDndrPJcYNaW/2sBeiXEOhQSIhLHukxct4nmfm0x+7ypYLsMv9u/pnXxe3FcwZaVyhK7eNDZCNR3tWaYvbHQryeFRLgUEioWxGpb9toLp9mdvhIbTBcCLy506l883Mt50qBf60tV1pzNXWnmOPffOXPPpFzvzR6RXvFGvnxI3wIl2SuStwfZOP12AxwbMQAvs5s5C/rswCZe4Dza/4u3X/Nr79Hvj9W98fqfg7cz4E/xTnwTxjg3MIbDdh8GSv5sxDnTjvE3ZYJeaO/b0g09dcj/CepUqL4aSTi7rAXaPPluAU48vVfK3H+sAvt6wJTPra0gGgtVIX42dyaGH8JcE4VaLh7F/Y1wCnJGOJ9RIu6IXR7UID30zJ14yvFj+YhITved/wlzM9F7SDJJVIwW8whJUZUjIT97UpUu4efQz7P/YqJe1lORmI1TXW9fJ6c4/j4AJPciVvuFOWe8QIuTjU1xtz4Ac0VkyS/blLtO2HOFcosNOBpLQHPqswJKtL6IxG8pUS6lxHtX0XG2zYqsoboqJ5hvGeT9ZkTznZNClzIDrbFdkeOgdwoSxElahdRjxiORGJ+if7oEoOM4ytNpWM072Baim7Lza/sEpuBw+GGieXJC0a6D2itWKYweZT34e0qFUTFYTvUEWgrEnoBJBX42hQT4lxGdGA9qW86KEkbordplZXPRgWE1O6z7EAroY2AGxOmiysFcLQb7UuQVqoreTTdKiXkd5Ihu6yW5/JejSgFwOmOiZ1FE71NG+TE9xLkXIC3tZhWS1uOGDDXoMyrJW3KpZC4sBpyPnRQWzBET9OsMiXeWTKqneGrE20nVp1CC4CR1zWCqH/Opcg6lON6jahs5BjLjrsohNT7M/+OqlD66vuNB9fsr5pYmbxg/OMBreJFEj9ETJDEoEvikQBGs4GsXQ0q/txWWp80FY0GcSzPf//19vX1FXIX4IgCRwCOVZ4q9CTCWcxax7t32Zg+VyoU9fvL/JXjrZQqAm5EmSOwxLybLeBGDTGdNqtd1E64+VzJ9wlEMBdOAgZ16ybmxy/pa9mlOH2MN0H1OP2YhvOPWXg9EZAgiWu16nxJSlyAGM16lirVWtKrBqVAa6/+rMxh1+dO0W1cqHOhfs+7c1nmh0wl4zUXeq3NzXhiUteD8dik1ECXsgOvN583M/xR15DlfB9ec7JhYnvuirlBgTjrVGZMkPKyi7iQFhUtrfnHCDSQ81ZjjquvI9TuS5ijlDj/jwEcMeMVL69a9TvEBNeT+KKBuoIRBXcVXJH3dXcpkmtF3o8COIfU5I0TE1JLgH2eUhcK4PQXY1ZJJxMFjsAAM7wJcSripU8FBYl9fKxfUm1mor6Ra+WLfzLfDdfKa2tmZIGPTQMMto8yNTDP8tQWO4vHSqFzbgbP6tjLdSNqFfO6oK5xOf5yLi0//s7aJGubFNlGUbYtmpjo2qEhf1LFjccG1vPCU0sNkzQq8YoRcBNo3UqAdcMNwFGtq9JC5ZjLa78SipK66W2YZ/3zKYdrWkvXzbwVgCxzyXDN3toZn0dW6Wgaobd9ktGBRWYntlhbOEK3danWAzUPzfNHFGnqvVnen3zeAhnl58r7Mg/5vALnh9ecbZpYGb9kuHWPpsIFihJHSI7qUmbVAmnE6ybQVlMWinm8tE+pOHhHaZ8qIdC+gCjPUjLftdFYOMJY5wrrn3UYduSC+OKMqTlyuGpSfjitZbMUJPYTFyqqykLcH+Xh9aQU32fSuiVeZ82EOLQQ5ixthJr3mQByP+ty/O2KCHTII9glh/SYZloqx5kd3WVv5VwDOPJ3Sv7JmmK+NrfmT+lrnic/qZPYsBoiPcvVJoDLD3l4Pc1X136YWyEpb1poKJ5gsm+XrYVzjrZMXBzfWW8sfxPUo/xdM3tzCbwxAxylwFGfN69Nci4sc06+zzzf5Nh/7j2loWCezLf9xATKey7F9YcsPB8VKAVOkHiI2Yn6RtZiuV+4Bzh/ikXG/Xv+/fcg98fn/vjcz4H7OfAPmQN/JADna3jz9cfmXfc7LRQKLiiFiKhEvhrm+OkQSwz1V3DnpyDH7Jlh30jEF6OJSPsvh/p/BXh+vr3rDzlRXxeY3wY4GYS7Z5LyppKq/E762seYm1hFt/kNgHMpCpxTZR7Z3ThPScagMlP0tSnC9cc8vJ+UECCGwWbDWA0WWYxDzSDHvlm1q6ibMluBOLLTLYalWpR3gF2FAkIBDsVE+pSo5JHEmDJqyrsYG5lGp9vn8tLItel2t09uzFX7z8k1S5+PEOVARkwrr3wriHQTP4Zq/J/U4P9MimrZHS4h1LGUKI9yBW+SXzRRmTlEb8MiCyOHbC8YlBxdKW/uFjrykkYUYDjeu0C3qWd79Yj1xX1W53dZnd9T5rLy8e7GMUd7BowCcywFrLn4VTfX5qJdgIMU3GuzV4z1HNJQMk9mbB+vAxp57inGxmJgWa8AiShw/GxKbgBOyusOilIG6apdZG7kWP3OhoNr9FKgHJ6jPzJgPDVyaZAWKrPEXYEbOWDysdyEa8W1tMKI34y0xljAidzAy8+Tm/mdZQFN58owM/VtB752Obg/yVa7uD6iNpGi1KmScPdyXviUkhHbRn3RCMNdyyyOiweCQcEbKdLUTb35Zl4BAP01RlEu7F+h2zSwtXzC2vwB6wsHrC8esLF0wM7aMbrtM473zzEcm7RjKsWzFLRSrMjxFGGRtBXptCJaPElGOvaoy58m7XW3BgS8pMVPWlnKkDanYJsagiVxRnnX3IIby7Xz+643y9dYfG/kZ9yqcCwtVPnaTv27LhoLpxju2FItVAer5jYLS4vByRWm03PN/0bUN+KBI+fMct7EzFg+Z9RAlsS/CyTRH16polZapwR+SYve+twVkwMndNatk5c0xEv/Ghx+SMHphww8nhTgJ2lnYohtX4q/fQGBjrm8Cawg7W0zpRm9tNdMMdG/wdainrP9a62Qs4S/ybE2D2lhEcB2vH/B/pae3XXxUJJztsfWso7dtSN0Gycc7xo5PzSDHPN5skAEKahNR9ec712jWzYxOyheIatUZIyTKcarQY2EOJbgb1NMgGrNEYgjHlaS8vX/EsBRa3udijOXtpmXXrXEBNeR+KKe2oJPTPSuc7R5pYGprwDOxYG0OJmY6t+nOm9UQTF/u1z8bUWZVq7MYzWAVkmIYwUhTmWEOBUT7FTAS59S8hO76a4TJc0x+6vn6rjdKDrMsE8Ay9H2Mauz60wOTjE7usD67Da6tVNOty843blCgKuMs5txhWHXpDxvBM5Ia4+6Fi0KojtFv6xxch3J15zvXLM8eqqMh0uSB0h72cG7ADGkr0CUNpIUJOAm0LqFAOv6W4BjW6wgR7AZ4BQmddFTP8uqGCqvGjg/kPlthiuWa1je146e1fkdJj4tMDuxprxu9jYMHImnzf41Z+b17VRayfZMHG5fcrRzycneFWc6k2pTlXZVBXVkjTGvuTdQQ8C2qB+PrjlYMbE8ZmSsU0dLyQL5HzTQq6WGaUop5YdjBm6illKKTvtCAuxyifQoJONtMw0FQ4x2LrA2tYN+R6+BCjNLUTzlHHQr4oOzQ3PpJHnxYrIuispcXB9k4flY1KtVBNgIsBEvnmbCnJsJcWxUoFXSCf2sNcVjgEMOwS4ZZMQ20Fo1yvzYNvtr5rVVrlGBVZb18Bw25g742DhFTkILb4PLCXMrxvtZHs7f5+D5JBd/+zzC3ApIftVITf4Iw53rau7JtX+4eanWf1mbL06vuDJcYrK0dVrWJQVwtNbc6wvxx7nApL/g6vSCy6MLzg8uMO5fKrCpWspOr9mdv1LgrDJjkpSoLl77yCZFES7fZeD5MF8BHAVqVSS93H9pACfM/l6B8/v+Ht3/330hdz8H7ufA/Ry4nwO/Zg78EwU4d9U3d2FNM2F2MgQafDUsihB5dLAMaQP6coi3i4Cbr4coTr4eYUqFoilRlP+C2fxUQRqHJiLVaNYezSAnwr7pRqFjab36+vHXnBjL13xdYMrHP1XgfAlw+tvHmJ9Y+3mAs3GKJB1JxGhx+gCxYY34WBXg8n0uXo+lyKok9KY9pV61pWnvX0COZhqqzoNtM6Ey1O6aHGc5rpK8JG0dlQQ6lBHuVcqr4GJiXhRRXtjGUP8kOzu7GI0Gri5NqnhX98lywyrFu+GatbkT9bvlxHfwNrCaKI9qFWvr97ga/6cVBFiVKnl/hEspb/wqSYioI/9DJ53VswreiKGnKm4sN8N3bsSlkBJFgRQMm8vHLM3sMjO2ztjgAsN9swz3zTHcP8fo4AJzk5ICta/MZs8OjZyfCUQRvwCL6aMZPsiuvfjMSNvL3BX9rVuUZozx4Xk7bwJaiHBrxN+mHj/ratVC5WdTqlKoogPqSX7ZrvxoWivmmOjbY2f5Ct3GFfubBvY3j1R8+MnBKeen57dpRpdXGrwxAxzThQYEDMeXHO/L952q7z/YuuBwS/OW2Vy4YOmznqnBQ+qKJ4l/0YSHVTrOD9Nxf5KP1zPZHdbibaN8KnkbUklpRh+DbYvIDrD4WygvDkuxaDmm5nYQgRAH2wJujlic3mVqeI2R3kVG+hYYG1hgfHCe6fFllmY32VzZU2bLAqgE2qni1rKDbylcBLrJcT24ZmXinJ66VYqShkh50cEbPwGFJfg9K8L/WRmBVuKPJK1PFohTr5kRm71tLNfStx6/vr6+ADgCSWzEA0cDOBlvO6nLn2CwZY2ZgX02Zs442ZQC+wr93rlKnzIenHF5YuTaYG6XsgA21a4g8OaKK/0V5yeXnB0YOdrVm5OfjOpcSZuTgLa5MT1DnXs0VyyS9b6X5z4V2H+fhOP3abg/zsNXCm87UbsV4W+fS6BTFu+fV1OU1k177SSTA+vK90kAnkW9YDllCpiZ4aMoow53jWytHLM0u8vc5CafR1YYG5xjaniR2fFVlqY32Vzc53DrjPMjs++PWTVkAUGWR5kjuiVpNTmiu3aZsrRhEiJbCHEswvdZHn7W2voixssWgKNUMqKUsXjh/KMVOI2EOWprkbR2SvuWJK5JwZ34opbagkEm+lY52hTYpsFIC2ARICL+I+IXNdm/S1XuMG+DJeknR5m0WwCOwM5Ae0nrKyfYqYRgp0KCnXN5IV4xiR101k6zOK5jZ/kUve428UcdJ1nn9Fec7Z+yv77L2twqO8s7nOycaV45J+J9YuJg3cj2wglb88dszh2yMatje+GA/ZVjjtbPMOyca+ucpe3mLsCR8yPXkHqta6XCmezepbnoM4Xx/SSEtxDhIi2CJfhbCRQXcNOsAI6kOSmwcwNw8njlV0JBUicf66dZntSxv3KCQWdUa5KaT+b1W7xT9IcGdFtHbCztsLd5zNnBldaqZoGFuyb21i7YWtazvnDMyoyO1bkDNhbFdPdUAeCz/SulXFLvQeaqZc01q4zUa4pQRtRS4kcmoLd9l+qcCRIjW4nyrFIJe6L4kmQq+ZsWbK/5FAlMDLTPx982iwi3PNKj66kv6GOkY5bVyQ3Oto8VGL+5XoSpGGF/ychEzzpNJaPkfGgnOrAC72cZOH+fhsejfHxkc0GMxSVgQDyYnJsIcZTocE156S9riV0eAQ6ZBLukkhFXR3vNCIsTW+jWTzHoxHtGWw8VzJZzaIS1mW266kfIel/H6wCt/crzSTZO32Xi8ThbXf9hbvkkvqynMnuAvpZFZj7tsDixz/rcIburxxxsnXKyf4bhWM/FmRGTtHVeSNuUeKhp7VPirXYtf5dP9ZyLofn+CcdbRxys6dhfPuBwzcDppkm1r62MGempWaUoYUileEW5S/JXPk5/n47HgzwzwKk131NJu7Vs+rRwD3DuC5Nv/R2+/9z9vLifA/dz4H4O/GFz4J84wLHAGwu4aSHMTkYzofaWYW7lsQCcG3jTpOTLImGWoYEbM7xxbCD4zghRN/u3UOenIEcMMRtR8cMCaBwsQ+CNNiLsm9GGABwN4ki7lTa+VOP8IZP06wLz9wOcKqryu+hvH//9AEcUOGaAU6IAToMZ4OTg9bhI+cuEOdSajUUtLWANWnKPMg9tJlxAmgI4LQriKANZOfYqMluSl6oJdKgk3KOMFwElREcUUpLXxkDPJFubu+j1Bi4Fhli8WyxtNLLjuHhKb4tIxruIDa1VxVeIfTW+j6rwe1KG/zOJwi0g3KWIN35lxEdUkfehlc7qKRZH9pWxpviTyM2vKl7lZ1tUNzvnrC/olMS/r3OajqZRmmsHVdR5dWkX1WXd1JR1U1vRQ3vTEAM9E0yOzrE0t8r2xg4nh0dcGAwK5KiiwryjLj9fikEx4h3v36ehZIbMODGd1FKp/G3ExLJatVD525aqtouXPjXEhTWRFt1BeeYQbVXTjHxcY2JglYnBRSY+zTIzMcf60gaHe0dcGjRDY5G5a61TJhUhfmm4wnB8zu7GAYszG4wPzTE2MM/YwCJjAysM967Q37GoPI9aqj6Tk9TNq6AKXJ+m4PQwDbcneXhZiQF1FaGuFWeVy7UAACAASURBVDz3KSc6pIzSjG4G2mbYXJBo7Eu1o285X1JgXJ6ZVCTtzto+i9PrjA/O0dcxQUfjMI2V/VQXf6SmpJvasi7qKzpprevhY9sgQ72jfB6dYXVhnYOdQy5PLzCJ8e9dfxiZFzI9zq7NhrI71OVPkRvXS2xwA8EORfg+lZ3eEiTWWTxrRDkjBsQCYW6um7ueWKpN8itj4zsGxup7zAlUCuRIspOttBQWEuUhKVTNFCf30VQ8QVftNINtEq27zMynZRbGl1mZXmZraYOjbR0XJwazSegtoBIljiTAnB2ecbB9yPrCJrPjiwz3TjLSN8tY/xLjAxsMda3RUTdPbfEkBalDfIhqJsStGIcfklQblfvjXNX2Jr5VQU7FhLgV8NyngIy4JhrKPjHau8zqrI6jbSnI7pShqvi95OJMz/H+ETtreyzNbjA5vMhA92d1LbTWD9FU3UtdeSdNVRL33EdX8xCD3eNMD8+zNruBbl3H2d4JVwKqRFF0p7gWZZZ+W1J7jEz17tNSMk12TBdRXuI5UqgMxyWJTVpbbiCOGd4IwFEQ52cAjgVc/3yMuNng2FEM2GU9r1VtNCGOVbzwquZdcDWJL6qpLehnvHeZw039lwBHruUvAM62Mu6ODq7A1y4b8fbytZH2lVr8xTjaroIgh1KCncTTKp8Qlxxe+hWQl9hGZ+0kC+M7bC8fcbpv4PrcXJgLyBMllvESSbs73hWV0yY7q1vsru6yu6pTsGxhYp3xvgX626bob52kv3Wc/tYxPnVMMN4zzfTgPMvjq+wt7WPYNdwAEqWME3BzB3rI547XTCwMSXrTIpUZw6S8aCfSrQI/qyL8raSNqo5AM8ARRY60iomvkrQZBTnm8dK3mPzEdrrqplia2GFv5QDDwRnXl0KPpK/J3IZzfs75iZ4T3TH7Wzp0W4ccbEt8tai7Tlme2WNqeJ2h7nn6O2fobZuiu3mcntZJBjqn+fRxjonBJRYnN9ha2ud4+wTjgV7FW4va8AZGmkGOUuIcXHO2ZWJOEvbK58iK6eZdYD1RnrUEO1Tj+0zaQysItCvXUvcciggQIGeTTrhbNmlvaqjL/8hwxxSrk6ucbevM7+v22pFjKKbSEz0rNJV8IudDK9EBpXg9S8Xp+2Q8HuXiayWvIZHi2r1EqFMDIY5i8K+ZjIuCK0Bgq2M6wS7JZMRV01YzyKJstqwfKSAmGxgK8pkBqwDftZkNuuqHyHpfzZvAfEJdc/B6moXTdxl4PM7C3y6bUNdc4sIryUtoo6HkE931k/Q0T9LfPslQ9ySfh+dZnlllZ32bo70DjCenXJ+LlEkWWVFzCg075/L0hJO9PXZXN1idWWJudIaJvnGGO0YY655msmeFmf4deusWqcocJvVFG699qgl3KlUAx+2HLLwfF6o2Z7UJpMzENS89C8AJs2sjzK6DsF9Mobo3Mf5D7hXvv/YPK37uj9f98bqfA/dz4I95DvzxABy1eyM7OC1aL7V6vIU4IfYCabShFDhmcBPq1ISMEKfGO9BGUkXqCZII7G8M2TVTw6FetRIJ0NHUOGaI8xXAiXCwwBvLYxOixAm3k/FTiPOHTJibQtRcWP4E4NgW4m8jCpwsUt5UUS0Ap00Azjq6zTPV6nJ7G6rd2O/dKHBmuQU4+bh8n433HYATbl+HGioKvcHcPibvTQBOC+G2LYTZtGpQzV4zOhaptNzABtlpSpww9wqi/Ep4HVpIUXYbfd2TbKzvcnZq4OL8K4Bj3sXdWj6lv22BorSPfIio55WPGLZW4f2wAt/Hpfg/KyLAJo9w5wJe+xQRF1pKVkwdLWWf+Ny/xtH6Ocb9K1X8q5th4zUnu6dsLu4yPbJMb9s4tWXd5KTWkZ5QSfL7MhLeFfPhbRHx74pJiCkmMa6UrNQqivPrqalsobujl8lx+d1XOT7ax3B2xtW5uIV+2f4jJsxzY0d01S8rFU7qmz5e+7WhAI6VBeCUEexYRqR7Ba/8qokJrSXlTRO5iW2U53RTVdhFTUk7deUttDV2Mv5pko3lTYwnRi70WisVEvl6aeLKeIXx9ILDvVPmP6/Q2zFMVUkz5QVNlOW1UJLbSlFWK3lpLWQnNZP2vpHo8HKC3HJxfpz8BcDxE0WBSxkR3sW8CiwgP6WZroYRFidXVaTs+dGF5k0hGWKnF+xv7rM0u8xI/zhtDR8py68nJ6WC9Pgykt6V8OF1EfFvCkl4W0BSTB4ZiYXkZ5RSWlBJY3UTQz1DLE7Pc7Czh/7wiCv9mWakKcWZeUi7kUTXzgzq6KpZpCztEwmRzYQ6FeHzNBe/Z8UEPKsgyMoMcL6CN+p6MavWBAD87LgLeszPQ8U3w76cYPsiIt1KiAmqJvV1E3nx7RQmt1KS3kJ5djN1xW201XTR19rLxOAY6wsrnO4ffwE25BoUHxz94Rn7W/uszK4y3DdGS20HRVkVFGfVUpLdTFlOB0UZHWTFt5Ic3URMRC2RPqX4OeTg9CAZl4fpeDzJxduqiACHUsLcS3nhV0JMRDmlWd30tk6zMrOnrn3DoVn5IS+u5ououE7R7WyxMDPHUO+wOm/VJa3kZ9SRFl9ByvtSkmLEeDyP5NgC0uOLyU4uoyS7hsaKVnpb+5gcHGd1ZoGjrW1Mekm6kx18sxrtQlNNieHrxrSBgeYVytIHFYgVr6pQ5wqtoFVx9eKRIdf2V21UdwGOPL/xJ7OcP0tL560iUCkDLRHjAnAcpKVTiudqgh0rifKqVGlqCS8qqSnoZbx3kcPN018AOFtU5g0SHVJ+B+CICbkkzFXhL+bRDiUEOYr6Jo9Q12xeB+STn9RCZ9048+NbbC8fcLovxbJADlnvRPEgsFIi5c84OzjgcGeHzaVV5ifmGOsfp6dlkKbKLspzm8hJqiI3qYq85EryUyopyaihKq+RxpI2uut6meybYmNmHf3OCVeHBtX6ci2pQmbAIY+iYtFvmlQq1Uj7Bg35E2RGd/LcvRw/qwL8raQdUXxwmgm0qVcmzcqs2U6UKoUEOuQS5VNEboKAqXEWxjfZWdnDcHAEV3pAzGvE/MrA9YWeS/0p+qMjjvd17Kxtszy7yueReQa7J2irG6CqsIO81HpyU+rISa4hK6ma3JRaCjIa1DVQXdRBR/0Awx/HmRubY3tpnbO9fa6NZxpssPjiyNsUBmFuFduU+da0QmnaIElRrUT7NxLqXIPPU2l7KyPAtpQg5X2jqW98rVMJc80g9XUltfmdDLdPsDK5xOn2LteX8r5kUmv/5HV0yydM9i7RVDJI7ocmogOL8XqWjNP3iXg8ysHPSpLgBOBo7cQhjnUKIElCY4CtvH4hAfbZBDqmEuyaSEZcBW3VfSxMLLO/to9+X+ajzA/5Oy2gT7yzzlmfXaW7YYDs+Cqig6RdKgfvZ5k4f5+B55NM/GyzCHHO5rV/EYkvq8hNaKQ4vYWi9HoK0qspzKikobKV/s4BZiam2Vhe43hPh8ko500miBxImZ8S5a5je3mF2dEJhjr7aKtpoSqvksKUIkpSqyhPb6Iqq5v8+DaSo+p541NBuFMxQXZiXFyA9+M8peoKtq3SUgKdxFRcQg+0e7cw+zbC7NtvAI7AnD8kRlxLoJKNvbsqbXl+X4jcH4P7OXA/B+7nwP0c+NOZA38cAOdGfmsBOM3mNimtPUprhzJ7sDjWEixRoU41iIFsqEsNIS7VBDtXEeRUSZBTBYFqlBPodDuCnMq5O4KdRBpfoW7+ZQdXZPgywm5ijOsId6y/iTIOl+JBqVO+frwFOP+QC+sXAY7cON0BOBYFzty4uYXKIq+33Ihewb6kUH2hwGnE55m0UOWYAU6lSvjRAI6mwNGMmTV4E2F3F+BoICfcDHAktSrEvpEgW006HuZWQaRPMS+DCyjIbKG3c4L1tR1OT/RciE+I5R7ZXHDIfaTI6Ic6FlXyUWJUI699xcuiCq8fy/F5VIrfU2nDKCRMTEO9SogJLCf9jcjgPzHaucrOvIHjjSvVsy91hUT3bi3oGO9foLVmkKKMJt6/KiTIMwE/1/f4urzHxzlODV+XD/i5xuPvnkhEQApvItP58DaLwtwqOlt7mP08w+72NscHRxjPxDfgjrzfhDLuXZ3VM9y1TWPJHLnxn3gX1KH531hpO8GSGhTkIL4ZZYS7iglzqSqQXgUU8CYkj5jIHN6/yibxXRa56cW0N3YzN7mglBvnp0ZMqhjUAI4AHWnv2lk/5FPvFOWFTcS8yOB1eCovQlJ5HphGhF8aYT7phHplEOSeia9DOu5WqTg9SMH5YSbuTwrwtpIWqiqCncsJ9ygmyq+Q7PhmWiuH+Ty0zPbiIWd7kqqkvd/jnXMWP28w0D1GbVk7GQmlvAhJJtAjFj+XWLwd4/C0i8PbIQYf53f4ub4lxDuG50EfiI5MIvVDLrWlTXzqGWF9QebqLsYjaV2QvoE7AOccZSi7OnnGSOc29fkTpL1uI8y5EJ8n2fg+LcRfktOsapQKRwyNb66Zuy1UZnhztxXS8vwu1FGfU2o72TWuU2bJklgT7lzKC89y3vhXEBNcQXRQAa8CMnnhl0Tc8wwyPxRQllNBe3070yOfOdjSafWfvBfzP/GeONo7YmNxi4mhGRorO0iPLyTcN4YI3w9E+iUT5Z9BpG82oR7ZBLpk42efhbdNJh5PM3B9lIbb40w8nwrAKVRxxKKWigmvIjWmgcbyYaaG1jnaOVeG4KImsSgxRN1kMmjQYHV+hf6uISoK6kn7UMS7qCzCfJO0a8ApBh+nd3g7RePn+o4gj1hCvd/zKjSZ5Jg8ijIqaapoZaRnhI25FS6OTrTYdEtLlbTsnGrR0qKKmB7Yp6l4ktTXLbzxr+G5RzXBDlX424gqQTxKfiXAURBOax8Vtd/tWqu1dH4L4ATb12ppUQ4CcKpUK1TCiypq8nsZ613kYONUg7yyRpqhgBwz8bSSFqqJfgE4Q0SHiAInB1/bInxVipwAnGr8bcvxl2hxx0JCnPMJcxMfokIKkls1gDO2ydaSjpM9i9pBgK9AHFFZiH/NhVKrnOlOWJtb49PHMerL2shNriDhTR4vg1IJdH1PsPt7Qj0/EOH9gZcBScSGZ5D8Oo/8xApayrsY75lie36bk61DTMcGuLoz6cwAR9pKt2cNzPTv0V4+TW5cF1GeZcqg29+qlADrGgIlQtxWvEyqlFol0F4zxw6wz+G5dwE58S2014wxP7bBzrJA12NUDjhihCMnXtpyRE0n88+I4cjAxtI2YwMztNb2UZLdSGpsGW9Cswh2TyTYPYFgD22EeScR6Z/Ky6AM4qLyFbiqKWqlu6mf6eEZ9la3MJ3ptfdmhoUWSKUBD5D498meHRoKJ8iO/UhcaAvhrjV4PSnBR1rFbIoItC8iwDYPP5tMfKxSCHXJIOVVJTV5nXxqn2B5YonTLQvAschDNQimWzllsneZptIhcj608CawBM+nAnCS8Hici6RMBTs0EewgCsF6gs0KMAE4gXbS8iiKphwCHdM0gBNbQWt1P/Pjy+yt7quWOtOZ+JrJ6wnAER+tC9bn1vnYOEROQg1vQwoId8/DxyoHFzEMfpyNr02OUkmFexTw0r+Id6GlxIQX8So4k0j/BMJ835Ecm0NlUR29nX3MTs6yu7HNpf4red71FUc7uyxMztDb+pGawlqy4nN5G/qBULeXhHvE8twriZc+WUS6ZxPqmEOATY5K6ZP2KX+l5ipR8fShDrVEODcR4dxMuJPmnacpcOQezgxx5NFONoDEF0fUNpYhbdmWcdsqfw9v/nQKk3/IvfL999zPj/s5cD8H/pTmwB8hwLmFNyFql7VOkyk7VBMgCUi2pYghr69tAb52+fjY5eFjl4u3bQ5etll42sjIVMPDNgNPm9vhZZPB7cjE2yYLb+tsfKxz8LUW34kC/KxF8VJMoG2ZimEVc90Q+1pC7WWn2LJD/NXjP2J36KYY/TkFjhnghCkT49sWql8PcAY1D5xnhTceOIHWVYQ51BNuL0Nr/9KSt5qItGsm0q6FCLMCJ9ymRVPj2LcQoXbZmgmxayRYAI5dLaEuFUrRERWQR356Mx/bx1lb2ebkWM+5+IRYPFXMAEcKqb31M4a7l6jM6Sf5VTNv/DWA4/FDGd4K4IgHjpgYlxDlXsYb3woSI+spTxvgY+08c0M6lZSiW75QSUG6FT1T/Su0Vn4iL6mB2MhCglwTsfshCuvvnmPzfRS2P7zE7sdX2D14g/3DaBwfvcPdNgZfl1iCfeKIf5dDZWkTQ/2jrCyusret4+zYqP3+lh1hpUy5Znv5gqkhHZ11KxSnjhEX2qFu7n3FZ8amXBVHQfayW1uqomyltSTAPgs/uxR87N7j7xxLiGcskQGxxEenUVvezNTwDCe6M6XCERAgO6eiwLk4u+D0wMjmso7ulhEyE8vxd3+Lp8Nr3G1e4fzsFU5P3uD0+C1Oj2JxevQep4eJOD9MwflBOm6PcvB8WoSvpNEIwHEqJ9S1hEivQlKjG6nO66e/ZYbZ4S22Fs4429MK3NWZQwY6Z6gu7iTtfTlRgWm4Wr/C+vtwrL6L5NnvXvDs719h9d0LbH6IwvZBJE5Po/Cwe4mvyxuighOUsqO1tpvPwzMK4pzs6rg+N9wU0xb1gHFP2qgumB08pK18hqx3HYS7FuL9JAvfpwXKByfAqpogm1oVWxtqd9tCdbOY37k2LeDmJ49mcCMwVtokBdCGOUhkeSUSuywQJ9y1mHDXAgIdUvG2jsHlcSSBLtFEhyWR/j6HmuI6RvvG2Fvfu30fFoBjuORg65Cl6XUGu8Ypya7ndWgKDg+DcXwYjvPjl7g+fYvrkzhcHn3A6UECTj8k4fIgFbdH4lWUhcfTHLye5eFtnU+QcwlvgqpIftNIYVonH5tFfbOvpaeZW2hUcSuqmLMr9Ad6DncOmRicpra0jYQ3uUT4JeDjFIPj45c8+/tIrH4nIwLr7yKw/SESh4fPcXochbd9NJF+ibx/kUVOYimt1V1Mf5rhaOuAi6PzGxCiPEvM7YTSxrg6dcrH+gUKEnuIj2zmtV89oc6SyFOpzNJVOpD9lxAn7GsFzt318+Y8WiDOLcAReCznLsyswBGAI62cQQ6VPPesJDqokvgoUSn2MtbzbYAjLTkCcA4tACd3iDfBFfjcAJxKfK3r8ZVUIVstcU9ioaWFKswtj+igYgpS2uiqm2B+TBQ4OrMCRwiXKB2EqGjiDlFb6A8MHG4fMz2yQHPVR9I/lPAyKIUgtzjcnr3G5neR2P/wHMcHUbg8foGXzRsCnWKJ8EwgJiyLopRaOmv6mRmaZ3thC8PeV/4tZoBzqZNI8QtWxo/pqZunMKGHl95l+Fnn4m+tGdcH2jRosdt2VQTalxNoX0yAQz4BDtlfApzxDU2Bc3iiKW+khUq57mqG6lf6SwyH5xzv6pkbX6Wjfoi8lFpiInMJ807Bw+YdNr+Lwvb7F9j/8AKHBy9xfvwG92dv8bKNVWvz27Bs0uPKKM9uoLd5iMWJJc72jjSDcDMsVMfRQkcBw941C6OHdNXMU5o2RPKLDp571CIx3z4CF2ykha+QAFuBLZn4PEsl1DmTlFdV1OR13QCck609ri8NZoqsvYDMa93KGRO9KzSWfCL7fQuvA0rweJKC43dJeDzKQ7yDxLxY2rU1gKOpUKWFKlAi20XR5GBpoUoiPbaSlqo+ZseW2VnZ53RXgKIF4AjEEdPzS9bnNvjY+InchFrehRQR4ZGPr3Uurj/k4Pk4F1+bfBVNHuxcSIhrAWHu+QS7ZeLj8AFXqxc4PA7iRXA8OakltDZ0MjE8ydbKpvLCuXP41NzUbe4wOTRBc2UruUlFvA1LxM/hOba/88Xh+xCcf4jC7eE73B5+wP1BMh4P0vF8mIPvkwICrEsItqsg1EE2uurMAKeJcCeB4eKDI+OuEses3v0FcCMeez8Pb+7VNzd/4+6uk/fP71VZ93Pgfg7cz4F/1nPgjwTgiLfK3XYpzaBSeRw4iERe5OyleEoyw8M0bL+Lx/p3sTz7u2ie/O0rHv/tC/OI4tHfynj+k/H4b58j48md8fS3L3j621c8++1rnv02GqvfvsP6t7HY/V08Tt+n4v4wG+8nhfhZlRFkW/1lTLkqIr9RSP6BF9QfBnAsKVTjzFlMjH9WgbONSqGS9oawJnyeFeHyfR7ej0q4BThfw5tbgKMgjm0LETIE5shQN2eycyYAp0G1R0jLRLhnEc/9cslNbeJj2xhry9ucHJ1h1MuO7a3awnJDLslQY70rVOcPkhrdytsAUVdV4a4AThl+z8oJsi1HookjXSp44VFJTGAtWW87VV9+Z+UcA03LjHdvMP9ph7Eu2TUV34JG3oUUEOGZia9dIk4PYnD8MQbnh+9xeRSP6+NEXJ8k4/YkBfenqXjaJuHjGI+faxzRzzPJz6ymramHidFp1pa2OD7Q/6RIl6js3bULZkcP6W1epyxjjLiwNnyeleLzVAxxNfAX4lBJiH0FwapFp5RAu1z8bNPwsYknwOmD2nF/HhBPQnQWDRXtTI/Ooz80KmAj8dRSDGoA55LTg3M2lw/pbh4jI75KKWA87N7ibv0O12cxuD59j9vTBNyfJeP+LA2Pp6LoyFatOF5PC/GxKlWKCCl2gyVVR0CFWxFx4TVkv2+jKreP9upJhtpXmPm0z9zoIR+b5ijP/UhidBkvg3MIcEvG5ek77H6Mxv5BLA4P4nF8mITTowScHn/A+UkcblZxeNhp6pxwn2SS3xVTkd+sWkZmRmbNu+zSRmVurTMXnxe6a/aXLlXyU1f1HDlxnYS7FeD9JAMf5YNTigZwam4BjiTLfeta+5YPzg0UuG2RVIo61TpZS5gqSDSIE+ZURqhTMQF2GXhZfcD18SuCXGN5F55GZnwhdaXNjPdPsb9xoM2NOxWSyXCJbvNQKZf6OyYpzmzidXAG9j9G4vTwFa5PYvC0SsTLKgXPp2l4PM7A/VEWHk+y8VTgRpQ3Evuej49NPqFupbyPbCAvqYv60mFGesWD51jzvRGoKEOmihFO9oxsLe0xN75MW20/2YmVRPgm4W3/DnerWJwfx+LwYxyOD+JwehiH86NYXB7H4vo0FrdncXjbfVCqiSj/VOKisinLbqCvZZiV6XUO1o8w6i61diSlHtBeU2DI9ryRkc51avPHyI77yPuwViLcahXQlGQeLY5bAI5A8G944CiYc2dX6eZcfRvgqELxDsAJEDWJfSWRHpW8CarkQ5SskXcAzl0TY5lvZgWOBnC2qbAAHNscfGyL8bWpwtemAT+bGgVw/GxFpVJIkJN4k+TyJqCIguQ2uuonWJjYZmflAL1OvGIEctwqYwSsCbzZXt5TBXxnwyD5qbW8Ck7D1zEGDxuBgzE4yPr0KBa3J3F4Wn3Axy6eAMdE5aHy0i+T9HflVOe10dv0ibnheXSrO5hEWWE5/8KMLGa/EvU+a+BTq5hL9/HGvwx/mxxzOpgANVHfVBMoBvTir+RQTKBjPoFOObzwLSQvSXtfS1Pb7K3pMBydmBU4Iokxv7crUSGes7t6yOLUJj0t45RktRATkUewe5L6/V0fx2H/nay9Airf4/YkXs17b5skfO2SCXJOI8o3i5jwXJKji6jOb2WwY5S1mTWOto64OLz4SWuwXGaSHrY+fcpwxzr1hZNkxfYoYCh/2/xsxPemiCAHUeFIC1U2vtZphLlmk/q6hlqzB87y5Aon2/tcX95Vp2jHcG9Zz3jPKvXFn8iMa+GlXwluj5Nx+F0S7o/ykajwUEcx7L4DcexrVBufAjii1nLIUx44QS5JpMWImq2P6eFFNQ9Ods9u237lOlLX0hXr85t8bBomN6GOtyHFRHgUqg0l1x9z8VSJdMUEOZYS7FxKiEspIa4lBLnk42Ofgpv1O5yfPedVWCr5GVV0NPcyNTrD1uqOMsZXp01OnQzTNXvru4wPTNJQ1kZWfAnRIan42b/B/rswnH58idujGLyeJuH9NE0BdGmZ8nlSiKi4gmwqVNhAqMS1SwiCAHBHrf38Ngn0rqGx2c/wG2qbL6HNt1qm7rZQ3VkfvrXm33/un3URcw+w7uf//Ry4nwN/inPgnzjAkXhws4TWDHCkPUciq4MdapQUP8ixgiDHMnxs85VHxNPfRvO7vwnjv/8nf37zV578zX904q//oyN//ReO/M1fmsdfOfI3f+XAX/+Vg3qU5/9Zxl9q4//8Swdk/Je/dOa//IUrv/kLd/7rX3jxf/2FD//tL/z5u/89lIf/+TXW//0DTt+nqwQK6X8PthelSD3BdvWqkAy2rdOije8oAixA5tdONsvXW2KOVVFqTsOStiLZUZQWKlHgJH8RI/5LLVQWgDNEbGizBnC+y1cKl0Drak15I6+jItE1Y2ZNfaMpcATg3B0axDEnhNk2mQFOHSFOFYS5FxHhk0NOciPdrWOsLW1zcnimWpBMZnNPy/2/3IgfbRuUkW9d0Scy3rXxLkgk6VW4fV+K16NyFSUuyifZ6Qt3qiLCuZKXHtW8D24k/VUHhfG9VKQNUp83QlvZBLW5A2S8beClrxS/CXg9S8TjcRKuD5Nwf5yK59MMvK1y8LG2FMdiVlqIt202XnYpeNq9JzIgjZQPpVSWtNDT+Ym5zysc7J3d1C2WOl3ixvc3L1icOuZT5xaVOeO8D2/F+6koRgrxty7V0lAcJKq9ilB7DeSIUa6/yPqtEwlwTCDMM5EXAUkkvc2jsbJLFXnG40uVYCQ7swrgXJmUsuJMd8nW8jHdTROkv6/Bx/E97tZxuFt9wP2ZBm48rdPxtpEiNF+9N19rUd2U4Gcj3gwi8a9BEnuCHSRZp4wQ52Je+pQTG1ZNyusG8hI6qMweorl8mtbKWQpTPhL3vIIAlxS87BNxs0rA6VE8Tg+TcHmSjvuzHDytC/C0zsPTOtusdEvD0zYFL7tEgtxSiYksICexitriVj51aS05VyfHX3qqiL/mWezpjAAAIABJREFU4TWap4qe3voF8j4IwMnD60k6Pk8l8aSEAOsqAiVO3FaiqeuVIs4CcG6uoTuGxer6u4EBXyrmlFm58lOR4kOggqSpyHyTc1aJtFRJ4ev1LAnXR28Jdk0gJiKb7IQyGsrbmRicQbdx9E2As79xxMLkBn2tUxSlt/IqMBf771/h/CAWj6dJ+NqIp0Uufjb5+FoVqOvS10oKNm1O+toU3KgLIzzLSIluUW0+PS1zzI5tI/Dza1WbgJTd1ROmR1b42DxCUUYjb8NycLeKxu77Vzg9iMX1UbICe57PMvG0ysTLWkYGXtbpeFqn4WWbgq9DEgHOCYR5JZEWW0Z9aTdjvTOsTm9rhsBiHH5TeGpF78HaFTNDe3TVLFCWMULKK2ndqcfXWtRoFapdR8UNSyuVKPbs6vjHKHAsAEfUmUH2tQjAEWVmhACcwEreP6+kKq+X0Z5FdBtnX3rgSB1ruFaxywebJsb7NIDzOqhCKTh9bATgSJJcI342teZEOUkWknY2MTHO5ZW/AJx2uuonWRSz39VDjIcCei29otpKIcfpYPOE2dEVupuGKc5sVAoVL7u32Hz3HPsfonF68AHXR6l4SLFsnY6fXSb+9hn426fhb59KqGs6cRFF5MbXUFvYxqeOUdZnVriUtjYVEW3255LuppNrpI3qaPWKiY/rVGcP8i5IVDY5BIi5rk05EretgJddOYEOpQQ5FikwFeyco1pzClI66Gn+zOrsnprfxmPxpBHQcee9meBox8DC5Cb97Z+pyOsk/mUJ/k7xOD6IxvHHWBx/iMfx+2RcH2prr6+NqA8lTSnb7BGTRYhrBmEeqUR6i9lvGQ2lXYz2TLE6s8HRltb6ZllzLY9XJ9fsLRuZGdqnq3aRopQh3gU3ImudpLUJkFJx744F6n3LehvhlktadB11Bb0Md0yzMrXKiTIx/grgnMPukp7RjyvUFn4iPaaFKJ9iXB4mY/d3Sbg9FIBTQ6hTmxniiB+f/N2yqHAEipUSKIomxwyCXJJJfVdJY3kvU0OLbC7ucSwAR28xu7YAnGs25rf42DRCTkK9GeDI2l2A64/5eD0pVmt4kKOoJ6UFtopglyoCnUrxsc/CwyYeV+tooiNzKMpuoLttiOnxBbZW9zCc3LYAC1CU07izustI7xQ1xe1KAfUmOAs/+/c4fP8a14dxeD1Nxs8mG39rURwJuJEUs3ICJeVL/U2WtVIDsgJlxd9KjZt7ILmnk/X2rqrm557fhTQ/9/y+cPm195L3X3c/V+7nwP0cuJ8D/3zmwP8vAOfrWO2vP75rUHcDcOyaUEbFAkjE28ChiiCHCgIdSwlwLMbTOgvrv3/H3/11EP/pPzjxH//tM/79v/qO//Ff/Ff+x3/xX/jX/8Nv+J/+/L/yb/6lZfyGf/OvfsO/+Zfa+Lf/8jfI+J/N49/9+W/4d3/+3/j3f/63/Ps/+x3/y5/9wP/6Z4/4D3/2lL/61w785n/z47v/Iwqr//4ep+8z8HpapAoGgTjSOiTFZKBNLUECcVRBqSkCvi4mf+liuvn639dCZWsGONEWBc4YcxOrWoz4LypwLACnGBcFcMoItK65TZxSiVoW5U0zz+1afjKUGkf54kgyVROhAnBsRIUj7W0VhLoVEu6VRXZSA10to6wuiofMKYbTc66M5hSb281pTnaNfB5ap7F0hKy4DmKCxYC6ClcFcCT6VtrWxJ/k/2bvPbzbuu583/cXvHlJbMltJrYlOcUlsWM5mWRSnTgzk9iptpNJ4iaJDZ29oxCFTYXqhRQ7QQIgQRIgWAB2qpFUo1hU2ESJvUnsFd+3fvscgKBE2b4zSe7cueRae4HFFsiDfQ7O/uxvoV37UmioPYlsEkEl2B9uwyF5KU5oK5CaWInsQ9VIiS9FXDBlBhyD9+8SIfrjYQR+lMLyA6LIuy+iEMZiBonUFHpK80taBrm4AOGCTAT5noAqNBVH9hqgT7fBXnoWrVe6MDY07Vo7uB+XF5yYHF7CnVszuHZhBDZDK07EVTGpe7i3gdkVNFJSc5Ry9hyqOZZQIw+pc/Qsl0EpOw1dWCr2qdKRejgfVSUXWKvYEi2QWa4J2y5lTVTLc6uYm1zBUO8szjiuI/1YBSIkBG04cBPml44IQTaixQYur0NGzTlWUC4DSftZyKv7Rptae0qgpt13KS128qENMiE21Ij98iKciK9AxuEGZB45g/1KCyJEqfD6w174/OkgRB+egGxXGoJ99AgX5CNaYoUyoBwKfxsU/hbIZbSgMCJCrEeYMIPl8CQq9Di5Lx+5KVY0lJ9HT1snFu9NstwHBiGIU1G25hTXMjPavYiLVb0wJjcgLjQXET5piPTVI1pQAIWQKr9trNmIICpZGuni5j5/GLxxNVR9ijKOhzoEcbjwXNe/RecxQQZ6rayQCw2I8E1HiNcxaEJScSzegNwUGyotZ9F+qYstzpkKwj0zKON1GQRwuloHcb7qBkxp9dinMEH64REE7U5GhG8WlBIT1LR4Josd2S5YYxPZLyhDw8xyNBRSExRSAxKjLcg5cQbV1g60XxxCf9d9zExQBZHHkzqBlRkn7t6aQFPtdRTr63BibwEUAafg9b4Wu34XC/FfjiPUK4fZT5X0PNIiqGT0aIZcwtlRI4XZCPM7zexdIX6HkKTRw5hmR335ZXRcpIyk+yxrigEcWgzyCpDZ0VXcuT6Dq2eGUGm+iZyjTdgXbUc0U31R05GVy6mhwOi/NsCRlDFlmUJiQWyoBQeUVhyLt8KadwFXXQBn9uEa8flJJwjgXGscRrH+EvZTXTTBT5GZARyFuBJyMeXgWBEtMkNO4bQyA9QB1EKVD0NKHWsB6m0fwfjd+1iaIhjg+aJw83qodxIXGzpQpK/G8b1GKAJPwPuPGvzlVyp4/+EAZB+fRrgfqa3o9c5ndeWUt0OV5pGCDESL05EYpcfJxHzoT1pQZzuHzqu3MDc2Aef8ItcQ5qoSn3UyEEoQ59bFYZYXdkxL1wI9ZysSFTELkFLMzTUK+41xtWsF5+KAqgAFGQ1orLmJge5JTA5NY2GawBT9bfRi8x9OYOzuDFou9KA0/zxSD1mhCU2D4M+J+PjXOvi8dxCSP6cg6JMc0PVQLiSwwoW6q0lFwld8R4syEO53HMHeB3BAlYW8lFLU2BrR1tyJoZ5xVrfuekrXI8GPqaEV9N+YxZX6QZTor+FEXDWo/Uklo+NXAG2QGZoAE2JkeVBKKMvFgIxDFagwNeJqw030dfRjenhywxaqkd45XD17BzbjZaQdqkJ8ZAECdp2G+M8pCNlN7yNl0AXUQRtQAy2pcPzJSkVBxqTCISUYnVccwFEFnmbHpqLwAtqauzHQPYapEa4VjVPD8PBtGRjoHsG5yhboT9pxSG1GXBgBHBNCdhkR4cOpJ9UUmBxQgZiACqgCKqDwL2HX23BxOsIkJ3EkMR+FeTW40NCKW+19GL47gTkKpXc1lvHgdbhvDJfPXYfVWIe0wxbsV+VBLk1FwCfH2WZHtFCPGKmZe4+gFjZxCdRiCnsncEOKGyp+IGhD9z50PSao7gLrlE/GXXs97/H+c5//z7kJ/6x7wM2fb77Wm3Ngcw5szoHNOfDgHPi7A5wHYc1GX3O/JLfjQrs1LohDAIcaHtgNkcwKlX8RgzcKfwMLZSWr1Hu/DMNb3/fCD779J7zx6q/xjRd/hle+/ia+8dKb+OYrP8Wr3/gpvvmNN/nxE3zzGz/Bq/x47ZWfgMa32HgTr7/8c7z+8r9j50vv4I2XfoNvv/guvv3iH/DDVz/BL77vj/f/TYldv9sP0V+SEeJFN4Q2tvBXEcARl7I8AQI4pMjR0GLSNR5UAnyKxNe9AP3cAKcejXUcwBkfnH5Iak47bVyIMSlwumHOuIyj2mpE+ZkR9JERkV4EcMrcIaEUWBwnrkK8mFPceAKcREk1aJAyh/6bWHElB3BELoBTAW2AFbEhBYiLzEVuSiXOVrfibi/Vcc9iYWaR1WCzDVyP9c306ByuX7oDR+EVDuBoOIAT9JEZ4XssUPjRYo+Op4MdU42EbgzLERdQjsTgMuwPK8Oh6HIcVVXgpLYCh+UEeAwI25UG6R9PIPCDNITtykGUdz7kvkWshpqgVYyoAjEiB2IozFNYxWphw33zEOx9GsqgDFaFnpFcAltRA64038Jw/5Rr7eB+XFlw4t7IAu52TqH1whBKDddwIr4SET5U0U55EwVgAIeyVaRl0NHvLi5HDNsBNyDKLwMqWQbiwjKxX5WNtMOFqC6hoMt+bARwVuacmJtwYrh3Hmcdt5BxrApRknSECdI5JYVAj0jKbhLR4t+KGNbMwwEqagBiLUD0O7ibgOhmnLIcLND6F0MXyAUtJ1KGiLICx3Q1OBZbg7gwC8IFOfD703GIPkiB7ONMBO4xIMSnEOGCEkSJKiCXVEEusSNaUoYocQkiRUUM7oRSCK9Yj7gIAwvbzDhShBrrWXS13MTiBOXgLHIqEgI4lPs67WTV1OO9i7hU0wNTSj3iQvUI90lFpG8Oov3WAM5aqxF3vq3lUq2HOewaw2AN96bgPs/oXHTbrB7+Gc07ApO08xzpm41Qr1PQBGfgaFw+9MllcBSfR/tFUnfMrFvX0gRZmV/B+MA0uttG0Fjdhfy089ivKIbsg2QE7yI7mJGBRI2MACW36CFFES2EVGILa9EhqEKLUVWACQdUJTCmXsAZRye62yYwcofaf1yhUvyUXAWWppzobhtDfVkrck46cEhbAIUsDcI/JcHvD0cQ8FEGC3pViSkzhpsbNE9UUhsUEivk4mJECvMR7peDEB+y4Z3EPpUBWccrUG5uxKWGTty5OYmFibXQZBfAmR93YqhrDjcuTqDBRvDtEg4oKhEtpIXn3wrgkPqhgv0tXFsUARwr9q8DOL2YGHigRvwBBQ4HcC5jv8qKCFEuD3BKoRBXQS7imqiiRYWQs3poA1T+euyNMrEaccrA6WkbZgqcxftz3ER2XyW49qT+zglW9WxIrcThuHwoAlMh/OAA9ry7H2KaE155zLJF4eIqGaewUMmK2fORlY7Ca+PC83BYa0LqQTMc5np0NLdjamiEBf5SVbl7gT7vZCB0adyJrivDcORfwok4CzQBOawqXEEgSmDhXhPK5vIvQEyACeogAzTBehyIKUB+ej0uVN/A3c4JTAxOYZ4pcGiHYO3iTefrcO8MLtZ1oTDrDCg0WhuSDelHR+H73iFI/nyaWXUjvIsh9+OgK1mhNbJS0LxXs8DfQsiZwi0VoT5HsVeeg4wjJbAZG9BU24bb14cwN0Fkd91TM7g9M7qCkZ4FtDeOgGxUyXtr2PlC1inWFkbwhmxh0lwoxNmII4Bz0I5yYxOu1N/E7fZ+TBHAWVq/80HHcfT2IlrODaDM1IKMI3VIjC5C0J50SD5IQ6gXZeyUQxdYz1Q4ZKNSyxyIkZYzdSOBSmrBUkopUygLqqA0pB4qgb2oiV0vBnrG2cYFAXr2wV/7CIj2dxHAuQb9KQcOaYoQF0bKyQKE7i5ApC/l/lFxQxXU/tVQyaqhlFaz626kuBBhohyEik/jSGIRzHn1uHCmHbc67mL47n3MT5ENl5+aPHQdvjOJK+duocR4DmmHy5EUUwiFTI+gXWTpJFhOFjSuSY7yq2LY9Ym7x6H7G66t09XeSZssnu8t9DkBnApeZfeoG3KX2uZRP9/8/oM38ptfb86JzTmwOQc258D/XXPgvyXAIajj2pVxARxOfeNgNqUYGQVTkvrGDKW/CQr/XAT7JWPXH3X49c+l+P7O/8DOb7yDl776E2x7bieef/Y1bHv+NWzf9i1s307jNWzb8Sob23e8iu3bX8UOfryw/VXQ+Mq21/CVbW/gq89/F1977gf4+nM/wdeffQsvfvnf8a0X3sWPX/fCr34cgj+/rYPPH44icFc25LRbzoIzS9lNGy2IqE7bBXD+MxDHvbD8VICTwVmoDlphNRDAaWEKnM8FcNIfBDjFHgCnChzAqX4I4LjgTSKFGosJ4FQiVlQJncgBjdABNQ2qdA6wIi60AAlRechLrcK5mjb0945g5t4cFqnFaQMLFUnJOy71cQDneB2OaOxMgRPIAxy5HykhCN5w9jr2ucTOQpfj/MuREFiKxGAb9oZYsS+0GPFBZsSIyB6WjZCPMhG+S48oLxODNwpfC5R+JVD6lUHpVwGlnwNKv0oo/aoQ5WNFmLcBQV6pkAdkYJ/GgJRjxSg01aD5/HUM3Ln38CJ9cRX3Rudxt+s+WhsHUWpswcl4BwMNkT65UAgKoJFYoZOWMnijE9uhFdmhplBXssr4ZSNGloX48BwkqXKRfsSCmpKL6GodYAvxdQqcVScYwJkkgLOAc44uZB6rRbQ0m6luIgSUmWJkVilmVyFgw2puKZ+Is/px8IsCuGmUQyejzBfKMKA8hxL2+ukoxyjYivjQUiRGlCMhvJzB01CfXEg+TIPs4ywE7jYgxNuMMF8Lwv1KESm0I0pUyR4jhWWIFJYgXFCMMN8ChPgYECHMhSYkDwdUBiTvM8FhrsPNy9exME51wRQOvWYloFYjUg5M9i3icm0P8k/XIzY0B+E+lMWQzRYVbgUOqThokcDya+zg7FCeFilXk5HH9zyAqvt8c3+Pk/yTqoyGRlzF1GVkG6DXM9TrNDTB2TgaZ0bOqXLYi2hHvQfjA2QvWb/AJIBDYKenbQxN1b0oSGvCAYUN/h+kI2RXLqJ9KQSUrFqc6kcjtkMjsrNdbFLi0AKQmoFISaAJzsdhXSmKspvRXH8b/d0zmBxewOKMh52FMl0ok2TSiRtXhmE3X8apfSUsz0QuyYTsw1OQ/iUVIbsMkPvaoKa/TVIFtZQASCWrRFYypUkpC7mOEBg5m5UgFYlyI04nlcKcVY+zjnZ0t46ygGsXMGBQluDRPSfG7yzidtsMmqr6YU5vwaGYKn5Okn3PghgKz6Yw+L+aAucBgCMmG6flrwBwKCDfBXAqWJW4C+AoJHnM/pMQYUTuyRoGy7pbhzDaN4n5ezQXKOyL/6CpPe/E7etjqCu/hszj5UjSmKAMzITskxMQ/vkEAnflIJLa1eh1IBDgT9fACqhkFJ5cjEgB2bmM0AYbsF9hwLH4PJTkVeHq2RZM3B3AytQ0VylOT0swgGJxZjgVTk/LMCrNl3Ey3gJtUA5iZJQJU4BoaqITEWSgavQCUCiuOigPmhACOPkwpdfjXFUHq6kfG7iHOQZwPP4umu5LQH/nFM5V3kReMsFeK7TBeQjalQLpX1JY8G6kN73H2BFD1z26jsso487O248JWJLCzQSqyg7zTUZ8pB6n9hXDmFaJmtJLuHHlDu4PPxwg71xwYn5iBZP9y+i8MomqoutITaqDOoCDNzFknSLlDYM3OVCIshEXYkR6kh1lhkZcrr+J3jbK2Zl4GOAsA2N9y7h2YQjlBa3IOt6AfQorQrwzIfsoA2HeBVCKKxAb2MABHBmdRw72/k9ta3T9JUimkHAAJ4YAzmEb7MVNaL/Ui0HWVrbE5VfRNOHPXQ7gjOJcZSv0pypxWFOM+PAippYL21OIKL8SKOg64V8LtX8dlNI6KCR17NobIbQgVJiLUFEaDidaYM49g8YzHbjVMYCR/inMk6LTQy1Hzzl6dwpXznejxNiI9COVOKgmO1Y+QvYQMDchWlDEgsHJckubU6xtkuXd0etn5667/nbEBTgQH+hAXKAdsQF2Lg+HcnHoPcY13CCHgM7/XTfem3/v5uu9OQc258DmHNicA/+VOUDrlf+HW7RQ+CzVONKjy5P8198J2Uhx8+D3uD9ovQJnY4BD9ikCOHkI8j2Fj95V4xc/FmDny7/DSzvewrZ/+g6e3PJ1bH38BWzd8hU8sfUreOKJF7CVxpM7sPXJ7Ww88eR2PPHEdjzJj6ee2I6nt+7AM1u/hme2vIJ/fPw1/NNjb+CfvvRdfPmLP8BXn/p3fOfrf8bPvyPBu28psOt3SewmjipmVdJSbtBONlM4/L0ATiZOH7QwgHOhrgW32u7gswFOF8wZn26hehTAISWOC+IQwFmDN3ZohAQkKAeIJOWUP0G11HkwpFXjfG0bBm6PYvb+HBfIu8Qt1D02cTE1MouOiy6AU4/DGjuz/QR+lI+wPbRza4N6nQKHrDFkcbFBK7FCKy2CWpyPGFEelMJsKASZkPtlIdo3C9GsuYiqT6n2lDz8+ZD7FbDFc7RvIaJ9ixDlY2Ej3NuIEO8MBHidQJR/MuJVWTh+0AijvhznG67hTu8YdwO8tgGN1SUn7o8u4G7XFAM4ZcYWnEqgmnYKvOYAjpoWlJJSprzRiiqgZceLAzhyF8CJyEGSOhfpRy2oLmlmCpzFKSeX2cFqgrk64nUAp7ILWcfqIJfl8O1p1JxGGUmFvFWlnC0q6Nhxo8KdF0MAJ5aaxwjgkL3L3wZdAAEcC7QBxSB7g0pWAKWUs9RE+OlZpXXA7jQEeWUhxEePMD8jIgQFiBAUIkJQjAgBqXQoXLyA/YyUNyE+2QjyzkCYXxpUgWlIiE7DkdgMlBrt6GhuxfzYGJwL826AQ4vP1VknFscpB2cJV+p7eYBD4b4piPDN5hpmyEIlpnnBS/llLnhTyZqJuFBiCr51eAwK1KTrDKfOYcCEgBqBRzbocwc/OICjFVdBI3JAKbCw/J1Qr1QGcI7EmpF9shwVhefR2tSD8f5PATjt42iuuQ1zejOSCOB8mI5QAjh+Zqa8oQY4ptaTOEC/E9kQOAWOmcvxCCpgVe/HE8phM17BtcZBjPcvY3ZiFaQAc3/QInAZmB13orXxLiz689ivpLYkPcvNCibL2yfZiPQ2QykgKFoDjaQaGmk1a9MhkKOS2JmyIFpoRaSAU+FECNMRF5mHYwnFyD5ZgUrLJWZ5nBqm2mM+u4NfGFIuydTQKmsRazk7CmtOG45oqhAtKuAsW+sADtkJ/1czcOg15ZqoWP4N30LFKXBKmc2J6r45gGN5wEL1aAXOhNtC9dkKHMrLYrlFkmymiMk5UYU6G9VtD2KkbwJzk9PACik6iKTwqrJZJ7rahlFpuYzkJCv2Kg1QBmYxtV/ALsqGMUJB14igGmgDq6AJIBViBVNFUftVlNCMKKERMYG5iIvIxn5VBgoyytBcexHDvX1YnLyH1dlFBlTY01IOzhynwultHUV10RUkJ1qhC9ZD7c8BnCi/Iu5a4QFwNEEUlp3LFDhk+Ttb2YHeG6MY7b+HufsPgCniDgvA7esTqC1tRfrhchxUm1m4c7gPQcoMFnhLVsdYaiyUVfGDAI6DXYtI3RYjKmFKxShBDiL8UqELzcFhXT7SjlhhM51Fy4UuZtNyzTVOwbkK58Iqg9wzo6vo65hGva0TmYcboA0ugDrAxOCNQqKHXJwDOeXuCLMQG2JAWlIFSvMu4FLdDfS03sG9wXGuDc99InGv2fjdZbQ1DcNe2I7sk2eYAi7MLxsBn2Qi3MfMzhUGcPxr2fkTI+VavaidijLyyOopF+dBIcsCAZy0wzY4ipvQcbkXQ7cnMDvBh/q7npemCwGxrlGWb6Y/VYXDWgviw4tBVsdwryJE+9mgpOtEQC1i/GuhlNYgWlTDwHm4oAihAj1CRak4kliMwjwO4HQygEMWuIffe8f6p3H1Qg9sxiYGcA5pbFAHmRHmnYdICoMWEnQtWVMYk2qVAA5BJPaeTMDGgYSgKuwNqUZiSBUSgioRF0DXY2r14zcKNiHOJrTahHabc2BzDmzOgc058F+aA+sAjk5CQbRcGC1X3cgtcP4rlGij//dBaOP59WcBHLVLgSMjeS8PcHxO4aPfx+DffuiH17/+G3x925t49umdePyLL+BLX3gej31xGx5/bBsef/x5PPb4c2x8acuzeIzG48/icX5sefxZbHnsWWx97Dk88aUdePJLX8dTX3wFT33hNTz9Dzvx9P/7z9i+5Wd4/YU/4M3X/fDbn0Ti49/sh/TDdESTJUBqewDgUGYPZ6H62ypweIBjrMcawKFAWNfdIPdIO27jgzO40TL0cAvVR9RCVQguxJgWulx48aMsVARxaHAAx8FAhIbAjbAcMULO+qENIMl3PhLluTCkVeFCXSsG+kY4gDPHSfwZvPFYd94bnkFbcx9r+8g8WotD6gqmuOIAThHkAtqxX8smIfsPZZOoxVaoxdSGkQ+VkOBNDhSCDCiFGYiRZEMjy4Uu0IjYIBPiQgoQH1qI+NAixJNKJ9SC+NASxIfaEB9axoYupAjqEAOUwRmIU2TjyD4j0pKLUVRQjabz7ejvG8cqOVY8IiBWlpy45wY4QyCAczLBzpQikT56Bow0PMDRicuhE9ndAEdFMEmQDbV/NhIicnBQnYuMoxbU2JrR1daPxalVrM5TZhA9Id3dr65X4BDAOV4HhSwHUSIaBHBI2l/EwmJjmJWPsqRosUQ33LycndXFE7wp5wCOS30js7DAXspkUUkpCNSIaFEug0OR7N/PQRQthqhZJYAqbAuhDaXFshW60BLEhtkQG2ZFbJgFurAiaEMLoAkxQh2cC22YHvuUehyNz0XqoTxUFdfg1pV2zI/zAMcF9tYBnGUO4KSSAuc/CXBYODEPdVitLcFqUtnYucBxXummcQMcF8TxBDiVboAT5k1hsjk4ElvIA5wLnwFwZtG7DuCUIoAAzu5cyP0KeYDD2bhIfagRO9YAjqgAav8C6EIKkBBZgOT9Fagwt6Dj8jDuj6xingCfp/ODXwDSgrblfB/MmWeQEJkDTWA25KIshHllInxPHuS+xYgR0i54LbTSGm7IaqCRViFG4mDKArnIxlQzkaTqEmUilqw7sYVIP1qKCnMj2pvv4P4Q1w7EVDg8wCH4RtXz432r6GicQGleB47pqlkrkIIanMSU9UMBqCXsHCaAw+VkcHNz7XN+p8aVUSR1tVCtPW4IcIScBUwXUsxym7gMnPPuDBwCoOwayZ/DpHCbm3RiYsCVgXMZ+2OsfAC4GdHRBKttAAAgAElEQVRilwKnnG+kIlWOCdEUPC3JRmxYHrKPV6HW1sIAzvDtccxOTMO5wmfF0GtCwdwzTnReG4aj+BJO7rcgUWGAKiibtcQFe1Goej6UlI8VVANNYBXUAaTCqWDfYwBHUIgoIYUn5yI2LAt75WkwpZWisaYZgz19mHcBHL78iv7GdQCn+AqS95YwmEeWIoUoH1G+hZALC1nWUoy/GerAfGiCjNAEk1KuAEYCOI4O9F53ARzKwFlv2aPj19M+hmrrVZxOKkGSKh/aYD0iqf1uD4H0fPY6xwdUI86/GnGu6neyw9J8ZxZFyo+jinMDooSZ0DIFkAkn9hWiMLsWFxtuYvj2FJcHxoAhXSSWQe18y2S3HHfi7s1ZnCnvQjY11tF1J5CCi6lyOwvRQhqZbDCAc4ADOBdrr6OntQ+TGwAcutyO96+grZles3bknDqLpBgbwgXZCPwkCxG+LoBTDy0BHHbuuACOjdnTogWUl5QHpYxCjFORdqQEDksjOq70YKiP5gkP3Fxv2W6AM7YO4CREWKCUFCPci6BQqRvgqAngSAjgVCNSUMHgeYgghwM4CQRwGpgCp/P6AEYHprE4swHAGZjB1Qu9sJmakHG0Cm6A42NwAxwVWfpc9ikGcFxqTs62ug7gBLsAjgOx/jzEcak9H4I4m7uxG90rb35vc15szoHNObA5BzbnwEZzwA1wdBKu1lEnreJaAlhTwN8G4Gz0i6z/Hm9d4DNwPBU4DwIcWkAG+STjo9+r8e8/EmDnS7/Fi9t/iuee3oktX9yBx77wPB7/0vPY8vg2bNmyDVu2Po/Htz6Hx7Y+i8f5sWXrs9i6xTW+jK2PP4cnH9uBpx77Gp7+0it4+ouv4un/byee+QcCOD/F6zvex0++5YNf/zgcH/16L/PBR9PulBvglK4pcP6mAMcIuZgsVJlIIQWOG+D0YXxwY4Az5gY4nSjIaMZRHSlETAj6OJevEV9roYqjEONHZOBw8IaqxKuYdUrrCW9YpowV2oBCxIUZkRidA0NaJc7XXmMAZ25qDksEcPgFhhviOKmdhQKAe1FqbGZhkUmqMlD2Q+DH+QjzIoBDCz7KTeCDjFmYMakvihEjKoBSaIBKlIsYSQ7U0mzoAnKQEGrAvsgCHFQU47DaimPaUpyIL2dw5VRiJZL3ViNlXy1O769D6v4GpB44g+T91Ti5rxzH9hYj5WgJ9OkVzD5V5WhE69UuDA3cexjgLPIAp5O3UBmu4mS8HZE+WYj01kNBCxgPgBMrskMnJNtZCQjgKAQ5awBHk4uMYw8DHHgCHH7BSRk45xjAqYVCls0ATrQwj4WEcgCH8iXoBruaWc/IdkbqDlceQSyDOCRvL4WWMnBYxTnJ5WlhQhW8pAgwQC7JhUKay9Umh5gQF1GAvfIitht9WFuGY3F2HE+oxMnEaiTvq2HHMGV/NVIOVCF5vwOn9pXj5N5SJB+wIfOYDYbTpSjWl6GxuhF9HZ1YnCQL1cMKnIUxJ+4xBQ61y9QjNmwN4FAuA7NQPaDA0cmoFvwBBQ597bH459pQXABnPcTRMjWOJ8CpglZczaxUKoGVVZhzAEePo3EEcCpQUXQBrc29GB+gxe1GFqpZ9HgAnIOKzwtwKC+GgljzmaJtr9yM1IN2VBZfw82WEcyMO7FIlggGcDgaSrCWvp4eWcXVc30oSG9g1jxNINlHchDhnY1ILyPIRkih47GSOuiktdyQEcyphlpSCZXYwRp2KLeGslciRVkcwNEVMhtIWUEj2pr6cH+IgnPXK3AIGsxPOHFvYBU3L02i3NSB47FVLPiXbDtKcRELa+YADinoHg1wCLK5Mo3Ya/jga8mrb3T+DnZtIEWWXGhhLT0cwCnGsXgLrHkcwCGb26MAzvjAKloah1FEIcauFiqxC+BwIcbRLMR4DeBEPwBwOq8NgAM4U3wtNQU6cceHQAMHcC7j1IES7FWaoA6mAPAMBHtnMYCjkNigDqBsk0pmoYqRlXMAR2RBFAM4lIWUx86FvYoMmNLL0FhzEUM9dzA/eZ9T4PB8xQVwVu470XNtBFVFV3AqkexNemahoprtSF8zg3QUlk1hwgzgBBq5di1lAaiF6qzjOnqvj2G0/z7mKNvnAYBDr/d6gGNigDPSLw3hezKh8DNBK7EhIaAK8f5VHMDhz0dXMxG9zmqqMheTSisXuhAD9ilNOBJfAGN6NRprr2Ow5z6nRlwglQodV5p7y1ie4eZb/61ZnLN3QX+8gZ0vBKKU0hxECTL4sTHA6f4UgDPRv+wGOPrks0hS2xAhzEbgLhfAqeBCjN3wkwBOOZRCAjhWyCm4W0wWKhfAscJhuYDrV7ox1DeG2cmF9RsuDwGcahzWWsEBHAsivC2IFlBuFafA4QBONaJFVYgUlCNcYEYoAzincSShiAGcpjMd6PIEOHSp8Ng8GRuYRcuF27CZmtcATmABQl0KHBEF3NvYvc2ahYoADp85Rtdcf7JOVTLlDalvmJUqwI44HuAwJY4L3jCY81mZOJs37uvvjTePx+bx2JwDm3Ngcw5szgEPCxUDOAzeEMBxQZz/jgCnlFlqqOpUIctnCgACOB//Xo1f/kiIb7/8O7y8/ad4/umd2PrFHXj8C89hy2PPY+uWbXjiiW3Y+sTz2PLkc9jy5LNsbH3yWWx9ghtPbH0WT2z9Mp7Y8iyefHwHnnr8a3jmsZfxzBdfxTNfeB3/+A/fwY4tb+Jb29/Fj1/1xjs/DMWH7yRC/EEq1gCOjduhclmo/g4AR+sGOHW8AqcPY0OfBXBu8QDHjiiBEUEf6xHpVQCVsITlyZDdhAM4lUxlQ21TbtUNU95w2TicfYqsU+VQC8oQI7BBJaBa52JW2xobmoeE6CzkpdnXAZzl+Q0ADtvpnMbVc92w5F5AygE79itsrBkn6BMTwr0KWbhwDGVmsKwW3u5DIZ+SQqhERigEesSI9dD46xEbpMfecAMOKsw4rrMiZW850g86kHW0Grmn6mBIOQNT6nmYMxpRmHURxdmXYcm5Cou+BUU5F1GQfQ6mrDoUGRtQZr2AmspLaLrQjs6b/RgbmcYKtXiQ2oC/CaYWKi7EeA3gnIirQIR3JiKo6ccNcMoQS5kJtHAWVkIjtCFGWMAAjiYgmyklDroATimvwJlexerCKmufcitw5qmFigsxPlfZiezjDwMcqggmGw4pb3QyUliQVZIUJZ4Apxyx1CQipSwSahch1Y6ZNbjIRQQJ88ByPmS5iAnMQ1x4PvYri3A01oaTeyuQmlQFUkvpTzbAkHIW+WkXUJjZhMKsJhRlN6E4pxFF2RdQmHUO5qwzKMppQKnpDKosZ1kDVUdzG4a6+7BELVQbAJz5MScm7yzich0BnDrEhWVzGTh+Hhk4GwKcByCOB7whCOAGOGQBcA++rWoDgKNjAKcKKqEV0b5GhHmnM4vJ0bgiZJ+yo6KoEa3Nt9cAjms3nQ8xpurqhwDOB+kIe0iBQ1CJMndcChwCOCZoAvMRH1GAAyozMo44UFPSiq62Mczdc2KJGpVIucQoAQ9SFoCp4VVcOduH/DRazOZAE6CHklqVfHIR7ZMPlV8JtCJS29UjVkqjDrGyOmiltcxSFSOpBJeFU4woUkWIshEbbsAhbSFOHypBaT6pjm7j3uACB3Dc5wQpxpxYvOfEzPAquq7eh6PgOk7EE8AxsrnFcn14FQ5ngaQqcVLfPAhyOJvbGsDxeF1dAIBfPOr87R4Ap5jZgkhNt1/JARwLAzjdGB+YeQjg0O87O+EEB3CGHgI4csoz4VuoHgY4WbwCpxI1thZ0tgxg6PYYZicI4MxzPc2eAKd1BJWWq0hOKsU+FanT8hAhykKoTw4iSI1HVhWZAyqZHSoZqRDLoZSUgp43UkAwLZ/Vl8eG52CvMhOm9HI01lzCUO9dzN+b4jJw6NpElydS4Mw6WSZR97VhVBZexslEC4MrlAtDVssIHzOz+RDAYQoc1kJlhDowF/sUBHAacNZxA7evj2O0fwpz9+fX16OToIMBnFFUW6/gdBJVt/MAx5cATgYUfkZoJSVI8K9EPA1ZFeJ4C1ystBrcqIJWWga1rBgxMhN0oSYkRptYTpCegvAr29HfNclgDb1eziUKNKa5t4TlmVUs3HNisGsW5x1dyD15BgkRZmiDDFBSNphfGiJ805giKFqQyVmoPBQ4HMAZg3NxzuPM5U4psimSAqfS0o7c5LM4qLExNWLQbh7gkKLSvw5aGdkQKxnUYACHcpMEZAsr4jNw+BrxI1Y4rOdxnTYD+kYxO0ng2uNpeYBzt2uc/c36UwRwSpAQQVl7BHCsPMCpZBYqtX8NlBICOJWIFJTxACcboaKNAc4SKXAeAjhzDwGcmIAChHjlsqZNCh5XU8A6tUxJuAwcyrrj7FMcvCGAwyAOD20I3LgGZ9Pl1J5cFg6fv8bycDYXJJsLks05sDkHNufA5hzYnAOfdw6sV+BIqxm8oVwBtrjhMyI+7z/21/rvuPBilwrHVSFOIcYeLVRUdcsAjgHBPinY9Z4Wv3pTgu+9+j5e/eq/4oUvfwdPb/kqnnx8O556YgeefuoFPPPMC3j6mR146h+348l/3IanaDyzDU+7xtPP4+mnn8czT23DPz75FfzTky/iy098E89u+RaeffwNPPul7+KrT/4UO194Dz9+zQvv/CAEH7yTAPEHpxHFFDhUF8rZqKiKmmrF/7YWKk6B4wI4FiMBnKu41fZpAGcaN1oGccZBAKcJR3UViBIYEPRxDiK9yH5k5QGOHXESBwsopqYpAjjcqEG8pAbx4hrEiWtY/g0F8TKAQ8obgjcCC1SiQlbZqgvNRXx0JvLSKhhcGrwzgrnpOTwEcEixsEI1tFO4fKYLRdlncXJvGfbJrVBKCxG0y4Rwb1pkkO2CmpIo8LUEOn8aVmhlhVBLTFCJcxEbaMTeiAIcUhbhRGwJ0g5UQH+sBvmpDSjKOo+SvCZUmC+hsvgqqktaUVfWgYaKmzjr6MS5ym6cr+7B2apbaKjsQK3jGs7UtKHp3HVcudSJ6x19uHtnDPcm5jYEOJOsheo+Wi8MotRwFSfiyhHulYGIPdlQ+JqYAidWwgGcOFo4C6ugdQEcIS2wXQBHj4zjxaghgNPej8VpyjhxARySV6xieX4Vs5OrrIXKE+BEC7PhUuC4AA6FPsfKSGFBSrtHABwKlBUXQ0mtVaRmooYdCTW30EIsH/HhZlYpfjS2FKlJlcg5UQ9T6jkUZTXClncJFQVXUFXUgtqSNjSUdeBMRQfO2q/jfOV1nHPQ5204U9GKs45raK5txdWzbWhv7sCdGz2YHBjCytS9NYDDswgKX50fW8XEnQVcruuGyQVwfE8j8lMBDqfYWMu9Wa++cV3j2LVGzCmSOIjjCXDWMnAI3ujENdCJCeCUINrPhHCfDOhC8nA0rhjZpxyoKGpCa3Mfxgfm3VDPtSTjQozXAE5hejOYAscD4Ggp3JoCmJmlhAM4VMOrElP2hJHVuidG5rNskazjDtSVtaGnYxwLU04sMzsQHTRSJHDWILK03B9axZWG22zux4bkMLCpFFNosgFyHzNiBKXQiSiwvAFxUhr1iJPVQycliFMDtaQaKnEFC2Kl3BWy58WGGXFQW4iUgyWwmc7jWmMvJgngMEUEQU1azS+zXJLlKSfmxkj5MYXKwus4mVDJFrIKqt8WF0BFtkeJhc+wIoDDtcrRI2VbcUDnUwCOW2XFLRzXARzBAwAnoRiWvHO4ep4AzjQHcPh4GlrMEoD4fACnjIEUqhGPFnMWKqbACScLFQdwbhHA6R3DzMT9DQFOV+soKi0tSEkqw/4Ysh8aESnOQagvB3AI1CgldiillH1DAcYeAMeviAUZqwKMiA0nO2IWTOkVaKy9jEE3wCGvFjf7CAywLKlJJ7pahuEwX8KJBAs0Qdks1DdaSJXUFGRM5zyvwCGAE2BETEAu9skLYDjNA5wbExhjAIeSkfknoKdxAZw2AjiXeYBjhC44G1G+qQjfk84Ajk5iRaK/g4M4skoGcOKk1YiT1iBOWos4WQ07ByhEXeNvZi2GCVH52Kc0IuuEHQ32Nty5NcFa+VbmVhm4weo8a69bnl3F4n0nhrpncaGqC3nJZ5AYWcCClBWSLIT7nuba6/zSEC3I+AyAQ+cS/7EKuBQ4DOCk8ABHlAMO4NA8phB4TrnmAjgU8qsSlTEVDl2HlSzE2AVwLKi0nsP1q7cwdGcEs5NznwJwOqA/VcMDHLrHsDKAIxdQy1UlC07mAA41pLkATgFCBVkIE6XgSEIhCvPq0XSmnSlwxgamsSHA6Z9DS2MfSk0XkXG0mkEqV4hxhC/ZcYtZC6haRg2GBHDKWVkBARymeCR4w5SP9LUrV62C2ac8FThk13WHGZMKxx1ovHnj/te6h978dzbn0uYc2JwDm3Pgf+YccMXO/B8PcBRSA0J8U+HzpwT84Zch+NcffIIf7PwdvvXSm/jq89/CjudewY5tr+CF7d/Ajh2vYMcLL2P7Cy9h21dexLYXXsR2fux44UWwseNFvLDjJXxl+zfx1W2v42vbvoOvP/cv+PqXf4gX/+kneHXbL/EvL/8JP/8OZeCE4+NfJ0JCAEdAVc0cwNkI3vxtMnCMiBZnwBPgnGcA5/anKHAI4AzgjOMmCjIaeYCTh6CPKdDUBJXQwuWhSCsYwIkXV7Kq8HUAh4c3cSICOGShojalcqhFpYhhjUocBNDQAiMsBwnRGTDQAqP+GobvjmJhZh4rCytrFipXrMsKMHJnChfrb6EgswHHE2xIjCqGQmpG8G5aMBPAIWuPFWqZlau7lhVDF1CM+OBi7A2z4ECUBSdj7cg+VA/z6UaU6q+g0tyGBttNNFZ24WJdD66cvY3Wpj60XbyLjssDuHFlCDdbhtHZOoqu9nH0XJ9EV8cYbrUP40bbAG5dH0RP5wj6bo9hcOAeJsZnMTuzjBVXDSt/r7+mwKEQ4yGUkYWKKXAyEOGVDSVZCMQWxEnKECe24/MBnCZ0td/1ADi0aCKAs4LlOQ7gDPXOgQBO1rEaKKRZiPIEOEJS4NDCmACGC+BQWDmnrouVVCBOSgqcUmjEVsSIiqAQ5kMtLURskAUJ4SU4pKpAcmINso6eQ37qRZToW1Bp7kBDWScuVPbgYu1ttJy9g7YL/eho7sfNy/3ovDqArpYBdF8bRE/bEHpaB1mbVue1fnS13sXtjru4e6sfQ939mBwYwfz4BFZnp9a1UDEWMePE3OgKJu7MM4DDFDjhOQj3I4CTwxadD1qoXC1ULsUGB6N5xY1LecM/fh6AQ9CGAE6shABONWLIFsEATiYHcOItyD5ViYriZrReJPsikQzX6o97XGYtVA8DnMB1AKcMsQ8AHK5GnGq3DdAGGVlV9SFNAbJP2FFf3sosLWSfWiE1wjIv8eABDgGJ+4OruFx/G6bTDYgNJmVaLlQiap4i+1QRU83pRARnGzwgTgMHcCS1UItroBJTmLGVKTXcAEdTyAJ4S4znce0CAbg5D4DD21oWuFwSqhPvbZtCVVEHyLLI1FwSIwcIxWbEiItZDo6Wb6JyQZxHApyH7HH8wpEWkAHUhlPOcp8oPJZCvDkFThELXrYygNPFAA6DXgRw6JRyggGdmQknxpiFilfgqC2s9j5KTBkmLgVOGWsddAEcuSQPcmkO4iIMyD5RhdrSFty6xilwZjwVOHyIMWXgkHKq0noNKQcrsD+GMqJM7HlCffV8yxQBHMq9oVEOlbScBRtHi0oQKSA1lJllT+nCcrFXkQWjG+D0Y/7eNJxz9Bpwc48BnDlODdXZQo1kF3E8vggxgVlQSKke2gPguCxUrL3JiBj/XOyNLoAhpQFn7aTAIYAzjbn7BHD4J6CncQGcdpcCx4IklQG64CxE+Z1GxJ40KP0M0Eks2OtvR6LMgQRZJeKlpMLhAY6kjr9GUauRDZS/pQs2M9XZXkU+Mo9Xor6iHX03J0Gh7stzK1xj1Ooce1yZW+EATs8sGqu6YEhuwN4oAji5UEgyEe6bgnAfum6kMSvVgxk46xU4HicwU4Yuoe3iEFPg6FPO4KCmBJGibATtykSETz6UIgpQ5xSOzPIt4ZoYCXKoRJTrY4GK5jyzUKUg7UgxHNazuH71JobuDGNmcvYRAGeChUdzAMeGhAi6xyhBhDeFI7sATg0I4KgkBHAciBKUMhUXB3CSGcApcgOcfnwqwLnQB9vnAjiUc7cGcCh43dX6R9cwbnhAHDfQoaw1T4DjGVz+P/Nme3MRtfm6bs6BzTmwOQc258BfYw644A09/rcDOFwrlkt9w1sJqNqWwldJgSMlCxXJyyk02AyF1MSyA8QfHcTH76rw7r/L8IsffYIffft32Pnym3jtpR/i1Zd+gG++/AN885Xv4xuv/AteeeV7ePkb38UrrvHKd/GNV76Lb/Lj1Ve+h9de+SG+9fKbeP2lt7DzxX/Dzq/9Eju/+it898X38ebru/H2v8jw3s/k2PVbCjFORSQBHFYlTjee5aAmFDZYRSpfs8lySLjdZJbp8BkJ3Nyx4JpxWLAqnwNBqgKNlMIEH1bgcADnsxQ4nw1w4hjAsYMDOARxPBQ4bvUND3BYW08FNC6AQ2okEe3gGhjASZRnwJhRgaaGaxgZGMXi7MIawPGAN7TQoIDKptqbMKbV4UisFQmRJD03I2R3PsJpl1hYCJWEvPhFUMvMUMsKoAs0Y1+kBUdiSpGc4IDp1HnYjW24UHEb184M4+bFCfS2TWPg1jyGehYw2reI8YElTAwuszE5tIJ7w6uYGl1leSJkS6KF3PSEE1Pjq5iaXMX0/VXMTq9ibtaJhXknlha5CAhPCxW1AN0bWWRVum0eACfSOwORXjlQ+uVDK7YyYBIncQGcal6BY4ZiAwVOdWkzOtv7seBW4KwBnKW5FdY+xACO4wGAI8iDnOXDUDYQwRkCGLXgsq648F66mHD5N2WIpQUTqW+EBZALDNAGFmJfVAmOaO3IOHQGhRmkrulEY2U/Ws6M4MbFSdxum0X/jXkMdS1i7PYyJu+u4F7/MqYGlzE9vIyZkWXMjq5gbmwVs2MrmBldxvTIEmZGFzE7voD5yXks3pvH8vQcVmdn4ZyfhXPx4RrxudFVjPfN41JdN8vAiQ/Xs4aaKIGer6TmmlFI8UbwhgEc6VpmyiNBDu1eU87WOgUOl4XDZeBwP9PRsZNwACdWUs2acuQCAoqZ0IXmsWyVnOTPBjiUu+KyUHEKHBsCCODs4kKMyTqyHuBUggCOUuQBcKLzcVhrhv6Ug6kRbt8YZ7vp6wEOKRM4Rcm9wRVcquuFKaUBuqAcFuYdIzKwPBIlD3BiCeCIeYDDK3GYAkdSC424BjEsB4cATj5T4OhCjUhSm3HqgAUlhnNoOd+DCQZweOXPChfAQ8GyBCsWJpy43T6F6uLrSN5bCaU0j+UqMYWXyAwVARxxCdYDHF4JxSxVawocpqhyq25cGUcPA5wYMdf+Q9XYsSFFOKAsxvGEYlgN53DlfBfGBqY41RLl9tApReK2Oe685wDOIIr0F7HfDXAKEP0gwBEXIlpiglxqgMJfj/goI3JOVaO29BooA4dZqCan4FzhLVTEBOh5Zp3obhtHlbUVKQftOBBTDG2YCVFiPcJ4gCNnCpw1gEMQh5qp5KISRAks3HUwwAQCOIk8wLlQQwocHuDMPwxwlu47QQCnouAiy21S+WeAmpnIQhvJFDi0CUEKnEKo/al+mzJb9CDVV15yPQ9wJjHWP/MwwKE/bV0GDlmo1hQ4EXvSofQzQie2Yq9/JRJllUiQVSGe4I2kBnGSWi6HSVzDQsUpFylGWghtkBlxEflIVJiQQaqz8jbcvjnBq854gOMk9coiCOAsTTkxRACnuotBp71R+dAxgJPBbJcEcMhKFcUsVEZ3CxWFGH82wBmEw9IGffIZJGmsiBBmIXBXBgdwhJQfRvapKmjI/kgAh4UyU1MTqVUIvJiglGVBFbQGcDpaPACOZ+mA20K1EcBxKXC4DBxtQDXU/tVMjSMXkSXahgiBCWGCTISJknE0wYyivDo0MwXOBgCH5iWrEZ/DVQZwLiHjaA0OqkuhIgvVHpeFysIrcKhdk1PgkMqIBeLT9ZZX36wBHC64eM065QI6ngBnU4Hz17ip3/w3NheHm3Ngcw5szoH/2XPAE978HwJwXBDHBXDIH29DjJRquyk4uACRQj0Cd5+C4C/78cm7Krz/i0D88ide+PF33scPdv4W39/5G3xv56/x3Z2/wnffeAf//MY7+M4bb7Pxz2+8DRrffeNtfO+Nt/Evb7yN77/xDr7/xm/wg52/xw93vo8fvf5H/Oi1P+NHr36An76+G7/4ngS//XE4/uPfNPD6/SHIPkxHpB8HcGIkpVzArj+3kKSdqf+M+oZOxM8LcDYKMX50Bg6nwDnruMmyXx60UJGChnbI1gCOA/ESTxUOZ53i1DfViGXqBFJz0IKrFDG08BDSgtMENYVshmcjkUI2MyvQfPYaRgfHsDS/gNXFFffCiRY1LHR1GRjqvY8L1TeQl1KDQ5pixIVzACd0TwGX00A7z5TTIM1HjMyIGGqXCsrDQVUxTu+zI+9kPRwFrbhS14877bOY6FvF7MgqKMDTZfFwWQuYSoK/eWWfrxdNfPpXXPkJt8vNb9YSwLk/soCBrmm0Nw2jnGrE4+yI8s5ElFcOVH75bAFDihcGcMSeFiozy+/RUOhypB4sA+e4BWsAx4llt4WKDtgqluZWMTOxisGeOWb/citwBJQNwwMcUiwxgEPnUY0HwCHAUYFYUt/ISqGTWhFDuTfCPET5UcVuPg6rbUg9WI3CzItoKO1GR9MkBm6trDumZM1gx5XyGwjGuY7npx+99T9lygSCDpwVwrlM2/lcjsvqtBOzDOAs4BLLwDmD+Ig8RArSEcWCmguhpB1uMcn6KziAw7cV0bm39oZGc9Rz8PBmQ4BD0NQ1COIQwCCsKHEAACAASURBVKkCwZtYSRUPcIyI8KUAcRfAccBe3Iy2i32YIAXOAx9kd/MEOFQjflBhQ8CHae4MnDULFf1uVQy6EXx7EOBQ61VuciXOONrQd3Mcy7NOViHOFDhYAla4Sm8CBZMDy7hY1wtjcv0awKEwVT8TVH5F0AhKmYouXlyHOHE94iR1iJPWca1UboCzXoGjCzUgSV2Ak/uLYTGcZZlVVJ1Oli3KW3EygEOtVKsM4CxOOtHXMYMay00k762CUmpk1jyFmAKoC1jr0BrAWWug4qA1fe0BcHj1Tdw6iLMGcOICHEyBQ+BLISjGOoCTaFkHcFhuEImlHgI4K2hp9AA4BDnEBHBKoBA7IBfzChxxIeSSfChkBigDcpEQbWJWl7qya+hsHcAwtQtNTgEugENzgq4bc050t0+gqqSNAzhqAjj5iJLkIsyPmt6ocppX4DAVDgUYl60BHMpgonY5HuAkyDNhSCvHIwEO8TSCzh4A54jODIUsg9VqM4Dja4ZcSACH4E0hYvzzueptaQ7iI0wsM+xMBSlwXACHCCF/4ePnOj1Hb8cEqq0tOJ1kYy1UVFUe5ZuOiD1ZDGDrxKVI9K9GgqwaCdIaxJN1SlKDWHEtdOJaaEXVzDpH7WQqCTVImRAbbkSi3ICMYxWoI9XZjTEe4FCTIRE4yo9ZYi19S9OUCcYBHGMKKXBcACcTYT4pCCOA45uOKAFd40xIS7LDlteIi7U30N16BxOPbKFaQmvzIOyWVuQkNyBJTQAnE4G70hHubYJCSPlh1dDQeUsAhzadXM1aEgr6pXuWAqhk2VAFnuYUOJZz6GjhLFQzZKH6VIDzYAaOBXJBKasv17Kw6yoWaCwXkSXaigiBEWGCDISJTuFoQgEPcNrQdf0zAM75O7CZCODU4qC6DCr/QoTsMbCmLVLhMfuU7NEAhyygnw1w+OybzRBjj/cnz/eqzc/X3rc3j8XmsdicA5tzYHMO0Bx4COBwbTRkqeArxN1BxrRjz9ks/t6Th+2KswYqTyUOB3A0LFeGgvRoh87ClBiUpxDum4mA3Sch+iAJu9/X4s+/jsDv/1WKX78lxK/eEuCdn/nh7Z/54u23fPDLt7zxi7e82ePbb3mDxjv8+NXPvfGrn/vg12/54TdvCfGbn4nxm59K8ds3/fHbnwTi3Z+G4o//qsAHb8di928OQPQfpxD8SQ6i/Uh1UgIGcJgSgKquuXYG96MnlPkM9Q0dc9fCRcvXG9PX9JqsKXBM7haq05+7hcoT4FAGjh1RfgYE8yHGDOCQ+kZagXgJKXA2ADi0Y8ryb+jGm+wlngDHAgWpkUSUm2JAYnQeDupyUZRbjcsX2jE+MoHlBapMpUpsbkHD4A0t/BaAu10TaLC3I/O4g4Vn6kIoTNeMsD2FXFMKqUrEBIdMiAvNZxL5I1oLCya2ZDehrqQDVxruoKf1HiburLBaWcrh4AIvueegBRQtekmGz9Q2Y05Mj65idtyJ+UnOarAwyf9swonZe07MTTmxMOPEIv2/C1xlMwuddEEL+lMWnJgaXcBg1zQ6moZRYWxBshvg6KHyK4BOUoJ4aTk7tnGfCnDykHHciurSi+hsH8DCNPe8LF+E0Y1VLDKA48Rgz/ynAhxOgUOvEe1uc/YpLluEgou55imt1AKtfyG0AbTgMeBYbCkLJbYZr7DK+famUfTfWmSWHAoVXr7vZAtRAjhszDhBsIW+vzTpxMI4Pya4Zhiy0VDFL9VKs+M8sYKFySUs3l/E8vQCVmfn4ZyfY1kWruNK84IycBjAubOES3WU5XIW8REGRPhRo4wBciHZ9cpYTgtrQ5E5oKUMGTY8L/qe8Ib73H2dcQcYr4UZrwEcromKcoMoN4fCj6mynmqOaSEYG5bLrDk5yQ7YLc1ou9SHiaH/VYCjh9zP7FEj7gI4BIsI4BRBLjKwWue9pMDRmZHDFDituH1jjClwll0WKgI4qxzAodfl3gCnwHkY4BihoupygY0FaceJa8HGOoBTA42EFDgPAJwwT4BzBlfPd8ET4HBNaVTt7PRQ4EwzBY7LQqWgIGORC+C4FDiuzJs1iLNhBs5G8IbADjXgBFBYN6kDHg1w6PdlChyCjxsqcFZw1Q1wihEpyXEDHDkPcKLFVkR7ABxVQC4So/OhT65BXVkrOlsHPxfAOX3IgQMEcMI/J8AR21j+jpwavAJM0IbpEU8W1bRynK+5jAFS4NyfhpMUODxfYecRWajuO3GrZRjl+RfZHFJICeDwChx3jXghYmRrAIfamxIiTWsKnBuPBjicAmcS1dZWnE4qQ5LKDF2QAZG+WQjfkwOlbyF04nIk+tchQVaLBGkd4in3htQ3BHBEBHCqoGbnMxfcTSpOXVge4qNzkX60DLVlLei9MYqFqVUsU5OhC+CsLLHrLzVRjdyeQ1NNNx4EOKS+CfdJRaQvXTuyERucj7QDDtjymnCx9ia6W+9iYnCCqQDX8ddVYKzfBXCuISe5HklqiwfAMTKAw11b6fpK5y8BHBfEITVOKWKkZqhkOVAFpiLtMLVQUYhxJ4bujOLRAIdCjCkDZ62FSiUtRoR3MeRCKkwohzagEhp/B8tKUojKECW0IEJgQJggHeGikziaSACnFs1nCODcZXN/XQYOD91H787jKgM4l5FxtA4H1RVQ+RchZA/BatqUKWHqYrWMgNTGCpyNVTgPK3Fo88A9NkOMN0HO57gf/nuvQzafz/P+afPzzfmwOQf+d8+BDQBOBdthdt18rDVQrdks/t6/NAcu1sMbtthiapZyaKRlbLFDQbYafwI5xayaNlKQgxDvNPh/chzCvxyA1x9iset9DT55T42P34vBR+8q8eF7rqHAh+8r8dH7CjY+fl+Jj99X4BM2lNj1ngq73lNj97ta7P69Drt/H4vdv4vHnt8lwue9JAj+cAziPyUj8KMshFMdr5CCe22s8YfVXEvp9+QhjieM8fj8s47r5wM4ZOPIwumDVlgN9Wisu4bOtjtcjTirFV67FaXF//igC+DcgjmjCcd0dkT7GRH8kR5RXgWIEZZw6hupfQ3g8Dk4CR42KgZwCOIQwGHKBlLg2Fj7lIKFYRoRF2ZCkjofJ/YVwFZQj2uXbmJybBIrS0t80Cmn2GAZDQtgTTp0c15ta8Hpg2WIi6AWlHzIRWZEeBXxAMcEuSiP5YHsVxTieHwJso9VotR4EReqOnHryhj6b02z0EmCMaRCYMoAWqgtgS12p8epaWYZg7fn0Hdriu2Gd14bQ1frBHra7qO3fQrdbRMsE+fmtUF0tQ/h9q0R9PeOY2TgHu6Pz2Fhhm/R8gA4zgUCQRzAud40DDsBnHhS4GQh2kuPGL8CxD4AcChDiAsxJvWLHuoAPRIic3FQY+ABziV0tg96ABx6PUmywsEksnqtAZxaKKTZbHHiqcBxAxypJ8Aph4bUYhQILbFAKytCfEgR9kYWIUlVhJwTdbCbW3D5TB87LkM986zRiIEbgjUUmksxGNOcRWZ6eBWTd5cw0juHgVtT6OuYxO12Op5j6G4bRVfrCLpaKWdoCF2tQ+jpGMTtG/2423kXY3dHMDs+idXZmXUAx52BM+bExJ1lXKrvQ37qOcSHGxHhl8msH1QVrRRR5gnVD5P6opJVpbsgzvpzbD3E+fwAh964CJwSxKFsCwsUBHD8OIBD1hx9yucFOBNorrkNc3oTkhQlCPgwFaG7PQGOgwXIa2k3X0wAp2wN4AQakBhlwiFtAbJP2lFfcQ0910e5uTHvxOoyEVECOKtc89CcE/dcGTgpDYgNyoFWlgu1OI/ZWVQEjQQliBWSnY9X1klqWZgs5SWRYotUBQRwlK4MHHEOdAzg5OPkgSJYDARwKBR4LQOHKbHA2bioMpvgXU8rhRh34ES8A9GiXAak5AzgmNk1g1PgbARwePUNg9drtjhmpeJhtjt7YyOAI/SwUPEKnKsXPisDZxlXLwygSN+M/epiRBDAkXAKHA7glCJabAHLxZGYmAKHARz55wM4ZNUiBU61rQ2nDztwQGOB7gGAQ4CIZeA8oMBRUD26qAQKSTEHcEL1iItKR15qmQfAmWEB0g8BnHtO3Lo6jDIPgBPNAI4JUb5FkAsLmQJnDeAYoCILVVQ+n4FzE3037vEWKv4a7np7cWXgtN1DtbUNp5MqkKQsYsHbkb45CN+TC4VvMYP9if4NSJA1IJ4Cs6V1TPW1HuBQZkwRy32K8c+Dlv2N2Ug7YkNN2dV1AGeVAZw5UKK8yzY42reA5toeBnDI/sUycKTUFpWKcNZClYloQQ5igwuQdqAStrxmNNfeQndrPyYGJ9k1yPVnsUc3wBmA3cIDHA0BnAxegWOEUkgWQG4jbO2RhzhiAjhlzBJGx1MVmIa0wyVcjTi1UDGAM/+wAmcZ6He3UFXhsNaChIhipjyO8CmEXEgqpTJoA+zQ+Nu5nCRRKaKFxYgU5CFckIZw8QkcTcznAU7r5wA4d2EzXUHGkXocVNuh8rcgZE8+Inzp9SB7ONnYCeC4rGHrLVQcwFkLM16vxllT5+iYxZXO581Fwfr3qM3jsXk8NufA5hzYnAObc+DhObAhwKF8DK3YdfNB4MYFb2jB8/A/8vf43oYQhylaCIpQ1gXVdpZDF0CjFBp/kvcWQSWlG+08RAiyEOKTiiDvFAR6JSNgzyn47zkJ/z0nIPPihr/XCQR4cyPQ+wRcI8j7BIK8TiLI6xSC9iQjaHcKgnafRtCuVATvSkPwrgyE7M5mCy8K/pX7kU2lBDEugEN2DjEtkGmsWQDcQOZzHlP3f/9pChwRD3CSrCgxNLgBzsTgNLuh9bwRdQGcm1cHcdZBAKcZx7QO1qgT/FHuIwFOgrgSCZIqViNOWThcZgHBmxpmKWEAh5RRFOJMagFBPmvNoQYQqlhOO8pVpna0dGFq8j6clP5LTTX8zh8DK3NOzE850dk2BHvRJRxPtEAdlMPsFnKBGVHeFkT5UoixAdHCHMSGGnE01oKsYw5Y886jsfoWetrGMTO6igVeHcIk6bSm5Qd9PUO123fm0X19HO2XB3DpXA/OVd9Avb0dDXZqSrqBs/abaChvR11ZC1PANDiuoKmhDS3NN3Gr/TYG+8a4HVNP2xBhlUUnZkYXMNQ1jRtNI7AbryElzo7oBwGOjFM3kQKHati5GnECOLk8wMnjAM6xElSXXkZn+xBbpC+R8of+FnouJ5gaiHJ6BnsWmEom81gtFJJHAByCbNIaHg7YoWE199Q6RbXhhdD4F2JvhAWH1SVI3l+GEsNFXGroxUD3DMsGIrUSASqmlqLwZlJMLYEpbu4PLWKom8DXKG5c6sfVc71oqrmFC9XXca6Sjus11Fe0oL7iKuoqrqLefgXnqq6gse4KLp29gq72Wxi7O4Cl6SnWKkPPweYG2U1muRajibsUxnsX+annERduQrhfFiIFJraIUYrsULFFUqUHwCGQs9H1aw3icACHs0qt1YhzKpx1ChweHnD5KwSKipnVjMJQqQXoeGIR9Cl2OCzNaL/Uh8nhB8gpAFLIEOToad8I4OR4KHA8AQ7l7bgAjhGaQAMSIo1I0uQj80Q5UyN0d4yw84bsdG6A41xlgagELt0tVCkNiAvSQ+vPARyVnwEugKMTViBOVI04MX9uS2sQS3OFBzgExijfK1pEjUs50IXnsd+BAI7VeAZXL3gAHDonmPKDU7cQwCHVVfe1+7AXtONYrB1RQsouIvUUWU9cAMfGGqjovcV93fP4fN17Dm+Rc6kRPwvgxLkycDwBzuC0OwOHASePEOPRfgI4/ShcB3DyQVBFLrYjWswDHGqhcgGcwDwkMoBT61bgDPWNY2YDCxXLwGEApx2phyuRpLVCF1GwzkK1EcBRSjgbFUEchcSCmMB8aEL1iI1MR25qKc7VXOIVOBsDHKrYvkkAx3QRh7RmyKUZ4ABOPqJ8SdFBbWekwKEqcS4DJ8ZfvxZi7HABnFnM3X8A4PDnanfbfVRb2nE6yYEkpQXaoAJE+uYifA+FZpdAK65GouwcEmRnES89wwVnU4W9uM5DgWODUkiWrlwGkDTB2YiNyEDqEStqyi6j9+YIywQjBc7qEnngeIBD1yQ6z+4uugEOqYcI4CgJ4LAa8QxE+mUhWqBHbLAZaQeqYMu7iObaThayPjF47zMATgtykuuQpCnmAU4awr0fBXDITkVWyCrW1kS5bSpZLlSB6Ug7bIPD0oiOq90YukPvJwsbA5xuysBphz7ZBXDoHsfM7MQEr2OkNmgDKqDxrwCVJ5BKhnLiIoW5CBemMoBzjAGcGjSfaUX3pypwFnD1fD9spqvIONKAg2oHVP7W/5+99+BqI8vXvT/mfe85p8O0I0GBYHJQJAeDwWBMTspCgBI555yTyTlnkXnu+u+qAoFxd0/Pve/MmSPWqhZ22xYq7Srt/dtPQPDrCoR7VUMmckDlVweVlO6BBHC4wz0D5ynAEUAOPT7CG/reA3D+OfPqJ/fRPzkP9fwdz3vlGQOeMeAZA//8MfAdwFGJaqCmRSSDOJz0l5MAcwqYf/ab9t1knoc4LL+DJgX+dax5JI5ATqATsYEOqPytUPhRdgplCVDwpgERtGPuq+MOkQ7hIh0ixDpE8gdBH+GIFuvADmprERkQ7UuHERRcSnAi2tuMKG8zW3RRzgJVb6tod0rsgIrBGx7gPKvBpdfy95zPh9f+JwBOQZqNAzgtJN8nL/+PAM4JXgY4OkS+rYCKV+Aw+5SkDoliOp4CHKoRJ9k7FzzJKXAIqtEuOtWH03mSSwxIjbYgN9UOXUEdWmsHMTe5hLPjE65+loc3tNij7BjXyR2O964wObQKq6EH6TEmtsAgtQ3ZS6LfU3UzD3BEpWwhm/fRgYrSNrQ6RzA1uIbdNbLgcNkpnAqAW0zeXYHZn072b7C6cIDxoWV0NE2gwT4Ie0U3KspbYSxuhqm4FebiNlQUt8NU2Ax9QR20+Q6Yy+rgtLSipbYHA11jWJxZwdHOCdcaIsAGeqrLOwZwthnA2Ua9AHDePlXgJD4HOAT+fAWAo0ViBAEcA4qz7GhyDOPb5DYuTu/xxwCn9QWAQyHGlM9AChJqSaFd4Vo2VpXUOiWugkJcgdhAC9LlduSn1kGX18oqqufGtnCyd8PZzx4W5nw2DVnJTm5xsEbgZgOj3bPoahhFs30QNRU9qNZ2oLK8DRVlzTCVNMJY3ABDcT0MxXUwltahUlcPu7kBddZGDHcPYW1+CZfHtHiiheEzgLN/Dw7grMNY0IO4UBPCvMoQ4W1ClI8NMt96KMQNUJF1QdrIK3AeAc7DdfQMCPwY4FDujZCBw9l5aMHBLUJo95l2o7WI8Cl4BDj5dai39mFy6I8BTp+gwJHb4P9LPkJePwU47DWQAkdEAKcGVEFM9yFVoB4JEQZ8VNLYcPJjYwuuY7LT3eL2mt4kWsXyAOcSTDU13L4Cc34H4oM0iHUHOF5mqL2rEetTg3jfBsRTnThr26KsnyYwFRApcKjOmuAFAzhlDwDna9ozgCPYkRjAIaAJFipLUPXbyBFqjBPIjKlBuHcZIr11iPIRAE41VOJHgPOje6T7+8j+DA9yXgI49O/JfSiU2wICOGnyKnxJotDlbjAFzub3NeJCiPHu+jUPcPqQoqpEuKQMEfQ54gZwIsVViBSZ3QCOFsnRZmhzXwI4ZKnjyasQYiwAnMw/AXAkVCHuZAcFGcskdsjJPswATjliIwrZfYoBnOUNXJzwAIen94KF6oIHOFQT/QhwtKzJLdK7mrMjPgc4ARzAMeS3o6t+jlfgCABHeAI+24eFM5+gqXoa+R8bkCa3IeaDBRHvDQh7bYLMy4EYUQsS/XqQ6NeNBGkX4iUEcdwBTgNreaNsJIIsZDmKCSlFQlQJirKsaK0ZxsrcLq7O7nBzcYO7Gxp0BHDITkX34EeAQ8HLCWF6qAM1oBpxUsyFe5Uggo0/LeKCLCj42AS7bhD9LfNuAMc9jIZ7bXvrVxgfIAXO9wAn/J2eV+Bw1kdOgUPXTyMDVgRwCBCTUljhp4MisBiFGQ7UV/dhamQRDPQdkK2YP5/0wGeArT8AnAZkxFQhMdzCNqmo9p2Am1JqQ0xADdT+FGhsB0EdasQkNXKYbz7CxdnISjaiStfMMugWpld/x0J1hZHuDdiNoyjO7ES6qgEKfzuCX1sQ/t4Ksmep/OqhIhWipI4BHJV7iPFzsCoo5L57FJR0f9986Ef3Bc/ve86jZwx4xoBnDHjGwL/7GHgR4KhE9QzgPEKcR/vSv+wJoUmBXz1YmKV/A8s/iPWvR6x/HWt/okYayqOR026lyAZq9YjypfaOakSJqtgRLapGNNkh6JBUQ84fCglVflZDIXY7RFYohIOky3T4WJnliOqz1WInf9SwEEb3Cty/eg4fFiw/AjhiI2SiEsSFlIIBHF07ev8kwKHJuKV4AFkxDYjyNiHoVz0i31qg8rU/5t88AJx6JEo4iEOtIRQ8mcDyCyjwtInl8lCDDGWDUOBqtI8BCqkB6cpqFH+uR5W2HT2t41iaW4Pr9IxPLOYnq3eckuR4/xLb6ycY7PoGU0krEqOokSUPJL+P8rJA7u2AzIeT1UeLS5AqN7Iq5brKXgx1zWBxZgtHOxcPuTqcCgC4ubrG0cEJ1ld2MDO5jK62Udgq21CSb0VelgU56SZ8TjYgM9GArEQTshMr8CWxEtkJRnyO1yIjrhS5aTpo8itRpa9FS20npoZnsbe+zy0YBNggAJydS2zPn2Cmdxv1+lHkkwKHAM4b3kIlfpqBQwqcGAI41DTkq4WawlAj9EiPodYVB5qcIwzgUAbOc4Bz6eKasjYXr9BRP4+SrD8COLSoqGfKMGpFUZL6RkK17Ga2yP2SUMuyLpzmAQy0f8Py3C5cx9zCwh2q3Jxd42TnCNtLm5ganEZ7XTcqNTUoz61EUZYFeZ8q8CW1Al9STMhONiIr2YDPyXpkJumQmaRFVooWeZl6lHw1wlBSgfaGdsxPzsJ1SPkTbgCHbBmkwCGAs36HwbYNGAt6GcAJfV+OCAKpPnbIfBuhoF1u2u1mAEeAOHQfe1nR8eT3n2Tg0L2QDq6NSgjSjWVhz9SiQmHJlAdBdc8EcMqRnWSB5gnAeSkD51GB8whwrA8Ah+CkWupgTS4cwGmGWtT8BOAoA/SsqjpFoUfhZzsabUOYG9/E2eENLs9vmDWRARyyL3FFUDjducNIxwrMBR1ICNbyAEcLuZcOivdGqMjW4uNAvG8d4kT1iCNVGJ+HRgtQUhAwC5WY7psGRIl5gBNjAgM4xg6M9C7gYJMCqPlAYGFdfwWWu3KyfYfZoQM49GP4pHIg5F0Jwr0IHBjZ/YLuswS/Ke+G3SuZvYKUSMLBTUyevGcCDOc/B9hnwYOFiguNlbN8pO8BzigphgSAQ4tm3gb5IsBRViJcTACHWqIo96YWkWI7IkVViBCRasbAWagCnwIcel/YwvyIgDWNB3oS7oHytxYo7Nc+iYIXAE44wQt3CxUPcDh482cBDk/R6DkpS4oUjkf3mBnZBgdwLIiWlnCbFvR8DwCHU3go/XgFToAGKdFmPAU4LriOb7h7Lf379FQEps7usTB+iqbqWeR/bMJHuQMxH6oQ8d6EsNcVkHnVIkZE9ql+JEh7kSDtRrykE3GSdsQyBU4z1L717DNV5mNiVlBlQDniw8uRqixHWa4THQ2jWFvYZ+opLgif1G5uAOfiHrurl6yFSvOlBbHBGij9yxEtLkWEdzEPcMoR6U3h9wLAGUJ/ywLmxzdxsHmM+ysCoW5f98DexhXGBtZRax1BeW4L0tSVCPctQuCrAnAAx8pn3whKZg6AkuJIzY56rnjBTw9lYAkKM5xMsTc1soSt1QOcHV7/AOBQBg41XxHAqURieAUUUhPC35P6kBoZrYgJIGWMHXK6Rn3IamxEuHcpwnxyES7OYvffKl3TnwA4BC43YTeO8QCnCQp/B4JfVyH8vQ0yUQ1Ufg0cwCEVjpg2AshCJWT9Cdfr3/EoXMeex79rc++vzik9f8+zyPWMAc8Y8IyB/55j4DuAo/StAQM4vAqHgzj/HQAOVSA/SnRJuksHVQhT6xMd5NVWSmqYT1wudjAPN/m42cEaReyQSygI0A6FlFoiuENFlcpSCkp2OyQO5mOnMEJ2MLUNp7ghO0oMhQk+HO4hnH99oDwsWJ4BHFrYcDXiAsApewQ4zbwCZ+MFBc7NHfY2SYGzwXZTv7dQVbhl4JDNpxYJYj7MWMKHGUsbkUANIn7NSPRvYQCNQkPp51FKqBGI1Eo0adYwi5Murwl1VX0Y7pnB2uIWLl0ufsbPT5DvANfpHfa2XFiZP0Bn0zTKchugDCpGwKtshLwtQZQXZfM4mT1LLtFDLi1GRowZ5uIWdDXS613D9soRXEfk7eH/ebaouMf56TnWlrcwNjSLlvo+GDW1yPqogzwsC1EfMhEV8BlR/lmI8s+GzP8rFP75UAYUQO7/FTL/z4j2T0dcZDYyE4tQnGOA1ViLwa5RbC1v/wDgXDwAnDr9KPLingMcKwsxZi1UFIxLyjcGcCqYqkMdqEVSpB6fYowoyXawzIf5yR2mIKLwZAZS6CXeAwLA2XADONHSMkT4kI2KFA4UEkv2PlLg0K4nB3BIJaagyntxFXvPqNErMawKhWlNsGoH0NUwi+nhdWyvHuHqnF+o0fqTdoWvgdO9U6zPbzB40+zogDa/Eh/VuVCGZEARlAX5hy+QBeRCFvgV8g9fIQ+ixxzIPmQjOjALipAsJMhykBaXh5y0QtRU1mJqaBxn+3u4p4BrQe3D52pQhsrhOlmoCOD0IDbEiND3tKMuKHAamAJHybe/kMqIqQgpyNgd4JCqxv3XLCj9JQuVO8DhrmsCOOxgAIfeKw0ifAvY4pJZqP5OgGOmDBw3Bc6LAEfsrsAxggBOXKgeyTI9CsiCYR3EzOgGTg9ucHFKAIdOHH/yCOCQZXD3DqOdq6go7EBijLNUnwAAIABJREFUsAaxARqoxBrIvbSQvzdA6VWJGG8HYn3qEOtbj1hRPag2nZq3KISVVE1UgywTVyJKpEeUpBSxYRqkxRjwNb0CVmM7RnrncbDJqd8EoQld3QR0yLZDOTzTA/uwaUfxUW5H0OtihL3TINKbAE4VSAlG99RHgPPCAvDJ++aei/P4GRD/HcChXLJKxAdXcQqcRE6BQwDnYPMMrHr9BwBnmFmo+pCitCBMXIpwBnCqOYAj4gEO1apTGLOfHopnAIdCjAngnL8AcFgGDgEcxyQKPjci/XctVFQfTgdvn5KQfep7BY4mn7dQCQqcqz8HcEhpGuVbgShvCuam4OCnAEcVoOUBTgenwJk9YoHVF8dkg+Xv4U8AzgkamQKnkb3XMR8qeQWOGTIvJ2JEFGDchwS/Hk6BI+1AnKTtsYHKtw4KH2pYMiDSuwSqwDIkRWnwKU4HfWEdelomsLl8wJgYBeHjjsb7JafAoWIs1z12Vi7R3fANpVlNUAWSDbcM0SJS3hQjghQ4pN7z0iD2QwUKUptg1woKnN8DOJcYG1hDrXUI5XnNSFNbEO5biMBX+Qh/p4Pct5rPgnNXMfPf07VEdiNmodJB+VyBwwDOjxQ4zwBOhJnVkYe/p89ZamSsRkwAzVcIsFQxm3H4ez0rdQgVAE7SHyhw+LeRgppHezbhYACng1fg2L5T4KhJgcPbqB4AzgPEEdQ1L1zDD0CW/t9fnxd5Fh+ec+cZA54x4BkDnjHwP20MvABwnFD51kNFAIcpcbgdaCHg81/jBNHO7LODD8GL4x9pQsC1PfHBwX5cjTcDOZJaHuI4QSBHOCgAkHzjdKioGpNAxHcHVRN/f1AbFh0sj+fhkZ5bONwWGX9xd+lhsfk7AIcmpnEh7gCHKmxXcfAjgLNxgpmRDZbzIoQYR3ob8OFXDSLemqDwrUac1MkdEifiJVR5TQc1U9UhXlqPBL8GJFAVbEAz4lldOk0gLVBKyDqlgUxcgpigMuSm2FBZ3o7OhjFMjyxie30P15fP1Al3lEtzh82VU8yMb6ORAow/OREpyYP4pwwEvSpC1HsTSOVEygelVAeFfzGyEyph03dhtGcem0sHON69YM1SbC7KLygo8+d4/wSzE4tobeiDodSJjKRyRAZm4M1/yPH6/5Pj3X+o4f1fcfD9KRHin1Mg+eUj/H5Jh/SXFIh/iYfoVxVCfBMRG5GJzKRC6Isr0dXcj7X5zRcAzi3Odi+wNX+C6d4tEMDJpcyPN6WIfKOBwutZjTjZmthimRQ41NylRQwtBCP1yOABTouTMmK2cXXGt1DxG/nuGTgbi5cPCpw/AjgEcpRiCgkl6xQnx1f6GZESWYXy7HY0Vk1gvG+d1VMf7pzh9opvs+HPKTVt7a4dYnp4AR31/dAX2ZCqykfA2xi8/d9ReP8fKnj/ZwJ8f0qF6JdUiP/2EdLfPkLyWyrEvyVD9LdE+L1JRJgkBcrQNCSrMmHRVmO0dwinuzu4v7x8EeAcMYCzDmM+ARwDr+IwIMrHChkt+sT1UJK6iOANAzjfwxtOTSNAHAFSvwRwhDYqCubmwawAcOheIShw/irAaVkGAZyPDOAUIPg1tdhVMGDMIDSzuTVB9Rzg+OtBFd7U7JaXZkNd5QCmhtdxvHsF18k1U5s9ABwetlH+zHj3KiqLO5EcpkVcoAYquka9yiF7p4fCywK1tx0xPnWI8a1DjIjsY9xnANnuKP+G7pMysQWRIi0iJcWICy9HeqweuRkVsJnbMdo3j4MtvgaZH590HVLIteuAU09N9OyiqmwYyVE2BPxWhJC3BHBMTOWoZEHaBMGpmYZbAApw/kGF4wZw2Pso5rLFWA4OqxavxwPA8RcUOBzASQj5+wAOLWRZBk75I8CJEBkRKSIVUi0iCeD4ViLCHeAEPFXgPLRQHZ1+p8BhGThTB2h2TKKQAE6s7XcycHh4I/4RwNEwC5VGyMBZXoeLLFTuAIevLn9Q4JjIQsUpcB4ADl1HoucAxwACOKnRFTDk8wBnRgA4FJbNr/yfABzKwJlCfloD0hRkoapAhJceYW+MkHnZESMm8N+NBL8uxPPwJk5CFeJNXAOVbw3kPpWI8taxljf1h1KkyrVMyVdR3oCBjinsrB493ntZKNgVcHvLoA61UG1RJljdHIozqLK+DDJxKaJ8CeDwh1cpgzgxgSYUpDbArnXLwNk4xv0lQSG3L6bAucD44CrqbUPQ5DciTW1GuG8+Al/lIuydFqT2EiyZwpxJ+Pym8aoWP7ZQKVmIMQHYXkyPLmJ79QDnhzy4Fp6WIuIoxJhZqAQFjgWJPMAhW1q0jxkKSRVi/Gnjie6DZFczIeydDqHvixHqzStwkoyoJAtVhxBifMrC/B82O/jn5ADOBhwPGTh1UFKI8WsjH2JMLVS1rJyBChrIissdwj318VE4B55HzyLLMwY8Y8AzBjxjwDMG/vEx8ALAeUGB41bh/c896c+gjTvEca+h5CHOE3jCgA4t6PjAPQrdY2ocUuRwB/1ayWrJa0CWKzY5YQCnltmw1DRZeXKQPYvUPW6TGPY9B3O41qmnAIcmcX/1HD6dAPJhyPwCR1DgvARw5lkGDk3ihdkg93hPCpyNY8yMrKOjbgbmoh6WSRHhpUPgL1T1aoDcx4IYCTVqUCaFHbFi7oghi4OYFllOxEpIjVCLWDaZ45U3Yj2UfjrEftAhMUyLDLUZhoImtDiGMTW0jLWFHRztHYO1hrj/WLfA4c4Nvk3tobdtAZXaHnxOrEaYby7EP2Wy0OgoLyPUYhvU0gqo/XVQfyjB1+Qq1Jh7MDmwhN21Y5ztX4F2ttkXTX6JO9wABzvHGB+chbOyDfmfK5CiLEGkXxZ8fkqE70/JkPyaBv/fMhH4OhsfXucg6HUugl9TcHUWAl6nwf91IiKkaUiUfcWXj2UwltnQ0zqM9UVOgcN2oYWnvXQHONuo1Y/ia1wdwl+XIOI11eiaECO2Il7q5Jq+COBQbouY2ssI4GgQE6hBcqQOGTEGlGY7QABngQc4t25ODJp8U6U5CzFe4gBO8ecWREnKEO5DKhxOgUM5CQqxnWUWxEibuPBJ2sEXkXWqAqS+UflTXlEVdF/b0WKfxMzwFls4HO+d4e6agiVICsPbJFz3WF/cx2DnNOymNhRmViI+Mh9+rxPx/j9i4ftzKvz+loVACv5+k4egt7kIep+LD+9yEPg2GwFvMhHklYnogCzERX7Bp7hcWA0OTPSP4Gxv93uAc87VkR9SiHHrGoz5XQxihLwjO4QeUT7UQlX7XQsVLexZExW/8OcWUdS4R4uMZ0HGTyxUArwRLFQ/AjhaXoHDhxgLCpwf1Yi77rG34WLtZr3NPMCR2eD3CwdwKKCbqVAIPPMAh6wXSjGFGFPArBFyKVWJa1nuzpekKtiNPRjtXcLu+hlODly4clGgK7/4JIBzC1we3GNmYBM1+n5kqS34GGFCbIAO0V5liHqr4wCOj+NBecMC7HkIJuy0s2wNkRkRvmUIF+UjPrIcWckmlOXZ0GDvwfTIMk52Lzl1GH8t0HVI1+Px9g3Wv7nQ37IGY0EfEsKq4P9rEYLfaBDpZWZKMJWEFGLPAA6DMlzrF4M4AsDhYbYA434McByQk81VVIUEpsCpxpcnCpwfZODs37PK6D8EOGShYlanFxQ4NW414n8S4MT9MMT4RwDHyjJwqEY8LpJCjJ0sxHh9aR2u4zPcXQl0hbtu7y7cLFQCwPErRbREx5Qc0QzgVD9R4NB9QR2gYwDHmN+Bbj4DhyrjL45fBjiLk8dosU+jOLMJGWo74oIrmF0p9I0eMi8rC/RN9O9EvLQdcZJWBnRiRARvuA0kUlrKvCsQ8b4MIW9zof5QjHS1DgUZFtiNrRjtncU+bU7QMKeXeE//ucbd9S2uyG55eI/V2VO02KaRl1qPSF+qDKfGuhKm6In0LkUkAziliAk0ID+lHjZtHwtd/za2jr31Q9yy68jtQ+oOONi6wPToOlrqRmEobsanWDP7jPL/Ww5C35Yxey9Xec9nZrHyArJTO7l8PBHZVQ1Q+JVC+SEfxZ8p36wf3yZXsLd5BFI0CZeu+2cYAZyuRgoxbkBGrAWJ4aTAMSDinZ4DOOJKxLAmTmrGewQ4Ye9LEOadiwhJNliIsbYZve3jmJ9ax+7GKWt8fBngrMNuGEZRZgvSlU4o/CsQ9FoHytyhpk0VawB1a9ikeZaEQvHp4Oco/LX6V+c9nr/31+eMnnPnOXeeMeAZA54x8O85Bl4EOEKI8WMGDkl/uR3qf95AcIM3D7vfvI3hya9p15Y7XgY4AsThQA5rT2AwhyTNfCUm//gUzDxasQRL1tPHlyAO93vcz/GP70b9FYDT1zyG+fE1HG6esV1Kt2koCODsbhxjengN7bVTMBV2I0PtQLhXOfx/KUbIay0LaKZmIuFQiyig+flB7UWUocKFFlOlcpRvKVT+ZUiO0CMzxoziTDtqzb0Y6ZrH1tIhjnbOcHlKGTXPdjdvgZ31K4z2r6O2ehSlX5uRojAj2OsrxD995hQ4XgaoyevvR2G7OsQGlyL/YzXqK/swO7KCw61TuI6uWQMJe7384pUsJHsbRxjonIS5tA7psZTfUYgI8Vf4/e0zAn77guA3eQh7X8SaSSJZuCXlI2gQ7lWIUK8vCHr/CbLAbKQoi5D7SY8KTQ3628ewubTrtojgzvL95S1OBQVOHwdwcmJrEfaqBGGvyiDjAQ7lqMRJazkLIAEcCZ1L8wsAx44W5xAWJreYAuc5wLm+uGetWptuACdSUoowbw7gRPpSBTsPcGii7dfIQKVCYuOyRyRmkPpGHaBnlb+0y95RO435iR1sLR/i5IAADkEB2sLnFkyU37E4s4P2+lFo8muRmWCEIigf0t/S4PNfBG9yEPyW8iYMiPDRI8JXx5Qb4b5lCPMpRohXAYMAqpACpCiKkJ1cCqe5HlOD4zgnC9WVGwjgLVSXZKFiAGcVxvxOxAZrEfKuEOFeBKmqWI24knaDSaLv34BYv4aH/JTHa6gOahHthPMAR0p/hr/PvQhw6M+6KXCYOoTuP4IChwBOIeLDnwKcicFltthjiyO3i++aBzjzE/vobVqGqbAPqQRwfqZmu3JEkE2QQAaDomRf4rIzyL4kAByZhNRnGqiDSpERV4HK8g6WVbSxeIiD7VNcnLqAOz4JlVdFUO37wugumqvGUPyxBp+VVUgMMjCAE/mWrFQVUPs6ESduZNk3cX6NiPNvZOcxhgJL/aie2MYaqOj9C/X9ioTocuSmW2Aqq0N7wxDmJ9dxfsjbF91f8ynlkbjwbewA7c55lGd3ISbIAunPRQh6rWWvWSG2MXBFjX2cAoezXsT5NYA7eCvGMxD3fxXg8DlWLANHADjd67CQAkdhQbioFO4KnCjeQsWFGH8PcNpqxlmeyjazUL2swKE2MncFznOAE/2QgfNjgKP6YEZMqBbxUcXQFjrR2TyItaU1nB+f4u7yZYAzO7IDh2kQn2IrEc0ADgX1VyCawsBFlANHwbgWqPzNUAUYoQ7U4aPMAgI4LMR4+pBZqC5PCOy6vdl8Bs7S1DHanTMo/9KCrDgHEsII4GgQSjXi3pXs+kvwbwOrqRc3cepf3zqofOug9q2F0seOqPdGhL4pQuBvWVAHFSIzTo+yHCvqqzsxObSAw51zHt7Q89MPcYObqxu4jm9xvHOL+fFD1FvG8SXBibD3dJ+g5imCOKXgAA7ZqEoQE6BDfkotrJpe9DbNYG5kFTur+7g+p8+pp+fvcOcS3yY30dUyyULvM+PNCPPJgd+vWQh5U8Kg06OF2snuE7QBQp+dSmqLEpHdiVq1iqH6kIeSbDsL9l+aXcfhzikuT28f7LHsrPKbEOsLhwzgaHMbkMkAjokBHLJtUaECqShj/KxQSp4BHC8COHmIkHzhAU4LetvG8W1ynQHf6zO+BdLtLaSg5pHuNdgNgyjKaEKa0gaFnxFBr8oQ/o4UP6SypY0UJ1MaC2o5DuAI98tHiPPPmzv+e07ePefT8756xoBnDHjGwP/cMfAdwHlsoaLGBN6zzRaVwi71P+tk8QDnCax5CeBwv8esTLQDRBYmmuzzChwCUbSwcz9YiwLLyOEqMZ+Cmafghnm93f7s97/mIBBnqXqEOuz5/8GdqIfFJy1OaTHJvy6yWZDnXSExMm+/u4VKADhHm+cvAJx77K4fYWpoFW01kzAUdOKTysbyRPx+LkTwq3JEehm4xb3IwvIjlGS1IQhASg6qmaWDwoR9LOygWm+aFEd450MdWIx0pQEFadWwlDShp3Ecy9PbuDy+Y7vxLLOA7Zg+zhpJJbC+6EJ3ywL0RZ3ISnKwRUngmxxIfspC8KtCRHnpESOpQmyAGXFBOiSElaIow4pm2wDmJ9ZwuufC1ckts2yw+TwBHFpPXgE7qzQBHkFJdjXUwbmQ+eWx8NmQ14UIe1uGKB8dC/ElC5hKWskOCvaVUTaEqBjhvjlQBecjPVaDgs8VqNI3YLBzElvL+y8AnJsnAKdGP4IvsbUIeVWM0FdliH5PSiIrC8KN8+MBDstpEQBOOWICy5EcqUVmjB5lNMl3DmFxcgvXp/dPslDpdT4HOEWfmxEhLkGodynCfbQQAA7lPNG4VT8AHCvkYi4Mk+2yB+qQrqiEuagD3Y1UFbzPxsnZ4Snuri+4k8kDnKvTe8yObaG2qh/ZKZVIiNIgQpIPya+fIfqJVExU0WvhLYkOKP1sUPpVQy61IFpCQbA6yPw0iI/Q4lOMDvnpBtRXtmB2eAoXBy8AHF6Bc8QAzgpbSFIwaci7AoR7adhzyUUOpq4jdVxcAAWaE8ThFv7sGuIDiVU/BDhC5s1z9c1zgEOg+I8Bzv4mnTN+bckP9etzuvbOMT++j56mJRgJ4ETbIP25AB8I4LwnNQq9TxyIYvXDLECYqoGrEEUteCItZNIyyP2L8VFhgLGoBd2NU1ia2cHO+iHOjs6AWzeAQwDs7B6rU4forp2B8WsL8hKdSAkzI5pUCG/KISOAIyJFWBPi/ZoQH9CE+MAmxAU0IjagntUTK6RWRIp0CPEuQLB3NpLkZSjKrobN3IK+jnGsfNvG5Ynbgpd/zRfHt1ifP8Z47ybqLJMoSG+Fwt8MyX8V4cMrAjgVbEGokjhZsDa9V0xRw7LNGriAegHGuQMc9j7ySgfWDsYH2j/JwHFX4FQjXf47Chwe4FBg9tnfDXCMTzNw8lrQVjOB+YlN7KwewPUDBY4AcIpYBo4NBHCiJFqEehMYpPZEK+SSWsiF/JvvLFRWEMChSveE6BLoCms4gLO4hrOjU9y+BHCO7zE7ugOnmQM4sgeAY0G0L0FdK7PkKP04gKMOMCImUP8AcLqpRvxHAOeWCzFemT5m9lx9XhtyEp1ICqfmwDKEviljbYJkSUzwb2GwUC2qg8LbyQ6lTw1UvjVQ+NgQ+U6H4Ff5zMqqDirAlyQjDEVOtNb0YW5sGSf7F48fIOy7W1xdXOPk4Aq7a5eYGtyBQz/CFEBBr/IR8rYQ4e+LEeVD9yaCOARwihEToEVeshPW8m70Nk5jZngZ28u7uDylzY8bDhLRv38HHO9eYnFmGwNdM6jWtyMr0YxQnyxIf85A8OsiRHubmDo1hmU5ORArJVUZbXzQ52YF5L4GKMRlUPgVQBWUi7IcOzoahrC2sIXTfReuz7ng8YcXxgMcgrNdjVPQ5nEAJymCAI4e4e9os8XIAI7aj9Q9VZBR+9R7I/t/YQRwfPIQKclBVpIZFk0LelonMDfxewCHasRXYDP0o5Dq7RV039Yh6BVlVmkZMCJQpKbqcr8aViJBkJXmOjQ/YXNJZm3k5mCehcY/a+7seV7P2POMAc8Y8IyBf7cx8ALAoUUK2TiE9oQGMBn9g83gnzEIXoI33K4WLaC+P3iwwyS8wg6QkHHxCKXcIc6f/Z6DPXx1JgvvE77n4A8tuNyPRxUPD5H+b1iongEctjPOAI6J5c0IAMeu60BfyzgWxtfwI4BDIcZkoeqsn0ZFSQ+y4msQJdIi6HUpUzTIxWao/avZEeNvBTv8rFCzoxo0WWS/F2BFbKCNBYQmhlUgOdKA7PhKaLJrYdO2ob1mEFMDi9hZOWTBkszOxUIv+W1bHrIwIDB+gNqqcXz5WItEmRmygHIEvc1DwN9yEfq2BDIfAjgWxAUYER+sRWJ4CYozrWi292FhchWne+e4Orl5BDi8fYqec3f1GD1NkyjPqUF8WAlU/qWIporVV5T5Q0oEMwhSqSSkBCA7GKcskotNiBKVI0KUj7jwUmQnV0BXUIv66m6M988z2xYLFKZ1K3tJ9yw74XTXhc35E0z1bcOpG8GXmFqE/FaMkN/K2M6ySlSNWGobYmCSC+Kmib5SbIJMVAZ1AKmYNMhQ61BKtbmOQSxObrLMAvcyG5ro37gpcNrrv4EATjgPcMJ8aCFoRBS1W5FSiqrDpQ1QSqhulqwSXBimkixpgRqkKcwwFbWhp2kaK3N72N0gIHCCu5tzLhiXXiM1hp3dY25iD/XWMeR+qkWywoJofw38fsuHhCDgayOifBxQS5v556vjM1SoCa6SQSW5nwkpMgu+JldDm+tAe00vFie+4eroAPfXV49WHBL+EMDZu8fh6i2GWpZhzGtHbHA5Qt7lM+VYlA9Zz+zsdVGAuTvAeQCgPMDhFDh0XyD1TSNT4NC9jst8eQnevAxwKKyb1YjzCpwvyZXQ8hYqUuAwgPMwLrglGSlwdtddjwCnoA8p0TZIfipA4G/lCHtH7z+NP2rPI7VgA1RiCmcmCxVVPJOaSodocRlk0iKWg1OeU49G6yAmBpawOr+F471j3N/wAIdfCbJQ10UXJrrW0FQxCuPXDuTEOhHrT3ZJA5S+VVCJnI/wnlRJfvXMKkqZYEqpA3JpFWRSA6KkJZAFFiAr2QxTWSPa6gcwObyArZUDLlPjYfXJfXOyd4n58R10NczBUtqP7IR6yKQmSH4qQdArHSK9aEefC4cX7BfuAEdQ4bhbqBjIFtUwNVWMmD4nqN79OcCh8/cIcOKDq5EmABxdN0a7F1hGGGXRPNyXCHad3+PcDeBUlvch1U2BE0Xvg6gW0SI712QormBQUggxToo2sRpxpsB5AnDIy8oDLn5MuwOcTzFWBnAiGcChcOznAOdZiDFVpEutUAWZEReuQ6KsFLqiWnS1DDEFztnxjwHOzOgOHO4AR0oKnKcAR+VXyRQ4DwAnmlPgdNcRwDnA/voZvlPgUNPV+T22Fs4x0rmKWtMItDntyIp1Qh1IWVVaKEQWqCUOxJFFkEJ9fZ2Qe9kh97JB4W2HwsfBBRh76xHhVYTQ99n4KNeg/IsdtZYODHVOYvXbFlxk33rydY9L1zX2t11Y/XaCwfZVVJQQIK2C/y+5CGJNUcWI9ilDNEEcbwrGL0JMgAa5SXZUlnags24c04OL2FrewfWZi1Pg8B9V9NZRS9TG8gGmR1fQaB9EcZYT6uAihHnlgpSbcl8zs0oxq6/YCjWBDnElVKIKKEUmqCUGJATrkRqtQ2asHlWaFgx2TGF7dR+uo0vO/iuEQtNr41WkGwuHDNI+AJxwAjg6HuAYoBBVsM9klYQ2VyoQ5WVCxHsdU5SG++QjUvIV2UkVqNS2obd9CvNTm8zKSUpK9kUP/L19Z9WF4a5l2A19KMqoQ7qyktW40/kLe1eGKG+CUBZWWEDFBfH+DYgntR5tkAkAh65NPp9KuP8+f/x3m1R7Xs8/Y43geU7PuPOMAc8Y+J81Br4DOOzDlilv/pUBzkvQ5vnv1fBBx8IbSgqiR3jDWmn4X1O7CoWd/pmDFr3fHz+GOP/PAA4LDOShEFPgUHsWAZxSFmJcmG6DQ9+B/pYJLIyv44gsVM8zcG7BrBYUsNnTPIdqbT9yUxug+mBEhI8WcqmJ1b4mhNqREOJgR2KIE4n89wnBdvZ9cpgTqRG1SIuuQ1ZMPfKSG1CS0YCK4nY0Vg6gv3kSs0OL2JjfwcnOOQMrzN8vTIhpsUQNNaf3ONi8w0DnGowlPYiLMiM6QIMIcSlC3lPQaREivcrZ7qVaUoG4AD0SQsqRFFGM4swqNNt7MT9B+RunuKS6a8qIccu/ode/v3aGwbY5mApakS43ISHYBKXIiPDfjIh4ZUbU20rIvagSnhYQdih8aeFH1dQVDGxFioqQGEUZDA5U69rR2TiGObKobZ1zLSwPC/V73NEu8M45NuePMdm7xQBOdmwNghnAKUXkO5pwU9ilHTEMRJKig+pYKWzVCBmzoZUiKbwcn1QalHyuRot9AIuTG6BwTvb6+HUgTb4fAc4FHgCOpAQhPrT7qkG4jwERvhWIFpHixsnAgIIWtrRbKzFDLjVA6a9lTS9pciNMTNExiZXZHexu7OP8+Bj3twRweLvEHdhCfWHqGK2131D2tQsZ8XVQBVsQ+Loc0p/LEfyqEpHv66AUtUHu2wyZbwOifWtZ3XekdzXCvSog97Pgk9KBkswG2LTtGGgZx/rsCm5PCUBQlczj+0gKkovdexyuXGOoZQnGvDbEBZch9B1VzJOCygQ5Ax8Oblc4oB5x/tRIx2czuGWmMPuUmLsv/Bjg8Gq3h7/HZ+AwCxUp/JxQiv8xgNPduARDQS+So60Q/5SPgL+VIvQtgS+yg9mhENVAKaKw6XooKHBaTMCtAjKxHtFiqkQuQnyYBgXpdlh1HehpHsfcOIGjg+8ADo2Zk807rE4cY6xtDQ2mCWg/tyMtyoq4AAtiCASIaOFcCwUpIETUUEZh73YG/mT03BRQHmhETIgWiTINSnKcqKvuwdjALFa+bbJ7yg3lTwnXN7c0xP7GGcZ6l1FjHkJJVgs+Kqi9yQDpz2UIfk0tQ5UMvAnqRUGBwyDODzJw6PNK/SLAocVkPeIDSH31CHDIfsa1UPEKHAZw5p8AHAHECgBnn9p4utchAJwIUSkiRQRDqxEtrkW02IFoMX1fgWipCXIaBLb2AAAgAElEQVRqoQrQ4iWAc3F8xkkBhZPzBwAnxIvq6c2IomuWV+CQCkfu3kIltkEureYBjp4povQM4AxjbWkdvwtwqEact1AxBc4PAU4FCOCQvfIjD3C6COBMCQCHOuPd3nB6XRcUWH2D+bED9Dcvw2kYRWlmK1KjqqAiq6aUAA7l4Dig9LVD4W2FzKv6yUHqToXYCKVfOdSBRchLrYJN14a+ljHMT6xgb/2IqVX4Ifbw4Dq7xtbqCWbHdtBeOwtNTjviQ82Q/OdXBP4tH2FvixHtWw6ZL0GcEkR7FyM2sBxfE62oKGpFq2MIk/3zTIHzkIEjvLw7+qy6xcH2GdYW99DXNgNLeRsyYs2ICSqH2t8ApaSCFQBwQLQSKrEFKtoMEZugFhuQEGhEhqISBakO6PPq0eIYxPTwEg62j3F1dv2duvKPAE7EOw2iCagwgEMbEKSUJVUsNYoZWAU7teRFSr+CAHM1zQ06Z5n9dX+LB0Z09oTPy2tga/kMw12LsBt6UZxZi0+qCij9S1kmXNi7YkR5U1izGSpJNWL9nEgIaEQiqfX8HoHcowrn0ULuATjCnNTz6FlwesaAZwx4xoBnDPy1MfAywJEI8IYe/9UVOD+wUbGAY+GkuMMb7jW5Axz6nkEc98cnQOep5YrUOt8rcQQFDldXLqhw/v8DOKSq4ABOfGgZCtPtcBg6MdBKAIfPwHkGcGhSeLh7ioWpTfS3z8Fh6kdRZj0SI6iiWMPqmZMiKpEqszJ7B1k8UqPtD9+nRFnxUWbDJ6UTn2NqkZPQwCbo5sJuOA0D6KyZxETPIlant3GwdozzvQvckLef5vq8TYHNuEnJcXqP4507rM6do8U5i7xPDQihWta3+Qh+X8RyXCJ9tJCR9FxEE2FOgZMQokNyRBlKMm1otvWDAptPKEPgmM/AEfJveAvVwcYZRrq+obq8HdnxFqREGqGS6pilKYysK290iKRGDy8jk8FHe5tZOGSUrxZRohJEivOQotCh/Gst6q39GOn5hpW5HZzuXT6ABm4VcYe7iysc75xi49sBJnrX4dANIDvWhuBXeQj+rRA06RYmwGoWEk1wgBbqVshFRrZDrPIrQ2KYBukKLYqZTWwQCxObuDnl1QLCogLUSnWHs8NbbC6dob1+GkVZdQiX5iHEJw+hPkUI86FAYx0ifc2ghTgHbyjnwgK5xMRCcRV+WigDyvBRZoKxsBXdDVNYmd1lYZ6UpYE7sivwW8P3YE1fK3PH6G5egKmsF1/T6hEfUYGg90WQ/lrIB16SnL/mAdpEeFkQ7kXV3zqEvC+H3E+Hz3E2GPJb0GQdwETvHHaWtnDnomwL2srnFxbkYji9x/nuDfZXXBho+QZDXhNiQ4oR+j4H4d7FiPLVs4pramFR+zkQF1CLWGpGIysSW+wLQIZT2HABxoICp5GDvKx1ibMBsL/D4A3tJHPHY7YFtc49BzhafEmugja/gY2PicEV7G9cPLwGYYV547rF3sYpyxfqafoGQ2EHkqPNEP30Gf5/y0MwNZV5UzUwwSEKZiaQQzDFxgCOglQ/Ys5GFelbCvUHDbITrNDnN6G+qh9jfd9AmSvMpig8KZ3Ga+D68B5HazfYmD7FYNMSbKX9+BrvREq4BXGBFijEtHNPhxlRrJrdhEgfIyJ9DYj0JcubDrGhOiTL9SxPw6JpRk8rWac2cbB1jPOjK7Z4F0AIYxX3wNbSCXqb52AobENWggPxYRaEe+vh/4sGIW9MiPKuBrNPsRY/3nbxBy1UfxrgUNugj5W1A8UHVSFNVoUvCdWw6bow2jWPg/UTMAUORTzR8ObBCilwXgQ4YiOixNWQiWshI4AjqUY0vScEcPx5gBNFCpxmCAqc3dUDcACHFCP8hfsE4EyhKJNqxG2ICzMjUqwBAZxwH1L/uQMcJwM4comDVYhHi22Q8QAnPkKPZEUZDMV16KZw9aV1nL8QYkyV6a6je8y8BHDEnAKHu0dUQckUOBQYb3oEOHkd6KqbwfLUPvbX6X7LZ2MJY43uu5cAtZ7tLF5gYfQQPXULqCzuxZc4Av9GxAWaoJaaOUuRjxkybxOivQyIei8ceqa2pKD6hFAtUmUa6PPq0OYcxLfRZdD5PD+4/G5jgn6E85NbrC4cYaRnDbUVYyhIb4LS3wCf//UF/j8XIPRNKWS+VPetg8xXC5lPOWIDDciJt8OY34qm6kGM9xLA2QdlmT180dtG+T4Xd0wpQ1luFNrdaO1H8WcH0pVGJIUbEcMUbXrIffRQ+OqgFGmhEmuhlmgQI9UgOdSA/GQHzAUtaKzqx3DnLFZmN3F2QHX2N5zdWYDz/HPSuNxYOOIVOI3IjK1E0kOIsY6zUJGqicaCpBpKcRWzOMtIweWrBW0+REnzkJNSDauhG4Nd81ie3cfh9jW7XtlrpPeN8qgv7rG5dIShzm9wGLtQmuVAhtoIVUAhQt5kI/xdPgt/JjWsSlyJOH8HEj80IulDM7M60nVJCiqywdH3T6ENhcc//p5n8i7MUz2PnrHgGQOeMeAZA54x8OfGwAsAh3ZKCHI08Y//CgCHXsxLNqpn8Eb4M78Lb9zh1PPvnyt0BNvV9wDn93JyHqGNkIHzaJ9iu8p/0UYlTILc6zrp53iwUElNkElKwQEcGxyGDvQTwJkggHP6/UT3HiyYdnV+B+P9i2hxjqKitB1fU+3IjK1CdqIduSm1KEhr4I9GFKTR0cBqYfM/1qMwvQGln1uYPN5Y0A2HYQit9km2uJ4ZXGfw4nTbhdvTuwfrlLBAeljDXN7jaPsKK7PHGKadbs0APqqqIf71C8S/5ODDmyJE+uhANiZaXJLigSaNsf4mxAfpkBhehqJPNjRWU4jxMg43T+A6vGLPxxb/goXqGjjeOWctWI3V/Sj/UoecRCtSIrjAYJmvhlV3y8U6KKhFS2KASmqE2o+USHrEhWgQH16Kr6mVqNK2obtpArPja9haptpXPmyXwQaacVON7SVO9k6xtXgAOhcNlmEUpdWxcGe5pBwqqQEx/hbelkY2oxrW3KEQUQWsCVE+GrbzTIuXNLmOvcYm6xDmx7dwffJcgXPPci5cRzfYXj1Fb+sM9IWNiIssgTq0lOUIxYYaERtqQVyIFfEhTiSE1CH2gx2qgErIpdRqpAMDOP7lSI02QZ/Xio66SSxObbP8jnPKVCGpFDup3BqUFoKby2cY6VtFnXUUuoJ2ZCbYoPhQjnDfEkSJ9VwQqp8dCimn3mCLXD/Dk4VucWY9HIZe9LdMY35sBQfr+7hzXQAUHkrnlBYWBHDO7nG+d42DNbJmLKC6vAOf1Aaog0pYI5M60Ax1QDViAqyICbAj1t+JGD8nd24JwPB5KYL6hgFqZp9qYjYqBna/AzgCvHGyFhlqXnuEOA5egUNgrAjx4Tp8SapmGRV11QOYGPgBwLm4wf7WCRant1nwcJW2C1kJlQj3zUW0pBiqAB3igiuREGJDfJAdcR+ciA2ogdrfDpUfwTcCOLQw0yOSxom/jlkcCj/VoKK0Fd1Nk1ie3cb1GR+GKkAw3tpyfXCPs807fBvaQbt9CsbcNuQl1yJTaUdSWBViAkhxQcG1JqgCDFAF0jk2ICbYgMRIEzLiKpGfbmM2wmZnP6uRP2DtOZe4ofwOgrQETUmsRd9f3GNxch+N1lHkpTnZv6EIMCD0vR6Bv+kR9paCc60sVJvunw/ZZXwOjmClYvYpCqd2z8Dh1VHs3srycshCRQqchkcFzh8BnA0e4Ahw+U8AHFLdCABHxgBOhRvA0SApyggNARznOMvAeQA4pGATvh4AziGaHQRwmp4CnPduAEdaAzk7nJBLCeIQwLGxfByZpArqIDMYwFGWw1DCA5xlHuA8y8ChgObvAA7ZSUmB8x3AoQyrCqj8aQGvR2qUBYa8dnQygLOH/bUTXB5TzpMb6KDr9Qq4Pr7HydYtdhcvMdW7iZbqCehzWvE1wYFMpQ2pEdVI+FCJ+A8WxAdWIC7Q/ORIDKnAJ0UlviZWM0hfV0GWt1lsL+3ifN/FjTW3pxVO6/H+LeYmdtFRPwdzSS+y4kkppYPP//oK/5+LEPpGw+6xCpEJcl86CChVIDvWCe2XFjgNvRjumMPmwh7uLugDhGduAky5useN6waXp5dY/baJ/rYJpgwqy6pBbpINn2SVSAwyIzHIhMRgIxKDDQzaUOtbepQZX+OsMOW1oKlyEMPtc2zjYWd1DxfH57i7vH7MU6MXJDwnAZxFAjjT0OY1sc9oyhSi7LuIt3q24UD5dGqC11LeAsyAbxVT60WJSxAlLUBOihVWQw8DOEszHMCh65N9uQEcCq4f71tAo7UPpsIGFHwk8KmBOqAAcR/KkRBsRHJINZJDHEgJqUdqSDOSg1qYjYoBHG9SVjk5eyN/vbI5jNv1+o/MhzyT/D83yfecJ8958owBzxjwjIF/vzHw3w/gEJwRgoyfABsu/+BxkNIkn8u34NQ2ApQiMCUcvwdweHjD6od5AEPhfL97CMDG/fEpvPlHJixs8sPXc1J7FqvqfAHgxIWWoSDdBru+Hf0t1ICy+gOAcwfXiYvlmyzNbmG8f4kFJNZWDMKu74PTNIS6ihE0VI1zRyX3WF85jvrKMXY0Vo+j1TGNjro59DYtYKRrFTNDm1ie2sX24hFOtly4pkpU2qClxRxNtt0mpPR7VPm9OLWD3pY5WPX9yEuvR0yIAaKfcyD5JR/BFC7sa4SCGlHElVxGjagKarJ4BWgRF1zCpPW1pm5M9C1gZ/mAhQfTjrrwXCQaoQUlgZbVuR0Md31DU/UQKku7UJrZiM9qGz7Jq9nOPC1QUqMqkRZdhU9yK1vU5iQ4UJhew6AP2VQ66scwMbiE1W87rNnq/NCFu8tb3F/TCpmata5xf30J19E5291fmd7DQMs8nPpBFKc3IjehHlnqOqRH1yIlrBYJH2qY5YfsMpRxwu2a6qBiu88UHEph0HY0Vg1hbmQTl0f3XBD0LZ1M7oTSc1+d3uBgi4dUtgGUfa1ByZc6lH1pRvmXDpRnd6M0sxdF6X3IT+lDproFSeEOKP15gCOlZhQKTjZB86UZzbZRTA+tYX1hF6f7J3woLj/Zp9N7RbXvl1ia3cdw7wqaHOMwlXQiJ9WBNHU10pR2pMlr8FFWh4+yGnyUO/BRbmOBotkJTuSm1qI8pxlOUz/L25kdXsX6/A6Ot45wS9kTV7Qbfc/GDQM45/e4OLzB8fYFvo1torNuEhUl7SAAlJtSj8+x9UiT1SEp3Ik4glNSCvS0QiV28AoaUuDUsZwbIaQ9VtqEOCkBHLovuGfg0J8leMOBG+GRAzgEceh4BDhRvsVICNMhO7Ea5V/rUWPpx3j/MvZeUODcXl7jePcUG4t7mB5eQXvtGCylbchNsaIgrRYlGS0oz+pGaUYPClK6kBPXgQx5K1LC69nrYgBHRACHQJ8RCj8TkiIs+BxnRVFmDeqrBjA5uMysfaRuowWaAE7pWiQlEylxtudPMd23jk7nFGoMQ7AU9aD8czvyU5qQm9yAnMRa0PuUk1SDvNQ6FKQ3oDynBZayTtRa+tBRP4KJgXlsLGzDdXiGWxd/rfPXG9mQLkjxs3mD4c5lWEq7kCI3QeZXhghfDULf6Zn6JsKrEjJfarTh751P8su4MOMHeEOqHLcFIdvh53f0ObsVD2/cAQ6fgfNgoZJV8wocAgKchYp+Vgab6B5FYIUgxz5nAxrteWahEhuYbYoDOHZegUMAx8iDSTeAQy1UE5tMMcIUOH8C4MSGmRFBCpz35bwCh+AnARwe3lAWkYSztTGQJKmEOsiE+AgdkpXlMJbUoadtGBvLG3CdUI04f83SKp0Chp8AnAEG5GT+pZAJAEdkYyo9aqFSkGXOz8zuESp/PVIEgFM7ze7ze2vHuDx2cTdYAe7yAOf25B6u3VscrV1hdfIQ411r6HTMoNYwgqqiPuiyOlGU2oKilBYUpjSjMLmJPRZ9bEZJWgs0n9tgKexCjaEfrfYhjHbNYWVmAyc7x7g+5W2yglKFQxDsdrizfonh7lXYDIMoSG9EarQVUb46SP+zCEF/K2d5Z3JfC/d5Iq5iapX4QLrX17D7s7moDV3141ic2sDl0QV/DyKvrwCTSUl6jRvXBfY39jA/vsw2S5qrB+HQ9sCU246y9GaUpjWi5GMDilPrUJbeAN3nZphy2mAr7UG7bQyjHfNYmtjE1uIujrYOcXFyitsLer5H5aH7Z9jG4jEHcHKbkRlbjaRwC1PehrP8tgpmyaVQYbWUNgTocLKCA1JYUl5WtLQQX5KsqNR2obd1FvOTO9jfuOQ2O+j88aCc7hHU5kh23aH2abTYBuDQdcCYW4+SdAfKM5qg/dwJQ1Y/Sj/2ITe2GxlRHUgJbn0AOHJvO5Q+VJvOKUuF+Qupb9xVOY9ztn+/CbbntXneU88Y8IwBzxjwjIH/F2PgvxHAoQHAq3CePD4fGBy4ecy7ESCNAG1+9Cj8OUGFI6hveO82a7HirBhcLbg7pHnp+0fo8zBx4Rcdf/WNpH+HQRsKoHUDOGq+hYpUFNGSUnAAx8oADssKYADnhJNlC5Ncery/xbXrCqeH59jfPMbG4j5TW0wPrmOibw1Tg5uYGd7G3MgOqG52dngHMw/HNvf/RndAAZykntlYOMPuigtHm5dMJXF1dMuCLGlRxFw3bmsINlG8ArMt7KycYrDjGyo1HficWMl2kUm94ftfuZD+Qtk3WshE1ArF1aMqRdVcFavYBJVUA1VAMb4kVMCq7cBwxyzWv+2y0GayGbEveuAn3tfnBDhOsTq7h8mBVXQ3zKDGOAg9Tbg/N6EovR55KU7kJTtQkFrDFDOlGY0wF7TDoe9Di20U/a2zmB5aweo3shYd42jnFGeH57g8c+H28gL3NxS+c8Uer88uWCvW7sop5kd3MNi6hOaqKVjLhqH70ov85HZkKpqQEtaAGP9ayHwckPlWQyaijBMDqBUqIdSI1Ggj8j86UG8ZwuzwJlwH91wOzjX/wnCL+5tbtit8dnDBwNJo7zc0OwfR7BhFq3Ma7TULaHMsodGyiBrDAqpL5lCa0Y8MVT2rCJZLKfdIC4VUg6RwE0o+N6KuYhAjXQtYmtnC0S5l4DytfSeocnFyh4PtC6wvHmNicA1tdZOo0nZDk9uC0qwWFKa3IC+lGfkfm5iCq/ATBwHMxV2w6/vRbBvDYNs85kY3sEUV2BsnONs7w/WpC3cuWqQRGOOl/a57FlBN1gmqo58Z3kBf8zwaqyZQXT4EzZce5Ca14ZOiHgkhdshF1PhC0M/GYAx3LQrXNqkLKXST4E0zD3CofU9oofpzAIcFhor1zGIXH6ZHVkI1Sr/UwWnuw2jvMvbW3SxUbEje4fbyCmeHZ9jbOMTq3DYmBxbR3TiJuoo+NFSOorl6Gm22BTRZ5mErm4IhZwRFqX34rGpFUihl0lSwXCayWNFBQCc2qAJJkWZkxFiYkq63ZQZr3/ZxvH3JKbZ45w5TxhDEoUX87g12Fk+xOLaL8e419DTMo6lqEnbdEKrL+lBR3A1jQTsqSrpQre2D0ziIJtsYeptnMdG/jKWpTeys7ON075R7r+g5aEgS3LsBA437a1dYnj5GK1OY1EPmV4TAN7kIelvCAE74ewtT31C+jzvAoUUet9AT7FTUJPZ4v//uvsqrb+IF9c0LAIcypxKCuRaqnMRq2HXdGOtewOHmGTsf7F7F//zs/BxwAGesZx1VFGKstCCCrJSi5wCnCtGkiJIamCVQEcABHM5CRXktfw3gBP8OwJExgGNFtIgyrCxQ8QAnRVHGAE5v2wg2VzZwcXKOezeAQyCPAZzDRwsVKaoeAI7Egmge4MgoDFfKNdRRhbSSBzh0v+ysncLS1C721o6YcoTRL3ajJ/p1z67Zu7M7XB3e4Hz7GgcrLmzNnWJ57BDTPdsYaFxGS+UMnJpRODQj3FE+DKd2BLX6MdSbxtFSPY3+xgVM9q5iaWIb24sHONk+xc3pJXdfoNuR8LlC8IHA4TWwOk820lmUfmlGqqIKMUFmRHjrEfiLBqGUt/SeMmoI7JJShRqiHEgIdOKTrAZfE2pR+rmO2ahmhpZwskPqrGvcX5GajQb2PXB7w/K57q9dcB0dY399D6uzm5gdXMFw2zw6HVNoNI2i3jCMWt0gnJo+1BsG0VI5hi77FAYb5zHbt4b1mT0crp/iZPsE5wcnuDw9xo2LGiPJx8R/WLt9fm0snqC7cYZZ8zJjrUgKr2StieFvTYjysrDXRK+FWqFi/Hj7qL+TVcHLJFpES4uRRTk/ZR3obJzCzCi1o5GSiT+JbufQdXDJNkMWx9cw2TOPweYpdDpG0GwZRFvVJDqqF9Bt20CdZhGGzAnkqHqRGtLGsnDoupV5cUHUHoDzeM/6q3M/z9/znEPPGPCMAc8Y8IwB9zHw3wzg/OjNe4Q2vw9umhEjoeP3II47wBHgDS/pF6T9LKOB8i/cD/c/873qRlhsuJ/8v/f73wM4VM/MAZySB4BDYY+9zaOYH1/BweZzgMOtsO5vbnBzccOCEy+Or3F+QNXXdNzibO+OtbAQLDinY5+r1aVqXeFwHd6zSmu2wy8obISJJz//fPGBt3K4Du6wOLXLFAPZSRaE+mTjw9scSH/Nhe9/5sP/11KEvTNCLqbGK2qosbPFuMLHCgVJ30UayCWFyFCbUFHcgt7GCSxObGBv7QRXx24/CM1P6SVfU3PSLc72r7G3Ri1AOxhoW0BD1SgDNJWlnTAWNLNsFVNBCyzF7bCWdaHFOorB1nnMDW9idWYfW8tHONw6w8m+C6f7Zzg9OMHp4REuz6hqm4J+SXJ0hbvLK1yfXuNs7xp7Ky6szZzg29Ah+hvXUGechv7LAHIT2pEW3YTYgDpEedkR7UMAh8ulUQeYEB9iQnKUGbkpDtSahzA9sIGz3Tu+Kp3kAvxBi4qrG1yfXeFw+wTrCzuYG1vBt7EtLE4cYHnqHAtjLkz1nmKo9Qidjh1UFk8iN6kF6g9GyHiAQxAnIdSMwrR62HS9TBkzN7YGssgQJHryxZ9TClCmEGqyb82MbqC7eQYN1hHYjQOwlPbCkN8JY2EnzMWdsJR2osbcz+qVBzsWMD20juWZPeyunuJs7wrn+5cgRZPriNptaHFxjfvLO25BeHkHyo+hoE/KHdpdc2H92ymmB/fQXb8Mh34SmuxefIlvRlKYDZHeetbEIvelFhgnU26QMk+4T8RKOOVNHAM4dG94DnCo0trx5OCqgR8VOARwFEyRUQYCOJ/jqlD0uRY2Yw9GupfYzygARLbYvL/B3dUlLk/Pmappf+MQGwu7bLd7enAFs0Nb+DZ8gMXRM0z1HqOnbhN1hgWYcseQn9yF1Mg6BviifSyQ+VLOhQ1ysRUKyhPx1yEmhAKvqZFqBDPD6yx75mz3hqlw2PPTZcEf95f3IJXEBWW9rF6wWujJ/g0MtC6iu2EW7TUTaLINo712Aj3NsxjqXMT00AZW5w5wuHGOq2OCtKRGoEW72/VGl9s1cEq5VjMnGOlaR5WmD+nqSvj99hne/zsDfr8WIOQtNRJR5tNjcxirIWZNe2R5e8zQ+N37JcEboW6cwA0Pbx5DjJ2Q+1qZQuER4Fjh0HVjvGeBhbwTsKGfWQBQ9OuLg3scbtyAARxNHz4q3AEOWVNqIBPbES1+BDiUEcQBHAOfgfNnAc70g4UqNtSMCFE5GMDx5gKTHxU41ARm5+1T1awBSyapYOHzpMBJUT4CnK2VzZcBzvk96N5NgJ5CjBnACShlFegEgx4AjrgS9G9ToL3Cj0LOdUiOrIA+tw0dBHAmd7C3dogLysci2QZTF5HXkVMj3rlucHt8jcv9K1zu3eJq/x7X9PmxcYeNaRdmencx0LjCYM5A0zIGmpYw1LKC0fY1THRtYm5wFxuzJzjeuGbjlFNJEUAh0sDDG+HeTpa9S+D6/B5z40dwVowiI64aMv9yRIq0CH+nR+grEyLeWRDtbYVS5IBKTJ/h1FzWgIQPDfgYUYsMpZ1Za+26Lox0zmJ3dR+XRy7cEkym+989PTcBFqKgBMdcuD27wNXRJU63XNj+dojF4W1Mdq5hrH0ZIy0LGGycw1jbImZ61rE4vIuN6SMcLJ/BtXOFu9M7BqSuTk9xeXqA6/Nj3F/x9lHhNfLX6yYDOLOs3Swz1oak8CoGb8PfmhHlVQUFqdhIGehXy8Lb4wMbERdAtt0qlnEWLS3B53grjMXtaKkZZ8B9a+mUfYaz+zo9Hw9xbs9vcb7nYvl12/N7WJvaxuLwxv9h7z2/2ti2rO/+F58Pzxj99r197nEgo0gOQpFkY4Ntcs4gCUlEkTMOBJtsjAkm5xyNCfMdc5dkMMfn3Nvdt/u53S2PsUch2UaqqlW7av32XHNhdmgV8x92sTh8huXRK4y0H6KpZAEFicO3AEfWDJ0vu4k1ehQ4/86S+T+c7zy/03Uv/72cwPO+J348MeCJgf/ZMfATgMPVTrcahdt/FA+c3zsRt8nY75dKucHN/e19kHN/f/mZkurnR1jjBjdNwsT0+9/dk/27oQ23f48L6T7AMVGRw5IuVRskgGOTFDiRZSh41QCntQ+DPeOitfbBJv1Lfki9pcSfD9qXTCgvhXni5emVMBr+xofKkxuxMsfVOT4Uc1xwRdU9TplMXwtbFK62iwfq+x9x9zWffdlU6PQGhxsnopTp4/sFdDcOozynFU9jyhHinY7gx7kIfliEoAflCOdqqR9LpygLp0cMJdlNMAY2io4behnVOYVI0VpQntmMVkc/hl9/EsqAw80z8Zz9vWyL34V55ldpP072LrG5eIT5yS2M9S/iQ98cBrqm8bZtEm9aJzHQPoX3XZ8x2juH6fcrWPy4ja2FI+FpszyzgbmpRcxMzmF+eh5Lc1+wuriA/e0NfD3dB3AmlVERqhA6HF/jbPcKh+tX2Fm4xPTQHvoa5mHP/4C8JAngxKmbEe1D6MA20cdDu0QAACAASURBVDXQK9j1w464kCokhFuFZL62rB8feuexuXCMoy3CDbe8iQeXK8RXon35+eEZDrYOsbW0LVqn761+xcH6NXaXrrE8dYnP78/woXsfzopPyHvWLRKzGFmp6GrEldqEMBuynjTCnNuFFscAht9MY2VuAxcnVJO4kif3uXXlM5fn1zjcORPtdWcn10T5EBP+ga45vG2bwbuOGWFiO9jzGWP9X/B5dFV0uFqd2xEqlMXpVcxPLWFhegmLM0tYml3G5sqmgGNXNBJ1JRY05r36eomL4yuc7l/jaOsaq7PnGHu7gY7az7DmDSInqRNJUXWIZhcfX5qhshMMAY4Ee91zhQRwqL7pFiqcHwGOu3yK5Ve3QwI4bP3O659Q8RbgxIeZkWK0C5+JquIe9HdRBbMvlbsJOMAMSZIJEOJcnJzhdP8Yh1tcxd/D5pcdbC8eYXfpAvvL11id/oaJN3voqVtEdeEECpMJcFqg8a+Cxk/yjdGzW5S8GQZ2nlFVwRRSiZxnjQKU9bVOCqXMyswujpksskzIbdTLS5ZJ7/kNrmgOvftNtIRendvHl4+Ef2v4NLyMyaEvosxrdmINC5+2sP5lTyhWzg8upNIsqsCuXBDxWvK/Ifw43bnG4kd2IJqH0/IBVF4lRlqhfpAF2Z+zEPywFFG+duho0CxnqUcL2Pr9LsD53sXmjunpD/Opy+RYKp26431zB+AkqKhcbBLJLTt4PQ2vwytDPXKS2KmvH5MDX3C4ceral9t5jMfl6z4BzhUmBtZQR4BDBY6cZuZU4LgBTgO0ilpoFXbRXp0mxsbgCjzTVqEy7ycmxj8roZqiBw4BThdexjYgPsKGKFk5Qn3KECkATu1342K9aOV+F+DUQCsAjhWJ0ZUC4NhKWzHYO4qN5XUBCn+jwPkOcDbQaHOVUP0AcOpFCZVbgaMnHFRVwaC24FlM9S3AmSLA2cP5wRElXS5w/RW4onLuK77uH2J/dRvrMyvYmt/CwcoRzja/4XTjGvtLF9iYOcXy5BGWJg+xNHmAxYl9LH3cx/LUAVY+HWBj7ggHq+c437t0KaRucHPpmgju3HA4x389usHexjWW587wtnMB5oI3SIyuQqR/MSL9KsRCQJR3DWJ866GjP4vww2pHHEsoVd1IDOrC07BmPIuqRYq2Cpa8DvQ1D2N+cgG7q9s4PziWlDHic3nDo6E776uEOOe4PvuKbwfnOF4/wfb8AVandrH6cQcrkxzbWP+0h+3ZI+wvnuFw+Qz7y0fYW97FzvImdlbXsbu+ir3NJZzsb+LyjECMc8WdP9fA+pdjMY9W5vYgLc4p5jiWFkd5VyPGrw76QMJmLibRG48d2DrENWUU/mNWUbr4Kq4W5TmdaHIMYqhvFkufdwXME88HfEZwQSPGzPXJJS72v+J06xRHq8fYWzjAzuwB9ua+Yn/+GodfrvH57QU6zKsoejoiARx1h4A2HoDz93nu+3s8O3p+h+dceGLAEwOeGPifFQM/Bzj/cG3Ef++g3ypv3AnZz9U198GN+/XvARz37+Xn3vHcofeOwm2cfLdtuev976Vdv/d9/2Pv/wzgmO4DHGUpEiLLUfjKicaqN8JckXX8R9s0mbzzMHj3R77vWuETEnQmde4hcQHptauMhYuPd//+d3/v3c/gc+H5Jc73z7C/doTZ8WW86xxDvaUHxen1SDFWig4Zql/TEPyQXXjKEekjrSpSYSB1p6Hvj1RWwZV5o4zqAwu0gcWiU1Pus1phtthZNyB1EFnY/14WISCO+/u4gAPLCE73r7C3fi4ejFdnD7D8mUqVHSxN7WD18z42546ws3CKg1U+xF7hYp9daY4xO7mEwdcj6G1/g/6+AYwMDmNqbByri19wfLDtWo12ETMpXxcJIk14rw9vsPb5AkOdq6gtGUX+8z680HXApG5ChDehQ60L4NB/QvKeMAaZkax1wJLXi9fNUwI6bS8f4vyA5/XeieX+XVwJ2T/NRWl4LD737AaXhzfYX7nGyqcrfBw4RattBvnJXdApy11dnMqgk5kRF2zDS0OtKNuy5Lejt3lYtKam2kh4/dwFdq74ubkk5LvCyT4VQGfYXjnBxpdjrM4eYenTIVY+H2J19hDr84fYXj7BwcZXofhibK7MbeLj8Az6e95joG8Y79+M4EP/mABkbGFO1c33PyJ/k9QSBIJMtKn0mB07xOvmL3AUfUD+8y6R3ET6lCHKuxJa/2rRHvuvA5zOOyVUdwGOE7GEiHKn8L0hxCHAif8OcNgRqhym4Eo81VjxMtaOksxWUb42O7GJ071rAUN/iMOba7Gif/P1mzhXl8cXYhX/kufrWDpnJxvX+DJ2hv7WNdSXfUTRi7d4rmlGpI8FUbw+/BtgCGyTWowrGgT0Y5L90liHotRWVJf1oadpDBODC1if28fpzoUwFBdKEx5QKqgYnwSrojyNqrtvONz6iv31U+ysHmNr6QA7q0fYWz/B4dY5Tve+gYBXCC7cCZ84OTwnV7g6vsTp9gXWZw/xoWce9ZUDyE5qFCoyrbIM6ge5UP2lAOFelaJVuoFQlnD2NwCnRSSCt6VUP7YiFg8jNDS+o775bl58F+CoXQCH7eUJcCLqhcoi91kjmqyD+Di4iKPNcwls3YlrxhWvd4LPyYF11FUM4YWxBhp5KWLkVsQI1U0TRBcoRS10Sgf0LDOi6XOoGc/1dpjzeoTSjMbj28v7kleMUMy5opm2Kqc3+DJ1iG7n9HeAExdhR1RgOUK9yxDpx+5fdwCOwulS4NQLiBQjqxblW6YQK55Em5FsLIettB2DvePYWN7E1+NzqbrJNU1w7qY/GJWVVOA0CQVOLXRBZUKBw25aWnk9aMysU7KEqhoGFdVdboDjmodaPmJBAJxdUUZEkCHA9fU5bq6+4ubsFIcbm1iensNk/whmRz5jfWYdh6snON+6wvn2Nb5u34hxvnWNs61rnG5K42TzGiebVzjdvsK3g2sJPH5XeHJH7swHDONvwMHmFean9jHYt4Q6yzCynrdAqypD0KMC6X5ClYpvA3T+TTAEMLbY7prPAN1IUPYiQdWFeHUjqHqMDS5FQUoDmqyvMfJmEkvTSzjc3BO+N9KZY+CTyLqhFY2Hr3BzfiUpjrYvcLL2FWcb33C+eYWL7Rtc7t7gav8GV4c3uNxjJ7hjrM6sYmb0E+Y/fsLizDRWvkxjd3MZFydHrgC5sxEA5wTvOmZhzu1FWlwjkqLqBbiVAA67rBHgcGHH1XhB0S7KrdmVjyoqvaoSKcYaFKS2wl7WBwJezk/H25yLbtVn4lM5z/I5wDU3XB/x+1/jaucaV9yXHWmsjF6hr3oLJc/GvnvgELx6AM5/7HnPk2x5jp8nBjwx4IkBTwz8Xgz8FODEEuDI3Sqcf2QFjhu0uMueuHV/7/tw5q+9vq++4e9m4Nz13XGDmt/buv/tf07A/RHAMan4UM8V4HIkRlaiKLUZTVX9+NA3jcXpbZzs/vjAe+ex8L/mx2t2JLnC3soRFqc28a59ArbidryILYM+OAsRAelQPUhF4J9SEfSgQCSoepFwNcGkkJI70XFL+Ai1iYdUeuGwrXiMfwnigsvx0kCfmFo4ijvxrn1SGGxeHN5IEIfP2vf/SFVkInG7OpFUCFQisKREJNButYILZomc4VoqC5n6sIiW2j5YSurRYG9DR1Mv3vUMYnZqBvtbO78pJ7n/0ewANPmOCpiPKEh+g5e6TphUTQh/XIso31qR2OoVTNokLxytvAIJ4VUoTu1Eq2MME/2rWJnZF6VZAr7d/4Dfe83qA0KcZYKBK3TVfkFBSo/wb4nwKUS0f5nobGRS2/A0yo6XJgeyn9XCWfUWY/1z2F07xgWTdwp/7nGjHyCgG1qJKgMXQOLxpJmuq0OR+/+zjGdtfh9DfZOos7bBae9EU003Wuv7MPR6AuyS9u3k/of9uIOED+szV3jftY76snEUpvTiWVS9aBcc8bhCtMU2yRr/fgocpRvgsMSqFga5DTpZJQzqSsSFVYpEOvt5A5ps7zExuIq99W84P3SV6Pz41f/wFfdrc+4KI727aDJ/RsmrfjyLbkbYY7bepgqHZUFtMCnaYZDT1NYBncKCxAieuxrkPG+AvbgXfc0f8XlkAztLpzjbubptG+z+dB5eF8TlVkAdJsw8V19dqh3+7E6if+d08PyyS9jWwgk+Da2Lkr+c505Ey/MR4ZePUK98qB/kI+QR1TdV0MkaYKJChsobMdruKHB+BDiSH84txBF+OG6Ao5LUN38McJwwyuuRFOFEmrEJec9b0FI1jKnBNRxvUrkmJazfD8lXCIBzKADOFuorP+CFsfYOwGGpEcunCDtqBMAxqG2ilCk2zIpkQw0s+X3oa/mEuQkq4dhu+95EREXg6Q0Wpo7R7ZxBUVoPXsY2Ii7cjsiAcoR4lSJCAByWMtG4WDIv1isJ61wAhz5Icrv43CfRViQbzbCXdmGo9xM2l/ZcpZau8+uCHQS6LA/7PLKDJtsI0hLqoFeXQ6eyCj8dLTvhKWicXAODygHuF8Eg4/tZjNShjgbii3cAzs0lAQ4Vel+FP8z1ySm2FlbwcXAcPc5ufOj+gLnRBewuHuHrjjTHfgeJPOiuGPwx9lwxdx8uuE+Sa8tzt/7lDO9fE958QH5qOxI1NgR75UH25xwEPygTnc4Ib/QBLTAGtsEkb0esKKHsRoKqR3hgGWR1iPGvQKRvLl4ZqcJpQ3fjEKbez2J7afe31839ydd9Tzm/kaATr5mf3Hr5fY9WLzA3toSBziEMvx3G5HsC60msLy5J7ebv7+OVtI/vOuZhzn2NtLgmJEU6YVDUQSiL/BqgZ9cnRStMCu5fK4yyVtAY3yAnwHGIMrjn2lpkPWtGWXYXWmvH8PH9hoCU9+flex8vvXTPDbwnuuaHvZkrDDgPUPp83AVw2j0KnL+T6vr3Htw97//nPGN7jqvnuHpiwBMD/11i4H8IwCFs+RnE+Wswx/337u1dWHU3iN1ghtv/N/CGAUWA44Y4NDGm+uZWgcM2zeyCUiF8OApetqDB3I+Brk/CA2Vv9bZjh/sBmR2hxUObe8sHMvdwP6C5X/PfMMFhAi7Zu/z4f/meyxiVq7s0EKYHDaXvJ9tfsbd6LMDNeP88XrdMoLq0B1nPHDCE5CLUNxVBj9Kg/Es6FP+ahdBHJdD42WDkyrwwa3Z13BImzkzwpHNtkksmtdrASpiYWERbkRZrR3FqI1psgxh5/QXrc0fYWz3H6e6lZPrL78l9YkJAyODeT/f+ufbjh32k3cGppJyhV8ji1B56W8ZQltuA9GflKMpyoKqsEY3VnRh6M4bFmWWc7p+Kh33peN05Zvy8K2Bv6RojvduoKR5DztMeJMe0w6hsdAEcllDVwcBVUyaGchtiAi2IDbYh+2kr7EUD6HF+wvi7FazMHOBk50rsm/iO9PG4n3DfSX64X2fb1wIKsIyqzf4Zuc86EB1QjDDvQkT5EeBUwah0IJ4tcKOrkGKogjmvEz1N4/g8tob1hUMcbH7FVwKJu/HgOq4iYXEpM0SsuY+xO5a4dcUKE0mhcBhaQpPjDfJSrSjKdKAstw7mwka01b0TLe53V2loLME49/ZuwnF1cIOljxd417oCR+EI8p9142lEHcIflyLicSW0fo7vCpy4793pOvDTEir+PTtViZJIqdOUu2zKvZXUN02IFy3K2enKAZ3MAoPSDFMwfWjMSI2vha3oDV63TGNmbBtr84dC2XLJc+S+5tzxxtfuuLyjaqFai2qpwY4t1JVOoSD5LZ5GNiL0sbu88A7AYXIvynpsiA2x4UmUHcl6BwpftaK+YgBv2z4L/6Tl6T3sr5zh28EdUOkGa/wOTDj5HXh9/LVBuxNXCdbXPfo8HeHLxKa49jpqx1Ge2YmkGCvUDzMR9CgXIY8LEUoo4WNBTAANWBtFCSjLQOPcg6rC70o7l+KOJVT3y6jc8MZlXizgzV0DY5cKJ4EKHCUVe06YFA1IimhEqpHtnlvRaB7G2JsV7CxdiK5cVBm4r6OLvRscrl1jc/4SH3pX4SgexHNdNWiwHh1oRozMIXnFuJUqBB1svR5iQ1yYTZjnMtHudk5hengTa3MHONo6l+YFXhMuZQPLtGZGD9FeO438l11IMToRGyYBnFsFTg30bIVO9Y2Cc70EcHTyWlCBw5by0jmvQrLBiqrCbtFRcHlmF4db30QnMO4XrztCas5jhxvX+Di4hQbzMF7F1UOvrhBdtAijJFWRC+CoWcpJ9Q29fcrxLMYKS16XKDNd/LQpSqjO9o9wc8lOVK7yqW/fcHV8htWZJXzofQ+npRGdtT0Y6hrD5/eLWPt8gKO1a/B4/0b54QY5PEZ3hzsW3fPJ+Y1QF9KbbXvpK8beraDJPoL8V21INtZAH1SBoEf5UP5SgNBHlYj2pWKzRUANk4xG5bzv0wPrLsCph8avEhE++XimsaDwpRN1FT142zaGmbFl7K+5za7Z1Y20hkTjj+HTd8bDf8p57/gGZ1TWje+iv30CdZWtaK/vxbvOAYwNjuHL9BfhN8Z5jn/EMeA1dsJ7zxH6mmZQltmLV8YmPI1oAMsCo71rofWjGo9lxgS6VOUR3kgAR8924iz1VDvwJKoOr2IbkfO8VbRYf9M2j/mJI+yvXUveapwX7sH2H+6ThFOci6kmpe/Zxyu8qd1BcdIokkK6hJ+QUODw+wR4PHD+uyQDnu9595nf87MnHjwx4ImBf+wY+AnAYYtdN9Dg9i7UcKtS/lF2yq3AcW/vQpy7P9/dn7s/3/03f7SfdwHO3Z8Jc+6+5s//ecfmLsARq2wugGOiB46SD/XV0CnNSIiwIi+5WbTIpgnp+MAylj8fCq8QGlfSgPho5xIH2xc43P6GI46dSxzvXAoYcLrLMgoOGhlz8O++4Xj7AkfbX3G0fSF+lt5jucWFSOj3111lMwssldkTHa2mR5ZBaDPYNYWOuiHUlPWgJKMJ6Yl8kCyDRp6LMJ8shHrlIuQRH7SLEelthtafvjcSwBGwSs6kjsobnrMOxCk7hK+FUVEHA5MXtRWJYVY811iREV8NS04n2hwf8L5nDp/er2Bpegc7y0fi+5+znIUlKm4gQDhzf/DhVEjGb3Cxc4O9pW9YnjrEp6EN9DZNwlzQgWRTOQzh2XhuKkZWilVAh+aa13j/5iMWZ9axvXKIvbUzHG4SYl3jdPsa5zs3YvV5ceIcrxsXUZk1gLTYdiRF0sPEiUjvOqGqoDJBOqf10MtroJXZYFI78MLQiIIXXbAV9ovOSx96FzA/uYO1uSNsLbJl+YX4vOOtK5xsX+Fs91oka1/3pLbRLE3YmL3C3MgJhrs3UVc2ioyEZkT6FSLMiwCnAtpAOwzKGpiC7IgLtSIh0oyc5EbYS16jq3Ecw2/mMTO2KcrODjYZG1fCQ4HtqumXxOPKROV23IikVRzjoxsBDo43r7G9+A1Ln44x3r+CZscgCtJqERuVhSR9AV7ElyHjmRWWwjZ0N41iengNmwsn2FnmPp7hcOMcJ1vSvl3Sp2TpGlP9++hwzKAiox+Zce1IDK1BxOMyRHqZofWTSqika+i2CxVj6bYLVZfoSiWVY7oBDlvgug2Lb7cSwGl2ARx2R6uGXl4Fg5JGrxbEhlhEsk/fl5ryAfQ0fQQNm2fG17G+sC9Kk1iSdLB5jqOtrzjZ/obTnUvhkyTOFdvEu8rdPg0do6tuEZbcYWQl9iA+rB6hdxU48jaYlASAjCFCv2qYghyi805ChA2psfUoTutETUk/OmrGMNAxg4+Dy1ia2sbm/KGAOSxVYbkQS+zc5XYEM7fnUIK34po5ldRqBEDC5Hf1HGuze5gfX8NI3wy66kfgKO5DwctmpBhYVlQG1YMcAW/CfViqZ0VMII8XY7wZsep2AXEEwBGlH7zWW0UHqu/lU/fgDc+ju624ZF7s8r+5D3DU7aAHDn3KRCt5pVN08Xqlb0H2kzbUlrzHQPsCFj/Sd+gK+6vXOFqXxvYCDZjPMTt6iL6mOVRk9yEhsgrhvoWI9K+AJpCtmalUoUEslSrVMAY5EBvqEAqaZH0dyrP70F4zibF3a5if2BGG0sfb5zjf57VJtRK7XF1jvH8HjVWTyH7egWe6ephCCXAqECaOVxViRAlVo5gjDGKu53xPn5paaAlwFA7EhtgFtGOb9srcbnQ7JzA9soG1uWPsLH/D0eY1znZuxP7tLl9j88slhvvWUFP6Hi+MddCpKqFT2sT+sMOVXlkPg6pG+HCxLIzGzIagEiTFmAXQfdM6gR8AzjeWc16ITnUspbs8+oqlqUW8a+uHrbAG1SVONFm60Fnbj8GOKcx82MDW/IUooSKAZdyJePsZ3HS9x/i7ogfb4YXwUVub28XnkTUM9czBWTWEwrQ2JGqsMARXIFpWJjqdEXay3FDr3wAj1WrydpjkHXcAThcSVYQ4neCigC6wCtH+JXgSYUFGQo24X7HDIduKTw8vY2N+D3trhzjdPcHl8TmuT7/hml5wvF7ccOPu/YTzIQHM/g2OVq6xMX2F6YF99DmnYC/qRPbzSlgKGtBS04P+nmFMDc9iaWZT3DsYIyc8Z5vXAnRPvd9CW/UkCgn6dE4khrkAjk8dtP5OGOjro6SyqF0qqxQApwXs8MZ4MapqER9WhyRNPVIMDSh42YXa8hG8bpnHxMAWZsf2sDpzjN3lrzhY+wbeL863pXvgt50bfNu+wbetG1xsXON87RpnK9eY7b9Eh3kF+Qnv8TSYQKxNKHA8bcT/854F/zOfMz2/23PePDHgiQFPDPzjx8BvAI6U4PwIOahucXtH/OOdVDe8ubt1wxhpKyVkP4M1P/67233k7/rZyfsR1iQoWnA7WnH/YP78d/zs9/5t7/0AcLhK7QY4ylZXsk8Zv0WU2mQnNaOq8DU6akcx2MVOP1tYmz3DxsJXrH05xcr8EZbnDrEyd4jVeY4jrLnG+vwRpHGI9S/0LDnA+jw75XDsubb74n2uKq/M7IkuQjQ+ZVvniYFFDL+eFSu0bTVDqCnvRXl2s1DcJGnLoFXlIlqWg0h/wptchHkXINynGJE+5Yj2ZUkUWz+zSwiTrhbXaIWJ7Z0FwOkUxpNSyQVX8OsQq65GXFAV4kPMSIqyIDuxHhWZHagrfytafw/3zWJubA1rc/vYXT7F8dalMCi9OLjBt0NpJZfeIyyf4vi2fyM8GQ5XL7E9f4bPHzbQ3zYtPDNKMlrw3FCJ8MB0yB8+Q6QiHXFR+XiZUIHyvCa01Pbj/etpfBpZFl2ulqYPsDZ3ivXZc6zPXmBj9hvG32yh2fpRlC8lxzQhIdQpEtoon3rha8KuPCYl279SIl8PnYx+ODVIDK8TD+4Zic2oyH6NxqpRvG2bxejbFUy938T8xB6WPx9hdeZIqI82vxxj88sptr58xdaXb1idPsfnD/sY6VlDb8MsrLnv8NJQj3CfAoQ+LkSUb6VICCnLp+Rer7RAryrHMx2NeRtRkdsJp3UQb9qmMTm0JtrHb3w5wd7aVwEA6avx9eAGd31cBBA4kY4pIRZVDSufTzA5uCHMjevNg8h7UY/YiDzIHsQj1D8ZMepMxEYWIPNZNewlUgnQRP8KPg+vC4XHyvQu1mePsTV3gZ2FayyOn2OwbRX1pWMoeNaNV7omxKkdiGDy5mWBzq9GmF//AHBcXl+3KhyaGVOVw7mCkOcuxKFxuWRa/l19o2xGgorv0aOJZVQOGBQ2GEW7ZSsIT17F1iMvpUUk1M6qQeE5MTYwj+nRFcxNbojyxpXZPazN7YlrbOPLAbYWzrCzeIW9ZZoAf8Ng1yYaKqdQ+OotXhrbYQquRYgb4PjXw0C4qewQJsBUcTFRY6zQP0mvsiIh3I4UfZ0wpS5Ja0d1yRu01wxjoOMTJvsX8WV8C5vzRwLkHG98xdnON1zsX0kwh6CTySgNzY9uwOuF55ieQ/trLJU6w9z4Fkb65tHrHEVtGcGNE89i2LK4AJH+BQjxyofyV5ZPlSHKv0oAAqPKCZOK8KYNcep2YbhK01XJu+NHgHNfecPzIubWv0GBk6BuQ7wAOC2Sj5aqCUnhzXipa0VmQhts+YPoaZjF1OAOFidPsDR1huVPX7H86QJzo8f4OLiL4b51NNsmUPCiQ5gTh3jlIdy3FNEBVZKRMVttq6huqIYxuBqxodWID69Gsr4BJRl9aKoax2D3EiaH1vHl4w42vtAD6hTbSxfYWrzG6swlBrvXUFM2irTEVjzR1MEQbEdUQCXCfSsR5W8TrcKpViK84ZzwHe7Se0fOjnUEOA48iXTgudaB0owutNhHMfx6GTOju1icOhZzz9aXa6zPXmJl+isWJ0/R374IW8EgknX0uzFDxzJcXvtUczGO1DUwBtlhDLII9Y0hqAhJMRWw5LWDAGdp2q3AocEvy6dcXjDfOK9eYH5iAd0Nr1GSbkHhKwuKU20oTa+Go6gdPc4xfOxfx/rMhVAknm5dC2jpni9E3LkhO7fnjEFCqAscrp9g+fMmRt/OoqP+g1AcZT+vR3xkBYK9s8FzFOZTgkjfSkT726U29byfKAj+3UO6j9D7JlHdjUR1J2KVjTDKq4UPWHyIFc9jbEiNo6qzCbXlfehtHBWfOTu2jLW5LRxuHOFk+xRf9y5weUD/Gwn8CyUX7yWHLt8bdjNbvMb8yCk+dG6gtWoKFVndeBlrgVadhvQnlagqbkF30xA+vJ7G1IclESvssMexMnOClZlTvO9ZRkPlCHKS2vEsuh7xwezgVg+ND719XMbMLoDD+yXLKwl1aBLOBQI2OzAF1Yk5hHGarG9Efko3qgoGRYz3Nc9i5PUKZka3sTC5JxlJi3n2GNtzR9idP8Lu3BG2P59hY+oSaxPsQnWAhsJZZBn7kajms2OrADjsQGVkSZeMEFxSD4utXFLVud/7ez8neX7f3/Y86TlOnuPkiQFPDHhi4L9vDNxnDv8k3VSl0gKxXLaKVQAAIABJREFUOv0PrcBxH/i78Obv8bP79/7+9v6B+73Xf8+Lw/3AIzpP/Qbg8OGehpNViA+zISOhEeVZ3WgwD6KzfhL9nfMYH9jAx6FNTAyuY2xgFaP9yxgdWMbYwDLGB6UxMbiMiYFlTAwuYWJw0TUWMDn4xTXm7/y8gPH+BYy+nceHvlkMdn8W7YY76obRWDUAW1EPitKakJZox5PoUmiVuQh6nArff05C4J9eQvmXLKG6CfcuQ5SvGTEBduhltTBQfcIWrzJ6YHDwgZCDAIcP33zw7hIdNuLVrYgPakScug4mdkpRmF3lVDakx9YgP9kJtgNvc7zHYOcnTA4sYG5sHSuf97C1cIydpRMBdPZWT7G/dob91XMcrF5gd+lMyPyZ3E4NLKOvcRzVJX3IfV6Pp5oKRMpy4P/nZDz6v0+gePACEbJMmMILkZ5kR2V+O1qqB12tl2cw/HoRE/1rmOhfx8Q7jg10139GVf4A0uNakBBeC5OarcMp32+Alh22qD5StYgkl4kb1Qpspc4V1NhgKUFMj29BaUYfass+oLV6Er1NMxjoXMDomxWMvV3BRP+yUFp8HFzBx4FVfBxYx2jfCt61zqGz9iMaKt6j6GUHkqLtCPPKR+jjIkSxYxPPgaJBJIYxMiuiA2nOa8FTjR0v42rFKnetUJV8wsjbZUx9WMf85DbYvWhj4RDbSyfYW6FZ9TkO1s9x6BrbiydYnTnEl4k9jLxeQmfdhCgxokdKfGQJgrxe4C//RwvfPyVA9fgFImXZeKKpRP6LJqEm66wbx5vmKQx1fcb4m3lMvlvGVP8Gpgf3MNy1jjbrJ1RmvEO6sQXPIuphUtgR5VWBaG8rdP61PwIcwkA5Ic3dMiquHksAR4K5vP7vmZjTyPzOkAAOYRvLqGqEEke081baYAqqQmKkHc91DqTG16A4vRWO0tdorxtGX+sE+rs+4cPrWYy9+4Lx/i8YH/iCicF5fBySYNz0h3186N1Eq+MzKnOGkJ7YiSRNI/QqB0K9KhHhbRMtuA1Uq6g6QCUeY4YQh/4obEUfE0hFRZX4LvRQStY5QPPeiqxOATe76kbF8fw0tIIv4xtY/rSJ9dltbC/sYnf5AAdrJzja4Gr8uTinPLeEu0vT+5gf38bU0LpoN95cNQRzbicyn9QiLrwUwY8z4P//vUDgnzOg+CUPql9LEO5tQUxgjTDrjgtuRVxQG+KC2hGn7kCsgDcugCNKJSUFzl1485u59CcA564HTgLVN3cADsuo4lTNeBregpSYVqSb2lCe8Q6N5km8a1vCSN+GGKN9mxjp28RAxwpeN82js24aVQUDSE9oRIyiVJSChfkQ4NAvxq2+IeiogSm4BrGhNYgPr8EzLRUO3agu/YDuhs/o75jHyJtFTH1YxefRDXwe2cbnkX1MDu6ho24WFTn9SDY4ERdRDZ3ahqhAMyL8LNAEsFMXSyobv8MbJuI0phWglaUximoxLySGVyNJUy1gE5U1vY0zeN+9jNHXG5gc2MGnoX18HNjFxLttjL3eQkf1NMoyXiMpmmVTZugUdhE7emUTDCo3wLHBEMTyqVLogwqQFFMKc14b3rSO3wM4rA+6BM3MWQ747fASs6MLaK/hvFmOtPgivDAW4LkuDxmJlagq6EB3/YSYD+dGd7H6+Ui0sj9Y+4rDjTMcb57geOsYx1vcErqfYXf5EGuz2/gyuYaR15/R4uhHWVYTXpgsMIYWIdQnC77//AKyf81CyOMSRPvboJcTcjbBKBScneL+Ie4honyKHahuAU48oSKVKgq7uGbiQi1IiKjEK5Mdha+aYC/qQatjUHi4TQzMYWFqDaszBKC72F06wP7KkTAnplHzEe8pS+fY/XKBrblLzAwd4l3zMupLJ1D0sg8p+hrEKHIh//UJ4qMKUJBaJ7zGupvG8LZ9SqiKRt4sYfTNMsbermL83Ro666ZgyX2HtNgmJIbVIFbNzlP1iPF1Qh/QLMqmhDqVQJcqHAFwWE7VAqOiGUZVE+ifxLiNEb5JNXiubURmYhuK03rhKB5Cq2MCb9vmMNT1BSO9C5h4u4Sp/iV86l/E9MACPg8s4NPbFUz2bmC8axddtkVY08eRGtMn1DdUzbGESpRPsaTrvnruXjnkb65rN6D1bH9nIfH3n0s9x9JzbDwx4IkBTwz874iB+9zhn+LEQzETGXcyczeh+T1lyv+Og3X3orh/4P7o9d3/9x/5+a8DHCYTkv8FV9xzaFSY2QlrQR+qywbgtH5AY9UwGqzvUW8dQp1lQIx6Sz/qrRzv0CDGWzRY34jhtL4W7cid1l44rT13Bl/3ocHch7qKXlEaZS/ugjmvHaUZLSh42YjMp3VIMdqQGFUJfVAJomWFCPXOg/Iv2Qh6yM4gpcKsWCNWSGvESqJR3ijaPUulE1KSzUSbHUMk2TsfwLsQp+xGnKoT8UzSglql7iEiibbDpKzCkzA7kjUOpJlqUJDcCHN2O+rKetFi60dX/Xu8aRnFQNckBrsnMdQzgfd9rtE7ifc9HzHQMYG+xg9or34HZ2UPKnNakPOMSZkZxuASRAQUQPkgB7JfshHsnY9oeRGMoWV4brCB5rVlOe2oKupBddkb1FXy2A/BaRmC0zyEBvMQzDm9yH7agqdR7PBiE2Uv9L2J8XdCxzbp9ABRMyGnuqoFenkT9HLJ+8KkrkdsSB2Stc3ITOxE4avXKM96B2t+Pxwlg6JbDjv/0APJaX2LRus70S650TKA+oq3sBf2oDKrA0Uvm/DK6EBscLkAOGFeJYj2q4Iwj1Y2iUROK69GjKxKlFFQVZAUU4O0hEYUpXbCVvgWTut7tFZ/QJdzBK9bR/Gug2qDSXzom8Lwa45JDL+ewIfecbxrH0Vv4zDaa96jtuwNyjI7kPXUiSStDTp1MYK9M+D3L8+geJCKUN8caOTFSIi0IjWuHkWv2mDN7YWjsBd1pX1orHyNJnM/ms3v0WIeRV3xe5Sn9iIztgVJETWID3LAILNB42OB1tcOfUCdBHDobSMGfW4kVV789zIq95znVhxyXvtbAE6jS4XDxK9WgBwm1EYVfYRuVREZiQ0oeNWCyrxOsMW4o6wXtZWvUW95jQZLHxqsvWiwdkvXXdWguFarS4eEiiMtsQ1PNCytYdJnRzi7ULGNeCC9LeiF1Q6TkqWUrSJRY2cnxovwvlDWwaSuQVxINZ5EVgvFVfbTJhS/aoU1rwv15W/Q5hhAT8MQXjcN4l3bIAY7h/C+exgfescw8uYjPvTxWplAf+e4OM9dzmG0VdPbZADW/B4UvmxBWlwtnkRaoFWUIORxHhS/5ED9ayGCHzG+rIjyo+lvA4wu5Y0onRLlU/cBjlv9dGfV/mfJ3O8AHEKc20EFDv11WEbFNuUteBLaLEoWqXzLedqF8sx3qC0dgdM8hobKUdSVj6Cm9ANshQMw575FeWYvsp4240k0O0MVC0VRhF+ZuC4MKrZur4VRKFVcACdEgjj0GklLaEVRao8oeawpH0SDdRAtjiG01nxAS/UImu3jaLCMoSz7DdISW2AKs0OntiJGQXBqFSofetwQQhDMudU3RqUEcIzKWgHujUpX2VyIQyiuaHDLhLy65L3YL+5bo8U1zKNwVo6goWIYFZmvkRHfLPxzNDIztHKq7hpgUDXDoG6AkaqioCpRPqUPKoY+KA9PY4phzmvBm7ax7wDnfP9EarF9TW8YybeFSsa5sWW017xFXrIFL01FSNLkIjYsA0+i85H7vBq2wh60VY+hxzmBd+0f8b7nE0beTGH0LUt/xzE+OIax/nGMvpvAyBvG3zB6mgbQVv0a1aXtKEqtwwuTGabQQmhk+QjxyhXwRv2gEOHeXBSoEebeBgIMUTrV9TsAhxCnE/GqFsRSHcYSUjXnRhviQqxI0tiRFl+HghdNqMxuR01pD5pt79DTyHvJMPo7CELH8aFnHMO94xhxjfddkxho+4Q3zZ/RWjWJqrwh5D9j18EWxIUS0hVC/TgVprBipCVWo9x137AV98Je0gdH6RvUlL9DXUW/WIypzO5B1pNmcR0b2RlMXgNdoKuzViBVZmwd3inujWyPzgUPqo74vnt+MCiaoZM3gkbVRrUTiRFNSNG1ICOhHQUp3ajIeg17Ub8ouawvewdnxVs0Vb5Bs7kPLZZetFp60VL5Ds3l79FYOgZrxnvkxb/B8zCWpbUgViy6NAv1DUHObwDOXTXOz65rz3secOOJAU8MeGLAEwOeGPjDGLjPHf5JasnaiQQBcO4nNB6A4wYw9w/cH712/5//6NYNcOJc6psfS6hYbsPSCYfwLmGilqKvRXpCPZg4ZjxpQObTBmQ8rUf6kzqkPalFamINUhOrkZroQJoYdqQl2pCWWIX0RKtrWJCeaEZ6YqVYNc1IrHBtzchItCA9wYLUODNemiqRYqhEUgy7YJUiNrQEhqBiaJVFAtxE+hciwq8YEb6lCGdrZ18zBLihtwOTOoIbRZMoC+P+3RrNstWrNH4EOF13AE4b4tUscWEpSw1MCgfi1DYkhFThaYQVKVqbMDfOfuJAfnItilPrUZ7lhDmvCZb8JlgLG1FV5IS10AlLAd93ojyrHoWvHMhOsuCVqQxJmmLEhhYiRp6PqIACsS9hPmUI86kQXhUxikoYgisRH2FBkrYKL0x2vIp3IC2hBhlPagXM4jYjsRbpiTVI1tuREM4V/ApoAthO2S6Mi3WBTWCyQXNqlpZQmcCkXC9vhl52RwavrkdCWAOeRjUiWdeEF4YmvDQ58Sq2AalxdUiLJ2ixIz2hSoyMBKqy7EiPt+OV0YoUnRlJ0RWiXW5MYBFCHxeASiieEyb+JmWLWPEXSg45IdOtv8eTqGqkGOqQntCAnOeNyH/hRFGaE6VZDajIbYSloBm24hbYS1pgL2pCVWEDLPl1KM2sQcFLB7Ke2vDSREVPJYwhZYhRFCOCpTbeuVA/zEKoTwGiAktFSUdsaJXohvVCX4O02Frhb5SZYEd2og3ZCQ5kJdQiK74eaYY6JEc5kBBcBYPcAn2gFbqAKuj8bND7V8MQWC8AjgQGCXEICdxllS4VDlfkf1Ac/i0AR/LBiVM2IlbJ49YAyZeJKqZqKQkMsSM+3CYUTDQVfhVXjdQEXnN2pD+xI/1pFdKfWJD2pBKpieVITTQjNcGG1IQapJhqkKixwxhahRilBRo5E3sbovztorW0Tu6EUQCcNqEwIPgzKLjazhhyDVUjTKp6mFhmGGxHYrgdz6IdSNE5kBZXjeynNSh8UYeS1FqUZdSgIssBc64D1rwaVBXUw1bUCGtBIypzG1CWXY/i9DrkvahBVpIDqXE2PNdaEB9WDp2iGNEBRQj3KRJ+N8GPihHmVYlIXxui/WuhDaSfEyEElWWtLsWQFOPCt4OlH/TAEeVrv4U4v5k/fwNwbjtRJYiuVBK8YQnV96FuQUJwExJDG/EkvAHJ2iakxbWIkpTc5+3IfNKMtDin8IRJ1hPYOpCkoSmxBWyBHuFbhDDvYlHeRLWKKajeNViWIpWmsMTNFFKDuLBaJMXU46WpUfzerKRGAXdzUuqQk1yHrGecj51ITXDiqbYahhArIgLLEBFQIdQ3Ghk9dtiFTlLE/QzgUPklhqoWJhXjzQ6TugpJ0bVIjWWnrU4UpHQiJ6kNmYncVyfSXSMj3okUXS0SwuxCfRPtbxHlk3rGsbpVJPcSmHIBHHUxdOrfApzdlX2cH5zi5pLuwpIBNiEODW7pi9RR24+85Cqk6IsQF5aNaNkLaBRpeKopRebTWpRmdKIiuwPmfELvNjhKW1Bd3oTaSidqzQ2oLm+AvbQBVUX1KMuqRt4L3pvK8Zy/LzwfGnkWwnwyEeqVg+BH+Qh6WIQwKu/82BnOpb75DnDocyOpcCQDYypwOpHIoe6Qyu1ULMuth0lVA5NKMnAmxHkaRQUbPaXsyHpSLToelqTXoTyrAZU5TljzG2EraISt0Al7YQNshQ0w5zhRltGEopctyHpCUNKAuNBa6BQ2RAdWuO4jedAHleJpDPerVtwvXsU5kGK04rnejBfGKlHGlZ5Qg2SdHQlhVujkldAEWKANcEAXwO5TBFRsHd6BeDXboVOhynukdJ+USiwliMP5Qe+COCzJiw12IiHMiadRDXiubcALQ72A5gSy6XEOpMfZkBFvRVaCGdmJlchNrEROgg05CXXIiW9Gmq4Fz8MlZSJLpght3EPAm/sKHA/A+cOH8t/Mc54kxnO8PDHgiQFPDHhi4F4M3OcOEsBRsZNAlwvieBQ4v3dDvX/wfvb69/7vv+f9nwEcdzmV5JfCVdlasfJvCuLKIcup2PqVZsGFCPdjx6dsITMP8clEsE8Ggn3SEeydhmDvVAR7v0Kw90sEe79AiHeKayQjxJvjOUK9n90ZfJ2MEK8UBD9KhvphMlS/JkPxSzJkf05GwL88R8CfXkD25zQofsmE+kEeqPCI8jeLVscGKhUIXNjFR8WVcapoCGK4gsgHabe5rOTHJEGcO94FLKNSdYBlEolBbUhQ83cQAtUJDwMDDWXlZujlZdDJi6GTF0Anz4VemQNjUC5iQ3MRH5GLhMhcJETlIFGTjYSoLMRFZMIUmgmdOg1RAS8Q4pUExV8SoPglCYpfUiD/JRXqBzkI9ykVJqY6Za1oH6ynikbFJK9cKEeiZPmIDMxFREAOIvyzEe6XhTDfTCnR8MlAqBcl/i7j5sdUIlUhJoCGzM3fO4vFB3UIiEOYw/cJcAhXWN4kDEwVbB9sgybQgqiAcldymYcQ/m6vdAR7vUKwF/eB4yVCvV4h1CsVoV5pCPNKR5gXv0cOQh/nI/RRESK8CZKqRfcvtnWmR4nktUFPDLYxZqJnRYy8EjGKMqG00CoLoVPlQxeUC31wNkxhPKb5SIopRFJMAZ5E83UWYsPSoFW+QoT/CwQ9fg7VgxQo/vJSHE/FLxkIepiDMJ9CRAWUIkZGM1UrDGo7DOoq6JWV0ClKoZUVIiYwF9H+mYjyS0WETxrCvdIR+igDoQ+zEPYwF2EPCxD+qBhR3hXQ+lXBEFgNYyDVN04J4NDkU3SYIoy+BThM5lhOJZm2u8sw/1aAI0EcqSOV1OmInWH0MnYOckCv4D5YoFOWi2OmURQgWp6LyMAshAekI8wvFSG+LxDs8xxq76dQPX4G5aMUKB6+hOJBGpQPs6B8mAf1I5aIlCPSvwoxMnYnYwmNC4gI5Q2TOLYMZgxJ11QCy5TUfE2lTjX0MrburoQ2sAwEd1peF4o8GFQ5MKqzYArOQGxIGuJCUxEflo6E8EwkRGYjLjwLxpBM6IMyRPId4f9KxJTq1xQofnkB2Z9ewJ+lK39Kh/oveQh9XIJIHxekFSV5NOAlgKS6rFkCTC7wJMEbJp/SoArUPdf94Tz5U4BDiCMNKm+kQYDDuYVzDMFoI2LVBFqSyoKlbsZgC3SqckQGFCHEKwfKXzOk8SADqgeZUD/MRjCvE68iRPiWQxNYJZQ3ccFNiAtuFElwbHADTMEEOnUwBtWKkirhHxPM318JnboUMUqe+2xEBGYizC8TId5ZUD/OgupRNpQPcqH4NQ9Bj4sR4VeJGKGGocKG6hvCwUbXILRrEOfUpKxHrKpB+IBRhaNX0C/HInyY4kLsSIyoRlwovXrKEOadD/WvmQh+mI2QR7kIo5ePdyEivEsR4V0h2rrTLJkKjdigDlHqZlTXCfUd24fr1SXiWn+qKYE5rxVvWiUFDgHOVzfAEW2TJIhDD5svExvorB1EXrIdz7RF0KkyoH70DMoHzxARkAVjcKkAm080FjzRlOGJpghJugI8N+QhJTYXKbHZeKbPQqImE3ER6YhRvUCY71Mofo1DwJ/j4P8vCfD95yfw++dkyP81A8EPCxHhYxbwRst9EYBTKi1iOZFR1nHHwNh9j5EgToK6Qxhes4wqTng01YlufDQn17G8TF4GrbxYumaUedCrcmEIyoYpJBvxYTlIjMzFkyiOHDyJykZiZCZMIVnQKbIQ5Z+NMO8cBPEc/2se5H8uQNADNwwsFfMpO3zFhlVApy4S9wvlgxcI+PMzKP6SAvWjVwj15nydjdDHeQh5VIiwx+WI9rFBG8CSY/pG0UuqAwlBXa7RjYQgDgKdTrEYIFQ4ilZxjqnqJAAmINSJe0kVNLIKRAWUIMI/H+E+/M7pCHn8EqGPUhDmlYwI72eI9HmOKJ9URPtmQ+NbiBjfCgHKdX510Lt8b26VNy1/27V878H0D697z7/1JDKeGPDEgCcGPDHwvzwG7jMHD8D5Bw8Id2JzV4UjKXGYpNHckg/7XDmsRmyQHbHBTB7N0HBl14+r4wUI9y1AmBj5CPOh2SPNhJlAZ4uOUOwKxRXNcJ8M10hHuA9H2p3B1xkI985EmBdXPzMR8jgTwY+yEPQgC+pfs8XDavBDmuOyRXUZovws0AbS6JUPxkxI3PBGaiEs4I1YBb0FOKznZ2LNh1NpSK/F+0rCHsIbJmdMWKngaUCsog4mlrDQUFZugUFe+R3k6BVFMCgLYVQXwhRcAFNwPkwheTCF5sAYkgNDUA706hzEyLMQ6Z8ugIf6wQsEPUxH8CMmPnywLUW0P1dBmVw5YVDViW4tBhXbuBNwVEAjKxXlFlEB7FhDxQ5HPiJ8OfIQ4cNEowQR7DLja4HGv1pI4elV4G4NLylwqKxogV5GgEMvHKkTDeGXnu2L5SxLsYnV2Ci/ckT48FgXIMw7T4IzPDfeWQjzzka4dw7CvXMR7pOLCB9+h3xE+BQh0ocAqVK0btcFsgSoSXwHE1svi7INJv9uiOOAVs6EkOUWFYiRl0KrkJRWOmUBDEGFMIUUIT6sGPHhRYgN5THOhUGdLR1TPyYhPJZSIhn0MBfBj/h9i8HvHyOzQCe6OXHlmx1wqmFQVkGvMEMnI3QohsY/H9F+2YjyyUGkdy4ivPIQ8bgAkV7FiPQqRbR3BWJ86Xtjh4H7I6P6xinMNKUVYUnh9SPAccXVd8N2d1noH5dQ3frhSAbHNDqmEaqJhqEKqnEkJQ59cXQK6/djFi0rRlRgASIC8hDunyMl877pCPZJRZB3GoK8MkRir36ciyCvAgR7lSDEuxwRflTgOFxGs4R5PFdSzIgtlTdMwJUtQk1AgCP5elDlRrWbHbpAK7SBhHVsGV8KnawYenkh9Ip8GJS5MKqyBcwxqnNgCsqFKSgPBrWUsGoVudAE5iDSj8kdPax4vWdD/WsOVL/kIPjXAoQ9YjwR3tigDWQJS73LkJtzlASXRGc5oSS8BTcEOJLyTgI4fzWJ+zcBHAneiHlCzXmHJspSaZlRtMm2CQP46ACq6oqEkiP4cQFCOLyk+Izw5RxWKeANrzujqh4SwGlCLCFOUCNMQU6YghpgDGJ5Sp0wNjaoCXet0CoroJGXIEpWgEied798MQ+HelN9RnBUjGAvKhQrRemUTk4jas7nhDWu+VIovSS1F/chTn07aOYudUOzC+VIXHANEsNqERfMNuMWRPqWCVAbRkAkylephKxEtJ8VGn8HYgJqoZMxphhPHaLUjfvIec2glhYC9EEEs2Ww5LeLrkzL01vYWz2QAM7VpWh77VbhEOAsTG6iq+498pOr8VxbKubW4McvoXpAmJsPnZqqxRrEhdsQF16J2LASxEUUIT4qH4maXAHW4yIJhrOgD8pEtDwdYb4vxYKB8i+EwK+g+CUdSpayPiSErrin7HR1MpRLJvimewCH9xH6XiUQfKh4PyHsc91LaPjLBRFFtTAn18s5N1VAJy+FTlECvbIIemUBjOoCmIIKEBtcgNgQjjzEhuTCFJwDvTIXGlkeIn25gFGAkEdFUP9agqBfyxDmVS7iScx5CjP0KjMIyrSKUnG/4P1G9WuGuKeGeeci0i8fkb7u+boCUT40/K+BPtAJkzAy532UChyWFt8ZKsIbmpzzXkJ4Q1Pj5u9luSzP0xPiyB3QyqzQBFYg2r8Ukb68R/F7ZwnYT1ge6Z2GKJ90RPvkuOBNKbR+FujYeMC/HsaAJpi++97cwhs+t/zV69nzbzzHyBMDnhjwxIAnBjwx8DfFgAfg/DcLlLsA50eII3VZYeJoUjBhoqReAjnsjKOln4I/H9b5cHZ3lCPav0w8sLF1arR/sWsUIdq/UAyNfyGkUQCNv3vwvSJo/Iuh8StGtB/9U0oR7cckh1CCEnZ2ACGcYBtch9RJSXw3riQ3C+VNrPCncHehIbiRlDWs3xdDqCLuAhy3aqIdkl8TE1TpoZuJarySfiROxHFlWlmLWHpDKOwwKqTSGgNLnRTlMCjLYFCVQK8qhl5VCL06HzpVAQghtIoCaGQFiPKXYEu4Nx/Ai8R+Cdl6oN0FbxpdSQ639Pag0ShXwalUsSBGZkZMIKXuFYgOKIfmh8EE2oqYAJ4bKiMo9Wfiz/ModRhjQsufCXWE+obmxjICHKouOKQOVXpXJxoa1vJ4sxV4lF8ZIn3ZhaVYjCi/EnCIc+TPUqlyaPwrEONvRgzPjz8Te3c5G7u1uEwvRScs6bOkEj0J5LDrDUEL95NyfpaC6RRl0IvjWg5jUDlMQWUwqktg4DFWFgoFjYZAi22YmYj4EmDxezIeCfe40k3AVyOOJb1+aO5sZCtjYVBthV7uUo8EFCPGvwQx/qWIoR8J25/7sf08wY0N+gAHDAE1LvVNA0yyRsTKmyX/GyHhv6vAYUy51Td3/W/+ugLHDXDYpvr+oGmuVFbFJJwgh+CFZTEWoWLSyMpFq+OowBKh/IgIKEQ4V779CxDuV4Qwv2KE+5Uh3K8CEf4WobzRBBKgERxSbcfzIgEcAhv3cHu98LpIoOqEnjMESvI6GKgK4rUYWAVtgAW6QDP0MgLOchjkZTDIS2Ag5FQUii2Bp15BwEMVWzG0smIXRJOu+Sh2+uE59Ga7diaVZmh8q6D152fUwECfDYUT0rGQwJL0PW9j/Lvy5l7p1F9N+P6tAMdVSkUj41hRJtMAxphRTQ8q4At9AAAgAElEQVSbauiVdmhlVYgOMCPSr0KMKH92gTJDw2tVZnN1fKLKkf+X0IZqlbujSbxvVEtzAuGuXlktujtpFZJ6TSMrQ3QgRzmiWC4VUImoALNor84uXZpAzh80JibodsMbQhuCpyYBqgmr49VNSAhyDxq5O0XpD6EhS4DigxuQGOoUW0J9baBddPnT+NnE9S7OUQA9VNjJyCmSepZvGti9SN4uVBqio5mqGga1FcagCuH/9VxXCWtBJ961TWJ5elsAnAsqcK6+SQDHpcJhy+/FyS101Q2jIKUOybpKAXhDvKloykKEXwm0SjtiQ52i5MzAxYagCuiDS2AIoadYPoxhuTAE54rSrRhlHqKpavTLEWqWUHaaIlzzKka4twTCY1hSJOZSxrsL3lDppXC3D3eV5H5X37UjngsBynYkiAWB23sJFZ2EZDQ1FiBHKS0KGBRWGBQWiHuJ0nUvUZbCoOKQ5juDinCnEFp5ETSBhNOci8sEYArzMiNceELZECOj55B0z+DcQMhLBSLvF5G+pQLycy6PCSQ4Mgt1FedJLY2tA+kbRzDL/eT11IY4paTCIcj5PuiPpaCRsXQvkdScbkWnZJDPTodUxeoVhH1UcfHexPs3IV8xonyLEO1bCI1fAWL8C6D1L4HWvwI6fwv0BOUBtTASJNHz5nd8b/7q9fzf7FnMsz8eKOeJAU8MeGLAEwP/r2LgNwCHSbEon/KUUP1NBOy/+sR9BzjuOnJ6pXAlm6a3CibefJiTyjgIctx+HPrAalEvL2rmBUxhq1QOPvCyy4lNlFfoZVZIwwK9zD2Y5N0f/Dvp39Is1iCnoaJDrFYKeCS8GVgixZVuPggz+XBDG3bNaROSbukh02W6eMd88T7AufUrubNK79r3eKoNhOKgBQkqDqm9c7yqEfFcoWaJAb+P6BLkAMurpNVUtsym6qMEMbIiaGTF0ASWQBNYCk1AmQu4SB41PD4ECyZ1A2JFCQb3RdoPdv6R9o2do6iCqhP/VnoYdh9fHmP6y0iD5SyijEwALWl1XVInMNm4HYQ3BqpvApugFwCHJShM2qlk4Jaf6YI5cnayYqLmEA/4MQEsy+KDOBUXVRIgYVciAih+FyYPbE/O7lZyJgJS0sMyHDFEPLlVE7efRTWAUOTQQJPxIyfQYnJLAENlEqEOSw44KqUhq3CV7pihDSSssULH7yK+B01a3QkrvSekuJFihrHDEhG+z9Vwnj/GqhR/IvZkdhhlDhjlNTDJa2GSs2SKqpt6GAMJb5yIlTchTuFeEZZiyO13cxtbhDf/doATp2CbXEISQhwqX6gKkzog0TSX++FWx4kkiW2KhbcJE3UbYkRZGv1tzIiWVSBaxq0FGlkVNOLvHYiR10BLUMdEi0o74WvDcy+dH0IRfpbb6yWB8MY1CJliFezq1gBDYJ1QJekDa6ALcEAfaBdDOo68ns3QB1ZCx4QxoALaAEK+cpGsaQMqoQ2Qzh9VPIRAjCUCIYOsGgbRQa4ORhlHvYgpk0ICD/xut0Oas4TXlVt1496657W/JZn7dwAc6Tvw2uV8RG8gzk+Er1SaEE5SicDyNJ6jGlE6qBdlqYSJDUJlE+cGNmq2QafZeAtig26HiSVrLD9UsTOgWynHLk9UODBpZ5JO+MmEnSV2hEfsIEjYQwjMc0xAx3mBQyqdIsCRwE0zEoKakcgR3IwnwS1iS5gTr24U5T/cin8Twr9rQZya9wW2meY1zlbZTd8Bh0j8mfArO2FSdMIg64AusF2o/qRyTSrh2FXNjNiQMqTorbAVdgvT4RU3wDk8A34GcD7uoLt+FAUpDeL/mYJLEeaTL1R3EX5mxBBSBbVCr2qAVlkNjcIKDX3BFCWIURZCo6THTYHwUYsKLEJUAH2WqIAkECcgr4JWzKuS0svIOHepvKTyYslTSag3XV5qLMf96TUv4qkV8Uo3xOH1xPJIllTxuPI+4locEQpPLgzw3kewzO59VAlWiHuKVKJYihgB7QlCWNZlRbSfHdF+1S61E881QSDN6Tl38312liPUd82NBNpchFCx41St6Dol/KwE2OO+SpCKvlFuEPpDSSLBlQveiHbiQsnZJC0IsCSXCwKMBz4zfFfwEhzWivJP3ve0vH8Q9gaYJeBL6BtoleaNQPqL8XrnPNskmRj/xPPmv/o5yfN5nqTKEwOeGPDEgCcG/ifHwG8BjoIP/h4PnH/kk/4biONKeiSII4Ecqg1MYjSJltwGmRP6QJodMolz3nmQ5wM9k3cmXEzk61xJMJPhPxquRFlRh1gFH2ypeuFDLpOIFtEZiuUbooQjqB3xHGpJaUM5N00VY90qG7FlZyn3cKlv+D6VEVxVFCumbnjDEhgm47dDgji3SSuVB+4hJPFUSLjg1nclgignIdhge+NKaQRwtZ2v+T5X5CXlEFfCY9XNiA9uQ0JwByR/Gjd4cnXRIZQS5SyELNJDsUHeINQ1ksKGQI3j3qq6UGpQQSGpEiSAQyUOPU1aYGRLVhoYszsVzylhndvIWkAk/j8qZmiA7BSrsvw8tpfVBbLNLP1Y6oUagt+HQ4I1LPORzKNF0uMCNhLAkT7/+2e5PodeRfQsIijk72DLd/FZMn4GR61YGdYFsiSMKgweP4IiDiqNuGrMZIv+FCz5+PF4SGV1t6Dve9JPk2qCQCYuwueoRpQE8edYRb2IQUl5RQVWowAWsVQ0Ce8bl/rmNwDnvgrHDW/4vvvG98clVEJ1IwAOvXUkQCRW89U0RWVXG0kpJuLCBViZLInEWMHkvl6UQ7Gtr1bBDlPViFFIsEbLpE5ZDx0hgBgs16Oyg+BGGgIcivjh8WHCSYDpvg5akECgxO/HVXpZo1gl50o5wZZBjHph8CygCz1DuKpPRRiVDFTR+Dug9bNLihqW+QVUg/BHStrqYZKzZNGJWAVLyCQVEkEWX4v3vkMt6bu5AZPYCsXAj52m3HPb7fF3n4efbP8NAEd0oWInKhdIkoCrBHJ4LAlbJEUToQsTaoJYHmtCniZx7ccFtSA+uFXMAXFBrYhV0ydK2vJndo1zv0ejZg6pG1gjdPIG4VnEduA6XiMsZxMwjvCIQM4N5fh5rvHd84Zx7wZhnF/dAKcFiUG3I4HzE2ED29wzDtStSAwiSJS6ckmxyPlUgjWcb+OV3YhX9SBe1StGrKIbRlkX9AGd0MvY/Y6xSsURDZutiAutxAuDDfbCXvS3T2Hl8w72Vw/x7WcA55QKnF101Y2hIMWJFL0NpuBKhPsUI/hxKcJ97dAQKKi6oFO2QatohEZeC43cDo3cIoEceRmEUi1QUirR70sooURnLsncmYDrdnGAZUQuRSfvGzTGdpffugDOXXhzW7LnKttzxRSbOUj+SW4/JQnqsERSml8Y8w0wiftmrUvZxjnPDq2AMITnHP8/e+/hHbeNtXHvX/p953v3fXc3tjRFknuTNFWucUvsFKe5N3VpRl1ySdySOE5iO5usE6cXp+5uqvx85wFwCZDDKSp2JBtzDg5IDgmSwAVw748XAIHMWTNErVfNc8aV4/RQNQ1g8y1su4eV59DmJ3rVsCi222ynCVUIGjlvU761pEAcPciUF1ngdVfpzZZzPgJweJV8CODHABvYX+g5qdj+ay85MwSUss+51ji8qqlP1XnVbrPtbu5FplkPTdXgRgPy6ITFDdXhoJ2Nqd/+P6cf8vnj5cnLgJcBLwNeBqwMhAHOCP5CBZorUBWVcudXoVouwhJWRAV46NV1cokJZDmB48pRHZrGkGVoZhhFjoHLfya45Ce/ojHQ6K0W+D/PdZcI1cawdkfXQ6E4HIorYUhQK2KkZBlXxk7g8QDgEOQ4EEcZHHwnrWRrcEMg4wIc/eW0qDzIqHRrl/iimeOA8xzIMBml3CovEwIIDh/g3A/GI4meCU00sgaUkq3mnFGTwtL4I7jhBJHTKLRowyefnkE+PW3g06QGU1yyVVzWm7UHTWdTWYEYBWA4z40aKkXYxkAPKhtcgJPluW6Q88yEr/zaqowU101e3XMEnU0j6Giiwk4IRBjEeSDMcALzZVYNLRBlX/2voZG6p+uWb1zzObk084H3Vdcq42BUpa+/8Jr3daCTAk8GQGkQRU8A7QWgPcb4FZlGv4Cp6FAAzk2h5zvSwwbF08xAAmf4knhjKY8Ylhu/CotLf+jLMOVVgq4v1vtmDgDHyKCuP5THMTOnhobgBZFjMSKNbLCMZT4KtSIMV4VJlpQR254aRjuXcQ9CGR2pMjpV4GpzOogHlhpaowxLbcArL7SUhjdFLoOuAE4ZeeZDk1khhqvWcNlhBrXsr455LENZXVlG58qSDitMvFIf5/+8RrcbdsU4VfcFtDKvVX4b7zjjJVdMGbhELwcJ8zXU5gpw6KUUGL1mhS6zUpcAF843FQoyv5AClwQ04zooD8Ixs5KWxFzlh6vG6RXBOpNjaon3zgQnIefQJD33CIewcKJgBjWhs5privNNucF4vBkvO1vGxgvHeIZob0N6HOpVgLTcM+9ZLhwaxD7cgJrUDAqpcyikzqOQuoBC8qIOqYvIJxkuIJc4j0zzOXQ2TaOzeVwN2ezg0Du1uuFp5FefwJ5MN86+dB6XJ27j0/fu4fvPXIBzX69EZVahuvP2N5jsewuHdnG1vG5kWk+Cq/e1/s8JrP17PzY1TaA99SraU+fQnppCe3oM7ekS2tMDaE/3oj3djS1qgt0eNXE3l77WUIngQeYLk2FDGqjr1ZfCQ3CrQxvpUwQksv4y2GFV7EN0P6KPhT4mcOUntoHNbN9L6GwaVnPStDcNgaBGhZWDaqLhLSt5bBjtPIfnq+fnJONsS0dVW71lxTDaGVj3gj5D9xf2A43tK9x+w92O60PUxwC1Gpf5MCD9ivRB8lEg+JDA89h/6PfSbXdJvafux/iRSINrBW8c7znqkctFd/LP6cvKy4CXAS8DXgaWmwxUABx2xFT6/DLiy0uYXYCjDFFThl0EKMlJtRR3pokQx8AbA3ByzWOBIaaNUAEzAnJkPxwr40wZCY4BpwwGKrl2dY8wiNHLt+b4PNWCGLxBbDxw1BAL3otGsvam0bEGOYExqIzCsBLuPo8COQp6UAkmCJAvjfziycC5aPjlkV+eK42EgizRqgAOIc60Xqo1RXjD99Lz9WQTnHPBARuuMi6KczQ2UEfgjo7l6yqHnRBsuK7y3ObSyzpooGLmOmjmvUcNvNETeKov0c756joBCgbeBLAoUOotTNGrgxHKyTxF+ut2AHICUEToZGAR31HeS4Zm0ZNIeROVtJeYwBsFjOQ9tVEWrEhGeKa+rPN5tMeRGjZkjHJd/hYWiJy4oDFsZAi8kTjO+4ZtAAFI9SAQUdUdtcIVDULCwzAEF3iovMmkvGjoc/UotWT7iII0BDUK1qTL6EyPIKNC2LCnp5UaRqc8yvjF3Hq/6Dl5nOdle25CvnkEDDmBLwJvY2KCXRUE8jSNIssQnDsWWopdtzms9wa0sm0QgMP8MwCH5aQgjsAbxvM19uYCcNRwSztPkJ3wOQJsogBH9lOjZmLv6rEqywDMEdpoSEdQF4QgHe2ho2GRATmmXCvKl95MDqTUXiBm0mx6PpmQa6aXVUkBOA3mCOdZThrkF1PTKKbOoZg6b8IFBXLyyfPIJc4hl5hRIZuYQTYxrYCv8tyk10WKHjinUVh9AnszPeh+6TyuTNzGZ/+8hx8++xG/Bx4494FZ4P7vwOyP93Hnra8w2Xsdh3YNYdeWsxrg/I0Ah544/di4cgLtyYvoTF9ApmUG2dYpZNvGkW0bQbZtGJnWQXS2cNU9eqsxpqcUwY0dgqsn7DVLZ5vlsxXEUX2IntvKAhzWTx0IcMXjS+qx7LsQR2COBZQy1NICHQ5hYtupIQlXguOKgQQ1GtgQ6BDctHMiegIRtoUKWGuPIbahBOAEN+0rSiruWFmCCk0OzCH4UZ61DtiX9nVesYFBEXijFkYIeYASUDn3Nv2DGgYZATdBHs63Xvvr5t8m+rzzeedlwMuAl4HHQgaqAhz95c574MzbuHjoFcg1RLUBSXiztYVK+6RS4pXXjfG+yTUR3OigvtaKwaW+nHPIRbUgX9bF8ArfV3sx0IgTjxcbK7iRmFAwKZsYhwS1RLjxUNAGrnbzt7BFG+1WySZMEsPFABzHG8cqkHw2rWSHIY7x5uBXdeU6zmFEHJrDuYMYZFiR9gjJm4mWFUxwVvcoBsYCv/ZqQ0G76+t5FwTg0PtFf011lG4Db6iMhxTyiBLOr6pqsmZ6oJghaJUQR+Z5sPcVgEKIQ5jE55IVV3Re6DwWTxr5OmsBDvNGAyMOSbAgRQMc2VfeP8p40bCK18uXYD3HiZ7vRB/j+xsPEGWE6GF+7pdlDam0V5F6TgVuCG8mUFArlEnZ0fDhsIZICMCA9s7SUNJ+JXYhjvXAERlmLHItsQNDYkCOyCTTVV4pIc8Htp9WNvTQFQ04Vb4pGKeBgHiAdKq5UzS4ybaMgPMrSVAeGsprg8PXhpFVk5fSC8lMoBx9PgfeEOLYek64wmAgzLxix4hVwEp7KaiJYGkYS3sSPBM9ROgV9OcBHAI/FYI6pocjaojSIMgxQEdBSfHOIZRRkMakEXjbcN/MZyUGsmpLCB80mGU6ygtHtQM07N35acQzjW2cnl9JoHXgLak8zOhlxtV/htUqQJmVEpeRXUlgpyEOvWq70tPoSs+gK31OhWJqBvnENHLNU8g2E/QTQmvPSNZleihyGGRn8rQaQiUAp+flC7g6eRufhwDOr8D9Wdz/A7j/G/DHD/fxr+tfYqLnDRzaKQDnBNb87TBa/+cY1v6tDxtXjiuAk0lfQK71PAqrz6GwehqF1RPIrx5FflUZubYyOK9QhvM9qTml6LUygby0xWbZbAvXtden/nigvevEa8bWeV2/dV8hnpy6L+Ex3Q647UJY3jXUsZ45ut9iO0sgQmhNQKPnGaLnipprRrwQ1fxDMhRWAxxeJ/2EgjaEOMYbh0tzd6zUoVPBnFIYpriQPJBtB5wHMF7LXDD8VmSySqy8eBwoxT5C9Q8BvJEPJSYO8k3yz8eV/YnPE58nXga8DHgZ8DKwcBlwAQ4/NP+Fhoh44GgvHP1VVX99ijNwFv4QviAXIw/DyqZy+05PghBHlSPBSfO4gTaMx5FnCIw3MersF3v5cq+NPwNuKB+Boib3jCi3aiJYHrPwxgIdswoI5yPgxItOUF9GBRqYoUHaoNcQQBvLVLK1d5D+8myNVzGmdaw9g7TC7noFiTwbKKSMOg5XILQx4EZ5NsiKORw6RSVbK9p6KI8BCgIX5Fll+Ij5wqveTTxbOExF3M2VAlwJc0Q5DjxWeJ7yHJD7m9gY/i4gYd65eZlTrv38KkwXf+0V4sIxDRMEOmlvIQV9jMGpDJFIWQi0CeLgvbXhot5PlHsFnky5ifdD8N4EcDJ8zAE+Jt/0c5oykzxWEMnMbSHlETyfMxzHAByRBXUfyXcTW8DXSL1zAU7l+ZIWh1HoIUU6ryn7GoJLHdBQSt6NsZ50VDw69JAeQhoBNqFYwQIafSZwmKNMnBxAErdujqKQiAtjKAR1fr4AZxyFRLjOFwXiqLpv+gnVVkj+lZUnE+fkEYijvKaCtqQyb207U+W/OXrgaMPVeuGoeYlUvoaHU+ll2ascEwM5uI7lZ84Vb51ITEPYNZq1BxmP6ToQlKl4VhHMGcCpy1gAg/FAVDDODHmVVX+aCHD0Es5cxpkeODkG5XVFOSDUn0BXahJdBDkm0DuTHjrsD7LsH9QEvxruEhB2cs6eJk7gfgq5lpPGA6cbPcoD5xY+e+8b/PDpD/idq1D9/gsUvXE8cD688RWm+q7jxSeHsbujG9k2Dp06jDYDcDatHENH8gIswCHEmUFh9SQKq8aRXzWKXJudbyib5nA143VohnPmlUegDJ+SeckMOFUeYcYrTA2ZDMuStBMCxmTfyp70cYwdmTdDrDQMl7otQ5tYrlJXNcSRSaMDYG+8DlX/puZOM22weDFy6KsKxvuGHpxNBtw066FL4f7CTnyvh0/Zfd2WaxnU/akB86YfcWUztK36AqefMrKvIZW07Q7EWUhd9tc6elVYRq0s+uM+L7wMeBnwMuBlQMtAXYBTTE+iSCNKKSxGMfed7RLtbEXZHEdgUHEoB43JxATyVM5NUAYYjTAV5DppGMTokliOu7Fcw9hRbCu26b7vGLERyCEGcCg2q0tZQKHnrrBKNo3XYTN0QACO+UodeOLQ2OHzOs8XzIdDOOACHMIaPRRFjGI9HwvvS0XVKrzutnazd/4LAIQ5FoAczjEkyr1+LlGCdRz+WqoBTti13b2vzRcBWzpNm4cOzBHvG+WBI4aGEyvPJznfPqNW+uXd7PlisLA90DBCD0fgvcWTRowg/Zym7DjUyQAcOU/yQD+3pMN7snwcgCNzILn3DJWJGBPGwBUZiIAbgTmST4vRCejhGCxfPReGwDINcET2HS+AyDtYg0mAAVeVCkMG7Tkik4zq1a5cw77yPXTdLCbHUEzoQGgTCsH8P249bmBb2gynnqu2hgDHBF3npK3QbUgxqQGOGo4WDKeS9kViuabBuFGAY+q65LWSSzWhMeFsGK7IOZWxlE84roBAAnicWLcl9j5SL3S5amPftjtu+WrQaeXV1BHl3TRqh8OpYW6c30iAjR4qJx5XCuRRFhRkk7nBdMy2mxBfwUc1FxrrkDbK2cZmONly82m1bHau5QQKq49jb6YbPS+ew5Xxm/js9tf4/tPv8fv3/8b93362AMfMgXP3na9xbuAtvLynhL2dPcivOol1fz+MVf9zDOv+3oswwLmAwqoLKNILZ9U0CqsmkW+bQL51XAU1/1BazzOUVUM/9baeEF9PjG+H7BJCmTZK2uWQ3qJlTrdV0ndIzP9cGYzWC9PfBe2UAUSm3xIwJ2VqAZ2GOlJ3KWOUBU6WLO27ahPd4afi9eLEFn5H2mvVBsccMx42co9oLCC5UuZ1Wi4QkjabsZZjPrt5j1Ceufnnt8Py5PPD54eXAS8DXga8DCxMBmoAHE7CSXgjwUAc30lHlLuFFcDiCrAomlrB1AbVhFLca0MWni/XVnsf+V/Hyjh0jDYx3hjXvpe+XowSTipaOxhAo1aR0t432gNHAI6Z/0PNi6MVcA4v0Io530We2z6XuNOL4qnvL/dxn0eU1FqxgTwyD4DyAKEBJBDCKLjyf0wsSnHI8yby1V6Uaz6zKP72+SsBjgYLDjhQ4Kz6fuC9E/oqq+fbCRT+UBrGmKRxHLyTm3c1toPzHWgTGFna6AoAjoJuAosYh+cA0s/mvn94uJRAGze2slFN1hs5LnIsRrXxdBJvJ1MPKGvKSFZ557yvMXqkDANjPlgNxgE2ssqUmgfF5KuCVO5z1q+Xto5KnZhLbOsP38emFbct6Y6CkyjrUEYI4oS8hgTgRA1n9/1itucLcNSwuzEUWnTQ3nUa5tjVqsL7eshTHFib27GG2joBnQpAas/IEIgWDyqZj8jMU0QIQ2hj27xwnrmdvWwTqHG4LIdgMRCSq/mKOFQpydXquHLcKXQmTyDXchyFVcewt/Msul+cwZWxd/Hpra/w/cff4vdvf8L9X38GZjn5DVSY/c99fPLuPZwfuoHD+8rYn+1FYfVJrP8HAc5RBXA2rxxFZ/I8sukLyLdeQHHVRRRXEeScQ6FtGvm2SeRbTWiZBFcwJLzJJDjXlw5qKK4BOmqYrpljSgGcoF3RbUTFfFZcsSs0v5ADGgNdR+TZjcP1QUCuaguDPi2uT3HbReM5mDYrXil5lo8Lbrvqtm92W/UbVYY/SX8hsWonxXMxGofafAsa5dpqsbRdKg7yKixzi6vf+LR9fnoZ8DLgZcDLgJcByoDocYzDQ6jMUuJdahiO44XjO+plAHCoaMYZVrWO8QstQ1go9DH5z3zFnXPa+r5hw8IYbRHlWZYiduNCqgQGNXmnmrCT8KY6wNGeOvT4kEqu80MmstSASYxpoxArpduAmgAwWGU5Cin0pKxa+bcTtHL4IZVyx63cbNvrI2lSAXe+nBLiBPtVlHOrOLvGvH5ffR95t+rARvLCxvqa8NdYSUdiNz05Ju8jwzzqxXI+Y0nDTVfAl/HAEYBj4JFM2KxjuV5ik7YZYqIm0Y1uG/BhZcOFB+62yE40do043leeXYYjyb6OtazR2LPXhWRBgRzXqBOjLy7WeauNUHkuqZtxdZvwVoL7v1wTrevRfTlvrvVerpP0CHE0wFlUiDMngGM9BQhs1BLRBuBwHqVq4MYenxuosUBOX1cb3LD8nfIOPMgMkKH3lGpztSeleE+qIbCcx4zzmTWNId+svazC7bgAtHAZFJMlMBSSwygkhpFXYUi1q7a97Uc22Y3OxClkUyeQbz2O4urj2Jc5ix4CHHrg3PoKP3zyHf747ic9hAqcAMcCnE9v3sOFobdxZP8Insr1omvtKWxccRRr/+84NvyjD1sE4KTOI9dyAYVWhvPIt55DvnUG+ZZp5FqmkEtPqcniOVF8NjmBTIIQx86jJh457kTttn2RNsep3wbc6Em/2Y7rPibYDwCjrWduHZZ6zfpv+xN7HwWJ3TINoI6u67rP0ACnmNZLletYAxztIcr2RdKMxAYAV4MrlceNl08MvNFQvHFwI2kH/ZB5Rm9UiKz42MuClwEvA14GvAw8WBkI2+tmDhy1gooHOEsY1FQTCmsk1v5C7hpz3BaDS2IxvGSfcfSa+ewzHT67KNKu4iwKdK14WBkcGt4YgCNwx/HAiQc42ggSxTusjFfLz+hx89wh5Z/Py/eQlXZkktYxcEnzAOYEMEmnGVLMIxCn3pdVrThbeGPzk88nz6xlwQKGMFgIHxcAIt4toszb465HjxgsOg/lfnx/AVoxcVDmcr7IauVzqfQDDyYZBmHPCxtpPO4+Z8TQiTWAXBDilGnwjG4+yvMylme2shTOR/uMcjycV7zOTeMUNhgAACAASURBVE+2zTPw/nPMQ1133boowIYxh09K4L6cJ/Va6nm1WM6ba/2X6yTdKDygl0M0OHkQm0eSV07cKMBxhqi4ngjKA0cZtAJwRpBP1wjB0LZK7yg9GXrl8RCYMQZ9zTJm+Ss5lPcklGG5mfJMTKKYmFRDYjksVg2NNfOZyXDYSoAj8ExDm2JyGMXkUBAKyUHYMIRCSod8qg85Lh/O+W/SJ1FoO4GuNSewP9uN3pfO4+r4LXx++2v8+On3+OOH/wB//Ibgx8Wo/nsfn938FheHNcB5Ot+HbevOYEvTcaz/+wlsWtGH9iau1nUOmeQ5ZFPnkEufRy7NeEaFbGoaORWmkEtxcmUCHA1xCHJs0CvihdsGx/uRACW2fsX1N6b9CpUDy8Ot/7oNYD23dVzKzGlTQvVZ+giJTV+RHkUI4gT9hqRn42i/wb5AgEr92ECc0PBTtvlzScOe6wGOLZf4dt3/7/PFy4CXAS8DXgYejAzEABwqFXryW06A6z1wHkzGPxiBrlQwrdEmxls0FmPLjcXwco9Fr5vrPtOSvBRjjcYKvwIvICiAY41BC2/ESOc9JV9cZVuOMZbnCschZVmBgBgjIF1GQYURFNJUzPWqSFTIA3jDr6WxIMEOzZqLEh0ozvJVV77Yh+4hUKMSKghcsLGcGwU4fD77n4U47jF5N503enhSrW05n7GbTnTbfW77n/YQkue0x3Vabtr1tkUGHWNLGWxyPCwLWkZcmdGyZPPQfV69rQ07nudeF5eu3LPRWKdhwWu0LkYhjuy757l1u962ex2349Jzz5H0pB1xvUD0toYUtt7GQ8i4vHKO1QU4XLFMBzsReWRC8BDAEZATF2tgaj1rKmGNC3HC4MbAVoEIITmrVubynpQdyVuT7wmJx1GUOYkIeRTo0e2s7tij4MwFOBbiFJJDsGEYhZQO+dQAcqle5FJnkW85g+Kq09i69jSeyvaj76XXcG38fXx++3v89Ol/MfsDJ70J8I3ywtEA50dcHL6Fo/sncCA/iB0betCRPIVNT5zGlpX96OAKfYkpdCankIkNAmwstFHDpJITamU9BWzMCoYW3ph2ITq8MxbgENa4EMeBz0E5SVkwduuyLht7zD0vplwDyG/7CXp/FSMAR/oN1c6H2vRImzYHLxzbZ9g+xz02l/6H5wbXOs9XrR/1x1258NteHrwMeBnwMuBlYOEyUBXgFI0HjkxizBUs1LwUVYxdXxgLL4yF52FUuRQFU8BFNI47X4wuxmKIMRYjol7sXhPe5vtpiCPKrRhwCwA4FV/ymaak75ZJ3LvKMfe8iJJMBVUprI4RJl9Vo/CGACdY1loAjrneUXQDmOMowvNWoAXiqNh9dhduVMIFDR7ccwSK2C+s+pnC54Shi3O/0HO4eRXddq5ReVIr/cr/KgCO5KGTv43XI5ETGlSu3PB4WCb0vsgL4/kAnLg0nWcI5LbWMZ2Gbrjd+hWtlwJZ3Dh6jnt9tW33GjetattyvqQn7Uk9iOO+c1w+xRyrCXAsvLEQR1YxY+yCnDhgE3Ms8MCR4VSVECcMbjgsKiL/DZWxK3+Sj5Kv1WI5L5rfUe8bQhzCGwE4sq29GxVMF6/G1CDyqX7k0z0otPaga1U3tq3txv7MIM4+/youDt/Gv974Gl/+kxDnP/jj+98w+9N9zP54Xy0h/suXs3j/2jeY7H4bLz05pq7btq5HLUm+eeUZtDf1o4PLpycnkUlOoDMRDpnEhDrO/5SnDYdMObCmEp467YXyMjFtDdsIyXdpuytigTj1AA7lMNoOyL4ro648C/g34MZAfttX6ImM1bC+APzX6DekrZO278+K5TlMHN9munnit30eeRnwMuBlwMuAl4HFkIEwwBnVy4jbIVRmAmOBN1SYYw0bXxhLJ19EmZxv7Jalm0YU/rj77nlx226aotgKvHkQsdwjbAiFFW95TvfZInAhUIxdI0zPVaEMfgVsjOdNsK09cALlPDYNpidgKApMGt+3aZj0Qgq1Y8zU9HQx5wWTA0fvr8FOGNxI2k5+qfdx8yluuwGjJPQONo/4rnGQK8gD57rwEJRw+YbrqSMnDx3gOPcW47KBWANQMdIZi+HOOGrcRyFL9P/ovptWND1JS4ZkubH8x1jSlLTkWSsBjkxurIdTuflRq8yc/xYEcOYBcZQMCryR2EKcSngTATjGU84CZved3W3nHRX0lrysFUs+M5a8FnjDWLxvXIBj4Y2eD4cg3bTHarjXMLQXTh8KLb0orurF1jU92N0+iMP7pjF87HVcGvknrs98gFuX7uDOWx/h7tuf4KMbX+DDt77Be1e+wvmBWzjz/Kt4Kqu9b/JtZ9DefAoK4DT3ozNZQpYTE6f0ilIZTlDsBD1ESoObMLyRNsiJBdqwLZL2QG0bgML6VQFuHGAT/S9UH8Nl0khfIuUceCWq+dEi/UMA/CPHg/4h2o6G28Sg/QvOf0j/S/46cbhtjeaX3/f542XAy4CXAS8DXgYWSwZiAU6BS0+b4C4brJUWn/mLlfkPKp14Y9tRdCMGvVVGo2UrkIOxC2zcbfccdzuaFvetkeJOajrf7cDYEKMjiO19wnnsPp9sh58zpPgrpTiqQNvJRtWQqQDcCMgJK+JawY6mIfvxUCIOVFQ7VluBr17mroxYr5ZqzxMDcVxj6SEYD/Xf3+SpI2Phsg+Xs/7PkZMHBnDi5My5b4PPG22o7X7UqBeAIrELV2Rb/ms0luv0/Cucg0WFYH4dgTlynqQrzyZwQcBCNG7E+ymm/BYMcKIQx/XKiW5rw9gOoQoDnHh44wCcELyJeZcaH0ZsWUs+xsVunrrgJgpv4gCOhju2PdXyqSaNTw0hlxpAvqUfhbZ+dK3uw86Ng3h26xiOP30OA4cvo3T8MsbOXMJ03yXM9F/GdN/rmO69jrHT13H6uQt4dmsZW9eeQa71FDqSJ7F55UlsXkGQ04cMJ6dvHUOuZQy5tG5nsqmxAOi40EZWyrNtlwNqHJCg2nBpk4LhpabORSFNrf1Q3eT10XKTus04+p/ej/YnMsQ28LapAnDq9RtBux997z9pv9r7++PxcuHzxeeLlwEvA14GvAwsRAaiuuFfuNwuAU4QkhNmpRVRVnyGLyTDH+S1gbIYKHHxkxaqeU2UAS4GvlaEK59NypyxC23cbfccbleTj7DROl9oE3edNTzEm8e9V/R55Hmjx/V+kIcOvHGNNm2oGVhQAW/cIVRmCIYZQuGmEd6uBk0W67gMjaoVN3ovN41Gr6l1nhjB84/rD1GJL2ctp46c/OkAp9Zzxhns7jEBJRILQHFjgSvVYvdcd1vOd+HNlAY4sSCH58v18jzus7qgQbYJGZyyqNqGRPJozgBnAvk0gzuUqlGIIwCH8uzKq/bAqQpwogCh0Xdzzot20tbDxs2/KLSJAzfihRP2vNHeOS5EM22h8sIpIZcaQj49iELrAIqrBrBt7SD2tA/jYKGMF3aN4sUnh/HS7gG8vLcXr+ztxct7BvHy7hJe2FnG/ky/gjdbmo9i88pj2LTiODY+cUJ54HQk+pBND6PQNop86yjyLaNQy7UbkGPBsm13FLgI+rc4bxMLcsXzxf1wMH8PHJHPiAwGw6mix00exjyrhTdmNbQIxFErn1XpN2x7VwNeufd8CNvV+/z4PPHn+3zxMuBlwMuAlwEvAwuTgahu+JdsM92NJ1BITqo4OleGz/CFZfiDzL8APlBpM18gq3kuyHE5z73WPqPADsYutJFt939uR/NGlN5oHGdszPUYjRdew7TF7Z/wMRLU/9Hnit8P8qAKvLGTlFqAE79qjcyfYc4LGXyu8SfbtSDHo/qfvPvCYmvQWMMtZLDVLH9HLh8awHHu2SC0iDbS1fcFmDAWiOLGAmMYi9cMYznunivb5lyBNQnCGzc4YCdIk9fI9fJMtSCOrscW4sTXz4r2ZU4AR+CNxPOAOMEwPldmqwOcxuZVqv2ulbBG2r1oeylwZi4xQQ7Pl3aUssnn0e0620Pd5pWQSw8j3zKEQusQiqs5HGoIu7cMYX92CE9l+/BUrhtP5U6psD97FvuzvdjbwVWnziKbPomNTxzFxieOKXizacUpbGk6g45EL7LpAeRbS8i38B4MZeTSzF/eW8DNKNTKfA4MEzgjcWWdj9SzWp42tf4L1VFJs3aZuXIa9ClBnxz17OK+9BcS67YsDAqtzNk2z0Ic955+u/Hy8Xnl88rLgJcBLwNeBpajDERtgb9km8vIJ+h1M4F8Yhy5xJgK+YT21liOL/k4PHOloti44R+FODa/ooBGwI3E7v/RBkCUXYnFO0bH1pNGviLPLe5KjaDI4CrYUXjD/ZoGfPiZgzysCnAiSrRacliU7rjYAxwBheHY5qM2Uuw8IhaS1T9Wzeuh0pgLl7OVb5GPuDjuGlfedR2onEjVThitoadcI+k1ei853wUf9bYFlkgsEEViATUGyoSAi/wn5zKWYwJpXHAj2/JfFAhJOvIs0WevXt9t+dg8iD2m5hQZQzFlQnrMWY45OonxBArK+6YawGnAEycW4GhZtka1rvOVMshyr/M+Mf+HAc5iQJso4JE05fm0vMqHm1xyBLkk66KGOLmWIeRbB5UnztY1fdi+rgfb1p3G1nXHsXXtYXStPYzimqMorj6BfNtJdCaPY9OKY1j7f0ex7m/0vuEQKg6f6kZnshdZrnTVMoRcmmEY2dSwvpfyQOGHCL0UuH4O/Sx6pcEy3Lhhzxq3v4jbrgZzKs5trCzDfYp5n9AE2vUATrQdtH2K6400H9ny1zRWhj6ffD55GfAy4GXAy8BSk4FYgJNT4GYcuWaGMeSaR5FLVBtm4wt1KRRqoCgGX/oeBMChch8Hb3g8KgdRQ1UAjj0eNk6qG3Sh81Ij6EqNqlBMccl7mx69b/SxUb2cNw28iueqfUzlowNwaJhFv4QGxlrE9T2YuNgcr+UKH06z8bIKg5Dlel04T6uBmMaOOwZNhZElRmmtMnfkJ3R93DUCYySWulAtlvPctOLu5/5fuR1tpKvvCyxxYwEpEguUcSGOe0zOYyzHDZxRXjgx4IbHK2CQpCPP0ijAqXz/qnVY1X8H4IRAThTgcF9DHFmVqjKWIS2VsYAEGs26DloZDtoDWXEqJEdS3nN4r1Cb5baLAlskjsKY+ewzLfcZtcy6ACebLCPLyYZTw8imh5TXTCbVi84kvWhOoL35CDY3vYRNK5/HxhXPYcMTh7D+Hy9g3d9fwtr/exmr//oKVv1/h7H2f49hwz84gXE3Opp5fT8yqQGdZmpIp58cBu/H9tEO9aUHDuGNgUmJMnKJCMCRd3ABjBxbSCzpVaQRX56hftgdvmQ8YtU7VQCcGIhT0e8IyHHaO6Zp7lG1joRkKf6Z/bU+X7wMeBnwMuBlwMvA8pIBawtQzx7FXzJNZQ1uCHE8wJkzAPizKoAochZAiKFRP67ugUNhFiO0XhwVfDEKJCbAkW0bh+BMsHqKa7REtwltwkGlq7xvHHAjK37MUYEN8jECcSqMNKOQR6GNux/kqxh2sbEd7mbPf9SPuUYIt+3E0HPfdtKqkK+oTFbbt/KoZbTaefXqQLX/3fSi9+K++3/ltm2koxAkbl+AiRsLTJFYwEw0lv/dWM5xIU6c141AHJ7vXi/PEfesMXW7Tl5U5JVAm5i4kBpHKKTjoI4cq4Q20blKXIhj66ojf079VkM5A3msLNOK96j63lpedDsp4EbiuQAbuaYyDss8PW3Fi2xMQRMLcAzESQ2iM9mHjuazaG+iRw3ntnkFG554wYCbF7H2b4Q3r2DN/x7Gmr8ewer/OYZ1/8ehVGexpakXnQmuQDWATHIQ2RC8KTkAJ+KBkxhR4IbwJgxwInWqKnSJnBeUT53jTK/i3MoyDfoOATcCWExfoWXGzEsXgjgx3psVAIfAUMCWyJwHOI3Xo8ry8tf6PPEy4GXAy4CXgeUpA6JbSxwFOAricAiVeODQQPGFvRTzIKQ8GoWxUW+NwBiJ/ZpXzSh1j8fJRFQpjlOCo+c0sh+FN7w3r1s4vGEacflYDd4E+RZS0KvBF1G6Ja523uNwXPJA4vkCHLleLxPM+TCsoRUnk7WOGRmq2765ct/ItntP9/ncbfecyu2FAxw28C5U4baAGYmj/8u+/M9YIE00ds/htlwrHQvjOIAjxwhyZFvHDbexzjCqgpkTJz42MKcqxKkDcEzacRBHrSwkwNiNpV2qK1OVZW7f35UT8WKUmMt+1wtybrU4LPd6hScNcPSqT9r7RUGcFAEOAcsQMskBdCZ60dHcjfbm09jSxImJj2LTCj3XzYZ/HMP6vx/H+r+dwLq/ncS6/zuFDf84i80reE0/OhOEQIPIqLSY3jAyCQYX4IyBZanaWc7HowCOhTh6CJWzRLjU/1jg4ubjHLdj0wuXWfV+Q7fn7IttfxFdXMD9T863Hl7WW9MDHFsvwvnvj/v88DLgZcDLgJeBx0MGtO1RTIZ17LAHjp8DZ9kAqzgFcnEADhuEWoZqtQYjoiTHKsFh42HRKh6NqAUYTaG8JNQJKd+LtB+FRY/TvuOpoOHYAgHOApZonp+c1KoP0f9cWYzUCTE468hqFG7U33cbdXdbwIrEAl5kv1os50lMgCPb0dhNw713GNDUf4fGzo8tPwN1ZAiOBjqON04sxGkM4FTCoRhwLBCnTrnGPnvFNa7MRCHMfOGNm6Ztg+3y3AbghCYRHlGeMWooFWFLAHH61WTEHQmCnDNqcmLOcbNpxRlseuKM8rjZ+EQ3Nj7Rg80r+tDeRPDjAhsBNxreKIBDDxvWjQCaabBuAY6GODKBcRjc8jrmU/Qd7XvWzveY62LTc+u1A/5VX+GAZeVJU98LNr6vjkIcD3Bql124TPy5Pj+8DHgZ8DLgZeDRk4E4gDNmhlApcDMBmQtHJjCmgvfoZcSjIdwh6GCAQ7xSWKlMBoDCQITaZSwGar18iyjCDSjBte9b736L+38oPx8nuPJQ3tV6zCgjbL5DqJxVaQIvrAoDeHHlIiyjUheqxdF7mzpRURei54X3FwY7XIjCbRewzHU7Cmtkv1o67r0bAzJzfddweYTzTf4jyBGIU0yPozK4kx8vcDu4V/yzyDM1Hrvt6FwATvTcOKhhn9HCGz2EKpccRy7FwJWgdJ+RTQrE0XPiKK8ZBXK0Nw2HRHUk+tHe3I8tTX3YvJKhH5tXDqjQ3jSIjuYhdCZKyCRLytuGwEaF5hIyJmQNwGG/xHJT/RPbpcRIOFTU/2r1i8ftu9bfdvO8GhCy6QV9RQTeWM8ZgpjKfrf+MQ9w6peVLQd/rs8LLwNeBrwMeBl49GVA2xzWA4c6+BjUKlQEN2olKhXza5xegcoDnKVbMQIlkoruAwU4jeZBPSW40XT8eY9eYxQjG4Qacw2hr+xLXU6cd36oAEfAiQtTqgGXxTzu3k+e4cHEdeuHAQFqtSoFcBYIaUKrXUXSelgAR9WVEgqpuFAP3oTritu/c/4bBW4kDrxwOB+ODKciyLEhkyxDh5KCMx2JEtqbh9HeFAnNJXQ085wyMm5oLiPjBK6ESW8b9mlStqH+rQIyO3VLee7UhlWSZu3YSbOivvK/mGdzAE4Y3giIWSjE8R44br77bSuDPi98XngZ8DLgZeBxkAH7wVgDHNHbDcDh8uFqGXEuJe5MaOgBztIVjpCCOx+A4yjFi9cIOEpwyNgOK8CLd7+lWz7+HaNlU0s25vNfNP2ltu+8U4VBWPtZ5+qVEn++C1S4LY3+g4qj93sw8IbvWrduhQBOBLjUgjHz+e+BAhzjDaLgjQDPKMCR404canvD+aX7DfuBhn289rxxPXB4zAQFcQhyRpFlSOmQSY0ik2QYQSdDooyO5nDobC6r4yGA44AbBXGa6IlTRla8bUy/pOcZEk8qiZ3ha6F3dOqaOh5+57ryomQqmoa7H04v6HsfOMDRIMjOyWaHbjX2TuHn9tf4/PAy4GXAy4CXAS8Dy0kG6gEcBW4mDcSRJXLlouX0oo/XswaKZINeOOKpE7ioi7LciFHU0Dmu0utuP17l4hvHuPJ25WGh23HpL7VjzjsuCYDzZ0Ac3nPxQU7d+rWsAQ7l2JEdbocAjgNqosej18XAjHiAIxDHATcCcCJDgQTgSKxAjoI5IwrmEOgEITGCTELDHQVxovCG3jkEOE1lBF444onDVcaiQK0ClkXyKeZ968pKqF+LS4/HKtsWlY/mw4kAlrAXzny8b+Qa8eBx59bx8CauHPyxStn0eeLzxMuAlwEvA4+KDAiLkY+k8hFWPHA8wIlV0pZ6BQgBHAfihEBNoGTaoVbR6xb3PaNK8KNSifx7LFxOorIxn/3lUg7Ou/0pAIfgRBr8aCwdwGLH0fu4+4sHcurK4bIHOJRxR35kOwpsZF/+r4gr64pt+60XTuBtE0AbATkCFGws4MaNXW8ceuUoTx0VG5hjQA5hThAMzNEAp6QAThzEkYmN1XC4CoATzafK960rKxVwJprv8WkG+Rj0rxHYEhx3+t05HYtJb9E/uMS/29zzzKfj88zLgJcBLwNeBrwMLL4MeICzLAFNPUEIlEij2M1nv949/P++QVp8GYgaSY3uL6eycN5pyQEcgpXFhjduei64cbcXB+LUlcdHAuBQ1h0ZmtN29Xpi+wiztLUaQiXAJhpbcCMT8LrgptY2z1f/G5DjzqGjtglzOMRKeeBYL5xKiKMnNq4OcKq/a105qYA3jadl8zEG0CygPw7SVbCHEMek76T5IN/Lp924DPi88nnlZcDLgJcBLwMPVgY8wHkkAQ6FJlD45rn9YAXPV2yfv4+rDDjG958GcASYuBDF3Xahy4PYdu8l2/JM84/r1qlHBuBE644jUxVAJ3pu/H6ov1ArPkWhDfcrwY0cqwVt+J8+bwz5tKRrjjlz6aj5dAzAyQQAhxCnhGy14VQcUhXrgRP/nnVlZAHwRtIO5eU8+9+5piH39vGfV+4+733eexnwMuBlwMvAw5EBD3AeWYAjAuQVQd+YiCz4eCnIgmNs/+kAx4UlAlIkfhDgxk1T7iOx+yzz264r348swFm4XIf6CePdIXCmkbg+wNHghgCHy7m7y5KH0ueqVtUgDlelkpWpZE4cAhc+bx1QUlc2FgHcRO9R75kW4//oPf3+wuuCz0Ofh14GvAx4GfAysLRlwAOcRx7gLG0B9A2EL5/HTQaWKsAhNBGYwtiFLQ9q273f/KCNOxly3brkAU7V/i4EExYZ4FhAY4ZnGQ8fgpxoEC8cDpmSJcW57QYuLV4r5A3cCb3TAwA0deXN37OqvPm8e9z6Pf++Xua9DHgZ8DKweDLgAY5XMLyS6WXAy8BDlgEH4oSGvNTu3FxY8WC2XaDyoKCNm657v4UBnIY6RQ9wqsp5CHbMA+AQ0lTzwnEBDj1vAmhDb5xIUOdyfpyE9sRxwU29bfHOEbgTBTkNyYhvC6vKiM+/2u2zzx+fP14GvAx4GfAy8HBkwAMcr6x4hdXLgJeBhywDcQCnfqf3YKCNC05coOKClge17d7PfY65bTfcWRqAwxWMZPLbBxXLKkkc3tPw8/3J9TAO4nCyXAtg6m9HIY69VoZNmaFUBDnp8crA4VWcQJlz44inTcQDR0COO0+OzJHD/3LN1kMnCnH4jsulPPxz+rLyMuBlwMuAlwEvA14GKmXAAxyvzHmF1suAl4E/QQZciNNY5/S4ApzKjqux/Iq9LpjwdgwWsjzA7WVUt0IAh3PKGE8ciS2MqQ1yBOLY88PwRnvhjCOfigvGQ0cgjgty6JXjwJz5AhwPcRZQf5aRPMfWf//8f0Jf5+XNy6KXAS8DXgYWVwY8wPGdmVdovAx4GVgWMvC4AZzF7eyM8uB44Tx4gLO8FJYKgCMTAzsgx0KZ2hDHnlcJb/QExuPIpSqDhjrOMCt5hsAbx0KcRgAOvXi0F85IaKLjByJbvh1dFu2oL/vl1S758vLl5WXAy4CXgagMeIDjFQ6vdHoZ8DKwLGTAA5xoB+b3F1upWVyIEwdvKqGNC3LEK6eQGocOWknhc8mQKvHCaRTgyHXucKrFzjefnq+LXga8DHgZ8DLgZcDLwMORAQ9wloXh9nCEwVc6n89eBpayDDxOAKeRcqgKG8Rro0bcSPqP+zkV+TvnyY2jAKc2vCHIEYDDOAA4HPZmylJgDCcsFpDjxtGJjIPzjQePC3Hk/SRt2Xfjx10G/Pv7PtHLgJcBLwNeBrwMLDUZ8ADHAxzvfeFlwMvAspCBxwXgNKIouEb2fLYbuYc/Ryssofyd06TGLsCpD29qAhxOehyFODKsao5xHMSRtKOxl4GlprT65/Ey6WXAy4CXAS8Dj7sMeICzLAw3X1Ef94rq39/XAS0DjxbEqVyFKq6co0a1u59L2hWHot4W0f18MjwPiqQTd09/zLY5kk8qDgEcF9DU224M4FiIo+fBqZinqJpXVRTiNNu5csRDx/XOqQ5xZM6cUQWMvBxYOfB54fPCy4CXAS8DXga8DCwFGfAAxwMc733hZcDLwLKSgUcH4oQBTlQpCMCBO3SHc6EEYQRZrkwUNd5r7VcDAOZ49Bn8vgYZtizqgZq4/xuHNwrgJMeRT1YBOGY4lVsuwbM58+QoeBczzMqFerFAz8iOmya33fv5bZ8fXga8DHgZ8DLgZcDLwJ8nAx7geMXMK6deBrwMLEsZWDyQM4LKtFy4wu3xIBSS41icYDugaCcYGNAhjw8Lb7JJC280xAnPieIa6hXbAQCKwIlaEGe+y48/AnUrKAsDVfQqUnGgJu7Y3OBNCOAk45Z3dxRGp0zsM46qVacqyjwG6tUEOFy9yoF9Sj6d+0Xl1e875fIIyLwvT1+eXga8DHgZ8DKwdGXA6s/FpKujj+EvdDvOJyeQT06aWJR2ucgX7NItWF82vmy8DDwOMlAJXkZjYEytY4Q3EqLnVUKcxQE30b6EfYqV18BwjsCbbHIUOowgkxxBJsFQRqa5h/Cl/QAAIABJREFUMsiwmVAsRjyHXUVCcE/nOYJncg33uW7HpbfMjum84Rw0OjQOcOYOb6IAJ+/mt5tv7nGz7ZZhsC1lHhPXAjgEQGqYlQE5Shbce7rP4rdD9TeoNz5ffL54GfAy4GXAy4CXgQcgA8JiRE+Xj6we4DyAzLYGildw6udFoIA7X0HdYz4P6+ehz6Plm0eU9UbLb/4QR8CNG1eHOAV6RCya9w0hju2A3HdV9dyBN9nUKFRIjmpwY+BNZ6IMFZrL6GRoKqnA5aVD4MYdSmOGXGmvHWf+HGd+HPdZ1LYx3IupMTQa7NwtjZdjxX3nIAMP8lrd7kYBzvzgDAFNvZCXIVQGGMW+W6RMdH5X5nXQZ8QCnBgPrCrnFVKjQdlXu1fscy6RMvTPVikbPk8Wlidz6aN8Xi8sr33++fzzMuBlICwDVn/2HjiPkqKVGsVyNCACZbsKtIn7PyzQDVTwZZo3c37PR0meH5N3ma98NwZwSOldOONCm+i2ex63NeG3wEU8aBYa2w5I2qu8O9+NgJvUKDIMBDfJEXQmGDS86WguQ4WmEjoMwCHIIcTRoYxMU9hDR+CNxGq4TQMAR55xTvEjILtaLl2AUx/C1IM0tf5vDOC4fZwZahWT1+E6xWFREmLgTdDvyDkS63sJvNPl30BfE/M8y6od9x5HDUP0ZVWuC5DLcH0K16FFywOvo81d7ty6KtsLKOdFK0v/DHMvS59nPs8akgGrP1cAHLql+yFUy1RJkwY8iOf2HrU66fj/RNF142qduxU6axDqY9pFP3ydvZ+bNrcpn2XkUw2G5AgKKQYq46L8zy1ffKf2qOSXyOBc32cEBcqRGIGNyl5w3gjyKZHjanIef7ye7IXhjAtgNHzpEu+RAOK40KbsDKPicfd6DXDCdVWDG21oc8LZuQXtxSNl4M5zMgoNcEaQS40gmyqrkEmVkUmV0Jk0ITGMDhWG0NFsQtMgOkzobBoEQ6ZpyIRhZJqGkWkumVBWw68aBjixc7G4zx2/zeE/KhhPErZv9cpxKf4vQ6d0bOBN8sFBnIYADhWcoH9j/sfXZdt/xNcr/s88r3WeKhOpP8E94++3FMtvXs8U9JEi29H3ddrBuu1h/XZPP+PyrB/zyt+GFPRonsu+tJ2y/yBiuYfE0Toi/ZiJ2a+pEKOPiXyoWKcTn2fmXkEdqyZ7D+J9l0+aFW2VU1eLackz9j3RMpP9aNnFlFmgs5j/Ysqw4jnqtKP6fP0hwOoTDzvfRZ6j8cN+jsf1ftF8j9t/XPNmru9t8y4W4OT8HDjLT+F2GnOr4ErDLXGkAWfjXK3zjTbkNfdLyKckSKfAe8l95StuNYNP/69WmUmMIjQRZbKMXLJkwjByqSETBpFLMQyokE/1IxwGkE8NIp8aUs9WSJUdiKMNxrAyYSuF7WSix+Za0Xi+Nf7jt+eT5nK65mG/f7TMGtmP5qd5ZiUzlBvK9rAJlCfKFQNlr79OEBnk9UyH9YP1TuqGjQP5V/VG6kw4Dstm+LktgDHwJjmOrpQOuqEXQCMQpzbAscaVm4cL8b6RdASmMh5BIc38YL6UkEsNI5saQjY1iEyqH50MyT4VOpK96Egw9KCdoZmhG+1N3eho6glC58oedDb1obOpH51NA8g0DyLTLCCnjGyCwaxiZSY2DsMKN8+rtVlzOe6mV7kdLlPJo1pxuNzD7dhi/KfvbfNEv2uO8GYpAJwGjWDb/9g6NpdjFviLcRTXbyxGfi9+GnN5T/fc4EOHMghN/azQEaQ9ZFuo20PdD0tbyHaRfTP/c9s9afvidIOovM81T6LX19qfa9rR82ulHf0veu189qNpRvfnk6Z7TTg9C+alrdL1R88fFtXHWL7SJ1LncvWuaPkbfdPRC3XbN45CygZ6vD04b7fwu9Zue908ituO023iznOPNXp/fY1bNyu2RedPj6HYMg4FcdLyMcSNpX/Vfayuk9Eyk7KTWHScmDIMyq9WuyqyEx8vLN/d/HS3G83buZznpj/Xbd4n7po4uYk7FnftUj42l3xt5Nyl/K5/5rPZvGsI4MiXMV3p/swH9/eObxCiXyVtByhftrULOTtfCQJFGA9HwhCySRMSQ8gkhpB1g/yXHES2IgwZ2ML7GEM1NY58agKFSOAxFZIT2jBIjiGnAI6FONlECdnksL6PGMzpPuRb+tC1egDb1w1h18YSdm8qY/fmMvZs5nYJu9YPY/uaYWxtG0ZXawnFljKKaddwtB1c7c7EVhZ7Xi05jGuIzbEACBAKlCNwp1aay/E/Jx9qvrdznoJd833XuHLisUqvETlmy5PnyX3praWhDcFNITWMQkhB7UM+3YdCax+KbZTBPmxbO4Cd64fw5KYSntxUNqGEnRtL2L52GF2rhpBvGUQ+PYx8msaPUWTNsCGBOblA/sfA7agBLe0wj4efXdd31ZjTY0BWjgrgDSc543ECHIE3jAlwwhDH5oPkR8y94u7f4DGthIpXHOFNWQXmTS5No68f2XQfsuleZNLdyLZ0I9fWg8KqXhRX9+mwpg9dQejH1tU6bFs9AIbtqwextW0AhZYB5JIDyCYGVfuVSRDi0AvHQhxtlFARdfOb2y6g4aT+0cBJ/mNCahJ5FUzbpto4tn/REL2fk8+BASPtuIkr8ljKaLFi5xmC/LD5YAHOBHKpWmF+XjqufGsZX/h7VRg9tYwPtz66ZRDADN1/SJpBXVGGVLhvCUBIrf+CNmfh7ynPIs+mYvd9ar23/GfOV8/OvtIBqxo+i7E+hELLEIqtQ6pt27ZmSPXDOzcMqzZv54Yydm4Ywc71o9ixdhTb1oyiq20UxdYR5NOcSLyMrNJDpN5ZGQu3a43mS1hu49v36DmNph09L5qOAO3ocXc/mka0z4vbl2skHTePeE85zljOjca10nXPlbTsPXR7x33t0UHPSF1uJWQT7MeGUWwdxtbVJWxbU8L2dSXsXD+MnRtK2LWhjF0bRrFr/Rh2rhvH9jXj2LZ6HFvbxtHVMoZCmm2fLnsNbiYd3VBADts8PmPcO/CY+/zVzomeJ+8ZF9cqR/dekWcS3Sb2OaPXufc190uNo2jglYpNuUo9DvRh9aFBJuHXqzIqvUHVUwKcMRRU3lKvNfqF+o/1mKGs9Q9+eEqzb+xHsbUPXW392L5mELvWl7B740gQntw4gl3ry9i+poyuthIK1F34gYV1lx8/1GIAZnVIZ966nNp2VyW0nptun2rrqJ2AVesooqfIx6b6sZWFmPyd89x9bhqyHS3HRvblWsbu+SKr1P9rBTkvKsNuWktl231Xd1vq1FxjN42l8o5L6Tls/niAE6pcS6mQ5vgsrsLJbTbagQKnO1/lyUIgkhh2gAyNGwkDyCRMaO5HZ3M/Opr6wqG5Dx2h0IuOZh06E73oTPQjw/QUFCqDDbqGN5MopBmmUExPqbiQmjLGjl7xTBkIiVFkVeAXcnYWGt5kkv3IprRhl2vtQX5VN3ZuHMT+7Cie2zaFF3bO4MWdM3hpF+MpPFOYwL6OUexcX1Kd0NZVZRRbaDBGFe2IceR8CVLKRUhRshUn3ChLWTmNrnTs9eKg45c0HpXY5EW995f/g3yYT4fllots607DKg2uAa4VVa1EyPkm39XzWHBTSA2ioL4q9iGf6kU+3Y1C61l0re7GtnU92LmxD3s7h3GwMIZDO6bxwk4dDu2cxrNbJ7E/M4qdG4dRXDWAQitBDr3IWC8EburhNgreKIBDiGmCeDyEYIIo2RoAWG87TrSrlUEdT6CYmrAwxwCcQmIEOhjlISFKhCO7FW2i5NFCY/PFznzRV6AsbZRDwhuCm1QPsumzyLacQbb1FPKrTmPrOtb1PuxuH8SejiHs7RzCvsww9mdK2J8t46nsCJ7OjuBAdhQHs2Mq7Nsygp3rhtHVRoVVewOwLckkOJxKIE7YCycogyDfRWZiQI2CN1PIJ01QbRnbMwkCciQWoBMBOQJKVPstxkudONQmLVZ7UVm2tu5omdMAh7C9TgjgztxAztwAjiuvcdtG1gROSBz0iRbg648csm/6A0KbIBgjKLhWe7IGxmUtSNPIfxX1bT5lqvNAfagRAy4Uy/vViw1cTWu4GgY32uuwkB5Q9Yrghkb77i1l1c4dLI6rNu/ZrVN4pmsGzxTP46nMDPZsmcLO9RPYtnoMhVYaf3pIIw1BDaqlnomi78pivbxwz5Xr42L3PNmOps08jB5z9+U6xuH+RfbDfYqc76ZhZFX6vbhY9YVyjb6XrhvRfOIz1LhH0KeyjXfriKQtcfQe0t4R0rIOj+qhrSy3xBA6E4MotA5j21p+rBjF3vYxPJUdx4HCOJ7pmsCzxSk8WzyHZ4sXcCB3Hvs7zmPP5nPYtX4a21dPKm8RepsSahdSk0ofpE5YTBHkSNtHPc18TAk9u/se1bbd95V3ZCx5JXGcnMgxOYexm0aj5SfP5l4raZp7qP56AsW06aul/2Z/bz5kZptHkFGhHOq31IcHtkfUZ1sIRwlwdDtFSMqgj4+jq20cxdaygq75dD8K/PjZ1outq3uwc30f9rWX8Ex+Ai9uP48Xtp/DC9vO4dC2c3gmP4V97ePYsa6MYpuGdqy7nWpIsgY5ymu4KsBx23/dZ9gPIZLPAnDkI5P2HrbexO4Hp7htATz6Ol3GNm1bLydQSMYFey7rmD1fyipa/m55cpvlHD3G/bjrRSZE52oklmvi7rEUjrnv6W67ecntuLyPHnOvkbSWwjsutWeQvJG6InVoDH+hchsdQiWKlRbKpfYy/nlUA6IURVf5FPLOjpJlasENAUummUMLGPpVIKzpbO5Dp4Ex7U09aF/ZjS0ru7F5xVkdVp7FZu6rwG2GM9jSZAOHNai06LHDr2yqkxFwM41iy4wJ0yikp1FITSujJ5ecVF442cSYAThlZNlR0AMoOYBMynyRbz2L3KozKKw5jb2ZIRzaOYkTB1/DmUNX0P3CFfS+eAXdhy7h6L7zeH7rJPa282vQCLavGVFfAPlVUX+RoIJg8ouxanBFeXBi1ajL/24clTtpaMWDw3jZBECAUCAaop440TSX677Ji0A5jb637Lt55CpdvL7Rd3fLRLZ1R6DbLRqfVHxFKdWwUP6zCjfvJ2WnvW4IbgqpfhRS9LrpQT59Brn0KRTaTmHbujPYtakbezN9eHb7CF7ZN4PTz1/GmUM6nD50GccOvornd0xhb2cZXWv6UVw1iHwrPU04VIjzvVABMwAnpb1usolxMOQkhGCCwBsTm7lWRI6LabpRT6CLgfBGARwDcRJjKCRGDbwhxCmjkCiZmNsMIsNxeS95u5CYRjXBlXXl5pe9QpqQpR85etykTiPbcgq51hPItR1H19qT2LW5G/uyAzjYVcKz28p4fscoXtg1hhefnMBLuyfx8u4pvLJnGof3zODInnM4uuc8DnVNYn/HCHasHUSxlemzPXIAjlmCXA+l0tBYARwFz5j/AinYLjmgJjmNfHIaOSdwP6/asbg4CnQIciohjjVcnLYnMGZijgUKYlxZzfVYXJlGZE3JoeRJA/E8II7oGeINVb0NEDmtHWtZE2hYD1zI/xrcqC/aEYATeLMSjJi5IXSbwXZDfxgoqlWruHJV7SDnay+DuZaXe77NA3mm4At8COAIMJb3jMbmqz2vUX0kY9ZT8bohvOEw5T4U0n3YtmYAOzcMYU97CQcKo6ofPrz/HI4duIhjT7+Go09dxpH9V/HCztdwsHABe9unsWP9BIptnJB8GO1NQ2oFOfb37Pt1+8x2WpR4LZPWkBNDLRpHlVhRZqvFcj5jNy3XOHSP620ti1JP5Bmj/YyuM5VGoJSXKaugX4z0f+7xwDDkPZmu24dV5lO4rkTuE3zpt7Jiz7fvFL7HlCoXATjZlC43wpuOBPuyYezaNIKnshMgsOMHtFf2nsPRpy7g6FOv4dhTV3Bs/+t4+cmrOLTtMg7mX8O+9ovYtX4GXW1sPyh/7K+m0NUyg670tAE4NOr4TA7AqcgX9z1kWxvD1qvU9SyV8pSyryYbvLdbtpI3Un6MRUeQshNdhrEcc3UZuVbSEhnR/XNXehIMW02s+m0DcAhvZIVFTtSvJutXOjE92PR8cSGA06phTgBv2sawddUEtq2ZQNeqERRa+ZGkB4XWbmxd3Y0d689iz5Y+PNc1iiN7L+Dsc9dw5tlrOPPMVZw+eBVH9ryK54rT2LN5FFtX8+MTdfFhtK8cVs+iPFnpGVQX4BAEsh/VQeo6jfpiEKRMpIykzNx6Gbct58l1ko6bNrcng1BITsIGCxG0/Ev5UA6kzKQMo7HIHmP3P7lOnkmekc9PuSw1GHiuyLFOI3wf954Pd1u3nfJ+0VjKgLGUg81/tyxkW5eHW/8kDx/uey2V/K39HJI3ku+S32P4C5dlrQZwFsutufbD+QKbV/7EfOkThZNf/EnOOQyK80F0Ng+Y+SEIbPqQSfRBe7iYMezKmOJQBhnOQM+XfmRb+pFrHTCB+30mEK70qCEPmXQfMqkBM48FAQ4NVAIcApsZFYot51BIM8wgn2KgQcTGfQICcNg5qMlHCZs4B0aKwynOorDmLLZt7MaTnb049OQ4Th+6hJHTb2Oy9xam+2/h3MAtTPW+i8Ejb+DkwddwaPs0ns5O4MmNY+hqM0qpQJwQwHGNJBnuZY4FnbqtOIGSETTcpjF3O/EKYEMw4Aan0w8UtkdB/l0lh+/ovjO3XaXHyQPmncqHaIdYK0+iZeIq1VR0XcVXDAULcSoBDp9nCNrzhvCmF4WUhjf59EnkWo6ja80JPLnlLJ4u9OHQkyWceHYG/UeuYrznJib7bgWhdOoGTj1/Cc/vnMKT7SXs2FBC1+qSAjicqFdBHAKcNF2OzfwiBuAIyJHYej2Y8+ito9zbNQDipLmEAOprnnzRY+eZmEAhMY5CVYBTQj5RNkEUkrnkdzT/q+/rYR0W3qi5ExS8GVCALJc+g2zLSRRXn8S29aewa8sp7Mt147kdwzi8fwKnnjuPM4cuovvFV9H78iX0v3IZA4evYvDwNQwdeR3DR95A6cibKB25jlMHXsOhbVPYvWkY21YT4nDuLAE4xgtHeeLQC0e8/giPNUDLEuAkNLxxYU0uOYO4kE+dg4Rcaga5tA759DTy6SkTJpFPM0S8cdi+GFijoJvAt5hYzrPKJfO7Vnk18l+0zNw6pBVa1iMtg1YRF4W8MjbnKojjfoWtvd0YwBEZrR2HYYaFFZXGhjVA9JAEATjjZkiC9maloVQBRqStMpPkK2iTHlVDdTlct2Zw++t5l184DzSwckCMgsTa0NPAWEPj+DxwrguGNXK4J+uNzPXVh1yqB4UWfrkfwP5MGc9tG8fhfedw+tBrGDj2Osqnb6B08h2UTtzC0LHbOHPobRze9zqe3foa9nbMYNvaMWUEbl4xoI3AZhfg6HbZtslUUMX4qRZHlVgxGCQW5daN5Ro3zahx6P6nt22dEyNfG3+6j9FQRT4MhEGU1MFovxjtB2Vf+kFeJ8M53b6rWv/F841MhPQQDTf0kA39v81X1n2p73IPwhutk2VT48imxtSk8p3JIQVv2hO92LaO3o8TeGHXeRx7+hLOHLqKAba/J99C6cQ7KJ+4jfLx99H74i2cePptvLTrDRzMX8buTecVwFEfitknpabQlT6HInXD1JSBVTLEikAkoh/Ie8l7BnBKv2MY4ISN3/BwA1ceRFZ0LPlhy9spP95XnkE9m+g2TtnJ/8Ez6nKUdEVetPcRAQ7zYNJ8dGFZjCHXPKpWUexYWYIOhJ4anHAlxgyHMglsbR1FsY3ebaPIc5hiiw481rV63ACcEgqt/EhyBl2rz2LXph48le3Hoe1lnDxwHoOH38RUzz8x1f0eJs++h4kztzF4+AZOPH0Zz3XNKE+rrWvKSnfZsmJIQxz2n0kuOjAaCZQXyg3DBLKpSR0cgCMAhQZ8V2LChHF0JcdMGEVXkmGkTpB6yjotZSrlWR0ayP3dWMolXHelbxQZkDjc9up6J/9J+8Ln4TafUeCNAJxhFJO1gkAekWFeL+2Q3Ofhx/IMOpb3lFjyX2KWg1sGUygmqwWBalGI8/DfceH61IN+ZpHJaL4bgJNVyprtJFzFaum/3IPOvKWZfnjMu1HazHw3Ct5wHhkFcAhvOCyqV036STjCMbHFtiFsW1vCzg2j2L1lHHs7JrGvcwr7szN4OncOBwrncbB4Ec9ufRXPdF3Ewa4LOFg8j6cLM9ifpZfBOHa3j2DnxhFsWzeCoiL+9DCgwTmJbIJKgSgGVA5cQ4j7U6DRVAlwBtCZ7EVn6gzyq8/iyY4BPLNjBIcPTKP/2OuYGXoPb57/DO9e+hK3rnyJ9659iZuXPseVsQ8x2X0b/S+9hSN7L+NAdgZbV9Flm94PNCLZEYsXjjWgtIFEgGPhTXyDzkrkyoLbsYviwU5dOniJCQcYXJDhKmxumst1W/LCfX/3vSUvInlAxSek9DTy/rYxswqSNTqt543rRSGKqnzJlLLkc/OZ+awCb7pRSJ+BhjfHkGs5jO0bTuBgVz+OHhhH35FXMdF3A5cn7+DdK1/h1rVvdHj9G1y/+DmmB/+J3lfexMt7LuBAcRq7No0j31JSqytRAdPGlAE4NHgDkMm6MI6MhOZxZBgUYCBoMJ5qyVFkzUS8ejiCVvhVXih4YwBO8xjyzaPIN4+gwBB44IQBDo3AsFw3UgaNnaMNTPtVX090Smjci3xLN/Ktp1FYdQK727vx7NZBHN4/hrMvnkPp5FXMDLyLy+P/wtWJO3h96kO8OfMR3py5i+szH+P6zCd4a+ZTHaY/w/WpTzF5+iZOHbiEA1nOxTGErZyHKG0AjgyjCgEcyVMCHOa9brOyiWhbdQ65pBvOI5c8j1zKDeeQS51DLs1AkBOFONUAjrjSV4/1PGJaya80MBorh8ryrVWHWJc0CK0ENbVgztwhjqtnxH8siijNYijFxGGYoQFOPLioBnDsnBL02FTDEgKPFsqwDtqYY7sxgiJDusEQABx5p/mUnVyr21t5JjeW4ZoW4EibY9/b/U9dq+bL0HNmuHU0l+pGLnVWDSHd1zGMF3dN4fRzlzB84i3VD1+buYsbr32Bty5+jesX7uHNme9wbvAzDB37ACcOvo3ntl3Gk5unkUkNY+M/+rBlxTA6FcCRtlnrnNKOW+NHjLS4mEqsGAyMXaNBDDnG7jmi+Ep6UXgj+/K/xKwnAm8Y09hw+xJdT7Qcy5d86VtYvm6/KAZ/XCz9IK9he850eR83n7gvzxK5R3AfRw9xQIc2JqPvZO+h9bQZZKmTJSeQSY0ikyyhIzmI9kQftiS6sWvLCA7tuoAzL7yJ8qmbODf4Aa5NfYwbr36Jty9+g7cvfo+3L/wbrw5/jfLxj3DqmVt4Yfsb2LvlggKjm/8xiI4VZWSamE/aqzFPYK48T42nJp859LGH+05w3olwqtK7QYxf+65hOXDlQwxNfcyWs7SNbvnJc1F/ieo1LE/nGeP0TFOeAg9o5CpZSoyrPjrXNIps0wgyK8sG3mjvm47mEjqay+hMGoDD+aQ4LYACOKMotGmAk2spg0GBnFb+z/MGkE13I5M6iR0b2L+WcPzANAaPXMFM3028PvEJ3rv8PW5f/g63Ln2Lm69+i2vjn2PizPvofv46Xtz5GvZ3zqBr9Rg2rxjE5pVDaG/m6pAjWj5CEEcAjgNvUpQlbQNQjoN3TxDgCMQZR1eCYQxdCRfelNGVjAsCd3iu2w6YskxMohiEKRQTTlDPYJ+jsh67dcutX0YOlJ7ktL9qn//pOmthIZ+L8icAh2CG4GaoTuA5PFfk2gIcplfZhz+cY9UBjtu+Ou1wtfx3y4LbCuxEIU403x/OO/5Zedv4faVNkn5M8j4CcLQnjhhD7Eh8hjaeyQ9T2OSLhUxypj1u1KSdahJierEMKC8bzlPT0XwWW1aewpamk8imzqK4qhc71g9iT/sIDuQn8Pz2c3hp96s4vO8Sjj19FScOvoFTz13HmUM30P3iOyqcfeEGTh+6jpPPvo6jT1/Gy3sv4tCuaRwoTmJP5xh2bCgrb4N8a0l3/k1ldNIltHlUKW2ZxAQyNJKUksDGnQYTjacxZBKjaqJRzlfRmSDA6UFH6jSKa7txcNsoTjx3EaUzb+G18Tu4dfUbfP7+r7j30Sy++3gWP3wyi28//A0f3fgRb5//Gq8Of4yBl9/Fiztew/Y1/NLOSZb1PCS1IY404G5sK442ntwyNo15qPOOKmcuuBB4YTr7UAfgprsct0VRdRUd992j+eIoPEHH2Oh7u2Wiy0or0KJMi4Ityi9jOcZzpHyZDp+bz0aljMOn+pT3TSF11gE4r2DXlpN4ed8IBo9fwsXRW3j70qf46OaP+O6TWfzwuQlfzOLLO7/h5tV7uFD6EP1HbuDw/svYn51Gvo11YsiBOA7AMa7G2SRhzRgy/BKn6g3dqVmHJOh5JNScLmpuFwJaetmV9SpuCtYQ2kggvNEAR0EcBXDE80ZiGRrSaN43fp4GzPzK73zVT/Yhm+xBNnkW+ZbTyvNm6zrCsQGceHYK5VNX8erITdy4+DHuvPUtvnz/Z3z1wa/45s5vuPfh77j34R863JnFvX/N4t4Hf+Cb9//AV+/9gbemvkTpyNs4tHVSTWq+fU0JBYIzwpsIwGF7o71wxAOHACcMcbLqi7RAZxfgONuENm6oBXCUF44ZSkUZXNIeOLouNe6Bo70p9UTHtb1u9BANfY6ut1rPiNc1IgpzqK112hADV6IeM/UADv+nN5yaDNSdFNTMKeGCEdm2xtpcAQ7bGhPm3fY7+RG8s4VLfMa5ARxzbQBwtPdN1qmn2eRpFNvO4pnCGE49exljZ2/i8vhdBa0/e/9n3Pt4Ft98NIuvP5zFlx/8gdtXfsFrI99g+PgdHH3qLezPnkc2PYT1f+vGpifohUOdJdwmi/GsjSBRUsUId2P5TxRYMRxcwzwKb3iOXMeuoVQQAAAgAElEQVRY0hNoI7Ecd2Ppa6TPqO6BI+8Q1hOi/WK0H5R9F+AIQI0CnGjfJW1xVA9x09Tp1gc40yC4pk6WYT+UHEEnh84k+rGluRtbEmewLzuO4wevYuzsP3Fl4jPcvvodPnvvF3z78Sy+vTuL76iPfTiLD17/HZfK32Lo8B0c2fMW9ndeRC5dxrq/9mDT3wbR/sQIsk2TyDYzjCPbPAYOHcol2BeJ/sB3CNdv+6GH78v30sEavAJvxPCVMq8mKxbg2LJjOUuZO/kbPAufK06vMc+q9AmWefhDoRpCq4bFjam5bvKcC6q5jGxTCZmVw8isGELnSh06Vg6hg543zcPooD7MeYgY6FWvAM6oBjirxlBoG1MeOIQ6zONsmucNI5MaQmeKH0FPozN5DHs6enHsqWmMnnoTV8f+hduXv8ZnN3/BT5/M4kfq0Hdn8cNHs/j0nd9x49x3mO69i55D7+KFHZexc+OUqrebVgwqj6DOBIfWjRqIQ3Aj8Ea8b8IQhx8BRP8iNNEeODEAR3nfuBBHYE005jmsoyxfKVvWeSlPF+Jwm7BA/ycQSWL9XLpehfVCyoCUv8RO21vRfmuZ0c8jbQzlUGCMQJzl5oEjdSgaS75LLO2vUwYENFFo4+47UM/Wv7h8l/x/nGNpk6QcJN89wImpqEtRUKySS0VXB84IzwkCBd5wjPkgOpoH0JHgMrxswLvRkTyNLc3HsWnlYWxuegXZ1uPYtv4Mdrf34WBXGS/tnsKJZ15F94t0ib2u3GHHztzGZM/7mO6/g/NDd3Fu6CPMDNzBVP8HGO++jeETN9D7yjWcOvQqXt43jWe2jWF3xxC2rR9AYRXv3YPNK3uwZWUftqzsR3vToBnPO6KNVHoZJAluBN7wOCdvK6sJ8xTASZ7Ctg09eGnfNEqnb+DazMf44K0fcO/uLGZ/vI/7P9/H/V+B+78Bs/+9j/9+MYsv3vsD/7zyH0z33MXx/a9jxzqm2a8mbSbEoYKrlWjOVxCdD8dWEtuJVzvmyojTsAcdfUT5cI+rxp/XuGk8CtsmH9x3rbYd5EE0H9z8rpYn7jmNApywwaA7a6ZDRYtlRaWMkxfr+W/0EKrTavhUtuUV7M2cxelDMzg/fBPvvfEVvrzzC365dx/4A6EfZfKbu7O4dfV7nBv6ED0v3cBz2y6gsGoYnQqocrlsGln8Gk5jnsNrptR4cQ1wCDzL6FSu01SYaPCYwHrNScZV4FBIzmFF77pBZJqGlDKYbSqDX/Pojp2jUmwCAY4KVJKjwUz26sqjrIix4FgZmax3nFSY8KYbnYnTyCRPIt96Al1rT2DHxuN4eW8ZpZNXcG36fdy58TXuffQLfvv2vqrb938H7jOfZ3Ws9n8D7v8CzP58H7P/ua/ag09u/I7z/R/i8O4L2NfOycw5qSMnL64BcJJRL5wIyKnqQShgZwZq+BSHUCkPHBlKJR44ZvgU4U3amQeHRkIAcNyJqGXbeuTIeeE2qVrdaPS4W4dkWz7c2NgO45PhVG7seuPM3ftGyb8YNaZPc2VQbzttqzKOarSrIaBhhwdpbxPX+yS8HQY47qSgYTBSCXDKc/TAWfoAh8uAZ1U71YtMohuZ5FlkDMB5Ycc0ho7ewNWJT/Gv6z8qo3323/dV3VR18leouvjtR7N47+pvuDD0FXpevI1nu15FrmUQa/73lPLCaV/JlW207IgBZRV4KqaipNaLRYltJHbTcgFN7e1wnWM9sf2NbOtY6pDEUg/j+0SRJRsTXtALhbqdC3Cs8au9cvgfz5F5nqyc27QIzA0AUZDD9rHMW3luMaq1JzS9oafVBzallyXL6EgOgUOnNjefwebESRzsmkTfK2/h8vhn+ODNf+vy//F+uF3+HQoI3L70O6a6P8fJA+/g6exFtaLoqv/3BNb9DyHOEDqeGEPnijFkVo4is5JARwMNDXAidTykK0Tag9B/kueMpRwEvEVjDeLsRKvu/1a/1vmsP3JYoOQAMrfNUXO96fJQAEetBEX9QoZzam/wYFqDpn50rOhF+4oetD/RreKOlfSSZx8/gE5OHs053FLae1cDHHra0PNmDMVVEyi0jSPfOqq8cghveD6v3dLUh/bmM2hPnEBH8jCezg2g7+XLuDZxB3dv/IDvP5rFH985dZf96W/AH/fu44tbf+Dt8z9i8uzHOHHgBvZ2nMOmJ/oVxNnCuXDUcC56aBHiuPDGHUJFiKNBjoL6xqPTzoFjPG+S7hAqDqXScKZWrAGs1Odo3ReQ4MYCb6JlHgU3lAGRG1eWTF1z61yF7i7XSRpyPeVVYGMJhWStIOeJjEsaf0Ys7yOxWz/itiVvxaNGYno8SZBj0ZjXSpq835/xvkv9nlIOUbn3AGdpCkxA8DVksNBGOhcOU+JXfEIcA3DUeOUBtHNi4lQvsi09yLV1o7imG9s30AW2B/ty/Ti0awTHDsyg5yW6Qr+BiZ63cWH4Ni6N/QvXJj/Cm+c+xY2LX+Cd177GzSv3cPvad7h19VvcvPIN3rn0FW68+jlen7mLyxP/wsWR25jsewelU2+h95XXcfLZy3hl70U8t43DrCaxa9MoutawE6JXzSA6E+xgjFsogQ2/hJshIdoLh3R/0HjgnMT2TT048vQ5jPe+gxuXPsfdm//G95/OYvan+8qAY6ejlMdfgF+/vo97d2bx0Vu/4eLgJzjx1Otqucv2lZxkuU9BHD1BIxUEur3rSSdVB2smqBTFyDbktuJUHqtW6aUBjourXfOoHY97dzlW7V2r5XX0fPc83fBbpZedsii9rgdOHMDhtUybhpV8WbMQR01g3HIcBDj7s93oefkiLk+8j7s3v8e3n/yO377XyqtLcAgUv/t0Fh9c/wGXxj/GAD1CdpxHoY1gtU95l3FSQA1dWX85tIZuxhOqHnAeqM5mveoHV3frTPQhxyVAuXzv6hK2rtGBQx+5lCtXWutq42prWulVw0gE1kTjxGjw9c9CHKucSMcpdWBRYqXgDiGv5sjqRSZ5Bp2JE8ikjmP7+tPYn+3FoZ2D6D98Aa+Nvov33/wcX3/4E/7zlanjUr8JcSTwGMEtAc5/74NG5B8/3MdH13/BdPcHeGH7tFrenfnDJdzjAY6ZB4dtjxqaJp44dj4cNaRKGZrRoaAcYuUEzuelQhy8EYAzbgCOO3+RhjhqBTFORi0rkkRiDXBcmY/Wh7nth8tV+pNoLPMumVitTEOvGbOUeDA/jgN05jj/zeIBHDGoaLhGoYs1cKuBHD0pqHjhcFJQgj+zIpOa1FcbxNqTzN6LBl0xzdDoEKoowGF7ONeyE4+5svJWoMErgXqA9r4xc22F5sMJQys7hEqu4TBjBpn/RnvKKYCTOIVC6xm8sGMSg0eu48r4XXxw/Xt8+/EfCtgosMq6aT6kEODcvvIzzg9+hu4X3sHB4jlk031Y89cT2Ph3LpQwbOZWYlstQRR4N3Zlvta2e427Xesa979GysA9v952ND3p93Ss2mjH2FfzMAXwhvUwDuBYYzMAOAoMWPmulH0ahK6MyXMzj2w/qQCOGs4+jYzywBlVXh8dyQG0J3uwJXEaW5IncHDrOPoOv6G8r1j+9+7+ofQw3If6kEHIzjaa3hw3X/sZ46fv4vhT19UwHPZjbf/Pcaz/aze2/GMY2eYJ5FSgB86o44HDOmIATlU44+avvFM0duWg/rb+oCPg2m0Lbf/Icgu8gPiMgc7oloHdpo7JMlGLiZjpDFi/OLl+sWUIXezT2waxddWACdwextZVJRS5jHdrSQ29zrWUjGeN9sLJ0TuQK1C1TaDQ6gCcFIdacZiTBjgdibPoTJ1AJn0Ez24dQun4Nbx94WN8+d5/8O/PZlWfqcqM5WYCoQ49Wm9f+g/O93+G08+8reaxWv+3Hqz/e58aSqWHdBHgjCETzHsj8KYyVh6XSqZtOVjPGTFIFxrbtC0MmMuxqPxYGQv3l6486O1Ku0DSkjREbgTQVIvlPLfOShoPM5bnZ1wtDwXYROMonKm1L9fKPeS+D/Ndl8u9JG+kngi49ABnzkrUXJWuOZ8fgTdcccYCHOlk+DXXABy6VqZI3/VqAe2JHnSmu5Fb1Y3i2rPYtaUXB7qG8OKeUZx8fgZDJ65iZvBdXJu+g7df/QS3r32BOze+wcc3v8Ont3/E5+//G1/962d8/eGvuHf3d+Uiy/ibj37FV3d+xhf/+g8+/eePuHvrW9x552u89+aXeOfS5wr8vDb6ISZ638Pg0bdx6rlrasK7vZ1jKKzSEyLTC2FLE8OQAjkyq71AnCw7BTXUpAedqZPYubkHx589j5mhW7j9xlf47P3/4qcvZpXiqIw483We2+x8fvxkFl/c/gOXy/z68zq2rh7E5idOo2Nlj/LEySdpTHIVHCreBDhaqaYSnOPErqZTDpeZrTzVG+u5nLNcGo35Pudc8qKRc6PP4V6jG3+r9FrF1M4fICBHwA5jrRDr8hQvHBpnHEpllhFnHUqfQLblMJ7K92Dg6CVcv/gRvrzzX/xEwPDvGIDDL5BfzqrhVW+c+wzDJ95RdSDf1of2pm4DcQYVVNDz2NAoJiAYV6tM6CGQHP7YjwyHGqV70LVqADvWl7B7M5dv5VxV49jXOYF9HRPYvXkcuzaMY8faMWxr4ySq9LApI9/MYLxuGAfwRiCOE5svSyLzFQpL8AVRviTOJZbhU/3Ipuh9cxIdzUeVUrmnoxsv7S6j+8UZnBt6Czev3MXXH/6An+/9jj/Ewy4KbQhuJBDg0Pvmp/uq7t95/b8YP/0eni1yGdQBdK1yJzE2S7KaOXDo7aeHUFmAzLbHwpwx0CNK5oYTjwE9V4QAHRMHAIdeN+J5w0mMI/DGrD6mJ5rXX2bZttMTsJhm4ITUJjgQ58ECHEcOErqfUR8GzITZehlxPdxJKeIByDEwhyBHoE7ov2rXhI/remv7N5FBG7tKrWPcKSPPhSl6eKpAlkpj1hpW0WFVsqqLWpKXX7cVwCmrPoIAUKdJLzId3LnMNMARkFMP5ugPB8oDNDAGo21b7X3pnwTaqH7LzHs3f4BDj5iSWrFNAI5aIY4fgZJn0Zk4pYY7Hto+oRYKuDL+IT64fg/ffvyrAqiBAc+6+gtAgHPryk+Y6f8IZ56/jgOFKWRS3Vj916PY8PezaF/JIc0a/Ony1zpNdWOBbbzb5j/o7dplYGVzfufFtq+BV7Xod+yfpC/TfVVQV4L22Mp0pbwbkFkBcKS/lLTpCaXnKuGQ0Qznv+HwqdQwOlL9aE92Y0vyJNpTx9VQ9r7D13B5/I4q/3t3f7EAx/GO/P7uLN599UeMnvwAR/Zexb72CXQ096Dt/zmK9f/bjY4VwyimuIQ4v8xPIK9WAaQxbDxdgrpRzZCV8pd3kTybWyzy5spgkMdBeUjbZA13Kf+gHIPykH5Rgxu2G5xEP5vgIiL0lu1FLtWvoM32tcPYtaGkhvru3VLGvvYR7G3Xy7Pv2TKOXRvHsH3dKLpWl8EpCQhxlCcO66kaRsUJjCeQbx1HrmUMhDr8XwBOexO94M8imz6JfNtRvLCzhPEzb+KfVz/H9x/9il+/0v2m8mw13q0EsfRsV8Pgrv2CS6Uvcfb5dxTAWfPXM1j7v93Y+I8BtDdxmgMX4Ii3jQtvZGgVbRQdaMNIPW7cy04M1vhY0lu8uLJOB+UceOCYcg5BV5ET+44iJ8svlneQ+iRtUSOxq2NHt6PX6/R1PZR7Mq4sA3/M5k946KAHOEtPYNyhPWalGTuG1lYqrVCPmrGvw+hM8YtJr5pwLtOqV2/auvEMnioO4JWnx9F75FVMDlzH6+c+wPvXv8RXd/6D7z79Ff/+6nf8+q3+ik1jiIap+rLNIUq/RL5202DiF++f7uO3H+7jl+9n8Z9vOAfIH7j38e/45PZ/cfvaPVyb/hRT/f/E2Rdfx3M7prB1fR9yq3rRkepVQ6s2raBBS0+bkppZX89qz9Up2AnQY4cQ6iR2tffg1KGLuDj6T3z47rf4+qNf8N9v9PMp7xsZWvG77nx+/nIW392ZxesTX+H0M6+ja1UfNv7tBLasOKM70OSAWmWjmC6hq4VfTvUXzFyiBB00xKlsMGwFko6/VlypFLiNlauQPiqNleRPbaWqVp7Z/yQtN3bzyT3u3k86COk4BNy4sfzHWFw3mZ4LcWgM8ktZD3LpkwrgPF3sQ+nUVdy6+pmS9V++0zKojBfXBWcWqj589v6/lbfa6NmbeOn/Z+89nNs6snXfv/LVC3XPeGZsBZIAiMBMEAABEsxUlhWoQDFHBOacFCzJVk62ZOWccya/V9/q7r03IMph6txTc+6xq9oNQki7d4e1fittWkJ1gCGF/Yi4khICRS8bgQhSqpkCNGEBw30UvKnyEt6o5KFNZcPYUjmBnTVz2F23IN5texuXsKdhCTuZUDw6j80Vs2gunUF9AcfJkd9GwqWMks74e9PMc5mC6VeFlS+EVCOs/l5PIZalTFOo8tL7phsRhnH62rCjJo3+PQuYS5/E2SNXce/yE3x8/tkKixSPG23VFWjDvcjZGD7FverlCj4/XcGVH19hsvsCtscmUFfIyjlDqCIMJrRxNvH8cwAcKx+OyYujewIdR0l3G+I4Q4fmFIDzsnS4E+AYeKNDp6TyWOZYqTwJhG4G4Dh7G+TYAMe5Bv71x9Y99kyh+reaJMpWlc+UEJ4JX74EOn/+37MVpi/33T8CcJw5KQhZDHRh7/TIUQpvtieOAByGOwi8cQAcerUJwLHhjcB/KwfGOHiOfAlxnEAn8/GX3gV/7j4qJdd43SjwIkn6majfgBxT7e4PeuCYggcxD8OnGOo4gho/q8TRC2cQUXcvqn192NMwjWFR4H/Fr6cf4unttwrgcP8zXhgfIKE1F489w3z6qhhxvo9PSy6Oon+2o3xdPyJ5rHClPLuyz8nVQ1rUHq/OB+febx5/bQzNv2f3zs9zPna+brXPzJqLGaDha7Ah83NsQ5yt8GWPgfrbnGWmp/yg32Ptxb8HcPjvTi8Bc638THUOqj0tC+B4CXBGUelNo9I7gIi3B5X+LuxqnEC64xiOzVwR+fHJzdfKiMF7v2x74TA859yhpxjvvoS2TUewOTyJiKsfgb+1o3zNIGKucTQULKIhsIg6/7yEkqprM7/1a2Np7o+Rgdnb12KuSfXOcXO+3vnYeQ+cj3/7NQYWWPcjS7FX+4uGN+J1row34bwBOQcbSkawKTSObdFJ7IxPo6V+Fnsb57CnYR4t9QvYXbeI7VXzYpxplOIgCuIwt40KqWK+m2nE/XOo9s/KY0IdATge5YETcTF9wiCqA72oLe5C2+ZJLA2dxc2zjyTVAI2dNH4IwOG94z3kbXyzInlxbp75hBMzT5DadxFbKudR8PdeFP2zX7xwwq4xAThRMbaafDfsDcAhvDFVquwE6vZcdK4Jc0/Zm/n5R3vne52fmf34a/Mp+3Vf/m2dlWbt/Zf2tmxm/w6zRr78rV+enf/qa7gWzBr42vpyytHOx2bdrdabz8zu7bX3n3cN/+q1/7u+z57rfwGcf3fCpwGOWGZXATjGekUhSDZK3wRivlFEfcOI+tKo9CVQV5bG5qpR7GqaQNfuBYz2HcehqfM4e+warl14gEc3X+Ld08/49PJLGCKbud7Q1bbu+L85rB0WFwlleKOAzqsHTOT6DtfOP8fZI/exOHIZyQM/Ye+GeWytnkJDmRIMQq6UuHoyzlfKI0ouHwNwaG2gB04PmsOD6GlZxNL4eVw5cw/3r77Ca3o/vNX5MQzAYfzu8xVxDX14+TOOT96Rcog1gQTK1nQilNOHqCuB6nzmOBlBrW8MdX4lXMcp/Hro1jumIc64ZQ1SArPZSM1Gk7kBqYPHuAOq/rcFC77ffJb57P+Kjfl/1+ZkNhczLl/b9LM3dfN6u7cPcfOZpnf+dvOcfdBnCr3mQDHghhUvzGPzb6Y3n6E/37hvixKYEsWFHjjf1yQx2nMM54/dxNPb7/D2iQ4fyFon4kL+4C2uX3yEE0vXMdp9EnuaZhDz9iGY04NQ3iDEOiaJxhn6SEWGAjQ9zxjuM4wqJlIuSKK+OInmYAo7a6bQtvkwBvecQqr1LIbazmOk4yKGDlxA/67TaN/8I/bWH8H2ykVsKGXuFAIcU2nK6X1DeDPjaGYOslfXnyEoaEUh22PB+tuqhMUwzqwmOX4YtkFBdhjVviSqdELFSs8B1BS2SejUaNcR/Dh3Cb+euYOnt19g+S3NgJn5bpbffMa7p2/x/N5zPLvzAs9uv8azW+/w7OYHPLn2AY9+/YgHP3/Ej1O3MLjnODZWMOxsAFVe5vHQAIdx+xa4IbzRTfYd5hFbvcl1iQXRwAkTMuSAOExCzVxGAnEIcr4Cb6QsNSGOUrwsgONVoZxSkloea68c7Z1DLx1pf+LcyriPRsFY7R5bAEclEbWATsb9tK2oxppqFHDLQ4fz+L/UA8d43xh4Q88581j1q4McA3HskCIpF24BHJU3icmvBd74DLyh5+aX36EAztdBjgVsrH0lOzzEua/9zmPxzF09hCoT3qwWQsX8G2aN2oUP+D4FcGwPnLhvBHENcBhCVWkBnCkMtR/HsenLEur49NarTIBDmeAjpLDAxWNPMJ/6Bb27j2J79QQqPT0o/Ecrytb2IpLHKnTGKm/v/QqoZ56j9nlg9mn2Zv93jheVtN9S1Mx7nJ9jvsv5nHld5mdTDlCfr+9f9v00yalNBaKMtWo+k/us83pNmKLZU0yluuxerS3b45pzV1UdNXuJ3WcBywwvAX535tks3p+sFuRdkBbzsgLVBKLeETG2Rbz9CHu7EPG1Y2fjKFLtR3B05hKunL6LJ7deYPk1BTANALRM+OwmAc4jjHWdx4GNBwVWRFy9KPjbAZSvGUDMNYaGggVpBDi1PlMBlOOkxj1z/7LPKc4H+6w3ZzjP9tXOeCNvmPeY3v48I4eZ+fgFqLYAvnnvKj095KWZPYXrj+cegSg9b5gLsh/hvF7UFaXEGENY077lEHp3HUNq/wkMt5/CUNsZpFvPIrX/PLq2/YSW+sPYHJ6VCq81BTR0sqQ78+LQ455eN3Oo9s2iyjeDKh/z0TCEijlq6ImfRoyFGAI9qC3qQOumMSykTuLaqbt4decdPj1VAEdy+BmAQ4fiVyuSH+fXE69xdPwuBlrOCEgKfNONwn8ogCMeOPkMs2MeS3pszSEmZcM1xPEZgMNy4/ytJkxTA0drLZk1ZtaHWYdmXX6tN68z7zOfY9ao6VcLVTL/5ujNmpX91XjGql7d18z5kjlX7LPReu3XIiasubvKHMr4N/19ek4Zj93MNWGu+Y/12e81f5v1ZvfmWvkbnXvF1+RnswZNb9ac6Ve7VvUddgVV853/nXWgP3Yf7HH+s683c914ov0VQmUdFv/6oP7Zm/AHX/8bAMeCN5Izg0qfcnflgRvzDyNWkEYVD4nqSbRuW0Si/Rhmh8/ix6Ur+OXMHdy99gRP7r7A6ydv8On1R6y8+4iVj3Rf0bFIoj1laaUOfpNxWsvu/xn4vIyVDwoEfXixjFePPuHp7fe4c/kVLvz4ED9MX8dE/wX07D6OnXXzqC3R1p38IX3YMOcHFSyGM/BAoKUhgUpvFxqCvWj/fgrTyR9x5ocruHHxAZ7ffSuWH7HKMx8GrfQmB87Vj7h++hWW0lfQvnkJVd4+lH3XjlBOD6KuQcSpHLOEusQgK8E7zgpVnhFUudlGUW2BHBVWxeoIVGwkDEXAi9mU1CalrIZzqPE64z3Nhmd6LWB45hD3zErpTOUNkQlwuLH+283H3/1N3FzMmJiNPFuo4t/m35y92ejZq8+whXZ707LHRCuzpuQ7e+u7zWeZz88a+1Uhjvpe9Z38Po6/CtWIe4dQ7etHlb8D26oHkWo/iB8Xf8bty4/w7N5rfHz5WcEGx/pYfv8eD24+woWfrmJh9BT69i5iW5whg50oW9eJUG4/Kt3MBUNwSWhAzxsDcMYkdCoeSKGhNI1N4SHsrBlDz44jmOi5iKMTd/HT7COcWniKswdf4OT8UywN3cF41xWJVd/fdBzbwouSD0eqrolXGecwPS2mdblWuqxnj7UZZ8dc/D14I7m3jEK4Sm8BnDFU+ziOCXHnrvIxfOoA6kvb0b5tHDPJH3Hh2DXc/uUBXt4nwGEiDSaHXsHKJyYp/4xXj57hzq83cPHEOZw5eganDp/FyaXz+Gn+An6cvYRj07/gh4nLGGo7jr2NTPDYh0p3DyrdCUnqzjBN5e3E8eYesxqsoSLrbFRomb/AtiB+IdxLCXgTQkSA42wmYTGFexvaOBUtlUxd509ZRXi0wI2cB39uXzACWnbP+Z39nFREkT1OQxxes6XsG6WEvfbGscKrnB5K2bly/hjQUWveCHBm/Tmv1SFo63WpoAgBjgE2BCvOZp43vfHIMcqtUn4NiJT7Y6pOSTUm5k4a002BGwVvsr7DN6Y9cBTsySx/bADTar0T4jiv9TceZ8wPFfarxkGHAFvlzrWSlOF9o4BN5rw3c52KJhtLiKsy4gJwuGYFgBqA04uW+kkMtR3F0alLuHzyNp7cfIblN4xn1Ar8isqB8uzWZ1w89hBzqYvo2XUY26pGEXZ3IPD3vShd041wbloncTf33Zwd3JfM3s3e7Odf36+s/dpSCs18yR5Ls8et9l3me5yKoVknBGbG44nzJ/t+OoGOuS9aCZSzJPt7+f3q+5yynPXY2kOcEIdrySiKZj1yDitQJ3uKmb9f3HfuzWrdfgE+NHBW+bsMwJlBzDchxkAaAiPeXoTy2xHy7seOhjSS7Uv4YeY8rpy+iSe3nmD5zXv79NPGPYZQnTt0H2NdZ3Bg4yI2hZiMvwcFf2tF+Roa0kZQ559DfWBe+gyAw70uY48yc4R99r1T57vKR2aSyjM3GUNbDWDn/uzci8w4sjdj6XyOj/XrHfBGlTqfQTXPTjk/9ZyRHHb2Xq8+k/eFVS6fAEwAACAASURBVE9HEKMXk2cQYVcPwq4uNAeHsbdxHgMtP2K8+zwW0ldwfOYWTi3ex8n5hzgx9xg/zTzBdP819O06jZ01SxJOxVAqVnKLsEiJVKYisJnTiYIJcAhNlBcOZYsoQ7B9A6gOdCFe0Iq9TSlM9R3GxR8u49HVx3h7/50Kf9PGEuNBRc+cB7+8w7mDDzA7+As6tx6T6rIFf+8WD5zy9UOI0JtVDE4EOKxaNi89kxZXC0yaljCvKlbF8hI8OfYY7REpe5esWa5TtUa+mJ8Ze0H2vsC1quaKtQdkQVUB+Pk0xo7pps6BL6F61rqVvdbImRrOaYOrNTfMHLHWpZlD+pyU5828M4DWzMmv9UaWMO9zzlGH965eH+q6s/e51f/+4sz/6meY9Wb2Kf7WrJBx6+/VriP7Gsy1sDdj5Lguh5zh/I1/5tr+Z7zWnCN/AZx/fwXZWFwdvbK+KCFZKX2m1OMoKglCvEnEAglUFydQWz6IPZtmkOr8EYemLuP88bu4+cszPL//Hh+ZL4JhUPRgYTjCR+3++nvMxj6mV39E6wsZECtRvF3B59crePd0BY9ufMS1869w7ofHmE1dQefO49gQmUAskBZvISlTySo7brpl0irOUuPjqPQkJYN+XWk/9m6awlD3UfwwywpA9/Doxrsvq1DR9fPuMu5ceIMLhx9jqveiJDNl/H7ptx0Ire8VSwiTqdZIErlx1AdMCNUYqt2s4mMavXHGpbHUo6rko5RgBV2MIMFDRFsJvLQiaYijQU6mMDqPuGdOBIBq9wyq3VSoVQiDAkNGYFx9A1ablDnozCb7X9H/0d9lxoSCjfOwXe2xFn4sAd281wjQprc3LeuwljVhl2CW8BJHZRBbCPgzv8P5fWb8qQjRc2QQVf4ubIkl0b9vCYcmzuPyqbu4f+0l3j5ZljnvVGA+vPqAm7/cx/GFixjqPoLWLVNoDiVQvr4NJWs6UJFDuJCWMEEFDBS0lPCpfIZPJVFbmMTG0DB21IyhbcssJnrO4sTcfdw6/xkPLn+WRN0Ukh/++hmXf3yP41OPMNV7DX07zmJX/DDqAgQPxpuMAEcBSANxjCCsQnM4lkZIse+1sfrYQq4RdnWvD95MpVCBEUv5FwHZBjjV/j4tULajuaILfbtncXj8LK6fu49HN57h7ePXWHn3ASufP2Pl0zJWPnKf+oT71+/hzLGzmB6aw3DvJJIdE+jfP47elnH07JpC944ZdG2fw67aMTSVUVjuRHB9F8K5g1LBS/JscQz4m0WIUMDpy99ulFrTa08FuQ4jfDgFEj42Agt75YmjnjOvcwgrqyjZ4oVD7w+Hgm7G3tn/vpCSuVaMZdnKfSQC22r7hbrnai/KAjdGuDJKYQb4yswdpBLq81rNdf+B3lLI7N+1+nUapZyCtlNhdirTNqzJBCnq9XYo1ZceOGqO83kNeBg2ZQCO5L0xEEd/h4851P44vJHcOU4lglVJeB0WdDB7TmZvCbQZCoVzrjDRMpuqnGX6+GrKvL53yguNQJONkFLBG+b9qQ3Q+4jXzpBHhh8mEfUMoNLFECoCnAkMtf2AH6Yu4vLJm3h84wk+v35nAxxKBsvA89ufcen4A8ylzqNn50FsiQ1L9Uvf31pQ8l0nwrnMBaLWopqnZv//rT3bnBnmtZwzTjBq4JyjdyaZ1QrL6t/Hzzafa3ozJ7k+FNhU3riOeWLu3xcWfKX8iUU+w+DDzzbXYZQ6XfXOAAcL4BgYTJCjAIG1d8nYKYBjwRv/FJQnmYKTVOZVhVIFrWV9GohvEpDLd7EC4jyqfQuokpYJcMLeHlTkt6Eifx++r08i0baAI9Nncfn0dTy59RjLb97Z8qABOLcIcO5hrOsUWjcyFIh5U7pR8M1+lH/Xi8q8YfG6qfPNgY0AR5oYZbiPOc4hGs6sv438Y8bRKJeOhPKSXD57HzZ7d9YZJuNonjOvMfuWuj80KFZ5mGhZJbe3QI6ZLwJwzD0yn0HPkzFU+QhwUoh4+hFydSPk7sSW2Bi6d/yA6YFLODZzG5eOP8XtS+/x+PoyHl1VZzrP+NOLLzDRcwX7mo9gU2QGDSUEOCO6uhQ9cehtQwOuzjVDgOObgkATn/LWYf68KsnhdwA761IYbj+EE3OXcOv8A7y49U481rlexQCqZff3D1kE5DWOTd7E0IEzUoCBRUgK/t4D5sEJ5rCwAq+P+scMovmziFJm5zjxLLRCuvRvsQCO8cIx61Ptw857a69Be40414v92LlGzfwwnom6515ugVdCHDbznPO1CowYY4mJemBBASMfqfXH/cDMjeze3Pfs3vk6p3zwFfAh+eSc7zGfZ+aohjjWejDX/vXenKf2ONMQzetX71n13/WepbxruQ6yfq8l5zjXmVMOcl6D87G5HtN/afgzv+v3evO7/+f0tnz3VwjV73oUZApSvz9J7ME1NPn33/PlouN7OHHNZmJ645rHA1wSbbJ8n7gwjgm8idDN2dePeMkgGkMJbKpOoWffQSyMXcSlkw9x79obvHq8LMDGOm3Nxu1IYEaHGoEwpsqLSRbq7PlvhD58Xzb04d+m8eFn4NOrFaiwqmWcOvQIIz0XsLNhHvXBMVQXDUuivJCUPGRSY1al4oEwgYgnjbC7D/GiAeyon0Tv3kOYSp3BiaWbuHHhlSQyZpnhT89W5CB6/2gF9395h0tHH+PI2A0k953CjupZRPL6UfptN0LrmQMnLZZalf9mAvUBnfSVQqx7TAMc9mzjkJLMVinmKcTdzhAUghuj+HOzJ8BxNIegZgEN7Xkj8EYAjoI4AoU04aeAaeZB5iZmBEp1kNiHnTnMfq93vv9feOxwC7XmuAGMznEwgk3G9TusifL8l781YywtF/lV1pUGOKZijzlkDcgxwq7d/9Hvdn6XWv+0ukvoj78XmyIpdH6/gMnBU/hp8Rp+PfsIj2++x0fmX3m9IvOccPTpvde48NMNzKRPomPHtOSfqinqQ8maNhR/24HgenrgaIBD4UYUKwpizPuk3J7rihPYEh1GS+MEencv4eDoFVw99RrvHi7LXJfYdYLSlyt4fHUZPx97i8Oj95Defwl76n9AHd2t6VHGsEB64dCTxAFxRIlxCCgcO6Vs6D3pD3rfWADEhCOxl+vRB7MIyDbAiQf6UFPQjdqiTmyu7EWidRE/zv2M+78+x8t7r/Hh+TusvP+AlU+fsPKRuXA4rp9w7eJNLE0eRWdLArs3dWFbfQc2xg6gKXQATRUdUoa8sawP8YJuRNydKFvbjrK1XQjlDKIyjwmjNVjidfkUrKIC+YU3jrkOyxPHKD9K2VQKlFMg4WNbEbOs6JYg5hBULKVLKVgMgSCgcYIbPnZCm8z1b58XmWeLc97ysdqT1P6gPWZ0cmKlvP7W2s+8/+q32L9XhdtMoMpjAy4zBxQc+61xyh43ZYyw9zH1uzKvzZzDDoBjKc1OkGMe65w3lqBOgd0hrOsxd46xDSh5nVQyjLWYFnTVTPJiyX/j09422b32zMgM2zKWX2evlQlRMBy/bVWhXN8rDVhtecAoHQ5ww/w9GuRkAhzeE70uJYTQ5IMaR8ytKrTxuglw6gunURuYQNzP5N9pgclRdybASR84gh8mL+CXEzfw6MYjfH71BlimMKElCwKcO59x8fh9qW7ZvXMJW6JpVOS2wvsfu2UPDOUkdQgQ5xuv0ZwHVNxWA/7O5wxs4fs4DgaoGC+r7N4ojOzVmlN7nf5OSwF3nBMamGSuIbWnWWvSWqu2pV4lI3cofhnXxu+zFVO1V8yhKn9OjFbsqSip8EuGYDqa9vKwAY5Zk/SYmhRwU+NnThTmQ2Sjgq2b3o9NkQjljaP3LQIcH+HNKgDHPyyh+KH8bgQ9B1Du2YNtdQkMts3jyPQZXD59DU9uPcLym7eWSCkPVoDntz7j3OG7GOs6iQMb58STNOLqEoBT9l0PKnOHZJ+ywI0BOD5tTLDWggZnonCqtaJkI+7h3D+MckkP1gXVJBzM4ZkiZa4d42HGJaM3+VoIQ1Tierkv+t7EPDOIuaelMcl9hiFE5ooCOApeK08YBXCGEfXSENmHClcnQp4OfF87gUTrjzg8eV0qu97/9SPePVEGVSuf2/MVXD/7GYvDt9G29Ri2RGfQUDaOmG8IFXlJhF30glEGT1XKm7CEIVXTUmKcZcar/QQ+lCf6EPW2YysNUC0LWBo6gwtHbuLuxZd4c08VA5EiAC9UIYAnV5dx/tAjTPdfQsfWH7A9RigzjMJ/9EoS44pc5q2clLAtVqBiGFWU40NPYt8M4v5ZxP0zMg+dHjh2GBX3WA1vrPVowITe76z9wOwL2X3mvij7ubUX6M/W+7540GpPWnUW2OeodR5myUJGtjQQh/uAvW7scywz548GaTLf9DyycgIx1YUCXKbPNPwY+OH8bKPrsTcAh/KEemztQ9ZacV7XbzwWOdAGONbYZXyOkVucvyfz95vrUL3JfZTZf2nMMZ/r6HUFYrnGjN/wG9eQ9brVZQYjO/yf0tty3l8A5z8V4NgDaym2GQro6hPoNxdghsXNVjiU0sEy25OyiTLjPJMCC7wp7sdmlghnpan9i5gdOYszx27hztVXePHwE96zqgvBC/8zkIVyFyMW3qzg04tlvH3yAS/uvcbjm89x7+pj3P31Me5cfmS1u1ce4u7Vh3h44zGe33+Ot0/f4LOEYTGESgGbjAgsulS/hyi5rx8t4+q5Nzg8dRsDB85gz8bD2Fw1h5qScYQ9aUQ8tCoogMOepRBD7kEwGfOGylG0bJhF957DGO8/g8NT13D+6CP8cuIJfjnxGJdPPMTFo3fxw+SvmOo7h/7dxyQhXFPZGEI5Ayj7rg/hnKQkiJXqU/5J1PmnpLFqDy2hrEBV7SbIYa+r+EjYlK0UWaFS+QyVMvG5GuJkHQJUpGzB1Jl3hG64+m/9GqV02fPInhtGwFebmRIcVJ4gCi+2QEjBTwug1vP291t5KizhwwghuncKmtnXYYRZ9k7IpKvk1PlmwWYOOydQoZCl/p5FjcS5q1h3gSu8dv3Z1ntE+bTHYdX15AQ4pmKPo7c+i99NgVA3811Wb0Gnr38fFTYqMlW+ATSVp7C7cQo9LQcx1ncCS+OXcPLgDVw+/QiXzzzEz6fv4uLJmzhx6GdMJo+ja/cMtsaTaAgOIOrrQdlagoUehHJZ1YUhRQQbyvot61rcrZn3qQ+1XMtRft8oelrmcXDsZ1w78xIfKOi9Vp5zBKhct6z48suPr3F47I7Ezu+uPYgqJjPPSyPqHtaVZVQokCXAcAwzxszhgWMpJkZoyOqzPW90XpmM/DJa4KCipK5ThVAJwCnsQV1xJzZH+5A6sISTC5fx6PpLvH74Fp9evsfKR25I9MBh+BQB8DKunLuFmeEf0LKlH5vi7WiMHEBN6X5UF+1HvLAdNYXdqC3sR5WP3k29qMhhG0AkN4Uoveq09w3hTY3fLuctwpAJqcq6DoE+An4U6FEeJrZAYwknEgJHoT+zyZoTbxSuV36GgVvaq0eHKclad4y5gfUWZMgSVszeYAstzvlrr3nn3sPf4vxbHjvWdeaa4dxQTSz64n2jrKcmV4opy67+Jswx12QsahTQVhkrec4Iq1S+nL9LCeX2da12bq4CcgzQcYKaVR8bWGaubVrBPAI9aYR7CgYoiGMDHIIcBXG0C/5X4A09f2yA44Q2zsfZ1mAnZMoWWtWYWB5tepztuWRC/JwKkVHuTa/XL+dfFsCJusfAqncG4NSxip2ULibAUR44MXrgSA4c5YGTPnAYRybO4ZcT1/DoxkMsv34DrGQBnNsfcfHYPcwmz6J756LsZQrg7PodgGNADasT2S07f5k6z7iuOO84Bjp/D70OrXtl7pnpeW9siKNgmMPwIp4g9AaZQ41p4qGi5umXnheOs00bbgyQ4HriGnOe1+p81hZrgTUGPmiIY6zcxgNHfsM8anymmp1ZT2Y/NsBRAZy4ABzut1TmGV5DaGHAhHmPUpzkDPQxufoc4n4DcObFAyfmm0HMP4GoAJxBhLzdKPe0otzTgm31gxhsn8eRWQVwHt16gM+vX2lrnpYtAby48xnnj9zFRM8ptG2ew5bIECrdBDj7kAFwvNrzxpIfuJeZNaBlMsplUh3UQFrOdSr8GuJIGBGBDcN4VCgPYYIK8VHjwNwwJheL8lBx/C3/psdKJ61XYfxziHnYZhFzzyCqAU5MSp6rJPfmTOB4cr9TZwBLazOUaQwx5qT0JhHO70OFuwOh/DZsrxvD4IGjODR5BZdOPMT9a+/w4bnO58iqim9Vdajr5z9hYfgGWjcfxsbIBGpLaOxMIJg7gLArJblwrHss0M4AnGmpTMUy4/TYYSLjSg9Dtwaxt2kCib0HMTN4EsemruDiDw9w6/xb8bi5evIlrvz0DCfmbmOi57zk59kcGUdd8Qgi7pR435StTSGcN4oYIQJDpYwXDgEOx4AAh5WxCHD8Tg+czPFWa9acGaZ3Gn0yFXx7/zMQVckrItut5j2s55D1PpPnTWSXVSCRyKkO4yvnpV7TZh1LqXuPgirmbOP9VvNMeUI5H9twx4YaArnMGehjuFlWs+aR+h6mlDBylUovYc5XMw5mrfyLvdYzKVPIXmoZsPi3fQ8yrsWn7rMKlVPQLuPfndDKkomcMCr7sZGv7GszMs6f6X9bblhNlvjv9pwt5/0FcP7TAI49qKsqmxkxmpkT5jcnpxPgULjU1FWsKvkEOHSfHJeqU1HfIKKBHtQH+7Fn0wSSnYexOEF4cwO3Lj/Dq8ef8ZGVWlhR6pMOl3ImLXvLUKdlyStz79enuHr2Ls4d+xU/LpzHsblz+GHmLA5PncGRmTM4OncGxxfO4NSRC7h85hru/Hofz+49x5unOp/OauFYGhKtvFvBo1vLuHjiJRbHbyHRfgH7Nh9Fc2QaEe8QKiXvzaS4Y9ITJ+wegSQ69iZRUzKEpsgottZOYv+WRfTtO4bR3rOY6D+Lib5TGO/9SUo8d+9cwJ7GSWwMD6O+mHlGEgiuG0RwbQKR3GER9Gr9U1DNVHxRgr0ot1Kxh0qCule1+TOykdd556CaKn2pSmAaiEOAo+eB06LugB2WICcVgJRwJwDICH6W5476LHtuKIBje08w5EoBoGqGYxlrpRH6TC/PU0DMFD5pPbKaCCQURAhxtDVclDp9kBG4mGaUPcc1EdjU++bQEJhHvZ+u0NkQR1smDSjwU1hTQMVWUvkaIwiz/wPrSb9erCT6s+t8M6jzmxLMtgJKq6TVjFLqtGab+/ZFr70YxCI/rEp5F6ewMTKCHXXj2L95Bj0tS0h1HMNk4jQmEicwOnAMw32H0H9gDnu3jGBjrB+xgjZEvB0I5XWhfF0PKtarKlSxfIZmaYAj4Rq0lg1L0sFKbzdqirqxqXIAu5vS6GmZwaHxC7h+/rl4m3EdyTomh32vEoZePvECh8dvIrn/DHbEFxBxJxFcnxCIwyS+VNCU9VkJv1TQpfIRx8zPe6IBjgMkGEtPRv9VeGNCMuxcM3yfUowzPXBqNcDZEuvDUPtBnD74K57eeoN3T95j+Y2CN0KY9b5BD6efT93C6MBhbK7tQU2wFbGifQh596DCvQ9hTxui+d1S4UolXE1IuXZJFO0y5dqp7PD38Hpnv4A4Yp1eBeAoiGMEDBtKUBkTIc4SUJR7vbjai5CnhHn1GgosprqVI3myFpaMkClr3gijVq8tk/ztlmKjHps9ytp7LBjiBLvZjzUwMeuZ+45e4xyXWv+cjA/nhMwLCcuwoUYVvcQ8Y1AV1Mas5LfiuSJeS9rTSQvKRtC1e7Mn2b0Nlv4IwMk8Q+0xUM/LGGkLo3PPVGOn9xoDdTnvrab3CSaZZole44mjgQCBjg1mfvuxDXFsaMNwRjsPAx//MYijrL7cszl/Jq1KalH3BKLucWkKphmPKMIMO1TBeHkRxBkvHAmdst4/hhg9T/l7fBOrhlAR4JgqVC3140i1HsbhibP4+aereHTjAZbfvNZChTIM0ev22a33uHDsDmaTZ9C1Y0EDnP3w/sdOFH/bjlBu4iseOKsAHFZ2Y9P5y5Q7P8Na1JjQw5CehrH8EamgxapzTB5L78nMRpDDkDsTcsZzgnsfzyXOe+ZjUa3WvwBphCcMF+A5Kt6zat6Kl5AjXLrWx0S8pjGMWuW4s702aKxQHjUSZknPDslhqOBNRoiCSYjuU/CGv6PGz/caLw+tbAkQVt4MhDYG4FT5pxAjwNEghzBHPDO4xzsbQTY/NzCvII7fCXDGFcDxE+B0CcAp8+wWgJPomMcPc2dF/nt48z4+vnwBLOs8SJrhvLz7WQxqU32n0LF1HlsqmeuQAGevBjhpkZmUkuw4/0W+4P6mPG8soxoT8uebcElWhuOeZAAOPQM0wPEyH8ushBZFxUNdyckCU+jtytCi7OZlwYwJgRJKAZ9R0MYzhyibexZRAhzXNKKuKcToJa4b56CBOBa88VCZZxLoUfGYYR4hlmEPejoQ8hLgjGDgwGEcnLqEiyfv4cHN1/j0mpZOHcqkUxDcuPAB88NXsXfjIppCI4gXJRH29KM8pxchVrJktTj/pPa4ccAbesAE5qTEOK81ms9iIb2oKezHxnAKu+pG0L5lGoN7D2Oi5zQWhy5jLnkJ0wMXMNF7FoN7j2FP4ywaylKSt6citw/l6wZQuoYlxIcQoXe6GGYMwJmW8aYSX805ZZU2J8AhGHPAG8uzVUF/G0Q7jBtiMFD7lQK05t+056fAPBWloGQX23vYnKWqt71PbfnbyM7Ke0sZQE2ooiP9gXcOtbK+lfxMeBN1TSLqJlBxeNeI99E0Kj1TqiqXZxpRNoa2GZDBJNMEXQK71HsJbmTtcf1JI0zV+YMEBE5LPtCoexKVrglpfCzyiJbB5Bod8ppl8HE+lwWyMmQIh47J53lemkqdPGvEKGMAsITnEQhynqn7zHudAXE00OPcMECH60lFjPx+r66HMpZD5vmtx1+Vh35bRsiWGf77/G3rRn8BnP9tAMehzGcohV9OKmsxZbu0cWLKxHVYCrX7vUlSpjLNq0TAUT8VxS40R/vRvXcOixNn8POZ27h3/RlePWGCYu1xQ4VIh0g5vWQY/vH83gfc/uUZLv10G8fmLmI6eQzJ9jn0t06jZ+8kOneNo7tlHH37JzDYNonR/nkcmv4J53/8Bbcu38OTO0/FG2flvUpoLF8EQ4tsy8yHlyu4d30Z5396jYXxu+jbfw5baxYQDbB6FhMXK4ATcU+BpQorXMOocKUQ8iQR9g0iEhhAbVlaPHK2107i+7pxbK8ZwrbqBDZW9qGmqBMVrlYU/KMFRf88gJJvu1H23SAq1g2h0kUBbgp1gdkMZZ9uzwoc8B7pBFHeGdSx+WYVpPAvoJ7Np1qdb0GAjgggltcI3++0EGulS9N/HgI8SKiwUMDjASGCo1j+KPzxAHECHMIbZX3i5ioWALH+MB6bAizz6ajkYqr6DQVdCpyZCfyYU0gplrYbMAVLCiNKIKGCYAAOhURlgaQypyyRSskTq50oiUrR4rXX++fRVLCIRg1xCGgIBpQFX7v0+6dFGawLOGGBY46LN4i+B78LcGxhzwAcA2/qAwri1Mn3MRQgs1klgp35RjL2AbNBOrwCRJjQAMKfQrxgEPGiftSW9qEpnMCWeBo7GkexvT6NzTUD2FDVjbpQGyIFe1GSuwu+v3+Pwm/3onRth8CbcF5SwviqvKzyMoq4fwxx5pwIjKPKz5xQfQJ84kUd2FjZi12NCXS3TOLQ5DncuPBE5X0y65nL+hPw4u4yrpx6hiMT15HYdxLbqmcRzOlH6Zo+hHKTiOaP6OSkme72tf5pZN4TdXhmABsNjq3nnJ4kBng4y3PzsePwVwrDuE5izIoYfagt6kF9SSe2VvVjuOMwzh6+hhd33+Hj80+Q/UM2J60MfgQ+vFjB+Z9uI9l5CHXhDlT49qLU1YLCtbtR8N0elKxtk+pekbwEKlk61T2kS7EbwKCERQr8VNhrAwQVWRBHAIstmBsBndcili9Jrq4AjrFe2wBHQVEKdrbCpgQWvkYEGGNJcwj//A4RlEQRVdYno5QQNNmQ06yVTIhjCx6ct2bOEtgYJXi1XgMd460nAIfK6xzquIYL5lFXwMezam4EOGa08NNTjOM5KmNLz64YE76LskyvB1XOWuV0ovBnKzZfhzdKMbYBDq+B1/LlWfn158yaVT3fL7l8tJW8WvZc577L/UndfwFWnAvSDLCikkvlgXNG54eRHDHKU87KkSNAdBWQ46NySVhKhXNM5aHiGBGiSmNeKv28JBhn9UM2Z24GdZ+NMiLV6dxUHijIjyOSN5bReK4JzPGocCiT10YScJsQBbkm5QWmAI6CP/TAIYwzAIf32uTAiTMHDvPqSRnxPqnGpwDOIRwaP4Off/oVj28+wPJbAhy7EhH3pKe33uHC0duYSZxG1455bI4mEcz9CsChIi7z1zl36X2zgBqv3RTEUQlqVZ48KtPcbwgVWQRhGATjVYTjXlUKnX3caqMyjwmqVN4gvR8QWjKZbmAB9QWLqGNjeesAk8EvosZnvtM+b5k0l7+Hv6/Wt6gbHy+AsgHXIHOmRPN4xurzlQDHeLuYsCmPATjOJKHKS4ehTTWs0BQgTCLMoaJJTw/leaTWGuclS0orgFPtn4YBOFEDcfzTqA7QK4MhVhpUsg/MokYU/XmBONUCcOYR89MDZ1xkskoNcMo8+1Hq2WUBnKPzZ3H57FU8uHEX754/xcqntyrxvBb1Xt37jEvH7mJm4DS6ts9jW5R5Uzo1wOlGZa4COCrkXBtLLHijlEkDb1RlUAVAmbSc4e+yxgS2KoWSYRxM6MvGkB4q1MwRU0kjJw2DhCkCVMZQ5WdTICfmo5cMQ5cJcOjVTgWcIUGENoQ386h0z6LSNYNKATiTiLomEHWNy9xTiq46F2SdWgq8AjhR35CEoTGPZ9FyDQAAIABJREFUUDC/HRW+VmyrT6O/7SCWps7h4qlbeHj7BT6/N/GHioXS8/TGxXeYG76M3c2zqC9PIVYwgAp3D0rXdyPkGhTjbU1gCnE23mNpBChzqCmYR23BgoTSRfNZQGFAIE7U242qQCfqiruxKZLEztpx7N84hz2NM9hZN4Ht8VE0BhMIezrh//se5P7fO+H9X60o/GcvytelEcpjAmUam/h9yvsi5lXeJwyf4nOcV/GA8cBRAEfGN5+Jj51NGaLNc2o+K9gjsFn2YAOmCWntRrAjRig5y+lRq2CNgrrKKGkZKpm/yGqUhVWzoamSmSk311jNAByuecK7KURyuf9qeUDDGXrUcK5F3IwYmEIlmwY4UQIbqRDGuUmAQ7BDjzCOnYFsBG2EqNwbDMBR48k8QxHXBMJ549IIcghxxBhkIE62fJb9t3mdeCDbYVNynhq9h1CX8g+hvmtczhieNVJgwwI4yquPe4vMswD3FN5jNe84H6zG+aDnhNMjKfOxSgFiAR6OidWMt+DXewtWOSDO12WEPyNP/Du/1pZ1/gI4f0pI/NpNtQdUWUHVQZQdDqP+7cvPyAY4VNKt57IADhUokmyWGRZ4w9ApWpwCadSWJtAY7seuDUMY6TuMk0d+wb0bj/D84St8eM3qUvpUFYCzguWPn/Dh9Ue8fvoez+6/xc2fH+Hs0euiJE4MHMPggQW07xjD7uYkdjQOYnv9ALbW9GF7XR92NPZh94Y+HPg+iUT7FKbTh/DD3Emc+/ESrl+6jsd37uP108f4+OYZVj6/BJAZH0330GcPlnHj8mf8dPg5RvquoGXjEdSUjqO6cBIx37QcmBH3NMKuSYQIcdwjqHAPo8IzhApvGtGCNOIlQ6gPDqMhmEJd2QBqS3tQXdgh1vjSda0I/H0fiv7RidLv+hFcO4RwDi2NBAqzX1Y+0KBNlAePfU/F+4YeOPmzqM2f020etfmm0QOHoVBqA1ThKRTElbVIBBAedm7dXOxpWaKArueKATmWNU8pMWoeOF2I1fvUQaTgTZVnHlXuecSkzSEmAscsoi42HjqmEdY42xQq80yblMf8d7oJ8/MlNIu/RwuNIjhq658StDXA8c2K901z4SIaCxZQH6AXjgY4Mn+dlmDlASLCJq3b4vau3P+tsB7HZqyuX32P08NArS0DcXSpZeNNIvkfbG8BKpWqAgItRlrJFEs6f4v6PaoKkLLmKIhnhEneByoUWpljOVAvqzqkEPUzrK9fcjPVlSXQEEqiLtiPeFk3YsVtCAf2o9y9D0Xr9sD/zxYUr2lD+fpeKHgzJEpGtZTpZX4dthHL1TmS34WQZz9iha1ojnTKeutqGcHBiVO4fuEBPr/SYZBa3qOy9Pzue1w++RCHxq+gf+9xbIlNomRdD4q+7Ub5egpuVMT4+9UYyLUzbFA80QjXNCiWA/Trh6ZAHANwtPeIAhwa2ujnMq1qPKip9DMZah9i3m5UBzpRW9SOLdFepOmBc+gKnt1+g/fPPkhFPEsZ5H71AXj/fAXnjt/FYMdR1FT0IuhtQ2neARStPYDC79pRSs+mHHobDaPSxZLhzPtjBD/bkkQhLxvgmHAqCg8UjNT12CCHApOzcQ8RCxwtS9qbTYEbKpNTYqGjlU4sdXytvF5732hrlPGqYG/DHfUaPqcAkR3mZQkrxhqVtU4EWNCLzj2NKjbXDGK68bGCvU7B1eH9okEO17h43wSUB454ZGmQQe+RmGcEMfewhORFXYThLI1LZZmKsgpbkfUm0EMBMyVoak8kq5qL/d3Gk+LPAhzn3qBCOu3wVBkD7mV5U5nNRQHVBjnKau4AllZ4m5k3Ct4YiLNaz7AdAVeyl5gKTro3HjtfeN4oRVQgDkEOc1RxvlpNKYYEE0pRZK+ssJE8wptxhHPHEM6zG4EOIY4UARAg4wQ5ao9VAJahCsqbTLx5WMlGXq9AJ6/FWYVKkhgT4DhCqHaLB85BHB4/jZ9PEODcx/JbhtDQWKOhqwCct7hw9BZmEqfQtWMOm6MJVOTth+9vO1HCipDigcPzgXCS+7zZbwkd7cqBCuAsIu5dRDzfVBma14UOaKWeQNSjyya705JbLOpJgy1mWn4aVZ60lHPmfBXoKPfOfL/ywqGXC0GO8b6p8Sl4Q1BDYCPnrYe9+h3x/CXUeJdQ6z+IOmlLqPOx8bfyXJ5BZe4UIoQ49Hal4igVe1ikQXl1xNyzcm7TGGM1HUolCqVvAbUCkhYyAQ4VNe2BQ6Mec94wZIVeN1HvJCL5Ewgzj2A+IQZhAgGFaTwXJ+T1VLTjBXOoDsyhyj+HmG8OUd80ov4xVPqHEPEPoMLbiTLPPgVwGgaQ6JyHATgPb93F+5dPgc9vgRUbQry69wmXjt/BzMApSS6/Lcp70oGCb/ag7LsvAY5ax0qOohwsTeQmrg0FP+P5rAA3IjkMVWgzlX2GKrGc9TQq82cQ8bDRe3scYTcrn40g7B5C2M0QfbYhRFjwI39YtxGRo+n9zXFS4GdGQRuBN/OodM1pgDMlCrzyiBhDVMCpOvtEOSXAF4BDpZ45HEcQzk8ilN+HoKcTpe79KPfuxdb6BPrbF7A0cwYXT1/DgzuP8en9By2kayPre+DqhReYTp/HjsYJ1JT2o9Lfg/K8DpSs60Awt0+up9o/LrluJOeReFxRqSYMWEA8sCg5aagvRFwpRFyDiLj7UOnpQczHMOZBNAeHsCU6jo3hUTRRni5LoSowgPKcTvi+2Q/X/7sX/m86UbJmUOBNhF5/nHca4Ig3hlSd0mE14oFDzy4q9joXk87HZCAOS6AzxyVbpXtcN7V/VbqZB9NuhMurNcnrJ550yqtMzm9tCJFzWc4bc9YoT3WeN2oNc/8wwFStbSlB74DFFsjRILYybxLhnHEL4kTF44a6yhQirknRVaivhPm3exoRj5qPAnGYJ4jwxsvnuD4VxPkSeNCLZ1LygBLcGHgTylUAh3+LF47ew9We/vvymjEEsOfZqUJHTfSBMlxwD+aZoAAOzxMN+KXaGe85oz10800g5qOuphr3G+4vvK+V+ZOSBoMpMKTRg8iALd2rsVMeS5kAR8Fp23BlR56sdq2WXGSu6z9Fp/9ST//3AUNO3ZSOBn+VEf+T1r7sm2sP6JcAh4qg89+z3+uwppoDS1NSWWgWwFF0WQ4qgTcK3PAA4kEULx5Cc2QI2+uH0NkyhYWJE7h87gZePX2F96/eYfkDXW702cB+eVkOi6cPnuPG5bs499OvODh1GkPdB9G6bQzf1w1iU1UvGkMdqCltRbykFdXFraJQVhfvR7x0H2qDe9EUbcWOxm60fp9AX+sYJlPz+GH+KM6fOIObVy7j6YOb+Pj2IQBCHC3cUb77CLx9voLH95bx89mPmBu9jfadJ9AUmUZd6YxQ3Ur3DCKuadkMw+5JhNwTqPCMI+gZQ9AzghAtCr4UKv1Jyf0T8XYjnN+GkLsV5TkHxCJf+M92lHzXi/K1SYTWjwmkIEmnJUusbIQTVF6oMElYE3tdJtyts7S7JxHnYwr+LrZpxN0ziLtndaPioF8rFlRjdVUCBwWPGBVKq40hxsTIpqqVvFcJrjU65MkGJJwfDoAjsf60MijAQmFPgA2Fi7wZRHKnEc6ZQihnEqH1E6rlTCBsWu6EHD60IqhGRWBcH0pUDKgkKBdRCani2NDiR3BjmonrF0vZtFSPaAgsoLlwSQBOAwGOhDFRKKfiYFvsqQBG3WxUsKnQq1AM26piPI1Msl0DM51riGNlwJfx2OGBpDyVCM7kO2mNle9iZTO6/KYRoXBPCy0T+9IiTsgm7vSMq2dlG3OwGYCjSoUqZZtWJLpZM3lfGpX5CUTyBxDx9qOSOVcCvQj7ulCR344y936U5O1DSc5+FK87gKI1bShb34NQnipnrTwWnOCGYVNpRPOZ6LAXIXcbgq4WVAZa0BhuxfaGLnTsTmFp4kdcu3DXBjhmSVNZuvMaP5+4g6Wxi+htOYyNlaMCNgL/6EDp2l6E6PWTT+s0K1zR2q5BjoY4kvyS1sw/A3AoxDkgjrJE2sIEIY4oVxTQmHAxbwCh3G5EXO2IettQHWjFxkgXkgfmcXLxZzy5yYpeb7H8loIs94sVAc8MEXv3bAVnj93DQPtxxIMDCHq7UObqQvH6LpSs7UH5+gRCuYQ3FHAmxL1dLHHG+iRWKSXoSciEFTZjvC60BY+QXH63AjgSpkL3Za04i6Kcpy1UIuCY71O99Xpascx7cinoU8jn2PA38LvsNSUeOhT6xT1aea3wNQTN6rWreOKIwKKVHAHEk4jRMpg3gWjuJCpzJhBZr1pl7qTlBaC8gxgeoC2SOvzLeMiY76OwJNZQenSIAM2xTSOSl0QkL2U1ejrR2yHuo2dDFsSRueGwhFqhZl8K1QqUqrVtFLnfEpoUwOH+qPZu4x6v4BXBzSQqaS3NcbZxeS6aN4GYtqBTWKUSRujHFskbkVbp0vuU7F9qnzL7ld0b7xwFqRX4+ArEEZijwI0Jo1IAZ1SU0xi/L0+1ytwRREzLGUE4Z1RaKGcUoVy2EbvljYhSFc4bAVuE16CvRSk9/O20WiugpuC5Cms091jCrwR40itIASneT/FgMR44ugpVlbcXu+vGkGo9iMMTp/GLBjgrAnB0cj1dRpwhkReO3sRM8hS6d8xhSzSBkGs//BrghH8X4NCLxTQFb2yAw2S1rHpDmEpFkAofk7omEXEzfHLQ0ZiAmSFgbNyDlQekeOm5GV6pYK8oOTzfuO50GLJUZpJyuYQ2pi0KwKnyLCGefxA1vkOo8x9GfeAw6i2Iw987JwaUSM4kwjxz82hNV8pMhNZ8F2EA27TAAYbpKJij4I5419IbIBvgyO8z4FHl/yGMEfnQpxSpsIeey6MozxtB0EXjFyGGaswryFZJIEuvOebNKZhFdWAWVb5ZRL1zqPRNoZLlqv1phH39CBqAk78L2wzAWTiLK+eu4tHte/j4+hmw4qhEBeDVvY+4dPw2ZgZOomv7LLbFDMBpQdmaLkRy08rbTjyOufaV/KVg9KTkIFRVQLlGRlDlHka1Zxhx7jesDOlV5zgT/0elYukEjKwoRj9ed14awbwUgnkJBPMGdBtEhYstgZA7KS3sTiHMcfGMIULwQgXcPYuIex4RF9scIq4ZAXGRPN5Lyktqz1CKLucgPceplCoPnohnHCF3GkHXgJxVJTQ45LWgxLMLm+v70dc+i6XZU7h45lfcv3MfH99rLzaun4/kYSu4fPYxJhKnsK1+GFXF3Qj5OlCaS8NFK8pyuhFmfkgv5Qd6F9lhYPRGkqTU/kUJZaFizdLjzGMTdrFCY59446gS40nUFqURLyS4SSDqG0DI3YfSdd0o+EcHfN+0o/i7PgRzeP+UVwaBmQAcDXEEAmqvG5X7RsMb8dawIQ4Bjij5cr/GJQlz2DWKsIv72DBCeUMI5dHLhy0lLZzHnDs8c9JKjnOltfFAr10dkmUZXsTTlYYRwgADbjJhjSo5r8BNtQWFFSS2Pf6oHxAmc11Oy9kRWj8K7sMKoI8j7FJgJZQ3DtUmEBKj85QCOfQCy59CpZdtUjcFVOkZxlLwnCdMRi1NxmIUobxRMFG02u+55xPWjwvQyQY4BnZwP/9q03JQJrzR6Qb89LSlh73y6qMMQ2OAOktY8YzeW8OyPsKEnx7qnSOo9DL6g7roKCJsHsJS1UL6OhhBIWNEr1ENpKTnd9AwxvVCGCVyJ/deZ1Py2m/JpNkAR67v/2iIY+tCf3ng/KfcaHtA/3WAYwRxRy/KulKoqHDQcsxDmgmLZQHRksC41vwk6sqGsK12DPu3TSHdcxg/HfoZ9248UZWkTHUpghuBN8rT+cObj7h97SFO/vAzJpJH0LF7ApuqexD07ELguy3wf7sFgW+36rYNgW+3Syv4bhsK1m5F4botKM3bioh/B+Jle9AcbcW+rbTMjGNubAmnj53CrV9/xZvnD5mRLcMyI4lX361IQuU71z7j6PwT9Leex5b4AppC84gX8uCk9w03wUmEaU3JH0fIM4qgZxjl7jTKXAMoze1B8fp2FK/bj6J1LShauxNFa3agaA09HvZLuebydX0I5VDxoEWS48nNigBnAXUMD6I7Mqm9ABW6sbOU+AiqaVmmcuJOI+ZKI8ZksLlsQyJox5jETapUKetQleUeP4JqWvgICtxpRJloLo8tqUI7GN7h4vMq/MB4CojQQtd/o3BaII8wQ1cQYlJObYFXwrem9XSxzB2V2OSytUmUrU2gbM0gStcMoGztIMqZ/2c9Y5cT4qVQkUtlN4kKthzTUgjlphHOI+wYEYsmD1tVzULHVnODZ+NmK7+TBJ8eOAtoLlpCY+ECGgo4vvRyoiJAAYtWel47vSMSoNAuSiDHxa0s+CYUQ6COwwqtvJcUxLFBqAE4XHcELhpyUfHwKEFPeQcoZZNW3oqcQQknYkgRIUqlm3mRlOcA8yPwt9IdW1zqNcQxoWIq5wGV2XEBH9F8HmAU/PoQdHWhLLcdJTkHULR+HwrXtiCwZid8330P/3c7ULB2D4rXUdDqRMjVj0oPy+aOqdLEEjrFMr0UvlKIEgixzKi7A8G8fSjN3YGwbwfqQ3uxta4N7bsGsTB+DNcu3JZKV1ZIpAaiT26/xMWfbmJ+5Cy6di2gOZxA4J974P1mL4q+a0cwt1cUG/4GJk9WuSKocNshVeKCLAep05U1+7EK87E8bIzHDXvuWbwfkmNBgRCGdVDQDeVw/vWgbN0BBHP2Iezeg5ivBc2hAxjcP40f5y/g4bVneP3oDZbfvNeZ0LUl8t0K3jxdwZmjtzHQfhjxYDeCPlZEaZPxL8vpQkUu8woxdIpCHeEglWujtHLdE4LYwoET4tQG6PJNYYFWbCpCfK/yOOHcVPAphXBuUq6Dc1hanprLKmSL3805nZJ1zt+iXp9EheQhYkgXIQfLNDNJLEP7VCgXfxf3JgueUpFhThoJXzSgx0AcXotS2kTZFu8N7kUjUpaXOb7C64dQsTaN4JoUgmvSEjoaziGYoMJBK+e4Zf2kVY0endLEW015qnF+KPBJ4TkppdhDOf2oyOF+2i9/cz3zmgXg+EdRw1BAWffac0VKVCt3c/FYsqARvRAIcYxwnR1GpbzubEhjDB3cC5xNe/ERTvB+s5oSLeK8To5DzhDC69MIrU/plkR4fVIS2fPe8B4ymXgoZwAVvLb1/WB1ODY+H3ERerIKE0M/hqVVigeSgjsq+a+GOAyZYk6OjOaEOSbUiiG8hMdMqm3OiSE5IyL8TTkJhNYPomL9oMrbtm5A9m+1h3MfG0RFLhu9WdgGpXFfI6Sl0hPOS4vAzX1cwJTlkaOUfOOFRmGZQIcAxEApznsFdwlwqCSrECqWEY+4elDl7VEA58AhHJk8i19OXJUQqpXVQqhu2h443TvmsSWWQsjVCv83u+Vs5j0wQEnA5qoeOMoTp1ogii51nc9qQ5xD9OyjZZiFDwjoBxFy9SHkIizvRkUe94VOhPI6Ec7r0I2PuxHO60Ekrw8RF+GOOhPUvkgvRd5TFaYgnnVMzsoktpaHDK33yguHHji1voOoDxxCQ8Eh1AeWUOdnOBUVRnq8jiOUM4KKnCE5X3nGKsWUiuqwQDcmhBUgIHkuaETR3nv0qKMHKPPqGA8celZoeZCygBo/5WWqcrxQmWLIeRpluQmU5AyglAUccgdRnjuIoG6cQyqHyjCq/BOIFzDsiuEe9BSYEUUz4h1BxJfKAji7sa1xEImuBRxdOIcr5+lx/QCf3tJIpwGeNiy8vPcRF4/fwfTAKXRun8NWApz8DgTogbOmWwMcFUpOo4wYSsSDgoYB5bFM+YhhmjGGxLJqI8/QfLYkqvJTiHmYyJf5XQhg0nLdQVcSwbxBlOf1oyy3F2W53SjN6URpTrtuHSjNYeP53Y3yvB4EXawQNSjAhaCLXiYSEkNo45oVY6JAN0I4A3Byed/YlLdC5r5KYML7wLHvQnFOK4pyWlCQsx1F7q3YVNeNvo5pHJw7iYvnruD+3ft4/057sX1ekXCqD69XcOn0Pcmrt7l2AJWFrQjm70MxP2dNC0rWtyHIeexJoFKMSsOyFghICDZVDhKGlFFJpocaAWdC4A3XRjCvHRV5HVbj3+W5bSjLaUPp+jYUr20X2aHouy6U59BzZ1jn2iGQUeE0lAcJb2oK5lBbOI8agkAJq1K5b1R+HnqG0WOH3l8qekA8k1igxMWKWikEcxMoz6HXT5/k96HBq2x9N8pzuhFky+1BRS7XNb2YexWA4tolkBVvOybIprFOy648+7k+JNkuwxG5dyzYTcrMG4Cje516gNDGCusTz0Aac8Ykf2bFuhSC61OoYMtJoSKXvz2NYO6Qanm856OocI0h5B5DmFBDAMcIIiwqwTyfXqW7hT1JgYhmD6/IHQBbkOOwfhBl6wfFMBWU7+F+wXnJvY7ntdrLBdjonFYil39hgDOgV8lthCH0vpE0B36mG1Dwpr6A3vY8D7j3KIATJlDL5TXyvCH8pMzbiwp3H0KefoQ9gwjnDyLk4boZRAWba1DB0lwm2mbILN/P6oO8z2rPk32PhgY35yTnA+cFZS+Gd9Jzy85LZ/Y6dW1fAiqjDzllhd8y/Pz3/zebN/wFcDTAqc2fwr/S1GSwB/TPAhx70ukM7MYLR+CNVoa44PSBTWGFVhMeVgQ3EW8CEVbGCQ+hZeM0+loPYnb0FC6euolnD0jzs/7T4Qif3q7gxaO3uHjqOqaHj6H1+2FsiJHu74HrPzZh7f/VjPX/z0bk/X9b4P5f2+D5j+3I/4/vkf+3HfB+8z28f98O79+3wv/tNhSt/x5lrp0I+3ajOdqBPZsH0HdgFPNjh3Dh5AU8unMXn96+xvJ7VpRRsb1MpPzuBX/DMq5d+oyD0/fQs/c0NlXNoSE4g+oCDW/ojqhdgCPeMYS9Iwh7hxHyplGRP4igpxdBdyfK3QdQ7tqHclcLyl27EXTtR4WrXQQ5WuIUKKBQphKY1gXmwA2Lyc+4AajwJlrhCF9o4UmjxpdCrT+JukBC+hpfEnFfCnFvGnHvkBJuGTrAWHuBAbREOxvdtQkKmD8gIXkEWIo67k+ixp9GjX8YNVR6fErpUd4qeh5wcxalRCsoVqiRCUfSAls+Y/4Zj0yLGgVYCjIJVHoSiHoTqPIlUB1IIl6QQk1RGrVFQ6oVp1FbnJbn4oVpVBcMSRhezJdG1EvvEvbDiPlGVay4JPxjzLM6NGjpE2Wd64eJgwNzaCggvGHeDIbjTKDWP4bawAhqAmnUBFJy3bz+Kgpe3hSYW6FG/p05YKjYqqSTSrHRZa81uDLrxKwv5YGjAI4JWVNeN0ohIjyTe8FwJ8uzhUJOArzGuH8YtQWjqA2wjanf6h9HLRN40iNFe6LQIkiPLXqWiIKTTwEphcr8QUS8fQh7uxHK70CFpx3l7laUufah1LUXJbSy5e2ReVnh6UQknyU8eT+GVK4b5rthiV4fEyOzulVS7lWVn5WqulHpa0XE14J46R5sqm5Dy6Ye9LYO4fDsT7jx890vAc4H4PHtF7h44iYWx86gd+8CNlclUJqzF8Xr9knpUlra4gVDiBeMIB7gmPM3MC8N5xT3GrXfqIOSB7lp2QBHwTsVGqPmgfHCoTs/15PJfcS/CVKUdwPhBqtDdSLmbUO8sBX1pfuxrboD6fZZnFy6iIfXnuL1o9f4/JoZHFXMJ/cMJmwmwDn34y2kug+jKdaDqtJ2xIo6EA10IervRTXXaeEwGorGUVfARKw6DwkVagphAnBoWTcQR1l3rJxIzPEi42E8D5hgmlWz0tJk7cvaTaK2IIW6whTqi9JoKB5CY8kwmkpHpDWWjsjfDcWcY3yv2gOqvHw8jBqZc+OSKLZWqv0oKJoJcPgbaRFT+acsbx25DgpiyrONa0X2H88QqljRTOf+4GMqPPSYIcyMcT0QGvjGUCW5HkYlH0TUy9ABhhEwnGAIUQkPHJJ5quZlClX8/ZIDhYIyK5lQaGYbRKVrUBQoKvmylwnAUV4cBCoKDFD4ZRgZQwtMHoLVAY5djSoL4GSUmzWhjyrfDte9gSG8Tip5VpOQmbRUkIv7h1Ab4D0ZRl0hk9uzDaGO+2JhEjUFCcQDCVT7B1HlG0SVP4EqfxLV/hSq/BwDKooqPIfnCRUFuvkzdIfnMvdECWERgMN8FDxrFBBUa4xAW3kjMoQnJveIFeII/ugRwjClhHVWcNyr/Wlp8QDX7RBqCrl/D6OWc6uE/RBqitOIF6XAfTxeOIzqghFU+Zk4dVTl/WCyaYYXUVgm4OLcYRghLa5aYOd9UjBKe0Rqr0kL4OTz9w0gkteDWH4PdokHziFJYswqVI9vPsTK2zeZSYzpFXjzPS4cvYOZxBl071jAllgaIdcBBL5pQemaToRzCbSVV5rsN1bIg8nbRAhiQz4FbVQ4niqbTI8Deh4MIepLiudA1NeLmL8XVYFexAuYtLVH8m3VFfegXlov6ov7UV88gPriQdQVJ1BXlERdUQq1HN+CEdQUqJxkBDkS5qEtxVJGWnJn6DAMyYGjct/UBZZQX7CE+sCCGIgkv5aEH3LM6fWoziKG33JeMddZlZ/wZBRVPl6D8iIxYSX05hCPPUItCW1kLh7mw3ECHDtUWMJjfSNS8UgpiAzbGUQFjQL5A6JoRbw8txKo9HKsknIWVgVGEC8YR7xwClUBFX5VmT+NSP64KJxhbxIhbx+C+R2QHDj5BDgJJLoWcXThPK6cv4HHdx/hsxPgabHzxd1PuHDsLqYE4Mxja4z7TCcC3+xFqQCcIWtfVl7FOsxUjAIEONxDCOPpNctwOAKbBKryB1GVP4Aqr2OtBpKoKkihivJMEVsK1cUJVBcPorq4H9VFvagq6tGNj3sRK+xDtKAP0UAfKv30pB1EJb26pZgGPQvoHcG8JtPiLUXvKcuLgF6Y2hMzI3RR9gOzz1JZH1CR485tAAAgAElEQVRygrcNFb59qPDvRqR4F3ZuHkC6fx7HDp/D5UvX8fD+Q3x4zzX0CSuf6SG/gvevVvDz2buYSB3H980J1FS0IVrcigrvPgnPDud3grkvZe0XcC7x/qu5JKFKkoOEJb5p+KWcyPCxQSlnzjDtSH4HwqZ52hH2tCOkW4W7XeSGoIBQeuskxNMn7mfYndrrJDkxZSUq3AJwFqR3AhyRGVnK3M/y5/TYGBHAyMgBhrOFPSmEaYj20pM+oUPTB1BV0I/qwj7EC/tRUzSA2mI2rtdB1JUkpNUWJ1FTyJyEaVQHKEepM4xnGqGuMk4wvI7GbxYccEIc45Hj6MXrjsU7CBUJOuiVTe9snjFKtozSKEtgRLDvTcsaYln3KCuN+eiVQlBDPYXghpECaYQkhI66WgJRztPCJKqLVOPeXVOsGmVySy4vSKMqMGTvETpnU8zHPU+FM1kQx2lY/Q0DnPHUkWuTNAf0PlYAhzK88zyQ88JD2UB5m7NITlXBAKoL+xHnvShNoK4shfryNOrKmMIjjZqStD6PjE4xhJifY8N1oO+3m2cADQwMaaRXz4h47jA0i/PJJGE3IOcvgJMdtWPzhv+xAOdfgTVff49OeGuFSunwDhPmYT2vFE5DAI1SqnqjuDt7RUzFMiXKFK1NiqKH6SLMsoBU+Ar6sal6BB27FjE+eALHly7j2qUHePkkM+8Mz1Ra7d+//IznD9/i5pWHODR7Ft37plEfbkdlwX6U5u6F7+8tyP+PPfB9sw+Bf7Si8Ns2ofDFa9pRvKYDJWvbJTypZN0BlKxvRXnuAVS42xD2tqG2vFO8eHZtGMBgx5QonFcvXsOzB0/x+uk78bghvHn5eBl3r3/CL+de4YeFu0h1n8OuDYuIlw6jqpCb/BhCLroiMoZ7HBFeNwXSwChihSOoKh5BTdmw5L9pDA+hKZxCc2QQzREmle1FY7Af9aWDqKFgSzhBwUaszCreneEiYnWngEX6K+7jhDFDAmlqAyk0FqewoTyFzeE0NoeHsDk0gs3hMWyqGMOG8jE0lY6hoXgUdYUU+KiYK2WNChsBBXuCi9rCNOqL02gsHUJz+TA2VYzIZ2ysmMDG4BQ2lE2hsXgKLN2qqiooaCMWZSuBpgI34hUg0IYHFIUhwgQKYikR7utLh9FcMYrNlUxEN4Vd9bPY0zSPvRsWsH/TkrR9Gxexd8M89jTPYXfjHHY1zGFH7Sy2Vk1jQ3gc9WUjqCkeQryQUIcbMEEOy1BqpY+eCXoslceFUo5V2VXCxglR5igIN5aMYkNwTI1baAwbOXZB9hPYHJrClsgstoRnsTE4g6aSKdQXGsXbKGfK88jOj6M2LwNwZO1YiURp2Wai3mHUFYyhsYTjO40NFVNoDk6iKTghjX9vCs1gc3gWG8qn0VQygfrCMdQFxlBHiONXnjhi3WR+ElE86R5Nyw7BVlLcjGuKBlFXOoj6YAKNoSSaIyk0RZJojDAX1SAaQoOoCyZQW5qUQ62mcBjxAgVOmPNGVZuigqwAWx3nSPkwmoIJNIU4jzuxtbYb+7cxPHEUE8l5nPrhPO5deyhlw8UDR+e1Wnm/IgDnl1O3cHj6HNKdh7BnwwhqSjpQXdSFhvIENkbGsCU6g02RGWyomEVT2Szqi+h5okJlqMSZfCXVIuwYgOPsDcxRe5MFcYzATeWQyrbkIVIKrIBJ7wTqCkYFbGyoSGNzZRLbqwexq74fbVtTmE4cxrmjl/H45nO8efxGAA6FWKnIwYJU71bw9tkKfj59B5Op49i1KYXNtdz3BrAxysTlaWyJjmF71Qx2Vi9iW2QeG4OzaCyeRl2BynkjYUqmGptAHAIc/kYqtARZTFJNjygFbWoK0qLcN5Rw3aq1vy06gR3VU9hdO4O9DbPY3zyP1g0LOLBhEQc2qta6YRH7mxewt3Eeu2pmsC06KfO+qWxEoIHaK7hfMIE14S2hqHK/p4KgKlxwzGl5p7LmaOL5RmhGEKDhsZewOY06rrfiMTSXTmBD2SQ2lE9hI1uQbRobK3jfZ9BQNoXa4nFUFYwi6qdglVKWNIIZCmmBBOKFSdSWDKGhbATNwTFpTeWjYGssHUZd0RDiAVXdz4Kx4oFD+KU8OAR46lA2FbaVBXB04lbmAzF5cJQSp0MpxPJJbyPlcSQ5m0yeGROORCgliWoJzgnMFQQhbKsrJFgbwcbgOLZWTmFH9Rxa6haxt3EJ+5uW0LrxIPZvWMI+3qsmJvCcxe76Geysncb38Slsjo6jOTSCegqmRQwtSMraj3gY5khYTmhOayiT7lOopoWZio1K8Cj5HyTvg7I6M2xR7SG0EjMEIClV0sJ5AwJt4n7+5jSaykaxKTyJbbEZ7KyZx666BbQ0LGFP00HsbT6IfRsPYf+mQ9i3cQktzQvY1TiLHfXT+L52Btvjc9hWNY9NkTk0lc+itohhDFRgOL/ows7foCzTIiDTq1cKBjDXARMZZwMc5jbi2CYF2tFrJZrfg521Y0i2Mokxq1AR4DzC8ltH+IwJobr5AReO3sVM4iy6dyxiS2wIIVcb/N/sQemaLoRzCde4p5g9Jju0TgMcr6rgIglimT+CwEnyMfBcSiPmT+o5m5C9bkM4Jd/1fc0odtWPoaVxHHubxrGveRz7N0xg/4Yp7N8wg33N09jTOI3ddVPYWTOF7bFJbI5MYkPFJBpKJlBToO4dwVelVOyiy7+exzJvdRJjhjixcpXkzlGGIeYUqy9UZ3sT12NwAhtD49gUGcPmyjFsqpzApsgkNoWn0RycRkPJFGoK6amgDSWUS2jQ4zoQgMMqVAyn4r6g8kJIRUHx/OLeNYpqAUJUntKI00BTmkZdeRr15SZX4AiaKsbQHJrAxvAkNoSm0Fg+ifrSSdQUTaI6MImob1Llj/GMIZw/hJA3gQpvH8rzO1DKHDj5LdjWkESiawlHFy/gyvlbeHz3MT5lATzu3c/ufMT5Y3cxOXAaHdsJ8Bh60QW/AJwehHNpCKMRTVXzk/PEnCXiFUajG8N9tUFG1vcAop5exDw9qPb3o7ZoEPWlKTRVDGNTdAxb4yxqMY2dDTPY3TyDlg0zYtzcs3EKezZOooVtwxR2N09jZ+MUttWOY1PViBhB64NDqC0dQnXhMAi2Yn4mPnbk9JBcHiofGuGa3bi+CODovcX7MIoaGoiKhlFXmkZDRRKN4QE0R/uwobobWxp60NM2ibnJ4zh36gpuXb+HZ0+f4dOn98o9fnkFnz8AH96s4OrPD7AwdRrtLePY3jSITfF+NEZ6URdkioMkNkRGsSU6jY3hKTSWTaK2yBRDUF4NqgqX9r6hp453AFWSu68PtSV9qC8bQGNwEE2hJJpClD1Ua6DsUsKzgCCbLSXVtBhCbsK1JGzP2u9mdSJeeuCoJNriccMKWBJqo9I9RPIJbWh8TqGSBsMA5+oQ6stH0My1EZvA9ppJ7KyfQgvXZ/M09nKtbpzVbQb7Nsz8/+y991Nb67Yt2P9iV3V1V1d39T3vnLPPTjbBmYzJGUzOwSQBElERSUgCIXLOOeccDBgQo2vMpYVln73frXffD+/267Orvi3ANtJa6wtzjjnmGCjPsKM4xYr8hE5kfzRL3Jv8wSDFyJhggg5kG7H1R2FTUR9JdSmj1byM57PHdwaJFpyytlTgJjHYgKRgPVLeGJH6jnG/GRlhFmQyfo/qxKdoq9z7TK7jSBtSw6xICrEg7p0iAB4V3IoI2scHNkiulhjSjLSoNmTFGpCXaEFxqh2lGQ5fjN6Nqk89KM/oQWlaN4qSu1GQ0I28OBeyP7qQGelAygcKB1OnSQGo+Ay+geBqfPbnryqIozJxVCYygRzuK2QtSStaIPc/M1Lem5DOmD3ahNx4ymsYUZxmkWdT8cmBymwXKj65UJ7lQlmGCyXpLhSnOlGY7JTzKDvGjswoK1JDzUh6b5Scgp0jBO4iX5KRyUFgkXs5gU/GY8o5qjC51Wv5Z/YNr1uJD1SGrvKq5tj/c77+/xjA+XMQ5j/GwFF+3zcAxx8RexZa/fcAHNHuYCXlD8AbX5sKqaVKoEXaGamirKToEPNai9h3TShINUNX069YEo7vYHf9DF/OvwmiqTwc7x1wcXSHnbVTTA2vQd/kQW6KDq//kSMtH6//Vom3f6/H+5/Y/0o6Y4u02QgtmwJoL9uh9Aq3giASP0fUSx2iAljh0YqNefyHOiSFV6MsuxWmZjfGB+axvbKPw+1LXBw+4PacDlR3mBk9RLdtGa2aUZTnupAa3YHwwEZEBJCGR7qdWcSLSUGUfstXBsS9MyIxxISUSDOyYnlQ84B2oiLbgYpsOypzrKj4xE2RAAaDJTPSw0xIequ2BSjCfayMsvLOpEBaY8SBgzoOBF1akPq+BZ8i21AQ14HSFD3KUk0oS+1EOYO9FBsKE6zIjbXgUxQ3cwNSPnQg8U0rEl6zYquwKfg1gZt0gjaRBuTGmFAQb0ZJcidKkmwoTuqSAL0g1olPkV1I/0BQiQg0N2RWngiUMBli5VYBcJgEKFRxttuwkqIVdkf822akh3cgN86MkjQ7Pud2o6GkD63Vw9DXjcGomYC5aUqGsWEc+vpRtNUOo+XzMLQVQ2gsG0R1fi9K0h3IjjMjLVIvbXmsYsWIxhArJD7xPzJ+KEgr7RZKPzS1RhjMsGUlXir9SpWbgE1BXCdKU+woTelCabIDJclOlKa4UJ7qRkWaB+WpHhTF9yA32oHMMLsPzGKVXQFx6MD1DcAhGq2IXSqvSgWSYoaJwWzhMCD5tUES2LyPTpQk9aIkpRfFKW4Up/TIKElxy89Kk3uRH+vCpwibJL4pb0xIfkNmBAEo/l4fSEEARwJ4JmwEy1rAyk86gYjYDuQnmVGSbkNljguVuS5U5jhRkeNAWVYXClNtyInvFGAsLcyM5Hes7HIuElBUKhuxQawqtSMz0oi8uE4UJJpQmKJHcXobPufr0VxjQ2dbN/ocw1icXMXxzpkwUoSZwvZIysTceHG8c4HlqW0MueZgaR5AfbEVuQk65MS3oijFogQIWR6UpnpQmNiL3JheZIQ5kfSW/fqKiCAPbukbl8DGl0w9J1dqkuV3oIqQnkJ3V1uW1JYsxcmGrUIKOEkQoSDOLony50921OZ1oqHYjLZqGzyWUSyOreN0+wJfjm/weE3XvCfFLc9np8p9Y2V2Dz2d46ivMKMyX4+KHAPKskwoz+xE1ScnarI9qM8dQmVaHwrjevAp3IG090qFiYnCs0C3JEVkvii6Roq4M1kqChsq/hWr8m1IDyN4q5d1W5ZqR3W2Cw0FvdCV9qO9agjG2lEY60ZhqhuDqW4cptpxGGrGoK8eRVvlMBqL+lCT3YOy1C7kx1qQGU4GCJlYrBwT6FWEpbnWmSAqrRNqgsik5pu1KZM4JnOyX0nrDZOfdtmzkt+0ISPEiJwoKwrjHChOcKE0qVvWGNdZmcx5N4qSepAT24X0iE4kfDAg9k2bMDgjXmoQ8bIeH4MbEfeW7moM6DsExChItAvAW5jYhcIEO/LjbJKIpn7QC6uOwHXiax+bjqwyMiJEf4oaVNTPIQNHEUdXGDiqkLKfYKu0pnC+KS54iuaUX5IqDBxlTxBnJ7YhCnijtPjEEzAnwy2IYEgTkt+2yr0m4MY9p/qTG42FQ2itGEfH5wkYaqdg1kzDVD8FQ9049LWjaK8eRkvlELRlA2go7pfAtDC5E5nRHUgJbUXCuxbEvmpGNNl3bNkQ1qMC4LBSr9LAVWFPcWChA5CAJ9RnYSLRgShWH9nqJHT5JmkLiA3SIZkFgwgD8uNtKE/vRl3+ALSlY2ipnEBb9RT0dTMw1M/AqJmBqWEWBs0U2mrGoKscRENZH+qLB1BbMIjqvCGUZfQjP74XGREuJL2ntglbKKjXwM9BRggDZGUds4rL50SW3DcAR0mcVXAsloWCl1ppPYoJ1AiA01LZC7dpCvMjazjaOIb3hsmn779nAOceswN7sLdMQ1PIuceWiVolgf+75jmBVwCcH8EbJali5VxpBaErCwEU7lVKG7kwRclefE0wvRUZBG7iO1CcZkZVjh2a4m7oKnrRWuVBe7UHhto+GGr7YagbhLFuCPqaQbRW9kNb6pHP9znbhbI0Bwri7ZKUpXygUCdjLrKtFAYTdRukHdDX2pQoNt9kypExR+o/GahWJL3qRHqIDdlRdhTEO1Cc7EBpmkMSz4osO8oz+TXfrweFiU5kf+xCepgNye86kfiaSQzPVh/Yxt9NG3HamT/r3/jOvmCKTvNcIbOyA/Fv2pD0oR3pkXp8ijUiN9GCvKRO5CdZUZBkQ1EKCzdMttwoTu1BbpwDGRE2JL23IP6NBTHBFP6ldosBkQFkDzQjIrARYQG1CHlJEWMVwHFjsHsOy7MEcE7wcMOCoU9oUYwygJPte0wPKABOTb4L2SqA85cKfPjpG4CjFgwI4PAZ87qVIpE6Dxmbca8kQ40sznp8DKgThlVqaDOyottRkGRGWaYd1QU90JR6oK0cQEvNENrrR9ChGUZHwxD0DYPyyu/58+bqQdSX9qIy14HCtE7kJprF2TQlTI/E9wYkMG4kc8Qn/CxMFjKj5Nwj8KQ4gjHpVVqsWQBgQc+A5PcGpZgWy6SXZiAmlOeYUFVoRF2FGZaOPgz3zWJtaQeH+6e4vrqG1/vNWtL7ANzfPmFz9QiD7jm0NboExKnIN6I4U4+CVD2K0jol+SeoW5zcjZwYziGCOBYkvFaAQIKdCkObMXsTYoK1wmZJj2jBp5g25CXoUZhsQml6J0rSO1GaTuDEKjEDE/bMKANSQ/XC3CZrjO1qbMUiw0WJAX1ixmqrEl2UfOLFscGKCxdb+qI4l9gyxPiebL7gFjl/aESSFkngje/rxOc8NzQlHugqB9DBNVo/DH39CAyaURg0I/K1vm4Y7TWDaCrzoCa/G2UZXQL6ZEabBICLfd0qHQoRL1oR+ksrIn5n7Mbnxr2EVvPfD7YpyhCGK9cV213JjtcrxZHX7b7ztROF8V0oTvLFsOndAraUpRNw4XpyIzfBicyPdqSEdyL+g14cc8MD66TdO/a9BqlROuQktaM0sxM1hS5oKwbQVjMKff04TA2TMDdOw1A3hbbqSTRXTKCheAw1eSOozBpCUXIfPkU75flSiFuegbA/VRajGpv9O6+qTAM7EgQEUdjISnyhrMGEV51IC7Ei+6Ny/nO/qs53oqHMBV1lL1prBtAhz2UMHfVjaK8bQ1vtGFo+80waRVP5KOoLh1CV7UFJWre4omZGWZD0wSA5RXQgZQjYbsXWLKV1kYY8nFNcb8JofWYT/TF48y8A509FjJUJzUq3MhRK8392ROs/AtAkB1rx3zMU0MYfxFFVoVUmzjfETL1/z+yb5+oiNw1lfGtl8DFwfC4lrJBzI46i6FpAE6KDmxD7rhHxoY0o+WSBQTuCsb4NbC6d4WT/C+7oPPXDfw9fnnC4fY2FyR30WKdQXdyJmHeV+Lf/NQH/+N9zEPT/fEbIzxQOY/8u7RSVPlr2kSqUti7fqyJQxoojFxwTetLhIl9qEf6iBqEvSpD+sR6achu6O8cwO7aO1blD7K5d42TXi/nJY3Rb59FU40bRJxPSKG74ug5vfq7C+180CCVwRN2MF0ZEiop/h9hZErxJi7IgO96Gksxu1BYOoLlqDO21Y+io4yY4jPbaQRFxrc7tQWkqabt2qUywws4KCUERAiRSdRedCwaurPCwtaYVyW91yAhvRmFCO6qyjNAUdaKxuAtNJS5oS3vRUNSDmlwXKjIdKE6xITfWhIyIDqF3Jr1rQeI7hdaZEtKBLEnKLQL6VGR0oTrHgfoCF+oLelCX14uanD5UZnhQGO+SCnnyWwsSXytUe3F8keqTAuBIlZ6i1QGtoqcibTmki79pBKsl+YlGVGZ3obHMI4ecvW0ans5FDHatYNi5jrGeTYz2bGLYtYaBrmV4rIvoMS/AYZiDrW0WHZpxSQTKsx3IS7YgK9YIVqTi3rUi9g3pra0K3VUcHBS0XElOKEZGUT++KnRnVuXZnsAEqiLdAU2+G5oCDxoK+9FYOIimomFoi8egK51EU9E4aj4NgYAKQReFOaFo6BCY+VZ5V7RwFIFT7ktE25XDlm1oSa+MAt6kvTci76MD5al9qM8fQ0PhOBqKxqEpGpPRUKT8TFMwhsr0PhTEOpBBNtU7o4+JY0YiNQF8bWKKqxCDdwro6RD3qhlpYe3IjTdKoFOd342m8kF01E1Cr5mCXjOJDs0kWqrHUV88iIpPbhQkOoT5khpC+jErJx34yCrUSx3Y2kTmTX68FRWZLlTnOlFX5ERjWRc66rvRpR/AoGscs6ML2F7ZxeXRtbgyCYDD9qJH4OGLFye7F1ib28VE/xK6zWMiSF5TYEZNgQ0NJW40V46ipXJS7sXn7FGUpg4j52MvUt53SXJCHQbFHpNJtMqK4CuBBKVCqiRa/sEBA9dvAI4/iEMAh6yy1Hd2pIc4UBjfg+qsPuhKh9BRPQRjXT8sTb1wdvRhoncWG7M7uNi7wu3pHR6vH/D09UnR8LpTGDh3F15sLR1ipHcWxuZutNY70FzthK7SBV2FG21Vg9BXj8NUMwtt0Riq0vuR/7EbmaEOpLxVzjM6sgmIwNY4n6YYq4l8HkxShQ0lrVjNyAxvR16sAaUpFlRnd0Fb4oa+egjWxnE4W6fhMc5jyLqMIdsyhm0rGLatYsi6gsHOZfSbl9Crn4ddOwVDzQi0Jf34nOUUICg9lO06zSBIJO1Joq+iJNYiaqq2Gsl991kJ+3rzCWzwHotmlOgpsR2TgHMbcqLMKE1yoOYTQawBNBUOo7l0DM1l49CWjKGxeBR1BcMozXAjJ96G5HAyGVsQ/boREYG1iAxiS1oDksN1yPzYJsBkWaYDNXm9qM3vU0ZeHz5/6kVRUpeA10lvFYZhEtt7XjPJUmybFbHyPwJwVPBGddBTQRz156pblTLHBET1gTeyZ8t+oIA3iogpWY+tiA9m+1MzEt9okfRWi6zIdhQlWuSeNxb3y7yw6+bQa1hFn3kdg9ZNjDi2MNy1iSH7GgZsK/BYFtFjXICjfQ625hm0Vg+hpqAbhalmfIrtQFoEK8WtiHnFKjY1DHgWf+vjV1x+fMmMiHk6EEc3ILZhUDz2hSLOKbpj1BT4nXpcjYh6yRafVnyKMqI4yY7qnF40l4/C3DAHZ/sqeozr8HRuYdC+gyHHDoacOxh27mCwaxO9lhXZv62tU7DoZmBsnIW+bh5NZdOoyhlDYdIgsqJ6kRLilPYYfl5FP0EBcJRqK/dtRbDyewCH56TCwFEBHOrKfAzQoDDRhOYKBcCZG17H0caJT3jcl8M/AzgPmB3Yh71lBppCN3JiDD4ApwIhf/cl8M9tUwpgSdDy+wTLLgKX8ll9sQbBM4I3sa/aEE+2WEgLsmI6UJhiQkWOFZpSF9pq+tGpG4NLPwW3cRoe8wwGrDMYtM5h0LaAIfsi+jsX0GOYQVfrBCyNo2j7PIjGYs5xgp1OZEXZkPiO9tLcI3xgnbT1kBlDkXGfrTfjI58eDRl11FRLem2W4kBRogufuSYL+tDIxLTCg5YqD5or+6ErH0RT6TBqcvtRktqDnBgyp6xIft+JhNcE2gj2k9H4J+8ljmFk8RmRwPXHVum3LUgLb0dugknYJ5/zXQJq1BS4UVvgEaCvoXQY2vJx1BeNoCzDg+yYLiR/MCP+DRknZD9Tw0OPiJdkD+gQHtiA0IBafKA4vwA4bWipJ4Azj+XZHRzvnOLhi4+BxWdPz4x74HjrHlP9u7DoJmUtZcfSkUmDYB+AQ9F5pbVFZVj9CODw+slaZespWYvcM5sQG1iPuOA6pIVqpS2vJM2M6nwHmio80DcMo7N1Ag7DDNydC+jvWsaAYwmDziUMuZTXAccy+rtW0GNZRGfrJFrrhlBf6kZlrhNFaXZkx1mQEWVGaphFgK2EN2wBIqNFEeCl4KqiUUZWFBmSZC6wOMiWcTItDUgLNUiRrzTdjpoCFxoretBS64a+qVfAmwH3NBZmNnCwe4qL82vc3REAVa1ifef6V2Bv+xRTYyvosgyhQ9cjrWuNVU7Ul7rQUOZBU/kwdBVkOA2hJNX9HGckviWAQ8kFiuOy8KpDBDWsXjchLaJVgJvSdAuqcrpQX9QDbXm/Mir6oavoh6a4F1XZLhSn2JETY0FamAHxr5kb6BD1olVAHM5PBdwiUM11ShFsO+II4Eg7nllaiiID2hDxskUBbwJ0iAzUIpbrlozjaBPyU+yozHWjsWIQes04rC3T6DbOod++hMGuZQw6ljHkWJHB2LXfvghP5zzsbVMS6zaW96Eq14WiVBsyPxLEaQdBHGqyfPhZKxo1BCVjKEbss5lX9hjOO//YhoCGAt7wfKGMQmJwC1LetCAn0oSSxC45XzX5/WgsHoSubBjNFaPQlo+isXQUmuJRVOb0ozC1G5mxdiRHGBD7XouIYLbPVSIpoh45SS2oyDWhscIFQ8MQHPpZeDqXMWBfw4hzE2Pd2xi0b8Fj2US3fh225lUYNYtorZpDXcEEilP6kBFBrSq2y3F9KCCvgIjSKuYfn/FrFt78fubT2VTiNpIGVHBEAXGkHe51F9JCmDt1oyxNOf91FQPQa4Zgax2B0zABt2UWfbYlDHStot++ij7rKno7V9FjWoHLsAJH+zIs2nm01UyisWQYn3M8KEpxCvOQADNzCnaRhL/QilYVxcQFlBKmvzKvlGtiDqp+xn9+VQCofzFwiEP8LxQvUiqw/98DcP5bwZv/HtDm+39rQ3LgnwE4/uANv1b62ZTWDxX9VF9VAMdvkgrSz6orA3cVwKFoVKO43sSHNCI5shGV+VZYOyYwO7qL/c1rXBzf4f6G5flvARXPhi9nT1ibP8KAa0HEjosy9Ih89Rl/+98y8ev/WYpXf2lC2C/s83Uh5mU3YqnOHtQjIyG4B4mv3Eh8xdceJAGB9k0AACAASURBVHAEU+HehdhAJ2ICuqSXkeh62IsaJIc3oTzHjDZNL5zmMbhtMxhwLWHEswGbYRKNn10oyGxDSowGsSG1CAuqxrtfP4swcRgrlCICZkTkyw7wAIh90y7gTX6yE5W5fWj+PAFryxL6bTsYce1htHsX4z07sgl2G5ZgapiBtmwUFZkeZEezKkB3DkWMUgRcWSkWLQLFFpdVXFbHU0O0yIttRV2+Fcb6XvSaxuAxT6O/cx4DtmX0mpckyO/UTqOtehQ1eT3ITzQjKUSLjMgOObSL+BmzPNAUDaG1ahwmzTRsulk42+bgNi6gu2MBXc0L6GycR1vlFKo/DSA3xiG0W2qjMBBQRDP5qugsUISOujQx1KR41YzUsA7kxJmE/VFb0I2W6kF0Nk/CbV7AkHMVU/1bWBrfx8rUIdZnj7G5cCZjfe4Eq7NHWJ4+wML4PmZH9sXdZ7hnEz2dy7C2zaFdM4HGimF8zvegON2FnHg70iIsiHtrwMdgvRzKivo8bTepE6C0fih6EOz1b0FaSLswb5qK+2DWTIAJVHfbCjzGLfSbdjFg3seg5RBu/R7MdctoKpwU0CU/tluYOInUIxFrYh6oCqCigDYKA0dZQ/y5wr5hiw5BmMwwi7B6dCXTsDZuwNG6g+6OXbiN++g1HaDXeAC3YR/d7Xtor5rH58x+fIowg5olKW+N0n7F91bYWYp+h7jTvOCcbEL8m2bkxJpQme0ADzVTE4ONNYy4DjDac4TR7iOMdB+iz7YLa/MymiunUJU9gILEbgnO6QTFAIgBFe08+fvy4qyozeuDvmYKndoZOPVz8FjnMdqzhJmhVSxPr2N7eQcnu6e4Ob9VAJx7KADHg+JWcXV8i4ONM6zP7WNuZB2jPQtwW6bQa5lDv3UNw449DNoO4WjbhqF2DY3FCyhNHUVGeLe0VoT/ohcQjiyVZ8HQZyDnz0AcBcDh3iRik6qrGG2Tgy0C3mSFOZEX7UZt9igM1XNw69cwZFvHqGMF4z2LmBlYxPrMNo7WT3HDfeviAY/XXtB5ivbh8nr7hIfrJ2EZrc5tYaxvBkPd0xh0zmLAPo9B2xKG7RsY69rFeNchurQr0BaOC7MrM7QLyW8oQEqXNrs4vihsMVbkyHJTXMWoN0L9muxos7RT1GR3o7msH+b6MThaCNgsYMS+iqmeTcz1b2N5ZA/rEwdYnzzE+sTR81ibOMLa+BGWRw4x27eLcecmBixrcLTMQV8zhsaiflRmdEslj20VpNor4LLqUkM3Kjtiee/poCF9+4p4q1So2epCUfUAanK1IOmNTlo9y1K60FQ4CHPdHBy6Zbj1Gxjs3MWQbR8DnXvos+zBpd9Ca/U0yrPdSIs2IDmiDckRLUiKIL2/FUXpFtQUci8ZgqFhEtaWBXQb1tBtWIerYw2O1lVYGhbQWDwsTLrUkA7RASKAQ40ZRfDW5wwjc0Fx3uKcUkRgVaBGBW78X/lnfwzgKAxE1eHqW9sU21TjgwncNONTpB7FSVZUZ3dDVzoIU/0kXG2L6LdsYMy5i9m+IyyNnGFl7BxrE+dYn7rA2tQZVidPsDJxiKWxfcwP72NmYA+Tnl0JqJ36eRibJqCrGkZNQZ/Qw3Pi7EgLt0qyG0nnFIoxkhVCJgvp50zmKOL5im4+VnG1YQWYbjgU64ygi1cgNckMyIy0ID++C5+zPWguH4OlcR7dHWsYtO1iwn2MucELLIxcYnn8CqtT11ibvsbazBXWZq+wMn2OhfFjzAzvibj3uGcPY+5DjLhO4bEco6v1AIb6XWjL1lH1aRb58UNIC+1G8ntFaJRMIYXR4mPgPAsdk/XApMAH4FBkXRg4OhEG/hjoD+BMY354A0cbp/B++eZAxViDCfzp5iNmBw5gb5mFprAXOTEsytTh1b9VijukJPAEKp/nujLnfwRwKMIqVVlqsb0yIf61Eckf2IZEpoATVTluAdENDeNwdMxKcWLEtYapvi3MD+9gcXQHy2PbWBnfxurELtYm97A+eYDV8X0sDu9gtn8LE+51AfN6jSvoalmCvnZWwO7yjEHkxfUiM5xsRUUMVpgxkviorETGbApzVtgwwgbVIz+mCzXZ/WhnrKKdhUs/jz7rIoYdyxjsWsWAbQP9nVuw6hahKxtHeYbnGcRJfMO2F6X1W22jUtopqYnDmJEgEs9Irgey4dqRKK2PzSBQUpXrUGKClinY2+fhMizDbd6A27yJXvMWes07khw2lU6gKKUHKaFmJLwzISaY4I1BxIBVAOcbA6cSIYHlyEtrR2u9B4M9C1ie3cXRzinuRXhewSBUAOdo8x6T/Xsw66YERCIjKCqwAcF/qcT7nxoR8RsF/JUkmiwr0TV6ZuBwb1ZiofhXBIjbkPqhHVmReuTFmlCaakVdQQ/aPg/BqpuCyzAnCeVIzxomBjYxM7KDhYl9LE8fYmXmAKuzB1ib4+shVmeOsDJzgsWJI4l9Rno24bGvwqFfhKlpFi2fJ1HPZPzTAAqTevEpyoVUuqO+6oQUOtgaSoFpAdYJOPIs4WftEH2tjDC2sZtQmeVEc+UQ7K0zMif7HYsYdi9gfHARS7Nb2N08xuXZDW5vvuL+/gFPdPeAF09PT3h8fMLD1yecHF1ibXkPEyNLGOqdQ79rDp6uBbitS/DY1iX+HbQfwqpbFbZGQaILaWEWJL+nu5hStCRjMOqlFhEv6pH0QYf8RBNq8p1o+TwAc9MkXPpFDNg3MaiOrk10G5Zh1EyioXhA2vDZpkS9mfBfKSasRRRNKHzzU2FNcT8hQEJwwYrYV2zHM4lLrjh9UX/wTQeSQo1Ij7YgP8WJitw+aMrH0F4/A1vbEnqt6xjp2cFk/y7mR5XYdXX6EGszRxLDrs8cY236CKtTh1ie2MfM4I4UJftsq+jqWIChYRrailF8zhtAYUo30qOs0iocSzD0FfcQFcDhqw80/A7gUOJJtuEr4E0zEoO1SH2rRVG8BfW5HhhqJmHXMoZfQX8nCwA7GLDuoq9zF27zDoyNi9CUjaE4042cJBuy4g1Ii2lGZrwO5bnslOiGrWMEvbZZjLpXMTe8h+UJXtMJNmbOsTl7gbXJSyyNXWJ+6BJTnnOMuk4xYDuBs30PHTXLqM2bQGFiH7KjewRokZY1ssCECcaYTMkZFeBG3aN8IA5zSrLIpL2Zcg1W0b5JfuNAVrgHBXEjqEibRn3eAlorlmBpWJGzv8/KGHcVUwMrmB3ewMLYDpYnD7AyfYyVyRMsT5xgafwUC6OnmBs+xczgKSZ6jzDk2JP9pqttFfq6WQG6KrM9KExxIusj280MiHujaOV8pFYO9YueHdV81+S7NqVVzC8//lcL1R/YiAcwgP+fEcDxB1kU0IXAyz+NABuS/1uH7/d882RXWDiJATZw0KJaBW/kVZxufA46z5NQZeH4TVChuvkDOB2IJJIeoEHM2wYkhjUgLaYRNaV2uCzTWJ49wtnBV9wwCbpTBTJ8FRGKCu7fY3pkC5a2YVTmm5ERoxOh1Z//j3y8/L+r8fZvVFZ34OPLXsQG9CIu0IP4IA8Sgj1IDPYg6VXfdyPxlfJn8UG9iAvqEcYObeaoUp7wQYecxA5U5lnQUNkFVg20n3ugq+lFZYEFn5K1iA2tQPirEoQGliPkZRU+/F6LMHEDYI8kqbwG6ZMkkk66f3acDZW5HjRXT8ChX8N47wk2Zh+wt/yIg9VHHK15sbd8j4XhcwzZd2FrXkZj8RgKE7oR/4o2hdQdIB2cFXcK4xHQUYZoELxvQXq4FiWpHdDX9mKoaw7r03vYnD3C9vwZdhavsDFzicXRE0z3H6Dfti7Mn4pPViSHaZCbYEDlJxeaSkdhrGcFVQnGp3qPMD94guWxM6xPnWN59BQzfccYcx6iu30DurJJFCV2SxUt7pUidqi0SlFrgb28FADrEK2b+DfUVCHjxoKa/B60146gq52J7CqmB/awMnWCrYULHKxd42z3DhcH97g+esTNyRNuTp/w5eQJ18deXB494Gz/ASc79zjcvMf28g1WZi4xN3qKMc8+3NYNdLYsoKVmEtUFAyhIdiIlzIS4t3rEvCKIQzYQ26moAUEmll3YWNQkiAnSIj28HZ8/uWCqH8OAZQUT3XtYHLzA5tQDtmceseMbq6MPGLKewFK/gvrcUZSleJAd6ZAKphKcMpH4rwE4bJ0ie8YoukQ50VZo8kZgbVzDmOMCs323WBx5wNrkIzamH7E++Yi18UcsjzygV78HXck4cqM7kUYA551BtFropsPgmELSMbR9fUGLXrLCGpD4vhnFqVZoywdgb5vFkGMbC6OXMgf3Vx7Bwfm4Nn2P0e4T2FvXoC2bRFk6KycU52YQxGSoCVEvG6UdqzyjGx010+iz7GCi5xCLo6fYmr/E/uo1jjYvcbp7gcujS9yc3+BB2ClKgsQkiYMaMdS2ujq+w9nuFxxtXmFv5RxbC6fYnr/C7uI9DpYfsTP3iNn+O/SZT2HW7KA2dwbZ0b2g20DoT+2I/JU6GNz/6crwvYCowsT5IRiQgEERKmb1XrEtJvPCIJWs9A9dyI12ozRxCG0VC/AY9rA0dI2t6RvszF1ib/EUh6vHON+5xM3RLR4uH+G99ioaP7dPCnhDIMfHwrk5+4qzvUvsrR9iZ+UIO0un2Fk4x878Nfbm77A//4iDuUeMdx3DWDOP0iS3gIHUwYl5aUPkL1ZE01qa1tGiXUItKUWUOuldG3I+UivDIa1PBB17jUuYcu9icYggzal85sPlLzhdu8XF1ld82bvHl90Hv/GIL7tefNnz4nrHi7P1Bxwu3WFn9gZLw6cYd+3CY1yDWTODhsJBqfKnhZiQ+FYRJqRNaJTYDCvBsCSyqvtOoFPaFHmP6XoVG9CMhFfNwhikRlddrhudmlmM2PYw03uKldEv2J19xP7CI3bnH7E194jl8a/oMW+isWIImfEdyIhrw6fEDuSm6FGeY0NLzSAc+nkJoqf6j7EweoW1qa9Ym/yK5dFbLAx9wbjrBJ1NS6jN7ZM2JWoEqWLNig4NRd0prkwggOwsmwLMiPjrnwE46s8J4HyrFiqsBibGSiVeFdVVhKXJvNEhPrgRye+10pqqLe2HXTeLgc51TPceYm3iCrvzX3G4/IjTDS8ud7y43PXiimPPi8s9Ly52H3G+c4/TrTscb9zhcO0O+yt32Ji7wuL4KaYHDjHQtQV72yJaqydRnTeA/EQXkkMsYk9MYIbtJhR9pU1sDJOXYMXNJzqQOm4GhP3aijBxjSJVnAKdbciOsYqejbZsHFbdEgase5gfvMT65B125h9xuPqI000vzrb5Gb243PeNAy8uD7w43/fidOcRR1t32F+/wd7aPfZWHrG7/Ij1mUcsjDxgsvcB/ZYbmBv2oClcQGHiID5F9yI1xCntg6IjQ0aXbzwz6KQV1gcO+AAcsgWFgaMCOJUeuE0zmB/e9AE4Psavz+nyzwEcaqD4AJxf9YgL+B7A8a+MK2cK76fSykTgJvGdEckhRgFv2DrChK1TNw9P5zomPEz6uO9x/7vB0cYdzrbucLZ9i/OdG1zsfMHl7g2u9u5wvX+Pq917nG/d4mT9Cw5WrrE9d4W1ySssDl9i1HkEV/sW9LVLqC+YRElKPzLCmfiR5s/WZhoNKAC2iIo/gymKjlZCcDuKE51oKR+TZG/MtYP5oUNszJ5ib/kSu0tfsLNwh+25B0y6T9DVwgR8VNp8qVPGtm8yJ0RXRVodfO2UZPtQAyeYAI6ihaWwh9tABjA1TYpSzGiuGkC3aQETfbuYGznByuQ1NucesDF7j7Wpe6xMfMV4zxksTcuoyu5HWoQZie+NIsRKwDGcgCPZ3nSZERHjOoQGfEZoYAXy0zrQpunDYM+iD8A5w9frW8VplOAdmaH3kLhion8XZh1jiB5p6YoM1CDoLxV4JwAOYx3GDbyvNokjFMFdxhRkPSki/4oRQwsyI/UoTbGhvsCNjuoRONvnMeLcwsLICVanzrExf4mdlWscbNzgaPsWp7tfcX5wj4vDe1we3uPq6F7insvDR1wcKmvoePsR++uMf+6wPHWF6cFTDDkP4OzYgL5uAfWF4yhN7ZfWFWoi0REo8je6u/HcU0BnFjCob8UW3NT3BmEnFiZa0VQ6AGf7Imb6CUJcYGPhHFvLp9hdP8HJ/hWuz7+KWLH34Qlerxfep0d4nx7w6H3EI4WM759wfXmH44NLbG8cYWP5CBtLp9hY5O/6gq2Fe2wvPGJn4RHj3ecwaZZQls7WSQtSPlALh/eRIrEsGNE9qBZpES3C1O6oG0aPaQFjPTtYGjvH7uIDdpeUsbf0gKWxCwzatmBumBH2WGEi2+zaEPKPOoT+TBCHrlSMnQkS8foJblIw2Sq6QQRvGCNGBZIJo5VWXbI+sxNsKM3qQUP5GIzaRfR0bmPEfYTZkQuszd5gd+Uehxv30n53sf+Ay4MHXB0+4vrIK+Pq0Cvf88+Ot75ib/UOWws3WBy/wITnCH22HVhbl6GtnERZVh8yo+1ijJL43o7YV4rDGueaImxM5pR63hAUpWsgDTU6QNMSgjcJwQ1IfdeIijQbOqpG4TGsY8K1j8Whc2zN3GJvUbn/PF/XZx4w3H0Ec/MiaooHUJ7rRFmODSXZZlQVdkLfNABP1xzmx3exNneC3ZUrnGx/xcWeF1cHXlwfevHlUDmXzne8sv/zHNhdfMQm9/ThewzazmDVbUFXNofKrFFphU98w+tRWi4VMI2t8AqQozBwvrFwlCKQ4jLFwiRj66Q3dqR9cKAoYRiavAUYa3bhaj3BoPUC054rLI/dYH3mWvKfg/ULHG9d4XTnFud797jY98rnP9/z4nzXi9NtL042vTje8EoszPm5Pv0Vi6NfMNZ9jB7jFsyNC9BWjKMypw+5CV1IizQj6YOqj0NpATL8GRP9C8D5DjcQAsj3pJBvmAMZOC+ovUGnCB6qavuUT1RQtFx+VET+H/G9/wV8//7fs3C+B2z89Wm+XbQdyYE/jAA7kv+Dw//38utvAI4ViQF+n9UPwKHjjTqUyorCyBG0UehufgAOFbyJpAfUIe6dBimRDchKaEJDlQN9jnlsrZzjy/kT7r88yQEqDBxfNYxJ0MHmjbBgGiqcyIhtFBeX979W4vf/qwTB/1aPDz91IPp3hw+86UV8YC8SgnzgjQA4HiS/VkeffJ302oPEV71ICHaDVRQuPtrLxb7WIilMi8wYHbITdciK1yIjtgmpHxsQ86EKHwKKEPD3LLz86ycE0/771yqEvWgQ2iNBIFY0CRAQvafFY9zbFhQks0VoWND6Cc8JdpcecX/2BO+NL9ljMvvlCefbXqxO3GKk6xDG2gWUp/Uh/rVBLFhpn62AOOzn9QNwXrcjNaQVmVHNqMq2wNUxifXpQzycP8F7+QTvtfI+j5dPssmebHmxOn2BHvM86ku6kBxRjZJ0A7QV/bC3LGCoaxdzg+fYnruXjZgJw93REx7Pn/DlwIvjNS+2Zh8x6T6HmQdvmgcC3ogTFOmEisZQ5AsGzlqpoMS+0iLxvQ5pEc1yCLMvuN+2ijkGhnNXONl8xM3xE+75mdU54NNJkbnw4/98VHfOjcfrJ9ydPeHqyIsjXtvMF0z2H8HduYkOzTQ+53mQFWMSpXnRzqA7hbihUdyY4otsGWAVmhtwIzIj29FQ1IvujnksDB5iZ+4OF1tePJ494fHi27jZ82Jt7AF9pn20lk+jOmsQeTEuJL3x9b9Ly5sPwCHY+Ww16ztwg+gkRQFXsm+ouWNHW8UUBiwH2Jx6xPGqkrDdnyrv+XD6hK9HT5Joz/V9gbluAQWxVqR9IJuAAsiKHTIBHAnM6DxBO0X26v5WJ8LFVdlOmJsmMNK9hbWpa0my5H7fKnOEX3858koiNew4Egpsde6QUPIJ3oT8o0FsMdk+kRrahvqCAbg61rE0ogQETCy9V34ABpkofzR8AI7KVOE64HvLK9cEQRA/IMR78ST3Y2HoAR7TGZrLl5Af7xGryPd/a0b4LwxM6TKlAjj+IM4fsXAYJLAaybYeI2LEtpi229QmMUn7UmFcPz5nTsDetI35vltcbHjxcPz0PA94neraev68ch+98N76mDgqG8ePkfN8rVyX/B0cl8r62hh/hKtlQzSWKFRNbamPLyyI+Nkklr5Rv9NFiBXEZpmrH4PqkRKiRXGKBY3FvehsmpRqPIHWk/VHXO96Zc5w7cr7qPuNL1FRgTR59bW1SRWawBOfx+UTbva9OF55xMbktQA5nY2zqMnpRVaUCSkMXoIJMHOesS2HwRiDS7o1ORBH2+QAp+jJEEyM/r1VHKEI4NBNJz/WgNbyYfSbN7ExcSvvw3Ul+5Y6J66fcHv8hLmRMxi048hJaZVRnGVAZUEnmms8cHcuYmHsVIIvgga3vv2Ka+aWwMG2V8CgIesBWsrHkR1lRHoo9a5UAWGfpThbnYJZkGAbMAEcFaD58VVl4CiJkIA9AuAoZ6CSpCqtCXT8YGsmnfUUl6gmxL3SIO5VLVJDNajN60JXyzTm+g+wPfsF5769Rl2Xskb4bNTxw9rh3HueU1dP+HqmrGEGptuLXzE9cAK3mYD9FCpzPNJiEfZbA8J+00qiy5Yqiu9/ZJsFrViDbNJiFf57G0J+aULor7Qq1iAyoF40hkrTHWitGkOPYQ3TfacC2vBckDnGOc11q35GYiNsS/EN+Vqde1wT/Ox+n59nIn/X2ZYXWzOPGHXewNK4iZrccRQn9yEr0uFr5SSASZaH0lpMHQ9Fx8yP3fFHAE6SCc0EcMwzmB/xATg3fgAOz5VnBo5/C9UPDBwfgCPuUmSbBftaMAQAs4qgLhNBcXhhu9Q7Op7QtEAv7RLaimE4OpYw6WGCfA2eyQ8XynOUvYT3hvdQ3Tu5j/B7dY3yVb1/X5Tz4evJE77se7G/9CiA5aD1EJbGFdQXjCIvlveN85CsJKNooykGCAT8vzHDFBODVlSk98DSMIfJnkNsztzgbNMr8YoA0uq6vHrC/vIjRl3H6KidReWnPtGlSXpvUgpOtNCmmHGgCuB0ic4ODQ8UAIcMYjqWtSLpPUWcdaj4ZBM27vTgPvbXvkpidXfqOxsun/CVhZxDL7bmH+Hp3IWmeAQZUSZJonivFUtktr20IiqoBVFBWkQEaRAWWI2woEoUpOsFwKFhxoqPgXN3dSPOgTJHfXPzcPMOE/07MOkm8LnAhaw4OojWI+gv5Xj3E89AuvkQwGFi7Uv8pSDEwpBi0kDNuXiK7r5tRG6cHvUF3TA3jGHQuoKF4SPsr9zLvsaY55FngLo387lySrKOSVDRJ8/zHAKpP/Pp9XBefD1/knvFAsz8yBf0W/dh0iyirmBUGLRJ70wI+UcTwn5uReSvRnz83YoYsXxnizXBqDakhRiRF2sVEXtT/TQm3Uc4XvfKfnJ/8YT7qyfQBZbzTj7b8wfi7XvEw+NXPDw84OHhEY/3T7i/e8LXGy9ur73iSnV//YRHnin+4/pJijNu4y6qc9g2Sf0SRYSW85Tt2ow1aDZC3ZvG0l50G+YxO3iAncVbXB94v8UKvpiBP1uZuILHsi77FM0wKOj+7m/VeE9b8V8UFo7oQ1HDLaATHwOswsgWdjZdh4JaEBnYhPCAOnx804DMWAPKsp1oqhyGrX0JI+4DrMwQgFbANN7/5+fHdaoQkv742al5Dfc9xlvHTzje9GJz4auAcE7jGnRV4yhO70aOuNvaECcaT9R5UgSeRXj+md1BIOcbgMN2vYTgJiQEa5D6vgG12U506eYw13eCndmv384Xv3XMObg5/xX9ji001w5BU+FGQ1UPmj670NHUJ7nX2twprhmjq/uUOkc5T31D9ifOX7/44eHsCRc7XgFyJt0XcLXvoKViFqWpfSJcLcCygJ4E1BSdMymGkIzxzDJS4jXGaoxpo39nC7IRKe+pz2dH9adRmOvXMWy7wtLwA3ZmlcLH7aEXEj8zD1LX14/7KNe8up9yn/U9F8Yg/Ow8jw5WWES6wVj3EZwda1IQqcrpRV6iDemRJjFOYd7DtS/6PpQx8ANx/sXAIYbgj3/wa1WyRQVwXipWn6qg4H8eDZwfP7jve3raf9fC5Pf3fAwYBUghoPL9SAqwQxldSAr445Ec0AUZgV1ICXT8MHx/Jv9WvZHfXv8UwCGSJiAOrerUr31Bq4+RI72JPgBH0T5gCxV7WZsQGVCLhBANMuO0Yi/YqunBiGcZB1vXAt5QrFg2P55bbLG4VQ6OreVLdFtmUZnXifiQWkQG1+DDL9UI/ksV3v29CeG/sHrOpK0XZNUkyHAjMbhXRlJwL5Je9SKZ43Uvkjh8bVVKOxUPYtImKX7bLHZzyWFapEY2ISm8UT5z7Pt6RARX4d1vpQj8Wz4C/1aINz9XIOT3OkSIGDIt0onAqgBOC8JeaERcszDViqYKVokXMDN4jMM1ryR/cq2+A5rXe73Pje4OY64jmOoXUJ5OcESPsF90iPitRaF/0gmEtrB0FgpQXFVSQ1uRFd2M6txO9JimsTV/IoeDBINkiPPQuFMOeiLPW4sXGHQuoq2uG/mpjWgo75K+3HH3LlYmr7C//CAVX25gz4kfQabLJ1zveaUyPNt/CUvjEkpSegT4ig7QQUYgBYq1+BikjIS3FOvTozi1U+ivpsYxDNpXhbrIauPZziNuT5Rk2P/z/lPg4hcwyJd+Pevqpnt79oSTbS+2Fu4wP3oOj21DtF2qcnuQl2hFeqRR7DoVNwGDBNpxrxygxSFdbaIDG5AR2YrGEjfcxnksjx3hcPmrJIGSUN0RnFDAhceTJ2xNPWLAsofWiklUZw0gL8aJxNd0RGE7Bi0cCeBwfVDYVBUP5Nf8uQ/A+WAUt5+iBBv01dMY7TrE3sKjVN0ZlCvv+y2h5vsuDd+is2FBhJbTPrQjhTbrtNkWMVZambO9gC2F1K9gL3UNUkK1qM5zwNYyicm+bWwtfJFgmPNOkPLFxQAAIABJREFU5geTLAoQnj9ha+EBI65DGBvm8Tl3ABmRnQj5uUkJgH7VIOqlBmlhbWgoGoTbuIm1ia8C7N0d+wI8zmn1UPf9XklAeHj6Dx72/sN3uPoY2c8BLJ8vE9vl0Qf0mU/QUjGP/LgeATbf/ZWBKcEXBjIu31Bad5RWHrY3qMGAWtFRKtDPAM5LA2JeqACOUQCcovh+VGdNwKHbwdLgVwHOCCQ9gzbcq74byrwgePMM4PDa/AKG53viSxTk2hnsMJi6fsLWJAGcdZSRRv2BmkYmRP9uRPjPBkT82iZsquiXWkkMUkQEswWl6WZJSh1t0xhzbWBl/ARHq3fgs/gn4Ea91+oz4Pe8Bn7vF3Tya/nZrfI7CIKcbTxiY+oCo44tdDaxtWQApWk9yIpihZAtiQyGqV1AbRKKtrL9iEDHHwA4r2mFrENBnAEdVSMYsW9jb/5BgJbHU98c8ps3BJ9Xps9gN0yiNLcDVUUmNFU7YdQNwm2dk3ZKJnwEclXwh/eW95Wg6/3Rk4BDXFtkFlAniPo7ia99FtgvW8UxRmlrVAEcH6P3T0Ecf/CG80lhoCoOYdRqoVgmEz3FDSXhTbPojaVHtCI3rgMlqQbU5dth041hwr2B3cUrCXQJOvGZ/LgGnteo+uzUV/WZ+uYi9wsmhAy0CeKwyjs7fII+2yb09QRx3EiLNIhbV/zbdnFUiSYrMciIaJ5dgZ3CzAl/0YLQ3xrwMbgJyaEtInxekWVDe80Ies0rmBs8wfb8nZwTnLvP8+iH/YR7yp8OPmP/wbVAEOrkSUCD1YlHDDvOYNWuQ1c2jYqMAWRF2pHy3iyW2XKPaZMrovmk1RMcUJhP8SK4TgMDHwOHGjhJJrRUetCrAjibp/De+LVQ+QoDp5vUwFFFjFUNHL8Wql9pzUuAkm0oKnhjk/PkI/VuqIdAF0TaQ79rEeCmIKUTVXkuafNzGhYx0beHjfkvAt7cqnPeLxl6ft5qcsHnza/V+6XeV/7cL+lgwYUgDqvPLASRdVZfMIz8OLom0mGKDBwTYgVgUV2SFCBT7K2DdNIqaW2ak/a9vcVbXO0r7EI5kxlL+D7TyYZXWuYM9bMi+pkbZ5cWO7IcqJ1EaQMyIEU0V8SSvzFwaLEd85LmCRTXb0ZauA6VOTbY26cwP3YoLC2ybzmfBfjjer5Wijxkiw7Yd9BYOoSMKKMiMPoM4LBY2IqoQDqeahEZpEF4YA3CA6tQmK5Hu6YPQz2LzwDO16svgPdRua++e3q4RQBnG6bmcXwudCIrjq5WdQj6L+V49w+NsHwIdCqD+jJsE2fsRztouh61IjW0AzmxRpSkdko8YdVNYNixiuWxQxys3ChnL0EHrl/12arPlK/qz7gv++3NP4ZB/J6/g+udbAiCOAsj1xiyH8CqXYGmaBT5cU75XGxvF1Hr3zvxkdp/FAdnW2ugAuDkx1pRke6EpXEWM/0nsh99d97xM/Gz+IFKT3w8jw+4fyCAc4+HewI4XgFx6Ej1yDWt7lHqNfHVNw6WHuEx7aE6px+ZbAn3MXDYIvyRxcAACtV/RnZsK5rK3ei1zGNx7EAYhwSr1TNVfSXgtz5zhX7rukgF0LU0+UMb3v71M979vQ6hvzSJg5DqxidtpLRcD2CbXLvY1se81ik5QHgzchL0qC7sRkfDOHosKxjv38fq7DWOtr24IqBx6dv7/J+Tuo5VsO3Hh+aLj3gvOb+5/s+4Vy/dYmrwCN3mNbTXTaGueBjFZCZFdSEpxIr4t2T0KWctWYhkrShMHIqH08mU5gAtwu6MD65D6nsN6nKdcLXOYXHoFAfL9wJI8FyU/UWdZ7dPwuYa79+GuXUEBt0grB2jcFomha22NH2I452vAsCp+7zc7z+6Pt/P1D2C70VgnqzMjZl7THsu4GhdBzUdKTRMxhXbsQnk0KFKYVOyGKeQMRTwg8wcFttYqOuQDoSMMDrw9aAmZwimukUMWQ+xMvogucnFthdfj325CwvC/uvMb+6pc/CfXjlf1bjs6gk3h14BM7fm7jEzcCbabubGOTSUDqE03SUOeR+DWhRnwSC2JP4LwPn3GDj+ZkkKA+elQnmOl1YqlYXzP1rE2A+U8UegCN78OHx/zrYlGS9tSHhpQ7y82pHw0jcCupD4PBxIDFBGUoADP45kAW6cSA1yITWIr06kBKrDIQDPMxAU2CWsHoWNo7ZQ+Rg4/iycZxDnx1YqRZDpRwCHySQV4KMCGhEVWIOUiAbkprSgIl8PU2s/pkbWcbrv08dQUV3fofT1+glfTp+wMnMGa/sUCtONiAz+jLCXNQj5tQ7vftIg7BcyUxgo0O2AgI0biUEcPd8GdXCCe5DE8UoZicHdSAymnSaTPVL5aBtI6+k2qO0+tM5jS1XcOy1i3jYiMrgeoS9rhHXz7tdqhL7QCHgj1oLUeQniQc4gmMLI1NSpQ+zbBhSmmEWozqGfwczgPg7XH54DE/Uw5GZOAGdj5gtGnfsw1s2iLM2N2OB2hP6sRcSvDIoowvYNwOHXdG8QAOdjM6rzLOgxTWJz7kgBiLhx8Z4yMKW9I4P6/a/YWj7BmGcR1vY+1JXq0dk6gFH3GrbmFVbGzZGy+clm7fdMuBmzuk0WDnUOzA3zInQb8ksDwn9vQOTLRkQHNiLmFTVSFNZNZnQ7Kj91obV6UDRSxt1bWJs+xen2/TfWDRMAdZPloeIfpP7RhqsGOvx76iHEv3f7JNWoS264m14sjp+j374BY+Mk6orYv2oHnRpYYaEmDq1HCeDQwYI/iwqoR0ZkMxpLutFrmsXK+AEOV25xd+ir9KiBCJk/J0/YnLpDv3kLzWWjqMr0ICfaLgepItZKG1EmEwRwFMvRxCCKR5JKrlRMkt7okR5iwKdII4oTrdKnPObcB4MaBuI8+OS++K6NwRSZQMsjX9DZMIeCOIskoilv25FEsUQRY1Vo8hQxVgCcJoT9Vo2U0EbU5NthbxvH9MCm0ErJenoOyHwVPVaTNudvRXhUz6r9p16khRnx/u8avP1rLUJ/qUPUy3qkhRPAGYDbuI41sifWvJJ48TCU//yCFHlG/s/pj56p+jPf838OEDh97yAAztLIV/Qa96ArmwK1l0J/bsSbvzQg7B+0N+Z97kZCkGKRy3WtDNURiYkEnwMHAwQlKKCTkrBwJDggA8eIjFAHCuP68DlzHF26bSwO3n0H4AiIx8/rO+jlewH3OE8e8XTnp4XDv6euoR/vCf/ML1DYGP+KLu0yihPsSHmraLPQsjb853ZZ/5G/NYoWR0qIDgWJelTnWdFR24de8yxmB3cEBDjd/Cpr9Dnw5rpSgSYmej6wSP7cx/55/ru8HvU5qUEM5xzZkcdPON+8x8b0JaZ6qce0jvbP06jI7EdamBUJb7h/msQxKPI3istSX4pFFQI4Vmnni37RitjAZiQIgKNFQbwe+uphjDm3cbj8IOAw30vmkG8+8muy7DYWTuC2T6K+0oTWBie6TKMY6V3G0tQB9tdupJL5vIf4kgz+W1kzp084WfVixH4AbcmQgEeJr3WID2pGLNtcCeAE6H26VAqAwwKQBJFk9f4piKO0TinuLlax4k6gS6BYKvM84H6tRfwbnTDgCA4XpZigKXbB1DCMXvMcZga2sD1/Kq0xApgz4PTt2WpSIvsh54qaqP9X2Gryd3zVVQJaBHH2V++xMnmJwa4tGBunUZ3nlr2Q7AW2RbFdIEocV0yICrAgMoD6bXRd0SAlrAVFqRY0lHTDoiX4voLFURYh7mWPItNTPSe+W+fqvCYrh3PINzj/ntcPr9MvIZTr5DVe+c6Zda+0j870XaFHv422yhmUp3mQS+ejULoekYFDEIJ7LVk4qqaLooPDxJQATsRvGkSrAE6VBx7LLBZGN3HsD+Bw03oGcOhCpdqIU6RXdaFSW6j8ARw7RJcnuFOYTLyPkWxvfN0qYtuJoc0oSLVAU9YLS8sk+h2rmB0+wNbitTwbtgcLSK/uEf7A9588c2E5cU1zjfr2EN5T3luuH8YRBFfWp+4w6jyETbuMpqJRYfZlR3IfJAunA7EvqUnFtka6JDUjNlCLuKAmVGY4YW2axkz/AfaWrnG9/yC/W/Z1v3UpAE7voQIMZruRE2tFwtt2hP9Koet2cQ4jyKW40n1roSLgxjZwWtLHvyKwSXZuE6pyO2Fvn8DC+J60Ecm9UZNNH0uZQMX+6gMGujbRWDaAjGg9kj7wLO/wMXAI4LAYQ7FRCs82ICKgBhEEcNIMAuAMPwM4J/h6/UUJONTregAOt24xMbAFU/MoPhd2ISu+HZGv6hD813K8+1kBcD4K88YKAewINgQpreJxtIZ/04L8+E7U5rnFKa7HMI/J3k1sTB/jZONGWK5cE7K+f4x1fLppd2QcEYjl4NrxsV+ezxLllFWOWgIB/HcEcfa90pbPFtJJ9ylsuhXU5Q2LxXwKRaZfkdXZiejfzIj+3SCs7tjAVtFRy4ulY6lD2o8I4LAd5vlsUOeb3/n85CX25RXmDQGc+3sFwHlQARw+M85RdZ5yrv4wCDb2GrdRmeUWweHk9xR/VnQeyTRlTBb5shLZsc3QVrjgtsxgcWwXB2tfhB0k8bMKJDwBBHDWZs7RZ11Gy2cm2HaxFX/zX6rw9m+1CP25yccQY3u/ogNGEIfseXEXeqlB/DstsmLaUZxhhqasRwSjqbVI/a6dlVsxNLk5U/TtZC/zrd3nfVq9Zt4z9fzldav7nQrwcM7xHt084Y6x+YFXJAEWxs8w3LMPW9sKmiomUJxOFrkDKaGdiH3NTgGaSbBVj9qv34SoFW01tuc2ID64Finv61GX1wVX2wyWRo5wsHIrYAQ/s9w3vj+f5x2kdW92bAvOzjF02yYw0reE+YkdMZc53rkDr1f2efXa1Gfqf018Dup/fr+bc4gsWrbVsq1qrPsQpoY5lFNvJ8aGtFDqHhFcVFrbFK0/snh5virsG9kzRPuzXYwHKN3QVDwOu25Fip4rYzc4WfOCrBsWQXiGyL1X7z/PI9/5yQLHA1nPjH94PvnO0+dn5X+N/Hdcg2cKkMPi++rkF0y4D+HsWEFT2QjyE22Ie81CDd1ACeAoblQqC+dfDJw/ZuAoII4wcChG6bMx/U8D4PwZeGNHYpAdSc/jG5gjFsMBVsS/5LAh7oU67Ih/YUe8gDhdSHjpUIYPvFFBHP9XgjnJBGuCXEgL7kYqR5A6XALkJBP0eakweJIJ4AR1PYsLKSwcKxII3sjo/F4Pxwfk/KimHc/PL5bJimgrk1jqNUQHNCAqqBrp0U0ozmpHfYUFDvMIFqa2cXVKuo2vai+b8ZOwb258m9r82DEMTWP4FN+O979WIOTXGqkOhks/KytwbOfqFoaNMGsI2AR1+4YLiUF+I5hAD79ngqckdwRwuOjo2hAbrJeWIAa2cW9aEfu6BTGvW/DxVQuig9lXrUNEoBaRgTpEMUAPppAV/51J6XkP6hQqHVvGwn6vQezbOhSkGNBU0YMu/QRmhnZwuH6nBETcyH3/cfMXAGf6EiOOHRjqplGa2i2VE9Jfw38AcKIDaI9NAKcVqWG0VmxGdb4F3aYJbMwdyOYkGxI3WMam98DdxSPO9q+wtbKP6ZFF9NqHYdDaMdg9jfW5IwFUJMBmEu6/GaufkQwNH5o+P3QOo2ZWgrbXf6vGh59rEf57HaKDNIh70yDiyOmROhSmGNH6eQCeziUsj59gf+VGemblfdRk0Xe4PScA/DkrbmQAXD/hgRsux3dJgN+BqH5WnksMiPhvvyhsnOWJMwx0rcPUNInqfLfYbX4MbsbHVx2IoUPDK4cE3rTZJUMsPVKLhhInes3TWJ3cw9HataD5EoSon5eVpVMCOFfoM69BWzqIivRuZFN8LohMDh5E3wM4icG0bOXcozMHARzq1uiRHmpAdpRBtDCMdRMY794VHQlpS2B1R2VQ8Zp8jIKV0StYG2dQEGcS6/jkt21IfEVaqQJAcM1R54AHIh1jwn6rQkqoRpydujrGMDO0gd3lC9EXegZKOBcfFZYW29p4z2gTWZZJ3YwOvPlrLV7/pRohP9dKUJUe0Sa2xW7jGtYmvnwDcHzz7TlA8A9Q1fv3QxAnQR3/zG88fy5Oxa8KgLM4fI0e/SaaSkaR/dGKDz/V4/W/1SH0pzYfgNPz74M4Pi0GHqpynySZUPRvPr5UAZwuFMT1oSpjDHbtFhYHbkQbRhg4TLB5wMu18GuCNQRt/IdX+TtqkKPubbIQ/RhPvA8MEBhAXD5hbfQG1oY55MeYRSeGiVXUr20I/7kV4b/QurkOES+qkBWtQ3VeJ4wNfei3z2JpbBcnm7fCNpFkkIGj372U++tbE3wfaQdka97Jk9IWxoCH7CJemwBRyueSgI2/y8c6479lmwbBurXJG/RZttFcMYncONLUCYYaRfQ27JcOEMQhK0oFcFg9+2cApwOG2mFM9GzhZP0eNwdK+xQ/u8wfH0BL6v32yiGGeqfQrrPBYRnAxNCSANEXhw8CTPOZ/NN/DCJ9LKLTNQI4e2gs7EP8q3rEBTUgNqAJMS90AuDE+wE4ZJcK0Mfkk5XA7wAchekrwsXUyZEAkxU3sm4IKihOPHEUNgzSSQU54V0T0iOakRPfhpqCLlhbxjHp2cTOwgVOt25we/Ioa1ueE+eK+h8/P++BL8hnsEmGHMd3ASiTOzVQVYNPzi3f/sm/Ty0aCgkPO7dg1k5BU9KPghQbEj5QL0Qngvt0ToykFTMZs0G0zK1HTlw7Gkp74OyYxFTfhmihnO88SFArQIJ6Vqh7L+edb06zYEDtsufBljZ+fpWxw+fsdwbysp/3b14rW2aombPhxfzgNbo7NtBUPIayVDdyPtqR/I6JHhkEjAEUAEfWtE/ImH/2DcCpFwZO6+c+eDpnsTi2heMtPwaOvLmybgiCKgDOJDSFtDnueHah+vD3Rmln5LxWGDgEcNg2xSKAHhEBrQgP1OHjGx0SQnVIidKiMt8OS8s4xvu3RE+E+j+8J3IG+taqzHeeY37PnM9PEgy/1t2Hc64RP70tdS9V17vffkK27Ob0HSa6j+BoXhXdtJIEFrO437UJAybmZTNiXmoRw7UQ2IjYoAbQedKqncRs/y72li5xfXAnZ7FMS7/nTM2I8d49dNRPoOIT7bbpCEUL5EZpb6Vm1z8BOOKSyPf3A3A+aJEW0YiqXAv+X/be+jmyJcvWfP/ltPXrrqpbFzJTzFKKmZUkZghxCEIhVoiZmZmZFWL4nm0/EUpl1q3unvllesZaZsdCmVVXykPu2z9fey2ZnxYmdjjbv1XPilwDdWsetflPnvv9tTsG29ZUcuWnQKOKH7cCHD+bHwGOmA9rACeP1Nhaqov7EYCzOrvD0Y4V4MgP/37tj7ZvmBjYoL5yhLzUFr6EVxHgWoDzb1kqbdRX/AidBN4ItKvT2qZEeeNiUOb+0hImRsB1xaK62WB57Jj95SvMhw/qfsr78cNzb72mopK+esF88sz5wR3nh3dcHN1yeXTL9dmD+t9e37k/e29knLjWWs2klhQ4MtZxTG3hvFJKxwe2E+3RotS5AR/qlYecamt1lPCGGpW+mRnbSkPJtPI6/FOAI/OI1D/3LwioeXx4VODm4cEKcB4tKpwXJF1cDjWu/ZPP/cUnNadnxXUQ7WUk0l2MraU9UhQZAnAK8bfP4ltIGfrsNrqbJlkY2+Jg7VJt1r2+N/KYvKDULGvTx/Q0LVCR20d6bJPy/3NVACefj+9KNUN25c8oPjsCceotlgcleNvqiPTWkxpXT2lOFy3VE4z3bbK9bFZejPcyf8p4K++bdV63/G71d5b3T+pWUefIGCif8h6rMVqug/x32mOtnm31zssjePuiQImocXZXn5jsP6HZuEJh2gipMV18CjQpQ2U/O7EpEIgjhtniOyftupKMZiDUqYxQ52JCnfOJ9iykMLGFDuMUSyMHHKyauTnWahPru6x+t7SN7ptZnt1koHuCscF51hb3ODu4VWO1GqdkXrHMRWp8l00CBeMt99d6LbQz+uG8rPOB1A+iDF6buqanfl0p6NJjO/ga2ES4m3hmis+n1nqpKRxlftU22iTVTTtHIzFetRTED9Bavsxs7xnb1tYw2UyQedBad8k9kust9+RGW0PciRXE8QviSSSfAollo0O1sArMeXtOMj9Zz8syPsh9lDY9aauaHRBfnwWyv3QS4VFJuJtRtfiKJYOmwtGUOP8DcP4LAEfMHiU1Qgqr7z44/28qcH4GOJZ4bolxfIU3VpCjQRwVNSwu8T/Bm1C7FkLtWtURZt+G9Qi3b+ftEWHfztsjyrGDaKcuYp273xxdxDh1Ee3YQZRDu0W1I+obDeBYIY4V4GjwRiBO049+OAJNLIeWsmNR4PwEcKSNJFh2QxyKCXTO43OInqyEasoKTHS1jLEyt8vNhYwMlkFNfUq/7TPmU4kZfmJ66JCqwlFiA6pw+TULz3cF+NjolVmeJLJIXGWUayfRrt1EicJG1DVWaOPYTrjliHBq4/th2ZGXnVZLlKbIryXFQiiqFncnOzs1CtLIDouYm6k0I+dagmWScaknVOKzXSWuUaJYxRTXCnBK8bHNI8Q9n+QYI/qcNlprRtTi+WjDrAaKt4O4DI5X+4+sT58x0rZJTcEE6dHtBDlW8vH3UmUgK1HsKspZClMpsh00yKQATkgFuuRGOhvGWZ/b4/HqSRvMLAtqGcjuLu84Ozxjd2OXpZllxgen6GkbYGFihePtS/X/f/tvejsWy/cyEIpqQ3Zfp/uPMOaP8ymwDse/ZuP2Wy4fbXQEOBYQ7lnCpwBJDTCSn9xCq3FSudaf7zypHRJrUWb9+WpisAAKATV3Z49cHd1xtidmuBccrJ9xsHHB4eYlR1tmzvZvuDq+5+78SZN1yuLt7YRo+cGyW3e08cDShLQQrFKpGyY52kSYp0ElU4W4inlnm2ofkEWM9FrH+pdSktFKT+Mkq1M7HK9fqt5764StPh9QlH9j6oze+kX0ab1kxrYpCXCw3Be1+yhFiChwRDbeqqJbI106iXARLwDZCatVyQ9x3tV8C6ghPaqBuqJRJrq3OFq/R6mgZNK3TPavxcHVC6tj55j0UySH1hHjWaFSyCRKPkz8NtRutLYzowCOnfhdZBPtU0hBShNtNSPMjqyxt3qmip3X6ybX71kg3wvrc6f0t8gO1qDawYrwrML1l3xc/paH17t8VVTF+VVSmNxNm2Ge2YFjNmfNKr1FfArUokP1uj/zfPOoKVJkIrUuMqyf1snVsjMi914Wm/IzBBRK+4tqqTh+YXvunrHOPZrKZilI7OWzXy1evxfg9rcCvP8w/ARwLEocUdg5t1sOiU1tVWOFZqZp3a2XRYYl1cmhRrW+xH1sITG4m6yYYRqKVpjsPFXSXDH5vT164uH0hqeLK56vrnm+vlVRxC+3DxrEEaDzYCkirItpKSAs56iujRSBsgiX8zx64e7gmZudZ2a6DpXh4Fe/KkIcSwi01eP/vgLfdxUKxAU6FBDsnEtKtAGDroO+5kkWx8XL40IVIOpevnkP1PWW3yvwU/nBSJvSLaerZk5WLjlavuBw8Vx9f7l5y+3e06vPj7VdzFp0SiGjQIK06Jy9qBa/+cELWipXVFqZQJwor0YC7I34vBPDTCvAkWRAgYkawAl2LCfctYxoLz3J4UZqCwaZ7N7gdPOO26NHrdC1FriWXbyn6wf2N/eZGZulvbmb0YEJ1he3uTyWa/8dclrHE+un+vfKuR+/cLQs7Y6bFCZ2EeygI9i+kGC7Eg3g2IkCp5pwx3oinBpVe7AV4IT/AHHewBu1O/gW4IiMvZFwVzlkDpFFnp4AhwKivPUK3mR+rcdY2Mdw+zI7ixfqOVeF/Vv4ZHkP5d6povPiRY25l/sPnGyZOVg742D9lKONM062zzkTo/BDMzcnt9yf3/NkFpD4fYFhfefkZ53uPLM6dcZwxwb1ZZPkJnYS41dFoLNsSIhpZw2+dnVKQRLorCfYtYDUmBpqivoY61pia/6I891b9TxZr7F65uQ+WRaPUiCL8erJzi3762a2ls7ZWjpTsG1r5Yi99WOOds44P7zg+vyax+tH9d++/jzLPVdFuPWduXxRppvjHUc0FM9RlDSojHZjvCTpUNrgxNhfWkctUFbmcNVC9SPASY1sQABOn2mOxXEN4Lzcvrn4spCW0ITNO2YHdmipnKA4pYP4kLcAR698REIdO7TYcOV700SQcx0BTkZ8HcrxERDiVUZMUAXxUQbKdF30ti0oI9eLo0dEXaGAo9xr6/2W98sCWR8upK34AfPBrTIwPt245HjjnOP1Uw7XjjjZOuZy/5ybkyseLm55vrYsyuS9kZ9jOeTZknaCzelbJjuPaS5dpuDrIHGe0sIr6ptKguzKCLQrIci+hGCHYkKcisn+3IypbJzZgW32ls8wH17zcmspJORGWe7R8eYj4z3bGItGyfzSytfgOkJcK/j4rhi/D5UE2oka9EcFTtgPAEd2rcuJ9JKI6GJyE+tUjbQ4ucXZgZnbC03JKL9SndMdaiNnf82sIppLM7v5FGgg0stAsLOoFKvxs6kmQBaCjgaCnCoIcCxVGzP+jt8BzogF4BzvnPBgNlsmJgtUeYajnWsmBtepqxwiN9XEl3AD/i75OP2agcf7QnxECS11nnMjgU5iBC7veqU693CPcmK8KyhK6VLpXQJvjtZuuDnS6rHX59zyjZyXvJsCNs8PHzjYumJz+YiVuR1W57VjbWGbrdV9DrZOOD244Or0hjvzw4/PkOVZUtfJUkuJGmFl/Iaumg2KU0bIiO7li6+YyjcR8KEOUXdafcmiPauJD6wnPaqZ6vwxRtq22V28V8bjomi+PH7g6uQW8/ktN5d33F7fc3fzwP3dgwZwHrXPx/tHHu4fub954NZ8x/WF/Dd3mE8fMQvMPdbUGHcnLzycvLAydo1JP09KhIDvcuWJpHlaizn4AAAgAElEQVQ1SZKptOUX4G+fybeQEvTZzXQ3jbEwvqbGwPvznygWcHP6wOr0vlLqlOV0kxYjKWXluPySg/uvGsDxt6tU9XOggxZoodqnHCrxdxDYl8fn4AoK0loxVY0y1rvKxsIpV8ciN/qpzrSCt3t4vH7h5vyZi8N7TnauVZ16sHHJ3toF++tXHIsh+f6j8pGRGkvNW38GsEVxankeNhbuGOrco6Z0Dl3yIIkRbYR7VuMr/pL2MrbJ+CcmzNJOJQEnlYQ46QlxKiLESUeUZz4FiSbaqyfUJs/h2oVKzJSxWn1Zx54HuDy5YGtti+nxGRZnxdbiiJsL7RmTf48oweQZNR8/cnFwowIqzvbOuTy85ObEzP3FjUr0kw2t1zrkWfrrnrV311rz3Wrrh/HuPepKpihI6iU5vIVIT3lvK5FNNGm9lPZrSUxTSaGyGepUS7hrDZEeNXwLaKQye5zhll32Fh6Vuf+rctd6Tm/qFQE08gyfbN+xv3bJ1uIJG/OHbC0esbN8zN7qqVpnnO+aMR/eqvXHq4LnzbCnrpk8BpcvqnV4ZeKSNuMCOV/bCXPTI6blYnnxXYFj2dj5kzjxn4UPsoa2rqf///n5T3iIihGXdA4FcCxtVK9Gxv9dAI4F3ohBsBwWiKN9ip+MdmgAR1PaiNomzL7VclihTTth4vVi3/F6hNt3EO4gRyfqe/mznXZEOnQS7STwpkcdcc69xLn0qu9fIY5jO9JqFeXURrSTfH5X4WgePVYFjuXzTx60V4Aj8MZBVDuW6y4mVGIWKgDHsYhA51y+hJaQnWSkoshET9soK/PbXJ/LSuft4KgZpgnAOd55ZmrgkMr8MaL9qnH+JReP34vw+VCu+qylZ1JaIqJcOoh26STKuYNIaVURWCMGem+OCMdWIpzksNwDa9uaSkv4nqJlpacCY8QlXdqiVMqA9XsBNXK4mAh1MRHmKhGsJgvAaSTIUXaBSvCxzSHEPZeUmEr0Oc201Q4wM7TC0ca5GsDVOVvHUgVw7libOma4dU1NomlREnFegZcY532Q+OYq1WctP18BHJH+u1US7VvB15BydMkNdDaMsT6/p1J/lBfHg7WH/JE7s5nL0xOO9vfY2dhkbXGFhak5dta2uTi80CYVuRWqcH7g0XzH49UNj1dmHq8uuTw4Z3/1REV8S3pUaWY/UT4G7P49A5e/y+5UNn4OOUR6F5MUWYUuqYmaYlmwSPF/qhblapffOoHILZeBXVqfLu452z1ne3mXxYkVpobmGOmepK91mJ6WIfraRulvH2eoa4qJwXnmx5dZnVtnb32Pi8MzHs23vDxaJlnLNZVFsxQMkuoxN3JAq1EmQlHh1BPt00iYh4lApxb8HWvxc9Dja59LjH8RJZnN9DaNszazxcnmOY9nzxqJt04OIhg7e2F96ojuOvED6SA92kScTy0BdpX4fjAoszUxZXsFOC4dlva9tleAE+luJM7byNcAI2lRddQVDzPRs87xxrUq4J+vZRVnKVKsBb4AnPFTTPoJkkMlgaqM13YQAXpqN1rAUYvySPK3K8bHJoton3wKUuppqxlkbnSZvdUjbs9+mp1epM3uWamx+loWqMiTCGKTSg9z/UWnAM7H9/kEOhUR41tO1mcTVXkDdNbMMtiyyETPqjIHXZ3ZZntpj8PNA872j7k5u+JJzEJ/2s2QQuL+SlKobjjZOWd3RUw9t5gfWWF+eI3F0W2Wxw+YH9qj37RIQ+kIRantaic9yktMlfPx/LUI3/dGNf6HO4kCx6rCsUKcPwc4Ec7NRLhIFGWTamkTY03Z9RNfjRhPE1/9O0gO7VUy3bbKJSa7dlR07/LYKquT82zMzXK4vs7lwaF6P55v7ni5e+TlUYNucm5qQSbP4OmtOr/t5X025nZZm95lZWKXpdFdFod3WRzcY75vj9bySfK/thLlXkyAjQ6/94X4yo7h+zJVmEV56fkSWEpRahNt1UPMDst9PODq8EobT+S5tzyjUmw+mx+4OrjgYPWAjelNFodXmO6eZ6RlmiHTBION4/TXjzLcPMFU5zyLA6usT2yzv3DI+eY5d0fXPJ3faYXZ4/PruyoLQ/Ep2Zq7Z6T9kLriJQqSRkkK6yLcTUxMqwlUc7IADwvAUSbGlQRLS5GrnijPUpLCqqgp6GO8a4Xj9SuuD294vpL+RBkgpJK94+XhlsfrK04P9tlYWWZ6YoKVhSUOtvdVutnzjUBCixmudcF/ZTF2lZ22nWclrV4bv6TDME/e5xaC7HMJsisg2K6YYLsyQgTg2BsJc6gj3LGBcMcmi+m4zB/Siy8qnBZ1aLJukXZb5d2yQyjgXwCOSMHlqEVMTIOcSwh0yudLUAXZ8Y1U6LrpbJhiYWyb053r1/EWKXTVDRP4IhDrgesTSWU7ZX1ul9mRVcb75hjsGKfbNEBP8wD9bUMMdY4w3jfO3OgcqzPLbK9scrJ7yM3ZBQImrM+iWnA/yOLmhcONW9ZmTulrXqEib5CEiDrCvCoJcq3C37EGH9ta/B1FcVpCqEc+GZ+NNJT1Mt2/wP7qAeYjswb6Lc+aAjc391wdX7C3LvHHa0wOzjHcPU1v6zjtDSO0Nw7R0TRIp6mf3rYBhntHmBgeZ35qhu21Dc4PT3i6uefl/klTtAkAlffnzTskRrpLw+f01q1TnTtJzqcu4rzrCLIvJ0hSj+ylLcikCn6lxhE17Q8KnGJSoxqo0vXT32wBONun6jpZpgv17iiAsyUAZ5uWynGKU9qJDzHib5eP61+z+fi7nkCbejXOhDm3q/YpMRcNdBaj20p8HErxdS4mKrCS5E916DKaaaoZZnJ4jYOdc26vHnmUXWIZei3ASOCIzF9Xx+ccbOyzNrvG/OgC0wOzjHdPM9w2wVDbGINtwwy2SbvzMLPDk6zMzLGzssbZ7iF3Z2a1waKumWWclQ0XWSSfbz6zPfPAUNMexuxpUoI7iPNsINJFVDilBIhyVoCmYyGhLkXkfDFhKh9ldmCD/eVjzIeXvNy9kY1YFq7Hm3eM9WxSVThExhcTn4NE1arH849CpRh+BTgK4siGmWa4KpsM3xU45UR6lhDjV0hOYg2ttYMsTq1zdnjJ7aUFUsgNknWhAByz+HVcMNC2QElmO3EBAoAqCXauUvHe/gIlxCdQ/AydKlVLt799Af4OeaTG1FJd1M9I1xJrs7uc7J7wcG0GLPOgpQbVAM4adZUD5KQ08im0XJkg2/8tBbc/8vG2k3dbAI6murL67YSqtvEy4vzKVcthd/0ca1NHnG5Ju8+Dds/fvje3L1wcXbO9esT8xAaj/fP0dU7QYRrAVNtFc10XzfVdtNR30tXSx0D3MONDkyzMLLKzvs358SmPt3faOyOnINdI6gSpqUQlc/7Czvw9o20H1BfNU5g4qjYmRNXg/6GWABsjgbYyJlcQ6W7gs281iaF1FCV30VQ2wXD7GjND28wMrzM9ssTM+AJrixvsbx9ycXrF9dUNd7d3SoXz9PSkqXEE3tw9cH56wd72PquLayxMLzM7vsz08DJTg6vMDG0xP7zP4sgJXTWLFKV0E+st9XwBQfZ6ZBNM6hgN4OTjb5/Ot9Ai9NlNdDcNszC+zMH6EXeybvjp6+b0hpXpbboaJynL6SA1pg5RQL4CHFGH2ctzITW0tFHVaN6Vymy8nAjvEtI/11Fd0sdQ5wKrs3scbV9yd/kGTKjn5IWn+3vM51cc752wvbrH0sw6k0MLDHRM0tc2rsa/7uYxelsnGOqaZaxviZmRDdbmBcadcnVyw93VHc93Qty0G2gFcAKEDjcfmRs7o6d5C2PRNNnxPcT41eEvnptKgS/jXxNBkvCorBXKCXIoIcixgCDHXCI98shPbKS9eozl8W0O1k4wH1/xci8Dq5BDqcnkubzj5vKM44NdttZW2FxbZXdjS82x+1uHbC7vsDS9zvTwEhMDc4z2TTLcPcpo7yhTQ9LyOMPa3AL76xtcHh7weHXBy80lL/dXvDxe8fJ0w4uAHMtzebp1z8LIIV11SxjyRsj61K48owLsZY0jQQ3i4diqNn4kJVQpo51riPGq52tAE5mxHUiYwmzfkQoYUV6Rstkpl9AKbqSl8OpFpZtuLhwxM7zJSM8iva2TtNcP0lLTS3t9H11NA/Q2DzLcOcZk/xTzo/Osz61ysL7L1cEpjxc32nxnKcNlrSJtijsLl4x1rVNdMEhSRLVSRgeoa29A7Disa0ilzP0fgPOnJsavLVQqXtXOpNqo/nsqcDRwI3318o+WicwKbaxmwIrICUiwRHBLG1KYqHEshaNmzmwtJr+nYoi8O8zBAiocBPS0au1WdmJ8LECmgxiXLmJduomTw7WHOFeBOl3EOItCR9qsNHgT7dxGlLOllUpUQq8my/8xHfwHgKPAiAZJxLD1O8DJ4WtoKbnJ1RhKmultH2N1fhvzmWxRvwU4II73VyeSLPTERP8hFXmjRPkYcfprDu6/FeH9XgM4Egco1yrSuV2DN87tRP4Ab1oJd7AcAnCsEO3Vk0i75uq6v3nRrP2LEgknZmESOa0O+bMczgJsmglzsR5vAI7DzwCnAn2OibbafmaGlzncONMkyTIRWL5k8Lncu2Ft8pChllWMulFSI1uQQU38RxTAkYnNRTPLkwlIzPpeAU5oObqUBroax9hY2NUAjlI/PPHy8KQmiftrM9cX51ycnnB+fMzpwTHHe0dcHF1wc36riqOHq2euT284P7jgZOeI4+0Djrb2ONzYYXNhi4WxDcZ712k1zihTRvEHsvnXdJz+lo77uwx87DOJ8S8m62sthvwuutSCZYeznVutILcUGqp4VbtPz9xfSKzfFWuzW4x0T2EydmIsNlGaU0NeSjk5SWXoUg3Kr6c0p4HqklZM1d10Nw8yOTjDxuIm5wcCce5VkaeeJXmcLLtbIo/cW7lnuH2HSt04KTHtfAlqI8KrFX+HZvzsq/G1L8XbLuc7wDGNsz67xenWhVKCvMI2S/EqxdHa5AGdNbKL0EpKRIOSAEv/v/cfFUgh+R3gCLTpJMq1m0jXHwFOrHcVXwKqSI2qoa54kMmedU42r7k9fVDqFW1it5jOyS63+YXViZNXgBPlUUa4SxkhItu3r1S70bKgtAKcAAVwMr8DnNoB5kaX2FsTgPOGpMlzqADOE+tzh/Q2z1Oe20tarBXg5OPyiw7vD4VqYSomukkRdeTFt1CR3UOVrpvqoi7q9J201vQy2DHK7OgcG4trnOwecXd5+/0dtxTKUmSaT2452DhhZWZLLVK7Goeo07dRU9xOfWk3jfoB6or7Kc1sJ/trAwmhklhSRohzifLj8f5D5PpibCfm0AJtrODmrfpGIM6PCpwIF0l5ElPPZiLdNN8SgThhzvVEuTcR592iIE52XC9lGUM0lo7RZhimzdhDR00bvaZWZofH2VvZ4Pb0UgOI9/LAvVmYyc6c+YWjrXOWpjYY7Jiku3GE9pohmisHaCoboKl0kKaSIRqLhilMaCUhSHahcvH+Iwvv33Px/r0Av/fFRLqX8S3YQObnGmpLehjtmmN35QDzscDVN7ux1iL+Aa6PzWwv7jLVN0dvwxAtFd3U5LeiT6unJKWWokQj+fGVlKbUUJ3bjKm0m57aEaa651mf3OR0/YTrg0uepIi50xYgr4vq6xeVFiQ7vIOtR9SXLJEXP0Scr4kgR0kJ0pIhQxzaLYbaNQTYVhDkoCfMRSK0S0gMM2DM72Wsc4nDtXOuDq54vLzWnC9fBMjK7zXzYL7EfHbK6cEBe1s7nOwfcXUqf/+kditFoi67bNfHT1ztP3GxI1HWjxyu3LMzd8P6xBXTXQc0Fk2SGS3m3rkE2hYQZGsFOJWE2BkJlRY6mU8cGlXrsMAbzQtHgzdqPlbeOJrKV/zetEJNAI4UbVb1ZjXBzhUEOxcT5KIjKcqIPruTttoJJgfW2Vk+484KhS2Fobx8Ulg/XN5wdXjJ3soBc8PL9DaPUV/WRWW+iaIMIzmJJeQmlVKQVk5JlgFDYR2mqjZ6WwaY6J9kbW5NjduPV7Kw0xZz6pkUoCdeC6cvKj51bvgYU8UMmV/aiPWrIcxDonNr8LGRT2kb1hPuVUT21zqaDUMsjKxzsnXB/dkbxcwTPF0/Yz4xs7Oyq8BNW30fhqImSnLr0aVVkxFfSWZCBVlJZeSk6MnPLEefb8RYVoeptoXRvlHWF9a4PDrj7sLMk4B4edYE4kjBLwX5E6q9bmfuFklGailfpCixn8++dQTY6VVrnqSciXG72rEVU+M3AMcaI/4d4MyzNLHN8fYZopx7/bIAFYlm/3OAk4P372UqGU5CEcJdrACnnkAnI36O5fg4FOHnWsiXCCO6tFZqygcZ6JxndX6fy9NbzRPEstCQ+yKbLHcX0iZzwc7KDjPDc3Sb+mkob6W6yERFbj0l6TUUpxspzqikOKOcSp2RxkoT3aYuxvtGWZtd5XT7hCer6lZOyTJPyT2XzYab3WeWBm9oK9sgL26IxIBWYj1qCHaQlrACbfH8CnCaaK4YZXZwg72VY8xHF7zc31r6CbSfLf/2461bRns2MBQMkP65CWlnCnIuxeP3AnzeVxBoa1Hg/ABwBHiKibLVxNgCcHwLyEmofgU450eyaP4J4FjG0/31cwba5inObCPWX69aZATg+NtIGl6NFhMtBuI/AZy0mDpqivsZfQNwHq+v/wTg3GgKnIohspObiA0p46N9Fjb/noTLb3l4yzjmYgU4ml9UgPgHuZUpr6svgQbKMnvpa1pic/aci70H1dJurSHkuX68euby8Ja1+T2Ge+ZoqOpVqvSCrBqykstI/VakjrT4QuTITddTqt6bBtqaupgYnmRrbZOrswsFAZRS8s37omofaSNff2Zh6Jye2i0M2TOkR/arpD3/9zUE2GjG+EH24ktmIMarii/+1cp4uTClHWNBP/XlA9SVd1NX0UZ9VQsD3SMszq5ytH/C5bmZ22sN4Eic+NPjEw93j0qZs79zyNz0Er2dg7TUd9FQ1UF1aSuGQnmuu6krGVRzalFKJwkhAt3y8fotF9/3JcqXR0E+hwqLAieDb6HF/yWAI7Xr8vQ2nQ0TlGZ3kKIAThmqheo38fArVUoPBXAsG6GBjtWEexiJ9a/ia6hBPVcdDZMsTe1yvHuF+fRezTPq/sl4rebYZ24ur9nfOmBxZpXh3knaGvoxlraQn25El1pFXmoVuSkG8tNrKMlppLKglbqyHnpaJpkZXWVn9ZDT/XOuL8w8P8hgrW1AqjFPVDFH4olzx+zIGR31a5RkDfM1RGBxuVJ9Sdqt+H8GyuaI+D5J7Ll9sbqWgQ7ZhLvnkp/YoAGciR0O1o+5knf5VpLXVB+cgjeySfJwY+bmUtYGxxzt7alna2l2hcnhWfraR1S9XVloojy/Hn1eNSU5lZTpqqjR12EyyljUydSgrD2WON/f4+b0iEfzKS/3F7w8XvPyKJtbGli83H9kY/aM8e5tNQcVJEmEfI0KupGkXQG/QXYtymhbfJoCbCWtrpovfo2kRbapAIXu2lXWJi4UoFYqVgXFRf38oDacHi7uuTq8YW12j6HOeeorBijTtZKfVkvGNz0pn4rI+FZCdqIeXUo5+txqqksaMVW1098yyOzQLNsLm1zsnnKvanEZUFHpvoerN2pTsaNmRm0oxvrqcf8tDe/3+fjbVRCiFKCa+uZ/AI6VHfyjAkfjIc38LyvAERWORHr+92uh+q7AUSob5REjffZyiGmiSFpl0fXz8f0heEv01PeOTapFQ+sRlPQF8Z5pUgk0ISJBs9OKUPHaiXZpI9a1nTi3DuLcuohz6yTWVcBOOwJtfjxaLRDnTXuXAjnWG/GPn/8c4Miuy08AJ6yUvNQaqvWt9HeMs7ogAOdWFWmq0LRUUwJwLo+fFIUe792nLHeYiI9VOPwlW/lxiLGvxOoKoZVCWqBNlAXeRDqK+uYNuHFoIdxy/BnAEcmaOoc3AEd5ZFii4ATgvB4WgKPgjQXghLuIea1WzEvbjKhjflTg/NcBzurkIYPNK1TljZAS0Yy/XRmev2oAR5Q3Ya51qq3LCnBCrAqc0HLy3wCcp+tHnm4eeL594PnuQe3U3F+L9NbMrdnMw80dT7eP2k6NSCQljlvk70f3HG1dsLW0z8rMBktTqyxOLDM/tsBk/zyD7XN0NcxSXTSiFvcBziW8/5dUHP6aiuvvqXy0TSMuqJiCNDEkHGFqYI3dlXOVkvBaKMs3MgneSVrKgxpo91ZPGOuZo07fQcbXUr6G5RLhm4qf8xd8HD8T4JZIiFcakX45JEQVkZ1YSWlOvSLp8u/aXT3g+vhW8/55U5Or3/mCaklaGLnEVLFEbvwAiRHdRHm342dvwsfOqKLgvW2zifYrVAqcvmbxEtrmbOfyx5YBy7/dCnAktl2X0ExiWB0RHgY+/lGK129l+H7QTBwFpIS7tBHp2kmUmySgSVKV1kIlCpxYiYAXc9PIag3g9MpC6eY7wJHZXFXj39sUrAAnKbSaKHc9Yc56gmTythU/g2qlCJB2oWAxJhUlmE0WUaLASa2nrVYUOBaAcy4riTdfFgXOmgXglOX0khprItzTgOvfC3H9RVoXSwh2EVNWia43qojU1OhaUqIMJEWVkhCpIyu+lKqiejqbepganmJ7ZVst8qwFrPU3yo7q6d4lq7PbjPbMYqrqpTizloTIfL6E5PEtrIiEcD3xoWXE+RcT4VlAkFMuAWJKaVuI73uR6leoAkbM/MJVy9RbcCPQxnpY26c0M81IVzEKlkVMG9HuLUS6mjQljnMDEa6NRLmbiPFq4VtgM6mRJnK+NKFLqEOXWIkuqQh9VgldDZ0sTSxyeXjB/aW2E2o9R1WEiafBhRjwHjDQPoWhsIXC9FpyE4ykf6ogJaqc1MhKUiOMpIZX89lHT6iTjo+/p+Hx91Q8/56J1685+NnkE+dTTkZsLfrMVrrqJ1ie3FbP+w+LdMuiTf5OFm7HWxfMDC7TYuilNKOO7M8VJAQXEOWRSbhrGqHOyQQ5xBPhnsq3QB2Z0eWUpjbQUt7HeMccWzO7nKwfc3skCh9RdGiLaa2ARXmUSNTs6uQDfU37VORMqSjhMNdaQp0FaIgEuk3tqAXYGfG3kQKzlFCXEiI9i0kIq1Tgb6Rjgf2VUy72L3i4NEv8oMjyVOH3dGvm3nzFw/U1D9d3PFw/8Hj9rEXSWvraRWV3vvuokkn2lm7Ymr1Sxd3S8BlzfcdMdh7QV7euIstTwhoIsM2zAJwSgm3LCbYTgFNFiCRc2NUSKnOKg6hwNCWpfGqbKVJbWA6lJhDVpsy9GsBRHmoqjcZIsHM5wa5FBLvlIa1T9WVDSo6/uXjC5YG021nfgu+fori7OjSzv3rM/MganfUjlOWYSI4uJS4wh1DPJHwcYvF1jCPY/RsR3kl8Cc0iO15Pha6BFmM3E72zbC/uc3t6rzYJBLhZ51X1TFqejd3FR3obNyhI7uNrcBNRHyU9qRafD9XK1DhUUrM+6hWgbTdOKUP3y90HBbPlOZAvec4kQeVw80K1ZTZX96NLNfIpNIcIv0wC3FPwsk/go2M8Ps7f8HP9SsjHBGKCU4mPySY7uYj6yhbG+6fYW9vn4uCM2zN51gT2a4sZ69WR8Va8cNbGzSp2vjxjmK8BdfjbFCswKIbtQXaycyvtBD+2UPnbyq50MWnRjVTlDzDQssDSxI5qQfuhNcgKcJQHzj+2ULn+NQfvP8oVKIpy6yXCtUMpb4Oda1VstZ9jKT6O+QS4FZDyqY7Kwj56WueZn9zhYPtSbUppF866CATZMLk4vFYQW5SR7XX9lGRVkxJTwLfQXGL9Mgn3SCPcM4Vwr0TCvOL5HJxG5rdC9LlGteAY65lma2Gfm2PNm0g9W1YwKPOsCDmvX9ife2Ko4Ziy5GnSw7v57C2QtRTf9zoCbPPVzn2YaxE5X02vAGdfARzZTX+jwJGTkFYjATjd61QWDJD2qZG4gCoCnUpw/y0f73flBNjWqbSuMEdJCJUkQKt5vJgYC8CRhZkGcKJ9878DnOl1FMARMG2dy+XeWFpVBOD0t81RnNFGjF+pBnCcBODIYk8DOJIAF/IW4NjnkRZTqwGc7iWlbDvdO+XpRgCOzLGWrxcQ09aJwU3qKkbITjIRE1SOp20W7/81Aee/S6t4OcEu0n7dgCz+xfxWAE6YWwXR3ga+BdVQkT3IQPM62/M3yttQFpnWL7WLf/jE7uqlMmKvLu0h5UsFUYE5BHun4eMaj4dDHJ7WwzGOQK94ooPTSIjLoSCrQkGR2fF5jnaPuTox8yDwziKseH3fBeLvP7M9e8dU9ylNJSvkfh5S9Yef8ikTgGNQGz+hzpVEuBmI9qris38V8aFVpERXkfqpkpRPJSR/zic1Xkd1eRPDfZPsbBxwdnLJtfmGp4dHXl5eeHp85uHuidvrR9aXd+jvGqWytJ7ctDJSvxbxNTKP2KAcvoQUEh8mRvzVxPqWEeiQj+vfMnD+90ylqPX/IEriBoJVClUhfvaZfFUtVCa6m0ZYGF9hf/2Yu3Prw2G9sii16/KUBnBKsjqUB6O0ULn+koe7pFB9KCVAWgdV6qh4c0o7XBXRkhoWVkvGlwZqSgbUWH24daU2YKxwTJVhVoXH3YtSiYlvjKxjasvb0aUZ+RahI8AtAX+XeMuRQLBHMpG+WXwK1pEcU0ZZXgsdjWPMjq6ztXyg2uIeru+/r4EsG1yy+SPGxuKHM957SE3JlPItk5SsIGf5t8umiMxn8mnA304Storwt9Phb59NmFsuOgE4NWOsTO5ysH6iapWnawEqMsaKwlXqFvmUd+1JqdgvTy9ZW9pipG8aU003el0D6d9KiQ7MJNI/jQi/JMJ84okJTCIhOpPM+AJKsitoqWljon+c7eV1TncF4hzzcisA5+YHgKPZMNywMnFKb+MqZZkDfAmsJUDUgHaiXq8j0KaZAJsGtTnnbyOA0e/AlPMAACAASURBVEhCsIm8r91U68YZbd/hYFnzElXPvTz7Aonubnm4kE0QM4cb54z3LlGr7yP1cxUxgfkEeaThafcZ13fReNnG4ev0hUC3eGL800mM1JEdX0qlroHOugFmBhbZETXy9q2KFJfxR3zF1ibP1RqtumCI9NgGgp3zsf/fSbj9XQCkbKbK/KOt3bX64Ht3h9UP539aqLSgKfH9/V/SM/f/lRYqVRQqcPMG3jhb5NdS9Dpru0fqU74XnxWXOu1TebOIP4vErtWp6EL5VAZPlv9O2gAkBUeOUIc61dMf5WIi2rWFGLdW4twE5LQT69ZGjGubgjvRorp5q7yxevRYFDj/WU/ePwAcawuV2q38DnCCnHOJD9eTn15HXUUHg91TrC/uai1Ulp1r61BsBTgHGw+M9exRljNM5McqHP+SjfuvhapAkKJNet/l96sWMFHhOLVhBTiS1qWpbzSAo+LY/0SBYz2/P4U4FpNKK8BRppUK7DQT5txMuIscAnDECFmDav8ZwBH/ApkUrIs9OWcpsi72ZFA7YKB5BUPeMMnhJvxs9Xj8qrWMBTlWE+YmZsnSdy19+Qblfh4jLVQCcFIb6GrSFDjiG/EW4AjEebq75+nujofbW26vbjCf3XBxfM3+5qmSdc6MbjDau0hf2yQdDUOYjD2YqsSFv5PGynZqSzuoKuhEeotzE1r5FFSNj30Rtv+WgetvmXjbZRHklk1yTCVVhV0K9qzN7mvtAhIHav2yLDQfLu442jxheXKDoY4Z6vVd5CYaiQnIIdQrg0DXFHwcEvG2S8LPKY1A1yxCPPKI8S/kW3gJaZ/KKcs10V43rFrTdpaOON+9Uz4qahErUOpUkzyuT5vpbdpAnzVKUmSbimYM8xD1jQlfO02B4+uQS2xAMaVZLfyHAEe60i40Bc6fARzPnwGOGBgrBY7E17eplJowZ4mAl4Kpkk9+FSRHGKktGmCiZ42TrWuljhH/GG1W154VayEuAKdJP0FiSDWR7npCFcDRawoHee8lvlzStRxlUizB2wJw8qWF6j9S4ABi0rc2d0Rv8wICcNJUikM17r9JYS5pY+WEuBqI8jIS42Pkk7+RL4FVfA7UExuQT4x/FknR+ZRkG2mubme0d4z1hQ0uj0RKa30AtE+BDEfbFyxObtLXOoWxuIPMr5VEeGcS6pFBuFcuUd7iIVJMuEcRIS5ioJyvUrBkUeZnIxBXClAxjBaAY1XaWKGNVXXzI7wRDxyrAifKTeCNvMPfF+KSIiSQLdLdpBISvgTUkhBiJCmigtToUtLjCtAll9Ba3cn86JJSj91diPrr+zv9FuCszOzTXj+GLqWW1LhyEiL0fA4qJs6vmE9+ej77VfDF10CUu3hR6PD+IwOv3zKUCsf3fTYhTgUkhRkplr78iiHGupfYXjrkTnrUrY+IZWdQkhUuD6TX+5zpwVVaqgYpSKklMbyYzwGSTKEj1CmHYMcsguzTCbBNJdQpkxgvHfGBpWTGGCnLaKa5rI/B5gkWR5Y5XN3n/vRKS9iyAgFZX4vh5sGzMt2e7D2lvmSB9Nguoj82ECFxxWL0addMgJ20VVXhZyvFeimhrhrASQyrxJjfw2jnkvJ2kX76R0mFeZbdfmmlkuLylqcbMVO85fZCzDzvON27Ynf1lNXZfeZHt5ns22C4fVXteHfXL9BRPU+bYY6W8lmaSmdoKJqmKnuU3Lh2vvgYCfigI9CmiGA7PaEOlYQ5VBEm6kaBn/a1SoUTLu+QFey/KmMF9L+BOgr4W5+bevW+yRgtUcJh7gJAyoj1L6EovZWO+kkWJ3c52rri5lR2Ct+8C5ZFwdWBWbXYjXXPq/tWlt1CWmwlUT46Qj2yCHBKwds2Hl/7BAKdUwh1TyfGN4fEyGJy4g2UZjXQVj3IVP8ye8snXO7dq1Y3uU+ygBdTYEm3E+gmSWL1JVOkRTcT/VHM8KuR9EQ/WQA7VCkAJWb0KZF1VOb20ds4x/zQLtsLl1rc+fkLx5sPLE8eqpaUJsMgBemNfA0vJsgjgwDXDHydMvF2yMLHMQMfpzR8nVMIdE8lzCed6MAsvkXpKMmppa2uj5nhBbYWtzjbPeLx0qy1U1nYtVwp5Tuw88zO3B0jrTsYckb4FlirjL0D5LkS37FXgKN5JgQ7WTxwLAAnVQCOboC+5gUWx3c43jrnRcZY65e8vsoDx5pC9aOJsQAcn3flykMk2l0AjihwGrV2DDF9dikl0K2ICL8SlV7UXD3J9MgOWyvi+XPLk3V8UDv4WkvQ+cEN6/P7TPQvKrhemt1IUnQRkd4ZhHtmEuqeRbBLNsEumQS7phHsmkKkTwbfwvPJ+KKnJKuOFuMAE73iq3TG2fadds8tbYWivrkVI+hV8di6xFS0Qk7MAPF+WtKd+N+8BTihCuA0YfpBgfNPAM7mLSMK4PST+qlReRQGCMD5VacBHBsBOFYPQlGzWQCOijH/DnAiPEt4C3CWpje4OL7i/uqN2kvujXjPCYjaOKe/dY6ijFaifUsJ96hQ75wGcGpVy4UGcAwEOYriQnxU3gCcrv8A4AAnuw9MDu5QVzFOTnIbccEGvO3zsP1LCq6/6/Cxq1ABCMEuAj2lBadSJV6FulYQ4VFBrI+BvHhJmptkuG2LpbFT9lduVXS41CN7q2bmRvbobVmgumSA7MRGogOLCfTIxs81A2+nNLwckn84/FxTCfFOJzook5QvRVQWNdLTNszi9Ap764eYj0WtaIG1llJL5qB78SxcfmRx+Ip2wzoF8SNEukkLlQB1DeAECsB2lESwKmRTKfqjAfG4+xxUzpfQUj6HFfA5PIdv0dmUFzXQ3zXO1toBp0eXqo3qURb/L6LAeeHh7oW76xdWF3fpbBmhMKeapM9FfA7PI9IvixDPDCK8dcT4lvDZXyLkZU4oxus3HR5/L8Dnj3LVfi4+PVp6rcSIZ/M1RI8+u+XVA2d//YS7nzegAPPpLUtTO3TUT1CS2UFydD1hCuDofgI4YusgyaMVyivpS2AdmV9a0Od001E/zfy4GGlfv0JwVbuIEFpaos8fONm/Ynl2m962CQxFLWR8K+dreAHh3ln4OCTh65CEn2MK/k6pBLqkE+KRTcTHPOICisj8Vk25rp2WmmFGexeU19HpwaVqJ5efr+YGmV9vNFPj8/1nFicvaK1eJCehixi/GiI8pU237jvAsTMQYFdGgH2xet4DHHIJd9eRn9hER+0EK5N7HK6Ll494XYoaWuTc0kKlQZuHm3uuL645O7pgdXGLga4JqsvbyE01kPyphNiQPII9MwjySCXII5lA90RCPyYTEyhjUQ7pX4qpyK+jo76HqcEpNhZWFMR5NJ9rsOhRfo/2Dkv8+9n2AzuLZkY7NqnSDZMQKmbSpQTaGxT4DbBpVj568oxKnRfuVqXWQ4VJvTSUTDHZvcvRmrSta2O2uj9Pj0pdJIpBabFbmtymvW6cgrRmIv2K8HfJwts+FfcPCbi9+4aXbSK+Dinq/kR8zOZToE7VSbnxRgx5bbQZhxhum2O2f5O1iVP2F++ZHxDbhEUMuQNkfzbxyb9SATPnv6Tj+Vu+2lC0AhwN3kh98D8AR0DNj8cbgKMUJ/+tU6jeKHDE1NQKcJw1/4Vw1Tdfb3GwlhezhhAxQ3KtIdStmlDXakLUYSTExUiwHM5SZGmH/J1EmIU4G1XiU5AQZmmnEMWGUw0RLvVEuTYS42Yi1r2FONl5dmtVQCfGtZVolxaiLC1TP17k72obSaP64bAUta/qFfnzTx44mvxcdsTENKyIIJdcEiLKKMxooKGqi5HeGTaX97m9EHxqraK0TzHOujx6ZH/9ntHuXcqyvwMcj18L8XkvLSMCqeqV+ZPm4fMd4ERKu5SKW38Dbxys9+Htw/T9HF/P5e0LZ00ZUW7oIv+1voxWgCMKCyvA0dqt/lOAs3n+5wBnVwM4/RKBmDtEUrgJX5tS3P5uAThO1cpjIsRFAzh+EsvrWokAnG8K4NTT1TjKxsIOT2aRfcourMiQpRASCeMTL09PPN8/Yj6/5njvnK3VQ6ZGVugyjWMs7aQ4uxFdajWZ8eWkfirWjrgiUuIKSY4pISm6nITIKj4FGQhxL8XrQz6Of83B80Mega46IkUKnVhHs3GIudF1peYRY7kfdpylgH1AtQosT67T0zRCha5ZLd5jAySePRkvGzlS8fqQxkebTHzscvC11+HvWKDMNcM884ny1ZH2yUClrp2uhnFmBtfZmj/jYvdZLS6lV3Vv+Y7l8VMGWtapLhwn41M70b61hHvWE+TSiK+dSbVQiQeOv5OOT0Gl6LNb6WuZYH1OvCouNfnz28dTJnIBOFOHdNT8qMDx+qOUtwAnVHygLABHS0hrU14ZUmCGuVQS5VlBnG85SeFV1BT1/wRw1LbCPyhwVhTAGVcAJ8KtlFCnUgJl98XG8k44NRHh0k6Ik6TKlCqAE+mTT35KHa21A8yOLrH7Zy1UAnCuxMT42AJwxAOnlWjvWrzeleP1h5jnCTSU6NZaIr2qifQ0qBjYMI9CQtyzCXJLIS4oi9ykcmr1Jvrbh1ieXuVsX3ZitFNR77rIUVUxfsbM8Bod9WOUZrcQHy6tbMl4vk/C2zYdf4ccAh11ygzW377QssMkOzVlym9IEr8E4mpeQ2+BzVtoI6obS4ytdfx1luQ68cAR5Y2mpBDwpSU6NCo/qzDxx3GtJcLdQIRHKdHeRXwKkJ3xPDK/FNNY0cn00DJne9fcnj/+0BapAM69psCRWNyGiiESo8uJCSgg4qOOELc8gp11hLgUqpaicBdZGIqaQBY/Wfi8y8TfJptA+xyiPAvJ+VJPbVEfg63zLEv7h7T2XX9v2ZLHU37n1dEjmwvHjPetYDIMUZDSQFxAPoGOaQQ4ZBJgl6N+R4BNLv42Ofh9yCLQLocwZx1RHkV88dOTHl1FYVI9xrxm+pqGWRlf4XLvhOern1oUhbFI0tDhM8sT17RXr6JL6ONzQBPRXpqpu79No2pr8LM1KBgtaYQK4HgVkxheibGwl7HuZWUiKO1gT9c32soWkZSLD46oBB95MN+r3dXzg2vWFw4Y71uirXaU6uIeynM7KUprIy+hhZyvzcqENTPWRGZsM5kxLWRFt5IW3sxXX5kLy/ATHyebEkLsy4lwqiJCYKpTDeGONYQ51ioz4+9eOKLEsR7WdlsN4miFmahmG7QNFUeBH0aCnDRjVTFYTYyUNqcehjoX2Vk54epYvMUs/fqWeyZjo+zQ76+cMNo1T21JF3kJNSSGlxHplY+vbQY+tul426Tx8UOq+t7fIYsg5xzC3HXE+BTyNbiY5Eg95dmtdNaOMze0yfb8GSdiiH7wrBZy0k6xOX3FbP8BbVVzynvik794heiVWb54QgTY12lyfGnJdCpGos+lUK3M6aXVMMlI2zpLo2fsLT0yM3BAR90sFbouMr/V8im4BH+XTFzfJePxIZOPdnn4Ohbj61iIr6MOH8ccfJ2y8HfNJMg9g3CfbNK/lFNZ0ExnwyAzQ9IWuM3d6blqJ1PjhWVjR66PnMfRyiNT3fvU6MaIDxaAU4C/rcBro/JdCrIXkCYbXNJq/B3gBDoWkxLViCGvn17TPItjO2puer55Q5X/GcAJ0WLEXf+Wi887ids2EePRp9SUIc4SQWwgQMxD3UsJ9y7hc1gFZboe+tuX2Vq+5HT/TgU1yKaCUq9ZFjKSrra7es5Y7xINFX0UpjWQEFlCsHs6Xh8S8LZJxc8umwB7MY7Pxd8hG3/HTIJcs4j4mEtsYD5J0bKwbaWtepypvk02Zk6RFC2JpJfjbEP8b+5UWktH5Rr65FES/FuJdhPQX46/jSgZdfhbFDj/twDOlgZwKvL7SY1rINZfEs2KcROA80c5ATZ1hL0CHGnzl/emSQFSaVeUGPMw53IiPEpUi2+2pYVqaWaDSwE4Zq3tQk29PwOctjkFRjWAU/7PAY6D/keAU6S1UIm3lChwnm9uLNTj+wR/uvvE1OAe9RWT5CZ38jm0Bj+nAhx/ycD9XSG+UlurBMsGgiX+WBmWVxLiXEGoSxnh7mUkhTVQkNSlYsQ7a8UfTrzPLticu2S0exOTYYLC9A5S4upUUpmvc65S+XjaZ/PRIYePDrnq8LLPQQ5vxyz8XDII9EgjJjiHnORKasvaGOgYZXlqndMdaY2xRJNbT0U2ms5fEP8oSSPrrdumOHmMaI8mBW+sAEdUDwJLtJpEakwD4eKt51VMhHc+4T45hPulEx2cTnFuLT1tY2ys7HNyeIH54oaH+wdenp94fnzhUVTVNy8sz+3SUj9EZlIlsaF5hPnKe5+mFtD+TtkEuxaq6xTiLKpMPb7vSxUclXZo6aiQKGnxdZENKD/7HL6qFKpWuprGmR9fZ3/9lLufTYxfpCX7jsXJHdrrJinO6CA5qoEwjwoVwvBdgSPQRnxwxEtGT5BTGYnhDRSn92CqnGC0W4yLj9TPkilIwQHLOCQG5Ec7V6zO7zHQOYOhqJ2k2FJCPNMIcEnFxz4Vz3cpfPyQho9tBn4OWQQ4ZhPglEOgU46a9z8FlpIaV0V+WgMNlf1q03RrWawM7riVxDlrypXMr5IqdvnCzvId/a0blGYPkhDeRJxfI+Fuor6pJ8CuhgBpPRIPGYEgjoWqZorwKKAg2aQBnCmxQTjj+kTguAxEb+i4pHdd3nG0d8ba0g6DvdMYy1pJ/Vqi7nuwVzr+rul4O6Th7ZCKj0MKPo7J+DunECzqwI8ZxAXlkJ1QjqGwiY6GXqaHptld3eTu4kxT+Vg99IQZXWk1w8nmPTMDe9QVj5EcUU+Q1LEylooP2weT8mnyszGoNZCo21OjmtFn9NFqmGF2YI+TDfFNs5yKuj+S5njN2e6x8lIc6ZqnqrCHxKgqPG0ycP41BZffBMKm4v4uXa0rBA4GOuUR4qYjwjOfaJ8C4iVo51M1JanNVOu6MelH6ayeY7BpHZN+mpKUbpLCaojyKiXYqRDv9zrcfxF1pngXGlQN+R3e/A/A0QQSb9fc8v1bgOMgrUPfE6j+W7dQvS4gvsMbSa2Q1hhxsA5R8cZVhLhWESJR1u5VhLjLYrmSYLcKgl0rCBIJnRwu2qf8nbj/B7vIwCREWyatUiTxQ6Llwl2qiXStJdqtgRj3JmJlZ9m9mRg3UeVIi5WWgqUAxs+gRv25iXCRlP98/LRDaQU4KkpcVDgSs+qgSSEDHQoVwEmMLKc4qwlTdS/j/XNsrx5wdykVjXXW0T6tAGdv7Y7Rrh3KsoeUAkc8cESR4vtepMziWdCgAE60U6tqBROQI4laGsARH6CfAU7zG28f+f5HgPMKcZQXkbSlWVvTmlSLmvqzAjyy46+pb/4fAZyfJPRSrJ7vXrM8sa92kityhkgMa8LnQwluvxQq0+ZgATjuDSq2XBQ4PwCcsHLVItPdOMrmwjZPZpEXyoLLAnDeGPyKueXFiZnttUNmx9dobRiiMKueuFAdgZ4p+LrE42n3CbcPMbh/iMHjQwyeNrF42X7F2z4ZX4dMfOxz8fqQi9vvebj8qsPXoZDwj8V8CdVTkt1Cb+s0W8uH3J6Ll4slTUlurRRisuN8Jy0ex4x0TVKe18DX0DxCPNLxeJfA+3+Jw/Z/f8Ppb6m4/56N13sdXu8L8HpfxMf3xZY/Zym4E+ldQOaXWowF3fQ0TjM3tMf+8r2KK9xbkjjYA7rr5zEWDJLzrZU4fyMBjnr8HSR1pRZfuyb8RIoq741LAV9CyijLbaOvZVIDONsX/whwLDvCGsCZ+qGF6p8BnHBJRnPpVklI4pUR7FhFiHM5kR5lxPro1WK2urCP8Z5VjreuuTnTFFQa9bD0Rluk8FaAI6oQATghTgI0xAvmDcBx7VCLyleA461DJwCnZoCZ0UV2V8Xs9K0MQHvvJG1gff6E3uZFynL6FfCK8W3A54PEw0qaXI1SgUV6SmSs5vUh5m2+9uINkIaXzTdCPVNI/VRERb60UfUxP76kTHytO0tK4v2stQ/IolZgQ5NhmNykRiJ9CrD/y1c+/OsXHP6SiMsv6doz8IdOed74fijF31b8r0R5Y41rF7NoATFWUPP20/Keyrv62vaiARs1ySowK+BGkg8alW9RsHyq9LxGtZj1sy3F+0OeKiRD3MSkW1M91BR3MdG3wunOLTdnWhT0q3xdnnELwJke3qOyoFfBTT9RJdhm4PkuA4/fM/H6QxaFhfi9l1YGbTHl8y5HwZsghxxCXXL47FdESVozHTXjauG5v3qK+fhWK17eDJsyhhxt3jDZv0FDxSB5SQ3E+Bfg9ls8H/4lFsd/S8T9F/HWKcTnD9ntzMfnnZgl6/D/kK/aKEIcJX60gK+BhaREFlFd0MxY1ySHa3vcn938+C4LiJWWnOsX9pYfGWjeUtGgiWEtxPqaCHFpwPd9nWon9LWpQK5joGMJoW7FRArAiTBQXdTLeO8KJ9tXXJ/KjqdFfSMmxpbWBjmvh6tHFacrsvapwTUaKwfIijcSF1hAhJdOwQwf22yVuObzIRvvd9n4fshV5xRsX0SIfYnyvPH/UITfHwUE2ZQS5lBJlEsN0a61RLrUEiFJF+qoI9xJEqkalIJVtTj/GcSxtE8pgOMosbcCQESSLz5NRhIiasj6Vk+TYYSZ4Q21oyvQ0rqQV8OhxddKdiWXJ3ZpNgySFltOuEcWfvbpuP+aguO/JeHy11Tcf83g4x85+MizaCP99gXKIyLIUUewLA5cskmPMVKV10Nfo+wc7rA5daFMnMXIdn3ykvGOLdoM05Rl9JASLipeUbPq8JNURzujev7FBDbArhw/2yJCXUv55FdJcng1uoQWagpH6alfY6r3hBbDPIWpnXwKKiPAJQf396nY/ns8f/zLNxz+lonH+yL8lTdMJb4Oerzti/hop8PLNhtPm3S87dOIDSwg65sBQ34TvaYhVqeWuT48Vqa5bxdPSkF0oqUqzQ8eUV80TkJIDb5i9m0jY0KV8lz5jwFOA5UCcJrmWXgFOFL5W74skED8k2SB0FI5RXFKlwJF/naFCMDxfV+hNnFiPfuJdGuzjLHlBDqVEuZZSkyAnuQ4I/Xlw8yM7CofC4HiTxbjYjX3yaLwVosXXp4+oLVmjOyEGnUtRGnq+NfPvPu/YnH490Tcf81WNYDPhyJ8bPLxsclV4Qi+9ln4OWYSrEISjJRld9NeM8Nk7zZbc5eqbedq95nNqSvG23dpLV+gLG2YtPA2ImUD0LaMAJtifN/nawDHTmuhCnUtJOerpsCZEQ+c1WNt1161UL0p0p7h0KLAEYCTEidjjQF/h2Jc/57Hxz8FOFK0C8BpUEq3fwA48eKBM8TyzCaX0hb0FuBI6WBJ6FEKnLY5CtNbifIpIdxdAzgBqoXqJwWOBeAE2OeRHlNLbVG/SlQTgHP2TwHOM9ODhzRUzJCX0sPXsHoCXUtw/S0Hrw/FqnYIcW1QCaShrpJGWk2Is4Fgp3Kl+BFYGOVVwbfgGtJimihJ76WxbJqBFlEKblNbMk7m12ZCvYrwddLh/iEb+1/Ssf8lA5c/BOQU4eOox9uhlI/2pXjZleJpm4+HTSbuNv+HvfeAinO9z321t0uas32zcpP42M45ybkpdnJOYjuJk3PTcxIf26k+KS7be281OtPpMLQZGJoKRSCK6G1ghmHoQ28SaiBUUEMFgSQkBEhISEKN567n/31D0ZZkJ8fOTe49rPUutLe0hKZ93/v+/k/JRawpH5m2KlQWNKG9oQ/jIxO4dfWOXJu99x/vW5rgkzaqucnnONY9C0fhSaRHqAAnyC2fG6pY+dknMFFaTgk37IjVVcKsL4HZWIjYkDwkRuWhvLAZA12juDp5C/O3lvBg6SGerjxRAQ7rxSEA5+KZWbS7jyAnox6JkQcQY8hHqH8uDDv2IdS3UO6nscH2tUEMs0+UHCsF3li0PUjgvkxTD7OmHJmWBqkRH+o8ifPj13Br6q60Inkfp3co9EAAzk30tZ1CTdEgcglwQl1i/5bsTLFQEU7T9lYvUQfMbSrI6EZTFUPCL2Py1C1pPKUCzLtXWfv7559h6uI8xkYm0eo8hPw9DYgx7IP/u8kIfm8X9B/kwLStEGE+xYjyL4U5qEIWP7eR/iWI9FOAjjWsDGmxFSjPYwzAKM4xZHh6GUtzzwXYyLVPnd/xmsFq8VNHbqHFfhJFexis3oPU8M41gCMV3FQT6QikapGgr0JKRA2q8now0HYal0/fwp3pe3h0l4HCG1SH6hP44O5jgXInjl1Am3sYBdkOmEOzEbg1CcHbMqDbkQXjznyYfAk/8hEWkK8MWIP2Iyp4P2INBdhjqUTpPhcaqz0Y6T2GK2cv4+EiAQ6VwmqmGZ9SRjawzpuDn/F5dDecEnurxVgn7XFxwa0wB3TBHNAiYegxAbVICW9AUUY3XGVHMNR2DhdGb+HedcXWLa+NPA7uwR7g9tQszhy9iN6W4yjP9cAaXgWfb2Zhxz9kIfDd/TCwcMWPeY5VMAfXSNC5lOxoeT8th9VUifSoWmTFO7Df5kbJLg+qsvtRX3AYB9K7JXA72r8Epq0Fsp8K31GOiJ3cu7HRjwHc3I8S3HiXd+i//n1NxLHhDK2Ajg+fR/+/8f/fAHCsBDhi11FCfqn8UJYyKfuhPgH6Lqkd9YYRK9/VkGJvmK/XtiPtRzxQ8LDRqTZWKMob1iDGapyICrQjzK8SoX7lCPErRYhfMUy+XAdg8i2C0acIBp9CGHYWQL8jH4YdBTDuLFTWjkIYtuVDv3U/9B/sh2FrIUzbihFG3+7OSsT41SIuwIGEIJdSm6pvQ4rRgxRDp/IYNtaCr4GcV4AbATneaeSG76oCRwE4bKQiwGkXL6sX4OSmNqCutAu9rYdx6ugF3Lh6R6SDaxdi9RcCcOaeYfbKY4wOXkd98VGkRDZKiHH4zhqY/Z0ShMdJaYq+CwQ46d4mLQE4PUih4ualtR7MrMCbNwEcr4Se2ULM0xjq0QAAIABJREFURJDv3g+c1EPTitEjS7FQvVqBk5euZODwMTPEmBfSTYoUbk42AJxDnZNoKD2KHKsHUQG1MG2jd5eblBbFmmBsEwuVmbkNXgWOF+B0KgDnxcMVpYmE6hvS7+fqdIbKh5VnuDVzB2dOXEKf5zjqKrqQk25HjCkPRv890PtkQLM9DcFbU6HZmgbttnTot2fAsCMTIT65CPMrlMapML8yhPpWIsyvGgkmOzLincjLaERjzSBGD57D7Wm2iyje7LWLLA+2j5/j6f3HmL44I57/A5n1iDPuQ3hADvRbc+D/zWxo3i2AcRtrq+2IDmhAlL8LUX7MmGmEQBKfSrlJWkxVyElyozK3WxQsQ23ncfrQLKZOP8SJwevobTyN+uJBHNjTjr0JhKJUpNQgOqgBMUGszVUATqyehxAV4JQS4JyUUOVFApz7Gzau6vuTkv6rZ6jAOY2KfT3Yl6xm4PjVIdxHycCJ1zAHhDX1VIYMwGYYlnBhSiwTWH+r57SuHulRdchJakBTFdUIlzE/s4xHEpynyly5i1BVSzzIXDq5AE89DzBNSA4hqOUBpg7mAE6zWGPYBZtpcA3gRAYWI4UAJ68NPS2HMTF6UQL0Hi6+AuAsqwCnZxKO0iMo2jOAjBgPogPdiPJnc0MbkkI8sIV1gptX+sfNwU5EB1UiIoATymwkhuYjL6MWjvJ2DHSM4PyJS1jghJA/jucldTH/4cblRfGbe1xjKN3XDVtUHTTv5yHg27nQvFcIw1al/jPCtxbR/g7EBlEqzHwhBnDSOsWbJVUQvKZugDVrv1YUNnLNXVPWqTfWNVWdmnGlZQhqJxJFvs12B6pHmhAdUC/16dFB5UgwVsIWWYUcWwOaa0Yk0HvhOtUhqzItk8emTvL5GWeu1Omjt9FQcQTJUbWI1hJ8liLUh4+rHOH05fvWw+zngtm/HmYChsAKxGvKYDWWITW8FDnWGpH0Hu09ixsXF3D35vKafUrejnx7qpLr6QtLONh1HjVFfci2sV2oHLoPcuD3j5nQvluI0G1ViOHP8muA2b8BsQFOxAY4EBvAeuQ6xAfVwmqogo1WxegS1Oa3YLjjKK5NXMXS7F08W3rJ1qDafxamX2D84C00V59E8Z4hZCb0IimkA1F+LYgKaER0oBPm4DrJyLCE1MIWuRngzKsA5zkDFkGI432zKNdGhl3PXl3ExZM3MNBxEjWFXUiPrUR0cD4i/AsQurMIpq0HlPXBAZjeP4DQrSWI2F6OaAZB+9Yglvc/hmQG1MES3IBkvRupJgKcNqQS4Bi8AIffFYjjBTmEOLaXIc4awFEazHgAYsNJosGF3XEtKNzlEZuHEojJHCgG9CqPR66HfM0eruLxAiXlK9Li5iobRHpMJSL98xC2s0DNhihB6FZuEqsQ7WeHma+V2AeZM0IbYa0oKKiuyop3oSqnFx01oxhuOY+x3mmcG7mDi0cXcbTzKjrrTqAuvx9FaW3YFeVAbGA5In3KBSBy80kFGsEobRW8piTq6mELdSIjukFyvipyhtFUcRq9rquwFx5DTnIHYvWVMPkUQftBAfy/nQ+/bxZC90EVwv2oyOhErLYdMZpmae+JDKxDuH8VQnzLEOZfjKSIKuxLqUfl/mb0uIdwfvQc7s/eFpk/K2ll+s1blxrGy8Po5Og8PPZx7LM2IyawDOZAPn5ObtuVal1d67oCR8v3nR0JersocFzlR3CQAGeMCpz7yqGXHyJ+hlSAszD9HBNHbqHHdQa1+4c3ARwqcKjCzQgbgc2k5IzxfZ1gqEVajANZyS6U7qNV9SQunaHNcVVgjVz7+FnhBPqRMlW/f/sFTh1mi9Mh7LXUId5YgjC/fAS/mw3/b+ZA914xwiT/rkmaDWMCnYgOrEN0YC2iAqsQGVgOs64CexLcMpxwVx5Df/NZjA1cw6XxO5gcncPRrsvw1I6jNncYRSk92BvdCkuwE3EE4bzW+FfBTHirqZRDnzWkGqVZnehyjWHi2EaAw1H3hvsgAc7MYxwfnoJzE8CpkbyRSB8CopcVOK8COA1ICbNLRlvJBoAjuS4PFduFesuV547WegE4vedQXdCL1Cg7kghwdG75eQw95b2BtcocXjI0nRaqlwEO2wBfB3Du3iTAmUdHw3lU5I0gK8mDeGM9TDt57a4VFaqF0QYmrjZFKU8oRlWH/LxaJJkcYkXaE9eMgvRu1OQfRmvNabTZJ1CWM4yMuGaEBZTB6FMK3bYyBL9fBs3WShh97AgPZJtZM6I0zYgMbkZEUDPCAx0I9a+AifBDW4j0uEpRr/Eee7j3BGYuzIrqWD4v3ieMt4WHq1i5s4rFqRcY65uFs2gcaREdMAe6EBvYqAIcqtcYJNyCBC2tLG7EUhWjqZL7FVVzZlMhLFFFKC9sw0DXOK5evI35W/fx4N6jDwEcvkYXz8yhvXEU2WkNSAgvQ7TuAEL8CmDYzqFcKSI5tAhU7uf82bRBS320NMn1Ktlp2jYp8TBrKpBpcUko8VDnKZwfn5YmMtZab/paBR7MP8GFkzfR13oaNYWDyE3xwCoApxqbAI6BOTKEHQSA9SjL7kd342mcPTaLG5fuYWnusQSMe68Lcm14znykx5gYm0Fv2wlUH+jG7sRahPhlY+c/2BD47b3QvVeA0O3liJAgb16XvHuzWkT5VSHStwIxgeWiLE4wlWB/uhtNtYdxfPgyrp5fxJ2ZFTy6q0B+XitkP0Fr2MIqLozPo7eZ0Pww8tMGkRFFmxFtVMx9YvNao5K9ZqiDxViD1Eg7qvf3YqD1NC7RQiUA5+ErAc7dO49w4cwMBnvGYa/owm5bFUyBe7D9mxb4f3c3tFvzEOLD/T6v22UID1D2MREBxQgP4EC3GCkxlchNrxew2NNyEOdOXMC92wtYJeBjKD1FP9zHqorTp3dXcW1iAX1NZ1C0ywMr97J6FwTg+HcihgAnsAExgTVIiXCiaFcnXOUjGGqfwMWxG7h3g9k669du7hlePLyvApxJ9DafQEVeL5IjHQj4TqHclzTvlcG0owYRfvVypjIHuRCncSMuuEHJEApiCDT3QHakhNYhI9KBvbFN2JfYjgJbD/bEcN9pF3Cj/24hQraWKfDGr17UNxJd8r8Bzisq0d8EcEQlQXjTJ2Fp/2oAZ1Ob1MZmKe+vlSamtdpqBprKwV+trmVujYGWKSXJPsy3Epr3C+HzT5nY9g+78P43UvHu31rxnb+1yHr37yx49+8S8e2/ScC3/joe3/zLWHzrr+Jkffuv4vHtv4zHt74eh299LRbf+mosvvO1BLz7NSve+7oN2/4mA37/MwvB/7QfhneLEbG9GnEBDbBqFcm4Td+JFAPbnJR/uxde/LO+vwxwNF0SCMmU+zgN22vKkJfqQn1ZN/raj+L08UnMTs3j6fKGKZh6ReaGbWnuKW5efoTjAzMKwIlohIF2nR2bAU6qvgvphl5kGPoUiKPrRaqWLVqvWhsVOF2iwHkVxNmoxFGmRirIUgGOtIjxuTIoAIeHRx6cmZVDKTqVD9FBZbCEloMAp66kC708PH8PgDM5fgsEOM6So8i2ekRxYtxWtQ5wwjySiSR5Ba8AOLQjzVy8DqXWmMFkyuFfLpzPScFXsbz0EBfPTqG/8ygqi1uwb1cdks2lCAvOhd4nCwbfbBh8c2D03YcQ31yEUhLvn49w/yJEBJTIxjEysBKRgdWyoYkKqkMyJ07JbhzIbkWr86BUfc/fnJcAZYFH6uvKC/mTpUdYur2I8ycuorW+T9Q3Ib67YdqxD4ZthdC/X46Q7XZE+tFfSkk/Jw2dQuVj/LsQ49+KaH8Xov3tsBgdoA0gP7VNJsTO4kMiyaRFgHL5sqxOZFnqkBpZBcIes6YKkQE1iOJmOKhFMnCowInVORBrqMTuxHrUl/bikApwFq4v4dlrAM7UmTkMtp5BZW4vcm3tSAl3I9K/HhE+TsQEcFOyDnCS9ANSPUvQTPAQz4Oerl4ATFpkrQT1UkUkdpIZqhFW8Owh5Q2qEmEjwBmfh6fuwwCHDVisYRSAEzIIi6Fd3ocEOPSdl+e1ort5BKePX8D1y7fxcJE2lU1bIAmznrpwFyM9l+AsO4qiPTxQdiIqoAmRfjygUinYieTQblhNHiQaCBNbYA6uR1RgGcL998MSdgD5uxxoqOjEkOcoLoxfxeJNVkpuuNkSJC4rFoLxkWvobDyFirwhZMQ2w7SzAvoPyiU4OcynDpF+fD55k+UGk+ol2jL53BLcdCJJrKi8pr5pKaHx61MRAllFYSe5VmKV5Oe3G4mUb2vYVtSJ2KBWxHBDzQMPA3hDHEiLaUBeGhtNxjB+iFkaz/BwYVUsYbLh8gKcx6t4fHcVZ44toLFqHGmxbsQZ6hCjsSMywC6biGj/RsQGtCA+yIN4qd/ma1gDi6FSakB3xZQhP60WbfYBnDp0EQsz9/FwfkVa5rxQls+rFxZdGL+DzsaTyKc03+xAnL4Kph0l0L1XgtDtdsT4NSEhiNk0nUhUM0MYBJ+o8WanESwyaLgSSaEHUJrViG7XEM4eO4dbV2axPLe8+dDL9+Vzqgdf4NzxO+h1X0BtwTHsTxmELcyDcB9+JhoQHehADCeu+looAKcGOTYXmmoO4cShS2AW0oOFB9KGgVUGi1KiyBOvovLh7587cQ1DnpNwlvcjL82FOMMBhOzME5Ab4VslIIytZJE+dkTutCPKx44Y33rE+jkQH9AggwtWhjPzJlnfhBRDswAcqnBSDC2w0UalWqmSxUrFeyOrxdfVOIQ43oEQCwiovlEq6PnepOTfJRlRWdZ2lO3rRWPVIRzuO4ur55n19kgg5tpGlhkVDOe9/gTT5+7hYMc5AdGJxjKYtu1H2I5SRO6sRbQvgVsjYv2bEBfYgvjAVsQHtimKEx5Ygzi9pvrOjj3RTShK7YI9bwjukiNoqxxDl/2ULFfRIZTv8WBfohO7ePA18PBeLkosArwEdXpo0fIQzENdAxJ1DYrVM6wRu2NakZfci9I9I6jZfwL7UwZgi2hGuF8l9NvKoNtaAe37VdC+X4eQnU2IDCC8GUSspk+sqtFB7YgM4J9vQKiPHaG+1fJe2JNQj6I9DWiv68OZw6dx78asSOGlVlxV+lOxQrve47kXuDw+j876E9iX1ISYQIZ81yCWG3GGX7JSlwDHoFqodA3yueWUPT+tE66KozjUeQEXTih5RNxnyBe/qddYwkjaw3pcEyrAaRPrpukDquXWM3CYf8digRi2mRlrsTveiQJK70t6cLB7AtfOLyh2uQ1tYPysPrn/HEu3n2L2ykOM9EyiprAfSRHViAwsQZhPCYxbS2H4oAJhOxyI9qM9ogdxwV2IC+6Q62+sRlHfxhDE6JxIj2lBXmoXKvYNwVE8gpbqoxJC7Kk7BkfRIEp2e5Ad78auSBdsVGkzM4hKBIKvwGrEBSlKrkRDNTYDnGmwXOABq4fZkrPxa5UAZ0UFOCPIy+hAmtkFs9YLcAiJvABnAMk6WqiohuZgT1XgBDch2aAAnLToSpRmN6NPVeA8WGRoOS3f6z+Uz50CcJYw0nsOVQU9SImqXQc4AQw/Va5jVhXgJP6LAc4iPM6LqMg7gkxrJ+IM9TBsL0W4Xw3MzIxhLqWpDQxsV5aqwtEpTXtWI/NwGpEW2Yy98R3Yn9qH0qxDKM0aQaalGwmmRhh3VMGwowaGHXUwbHfCuLMRoX4tiAjsQHRwF6KCuxAZ1I3IwB5EBLQiPMAhw10OAVJjqpGbWo+q/CYMth/D1TMzeHZPheuyneYbWsm+Yg4OFXgn+m/CWTSG1DDun5wwBxDSKvdVpcmIlc0dAnIIos20L+mqEWdi+Gs5UmIrUF3cjaGeM5ianMfC7WUsLz3GM6mlVmqiWW7E12jyzCI87lPI3dWGpCg74ox8f5cj1KccEf61cjAnMOLnla1DbFNKCO5CoqYHFk0f2F5INV2cpkH2bJmWRtSVDGKok/k01zE7df81CpynuHByFn2tZ1BdOIR9AnAaZQgaxmsphxQ6JyxGp9zLk0IdyIhxwV5IBfdFTJ29i4Ubj0Q5zr2yfPEexFvRCnD98hKODEzCWTmE/bubYYuuEUVK4HeyoX+vCKHbKhHt44TZ3yUtX7ymJhKu0OLEa1Qg1cMMUq5GnL4c2clNqC89iMGO85gYvYXrl5bxYP6FqDTlva/+Ewh1rp69i5HuK3BXjqM0cwR7YnthDmxFbFCz3HssBjesRhesJjai0ZZYj+r8PgE4kycZYnwXy4vLWH324cHdndllnDx+BW3uwyjJb0VKfCWM/tnw+XYGgj/Ik+wntq9Fc1gX7ESMxoHo4Dq1/KMGMRoq8O3ItNajcG8DWuv7MX54AneuzykNnSvqOYTvSu5XHq8KcJw5v4iBljMo3uNBcmidDD8IcGIE4DTDLIMfwigHina1o6FsEANtJ3B+9Bru3ti4p1TIEAHOnek5XDgxjUNdVJEfQ5a1G2F+Thi3ORCyo0HZk/g1I9qfCh8G4dOKxvtpi6JE09AG5YJV34BkYwNSQ93IiGjFnmgPUkNa5F4bubMG4durELXTDjMD2wMbodj529SBold9w+/ryhvvr/+3AkcR2TCyZYuSS9KHZE669QxLU+o/vYGD3g3XD/S7KG8U2JGiVyxIm7+/DG96ISBHJsNsoWFgMQOJm8UzHxVYC837Bdj6jXT8/V+Y8dd/Eob/8fta/OmX/fAnX/bBn/6uL/7s9/zwZ7/niz/58k780W9vxx98aSv+8Etb8Ue/tQ1//Fvb8cdf2o4//uI2/PEXlPUnX9iJP/uCH/77FwPxtS8b8Hd/GIlv/vcEbPurdAR/cz/Ct1cqm1otN64epDILx9iLFEIJgiav2uT7/f4KgGPR8KZAgFOlAJw0FxzlvRjwHMfE2CXcuragHHzW79PyK4Z5cZNzY/IhjvVPo+7AEaRsAjgNawqcVH23wJsMQ/8mgJOq6xE7FS1V/PU60PFCnA0AZ80qtkFRtPa4FQWOWMj+JQAnjQCnE70tI5g49mYFjgJwLr4a4PCQQYBj4iaVh+Z1BU621Ynq/HYc7BzD9UkV4DxRN0DezSmnfyvPsTi3hNHDE3BUeZCaUIT48HxE6AhvcqDfkSsyybCAIkQGl0i1Nu0xZm0FzJpKmcrEBFcjOpgX7Vqp3yb8SDc3Ii+jFRUFtMfRpnMJi7cpn1RzOtTXd/XxCzy4cx+3pm5hbPg06kraYY3IR8B3kqF9Pxum7SWI8K1HTCA9vW2wSl07J529iNf0gOFmkrwfTLuC4junvHJXjAuZCY3Yl9SM/NR2FGV0Ym98A6ymcoQTRFG6u7MQ4b5U9dQiKqBhE8Ax6+ph1ldgV0KdZLEc6h4XBc6bAc4dDLZNoCqvD3kpHUiJaEIUAY6vEzH0cnOjqu+BVd8HApwk/ZDUXfOwzAm3VCqb7EiNqEGWpR7uymGMSR6IAnDYTCCaZCivo9hVllcxOT6PjroTawqcBB0Dfe2I9qcNQgE4KV6Ao62HF+CU5baiq2kEp4+dxwwrqF8BcGjxuHbxLkZ6L8NZfgxFe4YUgOPfhAjfJpiD2YLWiaQQAhzaZDohORC6BsQEU4VTAGt4iQRMs/p4uOsYLp6cwr1Ztgtt/pDz8DR7dUmm0N3uM6gpOIRMSwcIBMN87YjwdyA6wAUz8y0IGDjZYF4Nf76qYnwztHkJ6LxS0tot+TlsC+Pi68VK8gRNlwCcuGAe3hSVUYLeCVuES9pW8jM60FZ3AuOHruPO9BMsz78G4CyuYuL4IppqTmF3YissYS7EG7gp5XuE0yaGeXpEQcrqVIZJWjgVDKlBRlQ1MhOqULzHiW7XYVwYu4aH88/w9P4LJVyRQJYfL9oxHhB4v8D4yHXQ0kArR7ypSj6vEX5VoILJHNAoLT02vRL2bDP0KgDa+1wKjGiBRTJRyhGvy5fQ5ubqbhwfGMPViStYmFnAs/vrUFiA1Qtg+c4LXDlzD4e7p9BcdQrFew8hNdKDsJ1OZdoVWIeY4FrEE+CEKgocL8AZO3gJczNLau7FA46NNwEcPr6ZydsSrl5f1on9GQ4kR5TAtCMTwe/uRcj2IkT71yCBVarSYtGI+EAX4gP5vREJgW5YgpqQpCWoaUOaqQNppnakGhXlzTq8aUYyK2V1Sh6O8p0Ah2ocxU71eoBDC5UCcKwMXEzpRG3RMDyuUYwdmpS2kcf3OTHcMIl8yuftOW5M3sPZY9fhcYyiIL0Zkf750LybI8pZNnBIy5XOI3Zk3k8TgzeuNqU9i5tObQPSw9zIim1FQbIHRSmdOJDK1YVCWweyYl1IDeGB/QBiA0pg9i9DjF8lzH61iA9sQGIwM+U6pPzAoqU90a1sZA2EOE7Y2LIT0YRdMW3YG+cRawKVILqtRdC8XyQQx7SzGmG+DkQGuBET3IY4XRditR1iV40Odgs4D/evFeVmqG8p4gxlSIvhQaYCjRXtODE8hoWZ63jxcBmrK8+UAzwPUJzaPliVgN6rpxbQ5RhHbrICcDihVRQFCsBheDsBjuytJKi0HonGehSkd6GRAKfr4j8D4BxEduI6wFFaqDqQGjIs1yGGbsYEEkTVICupEWX7OtBUO4zjQ8w8uiewkwcW+Zzw8MJmoMUnuH3tAS6duoOeplMo2sMDezFMO2i9KEOEbw2ieMAOpBKKqsB+WHS8B3aLtTNBpyhw43SNiGd7UIRbFF/7ktuRl8JGwCbkpzVKhtKumBpYjeUwB5QgNqACcQG1iCfoZyMcLY3BdgHGCTpC4w0Ap3EME8enMXvVC3BeOvR9COC0KwBHUwPjVqq6HIgLYJsbQ4wHkKztkyZQUTJzsKGhEujVAOfy2RkoAEdVYHn3Dq8DOCGqAucHCHAmji7A47yAitzDUt0cq7NDv7UYYb7VEuSfqG+VQRqjD5JDuFqUHEo9BzNOJQ/H1ICkEBds4Y1IjWxCekwzUqObEG+oFxWa5oNi6LeVw+RTgzB/hyhvIoLciAxuQlRwi6zI4FZEBrUhMqgREYG1CA8sRYyuGMmRZdhrrUDRXju6Godw8cQVrFDh95gB6SpVZy4Nw8sXVvHgxguMD1KBMwpbaDOi/OrFnkKFKe+tAlIE3tA23Cq5TrGEXwZmMtUiKaoGGYl22Mv6cajvHK5dWsTC7Yd4eH8Fz5/S66Pei1SAc2niLrqazyB/rwcpsU4khtUhRluDiAA+fwzbZeYO1flU/HUgPoirU0LvLZqXFTiVUADOAIY6T+P8+Axmp5ZeDXDmXwY4nRJDYdpWAwE4tIoyWoKB0+FsDXMhK7EZ7opjGBu8hrmpFTxaXFWGFCo8kSEC7XsPV3F5Yg49LeMoympFelytKFzD/Ipg+KAAYTvKwQFCHBtIg1pgketpq+SqJUm+GnOnaFNlzEU1YjVlAn0r8nrQ7hzF0cHLuDyxIM1TYqFSFSt8+1PJd/3SA4wfuolu1wXU7D+OrIQ+GRTyNSRIZg5qEvOLQmnBrUdGDM8E/ehvPS2W0dd+lgHMztzHkeHzqKvqxb7dTliiymBgnMF390JP625gLRIMrUjgedXQhHi9W5r3YnVOSH6kzo6k8Fqkx9UgK7kazgoPjg6cwM2rN7CytCyqe+810Pt88r15c/IehtrPSvOdLbweFmOjvP9i/D1ioTIHEcDbkRblROGuDjhLhtDfMo5zx6exOLO8QdVNmeYTvHj4AIs35nFl4ibGhq7IHu3A3n55D3DPFR3kRFSAA1HcW/o3IIb7Sw4HpQyjEYlUMdGKpuFAoxbxwdWiuLGwZU7fIPfY+CDm6NXD7E+VoQvxQW6J87CIIpx5jOpg8ZX7zXWY8yqIwzP3D5RT/Jv5+96gwBH5vMCbjRCHhOeHaKFS1TcCbVTlilfBQgAiy9vmpO9Fip4AhxtmSvo9SKIE09Ai4Wvxeqd48ny+uVfgzZ/9TiD+229sxRd/5R/xuf/0dfzqf/wqPv8LX8Ov/eLX8Wv/+ev43C98Fb/y81/BL33mz/HLn/lz/Mpn/wK/Kusr+NXPfAWf4/r0V/D5T38Nv/bpv8avf/pv8Vu/+G38wed34C++qME3/jBKII7+3SKRTpMOc5KYxkBjU5+AHC/A+X7eTGugZyPAEfVNFyyciKkAJ95Qiry0RjjL+zDYOYqzJ67g9vTiGwHO9cnlDwMcWqgCCHA4Ie2AAnD6kWEYQLq+H2k65uCswxvaq1K5BOJ44c1LFqo1gMPAys0QRzYdtFBJtazye/8sBY4KcHpaRiT0dG76rmxINx5nucljBs7k+KxMApwlR5Bt7diswDG0IEWsK+0CcHiwtKoWKi/AOdQ1hhuTNxT7lFd9wx/Eezr9ycvPcHtmAcM9YyjOdSI0KB1Gv93Q78yCdlsu9DsKEOpfgmjKjvV2xBsoD+cG2CHf42jX0dph1trle6yO0u967ElswoHMDtSV9WHAM4YLp65iaX4JeM4fvP5Iny0/x+LNe5g6fx0Hu8dQntcEsz4H2//egqB3sxG6swKxtMiwio8hsyF9sIX0SxCwRduLhOAeUUcksslCz8Mmpw68IbPdplZZRjYWMVysGPr3c+D3D+nw+8fd0Hx3P0Iob/WrE1tHDMPSNF0wa5th1tbBrCvHrng77MU9IMC5MjGDhZl7r1bgMCtm4g6GCHD2E+B4kBrZLDeHSN8G2XxvBDhWqVIdVOFAq0g21xp5ImqQmViPxsohjA1NYm76AZYXWZnMFDtOPpWDzCsBjokVuesAJ24N4AwpCpwPAZxDOEWAc+kWlhc+rMAhwJm+eBeHey+jYQPAiVQBTkxQGxLYwmbqUVc3kkzdSGDWBTMuAouQFF6Kwj0NaKzuxsHu45g8NYWlW8ub3gfylny8KlLo00dm0NM0gdqiw8hO7kas1oUoSmdZjUk7B5v1CBeMnbCF9MBm6oaNtd8MIKYaiMvgQbKhQ/3OX3vXBojzoRvqZniTZGDVbS8sOr7HCAo59eYUsFmqR2ngFNEEAAAgAElEQVRXTI1qxJ74JhTu6pTNwcmRGdyZXsHy/PMPKXAIHrgZnDi+gGb7aey1diA5Qsk24+ErTsPMASWA2WYcUJqwDM3SKkZbwZ5YO3KT61CxrwmDraO4emZW3ov8e/lekKngUyUMmhO6+evPcKT/qoRjMlA8KrgU0UFViAmyIzbYJcov5gSlhQwhLWQQaSEDSAvpR1pIH1JNPUiRQGfagagCKkNMUB5ykqrhKGnDUMchnBulCucWVu4SLKqHUn62GYK4uIrrFx9IWGcXrQf7DiM9ugMhO+wI9yUwZZtIDeINNbCE1oiFKsfWKPaRsYOTuD29hKU797HygG1lywrAETqlbJwvnZlBd9MICvbUITWmGNHB+xD83XQEfHMXTNuKEBtYh2QjQYuyYaaylIdEWcGtSNK0IUXucZ3YFdaN9FCPQBzJvllT33gBjvc7QY6qwvkQwGGmEqdr3Kwp1yIF4DQgOcyNgoweNJQfwUDHGZw5PoXZqTt4svwI2GBl5Wt4b/YJrpyZw/H+STRWHESWxQnT9n0I/GYWwrZXyMYwI2wAqaY+gW208Vq1HlgJ/eQg3C6PkXAqWdck+SbpoU3YTdAS3oi00AbYjPWwaGsQ61+OiO1FMH43F6EfFIm9LIbBiwFOUfHwXs0gbyuvvbpWWPXNcn216KjEYdAxQybtiNPSRlKDCP9yGLcXIfi9PLFPUWLPjT439XF6woVmJNDua2hWN/wNcp2NDq5GRGAZIgKLwWbBlOhiZFqL4SxrwujgMcxPz8hGfA3g8C1GMeKy0qQ1dXoR3c6XAU6D2Al4ECXA8SqbJaxUWw+LyfEKgPOShYq3qqeAosCZQ4/rLGr3vwxwHEgIbkeKaVAUgFRSMpvGGlqD3NQm1Bb1gI1cp45cFUhD8CSfFfUeyMexNPcIMxcXcerwDFpqj2GfrRkhtMTzEOhTKVNn5n8wl8vKYaR+SBr25PpEpa/BIw00iQZmkDXKoY1gOS3ahZQIZkyVI95QiBiGwfsyPykHundzEPJBEaJ2ViGOBRCBDUgIdshBhZaARIJVYw2SQqtRmt2JrsYTOOsFOAw+fXlq/xqAE/MhgEP1zUaAQ1X2awBOjqLA8QKcZw9fbIL+6wqce+sKnMhaJP0LAc6912TgUE24GeB0SDiz/oMDCKMdM9ApFr0kXm9MbbCFcFGJw2s3ATxbsbiYteIA90xmbS2ig6sQEViBEN9S6LcXi9resKMUYQHViNY6YNY3wKx3qasRZj2XG2Z9E2J0DgkDj2YgrvEAbNHF2G0pRf7uSnga+nF+9CIe3nkINlcy9N3bMPTi4Qtpn1u++QInB2fhKDoujT5UKTJHch3g0H5IeNOFeCpfCKN1DbK3s4bXwxbDNion6iuGMNJ/AdOX7mLx9iM8vP8EL57xZqAAHL6/qcC5fPYeulvOojCrC+nxLiRFOAQGRQfbxWaawOu0oRMWvWJVVgCOosKxUIUjGThs0XRg3ULVD6+F6o0AZ3wWvS1nUF0whFybF+DUImynHdEqwJHK98hGUW/npbSh3X5CAnXv33qB5wyZ38greV1YUQoJzp24iZb6w9ibVIeEkFLEaJnRWIywnaWIkogKFyzBbeI8SJKGXKo3lcXhAW26Vj6vmhrEBJciPaYOB/Z2oKHyIAY9Ezg/fguLNxUl1UYFztPlVdy+9gjnx+Yx7JmCs+QkcqwDokxmQUCCjmcBN5JDGUDNe1A9MswKwOlrOYXzJ27g5pUFGZIoIcbre3L+6sbUEob7JlBe1IHdNjviw0tg8MtF4Ps58n41a51IDvWILdoa0gaLqUXKdOL1jfI+YZ6QDGaiKpEeX4aaAy042HMM05PTeLjI66yaJ6T+WK8KZ/byEg56zqEsuxspEQ5YjRzctiLGzwNzIMPj2RTGAgkXCjI6UHdgEN2Ey0euYX5aATiyDxJ7Ki1UD7F06x5uTM7h/Oh1DLafBbMcc1M6sSuuBalRbvC1TzQwkoKfUQJXl3xuk42NSAlxI9nUIMUgCZpqxAVWIJZB+QHVYjmNC6TN3IkE7qcIIbUs0eBr2oakNTv/m9U3b1LhvHz2/H7O3/8+/sy/FYDzkm3qXw5wqL4hyWwAD8Emn2J88I0UfP0PDfjy57+DX//Fv8EvfupP8bPv/DZ+5ie/iJ9950v4uU9+CT/3f3wJP/vJL+Fn3vkC/s93flO+/+w7X8DPvfMFfOqdL+I/vPNFfPqdL+Ez7/wWPvOTX8ZnfvK/4bOf+AP88k9/Bb/52f+J3/vl9/CV39LgW3+RgMB/ykGkTxXiAunrbkNaiBfgEDatK3C+1xtk7U33GoBDKbYELupLkZuqKHAGPaOYGLssFdY8NL78xUPK/blnYqFiBg5DjNOimsRfHeFTi9g1gOORBi2qbzYDHAKbXqRxvQxwtN1I0XatLZu2C7YNAc3reTdq9o0Kb/5fAzhbVQvV9wA4Nfnt+BDAUVUPcpF7vIqVpWeYnVrAYOcYinJcCAncDZN/Noy+TGgvRohvBSKZqaBzINHUiEQjl9J2xs0wJduxhB2U12prZcUbapFpbUJZbhea7MMY6TuNy+em8WDxPvBi82tL+fjczF1cOj2NwY4xlOW2IM5YAL9vpUH3QT7CfWsQJ2oLNgExJLpXKrGtcrDuFYktM0oUGw2zDihddsMqDRBOaYGwGhywGOqloSR8Zwn07+XD8H6RAm98eRN3ISaIMnRajDghboYocFSAwzakNQXOzD08XeIIePM7lLk4BDiiwHkJ4ESoAIcZOBZ9t2zCXwY4osDR1SOJlcobAM7oBoDz5IcCcEb+2QAnLboTEX5NCPfhxLsd8bQbGXuVZeqF1dSLBH0zzJpaSfZPCi/bAHCOYfL0FJZufxjgUEZ7+9oSTh+dQU/zBGoLD4tcnRJdtl5FBxJytCBBDshKM1SyqRtJRq8KxyONXkkCbtqRbPAugpyXYM6mqYg6BZFaW1bbUn1DeNMnaimZdmt7EB/cDb6GDG6OpzLG5JIcrl1xbuSnt6PVPgoCnPmZFSwvPIMcOFQ3KDcoVBgJwDk2j+baU9hjaUdSuFs+V2yuIahUVEWsMh8UC5hVz/9XJ7kQmfFUDTTAXtSBQ52nMH3ujhxiuUnm3y9QlgG/y6vikb997QlGei6jYn8/Yg0ViJTA02ql/YlqPVqBCHBMQ0gzDSLNRDDQr6gujRw6cLhAgMM2Cyru8pFtrYa9qAV9LUM4deQMbly6iZW7lEMo4Mb7qXiytIrZyw8kO6Sv+QKq9x9GRkybaoOrRmRANcyaasQbFLWCLbIG2ckKwBkdvqQCnAdYebCM1ecPVcmFojyj1W7y9DQ63Yewf5cdaeYSqWc1bc+C4X3ew0rlMJpi5ONrl/yaJG6o2E7GpelAstaDFFGZdkobY5qJitN2pBgYXrzRPrUR3qwDHGUTruTgKPfDlwCOjhYqt0j+CXAO7OlDU81xHO69iAsnZ3D7+gKesvHmxTrQ5iFh8caKQPuRrnMyXcxKbEDoznxovpOL8B3VSAxm+cAAUox9ouBV8uU6kaTxqIuPrV1UQgxh5uNJNbUiPbQFaSHNSDHSLtYIq9aJ+MA6mH2rEbm9AtE7qxHrV4+EQBcS2eRG2KXtUHPeaAujFJwQh7k4LiQQ6hHeaGphDuYUnVbUcoT7lSDEp0gy0ZiZkWCsQ1KYS0BlSmQL0qLbkBbdgtRoqg/cSIlqQHJEPazhNbCGVyIjjnlS1SjaW43WOg9OjZzA4nUqcB5sVuBsBDhnFtHdMI5cG+1LDAqtAbMMmAfxeoCzUYHjtVB9PwDn0GYFjo8KcIwEOLSb0B5YgeSwGuRnNMNZ1of+9jGcG7uG+etsfNkAOvmRWVmV1rqrZ+9gbGgK7iq2yrSIDdDIzCbe/9guSYgm0nsOJYeQLArOXkjW3iaA44ZVWoMIb1xIDqsTq3CCoQSxmgOI8i9C2A5mNRQiYkcFzH51SAhqRGIw1XicNDtkwqwAnA0KHMnAmZZD34N5HpQ2nmiVz/5mCxUVOCwGqIFxWwUifWgr4OdvEMk6LipxeiDv39cBHNVCdfks9w5vslD9awCceXic51GRO4IsS4dkdxm38fWpRmxQgwBCAcYEOLJakWQkwKGyxCWHQyo24/UO2d9TrRwVpAAcNnYyR4QZl5HBlYg12GEJcyIpshHJkW7Yoppgi6Y9h6tFVnJ0I5Kj6pAUWYEUczn2JlVi/+4alO93oq/1ICZPXsHjBQIcZg5SlqksqhyeLKziPhU4AzfhKDwmLVORvhsBDoesHUjQdiGRQzLGAOhawcM5y1Ks4Q4kR9dhl8WBuvIhHOpTAc7cYzxaAziq45VKlccKwOlpPYvCzC6kxblgjXAiVs9YARXg8PpsVGIHuJ9bBzidsEijcLcaYsy9JkOMnagrIcBRQoxnp+69XoEzPos+ATjDyLV1ISmkCaatdgXgiIXKIYf49OhGZCY0oyjDg07nSZw7fgvLcy9EtbQR4Gy6lx+/jmb7YeyxUOFaBjPbwvzLEOFTIU1a8YFuud8kaT1I1nbIgNmmU67PyjCgGYkaNmhWISawBGlRdhTuboOjbAj97adx7sQsFm8y9Fe9bqg3WJ6R7lx/jEun7+Jo73U0VpxGbvKAlEtQfRinVbLXqCyizdsaapdMrqr8PvS1UIFzE8yQu/+qz7IAnPs42HcOlcXdyEylAqcSoYEF0NLKy5B7DdXsDO5uB1twEw3clzXJvohV7HFah2qNrkJ6XBmqi1pEfX3t4jU8WFjCs+WnipqSj4fbadVGNXvpHoY9Z9cBjklV4AjA4aDTjXgd86Rc2J/Sjqq8XrTZj2B8+CpuX+U9Qv27+PexZffxCh4vsonqAW5euouJY9cx7LmA5pox1B84jKq8YZTs7UNheg/yU7qRl9yNfZYu7EvsRE6iBzkJHciMbcWuKDdSwxxINtph1dnFnkzLaXwQlTkuWLQMK6aiSoU3ol7mMGej+mZdbeOFNi9/X1PhMMPxJeEA//t7nb3//fz+GwEON1Ne9Y3XRvVDUOC8Ct7QPvV9K3B6VAUOp8n8ELgRR7KuqYF+ez6+/Vdx+NPf8cHnf/4v8fM//Qf4qR//r/iRj3wWH3vrU/jY28r6+Ec+hY9/9D/gR7g+9mlZP/rRT4Prxz72Gfz4xz6Ln/j4z+MTH/9P+MTH/jN+4iO/jJ94+3P4mR/9bfzCJ/8Ev/6pv8bvf+4D/N0fhWHnN3aJTDvGnxM6bvhYL8mWgh8gwGGGSbBTAA7r9ATglHktVJdx69qrFTj8YD6Yfw5poRq+iYbSUfF6h2yvAG8+sYEuVYHjBTgDyDAMritwtJsBTpoochhsvA5vvODmQ/BGWs046fz3AXAyYhpABQ4BzkjnRgWOKtfnNZP2qYc8UD7F9csL6O84gaIsN0ICMhHivx+hfsUIZchaQD3MvBkYW5AU2g5rSLtIhS0GKi+aEadzIVamImweoo2KSp0qZCe7pRXC03gUo4fO49rkTTxc4qF9M/l4fO8FZqfuyga3t5UApx2JoaUIfi9Lsjoob0yQFqAuBX4YNlpbFHsLKyalNUjHyTchDoN7m2ClGkddFk6ANfR51yGKknRfNm44wGmF1C5qGJDnQZxWATixbCTYBHCUEGMqcJ4urR+65H7Kek6GGL8B4NBCJQBH1y3ydwIcq1ZR4CRKVa+SgZMU8maAsyoKnHULFadDmyxU37cCpxJluW1ioXqjAufRRgXOcbFQCcDxbULYTgb6tiNe2w2LoQ8WYx+sxn5YTf3gRM2sqRO7lgJw2EbQjUM9x6Tm9P4cVRWbv3iYuX3tHs6oAKemcAR7Ez1inQr1cSCK0lYtr5OcOLOWmuCGE+hOmX7zAMVGL+Ww2YZkg3fxEO9dPIx6g469k5F1gMNMImVtBDh9SNT2IUHTiwQGGrNhiK1hJtoVXEg3u5CX2ooW+3GcHJnG/PXHeLj4TAK75YCuft4E4CwwA+cOmmpOYndCK6xhjUgwUlnEfCc2f9AW1otUE6fsfF8zKNou1brZlgaU7G2Gq7wXR/vO4sbkomKd4llKBSj8XK+o9qkblx9juPMSyvb1S7hpBPNNJOCV+SjNEvhMG2+acQipxkGkGgeQauhDimGDbZaV2Mxx0FUhTluILEsNqgua0enqw9jwuORrPV58vPmFpEbswSrmrj3AhbHbGGqfFBiXYW6RIPIwnwpEBlRKlkG8oQqWkGokR7wEcK4t4d6cCnCesUqcJ3YlxJRh1xdPTsPjOoi89Fqkm0uRaDogVqMIn0KZkFl1LqSaOpBioNWJzYQKtFGaEwnouYHoksD7VGMXUg0M7+dk9CWAIzk4rBX3LtqL29Qp6vcCOASOTtjC3SjJ6ke7YxxjB6/i8tlZzDMAeoWPSyXqfI9Q7XH9Ec6N3pCcgrqiAWQmuBDhWwQGJDL/JiGYNq9BpHB/wwOwpkuW8rg8sGn5b/IghUvPQH8ugql2pPDfTQURm+M0zWJZSaA11d+l2MqC2eLIw4UHSVze+53Oo3x+DG1IMjSLrS9BS9sDlVxKGxJb2aIDyxHNqnBtOeIMVbCG2ZES5ZQ66d3xzdib2IIsaxuyk9qQndyK7OQWyXzItLqw1+LEXks98tIdKMl2orqwAT1NfTh3/BTu3bz5YQvVJoCzIAAnz0YIrwAcZuD84AEOq9YJcNrXMnCifLiBbwMVc1QqUSHA58EWUY0De1rQVD2EQz2nMHnqOu7ObgjZVD8xHEyxcY1NN0d6L4lKK9vaKtZeqkNpg2UIL7O6kiUzkeBjSFQsCmTmoZcwux0WQ4sMAS1GTpVdSA7l4Y02hBokGCoQpyuHOagcUX60NFUgxs8uVjkLg+75ftDQLuBEopbhobWqhaoKpVmetRDjtan9GwHOYeTtYgZOowx0TAQ4vgSG/AwOwqbj4uPo3ZCBQ+vfhy1U3haqV4YYU22ohhivZeD8MBQ4N5iBdAce5zlU5B4SJTQzlEJ2sN2uRrKheK+xERhvADhse7UamtRGKhcE4DBbT8usEA43FIBDaBNNO7quCgkhtRKymhbrwq4EN/ZYmrE3qRWZyW3qakdWcjsyk5tFAbPbWotMWw0K9tajfD+HoS041H0MVyeuYWXxkVhVeJDFKvcsCowgwFmaYQbOddQXHkVyiAuRsh/yKnDa5D6XqO2BRdcv+65EQ4fYZRJNLljDnUiOqkd6ggP2UgKci7h26R4WbxPgPMULNpzyiz+SvQsEOOeWsAnghCsAh/boWL7vqDw10rKsZM4xA4c5cMxms0iQ8cs14gQ4fWqN+DXZQ24KMeY2U0KMn+KCCnBqCrwApxmmrXUI21EnGTisDmfw9S6zGzlJLSjN7EZv4ylcPHEbj+YV+xTvq8oX1Tircn+jTZrDJnf1YexKqEe8sUKiBaIDKhHlR4WGE4lBLfIe55lCrs1syeU1VkMVaDOsbNsKYgh9JaIDipESWYv8jBbYiwfQ03ISZ0dvKABHETV5/xESfL44+wRT5+/jxPAttFRPSAYZCz4Ybs4CHDo5FLW8HYmmGqRE1aFyf58EOl8YvynPGT9Xq0/XHpz3IeLG1DIO9V9EdUk/stPdSI6pRYSmBIadhPPMjmRcQYei/NNzP0RldJNitROHhQMWkx3JkVVIiy1DVWELhjqPYur8FO7fuYenDxhkrD4c7/vk0apAljWAE+mA9WWAw/wgfQNSIxqRk9QqVitX2TCO9V3Czcn7awHx/LvFnrzyDM+Xn+HJvedYvvMUt67cx6WTcxgfnsZI1yX0NZ1FR904miqOw1VyDPWFR1CbdxhVOQdRkTmI0t39KErtRp61HVlxTdgVyQwc2qcYT0CQwww0njNaRXmr7C07JILknwtvvDBnE8R5Bcj59wNp3gSc/g0BnFerbjaCnNdZqAhwKPnnxYv2KZfYUKKCK6H5IAd//z/C8bv/5dv4zE/9Pn7yRz+Pj275WWzZ8jFs2fL2S+sj6v//OLZs4eKf4fo43tryI3h7y4/iI1t+DB/Z8hN4e8s7eHvLT+FH3voF/NTHfgM//4k/wG/+x2/gq18Oxvt/lQTj1iKR/iVqmpH6GoDzvUjgGjl8lQJHBTjmoErE6kqwL4WNTD3o7ziOM6NvADhPmLz+ArennmD80C24ysewK7YVYTurEOXHAL5GsWdx85mq7xH1jQJwBpCm60eqtk9R36gKnI0Ax6bhhl5R3WyEN9wUcxMrrWYaDyyUqav/7VXleB/rv6qFSlXgcMNGC1WSSbVQBSkWKjaE5GwAODcnbygV4k9UgEPizc3Pg1W5qE1fnEdf6zgK9zYjxC8HoRJOzFYLJ8waelzbJaQ2JbwXttAeschYDF1ia6K0VvI7gu2gFJ4b+FhdGXJS3Kgv7Ud/+zjOHL+Mm1O3sfKAjTKbvx7efYHrlxZx6ugUutxsHuqUJhK2E4T7VsEcxAlWNyzMItF5FyGIuggPtF2w6tgcxPpnHjipGmB+SOtrFuXozFChvL5DzRFg2DRXF+J0LUqlpL4cuxPsqC/pWWuhWgM46h5FHg0DeKWF6nUKHCXEeCPAsWj7YNUOKBk4Wm6YWDvKSVDdKxU4DxZXQAXOqwDOxY0ZON8XwCmBLWojwLkgmRyvtFC9DHB2DyEtqlPUN6E7mhHlz0NLNxIJcAwDsHISbRxEvL4dZg3zdkqQFF6Owj0uuL0A58xV3J97sPmNwP3Wk1VpRiDA6W2eQHXBCHbHdyDUpw6mHfWSo8HWKyVnp0vJQ2JQuF553RkGzdeVU5AkPQ89XPw1FxUEhDtcXoDjnY4wvJjKKC4CnF519UFAGzexun4kavuRqOuSzSanTRZTI5Lpm492IsfWhOaao2AA8wJrxBdZ+y5jINnJcgNIgMNw49NH5+CuHkdGfCsSQ5mB45IgTGb7EFYmG/qQGnJQAA5zR3hItoXZsS+pERX72tFaO4QTw5O4dXVJsV5u2NyRB6zcX8XdW88xffEhBtonUZLdLwcEVmVGB6r2KR7adB6x8aYaBpFqGECKoV/AgGLv9eaedcBKH7iO9ZrFyLTUoCLPjda6bhzpP46pc9N4tPjhzzWl+gvXl3H51B3ZKNUfGMGu2CYYtpYgdGcpIv1Zw16BzQDHJSG/o0PMQlMAzuMHD7GqApzV58xzoHT9BS6Msxr9IPal1iIjthzJYWWIp50guBwWbR1sBjcUVY3aqrgWNsyNQ6+6etSJFlsXCXq8AGcd4njVODZ9M5RFdc73Ajh8HzYjnqoqjUMATnnOILrdp3Hm+HVMT87h7tw9PH/C520DwGHt+8xDnDk6je7GExI4uTfehShWlH5QAoEFQfx3qgoGbS+SNN1IVu9fKbpOcKXqusAgfy4J5Od9bE2h41XqeJCsYRC/sslS9jCEebRM0+KtDLyokKA1jBZEm5FwkddUNxK0HMLYJe+FryWVNzHBtOoQ3NQiJaoeGbFO7ElsRHZSE3JTWrA/vRWFu9tRtKcdB/a2ozizHQcy21C0twWFe5pQuNeNirxm1JW0wF3dhoOdVBKcxf1bswrAYYOi+l4XnkcL1d1VTJ1ZQLdrHHkpVP2VwhzMtpcfNMC5gx4XAc7IBoBTKq9JfBAP77xGcAhQj+jAUqRE0nrUhnbHQRwfOour52Zxf44W2M2XPV4T5qbv4/zoTQx3XICj+DCyLG2I9KtCGMsZAlyKUo7tlgaqrgZg0yoAh0HASXraPgmwCXCUnB8qUC1GBus2SKh/opFhoKwSrkG8thZxVD2I9J9DLyrMqLRqg5W10cx7UAGOEmJciZIsDzpdozhz7BpuXplXbBcvAxxpofKGGK/XiFOVS4AT5dugABzdEGz6Idh0A/IZZKwB1QlWzZsBDmvEn2ysEffuYX6QAOfmIl5QFSc0fP11uicAZw4ex1lU5h5EjrUDrDgO21kGVhonaBgGTRsdAY4CcZJNrUjm0EsssG4kUr2iY1U1LUB1iAqqRXhAFcIDKxGjrUY87d7hdUg1O7A7oQFZNjf2pTVjf0YrCva0ozDTI6so04OirE4UZrYhf48b+3c7UJjpQEWBG/XlbWh19OD40Lg0ej65x5Y7Dnu8dF8JMV6Zf4G7088x2jeDuoIjYhGJ9K1Zs1AxLJjB/RZ9L6z6QVhlONMJCyvSQ9ywhjcgKbIeafH1qC0ZxMFeFeDMreDRg5cADu97K8CVc/fR03oOhZndigKHAEfHBlACHD4/HaKuVjLnGKrvkSBjaaKSNkiPomikyltTIuG4dSW9KsCZem2N+PL8U1wcv4W+lgnUFBxErq0bSSHNCNlaLwAnyr8OcZo6yYnZE+vG/pQ2VO7rxUDLBC6fnMPKotoaJ4yDpOG5PKdPlp7j/u3nMrBxVY4gI96BeEMlYjVU0vC5JBxthDW4DdJ0K4NiFeJo2pHEPJygJiQGuBAfYIfZv0Is/rbwGuSlNqGmsA9d7hNyv1i4SVqtACnvu/LFilLqcuPyQ5w5cgft9edQkDaI6AAqleulRIKgME5nRzw/94Zq2CLt0sLERq6LJ29Je9uDBd5fXwVwHmJk4DJqy4YkeDo1zokYfaWiEgvka0BVGc8d3Lu2SFELLeBUC9LmRICUaLQjOaIaaeZyVBW0YNBzFFfOXcW9ubtYWXqsqBH5sMgX+RAfreLGpbsY9rCZrQspAnDWLVQKkG+WzxLzTzMTWlCQ3o7agj4Jop85vyR/B/8uATiEOGy7ku/Kz3i2tIoHt59h/tojzJy7i4ujt3By6BqOdF3CcOsF9DWeRWfdGbRVnYC79BgcBSOozhlE2e4eFKR0ICehGbujaK3iEKxOrpccFlOpbKOlX+z7G23631t14wU3L3/fBHI2qHH+fwBw+KR5FTjeAOMfrgJHNkDe8OLXKXAkC4fTzfUlGTgEOPQQGmifqkVUcDmCt2bi778agt/7jX/CZ3/6d/HOj/0SPvrWJ7Fly1vYsmXLaxZ/z7v4Z97CW1vexttbPnjLJ6gAACAASURBVIKPbPkY3t7ycYE5b235cXz8rU/hkx/7HD7ziS/jv/78X+IvfscP3/3LBOjf3w82eLB1YiPAkbas7/NN5IUaSsMAWwbURZIuKfNOCMDRliDH5oS9uAs9rUdw6uhF3Lw6jyfLm1UavGiJAucNAIcViJKBo1UATrphEBmGIaTrNwIcQhwvyOlBKi+qmm4IwNF0wqYuZZqptEytARytAnAE4kjegAJz+Pvy5ylvoyLr+2mh+l/NwHkFwEnUtUiDQJLRJRWvmwDORRXgrLB1Sp2M0Jt8nwDnGaYvLAqVL9rbprZLlSEygPWkTYgN7kACN4nMXAgdQLKJh3ROZfpEWktLC1sYYoIZSlqF6OAyxOpLsC+1EY6yfgx4xsUad/PabawsM4x089fDu6uYubSEk0dm0Ok+idJ9PRJ+ZtheJFkZ5iBOfHvl58l3FeIoh20euAlvXrU6BeoQ7LxuWXRUOPBArqwEXRfidd3SohSvZ2BfBfYk1kkL1UjPKUyduw6GGIsCxwtw1EkPbwzMrhhoO4PKvD7kvhxiHMAWFzYmdYHZPVbtugKHwIm1pxYDfcVsyKpdy8DxWqgIcFYIcFjrwFOAetOjAud/DeAcxqlj3x/AcZYfR9HuIaRuAjgdiNP0IFE/AIuBTVdDsBqHEa/zwKxxIDKwFEkRFRsAzlFceh3AWXkO5kGdOTa9AeB4EOpTD9N2ByL93Yhn1pGxCwSIEmgtTW9egNOq+o8JbprV9b0gDlU4XoDTA6vWC3A2wxsF4AwowaHcuLwS4BzB+MiUtFY8uruC54+4k1Ber80A5zbc1SfWAQ5zDoIbwfe6AnD6kRpySGrQE1lFrQKcXJsblXketNcdxPihS7g9dV/56zds7vhzHi2tYuHmc0ydf4j+tkkUZw0gWlONcL8qqUDnJouqL+an2PR9SFMBTqp+ACl6BeJIPhtbYiR3g4cPFeAk1qA8twkt9m4c6RvF1XPXXg1wHj3D4o37uDoxJ8oCZ8kI9sS7YdrO4M8SRAaUiRQ+Tl+BxJAqJIVXI8u6DnBuX7uPpTvLCvR9Tl00tfjKZkwBONPocB5CbioVOOVICi1DnKYEsUHlSNTYYdO7kWbsQJqB9wOCDcIMBtf3IkXXjxRtP1J0fbDpvBCHyhXCEcIZApyNEMcLb/idAIe/R9izUYHDRknuOwgFvQBHUScytLR83yC6m05jYvQ6pi/N4d6du3j+lNfD9c0zXzsBOEdUgLO/H3sT3IjyLwMbj6J8ONHtRIp+WA7ADIJNCu5BskZRkRLcpOm5upFGZZWe2W99sGl7kazpe8Xqh03TD5t2ACk6vvZUYCkrRWABlbcEeUpOH22IBDhWHkppwzK6YAtzi5V5d2wLcpI6UJjRg7LsQVTnH0TdgRE0lB2Bu/IommuOoc1+DB2O4/A4RwUIdDWOoauRvz4OT8MxeFxH0NdyFMOdx3Ck7zjOjU7g5uUpPJq/gxePaD9SrQQ8vHtDjBc3AxwGgcauARwljPXVGTgvW6hYI84DgHph53VdnQwrGTgEOOc/BHAifTiJbYXN2Cfqz3hpmyxBalQVyve1wdPAVrVzmL4wi+V51Wq44RZIS4sCcGYx3HERjuIjyE70INKvFuE7WBHvlowjWub42RT1ilZ9/dUmJwXgsATDC3Co+uD9RAn1l3wHqUhmbhFzbph3Q9l/s4AbNo6yWc2qZVMibVR1SNDVIpHqOFMlSjI71gDODS/AYdPQxq8XwG21haqhygtw3DKQDNleqQCcII+oh+T9SxAlKrJ1gGPVKAocwurUqEpIjXjLUUxOXMP9hQcKwPH+2B8gwBkfmgRrxO+9EeDcRofjDCr2DSPbwuB+O0J3lCBGwtJdisKNChwjYR6Vnyq84X1Cz9eClc6KBZ2h5skRLUiJbkNGnAc5tm4U7u5HWe4QqgsPor50BI1Vh9FsP4rW+uNod47B4zoBT6OyOt3j8DSOot11BG3Og+hoPIjetiMY7h7FsaGTmDx9FXdm7uDZ/RWwuU1yB1XPNw/JVODcUxU4jqIjsIW6ZGgbE+BEHIcIXoBDlbB+GFZDv9xzE41tSAxpkuB9a6QDqXEO1JQMKQBncgmLt1fw6P6zzQqclwBOQWYXUmMbYRGAw6bKesQy+03vQTLVu2KNZ2kAA4w9SpU4AZ+Olq5GxGlrYNYwI6teyiWGOk/g/PhV3JpaBNXca1/qvmx5/pnAiv6WCdQWHEReSg+SQ1oRstWxBnAYipsU4sAeWqFT21Cd1ytW+Cun5/D03qpiyxHwyk3XU6w+foaVe89wb/YpThy8BmfZIaTF1CNWWyn3ahZIKOqbZiRrOtTiFGZwdiOVkJ0qHE0bkgXiuJHA5yCgCtH+JUgRgNMs6vXOxjGcOXYdCzdeBXBe4P78U9y6SsXmAjqd51GYMYiYQCoA62AOVrIp43S1iBOAUyWtUKzR3gRwqNKSzKK1Z06USzenHmNk4Apqyw4id1cHUmMbRCEW4suzAW1vHKx2CMThIFRUyQQ5zAcMpnWYALlelLVp5gpU5rcqAOfsFO7dXlQADvPA+DneBHAWMdRxBiXZnbBFMKvMG2LMBlDmMik5sWx0o2Iq29qEkkwPuhrGcfYoP8PPJX+Pw+k1OxVfO741+HMer0rpwuOFJ5IxOnflHmbOzePK+G1cPH4LZ0du4tTQDYz1TeNo5xQOtV3GQOMFdNdNoL3qJNzFo6jLO4LyPYdQaBtEbmI/Ms19yAjvRWpIN2y081MRKe9ZKlnV/eUrmqdeBjav+u81iPN9nr3//cCdNyhwpIVKxxpxZUldoWxGmeOiSKd/YA/0VTaq1wKcdXDjhTh8wZNN7ZJan2j0ApwyATj/8LUQ/N9f+Cf8p5/7XfzUT/xf+PhH33kNuFGBzlsfBjtvv/UWPvL22/jo216IoyhyfuStn8EnP/5L+PQnvoT/8tmv4s9/ewe+8/VYaN/LRbhPhbR3rAMcpe6cNbteOPOm58/7Zz4McLrB1gxm4HgBTnaSAzVFHnQ2HcKJkfO4fnkOK/dfGlPx86daqBQFzuyaAid0BzcHdYgNoHefskTKx3vw/QOcrnVww4mkTCXXoYwSEsnmh3WAQ4hjCe5QFDmcbHLCyefmhwhwHBtDjF8GOEYCgBbEBbml4nWXqsCpVS1UNy8wQ2BF/PabAM7S/8PeewDHtd1nnpZtyZLt8e6Ux5blMF5rvJ5dr6x1kCXbMyOHKdfafgpWTdnrtfwiHx8DgAYaOQPdyCAfwQCSYAKRc845ZwaASEQgQYAAiJwziMa39f3PvY0GCPI9acdhas2qUw2G98hG33vuOb/zhX3x+D4fXEZdcR/uXKyA55k78DqTrEJjz1IqTqVNLYKNhDeNCOEG3akBQY4NMBmYCcJGjjx5YBDg+DrEwd8pFtekXYzh1F3o63yKKSvAOQznFMBZw6P2SVTk9SDuah1CPFjReRceH2XAz44nvkoBEcx/h6gjuMlWiglV/czcEl09UW297/X7/7hXXWWh4BABRDXMDKt1ZABvEUzGLAQ6J4msPyu+Fm01bwA4FkguDh/49cW9csrBGnG2gbBG3OMkE+55OqGdbBESEOA4NijFB20qtHxJ1WjmKwBnZnwNOsCxvAHgXAkqQOgPrMBpQ3fHpwc4twhwvCrg/mE+XD9glXgZ/O0VwDGzVcvYhGDnFgQ6Vogyi7krIZ5JuBXFfBNaqDrwpGcEq7OrNisG9SVDSt8McGgn4ukn4Q2tU5WSC6Err8wGAhwqS2wBDkGODnGowtGVOLoK5yjAIVyjAkcBHLn22PpiqIeZg80vVoCTqylwshAdlo/CtHY8an2GhckNbC5tYW+T4IGrFLXxPFDgHAU4vH90gMNGLQVwaA9jEC43pGHuGbgWXoDkmAqUZbXgUetTsSiRa9iezvHnG8v7mJ98iWf966gtGkbspXppiWPlrc+ZLJknBOAYCAMUwBGIYwtwtOZGzm2UCJt4cm+IxUVTGhKuFqAwvQptGsDZWHwVzO5v7WBpahWjj2dxr+4JcuJbccmcD7cPY0Wt4X027gDguCQh2D0Zl4JykJtExcITCXxdmdvA9tqWtEkI6NA21Dur+xjsGtcATjrO+SUixDUO/vax8DsTD7N9OkId8xFpJMCp0NQoOrypRTiBhUALboipxuGagHXgVCQRzByFOJ8EcPjfvw7gZCLMkwCnXgBO/8MJaXxbnl/C3kvaCPUdqbK1KoAzjqq8LqTE0ELFJrtEuL6XAJ+TuTDb0fbVgjDaaNjo41CNMAE4VYh0rMQ5Jw4+/2pxzliPSKcGhNO24tCIUPtGhDo0IsyhSUaoQxPUaJafhzs2ItyJQymyIqjIkmYyPtsUmAoxFoHWkFCXAkR6FuGKuRK3z9cj+Vobcu52oiS9D7UFT9BU9gxtVWO4X/ccXU0T6G6ZQG/bhLRrPb4/icEHLzDUOYWhzhcYfPgCAw8nMfBwAsOPxvGsbxzPB55jZvQFVqfnsLvC9qaNA4DD68AG4DzrnRcFTkxEIfwNCfB3SFOnwNYMHLYXai1UTrkIZIixsx5i3I6WigFpZfnvBXB87e4i0icZiddLUJXfiu72AUw8mcHmMSHxAnDG1jDwgABnGFl37uGKuQJeH2XA40Qm/E8XIpj5fKKOI2hrRDgBjliRNBudBg6Dnfg+aaNiDpyCOLTlCzgwMhODKk9mX/FQpAghzOJwokKN6+JyATom+xzVRGVIhcmJACdRAE55zn30doxh8uk8VubWYDkO4DxXChwBOBfKcc4/H2xrUgAnFyY7As8mAZC8xghweO+JZY+NLVQDGVm3rQOcAlQT4PSOYWWedspd4bjytNDmgk+2UJVI8PfhGvEUBDgk4Na5YhSntoEA5/knAJzethmUZfYgIboRl820jKTC9f078PkoBfyeiUVRMrcIb6j4JORU8IZNNgQ4DJUNcWP7VBmuhNTi5sctSLh2H1lx3SjJeIzaoqdoqniG9tpRPGwaw6O2cfR0TKDv/gs8fjiFxw+nMdA5LcG2DM/tfziOvgdj6H84iqHu5xh5THXfFObGF7E+vw7Lxq4CODa5g3wG7S5qGTgNk8i+cw/h7nnwOZ0Gv7M5COAhggAcKpwJVJpl7RdkrIKZ60yXQgVwPA8ATlP1EMaGV7Aws42NYwAO71VdgXPz4gHAYc6g99ksKSYwEyIS4FDt41gNs0OFwBsz192yGS6FycBsF2anEeCodtDXAhxeJPvAxsJLDD+aRl1xP9JuteB6eA1C3Yrh9n423E9kinqfSsIQ10xcDMjHzcgSpN6oQVNpH571agBHKwhQz6AdWDZ3sbX0EkuTu3jQ+AyZsc0I9eIBZqLkb/nbZSLQjuqbYsnSpAPgnBwcVyPSkfN0BSIMZQh3KEaYfQGC7NgClwLfM3EI90jD9YhCpNysgQI441iYPJoxty/tsWsLu5h9vo2hriVU5Q3i9oUGafTyteO/JV0yCP0dUxHgxKy5HxTgbKO1fhRpca249nG5QDcfxxS4nkyQw12qbcS6SfWzrF+5B6Eah9CccQEsE8lCmGcaIv2SkHSjBA1l9zAiAGcJW6IOOwJwttistYiGsl7EXi5HqEeGKAmZgckacQE4BgIc1a4VwQZQvxzJPstNaEFb1RBG+xYxN7Yh7Zy81kWxaXvApcEitrNZ1l5iZ3EXGzPbWJncwtLYFuZHtjE7vI2pgU1M9G5g7NEant5fwUDbInoaZvGgagotReOoyXqGksQhZFzvRmxkBy77NSDSrVoAjsBGtrMy28tAiPPDq3AIdY5CnDftvf/H+b1PADiUwusbt39UgKMHC2kgR0mRbe1T/FpZqCKYbSCjVrIG+DWbPsJcyiW5npVpTKj3sU+Ew3tX8Ld/5Yn/8rv/gF//0h/j5//NV/D5z/6CZpGiZYrjx2V85kc+i8985nP40R/9HH6Ur6K2oeLms/jxz/wEPvdjn8dP/NgX8NnP/CR+/Ef+DX7sR/4nfP4zv4x/+7nfFAXOV37pLfzX3z2Jv/+LADh8/yo8TiTKBHQY4PCU+qCR6U0XymsBjqFK0tgPAI6aiJNulqA0pwH3m3sxNjyFrRVaDw7v7ygZZAbOzOg2upo1gONXBLcTKgOHLVQmu0JpNFAAR8/AOV6BIw1UzEFwqESovQZuHCgrZ9iYLcCpFNtUEINyHXgiUAYzVSl2rG4tQ5C9agARiCMnlaSw1Sqbw4nKDtpyCuHPkzn7eAS5JeD6p1TgLL3YwJDWQqUDHM/TDAU8HGJMC9VxAEdCjMsfYlIHOGyr0TZ8fKiKnHDWgucDy6gv7kfspUp42d2FNx8mdqxrLkUAAQ690E58uNYjiEoLRzUOA5xMsE7c1yEeAca7ql0ssQ5NlV3oJ8B5TgUONyw2pyQA2JYz/kQHOL2Ii64DWw5eATj8+zm4sRZpLzNKmIejhc7K/X5wz4dQXn5oHABdfV5Qld5aVTQtWvRfc7Ae0TkbJpckXArOkpa09toejA1MYmFyBburPDbQrk++WoCd5X087ZmRisbEa9W4SoDjkS8NVx4fEuCwmrBESZNF5VGHEMcG9R6MPD0tRAgzVdyzXg9wNrdhBThcwLL8R1PglGZ04koQ/x88PaW1QdWIs4UqiFaKQzXidxEqFqpSVBQQ4Axh/Mns8S1UYqFaRGvNE6kRv3WhHuFeZXA7kQuX9/PhdYoArwomWoysKhxdgZMtwbmhBDgX85GfUv0JGTgWawZOTWE/Um624ULggQLH+7QCOMFWgMP8By40aZ8rA2sbVQMAgY3t0C1Ur4E3VgVOlVLgGFhbqqpLgxxrVW0v7VO6hcqJ+U/MwMlBqEcWInwywArswrRWdLWOHAE4VOC8FOWbLcDJs1HgBIiF6jUAx5FV7akI80hX0uqblSjP4aZwRNrJBMjyGtSuR/K99cU9zI3v4GnvKmoKh3DnIgFOGjxPpcLXFuDwNJBKDdno1yHCiaNWU6Ywn4Iho8wgoIyZTUN3ERWYhvjoAhSkVaO15gGe9T/HxsKrFqr9rV0sT6/h+eAcHjSMIC+pHZeDC0V943mabVgHChyTcyKC3JIQZc5GTiIBzlPMPF/Hytwmdta5eNUmLV7zLwEFcCZQlt2KaxEZiPRNQpBLPHzPxsLnVDwCz6YjxFAguTbKSlQl75PWMKpulAKHm2FdgaMf6tgCHEKcoyBHV9/w16nW4YaUFiRuQt8McBKvNaC6sBePH05ifGQWBDiWl8cpcDbR234Y4PicIcBJhM9HPKTgeuIA4IQIwCGcouqmCueMOrypwznnBgVwGBpLgKONMAEAhDfq10IE7DQg1EBbi2ajo42PDZQuXJQyHFxZp4KNuZKZEe6Ri4uBRbgbVYfs2PuozB5AS/kYHjXNYrhzDWP9W3gxtIvZkT1pcVqasGDlhQVr0xZszFiwObePrQU1Nhf2JWtiY179fHtpD7srL0VBYFnfVPYpAhzWiFMOr52kWlb3sb2wj2e9C6jO7UZMBOuOE8US4W+XD38GrIvqkfYilkQwaJOn+FmHAE5zxWMMdE5gbnxJtaPoSw9N5fE6BY7zu3E4UOBQlVoMNgkS4JzzTUHyjTLUFHWg7/4wXozMYWvpANbpfwUBztwxAIeHUp7HAhwCuSYF5fQcGdqQtAY0sVJRAebEpj5udthMxUGgw0wWtv0w3L1M7PthctjIZiuC2iI5XKPFiq04OsCh1Y0Ap6djFBNWgMN5TfuhPQNnrACnHTculON8ADMdmRWTDJ9TBDhU/B0AHMJjKrxCeUptYHuaLcBJxN3LBagubFeh92ykWyWQ0P5O7fn3eoBTgACpEedGirZapRozswqbbTIOibh1rsQG4Ey9UYHT2zatAZwGsW4QJDizxetkCkx2VOCUicWQWTiEN1TdsM2GtimGrgY5Z0uYd7hXHi4Hl+Pu5SZkxnWiLHsITRXP0dUyhyfd6xgb2MLU0x3MP3+JpRcWrExZsDpjwdqcBcxcoQV3Y1G98udr8xYZG4sWUaBwbny5zqwbi1Yhzjpl/YPSFAhL+1hjC1XjNHJjHyDSswC+Z5jDkguuFxhgbKY13VaBY6yEmTmdLvkSsBzsmYGIgAykxjagqXoQo8PLWJjZwsbRDBwu5XeAkQFlobp5sQIR/rny/zgEcLi3cG6QwwtVGlAFs2Tf8LpUz/cDBc5RC9XYqxYq7S1vLOxKvlR9ST/Sb7cgJoIxACU2AIeWYjaXZeJiYD5unS9F+s06NJf1Y6xvHnsrBw2PMvFYdrG3sYvNhV0sTOzgXv0zpN1ulMYlhrj7nqVFMVsaiXhNc15mE64aNQquUyXJqAfHUoTzwMmee5dU+NF66akATvKNGpTnPpSMnXkCHF1FIs95BXDWF19ifnIXT3tWUVMwjNiLTfCzZzX26wBOmtVCNdg9jamxZaxRgbN35MB8H3gxuqMATnwbrp2vQLhfHnwMtgCnECGSQcgDJ45yCXGnjZSWKh48BbtkSwueDnDqNYCzNEO1FO19au3MZzqvEapjCHDqS3ulwZYZanSliMr/dAUCWC1vKJE5je1UbI+iYp3tWolXa1Ce3YXOpjF5HhB6MePT9nDr4C7QvtLVP3ymcJmxuS+lEJa1fewt72NvcR8v+UyatmBtwoLFZxbMDO5hrGsbA61r6KxZRE3WKDJjHuF6cAPOeZZLBpaZ+70z+Qi0L4LZgQ2O/wpwXuUFbwQ4zC3hKTc3bXxA6IMLLX2x9qaAnR/i95w0C41moxI7zSGoo+Xg2NinRIXjXIVwF9bhMhyWLVTZ8DOkwnjiBt7+rgn/1386jd/5je/i13/xm/jiz3wFX/jsL+DzP/7v8PnP/hy+8Lmfwxd+4ufxkz/xC/ipz38JP/2FX8RPfeFL+KnPfxE/+RM/j5/+/BfxM1/4Ev7tT/4SfvanfgX/8+d/FT/zuV/DT//4l/HvPv8V/PJPfwP/68/+Gb72a/8Nf/F1O/zDX5rh8PfX4XEiSbrsuXhTIcZ6HsIPr8AJNVSBg80WlOn6M22fJ7pB6Yi/XojCjBq01Xfh2cAENvRWE5s7jgBnfYEWi110t00jP6kTUQHF8DjJDBxKjTkJFggg4sUSebSFSkKMdQsVpeU1CDfw9LJSMgREeXNEgaPUHSoDh/BGkvk1eBNoV3oAcbRsHKGl0pT0jwNwLgeXQQGcJKmnZvJ7uHu5ZOAcAJy8QxaqFgE4k7Bs7FgDvjip6QCHKfvjgyuoL3mMu5er4G13Fz52qQJw/FmZrAEcMzeyTkoJo1QJ9WC1slLg5IvnlhtNXwcGWLIePh85ifVoqnyEx11sF5vGzjpzT2xWE4AsRhTAeYGKPB3gZGkAJxN+hB4ERgKONIBjo8SRxqBX4I0Oawhx9K/VXHD4z2tKHsnYYc6ODnBYiZilAZxMAThttgDHtoVKW7zqAIcVjQlXq3E1tASh7nnw/Ihe+UwN4GgKHFER6QCnRh5+zMBimGA4gYBmocpNasT9pmFYFTjHABw28tBCVZrR9QMCnGQJjK4oaEf3vWGxdawdc0LMBfLY0AJaa4aRndAhUu9wr1K4nciGy/sMWC2Cnx2DfWtgdtIhTiMCHcusCpxQz2TcvliA/JQatFQ/wJOeMazMsFnI5gYng5AQ41X0dkzgMMDJgOsHWfDWLFSivKECh41e0uqlA5xSgTgqA0fPvtHhzRvyb2wBDjNwCCxl2IAcQy14DxDImhna58QWqiw5JYrwTUN0eA4K01rQ1foUC5PrmgJHM2NDhe++CeD4OiiAwxwmKnDCXVsEAjMsWQCOZzpiIguQdqsCFbmt6Gl/irnxVWWJ5PdR+15advbBhR1P5p70rKC6YNAG4KQJwAlkrhRDFKWJiRv+OkQaa5XlhgBHTsbfBHAKUZBWg9aah68HONu7WJ5ZA/O1HjY+ExvNlZAieHwUJ/YpH7sE+DHs1ikRAnBckxBlOgpwtjSAo/k+NYBDgDrYNYmy7HZci8hEpC+VAlw8M1Q0Hqz2pB2ELVpSKOBUDYE3NgCHoCLsWICjZ+FQiaOrcWxhji28oWpHgzdS76xbqJjBRTscLVSZoF/fCnA6JzExMoeVheXXApye9glU5j1Cyg1NgfMKwGmWHBEqcEIc1OfEQwvapghw5LOUz7QeEVTgEOAwN0UbzFBRCh7+WgNCHAh46kBLlgTL0jbFQyfnKkS48ICpFKHOBdLsF+GRh4sBhbgWWoL4yzXIT7yP+qIhPGqaxvDDZUwMbGNxXIGazTmL2DVeLu+DQxbFK/sgeOEimTXgMvg1F836r22o4FA26Fg2eeLNMNZNqUMWgMMF/5b6/yiAs4jq3B6xPwQwbNshSwLB/c8yR4MZZwzbVBkxBK8BhmzZUN84V4ncxHYogDMuygXLlg1k4QL/JWvE99DXPosqycBpwWVzqdyTzu/Fw+sjWqi0DBynElH3SB2wHwFOuVjC+h88wdTIHLaXDuxy+sx3COCUHihwbAEOISrDxWlxjHBsQIQOcAS4qc9MZeFQkXj4RJxzlRpU5tD+xs+SeUaVCOPaU9ajjBugvb1YAzish1cAx+yciNiLRwHOKiw7OknR5h5aqF4BOKyetgE49hrAMTYh3Mgg7rrDAMdwGODEagBnqGcUy3Or2Frd+QEBTikCz+oAh8UG+TBL+LYtwGlHV+MTPH/8SQBHU+Bc0QCOQwqc37kDr5MpctgZrmVEUX1D5Q0zb1hLbHLKBAOPw71yEWUqwrWIciReb5QMtNriJ7jfOIWBzmWMD+1gcdKCNcLNhX2x7tDizsH7Qsb6vmxKec1wc8pnP60ifOVBDn9NVAc8qOOgZV5j3/r1JhYSDeB0N84g926nKOn8zmTBXyt0OAA4PCxrVKoYY7kUWZjZROqehSDPNEQEpCE1tg5N1QMYHV5SAGfl1QwcHeCwRvyGAJycIwCnSKzloc5UeDMMnKUB1QoiMYtHK6mQVj8DlSVxNi1U3RjoZjo+QgAAIABJREFUeo7poy1U2hveWNzBcPcU6kv6XgE4VLjJ3sEuTQ6+CHBuny9Fxq06tJQ/VgBn1QbgcO1qeYm9jZei7FmY2BWAk3q7CWa3dHieSYSvXToC7HOUPZEqGwHrGsAx1uK8sQbnjIQ6FYh0or2KroEcBBLgaAqca+GFSLpRjbKcB+jueC5tU3JQY4U4BDgWbCztiVV6pI9K26eIvdQMXx3gaK2wVgWOc7LKwImpQU1xNwYevcDU2BJWFzfeAHDGkMbKbQ3geDukwOVDKnAIQQsR6sxWWNprOTSIw/xJQhbaM11yBOCc86UCpxg6wKGFygpwtDn2MMDpkYy0YDfaODWAc4YAp1ws9JzPqDDk75mNGQj1yMSNyDJkxrZIzhxVUSN9c1h8sY1tPndYA68dXlsVOfoNob9q63jZG/GAgFCHyzeCHd5XK/vY44HDtAXLYxbMDO3hec9LdNUtoCrzKVKvPcCN0HpE+VZI+xz3o4HMOHIokQiH4+xRn+rXNPgjKhzNAfMqDPkheIUuPPlne/2XBnD4jThip6J3/OCbrf2DxVOuLShFgaMATrhrmeTgUGrJqkGP03E49f98jL/9Sy/8+R+exB999W/w1f/w5/i1X/h9/Puf+x386s//Dn71i7+L/+VLv4df+8Wv4cu//Pv4D7/ydXz5l7+GL//S7+HXvvS7+PIv/h5+/Ze/hv/4K1/H//bvv4Hf+MU/wK9/8Q/x5Z/9I/zHL/4Zvvorb+FrX/5v+JPfeh9//U03vP/tcDh+/wY8TyQLwFELOKqEDlpJrO/xDR/8UQWOwBttoRtiKJO6ygCHZPg7xiLKlIrY6DzkJJejiQ01faNYlWCtg00J7zECHAbeLr7YQ/+9eRSndeNKcDm8z/BUOQP+ZxmSl48gXbboRAl5nTUHIIIAx2aEGxTAYQuVNJLIa6WEOlKBw0UTA4tpl6LqxmRfBkIb6zhbYgU4hDv8s28COAyu0xU4187lIONuBaqLWtF774nYRoRG65MJ3/pL4KgCRwc4xvePAhxCDt1CRYCTi+jgHKTeKENLeScmBzWAY/NA5+TE5qT1OQvGh1bRUDIgAMfHnqfjrGFlGBk9p0qB8yrAqYNZAzj0vPrbZ8milsGkAU7xVoDTXGUDcDYIcA7T/vXFfYwPr6GrbRLlub24G10vNZXGD+Lg8REBDtVFuupHB0hURdDqomWWvPJagxDCWxlUEeiDfnsOqqoOcnMOsnBYR14BtmsxvZ8bSzajWC1UAyoDhxkc+oZZXjUFzpNuhuUR4FQhOkQHOOnw0AAOT4MZvkz7VohjHUKcGlVdtbFcQswJcMJeA3BWmYHzaQCOawYCNQWO9+lMOVELcqpAmBvzaUrhb8iE51kqcJIRd60MFQUdeNRBgDMHSnKt70u7FhXAmUdr7RCyE9px60Itwj2L4fpBJpzfy4bnR4XwsyuDifYzpxqY6Zc3NiDQkSHGzMCJxwHAqUVL9UOpjF+ZYQOPzQXPa35LNRf1dkyipvCxjQKHACfzDQBHayFjPg5PcllJKlYp/fV18MbGn6xZ8sSaJ8HYGsRh/hLVOARUAnDK5WTbxHwJF3q80xHhm4ro8CwUpjWhq/XJMQDHIvezFeDcm4UocEwqxJgKHAIcX3sG37JdSwGcYGcqm/KlijfMM13uqdRbDBRtQU/7E8nskAXJIYBjwfoipdWsGLUFOOnwPHUY4LDWlAqVc8YaRHI41SCCkENARzXCrAoc5iLwpDIOUYHpiI8uQkFaLVprOjHSP46NhVdbqPaPApzkDlwJKYbHKXroE6AADucKApwkBL0CcDawMkeAQ/8/N77qNI3zogI4LzSAk4VI3xSxeviejYf3qUQEnM1CsIEV2np5gPaeNICjwA3VN7RPMQNHP9TRbVSEOPrQQY4Oc3TlDf8sn+88HGLOHjfSVELQasQ2PAVwGGqrA5waKnA6X9gAHCqXDuZDvre58U1YAU5MAy7SQnUmES5WBQ6hlAZwOIcIwKGyqFrC+xngL5+hZN8w/4Zgpl5ADQEOwY0MCZLl17a1zpyXqGg8CC6mvTvMtQhhbnkIc8/GZVMh7l6sRlZsi1Tttlc9xeN7s5gc2sL86B5WpyzY4cklF85c9HIDuqLChglbNmct2NAGDw84uGnloNKAmWwb8zvYXNjCzvImXq5uKAWOABwFQg8pcCQDZxnVub24EVEmFosAB7bpFML/bAUC7QmX2VqnGlOYXXUY4LShuaIfA53PMTe+AMsWj4W1HzrAGd9Db/sMqnL7kRLTIiHDhKpGAThZCHRgcK2aX6nuIcCJ9E39ARQ4DDGeQvMxACfgdKGsRRTAqdMADm1UVOIoxZS69pQi9QBol2sB/QRYyt4Q5MQqcqqpKlVWn9jjFLDltcuNl8k+D4H2mRJ2TAXOvxyAs4KtlR2x0vPTkWuAOX4b+xLGetBCRTtMNsxsxGFD4tlSTYFjA3DsUxHgkIRb50pRnGoLcKjA4j15+JBJQozbZlCe2YvE6EZcMpVIFovzu7HwPslmuAKwyS7cmZtYtmAylJeby2yYXTIR7JaJy8HFiLtSJ4cgFXl9aKsdRd+DOYwNrmNm7CVWZy0yr9kCG37Ng6FNKm7m90WBo1Q3VN7sY3XOgpXZPazM7mJ1bhdr8zuiot1eIWSwHLLM65c0n0G8P1kj/qhhBjmxXYj0KAbzb/zP5iPAnhYYKnD4TKV1niHGNXL/mJ0LYXbJgdk9E0GeqYgISEVqbC2aqh9jdHgRCzOb2OBnZNtCpStwHq+gqqgPNy6WI9wv+xiAU4UQ5yaEMEdPCgNqDkMcrssMvHdVjfjFIK6h69FY0YuBrglMj64erhHX3vDG4jaGe6ZQX9qHjDtU4FQjzF23UGXAi4e/Z1MR7JKBiwF5ck1QgdNynAKHzyA2eW1asLloweLkHu43PEfanVYEeWTC2472Lt47eVKmEMJMNUMVIgx1kr15zokApxbnnQnamVVGFQ6tg7kCcHxOxyHMPRVXwwuQFFON0pz76Gobw8zzTQUU+Bjk42JfAZz1JQsWpiwY6d9ATdFT3LnUDB/7LKXA0QGOFmBscklBuHc6EmOqpd3qceckJkcXsLKwDsvREGMLMPlsGy11o0iNa8NVAhzfPHg7pMLlw0R4n+F7pJW2WoM4VQJxRInDJlDmaTkWIdSFB5KsL09EUkzxgYVKQowZ6aAe6/q9zLayiWEqcHpw+2IpCHBMAnCK4XemUgEcXpssH2HMAZtlqW5zycRFUxFuR1Ui/XYjyrIforV6EP0PxjE6MC9lD0tTW9hcsMj9JAcFtFfR4a6dsfHZK4NL4KPDBuK8XNzHNp9bVMc9t2CsZweP6udRmzuCnNhHiD3fhI+9S+FPWxzDuWkNFXXkcSoctmVzzcBh8/uENkfGvwIcWWj9IypwdKAhSpwDpcrrYIYsHOWkiwCHFgfaqBhmXCheWT+HNLCJ5+TfRuDv3vLGt//UDn/2jbfxja98B1/7zbfwtf/jLfw+x1e+ha9/9dv4xv/5HfzBb39XXr/x1W/j6195C9/4rW/hD776bfzRb38H/+m3v4s//K3v4g9+86/x9d/4Hv7of/9bfPMrb+PPfvsEvvUHBvzdn/vj5PcuwPj2bXidSBUYQnktGyn0oS+IBeLo7/eY1+PfM1U8tLiUw0wvK+umHWPxcWCypOinxxeirrwVA91P5bRFbqI9fWdCCr6PrZV9rMxYMNi5hPKsPlwPrxJrUoA9cx1ypInKbM/avnLZjFhPlSW4ksDmyJBQS8rP9VGlAht58+jwRlPcBJwtwdEhKhye8DEThxsv3oQ2ChxaV3QLlTQP2MfD7JYAApz0fyqAU0GAwxrWXTVh8SGgyY+5uObCYGJ4HQ2lA4i7Ug0CHF/7NMnkYGaLDnC4gVUWJg2iiKWEYXNU6RDgsJWE6fSJCHRKELVAbmIDCHAGRIEzhZ0N5p7wH3DwY32BNdWr6Gq1BTjZMH4QD4+PsuArAKcBZkcO5pDQyqIAjrK5qI32IZjjUI3gQ4PWPaq/NHCjwTkBdPycWVOuDS72TaxudspAoDFBQoyz4qk26MazxxOYn1iRFhwrfNDI/c7KPgTgFHZLgj43q6FuufA8mS5ZBr5Uj9jzwUbVCNtD6hFKgGM8rMDRAc5FcyZsFTjHAhw2ia1TjWCjwBGAkyEWKgIcf7FQEeDUw2wshZ8AnDiEeqfYAJwnrwU4uxsWjA3NobVmENkJbbh5oQZhnkVw+SAdzu8x3ydfIFugYyVMzBFyUm1UrwKcQuSn1KGluhNPeiawMsOn58F1wK8IcGbG1tHb8QI1BQNIvdmuWaiOBzjqwaeCrEVmzXwcATg6sNFfmXfDwc01B8GNDbwRBQ6BnspV4muQSMh1iMP2MwIcDgb3EfBlI8g1Q0L6InyTER2egcK0xmMAjoIPXBwcAjipXThPgOOei0MAh6HbNgAn0IkAJ0W85Ncj81ReWF4zetqHMTe+rDze+jTJW3tnTwGc8S081QFOVIN44g8ADgPfOU+WSsCvst0oBYdAHJkvNYDjQIhtC3AyEB9djMK0OrTVdOFZ/wQ2jqkRt2zvWBU4PBHLS+7A5ZBiuEsIIuumWbmahACnJAE4DDJWCpwW3G8cwcxzG4Czx9XVUYAzhbLsDlyLyJbNcpAL2z9YkZwsz4Jgytdt1a4amFLhzLYtVPpJEGGMDnBopbIdOsw5Cm909Q3hjQ5weF1pAMeRmQ2ZoG0i6XoDaop6MSAAZx4rCyuqXesVgLNlBTjJNwhwCq0Ax1ssVLYApx4hVM0YFIiiHY6Dawtaq4Lt+VqLEAcN4OgNQMy5Maq8GzZaSauVXksuoe9l0mrETBXJvHHNRzjb1ryYG1OKnLgWNBT3obtlHKO9i1h4viubQlHWbGgn1pokXk4v19SmcX3GgqWJl1gc38X82DbmRjcxO7qBmdF1zDzjWMPs2Armx5exOLmI1dklbC2tYG99HfvbbC1RINQKcLT/72jvCqpz+3Ajolw21gFU+NoVw/9sJQIlw60CQUZCHBU+HmBgO1M2lAJHBzhjmBufh2XLBkZqAGd+/CV626dRKQCnGZeCSuCrARzPU1yolyHMlflfZdIyxIMaApykmDLUFLaj994QXjydxdaijbpHm/qUAmcVgxrAyb7TgWhzheT60UJlC3AinbgRbECkoRERAnAaJICaajJef7QM83BA2hoNrGOu0AafaQQ6tDlwo1CNUGONtN2FiQqG/z2fj6UwOeTD5JAlNlyTU8o/O8CpKmzHUM8zLM2uYHNlWw7yZB+tZVl8OoDDU3ACHLZsZSPAPs0G4HTYKHCOBzgr1haqPiRGN+GyiUqrNNUM91E6VGNrtazjCXCkccqJLTxZAm/CvDNxK6oSucn30Fg+jEftUxjpX8bs+A54gLWrBa7KuoxTnWbnIMAhvFmZ2ZUclPmJbcxNbGN2fFs29bSZTo+tSmPfzHP+/5YxN7GM1bkN7Kxo6z3Z8GsXG6dQLcR4ZcKCroZZZN/pQoRHCXzP5LwKcJy4NqlHsLFa7h2zcwHMLoQv6Qj2TEZEQDJS71ajqbofo8MLNgBHg9KaYpKb5BEd4ESVaQAnC8pCxcprNoJWI8S5GSFOTWLR5xrP7KAgjnoWV6ooAkMmfB14sJaHjLuNaKzow0DXJKZH10RtcXhFQYX3FoZ7XogtJ+NOM24cBTisTyfAcU5HlH8ubkWWIP1GLZpL+zHaOy8xA7LJ1zn7/r7Uom8t7YvF7UHjBDLudiDEKxc+POQwZMv6MYhzpyPjGBjToMLkGSp/3plDqXBUPpsCOAFnU+F96i5C3FMkTy8xpgol2ffQ2TqKqTHaRzWwwH+HZR8vty3QAc6z/k3UFI1YAY43C0X4b3FMR4AxDYHOqTC7pojVmwCnqpDFIuOYeDaP5YU1WF4emZcswAQBTu0oUu+24eq5SoQR4NinwuUEAU6WZN2EudRIXASVfFYVjqyvuNYqQZhrngI4PgcAh6UHK7PL2JaAbe3T0u5lXpsEOHUlPbgdVSa2NB3g+AvAqUAgD9QZaO1YrNWWK4timGcuLgTm42p4ERKuV0mbZXVRJ9rrBqQ971n/LKafrWJhYgOrDNteeCkqN1GC6jCH32N9EOJowEzAjg5xqBKlglQbBKET/bvob1tGY9EYsm4/RHRQpbQRSrMsW1GlJdAG0MgcrOBNKPeLRyGOtZRFZauqA5UDpnAgCrEViPyP9rW+7tJflUuKh2k/Iq0z/9QWKhuQYQUY4u1Vag5RdDiwzlM1MikrF6XKKrla2ai4ECiRkC3Kw7zPJsL4QQzO/P3HePd7gfibv3DBW3/8Ef7yv5xQ45sn8Jff/BB/9Scn8daffoRv/dkpvPWnJ/FXf/Ih/uqbJ/BXf3wCb/3Jh/j2n56U8a1vfoS3/vNH+Ms/PIVv/ZEdvvOfHfG9b7rg7/6rr6hv7P42Gi7v3IX3h6zAKxCYocObCBcFcgTiOHHhqi6o4y4m/ff0EGPre3Wi15qNMXkINKQgwCkW5wOSEPNxOpJv56CqqAF9nYNYnFmGZZuLNT4BeBqi1cey9nqeWSOrqM4bxK3ztXLKYTIweC8HAWdyYLbjiRVPlqsOTpW1DQktU4Q4YQ4ayOGvW0+d1eKXGUb89+vqG115w/wSAThU4RDqUJUj6hwqdPieDgBOiHO1hKwS4JgcKxBgYL1pBryPATg9955g5vmSTBy2Dx8uUnUFTlPFILLutuFycCk8TqdAKXAyxduuLFSvU+CUo6WiC5NDBDgv1eSk9pNCnjl5bc7vY3J4A41lQ4i7UiMqIalhtc+T0F2TQ6WchCiAY9PMYwNwAqU+kA9iyluTDgBOUgNaNIAzM06As3IotJMLMZ4kjQ2torNlEmVU4FypR7C7AjjuRwGOoR4m/r0OfLjXIsihBkEO1a+MV+FNJYIdOCoQrDeIOZQhiIOKMG2YaO/jcMxHoBMffvGIMqdLzX1L9SOMPB6XBdIhBQ4/tH1WGyuAU1P4CHFXKnEl+CjA4SaYGyPm9lCyToDTJIto+odpoQrRFTjeaaL80QHO9PgaCHC2Nrexp4cY89Y4AnAuBxUi2DUTgczROpsOK8Ax2gKcLHiyGUoADsPD7+HRvadvADh7GB2aRWvNALITW3HzQhXCPAvg8n4qjO+mw+NkLnzt+DClcokWNJ7W1SLQscRGgZMiVcH5KfVyPTzpmcTKDHd4tle8DnA20dsxhZqCQaTeaEdUYDncTzIIMwPep/NgIqAR77V+ckEbHGXWCsTx82SInAI2+qsObl4Db6wA56gqS6uq15rPuMhkgDFD9BhyHeSaJjWZEb6JiA5PQ2FaA7pah7EwuaZZqLgKUCu/TwY4ufCxL0SAqAXqEObaAipwBOAYWAGaJrlSqbdLUZ7bKLlFs+NLBwBHvpX7sOy+xPriDmYJcHpX5PsYe5Hhhhnw/Chds1DpAKcEEU7lOE9Jt1FvLmJbhgLaVOCwoloBnAz428fjYmAm4qNLUJhWj7ZaQs1J8JTz6A8CnKWZVYwNzkvQY15Sh5xCE+B4UoFDgOOYhABjMkzOyQhyS0aUOQc5ibYAZxvba5QKagBHkzarDBwCnHu4FpGLSN80BLmkwPdsErxPpSKAAZK0hxGEyWDWBgdblXTVja680dU3+gKIEOfIsIU58nv8szq80dU3BDi8t3WAUwSTBnAiNIDDKtuBrhdgk48COIQF+s5ALRjnxnWA043DACcBBDgme6ondAWOBnC0WnRlE6uW7KJg+yqYz1YiyK4GwQ51CDFoShsqbwhtaGGRwfp4zkVUcPD6L5M6bCo6qWKgIpgtNZHeObgQkIWka1WozutCX8c4pkdWRTlDCPHKD85PXPSu70sV78rUrsCa8YEljPUvSEDo0+5pPHn0AsMyJjH8aAJPe8fFljc2qIUYz8xjd3UV+9vb2N9VAIffMtpD+PyimmC0l+uBx7gRwWctG1Jy4W9XAv+z1QhkPhfVBEZCHNbeFiLAoCq2DwBOHwY6RzE7PgvLFnOJDhQYvH3nx3dlTqrM7UPyjaMAJ1uC1cPcmhDkXI4Ax2x5hhLgJMaUobqgDT0dg5h8MoOtRRvbkfYNex3A8fkoA14fKIBDRbA6jCLAqUekAwGOrsJpUI1kjvUIcaT9RNmMebhhdqgGbc58hqvnOFUVvEa5DqtTQ+APrw+qWfksLADXUybHDPxwAGcMOcl6Bo6thSoPJquFqvlTW6gIcAa7DwDO3tYByOXG+jDAqUG4FxU4OTDzc7YqcAhwNGuTFeAk49a5MpSkEuA81SxUrwc4/e2zqGCN+NVmqZEPNKTD5d04+JzKFIWPRA24VokCh/c9VfTBblkI887C+cAcpNxqQG3xYzx+OIcXI5tYmbNgb/OY+4bLCeYTUh09vy3PkqlnixgbnMWzgVmMPJ7Fk/5ZPOmbwXDvCwz3TmC4dxxP+sbxtH8czwbGMTu+ILlkshmVDb9+sSk7yPa81kJVP4vs248Q4V4K39O58DtbgAA+y3UFDrMGBeBUIYgNVM55MLtmIcg9DUFeSYgISETq3So0VfdhdHgeCzMb2FjdhkVvNToEcJZRVdSLG1GlCPfLEhvWAcDh+pWwqEUDOA3Wgzozr2PaqdgQaiiCPxVuDiyXyEfG3SY0Sn7VFKZH17FNa/uRHxuLmxjumURDaS8E4ETaKHA+yIAXAc6ZFAQZ0xHll4ubEcVIi6k5ADhsoeJSRZ+mCXW39gUWLU9Z8LB5Cplx9xHqkw9fx0wEMPfIibCwVDUQMTrCnvsNZVE+71yH8y61OOfM/QkD9pn9lAsCHC8CHLcUXAnNFwhRnHUPD1tH8WJ0XaCR/Du4fheAs4/15X1NgUOA8xS3LzbByy4LXszAsQE4plcATid6HzzH+MgcluZXsffy8LzE9zo+soXmQwAn3wbgZMNkKEG4Sy3CdYhDEEErFcOo2ShG+7EGcCK8E5F0XSlwBODMLWOHdkidG2lrWQKccSvAKZX1FQFOoIOC8QF2lQhkO5kcspYigNeDAw+Pc+R+M7lkINg9FRfMObgZVYzk21UoSG9GbUkX7jcOY6BzEiN90xgfmsfMs2UsTqxjbWYbWwsWZVmkYlS3+PIgQrddcRmnHUjIr/Fr+RzU842ZbrNP99DftoSKzMe4LfvSTATRZu90AHAIzxVAJ8zh98tm2KpwBOCo9lxdifP/LwWOnLD/E2fgHAtwFOQgvNEBDjeROsShnUNgBzdY7I93LlONVC4M3OOFSdl7HIzvx8Du+xdw4m9C8A/f8cH3v+2lxne88P3veuMfOP7aB29/z1de+fPvf9cL//BdL7z9115456+98e73vPHOd33wzrd98fZbfnjnW4F479tB+OA7oTj1vfNw+L+vwuXtO/A6kQy/U9mgkoUWsAhjtXUoBQ5VKgcAR4c1tiBH/7VDAEfLIbACHMcUBDrF4pw/FSkpiI/JQFl+DXru92N+agF7Wwws5LG1wqC8YfjA40Z5fHALDSUjiL/chHAPtvfkwcRF25lsycEJceDJsgI4lJPrGxIrwNGVOBrAUQtfBkwryT3fHwObpW1KEvAVnKFkMMKtDue9GnHRtwXnPRsR7kqfMBdGGsBxphRUAzhGqhIUwOGE6m0XB7NbvFWBU1XUip77rwc4iy82MNg1BQKczEMAJ1F8qAwnJMAJduEDsFBrobK1UCmAMzE4dQBwOGlywuS+yApwNgXgxF+pkRBiHeBQMfIqwNEgjjTz8JSPf6YQcupJgGNPZVUiYiILkUuAU92NwUcjmB2fwu7mawDOoA5w+nD3SgOCPXJg/CAB7qey4Sv2nEaYHRthNjTA5FAPs8NRiEOQo2DOYXhDJQ7VNzq80QFOmYRdB78G4gQSMEr9YhwumNKQcbcKzdVdYheh6mHb1kLFBcMPAHBU8DIBTgNCjQcAJ9hZhRiHumch3OuTAQ65Jj/DXU2BU5LRhctBRccAHHqFXw9wygvu41EHAc481ha4UT68AtrZeInRoRm01j5GdmILbl6olHYtlw9SYHwvFR4nc+BrVySZN7zW2eYVZKxBoGOxyImVhSoFt6OKkJ/SiJaqbjzpnsLK9K7tPkn+UkpoZ8a20NsxfQBwAg4Ajo8GcFR4ni3AUeoqaYqz1jceB22OqG7kJEQ7GbE59RAgpFXMK3udAjmqgUoFGNNiF+SaCgY0R/jG40pEKgrS69HVNnQY4FjUZvBNAMffmad3BDjc8JQjyLkOYW4EOJVWgBPqlYprETlIvlmM0px6PGp7jJnnC2ohYf3MCLpfYmNpFww+fNa/qlqoLjbC1y4TXh8xKyxLNWTQw24gwCnDeWOFJulWQbj6fKnqpzWAw1pS+3hEBWYhPrpUYJUAnP4XxwCcfbzc2sbC1DJGHs+go44B2K2IMhfA9cO78DgdD287TYFDgONCgJOiAZxWTYHDTQ4BDuvDuWPXbBO7CpYOdk2jLPs+rkXk4ZxvBoJc0uBrlwzv0+kyDxLohRnrtcGsDR3g2IKbo/BGhzif9HoU3lDBwPE6gJMrChwBOJ1TAnBWF5hfRIBjAwvEQkWAM4nKPAVwokyF8DqTCOf3CHBY/8z31aTmD27aJbeGdjBaYRSg4mY8iODibBXMdtUw29ciyL4OQQ4cBN+1CDZwaPlg3KgJBKWatFjmc9oAArmRd8pGhFcuLgfl4+b5IuQlNaG95jGeD8xibXZbNpqch9QNrKAy7+OtxU3MPV/AaP84+u8/QWfTY7RVP0Jd8X3UFt5DdUG7NDRV5regIq8ZFXlNqMhrRE1xMxrKW9BS3Yq++92YfDqKjYUF7G8dKHDk+UU4tKYadUZ716TiOyaiUg4R2IrIAGO/MzUIsKuWtkQeplAhY6LV2JALNn4KwEloRVMFrW18Rs1gb4tB+/puTV1z8+M76OmYQmVerwCci0El8HFIhdN78fA8RYBTjjC3ZgQ7VyApVt+fAAAgAElEQVTAMUcAToRvChKvl6JSWqgeY2J4GpsLr6oOGTY7P/aqAueTAI5S4RDiNIKZRqGOjQhxbESwYyOCDA3yWfOQw2Rfg0B7DWQ5MFeE1mPmr3Hw+tEH18tUEhPc5cKsAxxjguRRlGfbhhgfycDhh2+tER9DTko7bkRV4HxAAQKcMuAmIcYK4IQ7NSPCqAEcwlXbEONjMnD+PwGcs7S985qm8pUbqUKYedjnkC5BxrfOlaMk9Z4COP3MwFmCZeNVCxXDt/vb51CZ3Y/kay24YuZ1lAGX9+LhcypLrGeRbvUId+W6j4cZOTA509qZh8uhxbh1qQJFmQ9wv2kU40/XsDz7ElvMVrHdwGpwen1hHVPPpjD0aAiP2rpxr+Ehmqo7UFfWiprSNlSXtKOquAOVRW2oKGxBRWEjKosaUVPajIbKVrTW3sPgo2HMskZ8XYV+W6cYsq+NfQjAmbCgs34eWbe7Ee5WJiHTfme4hixRKgexULHggYpaLcDYORcm1wyY3VMQ5JmA8IB4pMRWoqm6B6PDc1iYWf9EgBMTVYow30yY3TJFDex1lsptrl9rEercKhCH1eVsOdXV1gJxHKsQaCiGv0M2fB2ScTGoEBl3W9BQPoiBzhlMj24cD3CWNjDcMyG2nIzYJtx4A8C54JeDG+FFSL1ejabiPjzrmcPukm2NuJrmCMZ4iEA3QGfLNDLjHiDEuwA+Br6nXAQ6FkHsigxmZs6mgdZHxjkwWL5OKXBoo3Lm/opNRXkIsEuD9+k4hLinigInQVfg2FqoOC1pz3rm+hPgzE3uYbhnDZX5w7hxoQEeZ9LhcSYN3vZp8HVMg79TKgKMKfKMDfNOQ8L1SlQWdqL3oQI4ywsEOPqFqL0/AThU4Iwh9W67KHDCfQvg7ZAG1w+T4cO8USpcXeoQ7lyr2TGpJNEADt83AY5LHsLdM3AAcO6DAGd1bgW7az8swOGhuDoYZyNpgIF2xkIEOuVJ8YjZNQMRvlm4GJKLmAsFiLteivS4GhSkN6EyvwN1JQ/QXNWFjvoedDY/Rk/7EAYejmD40Sie9jAA+TnG+sfxYvgFZkdnsTS5gPWZFWwvrsOyuiUh9wJGtbM53k/S6jZlwWjvBppKR5B2oxUX/ItwzrsE4W7l1rZUHeDoEIfKGyvE+VeAI+snUeBw8fZPHmL8BoCjQAY3j7aDm0pl1ZF2J1JLY5k0BIQ4sw6SeQg54GaaHnj3k7dhfP8aHN6Jgv3bH8P+7Quwf0cb70bBgeM99Wr/rvp1h3cvwMDx3gU4vhcFx3cvwvGdS3B8+zKc3o6G8Z1rcH4nBq7v3obnB/HwOZkCv1OZMJ3NR7ADwxoPGrRUGOTx8IbA5lMBHPFcE3Tkw+SYhkCnOET6JSI6Ihl3otNQnFONrvY+zE4u4OUmGwdIbQhw+Ko2rLxhpkd2paI0NaYVH/sxyJeJ/znwO52BQLtcBDsUI5whYYRPBEdWixQn0xqEGVTTSrgtwGE7mNjFlCqKJ6k8kaLlIsylEhHu1fjYuw6XA5sRE3oPsee6cC24HRd8WK/N4ECGJJbLIi7EhScWlTBbAU6BEHEvuziY3OJxNTJbLFT/PQBOmAZweLLI5o0Q5yMAp7wLE2KhemklyrIAPgRwlAInPvo4gEMZNjew6mGu1ytzAcimN6pXmDofSIBmn3EI4OQlNaK1ugdD3c8wNzGDvU1m4PDz1H5YqMCxYGxwRRQ4koFDBc5rAI6JAIcqHBuAQ5mtDm+UGkezSwm00cFNuRXYBHMxx2AxbbDWnrYbgiiBUQYm3bMNLg3+jnH42JSKdAKcqi487X8uMuXtFbWh1N+GAJzVfTzpmYZS4FTgSnARQt1y4HkyzWqhYpZSiJHy9VqEGnWAw1Mu5jPQOpmDUHdaLghwMpCT1Ih7TcMQBc7SgQJnn61Ge6pdWQGcBbwe4LCBRAM4zrRQ6QqcVNy9VgEFcEYE4Kx+AsChOuKWAJw8uHyQrAGcbPjZFUrmjcj0aRs0Vssihqozr7NxKgNHAA4VWTrAIZy1fgflCwE4o9vo7ZhBTcGQjQInDa4fsFWLChzaOpg1oE4xeJLBzaeyw+mVo6xuJKyxHUclrEd+rnmORb0pNhJCWduhQRxpoMqTYMog1xSEeiYiwo8AJwUF6XUawFk9UOAcATi0DPYwA8fGQvUqwKmVDSHnEC5M/Az8e1JwNTwbiTeKUJJVi87WfkyPzb8KcF6+xNbKrgT4jT5eQ13xMO5eIsBRp40K4OSKqibUsVgAzjkrwKEvv8o6Z1KBE6JZqAKtAIcKnAOAMyIAR9/B65/nPnY3tzE3uYwnvdNorRmSxfbHgfkwfhALNwYZn7UFOClycnYxKBe5ia2i2Jl9fgBw9kWBcxzAeYBrEfk455uJYFcCnBQN4BSIIov3mII4RwAOs+qOPLM+/c/53+qlCLp16nUAh41HmVAKnEYcAJwFrM6vYf+lFgKgfdsI+XQFToUV4BRIZpAKzKXStEKBX6cGpbiQzTiBsK4yqhU1hQAcu0qYzlbCxJNLuyoE8pW5MBx25TDZUz2qWl4UiC/S7LCcy7MlmJ4htOf98nDjXAlSblShKv8B+u+PYvHFmgpM5dpf21SIInBtF+vza3jx9AV6O/rRUNaKwrRKZMaVICkmH7cvZuFWFC0lGbh5IR03LqThxsepiPk4GTEXknE3OhXJtzKQGZ+NutJaDHT1Ynl6BpbNYwDOusrWGetbEwVOTLgCOMxv87MFOA4MqK+E2alM5qUAB1Zq5yAmshI5Ca1oLO/B44cjmBGAc/gZxXl2fkIBHIbsJ99owiGAc5oS/gqEu7cg2KUSAU4EsQmI8ElFwrVSyavqau3H+OAUNua3rN8r/U6xbOxhfmxFatWbS4eQfacd0eZy+HyUDq8TGQg4XaAs4ZJRxRKGekQaaKNimLFWES/qKh3gNCDIwEMOBW9M9lViIwuw0/KA+L2gvU5ATi2CCfNkqIIJ1ovz5NjM8F2nFJgIcKJKRO2mWqjmsDKnAxwrOZa5nNae+81jyNUAzseBNgDndB7M9hWS30SAEyHqLyrkaK1nu2mJBI+rGvE02fTFXs5HVWEbBsVCtYzNlS0cUuBQqSIZOCuQDJwbhxU4gYcADrModICTIS1btgBnrH8aS5M6wDkAePycCHAed8yjMvsxkq+1IjqIlrRMuL6XAJ/TWQhyLMU5dwVw2HTGHCQzW42CinDncjUyE1rQWDmIoZ45LDPrRrMZHtw3exLWvr64jrGhMdxveoDSnHJkJuQi9U42Em5kIPZqGm5HZ+DWlUzcvJKFG5cyEHMpFdcvJuHm5WTExaQjJTYbOSmFaK3twLOBMWwvq5wRXsPyQwc4C/ughUoHOGFupfA+lQu/M0qBQ6WwysDRAU6FNHOaCXBcMmB2S0aQZzzC/OOQEluBxkMA5/gMnKePlQInJqrECnB8DZl4E8BRbad1KnvOUG0FOD72ybhoLkB6bDMaywekWv31AGdTLNsNZT3IuNOEmMgqhLkVwfW9LLhrChy/M6kINqbjgm8OYsKKkHK1Co1FvXjW/RqAw4OztX2szRLgzCAj/gGCvfLg5ZAOX0MOAhwLJHeKawlC/XBHhsmzKIAAh+qbGpzTAE4k1dc8MLRPg8+ZOIR66ACnAiXZHXjUPoY5vUZc+xj5ws+UAGdm/CUGulZQljOI6+fq4HoqFW6nUuB5NhU+hlT4OabA3ykZAcZEWUPEX6tEZcFDG4CzBsveYYDDvdb4iC3AqQIBDi1ibidT4HMmV9b9EVaAQ0UfWxj1g32+7zKEueQrgONFBQ5rxAlwnmN1bhW7a7uHASbP8z6VAkcBHCqylBKHc3oJAp0KEEi1jnOGWPxCfdIRGZCGC8HpuBKRiZgLObh9OR/x14uQcrsUmfHlyE2uQmF6LcqyG1CZ34SawhbUl7Shqbwd9+oeoru1BwMPBzD2+Bnmn09ja2EFlo1t1e6mW9oYps8wccK0Z3voapxCUfIj3IyswRVzNc5780BfKW8E4GgHhZKX+oMAHM3xcnSv/enXLYf36P/8/x3XUbbDxkL1zw1w9G+OVYkimSr6ZrICwfYVCNKGWDrk1LgMDL2if57+wWAnPkgLEOSUC7NTtgST+htS4GufAF/7eGn7YeOPr+F1Ix5+BjX8HRMQICMRgY7JMDmmwGRIg9lA/24GzA6ZMNtTdcOaUsKbYnUDHm3UsrmIqChi/aP+HvX3zFf9115R4BhJa3kxc4KjRDcRET7JuBSaipsX01GQUY2HLf1ysry7/hIW1obqAMfmBHZhfA8PGp4rW1EQSWcegoyZ8D2dIgGWQYYChDPhnS0krFCWBbtaeKvgSnViqZ/KqqyEakRQYePC9g0ukhn4V4xQ5xJEepThgm8los21uB3ZiqQrnci61Y+4qPuIDqpDmHsRzMZ8BDlzE16CYJcykVObjeWiwPE3qMlPAI5rnAZwyqEAzvBrLVQHCpyh1ypwDgEc+zwEu+RKiPGV4BykMMS4olMDOLtvADjraCobRHx0NfwcWO+bCn8Hpr/zNJb5MEp6LfXd4rVXJ3gEOEGSaVQMaQhgkLFdKvwNyVLpmpfUjLbqfgx3j2F+Yu4VeTo38GtzexgbXEJn6zjKc7txN7oWwZ5ZMJ6Ih8cplYFjcmyAGspCZeLilP5oGdUSphwkVg+qppRyiuqaw7CmWK7rYIciqSsNZn2joUhqp1VqPpPzS8VCdQjgBKYinadMlZ140keAs4TtZdITm6cpjWEawKkt6kLcFQKcQoS6ZcPzZCo8TmTA9wwDzQ4DHJ6khxgZFFimal6lapSNFanSzpaT1IB7TUOYHl/FytKW1UJFgGPRAM7O2j4GOudRnPHIqsDhposWKi9m4DgogBPKDBwN4HiIheoowFnA8QBHs1DVDiAn6SjASYHHSX7mBfIQNUk+TLmASy5imOPlxcBkzyTcvsgMHM1C1T35egWOFeDYWqgUwPE+nSve62AueghxtBwbLpR09Q1fgx1tAc4RUGNV3egKHu33jwE4h5U4BwBHMg6MGTiswDkMcDaWtrC3edhCRQXhmwEOa3c5d7DqlJaMCivACfHkwi4HCdeLUJRRh4fNjzE1uqAWQdZr0SLAm3W7S9M7eD64hoaSYcRdrpdcK89TKRL4Trsp58gQxyKEO5Ui0pmwm6eBaq7U50vVGshNPsMtmYt1FxcCmYGjLFSttd0YefxCLFuH7wZgZ2MbM+PLGOyeQlPlINLuNOOcXz4c34uF64dx0tjhZ+DCMgVm11QBOJeC8pCbpAOcDazObWPHxkLFRSshAVWYSoGjARw/Apx0+NqlCsAhyGb+RyitQcY6lfXBZ48oVNRC0/Z59em+fnWxcRBerIMcXYFThiCGLIpVmK1y+Ui63oTawn4Mdk7jxcgiVufXsb97GHxZAU7HBCryupB0ox5Rpnx4nk6A8d14eJ7MBjfizIlgAHowLTOamoKWGB1WsXmP86GJrR1nS8X663+mGP5niuB3uhC+pwrgd7oA/mcKEWhXBJO0DxUiwJ5NaDnwPZsF37OcQ5hploaowALEXalCfnKLUt8MzuLlqvWis370BLDr85uYGZ1H3/0hVBU0ISEmBxfMdxDqFQN/YzTcT0dp4wLcz3B8DLcz5+B6OgJuZyLg73weoT4XcSE4GpnxWbjf2I658QnVRCW26gMFqb5wHutbRXVuP66Hl6sgfTvaQUrgd7oG/nbVCHSoEguGAByR3WsAJ6ISOfEK4PRbAc5hBQ7n+fmJXVHgEOAk2QKc9xPgeZp2mUqEe7Qh2LUKAU55Yg8M90lD/NVSqbp/2NyPsYEprM9tvfLcoLV5fmwJgw8m0Fw2gOw7bYg2l8L7o1R4nkhDwOk8ZQlnu5ioiesQYaiXoUKM9SBj/ZnMfDgerNA2xRYuWr6LEWBHdUWhfM2DBJkv+ay0r5Ihh4u0p4r9Qwc4yQh0ircBOM8w8ZQAZwWWHcJHm2vAAslmedA8qgGcchDgBBoz4PZhMnwJcBwUwIl0bkaEcwMinHWAU64BnAKEOmcjzJ0AJwGxl/NQVdiKwZ4RLM0uCcDhNca/lveKWI2sAGcArF7WLVQMEVcAh/OXCrbne2P+YqAEzabh9rlKlKTeR2fjCKwAZ/1wsDgv7lUBOAuoyhlAyvU2RAfRLpwJl/cJcLIVwPGoR7hblbTj+BvSEeSWgWuRpUiLbUJFQTcedUxiemzLGsJsvWnAuXIHK/OrmH4+jwctPchPK0N0xB2EeF+CyT0Kfs4X4O34Mbwco+BpuAhPw2V4OETBzf4cXM+GwdMQjkCPjxEReAXR526hOKsCfQ8GsD6/odZ8+pkZv3W2GTj1c8i6/UhiG6ju4xqFFioFcNjsqAEcUa8VKwuVC9UzKTB7JCDMLx7JdwhwejE2PI/FmQ1s0hpzTIgxAU5lYS9iLigFjsktE76GDHidyYKfPTNwahDq0oJQ5uAYmxDsxDBjllcwc1G1UlGBw2IE2m+jzHlIv9twOAPnOAvV0hae9L6wWqhiIqoQKgAnE+4fpIuFyu9sGoKNGbjgk4vrIUVIulKFhsJejDyaE4umtHnpEIzwRLOuM86hq20KGfEdCPLKgqd9CnwcMuFvyJOMFq5DCDYIKyOM9QJwVEOgDnCqcM6Zf4a5U8ziuYswz2RcDc9FQkwZSnLa0H1vFAtTr1qUeQ+sLzHAexd9DxZRnNmP6PAaOH+YDNeTyfA4w39Lqhz+0Krs75SAYI9kxF2tEIDT9/C5ZOCsLK5peyybK9KagTOGtLvtuHauChF+hfJ5uZ9Mhe/ZPA3g1COcamEeSIqSjgCHbcPcA7LprgBhblSUJyPpehkayh5KjuTq3Dp211QgvfythyxUC6gr6cbtKFqoUiXEONChSPLMlIVKBzhUe7NQgmvnEpiMCuAEGDPgb0yGnzEevk534ON0C77GGzL8nGNgcr+FUO9YRAbE40JQEi6HpuJaZDpuXshC7OVcJFwrQMrNIuQmVqAsuw71JS3obOrGaP8oVqYWYVnfAVWTvI9EiaNn5mxDAvqHO1dQWzAs88Stc024FFCHUB7oiz1Zc2rI4SLVSlUSoSLgizBHs/LTNqUfJFotVDb76k+3XvmXBmyO/nts11Nqb07YqTJwdAWOViHM31BDXWD/1N8AAg0239jaqBTEoTKADUYcOsA5DHGoxAkmHDAqkGNiuOqRQZmq7WB+BwfrC03GdKlaY91akHMmgp2zEGLMQYgxFyHGPIQ4ceQj2LFATkC4wQ0xlKob0KZVywpl9AtJAM4BrLH9nh76s3IzU7lDe5IOcAphdsoCWw7CPFPwsSlFLAgZ8RVoqXmE0aEprM5vYHuNp25c5GrWDt7oe5C2isf3p1GR8wgJ0bW4GlqMcM8s+J5JgP/ZNGm54uaETSKhHNzQyb+Xnz9l5jyR1U5lRRWkZRFJOwMXyFzIFEpWT4R7Ia6YKnD7XD1SrrYjL64HFRlP0VQ0hbL0J8i81YnYC424FlqNS+YqfOxfgUivCoR5VCDYtRImYyUCHAuFXnvaxSHwEMBpQc/9HxTgJMP4vrJQcdIKcy8TaCQKHB3g+ObgSnC2AjjlBDjH1IgfUuCsobFsAPHRVfAz3JXQVEpVCXCUH5oPc4bvMieBAbxsUKLnntJ7/l6Jagjgqa1dGvwdUnA1tAiZsQ2oL+lC372nmB6dwcsN5gvYnNrtAxsLFkw+WUHvvUlUF/Yi4XodQr0yYTxxF+6nMuAj+Sp1MDnWw8R2AhlapbPmj5aayUMZKMrzHmosQYRLGc67VyDKqwrn3csR4UJlGXOSeL0XIIiAw6FIsw6UgKHMAnAMafBzvIuPDwGcMcyOswLxiHpkXwGcp71TqCt+hISrlfL+2drixYX4h7Su8KFXKva6UGfKhRu0xQozYxiumSehhyHu6Qj3TsWl4AzkpjTifvMwZiZWsWoFONvQAc7eDrCzvo+BLgVwLgUVI9g1CwGOmfA5mwGv01nwswE4JudS+BqycAjg5N9Hl1ioCHCOKIu4uFzXAc4gcpJacSuqCuFe+XD9IBnO76VI0LSfXb5YpkxsO5EHajloQyMI9LaLFZXKrah85CXXormyE8PdE1ieZruQzaKBV4a0UG2j796sWH/SbnXgoqkCHh+lw+1Eupz80Acf7EwQpiCO1FdqShsumA7gjQIzStbLh6V6YB48LLUQOXlwquYd9bBUwXGi6pEHqQo25vUvw4kNCPkw21qo/BIQHZGKQt1C9WIVAnC2DszzYqHa2seGKHDmlALHrIUYi4WK1gsCnFIEsd3BrVEgsFLgaIAjOAt3LhUgJ7EKHXW9mHgye9A0wpBfUSruYmdtV5pJJp9soLniCRKuNiDAiUrOFPjZZSDAPhsmhzzVFOHEsF9urNQgYFcLCb2Fj0HmVGSkwNeOrYEZYg0pyW7CvcY+jA1OY/OYeuTN1W1MjCyg5944qot6kRjTgHCfPDi+dxeuJxPgeTZJTgcDjQxYPApwRjA7RoCzpQEcgjCLzP2vApwCnNMBjr0twKGFTilTqE5hxgufQRyycPrUCpyjC42DxcYbAY5jERhiyErrcI88xF+pR0VON7pbn2NsYFbygfZ3Dp988r0tTO7gcec06ssei2qJwc+0m7HxyPMjHeAoq0ywo1JbEORwPmauDSGOAjgVMNECy1YZeyokGeBaqB1KMJ9A2bWV2pcbqAJQmeJnlykB6D52adJG6O+YJu1hqbcaUJn3EI9an2Lq2YI0sRy+exVYmxldkjyZxvKHyIwrx+WwZAS4XIOP4So8TkfD+UQ0nD+8ApcPL8PlJMdFOH94AcYPP4bzyfPwcYxCkOdlnDddRfrdbHTUt2P2+YRYW/a3tUU/D3M4fbBKeXkfY32qhep6eKnY6PzO5sDvTIlmoaoBc2DMks/FGtoi0GJlcsrB9YhKZMe3oqGsF/0PnmHm+Sz2jmTgcI4SgHNvChX5fUi62YyLwaXSyOL4XgI8TucigNeSexuCXKrg78iwzyTJrIq9VIKi9Ea0E3T2TmBtdvOVOc+yuYvFiWU87XqBjqph5Md34HpoGfxogzjJLMI8hDiWIoKV7lQJOzEQlXXidQh3rBPrHK9D9Sxm+DrzwKhEpKKUtqFihLoUI9yNowThzFh0LpV1EUNWqVwMtufhIudOpVIJlrUm2wwVwLl9oRilWR3obh/B+JMZLM8tw7Jz2P7HxzqVAp2tz5Gffg+3L1fhgrlQTsQVwOHhYAXCnZoQITXizF4iVOWarAwhPExxyEOIMROhbskI94rDnUs5qCpswVAPiy2WsLWyeTjE2FaBU30Y4DDDKZA18nYEOFozjgCcPAQ6ZCHQkIHb56pQnPoAnQ3PMNo/IwqcPQE4hx9MYqHqWEBF9iCSrrXhSlAFAh0JcNh6lymHChFutQhzZdV2PnjPcA1z+1Il8tPuoaVmCEM9s1iYeqkUk7Y3zv7/y95bf7edtVm+/+T9Yaanu9/qgsTMDDLIzE4cx8wgsMAiS7ZlppgZY2Zmhj1rn69kK6mk3renZ+b2Xbe91nfJqUqVLemrc57zefazN0SU89bqPmbHV9Bq6kPFZy3kYXkI9UxHiEcmQjyyEOyeg2D3jwh2/4Rg988IcvuIgHdZ8PsjFUHuqYgOzEJSVB4+pJTApG7B9NA8rg6vpYOmUyXnVOA4PXA69tFQPIbkIDMi3mlEjRIjgC4VOBLAYa1Hzzm5v8MDRwCcesSHVCM5qgq1JS3oapnG2uIRjvecAMcpyXsDbcLE2DiDgmwrkqM0IMCJ9FYhzF0Npi9yLCbRn6EOjC3vQoJfJ+R+b6mj9J/jZ5cpTzQJp1+asrwD3c2OGPF1jra71JaO1/jm9B5fp3fRZZ2BqqwPBWncW6nAkQBOxDulODMQ4GSEa/ExrhHlmVbYNeNYHN4Vh3KuM1ybnV/8npHt1ycvmBzcg7Z2CMkyDaL86hDtq4KMY3qsLX2tSCJ4Jaz074SUJMfPMKEOLR5a4FTgxBG2eJYJ2FGQTuWVDS3GL5gZXccJAx+++2I9cX70jI2v1xjr30dj/RRyE1vh/2sNAn+vEV6ZkV4EOFThcK+tgjykFhV5BDhjrybG58c/AThOBU6ZBHDSoo2I9lEh9J0C0QLg2JASwM+wdI7i/vp6zhbWILQF4XSEFqnh9aj53Iwu6zhW53bE2e7h0uFnxufF9ZwKnFt64Bw5AI7FAXB4DjEh2p0x4jQxZlJsC5isKmetSU9CGof7GxBHhVhAA2ICFIj2r0GkXzkifEsR7l2MMK8ihHoWIsKnGNH+peIcJg+pQhK9nKJrkRFTj5x4JT4kqkUwQmmOHtWfOaZnQbOuC6M9k9iY38DF3hluj2+F/YQAOCzxWJ48QvJjm7pCn3UdmrIxlGf3C4DDJrfkmyqFpchpqcJztAA40tlTqkvf0qfEuiz+3ptIwlnLinRrR8L1N2dt59nc5dH1XP6f6/vv6yoXBY5UfDvmu0XE538egENJ+usolRe/lwBOog+7xrycxptOJY4ZCf40xTIKRQ67v6ID7KcTJomxNExzvfjPHVc8N2LG3YpLjwQCG/9GJPobkORvRJK/CUl+vMxIZPQuLwFvCD2ahQeOtMG+gZqf3TCuN4f0dxw01imTcnjgSAock4hZjPNTICG4DimRtUiPrUFVgRl20xDmxtawv8ku5QUeGeso2i2Os/8LcHvygvW5Ewy2fYWpfgiVH+3IitEi0r0c0R41oNxf7kMjS6osqLzg2AxfW35o2l47lclcfBwARzq08L3gSA2ltuz2q5ARYUBJehsaSgbRrJ5BX9M6JnuOsTJ+j8meE/Q2bcCqmoGmfATVn/pRmNaJnDg7MqLsSA6zi6Iuxs8k6HWYZzlig8qRl9YARTkVOH8BcB6Bk50b0WnuaXZV4BDgVCHcnXDOgORQAhyzMGdkYSoUOK8Apwl9ttG/D3C+XqDHNifSkzcgumMAACAASURBVKJ9yhDNiHfvBsR4c4yPhSBNmqmcYodZAjj0cOH3kkqBY1Ts4PIAQK8MBT7EG1Fb0AZrwxeMdM9hc2kb9+fsbn67yd5RBrp2icWJPXRZZ1Fb1AmmNvj/VoqQ9wpEeBgh825DrE874rihv17tIhmIXSJp1IW/I8Ebo6PN4v5ODTIjO7IZ+XEdKEnuRX5sO7IjCHEMSPLj/dEo1AWMAY3jIUdEaDYhxlsP2U8Azv6GBHBcZcl8Sg8Xz8IgrdMygZqCVnxOMSM1TCtMVcP/YJGgF7Ja+iO9ApzAXhHRGe9nEZ9hAlcCHMZSf0hSQ1/fjZH+JexvX+Li7Ba3t3d4fL7DMx6EAudHAEfuADgR3wCcFiQFd+EN4FQiMYIKHBusjUMY/7KM9SUHwPmuULi7esYaU6jaF6GrGUBJth0pEY0I+q0OAf9WhzAR9U6Aw1hHJnBYRNoV1ySmJ0V4liApvBLF2Troau3oaRnG4sQGzvZoYvHtDyPAOdy4x9zwIToty1CWDiNX3oIwNxVC/lA5AI4RCQEEYVYkBXC80ekfRsWF8/oZrOHh/UeXA+Y4VDjOrocUqfyWTCXSqcSoJAEOTSpZqNciVYyBKmBUdmF8YAnHOxe4OaPc3+l29yKWMBYoLPqmhg7RWD+OzHiLlEIVoEWkNwEOR/dYKLciKagT8gCrUODIfOohD64X6peCdC0UJVb0tIxjbX5PFJJM6HsRPjHsft7j4YpjLI/YX7vFgH0FdYU9IjGL0cf0VWNqH6EMD/QiLeL1dZNePzE+KpR1NGo3C+AT7VWDSM8SMdpXV2xDq2EA4wMLoiMvPKG+fStxeXKL1YUDDHUvw9IwhrKP7UgK1wvfkOA/qhHuUScOOrEBCsQHKd5GqIQCxwlwbsRoAXij/wTgfEo1IC2afkRKkQwU4a6EzJOeaFwLCJwp72bizv8KwPm+yJDgzdsIFYtWVxNjqrrYAKARMJUtesi8GkQaXWlWG4z1w/jStoSlyW0cbdOon5Xf2wvHQvB07xHLM8cY7FyFoX4Yn9NtiPKpRcCvVQh7x3QlHqy6HRcBThcIcqjIYaodzdFFEpGPDXHeVBWaIHeYEacEG5EZacOH2DZ8knciV0aJt00c7BMCqKLjyLYSTngT5V0vDuCf06xi1KiHcdujmzikefats63/9vtfHj5geWYXffYp6Os6UZyjR2J4OULcqLwpQOj7EoS+r5Aut3KEupci1K0YIW6FCH7/GSFu+ZAFFCA5sgg5iaVoqDJiqGsYBxvb4MH6TwDHMUK1NnOMVt0k8pPNiPKsQZSHBtEejJ6lB04b4ugJ5AJwohmVTYCTQoAzIA53syNrPwY4YoTqEVNDe2gxzKK2uA/ZCU0I91bA55cqBHP0hM254H7EBdgh821EhGctEsMUKMoyQVvdAcKspYl1nO9dffN+8w8vt/c42znD2swexrpWYK4fRnEazTqViHJTiljvJD82Ipx+hO1I9etEih/9YyRVmUiQ8uWos+T3QnAj9yWsMyA1xISsqCbkxTWLdKucaBsyw61IDWpCMtdQXypV2Uh0+sQYkOCvRTybgL70s6tAcaYJFvWASNjbWNrD2cEpnu//rFQ52rnHxOAmzJpRlOe3IyfBjLhANYJ/r0GkG/2bbCLCPkUYafPgx7qc/pAWyL0MiPfUIsFXicTAaiSFlqE0R4NWQy8Wp75KAOeC94D0gRFqPCfAWTtHv30OtYX2VwWOBHDogeMKcMyI92lErDdNWNUoTrfDXD+KUQKcmX0B0p6uvgNTjhGqmcFj2BrmRY2XG0+zajU41hj2jkDciKSAFiQGNoFjRrH+CqRFN6DqcxuaG8fFGMz64inOD58EhH77xEgNyYPdM8xOrKKrZRSqqmbkJtchzDMXvr+mI+CPHAS75SPMoxhhnqWgijvCqxphHmUIcS9A0PsPCPXMRWxwPlJjivApvQoWTTtmR5bEyJ5QCjgBzrNkunp7+IKT9WeMtu9CXTyC5GCj8PJhkynGi+sWFVqs92gs3CGSVRP8+dwaER/I8TAF4kNqkMR46OJWdDVPY2X+EEe717hh+pVTrcKDOXtdty9YFilUsyjMaUZKtBZxwQ2iqRnqxiZdI2IYBCJqOwe4cdZ5TgXOK8BRiaTUbHkDlOV2dDePYX58FXvrJ5I3oeuLC+Dm7BHL0/voIhAvY31ud4xQqYQCJ/I9PdNUSPBTIzWkAdlRWhQmGWGtH8ZM/yYudh5FEpVQfj2xgyzBHL6ut2cvmBnZg0E5jMwEPWKDFYgJUEHm2yCM1Bl3LQBOYDdSCTv4eRUqgxak+FLtSqBJDxwd4rzZXClFWlQtirP1aKhqQYd1CPOslQ7+DHCe7oCj3Xssz57hS8cmGipHkRVng98v1Qj4zQlwFBLA8XEAnOAalDsAzuzYJrZXGSP+gxSqJ2CLAKdtDfVlA8hPawUBjsxXLYIQoj0aBSSm+f0bwKEK1bEfMvyA574AM1JCdEiNUKLmcyu6rJNYnd8TjRmRvObcQlzuEwngTKAk2wyOqMf6EeAYEe1uEwAnjqpBNo0ZhiMCJVhzGiEPMEAe2IiEYJ0w2o4NVEAWQCVOFSJ9KxHuXQGev8K9KhAhJleqEe3nCFEIrBP1VWIo6zmFiFvPiKGNAVXPdagpbESLvhsT/dPYXNwS68TdyZOkwHHAG97zbCZszt1gqG0bxpppVH0YRE5Mu0hEjHIzCSUsQ3H+PsCRmmeEOJLwwHH+FgnXko0Jz+U8c//sPO78567n8v9c339fW30HcNiRcF6vZFAkR0hP/P/mk3G+mHwzxOUKcQgWCG9eAY5kvkmzXxaC0iXF4XIRTfCnwTG7wDy0m14vHqDEIYrpEew0+Jkh9yf8ka7EADMS/bmg8GoSV7K/FeISShXKWJ2XNB717Q1CQy4XGuhy8/zotRRSutfXW+p8SiaL7Iw7oxZViKdDenCNmKktzNbDqOrFSM881hZ2cbRzjLsLFj3fFro03z1cv8bC2C76muehrejBpyQ9Ij2KEeVeAZlnHeI4EuZN2a50OI9nhDN/fwKcgC6kBHQhmfI/qiH8eUhrRry3BbFelBnrBARi1Hm2TI+6T51oaZjEaMc6vo6eYf/rE24PnnG4+ozVqStM9u2hp2kZpvop1HweQH5KB3Li25EW2YaE4DbE+ptfAU7MK8Cx/m8GOAbRWaSPSkaUxqHAaUKvADjsYHJ209FJ4D7kqsD5eo4e2ywq85oR7VMqkqRk3mrEeBsQ62MRs6auAIdjCez20stF8iAhWWanlxL8BnFIzJIZUJ7bDEaJ97VMYGVmAzenl98VsJJy5XjrBquzh+i3L0BZ1oXUKBX8fytG8Ls6hLs3ItqrFTE+bYgjMArokC5/muu1QU7TXAeNf429DTCK+duMMBPy4+2oyOiD8tMYytMIcajEMSDZX4cEmjXyvfbUI5ZjQALiEODoIPPm5lfxJwUOAc7N6YMoTMThi/WkADhPWJnZRbd1EvVFdhSmWURyS6R7PUSXx5PgtQlJgXYkBVJW2YUkB8CJ87VIXRs/JbiJpEbX42OKGo3KHowOfMXBziUuvwM4PK87Ac6ciwLnzwDHJFRgEsCxIspHg1CPSiQ4AY5+CGOvAOc7ogLg9vIFqwtHDoDzBSU5dqRGGBD8ex0CnQCHByJfo0hf4LpDSWusr0bEREd4FSMpvAJF2Rpoa1rQbRvE4sQaznYJZr+ttnh/MvFlfvQI3dYVqMtH8DHBLgy7Q95xpIOdLQIciyiWkwOtSAmyISWoGSLFL7AFyQG8nOZw9Bt6AzZOBcaPH98gjhNAOwGOFFdOOTkvel0ZHBGxCiSH1zkAjhJGVRcmvhDgXEoA586hHuRBjSNvDoAzPXyIRsWEBHBC9YghwPFheg27+Xz9WpAU1O4CcBSID6xDWrTUIar8ZEK7eQRfp7dxf/GM59snvDyIFpbwVXm8fsDN6RNovjrUuQpVaR+SwpiQoXCk9KglFY7oEpqlaHXnWJo/PcoY1UnljUUcTqK92PWsQoRnEXITVcITqtM6gpnRFeytnwo/gG/eyRfg9PAGi1M76G6Zg7aGqTStSAjRCfVg8LtaRHDUkirRAOW3AKe6DyPdyzjYuMLF4Q3uL2+lG50voHOE6kIaoWrSjOAV4ATTQL0OEsBhQWxxqAYJnd/uA9GQcBQ/P9q3vv1n3xcZ/LOzGeR8dEIcPn4LcGIJcDwbhLdVYWoLGsr70WGewszQGvbXj6URO37keDm6kBcHz9hcvMDU4A5s2knxeYvxk+Jbw94T4PC9cZq6S8buBDo0r30brWKUtE14k3EMOzGQSVJaZEaZkJ9oR3lWH6o/DKI4rRt5Yk6/CckhNDvnOAOVN7WI8qoTxT+9PEqyW2FWjWCkZxmrs3s42T3HC+HTd1/H27cYH1iFUdWH0o9mpMfUI8yzEF7/+gH+vxYj9H0NojxpTN+AKEbdEhZRNeVZA5qdh3uWC5P/9BimutVAV2PFcPcYDtZ38HjJcRCHgoGvFRU4ToAzfYgW7Rjyk4yI9KgWI2AEODICHC8CHGmfiPdrFj4a9MghrMonwKlwApx17G8cSg0jl4WJt93R1iOmh/fRYpxDbUn/K8Dx/lsVgt7pEMXx3cB+xPpTwdqIcE96OjE1jj4drcKweW54BWc73+9/zyJd63L/HFsLB5gZ2EBzAzu3dqGalnkqRSMpyd8s1jehwqFPnyNZjaPfTDElNKTiRhqFo9KqEQlUVgfqkBlpRH6CDeVZ7ajIakdhcis+xliREWZBWpAFyf5NYkQrwYeqVUIf/rcaxPsqEetTA5lPBYoyjDAr+zHWt4j1xR2c7p/g+Z5qWucJTLoRjvceMDW8Dat+AtWFnfiYbBFeME6AQ0PhJB+OfHWK2HPxefEhQDIhntDVowHxhNUBlUgMKUFJjhothm4BcM6PTnBHgHP/HcC5esGuE+AUsGn2lkL1NkIlJROyPqDSjIEXcT4NojFnrhvFaOcqVqf3cbx1BgngfLsxne08Y/rLMaxqNrn6kBNvhcxHBf9fKhD6hwK8n6gGoP8NzbHjAuuRGaeBoqwTXc2zWJrex97GlRh5cfHHFi8aMzo2Vw8w0D0DTV07CrK1iA8tg9e/ZuLdf0uHz9/yEPyuFBFetYhiTeLdAJkP7zmGYlQh1KMYET6FSAgrRWZ8JQqy6mHVdQtPp5ujuzcFjkO9LpJH959xvPqE4bYtqIuGHQBHLZQVrIEIAlnvCfhNRZ+/XaheeUiOD9AgPkgpAZzIGtQUtaLTNoXl2QMc7lz9HODMXqDVNIeinBakRusQ7wA4Ie+ViPCgwpN1JtOm2oTnjZzmybx8pUtS4DDNlWtGGbLlSijLW9DdPIz5ia/CD+7u/Nv7kS8wIcvyzKFQmEsAh+PJVOCoEEpV73sVYr3UAuAkB6qRHqLCxxgdDJX9GO9axcn6Le6On/AsDKG5xz5Ja8/Ni1D8zI/vw6Idxcc0IxIjVIgLJsChFyQ/Q7RgaEV6UI8AOBzhFZMY3jxL2ZAsLp5LaKxdi2ivEmTIalH+sRGNCjt620axNLOJc45efvf1cPOCvY0bzI0dosu6jPqSQaRHW+D7r9UI+LUGoRyX5tgzG5E+tYj2qUJ8kARwWg1jmBvbegU4L9+ZGPMe3XYCnFICnBakR5sQ49uA8PcqyDyoXLdBNMAd0wySiX87kmiqL4z17UgObBL+pGkRatQUtAmlI5PUOBotAM4PQN/m0qFIjSrOMYvoc6a5ybwMiHKzir1PABz6k/oT4PAczGkOgwA4icFsaBuRGKqHPIQqLxViAhWI9qMfUB3CPWsR5lEnxstC3eoR6l6PMPd6hDMBjP55joseS5HeFYjyKUaU7ydkxlWgrtiIdnMfZobmsbW0j6uDe3EfCG0Bnwen1y9esLN0j/HufdjUC6j9NIwcWRsi3jUi7Hc9ot5zZNkiXjueN6m6kYKBeD6WRqhYe5IRCAWOYANvZ24BbVymYl7P5zzXul5/52z+bY3z/WjT/60/f19buQAcqYNKgNMuIM5/FoDjNDN2BTmv37/KpSR4IsWxsZvnvKSRgESxYUsSsnhRpLFQe7v4zyQ6yUKcZp9vl9ShbhFxb1TCiAhz1xvCIctyfYOd8En43bgAHNe/8+PvXd4gh3SdCxhvWnZ92RWM89Ug1p9xzbWICajExxQt1JUd6G6ZwNzYKnZWKc8//xPA4UHv8uAeu8tnmB3eRKt+FJV5ViSFVEEeUIN4fwXifRukjdqr0QFl2H2xia6CZCBLfwTCGyk+lfHDotvsSX8eHRIC1EgOVqAw1QxD9QAGWxexOnGIw9U74eLPApLE9Wz7GduL15gZ3EOHiWlRQyhIb8eHhDakRbVBHmxHjL8JkT5KhHqWIybQqcAhwOn9+QiVUODcfqfAsSDU7XsFTpNQ4MTSc8RLJw4L6QLgNKCusAl9zVTgOAAOPQRYd7sCHMcIU49tBpV57PaWINq7GjJvFWK8G0XBS28TKWXLLjxbBMAJ6Bb+EjwcCfjlQ/hlgMyThww1MiL1AmLUFbaI1JGFsRVcHpzj5UmaYXfWyCzCL/bvsbN8hrHeFehr+5Et1yHUvRxhHlTgNCLSsxky71bE0tuBnjEBErxh4hHllGJsh/cUR6MC2XU0ICPciLw4K6qy+6AvnUSrcgWGsmnUf/iCkqQ25MXYkB3ehNQgExJJ8H0kL4gYzybhBUEFjjAxjlVAUcZCYRSLU+z0HOH6hEqHN0UYnwsloRsLB8IjQl/dg/LcFmTHEKopEOnGLh0VOBaRLJQY0IaEgE4kBPQIaT/jEKO81JD5KZAYpkRmrAoFmTqYNf2YGFoR8ZxXF/e4u6MC5xZPuBfBPD8HOA2gAidMjFD9HOCUfbKhST8kIJGkwPkBwLkgwDlGf/sSdLWDKMlpEwAn5Pc6BP1ah7D3anHfxfkR4NBvh35QJhHrGO1TjQivIiSFl6E4uwG6mmZ0N38RAOd87wcAh10lV4BTNoIPCa0Ic1ch+A+FmJeXEcr6cazUjMRAjgQQ4vBqFmbjNByXAI50aE+mSaY46PCw43K5jNS8AZ23TdQJoJ3whve5uNc5XunPgoEKHKUAOGmyGuSlqWBUdf8E4EjjP893L7g+fcErwJE3QS4Ajs4F4PD1syEpqA3ygCZJgeOrQFxAnfBGyopViA5ds+4L5kbXcXV8LxIdnu/uGNMnko2ebx8E2Dnfe8Jk/xYMdcPIjTciOUwDeWADYnxYYFJhR1Nops5QikzfFh5CJFmySPzwMoFqBZpR872M9itFfpoWurpODHRM4ev0Fo62L4WR6Gt9ybMPx052rzE9sokWwwRqi7uQl2JDfJAOAb/WCWVdJJVA/mrJFDNIKSlw4mli7AA465cSwGFHnO1GVpVctx4k6EsPHAKcPKcCJ1iJKCq+3Hmgos8FVQicJed+Q6DH+8BZKHFv+nuFisv+JVSknM+Wrrd6whXi8HvePw4FDo3d+drxUOqnQV5iE+oKOtHEaNieJTH+JowcWfw5CkA+N47Y7a/f4ev0KTqbFlFd0I3EUA1YcEa4E5DbEOvdiVhvJvLR1J0Qh4qcHiSIi94R9EChn5cBSUEGpIcbkC1rRGFKM2o/9aGxcgpN9QvQlk2gJm8QxeldYiwkPdIIeaBajNtxLeIlD1Gj7IMdVu0YJr+sY2PxAGdcx4UHyrcH3f2NGwx2foWyohMfUxsRH1oL/9+L8P6/f4IvD7vv2Jlm/CuBNSOB6ZOlR5RXAyI8JZhDgJ0Vp0JBhhqGejtGe6dwsLGLp8sbvBBUOtZdJ8C5P30Wh+8W7Qg+JTUiwqMCke70HKPs3qnAeQP9wkfDk0o3HfJTWqGp+IJu6yxmRzawv3GExxuXwxJB0RNwuPWIKReAk5XQhDAvBTz/tRKBf2gR4dWMuIA+xPjZEeVjQLgnm1IKfEyiDN8Ki7obE32LONo4e1MniA/MI17ub3F7dInD1RMsU4VqmkFdfpdQb3LMXKimA0xi7CmZZvTiHnYob0TRz7jZJjEGHMPn5akVvokpwXpkRDSiIMmK2rxOMZplrBqGurAf1TndKE5uR35cG3Ii7UgPakFKgFWMPFChTYAjQiYcAKcw3Qijok801lbnt3Cyd4SnO6ppv4V4pwePmJvYR1vTLJQV/fic0YyEEI1Q4ES81yDGg0poRpZLseXioMIQAY7Q8DProUaCbz2Sg6uRFlmByk9atFv6sDy7jMuTU9xf3eLl4c8AZ2/VocApaPsJwOF4mFWoc5nWSWUco9KL09tgqhvDSIcT4Jzj6frPagcCnKkvDAqYQ8XHPmTFNgmQ4Pu3ctHIYCqPaHT5cYyjAXFBtaBCRF3Vhf72eeEPc7RDdYqj9nldLAEmOK8s7aGjZRzVxVZkJ6kQ6VeK9/89B7//P7nw/pdihLzj4V4rIpM5TsL0HZm3AZE0pvegB2Y5kiKqkZOoQOkHLVoaB7Awto6bowcXgMMmwgueL55xvf+Eo9VHDNs3oCocRFKQXjSZot21widJ8gzieaNdNDuTRL0iBS0QUDHphz44SZG1qCmyo9M2g5W5Ixzt3ODmwmW83AGl6f22/ApwWpESzTSrBpGUFPyuHuFuhLp8Xs0iMY6whiBHeN/Q/0Z44FCFTTW2ApHepciRK6CqsKG75QvmJxawt34Aer99/yUBnGN0WxegLhtAYVqb8KwM+kUpAE6Umxpx3g1I8GtAkr8KKYFKZEU0QFPSg6HWJewtXeBy7xYPNITmmvdE813J/4QG2suzR2izTKH0UzOyEvRIjtSAPiwMDeAYFc9gqYFdQqnCfYPWGQleNiR6WZHkzYvm3VrE+9Qi1qcUOfH1qC00w6bvwnDvJNYWtnHJ9Lrvvm4uXrDx9QwjvRuwaqdRkdeN5HDjK8AJe18v7lGZr1IAHHqlfg9wdtaouqaZ/revG9e87eU79NnXUF/SLxQ4BDhUw0S4qRHjSYDDCY1O6fJ1QtkOJInPNv242Ci3ITXUiLRILWoLOoTCfnX+COeHD2L0X6zlfF68TxxKrc2lAwfAMQmz7Fg/NWRejYh8b4XMg2c0pjoR4FhFDcian/VYapgFWTJ6T7YjV25HdnwzMuOsSIs2IyHUgJgAHSK8GODBvVSD4HcaBP/BqwHB73ipEfwHL9aaSgT+XgH/3wrg92sOYoOKRP1qUrVjqJPN6G2c73Itetu7+RwIR/e+3mOy9wCt2iXU5Q8jO7oNYb9rEfJvWkS8MwgVTryYBuHr1yp8VyUj97fa08kExNSI65nbcVYXHoWOJpQT+Dj/Gz66Ci3+fp3z9+qg/1P//s/1FT8fwgOHDs9O9Y306CiuXhUh/6d+qR//f98gCCVPDhWOU43z3Qv+7d/lGJKjSBTwg7P89B9hApCDTrO7JLxASK656EkLH+P/eEkjL5IHgJCSu/gCSCa+LAakgsBZoL65Q/9covX3bwzXN0hS4AjJrDARbRISX456kbAKiBNQi5wEPWqL7WjWD2Kkdw5rC5u4PDp1AJy3YlEU8edPON+/xfbSMUa7l2BRfUFZjkV04bJkjUgOZleEYwKUzNJdnz4ATnd9J9l3AAABwNjB4iHeKGTHuTFmFKZY0FDShW7zJBaGN3C4eoarvQc8nztklIxkPHnB+e49VmeO0Ne6JCTZ+WnNYgFJDm9GjH8zItmV86pHiEcZZIFlwhVdUf6/CnBqHCNU9DhqRHIoAQ7VVxLAkQdqIQBOIgGOBX3NI9hapAkkEwkcc6eOjZXw5Ob4CTtfz9Btm0blJyo0iqUocW962TBFhSovvl+EX61IFIdgxgd2S34LrwCHHXsTRBHppUVqmB55CQaU5ZjRpO4VcX2nOyeSDJ6CKh5auOhRinryhFOOi43toEU/jpIcGxJClIgN0CLahzG6VkR62hDt3YJYxkmKq0Vs+DHeTI0yi82SqW1JQY3IijKLrmNVbjfM1TPoN+9gvu8Go63H6GncRHPdAvTFE6jNHURRYhdyouxIC+EMNmWa0ghVLDuQ/tXIildCUdqMjiaO9n3F9soBro5vX39/PgdenIneXzvDzOAGOkzTaOC8dTJhKjtbBDjs+kmHZSkGkZ3hTsi8bYj00CPMjZ47HCdU42OyDpX5TWg1jWJuYhPnx/e4vXnE/f29BHBe7vD4+CKKv7urF8yNH8OkmgA9cORBGiHhjfBQI4yFEe9pJjYFdyMuwEWBE14PCeAM/hzgcFzxFeB8FQCnNKcdqZFG0Mwu6Nd6hL+nUTLHNtnBd16MdeShvxIRXgVICi8V3dTG+mb0tQ7i69QaLvYpwf/2izPFHKGaFSNUX6EoGRSy4KA/GFleg+DfFcKYmUar9Cli5yUxwASqC4WvA0cCxDiQdGAnnBWjM05wI6ANR2naROQyFYGMseUlQRyuWc41720E1AlvJIDDwoEAR+MAOPVIi65FXqoEcMa/UeCw8OIuz/YMhIHlK8BRTiDLCXACdUIZFenFBAtCMCuSguyQBzCynKocKlXqRUJFWnQ9PqdpYVL2YqL/K463LsDo2Yfra7w8XYsf8vLw5PhcvWBp4gjtpjlUf+rCpyQb0qMMYFFEk2uqIehfJPNiPKsZMT68pMN1tBcPCI0Qv5MfjSsVSAyvQfmnJrE+Tw0tY3v5COcHNGt2rM18cChK9reuMNK/Ar3iC4pyWEyZEBugQ9AfDt8DGomKbq40/sQRMfoaaKt6Mdz1FQcC4FzjgZ4Uzy4A59EV4IwKgJPKEaofAhw2MXgQcQKcb+XHf72Hue5f/N4Ja/7qkaN7rgCnUQAcNipyY02oyG2FrqoH/fYZ4QVweyYVgSxeuR7ykT4Op7vP2F6+xVDnBjRVI8iOt4iEDUKPKE8bIt3aEO1BaXYnYr0IcrodVw/iBNzhCI5ZKAnTwozIkzehLKsFDaX9aGmYw2DLLia7z/CleR8djetoUixCXTyK0uwu5MRZkBKhSHaluAAAIABJREFUQ0JIA+IC2WlXoexjK6zaUUx+WYVzhOZFKDC4iL99ETwNdq1BXTmAT2k2JIRpEPhHFdz/qRR+PDC9Y8pVO2K82xDjQ1UlwXwTIj25xusQ7qFGYogWOXI9irMNMBOI9s/haPMAz9e30t7h2DcEwLl+wf3pI1and9GsGUJeog7hbmWIcCM0p1mzHTIPO+K8qdK0w1WBQ4DzOcUOTeWgSLKZG90E1ZVPNy6HJcfB4mDrEZND+2g2zKK6uA+ZcguCPerg/i8s8jUIpzdDQD9i/NoQ7WNChJdKqBSy4zUoyTFAU9mCwfYp7CwdSCpYAeykLsrLwy2eLm5wuXeN/eULARMaq4bwMaEJaeEGJAU3Cg/EeB8qayxidCHBR1IMS55fHHF2wEJ37jVKJAfrkBtjQlGqDarCXrSoJzDUuoyx9nX0W5bRrllEU80sGgrGUZU5iIK4XuRGtiMjVEqO4Z4lAI5vjQi/IMAx1PdgqHMWy7PrONrdx+PdBR3SHF0M6R64OH7GytwJvnStwaSeQNnHDqRE6BH8ezXCCe/czYj3bIPckzH3LZBznIAGtp4GxHloEeehQgrTm2LU+JyqhqbShoH2UWx+3cDtxQWebhyNE/44fl6oIqaJsVDgzIuR7R8rcJwApwkcrYrzbkScjw7Fae0w1o5hqH0VK1MHONm+AD2Jvv863X7G1MAxzKo5lH/oRWaMBRFe9fD65xIE/lojvOYEWKFpub8SsYFVyJIroa5qx0DnDDaWD3C0e4nrcxfjVscPub97wfLiATpbplFT0obcZB426+H1LyVw+2+l8Psbx5QlhQrVdzLPFjEWyJQ1mvoLY3rvGqREKpFH89vPFrSbR/F1YltSjryqrglwnvF8+YSb/Uccrz5gqHUdis/9kPs3IPS3WkS8U0Pmzrh33mdUCLSLOo8j31Q8xPtzbyLA4Xpbj+QoBepKOtDTMi+CKE7373F3yTktx5NzAThfZy7QYphDQVYLkiK1iAlUCTVEIA1333M/ogcXazlCnBY4IY5Q5Pg4TYyN4Dgxm4y5CXVQVzShp7UfC5Nz2NvYw93Ft6bw/C24nq7MnKDbtvgKcJKCjQj6hemgSkS7qRHvo0GinwZJ9F/yVyIjVA3l5w70WmaxPn2I441z3Bxf4eWOKXISoBJr9j2VKhcY7F6Gtq4fJR9bkJNghDxYjQh6QbLmI6ChVyTHOKnU87JB7mlFAi+vJiR60b6CnqT1SAyqkO77KjYMv2B2bAE7a/u4Ofvz82LgxMLUHjqaZkVAQH5qM+RBOvj9a40Ybw93V4oxP9ZhMl964VS/Ahx64FCBs7P6E4DzSIBzi377GhSlA/ic1oqMaLPwDWPqGs3QBcAhuBEQpwvJfl1I9u1CEv3YfHgR/jUjNdSM9Eg9agu70GWdx+rcsRglZPiGOAM4bxW+rHcv2Px6gI6mMRTnEODUiFolWgCcJgngULFHtTB9EKnEDjAiJcSEj/HtKM8ehKp4BsriadQVTqLm8zhKc4fwIZFwqwXRPmZEepkR7m5B6PsmhL5rEo9hbk2QLgvC3HiZEfJeLbz6CHHigspRlKWHob4DA+0T+Dq1jbPdu9dmuLPmoQJn9+stJnr20KKhAmcI2dF2hP2uQ8ivOkQ6AA7VS0xJdQU4EpSRak8njPn/McChKaVzhMqORF9ezoL8x5Dlr4u5/9h/8y2UcR4Q/vqREEXqArNgpDxbSroQBrK+zsQBghzpktzaJdMvEfXsAm/eII6rqaP0fbIv5zKdMnBncer82T//Hf+x18u1CJYOSYIYstPLUQG6r/tpEeuvRkyAEplxejGjqa/rRk/zuIifPts7Ef4HzoMyH0WxSwuNqxdcHT5gdeYAg+2LsCgGoSjqRFG6DZlRjUgM1EDurxFx7FxMY7wMiBE+J1bE0qxW+DsYJaWJoysT76dFTowZpZktUBV3o003hqm+VewuneD28AFP5y9SV8NRQBBAcKTrYO0WI91r0FUPIi/FioyYJiSEWBDpbUaYpxahHrUIdi/5dwGc051bLI7vgR44DeVf8IGdv7+jwJEATgM+JqpfAc62E+DcS0oA8RpSDSoAzvMrwKnIa0Kkd9ErwIn21gkDMUY30/HdCXCSmB5BgBNA80waGdM4UVIwcQSNahMCtGyZDvnJ7Kp3YKRrFgdrR5IU9bWokJIk+PrdnnJ04AT9rfNQlnYhR94ovFZi/Qk39Ahzp7eAEVFUBdDUjJdQCBiFAoRjHvT4oHHwR7kZFdl26CuG0N+0gZXRO1xuPONg4QnrY4+Y773AYNMumuuXoMwfQ3FKLz7IOpASzOLIIu4HpkzEB9YiR65GfUkL2kxDmBpaFCaOl0d/BjhUhZ3uXmFt9hDjPWuwqsdQnmsXseAy73rEiJE0Jns1QiZ+dwuivWxCYRTqphTRylE+lUiTqVCYaRBjKt2M3J7bxfXFEx7un/Hw+IDH5xsBcR4fnwXAoYnx9yNUnMF2Ahy+TnFU7f0J4NSh7JMVTfovGB1YxNrSIS5pYuz69ROAk+YEOL/RNFZSckjFHQs86YqljJgAx7NAuP6X5mpgUNgx0D6C5ZkNXP5AFkyAc7BxjemhHbSZZkVkb7qsEX6/lsPrX3gIrEDQ7zUCdjElJ8ZbI/zAOCYipac0SebGNCwWce0/BjgSuPkW4nwLcJzrn/NRUt98D3AShQJHAjifHADn2xEqJ8CRvA9cFTgGF4ATG6j9BuDIHQocev0Q0tJvIc6ffmH1QvHzIbFBjCYOdsxh++sRTvdo4MhO2rcFJj/f218vMdK1DpNiDFX5XfiQaBEH8zD3GoS7MyWM94oOEZ6MP6Y0n48aRHiqEcYkMy8FYoMUSI5UikMJfQd6WqT0qZPdKzGqJfwBHOBGHKyegN31S/R3LKK+tBM5iQZhYMyI5dD3WkRSpeerFX4RBI7yIJXk8ROneQU4+z8DOE9OgLOPJs0o8lIM+DPAoSSd3i8OgOPPtIc3BY6QIv8HFDhvjY43VY4EeJwAhyayHCltRLS7RnhuZEYZUJhmRV2hXRidL05s4fKIqpKXNyn2o8Mc8/gFJzvPmBnah1UzhaLMVqREGBAfaBSd6tA/zIh434QoGjtyvNTLLq5Y8dgi9jgZO/ZeCmRFGwS80ZT1oE0/jYnuPWzO8AD3jK3ZJyyN3GGy5xydxg3oKydRmt2JXHkT0qINkIc0IJ4KnI9U4BDgrL0BnIfvPFBegL31Gwx1r6KhegCfM21IitAg2K0SHv9cAv9/q0fYex7S7ALexNLY0ZejKCZEeupAL4xQtzokhKqRm6hD2QcTmjS9mBqcx8n2kdSAuJdAKJcoNnGYBnJ/do+V6W3YNNwfGxD6vhhh72sR/l6HiHc2YX4Zy4PTqweOWYy8OAGOtnIIPbZ5zI1tCoN6QoLXLyfA2bzD5NAOmg1TqC7qQnq8AUHulXj/P4rh+6sSYR4EbN2I8WtFtC/97qgsUyJd1oBPKVopQtcyirWZHTxdcGyKnljixCLUZS+3rCuecLX/hPnhXbRoJ1GWbUdOnElE0HMvkrzltKKG4WijaDb50PxV8nQhuIlwo/9PNdLCNShMbUL9Z45+T2C8cx1bs+c4WLrFxuQVFr+cY6L9GF26TRjLF1CTNYaihF7kRrUgJYjmqyrE+ykQJ0yMq0GA01jbg4H2aSxNr+Jwex8PN38GOITTWysXmBrZRZtlDrXFPUiX6RH8R7kw849iYpd7E+I8eEngRoxOeWoQ56lCnGc9MiPUKEjRoybfDKumGxMDM9jf2MXDzRWE6bcTDjhVxN8AnJ+NUBHgsH75AcCpGcVg2wqWp/YFwPmRt9PJ9jMmB/ZgVk6i7EMHuCeFeVTB458+iz2Jxq5UCcT4asFxR6rJM+MVaKhqx5fOWWyt0B+GAOfx7dDnuMnubp+xvLiHztYp1JbZ8THNgLgQBfx+LYPXv5Qh4Lc6hLtRfWNCtKcZBDdR7lZEuBkQ9l6FkHdVIpGW47UFGY2oL7Gh2zaOlekdSD4dkv8aPcQo232+fMT1/gOO1+4w2LqC2vwuxPrUIfCXcoT+XocoxwFdJGbSbDeQdV672FepfI0jwAlUQx6iQEoUm1td6LMvYuvrBS4OH0VE+jefHyY2Xb9gafoEzY2T+JxpQWKEAjL/GoS4VyDgtwqEvK9HhIeUekrPRSqM4nybhbFxnE+r8LDiIxUtAuD4liA3sQ7qyib02JlSNof9zX8U4NDE2IhAJ8Bxb4Dch4EuOiT6NSDRV4nUIBVqP7ShXTeBheFt7H09xuXBBV5uuZ87b0DpWyZEzY7toK1pCqqqHpGylRguKaPYJKEalMlesUz38uSoIC+jgJZyz0bIvfhzqfavQ2p4JcpydbCoOzDSO4G1hTUc7Zzg/tJRlzmahfwVjvfuMP5lHQblIAozrUiX8bVpgP/f6kSjK9JDLXy+4gMaEOvHZFgHwPnYDOcI1c7qkUOB47LmcW11Apy2N4CTKeNotw5R7j8GOJKv1Y8BTkaUHnWFXQKUr84T4DxCABznxJtjnWWf5s8ARyXW68j3Fsi4bjApjxYgDCMJMiE52ICMCBPKMnuhq5hFh2EHHYZt2PUbaNauobF2HhUfh5AdTysLo1ifCXHC3C3iCicI9WxCpBcvi7iiCHk81Ahxq0Tgu8+ICy5DYZZGxI73to5gcXIDp7tUO781rPiePF68YHvpBmPdu2humEfNfwGcv1A6u7KBtwbZmwKH84YC4tiR6NMqXQKK/MdgzD8GLv53/Iy3w4OkwnGBOA6Ak+gKcqjKEZekvHlV3/A1EK+FE2Q5Ic3PHp0/l49/Bjj/vuf/ozeJ6ijKWc1i5EUYMhOy+KvFZl+YYUFdsR023QCmBpdwtHUiARvRtXL5wDgWMx76znbuRAdluGMZzZox1Bd2Ii/BiNRwGkiqxTw5xwaYCBIj4rF5iKbPiQaS14sScX5KyAMUSApWoiDFAlVJF1q1Yxjr+oqN2UNc799/U2w7qauASU/A5f4zJvt3oK8h8TUjNdqEuEAjwjwMCHFTI9itGkFuJZAFlCIvVQ0qcOymPkwPLeFg41QYYr1ufI5FlKoUApzeV4BjRZgbzWOrEeHO5+VMoTIjzpfjOtIIVUaUBHDqqcBpGcH20pYwSxTyY+dewPrxmuqXZzG+RAVORZ5VAJxI70ppfpbQS0h2OTtO6aJDgUOAE0gPoU7hKSTMXX2kuE4xh+9N3wWdeP2zZAooiprR3zKJ7aV9UXDzYCkc3AnBqMahn8HtC463rjE5sAaLehCluVbkJhiREKpFmLsCYe5KhHuoEOHJGEn6hfCi8SsfKb+nb0MlUiNUKEqX3r92/RQWhk5wuSUpjyhxfDx4wcX6MzYmHjHSeghr3RJqPwyjILEX6eF2yDwtiPWmSS0VFgrkyrWoK25FS+MgxvoXROoOZ3hfuwfO+/Ce4w/3OFy/FGqsHuscFMWdyIhWIy6AaR6SaStnuDkuxUNsBJU37kqEvOdGUSRGVNi5q/hkFoa4Q92zont3f/OC58cXPD0R4FzjgRDn8QmPdxAb4ZwjRlxS4DCFygXgeJkdAKcHcQEckaMHTgUSw2tR/smCJl0/Rgfmsb54gMtj547quBOdAGf+CAPtS9DXDoIKHAIczv4H/6ZAhJsGMfRS8Zc8t/goRqh8GxDlXY1wz0IkRZSj9IMOlKAOdjKJYAtXRy6dbuePuwP2Ny4wNbSOVuM4qgvakBqtgs8vn+Hxz5/g87ciBPxahuA/qhDuVodoLyalaEVcM6XxnDmnsR3HToWr/08UOH8NcFzXP34vrZNOFU4CTQcdChwCHEa+U4FDgGOiYmBgCScODxwx1iQUOG8A5+bkBTPDBzAox5GdYEFCmA4EONE+GkTRj4mmsxyVCGpHYgBjMo3iObKTFh9AiFMrzIyVpe3Cb2l5akf4qVwzDvSBrylvSOmBn62T7QcsjB+ip5nv3xCKs5uFwSoT8UI9qsU8eIi7EqEeCoR5KoRSMNyLJprVCPWsRLhPJRLC65AlV6EgSwujqgejfYvYXz8V6VOPzs8yVSTO6wHYWj5Hp3UGpR+sSIqgIkEj4lUj3KkUpAqoUTIdDNJKACeIJs0aaCp7MNy5hP01HggcChwuFoIMSfCe8/MLEw6Ak0qA0/CdAkcCOPGuACfAAXAIcxzy47/ey77fu6Qi42fwRmq4uAIci/DVinZn40CD1LBGfJSbUJrdBJt2GDND6zjdvYZ4/QhxnCocGrNeSQaZK7MnomtZW0TwZkZKuAHRPloE/0YzSR0i3SX1FJsSQkXlaYHM0wgBb7zpd1SNvIRGKArb0aodwXj3Orbnb3F/RPPcFzydvuBq9xmHK0+Y+3KFDuMalCXDKMxoQ3Z8E5LCJK+Ksg9tLiNU+zg/OBOjP9Lsl/PDy8/uFUZ6V6Cr70dxbhNSY9TCONL7b4UIpAqDMIsHNF8LYv1MiPU3CJDH9TvUnfdiBZLC65GXqkV1gRkthj7MjiyCTRxCjpeHt+6+BHCecHd2h5XpTdg0/fiQSAhUiLD31Qh/34CIdxZEeViFQSrVNxwTFIkmPFQ5FDgC4DQvYH5sCwebp3im8bjzyzGyd7B5jcmhDTQbx1Bd3IaMeA2C3Evx/n98ht9vNWKPjw1oR6x/M2L8aPZJ9ZIKyeEqZMWqUZTZiGbtIBZGN3DLsZYrGmAyyobA9V4oI5wjYRvzp+hrXoCypBsF6U3IlOmE+i7CoxqRHvWI8uRIgVS7xPg0gKCcDYJIT66JZVJSXKwGVR9bYa4bxHDbsoA3j8cvkmfQ4QsuN6VmxlzPFbp1O9AVzaEyY0iMVKXRC8lXJRQ4cX51iPOvRUEaY3W70dfKxJ8VcVi+vybAoVrlbb2hWvNw+wbLVOF0rkBT3S9U1WFupYhwq0aUm0ooPGLd9Yil4sazAfFUK3kxObQOcp8afIzToOqDBY017ehpHsbi5BJO9g7F/fbyxA+J481xATj7684RKgIcBRKDtOB+QA+cOGFi/KbAkczFJQVOUWo7DNWj+GJfxvLkPk53LvFCSPjdF9fQyYFNmFXDKPvQjHQZ4XYp3P/pA/x+KUHou1rxnvD9kPnWQuZfjsy4eqHA+dI5h+2VYxzvXuH67BHPzmBC/hjur9ePWF7cQZd9AvWVduRnGZAUqUCwWxn8fi1F8PtqMV5I3xuOyEcStrvphBdJ6PtqBL8vRZRvGTJiFSjJNUBTbUd/2yTW5nZxf+YwWn3kCZmfHQKcB1wf3ON4/VakntXmt0PmU4mAX4oQ8jtj0Wnqy8RMemW2ivGf5MA2MbJCxSsbrgQ4VEgTnCvLuzHQ/hU7q1e4On7CE2PeXb74OZUAzj6aG0fwOVOPxIhqyALKEeJejIDf6Y1VJcbBYgkj+b75UlHNwzrDMSRFDlU5sT5M+GKqUglyk+rRUNWEXvsAFv8C4NxTQTzrqsAhwDEg8G/1kgLHXYME30YkMdzFVwO5twrJ/ipUZLbCphjBhDNifudMQGSXpya+vT59xvrSCUYHVmBrHEFVYSvSYpSI8CoT6YGRHlQDMuGLCYIa4c0p/Dm91OLel3srkRxUj/SIWuTEVaOuUPJamRtfEJ+zi6PLbxWuDqXm3uaNGGuvKWoX7wPXG/q/BVDp+AeBrwbx/nqwocvmj8y3GvKgalR8tKG1cRRzoxvYWWXT7kIEH7g+r28BzhcUpNuRKWM4hR5RXMM9mRhHqw8akdOcmSNi3eJRKHA4JuktjVClhZmQEU2A04lu2xycI1QPrBtcyk2x/wmAc4iOpvFXBQ7TlekxxbVc5mEWiW9UXBPe0CohLawRH2LNUBYOor1xDbMDV5j9coWp/jOM9xyjx7YBbdUECjI6kBCqR1xQI2R+BkR6MxmY0egS1JH5mRDjb3RcBkT5snlVjhCPT5CHFaMwWwVtrVWM7C1MrOJkl4pnl/PoM/Bw/oKtxWuMdu2gSTmH6o9fkBVlR9gfOoQKBY5RjFD9lwKHfOT7+kqqs10ADoEFIc7/VwEO4cnbAcJVhUM1juTv8/boVN28jU45wI3Du8F5AOGj81Dy7eP3B5c/wxsn0PnrwtcVXn3/JvFnUNIudUPkwmyZmwKd+zVIDpfk00WZjG+zCxXOyswmbk5u8Hj5KEGO7/ZXfvBvj59xsHaF5ckDkSjQbpiCrnIAtfmdKM9tQ1E6Y51tyEtoxgd5C3LjWvEhvkXM/eclWJGfbENxRjPKc1tRk98GY+0XdJqmMN69jJXJXRytnQupNossseg4fgch4b14wfXhM9ZmT9FpmUVFnl34mHDGN9qXnW0DwjwaEOpeIxQ4MYH/HoBzLQEcm6TA+ZhAgFOPgF9qEOHeINQOUoz43wM4m6J78E0BzJGOHwCcCO8iEdMoDNBEocjZZAfA4TgClQ1CgcPIWo7zUYHD+FIaKdLM2BFf6q9HUpAKyaE1KM8xoVX3BQujqzjePMXN0b1QMbGAEQCMEOcOwtdodfYAQ51LoruuLOsVsdUZMUakRBqQHN6IxDC9WIgTQw1IDDUiKcyE1CgDMmMakROvRxmNk6v60GGcxETvOrYXrnF36DCRpVrq7AUPBy/Ym3/CeMchrPXzqModQL68E6khzZL0ngU+u6gBdciMUQtvIJOyD/1tU1ia2n6j76xVHBfh2P3FIy7277C/doXJgQ206MdQnW9HQboFeUkmZMcZkCFrRFqUEamRJqREWJAayRQdrSgKP6XqUVfSiiZNv+h2LkyuY3/rFA+3T3h6fMLT0z2eXqjAucHjwxMebl9we/6C2dEDmJTjjhGqN4DD2fJoAXBYtLwBnDABcDgOY4ZV14ex/lmsL+7h8tjl8MId/Rm4PX8Wm+5A2xL0NU6AY0LoH1K0twA4jCimsbrLFePL8a0ahHsWIzG8AiU5OjTWt6HPPoqv0xu4YKTud1/8PB1tn2N+YhM9rdPQ1fWikGkEoYwiroU8hIcirVAjpIQbhWFdUogJiUFmJAQQINEHSQI44j79hwHOW0KRdBB3XQv/CuCokBKuQLoAOGqYlD2YGPj6U4DDzvvNKQHOPozKMWTLzUgMpemlBHBYoHMckLGtyYGdSAygcoDmjnrE8VDop0R8QB3SIhTinmxq+ILRngUsz2ziaPtYMmBlUgYLCwccvTx8Ft3RmeE9dDcvQl8/hNIPrchONCA91oDkKHqV8NIhLrQBcSEqxIepkBipQrJMjcwEDQpzTKgpaYFe0YVe+yQWpzZxuncpfJ8IXgWE5XriMHZkJ3Z2ZBsm5SByErQiwpQRpDKqz7zYYeVomBHyAEKcbwGO9huAcyWNUP0lwDEiNYrJKJIHTrg7D7ns5nJc0UWB8+8GOD8qMv68n7kWImJ/FCNbkqksDUFlHo1CkchRGI72fko2QF/Th8GORWwuHeN8/w73HMl1NHfFa0mrBY5krl9jenAbbaYZMepTmdeNvEQ7UsObkBLWhJRQC5JDHFewBUlBFqSEWJAZZcaHeDMKUkxQl3SiTT+K8e4lrE0f4mzzGc8XUvFMkP5w/ILL7Wcsjd6gy7SK+iKa8DcjQ2YUaR70cyjKkkyMR3tXRPLZ6b4T4Lhsxs/Aye4t5id20Nk8DU1tr4A4KdFKkQJCP51Yfy3kgWYkBJmQEGxAYogeiaE6JIVpROwyD4SFWWYoy9tg1fVjuHsGa/NbuOIh454Gxg6Aw33jgUrYR9yf32JjcVeoDqoLbMiIUSMt0oCUMPpI0S+NhsDs5nM0lhHijeL+YEre5+89cGgs7fRA4bruADjHHO+d3EaPfRp6RR9K85qRGtMAGQMYaIgf3oKM6D6khNPzzoIYNqQCmCioQmqkCnlJOjTW9GK0axH7K6e4OrjE08W1QzFHkCN50/Hzc7h+hZmhbXRYZgUwr/7cgU+pFmTIdEiN1COZ+19wI5JC9EgK1SI5XIPUiAakRzUgU8bGjQ51hW2wqUcw1LaE5fEDYcQq3nNCu3Np/7vYeMbXwRv0NG5DUzCF8tQ+fJRx3aF/VL1QZUgx4rXITzZAVd4hjNMnBxewvbKNm4vzt/FQx2v1cP2C86MH7G1cY35iH53WWajKe5CfYhKq2BxZE7LCbcgKtyIrwoLsSBNyo4zIizXhs9yEomQzlAVtaFL2o9c2jpkhKju2cHVyIr1WrgDHoYCWPidnrylUAuAE0kCWfhNNoHEyPSdozM5AECq/xQiVtw6vAKdVAjgn25dizPybbYmG7Lt3mBvdQptpDOqKTpTkWERdEBtQA3mgColBOiSHmJEYpEd8YD1i/MuRHlMLRVkrelqnsDq3h/3Nc1ydEEI6Dn18zfgK3j1ha+MIY0NLsBmHoKhoR36GEfKwehGFzHEqHs7jgwgFNYjjvUWIEtAgkvcSQuuQGadCeZ4FVK53WscwM7yKvbUTPDKqWYyrOwqt50c80+D++B6nW3eY7NuAWTGE/GQT0iIImfXCs0SKm7c5vORow8B9qEmMLEsKnAYk8N6OUgsFTr9Q4Jzj4vAej9cup3KWR7y9b16wtnAgvC1rS6zIz9AgJ0GNtGgFksIUSI3QITPagpyYVmREtiAlxCb2coY7OCEOR6s4Ni3zrUO0Xwk+uAKc6Xnsb+7j/vK7+oVDfg6A0/M6QvVngCMnwKGK11cHuXcDkvwaUJRshaa0Bx2GcUz1f8XW0q4I4ZCUc293CEEEzZuX5/cx3PsVVv0Iaovb8CnViFy5ERnRRiSHGIVShKr0lGAdUkO0SA/VIjNch9xoPQqTjaj8YIaqlHClF+NMO1rewPnhCW4vODoq1Zmsj/gZu6Yx89wJWk1TKMxuQmxgLaJ86CWkQvCvGoTT7N6LCcR6MerNtN8YJ8DJs6LVMIL50Q3srh7i6uRCCgl4e0rijLOzfIuBtjUoywYF0M+KYQrZDwCOY4yKEEeYk/t0IMG7DQnerWKsnR44GVFa1BUyjW8Wa/OH4j75GcDZWj4SAKck1wR5MBu68YkFAAAgAElEQVSf9OvTI+I9lZMmkXbLBlp6uBl58TaUprdBUdAHu3YBE92H2J5/wvbCEzbm7rA6w7X0CD22FRjqJ1CZ34uirC58TO5ARqwdqVEtSI6wIiHMBHmoAQlhBiSGGZDE0dWIBiRFckywEnnpCigrLLCbezD+ZQrri9siSct5FnQ28ZmOvDJ1hh7rMtSlQyhKp5eZTQCcsN8kE+MYD8nE+L9GqL6vpX4IcJyqEypwqPyQAMI/DiBcYcT/G9/z93UFLm8qnD9BHIfnjTM61RXYSEackpmj83sJDrkcVIQhnsNIiSMIrtd3Spx/3+v37Rsl/X8lnwApUYuJWY2I9dMhIUiDlHA1MmKUKM7Sw6LqwXjfPPbXDnF5cC06SEL54rLY8M9iFOjoGSebd9iYO8Xs4A6+2L+i3TgLq3oCjTXDUJcOQFE0gLqCAVR/GkDt537UF/ZDWdwHTUW/iCNv0Y6jyzyNkc6vmB/ewub8EY7WL3B9cIfnK4cBsEPBwl+BAOR89xFbC2cY6VqGvroPuQla0Z0J96wRYwiRXkZEeGoQ5kHJaAliAsuEAkf5DyhwznausTS+iz7bIjTlg/gotyH8vQIBv9SK0RV25wXAoVcGCxYvJk8whcqpwDGjr2UY20v/AMBpnkbFJysIcCK8KhHppRCKAJmIEv8O4PhzNrrzO4DTIo0siEMTvV8aJRm2XwXyk7VorOnEcMcM1md3cLp9KQy/xNvoAnHuz59xtHWF1dkjTAxsoLNJKmLL89pQmNmMvNQm5CSYkBXPy4JsuRU5Cc34nN6K0g921BS0w1j/BTRknhlcx9bCCc53HsWBxVmMs0hmMXuy9oypnkNYFTOoyO7Bxzg7koKaEP5OL2baozjD7FOL1Ejei2ZoKjvFbPns8DqONinnfoM34v/9+Iznm2fcnz/h8ugR6/NHGO1ZRrN+FLqaPtSXdKIirxVFWTYUZPB3bkF+aqsYjyj70ILK/BZoqrpgN41itHcRyzPb2F07wtnRJR4osX98wJOIEL+RIM7DI6jMoWydh3OjchS5chMSRIy4pMCh9JoAh2bPScG9iPO3IdqHRm7lSAyvRvknE6y6HowNzGBjcQdX35vlCYDzJDZdJ8Apy2lHeqQrwOFhmUkAHLOTIsTj/ZuEnDzKuxbhniVIDKtEUZYO2ho7upqHsTC5hrN9pph8+0XvFh4OV+a2MdK7gBbjMJQVHficacSnNBMKMmwozm5HcVYnPqe244O8FZmyZqRF2JAc0iTSqSiTl4zb6Xvyj45Q/QjguK67jn1EwHBJgZMQoEFikBPg1CE/VQI4kw6AQ3+T7xU4LMReAY5iFNlyJiZoERdAdYpGpMhxXJHwiQo3joFR8SOSU5hU4UPFUR0Sg+tRmGaCurwDbcZhTAzwoLOL27Mr8DUURTub1fdSCsfJ7gN2Vq4wN7aPXvsSTA2jqCvrRVl+J/Iz7chKtCEjwYy0+EakxGqQkaDBh7RGFOSYUFFog6auGy2mYQx0zmJufB2UXl+fOBJO+DlwdKIeLjhGeIetpRMM2OdQV9SGhJBqBP5WJLwaooVBPL1IpAKdaV7yAB7q6YMjKXD+IwAn0oupdQQ4jB7lvsJ1iCb+jDS1iwPJq3H/3x2hcu733+5frsDm+++/ATi+HCm1CO81ju4mBOqRGq5DVqwW9cUdIo2Kh8Ld1XNcHj6+Am2xljjgG6Pgt7+eY254F/2ty7BppqEqGUJJVhcKU9uRl9iCnFgrsmOakM3DcXQTPsRZUZJhR11+J/SVfeg0TWKqdxmbc/s427rB44k0siXgOceQLyWIszZ1h27LKmoLevEhgT44erD7GenNNKVmNNYNYajzK5ZndnCyS8Woi9qLH+NnCAC8sXyMyeF1dNim0FDdjYIsE1JlKqRENSA5XIfkUAOSwzgeq0dqlA4ZMTrkJjSKA09xjkWsgW2va+AWDreOcc/0Id7Xj44RYHF4fxYpXkwp2109wGjvAkyqAVR+ahNjYAUpvfgYP4CMyC4kcTRW+AfpEeWhRaSHBgQ4+clMBhtAp3laJIPtrR3ikabZLw6zWQfAOTu4EfG3VJ7ZzWPQ1vai7GMLPqY04VNKG/JTevE5ZRg5cV1ICbciLoCjNCrE83AfrESmTANFcTu6LJNYGt/Eweohbo7P8CLG0HjClp4XP6+MYhfv+egeBtqXYdNNQFneK6Dr5wwbchMtSI8xIjPWgKy4RmTL9chLNqAo04KKPBtUpR1o1Y9htGsFazNHOF6/lkIXHKDVuf89Hr5gfewefcYtqD6NoSSJXnBcOxsg86xFjFctmMJJiPMxsRF1Ra2waQcw3DONtcUNXJ06vQklc3G+/xyBuLt8weXJE/YIokZ20G2dg6F2EOqSftR87EVpWhdKUjr+J3vfwdXWuiU5f/LNmp7ufu8GR0w02OScccTGNmByzkiInHPOOQeBkJAEiBxFzap9zsGy773v9ZuOM6tZ6yxhDArnS3vXrl2FjMR2ZCa1IvtNKwo/dKAstRu6rD60V01gtH0BS+ObMK2YcWjex9XJMe7IVnKyb46DorL97oFODcBpRXxAMWJ8KxBJtsbTemHhEMBh0TDW5wcAJ6EDNXnjGBIAx6K0UF26gJLqvD7aOxfHPxZwGqoGUJHbiZyUZnxJrMOnuAYpBCaHt8q5GPuS4uxZSAzJQ0F6Izrqx7E0tY3djQMc718pAI7Lsee8ccJuPcLqogkj/UtorBlFYWY7PiTokRhWjoTQMsQHlyEukFc54gIqRIw+KaQKbyOrRC8v62M99MW96GmewewY2xxtcr5Ke6a0eahB1i1Fja9xeXiNY8sVVqf20Ne0KPFv7odOZLzuwOe4diRHtOFNUBsS/FpBvSUpzFHywIvM5GpE+5KdrAA4xRnd6GtZhHHpQF7z+lSjGCmVLSa5BPl3160Y65tHbVknijMbkfu5Hhnv65D2ug5f37TIms15P4DU+G68C2sVMFpx2SWI04QIT1q3V38DcOKLUZHfiL72QSzPkq1CAOe3+kXfATjZFDHWAJwiVQOnCtGeNWJkEeOlQ7RHlTBxPkbUIvd9C3S53ehtnMDS5DoOzHQOpPYJ76cyiFyzZFbZzafYWLJiYmAd7fXT0BUOoji9B5nv2vExpgnJkQSoDOJq+z68GilRFBivRdbrepR/JWjZj56GMUwPLGBrcQuHe3ZcnZzils57KuhH8OZ4nyy3K0yPmKAvGca7GBp+ZIm5CQ0lAh7qhZnJVv0oH70LgJOHGL98MXtpI4AzYQT3u7PDYwgtzGVOcswEwOnYQln2CNLedOBteCOi/wjAUUEcMnLIvIl2p8ZVszgeJ/rrxRW3KK0DPU0E5K04tl9CABwtl3JZzzurdnQ1TiEjuQ7RLwsR4eMK4BiEhRXto8Pb0Dpkv+lEVeYw2qvmMdVtxvbcBc4sFOl24tjsxKHpFrtrdCu2YbjTiLaaJRhKZlCSOY6sD4P48qobyXGteBVRh4RQPZLC9XgVocfrSD3ex+mQklSF1HcVKMysQ1NND0b7JrG2QAai/d6JVgNveM9ObU4sjNnQXDWLrPed+BDVLMXFgIe6by5U/w3gqG1VP8ZV/yIAR2GV/H0AhBbI/Wc8agCLazKh6dUoj9+0fjTGDR81zQYNtFEdrKQ6+NvvvwNrXIEb1+9dQJy///59GywlyOXiVg5UsXZkb63QJ9mvWYhw7yy8Ci1AUXoTuurHsTK1BevmIS4P1WDBZbPht7KIGIye3eHcdge78RLbC0dYHrdiqm8Hg61r6KxdQJtuHk0Vc6grmUFD+Qyaq2bQXjMrLhRjXWuYHzaKo8Xu6gEOdk5Fxf/myCnPy+SLr6Nt3HzdmyOKVh1hemADzVWjyEqmvkQO3P7pPbx/yYA/hWk9DQjxqESgeyH83TIQ4ZuFT4mknv7tFirH7unvADil8P2pSFpXaBcf/6JREleCOXSVIX2YNuIpseUoTqvDwD2AQ8FMtYKp3jMGcxeHPFyP0NM89xsAh5oYodS00azENQaODwXKOhUbca82RMshrzm+0P2nVcSjw55RIyUDb0KLUZTahI6aEcwNr8KyfiB0w/thVDdwHvSsRp/YbmEzXmBxwoK+1iXUlAwJVTfvSzvS3zfhy+tGpL5pRtrbNnx934mC9F5U5Q+KK81A2yKWSIffovDcjSQo92ALYz8G/9xszU7MDxLAmUHmm068D2dSacDLXyvh/7BMWnSCn+Uj9mUxPiXoUZjWioaKQUz2r8GycXxfMWfgCidVjBWBQr4WP8eh+UKAj8nBTfS1LKBZPw5dUT9KMrtQlNEFtkXQYaYsuw/6okE0VI2gp3kWsyOb4CF2ZD3H6cEFLk4ucX15hdubSzidF7jDOZy4wM31NS7P7nCyf4eZETMMxWNIjvoG4LAHWgFwCKwoAA7djTQAJ04AHAOaqnswOTgD44oJp/s/sGII4DhusLlgxWD7sjBwNAAngAycX8qEzq0ky6zQNYoAL18n3EuHEA/aM2Yixj8PqW/oMNeCjoZhcZg7MJ/cD7/2zd3tFY73Hdhe38XcxCoGOmfRpB9BWV6XjH9F3hCqCyZQmTeJwrQRZLzrw8fYTrwNb0NSYLNYi7M/mgAO5+DfB+CoIM53gPm3fVeYnPcADp0PKoVhRgbO61AFwKkr7cVfB3DuoLRQWWAoGcO7aLLIKsWaNdyTVTO9MNgoFJ7g2yvriy4WUZ6sGutER4lW2ZHepFtXIftDA6ryO9DXMonV2S0c24/hZMBHjQ3uV2JMdSdtdueOO+xtX2B51o6R3i20NSxAXz6JwuxBpH/swpf3Lfj4pg7vk6rx6X0NstMaUJzXhpqqPnS1TmFyZA0by3vY2z4Ud6gbtoFosToHkO0A+3eipzU/soWmymGkvaqB/+NUPP1fH+D7cz5C3eim043Y521CyRc7drIynv8egMOKrisDR2mf4Pr9voXqGwOHbZTfAByCihqAoziT0Z3s7wdweO5/O7/+2vffABzuhWwpbUSEO1syCSbppEBBkVWyBOvKRjDavYL1OStov80ixI9fTMA4X9jCsbVwhKk+EzoMS9DljaEkfQBMutJeNeNLQiO+xDfic1wDMl63oPxrL5rKxjHQvICF4S2YV+y4sCmvIXuh9kLcD7lfnd1hZ/EGvY0byP/cjbcRnJdsVaXVaiE+xnGeDckesDJtgt1EMXoXAEfNDy+Ob7FvOcP2+gGmR7YE7CjJbseXNzX4GK/Hu0gdkoJ1eBVK4IYJaKUkoLSMzU9rQWV+tziQTA2tY3vFjv3dY5wdnCtzmiwVAjjadXUjorZXp+ewmQ6wOGVEX+sC6somUJU7ieLUSWS/m0JKzAASA1tFkD/wSYUwV4Pd2HpUjc9xLSjL7EeHYRozQ+swr1txfUJ3PPWc1M6K/XNpN1iY3MBwzwLa66ZQUzKM0qwBFH0dQcGXceSlTCM1cQBvwloQ5ctEswzh3qWI8C5BnH85spOb0VA2hLHuRWzMGeEw23F3qVlxK4ARzyW2z53uO7G/e42NRQfG+41oq5tFVeEgCjO6kfGhFR8S6pFC8OBVLb68rkFWSgNKszpQW0qb+klMD25I8eLc5hRrW84jAVm1RIljfnIH09wtBuq2UfJxBGnR7XgbaEA0XWue5iP0WQHC3AsQ7lmI91HVYnBQU9KN/vZJrM5t4Nh+oCR+tzdKC5h6rwQ8VoHjva1TrEyRFb2J7rolNBRPoyJ9GKWpAyhL7UdZai+qMvpQXziC9spJ9NfNY6Z3A5szFhGOPbWd4opMpSsKmRPA4RtnDEjWH/e4O5WpRgCHmmk/ADhPCOA0INqjUWmr9WErFfdSxUo8PaEDehXAWZ0igPMDA0e9X9yHWHWfHJxHb8s4WmtGUFsyiIrsPhSl9iLnfS/SE3uQHNkoBchw70zEB9JFq05ar2eG1wVUoR00x/i7r7s7nB5dwLJziNVFC4Z6llBXNYTc1GZ8fl2D5PgqvIkqR1J4CV6FlwnD7E14hTicpb+rRe6XRlQXdqO7eRrz49uwbB3DYb3A5bEC5N/HJ3xh5xXEofDoGqf2a+ysODA7ZEJv4zIayqZRlT2GgpRBfE3swYeITiS9bEWke5OIGtOAgTH6NwCnHAnBFSj6ytaYeWwu2HBgPhawVYA2sZhn6zDbuG5h27ZhfnwZ3U1DaKjshqGkG1X5XSjP7kZl9iCqcsZQnTON/JQhfI7tEHYhjREiOV5eii6OADjehQoD5x7AGRIAx7pj/ZsADsFaAjjUTfH9iyuAY0Csdy1i2AruoUOMRzXeBOrwmSYc72pRV9KD8d45mDfMuDg6U/cjdRQ5R9gmdkH22QV21g+wMGHCcOcqWvUzqMwdQnZyB74yXk2iyYoeqQl6ZL2pQ/7HRgFvWiuHMdo5j9UpI8zrbFE9xI2YElwr8SrX1i3AM9xmusTm8hH6O9ZQlNGNmIBiePwlFc9/yYP/wwoEP2EbLVvQDFJEpQYoGTgRXnmIfZGPvJRGtNeMiUuZZcuGc8excgi4TEpOld31Swx1GFGeM4r0N514F/H7AI60UbGVyosOVB2IcW9D1LMWRD0jaFqLhJc6vA6hHmc7eptnsLW0B2pISuvw7wE4a/voapxG5od6kF0W+ZwmGToEPyEDpwYRntUiuv0hsh6laQPoqF7AbJ8ZO/NnEtNrcY8A1dJhQGmGS2wvn2Bxwo7hrh20GlZQVTiNgvQhZHzoxIfEenEQexerR3K8Hh8S9UhLNiA3rR4lOY2o13VjsHsCy3Or2Nux4Mh+jCvGPwxJtOsaONpzYmrAJDIayTHUL1PyWgI4gQ/1CH1Si4j/BnD+XgCHgsYaA+f/PQDnW8D4LZn4npXjWh3WgBs+/gjeaHbkf/T4W2BH3Lz+zUAcBQC7B4skydJaqbhJEyhgy0chgpn0vcjB17d61BR1Y5C0/Ukj7MZjCXS5aORg0jYdxr5qtYyBMBOJI/M1rBtnwshZnrBiZsiEyb4djHVvY7jDiNGuLUz0bmF6YAsLY9vYmDPDvGbHgekIp9YzRavlTE2EhIaqvoZa2WZQRLrz7NAGWnXDKExrwvvoMmGwuP3je/j8kiHaLeHetUqfv0chAgTAyVQBnCa01Q6IBo7tDzRwHLsnWHVh4KRENyPocRl8fypG8BP2eRPAoSI7GTIGRKgAzpvfADhG3F0oVUztvvFAUAAcOp4oLlTZnxoRqDJwgtxLRNg0hAAORRPFnl5toaKrz3PF2SyGAI5Hqyjs83u2VJE5EEXLx2fUSMlCYkAhMt/WQMdks3EcKxNGOExn35gC6gElm6FaVSNTgdVpVqkH2hfQ0TCNJt04DKXD0BXzGoO+ZAqGsllxN+htWcZozxqWJlnV3sfZ/rkwYiRhUQ8KHrRMVkgjN68cY6h1BWUZDLxIuWf/tQ7+D6sR8KhcBF6DnxUIMyA5uhpZyQ3CwmGguLVgA9kGksByLrJiyxurAUSsyhxew7ZzjK0lGxYmdzDev4a+tjm010+hrXYKrYZptOhnhGY+2LGMif51LEwYRWPHajzAkfUEJwcUPjzBxekJri5PcXt7hjvwusDtzQ2u7xk4ZtSWEMAxIMavXERvNQAnzJ0ATjviXvRLKwGF7gKfZSEuKA85n2vQrOvG1NA0tld3pFVRW1LyeEcK8q18huGOFegLR5D9vhOvgusQQLtFAjiPCSyQ5UP7aTpV8GpGuJceIR4lCHyWjeiXeUhJrERRRiOadH2YHlmGbefw+zXMgM95IdR8264Vmys7YMI02r+E7pZZdDcvoK91DQPt2+ht3kZ9+RJKMiaQ/roPH6I7kRTULNo7kZ60j2bA/h8D4CQKgFOML4kV0AAcaimIw5CIHrJ8pmjgcM8gzXZu1AxDySjeRekQ+7JcABw6qIULgNMo7h+Jz/tVl7dWRHvRIa8GERSmfVaKCK9ivAoux+cEHXJTatFY2YeJ/gXsbljAnvmLo0vcnDmV+Ulgm4nOhVIZN2+fYXXhAFOjZvR3baKlYRm6ihlUlY6jrHgAxQVdqCjtQV3NANqaxzDQO4PZyXVsrlqwbznGycGFBMp3V6puixrAcC+xbZ5gbngTHYZRFKc3g/pXvr98xtP/mQK/nwoR5laP+OcaMEVwhYLX1QLgxPiV4H1Epbg0jXWvwLp1jBPbKW7OmFATLOAmoQSyN6KBYxVdlpREBcCJ9CtF0LNC0Qhjgk7mYKQXrdHJwGlBHMGbewDn36cY8z2A04Io6jd40Pab+6eiqxXtV4IvifUoy+pBa82krHtWr0/tKsPThQCg7dFXjjscmq6wNe/AzOAu+pvJLl1AU/kkagqGoSOwmTuE6pwh1BaOosswh4muVaxO7MCyYsep5UQRj//h3ORtvXHc4cTsxMKwFY3lk/iSVIfYlyUixhroVoSAp8V4H12H4owetNdOYXpoDbsbe7g+IfjADU8talDE8YL0/ivs751hc9mKicFVtNdPQFfUh/KcHhSldyM3pRN5nzqQ/6UdhWltKM/pRG3ZAFoNY+hrm8Xk4Kq06NHtz7qzjwPLAU72HTg/Osb12bkqZEsg5xbOq2vcXFzKnN9Z38fChFkqrZ11G6grWUV5xjIyXo/iTWib2DD7PyqVNgPqx1E7KyW2CSVfe4WxMtW/it1VC66P6eSmgATafn51fIkDswM7axaszO6IRtdI1wZ6GtfQUr0MQ9EiyjLnkfluCO8iWkToNcS9VLHx9SwT4C41sQHlmV1o0w9jsn8eOytbuHTYcXfNVioCEyqIo7ackMmyb77C+uI+JoeM6GldREvtDAzl4yjLG0R5/gAqC/tRVdSHuoohAb6GOhcwM7wB45INDvO5UnjS2r45TuqZzzbic7MTS4P7aC6Zw9e4NrwNrEE8mSseRQh1y0fYswKEexTJXvMmvBJf3xpQwhYPwwBmxxZFyPj2kuNBZyg+sTod1LOcbTMntitpJ96cO8D8kAWj7VvorllBp24RXboFdOnn0Fs7j9HWVcz2GrEyaoZxzgrL2j4OTQ6c2I5w4TjCzfkR7q41Fo6CgHBtcA+6PXOKHtdgOwGcFsQHkoFTjki22zypEwCHQKrooqkATpQAODqkxrWhKnsYvQ2LWBzbgY027xfK82vnn5I4n8K6vSd25nNjS5gcWBadrr6mZTRXzKIqa1xpw45tQVJQOcK8MhD1IgPpb6ugL+qS1uvlaba5EiD67oSVxXN1fgXH/iks2w4szuxgqHtJ9FSqi/tQltuFwoxW5KU2IT+1GQVprShKb0NFbrcwzrhu+ttmMTW8jrV5MyxGB+zmYxzZTnF+dIarM5UJSnrU3bWsH7Y5XThusW86x/byEZbGbRjt3Ea7fhm63CnkkZ0Q241X/q0If1qPcDe6nFEb6RuAQ+H5+MByWccdddNYnjLBatzHORkdZEuJQxnn9ZXMkWPrPrZXjJgdncdYzzSGOmbQ3zKL3sZ59DQsoaduDd21W6jJn0X2uz68DmkQBglbbamZpbgj0mGzAKHeX/GBAE5BI/o6hrEyt4w/BHCO7rAxRy0UyhAMIp0Azssa+P1EAKcM1CijLlKsdx1iqD/nQRBHjwS/arwNqcTHmHKUZjShq2EEy9OrsJusODvkOGrogzqe1EA5v4XDdg7zpgOrs1ZM9hvR07SEhvIpGIpHocsfQFVOD3R53agr7kdL5TC6a9neuoj16W3YjPsy57m33lHgkAHxrXrOXgIUTF5bsGO0fxN1leNSzKQYtPuf0/D8lwLFrYyOeHTbpR4gGa4UndYAHL885KU0oL1mFEsTm9jbtOLccaSer9wglC++7I8ADhk4mgYOHX2jyLDxVGzDCeJIRwgdkd1bEeXWLELlsd50iKIrbhmK0ymJMQPj8p64uLL1kWtLvlQQjPHRzqoK4CS7AjjUfaKIt14MaaJ9yvAxyoDStB506mYw278N08IRzve0lsFvhVo+56XjFkeWK1g2L7A668Dk4B56W4xoqVmGoWwS5XkDKMrqRnF2N0pzulGW1w19aS8a9ANobxzCUO8UFmdWsLtlwtH+AS5OziWmkvPhStF4opTBztopmIfkf+lArD9dghUtosCH1PCpUQCcZwSU/9uF6huuoRXHXBg4BAmEhaK6T/3/AuD8NSvTbxViDbzRABkNsFFppNIL/OP32u9oj9rffnsU4OVfxcL5EcChUwxfr0kSFIqmhXtUI5RJ/9McRHjnIDm6HHmf6mGgfVvbNFand+DYPRe1bzkIv+0598G9VlGkoBQtsh1mCsueYXf1GDvLxzAuHmNz/hhbiw7sLDuwu3YI69YhDnePcGo7wdXRKW5PL+5FE2WT0YJqsb124oI9xKZTLIxuoq1mGPmfDXgfXYiYl9kIeJoKr58+Ka5CHnQyokOFDiGeRb8L4MyNrMC29fsixkcC4OxioGlZAo2U6BYEPS4XBf3gxwRwKOalADhMhii+G+dXDQXAKUNxWi36m0dgWv4DAIeMpQMFwOlumkV2igLgBD7LQeCzYgSSbv6sRiyGlWSoRRUypiAoe6Npj9iKKI8WRBHE8WwTy0T+nKyBcPcKhDzNQ9yLQqTElCP3Yw0Mhe0Ybp/B1rwFF/u3YDImY6kFgEwIGcSSLmq7gHnzAKtzJixObmN21IjJwS2M929jfGAXEwN7mBy0Y358Hytz+9ha3od1+wgn9jPRMrgPLNV5wja4k71T7K3bMD2wLAn3l0S6oOUgzLMIIc9Yoa0RoUCKRYZ6FCPatwSvwyrxOUGPvM9NaKsZF8DPYbnENR1FRPSQL6BctOrkzy5PriXZPbCcSlBlXLVhdc6MxckdqdDMj+9ifsyMlRkrtpb25dC3Gh3YM+5jz2iFddsidq2HNhuOD+04PzvE9c2xADjAJe6cTtxeAZfHd1gY30MdW6gI4PiWi37PbwAcv+8BnPigPOR9rkFLTTdmhqexu2bCpYOB1/df7KPfXrZjpHMVhqJR5CR34gXhAXcAACAASURBVDUBnAcEcMoR/EQnCSoTZV5R3i2I8qHduwGhHmXSjhf1Ih/JceXI+WyAvrQDo31z2N20uVQjGTSzdeEU1xfHOHU4sL+3D/O2HcZVK1bnLFid28fGwgm2lq6xOn2JwfY91JYui8PAp/huJAU3IdxTrxzyYt1JFs7f00L1L2fgxPqQgVMlAulJgaXCMEtNqkR9WR9mBtdwuHuqADiX7C/6HQBnZBeG4hG8i6wWnYxoOkWwRcq9Rujqcd6dSPQZUEQBRWNKcTWidkOEeyUivagtVYY34dTXKENZFm3uhzE/sQLTuhkHFgcujhisK2tJquLXEMbW0cEtrOYrbG+eY2XxGDOTBxgZsmKofwf9fRvo7VnC4MASxsdWMTe7jtUVI0zbVhwwmTq5ws35jSROgqcwibpUxFHPbTdYndpBV90oilJrkRJTihjfHPj+nArP/52Ol7+UItyNNu/9oCuiMIu8ahFNZsrzCsT6lSI5sgq6vD6MiY24C4ADFcDh1LyF7BnLUxqAUyvtOZG+dNQqwMvHhQgSAIfVWwI4FF//DwRwWCzyps0z90Puiy2KjoOXQQRAI31K8T6qRlg4TMI66qYwN2IU2/Tv9kJ1GQroTKbkwS0OTRfYXTnG+vQ+lsb2MDtowkTPJsa71KtzC9O921gZt8C0uI8D2t9aT+E8Ju2fG6vL2r4Drg/vYNsgy9MiLV0FX1oRF0hRXhYfcoSWH/SM88yA7I+t0BcpOmDr80acHhzh7vZbSwufmYE/k5izoytJIrdWrJgfN2Ksd1XYO71NS+g0LAobtrt+Hj2NcxjsWMTU4DqWpnawubSHzaVdbCztYGPJiI3lLWyubGBrdQO7W0Y47HZcn6ssQdVV5+7mGlenVziyXWLPeIGN+VNMDxygt9GC+hIj8j9N4X1kB0LcK/DiAS2LGV8o4ufUlCtK7UZT5SjGe5aws7wr5z/ZlNKqo51JFze4PDrHse1IWEFW4ynMa+eisTDZZ0NXnRE1hUvI/TiM95EtCPMi+5HCwtR9qpTWg+TIWmS9b0R5dgs66wYwPzoL2/YWLo9scF6cQAR65SaqWOUNcH5yB7vlAsY1B5bmrJidMGN8cAeDPVsY6t3EcO8GRvvWMDW8gYXJbawvmLG7vi/6VFeq49F9/MIkieeS/RL7myfYnLKj1zCP4pROcd2JdC9ABEGbZ67gTQk4X5OCy/ExrhIZydXQFbdiuHtCNDrOjwiu0I1FBT04v4jp3dzBeXGLy6Mb0bU72LmEZe0MxrljrI4dYmV0HyujNqyM7mF9woqd+UPYVk9xtH2JQ+MJ7Fv7sG5aYN8xw2G14PRwD1dndtxdnSgbmnqflLWhAThzwsChqLwrgEMdHAXAaRLWd4xXPaLY8uuhw5eYVpSl96NdN43pATKw7LhlC9D9l8KsvT0jG/YA9l0zLFu72F2zwbR8iM1ZB8Y7TWitWEJp6hjSE9sEWA/xSEOo12dxSipMr0eT6MxRO8gugr4Cdt2/hhO319c4P7nE0f45zNsOrC9aMTu+g7GBDQx2LaOnZQ6dDVPobpoRlm5fy5yw92aGt7AyY8bGghUbsnYs2Fw2w7i2i+0NE0ybO7Bb9nB2dHjfrkdGjPPSKQyI80Mnjvdusb9zi825ExFfbalcRmn6GL4m9uJ1QCtCHxsQ+kSP8Gf6ewZO1PNKpT3Qvww5KS1orh7F7PAaTKu7OLHv4+7mVDnP5Uwn6+gCV8dHONqzwbJpws7KDoxLu9ia38PGjA2rk/tYHj3EwtARuimknj4qbKZoX53o7lDYX1y4PKkrmY8Qr3QkxxehoqAJ/Z3DWJlf+UMAhwzz9Vk7ehuXUJE18A3AoSveg3KEuenAORHn04hYL+qT1CHa3YBYnyokvCxFUlAhMt/rUFvaIfN+bX4NNhPZPj/GSgQkbnF1eo2T/UvYTWfYXnFgZdqOmUGLFI/HezYw0rmCsa5lTPevYWF4A6uTRpiW93Cwc4CLA4raX+DuiuCNGjuQwXQF0G10e+1A1ntt5ShyvrThTbQOQR758PopCy8elCL4aS0iPdoRRVdYb7o1KXGKADieuYj1y0Xex3q060dEj9KyacH5oQO442fRkhxlHzevX3zHwCGAQ61S2oiHuRkQ5d4kVuG0CyfzhlesB1unWhDp1oRIt3rEeNcg4WUlXoeUoji9Ff0tM9he2cPZwYXoIv0I4LAIROYl53rmPYDDPVQFcNx1iPCqQMxzxgo6FH1uRVPZkLRcbkzt4ohFYXH4U/ZQQYjIELy8Fb2+swMn7KZr7KyeY2X2BLNj+5JHDHVvoK9jBf0dy6Bz5mDXEsYGljE1uoL5qTWsL23BvG2GY38fl2cnuL2k8LyygFkYO96/xd72GWbHTKirGMWnROqeZiLgab44xRHACX5sQNhTOpFpAE7Lf9uIf8dsdgVwfP5fBXA0NOrHR8Wu9EfmzY///gbiMIjUwBcFlInzbsIfX3Rtcb1+0MAhC+ffALxh69U9A4fPKeNEAKdRWgfEdlXsMPMQ6p6D+IAifIguR8Y7PWpL+jBK4Ufa0JmvFBBHQ3C1w5B7EJMKtSpMYICbAu3dLg/vcHlAUOcO53aFpcOfEeihjTV/TxIe/i2fl8/lGvDyNchIcNzCtnWMtWkzuusnUZTWiMTgbAS5f0LA0y948SgNfg8yEeRWLE4r7L+P9q1FmCddlLIR8TxbrEVLs1rRbhjG3Mi6PB+RYtfX42fghrQ6sYeBplVUZY3iU3Qrgh6Vw+8vJQh+XC0giQA4Pg1SRWCVPt5PhzchVUiJqURxWgMGmsdhWjbBec5KiPrZ1GCbbKWzfaLex+hunEP2xyYEun9FwLNcBLgVi2tIkBvdrRQaKxN0JkPUlCDLhomKJCnuzYh0b1ZYOJ5MXsjAqUMEHb+eFSPapwhJgcVIjixBTnINmioGMd2/DuvGCU72ruX+u5wfch84BhyTS4cTdH1y7LEKeg276Qb2HW7EyrW/64Rjz4kTjqnjTqFmuuR7MjXUwPJi/wa7q3bMDa9JJTQ3pQ7RL3Lg9ZeP8HuQjcAndPggkFiLcE+duDlFPS9HQmAl3oRX4UuSQTR2xnoY7J2I1g3fo+sXx8154ZQ2KlYhebHlg/TXk30njqy3QrXke3awX3f/TmHzUGDylFVLJiJ2GFe2sbtpwp7JjH2rFSdHB8LCUSpbymHL1yIVdWlyH/VlU/jAFipfggEVCH5agSDaj7o3INq7A/F+/eJuRJZb0LMcJAQVIP9LHdoM/ZgbXYCZVfUjl+qj+qHIWDKtODDWtYHa4nHkfujG6+B6+D8oxctfyhQAx7MeMT7N8vzRPi0C4kR40eqdbRgFiPQrxNuoCqS/q0FZbhsGOuawvcoWAnU+CoOJNDdWc6/hvLzBzfmttP6wsnF2qIj/sr2Oa/nU6sTK5DW66k0oSp9A6qteAXCofRL4uFKceKI9/20BHO61yv7aCAXAqUa8XwWSAsvxltbvSTo0lA5iZnBTEu3LwxvcXXAzUuaH7EkqA2d+xCIVuXcR1Yh9Ua70qXtWI9LdIGLg8QLg9KuuDhSqb0G0ZwOiPWsR5alDpCeTwlLEvihCXEAevr7TQ1/cLe4ji5Ob2N3Yx+k+gRYVwOGwqvsiuxDI3GJyeHTABNGJ3R0nTEYntrcuYdw4xY7xFHvmMxzYL3B8dIWLMwUs5HO47ouyRsl4tJN9c4bRDgbJ1ILIQoRnGgIfp+E5ncP+nIPAB5WIcOMZ0ysC/LT4JoNQABzfSsS9KMOHKB10+YNgoEvQ/dR2idszVQBAnY98TQIdy1N2NFZOISWhDgnBFI0tBa3RXz5iG1W5OF0RwCEr7D8UwJFzl3sg26g4bmTi0E1FcRMLp01tUBU+xBjw9V0jqvL7QRHQjXla1d4q5xo/svalMkt5Ptwe3+Hq4A5nVrXHf/sW9s1r2Dd43cC+cYtDo1N0AKh1w/2df8e553q28Kn5syPTDZbHLehpmEMZ7d6jK/Hi8Rc8+6eP8P45Ay8e0dmvSlznviQ1oiC1DS36UcyPrePQfCSCr989N48wpVNDEo6T/Vvs71LU/RymlVNszR9jbeoI69MObMweYXPeAdPqKQ5MN9Iqxn3y0HKFnbV90cmaG1/BzOgCJodnMD+5CPOWBRfHP1AY1NeU+3OiGArsLN1ipp+izHZUZC7gU2w3Qt0r4PdrgbjncZ+nIOqHqCYUfO5GfekIRjuXYFy04PLwQs5/1xiA3wsIyv2K91O9yF7aXb7FZI8DLRWbKPoyLuYIZN+8fFiAoCelwvyhW2RSkB7J0Tqkva5GNZ2C2kaxPr+KA7MVlw6FjaoN+f3jPavpDg6bE/sWJ/ZMTuxu3cJsvIHFeIW9bSaLV3BYr8UFiHukxDKcQ9x6tIsE0dM7HGydYJVMpeZlVGf242OEHv4/fYXvP6cj4NcchD4pRIR7KSI9yxHlzX2mCvEBFXgdVob3MSUoSK8TsJh24g6rA5dHl7LP3L9nuVe8R9SEuxMWkLg/Mv6yKcyfs10ntOvC4sQt56payOG8dZhOYVrZxc6qEXvGHRxYdnF6aMXt+akCrKkvJnPt/A57m0cYbF9AUVqbiMrHigaOwsD5PQCH+yh1Tz5FtaDocx8aSsdF93B7yY7rY5czkPdO1p9TCnpMrm9OlHhFtKMO7mCcvcRQ8y502Uor9uvgSgS5pSHgaQoSg3OR9qYKZTmt6BMxYysuKVjOl/iWL8v3si+zoHastEUzPrDtXGN34wxbSw6sz7OAcYitxSNpB7FuXUkcwfG+dNzhYPca26uHWJrZweLUJuYnVzE3sYiN5U3s79lxc6ECDtp+wrnscnFPWZ86w0DTDvS5s8h+O4g3Qa0IZvL5SCdi7GQRRvuQhUNno3LEvChH5rsW1JWOYKybe5gJDsuhAkDIB+SLsdWEIAR1vm5we3KNm+NbXNNM4vAOV/t3OLU4RVzdtnaLqZ4D1BbM4XNsqyIK7cO2GYO08od6lIuzZbDnVyTHlaCyoAUDneNYnd+AbecAV8euN1WZJIqwLAHdZVRkDSE9qR0JLw148XMJAh9WINyN+jeNiH9OVkQjoj3qxeY70oOFErqwZeNDTCmKvjagWU8Hxllppzvd/xHAUScl5z9ZGTwXD+9wYqUWixN24y1sm9fYWz+HdeNc9DqPd29wbnXKfZC9WovPlZBBnpBzhS5ejIEoDN5imERGSiNeRVYg8kUxXj7Nhc8vucIap4OquA+LW2wjYp/XSqEpyqsEEZ55KoDTgDbdKBbHNmHZsOKMIsbUlnL54n5nXr/CUMc2yrPHkPa6E2/CGkWnNPBROUKf1iDyWSNiPAjatCPWU71YxH2msG8i3eoQ460XBs7rkDKUpLejv3UOptV9YX9J3Kzlb5wmV0rRlsXCzvrpPwBwCCJWCUD7PlyPvOQ2GPIH0Vs3i8XhHdg3TpUzT7uP2me6X8fKazBPOLY5cWB2wrrtxO7mFXbWz7GzfgbTxqlcFuOpiFMf7J3ieP8c58eXuKETosY2VMeIdujWnQuszNrQ17Yk8gjxwSVw/+cUeP+UiRe/FoMATsjjWnGginRvlCIdYzq6YSY+b0MCNQ/pjKnm2bGezeAVQ6YO41g19xb3TO9W9XeJESh5ufb72mOc69/8i7X+/n1YyX8st/JbjIMxNpk5/0MBBlwFjP+rt1D9+GG0f//LgJsfgRwRKL5vfdKAmb8TwHEBbLQJpD3+8aD87UlwD+CwKioATrOiF+BJvYAahNIm+mkRgt3yEeFdgNiXhXgVWoyCzy1oqZ7A9IARtNp07F7JoS+JiZqc3B+MLhsgAxgJwDRQh3kiLzWp+TGw1da8/Fw5f+7/nsms3XiKpXET+lvoDNKpWpd+hMc/v4H3z58EvAl4UqBW3ugK0oEYP4q6ViDQLQ8Rz/NFU6UsqwPthnHMDRsl+ZENzeX84Xs8Ml1ibcKGgcZ1VGeN4VNUK4IfVSgAziMVwPFlZalBertZWYr3q8HbEB0+xVSjOK0Z/U1T2FmywHmmWUoqQR2fn8DVmf0OOysn6GpYQNbHZgS6ZyDALVeo8wFPKhEkVrXsf25ANCnIWksCDwlJUAje0K6Tbg90X9ESF1a6yM4oR5RXMWKeFyLuRR6SI0tR9rUd3XUzWBq3YHf1CMd7NwJ4uAbN340bN+S/drm0uMm4aeOvBfeqPtLehtriUTuGkowWvI+ihfcXPPrTW3j9ORMBjysQ6dWCSG9WG2oR4a2TYCXmRaUIB74Nr0bJ1x501S9gedIq9PCzfaUXVuaNGhxRA0feLw8ovhde/D/OQW3+aZ9HO8T4a9fAsfUSW0u7mBtbxOrcGoxr27CYLHCQunmuaSaoY0iG7dkdlicP0VA6gw+RdGMigFOJEAFwaGHsCuBQzLNaLGcTg4uR/6UR7YYhzI2uwLKx/70ukboQGFiYVo4x2rWJ2uIJ5CQTwKmD/4MSYVWQgUPh3ZjnBPcI4rD60yK963ytUM8ScQh6FVaJT4l6oYF3UMxzhtUfRcPg7pLUV05IhTKsxX7yqN2/+4WpBJ6mlVsMtllRljmF9Ne9SApqkPYI/wekRevFeUQYON5k1nx//dZGXPv/v73fihimdy1ifXSI96vCq8BKvAutRHqiAQ2lI5gZMOJw5woXB4p2lvYZOLZc42zvnBveQ03hGN5GULOqXGjOpPxHurMPv1n6yZO8CeD0KL3lAh41I+Y7EIdtIIUI88rG++gKua/1FYMY7FjA0tQurMZzSRYYSLKFSuaeBsDwXqr7ogboUBCbmkoXp3cijk12l6xHbS253H9tLnNunFhuYFk7wfKYBS0Vo0hPYgtiMnx/SobvX77A9y+ZePlzoSQBkW40EugFHRMVAEehecf60sWlAh+iDdDnD2O8ewu2zQuc2m5we8qFo764up4J4i1PHaCxakYcehJCqr4BOA8LBYilVfl/CgPnvnCi7IMxXs1SoKAgJ/v3wzyo91OBhKAqGf+cj21oqpzC1MAOTKsnODRfCaB7D5bxbHM53+73ELU4wTH47tLafjXQRls/fOTzcP9h69ThHbbmHBhoZmtBD74kGhDpm4dn/5iMh396D88/Z+Dlo2KEeeoRH1CDd1EUEa9DdUEfhjsXYVq14/zgSgHMtfhfey3t3GQHhQpMXxGEpYW12YkTi1NA2DPbHfhznqva3k/XtM1FO0Z7F0VvhaLn7Q0D6O8Yx8qcEUdWtji5AInaa6qPBFcOt51YHb/BYJMDupxlfInrEftt358L8PIB23ppZ16D9xFNwiiszhtEX9M8VqZMOLacwsn2Q+7hP54rrmtA/f7Y5MTSyAXadUaUpE0IgEO7b79faWVOAIcsAoM4FdGggbFMjrQ99mK8bw6bC9uw7zhAAX/5XL/zGtp645nCTpgbFgYuyHJRLjlrfnyv2v3gmKvn34nlCutTFgy1LErik5HYgBjvIrj/KQXuf/oCvz9nI+RxCZi8RtGRx0ePGF8DYl9UIc6/FPFBBUh7q0NtOVs2l7CzZsOh5VQKFN+dcdrZpr0nbf6qc+8eOOA9JojgAmiwvcu2cYyl8TUsTixhc3EdZrKvrBSpPVXmibYdqExdatINMHlKa0dCYCli6dYkLVS10gIU5U52hRL/km2hATgfwpuR875LWg97GxawMmXB8Z4iNCxjr8WM3De1vZP3VftyAvubTkx176M2fwE573rEcSfwaTr8Hn5ElF8W3kWVIjO5Fk1kqYzu4NByI4UJiRH4nHw+bb1or8dH3jNXIMDmlFiNgISsGYKz6rrj46HZidVZO4Z7yCCYQ1/HJHraRjA1Mi+acudH1BFSn5fjQIKHS7zJPYRg5Fi7BbUF88h9NyQATuCv1Qh6UIUwnqnUiPGpQZR3NSK9K0R8PjWpCVW5AwIC0+DBskEQjBPuW9HgN+tInZvy2V3a+i72nFgeuUBTKddsG3guUDss3IN5gR4h7mXfAJzYUsUyvWMKq3NG2LaPRD9RnlO9p3wPLNauTR+ip34F5ZnD4jyW8LJOik9BD6vEEpt6nAnPWfBuQrRHAyKpIfOMhaAihLhnIym4SIokFXmt6KgfxuzoOvaMRwqL5If7qE0Nvg8ZQ3XOu4KZsl//XsFY+2NtTqgMa4f1FpbNSwx1raMstxeJ4aUI8clFoHsuXjzKhd+DQgQ/rUKERwPifDqlXTjueSPinqsMHA3A8c1D3gcVwOFYrdtxtu/C/lNfn+txd+0ag+3bKMsaQ+qrTrwObRDdMAFwnuhFHDzGnW5TbcK8iSX73qMF0c8U9k2kW60LgFOOkvQO9LcsiMEBWf8sVsq65xJQ9yj+zLhkR2fdlDi9xbwsUkSMhYFjQJic6TrE+FbhbagBGa9aUJbWgyYCsB3r2JrdFzCQwLqcKdoZqN1X7ZHrgGuM0/TiTgAyAjGuF0Ez7rGyP3EM1TUpZyi/58+47ixOrM3tY7hrHYbSEaS+qUeoVy4e/c/38PjHr/D9uQhBjxT9GzpQcS9ijHcP4BC88WkTMOffDMDxcgF9/v8AcFoQQxcqz/+qGjgaYMPHv5ZEfC9g/FvgRtPJcXWT+tsATrwXkwbX628DMf+3IA4BHA1JpNAomUIS5Ho2gfoVbAMKc69EmEcZIrxLpYWFPflU/C/N6EZz1SSG2lewOLYL06oDDvMlTm23wrD5jkmjHrqy8NTFpi06edSCBtfDhAvcNek/VZg7PNQJ3BgX7ZjoXUVL9SiKv7biS5IOcQH5AgJ4//wZfr9mwP9RvlC0qQJPwCP+RSdinrO9oxpBboViA/w5oU40ENoNU5gb2oFt4+K7IFbewyVwtHONtfF9DDZuojprAp+i2hD8sAJ+fy5BMAEcjzok+JJp0Cg00CiPWiT4GfAuxIDPsTUoSW1Df+MMthes4rwkm5oaMJFqSPYRKwTby6forF9E5ocWBDwj9S9P9Ht+BHBoxRkj4tOcUy7j5t6ASHdSldnqoQE4tBM3INKzClGs5nmVIMqnEEmBJch804Dq3D50GmYx0buB9Vkb7NvnOLXfSjVJ3qdrEKgFNFoQpQUi/Ll28RBwDRRVRJ/aEnzu7WUHJvrW0Vw9gsLUJqTE0QozHy8epsP9H79IdTbYjWPWJiBElE8jIn3qwOop1fej/aqQEFiNjLdt0OWPoKdxETPDO9hePlDsM08VMEICM95jvn9tjmnBjPb+XA4AJgr8vGQVcC6vzu5gqHMCrYYuDHePY2F6BaYtCw6shzg7Occt6f2cpnyOS0iytzh2iLriaSRHMCGuRIRHpeK2wvYmHhjeHUjw60fM81ahQYd6FCIppAwFX1rQqh/BzNAqTCv7Ai5oZ5wEQk4yzu5EQHWobQP6/DFkvevEq6DaewAn5KkOUV71iCWA85wtdgqAE+3DHnYDop6zTagKSSE6vI/WI/1tPQwlw+JoY1w+hG37GEfWUxEHpIUvK7favftxbCUoZBsjD/qFW/Q2mVCUNooviZ2ID6hFwKMSvPy1BCFPdEKb1wCcOGoA/QDi/AjqxHvzEP1re6+6t0oLah1iffTCdnsVwF75aqQl1KG+eBRTvduwb13hzOa8Z1PI5+A4EzC1OjE7sAd9wRjehhHAqVD61D35nmvljKIgIAGcRO9eJHh3Id67AwTlmYTEEEj1okV2tbRSUfT9VWglUl/VCUOipmQQPc3zmBnZgXGZLXlncOzdiN08XSxcwZz79aLOR4I5Atxwv9SSC3VP5L3n5+BcJXORzDnL2hGWRk0Ybl1CU9kIClKa8Ta0BAEPPuHFz1/w8udM+P+cj0BqDTw2IPIZCykKgCN7vjeZWwbE+emQ4F+FjzG14hg43k2nniuh94vWlPp+5D6STu4g6+wQjVVz+JzUhKRQHSL9ykHNlpePikSoVgn21BYqskulYPDtXPy/Pb/+2t9JgUMtnFBzQ7sUJzEKS+oRzsquTxViXlQjPqAaKfENKP7ah+aqaQx3rmNhnMyDA9D55vzgRuYQg39ZE9q4aAEo9xjuNQw0tWSe3/Pn2j3jo7bHcNx2r2BdO8XapB19jUuoyunHp3iDJL7B7rnw+nMq3P8pXd0PWWmvR9zLWiSF6PE2shp5pK1XjWKyfxVbi1bYtk9wtq8CHj/seTJefH0VQOD8Z3DNizpkZBTxohkAzyIWE9bnDkTzTFfcifK8FpTlNqM0twm1Fd0Y6VkQMVhWUMnWuT/PtM+rzk/7hhMLQxforjGjPH0GH6M6EPyU4v8FwhwMflINtmW8Cm7A5/hm5H5sQx33pI5FbM7vYn/HgZO9czFNkGRLu6c/JAS8x3QznO0/RnPZKgo/jSA5ohnBT7kP0SmyXJLPaJ96xPjqEfuiAnH+FMXXoSi9BQ0V/ehvm8bs6AaMy3ZxcDs9cCoOLUzOXc472ft+79xzOf/uf0c7Xy6VteLYvYR59RCLo9voqZ9FdU4vvibV4U1QJcLdCuDzDxl4/o/ZCPilCGFPK5VE3YvJX50iDM9WFlpZ+xYIWFz4tRUt+hHQlWttdlfmAN1+RNfC5V7JHqKBbdr5zPngenFu8Owku3L/DublU8z0r6Hd0IeuBloqT2FjcQ12k0XVHlFPKD7fhcJe3V07Rn/LEgpTO5AQUIYYAjgeOtlzpG3BnQUu6uCo+6cwcAx4E9SA1LgW5H3sEBfS4fYVbM7bxIDgzH6ttEjz83CNyZn+PajHdWZdu8VEpxU1uXPIftuNNyE6BLllwu/hZ4R5ZyEhuEiEuqkB1dO0gOVpG8wbpzjcu8LFMffjH4pTnGPq3iv7M0E7xgdcJycqW5ygrfo9f+6gntGkDZ2NsyjL60B5fjsqClpRWdSM9oZBzI6xxegAl8c3ArjKfqKNE8fiFsKE2Zw5w0DjFqqyJpD5qhev/JsQ+GsVgh5UqwCOQYwe6BpLJkSUTyU+RNcjL6UDNYWD6G2axcLYJqxbbAc6x9XRFW5PbpV1qt1Hvp42Z7VzWj6zwwAAIABJREFUh0549jucmpyYHzhGbeGcuOlFUAjcQ3FnJIATqgI4IZ6ZSI4pR3luO/pap0F9ob3NY2G8yD3T5toN5LxdmXCgy7CC0vRhpCV0IvFlPQJ+rVCKCu51iPdpQcJzng0K6yHqWT0i3am3Qj3BQiQElSIlvhrZKXWoLOhAR/0EpoY2pGV/b/sER7YrYRxK7OeyF93vf9p85/+5Xtq9cHnkc3AtnR7cwmY6w8aiXc7y/rYVVBcNIfVtAyJeFCDAPQcBbvnS5hpIpt8zxQBBaRsnS55uljWI8SYYW4wIjzzECoDTiLbqcdkLzKvU3KGjqhJTajEf5/rOyjX6W4wo/jqCz4kdSAqmDlEVAh6WI0QAnAYRLP4G4LQihgVcFnPd6hHpRmFoPRL9aRHPboBOMYuhw6zDci1jJfOQc0A9H3jWUwy73TCJ9Le1iPQrECH4UPcqBD3RAByCyjq8CjTgY1SDgDh0XGwsGcdg8zKWRndhnLfBStc3y7EwKrm/8L7f749aHM77zvFQ1zfv/f3FopfLWHGdEtC5PnXi9OAaBxYy5c9Fd436jFWFA8hIbsKr8EqRDfD4p1T4/JQrOkshj2ukfSrimZIjEcBhrMA8WNg3PmTfMPdXO11UBs49o+aegfMtd/7u97W/+53Hvxav/Of+nyvmoeAev8PAIZWZVngEcEhL+q8I4Lh+EA2AcX38EbT58d+uv6t9T/cqdTLct0Z9z8CJ925CvJd2uYI3/P4/A8BRKpXUdWHFSg4J7ypEeHETLcWrkGp8TqhHbkobqvP70VE7LTbNK9NmSaLt26TcOyW54OGmHLjfL1BZkNpmqQVHGoCjBho8ALixMKDk85HKvzFnw+zQJvqaZ6Ev7EXGOwPiA8mmyRbB5RcPM+D3IAv+j0ibLkPoMwaIdcJEiPPtEmYCK34hbiWI9i0T15Byul/UzGJu0Azr+rVCIVbfmyRLVLXfvsXa+CGGGo3QZU3iU2Q7gh6Ww/efSxD8sEoBcJ4rAE4sEzuPOiT61eF9aB2+xNahJLUTfQ3z2JqzS8WV9tlaMsCggJWc4z0njIvn6KhbQub7VgS4ZcGfAM6TEhDACRYGDgVBSc8kGKAEQkow1KhULNx54Cm95hq6zIApmkJqnjWIor6HRznC3dlOVSJWil9fN6IorRON5WOgRfXSpEVcEew7BHJuJFjR3ivHRA6Xe3RB/YZjpx3W2qbMgEdtW2I117R2LEnRUAc1b4aQ/bEeiSEFCPPORCADrQeZeP5zDmjBGEbtFI6Xbwein7ci2ofCbfWgGwJt7qOeV+F9ZD2ykjtQkdOPdsMMJgc2YVw+wKHlAmdMuDj3uPFzLF3mmnwGfg7t/apBDEHHUzqnbV9gbcaM/rZJ6IoakZtaisbqDoz1z8C4Zobd4sCJ4wLXV07R2LxlJVZNpudHDlFbNI33YZxzBHCqEOpWJUyUCHeyphQAh+4/4lbmWSJOZQWf29BSPYap/jVsLx5IVVyCDpd7yQrW+swh+pvXUJkzIlajSYEG+D8oFhvOkKfVAuDE+TYj1gXAIWgZ69eI+Jf1SAioRWJQjQriGFCY1oXm6kmM926I+OHOKoNmh4goXx3fKAGfy+F5f5Cq95MJ3/rMNbrqNpH3aQAppFu/0OHlw0K8+KVY2gsjPai3ogA3CoDThjgNpPm9x78KnGv7Kp+D+2g9Yr1rEO+rxyt/Pd6G6JEa34DawnFMdO3AunYlNpZSKWXyrSYpTFzJQJju2xMnoTehrCZVINqbIKde1i/PqASvLgFwkrx7kejd8wOIozJxvLhHKpXQOP9KvA5T7JgZRFTk9aO1Zhoj3RTGNsO44sCB5Uocy6iZxLY+uacawKiBNGrl78e1JkGxuq6YZNu2zrE1b8d03wbadeMoS2/Dl7gqvAkqRpRXDgJ+TYf/L5kI/DUfQQ9KEPKoGuFPSE1vQ6xnj7RQCXBPy/fnFGKvQWKADikxDdDnj2O8awd76zc4MjuVQE9LNqi3I3owd1gcd6Cpah6pr1vxKsyA6BdsDSyG/+NisYkOczcgStPAETDFpX333+l8+wbgaEUT7s0UUaZgM4WMFadAtu9wP4nwrpJ18SmhCXmfOlGdPwgKgk70rWF9zgzLxiEclgsBrLS9UJI7bbw0IEdNhGS/0dYN14qa/DE5vrI7cbB5ivWpPUx0raON9tTpXUiJrUGYVx4Cn+bgxcNs+PycDV+yR55UiPV2jG8zYvxqBXCKeclExiA26M3VYxjpWsbylFmYiGwXEGcRdjjxLNXOVXVO3SczWjKsJq48h9jWfGBiIeEcQ52rqCroxoeEYryLLURyQjFSksqQm1qPlppRzI5swUZQ0nyBM/uVtH0KyKLOaZ79eyu3mGZbU9kGClPG8D6sBYGPS/D8L/kgQy/kqV5Yg/Ev6vAmtBYfYwwoTG1HcyVbQRaxOr0N04odBztnOLc7pd1DkgC+Z95XNXbge99bvcVklx31hQvISx7Au7BGhDwtgf+DIoSwKu5ZJ0WPaJ9atfWkBEkhFUhJqEH2xwZU5HWitWYcI90rWJq2YHvNIWKlTO6lAkwgh/fwj75c77N2r9VxZ0KknCt7oDB4azXdy9qFnRvlnYcwtzwEPsjHiz/nw/8nrtNKRDyjDhfPempoUF+PmmYGEcYP8ShAXABt5TkH2tBQOYThziWs0jXKdC5tm9r5pyVLP84D7eeyp/CcYWJ0cIfjHSfMSzeY6jWiuXIQeZ8rUZatR2dDL+bHF2DZsuDYeiJzTM5Q/h3bVA7usLN8BOorFXxRAZznPAPZtmAQIeNItpZ4ugI4daKDk/iiFu9C6/Apth6FnzvRVDEBupGuzezBvH6EI8ulACd8HbKy2AIkYKnL3DUv3WK0zQJ99gyy3hDA0SP4WQ78HqYh2D0Lkb4FSAwuQ2ZyM/RFwyJyOjNiwtbSoezJHGdaKsuezOd13ZM55lpBUV3PWrJJ4JN7MdfN2uwRuhoXUJDeisTwAryNKcLHxGJ8eVuKivxm9LVNYH3ehEPLmcRWjG05d+8vCk7vOrE8uo9O/TJKU4eQFteJxBcNCPi10gXAYcsrHfXIbtSJJsorFgvjG5DzsQU1Rf3CTF+e3IZl40BcvdgGe+1Q3NBknfJ1uVZVQIcJs4hqs41qy4np7gPocibxJoTxciFC3EoR6kY2jE4AnFCPAoR4ZAmQWJZNa+ppMbowr50I2+Y+7uL9uoYwMpbHHOjQr6D4yxBS4wngNCDwQRWYWEd5NIi4fYIvYwXmis3Ckohiu7IwsGnbXiEt9B/iq8XeuiynE43Vo+hvX8DMqBGbS3bsmy9A8JXgi7ZXy3vh3vx7uYYWB2rjSvBAPdv2d2+ws36MhSmz6LLUVowiL60dKUm1iAsugz/n12NVo4z35xnjazrI0QCB0gbUOFVamKK9KhDlqQE4+chLbkJb9QQWR3awu3KA470zOM9VkwX1HOH7MC5eobdpCwWpQ0iJa0NiUB3CPFQA57EeEU8bxC48xr0VsR5Kh4sC4DQgyq1O2tDifPRI8tfhbVg1ilI70Vk7h+XJPXGYZd7B9r/74gJlLhx32Ji3oVXPtngDInzyEeZZArbGKwAO8xADYp4bRIg6KbBG5snHqFrkJrehOqdfwKmhtjnMDq1hc47Ank0+430hSN2/ZUy0MdAe1Zj8xznENSex9rFTLNBFpHpuD1NDRrTVTqMkqwucGzEBRQjzzkfAk1z4/pKDlw/IvqkU8WKyb5gjUcD4NwCOtE+5ADheJJsobVS/aYe6J0AoOfp9nq/l+z88/nvm8v+653bFPf4agEPwxoPXf3UAxyVJ8PoRpNH+zUosL+3f2qPr3/L7PwZwvgdu/uMBHNLEBD3kpKWWCt2yVL0ApT2nGZIM+jbKYiX6SoSZGjJUvk8IKsOnhBoUpbejvnwYfS3zmB7awsacXZgWx0R31f5/bkSymfKw+LEC8DubqgSZpK2SmcIe5K1LrM3YMNq1KkJtJV/b8CGWfbhpePineDz9B7bekL1BAICV33KEuukQ7l6LSE8G7W1imxvt3QpaybI9jPS/1MRWVGQOo0O/iNkBqwSBDLLlcGYAzkri+R0cRifWxo4w1LQDXea0AuA8KIfvP5Ug+EEVotzrkEAAhxu2F6l59XLoJoc2IDW2EaWpPeirX8TmzAEu7UrlkwGu3JcTza3Lia2FC3TULiPjXRv8n2bB/wk3oR8AHAnqNACHwRBZP9yQuDHVidUk7Tppn6tUnpVqF2076foQ/qwKIU9KEfK0WGwN41+W4XVoOXI+tMBQPIz+1iXMjuxgc8EO2/apgCHXJ2z7Utw5/ih+lQDRtTKpVTD272A1XopQcHfjPCrze5H2xoDol7nw+Ms7PPmHt/D48xc8/yUH/o9LEfKsBpGsnvv1Is6vG7G+HYgRwIPsqTqEkML7rBIxfjq8DqtBSnwtijO60SYWtEZsrxziYPdCDqHfHAA/HuIu75dsBibEa7N7GO6cg66wBZ9f5SAuOBmFGdXobhnBxpIZVtMxjvYvcUlNJ+qYEIBTg7jZwUMY8qclGI32UgCcMDdWzWiXyvGgUGU/4p63I9qHekblIHhQ8LkDzZXjmOhZB51CjnbVSjoDSbVqz4N2eeIA3fV07FJsOBP9a1QApwShbtWSnMb7tSDO9xsDJ86vFYn+rXgd1IpXQU1ICKC7jU5YB6mvmlCa2YumqgkMtS9hcdwowpAOM6v5SguJtna1ioisX3Udsw1pZeIMbfpVZCV3IzmqEdG+lXjxaz782K7zuAqRQifWAJwf98d/xb8FEG9AHC0yfQ145W/A22ADUuOaYMifwFjHDszLV9LTT20YWW+q8xn7/Y9MTkz27KE6ZwyvgxUB32jvagE6uZbiVADnlXc/krz7kOTTiyQfVxCHrcFsO2X7g9KCREZHhHcZwryLEBdYhk+JdShIo7vPKHpblzA3ZoJp/Rj23Ssc250ifM37KsnCHy6sb/+h7UcM/O3GK6xP2zHRvY6WylGhZCf458L3p/d48dMnvPwpDQE/ZyHo13wEPyhF6KMqhD+pFWHDaHf2yhPA6RT9LAIbcb5sM60Fg7FPsY3Q8x527cKyRtFepW1AmJUM/NXKM0HFxbFjNFUtIu1NO95E1CHmJVsDSxHwuATBblUIp1CpgMhq26criPPvCuBo4A1bW5sRz/35ueK2yOIEA23axYe4VYvGWChFbv0qkRhciY9xejnXGqtGMNK1KI56u2sOaSXTAARJgL8NjfKdmrjz/+QMc0kKmBxdWKkxcYnNaSuGWpZgKOhH5tt6vAouQ+DTTDz603u4/cNneP2ZYpiFCHpaiXBPCqI3I/5lO6Kf1yHciwAZE3ievwZhe9WVDWOoYwVrM2SPOEXHhuv2d9/jj+9Z/TfX9bFFAW+mBsziMkjHOt8nb/H88VsEeHxCmG8G3kaXoSynC30tCyJKurvqwD7ZP7YLRUNCPdf4eXcXbjHebkVd/gJy3w4I0yLgYRGe/4UMnAqEPiUzlPpIBtGfogPapzg9eL63VA9juGMeC6NGbC8dSDskNbeo18GE9z6OIFvNcQfTwi1GWs3QZ08h500P3gbXC4AT8JB7I9mJ1KJoRZQ3K9jVCHlWijDvEkS9KEFcUAmSY6uRl9oGQ9kwelsXMD1ilMr+wd7lPUPjD27db3/MecACFM9AtQC1OX8IOggaigeQnVyP16ElCHySjqd/egfP/50Kvz/nIeiXcoQ+0iHiKcV9yVYhe0xxnIx93i7FG559/o/zQRAnPrAMH2KrkZPSKDHYaDfZWAfY3znHxaFqX/3bd/fbnxCcuIRoNpkWLjHbZ5XzKOdjDWIDUpAclynFjPG+aZjWzDjYJcvCqQDiPP8cSvHDuHgkTkb5nzqREFCOGAI47jqE3Du/sG3qtwAONTrifKuQ4F+BT3G1KE6noPUEhjtWsTRhhmX9RGLJK4dTgEK2BgmjRAAdhTW0M3+N4RYTqrOmkPm6SxLKUPd8vHiQAf8nWQh6loswb2rA6ZH9sQ1VBYPobJjHzDD35FPR4vg/7L0Hd5tXliX6fuab6e6qsgITiAyQIJEzmKMYxAQG5AwCYM7KOUdblm1JlmQ5lV1V82a9mf3WPheQaLk6vJqqng7jte4CJUsU+OH77j1nnx0oJSajTO4tAjj/1H91aQ5r1O9esHb7GVcPniO1cISQfQVNfxeB/tQAbNpReDomMT2UQTV7Drcvf4aXn32P717+SYAOOZcIEpIN9/3/xLvP/xvunnuFzfRdLI8cij9Q2FSB7UQDwOEgjgMiJp6yvqU3DRmkBfR2FzDoziM6uoFq8jIu7z7Ck5sv8dXjb/Huyz8IG/VP3/4PGRjyfcsgsX4Nua/992//J36iT8xn6rnNzFwT89vO01F0NRMIzcClKSgApz0Gh3YBw74cUrP7OKjdxf2rL8Rji+zWBsDVeCUw9fjqj9jJP0X8zGVMBQ/Q07Eh3j6uloqwxsOmHfSY98TXkcN+Jiwx+TFgqiJkWUWoo4hgZxbBriR6XSmM95awcGYD6cV97FRu4eZFsnG+xzcv/oTff/P/KhCH5ytrefYZfC7/3H/cr7lXs9bideBn+up/4MsnP+P+9Tc42n6C7MpFnOlbhcschU27AHPzHHS/m4HxxAI6m+NwaujVQ/CmJiwzMuOlFyDQwchtst+1CsAJmVewNLKJncItPLr6Soy4f3j1B/Ekalyvxnt5/uC/4aD2HEsTlzAa2BaGs7M9JwwcO5NHW2vwa7ZBAIf9NQdP9GzxM/WUKVWaMkKmEvpsJRnYxiaZwHcP969+jZdPf8K3L/47KKElg4w/N8Eb9m2f3XuLzcINTPSURIrk0MZhrwM4znYCOBX5XAImgohM7kzBo02gryuLM8ESoiNrKCztY7d8BdcP7+HJ7c/FnJw+mjyb5OdsgKR/7nPh73Edq9G5n3Lw9dM7Jsv+hCd3vsaVgyfYKF7H8vQ2+r1pWFomofmHURg+OQvr6UV0tSTFh5JyfncbJXlrAgz6dSqB6gMDp86+Oc7AIQjTAHDqgMz/GljytyNk/OXv6z8sgNMAZRpgzT/22vhzfP24ITkG4IgebhN/DriJ6Deg1iYi+uOLU+AP6y//kP7cjfPhhiVaLIgxWVL19I6gcQch844qevQbElHsoJEuPSKMefitGQx4ijg7sI74zB7KCU6bb4AGWTcvfIoH157j6Z2v8MWjV3jx9DVefvYar569xuvP3+DNF0R/3+Ldi7f49uU7fPuSB9qP+PbFj3jz/Hu8+uw70WA+uf0Kdy99IY7xZPqU4xexMrUtm0q4i2lSM2j9L8No//tJGH+3AOupuPin2FuLcErkIk2Z+TPtI2SkZGBXGkp3OyfuJTAOfGnsnEybD1af4PbR1/ji7k94+egPePXoJ3z9+Ed8ee9b3D36CkerT1BZuoXFwXMY7FqH/VQWnb9VBRcbvoiRgNgGQiwUdWvosaxhyL6OM751LI3soRa/jivbjJR9LbT5z+9+j+f3acL8Ax5d/xb3Lr/Fxe3nyEWvYDRQgbV5Xgq1rtaUpDE5NZwaU7JBuQObxjUEZfHAYGFErxuCNPyaxV8DwGER2NCdU2JFE68y3G1kbOQRZOFkY7pTDSuT+yjFL2OnfAsXdu7j9qWneHz7czx7+BwvPnuON19+gW9evsJ3X7/DD29/xA9vf8Z3b/4ok8pvXv4/kj7yNY0yP/sZnz/6AY9vv5UCidPc9cJNJOcPxb+hz5WRQ0H/yQS0v5mC8RNOnRPobmVxQKYNddfnEDQfIWA6QMC0B59xBx7dpvx/h4axx6viCdHvLmN2aAvZxQsC4nAafffKc3x27yVefvoNvn5Gj5zXePvV13j3goka3+Ddi+/w7qsf8Ob5D/j62Q/COHp08wWuHT7CXvUairEdzI9l0O+Zhq9jCDPDCZTTu7hydB+3r32K+7c/x+MHX+Hpw1d4cu8NHt/+Fg+ufYfd0lPEJi6iv4sHeR7e9gLcBBKZFkDgmgCO+YJEvweMjBXPo89exNzgFnLR89gp3cKV3U/x8OprfPX4Z1nPH/6IZ/e+w70rr3BQe4TC0hXMDm5jmM2ymZOYBGynOR3j51lFkIwbY90Hh0UEI5utCsTp6dxEqGMNQUsFoY4yRv3rmB/eRXrunLCi9qs3ce3gHu5efoyH15/h6Z0v8fn9V/j8AdfX9fUWbEa+ePATHt8goPQpMvOXMRasosdONgONQ5fRcTIBRwvlhZxGEcBRe8yv98iP98x/4a8FwOH3roIFbq+1goGuVYx7CZhewk7+oTzPD6+8xtObr/H5Pa43+OzON3h64x3uX/pG/szK+BF6bQUEjDkE9EUBcAIEnXTb6NEfgABOHwGcOojzgYmjIjsF+Ob1NpElpvyaXDQ3tuYx6K1gaoCRs/soJi5JbP3lg8e4dfkZHtz8SlK9vnj6Di+ff4+3L34vzxKfp+/f/Anf0yz89Z/w7dd/wLuXP+PNl78XSc+ze2/x6MYrMfs8qNxFLXEJicltnPHn4TdEYf3dBDo/mUHXiUXYT8bhPJ2Bq6kAT8sqvG3rUuwFtYwdPQembNEEPURww7wmRXK4o4ghdwXxqQvYLjzCnQtv8ODqW5nYPbv3Bs/uf4PP7n2LT+/8IPf8XuVTJGYuYdBXRairCK8pB3t7WoHomqJIZJhAJSlU7+VMdRbO3xzAqbNdjWSBqcV908c4eKYttlfh1FQkNUS8eowlBDsL6HcVMT1ASdUeKukL2K+RmfEQD69/js8fvMCLp1/j7Rdv8d2rd/j+1bf44RVB1+/x48sf8MOL7/HDV9/juy++x7tn3+LN03d49egdPr/1Cg8vP8etg6c4XL2N4uI5zA+uYdCVhd+UQGdTFO3/dQb6f4jCfCKGzqYMKCVlbC/9rIKWXfhM9D8oSapcsCOHIW8Z031riE3vo5q+jvNbj3Hvyks8uf0azx99g1eff4e3X/2Ab1/+Ht9//TN+fP0H/MhXrlc/4bsXTBr6CW8+/xnPH3yHu5de4PzmE9QyN7AwvomIMwbDqWEYTo2go3UKdv0CIg56iWygHL+E85sPcXX/Me5ceIJH1z7Fp7e+wOd33uDLez/g2c0fcW37K2ymmLB0CVPBHfR2rMLenETnSZr/c8DCz4JSz5qSx5rToKH43FANmfld1NJk+N7A5Z2HuHPxczy4+qU0pM/uvsHz+9/iywc/4sXDP+LZrZ9wdesr1GJ3sDR0IGduxMLEkcbeSFkDWSy7Yqbt1lXg5N5MNqelgKCtgH7PKib7N7E8vS80/K3VG7i4/wB3uQ/ef4EvPn0r8dLvXv+Eb9/8jO/e/ozvv/kZ37/9Cd+9/T2+ffN7fPP1DxK5/vWX3+DFs9d49vCVpDbevvRcwglKsYtYGN3EiI+m6Ql0NS9A/3dnYfntImwnCLKW4G5hQgoZtZRA7ygzVMM+gsYDeHn+acriMebUphBgsoyriIneqrCx1jJXcWH7AW5f/BRPb3+BF5+q+/TdV9/gu5fv8MOrb/GjrHf48dU7/PDiO0lIe/ecNc+PeHDpFS6sf4pa8jZiEzsYDSTgMY2i1zWN5ak8tkpHuH7uLu5eeYLHt77AM6YS3vkaj2++xP1rL8T/hhHNlNn7TAkZUBDAcbdW4NGwPlEeOO9Zw1KzkE1SRtBUQMiawxDP88EtpGZ5Jl3DQfWeRIU/uv4lHt8iU/QzPL1DttGneHbvBZ7f/wbPbn+HK9tfoBa/g6Xhcxj3byDSWYCjfUWeq+42skUS8BgzIkEcj6xhfmwH2aWL2CrfwZXDz3D/xks8vvM1PnvwBl88+fDsfP/6J/zw5mf88OYP+OH1H/H9138Uqdrrz38UH5Ent77GjaNnOFh7gGLsktQ3LvMiWv5+CIZTY7C1T8JtmcFIOIX47KZI3q4cPBK2LcHJz+68xme3X+GzWy/x6c0XuH34FDu5G0hPH2AqtIZBu/IwoU+Mo6leS3BPYIqXvgqvjiAOJVUlhKys5QiCrWJlcheV5GXsV+/g0s5D3Dr3mYChj298gSe3vsCnt59LZPbnd1/j+b13+PyOSiW7f+Eb3Dp4g43kfcwP8HlJoOPUArqbY3C2ZYRR5daSJU1friUMuLJYHNtCJXFZpDn0TXt8/R1ePPqTPJtfPfwjvnrwRzlvD8qfIjvLlLB9DDuZSFiDvakkshbK/nkWhU17Ui80UlUFxDRtIGRdQ9BKBmIRPitBnAz6vUWM91QwO7qO9OIh1gvXcX7nIW5c+Az3r3+Bz+6/krSlr599g7dfvsN33Ku//gE/vv4JP739kzC7CKRxcPf6Gest1lrf49Gtd7hx4QUON5+ilr+DZPQipoa4H+ZEMtXZtgRryzIsp1ckTY/ABpn93MekLmcPwPqbknb6bhlKCBpYX6Tg062AAM5sbxXFxfM4t3YfNw6f4P7Vz/Dp3S/w/NFLPH+oaqxn9yhJfI780g2ciWyjx16B38yEzxy6hFFShqeNDJzjAA4JEhsIsA9oryKgXQUZOD0dBfR15zE3uCkBBUfrD3Hj3HPcvfyV1BOf3nmDp7df4cmtF3h860uc27yD1DyHMil0awiAxmBvoy8qBx/04uTQin6cHESW4dEWxDIg3JEXoGgiXMLi2Bpy0R2sZw9xtH4F14/u4eGNZ3j+6BW+/PQ1Xnz+VhJOX7/4Fm9efodvXn0v6+3L72QfffPVO7z+8h2+/uIdXj1/hy+evsHTe0zB/QxXjx5gp3INhdgRomc2MBoqwN8Rg6VpBvrfnYXlVFQSCHmtyBpza1YFvBHz4jr7hoSFBoDznszwMYAjf+bfg5fNn+vx/yW/9x8awPnHQJuPf78B4nxoPpSHDi9O4yI25FENto16/QDeEMQ5Dt7w6w/gTePrD9+v8X3/8lc2H2o1AJxGwssOgpQ9cDM17QmDxavbglurWBAeam+NOYRseQz5Spjsq2JhbB1xKhCjAAAgAElEQVSxs1vIRHdRThxiPXceu5VLOLd5FZd2r+PK/g1cO7qJG+dviZ76zuXbuHeV6x7uXXmAe1cYY/oQVw/v4cL2LezXrmM9dwml2BFSc7uIjq9hsreEAU9GDJVd+kV0NM9D/9sZGH8XheVkHB2nM+hqLsLRUoGrbQ2edsbH7iKgP5DUFQI5TMXxaoker2LIuY7pyA6Wx46Qm7+CteRtHJQf4ajCdQ9Hq7exV7iOyvIlJCcOMdezjVHnGsLGMhyncug+wal2GWz4FIDDB52O5evoMa8L0DPmXsdMzxYSE4dYXbkstEki7zvF29gp3sFm/jbWMrdQSd5AJnoJ0wObCHdlYTlNAGdFNULtnLJwo1STFx4SAZGP8IBQS4E4lEoR0GlozSkdUCbVYdEW16mp2nUltWL6DJsWSwH9TrJZapgd2hA0O7vI5uUQ26vncLBxDhf2zuHa+XO4cfEybl2+gTtX70kyyZ1rT3D76qfSlF47/xRsUHmI7tbuYC1/DYX4BcRmd3F2qIZBXw4+6wqchiXY2qIwn6JMYAkdTZxG0i9mFS6+Nz0ZKkcIcJkOZfmN+/Dqd+DWbsKlYSNThd9cRqizgGFfBbNDm1LEluLn5b7Zq7KYvYErB7znbuLOlZu4e+UW7ly+g9uX7uHmhfu4enBH7rWDtRtYy55DJrqB6HgeE70x9Htm4bOOwWUcwnAwiqXpMlazR1grXsBa+QLWKxewvnoJa8WrqOVuoJq6hfjUeUyFt9BjLcEvaUZFeNpoNFeBX0tGwAcAJ2hal2vfYysJdZwMqMz8gaRrbRVu4WjtEQ5qD7BXuYvt0i1UUleQmDnA2f51DLhoNsuiIY2upgS6m9Mg04d0YwXuKbZDQKa3ipEjzBzLJoLmdQRMNQQtVfQ7ahjzr+Fs3zoWxzaRnNmSZJZqak8O3a3CeeyUL9bXBeyUCZJdFtPvvdU72MjdQmr2CFO9NYRtKfhMSTjbE7AxgagpJYkZCsCpgzcEcX4Fcv+lv0cQgABODWGTkiz2dVQw7FjDfP8+sjNXsJm5i+38HewUb2K3fE0WdedbuVtYS91CQj4v+mPlRaNOAIcFT5B07gaAoz8G4BjIwmnIqQ4RMR4gbNxD2MS9klK/DXgNVbh1ZXhNJYRJLfZUMB6uii6b1Prs8h7K6SOsFy9id43MnJu4fHgXNy4+wJ1rj3H3+lPcY3T4jWdSqNy+/BQ3LjzGlcMHONq4JV5JleQFZOf3sDy+gZneMkY9WUSscbjaouj8ZBbdJ5fgOJWAm1KM5hK8rRX42tYR0GxJWkVId4iw/hzC9PUx7iJs4j1CGRVp0XmRUZ3t20Z69jK28vexXbwr4OLO6g3slG9gq3QLG4U7cs+vTJ/DeGQd/o4s3Ma0RDc72jPCkqP/mNqzeD9+xMBpFE/vz8e//Bz7+EwMcVgiAB8BHN4jZGopwFt5FxHY34BHuwGXZgOOtg0427mnVOAzlxCin5KnKDGksyMVxGY2UFjZxXr+CHu1izi/fRlXD6/i1sUbuHPpFu5duYMHV+/hwRVKce/i3oW7uHN0Bzf2buHy1k2cr13HTv4iqrFD5OZ2sDRaw0SwID5kbt0y7G1MZVqB+ZMYrCdTsDWRql4UcMnN/dCwJZ5gPrm/FEhIBmJPdwmD7jLO1CXN2eh5rGWvgZ/T4cZNXNy7jatH3OseCCj74Bobhce4f+Uh7l2+j5vn7uHK3l2c37yN7dI1lGLnEZvewfRAFX3uFJyGeeg/GYX+xBlYmmfQpVmEz5oSD635kQ3Zr4pL+6gkKHvZx3b+HHbyl7FbuIHtzC0UFy5hZfQA08FNDDuqCJuZopOBo5meL0xy20TAsIuAkXshPc6y6O2mn0cRZ/tXsTheQ+Iswe09rMbZ0F/AevYitgqXsVO8jr3SbeyX72M7dw/5+UuIDu5gxFVCj5XeK2l0nY6j63RaGJD8t4JG1jL0H1sXNqfHUIXPTNZYDRF7DQPeGsYiNUwP1bB0dgvZlT3UCkfYWbuMc7s3cPX8Pdy88hC3rz/C3ZuPcf/2Y9y7+Qh3bzzEnesPcfPKPVy7cBuXDq/jaOcKtqsXUckeIbe8j6XJDUz2ltHnpHk2m984ulti6Di5gu7T9KZScgifhtKRbfhZt+j2ZPgUMBwiYDiCV7cjoJejrSgyCvqahW1FDLjLmOytITq+ifT8LlaTh9guX8D57Su4engdN3n+XbqN+1fu4v4Vvt7CfZ6HF27hxuFtXNm5i6PqLVTiFxGf3MNUpIZBdxrBjnnYtSMIdE5iojeO5FwFlfQeatkDrOfOYaNwEev5i6hlL6KavoDMwj5mBmsSd072i6s9DSaakk3EiTebcTITRM5I5rCOcu8q/OLzUxKz3N6uEkZ8VUz3bmBhdBuJs3vILx+iljmPtdwh1vO72ChuYau0i+3SOeyWr2K7cBOF6EUsjuxhzLeOvq6yhAg4NBwKMcEtJcw1n4nPdgm9zjIGfWVM9a9jeXoP+dgFqVW2ytexV7uBo61buLR/B9fP38cdPivXHuPetSe4d/Up7lx5gmtH90VifbB2DbXseWQW9xA9s46xSBGhrgQ6NfPQ/GYCptPTEkbhNi+gz53AZH8RK9MbyC3toRw/RC19Hlv5S9jKX8BW7jy2skdYXd5F7Aw9gUro78oibM7Bpy3A0cw0r7KY/cqQTjzY6G9YE0aOT1+SgZDPmEZvdx7jwVXMD2+IoXR24UAsB9YyfH7OYyN/DluFI2wXz2O7cAnbhavYzt/ARvq6DBpXl65jaWQfw64SnG1L6Dg5LwCOS0MZUFnSFz06XtMV9HZnMNVTRXxqF8XlC6ilrmO7cBeH1cc4qDzEfvkB9kr3UUvcRHLqAmZ6djDkWEOPtSr1CuV1ZJFwwCVDHuMugqz/dDvwa7cRYI9i3kLYuoWgdU1qPo8pD6+ZIE4Ova4ChgJFTA5UhY2TmN9FIX6ItfwFMTp+XwNeuIW7V9hv3Mf9a4/wkGfs1U9x++JTXD96gst7j3C0cR/bq7dRy95AdvkiFqf2MTGwjn5/GYHuLOyGOCzNi+hoicHGlF5NHk5tSc4N1l2s6ciIJ/OGPUCIseiGCsKmMsIEKJkYZkggZI7jjL+I5dFNFJeOUE2ex0buPLbL57BbuYDdylXsrPKsv4v0AnuCbYS7i/CYcuBwiCw8DscYI06w9z37RtQtBHDUEDmoqyKorwiAEzbnEe7IYDzAfZX2FxflrFjPXcNG/io2C1ewWbhcf6bPIb2whck+DuoX0dkyCwbCUNkgNXo7feQ4gKS3HUH4Wh1ILCNoLqLHlke/I4uxQB4zA/RrKyE9X0M5uY3N0hEONi7j3M5VXDi4jivnb+LqxVu4fuk2bly+i5tX7uLG5Tu4cek2rl+8hWvnb+PKudu4fHgH57ZvYGv1IlbT+0hFNzA/XsZoOINw9zI85iXYtTST5jB4SZg3BG8IrLHf8zLVTEtgXEmnyJ5+L3s6rkbh1/Xa5P3/P/brj2uNf/+//g8L4LCxaAAz/9zrL5uQD+DNcQCHN0YDxPnw+mvQpgHi/Bq8IYjz17xhBLwRmVdDQkUZVb3ZOgbgEMyhDMmn34ZHvyYNCjcSTjQYYUg5VZ8rjUEvIy7TGAulMT2QQ3S8hPhMFZkoY1qpPd5GJbWDWmYPG/k9bBX3sVk8xGbhSA6VavoQucVtxKZrmBsu4kyELJ8YgrYoPMY52Ntn0NE8BfOpKVhOzcB6agEdp5bQcTqOztP1wpcATmsFLg1BCgXg+HX7COqPpBjy67dkAktjwN7OGoZcaxj3r2MqsonZ/i1Eh3cQHd7EwmANc/1lnI3kMebJot+WRtCQgI/FQEsGjtM5OE8XpTnihhkhTZ9NA1MWdBvoMa2jv3NdGspxLw/kNcz01rAwVMPsQBVn+yqY6lnFRKSMM+EyxkOrGPLRjJkO/yswnaCXD1FvbkLUATPFSDFvFIDDhuSXAA5BnA/gjWLgEMCJCDV1FxE2mowdFz8q0lO5CSsPD0pfAh1Z+fd7GVUazGCiL4u50RyWpvNILuSRjxdQTK6inF5HJbuDam4f1dwRqrkLqGQvoZA4h/TiAWIzO5gbq2Gsp4BedwLejkV062ZhPj2J9t8wGndawBsFUJFhlJPJoktLaQNNtHmvHcBvoG/RAYJk4Rj34DPsyBTS3b4u0gxS4d16dQ/2OXIY9hN8KWJ2qIzFM6ty7+WW1rGa3MJmYRebxT2s5/bk/ltN7CIb3UBsuor50SLGwwn0OufhNo2ju30YXZphdLUNobt9BKGuOYyEEpgbW8XseBnTo3mcGc5gbCCNsb4cxiIFjAbLGGTzYCsiaCRltgAfDfg0nJJU6wDOHnrM5xARYJQT4VVEOkoYcJbF/2Gqt4KZgTXMj2xi8cyOTGtnhmqY6i9jLJRHv4vgJYv/FTjbSWlOopuysxY1ZeBBxc+UGmzVMG9JWpl8zeQymmyTvUUzT6Ell2VS2dudxYAzjWFPEqOBOM6E45jsSWK6L42zAxlZ0wMZTA+o5/rsQAkzgxXw/Q56aASbRLdmCZx0drcmJLZXfCc0jOPmfUg9+C/3yP91IIcADht0Aqo1RMwKxOm3VTHu2cBMz7YUoYsjW1gYrmFuqCjFxEx/CWd7K5iKVDHqXUVfVxFBUw4BQwEBfQkBXQVB7XodwNlHr/4c6IHTazhSy3gkUqpe0yG4ekz7iJj3EBa24qZINBi36qY3gTEHnzkDepaEu5PocyUx6EtgvCeDs0MFRCcqiM+uI7O0jVJiH7XcEdby57FRvISt0hVsFC6hmj6HUvwAmeg2Fs9UMdWXx5Anjp6uZQTMC3BpZ2FvmUV30xy6Ti2g6+QiHKcTcDPJjYklTKHQ0OiQhR0ZoXsIGwg+8T48kHsxYt5GyMT9hXToHHwm3g+rmOrZxNL4gZjLzo+sYXa4jLODJUz1lTHRsyr3fK8jB685KUaONC+mfIpR0W4m3+kon+JU8jh4U2/i/qYADu8N/juKgSMFtX4N9CdTnmAbkr7l1W7C005AeBMu2VMq8rl5jFl4zSn4rXEEOpfQ64phJJjE1EAGC2N5xM4WkYmuohSvoZLawFpmG5v5fWzm9rCe2cVacgeVlS3kF9aRnK5iaYz3XAYjnhginQvw6mdhZ0LOiSkYfzMFC2n4p9nQc8JZgKONU1ZOOqtwa9fgYbAA495ZMBvJVlmF31wUuaLPnEKwI4V+VxZjQTKHKpgfrWJpsobE3Dqyi1soxbnnHWGT91buCOuZA6yl91BY3kJyhr4ZBAByGPQkEOhYhF07i46WaZhOTkD72zHoP5mE+TSlKItwaGPwWRLo6U5hyJvBWCCFiXAS070JnO1NYboni+lIAZPBIsY8RQzaC+i15hE25RGg4Xd7QVIufWSJUrZt3PuFN03AkkW4M40ePi/OBAY9SQz70hgNZMTw/kw4i4lIFlM9BUz3rmKmr4bpSA0j7iJ6rAQMVuBqZdpaDLaTcfGqo4RVGDjGXfk3yQZjmiN91QLmGoIdNQQ7Kwh0coqbgr8zJufWSDiNqaEsFiZKiM+vIRfbRjmzj2r+AOulI2xWzmFj9Ui+rhUPsZrdQz5Os+cqls6WMD2cwXAojrBjEV7rAmj6ymbIenoetuZlMVl28Xyn8Smn5e00b6UEYgcB3TEAR68AHJ9uV4GO7crDySt+cDkErRkB0HvtCfS7YxgPJzE3kpX7NButoBRbQzW1hY0ck9a2sZHdwkZ2E5XEBvLRNSSna1gYLmPUl0HQugxb8yw6m6fR2TyBjuZRuAyT6HEsYDySwMxQFlP9GUz0pjAWTmIslJJ6byyUwaA3hVBnHE7tEjqbFuFoTclnTSk3gzIo1ecgiWa1ioVDYIfnFVkmBF2KAuKEOnLCJOm1ZzDAWsTHmjKF8UgcZ3pXMNm3iKn+ZUz3p3G2vyC11Ji/LFLYoCkPn577D0HktCyavnqZ5GUhQMtGOAOXMQ5/B6U4WYyGipgaWAXB2uhEDStn15Ba2EIhtosqn5X8EdZy52TxOcotbWNluoLZYf7dOCL2KFyGGXS2TsHSNA3jCbIAZmFpWhAjVSaeBjpjYqTc705g2J/EaJA+VvRh5EphMpLEFH2m/HH021cQNC/Do12GW5OEm94zrWRNV0Qm32BZk2lNiRH3WBU6koWjLQG3Lg6/OYGwjc9QCoMe9fzIs9OTwWRvGtN9KTnfp3ozmIzkMBnOYyLI1NQSxn08F5kgFkfnqQVYP4miuykh8vuAgYwpShKZFJoUUGDIXcBUpIK5wTXMD61jYXhDauj5wXXM9q/hbG8VZwIcmBYRseZBebliJ5fE4NpDAKedjBWGqaghcqAO4AQFwNlGpGNLWDg+E73CcnDqU3Abk/CaE/B1xBG0xRC2r6DHuYyhQAJTA1lEz5SRmK0ht7SBcmJbar+N/IH0GpuFC1hn3Zo8h8LKIZhGuzS5ibNDVbkfepxpuC3L6Gibg+HUDAwnZ2E4MQ/DiSis9Hdrz8uwhszboHkDYcsmIuY6OMn6q50s3prUJj1Sm5QRqYM4YVMKg/asgDiz/auYHShhdjCHmaGUrLODOZwdLGO6vyqG9YGONLo0MdhaaQ+RgoP3AweDlEgxHKMhn6oDONKD6NcRZm1kIIhURpCG/cY0+roLElIwO7gp9eXscA3TA2VM9hUEsJnsYxBDFsO+BIIdrOnmYDk5J70IUwOdbVW422vwEHwVbzsF4giLklJyQxF+Y0ZCYphc3NO9jH7XIoYDy5joTWJ2JIvFqRJic6tILtaQjW8gn9xCMc29dQermV15LaW2UUxuoZDYQj62hezyNmKzNcyM5DASWkHYPgeXcQrWljG0/2YQ+t+dgVnCa5bQ3ZqUAB7KCylvl35JrC3+DHjDOqQO2nzcW/8fAEc26jqr472J8b/VFKrjSNTxZuOfAm+O/zmVnPJL8OYvBXD+PHjzVwdwpIhmUf/RYrNFAEdAnF1h4wTqIA6nVwQUXNoC3LosPAY+rPTFSSDAgreDkeNL6HXEMORNYjycwVRfDjODecwPFxEdK2HpTBkrk2XEpmlaWMHKVBUrkzVExyqY7itgxJ9Cr2MFAWsULv0sbK1TsJ6ehPnkJIyf0FV8GpaTC7A1rUiqhb0lA3tLHoxSdpDi11YDTWO92m34dUTzCeAcSmyumGnTq8a4hrC1hl5bTeKYBxwVDDrLGHLSDDWPfoJSthgi1mUEDMvwtC/B0bwEZ3MCLhrt1mUJfg39MtYRMSgAhyAdAZyIcQN91nUM2CilqmHIUcaQo4BBZxZ93WlEbEkErXFZASuvXUIKY5c+hq7WFVhPLan4U40yp20YF6vXNXn/jC1vLCWnqk8AxDxNpa9I4WTaAc3hCOBw4i5yOcO2NPpCwyUFkjpWPaVNpE2zcWHBQSbKCkZCMUz0xzAzEsPsWArz41ksnCkiOrGK6EQV0Yl1RCc2MDNSw0TfKkZCBRAE8lmX0aWdg7lpCoZPJtH+D2fQ+l/GhTVlPb0MRxupuAVJCPFoSQdeF4DGRwBHx2npvqKQG0khV9NTTlJpjEsvI7f83Qy8hjQClpQqYh1JDLiTUiSx6CPwIE3XNH0t1H23NFHGwigb0SxG/An0Opbht8zCoZ2A5fQwzCeHYD09ClvLGdjbp+GzLKDHGcdwMIvBQBo93hiCzkX47FH4upbg64zBb02IMbbfmJVGxa/j5LEML/XCYt7IhpKfwVGdsUGzYWq7y+jpLImUiswaRqRTRjDk5WSVnhwpBG1x+CzLcBuW4WjndIGHVFzufUcrD/MC3JqSTBo4kfsgoasDOOJBooo90WgL1ZemniUlHTIxMSSOoGUZQWsUoY4FhDujiNiiiHQt1lcU4S6uJUS649LAhW1pxbrRxpTcr5ngDc1JCSiVlB8TJ98Sv/3LvfKvAuDIFEUxLAjiNJg4A101DDtrGPfVMOolSyGDPkcMvfZl9PK9dyXRY8sgbM0gZMp+YN/oViFTqzqAE9HvoVdfB24MR+gzch2iz1Rf5gP0msmq2kXErKRIBMjEY0XP9Ax6mKTh0iXgNqzAY1yC1xxF0LaMXt5PfvqY5DA9UMTcyCoWJ2oSKb8ytSHpOMuTm4iOkRW3iskexl0nEepcUqBN2wxsTWfRcXIaHSdmYDu5gG4+U4ycbsnA21aEX1ORQpKaeBUcwDOKAM4BIvTzMe6rPcFMqR0BnFV4aHSuZxNN08NVjAfXMOIvY9CTlYY60h1DyBYDo0V95jhkv9KswNpEw/U47G1peaY5+eJ1UKDzcTnnvxKAI9P+OiNSKO30BVPLryeAswWfbgte7RY8WnqjbcpU0aMvw63Pi9ebQ7cCu3YBbmNUPLsi9iUMeJYxGophsi+O2eEkFsayWDpTQGyqjNhkGcvjJSyNFrEwlMfZHk48aRYfQ7gjCo9uFramSVhOTMD02wno/34SeiZNfaL2enryuKU5597GxcaMAA5ZiRsKwDFRAlmF31SC18D7Kwm3np9FQoxtmWo34OEgJYMxnr39eQHeFsd5xq5heaKKpfFVLI6VMNOfw1gwhX4nz2x11nY2U9I6AeMnkzB8wn17GuaTc+hoWkSXsBkScOnj8BpjCJi5XyzV94p5RGwLiHQsImxdRsgcQ9AYR0CfhF+bgl+bFVCbZvo01RdZr0lJJpTpOqViJTEY9pm4ryTgM8fgN6/Ab1lBgMu6IgBDsGMZoQ5e0wQinWmErWn4DQlJcuo+vYjuU2S1JNB9OgVHcx6etlWZvLI5DFCyxWtJBoNIt2oIWAjk0KwzB5c+AYduSSa6Adsyelw0o6WBcwZnh4uYP1NGdGoVy2criM1VsTJTwdLZChanK5g/Q9Amh/HeFAYDMQFuPJY5dLVPCyBmOX0WphMzUrt0NcfQYDT46WlCzxs2YGQdMAq4AeDo9hGQ2uVIzkQv71MZdJCVS/P0HLyGFJiC5zEswmOYRahzXpqmseAKpvoSmB1KITqWxfJEAcsTeSyfyWH5TBYLwxlME4jxJ9Fnj8FnpIfgDHR/PwHj7yZhOTUlQI5TNwu/NYpe5woGPXE5L0O2qMRz82z0WaPwW5fgNS0JeMPzqZN7UWsGXpFwbH0Ab8gEfg/grNcBHDZZTNtiPHYBPgKohjS8xgR8Jt4Dy8IECHQsINg5i5DtLEJdswjbogizce9MImhJw2egxCcHN9NT2/NwUT5BaQfZKaaKfM4eQ1EkiF1tfLZX6rVOUqSBfe40Bn0ZjASzmOjNS2T1Ap+ViaqspQmynKryTA37CdwswGMmM20SxhNj0P5mDPrfEcCZh/mUSiaid4inDjR4zQQ/l+GzLMr1ZH0btC4gaJ1HyDqHkHUWAfMcvPoFuDRROFqW4GQz2sZ7uCKx2j56mwijieEUjf1sXa6zS5NXNUFbAs72GFzaFbj1y/AaWd/wGV9BqHMZYduSnOm8fqHORXmG5eznM8s6wJQU8MbZugLbqSXYTq3AyTNFW5JUJX5W9Ivz6TOS3tjblQdBnFFvEcOsXVw5DDiyUkP32JKIdCQQNCfgNyThaefPQ1ZcHq6WkphbUwZEAIeyKakV5P7nM8A+ZAdh8zZ6OrYRsq4LeM392clzVZ8UvzmerW7jElzGBbiMZE4voMexLCAEh8BnB/JYGC1ieYLSMrUPxibXsXRmXfyoZgY4SC1iyJtFpDsBr3kZ3e0LMJ+eQftvJtH6dxw8zkL/uyjMJ2OwtWThaniTWbcRtmwjwmUmOMl6h6AK+wJaK9TQa+aqoIesXCPB7Ax6O7IY6M5ixE3ZYAoDzhX0OhbQY2di2iJ4zoa7CFDF5T7l+dpBZjPDWegtx7qyvaYYJXXvG/r2kX3KQTL7EfYgEcaYGysIEljhuW4lO4YstwpG/avimdRrJ/DKc30FYa4u3i9LcOkWYWuhCbA6n5h06ib7nRHv9C57D+B8AHG8UvfQeD8Bj9x7i/CZ5xHonEfEsSj9xHAwgbGeFCY4FBzJ4exoHrNjBcyNc38tYW6sKL+eHc1jZjgvw67pgTzGwmn0uvj8zKK7fRKW0+wlRtD6fw/JnsVBSHdLEm6CawyjMDCl9FhPRE8xGhPXh0cNgOZj4OY/16+P4x78WtXpYf02/i82j6pZVFN/QVgZI16/gP+2LtSf/0H+uUZDATYN8Obj78Fff8ya+cC+YbP/awbOPw7e/KsCOMdAHDJwuAKGHfgNm1L8cLrKh5UJLH5THn6muJjTEjfuN8cRYnHVlUSfI4UBVwqD7hSGPCkMe1MY8aUw6k/J9G6MjB1/BqP+LIa9WfQ7U4jYOEFYhpcJDdpF2Nui6G6lpnERXc3L6GomcJMUM2I3texS+Jbh0lTg1hC84YG2Cb9OAThCRdYdKACHRZx4g2wibNlApGMNPbYaejoroCks3dp7OnIIW9IImRMIGmPw6+LwahJwtybgYVHSmoevrQy/pqrkFvQwqm+aDZZVxLAhMqo+6xr6Oqro61iV793bye9Ng78UvIYEPAaaCSfgNSbhMdIkmm7vlBQlBWn3aNkIcVqpjIv5SpqmeDqY1hE2bahFI1IjJ11skBR4I5MvoSyzaKqDcoZdBGWp68BpJI0dvYaKFDo8HN0ygU4j0JFEqCuOHqcCcoYCyxgOxsANeCSYwkgoi5FgTgwgh4NFDHjz6HXmxMDVZ0nCqV+BrW1RUlUsIpeKStJUZ1NcwBua7/mYpkCDZZkkqcZK6LNCH9+rAzi8//h+CTptwieHB/8OGQMFiX8OWHIIdTCpIC1RvD123ntxDLoTAtKMhxIYDyUxzolhMI0RP82bOaVahs8chVs/D0f7rBhLdrXMwN42B2d7VA4hgpM0jOxz5RBxZhDgIW9bgbtjGR5rDF4Lm420SADI5KDmOUBdOhkxpIhr18TNnnshWQ8CokkqDmO/Gd9cBr1HIrYCIjbq2BmVnkeok3T7lBSZNGB06kgHZ5OcgINFkCYHN31GyPKRA9ah4TUAACAASURBVJ1mgJTRHfNAkshrRVcnZV2xtMjUqhfNBjKGcgiaMlK4BUxszFakOWODJsUzX01L8JmWVENlTiBgIeijAFxnewbdrWnRSNOLxyXvh++FBSb3ewXi/3N76f+//0/QWem9GwwLoSwb63KqzgoG7BVh2BCsCXFKxwbQQqAqhaCZP68Cb/z6AgI69ZkFdSy8NuoMHAI4h+g1HH4E3Byi33yAPgsBHLKq+LlyukyZBllOtXpKiIp5ZYKfR5+Ch+CoYUUaU+6PZDHQQJzgyIiPzXQeY8ECxoNFjIdKGAsUMezNCQAlRZY1Di9BvLYoHK2ccC/B0bICR0scThrMt2bgbsvDKxP9iujgySZSSQos7ng47yJs2K/Lp5RhZMhEo1QWYdS2F8WUMEiJTlcZg07GPPPeJNON+3IMXlMMHmMcbn0CTm0Cdk1C9OYEb2h2z5huGmwqTwDFBmtEeb/fk/7GNYAUZ5z8S9GmzOXJlAxQyqHj2bClzgf9Dvyk7OtpcNvYC8mgKsClo1E+AYsYPGwkrcsCvkXsy+hzLWPQu4xhf0y8Qs6EUjgTTGM8kMKYL4kRTwIDjjh6bTGELCtyljk1Udia5tBxah4dJxdgPbmoJDTNTDBhHL3yFqBclhR1Lq9uTSQ/fG9M45OJr2UNATPTlIoCtjHml6AHn8lgR1qAbEbu9jiS6OfZ68lgxJfFaIDyJJ61GYz6Uhh0JdDTFUPAsgSPfhEOzQJszTRQnoOV7/F0FJ1NBIxXYG9LwsUEQ51qrn3GlHj3+LlfkGFgXkTAtIiAcRkBnpmGBPy6pDBWvZo0fO05ENQO6qt1P6IthAX4pEx7W+QHZMWKlKrexPtMHA6pn41DIrWS8BnV8hvTMu31s3FvZ1PIBKc4nM0puJje1VqAp60Mn4Z7MD97fu4bsh+TxSDyE/Fy4L+7Kmk+HGIQcPUQPOBQysZGPSGSsiF/BiOhDEbCGYxGUhjrScor/U1GQmkM+lPo8yQQ5vDJtgQvmyADGU1RYSh2tTFVjObUqnYha0IlRLIRW4e/neajZOEQxKkzcAjg6Dh8OoJftwefAI5souh9ovYXmuF79El49GzYo/CbVWPea1/EgGsJQ55ljHhXMMp71c/XFYz5lzHsWUa/YwU9Nu73K3Br2agt1hlhrLeW4Ghflj0rYIlLPcd9KGxLwG/hHrAkCTUeI8FpAkhx8UYhC8TO/YiyXi3BOk652QuoabfaCyipIoBTP5NkUk4JMEEc1gV5+AxZUBJEaa7PFIfPxL1zSc5rntleE8EJ7okpeIURUhBTX3rCsD5l7aTqVDKYCXrW4DGURX5m16RBHyGazvstZLZmECKo38WAjhT6nARCMxj2ZTES4KBHrWFfRoDskI3vYx4Et2ytM7CcIjjHFNRFsL7p4lBRw3uJZ2QWXmMGXrmHkwJ8cw8lsMIa12uIwmtYkOXRLcjn4NKswNmagKstKz56Ho0KqGiANwJGy6CO4DjrppqAOKyHXQJgZeBqT8GlJcCblMAKXkf6bbE+5z7u5/U0cm9agVcfg5dAsD4Jnz4FrzYFN9mUjMZu5fNVlJqBdeeHZKU8yHgiq6avq4B+e0GYOz0dGYQtKYTMSQSMBG7iKoGpPQm3hs8pn016Iyn5lHiz8b5vADj1eoF1Q9i4g4hlRwCcsHVD9j0v9z2mfPKaGtPwEnAi4GuKwWdeFqA3bOOQKSH9xJAnLfvfeCCH8SDlZeqMHfUXMOzJYcCVQW93SnoWr4kMshV0UTrWtAjTySiMJ5ZgORVHZxO93QgOlgVMD1l2ELbsyvvjAEdqANbeZB0LA0cxYHpMNfSYqogYywgbigLiRMw59NLDqiuLXoJcnQpED1gXZD/m80UWlduQgIOy9BbeU4p9w36HbGthYnE4o6XZrvI0jRi30cNl2kKvmWsTERMHvWS5FREyl9BrW8WAQyX/9XQVELTwOvL6qcV/l7/mcMCpYeBJth4KU5UwDgbD+PRk8HPxa4I5BHU4hOUzl5fBlexJhrg8p34OBztXEOYAzZlAryuJPk8S/b4UBvxqcf/kGvAlMeBNYMBDRmEC/fyzzqR8ngQhvaZFuLQLsLdxiD8P64k52AgYtxAgZJIohwQErWpy1vD5kD3nz4A3rBN+3aP/Z/q9jzGLjwEcNo4EA4RCT4S1wfb4t3iRfv3D/BKgaQA1x18//jvHf/3P/4wNb5t/6etf+2ZTKGTjM6m/ymGrPrOG/OEDiEPTQR4YnGBxwkofFWWiJtMTToT0aXWoMuWBsiMDD4cVePRL8OgW4dGzMV6ARz9ff+XvsYhcBj0BXO3LcGq4VuDUxOEieCIu5xmZarNpp+aXE2N5D2LoRj0w14b43HDCKiaA+mOFkH5PgRdi3EltLc07NxCykAZJA1hS7ohU5+DTpeHTJuFtT8DbnoJXk4FXk4VfU0CgvYygtoqQeGUo9k2PADgE3+oAnYGgDqVVa4iYqggbaSZWRFCiirPy8zg5KZFpCYtjHracXvPgJYOBkYSMWWUj1JDENNBkAjgbCJs3EBHqpqJvksIpfhb0vSFI1Vh1c2peD9VMKxCHgJyAIsYtOZC8hjXQF4BFDg0eeUB6eDia48Km8XcuyiTa37Ekv6afjc9CACMuf8ZtjMOp58Qghu42Tipi6GhakckBU2nY5JP+SXmFT7+KIL1YSEFlTCqNYA38zGg6XZ9A6qmHVvIbUmkl8liSf3g9KAUii4TfR2lv6eUTtDKhikAiD6MVAR18pgX4TPNScLG4ZcNCYIITS4I03a3zsLdG4WhbgqudhWxc6MGc6DFJI2AmTT2PYGdevD48lhScpjgcxhhcxqR4f4hchjIcMaxblUaFbA4mkkl0qjACdhChZwplbAb+vATmWLTSe6QEv4nTR9K9c1L0ufUsMpOwayhN4SK4l64Dl3nxF+CE08dinsZy7aTU1qe51EGzmCCAI43rmngOqPfD96T00WKyRx8kee85sBliEeeVyMs4XJzk/WLF4Wbhy8KUB7WWBSP9OkpS2DD5zUMjajYk9LggLfpvBOCQVUJwQn4+mWDXEGDDILrvVYTNNMfMg42e1xCHR4pUmmumZHro05EVkIdfW4RfW1byKR2pzwrAieh30WtglPgB+owH6Dcp4KbfcgC19tFHAIcMN5qYm5h2REB1XSRJASOfXQKwbCyYSpaBW5uEh/sKC2VDEn4ji+okAgTF6stvisNvjH/YN3Ur7/dER+sK7M2quHdzT9KyYCmqooURpgQN5Z4jaKiSFtTAhHt7nQUlptI0i+QeTxkL70UCOAoUlb2V9wT9YKwluf/ZJLl0MSluRb7XnhCgmWk+SqqQFfDGLU2lAm8U+4b34b8uA4dnZGO6Jn44AuKwsOa9ohYBHGXYT3BYMfyCph0ETPSboe8ME59KcOo47c3AyWJWF4OLZ5RhEW7jAjymOXhN8/CbWXQvKhDDvAi/aRF+4yK8+kVpxpyaJQHbupuXYTu9It4s3c1JYSjINFW8gniO8jNgKhNZXARuFE1dwBvK0LhPmtfr55WSUjFilykgXjaJBsVG9HDSTUaKsDJWpAH3GBrN4hJ8xiX4yQaT97cIR9si7K2L6JYByRJsTcviz+JoYwGfhlvLZ70An6EkLBlJazPkhPHA+9iji4vcg5IPjzZWPzOT8GhScLel4W5Nw6vJC6hNxigTGzmtpvQwYtkX+WEDRBTwU6K+2SQxIlnFJPPMF5YoWW1aSmR4hmbh1mSlySV4SZaAszkDd6sCMfksBLTcE2j0SYCEi9N+ldioTP+VFwebfgWI8N9R+y+be2FPsLGxJODvoOxmBT6yTSwL8FhmQYaNx0wmxiLcpkU4DVF0axfQpYnC3s60KALvSbh0abi5B5A1w8EXfV84IeYZQCmMdg0+zTp8GoI4DRnVHgJ1AMevawA426qJYuqkvOdi3ZOEn1MCbn4GZBDpo/Aa5+EzzsFrmK2vGXgNjXVW2Dpu3bwMyehZw7hoewvNldko8vvxfqLJNlM7s3K2Bq1qUOeVYRPlvFysX+o1TDuTili/UCq3KiCZGNFK88TzqH4mNQYLwiLhOUipJRcHC/Wa0qAGhD5hGWUFBOdkX+1DMTh4JtYlu+8jnGUfU+wANRRSzxKHVFz0ueIe5SLQoSXrmMmBjTM3A7chKSwstz6mWJNkX79fS/BwqKiLwskGUjOH7rZ52FoWRDLW2bSiGKgafu9VsRog+4esH8rdGP7hM+YEzGFz69IyvWgZDu4PbYtwtkXFc8bVRnYWwY4sPLyOGjJ5GVBByRmHIgq04fne8DhUIA7rX5Wwx6AOspDExLVdPTM8s906grC8F+ufWXsCTo2qQ11t/DcVcONpz8DLZphnI6UoBF4NHBhyQEj5PuXRRZEeh+jFZMkjbMkhaMzAz2FFOyVXcbg1McjP0xqHqzUFt4A3eXhaKS2viAmvuufrAA5ZNw0WgDCWFUDS27GNiJW1umLL+U1FqZf85rycT5ReEogIWtMyoAmY62AVz1KCVqb6+Vr/Ws5Xo+pLGj2HQ7OM7lY1IGaaLWtWW3MaXS2UpvF6EhjkcIbydzJvuH/t1dntBJvqICXPXQlCWENYX0PEwFVF2LCKsKGMkJ4gTgFhUw5hPldUMHCv5n6si8KtW4JbtyIsFmc7az7aDKjambWW1HzCtFYDCcWu3UbEuCO1SK95B32WHfRb1ephiiGZm4YKQqYKwpYqIh1VhDvoBUqpMy0MUgrwa+dzn4anvucTkOE99b630nOIysU+kGxGNVBl2qhiBhI4pfk+Bzl8ZtMSbMEBtQBtrMstCekZPASBLStwm5fhNpFBxaQ4tYc69VE49Qtw8lnTEbDhc8e9jdeJNRxrVCZfUTablTpIBgT8GUU2Rt87srPrQQYyzDleF3z4+q/dU//7+n7H8Qp+fRzAEbpkXYpzDMQRyc4/ojv73//Df/wD/aW//ufBm8bP+i8Fb/7aDJzGv/+h2P0IyKlrUlnoE8ChpIpMCDbT0myLtwH9DdhQH4s15OGoycHJqXxLEl3Uo59aRseJKCyfzMP8uzmYfzsD02/PwvTbGZipF/7dHKyfLKDjxCI6T7HQZTFBuiVpsQqwYUNE5/qwZV3ACwIYkqAik+8G64TARV1LK5RpmhizEKIhIAEcTp059dsFC3ZF3yaSzMOPzSe1pUwPotFiBp42Ajc5+NoLCJBCSkqwroqwfg0RPcGbDRC8+QDgKAaVSKn0NDJjI8UisgJ/Ow9iJgnk4WzJorspJR4mdspO2jLSmPPQVcAN/SNo8Ndg3/DQ/gDgsFGMmDfRY9mqr230NGic72nKbFK2wEhGaVgkkYugiLoOvBYBLsrjjDvw0eDRsAWPfh0uJnVoi3C0Z2FvT8HeHkM35TuaRXS1LcLWSnMztTqaaR62JOZhllPLsJxcEVNp66kkOk6nxZSTByCnhsrsjeDTpqKdWraFIismy8ZfaqCDumPMPZnesXiob8hMleFkgek5BN/MPIzU9I4SEFc7Y4xX0NXCKfIcrCenYT01jc7Ts5L+0d3CaXhUabxPLKCraRnONh4KOQQItJkIAFQRttTke/sZl2gswmXIw6HPoFuXQrc2BYcuAxeLQD2nhiWhgjMZSQ4R/ZqkkilNMqmtnJJQwsafk+AGadCcBNfjQLUsEgpSdNHxn6ZxnLqoQiIFO5sUDUETTmBYHPP+aCSQUUdefQ/gkG0gTKy6qbawbwTkWENQW1OLbBMx2mMjUUFAX5aijPe6R57fDOzNqV8u+j+1UgvNZ7wEF9PRaGonBzglcBtiTkrwhgf93xzA0XJizaZnA35pgBrXYVUo3x4W6dJUsBlNwiUFKieKWVWgthO8KcGvZdxnVZkP6gjCboMATo9hH73GffSZ9oV1Q+bNgGW/vvbQZ95Fr4nFEyddarFgilAXL0UT2U6clpElpZ59+l5wX3O1puGkpr0lAXtzHN1NbO6XYaOx94lF2S/l9RN+vQTbyRV0nUrIvkEKuk/kCYqFp5h3CihR/i8KxJV7TUC0D0BaA1BT+yTBG7WHN3Ts4osl7DZO+Itwa1ngcfqYQHcLV1J8juwCyGblnnSJ5w0ZcQ3mjdq3FHjzAcB5/97+xgycj882BfapvVD2w3rKT8hAE+oDhM0HCDHCtn4u+I2b781uaVRJ48jutpQwKGzc95rn0dE0g86m2WNrThg2nAraTi/ARgYLJ/I8z5iQeJpnoZL7isRQwHkC0Yy4ZpQ7ZZVMkGNqlwJz1GsdvJE/Q78F/lmyccgcIbuARpr8nPJwtbPZUJ+VrTkmUpaOk4pVYflkTqaVNCXtPDUP26kF2E4tqnV6Gd1NcUmJcrQqdp86dznUoLylIoB7yKRAcz+bax2bdDbrvI8TcLYkpEnjfe0mG4yxw5Qb0I9JU5TnK2IiM5Vn1S56LHvosdBDahehY2ex+vnZeNckOlsYSZSUta8Ku8/ZVgbp/DTytDcznScvUilXcwHulgK8bQwWYMNEHwgVKkBPCj8jZJmGJEvtlQ3gWyba9JpjFLBBsYt5PennRBNcsszsGk7meebNwdo0DdOpMzCfIpX/LCxNsxI8YD41D9OJBZhYxzQTdCc7hg08z6YqAtwTzPXPW1izBNjJClqHj1KSXwE4B4qBIwAOQxh2BBiXhkrMOWnSqRJgyEJyU6KiYWLQIuwtC+hqnoXtNKWWU7B8MiHyPeuJCVhPToj8svMUp9dRdFGO3sLIdTIMKP8kgMbhSONsZdQ7ATwChpTP817LyfUhSKAW91qe88o4VMl5FfOJII5aZN40lpIA8Rz8UNtw32gs+uMQVGbjXJbvzcGWg2djS0aepc5TGXSczMLezP2VzxJr1R15ZY2qGOOKJSBsNmG2keFWUSwdYQyUZajEn4FDJltTAp2nY7BKLRMVRhLlGZYT87CcJEON7LR5dNLLiNLC5pjsiXxf6tqxyVeeVQqIZQAFn1P6DZbhNZTUntqWQTdlx9z7m2n8z88gLnWzW0OfmCK8UjNyMEOWFsG+RpKXCqcge+197WRUkmkyIjzamjDS+awwuYysWO45Tk0RjrYC6FFnb83BTt+tZr4P1uoZOAiCknGjyQtwQ7ZN2KjOmIiJfoqUw7EG47m2KjWDmP+TCS3eNnzuKV1MCBuOjDj76TgcTUkBV10tBG8Y7MBURD6LDdZZ3aNNfNp2pE7isIuy/x4LE+y20WPdRMRKALuKIGPFLWWELCVZYWsREbKYO4oIWZjIqIakjlaywXi2cv9lH7Ii+3Enz1gyIE8swkpfmxPsPfh7K+g8qczPacsgbGKy9Qg0cjBqpqRLAc8RyyEiZuUjJ2EAHHzzXNNtIMT0TR3DEAjiKCAnrGffsIqQvoygnmxE+u7RpDoNt4BolB0S7F+Bo5UsdT6PamAndd978Kb6wetSYq6ZgEvmzQ56zbvos+yh37qHgQ6uXfSyL2CPVDfyp30EF897j3ZV2DXdzXm1l7bw3qAkn2bVBFQ3FUNS+j4OfJUS4/3wV6TIDARYU944Iu9UzyuZuNz7PATfDQRLKT3jMIT9REJ8fTpbl2FtWYSliaEm8zCdnIPpBNcsTJ/MwvzJHCyf8NnjWcrrwwEYPQtLCDGJlgEW5jVRO8jZwhqMfqTvPUkVC1c+lzoDsMEEbNQI/7lfP8Y3jgE4yrBM+W6oG5wPZmPxL/7LQY7/yH/2fzeA8/G1/YUnzjE2Dj08GvKb98ycukGpgDpEZdnAUSNJoytKmhg93lqCkxtDcw72pqwYCzJ5gRp1tfh1BnYWt01ZKcbEZZ0bPSOj67IQJR0imnyMaVJPWXrPNhHWSQPAUY72dLUX8OYYgENPFTFQfL8hKXkOJxxiDiegC2P4OMVjY6eWGJPVN2UBcAjiSAz8r1PD1HWlJw617RsICE16TbTMPMBoTMdrQ0MyHq6kRooURuiRRL1ZwJBi3JhiN0AcyqXYILJR/MC+UVPNYwe6AAXqWij2TQO82UfQcCArQKNgwwH8+gN49fvw6Pbh1u3CrduSRCgaCzu1q3AQzNHm4NCyoM29X3ZNVsULCuCQQ1dzDramvKSB2VtYaDcSwTgVo8SEhqkEbeq+PEI9VVTZDyy9HQh4I4d5AymvG0QLKEZwoi4jY/PTMOYlg4AyEB2vJQEG1cxwqsgCiYUSDwBqsMleYKHiaEnD3kSacE7AO/FnoLcQmRQyJaa3yZY0VZyMu/VVOHVldGuL6CbzhP+WnvR7stEIhNTq5nH1CQBNrWURFFAHLQ8TTiJZ0IofByVhlEroOMWowa2tSlQjwREWXw4NG0hODVWUPKmqH7xuWCBzMscpLou8BuOH4GHjmtFYmwXFL1dYv6H00vXDjs0O3yuLRBaL9O6hAbMsTRWkcXtEosiEN5qEb4hUkT4i9JuidxEbjACft/pz1TAlJGjQmKz91V4J8JF2TckBG/L2LZEgEMzhHkSKsUogYMwlGwtOr7iUPxFBGwFu3j/jZN/QwLgB4Oygx7CHXi7jHvq4TLuy+s274Ooz76DXtIUe4xZ6TJtqEVhtFBJsIPn8cnIqTADFAGiAbT4yp8TomkUtaeUFuFoKcDbn4WziytVXHi5JIikJ9Zz7K9kkKnr1l0OSX+3N7wEcxYAVKbOAa8dMrgkY1A0xlaRRNR1kALIh497EOM4PS+1Xbibkacle4HPHZotF38fgzZ8HcD4+d/66v1aFyS/OMl6HX9D0CaYS0GeculoNNo7fSNP0XZV8p9sQQ2GHpgx7W172O5pJ0odKQC02CbKSIu2leaujJSUNkb05i26ee/Rna2GxXBUfATLUuB+yKQhySRIYX38N4CjzcU59CdxwCq2Yo2QvEhBnIc7PTH1WBDmYHlOEvaUg/zZBI9upFGynkujimdukQFlHc1o1bARZWvNwtxGQIThcFnDQz1Q7kVoqdgT3Ni4lw6RUk8Ak41lLIvNwE6SXVRIZn0rgY7pSCf72iphIswGk5FBkhyKhYgqaWgTPBECjXFs893bh09O4nh5F25Iq6dZsKdPptg24Wtfhal2Dq6UGd2sVntaqhArQl06MyOtsWDZU/DVBHEobOB1vLD6TskTKwym1AhP4LLCBEa+19rIC1nnuaSgVTaCrdVn8IfjaXTfvtrMhb83CxjOQDRHPdoncXVP+RcKi4ufLZoqDpjqoIbG/bGS5RygJVVDHs5refQfi3xfUn5M0TQ6i/CINJ9OY+z7TvPh5FSWtiIw8gtMeMqfaCKjF4WiJCWuvm8wqmidTgk5ZHP9fa0rioemzIvWHSLMU84tMA4Jpv1yKIaauDYH744v7rWJCqzpKsaGF9VY/74SBc0zi/Qt23nuQpwH28Hyk7I1Tfp41G+JtyBADhlQ4W9dgb67B3rwGV9smvLxmDD0wsqahZx6lkZRIbsuz5hO5B8GcXy96erwHPVrL8rwSHGQ908WatQ5yEOjolme78azTk4QgZlEARn4P+vQx9IOJZ2pxn2UN8QGgVcbDDNuo76ttxfpQpCjGzxJIUD/nRe7XYN80WJVsRqX2Zb2nvGK474vNAX9eHZlaW/BwtXNtws2lYfLeOlxt63C21eBsrcLZWoFTalEaJTPpiudjRdjDwrrhcIJhGEYOKrhYxxDE4YCqJozXIMEcSlcIMnAA2ppTqyUHt6y8kjSypm8leFOFT0Nm3Kac25QBKZP9urqB7Bth4Ki9gsPJHrLNLQrACVkq4ArXV8RSQcRaQY+1An4dNHLApX4W1YOU4eT+21yWRC9HM/sRAr8KwLI3sQ7Mwt7EHoX/j7U56511ScjiPcSBZ9i0rwB/E8GbI0RMhwgbKUneFfYCzzDF4GUdUV/SI3Dgy1WTRTAnJJ57ZWEAk+3k0VAulxHmFQE8N4d1YhFBcL4k5yyHfY2kWRVVzhpP1ZVk4CgAhyDOLvqtahEwD/P+q8uHFZCqhrpM//JoNuBuW4e7bQMeLs2mBMA0ajn+3LI4CJdeiv0U6zzFxlG+lARxOMCjX5uSVAmTlGxSY00Wa2SPgUEBq3DpStJT2NsL6G7LoYv7ZksGtiYCqCkVSsNgmtNME8yg+7TqCymJpS0Ba6qQGDXXa9iG749hC5H35JC6GqKBM8gAVUnNPsSG/xMYBL/P8b/T+D7/oV7/OQCnbpwqAA4jS2lKJSDO/wFwGgXrvz0Ah01znY3zCwCHlHsWvfXCl8XvsUWghI0atax+7ZYk79CcjE7pvnYF6rAp9FLPK4vNS0WAHoI9H5ZqFtmkCG1UvDSUHEC01EJVrCdnNWRC71+V75KSB6j3qt5XA7z4wD55z0CpN5w8AKUh4j167LBSBsWMfV//sN6j6w0ApxEDv4UeA6f3anGST30qDcZ4SLHR5KH1i2tCsIsshnZqTBuLhUCDMlufurynzioAR0CcBpDD6QgBh2MTmYYH1Ydrwc9rv77qAA7TnvSHkvrk1R3Ao+UikLMLDwtnxsfryMipCS2Y5m1uNnX66ocl0yyablbh0lSlSVEx7iy6WExwwyf4p9hPYRMPQlJP1YSlAe42ABzee9Jo0ZStbsymQBACDIrVJK/vGUkEuWjSy2vG68fYwFVpRjgR5EFIRhhf2cArjSyBQTKi1CHPSSwPRh6KLIxEey3vcV+aioCRxdimYidpq7C3r8pyanktOHFrNK2c+qrJbwMQaQA4ClBRgFQDwFEgTr1QJR1VKKlM4uKiYfiHxV8rvbEyMGyYGBJwOb6EfUNw5vgSWRClQR8W9x3eq72k3hp3ZbFoEpZQQ27HhleaXsXgUAe3AmnEO0S3K58vP2M/D/j3DbHaGwTw/ZtIqOqAkIA4OwgyuUWWAnP8ZOWIjIvPkJrkEpzjUg2oakTJjlOLTDkCXLw+CmyL6AngkIXDxa935Fr1GreFdUPmjVpbAuIQyBEwh02qADj1YqJeUMieKntBg+nIe6EO8LIw53SVex2bSjIGOHXl0tSX/B5/rvq0kp/Vu1VLawAAIABJREFU/8femzjHUWRtv+/feSNuxBf3/WYGW+pNsoHBttZWt2Q8wLAMMMAwDIvxbmvfJdtgwOwM+z4YGDD7vnh5bjzn5KnMqq7ulmTJyCYVkZHVpe6qrMyTWXl+ec5JTh5dEstCGY/Z18z10FuxiTWeuJbSMs8rSVwRpywxiduBbE3LvtQkicsqoUE6GWzWOF2ENpbUOlKtb3wQY3v/bUyeBTgeIMpk2723xBqRYD+xSmTgdE0Mos7UX+JYOCs7QlEhZwwEmrJLoFQGS5XEWGxMVOSYjskqJrd67dnK2GyEnrRO46SXyp1aCYhFKxUwWk1KItDxVlFSl+Ji6gAO3fTE9Zcro1QI6X7qLKjYfgTBDH7cOSH33bVlDLsI0q85JorLrmuOoofxJwhauMpKNxdxu9N+wVVajXPBd46Oq7qzoTsm6AiSuP7QJSl5p6tliwFKghuBN3xf0q24a97FjGIAYwX3CnC8clTrOo5a1wkMVbj70rIL3ruEvk6mRU0di+jrWEB/xxz6qXB0zDgXJPYdAlOOf24CbyviXBWXAOXB/+w7bhMCWZ0VS1DnHsBdERP3ALXUsBgju7gjpbgJ0PqS1hUau4i7tPR0TkG2gLd4EWIN4hR5cRVWMCFuprKKri5+6g5BWXXv6tJx1EonQYCjMIeyqsGYdcynBSbfXUx0a6EVJRVw7oJ4GP2dXJhIJ0IeSeJ6SUuq0NqXsheUMzmmq6UmtWzR+hHAksTD4JjirH6T3I8zCm/MMtAARODuLWOS/V6tN+19o5tR+HdNf2ER/SILCyIHA4UlWYQaqpxM5IbvIwU4hDhaXn023Z5arQgUeij4YGBzt0lCJ/vrJHo7CFxp/UXrbM4paOnDhTb2ccIydWnhvE1i8znLU74buYgh7z9arxNOym6F3LGQoEnnE/obQi+6xOl4K+Oouc1LnCBdUEncp7gQJtYEjG3IwLke4PB+YlUt1tUMgE2rrSV5R2udLaCvU+uMMJTKOqFOXwfn57q4oItA3LVPrXi5a5ifD9NdWCGO7rzKORnfnbbYyf4+hoEOxp5Kp36xuhlH/5YJsTbjQgvnwwm8ce5Tqh8uYrjCRLcgjhmc33Kxktb3k846Wi2kh7dNYoRp+xR2b5/CyDbuTKm7DurCFvUPjhE6VnidRF0p+zu40QTngWPi1tW7ha5dHNP4O+ox6tLPubNYaxLaWKocR53vEtmx2Om0nD8k86wQ4lBX0ESrHIFf4mbPWIk2HyVAV4hOkM55rCQBtYTmnLe4RV23MOgBDmPfEOKEIEfbi9/hvEETj6mjcav2RZeWMMg+JGkZgwXG3gpS4sWgc/mkXxLiFLXfELJKbJzyLAZpDWaJC6B0Te6axUBlRlJ/eVp3NC5Noqc4gZ7COHZ1csGSixyjmrjwL2CRsZIU+tEFlmM7n6fGBSwmbkFfYJ1bmkO9yMT/B4AmhDGp4+A7IZxJfUch2MbMVZrcPyzLhh03AhwNGzOP/xELHFldoaKmypp1SiFbG1ao37JC1nbvlUCcyyk8CcARkKOTXwEcDuBw0MomhTnctYvf96u90tmoxMjLxlF8Z46pJpkaeJfuMRKzI4nboebFQo7Fd1qjiEt8FxeQioqIRRaXWA8p4GSwifAmSHyxJiRZV2n44iNUGOlexu5ty7hx+xL2XLsspoj0JxU3CaG8DuQYwDHKzp2nJCi1AziEOBwASoEiIcqw7sKmZebkhqtUOvEWhYkrOElSywpO1DRQqypEiRtR4k40oyaDUqdqgWN1LcBUwCljrwTKXplWOPaC5yR5Gf0EN4UlSVzN4uqnulWpO4EE1eQqvQTTnEFVFAhdVZKVYwlwzQkQV8w4SaJlwHH3smPOxJcgd79xY4KUjdDAFCyDN8xdXSUTXLVmILzR+nBKhoAu1pvBG65KMnEVRldiBkt0BeB2ihOo0w2ve0pcXLiKNMRVJPOXpTUKJyiMa+IADhUMKlr0+eXqQk9hEjs6xiTxxdMvVge6csu2oYkxlRVLFoeDOeNwWPIrkzZhdhPLyjwIjJjoa21JXogSO8cmxc4U3ZR/ygkhhLP68QBHFZlswPSR8pwACVqV7O5aakgj7A+uT7Bf0N2B+bBrQ1otCATlRJmrnw7esJ3lO3RVdAGz86xu2scXC2ONmXw0yQ3kyA4WBKWcrDiwZaCPbndJ4gqiruJQUVV40wzgLIAwR6Es+7dLFdafgzjdHuYwgCCTrloS4nAs4ETKgzICs91S78w5vugY46EQJ2MuIGECkzl28jo2uWUfsj7N/q31rYDdARw3DqcVK5MfHX8SgOOsD7PgRhUK765qbqsCELiq7ABCI7jheJUPb2TSuaHvfz/2er/uwDSfsai4ssj3gFmOOdAsCpDExVnGUNdxVMUaZ152gtKgws46qUTFN0x0H2NSazwFygQqhMusB4IaxnzhGKjuWmp14mBGAHAS+OXgDWG91TsBjiTGo6DlDvOkHRTGqRWsKqGyOOAWSDROFhUGWtdwi9lpqFsTXXHnsXv7giSJp8Zr0nXAja9m4aeuHKpE8N0k1haEpXxOl8yyZajIvkVrRO7MSMs0Azi6rTQXZOQd5cZacUnoPoF69wmFOIRoJQbvXQySfhblQ/q7s8Bz7pS0duXEPgE4hDROoZIxkP3Xgn0GOeGSJSrGVJLVAoTgTeOLMO4Mg+drPKAj6mpL5fHaWdnquL6NVlRu/E7ghxvfHUQQ91Jxm9LFLb4HpD+IwqAyqnMrDTZeK59ArXwStfJxWTBTF0hacfJ9yHbgwgPfYbQAVXdYsYjgO4/uGnzvlRmDb0xiYNS6x1UBFkXY3NEpUwwsTRcR59JGV3kDMVwgcwAnm8sGCQIhFSTIwpGrP85zdI5m0MHPA1U/YPs7iGNQWeJT8XcaD0+fV98vfMdw3pJOanVD4FfrIsDh4hTnNt4CR64li3xuQwdn7aX9z/XFigsR4MpO9xFNCkk5ztUkZp/G6hkUdzsGh6VLio51AojEMoHjL+c/BDbqciPvz+3LGNm+LLFTWNcGbZN+nPRp9muz0tI5os6DdSFTAQ7rzfpRGLNSrQoNNiQWSbIjq/WjBQwUmObEwoSWGLSe57sgmQsRasp8KLS+0fk7+/FuLhiKNQ7nHS7+nLNUN4t1y2nFPkgX760TifVNsjuiWFrroobMCZyXhljg8H0n/VIBDsMmyNytewrDTNsIbKYl7d4+DUnb6Kapc2LvssuFDKeb8Pqii1AW6dqu1kQ61nEcY9KFEi5Ec2GIv2Wf5CYUw5zTSuJiqMGbBQ0kLMDANqexxTKFOLTUH5GQCzMSHzOEOAzuLtaOmdwWncRVnvNMB9VNH7BFQVrY+ADGPhYOYY5a38wmMbYGOriArBCHwdL5DPo8HF80CSwWgEOos4gqkwRW1/dlaIGjcDQApOw/nLdSvpm2MfEz0xyqTA7oDHYp0KFOofHf/CISFyJkAV8WDPgeUtkXfZL6H8d7cZ2fQrVjGkOd06h10n2NycEdtxgn9eN0Mp2POf1MxlzOFTJ6e2B9I3pcs+9lf3fFfQ7nSTy2OXcAcBJlrcsmnhSqnEq74h4+0+hXevkDoRVLF7PCCd0fJJaRrtJnAY6t3HtlzVaavdlaYoZpkyUxzaSpGl9GCnLEOkE6q3tZBfBGwAdfxA7gyKpOcqwvNCm7DNL6UlMLFA7C6WQUmTm/Q0uQke4l2Vlm97Yl8SWlPykHQVHIEoBjECd0SdFVPQFxNlA4eCNWOFaXoVuQPIcpUea6pdBGJsJu4LLBWiZsDtokE3wq63KOL9tgcuRgmQEcDZ6rSh7bjYO2rT7TvUxXaZbQX3CTZbGo0JVigguaHYtppDMDp8m/gQVOQuSY0EGIPFeZTaEPAI576SlIcmURRcqBtlDOzFLBrHBkkhu4BMnkVUGOWN9IUDe1wJFtWbkiyXgcJSoqDBg5pkHcZLtpvvzp1z0t/t2iyLs6JBzUlzsnDtz2m5MhXTHj5Iz10FecEpcKrsYTatFqwZRXATgBvJHJQeLKRHhDSwsHcUL4mLi7UXHwq50yWU7q17lbyESUyhUT+4gmAzfMQ3iTBTchaFQAEcIEBTkJvDGIIyCHgFPBm1hRCTBwSnDS3raSzvZluyrMTQNhm6xlXx55n+2ForkfW3JADlfwEkjKoIg2IdUJuCjBLu6E+vGry5hadVHpc4qfe+kT2NDqJoQ3CWh3prriOkWQQ8VUgIu6U3l4YwBH4YtZOgnAEYhj8CYnD5RLUzjTAIcWONaPPKxmnasFjofoqkjp6raOry6gbxKPwrnOJS6kbhziGORcOCXOjgADVS5E6XDgIBmPmlrdOMsbjvOXJf5NKEtOhkQx4BzEybCAfGcxSuAcuP+Jokg3265l585jbhhmHk6rOwdqklxdjdSFTON4iFUT5ZB9VoLnq/VhLQE4VOJMkXNbLLNOBXw5YC8yqxY3hDUaLFvdiDVovQM4DpYliwJ00xJXQnUn1DgaGjOLwcz5XmGfYHwErnDT1J7xEnZv49inyo0pNQJxDM44iz+OO2oFEiy02P8cTFagrBaJ0ifce59jrCzEyHufAGtRLTIJt7oV9tMSJwTEZoVhubSXuVFmYL8CHM7PzApHV2c59gmAJXh1/cty9jGbn3AOQSVZAYZZqDlLPsYLc9teM0ZMnQrk9jkMc0vhbrWkMsihFh8e4Oj1XCBtURL1fcA+ofdWQOvnVm7HOIE4TmFk/5Y5jwI2AdCE0BK8k1DOwRzZiYW7sYyjVuHuX27xwhYwxJpB49EpvFHXZrYFoYy5xbPMrAt+NjiTzg3cMA++I/MbdRdmecW6yQEHfcc6i2GTBZvnsS3l+Wz8cvM2WQxwc5csxBEXyOOyYCRukLRAkUU6dfWQd6p7rwpwEqsVPmeY7DncO9jesxIsWPsYXavVQk0hDmM/itudABytpwQ8CcAxudaAtxr3SQPfeoBDmQnGVDuW7dbd/NC9ywyGaV2m57s27gvA53uhYaFlURZaNI6SujurOx7daxTqs0/rfJI54YbOzamvieIrfUStYghwOC/WOQ5BLa1ZaVnSmHieUFctSjWoOBdZ+L4O5wZpgKOLHglskbFPNwmQuVvXtMzjOJcbcWl3t8IbiUcncEbdYHQeQr1Tk/VznTMbxHFzKda1BLtXi3l17XIARxZBFf7rgjUBjs47QmsPm3MZOGauAMfiZaoOYXUlANZArIAaD2sM2pjljbyPZRGT8xeOb9pG9ky6C5Wbj9Ain3JMyzDZ5U5jDimYUqtqBThuIUgs9Alx6L7J2KEhuFkEXTv1nL43Zb4vYNqNlbyXARyBOAZuLFd4Q4DDBWBa5TDenLhpSiw+G2tVH+Kz6gK26oLJ8zoLHAlNQYtLSQG8yQIcqSOvh6Zcoyjjebq7QZswz/veFX0unCfx2ObbEeDkC8VmbewA3phw+4E1oyy5ibCaouvAViehNsItppDpgZnXEmuUoDOIr6J7QcgLSRRdp5g62qpKqq5Q2cQ/zGVi4BQCOS8WOOyofrC2wZsDbRbi2Gd+h7BDVrcZOK3LUWzm8tLiIKkDvSrH6k6lcUVU+dPJolPY7HlDCxyDOKny6vNy0i2DlRuwdGJsliamaIYvVz0nL1x56bqXbQJudGXGAxwqex68yUs+sErioGwTY8lt8mM7VdnqoaxK2WqcBgwUNwCZ8Jl5JXNdKSPRr3NlrKJkX7cw5qTUW3Dpi9Ast0JZC2XIFD/Kggc5Ci8YDJGKpq5GqouMrmhwZYMTWEs1ApwKTWyz8EYnLJRDlX8FegIhbDLrgiISZBHiGLzhyoEpsPkAx7dfAnBCeGOT1VYQhy9FJplUugm1TXhdv7F6UfnkKpBZhdmKkOVmRUJAoZCCAfB8MtCen4scCZxRaOBXSNXF0v/fAdRE7sP2zL44Wn22l0ooG82O9R6iABCUsY4EDGf6jp1z1krm5mZ9mC90tbgxy5t5Nct14zfHrlRyINqf0/6on93klzDIuWKZS5bmqkwKTMtCG2mfAAI5ax4/vmmfNqsbGeuaABxVipxy5CCiTtydJVcCc3xwUZ0s6uqyAJyKU2QsN+gu4MbAg1lUhrlT4IKxL3fStG7vx1Ce2gAc59pK11/CfIVfbsW/QrdAt1ufxaySAMMae0ZdJxXWhOCGQFeCQ4t7mgM4hJlNFEZVfJ1bSRAjRJQpV9e6Gu5iwAmsD9oiaYfgfSKKiEEWZ41mgMXFEJDxymJcOAtOWfGWlXUdZ22MFRc/sQb10Fj/x3tmzomZux/39N2pkEIUMgfy5P0tSr3CcgHDAs3V3VZcQsRKyhQHn9u722I6qcVmxmxe5Cm0QDTrObVu833O3vM6yddFIO0r6m5o72eDdBMY4qYKlUmxylCrKKeEyzgdQgAbrw1MUNnxlpict4iCLPMPNwcRV1Z7R/J9qe9MfW9z7HPul24hw1yKaVGqSUGOBNM3sMPYbi4xwLoqwRpLTxVZv3gh8sh3jbybtPwGYLRu1IVdjlPQJjgftjHLa+/WYI5CpVMW7+Q+rJPgnu69wXYWJVnGNQPVBnLSLvES21CsdDgHMfCjbZEuq8GaTO7mjyyrf9e6cUz6ob5HBNoIvCE483MH+Y1YPfpFwdQCtiwK0dqb91VgaztJ5eUCcsUyifLn3meuLpu2h1tkFZfaYJGFViSsSwOfCqzVykrbWa9vC6nNAY7BAcqqzov1/akWrbog0njM8YNxIJlsHuR1DH1vq47gIIvM4W0eq0BZrJvdTo+2ADdcUStCDWI7gxFCaY5pIYCSebi9B+x9zLJr+cVaWuYCZrnh3PdtIZGKrXMvNxdcXbB281b5noIAXcTVzUwIcgziCNRJNjzhmGQQJ7AEJohNWZibDpDNdbxVoBHMNQIIbUDHAI7G1yI81mD+6hrv5mzZ+ThdODmHp2W1pVBfcKEyFFZ6eKPAOliAdIuPVVolOleqxLVKwI3GBGN8KN1Mge9Km4/wmf1z6vzWASuOmeIiRfcpZ3GTcqOydlTdUSFXE4DTbM4R6KqiEzT73hV9Ppwn8djm2hHgXGEAR4U7NBfzg2ujsqQNrdDG4A3P1dwquJB1G/zc4KbnTBHXwS6ZiLjOmFgnOOVLO63/jX6/xeek03mAo+45ptjZqk46t0mJwSuZUImyZIO8G+hT1g1ugior9zzWiWKjYuI7idZppvycfCcvZz8RTiwqnNVN+GLVeskMbg6GJc8gEyUFUwJyHNQKgZZCLX2x6wveXM1s8qMKDbcb52CtK1mhkkNFxyk7FktCcg3AWBMffm+aWSstwyedhIm/v8A/vgxXC3JMyeBgr37PFmxTc+4IxcTtBd3uUDLJdYGQU3UbrBKGdSUTR4UnNKlmcMdwl5jQ+mYlAEfbTl8sKs8B2BCIo6ufyaphCuz4iaXvCyo/yWqzk8N8gJOGN4k5qYvZ5KGFwgYPCnx/MkiQ5G6ibJPs5PwlW9/4fuNfLH4ssnNyX5vkJ7DI+lgIEXKOHfzydRdMwoI6MZ9qycMXdtLnwnHCJoXplR2tWx8fSyx8aOUjaQ4jjEkUpPRE0O1ul4xvHK8dIEtyrzw1yI4oFaYceeUxGW+T8ceUhCB3cEDHH+sjlufUafJ9950A2pjMNo6R6bq69P/nyI70aYWStDCz94IEwCbEEeWR/dzGNx8fQ8Y/cYkJJ5qENGHSyafBXCpf4oLBuhWFMLy2jqX23lFlrEmdihuDuTOYpWWT7xr8adqeHqrIOCRKDhUAtwAQyLO0dwoO6zjjx6+Vf5b+FV7bAQgCVtaBLJ5YMGOxeOQ7xbWDKeI5uZ+jcAy1Pt8oS2o5R4AdjH+JBY7Ca1F4pH/56yQAQ9pQ3UtUeVeLF4sZpNZ9fBY/PnsAEJwzIBKWlfMMZ9kQWth5hdFADmEtx7/wWXlP16bJu8zebcxtO93wnB0b1LY+bDKl1lHyLK68afm074W5/03qu/ac0vZOgQoAThrmcczxdZ9uW14/O975fqpzEJuLhOf9mMh7+f7W6jh8Lju2OnL1LXXNegzrUL9rQJHl5WKGWvJyAU3nYr4MgVy0lJtgLGZ7iNWalcvycA7RWE86z8tbwLR5sNV7+Jw8tvdaxgJHYtMQ4PC8vu+S+WpqUSQz3qTGE97Tl9vCDdicg4sdOpe1MoRjII+9mzoBDHeE9cmNacm7OnwfUA7dGCG5zdvD3MYQ+53BH50HJXMdmXdojEuzwNExhu95vrMtLotbPHPu17qww/uptXSq7hKZMtlSS0yDGZIngNyV2bVTaPmrcEqfIxwbwzpP5k5J33KANDUft3m5gzoJCFSdIIE4WZdRfrY4OHm5vR9drlanfhFI3g2BvOTOCSTWjcW8mcNw8Fni44heaW0Z88Y6NPm2PAfgcFVFKbROnlSZ5A9ihW6aOnCKQQhwbEBNOrlTkkxxasiLC6g3BTgWzNdyI9zZ3CsXOlG0F8sKcxnI3EQheQH4lwQHL4utooNa+n820DW2Szi4exeVZOCVAdUU6fZybfdJ5fJizrxAZYKrcChv4pwe5LSOdOC2OrA8UMCDl6YO5l4pzrZ14+f0YK7wJYQyDLoYphOolZjCc+H39aWg1+FxGuCkZcwGGR07tO6yEypbgWQerkIavOE5m9S6F6S8JEL5MpmwetHJnp+QeyXOlDU/kUtPKsS8mC9kN6nRAGzWnnbP7P3CCSiPVdkLc5kku8mul1WVUTPfbQQ4CgHU6katbxQq2GqR5WE9++P0i9/KvLrcxpS15fpyoUw2lwstb8t+FbS3r7t0n9XJF+tDJ2IGcGRSsMb3lr+m1bPmIbThcbPvNZY1U/du9TiUE5PZUF5EKTU3PsuTiZIpBGvMUwqYybfPG58hXe/r938vtylZ4c4mLoaQKcMJxEoATmN/s3rUPJhkGjBpmmeVtPDaGWXM6i4BYPouELiSQByuRKtCk6so2zXyclP8krYOAYxvo1TfybvOCs61akd/fXd/eWdTGTQFt8mYJ++tYEzOvMea3TPbn8LxzwdxJ7BWJdWu48tpfcHezV6x8uO+/c++G+St6svNV9IAR+G5KrB8b5vlrFMe2c9T1wzb0eo0r4x556zc/N36ykDqeql5mc1JLLf72nPY58z4lmlvex/58c7em76PGSxJFrUCC5tU+do9e97cLNVPrczMtdwCIpxVlek8Uh7ey/XF9LiSHSvyP9tv88u/sjqzMvo8LL8/tr5gc0rpIynYaACH43g4R847bjbWh2O1yYT2RwM4fOYESMixQef0ffzcR2FtUn55Z9t90uXQ8SHv+/ye/aYx93WXBTg2t9J3uQXXtXGILpxmqZuts2SOn5It10fduJ0PcBoBvNRXZq6iMtNORmyMtTw753cLvnRflYUh628qrym3URe8nWEYsuftc7YPJPLNPpIpP9vT6rFVLu0u4RfUIjPvOmnZSMvE7+d/abnWcAFqda5BjOm24VI4cZKOmdM4v5+K22QCk/eCTQavZoqTxYdwuQM4hDjtE10S0okEO/+ltMq6Sq1SuxdCixf0ymUufFkQ4tiEI8xXWdamfSC4F2OqBC/HZJBP7u/rTZ4l7/nDc9KurcqpndrDm+wATtBCCGNQxiDNSnL7jeUGcwzk2L3s5ZHOvTLGMvIZXD1J3BnbAYo7nljitt5h4nm/S5T+3uoiHMy8zCcv6mTSZQqci1GRWTERc9wgDk6yQpqsTJm8hPdNP6fVvbgJ2Ep0svpqL2D7vc/TE5isC5W9/NxqmoMTzV6EK+8X/v7r/5v8NknLgX1n/cqRrZNLfa7s9exzCt4UcyxwMhOWvHKkxsxERtOTf5kUtRgDw2vk3ePKO2cywTwAfgL1HSyQ/uQDpSd10HI1PF2v2cnnaj4nE1XXLuk6DhSUFlYy6d+sn/xv5HWTejZ5tLmHs8ygkqtKudZ1tp74+9WUz/qaz811NJ2Lctr23bi6e6+onLZ4Zu4P4bs673iVz7+iMlyOa+Y9i5zz85dQNlqVO/yeyUe279l55iHAyVNsW91rzf9zz+stuzgXzTxrk/E6+yzhZ3ku6zuXo914j0zbJSAnOb+O/SK5psGc4NqptgzOZ96Tvq/bnOfS82ZyYLJo/+e9CYatDFz8oWWIfk7Pvew3K8n1Pjp39AuBtmCYA3AuSTbs/WnzUs7Lwzm6WXM5Cy4Zs/270eBMszyUZztO+mvu+1Db2up0JbnUabgL1SXVR1rWVtJeV8Z3rJ01TwEc9fH0AEdBjlkC8AdXa6Vcoc8VDo4JvLEG1o6cmgy7ibFv9AzQkZ1b2p2z61ueecGtVUaSZ/FtYQNtNl+dHHqw0gykrO56vnyNv/P3SkCFAIvGOrJnSq6RPL+9BA1kraTfWVuEg3c4gGcBDmHMWuANf7cOACeBJQQzBm4sD+ENjwOAY/AnkTF7buY5AIcvlmTCpRAn5dbmQE4W4LDtVFbMEsc+s+3tnlbXjbnFejCokwClJspGGuI4092GCY6fYDR7GSaylNRPK1ndqP9Z/VgeKONJ3dn/1rcMVi/rVQ92vWyegjjOhSr7HfvcrCzW/yXPAAiT2dR3AgUge77ZPa6s8yYTzEOZsfkHz3kQmq0D1plNLDcql3YJ2qGhflPgJgQ6bjX6N+2Xa+9rDXXtxlUqiWoxoeMs6z1PdhvqqU09WN9J5xz/XBJlyz6v/blWW67k+83e1a0U2TbPnFx7s32v4ZnYP/PnM+2eIZGj5J2c7rOJ7Lj/K4S4jO2b166uPdqVvdmYkzxTUGft6mnd/p/3PNae6y1nyb1y2kv+13g+3b8NmKxfvtJ61HK0ml/5/630mvyeykwW4NgCpr0f1uvdYO/PcD5qOgBzO58PcCi/lwJv+Kx5dbPSNs77bTyXV6fWzvb+M319Hv+jQbk0cF8a3nASpQN3rNS8Sl2fc8lLIun8NgisJLfJbZhrp22YEGcnyKsCO16AvDK7Ps+/MbLVHKqs//024l559d2Hh6WiAAAgAElEQVTsnA3SzG3wNgpv4MUsaVab2++Z2zUtD18Qvgxe7qy8lBO+uGwVIgtwsvBmrQCHZbB+YEpFYHqeMne1sqStptIAju1qMm7P4p/TvxybnbOy2G+Z2/U01xedmgWv9KUXfi97vd/uc/iMdqzKtx8v7Hy6DtajzKyT9biOXSOs4+xxK/epduVIjfWh4mvHwTvAynJ15yYTYZ4nN759m9VhM2XqUs+3BTgie3nvAF/mq6ENU/VOOTWZtTyQXX53Lc+c7Wt5n9dy3fibtbXHetRbIjdOTrL9cWX967cpf7uyZ5+Fn5N+EfSH9ajHq+kaef16Pc6tto7a3XO11+P3VWY0OC/dfm0BOT23XE95tndnOA/Nzs3T1pJZuc1CnOz/m8l1q3G+Xd3y/2up39/nb6yNI8DZFEKTvBiSDm8dP5v7AcAGAp+H3/XKoldi9VyjAuVXOmmVowq3F5DG7+f970rofOGkeqPLu173yqvrVue0/TxM4MC9HhAnBDd2bODGcrtX+mWRBjhW7wZwVgJv1gZweF8v+65vpKBN2kc5AUqJhU/Yhjy2sjO3Nghfkis5btYHw2tfLcdWR63yK+dZVzIByX4nLTONz9ow7jeZ8LeaGLW7x5X1/1ayEv7P12XLOgzrM+c4b2La7tzKFcxw/PDlvbLao3m5G+qdcxer45x5zNXy3PE5msvESuomkRsnK9n+tvL+dWnlWElZs99pV3Z7lqQfNOkP2evGz5e/LS9XnScyk1h0qw7nAc56P3v4nsybk+ocNHR3NbkNc4M44Tk7TuQ7M85frjqN9/FtrBap0QIno6Ctd6dqfT3fyUMI444tYOVK82DHBN2NQc3lvDLr4Y4oohZfwOVXL8Bp3Qabb1DwndQDg3bnmgGcZhDHYMxqc4M22dwgjr440vCGZWcbGLyh1YsBnDyrm/Dc6l2ocgGOKBmBFY57qSZlWhW84fPkvSBbn2vflleanDYrbztZNXlo9vvNez4LasLPqx1HWo79wQRptde9cr/fTm7ScrHS+kt9zxSrNbhcbWYF83K3eapOA1nNnr/c5Yr3S/eRzVQfiWxcpQAnUW75fE36xGZqj1iWje0raRnwC/AbV+/Z92eoE3jdrx3AMViTzbPvv/D5Nu6ZNraNrrxy+zaOACe1sv7bCErSCVpAmmphBq3SUMHvBCU7dDSAHK6Qmb+668jmC1sOtx/2wvH7UTZ/m3ZvPXDktYNZSDXPvfVNCBIMrITAZb2gTXhNHtu9wjLas7CeLx/Aofx6cGlWODaxyqyE0KUrF+BkZcOehXlYx+2PPdCyugmv1eo4W4Yr6XPec11J5d/YsiZjf5PJPv/fepy4Gv+fJzM8l/+sK6nDhu+sEeJkJ7DNyvR7Od9Qrxk5/r3UQ3zO/L6ZrZdEXq5CgBPhzcpkICsTV/vnRObd2Ljxz9v4/vTz4OzurOkYVFlgE37OvvvC59r4Z4qy5evYt28EOE0mhb6yNl5wko7QDOC0gTcJ2Al/X5pFtTQnSS1xtKPqSyYAOQnECQLnBoFWk7JlJmarPX856/PquJfvpArSTOlvnufDGwMLBlaywGW9P9t9rJz2HNaPDOCs1Ppm7S5UIYDMymsDrMkFOFZmy+1ZmOvzta5zq3vN7Tft8/A+dmxliPnV0b8b2zGU0av1GTf6ucI6bHvcRIkMJ63Z4+wkdqOf50q5fraur5Ryx3I2jkMbWSeJnDTpe5u5f62o7HyuzFx5I+szXvvyyu+VVd82d8wsZK6D5anJ+JVVH1eLrPh2jQBnswOcwmxLy5sE3hDyGMApzWJI0hxCeBNORlPWOKFlDqPFZ15A1lkvJY8dfbWDh++kpvCvBhbkf9fgynpDm+z1eJ88gGPwZjXuU5cCcFiGsB6zbRDEqWgAONnv8nN4rdUDnPw2SUMeX2/typ5XvngujjNRBtrJQOo9tsrJ7GZWMNs9d/x/7Bu/tQwkfS8H4OjiYhqA/NblDe/fquycW8exIfavUF4203Eiu9Tt1vLOC8DkZnqu32dZvB4SAc4mBjgpOLMSK5w8gJPTWRN4UwktcXwQQnaKVIdn9PS1pOA6v8+OdikvNN9JV2qBY6Anm6fBgUEc5lnwsh6fFd54EMHnsHpYC8CxrcVpsWNuTnY95mE9eaji6yD8f1gWu0YAcVIuVPb/MM9eS+/n77Wen7P3CssRj71MxbqIdbE6GUi923IUyXChIzze7ApmlIPVyUGsr8tfX6363mbvX1dy2aOsX35Z30x13kp2w3dc3nG2X26m5/p9lsXrBjkAZx61StxG/HIKhu9c6Tg2qwE4EvcmBXDyfRsF3hDcWEqsbwKAk7XCcfAmdQ+7V4tcd8hSGHQ56/PquJfvpB5SXBogaA5yQqjT7rgd5FGAo1DDnsFennkAh4AmDFicPbYAxmsFOKwzK4flVh7LsxDHzmdz+32YX1qb5MOf8Pp2nC1L/Hx19PPYjpe7Hf371i1U2Or5SvJgUYLXudxlj/eLdX4ly0Dbvhf0r832nKspexwbYj/d1PIbvOvyoE14LgKczSbLphPkbiNO5X5RUr2yiHplAXXboYjxUuKkZe11QDCSijmjgpF+MaQhjgQnZoDibGoGTkLXKemkgZWNQZvKvLYr21ZSEP+mPI/hsIzBCzVdzoyVTpvvRblZ7SDgO2kjgFjL/zxoSIOcRheexv+3gzrh/8N4L+F44SAJrWgkEchYHByzsmmWO3izJgucPIATlsvaxSCOfW6Wr6XuL/U3zcoSz8dxJcrAWmSg4V0WTGiTCWv2XOYdt5b7xt9Eef09y0BDv2Ofsn62yfvXlVz237PMxWfXMbdBfq3fuTyENuFxtn/G+vyt32Fen2higRMBzoYIaQhG7Djz0tJOloE4zWCNO9/QMZOXYmBpE0KbrgXULSUQZwHDlUyyMkq+OmCTLdOG1OdVDxN9R10fiGPX8zAn3wJE/98IcghnQliTd2zXtnvZYHd5AQ7L3vhsVibLrWyrye23lytfTdnid+M4E2VgJTKQfT+t5fNK7hO/E+UxykBaBlba1zZjvV3JZd+M9RnLlO4bG1kfTWU3ADkhuLHjCHAuXxutrP297hEBzuWEAA6IDBOmGBzJ3N93svYQJ3RR8r+zFQ0Pb8TKxoBNkA93LSCbRroWICmEOVZW5pnyxs+Xo3P7Dru+IGcl120Fc/Ktd7z7lNVNFt5YEOOVWOCYpY6z3JE4NXZd5uEzpMvaCHAM6oS/Ca+1muPwGht1vJryxO/GsSjKwGpkIPXOzF1Iab5osZr7xO9GuYwykJaBdn1vM9fXlVz2zVyvsWzpPrIR9dFUdltAnAhwNr5dVtfWXt9oADj1ruhCtbrKzGncBlcpD2zonkSAk3JTCgGJgySNHW0WCmzCvMkEUzpjBuDQ0iaAN+GxQJwQ2GSPs+Vbl8859Rbh0GUCZH4ASAOQVufzYU3WSsfDG17L2rgVwGkHcdYOcPLLtp4Qx54v5r6tY13EuogyEGUgykCUgSgDUQaiDGxmGUjpmU1cqSLA2Wwy7PW0CHASJXOdGqkFvKHVzYoATgJItEypTpazUsgBIvUdAzgMUOzi3misG4t5k583uFAJyFkBcErK60FVYmHU7n/rXf/xegE4WQ+Z9oMF4UwWimQ/rxzghFY4Bmma5aH1DWFQ+Fzp8uWVUctk4IZ5+Bseh9eLx7E+ogxEGYgyEGUgykCUgSgDUQaubhnI6o7mOmW5AJxAx4zy8FvLg9dfcgCO7kDFQMYxiPEaGsoBCwE1BmzCnNY3zgKn6XeC7ycgJOhAqQ4XKJ/J+QDg1CX2DYMWryxJ2ayMYR6UaSXlbved8LnigLAGOQvafWPrzw8WeXDEAxyLh2OgxH5nz5ZnhbNSiLN6gJOGNlaWMLdyWe7Kd9nq1e6bzcMytjrO/i5+3th+EOs31m+UgSgDUQaiDEQZiDJw9chAojdSx8xY4djn8Dux7X/rtvd6QUuAIxAn2YGKu1Hxh7914Tf5/UOAEwIQOc4ECc66KuV9Di1YWtR92MHkOIA4snV4sl14sCuVO5fAlPBewXE7GLOW/yf3bPFMUdY2i6zbgJFngWPghjm3GGfeBuIwjk2yG1U7iOPgTbID1coscBTgWLktz9anQRtXHomvE5xblWyGv1vpcbY8Vs6V5tnfx89xzIgyEGUgykCUgSgDUQaiDEQZaC8D+bqjhznZ/8c6bV+nG1tHXj9oADjD3d4Cp1ZegCUq3AQBG1uw37pi1uH+DnwI1GgCcJJAwRYwOJtnQU4AU5pBtGwnSz47opqQ1OznHMuepI2z9133z+tQ36tSsuP9kraVemsFGsK6sgEjC3AM3hDchGktECcANSm4k7W+YZnzysYyKjhqDpD4u+CZ8+7TFBSF9wyPg+uF126AVJnnS74bXsvq2XIDYb7e02Aq/G2LcqTqK/xNPE7LUqyPWB9RBqIMRBmIMhBlIMrA70cGEn0xRx8M/xdlYjPIhOkH81ghwJlHrUSAsxkKv8nL0BTgqPXNSNciVpYWMBKCnBQ8aayDsJMNcXvxEtNcyxT+ptmxdFiJ6zOH+nrmUanMgIjGNt24wbKVsh/+z8pkA4YHJH478RDchMcrgDjNAEre+VzgwfJZ2Zj78vnz9gzuuRqunY27kwVG9vtsHtZTcNxw/Qy4Cf/f8Ez2LP45vItaCHHse1am4P7JNfPO2fdjvnF9K9ZtrNsoA1EGogxEGYgyEGXgypCBZvpf9nxsz83Qnjb/bwJwGPuG7lNmfSPwhgAnKt3tle4WAIdAhvBmd3fr5AFPCHEyO1dl2iLpaIQ365SSa7ahslEuNkOnXmkZ8hT7ZufCa9qgYSAhz/pmefWWOCHQaHacghJhmXhs5WLuwYc/z++450uun4U2U6iXLfF/IcTJ3i+4nl2Xuf0mlWfvE37Ou0f6GTy8sbrOQjErW7P2yztvv4l5nIxEGYgyEGUgykCUgSgDUQaiDGQ2wwn0vlg3m6l/eJ0n1wInApxLaKymAIfBixXIeECziN0EOkEK/yeuVokVTgbgmEWOAzlp2DKL2jpAHN22vHmnTt/zEuosA6PiYLGRdZmn1Lc6Z2WxQSMEOKHFDeGNpfB8FjrwOnZNd98U9MixWAlBSfJbu4aVi3kW4Nh3DLCEAMWADfNJl+ycgzhyX7tGmAf1lVv28D6tjmccXLJrE5Jb/TIPwY3VqdVnTj2m6ikoY+q83SvmXg5jXcS6iDIQZSDKQJSBKANRBqIMRBnYzDLgdZ4IcBoUwktsuDYAx7bqVmsctchJQ5vQxarRAoexdZIAwHYckNIUVFkPiFOcRS7IKc6hxhTcO3b6S5Sd9ZbF1PUyCn0ueMjAkxTAsEHDAANBgkEFAzfHcyCOQQiFK2oZE9ZTm3Kl4AO/G/6Wx1Yuy7P3MXjDZyNMMUgTghsDOMx5fhr1UhauhPcNy8zvBUnqtRW0cf9LfmPPlC3/IuolS0uolyzxHJ+R37cyheXhsSuPtXHbOrTrxNzXaayLWBdRBqIMRBmIMhBlIMpAlIEoA5tFBkxXaOlCZQGMXfyb6EIVKEwtGrIlwHFbiAc7Uq08oHH424w1TqLIpYHKeljhpN2x5jBk4CbMk/ubYLWon+S77b5j12qXt7vOlf7/Vs+/kmfLKPem1K8kXxPAMYhjcIe5WY0YeOAzWdkz5TPYYOWzz6ncfmt5to7sPvy/AZwQ3oTAJns8hXopC3Ds+nY/d10pUwBvDJysOOez2zV5D4NPBm6YG7ix3ABO5hlT9WN1Grpp2bmV5laubG510SrP/iZ+9u0c6yLWRZSBKANRBqIMRBmIMhBlIMrAamXAz72bWOBwJ6oIcNYkWGYV0ybXXapWuq14Bti0uTaDDYeWMe2P1eWKwCednJVNCGvyjhusIEzAViuY/L79drX5Wu61iX8jAaPN2moB9XI22f/CPBtomvAiTBkLG7FIIdjIns8q/lZPbJNmFjgEN5bMIscgziqtcAxGsFx23JBbmSzPygvPG7zh8xjAyQKbzOdceGOwhPew+xkICQGOnVtpbtfSelWAE8KblQIc96wNdRQ8f97/VnTOymh5tp6bfbbvx9zLTKyLWBdRBqIMRBmIMhBlIMpAlIEoA2uRAT/nXgXAUSgQK3wFFd4WsHilW0BOWQFN3nGDu9Qqrt1sy/GwDdvDHYNBoSWWP24PXFZQX4lS7AWz/XXzvruae/2W322j4DeFN4ttQE4rgJOFNM6VJxfiBFBHlHyrK9b5SgDO5bDCYR1auSwPZYLnDGCsDN4MlyYxXJrGcGkGw8lz6zMrXOGx3ov/16Tf9Z/tPPO5psmu4+U8a31jFjfZPM8Cxz1rHpBpBudWcj65Xl79hnWdd2y/sbyNzCf3yvueXSPmXm5iXcS6iDIQZSDKQJSBKANRBqIM/H5kwM+3cwDOPOq0vhELHG4dTmXdlPgoJOsmJCkl3QOdfGDjFPNQYQ1+nwd+eC7/Wjnng/YN21qPPazxu5GZspnOh0sLbl96881j3lyJbfxf+Ds9VsU5fR9/zguyKsKbXT7zlNPMObGYYXsT6i24tIjhcl6y/4cAcA7DZUuzGC7PYrgyk0nTGK5kkwEeAzuhFY7VK+s7C3DM2obQ5sT6WuEYZFiVcm8ywTLnAZwJ1MuW1PpmuExwwzSVAjgqn5TpRahsm3ybTBPSZAGO/a9dbrLOa1rifZbapGxZwj6WkSWpN2vXNeZyjbD9VQZ8H8z2zbD+7Xd55VrtObtWzNftHRS+T+JxAmdj/cY+FmUgykCUgSgDUQaiDGw+GbA5dlaHmMf/DHfPo1Zx4IbwJuOOs/ke5uoTsEaI4gFa+D9pixWCmjzI4wFP/vWbAxwPdepFJ0zMi6bgmmBZ3k6Z5f/tu5abUptVELOfvTBvfojjlVYNDG0BomdQK1nSc75t6TpFeLMkqV5eks+hS1W6bVvBmyy0afysrkYhxCEEsT7GujaAY+5RBDjmOkWAk4U49r12sXB4D18/Hr6EICnz/+T7Vr4wbwdvFOIMlyccvMkCHJNJymEWmpg8ZwGOnbffmiw3y72M6z0uFeDwvtk6agVurJ3z8my9s26tr2X7YPjZvpNpi4ZyZcu5ks/hNeNxfBdHGYgyEGUgykCUgSgDUQaiDPxeZMDm2KZXmB4xj/+pd80FAEcV+6HAQiMKySUISWA14+FJYBET1HMIatodt22TlpCnObxhwOKGVJjHkEu1wjwsCcgRiEOQo2m4SAELldpWxyaMlptQap5d8ed2y/6cF+i2dfGbrTQHCmrZYE02n8ZQcUaSxiJi2/DZ6DpFcKPuNLLVdFEhmijVSfuaC5XBCyrhWeV8CvXKFIZTyYOcesW+b4q/Azi8h5RltQCHgMcgTrtYOOxb6XpqGp+nIT5Otl9aHfB5bMcpQptxl1YDcGgVYxDH5FNhiXedMtm2/6fl11vZZM/zupbaWd9YOawsvBbvZ/fWPFWHDKhslkxJbm3cLg8hDuuXMmDtn59rnzRZsTaxtsgrS5tzoTzIsV0z5pt3rIttE9smykCUgSgDUQaiDEQZiDKwvjJg8+usrmEApzyPofIcCG400SpAFf31LcjvrGETRTuANqlzGZjSEFQ4E2S45NvF2qdVLpYUze6XCU6s4GYW1UJO6pxF1aWhzjkMFeZQC1K9OJdAHMKcRuuaVueyCm4IavKOvTCrgrlZZSoAE9JuHt4otCG8cakwg6qkWd31i7CqrBCH8GaoOI9qwe8I5iEOAQ6V5VBhzlfSG12oDOKou5Uq/byOunMpcNT6112lDMpkLXDMGqd5HByFb9ZuYXul66gRPOQo+ykFP3MtARZZgGPuU8zNdcpyulCZSxRhCOWU8mjgxIAJz/P/2Xg39n1+j5DFgIwBmrzcvuPy4hKGW6VUWbLlCZ/fjjN1yvpKQE5OfYb/SwETbS+Bh00gThqo2v2Z55RhTefCa8bj+C6OMhBlIMpAlIEoA1EGogxEGfi9yIDpTqZHUw9QXUAscATeJACHSuSsgBzCgSgka6yDQBEelm3Eg23BA6jiAYyrd9b9JSZ112kEcP5ehAF2H291I/CmcxaDnTO5qdo5E4Cc2TTICSFOC/eo5pYJKpReKVwJvKFgr7F9LsvvvCKrbaIQx6xuCHCqBZc6pzHYyWO2CyGK7kJVc/CGbWL/09hEBv/MLWsWNVr6JAp5BuQkMXCy8XH4mXFzmOZcInB0u2CJ8k5LmhDghBDHAA7PhdY37VyofN00uE8lz9AEOKRggLW/gQoDOGaFE+w6lcS9IbjJi39jQCYLcMzixQBOCHsotwZhljFctGTnLjFPAE7WCkcH8/WHmPayyLe4URgX9k37vrUD80zbrvlzeM14vLnHutg+sX2iDEQZiDIQZSDKQJSBKAPrJwM2x44AZ+MV/gDcSGBagTfZ7cPT24QbWPFQxeDK2vKVABy7Z81Z4pjrVDsLHFriDBEyFGa9FU4G3rSGMKHyl91C2T6H37FjL8SqtPLzlTBINCqz6Xg4bGNngdM5jaECIQ/j2qjMENawTQY7pkGAJvXONku53xnE8VY+WauLbGBjs7jRoMchwCFodPCGEKdkblAGcJgbrGkPb/KtbzJ10g7Y5P0/Dwok38tCHMKcKdQF2hi48btPeZckAzhZWNIK4BDQENocb5IM6Kwld/AngTievJuVm+8LYf+wPpPOW1nT5P/P2p65Bzq+f4f3zPbFTBvntVfLc9nrxc9XxngX2ym2U5SBKANRBqIMRBmIMhBl4NJlwM+zvZdAtMDZGAAQWNeo5U0W3tjnAOI4ZbwB4DhQYsCkaZ5Y0yjwWTnA8cGJCQqaQZz8+zo3KgFALg5OKk6NKY8GZcLctkoOz4XH9lvLvQB7hfUKGxgE7GnMGt0xTI/pssT2UoijMXHY4U12CGroXjXQQQudwApHYuIYyGkGcGiVYjtTOUiTuFzp//T/3vJG7mvWN3TjEoATwpsQ4BjIybO8UaVflX1rP2uzQLlPoEsTa5tW/88CgOS7Zn3kwI3Am2nUZctwAzd0ncq6T60G4HAAXXTuT4Q3JzBcPOkSjy3lgZ3VwBxzuTJ3rBDiWP9I5+ryFsKXbPut9HN4DQ9x0m2abVtr45hf+os71mGswygDUQaiDEQZiDIQZSDKwO9TBmyOHS1wNgbamCVIYH1jCjitGUa6MqniIY7GGlFFPAQ4GhPFYqO0z8PfNgM4JvzqgpOGN3aO8VYM5OTlZrGTyl2wU78yH8IYgzUrze23oVLqBXj9XUYu06DIrb6dK90IZcIlnjOIQ3cqghxClZGueUk8VoAzlXKxSrdNaKlFKxyDM7ZDlQM0CUSy/zN330nc/HSrco3B0wzghBAnCwNM6VeLjUbg1greGHhplWdATwhxEoDD7wTXKBHezEhSaGPghi5RZl2THRyVcCvxtu+ELlT8Py1kDN48huHi4xguMmcymMPcYA7zEOisBOQ0s8JRkKN9zvoM82x7hJDNLKaa5fbd7DWybcq+GfbJy9SHbJyN+ca+x2L9xvqNMhBlIMpAlIEoA1EGogz8xjLg59rRAmcjhTEEOGWLeWOwJidPrHWcJUVoSSPBhNPghu41Yg0Tfs8dyy5GgVuNwJrk+mrt47efNiU9jHNChTuENw4K5FkBhfcUdx7CIAMupkw2AzbLqJcs6Xd8fBVTFHW1P73Sf4UriYE8GLxhru5KzgpHAA7Bg8ajGeEOcSXKwDQGOhzAERc2C2jMejfZcXGNKCNFhTgKcryljwYozn52smGufnTdkgDKDKKcBwSyyn342drP4E2o6Fv7OYCTgi0Z4BLCl9zjHIjTcD33HQduFOAYgLHcwEwevGkFcPh9Wt8QwBDIENIQ3pxyicftYI6BnBVCnMSNKnTvYv1qGxHcWGp0cbPt3k+iVmqW7DsGd/Ksqlq1q7VvzA2UxzzKQpSBKANRBqIMRBmIMhBlIMrA2mQgApzLR9ACaCIxcKi45yS1vHG7U5kSzjwHzoTWNXacsrIJwJFdV+5pSrlZfHQtYiRIw5VFibXCgLVigWPWN8lOVAxarMF1GWBXg+wqVCIkYFkUICjA0TgZVOJDpZ7HtrLPPFQQ7bx93wDAVQpwBOI0gjy1wCGc012pGJ/GYtbUSgxsPIWBjkmtf3GjIqxRCyqBXGw/2QKeu4V56OdlxUEeg5d5MkqY5OCNApxwG3Nrn1a5tZ0q+Qr0bOCxgXs94I1Z1mQgTlOAE1j8yC5SBm0sN3jDXK1afM5z9j2DPsxDgEOrGlrcPIHh4pMubwdyzCLHIE6z3OCOuVGFwZVZ1iy8UTDq+xihjAGbx1Artkilx9x3+Zuwj1qba5/Mb1dr35iv7QUd6y3WW5SBKANRBqIMRBmIMhBlIMpAKAOmR5muYnqK20Y87kIVVtY6HedAFYMrPvf3Siwp2kAc+54IeGLVoYFnac2hyax/1A2HLly7uxsT3XS48xAtPmjpMURYIDsjTWGwQGAwgYGGNInBwpR8T7bBTrY2XynEoXJo4Ia5UxCLixgqLqLGFARMNdcue+52+abt+A6a+DaytqIVhYI7c6FiW7BNmCw2jljgJHFwDOCoBRWBC+vJgh0rbCN8I8wx9yqDOLalvQGbbE6op1uYr8wCJw/c5FneUNZXC3CCGDaMY9NgjbMCiGP3dLkHMgZmmNvAaLlZ39h3CG0sZg5znud3zIUqtMBpZn2TB22WMVxql7LwJrTAWQnAyUCcBNQY2Anz0Aon6JtJIGu2a7O29WPZpu2DBjBjfvkWNGJdx7qOMhBlIMpAlIEoA1EGogysQQYiwFlDpa1NIWkHGFr9XxT5xBpnVqxx1NLGLF1cmRIYoOAmG2NnuGsBw4QzXXOSRrrn0JhmMdw1g3plGrXyFIZKk6gyFScwWBzHYGEMA4Ux9HeOZhLP8/8TqBYV5FQF/ujW80OlBQyVFiUlcEYgjUGbLMBRRVF+U1zAUNFbcAq7FKMAACAASURBVCjIUUuTVvWW979Np0QGVi9q6aLgRLbrTqCdd38ydycFOD6I8WAnY+IQyrBTEwItYqRCRX5e3Ou4W5UktxU8dw3T7zu3K8YuEUubfHBDeJMGOAZomuW+vdLKPcsX9qHAEqbBWsasaizPgpvws33H8jYQR1yognvnWuEYqDGAw9zOhfCGAZAtCLKHOPXicdSLJ1EvPuZyHjOdcIn/t7SMOl2vBNyEcKbdcTqIsdY12z0f4tRLx6HJoIwDNcWTqBVP5KeSWt8kLo5lZ4UlQM/gTQQ4abkOZTwex7qJMhBlIMpAlIEoA1EGogxEGVgPGYgAJ6NMrkelNl4jDySs5hyFPfv9pAMYtAndcLLBkZPPHt7Uu2Y9pClPYkjSBIbKlsYxVB5DtcQ0isEi0zFJA8VjkFQ4iv7CMU2dzEfR7wDPQGECA8UpDBanMcjtsItzqAYQZ6i0BE0BwCkfRz1JjQCHECgNbwhxGuum1bmk3lIQobHNLt/3zPLF8nCrbn0+cUlzW4QbwKFrlFjW0JrGwZhqQS1wCGIIb0a6qGgTfs3JTlWyW1XyXW75rvBG3a681Y7BmsY8UNwT64sQ4Fj7aN4IbrLwhvUeQJRLAjirtMTJATgsy7ADOa3aX79DixsDN9yCfNJ95vPwOR1AEUCTATYJRLGYT8x9rBo9NgizkjyEKDxeRN1Zrfnr8h7Wv06gXj6BuoAZhTZDheOoMnUuayosY8ilWlF/O1w+juHyMobLSxiuLAnQU6hnMmsvlN+yP8V7t5Ld+L8oH1EGogxEGYgyEGUgykCUgStZBmy+bQvM0YVqQ4BOGijY1s4ryRVOJJ0ssNagu5XGs1GXGwmAK6DGxbPpXsRI90ImzWO4m9Y3s6hVpgXaDBbHPIQRIHMUA4WjGCwewWDpKAZLzDUNlI6AST6X9XigeAT9hSPos9R5FH2dx9BHC53COPoLkxgoEOLMYrA4LxCnWlqEpGIAccrHUTN4UzmOemUZtTL/v4iqs8BRKx7CAWd9U55Hjdttt0sZyJPU52aBOKl2NWVYLRqSZ3XPbLv81FxcIkKbMIkFDgFO1xJ2dy+LJQ6vQVgjgIfQxpJcgwGqHXBxVjaioFNJz0l1sb5QqKDbUudBm9AawwaZ7EAZgBuDOGsCOJOoixuVWeOYBY7lTSxxmgCcFFDKlQ+WmztX8fq8J+/PNKGfk+sGECcJzp0HagzcZEFNFsqs7nONbSpuh+765WWBowphTmA4BXAIb5Yx2LGEga0LkgY7FjHYuYhqYQkCcMrLGKkc19S1LPI1QogTxMtKXEBz6y3b/vHzphuHYrttyPs/tnPs61EGogxEGYgyEGUgysD6yYDpVhHgbOjELQE4KwxEnA0yKw2eY2kTQpvdXYvY3Z1OIxmII/Cmexb1rhkMVSZBeNPXcQQ9Ww+6dAA9W/ejt/MA+gsHMFA6iEGXBsoHwTRYOYQqU9dhDFYOY6B8GP2lw+grHkZf4TB6Ow+jp5P5UfR2jqK3cxx9hUn0F2YwSEuQ0gIGi0wEMwQ5hDSENbQOOIG6wBsCHAIdB3DEcocuWPOayvMYkjSHJE5TufE4BXYCiLN+HWi9BiPfEQ3QqPVKWmlPxwCigk74QjgTABq6UJUXsbtrWQCOt8Jx37XfSK6uaQpwFCDkQZvwXBrgeHjjy5t9lmwd5YAbApwGeEPwYhDGYIlBGssNnqwV4jQpiwGl3Fy3HU8DHMKbcQdyphzg4XOzLtiGhChZQMPP6fZdr88Kb8ztkPeh5RQtZ45juHIiSexvDEw8VFzGYKfCm75r5sDUv2VeQU4n5Ywwbxm7u45jdzeTytburiUJfu4hjrMiiyBgQ98nm2/8yvbx+Dm2UZSBKANRBqIMRBmIMhBlYGNkwOtacRvxDVQ6Lhng5MAbBr0NAQ53kSLE0d2kGOuGljnzqFfmUGNi4NvyDIbKU6iWJzFYouUNIcsh9HYexEDpMIa6jmJ4+yj2/HkcN++cxK1907h9YBZ3DM7hb9V53FlbwF31Jfx9ZBn37D6Ov48cx13Dy7iztow7qsu4fWAJt/Yt4pbeRdy0cwF7bljA7usXMHztAurdCxiqLIgFDgHOAFNhHgMFwpwlVIvLHuQQ4nQdR62y5CAO4Y1LCbwhxGmENtlzCcTZpABHZcPi+YS5s4qhki/bdoeuSgzsvCDgplqYx2DnHAY6ZtC/lduKq0uVuUUJ9ElBGwM5Ht5IkGjn8qOwhsq+pbQljgc4QflSAWxtUMkO2i1gSS68aQdwQnhjxwZ2mIfwh8etLHEIZVqUL/U/KxfvwfsavBlzxw4mpSxxrE42KvcgSNvbtW0hsMJJ2tNBHLHAOS7WNbSyGehYQN+WOfT8cQY9f5xG7zUz6N8yi4GttNyiDBLWEOIQ3tC6awke4PD/Ht4k453rcxvz8srKV/wc6znKQJSBKANRBqIMRBmIMhBl4GqXAa9PRIDzWwAcBp0NU8ZCh4qQdMKMu1QCbwhxEmgTwBsDN4Q2pRlUS3RhmsJAkXFp1GVKXZ4Ib/ajr7gftW1HsGfHGG7rn8ZdtXnct2cZ//rrSTx8xynsvfNJ7Lv7NA7c8ywO3fccjtz/Ao498BKO/PMlHL7/RRy870Xs//sL2Hvnc3jo9mfwwF9P476/PIm7h0/hjupj+GvvCey5YQn17QuoVuYwUJpFf2EWfR0z6OucRX+ngRwGOV5CrbKMetcy6gJwFjHEOC5JMuublQEcAp0sxNlMA1tK2eUW4EkKAIkEiyXA0XMKb6hYz0psm4Gt0+jbMoneP41L6tsygf6t3GKcW7zbjlMaH0etdcxqxyAOr62uNh7cGMBh7iFOPsDxA4lanYQDZwaMNAMpueezECYENAZtwjz8fxbiNAE4ufdt9908eEOAQyscAh2WyUEjgT9hfazXcbbOVTbSAMfc48wKx9pR25ZtTlmqds6jf+ssev80hZ1/mMDOP4yj548T6L1mEv1bpiXGEt3waM0lrnninucAjtvhTgBOAknNPTTjAmrjWcyjhU6UgSgDUQaiDEQZiDIQZSDKQJSBNciA1wEiwNlAAUqU9BDQFLgLEJXrdDL3KQlcyzJl4E3W6iYLcAh3GOh2qDzrwA3dl8YlJk0v3aU6DqGnYz96Oh9FT+cj6Ck8hP7yw9h9w0HcNjiOe/fM4cHbl7H/nlM49q+nMbn3eczsfwnzh17F0tE3cHzsLTw2+S5OTf8Hj0+9j5MT72F57B0sHn0Lswdfx+Sjr2D0oZdw8B/P4eE7T+P+W07hruET+GvfAnb/eRrVrgkMlOlWNYZdW0fR0zGO3o5J9HVOC9QZLM2JpU69awm1rkXUKi6VF1ATiLM6gJOFNwkUa9re1inWS9FufR0vGz6gsFjPEOS4naFkRyrG+xEF2bYQn9Lt3DvG0LflKHr+dAg7/3AAO//vAez6wyHs+uNh9PzpKPq3jGGwcwqJXCUxcwhxVgJwTOnX3AMcD5TU9cfqjXn4zAHAWRMsWQ3EaQVwWljhlGfgd3Uz+NAk53fLukNbrTyJmkAbwpsxOa6VJ6Dnp9z3+P12119NIO7QSsuOA3hDSzVCGcabomudxI9y1lu2a1RZLWYE7jLQeOcE+raMYtcfj+KG/z0kaecfjoj89F0zisHOSQHBtOqTne2cBQ5hDt2nKJ+1UD5LYd21fra0rIRyE49j3UQZiDIQZSDKQJSBKANRBqIMRBlIy4DXuSLASSmd6ysoXkmn8h0ksb7xAGeowO2i9f+1IpWgHIATBiruWtQgogwk6lK9wsC+IbwZQ2/HUezaegg9nQfF2qa/vA/VbfswfP1+7Nl1ELcOHsG9N01j793LOPbgk5je/xyWjv0bp6Zfx+mFt/Hs8nt48eQZ/PvUx3j1qU/x+tOf483nvsQbz36B157+HK88dRYvnfoEz534EE8v/gdPzL6L4+NvYu7wa5h89GUc/ufzeOSu0/jHzY/jzvoy/tq/gD07ZlG/dhLV7kkMlCfQV2CaxEBpGlVazXTNo1ZZcIkQRwEOIY4oixLAeL2CGPuO4GPQ2Ln1lYV0B/TKLXeKYhILHAlYTLcYdUsZLlMOKA/TGCpOoloYR7U4hmrxGKrFo6gWj2CoeBhDpYOolQ6jVj6KemUMw12TGOmaxu7uOdy4TZVvAkG5lwQy5vGixDjJt8BJwxta4ijAUWudMIhxGuKEdeYAzprgjVnCtIM4WXBjn7O/a4Q4AlcEysxKv2HfaZ2yAIfAZhy1BODwOAtxCHyYHMxJ5Zn7JVYsXjZ0/DBYk5dnAc58AnAE5BDiOBdEBZq6A129Mol61wTqXWOoVUYxVDJ5Ooah0qg8U70ygeHKFEa6ZrC7ex67GRhdrP50J6q6ARyRWZZZrf4IhxIoJnUaWMKZVVx0scrAzrDfxOPsWBk/R5mIMhBlIMpAlIEoA1EGogyYjhqDGG/oRLopwAlhTuY4D+Co9Y25SnloI6vgVKRorUIFvUSAQ5epcQkmTHizY8s+9Bb3Y7B7P+rXHcDNfUdw1+4JPHD7LPbffxwT+05jefwlnF56Cy8+9h5ePX0Gb7/wX7z/8mc489qX+PjNb/DpOz/g7Hs/4vP//IIvPziHLz74FZ+9/wvOvvcT/vv2D/jwjW/w/qtf4p2XPsMbz53FK6c/FbDz9NKHODH5HuYOv4nRh1/GvnuexT//+gT+Vl/Gzb3zGL5uCn2lMfQWxtBfnMBgeRrVyiyGJH4P4/jos9VpXeRSGuK0ADk5CnG64/tO0Ahv7H8bNVh6SwWFN1SACW0WdItmcU+Zw3CFQX5pRTOOauEYBgsENkdQKx/BcNdh7Ln2KG65YRS394zjb/1TuHNgDncPLuHuweO4s/8E7ug9idt3ncQtfz6OG7fRYsJBHHHXCgFOvttUowuVAZwlGMTZWIBjcWdCIGOQplkefjd7rGAoDVQyIKUpyFkpwDGIQysdWuNYMpiTBTp2/xzQYbDSdl9L5Rl440CNghsFOdz9TXaA49bzhIC0HKqMY2TbOPZcN46bbhjDLTsncOuuKdzWO4Pbemc19czh1l1zuGXHHG768zz2XDuP3QSB3eyPGp+qVuaYwzIQLM3J2EN4owCHdWXP1SR31jori0G0Uf0wXjc9Jsb6iPURZSDKQJSBKANRBqIMRBnYnDJg+mkEOJcV4BicUbATWOQEEMe+E7pQiftCEPMmsboRJYqKFGPIcKvuGQyW6DpFF6XDAm/+/KeH0Vd+FLXr9uMvvYdw954J7L13CRMHnsKJ6Zfw7GNv4Y3nPsKZN77Ep+9+gy/OfI9vP/kJP372K3758gLOfX0RF767iAvfX8SFHy7iwo8u8fj7izj37UX8/NVF/PD5OXz76c/4+r+/4KuPfsUXZ87h47d/xrsvf4dXn/4Szx7/L5bH38XYIy/jkbuext9vPIGbemfQ33UMuwpH0FscRX+JEGcK1coMaozn07WAetei5gHEEZjjgqcq0MlaLTR+buyIvhM0Bzj8znoPYmqZYlYKAnDE4orBpwnpliRINeHNcIXKPq1ujmGg8xAGOvdjqHQAw10Hsefag7itZxT31Kbw4E3zeOTW43j0tsex747T2Hf7s3j4lmfwwI1P497aafyt9xRuvp5BbBXgyG5FJbrXcHtrbjPdDuDwOwpvuLMRrXY0qTuVhzhWV3nWN41WME0DDDdY7WRBTDN4w/PZ72Y/Z61hDDDkAZTwHL/H3xLIEM7kWeCYFQ7/F4KcZjDHypJXBsLJ9lY3SXykDMAReFOYw4AkHReqtK7pOoYbrx/FLbvG8LfBcfy9PoN/3LiAf918Ag/efBIP3HQC/9xzAv/YfRx3V5dwW8+iQJwbtzNAOscaxs/RXeJo3UOAowHELVg6A6Znn8ueL5OngkQ7mWl6zmQr5us/JsU6jXUaZSDKQJSBKANRBqIMRBnY3DLgddfoQrXuCroX/tACx8AMz1E4wv+Z+xRz+14W4Mh24Q7iiOVNCt5wW+45DBanxZKlr3BM3KZ2dDyKG7Y+hOr2/bip/wju2jOOvfcuYvrwaTy1/Bpee/4/+ODNs/j8w2/x45e/4ty35wXSXPwFuHgewAWs7O+ifv/ir4D89mfg4k8Kd747ewGfn7mAD974CS+f/hyn5j7A1IHX8Oi9z+CuG5cxfMOEQJz+yigGymPiVlWl1QmtTwKAI7tryQ5bBB3B7jdipRDUZ1i3TV01fAcweDOcbO2c/Z9vz0vq1M4dyiwOPMAxlznuLsYAscsab6RrBsMVKv6jqBYOob/jUQx0PIJ65VHsuXY//rrjIO4bnsCjty9i/IEnMPvIc5h/9N9Y3P8GFva9jckH3sDhu1/BI7e8iPtqT+O2nScx0sX4NwSHBAOrBTgO9nDr9wzAUYiglhgGJ+X5EhATQhRzj1ptHl5jJQDHYtGEli/BMV19BDQoVMjuYtb42QDFlOzoNlSewFCDCxUBTjalYQ6tYLgjnKbQGofXD3dXo0VdmMKA3u6Y0MZiRBnAKXHHN7W8IbzpZ7DwAq3yxjBYOoJ692HcvPMY7hwax/1/mcIjty/h0L1PYOLB5zH54AsYe+B5HPvHszh099N46JZTuKd2ArfuWsRfrl8UC5yh0iIGC4uoFgmNzb1RY2/pbne09FGI43M+WwBvUrFyvDWa9Y3m+Tr1xQ0c8y9pjIjl2gBgHmUmymSUgSgDUQaiDEQZiDJwNciA11EjwNnASXMCaTJQgZ0o/T8FN60AjgUtzlrfUKGqUqkqzqG/MInezlH0Fo6gr3QY/ZWDGNx2ALcOjeOBOxZx5OEnsDjxAp557E28+e8z+Pj9s/ji46/w/Rff49fvfsaFH3/BxV9+xcVzvwIXzgEXzgMXLwAX23EcfucccP4XXDx/Xg4Jcy78dBG/fHMR339+EV9+fB4fvvUj3nzhazz/+Fksjb+LIw++hH/89RRuG1rEnl2zqG2fQH9pDIPlSVSpXDOuT2B5Q3CThTcEXY316es3f8DyHYAARzpBkflCkNz2yLZNsrtPcj0BMsF3msmRfI+xbDSeDWPaUEHNAzhigVOhBQ53/pnBCOOUlBnr5gD6Ox7GQOFB7N6+F7fuOoC7q0dw4M5FzO97Gs/OvYlXTp7B64+fxVtPfIM3Tn2PZ6Y/x/KBDzF639t46OYX8be+xzFcnkX/1kkMdnIXtDyAE8a+WQx2oVIrHbHWEYCTB3EM4NizMRaKQpK0VUwzcEN3sWzKftcBmNI0aqWpxuTcleh2lvv/5Df8/bS4+9DtkHAhBU/EJYhuQWFyMV7K6opkAGeoPOZAzri4J9UqjCsTJjtP4ONSibmCHO/OFZTB3ZdQljDGgxwf08Zi2yjAcVZ4bse2amlBXKcE4BQIcCbRTyuu0kEMbz+A2wdH8cAt0zh83xJm9j2Nx8dfxwvLH+Kl4//Fcwsf4vTMf/DY6DuYfPBl7L3tadxVPYG/7lzG7u1LAm76OxTicOxRVyoCHLpPsU6nXM7jEOb4GDkpVyuJmeNkpan1jVnnXA0v3/gMyRjabMyM5yPIijIQZSDKQJSBKANRBqIMZGTA668R4GygcKQhTRoqNP6veRDjMAaOWN9IAFEqT1xtV2WtvzCN3s4x7Oo4jJ7CAQx0HUL9+sPY03sE/7x9AWP7nsGphTfw8jNn8P4bZ/HZR9/g+69+xM/f/oxzP/yKiz+fx8VfCV/OAxcuKLwRGGPnLyiYMcucBOrwwAEcMcHhb4NTP0Pcrn759iK+++wCvvjwAj5+9zxefeZrPDZ7BqN7X8WDd57GnSPL2L1jEn2lIwpxSlRwuWo/h1plDgzSLCkFVEyQWbcGEBgQOF3X+QqD/XYe9aIlB3AkFg3j0TAFkIYQxuQlATP8vwM09j/J3fXtf5IroBClXRRX1+YCdHgdWhYRUs0pwOmeRL3CALMKcAYLD+Ev1+/DnYNH8MBfJjDz8JN4YeEtfPzyl/j63V/w3X8u4KcPL+D7Dy7gzAvn8eLCt1ja/zEO3vkq7h58ArXSJHr+NCpbjVcLrCPnFhUGKS4T3ITJdqEKLXCaAByp9zyAkwUx9jkLbPg5AF0OdpmrldabwpdGQOPOOxcqAhp+Z6jYJIUARyCOs34RaMJYUtk0I3FkGGOqWpp0aQLV8rgAnKGKgRrLDeLYZ7XYodVOtcTEz7TEmUatQosVD3AU3NCizieCnGwSiENLPMbAYmyayiKGyouolhcwWFrAQHEOfYVZCRLeXzyKgdJ+DF93AH8fmcCBe5Yxf+g0nll4C2898xk+e+scvnjnPD55/RzOvPQT3nrqazwxcQZj/3wF9994Crf3ncCe65YwWFxA39Z5DBQ49ui9h8rqvql1w/phmnZxdwJrHImRM41qkUG5XZJ2YlspxLG2lrwB6AT9L9XX4vlkXIr14sfoWBexLqIMRBmIMhBlIMpAlIGrRga87hoBzoY0qq9gddFprWAkMCfjQiVuVM7qJHQhYgwYWqao4kQlbxZ9nRPo6TgicW92FR7B0LWMeXMYd4yM4vCDj+Px+Tfwzsuf4+yZH/H9F+fFOkYsaxxsQQhmjMnQJcoBGIl989NFcY0SNyn+L/ub0FCH1zDIY9c75673w0V8/uEFvPHidzi18BHG972KB+44hZv6JtFbOiAQZ4CWOLKaTysOdb+oE+QYEEnaTetaAQ5jvLiUCvjqgA5hUPI7tolrpwTgOCuc8gJGMhBHQY5rR1cG7hKlO0VpubQdCZJcCuGPlJ3PwedR2GCWCGIF4iAO457wOYdpgdM9LbtKVYuH0dfxKAaLe3HLjsO4b2QC+/62gFPjL+ODFz/D+S8uSjtJe7DazwM/f3wB/3n+HE5PfYWx+9/GvfUnZAerHf/3IHqvGcVAJ8ugLjAGjixQNHO1dCJQYvBjtwuVi5nD2DkNblRW77KrloEpKuWEMjOo0yUuTM7aRts2aFeCOle/yVbw5n4jdWSWHgEEIAzIgACx/ihOoVqwxF28mKZQ5Xn5jQbdVSsc5wYk4IawxoCNgggFEgYnAoAjIGYCQ5UJ1Ji6mMZR4w5PknhMiKMAh/BmkDuJlQh/JjHEOEcucDetgKrleQzSHbI0h4HirEsO5Mh5/l8T4e1QmeBmCfWuZQdxljBUWXQAZz4AOASj+7D7+gP4582zmHjkSZyefw3vvvAZvj1zQSAr+/WFby/i3GcX8c37F/D6E99g6dC7ePjWp3HX0GP4y5+XMFCYRc+WGfR30jpoAbWuRQxVCHBmMcityV0ygKPBkx3EIdQpMmkbKFzTvqDAzYG9BnBDC5zsGBqOsdn/xc+N9RXrJNZJlIEoA1EGogxEGYgyEGXgypUBP/dtDnDMfSAMsNvg+uMtHRII0TTuyO9BYHzFJnDAIEGDAuLrI6k7V9fyWZRYtToR1yHGf2FMGIE3GjiUStNAcQZqfTMqcW92djyC/q5HcPPAEdz312kceGAZS5Mv4d9Pf4D/vv8dvv38HH757iJoLJP8GWChAvfDRfzy9Xl8/9nP+PKjb/Hpe1/gwzc/xZk3PsUHTK/r8YdvfYqP3zmLT947i8/OfI6v/vslvj37DX766kec+/6cAoVzmTg6gaEO3ao+fvccXn/ha5xa+ADH9r6Ie29exu6do6hfO4pqZQx9nWMY6GQQX8YzcRCA9ZKqSwMmuiuPWCUUgx16kp18KKtmHRIohFl4U1J4Q4BjSeq/PK+whvd28IEuSRJs2AEHceeS+2Xvb8FwCXDoBqSWF4QMqugSFtDFhInfJeyYwXAXlXs+/1H0dx5EtXwAt+w8inuHJ7D3jjk8NvoS3n/hE5z//ILUdxKz6CLwyycX8MGL5/H0zJcY++ebuKf+OAYKR/Hn/92HnmuOYrAw5a2aBJooOFJLJ7N4crt/ZSCOwBvGQCkwLbjkwFlhDtXCLKqFGUmEVKwvDcrMnbUIp+YloDKDKieWVckx76lWT+GOY4Q6AnSkjma8FYezZDLIZznvK7DAAI6DNgoQDCQoqEkBnDItbxTe0DpGLGRczthMKSsTfrZ4NoxXJEkBTr1rHD5xy24FPLTYqZYCgMNrG8DpckCkawHVyoLCHAKdMJXmMVDUZACH8KbedVwhTtcyhrqWUC0vYqA0j366VhYn0V86iv7yPuy+4QAevG0ecwefxYuPvYMPX/0aP356QcYDgj/2//NfXcS3H1zAq6e+xOy+13Hv7uO4tXcBu6+lO9Y0erdOo78wKyCJILlK8CRj0TQGitMSi4vxuJgaAY4BNc3TEMe53DUAnMY+v5rxNT1eZK8VP8f6iTIQZSDKQJSBKANRBqIMRBnY7DLgOUNLgMNJebWoyQLtJrChDcz5/QmBr9RG5cL+ly8YSZ2GAEdAgSqyYhHhAA7jwuhKPVe8qUgx7s0EejqPoqewH7uKD6F2/SO456YJHH7oJE7MvIgXn3oX/3nzM3xz9heBNxd+zoAVB3DO/3AR3539FWff/w7/efUsXn/2Azz32Js4Nf8SHpt9ESemnsfyxHM4PvkcTkw/h8dmn8OTiy/gucdfwSvPvom3X34PH7/7Kb7+5Fv88u05tdYhxDFLHBIjQpzzwK/fX8Q3n13AJx+cx+svfIUT0+/gwP10pZrDTb1TqG0bxa4th9Gz9ahAHMbWEAsOc6uhu46keeiuSowPwsCqTG6baxfgVUGAj8GRgJziHOqS6ELl3adGyosY4Y5QFQYW1kQLHLG2EYsStSoZLjPQMOEEwYSDEXZPCRLMMjGGSTbIqwtiG7iaKDQgdHDlFNcaAoQJUfgHi0dRqxzFLTtH8ff6BB66dRrHjz6Hd579AD9/8pNaULCu+ZcCOJ9j7IHX8Pf6CfQXDuK6/30Eu645hMHiBIa75lBnqqiFk8CPxEomhDi0wnGWOKzb4hKqhUUMds5jsGNO8monAyQvoNrJzzPo3zqNgQ6CErWgMnAjO6p165bU3JaaOxulkmwdr65ksk21cysT6yDZlWkOHIsGC/7agqO/MAAAIABJREFUYpmV1L+CHkIZ3pvf0++ay44DO2IJQsCg7ktq7UM3Jn524EasY6ZRF5DGcwHAke/MYIjuTxVCGAbeJsRRKxwPbwhyPMBRFyoCnHGxwKk6gFPrnkeNW71vX0Z92zJq25Yw1L2Eoa5FVLsWMFhZEJBDKEOAQzBDN6khsb45jnr3CdS7j6PGRIhTWcJgeUEhTmkK/WUFOHt2HMAjdy5iafQFvP70B/j07e/xKy24aE13UeNWnf/6oljl/Pvkpxh78AXc1j+JG68fR617Av3FKfR1Mlg6d7aaTeANjwmTCXAkFWiN4wGOuomp9c1gYRJMZhHlIU4ewMmOmTaW5uXZ78bPv7/3cGzz2OZRBqIMRBmIMhBlIMrA1SgDfu67MoDD1fTAEsdgTja3HZQMSPy+hMdXanOAw+80CpTVF+uPdZp8TqxwQusbhQGDJYM34+jZOopdnYfQU9yL3vK/cGPPXjx8zxyWJp/Day+8jzNvn8VXn3wv0EQsb6ishX8O4Pz89UV8+v4PeOuFT/HsiXewPPEixh89hUfvm8PDd0/jgTsmcN9fj+Eftx7D/bcfwwN/O4ZH7h3Hsb0LmBt9HKfmn8Mrz7yFD9/+FN9+9iN+/f687EqVWIYE92TMYwly/N1FfHrmPF46/Slmj7yCB+88ib/V5zB83Shu+OM+7PjjQfR1HMNQifFgAhcLcTcjxFGAM1QkLOH2xkzLst0xA6wSPBDgSIwRiXtCRX5GksirAzgWwJjwZnclTAvYLRAnBDgzEHgjAMcgDq1KaD1igV1tq2UFON6qg/CGQZqZFAjQVYztqVYLM4niS+VevsPvlsZR7x7HLbvGcXdtAv+6ZRKLh57CG6ffwfcffS1bvdMFRv4uAj9/cgH/eelXnJ49i9EHXsHd9SX0du7Dtf/fg9h1zQEMlsYxvG0W9e4Z1LsYMDrYoYn1LCDHQRyBKA7isH6LhDcLGNg6h/4ts5IPdsyjWlgQoNO/dQZ9W6YkDRboRuWsbhy42b1tCWEa6V4C03CXJtahJLafHbuc7mkciwY6dUxi26p7IUGQs1BjezOWDYFCpwEc7y4lbUEAIYDBAZwKYy2pi5sBHIKb4e5pjGybkbxWUYsbgS4EPfxNFwNtzyrEoTUOIY4DNmyvevdEkkI3qhDgVHnf7gXUr13GyPUnMXLdCQxfewL1a4+jtn1ZQE61a1EgzkBZ4U0ftwc3gNN9AsPbTqK+jRDnhECcoa5lVCuLGCDEKU9hoHIUA5V92LPrAPb9fRmPTb2M9/79Cb784Cec/8YTVrpM8vM3Zy7gheNncOi+JzFy/QEMdh3AACFQUSFOP0FNaQaDZVriGLyZwUCBaRoD3PmK1k8ukHEjwJlIIM5QcVJi4ogbFS3tEguc7Fi5kjE2+5v4Oe+dE89FuYgyEGUgykCUgSgDUQaiDFxJMuDnwWsCOKr4ui2vLW6L5YFlzu9HKHyFKrxZQH0V21InwCYBOCHEMTcTdS0R65sSV7mn0FcYR0/nKHZuPYre4iEMbtuH2p8fxh0jB3Fs73E88/ir+Pj9T/Hlp9/gp29+VrcpxrwxfY3bf/8Cscr54YuL+Oid7/DK6Y9wcvp1TOx7BvvvO4F//HUKt9UO4ZaB/djTsxfDNzyE4R0PYveuf+HG3gdwy9BD+PvNB/HQ38dx5OF5LE0+heefeA3vvfYhzp75HN9+/i3O/fgzLp7n8n763maN892XF/Hua1/jyaX3MPboC3jgtpO4qWcSO67Zhx1/OoC+jqMSM0R3MzIFj9YzhDd03VkQqDBUJFxY1iQgR7c6NmXeYm/QjYYQh3IsFjiyExVdphaxWwDOEm7klt4JyKEVzjxGJObNrIM30xhmwNwKE4GHwg5ajYgVEK1UitxumVs6q7UUQY0EwKVVjSSFOApwuNWzs15Icp6zRDAwiZt3TuAuAziHn8Qbp9/Edx99jgvfnVNYRoJzEfhJAM7POD37Xxx74CXcWZvDzq0Pofv//AM7/rQXA8WjGN42jXr3NGpdUyCc8EkD63qIoxYxZoVjAKd/6xz6rplJQRw5t2UGvdcowCFoYf1zS/gEtASWN/WuNKSRgLwC3ZYchHMxXhikt8z2nMcgd1bqIJihtRL7h1rx2LW0DRT0ECAxiYuac8MizFPrQpbNWR85gCPwxrUTQRvdoupdmhO6DZaYCNxmxPpE4r/wegLbGHhbrXBogTPczTThEq/j4uRIPByNgSOAjlY83QuobV/C8HUnUb/2hIKbbUuodi9hsGsBA5V59Jfn0FeaQ19xDr2sgyJdrZZR7z6J4W2PoU6I030CQ13HUa0sCbzpL82jr8Tg4IfRV96LPTv3Y9+9yzg1+wo+eO0svvnoF4F/Dv2JDF345iK+PnMezy69h313H8dg14PoKTwswdF7Oo+htzDuAiNPoV/GohkMCMSxmD0GcaYFTIobmgRqNgscwhtNaoVDgMOdw3Rb9XyA02yMzZ6/kl7Esay/nzlCbOvY1lEGogxEGYgyEGUgysClyICf87YEOOqGEF2o2gubr1ADOAwQmw9xGhsuC3DEssnFIFLXDiqpDFw8L9trD9AlojDhdp06ip1bD2Og6zB27zyM22qH8eDdk1iYeAqvvfgWvj77BX742iAK/ZccRCG8OXcB33z+PT5853O8+vwZnFp4HZMHnsHDdx/H3/dM47ahUezpOYja9XtRvfZh9Hc/hN7Kv9Bb+Sf6uu5H/7b7UL3uH9jd8y/cMvQw7tyzHw/fM46x/QtYnn4Czz/5Et557R18/t//4qdvv8G5n36Se5qViAGcH769iI//8z1ee/4sTs2/h6MPPoe7RxbRXzyI3o5D6C8Q4IyjVp4EIQ5jz4zQ8sLVcY3xWAhLCksYLCy7xK2OqezPOasAWgToqj/jv1QLDuLQOkTi3tDqhtBmCTd2WTJLHFrhzGOEu0OJyxQtcKbB7arrVNglgK7CgCpdWwrzGKCFClN4/+IkBkoTGCxzq3S60Eyg6qw6BmklwXYtsm196uucQm/HJHo69Lc37ZjE3fVJPHjbNJaOPoW3nn0LP3zyOS788IuPa3QR+PGTC3j/pR/w1OxHOPLP53FHdQp//tP9KP+/d+O6P/wLfYWDqHWrW4wG3lVApHFcCCHosmaWOGpdpLtT0T1tCVVngdN3zSx8ItDhZwKcafRtndEtywl1ZUv4ebGGMsDCXZYYFHegky5RDNTLYLjmCucBDgP1SuKOa4V59HfOoo9xWPg7Ajy6rclOTLYjE3dj4i5JhGeMz0KrNVrKKAzVsvCc7nCmu5zR9Yqgh64/ExgojGGgMIqBwjHQfY15f2EU/YVx6XvSRhJkmMGGzXWIllIa0JiwxsMbHk+mUr2L1jpTqFamMViewWDXHKrdi6huW8Zg9xL6KwvoK8+htzTr0gx6ijPoKbjUGQCcbY9hePvjAnIIbwYrS+gvLaC3MIddHdPY1TGGXYUD6Ck+hN07HsW+exZxavZlfPDap/jm459x4Xsjugp0aYHz1Qe/4OnFt/HIXQvoK/0DO7bejxu2PIwbrjmAHVuOYOdWxtwiyKHMEuDMaaweCbCsddLoQqUA08ObCVQLTHkAJxwnL218bT9+h/eKx7G+ogxEGYgyEGUgykCUgSgDUQY2mwz4+XAbgKPxJgQohK49gZVNAh8y534/je4r0+ANwc2lAxxaFzBRsVTlkwqnuE4VJ9BXoAJ1FLs6DmFHxwEMXXsYtw6N4f7bpnFs7wmcPvEyPnj7I/zy/Q84/xOVe0YodfDG7Tx17scL+Oi9z/HcE29h+shT2HvfIu68cQy16/ehp/Qgdhb+hR0dD+DPW/+J67f8E9dfcz+u/dP9uO5P9+Haa+7Bddfcheu33okbCndiV/ku9G27Czf23Y+/7XkED9x1CGMH5/DUiWcE4nzxyVl8/9W3OPcTt6/y5ZAdk364iK/O/oqP3v0Rrz//JRZH38C/bnsMtW1HMFA6hMESAc4ohkoTAnBoCUPrGAYZlrqm4k9LFwEnc6LY93dOo69jEn0d4+jrGEVf5zH0dR5BfyeV8VEMFsbdqj8tauYDcLOMG7tdEpBDiKNuVLu75rG7ixCHblO0FpiUMg0VJ0Tpp2I60DmB/o4Jd2/mE+jrpNI/hv7iqKSB0jEMlhmoeRxDXdyJiK45BDSTGnC2wPYdR2/nKHo6jmHnliPYcc0h9HYexJ4bjuLu4VE8/LdJHB97Em8//xZ++uwLXPzpV9nmXeDYBeCHTy7gvZe+wZNz7+Pw/adx6+Aorv3D3ej8f27F9v+9D7s69qLadRRDXaOodY95Nx9nbaIAR62LhrtmJV4O4/wwESoyUPFAB61sJtF7zQR6/jSOXX8ck7znmnH0btVnH2C8E+4mVlF3MFqwDFB+O8fQ23EMuwgDrjmCnq1snzEBJAK5uFW3WLXwdxpLhTscMeYT67RnC9t1Utx1DNAoyFGYw2PdpY2Ah/1nAbTQYbwdWgPVu+Y1/o/EAZpFvWtWoBVdywgM+zoOoXfrAfRs3YeeLXs130qgeBi9HUfR28HyE6IyJsyk7AJHqMpnI5ijtVQ9gTZTGN42hZFt0xjZPo3d22fE+qnWTYAziQG2fWUK/ZVp9Fdm0Fuewq7iBHZ0jmFHx5jmnWPY2TmOnZ0T2NU5iV2se8KpyhKGt5/EyPbHxHWKblP9JVroTGHn1nH8+Zqj+POWA7hh68PYUbhfLOj2/v/svYdXm2ue5zn/2vbsbM85u2e6e3ZOz3RPd9etqnuvbUBCIkchQGQw0QlsTM4ZkUEiRxswGLANGGOCMTY5J332/J5XMvimrtqpmqquls957osxF0mvfgrPR9+Q08FA+xTvX22wt378kwBna/mYka45Cu+3EKZ7QOCthwR8/4SA7woI/L6U4NuVChzLjJoFeMl9JIBZQWaxVUlGjtx3WgOVWKgkwFuyrFyqG+3oCjN2VYnfCBhXdtP/9efXfz+vRX9ubzTc18c9e+4ZcM+AewbcM+CeAfcMuGfgDzMD1++Jfx7gGDq08FWBCDfgzR/mCvw53ZHXJ+OXs2t+159zQoWfUeD8HPBS3/9hztAXgKMpGMT6IZths64Rk1etyr0J86og1KuUhNAasu+2UpnXg61pnJnxt3xe34bLq2twI7v7m01QOxfMTb6nrX6U7LR6Ek2lhOqf4vVP97n195l4/MMDdP/0CMOvHmP41RMM3zzB+E0uxm8eY/jmEYZvHuD9TRb6X6Wj+1UKul8l4X87lXCfTGJDH5CTXoa1ysbzwWmW51f5tL7N8Z6EbDhDlJ2BxhdncLzvYF9CjZcuGLOvUpU7RkqowIVaYoy1GsTRN6oGJ1HCiEpGAoal+elLYK1Yj5R6pdmplBDFRB2Rulqi9NVE6auI9q7G4l1HjEF+VzPxxjZlj0ry6UZWsu/1SvK1k+jjBDgK3nSS6NtBgo8oU6QZSwsYtugbidY3qKUpNUStIZdbR5S+HgVsvOtVhojkiMiKNtarpq0YnyYFcSzGJqXO0TJGBPbUY9bXEqGrItyznFCPEszeJdw1VfI4uZ6KnBYGWkZZml7gbGsHx9mFdl6d9/GhAjjbjHS+pTZ/iExLLQHf30f3T2kEfJdDhK6EWL864vwbiPe3khDQTLy/Bh1EhaNZgUSF06rasFStuao2d7ZjSX6PgCtRpHg2EHGnnvDbtUTcEfhRT5ScD6mCV2qjRqKNjUQZ6on0rsWsr8bkJQCgnJA7JYTeKcHkVU6kdxXRxhpifOuI9asn3r9RXbc4P1EKCeyyqt8pSpgIAT6eAnwEnolVR9QzmtJGgzcCcDSII4ofl80qXuxbanUR79tJvG8H8ZID5Cs5QAJfGoiS2navCiI8BdwUEX4nXx0jPMuUcsmsqyFCQsM9Gwj3aMSkYI6zNU0BHFEwafY0sahJjo7Y1RL821STU1JgB4kBbcT5tWDxaSLKaFUr0seKrAhDA+H6OkI8a9QK9aohTFdLuK6OcF0DYV6NhAuY1Lch2TgCcGTFit3KKDarFky6RsI8a53wpkTBWJPhCYmmQspzOxjqesHqmw32No5V85QCf8JVz+Biz8HW+wNGul5S+MBKuD6TwNuZBDghTuCtfHW/hXtVYvaWebYSLSHOPh1qSaaPUhV9BXCc+UKuKnGnCk4p4b6o4TTrqPYa88Pn22tFo8Dx3weQ/+W9Zv05vX66r4t7vtwz4J4B9wy4Z8A9A+4ZcM/AH38Grt8b/w4ARwM5CjC4QnUNznaknzv+RFDvH/9G/b6DIxacP9y6qb65tk65Nh2uqms5/lz1ugbKbqqdRH2jbB4qu0PsU5IxIeqMekwSWqxap8oIl419ZD2FD220VI8x2vuaxbl1dj/tw5V4pVzbM60B6uzQwf7nM1YWtxm0z1KWayMhvBizbyHBHgX4/Dofw6/kWIT/b0sI/K6MoO9llRN8q5zg22UE3y4m6HYhQbfy1Sfzft89wvfb+0qRE2Z4SKT/Q9Ljiih50kR74wDPB2dYmFvh04ddzk8cXJ45uLy44vLykovzK86OHZxI9sbGFS9HNmkun+JhXDt3w6zE+4uNpYZIz1osequyUCVK4K1YYgyiUtIaglyb5oSANpKD20kN7yDT3ElmZDuZkW1kRraQaW4lM6KdjPBO0kO7uBtkI9n/pn3qawWOaqEyXNunEkSN8kV906hAkFhlEgOaSQluJTWsjXRTG5lmucwOsiI7yYrqIjOyizRTO8khLSQEWonzb1Tql2iDBniivBuI8m4kSqxivk0kBDaTEtZCangzqRFW0syN3IttJD+jleqnXbRV9TLRN8Xam2UudvcUwHE1CUlA9NHmJStzO7wYWKajZoLCex0kh5di8SskOayarOhWshO6eRhr516UTdVE3w0VsCCZOE4rkMGKtCbFGrQmpQS/ZpICWrkb1E5aaCeZJhtZ5m51TA/vIjW0g7SwDtLk3Jo6SQ1vJzG4mVj/BqJ8aon2qSHat5po30pi/KuIC6omIaSaFFMdGVFN3I9rIyfZxpMUO7l3u8lL6+Vpah+5KX08TurjUXwvWVF2UsM6kfs4zqVg0TcpZY7YmrQQXbFWiVpN7FNOBY7TZvW1+kaUN/J7BLC0kBDQQnJQC2JTSwtvIi28kTRTHammGtJMDaRHNJFhbtNuV1AroqBRqilvCfaV62AlSq8ph2J9nKoeZ0C0qKxUKLIT5IgSJ96/hTiBZgHNJIa2cTeik/RoG6mRXdw1d5Is8/JldZAU3k5cUCvRvs1E6K0qfyZaFFG+ruaqLmJ827H4tRAf2EqS+p1tpEdLjpWV+wl1Koi8vW6YqZF5Npe3OPp8qtriXM8QEnJ+ue9gZ+2AF4OvqS+2kRYtwehaOHpSeCXJ4bWkmKykmltJM3eSEtZBQlA7sX5tWIySDSRATXKCboYYa1lDUu8u+VMSMO16rlMfEEijnLf2PPlzz6UucOM6Xj/PXr+g/fm9zvy+r0vun3ffh+4ZcM+AewbcM+CeAfcMuGfAPQM/nIHr97u/E8D5veCNC+r8GUOceJV78oeDN66TeL2hcIEb19EFcH4e3kigrmtD8wXyOMGNVoMt+SCStWElQj6N96rApC/DZCglwqeIzLgGKvP76WufYW5ihfUlyb45ce3Lvhxlg7a/dcHa212mxpZpqhrhfnIDAR6PCLiVS+CtYoJuVRF8q47QO/Va1oUoDFTehVXbqIrNxLtBVRObvQUklRPmWUyIRz4hXk8J1T8mzPCI+PB8Ht6tpvRpK631gzwfesXy4keO9s45PTrn7PSMi4tTLi8uuDzTWqlOdh0sTO3Q3fiawsxe7lsExogqohLTnUqivBqI9W7DBXDEliG5I6KeSPBvITmkjfSIDh7EdJOb3E9R5jBFWYMUZfVTnNVLYUYf+an9PE0aICdugHvmPlKDupF8FxViLLYpp4VK1DeaTatDgaI4achR1dLSJGVVtq5YYwNJQc2km9p5EGPjcUI3eXflcuTyBinOGqI4a1hdjyfJfdyPsZFqaiMxuIkYv3oi9aLmqMasqyVSX6cUK4lBLWREdpCdYCP3rp38DDtF9+xUPO7GWjqArX6E4c7nvJl4w6flda4ODnCcX2hV0KJqOofjTxd8WNjl1fM1BttnaSwZIi+jhZzkJgoy7ZQ/GqEmd5LSB8/ITx0mJ76PrEg7d0MkcLiZaKeqyKKvx6KvI86ngUR/K3eDm8mM6CA7tof8lCGKM55RlD5KYdow+alD5Kk1yNPUQbITe8iIaiMhpJ4o3wqi/SuICawgLrhcZStlxNTzMMlKbkYbxQ+7qXo6REPxc6wl4zSVTdJS/oLm0hdYi6eoL5ii+skkxVljPEkaICuqm7thnQqCROrFziSqHM26FeEhtiYJ0pWw4muIoyxWqpGs40v9twQ4K6uTXzMpIa0KumXH23ma0k1eqp38tC4KMjooyLBRmCH36yC5KQPcj+kmJUyqv1uJ8Wkh2iD2tyYNWhjatHYqn05ijFrAsVjHJLBYuzyBN60KGCUEtZAc1kqmpYtHSX3kZQ6r9TRjiNz0IZ6kDar1WM5nSj8ZFruCPZFGK5HGZqLl9/sIuLER69dJbEAb8UGtpEd1kZ3US+G9IUqzh6jIHaC6oI/mqmFGu2dZnFlld+OA071zpHnK9UdmRzJx9j8e8mriLbbGIQof1PM0U1YTTzPbyMvoIi+zh/zMAfLSh3gY30uqqYs4f7FRaedCwJaCWV9aqLR6d6mqd1lDFaAWaCNh5E5Vze9zvH6+vX5Bc7/Y//DF3v1390y4Z8A9A+4ZcM+AewbcM+CegX/7M3D9ftfFHrQW5U7+Q5zI4MU+5VxaiK5sYDXVjeSF/NJy/dyf6iRpcEaCaX9p/RjeJEiQ7f+P5aqfdh2vNxUueCPHrwGOC9T83PEmMHPBG7k/pJJXclREfRPmWYbJuwSzTzGR/oU8TLFirRxlfHCJ9wvbbG8ccnZ44dqXfTmKReLT2gmvXqzT0/aSwuwuLMHFfPv3KXj+z2z8vy3H5NVKlMFGtMGmqohjJCBWap79uknw7yEhwE5CoI3EQFFCyCfvTcoSJDAnXFdCiGcegXeyMfk8IcFURFZiJYWPW+hqecbc1DI7n4852Dvm5PiY8/MjLhW9cdq7TmFt/ohRm5bdkptiIzVcckmkUrwMs0edqhxOkFpptRkUK49kuFhJDm4hw9zOo3gbBRkDVOU8o6n4Jc0lUzSXTNJcMk5j4Ti1ueNUPhqnJOM5j+OGyQjtJd7QpVaChBg7AY5chsy6al3z1jI8YqUq2mmdivauJ9angdSwVh7G2shP66fswRA1uWNYi8YVfGgunaa59CXWopdUZD/jaeoA92K6lLom1l8afSqUlcjkWYVYcyQbJzW8jZxEOyX3B6nOHaGhaJTWijFsDc8ZbH/BRN8Mc89es/rmPfsSYHx8hONCMnAcKgdHNuQnW2d8Wt7j3cwGLwbf0tcyjbV0iLqCIVrKJumqeUNP/TtaSl9T/XiKwvRRsuP6ECVNvChLVIBvHVFeNURJ4LFPLUmB9aSFWXkU00lR+iC1TyZpLpqjqWgWa+EMjQXTqvGqNu8FNU8nKXk4wqOkLpJNdUT6lWAJLCUutJREUxmZcdU8ybBS8riD2uI+WmvG6Gl+yYhtgTH7Is+6lxjvfcez7neMdL5jsPUd3Q1vaS6ZoyJ7gqd3h7gX3U1ScCtmvZYTFHa7jrBbDYTdsmLyaCbSS6rCNYAjShxtyd+1sGLJHYoxajXfYiNLM7WRk9BNSdYQ1U/GqMsbpaFgBGvxMNbiMazFE1iLphVIyk8f5p6lh6TgduIDpEVKHjPNyjpkUfXiNmKMXUQbJNC3VWXkCNAQ0CNV42KfUvAmtIX0yHZyUnopfjhKbeEUtYXTX1aNgKv8KaryXlD2eJzHqQOkRXVh8bMSpexXcrkdyjoV69+u4E1yeCvZyb2U5TyjuWKGrvo5upvm6G+fZaz3DW+mVtlY3uZ455TLo6uvwq8VwDm65GjriHdzK4z1vKCp0k5jWTeNZf00lg3RWDpGY8k4DcUvqCuYpjBrlHsx3cQFtBAtVkCDgF4N9qr8IwFack7ksSRLPR9qNqhYbxu/23L9/PXx+rn2+gXtT/W6475c9xtD9wy4Z8A9A+4ZcM+AewbcM+CegT/eDFy/3/39AY4zyNQVaHoT5nyBNwJ7/gQKnF+GNjeBzjXASfDu1MCNvouEX1r/Ctz5KYBz/Wmya9PSQaz+62Don4I4LoDzZcPjVA1cA5xKwrxKiDAWExVQTGxoEbn3WuhomGB2Yo3N1UMOts+4PJW04q//nB86eD+/x2jvAvVlIzy624LZt5jv/j4T3T/lEfBtLRFeNqK9+7EYZPURY+wnzqefeN8BEv0HSAoYICmwj6SgXhIDxb7RicVHKoUbVZ15mFcpwR75hBsKFBxKiiznUVoDjVWDPB+eZ31lm61Puxzs73F6vs/V1ckXm5fYfzaXT5kaXKOtcoqSB/3ci5ZP9StU/XWER40KQY0TS4zaiEsGSh0J/o1kmFvJSbBRfG+AhsJJuuvneW5b57l9lef29zy3v2Ok/S19jYt0Vs5jzX9FUdo49yIGiJH7RSdPOl0k+Ej2jVi0tIwdpY4S64eyhEjujaa+EfuT5LTct3RRmDFAXd4E7RUv6bO+4ZlticmeFSa61xjv/sBo5zpd1QvUPn1BfvqggjiJwY1ECJi6XaJybsy6aizGeu5Fd1B8b5Cmkkns9TMMtr1ioneemdFF5ieXeDezzNr8Kttrm5xs7+I4PcZxcYbj4vILwDnbPWdv45CPS9sszWwwM7rMmH2e0a5FJnrXeDm4zdzwAaPtH+msWqL2yUsKUke5F9mt2pKkul2CfM2elUR6lRPnW83dkHruRTZRkNpNw9MJeure8qxjg2cdHxjrXFdrqG2FvuZ32BsWaSqbpuhhH5lxVmJDS7kbXcX95Hpy7zWhG1FYAAAgAElEQVRTnteFtWoAe8tzhrtnmBiaZ/b5MgvTH1h8ucHblx9ZmvnI4tQm8xOfeDX2menBT+o89jQs01q2QNXjafJSR8mKEpDSQaxvm6q3DlMhyhIs3OqscBdo4wI5chSLj4Qra7k68X5WEgOt3Ld0UnJvmKbiaXrq5xlomme4fZ5ntnnGut4y2rnMSPsq9rq31ORO8Th5gJSwdhKC2pQS5xrgdBLr062WxdClWqqkTUwCmEWZIhAnTuxoIa2kRrTxIN5GyaNRmspn6WtdZqB9hcGOVYY61xlsX6O35T126xJt1W8ozXnO/Xg7Fv8GIo0NmA1NmCX7SdRhge0kh7eTFdOpfl9r1Ryj9lWmhj4wM/aBVxMfeDv7kQ/vdtjdPOTs4JyrEyfAceZROc4dOI4vON075uPyJm+m3jLW+4LR7mnGeuZ41vOGse5FRmxLDHW8p695mbqClzxJGSQhWDJ9rEQZJMPJGUitFxuVwE8XwOlCZRIZBMDateXdTZxaWsOZtJx9vW5CHjfA+VO8trov0/2G1D0D7hlwz4B7Btwz4J4B9wz8KWfgDwxwvgI3Sqnzp7lxvy/AUfDml6DNjX9L1HeR6G37yZXgbVNV1D+EOBrAccIbsQkoePM7AhxX+5T61FpTRMnGU9QGJq8KwryKiPQtJjakhKTIEopy2ulpn2ZxbpO9T+ecHlxdtxLdYDiHWw5eTW7S0ThFwUMbaZZGTMYyvP5JwonLCfquGZNHL1G6IbWi9YNY9IPEGIaIMw6R4DtMot8wif5DJAUOkhjQT5xvNxZDh6rAVu1YOgldLVe/N8q/jNiQctLjaqks7KXP9pKF1+usr26ys/2Zk7MdrhzHzloqTYWzs37O6/FN+pvfUJs3Sk6ibM4rCbtdggAcaXoS4GLRS+WzFjqbFNTAg9gOCrP6aCh8Tl/TAjNDn1h/dc7661PWXx2z9uqQd1N7zA1vMW7bpLd+lZqcWR5FDxEtlhvPNg3iiBpHqW9c6iktZ8ciCgq9VbMXqRapepKCmniS3EtN7jjddW94blvhzbNPrL8+YHPxhI/zZ3x4c8H7l+dM9n5Wm//q3Bfk3u0j1dSMWV9OyO0iwj20gNwYn3pyEuzU5j2nv2meF/3vmZ/4wPr8Z7ZWdthZ22FvY4fDzR1Od/a5OjzEcSb17NI0doHj/ArH2RWXh+ec7pxysHnE9uoBH5f2WX29y8qrAzbmL/j89oqtpSsWxs941vmZjoq3VDx8waOYPmUpCr9drSxrEXK9vEpI8K8iw1RPTlwLlY8G6K59zcv+z7yfPmfl5Tmrs+eszZ2zNHXEq2e7TA1u0t/2lrriMR6nt5ISXUFORhNleTaaa4bp7ZhifPgNb16usLL4kfV3W2yu7LK1fsC2Wvtsr++ztXLIp3fHbCyesPb6lKWpY16PHTHdf8Bgy0day5aozJ4hN+UZmeY+4vw6CPdoIsKzBWkii9JreTg3FTjSjqSqwvUNxPo0khAg6i0rj5O6qc+fZLD5HTODn5h/9pl3U9usvdphZXaP5ekDll4c86Jvi/bKeQozR0iNaCcpWACOAExplJIcmC5ifXrVshjsqmLbrGvV6uGlscnbqgBOilinojvITeuloXiKgfb3vBrfY/7FIW+nT3g3e87b6VNePT9ganibEds6jSUveXy3F0tAHWZDLRH6BsKl+czQQkJwOxnRXTy+20NjyRQjXSssvTxgXeZw+ZTPqyfsbpxyuHXG6f45lyeXXJ1eqnlR6i01Oxc4xNp4eMLB5102VzZ5/2aN928+sDr/ibWFHVbf7PFudp/FFwfMje5hr1+i5MEzkkNbsfhKcLXWtiZh60p1pGxUAnBc8EbATbdz9RBv6CXO0KMgTqx39w/gjQvm/BjiuBU4f5rXWfebN/d5d8+AewbcM+CeAfcMuGfAPQP/u2fgjwVw/gSqm5vD82OAc6200aRGrr93fQVcBLwk6G2/uDR4YyfR+8dLAzjXEMe1sfgCcPSdmsLjd1DfiCLn6wwcLTvi2kJVpxp7wnSFRAcUkxhRSnpcORUFNoZ75lS2zcm+g8vTr8OLFcO5gu0PV4wPvKe6cIiMuAbiQqsJ9y7H5zdF+H9bTcgtATjdRHr2E+nVT5RXH9G6Piz6fmINA8T7DJLgO0ii3yBJAYMk+rsATieRskmVkGVvUQbUYDZWYvYtx+xXQoKpgoLsDtqtz5gaf8vS4iqfNjc5Od3FwddZPQefrlie22W85z2t5S/ITxPLVjVhdwTgVBPpaSXKq50oqW3WSY5MFcmhjTxOtlGVO4Ktfo6XQ5t8XLjkat+hrT0HV3sOjjev+LR4ydLkGS+6d2kpWuRx/Ahm2fTfblLV2JLRoUFJ7dzHerdjkaYjFVQrwEhqwOux+NRzN6yFoqwh2ivmmOj+wNLUPjvvr7g6cKhw2Ksjh/r67LOD1blLJno+01Y5T+mDMbIs7UQaJBi6kDCPUsy6StW+lJfaT3vlHNP9H3k/e8DO6rm6DWJvUetM2oIcOM4ucZyf4zg/da5z7XtnV9plHztUw9DlgUMF00q7kJwDuW7qeh062Hl3xfzYOUPNH2nIe8WTxCEFcEK/qyTsVjnhd0qJ8CwmMbCae1FW8lI6sBY+53nXGutzl5xuXnH+2cHlzvX53Xp/xerrE16ObNDVOE1xThdZSdWUPbXR0TjO+OBbFmY2+bhyyPGug6sTB45TWWjrxMGV87rLdZXrfbbj4OSzA2nX2lu9YvvdFe+mLnnRu0dvwxr1+a95eleay7pUO5RW3S45LHK/SaW15OGIjUrCjVtUlpTcj2KBSwxsVGHZBel9dFW/YnZoi4/zl+yuXHGyecXlnoPzbYf6+vDDFSszFwy1rlGRM056ZAfJoW3E+jcT6W0lSlqWRLnl20ucr6jX7CpMWT02pAJdZ1Uh5LF+VlJNrTyI76T4wSC2+jfMjm2ztXLF3ocrjj5dqdt8/MnB9soVa/OXvB7fx9YwT8G9fmICqonwriJcYKk0cnk3kRzWwYP4boruDysF1OvxPQ42rtT5k/tbnV/XDKmjzJAAPwE5koUj6+zLujw64+LgnLO9c87lOcU1N/sOTrccHHy4YvPtJc/tn6l9Ok2qqZ0YAThGJ8BRtfBaS5icd01RaCNOqs+N3cQbe4g39mpLII53DxrA+SmI4wY4N1/r3F//737T5L4898y5Z8A9A+4ZcM+AewbcM/CnnIHfEeC48m/k6FLZfLFOuaxUrtDiP5Ft6uYg3QQ4X1WC6zuJU0GZcuz6wbIRr//llaC3k+ACN4ZuEg3dJDmXAB35t3hZ8nu8bcSppWXgqLyHrwDOv67A+QriqI2PBhIs3i0q6NakKyNcX0BscAmplgoe3K2mvryX8aEFNtcOuZQN8Q/jby61iuCN5TMGuxYoeGhXyphIv3KllglUDVN1hN1pJcLDTqRnr3P1EOXVg0XXS6x3H3GGfhJ8+kn0HSDJf4BEP7FXdWPx7lKZI1H6FpWBESWbOGMNEcYKTIZiLEGlPM5sobFqiJH+WV7PLrHxYYPj0z3g7IZGCI63HawvHjE39hF7wxwl9/tICqkh3KMEk0c1Zo9GIj3aMEuVspfUUleQEtbA0zQbdYVj9LfOszC5ozb7X/1iBwoMHG1csfH6krmhQzrK3/IkYRjT7XrCv6/H7GFVsEYq2yVw1bXk3EuAdKSXACMJG64jxkcailooeziCvX6euZHPfFw4U5v9ry5X9EWnsL18xezIPva6JSpynnMvph2zdymB3+cReqcYs66COL8GCjMHsdfN83psh82355xtObSA4h/9UlEsidJK1DeyBOY4FRUCeWST7jpeoM2EuOpcrWRXcLJxxfLUJWMdWzQVzvMkcVgFQgf9tpyQ70o11ZNnMUlB1TywNFGQ3kVb+QtmBjfZFVB1LBCALxXmjhMHp9sOtteuePtym2H7G+rLBsh/1ExL7SjPBhZZfrPL9ocLzg7kDvnhjXL+Xa6jLJlb1+0QsOMEYgKiBOSszFwyM3RAn3WNmtwZHsWJCqcZqWePNmjNUGJdEoij2ajalX1Kas4j9bXE+taRFFRPmqmRkvv99FsXeDd1yPHGFZe7GoST6yGPJ7ltApREufTc/pHqJ+Okm1tJCmkmxk/AjPxOsUiJharnGuA4c3Ck8lx+JsKrlli/BtIimnmU2EH540EG2hZ5N3PIuQAtgS1yTsXWdIb63u76Fe/njulrXaTwQR/RfmWE60oJ86wg5HYNZn0jKeHtKri4PGeMwbZllmdPFbhTzwWu+9x1uuU2qXN7xdXpBZfHp2o5Tk9xnAkQlFnSVHyq3ezm/ST/7ykKKh59uOLl4D6NhbMqnDvGt0HBTWnKi5DHipeVaKkMl5B2yb4R65QCOC5400e8se+LCscNcP6Ubwzcl33z/YT7a/c8uGfAPQPuGXDPgHsG3DPw5zMDvxfAua4M1+CN2EtcSws0dsGdnz7+ke94J0D6KovHW7uByrKk07JNYnSdfL26iNV1EauXZfuy4nQ2frjidQJ4BNQIvOkh6caSv8v3BeDE3QQ4KuPhBsRRwZ03W6jaVTjuT2Xg3PzeTTVOjEEsIXVE6CTAuICEsFKyEqt5+qCB1vohXj5fYvvjiRZK+sMN1xlcHDlYmT+mu+U1j9M7MfuVYjIIwKkk5E41YR7S5tNGlJedaF0P0V6yutWy6HqI1cun5PKJeR8JPn0K3iT49hFn7MaiF4AjNeetKuMjWgJWfeoxG6swGcqwBJbzKLWZ6uI+ejommZ6YZ21lneMTATjnrm2lOkoT1ebyCYtT2wy0zlOeM0BKeB0mz1JMntVEeDRi9mglQrVj1ajzkRxWS25qB7WFQ/S1zDI/+Zn9DdkBfw0EZHO8v37F2twZ0/3btJS8Jjuun7Dvawj9roaIO41E65pRdceGNlwgRwCO2KcidfXqPog21GLxqeVueBOloqCom2NmeIMP84ecfv6x+kk2vJ+XZLO7TUf1G0oeDJMR1Uy4VyH+v32ibFQSaBzn30BB+gCdogQZFivWkQJRAkrUHznIkvtX1uUljksJMJYlGTiy6XZcQw9RtQjQu7lcUOcU9t5fsfDsjMHmDeqezpId24/ADf9vSgn6toSwW8VKgSMA536MC+BMMjO4oSmNfghwTuF0x4HY4N6/3mZqZImethc01w4x2veaxdlP7H68UpXxl/L/Cmh05a8IJHEpRORrAQyu5fo3ue6nmkJHwJaondZfXTI7tE93/TKVOS94GCutSJ2qojvCs041VGkQR+CNrCaloorUVxPnV0NycB3pEfWUPehjoHme9y8PlLJIlDdyv8mfmwBH7sexrjXKs0dIDmsgPrCeaKPMp+RHiQqnHYvRToxPjwoBF3uh2LYk2NfsJT9XRYxvLammRh4ltlLxuI/BjnlWXoklzqmUkfMiY3R+DXCW5/bpaZ4jL6sLkyFf5UwF3y4h6FYlEbp6xJKVndRNec4I/a1vWZo+4GxbO1cK5F1ez82X86qA3zlXJ6dcnZzgOD3RAI5AnLMLp0LHeb+47itpqjrSVFcH61dM9+9QnzdNSqjAq1oidbVEeNZi8pAcpUaiFEBzARzJlbKr54wvChyDG+D8+bw5+CO/Xv+JVbru8+y+f90z4J4B9wy4Z8A9A+4Z+Lc5A78zwNFaQxSYUYobF7hxHb9upPppgCMX9kcaFKcy6Ca8ka/lerjgjcWrA1nRX1Yn0V6dWNTqwqLrIkYtGzE6G7E3l5eNWOeK13WT4C3wpvdHSwEc/TXAiTfY0ZYEdbpyVCQL5ybAka+vq8O/QBud5LB8/X0t1LgTBXC8JfeilAhDPinmcrLT6lRNt731Ga+nVzjYkj5ubdP55b+XIJtlASNvZ/dpr5vlXkIrIboiQnVlhHlVYfKqw6wsSWIXshOj78Gi78aiE4BjV8cYfTex+h6VVyHWB4E4CZL1YRCAYyPKq1OFxloEfEi1so9UHdcSYagkOqCSe4lNlD6109owwvOROZbfrXF8vA/ckAs54GxfFBznrLzeZ9S2RPXTYVIj6jHpSlXltsmjEdOdVkweDWozbNKVkBRazePUVmoK+ultmeLNxIayovwUwNldu2R55oDJno9Yi2Z4EGMn5LtKgr+twnS7TlmzpPJZzneM3BZjG9JYJKG3kfp6orxriTbWYPGp4W64ptzoqn3J9NAaa693OP50/iPFjAAUsZu86P9IS/kMBVl93I1oIOROHr6/ziH4VoEKNI73b1BtVu2VM0z1r7M8s8vOyomy8XyBGy77i4I1F3DpWlc4LjW1jvpZUUncsCJ9sZOJFeZQs5aJnWxu6ICe+vdUZb/gflQ3ER61+P5zEYG/KSZUAI6XpsC5H2OlIL2T1vIJXg6uXwMcuT4CB4Q5CMDZvWJn45j1t9vMv1zjxegCY/2vWJjZYOuDWHRuqGrkOp441HwKYLyQ6yVgR37GCXG+wConuFLw4UKDCBc7DqWW+fDmkqn+bbqq31L+cJzshD6SQ1oJ96gi7E4NEWK7E7jo3aYyaERFJda7OL9qkkNqyDDXUv6oh6HWN6zO7itbmLLBOcVh6jJFgXPo4PPbS0Y63qmA7figaiy+VZj1VYTeqSJc7Ew6uRwbFnlcGLpUPpTFoAEcUd+Ee1Rg8anirqmOhwlNqh5+uPMVa/MSSH1DKeUER6LK2V0/593sFnbrFE/SmwnxfIT/9zkEfJdP4HdlmLxqSAlrJjvJRnnOMH3NCyy82OFILGBiIxRVj0A8F7xT51ZAnyi2zrk6PeVKwRsBOM4ldqqTc66OL6/vE/n/5TzsOzjfcrC/esWL3s/U5k6SFCTBxVVEeFYRLufidg0RHg1EejkVUGIhlbpwFV4sGTjyXCINcKLsc1uo/mivk3+s11/37/3jvbdxn1v3uXXPgHsG3DPgngH3DLhn4KsZ+D0Azg/hTYJPFwk3FTguK9VPHL8Ana8u/A8Ic35CfXMT4Fh0Am7aifLsUCtSHTuJ8uzUYINXF9FeGsSx6GzIivFyLbv2taeNGE9R5bgATh9Jhj6SnUu+TvTuIV7f7VTgaPBG6qi1INxriKPZurTb74I5X8CNvh2Lru1HS/5dflZBKYOoW2qV7cZszCfNUsHT+1ZqS7sYsE/y9tU6x3vOnfQXeqNtlsWuItkyryd3aKqYJjW6Cf/vCwi+U0a4l1gwnPkdyuZgJ9ag5VHE6OxY5Jzo7GrF6gRUSWuMFj4qAaRie7Do7ETJ+RS7mDTO+LRj8WlGrFSShxPtV01GrJX8hx3Ulfcx1DfN0uIKR0cH1/4buc5XcH7gYP/jJR+XTpjof0994SjpUQ0KXEmFugCc8NuyOa8n3FMauYpICK0gJ7WJ6oJuulvGeTWxwu4H2W1qSgbXUTbgWysnvJ3a5pl9RYXW3ovuJPC3pQT+tpyw2zVE6hoVsFEQx3gD4BgaifKuI8rgAjhVpIQ3UHy/h46aF0wNvGNl7hMHH480FZTrPhAlxYmDj4vnjPeuYy15wdN0O0lhNQTeeozhVw8J+j5PAzgBDeSl9tJSOsVz+zILE5tsLO5yuHnM5f45V4fnXB3JxlpAiNhchGTIuoQrp/JHFC0CVU4kzPiS870rZWs6/uxAMlVEISTqFcmvWZ29ZLLnM52Vi5Tee0a6qZOw7ysx/GMB/t8UEvJ9kQZwgkWBYyU/vYPW8udMD66y/f7ixxaqMwene6fsftxnc2WbtbebLL3+wNu5D2ytHysV2JfTco6yUR3tODjcvuJg65KDz+ec7F5x4cwQUrdDRlruxx/+kdspIEKg0ScHK7MXjNs3aSt/Q3HWKGkR7YTeKSfkViUmj3oVIhwlAEcakvQ1ROpF8VRJSmgVmZE1VGTbGWl/xfqrPS5EuSJqGKc4TAEcgU2HDj69PWO4fYHCLDsWv1IiDfIYKiP4Vjmhd2oxeYp1q5NobzvRAnCMHcQYW4mSljYvyXIqI9pYTkp4NQ8TGqh40sWIbZYPi9tfFEmumyq373xPFE3HLM1uYGt8Rs7dOgJuZeDzm3v4/fYJAb8txuQlOVBWshM7Kc8epMf6itfPNxFYeSL39Y6Dsz2HOq+a7U1A3zXAUdYpBXCOcZxp6+r4mMvDY873jrmQ2dvXsp1c8EZsZpJFNG77SFXOcxL8Re0kuUkVhN2SDKUqTHfqifSUHJw2LCoDTFqkJM9Ggow1gBPnBjjuNyh/rPcI7t/rni33DLhnwD0D7hlwz4B7Bv4iZuDfA8DxluDgTixqdSmFiEAGtfR2pTIRpYmmNukmxltbKoNBL3ahHmJ13cR6aSte30uioZ9knwFSfAbVuquOAyQZ+5S1Kl6sVC71jQvgGJ0AR5Q4al1b0n4IcX4J4AiYijVqACfSUEKkTx7pMeXkP2ykrryTwe4J3r5e5WjXCS1cO0DZ+56hLCtiXZkb36GxbJoUczO+3xYRdKucMA/JkbEqm0eMDLiz4ldV+eoF3ohK6VqdFKeXzB+5rQJxtHMV41LqyM+r69qBRTatBitm7zqifWtJj2ki734HNSW9DPZM8XZeAM6h09fhvMIOzep18PmKz+9PeDG4QkPJGJkWAUFlROhqMHk2En5HAziSASKBzgmhZeSkNlJV0IW9ZYxXE+/Y2Ti6PgtOkHN5dMnWqrT8fOKZfZn6gnGyotsJ/G2Jgjhht6uJ1DVgMTShAZxWTYFjFHuIC+DUEG2sJtqnkuTwOoru2+moGefF4Fvev9rgYHMPx8UNVZHzPthcOufFwDotFdMUZPWQaq4jxCMX399kE3K7QDVSxQc08Ci+g4qcATqqJhhoneJ5z0tmx2ZZnH7N+9dv2Vh6z9baB44+b3EpLVQXQmtuyK4UMLrkdPeYvY1dPi5tsjy3xuvxd8yMvmNubJXXzz7yavQTI+3vaCufofzhKNnx3SQGNik1ku8/F3+lwEkMquJeTCN56e00l40xNbjM1vuTLwBHQINcB8fZGeeHxxxu77Ozsc3HlU1WFtdZer2q2qY+LG/zae2AtXc7vH21wezkMi/GFngxNs/k6GsmR18xO7HA/Mtllt+s8XH5E3sf9znfF2DlBCqumyq305mRI3k1Ym1aGD9ktOMDDQUvyU7oJtK7Sqm0IrwanC1QUufdgFS2m/VlxPmVkxJaQWZkJRXZXQy3zbD2aovzbYEVWqOXUEUFipwKnM23xwy1vaYwqxOLXzHyeJRcKoFF4R5io2omUq8BHKXAMbYT4yMAR/JvRBFUQrSxlJTwSh4m1FLxpJ0R2xQfFjevAY4TWAlAOt09Z3t9j6XZVWyNIzxOrSbwTjp+32bh/+1jAr8tItyzgoSgOu5ZmsjL6KSheJi+lmlejizxZmKZ+RdS0f6Od6/fq/tk//MeF8eiunHmJykb3pmCN1cnB5wf7rC3ucHGu2WWZt+wOD3P4tQCC5OLvBlf4tXYe2aH1pjsWaW19AW5yaI4qsB0pxTTHTlWKSVXpJdYEluQ5xXJwFEKHAVwrhU4XwOcnwsydocYuxU6f8APf/4i3si5z4f7MeGeAfcMuGfAPQPuGfj3MwN/oQBHU/wIgHA1nogFTCT7NiXbF+m+2H1kxTiX+rvRTqyxW6041YyigYk4qbRVyppuEiT3xthHiu8Ad/0G1Ur1GyLVb5AU336SxFJkkJ/rRlPfOBU4Uketlst2ph1d6iQN4jgtUz+jwJHBlPyhOKM03NSqDWOUbx4ZseUUPGqkobKT4V4BOCsc7p5oFqobioWrUzjecbC9fsXLsS3qi6dINjWr5qnA7ysIuyO5FVKPrW20BODEC8SRjZZkBElWkHOpfCAXwBEVjjTH6HsQgGPxEiuVXdukGTsV+IjyblLV59E+daRHN5Ob2UFlYTf9tikWX69wdCiQ5caVFZxz7OBo+4rttVNejqzSVPaMrNhGonzKMHvXqJyRcI82wj3rCfOSDJ8C4sNKyU6tp6qwA3vLCHOTS+xsCBy68ccBl0fnbK3u83Z6k2f2JeoKnpFlaVWb4EDJfLlTpWxSFmMTFmMzFmOL2nzL36VdJ8ogCpxqooxVRPtUINk7RfdttNc858XAAu9frXOwuauChW9cslJyfF4+Z3p4g46aWUoe9pMR3Ui4Ll9twkM9Con0rkQsVJmRzTxJaaPsURfVeZ3UF7fTXNFKZ0M7Q519vBh6xvzUrNpcH21tqwDjm5clX18enrG7scPqwhpzz18zbBunvaYXa5mdpvI+WiqGaSkfpepJPwXpNu7HtJIS0ki0dy3B31Z8ycAJva1ZqBKDKsmy1PM0rZWmshFeDCzxeflQs+YoS5SogCR/6ZjL4yPODg4VxNn6sMX6u4+8e7PG6+klpp694dnADH1d43RYh2io7Ka6pIPq4naqi1upLm7BWtVFR2Mfve3DTAxNszizxOfVT5zsHHF1JAG7N1RmzvwcsQhJOLU0j80/P8Ret6jawWL9a4n0FrWNQBtpgZIgYbELVmLWlRDrV0pyaBmZUeVUZHcw3DbN2twmZ1uiONGambgSyHGprF5iq9pcPGSwbY6CzHaifQvV4zFCX6bUYCZP+d0SeN2lFDgCcGJEgfOTAKeCh4nVVDxpZdg2yfriBy3DSG6eQCq5bacOTraP2VrbZml2GZt1iCdpVQR7puP/fRaB3z0m6Dupoa8g1r+a1AixZVkpvNdGdZ6d1qp+OuoH6Grsx9bUx0DXMDMTs6yLfXHvgIvjYxVYrF2YtJodc3G8x+H2JquLC7wcG6ev3Y7N2kVHXSetlR00l3fRVNKNtaifurxB8lI7SQuvJ8JDMpNKFMAxe9YQpWtQzW1iQ4w13ngu/l9S4IiCR1uutr+vAuvdG3P3p2zuGXDPgHsG3DPgngH3DLhnwD0Df3Ez8BcDcDRYE2d0HlVrUJvWHGRsJ1aWTwdxvp3a8usizrlifTv54Yrz7SLer4sEPxvxvl3E+YhqRjYeHSQYbST5dpPi38vdgD5SA/tJCxwkLcWDyw8AACAASURBVGCAu/59JPv2kugj8MYJcASCfIE3NvX/y+8Q+5nLgqYgjjMXR1mpdD+2Ucn3BUhJcHScsvPUEWUsJcovn8y4SopymrBW2xnpm1QKnIPtEy2PxKVSkM38iYPDLQefV66YGv5EbeEkiWFWjN8UEPCdKAdqVL2xABxpXZLrrSCUtGk5AU6crosvSy/f11Q4YqcSyCUAJ8ZLsnPkk/IutWGTjVuUt2yaG4g21pMW2cTj9HbK8+z0dk6y8Or9TwIcZYnZdbC3ccbsszVaKp9zP95KtG85kYZaInRWNIDTQJhXBaFfAE4DlYWd2FtGfxrgyLk4OmNrdY/F6Y+M2d9SVyB13i0EfFtIwLfFhN2pdAIc688AnFqijAJwKonyKSc5rIbCe1201zxjcmCeZRfAufw6mFkUHNsrl8yObWJreE3F4yHuxTZjNogK6inhXiVEG6qJ97eSHNJAmrmOrJga7sWWcy+ukKz4J2Sn5lLxtIq22jZG7IMsTM+xs76hVC8/BDinu6dsvNvk1cQC/R1j1Ba18uhuMWnRT8mIKSQrtpR7sZWkRpSTECSWnhIivKR1qgxpoAr8TQXB35UptYhJV0RCUAWZljpyU5uxlg4x0b/I5vKBM1tFU97I5l/lp5yecXVywdn+OXufjthc2WP5zScmh+exNY9RVdRJ/sMGHtytJMVSSLwpl3jTY+JNOcSbskmPzycnvYzC7FqaKm2Mdk/yduY9O2s7nGwf4Ti+oW4S9iegQ6Kf9rWq709vr3hm/0B17nMVMmzxrSXKIACnScE/yaGJ8Kog4gbAyYisUABnqG2a1blNTj+fcbUnVrVTuNQCfVWe0L5Y4Q4ZaH1FfmYHUb5FRBpKidBXIPa+CF2jysCJ0tuIFmuhABwfaaWSx4LLQuVS4AjAqdEATtcE64vrzuYnLdxZZkbyZ462Dtla3eLtzDsN4KTXEOqVSeCtBwR9n0vw96VqbkUVlhBUoyBOpqWW+wm1ZN+t4XFaNU8yK8i9V0ZZXh097QO8nn7D3ucdTg+OVPi1a36kzezscJ+djx+Ym5zC3tJFyeNScjMLeJQss/OYtMgnpJkLSDOXkGqqIN6/TMGw4G8LCflOAE4VUbp6YgxNSjUY59Nx/XxqFJjuslDdVOH0oIC5QHNv+431Y+WNPL/8NLyRFzb3J1Huc+CeAfcMuGfAPQPuGXDPgHsG3DPwlzYDf3EAp504BWzkk16xvEgAbYsWPCvhs2J/8XGtJqQhSWtJaiLKaP2yoo1WopXyQuwzTSq0NkonLSqNWPRNxBpaSfDpING3kyQ/Gyn+AnS6SfbrJsnXTqKPnQSf6+wbUbHchDjX7V2iwnFZqZzBxlKzK0sCjG8ocRTA+UqBowGcaL98suKrKHncQkttD2MD00rlcLhz+mOAc+zg4PMlH5cvmBzcoDp/nITQRg3gfCubdMl9sWLxbv0CqwTgJMj1l82SVK/fADha7brUpsuS7J9uJBtHAI5q9FJBpZ1YVN5IM2JfiTLUk2puIie1jdJcGz0dE8zPvefoQFQyXytwZDN+vu/g8NMFr8fXaase52FiExb/Ci0U2QlwwjxdAKeQ+LAyslO/BjjbP1TgOAHOZwE4LzcY6xaAM0qmpZmAbwsI+LaI0DsVmuXLKACnCYtPCxafVjUXmgKnlkhjFZHGCqUISgqrpuBeJ23VToAzt86+UuDcgAxyCwXgrF4yN7aJ/QbAifAuIvD7p4R7lqpsnTj/JtVqlBBcTVJoGQkhBcQF5xATmEWi6R5P0ouoLWqkp6WXuecv+bzyAcfp1zXsshk/3jpjdf4jL0be0F4/QMHDWhLCHxHhk0aU331iAnOIC3pKtF8eZu98wjzyVZV56O1yQm9VE3qrhrDbFYR5lBDuWUhcYBkZ0TU8vmulsWSAif6FGwBHwnGlBUvUHM72ohMJzb5gc2Wft7MfmRxaoss6TnleF/eSKkmOLCI2NB+z3xPCjNmEGR4SZrhPmCELS1A2SeanZMYVU/iwgZbqXsZ6plmYXmbz3SdOtrSMIbFPSc24AjgKdjiQevGDjStmhj/TVDZFVkwriSGNxPg1KpugyVOCr2swOQFOjChwQsrJiKyk/FEnQ20vWZ37xOlnUeBoWUNfAI5UmO872Fg4pL9ljqfp7Zh9ijArgFOpgoQFLpp1krNjdwIcaaPqIta3g2ixE+pqCPMQYFbGXVOlAjjlosDpmmBtQQCONIk5AY7Yw46vON4+0hQ4c+/pbhrhaUYtYfp7BN1+RPDtfEJuiXWrRs1PbEAdiaF1yFwmhVeQHFFKSmQxqTGFCow9uVdOe2MPM5Ov2f20y+nBibq8LwDn7JzT/X0+ra0zOTKOtbKZBymPuRv9gPjQe0T5ZWI2ZBFpfESUMZdoQyFmXRHhd4oVvAm7VYHZsxaLt5VYYwuxxjb13BznIwpCgdCiiBQwLtZSycHRrJhyjPtJcHOtuPllcON6UftLe7F23x73G1D3DLhnwD0D7hlwz4B7Btwz4J4B13vdTuK9ZXU5Vyf/QT4tlRBaFUTrDAmOu9FA9ecVYtyhbQ5uwJtYnxZinHklZn09EbpaTPoaTPpqTPoqwr+sSsL1lYTpJEOlglCx4niVE66rwKSrJNxLNq/lhN4uI/RWGeF3KolQtoA6ovWNxHg3K0tTvLFdQZ0En05unhst70bLvYk3dKHWjfBnl4VKhtGVhRPrgjg3jvK9myHGFoOoCUqJ9ivgXkI1ZblttDcMMD40y/uFDxxLBo5sam8wkYujK/Y2L/iwdMp4/zpVec9ICGlQAEcabMLv1GpWB+9W4g0dJBptJDoBToK3VKh3XS+dfO2qVdeq1RXE0dkVxPkhwInUNWkBsvo6UkxWslPaKH3SRXfbBPOzyxzuHcCVSChcW0gtgPfyUKwjlyy8+EBH7QSPkpuJCRBoIi1cTYR5tCEAJ1QpcAqJCyvjUWojlYVaBs7c5Dt+CuBcHJ3xeXXXCXAWqS0cIdPSRMC3+UqFIzkmZtUy1ajBPJ9mBXEE7kUZ64k01BBpqCTSWE6ksZTE0EoK7nXQVj3GRP88y78EcESBM7qJrX6OspxBMi1Wwr0KVBBt6J0SIr1rifNrJtavXtlhYv3LEFBnNmYT7p1BdEAWD5IKqXjaiM3ay8zYDJvvfwLgOODg0xnLrzZ53v8Ga2UfOWmVmP0y8b+TRIgunQjjA6L9nmA25GLS5RHqUaBgjVRcm70aMHs1qsp2ATihHnnEBJSQFllFdkoDDSX9TAws8On9gdZs5LRQOS7FQiVZNVfKBre/ecbS7CZjPW9oqRqjJKeTrPgaogKeYjLmYjLmEW4oIFwAkv4pobrHhOgEMj3GEphLQmge9+LLKX7URFNFD4Md47waX+TT8meuDi+0y3Y2YKmMGmfrllRnz0/s0FU3R26anYyoVhKCmon0bnQGX1dj8ipXCpwY3zKSQspJN98EOJ8527rgal8uQzqzJRvmUqvO3neoqvi+ljmepLVjMhRi0pcSLs8ZUp3tJZCmnUgFcHqJEWumr404v06l6JL2K5NHORafcu6aqr4AnKGuCVbn17TqbgE4LjB16uB094TdD7u8f71Kb8sYeVkNmLwfEOLxmNA7RYTdqcbkWa9URgKq4oMaiQuqJSawEktgKbEhxSSYCkmJLiA7o4LW+j5mJufZ29rn7PBMy91xPvyuTi+UterjygfG+p9TXdRASvQDYoKziPTNJEyXSajHPcI8szF5PVXwRpRbJg8t98bsKS1ujcQYmhVMV0BdmtzERuW0tqrnRqVOFNjdrS0Fc0R541LcXIOba7WN1uj3tWXq+sXM/cLufnPnngH3DLhnwD0D7hlwz4B7Btwz8Jc5A9fvef9NAxyx+yiblFFT3gi8iZUNt1ELzg33qiLEo4xgjxKCPIoI8igk0KPgxson4E4e/ref4n8rVx0Dbj8l8HYeAbee4v9drtpc+/3mCQHf5hH0XSEht0oIl02+ZzVRXnVY9I3EGgTmtCmQc62scYUW3zy6VDeuo/YA+wJwvsCc6xrxa4AjuT6iKpIMnFKifQu4n1hL+dMOupqGmRx5xeriJid7IkX4GuCcH16xu3HG2sIRz3pWqcwdIz64HuM3+QRJBbGHfGLeTKyhjQRjJ0lGu1qJBgE0NwCOwBsFcLpI0NtI8HYBHE2NE6caqjQLlWyyLPo21UATfqeeCK86ksOsPEpupeRxF92t47yZecfB7j5citTga4AjmSZSn7z08iNd9ZPkpDQTG1hJlG+dBnA8rwFOiO4GwCnowtYyxs8DnFM+rwnA+cBotwCcYQVS/L/Lw//bAkI9bgAcH1Hg3AA4hjoiDdWYDRUqUNlsLFbtV/lZ7bRWjTLR/4Z3c2vsfZQMnB8rcLZWLpgZldszS+mjftWsFeKRh4+qES/GrK8l1lcUPw3KpiX3s2TkBHtIZXQ6oboM0i2FlOQ00lHfy9TIDB/ffVCNVNdnT1Ol7H08Z3Fmk5Hu19QW95AZX0agZwpev4rB97tkgj2zMHlnE657QphnHiF3Cgj3LEcAg1Kw+Ujtda1SBgXfziXar4i75goeJddSX9zLxMA8n1YOb9RSSwuW5NNcqNyWyyMHW2snTI0sK3jzJK2Zu5GVmIxP8f4mA8Ov7+P//VNCvUoI1ws8LSHEs4Bgj1xCPXMJ0z3B5P2YuOB8MmPKeZpRR21hB0OdEyzNrHCyfcLlwZXKpXE1VSmII7zl0MH7uQMG2hYoyx7gUWIXKeGtRBoaVUtUmEfVNcDxuwY4kjk09FWI8SWOU3k8SdvXlQI4F3sO1ucP6WmaI+duO2H6QsJ0pYR5VRLmKefLSoSXAJxuor0F4PQQ52cn3t/mzMGpx+RVSYxvBakR1TyUx/CTVnW7VgTgqGYxqYPXHsNy2yTAee/jHqvz6/S1PafgvpUIY7Z2njxLFTiK8LISJcHbvs3EBjQT7VtPhLGKMH0pET7FWIKKiDcV8CC1kpa6fmYmFznYPuLs6OIrgHN5csnRziEflj8wZH9GyZNaooMyCTOkEeyZQcD3mfh/J9atx4TczifsTrGCN2avm5k3Lngj4KZVKfu0fC0N4mgfCLjUiQJxtOY+pcr5Ub7NvwZtXC9m7jcrf5lvVtz3q/t+dc+AewbcM+CeAfcMuGfAPQM3P8D8twtw1Ke5AnCu4U2MTzMxvmKLknDbCgK+L8T4m8d4//oRul/fx/ObTDy/ybix0vH4VRp3/iWV2/98V607/5yKxz+n4fFPqdz+x7vc+h/J3PrvKdz5hzQ8/zET3f+8j/FfcvD75ilBvy0i7FY5Zo8apcqRkGGxcmnqmhvgxtu1yZDjLwygQfu3L0DHqb5x/T3Gu0WFzEZ6lxDlm8+DxFoq80VxMsrU2DxrS5843ZeN9A8BziU7H05ZeXPAqP09FU9GvwI4EZ516hNzBaEE4PjYSTZ2k2Swk+htI0EvwMapwtFpXwvYSfSWfxeI47RT6SQbR8vNUQBH14bZo4nwW/XIZUjN8aOkNkpyBOBM8OalBnAcl19DJ5X9ceLg8sDB8uxH7I2TPE5tJi6okmgBON5NhHq2EeolCpxKNIBTzqO0RioKbNhanzGrFDg3WqgU4XBwcSQAZ4cFBXAWFMDJiGnE/zsBdvkK+EV41xDl00C0j1VZ78RGFW1sJNJYh9lQhdlQjtlQQoShiPiQMvKy2mipGmW8/w1LCuDsfR20K3fHBWgAZ4POuhlKHvaRFllP8O2nGH+VQ9D3JSpcN9ZX7FpWFbxr0pUT4lFAwPc5GH+TReCde9yNLKboYRPttf28GJplY+kDV8enX/EbuazdDxfMT31isGueyvxeUi2lGL9L5tY/RqP/dRIBt0VNkU2o1xOlsAm5U6hsRdGGBuL82on371C2t3CvUoIUwCkkxVzGw+Qa6op7FMD5vHqo1ZULq1IqKrFRaWG/UgO+sXTEsG2e0pxudZ7C9XkYf/2Qb/9bKnf+xwN8fl1A6J1qIvQNhHtVK3gWfLuY4NsF6rwE336CSZ9LbGAeKRGFqn2ptbqfl6Pz7KzvK5uYzIhY7lyKFVcezoe3RzzvXaa+6Bl5Gb2kR7arSvuQ29WEeYjdqRyzvpQYATjBFaRFVFL2UADOLGuvtjnfkRpxyfYRuHiB49yhVEUCcNbeHNLd9EoBnFB9IaFepYR6VhLqIQCniQhdB1H6HiyGfmJ9eon36yEhwE6sbxtyfqWJKsavitSIGh4m1lH+pI3BzglW5le1evjzK01F55ybi4Nz9jcPWFv4QH/bOIX3mzAbcwjzyiPcqxyxhangZIElPm3E+LVhNloJ09USeKeCUH0ZZv8SYsOKuZ9SQ3PtEDOT7zjYOeHsSGvYcj48uDi+4nD7iPV3G/R3PSP/YS0mn3QCPbTQZN/fPsD3Nzka0P6+CLHcmTyczW3eYl2V5+M24nw0uK4Ajr6FKF2z06LZTrxPJ/E+XST42JTlVAt9d7b1ffVcefN58+e+/oXn0196rnX/2y+/FrnPj/v8uGfAPQPuGXDPgHsG3DPgnoE/oxm4fi/8bwPguKxcPzo6AY7TNiXKG8kpEQuU72/zuP2PWfzL3ybwj/8lmr//v8P5r/85kL/7a3/+7j8H8F/V8ufv/tqPv/2/fPkv/8nI3/wnH/5WLV/+9v/05W//ow9/81c+/M3/4cPf/ZU//+9fBfHf/mMo//DXkfzq/4nn279NxfO/P8D3V/mEfl9BtFgHvCUIWEKHnZ8wq6yHm58i39hw/Oj2uJQ5GuhxgRvXUWxbUfoazHqpEb8GON0tz5h+tsD60uffEeCMKIDj800Bwd+XEeFZqwBOvKGNRGMnyQrg2J0AxwlsnBBHQI5AHQE3AniSDN0axJE8HJ1NKXTi9J3ESn271//H3nuAx3Wed74UJUu2bNly4tjO2pvYjuJ447LZjSXZTpOzTuKW5F4nlmXLji2KBWUqep8+KOwgKbGh994HvbOLJMAudoIN7AALGkngt8/7nRlgSJF2bpK7ibMHz/M9MwRADM6Zdwbz/eZfWnGE+UheUo89vI7lST42LmujdHM3va2DHD90ljujtzUFTgA6STW0qCjGZ1SN89DhS3Q3DLB5RRPeuDKcUTXYzI2zACfBGAxwGikv6KKrWQDOEDeCa8SDAM5VBXAusqvnONUF2xGAEyVqq0W5JISVYDNX4YyowxXRoLKSRIXjFIBjqcFuqcBmLsVmKSLFkk9qQgm5a1portypAZzDFxi9NKqsMMFURQCDAjg7h+moPUjxBgE49SSE5hLxiyzil5SosGdPRJuy2jgtDcrKJZa++LBCpQhLMuWR6ammZFMH7XW72L/jKJdOX1bNTMHQTgDOyPA0x/ePsK3rNJUF21ntrSIqJJOwn6VjXbiKuPD1pFiyEaiSZCggMaxIKUOk6lpAgzeybRbgxIfl4I4pZKWzjA0rqqkr6WNg+3GuX5AKcy2DRjvW+8zcn+H++AwTIzOcP3abvuaj5K7pwhkhkKOQ6DfzMP00h4hfFBO/WMKEpV67QwEIm0EsTjUkh1WSHCb19sXYTIWq6Sk1Lp813lJqCiTU+BAXTlxh5OJtJkbua1aqINugnOtr58Y5sPMCbdUHKNkogdGduCLqiQ+pJEnsPoYyHOZSvFFlLE2oYLWtktL1XWxvPcKFd0f8AEdqy2UwJZcGdTsCjC6eGKfXd4xNKztJMheTpBREVSSG15FsaMZu7MRp7sdt2Yo3op+06D7SY8RK1aaq6KXC3BtVxUp7NRuW1lGe3YZYqJQCZ0IsTTMahH0A4NzyK3D2kreuUamnEsNylM0zOUzCkVtwmNtwRXTgierEYW0j2eQjPryeZGlOi60hXRQ/qxppqZPHx1luj0wydcdfzS534DTck7ysGxNcPHOVrZ2DFGxsxBa1ngTDeuLCsohdnE/s4mISQsr81q1aZbfTAE0rHnMbHhUm36YC0TUFTjNuk0+pcQRwK7tppAAcP8Txt/Qpa5X+QuE/0AuFoL9V+v2i3y/6DOgzoM+APgP6DOgzoM/Av+MM/DoBHFGlPA50qOybFsQ6FYA3ssmODy0m7PV1/OT7br7351F862vh/NkfL+ClL/+YF7/0I1768o94+SuvqfXil1/lq1/6IX/8xX/kq/714hd/yIt/6F//7Ye89N9+yMtf+DFf+8JP+foX/olXvrKEb79o4e//NJ6f/LWbJT9YoyCOTYJEDY3KiiSBxtJKJSBH25gETrr/RXHQcaVZOlSo8VzI8aNBjmyCBODYjEXYrbm8nV5DZV4XvS27OegHOFOPUeDcGJ7wK3CGKM/aSXp8HZFvCMApU+oYsYGlWdqCAI5fgSPgJqC6UfBGAzgCb5ZZetRSKhw/wJGwY6+xA6+xHZdsKsOaSAnRAM6KJB+blrdTltVNX9s+Thw+x/itMU29Iftk/1KNQo8DOJE1pPgBToKhHgE48cZCvEmiwBGA001Xyx4/wBkPZijqumTgXL0wOqvAqS7Y4Qc4OUQ9oMDR4I0Wft2igq4dFlH/VJJiLiXFXESKWQKGS8hZ04KvYid7+o9x6tAFRoZHlRUm+MYfADg1BxTAWe2sJzE8j8g3solfIk0+DXgi2lVosgQoCzSyWyTDSTKainFElvJWeiPl2b101u/hwI7jDJ+6zP07DzaPzQKcA6Ns7xqiqnAna9LqiA1/G+MvVhC15C0SjVnYIwo0EGUqJim8lBRjlcqJcVtbkeUw1yEqIPkdvXHFrHJVsGllLfVl/QzuOMH1i2OzVp/Ascpx3r0jodkznDo0QmfdQaW6EotWfEihAlVxiyRbSjb+zbjNXcpmJG1NTrHcGZtwGEVRUoOADruoRyyFOKw5ZCTlU7yhhR7fHo7vO8vwqevcujKpYJ/cbvCHWAaPDV6hv/kY1Xm72ZDRjTuynvglFQrg2IxlOC1lpEaXs2wW4HQ/CHDGBG6It0+zNEk7mtizhk9O0tdynKzVXSSLzc0kOVrVJIWLPaoZh1SIW/rxWLeRGrmF9Oh+MmJ6SFUARzJyqlTG0Sp7DRuWBQOc88xMTqoq8YClUI5LFDg3L9/i3LGL9DXvIXdtI8mmjcSH5iCZTcmhDdjCW3GY2nFZu/BEdeOwdpJibiPR0IwtoglPfANLHXVsymyhtX4vh/ed49aNSSYF4EgGtoBTue/GZrhzY4qLQ9fZ1nWAwk0tOKI3k2TOIslYQGJ4GUnhkiEkden1OExNOE0+XKYWXKZW3KY23AJxFMjRsm8E4sgSZU5aRDvpkR1kCMAJQByVDeZ/ztNfGPw7vjDQgc0vVcjqs6nPpj4D+gzoM6DPgD4D+gz8u81AgCU8HGLc+R8wxNgPb+YgRwB2SKuJKHBaEOuU29qA01KL3VyJ5Jm88YMMvv/nUbz8h//Elz/3A37vt/+aTz7/dT7x4Zf45PMv89sf/Rq//RtfU9c/8ZEX+a3nvsrH/esTz32VTz73Ir/93Ev8lw+/xKc+/DKfeu4bfPq5P+PTH3qFz3/se/zRf32Vb3x+Ad/+qlVBHFHiJCwuxR5Wi8fcwlJpporq0VqppHVl1hoQADjahkU7Lmml0urFAxXjGszRvkcpcGRjZPLhMMrmSZpvclRNsAI4rXs4uOckw2eu8yiAM3X7PgrgHBUL1eMATrMf4HQq+1TAQiVZN5J7oylvguCNtYdl1l61RIWTrgBOt2qrEoAjbVouUSOENZISUofdUMuK5CY2r2ijPLuHLe37OHnkHBO3x2Bmeg7eiGNlKqDACVioBtgUUOA8FuCUs2FlE+UFPXQ171UKgxuXJoL39Or6vbG7XD1/ixMHL7Or5xTVBbvI9DQp9U2UZByFlWMzS9NXI07VXNaCSyqgrVL5XafUOSmmMlJMkt1SgDeuhJzM1gcAzo2LI1r4bXCuT7ACRwGcPjJd0qIlIcY5StGgAI61HY/YYCKblR3QFVmvgptF+eONq2Ljshaq8rbQ3TjAgZ0nFMC5e0van/zBt34Fk1LgCMDpHqK66B3WZTSQYNqEZeEaYsM2qfBdV3QZjgg5XgntriTFWKuamlxmsfoIwKlX8ChZMobiS8n0VJG1up7G8i3s33mSG8PjGsAJPsvTMHVbMpfucnTwCr7yvax2CSzcqNQbon5xmOpxWySvqpO0yD7SI/tJjejBa+3EbWlVX3NbGnGZ63CYxO4kCqFNOCI3s3lVHc2VWxncLna1YaUCksDrhwHOnWv3OXNkhHd6h1QbVdbKPjxRAnBE2VOGzVSGy1pGakw5yxIrWW2ronT9IwCOqGGEbgSqysdmuHRqiv7WE2RndpMSUUqKRQLRq0kyCPBqxmHuUvap1IhtpEVtJT2mn4xYycJpQ45Lmqi80VWsctSwcVkdFTltbO8a4NyJc8xMCowLyoSahru37ymAM3T0ooJXOZmNJBo3EheizU1SaAMpYa3YTR1+gNODM6ITm6WdZIFiUc2kJTWx0t1I9rp22hsHOLLvvAZwJEdIHHhyfPcCAOcuw0MjbO8+RPHmNlxx2dgi8rBZS7Cbq9VcOC3S0ubDY23BbWlBzYyp1Q9xBOS0qhY6LbxYmqiC4E1UJxmyZgGO9pyuQP2/2x9FHV7o8EKfAX0G9BnQZ0CfAX0G9BnQZ+A/6gz8ZwI4Yp+SWnBrHXazNE2VEfHmZn76dy6+9bUwvvzZH/C5T36Ljz/3Es8+8wd84H2/xweefoFnn/n9h5Z87gWeffoFPvj0C3zo6Rd47pnf5yPv/zzPv/8LPP/MF3n+6a/w/Pv+B//lg3/K7//md/ij//pD/uJLi/mHV5JY/A+riX2zkJTQKtymZpZGdc8CHFHgBGxQwZepfhVOAOIIvBFbwcNKHPk/UifuVuoEDeDYBOCkVasMnJ6HFThB4ED21nfHphm5NMn543d4p+ecCjHOSKwnakEBCYul9leg00MAx9Lrt0YFARy/fWpWfeMHOBkK4PSQppqoOvEY23FLlXK49p6fxgAAIABJREFUD3tYA8lKbVHDipQmsla2UZ7Tw5aOfZx69zwTt8dhxr9B1uJGUEqHsRnu35rmzKFLdNUPsHF5I57YUhyR0igmFqoWHlTgBAOcAQ4PnuPGpQezYeRc3Bu7z9Xztzlx8Cq7ek5TXbCbTHczAm+iFhaREFaJzSwhwmKbEnij2VKc1mbslgZsJlHEVGjNQ6ZCvHGlZCuAs4s9/cc5eegCAnCmJySYZY5sCGC4fu4uB3depL16H8Xre1WI8aMBTpsKvPVE+pTtx2HVrFsBgFOZ+88FOLIJP0N1sQCcRhLMm7EsXEdceLbajLtjqnFGVWvV7CYJh5aqbR8O2fSbBAhodsRkYyFp8WWs8VaTndlAY/lW9u04qVmo7vqVU+pQpf56WgVp3xie4t2BywrgCKiS3JT4JQWkhFfgMktLkSgyZOPeS5q1T1VKey1a9bwAUI9F2uSacIltTVqeDDk4o3LYtKKOhtJ+dvUe5vj+81w9d4t7jwA4Y9fvc/bYKINbz9NScZCc1f14ouqIW1yiarxtRq3KWwM4YqHSAM621iOcf3cEabKSIG1lEVPyFD9YFIBzeootbSfJWdODPbIMm0XmQQM4NmMzTqUq2kJq5DbSo7aQEdNHRmyPBnAk38hUTWp0tQZwlgvAaWVH114N4Ew9CHBkbqZu3WXk0i3OvDtMj28v2ZmNJBg2Erskl/iQchJDGkkJa8Nu7MBp6cIdqSlwbGaxUbXgiPSRmtjIClcDWWtaaWvQAOet6xNMBAMcUfuMzTA2co9LZwX+HaZocxvOmGxSLLmkmItJMVZiMwpYE2DuU/DmUQDHYxY7VaB9SsKLtTpxUeBk+BU4cqnsVGoORGnpXzrE0d/d0mdAnwF9BvQZ0GdAnwF9BvQZ0GcgaAb+0wCcVmWf8kRIA4vYW6QWvBjzG2/zo+8m82f/4w1e+ORf8wmBN09/nifnfYwn5j3PfLU+yvx5H+XJeb/Bk0/8Jk/N/xhPPSHrN3lq3m/yvid+k6fn/xbPPPlx3v/kJ3nmiU/xzBO/wzNPfJaPPPUVPvXBP+H3P/ZtXn7hdf72G5Es+PulRL2RQ9ISycJpImMW4IiFqkMDOCYNxAiM8Zrn6sHl6wFo88sAjvxcuyglDAXYzNmsS6uiPLdDhRgfeOe4ysARtU0wONCgxQyjl+9y8eQ4e/rOU5W7m6VJjUS/WUjCkjmAk2ppI93SyVIVYOwHOJJrY+iYtVFJ/k0g+0YUOEut8n29pJt6kBYqr9EPcAwCcJqwh9WTHFKtVAcrUxoVwKnI7WFrx35OHz3P5J1HAJwJCZCVFqppTh+U0N+9bFjWiDumFEeEAJyGRwCcCjas9FGe30tX8yCHB85z45J4Q4I+ZrQN6tXzY5w4eI1dPUNUF+xhtbuVqEWFRC0sIUFCdc1SGS62qTZcVlE1dOK0tGI3N2GTGmhVRV9GsqlI5fJkZ7bhq3hnFuBcfxzAOTvFwR2SyzLoBzgStp1PtChwJAzbIGCjA49VAzjSKCS5LQJwBBIIwJEWr38JwFm7tJEEUxaWhW8TZ8jDHiE/rwF3dAPOyEYcogwx+7AbW1QNtmSq2AwawEkxFpGeUM4ab42CB03looA5wdVztxVsU5BDIBz3mJ68y9jIpGqgenfvJQ3guOuJXphFQkihCvAVFYoCOEpd102quUfVR3skN8kk9hv/hl+USEpZV4HNVIA7Ol/VmNcU9rGlfT+H9w5xaegmdyXI+CEL1diNe5w/fpMDO4dprz5MbqYAnFpiFxUjle02YwlOaymp0WV+C5Uf4LQc4fyRIIAT+LmBbCY/wNnadpLctT04osqxW8VWV02yoRab0YdLmpQitpAWDHBiupWFSqyeCuDEVLPaWcPG5bVU5Layo3sv50+eY+buBEiotx/+ybmdvHmXG8O3OP3uMN1Ne8nKbCLBsEkDOEs0gJMc2obN0IHDLFk/3TgsHaSY2kgyNmOPaMKb0MCygIWqbg+HBoa4eW2ciVv3NQWO30IlGTjjo9NcPneL7T1HKNrUhiM6iyRzDknGIpLCtbp0m98+5VLB6mKfasFlDChvNAuVtASKAkcBOXOLgjhpkoGjAI4f3oilKkIywwIApy3oD9V/1HdA9N9Lf3dOnwF9BvQZ0GdAnwF9BvQZ0Gfg/+QM/KcCOD48ERIwKxv7MhLCCzD8fA0/+KsYXvria/yXj3ydDz3zBZ6c91vMm/c+5s2b/9B6yv/5Z5g372n/dfk+Wc8wf977eXLeB5g/71nmz3tOAaCnn/gdnn/qy3z6g3/CVz79//A3Xw3nZ99zq3aqhEUlOI0NZEQGFDgawBFoE1iiTtEgTrsCOxrA0SDOPwfgJIcXkGLOZm1qJWU5bXT5drJ/1zHVQvUogHN/YoZbV+8xfHqCgS0Xqc7by/LkJmIWFpG4pEIFDIsCJ9XsBzgWyb/pJSOQa2PoCLJRzQEc+R71feZe0ky9pBp7NIBj8OffSCNPWB3JIVUq92OlrYHsVa1U5vWwrfMxAMcfYCzqm6mR+5zcP0x7zR7WL23AFV2C3Soqq4cBThHeJAE4zZTn9/kBzgVuXBJ5SNDHLMCZ4MSBG+zqOUd1/gCr3e1ELSwmamEZ8aGiRBH1jbQWaZYUl7Ubp6VdAQ5pTBK1heSeJJuK8caVk53Z/l6AMy4+sLnblo34tbNTHNhxnraqAYre7mG1s46k8HyiF7wX4LjFmqIsXHMAxxMbADj9dDX8EgtVIMT44A22d59WCpy1GU3Em7KxLNpAvKEQR1QNqfEteGNbcEe14IxoxSGQythKSngbKWEtqt0oRYChqYT0hArWptaSs6YJATgD245zeegmUhc+PSlNRgIdJrk7PsHt6+MIJDuyZxhf2R7WeBqIWZRNYmiRmgOPRXKiWvxQswuvsRuPsRO3oR23QXucpFq1rJRUVateg1Sqe2KK2bCsAQGAXU172b/rNBdPjSqAQ1CIsZz1sZG7XDx1iyN7LtNVf4T8tQJwaohdWEBCiAQ2F+O0lDwS4Jw7coPJgAInAHDESCWzGQA47SfJW9eLM7oCe4SotjSAYxeAI5bJiC2kR20jPXoLGdF9ZCiAI9lCDwKcTStqqRSA07OXC6fOMnNvHKblXGoER25zYnSK68O3OHVkmK6mATavbiI+fDMxS/KIW1JB4pImggGO09qF3dxBilGa2pqxWRvxxNWz1F7DxlU+Wmrf4eDeM4xevfMAwJF5leeK8ZszXD53m+3dRyjc1IY9KosEYzYJ4YXEh5YpyJkcLhk4jSr/xiktU7MAJwBv2lWIsVcpcVqUKtFrbiHV0qqAjUCb4KUBHGnwE4CjQxz9BdH/yRdE+m3p86bPgD4D+gzoM6DPgD4D/9Fn4D8bwIlswGkVgFNKgiEfw89X84O/ieLlL/2QT330JT70/t/jyXkfZt68J5g3b94jlnz+ySCwI/+WNZ/5855k/rz3MX/e0zwx7/3Mm/cB3vfEJ3juqT/gt599kS9+6rt8648X8/p37Jh/tp64hUU4DHVkRHaxNLJbWaIE0ATgjUfepfYDnADEmbVSqTDPgIVqLtTT64c/AQVOcng+KaYs1ngrKMlqob1hGwM7jnD+1GUmb829ex/ABwrgXLvP5TNTDG4dpqZggBUpPmIWFZEYUoEtvE7l68jtpJk7EUvUUqWq0ZqlUmcBjrRPPQhwMiy9pM8CnF68xi7cykLVgiO8EVtYLUkhYruoYKWtXgGcqvwetnUFARyRG0gGh4iH/ADn3s1pJq7f4/jgBVqrdvNWeh2OSKnuriTZKOG/YqGq84cYawBn/SzA2cfhgYvcuCQVSf4P2Q8LwLkzw9XzU5w4MMrO7gtU5+9jtbuTqIWlRC2sID60DptJ1DcdyEbYZe3BZe3FaenEbm5RNqMUUw3JJsk9KcUbV0F2Zge+it3s6T/ByYMXeaQCRwDO0CQHtp+jtWovhW93s9pRqwBOVADghIsypQO3RZqoNIATaL8SdZkCOEtbqMjxA5wdkoFzhXu3Jh5ogxJYNHJpmuPvATg5WBZuIl4CkaPqSU1oxxvbjjuqHWdEOw6BVMZ2UsLbVaZKSngDKQYBZgJwKlnrrSP3AYAzqiw305P3mbkrd9wEd8c0gHPt/LgGcMr3stbTQOyibJJCi1WGk9fSSKqlRVNaSO28Um514TFoEEcazMR2KDAzNaIFt7UWh6UMT0wJb2c0UpbTS0fDXgZ3nOb8qVGmbs3l/6h7ewbujExx8fRN3t17he4GATiSgVNFzJv5JCwpIDm8EIe5GK+qEa9gVUoVJW93s7XlCArgXPdbqAIA52EFTgDgxGgARxQ4SeE12Ixi++okNbKfjKhtZCiA00tGTBepkRrAcZprSPMrcBTAyWthZ88eLpwWgCOh3ncfADjjo1Ncu3ibU0cu0dU0yObVzSQYsohdIsHQlSSG+EgObcdm6MRuErXYHMBJkhyqAMCxzQGcQ3vPcPPa4wHOlfNjbO95l8JN7dgiBeDkaAAnpJyEkGqSw+qwGeYAjhZi7A8wtgi8aUcgnFLhiCXOJMBOAziiwkmLkDYqbcl1+ZyCNzrA0RVIulxanwF9BvQZ0GdAnwF9BvQZ0GfgoRn4tQY4QYAjQuqOfXijGnBGVJFiKSXRmI9pwWpe/V4Mf/bV1/i9T3+D3/rI53n/0x99LMB5Yv585j/5JPPnz0euP/HEE8yf/wRPPTmf9z31JE8/9RTvm/80T/nVOO+f/wk++szn+dRzf8yXf+fbfOurb/L6d5Ix/XQdsQvzcYTXqoBOUeFIro1moXqvAicAcCTjRr4noMQJXBcSGsi/ke+dAzgFCuBkesoo2uijpaaP3VsPcvb4MOM3ZSMdoBbapQCc29fuc2XoLvu2Xaa2YJAVNp9mJ1lSoSCL09CEwKVUlXMj1eCaLUqqwVMNnX4FjgZwMizdCvIIvHkvwBE1hWTgtOAwCMCpISmkHGn9WWGrJWtVC1X53Wzr2sfpo+eYvDOmhagEA5yxGaZG7zN29S5H956nuWKXsu/YLIUkK5tcHYnhsokNBjiVrF/ZQnl+P13N+zk8MMz14SA72QMA5y4nDtxiZ/cw1fkHWO3uJmphOVELq4iXRh+TqG+6cCp404fL2u/fFLdiMzWRYqol2VRFsqkMb1wl2ZmdfoBzklOHLnLj4uh7Q4zvwdWhCfZvP0tL5R4K3+pilQI4eSiAs7gMuwAccxDAUTk8/iYqATgxVaxXAGeLpsDZcZJLp65w//akZiHy3+8CwjSAM8J2yfkpfoe1GT7iTLlYFm4mzlCKPbKR1PguPDGduCI7leXGbu5UFdh2Qxe28A5shiZSDNWkGEsfAjjbGNx2YlaBMxNQ4DDJvYlJ7tyY0CxUA5dprtAAzqwCx1CFAJw0Swvplg5tjmYteKLG6cRr0laquVPZb5ySzyOZNZElvJXeRFl2Hx0NgwzuOMP5k6NMBgBOYO6npUlpQqlzxMbV3XCY/LW9eCIriF6QS/ziPJLDCrCbCvFElpARV8aK5EqK3+piS/Nhzh6+waQAHMnWCYQYC8ARUZVS4Eyyte0Euet6cEaLhaqCZGMliWGSTyXhyx2kRQQATj8Z0b2kRz8EcGKryXTVMAdwdnPh1Blm7t2BaclP0g5GgOb46F0FcE4euUxX0z6yMltINOYQF1pEQqjUrrdiC+/CbuzCYerCEQA4pjaSAxaq+HqW2WvZtNpHq9SIDwwppdTk7fvKBidwU+ZGKuBFgaMBnKMUburAFplNojFXNaElhkmLV61SZwUUOC5zixZYLM9XCt5IwLx/SaW4qHAUwGlVChxpu1MQR0COhBv7l6hz1NIVOA/9wf6P/o6Q/vvp71rqM6DPgD4D+gzoM6DPgD4D///OwOMATsevQwuVH+BI2G9EG2KxSI1qxBVRi90itpYiLAvf4vW/T+Jb31jAl1/4G373k1/lox/8DE8/+SxPzX+Gp+a/n6ee/ADve+oDPP2+Z3nm6Q/xzDMf5plnPsQzT3+QZ9TnnuUDz3yQD77/OT70/o/w7NMf5QNPfoxnnvg4H37f5/jEs1/hM89/gz/6zN/y1y8u4fXv2DC+vo7YNwtUKLCoBx4EOHP5N0qBY5wDOoFg44ASR+78wOcC6hsBOBJiLBk4ykJlymKVq5T89Q00Vnazs0+AyAWVP6IAjj9DQxDO9OQMd65Pc+XsXfbvuExd0SAr7T5iF4uFqpyU0Boc4Q24Dc2kmtpJN3eTYQ40Sz0McATezAEcpb4x95JqEvWNpsDxmNpxi4rFIOGq1SQuKSPFUMqKlBqyVjVTmd/Ftq5BTh87Owdw5PcViOO3qUyO3OP25UkO7z5HY+kOVruqSDLlkyS11obaIIBT7q8RD7ZQBSlwAufhVwKcCj/AadQAjqU7COBswWnpxm5u8wOcuiCAU6UBnPI97OkXgDOsAZxJUVHMgTQ5rqtD4+zbNkRLxW4K3upEaqQTw3KJeiNbNZjZ5D4wt881MUmVuKUeu9gDzeV4Yir9AKefrvq9HNh5kkunrzI95rdr+W9PNuKjl6c5cWiU7T0SYrybtRnNxJnyMC/MIi68DHtkE974btzRXaqxSCw3ot5QEMDYrS4lz0UDOAEFTu2sAmdw23GunL2pVXhPzYA0J3GX+5NTjI9MMTIsNd5Xaa4YZK2niZhFOSSGFGM3VOGxNKkNfIa1i2XWfpZZt5Bh6SPd3EOqSYM4AgGlyUxmyBZeRVJYEQ5rIevSGijL7qWjcZDBnRrAmbjpDxsW5iHnYPq+ghMXTo3w7oAf4Kzpwa0ATg5xi/JIDM3HZijAZS0mLaaM5YmVFK3rYovvCGcPjzBxfYb7kq3jrxEXi9ZsjfipSba0nSBnbTeOKGmhKiPJIMqUSkS15DK3K4CzNHpOgRMAOBLM7DTXkh5bS6arls0r66jMa1UKnPOnhh6jwHkQ4GSvaSXJnEtCmGTS1GAztCLQzSEAx9yplt3cjs3UqgCOI6KR1IR6ljlq2LzaR1v9bt7dd5Y7IxNM3ZnWAI489u75Ac6oAJxxdvQco2hTJ/aoHBLVY6+UpPBqksPrlfpG7GJin5IGKlGNSSh1ANykqWBigdICpzWIIyBnFtJYAuDmIfWNrsDR4Y3+jps+A/oM6DOgz4A+A/oM6DOgz8BDM/CvBDiBwN3Ai/THXQYrSf7FRMqvTAmoUwK3rbJiIttJi2whNcqHO1KyQqpIMZcStXgTb/yjh7/9ppGvf+VVvvS5v+J3P/7HPP/sZ3juA5/mw7Ke/a985IO/w/Mf+l0++txn+Y0Pf47feO6zfPRDn+GjH/wdfuNDv8PHPvwZPvGRz/LJ53+Pj3/oBT727Of5jWf+gE9+8H/wmef/hD/4+F/x0gs/5DsvG/nZd52YfvI2sQsKVJW4tKuIjUr9noEQY7MGbTSAo+XgKBWOCjUOgjYCcIIyc+R7FMAxNeEwaiHGYqFa6Swhd10dtaXtbOvey8kjZ7l9w1/tHAQPpCZ47MY0187f4+CuKzQUD7La2Uz8ErFQlZEcWoU9rA5XeBNeYxvp5i4FcdJUhfgjAI4f8DygvnkUwDH6AU5IKSmGElbYashe3UxVQTfbuvdpAGfsjp/c+C/uaTan8ev3GL04wYGdQ9QVbWO5vYKEcLFxlJAYXkNiuI8EQy0JxjLijQV4k8qCasQHODxwgRvDQRk4cj4esFDdZFf3xSAFjgCcauJDJai4XQEbp7VXqW9c1gDAkU2xjxRTvaqNlhwcCRbOzuzCFwxwhm8yMxlk3/Lnp1w5IwDnDM0V71CwroOV9moSw3KIeiOL+MUlKi/IbRYbSgsuiw+XVJlb5trV3AJwMpqpyO6j0w9wLp++xvTYg7BIYMNNBXBuskOCmov3aADHmI/5zWxiw8uwSbBtXDeuqC4c1k5STO3YjGLB6cZp6sVpEojTjM1YQ7KxhPT4CtZ6JQOnUWXgDG4PAjiqiUroyT2mp+4xMXpP1Ygf23eNlsp9rPX4iFkoAKcEu6Eaj9mnQmszInpYHrmV5ZHbZyFOmlmCsDvwGNpwhTfjCKsjJbRcARe7JZ+1qXWUZffQ2TjAvp2nOX9yhAlR4Ihz0J+DIzXct66P8R6AE1GhgsZjF+aSEJJPSngBLkuxPwenksK1XfT7jjB0eHQO4AicYlqz902iMn+GTwvAOU7O2i7sUSWa8i+8jPgl5SSH1eMSK2JEP0vFQiUtVFG9pEd1kxohob4+nOZ60mPrWOOqZ/PKBirz2tnZs5fzkoFzN2Ch0uBfQIFz9eJtTh7WLFQCcJItuSSGS5ZPjcotchg7cYh9yixBxpLXJGqxZlJMTTgjG0hLqGO5o4asTB/t9bs5uv8s46OT3BWVkZS1PQxwzgUAjhxjLknmApX7JFXzKUapQm9SjWUq+0a1TbWTqixvAtYDK6DCkXdkAtk2YpUKBjltiCLnQfuUnoHzL/57qb/YeejFjv5uoD5L+gzoM6DPgD4D+gzoM/CfYQb+PwAcqfZVIEa9oxqwLwUuJXj38evfBOAEqVHU8AVbjZQCp5XUqGY8kZoKRyBOfFg+YT9dxet/l8L3XzHwzZd+xktf+jv+8LN/wR/87p/whc/8qVr/7bN/xh/+3p/zhy/8BV/8/Vf44gt/of37c3/KFz/3Z3z5hT/nv//+X/BHn3+Fr3zum3zpd/+SL376f/HfP/NdXnzhB3zjCz/mr/7nYv7xm3G8+fcZWH66ifg3i1UocNoswOlSDVNyLmbtUEHqmwCcmQU5jwA3AnO0/+tTGSI2YyE2cxYrHMVkZVZTWeCjr30Xxw6e5ta1Ow/kocg2UKwfolIYvTTN0b03aK44yDpvK0nhpSSHi4WqSrNRiYXH2Eq6uTMI4HSSatSWAJ10U7f6mqglRH0zm3/jBzgelYHThsvUjMPQoKknQsuwmUpZ7aoj/612Gsq3sKv/IEMnLzA57rdQqV9U24TLpvL21Smunr3N3i0nqcrrJz25hJiQLOJCi0kQVY8CODUK4CSY8klNKmXDqkbKC7rpapaaZKkRD2qh+iUAJ9PdQ9TCSqIW1jwG4GzFaelBUzUEAxxpcpJqbQE4e+cUOI8FOGPs23qG5vJd5K9rZ6W9yg9wNhO/uBhbeK2yorgtzbgtTbhVA1MtdnMVNpNkwFRoACcnCOCcucb0+EO5R9MawDl56BY7es5SU7yXtRmtxBkLFMCJCSsnxdqEO6YLZ2QndmksMmoAxyEAx9yH09yDwxQMcMpZ660hJ7OBprJ+Brcdm1PgzAKc+wrgjI/eU/Ds6MA1miv2s9bTTMyiPJJCpWmrVjVQCYjNkBr6iG0ss25nqWUL6ZZeBOCkmjoUSPQYmnEa6rCFl5MUlo/dkse61FrKsrvpaNzD4M5TnD954xEWqmkFMi+eHuXo4GV6Go9QsG7OQqUpcApIMRThspaSFlPB8sQaitb10O87ytDhm0EAx6+iEsARsFCdmWJrxwny3urGGVOKzSoKnADAqcNpFFVJHxmRW8mI7Cc9soe0yC68VoFz8hiuJy2mnkxnA5tXNCqAs6N7wN9CNf6eFiqxUF29cIsTh4fpbBoge00zyeYcEsMLSTZUY5fzJBXiAh7NbTjN0pjWrABLiqkRZ2Q9aQm1LHdUsTmzkfb6d3h33xBjosC5rSlwBH4JLJIa8fFRCTGeYEfPcYo2dWOPyifJXKhyn1KM9appS9Q3Mh8BgOMW65S14wEVjlLjzFaDBwEcgTnB0Cb4um6f0gGEDqH0GdBnQJ8BfQb0GdBnQJ8BfQYemoF/NsAJyjRQVddzdde/DNwEvvZvAXACVqJZwBEAOioks43UiFZSI1vwRjbhiWzAFVmn2qgiF24m9PUV/Pz/dfDDb0fx/VdC+MuXX+eVF3/EKy++xisv/ZhXXv4x3/zaT/jLr7/OX37jp+rym1/7Ma+89BrffPk1/tfXfsy3vv4T/uobr/Otl3/K/3rxZ3zzf/6Mb311Ad9+OYTvfd3IP7wSwz99103oDzOJ/KdsEhaV4givV2ArPUIUONqSDesDEOcRoCYY5qjrftAj50DglTRFOU3V2E1F2CzZLLcXsXFlBSU59XQ1b+PIvhOMXrmlNmKqlce/95SNmVQt37k2w8kDN+moOcKGDAnlLcdurFSQRbJqnOENeIwtKshYbFSaAmcO4KRKHo7Uipu6SfMDnDRz36x9ymPswW3sxKWsLz7shnr1s5PDynBYy1jrbaRkUzettTvZu+Ndzp8ZZmoiCODI7zsNU7cFNk1w8eQou3qOUprVjSe+kMhFG4lZUkB8aBWJ4VKlLACnlARTHqlJJWxY1UB5QSddLbs5PHiWG5cm/GfAb63xK3CunZ9UIcZzChw/wHlTAI4Pm6lDARunVfJvtuCyBgBOh1/V0ECyKFNMAnCkWrvbD3BOKQvViACcqUcpcMYY3HoaX/lO8te2sdIuuSnZRL0hAKcIW3iNUt+4LT6katttrcdpqcFurlQATAM4Pipy+pSF6uDOk1z+pQDnNjt6zlFTPOAHOIWYFuQQE1pOsrkJp6hvIjqxqcaiAMDpwWXuw6UATotfgVNMWnw5azzVZK+uo7Gsj4FtR7kyNKpZqIIBzuQ9xkbucf3iXd6dBTgtxC4uICm0XMELr8o86SJdIId1G0st28kQgCNh2H6AkyrQ0tSM21iH3VCGBHc7rHmsS6uhNLuLjobdDO48yflTGsB50DY4w9jopKoYP7bvCr1N71K4TkKMK4lRGTj5JIUWYjMW444oIz22ihVJdRSt62OL7xhDh28xfi1goQoCOCqneYbLQ3fZ1nmS/PU9uOLKsEeUkSxKsCWiZhOAIwqTXjKkiSqijzRrD6lWyfNpxWXy4TDUkxpdz2pHA5uWN1KR28GO7kHOnTjH9KQo6OaAnCiLBIgpgHPoIp2NUiPuI8mcRUJYPsnhldgNPgWNnKZWBW+c5hYcUglvEjtgPc7IWtISqlnuqGTz6gaGJvO5AAAgAElEQVTa63fx7uAZxkbGkda66Qm/gmkKpcgZG3kQ4DiiCkg2F5FsqiTF2IBNcnWMzTjEJmmSY2pFlGMeS7taszk4Crb768HVc1gQxAmGNrPX/zO8O6Ifg/4unz4D+gzoM6DPgD4D+gzoM6DPwL/1DPwzAY68INdejD8YshsANL/q8t8K4MzCjAfam9pUw0mqtdUPcZrxRkmgcZPKwxHbR8ySbCxvrCPkJ0v5xQ/svPa9aF79TiSvfte/vhfFj74XzY++H81rfxujLl/9XiQ//G4Er343gte+F8mPvx/FT74fxY+/G81r347mR38Tw2t/E89Pvp3ET79j542/9RLyj6sw/2QDMb/IJ2lxhQIhcm4C8EazUvmVOAJiHgNvxFr18JLvVQBHFDwWyZuQDX0xdksOy2wFrF9eRsHGatoa+ji09yg3Lgk8kFYev+QEraFHwkknR2cYOjJGT+MxslZ2446swmnRKr5todU4w+vxGJtJM3f4bVSdpJkCAEeDN16D1hqUZuohTW245wCO29CDy9CB0yABxpLXI001EuxahiuynPUZPipy++nyDbB/9wkunr3M3clxjdoEUMsMSlFx/eIYQ0evsbX9MIUbOnBE52FZ8DZRi/KIk+ad8CYSDdUkGEtIMOWSmlTMhlV1lBd00NWyi8ODZ7hxSX62/yNIgfMegOMSgFNF1MLaRwCcrbis23BYelU1szRUySZWAziVjwA4lxgZvvVegDMFl0/fYXDLKZrKdpK3tpUVtgoSw7KIemMT8YsLsYVX4zb71TeWBtzWOpyWauzmCj/A8Z/D7GCAc53p8aCwZjncWQXOHXb0nKemaJC1Ge3EGYswLcglOqScRFMjjshObBaxT3WQbOjApkJwe3BZ+nFZenFI65ayUAnAKWONp4rs1bU0lvUysPUol4dGVOitKFOY1qxG9yfvc+fGPa5fuMuRPdfwlR9grbeVuMVFJIdV4jQ14rW2K6iRbukn3bKNdPN20s1bEBgoAEfmL00pMZrxmASIlGEz5OGKzOOttGrKsjtob9jF4I4TnD91ncnbWotZ4K6Wy/GbU1w+e4sTB67S5ztK0dv9eKOlhSqPeIFJYVJpXqKCjTPialiZ3EDxW1vY4jvB0KHbCuDcUxk4c/OjbFqTcOXsPbZ3n6JgYy+ehDIckWUq0Dpe2RFr1fynCuC0biHN0kuqpRuvuRO3ABaDD3t4A6lRDay2N7JpeRMVuZ1s79rH2ePnuS8A597c/RkAOFcu3OL4wQtKebQ5s4lE02YSwvJIDi/HbmjEYRSY0oJTIK+ofMyN2E312Ex1uCJrSEuoUjbEzavraavbybuDp7lzY0y11slzg0BesVKJ+k0BnLMTbO8+QdGmHhzREh5eQoqpStmnbMYWzbYlx6OWKO78DVQKNM/93VB/Px77DkqQEuex3/Nv/cdP/3n6Cyp9BvQZ0GdAnwF9BvQZ0GdAn4Fftxn4NQQ4wWBDwk0VBDG34rW0Iu/oeyNEhdOsWqncEQ3YTVUkhZcQtySXiAXrMfxsFYt/lMaiV70sejWVRT/yr9fSWPRaGot/nM5iua4+72XRj7wsfs3Lkh+nEiLrR2kseTWdJT9MJ+SHSwl9dTlhr67C+NparD/dRMwv8khYWIotpBqXoYk0S4dqoJIMHAVypCI82Eb1CIgTfIyB63Kc8gATIJQq1dKWGlV/bLfmkJGcz9r0IrLXleGr7mRw1yGuXLjOvfG7GkCYllCQaQIBrPfvzHDxxBTb2k5T9NZWlibW442qxSGNSksqsIfW4Jbf3dxOuqVTKXEUwFEQRwM4UvmcapSwWVFLyOojValw+vCa+pQCRwCONCqJJUgAjs1Yjje6kk0r2qgt3s7WzkMc2X+GyxevcvfuewHO2Oh9hs/c5Oi+CyrrJGtNM0mWLAw/X4v1zRxillSQGN5IgqGKBGMx8cYcvElFrF9VS3lBG10tOzk8eJobl0TdE/QhWbtjM1w7P8GJAyNzGTgBgPOmAJxmbKqKWarD+5X6Zg7gdKqKcckASTZKE5Uf4KwWBc4Ae/pFgRMAOIH+ae32BXIIwBlQAGfHgwBnwUYSZgGOX31jqVf12QqwmcuxmUrwRJexPqOJiuxeuur3oClw/AAn6DDlLr91eZqTh8bY0X2BmqJ9rM3oUADHvCCPmNBKkgUGRvfgjOzGbunGZurS8m/MfbgtWzSAI7DKIPXYhaTGlZLpriB7dQ2NZT0MbHuXK2eDFTgawJmenGZs5D43Lt7n6EDArtdO3OJiksOqcBiblM1GVClpSnWzg3TzDtLMW0kz9Sl1l1ioUk1SM96Ex1SD01iKzZiHKyqXt9OrKc/poKNxF/t2neDC6RsKOgQHRsupGLs5yfCQzJBmoZIWKgkxjnpDQoxFgSMApxR3RKUKFF6R1EjRuq30N53gzMFbjF2Z5q5k6wRceNLSJMKYRwAcZ1QZKeYyEkPLSQnTAI5XqdTksdGDV3J9TJ24lTKtWT02JHh9td3HpuXNVOR0s71rP2ePX+D+hFTCi1/Lf4feF/vjfa5eFAuVKHD2kJXZSKJpEwmhuYi6zR5ej0PZqCRUWEKSm3CY5TmwDpupBldkFWkJlaxwlJO1up72IIAzdeverIpKjnUW4JybZEfPSYo39+GMKVI5PynmGlKMTdiMrdiNbTikGc/cjssslxrAUUqcgEryMZf6C4ZftxcM+u+rz6w+A/oM6DOgz4A+A/oM6DPw7zsDv+YAZw5utCpbgkAcj7XFv5pxq6aXOuzGKpLDy0gIKVQtOJELNhDxxnptLVivwE6EfO7N4BX4/Hqi3lxP1ML1RC/cQPSbsjYSvWATMQs2E7Mgm9gFOcS9mUfComKSFpdhC6nSVCymZgVrAoHL2qWWJaRl2cw1UAWrcQLHJZcBSBVQ32g/Q46zFqelBIc1l/SkPFZ7C9iwqoi6slZ2b93HxTOXmbg1oeqcZ+6Lt+X+bECpbD6vDk2zu/c8VTm7WeNqYWl8A05zFQmLSkgJqVTqoVRzq6p4TjNrEEeagbQl+SQPwhulmrD0kybL2o/H3IVT3qEPl/ybGmzGSuymMtLiKsnJ7MBXuZvdW49z4sgFrl2+wb17YnPSKpPVlnUGbl2fYujYNQa3n6KxYgdvLasjNnwDIa+vxvxGFtGLy/wAp5IEYxHxxmy8SQWsX1VNeUErXS07ODx4ihuXJCA56EMAzh0Jcx7npAI4F1SIcea/CuDUkv0wwLkkCpxHAZzbDGw5SVPZ9iCAs5koAThLCmcbmtxKfRMMcMpIEbtPVCnr0xupkBDfOj/AOf1oBY4AnFOHxtjZfZGaov0K4MQbizG/mU9cWDW2iFa8cf24o/twRfbisPbgtPQq9U0A4IhNRiw68aH5eGOKyXSXk726WgGcQbFQnZW6dE29od2FAjy0DJWR4WmODY7SUnmYdd4OBXCSQqsVwPEoa02vAjhpZgE4O/0AR9rMZNbaSDU14zU14DZV4TSWYDfm4o7KZf3SairyOujy7eLA7hMMD93g3niAdszd13dGJ7h4eoQje4fprD9IbmY3TkspET/PJnZhHokhYqEqxW2tJD2mluWJDRSu3Upv43FO7b/J7cvTTEm7lTyE/B9zAOcu27tPUrCxB09CKc7oMmzSgKfypOpwhjfjMUibVg9eo4Qyd+GZBTiaOi0tysdqezOblrdoAKfzAEPHL3JvfNJfXe6/0WlRpE1zbfg2J48M09UkFqpGkkwbiQ/NISmsRIFSR7gEnDcqhZPT3IDDXIfdVIPNVIUrspL0xApWOsvIzqyno24HRwdPM3ZjDAVwAhaqoAycKwrgnKJ4cz+uWAlqLiPF5Ac40nplkqwdaUyT2vsOrT3ND3E8jwE38lwWWP++fwD1FyD6+ddnQJ8BfQb0GdBnQJ8BfQb0Gfh1moFfd4BjaMVtaMFtbFF11R5ziwpG1cJfxYLiw2VpwGWRDJFaHOYq7KZytWFLMZYQvMRqlWzSVopJbALaEsWD3awth7kUh7kMp7kcp7lCW6ZKXLI5MlXjMtbiMtbhMsqG04fU5YraRlnMAiHQs01Uv9pCJUHCAbAjG56AHUtq0z3WelyWchzWAtISCljhLGTt0kIqC1vY0TPI2RPDyhoxdWeCmbsiH/DnafhbZkaHpzmwfZjGkgE2LWtnZUo9LmsZsW/mkri4BHtYDV5pCbJIO0xgqGVgukhV9pYepSoQZYFaEjxr7SM9op+MyC14LZ0q50PBG0MFdlMpDksxGYnlFLzdSbu0J+0+w9CJS4xcu8n0ffkdgzbgMzByeYrjBy+zvetdKgv6WeWuImLRWyz60SqM/7SZqEUBgFNFvCEAcApZv7ImSIHzGIAzdp/r58c4eeA6u7rPUZO/j0x3J1ELy/014g1ajbi1K6hGvP8RNeLVqpVnLsR4rkZ8RGxsdx8COJNw6dRtBvpPqlr0vDWtrLRVkBSepaBg4pIi7IYaPJZmVbMtAcaahUqb3QDAeTu9kfIsATi7ObDjBKqF6hEWqltXAgBHFDiahSreWIjlzVziDZU4o1tJT+wnNb4Pb4yoU3pxR8jqw23tVxk4NkOTUpWIis0dXcRqVzlZq2poKutlcPsxrp6Tti1NmaLdhRrwmLw5o7Vg7b9JW9Vh3koVBU4RSaGS19KAxyz2x25SLWKZ2kaaeTtp5n4/HJQA4xa8xiY8xjrcxgqcxiLsphw80TlsWFZFZX4HPS27OLjnBJfP3WB6Mmh+/Nzj9siECjg++M4F2mr2k7VS8ouKsfxsE9ELckhYUoDNUIzbWk5aTDXLEuooWNNHT/1RTuwb4ealxwOcy2cDAKdbARxXTBl2azkpBsmTqsMR7sMV3oE7vAu3oRO3oUOpb+T5yiXV24ZG0qIbyXTMKXC2de5n6NgFP8AJICPtoSGZUKImO/XuJbp9EmLc5Ac42RrACatRwFRCwx1GAbL1OEwawBElolglMxIrWeWsUCHUHfW7OCoZONfHuXvrvgbh/CHGolCTEOMr56bY2XuKkqx+3HEl2KylpJirSZFgcoPk37Tikswba4e2LO1aDo5k4QSBmgCwCQSxB/6tv2AIPLfql/os6DOgz4A+A/oM6DOgz4A+A/oM/KoZ+DUEOKJImVWoGFpxBQEct6lFC3+V/BCVISI2FB8eaxOeiEY8EQ14IurxRNbhjqzFHaEtV0StysuZvZSvRdbiiZpbYjFKja5ToaNp0Q2kRzdqK6qJ9Kgm0iKbSLX68MrtmQXetPgbVvztXQJwHtrQBODMey79xxj4vGx6JEdI1Ddank473ogGXNZKHNZivHFFZKQUstJdSHGWj97WPZw4dI7rl24yNjLG/Qn/7lr2t/41dm2G44NX6Wk8ROnGftan+0iLLSduURaJogIJr8BtalDHI8ciwavy+3jN0qIlEEfAjWR6dOMxdWmfs/aQHtFLRlSfatpxmupJCdOAmSuilNTYEjLd1VTm9rK145CqQ758foQ7o3eYmQ6SOMiveR+unp/gwO7zdDQMUrChk/SUcswL3mLhq6sx/CybqEXlJBokxFgATjFxosBJFIAjFqr2OQvV8O3Z4w4c//TYfW5cGOP0oevs6TtHbeEgazwdRC8qURAnPrRWVYU7LG04rR04rV24rN04LFLNLPaqek2JYKpQlfWp8ZXkrumkuXIPA1tPcubIMKOXBeA8FGI8CZcVwDlFU+kO8te0sdJWSVJ4DtFvZpEYUozD39DkscrsBkKMJbS6nGRDMa7IUt5OexTAefC25Fg1gHOHnd2SgTPA2nRpoZIa8SziDeU4o32kJ/WQGt+NN7Ybd5RkIvUoiOOR8GaxVRkaSQwtI3ZxDu6oQlY7NYAjGTj7th3j2rlbjwQ4U7dm1O1LYHZbtQCcNhViLBYjyUVymZvxqHmSWeonTeXfzKlvvMZmPAJDDTW4jBJ8XIDdlIUnJouNK6qoKepkS8dujgye4sqFEaalBvuhj5vXx/0qriGaK/ayaVmbap2SprjoN7JJWJKvAI7Ay7ToSpYl1JCf2UN33RGOD15HQKdkRs1aqPyzqVmo7rKj5xRFm3tITS7DHVuGI0IAToVfDSMApx13eCceQwceowZt3aZmXMYmnMZ60mMayHQ0sXm5j8pcsVAd4Ozxi9yfmNLq0IOOZ+rODCOXxzl99DLdzYMPApzQYlJEURVWr0LDpeFKHn8OUy12Y7VSIbojK8lI8AOcNY10KoAzxNj1Ce7ekhYqLUNIFEbT4zNM+AHOrt5TlGb144krwW4twWauJEWCyQ1i0xLFYzupkZ2kRmjtUwJuAisAatSl3y7qFXDnfy7U/0j/qj/S+tf1GdFnQJ8BfQb0GdBnQJ8BfQb0GQjMwC8BOBIwqtpEHgqjDAQSK1XJL6kOD/564P/8a068vOAPAA1RpsxCnMB1kwAGsVIJbNBAjsciipxmPFZZshn24YnQYI47QjbGAkG05ZSqZv9Sn4toQDJ0pNEqsLyRDXgjG0kVWBPpI01WRLNaqRHNpFqbVcCwAA9R32gVuY8HOIFNzOzlI/JwAl97L8BpxGWtxmktxR1djDe+kLTkfLLX1tNSu13lgpw/dYWRy6NM3RnzB3fMAZyp0RkuHL/J4NYhOmr2UbKhh0xXLcmGHJLDC1Tjj9MoqqJ6nPJuvmzWDD5ccr5NHcoiJeDGbezAZWjHJZtTs+TzdKqqZAFnsnFMCi3WlDcJ5axxV5O3toW22t0c2HWGq+dvc/v6BFNjUxqxCdqsimXlwqnb7Ow9SVXBNtYv9ykLh+mNt1n82jpMP88jenElSQYfiYYa4g0lxBly8CQW+QGOP8R44DTXL97SNsPi0PIDLAn8vTk8zrmjI+zfdpHG0n28ldZOzJIiBXGk4SrFVI/d4sNhaUGBHEs7DkszdrOoc2pJkVpvyaWxlJCeWEn+W1201wywf+cZzh67zC1pArv3IFSRjb8KMe4/ja9UWqjaWWUXi18uMW9mkxRS6m9oasOr7ICBGvEa7KaKBwBORVYPXX4L1ZVAC1XQOVQZOEqBc5ud0kJVtIc16T5iDTmYFmwkNrwYW2Qd3oR2PLHtuKLbcUa047J24o7oxiNKHEs3dqMAnHLiFMApYpUAnJU1NJb0Mrj1GNfO3nqvheruDAJwbsvtK4BziHXeFlUjnhBSQkp4NU5jowr0FVuR19Trz1DqJlXmyNSK1yTqm3rchmqcBjkvediMm/DEbCZrVRWN5d3s6t/HiUNDXB8eZTqQUxN0Dkau3FGgcFfvCeqLd7EhQ/J8irD+TOyPOSQuycduLMLtrxFfllBFfmY33XWHOD5wjdGL95kYeTTAuXr+Hrv6TlOa288yRyXehAoFcJLCShVMETuTgBuxT6VKfpQcl0XghQDmRlymOtJj6lijasR9VOV1s6P7IOdOXtKsd0GOQplbyaUZvTLBmWNX6WkZJGettFCJhSpLZfmkhFZhC6vDHl6ratodRoE3NVr+lKESV0QFGQkVrHJUkJspAOcdjg2eZfz6JCqoWQCYKPTEbekPO796bgoN4PThjivCbi3GJmHa6mc34rK04I1oJz1KHvcCcTrRasMlbH3OKqWgjQ5wHqqBDPwh1i//Na8N9P+rz48+A/oM6DOgz4A+A/oM/N8zA78U4IgEXpPBz0rhxUrjtwIFA5pfdT3wf/41J1aBjEcADg3q+OGNAjhaHo5SjEgmjlqatWouH6cFt1UsVs24LD7V1qI1tshm3YdTrFdWn/Y9AfDjv/SKykZAjbVFLQkUVg1YFgE2/jBlCVWW9pzZ8zX3oAoAmcdeBh1j4Hs0eKOFIWeoOnJR4MjvV4vTKpvGEpxRhThicnlrWQ01xX1s7z7I8YNnuXz2KuM3RYHit1D5XSbTYzOMXJjkzOFr7O0/ha9sF5tXNOGMyMVuLlDNPDZDuVYtLhk2YXXYwhqULcRplLYZqQnvwGFowx4mWTfNqjZZwku91jb17n+Kyh3KwxUhqo0qclY3UVvQy46uwwy9exWx2GjNN5IMG7RbFSHAxAyn3x2lu/kwOWvbWe6oJsVaiOkXGwj5yQYsbxQQu6SaZGMzicaHAY60UEmN+DscGjjFtQsCUvyMyA9xZibuc/vKBMMnb3Hkncu0VO5n/dJ2YkMKiF5cSHyYVEJL81M9dmnyUY0+Am8asZlFnaPBG7u1FHtEMUuTKyna0EVXwyCH95zlwokr3L52+70AZwquqBaq0/jK3qFgbQer7DUkG/KIeTOHpJAyBcw0NUMrnohmpR4TC6AGcEoQNdPbaU1UZGkhxod2nkQAzsz4g7BIARyVgXOLnT1nqSnazZr0RmLCN2N4422iQ/NItlbiim1SShxHhA+7pVnBKoE4nogePFYJNW4kKayceLFQRRWxylHO5hU1NPgBztVHAZypGRX+e+eqAJxR2qoPss7rI3phDvFLpImqAoeocIw+3KoqvAsJ/JWWplTVPNWC19SIW2yJhiqchmLshlxSjBvwxG4kZ001LTV97Nt1iDPHzjFy+dZ7Ac4MXLt0i6P7L7Cl/TDV+dt4K9WnAE7EP20m9s0cEkMCAKeEtJhyliVUkp/ZSXfdQY4PXGXk4j3GR0Sd4qdCMqpiM5qC6xfvs2frEJUF21jlqSEtqRJHhGRtFZMcWokjvFGFFqdJE5VF6tJFQdeuQK9HrJ3mGjJia1njaiBrZRNV+d3s6jnIhVNX1Lwq2BiAUQJwxmYYvTrJ0PFr9LbuI3edj2Q/wEkMLVS3mRIqj9UaZcNzGOSyGlt4pVLCieUyI76C1Y5KcjOb6KrbzbHBc4xfn5oDOP7jk8efPD6vng8AnF7csYXYrUXYVZh2DQ7JJrI2kxrVRkZMJxnRnaRHdpIW0anUgvK8Jc9hs+BdBzg6wNFbxvQZ0GdAnwF9BvQZ0GdAnwF9Bv4VM/ArAI5sxh9oE5kFEv5cl//DCpxHbQYe3BwIvNHAk3rHV97ttrThmV2S1dCu2m9EXSSNKVJ/KzkOwctpbsUly9KGW61W3LMwKACFtJ8tP//Bd5q1d53/JbAqAGxUTkRQbbiWo6PZpzSA04E3QkKaJdunGrsEi1oKSTLnssJVSfHmTtVSs2/ncWXHuH39Jkw/CHBkAzo5IhBnQkGcLa0HKdnYQXpiIR6BQZYiUgwlqt0mObSC5NAqksVWFNaIXTXdtCt4YwtrJiW0iZTQRuzhTThNYluTbIwaUgzFJIZmkxpbyIaldVRkd9LdsJvDu89w/cLYA5nFgX1qQAEgaoOj+67RVDnAak8dzpgS4sLzMP1iM+E/zcK6oIS4kFqSja0kGmtJMJQSZ8jFk1DM+pX1GsBpfodDe09x9bxUqvszWiSSRjapk/eV8uDq0B1O7LtKR+0BNq9oJz48n9iQfBLCikk2SutTtWrwsUkYrF95YzOLOkeUN6V+eFbMcnslpZt76PXt59i+C1w6fY2xG3ceA3DG2Lf1DM3luylc18lqRy0phnyVPyTnWhqElJIhQkBdi1KNuSySZVJJcrgAnLL3AJyrAnAmHg1wTh66yY6eIaqLdpGZVk90+EbCf7GGyJAsEs0lOKJrsUfWYbOILaxBwSqnKPCUCqdLVVFLTXV8SB4eP8DZtLyGhuJeBrYc4+rQLQXcVNCvivzRQozv3p5BrHqnD47SXnNQwZOYRdnELykgKbRM1aVLXotLWaXaFexQsy/2Q7MPj6ket7EGl0Fgj7RFZWMzvU1q3Eby366hq3Er7+47xoXTw9wUWPawAmcarly4ycE9Q3Q17qNscx9r3Q2khBcS+fMs4hbmkhQqAKcQd0QxaTGlLEsoJz+zne66AxwbuMLIxbuM35hWx6dmNABw7sKN4WkGd5ynrnQX65Y2stRWjTOyjLjFhSSGlCuAk2ruJMPaS0ZEN0sjO1ka1U56hEDgetzmapbG1bDOXU/OKh81BT3s7jvM8Jlr731s+AHOzatTnD1xnb7W/eSua1YAJyFsM4mhBSSHlJMSKjaqauzh1X54U0VKmDx+teyu9LjyIICzh+OD55m4fpf7UpUukEoAr6hwJmZUePM1BXBOUprVgzu2ALu1ALulVLX7ScaOqBrTotpYGtvJ0pguMqK6yIjsVlbPfw7Akee8f8lzpf5/9POmz4A+A/oM6DOgz4A+A/oM6DPwf98M/BoCHA1yiA3hl61g6b68CyyrUwXrei1dKjjVY+nGbe7GZe7CZerCKa1J/uUyd6rPuy1duC2danksnQQv9fP+N3vv4dVWmq75zh9517r3zPR0V1eVEyAkASaDMsngbHAg5yhEBpHBYONAtE02OeccDPzuej9JBruquurM9JkzXUes9a2thLb23q+29P30vM8jz3llyGTNNS537NeiugK/viqSrhgbq9u+88hRkxvxvvH437g9cOKU14SoiKox6ysw6cow6IqI0uWRaLST+cRJVVEX3S2DjA1JjPY6F19O4Owyllj5XOxfcLhxytrMDh96pmhyvCc3xcmLxFoeWqpICHN5ACmlj0qzEU+NGkz+9Zj9GzFpnArmGHxrMPhWq2Qhi1ZafmqUV1BCWDmJkSU8v1NFWUYbrZXvGOgeZXZ0hf2141+dpIqPyfHuBRuL57zrnKUoowtraCHhmhyCb2UT8HMW+p/yCL5RSrhvNdH+TqL8K4nwLyZMk40tvIDkJAf5GU6cVa8ZfDvB6ty2ShES5YRMTrkQwHDO6fYXdpdOWBjdpds5Rv6zDmwhkiSUT7RWDK0LidKIEke8Z0qJ8isjyq+YSL8CIv3yMOjysN6W9qkikuMqceR186ZtlLmxDTYXdzneOeTi7FuoIpBjbfqQwa4Z6ovfk/eklYemCqL9cgn5ORsFcDQywW9BtTGqNipRhVWqSXOUbyGWQAE4tZSkd9Ba2cvHN5P8GsCRYyweLuODG3Q3T1Ka84r7MeUE+STj87f7BNx4Tph/LsagUqIDyojS2YnUOBTEkbYxq6hw9E1KaRHtV/DBGbkAACAASURBVEyEpFDdzlctOC8Sy6nMa6ev0w1wDl1tRhdfXITj4vQCUXlJq97SxBGvnJ/Jf/qS+LByYm7bsQh49CtT7T6iVLH41Sk/K4u06fnJdUlxsmP2LcHkU4jFP5+YoBwSwjJ5EptHea6TN219zE3MsbG0weH2oSspyq0wE9gizHJheoP33WNUF0vil5O70dLWl+0GOBlE3crGrM3FFpBHXHABd8ILyXrQQLNjgNHeZTYWjjnYPOPsyP3EshAFzhfYXTtnpH+N5pqP5L3oIOVOPfHhdpV2F3GjGKOPtH9JkpacF0S92ECcAI+AamIDKogNLOdBdBXpd51IO1yj4y1DbyZZnd9ytfxd2ZbLFqoTtwLHBXCidS8I90kj0ieX6FvFX+GNyc+OUuD4SlvVJcBJCC3hgbGUrIe1qnYmBxY53brgfN/dJuZR4IgHztYFq7NH9LRNUJDaijUkG4Mui2h/gbtFqjVLTJKtOgGO9cQFNpIQ1Myd220kBLYo03UB7CpNTwHpS++br7D6yjnv67nSC3W8UMtbA94a8NaAtwa8NeCtAW8NeGvAWwO/UgOXnCFO24QnLVou/zdpg/m/UYHza1/8f+s28Zy4BCxitNtKjK5NDZuuHZu2Det3w+K5rmtT/h8CeWzfDfU8bgPfby/LOr6HOG4y+j2s+a3r309orgCcyzhyMQuVKHFp9arCpHdg1JcRLS0S0eU8u1tLYXoLjRU9DL4ZY2V2lYvjEy5Oz9XkU/3K7m4DkUn2wcYJc6Pr9Hd/prmyH0fOa3KetJEc28Dd6BoSwquJDXZ5Aln0Ne62s3pM/qK0qcGoqcLoX6lSbyx6l0l0UmQ1ybZa0u/VU5bZQVt1H4OvxpkdWWJzYUel3qjX8VV644Irx3sXbC1/YXp0l9baEdIeNBHqm0ngtQyCrucQfKOA0FulRPhWqvYpg66RaG01kdpSIvxzVRrQ4wQH2S/qqbF30fd6nBWJuT690kJ1LilJ55zvn3O8cc7G7DF9ndNqu++bHcSGFWMJKiBam0ukXw4RvrlE+OQpVU64Ty7hPlmE+2Rg0GUTG1bAPVMp6Q/qaCh7x+Dradbn9tlfP+LLvkCzXwKc9ekjBrtnaSjpJS+5TXmgGDR5hP6co5ROZgE4uhZ3sk8DVr0H4NiJ8ivCEljM07g6St0A59ObSdZnRIHznRG0qEQWThntX6HTOUpRZgd3bSUE3HzErb8mor+eTKh/JtFBhUTpi4jUlhAhoEr8TbRynBux6hu/ApxI3xxiggu+ApyK3HZ6O8dYnd5VsEYUHKrGzr8gAEdUHAIGNufOGexeorZwgNTEFh6Z60kIrcCoEbPfUpV4ZvKpRIZRWoDEy+VmMYZbhRhu5WG4lYNNl0tSRD5PbIVkP3HQVPGSj+9HFJw82Nrj9EDq2w3o3G1yEmw2M7FKd8tHijJaeXqniviwYsKvZxH41zTCfs4k2icHizYfW6AocAq4E1ZI5v16msr7GXm/xPrcMfsbVwCOIkOuWjrYkja/HeWDU13SS3ZyG/dMlch+ihCfn5tlmG5VY/Gpw+pXg1VTiU3rIFZfTnxQOQnBdlJsNRSktKoWxldNHxkfnGNzaeeXLVTnIClUWyvHTI+t014vLVR1ROtfEOGbTpRvrgI1Rt9yBW5MmnLMGrksAKdUwR2LtpiEEAE4ZWQ9rKO9qp+poRXOdy+U543afwJwxMRYUqg2L1ie3ud1yygFL5qwBmdg0Kardr/IW7lEyjb6lCi1jxgyx+jqiA9sJCm4jTtBrQrgCLwRo3nxLZPztMDo75U5nvO3F+B4f0Xz1oC3Brw14K0Bbw14a8BbA94a8NbAP6qBfyGAIwfS80X/jyyVYeivARwFY9qw6VwjRkDOL4bn/lZEqeMCOC0u416tK3FJkpi+HS41zq8qcK6ob5SaxgNlvlt+VeZ4aKN7svO9x5CYhCqAI21UATXfQJy4cAePYqpIf1hHeV47b9o/Kj8WSV1SE2zhCe4JrqddSdJndldOmR/b5tPbRaVGqS54R9bjVpLj6nhgriExqpK4UAcxtyXBq0IpQkw6Uf+UKwWQSVeq/HgsAcXYgsSwuIqsR43YszoVvBl6NcnC2Cp7q4ecbrsii78HOKKQ2d+4YOHzAUNvVqgq7OO+tQq/vz7F738+J/DnXMJ9y4iWViZtHUadE5OuGYOulmhdOZHaAmzhxTyMqyAjuZ6Kope87xpnefo7DxwBOGfSMuICDAer54y8W8Rp7+XF3RrumUuJDcvHoMsg3DdVmcSG3Uwn9EYGoTdSCbn5nJCbTzHo00mIzOdJnJ38Z02qfWp8YJGD9S/uWGZxg/0uRvwU1meOGeqep6Gkj/yUdh6ZqzH6FxB6TXxMRDkhEeJSe02q/U9a0sxagWTilVOMpAk9ja+nNL2T1qo+vgKcYzfAEeWGTMSPYX3ukE/vF2ir/0BBegtJlgL0N+5x86/x6K49JESTSnRgLpG6fCI0BYT5FhEpvin+EkNdj0XXgElbiUFTolRCsSGFCuA8v1Ou/Ix6O0ZZndpxAZwjUXGccXF2gihxFDQ7hgPxwRnYpqv+M/aMd2Tca+O+oQqTvyQn5RHtU0D0rSKibxYRdSOfyOs5hF/LIOJ6OpE30oi6+YKYwEwemgvIuG+nPKeB1y3vmRmd4WRPUtZOlaJKwIPyp3ErrU4PL5gcXqG1doCMx3XcN0uUdgEhP2cS+NcMwn7OxuCTj1VXREygpFAVkRBaTMY9J41lAwy/XWZ99oT99XPl1fSVNcr+PXcpxZamD/nUt0Rn4whl2V08iROwKN46WUTezCf6RgmGm2UYb5Vg8i1SZswx+iISgotJiigh9U4NjqxOOmoGGOqZZHZsmR0xv5b4eXmfev4E4OwK3DxianSNtrpBsp7UY9CnEumXodoVjb5lyldIvIXMmgplkiwqHINPGdG3SrBoS0gIKf0KcDqqB5n5uOZS31yJgZf9eLZ/wf76GUuft3jV/IH85/VYg18QrX1GpG8q4TfSCbueSfj1HCJuFBB1s0QZnscFNHA3pI3E2x6AU4/Zt06pcOS8LT5A3wMcUenIfd4Pa+8+8NaAtwa8NeCtAW8NeGvAWwPeGvDWwD+qgX8xgPP7B/Nyg34d4LhVOEqJc6nGuVTliGnrd/BGlBCe9iltM7YrLVOX6h6P6kaWV1+De+d7QIxqhfoH/kFfVTme/7s0jf4Kd9RjxAjV6fZHcUEcc0ClUuLEhDhIMjh4FOsgJ6WB5qr3DPfOsL28z9HWF9dEWya47km+muifwenOBdtLpyxO7DH8bplXjePUlfRRmvGK/KedZD5s5XliEylxjTyxNfLQ7OShuYGHlnoeWet4bKsjJa6eZ3caSLvrpDitg7qSt3Q1fGDo9WdmR1bYWdrjbPfLN7/2e+anshTfm8WpfQZ7Fmmu+kR2SgexYXZ8/scz/P6SRtC1AqI0FZj0dZj0DZikvUffjFFfh0FfQbS+BFtYKfdsFTy7X0txdhuvWkeYG990rdPjg+MBOKLKObrgy/YFc8MbvGkZpSK3m9yUJp4nVam0pYSIfOLD8okNLVDqk9jQfOLC8oiPyOOhTZQ3tRSnt9Jgf0ffy0nmxzdV25AoGKRN69cBzglD3Qs4SwfIT+ngkbkGg0bAQh6RN10AR5RfVm2zUsFZFMCpxijtTX6lWAPtPI2rpzi1g2bHOz68Hmd1epULFRd/xcfkGDYXjxgfWuZ16xiVRd28eFiJJTydyIBnmEOziTcUc9daQUK0A1toOaZAOyZpy5P2PF09ZgFlkril2shyEIAjJrjPEx1U5HbQ2yEKnKsA5wsXZ8dcfDl3AZwTONm8YGn8iA+vVumonqQ6t5+8Jx0kW2t4EF3J3YgKEkMdSpESf7uE2KBCYgLziA/O405YPncj83kWV0bB01pqClt52fCGkd5R1uaWOT86UrBDoINqbRJl2SmcHV+wv3XBx94Fakrf8DjOrtRSRq1L6RT8QzYR1/Mw+hZh1ZUQE1BCXFAJ8cElpCU6qSvsY+DlPAvj+2wtnSJ+PurPDW8EFEm9biwdMz26weCbGZqr+ilMbeWhpYy7UXa1XUmh1SSF1JAUWsndMAf3Iuw8NlXwPK6ajLt12NPbaavsZaB7nOlPi6zNbSCKoosvp78JcKZH1ml3AxyjXhQxmcqvSgyLTX6VWMRcWFuFVSdJV5Ii51AqGavOTkJoGfcNZWTer6OtcoDpoVXO964ocKTLUhQ40l65+YXVmW16X45Qmd/K45h87hnzSIpyeQXdCS0jIaSchNsVxAdVced2HYnBjSSFtHInqEWZuFs1LoAjCYFyDosPkNYqV0uZx7/M5Xv0jz6ovPf9/mefdx9595G3Brw14K0Bbw14a8BbA94a+PPXwCVr+L++heqPHYzLDfKAlKuQxaWYkaQb99AJxHENl+LBrbhR6gcBN1fgjVJDiCLC46lzufSs69vllTfQ/yrA+f5X6auAR+8kNsBJTKAkFdViCZAEqSqsQQ5iQsuIjygmOd5BeU4nr5o/MfVxmbWZPdUWocxePSocmZW6U5/Er2R3+ZylyX3GB9bo65qlu3Gc1qpP1JUOUJH3DnvWW4rT3lDwvIfCF68pSn1NSforyrJeU5n/lrqSXpocA7xqHGXw1RSTg4ssTWywvbjLyfYxF0fuib0HIrknjPKatlfO+fR+mcaKIbKTW3lgqVbePpq/pKP7Ww4h10uJ9hffnwbEZNesb1YAR2COMaAKQ4Ada2g5iaYKHsVXkf2sidbaISaGVpRhs/L5OL5weQEJxBHFxolrsro5d8DEwBJvWkdpqeyjMq+LgmeNZD6sJf1+LalJNbxIrCHtXi0ZD+vIflJPaUYb9aVvednwiYFX00x9WmNj/kAl+kgM88WJe10eWCa7+kQ8cDwAZ5D85E4emmrUBDz4xzwirosBbT1W/1ZX0pe/JH5Ju1oNRv8KDBo7tqByUmLrKXzeRkPpa/q7RliaXOL84BDO3T5HosA5hb21U+bGtxh6O0d7wwfK81+S+qhK+QSlPqwjK6WF3OcvSX3QyuO4RhKj64gLqyXmdh2WgHpM2lpl4iytW9JOFiMAx1zKi6RKKvM66e0UeORuoVIKnC9cfBGAc/YV4JyJcmT+nLlPBwz3bPCmaZaW8mEqst5R/LybvMftZCQ18SK+nudxNTyLreJprIO0xCqyH9SQn1xPZU4rbVVv6O38yMTgZ5anlthb3+Li5Eity6NWEbAifjVHOxesL57y7uUUJVmdxEWIoiqbSN9cwq7lEfazqEaKMWlKsensxATYiQ20Ex9UxvM4J5XZ7+hp+szEwDor0wcqiUqtQ3av2wPny8EFexunrM7vMT26Sn/3JK3V/aplsPBZO3mPO8i+10FWkoxWsu42k32/kaKUVhwZndQVvKKzZoDB7glmPi2xMb/F3voup/sHCoJ9k8z2VYFzjACcjvohsp80YNRnECXx6n6FKnlKRbNra5C0vNgAScurcYEcbRUxARXKNPledBnpd2tpcfQx2b+sAKbrveHeNmFHhxfKI2p7aZfR3inaa95SlFpHfkodeU8ayXvcQu6DNrLutpN+p50XsR0km9p4ENlKYrA7bUvXgFUj3kZ1yrNMlITijXNVheOBN14FzpXPi+/P+97rXnWWtwa8NeCtAW8NeGvAWwPeGvDWgKqBS97xJwA4lxtzCVJcyphLiHO17ekS5Pw6vLlqWuwCN5JcJUOlTsnS3db1u3DpfwHgeJ77+6Val7Rk6RuJlUjiQA/EkQl3LWbxw1GpVDnEhReQ+aiButK3vO+c4PMHiUU+VZMzNRl1iwqUsuCKJ454X0h88tLnA6Y+bjH8foW+l7O8ap7kZcM4bTWjNFUM01w5TGv1MO21w3Q3jvK+4zMfeuYY719mbnST9dk99tdO1ETw/ODcDTSutHAJ0JBf+0UFs3fB4udDuhrHyXnaSkxoIdG6fEJu5KH7aw5BPxYRfkuSoOqxBjRhCWhWAMesa8GkdyIR2KbASqyhDuKiHCSaHTy/X0+9vY+Pb+fZWTrleFMUSNLiI8oY1+tQEOdYVCJnCMSZG9lg+O0cb1qGlbqlprCbqrwuHNkvsWd2UpnXTW1RD077O142fGSge5rPQ2ssf95ne/GEI0ksEvWN8oPxwKLLbZa2rbUpATiLOEuHyE9+qQBOlE8Bt/+eS/g1SRGqx6Jpw6xpRiLbTZp6ZRBt8hcjY1cbW3JMPfkpLdQUdvG+4wML4/Oc7e25TJPdcE5Aw/H2OWtzB0wNrzPQM0Nn40eqS3twFHRTXfKO+vJBnBWfKM/tIyflFSkJrdw3NZEQ7sQaWK8ixKM14r1TQIRvNjEhBSoS/sXdKirzX9L3ckKBQbXNCuCccvFFVDFfXMlf4lW9f8HxxgU7C+esTp4xM7TP8Os13rXM0Fk9SmPJAJVZPZSmdlLyoo2SF80Uv2ikIquNusKXtJT38KZ5kNH3n1maXGFvdZfjrT3ODg7Uur5ROZ3Dl/0LdtbOmZ88oMM5SuaTJqK0aYTcSCXsRjYRNwqJulmqVCkW/3JidBXE6B3EBjiICywnxeqkNLWHtqoRhl4vMDe6zd6aKIsuAYeAuLPDC453z9jfPGZzaY+Z0VU+vpnmddMw7dUfaSoboi5/gOrsPqqz31Gd00N17iuay97TVTvE+9YRRt/OsDC6qtRppzsnnB0cKyjF2aFrZS7dj6pXUQFtLx8zM7JBR/0Hsp9I3Uu0ejYGTZErlt2/BpuujtiABuKDnMQFNhAXWE9sQC3xt6tJDKvgXpSdtMQamsp6GXu/yPH6BWc7bhWOQE0Zyr/ojIP1feZGF+jvGqbF8Yome496/c1lgzQVD1GXN0RlxiBlz/rJTuohxdShkrbEsDlG3qsaaZ+SVDGnAjfijXNVhXP1/Pa751HvFxfvFxdvDXhrwFsD3hrw1oC3Brw14K2B/9I1cMk8/oQA53t4czUx6irIufS2cSlufk114wI3vwA4bojzuxOP/y2A40R5RLh9ImTCE6sAThNxgU3EBTURG9iolDhWUUzoKpTRbditLAy6HB7HVFL4op1GRz/9XbPMj+1xvOkymJWJmmql8kwSPe0hokpxJwjtrp6zOnPE7Og24wOrDPcuM/Rmkf7ueQZezTP0Zp5P7+YZG1hidnSDlZk9dpaPOdo6c4GMq54736/H7bUhUdPr82d8erdKVWEv9y0OAn56RuCPGQT+mEvQ34sJvVZOlMSUi7FuYAvWgBYs+lbMMgIaMQfWYwmqwRpSiS3MQWyknYdx1Tjye3jXIRP/XXaXDjnbEbNbmYm7t92tqBDFgbRSHax9UZ4unwcXGega503zR141fqCrfojO2iFeNw7zrm2Mga7PjPUtMj+6xeb8sYrLPtqU6OVzvuydc34owOr8G1gk0EgmxmtTpwx1L9FY9oH8lC4eGmuJ9Ckg6Ic8wq6JiW8DFk07Zk0LZo2kfQnAqcWkqUZSf2JuV/LEWkfO40YqctroaRlgdmSak51tV9rYuchvXNDoTFQi619YnT1gemSTT70LvOucVMlCbztn6O1epK97mdaaSRy5A2Q+6iY5ro2k6EZsQXUYNJUqgSvSN58In6yvACf1bjVV+V0K4KzO7LmO9TcA54SLY9kPF6pFR5KOjtbO2Vs8Z3P6nOXxY2Y/7jH2bpX+jhm664dpq+intaKXFsdbWhyv6arr5V3rEEPdI3wemmFtZp2TrUPXfhVA9EXo0LegQ1RH4qO0NHXEp941akv7SU6oJujaUwL+/lwlfUXeFONdh/KJsWrFfLeKGF0lMfoKYvUVPDI0kPf4JbUF/bxunGC8f4WNhSMXkJK2O2nFc4/zo3O+HH7hePeYreVdFic3mBxaYvjtPAOds7xrmuJ1/SQ99eP0NIzyxjnMQOeEAjczQ0usTKyzvbDD8eYh5wJvVEvYoZjQuCRUnvfMOaqNa2f5hNnRTTrrP5L9pNENcHIw+BW7AI6mFptOEqGcJAQ3kXC7kXiJLg+S67UkhlWSFFHG8/gq6ove8un1LLuLJ5xsiKm3W5XmhrmiljvbPWJzboPpD7MMdo0w+HKcoZdTfOyaZ7B9kXfOObqqZmgunqTs6QAvbC+JD6zH5i8x8LUK4Nj864nVOYkPkBj1KwDnCgRX57X/0h/GXgXO736OeuvD+4XdWwPeGvDWgLcGvDXgrYH/8jXwpwE4lxviUt/8I3hzFeRcXhZvGzU8fjdfW6Y88MapDGVtOicxV4fWqX5dll+YL8d3X8b/CQDHZfR5GcMrRsZxAc3EBTYTG9hETGAjtgAnZl0NBn87kb4FGHUF3DVU8OKuk6LUTporPzD4aoGFiQMVL3287QI5HjWKZ6mUBjJRPUYpYw42zthaOlJwZunzLvMTO8yMbjE7tsX8xCYLnzdZmdlSKoT9jSNOd0XlIy000pvlmYHK0kVNxEBYVDenuxfKZHfywzq9XbM02IfIfNSCLaQY3Q8vCPwxh5CfSwi/UUWUTx1GjROLRFsHtGINaFUARyCORRQ5QU4s0vYTXI0lpAJLqJ1EcyUFaS9pr/vEWP8Sy583OVzb5+JEJAZfX456jTIhF9hwtnvO/uoR6zPbzI0s83lwgcn+Bcb7FhjrXWBqcJm54XWWJrZZndplfXafrYVDtpdkiF/KLltL2xxsHnAmyUhnbpjiXp8yFp7+wodXKzTbP1Hw9BWPTPVE+RZx++/5hF+vwOjTiNW/E4t/G5avKpw6BXCMkmQUWMFDUzXp9+ooTmukq+Ednz9McLS5wcXpkatFzL3blR+M8jMRA9xTVmYOmRvfYXpkh9mxfebGBc4d09u1QqNjjPznb3iW2ME9o4CyGqJ8y4n0LXancGViEwWOxU7qvRqqCrqV7883LVSnQuUEdhxzfnCCqEoO10/YWTxmffqQ5fED1j4fsznzhZ35czamj1kc3WFqcJWx9/OMvpth+M1nPvWMM9Y3ydSHGeZH51mbEeXNFmd7B1yciEmywJsjuBCAIzIuF7ASo9/Fz3sMvlmgpeYTec87SDSUof/xKQE/pBJ6LY/oW3bMElWuqcaqrcamq8EmS20VNm0ld8NrSU1opuhZFw2lffS9nGJhYlsBFKlbBW88ahzx+jk55ezoiKPtA3ZW9lif3WFpYou54S2mBjb53LfB9MAGsx/Wmf+0zvL4ButTW2zPbbO9sMnWgrTeLbG1tMj+5iqnBxtcfNlxvQGvvH2UsmjlhNmRTTrqXADHqMskyjcHg28xRl/xu6nFqhXFjZP4oCY3vHFDnNt1JIRIG1UJyTYHlTld9LaNsTyxxd7SkVLhfIW6nlaxkzNOtg7Ynt9icXSZpdF1VsZ2WBs/ZHn4iKnePYa7tnnvXKU2e4SM+G5itNUqUUz2sVWAkgfg6JtJCBSA06zSqATaXB3eCfx3nxv/5b+gePeH9z3hrQFvDXhrwFsD3hrw1oC3Br6tgUvu8S+uwLncEA/AuWybuoQ0v3fbtxBH2qa+hzcNxEhrwHdD2gUu4Y0H5Fwptn8WwBEVjiex5SvAabmEOAFNWMXgV6UGlWLUFRMbalcQJzmuhuL0LuVnI20hM8PbbMwfoyCOqAk8MdtXwYawB5moCsiRNpjtCw63JHb8gj1RU6yfq4jlw80zjrbP1WNU69CvqW6uTETlMSfbX9heOmZicIXuxhEcua9Ju+/kTrSdcN9sNH8RgJNH+I1yjH4NmLVNWMSTKKAVW0AbVhn6NhfECWzGGtSI9XYDlts1mIIqMAaWERtRTsajVmpLennfLjBgmZ3FPeXD8w3A8WynW10hSoTTrXMOVk/YXTpid/GYnYVjduZPOFg552TjQkUvi6rkYO2M7cUj1mZ3WZneZPHzKnPjC2wsbnK8I/4sVzZcmNEJbMyc8+n1Oi3loxQ96+GxxUm0bwnBPxYSeaMSk18TMbqXWLXtWPyvqHD8ajD6VWLVV3AvuopnCTXkptTRVv2Gsf4J9tc3XXHx4i905U8dQw+g2heV0IXyBDreci1FAfV56Igu5zxlmf2k3evivqkZS0A1ET52wm9J+5rEp18CHPECqip49esA5/yYi2MBeQeqBWdrYYflyQ1mPq4y2b/C8sQ++8suxYdEWH9R6pwL9pfP1L7emttnY1bgxi67y3scrO1zvHXI2d4x54cnCpgok18pWqR4z11tP8ews3LKcN8SzdVD5D1v51FsFeagfLR/e4r+hzTCrhVg8JWEJgEdtdg8w19ivquw+FUqQ96Hhlpe3Gmg+EUHnfWfmBxaQUCmtPrJMVT1I/tY3i/nsv4vXBydcbZ/xumOxNNfcLjiUhztL5xztHLOlw13q5K0K0l09+4FR+vHbMytMTc2xfTIGKvzsxxsrXJxuvutAkdWsy9pcV+YHdmmo+4TWY8bMWgzifTJIdqnGKNPBSa/Wqz+9eocFRfQSFygwBsBOU3E364j/nYFccHFPDSWUZraTnf9B6YGl1if3uV007Ufr5SOuqjgprwnJE5984KzLdd2nK1fsDdzzsqnMyZ6jmgtmSE7sQeLXwXR4uV0qwKLX41qpRIFjnjgfG2fknOr/7fqwm8/nK6cP70gw/trm7cGvDXgrQFvDXhrwFsD3hrw1oC3Br4JTPICHHe61KUK55cA53tw47n+fwrgfJ3wSPFKolVAM/GBHoDTTEyAqFMasOil1cjhjvcuxqQvxByYT0p8LaUZ3bTViOnuHDMj2+wsu1udrsQIfz+B+3rdA3dE7SCQwK16UEu57w/+SXvW7rIr8ept2wT27Jfct5QSrcsg+GYa2r8+w+ffnhP493yifMR8tZXYwHZiZAS0K4BzCXFaVUuVLagJW7ATS1AtxgAHkdoSTLdLSU5ooCSjm9aqD3x6M8f6lKvd5+tr/rXXrSblbi8QgTqe8SstZ5LgI/46S5PbzIysMjE0x3DvBIuTK+yvH/4S4JzC5uw5w2+2aKuYL0t3nAAAIABJREFUoPj5O55YmzD4lRLyUxGRN6uU902svgubrgNJozL7u9qojH41GERloa3gTnglT2xVpN+vodH+huF3n9ld3nW1bn0HjX73sFzA+uw5/S+3qMofJutRDw/N0ppWTdjNUsJuFhJ2K5ewrwCnnLT7tVQXugDOipgYS/uNqFO+SGGccHF0yNHWLttLmyxNrvJ5aJ6PPVMMvPzM4qjAgl/b8S4gItBLqVxkv8tlj9rFDdguTs9BlE0eiiJqkWNX9LUYDve0jlOY3s4dQzGmoFxCbqaj+UsK+r9lEHZdvGKqsOkasGnrsWnrFMSxamqw+FVh8qnAqnUQf7ucpMgynt+pob7kHR/ezKmUNjH6lnX94s/zcjxLeU2e7fgHx0PqZ31mi9G+MQZf9zMzOsH2ygoXxwcgrXBX/uR9s7dyzuzwLh21I2Q9asLgn0XkrRyibhVjEIDjW4tFI9vUoJLqBOLEB0orVTPxQfXEBjpUZPrdyBLyk5tpLu/l46spFkc3OVr9dYBz5SX84qKCnUvnrI6c8bpmlbz77zDeshP+UzHRN8qx+FW7AU4DcfrGb9Q3kkxl0zS4WkS9H8jeL2XeGvDWgLcGvDXgrQFvDXhrwFsD3hr4AzXgEq+4AE4Tcdpm5PJ/i9E7VYSxVevE5pG6i/eKR00iCpA/ODz/8x/3C+u/R4Fz1f/mH6lzRAXhMSz2tE39Un0jEOf3Ac7lflPgReDLr0WKu/eteNxctha4fqX+6oPjUeBcBTgBLcQGeACOU6lwLBIDLXHQOrtS4hh1hdwzVZL+oInSzFc0OQZ52z7J2MAyi593WJ8/VL/uH8mv6zIZl0mqABqZQ3raKWRCenW4J9ZfYc7V+aZnAus2sZWJ7+HGF7YWDpgZXuNDzwzdzmEq8l6Rer+OmLB8IvwyCLkhvjcZ6H/IJvRaKdG+tdj0rW5wI/DmCsDRiwrnCsC53Yg1qA5TYBUGfTnm2+U8sNSS8aCFsoxuOmo+MvxmnrXPexytniklgQIPAq88KqSr2yeXPfBG7v9uiMpiaWKXT29neds2zLuOYd53fqK36yOfP86xsbDjAhvyf7IOMYbduWBp7IyBzlWcxSPkJ/fwyNSIwa+M0J9KiLoprT3NCMCJ0XVgc8eJm/2dmPzqMPq6fHDigkWFU0FybAX2rA66nUNMDs2zOr3J7vIBJ9su/xm1XhGqXAVu7mMqt8nxlNaxhbEzeppXKE3v50VSF3cNTZh0AnDKCLtZ5AY4WdiCC3lgKVdpXKqFqnOClSk3wDn2AJxTF8DZ3GFrYYO50UU+9IzRVd9Ho72H/peTLIxuu5RMqtZc3itf6+wrqLkCcL4/FsfnXBxJq94x++t7rMxsMvVphXcdE1QVvuZpYjWmoBwi/bMJuZFJwN/TCf45l8ibZarNSFohXRDHBXKk1UfUImbfKiyaCpVKFR9SwiOLg9J0UeF8ZHxgkcXJLeXxdCZKHGVUfeU1ekCTe7+qevHU1hX4J3UgSpbdxXPmR7b48HqSzvoeWms6GHozxOLnOY53xKT51LWOM5fR9+H6BWszJ0wMbNLi+EjGvUaiNVlE3Mwl+lYJRp9KzH4CcARMyblKkupEhSMKnGbiguqJCazAFlDMnbBi0pPqcGR10lHdz1D3ZxaGNzhc+eJSB4kRt9StbIvneAig82yz+zV9Wbtgd+qcuYEzOsvnyUp8TfSNUsJ+LFIqHGmjitHWqXPkJcCR85uc1/7jAc7lefTqOdWr7vmP+yz27lvvvvXWgLcGvDXgrQFvDXhrwFsD/7E18D3AEYjzpwU4V+HN1cu/BnIkJtz9pf+r580vAY6CN7/XQiUk8Qr8+gpxvgdgvwdwtG4PHPdzxembiVPw5hLgxAS4vHBsgXVYJVpcL8lNZUT7u9qp7psreZZYR97zVqqL3tDZ8IG+7klG+haYHl5neXqPraUTFTf+NVlIJm6e4ZmQesCGB3h4Jq+e6zJJ3XO16GwvSaLVHlMfVvnQM83L+g9KvZGT0khKQiUJUcVEa7MIvSUAJ5uQ6/mEXi8mUlKnNPVYda1Y9e3Y1GhzKXA88EYvLVUt2IKaibndiE3aqIJqMQdWYbldyZ2oah5aa3l2pw67QJzqj3x6Ncfcx002po5Um4tAFVFCyPYKzJCkqK9AxzN5le0WALPnavk5XDtnYWxbmfg22F9jz26mtqST5qoeuhr7+PTuM4uT6wpaSYz2lx1py7rgYPmcqcFD3jQuUJ07RPaDVzwwODH42Qn7uZToWzVYNC0ugKPvwKZrw6ZtweLfiFlTj9HX1UZlC3AQH2onKaqUrEdOtT9fN3/k49sppj8tszq9ze7KEYcbJ6pd7WzflY6ltlG2U8a+a3tONi+YHDylvWaW3OQ3PIlp5U5EA0ZtFeE3pYWqmLBbeYT7eACO2wMnv5veqwDnqwLHBXAON3fYXFjn88cZelr6qS5sJe9pFc6yV/R2jDE7vMHm7BFHa67X8g0g+76+rgIcpbY542jrkL3VbebGFhh8PUZ7XS/lOR2k3q8mLqKAUJ80Qm66a+paLuE3i13tU9q6r3BWgRxtg2o7svjVYhajbD9R4ZRhCygiMaKEjAcNKn2syznE0OspZoZX2Vo85HDzC6e7Z27DanfNeCCfp17ExPnqOLhgf+mcxZEjhntW6GkaU1CrLKuB0qwaXjp7GB+cZHd1h4ONI463zlXtCLzZmD1lfmSfT29WaCgZJDXRSZSvB+CUYhL45OdKfbIKHBGAo28kLqCJ+EDxy6onJkBa8EqICy7iibWC7If1lGe20l7Zy+DLSeY+rrExvcve4iGnG6ec75xztu1um/K0T2272r/ONi44mDtndVhaqE5oLpoiPa6LyGvFhP5QQNT1MgXEvgU4Aq+9AOc/9kPd+6XJu3+9NeCtAW8NeGvAWwPeGvDWwJ+7BlzQ5lKF0/hnVOBcBTa/dvl7iPNPBjgeOdgVkPMLBdM/ADge7xuBSh5F0zcAR99CjN6lwhFT49ggVzKVWV+Nwd9BpG8JBm0xpsBCrCEF3DOXkfaglpLMNurtPXQ2DNHbNcFo/yJzY5usz4l/ybnySxGzYfH+EGWOmvh7fon3TFY9k+0rShPxWdlfkzjyc+bH9vn4dpFXTaMq0jzvWROPbGUY9OlEaNIIvZXK7WsyMgm9KWlHpUT7VWKU5CX/RizaViy6Nqwy9G3YFLxxq288ACewmZigpq8Qx3a7HtvtGmzBFdiCy4gJLuJZfB2laV20Ooboa5tW5rLrn085Xj3nVPxJttzeJKI+kG2U7ZFtU365rm0X/5vthWOWJ3YY7J6irqSb1Ht27luyyXjkoCy7iaaKHno7R5n6uKz8cQ7XLthfOWd36Zz1qXNG3mzTWT2FPb2P9KSX3ItsUAAn/FoZ0bdqle+NKHBi9Z3E6NuJEYClbcKsacDoW6vMak3+5Zj1JViDCnlsqyTnSROV+dIe18/7jjHG+udZGF9XipytxW3213Y5WD/kcP0LAgPUWBOfn3MV7/2xZ4+G0nFS73YqA+PYkDoM/lWE3ypXACfcJ59wn2xsIYU8MNtJvVtDZX6XG+C4lUYCvs5E6uMGOBvis7TOWP8krdXdZCfbuWtMIye5grqSl7xtHVbm0CufdzlavwrQriifZP9fhTdy/UjAoHgo7bI8tcLgq2Gc5V1kJ1dx31qANTiLkFvP8f9bMoE/phFyPZdIn2IMfg7M4nUjQFYvCju3yk4rfiwNWPzqMfsKxBEfojJMmgJsgfk8spST9agOe3YbbdW9DHRPMDuyxsbcHnurh5zsiBLInbh1FdbIZQ8o23Gpbo5XL5j/dEhf+wKNpUOUpb8k63EtyfEFPEsqoKa4lYFXI6zNSrrZHlsLR+wsfmFj5pT50X0m+rfo61igOr+PZ3H1RN7KIuKGGDOXKWNmiewWeKMAjgBfOd8ESGKdmJ03EKOvxKorwRZYSFJEMY8tZaQmllOW3kKr4x397WNM9M6xMLzK5vQ2+4sH7C8esb9wwv78KYcL5xwvnXO6fM7h/Dnro2fM9p3ysX2X+twxnlvbCf+pkJC/5RN1TQBO5RUFjiRR/ScBHKVcvGIC7zkfe5d/QKbr/QL45/4C6D2+3uPrrQFvDXhrwFsD3hr4V6yBS3gjMOdPB3CuAptWYrSecfX2fwBwlPJFJkOXCpyvyps/0kL1/STht9rQfhXg/Ir0/+v/uxU4elFsCMBpUT44sTJZE0VKYCMWfR1G/2qi/BxEaUqJ0hQQ5Z+DNSSf+5YyXtyvIv+FE0deB07HGzqdg7zvHOPT+xmmh1dYmNhQ0cjLU5I0takmlhvz22wu7rC1KGqdA5VQtSkpQ/PHrM4eqwjnmdE9JgY3VTT4m7Zpmis+UJ7zmsxHTTywOjAH5RDwU7IrLvynVIJ+zlAT7QiZaGsqMGvrMGudmPybMfuLD0wbFm2bG+S0ulQ5uhasehnNWAOasEkSl4wgJ7agBqyixAmowKgrJVqTz92oCtKTmilNfU1jyRBvGqcY6Vlj/uM+SyP7rIwfsP75gM2ZQ3bmjtlbPGF/6ZjdhQO25nZZn95k9tMSI++m6G0fUUqSrCdVxEemY9A/4Z4lh4xHFZTnttJS9Y73HSOM988r5dHnwXUm+jb51LNBV+0UVTmDZN3v4omlmYSQWgy+5UTcKMfgU+cGOC8VwInVtxMr0ErnVuH41WP0q8aocWD0L8WkLeJOZDliUp31uImyzE4ayt7Q5Ryk7+UIH96MMNo3yuePY0x9/MzUxzmmPy4z9WGDqQ/bfB7aZ6z3gM7aWUrSenlocRIfVoM1sBqDpopInwoF1CJ8CojwdQGc+2Y7L+5WU5EnAGeclakrAEd54FwqcATgjPZN4Czv4PndAsy3H/I4Npu8Z5XUFrfT5eznQ88kcyOrrM/ssTl/oBKr9lZOkbG7fKLGztIx24uHqgVvbXaH2ZFlRvs+09/9kUZHF3nPqrlnzsEY8IIwn2cE/PgUzV+eEvhjJmE3CjD4lWPyr8IinjeqNbKZGJ0MF8ix+Tdi1Tix+gnIERVOOSZNERZ9PomRxSTH2Ml4UEV5diutVe8UoBvpnebzx3nmJ5ZYnVllbXad9blNNua22ZzbYXN2l83ZPdan91ge32N+eI+pgR3eNs9QVzhA9qN2nsbV8MBcQkJkFvfM2RSm1tFW85aR91OM9s4y2jvPaO8Sn3oW6eucpafpM60VwxQ+6+KhqYrwGwJw8jH42LFoal2tSartVVpf3QBHlHrKK8tJTEA1Nr2dmMBiEkKKSIos5IGxgIx7ldjTm2ksfU13bT99rZ8Yfj3O595pJntnmXw/x+S7BaberzDTt87cwDZT77cZfrlGr3OJzvJpSpJ7eRTlJOzHAkJ+uApwaomVaHP9fx7A+dqC6lZV/it+SHtfs/fLpbcGvDXgrQFvDXhrwFsD3hrw1oCnBgTaXB1/Mg+cq6DGA288y6v3uSBOrFZiyKW3zFMg7mQpD6z5teU3MeLyeM///sryK4D5zkfoCsC5+v9KdSPP9/X+JmJ14pMiwwVvvgIcBXFaiA10wRyrvhGTth6Dfy0G/wqi/UuJ8i/AHFRAfGQx962lpCSWk/awipyndRRlOKkoaMPp6Kaj/h3dzX28bu3nTXs/7zoH6O0aoL97kIHXMj4w8HqYgdej9HWP8bZzlFeto7xsHKGleoh6ex8VBW8pSusi41ELyfF1JBkc2IKLidLkEvRzulLdhNzIIUwpb0qI9nNg0tZi0TtV8pQAHKNfKyaNa7hgTgtmaSvSNruGThKqBFbJkKhxMXKux6yvxaStwuhfjkFTQnxIhYrsTr3TSkFyN1XZvbTYP/GqboI3znHeNY/R2zpGf8cYQy/H+PhqnI+vRhns+khfxwDvWt/RXtNFbVELJRl1pN0rJdGQSaT/Y0Ju3ccWmsqjmEIyH1dRnO6kqqCdJsdrWirf0ezopdE+QF1RP8UvXpGe1MoDQy0JIVUqVUoATtStSoy+9QrgSApVjL6TWH0HAnFiRGkk2+vvxKypw+QvXjguiBMTXE5SVCWPbDW8SKojJ8VJaWYTVYUt1JU20VjhpK22gbaaZtpqOmirfkVr9TtaKvtpdnygofQTRS/e8DS+mZgQByZ9OUZJMvOvJtqviig/O5F+hUT45RATUoQAnOdJVVTkvuR9x28DnKPNXbYWN5UCRwDO08Q8ovV3SYh6RnJCjlLk2LPrcZZ30t3YS9/Ljwx0j/ChZ1y1oH18O8mHN5MM9Uww8Hqcvq4R5THU0zJIe20P9WXtOHLryXpi56EtD8vtF0T4PiXkxnMCf0xF/0MGIdfyiLxV6oY3tVi09VhFcaNtVsMFca7AW/9mFXttFdjjX4ZVV0R8SBF3o4t4bCsm40Glim6vLeqkueIV7XU9dDe/5U37e9519NHb2U9f1yD9L4fo63SNd22DvHIO0lE9SJN9gNL0blKTmkiMrCQ2pISYkAJswdnciczhxd1ySjIaaSx/hdP+GmdZDw2lb6gt6qEy9zX2zC4Kngr4qSM+pJSwawJwCjD6OpQps6jy5DzhURfFfpNW10hcUB1xQZXE3bYjHj93wgpJjMjjiaWYtEQHeU9qKEttoCq7GWdRGy32TlpKX9Jc0kVTcTctpT202XvpdAzQVjZIQ14flenvKH7ymufWZhKCKgn7MZ+wvxcSrVqoKrH5XwU4jcSJAur/sAeOF+D8yufQP/qM8t73jz/DvfvHu3+8NeCtAW8NeGvAWwPeGvhProGr8OZfUIEjX06vGhkLgLkyKVOXr4IagTdXr18+9hLeXAU48vyeePA/svydL8t/FOBcabe6Cm9cBsgegPNbEEcSqZqx6gVuODHrGjBpqzFqHUT7l2DUF2G5XUhMaAFxkfkkROeTaMznrjmfJ/HFpN4vJ+dptVIEFGfUU5rVgD1HlDqNVBY0Ul0kgKCZysJWKgvaKM9tozijhbznjWQ+aeDZ3WoexThINJQQE1aIKTCPKP9swm5lEXozy+11k03YzTwifIqI8ivF6O/ArK3Gqq/HFiCvuwmTphmDr2sY/QTmNGHUyO2NKp1JEppEqWP2l+1rwORfj8lfAEctRv8ajJoqpVYRk2CLvpy42xUkhlfxyFjHi/gmch62Ufz0JaUvOihLbac8o42KrDaqctqpye+gJq+NypwmyjPrKE6tJONBMY9s2cSGPcege0LIzYdof7iL/9/uEe73lJjQLB5YingSW8KzO6Wk3isj7V45L5IqeX6nmpTYGmU+HHu7FINfEdG+xUT7lBHt48AgBsV+0v7SogyMYxS86SAuoJ3YgDalsLLpmpW5uNlf2ssqMWjsyqjaEliGLbiU+PASkgzFPLQWqbac50l5pN3PIfNxFpmPc8l4VETGQzvp9ytUG9TzxHqexju5a6jGFmQnwqeQiFslRPk6VAub0V9aqSqI0hQT6ZfnAjgmO88Sq3DkdiqAs/pVgSPtTi7X24sj8W/ZZ3tph/GBaZzlL0lJKCDM7y7GwMfEhD0jyZhOcnw+mY/LKUqrw5HboqBXbclLGspeUVfaTU3JS6qLOqnIb1M+Q8UZDeQ9ryb9YRnJ8XncNaW5j0UKoTcfE/RzMkE/vSDopwxu/+TyvYn2dWDR1WLVCbyR/duI1b/pK8CJ1bkgaJy+BRniGxOjq8Gmc2DVlarUptjb+cSF5nDPUEBybAmpd8vJelxB7tMKClIrKc6ooiyrhvKceipynWo4chooF2+b9DryUmrJuF/N07hK5Vtk0ucR/HMGwdcyiPDNxKjPIiYkh7vGApLjSki7LzVTzrM7dlLi7DyJsfPIaueh2c49g53Y26JUyyP0pywibxRh9qtUCpeEwBYV1S1wVyCOLMUnSxQ4rijxBhKCBRxWEh9cprxwYm/nkhCaS2J4LnejcnlozCPZms+L+ALS7hSRGl/Ei7ginscUkRpXRsadSrKSasm8U8sLWw1PjFXcD68gLsCOyadYKXDCfyoi+oZdtVAJwInR1RGnbyBeTJUVwJH0qXpsYrjs36CAzlVo/c+67PEx8wKc3/lM+k/+AvLPOt7e5/EeZ28NeGvAWwPeGvDWgLcG/qvUwJ8M4Hhgzu+BnEtwE6NUN67HX8Kgq2+APwJufkd54/mS7AE4njQq9/IS0rjX+xuPE4Djeqz82u4ebjWO8sGRyZu+GZu+6esQJY60U5l0VRi1dgxa8cQpxKDLJVqXRZS/+NGkEu73DIM+FVtIJneicrhryueBtUApS57EFZGSUMSzxGKeJ8ko4VliKSl3yngcV8o9cxHxkXlYgrOJ1KYRfPMZur8/RvM/H+H3l8f4/vcn+P2PFHR/S+P2TwJvClzeJAIhtBVIcpY1oA5boJOYwGasAnD8mzD4NrqHE4OvE6OfE6NG2qsE3MiyAZOmXg2jpk6BB4NfLQY/Mfytwih+On4OjH52jH5lGDUlWHSlSoWQFO7ggaGCR0YHj012ki1lpNjKeBZr50W8nefxZaTEFPLYkst9QybWoKeE3byP5i9x3Pq3OG7+f3e4+f8m4fPfHxD4cwqRmjRswTlYb8tj07AEPccc8ByTPhWjNsOdGpRJyM+ZBP2QSchP+URcL8XgI74r4n8j6W8t2HSiupH2qXYFcL6HOBYFq2pUu1m0n50o3xIifEUlk0uUJpNobRpG/TPMgcnYgh8RG3afmNDHxISkYAt+gSUoHVNAFgZtDlGafMJv5Sm1SuDfcwn+uZgIMZD2r8escyoVk0G13+UTG1LMfQE4dypx5HTS2zHO6vcpVBfSQnXMyfYhu8v7TAzO47S/Ijm+SCmVwnwfEqF5RJT2MZbg5yREZaoaS04o5VlSOan3K8l4VE3agype3KtQt8l9D2yFJBlziYvIwBz0lAjNA4Ku3yHwpyT0f7+H7m8P0f+QQpD43vycQ9iNIqJ87Jg0VdgECuoaXMl6CuCICkd8rlwAVMBNfEArCQGtLp+WgHpi9PJ/5Vh0xZi1eRj9MzHrM7HdziQuNIuEiCwSozO5a8zkvjmTh9YcnsTmkRJfSEpcAU9i83lsy+OBKYeE8CwsAelE+r7g9k/P0Pwlmev/z2N8/y2FoJ9SidJkYwnMwxqUrZ4/JjgdS2AqRt0LovyeEen7nEjfVKJ804nyzSTiZhZh17IJ/TGHqBslmP2q1Ou+E9SKQByPEscFcFwtVJJElRDcyJ2QBu6E1hB/WyLFi7Ho8zBpszD4pRHl85Ro3xSMmhTM2hQsumTM/skY/ZIx+CRj8nuG1T+NGAFOuhzMftkYbmURKUqgn3MJ/ymf8B8LibxWguGmxIhXYfOvUT44HoATexXgCMTxAhzvL1iez0bv0lsL3hrw1oC3Brw14K0Bbw14a+AP1cCfFOAIyPFAmW9hjUzcvoU28jgP+PEsrwIcufx7EOf7x//G9a8AxgNivlt6ivb3HueBN+7l13YQ5e0hAMcFcUSF41LiyIS8BpO2EpPWgVFXhlFXglFXhEGbT7S/TP6zMOpysAblERtaQHx4IQkRhSRGFZIUXUiSoVCpBO4ZC7hnlMtF3DUUkxhVTFxYIdbgfEwBeURqsgm9mUHQz2nKSDbwx3QC/55B0I/ZhFzLJ/xmEdEKpkjLVCUWnShvat3qGye2gEasOlHZiNrGMxqVqbFLdSPmxo2u1imtEwEaZhkCHUSFo4BOHSYxQtbUYNJUY9JUYtJIspWY2ZZj0zuIC3JwJ8RBoiQ6hZVxN7yEexElPIgq5aGhjAeGUu5FFZEUkc+dsBws+gwibj0n8O9P0P8tBf0Pz9H/INuYoZRF0ZpcLAH5mANyMOkzMepkEp6GUZuJ0T9HqSaifPKJuFlI+LUiFWst8MasqVXqEIEK4ncTo2tzDb0ob2S0KjNjMTR2qXBk34jaSJRG4ldTSbSmXLXIGfwLMWpzMeqyMOvTsAQ+xxqUgjXoOZbANMwBmZh0ORi1+Rg0hUT5lRDhU0L4jRIV3R5+w0G0pDGJgbROWvDECLuMaE0BcaElPDCLOqSKytyX3wEcUeC48uMvjk843T5mb+WIyaElGsvfkpJgJ9QnmXC/FCI0T4n0f44xIB1bSDYJkQUkGYu5aypV3kwPrGIMXcY9cyl3TSUkGgQO5hMblovldpaCjBF+0i71mODrKQRfe07wtVRCrmURdiOPiFvFRPmUK3gnaiWrzpXKZNU6ryhw3ABHvX9c6pt4pcIR5UoDsfoat+mv3Q1x8hXssAbmEROUS2xwDrEh2QrmxIdncScih6SoXO4a8rkbnUdSVB6JkbnEh+VgC8rGqM0i0ieTkOsZ6r2g/Us6gT9kKRWaUVuINbAYa2ABlkCpoSxMuiwFjaL9MhS0iRZY4peLwS+PaJ98om4WEnm9CMOtciyaGpV0Fx/QjJiji/rGJq1iyu+nSZk2uyLFG4i/XU/87VpigyqJCbRjDSjGrMvHqMkh2jcDg286Rr80TJpUzJpUjH6pGHxeEH3zBUafdMx+WVj9c7H652P2y8dwK5+oG/lEXS8k6noxUddKMdwQNU4FVo3EiEsLlShwXD444h8WIzHnWoE39e7LfxB+e86Nf3DpVeD8xmfQH9x//1V+wfJup7dOvDXgrQFvDXhrwFsD3hr4V6uBPwHAkaLzgJffWl7CnEuw81uP/eNF7Nl5/66D/ptw5rv1/ubjvoM+X30v3JM3mcBdGaJmkfQdi65etZOYddWYdVVY9BWY9Q7VhmPSShtTMSZRHOhlMukZRVglvSqwAGtgPjKBFcCjloH5WGTSGVCISS/goIBo/wIifQuIuJWvJtNhN/IJv1FIxE2JBi8l2rcco6ZCQRuLrkaBG5u+zqWS0Ddg1TvVaxWAY9E2XQ7ldSPbIUCqEZsMiU3Xu2CPbJ+aoCug48QiyUJqiK+MgJ1azF9hThVm/0osWgdWbTk2XRk2XQkxuiJi9UXEBRYTH1RCfFAxcUFFxAYWEhNQgMk/jyifXMKuZxOpDwlvAAAgAElEQVR2Pc+1XbdKiJQ2KL9S5bVj0hZj9M/HoIBNJgZNJkZNLkZNAUZNMUZpF/MrV5HWJr9qLP5irOtORhIPI4E1Am0UrBHvmxb3EHgjowWraqWSfeOGV7p6TLo6BVsEzsmxNGkLMelyFQgw69Mx6TIwarMx+udh0BQQ7VdMtG8ZUT4OonwqlAdP5K0qon0FesnzigqqGbNWIJH4CBUSF1rKQ4tDtV9V5XXR1znB2vSeiiSXtK6LswvgjIvjL3zZOWV/5YSpoTWaHH08u1NFuJ+ovNKI0KQTqckgWpeNMSBXeTJJS58Ma3AR1uBCLDJui/FxAaYgFxg06nPdqqEsInwzCLuVRvitTCJu5RDpU0CUr6RN2VV9CbSTY+7yvWlQpr7ifyMtVDbVQnUV4LiUbHECc/QegFNHjBviCOyz6sQTp0T54lh0hVh0+Vh0uZhFiaLPxhKQjTVQYE2uGnJZbpP7BN5F+0kalrR15RN2vYCQa0Uq2lxAprT32QLLsQaUYNbJ+0gUP3Kc8hSwMfjlY/QrVMbKJk0JJqkhXztGn3LVPmV1GxjLOUighbQMWb8f2gb1HosJqCMmoJaYwCpsgRVqvaJIM/kXYRQg45uL0TcHo282Rt8spbCJvplF1PVMom/mYPTJw+xXiNmvCJNPEcZbxRgkov1mKcabdow3yxW8uVTfuABOrAI4DbgM4AXceIanhcoDyL87B/5vwAYvwPnn7ct/1+fb/8Yx867He8y8NeCtAW8NeGvAWwPeGvDWwO/XgIdBeJb/YibG32/gb0GZf8/t3z/nb1/37LT/jEKTmGBPks73y18CHAEdAkcaXENf71K86Ouw6msUUDFrqzBrK1wGuRo7Bj8BLgJe/n/23oNLjiM7E+VfWmnfk5vhOI13HHrCe3R3+ap2aFfVXbYBEIYgQQcChCO8a+9hCHJm9fQ0I42ko3d25KWVVnZ2JI0l+b1zb+TNjMry7bv69jnfiaw0kZE3vnBf34ggAYaEmEvooUWHWcC4gEMHLzrnLqOnhUSbqzwtqq/tBvrbbqK/7RaSvJ7KCIbCYywekViTi08jn5gpQi4+gyyLN9NGmOC0zvIuU+xNFDfbpOcScyCY7dJNmIuTl4Ej6kSN54H5fjmeZXGHBJ0h8s6h7bjbRpFsuYuBg7cwcPAGBg5ex8DBKxg4eBXJlqtItl5DigaobdeRClzHYPAGUoGbSAZuO99EtiLbzSITn8VQlISOEfYsGgjQ9J330N92EQNtF5EMXEEqeAODoTtIh0eQiUzwlB5Kcz4+j+F2WqPkPgqJ+yjE7yMfJ4+bBZ5OlQ7T4sXzvDZONkKCjgg595GlxY0JcQPP08pb5DgVusqeGwMB8ty4iv7Wa+hvvYn+VrNwcn/rOJJttD05eS7RtDQShmgal7P7VYTWFLqLZOgahg/dwquZEZw9PoGx9z7EDx7/eXkB5xcf4aOffIT/+qeP8Jd//G+Yv/dD0Ho7vaHz6A1dRG/oEnpDl9EbfI/RE7gCQjdN5Wq5hM6DF9F58AI6Wy6gi9B6Ed2tF3Go9RJ62mjdJMJ76Gt7j4WoVPgW85Y9uaITjhg4w8KYiHq8eLGIN2ESbzwUib+89tSM44UzhXxsgr1x8jSlKnoP6fBt5kIycNXxhLmEvpYLBiQkMS6gt5VwHr0tFF5iQbOPbN92kxfrpvWZyLuJyl4uQaDyQWsPUZm7imTgGpIBEvyMYDMUus2eY+nwXcOh8CgytC16eMJZT2YamZBBOjiNIdoW3UaIhMxJpCPkkUQ2Gkc2bt6bid1DOkI7b91CKnCDeZ9svYqBFioP5F3zHnr3X0b/AfpNos0NpFpvItV2CymaptZGos1dXvOGhBvyvDFTpxzxJjqJfMx44ZCQQ7tSEWRnv1Lvxsr1bSP1rAo4y2PHRmyu96rNlQPKAeWAckA5oBxQDqw8B0SDkHCDCzjlDFZLvCn3TO1zYjAJ15KsZrDiDUp5Og5NyYnMIcNTc+YdrxwKi0HeLjTA5alI5K1COx2FJnjL6mRgFAPsoXEX/S13eNpP38GbHPa33EZ/K+GOsxDvPWfNGVp7hhblHefpQeQBwwvIRkmkmWWxJR+fg4CEDBZg2IuGBBwSmZx7SZxxMYtcfJa3SKdt0hn02y/eON8tuw2JF0uGxJDQPIaCcxgMTCPVOoFUyxhSLaNItYxgsOUuBlvvYYimNQUIo0iTlwyDtpee4EExLTY8FJrlHaMy0QcgDEVoqhdt8z3KAk8yQOKVDMJvYZC3pqbdjZzpUuQ1RWsX8SKz9zGceGAJOGZbePa2IU8Y9sIh8YZEFcJDB4+QjRHeRzb+mI8z0fs8vWyIFnMOjfH6P5yWtttItt3FQNsIkm0k2kwiRWsKBeYwFKJtyil+E28u+oh3waJ3kQfLYMgIOIe7b7OAc+7EJMavfA9/+Pgv8C9//Z/4+L8+wSe/IA8cAPgYn/ziY/zq/3yM//zfH+MvfvjvmLvzJ3jjyCx6ApfQG7yC3uA19IVuoC90C30h2uXqLnoDd3Co7Ta6W2+i6+B1dLfcwKHWG+gJ3GT0Bm6iN3CLvZ36A7fZk4u2B0+FRByc5IWKSQRkjokHGnPBrHnDnjck3lgCDok3nphjPFjyLOLQzm8i5NCUKvIYmUCGhMjgCAZJmGy7g2TrLQy03MQAeZe13sBAm4CmcRFusKfTAAl/tBYTCTfhcQxFyBtuCtnYNHKJaeTiUyysDIVHeVcpWrdpMHgPQ/R9oRGkQyTYjDFoYeAseW4xzDoy5HXDnjci4oSmkXbAQk7IlGviBQk5megksrTWT4I8c0jYoamGoxgM3EOK0HYXqdY7DFrPhpBqoXP3MEjlwyojVE5oHR4qJ5nQuCve5KKWgOOIODSdSgQc443jeOWUTFGtXf/Wqm9VwFm6DWvZWK+rjZUDygHlgHJAOaAcUA6sPgdEf5CwCQWclTGqGEzCtSavO2BxBQwj4NhCBk3NcddYESEg8gCZyH1kwoQFFjlE6BgKzmIoOMM7JJHoMUieGoFJDMp/951BIk/ZoO2BeZtmIwjRdBWGiEi0fbOzRo+3Zo9M+3K8ZCJm6pMIOOxpw143RrzJkqcOYxrZOA0+zfomNA0pK+93PS1orSPadeyBET8iD5EJG6RD95EOziMdmEU6MINMcAbZ4DRyoRnkQrPIh+eQD88jx1hALryALNmHngs9wFDoIYZCjzAUet/BA941i8QdEr+GQqMYovV2QjQQp0WKRx1BawppmqJEW1bTbmhR43FDnjfG+2aBRR13a3jOL8mzh8hxnj1CNvq+gw+QjX2IXOy7HGai7yMdofTNY5AWfA5MItk6xqJNqo3ybgaDJNoEKZ/JFu8jF3mMfOwDFOIfOqDjD3ghZRLTaO0g2jWLBJzXMqN49+QUJq5+Dz/88C/xb3/zX/j4p0bAgQg4P/8Yv/zxx/iPf/gYf/6H/46ZW3+K1wuz6G59Dz1ttKPVbQzwtvZjGIhMIBmZwkB4Gn3BSfQGxnGodRS9gTH0h8aRjEwahCeQpN8h2l1sArRgNe04RmsB0VQyEiE9rzPahcnA4769DpYn2ngCjhFvpAx5i4rPwez8RiKwMz2JRJLgFNLBSQwFJjBEawYFxjHIO4mRcElrLjkI01QuWlNogj22SBCjneE4vc5C4yJQEoeNl9gk0iGDTGgKGRJAecFfs24MLfwroC25DbzpU94UKkvEIc+zIIGEPbIZlR3ygqPFzp2peGRTuh6YxFCbg8Ak0rS9PX+vP5xkG6RpAe7gBGgqF+86FZnghYuNWEOCTTmQF45MqZous8bY0utsyUuyR47gTDFb63pa37/0vFUbqg2VA8oB5YByQDmgHNjMHBD9QUIVcOqcwy8Go3A9EEgGKCyiuCKG44FDCzfzArnegrgk5LBXB03LccQbEnDIU8WDiEBOyOvtmKlbZr0Qa6ti2q6YpgElRISQgbTlCVQkLpFnhIFZYNZbu4bXhYnPodC+gEL7PA80yfvGeFg4wg3vLkQ7DE0hS4ui0qDUHdySRwK91xNxcpFHyEVIsHiEbPghfzMJM7QmUiEyj+HoHA7H5nE4voAjvCvRA2dnovsoxO4jT2vQOEIXCUCDwQdIBQwGgwsYCs3xTlLs4RAZRyYyigwt0kxiDg3qaZBPg37Xg8dJH29f7dgsbkLe/plFnQe8jXg+9gAEEnBcEYe+I/o+crHHyMVIcHmMbPQRMpEHLBCR8CYCTqptgsW3dIhENRKPiAePUIg9xnDiAxxu/xBHOr6LIx0f4nD7BxhOkKhDwh554NzhaT2Hu2/hdHYU509OYera/8Aff/hX+Pe//Sk++dkn+OSX7HzjrIHzEX7577/CT/7Xr/Cj7/8rpm/+EK/lptBF0+9oml2QdkIbRSo6gcHoNIZi8xiMzSMVmcNAaAb9wSmkaHt4EjjaF5Btn0c6PochWtiaztPaP+Q1xjDrItniTZGHmbNguSxcbkJbwBHhRgb5niBiPHHMelO0ODCLOiwCGG8Xj2tmW2xXaCEusnfNFHvW0JRBmiLI3mPkMSYeZbTgsA0WoWgtJ1qzx6zbY0RJep/NbTo2aRDxRgQnnlbplCkRcsgTh7xwiHspmj4YJC8cKkOzyCdo1zoST8mrjLgposwUSDyi99I7zPdTPWfsw+khcSk0ycINheQRxGvbOFOk2NPGnTIlIo5MobLEG/J0KvLAWZ761K0PVcBZF+3TemgjNQ3LU7bUjmpH5YByQDmgHFAOrC0HbB2CjlXAWSeCTKMFwx2wsKcAeQsYkBcMb5vMXgnG68N44jhrqshCuZZ4k+VBPg30jQjDW5bzduUk2BjRZtgRbA7T9sWCjgd8zEIOiRG0rTlN2SLhhgQVJ00cykCTdweShVenkaHBJU1jic/x+jCegEPTY8TrhgbJzlSQCE3REi+FSeOtwINP8sqxBByaGhR9bBB5hHzkAfKR+yhEF3A4RqLNPI7EF3A0cR9H2x8wjiQe4HDiAW8zTSIOLy4cIS+c++zFkgosgDBI3jwkjrAXEKVtArnoGLK0wxZ5YdCiujSljAUcWp+ExCq6nzyQzNbvZkoVTasSUcyZWhU34o0RcMwOVTyViqdUkaBjpjzlaDoVeeiwyDSPIdpuPTCJVBt5h9DAnGxLXKA8IVHqIYbj7+Nw4jGOtD/G0Y4PcKSDxJz3MZx4xGvx0Ho9gyGasvQeDnfdxGuZEUvA+Uv82BJwzCLGv8InP/slfvlvv8BP/v4X+J9/8M+YvvGHeC07ia4D59HTegX9tANZZJQXXiZRJh27j3TsAQaj95EKzyMZIrFmAZnEA+Q7HzFy7Q+QiS8gTQs4O6ApZiTWyO+M4+HFAo54e7kCjhFtvPVuvErX82KRHZKMSMHlj6dTGS8cEUnovJQ1Cvk3r0c1Y9Z1Ic8WQlzWeSKhhGBNGywn4jjiKImadvxyLKKNHfL7fWmsKuKw91w5AWcGafLMYVHGL97M8jblwySokujjbAUuO0nJVK5i8cYWaES0kZCuzRTDFXC8fGm0/vPfb9tNPXCWz65+O+tvta1yQDmgHFAOKAeUA8qB1eVAkYATVQFnw/63UgYsFHpeOI6Qw2t9iEcMiTgLyNEUHgc0qC8H9lhwpqN4Ig7t1ENbFltiTpy8bwyM9wjt6GN29+EFlit43ogHjgkdEYf+yx+dZaGIFvgtJEjUIG8FM0Bm7wbycKB1PCIW3HVBZI0QGpDTwJ2+8wF73tB0IYNiAWc4Oo/DMfHAMV445IlDGHa+lb7fiFHkpURTkBZYxDHTkeYccYREIxFwxlnAyZI3Tngc6dCEM0imaWJmS2sWtpz1cEjA8UScBZ5S5U6tcjyARHgTIYmEHJ4SFyMhh9bHIVGOdq6i9NCgnLwvSLyhQTl5bjhiXoTyhsSrByjEHmCYEC8G5V+G1kYJ3sJA22UMd97Aq+m7OHd8AhNXvosffvAX+HdaA8eZQvXJrz4CPv4FPv7pz/HLf/05fvJ3P8eP/uCfMHP9Bzidm0DXgXd5F6a+Ntqxa8RMhQrT9vD3MRh+iMHwA6TCJOLcxxCtLRR/hFz7+4xs4iEy5BEUve8hYhZ6pjWCDHwcpvxiAafY46a4gRGPGxFvjMcJiTreWjgkWtgeOCKweKKQW/ZI3OBdxWhnsUow6zaZtZ8sLxwWcBxPt3Jl2BE/c2F5vwklbTTVS44lPezB4+xIxVzgtXDMmlS8zXicplARJ4iPJPCJEGq82mihYRJbSLgpkCjFwovYyuwk5XreRGyBxhZw5Jiuy7HE5YQs4Cxvw+faQD1wNmybVlxWl5cfGrfaUzmgHFAOKAeUA8qBjcoBFXA2qMeNn3AyYLFD1+PFXazVEXFczwQZ5PpDb9DLIogzuCQxRgaJHPK2yyToGFGHRRsWbsx97u5Y7vsp3uIBqP1bhByKjzx9SBQqK+DwlCkSbyaKETaLqJq1OGiASe8SLxyaQmULOA+NB05kwZlCNYvhqGAOw5SGKIEG8GYQLwvestjl7A5FYo6ZrkXvogE87bAziVx0nEHeOCadk8ZTiBZplgG6s3YL28mZTsM2FC8cWRsntoA8T4Gjb/GENz5218khYU5EDBICnDVbeGDuTbshTpBdxBtFvknymYSqAr2PptaEae0eWpj3EoY7ruHU4B2cfXkM45c/wB+9/yP8G20jTosY/5ymUf0K+Ohn+Pi/foZf/svP8JO/+yl+9Af/GzPXv4/Xc+PoJgHn4GX00WK/gXtIBiaQDMwgGVxAKvgQqdBDDBLCD5GmqWC0OHPiMXKJx8jG33cWaX6ITPQhr/NDCy8biIBjQleIZAHH5rHdSHnTdowHjogSloBTJOKYNXCYT2S/ooWQKU/kPTS9kCBio0yH8oeO0OPsJGfEQROHLLZc4rHGHmylizBTOTQ89cpmuTJlRBojkrKwQ9wj8caZumWmbZl05iKOPVzBRYQXEWlkG3ArdHaW8kQaecYf2uKN8ejx12XL8VtsQB5L6oFjc1+Pl4NfGofySDmgHFAOKAeUA8qBteKACjhNIuDYBJLBC4UyXckbZMpgk0J78GmfL3dcWXix31d67I+rRjwkltgCTpy8eaxBsSve2ALOOMjTJRsZQ44xbtbj4DU7LAEn/Bg5QuR95CNGwDFr4MyhQGt9WDDrctACq84AlOOStIvtKG6xIaVx2ifg0FQqAgk6ZutkXnCZB80yeDZhiYjDHk4kpJB443gzucKb36b+35JOCR1PExnMuuE0suSZQ1NngpMckqDBU2Vi5JFBCzBfx0DrRQx3XMWp1C28c2QUYxcf448e/U/821/+BB//5yf45Ge0+9Qv8MlHP8XH//lf+OW//JQFnD//g3/E7I3v443cGHoOnEMvCTgt19HfehcDreMYaJ3BQOt9JNseIRV4hEFaFDr8GOnIB8hEzQLN2fiHyMbomNb4eYxM5BHSYVpE+gFPZSMRRxbgNjt2OSKWCDgkPjqioSkjnnhTUWxwp/TQvaZxkjhIWDECjuS9hJIHxqNF1mQSrxZvQWJnPRvH08wIop6wytMXaQpjaA4ZB3Qs53lhbYcHhhfVBRxJtxGWijlnxBs6J4IS8dfA2MYTbMzuUbKWDYX+a36hptzixJbtV7jele9WAUc7V1KGNVQuKAeUA8oB5YByQDnQDBxQAWeFBxJrQRIZvJQLxfOierg2hZvTK2t6yBQt9u4x/6nnwWVE/utPi6bSjjcEEmxIvBm1QOdpEEkDa9qN6hFy4Q8Y+bAn4NACxoWICDjTKEQMWLhh8UYGqp6QQ3lqbOsN2o33jRFwzM47It4UCzj0DUbEmXFCb1BdTsSRqWg80I9YniBuGiQtpaFwz6RVRJxp5MOEKUYuPIUcLUIbmkAubAbmPF2GFokO38NQ8DpSgYs43HkNrw7exrmjY5i4/AF++P6P8OO//g93ChU+om2ofoFPfv4LfPTjn+On//Bz/NUP/gkLt36Atwrj6Gs5h/6Wy+gnD5zWe0i20g5Zc0i2PkSq7TEGAx9gKPQh0uHvIhP5LjLR7yEbcxD9LrLRD5GNfoBM5DHvnpUOPXJ2FXuATNjspCZbxrMXjivgOHYhXokwU8WzxBV15F5XwJG8JgFHRBvyhiJuUTjPC2IboZTeKYsdFy9C7Hq3uB4+5DljBLq87V3Fnl3zLNzQYtsurHeR+GgEIM/7hr3FfHWaWw+wx4/Ht8oCjnCeQlu0oWPaHtzeIlzutQQc13Yi2Kx+fSLfrALO6tte6h0N1fbKAeWAckA5oBxQDigHlp8DKuD4BjvNQDIZvJQLN8T3OVO1eJ0dPrYFHJreQSKOJeDwdKUx5KKjFvwCzkPjfRN+DFvAKfbA8Qs4MjiV0BZxPO8MFgZcUUAGvWag63nfGA8cI+CUF3GMl4TZWl2mn7kD9BKeyuC4UuivLOg+x6MoMoUCYxJ5Em1oByHeRUgEHPpO8nC6i3ToOgaDl3Gk6zpOD93Fuy9PYPK97+FPHv8l/g9tI067UP0K7h8tZvzxf3yMX/7TR/jbH/4rHt75Id45PIWB1vMYaL2CZOtNJNtGkGyjBZbnkGp7hMG2DzAU/BDp0HeRYQHne0bAIRGH4Qg4kQ+RIe+csBFxMmHadYsWbqYd1axFuWkqGXvekC1F2HLs5OaTCA6StxKWz+NiDzYScGQqmyfgGG8sukZij/deM01L1tuRKURmWp6Z/kRCjJkmRwtrEzxhSN4joRGPWLxhQa+6eCPl3U1PGRHHTPky076In8XeNdUEHJ/N6Nki8cbPwdX7Ld+rAs7q2Vy4pqHaXDmgHFAOKAeUA8oB5cDKcUAFnJKB8coZezWJLAMYCVfz3cvzLhmYOvnhbDfMXjjuIFMGlySWjDsYQz5KoGs0oKSBNA1+aSFj2kZcvG/MLlSegDPrTqHyhA5H8OD3+Qf2JAgIV/zigAxsSbQReNNTij1wyBPHeEXINJdyofcueSeFznvLhvZ99r0iUomIM4U8LUBrLUJb4O8lAYe2Qb+JoeAVHO2+hdfTYzh/fAbTV34ff/rB3+Inf/NLfPJz2ULc1XDYK+ejf/0E/+tP/wPvj/wZzh2dQzJwGcnANSTbbrGAQ1ubp9pmMRh4gKHg+0iHHiMdJg8bEmnI20a8boznTTbyAbKRx8hG3nfwCFnaSr1IwKEFnEm8cUQUa/oUiSjFQpsIOH7BonI+eyKOEVE8EadYVCkWcDwhp3hhZEfAoemCzoLfrogjYo4dutPnjKdPsQedP69Lf0s9wKEl4njijS3g+G3i8dmUK/u3ZUexMYf+NJD3WC34n1n8b/leFXAWb8PydY7Gp3ZRDigHlAPKAeWAckA5sJYcUAHHHYQrEdeSiN67/YM8O1/8g3AaSJYTcUjMsQUcGmCTV8NDhmwhTlNWPE8GEnDoXbYoslgBxxvUiuBkQllrxA5l4Vtf6HpwGAHAs49tD3967bTb93nnZYqYCUXEcaaNuUIVeYmQl9MoMuHbSIev42j3HZweGse7R2cxdv738Puzf46//+Mf42f/8DF+9c8f4aMf/wof/+cv8dG//gL/+Xc/w7/86Kf4s+/+I6av/ABv5sgD5xIGWq86As49pAITGAzOYihI27LTdKj3eXoUrXNDU6WKUU64MeKNEeZsjxVHvLG9YNgLx88dL4+89V6sc0U8sG0p/BQRp1i88UQK7xkRE1wBh7bjZsgC4P5FwJ0pVTK1ikKeoiXvtkPvPZU5IiKScMzmnxx7AqM7jczlg2WXcueKbEV29qfJTm89x/7nG/8tNlcBp3HbleafxqE2UQ4oB5QDygHlgHJAObBeOFAk4ER0G/Eygw8l6+qRtdLgzs4D/0C8nIgja3QYDxyzDo7xwvHEG5qqUizgFH+nJ3oYTx7rd8mAtfI1d/qMu1CsDJhlMF0m9Ik3NBgtTpv/d+X3FwtStFCzeOCUhu6Cze520WM8jSoTuYWj3ffwWmoC7wzP4uZbH+L+7T/GHz36a/zVD/4Zf/vDf8Tf/+k/4B//v7/H3/zR3+FH/8/f4k8e/w0ej/4Zbrz5AU72j6D3wEX0HbyKgRaaQnUPgwHaVp12RbqPbPQRIxd9H35ko++b6663DU2ZMkJcefGmjIDD9nRsVE6AqHSuKJ9tmwtPK4k3dN3cL0IC5yGv71ROvLGFHPE884U1OWCnr/TYTUcRDz3RxhYaawo4RXbxc8//brFVo6E/nsZ+y/eqgNOY3YS3GqrdlAPKAeWAckA5oBxQDqxPDqiAs8SBkRJ7OYldbZBnv8c/GPeLOCTg0DkScOheWpOEBttmfREj3Ih4Q94N9nvt99Cxf4Baz+/iOGQwWTQ1qoxI497nu9YYx6qlr1S0sQWdom/l3YhoAeZRZKN3cbRrFKeSk3grN4vLrzzEyLu/h/u3f4j/MfVn+L2ZP8Hvz/4R/t/57+N7U3+A90e+j/mb38fds9/DO4dnUGi/ie69F9Cz/wr6D95Asu0uBoPjyNBuWDRNKPbQwiPkYhaiD5GLyjo3NA1OIF43FJI4Z8QUb5qT5XVi52Elsabcefs5Prbz1eaM/9i7ryhPeeqST6xxvXFE2KGpVQb29Ka8M4XQ5JEXfyPcKBYTy4g3Jd/r51Kj7/XbpZHfjb6r+H6xuwo4xXZphC96r9pOOaAcUA4oB5QDygHlwPrjgAo4KuC43gJrW0DLDe7qEFfcgTcJNraQYws4VPBExJHpKP7Q/35/YfUPZv2//fdLfN55GVTWGy4+P/xpk9+VBByzhXqRgMPbXNP257Q49AiOdo7hZP8kXk/P4Ozhebx36iFuv/0BRi98iNEL72Pswn2MXZzF3XPkoTOHq6fncfbINE7038NQ6Bo6d1/AoX1X0OcIOEOhcWRoUeX4PAqJByjE7yMff2AQe4CcIPoAueh9A3dxXxFvihf2LSfeGBvK99PUsBrTgezrZQUNLz+Lhb/S/KZ3F+W1I+DkeGc1EnIs0YaPZ1aNPxgAACAASURBVJCPzSBHcLbzlrAo3UXpstNT69hZSNmN21tYm95TnP+V4ir/naVclfsk9Jc3/2+5zw4rpaH2ebG7Cji1bVWad/qM2kQ5oBxQDigHlAPKAeXAeuWACjgq4KxTAceenkKDvUoDO3tQLiKOhPagVCohOx46rjWQlOcktMSAsgNpiV/ild8SSjwrGfrTSHYwsD1uzLERbwrut5h0sbcGL8BsvHAOd4zjWM8kXhmYxun0NN7KTeOdw9N49+VJnDs6irNH7+Ds0et4u3ATb2Rv4bWhOzjedxv5BHncXEX33svo3X8N/S23kAqQqEPTs6aQj8+ikJhHIT7PYk4+Rh45C8gxSLhZ8OB62Yi3jdi4Bj/cb7O50oCQU7e4UZqnIiQY7ytnh7HYHEjEycVmHTiiDYs308jFyDtGFr/2QterTEQm/q7Sd5ZvbBxOyLMsdlrlpOgb7TiFtxKKzeW3hNWf8TykvLyTc14ZlLgktONs7FjsrgJOY3Yrzx2NQ+2iHFAOKAeUA8oB5YByYL1wQAUcFXDWrYBT38Cu0sDUGaC7g3e70pEBooQyKPULAXTdfk6O/QIJ/ZZrteKU+1YyLJc+7xyJNX74p+aY6Ta0kPEEspExFBLjONo5geM9kzjeS2LOPbx8iNbGuYrDXRcx3HEO+fa3kIufQSZ6FkPhd5EKXuTFi3sPvIeefVfZ+2ag9Q4GA7S71bgRcGIzKMTnkCewZ8o88rF55Bgi3swjxztLeYP/+rghNva+vdjLZLHnJd7aoQgJHNKuWIS6BJxiEceIN45XWUWxpVp6nG91BRwqH1Z8FeMUPlNolxN/WbHfbT9T7jl/PPLb/5wdZ2PHYncVcBqzm1eP6XNqC+WAckA5oBxQDigHlAPrkQMq4LgDbyXo2hLUP3iTgV+583ZeWYPwosFpIwJOve+S91rvLPGCsNPrH+TSNYljpUN/Gr3ftcQbSqMRcKaRjdB24uPIx8YxnBjHkc4JFBJ3MRQiQeZddO15Ex27XkVix3FEtx1GdOtRRLYcR+SlVxDb+jrad55F957L6DtwAwMtt5Fqu4eh4CjSYRKGpngaUbGAQ0KOEXHYE4eEG4HlgWPEBNvWclzOrt63L13AKRd/5XMiJNgCjhGqRLCSaVRm+pSZQlW6Pk3RFKqKYkvldBjeOXaou5xQfGJXCesRW+ReO5TnKoX2vXRc61uqXxe7q4BT3U5LtbM+r/ZVDigHlAPKAeWAckA5sLocUAFniQMFJexyEtY/iCv3u9z7rAG6PTgt630jz5eL239O7i0XWu8s4pA/Dv/vcnGt1Dk7jcXHIuL4PW+Ez2YAPI1cxHjh5GMTKMQnMNw+gXycBBjaTeo99B08j579Z3Fo71vo2nMaXbvfQOfut9C56x107T6Pnn0k3lzHQMsdFm8GA6PsfZMJkzBEIhsJGI6YIR4qItjYIYs3tI6R357272p2LP7+xQk51eIvf02EBCPg0I5jjheOE8qCxbxIccxZA8fdKcoTcooEnKq8Lp8Ok6+WDcqWE7ruf962b7XjxT5XKU5/fI39FrurgNOY3UrzX59XmygHlAPKAeWAckA5oBxYTxxQAadkwKIEXVuCVhrQyflK+WMNTt0Brpwr94zEVy0s91w95yrFWc+z6+MeMwCmRW+NiJOPTiIfm0QhTiFNf6J1bGg9m2tItV1BsvUSBlouoL/lIvoPXkb/wSsYOHgdSVrzhqZNtY1gKDCGNO0+FZpENjzFAg6tDWNEDLNttl/gMN43RriRRYpLvULE3uvDdv7yI2KCtwsZiThlto93hRvZat5ZdLiEz9V4XcsG8my5sNKzYt9KYbnnKt1bz/ly8TV2TmyuAk5jdvNzV3+r/ZQDygHlgHJAOaAcUA6sLw6ogKMCTpn/uq81SSsN8mqlyz8orXU/XV/su6rFXS7Oavevz2vuttMk4vDORbLoLe1ORSLOKLKREWQjd5EJ30EmfNsJ6fc9ZEK01s0Ye9ykQxMs3LDnDYk3YRKGRKjwixmWlwrvHuYJOOXza33aTxo7ERPKizb+bxebVBNvluN77bJST3zlOE3naj1b6blK52vFV991sbkKOPXZq3Y+ajxqI+WAckA5oBxQDigHlAPrgQMq4NQcgChR1wNRNQ1ryUMz2JctrUXMMTslkZhDO1XZoHOTzvQrEmsE0yzcuOINCTj+rbYr/G6W/K/3e+m+ZvnmtfgOsbMKOMqjteCfvlN5pxxQDigHlAPKAeXASnFABRwdKOlAUTmwIhyQQXQuPAMaSPNguoxos1KVm8a7eRtO4Z4KOJuXA1r+Ne+VA8oB5YByQDmgHGhGDqiAo4P3FRm8N2Nh0W9qrBGQQXStUO3amF3VXrXtJZxTAae2rZRPaiPlgHJAOaAcUA4oB5QDG4cDKuCogKMCjnJAOaAcaCoOqICzcToh2mHUvFIOKAeUA8oB5YByQDlQPwdUwNGBW1MN3LTw11/41VZqq2blgAo4yu1m5bZ+l3JbOaAcUA4oB5QDm5sDKuCogKMCjnJAOaAcaCoOqICzuTs22rHV/FcOKAeUA8oB5YByoFk5oAKODtyaauDWrAVVv0sbIeVA/RxQAad+Wymv1FbKAeWAckA5oBxQDigHNg4HVMBRAUcFHOWAckA50FQcUAFn43RCtMOoeaUcUA4oB5QDygHlgHKgfg6ogKMDt6YauGnhr7/wq63UVs3KARVwlNvNym39LuW2ckA5oBxQDigHNjcHVMBRAUcFHOWAckA50FQcUAFnc3dstGOr+a8cUA4oB5QDygHlQLNyQAUcHbg11cCtWQuqfpc2QsqB+jmgAk79tlJeqa2UA8oB5YByQDmgHFAObBwOqICjAo4KOMoB5YByoKk4oALOxumEaIdR80o5oBxQDigHlAPKAeVA/RxQAUcHbk01cNPCX3/hV1uprZqVAyrgKLebldv6Xcpt5YByQDmgHFAObG4OqICjAo4KOMoB5YByoKk4oALO5u7YaMdW8185oBxQDigHlAPKgWblgAo4OnBrqoFbsxZU/S5thJQD9XNABZz6baW8UlspB5QDygHlgHJAOaAc2DgcUAFHBRwVcJQDygHlQFNxQAWcjdMJ0Q6j5pVyQDmgHFAOKAeUA8qB+jmgAo4O3Jpq4KaFv/7Cr7ZSWzUrB1TAUW43K7f1u5TbygHlgHJAOaAc2NwcUAFHBRwVcJQDygHlQFNxQAWczd2x0Y6t5r9yQDmgHFAOKAeUA83KARVwdODWVAO3Zi2o+l3aCCkH6ueACjj120p5pbZSDigHlAPKAeWAckA5sHE4oAKOCjgq4CgHlAPKgabigAo4G6cToh1GzSvlgHJAOaAcUA4oB5QD9XNABRwduDXVwE0Lf/2FX22ltmpWDqiAo9xuVm7rdym3lQPKAeWAckA5sLk5oAKOCjgq4CgHlAPKgabigAo4m7tjox1bzX/lgHJAOaAcUA4oB5qVAyrg6MCtqQZuzVpQ9bu0EVIO1M8BFXDqt5XySm2lHFAOKAeUA8oB5YByYONwQAUcFXBUwFEOKAeUA03FARVwNk4nRDuMmlfKAeWAckA5oBxQDigHynFgDvlIKQqROXiYxRO52AwyEYNsZBbcEY7OIh+dYxRic6gX8oxmSLkM0XPKC+WAckA5sBIcUAFHebUSvNI4lVfKAeWAckA5oBxQDqw8B0pFG1vI8cQbEnJUwGmq/0KvPLm0AKuNlQPKgfXHARVw1l+eaDnRPFEOKAeUA8oB5YByYPNyYAb5iKAWD1TAUVFGp4coB5QDyoFNxAEVcGp1DPT65u1Aat5r3isHlAPKAeWAcmB1OSDCjR1WyoNy4s088hEPhcg8PMzpFKrVzcxKGafnNR+UA8oB5cBiOaACjnJnsdzR55Q7ygHlgHJAOaAcUA4sLwds4cY+Lmdnv4DjCTci4njiDQk5KuDof+k30X/pl7dgliuAek5trBxYCw6ogKO8Wwve6TuVd8oB5YByQDmgHFAOFHPAEmyi09Y0Kjpfzla2gFMq3pCIowJOWcOVM6aeK08ytYvaRTmgHFhfHFABZ33lh5YPzQ/lgHJAOaAcUA4oBzYnB1TAqaBUaYHYnAVC813zXTmgHCjlgAo4pTZRnqhNlAPKAeWAckA5oBxQDqw2B1TAUQFHPYaUA8oB5YByoCoHVMBZ7c6Jvk87xMoB5YByQDmgHFAOKAdKOaACTtVOe6nBlERqE+WAckA5sNk4oAKOcn6zcV6/VzmvHFAOKAeUA8qB9cgBFXBUwNH/vCsHlAPKAeVAVQ6ogLMeOzCaJu1YKweUA8oB5YByQDmw2TigAk7VTrsWiM1WIPR7lfPKAeVAKQdUwCm1ifJEbaIcUA4oB5QDygHlgHJgtTmgAo4KOPqfd+WAckA5oByoygEVcFa7c6Lv0w6xckA5oBxQDigHlAPKgVIOqIBTtdNeajAlkdpEOaAcUA5sNg6ogKOc32yc1+9VzisHlAPKAeWAcmA9ckAFHBVw9D/vygHlgHJAOVCVAyrgrMcOjKZJO9bKAeWAckA5oBxQDmw2DqiAU7XTrgVisxUI/V7lvHJAOVDKARVwSm2iPFGbKAeUA8oB5YByQDmgHFhtDqiAowKO/uddOaAcUA4oB6pyQAWc1e6c6Pu0Q6wcUA4oB5QDygHlgHKglAMq4FTttJcaTEmkNlEOKAeUA5uNAyrgKOc3G+f1e5XzygHlgHJAOaAcWI8cWE0BJzyDbGQG3BGOziIfnWMUYnOoF/KMkmk9kknTpLxUDigHmpMDKuA0Z75qedV8VQ4oB5QDygHlgHJgY3FgFQScbHQamTBhBpkIiTizyKmAo54/OmVDOaAcUA5sEA6ogKOdu43VudP80vxSDigHlAPKAeVAc3JglQScdHgaaRJw2AtHBZzmJJNWEpqvygHlQHNyQAWc5sxXLa+ar8oB5YByQDmgHFAObCwOrJWAY3nh1Dt9iu7TKVRawDZWAdP80vxSDjQDB1TAUR43A4/1G5THygHlgHJAOaAc2OgcWFUBx0yj8q+DowLORieRpl8rQuWAcqC5OaACTnPnr5ZfzV/lgHJAOaAcUA4oBzYGB9ZCwLEWMyaPGhVwtLBsjMKi+aT5pBzYrBxQAUe5v1m5r9+t3FcOKAeUA8oB5cB64sAaCDhmHRyzGxULOCTi1AmdQrWeyKNp0cpMOaAc2BwcUAFnc+SzlmfNZ+WAckA5oBxQDigH1jcH1krACZduJ+6KM8724lV/b5CdS9Z35mvh1PxRDigHlAP1cEAFHOVJPTzRe5QnygHlgHJAOaAcUA6sLAfWWsAhIaYewca9RwmxsoRQ+6p9lQPKAeWAnwMq4Cgn/JzQ38oJ5YByQDmgHFAOKAdWnwNrKOBkaScq9aTB6me6FjS1uXJAOaAcaIQDKuAoXxrhi96rfFEOKAeUA8oB5YByYGU4oAKOCigqoikHlAPKAeVAVQ6ogKOdsJXphKld1a7KAeWAckA5oBxQDjTCARVwqnbalUyNkEnvVb4oB5QDzckBFXCaM1+1vGq+KgeUA8oB5YByQDmwsTigAo4KOPqfd+WAckA5oByoygEVcLRzt7E6d5pfml/KAeWAckA5oBxoTg6ogFO1096cma6FWfNVOaAcUA40wgEVcJQvjfBF71W+KAeUA8oB5YByQDmwMhxQAUcFHP3Pu3JAOaAcUA5U5YAKONoJW5lOmNpV7aocUA4oB5QDygHlQCMcUAGnaqddydQImfRe5YtyQDnQnBxQAac581XLq+arckA5oBxQDigHlAMbiwMq4KiAo/95Vw4oB5QDyoGqHFABRzt3G6tzp/ml+aUcUA4oB5QDyoHm5IAKOFU77c2Z6VqYNV+VA8oB5UAjHFABR/nSCF/0XuWLckA5oBxQDigHlAMrwwEVcFTA0f+8KweUA8oB5UBVDqiAo52wlemEqV3VrsoB5YByQDmgHFAONMIBFXCqdtqVTI2QSe9VvigHlAPNyQEVcJozX7W8ar4qB5QDygHlgHJAObCxOKACjgo4+p935YByQDmgHKjKARVwtHO3sTp3ml+aX8oB5YByQDmgHGhODqiAU7XT3pyZroVZ81U5oBxQDjTCARVwlC+N8EXvVb4oB5QDygHlgHJAObAyHFABRwUc/c+7ckA5oBxQDlTlgAo42glbmU6Y2lXtqhxQDigHlAPKAeVAIxxQAadqp13J1AiZ9F7li3JAOdCcHFABpznzVcur5qtyQDmgHFAOKAeUAxuLAyrgqICj/3lXDigHlAPKgaocUAFHO3cbq3On+aX5pRxQDigHlAPKgebkgAo4VTvtzZnpWpg1X5UDygHlQCMcUAFH+dIIX/Re5YtyQDmgHFAOKAeUAyvDARVwVMDR/7wrB5QDygHlQFUOqICjnbCV6YSpXdWuygHlgHJAOaAcUA40wgEVcKp22pVMjZBJ71W+KAeUA83JARVwmjNftbxqvioHlAPKAeWAckA5sLE4sFYCTmQG2cgsqFO8sQym6dX8Ug4oB5QDm40DKuAo5zcb5/V7lfPKAeWAckA5oBxYjxxYfgEnH5lHwcUcnshEp5EOG2TCMyBkV1HAkY63hkYwUzvUZ4flrrCa1e7LbSeNbz02FJomKb/Z8AxyhGX8B4TEpWF9dbPaSe2kHFAOKAeUA8oB5cDm5cDKCDgi4uQjRQLODNKrLOBs3ozVQr0ceb8cYsJypGMjxLEcttI4VChZrxyQMrjcAo7Eq6G2WcoB5YByQDmgHFAOKAeUA1U5ECX70D8SHREnOo28HHNYbiwxBxJlDOZBQk11zOGJbGwa6YgRb1ZTwCn78fTRCrVBJQ44/1W3ubPUAaUdl3tc6f0b5fwK2Gmpdtbny1XYem65eCFldzkFHImzKNwodYCmU9tR5YByQDmgHFAOKAeUA6vHARp/sb1XQcDJxGaMgEMiTsRMocqswhSqok6x+8Hy4RqqkFWBAz5xYqkDwCIeNlslZ9lqqXbS51VsWc8ckHK8YgJOs9UN+j2r16FTW6utlQPKAeWAckA5sIk4YAQc9sJZpAdOrooXTo6nUPkFHBZvVn4R43ICRTY6C4XaoBIHXM4sozAhAz8Oncq10vs3yvmVsNN6Hrxr2lRcknK80gLORqkDNJ3ajioHlAPKgXXIgcgsbxJDG8Uo1AYbkgO1xuqVuO1/zr7Pf63ab/u5SsfVnl/Ba+74KzqDnIN8dAbF8PfZZeoUhc7Uqeg88gT57QtLBBzyvMk4xpABbT46h5VCLjoHA+NtoY3NOmxsVpDoi8lvt3A4Is5SBu8y6BOuU9yLSdN6fMZvJ/rGpdhKn1X7rWcOSFleLgFH4mvGumE91leapuZpezQvNS+VAxU4UGmwqedVzNpoHKg2Xqr0Lf5n7Pv81yr9tp+pdlzp+VU4b8ZfRsApFm5IyJm1YOsrjmAjwk10AfkiFAs6loAzi3RklsUbFnDYE8GOeOWObRFHK/0Klf4qEG6j2N4vTCxlUNnMgzS/nehbl2IrfVbtt545IGVZBRxtQzZKW6bpVK4qBzYRB5zBpvlHOf2zXKE22JgccL2Gyo1NWUso/138nDxj3Sc7X9esDxsoQ0XvkneuQuiOvZwZHcWCTSXxhjQWI9AUovMoxBaK4Ak588hF50FTq7JmFyoj3qQjc8hE5pB1vWKMaFOIzWGlkI+Zd9gijv/jN+dv8UzS0ONGeVt4q3bbLmj1HXtxe+vt1KxAVqECWI40uOVmGaebrecBvKZtcwtMlQQcOb+kcIOU+eWoNzSOTTSgVF43jcetltsNUG5VsDGilbPbcWYxodpw3Qh/LLr42xBnCRYW5vz5K8uzyDO+vCwbn9wroe+Zovf4rtUVn8S7LKGMUZ0dutjTxtFRoo6OQmElsNZC4o0l4MTvs5CTjy2AwOJNdB5ZQmQWT2SiJOCQeDPPoAt0E6tBHJFEuPxCjgg4NEXLG0yvxLE3QHcHtotaTGol0qZxNpr3yz2lz35/s3SEXJ6rgKOeR5vA+2pJAo1VRsrFY/47ROvCrTNEZ5BlbIDBy7J0kPQ7m6V90u9QLm9cDki921h7UOxtMo1MZBMiPI3MUrEZ7bYq31zqNVOxz1PU93DKgy3eUHr9+czfUFx27DJhBBe6XqVuLBJpKr2DypX5Fi9OeS/FXZzeit9Ys7/nX79pDjStieEuPWN51bjnSNSxPXHkWJxmbAHHeOIYAcdoM+RkQ842tOnUE5koHTjiTcRxzyEBp0i8WUERp+ijzAcs9wC9OD4xVr3haqSJ3uGIZhquiS1ExKHCUbUCqVa5rKNrKuBsbo+UzeaRVE54Weq5cg27dAzWQ1jaOZFOij90/oERm0VuMSha3J3itv4hspj46Bm3rvSntZl+m46oWxdvim9upvzTb3EHOzLo0dAMAIsGq6UD38rtwyYUbUR8cAb06fA0lgIWBiRODZdZCCwnfFQSVKz60RVWDL/L5a/Jt0rPOGIo1y9V2k3feyhOeZcrGBVxwhJZ7brLjaeRsmvZxhV3bBFnjqc1GQHHG8/zlChey4bOkcDjeOhE7F2qRI8gRxnRWxZQiBvPG/a+iRnPGyPezGIoPE0CDok3C4xshFx0vAdoHtZwXCKjcPm9cCTOYpFlhUQTmrIVm20QK5SWEuHKy3BPzPEvYqS/vbmACyhElwbPzsb2nohTqcLaOOfdQYNbWaigsdlEjc32vV7D6DSQ4RnQmjhLBjfW67PjXa+IsyjhxhZnWHzwOl9cv9jXF3G8GQaHvAuF2MZnw83w/fqNXplRWzSRLYoGieuzbVh3Hj482J5COrw0qICzsnyz+xRF7Ze0YxSWiCFemqrlL3HSrQd9ZShrXSt6r91usvDivKsMnypxw46bvm/pZcMWc2aRDTtgDxwZyxePT3m8ydflfuqbTiMXIcjixqRREOZZvGEBJz6PXMzAONyQeDODQVfACd9HJnIf2ch95KL3ea6VLKIz7KpBxUIGz+MiQceaz1VJhLHvEcFmbcJZFGKNYuVEK88GRiTzBAUVamyhxj5eqmhjP8/xxoy3GdleBRwVeTab6LGRv7dEsLHESrq2aOEmNINMqIx7boWOZyY8hYqITCGzIjCdmKKOid2pso5NZ2hmcd43bqetePDlxUnxLgJW+rJR6tQ1I4zNXPsUfXOxPd1Ord7jdfDVFmqL9coB3+Bz6QNCbwDcvHGZdrLaAL+ea9zWcpu6GWy2et9o9yW89r28wwP/Q8gum255qJzHJt+mwe9x7/e+z/QBnK23uU/heAyXfQ/1z0qFwErcMN8m4pFJw1LLGQtd/M89EWTIu8YsMOyNWe8jT5oKY8EsPsxijxFvsqFpZENTjpAj246TQDaHXIxAwo1Zm9gVbyIzSIWnkQpP4YlsdAEZFnAeeAJO1CycY0Qc43nD4oyspeB4j7AAUeJJYg0CrUV86HlXyFlBTx5PFCknvNjizQwKsUqw76PjcnEt1znHw4lWnnZRrNzZooMeL59tjIBDHmfqgbORB/KadqvOlTq6ycNa4k1ukZ43LNwEp5EJTSEdmnJDOk6HJksRnqz4H0WvM7FyIk49wofXGVuE0GKJM0ZkkE6QdDYWF7px0X/dmhWuKGVs5Ik0zShW6TfVUxb1nibjSbPWXSvwXe4/MsoMvOsRbuie0jbVCABN24b488FtU1aiHDltuSOe8GyV+CzyjDnk4wJzjv8x4YorJj1GFDH9HX+euvlfJN6Ye0VgKdtXqesdph9WzA+HG5Vs5rdtQ79lapZMh5L1b2wBR4QbE+YiC8iG55ENzSIj/ySkPmZwEpnQJHObPHKKhKGo2R2c1rsZipDXjRFvkqEpJEOTJODMIxu+z8hFvBd6QoERFmi3H5rbJbv+eGKDd12uSedaflPov3/1f1sCUnQWhbpgP7Ma37B84oSXfxpnLVsYtdQWcaw1Huz1HjbSseWNoALH5hM4Nkue58KzKI8ZkIjTCOi/IZngNNKBKYPgFNICR9ApK+JYYg8JPy64w1nOk2fp57wOaz0CiqnPGp8+7P33zZ2SGa3nfbXvYTHDmZrmfUuziTmmk8ed0mWym8ZVm1tqI7XRanDAE2TVm64uW8hUZP/itov8bdoNe+2U5s8Hw+uVGZ94/YM5FOKCeRQS8xi2QL/pupnqI2kxdY4RH6r3b8q391JneX0OSY/d95D4KQ53zRvpf1n9rXLcsOuEuvjqCkelvOK4eIwlwo3lfRNZQJ5xH3nSVBywgBOaY/EmTf3M4BSGgpMYCkyYMEj/IDT/OJR1fdLhGV7rhqZLEVIs3ExhIDSJgdAEnqAdp7JhUobIvYem7tz3rS0yj0Jk3gg34TnkCX5BJlLmHvte57qINsPReawd5jAcbRSNpVe+s/6wnMhC+aBYeRt4tjfTqEhBtQvlxj22BVQ99sRntUX9tihQXb8O4eZheBZ5H8oLOpWEHu98lv4rUknAESGHw0mkqbFlOA2uLdzIsdMx5f+q0H9WlhPcGXYWwatDWOZdD3hutXS8GgyL3uF0tqxF+GgOdyNw/8u0nDZZb3HZeeS3jyvoSAdYQ6+jrrZQWygHmo4DTn3o1v3L9JvbnaL2qbm5Y9pyEjoabMPrvJ9nnMTnef1bI9wsYDhhgwQcZ2YOx2kW4GW+2nlab3ssbaPszMTr1FrfZuetL35PxPEEHduLxe2TcHsrvFhq/8XzujH9UNJHBCLeUOiJN3RsPHBIwJlFOjgDEnFYwCERRxCawpAgPO2JN6EpS7yxBZzIHLv15MLzyIW9lxciCyzckHjDCM8b8SZMHXpzbji6AAPnHhFyHPFG7qPQCDZy/2qH8n47rEfEse9v9Nh8Yy3vj+Lrm0+wGY7ex1rCCERGxDGLTDlipeVt5g4WN/C59TgI1zStT3HEy5fiet2uz9fHcQX7yT8PfMKOX+jJh2aRs0ACTjZoRBwSchjkleN45rC4UyTkeB46mSB53jj3WiHPcS7pyCzDwsrkekPzOwAAIABJREFUXcQdnzq8y5ypzO66dY1OCXanSZt3iYdt7dBJo88TKlsyvW2Zxa0Se69V/CafJZ8q2WuzeMvpd9ZRVpt86mvTckAGn25dWbxmaKX1QTfPeWeXHcszvHx9KO1aPaEMpCuUK80T3vWoEY55y4XMg9a/pU2MhuMLOGyBfvN5a31c6lvwe9z8bayPI20k1w92f0XipXMV45b2vfidrnjjPmfH4XGnPA/ruS7/CPWLN5Zw465/84CdY0TEoUWPM4wZ3kWLhCj2uolMI21hKDINwmBkGqnIFCMZmYLgCV4sJzwHI+CQSGMgHfRhEXLkfNgRbyILOMyDb0eMiSzA3LuAAt3D95lzdF7EHnpmdUHpFMzjcHTlUEmkWnkvko0l/KylYFPp3UZIk4IoBXPjh95gvMJgdx16V2ia10NezWOYhHdXfG9UwF6F+yV9vpDbLvpHAws5pR46rpATmoUn4swgRwJOFbjzlh1xx7jBOkKPCD68fg4tTFc9LhExFust5D7HA74a9RSvP2evsba4Y9NBM+/iTk/F6WueVxOlU761KKxlnya57uaT31buQL1G3m3gfxo0wz8+9BuUnzU54K7vubh6tX5P/WaIv4Kw5dRzjQymjRhYgZ/OwL/5bFvBfq7QsbTrrr1IvGEUizci5LCIQ7tUi4jjrN8qfYSK7Z6/HfT/lv5MUZ9FxCGaDVHct7D7F1XfKfEyzxxxxv/uRn677bKMGY3zCzm+kLdNgeDOoHmAQuwBClFHxInSbt9m05xslBYotnY3js0iQ+veRGeQZkxjKDqNQQup6DRSsRmkYtN4glUtTjiJOM4UKe78GhFmOEyCjCPKhBxhJkzCTBkhJnKfz9MzDLqn3H2rLuJQWkXEoXAxIo79fO3jSkKBni/vcbO6op7HXS8/yOPMeJ157nBSODdeKAKsHboDct+AV89bYoXaxhNuIlRP1q7r1ts9wmcScAqhyiDxxhVwgp43Dgk8JZ2B0KwnytheOoFpZASOiENePDUFnBAJRmXeU+7dFc9V6Ly6nQtznesAd4H8JQwCZCo1TTHlf/rUTn89dmjme6rlcc2BoS8f9f76+K52UjutJgeWrX5djjp6I8dhtS9Lzb+mzpNltJPfzrbd2OkitoDDNTDMQg7NYjB9C4qz3v5BST/L0SIoDomPQ+ubFxd3cZ24uDi8/g4vJUNOLTzrSMaOjnDjijdGtBmOPcBw7CEoLMQJ9xn5+AIM5pGjrcIJiTlk43PIxGeRiRkRZyg2A8FgbAaD8VnGUGKGBBxnoUenM2s6tdTpJbHGCDEs4IQWzLkQnRNh5oERaESoIQEnXAoWcax7Vv/3Ag6Tx5CLeRymwckywwwcLK8jFgSsTBVlTkNWKEkEtLHavPDebXmPUUXheJtt1ND1gBNPuPA8hhVqg8VwYCMKWvydItzMohAqDxFw8iTeCKwpVfb0KjrOBglmmhVPtQrMIOODe50Fn1necYB2HbBhx2s6At4/Thr/Xec0N3e6s+cNK16x9YTSiaIOnqkXK6c5F5ormprm2a7Yfq6tLJs2xzmT314+l7cVd5ZlivoyhrXyh66v1Ls1XrXtZuRAPXWo3lO77bHbmaXyqFntzbMFlrG9KLIzr09r5ROLN/dxOFYL3jMSXz19Ga+NtP+BRh7oTj1KopAcO2E98RbfUxoHxWnuKX/N/87yv8sIN/6xPXvhGCGHvHDysfsG8fss3ohwQ6KNIBObRZpEG8vrhjxwjOeN8b4ZjM9g0Ag408iFCMaFnAzqF3EKIt4E51nEIe+acoNtI954oo/riSMeOWsa+gexcxgOrwTES4mI4XgvsZAjma2h8egSzy4TrhVXXO8yFjvKd7SLK4ONcw97IDhTSeh4Zfiu8TarXf382TC/LcHGFWncdk3aNycMziBnoV4BwfW6Ee8bJ3SfrzIFyLS1psPiTueqtV5PyXWvHqo95dD8Z2wpGwd4HWvvvaZeLJ6iVtohc6amNZ1I4wl5bp77vrF6Pss0yRoeUW7nVe6vHJa2U16nWPLF8K1yHLW5pM+qjTY7B6xBX0MbsphBbi2PhtW8zt4Trri/0tOeK39/uXRwm7NIj6Litq7ye1fT1svxLiNK1WgzFmkzsnc5u9E76027mz5fu1XcNpk+g7RJ/rC4jbLKmrXcQ3F8/j6JXT/Zz1c5Lvl2vy38v0WwKp1RQtOnzC5U3prCZodv2iRqAfnYAnIxz+vG87gxwk2ahZspDEYmkbKQjE4hRYhNIRWfRioxhSdykSlkQwKau2/m70vng43p/ldyhsUd7sjTf2blo+3MsqZh1TLy6lwv7mDmwzNYLZR3D/PcsPS6P2/W4rd/q+Fp5MLNhXx4Ggq1QcMcYGFfBP6NFE4hF6ofbvtHCxEzJpEJVoe3C5XsRiX3m23E3TjdtlXaWAnNwntLqmuc3RvIi7YqorMoLAN49wvZMYI9d0vrSd4BgvsQ8p0m5K3VG7BvLfuv3+veNvKGA6U24jznPKsvX4zdaQHG6vnMizbygtHOoo5l8oHyx3t/9fhqvU+vq/02JQecxXEbq1PnQAvEDq9T8AL3y9BGVLVJA9/Oi+kuV3oaeO96zR87Xctqm6o2pmlMhrdF3I3PYdgPn42FB9x28QLE1qLUThtUqa8gbZSpW2q3e+XrILNotqSjauhLu23r6seyTpCECyjE5nkxZ9rJmBYqzkZ8oB21YrPIEuIzjEx8GpnYFNKxSQxFJzAYnUAqMo5keMwgMoZkZAwD0TEko+NIxiaQjE8glaBtxCOTyIanDKSzKY08hzLA9TqcZDBjEMpg0wmxOxleR0KeXcvQ7kBNIRdeDCaRC1dChfgiUyBxLBeZXkVYhcTuaJccN5YmLmiVdvaITCNbC/SsdY9nE5Pe8gWwUueosbR77zLP5SPTsMHXo9PIRaeWDdnoFNYS7re4HJxCPqJQGzTGAWobKtd7G/9aNkxtn4dMeAKZkEE6NIFGwc9SHOGJonjtd7jH1O6SfamMLqnumUY+VhuF2AwK8SUgNuO9p1J9Sf8MYng2db83PIkMw9hH7GyHjdp7Kffb77XzjTng5GHFY4cj9JyXhnGkQwJz3ovXfLttC7IT5Xs+Wjvv6J66OSL2534dvdextxs6ecP8M/m1dA46HLbz3+E3cbzou63yVnLefsZKn+GU6U/JsT+0v4FtatuV7eezoW2nkveassl5tOTyudTyvcrPW30G26Z1829Jddkqf+si01pXmfXVyYXYNJZcBy+l/q72rF23+9JdT9tS7z0N2WAZ02TbfTg+g2qo2UZSO7oaqJRfy2iXevPN5rttS7KV35ZF9rPS6tYfdr3r7xM4v7nNKKp3p007SXW6Xa8TV8vV7U65Lrm3DLeZk5VsXfP8LApxH3gLdNqmnMa81L5aY186F5tGLj6NXIIwhWxikpGJTyAdH8dQbAyDsVGkoqNIRkaQjNzjcCA6AkZsFMk4YQyp9lE8kY16jZXdKHqVNzV8NPi1G3sxJHXqnI6da0jnfucZec6Oe22O5TslnEA2UgPhcWTqQLl4cpEJ5KKTjHx0EvnY1Cqgjs6gS/oKjWXFzoyxWyYyCRumsE26goVbUEXEsO7ne6XRlMIUny4/sOBKssz3cCGukHaJ2wrz3EmmjrKDcvkQn0LeQS4+hcVjErm4g9gkcusE+Rjxb2VRiE9BsQFssBgeUP21DpCLUp26/MhGJ8CIjCOzRGQj4yYuibNCyN+xhPqBy3N8kuutquUuMYVCYpox3D6NRcF5nuOx6kpTZ3r1SvX6zuSba2vbLvXYnNvhMWTCS4Tzrkr55PIrNoGcDTu9dGynOTyOdHjMQ2gMQ6FRpENjBvY1Sr+kQbi8BB6Utbmk1U5jjeNy+WI4Snao0pbJu6IT7ndRHrn2CI2yLcge9YBt5jzPfa8a6RZbUkjfQGmVtpxCKhv82/kG+U5+riidpXklccszdliXbfx2s2xlx9XI8aLeW5SO6uXQ5jWXEV99ZspEFT4Uvas577PrXptr9R5zfc31MtXNaws3zauQb43ZzbQr1eqeRuIzdjbtoLSHlUOTJ65tuA5ZGy7zNzp9tmq2WJlrXl1h6h1jA2N34W0lm3pjqjyNiRx+uXUd1Su+up3rG6eOLH1fcb1elDdL4C6lTeLyl0U532iYi00hG6MxsQM6pjSSHRJTyLcTJpFrn2Bk28eRbR9DJjGGdGIUQ/ERDMZHkIrdQyp2F0kHAxTG7yGZGEGyfQSp9nsk4FDn1XoZDX5jAs/wlAGcCNdYco8/9J7x4nU6yMvQgLkEWHRc1NDXAnWyDNLhURCGwiMGoREMWaBrdG82SpB4zXEuNg5CPj7hopCYxMpAClT10JCRSCsVpNVRZZvKNxSH0plx7RIZQ5psFB1nZGMTIHAD74gY9Juu+++Te/LUeLVPM3hg0VE8wOBrTgNH9xalnQue8x11Hzv5kJhE3skHKkgFB3S8HOCCmZhAbo2RT0xgpVFITBg+W3YUe2rocWtd2YLzbOW5sSLci48jt5yIjSMbG1te1Jm+JdunfcKtu6rxa7hjCsuBQgfV16Wc5jqzzrrGrRMtG2Wr5EEmSm3M6LLC5LePR/70t08gL7Cucfrj48gSYpQ2k750ZBTpiNNHCN/DYMjDUPgePIzwfXQ/f5fLPRNnJW7z++S9TljpXjovnC5nOzuddEwovs8uD863+t7tpodtMIp0tPT7U6G7SAXvIhm4UxOp4B0Y3MVgiCA2MzaVdLr9sCKb3uNvoG+mb6dyZfOUfrv5JunlvJI8MumktFKavfeXsY2bX2SjGraxbWZxPENpWAQkTzm047bKUjVOGF5Y3HBsIXlvOGzyUezt8jQ66nCq+Jtrva85r3t9uyXX4VbdshZxSbnY0PlUrw3LtF12PUHHdh7YtvHX91JmVia0699ax752rIG6oFye220x11F22+vWfeadZCu//ezf3H46eZOLT3DdTHVXSbz8Dvs7rW/y560vDyW/7G8xeeXVc0X1pu8b5DmJxw7t/Jf7yof0bR6890tdOYEsXadvoT5F5wQKnRSOI9c5hlznKLIdhBFkOkaQbr+Hofa7GEzcYaTid5AUJO4g2X4XqQ6DJ8wAnAbaxWqRDMjtMBMbx6LgDPJlsL+sIaWpbPxjSEeLUVTYYqPIlAURzFxLR0cwRA195J4BdcwYdzEYNqBrdB91YDJR6ggVIxsbQTY2ilx8JUAdFhsW8X0FOZ+gjo19fQw5l8xUeKSTLB3M4lA6oOb7HTuQXeTbKa74GLL0Dgf0m2zJnTu2afE9bgfZ6ShzBSqdZgql48WhF6/E74a+bzWdS+ddzjdKnmbjoxDkEqPIJcYM2sdYBSUldPEYRSaxfpBNjIJhfbN8uz+syE+2EdmpGsaQb1dsBBsYvlfLy3VwrUJdaTg7gmx8BJnYcuEeMjGqw5cBFE+M4htBhtJYBfQN/D1SRhcRcplsH0OuClxOdoyjsATkO8bLlnH33VXrh1JOefWSYyfOT7GfyRPOFzdv7iIdXQ44cbP9vTxw24IK9Zj3ndTGURsi6Ta8GYrexVCE+gR3kArfRipUBuHbfJ3uoXvpGeYdc6aUN4YjThqlDrd4YqfZTZ9wQe5zv9MrN8RRSS+nQd7vfJPNzZocE1vEqA/kxWvbIRm6hWSwBkK3XJvRs4MRz0bGTsZebOPIHdeOYmu6n95PaRdbCPepzqOyRuWR0yj5FLrtpOsmBgIOgjfNOSc9RWlx8szNN6oznDLvD4m7fvA9S61n3DrGH79X33i8cfKc8tUqX8w5K6/K2tTiquQFP1fmu6S+8793M/+uVvevu2sVOOzn9Hr7bcqXx3u/XT3+mX6wqS+lLSrTbrp1pqkrOD6xDZUXqjesuofrBqqrlhv0Dqn/qM6pBLc9rFwPNZRnTt3ktg3yvXZ6nDaLyjzVqVLXVgz9NrXsKfWQG7p1i5ennIdOHHZbRHU7v9ON33umXF3nvkPq30rvknZdQmrXJM0VQ7qnPExaKG3OWKx9FLmOUeQ6R5DrGEG24y4yHXcY6Y47SLffxlD7bQwmbiGVuIlU/CaS8ZsYiN9Af+wG+uM3MJC4iYF2gyeGIqMYioxhKDLuEzxGkY6NYig2woN0GqgPRu4ZRO9hsBHQc879FM+ygdImsOKVdKYid0EYtBG9i0HqbBEoQ+L3TEc7MWI6ZmTg9lFkCTwYH0Em4ZGD7qcMpecHo3e8uOh3xHTeTIeFOgI3HFxHMnAdAy6uYaDNoL/tGjxcRX9bvXCea72G/tbr6G+7joG2GwaBG9whSQYoDTeRCprO0WCIOpA+0DnpbHIn6ybouQGKo+0G+gmt19HXeh29rdccXEdfG+EGBiju8G3TGaVCQcSnQtUuQsYI0lIYnMJG1+kc2Z/yImXZbYA6exa4g0YViOQZVSBlChLFxXlq5QPFNRC8if7ADUZf4DoE/Xx8DX0BgXMteB29i0XgGnrXCbzvku+7hv5AJVwH2aMEwevoD17HQE3cwEDoBpKKdWEDyouKCFKZrSdP1+Ye4hujHB8d/pbj9qLOtV1DH+MqetuWBxyfU6dULm9UDu2yRXmyCLjl7SaSoSoI30SKELm1eDhxJMM3UQJ+d6Xy76TLfs66v4inLjct/jrnXF4IPxYZMved8mHqq9L0sa3EZhL6bVfme9xvsdLmlTXrmzjfjB0lXwYjtzAYpXb0NoZid5AmxO8yMom7yCTuIUtoNx0/7gRSR9BBvnMUBmModI4h74LOm9/mvLnPPDeCfIf5Xegag4dxDHeNY7jb4HD3BIpwaAKHLch93vPy/lGOX95FHdbKMN8i30Fp5fg47ZJm6qNRn436btQnuM18drlfhuNkV+a9k1+SR+XrmPLtY1HdInWG22+QZ5x6S+pWqw72eFbMAUlLo6EdHx3T8+45m5dy7JQ3fk/whqlfiaN2H6jtmlcHtl5Fr6Dtqqkjqe7lbyv/DZwH8r7NELr1rdjeCxvNzzW9X+qqsu1tmT5hQ/d5fU+3DEn5KRdaZYruN+1nmTRImqvw0SsTlC/Fda20g1znUr3rwK4npFxJPUHp4T5C61X0tF4xaLmCnpWC8w63HEp5lNDqr5g+jPRllhYW9YPkXRT664HQDe5TSLtFdbHbfjntmJxz6+DILdN3sOsjq570lwO7TjPtZIU+TKW+ifMeyUt//MUcqVB+La4ttQ8yELoOQjJ8HcnINQxErmIgfAX9kffQHzboC7+H3vBl9IYuoSd0CYeCF9EdvIiuwEV0Bi4YBC+ikxC6gCeSYTMAT9F/jmggTaCGkXELqegtJCM3MRC5gf6wwUD4BhiRG3yertUCxcGI3kRykUhFb3J6KE2pmMGgE/JvJ15OC6f1OvrDHgbC1zEQIeNdRzJ6HYOxmxhK3GLVi1SwbOdd5LoI9xzcNefovHON7sm03+HnBuM3MRi7gRQheh0DYWrkrqC37TJ6Wi/iUMsFdB88j64D59C1/xw6959Dx76z6Nj3Dtr3OthzBgkbu88g0Sj2vIN2xll07D3H6Nx3Dp373kXX/vPoPngBh1ouoqf1EnpbL6Ov7TL6Au8ZtL3Hv02aL6Gn5RIOHbzIz3QfOM/Pd+5/Fx373kX73nNo33cOHfvfRSddo3gprtAVtinZYiju2TPTfhtFEKUxcZttRvbqDbyHQ62XOK6O/efBOHDei7/1EnoCl9EXvIL+0FUMRK5x3lH+FYELxDXOA7qX0tXVchGdBynOd9G+/xwS+846eAeJfQbxfe8gvtcgtvcdMPa9g9iicAaxvRb2neF4+B30njWCfGu5sJ24uO9sKfafRfv+s+hgUJ5XwIFz6DhwDp2KdWUDypOyqJSP6+a84R1xrywvmate+S3H6bU855Ynp/zwd5Q55vK01LJz8F10HnwXXbXQch7dK4iulvOohHrfaz9f7zNLuq/1AroFtm3knC881HYBPW0X0RMgUJtUG72BS2AEL6GXcRm9QWrLHISo7byM/pDpvA1EqB29imT0KlKxaxiMX8dQ4gbShPabyHTcQrbzNnJdd5DvvoN81x0+Nr/votB9F4VD9zDcIxjBcM8IDvcKRnG4lyC/KRzFkT7CGI4S+m2M4+WBSpjAsYEJHEua8OWBCZSi0rP1nDdx0/spTZRG+pZ8N/XJbrM9yD5kK+r8kh3JtmRvk0dOXlGeVUPgopdHTn7x820XcYg4INymsiZ1qr+ulLZPyqHwqfUCx3HIeT+FBtRvWgzkefNNblzyHgrLxety2asHuLw59QfVIcXfZrX7Rd/mPV9U9uT95d7drOdcm1r1CJ/zbGTXaZWP66i/hVcrEHLeO/m/3H04t//hlpcKbXu5vma1ttHhrWn3TNtTxEcpfxQKN7lsOHWBXebdutnU1VRXUBmi59wyQmnZf477w9S+J/YKziCxdyUh7/GF3GenfnuFvnu5/nxD50rj5nEA1QXEwZbzXK+Rrahd4/ZM2jFqyyxQvSztHbeBUj8X1YmmnuT2WPLOKl9UD0sdWqsu99pnaau9+t+tL6lOsnlhvcvmkeR/zf5VtXJ54F10HaD6lcr5OYOWc+hqOYvOlnfQ2XLGRUfLGRDaW95G+8G3kTjwNuIH3kLswFuI7n8Tkf1vMML73wDjwBt4oi982VF/SAlyEH0P/Q76opfRG7mEnvBFHApdcNETusDn6Hwl9IYvojdCuIQ+QfQyKM7FoD96Gf0xwnsYIMQJV0wYozSbeIvTex49IQKl9wJ6IxfQR4hexED8MlLtVzDUeQ2Z7uvIHrqBXM9NRr7nJgj0O99zC/neWyj03uJz2e7rSHdexWD7FaQSlzEQv4T+6EX0ht9Fd4Ay5W20H3gD8X2vI7rnNUR2n0J41ysI7TyJ4I4TCGw/jsC2Y2jbegytW19G6xbCUbRsOcohHdcP83zbFoqP4j2B4HbCSYR2vILwzlOI7n4N8b2vI7HvDbTvfxMdB95C58G3S9Bx4E2+TvfR/bE9p/nZyK5XOZ7QzlMghHe9isie04jtfZ2J1h04yzYdiF1CKvEehjqusH3SnddAyHQVg+xG9joUPMfPx/e9gfDu1xDY8QojSO/ZdYrPxfbRO95CZ+sZ0HsOhc6hN3IefQ7omNATfhc94XN8vbPtDBIH30R032mEd7/KcQV2nkTb9hNo234crYxjaN12DC3bjuHgtpeLcGDby1gqTJzHcHD7MbSsMVq3H0N5HGd7kE3adhQjQDwl7KyOIHF610mEiN+KdWEDyg/GzpMg3pdH9Xytle8ret3hnp+T6/V3YMdxeDDlJrjzBCrjJJar3IR3v4IwtS9VENlzCgZUb68eontfhcFriO7zYe9riJbDvtcQY5xGbP8qwHlfSfqs9FJ64vtPI3GA2qI3DFreRIegldonB21voavtbYPA2+gOULtVAcEz6A6ewaHgOzgUegc9obPoDZ9DX/RdDMTOIxm/iFTiEgbbL2Oo4zLSXVeQ6b6K3KFryPdcR45xDbke87vQewOFvpsY7r+FwwO3cGTgNo4kb+No8g6OpgxeTt3Fy4Mejg3dA+F4CUZwPG1wIj2KImRGcSIzipOE7BjjlewYXskRxhmncuM4lbdAvyvBvs89nsCp/ATHTe+mNNI3HO6nPth1ZLqvYLCD+l7UpzvHNiS7U55QHiUIB173IPl28A2Tb05+0TOUP5wHwXf4uCvwNudne8ub/Hxs32nmKpUdU86K2zoqg9THo+vMd4e/wpnEAYczVhpcHi3hXMk32t9rHccPvM78dcuTcJvLn0mzqRdK6xGpN+zvcuNxyid9Z3y/ZWvr3UV5sEHPs/3IhjYq1U3EFbFvtXDva4jsfdVgFevk0vr/lBmfVGk/qrUt1a+9gtBur6xIn4TbvqL2sUx7yO1a+bZNOEn2c9sXX1ti2pDXuA0x/DzN+WfX4W797dTjXN4PvsH3EcepfZIyb/q2Jp3c95E+CvWdVwmmj1FP/6Ja36ORa+Z7Q7tOguu4PafY3qY9NG0h2ZDbPmn3Am+D6k+G0w52tpn6lO7rIIi9D5q6mutrrqNeL2nz/XlXWp8U160Stz9st99ltQ1SN9J7uF6T/oDLp1cRpXp9sdj9KqK7X0WEQWWN2goC2ZRwAuE9xQjtPgFCcPcJBHadQNuu42jdeQwtO4/h4A7Cyzjg4Inu4NvopoYrZDoR1JE4FH4H3Ywz6A6fQVfobXQG30JH4E2D4JvoCL6JzloIvYUuQfhtdAsiZ9C9SByKnAEjegaHou+gJ/oOh3SO4ue0ht7itHF6KY2hNzkddL0negZ98XfQ334Oqa4LyPRcRn7gKoZT13Fk6AZeTt/CsextB3dwLEu4jZczt/nakcEbKAxc5ecGuy4g2XEOfXFKE73zNOItryCy7zgCu46gZXsB+7dksef5Qex6Nont3+nH1m/34KVvduPFb3Th+a914LmvtuPZrxASeObL8QZA9xs8++UEx/HcVzrw/Fc78cLXuvDi1+kdh7D1W73Y/lQ/dj2Twp7nh7D/xSwObs2jdfswAjsPI7jrKMK7X2aEdh1FYOcRtO04jJateex/KYu9z6ex69kUdnwniW3fpvT3YdtTA9j+dBI7Kc4XMjiwNY+2HUcQpHj2Hkd0/0nED55ykWh51T2OHSD7nEDbziPYvyWP3c+nsfPZQWz7ThJbvt2HrRL3s4PY82IGB7cVENh9FNH9JxBvOYWOwGvoCr2B7vAb6Aq9js7Q6+gInkai7RRiZPv9x9G26wj2b81h1/OD2Pb0ALY81YcXv3UIz5PNHTz39U4Qnv16B575WjGelt90rS504pmvl+LZb3TCoAvPfmP18Nw3umBDvrlc+MI3u1GCb3XjRcYhvPTtWujBS9/uwZanFOvFBpQfBrXybn1ep7JqIDxcyVDetfjwpW8dgo0t3z6ELVQmquGpHmx9qnfx+E4vtjH6sO3p6tj+dD+2P9OPHUvGAHY84+DZAexwsPO3TuEIAAAVy0lEQVTZAZRHEjufS2JXTaSw6/kUdgteGMQewotDjL0vDmHJeGkIe19KY59gSxr7tmSwn7CVkMUBwbYsDmzL4SBhew4t2/No3VFA265hBHYfRpCw5whCe48ywvteBiGy/xgjeuA4CLGDBGq3TiJBaH0F7W0EascIr6IzSKA27TV0h0+jO/w6eqJvoDf2Jvrib2Gg/QySHe8g1XkWQ93vIn3oPLK9F5Hru1SEfP8lFAbew3DyCg6nruLI0DW8nL6OY5kbOJa5iePZmziRu8U4mb+Nk4XbeKVwB6eG7+LU4bt49fA9vHZkBK8dHcHpo6N4/eUxvHFsHG8en8CbJybwFmMSb52cxNsnp/D2K1M488o0zpyaxjuvzuDsq7M4+9oszr02h3On5/Du6Xm8+3pjOP/6PIrwxgLH+/bJSU7PqcP3OP1Hh67xt5ItyD6HIm+wTcn+lEeSZy078iC07iygbeewlX9H3PyTfKP8oufpN+Ur5TE9R3ERR4g7xEni6K7nCIbbht+Gu3SdeLpvS5o5dWBbtigtxCHGzgLHTfE3DCcO/rbteY6f0khcJc7SO0uwNcv8Zp5vyTDvKY1UFui7pGxxeaNvdCBlkK9TuaHvcsuLlBur7JR7dxOd278tCwbbM4N9W8WWXrh3i2NT4stLQx6cukzqNAp3v+jZWmy+JiFxul4w96UM1Aq9MkLtA7cX3H6Ua4vMdW5HnLLF7YaVLm4fXkgxP/3cZB4Ln606XvjKdTuVR6nTnXrBLn9UpqgsUfmhskLlg/PphUG2D5V1/g7nG6hN3f5034qgWptu3knvrvz+as/Xe83rNwyYdvx5Gu8Ncp1B9qH6hutXahulft11mNtIaicFVCczdg6b+m5HgZ+jdpXrLsfmlEeUX9QuS/3kb6/NNXNPSd5abXVJPSvvdPLY1JemzTfvlHJr+hpSF9L37n7egMbxZfHcIPa4SDnHKex+bhC7n025oHH07meTrAXsenYAgp3P9sPA9KOknGx/ZgCEbU/3Y+t3+rHlO314icaygu/04YlE20lu/KhT0R5wEDyF9uApJBzEA68gFjiJaNsJRNuOI9Z2ojYCJxBnnEQ8eBKJ4CsGoVeQWATaQ6/A4BTaQw7Cp9BOCJ3iOOk99M4YwUkj/U4ET/KznZFTOBQ/jb6ON5Dsfhvp/nPID17E0exVHB++gVeO3MKrx+7i9Il7OH1iBK8TTo7g9PF7ePXlOzh19DZOFK7jSOY95JLnMdhzBv2db+JQ/FV0hE8g1noUwX15HNw5hD1b+rH9uW68+FQCz3w9jKe+HMA3vnAQX/nMXnzxU7vwhd/agc/+xlY8+X+9hE//9xfxqV9/Ab/za8/jU/+tXryAT/03B7/2Aj79ay/i07/+Ip789Zfwmf++FZ/9v7fhc7+xHb/727vwlSf34hufO4hvfzGAZ74WwQvfimPb053Y8dwh7H6xD/u2DmD/1iT2bRnA3pf6sfuFXmx/pgsvfTuBZ78ewVNfCuIbn2/BV57cjy99ei++/OR+fPUzB/C1z7XgW18M4DtfCeO5b8TxwrfaseU7Xdj2zCHseK4XO57vxU5GH//e/mwPX3vpKRI24njqyyF8/QutHM9XPnsQX6J4P3cQ3/xiG56iOL8Zx7Znu7FnaxKtuzMI7i8g2nIEieAxJELHEA++jFjgKCKthxHYn0fL7jT27Uhi+/PdeO7bMXzry2346ucP4ItP7sHnf2cnPvub2/GZ39zm4snf3Ionf2MrPs3Ygk/9ho2t+NRvbsWni7ANn/6tYjz5W9vgYTue/K1ifOa3t2Mt8Nnf3o7P/vYOxud+ewcq4fO/s4Nt8/lP7YSNL3xqJ77wqV343U/Xxhef3A3F+rDB7z65G7/7aULtfFvP9xD3VgPLYYMvfnoXPOzGl56shT340mf24MuMvfjyZxaBz+7FVxx89XP7UAtf+/x+LBVf/8IB+PGN3z2ISvjmF1vg4kst+GYFfOtLrfj2lwlU77fhqa8G8J2vBvH01wQhPP31xeOZr4e4DX72G2E8+40InvtmBM99K4rnvx3DC0/F8eJ3Enjp6XZsebodW5/pwNZnO7Dt2U5sf64L25/vwo7nu7HzxUPY/VIP9mzpw96t/di7rR/7tg9g/44kDhB2pnBw1yBaCLuH0Lp7CG170gjszSC4L4vQ/hzCB/OItBQQbR1GrO0w4gFqy46iPfQyoyN8DJ2R4+iKnkB37CQOxV9Bb/ur6O94DQNdryN16A0M9b6FTP8ZZAfOIpcUnEP+/2/XvN+aTLM+vn/Czr6zzoh1QEFaCJBACqQnpJMEEnoVgRS6BRUpijQbIFJEQEVUFMWGvTvOvDs768x/9N3r3E8eCKjosL478+61P3yuO+V57nLO93zPEy7ap9DTcQF9Ry7iWOc0TnTN4GT3ZfT3XsVA3ywGj13D6ePXMXRiDmdOEjcwfOomRvrnMTpwC2ODtzE+dAfnhu9hcmQBU2MPcOHsQ1wcf4zpiSe4REw+xczkU1yeeoYr55/j6oUXmL34ErPTr3D90mvMzbzB3OU3uHH5e9y88pbj6lvcvPoW81d/+Hxmf8D87A+4Nfsjm5fWOz/2EONDdzHcf5Od5fjRGRYDigvFi+JaVdKO4pxmlkvKY6F7L5e/HC5/pXkHUJZ/kOWwvKBleQ5L2rCrqJV9Rt/RtZR/0kROZgPTS5a1lmmStEkaZVo1+oJarQF9T9oiXeU6GpHnalrUFOmJ7SeoKdLVb4bude9j89B8pFM6J+2RoPXynE1Mz6TpRUjjjkamdToLQXv02OvZudy2erbvbFsdqw+uTrhaoc+W1UuwZqhueGhuRuia/3GvG5HjXDqzxxGMYTCW7sxgLO31yCYolkGybHVMG6QPwsXzL3jav+KH791rroHTHFgdU4Dp3Mm8mfz503A1wtUJeTuHF+T3K+H9f7EHGP2LfYD2xvUCrgcs8/MQfZKn85C2GUG9s3pc4fOsJqkug/C1xGrI0cjVB+XOQnVfw87vMPpXnKMadsOXhfXJ0N75kddW/cd7L/VkC/GBnv25n9Ec1L+5/sz5HMWedL3kcbuRz3rmcn+jnsnDfO4jvkV9l48385NlvrRUQ3wthY7LfCnYx1f2cPJcxgq/5NelNeks5IH0bMBqlPJNtWkOQs8iphpGlqkGDGMNsowBhssYgCsjFD9cBj+chgCcbFz52g+Hwceh9yFT7w3ig13vg13H44dNR3838MGi8cGs8cJEqL0wqr0waX34EzW+uupeDm8v6ghfH+p8vagNUuPtQcDbjUB1F/zVXWwMeLtQsyrdqPF2o8bXjVpfD4e/B3X+3i9HoBd1hJ/22sPWoz0u4u1Cra8bdYEeNNT0oan+OJp396Ol+TTaD42g6/AEjvVcxMCJKzgzeB2jZ+YxPnoHk+P3MDW+gKlzCzh/7j4mz97D+MhtjA3dxFD/NZzsvYSeI5M4fGgULc2D2Nd4HA013QhUdaCy7CBKC/YgP7sOLpsXVkM5dMoCKKVuSJMdEAnMSIg2IC5Si+gIFSK3pmP7ZjkiNsoQvkGC8LDPRYrwMI6IMCkiNsiwjdgoR+TmNERtUSD6OxXit+uQFGNEaoINaSIXNLJcZKiKYDOUw2WugifTj4LsOhS6G1CQXY/8rDrkOskoq2DVl8GgLIRK4oEsyQlRnBWJ0SYkxVqQHGeFON4OSaITclE2lJIcaOT50CuKYFSXwKwtg1lbDrOuHBZdOUzaMva5QVkMbRr94cQDSZILIoEdIoENyfE2JMXbkJroQFqqG+q0fBi1pXBaq5Hvrkd5UTMqy1vgr2xHnb+TUeM7gkB1B7yVrago3Y/igt3Ic9ch01oJvboQ8tQsiIRWJMQZERulQ1SEagVKREYosT1cwdgWrsAi9DmxjVAhktj+PlHb1YiKVGMHQ4MdkX8AojSIjtIgJkq7SGyUFh8ibocO7xGtQ3y0niGI0UMQY1idWAMSYjP+y+8aAwMEsZ/I06fy+If5njSnR/z/MYu6ZvolDa8dYawBwtgMRmJsBhLjjKuSFGdEUrwJyWtFYEKywMz6iSjBjNUQJ1ggFvJYIRb+NlKEVqQkcqQm2pCaxCFJskOSzCEVZeJDyMQOfAp5ihNpqS6kS7KgkGZDKcuGSuaGSu6BmkgjctaMJj0HWkUutIo86JT50KvyYVAXwqgtgllfAmtGKWymctjNO5FproDDsgtOayVctipk2auR7fDC4/QhxxVAXnYN8t21KPDUoTCnHkW5DSjOa0RJfhNKC3Yzygr3oLxoL3YW70NFSTN2le1HVflBeCuof7UiUN2OWm8H6v1H0BDoRGPNUTTWEl1oquvC7vpu7Gnoxd7GPjTvPoYDe0+gpfkUWg8MoKPlNI60DuNo+yiOdoyiizg8hu4jZ9HbOY6+rgkc757Eyb7z6D9+EYMnZzB06jLODFzB8OAsRk9fw+jQdYydmcP48A2cG72JibFbmBq/jQsTdzE9tYCZCw9xdfoxrs08xdyV57gx+wI3r73E/PVXuDX3GrdvvMGdm9/j7vxbLNz6Affv/IgHd/+GR/d+wuP7f8eT+z/jyYN/4OlD4h3j2cN3+CiP3uHZo18Yzx/9guePiV8ZD+/9hNs3XmN25gmmJxcwMTaPkdOzGDg+jZ4j42g7MMBi5atsZTlw2atgNZTCbipneWQ5zKyGOySHuVlcHvPctctyme+uY/nNcfmRnell99uMZTDpipGhKWS60aTncjqUe6CScxrVpHH6Im1lqAvZ9RZDKawZZbAZy2E3heqK0xbpay2QNh2WCqZTmpfOSWvQWgSta9EvhzRO0Dl4SPsEnYvHoC4AQ1UAAxF8z39P1/P3h478/DSuXPv/63uzoRShmCh++hIY9cUw6jgydEXIoBhqi2DQFsKg4dBrCqBXF0DHyIdOlQ8tIw9aZR40vzeKPGgUecwPNYpckKbJI1dl0X95P/6MkdUIVydKmRsMaTbzePL5UJTM97lr+LrivJ/fF+ffnIeH+jjpdEnDvFZp5DVOWmXaXKzJpbrkaojqaCfrAXwN0fV0P9UA1TWty9U+ncez6llCz7Wm15Is1gupH6ZLqC8uQX1ykRQnqHcyxA7IQ1jsuSIHZKLMNeBg8/J9mXoy9WGKA/VPii/F9cM+x/dQ8ioe8qxgnI3kWcv9iuJN8/GeRHGndQiKv05JOV+OXpnPfIrPtVlXwvbDee6S71JP59cO9UvaO61J9/N5pjXoWYGriVyQtzPkOdDKc5cjy4GGkBIeDokHaokbahpT30eV6sESbihTiGwoUtxQiDnSxR4wRDlII5JzIEv2QJrEIUmi384eSJNz8KdAdRt7oKAfwgFvEF8HAr7D8Ps6GD5fO3zednir2+CtboXX2wbfR2mFz0u0wefj8Pva4Pe1w+9vR+ALQvMxaH+0lpf2xq1N69FatTWHUV/ficbGLuzd04uDB06ivW0QnZ0j6Os7h/7+ixgevorxs3OYnJjHhfN3MD29gEvTC5ghLt3HxQt3MTUxj3Nn5zA8dAWnTpxHT9coOtoGcXD/MexpOoraQCuqK5tRVtKAgjw/sp0VsJqLoFO7kSa1QZycgYR4FXZEyrDtOzG2bkrExvXxWL8uBt98HY11f4nC119F4q9/XhvrvorEuq+iGN/+TzTWfx2DsL/GYvP6BERsESN6mwyCGCXEQgPSUq3QKFww6nJhNxchK3MnPK5dcDsrGFmZ5bCZqDHRQ7MTshQLREL6Qa9E9Pa0ENIRF6WEIEYNYZwWyQI9xIlGSERmSMVWyIgUDqnIgtRkM1KSjEhOoB9KWsTuUGIHzReZjpgoBXsvFOiQKjIjXe6ATpMDu7UEOe5qlBbXo6J8N6qrmlHjb0HA3wKvdz+qKvdi584mFBYG4HFXIjOzBDqdG1KpBQKBClGRUoSHi7FpkxBhYfHLWB8Wj2/XxzG++TYWxLog36yPA/EtXRMWj/UbBBwbBQhbJAFhGxMQtikBGzYJP81mITb8G9i4WYhNmxMZm7ckgmfLliSsZOvWJGzdmrzId1uTwfhOhHAinBCvSkSEGBERKdj2X36XGFDsGZ/I06fy+OW+53XzRx85XfP6XYwjH89Vxve0Hp6C7REckdtS8SmitqUiaruEsWO7BL+ZSAl2REoZ0VEyfIqYHXKsldhoOWKj0xAXQ6Qz4mPTER+rgCCOUCKBiF+JCkIBjxpCwRKJCWrwJAk1SE7UQpSkgzhZjxSRAaniDEhSjAxpqgmETLIWzJBLCQvSZFaky21QpNmhVGRCrXRCq86CXueG0ZADU0YuzMY8WEz5sJoLYLMUwm4tQqatGM7MUrgcZchyliPbtRPu7Ap43LuQ66lEbk4V8nKrkZ/rRUGeD4X5PhQV+FFcGEBJUS3KSupQXtaAXTubULVrD7xV++D37keN/yBqAy2oqzmE+tpW1Ne1oqG+DY0N7Whq7MCepsPYt6cT+/d14eD+Hhw62Ie2QyfQ0XYKRzoG0Hl4AEePDOJo52l0Hx1CT9cw+npGcKxvDCePj6P/5AQGB6YwNHgBZ4amMXLmEkZHZjA2ehlnx65g/OwsJsavYWpiDuenbmD6wi3MXLqDq5cXcG32AW5cf4T5G09xe/457tx6gbu3X+DenZdYuPsa9++9wYP73+PRg7d4/OhHPH38v3j29Cc8f/Z3vHj2M14+/xkvX/wDrxjv8Orl6rx++Q6vX/3CePPqF7x59SvevP4Vz578DQt3X7G90N6mJucwNnIZg/1T6Ok6g9aWYyxWVbv2oiDPy3Km12YjQ+9ZzCflkvLosJcs5dFVDnfWTrizKuChXGbvYu9dzjJ2DV1vMuaB5iKdKNPtSJdbIZOYmRZJm0yj4gz2ntcXXUfX6zRZMOjcbB9GA6erUG3RntYCaZPmIa2SZjP0hIetRXuldQktoeZxQat2QaMinGx/aqUDhEpBZEKZzmNn9cFqJN2++DldR9fT/cvh5qb5l9bj1/0PGTVZ0FD81C6o1S6oVE4OpQPKIApFJoh00km6HWlpNg65DXK5lSGTWSGTWTikFsiC0LPqvwt+TW40QyY1M02Tt/I++/GR82Pelz9npBrhIV8nyOPfh/uO9/5F/081Br2f83CuzkK93AZFiE55HZOmOUi3pFkXqwuqEfJ7g87D6oZqh4OrI/pOp8lm9UJ65+ue1l1Z+9xZ6DxfGi4+1A8/BPVKIklIaBiJCRosh+uvS/2X78OfP1J/5noz9WUD68UUgzS5lcVcpXSwOJHfUFyX/I7rpeRRvE9xXkU+tdyrOM/gfYn3IvJaG+vXLO4hGg19BqC+zvd0zne5/azcC1ubeSWXa9on75XkZaQTBdWrzAq5zMKeF9g69MxBpAQRmyAXm4OYIBeZICOSjRxJGZASiRySxAxwGCGh38WEcIlUIf1jRQYjJcGIFIERYoEJ4njCzBDF0z9LWJAUa0ZiEGGsGcJYCxLjLPgnXq+aViiuIVMAAAAASUVORK5CYII=)
# + [markdown] id="zKCKwjjGsc0q"
# 
# + id="qFQ-k4sWsKW0"
# Libraries
import qiskit.quantum_info as qi
from qiskit.circuit.library import FourierChecking
from qiskit.visualization import plot_histogram
# + id="VxgoWClNs3fI"
# Inputs
f = [1,-1,-1,-1]
g = [1,1,-1,-1]
# + [markdown] id="teY_2L7wtB_u"
# 
# + colab={"base_uri": "https://localhost:8080/", "height": 92} id="8ktU_niZs9Jf" outputId="bafe30a0-c737-4608-8317-066c893999a4"
circ = FourierChecking(f=f,g=g)
circ.draw()
# + colab={"base_uri": "https://localhost:8080/", "height": 337} id="_gkYGwlWtNAz" outputId="8f377274-06c6-40a3-98de-0b70788ef8fe"
zero = qi.Statevector.from_label('00')
sv = zero.evolve(circ)
probs = sv.probabilities_dict()
plot_histogram(probs)
# + colab={"base_uri": "https://localhost:8080/", "height": 337} id="WupHwbfatoKx" outputId="5c747277-f8ac-4c15-9925-ddebaab12948"
# Inputs
f = [-1,1,1,-1]
g = [-1,1,1,-1]
circ = FourierChecking(f=f,g=g)
circ.draw()
zero = qi.Statevector.from_label('00')
sv = zero.evolve(circ)
probs = sv.probabilities_dict()
plot_histogram(probs)
# + colab={"base_uri": "https://localhost:8080/", "height": 331} id="QhIVN26kuSSD" outputId="933e4da3-4ba8-40d1-8dc7-d7e3c567f8ed"
# Inputs
f = [1,1,1,-1]
g = [1,1,1,-1]
circ = FourierChecking(f=f,g=g)
circ.draw()
zero = qi.Statevector.from_label('00')
sv = zero.evolve(circ)
probs = sv.probabilities_dict()
plot_histogram(probs)
# + colab={"base_uri": "https://localhost:8080/", "height": 337} id="mtUdd8uXxOsO" outputId="5874bc9c-5e3b-4eba-807e-a8423de83137"
# Inputs
f = [1,-1,1,-1]
g = [1,-1,-1,-1]
circ = FourierChecking(f=f,g=g)
circ.draw()
zero = qi.Statevector.from_label('00')
sv = zero.evolve(circ)
probs = sv.probabilities_dict()
plot_histogram(probs)
# + colab={"base_uri": "https://localhost:8080/", "height": 337} id="qeLALWvHxoKM" outputId="72a8a71e-0888-45bb-933b-5a4e08262524"
# Inputs
f = [1,-1,1,-1]
g = [1,-1,-1,-1]
circ = FourierChecking(f=f,g=g)
circ.draw()
zero = qi.Statevector.from_label('00')
sv = zero.evolve(circ)
probs = sv.probabilities_dict()
plot_histogram(probs)
# + colab={"base_uri": "https://localhost:8080/", "height": 331} id="YUfuj8S7ySCt" outputId="d4cb696a-494b-4949-ecb5-9dc8a2fcb188"
# Inputs
f = [1,-1,0,0]
g = [1,-1,0,0]
circ = FourierChecking(f=f,g=g)
circ.draw()
zero = qi.Statevector.from_label('11')
sv = zero.evolve(circ)
probs = sv.probabilities_dict()
plot_histogram(probs)
# + id="lOm0Ah01ydIt"
| 1,318,522 |
/code/chap09mine.ipynb | 15b56ad3e9c126a6c446ce1a7f53c77cf3527ff8 | [
"MIT"
] | permissive | chiunaomi/ModSimPy | https://github.com/chiunaomi/ModSimPy | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 176,880 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# # Modeling and Simulation in Python
#
# Chapter 9: Projectiles
#
# Copyright 2017 Allen Downey
#
# License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)
#
# +
# If you want the figures to appear in the notebook,
# and you want to interact with them, use
# # %matplotlib notebook
# If you want the figures to appear in the notebook,
# and you don't want to interact with them, use
# # %matplotlib inline
# If you want the figures to appear in separate windows, use
# # %matplotlib qt5
# tempo switch from one to another, you have to select Kernel->Restart
# %matplotlib inline
from modsim import *
# -
# ### Dropping pennies
#
# I'll start by getting the units we'll need from Pint.
m = UNITS.meter
s = UNITS.second
kg = UNITS.kilogram
# And defining the initial state.
init = State(y=381 * m,
v=0 * m/s)
init
# Acceleration due to gravity is about 9.8 m / s$^2$.
g = 9.8 * m/s**2
# When we call `odeint`, we need an array of timestamps where we want to compute the solution.
#
# I'll start with a duration of 10 seconds.
duration = 20 * s
ts = linspace(0, duration, 11)
ts
# Now we make a `System` object.
system = System(init=init, g=g, ts=ts)
# And define the slope function.
def slope_func(state, t, system):
"""Compute derivatives of the state.
state: position, velocity
t: time
system: System object containing `g`
returns: derivatives of y and v
"""
y, v = state
unpack(system)
#print(t)
dydt = v
dvdt = -g
return dydt, dvdt
# It's always a good idea to test the slope function with the initial conditions.
dydt, dvdt = slope_func(init, 0, system)
print(dydt)
print(dvdt)
# Now we're ready to run `odeint`
run_odeint(system, slope_func)
# Here's what the results look like.
system.results.head()
system.results.tail()
# The following function plots the results.
def plot_position(results):
"""Plot the results.
results: DataFrame with position, `y`
"""
newfig()
plot(results.y, label='y')
decorate(xlabel='Time (s)',
ylabel='Position (m)')
# Here's what it looks like.
plot_position(system.results)
savefig('chap09-fig01.pdf')
# **Exercise:** Add a print statement to `slope_func` to print the value of `t` each time it's called. What can we infer about how `odeint` works, based on the results?
# **Exercise:** Change the value of `dt` and run the solver again. What effect does it have on the results?
# ### Onto the sidewalk
#
# Here's the code again to set up the `System` object.
def make_system(duration, v_init=0):
"""Make a system object.
duration: time of simulation in seconds
v_init: initial velocity, dimensionless
returns: System object
"""
init = State(y=381 * m, v=v_init * m / s)
g = 9.8 * m/s**2
ts = linspace(0, duration, 11)
return System(init=init, g=g, ts=ts)
# And run the simulation.
# +
#system = make_system(10)
#run_odeint(system, slope_func)
#system.results
# -
def sweep_velocity(velocity_array):
velocity = SweepSeries()
for v in velocity_array:
system = make_system(10, v_init=v)
run_odeint(system, slope_func)
velocity[v] = system.results.y
return velocity
velocity_array = linspace(0,25,26)
velocity_sweep = sweep_velocity(velocity_array)
velocity_sweep
# +
for v in velocity_array:
y = velocity_sweep[v]
T = interp_inverse(y, kind='cubic')
T_sidewalk = T(0)
plot(v, T_sidewalk, 'r-', label = 'sweep')
sns.set(style='whitegrid', font_scale=1.3)
decorate(xlabel='Initial Velocity', ylabel='Time')
legend(loc = 'best')
# -
# To figure out when the penny hit the sidewalk, we use `interp_inverse`, which return a function that maps from height to time.
# +
#y = velocity_sweep
#T = interp_inverse(y, kind='cubic')
# -
# `T(0)` interpolates the time when the height was 0.
T_sidewalk = T(0)
T_sidewalk
# We can compare that to the exact result. Without air resistance, we have
#
# $v = -g t$
#
# and
#
# $y = 381 - g t^2 / 2$
#
# Setting $y=0$ and solving for $t$ yields
#
# $t = \sqrt{\frac{2 y_{init}}{g}}$
sqrt(2 * init.y / g)
# The estimate is accurate to 4 decimal places.
# We can double-check by running the simulation for the estimated flight time.
system = make_system(duration=T_sidewalk)
run_odeint(system, slope_func)
# And checking the final state.
def final_state(results):
"""Returns the final position and velocity, with units.
results: TimeFrame with y and v.
returns: y, v at t_end
"""
t_end = results.index[-1]
y, v = results.loc[t_end]
return y*m, v*m/s
# As expected, the final height is close to 0.
y_final, v_final = final_state(system.results)
y_final
# And we can check the final velocity.
v_final
# And convert to km/h
km = UNITS.kilometer
h = UNITS.hour
v_final.to(km / h)
# If there were no air resistance, the penny would hit the sidewalk (or someone's head) at more than 300 km/h.
#
# So it's a good thing there is air resistance.
# **Exercise:** Try changing the initial velocity and see what effect it has on the time to hot the sidewalk. Sweep a range of values for the initial velocity, from 0 to 25 m/s, and plot `T_sidewalk` as a function of initial velocity. You might find the following function useful.
#
# Things might go horribly wrong for the larger initial velocities. What's going on?
def flight_time(system):
"""Simulates the system and computes flight time.
Uses cubic interpolation.
system: System object
returns: flight time in seconds
"""
run_odeint(system, slope_func)
y = system.results.y
inverse = Series(y.index, index=y.values)
T = interpolate(inverse, kind='cubic')
T_sidewalk = T(0)
return T_sidewalk * s
# Solution goes here
flight_time(system)
# ### With air resistance
# Next we'll add air resistance using the [drag equation](https://en.wikipedia.org/wiki/Drag_equation)
#
# First I'll create a `Condition` object to contain the quantities we'll need.
condition = Condition(height = 381 * m,
v_init = 0 * m / s,
g = 9.8 * m/s**2,
mass = 2.5e-3 * kg,
diameter = 19e-3 * m,
rho = 1.2 * kg/m**3,
v_term = 18 * m / s,
duration = 30 * s)
# Now here's a version of `make_system` that takes a `Condition` object as a parameter.
#
# `make_system` uses the given value of `v_term` to compute the drag coefficient `C_d`.
def make_system(condition):
"""Makes a System object for the given conditions.
condition: Condition with height, g, mass, diameter,
rho, v_term, and duration
returns: System with init, g, mass, rho, C_d, area, and ts
"""
unpack(condition)
init = State(y=height, v=v_init)
area = np.pi * (diameter/2)**2
C_d = 2 * mass * g / (rho * area * v_term**2)
ts = linspace(0, duration, 101)
return System(init=init, g=g, mass=mass, rho=rho,
C_d=C_d, area=area, ts=ts)
# Let's make a `System`
system = make_system(condition)
system
# Here's the slope function, including acceleration due to gravity and drag.
def slope_func(state, t, system):
"""Compute derivatives of the state.
state: position, velocity
t: time
system: System object containing g, rho,
C_d, area, and mass
returns: derivatives of y and v
"""
y, v = state
unpack(system)
f_drag = rho * v**2 * C_d * area / 2
a_drag = f_drag / mass
dydt = v
dvdt = -g + a_drag
return dydt, dvdt
# As always, let's test the slope function with the initial conditions.
slope_func(system.init, 0, system)
# And then run the simulation.
run_odeint(system, slope_func)
# First check that the simulation ran long enough for the penny to land.
final_state(system.results)
# Then compute the flight time.
y = system.results.y
inverse = Series(y.index, index=y.values)
T = interpolate(inverse, kind='cubic')
T_sidewalk = T(0)
T_sidewalk
# Setting the duration to the computed flight time, we can check the final conditions.
condition.set(duration=T_sidewalk)
system = make_system(condition)
run_odeint(system, slope_func)
y_final, v_final = final_state(system.results)
# The final height is close to 0, as expected. And the final velocity is close to the given terminal velocity.
y_final, v_final
# Here's the plot of position as a function of time.
plot_position(system.results)
savefig('chap09-fig02.pdf')
# And velocity as a function of time:
def plot_velocity(results):
"""Plot the results.
results: DataFrame with velocity, v
"""
newfig()
plot(results.v, label='v')
decorate(xlabel='Time (s)',
ylabel='Velocity (m/2)')
plot_velocity(system.results)
# From an initial velocity of 0, the penny accelerates downward until it reaches terminal velocity; after that, velocity is constant.
# **Exercise:** Run the simulation with an initial velocity, downward, that exceeds the penny's terminal velocity. Hint: use `condition.set`.
#
# What do you expect to happen? Plot velocity and position as a function of time, and see if they are consistent with your prediction.
# Solution goes here
condition.set(v_init = 25)
system = make_system(condition)
run_odeint(system, slope_func)
y_final, v_final = final_state(system.results)
plot_position(system.results)
# Solution goes here
plot_velocity(system.results)
# ### Dropping quarters
# Suppose we drop a quarter from the Empire State Building and find that its flight time is 19.1 seconds. We can use this measurement to estimate the coefficient of drag.
#
# Here's a `Condition` object with the relevant parameters from
# https://en.wikipedia.org/wiki/Quarter_(United_States_coin)
#
condition = Condition(height = 381 * m,
v_init = 0 * m / s,
g = 9.8 * m/s**2,
mass = 5.67e-3 * kg,
diameter = 24.26e-3 * m,
rho = 1.2 * kg/m**3,
duration = 19.1 * s)
# And here's a modified version of `make_system`
def make_system(condition):
"""Makes a System object for the given conditions.
condition: Condition with height, v_init, g, mass, diameter,
rho, C_d, and duration
returns: System with init, g, mass, rho, C_d, area, and ts
"""
unpack(condition)
init = State(y=height, v=v_init)
area = np.pi * (diameter/2)**2
ts = linspace(0, duration, 101)
return System(init=init, g=g, mass=mass, rho=rho,
C_d=C_d, area=area, ts=ts)
# We can run the simulation with an initial guess of `C_d=0.4`.
condition.set(C_d=0.4)
system = make_system(condition)
run_odeint(system, slope_func)
plot_position(system.results)
# The final height is -11 meters, which means our guess was too low (we need more drag to slow the quarter down).
final_state(system.results)
# `height_func` takes a hypothetical value of `C_d` and returns the height after 19.1 seconds.
def height_func(C_d, condition):
"""Final height as a function of C_d.
C_d: drag coefficient
condition: Condition object
returns: height in m
"""
condition.set(C_d=C_d)
system = make_system(condition)
run_odeint(system, slope_func)
y, v = final_state(system.results)
return y
# If we run it with `C_d=0.4`, we get -11 meters again.
height_func(0.4, condition)
# Now we can use `fsolve` to find the value of `C_d` that makes the final height 0.
solution = fsolve(height_func, 0.4, condition)
solution
# Plugging in the estimated value, we can run the simulation again to get terminal velocity.
condition.set(C_d=solution)
system = make_system(condition)
run_odeint(system, slope_func)
final_state(system.results)
# In this example, the terminal velocity of the quarter is higher than that of the penny, but we should not take this result seriously because the measurements we used are not real; I made them up.
| 12,490 |
/.ipynb_checkpoints/Intro_to_Machine_Learning-checkpoint.ipynb | f27247fafff149de02d4b26f5cec548ad022f4b5 | [] | no_license | vigneshhere/ai-workshop | https://github.com/vigneshhere/ai-workshop | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 8,199,638 | # ---
# jupyter:
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# extension: .py
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# language: python
# name: python3
# ---
from requests_html import HTMLSession
from requests_ntlm import HttpNtlmAuth
session = HTMLSession()
r = session.get('http://hkvkms001/default.aspx',
auth=HttpNtlmAuth('hkv\\hoek', 'w8woorden'))
taken = r.html.find('.hkv-projectTaken-webpart-data', first=True)
taken.text
'
| 560 |
/.ipynb_checkpoints/2.4.2 Examples Involving Randomness-checkpoint.ipynb | 22dedc525ca7817bf1c94937fef8ad2d3883b664 | [] | no_license | dharnitski/edx-PH526x | https://github.com/dharnitski/edx-PH526x | 1 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 15,973 | # ---
# jupyter:
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# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# +
import matplotlib.pyplot as plt
import numpy as np
import random
rolls = []
for i in range(10000):
rolls.append(random.choice(range(1, 7)))
plt.hist(rolls, bins = np.linspace(0.5, 6.5, 7))
# -
ys = []
for rep in range(1000000):
y = 0
for k in range(10):
x = random.choice(range(1, 7))
y += x
ys.append(y)
min(ys)
max(ys)
plt.hist(ys)
| 641 |
/FeatX_OSELM_comb.ipynb | 22a258b81403d43c6a35484386e4a3e4ff05a862 | [] | no_license | aguntaka/NeuralNetworks | https://github.com/aguntaka/NeuralNetworks | 1 | 3 | null | null | null | null | Jupyter Notebook | false | false | .py | 23,980 | # ---
# jupyter:
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# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# name: python3
# ---
# + [markdown] id="LLjrHVQARPU6" colab_type="text"
# # imports
# + id="y3sYTTanvuP1" colab_type="code" colab={}
import numpy as np
import scipy.io
import pandas as pd
from random import randint
from time import time
sin=np.sin
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import MinMaxScaler
asin=np.arcsin
from numpy.linalg import inv
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
global c_i
c_i=2**8
# + id="fK8j9IOxww1b" colab_type="code" outputId="db15cb89-5820-48e1-b621-941bf3a8883a" colab={"base_uri": "https://localhost:8080/", "height": 34}
from google.colab import drive
drive.mount('/content/drive')
# + id="lLIZ9dTcOOZ4" colab_type="code" colab={}
######optional to save all features in a seperate folder
import os
##### choose ur own folder to save them
os.chdir('/content/drive/My Drive/final project /data-channels')
# + [markdown] id="Wl1JpWMXC1Nr" colab_type="text"
# #Custom function
# + id="zFkDHDsaC4CF" colab_type="code" colab={}
def randperm(n):
r_perm=[]
while len(r_perm)!=n:
k=randint(1,n)
if k not in r_perm:
r_perm.append(k)
else:
pass
return r_perm
# + id="ngZzCwQFDghl" colab_type="code" colab={}
def noofclass(fea,label=True):
n_class=0
labels=[]
for i in range(fea[0].shape[0]):
labels.append(fea[0][i])
if fea[0][i]>n_class:
n_class=fea[0][i]
else:
n_class=n_class
if label==False:
return int(n_class+1)
else:
labels=np.matrix(labels)
return labels.transpose()
# + id="kmyQzBAyWEFt" colab_type="code" colab={}
def linear_kernal(a,b):
return np.matmul(a,b.transpose())
# + id="z_GDJwf165lA" colab_type="code" colab={}
def normalize(a,b):
temp=a+b
min=np.min(temp)
max=np.max(temp)
#print(min,max)
u=2*((temp-min)/(max-min))
u=u-1
return u
# + id="mnKy1NNEW7WG" colab_type="code" colab={}
def sigmoid_inv(a):
temp=1/a
temp=temp-1
temp=np.log(temp)
temp=-temp
return temp
# + id="X6wktKGcrSX4" colab_type="code" colab={}
import math
def sigmoid(x):
return 1 / (1 + np.exp(-x))
# + id="QlcPLsCqcaTS" colab_type="code" colab={}
def normalize_inv(a):
return (np.max(a)-a)/(np.max(a)-np.min(a))
# + [markdown] id="gVTUzKf232LO" colab_type="text"
# # Morres-pseudo inverse
# + id="99xlc6yS6srC" colab_type="code" colab={}
def relu(features):
return np.maximum(0,features)
def sigmoid(features):
return 1.0 / (1.0 + np.exp(-features))
def linear(features,weights,bias):
return np.dot(features, np.transpose(weights)) + bias
# + id="AGMvzguk4Xuq" colab_type="code" colab={}
### function for morres-pseudo inverse
### Input: features, target, weights, bias
### Output: Morres-matrix, Beta (output weights)
def pseudoinverse(features, targets,weights,bias,forgettingFactor=0.999):
(numSamples, numOutputs) = targets.shape
assert features.shape[0] == targets.shape[0]
H = linear(features,weights,bias)
H = sigmoid(H)
Ht = np.transpose(H)
M = (1/forgettingFactor) * M - np.dot((1/forgettingFactor) * M,
np.dot(Ht, np.dot(
pinv(np.eye(numSamples) + np.dot(H, np.dot((1/forgettingFactor) * M, Ht))),
np.dot(H, (1/forgettingFactor) * M))))
beta = beta + np.dot(M, np.dot(Ht, targets - np.dot(H,beta)))
return (M,beta)
# + [markdown] id="mlCW8jxCBwdO" colab_type="text"
# #sample_g
#
# + id="jl_51itLBzup" colab_type="code" colab={}
def sample_g(fea):
nclass=noofclass(fea,False)
train_per_image=100;
fdatabase_label=noofclass(fea,True)
tr_idx=[]
ts_idx=[]
tr_list=[]
ts_list=[]
idx_label=0
for i in range(nclass):
idx_label=[]
for j in range(len(fdatabase_label)):
a=int(fdatabase_label[j])
if a==i:
idx_label.append(j)
num=len(idx_label)
idx_rand=randperm(num-1)
tr_num =train_per_image
for k in range(tr_num):
temp=idx_label[idx_rand[k]]
tr_list.append(temp)
for l in range(tr_num-1,num-1):
temp=idx_label[idx_rand[l]]
ts_list.append(temp)
return np.array(tr_list),np.array(ts_list)
# + [markdown] id="FliEB3qgJ5UQ" colab_type="text"
# #First General Layer -
# Subspace Feature Extraction
# + id="sqN759KOJ4rE" colab_type="code" colab={}
def step1(training,testing):
X=training
y=testing
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42)
print('traindata : ',X_train.shape,'testdata : ',X_test.shape,'train_label : ',y_train.shape,'test_label : ',y_test.shape)
return X_train, X_test, y_train, y_test
# + id="EG1LRa2e575f" colab_type="code" colab={}
#weights and bias generation on random
#for the first layer
def step2(data_set):
dim=data_set.shape
weights=np.random.randn(1,dim[0])
bias=np.random.randn(1,1)
H=np.dot(weights,data_set)+bias
#print('weights : ',weights.shape,'data : ',data_set.shape,'bias : ',bias.shape,'H size : ',H.shape)
return H
# + id="gJmf8wLK8b3i" colab_type="code" colab={}
def step3(subspacefeature,labels):
temp=inv(c_i+np.dot(subspacefeature,subspacefeature.transpose()))
#print(labels.shape,temp.shape)
temp=np.dot(subspacefeature.transpose(),temp)
#print(temp.transpose().shape)
y=labels
weights=(y*temp).transpose()
#print(weights.shape,'-weights')
temp=weights*subspacefeature
ind = np.ones((1,subspacefeature.shape[1]))
y=np.matrix(y).transpose()
y=y*ind
#print(temp.shape,y.shape,'-v')
bias=mean_squared_error(temp,y)
bias=np.sqrt(bias)
return weights,bias
# + id="61yKCqzSDFE8" colab_type="code" colab={}
def step4(weights,bias,subspacefeature,labels):
ind = np.ones((1,subspacefeature.shape[1]))
y=labels
y=np.matrix(y).transpose()
y=y*ind
e=y-(weights*subspacefeature+bias)
return e
# + id="jcaQphX0DpQI" colab_type="code" colab={}
def step5(error,weights,subspacefeature):#step 5 &6
#c_i=np.random.randint(1,10)
temp=c_i+np.dot(weights,weights.transpose())
temp=np.linalg.pinv(temp)
temp=np.dot(weights.transpose(),temp.transpose())
temp=np.dot(weights.transpose(),temp.transpose())
p=error*temp
p=normalize(p,subspacefeature)
#print(p.shape)
return p
# + id="OUOTltJfEW42" colab_type="code" colab={}
### APPLY BOOSTING PHASE HERE
def step7(weight_fl,bias_fl,input_data,normalized_error):
#c_i=np.random.randint(1,10)
a=weight_fl
b=bias_fl
x=input_data
p=normalized_error
temp=np.ones(shape=(p.shape[1],x.shape[0]))
#print(temp.shape)
p_temp=p
p=p*temp
#print(p.shape)
a_temp=(p*np.dot(x.transpose(),np.linalg.pinv(c_i+np.dot(x,x.transpose()))).transpose())
a_f=a+0.5*(a_temp-a)
temp=np.array(a_f)*x
#print(p.shape)
b_f=np.sqrt(mean_squared_error(temp,p_temp))
#print(type(a_f),type(x),type(p))
h_f=np.array(a_f)*x+b_f
return h_f,a_f,b_f
# + [markdown] id="fu3o_LTHbMQ1" colab_type="text"
# #Second Generel Layer -
# Subspace features combination
# + id="PF_kva9BRKor" colab_type="code" colab={}
#def fea_comb(subnetwork_nodes,data_channels):
def fea_comb(feature_array,subnetowk_nodes,data_channels):
for i in range(1,data_channels+1):
for j in range(1,subnetwork_nodes):
#temp="ch%s_fea_%s"%(i,j)
temp=feature_array
if i==1 and j==1:
#h=np.loadtxt(temp)
h=temp
else:
prev_h=h
#h=np.loadtxt(temp)
h=feature_array
final_h=linear_kernal(h.transpose(),prev_h.transpose())
return final_h
# + [markdown] id="-XB-LpsBUq2H" colab_type="text"
# # Third General Layer -
# Classifier with Subnetowrk Nodes
# + id="3o1az7DEU3_B" colab_type="code" colab={}
def last_layer(final_features,test_labels):
c2=2**12
C=3
N=10
#step 1
h=final_features
for c in range(C):
if c==0:
prev_e=test_labels
else:
weights=c2+np.dot(h,h.transpose())
print(prev_e.shape)
temp=np.ones(prev_e.shape)
print(temp.shape)
temp=prev_e+temp
print(temp.shape)
temp=sigmoid_inv(temp)
print(temp.shape)
temp=np.dot(temp,np.dot(h.transpose(),np.linalg.pinv(weights)))
#weights=sigmoid_inv(prev_e+np.ones(prev_e.shape))*np.dot(h.transpose(),np.linalg.pinv(weights))
bias=np.sum(np.matmul(weights.transpose(),h.transpose())-(sigmoid_inv(prev_e)) )
bias=bias/N
g=normalize_inv(sigmoid(weights*h.transpose()+bias))
in_pro=np.inner(prev_e,g.transpose()).transpose()
near_int=np.around(g)**2
output_weights=in_pro/near_int
e_c=prev_e-np.dot(output_weights,sigmoid(weights*ouput_weights+bias))
return e_c
#step 2
# + [markdown] id="kEyuN2mNn16f" colab_type="text"
# ⟨,⟩ - inner product
# || || - nearest integer
# + [markdown] id="Uuyjdv2cwswN" colab_type="text"
# #main
# + id="iPNynzA9wrq0" colab_type="code" outputId="094bd8a6-dda3-4d19-bbcb-c2d07c06c695" colab={"base_uri": "https://localhost:8080/", "height": 102}
mat = scipy.io.loadmat('/content/drive/My Drive/final project /Matlabcode-feature-matrix/spatialpyramidfeatures4scene15.mat')
featureMat=mat['featureMat']
labelMat=mat['labelMat']
labelMat=np.argwhere(labelMat==1)
temp=[]
b=[]
for i in range(len(labelMat)):
value=labelMat[i][0]
b.append(labelMat[i][1])
temp.append(value)
featureMat=featureMat.transpose()
labelMat=np.array(temp)
fea=np.column_stack([labelMat,featureMat])
fea=fea.transpose()
print("label Shape: ",labelMat.shape,'\nfeature Shape: ',featureMat.shape)
print('fea Shape: ',fea.shape)
train_idx,test_idx=sample_g(fea)
tr_fea=fea[: , train_idx].transpose()
ts_fea=fea[:, test_idx].transpose()
print('train fea: ',tr_fea.shape,'test fea: ',ts_fea.shape)
train_data=tr_fea
test_data=ts_fea
train_labels=labelMat[train_idx]
test_labels=labelMat[test_idx]
print('train labels: ',train_labels.shape,'test_labels: ',test_labels.shape)
# + [markdown] id="1nvvYxQsDYob" colab_type="text"
# matrix shape - (rows,columns)
#
#
#
# + id="TmLHBPOpANGo" colab_type="code" colab={}
train_inputs,test_inputs,train_labels,test_labels=tr_fea,ts_fea,train_labels,test_labels #step1
data_channels=10
subnetwork_nodes=3
##First layer call
for i in range(1,data_channels+1):
for j in range(1,subnetwork_nodes+1):
start_time=time()
sub=step2(train_inputs) #step2
w,b=step3(sub,train_labels) #step3
error=step4(w,b,sub,train_labels) #step 4
norm_err=step5(error,w,sub) #step 5 and 6
h_f,a_f,b_f=step7(w,b,train_inputs,norm_err) #step7
###
### AFTER BOOSTING PHASE IS DONE FOR THE FIRST TIME
### SAVE THE WEIGHTS IN A FILE
###
### AFTER THE FIRST BLOCK OF DATA IS SENT THROUGH WE WILL USE THE PREVIOUSLY
### SAVED WEIGHTS FILE AND ADD ON TO IT AND SAVE IT
###
### THAT COMPLETES THE SEQUENTIAL LEARNING PHASE
###
end_time=time()
h=fea_comb(h_f,subnetwork_nodes,data_channels)
train_time=end_time-start_time
print('train time : ',train_time)
# + [markdown] id="BByAxe7_uOTb" colab_type="text"
# # Print statement
#
# + id="PWxXhGzX0jN2" colab_type="code" colab={}
print("------------------------------------------------------------------")
print('***train data*** : ')
print("------------------------------------------------------------------")
print(train_inputs)
print("------------------------------------------------------------------")
print('***sub*** : ')
print("------------------------------------------------------------------")
print(sub)
print("------------------------------------------------------------------")
print('***error*** : ')
print("------------------------------------------------------------------")
print(error)
print("------------------------------------------------------------------")
print('***norm_error*** : ')
print("------------------------------------------------------------------")
print(norm_err)
print("------------------------------------------------------------------")
print('**h_f*** : ')
print("------------------------------------------------------------------")
print(h_f)
print("------------------------------------------------------------------")
print('***h*** : ')
print("------------------------------------------------------------------")
print(h)
# + id="cabo-L9_pKv6" colab_type="code" colab={}
print('weights before updation\n',w)
print('bias before updation \n',b)
print('-----------------------------------------------------------------')
print("weights after updation \n",a_f)
print("bias after updation \n",b_f)
# + [markdown] id="rzf2_Q3juSpZ" colab_type="text"
# # final output
# + id="WRa__GqGy2mQ" colab_type="code" colab={}
last_layer(h,train_labels)
| 13,035 |
/.ipynb_checkpoints/02_Face Detection-checkpoint.ipynb | fc8a7f2f4a69ee98faec465c54be3a5c4cd1a416 | [] | no_license | Sophie52052/computer_vision | https://github.com/Sophie52052/computer_vision | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 14,132 | # ---
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# name: python3
# ---
# +
###Face & Eye Detection using HAAR Cascade Classifiers
# +
import numpy as np
import cv2
face_classifier = cv2.CascadeClassifier('Haarcascades/haarcascade_frontalface_default.xml')
image = cv2.imread('images/Trump.jpg')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
faces = face_classifier.detectMultiScale(gray, 1.3, 3)
if faces is ():
print("No faces found")
for (x,y,w,h) in faces:
cv2.rectangle(image, (x,y), (x+w,y+h), (127,0,255), 2)
cv2.imshow('Face Detection', image)
cv2.waitKey(0)
cv2.destroyAllWindows()
# +
###Let's combine face and eye detection
# +
import numpy as np
import cv2
face_classifier = cv2.CascadeClassifier('Haarcascades/haarcascade_frontalface_default.xml')
eye_classifier = cv2.CascadeClassifier('Haarcascades/haarcascade_eye.xml')
img = cv2.imread('images/Donald.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_classifier.detectMultiScale(gray, 1.3, 5)
if faces is ():
print("No Face Found")
for (x,y,w,h) in faces:
cv2.rectangle(img,(x,y),(x+w,y+h),(127,0,255),2)
cv2.imshow('img',img)
cv2.waitKey(0)
roi_gray = gray[y:y+h, x:x+w]
roi_color = img[y:y+h, x:x+w]
eyes = eye_classifier.detectMultiScale(roi_gray)
for (ex,ey,ew,eh) in eyes:
cv2.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(255,255,0),2)
cv2.imshow('img',img)
cv2.waitKey(0)
cv2.destroyAllWindows()
# +
###Let's make a live face & eye detection
# +
import cv2
import numpy as np
face_classifier = cv2.CascadeClassifier('Haarcascades/haarcascade_frontalface_default.xml')
eye_classifier = cv2.CascadeClassifier('Haarcascades/haarcascade_eye.xml')
def face_detector(img, size=0.5):
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
faces = face_classifier.detectMultiScale(gray, 1.3, 5)
if faces is ():
return img
for (x,y,w,h) in faces:
x = x - 50
w = w + 50
y = y - 50
h = h + 50
cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
roi_gray = gray[y:y+h, x:x+w]
roi_color = img[y:y+h, x:x+w]
eyes = eye_classifier.detectMultiScale(roi_gray)
for (ex,ey,ew,eh) in eyes:
cv2.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(0,0,255),2)
roi_color = cv2.flip(roi_color,1)
return roi_color
cap = cv2.VideoCapture(0)
while True:
ret, frame = cap.read()
cv2.imshow('Our Face Extractor', face_detector(frame))
k = cv2.waitKey(1) & 0xFF
if k == 27 :
break
cap.release()
cv2.destroyAllWindows()
# +
###Merging Faces
# +
import cv2
import dlib
import numpy
from time import sleep
import sys
PREDICTOR_PATH = "shape_predictor_68_face_landmarks.dat"
SCALE_FACTOR = 1
FEATHER_AMOUNT = 11
FACE_POINTS = list(range(17, 68))
MOUTH_POINTS = list(range(48, 61))
RIGHT_BROW_POINTS = list(range(17, 22))
LEFT_BROW_POINTS = list(range(22, 27))
RIGHT_EYE_POINTS = list(range(36, 42))
LEFT_EYE_POINTS = list(range(42, 48))
NOSE_POINTS = list(range(27, 35))
JAW_POINTS = list(range(0, 17))
ALIGN_POINTS = (LEFT_BROW_POINTS + RIGHT_EYE_POINTS + LEFT_EYE_POINTS +
RIGHT_BROW_POINTS + NOSE_POINTS + MOUTH_POINTS)
OVERLAY_POINTS = [
LEFT_EYE_POINTS + RIGHT_EYE_POINTS + LEFT_BROW_POINTS + RIGHT_BROW_POINTS,
NOSE_POINTS + MOUTH_POINTS,
]
COLOUR_CORRECT_BLUR_FRAC = 0.6
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(PREDICTOR_PATH)
class TooManyFaces(Exception):
pass
class NoFaces(Exception):
pass
def get_landmarks(im):
rects = detector(im, 1)
if len(rects) > 1:
raise TooManyFaces
if len(rects) == 0:
raise NoFaces
return numpy.matrix([[p.x, p.y] for p in predictor(im, rects[0]).parts()])
def annotate_landmarks(im, landmarks):
im = im.copy()
for idx, point in enumerate(landmarks):
pos = (point[0, 0], point[0, 1])
cv2.putText(im, str(idx), pos,
fontFace=cv2.FONT_HERSHEY_SCRIPT_SIMPLEX,
fontScale=0.4,
color=(0, 0, 255))
cv2.circle(im, pos, 3, color=(0, 255, 255))
return im
def draw_convex_hull(im, points, color):
points = cv2.convexHull(points)
cv2.fillConvexPoly(im, points, color=color)
def get_face_mask(im, landmarks):
im = numpy.zeros(im.shape[:2], dtype=numpy.float64)
for group in OVERLAY_POINTS:
draw_convex_hull(im,
landmarks[group],
color=1)
im = numpy.array([im, im, im]).transpose((1, 2, 0))
im = (cv2.GaussianBlur(im, (FEATHER_AMOUNT, FEATHER_AMOUNT), 0) > 0) * 1.0
im = cv2.GaussianBlur(im, (FEATHER_AMOUNT, FEATHER_AMOUNT), 0)
return im
def transformation_from_points(points1, points2):
"""
Return an affine transformation [s * R | T] such that:
sum ||s*R*p1,i + T - p2,i||^2
is minimized.
"""
points1 = points1.astype(numpy.float64)
points2 = points2.astype(numpy.float64)
c1 = numpy.mean(points1, axis=0)
c2 = numpy.mean(points2, axis=0)
points1 -= c1
points2 -= c2
s1 = numpy.std(points1)
s2 = numpy.std(points2)
points1 /= s1
points2 /= s2
U, S, Vt = numpy.linalg.svd(points1.T * points2)
R = (U * Vt).T
return numpy.vstack([numpy.hstack(((s2 / s1) * R,
c2.T - (s2 / s1) * R * c1.T)),
numpy.matrix([0., 0., 1.])])
def read_im_and_landmarks(image):
im = image
im = cv2.resize(im,None,fx=1, fy=1, interpolation = cv2.INTER_LINEAR)
im = cv2.resize(im, (im.shape[1] * SCALE_FACTOR,
im.shape[0] * SCALE_FACTOR))
s = get_landmarks(im)
return im, s
def warp_im(im, M, dshape):
output_im = numpy.zeros(dshape, dtype=im.dtype)
cv2.warpAffine(im,
M[:2],
(dshape[1], dshape[0]),
dst=output_im,
borderMode=cv2.BORDER_TRANSPARENT,
flags=cv2.WARP_INVERSE_MAP)
return output_im
def correct_colours(im1, im2, landmarks1):
blur_amount = COLOUR_CORRECT_BLUR_FRAC * numpy.linalg.norm(
numpy.mean(landmarks1[LEFT_EYE_POINTS], axis=0) -
numpy.mean(landmarks1[RIGHT_EYE_POINTS], axis=0))
blur_amount = int(blur_amount)
if blur_amount % 2 == 0:
blur_amount += 1
im1_blur = cv2.GaussianBlur(im1, (blur_amount, blur_amount), 0)
im2_blur = cv2.GaussianBlur(im2, (blur_amount, blur_amount), 0)
im2_blur += (128 * (im2_blur <= 1.0)).astype(im2_blur.dtype)
return (im2.astype(numpy.float64) * im1_blur.astype(numpy.float64) /
im2_blur.astype(numpy.float64))
def swappy(image1, image2):
im1, landmarks1 = read_im_and_landmarks(image1)
im2, landmarks2 = read_im_and_landmarks(image2)
M = transformation_from_points(landmarks1[ALIGN_POINTS],
landmarks2[ALIGN_POINTS])
mask = get_face_mask(im2, landmarks2)
warped_mask = warp_im(mask, M, im1.shape)
combined_mask = numpy.max([get_face_mask(im1, landmarks1), warped_mask],
axis=0)
warped_im2 = warp_im(im2, M, im1.shape)
warped_corrected_im2 = correct_colours(im1, warped_im2, landmarks1)
output_im = im1 * (1.0 - combined_mask) + warped_corrected_im2 * combined_mask
cv2.imwrite('output.jpg', output_im)
image = cv2.imread('output.jpg')
return image
##
image1 = cv2.imread('images/Jay.jpg')
image2 = cv2.imread('images/Trump.jpg')
swapped = swappy(image1, image2)
cv2.imshow('Face Swap 1', swapped)
swapped = swappy(image2, image1)
cv2.imshow('Face Swap 2', swapped)
cv2.waitKey(0)
cv2.destroyAllWindows()
# +
###Run Our Facial Recognition
import cv2
import numpy as np
face_classifier = cv2.CascadeClassifier('Haarcascades/haarcascade_frontalface_default.xml')
def face_detector(img, size=0.5):
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
faces = face_classifier.detectMultiScale(gray, 1.3, 5)
if faces is ():
return img, []
for (x,y,w,h) in faces:
cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,255),2)
roi = img[y:y+h, x:x+w]
roi = cv2.resize(roi, (200, 200))
return img, roi
cap = cv2.VideoCapture(0)
while True:
ret, frame = cap.read()
image, face = face_detector(frame)
try:
face = cv2.cvtColor(face, cv2.COLOR_BGR2GRAY)
results = model.predict(face)
if results[1] < 500:
confidence = int( 100 * (1 - (results[1])/400) )
display_string = str(confidence) + '% Confident it is User'
cv2.putText(image, display_string, (100, 120), cv2.FONT_HERSHEY_COMPLEX, 1, (255,120,150), 2)
if confidence > 75:
cv2.putText(image, "Unlocked", (250, 450), cv2.FONT_HERSHEY_COMPLEX, 1, (0,255,0), 2)
cv2.imshow('Face Recognition', image )
else:
cv2.putText(image, "Locked", (250, 450), cv2.FONT_HERSHEY_COMPLEX, 1, (0,0,255), 2)
cv2.imshow('Face Recognition', image )
except:
cv2.putText(image, "No Face Found", (220, 120) , cv2.FONT_HERSHEY_COMPLEX, 1, (0,0,255), 2)
cv2.putText(image, "Locked", (250, 450), cv2.FONT_HERSHEY_COMPLEX, 1, (0,0,255), 2)
cv2.imshow('Face Recognition', image )
pass
k = cv2.waitKey(1) & 0xFF
if k == 27 :
break
cap.release()
cv2.destroyAllWindows()
| 9,872 |
/part04/3.b 실습 - Files operations.ipynb | 5d1dce563928f38e255f139138b071e1ebf63be1 | [] | no_license | soogroup/python_sun | https://github.com/soogroup/python_sun | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 14,078 | # ---
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# ---
# # <font color='red'> *실습* </font>
# ## <font color='red'> FILES OPERATIONS </font>
# # <font color='blue'>1.fruits_input.txt 파일의 내용을 출력하세요. </font>
with open('fruits_input.txt', 'r') as file :
print(file.read())
# # <font color='blue'>2. fruits_input.txt 파일에서, 줄바꿈('\n') 및 '' 을 다 제거하세요. 그리고 한문자로 되어있는 문장도 모두 제거하세요. 과일이름만 남도록 하여, 새로 fruits_out.txt 에 저장하세요. </font>
fruits_list = []
with open('fruits_input.txt', 'r') as file:
for line in file :
if line == '\n' :
continue
elif line == '':
continue
elif len(line) == 2 :
continue
fruits_list.append(line)
fruits_list = [ word.strip('\n') for word in fruits_list ]
fruits_list = fruits_list[2: ]
fruits_list
with open('fruits_out.txt', 'w') as file:
for word in fruits_list:
file.write(word + '\n')
# # <font color='blue'>3. fruit_out.txt 에 저장되어 있는 과일과 my_fruits에 있는 과일을 비교하여, my_fruits에 있는 과일이 존재하면, 해당 과일의 이름을 출력하세요.</font>
my_fruits = ["Apple", "Pepper", "Orange", "Watermelon", "Tomatoes"]
with open('fruits_out.txt', 'r') as file :
for line in file :
if line.strip('\n') in my_fruits:
print(line)
# # <font color='blue'> 4. 유저한테 다음 문장을 입력받습니다. 유저의 문장에 콤마(,) 가 들어있으면, 이를 잘라서 names_out_file.txt 에 write 하는 프로그램을 작성하세요.
sentence = input()
sentence_list = sentence.split(',')
sentence_list
with open('names_out_file.txt', 'w') as file:
for word in sentence_list:
file.write(word.strip() + '\n')
# # <font color='blue'> 5. "S&P500_Stock_Data.csv" 파일을 읽어서, (1) HEADING(즉, 맨 위의 데이터를 설명하는 컬럼 이름) 출력, (2) 그 아래 있는 실제 데이터를 맨처음 데이터부터 5개까지만 출력하세요.
import csv
with open('S&P500_Stock_Data.csv', 'r') as csvfile:
csv_data = csv.reader(csvfile)
stock_data = list(csv_data)
len(stock_data)
print(stock_data[0])
print(stock_data[1:6])
# # <font color='blue'> 6. 유저가 문장을 입력하고 엔터를 치면, recoding.txt 파일에 유저가 입력한 문장을 저장합니다. 단, 유저가 "STOP"이라는 문장을 입력할 때까지, 계속 유저의 입력을 받아서, 파일에 저장하는 프로그램을 작성하세요.
while True :
sentence = input('문장 입력 : ')
if sentence == 'STOP' :
break
with open('recoding.txt', 'a') as file:
file.write(sentence+'\n')
| 2,473 |
/Regression-Project .ipynb | ef465f8ea68ac74974f93b57691bd6b2a2c71b48 | [] | no_license | maghrabi-es/Regression-Project | https://github.com/maghrabi-es/Regression-Project | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 6,555,503 | # ---
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# language: python
# name: python3
# ---
# +
from bs4 import BeautifulSoup as bs
from urllib.request import urlopen
import pandas as pd
from selenium import webdriver
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
from selenium.webdriver.common.by import By
from selenium.common.exceptions import TimeoutException
import time
import requests
import scrapy
from scrapy.crawler import CrawlerProcess
import json
import numpy as np
import matplotlib.pyplot as plt
import re
from sklearn.model_selection import train_test_split
from pandas.plotting import scatter_matrix
import seaborn as sns
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OrdinalEncoder
from sklearn.linear_model import LinearRegression,LassoCV,Ridge
import sklearn.metrics as metrics
from sklearn.model_selection import cross_val_score,cross_val_predict
from sklearn.ensemble import RandomForestRegressor
from sklearn.preprocessing import LabelEncoder , StandardScaler, PolynomialFeatures
from sklearn.metrics import r2_score,mean_absolute_error,mean_squared_error
from sklearn import linear_model
from sklearn.linear_model import Ridge
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.model_selection import KFold
from sklearn.model_selection import train_test_split, KFold, cross_val_score, GridSearchCV
# -
# # Udemy Website
# # Overview
#
# Udemy, founded in May 2010, is an American online learning platform aimed at professional adults and students. As of Jan 2020, the platform has more than 50 million students and 57,000 instructors teaching courses in over 65 languages Students take courses largely as a means of improving job-related skills. Some courses generate credit toward technical certification. Udemy has made a special effort to attract corporate trainers seeking to create coursework for employees of their company. As of 2020, there are more than 150,000 courses on the website.
# # Dataset
# The dataset was collected by using web scraping methods like selenium and beautiful soup to collect more than 5K useful records from Udmey website.
#
# This dataset contains 9899 records of courses from 4 Levels (Beginner, Intermediate, Expert and some of them suitable for All Levels) taken from Udemy. Udemy is a massive online open course (MOOC) platform that offers both free and paid courses. Anybody can create a course, a business model by which allowed Udemy to have hundreds of thousands of courses.
# # Objective
#
# The objective is to analyze dataset based on several variables, determine what variables affect Courses Subscribers the most, then build a model that can predict the Subscribers of a courses.
# # Goals
# Predict the Subscribers of Courses based on the features available.
#
# # Phase I - scraping
# # Inputting the URL and scraping Method
# +
#url = 'https://www.udemy.com/robots.txt'
#response = requests.get(url)
#print(response.text)
# -
ch='/Users/elyasm/Downloads/chromedriver'
de=15
driver=webdriver.Chrome(executable_path=ch)
driver.get('https://www.udemy.com/courses/it-and-software/?p=1')
# +
def extract_text(soup_obj, tag, attribute_name, attribute_value):
txt = soup_obj.find(tag, {attribute_name: attribute_value}).text.strip() if soup_obj.find(tag, {attribute_name: attribute_value}) else ''
return txt
rows = []
for page_number in range(151, 625):
#f'https://www.udemy.com/courses/it-and-software/?p={page_number}'
page_url =f'https://www.udemy.com/courses/it-and-software/?has_closed_caption=true&p={page_number}&sort=popularity'
driver.get(page_url)
time.sleep(7)
try:
WebDriverWait(driver, de).until(EC.presence_of_element_located((By.CLASS_NAME, 'course-list--container--3zXPS')))
except TimeoutException:
print('Loading exceeds delay time')
break
else:
soup = bs(driver.page_source, 'html.parser')
course_list = soup.find('div', {'class': 'course-list--container--3zXPS'})
courses = course_list.find_all('a', {'class': 'udlite-custom-focus-visible browse-course-card--link--3KIkQ'})
for course in courses:
course_url = '{0}{1}'.format('https://www.udemy.com', course['href'])
course_title = course.select('div[class*="course-card--course-title"]')[0].text
course_headline = extract_text(course, 'p', 'data-purpose', 'safely-set-inner-html:course-card:course-headline')
author = extract_text(course, 'div', 'data-purpose', 'safely-set-inner-html:course-card:visible-instructors')
off_price=extract_text(course,'div', 'data-purpose', 'course-price-text').strip('Current price$')
orig_price=extract_text(course,'div', 'data-purpose', 'original-price-container').strip('Original Price$')
course_rating = extract_text(course, 'span', 'data-purpose', 'rating-number')
number_of_ratings = extract_text(course, 'span', 'class', 'udlite-text-xs course-card--reviews-text--1yloi')[1:-1].replace(',','')
course_detail = course.find_all('span', {'class':'course-card--row--29Y0w'})
course_length = course_detail[0].get_text(strip=True).strip(' total hours')
number_of_lectures = course_detail[1].get_text(strip=True).strip(' lectures')
difficulity = course_detail[2].get_text(strip=True)
r=requests.get(course_url)
s=bs(r.content,'lxml')
student=extract_text(s, 'div', 'data-purpose', 'enrollment').strip('student').replace(',','')
rows.append(
[course_url, course_title, course_headline, author,off_price,orig_price, course_rating, number_of_ratings, course_length, number_of_lectures, difficulity,student]
)
columns = ['url', 'Course Title', 'Course Headline', 'Instructor','off_price','orig_price', 'Rating', 'Number of Ratings', 'Course Length', 'Number of Lectures', 'Difficulity','Subscribers']
df = pd.DataFrame(data=rows, columns=columns)
df.to_csv('Udmey Courses1.csv', index=False)
driver.quit()
# -
# # Phase II - EDA
df1=pd.read_csv("Udemy Courses.csv")
df=pd.read_csv("Udemy Courses1.csv")
udemy=pd.concat([df1, df], join="outer")
# # Amount of dataset
print("There are {} rows and {} columns in the dataset.".format(udemy.shape[0], udemy.shape[1]))
# Check for null values and data type:
udemy.info()
udemy.isna().sum()
# Data Cleaning:
udemy['Course Length']=udemy['Course Length'].str.strip(' total min').astype(np.float)
udemy['off_price']=udemy['off_price'].replace('F',None).astype(np.float)
# Fill Nane Value with mean:
for column in ['orig_price','off_price','Rating','Number of Ratings','Subscribers','Course Length','Number of Lectures']:
udemy[column]=udemy[column].fillna(udemy[column].mean())
for col in udemy.Subscribers:
val = udemy['Subscribers'].mean()
udemy['Subscribers'] = udemy['Subscribers'].replace(0, val)
udemy.isna().sum()
# # Statistical Summary
udemy.describe()
udemy.min()
from pandas_profiling import ProfileReport
report = ProfileReport(udemy)
report
# # Features in the DataFrame:
# course_title: Title of course
#
# url: Course URL
#
# price: Price of course
#
# num_subscribers: Number of subscribers for the course
#
# num_lectures: Number of lectures in the course
#
# Difficulity: Difficulty level of the course
#
# content_duration: Duration of all course materials
udemy.drop(["off_price",], axis=1).hist(bins=30, figsize=(20,15))
plt.tight_layout()
plt.show()
# Initial observations from the histograms:
#
# Most course durations are between 0-60 min.
#
# There are usually around 1-50 lectures per course.
#
# Courses tend to have few reviews. There are probably a handful of courses with a large amount of reviews since the X axis goes up to 10000 while over 9000 instances are represented in the first bin.
#
# The majority of courses are in the same range of subscribers. The instances farther up the scale were probably more successful or perhaps courses on a trending topic.
#
# Assuming the prices are in USD, the range is between 0-120 dollars. The plot shows the most common price roughly 20 / 55 / 95 USD.
# # Exploring Attribute Combinations
df2 = udemy
df2.head()
corr_matrix = df2.corr()
corr_matrix["Subscribers"].sort_values(ascending=False)
# +
attributes = ["Subscribers", "Number of Ratings", "Number of Lectures", "Course Length"]
scatter_matrix(df2[attributes], figsize=(12,8))
plt.tight_layout()
plt.show()
# -
# scatter plot of the strongest correlation in the corr matrix
# the alpha is set to show the distribution more clearly
df2.plot(kind="scatter", x="Number of Ratings", y="Subscribers", alpha=0.1,
color='b', figsize=(10,5))
plt.title("Reviews and Subscribers Correlation", size=20)
plt.xlabel("#Ratings", size=15)
plt.ylabel("#subscribers", size=15)
plt.tight_layout()
plt.show()
# +
sns.lmplot(x = "Subscribers",y="Number of Ratings", hue = "Difficulity", data=df2);
# -
# # Correlations with num_subscribers Attribute- Overview:
#
# The strongest positive correlations (0.1 or more) are:
#
# *Number of Ratings
#
# *Number of Lectures
#
# *Course Length
#
# The strongest negative correlations (-0.1 or less) are:
#
# *off_price
plt.figure(figsize=(10,5))
sns.scatterplot(y=df2["Difficulity"], x=df2["orig_price"], alpha=0.1)
plt.title("Price by Course Level", size=20)
plt.xlabel("price", size=15)
plt.ylabel("level", size=15)
plt.tight_layout()
plt.show()
# plot subject by number of subscribers and level
# the black bars represent the error
plt.figure(figsize=(10,5))
sns.barplot(x=df2["Difficulity"], y=df2["Subscribers"])
plt.title("Level by Number of Subscribers", size=20)
plt.xlabel("Level", size=15)
plt.ylabel("subscribers", size=15)
plt.tight_layout()
plt.show()
sns.relplot(x="orig_price", y="Subscribers", data=df2, kind="line", aspect=2, ci="sd")
plt.show()
sns.distplot(df2.orig_price, bins=10)
sns.kdeplot(df2.orig_price, shade = True)
# +
sns.catplot(x = "Difficulity", y = "orig_price", kind="violin", aspect=3, data = df2);
# +
sns.lmplot(x = "orig_price", y = "Subscribers", hue = "Difficulity", data=df2);
# -
#Top 30 Most Popular Courses by num_subscribers
top_course = df2.sort_values(by='Subscribers', ascending=False)[:5]
figure = plt.figure(figsize=(10,6))
sns.barplot(y=top_course['Course Title'], x=top_course.Subscribers)
plt.xticks()
plt.xlabel('Subscribers')
plt.ylabel('Course Title')
plt.title('The Most Popular Courses')
plt.show()
# # heatmap
sns.set_palette("RdBu")
correlation=df2.corr()
sns.heatmap(correlation, annot=True)
plt.show()
# # Observations:
# *All Levels and Beginners are the most common level, representing over 50%.
#
# *Price variations according to the level of the course also show that Expert is the least common level in the data. It is also the only level that does not provide free courses. The other levels are dispersed more frequently throughout the line.
#
#
df2.shape
# # Label Encoding
le = LabelEncoder()
le.fit(df2["Difficulity"])
list(le.classes_)
pd.get_dummies(df2["Difficulity"])
# # Modelling
# Reviews prediction by subscribers
#
# preparing data for train test split
#
# Linear regression
# Lasso Regression
# Ridge Regression
#
#
# Splitting the Data:
#
# Before further analysis let's split the data into a training set and a testing set. This will ensure avoidance of bias that could occur from learning the data as a whole.
#sns.pairplot(df2)
X=pd.get_dummies(df2, columns=['Difficulity'])
X=X.drop(columns=['url','Course Title','Course Headline','Instructor','off_price','Rating','Subscribers'],axis=1)
y=df2['Subscribers']
print(X.shape)
print(y.shape)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# # Baseline Model
# Linear Regression
reg = LinearRegression()
reg.fit(X_train,y_train)
train_pre = reg.predict(X_train)
preds = reg.predict(X_test)
print("Accuracy on Traing set : ",reg.score(X_train,y_train))
print("Accuracy on Testing set : ",reg.score(X_test,y_test))
print("__________________________________________")
print("\t\tError Table")
print('Mean Absolute Error : ', metrics.mean_absolute_error(y_test, preds))
print('Mean Squared Error : ', metrics.mean_squared_error(y_test, preds))
print('Root Mean Squared Error : ', np.sqrt(metrics.mean_squared_error(y_test, preds)))
print('R Squared Error : ', metrics.r2_score(y_test, preds))
plt.scatter(train_pre, train_pre - y_train, c = "blue", label = "Training data")
plt.scatter(preds,preds - y_test, c = "black", label = "Validation data")
plt.title("Linear regression")
plt.xlabel("Predicted values")
plt.ylabel("Residuals")
plt.legend(loc = "upper left")
plt.hlines(y = 0, xmin = 10.5, xmax = 13.5, color = "red")
plt.show()
# # Polynomial Features
# +
poly = PolynomialFeatures(degree=2)
X_train_poly = poly.fit_transform(X_train)
# Apply polynomial transformation to val set
X_test_poly = poly.transform(X_test)
# Fit a model using polynomial features
lr_poly = LinearRegression()
lr_poly.fit(X_train_poly,y_train)
y_pred0=lr_poly.predict(X_test_poly)
print("Accuracy on Traing set : ",lr_poly.score(X_train_poly,y_train))
print("Accuracy on Testing set : ",lr_poly.score(X_test_poly,y_test))
print("__________________________________________")
print("\t\tError Table")
print('Mean Absolute Error : ', metrics.mean_absolute_error(y_test, y_pred0))
print('Mean Squared Error : ', metrics.mean_squared_error(y_test, y_pred0))
print('Root Mean Squared Error : ', np.sqrt(metrics.mean_squared_error(y_test, y_pred0)))
print('R Squared Error : ', metrics.r2_score(y_test, y_pred0))
# -
std = StandardScaler()
X_train_scaled = std.fit_transform(X_train_poly)
X_test_scaled = std.transform(X_test_poly)
# +
m = LinearRegression()
m.fit(X_train_scaled,y_train)
y_pred2=m.predict(X_test_scaled)
print("Accuracy on Testing set : ",m.score(X_test_scaled,y_test))
print("__________________________________________")
print("\t\tError Table")
print('Mean Absolute Error : ', metrics.mean_absolute_error(y_test, y_pred2))
print('Mean Squared Error : ', metrics.mean_squared_error(y_test, y_pred2))
print('Root Mean Squared Error : ', np.sqrt(metrics.mean_squared_error(y_test, y_pred2)))
print('R Squared Error : ', metrics.r2_score(y_test, y_pred2))
# -
# # Lasso
#
# +
lasso_reg=linear_model.Lasso(alpha=202)
lasso_reg.fit(X_train,y_train)
y_pred=lasso_reg.predict(X_test)
print("Accuracy on Traing set : ",lasso_reg.score(X_train,y_train))
print("Accuracy on Testing set : ",lasso_reg.score(X_test,y_test))
print("__________________________________________")
print("\t\tError Table")
print('Mean Absolute Error : ', metrics.mean_absolute_error(y_test, y_pred))
print('Mean Squared Error : ', metrics.mean_squared_error(y_test, y_pred))
print('Root Mean Squared Error : ', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))
print('R Squared Error : ', metrics.r2_score(y_test, y_pred))
# -
# # GridSearchCV: Lasso
alphas = np.linspace(0.001, 1,100)
kf = KFold(5, random_state=42, shuffle=True )
params = {'alpha':alphas}
gs = GridSearchCV(linear_model.Lasso(), param_grid=params,verbose=100, cv=kf)
model = gs.fit(X_train_scaled, y_train)
model.best_estimator_.alpha
# +
y_pr=model.predict(X_test_scaled)
print("Accuracy : ",model.score(X_test_scaled,y_test))
print("__________________________________________")
print("\t\tError Table")
print('Mean Absolute Error : ', metrics.mean_absolute_error(y_test, y_pr))
print('Mean Squared Error : ', metrics.mean_squared_error(y_test, y_pr))
print('Root Mean Squared Error : ', np.sqrt(metrics.mean_squared_error(y_test, y_pr)))
print('R Squared Error : ', metrics.r2_score(y_test, y_pr))
# -
# # Ridge
# +
Ridge_reg=Ridge(alpha=202.02118181818182)
Ridge_reg.fit(X_train,y_train)
y_pred1=Ridge_reg.predict(X_test)
print("Accuracy on Traing set : ",Ridge_reg.score(X_train,y_train))
print("Accuracy on Testing set : ",Ridge_reg.score(X_test,y_test))
print("__________________________________________")
print("\t\tError Table")
print('Mean Absolute Error : ', metrics.mean_absolute_error(y_test, y_pred1))
print('Mean Squared Error : ', metrics.mean_squared_error(y_test, y_pred1))
print('Root Mean Squared Error : ', np.sqrt(metrics.mean_squared_error(y_test, y_pred1)))
print('R Squared Error : ', metrics.r2_score(y_test, y_pred1))
# -
# # GridSearchCV: Ridge
alphas = np.linspace(0.001, 1,100)
kf = KFold(5, random_state=42, shuffle=True )
params = {'alpha':alphas}
gs = GridSearchCV(linear_model.Ridge(), param_grid=params, cv=kf)
model1 = gs.fit(X_train_scaled, y_train)
model1.best_estimator_.alpha
# +
y_pr1=model1.predict(X_test_scaled)
print("Accuracy : ",model1.score(X_test_scaled,y_test))
print("__________________________________________")
print("\t\tError Table")
print('Mean Absolute Error : ', metrics.mean_absolute_error(y_test, y_pr1))
print('Mean Squared Error : ', metrics.mean_squared_error(y_test, y_pr1))
print('Root Mean Squared Error : ', np.sqrt(metrics.mean_squared_error(y_test, y_pr1)))
print('R Squared Error : ', metrics.r2_score(y_test, y_pr1))
# +
Ridge_tr = round(Ridge_reg.score(X_train,y_train)* 100, 2)
lasso_tr = round(lasso_reg.score(X_train,y_train)* 100, 2)
Polynomial_tr=round(lr_poly.score(X_train_poly,y_train)* 100, 2)
lasso_te = round(lasso_reg.score(X_test,y_test)* 100, 2)
Polynomial_te=round(lr_poly.score(X_test_poly,y_test)* 100, 2)
Ridge_te = round(Ridge_reg.score(X_test,y_test)* 100, 2)
# -
model2= pd.DataFrame({
'Model': ['Polynomial','Lasso','Ridge'],
'Train Score': [Polynomial_tr,lasso_tr, Ridge_tr],
'Test Score': [Polynomial_te,lasso_te,Ridge_te]
})
model2.sort_values(by='Test Score', ascending=False)
| 18,183 |
/train.ipynb | 9df412358cfc8c508ceed229256c11aaa71cff82 | [] | no_license | cho0h5/covid-cough | https://github.com/cho0h5/covid-cough | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 33,319 | # ---
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# name: python3
# ---
# Loading and displaying an image
from PIL import Image
img = Image.open('/home/dvveera/dvveera_hdd2tb/orl/s1/1.pgm')
print(img.format, img.size, img.mode)
display(img)
# +
# Load the dataset
import load_dataset
import numpy as np
from PIL import Image
dir_src = '/home/dvveera/dvveera_hdd2tb/orl'
data_label = load_dataset.load_data_label(dir_src)
# Training, validation and test data with labels
# data_label = list(train, valid, test, mean_train)
train_storage, train_labels = zip(*data_label[0])
valid_storage, valid_labels = zip(*data_label[1])
test_storage, test_labels = zip(*data_label[2])
mean_storage = data_label[3]
# -
# %%time
# Principal Component Analysis by Snapshot method
# Converting images to a one-dimensional vector
train_1d = [t.flatten() for t in train_storage]
# Calculate the mean image
mean = np.sum(train_1d, axis = 0) / len(train_1d)
# print(mean)
# Mean-centering the images
train_center = [t - mean for t in train_1d]
# print(train_center)
# Create data matrix
data_matrix = np.transpose(train_center)
# print(np.transpose(train_center))
# Create covariance matrix
covariance = np.dot(np.transpose(data_matrix), data_matrix)
# Check for symmetricity
if(covariance.all() == np.transpose(covariance).all()):
print('Symmetric')
# %%time
# Compute eigenvalues and eigenvectors of covariance matrix
eig_values, eig_vectors = np.linalg.eig(covariance)
# print(eig_values)
# %%time
# Multiply data matrix by the eigenvectors
v_cap = np.dot(data_matrix, eig_vectors)
# Divide the eigen vectors by their norm
tr_v_cap = np.transpose(v_cap)
v_n = [a / np.linalg.norm(a) for a in tr_v_cap]
# %%time
# Sort eigenvalues and eigenvectors
eig = list(zip(eig_values, v_n))
sorted(eig, key=lambda x: x[0], reverse = True)
print('Sorted eigen values and eigen vectors from high to low')
# %%time
# Remove close to zero eigen values and their corresponding eigen vectors
eig_values, v_n = zip(*eig)
temp_values = []
temp_vectors = []
for e in eig_values:
if e.real < 0.000001:
pass
else:
temp_values.append(e)
idx = eig_values.index(e)
temp_vectors.append(v_n[idx])
eig_values, v_n = temp_values, temp_vectors
eig = list(zip(eig_values, v_n))
# %%time
# Create projection matrix
V = np.transpose(v_n)
# %%time
# Project training images into subspace
proj_data_matrix = np.dot(v_n, data_matrix)
# %%time
# Identify test images
# Test images are mean-centered
valid_1d = [t.flatten() for t in valid_storage]
# Mean-center the images
valid_center = [t - mean for t in valid_1d]
# Create data matrix
valid_data_matrix = np.transpose(valid_center)
# Project test images into the same subspace as training images
proj_valid_matrix = np.dot(v_n, valid_data_matrix)
# %%time
# Calculate L2 norms
tr1 = np.transpose(proj_valid_matrix)
tr2 = np.transpose(proj_data_matrix)
l2 = []
for a in tr1:
l1 = [np.linalg.norm(a - b) for b in tr2]
l2.append(l1)
# %%time
# Find the index of the training image that matches closely with the test image
idx = [l.index(min(l)) for l in l2]
# %%time
# Find the label for the test images
out = []
for i in idx:
out.append(train_labels[i])
# print(list(zip(out, idx)))
# print(list(zip(valid_labels, idx)))
# %%time
# Find the accuracy
correct = 0
for i in range(len(out)):
if out[i] == valid_labels[i]:
correct += 1
accuracy = correct * 100 / len(out)
print('Accuracy(%): ' + str(accuracy))
# Identify test images
# Test images are mean-centered
test_1d = [t.flatten() for t in test_storage]
# Mean-center the images
test_center = [t - mean for t in test_1d]
# Create data matrix
test_data_matrix = np.transpose(test_center)
# Project test images into the same subspace as training images
proj_test_matrix = np.dot(v_n, test_data_matrix)
# Calculate L2 norms
tr1 = np.transpose(proj_test_matrix)
tr2 = np.transpose(proj_data_matrix)
l2 = []
for a in tr1:
l1 = [np.linalg.norm(a - b) for b in tr2]
l2.append(l1)
# Find the index of the training image that matches closely with the test image
idx = [l.index(min(l)) for l in l2]
# Find the label for the test images
out = []
for i in idx:
out.append(train_labels[i])
# Find the accuracy
correct = 0
for i in range(len(out)):
if out[i] == test_labels[i]:
correct += 1
accuracy = correct * 100 / len(out)
print('Accuracy(%): ' + str(accuracy))
# Print images (true / predicted)
j = 0
for i in range(len(out)):
dis1 = Image.fromarray(train_storage[idx[i]])
display(dis1)
print('Predicted:' + str(out[i]))
dis2 = Image.fromarray(test_storage[j])
display(dis2)
print('True:' + str(test_labels[j]))
print('-----------------------------------')
j += 1
# print(list(zip(out, idx)))
# print(list(zip(test_labels, idx)))
# +
# # Write PCA output vectors to file
# f = open('/home/dvveera/pca_train_data_with_labels.txt', 'w+')
# f.write('Training data\n')
# train_file = zip(np.transpose(proj_data_matrix), train_labels)
# for i in train_file:
# f.write(str(i))
# f = open('/home/dvveera/pca_valid_data_with_labels.txt', 'w+')
# f.write('Validation data\n')
# valid_file = zip(np.transpose(proj_valid_matrix), valid_labels)
# for i in valid_file:
# f.write(str(i))
# f = open('/home/dvveera/pca_test_data_with_labels.txt', 'w+')
# f.write('\nTest data\n')
# test_file = zip(np.transpose(proj_test_matrix), test_labels)
# for i in test_file:
# f.write(str(i))
| 5,657 |
/THE ELIF STATEMENT AND LOGICAL OPERATORS.ipynb | cb0140fb589314c98a0b40b0030820b9b079adc9 | [] | no_license | joyson951/THE-ELIF-STATEMENT-AND-LOGICAL-OPERATORS | https://github.com/joyson951/THE-ELIF-STATEMENT-AND-LOGICAL-OPERATORS | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 2,203 | # ---
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# ---
# # NLP Basics: Vectorize Text Using TF-IDF
# ### TF-IDF
#
# Creates a document-term matrix where the columns represent single unique terms (unigrams) but the cell represents a weighting meant to represent how important a word is to a document.
#
# 
# ### Read In Text
# +
# Read in raw data and clean up the column names
import pandas as pd
import re
import string
import nltk
pd.set_option('display.max_colwidth', 100)
stopwords = nltk.corpus.stopwords.words('english')
path = "/Users/luisek/Documents/GitHub/Advance-NLP-with-Python/Ex_Files_Adv_NLP_Python_ML/Exercise Files/"
messages = pd.read_csv(path+'data/spam.csv', encoding='latin-1')
messages = messages.drop(labels = ["Unnamed: 2", "Unnamed: 3", "Unnamed: 4"], axis = 1)
messages.columns = ["label", "text"]
messages.head()
# -
# ### Create Function To Clean Text
# Define a function to handle all data cleaning
def clean_text(text):
text = "".join([word.lower() for word in text if word not in string.punctuation])
tokens = re.split('\W+', text)
text = [word for word in tokens if word not in stopwords]
return text
# ### Apply TfidfVectorizer
# +
# Fit a basic TFIDF Vectorizer and view the results
from sklearn.feature_extraction.text import TfidfVectorizer
tfidf_vect = TfidfVectorizer(analyzer=clean_text)
X_tfidf = tfidf_vect.fit_transform(messages["text"])
X_tfidf.shape
# +
# How is the output of TfidfVectorizer stored?
# sparse matrix
# +
# tfidf_vect.get_feature_names()
# -
| 1,782 |
/Mathematics/Number/Number 3.1b - Factors.ipynb | 353a307127f36192a12bf2007f1aa485fd042706 | [
"Unlicense"
] | permissive | misterhay/Callysto-Grade-6 | https://github.com/misterhay/Callysto-Grade-6 | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 5,547 | # ---
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# language: python
# name: python37564bit8eca2ece5df347009757f05252a386a2
# ---
# # NNで株価を予測モデルを作成する
# +
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
#import pandas_profiling as pdp
import seaborn as sns
# %matplotlib inline
# tsvファイルの読み込み csvではないので、delimiterオプションを設定
df_train_law = pd.read_csv('/Users/apple/python/stock/StockModelpytorch/data/2017_1321.csv' ,index_col=0)
# -
# ## 欠損値チェック
df_train_law.shape
df_train_law.isnull().any(axis=0)
df_train_law.describe()
# ## 価格のヒストグラムを作成
# * bis = 50を指定しないと、棒グラフが細かくならない
# * 時系列データはヒストグラムが意味をなさない
df_train_law['open'].hist(bins = 50,range=[18800, 24000])
plt.figure()
plt.plot(df_train_law['open'],label="open")
plt.legend()
plt.grid()
# +
from sklearn import preprocessing
np_name = df_train.name.values
np_item_condition_id = df_train.item_condition_id.values
np_category_name = df_train.category_name.values
np_brand_name = df_train.brand_name.values
np_len_description = df_train.len_description.values
# -
np_name
mm_np_name = preprocessing.minmax_scale(np_name)
mm_item_condition_id = preprocessing.minmax_scale(np_item_condition_id)
mm_category_name = preprocessing.minmax_scale(np_category_name)
mm_brand_name = preprocessing.minmax_scale(np_brand_name)
mm_len_description = preprocessing.minmax_scale(np_len_description)
print(type(mm_np_name))
mm_np_name
# # 価格との相関を調べてみる
# +
plt.plot(mm_item_condition_id, log_price, "r.")
plt.title("name and prices in wineries")
plt.xlabel("item")
# -
cor = np.corrcoef(mm_np_name, log_price)
cor
# ### 正規化した目的変数をdfに入れ、もとのは取り除く
#
# +
df_train['mm_np_name'] = mm_np_name
df_train['mm_item_condition_id'] = mm_item_condition_id
df_train['mm_category_name'] = mm_category_name
df_train['mm_brand_name'] = mm_brand_name
df_train['mm_len_description'] = mm_len_description
df_train_drop = df_train.drop(["name","item_condition_id","category_name","brand_name","len_description"], axis=1)
# -
# # NNを組んで見る
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
from sklearn.model_selection import train_test_split
import numpy as np
# ## Dataを組むまでの流れ
# 1. Datasetクラスを作成する
# 1. 入力データと正解データにわける tensorにすること pd->np->tensorに変換する必要がある
# 1. \__len__と\__getitem__を用意すること
# 1. Dataloaderを準備
# Datasetクラスで作ったやつをinputするだけなので難易度は低い
#
# ### datasplitする前に正解ラベルを一緒にしておく
df_train_drop['log_price'] = log_price
df_train_drop = df_train_drop.drop('price', axis=1)
# ### 140万のデータを訓練させると時間がかかるのでデバッグ用に1000で行う
# まずランダムに並べ替えてから1000を抽出する
randsort_df_train = df_train_drop.sample(frac=1)
debag_df = randsort_df_train[:10000]
train, test = train_test_split(debag_df, test_size=0.25)
train
# ### datasetクラスを作成する
class PdDataset(torch.utils.data.Dataset):
def __init__(self, data , price , mode='train' ,transform = None):
self.data = data
self.label = price
self.num = len(data)
self.mode= mode
self.transform = transform
def __len__(self): ###データの大きさをreturn するlenメソッドを必ず作成する必要がある
return self.num
def __getitem__(self, idx): ###正解データと入力データが対となるようなgetitemメソッドを必ず作成する必要がある
out_data = self.data[idx]
out_label = self.label[idx]
return out_data, out_label
# ### 入力データと正解データを作成する
# 正解となるpriceを除き、正解用としてSeriesにする
X_pd = train.drop('log_price', axis=1)
y_pd = train['log_price']
# +
### デバッグのために、データサイズを小さくする
#X_pd = X_pd[:10]
#y_pd = y_pd[:10]
# -
X_pd
# ### datasetクラスに渡すために、tensor型に変換する
X_pd = X_pd.values
y_pd = y_pd.values
X_pd = torch.from_numpy(X_pd.astype(np.float32)).clone()
y_pd = torch.from_numpy(y_pd.astype(np.float32)).clone()
# +
print(X_pd)
print(y_pd)
print(X_pd.shape)
print(y_pd.shape)
y_pd[0]
# -
# ### XとyをDatasetクラスを継承させて作った自作クラスにinput
# +
trainset = PdDataset( X_pd , y_pd )
# -
dataloader = torch.utils.data.DataLoader(dataset=trainset, batch_size = 32, shuffle=True)
# ### 中身の確認
# +
dataiter = iter(dataloader)
inputPd, pricePd = dataiter.next()
# -
inputPd.shape
# ## モデルの構築
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
### 説明変数の数は 6 , 回帰モデルなので出力は1 とし活性化関数を通さない
self.l1 = nn.Linear(6, 100, bias=False) ### 入力層 1層目 : 説明変数 , 2層目の中間層のニューロン数
self.l2 = nn.Linear(100, 100, bias=False) ### 中間層2層目 : 2層目の中間層のニューロン数 , 3層目の中間層のニューロン数
self.l3 = nn.Linear(100, 1, bias=False) ### 出力層 : 3層目のニューロン数 , 出力
def forward(self, x):
### 出力のときだけは活性化関数を通さないことにより、実数がそのまま出力される
x = F.relu(self.l1(x))
x = F.relu(self.l2(x))
x = self.l3(x)
return x
model = Model()
model
# 損失関数
criterion = nn.MSELoss() ### 損失化関数は二乗誤差を設定。 回帰モデルなのでOK
# 最適化関数
optimizer = torch.optim.SGD(model.parameters(), lr=0.1) ### lrは学習率
# +
ls_loss = []
for epoch in range(3):
model.train()
for i, (inputPd, pricePd) in enumerate(dataloader):
inputPd.long()
pricePd.long()
print(inputPd.shape)
# 勾配の初期化
optimizer.zero_grad() ###勾配を初期化するのはミニバッチで、ランダムにやるために必要で、ここはその名残かな
y_pred = model( inputPd ) ### ニューラルネットで計算したoutputを保存
#print("y_pred : ",y_pred)
loss = criterion(y_pred, pricePd) ###教師データとoutputから損失関数を計算
# バッチごとのロスを記録する
ls_loss.append(loss)
# 勾配の計算
loss.backward() ### 損失関数を微分 = 勾配の計算
# パラメータの更新
optimizer.step()
#print('*** by optimizer.step()')
print(model.l1.weight)
# -
# ### ロスの確認
plt.figure()
plt.plot(ls_loss,label="loss")
plt.title('Optimization function error') # 最適化関数の誤差
plt.legend()
plt.grid()
# ## できあがったmodelでテストを行ってみる
test
X_pd_test = test.drop('log_price', axis=1)
y_pd_test = test['log_price']
# +
X_pd_test = X_pd_test.values
y_pd_test = y_pd_test.values
X_pd_test = torch.from_numpy(X_pd_test.astype(np.float32)).clone()
y_pd_test = torch.from_numpy(y_pd_test.astype(np.float32)).clone()
# -
testset = PdDataset( X_pd_test , y_pd_test )
dataloader = torch.utils.data.DataLoader(dataset=testset, batch_size = 32, shuffle=True)
#model.train()
for i, (inputPd, pricePd) in enumerate(testset):
print("@@@@@@@@@@@@", i)
print(inputPd)
y = model(inputPd)
print("y_pred : ",y)
if i==4:
break
| 6,705 |
/Experiments/Extended/Test_HF/TEST-WATTVW_FOV-AR=5-COM=20-BEST_PPO.ipynb | 2b5e4f623bfe2374ef408e62d980bdf852307fc2 | [
"MIT"
] | permissive | KRIS2011/Asteroid_CPO_seeker | https://github.com/KRIS2011/Asteroid_CPO_seeker | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 407,870 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# # Test Recurrent Policy with Extreme Parameter Variation
# +
import numpy as np
import os,sys
sys.path.append('../../../RL_lib/Agents')
sys.path.append('../../../RL_lib/Policies/PPO')
sys.path.append('../../../RL_lib/Policies/Common')
sys.path.append('../../../RL_lib/Utils')
sys.path.append('../../../Env')
sys.path.append('../../../Imaging')
# %load_ext autoreload
# %load_ext autoreload
# %autoreload 2
# %matplotlib nbagg
import os
print(os.getcwd())
# + language="html"
# <style>
# .output_wrapper, .output {
# height:auto !important;
# max-height:1000px; /* your desired max-height here */
# }
# .output_scroll {
# box-shadow:none !important;
# webkit-box-shadow:none !important;
# }
# </style>
# -
# # Optimize Policy
# +
from env import Env
import env_utils as envu
from dynamics_model import Dynamics_model
from lander_model import Lander_model
from ic_gen import Landing_icgen
import rl_utils
import attitude_utils as attu
import optics_utils as optu
from arch_policy_vf import Arch
from policy_ppo import Policy
from softmax_pd import Softmax_pd as PD
from value_function import Value_function
import policy_nets as policy_nets
import valfunc_nets as valfunc_nets
from agent import Agent
import torch.nn as nn
from flat_constraint import Flat_constraint
from glideslope_constraint import Glideslope_constraint
from rh_constraint import RH_constraint
from no_attitude_constraint import Attitude_constraint
from w_constraint import W_constraint
from reward_attitude import Reward
from hf_asteroid import Asteroid
from thruster_model_cubesat import Thruster_model
from sensor import Sensor
from seeker import Seeker
landing_site_range = 10.0
landing_site = None #np.asarray([-250.,0.,0.])
asteroid_model = Asteroid(landing_site_override=landing_site, omega_range=(1e-5,5e-4))
ap = attu.Quaternion_attitude()
C_cb = optu.rotate_optical_axis(0.0, 0.0, np.pi)
r_cb = np.asarray([0,0,0])
fov=envu.deg2rad(90)
seeker = Seeker(attitude_parameterization=ap, C_cb=C_cb, r_cb=r_cb,
radome_slope_bounds=(-0.05,0.05), range_bias=(-0.05,0.05),
fov=fov, debug=False)
sensor = Sensor(seeker, attitude_parameterization=ap, use_range=True, apf_tau1=300, use_dp=False,
landing_site_range=landing_site_range,
pool_type='max', state_type=Sensor.optflow_state_range_dp1)
print(sensor.track_func)
sensor.track_func = sensor.track_func1
print(sensor.track_func)
logger = rl_utils.Logger()
dynamics_model = Dynamics_model(h=2)
thruster_model = Thruster_model(pulsed=True, scale=1.0, offset=0.4)
lander_model = Lander_model(asteroid_model, thruster_model, attitude_parameterization=ap, sensor=sensor,
landing_site_range=landing_site_range, com_range=(-0.20,0.20),
attitude_bias=0.05, omega_bias=0.05)
lander_model.get_state_agent = lander_model.get_state_agent_sensor_att_w2
obs_dim = 13
action_dim = 12
actions_per_dim = 2
logit_dim = action_dim * actions_per_dim
recurrent_steps = 60
reward_object = Reward(landing_rlimit=2, landing_vlimit=0.1,
tracking_bias=0.01, fov_coeff=-50.,
att_coeff=-0.20,
tracking_coeff=-0.5, magv_coeff=-1.0,
fuel_coeff=-0.10, landing_coeff=10.0)
glideslope_constraint = Glideslope_constraint(gs_limit=-1.0)
shape_constraint = Flat_constraint()
attitude_constraint = Attitude_constraint(ap)
w_constraint = W_constraint(w_limit=(0.1,0.1,0.1), w_margin=(0.05,0.05,0.05))
rh_constraint = RH_constraint(rh_limit=150)
wi=0.05
ic_gen = Landing_icgen((800,1000),
p_engine_fail=0.5,
engine_fail_scale=(0.5,1.0),
lander_wll=(-wi,-wi,-wi),
lander_wul=(wi,wi,wi),
attitude_parameterization=ap,
position_error=(0,np.pi/4),
heading_error=(0,np.pi/8),
attitude_error=(0,np.pi/16),
min_mass=450, max_mass=500,
mag_v=(0.05,0.1),
debug=False,
inertia_uncertainty_diag=10.0,
inertia_uncertainty_offdiag=1.0)
env = Env(ic_gen, lander_model, dynamics_model, logger,
landing_site_range=landing_site_range,
debug_done=False,
reward_object=reward_object,
glideslope_constraint=glideslope_constraint,
attitude_constraint=attitude_constraint,
w_constraint=w_constraint,
rh_constraint=rh_constraint,
tf_limit=5000.0,print_every=10,nav_period=6)
env.ic_gen.show()
arch = Arch()
policy = Policy(policy_nets.GRU1(obs_dim, logit_dim, recurrent_steps=recurrent_steps),
PD(action_dim, actions_per_dim),
shuffle=False,
kl_targ=0.001,epochs=20, beta=0.1, servo_kl=True, max_grad_norm=30, scale_vector_obs=True,
init_func=rl_utils.xn_init)
value_function = Value_function(valfunc_nets.GRU1(obs_dim, recurrent_steps=recurrent_steps), scale_obs=True,
shuffle=False, batch_size=9999999, max_grad_norm=30,
verbose=False)
agent = Agent(arch, policy, value_function, None, env, logger,
policy_episodes=30, policy_steps=3000, gamma1=0.95, gamma2=0.995,
recurrent_steps=recurrent_steps, monitor=env.rl_stats)
fname = "optimize_WATTVW_FOV-AR=5"
policy.load_params(fname)
# -
# # Test Policy
# +
env.test_policy_batch(agent,5000,print_every=100,keys=lander_model.get_engagement_keys())
#env.test_policy_batch(agent,10,print_every=1)
# +
values = np.random.normal(size=1000000)
print(np.mean(values))
# -
lgoritmo Grow y el algoritmo Shrink encuentran los conjuntos más pequeños de generadores?
# <span style="color:orange">Pensemos en el siguiente ejemplo de un grafo:</style>
# 
# Los puntos se llaman nodos, los enlaces se llaman aristas.
# Cada arista tiene dos puntos finales, los nodos que conecta.
# Los puntos finales de un borde son vecinos.
# Un **conjunto dominante** en un grafo es un conjunto S de nodos tal que cada nodo está en S o es un vecino de un nodo en S.
# Algoritmo Grow:</style>
# > <span style="color:orange">S=∅</style>
# > <span style="color:orange">repetir mientras sea posible:</style>
# >> <span style="color:orange">mientras que S no es un conjunto dominante, agregar un nodo a S que actualmente no sea adyacente a S. </style>
#
# <span style="color:orange">Algoritmo Shrink:</style>
# > <span style="color:orange">S = todos los nodos</style>
# > <span style="color:orange">repetir mientras sea posible:</style>
# >> <span style="color:orange">Si hay un nodo x tal que S - {x} es un conjunto dominante, eliminar x de S</style>
#
# <span style="color:orange">Ninguno de los algoritmos tiene garantizado encontrar la solución más pequeña.</style>
# # Bosque de extensión mínima
#
# 
#
# Una secuencia de aristas [{x<sub>1</sub>, x<sub>2</sub>}, {x<sub>2</sub>, x<sub>3</sub>}, {x<sub>3</sub>, x<sub>4</sub>},..., {x<sub>k-1</sub>, x<sub>k</sub>}] sin repeticiones se denomina **camino** de x<sub>1</sub> a x<sub>k</sub>.
#
# <span style="color:orange"> Ejemplo:
# Tengo 4 caminos para ir de “Main Quad” a ”Gregorian Quad”:</style>
# * <span style="color:orange">Dos caminos a través de “Wriston Quad”:</style>
# > <span style="color:orange">Main Quad - Wriston Quad - Gregorian Quad</style>
# > <span style="color:orange">Main Quad - Wriston Quad - Keeney Quad - Gregorian Quad</style>
#
# * <span style="color:orange">Dos caminos a través de “Keeney Quad”:</style>
# > <span style="color:orange">Main Quad - Keeney Quad - Gregorian Quad</style>
# > <span style="color:orange">Main Quad - Keeney Quad - Wriston Quad - Gregorian Quad</style>
#
# Un camino de x a x es un **ciclo**.
# <span style="color:orange"> Ejemplo:
# El camino Keeney Quad - Wriston Quad - Gregorian Quad - Keeney Quad es un ciclo.</style>
#
# Un conjunto S de aristas genera un grafo G si, para cada arista {x, y} de G, hay un camino x-y que consiste en las aristas de S.
#
# Un conjunto de aristas de G es un bosque (forest) si el conjunto no incluye ciclos.
#
# <span style="color:orange"> Ejemplo:
# El conjunto de aristas {Main Quad, Wriston Quad}, {Wriston Quad,Keeney Quad}, {riston Quad,Gregorian Quad} es un bosque.</style>
#
# En la realidad, cada arista lleva asignado un peso.
#
# <span style="color:orange"> Ejemplo:
# En el grafo que estamos siguiendo de la imagen, los pesos de las aristas son:</style>
# * <span style="color:orange">{Main Quad, Wriston Quad}:3</style>
# * <span style="color:orange">{Main Quad, Keeney Quad}:5</style>
# * <span style="color:orange">{Keeney Quad, Wriston Quad}:4</style>
# * <span style="color:orange">{Wriston Quad, Gregorian Quad}:6</style>
# * <span style="color:orange">{Keeney Quad, Gregorian Quad}:8</style>
#
# Para calcular el mínimo bosque generador del grafo:
# * input: un grafo G, y una asignación de pesos de números reales a las aristas de G.
# * output: un conjunto de pesos mínimo S de aristas que generan el grafo y un bosque.
#
# <span style="color:orange"> Ejemplo:
# Supongamos que queremos diseñar la red de internet para el campus universitario.
# La red debe lograr la misma conectividad que el grafo de entrada.
# Una arista representa un posible cable.
# El peso de la arista es el coste de instalar el cable.
# Nuestro objetivo es minimizar el coste total.
# ** Algoritmo Grow**</style>
# > <span style="color:orange"> def Grow (G)</style>
# > <span style="color:orange"> S: = ∅</style>
# > <span style="color:orange"> Consideremos las aristas e en orden creciente:</style>
# >> <span style="color:orange"> Si los puntos finales de e aún no están conectados, agregue e a S.</style>
#
# <span style="color:orange"> Colocamos las aristas en orden creciente según sus pesos: 3,4,5,6,8.
# Cogemos la arista {Main Quad, Wriston Quad} y la añadimos a S.
# Hay que añadir el nodo Keeney Quad. Se puede hacer:</style>
# * <span style="color:orange">{Main Quad, Keeney Quad}:5</style>
# * <span style="color:orange">{Keeney Quad, Wriston Quad}:4</style>
#
# <span style="color:orange">Luego añadimos a S la arista {Keeney Quad, Wriston Quad}.
# Faltaría añadir el nodo Gregorian Quad. Podemos añadirlo:</style>
# * <span style="color:orange">{Wriston Quad, Gregorian Quad}:6</style>
# * <span style="color:orange">{Keeney Quad, Gregorian Quad}:8</style>
#
# <span style="color:orange">Luego añadimos a S la arista {Wriston Quad, Gregorian Quad}.
# Por tanto, el mínimo bosque generador del grafo quedaría:</style>
# > <span style="color:orange">S={ {Main Quad, Wriston Quad}, {Keeney Quad, Wriston Quad}, {Wriston Quad, Gregorian Quad} }</style>
#
# <span style="color:orange">** Algoritmo Shrink**</style>
# > <span style="color:orange"> def Shrink (G)</style>
# > <span style="color:orange"> S: = { {Main Quad, Wriston Quad}, {Main Quad, Keeney Quad}, {Keeney Quad, Wriston Quad}, {Wriston Quad, Gregorian Quad}, {Keeney Quad, Gregorian Quad} }</style>
# > <span style="color:orange"> Consideremos las aristas e en orden decreciente:</style>
# >> <span style="color:orange"> Si cada par de nodos están conectados mediante S-{e}: quitar e de S.</style>
#
# <span style="color:orange"> Colocamos las aristas en orden decreciente según sus pesos: 8,6,5,4,3.
# La arista que más peso tiene es {Keeney Quad, Gregorian Quad}:8. Si la quito aún podría seguir yendo de Keeney Quad a Gregorian Quad.
# Luego S= { {Main Quad, Wriston Quad}, {Main Quad, Keeney Quad}, {Keeney Quad, Wriston Quad}, {Wriston Quad, Gregorian Quad} }.
# La arista siguiente con más peso es {Wriston Quad, Gregorian Quad}, pero si quito esta arista Gregorian Quad se queda desconectado, por tanto, la mantengo.
# La arista siguiente con más peso es {Main Quad, Keeney Quad}:5. Puedo eliminarla ya que pasando por Wriston Quad siguen conectadas.
# Luego S= { {Main Quad, Wriston Quad}, {Keeney Quad, Wriston Quad}, {Wriston Quad, Gregorian Quad} }.
# La otras dos aristas restantes: {Keeney Quad, Wriston Quad} y {Main Quad, Wriston Quad} no se pueden eliminar.
# Por tanto, finalmente queda S={ {Main Quad, Wriston Quad}, {Keeney Quad, Wriston Quad}, {Wriston Quad, Gregorian Quad} }.</style>
#
# <span style="color:blue"> Ejercicio 11:
# Supongamos que queremos diseñar la red de internet para el otro campus universitario.
# La red debe lograr la misma conectividad que el grafo de entrada.
# Una arista representa un posible cable.
# El peso de la arista es el coste de instalar el cable.
# Nuestro objetivo es minimizar el coste total, usando el algoritmo Grow y el algoritmo Shrink. </style>
#
# Los algoritmos Grow y Shrink prueban si un vector es superfluo (no necesario) para el conjunto generador del espacio vectorial V.
#
# ** Lema del vector superfluo**
# Para cualquier conjunto S y cualquier vector v ∈ S, si v puede escribirse como una combinación lineal de los otros vectores en S entonces Gen(S - {v}) = GenS.
#
# **Dependencia lineal de vectores**
# Se dice que los vectores v<sub>1</sub>,... , v<sub>n</sub> son **linealmente dependientes** si el vector cero se puede escribir como una combinación lineal no trivial de los vectores:
# > **0** = α<sub>1</sub>**v<sub>1</sub>** + ··· + α<sub>n</sub>**v<sub>n</sub>**
#
# En este caso, nos referimos a la combinación lineal como una dependencia lineal en v<sub>1</sub>,... , v<sub>n</sub>.
# Por otro lado, si la única combinación lineal que iguala el vector cero es la combinación lineal trivial, decimos v<sub>1</sub>,... , v<sub>n</sub> son **linealmente independientes**.
#
# <span style="color:orange"> Ejemplo:
# Los vectores [1, 0, 0], [0, 2, 0] y [2, 4, 0] son linealmente dependientes, ya que:
# 2 [1, 0, 0] + 2 [0, 2, 0] - 1 [2, 4, 0] = [0, 0, 0]
# Por lo tanto:
# 2 [1,0,0] +2 [0,2,0] -1 [2,4,0] es una dependencia lineal en [1,0,0], [0,2,0], [2, 4,0].</style>
#
# <span style="color:orange"> Ejemplo:
# Los vectores [1, 0, 0], [0, 2, 0] y [0, 0, 4] son linealmente independientes.
# Consideremos cualquier combinación lineal:
# α<sub>1</sub> [1,0,0] + α<sub>2</sub> [0,2,0] + α<sub>3</sub> [0,0,4] =[α<sub>1</sub>, 2α<sub>2</sub>, 4α<sub>3</sub>]
# Para obtener el vector [0,0,0], α<sub>1</sub> = α<sub>2</sub> = α<sub>3</sub> = 0
# Es decir, la combinación lineal es trivial.</style>
#
# Los algoritmos Grow y Shrink devuelven un conjunto de vectores linealmente independientes.
#
# # Bases
#
# Si finalizan con éxito, el algoritmo Grow y el algoritmo Shrink encuentran cada uno un conjunto de vectores generadores del espacio vectorial V.
# En cualquier caso, el conjunto de vectores encontrado es linealmente independiente.
#
# Dado un espacio vectorial V. Una **base** para V es un conjunto de generadores linealmente independientes para V.
#
# Por tanto, un cnjunto S de vectores de V es una **base** para V si satisface:
# * GenS=V
# * S es linealmente independiente
#
# <span style="color:orange"> Ejemplo:
# Dado V=Gen{[1, 0, 2, 0], [0, −1, 0, −2], [2, 2, 4, 4]}. ¿Es {[1, 0, 2, 0], [0, −1, 0, −2], [2, 2, 4, 4]} una base?
# Por la definición del ejercicio es un generados. Ahora bien, ¿los vectores son linealmente independientes?
# 1 [1, 0, 2, 0] − 1 [0, −1, 0, −2] − $\frac{1}{2}$ [2, 2, 4, 4] = **0**
# Por lo tanto, son linealmente dependientes, luego no es una base.
# ¿Podríamos encontrar una base?
# Para ello vamos a usar el lema del vector superfluo:
# [2, 2, 4, 4] = 2 [1, 0, 2, 0] − 2 [0, −1, 0, −2]
# Por tanto:
# Gen {[1,0,2,0],[0,−1,0,−2]} = Gen {[1,0,2,0],[0,−1,0,−2],[2,2,4,4]}
# Para ver que estos dos vectores son linealmente independientes:
# a<sub>1</sub> [1,0,2,0] + a<sub>2</sub> [0,−1,0,−2] = [a<sub>1</sub>,-a<sub>2</sub>,2a<sub>1</sub>,-2a<sub>2</sub>] y para que devuelva el vector **0** la única posibilidad es que a<sub>1</sub>=a<sub>2</sub>=0.
# Por tanto, {[1,0,2,0],[0,−1,0,−2]} es una base.
# </style>
#
# <span style="color:orange"> Ejemplo:
# Comprobemos que la base canónica de $\mathbb{R}$<sup>3</sup> {[1,0,0],[0,1,0],[0,0,1]} efectivamente es una base.
# Es generador de $\mathbb{R}$<sup>3</sup> ya que dado [x,y,z] ∈ $\mathbb{R}$<sup>3</sup>, [x , y , z ] = x [1, 0, 0] + y [0, 1, 0] + z [0, 0, 1].
# Son linealmente independientes ya que a<sub>1</sub> [1,0,0] + a<sub>2</sub> [0,1,0]+ a<sub>3</sub> [0,0,1]= **0** cuando a<sub>1</sub>=a<sub>2</sub>=a<sub>3</sub>=0.</style>
#
# <span style="color:orange"> Ejemplo:
# Comprobemos si {[1, 1, 1], [1, 1, 0], [0, 1, 1]} es una base para $\mathbb{R}$<sup>3</sup>
# Para ver que es un generador basta con comprobar que estos vectores se pueden escribir como una combinación lineal de los vectores de la base canónica que sabemos que es un generador.
# [1,0,0] = [1,1,1]−[0,1,1]
# [0,1,0] = [1,1,0]+[0,1,1]−[1,1,1]
# [0,0,1] = [1,1,1]−[1,1,0]
# Luego, {[1, 1, 1], [1, 1, 0], [0, 1, 1]} es un generador.
# Falta ver que son linealmente independientes:
# a<sub>1</sub> [1,1,1] + a<sub>2</sub> [1,1,0]+ a<sub>3</sub> [0,1,1]= [a<sub>1</sub>+a<sub>2</sub>,a<sub>1</sub>+a<sub>2</sub>+a<sub>3</sub>,a<sub>1</sub>+a<sub>3</sub>] = **0**.
# Para que esto ocurra:</style>
# > <span style="color:orange"> a<sub>1</sub>+a<sub>2</sub>=0</style>
# > <span style="color:orange"> a<sub>1</sub>+a<sub>2</sub>+a<sub>3</sub>=0</style>
# > <span style="color:orange"> a<sub>1</sub>+a<sub>3</sub>=0</style>
#
# <span style="color:orange"> Y esto sólo ocurre si a<sub>1</sub>=a<sub>2</sub>=a<sub>3</sub>=0, por lo que los vectores son linealmente independientes.
# Por tanto, {[1, 1, 1], [1, 1, 0], [0, 1, 1]} es una base.</style>
#
# Queremos probar que cada espacio vectorial V tiene una base.
# El algoritmo Grow y el algoritmo Shrink proporcionan una forma de probar esto, pero:
# * Si el algoritmo Grow termina, el conjunto de vectores que ha seleccionado es una base para el espacio vectorial V. Sin embargo, aún no hemos demostrado que siempre termina.
# * El algoritmo Shrink implica que, si podemos ejecutar el algoritmo comenzando con un conjunto finito de vectores que generan V, al finalizar, habremos seleccionado una base para V. Sin embargo, aún no hemos demostrado que cada espacio vectorial V esté generado por un conjunto finito de vectores.
#
# ** Lema de representación única**
#
# Dados a<sub>1</sub>,...,a<sub>n</sub> una base para V. Para cualquier vector v ∈ V, hay exactamente una única representación en términos de los vectores de la base.
#
# <span style="color:orange"> Ejemplo:
# Una base para un grafo es un árbol generador.
# La representación única muestra que para cada arista xy en el grafo, hay sólo un camino de x a y y que es el único camino.</style>
#
# # Cambio de base
#
# Dados a<sub>1</sub>,...,a<sub>n</sub> una base para V y c<sub>1</sub>,...,c<sub>k</sub> otra base para V.
# Definimos f(**x**)=[a<sub>1</sub>|...|a<sub>n</sub>]$\cdot$[**x**] y g(**y**)=[c<sub>1</sub>|...|c<sub>k</sub>]$\cdot$[**y**].
# Ambas son funciones invertibles por las propiedades intrínsecas de las bases.
# Luego podemos calcular:
# > (f<sup>-1</sup> o g)(**x**)=[a<sub>1</sub>|...|a<sub>n</sub>]<sup>-1</sup>$\cdot$[c<sub>1</sub>|...|c<sub>k</sub>]$\cdot$[**x**]
#
# Esta función mapea un vector en la base c<sub>1</sub>,...,c<sub>k</sub> y lo convierte en un vector en la base a<sub>1</sub>,...,a<sub>n</sub>.
#
# <span style="color:orange"> Ejemplo:
# Calcula la matriz de cambio de base de la base [1,2,3],[2,1,0],[0,1,4] a la base [2,0,1],[0,1,−1],[1,2,0].</style>
# <span style="color:orange"> Ejemplo:
# En la vida real, el cambio de base se utiliza para muchas cosas, entre ellas, para eliminar la perspectiva de una imagen.</style>
| 20,398 |
/Practice_Problems/Lecture_15_Practice_Problems.ipynb | 3b122666237435bdb2e3aee3f2a49de71b8e2ff7 | [
"CC-BY-4.0"
] | permissive | ichit/Python-for-Earth-Science-Students | https://github.com/ichit/Python-for-Earth-Science-Students | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 3,528 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# + [markdown] id="wm7TgCNRzYpJ"
# # Multi-Class Image Classification Deep Learning Model for [PROJECT NAME] Using TensorFlow Version 1
# ### David Lowe
# ### November 24, 2020
#
# Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery. [https://machinelearningmastery.com/]
#
# SUMMARY: This project aims to construct a predictive model using a TensorFlow convolutional neural network (CNN) and document the end-to-end steps using a template. The [PROJECT NAME] dataset is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.
#
# INTRODUCTION: [Sample Paragraph - The Rock Paper Scissors dataset contains images from a variety of different hand poses with different races, ages, and genders. These images have all been generated using CGI techniques as an experiment in determining if a CGI-based dataset can be used for classification against real photos. All of this data is posed against a white background, and each image is 300×300 pixels in 24-bit color.]
#
# ANALYSIS: [Sample Paragraph - The performance of the baseline model achieved an accuracy score of 100% after 15 epochs using the training dataset. After tuning the hyperparameters, the best model processed the validation dataset with an accuracy score of 82.26%. Furthermore, the final model processed the test dataset with an accuracy measurement of 82.26%.]
#
# CONCLUSION: In this iteration, the TensorFlow CNN model appeared to be suitable for modeling this dataset. We should consider experimenting with TensorFlow for further modeling.
#
# Dataset Used: [PROJECT NAME] Dataset
#
# Dataset ML Model: Multi-class image classification with numerical attributes
#
# Dataset Reference: [http://www.laurencemoroney.com/rock-paper-scissors-dataset/]
#
# One potential source of performance benchmarks: [http://www.laurencemoroney.com/rock-paper-scissors-dataset/]
#
# A deep-learning image classification project generally can be broken down into five major tasks:
#
# 1. Prepare Environment
# 2. Load and Prepare Images
# 3. Define and Train Models
# 4. Evaluate and Optimize Models
# 5. Finalize Model and Make Predictions
# + [markdown] id="yjz77tzczYpL"
# # Task 1 - Prepare Environment
# + id="yymMW8D4zYpM" outputId="cf55199e-d4de-4677-c5e3-693aad08aa93" colab={"base_uri": "https://localhost:8080/"}
# Install the packages to support accessing environment variable and SQL databases
# !pip install python-dotenv PyMySQL boto3
# + id="WTBLbPj6zYpP" outputId="bc442e0a-8105-4ffb-a148-13e9cbfc4193" colab={"base_uri": "https://localhost:8080/"}
# Retrieve GPU configuration information from Colab
# gpu_info = !nvidia-smi
gpu_info = '\n'.join(gpu_info)
if gpu_info.find('failed') >= 0:
print('Select the Runtime → "Change runtime type" menu to enable a GPU accelerator, ')
print('and then re-execute this cell.')
else:
print(gpu_info)
# + id="NPBJM3QLzYpR" outputId="3b9912d6-2938-400a-8a2e-b58f6295ffa8" colab={"base_uri": "https://localhost:8080/"}
# Retrieve memory configuration information from Colab
from psutil import virtual_memory
ram_gb = virtual_memory().total / 1e9
print('Your runtime has {:.1f} gigabytes of available RAM\n'.format(ram_gb))
if ram_gb < 20:
print('To enable a high-RAM runtime, select the Runtime → "Change runtime type"')
print('menu, and then select High-RAM in the Runtime shape dropdown. Then, ')
print('re-execute this cell.')
else:
print('You are using a high-RAM runtime!')
# + id="VDwThQ4vzYpT"
# # Direct Colab to use TensorFlow v2
# # %tensorflow_version 2.x
# + id="Rt62MLGBzYpV" outputId="03185adc-09d5-4140-c144-eb75eb0aa4f5" colab={"base_uri": "https://localhost:8080/"}
# Retrieve CPU information from the system
# ncpu = !nproc
print("The number of available CPUs is:", ncpu[0])
# + [markdown] id="JU6CoUSHzYpX"
# ## 1.a) Load libraries and modules
# + id="5vLXzBGlzYpX"
# Set the random seed number for reproducible results
seedNum = 888
# + id="7OPrZkvnzYpZ" outputId="68371d26-81e4-428f-ecf5-f9bf9010a862" colab={"base_uri": "https://localhost:8080/"}
# Load libraries and packages
import random
random.seed(seedNum)
import numpy as np
np.random.seed(seedNum)
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import os
import sys
import boto3
import zipfile
from datetime import datetime
from dotenv import load_dotenv
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report
import tensorflow as tf
tf.random.set_seed(seedNum)
from tensorflow import keras
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.callbacks import ReduceLROnPlateau
# + [markdown] id="j_DlsgItzYpb"
# ## 1.b) Set up the controlling parameters and functions
# + id="YidrXfyszYpc" outputId="1e2ee8a2-1ed8-4669-cfbe-564155bf5d3f" colab={"base_uri": "https://localhost:8080/"}
# Begin the timer for the script processing
startTimeScript = datetime.now()
# Set up the number of CPU cores available for multi-thread processing
n_jobs = 1
# Set up the flag to stop sending progress emails (setting to True will send status emails!)
notifyStatus = False
# Set Pandas options
pd.set_option("display.max_rows", None)
pd.set_option("display.max_columns", None)
pd.set_option("display.width", 140)
# Set the percentage sizes for splitting the dataset
test_set_size = 0.2
val_set_size = 0.25
# Set various default modeling parameters
default_loss = 'categorical_crossentropy'
default_metrics = ['accuracy']
default_optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)
default_kernel_init = tf.keras.initializers.GlorotUniform(seed=seedNum)
default_epoch = 15
default_batch = 16
default_image_size = (150, 150)
input_image_shape = (150, 150, 3)
num_classes = 3
# Define the labels to use for graphing the data
train_metric = "accuracy"
validation_metric = "val_accuracy"
train_loss = "loss"
validation_loss = "val_loss"
# Check the number of GPUs accessible through TensorFlow
print('Num GPUs Available:', len(tf.config.list_physical_devices('GPU')))
# Print out the TensorFlow version for confirmation
print('TensorFlow version:', tf.__version__)
# + id="1HXEz_jAzYpf"
# Set up the parent directory location for loading the dotenv files
# useColab = True
# if useColab:
# # Mount Google Drive locally for storing files
# from google.colab import drive
# drive.mount('/content/gdrive')
# gdrivePrefix = '/content/gdrive/My Drive/Colab_Downloads/'
# env_path = '/content/gdrive/My Drive/Colab Notebooks/'
# dotenv_path = env_path + "python_script.env"
# load_dotenv(dotenv_path=dotenv_path)
# Set up the dotenv file for retrieving environment variables
# useLocalPC = True
# if useLocalPC:
# env_path = "/Users/david/PycharmProjects/"
# dotenv_path = env_path + "python_script.env"
# load_dotenv(dotenv_path=dotenv_path)
# + id="W-a57IMqzYph"
# Set up the email notification function
def status_notify(msg_text):
access_key = os.environ.get('SNS_ACCESS_KEY')
secret_key = os.environ.get('SNS_SECRET_KEY')
aws_region = os.environ.get('SNS_AWS_REGION')
topic_arn = os.environ.get('SNS_TOPIC_ARN')
if (access_key is None) or (secret_key is None) or (aws_region is None):
sys.exit("Incomplete notification setup info. Script Processing Aborted!!!")
sns = boto3.client('sns', aws_access_key_id=access_key, aws_secret_access_key=secret_key, region_name=aws_region)
response = sns.publish(TopicArn=topic_arn, Message=msg_text)
if response['ResponseMetadata']['HTTPStatusCode'] != 200 :
print('Status notification not OK with HTTP status code:', response['ResponseMetadata']['HTTPStatusCode'])
# + id="gCnz7I5wzYpi"
if notifyStatus: status_notify('(TensorFlow Multi-Class) Task 1 - Prepare Environment has begun on ' + datetime.now().strftime('%A %B %d, %Y %I:%M:%S %p'))
# + id="-b-SwoTszYpk"
# Reset the random number generators
def reset_random(x):
random.seed(x)
np.random.seed(x)
tf.random.set_seed(x)
# + jupyter={"outputs_hidden": false} pycharm={"name": "#%%\n"} id="TkZYm2ZmzYpl"
if notifyStatus: status_notify('(TensorFlow Multi-Class) Task 1 - Prepare Environment completed on ' + datetime.now().strftime('%A %B %d, %Y %I:%M:%S %p'))
# + [markdown] pycharm={"name": "#%% md\n"} id="CMKAG-YIzYpn"
# # Task 2 - Load and Prepare Images
# + jupyter={"outputs_hidden": false} pycharm={"name": "#%%\n"} id="xHtS66j4zYpo"
if notifyStatus: status_notify('(TensorFlow Multi-Class) Task 2 - Load and Prepare Images has begun on ' + datetime.now().strftime('%A %B %d, %Y %I:%M:%S %p'))
# + id="CqQCL1KyzYpp" outputId="bd6c9eb3-ce38-492a-bdd6-932ccebc98aa" colab={"base_uri": "https://localhost:8080/"}
# Clean up the old files and download directories before receiving new ones
# !rm -rf staging/
# !rm rps.zip
# !rm rps-test-set.zip
# + id="iI1RPsfzzYpr" outputId="8a2656d1-37ca-4c38-cf86-25cc7ef4d88c" colab={"base_uri": "https://localhost:8080/"}
# !wget https://dainesanalytics.com/datasets/laurencemoroney-rock-paper-scissors-dataset/rps.zip
# + id="oV8WG_1EzYpt" outputId="5520e718-1e17-4aeb-b8a3-bff472e871f6" colab={"base_uri": "https://localhost:8080/"}
# !wget https://dainesanalytics.com/datasets/laurencemoroney-rock-paper-scissors-dataset/rps-test-set.zip
# + id="bbOS-IbxzYpu"
staging_dir = 'staging/'
# !mkdir staging/
# + id="fOWPVRdszYpw"
# Unzip and put the files into the staging folder
local_zip = 'rps.zip'
zip_ref = zipfile.ZipFile(local_zip, 'r')
zip_ref.extractall(staging_dir)
local_zip = 'rps-test-set.zip'
zip_ref = zipfile.ZipFile(local_zip, 'r')
zip_ref.extractall(staging_dir)
zip_ref.close()
# + id="rr3kRedTzYpx"
training_dir = 'staging/rps/'
validation_dir = 'staging/rps-test-set/'
class1_name = 'rock'
class2_name = 'paper'
class3_name = 'scissors'
# + id="a-ivUw4bzYpz" outputId="4e4a79b3-2b1e-489f-ef01-e4471b960419" colab={"base_uri": "https://localhost:8080/"}
# Brief listing of training image files for class 1
training_class1_dir = os.path.join(training_dir, class1_name)
training_class1_files = os.listdir(training_class1_dir)
print('Number of training images for', class1_name, ':', len(os.listdir(training_class1_dir)))
print('Training samples for', class1_name, ':', training_class1_files[:10])
# Brief listing of training image files for class 2
training_class2_dir = os.path.join(training_dir, class2_name)
training_class2_files = os.listdir(training_class2_dir)
print('Number of training images for', class2_name, ':', len(os.listdir(training_class2_dir)))
print('Training samples for', class2_name, ':', training_class2_files[:10])
# Brief listing of training image files for class 3
training_class3_dir = os.path.join(training_dir, class3_name)
training_class3_files = os.listdir(training_class3_dir)
print('Number of training images for', class3_name, ':', len(os.listdir(training_class3_dir)))
print('Training samples for', class3_name, ':', training_class3_files[:10])
# + id="mpGhJvRHzYp0" outputId="c2e43681-4477-45f8-e5b8-53ed02672ea1" colab={"base_uri": "https://localhost:8080/"}
# Brief listing of validation image files for class 1
validation_class1_dir = os.path.join(validation_dir, class1_name)
validation_class1_files = os.listdir(validation_class1_dir)
print('Number of validation images for', class1_name, ':', len(os.listdir(validation_class1_dir)))
print('Validation samples for', class1_name, ':', validation_class1_files[:10])
# Brief listing of validation image files for class 2
validation_class2_dir = os.path.join(validation_dir, class2_name)
validation_class2_files = os.listdir(validation_class2_dir)
print('Number of validation images for', class2_name, ':', len(os.listdir(validation_class2_dir)))
print('Validation samples for', class2_name, ':', validation_class2_files[:10])
# Brief listing of validation image files for class 3
validation_class3_dir = os.path.join(validation_dir, class3_name)
validation_class3_files = os.listdir(validation_class3_dir)
print('Number of validation images for', class3_name, ':', len(os.listdir(validation_class3_dir)))
print('Validation samples for', class3_name, ':', validation_class3_files[:10])
# + id="y8e9nU29zYp2" outputId="a5ed1a3a-c1c5-4156-a7af-98ab2ea4fc01" colab={"base_uri": "https://localhost:8080/", "height": 520}
# Plot some training images from the dataset
nrows = 3
ncols = 4
# Set up matplotlib fig, and size it to fit 4x4 pics
fig = plt.gcf()
fig.set_size_inches(ncols * 3, nrows * 3)
reset_random(seedNum)
number_elements = ncols
random_training_class1 = random.sample(training_class1_files, number_elements)
random_training_class2 = random.sample(training_class2_files, number_elements)
random_training_class3 = random.sample(training_class3_files, number_elements)
next_class1 = [os.path.join(training_class1_dir, fname) for fname in random_training_class1]
next_class2 = [os.path.join(training_class2_dir, fname) for fname in random_training_class2]
next_class3 = [os.path.join(training_class3_dir, fname) for fname in random_training_class3]
for i, img_path in enumerate(next_class1 + next_class2 + next_class3):
# Set up subplot; subplot indices start at 1
sp = plt.subplot(nrows, ncols, i + 1)
sp.axis('Off')
img = mpimg.imread(img_path)
plt.imshow(img)
plt.show()
# + id="mca1077xzYp3" outputId="9d35b76b-6ed4-4f27-ff33-0a0da0e5b175" colab={"base_uri": "https://localhost:8080/"}
print('Loading and pre-processing the training images...')
training_datagen = ImageDataGenerator(rescale=1/255)
# training_datagen = ImageDataGenerator(rescale=1./255,
# featurewise_center=False, # set input mean to 0 over the dataset
# samplewise_center=False, # set each sample mean to 0
# featurewise_std_normalization=False, # divide inputs by std of the dataset
# samplewise_std_normalization=False, # divide each input by its std
# zca_whitening=False, # apply ZCA whitening
# rotation_range=10, # randomly rotate images in the range (degrees, 0 to 180)
# zoom_range = 0.1, # Randomly zoom image
# width_shift_range=0.1, # randomly shift images horizontally (fraction of total width)
# height_shift_range=0.1, # randomly shift images vertically (fraction of total height)
# horizontal_flip=False, # randomly flip images
# vertical_flip=False, # randomly flip images
# fill_mode='nearest')
training_generator = training_datagen.flow_from_directory(training_dir, target_size=default_image_size, batch_size=default_batch, class_mode='categorical')
print('Number of image batches per epoch of modeling:', len(training_generator))
# + id="MvjC0IBKzYp5" outputId="2320141b-0012-4f2c-af73-076d23a6450b" colab={"base_uri": "https://localhost:8080/"}
print('Loading and pre-processing the validation images...')
validation_datagen = ImageDataGenerator(rescale=1/255)
validation_generator = validation_datagen.flow_from_directory(validation_dir, target_size=default_image_size, batch_size=default_batch, class_mode='categorical', shuffle=False)
print('Number of image batches per epoch of modeling:', len(validation_generator))
# + id="_C7U2LGuzYp7"
if notifyStatus: status_notify('(TensorFlow Multi-Class) Task 2 - Load and Prepare Images completed on ' + datetime.now().strftime('%A %B %d, %Y %I:%M:%S %p'))
# + [markdown] id="Q1zN_4yTzYp8"
# # Task 3 - Define and Train Models
# + id="Do8vW0SIzYp9"
if notifyStatus: status_notify('(TensorFlow Multi-Class) Task 3 - Define and Train Models has begun on ' + datetime.now().strftime('%A %B %d, %Y %I:%M:%S %p'))
# + id="TxzTYyrwzYp-"
# Define the function for plotting training results for comparison
def plot_metrics(history):
fig, axs = plt.subplots(1, 2, figsize=(24, 15))
metrics = [train_loss, train_metric]
for n, metric in enumerate(metrics):
name = metric.replace("_"," ").capitalize()
plt.subplot(2,2,n+1)
plt.plot(history.epoch, history.history[metric], color='blue', label='Train')
plt.plot(history.epoch, history.history['val_'+metric], color='red', linestyle="--", label='Val')
plt.xlabel('Epoch')
plt.ylabel(name)
# if metric == train_loss:
# plt.ylim([0, plt.ylim()[1]])
# else:
# plt.ylim([0.5, 1.1])
plt.legend()
# + id="M2nXRu9mzYqA"
# Define the baseline model for benchmarking
def create_nn_model(conv1_filters=192, conv2_filters=128, conv3_filters=64, conv1_dropout=0.25, conv2_dropout=0.25, conv3_dropout=0, dense_nodes=512, dense_dropout=0.25,
n_inputs=input_image_shape, n_outputs=num_classes, opt_param=default_optimizer, init_param=default_kernel_init):
nn_model = keras.Sequential([
# This is the first convolution
keras.layers.Conv2D(conv1_filters, (3,3), strides=1, padding='same', activation='relu', kernel_initializer=init_param, input_shape=n_inputs),
keras.layers.Dropout(conv1_dropout),
# keras.layers.BatchNormalization(),
keras.layers.MaxPooling2D((2,2), strides=2, padding='same'),
# This is the second convolution
keras.layers.Conv2D(conv2_filters, (3,3), strides=1, padding='same', activation='relu', kernel_initializer=init_param),
keras.layers.Dropout(conv2_dropout),
# keras.layers.BatchNormalization(),
keras.layers.MaxPooling2D((2,2), strides=2, padding='same'),
# This is the third convolution
keras.layers.Conv2D(conv3_filters, (3,3), strides=1, padding='same', activation='relu', kernel_initializer=init_param),
keras.layers.Dropout(conv3_dropout),
# keras.layers.BatchNormalization(),
keras.layers.MaxPooling2D((2,2), strides=2, padding='same'),
# Flatten the results to feed into a DNN
# This is the last neuron hidden layer
keras.layers.Flatten(),
keras.layers.Dense(dense_nodes, activation='relu', kernel_initializer=init_param),
keras.layers.Dropout(dense_dropout),
keras.layers.Dense(n_outputs, activation='softmax', kernel_initializer=init_param)
])
nn_model.compile(loss=default_loss, optimizer=opt_param, metrics=default_metrics)
return nn_model
# + id="OcVElo2TzYqC" outputId="087b6a64-6e9f-4dbf-f177-2959571e9efa" colab={"base_uri": "https://localhost:8080/"}
# Initialize the neural network model and get the training results for plotting graph
startTimeModule = datetime.now()
learning_rate_reduction = ReduceLROnPlateau(monitor='val_accuracy', patience = 2, verbose=1, factor=0.5, min_lr=0.00001)
reset_random(seedNum)
nn_model_0 = create_nn_model()
nn_model_history = nn_model_0.fit(
training_generator,
steps_per_epoch=len(training_generator),
validation_data=validation_generator,
validation_steps=len(validation_generator),
epochs=default_epoch,
callbacks=[learning_rate_reduction],
verbose=1)
print('Total time for model fitting:', (datetime.now() - startTimeModule))
# + id="B3TrVKk4zYqE" outputId="374c9a14-c1c7-4862-a95c-7ed9d4230fb3" colab={"base_uri": "https://localhost:8080/"}
nn_model_0.summary()
# + id="xPh-jQdtzYqF" outputId="5d406275-279f-4ece-fa39-1ed6f6130d4a" colab={"base_uri": "https://localhost:8080/", "height": 433}
plot_metrics(nn_model_history)
# + jupyter={"outputs_hidden": false} pycharm={"name": "#%%\n"} id="QGtVTxNYzYqG"
if notifyStatus: status_notify('(TensorFlow Multi-Class) Task 3 - Define and Train Models completed on ' + datetime.now().strftime('%A %B %d, %Y %I:%M:%S %p'))
# + [markdown] id="bWEbAyjvzYqI"
# # Task 4 - Evaluate and Optimize Models
# + jupyter={"outputs_hidden": false} pycharm={"name": "#%%\n"} id="_8DuSNAtzYqI"
if notifyStatus: status_notify('(TensorFlow Multi-Class) Task 4 - Evaluate and Optimize Models has begun on ' + datetime.now().strftime('%A %B %d, %Y %I:%M:%S %p'))
# + [markdown] id="AvGcXb6xzYqJ"
# ## 4.a) Alternate Model One
# + id="79R9HvH-zYqK" outputId="af9dc892-8c32-425c-d9b2-c1c49519ddb6" colab={"base_uri": "https://localhost:8080/"}
# Initialize the neural network model and get the training results for plotting graph
startTimeModule = datetime.now()
reset_random(seedNum)
nn_model_1 = create_nn_model(conv1_filters=256, conv2_filters=128, conv3_filters=64, conv1_dropout=0.25, conv2_dropout=0.25, conv3_dropout=0, dense_nodes=512, dense_dropout=0.25)
nn_model_history = nn_model_1.fit(
training_generator,
steps_per_epoch=len(training_generator),
validation_data=validation_generator,
validation_steps=len(validation_generator),
epochs=default_epoch,
callbacks=[learning_rate_reduction],
verbose=1)
print('Total time for model fitting:', (datetime.now() - startTimeModule))
# + id="wkcO9eapzYqL" outputId="a9159735-e4f0-48b8-82d2-d4f31c6c82cc" colab={"base_uri": "https://localhost:8080/"}
nn_model_1.summary()
# + id="OpH8JS_CzYqM" outputId="68ffdc6c-b995-4d8e-9b17-23124c756dbd" colab={"base_uri": "https://localhost:8080/", "height": 434}
plot_metrics(nn_model_history)
# + id="fF1O3KjHzYqO"
if notifyStatus: status_notify('(TensorFlow Multi-Class) Task 4 - Evaluate and Optimize Models completed on ' + datetime.now().strftime('%A %B %d, %Y %I:%M:%S %p'))
# + [markdown] id="MJJTdK-hzYqP"
# # Task 5 - Finalize Model and Make Predictions
# + id="PzeqrJJTzYqP"
if notifyStatus: status_notify('(TensorFlow Multi-Class) Task 5 - Finalize Model and Make Predictions has begun on ' + datetime.now().strftime('%A %B %d, %Y %I:%M:%S %p'))
# + id="SEHPWfiAzYqR" outputId="df2473f1-db8a-4579-f37c-90176474cbbe" colab={"base_uri": "https://localhost:8080/"}
final_model = nn_model_1
# Display a summary of the final model
final_model.summary()
# + id="jIdipqhtzYqT" outputId="db6b80aa-e214-41ad-b6b1-9b57a51e0e45" colab={"base_uri": "https://localhost:8080/"}
# Print the labels used for the modeling
print(validation_generator.class_indices)
# + id="95uPbf4yzYqU" outputId="25af936f-e5e4-4f7c-a608-e8b29ae6a712" colab={"base_uri": "https://localhost:8080/"}
final_model.evaluate(validation_generator, verbose=1)
# + id="MYTjRVNQzYqV" outputId="f5e3827e-30af-4fd2-b5a9-3dfd1c210e19" colab={"base_uri": "https://localhost:8080/"}
test_pred = final_model.predict(validation_generator)
test_predictions = np.argmax(test_pred, axis=-1)
test_original = validation_generator.labels
print('Accuracy Score:', accuracy_score(test_original, test_predictions))
print(confusion_matrix(test_original, test_predictions))
print(classification_report(test_original, test_predictions))
# + id="COGCI66wzYqX"
if notifyStatus: status_notify('(TensorFlow Multi-Class) Task 5 - Finalize Model and Make Predictions completed on ' + datetime.now().strftime('%A %B %d, %Y %I:%M:%S %p'))
# + id="OeNtc6N2zYqZ" outputId="e45b3c46-e1f2-40d2-b58a-ef708d917951" colab={"base_uri": "https://localhost:8080/"}
print ('Total time for the script:',(datetime.now() - startTimeScript))
| 23,128 |
/Sk-learn Data generation.ipynb | ff16630f9abe79cc31d93454accafbe9667ed467 | [] | no_license | amitagarwal2208/MultivariateRegression | https://github.com/amitagarwal2208/MultivariateRegression | 1 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 211,515 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# +
#sklearn = scikit learn
# generating data
from matplotlib import pyplot as plt
from sklearn.datasets import make_regression,make_classification
# +
# make_regression?
# -
# house no. is not informative
X,Y = make_regression(n_samples=400 , n_features=1 , n_informative=1 , noise=10.8)
print(X.shape , Y.shape)
print("1")
plt.scatter(X,Y)
XC , YC = make_classification?
# +
XC , YC = make_classification(n_samples=400 , n_features=2 , n_informative=2 , n_redundant=0 ,n_classes=3, n_clusters_per_class=1 , random_state=5)
## n_features = n_informative + n_redundant
## n_clusters_per_class tells how many clusters do we need of each class
## random_state produces same kind of graph everytime.
# -
print(XC.shape , YC.shape)
# +
plt.scatter(XC[:,0] , XC[:,1] , c=YC)
## Aim : to find seperators that separates each pair of class
## if k classes --> kC2 number of seperators needed
## basically a seperator to seperate each pair of class.
# +
# Blobs DataSet
# Used in unsupervised learning
from sklearn.datasets import make_blobs , make_moons
XB = make_blobs?
# -
XB = make_blobs
XB,YB = make_blobs(n_samples=1000 , n_features=2 , centers=5 , random_state=10)
# +
plt.scatter(XB[:,0],XB[:,1],c=YB)
# eg. : Amazon customers who are active in diwali seasons
# Customers frequent electronics buyers
# Customers more likely to buy furniture
## ====>> UNSUPERVISED LEARNING
# +
## Moons dataset
## Non Linear Dataset
# make_moons?
# -
X,Y = make_moons(n_samples=100 , noise=0.15 , random_state=7)
plt.scatter(X[:,0],X[:,1],c=Y)
plt.show()
# +
# Revision
X,Y = make_regression(n_samples=100 , n_features=1 , n_informative=1 , noise=1.8)
print(X.shape , Y.shape)
print()
print(X)
print()
print(Y)
# -
plt.scatter(X,Y)
plt.show()
XC,YC = make_classification?
XC,YC = make_classification
XC,YC = make_classification(n_samples=300 , n_features=4 , n_informative=2 , n_redundant=2 ,n_classes=3, n_clusters_per_class=1)
plt.scatter(XC[:,0] , XC[:,1] , c=YC)
plt.show()
# +
## Subplots
## used to put all graphs and plots in a single plot
plt.figure()
plt.scatter(X,Y)
plt.figure()
plt.scatter(XC[:,0] , XC[:,1] , c=YC)
plt.show()
# -
| 2,429 |
/Mid-Point/Presidential Election Data.ipynb | e5e608e98ce38c818b53e718a8bed71ebc0efb7b | [
"MIT"
] | permissive | sayaaoi/Open.Data.Mashups.Project | https://github.com/sayaaoi/Open.Data.Mashups.Project | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 27,047 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# + [markdown] toc=true
# <h1>Table of Contents<span class="tocSkip"></span></h1>
# <div class="toc"><ul class="toc-item"><li><span><a href="#Presidential-Election-Data" data-toc-modified-id="Presidential-Election-Data-1"><span class="toc-item-num">1 </span>Presidential Election Data</a></span></li></ul></div>
# -
# # Presidential Election Data
# Determine wheather a state is regarded as conservative, liberal or swing.
# import necessary packages
import pandas as pd
from bs4 import BeautifulSoup
from requests import get
from requests.exceptions import RequestException
from contextlib import closing
import csv
# 1996 ELECTORAL AND POPULAR VOTE SUMMARY is in html format
def simple_get(url):
"""
Attempts to get the content at 'url' by making an HTTP GET request.
If the content-type of response is some kind of HTML/XML, return the
text content, otherwise return None.
"""
try:
with closing(get(url, stream=True)) as resp:
if is_good_response(resp):
# r.text is the content of the response in unicode,
# and r.content is the content of the response in bytes.
return resp.content
else:
return None
except RequestException as e:
log_error('Error during requests to {0}:{1}'.format(url, str(e)))
return None
def is_good_response(resp):
"""
Return True if the response seems to be HTML/HTM, Flase otherwise.
"""
content_type = resp.headers['Content-Type']
return (resp.status_code == 200
and content_type is not None
and content_type.find('html') > -1)
def log_error(e):
"""
Print log errors.
"""
print(e)
# Download 1996 presidential ELECTORAL AND POPULAR VOTE
url = 'https://transition.fec.gov/pubrec/fe1996/elecpop.htm'
response = simple_get(url)
if response is not None:
htm = BeautifulSoup(response, 'html.parser')
# cast to string
para = str(htm.find_all('pre'))
temp_content = para[para.find('>AL'):]
table_content = temp_content[1:temp_content.find('<st')]
table_content_li = [x for x in table_content.split('\r\n')]
content = []
for row in table_content_li[:-1]:
a = row.split(' ')
if a[1] == '':
a[1] = 'n'
if a[2] == '':
a[2] = 'n'
content.append(a)
# process exception: different length of row
content[8]
update = content[8][3] + ' ' + content[8][4]
content[8][3] = update
temp_row = content[8]
del temp_row[4]
temp_row
# replace exception with updated row
content[8] = temp_row
# convert list to numpy array then to dataframe
df96 = pd.DataFrame(content)
df96.columns = ['State', 'Clinton','Dole','Popular vote','Total Popular vote']
df96
# +
# export csv file EXECUTE ONCE!
# with open('/Users/amberwu/Downloads/UIUC/Course FL 2018/Open data mashup_data copy/Presidential election data/Election_data96.csv', 'w', encoding = 'utf-8') as csvfile:
# df96.to_csv(csvfile)
# -
| 3,267 |
/notebooks/08_hovmoller_space_time_visualisation_advanced.ipynb | 612a0eeedcccd5207b65b13d27361b768201f2f9 | [
"Apache-2.0"
] | permissive | alexismc/agdc-v2-examples | https://github.com/alexismc/agdc-v2-examples | 0 | 0 | null | 2016-11-16T01:14:07 | 2016-10-29T01:35:30 | null | Jupyter Notebook | false | false | .py | 582,527 | # ---
# jupyter:
# jupytext:
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# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# name: python3
# ---
# + [markdown] id="view-in-github" colab_type="text"
# <a href="https://colab.research.google.com/github/albert-yue/gcn-explainability/blob/master/notebooks/experiment_r52.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# + id="FNmaPYq5QeVO" colab_type="code" outputId="9577d3e0-d82c-4d73-86b9-27204d6291c7" colab={"base_uri": "https://localhost:8080/", "height": 125}
from google.colab import drive
drive.mount('/content/gdrive')
# + id="_qgqRZORRtWe" colab_type="code" outputId="d3971847-1533-44f0-886f-dc660a0abda4" colab={"base_uri": "https://localhost:8080/", "height": 35}
# %cd gdrive/My\ Drive/MIT/gcn_explainability/
# + id="97FxfI2NSApc" colab_type="code" outputId="7670d7bb-c009-4581-bfd5-fcd5995c003b" colab={"base_uri": "https://localhost:8080/", "height": 228}
# !git pull
# + [markdown] id="jkC8bb4BSMWw" colab_type="text"
# # Set up Vocabulary and Labels
# + id="Or532CXhSLuu" colab_type="code" colab={}
from src.data import save_all_labels
save_all_labels('data/r52-labels.txt', 'data/train-r52.txt', 'data/test-r52.txt')
# + id="isNumAIlaS6S" colab_type="code" colab={}
from tqdm.auto import tqdm
def save_vocabulary(out_path, train_path, test_path, doc_freq_threshold=5):
word_freqs = {} # counting num of docs
for data_path in [train_path, test_path]:
with open(data_path, 'r') as f:
for line in tqdm(f.readlines()):
line = line.strip()
_, text = line.split('\t')
words = text.split()
for w in set(words):
w = w.strip()
if w in word_freqs:
word_freqs[w] += 1
else:
word_freqs[w] = 1
vocab = []
for w, freq in word_freqs.items():
if freq >= doc_freq_threshold:
vocab.append(w)
with open(out_path, 'w+') as f:
f.write('\n'.join(vocab))
# + id="FCBFetAoTsd6" colab_type="code" outputId="ed27c8ff-49c2-45fc-c71a-1b3941633c63" colab={"base_uri": "https://localhost:8080/", "height": 116, "referenced_widgets": ["a10bd2b9b03c4cd6a5f3893aa5cceb84", "ffe4561c945e449eb5c5d776c62323db", "1f567de3fb4947f7827b8a7814c13e1f", "94fb45595cc04ba2ae768afc4669d6ad", "b606568240354e69862b530209b15cd6", "394ec8d8e9394cf39492991996162c68", "18faaa221c314027bba5f9211b16ff5d", "5a83612ab43341e18b2a09d833f298aa", "2bc5ecc5ac184170a523787c8949f44f", "10785718523249f0886a7654ccc8c402", "0edbc64fe4cb40f5a9d6ffdb36b97a1c", "e9d600614c3842e280939e924298a2ec", "34e3ac812d104224810f77b8ed6fe435", "4a22c24858f446edbb80b587761325a0", "cd21991e83fb4b18bb3b9a3bdf42b37a", "000c8efd74f844a4bdc0660dc21886b2"]}
save_vocabulary('data/r52-vocabulary.txt', 'data/train-r52.txt', 'data/test-r52.txt')
# + [markdown] id="iwjCOx7fSDXA" colab_type="text"
# # Training
# + id="bGSD7FeHSCVH" colab_type="code" colab={}
from src.data import Corpus, get_data, get_vocabulary, get_labels
from src.models.gcn import GCN
from src.preprocessing import clean_text, build_adj_matrix, normalize_adj
from src.train import train, evaluate, accuracy
# + id="JwhbvGxhUTJH" colab_type="code" outputId="50472996-a767-47f6-92dd-0317e7a06b9d" colab={"base_uri": "https://localhost:8080/", "height": 70}
import nltk
nltk.download('stopwords')
# + id="PcBq34leUtem" colab_type="code" colab={}
from matplotlib import pyplot as plt
def plot_loss(train_losses, val_losses):
plt.title("R52 Loss per Epoch")
plt.xlabel("Epoch")
plt.ylabel("Loss")
plt.plot(train_losses, label='train')
plt.plot(val_losses, label='val')
plt.legend(loc='lower left')
# + id="eQtRS_LGSCuJ" colab_type="code" colab={}
seed = 0
val_split = 0.1
vocab = get_vocabulary('data/r52-vocabulary.txt')
labels = get_labels('data/r52-labels.txt')
corpus = get_data('data/train-r52.txt', labels)
test_corpus = get_data('data/test-r52.txt', labels)
# Split validation set
corpus.shuffle(seed)
len_train = int(len(corpus) * (1 - val_split))
train_corpus = Corpus(corpus[:len_train])
val_corpus = Corpus(corpus[len_train:])
num_documents = len(train_corpus) + len(val_corpus) + len(test_corpus)
# + id="kTLECP_UUPH_" colab_type="code" outputId="008347a6-80a7-41f9-ac25-bf9ff70db22e" colab={"base_uri": "https://localhost:8080/", "height": 116, "referenced_widgets": ["0474518c7e004901bfc5070d2652f115", "8fbdb0541b274be4923e66ec36500cdf", "f88058eb82e54d8998aec1f9089cef8d", "cbc2aa5372684ca8a4678730f0b35b55", "0e6a25de7c374dbc9921f7c9c9933cb4", "5d485be8ac00475cab772d769ce5d49e", "93b11a455e724f31bdf3296e48a9db5a", "8342da9730f3400582366a1f1959d282", "5230f3087fd14acc8a813f6030fd36be", "074ea62a1d414ac4a643a26ae0b08883", "4ad840db3d434b3ab8bc7ab5f71c0303", "e55079e3f28444c8aedbbffff5e2ee08", "40ceaba1915b477ea4d147847d5a559b", "33781ed753a14312a77d9444169ce524", "061122b873d34915abd95d6b8dc26750", "4cc2224097ce48f0b2ab436c31fc14bf"]}
# Mask out unknown words
clean_text(corpus, vocab)
clean_text(test_corpus, vocab)
# + id="N27VJehTUR6j" colab_type="code" outputId="e990a9ed-a2de-46c0-8dc7-0c4508775954" colab={"base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": ["39f3b726b0814fef9b1b6d6c05a050b3", "90b76b6ae2074ba98790b83c1254504c", "20be20e368744008851e4db834533e1b", "2ae8a6db0c784f689cca6a2a7181440b", "a7314afdffd8494ea792c2be3e54bc21", "f4bff4ee3a5c4565b8016228d3f12b9b", "c6afb712045d499aa2dfb7eb36075762", "edc949009d7844eebdcbed0994b07f57", "0faf07367f04449ab2f87a971e39313a", "f18963c7255a421595243c4d81472672", "2f6f92bb698841d8abed48723a56d88d", "8d40d2ffceba49db853429ac5de928c4", "ea7ac63b53814dd593c01821842a4b94", "7789e320c3c44c099560c56f828dd11f", "75b88bd94efe414385dd40e25c44f1d9", "1f6b8aa3a61e4dc78bdb5e79761db47f", "034a75c89766467b84ff18bc3b4b0470", "017299615ccc4d1794f4b6eb6c6942ff", "5a90104890c24e60adbff7c7c10b911e", "21af429aab4a41fa9295eda15891b061", "71b635261f4b4f358dca3cf65fa535f0", "13c7dc1dc5654826bb3be77c53a30d6d", "67de8e14fdd541479d7b3e17bf55392c", "46df7e3a56944e9b8310decd318f6f7f", "d85e8e7e03d04de3acfb9b4e51bdbdc8", "59d76a6d1f674b9d9b85a4b78a243d95", "2bfa1975d8304fc39dd995ebc7e7137a", "656f321aaae441568128209f171d8da5", "9f284f68ac2941a28543d708d58312cc", "2da732b21d6a468fb97c0f766f4caf60", "d16fcc8c13a745d0b758a2961d374cf5", "b34026d4142f4fc9aebcbd8e01069785", "becaca34a0a24ca1a0a8878d5c03ae8d", "717aef34d5d14f8192ea7585df40bde0", "6ef957d17c3c41febecfd0b236b0a6c4", "36abe153025e49ed90998ea80e546849", "2f2b61984a544887b52980e335e6ecb8", "5abaee632b5c469793b0113ac4cd3210", "458955f1c1534f5c9a9db5874ee8d473", "ca0341a9bee54948aaee15096986cb42", "e1d50281d0e348679c2f5c9b21cf3615", "d0f7ab3722ca41949ef183326f8a16fa", "4aeee818304646bf84cc4b2f039070e9", "f6611c1b8aef484295e8718037b32a36", "3906161c20364f0dadafa48847adbb27", "adc0f7bca37140c99fe6275f0f33852c", "d80b8e50eef54222b0e695e224c27381", "585756d320854801b30cc93828aaee77", "eefea879d41c477ebdd1cfb18e7fd89f", "9b05a9d633f249298e0c72b21c3b9b2b", "eaf21ee08e7440eebe352bc97aa843a9", "92d9846b966047af8ef1714123053c19", "b033d42c74da45bb9fa51862c03b0bb4", "47e876d3a6ac414fad368a7b9c5f30b8", "8abccd9d511045aab3a3f5464171af0f", "d8777b78b7d7479eb1ce3fea75fefa83", "350374c104ae4453988dcd4b108ef730", "27c4f4d9e576442c985e946aa17182ad", "6ca7146530884fd9aa78b47950482c17", "cfa36d8e8b1f4e129b28292a0259b7bb", "2bf5d1bbd3a142238579b0a7b14de310", "e8e4c36ef1f44fd480222d1dfe2193fe", "a9bcbd589a084b50982cef994ea19a01", "47b6caea7c7d4d41b531c651aeefe78d", "dcd3d6bd51004d06948590b87e512a2f", "aeb976a2cc494d37b428523bb09ef1ec", "8d12366d9d9e4c0e96a4af091110081c", "b65e023a520b4545bf606efd33270472", "eb0c0341bcba4e5e907c298f841aaaf3", "e89821a4f37d41d4a7347143d74b6a3b", "88eed407697b47a0ab20998050da4065", "414d64842fdd4bfcbabd7eb0edeab8c0", "3932c5b59cfb42e8a0ef41a87b298dd6", "550f179b37e74c5b898abf03eeabb856", "c75326978ae1439086f247b61be2d39f", "3288e25388af457eab7122a2b8351db3", "12735c907c05490bba6c1fc3b1435121", "33126018a4eb4611983ad3d0c0a4c360", "aa379a36b96b47f4a6e9a8fafbf39001", "9fcb43ff86d945e4b63e0202a1b45120", "93b98b87e704436fb83738eececbca7f", "8e4e2a2b100a414a91a252b03cace6ba", "d73a765757324d079ec59b65773212aa", "605fc9070d454d65aa56183cdbad0b15", "53130fc0d11945b19af01a2e7c1c9308", "76d9ca7c7fe54878bf91b9bfdd7bf1c3", "37d22261c3e7408aaec3ed5645d7fe87", "6beeab9821b3423a892db84dbe08ede2", "d32a23f5274b4b5dbe2bb940de8f53f7", "cac322d304bb47c19f9eb4bdf64696bf", "8fbba0fb508e42a9acdba5f8e5b80d42", "78d118f3943d4b709cf78e87191ba3b1", "22267d2f02d54c818c4670144787fa24", "fb9e2f3579bf453f8667da601762a79c", "adedf8d4551a42d1b723c2b9e8639d12", "3d02a205e78e4fd7bb2907b6e05e430b", "941d0a1742e9413ea085d285684dfb62", "22559e71754d43ba93228b2a8e6608ee", "b9af961138554112a2aae2b716b7a5cd", "831bcf3a02764a749c4fd28b24df4d40", "b09df1325ef64116ad893e47df3cca18", "6a85fbad12974d1489958aa33ca210cb", "0ba61f8315f844e49b8448de2829a678", "4fd437ff15234d00beb985a7255ab0e0", "5e8575726fd44ec59504579d9f182221", "8c9289b7818a4305a66770f0cdc7a0fc", "d577451d7adf40d6922e41c395cd4baa", "5927cf5dd49d4c5495520f659d7d35d1", "e0055d48d44c4a9c9e4714b90d2a35b5", "00ce7e45d1874cecb3ac55df8c6bf77e", "ad442743b923465da4c24ba288c645fa", "7600211491aa41518fee2b20f9dc4a26", "24577368e5f740298de5cb7b2f409018", "35127f4e05a747dab2250bf25fdbcb47", "297ffb136b444227ab74ca8dc4937da8", "8bd1dbb40a8f4a58a6d117684909e260", "eb1efadaef48459889b2bed65dceab56", "ea3fee5d0c3744e380b111c7f499abe9", "1e189edc0a024e99af8413972bd948d0", "637db436a22a4953aa9c755f6e1e9e81"]}
train_adj_matrix = build_adj_matrix(train_corpus, vocab, num_documents, doc_offset=0)
val_adj_matrix = build_adj_matrix(val_corpus, vocab, num_documents, doc_offset=len(train_corpus))
test_adj_matrix = build_adj_matrix(test_corpus, vocab, num_documents, doc_offset=len(train_corpus) + len(val_corpus))
# + id="SOjgS6dfUZz_" colab_type="code" colab={}
from src.utils import save_sparse_tensor
save_sparse_tensor(train_adj_matrix, 'data/r52_train_adj_matrix.pt')
save_sparse_tensor(val_adj_matrix, 'data/r52_val_adj_matrix.pt')
save_sparse_tensor(test_adj_matrix, 'data/r52_test_adj_matrix.pt')
# + id="UhvV9fqAUck3" colab_type="code" colab={}
from src.utils import load_sparse_tensor
train_adj_matrix = load_sparse_tensor('data/r52_train_adj_matrix.pt')
val_adj_matrix = load_sparse_tensor('data/r52_val_adj_matrix.pt')
test_adj_matrix = load_sparse_tensor('data/r52_test_adj_matrix.pt')
# + id="omSQMaiDUeHu" colab_type="code" colab={}
train_adj_matrix = normalize_adj(train_adj_matrix)
val_adj_matrix = normalize_adj(val_adj_matrix)
test_adj_matrix = normalize_adj(test_adj_matrix)
# + id="G2uG4kNVUmrC" colab_type="code" colab={}
hidden_size = 200 # hyperparameter
dropout = 0.5 # hyperparameter
epochs = 700
lr = 0.02
num_vertices = len(vocab) + num_documents
model = GCN(num_vertices, hidden_size, len(labels), dropout=dropout)
# + id="RZYEiCo1UoYo" colab_type="code" outputId="27246129-e172-4dcb-a0d3-e603888932e4" colab={"base_uri": "https://localhost:8080/", "height": 527}
# %time train_losses, val_losses = train(model, train_adj_matrix, val_adj_matrix, train_corpus.labels(), val_corpus.labels(), len(vocab), epochs=epochs, init_lr=lr, plot_every=5, print_every=10, save_path='r52_train.pt')
# + id="qnkbRp_1UxN_" colab_type="code" outputId="9c40fcf2-7d1c-4d8e-ebd2-2646e614ec7e" colab={"base_uri": "https://localhost:8080/", "height": 295}
plot_loss(train_losses, val_losses)
# + id="84t7ontsUzMz" colab_type="code" outputId="81d653d3-5161-451e-c0c4-08ea34f8e20a" colab={"base_uri": "https://localhost:8080/", "height": 35}
test_start_idx = len(vocab) + len(train_corpus) + len(val_corpus)
test_loss = evaluate(model, test_adj_matrix, test_corpus.labels(), accuracy, start_idx=test_start_idx)
print(test_loss)
# + [markdown] id="kVmy8GKn1wZB" colab_type="text"
# ## Full Matrix
# + id="IM3XVrgY1yYC" colab_type="code" colab={}
full_corpus = Corpus(train_corpus.data + val_corpus.data + test_corpus.data)
# + id="p-YLSgp02I7j" colab_type="code" outputId="97bd3955-9e67-4c28-d26d-3d37ac84685c" colab={"base_uri": "https://localhost:8080/", "height": 388, "referenced_widgets": ["4d13759fc99846b9a8e8270cb75d04c0", "2283acacf6214c8a8f0d65d4e6bbe3b0", "245693734ef24878a9091cf872528c07", "40ae1ffa579b4ae292fbca93fd9afce6", "790459ff1dde478db8cab3537375bfa6", "0fe7637584f646449421a8066cb78b0a", "dc1acccf6c9549bab144e02c6ef56791", "b54e5a5063cb4ee08c290b93c6f049ed", "f603a834980d4c93880dc3b99cd8861c", "f9971066b0e3456fad9cbb65480b14e7", "ff15ee008caa44fb8189f89dbe483a4b", "4aefcf95b59642d2af7db8a24bda1023", "cbb0bf90a4fb429eb0379bce1b462644", "a19316a821c942589f2ba1b27bab65eb", "b91f11c6858b43d18eb22012e741beba", "6dc7b65b55bc4b8d8573ac1cbea31b0e", "5962d17248f44d068a2ca7c431fb0648", "492314fefe6649a598941b4caa426fed", "d325e9201a2f48e2abea5a8bd40f6ab4", "93dfa06df95f4e5e9ec2b53b6df42338", "35b7b1b050504db69fe8d10188bd2c08", "f948080decd647fda030db9e97968317", "462d8118ec68449d8487e5fc39b7d107", "fc493271081047ab972c9c1fe32f8bf5", "c23a13c28c454cc995def5f96fe4c44f", "cf367d336f4e4b828a52ebb5bffb7d6c", "e5983f399aba4baca0469d2f90251bc0", "5565040106674b3eb47fe7eccb574786", "326f9a107d7e498e95f7551806e41b6d", "9f8e7a64eeea4b98823745c4bbba5514", "6436308579164eaca9da471a58cc82a5", "370aecf415fd45b08aecbedd12b0a35d", "e8b99246a45a4b018010e9f72b00de74", "f1a292790bb74920858b1b90c7fe9278", "84c3e54ec34b4e599ca2c595571b5d87", "c1316e344dd44f349189819bd2c47026", "813ba1082e284de3b7612c48a05107a9", "0e0748c1f73347d490b048fcfed32137", "009418eef2fd48b0b9bb190a25f83ad5", "bfa4e6425b0148f4843eaccd840d4383"]}
# %time adj_matrix = build_adj_matrix(full_corpus, vocab, num_documents, doc_offset=0)
# + id="hn6_GLea2LOa" colab_type="code" colab={}
from src.utils import save_sparse_tensor
save_sparse_tensor(adj_matrix, 'data/r52_full_adj_matrix.pt')
# + id="0WSP2OAe2QvI" colab_type="code" colab={}
from src.utils import load_sparse_tensor
adj_matrix = load_sparse_tensor('data/r52_full_adj_matrix.pt')
# + id="FWseqkbG2Vu_" colab_type="code" colab={}
adj_matrix = normalize_adj(adj_matrix)
# + id="RkHixuvb2X0I" colab_type="code" colab={}
hidden_size = 200 # hyperparameter
dropout = 0.5 # hyperparameter
epochs = 700
lr = 0.02
num_vertices = len(vocab) + num_documents
model = GCN(num_vertices, hidden_size, len(labels), dropout=dropout)
# + id="TrQ7sDZT2e68" colab_type="code" outputId="28ae3308-1fd1-4f09-e3c0-39d16dce290f" colab={"base_uri": "https://localhost:8080/", "height": 1000}
# %time train_losses, val_losses = train(model, adj_matrix, adj_matrix, train_corpus.labels(), val_corpus.labels(), len(vocab), epochs=epochs, init_lr=lr, plot_every=5, print_every=10, save_path='gcn_r52_full.pt')
# + id="gzTH_wrw2nAt" colab_type="code" outputId="d32c547a-38e3-447e-f79a-4d452237eff3" colab={"base_uri": "https://localhost:8080/", "height": 295}
plot_loss(train_losses, val_losses)
# + id="1Vs0YUgW2wx4" colab_type="code" outputId="5f35e1ac-1c5f-45b7-c25c-30f83d5b25b2" colab={"base_uri": "https://localhost:8080/", "height": 35}
test_start_idx = len(vocab) + len(train_corpus) + len(val_corpus)
test_acc = evaluate(model, adj_matrix, test_corpus.labels(), accuracy, start_idx=test_start_idx)
print(test_acc)
# + id="lW0Lv_5c2glT" colab_type="code" outputId="3e76b21b-e760-4295-a4d3-b4547e631021" colab={"base_uri": "https://localhost:8080/", "height": 72}
print(train_losses)
print(val_losses)
# + id="KBKzMe4w-xFb" colab_type="code" outputId="b8b4a6b2-8911-4cb1-9fb8-460131adfe4c" colab={"base_uri": "https://localhost:8080/", "height": 1000}
import numpy as np
repeats = 10
repeats_test_acc = []
for i in range(repeats):
model = GCN(num_vertices, hidden_size, len(labels), dropout=dropout)
train(model, adj_matrix, adj_matrix, train_corpus.labels(), val_corpus.labels(), len(vocab), epochs=epochs, init_lr=lr, plot_every=5, print_every=10, save_path='gcn_r52_full_repeats.pt')
test_acc = evaluate(model, adj_matrix, test_corpus.labels(), accuracy, start_idx=test_start_idx)
repeats_test_acc.append(test_acc)
repeats_test_acc = np.array(repeats_test_acc)
print('Mean %s and std %s' % (np.mean(repeats_test_acc), np.std(repeats_test_acc)))
oc_auc', n_jobs=-1, verbose=2, cv=3)
model.fit(x_tr, y_tr)
print(model.best_estimator_)
print('train AUC = ',model.score(x_tr, y_tr))
print('val AUC = ',model.score(x_val, y_val))
print('Time taken for hyper parameter tuning is : ', (time.time() - start))
print('Done!')
# + colab={"base_uri": "https://localhost:8080/", "height": 118} colab_type="code" id="wiLnXbe5Fn44" outputId="c217212b-a460-43a1-e392-218ca297059e"
# train model with best parameters
dt = DecisionTreeClassifier(ccp_alpha=0.0, class_weight='balanced', criterion='gini',
max_depth=15, max_features=None, max_leaf_nodes=81,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=5,
min_weight_fraction_leaf=0.0, presort='deprecated',
random_state=23, splitter='best')
dt.fit(x_tr, y_tr)
# + colab={} colab_type="code" id="3VMsoFOGFw_q"
# create dataframe for features and it importance
sorted_indices = dt.feature_importances_.argsort()
features = x_tr.columns[sorted_indices]
dt_fea_imp = pd.DataFrame({
'features' : features,
'importance' : dt.feature_importances_
})
# + colab={"base_uri": "https://localhost:8080/", "height": 850} colab_type="code" id="FKWqDdQAFxqt" outputId="1ff3e2e6-5141-4aa7-cd90-38461a052e33"
# plot feature importance for DT
plot_feature_importance(dt_fea_imp, 'features', 'importance', 'DT')
# + [markdown] colab_type="text" id="EfAHKnMpi3Gj"
# - From the feature importance plot we can say that, lyrics_count has maximum importance.
# - Let's take the features with positive importance like composer_count, song_lang_boolean, isrc_missing, source_system_tab, msno, song_member_count, source_type, third_genre_id.
# - Let's try other model using the above mentioned features only.
# + colab={"base_uri": "https://localhost:8080/", "height": 67} colab_type="code" id="XLJYiw3SFz9v" outputId="48db5b71-d2d3-494e-aee6-963c6e3559c0"
# Apply model on test data and save predictions
predict_and_save(dt, x_te, 'dt')
# + colab={"base_uri": "https://localhost:8080/", "height": 834} colab_type="code" id="imYUeTF-kzBo" outputId="fe5357e3-3037-4802-c1d0-d7022c7ce641"
fig, axes = plt.subplots(nrows = 1,ncols = 1,figsize = (20,15), dpi=300)
tree.plot_tree(dt,
feature_names = x_tr.columns,
class_names=['label - 0', 'label - 1'],
filled = True);
fig.savefig('tree.png')
# + [markdown] colab_type="text" id="QCVZr0Zln9J5"
# ### 4. Random Forest
# + [markdown] colab_type="text" id="hqU1XqBuoB2g"
# - We will apply random forest on our data sets.
# - As hyper parameters tuning will take time for random forest, we will do one by one with list of parameters.
#
# + colab={"base_uri": "https://localhost:8080/", "height": 991} colab_type="code" id="t1LvkuF-mvZh" outputId="cf01871d-cbc3-4e4c-a4d5-8e4089fdeb9c"
# Hyper parameter tuning using GridearchCV on n_estimators
start = time.time()
parameters = {
'n_estimators':[10, 50, 100, 200, 300, 500,1000]
}
clf = RandomForestClassifier(random_state=23, class_weight='balanced', n_jobs=-1)
model = GridSearchCV(clf, parameters, scoring = 'roc_auc', verbose=2, cv=3)
model.fit(x_tr, y_tr)
print(model.best_estimator_)
print('train AUC = ',model.score(x_tr, y_tr))
print('val AUC = ',model.score(x_val, y_val))
print('Time taken for hyper parameter tuning is : ', (time.time() - start))
print('Done!')
# + [markdown] colab_type="text" id="XD5QI9TMC8tN"
# - We can see here our train auc is 1.0 and val auc is 0.65 which means our model is overfitted.
# + colab={"base_uri": "https://localhost:8080/", "height": 790} colab_type="code" id="rfKZbBELqDbt" outputId="a8359e7a-1b8c-4341-9e86-870828015fe2"
# Hyper parameter tuning using GridearchCV on max_depth
start = time.time()
parameters = {
'max_depth': [4, 8, 10, 15, 25]
}
clf = RandomForestClassifier(n_estimators=1000,random_state=23, class_weight='balanced', n_jobs=-1)
model = GridSearchCV(clf, parameters, scoring = 'roc_auc', verbose=2, cv=3)
model.fit(x_tr, y_tr)
print(model.best_estimator_)
print('train AUC = ',model.score(x_tr, y_tr))
print('val AUC = ',model.score(x_val, y_val))
print('Time taken for hyper parameter tuning is : ', (time.time() - start))
print('Done!')
# + [markdown] colab_type="text" id="YE97wdcDDH8v"
# - Here difference between train and val auc is very huge which tells us about overfitting.
# + colab={"base_uri": "https://localhost:8080/", "height": 588} colab_type="code" id="JiNKGwX0qDXw" outputId="2181b6b8-cbc9-42a7-fd3f-f7424252e7f3"
# Hyper parameter tuning using GridearchCV on min_samples_split
start = time.time()
parameters = {
'min_samples_split':[3, 5, 10],
}
clf = RandomForestClassifier(n_estimators=1000, max_depth=25, random_state=23, class_weight='balanced', n_jobs=-1)
model = GridSearchCV(clf, parameters, scoring = 'roc_auc', verbose=2, cv=3)
model.fit(x_tr, y_tr)
print(model.best_estimator_)
print('train AUC = ',model.score(x_tr, y_tr))
print('val AUC = ',model.score(x_val, y_val))
print('Time taken for hyper parameter tuning is : ', (time.time() - start))
print('Done!')
# + [markdown] colab_type="text" id="5Y5yWcFCDRGH"
# - Again we can see overfitting of our model.
# - Lets try first above parametrs and check the performance.
# + colab={"base_uri": "https://localhost:8080/", "height": 151} colab_type="code" id="oSsh3Et1qDVQ" outputId="ae7fa3d2-6988-4379-9efa-5dab0d3114ef"
# train model with best parameters
rf = RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight='balanced',
criterion='gini', max_depth=25, max_features='auto',
max_leaf_nodes=None, max_samples=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=10,
min_weight_fraction_leaf=0.0, n_estimators=1000,
n_jobs=-1, oob_score=False, random_state=23, verbose=0,
warm_start=False)
rf.fit(x_tr, y_tr)
# + colab={} colab_type="code" id="BKsOn5UqqHaS"
# create dataframe for features and it importance
sorted_indices = rf.feature_importances_.argsort()
features = x_tr.columns[sorted_indices]
rf_fea_imp = pd.DataFrame({
'features' : features,
'importance' : rf.feature_importances_
})
# + colab={"base_uri": "https://localhost:8080/", "height": 850} colab_type="code" id="ZdmkLZkNqHdk" outputId="8cfb6c0c-110e-4f18-9754-435cfd341224"
# plot feature importance for DT
plot_feature_importance(rf_fea_imp, 'features', 'importance', 'Random_forest')
# + [markdown] colab_type="text" id="L1Wpd1VcQlXw"
# - We can see from the above plot that all features have positive importance.
# + colab={"base_uri": "https://localhost:8080/", "height": 67} colab_type="code" id="XIogOmNCqYV0" outputId="020a391a-504d-4c15-c7dd-4b82252cb39c"
# Apply model on test data and save predictions
predict_and_save(rf, x_te, 'rf')
# + [markdown] colab_type="text" id="BOvoRoxArC9X"
# ### 5. XgBoost
# + [markdown] colab_type="text" id="-BONiUYwrHJs"
# - We will apply XgBoost for GBDT.
# + colab={"base_uri": "https://localhost:8080/", "height": 252} colab_type="code" id="GLvXzYORrhne" outputId="7b169c2e-4c11-4775-de40-077345570d74"
start = time.time()
parameters={
'learning_rate' : [0.05, 0.10, 0.15, 0.20, 0.25, 0.30],
'max_depth': [3, 4, 5, 6, 8, 10, 12, 15],
'min_child_weight' : [1, 3, 5, 7],
'gamma' : [0.0, 0.1, 0.2 , 0.3, 0.4],
'colsample_bytree': [0.3, 0.4, 0.5 , 0.7]
}
clf = xgboost.XGBClassifier(objective='binary:logistic', random_state=2)
model = RandomizedSearchCV(clf, parameters, scoring = 'roc_auc', n_jobs=-1, verbose=2, cv=3)
model.fit(x_tr, y_tr)
print(model.best_estimator_)
print('train AUC = ',model.score(x_tr, y_tr))
print('val AUC = ',model.score(x_val, y_val))
print('Time taken for hyper parameter tuning is : ', (time.time() - start))
print('Done!')
# + [markdown] colab_type="text" id="_qEtACooZP4O"
# - As our model is overfitting so we have to be more careful during parameter tuning.
# + colab={"base_uri": "https://localhost:8080/", "height": 134} colab_type="code" id="aKlTPaHjriPk" outputId="d15fc0f7-89c0-4d92-de42-5c8026ff1048"
xgb = xgboost.XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,
colsample_bynode=1, colsample_bytree=0.5, gamma=0.3,
learning_rate=0.05, max_delta_step=0, max_depth=15,
min_child_weight=7, missing=None, n_estimators=100, n_jobs=1,
nthread=None, objective='binary:logistic', random_state=2,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,
silent=None, subsample=1, verbosity=1)
xgb.fit(x_tr, y_tr)
# + colab={} colab_type="code" id="fGoJqCUZrii_"
# create dataframe for features and it importance
sorted_indices = xgb.feature_importances_.argsort()
features = x_tr.columns[sorted_indices]
xgb_fea_imp = pd.DataFrame({
'features' : features,
'importance' : xgb.feature_importances_
})
# + colab={"base_uri": "https://localhost:8080/", "height": 850} colab_type="code" id="VifCEHoTee6E" outputId="6f5bdeb4-b266-4d4f-a04a-6ab549cb6306"
# plot feature importance for DT
plot_feature_importance(xgb_fea_imp, 'features', 'importance', 'XgBoost')
# + [markdown] colab_type="text" id="HoneC8l3epd-"
# - We can see that all features have positive importance.
# + colab={"base_uri": "https://localhost:8080/", "height": 67} colab_type="code" id="b0U2sId-rizs" outputId="8a7450cd-80d1-4f60-d446-cf0f167574cc"
# Apply model on test data and save predictions
predict_and_save(xgb, x_te, 'xgb')
# + [markdown] colab_type="text" id="KheiVUuorM85"
# ### 6. LightGBM
# + [markdown] colab_type="text" id="yUAuY8zcfF2C"
# - Lets try lightGBM on our data points.
# + colab={} colab_type="code" id="FF9LUGgBrkIu"
params = {
'objective': 'binary',
'metric': 'binary_logloss',
'boosting': 'gbdt',
'learning_rate': 0.3 ,
'verbose': 0,
'num_leaves': 108,
'bagging_fraction': 0.95,
'bagging_freq': 1,
'bagging_seed': 1,
'feature_fraction': 0.9,
'feature_fraction_seed': 1,
'max_bin': 256,
'max_depth': 10,
'num_rounds': 400,
'metric' : 'auc'
}
# + colab={} colab_type="code" id="6wPwRK-1JURj"
tr_final = lgb.Dataset(x_tr, y_tr)
val_final = lgb.Dataset(x_val, y_val)
# + colab={"base_uri": "https://localhost:8080/", "height": 1000} colab_type="code" id="QbzWj4bbrkFV" outputId="c47d0d1c-81ec-4bdb-b5b1-854066bddab8"
# %time model_f1 = lgb.train(params, train_set=tr_final, valid_sets=val_final, verbose_eval=5)
# + colab={"base_uri": "https://localhost:8080/", "height": 67} colab_type="code" id="YONqRWGvrj4D" outputId="75d1e64c-c7b1-4341-e01e-8bb5beeff8a8"
# Apply model on test data and save predictions
predict_and_save(model_f1, x_te, 'lgb')
# + colab={"base_uri": "https://localhost:8080/", "height": 34} colab_type="code" id="ZXjxt8k0BOpP" outputId="862099c9-2acc-4c0b-d73a-0f33d2abd51e"
import joblib
# save model
joblib.dump(model_f1, '/content/drive/My Drive/CS-1/Data/lgb.pkl')
# + [markdown] colab_type="text" id="m7xZkvsC8akm"
# ### 8. Voting Classifier
# + [markdown] colab_type="text" id="Woa5ocxp89Ro"
# - We will use voting classifier from sklearn with DT, RF and XgBoost.
# + colab={"base_uri": "https://localhost:8080/", "height": 571} colab_type="code" id="orHwcw9R8eYO" outputId="be8677c1-f12c-4922-876b-2bdc4f83c9d9"
# Lets use voting classifier
voting_hard = VotingClassifier(estimators=[('dt', dt), ('xgb', xgb), ('rf', rf)], voting='hard', n_jobs=-1)
voting_hard.fit(x_tr, y_tr)
# + colab={"base_uri": "https://localhost:8080/", "height": 67} colab_type="code" id="L8Qdu4DC8eT6" outputId="6e787990-2deb-45d0-a7a3-b1ed40cab90f"
# Apply model on test data and save predictions
predict_and_save(voting_hard, x_te, 'Voting_hard_Classifier')
# + colab={} colab_type="code" id="jrh8GJB88eMU"
estimators = [
('dt', DecisionTreeClassifier(ccp_alpha=0.0, class_weight='balanced', criterion='gini',
max_depth=15, max_features=None, max_leaf_nodes=81,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=5,
min_weight_fraction_leaf=0.0, presort='deprecated',
random_state=23, splitter='best')),
('rf', RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight='balanced',
criterion='gini', max_depth=25, max_features='auto',
max_leaf_nodes=None, max_samples=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=10,
min_weight_fraction_leaf=0.0, n_estimators=1000,
oob_score=False, random_state=23, verbose=0,
warm_start=False)),
('xgb', xgboost.XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,
colsample_bynode=1, colsample_bytree=0.5, gamma=0.3,
learning_rate=0.05, max_delta_step=0, max_depth=15,
min_child_weight=7, missing=None, n_estimators=100,
nthread=None, objective='binary:logistic', random_state=2,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,
silent=None, subsample=1, verbosity=1))
]
voting_soft = VotingClassifier(estimators=estimators, voting='soft')
voting_soft.fit(x_tr, y_tr)
# + colab={} colab_type="code" id="6Z1Prl6t8eHD"
# Apply model on test data and save predictions
predict_and_save(voting_soft, x_te, 'Voting_Soft_Classifier')
# + [markdown] colab_type="text" id="lqt5c6SO8a1T"
# ### 9. Stacking Classifier
# + colab={} colab_type="code" id="2rzmr41u8lz_"
estimators = [
('dt', DecisionTreeClassifier(ccp_alpha=0.0, class_weight='balanced', criterion='gini',
max_depth=15, max_features=None, max_leaf_nodes=81,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=5,
min_weight_fraction_leaf=0.0, presort='deprecated',
random_state=23, splitter='best')),
('rf', RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight='balanced',
criterion='gini', max_depth=25, max_features='auto',
max_leaf_nodes=None, max_samples=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=10,
min_weight_fraction_leaf=0.0, n_estimators=1000,
oob_score=False, random_state=23, verbose=0,
warm_start=False)),
('xgb', xgboost.XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,
colsample_bynode=1, colsample_bytree=0.5, gamma=0.3,
learning_rate=0.05, max_delta_step=0, max_depth=15,
min_child_weight=7, missing=None, n_estimators=100,
nthread=None, objective='binary:logistic', random_state=2,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,
silent=None, subsample=1, verbosity=1))
]
# + colab={"base_uri": "https://localhost:8080/", "height": 622} colab_type="code" id="gNArQGC2En3H" outputId="c3e3748c-85e5-4db5-d190-259f6b108e0a"
stacking = StackingClassifier(estimators=estimators, verbose=1)
stacking.fit(x_tr, y_tr)
# + colab={"base_uri": "https://localhost:8080/", "height": 67} colab_type="code" id="-4zBhfnX8mhZ" outputId="dc5ef855-6734-4abb-f123-b9afde947b3e"
# Apply model on test data and save predictions
predict_and_save(stacking, x_te, 'stacking')
# + [markdown] colab_type="text" id="au-Leh-RIJoB"
# ## 2. Feature Extraction and Selection
# + [markdown] colab_type="text" id="O3NFFtowG-X2"
# ### 1. Feature Selection using DT
# + [markdown] colab_type="text" id="H1V9kpRZH5Be"
# - From feature importance (using our decision tree model) we have selected features which are having higher importance.
# + colab={} colab_type="code" id="DTNLUFuHF7EY"
x_tr_new = x_tr[['composer_count', 'song_lang_boolean', 'isrc_missing', 'source_system_tab', 'msno', 'song_member_count', 'source_type', 'third_genre_id']]
x_val_new = x_val[['composer_count', 'song_lang_boolean', 'isrc_missing', 'source_system_tab', 'msno', 'song_member_count', 'source_type', 'third_genre_id']]
x_te_new = x_te[['composer_count', 'song_lang_boolean', 'isrc_missing', 'source_system_tab', 'msno', 'song_member_count', 'source_type', 'third_genre_id']]
# + [markdown] colab_type="text" id="vAGoWvbLeCcT"
# <h3>1. DT</h3>
# + colab={"base_uri": "https://localhost:8080/", "height": 118} colab_type="code" id="WRlvglFfHFW3" outputId="0046169e-8690-4e39-9bdb-31ed894ef10c"
dt = DecisionTreeClassifier(ccp_alpha=0.0, class_weight='balanced', criterion='gini',
max_depth=15, max_features=None, max_leaf_nodes=81,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=5,
min_weight_fraction_leaf=0.0, presort='deprecated',
random_state=23, splitter='best')
dt.fit(x_tr_new, y_tr)
# + colab={} colab_type="code" id="DoZfNx1dHJQK"
# create dataframe for features and it importance
sorted_indices = dt.feature_importances_.argsort()
features = x_tr_new.columns[sorted_indices]
dt_fea_imp = pd.DataFrame({
'features' : features,
'importance' : dt.feature_importances_
})
# + colab={"base_uri": "https://localhost:8080/", "height": 850} colab_type="code" id="K2Oa7JBXHJIj" outputId="2c564aea-c4bb-4bba-d141-13d3ac9c9079"
# plot feature importance for DT
plot_feature_importance(dt_fea_imp, 'features', 'importance', 'DT')
# + colab={"base_uri": "https://localhost:8080/", "height": 67} colab_type="code" id="tBfj9HMZHVc9" outputId="3e08791d-d5df-4c60-b98b-a56c99dbc711"
# Apply model on test data and save predictions
predict_and_save(dt, x_te_new, 'dt_new')
# + [markdown] colab_type="text" id="X5Mm4ft3cUTp"
# <h3>2. LighGBM</h3>
# + colab={} colab_type="code" id="zDUtoKtKcKAL"
tr_final = lgb.Dataset(x_tr_new, y_tr)
val_final = lgb.Dataset(x_val_new, y_val)
# + colab={"base_uri": "https://localhost:8080/", "height": 776} colab_type="code" id="W5W-smBzccJN" outputId="b830a5b2-9338-462d-95cc-f55ea6095336"
# %time model_f1 = lgb.train(params, train_set=tr_final, valid_sets=val_final, verbose_eval=5)
# + colab={"base_uri": "https://localhost:8080/", "height": 67} colab_type="code" id="gLzp4U51ceWy" outputId="39c9579a-0702-4cb2-a679-61e134980d1f"
# Apply model on test data and save predictions
predict_and_save(model_f1, x_te_2, 'lgb_dt')
# + [markdown] colab_type="text" id="NVIIOaOVKFMp"
# - When we use the above extracted features in DT, it gives better results comapre to previous features.
# - But when we apply the above features in LightGBM, it decreases the score little bit.
# + [markdown] colab_type="text" id="HCMbLEvHKxgM"
# ### 2. Feature Selection using SelectKBest
# + [markdown] colab_type="text" id="xXCOEl27xras"
# - We will experiment with selectKbest method which selects best k features score and use it.
# + colab={"base_uri": "https://localhost:8080/", "height": 104} colab_type="code" id="jD4JQqq1K7tG" outputId="f85efe1e-35e5-4207-f6c8-a395a87a734d"
selection = SelectKBest(f_classif, k=20).fit(x_tr, y_tr)
x_tr_2 = selection.transform(x_tr)
x_val_2 = selection.transform(x_val)
x_te_2 = selection.transform(x_te)
# + [markdown] colab_type="text" id="G85_tRKkx1OJ"
# <h4>Light GBM</h4>
# + colab={} colab_type="code" id="7PL_Iyf1OJTO"
params = {
'objective': 'binary',
'metric': 'binary_logloss',
'boosting': 'gbdt',
'learning_rate': 0.3 ,
'verbose': 0,
'num_leaves': 108,
'bagging_fraction': 0.95,
'bagging_freq': 1,
'bagging_seed': 1,
'feature_fraction': 0.9,
'feature_fraction_seed': 1,
'max_bin': 256,
'max_depth': 10,
'num_rounds': 200,
'metric' : 'auc'
}
# + colab={} colab_type="code" id="4vItVbZzOJTX"
tr_final = lgb.Dataset(x_tr_2, y_tr)
val_final = lgb.Dataset(x_val_2, y_val)
# + colab={"base_uri": "https://localhost:8080/", "height": 798} colab_type="code" id="dyLvICGgOJTc" outputId="c3fb5184-03a3-478a-aeb4-f6ae138c0323"
# %time model_f1 = lgb.train(params, train_set=tr_final, valid_sets=val_final, verbose_eval=5)
# + colab={} colab_type="code" id="3woWXcRobKmj"
# Apply model on test data and save predictions
predict_and_save(model_f1, x_te_2, 'lgb_selectkbest')
# + [markdown] colab_type="text" id="RxOMAQfKK8sz"
# ### 3. Feature Extraction using PCA
# + colab={"base_uri": "https://localhost:8080/", "height": 50} colab_type="code" id="IRY4K896LB89" outputId="040200bc-e253-4e83-8eae-b55fbf177284"
pca = PCA(n_components=44)
pca.fit(x_tr)
# + colab={"base_uri": "https://localhost:8080/", "height": 285} colab_type="code" id="gGIoPjmYLB1v" outputId="046b3675-efa6-4040-92b1-2a93ef7a3adb"
plt.plot(np.cumsum(pca.explained_variance_ratio_))
plt.xlabel('number of components')
plt.ylabel('cumulative explained variance');
# + [markdown] colab_type="text" id="kCwmesxkYpF4"
# - From the above plot we can say that < 10 components covers more than 98% variance.
# - Let's take 10 components and again apply pca on top of that.
# + colab={} colab_type="code" id="QOFGY9tnZCl2"
Pca = PCA(n_components=10)
pca = Pca.fit(x_tr)
x_tr_3 = pca.transform(x_tr)
x_val_3 = pca.transform(x_val)
x_te_3 = pca.transform(x_te)
# + [markdown] colab_type="text" id="R_dsGo1vx6GC"
# <h4>Light GBM</h4>
# + colab={} colab_type="code" id="y0TlIFcsZCaz"
tr_final = lgb.Dataset(x_tr_3, y_tr)
val_final = lgb.Dataset(x_val_3, y_val)
# + colab={"base_uri": "https://localhost:8080/", "height": 776} colab_type="code" id="apRIWB_VZhsj" outputId="ba1e13d7-d017-4cab-db05-482baac548d1"
# %time model_f1 = lgb.train(params, train_set=tr_final, valid_sets=val_final, verbose_eval=5)
# + colab={"base_uri": "https://localhost:8080/", "height": 67} colab_type="code" id="au0FZk4va6pC" outputId="47383974-1165-4834-a856-e8facca41b94"
# Apply model on test data and save predictions
predict_and_save(model_f1, x_te_3, 'lgb_pca')
# + [markdown] colab_type="text" id="JFpzUkjeyCfk"
# <h4>Summary</h4>
# + [markdown] colab_type="text" id="uvxq0mvhyG_8"
# - From the above feature selection methods, SelectKbest gives the best result compare to PCA and DT based features.
# - We will go with features selected by selectKbest model for deep learning based architectures.
# + [markdown] colab_type="text" id="AnlDktyGefnk"
# ## 3. Deep Learning
# + [markdown] colab_type="text" id="ZXYVOcutequg"
# - Let's try Deep learning models on our data sets and see the results.
# + [markdown] colab_type="text" id="En_Q-ZjKGXYq"
# ### MLP model
# + [markdown] colab_type="text" id="5qUyohYrI0BV"
# - Let's take our data into tf.data pipeline to apply Deep learning on our data points.
# + colab={} colab_type="code" id="t1HoNrQxNSot"
BATCH_SIZE = 64
FEATURES = 42
# + colab={} colab_type="code" id="CsYKGiJ_HTUt"
train_data = tf.data.Dataset.from_tensor_slices((x_tr, y_tr))
val_data = tf.data.Dataset.from_tensor_slices((x_tr, y_tr))
test_data = tf.data.Dataset.from_tensor_slices((x_te))
# + colab={} colab_type="code" id="ZKM2NlosGb1_"
tr_batch = train_data.batch(BATCH_SIZE, drop_remainder=True)
val_batch = val_data.batch(BATCH_SIZE, drop_remainder=True)
te_batch = test_data.batch(BATCH_SIZE, drop_remainder=True)
# + [markdown] colab_type="text" id="dZ-HsI7mS4MX"
# #### Model Architecture
# + colab={} colab_type="code" id="HqHWE8k2FA9E"
def define_model(BATCH_SIZE, FEATURES):
'''Function to define model'''
tf.keras.backend.clear_session()
tf.random.set_seed(1234)
input = Input(shape=(None, FEATURES), name='Input_layer')
x = Dense(256,
activation=tf.keras.activations.relu,
use_bias=True,
kernel_initializer=tf.keras.initializers.glorot_uniform(seed=78),
bias_initializer=tf.keras.initializers.zeros(),
name='Dense_1_layer')(input)
dropout_1 = Dropout(0.5)(x)
x = Dense(256,
activation=tf.keras.activations.relu,
use_bias=True,
kernel_initializer=tf.keras.initializers.glorot_uniform(seed=25),
bias_initializer=tf.keras.initializers.zeros(),
name='Dense_2_layer')(dropout_1)
dropout_2 = Dropout(0.5)(x)
output = Dense(1, activation='sigmoid', name='output_layer')(dropout_2)
model = Model(inputs=input, outputs=output, name="MLP_model")
model.summary()
return model
# + colab={"base_uri": "https://localhost:8080/", "height": 980} colab_type="code" id="yV5Vb23JGVuz" outputId="c2243762-ede0-4c4d-9cf2-2d48774eea3e"
model = define_model(BATCH_SIZE, FEATURES)
tf.keras.utils.plot_model(model, to_file='model.png', show_shapes=True,
show_layer_names=True, rankdir='TB', dpi=96)
# + [markdown] colab_type="text" id="gCtzI3KvS9A9"
# #### Loss function and Optimizer
# + colab={} colab_type="code" id="U9t19YdBezuX"
loss = tf.keras.losses.BinaryCrossentropy()
optimizer = tf.keras.optimizers.Adam(1e-4)
# + colab={} colab_type="code" id="jJkuJu__UtM3"
def auc(y_true, y_pred):
return tf.py_function(roc_auc_score, (y_true, y_pred), tf.double)
# + [markdown] colab_type="text" id="UQMynWZPUbGT"
# #### Compile and fit the model
# + colab={} colab_type="code" id="upD-X4WYcKji"
from tensorflow.keras.callbacks import EarlyStopping
early_stoppings = EarlyStopping(monitor='val_loss', patience=2,verbose=1, mode='min')
# + colab={} colab_type="code" id="ddjKZl-9V9pg"
# %load_ext tensorboard
# + colab={} colab_type="code" id="FkUI6TVpX0Cv"
log_dir = "logs/fit/"
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)
# + colab={} colab_type="code" id="p_yegU8bUSsW"
model.compile(loss=loss, optimizer=optimizer, metrics=[auc])
# + colab={"base_uri": "https://localhost:8080/", "height": 222} colab_type="code" id="9DZITwKSUsSA" outputId="728dd12b-9ea3-4f3c-b5b9-77410c67c484"
EPOCHS = 10
print("Fit model on training data")
history = model.fit(x_tr, y_tr,
batch_size=128, epochs=EPOCHS,
validation_data=(x_val, y_val),
callbacks=[tensorboard_callback, early_stoppings])
# + colab={"base_uri": "https://localhost:8080/", "height": 821, "resources": {"https://localhost:6006/": {"data": "<!doctype html><!--
@license
Copyright 2016 The TensorFlow Authors. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
--><html><head><meta charset="utf-8">
  <title>TensorBoard</title>
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href="data:image/png;base64,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">
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">

  







































































































































































































<style>
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto'), local('Roboto-Regular'), url(/font-roboto/uYECMKoHcO9x1wdmbyHIm3-_kf6ByYO6CLYdB4HQE-Y.woff2) format('woff2');
  unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto'), local('Roboto-Regular'), url(/font-roboto/sTdaA6j0Psb920Vjv-mrzH-_kf6ByYO6CLYdB4HQE-Y.woff2) format('woff2');
  unicode-range: U+0460-052F, U+20B4, U+2DE0-2DFF, U+A640-A69F;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto'), local('Roboto-Regular'), url(/font-roboto/_VYFx-s824kXq_Ul2BHqYH-_kf6ByYO6CLYdB4HQE-Y.woff2) format('woff2');
  unicode-range: U+0370-03FF;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto'), local('Roboto-Regular'), url(/font-roboto/tnj4SB6DNbdaQnsM8CFqBX-_kf6ByYO6CLYdB4HQE-Y.woff2) format('woff2');
  unicode-range: U+1F00-1FFF;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto'), local('Roboto-Regular'), url(/font-roboto/oMMgfZMQthOryQo9n22dcuvvDin1pK8aKteLpeZ5c0A.woff2) format('woff2');
  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2212, U+2215;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto'), local('Roboto-Regular'), url(/font-roboto/Ks_cVxiCiwUWVsFWFA3Bjn-_kf6ByYO6CLYdB4HQE-Y.woff2) format('woff2');
  unicode-range: U+0100-024F, U+1E00-1EFF, U+20A0-20AB, U+20AD-20CF, U+2C60-2C7F, U+A720-A7FF;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto'), local('Roboto-Regular'), url(/font-roboto/NJ4vxlgWwWbEsv18dAhqnn-_kf6ByYO6CLYdB4HQE-Y.woff2) format('woff2');
  unicode-range: U+0102-0103, U+1EA0-1EF9, U+20AB;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 700;
  src: local('Roboto Bold'), local('Roboto-Bold'), url(/font-roboto/isZ-wbCXNKAbnjo6_TwHToX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
  unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 700;
  src: local('Roboto Bold'), local('Roboto-Bold'), url(/font-roboto/77FXFjRbGzN4aCrSFhlh3oX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
  unicode-range: U+0460-052F, U+20B4, U+2DE0-2DFF, U+A640-A69F;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 700;
  src: local('Roboto Bold'), local('Roboto-Bold'), url(/font-roboto/jSN2CGVDbcVyCnfJfjSdfIX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
  unicode-range: U+0370-03FF;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 700;
  src: local('Roboto Bold'), local('Roboto-Bold'), url(/font-roboto/UX6i4JxQDm3fVTc1CPuwqoX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
  unicode-range: U+1F00-1FFF;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 700;
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  font-weight: 700;
  src: local('Roboto Mono Bold'), local('RobotoMono-Bold'), url(/font-roboto/N4duVc9C58uwPiY8_59Fzwn6Wqxo-xwxilDXPU8chVU.woff2) format('woff2');
  unicode-range: U+0370-03FF;
}
@font-face {
  font-family: 'Roboto Mono';
  font-style: normal;
  font-weight: 700;
  src: local('Roboto Mono Bold'), local('RobotoMono-Bold'), url(/font-roboto/N4duVc9C58uwPiY8_59Fz1T7aJLK6nKpn36IMwTcMMc.woff2) format('woff2');
  unicode-range: U+1F00-1FFF;
}
@font-face {
  font-family: 'Roboto Mono';
  font-style: normal;
  font-weight: 700;
  src: local('Roboto Mono Bold'), local('RobotoMono-Bold'), url(/font-roboto/N4duVc9C58uwPiY8_59Fz_79_ZuUxCigM2DespTnFaw.woff2) format('woff2');
  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2212, U+2215;
}
@font-face {
  font-family: 'Roboto Mono';
  font-style: normal;
  font-weight: 700;
  src: local('Roboto Mono Bold'), local('RobotoMono-Bold'), url(/font-roboto/N4duVc9C58uwPiY8_59Fz4gd9OEPUCN3AdYW0e8tat4.woff2) format('woff2');
  unicode-range: U+0100-024F, U+1E00-1EFF, U+20A0-20AB, U+20AD-20CF, U+2C60-2C7F, U+A720-A7FF;
}
@font-face {
  font-family: 'Roboto Mono';
  font-style: normal;
  font-weight: 700;
  src: local('Roboto Mono Bold'), local('RobotoMono-Bold'), url(/font-roboto/N4duVc9C58uwPiY8_59Fz8bIQSYZnWLaWC9QNCpTK_U.woff2) format('woff2');
  unicode-range: U+0102-0103, U+1EA0-1EF9, U+20AB;
}
</style>


<style>
  html,
  body {
    margin: 0;
    padding: 0;
    height: 100%;
    font-family: Roboto, sans-serif;
  }
</style>





























































































































































































































































































































































<style is="custom-style">
  :root {
    --tb-orange-weak: #ffa726;
    --tb-orange-strong: #f57c00;
    --tb-orange-dark: #dc7320;
    --tb-grey-darker: #e2e2e2;
    --tb-grey-lighter: #f3f3f3;
    --tb-ui-dark-accent: #757575;
    --tb-ui-light-accent: #e0e0e0;
    --tb-graph-faded: #e0d4b3;
  }
</style>










































































































































<style>
.plottable-colors-0 {
  background-color: #5279c7; /* INDIGO */
}

.plottable-colors-1 {
  background-color: #fd373e; /* CORAL_RED */
}

.plottable-colors-2 {
  background-color: #63c261; /* FERN */
}

.plottable-colors-3 {
  background-color: #fad419; /* BRIGHT_SUN */
}

.plottable-colors-4 {
  background-color: #2c2b6f; /* JACARTA */
}

.plottable-colors-5 {
  background-color: #ff7939; /* BURNING_ORANGE */
}

.plottable-colors-6 {
  background-color: #db2e65; /* CERISE_RED */
}

.plottable-colors-7 {
  background-color: #99ce50; /* CONIFER */
}

.plottable-colors-8 {
  background-color: #962565; /* ROYAL_HEATH */
}

.plottable-colors-9 {
  background-color: #06cccc; /* ROBINS_EGG_BLUE */
}

/**
 * User-supplied renderTo element.
 */
.plottable {
  display: block; /* must be block elements for width/height calculations to work in Firefox. */
  pointer-events: visibleFill;
  position: relative;
  /**
   * Pre 3.0, users could set the dimension of the root element in two ways: either using CSS
   * (inline or through a stylesheet), or using the SVG width/height attributes. By default, we
   * set the SVG width/height attributes to 100%.
   *
   * Post 3.0 the root element is always a normal div and the only way to set the dimensions is
   * to use CSS. To replicate the "100%-by-default" behavior, we apply width/height 100%.
   */
  width: 100%;
  height: 100%;
}

/**
 * The _element that roots each Component's DOM.
 */
.plottable .component {
  /* Allow components to be positioned with explicit left/top/width/height styles */
  position: absolute;
}

.plottable .background-container,
.plottable .content,
.plottable .foreground-container {
  position: absolute;
  width: 100%;
  height: 100%;
}

/**
 * Don't allow svg elements above the content to steal events
 */
.plottable .foreground-container {
  pointer-events: none;
}

.plottable .component-overflow-hidden {
  overflow: hidden;
}

.plottable .component-overflow-visible {
  overflow: visible;
}

.plottable .plot-canvas-container {
  width: 100%;
  height: 100%;
  overflow: hidden;
}

.plottable .plot-canvas {
  width: 100%;
  height: 100%;
  /**
   * Play well with deferred rendering.
   */
  transform-origin: 0px 0px 0px;
}

.plottable text {
  text-rendering: geometricPrecision;
}

.plottable .label text {
  font-family: "Helvetica Neue", sans-serif;
  fill: #32313F;
}

.plottable .bar-label-text-area text {
  font-family: "Helvetica Neue", sans-serif;
  font-size: 12px;
}

.plottable .label-area text {
  fill: #32313F;
  font-family: "Helvetica Neue", sans-serif;
  font-size: 14px;
}

.plottable .light-label text {
  fill: white;
}

.plottable .dark-label text {
  fill: #32313F;
}

.plottable .off-bar-label text {
  fill: #32313F;
}

.plottable .stacked-bar-label text {
  fill: #32313F;
  font-style: normal;
}

.plottable .stacked-bar-plot .off-bar-label {
  /* HACKHACK #2795: correct off-bar label logic to be implemented on StackedBar */
  visibility: hidden !important;
}

.plottable .axis-label text {
  font-size: 10px;
  font-weight: bold;
  letter-spacing: 1px;
  line-height: normal;
  text-transform: uppercase;
}

.plottable .title-label text {
  font-size: 20px;
  font-weight: bold;
}

.plottable .axis line.baseline {
  stroke: #CCC;
  stroke-width: 1px;
}

.plottable .axis line.tick-mark {
  stroke: #CCC;
  stroke-width: 1px;
}

.plottable .axis text {
  fill: #32313F;
  font-family: "Helvetica Neue", sans-serif;
  font-size: 12px;
  font-weight: 200;
  line-height: normal;
}

.plottable .axis .annotation-circle {
  fill: white;
  stroke-width: 1px;
  stroke: #CCC;
}

.plottable .axis .annotation-line {
  stroke: #CCC;
  stroke-width: 1px;
}

.plottable .axis .annotation-rect {
  stroke: #CCC;
  stroke-width: 1px;
  fill: white;
}

.plottable .bar-plot .baseline {
  stroke: #999;
}

.plottable .gridlines line {
  stroke: #3C3C3C; /* hackhack: gridlines should be solid; see #820 */
  opacity: 0.25;
  stroke-width: 1px;
}

.plottable .selection-box-layer .selection-area {
  fill: black;
  fill-opacity: 0.03;
  stroke: #CCC;
}
/* DragBoxLayer */
.plottable .drag-box-layer.x-resizable .drag-edge-lr {
  cursor: ew-resize;
}
.plottable .drag-box-layer.y-resizable .drag-edge-tb {
  cursor: ns-resize;
}

.plottable .drag-box-layer.x-resizable.y-resizable .drag-corner-tl {
  cursor: nwse-resize;
}
.plottable .drag-box-layer.x-resizable.y-resizable .drag-corner-tr {
  cursor: nesw-resize;
}
.plottable .drag-box-layer.x-resizable.y-resizable .drag-corner-bl {
  cursor: nesw-resize;
}
.plottable .drag-box-layer.x-resizable.y-resizable .drag-corner-br {
  cursor: nwse-resize;
}

.plottable .drag-box-layer.movable .selection-area {
  cursor: move; /* IE fallback */
  cursor: -moz-grab;
  cursor: -webkit-grab;
  cursor: grab;
}

.plottable .drag-box-layer.movable .selection-area:active {
  cursor: -moz-grabbing;
  cursor: -webkit-grabbing;
  cursor: grabbing;
}
/* /DragBoxLayer */

.plottable .guide-line-layer line.guide-line {
  stroke: #CCC;
  stroke-width: 1px;
}

.plottable .drag-line-layer.enabled.vertical line.drag-edge {
  cursor: ew-resize;
}

.plottable .drag-line-layer.enabled.horizontal line.drag-edge {
  cursor: ns-resize;
}

.plottable .legend text {
  fill: #32313F;
  font-family: "Helvetica Neue", sans-serif;
  font-size: 12px;
  font-weight: bold;
  line-height: normal;
}

.plottable .interpolated-color-legend rect.swatch-bounding-box {
  fill: none;
  stroke: #CCC;
  stroke-width: 1px;
  pointer-events: none;
}

.plottable .waterfall-plot line.connector {
  stroke: #CCC;
  stroke-width: 1px;
}

.plottable .pie-plot .arc.outline {
  stroke-linejoin: round;
}
</style>










































































































































































































































































































































































<style>/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
 Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
     http://www.apache.org/licenses/LICENSE-2.0
 Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

.tensor-widget {
  font-family: monospace;
  font-size: 14px;
  overflow-x: hidden;
  overflow-y: hidden;
  position: relative;
}

.tensor-widget-dim {
  border: 1px solid rgb(160, 160, 160);
  display: inline-block;
  font-size: 12px;
  height: 14px;
  line-height: 14px;
  margin-left: 15px;
  margin-right: 15px;
  padding: 2px;
}

.tensor-widget-dim-comma {
  color: rgb(128, 128, 128);
  display: inline-block;
  font-size: 12px;
  height: 14px;
  line-height: 14px;
}

.tensor-widget-dim-highlighted {
  border: 1px solid rgb(100, 180, 255);
  font-weight: bold;
}

.tensor-widget-dim-brackets {
  color: rgb(128, 128, 128);
  display: inline-block;
  font-size: 8pt;
}

.tensor-widget-dim-dropdown {
  background-color: rgb(255, 255, 255);
  border: 1px solid rgb(128, 128, 128);
  box-shadow: 2px 2px 2px #b0b0b0;
  cursor: pointer;
  width: 180px;
  z-index: 1000;
}

.tensor-widget-dim-dropdown-menu-item {
  border-bottom: 1px solid rgb(180, 180, 180);
  font-size: 12px;
  padding: 3px;
  user-select: none;
}

.tensor-widget-dim-dropdown-menu-item-active {
  background-color: rgb(100, 180, 255);
}

.tensor-widget-dim-dropdown-menu-item-disabled {
  color: rgb(128, 128, 128);
}

.tensor-widget-dtype {
  align-content: center;
  color: rgb(60, 60, 60);
  display: inline-block;
  font-size: 8pt;
  height: 48px;
  line-height: 22px;
  max-height: 22px;
  padding-left: 14px;
  padding-right: 10px;
  position: relative;
  vertical-align: middle;
}

.tensor-widget-dtype-label {
  color: rgb(128, 128, 128);
}

.tensor-widget-header {
  background-color: rgb(252, 252, 252);
  box-shadow: 2px 2px 2px #b0b0b0;
  height: 40px;
  line-height: 40px;
  max-height: 40px;
  position: relative;
  vertical-align: middle;
  width: 100%;
}

.tensor-widget-info {
  align-content: center;
  color: rgb(0, 0, 255);
  display: inline-block;
  font-size: 8pt;
  height: 22px;
  line-height: 22px;
  margin-left: 8px;
  max-height: 22px;
  position: relative;
  vertical-align: middle;
}

.tensor-widget-menu-thumb {
  color: rgb(32, 33, 36);
  cursor: pointer;
  display: inline-block;
  font-weight: bold;
  font-size: 16px;
  margin-left: 10px;
  margin-right: 5px;
  position: relative;
  user-select: none;
}

.tensor-widget-menu-thumb:hover {
  color: rgb(227, 116, 0);
}

.tensor-widget-shape {
  color: rgb(60, 60, 60);
  display: inline-block;
  margin-left: 12px;
}

.tensor-widget-shape-label {
  color: rgb(128, 128, 128);
  display: inline-block;
}

.tensor-widget-shape-value {
  display: inline-block;
}

.tensor-widget-slicing-group {
  background-color: rgb(250, 250, 250);
  border-bottom: 1px solid rgb(190, 190, 190);
  display: block;
  height: 18px;
  text-align: center;
  padding-bottom: 5px;
  padding-top: 5px;
}

.tensor-widget-tensor-name {
  color: black;
  display: inline-block;
  font-weight: bold;
}

.tensor-widget-left-ruler-tick {
  background-color: var(--ruler-background-color);
  border-bottom: var(--border-style);
  border-top: var(--border-style);
  box-shadow: var(--border-style);
  color: rgb(110, 110, 110);
  cursor: pointer;
  display: inline-block;
  font-size: 12px;
  height: 29px;
  line-height: 29px;
  margin-left: 0px;
  max-width: 45px;
  text-align: center;
  user-select: none;
  vertical-align: middle;
  width: 45px;
}

.tensor-widget-top-ruler {
  height: 24px;
  white-space: nowrap;
}

.tensor-widget-value-tooltip {
  background-color: rgb(240, 240, 240);
  border: 1px solid rgb(160, 160, 160);
  box-shadow: 1px 1px 1px #b0b0b0;
  display: none;
  font-size: 13px;
  padding: 5px;
  position: absolute;
  user-select: none;
  width: 240px;
}

.tensor-widget-value-tooltip-colorbar {
  height: 24px;
  width: 95%;
}

.tensor-widget-value-tooltip-indices {
  font-weight: bold;
}

.tensor-widget-value-tooltip-value {
  margin-top: 20px;
}

.tensor-widget-top-ruler-tick {
  background-color: var(--ruler-background-color);
  border-bottom: var(--border-style);
  border-right: var(--border-style);
  color: rgb(110, 110, 110);
  cursor: pointer;
  display: inline-block;
  font-size: 12px;
  height: 24px;
  line-height: 24px;
  padding-right: 2px;
  text-align: center;
  user-select: none;
  vertical-align: middle;
  width: 45px;
}

.tensor-widget-value-div {
  border-bottom: var(--border-style);
  border-right: var(--border-style);
  cursor: pointer;
  display: inline-block;
  font-size: 80%;
  height: 24px;
  line-height: 24px;
  max-width: 45px;
  padding-right: 2px;
  text-align: right;
  user-select: none;
  vertical-align: middle;
  width: 45px;
}

.tensor-widget-value-div-selection {
  font-weight: bold;
}

.tensor-widget-value-div-selection-bottom {
  border-bottom: 0.5px solid blue;
}

.tensor-widget-value-div-selection-left {
  border-left: 0.5px solid blue;
}

.tensor-widget-value-div-selection-right {
  border-right: 0.5px solid blue;
}

.tensor-widget-value-div-selection-top {
  border-top: 0.5px solid blue;
}

.tensor-widget-value-section {
  --border-style: 1px solid rgb(140, 140, 140);
  --ruler-background-color: rgb(210, 210, 210);
  -moz-user-select: none;
  -ms-user-select: none;
  -khtml-user-select: none;
  -webkit-touch-callout: none;
  -webkit-user-select: none;
}

.tensor-widget-value-row {
  height: 25px;
  line-height: 25px;
  white-space: nowrap;
}
</style>


























































































































































































































































































































































































































































































































































































































  </head><body><custom-style>
  <style is="custom-style">
    [hidden] {
      display: none !important;
    }
  </style>
</custom-style><custom-style>
  <style is="custom-style">
    html {

      --layout: {
        display: -ms-flexbox;
        display: -webkit-flex;
        display: flex;
      };

      --layout-inline: {
        display: -ms-inline-flexbox;
        display: -webkit-inline-flex;
        display: inline-flex;
      };

      --layout-horizontal: {
        @apply --layout;

        -ms-flex-direction: row;
        -webkit-flex-direction: row;
        flex-direction: row;
      };

      --layout-horizontal-reverse: {
        @apply --layout;

        -ms-flex-direction: row-reverse;
        -webkit-flex-direction: row-reverse;
        flex-direction: row-reverse;
      };

      --layout-vertical: {
        @apply --layout;

        -ms-flex-direction: column;
        -webkit-flex-direction: column;
        flex-direction: column;
      };

      --layout-vertical-reverse: {
        @apply --layout;

        -ms-flex-direction: column-reverse;
        -webkit-flex-direction: column-reverse;
        flex-direction: column-reverse;
      };

      --layout-wrap: {
        -ms-flex-wrap: wrap;
        -webkit-flex-wrap: wrap;
        flex-wrap: wrap;
      };

      --layout-wrap-reverse: {
        -ms-flex-wrap: wrap-reverse;
        -webkit-flex-wrap: wrap-reverse;
        flex-wrap: wrap-reverse;
      };

      --layout-flex-auto: {
        -ms-flex: 1 1 auto;
        -webkit-flex: 1 1 auto;
        flex: 1 1 auto;
      };

      --layout-flex-none: {
        -ms-flex: none;
        -webkit-flex: none;
        flex: none;
      };

      --layout-flex: {
        -ms-flex: 1 1 0.000000001px;
        -webkit-flex: 1;
        flex: 1;
        -webkit-flex-basis: 0.000000001px;
        flex-basis: 0.000000001px;
      };

      --layout-flex-2: {
        -ms-flex: 2;
        -webkit-flex: 2;
        flex: 2;
      };

      --layout-flex-3: {
        -ms-flex: 3;
        -webkit-flex: 3;
        flex: 3;
      };

      --layout-flex-4: {
        -ms-flex: 4;
        -webkit-flex: 4;
        flex: 4;
      };

      --layout-flex-5: {
        -ms-flex: 5;
        -webkit-flex: 5;
        flex: 5;
      };

      --layout-flex-6: {
        -ms-flex: 6;
        -webkit-flex: 6;
        flex: 6;
      };

      --layout-flex-7: {
        -ms-flex: 7;
        -webkit-flex: 7;
        flex: 7;
      };

      --layout-flex-8: {
        -ms-flex: 8;
        -webkit-flex: 8;
        flex: 8;
      };

      --layout-flex-9: {
        -ms-flex: 9;
        -webkit-flex: 9;
        flex: 9;
      };

      --layout-flex-10: {
        -ms-flex: 10;
        -webkit-flex: 10;
        flex: 10;
      };

      --layout-flex-11: {
        -ms-flex: 11;
        -webkit-flex: 11;
        flex: 11;
      };

      --layout-flex-12: {
        -ms-flex: 12;
        -webkit-flex: 12;
        flex: 12;
      };

      /* alignment in cross axis */

      --layout-start: {
        -ms-flex-align: start;
        -webkit-align-items: flex-start;
        align-items: flex-start;
      };

      --layout-center: {
        -ms-flex-align: center;
        -webkit-align-items: center;
        align-items: center;
      };

      --layout-end: {
        -ms-flex-align: end;
        -webkit-align-items: flex-end;
        align-items: flex-end;
      };

      --layout-baseline: {
        -ms-flex-align: baseline;
        -webkit-align-items: baseline;
        align-items: baseline;
      };

      /* alignment in main axis */

      --layout-start-justified: {
        -ms-flex-pack: start;
        -webkit-justify-content: flex-start;
        justify-content: flex-start;
      };

      --layout-center-justified: {
        -ms-flex-pack: center;
        -webkit-justify-content: center;
        justify-content: center;
      };

      --layout-end-justified: {
        -ms-flex-pack: end;
        -webkit-justify-content: flex-end;
        justify-content: flex-end;
      };

      --layout-around-justified: {
        -ms-flex-pack: distribute;
        -webkit-justify-content: space-around;
        justify-content: space-around;
      };

      --layout-justified: {
        -ms-flex-pack: justify;
        -webkit-justify-content: space-between;
        justify-content: space-between;
      };

      --layout-center-center: {
        @apply --layout-center;
        @apply --layout-center-justified;
      };

      /* self alignment */

      --layout-self-start: {
        -ms-align-self: flex-start;
        -webkit-align-self: flex-start;
        align-self: flex-start;
      };

      --layout-self-center: {
        -ms-align-self: center;
        -webkit-align-self: center;
        align-self: center;
      };

      --layout-self-end: {
        -ms-align-self: flex-end;
        -webkit-align-self: flex-end;
        align-self: flex-end;
      };

      --layout-self-stretch: {
        -ms-align-self: stretch;
        -webkit-align-self: stretch;
        align-self: stretch;
      };

      --layout-self-baseline: {
        -ms-align-self: baseline;
        -webkit-align-self: baseline;
        align-self: baseline;
      };

      /* multi-line alignment in main axis */

      --layout-start-aligned: {
        -ms-flex-line-pack: start;  /* IE10 */
        -ms-align-content: flex-start;
        -webkit-align-content: flex-start;
        align-content: flex-start;
      };

      --layout-end-aligned: {
        -ms-flex-line-pack: end;  /* IE10 */
        -ms-align-content: flex-end;
        -webkit-align-content: flex-end;
        align-content: flex-end;
      };

      --layout-center-aligned: {
        -ms-flex-line-pack: center;  /* IE10 */
        -ms-align-content: center;
        -webkit-align-content: center;
        align-content: center;
      };

      --layout-between-aligned: {
        -ms-flex-line-pack: justify;  /* IE10 */
        -ms-align-content: space-between;
        -webkit-align-content: space-between;
        align-content: space-between;
      };

      --layout-around-aligned: {
        -ms-flex-line-pack: distribute;  /* IE10 */
        -ms-align-content: space-around;
        -webkit-align-content: space-around;
        align-content: space-around;
      };

      /*******************************
                Other Layout
      *******************************/

      --layout-block: {
        display: block;
      };

      --layout-invisible: {
        visibility: hidden !important;
      };

      --layout-relative: {
        position: relative;
      };

      --layout-fit: {
        position: absolute;
        top: 0;
        right: 0;
        bottom: 0;
        left: 0;
      };

      --layout-scroll: {
        -webkit-overflow-scrolling: touch;
        overflow: auto;
      };

      --layout-fullbleed: {
        margin: 0;
        height: 100vh;
      };

      /* fixed position */

      --layout-fixed-top: {
        position: fixed;
        top: 0;
        left: 0;
        right: 0;
      };

      --layout-fixed-right: {
        position: fixed;
        top: 0;
        right: 0;
        bottom: 0;
      };

      --layout-fixed-bottom: {
        position: fixed;
        right: 0;
        bottom: 0;
        left: 0;
      };

      --layout-fixed-left: {
        position: fixed;
        top: 0;
        bottom: 0;
        left: 0;
      };

    }
  </style>
</custom-style><dom-module id="paper-ripple">

  <template>
    <style>
      :host {
        display: block;
        position: absolute;
        border-radius: inherit;
        overflow: hidden;
        top: 0;
        left: 0;
        right: 0;
        bottom: 0;

        /* See PolymerElements/paper-behaviors/issues/34. On non-Chrome browsers,
         * creating a node (with a position:absolute) in the middle of an event
         * handler "interrupts" that event handler (which happens when the
         * ripple is created on demand) */
        pointer-events: none;
      }

      :host([animating]) {
        /* This resolves a rendering issue in Chrome (as of 40) where the
           ripple is not properly clipped by its parent (which may have
           rounded corners). See: http://jsbin.com/temexa/4

           Note: We only apply this style conditionally. Otherwise, the browser
           will create a new compositing layer for every ripple element on the
           page, and that would be bad. */
        -webkit-transform: translate(0, 0);
        transform: translate3d(0, 0, 0);
      }

      #background,
      #waves,
      .wave-container,
      .wave {
        pointer-events: none;
        position: absolute;
        top: 0;
        left: 0;
        width: 100%;
        height: 100%;
      }

      #background,
      .wave {
        opacity: 0;
      }

      #waves,
      .wave {
        overflow: hidden;
      }

      .wave-container,
      .wave {
        border-radius: 50%;
      }

      :host(.circle) #background,
      :host(.circle) #waves {
        border-radius: 50%;
      }

      :host(.circle) .wave-container {
        overflow: hidden;
      }
    </style>

    <div id="background"></div>
    <div id="waves"></div>
  </template>
</dom-module><custom-style>
  <style is="custom-style">
    html {

      --shadow-transition: {
        transition: box-shadow 0.28s cubic-bezier(0.4, 0, 0.2, 1);
      };

      --shadow-none: {
        box-shadow: none;
      };

      /* from http://codepen.io/shyndman/pen/c5394ddf2e8b2a5c9185904b57421cdb */

      --shadow-elevation-2dp: {
        box-shadow: 0 2px 2px 0 rgba(0, 0, 0, 0.14),
                    0 1px 5px 0 rgba(0, 0, 0, 0.12),
                    0 3px 1px -2px rgba(0, 0, 0, 0.2);
      };

      --shadow-elevation-3dp: {
        box-shadow: 0 3px 4px 0 rgba(0, 0, 0, 0.14),
                    0 1px 8px 0 rgba(0, 0, 0, 0.12),
                    0 3px 3px -2px rgba(0, 0, 0, 0.4);
      };

      --shadow-elevation-4dp: {
        box-shadow: 0 4px 5px 0 rgba(0, 0, 0, 0.14),
                    0 1px 10px 0 rgba(0, 0, 0, 0.12),
                    0 2px 4px -1px rgba(0, 0, 0, 0.4);
      };

      --shadow-elevation-6dp: {
        box-shadow: 0 6px 10px 0 rgba(0, 0, 0, 0.14),
                    0 1px 18px 0 rgba(0, 0, 0, 0.12),
                    0 3px 5px -1px rgba(0, 0, 0, 0.4);
      };

      --shadow-elevation-8dp: {
        box-shadow: 0 8px 10px 1px rgba(0, 0, 0, 0.14),
                    0 3px 14px 2px rgba(0, 0, 0, 0.12),
                    0 5px 5px -3px rgba(0, 0, 0, 0.4);
      };

      --shadow-elevation-12dp: {
        box-shadow: 0 12px 16px 1px rgba(0, 0, 0, 0.14),
                    0 4px 22px 3px rgba(0, 0, 0, 0.12),
                    0 6px 7px -4px rgba(0, 0, 0, 0.4);
      };

      --shadow-elevation-16dp: {
        box-shadow: 0 16px 24px 2px rgba(0, 0, 0, 0.14),
                    0  6px 30px 5px rgba(0, 0, 0, 0.12),
                    0  8px 10px -5px rgba(0, 0, 0, 0.4);
      };

      --shadow-elevation-24dp: {
        box-shadow: 0 24px 38px 3px rgba(0, 0, 0, 0.14),
                    0 9px 46px 8px rgba(0, 0, 0, 0.12),
                    0 11px 15px -7px rgba(0, 0, 0, 0.4);
      };
    }
  </style>
</custom-style><dom-module id="paper-material-styles">
  <template>
    <style>
      :host, html {
        --paper-material: {
          display: block;
          position: relative;
        };
        --paper-material-elevation-1: {
          @apply --shadow-elevation-2dp;
        };
        --paper-material-elevation-2: {
          @apply --shadow-elevation-4dp;
        };
        --paper-material-elevation-3: {
          @apply --shadow-elevation-6dp;
        };
        --paper-material-elevation-4: {
          @apply --shadow-elevation-8dp;
        };
        --paper-material-elevation-5: {
          @apply --shadow-elevation-16dp;
        };
      }
      :host(.paper-material), .paper-material {
        @apply --paper-material;
      }
      :host(.paper-material[elevation="1"]), .paper-material[elevation="1"] {
        @apply --paper-material-elevation-1;
      }
      :host(.paper-material[elevation="2"]), .paper-material[elevation="2"] {
        @apply --paper-material-elevation-2;
      }
      :host(.paper-material[elevation="3"]), .paper-material[elevation="3"] {
        @apply --paper-material-elevation-3;
      }
      :host(.paper-material[elevation="4"]), .paper-material[elevation="4"] {
        @apply --paper-material-elevation-4;
      }
      :host(.paper-material[elevation="5"]), .paper-material[elevation="5"] {
        @apply --paper-material-elevation-5;
      }
    </style>
  </template>
</dom-module><dom-module id="paper-button">
  <template strip-whitespace>
    <style include="paper-material-styles">
      /* Need to specify the same specificity as the styles imported from paper-material. */
      :host {
        @apply --layout-inline;
        @apply --layout-center-center;
        position: relative;
        box-sizing: border-box;
        min-width: 5.14em;
        margin: 0 0.29em;
        background: transparent;
        -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
        -webkit-tap-highlight-color: transparent;
        font: inherit;
        text-transform: uppercase;
        outline-width: 0;
        border-radius: 3px;
        -moz-user-select: none;
        -ms-user-select: none;
        -webkit-user-select: none;
        user-select: none;
        cursor: pointer;
        z-index: 0;
        padding: 0.7em 0.57em;

        @apply --paper-font-common-base;
        @apply --paper-button;
      }

      :host([elevation="1"]) {
        @apply --paper-material-elevation-1;
      }

      :host([elevation="2"]) {
        @apply --paper-material-elevation-2;
      }

      :host([elevation="3"]) {
        @apply --paper-material-elevation-3;
      }

      :host([elevation="4"]) {
        @apply --paper-material-elevation-4;
      }

      :host([elevation="5"]) {
        @apply --paper-material-elevation-5;
      }

      :host([hidden]) {
        display: none !important;
      }

      :host([raised].keyboard-focus) {
        font-weight: bold;
        @apply --paper-button-raised-keyboard-focus;
      }

      :host(:not([raised]).keyboard-focus) {
        font-weight: bold;
        @apply --paper-button-flat-keyboard-focus;
      }

      :host([disabled]) {
        background: #eaeaea;
        color: #a8a8a8;
        cursor: auto;
        pointer-events: none;

        @apply --paper-button-disabled;
      }

      :host([animated]) {
        @apply --shadow-transition;
      }

      paper-ripple {
        color: var(--paper-button-ink-color);
      }
    </style>

    <slot></slot>
  </template>

  
</dom-module><custom-style>
  <style is="custom-style">
    html {

      /* Material Design color palette for Google products */

      --google-red-100: #f4c7c3;
      --google-red-300: #e67c73;
      --google-red-500: #db4437;
      --google-red-700: #c53929;

      --google-blue-100: #c6dafc;
      --google-blue-300: #7baaf7;
      --google-blue-500: #4285f4;
      --google-blue-700: #3367d6;

      --google-green-100: #b7e1cd;
      --google-green-300: #57bb8a;
      --google-green-500: #0f9d58;
      --google-green-700: #0b8043;

      --google-yellow-100: #fce8b2;
      --google-yellow-300: #f7cb4d;
      --google-yellow-500: #f4b400;
      --google-yellow-700: #f09300;

      --google-grey-100: #f5f5f5;
      --google-grey-300: #e0e0e0;
      --google-grey-500: #9e9e9e;
      --google-grey-700: #616161;

      /* Material Design color palette from online spec document */

      --paper-red-50: #ffebee;
      --paper-red-100: #ffcdd2;
      --paper-red-200: #ef9a9a;
      --paper-red-300: #e57373;
      --paper-red-400: #ef5350;
      --paper-red-500: #f44336;
      --paper-red-600: #e53935;
      --paper-red-700: #d32f2f;
      --paper-red-800: #c62828;
      --paper-red-900: #b71c1c;
      --paper-red-a100: #ff8a80;
      --paper-red-a200: #ff5252;
      --paper-red-a400: #ff1744;
      --paper-red-a700: #d50000;

      --paper-pink-50: #fce4ec;
      --paper-pink-100: #f8bbd0;
      --paper-pink-200: #f48fb1;
      --paper-pink-300: #f06292;
      --paper-pink-400: #ec407a;
      --paper-pink-500: #e91e63;
      --paper-pink-600: #d81b60;
      --paper-pink-700: #c2185b;
      --paper-pink-800: #ad1457;
      --paper-pink-900: #880e4f;
      --paper-pink-a100: #ff80ab;
      --paper-pink-a200: #ff4081;
      --paper-pink-a400: #f50057;
      --paper-pink-a700: #c51162;

      --paper-purple-50: #f3e5f5;
      --paper-purple-100: #e1bee7;
      --paper-purple-200: #ce93d8;
      --paper-purple-300: #ba68c8;
      --paper-purple-400: #ab47bc;
      --paper-purple-500: #9c27b0;
      --paper-purple-600: #8e24aa;
      --paper-purple-700: #7b1fa2;
      --paper-purple-800: #6a1b9a;
      --paper-purple-900: #4a148c;
      --paper-purple-a100: #ea80fc;
      --paper-purple-a200: #e040fb;
      --paper-purple-a400: #d500f9;
      --paper-purple-a700: #aa00ff;

      --paper-deep-purple-50: #ede7f6;
      --paper-deep-purple-100: #d1c4e9;
      --paper-deep-purple-200: #b39ddb;
      --paper-deep-purple-300: #9575cd;
      --paper-deep-purple-400: #7e57c2;
      --paper-deep-purple-500: #673ab7;
      --paper-deep-purple-600: #5e35b1;
      --paper-deep-purple-700: #512da8;
      --paper-deep-purple-800: #4527a0;
      --paper-deep-purple-900: #311b92;
      --paper-deep-purple-a100: #b388ff;
      --paper-deep-purple-a200: #7c4dff;
      --paper-deep-purple-a400: #651fff;
      --paper-deep-purple-a700: #6200ea;

      --paper-indigo-50: #e8eaf6;
      --paper-indigo-100: #c5cae9;
      --paper-indigo-200: #9fa8da;
      --paper-indigo-300: #7986cb;
      --paper-indigo-400: #5c6bc0;
      --paper-indigo-500: #3f51b5;
      --paper-indigo-600: #3949ab;
      --paper-indigo-700: #303f9f;
      --paper-indigo-800: #283593;
      --paper-indigo-900: #1a237e;
      --paper-indigo-a100: #8c9eff;
      --paper-indigo-a200: #536dfe;
      --paper-indigo-a400: #3d5afe;
      --paper-indigo-a700: #304ffe;

      --paper-blue-50: #e3f2fd;
      --paper-blue-100: #bbdefb;
      --paper-blue-200: #90caf9;
      --paper-blue-300: #64b5f6;
      --paper-blue-400: #42a5f5;
      --paper-blue-500: #2196f3;
      --paper-blue-600: #1e88e5;
      --paper-blue-700: #1976d2;
      --paper-blue-800: #1565c0;
      --paper-blue-900: #0d47a1;
      --paper-blue-a100: #82b1ff;
      --paper-blue-a200: #448aff;
      --paper-blue-a400: #2979ff;
      --paper-blue-a700: #2962ff;

      --paper-light-blue-50: #e1f5fe;
      --paper-light-blue-100: #b3e5fc;
      --paper-light-blue-200: #81d4fa;
      --paper-light-blue-300: #4fc3f7;
      --paper-light-blue-400: #29b6f6;
      --paper-light-blue-500: #03a9f4;
      --paper-light-blue-600: #039be5;
      --paper-light-blue-700: #0288d1;
      --paper-light-blue-800: #0277bd;
      --paper-light-blue-900: #01579b;
      --paper-light-blue-a100: #80d8ff;
      --paper-light-blue-a200: #40c4ff;
      --paper-light-blue-a400: #00b0ff;
      --paper-light-blue-a700: #0091ea;

      --paper-cyan-50: #e0f7fa;
      --paper-cyan-100: #b2ebf2;
      --paper-cyan-200: #80deea;
      --paper-cyan-300: #4dd0e1;
      --paper-cyan-400: #26c6da;
      --paper-cyan-500: #00bcd4;
      --paper-cyan-600: #00acc1;
      --paper-cyan-700: #0097a7;
      --paper-cyan-800: #00838f;
      --paper-cyan-900: #006064;
      --paper-cyan-a100: #84ffff;
      --paper-cyan-a200: #18ffff;
      --paper-cyan-a400: #00e5ff;
      --paper-cyan-a700: #00b8d4;

      --paper-teal-50: #e0f2f1;
      --paper-teal-100: #b2dfdb;
      --paper-teal-200: #80cbc4;
      --paper-teal-300: #4db6ac;
      --paper-teal-400: #26a69a;
      --paper-teal-500: #009688;
      --paper-teal-600: #00897b;
      --paper-teal-700: #00796b;
      --paper-teal-800: #00695c;
      --paper-teal-900: #004d40;
      --paper-teal-a100: #a7ffeb;
      --paper-teal-a200: #64ffda;
      --paper-teal-a400: #1de9b6;
      --paper-teal-a700: #00bfa5;

      --paper-green-50: #e8f5e9;
      --paper-green-100: #c8e6c9;
      --paper-green-200: #a5d6a7;
      --paper-green-300: #81c784;
      --paper-green-400: #66bb6a;
      --paper-green-500: #4caf50;
      --paper-green-600: #43a047;
      --paper-green-700: #388e3c;
      --paper-green-800: #2e7d32;
      --paper-green-900: #1b5e20;
      --paper-green-a100: #b9f6ca;
      --paper-green-a200: #69f0ae;
      --paper-green-a400: #00e676;
      --paper-green-a700: #00c853;

      --paper-light-green-50: #f1f8e9;
      --paper-light-green-100: #dcedc8;
      --paper-light-green-200: #c5e1a5;
      --paper-light-green-300: #aed581;
      --paper-light-green-400: #9ccc65;
      --paper-light-green-500: #8bc34a;
      --paper-light-green-600: #7cb342;
      --paper-light-green-700: #689f38;
      --paper-light-green-800: #558b2f;
      --paper-light-green-900: #33691e;
      --paper-light-green-a100: #ccff90;
      --paper-light-green-a200: #b2ff59;
      --paper-light-green-a400: #76ff03;
      --paper-light-green-a700: #64dd17;

      --paper-lime-50: #f9fbe7;
      --paper-lime-100: #f0f4c3;
      --paper-lime-200: #e6ee9c;
      --paper-lime-300: #dce775;
      --paper-lime-400: #d4e157;
      --paper-lime-500: #cddc39;
      --paper-lime-600: #c0ca33;
      --paper-lime-700: #afb42b;
      --paper-lime-800: #9e9d24;
      --paper-lime-900: #827717;
      --paper-lime-a100: #f4ff81;
      --paper-lime-a200: #eeff41;
      --paper-lime-a400: #c6ff00;
      --paper-lime-a700: #aeea00;

      --paper-yellow-50: #fffde7;
      --paper-yellow-100: #fff9c4;
      --paper-yellow-200: #fff59d;
      --paper-yellow-300: #fff176;
      --paper-yellow-400: #ffee58;
      --paper-yellow-500: #ffeb3b;
      --paper-yellow-600: #fdd835;
      --paper-yellow-700: #fbc02d;
      --paper-yellow-800: #f9a825;
      --paper-yellow-900: #f57f17;
      --paper-yellow-a100: #ffff8d;
      --paper-yellow-a200: #ffff00;
      --paper-yellow-a400: #ffea00;
      --paper-yellow-a700: #ffd600;

      --paper-amber-50: #fff8e1;
      --paper-amber-100: #ffecb3;
      --paper-amber-200: #ffe082;
      --paper-amber-300: #ffd54f;
      --paper-amber-400: #ffca28;
      --paper-amber-500: #ffc107;
      --paper-amber-600: #ffb300;
      --paper-amber-700: #ffa000;
      --paper-amber-800: #ff8f00;
      --paper-amber-900: #ff6f00;
      --paper-amber-a100: #ffe57f;
      --paper-amber-a200: #ffd740;
      --paper-amber-a400: #ffc400;
      --paper-amber-a700: #ffab00;

      --paper-orange-50: #fff3e0;
      --paper-orange-100: #ffe0b2;
      --paper-orange-200: #ffcc80;
      --paper-orange-300: #ffb74d;
      --paper-orange-400: #ffa726;
      --paper-orange-500: #ff9800;
      --paper-orange-600: #fb8c00;
      --paper-orange-700: #f57c00;
      --paper-orange-800: #ef6c00;
      --paper-orange-900: #e65100;
      --paper-orange-a100: #ffd180;
      --paper-orange-a200: #ffab40;
      --paper-orange-a400: #ff9100;
      --paper-orange-a700: #ff6500;

      --paper-deep-orange-50: #fbe9e7;
      --paper-deep-orange-100: #ffccbc;
      --paper-deep-orange-200: #ffab91;
      --paper-deep-orange-300: #ff8a65;
      --paper-deep-orange-400: #ff7043;
      --paper-deep-orange-500: #ff5722;
      --paper-deep-orange-600: #f4511e;
      --paper-deep-orange-700: #e64a19;
      --paper-deep-orange-800: #d84315;
      --paper-deep-orange-900: #bf360c;
      --paper-deep-orange-a100: #ff9e80;
      --paper-deep-orange-a200: #ff6e40;
      --paper-deep-orange-a400: #ff3d00;
      --paper-deep-orange-a700: #dd2c00;

      --paper-brown-50: #efebe9;
      --paper-brown-100: #d7ccc8;
      --paper-brown-200: #bcaaa4;
      --paper-brown-300: #a1887f;
      --paper-brown-400: #8d6e63;
      --paper-brown-500: #795548;
      --paper-brown-600: #6d4c41;
      --paper-brown-700: #5d4037;
      --paper-brown-800: #4e342e;
      --paper-brown-900: #3e2723;

      --paper-grey-50: #fafafa;
      --paper-grey-100: #f5f5f5;
      --paper-grey-200: #eeeeee;
      --paper-grey-300: #e0e0e0;
      --paper-grey-400: #bdbdbd;
      --paper-grey-500: #9e9e9e;
      --paper-grey-600: #757575;
      --paper-grey-700: #616161;
      --paper-grey-800: #424242;
      --paper-grey-900: #212121;

      --paper-blue-grey-50: #eceff1;
      --paper-blue-grey-100: #cfd8dc;
      --paper-blue-grey-200: #b0bec5;
      --paper-blue-grey-300: #90a4ae;
      --paper-blue-grey-400: #78909c;
      --paper-blue-grey-500: #607d8b;
      --paper-blue-grey-600: #546e7a;
      --paper-blue-grey-700: #455a64;
      --paper-blue-grey-800: #37474f;
      --paper-blue-grey-900: #263238;

      /* opacity for dark text on a light background */
      --dark-divider-opacity: 0.12;
      --dark-disabled-opacity: 0.38; /* or hint text or icon */
      --dark-secondary-opacity: 0.54;
      --dark-primary-opacity: 0.87;

      /* opacity for light text on a dark background */
      --light-divider-opacity: 0.12;
      --light-disabled-opacity: 0.3; /* or hint text or icon */
      --light-secondary-opacity: 0.7;
      --light-primary-opacity: 1.0;

    }

  </style>
</custom-style><custom-style>
  <style is="custom-style">
    html {
      /*
       * You can use these generic variables in your elements for easy theming.
       * For example, if all your elements use `--primary-text-color` as its main
       * color, then switching from a light to a dark theme is just a matter of
       * changing the value of `--primary-text-color` in your application.
       */
      --primary-text-color: var(--light-theme-text-color);
      --primary-background-color: var(--light-theme-background-color);
      --secondary-text-color: var(--light-theme-secondary-color);
      --disabled-text-color: var(--light-theme-disabled-color);
      --divider-color: var(--light-theme-divider-color);
      --error-color: var(--paper-deep-orange-a700);

      /*
       * Primary and accent colors. Also see color.html for more colors.
       */
      --primary-color: var(--paper-indigo-500);
      --light-primary-color: var(--paper-indigo-100);
      --dark-primary-color: var(--paper-indigo-700);

      --accent-color: var(--paper-pink-a200);
      --light-accent-color: var(--paper-pink-a100);
      --dark-accent-color: var(--paper-pink-a400);


      /*
       * Material Design Light background theme
       */
      --light-theme-background-color: #ffffff;
      --light-theme-base-color: #000000;
      --light-theme-text-color: var(--paper-grey-900);
      --light-theme-secondary-color: #737373;  /* for secondary text and icons */
      --light-theme-disabled-color: #9b9b9b;  /* disabled/hint text */
      --light-theme-divider-color: #dbdbdb;

      /*
       * Material Design Dark background theme
       */
      --dark-theme-background-color: var(--paper-grey-900);
      --dark-theme-base-color: #ffffff;
      --dark-theme-text-color: #ffffff;
      --dark-theme-secondary-color: #bcbcbc;  /* for secondary text and icons */
      --dark-theme-disabled-color: #646464;  /* disabled/hint text */
      --dark-theme-divider-color: #3c3c3c;

      /*
       * Deprecated values because of their confusing names.
       */
      --text-primary-color: var(--dark-theme-text-color);
      --default-primary-color: var(--primary-color);
    }
  </style>
</custom-style><dom-module id="paper-checkbox">
  <template strip-whitespace>
    <style>
      :host {
        display: inline-block;
        white-space: nowrap;
        cursor: pointer;
        --calculated-paper-checkbox-size: var(--paper-checkbox-size, 18px);
        /* -1px is a sentinel for the default and is replaced in `attached`. */
        --calculated-paper-checkbox-ink-size: var(--paper-checkbox-ink-size, -1px);
        @apply --paper-font-common-base;
        line-height: 0;
        -webkit-tap-highlight-color: transparent;
      }

      :host([hidden]) {
        display: none !important;
      }

      :host(:focus) {
        outline: none;
      }

      .hidden {
        display: none;
      }

      #checkboxContainer {
        display: inline-block;
        position: relative;
        width: var(--calculated-paper-checkbox-size);
        height: var(--calculated-paper-checkbox-size);
        min-width: var(--calculated-paper-checkbox-size);
        margin: var(--paper-checkbox-margin, initial);
        vertical-align: var(--paper-checkbox-vertical-align, middle);
        background-color: var(--paper-checkbox-unchecked-background-color, transparent);
      }

      #ink {
        position: absolute;

        /* Center the ripple in the checkbox by negative offsetting it by
         * (inkWidth - rippleWidth) / 2 */
        top: calc(0px - (var(--calculated-paper-checkbox-ink-size) - var(--calculated-paper-checkbox-size)) / 2);
        left: calc(0px - (var(--calculated-paper-checkbox-ink-size) - var(--calculated-paper-checkbox-size)) / 2);
        width: var(--calculated-paper-checkbox-ink-size);
        height: var(--calculated-paper-checkbox-ink-size);
        color: var(--paper-checkbox-unchecked-ink-color, var(--primary-text-color));
        opacity: 0.6;
        pointer-events: none;
      }

      #ink:dir(rtl) {
        right: calc(0px - (var(--calculated-paper-checkbox-ink-size) - var(--calculated-paper-checkbox-size)) / 2);
        left: auto;
      }

      #ink[checked] {
        color: var(--paper-checkbox-checked-ink-color, var(--primary-color));
      }

      #checkbox {
        position: relative;
        box-sizing: border-box;
        height: 100%;
        border: solid 2px;
        border-color: var(--paper-checkbox-unchecked-color, var(--primary-text-color));
        border-radius: 2px;
        pointer-events: none;
        -webkit-transition: background-color 140ms, border-color 140ms;
        transition: background-color 140ms, border-color 140ms;
      }

      /* checkbox checked animations */
      #checkbox.checked #checkmark {
        -webkit-animation: checkmark-expand 140ms ease-out forwards;
        animation: checkmark-expand 140ms ease-out forwards;
      }

      @-webkit-keyframes checkmark-expand {
        0% {
          -webkit-transform: scale(0, 0) rotate(45deg);
        }
        100% {
          -webkit-transform: scale(1, 1) rotate(45deg);
        }
      }

      @keyframes checkmark-expand {
        0% {
          transform: scale(0, 0) rotate(45deg);
        }
        100% {
          transform: scale(1, 1) rotate(45deg);
        }
      }

      #checkbox.checked {
        background-color: var(--paper-checkbox-checked-color, var(--primary-color));
        border-color: var(--paper-checkbox-checked-color, var(--primary-color));
      }

      #checkmark {
        position: absolute;
        width: 36%;
        height: 70%;
        border-style: solid;
        border-top: none;
        border-left: none;
        border-right-width: calc(2/15 * var(--calculated-paper-checkbox-size));
        border-bottom-width: calc(2/15 * var(--calculated-paper-checkbox-size));
        border-color: var(--paper-checkbox-checkmark-color, white);
        -webkit-transform-origin: 97% 86%;
        transform-origin: 97% 86%;
        box-sizing: content-box; /* protect against page-level box-sizing */
      }

      #checkmark:dir(rtl) {
        -webkit-transform-origin: 50% 14%;
        transform-origin: 50% 14%;
      }

      /* label */
      #checkboxLabel {
        position: relative;
        display: inline-block;
        vertical-align: middle;
        padding-left: var(--paper-checkbox-label-spacing, 8px);
        white-space: normal;
        line-height: normal;
        color: var(--paper-checkbox-label-color, var(--primary-text-color));
        @apply --paper-checkbox-label;
      }

      :host([checked]) #checkboxLabel {
        color: var(--paper-checkbox-label-checked-color, var(--paper-checkbox-label-color, var(--primary-text-color)));
        @apply --paper-checkbox-label-checked;
      }

      #checkboxLabel:dir(rtl) {
        padding-right: var(--paper-checkbox-label-spacing, 8px);
        padding-left: 0;
      }

      #checkboxLabel[hidden] {
        display: none;
      }

      /* disabled state */

      :host([disabled]) #checkbox {
        opacity: 0.5;
        border-color: var(--paper-checkbox-unchecked-color, var(--primary-text-color));
      }

      :host([disabled][checked]) #checkbox {
        background-color: var(--paper-checkbox-unchecked-color, var(--primary-text-color));
        opacity: 0.5;
      }

      :host([disabled]) #checkboxLabel  {
        opacity: 0.65;
      }

      /* invalid state */
      #checkbox.invalid:not(.checked) {
        border-color: var(--paper-checkbox-error-color, var(--error-color));
      }
    </style>

    <div id="checkboxContainer">
      <div id="checkbox" class$="[[_computeCheckboxClass(checked, invalid)]]">
        <div id="checkmark" class$="[[_computeCheckmarkClass(checked)]]"></div>
      </div>
    </div>

    <div id="checkboxLabel"><slot></slot></div>
  </template>

  
</dom-module><dom-module id="iron-icon">
  <template>
    <style>
      :host {
        @apply --layout-inline;
        @apply --layout-center-center;
        position: relative;

        vertical-align: middle;

        fill: var(--iron-icon-fill-color, currentcolor);
        stroke: var(--iron-icon-stroke-color, none);

        width: var(--iron-icon-width, 24px);
        height: var(--iron-icon-height, 24px);
        @apply --iron-icon;
      }

      :host([hidden]) {
        display: none;
      }
    </style>
  </template>

  

</dom-module><dom-module id="iron-a11y-announcer">
  <template>
    <style>
      :host {
        display: inline-block;
        position: fixed;
        clip: rect(0px,0px,0px,0px);
      }
    </style>
    <div aria-live$="[[mode]]">[[_text]]</div>
  </template>

  
</dom-module><dom-module id="iron-input">
  <template>
    <style>
      :host {
        display: inline-block;
      }
    </style>
    <slot id="content"></slot>
  </template>
  
</dom-module><custom-style>
  <style is="custom-style">
    html {

      /* Shared Styles */
      --paper-font-common-base: {
        font-family: 'Roboto', 'Noto', sans-serif;
        -webkit-font-smoothing: antialiased;
      };

      --paper-font-common-code: {
        font-family: 'Roboto Mono', 'Consolas', 'Menlo', monospace;
        -webkit-font-smoothing: antialiased;
      };

      --paper-font-common-expensive-kerning: {
        text-rendering: optimizeLegibility;
      };

      --paper-font-common-nowrap: {
        white-space: nowrap;
        overflow: hidden;
        text-overflow: ellipsis;
      };

      /* Material Font Styles */

      --paper-font-display4: {
        @apply --paper-font-common-base;
        @apply --paper-font-common-nowrap;

        font-size: 112px;
        font-weight: 300;
        letter-spacing: -.044em;
        line-height: 120px;
      };

      --paper-font-display3: {
        @apply --paper-font-common-base;
        @apply --paper-font-common-nowrap;

        font-size: 56px;
        font-weight: 400;
        letter-spacing: -.026em;
        line-height: 60px;
      };

      --paper-font-display2: {
        @apply --paper-font-common-base;

        font-size: 45px;
        font-weight: 400;
        letter-spacing: -.018em;
        line-height: 48px;
      };

      --paper-font-display1: {
        @apply --paper-font-common-base;

        font-size: 34px;
        font-weight: 400;
        letter-spacing: -.01em;
        line-height: 40px;
      };

      --paper-font-headline: {
        @apply --paper-font-common-base;

        font-size: 24px;
        font-weight: 400;
        letter-spacing: -.012em;
        line-height: 32px;
      };

      --paper-font-title: {
        @apply --paper-font-common-base;
        @apply --paper-font-common-nowrap;

        font-size: 20px;
        font-weight: 500;
        line-height: 28px;
      };

      --paper-font-subhead: {
        @apply --paper-font-common-base;

        font-size: 16px;
        font-weight: 400;
        line-height: 24px;
      };

      --paper-font-body2: {
        @apply --paper-font-common-base;

        font-size: 14px;
        font-weight: 500;
        line-height: 24px;
      };

      --paper-font-body1: {
        @apply --paper-font-common-base;

        font-size: 14px;
        font-weight: 400;
        line-height: 20px;
      };

      --paper-font-caption: {
        @apply --paper-font-common-base;
        @apply --paper-font-common-nowrap;

        font-size: 12px;
        font-weight: 400;
        letter-spacing: 0.011em;
        line-height: 20px;
      };

      --paper-font-menu: {
        @apply --paper-font-common-base;
        @apply --paper-font-common-nowrap;

        font-size: 13px;
        font-weight: 500;
        line-height: 24px;
      };

      --paper-font-button: {
        @apply --paper-font-common-base;
        @apply --paper-font-common-nowrap;

        font-size: 14px;
        font-weight: 500;
        letter-spacing: 0.018em;
        line-height: 24px;
        text-transform: uppercase;
      };

      --paper-font-code2: {
        @apply --paper-font-common-code;

        font-size: 14px;
        font-weight: 700;
        line-height: 20px;
      };

      --paper-font-code1: {
        @apply --paper-font-common-code;

        font-size: 14px;
        font-weight: 500;
        line-height: 20px;
      };

    }

  </style>
</custom-style><dom-module id="paper-input-char-counter">
  <template>
    <style>
      :host {
        display: inline-block;
        float: right;

        @apply --paper-font-caption;
        @apply --paper-input-char-counter;
      }

      :host([hidden]) {
        display: none !important;
      }

      :host(:dir(rtl)) {
        float: left;
      }
    </style>

    <span>[[_charCounterStr]]</span>
  </template>
</dom-module><custom-style>
  <style is="custom-style">
    html {
      --paper-input-container-shared-input-style: {
        position: relative; /* to make a stacking context */
        outline: none;
        box-shadow: none;
        padding: 0;
        margin: 0;
        width: 100%;
        max-width: 100%;
        background: transparent;
        border: none;
        color: var(--paper-input-container-input-color, var(--primary-text-color));
        -webkit-appearance: none;
        text-align: inherit;
        vertical-align: bottom;

        @apply --paper-font-subhead;
      };
    }
  </style>
</custom-style><dom-module id="paper-input-container">
  <template>
    <style>
      :host {
        display: block;
        padding: 8px 0;
        @apply --paper-input-container;
      }

      :host([inline]) {
        display: inline-block;
      }

      :host([disabled]) {
        pointer-events: none;
        opacity: 0.33;

        @apply --paper-input-container-disabled;
      }

      :host([hidden]) {
        display: none !important;
      }

      [hidden] {
        display: none !important;
      }

      .floated-label-placeholder {
        @apply --paper-font-caption;
      }

      .underline {
        height: 2px;
        position: relative;
      }

      .focused-line {
        @apply --layout-fit;
        border-bottom: 2px solid var(--paper-input-container-focus-color, var(--primary-color));

        -webkit-transform-origin: center center;
        transform-origin: center center;
        -webkit-transform: scale3d(0,1,1);
        transform: scale3d(0,1,1);

        @apply --paper-input-container-underline-focus;
      }

      .underline.is-highlighted .focused-line {
        -webkit-transform: none;
        transform: none;
        -webkit-transition: -webkit-transform 0.25s;
        transition: transform 0.25s;

        @apply --paper-transition-easing;
      }

      .underline.is-invalid .focused-line {
        border-color: var(--paper-input-container-invalid-color, var(--error-color));
        -webkit-transform: none;
        transform: none;
        -webkit-transition: -webkit-transform 0.25s;
        transition: transform 0.25s;

        @apply --paper-transition-easing;
      }

      .unfocused-line {
        @apply --layout-fit;
        border-bottom: 1px solid var(--paper-input-container-color, var(--secondary-text-color));
        @apply --paper-input-container-underline;
      }

      :host([disabled]) .unfocused-line {
        border-bottom: 1px dashed;
        border-color: var(--paper-input-container-color, var(--secondary-text-color));
        @apply --paper-input-container-underline-disabled;
      }

      .input-wrapper {
        @apply --layout-horizontal;
        @apply --layout-center;
        position: relative;
      }

      .input-content {
        @apply --layout-flex-auto;
        @apply --layout-relative;
        max-width: 100%;
      }

      .input-content ::slotted(label),
      .input-content ::slotted(.paper-input-label) {
        position: absolute;
        top: 0;
        left: 0;
        width: 100%;
        font: inherit;
        color: var(--paper-input-container-color, var(--secondary-text-color));
        -webkit-transition: -webkit-transform 0.25s, width 0.25s;
        transition: transform 0.25s, width 0.25s;
        -webkit-transform-origin: left top;
        transform-origin: left top;
        /* Fix for safari not focusing 0-height date/time inputs with -webkit-apperance: none; */
        min-height: 1px;

        @apply --paper-font-common-nowrap;
        @apply --paper-font-subhead;
        @apply --paper-input-container-label;
        @apply --paper-transition-easing;
      }

      .input-content.label-is-floating ::slotted(label),
      .input-content.label-is-floating ::slotted(.paper-input-label) {
        -webkit-transform: translateY(-75%) scale(0.75);
        transform: translateY(-75%) scale(0.75);

        /* Since we scale to 75/100 of the size, we actually have 100/75 of the
        original space now available */
        width: 133%;

        @apply --paper-input-container-label-floating;
      }

      :host(:dir(rtl)) .input-content.label-is-floating ::slotted(label),
      :host(:dir(rtl)) .input-content.label-is-floating ::slotted(.paper-input-label) {
        right: 0;
        left: auto;
        -webkit-transform-origin: right top;
        transform-origin: right top;
      }

      .input-content.label-is-highlighted ::slotted(label),
      .input-content.label-is-highlighted ::slotted(.paper-input-label) {
        color: var(--paper-input-container-focus-color, var(--primary-color));

        @apply --paper-input-container-label-focus;
      }

      .input-content.is-invalid ::slotted(label),
      .input-content.is-invalid ::slotted(.paper-input-label) {
        color: var(--paper-input-container-invalid-color, var(--error-color));
      }

      .input-content.label-is-hidden ::slotted(label),
      .input-content.label-is-hidden ::slotted(.paper-input-label) {
        visibility: hidden;
      }

      .input-content ::slotted(input),
      .input-content ::slotted(iron-input),
      .input-content ::slotted(textarea),
      .input-content ::slotted(iron-autogrow-textarea),
      .input-content ::slotted(.paper-input-input) {
        @apply --paper-input-container-shared-input-style;
        /* The apply shim doesn't apply the nested color custom property,
          so we have to re-apply it here. */
        color: var(--paper-input-container-input-color, var(--primary-text-color));
        @apply --paper-input-container-input;
      }

      .input-content ::slotted(input)::-webkit-outer-spin-button,
      .input-content ::slotted(input)::-webkit-inner-spin-button {
        @apply --paper-input-container-input-webkit-spinner;
      }

      .input-content.focused ::slotted(input),
      .input-content.focused ::slotted(iron-input),
      .input-content.focused ::slotted(textarea),
      .input-content.focused ::slotted(iron-autogrow-textarea),
      .input-content.focused ::slotted(.paper-input-input) {
        @apply --paper-input-container-input-focus;
      }

      .input-content.is-invalid ::slotted(input),
      .input-content.is-invalid ::slotted(iron-input),
      .input-content.is-invalid ::slotted(textarea),
      .input-content.is-invalid ::slotted(iron-autogrow-textarea),
      .input-content.is-invalid ::slotted(.paper-input-input) {
        @apply --paper-input-container-input-invalid;
      }

      .prefix ::slotted(*) {
        display: inline-block;
        @apply --paper-font-subhead;
        @apply --layout-flex-none;
        @apply --paper-input-prefix;
      }

      .suffix ::slotted(*) {
        display: inline-block;
        @apply --paper-font-subhead;
        @apply --layout-flex-none;

        @apply --paper-input-suffix;
      }

      /* Firefox sets a min-width on the input, which can cause layout issues */
      .input-content ::slotted(input) {
        min-width: 0;
      }

      .input-content ::slotted(textarea) {
        resize: none;
      }

      .add-on-content {
        position: relative;
      }

      .add-on-content.is-invalid ::slotted(*) {
        color: var(--paper-input-container-invalid-color, var(--error-color));
      }

      .add-on-content.is-highlighted ::slotted(*) {
        color: var(--paper-input-container-focus-color, var(--primary-color));
      }
    </style>

    <div class="floated-label-placeholder" aria-hidden="true" hidden="[[noLabelFloat]]">&nbsp;</div>

    <div class="input-wrapper">
      <span class="prefix"><slot name="prefix"></slot></span>

      <div class$="[[_computeInputContentClass(noLabelFloat,alwaysFloatLabel,focused,invalid,_inputHasContent)]]" id="labelAndInputContainer">
        <slot name="label"></slot>
        <slot name="input"></slot>
      </div>

      <span class="suffix"><slot name="suffix"></slot></span>
    </div>

    <div class$="[[_computeUnderlineClass(focused,invalid)]]">
      <div class="unfocused-line"></div>
      <div class="focused-line"></div>
    </div>

    <div class$="[[_computeAddOnContentClass(focused,invalid)]]">
      <slot name="add-on"></slot>
    </div>
  </template>
</dom-module><dom-module id="paper-input-error">
  <template>
    <style>
      :host {
        display: inline-block;
        visibility: hidden;

        color: var(--paper-input-container-invalid-color, var(--error-color));

        @apply --paper-font-caption;
        @apply --paper-input-error;
        position: absolute;
        left:0;
        right:0;
      }

      :host([invalid]) {
        visibility: visible;
      };
    </style>

    <slot></slot>
  </template>
</dom-module><dom-module id="paper-input">
  <template>
    <style>
      :host {
        display: block;
      }

      :host([focused]) {
        outline: none;
      }

      :host([hidden]) {
        display: none !important;
      }

      input {
        /* Firefox sets a min-width on the input, which can cause layout issues */
        min-width: 0;
      }

      /* In 1.x, the <input> is distributed to paper-input-container, which styles it.
      In 2.x the <iron-input> is distributed to paper-input-container, which styles
      it, but in order for this to work correctly, we need to reset some
      of the native input's properties to inherit (from the iron-input) */
      iron-input > input {
        @apply --paper-input-container-shared-input-style;
        font-family: inherit;
        font-weight: inherit;
        font-size: inherit;
        letter-spacing: inherit;
        word-spacing: inherit;
        line-height: inherit;
        text-shadow: inherit;
        color: inherit;
        cursor: inherit;
      }

      input:disabled {
        @apply --paper-input-container-input-disabled;
      }

      input::-webkit-outer-spin-button,
      input::-webkit-inner-spin-button {
        @apply --paper-input-container-input-webkit-spinner;
      }

      input::-webkit-clear-button {
        @apply --paper-input-container-input-webkit-clear;
      }

      input::-webkit-calendar-picker-indicator {
        @apply --paper-input-container-input-webkit-calendar-picker-indicator;
      }

      input::-webkit-input-placeholder {
        color: var(--paper-input-container-color, var(--secondary-text-color));
      }

      input:-moz-placeholder {
        color: var(--paper-input-container-color, var(--secondary-text-color));
      }

      input::-moz-placeholder {
        color: var(--paper-input-container-color, var(--secondary-text-color));
      }

      input::-ms-clear {
        @apply --paper-input-container-ms-clear;
      }

      input::-ms-reveal {
        @apply --paper-input-container-ms-reveal;
      }

      input:-ms-input-placeholder {
        color: var(--paper-input-container-color, var(--secondary-text-color));
      }

      label {
        pointer-events: none;
      }
    </style>

    <paper-input-container id="container" no-label-float="[[noLabelFloat]]" always-float-label="[[_computeAlwaysFloatLabel(alwaysFloatLabel,placeholder)]]" auto-validate$="[[autoValidate]]" disabled$="[[disabled]]" invalid="[[invalid]]">

      <slot name="prefix" slot="prefix"></slot>

      <label hidden$="[[!label]]" aria-hidden="true" for$="[[_inputId]]" slot="label">[[label]]</label>

      <span id="template-placeholder"></span>

      <slot name="suffix" slot="suffix"></slot>

      <template is="dom-if" if="[[errorMessage]]">
        <paper-input-error aria-live="assertive" slot="add-on">[[errorMessage]]</paper-input-error>
      </template>

      <template is="dom-if" if="[[charCounter]]">
        <paper-input-char-counter slot="add-on"></paper-input-char-counter>
      </template>

    </paper-input-container>
  </template>

  
  <template id="v0">
    <input is="iron-input" slot="input" class="input-element" id$="[[_inputId]]" aria-labelledby$="[[_ariaLabelledBy]]" aria-describedby$="[[_ariaDescribedBy]]" disabled$="[[disabled]]" title$="[[title]]" bind-value="{{value}}" invalid="{{invalid}}" prevent-invalid-input="[[preventInvalidInput]]" allowed-pattern="[[allowedPattern]]" validator="[[validator]]" type$="[[type]]" pattern$="[[pattern]]" required$="[[required]]" autocomplete$="[[autocomplete]]" autofocus$="[[autofocus]]" inputmode$="[[inputmode]]" minlength$="[[minlength]]" maxlength$="[[maxlength]]" min$="[[min]]" max$="[[max]]" step$="[[step]]" name$="[[name]]" placeholder$="[[placeholder]]" readonly$="[[readonly]]" list$="[[list]]" size$="[[size]]" autocapitalize$="[[autocapitalize]]" autocorrect$="[[autocorrect]]" on-change="_onChange" tabindex$="[[tabIndex]]" autosave$="[[autosave]]" results$="[[results]]" accept$="[[accept]]" multiple$="[[multiple]]">
  </template>

  <template id="v1">
    
    <iron-input bind-value="{{value}}" slot="input" class="input-element" id$="[[_inputId]]" maxlength$="[[maxlength]]" allowed-pattern="[[allowedPattern]]" invalid="{{invalid}}" validator="[[validator]]">
      <input aria-labelledby$="[[_ariaLabelledBy]]" aria-describedby$="[[_ariaDescribedBy]]" disabled$="[[disabled]]" title$="[[title]]" type$="[[type]]" pattern$="[[pattern]]" required$="[[required]]" autocomplete$="[[autocomplete]]" autofocus$="[[autofocus]]" inputmode$="[[inputmode]]" minlength$="[[minlength]]" maxlength$="[[maxlength]]" min$="[[min]]" max$="[[max]]" step$="[[step]]" name$="[[name]]" placeholder$="[[placeholder]]" readonly$="[[readonly]]" list$="[[list]]" size$="[[size]]" autocapitalize$="[[autocapitalize]]" autocorrect$="[[autocorrect]]" on-change="_onChange" tabindex$="[[tabIndex]]" autosave$="[[autosave]]" results$="[[results]]" accept$="[[accept]]" multiple$="[[multiple]]">
    </iron-input>
  </template>

</dom-module><dom-module id="iron-overlay-backdrop">

  <template>
    <style>
      :host {
        position: fixed;
        top: 0;
        left: 0;
        width: 100%;
        height: 100%;
        background-color: var(--iron-overlay-backdrop-background-color, #000);
        opacity: 0;
        transition: opacity 0.2s;
        pointer-events: none;
        @apply --iron-overlay-backdrop;
      }

      :host(.opened) {
        opacity: var(--iron-overlay-backdrop-opacity, 0.6);
        pointer-events: auto;
        @apply --iron-overlay-backdrop-opened;
      }
    </style>

    <slot></slot>
  </template>

</dom-module><dom-module id="iron-dropdown">
  <template>
    <style>
      :host {
        position: fixed;
      }

      #contentWrapper ::slotted(*) {
        overflow: auto;
      }

      #contentWrapper.animating ::slotted(*) {
        overflow: hidden;
        pointer-events: none;
      }
    </style>

    <div id="contentWrapper">
      <slot id="content" name="dropdown-content"></slot>
    </div>
  </template>

  
</dom-module><dom-module id="paper-menu-button">
  <template>
    <style>
      :host {
        display: inline-block;
        position: relative;
        padding: 8px;
        outline: none;

        @apply --paper-menu-button;
      }

      :host([disabled]) {
        cursor: auto;
        color: var(--disabled-text-color);

        @apply --paper-menu-button-disabled;
      }

      iron-dropdown {
        @apply --paper-menu-button-dropdown;
      }

      .dropdown-content {
        @apply --shadow-elevation-2dp;

        position: relative;
        border-radius: 2px;
        background-color: var(--paper-menu-button-dropdown-background, var(--primary-background-color));

        @apply --paper-menu-button-content;
      }

      :host([vertical-align="top"]) .dropdown-content {
        margin-bottom: 20px;
        margin-top: -10px;
        top: 10px;
      }

      :host([vertical-align="bottom"]) .dropdown-content {
        bottom: 10px;
        margin-bottom: -10px;
        margin-top: 20px;
      }

      #trigger {
        cursor: pointer;
      }
    </style>

    <div id="trigger" on-tap="toggle">
      <slot name="dropdown-trigger"></slot>
    </div>

    <iron-dropdown id="dropdown" opened="{{opened}}" horizontal-align="[[horizontalAlign]]" vertical-align="[[verticalAlign]]" dynamic-align="[[dynamicAlign]]" horizontal-offset="[[horizontalOffset]]" vertical-offset="[[verticalOffset]]" no-overlap="[[noOverlap]]" open-animation-config="[[openAnimationConfig]]" close-animation-config="[[closeAnimationConfig]]" no-animations="[[noAnimations]]" focus-target="[[_dropdownContent]]" allow-outside-scroll="[[allowOutsideScroll]]" restore-focus-on-close="[[restoreFocusOnClose]]" on-iron-overlay-canceled="__onIronOverlayCanceled">
      <div slot="dropdown-content" class="dropdown-content">
        <slot id="content" name="dropdown-content"></slot>
      </div>
    </iron-dropdown>
  </template>

  
</dom-module><iron-iconset-svg name="paper-dropdown-menu" size="24">
<svg><defs>
<g id="arrow-drop-down"><path d="M7 10l5 5 5-5z"></path></g>
</defs></svg>
</iron-iconset-svg><dom-module id="paper-dropdown-menu-shared-styles">
  <template>
    <style>
      :host {
        display: inline-block;
        position: relative;
        text-align: left;

        /* NOTE(cdata): Both values are needed, since some phones require the
         * value to be `transparent`.
         */
        -webkit-tap-highlight-color: rgba(0,0,0,0);
        -webkit-tap-highlight-color: transparent;

        --paper-input-container-input: {
          overflow: hidden;
          white-space: nowrap;
          text-overflow: ellipsis;
          max-width: 100%;
          box-sizing: border-box;
          cursor: pointer;
        };

        @apply --paper-dropdown-menu;
      }

      :host([disabled]) {
        @apply --paper-dropdown-menu-disabled;
      }

      :host([noink]) paper-ripple {
        display: none;
      }

      :host([no-label-float]) paper-ripple {
        top: 8px;
      }

      paper-ripple {
        top: 12px;
        left: 0px;
        bottom: 8px;
        right: 0px;

        @apply --paper-dropdown-menu-ripple;
      }

      paper-menu-button {
        display: block;
        padding: 0;

        @apply --paper-dropdown-menu-button;
      }

      paper-input {
        @apply --paper-dropdown-menu-input;
      }

      iron-icon {
        color: var(--disabled-text-color);

        @apply --paper-dropdown-menu-icon;
      }
    </style>
  </template>
</dom-module><dom-module id="paper-dropdown-menu">
  <template>
    <style include="paper-dropdown-menu-shared-styles"></style>

    
    <span role="button"></span>
    <paper-menu-button id="menuButton" vertical-align="[[verticalAlign]]" horizontal-align="[[horizontalAlign]]" dynamic-align="[[dynamicAlign]]" vertical-offset="[[_computeMenuVerticalOffset(noLabelFloat, verticalOffset)]]" disabled="[[disabled]]" no-animations="[[noAnimations]]" on-iron-select="_onIronSelect" on-iron-deselect="_onIronDeselect" opened="{{opened}}" close-on-activate allow-outside-scroll="[[allowOutsideScroll]]" restore-focus-on-close="[[restoreFocusOnClose]]">
      
      <div class="dropdown-trigger" slot="dropdown-trigger">
        <paper-ripple></paper-ripple>
        
        <paper-input type="text" invalid="[[invalid]]" readonly disabled="[[disabled]]" value="[[value]]" placeholder="[[placeholder]]" error-message="[[errorMessage]]" always-float-label="[[alwaysFloatLabel]]" no-label-float="[[noLabelFloat]]" label="[[label]]">
          
          <iron-icon icon="paper-dropdown-menu:arrow-drop-down" suffix slot="suffix"></iron-icon>
        </paper-input>
      </div>
      <slot id="content" name="dropdown-content" slot="dropdown-content"></slot>
    </paper-menu-button>
  </template>

  
</dom-module><dom-module id="paper-listbox">
  <template>
    <style>
      :host {
        display: block;
        padding: 8px 0;

        background: var(--paper-listbox-background-color, var(--primary-background-color));
        color: var(--paper-listbox-color, var(--primary-text-color));

        @apply --paper-listbox;
      }
    </style>

    <slot></slot>
  </template>

  
</dom-module><dom-module id="paper-item-shared-styles">
  <template>
    <style>
      :host, .paper-item {
        display: block;
        position: relative;
        min-height: var(--paper-item-min-height, 48px);
        padding: 0px 16px;
      }

      .paper-item {
        @apply --paper-font-subhead;
        border:none;
        outline: none;
        background: white;
        width: 100%;
        text-align: left;
      }

      :host([hidden]), .paper-item[hidden] {
        display: none !important;
      }

      :host(.iron-selected), .paper-item.iron-selected {
        font-weight: var(--paper-item-selected-weight, bold);

        @apply --paper-item-selected;
      }

      :host([disabled]), .paper-item[disabled] {
        color: var(--paper-item-disabled-color, var(--disabled-text-color));

        @apply --paper-item-disabled;
      }

      :host(:focus), .paper-item:focus {
        position: relative;
        outline: 0;

        @apply --paper-item-focused;
      }

      :host(:focus):before, .paper-item:focus:before {
        @apply --layout-fit;

        background: currentColor;
        content: '';
        opacity: var(--dark-divider-opacity);
        pointer-events: none;

        @apply --paper-item-focused-before;
      }
    </style>
  </template>
</dom-module><dom-module id="paper-item">
  <template>
    <style include="paper-item-shared-styles">
      :host {
        @apply --layout-horizontal;
        @apply --layout-center;
        @apply --paper-font-subhead;

        @apply --paper-item;
      }
    </style>
    <slot></slot>
  </template>

  
</dom-module><dom-module id="tf-backend">
  
</dom-module><dom-module id="tf-storage">
  
</dom-module><dom-module id="tf-tag-filterer">
  <template>
    <paper-input no-label-float label="Filter tags (regular expressions supported)" value="{{_tagFilter}}" class="search-input">
      <iron-icon prefix icon="search" slot="prefix"></iron-icon>
    </paper-input>
    <style>
      :host {
        display: block;
        margin: 10px 5px 10px 10px;
      }
    </style>
  </template>
  
</dom-module><dom-module id="iron-flex">
  <template>
    <style>
      .layout.horizontal,
      .layout.vertical {
        display: -ms-flexbox;
        display: -webkit-flex;
        display: flex;
      }

      .layout.inline {
        display: -ms-inline-flexbox;
        display: -webkit-inline-flex;
        display: inline-flex;
      }

      .layout.horizontal {
        -ms-flex-direction: row;
        -webkit-flex-direction: row;
        flex-direction: row;
      }

      .layout.vertical {
        -ms-flex-direction: column;
        -webkit-flex-direction: column;
        flex-direction: column;
      }

      .layout.wrap {
        -ms-flex-wrap: wrap;
        -webkit-flex-wrap: wrap;
        flex-wrap: wrap;
      }

      .layout.no-wrap {
        -ms-flex-wrap: nowrap;
        -webkit-flex-wrap: nowrap;
        flex-wrap: nowrap;
      }

      .layout.center,
      .layout.center-center {
        -ms-flex-align: center;
        -webkit-align-items: center;
        align-items: center;
      }

      .layout.center-justified,
      .layout.center-center {
        -ms-flex-pack: center;
        -webkit-justify-content: center;
        justify-content: center;
      }

      .flex {
        -ms-flex: 1 1 0.000000001px;
        -webkit-flex: 1;
        flex: 1;
        -webkit-flex-basis: 0.000000001px;
        flex-basis: 0.000000001px;
      }

      .flex-auto {
        -ms-flex: 1 1 auto;
        -webkit-flex: 1 1 auto;
        flex: 1 1 auto;
      }

      .flex-none {
        -ms-flex: none;
        -webkit-flex: none;
        flex: none;
      }
    </style>
  </template>
</dom-module><dom-module id="iron-flex-reverse">
  <template>
    <style>
      .layout.horizontal-reverse,
      .layout.vertical-reverse {
        display: -ms-flexbox;
        display: -webkit-flex;
        display: flex;
      }

      .layout.horizontal-reverse {
        -ms-flex-direction: row-reverse;
        -webkit-flex-direction: row-reverse;
        flex-direction: row-reverse;
      }

      .layout.vertical-reverse {
        -ms-flex-direction: column-reverse;
        -webkit-flex-direction: column-reverse;
        flex-direction: column-reverse;
      }

      .layout.wrap-reverse {
        -ms-flex-wrap: wrap-reverse;
        -webkit-flex-wrap: wrap-reverse;
        flex-wrap: wrap-reverse;
      }
    </style>
  </template>
</dom-module><dom-module id="iron-flex-alignment">
  <template>
    <style>
      /**
       * Alignment in cross axis.
       */
      .layout.start {
        -ms-flex-align: start;
        -webkit-align-items: flex-start;
        align-items: flex-start;
      }

      .layout.center,
      .layout.center-center {
        -ms-flex-align: center;
        -webkit-align-items: center;
        align-items: center;
      }

      .layout.end {
        -ms-flex-align: end;
        -webkit-align-items: flex-end;
        align-items: flex-end;
      }

      .layout.baseline {
        -ms-flex-align: baseline;
        -webkit-align-items: baseline;
        align-items: baseline;
      }

      /**
       * Alignment in main axis.
       */
      .layout.start-justified {
        -ms-flex-pack: start;
        -webkit-justify-content: flex-start;
        justify-content: flex-start;
      }

      .layout.center-justified,
      .layout.center-center {
        -ms-flex-pack: center;
        -webkit-justify-content: center;
        justify-content: center;
      }

      .layout.end-justified {
        -ms-flex-pack: end;
        -webkit-justify-content: flex-end;
        justify-content: flex-end;
      }

      .layout.around-justified {
        -ms-flex-pack: distribute;
        -webkit-justify-content: space-around;
        justify-content: space-around;
      }

      .layout.justified {
        -ms-flex-pack: justify;
        -webkit-justify-content: space-between;
        justify-content: space-between;
      }

      /**
       * Self alignment.
       */
      .self-start {
        -ms-align-self: flex-start;
        -webkit-align-self: flex-start;
        align-self: flex-start;
      }

      .self-center {
        -ms-align-self: center;
        -webkit-align-self: center;
        align-self: center;
      }

      .self-end {
        -ms-align-self: flex-end;
        -webkit-align-self: flex-end;
        align-self: flex-end;
      }

      .self-stretch {
        -ms-align-self: stretch;
        -webkit-align-self: stretch;
        align-self: stretch;
      }

      .self-baseline {
        -ms-align-self: baseline;
        -webkit-align-self: baseline;
        align-self: baseline;
      }

      /**
       * multi-line alignment in main axis.
       */
      .layout.start-aligned {
        -ms-flex-line-pack: start;  /* IE10 */
        -ms-align-content: flex-start;
        -webkit-align-content: flex-start;
        align-content: flex-start;
      }

      .layout.end-aligned {
        -ms-flex-line-pack: end;  /* IE10 */
        -ms-align-content: flex-end;
        -webkit-align-content: flex-end;
        align-content: flex-end;
      }

      .layout.center-aligned {
        -ms-flex-line-pack: center;  /* IE10 */
        -ms-align-content: center;
        -webkit-align-content: center;
        align-content: center;
      }

      .layout.between-aligned {
        -ms-flex-line-pack: justify;  /* IE10 */
        -ms-align-content: space-between;
        -webkit-align-content: space-between;
        align-content: space-between;
      }

      .layout.around-aligned {
        -ms-flex-line-pack: distribute;  /* IE10 */
        -ms-align-content: space-around;
        -webkit-align-content: space-around;
        align-content: space-around;
      }
    </style>
  </template>
</dom-module><dom-module id="iron-flex-factors">
  <template>
    <style>
      .flex,
      .flex-1 {
        -ms-flex: 1 1 0.000000001px;
        -webkit-flex: 1;
        flex: 1;
        -webkit-flex-basis: 0.000000001px;
        flex-basis: 0.000000001px;
      }

      .flex-2 {
        -ms-flex: 2;
        -webkit-flex: 2;
        flex: 2;
      }

      .flex-3 {
        -ms-flex: 3;
        -webkit-flex: 3;
        flex: 3;
      }

      .flex-4 {
        -ms-flex: 4;
        -webkit-flex: 4;
        flex: 4;
      }

      .flex-5 {
        -ms-flex: 5;
        -webkit-flex: 5;
        flex: 5;
      }

      .flex-6 {
        -ms-flex: 6;
        -webkit-flex: 6;
        flex: 6;
      }

      .flex-7 {
        -ms-flex: 7;
        -webkit-flex: 7;
        flex: 7;
      }

      .flex-8 {
        -ms-flex: 8;
        -webkit-flex: 8;
        flex: 8;
      }

      .flex-9 {
        -ms-flex: 9;
        -webkit-flex: 9;
        flex: 9;
      }

      .flex-10 {
        -ms-flex: 10;
        -webkit-flex: 10;
        flex: 10;
      }

      .flex-11 {
        -ms-flex: 11;
        -webkit-flex: 11;
        flex: 11;
      }

      .flex-12 {
        -ms-flex: 12;
        -webkit-flex: 12;
        flex: 12;
      }
    </style>
  </template>
</dom-module><dom-module id="iron-positioning">
  <template>
    <style>
      .block {
        display: block;
      }

      [hidden] {
        display: none !important;
      }

      .invisible {
        visibility: hidden !important;
      }

      .relative {
        position: relative;
      }

      .fit {
        position: absolute;
        top: 0;
        right: 0;
        bottom: 0;
        left: 0;
      }

      body.fullbleed {
        margin: 0;
        height: 100vh;
      }

      .scroll {
        -webkit-overflow-scrolling: touch;
        overflow: auto;
      }

      /* fixed position */
      .fixed-bottom,
      .fixed-left,
      .fixed-right,
      .fixed-top {
        position: fixed;
      }

      .fixed-top {
        top: 0;
        left: 0;
        right: 0;
      }

      .fixed-right {
        top: 0;
        right: 0;
        bottom: 0;
      }

      .fixed-bottom {
        right: 0;
        bottom: 0;
        left: 0;
      }

      .fixed-left {
        top: 0;
        bottom: 0;
        left: 0;
      }
    </style>
  </template>
</dom-module><dom-module id="dashboard-style">
  <template>
    <style include="iron-flex"></style>
    <style>
      :host {
        --sidebar-vertical-padding: 15px;
        --sidebar-left-padding: 30px;
      }

      [slot='sidebar'] {
        box-sizing: border-box;
        display: flex;
        flex-direction: column;
        height: 100%;
        margin-right: 20px;
        overflow-x: hidden;
        padding: 5px 0;
        text-overflow: ellipsis;
      }

      tf-runs-selector {
        flex-grow: 1;
        flex-shrink: 1;
        left: var(--sidebar-left-padding);
        max-height: calc(100% - var(--sidebar-vertical-padding) * 2);
        overflow: hidden;
        position: absolute;
        right: 0;
      }

      .search-input {
        margin: 10px 5px 0 10px;
      }

      .sidebar-section {
        border-top: solid 1px rgba(0, 0, 0, 0.12);
        padding: var(--sidebar-vertical-padding) 0
          var(--sidebar-vertical-padding) var(--sidebar-left-padding);
        position: relative;
      }

      .sidebar-section:first-of-type {
        border: none;
      }

      .sidebar-section:last-of-type {
        flex-grow: 1;
        display: flex;
      }

      .sidebar-section paper-button {
        margin: 5px;
      }

      .sidebar-section paper-button:first-of-type {
        margin-left: 0 !important;
      }

      .sidebar-section paper-button:last-of-type {
        margin-right: 0 !important;
      }

      .sidebar-section > :first-child {
        margin-top: 0;
        padding-top: 0;
      }

      .sidebar-section > :last-child {
        margin-bottom: 0;
        padding-bottom: 0;
      }

      .sidebar-section h3 {
        color: var(--paper-grey-800);
        display: block;
        font-size: 14px;
        font-weight: normal;
        margin: 10px 0 5px;
        pointer-events: none;
      }

      paper-checkbox {
        --paper-checkbox-checked-color: var(--tb-ui-dark-accent);
        --paper-checkbox-unchecked-color: var(--tb-ui-dark-accent);
        font-size: 15px;
        margin-top: 5px;
      }
    </style>
  </template>
</dom-module><dom-module id="scrollbar-style">
  <template>
    <style>
      .scrollbar::-webkit-scrollbar-track {
        visibility: hidden;
      }

      .scrollbar::-webkit-scrollbar {
        width: 10px;
      }

      .scrollbar::-webkit-scrollbar-thumb {
        border-radius: 10px;
        -webkit-box-shadow: inset 0 0 2px rgba(0, 0, 0, 0.3);
        background-color: var(--paper-grey-500);
        color: var(--paper-grey-900);
      }
      .scrollbar {
        box-sizing: border-box;
      }
    </style>
  </template>
</dom-module><dom-module id="tf-dashboard-layout">
  <template>
    <div id="sidebar">
      <slot name="sidebar"></slot>
    </div>

    <div id="center">
      <slot name="center" class="scollbar"></slot>
    </div>
    <style include="scrollbar-style"></style>
    <style>
      :host {
        display: flex;
        flex-direction: row;
        height: 100%;
      }

      #sidebar {
        flex: 0 0 var(--tf-dashboard-layout-sidebar-basis, 25%);
        height: 100%;
        max-width: var(--tf-dashboard-layout-sidebar-max-width, 350px);
        min-width: var(--tf-dashboard-layout-sidebar-min-width, 270px);
        overflow-y: auto;
        text-overflow: ellipsis;
      }

      #center {
        flex-grow: 1;
        flex-shrink: 1;
        height: 100%;
        overflow: hidden;
      }

      ::slotted([slot='center']) {
        contain: strict;
        height: 100%;
        overflow-x: hidden;
        overflow-y: auto;
        width: 100%;
        will-change: transform;
      }

      .tf-graph-dashboard #center {
        background: #fff;
      }
    </style>
  </template>
  
</dom-module><dom-module id="tf-option-selector">
  <template>
    <div id="wrap">
      <h3>[[name]]</h3>
      <div class="content-wrapper"><slot></slot></div>
    </div>
    <style>
      .content-wrapper ::slotted(*) {
        background: none;
        color: var(--tb-ui-dark-accent);
        font-size: 13px;
        margin-top: 10px;
      }

      .content-wrapper ::slotted(*) {
        background: none;
        color: var(--tb-ui-dark-accent);
        font-size: 13px;
        margin-top: 10px;
      }

      .content-wrapper ::slotted(.selected) {
        background-color: var(--tb-ui-dark-accent);
        color: white !important;
      }

      h3 {
        color: var(--paper-grey-800);
        display: block;
        font-size: 14px;
        font-weight: normal;
        margin: 0 0 5px;
        pointer-events: none;
      }
    </style>
  </template>
  
</dom-module><dom-module id="iron-collapse">

  <template>

    <style>
      :host {
        display: block;
        transition-duration: var(--iron-collapse-transition-duration, 300ms);
        /* Safari 10 needs this property prefixed to correctly apply the custom property */
        -webkit-transition-duration: var(--iron-collapse-transition-duration, 300ms);
        overflow: visible;
      }

      :host(.iron-collapse-closed) {
        display: none;
      }

      :host(:not(.iron-collapse-opened)) {
        overflow: hidden;
      }
    </style>

    <slot></slot>

  </template>

</dom-module><dom-module id="tf-category-paginated-view">
  <template>
    <template is="dom-if" if="[[_paneRendered]]" id="ifRendered">
      <button class="heading" on-tap="_togglePane" open-button$="[[opened]]">
        <span class="name">
          <template is="dom-if" if="[[_isSearchResults]]">
            <template is="dom-if" if="[[_isCompositeSearch(category)]]">
              <span>Tags matching multiple experiments</span>
              <template is="dom-if" if="[[_isInvalidSearchResults]]">
                <span>&nbsp;<strong>(malformed regular expression)</strong></span>
              </template>
            </template>
            <template is="dom-if" if="[[!_isCompositeSearch(category)]]">
              <span class="light">Tags matching /</span>
              <span class="category-name" title$="[[category.name]]">[[category.name]]</span>
              <span class="light">/</span>
              <template is="dom-if" if="[[_isUniversalSearchQuery]]">
                <span> (all tags)</span>
              </template>
              <template is="dom-if" if="[[_isInvalidSearchResults]]">
                <span> <strong>(malformed regular expression)</strong></span>
              </template>
            </template>
          </template>
          <template is="dom-if" if="[[!_isSearchResults]]">
            <span class="category-name" title$="[[category.name]]">[[category.name]]</span>
          </template>
        </span>
        <span class="count">
          <template is="dom-if" if="[[_hasMultiple]]">
            <span>[[_count]]</span>
          </template>
          <iron-icon icon="expand-more" class="expand-arrow"></iron-icon>
        </span>
      </button>
      
      <iron-collapse opened="[[opened]]" no-animation>
        <div class="content">
          <span id="top-of-container"></span>
          <template is="dom-if" if="[[_multiplePagesExist]]">
            <div class="big-page-buttons" style="margin-bottom: 10px;">
              <paper-button on-tap="_performPreviousPage" disabled$="[[!_hasPreviousPage]]">Previous page</paper-button>
              <paper-button on-tap="_performNextPage" disabled$="[[!_hasNextPage]]">Next page</paper-button>
            </div>
          </template>

          <div id="items">
            <slot name="items"></slot>
          </div>
          <template is="dom-if" if="[[_multiplePagesExist]]">
            <div id="controls-container">
              <div style="display: inline-block; padding: 0 5px">
                Page
                <paper-input id="page-input" type="number" no-label-float min="1" max="[[_pageCount]]" value="[[_pageInputValue]]" on-input="_handlePageInputEvent" on-change="_handlePageChangeEvent" on-focus="_handlePageFocusEvent" on-blur="_handlePageBlurEvent"></paper-input>
                of [[_pageCount]]
              </div>
            </div>

            <div class="big-page-buttons" style="margin-top: 10px;">
              <paper-button on-tap="_performPreviousPage" disabled$="[[!_hasPreviousPage]]">Previous page</paper-button>
              <paper-button on-tap="_performNextPage" disabled$="[[!_hasNextPage]]">Next page</paper-button>
            </div>
          </template>
        </div>
      </iron-collapse>
    </template>
    <style>
      :host {
        display: block;
        margin: 0 5px 1px 10px;
      }

      :host(:first-of-type) {
        margin-top: 10px;
      }

      :host(:last-of-type) {
        margin-bottom: 20px;
      }

      .heading {
        background-color: white;
        border: none;
        cursor: pointer;
        width: 100%;
        font-size: 15px;
        line-height: 1;
        box-shadow: 0 1px 5px rgba(0, 0, 0, 0.2);
        padding: 10px 15px;
        display: flex;
        align-items: center;
        justify-content: space-between;
      }

      .heading::-moz-focus-inner {
        padding: 10px 15px;
      }

      [open-button] {
        border-bottom-left-radius: 0 !important;
        border-bottom-right-radius: 0 !important;
      }

      [open-button] .expand-arrow {
        transform: rotateZ(180deg);
      }

      .name {
        display: inline-flex;
        overflow: hidden;
      }

      .light {
        color: var(--paper-grey-500);
      }

      .category-name {
        white-space: pre;
        overflow: hidden;
        text-overflow: ellipsis;
        padding: 2px 0;
      }

      .count {
        margin: 0 5px;
        font-size: 12px;
        color: var(--paper-grey-500);
        display: flex;
        align-items: center;
        flex: none;
      }

      .heading::-moz-focus-inner {
        padding: 10px 15px;
      }

      .content {
        display: flex;
        flex-direction: column;
        background: white;
        border-bottom-left-radius: 2px;
        border-bottom-right-radius: 2px;
        border-top: none;
        border: 1px solid #dedede;
        padding: 15px;
      }

      .light {
        color: var(--paper-grey-500);
      }

      #controls-container {
        justify-content: center;
        display: flex;
        flex-direction: row;
        flex-grow: 0;
        flex-shrink: 0;
        width: 100%;
      }

      #controls-container paper-button {
        display: inline-block;
      }

      .big-page-buttons {
        display: flex;
      }

      .big-page-buttons paper-button {
        background-color: var(--tb-ui-light-accent);
        color: var(--tb-ui-dark-accent);
        display: inline-block;
        flex-basis: 0;
        flex-grow: 1;
        flex-shrink: 1;
        font-size: 13px;
      }

      .big-page-buttons paper-button[disabled] {
        background: none;
      }

      slot {
        display: flex;
        flex-direction: row;
        flex-wrap: wrap;
      }

      ::slotted([slot='items']) {
        /* Tooltip for descriptions and others break with more strict ones. */
        contain: style;
      }

      #page-input {
        display: inline-block;
        width: var(--tf-category-paginated-view-page-input-width, 100%);
      }
    </style>
  </template>
  
</dom-module><dom-module id="paper-dialog-shared-styles">
  <template>
    <style>
      :host {
        display: block;
        margin: 24px 40px;

        background: var(--paper-dialog-background-color, var(--primary-background-color));
        color: var(--paper-dialog-color, var(--primary-text-color));

        @apply --paper-font-body1;
        @apply --shadow-elevation-16dp;
        @apply --paper-dialog;
      }

      :host > ::slotted(*) {
        margin-top: 20px;
        padding: 0 24px;
      }

      :host > ::slotted(.no-padding) {
        padding: 0;
      }

      
      :host > ::slotted(*:first-child) {
        margin-top: 24px;
      }

      :host > ::slotted(*:last-child) {
        margin-bottom: 24px;
      }

      /* In 1.x, this selector was `:host > ::content h2`. In 2.x <slot> allows
      to select direct children only, which increases the weight of this
      selector, so we have to re-define first-child/last-child margins below. */
      :host > ::slotted(h2) {
        position: relative;
        margin: 0;

        @apply --paper-font-title;
        @apply --paper-dialog-title;
      }

      /* Apply mixin again, in case it sets margin-top. */
      :host > ::slotted(h2:first-child) {
        margin-top: 24px;
        @apply --paper-dialog-title;
      }

      /* Apply mixin again, in case it sets margin-bottom. */
      :host > ::slotted(h2:last-child) {
        margin-bottom: 24px;
        @apply --paper-dialog-title;
      }

      :host > ::slotted(.paper-dialog-buttons),
      :host > ::slotted(.buttons) {
        position: relative;
        padding: 8px 8px 8px 24px;
        margin: 0;

        color: var(--paper-dialog-button-color, var(--primary-color));

        @apply --layout-horizontal;
        @apply --layout-end-justified;
      }
    </style>
  </template>
</dom-module><dom-module id="paper-dialog">
  <template>
    <style include="paper-dialog-shared-styles"></style>
    <slot></slot>
  </template>
</dom-module><dom-module id="tf-color-scale">
  
  
</dom-module><iron-iconset-svg name="icons" size="24">
<svg><defs>
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<g id="add"><path d="M19 13h-6v6h-2v-6H5v-2h6V5h2v6h6v2z" /></g>
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</defs></svg>
</iron-iconset-svg><dom-module id="paper-icon-button">
  <template strip-whitespace>
    <style>
      :host {
        display: inline-block;
        position: relative;
        padding: 8px;
        outline: none;
        -webkit-user-select: none;
        -moz-user-select: none;
        -ms-user-select: none;
        user-select: none;
        cursor: pointer;
        z-index: 0;
        line-height: 1;

        width: 40px;
        height: 40px;

        /* NOTE: Both values are needed, since some phones require the value to be `transparent`. */
        -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
        -webkit-tap-highlight-color: transparent;

        /* Because of polymer/2558, this style has lower specificity than * */
        box-sizing: border-box !important;

        @apply --paper-icon-button;
      }

      :host #ink {
        color: var(--paper-icon-button-ink-color, var(--primary-text-color));
        opacity: 0.6;
      }

      :host([disabled]) {
        color: var(--paper-icon-button-disabled-text, var(--disabled-text-color));
        pointer-events: none;
        cursor: auto;

        @apply --paper-icon-button-disabled;
      }

      :host([hidden]) {
        display: none !important;
      }

      :host(:hover) {
        @apply --paper-icon-button-hover;
      }

      iron-icon {
        --iron-icon-width: 100%;
        --iron-icon-height: 100%;
      }
    </style>

    <iron-icon id="icon" src="[[src]]" icon="[[icon]]" alt$="[[alt]]"></iron-icon>
  </template>

  
</dom-module><dom-module id="run-color-style">
  <template>
    <style>
      [color-class='light-blue'] paper-checkbox {
        --paper-checkbox-checked-color: var(--paper-light-blue-500);
        --paper-checkbox-checked-ink-color: var(--paper-light-blue-500);
        --paper-checkbox-unchecked-color: var(--paper-light-blue-900);
        --paper-checkbox-unchecked-ink-color: var(--paper-light-blue-900);
      }
      [color-class='red'] paper-checkbox {
        --paper-checkbox-checked-color: var(--paper-red-500);
        --paper-checkbox-checked-ink-color: var(--paper-red-500);
        --paper-checkbox-unchecked-color: var(--paper-red-900);
        --paper-checkbox-unchecked-ink-color: var(--paper-red-900);
      }
      [color-class='green'] paper-checkbox {
        --paper-checkbox-checked-color: var(--paper-green-500);
        --paper-checkbox-checked-ink-color: var(--paper-green-500);
        --paper-checkbox-unchecked-color: var(--paper-green-900);
        --paper-checkbox-unchecked-ink-color: var(--paper-green-900);
      }
      [color-class='purple'] paper-checkbox {
        --paper-checkbox-checked-color: var(--paper-purple-500);
        --paper-checkbox-checked-ink-color: var(--paper-purple-500);
        --paper-checkbox-unchecked-color: var(--paper-purple-900);
        --paper-checkbox-unchecked-ink-color: var(--paper-purple-900);
      }
      [color-class='teal'] paper-checkbox {
        --paper-checkbox-checked-color: var(--paper-teal-500);
        --paper-checkbox-checked-ink-color: var(--paper-teal-500);
        --paper-checkbox-unchecked-color: var(--paper-teal-900);
        --paper-checkbox-unchecked-ink-color: var(--paper-teal-900);
      }
      [color-class='pink'] paper-checkbox {
        --paper-checkbox-checked-color: var(--paper-pink-500);
        --paper-checkbox-checked-ink-color: var(--paper-pink-500);
        --paper-checkbox-unchecked-color: var(--paper-pink-900);
        --paper-checkbox-unchecked-ink-color: var(--paper-pink-900);
      }
      [color-class='orange'] paper-checkbox {
        --paper-checkbox-checked-color: var(--paper-orange-500);
        --paper-checkbox-checked-ink-color: var(--paper-orange-500);
        --paper-checkbox-unchecked-color: var(--paper-orange-900);
        --paper-checkbox-unchecked-ink-color: var(--paper-orange-900);
      }
      [color-class='brown'] paper-checkbox {
        --paper-checkbox-checked-color: var(--paper-brown-500);
        --paper-checkbox-checked-ink-color: var(--paper-brown-500);
        --paper-checkbox-unchecked-color: var(--paper-brown-900);
        --paper-checkbox-unchecked-ink-color: var(--paper-brown-900);
      }
      [color-class='indigo'] paper-checkbox {
        --paper-checkbox-checked-color: var(--paper-indigo-500);
        --paper-checkbox-checked-ink-color: var(--paper-indigo-500);
        --paper-checkbox-unchecked-color: var(--paper-indigo-900);
        --paper-checkbox-unchecked-ink-color: var(--paper-indigo-900);
      }
    </style>
  </template>
</dom-module><dom-module id="tf-multi-checkbox">
  <template>
    <style include="scrollbar-style"></style>
    <style include="run-color-style"></style>

    <paper-input id="names-regex" no-label-float label="Write a regex to filter runs" value="[[regex]]" on-bind-value-changed="_debouncedRegexChange"></paper-input>
    <div id="outer-container" class="scrollbar">
      <template is="dom-repeat" items="[[namesMatchingRegex]]" on-dom-change="synchronizeColors">
        <div class="name-row">
          <div class="icon-container checkbox-container vertical-align-container">
            <paper-checkbox class="checkbox vertical-align-center" id$="checkbox-[[item]]" name="[[item]]" checked$="[[_isChecked(item, selectionState.*)]]" on-change="_checkboxChange"></paper-checkbox>
          </div>
          <div class="icon-container isolator-container vertical-align-container">
            <paper-icon-button icon="radio-button-unchecked" class="isolator vertical-align-center" on-tap="_isolateName" name="[[item]]"></paper-icon-button>
          </div>
          <div class="item-label-container">
            <span>[[item]]</span>
          </div>
        </div>
      </template>
    </div>
    <style>
      paper-input {
        --paper-input-container-focus-color: var(--tb-orange-strong);
        --paper-input-container-input: {
          font-size: 14px;
        }
        --paper-input-container-label: {
          font-size: 14px;
        }
      }
      :host {
        display: flex;
        flex-direction: column;
        height: 100%;
        overflow: hidden;
      }
      #outer-container {
        contain: content;
        flex-grow: 1;
        flex-shrink: 1;
        overflow-x: hidden;
        overflow-y: auto;
        width: 100%;
        will-change: transform;
        word-wrap: break-word;
      }
      .name-row {
        contain: content;
        padding-top: 5px;
        padding-bottom: 5px;
        display: flex;
        flex-direction: row;
        font-size: 13px;
        word-break: break-all; /* makes wrapping of hyperparam strings better */
      }
      .icon-container {
        flex-grow: 0;
        flex-shrink: 0;
        padding-left: 2px;
      }
      .checkbox {
        padding-left: 2px;
        width: 18px;
        height: 18px;
      }
      .isolator {
        width: 18px;
        height: 18px;
        padding: 0px;
      }
      .isolator-container {
        padding-left: 6px;
        padding-right: 3px;
      }
      .checkbox-container {
        padding-left: 2px;
      }
      .item-label-container {
        padding-left: 5px;
        flex-grow: 1;
        flex-shrink: 1;
        width: 0px; /* hack to get the flex-grow to work properly */
      }
      .tooltip-value-container {
        display: flex;
        justify-content: center;
        flex-grow: 0;
        flex-shrink: 0;
        text-align: right;
        padding-left: 2px;
      }
      .vertical-align-container {
        display: flex;
        justify-content: center;
      }
      .vertical-align-container .vertical-align-center {
        align-self: center;
      }
      .vertical-align-container .vertical-align-top {
        align-self: start;
      }
    </style>
  </template>
  
</dom-module><dom-module id="tf-wbr-string">
  <template>
    
    <template is="dom-repeat" items="[[_parts]]" as="part">[[part]]<wbr></template>
  </template>
  
</dom-module><dom-module id="tf-runs-selector">
  <template>
    <paper-dialog with-backdrop id="data-location-dialog">
      <h2>Data Location</h2>
      <tf-wbr-string value="[[dataLocation]]" />
    </paper-dialog>
    <div id="top-text">
      <h3 id="tooltip-help" class="tooltip-container">Runs</h3>
    </div>
    <tf-multi-checkbox id="multiCheckbox" names="[[runs]]" selection-state="{{runSelectionState}}" out-selected="{{selectedRuns}}" regex="{{regexInput}}" coloring="[[coloring]]"></tf-multi-checkbox>
    <paper-button class="x-button" id="toggle-all" on-tap="_toggleAll">
      Toggle All Runs
    </paper-button>
    <template is="dom-if" if="[[dataLocation]]">
      <div id="data-location">
        <tf-wbr-string value="[[_clippedDataLocation]]" /><template is="dom-if" if="[[_shouldShowExpandDataLocationButton(dataLocation, _dataLocationClipLength)]]"><a href="" on-click="_openDataLocationDialog">…</a>
        </template>
      </div>
    </template>
    <style>
      :host {
        box-sizing: border-box;
        display: flex;
        flex-direction: column;
        padding-bottom: 10px;
      }
      #top-text {
        width: 100%;
        flex-grow: 0;
        flex-shrink: 0;
        padding-right: 16px;
        box-sizing: border-box;
        color: var(--paper-grey-800);
      }
      tf-multi-checkbox {
        display: flex;
        flex-grow: 1;
        flex-shrink: 1;
        overflow: hidden;
      }
      .x-button {
        font-size: 13px;
        background-color: var(--tb-ui-light-accent);
        color: var(--tb-ui-dark-accent);
      }
      #tooltip-help {
        color: var(--paper-grey-800);
        margin: 0;
        font-weight: normal;
        font-size: 14px;
        margin-bottom: 5px;
      }
      paper-button {
        margin-left: 0;
      }
      #data-location {
        color: var(--tb-ui-dark-accent);
        font-size: 13px;
        margin: 5px 0 0 0;
        max-width: 288px;
      }
    </style>
  </template>
  
</dom-module><dom-module id="paper-spinner-styles">
  <template>
    <style>
      /*
      /**************************/
      /* STYLES FOR THE SPINNER */
      /**************************/

      /*
       * Constants:
       *      ARCSIZE     = 270 degrees (amount of circle the arc takes up)
       *      ARCTIME     = 1333ms (time it takes to expand and contract arc)
       *      ARCSTARTROT = 216 degrees (how much the start location of the arc
       *                                should rotate each time, 216 gives us a
       *                                5 pointed star shape (it's 360/5 * 3).
       *                                For a 7 pointed star, we might do
       *                                360/7 * 3 = 154.286)
       *      SHRINK_TIME = 400ms
       */

      :host {
        display: inline-block;
        position: relative;
        width: 28px;
        height: 28px;

        /* 360 * ARCTIME / (ARCSTARTROT + (360-ARCSIZE)) */
        --paper-spinner-container-rotation-duration: 1568ms;

        /* ARCTIME */
        --paper-spinner-expand-contract-duration: 1333ms;

        /* 4 * ARCTIME */
        --paper-spinner-full-cycle-duration: 5332ms;

        /* SHRINK_TIME */
        --paper-spinner-cooldown-duration: 400ms;
      }

      #spinnerContainer {
        width: 100%;
        height: 100%;

        /* The spinner does not have any contents that would have to be
         * flipped if the direction changes. Always use ltr so that the
         * style works out correctly in both cases. */
        direction: ltr;
      }

      #spinnerContainer.active {
        -webkit-animation: container-rotate var(--paper-spinner-container-rotation-duration) linear infinite;
        animation: container-rotate var(--paper-spinner-container-rotation-duration) linear infinite;
      }

      @-webkit-keyframes container-rotate {
        to { -webkit-transform: rotate(360deg) }
      }

      @keyframes container-rotate {
        to { transform: rotate(360deg) }
      }

      .spinner-layer {
        position: absolute;
        width: 100%;
        height: 100%;
        opacity: 0;
        white-space: nowrap;
        color: var(--paper-spinner-color, var(--google-blue-500));
      }

      .layer-1 {
        color: var(--paper-spinner-layer-1-color, var(--google-blue-500));
      }

      .layer-2 {
        color: var(--paper-spinner-layer-2-color, var(--google-red-500));
      }

      .layer-3 {
        color: var(--paper-spinner-layer-3-color, var(--google-yellow-500));
      }

      .layer-4 {
        color: var(--paper-spinner-layer-4-color, var(--google-green-500));
      }

      /**
       * IMPORTANT NOTE ABOUT CSS ANIMATION PROPERTIES (keanulee):
       *
       * iOS Safari (tested on iOS 8.1) does not handle animation-delay very well - it doesn't
       * guarantee that the animation will start _exactly_ after that value. So we avoid using
       * animation-delay and instead set custom keyframes for each color (as layer-2undant as it
       * seems).
       */
      .active .spinner-layer {
        -webkit-animation-name: fill-unfill-rotate;
        -webkit-animation-duration: var(--paper-spinner-full-cycle-duration);
        -webkit-animation-timing-function: cubic-bezier(0.4, 0.0, 0.2, 1);
        -webkit-animation-iteration-count: infinite;
        animation-name: fill-unfill-rotate;
        animation-duration: var(--paper-spinner-full-cycle-duration);
        animation-timing-function: cubic-bezier(0.4, 0.0, 0.2, 1);
        animation-iteration-count: infinite;
        opacity: 1;
      }

      .active .spinner-layer.layer-1 {
        -webkit-animation-name: fill-unfill-rotate, layer-1-fade-in-out;
        animation-name: fill-unfill-rotate, layer-1-fade-in-out;
      }

      .active .spinner-layer.layer-2 {
        -webkit-animation-name: fill-unfill-rotate, layer-2-fade-in-out;
        animation-name: fill-unfill-rotate, layer-2-fade-in-out;
      }

      .active .spinner-layer.layer-3 {
        -webkit-animation-name: fill-unfill-rotate, layer-3-fade-in-out;
        animation-name: fill-unfill-rotate, layer-3-fade-in-out;
      }

      .active .spinner-layer.layer-4 {
        -webkit-animation-name: fill-unfill-rotate, layer-4-fade-in-out;
        animation-name: fill-unfill-rotate, layer-4-fade-in-out;
      }

      @-webkit-keyframes fill-unfill-rotate {
        12.5% { -webkit-transform: rotate(135deg) } /* 0.5 * ARCSIZE */
        25%   { -webkit-transform: rotate(270deg) } /* 1   * ARCSIZE */
        37.5% { -webkit-transform: rotate(405deg) } /* 1.5 * ARCSIZE */
        50%   { -webkit-transform: rotate(540deg) } /* 2   * ARCSIZE */
        62.5% { -webkit-transform: rotate(675deg) } /* 2.5 * ARCSIZE */
        75%   { -webkit-transform: rotate(810deg) } /* 3   * ARCSIZE */
        87.5% { -webkit-transform: rotate(945deg) } /* 3.5 * ARCSIZE */
        to    { -webkit-transform: rotate(1080deg) } /* 4   * ARCSIZE */
      }

      @keyframes fill-unfill-rotate {
        12.5% { transform: rotate(135deg) } /* 0.5 * ARCSIZE */
        25%   { transform: rotate(270deg) } /* 1   * ARCSIZE */
        37.5% { transform: rotate(405deg) } /* 1.5 * ARCSIZE */
        50%   { transform: rotate(540deg) } /* 2   * ARCSIZE */
        62.5% { transform: rotate(675deg) } /* 2.5 * ARCSIZE */
        75%   { transform: rotate(810deg) } /* 3   * ARCSIZE */
        87.5% { transform: rotate(945deg) } /* 3.5 * ARCSIZE */
        to    { transform: rotate(1080deg) } /* 4   * ARCSIZE */
      }

      @-webkit-keyframes layer-1-fade-in-out {
        0% { opacity: 1 }
        25% { opacity: 1 }
        26% { opacity: 0 }
        89% { opacity: 0 }
        90% { opacity: 1 }
        to { opacity: 1 }
      }

      @keyframes layer-1-fade-in-out {
        0% { opacity: 1 }
        25% { opacity: 1 }
        26% { opacity: 0 }
        89% { opacity: 0 }
        90% { opacity: 1 }
        to { opacity: 1 }
      }

      @-webkit-keyframes layer-2-fade-in-out {
        0% { opacity: 0 }
        15% { opacity: 0 }
        25% { opacity: 1 }
        50% { opacity: 1 }
        51% { opacity: 0 }
        to { opacity: 0 }
      }

      @keyframes layer-2-fade-in-out {
        0% { opacity: 0 }
        15% { opacity: 0 }
        25% { opacity: 1 }
        50% { opacity: 1 }
        51% { opacity: 0 }
        to { opacity: 0 }
      }

      @-webkit-keyframes layer-3-fade-in-out {
        0% { opacity: 0 }
        40% { opacity: 0 }
        50% { opacity: 1 }
        75% { opacity: 1 }
        76% { opacity: 0 }
        to { opacity: 0 }
      }

      @keyframes layer-3-fade-in-out {
        0% { opacity: 0 }
        40% { opacity: 0 }
        50% { opacity: 1 }
        75% { opacity: 1 }
        76% { opacity: 0 }
        to { opacity: 0 }
      }

      @-webkit-keyframes layer-4-fade-in-out {
        0% { opacity: 0 }
        65% { opacity: 0 }
        75% { opacity: 1 }
        90% { opacity: 1 }
        to { opacity: 0 }
      }

      @keyframes layer-4-fade-in-out {
        0% { opacity: 0 }
        65% { opacity: 0 }
        75% { opacity: 1 }
        90% { opacity: 1 }
        to { opacity: 0 }
      }

      .circle-clipper {
        display: inline-block;
        position: relative;
        width: 50%;
        height: 100%;
        overflow: hidden;
      }

      /**
       * Patch the gap that appear between the two adjacent div.circle-clipper while the
       * spinner is rotating (appears on Chrome 50, Safari 9.1.1, and Edge).
       */
      .spinner-layer::after {
        left: 45%;
        width: 10%;
        border-top-style: solid;
      }

      .spinner-layer::after,
      .circle-clipper::after {
        content: '';
        box-sizing: border-box;
        position: absolute;
        top: 0;
        border-width: var(--paper-spinner-stroke-width, 3px);
        border-radius: 50%;
      }

      .circle-clipper::after {
        bottom: 0;
        width: 200%;
        border-style: solid;
        border-bottom-color: transparent !important;
      }

      .circle-clipper.left::after {
        left: 0;
        border-right-color: transparent !important;
        -webkit-transform: rotate(129deg);
        transform: rotate(129deg);
      }

      .circle-clipper.right::after {
        left: -100%;
        border-left-color: transparent !important;
        -webkit-transform: rotate(-129deg);
        transform: rotate(-129deg);
      }

      .active .gap-patch::after,
      .active .circle-clipper::after {
        -webkit-animation-duration: var(--paper-spinner-expand-contract-duration);
        -webkit-animation-timing-function: cubic-bezier(0.4, 0.0, 0.2, 1);
        -webkit-animation-iteration-count: infinite;
        animation-duration: var(--paper-spinner-expand-contract-duration);
        animation-timing-function: cubic-bezier(0.4, 0.0, 0.2, 1);
        animation-iteration-count: infinite;
      }

      .active .circle-clipper.left::after {
        -webkit-animation-name: left-spin;
        animation-name: left-spin;
      }

      .active .circle-clipper.right::after {
        -webkit-animation-name: right-spin;
        animation-name: right-spin;
      }

      @-webkit-keyframes left-spin {
        0% { -webkit-transform: rotate(130deg) }
        50% { -webkit-transform: rotate(-5deg) }
        to { -webkit-transform: rotate(130deg) }
      }

      @keyframes left-spin {
        0% { transform: rotate(130deg) }
        50% { transform: rotate(-5deg) }
        to { transform: rotate(130deg) }
      }

      @-webkit-keyframes right-spin {
        0% { -webkit-transform: rotate(-130deg) }
        50% { -webkit-transform: rotate(5deg) }
        to { -webkit-transform: rotate(-130deg) }
      }

      @keyframes right-spin {
        0% { transform: rotate(-130deg) }
        50% { transform: rotate(5deg) }
        to { transform: rotate(-130deg) }
      }

      #spinnerContainer.cooldown {
        -webkit-animation: container-rotate var(--paper-spinner-container-rotation-duration) linear infinite, fade-out var(--paper-spinner-cooldown-duration) cubic-bezier(0.4, 0.0, 0.2, 1);
        animation: container-rotate var(--paper-spinner-container-rotation-duration) linear infinite, fade-out var(--paper-spinner-cooldown-duration) cubic-bezier(0.4, 0.0, 0.2, 1);
      }

      @-webkit-keyframes fade-out {
        0% { opacity: 1 }
        to { opacity: 0 }
      }

      @keyframes fade-out {
        0% { opacity: 1 }
        to { opacity: 0 }
      }
    </style>
  </template>
</dom-module><dom-module id="paper-spinner-lite">
  <template strip-whitespace>
    <style include="paper-spinner-styles"></style>

    <div id="spinnerContainer" class-name="[[__computeContainerClasses(active, __coolingDown)]]" on-animationend="__reset" on-webkit-animation-end="__reset">
      <div class="spinner-layer">
        <div class="circle-clipper left"></div>
        <div class="circle-clipper right"></div>
      </div>
    </div>
  </template>

  
</dom-module><dom-module id="plottable-style">
  <template>
    <style>
.plottable-colors-0 {
  background-color: #5279c7; /* INDIGO */
}

.plottable-colors-1 {
  background-color: #fd373e; /* CORAL_RED */
}

.plottable-colors-2 {
  background-color: #63c261; /* FERN */
}

.plottable-colors-3 {
  background-color: #fad419; /* BRIGHT_SUN */
}

.plottable-colors-4 {
  background-color: #2c2b6f; /* JACARTA */
}

.plottable-colors-5 {
  background-color: #ff7939; /* BURNING_ORANGE */
}

.plottable-colors-6 {
  background-color: #db2e65; /* CERISE_RED */
}

.plottable-colors-7 {
  background-color: #99ce50; /* CONIFER */
}

.plottable-colors-8 {
  background-color: #962565; /* ROYAL_HEATH */
}

.plottable-colors-9 {
  background-color: #06cccc; /* ROBINS_EGG_BLUE */
}

/**
 * User-supplied renderTo element.
 */
.plottable {
  display: block; /* must be block elements for width/height calculations to work in Firefox. */
  pointer-events: visibleFill;
  position: relative;
  /**
   * Pre 3.0, users could set the dimension of the root element in two ways: either using CSS
   * (inline or through a stylesheet), or using the SVG width/height attributes. By default, we
   * set the SVG width/height attributes to 100%.
   *
   * Post 3.0 the root element is always a normal div and the only way to set the dimensions is
   * to use CSS. To replicate the "100%-by-default" behavior, we apply width/height 100%.
   */
  width: 100%;
  height: 100%;
}

/**
 * The _element that roots each Component's DOM.
 */
.plottable .component {
  /* Allow components to be positioned with explicit left/top/width/height styles */
  position: absolute;
}

.plottable .background-container,
.plottable .content,
.plottable .foreground-container {
  position: absolute;
  width: 100%;
  height: 100%;
}

/**
 * Don't allow svg elements above the content to steal events
 */
.plottable .foreground-container {
  pointer-events: none;
}

.plottable .component-overflow-hidden {
  overflow: hidden;
}

.plottable .component-overflow-visible {
  overflow: visible;
}

.plottable .plot-canvas-container {
  width: 100%;
  height: 100%;
  overflow: hidden;
}

.plottable .plot-canvas {
  width: 100%;
  height: 100%;
  /**
   * Play well with deferred rendering.
   */
  transform-origin: 0px 0px 0px;
}

.plottable text {
  text-rendering: geometricPrecision;
}

.plottable .label text {
  font-family: "Helvetica Neue", sans-serif;
  fill: #32313F;
}

.plottable .bar-label-text-area text {
  font-family: "Helvetica Neue", sans-serif;
  font-size: 12px;
}

.plottable .label-area text {
  fill: #32313F;
  font-family: "Helvetica Neue", sans-serif;
  font-size: 14px;
}

.plottable .light-label text {
  fill: white;
}

.plottable .dark-label text {
  fill: #32313F;
}

.plottable .off-bar-label text {
  fill: #32313F;
}

.plottable .stacked-bar-label text {
  fill: #32313F;
  font-style: normal;
}

.plottable .stacked-bar-plot .off-bar-label {
  /* HACKHACK #2795: correct off-bar label logic to be implemented on StackedBar */
  visibility: hidden !important;
}

.plottable .axis-label text {
  font-size: 10px;
  font-weight: bold;
  letter-spacing: 1px;
  line-height: normal;
  text-transform: uppercase;
}

.plottable .title-label text {
  font-size: 20px;
  font-weight: bold;
}

.plottable .axis line.baseline {
  stroke: #CCC;
  stroke-width: 1px;
}

.plottable .axis line.tick-mark {
  stroke: #CCC;
  stroke-width: 1px;
}

.plottable .axis text {
  fill: #32313F;
  font-family: "Helvetica Neue", sans-serif;
  font-size: 12px;
  font-weight: 200;
  line-height: normal;
}

.plottable .axis .annotation-circle {
  fill: white;
  stroke-width: 1px;
  stroke: #CCC;
}

.plottable .axis .annotation-line {
  stroke: #CCC;
  stroke-width: 1px;
}

.plottable .axis .annotation-rect {
  stroke: #CCC;
  stroke-width: 1px;
  fill: white;
}

.plottable .bar-plot .baseline {
  stroke: #999;
}

.plottable .gridlines line {
  stroke: #3C3C3C; /* hackhack: gridlines should be solid; see #820 */
  opacity: 0.25;
  stroke-width: 1px;
}

.plottable .selection-box-layer .selection-area {
  fill: black;
  fill-opacity: 0.03;
  stroke: #CCC;
}
/* DragBoxLayer */
.plottable .drag-box-layer.x-resizable .drag-edge-lr {
  cursor: ew-resize;
}
.plottable .drag-box-layer.y-resizable .drag-edge-tb {
  cursor: ns-resize;
}

.plottable .drag-box-layer.x-resizable.y-resizable .drag-corner-tl {
  cursor: nwse-resize;
}
.plottable .drag-box-layer.x-resizable.y-resizable .drag-corner-tr {
  cursor: nesw-resize;
}
.plottable .drag-box-layer.x-resizable.y-resizable .drag-corner-bl {
  cursor: nesw-resize;
}
.plottable .drag-box-layer.x-resizable.y-resizable .drag-corner-br {
  cursor: nwse-resize;
}

.plottable .drag-box-layer.movable .selection-area {
  cursor: move; /* IE fallback */
  cursor: -moz-grab;
  cursor: -webkit-grab;
  cursor: grab;
}

.plottable .drag-box-layer.movable .selection-area:active {
  cursor: -moz-grabbing;
  cursor: -webkit-grabbing;
  cursor: grabbing;
}
/* /DragBoxLayer */

.plottable .guide-line-layer line.guide-line {
  stroke: #CCC;
  stroke-width: 1px;
}

.plottable .drag-line-layer.enabled.vertical line.drag-edge {
  cursor: ew-resize;
}

.plottable .drag-line-layer.enabled.horizontal line.drag-edge {
  cursor: ns-resize;
}

.plottable .legend text {
  fill: #32313F;
  font-family: "Helvetica Neue", sans-serif;
  font-size: 12px;
  font-weight: bold;
  line-height: normal;
}

.plottable .interpolated-color-legend rect.swatch-bounding-box {
  fill: none;
  stroke: #CCC;
  stroke-width: 1px;
  pointer-events: none;
}

.plottable .waterfall-plot line.connector {
  stroke: #CCC;
  stroke-width: 1px;
}

.plottable .pie-plot .arc.outline {
  stroke-linejoin: round;
}
</style>
  </template>
</dom-module><dom-module id="vz-chart-tooltip">
  
</dom-module><dom-module id="vz-pan-zoom-style">
  <template>
    <style>
      .help {
        align-items: center;
        animation-delay: 1s;
        animation-duration: 1s;
        animation-name: fade-out;
        background: rgba(30, 30, 30, 0.6);
        bottom: 0;
        color: #fff;
        display: flex;
        justify-content: center;
        left: 0;
        opacity: 1;
        padding: 20px;
        pointer-events: none;
        position: absolute;
        right: 0;
        top: 0;
      }

      .help > span {
        white-space: normal;
      }

      @keyframes fade-out {
        0% {
          opacity: 1;
        }

        100% {
          opacity: 0;
        }
      }
    </style>
  </template>
</dom-module><dom-module id="vz-line-chart2">
  <template>
    <div id="chartdiv"></div>
    <vz-chart-tooltip id="tooltip" position="[[tooltipPosition]]" content-component-name="vz-line-chart-tooltip"></vz-chart-tooltip>
    <style include="plottable-style"></style>
    <style include="vz-pan-zoom-style"></style>
    <style>
      :host {
        -moz-user-select: none;
        -webkit-user-select: none;
        display: flex;
        flex-direction: column;
        flex-grow: 1;
        flex-shrink: 1;
        outline: none;
        position: relative;
        white-space: nowrap;
      }
      div {
        -webkit-user-select: none;
        -moz-user-select: none;
        flex-grow: 1;
        flex-shrink: 1;
      }

      #chartdiv .main {
        contain: strict;
        cursor: crosshair;
      }

      :host(.pankey) #chartdiv :not(.drag-zooming) .main {
        cursor: -webkit-grab;
        cursor: grab;
      }

      :host(.mousedown) #chartdiv .panning .main {
        cursor: -webkit-grabbing;
        cursor: grabbing;
      }

      #chartdiv {
        contain: strict;
      }

      #chartdiv line.guide-line {
        stroke: #999;
        stroke-width: 1.5px;
      }
      #chartdiv:hover .main {
        will-change: transform;
      }

      .ghost {
        opacity: 0.2;
        stroke-width: 1px;
      }
    </style>
  </template>
  
  
  
  
  
</dom-module><dom-module id="vz-line-chart-tooltip">
  <template>
    <div class="content">
      <table>
        <thead></thead>
        <tbody></tbody>
      </table>
    </div>
    <style>
      :host {
        pointer-events: none;
      }

      .content {
        background: rgba(0, 0, 0, 0.8);
        border-radius: 4px;
        color: #fff;
        overflow: hidden;
        pointer-events: none;
      }

      table {
        font-size: 13px;
        line-height: 1.4em;
        margin-top: 10px;
        padding: 8px;
      }

      thead {
        font-size: 14px;
      }

      tbody {
        font-size: 13px;
        line-height: 21px;
        white-space: nowrap;
      }

      td {
        padding: 0 5px;
      }

      .swatch {
        border-radius: 50%;
        display: block;
        height: 18px;
        width: 18px;
      }

      .closest .swatch {
        box-shadow: inset 0 0 0 2px #fff;
      }

      th {
        padding: 0 5px;
        text-align: left;
      }

      .distant td:not(.swatch) {
        opacity: 0.8;
      }

      .ghost {
        opacity: 0.2;
        stroke-width: 1px;
      }
    </style>
  </template>
  
</dom-module><dom-module id="tf-line-chart-data-loader">
  <template>
    <div id="chart-and-spinner-container">
      <vz-line-chart2 id="chart" data-loading$="[[dataLoading]]" color-scale="[[colorScale]]" default-x-range="[[defaultXRange]]" default-y-range="[[defaultYRange]]" fill-area="[[fillArea]]" ignore-y-outliers="[[ignoreYOutliers]]" on-chart-attached="_onChartAttached" smoothing-enabled="[[smoothingEnabled]]" smoothing-weight="[[smoothingWeight]]" symbol-function="[[symbolFunction]]" tooltip-columns="[[tooltipColumns]]" tooltip-position="[[tooltipPosition]]" tooltip-sorting-method="[[tooltipSortingMethod]]" x-components-creation-method="[[xComponentsCreationMethod]]" x-type="[[xType]]" y-value-accessor="[[yValueAccessor]]"></vz-line-chart2>
      <template is="dom-if" if="[[dataLoading]]">
        <div id="loading-spinner-container">
          <paper-spinner-lite active></paper-spinner-lite>
        </div>
      </template>
    </div>
    <style>
      :host {
        height: 100%;
        width: 100%;
        display: flex;
        flex-direction: column;
      }

      :host([_maybe-rendered-in-bad-state]) vz-line-chart {
        visibility: hidden;
      }

      #chart-and-spinner-container {
        display: flex;
        flex-grow: 1;
        position: relative;
      }

      #loading-spinner-container {
        align-items: center;
        bottom: 0;
        display: flex;
        display: flex;
        justify-content: center;
        left: 0;
        pointer-events: none;
        position: absolute;
        right: 0;
        top: 0;
      }

      vz-line-chart2 {
        -webkit-user-select: none;
        -moz-user-select: none;
      }

      vz-line-chart2[data-loading] {
        opacity: 0.3;
      }
    </style>
  </template>
  
  
</dom-module><dom-module id="paper-dialog-scrollable">

  <template>
    <style>

      :host {
        display: block;
        @apply --layout-relative;
      }

      :host(.is-scrolled:not(:first-child))::before {
        content: '';
        position: absolute;
        top: 0;
        left: 0;
        right: 0;
        height: 1px;
        background: var(--divider-color);
      }

      :host(.can-scroll:not(.scrolled-to-bottom):not(:last-child))::after {
        content: '';
        position: absolute;
        bottom: 0;
        left: 0;
        right: 0;
        height: 1px;
        background: var(--divider-color);
      }

      .scrollable {
        padding: 0 24px;

        @apply --layout-scroll;
        @apply --paper-dialog-scrollable;
      }

      .fit {
        @apply --layout-fit;
      }
    </style>

    <div id="scrollable" class="scrollable" on-scroll="updateScrollState">
      <slot></slot>
    </div>
  </template>

</dom-module><dom-module id="tf-markdown-view">
  <template>
    <div id="markdown" inner-h-t-m-l="[[html]]"></div>
    <style>
      /*
       * Reduce topmost and bottommost margins from 16px to 0.3em (renders
       * at about 4.8px) to keep the layout compact. This improves the
       * appearance when there is only one line of text; standard Markdown
       * renderers will still include a `<p>` element.
       *
       * By targeting only the top-level, extremal elements, we preserve any
       * actual paragraph breaks and only change the padding against the
       * component edges.
       */
      #markdown > p:first-child {
        margin-top: 0.3em;
      }
      #markdown > p:last-child {
        margin-bottom: 0.3em;
      }

      /* Pleasant styles for Markdown tables. */
      #markdown table {
        border-collapse: collapse;
      }
      #markdown table th {
        font-weight: 600;
      }
      #markdown table th,
      #markdown table td {
        padding: 6px 13px;
        border: 1px solid #dfe2e5;
      }
      #markdown table tr {
        background-color: #fff;
        border-top: 1px solid #c6cbd1;
      }
    </style>
  </template>
  
</dom-module><dom-module id="tf-card-heading-style">
  <template>
    <style>
      figcaption {
        width: 100%;
      }

      /** Horizontal line of labels. */
      .heading-row {
        margin-top: -4px;
        display: flex;
        flex-direction: row;
        flex-wrap: wrap;
      }

      /** Piece of text in the figure caption. */
      .heading-label {
        flex-grow: 1;
        margin-top: 4px;
        max-width: 100%;
        word-wrap: break-word;
      }

      /** Makes label show on the right. */
      .heading-right {
        flex-grow: 0;
      }
    </style>
  </template>
</dom-module><dom-module id="tf-card-heading">
  <template>
    <div class="container">
      <figcaption class="content">
        <div class="heading-row">
          <template is="dom-if" if="[[_nameLabel]]">
            <div itemprop="name" class="heading-label name">
              [[_nameLabel]]
            </div>
          </template>
          <template is="dom-if" if="[[run]]">
            
            
            <span>
              <span itemprop="run" id="heading-run" class="heading-label heading-right run">[[run]]</span>
            </span>
          </template>
        </div>
        <template is="dom-if" if="[[_tagLabel]]">
          <div class="heading-row">
            <div class="heading-label">
              tag: <span itemprop="tag">[[_tagLabel]]</span>
            </div>
          </div>
        </template>
        <slot></slot>
      </figcaption>
      <template is="dom-if" if="[[description]]">
        <paper-icon-button icon="info" on-tap="_toggleDescriptionDialog" title="Show summary description"></paper-icon-button>
      </template>
      <paper-dialog id="descriptionDialog" no-overlap horizontal-align="auto" vertical-align="auto">
        <paper-dialog-scrollable>
          <tf-markdown-view html="[[description]]"></tf-markdown-view>
        </paper-dialog-scrollable>
      </paper-dialog>
    </div>
    <style include="tf-card-heading-style">
      .container {
        display: flex;
      }
      .content {
        font-size: 12px;
        flex-grow: 1;
      }
      .name {
        font-size: 14px;
      }
      .run {
        font-size: 11px;
        width: auto;
        border-radius: 3px;
        font-weight: bold;
        padding: 1px 4px 2px;
      }
      paper-icon-button {
        flex-grow: 0;
      }
      paper-dialog-scrollable {
        max-width: 640px;
      }
      #heading-run {
        background: var(--tf-card-heading-background-color);
        color: var(--tf-card-heading-color);
      }
    </style>
  </template>
  
</dom-module><dom-module id="tf-downloader">
  <template>
    <paper-dropdown-menu no-label-float="true" label="run to download" selected-item-label="{{_run}}">
      <paper-listbox slot="dropdown-content">
        <template is="dom-repeat" items="[[runs]]">
          <paper-item no-label-float="true">[[item]]</paper-item>
        </template>
      </paper-listbox>
    </paper-dropdown-menu>
    <template is="dom-if" if="[[_run]]">
      <a download="[[_csvName(tag, _run)]]" href="[[_csvUrl(tag, _run, urlFn)]]">CSV</a><a download="[[_jsonName(tag, _run)]]" href="[[_jsonUrl(tag, _run, urlFn)]]">JSON</a>
    </template>
    <style>
      :host {
        display: flex;
        align-items: center;
        height: 32px;
      }
      paper-dropdown-menu {
        width: 100px;
        --paper-input-container-label: {
          font-size: 10px;
        }
        --paper-input-container-input: {
          font-size: 10px;
        }
      }
      a {
        font-size: 10px;
        margin: 0 0.2em;
      }
      paper-input {
        font-size: 22px;
      }
    </style>
  </template>
  
</dom-module><dom-module id="tf-scalar-card">
  <template>
    <tf-card-heading tag="[[tag]]" display-name="[[tagMetadata.displayName]]" description="[[tagMetadata.description]]"></tf-card-heading>
    <div id="tf-line-chart-data-loader-container">
      <tf-line-chart-data-loader active="[[active]]" color-scale="[[_getColorScale(colorScale)]]" data-series="[[_getDataSeries(dataToLoad.*)]]" data-to-load="[[dataToLoad]]" get-data-load-name="[[_getDataLoadName]]" get-data-load-url="[[getDataLoadUrl]]" request-data="[[requestData]]" ignore-y-outliers="[[ignoreYOutliers]]" load-data-callback="[[_loadDataCallback]]" load-key="[[tag]]" log-scale-active="[[_logScaleActive]]" request-manager="[[requestManager]]" smoothing-enabled="[[smoothingEnabled]]" smoothing-weight="[[smoothingWeight]]" tag-metadata="[[tagMetadata]]" tooltip-columns="[[_tooltipColumns]]" tooltip-position="auto" tooltip-sorting-method="[[tooltipSortingMethod]]" x-type="[[xType]]">
      </tf-line-chart-data-loader>
    </div>
    <div id="buttons">
      <paper-icon-button selected$="[[_expanded]]" icon="fullscreen" on-tap="_toggleExpanded"></paper-icon-button>
      <paper-icon-button selected$="[[_logScaleActive]]" icon="line-weight" on-tap="_toggleLogScale" title="Toggle y-axis log scale"></paper-icon-button>
      <paper-icon-button icon="settings-overscan" on-tap="_resetDomain" title="Fit domain to data"></paper-icon-button>
      <template is="dom-if" if="[[showDownloadLinks]]">
        <paper-menu-button on-paper-dropdown-open="_updateDownloadLink">
          <paper-icon-button class="dropdown-trigger" slot="dropdown-trigger" icon="file-download"></paper-icon-button>
          <paper-listbox class="dropdown-content" slot="dropdown-content">
            <paper-item>
              <a id="svgLink" download="[[tag]].svg">
                Download Current Chart as SVG
              </a>
            </paper-item>
          </paper-listbox>
        </paper-menu-button>
      </template>
      <span style="flex-grow: 1"></span>
      <template is="dom-if" if="[[showDownloadLinks]]">
        <div class="download-links">
          <tf-downloader runs="[[_runsFromData(dataToLoad)]]" tag="[[tag]]" url-fn="[[_downloadUrlFn]]"></tf-downloader>
        </div>
      </template>
    </div>
    <style>
      :host {
        margin: 5px;
        display: block;
        width: 330px;
      }

      :host([_expanded]) {
        width: 100%;
      }

      :host([_expanded]) #tf-line-chart-data-loader-container {
        height: 400px;
      }

      #tf-line-chart-data-loader-container {
        height: 200px;
        width: 100%;
      }

      tf-card-heading {
        display: block;
        margin-bottom: 10px;
      }

      #buttons {
        display: flex;
        flex-direction: row;
      }

      paper-icon-button {
        color: #2196f3;
        border-radius: 100%;
        width: 32px;
        height: 32px;
        padding: 4px;
      }

      paper-icon-button[selected] {
        background: var(--tb-ui-light-accent);
      }

      .download-links {
        display: flex;
        height: 32px;
      }

      .download-links a {
        align-self: center;
        font-size: 10px;
        margin: 2px;
      }

      .download-links paper-dropdown-menu {
        width: 100px;
        --paper-input-container-label: {
          font-size: 10px;
        }
        --paper-input-container-input: {
          font-size: 10px;
        }
      }

      paper-menu-button {
        padding: 0;
      }
      paper-item a {
        color: inherit;
        text-decoration: none;
        white-space: nowrap;
      }
    </style>
  </template>
  
</dom-module><dom-module id="paper-progress">
  <template>
    <style>
      :host {
        display: block;
        width: 200px;
        position: relative;
        overflow: hidden;
      }

      :host([hidden]), [hidden] {
        display: none !important;
      }

      #progressContainer {
        @apply --paper-progress-container;
        position: relative;
      }

      #progressContainer,
      /* the stripe for the indeterminate animation*/
      .indeterminate::after {
        height: var(--paper-progress-height, 4px);
      }

      #primaryProgress,
      #secondaryProgress,
      .indeterminate::after {
        @apply --layout-fit;
      }

      #progressContainer,
      .indeterminate::after {
        background: var(--paper-progress-container-color, var(--google-grey-300));
      }

      :host(.transiting) #primaryProgress,
      :host(.transiting) #secondaryProgress {
        -webkit-transition-property: -webkit-transform;
        transition-property: transform;

        /* Duration */
        -webkit-transition-duration: var(--paper-progress-transition-duration, 0.08s);
        transition-duration: var(--paper-progress-transition-duration, 0.08s);

        /* Timing function */
        -webkit-transition-timing-function: var(--paper-progress-transition-timing-function, ease);
        transition-timing-function: var(--paper-progress-transition-timing-function, ease);

        /* Delay */
        -webkit-transition-delay: var(--paper-progress-transition-delay, 0s);
        transition-delay: var(--paper-progress-transition-delay, 0s);
      }

      #primaryProgress,
      #secondaryProgress {
        @apply --layout-fit;
        -webkit-transform-origin: left center;
        transform-origin: left center;
        -webkit-transform: scaleX(0);
        transform: scaleX(0);
        will-change: transform;
      }

      #primaryProgress {
        background: var(--paper-progress-active-color, var(--google-green-500));
      }

      #secondaryProgress {
        background: var(--paper-progress-secondary-color, var(--google-green-100));
      }

      :host([disabled]) #primaryProgress {
        background: var(--paper-progress-disabled-active-color, var(--google-grey-500));
      }

      :host([disabled]) #secondaryProgress {
        background: var(--paper-progress-disabled-secondary-color, var(--google-grey-300));
      }

      :host(:not([disabled])) #primaryProgress.indeterminate {
        -webkit-transform-origin: right center;
        transform-origin: right center;
        -webkit-animation: indeterminate-bar var(--paper-progress-indeterminate-cycle-duration, 2s) linear infinite;
        animation: indeterminate-bar var(--paper-progress-indeterminate-cycle-duration, 2s) linear infinite;
      }

      :host(:not([disabled])) #primaryProgress.indeterminate::after {
        content: "";
        -webkit-transform-origin: center center;
        transform-origin: center center;

        -webkit-animation: indeterminate-splitter var(--paper-progress-indeterminate-cycle-duration, 2s) linear infinite;
        animation: indeterminate-splitter var(--paper-progress-indeterminate-cycle-duration, 2s) linear infinite;
      }

      @-webkit-keyframes indeterminate-bar {
        0% {
          -webkit-transform: scaleX(1) translateX(-100%);
        }
        50% {
          -webkit-transform: scaleX(1) translateX(0%);
        }
        75% {
          -webkit-transform: scaleX(1) translateX(0%);
          -webkit-animation-timing-function: cubic-bezier(.28,.62,.37,.91);
        }
        100% {
          -webkit-transform: scaleX(0) translateX(0%);
        }
      }

      @-webkit-keyframes indeterminate-splitter {
        0% {
          -webkit-transform: scaleX(.75) translateX(-125%);
        }
        30% {
          -webkit-transform: scaleX(.75) translateX(-125%);
          -webkit-animation-timing-function: cubic-bezier(.42,0,.6,.8);
        }
        90% {
          -webkit-transform: scaleX(.75) translateX(125%);
        }
        100% {
          -webkit-transform: scaleX(.75) translateX(125%);
        }
      }

      @keyframes indeterminate-bar {
        0% {
          transform: scaleX(1) translateX(-100%);
        }
        50% {
          transform: scaleX(1) translateX(0%);
        }
        75% {
          transform: scaleX(1) translateX(0%);
          animation-timing-function: cubic-bezier(.28,.62,.37,.91);
        }
        100% {
          transform: scaleX(0) translateX(0%);
        }
      }

      @keyframes indeterminate-splitter {
        0% {
          transform: scaleX(.75) translateX(-125%);
        }
        30% {
          transform: scaleX(.75) translateX(-125%);
          animation-timing-function: cubic-bezier(.42,0,.6,.8);
        }
        90% {
          transform: scaleX(.75) translateX(125%);
        }
        100% {
          transform: scaleX(.75) translateX(125%);
        }
      }
    </style>

    <div id="progressContainer">
      <div id="secondaryProgress" hidden$="[[_hideSecondaryProgress(secondaryRatio)]]"></div>
      <div id="primaryProgress"></div>
    </div>
  </template>
</dom-module><dom-module id="paper-slider">
  <template strip-whitespace>
    <style>
      :host {
        @apply --layout;
        @apply --layout-justified;
        @apply --layout-center;
        width: 200px;
        cursor: default;
        -webkit-user-select: none;
        -moz-user-select: none;
        -ms-user-select: none;
        user-select: none;
        -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
        --paper-progress-active-color: var(--paper-slider-active-color, var(--google-blue-700));
        --paper-progress-secondary-color: var(--paper-slider-secondary-color, var(--google-blue-300));
        --paper-progress-disabled-active-color: var(--paper-slider-disabled-active-color, var(--paper-grey-400));
        --paper-progress-disabled-secondary-color: var(--paper-slider-disabled-secondary-color, var(--paper-grey-400));
        --calculated-paper-slider-height: var(--paper-slider-height, 2px);
      }

      /* focus shows the ripple */
      :host(:focus) {
        outline: none;
      }

      /**
       * NOTE(keanulee): Though :host-context is not universally supported, some pages
       * still rely on paper-slider being flipped when dir="rtl" is set on body. For full
       * compatability, dir="rtl" must be explicitly set on paper-slider.
       */
      :dir(rtl) #sliderContainer {
        -webkit-transform: scaleX(-1);
        transform: scaleX(-1);
      }

      /**
       * NOTE(keanulee): This is separate from the rule above because :host-context may
       * not be recognized.
       */
      :host([dir="rtl"]) #sliderContainer {
        -webkit-transform: scaleX(-1);
        transform: scaleX(-1);
      }

      /**
       * NOTE(keanulee): Needed to override the :host-context rule (where supported)
       * to support LTR sliders in RTL pages.
       */
      :host([dir="ltr"]) #sliderContainer {
        -webkit-transform: scaleX(1);
        transform: scaleX(1);
      }

      #sliderContainer {
        position: relative;
        width: 100%;
        height: calc(30px + var(--calculated-paper-slider-height));
        margin-left: calc(15px + var(--calculated-paper-slider-height)/2);
        margin-right: calc(15px + var(--calculated-paper-slider-height)/2);
      }

      #sliderContainer:focus {
        outline: 0;
      }

      #sliderContainer.editable {
        margin-top: 12px;
        margin-bottom: 12px;
      }

      .bar-container {
        position: absolute;
        top: 0;
        bottom: 0;
        left: 0;
        right: 0;
        overflow: hidden;
      }

      .ring > .bar-container {
        left: calc(5px + var(--calculated-paper-slider-height)/2);
        transition: left 0.18s ease;
      }

      .ring.expand.dragging > .bar-container {
        transition: none;
      }

      .ring.expand:not(.pin) > .bar-container {
        left: calc(8px + var(--calculated-paper-slider-height)/2);
      }

      #sliderBar {
        padding: 15px 0;
        width: 100%;
        background-color: var(--paper-slider-bar-color, transparent);
        --paper-progress-container-color: var(--paper-slider-container-color, var(--paper-grey-400));
        --paper-progress-height: var(--calculated-paper-slider-height);
      }

      .slider-markers {
        position: absolute;
        top: calc(14px + var(--paper-slider-height,2px)/2);
        height: var(--calculated-paper-slider-height);
        left: 0;
        right: -1px;
        box-sizing: border-box;
        pointer-events: none;
        @apply --layout-horizontal;
      }

      .slider-marker {
        @apply --layout-flex;
      }
      .slider-markers::after,
      .slider-marker::after {
        content: "";
        display: block;
        margin-left: -1px;
        width: 2px;
        height: var(--calculated-paper-slider-height);
        border-radius: 50%;
        background-color: var(--paper-slider-markers-color, #000);
      }

      .slider-knob {
        position: absolute;
        left: 0;
        top: 0;
        margin-left: calc(-15px - var(--calculated-paper-slider-height)/2);
        width: calc(30px + var(--calculated-paper-slider-height));
        height: calc(30px + var(--calculated-paper-slider-height));
      }

      .transiting > .slider-knob {
        transition: left 0.08s ease;
      }

      .slider-knob:focus {
        outline: none;
      }

      .slider-knob.dragging {
        transition: none;
      }

      .snaps > .slider-knob.dragging {
        transition: -webkit-transform 0.08s ease;
        transition: transform 0.08s ease;
      }

      .slider-knob-inner {
        margin: 10px;
        width: calc(100% - 20px);
        height: calc(100% - 20px);
        background-color: var(--paper-slider-knob-color, var(--google-blue-700));
        border: 2px solid var(--paper-slider-knob-color, var(--google-blue-700));
        border-radius: 50%;

        -moz-box-sizing: border-box;
        box-sizing: border-box;

        transition-property: -webkit-transform, background-color, border;
        transition-property: transform, background-color, border;
        transition-duration: 0.18s;
        transition-timing-function: ease;
      }

      .expand:not(.pin) > .slider-knob > .slider-knob-inner {
        -webkit-transform: scale(1.5);
        transform: scale(1.5);
      }

      .ring > .slider-knob > .slider-knob-inner {
        background-color: var(--paper-slider-knob-start-color, transparent);
        border: 2px solid var(--paper-slider-knob-start-border-color, var(--paper-grey-400));
      }

      .slider-knob-inner::before {
        background-color: var(--paper-slider-pin-color, var(--google-blue-700));
      }

      .pin > .slider-knob > .slider-knob-inner::before {
        content: "";
        position: absolute;
        top: 0;
        left: 50%;
        margin-left: -13px;
        width: 26px;
        height: 26px;
        border-radius: 50% 50% 50% 0;

        -webkit-transform: rotate(-45deg) scale(0) translate(0);
        transform: rotate(-45deg) scale(0) translate(0);
      }

      .slider-knob-inner::before,
      .slider-knob-inner::after {
        transition: -webkit-transform .18s ease, background-color .18s ease;
        transition: transform .18s ease, background-color .18s ease;
      }

      .pin.ring > .slider-knob > .slider-knob-inner::before {
        background-color: var(--paper-slider-pin-start-color, var(--paper-grey-400));
      }

      .pin.expand > .slider-knob > .slider-knob-inner::before {
        -webkit-transform: rotate(-45deg) scale(1) translate(17px, -17px);
        transform: rotate(-45deg) scale(1) translate(17px, -17px);
      }

      .pin > .slider-knob > .slider-knob-inner::after {
        content: attr(value);
        position: absolute;
        top: 0;
        left: 50%;
        margin-left: -16px;
        width: 32px;
        height: 26px;
        text-align: center;
        color: var(--paper-slider-font-color, #fff);
        font-size: 10px;

        -webkit-transform: scale(0) translate(0);
        transform: scale(0) translate(0);
      }

      .pin.expand > .slider-knob > .slider-knob-inner::after {
        -webkit-transform: scale(1) translate(0, -17px);
        transform: scale(1) translate(0, -17px);
      }

      /* paper-input */
      .slider-input {
        width: 50px;
        overflow: hidden;
        --paper-input-container-input: {
          text-align: center;
          @apply --paper-slider-input-container-input;
        };
        @apply --paper-slider-input;
      }

      /* disabled state */
      #sliderContainer.disabled {
        pointer-events: none;
      }

      .disabled > .slider-knob > .slider-knob-inner {
        background-color: var(--paper-slider-disabled-knob-color, var(--paper-grey-400));
        border: 2px solid var(--paper-slider-disabled-knob-color, var(--paper-grey-400));
        -webkit-transform: scale3d(0.75, 0.75, 1);
        transform: scale3d(0.75, 0.75, 1);
      }

      .disabled.ring > .slider-knob > .slider-knob-inner {
        background-color: var(--paper-slider-knob-start-color, transparent);
        border: 2px solid var(--paper-slider-knob-start-border-color, var(--paper-grey-400));
      }

      paper-ripple {
        color: var(--paper-slider-knob-color, var(--google-blue-700));
      }
    </style>

    <div id="sliderContainer" class$="[[_getClassNames(disabled, pin, snaps, immediateValue, min, expand, dragging, transiting, editable)]]">
      <div class="bar-container">
        <paper-progress disabled$="[[disabled]]" id="sliderBar" aria-hidden="true" min="[[min]]" max="[[max]]" step="[[step]]" value="[[immediateValue]]" secondary-progress="[[secondaryProgress]]" on-down="_bardown" on-up="_resetKnob" on-track="_bartrack" on-tap="_barclick">
        </paper-progress>
      </div>

      <template is="dom-if" if="[[snaps]]">
        <div class="slider-markers">
          <template is="dom-repeat" items="[[markers]]">
            <div class="slider-marker"></div>
          </template>
        </div>
      </template>

      <div id="sliderKnob" class="slider-knob" on-down="_knobdown" on-up="_resetKnob" on-track="_onTrack" on-transitionend="_knobTransitionEnd">
          <div class="slider-knob-inner" value$="[[immediateValue]]"></div>
      </div>
    </div>

    <template is="dom-if" if="[[editable]]">
      <paper-input id="input" type="number" step="[[step]]" min="[[min]]" max="[[max]]" class="slider-input" disabled$="[[disabled]]" value="[[immediateValue]]" on-change="_changeValue" on-keydown="_inputKeyDown" no-label-float>
      </paper-input>
    </template>
  </template>

  
</dom-module><dom-module id="tf-smoothing-input">
  <template>
    <h3 class="title">Smoothing</h3>
    <div class="smoothing-block">
      <paper-slider id="slider" immediate-value="{{_immediateWeightNumberForPaperSlider}}" max="[[max]]" min="[[min]]" pin step="[[step]]" type="number" value="{{weight}}"></paper-slider>
      <paper-input id="input" label="weight" no-label-float value="{{_inputWeightStringForPaperInput}}" type="number" step="[[step]]" min="[[min]]" max="[[max]]"></paper-input>
    </div>
    <style>
      .title {
        color: var(--paper-grey-800);
        margin: 0;
        font-weight: normal;
        font-size: 14px;
        margin-bottom: 5px;
      }

      .smoothing-block {
        display: flex;
      }

      paper-slider {
        --paper-slider-active-color: var(--tb-orange-strong);
        --paper-slider-knob-color: var(--tb-orange-strong);
        --paper-slider-knob-start-border-color: var(--tb-orange-strong);
        --paper-slider-knob-start-color: var(--tb-orange-strong);
        --paper-slider-markers-color: var(--tb-orange-strong);
        --paper-slider-pin-color: var(--tb-orange-strong);
        --paper-slider-pin-start-color: var(--tb-orange-strong);
        flex-grow: 2;
      }

      paper-input {
        --paper-input-container-focus-color: var(--tb-orange-strong);
        --paper-input-container-input: {
          font-size: 14px;
        }
        --paper-input-container-label: {
          font-size: 14px;
        }
        width: 60px;
      }
    </style>
  </template>
  
</dom-module><dom-module id="tf-scalar-dashboard">
  <template>
    <tf-dashboard-layout>
      <div class="sidebar" slot="sidebar">
        <div class="sidebar-section">
          <div class="line-item">
            <paper-checkbox id="show-download-links" checked="{{_showDownloadLinks}}">Show data download links</paper-checkbox>
          </div>
          <div class="line-item">
            <paper-checkbox id="ignore-y-outlier" checked="{{_ignoreYOutliers}}">Ignore outliers in chart scaling</paper-checkbox>
          </div>
          <div id="tooltip-sorting">
            <div>Tooltip sorting method:</div>
            <paper-dropdown-menu no-label-float selected-item-label="{{_tooltipSortingMethod}}">
              <paper-listbox class="dropdown-content" selected="0" slot="dropdown-content">
                <paper-item>default</paper-item>
                <paper-item>descending</paper-item>
                <paper-item>ascending</paper-item>
                <paper-item>nearest</paper-item>
              </paper-listbox>
            </paper-dropdown-menu>
          </div>
        </div>
        <div class="sidebar-section">
          <tf-smoothing-input weight="{{_smoothingWeight}}" step="0.001" min="0" max="0.999"></tf-smoothing-input>
        </div>
        <div class="sidebar-section">
          <tf-option-selector id="x-type-selector" name="Horizontal Axis" selected-id="{{_xType}}">
            <paper-button id="step">step</paper-button><paper-button id="relative">relative</paper-button><paper-button id="wall_time">wall</paper-button>
          </tf-option-selector>
        </div>
        <div class="sidebar-section">
          <tf-runs-selector selected-runs="{{_selectedRuns}}">
          </tf-runs-selector>
        </div>
      </div>
      <div class="center" slot="center">
        <template is="dom-if" if="[[_dataNotFound]]">
          <div class="no-data-warning">
            <h3>No scalar data was found.</h3>
            <p>Probable causes:</p>
            <ul>
              <li>You haven’t written any scalar data to your event files.</li>
              <li>TensorBoard can’t find your event files.</li>
            </ul>

            <p>
              If you’re new to using TensorBoard, and want to find out how to
              add data and set up your event files, check out the
              <a href="https://github.com/tensorflow/tensorboard/blob/master/README.md">README</a>
              and perhaps the
              <a href="https://www.tensorflow.org/get_started/summaries_and_tensorboard">TensorBoard tutorial</a>.
            </p>

            <p>
              If you think TensorBoard is configured properly, please see
              <a href="https://github.com/tensorflow/tensorboard/blob/master/README.md#my-tensorboard-isnt-showing-any-data-whats-wrong">the section of the README devoted to missing data problems</a>
              and consider filing an issue on GitHub.
            </p>
          </div>
        </template>
        <template is="dom-if" if="[[!_dataNotFound]]">
          <tf-tag-filterer tag-filter="{{_tagFilter}}"></tf-tag-filterer>
          <template is="dom-repeat" items="[[_categories]]" as="category">
            <tf-category-paginated-view category="[[category]]" initial-opened="[[_shouldOpen(index)]]" get-category-item-key="[[_getCategoryItemKey]]">
              <template>
                <tf-scalar-card active="[[active]]" data-to-load="[[item.series]]" ignore-y-outliers="[[_ignoreYOutliers]]" multi-experiments="[[_getMultiExperiments(dataSelection)]]" request-manager="[[_requestManager]]" show-download-links="[[_showDownloadLinks]]" smoothing-enabled="[[_smoothingEnabled]]" smoothing-weight="[[_smoothingWeight]]" tag-metadata="[[_tagMetadata(category, _runToTagInfo, item)]]" tag="[[item.tag]]" tooltip-sorting-method="[[_tooltipSortingMethod]]" x-type="[[_xType]]"></tf-scalar-card>
              </template>
            </tf-category-paginated-view>
          </template>
        </template>
      </div>
    </tf-dashboard-layout>

    <style include="dashboard-style"></style>
    <style>
      #tooltip-sorting {
        align-items: center;
        display: flex;
        font-size: 14px;
        margin-top: 15px;
      }
      #tooltip-sorting paper-dropdown-menu {
        margin-left: 10px;
        --paper-input-container-focus-color: var(--tb-orange-strong);
        width: 105px;
      }
      .line-item {
        display: block;
        padding-top: 5px;
      }
      .no-data-warning {
        max-width: 540px;
        margin: 80px auto 0 auto;
      }
      .center {
        overflow-x: hidden;
      }
    </style>
  </template>

  
</dom-module><dom-module id="tf-custom-scalar-card-style">
  <template>
    <style>
      :host {
        margin: 5px 10px;
        display: inline-block;
        width: 330px;
        vertical-align: text-top;
      }

      :host([_expanded]) {
        width: 100%;
      }

      :host([_expanded]) #tf-line-chart-data-loader-container {
        height: 400px;
      }

      h1 {
        font-size: 19px;
        font-weight: normal;
      }

      #tf-line-chart-data-loader-container {
        height: 200px;
        width: 100%;
      }

      #buttons {
        display: flex;
        flex-direction: row;
      }

      paper-icon-button {
        color: #2196f3;
        border-radius: 100%;
        width: 32px;
        height: 32px;
        padding: 4px;
      }

      paper-icon-button[selected] {
        background: var(--tb-ui-light-accent);
      }

      .download-links {
        display: flex;
        height: 32px;
      }

      .download-links a {
        font-size: 10px;
        align-self: center;
        margin: 2px;
      }

      .download-links paper-dropdown-menu {
        width: 100px;
        --paper-input-container-label: {
          font-size: 10px;
        }
        --paper-input-container-input: {
          font-size: 10px;
        }
      }
    </style>
  </template>
</dom-module><dom-module id="tf-custom-scalar-margin-chart-card">
  <template>
    <tf-card-heading display-name="[[_titleDisplayString]]"></tf-card-heading>
    <div id="tf-line-chart-data-loader-container">
      <tf-line-chart-data-loader id="loader" active="[[active]]" color-scale="[[_colorScale]]" data-series="[[_seriesNames]]" get-data-load-url="[[_dataUrl]]" fill-area="[[_fillArea]]" ignore-y-outliers="[[ignoreYOutliers]]" load-key="[[_tagFilter]]" data-to-load="[[runs]]" log-scale-active="[[_logScaleActive]]" load-data-callback="[[_createProcessDataFunction(marginChartSeries)]]" request-manager="[[requestManager]]" symbol-function="[[_createSymbolFunction()]]" tooltip-columns="[[_tooltipColumns]]" tooltip-sorting-method="[[tooltipSortingMethod]]" x-type="[[xType]]">
      </tf-line-chart-data-loader>
    </div>
    <div id="buttons">
      <paper-icon-button selected$="[[_expanded]]" icon="fullscreen" on-tap="_toggleExpanded"></paper-icon-button>
      <paper-icon-button selected$="[[_logScaleActive]]" icon="line-weight" on-tap="_toggleLogScale" title="Toggle y-axis log scale"></paper-icon-button>
      <paper-icon-button icon="settings-overscan" on-tap="_resetDomain" title="Fit domain to data"></paper-icon-button>
      <span style="flex-grow: 1"></span>
      <template is="dom-if" if="[[showDownloadLinks]]">
        <div class="download-links">
          <paper-dropdown-menu no-label-float="true" label="series to download" selected-item-label="{{_dataSeriesNameToDownload}}">
            <paper-listbox class="dropdown-content" slot="dropdown-content">
              <template is="dom-repeat" items="[[_seriesNames]]" as="dataSeriesName">
                <paper-item no-label-float="true">[[dataSeriesName]]</paper-item>
              </template>
            </paper-listbox>
          </paper-dropdown-menu>
          <a download="[[_dataSeriesNameToDownload]].csv" href="[[_csvUrl(_nameToDataSeries, _dataSeriesNameToDownload)]]">CSV</a>
          <a download="[[_dataSeriesNameToDownload]].json" href="[[_jsonUrl(_nameToDataSeries, _dataSeriesNameToDownload)]]">JSON</a>
        </div>
      </template>
    </div>

    
    <template is="dom-if" if="[[_missingTags.length]]">
      <div class="collapsible-list-title">
        <paper-icon-button icon="[[_getToggleCollapsibleIcon(_missingTagsCollapsibleOpened)]]" on-click="_toggleMissingTagsCollapsibleOpen" class="toggle-collapsible-button">
        </paper-icon-button>
        <span class="collapsible-title-text">
          <iron-icon icon="icons:error"></iron-icon> Missing Tags
        </span>
      </div>
      <iron-collapse opened="[[_missingTagsCollapsibleOpened]]">
        <div class="error-content">
          <iron-icon class="error-icon" icon="icons:error"></iron-icon>
          <template is="dom-repeat" items="[[_missingTags]]" as="missingEntry">
            <div class="missing-tags-for-run-container">
              Run "[[missingEntry.run]]" lacks data for tags
              <ul>
                <template is="dom-repeat" items="[[missingEntry.tags]]" as="tag">
                  <li>[[tag]]</li>
                </template>
              </ul>
            </div>
          </template>
        </div>
      </iron-collapse>
    </template>

    <template is="dom-if" if="[[_tagFilterInvalid]]">
      <div class="error-content">
        <iron-icon class="error-icon" icon="icons:error"></iron-icon>
        This regular expresion is invalid:<br>
        <span class="invalid-regex">[[_tagFilter]]</span>
      </div>
    </template>

    <template is="dom-if" if="[[_stepsMismatch]]">
      <div class="error-content">
        <iron-icon class="error-icon" icon="icons:error"></iron-icon>
        The steps for value, lower, and upper tags do not match:
        <ul>
          <li>
            <span class="tag-name">[[_stepsMismatch.seriesObject.value]]</span>:
            [[_separateWithCommas(_stepsMismatch.valueSteps)]]
          </li>
          <li>
            <span class="tag-name">[[_stepsMismatch.seriesObject.lower]]</span>:
            [[_separateWithCommas(_stepsMismatch.lowerSteps)]]
          </li>
          <li>
            <span class="tag-name">[[_stepsMismatch.seriesObject.upper]]</span>:
            [[_separateWithCommas(_stepsMismatch.upperSteps)]]
          </li>
        </ul>
      </div>
    </template>

    <div id="matches-container">
      <div class="collapsible-list-title">
        <template is="dom-if" if="[[_seriesNames.length]]">
          <paper-icon-button icon="[[_getToggleCollapsibleIcon(_matchesListOpened)]]" on-click="_toggleMatchesOpen" class="toggle-matches-button">
          </paper-icon-button>
        </template>

        <span class="collapsible-title-text">
          Matches ([[_seriesNames.length]])
        </span>
      </div>
      <template is="dom-if" if="[[_seriesNames.length]]">
        <iron-collapse opened="[[_matchesListOpened]]">
          <div id="matches-list">
            <template is="dom-repeat" items="[[_seriesNames]]" as="seriesName" id="match-list-repeat" on-dom-change="_matchListEntryColorUpdated">
              <div class="match-list-entry">
                <span class="match-entry-symbol">
                  [[_determineSymbol(_nameToDataSeries, seriesName)]]
                </span>
                [[seriesName]]
              </div>
            </template>
          </div>
        </iron-collapse>
      </template>
    </div>

    <style include="tf-custom-scalar-card-style"></style>
    <style>
      .error-content {
        background: #f00;
        border-radius: 5px;
        color: #fff;
        margin: 10px 0 0 0;
        padding: 10px;
      }

      .error-icon {
        display: block;
        fill: #fff;
        margin: 0 auto 5px auto;
      }

      .invalid-regex {
        font-weight: bold;
      }

      .error-content ul {
        margin: 1px 0 0 0;
        padding: 0 0 0 19px;
      }

      .tag-name {
        font-weight: bold;
      }

      .collapsible-list-title {
        margin: 10px 0 5px 0;
      }

      .collapsible-title-text {
        vertical-align: middle;
      }

      #matches-list {
        max-height: 200px;
        overflow-y: auto;
      }

      .match-list-entry {
        margin: 0 0 5px 0;
      }

      .match-entry-symbol {
        font-family: arial, sans-serif;
        display: inline-block;
        width: 10px;
      }

      .missing-tags-for-run-container {
        margin: 8px 0 0 0;
      }
    </style>
  </template>
  
</dom-module><dom-module id="tf-custom-scalar-multi-line-chart-card">
  <template>
    <tf-card-heading display-name="[[_titleDisplayString]]"></tf-card-heading>
    <div id="tf-line-chart-data-loader-container">
      <tf-line-chart-data-loader id="loader" active="[[active]]" color-scale="[[_colorScale]]" data-series="[[_seriesNames]]" get-data-load-url="[[_dataUrl]]" ignore-y-outliers="[[ignoreYOutliers]]" load-key="[[_tagFilter]]" data-to-load="[[runs]]" log-scale-active="[[_logScaleActive]]" load-data-callback="[[_createProcessDataFunction()]]" request-manager="[[requestManager]]" smoothing-enabled="[[smoothingEnabled]]" smoothing-weight="[[smoothingWeight]]" symbol-function="[[_createSymbolFunction()]]" tooltip-sorting-method="[[tooltipSortingMethod]]" x-type="[[xType]]">
      </tf-line-chart-data-loader>
    </div>
    <div id="buttons">
      <paper-icon-button selected$="[[_expanded]]" icon="fullscreen" on-tap="_toggleExpanded"></paper-icon-button>
      <paper-icon-button selected$="[[_logScaleActive]]" icon="line-weight" on-tap="_toggleLogScale" title="Toggle y-axis log scale"></paper-icon-button>
      <paper-icon-button icon="settings-overscan" on-tap="_resetDomain" title="Fit domain to data"></paper-icon-button>
      <span style="flex-grow: 1"></span>
      <template is="dom-if" if="[[showDownloadLinks]]">
        <div class="download-links">
          <paper-dropdown-menu no-label-float="true" label="series to download" selected-item-label="{{_dataSeriesNameToDownload}}">
            <paper-listbox class="dropdown-content" slot="dropdown-content">
              <template is="dom-repeat" items="[[_seriesNames]]" as="dataSeriesName">
                <paper-item no-label-float="true">[[dataSeriesName]]</paper-item>
              </template>
            </paper-listbox>
          </paper-dropdown-menu>
          <a download="[[_dataSeriesNameToDownload]].csv" href="[[_csvUrl(_nameToDataSeries, _dataSeriesNameToDownload)]]">CSV</a>
          <a download="[[_dataSeriesNameToDownload]].json" href="[[_jsonUrl(_nameToDataSeries, _dataSeriesNameToDownload)]]">JSON</a>
        </div>
      </template>
    </div>
    <div id="matches-container">
      <div id="matches-list-title">
        <template is="dom-if" if="[[_seriesNames.length]]">
          <paper-icon-button icon="[[_getToggleMatchesIcon(_matchesListOpened)]]" on-click="_toggleMatchesOpen" class="toggle-matches-button">
          </paper-icon-button>
        </template>

        <span class="matches-text">
          Matches ([[_seriesNames.length]])
        </span>
      </div>
      <template is="dom-if" if="[[_seriesNames.length]]">
        <iron-collapse opened="[[_matchesListOpened]]">
          <div id="matches-list">
            <template is="dom-repeat" items="[[_seriesNames]]" as="seriesName" id="match-list-repeat" on-dom-change="_matchListEntryColorUpdated">
              <div class="match-list-entry">
                <span class="match-entry-symbol">
                  [[_determineSymbol(_nameToDataSeries, seriesName)]]
                </span>
                [[seriesName]]
              </div>
            </template>
          </div>
        </iron-collapse>
      </template>
    </div>

    <style include="tf-custom-scalar-card-style"></style>
    <style>
      #matches-list-title {
        margin: 10px 0 5px 0;
      }

      #matches-list {
        max-height: 200px;
        overflow-y: auto;
      }

      .match-list-entry {
        margin: 0 0 5px 0;
      }

      .match-entry-symbol {
        font-family: arial, sans-serif;
        display: inline-block;
        width: 10px;
      }

      .matches-text {
        vertical-align: middle;
      }
    </style>
  </template>
  
</dom-module><dom-module id="tf-custom-scalar-dashboard">
  <template>
    <tf-dashboard-layout>
      <div class="sidebar" slot="sidebar">
        <div class="sidebar-section">
          <div class="line-item">
            <paper-checkbox checked="{{_showDownloadLinks}}">Show data download links</paper-checkbox>
          </div>
          <div class="line-item">
            <paper-checkbox checked="{{_ignoreYOutliers}}">Ignore outliers in chart scaling</paper-checkbox>
          </div>
          <div id="tooltip-sorting">
            <div id="tooltip-sorting-label">Tooltip sorting method:</div>
            <paper-dropdown-menu no-label-float selected-item-label="{{_tooltipSortingMethod}}">
              <paper-listbox class="dropdown-content" selected="0" slot="dropdown-content">
                <paper-item>default</paper-item>
                <paper-item>descending</paper-item>
                <paper-item>ascending</paper-item>
                <paper-item>nearest</paper-item>
              </paper-listbox>
            </paper-dropdown-menu>
          </div>
        </div>
        <div class="sidebar-section">
          <tf-smoothing-input weight="{{_smoothingWeight}}" step="0.001" min="0" max="1"></tf-smoothing-input>
        </div>
        <div class="sidebar-section">
          <tf-option-selector id="x-type-selector" name="Horizontal Axis" selected-id="{{_xType}}">
            <paper-button id="step">step</paper-button><paper-button id="relative">relative</paper-button><paper-button id="wall_time">wall</paper-button>
          </tf-option-selector>
        </div>
        <div class="sidebar-section">
          <tf-runs-selector selected-runs="{{_selectedRuns}}">
          </tf-runs-selector>
        </div>
      </div>
      <div class="center" slot="center" id="categories-container">
        <template is="dom-if" if="[[_dataNotFound]]">
          <div class="no-data-warning">
            <h3>The custom scalars dashboard is inactive.</h3>
            <p>Probable causes:</p>
            <ol>
              <li>You haven't laid out the dashboard.</li>
              <li>You haven’t written any scalar data to your event files.</li>
            </ol>

            <p>
              To lay out the dashboard, pass a <code>Layout</code> protobuffer
              to the <code>set_layout</code> method. For example,
            </p>
            <pre>from tensorboard import summary
from tensorboard.plugins.custom_scalar import layout_pb2
...
# This action does not have to be performed at every step, so the action is not
# taken care of by an op in the graph. We only need to specify the layout once
# (instead of per step).
layout_summary = summary_lib.custom_scalar_pb(layout_pb2.Layout(
  category=[
    layout_pb2.Category(
      title='losses',
      chart=[
          layout_pb2.Chart(
              title='losses',
              multiline=layout_pb2.MultilineChartContent(
                tag=[r'loss.*'],
              )),
          layout_pb2.Chart(
              title='baz',
              margin=layout_pb2.MarginChartContent(
                series=[
                  layout_pb2.MarginChartContent.Series(
                    value='loss/baz/scalar_summary',
                    lower='baz_lower/baz/scalar_summary',
                    upper='baz_upper/baz/scalar_summary'),
                ],
              )),
      ]),
    layout_pb2.Category(
      title='trig functions',
      chart=[
          layout_pb2.Chart(
              title='wave trig functions',
              multiline=layout_pb2.MultilineChartContent(
                tag=[r'trigFunctions/cosine', r'trigFunctions/sine'],
              )),
          # The range of tangent is different. Let's give it its own chart.
          layout_pb2.Chart(
              title='tan',
              multiline=layout_pb2.MultilineChartContent(
                tag=[r'trigFunctions/tangent'],
              )),
      ],
      # This category we care less about. Let's make it initially closed.
      closed=True),
  ]))
writer.add_summary(layout_summary)
</pre>
            <p>
              If you’re new to using TensorBoard, and want to find out how to
              add data and set up your event files, check out the
              <a href="https://github.com/tensorflow/tensorboard/blob/master/README.md">README</a>
              and perhaps the
              <a href="https://www.tensorflow.org/get_started/summaries_and_tensorboard">TensorBoard tutorial</a>.
            </p>
          </div>
        </template>
        <template is="dom-if" if="[[!_dataNotFound]]">
          <template is="dom-repeat" items="[[_categories]]" as="category">
            <tf-category-paginated-view as="chart" category="[[category]]" disable-pagination initial-opened="[[category.metadata.opened]]">
              <template>
                <div>
                  <template is="dom-if" if="[[chart.multiline]]">
                    <tf-custom-scalar-multi-line-chart-card active="[[active]]" request-manager="[[_requestManager]]" runs="[[_selectedRuns]]" title="[[chart.title]]" x-type="[[_xType]]" smoothing-enabled="[[_smoothingEnabled]]" smoothing-weight="[[_smoothingWeight]]" tooltip-sorting-method="[[tooltipSortingMethod]]" ignore-y-outliers="[[_ignoreYOutliers]]" show-download-links="[[_showDownloadLinks]]" tag-regexes="[[chart.multiline.tag]]"></tf-custom-scalar-multi-line-chart-card>
                  </template>
                  <template is="dom-if" if="[[chart.margin]]">
                    <tf-custom-scalar-margin-chart-card active="[[active]]" request-manager="[[_requestManager]]" runs="[[_selectedRuns]]" title="[[chart.title]]" x-type="[[_xType]]" tooltip-sorting-method="[[tooltipSortingMethod]]" ignore-y-outliers="[[_ignoreYOutliers]]" show-download-links="[[_showDownloadLinks]]" margin-chart-series="[[chart.margin.series]]"></tf-custom-scalar-margin-chart-card>
                  </template>
                </div>
              </template>
            </tf-category-paginated-view>
          </template>
        </template>
      </div>
    </tf-dashboard-layout>

    <style include="dashboard-style"></style>
    <style>
      #tooltip-sorting {
        align-items: center;
        display: flex;
        font-size: 14px;
        margin-top: 15px;
      }
      #tooltip-sorting paper-dropdown-menu {
        margin-left: 10px;
        --paper-input-container-focus-color: var(--tb-orange-strong);
        width: 105px;
      }
      .line-item {
        display: block;
        padding-top: 5px;
      }
      .no-data-warning {
        max-width: 540px;
        margin: 80px auto 0 auto;
      }
    </style>
  </template>

  
</dom-module><dom-module id="tf-image-loader">
  <template>
    <tf-card-heading tag="[[tag]]" run="[[run]]" display-name="[[tagMetadata.displayName]]" description="[[tagMetadata.description]]" color="[[_runColor]]">
      <template is="dom-if" if="[[_hasMultipleSamples]]">
        <div>sample: [[_sampleText]] of [[ofSamples]]</div>
      </template>
      <template is="dom-if" if="[[_hasAtLeastOneStep]]">
        <div class="heading-row">
          <div class="heading-label">
            step
            <span style="font-weight: bold">[[_toLocaleString(_stepValue)]]</span>
          </div>
          <div class="heading-label heading-right datetime">
            <template is="dom-if" if="[[_currentWallTime]]">
              [[_currentWallTime]]
            </template>
          </div>
          <div class="label right">
            <paper-spinner-lite active hidden$="[[!_isImageLoading]]">
            </paper-spinner-lite>
          </div>
        </div>
      </template>
      <template is="dom-if" if="[[_hasMultipleSteps]]">
        <div>
          <paper-slider id="steps" immediate-value="{{_stepIndex}}" max="[[_maxStepIndex]]" max-markers="[[_maxStepIndex]]" snaps step="1" value="{{_stepIndex}}"></paper-slider>
        </div>
      </template>
    </tf-card-heading>

    
    <a id="main-image-container" role="button" aria-label="Toggle actual size" aria-expanded$="[[_getAriaExpanded(actualSize)]]" on-tap="_handleTap"></a>

    <style include="tf-card-heading-style">
      /** Make button a div. */
      button {
        width: 100%;
        display: block;
        background: none;
        border: 0;
        padding: 0;
      }

      /** Firefox: Get rid of dotted line inside button. */
      button::-moz-focus-inner {
        border: 0;
        padding: 0;
      }

      /** Firefox: Simulate Chrome's outer glow on button when focused. */
      button:-moz-focusring {
        outline: none;
        box-shadow: 0px 0px 1px 2px Highlight;
      }

      :host {
        display: block;
        width: 350px;
        height: auto;
        position: relative;
        margin: 0 15px 40px 0;
        overflow-x: auto;
      }

      /** When actual size shown is on, use the actual image width. */
      :host([actual-size]) {
        max-width: 100%;
        width: auto;
      }

      :host([actual-size]) #main-image-container {
        max-height: none;
        width: auto;
      }

      :host([actual-size]) #main-image-container img {
        width: auto;
      }

      paper-spinner-lite {
        width: 14px;
        height: 14px;
        vertical-align: text-bottom;
        --paper-spinner-color: var(--tb-orange-strong);
      }

      #steps {
        height: 15px;
        margin: 0 0 0 -15px;
        /*
         * 31 comes from adding a padding of 15px from both sides of the
         * paper-slider, subtracting 1px so that the slider width aligns
         * with the image (the last slider marker takes up 1px), and
         * adding 2px to account for a border of 1px on both sides of
         * the image. 30 - 1 + 2.
         */
        width: calc(100% + 31px);
        --paper-slider-active-color: var(--tb-orange-strong);
        --paper-slider-knob-color: var(--tb-orange-strong);
        --paper-slider-knob-start-border-color: var(--tb-orange-strong);
        --paper-slider-knob-start-color: var(--tb-orange-strong);
        --paper-slider-markers-color: var(--tb-orange-strong);
        --paper-slider-pin-color: var(--tb-orange-strong);
        --paper-slider-pin-start-color: var(--tb-orange-strong);
      }

      #main-image-container {
        max-height: 1024px;
        overflow: auto;
      }

      #main-image-container img {
        cursor: pointer;
        display: block;
        image-rendering: -moz-crisp-edges;
        image-rendering: pixelated;
        width: 100%;
        height: auto;
      }

      paper-icon-button {
        color: #2196f3;
        border-radius: 100%;
        width: 32px;
        height: 32px;
        padding: 4px;
      }
      paper-icon-button[selected] {
        background: var(--tb-ui-light-accent);
      }
      [hidden] {
        display: none;
      }
    </style>
  </template>
  
</dom-module><dom-module id="tf-image-dashboard">
  <template>
    <tf-dashboard-layout>
      <div class="sidebar" slot="sidebar">
        <div class="sidebar-section">
          <div class="line-item">
            <paper-checkbox checked="{{_actualSize}}">Show actual image size</paper-checkbox>
          </div>
        </div>
        <div class="sidebar-section">
          <h3 class="tooltip-container">Brightness adjustment</h3>
          <div class="resettable-slider-container">
            <paper-slider min="0" max="2" snaps pin step="0.01" value="{{_brightnessAdjustment}}" immediate-value="{{_brightnessAdjustment}}"></paper-slider>
            <paper-button class="x-button" on-tap="_resetBrightness" disabled="[[_brightnessIsDefault]]">Reset</paper-button>
          </div>
        </div>
        <div class="sidebar-section">
          <h3 class="tooltip-container">Contrast adjustment</h3>
          <div class="resettable-slider-container">
            <paper-slider min="0" max="500" snaps pin step="1" value="{{_contrastPercentage}}" immediate-value="{{_contrastPercentage}}"></paper-slider>
            <paper-button class="x-button" on-tap="_resetContrast" disabled="[[_contrastIsDefault]]">Reset</paper-button>
          </div>
        </div>
        <div class="sidebar-section">
          <tf-runs-selector id="runs-selector" selected-runs="{{_selectedRuns}}"></tf-runs-selector>
        </div>
      </div>
      <div class="center" slot="center">
        <template is="dom-if" if="[[_dataNotFound]]">
          <div class="no-data-warning">
            <h3>No image data was found.</h3>
            <p>Probable causes:</p>
            <ul>
              <li>You haven’t written any image data to your event files.</li>
              <li>TensorBoard can’t find your event files.</li>
            </ul>

            <p>
              If you’re new to using TensorBoard, and want to find out how to
              add data and set up your event files, check out the
              <a href="https://github.com/tensorflow/tensorboard/blob/master/README.md">README</a>
              and perhaps the
              <a href="https://www.tensorflow.org/get_started/summaries_and_tensorboard">TensorBoard tutorial</a>.
            </p>

            <p>
              If you think TensorBoard is configured properly, please see
              <a href="https://github.com/tensorflow/tensorboard/blob/master/README.md#my-tensorboard-isnt-showing-any-data-whats-wrong">the section of the README devoted to missing data problems</a>
              and consider filing an issue on GitHub.
            </p>
          </div>
        </template>
        <template is="dom-if" if="[[!_dataNotFound]]">
          <tf-tag-filterer tag-filter="{{_tagFilter}}"></tf-tag-filterer>
          <template is="dom-repeat" items="[[_categories]]" as="category">
            <tf-category-paginated-view category="[[category]]" initial-opened="[[_shouldOpen(index)]]">
              <template>
                <tf-image-loader active="[[active]]" run="[[item.run]]" tag="[[item.tag]]" sample="[[item.sample]]" of-samples="[[item.ofSamples]]" tag-metadata="[[_tagMetadata(_runToTagInfo, item.run, item.tag)]]" request-manager="[[_requestManager]]" actual-size="[[_actualSize]]" brightness-adjustment="[[_brightnessAdjustment]]" contrast-percentage="[[_contrastPercentage]]"></tf-image-loader>
              </template>
            </tf-category-paginated-view>
          </template>
        </template>
      </div>
    </tf-dashboard-layout>
    <style include="dashboard-style"></style>
    <style>
      .resettable-slider-container {
        display: flex;
      }
      .resettable-slider-container paper-slider {
        flex-grow: 1;
      }
      .resettable-slider-container paper-button {
        flex-grow: 0;
      }
      .resettable-slider-container paper-button[disabled] {
        background-color: unset;
      }
      .x-button {
        font-size: 13px;
        background-color: var(--tb-ui-light-accent);
        color: var(--tb-ui-dark-accent);
      }
      .no-data-warning {
        max-width: 540px;
        margin: 80px auto 0 auto;
      }
      paper-slider {
        --paper-slider-active-color: var(--tb-orange-strong);
        --paper-slider-knob-color: var(--tb-orange-strong);
        --paper-slider-knob-start-border-color: var(--tb-orange-strong);
        --paper-slider-knob-start-color: var(--tb-orange-strong);
        --paper-slider-markers-color: var(--tb-orange-strong);
        --paper-slider-pin-color: var(--tb-orange-strong);
        --paper-slider-pin-start-color: var(--tb-orange-strong);
      }
    </style>
  </template>
  
</dom-module><dom-module id="tf-audio-loader">
  <template>
    <tf-card-heading tag="[[tag]]" run="[[run]]" display-name="[[tagMetadata.displayName]]" description="[[tagMetadata.description]]" color="[[_runColor]]">
      <template is="dom-if" if="[[_hasMultipleSamples]]">
        <div class="heading-row">
          <div class="heading-label">
            sample: [[_sampleText]] of [[totalSamples]]
          </div>
        </div>
      </template>
      <template is="dom-if" if="[[_hasAtLeastOneStep]]">
        <div class="heading-row">
          <div class="heading-label">
            step <strong>[[_currentDatum.step]]</strong>
          </div>
          <template is="dom-if" if="[[_currentDatum.wall_time]]">
            <div class="heading-label heading-right">
              [[_currentDatum.wall_time]]
            </div>
          </template>
        </div>
      </template>
      <template is="dom-if" if="[[_hasMultipleSteps]]">
        <div class="heading-row">
          <paper-slider id="steps" immediate-value="{{_stepIndex}}" max="[[_maxStepIndex]]" max-markers="[[_maxStepIndex]]" snaps step="1" value="{{_stepIndex}}"></paper-slider>
        </div>
      </template>
    </tf-card-heading>
    <template is="dom-if" if="[[_hasAtLeastOneStep]]">
      <audio controls src$="[[_currentDatum.url]]" type$="[[_currentDatum.contentType]]"></audio>
      <tf-markdown-view html="[[_currentDatum.label]]"></tf-markdown-view>
    </template>
    <div id="main-audio-container"></div>

    <style include="tf-card-heading-style">
      :host {
        display: block;
        width: 350px;
        height: auto;
        position: relative;
        --step-slider-knob-color: #424242;
        margin-right: 15px;
        margin-bottom: 15px;
      }

      #steps {
        height: 15px;
        margin: 0 0 0 -15px;
        width: 100%;
        box-sizing: border-box;
        padding: 0 5px; /* so the slider knob doesn't butt out */
        margin-top: 5px;
        --paper-slider-active-color: var(--step-slider-knob-color);
        --paper-slider-knob-color: var(--step-slider-knob-color);
        --paper-slider-pin-color: var(--step-slider-knob-color);
        --paper-slider-knob-start-color: var(--step-slider-knob-color);
        --paper-slider-knob-start-border-color: var(--step-slider-knob-color);
        --paper-slider-pin-start-color: var(--step-slider-knob-color);
      }
    </style>
  </template>
  
</dom-module><dom-module id="tf-audio-dashboard">
  <template>
    <tf-dashboard-layout>
      <div class="sidebar" slot="sidebar">
        <div class="sidebar-section">
          <tf-runs-selector id="runs-selector" selected-runs="{{_selectedRuns}}"></tf-runs-selector>
        </div>
      </div>
      <div class="center" slot="center">
        <template is="dom-if" if="[[_dataNotFound]]">
          <div class="no-data-warning">
            <h3>No audio data was found.</h3>
            <p>Probable causes:</p>
            <ul>
              <li>You haven’t written any audio data to your event files.</li>
              <li>TensorBoard can’t find your event files.</li>
            </ul>

            <p>
              If you’re new to using TensorBoard, and want to find out how to
              add data and set up your event files, check out the
              <a href="https://github.com/tensorflow/tensorboard/blob/master/README.md">README</a>
              and perhaps the
              <a href="https://www.tensorflow.org/get_started/summaries_and_tensorboard">TensorBoard tutorial</a>.
            </p>

            <p>
              If you think TensorBoard is configured properly, please see
              <a href="https://github.com/tensorflow/tensorboard/blob/master/README.md#my-tensorboard-isnt-showing-any-data-whats-wrong">the section of the README devoted to missing data problems</a>
              and consider filing an issue on GitHub.
            </p>
          </div>
        </template>
        <template is="dom-if" if="[[!_dataNotFound]]">
          <tf-tag-filterer tag-filter="{{_tagFilter}}"></tf-tag-filterer>
          <template is="dom-repeat" items="[[_categories]]" as="category">
            <tf-category-paginated-view category="[[category]]" initial-opened="[[_shouldOpen(index)]]">
              <template>
                <tf-audio-loader active="[[active]]" run="[[item.run]]" tag="[[item.tag]]" sample="[[item.sample]]" total-samples="[[item.totalSamples]]" tag-metadata="[[_tagMetadata(_runToTagInfo, item.run, item.tag)]]" request-manager="[[_requestManager]]"></tf-audio-loader>
              </template>
            </tf-category-paginated-view>
          </template>
        </template>
      </div>
    </tf-dashboard-layout>
    <style include="dashboard-style"></style>
    <style>
      .no-data-warning {
        max-width: 540px;
        margin: 80px auto 0 auto;
      }
    </style>
  </template>
  
</dom-module><dom-module id="iron-autogrow-textarea">
  <template>
    <style>
      :host {
        display: inline-block;
        position: relative;
        width: 400px;
        border: 1px solid;
        padding: 2px;
        -moz-appearance: textarea;
        -webkit-appearance: textarea;
        overflow: hidden;
      }

      .mirror-text {
        visibility: hidden;
        word-wrap: break-word;
        @apply --iron-autogrow-textarea;
      }

      .fit {
        @apply --layout-fit;
      }

      textarea {
        position: relative;
        outline: none;
        border: none;
        resize: none;
        background: inherit;
        color: inherit;
        /* see comments in template */
        width: 100%;
        height: 100%;
        font-size: inherit;
        font-family: inherit;
        line-height: inherit;
        text-align: inherit;
        @apply --iron-autogrow-textarea;
      }

      textarea::-webkit-input-placeholder {
        @apply --iron-autogrow-textarea-placeholder;
      }

      textarea:-moz-placeholder {
        @apply --iron-autogrow-textarea-placeholder;
      }

      textarea::-moz-placeholder {
        @apply --iron-autogrow-textarea-placeholder;
      }

      textarea:-ms-input-placeholder {
        @apply --iron-autogrow-textarea-placeholder;
      }
    </style>

    
    
    <div id="mirror" class="mirror-text" aria-hidden="true">&nbsp;</div>

    
    <div class="textarea-container fit">
      <textarea id="textarea" name$="[[name]]" aria-label$="[[label]]" autocomplete$="[[autocomplete]]" autofocus$="[[autofocus]]" inputmode$="[[inputmode]]" placeholder$="[[placeholder]]" readonly$="[[readonly]]" required$="[[required]]" disabled$="[[disabled]]" rows$="[[rows]]" minlength$="[[minlength]]" maxlength$="[[maxlength]]"></textarea>
    </div>
  </template>
</dom-module><dom-module id="paper-textarea">
  <template>
    <style>
      :host {
        display: block;
      }

      :host([hidden]) {
        display: none !important;
      }

      label {
        pointer-events: none;
      }
    </style>

    <paper-input-container no-label-float$="[[noLabelFloat]]" always-float-label="[[_computeAlwaysFloatLabel(alwaysFloatLabel,placeholder)]]" auto-validate$="[[autoValidate]]" disabled$="[[disabled]]" invalid="[[invalid]]">

      <label hidden$="[[!label]]" aria-hidden="true" for$="[[_inputId]]" slot="label">[[label]]</label>

      <iron-autogrow-textarea class="paper-input-input" slot="input" id$="[[_inputId]]" aria-labelledby$="[[_ariaLabelledBy]]" aria-describedby$="[[_ariaDescribedBy]]" bind-value="{{value}}" invalid="{{invalid}}" validator$="[[validator]]" disabled$="[[disabled]]" autocomplete$="[[autocomplete]]" autofocus$="[[autofocus]]" inputmode$="[[inputmode]]" name$="[[name]]" placeholder$="[[placeholder]]" readonly$="[[readonly]]" required$="[[required]]" minlength$="[[minlength]]" maxlength$="[[maxlength]]" autocapitalize$="[[autocapitalize]]" rows$="[[rows]]" max-rows$="[[maxRows]]" on-change="_onChange"></iron-autogrow-textarea>

      <template is="dom-if" if="[[errorMessage]]">
        <paper-input-error aria-live="assertive" slot="add-on">[[errorMessage]]</paper-input-error>
      </template>

      <template is="dom-if" if="[[charCounter]]">
        <paper-input-char-counter slot="add-on"></paper-input-char-counter>
      </template>

    </paper-input-container>
  </template>
</dom-module><dom-module id="paper-toast">
  <template>
    <style>
      :host {
        display: block;
        position: fixed;
        background-color: var(--paper-toast-background-color, #323232);
        color: var(--paper-toast-color, #f1f1f1);
        min-height: 48px;
        min-width: 288px;
        padding: 16px 24px;
        box-sizing: border-box;
        box-shadow: 0 2px 5px 0 rgba(0, 0, 0, 0.26);
        border-radius: 2px;
        margin: 12px;
        font-size: 14px;
        cursor: default;
        -webkit-transition: -webkit-transform 0.3s, opacity 0.3s;
        transition: transform 0.3s, opacity 0.3s;
        opacity: 0;
        -webkit-transform: translateY(100px);
        transform: translateY(100px);
        @apply --paper-font-common-base;
      }

      :host(.capsule) {
        border-radius: 24px;
      }

      :host(.fit-bottom) {
        width: 100%;
        min-width: 0;
        border-radius: 0;
        margin: 0;
      }

      :host(.paper-toast-open) {
        opacity: 1;
        -webkit-transform: translateY(0px);
        transform: translateY(0px);
      }
    </style>

    <span id="label">{{text}}</span>
    <slot></slot>
  </template>

  
</dom-module><dom-module id="paper-toggle-button">
  <template strip-whitespace>

    <style>
      :host {
        display: inline-block;
        @apply --layout-horizontal;
        @apply --layout-center;
        @apply --paper-font-common-base;
      }

      :host([disabled]) {
        pointer-events: none;
      }

      :host(:focus) {
        outline:none;
      }

      .toggle-bar {
        position: absolute;
        height: 100%;
        width: 100%;
        border-radius: 8px;
        pointer-events: none;
        opacity: 0.4;
        transition: background-color linear .08s;
        background-color: var(--paper-toggle-button-unchecked-bar-color, #000000);

        @apply --paper-toggle-button-unchecked-bar;
      }

      .toggle-button {
        position: absolute;
        top: -3px;
        left: 0;
        height: 20px;
        width: 20px;
        border-radius: 50%;
        box-shadow: 0 1px 5px 0 rgba(0, 0, 0, 0.6);
        transition: -webkit-transform linear .08s, background-color linear .08s;
        transition: transform linear .08s, background-color linear .08s;
        will-change: transform;
        background-color: var(--paper-toggle-button-unchecked-button-color, var(--paper-grey-50));

        @apply --paper-toggle-button-unchecked-button;
      }

      .toggle-button.dragging {
        -webkit-transition: none;
        transition: none;
      }

      :host([checked]:not([disabled])) .toggle-bar {
        opacity: 0.5;
        background-color: var(--paper-toggle-button-checked-bar-color, var(--primary-color));

        @apply --paper-toggle-button-checked-bar;
      }

      :host([disabled]) .toggle-bar {
        background-color: #000;
        opacity: 0.12;
      }

      :host([checked]) .toggle-button {
        -webkit-transform: translate(16px, 0);
        transform: translate(16px, 0);
      }

      :host([checked]:not([disabled])) .toggle-button {
        background-color: var(--paper-toggle-button-checked-button-color, var(--primary-color));

        @apply --paper-toggle-button-checked-button;
      }

      :host([disabled]) .toggle-button {
        background-color: #bdbdbd;
        opacity: 1;
      }

      .toggle-ink {
        position: absolute;
        top: -14px;
        left: -14px;
        right: auto;
        bottom: auto;
        width: 48px;
        height: 48px;
        opacity: 0.5;
        pointer-events: none;
        color: var(--paper-toggle-button-unchecked-ink-color, var(--primary-text-color));

        @apply --paper-toggle-button-unchecked-ink;
      }

      :host([checked]) .toggle-ink {
        color: var(--paper-toggle-button-checked-ink-color, var(--primary-color));

        @apply --paper-toggle-button-checked-ink;
      }

      .toggle-container {
        display: inline-block;
        position: relative;
        width: 36px;
        height: 14px;
        /* The toggle button has an absolute position of -3px; The extra 1px
        /* accounts for the toggle button shadow box. */
        margin: 4px 1px;
      }

      .toggle-label {
        position: relative;
        display: inline-block;
        vertical-align: middle;
        padding-left: var(--paper-toggle-button-label-spacing, 8px);
        pointer-events: none;
        color: var(--paper-toggle-button-label-color, var(--primary-text-color));
      }

      /* invalid state */
      :host([invalid]) .toggle-bar {
        background-color: var(--paper-toggle-button-invalid-bar-color, var(--error-color));
      }

      :host([invalid]) .toggle-button {
        background-color: var(--paper-toggle-button-invalid-button-color, var(--error-color));
      }

      :host([invalid]) .toggle-ink {
        color: var(--paper-toggle-button-invalid-ink-color, var(--error-color));
      }
    </style>

    <div class="toggle-container">
      <div id="toggleBar" class="toggle-bar"></div>
      <div id="toggleButton" class="toggle-button"></div>
    </div>

    <div class="toggle-label"><slot></slot></div>

  </template>

  
</dom-module><dom-module id="tf-graph-minimap">
  <template>
    <style>
      :host {
        background-color: white;
        transition: opacity 0.3s linear;
        pointer-events: auto;
      }

      :host(.hidden) {
        opacity: 0;
        pointer-events: none;
      }

      canvas {
        border: 1px solid #999;
      }

      rect {
        fill: white;
        stroke: #111111;
        stroke-width: 1px;
        fill-opacity: 0;
        filter: url(#minimapDropShadow);
        cursor: move;
      }

      svg {
        position: absolute;
      }
    </style>
    <svg>
      <defs>
        <filter id="minimapDropShadow" x="-20%" y="-20%" width="150%" height="150%">
          <feoffset result="offOut" in="SourceGraphic" dx="1" dy="1"></feoffset>
          <fecolormatrix result="matrixOut" in="offOut" type="matrix" values="0.1 0 0 0 0 0 0.1 0 0 0 0 0 0.1 0 0 0 0 0 0.5 0"></fecolormatrix>
          <fegaussianblur result="blurOut" in="matrixOut" stddeviation="2"></fegaussianblur>
          <feblend in="SourceGraphic" in2="blurOut" mode="normal"></feblend>
        </filter>
      </defs>
      <rect></rect>
    </svg>
    <canvas class="first"></canvas>
    
    <canvas class="second"></canvas>
    <canvas class="download"></canvas>
  </template>
  
</dom-module><dom-module id="tf-graph-scene">
  <template>
    <style>
      :host {
        display: flex;
        font-size: 20px;
        height: 100%;
        width: 100%;
      }

      #svg {
        flex: 1;
        font-family: Roboto, sans-serif;
        height: 100%;
        overflow: hidden;
        width: 100%;
      }

      #hidden {
        position: fixed;
        top: 0px;
        visibility: hidden;
      }

      /* --- Node and annotation-node for Metanode --- */

      .meta > .nodeshape > rect,
      .meta > .annotation-node > rect {
        cursor: pointer;
        fill: hsl(0, 0%, 70%);
      }
      .node.meta.highlighted > .nodeshape > rect,
      .node.meta.highlighted > .annotation-node > rect {
        stroke-width: 2;
      }
      .annotation.meta.highlighted > .nodeshape > rect,
      .annotation.meta.highlighted > .annotation-node > rect {
        stroke-width: 1;
      }
      .meta.selected > .nodeshape > rect,
      .meta.selected > .annotation-node > rect {
        stroke: red;
        stroke-width: 2;
      }
      .node.meta.selected.expanded > .nodeshape > rect,
      .node.meta.selected.expanded > .annotation-node > rect {
        stroke: red;
        stroke-width: 3;
      }
      .annotation.meta.selected > .nodeshape > rect,
      .annotation.meta.selected > .annotation-node > rect {
        stroke: red;
        stroke-width: 2;
      }
      .node.meta.selected.expanded.highlighted > .nodeshape > rect,
      .node.meta.selected.expanded.highlighted > .annotation-node > rect {
        stroke: red;
        stroke-width: 4;
      }

      .faded,
      .faded rect,
      .faded ellipse,
      .faded path,
      .faded use,
      #rectHatch line,
      #ellipseHatch line {
        color: #e0d4b3 !important;
        fill: white;
        stroke: #e0d4b3 !important;
      }

      .faded path {
        stroke-width: 1px !important;
      }

      .faded rect {
        fill: url(#rectHatch) !important;
      }

      .faded ellipse,
      .faded use {
        fill: url(#ellipseHatch) !important;
      }

      .faded text {
        opacity: 0;
      }

      /* Rules used for input-tracing. */
      .input-highlight > * > rect,
      .input-highlight > * > ellipse,
      .input-highlight > * > use {
        fill: white;
        stroke: #ff9800 !important;
      }

      /*  - Faded non-input styling */
      .non-input > * > rect,
.non-input > * > ellipse,
.non-input > * > use,
/* For Const nodes. */
.non-input > * > .constant:not([class*="input-highlight"]) >
  .annotation-node > ellipse,
/* For styling of annotation nodes of non-input nodes. */
.non-input > g > .annotation > .annotation-node > rect {
        stroke: #e0d4b3 !important;
        stroke-width: inherit;
        stroke-dasharray: inherit;
      }

      .non-input path {
        visibility: hidden;
      }

      .non-input > .nodeshape > rect,
.non-input > .annotation-node > rect,
/* For styling of annotation nodes of non-input nodes. */
.non-input > g > .annotation > .annotation-node > rect {
        fill: url(#rectHatch) !important;
      }

      .non-input ellipse,
      .non-input use {
        fill: url(#ellipseHatch) !important;
      }

      .non-input > text {
        opacity: 0;
      }

      .non-input .annotation > .annotation-edge {
        marker-end: url(#annotation-arrowhead-faded);
      }

      .non-input .annotation > .annotation-edge.refline {
        marker-start: url(#ref-annotation-arrowhead-faded);
      }

      /* Input edges. */
      .input-edge-highlight > text {
        fill: black !important;
      }
      .input-highlight > .in-annotations > .annotation > .annotation-edge,
      .input-highlight-selected
        > .in-annotations
        > .annotation
        > .annotation-edge {
        stroke: #999 !important;
      }

      /* Non-input edges. */
      .non-input-edge-highlight,
.non-input > g > .annotation > path,
/* Annotation styles (label and edges respectively). */
.non-input > g >
.annotation:not(.input-highlight):not(.input-highlight-selected) >
.annotation-label
/*.annotation-edge*/
 {
        visibility: hidden;
      }

      /* --- Op Node --- */

      .op > .nodeshape > .nodecolortarget,
      .op > .annotation-node > .nodecolortarget {
        cursor: pointer;
        fill: #fff;
        stroke: #ccc;
      }

      .op.selected > .nodeshape > .nodecolortarget,
      .op.selected > .annotation-node > .nodecolortarget {
        stroke: red;
        stroke-width: 2;
      }

      .op.highlighted > .nodeshape > .nodecolortarget,
      .op.highlighted > .annotation-node > .nodecolortarget {
        stroke-width: 2;
      }

      /* --- Series Node --- */

      /* By default, don't show the series background <rect>. */
      .series > .nodeshape > rect {
        fill: hsl(0, 0%, 70%);
        fill-opacity: 0;
        stroke-dasharray: 5, 5;
        stroke-opacity: 0;
        cursor: pointer;
      }

      /* Once expanded, show the series background <rect> and hide the <use>. */
      .series.expanded > .nodeshape > rect {
        fill-opacity: 0.15;
        stroke: hsl(0, 0%, 70%);
        stroke-opacity: 1;
      }
      .series.expanded > .nodeshape > use {
        visibility: hidden;
      }

      /**
 * TODO: Simplify this by applying a stable class name to all <g>
 * elements that currently have either the nodeshape or annotation-node classes.
 */
      .series > .nodeshape > use,
      .series > .annotation-node > use {
        stroke: #ccc;
      }
      .series.highlighted > .nodeshape > use,
      .series.highlighted > .annotation-node > use {
        stroke-width: 2;
      }
      .series.selected > .nodeshape > use,
      .series.selected > .annotation-node > use {
        stroke: red;
        stroke-width: 2;
      }

      .series.selected > .nodeshape > rect {
        stroke: red;
        stroke-width: 2;
      }

      .annotation.series.selected > .annotation-node > use {
        stroke: red;
        stroke-width: 2;
      }

      /* --- Bridge Node --- */
      .bridge > .nodeshape > rect {
        stroke: #f0f;
        opacity: 0.2;
        display: none;
      }

      /* --- Structural Elements --- */
      .edge > path.edgeline.structural {
        stroke: #f0f;
        opacity: 0.2;
        display: none;
      }

      /* Reference Edge */
      .edge > path.edgeline.referenceedge {
        stroke: #ffb74d;
        opacity: 1;
      }

      /* --- Series Nodes --- */

      /* Hide the rect for a series' annotation. */
      .series > .annotation-node > rect {
        display: none;
      }

      /* --- Node label --- */

      .node > text.nodelabel {
        cursor: pointer;
        fill: #444;
      }

      .meta.expanded > text.nodelabel {
        font-size: 9px;
      }

      .series > text.nodelabel {
        font-size: 8px;
      }

      .op > text.nodelabel {
        font-size: 6px;
      }

      .bridge > text.nodelabel {
        display: none;
      }

      .node.meta.expanded > text.nodelabel {
        cursor: normal;
      }

      .annotation.meta.highlighted > text.annotation-label {
        fill: #50a3f7;
      }

      .annotation.meta.selected > text.annotation-label {
        fill: #4285f4;
      }

      /* --- Annotation --- */

      /* only applied for annotations that are not summary or constant.
(.summary, .constant gets overridden below) */
      .annotation > .annotation-node > * {
        stroke-width: 0.5;
        stroke-dasharray: 1, 1;
      }

      .annotation.summary > .annotation-node > *,
      .annotation.constant > .annotation-node > * {
        stroke-width: 1;
        stroke-dasharray: none;
      }

      .annotation > .annotation-edge {
        fill: none;
        stroke: #aaa;
        stroke-width: 0.5;
        marker-end: url(#annotation-arrowhead);
      }

      .faded .annotation > .annotation-edge {
        marker-end: url(#annotation-arrowhead-faded);
      }

      .annotation > .annotation-edge.refline {
        marker-start: url(#ref-annotation-arrowhead);
      }

      .faded .annotation > .annotation-edge.refline {
        marker-start: url(#ref-annotation-arrowhead-faded);
      }

      .annotation > .annotation-control-edge {
        stroke-dasharray: 1, 1;
      }

      #annotation-arrowhead {
        fill: #aaa;
      }

      #annotation-arrowhead-faded {
        fill: #e0d4b3;
      }

      #ref-annotation-arrowhead {
        fill: #aaa;
      }

      #ref-annotation-arrowhead-faded {
        fill: #e0d4b3;
      }

      .annotation > .annotation-label {
        font-size: 5px;
        cursor: pointer;
      }
      .annotation > .annotation-label.annotation-ellipsis {
        cursor: default;
      }

      /* Hide annotations on expanded meta nodes since they're redundant. */
      .expanded > .in-annotations,
      .expanded > .out-annotations {
        display: none;
      }

      /* --- Annotation: Constant --- */

      .constant > .annotation-node > ellipse {
        cursor: pointer;
        fill: white;
        stroke: #848484;
      }

      .constant.selected > .annotation-node > ellipse {
        fill: white;
        stroke: red;
      }

      .constant.highlighted > .annotation-node > ellipse {
        stroke-width: 1.5;
      }

      /* --- Annotation: Summary --- */

      .summary > .annotation-node > ellipse {
        cursor: pointer;
        fill: #db4437;
        stroke: #db4437;
      }

      .summary.selected > .annotation-node > ellipse {
        fill: #a52714;
        stroke: #a52714;
      }

      .summary.highlighted > .annotation-node > ellipse {
        stroke-width: 1.5;
      }

      /* --- Edge --- */

      .edge > path.edgeline {
        fill: none;
        stroke: #bbb;
        stroke-linecap: round;
        stroke-width: 0.75;
      }

      .edge .selectableedge {
        cursor: pointer;
      }

      .selectededge > path.edgeline {
        cursor: default;
        stroke: #f00;
      }

      .edge.selectededge text {
        fill: #000;
      }

      /* Labels showing tensor shapes on edges */
      .edge > text {
        font-size: 3.5px;
        fill: #666;
      }

      .dataflow-arrowhead {
        fill: #bbb;
      }

      .reference-arrowhead {
        fill: #ffb74d;
      }

      .selected-arrowhead {
        fill: #f00;
      }

      .edge .control-dep {
        stroke-dasharray: 2, 2;
      }

      /* --- Group node expand/collapse button --- */

      /* Hides expand/collapse buttons when a node isn't expanded or highlighted. Using
   incredibly small opacity so that the bounding box of the <g> parent still takes
   this container into account even when it isn't visible */
      .node:not(.highlighted):not(.expanded) > .nodeshape > .buttoncontainer {
        opacity: 0.01;
      }
      .node.highlighted > .nodeshape > .buttoncontainer {
        cursor: pointer;
      }
      .buttoncircle {
        fill: #e7811d;
      }
      .buttoncircle:hover {
        fill: #b96717;
      }
      .expandbutton,
      .collapsebutton {
        stroke: white;
      }
      /* Do not let the path elements in the button take pointer focus */
      .node > .nodeshape > .buttoncontainer > .expandbutton,
      .node > .nodeshape > .buttoncontainer > .collapsebutton {
        pointer-events: none;
      }
      /* Only show the expand button when a node is collapsed and only show the
   collapse button when a node is expanded. */
      .node.expanded > .nodeshape > .buttoncontainer > .expandbutton {
        display: none;
      }
      .node:not(.expanded) > .nodeshape > .buttoncontainer > .collapsebutton {
        display: none;
      }

      .health-pill-stats {
        font-size: 4px;
        text-anchor: middle;
      }

      .health-pill rect {
        filter: url(#health-pill-shadow);
        rx: 3;
        ry: 3;
      }

      .titleContainer {
        position: relative;
        top: 20px;
      }

      .title,
      .auxTitle,
      .functionLibraryTitle {
        position: absolute;
      }

      #minimap {
        position: absolute;
        right: 20px;
        bottom: 20px;
      }

      .context-menu {
        position: absolute;
        display: none;
        background-color: #e2e2e2;
        border-radius: 2px;
        font-size: 14px;
        min-width: 150px;
        border: 1px solid #d4d4d4;
      }

      .context-menu ul {
        list-style-type: none;
        margin: 0;
        padding: 0;
        cursor: default;
      }

      .context-menu ul li {
        padding: 4px 16px;
      }

      .context-menu ul li:hover {
        background-color: #f3913e;
        color: white;
      }
    </style>
    <div class="titleContainer">
      <div id="title" class="title">Main Graph</div>
      <div id="auxTitle" class="auxTitle">Auxiliary Nodes</div>
      <div id="functionLibraryTitle" class="functionLibraryTitle">
        Functions
      </div>
    </div>
    <svg id="svg">
      <defs>
        
        <path id="reference-arrowhead-path" d="M 0,0 L 10,5 L 0,10 C 3,7 3,3 0,0" />
        <marker class="reference-arrowhead" id="reference-arrowhead-small" viewbox="0 0 10 10" markerwidth="5" markerheight="5" refx="2" refy="5" orient="auto-start-reverse" markerunits="userSpaceOnUse">
          <use xlink:href="#reference-arrowhead-path" />
        </marker>
        <marker class="reference-arrowhead" id="reference-arrowhead-medium" viewbox="0 0 10 10" markerwidth="13" markerheight="13" refx="2" refy="5" orient="auto-start-reverse" markerunits="userSpaceOnUse">
          <use xlink:href="#reference-arrowhead-path" />
        </marker>
        <marker class="reference-arrowhead" id="reference-arrowhead-large" viewbox="0 0 10 10" markerwidth="16" markerheight="16" refx="2" refy="5" orient="auto-start-reverse" markerunits="userSpaceOnUse">
          <use xlink:href="#reference-arrowhead-path" />
        </marker>
        <marker class="reference-arrowhead" id="reference-arrowhead-xlarge" viewbox="0 0 10 10" markerwidth="20" markerheight="20" refx="2" refy="5" orient="auto-start-reverse" markerunits="userSpaceOnUse">
          <use xlink:href="#reference-arrowhead-path" />
        </marker>

        
        <path id="dataflow-arrowhead-path" d="M 0,0 L 10,5 L 0,10 C 3,7 3,3 0,0" />
        <marker class="dataflow-arrowhead" id="dataflow-arrowhead-small" viewbox="0 0 10 10" markerwidth="5" markerheight="5" refx="2" refy="5" orient="auto-start-reverse" markerunits="userSpaceOnUse">
          <use xlink:href="#dataflow-arrowhead-path" />
        </marker>
        <marker class="dataflow-arrowhead" id="dataflow-arrowhead-medium" viewbox="0 0 10 10" markerwidth="13" markerheight="13" refx="2" refy="5" orient="auto-start-reverse" markerunits="userSpaceOnUse">
          <use xlink:href="#dataflow-arrowhead-path" />
        </marker>
        <marker class="dataflow-arrowhead" id="dataflow-arrowhead-large" viewbox="0 0 10 10" markerwidth="16" markerheight="16" refx="2" refy="5" orient="auto-start-reverse" markerunits="userSpaceOnUse">
          <use xlink:href="#dataflow-arrowhead-path" />
        </marker>
        <marker class="dataflow-arrowhead" id="dataflow-arrowhead-xlarge" viewbox="0 0 10 10" markerwidth="20" markerheight="20" refx="2" refy="5" orient="auto-start-reverse" markerunits="userSpaceOnUse">
          <use xlink:href="#dataflow-arrowhead-path" />
        </marker>

        
        <marker id="annotation-arrowhead" markerwidth="5" markerheight="5" refx="5" refy="2.5" orient="auto">
          <path d="M 0,0 L 5,2.5 L 0,5 L 0,0" />
        </marker>
        <marker id="annotation-arrowhead-faded" markerwidth="5" markerheight="5" refx="5" refy="2.5" orient="auto">
          <path d="M 0,0 L 5,2.5 L 0,5 L 0,0" />
        </marker>
        <marker id="ref-annotation-arrowhead" markerwidth="5" markerheight="5" refx="0" refy="2.5" orient="auto">
          <path d="M 5,0 L 0,2.5 L 5,5 L 5,0" />
        </marker>
        <marker id="ref-annotation-arrowhead-faded" markerwidth="5" markerheight="5" refx="0" refy="2.5" orient="auto">
          <path d="M 5,0 L 0,2.5 L 5,5 L 5,0" />
        </marker>
        
        <ellipse id="op-node-stamp" rx="7.5" ry="3" stroke="inherit" fill="inherit" />
        
        <ellipse id="op-node-annotation-stamp" rx="5" ry="2" stroke="inherit" fill="inherit" />
        
        <g id="op-series-vertical-stamp">
          <use xlink:href="#op-node-stamp" x="8" y="9" />
          <use xlink:href="#op-node-stamp" x="8" y="6" />
          <use xlink:href="#op-node-stamp" x="8" y="3" />
        </g>
        
        <g id="op-series-horizontal-stamp">
          <use xlink:href="#op-node-stamp" x="16" y="4" />
          <use xlink:href="#op-node-stamp" x="12" y="4" />
          <use xlink:href="#op-node-stamp" x="8" y="4" />
        </g>
        
        <g id="op-series-annotation-stamp">
          <use xlink:href="#op-node-annotation-stamp" x="9" y="2" />
          <use xlink:href="#op-node-annotation-stamp" x="7" y="2" />
          <use xlink:href="#op-node-annotation-stamp" x="5" y="2" />
        </g>
        <svg id="summary-icon" fill="#848484" height="12" viewbox="0 0 24 24" width="12">
          <path d="M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z" />
        </svg>

        
        <pattern id="rectHatch" patterntransform="rotate(45 0 0)" width="5" height="5" patternunits="userSpaceOnUse">
          <line x1="0" y1="0" x2="0" y2="5" style="stroke-width: 1" />
        </pattern>
        <pattern id="ellipseHatch" patterntransform="rotate(45 0 0)" width="2" height="2" patternunits="userSpaceOnUse">
          <line x1="0" y1="0" x2="0" y2="2" style="stroke-width: 1" />
        </pattern>

        
        <filter id="health-pill-shadow" x="-40%" y="-40%" width="180%" height="180%">
          <fegaussianblur in="SourceAlpha" stdDeviation="0.8" />
          <feoffset dx="0" dy="0" result="offsetblur" />
          <feflood flood-color="#000000" />
          <fecomposite in2="offsetblur" operator="in" />
          <femerge>
            <femergenode />
            <femergenode in="SourceGraphic" />
          </femerge>
        </filter>
      </defs>
      
      <rect fill="white" width="10000" height="10000"></rect>
      <g id="root"></g>
    </svg>
    <tf-graph-minimap id="minimap"></tf-graph-minimap>
    <div id="contextMenu" class="context-menu"></div>
  </template>
  
</dom-module><dom-module id="tf-graph">
  <template>
    <style>
      .container {
        width: 100%;
        height: 100%;
        background: white;
        box-shadow: 0 1px 5px rgba(0, 0, 0, 0.2);
      }

      .vertical {
        width: 100%;
        height: 100%;
        @apply --layout-vertical;
      }

      .auto {
        @apply --layout-flex-auto;
        @apply --layout-vertical;
      }

      h2 {
        text-align: center;
      }

      paper-button {
        text-transform: none;
      }
    </style>
    <div class="container">
      <div class="vertical">
        <template is="dom-if" if="[[title]]">
          <h2>[[title]]</h2>
        </template>
        <tf-graph-scene id="scene" class="auto" render-hierarchy="[[renderHierarchy]]" highlighted-node="[[_getVisible(highlightedNode)]]" selected-node="{{selectedNode}}" selected-edge="{{selectedEdge}}" color-by="[[colorBy]]" progress="[[progress]]" node-context-menu-items="[[nodeContextMenuItems]]" node-names-to-health-pills="[[nodeNamesToHealthPills]]" health-pill-step-index="{{healthPillStepIndex}}" handle-edge-selected="[[handleEdgeSelected]]" trace-inputs="[[traceInputs]]"></tf-graph-scene>
      </div>
    </div>
  </template>
</dom-module><dom-module id="tf-debugger-continue-dialog">
  <template>
    <paper-button raised class="continue-button" on-click="_continueButtonCallback">
      <span>[[_continueButtonText]]</span>
    </paper-button>
    <paper-dialog with-backdrop id="continueDialog">
      <h2>Continue...</h2>
      <div class="continue-to-type">
        <div class="continue-to-type-name">
          Over Session Runs:
        </div>
        <paper-input id="continueNum" class="input-box" label="Number of Session Runs (including the current one):" always-float-label type="number" min="1" step="1" value="{{continueNum}}"></paper-input>
        <paper-icon-button class="go-button" icon="arrow-forward" title="Session Runs Go" on-tap="_sessionRunGoButtonCallback">
        </paper-icon-button>
      </div>
      <div class="continue-to-type">
        <div class="continue-to-type-name">
          Till Condition Met by Watched Tensor
        </div>
        <paper-dropdown-menu id="tensorConditionDropdown" class="input-box" no-label-float="true" label="Tensor Condition" selected-item-label="{{_selectedTensorCondition}}">
          
          <paper-listbox id="tensorConditionMenu" class="dropdown-content" slot="dropdown-content">
            <paper-item no-label-float="true">Contains +/-∞ or NaN</paper-item>
            <paper-item no-label-float="true">Contains +/-∞</paper-item>
            <paper-item no-label-float="true">Contains NaN</paper-item>
            <paper-item no-label-float="true">Max &gt;</paper-item>
            <paper-item no-label-float="true">Max &lt;</paper-item>
            <paper-item no-label-float="true">Min &gt;</paper-item>
            <paper-item no-label-float="true">Min &lt;</paper-item>
            <paper-item no-label-float="true">Max - Min &gt;</paper-item>
            <paper-item no-label-float="true">Max - Min &lt;</paper-item>
            <paper-item no-label-float="true">Mean &gt;</paper-item>
            <paper-item no-label-float="true">Mean &lt;</paper-item>
            <paper-item no-label-float="true">Standard deviation &gt;</paper-item>
            <paper-item no-label-float="true">Standard deviation &lt;</paper-item>
          </paper-listbox>
        </paper-dropdown-menu>
        <paper-icon-button class="go-button" icon="arrow-forward" title="Tensor Condition Go" on-tap="_tensorContinueGoButtonCallback">
        </paper-icon-button>
        <paper-input id="ref-value" class="input-box" label="Reference value to compare to" type="number" value="{{_tensorConditionRefValue}}" hidden="[[_isRefValueInputHidden]]">
        </paper-input>
      </div>
    </paper-dialog>
    <style include="dashboard-style"></style>
    <style>
      :host .continue-to-type-name {
        font-weight: bold;
      }
      :host paper-dialog {
        width: 36vw;
      }
      :host .input-box {
        display: inline-block;
        position: relative;
        width: 80%;
        font-size: 110%;
      }
      :host .go-button {
        position: relative;
        width: 15%;
        display: inline-block;
      }
    </style>
  </template>
  
</dom-module><dom-module id="tf-debugger-initial-dialog">
  <template>
    
    <template is="dom-if" if="[[_open]]">
      <div id="dashboard-backdrop"></div>
    </template>
    <paper-dialog id="dialog" no-cancel-on-outside-click no-cancel-on-esc-key opened="{{_open}}">
      <h2 id="dialog-title">[[_title]]</h2>
      <template is="dom-if" if="[[_hasCustomMessage]]">
        <div class="custom-message">[[_customMessage]]</div>
      </template>
      <template is="dom-if" if="[[!_hasCustomMessage]]">
        <div class="code-example">
          <div class="code-example-section">
            <div class="code-example-section-title">
              <a href="https://www.tensorflow.org/api_docs/python/tf/Session" target="_blank" rel="noopener noreferrer">tf.Session</a>:
            </div>
            <pre class="code-snippet">import tensorflow as tf
from tensorflow.python import debug as tf_debug

sess = tf.Session()
sess = tf_debug.TensorBoardDebugWrapperSession(sess, "[[_host]]:[[_port]]")
sess.run(my_fetches)
          </pre>
          </div>
          <div class="code-example-section">
            <div class="code-example-section-title">
              <a href="https://www.tensorflow.org/programmers_guide/estimators" target="_blank" rel="noopener noreferrer">Estimator</a>
              |
              <a href="https://www.tensorflow.org/api_docs/python/tf/train/MonitoredSession" target="_blank" rel="noopener noreferrer">MonitoredSession</a>:
            </div>
            <pre class="code-snippet">import tensorflow as tf
from tensorflow.python import debug as tf_debug

hook = tf_debug.TensorBoardDebugHook("[[_host]]:[[_port]]")
my_estimator.fit(x=x_data, y=y_data, steps=1000, monitors=[hook])
            </pre>
          </div>
          <div class="code-example-section">
            <div class="code-example-section-title">
              <a href="https://keras.io/models/model/" target="_blank" rel="noopener noreferrer">Keras Model</a>:
            </div>
            <pre class="code-snippet">import tensorflow as tf
from tensorflow.python import debug as tf_debug
import keras

keras.backend.set_session(
    tf_debug.TensorBoardDebugWrapperSession(tf.Session(), "[[_host]]:[[_port]]"))
# Define your keras model, called "model".
model.fit(...)
            </pre>
          </div>
        </div>
      </template>
    </paper-dialog>
    <style>
      /** We rely on a separate `_hidden` property instead of directly making use
          of the `_open` attribute because this CSS specification may strangely
          affect other elements throughout TensorBoard. See #899. */
      :host([_hidden]) {
        display: none;
      }
      :host,
      #dashboard-backdrop {
        position: absolute;
        top: 0;
        bottom: 0;
        left: 0;
        right: 0;
      }

      #dashboard-backdrop {
        background: rgba(0, 0, 0, 0.6);
      }

      .code-example {
        margin: 10px;
        font-family: monospace;
      }
      .code-example-section {
        padding-bottom: 15px;
      }
      .code-example-section-title {
        font-weight: bold;
      }
      .code-snippet {
        padding-left: 1em;
      }

      #dialog-title {
        padding-bottom: 15px;
      }

      .custom-message {
        margin-top: 0;
        margin-bottom: 15px;
      }
    </style>
  </template>
  
</dom-module><dom-module id="tf-debugger-resizer">
  <template>
    <div class="bars">
      <div class="bars-rotator">
        <span class="bars-text">| |</span>
      </div>
    </div>
    <style>
      :host([_resizer-identifier]) {
        position: absolute;
        background: #ccc;
        user-select: none;
      }

      :host([is-horizontal]) {
        cursor: row-resize;
        height: 10px;
        left: 0;
        right: 0;
      }

      :host([_is-vertical]) {
        cursor: col-resize;
        right: -15px;
        top: 0;
        bottom: 0;
        width: 10px;
      }

      .bars {
        width: 80%;
        text-align: center;
        position: absolute;
        top: 50%;
        left: 50%;
        font-size: 5px;
        transform: translate(-50%, -50%);
      }

      /** This block prevents the bars rotator from having a height that is
          the entire viewport, thus occluding it and giving it an undesired cursor
          value. */
      .bars-rotator {
        display: inline-block;
      }

      :host([is-horizontal]) .bars-rotator {
        transform: rotate(90deg);
      }

      .bars-text {
        transform: scaleY(15);
        white-space: nowrap;
        display: block;
        font-weight: 400;
      }
    </style>
  </template>
  
</dom-module><dom-module id="tf-op-selector">
  <template>
    <div>
      <paper-dropdown-menu id="filter-mode" no-label-float="true" label="Filter Mode" selected-item-label="{{_filterMode}}">
        <paper-listbox class="dropdown-content" slot="dropdown-content">
          <paper-item no-label-float="true">Node Name</paper-item>
          <paper-item no-label-float="true">Op Type</paper-item>
        </paper-listbox>
      </paper-dropdown-menu>
      <paper-input id="filter-input" label="Filter Regex" always-float-label value="{{_filterInput}}"></paper-input>
    </div>
    <paper-spinner-lite active class="spinner" id="loading-spinner" hidden="[[!_isLoading]]">
    </paper-spinner-lite>
    <div id="selector-hierarchy"></div>
    <style>
      .indented-level-container .content-container {
        margin: 0 0 0 20px;
      }

      .level-container iron-collapse {
        padding: 0 0 0 20px;
      }

      paper-checkbox {
        display: inline-block;
        width: 18px;
        height: 18px;
        margin: 0 8px 0 0;
      }

      .op-type {
        padding-right: 10px;
        color: #444;
      }

      .op-title-leaf {
        text-decoration: underline;
        cursor: pointer;
      }

      .op-title-leaf:hover {
        color: blue;
      }

      .partial-checkbox {
        background: #f57c00;
      }

      .node-expand-button {
        margin: 0 0 0 -13px;
      }

      .level-title-text {
        display: inline-block;
        font-weight: 800;
        margin: 0 0 0 -1px;
      }

      .op-description {
        font-weight: 300;
        margin: 0 0 0 27px;
        padding: 10px 0;
      }

      .spinner {
        width: 20px;
        height: 20px;
        vertical-align: middle;
      }

      #filter-mode {
        width: 150px;
        display: inline-block;
      }

      #filter-input {
        width: 250px;
        display: inline-block;
      }

      .highlighted {
        color: red;
      }
      .highlighted > .op-type {
        color: red;
      }

      #selector-hierarchy {
        width: 100%;
      }

      [hidden] {
        display: none;
      }
    </style>
  </template>
  
  
</dom-module><dom-module id="tf-session-runs-view">
  <template>
    <div class="session-runs-div">
      <div class="section-title">Session Runs</div>
      <table id="session-runs-table" align="left" class="session-runs-table">
        <tbody><tr align="left">
          <th>Feeds</th>
          <th>Fetches</th>
          <th>Targets</th>
          <th>#(Devices)</th>
          <th>Count</th>
        </tr>
      </tbody></table>
    </div>
    <style>
      :host {
        display: block;
        padding: 20px 0;
      }

      .section-title {
        font-size: 110%;
        font-weight: bold;
      }
      :host .indented-level-container .content-container {
        margin: 0 0 0 10px;
      }

      /* TODO(cais): This needs work: the table shouldn't get too wide when
         there are many feeds/fetches/targte names. */
      .session-runs-table {
        align-content: left;
        align-items: left;
        text-align: left;
        font-size: 90%;
        border-style: solid 1px black;
        table-layout: fixed;
        width: 100%;
        word-break: break-all;
        padding-top: 3px;
        padding-left: 3px;
        padding-right: 3px;
        box-shadow: 3px 3px #ddd;
      }
      .active-session-run {
        background-color: #ffffe0;
        font-weight: bold;
      }
      .sole-active-session-run {
        background-color: rgb(172, 232, 188);
        font-weight: bold;
      }

      .node-or-tensor-element {
        text-decoration: underline;
        cursor: pointer;
      }

      .node-or-tensor-element:hover {
        color: blue;
      }
    </style>
  </template>
  
</dom-module><dom-module id="tf-source-code-view">
  <template>
    <div id="fullStackDialog" hidden$="[[!_fullStackShown]]">
      <div id="full-stack-title">
        <paper-icon-button icon="filter-list" disabled="true">
        </paper-icon-button>
        Full Stack Trace of Node:
        <div id="full-stack-node-name">"[[_fullStackNodeName]]"</div>
        <paper-icon-button icon="close" id="close-full-stack-button" title="Close Full Stack" on-tap="_closeFullStackDialog">
        </paper-icon-button>
      </div>
      <ul id="full-stack-content"></ul>
    </div>
    <paper-tabs id="source-files-tabs" selected="{{_filePathSelected}}">
      <template is="dom-repeat" items="[[_shortFilePaths]]">
        <paper-tab id="[[item.id]]">[[item.name]]</paper-tab>
      </template>
    </paper-tabs>
    <div id="source-file-content" class="source-content">
      <template is="dom-repeat" items="[[_fileLines]]">
        <div class$="{{item.sourceClass}}" id="source-line-[[item.lineno]]">
          <span class="source-line-number" id="source-lineno-[[item.lineno]]">
            [[item.lineno]]
          </span>
          <span class="source-line-node-toggle" id="source-line-node-toggle-[[item.lineno]]">
            [[item.numNodes]]
          </span>
          <span class="source-line-text" id="source-line-text-[[item.lineno]]">
            [[item.text]]
          </span>
          <div class="source-line-nodes" id="source-line-nodes-[[item.lineno]]"></div>
        </div>
      </template>
    </div>
    <style>
      #source-files-tabs {
        position: relative;
        height: 8%;
      }
      .source-content {
        position: relative;
        height: 90%;
        font-family: monospace;
        font-size: 90%;
        overflow-x: scroll;
        overflow-y: scroll;
      }
      .source-content :hover {
        background-color: #ffff00;
      }
      .highlighted-source-line {
        background-color: #ffffe0;
      }
      .source-line-number {
        display: inline-block;
        color: lightblue;
        width: 2em;
        text-align: right;
        padding-right: 1em;
      }
      .source-line-node-toggle {
        display: inline-block;
        color: blue;
        width: 5em;
        text-align: right;
        padding-right: 1em;
        text-decoration: underline;
        cursor: pointer;
      }
      .source-line-nodes {
        padding-left: 4em;
        text-decoration: underline;
        cursor: pointer;
        color: blue;
        margin-top: 0em;
        margin-bottom: 0em;
        margin-right: 1em;
      }
      .source-line-node-entry {
        margin-right: 1em;
        background-color: yellow;
      }
      .source-line-nodes span {
        text-decoration: none;
        background-color: yellow;
      }
      .source-line-text {
        display: inline;
        word-wrap: break-word;
      }
      #fullStackDialog {
        z-index: 1000;
        position: absolute;
        top: 10%;
        left: 50%;
        width: 45%;
        height: 85%;
        background-color: white;
        border: 1px solid gray;
        font-family: monospace;
        box-shadow: 3px 3px #ddd;
        overflow-y: auto;
      }
      #full-stack-title {
        font-size: 110%;
        position: relative;
        width: 100%;
        background-color: #eee;
        text-align: center;
        font-weight: bold;
      }
      #full-stack-node-name {
        color: blue;
      }
      :host #full-stack-content {
        padding-top: 1em;
        padding-right: 0.5em;
        margin-top: 0.5em;
        font-size: 90%;
        word-wrap: break-word;
        overflow: auto;
      }
      .stack-frame-clickable {
        color: blue;
        text-decoration: underline;
        cursor: pointer;
      }
      .stack-frame-nonclickable {
        color: #555;
      }
      #close-full-stack-button {
        float: right;
      }
    </style>
  </template>
  
</dom-module><dom-module id="tf-tensor-data-summary">
  <template>
    <span class="section-title">Tensor Value Overview</span>
    <div id="tensor-data-div" class="tensor-data-div">
      <table id="tensor-data-table" align="left" class="tensor-data-table">
        <thead>
          <tr align="left">
            <th>Tensor</th>
            <th>Count</th>
            <th>DType</th>
            <th>Shape</th>
            <th width="25%">Value</th>
            <th width="25%">
              Health Pill
              <paper-toggle-button id="show-health-pills" checked="{{_healthPillsEnabled}}">
              </paper-toggle-button>
              <paper-card>
                <div class="health-pill-legend" id="health-pill-legend"></div>
              </paper-card>
            </th>
            <th width="5%"></th>
          </tr>
        </thead>
        <tbody></tbody>
      </table>
    </div>
    <style>
      :host #tensor-data-div {
        height: 100%;
        overflow-y: auto;
      }
      .section-title {
        font-size: 110%;
        font-weight: bold;
      }
      :host .indented-level-container .content-container {
        margin: 0 0 0 10px;
      }
      :host .tensor-data-table {
        align-content: left;
        align-items: left;
        display: block;
        text-align: left;
        vertical-align: middle;
        width: 100%;
        padding-top: 3px;
        padding-left: 3px;
        padding-right: 3px;
        box-shadow: 3px 3px #ddd;
      }
      :host #tensor-data-table th {
        vertical-align: top;
      }
      :host .active-tensor {
        background-color: #ffffe0;
        font-weight: bold;
        border: solid 1px #888;
      }
      :host .highlighted {
        color: red;
      }
      :host .health-pill-legend {
        float: right;
        font-weight: normal;
      }
      :host #show-health-pills {
        display: inline-block;
      }
      .value-expansion-link {
        text-decoration: underline;
        cursor: pointer;
      }
      .value-expansion-link :hover {
        color: blue;
      }
      .health-pill :hover {
        cursor: pointer;
      }
      .tensor-name {
        text-decoration: underline;
        cursor: pointer;
      }
      .tensor-name :hover {
        color: blue;
      }
    </style>
  </template>
  
</dom-module><dom-module id="tensor-widget-style">
  <template>
    <style>/* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
 Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
     http://www.apache.org/licenses/LICENSE-2.0
 Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

.tensor-widget {
  font-family: monospace;
  font-size: 14px;
  overflow-x: hidden;
  overflow-y: hidden;
  position: relative;
}

.tensor-widget-dim {
  border: 1px solid rgb(160, 160, 160);
  display: inline-block;
  font-size: 12px;
  height: 14px;
  line-height: 14px;
  margin-left: 15px;
  margin-right: 15px;
  padding: 2px;
}

.tensor-widget-dim-comma {
  color: rgb(128, 128, 128);
  display: inline-block;
  font-size: 12px;
  height: 14px;
  line-height: 14px;
}

.tensor-widget-dim-highlighted {
  border: 1px solid rgb(100, 180, 255);
  font-weight: bold;
}

.tensor-widget-dim-brackets {
  color: rgb(128, 128, 128);
  display: inline-block;
  font-size: 8pt;
}

.tensor-widget-dim-dropdown {
  background-color: rgb(255, 255, 255);
  border: 1px solid rgb(128, 128, 128);
  box-shadow: 2px 2px 2px #b0b0b0;
  cursor: pointer;
  width: 180px;
  z-index: 1000;
}

.tensor-widget-dim-dropdown-menu-item {
  border-bottom: 1px solid rgb(180, 180, 180);
  font-size: 12px;
  padding: 3px;
  user-select: none;
}

.tensor-widget-dim-dropdown-menu-item-active {
  background-color: rgb(100, 180, 255);
}

.tensor-widget-dim-dropdown-menu-item-disabled {
  color: rgb(128, 128, 128);
}

.tensor-widget-dtype {
  align-content: center;
  color: rgb(60, 60, 60);
  display: inline-block;
  font-size: 8pt;
  height: 48px;
  line-height: 22px;
  max-height: 22px;
  padding-left: 14px;
  padding-right: 10px;
  position: relative;
  vertical-align: middle;
}

.tensor-widget-dtype-label {
  color: rgb(128, 128, 128);
}

.tensor-widget-header {
  background-color: rgb(252, 252, 252);
  box-shadow: 2px 2px 2px #b0b0b0;
  height: 40px;
  line-height: 40px;
  max-height: 40px;
  position: relative;
  vertical-align: middle;
  width: 100%;
}

.tensor-widget-info {
  align-content: center;
  color: rgb(0, 0, 255);
  display: inline-block;
  font-size: 8pt;
  height: 22px;
  line-height: 22px;
  margin-left: 8px;
  max-height: 22px;
  position: relative;
  vertical-align: middle;
}

.tensor-widget-menu-thumb {
  color: rgb(32, 33, 36);
  cursor: pointer;
  display: inline-block;
  font-weight: bold;
  font-size: 16px;
  margin-left: 10px;
  margin-right: 5px;
  position: relative;
  user-select: none;
}

.tensor-widget-menu-thumb:hover {
  color: rgb(227, 116, 0);
}

.tensor-widget-shape {
  color: rgb(60, 60, 60);
  display: inline-block;
  margin-left: 12px;
}

.tensor-widget-shape-label {
  color: rgb(128, 128, 128);
  display: inline-block;
}

.tensor-widget-shape-value {
  display: inline-block;
}

.tensor-widget-slicing-group {
  background-color: rgb(250, 250, 250);
  border-bottom: 1px solid rgb(190, 190, 190);
  display: block;
  height: 18px;
  text-align: center;
  padding-bottom: 5px;
  padding-top: 5px;
}

.tensor-widget-tensor-name {
  color: black;
  display: inline-block;
  font-weight: bold;
}

.tensor-widget-left-ruler-tick {
  background-color: var(--ruler-background-color);
  border-bottom: var(--border-style);
  border-top: var(--border-style);
  box-shadow: var(--border-style);
  color: rgb(110, 110, 110);
  cursor: pointer;
  display: inline-block;
  font-size: 12px;
  height: 29px;
  line-height: 29px;
  margin-left: 0px;
  max-width: 45px;
  text-align: center;
  user-select: none;
  vertical-align: middle;
  width: 45px;
}

.tensor-widget-top-ruler {
  height: 24px;
  white-space: nowrap;
}

.tensor-widget-value-tooltip {
  background-color: rgb(240, 240, 240);
  border: 1px solid rgb(160, 160, 160);
  box-shadow: 1px 1px 1px #b0b0b0;
  display: none;
  font-size: 13px;
  padding: 5px;
  position: absolute;
  user-select: none;
  width: 240px;
}

.tensor-widget-value-tooltip-colorbar {
  height: 24px;
  width: 95%;
}

.tensor-widget-value-tooltip-indices {
  font-weight: bold;
}

.tensor-widget-value-tooltip-value {
  margin-top: 20px;
}

.tensor-widget-top-ruler-tick {
  background-color: var(--ruler-background-color);
  border-bottom: var(--border-style);
  border-right: var(--border-style);
  color: rgb(110, 110, 110);
  cursor: pointer;
  display: inline-block;
  font-size: 12px;
  height: 24px;
  line-height: 24px;
  padding-right: 2px;
  text-align: center;
  user-select: none;
  vertical-align: middle;
  width: 45px;
}

.tensor-widget-value-div {
  border-bottom: var(--border-style);
  border-right: var(--border-style);
  cursor: pointer;
  display: inline-block;
  font-size: 80%;
  height: 24px;
  line-height: 24px;
  max-width: 45px;
  padding-right: 2px;
  text-align: right;
  user-select: none;
  vertical-align: middle;
  width: 45px;
}

.tensor-widget-value-div-selection {
  font-weight: bold;
}

.tensor-widget-value-div-selection-bottom {
  border-bottom: 0.5px solid blue;
}

.tensor-widget-value-div-selection-left {
  border-left: 0.5px solid blue;
}

.tensor-widget-value-div-selection-right {
  border-right: 0.5px solid blue;
}

.tensor-widget-value-div-selection-top {
  border-top: 0.5px solid blue;
}

.tensor-widget-value-section {
  --border-style: 1px solid rgb(140, 140, 140);
  --ruler-background-color: rgb(210, 210, 210);
  -moz-user-select: none;
  -ms-user-select: none;
  -khtml-user-select: none;
  -webkit-touch-callout: none;
  -webkit-user-select: none;
}

.tensor-widget-value-row {
  height: 25px;
  line-height: 25px;
  white-space: nowrap;
}
</style>
  </template>
</dom-module><dom-module id="tf-debugger-line-chart">
  <template>
    <vz-line-chart2 x-components-creation-method="[[_lineChartXComponentsCreationMethod]]" y-value-accessor="[[_lineChartYValueAccessor]]" tooltip-columns="[[_lineChartTooltipColumns]]" smoothing-enabled="[[_lineChartSmoothingEnabled]]"></vz-line-chart2>
    <style>
      vz-line-chart2 {
        height: 300px;
        position: relative;
      }
    </style>
  </template>

  
</dom-module><dom-module id="tf-tensor-value-view">
  <template>
    <paper-toast id="tensorValueToast" text="" always-on-top></paper-toast>
    <table class="tensor-value-view-table">
      <tbody><tr>
        <td colspan="2">
          <div>
            <paper-item id="tensor-name" on-tap="tensorNameCallback">
              <span class="tensor-name-text">[[tensorName]]</span>
            </paper-item>
            <paper-icon-button icon="close" class="value-view-icon-button" id="value-view-icon-button" title="Close" on-tap="closeButtonCallback"></paper-icon-button>
            <paper-icon-button icon="forward" class="value-view-icon-button" id="value-view-icon-button" title="Continue to" on-tap="continueToButtonCallback"></paper-icon-button>
          </div>
        </td>
      </tr>
      <tr class="tensor-value-value-tr">
        <td>
          <template is="dom-if" if="[[_useTensorWidget]]">
            <div id="tensor-widget"></div>
          </template>

          <template is="dom-if" if="[[!_useTensorWidget]]">
            <paper-item id="debug-op"></paper-item>
            <div>
              <paper-input class="inline value-card-input" label="Slicing" id="slicing" value="{{slicing}}" on-change="refresh">
              </paper-input>
              <div>
                <paper-input class="inline value-card-input" label="Time Indices" id="time-indices" value="{{timeIndices}}" on-change="refresh">
                </paper-input>
                <paper-button raised id="time-indices-toggle-button" class="tensor-value-buttons" on-click="_timeIndicesToggleButtonCallback">Full History</paper-button>
              </div>

              </div></template></td><td class="tensor-value-view-td">
                <template is="dom-if" if="[[_isValueScalar]]">
                  <paper-input class="inline" label="Scalar Value" id="value-scalar" value="[[_dataScalar]]">
                  </paper-input>
                </template>
                <template is="dom-if" if="[[_isValueLineChart]]">
                  <tf-debugger-line-chart data="[[_lineChartData]]"></tf-debugger-line-chart>
                </template>
                <template is="dom-if" if="[[_isValueImage]]">
                  <img class="value-image" height="250px" width="250px" src$="[[_dataImageSrc]]">
                </template>
              </td>
            
          
        
      </tr>
    </tbody></table>

    <style include="tensor-widget-style"></style>
    <style>
      .tensor-value-buttons {
        height: 75%;
        font-size: 10px;
      }
      .tensor-value-view-table {
        width: 500px;
        display: inline-table;
        border-spacing: 5px;
        padding-top: 3px;
        padding-bottom: 3px;
        padding-left: 3px;
        padding-right: 3px;
        background-color: #f8f8f8;
        box-shadow: 3px 3px 1px 1px #d8d8d8;
      }
      .tensor-value-view-td {
        width: 350px;
      }
      .value-card-input {
        width: 150px;
      }
      #tensor-name {
        display: inline-block;
        position: relative;
        width: 50%;
        cursor: pointer;
      }
      .tensor-name-text {
        color: blue;
        text-decoration: underline;
      }
      #debug-op {
        font-size: 90%;
      }
      .value-image {
        image-rendering: pixelated;
      }
      .value-view-icon-button {
        display: inline-block;
        float: right;
        text-align: right;
        width: 20%;
        text-decoration: underline;
        cursor: pointer;
        font-size: 90%;
        color: blue;
      }
      #tensor-widget {
        border: 1px solid rgb(160, 160, 160);
        /* box-sizing: content-box;
        -moz-box-sizing: content-box;
        -webkit-box-sizing: content-box; */
        height: 280px;
        width: 484px;
      }
      #slicing,
      #time-indices {
        --paper-input-container-input: {
          font-family: monospace;
        }
      }
    </style>
  </template>
  
</dom-module><dom-module id="tf-tensor-value-multi-view">
  <template>
    <div id="multiView">
      <div class="section-title">Tensor Values</div>
      <div id="multi-tensor-view-container"></div>
    </div>
    <style>
      .section-title {
        font-size: 110%;
        font-weight: bold;
      }
      #multiView {
        background-color: #fff;
        padding-top: 3px;
        padding-left: 3px;
        padding-right: 3px;
        box-shadow: 3px 3px #eee;
      }
    </style>
  </template>
  
</dom-module><dom-module id="tf-debugger-dashboard">
  <template>
    <paper-toast id="toast" text="" always-on-top></paper-toast>
    <tf-debugger-initial-dialog id="initialDialog"></tf-debugger-initial-dialog>
    
    <tf-dashboard-layout>
      <div class="sidebar" slot="sidebar" id="left-pane">
        <div id="node-entries" class="node-entries">
          <div class="debugger-section-title">Runtime Node List</div>
          <div class="toggle-source-code">
            Show Code:
            <paper-toggle-button class="toggle-source-code" id="show-source-code" checked="{{_sourceCodeShown}}"></paper-toggle-button>
          </div>
          <tf-op-selector debug-watches="[[_debugWatches]]" debug-watch-change="[[_createDebugWatchChangeHandler()]]" node-clicked="[[_createNodeClickedHandler()]]" force-expand-and-check-node-name="[[_forceExpandAndCheckNodeName]]" force-expand-node-name="[[_forceExpandNodeName]]">
          </tf-op-selector>
        </div>
        <div id="source-code-view-div" class="source-code-view-div" hidden$="{{!_sourceCodeShown}}">
          <div class="debugger-section-title">Source Code</div>
          <tf-source-code-view id="sourceCodeView" request-manager="[[_requestManager]]" debug-watches="[[_debugWatches]]" focus-node-name="[[_sourceFocusNodeName]]" node-clicked="[[_createNodeClickedHandler()]]" continue-to-node="[[_createContinueToNodeHandler()]]"></tf-source-code-view>
        </div>
        <tf-debugger-resizer current-length="{{_leftPaneWidth}}" min-length="[[_minleftPaneWidth]]" max-length="[[_maxleftPaneWidth]]">
        </tf-debugger-resizer>
        <div>
          <tf-session-runs-view id="sessionRunsView" latest-session-run="[[_latestSessionRun]]" session-run-key-to-device-names="[[_sessionRunKey2DeviceNames]]" sole-active="[[_sessionRunSoleActive]]" node-or-tensor-clicked="[[_createFeedFetchTargetClickedHandler()]]">
          </tf-session-runs-view>
        </div>
        <div class="buttons-container">
          <paper-button raised class="continue-button" on-click="_step">
            <span>[[_stepButtonText]]</span>
          </paper-button>
          <tf-debugger-continue-dialog id="continueDialog" session-run-go="[[_createSessionRunGo()]]" tensor-condition-go="[[_createTensorConditionGo()]]" force-continuation-stop="[[_createForceContinuationStop()]]">
          </tf-debugger-continue-dialog>
        </div>
        <div class="container">
          <tf-graph-loader id="loader" out-graph-hierarchy="{{graphHierarchy}}" out-graph="{{graph}}" out-stats="{{stats}}" progress="{{_graphProgress}}"></tf-graph-loader>
        </div>
      </div>
      <div class="center" slot="center" id="center-content">
        <div id="top-right-quadrant">
          <paper-tabs selected="{{_topRightSelected}}">
            <template is="dom-repeat" items="[[_topRightTabs]]">
              <paper-tab id="[[item.id]]">[[item.name]]</paper-tab>
            </template>
          </paper-tabs>
          <div class="runtime-graph-device">
            <span id="runtime-graph-device-name"> </span>
            <paper-dropdown-menu id="active-runtime-graph-device-name" no-label-float="true" label="Device name" selected-item-label="{{_activeRuntimeGraphDeviceName}}">
              <paper-listbox class="dropdown-content" slot="dropdown-content">
                <template is="dom-repeat" items="[[_activeSessionRunDevices]]">
                  <paper-item no-label-float="true">[[item]]</paper-item>
                </template>
              </paper-listbox>
            </paper-dropdown-menu>
            <paper-spinner-lite class="spinner" id="top-right-spinner" hidden="[[!_busy]]" active="[[_busy]]">
            </paper-spinner-lite>
          </div>
          <paper-progress id="top-right-progress-bar" value="0"></paper-progress>
          <template is="dom-if" if="[[_isTopRightRuntimeGraphsActive]]">
            <div id="graph-container">
              <tf-graph id="graph" graph-hierarchy="[[graphHierarchy]]" basic-graph="[[graph]]" stats="[[stats]]" progress="{{_graphProgress}}" color-by="structure" color-by-params="{{colorByParams}}" render-hierarchy="{{_renderHierarchy}}" node-context-menu-items="[[_createNodeContextMenuItems()]]"></tf-graph>
              <div class="context-menu"></div>
            </div>
          </template>
          <template is="dom-if" if="[[_isTopRightTensorValuesActive]]">
            <tf-tensor-value-multi-view id="tensorValueMultiView" continue-to-callback="[[_createContinueToCallback()]]" tensor-name-clicked="[[_createNodeClickedHandler()]]" get-health-pill="[[_createGetHealthPill()]]">
            </tf-tensor-value-multi-view>
          </template>
        </div>

        <tf-debugger-resizer is-horizontal="true" current-length="{{_topRightQuadrantHeight}}" min-length="[[_minTopRightQuadrantHeight]]" max-length="[[_maxTopRightQuadrantHeight]]">
        </tf-debugger-resizer>

        <div id="tensor-data" class="tensor-data">
          <tf-tensor-data-summary id="tensorDataSummary" latest-tensor-data="[[_latestTensorData]]" expand-handler="[[_createTensorDataExpandHandler()]]" continue-to-callback="[[_createContinueToCallback()]]" highlighted-node-name="[[_highlightNodeName]]" tensor-name-clicked="[[_createNodeClickedHandler()]]" get-health-pill="[[_createGetHealthPill()]]">
          </tf-tensor-data-summary>
        </div>
      </div>
    </tf-dashboard-layout>

    <style include="dashboard-style"></style>
    <style>
      :host {
        display: block;
        position: absolute;
        left: 0;
        right: 0;
        top: 0;
        bottom: 0;
        overflow: hidden;
      }
      paper-toast {
        text-align: center;
        font-size: 110%;
        width: 40vw;
        margin-left: 30vw;
      }
      tf-dashboard-layout {
        --tf-dashboard-layout-sidebar-basis: auto;
        --tf-dashboard-layout-sidebar-max-width: none;
        --tf-dashboard-layout-sidebar-min-width: none;
      }
      .debugger-section-title {
        font-size: 110%;
        font-weight: bold;
      }
      paper-tabs {
        color: #555;
        font-weight: normal;
      }
      paper-tab.iron-selected {
        color: black;
        font-weight: bold;
      }
      #initialDialog {
        /** This matches the default z-index of paper-dialog backdrops. */
        z-index: 102;
      }
      /** Resize the region for the graph as the user resizes the region. */
      #graph-container {
        height: calc(100% - 120px);
        /** Clip the minimap if the height of the graph container is small. */
        overflow: hidden;
        position: relative;
      }
      #graph {
        position: relative;
        display: block;
        width: 100%;
        height: 100%;
      }
      #tooltip-sorting {
        display: flex;
        font-size: 14px;
        margin-top: 5px;
      }
      #tooltip-sorting-label {
        margin-top: 13px;
      }
      #tooltip-sorting paper-dropdown-menu {
        margin-left: 10px;
        --paper-input-container-focus-color: var(--tb-orange-strong);
        width: 105px;
      }
      #x-type-selector paper-button {
        margin: 5px 3px;
      }
      .runtime-graph-device {
        align-items: center;
        display: flex;
        flex-wrap: wrap;
      }
      #runtime-graph-device-name {
        font-size: 85%;
        word-break: break-all;
        display: inline-block;
      }
      #active-runtime-graph-device-name {
        font-size: 85%;
        width: 350px;
        display: inline-block;
      }
      #top-right-progress-bar {
        width: 100%;
        display: inline-block;
        vertical-align: middle;
      }
      .line-item {
        display: block;
        padding-top: 5px;
      }
      .no-data-warning {
        max-width: 540px;
        margin: 80px auto 0 auto;
      }
      .sidebar {
        height: 100%;
        overflow-x: visible;
        position: relative;
      }
      .center {
        position: relative;
        height: 100%;
      }
      tf-debugger-resizer {
        right: -10px;
      }
      #center-content {
        position: absolute;
        right: 0;
      }
      /** The resizer should have no space to the left of it. */
      #center-content tf-debugger-resizer[is-horizontal] {
        margin-left: -23px;
      }
      .context-menu {
        position: absolute;
        display: none;
        background-color: #e2e2e2;
        border-radius: 2px;
        font-size: 14px;
        min-width: 150px;
        border: 1px solid #d4d4d4;
      }
      .spinner {
        width: 20px;
        height: 20px;
        vertical-align: middle;
      }
      .node-entries {
        box-shadow: 3px 3px #ddd;
        box-sizing: border-box;
        height: 80%;
        overflow: auto;
        padding-left: 3px;
        padding-right: 3px;
        padding-top: 3px;
        position: relative;
        vertical-align: top;
        width: 100%;
      }
      .source-code-view-div {
        position: relative;
        height: 40%;
        width: 100%;
        vertical-align: top;
        overflow: hidden;
        padding-top: 3px;
        padding-left: 3px;
        padding-right: 3px;
        box-shadow: 3px 3px #ddd;
      }
      #sessionRunsView {
        position: relative;
        width: 100%;
        overflow: auto;
        max-height: 25vh;
      }
      .buttons-container {
        padding: 20px 0;
      }
      #tensor-data {
        position: absolute;
        bottom: 0;
        left: 0;
        right: 0;
        padding: 20px 0;
        margin: 0 0 20px 0;
      }
      #tensorDataSummary {
        position: absolute;
        bottom: 0;
        left: 0;
        right: 0;
        top: 0;
      }
      #top-right-quadrant {
        height: 66%;
        overflow: auto;
      }
      .toggle-source-code {
        margin-right: 1em;
        font-size: 80%;
        float: right;
      }
      .context-menu ul {
        list-style-type: none;
        margin: 0;
        padding: 0;
        cursor: default;
      }
      .context-menu ul li {
        padding: 4px 16px;
      }
      .context-menu ul li:hover {
        background-color: #f3913e;
        color: white;
      }

      paper-input {
        width: 200px;
      }
      .inline,
      paper-item {
        display: inline;
      }

      vz-line-chart {
        height: 300px;
        position: relative;
      }
      [hidden] {
        display: none;
      }
    </style>
  </template>
  
  
</dom-module><dom-module id="paper-material-shared-styles">
  <template>
    <style>
      :host {
        display: block;
        position: relative;
      }

      :host([elevation="1"]) {
        @apply --shadow-elevation-2dp;
      }

      :host([elevation="2"]) {
        @apply --shadow-elevation-4dp;
      }

      :host([elevation="3"]) {
        @apply --shadow-elevation-6dp;
      }

      :host([elevation="4"]) {
        @apply --shadow-elevation-8dp;
      }

      :host([elevation="5"]) {
        @apply --shadow-elevation-16dp;
      }
    </style>
  </template>
</dom-module><dom-module id="paper-material">
  <template>
    <style include="paper-material-shared-styles"></style>
    <style>
      :host([animated]) {
        @apply --shadow-transition;
      }
      :host {
        @apply --paper-material;
      }
    </style>

    <slot></slot>
  </template>
</dom-module><dom-module id="tf-graph-debugger-data-card">
  <template>
    <style>
      :host {
        font-size: 12px;
        margin: 0;
        padding: 0;
        display: block;
      }

      h2 {
        padding: 0;
        text-align: center;
        margin: 0;
      }

      .health-pill-legend {
        padding: 15px;
      }

      .health-pill-legend h2 {
        text-align: left;
      }

      .health-pill-entry {
        margin: 10px 10px 10px 0;
      }

      .health-pill-entry .color-preview {
        width: 26px;
        height: 26px;
        border-radius: 3px;
        display: inline-block;
        margin: 0 10px 0 0;
      }

      .health-pill-entry .color-label,
      .health-pill-entry .tensor-count {
        color: #777;
        display: inline-block;
        height: 26px;
        font-size: 22px;
        line-height: 26px;
        vertical-align: top;
      }

      .health-pill-entry .tensor-count {
        float: right;
      }

      #health-pill-step-slider {
        width: 100%;
        margin: 0 0 0 -15px;
        /* 31 comes from adding a padding of 15px from both sides of the paper-slider, subtracting
   * 1px so that the slider width aligns with the image (the last slider marker takes up 1px),
   * and adding 2px to account for a border of 1px on both sides of the image. 30 - 1 + 2.
   * Apparently, the paper-slider lacks a mixin for those padding values. */
        width: calc(100% + 31px);
      }

      #health-pills-loading-spinner {
        width: 20px;
        height: 20px;
        vertical-align: top;
      }

      #health-pill-step-number-input {
        text-align: center;
        vertical-align: top;
      }

      #numeric-alerts-table-container {
        max-height: 400px;
        overflow-x: hidden;
        overflow-y: auto;
      }

      #numeric-alerts-table {
        text-align: left;
      }

      #numeric-alerts-table td {
        vertical-align: top;
      }

      #numeric-alerts-table .first-offense-td {
        display: inline-block;
      }

      .first-offense-td {
        width: 80px;
      }

      .tensor-device-td {
        max-width: 140px;
        word-wrap: break-word;
      }

      .tensor-section-within-table {
        color: #266236;
        cursor: pointer;
        opacity: 0.8;
        text-decoration: underline;
      }

      .tensor-section-within-table:hover {
        opacity: 1;
      }

      .device-section-within-table {
        color: #666;
      }

      .mini-health-pill {
        width: 130px;
      }

      .mini-health-pill > div {
        height: 100%;
        width: 60px;
        border-radius: 3px;
      }

      #event-counts-th {
        padding: 0 0 0 10px;
      }

      .negative-inf-mini-health-pill-section {
        background: rgb(255, 141, 0);
        width: 20px;
      }

      .positive-inf-mini-health-pill-section {
        background: rgb(0, 62, 212);
        width: 20px;
      }

      .nan-mini-health-pill-section {
        background: rgb(204, 47, 44);
        width: 20px;
      }

      .negative-inf-mini-health-pill-section,
      .positive-inf-mini-health-pill-section,
      .nan-mini-health-pill-section {
        color: #fff;
        display: inline-block;
        height: 100%;
        line-height: 20px;
        margin: 0 0 0 10px;
        text-align: center;
      }

      .no-numeric-alerts-notification {
        margin: 0;
      }
    </style>
    <paper-material elevation="1" class="card health-pill-legend">
      <div class="title">
        Enable all (not just sampled) steps. Requires slow disk read.
      </div>
      <paper-toggle-button id="enableAllStepsModeToggle" checked="{{allStepsModeEnabled}}">
      </paper-toggle-button>
      <h2>
        Step of Health Pills:
        <template is="dom-if" if="[[allStepsModeEnabled]]">
          <input type="number" id="health-pill-step-number-input" min="0" max="[[_biggestStepEverSeen]]" value="{{specificHealthPillStep::input}}">
        </template>
        <template is="dom-if" if="[[!allStepsModeEnabled]]">
          [[_currentStepDisplayValue]]
        </template>
        <paper-spinner-lite active hidden$="[[!areHealthPillsLoading]]" id="health-pills-loading-spinner"></paper-spinner-lite>
      </h2>
      <template is="dom-if" if="[[allStepsModeEnabled]]">
        <paper-slider id="health-pill-step-slider" immediate-value="{{specificHealthPillStep}}" max="[[_biggestStepEverSeen]]" snaps step="1" value="{{specificHealthPillStep}}"></paper-slider>
      </template>
      <template is="dom-if" if="[[!allStepsModeEnabled]]">
        <template is="dom-if" if="[[_maxStepIndex]]">
          <paper-slider id="health-pill-step-slider" immediate-value="{{healthPillStepIndex}}" max="[[_maxStepIndex]]" snaps step="1" value="{{healthPillStepIndex}}"></paper-slider>
        </template>
      </template>
      <h2>
        Health Pill
        <template is="dom-if" if="[[healthPillValuesForSelectedNode]]">
          Counts for Selected Node
        </template>
        <template is="dom-if" if="[[!healthPillValuesForSelectedNode]]">
          Legend
        </template>
      </h2>
      <template is="dom-repeat" items="[[healthPillEntries]]">
        <div class="health-pill-entry">
          <div class="color-preview" style="background:[[item.background_color]]"></div>
          <div class="color-label">[[item.label]]</div>
          <div class="tensor-count">
            [[_computeTensorCountString(healthPillValuesForSelectedNode,
            index)]]
          </div>
        </div>
      </template>
      <div hidden$="[[!_hasDebuggerNumericAlerts(debuggerNumericAlerts)]]">
        <h2 id="numeric-alerts-header">Numeric Alerts</h2>
        <p>
          Alerts are sorted from top to bottom by increasing timestamp.
        </p>
        <div id="numeric-alerts-table-container">
          <table id="numeric-alerts-table">
            <thead>
              <tr>
                <th>First Offense</th>
                <th>Tensor (Device)</th>
                <th id="event-counts-th">Event Counts</th>
              </tr>
            </thead>
            <tbody id="numeric-alerts-body"></tbody>
          </table>
        </div>
      </div>
      <template is="dom-if" if="[[!_hasDebuggerNumericAlerts(debuggerNumericAlerts)]]">
        <p class="no-numeric-alerts-notification">
          No numeric alerts so far. That is likely good. Alerts indicate the
          presence of NaN or (+/-) Infinity values, which may be concerning.
        </p>
      </template>
    </paper-material>
  </template>
  
</dom-module><dom-module id="iron-list">
  <template>
    <style>
      :host {
        display: block;
      }

      @media only screen and (-webkit-max-device-pixel-ratio: 1) {
        :host {
          will-change: transform;
        }
      }

      #items {
        @apply --iron-list-items-container;
        position: relative;
      }

      :host(:not([grid])) #items > ::slotted(*) {
        width: 100%;
      }

      #items > ::slotted(*) {
        box-sizing: border-box;
        margin: 0;
        position: absolute;
        top: 0;
        will-change: transform;
      }
    </style>

    <array-selector id="selector" items="{{items}}" selected="{{selectedItems}}" selected-item="{{selectedItem}}"></array-selector>

    <div id="items">
      <slot></slot>
    </div>

  </template>
</dom-module><dom-module id="paper-item-body">
  <template>
    <style>
      :host {
        overflow: hidden; /* needed for text-overflow: ellipsis to work on ff */
        @apply --layout-vertical;
        @apply --layout-center-justified;
        @apply --layout-flex;
      }

      :host([two-line]) {
        min-height: var(--paper-item-body-two-line-min-height, 72px);
      }

      :host([three-line]) {
        min-height: var(--paper-item-body-three-line-min-height, 88px);
      }

      :host > ::slotted(*) {
        overflow: hidden;
        text-overflow: ellipsis;
        white-space: nowrap;
      }

      :host > ::slotted([secondary]) {
        @apply --paper-font-body1;

        color: var(--paper-item-body-secondary-color, var(--secondary-text-color));

        @apply --paper-item-body-secondary;
      }
    </style>

    <slot></slot>
  </template>

  
</dom-module><dom-module id="tf-graph-icon">
  <template>
    <style>
      :host {
        font-size: 0;
      }

      .faded-rect {
        fill: url(#rectHatch);
      }

      .faded-ellipse {
        fill: url(#ellipseHatch);
      }

      .faded-rect,
      .faded-ellipse,
      .faded-series {
        stroke: var(--tb-graph-faded) !important;
      }
      #rectHatch line,
      #ellipseHatch line {
        color: #e0d4b3 !important;
        fill: white;
        stroke: #e0d4b3 !important;
      }
    </style>
    
    <svg height="0" width="0" id="svgDefs">
      <defs>
        
        <pattern id="rectHatch" patterntransform="rotate(45 0 0)" width="5" height="5" patternunits="userSpaceOnUse">
          <line x1="0" y1="0" x2="0" y2="5" style="stroke-width: 1" />
        </pattern>
        <pattern id="ellipseHatch" patterntransform="rotate(45 0 0)" width="2" height="2" patternunits="userSpaceOnUse">
          <line x1="0" y1="0" x2="0" y2="2" style="stroke-width: 1" />
        </pattern>
        
        <ellipse id="op-node-stamp" rx="7.5" ry="3" stroke="inherit" fill="inherit" />
        
        <ellipse id="op-node-annotation-stamp" rx="5" ry="2" stroke="inherit" fill="inherit" />
        
        <g id="op-series-vertical-stamp">
          <use xlink:href="#op-node-stamp" x="8" y="9" />
          <use xlink:href="#op-node-stamp" x="8" y="6" />
          <use xlink:href="#op-node-stamp" x="8" y="3" />
        </g>
        <g id="op-series-horizontal-stamp">
          <use xlink:href="#op-node-stamp" x="16" y="4" />
          <use xlink:href="#op-node-stamp" x="12" y="4" />
          <use xlink:href="#op-node-stamp" x="8" y="4" />
        </g>
        <g id="summary-icon" fill="#848484" height="12" viewbox="0 0 24 24" width="12">
          <path d="M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z" />
        </g>
      </defs>
    </svg>
    <template is="dom-if" if="[[_isType(type, 'CONST')]]">
      <svg height$="[[height]]" preserveaspectratio="xMinYMid meet" viewbox="0 0 10 10">
        <circle cx="5" cy="5" r="3" fill$="[[_fill]]" stroke$="[[_stroke]]" />
      </svg>
    </template>
    <template is="dom-if" if="[[_isType(type, 'SUMMARY')]]">
      <svg width$="[[height]]" height$="[[height]]" viewbox="0 0 24 24" fill="#848484">
        <path d="M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z" />
      </svg>
    </template>
    <template is="dom-if" if="[[_isType(type, 'OP')]]">
      <svg height$="[[height]]" preserveaspectratio="xMinYMid meet" viewbox="0 0 16 8">
        <use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#op-node-stamp" fill$="[[_fill]]" stroke$="[[_stroke]]" class$="{{_fadedClass(faded, 'ellipse')}}" x="8" y="4" />
      </svg>
    </template>
    <template is="dom-if" if="[[_isType(type, 'META')]]">
      <svg height$="[[height]]" preserveaspectratio="xMinYMid meet" viewbox="0 0 37 16">
        <rect x="1" y="1" fill$="[[_fill]]" stroke$="[[_stroke]]" class$="{{_fadedClass(faded, 'rect')}}" stroke-width="2px" height="14" width="35" rx="5" ry="5" />
      </svg>
    </template>
    <template is="dom-if" if="[[_isType(type, 'SERIES')]]">
      <template is="dom-if" if="[[vertical]]">
        <svg height$="[[height]]" preserveaspectratio="xMinYMid meet" viewbox="0 0 16 15">
          <use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#op-series-vertical-stamp" fill$="[[_fill]]" stroke$="[[_stroke]]" class$="{{_fadedClass(faded, 'series')}}" x="0" y="2" />
        </svg>
      </template>
      <template is="dom-if" if="[[!vertical]]">
        <svg height$="[[height]]" preserveaspectratio="xMinYMid meet" viewbox="0 0 24 10">
          <use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#op-series-horizontal-stamp" fill$="[[_fill]]" stroke$="[[_stroke]]" class$="{{_fadedClass(faded, 'series')}}" x="0" y="1" />
        </svg>
      </template>
    </template>
  </template>

  
</dom-module><dom-module id="tf-node-icon">
  <template>
    <style>
      tf-graph-icon {
        --tb-graph-faded: var(--tb-graph-faded);
      }
    </style>
    <tf-graph-icon id="icon" type="[[_getType(node, summary, const, type)]]" height="[[height]]" fill-override="[[_fillOverride]]" stroke-override="[[_getStrokeOverride(_fillOverride)]]" faded="[[_getFaded(renderInfo)]]" vertical="[[_isVertical(node, vertical)]]"></tf-graph-icon>
  </template>

  
</dom-module><dom-module id="tf-graph-op-compat-list-item">
  <template>
    <style>
      #list-item {
        width: 100%;
        color: #565656;
        font-size: 11pt;
        font-weight: 400;
        position: relative;
        display: inline-block;
      }

      #list-item:hover {
        background-color: var(--google-yellow-100);
      }

      .clickable {
        cursor: pointer;
      }

      #list-item span {
        margin-left: 40px;
      }

      #list-item.excluded span {
        color: #999;
      }

      #list-item span.edge-label {
        float: right;
        font-size: 10px;
        margin-left: 3px;
        margin-right: 5px;
      }

      .node-icon {
        position: absolute;
        top: 1px;
        left: 2px;
      }

      .faded span {
        color: var(--tb-graph-faded);
      }
    </style>

    <div id="list-item" on-mouseover="_nodeListener" on-mouseout="_nodeListener" on-click="_nodeListener">
      <div class$="{{_fadedClass(itemRenderInfo)}}">
        <tf-node-icon class="node-icon" height="12" color-by="[[colorBy]]" color-by-params="[[colorByParams]]" node="[[itemNode]]" render-info="[[itemRenderInfo]]" template-index="[[templateIndex]]">
        </tf-node-icon>
        <span title$="[[name]]">[[name]]</span>
      </div>
    </div>
  </template>

  
</dom-module><dom-module id="tf-graph-op-compat-card">
  <template>
    <style>
      :host {
        max-height: 500px;
      }

      .incompatible-ops-list {
        height: 350px;
        max-height: 400px;
        overflow-y: scroll;
        display: flex;
        flex-direction: column;
      }

      iron-list {
        flex: 1 1 auto;
      }

      paper-item {
        padding: 0;
        background: #e9e9e9;
      }

      paper-item-body[two-line] {
        min-height: 0;
        padding: 8px 12px 4px;
      }

      .expandedInfo {
        padding: 8px 12px;
        font-weight: 500;
        font-size: 12pt;
        width: 100%;
      }

      .node-name {
        white-space: normal;
        word-wrap: break-word;
        font-size: 14pt;
        font-weight: 500;
      }

      .subtitle {
        font-size: 12pt;
        color: #5e5e5e;
      }

      .toggle-button {
        float: right;
        max-height: 20px;
        max-width: 20px;
        padding: 0;
      }

      .non-control-list-item {
        padding-left: 10px;
      }

      div.op-compat-display {
        margin-top: 10px;
        display: inline-block;
      }

      svg.op-compat {
        width: 250px;
        height: 25px;
        float: left;
      }

      div.op-compat-value {
        float: right;
        height: 100%;
        font-size: 14px;
        color: black;
        margin-left: 10px;
      }
    </style>

    <paper-item>
      <paper-item-body two-line>
        <div>
          <paper-icon-button icon="{{_getToggleIcon(_expanded)}}" on-click="_toggleExpanded" class="toggle-button">
          </paper-icon-button>
          <div class="node-name" id="nodetitle">[[nodeTitle]]</div>
        </div>
        <div secondary>
          <div class="subtitle">
            <div class="op-compat-display">
              <svg class="op-compat" preserveaspectratio="xMinYMid meet" viewbox="0 0 250 25">
                <defs>
                  <lineargradient id="op-compat-fill">
                    <stop offset="0" stop-color$="[[_opCompatColor]]"></stop>
                    <stop offset$="[[_opCompatScore]]" stop-color$="[[_opCompatColor]]"></stop>
                    <stop offset$="[[_opCompatScore]]" stop-color$="[[_opIncompatColor]]"></stop>
                    <stop offset="1" stop-color$="[[_opIncompatColor ]]"></stop>
                  </lineargradient>
                </defs>
                <rect height="25" width="250" rx="5" ry="5" style="fill: url('#op-compat-fill');" />
              </svg>
              <div class="op-compat-value">[[_opCompatScoreLabel]]</div>
            </div>
          </div>
        </div>
      </paper-item-body>
    </paper-item>

    <iron-collapse opened="{{_expanded}}">
      <template is="dom-if" if="{{_expanded}}" restamp="true">
        <div class="expandedInfo">
          Incompatible Operations: (<span>[[_totalIncompatOps]]</span>)
          <iron-list class="incompatible-ops-list" id="incompatibleOpsList" items="[[_incompatibleOpNodes]]">
            <template>
              <tf-graph-op-compat-list-item class="non-control-list-item" item-node="[[item]]" item-render-info="[[_getRenderInfo(item.name, renderHierarchy)]]" name="[[item.name]]" template-index="[[_templateIndex]]" color-by="[[colorBy]]" item-type="incompatible-ops">
              </tf-graph-op-compat-list-item>
            </template>
          </iron-list>
        </div>
      </template>
    </iron-collapse>
  </template>

  
</dom-module><dom-module id="tf-node-list-item">
  <template>
    <style>
      #list-item {
        width: 100%;
        color: #565656;
        font-size: 11pt;
        font-weight: 400;
        position: relative;
        display: inline-block;
      }

      #list-item:hover {
        background-color: var(--google-yellow-100);
      }

      .clickable {
        cursor: pointer;
      }

      #list-item span {
        margin-left: 40px;
      }

      #list-item.excluded span {
        color: #999;
      }

      #list-item span.edge-label {
        float: right;
        font-size: 10px;
        margin-left: 3px;
        margin-right: 5px;
      }

      .node-icon {
        position: absolute;
        top: 1px;
        left: 2px;
      }

      .faded span {
        color: var(--tb-graph-faded);
      }
    </style>
    <div id="list-item" on-mouseover="_nodeListener" on-mouseout="_nodeListener" on-click="_nodeListener">
      <div class$="{{_fadedClass(itemRenderInfo)}}">
        <tf-node-icon class="node-icon" height="12" color-by="[[colorBy]]" color-by-params="[[colorByParams]]" node="[[itemNode]]" render-info="[[itemRenderInfo]]" template-index="[[templateIndex]]"></tf-node-icon>
        <span title$="[[name]]">[[name]]</span>
        <span class="edge-label">[[edgeLabel]]</span>
      </div>
    </div>
  </template>

  
</dom-module><dom-module id="tf-node-info">
  <template>
    <style>
      .sub-list-group {
        font-weight: 500;
        font-size: 12pt;
        padding-bottom: 8px;
        width: 100%;
      }

      .sub-list {
        max-height: 300px;
        overflow-y: scroll;
      }

      .attr-left {
        float: left;
        width: 30%;
        word-wrap: break-word;
        color: #565656;
        font-size: 11pt;
        font-weight: 400;
      }

      .attr-right {
        margin-left: 30%;
        word-wrap: break-word;
        color: #565656;
        font-weight: 400;
      }

      .sub-list-table {
        display: table;
        width: 100%;
      }

      .sub-list-table-row {
        display: table-row;
      }

      .sub-list-table-row .sub-list-table-cell:last-child {
        text-align: right;
      }

      .sub-list-table-cell {
        color: #565656;
        display: table-cell;
        font-size: 11pt;
        font-weight: 400;
        max-width: 200px;
        padding: 0 4px;
      }

      paper-item {
        padding: 0;
        background: #e9e9e9;
      }

      paper-item-body[two-line] {
        min-height: 0;
        padding: 8px 12px 4px;
      }

      .expandedInfo {
        padding: 8px 12px;
      }

      .controlDeps {
        padding: 0 0 0 8px;
      }

      .node-name {
        white-space: normal;
        word-wrap: break-word;
        font-size: 14pt;
        font-weight: 500;
      }

      .node-icon {
        float: right;
      }

      .subtitle {
        font-size: 12pt;
        color: #5e5e5e;
      }

      .controlLine {
        font-size: 11pt;
        font-weight: 400;
      }

      .toggle-button {
        float: right;
        max-height: 20px;
        max-width: 20px;
        padding: 0;
      }

      .control-toggle-button {
        float: left;
        max-height: 20px;
        max-width: 20px;
        padding: 0;
      }

      .toggle-include-group {
        padding-top: 4px;
      }

      .toggle-include {
        margin: 5px 6px;
        text-transform: none;
        padding: 4px 6px;
        font-size: 10pt;
        background-color: #fafafa;
        color: #666;
      }

      .toggle-include:hover {
        background-color: var(--google-yellow-100);
      }

      .non-control-list-item {
        padding-left: 10px;
      }
    </style>
    <paper-item>
      <paper-item-body two-line>
        <div>
          <paper-icon-button icon="{{_getToggleIcon(_expanded)}}" on-click="_toggleExpanded" class="toggle-button">
          </paper-icon-button>
          <div class="node-name" id="nodetitle"></div>
        </div>
        <div secondary>
          <tf-node-icon class="node-icon" node="[[_node]]" render-info="[[_getRenderInfo(graphNodeName, renderHierarchy)]]" color-by="[[colorBy]]" template-index="[[_templateIndex]]"></tf-node-icon>
          <template is="dom-if" if="{{_node.op}}">
            <div class="subtitle">
              Operation:
              <span>[[_node.op]]</span>
            </div>
          </template>
          <template is="dom-if" if="{{_node.metagraph}}">
            <div class="subtitle">
              Subgraph:
              <span>[[_node.cardinality]]</span> nodes
            </div>
          </template>
        </div>
      </paper-item-body>
    </paper-item>
    <iron-collapse opened="{{_expanded}}">
      <template is="dom-if" if="{{_expanded}}" restamp="true">
        <div class="expandedInfo">
          <div class="sub-list-group attributes">
            Attributes (<span>[[_attributes.length]]</span>)
            <iron-list class="sub-list" id="attributesList" items="[[_attributes]]">
              <template>
                <div>
                  <div class="attr-left">[[item.key]]</div>
                  <div class="attr-right">[[item.value]]</div>
                </div>
              </template>
            </iron-list>
          </div>

          <template is="dom-if" if="{{_device}}">
            <div class="sub-list-group device">
              <div class="attr-left">Device</div>
              <div class="attr-right">[[_device]]</div>
            </div>
          </template>

          <div class="sub-list-group predecessors">
            Inputs (<span>[[_totalPredecessors]]</span>)
            <iron-list class="sub-list" id="inputsList" items="[[_predecessors.regular]]">
              <template>
                <tf-node-list-item class="non-control-list-item" card-node="[[_node]]" item-node="[[item.node]]" edge-label="[[item.edgeLabel]]" item-render-info="[[item.renderInfo]]" name="[[item.name]]" item-type="predecessors" color-by="[[colorBy]]" template-index="[[_templateIndex]]">
                </tf-node-list-item>
              </template>
            </iron-list>
            <template is="dom-if" if="[[_predecessors.control.length]]">
              <div class="controlDeps">
                <div class="controlLine">
                  <paper-icon-button icon="{{_getToggleIcon(_openedControlPred)}}" on-click="_toggleControlPred" class="control-toggle-button">
                  </paper-icon-button>
                  Control dependencies
                </div>
                <iron-collapse opened="{{_openedControlPred}}" no-animation>
                  <template is="dom-if" if="{{_openedControlPred}}" restamp="true">
                    <iron-list class="sub-list" items="[[_predecessors.control]]">
                      <template>
                        <tf-node-list-item card-node="[[_node]]" item-node="[[item.node]]" item-render-info="[[item.renderInfo]]" name="[[item.name]]" item-type="predecessors" color-by="[[colorBy]]" template-index="[[_templateIndex]]">
                        </tf-node-list-item>
                      </template>
                    </iron-list>
                  </template>
                </iron-collapse>
              </div>
            </template>
          </div>

          <div class="sub-list-group successors">
            Outputs (<span>[[_totalSuccessors]]</span>)
            <iron-list class="sub-list" id="outputsList" items="[[_successors.regular]]">
              <template>
                <tf-node-list-item class="non-control-list-item" card-node="[[_node]]" item-node="[[item.node]]" edge-label="[[item.edgeLabel]]" item-render-info="[[item.renderInfo]]" name="[[item.name]]" item-type="successor" color-by="[[colorBy]]" template-index="[[_templateIndex]]">
                </tf-node-list-item>
              </template>
            </iron-list>
            <template is="dom-if" if="[[_successors.control.length]]">
              <div class="controlDeps">
                <div class="controlLine">
                  <paper-icon-button icon="{{_getToggleIcon(_openedControlSucc)}}" on-click="_toggleControlSucc" class="control-toggle-button">
                  </paper-icon-button>
                  Control dependencies
                </div>
                <iron-collapse opened="{{_openedControlSucc}}" no-animation>
                  <template is="dom-if" if="{{_openedControlSucc}}" restamp="true">
                    <iron-list class="sub-list" items="[[_successors.control]]">
                      <template>
                        <tf-node-list-item card-node="[[_node]]" item-node="[[item.node]]" item-render-info="[[item.renderInfo]]" name="[[item.name]]" item-type="successors" color-by="[[colorBy]]" template-index="[[_templateIndex]]">
                        </tf-node-list-item>
                      </template>
                    </iron-list>
                  </template>
                </iron-collapse>
              </div>
            </template>
          </div>
          <template is="dom-if" if="{{_hasDisplayableNodeStats}}">
            <div class="sub-list-group node-stats">
              Node Stats
              <div class="sub-list-table">
                <template is="dom-if" if="{{_nodeStats.totalBytes}}">
                  <div class="sub-list-table-row">
                    <div class="sub-list-table-cell">Memory</div>
                    <div class="sub-list-table-cell">
                      [[_nodeStatsFormattedBytes]]
                    </div>
                  </div>
                </template>
                <template is="dom-if" if="{{_getTotalMicros(_nodeStats)}}">
                  <div class="sub-list-table-row">
                    <div class="sub-list-table-cell">Compute Time</div>
                    <div class="sub-list-table-cell">
                      [[_nodeStatsFormattedComputeTime]]
                    </div>
                  </div>
                </template>
                <template is="dom-if" if="{{_nodeStats.outputSize}}">
                  <div class="sub-list-table-row">
                    <div class="sub-list-table-cell">Tensor Output Sizes</div>
                    <div class="sub-list-table-cell">
                      <template is="dom-repeat" items="{{_nodeStatsFormattedOutputSizes}}">
                        [[item]] <br>
                      </template>
                    </div>
                  </div>
                </template>
              </div>
            </div>
          </template>

          <template is="dom-if" if="[[_functionUsages.length]]">
            <div class="sub-list-group predecessors">
              Usages of the Function (<span>[[_functionUsages.length]]</span>)
              <iron-list class="sub-list" id="functionUsagesList" items="[[_functionUsages]]">
                <template>
                  <tf-node-list-item class="non-control-list-item" card-node="[[_node]]" item-node="[[item]]" name="[[item.name]]" item-type="functionUsages" color-by="[[colorBy]]" template-index="[[_templateIndex]]">
                  </tf-node-list-item>
                </template>
              </iron-list>
            </div>
          </template>

          <template is="dom-if" if="[[!_isLibraryFunction(_node)]]">
            <div class="toggle-include-group">
              <paper-button raised class="toggle-include" on-click="_toggleInclude">
                <span>[[_auxButtonText]]</span>
              </paper-button>
            </div>
          </template>

          <template is="dom-if" if="{{_isInSeries(_node)}}">
            <div class="toggle-include-group">
              <paper-button raised class="toggle-include" on-click="_toggleGroup">
                <span>[[_groupButtonText]]</span>
              </paper-button>
            </div>
          </template>
        </div>
      </template>
    </iron-collapse>
  </template>

  
</dom-module><dom-module id="tf-graph-info">
  <template>
    <style>
      :host {
        font-size: 12px;
        margin: 0;
        padding: 0;
        display: block;
        max-height: 650px;
        overflow-x: hidden;
        overflow-y: auto;
      }

      h2 {
        padding: 0;
        text-align: center;
        margin: 0;
      }
    </style>
    <template is="dom-if" if="{{selectedNode}}">
      <paper-material elevation="1" class="card">
        <tf-node-info graph-hierarchy="[[graphHierarchy]]" render-hierarchy="[[renderHierarchy]]" flat-graph="[[graph]]" graph-node-name="[[selectedNode]]" node-include="[[selectedNodeInclude]]" highlighted-node="{{highlightedNode}}" color-by="[[colorBy]]">
        </tf-node-info>
      </paper-material>
    </template>
    <template is="dom-if" if="[[_equals(colorBy, 'op_compatibility')]]">
      <tf-graph-op-compat-card graph-hierarchy="[[graphHierarchy]]" hierarchy-params="[[hierarchyParams]]" render-hierarchy="[[renderHierarchy]]" color-by="[[colorBy]]" node-title="[[compatNodeTitle]]">
      </tf-graph-op-compat-card>
    </template>
    <template is="dom-if" if="[[_healthPillsAvailable(debuggerDataEnabled, nodeNamesToHealthPills)]]">
      <tf-graph-debugger-data-card render-hierarchy="[[renderHierarchy]]" debugger-numeric-alerts="[[debuggerNumericAlerts]]" node-names-to-health-pills="[[nodeNamesToHealthPills]]" selected-node="{{selectedNode}}" highlighted-node="{{highlightedNode}}" are-health-pills-loading="[[areHealthPillsLoading]]" all-steps-mode-enabled="{{allStepsModeEnabled}}" specific-health-pill-step="{{specificHealthPillStep}}" health-pill-step-index="{{healthPillStepIndex}}">
      </tf-graph-debugger-data-card>
    </template>
  </template>
  
</dom-module><dom-module id="tf-graph-board">
  <template>
    <style>
      ::host {
        display: block;
      }

      /deep/ .close {
        position: absolute;
        cursor: pointer;
        left: 15px;
        bottom: 15px;
      }

      .container {
        width: 100%;
        height: 100%;
        opacity: 1;
      }

      .container.loading {
        cursor: progress;
        opacity: 0.1;
      }

      .container.loading.error {
        cursor: auto;
      }

      #info {
        position: absolute;
        right: 5px;
        top: 5px;
        padding: 0px;
        max-width: 380px;
        min-width: 320px;
        background-color: rgba(255, 255, 255, 0.9);
        @apply --shadow-elevation-2dp;
      }

      #main {
        width: 100%;
        height: 100%;
      }

      #progress-bar {
        display: flex;
        flex-direction: column;
        align-items: center;
        justify-content: center;
        width: 100%;
        position: absolute;
        top: 40px;
        left: 0;
        font-size: 13px;
      }

      #progress-msg {
        margin-bottom: 5px;
        white-space: pre-wrap;
        width: 400px;
      }

      paper-progress {
        width: 400px;
        --paper-progress-height: 6px;
        --paper-progress-active-color: #f3913e;
      }

      .context-menu {
        position: absolute;
        display: none;
        background-color: #e2e2e2;
        border-radius: 2px;
        font-size: 14px;
        min-width: 150px;
        border: 1px solid #d4d4d4;
      }

      /deep/ .context-menu ul {
        list-style-type: none;
        margin: 0;
        padding: 0;
        cursor: default;
      }

      /deep/ .context-menu ul li {
        padding: 4px 16px;
      }

      /deep/ .context-menu ul li:hover {
        background-color: #f3913e;
        color: white;
      }
    </style>
    <template is="dom-if" if="[[_isNotComplete(progress)]]">
      <div id="progress-bar">
        <div id="progress-msg">[[progress.msg]]</div>
        <paper-progress value="[[progress.value]]"></paper-progress>
      </div>
    </template>
    <div class$="[[_getContainerClass(progress)]]">
      <div id="main">
        <tf-graph id="graph" graph-hierarchy="{{graphHierarchy}}" basic-graph="[[graph]]" hierarchy-params="[[hierarchyParams]]" render-hierarchy="{{renderHierarchy}}" devices-for-stats="[[devicesForStats]]" stats="[[stats]]" selected-node="{{selectedNode}}" highlighted-node="{{_highlightedNode}}" color-by="[[colorBy]]" color-by-params="{{colorByParams}}" progress="{{progress}}" edge-label-function="[[edgeLabelFunction]]" edge-width-function="[[edgeWidthFunction]]" node-names-to-health-pills="[[nodeNamesToHealthPills]]" health-pill-step-index="[[healthPillStepIndex]]" handle-node-selected="[[handleNodeSelected]]" handle-edge-selected="[[handleEdgeSelected]]" trace-inputs="[[traceInputs]]"></tf-graph>
      </div>
      <div id="info">
        <tf-graph-info id="graph-info" title="selected" graph-hierarchy="[[graphHierarchy]]" hierarchy-params="[[hierarchyParams]]" render-hierarchy="[[renderHierarchy]]" graph="[[graph]]" selected-node="{{selectedNode}}" selected-node-include="{{_selectedNodeInclude}}" highlighted-node="{{_highlightedNode}}" color-by="[[colorBy]]" color-by-params="[[colorByParams]]" debugger-data-enabled="[[debuggerDataEnabled]]" are-health-pills-loading="[[areHealthPillsLoading]]" debugger-numeric-alerts="[[debuggerNumericAlerts]]" node-names-to-health-pills="[[nodeNamesToHealthPills]]" all-steps-mode-enabled="{{allStepsModeEnabled}}" specific-health-pill-step="{{specificHealthPillStep}}" health-pill-step-index="{{healthPillStepIndex}}" compat-node-title="[[compatNodeTitle]]" on-node-toggle-inclusion="_onNodeInclusionToggled" on-node-toggle-seriesgroup="_onNodeSeriesGroupToggled"></tf-graph-info>
      </div>
    </div>
  </template>
</dom-module><dom-module id="paper-radio-button">
  <template strip-whitespace>
    <style>
      :host {
        display: inline-block;
        line-height: 0;
        white-space: nowrap;
        cursor: pointer;
        @apply --paper-font-common-base;
        --calculated-paper-radio-button-size: var(--paper-radio-button-size, 16px);
        /* -1px is a sentinel for the default and is replace in `attached`. */
        --calculated-paper-radio-button-ink-size: var(--paper-radio-button-ink-size, -1px);
      }

      :host(:focus) {
        outline: none;
      }

      #radioContainer {
        @apply --layout-inline;
        @apply --layout-center-center;
        position: relative;
        width: var(--calculated-paper-radio-button-size);
        height: var(--calculated-paper-radio-button-size);
        vertical-align: middle;

        @apply --paper-radio-button-radio-container;
      }

      #ink {
        position: absolute;
        top: 50%;
        left: 50%;
        right: auto;
        width: var(--calculated-paper-radio-button-ink-size);
        height: var(--calculated-paper-radio-button-ink-size);
        color: var(--paper-radio-button-unchecked-ink-color, var(--primary-text-color));
        opacity: 0.6;
        pointer-events: none;
        -webkit-transform: translate(-50%, -50%);
        transform: translate(-50%, -50%);
      }

      #ink[checked] {
        color: var(--paper-radio-button-checked-ink-color, var(--primary-color));
      }

      #offRadio, #onRadio {
        position: absolute;
        box-sizing: border-box;
        top: 0;
        left: 0;
        width: 100%;
        height: 100%;
        border-radius: 50%;
      }

      #offRadio {
        border: 2px solid var(--paper-radio-button-unchecked-color, var(--primary-text-color));
        background-color: var(--paper-radio-button-unchecked-background-color, transparent);
        transition: border-color 0.28s;
      }

      #onRadio {
        background-color: var(--paper-radio-button-checked-color, var(--primary-color));
        -webkit-transform: scale(0);
        transform: scale(0);
        transition: -webkit-transform ease 0.28s;
        transition: transform ease 0.28s;
        will-change: transform;
      }

      :host([checked]) #offRadio {
        border-color: var(--paper-radio-button-checked-color, var(--primary-color));
      }

      :host([checked]) #onRadio {
        -webkit-transform: scale(0.5);
        transform: scale(0.5);
      }

      #radioLabel {
        line-height: normal;
        position: relative;
        display: inline-block;
        vertical-align: middle;
        margin-left: var(--paper-radio-button-label-spacing, 10px);
        white-space: normal;
        color: var(--paper-radio-button-label-color, var(--primary-text-color));

        @apply --paper-radio-button-label;
      }

      :host([checked]) #radioLabel {
        @apply --paper-radio-button-label-checked;
      }

      #radioLabel:dir(rtl) {
        margin-left: 0;
        margin-right: var(--paper-radio-button-label-spacing, 10px);
      }

      #radioLabel[hidden] {
        display: none;
      }

      /* disabled state */

      :host([disabled]) #offRadio {
        border-color: var(--paper-radio-button-unchecked-color, var(--primary-text-color));
        opacity: 0.5;
      }

      :host([disabled][checked]) #onRadio {
        background-color: var(--paper-radio-button-unchecked-color, var(--primary-text-color));
        opacity: 0.5;
      }

      :host([disabled]) #radioLabel {
        /* slightly darker than the button, so that it's readable */
        opacity: 0.65;
      }
    </style>

    <div id="radioContainer">
      <div id="offRadio"></div>
      <div id="onRadio"></div>
    </div>

    <div id="radioLabel"><slot></slot></div>
  </template>

  
</dom-module><dom-module id="paper-radio-group">
  <template>
    <style>
      :host {
        display: inline-block;
      }

      :host ::slotted(*) {
        padding: var(--paper-radio-group-item-padding, 12px);
      }
    </style>

    <slot></slot>
  </template>
</dom-module><dom-module id="paper-tooltip">
  <template>
    <style>
      :host {
        display: block;
        position: absolute;
        outline: none;
        z-index: 1002;
        -moz-user-select: none;
        -ms-user-select: none;
        -webkit-user-select: none;
        user-select: none;
        cursor: default;
      }

      #tooltip {
        display: block;
        outline: none;
        @apply --paper-font-common-base;
        font-size: 10px;
        line-height: 1;
        background-color: var(--paper-tooltip-background, #616161);
        color: var(--paper-tooltip-text-color, white);
        padding: 8px;
        border-radius: 2px;
        @apply --paper-tooltip;
      }

      @keyframes keyFrameScaleUp {
        0% {
          transform: scale(0.0);
        }
        100% {
          transform: scale(1.0);
        }
      }

      @keyframes keyFrameScaleDown {
        0% {
          transform: scale(1.0);
        }
        100% {
          transform: scale(0.0);
        }
      }

      @keyframes keyFrameFadeInOpacity {
        0% {
          opacity: 0;
        }
        100% {
          opacity: var(--paper-tooltip-opacity, 0.9);
        }
      }

      @keyframes keyFrameFadeOutOpacity {
        0% {
          opacity: var(--paper-tooltip-opacity, 0.9);
        }
        100% {
          opacity: 0;
        }
      }

      @keyframes keyFrameSlideDownIn {
        0% {
          transform: translateY(-2000px);
          opacity: 0;
        }
        10% {
          opacity: 0.2;
        }
        100% {
          transform: translateY(0);
          opacity: var(--paper-tooltip-opacity, 0.9);
        }
      }

      @keyframes keyFrameSlideDownOut {
        0% {
          transform: translateY(0);
          opacity: var(--paper-tooltip-opacity, 0.9);
        }
        10% {
          opacity: 0.2;
        }
        100% {
          transform: translateY(-2000px);
          opacity: 0;
        }
      }

      .fade-in-animation {
        opacity: 0;
        animation-delay: var(--paper-tooltip-delay-in, 500ms);
        animation-name: keyFrameFadeInOpacity;
        animation-iteration-count: 1;
        animation-timing-function: ease-in;
        animation-duration: var(--paper-tooltip-duration-in, 500ms);
        animation-fill-mode: forwards;
        @apply --paper-tooltip-animation;
      }

      .fade-out-animation {
        opacity: var(--paper-tooltip-opacity, 0.9);
        animation-delay: var(--paper-tooltip-delay-out, 0ms);
        animation-name: keyFrameFadeOutOpacity;
        animation-iteration-count: 1;
        animation-timing-function: ease-in;
        animation-duration: var(--paper-tooltip-duration-out, 500ms);
        animation-fill-mode: forwards;
        @apply --paper-tooltip-animation;
      }

      .scale-up-animation {
        transform: scale(0);
        opacity: var(--paper-tooltip-opacity, 0.9);
        animation-delay: var(--paper-tooltip-delay-in, 500ms);
        animation-name: keyFrameScaleUp;
        animation-iteration-count: 1;
        animation-timing-function: ease-in;
        animation-duration: var(--paper-tooltip-duration-in, 500ms);
        animation-fill-mode: forwards;
        @apply --paper-tooltip-animation;
      }

      .scale-down-animation {
        transform: scale(1);
        opacity: var(--paper-tooltip-opacity, 0.9);
        animation-delay: var(--paper-tooltip-delay-out, 500ms);
        animation-name: keyFrameScaleDown;
        animation-iteration-count: 1;
        animation-timing-function: ease-in;
        animation-duration: var(--paper-tooltip-duration-out, 500ms);
        animation-fill-mode: forwards;
        @apply --paper-tooltip-animation;
      }

      .slide-down-animation {
        transform: translateY(-2000px);
        opacity: 0;
        animation-delay: var(--paper-tooltip-delay-out, 500ms);
        animation-name: keyFrameSlideDownIn;
        animation-iteration-count: 1;
        animation-timing-function: cubic-bezier(0.0, 0.0, 0.2, 1);
        animation-duration: var(--paper-tooltip-duration-out, 500ms);
        animation-fill-mode: forwards;
        @apply --paper-tooltip-animation;
      }

      .slide-down-animation-out {
        transform: translateY(0);
        opacity: var(--paper-tooltip-opacity, 0.9);
        animation-delay: var(--paper-tooltip-delay-out, 500ms);
        animation-name: keyFrameSlideDownOut;
        animation-iteration-count: 1;
        animation-timing-function: cubic-bezier(0.4, 0.0, 1, 1);
        animation-duration: var(--paper-tooltip-duration-out, 500ms);
        animation-fill-mode: forwards;
        @apply --paper-tooltip-animation;
      }

      .cancel-animation {
        animation-delay: -30s !important;
      }

      /* Thanks IE 10. */

      .hidden {
        display: none !important;
      }
    </style>

    <div id="tooltip" class="hidden">
      <slot></slot>
    </div>
  </template>

  
</dom-module><dom-module id="tf-graph-node-search">
  <template>
    <div id="search-container">
      <paper-input id="runs-regex" label="Search nodes. Regexes supported." value="{{_rawRegexInput}}">
      </paper-input>
      <div id="search-results-anchor">
        <div id="search-results">
          <template is="dom-repeat" items="[[_regexMatches]]">
            <div id="search-match" on-click="_matchClicked">[[item]]</div>
          </template>
        </div>
      </div>
    </div>
    <style>
      #search-container {
        width: 100%;
        overflow: visible;
      }

      #runs-regex {
        width: 100%;
      }

      #search-results-anchor {
        position: relative;
      }

      #search-results {
        color: #fff;
        position: absolute;
        max-height: 200px;
        overflow-x: hidden;
        overflow-y: auto;
        text-align: right;
        max-width: 100%;
        box-sizing: border-box;
      }

      #search-match {
        background: var(--tb-orange-strong);
        padding: 3px;
        float: right;
        width: 100%;
        box-sizing: border-box;
        direction: rtl;
      }

      #search-match:hover {
        background: var(--tb-orange-weak);
        cursor: pointer;
      }
    </style>
  </template>
  
</dom-module><dom-module id="tf-graph-controls">
  <template>
    <style>
      :host {
        color: gray;
        display: flex;
        flex-direction: column;
        font-size: 12px;
        width: 100%;
      }

      paper-dropdown-menu {
        --paper-dropdown-menu-input: {
          padding: 0;
          color: gray;
        }
        --iron-icon-width: 15px;
        --iron-icon-height: 15px;
        --primary-text-color: gray;
        --paper-item-min-height: 30px;
      }

      paper-button[raised].keyboard-focus {
        font-weight: normal;
      }

      .run-dropdown {
        --paper-input-container: {
          padding: 8px 0 8px 10px;
        }
      }

      .color-dropdown {
        --paper-input-container: {
          padding: 9px 0 0 13px;
        }
      }

      table {
        border-collapse: collapse;
        border-spacing: 0;
      }

      table td {
        padding: 0;
        margin: 0;
      }

      .allcontrols {
        padding: 0 20px 20px;
        flex-grow: 1;
        overflow-y: auto;
      }

      .legend-holder {
        background: #e9e9e9;
        border-top: 1px solid #ccc;
        box-sizing: border-box;
        color: #555;
        padding: 15px 20px;
        width: 100%;
      }

      .toggle-legend-button {
        max-height: 20px;
        max-width: 20px;
        padding: 0;
      }

      .toggle-legend-text {
        vertical-align: middle;
      }

      paper-radio-button {
        display: block;
        padding: 5px;
      }
      svg.icon,
      tf-graph-icon {
        width: 60px;
        height: 18px;
      }
      .domainValues {
        margin-bottom: 10px;
        width: 165px;
      }
      .domainStart {
        float: left;
      }
      .domainEnd {
        float: right;
      }
      .colorBox {
        width: 20px;
      }

      .image-icon {
        width: 24px;
        height: 24px;
      }

      .help-icon {
        height: 15px;
        margin: 0;
        padding: 0;
      }

      .gray {
        color: #666;
      }

      .title {
        font-size: 16px;
        margin: 8px 5px 8px 0;
        color: black;
      }
      .title small {
        font-weight: normal;
      }
      .deviceList,
      .xlaClusterList {
        max-height: 200px;
        overflow-y: auto;
      }

      #file {
        padding: 8px 0;
      }

      .color-legend-row {
        align-items: center;
        clear: both;
        display: flex;
        height: 20px;
        margin-top: 5px;
      }

      .color-legend-row .label,
      .color-legend-row svg,
      .color-legend-row tf-graph-icon {
        flex: 0 0 40px;
        margin-right: 20px;
      }

      .devices-checkbox input {
        text-align: left;
        vertical-align: middle;
      }

      .control-holder .icon-button {
        font-size: 14px;
        margin: 0 -5px;
        padding: 5px;
      }

      .button-text {
        padding-left: 20px;
        text-transform: none;
      }

      .upload-button {
        width: 165px;
        height: 25px;
        text-transform: none;
        margin-top: 4px;
      }

      .button-icon {
        width: 26px;
        height: 26px;
        color: var(--paper-orange-500);
      }

      .hidden-input {
        height: 0px;
        width: 0px;
        overflow: hidden;
      }

      .allcontrols .control-holder {
        clear: both;
        display: flex;
        justify-content: space-between;
      }

      .allcontrols .control-holder paper-radio-group {
        margin-top: 5px;
      }

      span.counter {
        font-size: 13px;
        color: gray;
      }

      .runs paper-item {
        --paper-item: {
          white-space: nowrap;
        }
      }

      table.control-holder {
        border: 0;
        border-collapse: collapse;
      }

      table.tf-graph-controls td.input-element-table-data {
        padding: 0 0 0 20px;
      }

      .spacer {
        flex-grow: 1;
      }

      .color-text {
        overflow: hidden;
      }

      /** Override inline styles that suppress pointer events for disabled buttons. Otherwise, the */
      /*  tooltips do not appear. */
      paper-radio-group paper-radio-button {
        pointer-events: auto !important;
      }

      .legend-clarifier {
        color: #266236;
        cursor: help;
        display: inline-block;
        text-decoration: underline;
      }

      .legend-clarifier paper-tooltip {
        width: 150px;
      }

      /** Otherwise, polymer UI controls appear atop node search. */
      tf-graph-node-search {
        z-index: 1;
        width: 100%;
      }

      paper-dropdown-menu {
        flex-grow: 1;
      }
    </style>

    <div class="allcontrols">
      <div class="control-holder">
        <tf-graph-node-search selected-node="{{selectedNode}}" render-hierarchy="[[renderHierarchy]]"></tf-graph-node-search>
      </div>
      <div class="control-holder">
        <paper-button class="icon-button" on-tap="_fit" alt="Fit to screen">
          <iron-icon icon="aspect-ratio" class="button-icon"></iron-icon>
          <span class="button-text">Fit to Screen</span>
        </paper-button>
      </div>
      <div class="control-holder">
        <paper-button class="icon-button" on-click="download" alt="Download PNG">
          <iron-icon icon="file-download" class="button-icon"></iron-icon>
          <span class="button-text">Download PNG</span>
        </paper-button>
        <a href="#" id="graphdownload" class="title" download="graph.png"></a>
      </div>
      <div class="control-holder runs">
        <div class="title">
          Run <span class="counter">([[datasets.length]])</span>
        </div>
        <paper-dropdown-menu no-label-float no-animations noink horizontal-align="left" class="run-dropdown">
          <paper-listbox class="dropdown-content" selected="{{_selectedRunIndex}}" slot="dropdown-content">
            <template is="dom-repeat" items="[[datasets]]">
              <paper-item>[[item.name]]</paper-item>
            </template>
          </paper-listbox>
        </paper-dropdown-menu>
      </div>
      <template is="dom-if" if="[[showSessionRunsDropdown]]">
        <div class="control-holder">
          <div class="title">
            Tag
            <span class="counter">([[_numTags(datasets, _selectedRunIndex)]])</span>
          </div>
          <paper-dropdown-menu no-label-float no-animations horizontal-align="left" noink class="run-dropdown">
            <paper-listbox class="dropdown-content" selected="{{_selectedTagIndex}}" slot="dropdown-content">
              <template is="dom-repeat" items="[[_getTags(datasets, _selectedRunIndex)]]">
                <paper-item>[[item.displayName]]</paper-item>
              </template>
            </paper-listbox>
          </paper-dropdown-menu>
        </div>
      </template>
      <template is="dom-if" if="[[showUploadButton]]">
        <div class="control-holder">
          <div class="title">Upload</div>
          <paper-button raised class="upload-button" on-click="_getFile" title="Upload a graph pbtxt file to view the graph">
            Choose File
          </paper-button>
          <div class="hidden-input">
            <input type="file" id="file" name="file" on-change="_updateFileInput" accept=".pbtxt">
          </div>
        </div>
      </template>
      <div class="control-holder">
        <paper-radio-group selected="{{_selectedGraphType}}">
          
          <paper-radio-button name="op_graph" disabled="[[_getSelectionOpGraphDisabled(datasets, _selectedRunIndex, _selectedTagIndex)]]">Graph</paper-radio-button>
          <paper-radio-button name="conceptual_graph" disabled="[[_getSelectionConceptualGraphDisabled(datasets, _selectedRunIndex, _selectedTagIndex)]]">Conceptual Graph</paper-radio-button>
          <paper-radio-button name="profile" disabled="[[_getSelectionProfileDisabled(datasets, _selectedRunIndex, _selectedTagIndex)]]">Profile</paper-radio-button>
        </paper-radio-group>
      </div>
      <div class="control-holder">
        <div>
          <paper-toggle-button checked="{{traceInputs}}" class="title">
            Trace inputs
          </paper-toggle-button>
        </div>
      </div>
      <template is="dom-if" if="[[healthPillsFeatureEnabled]]">
        <div class="control-holder">
          <paper-toggle-button checked="{{healthPillsToggledOn}}" class="title">Show health pills</paper-toggle-button>
        </div>
      </template>
      <div class="control-holder">
        <div class="title">Color</div>
        <paper-radio-group selected="{{colorBy}}">
          <paper-radio-button name="structure">Structure</paper-radio-button>

          <paper-radio-button name="device">Device</paper-radio-button>

          <paper-radio-button id="xla-cluster-radio-button" name="xla_cluster" disabled="[[!_xlaClustersProvided(renderHierarchy)]]">
            XLA Cluster
          </paper-radio-button>
          <paper-tooltip animation-delay="0" for="xla-cluster-radio-button" position="right" offset="0">
            Coloring by XLA cluster is only enabled if at least 1 op specifies
            an XLA cluster.
          </paper-tooltip>

          <paper-radio-button id="compute-time-radio-button" name="compute_time" disabled="[[!stats]]">
            Compute time
          </paper-radio-button>
          <paper-tooltip animation-delay="0" for="compute-time-radio-button" position="right" offset="0">
            Coloring by compute time is only enabled if the RunMetadata proto is
            passed to the FileWriter when a specific session is run.
          </paper-tooltip>

          <paper-radio-button id="memory-radio-button" name="memory" disabled="[[!stats]]">
            Memory
          </paper-radio-button>
          <paper-tooltip animation-delay="0" for="memory-radio-button" position="right" offset="0">
            Coloring by memory is only enabled if the RunMetadata proto is
            passed to the FileWriter when a specific session is run.
          </paper-tooltip>

          <paper-radio-button id="tpu-compatibility-radio-button" name="op_compatibility">
            TPU Compatibility
          </paper-radio-button>
          <paper-tooltip animation-delay="0" for="tpu-compatibility-radio-button" position="right" offset="0">
            Coloring by whether an operation is compatible for the TPU device.
          </paper-tooltip>
        </paper-radio-group>
        <span class="spacer"></span>
      </div>
      <div>
        <template is="dom-if" if="[[_isGradientColoring(stats, colorBy)]]">
          <svg width="140" height="20" style="margin: 0 5px" class="color-text">
            <defs>
              <lineargradient id="linearGradient" x1="0%" y1="0%" x2="100%" y2="0%">
                <stop class="start" offset="0%" stop-color$="[[_currentGradientParams.startColor]]" />
                <stop class="end" offset="100%" stop-color$="[[_currentGradientParams.endColor]]" />
              </lineargradient>
            </defs>
            <rect x="0" y="0" width="135" height="20" fill="url(#linearGradient)" stroke="black" />
          </svg>
          <div class="domainValues color-text">
            <div class="domainStart">[[_currentGradientParams.minValue]]</div>
            <div class="domainEnd">[[_currentGradientParams.maxValue]]</div>
          </div>
          <br style="clear: both">
          <div>Devices included in stats:</div>
          <div class="deviceList">
            <template is="dom-repeat" items="[[_currentDevices]]">
              <div class="color-legend-row devices-checkbox">
                <span><input type="checkbox" value$="[[item.device]]" checked$="[[item.used]]" on-click="_deviceCheckboxClicked"></span>
                <span>[[item.suffix]]</span>
                <template is="dom-if" if="[[item.ignoredMsg]]">
                  <paper-icon-button icon="help" class="help-icon"></paper-icon-button>
                  <paper-tooltip position="right" offset="0" animation-delay="0">[[item.ignoredMsg]]</paper-tooltip>
                </template>
              </div>
            </template>
          </div>
        </template>
        <template is="dom-if" if="[[_equals(colorBy, 'structure')]]">
          <div class="color-text">
            <div class="color-legend-row">
              <span class="label">
                colors
              </span>
              <span class="color-legend-value">same substructure</span>
            </div>
            <div class="color-legend-row">
              <tf-graph-icon type="META" height="16" fill-override="#eee" stroke-override="#a6a6a6"></tf-graph-icon>
              <span class="color-legend-value">unique substructure</span>
            </div>
          </div>
        </template>
        <template is="dom-if" if="[[_equals(colorBy, 'device')]]">
          <div>
            <template is="dom-repeat" items="[[_currentDeviceParams]]">
              <div class="color-legend-row">
                <tf-graph-icon type="META" height="16" fill-override="[[item.color]]" stroke-override="#a6a6a6"></tf-graph-icon>
                <span class="color-legend-value">[[item.device]]</span>
              </div>
            </template>
            <div class="color-legend-row">
              <tf-graph-icon type="META" height="16" fill-override="#eee" stroke-override="#a6a6a6"></tf-graph-icon>
              <span class="color-legend-value">unknown device</span>
            </div>
          </div>
        </template>
        <template is="dom-if" if="[[_equals(colorBy, 'xla_cluster')]]">
          <div>
            <template is="dom-repeat" items="[[_currentXlaClusterParams]]">
              <div class="color-legend-row">
                <svg>
                  <use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#unfilled-rect" x="0" y="0" style="fill:[[item.color]]" />
                </svg>
                <span class="color-legend-value">[[item.xla_cluster]]</span>
              </div>
            </template>
            <div class="color-legend-row">
              <svg>
                <use xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="#grey-rect" x="0" y="0" />
              </svg>
              <span class="color-legend-value">unknown XLA cluster</span>
            </div>
          </div>
        </template>
        <template is="dom-if" if="[[_equals(colorBy, 'op_compatibility')]]">
          <div class="color-text">
            <div class="color-legend-row">
              <tf-graph-icon type="OP" height="16" fill-override="#0f9d58" stroke-override="#ccc"></tf-graph-icon>
              <span class="color-legend-value">Valid Op</span>
            </div>
            <div class="color-legend-row">
              <tf-graph-icon type="OP" height="16" fill-override="#db4437" stroke-override="#ccc"></tf-graph-icon>
              <span class="color-legend-value">Invalid Op</span>
            </div>
          </div>
        </template>
        <template is="dom-if" if="[[_statsNotNull(stats)]]">
          <div class="color-legend-row">
            <tf-graph-icon type="META" height="16" faded></tf-graph-icon>
            <span class="color-legend-value">unused substructure</span>
          </div>
        </template>
      </div>
    </div>
    <div class="legend-holder">
      <paper-icon-button icon="[[_getToggleLegendIcon(_legendOpened)]]" on-click="_toggleLegendOpen" class="toggle-legend-button">
      </paper-icon-button>
      <span class="toggle-legend-text">
        [[_getToggleText(_legendOpened)]]
      </span>
      <iron-collapse opened="[[_legendOpened]]">
        <div>
          <table>
            <tbody><tr>
              <td><div class="title">Graph</div></td>
              <td>(* = expandable)</td>
            </tr>
            <tr>
              <td>
                <tf-graph-icon type="META" height="16" fill-override="#d9d9d9" stroke-override="#ccc"></tf-graph-icon>
              </td>
              <td>
                Namespace<span class="gray">*</span>
                <div class="legend-clarifier">
                  <span>?</span>
                  <paper-tooltip animation-delay="0" position="right" offset="0">
                    Encapsulates a set of nodes. Namespace is hierarchical and
                    based on scope.
                  </paper-tooltip>
                </div>
              </td>
            </tr>
            <tr>
              <td>
                <tf-graph-icon type="OP" height="16"></tf-graph-icon>
              </td>
              <td>
                OpNode
                <div class="legend-clarifier">
                  <span>?</span>
                  <paper-tooltip animation-delay="0" position="right" offset="0">
                    Node that performs an operation. These nodes cannot expand.
                  </paper-tooltip>
                </div>
              </td>
            </tr>
            <tr>
              <td>
                <tf-graph-icon type="SERIES" height="16"></tf-graph-icon>
              </td>
              <td>
                Unconnected series<span class="gray">*</span>
                <div class="legend-clarifier">
                  <span>?</span>
                  <paper-tooltip animation-delay="0" position="right" offset="0">
                    Sequence of numbered nodes that are not connected to each
                    other.
                  </paper-tooltip>
                </div>
              </td>
            </tr>
            <tr>
              <td>
                <tf-graph-icon type="SERIES" height="16" vertical></tf-graph-icon>
              </td>
              <td>
                Connected series<span class="gray">*</span>
                <div class="legend-clarifier">
                  <span>?</span>
                  <paper-tooltip animation-delay="0" position="right" offset="0">
                    Sequence of numbered nodes that are connected to each other.
                  </paper-tooltip>
                </div>
              </td>
            </tr>
            <tr>
              <td>
                <svg class="icon">
                  <circle fill="white" stroke="#848484" cx="10" cy="10" r="5" />
                </svg>
              </td>
              <td>
                Constant
                <div class="legend-clarifier">
                  <span>?</span>
                  <paper-tooltip animation-delay="0" position="right" offset="0">
                    Node that outputs a constant value.
                  </paper-tooltip>
                </div>
              </td>
            </tr>
            <tr>
              <td>
                <tf-graph-icon type="SUMMARY" height="20"></tf-graph-icon>
              </td>
              <td>
                Summary
                <div class="legend-clarifier">
                  <span>?</span>
                  <paper-tooltip animation-delay="0" position="right" offset="0">
                    Node that collects data for visualization within
                    TensorBoard.
                  </paper-tooltip>
                </div>
              </td>
            </tr>
            <tr>
              <td>
                <svg class="icon" height="15px" preserveaspectratio="xMinYMid meet" viewbox="0 0 15 15">
                  <defs>
                    <marker id="dataflow-arrowhead-legend" fill="#bbb" markerwidth="10" markerheight="10" refx="9" refy="5" orient="auto-start-reverse">
                      <path d="M 0,0 L 10,5 L 0,10 C 3,7 3,3 0,0" />
                    </marker>
                  </defs>
                  <path marker-end="url(#dataflow-arrowhead-legend)" stroke="#bbb" d="M2 9 l 29 0" stroke-linecap="round" />
                </svg>
              </td>
              <td>
                Dataflow edge
                <div class="legend-clarifier">
                  <span>?</span>
                  <paper-tooltip animation-delay="0" position="right" offset="0">
                    Edge showing the data flow between operations. Edges flow
                    upwards unless arrowheads specify otherwise.
                  </paper-tooltip>
                </div>
              </td>
            </tr>
            <tr>
              <td>
                <svg class="icon" height="15px" preserveaspectratio="xMinYMid meet" viewbox="0 0 15 15">
                  <path stroke="#bbb" d="M2 9 l 29 0" stroke-linecap="round" stroke-dasharray="2, 2" />
                </svg>
              </td>
              <td>
                Control dependency edge
                <div class="legend-clarifier">
                  <span>?</span>
                  <paper-tooltip animation-delay="0" position="right" offset="0">
                    Edge showing the control dependency between operations.
                  </paper-tooltip>
                </div>
              </td>
            </tr>
            <tr>
              <td>
                <svg class="icon" height="15px" preserveaspectratio="xMinYMid meet" viewbox="0 0 15 15">
                  <defs>
                    <marker id="reference-arrowhead-legend" fill="#FFB74D" markerwidth="10" markerheight="10" refx="9" refy="5" orient="auto-start-reverse">
                      <path d="M 0,0 L 10,5 L 0,10 C 3,7 3,3 0,0" />
                    </marker>
                  </defs>
                  <path marker-end="url(#reference-arrowhead-legend)" stroke="#FFB74D" d="M2 9 l 29 0" stroke-linecap="round" />
                </svg>
              </td>
              <td>
                Reference edge
                <div class="legend-clarifier">
                  <span>?</span>
                  <paper-tooltip animation-delay="0" position="right" offset="0">
                    Edge showing that the outgoing operation node can mutate the
                    incoming tensor.
                  </paper-tooltip>
                </div>
              </td>
            </tr>
          </tbody></table>
        </div>
      </iron-collapse>
    </div>
  </template>
</dom-module><dom-module id="tf-graph-dashboard">
  <template>
    <paper-dialog id="error-dialog" with-backdrop></paper-dialog>
    <template is="dom-if" if="[[_datasetsState(_datasetsFetched, _datasets, 'EMPTY')]]">
      <div style="max-width: 540px; margin: 80px auto 0 auto;">
        <h3>No graph definition files were found.</h3>
        <p>
          To store a graph, create a
          <code>tf.summary.FileWriter</code>
          and pass the graph either via the constructor, or by calling its
          <code>add_graph()</code> method. You may want to check out the
          <a href="https://www.tensorflow.org/get_started/graph_viz">graph visualizer tutorial</a>.
        </p>

        <p>
          If you’re new to using TensorBoard, and want to find out how to add
          data and set up your event files, check out the
          <a href="https://github.com/tensorflow/tensorboard/blob/master/README.md">README</a>
          and perhaps the
          <a href="https://www.tensorflow.org/get_started/summaries_and_tensorboard">TensorBoard tutorial</a>.
        </p>

        <p>
          If you think TensorBoard is configured properly, please see
          <a href="https://github.com/tensorflow/tensorboard/blob/master/README.md#my-tensorboard-isnt-showing-any-data-whats-wrong">the section of the README devoted to missing data problems</a>
          and consider filing an issue on GitHub.
        </p>
      </div>
    </template>
    <template is="dom-if" if="[[_datasetsState(_datasetsFetched, _datasets, 'PRESENT')]]">
      <tf-dashboard-layout>
        <tf-graph-controls id="controls" class="sidebar" slot="sidebar" devices-for-stats="{{_devicesForStats}}" color-by-params="[[_colorByParams]]" stats="[[_stats]]" color-by="{{_colorBy}}" datasets="[[_datasets]]" render-hierarchy="[[_renderHierarchy]]" selection="{{_selection}}" selected-file="{{_selectedFile}}" selected-node="{{_selectedNode}}" health-pills-feature-enabled="[[_debuggerDataEnabled]]" health-pills-toggled-on="{{healthPillsToggledOn}}" on-fit-tap="_fit" trace-inputs="{{_traceInputs}}"></tf-graph-controls>
        <div class="center" slot="center">
          <tf-graph-dashboard-loader id="loader" datasets="[[_datasets]]" selection="[[_selection]]" selected-file="[[_selectedFile]]" out-graph-hierarchy="{{_graphHierarchy}}" out-graph="{{_graph}}" out-stats="{{_stats}}" progress="{{_progress}}" hierarchy-params="[[_hierarchyParams]]" compatibility-provider="[[_compatibilityProvider]]"></tf-graph-dashboard-loader>
          <tf-graph-board id="graphboard" devices-for-stats="[[_devicesForStats]]" color-by="[[_colorBy]]" color-by-params="{{_colorByParams}}" graph-hierarchy="[[_graphHierarchy]]" graph="[[_graph]]" hierarchy-params="[[_hierarchyParams]]" progress="[[_progress]]" debugger-data-enabled="[[_debuggerDataEnabled]]" are-health-pills-loading="[[_areHealthPillsLoading]]" debugger-numeric-alerts="[[_debuggerNumericAlerts]]" node-names-to-health-pills="[[_nodeNamesToHealthPills]]" all-steps-mode-enabled="{{allStepsModeEnabled}}" specific-health-pill-step="{{specificHealthPillStep}}" health-pill-step-index="[[_healthPillStepIndex]]" render-hierarchy="{{_renderHierarchy}}" selected-node="{{_selectedNode}}" stats="[[_stats]]" trace-inputs="[[_traceInputs]]"></tf-graph-board>
        </div>
      </tf-dashboard-layout>
    </template>
    <style>
      :host /deep/ {
        font-family: 'Roboto', sans-serif;
      }

      .sidebar {
        display: flex;
        height: 100%;
      }

      .center {
        position: relative;
        height: 100%;
      }

      paper-dialog {
        padding: 20px;
      }
    </style>
  </template>
</dom-module><dom-module id="vz-distribution-chart">
  <template>
    <style include="plottable-style"></style>
    <div id="chartdiv"></div>
    <style>
      :host {
        -webkit-user-select: none;
        -moz-user-select: none;
        display: flex;
        flex-direction: column;
        flex-grow: 1;
        flex-shrink: 1;
        position: relative;
      }
      #chartdiv {
        -webkit-user-select: none;
        -moz-user-select: none;
        flex-grow: 1;
        flex-shrink: 1;
      }
    </style>
  </template>
  
</dom-module><dom-module id="tf-distribution-loader">
  <template>
    <tf-card-heading tag="[[tag]]" run="[[run]]" display-name="[[tagMetadata.displayName]]" description="[[tagMetadata.description]]" color="[[_runColor]]"></tf-card-heading>
    
    <vz-distribution-chart id="chart" x-type="[[xType]]" color-scale="[[_colorScale]]"></vz-distribution-chart>
    <div style="display: flex; flex-direction: row;">
      <paper-icon-button selected$="[[_expanded]]" icon="fullscreen" on-tap="_toggleExpanded"></paper-icon-button>
    </div>
    <style>
      :host {
        display: flex;
        flex-direction: column;
        width: 330px;
        height: 235px;
        margin-right: 10px;
        margin-bottom: 15px;
      }
      :host([_expanded]) {
        width: 700px;
        height: 500px;
      }

      vz-histogram-timeseries {
        -moz-user-select: none;
        -webkit-user-select: none;
      }

      paper-icon-button {
        color: #2196f3;
        border-radius: 100%;
        width: 32px;
        height: 32px;
        padding: 4px;
      }
      paper-icon-button[selected] {
        background: var(--tb-ui-light-accent);
      }

      tf-card-heading {
        margin-bottom: 10px;
      }
    </style>
  </template>
  
</dom-module><dom-module id="tf-distribution-dashboard">
  <template>
    <tf-dashboard-layout>
      <div class="sidebar" slot="sidebar">
        <div class="sidebar-section">
          <tf-option-selector id="xTypeSelector" name="Horizontal axis" selected-id="{{_xType}}">
            <paper-button id="step">step</paper-button>
            <paper-button id="relative">relative</paper-button>
            <paper-button id="wall_time">wall</paper-button>
          </tf-option-selector>
        </div>
        <div class="sidebar-section">
          <tf-runs-selector selected-runs="{{_selectedRuns}}">
          </tf-runs-selector>
        </div>
      </div>

      <div class="center" slot="center">
        <template is="dom-if" if="[[_dataNotFound]]">
          <div class="no-data-warning">
            <h3>No distribution data was found.</h3>
            <p>Probable causes:</p>
            <ul>
              <li>
                You haven’t written any histogram data to your event files.
                (Histograms and distributions both use the histogram summary
                operation.)
              </li>

              <li>TensorBoard can’t find your event files.</li>
            </ul>

            <p>
              If you’re new to using TensorBoard, and want to find out how to
              add data and set up your event files, check out the
              <a href="https://github.com/tensorflow/tensorboard/blob/master/README.md">README</a>
              and perhaps the
              <a href="https://www.tensorflow.org/get_started/summaries_and_tensorboard">TensorBoard tutorial</a>.
            </p>

            <p>
              If you think TensorBoard is configured properly, please see
              <a href="https://github.com/tensorflow/tensorboard/blob/master/README.md#my-tensorboard-isnt-showing-any-data-whats-wrong">the section of the README devoted to missing data problems</a>
              and consider filing an issue on GitHub.
            </p>
          </div>
        </template>
        <template is="dom-if" if="[[!_dataNotFound]]">
          <tf-tag-filterer tag-filter="{{_tagFilter}}"></tf-tag-filterer>
          <template is="dom-repeat" items="[[_categories]]" as="category">
            <tf-category-paginated-view category="[[category]]" initial-opened="[[_shouldOpen(index)]]">
              <template>
                <tf-distribution-loader active="[[active]]" run="[[item.run]]" tag="[[item.tag]]" tag-metadata="[[_tagMetadata(_runToTagInfo, item.run, item.tag)]]" x-type="[[_xType]]" request-manager="[[_requestManager]]"></tf-distribution-loader>
              </template>
            </tf-category-paginated-view>
          </template>
        </template>
      </div>
    </tf-dashboard-layout>

    <style include="dashboard-style"></style>
    <style>
      .no-data-warning {
        max-width: 540px;
        margin: 80px auto 0 auto;
      }
    </style>
  </template>

  
</dom-module><dom-module id="vz-histogram-timeseries">
  <template>
    <div id="tooltip"><span></span></div>
    <svg id="svg">
      <g>
        <g class="axis x"></g>
        <g class="axis y"></g>
        <g class="axis y slice"></g>
        <g class="stage">
          <rect class="background"></rect>
        </g>
        <g class="x-axis-hover"></g>
        <g class="y-axis-hover"></g>
        <g class="y-slice-axis-hover"></g>
      </g>
    </svg>

    <style>
      :host {
        display: flex;
        flex-direction: column;
        flex-grow: 1;
        flex-shrink: 1;
        position: relative;
      }

      svg {
        font-family: roboto, sans-serif;
        overflow: visible;
        display: block;
        width: 100%;
        flex-grow: 1;
        flex-shrink: 1;
      }

      #tooltip {
        position: absolute;
        display: block;
        opacity: 0;
        font-weight: bold;
        font-size: 11px;
      }

      .background {
        fill-opacity: 0;
        fill: red;
      }

      .histogram {
        pointer-events: none;
      }

      .hover {
        font-size: 9px;
        dominant-baseline: middle;
        opacity: 0;
      }

      .hover circle {
        stroke: white;
        stroke-opacity: 0.5;
        stroke-width: 1px;
      }

      .hover text {
        fill: black;
        opacity: 0;
      }

      .hover.hover-closest circle {
        fill: black !important;
      }

      .hover.hover-closest text {
        opacity: 1;
      }

      .baseline {
        stroke: black;
        stroke-opacity: 0.1;
      }

      .outline {
        fill: none;
        stroke: white;
        stroke-opacity: 0.5;
      }

      .outline.outline-hover {
        stroke: black !important;
        stroke-opacity: 1;
      }

      .x-axis-hover,
      .y-axis-hover,
      .y-slice-axis-hover {
        pointer-events: none;
      }

      .x-axis-hover .label,
      .y-axis-hover .label,
      .y-slice-axis-hover .label {
        opacity: 0;
        font-weight: bold;
        font-size: 11px;
        text-anchor: end;
      }

      .x-axis-hover text {
        text-anchor: middle;
      }

      .y-axis-hover text,
      .y-slice-axis-hover text {
        text-anchor: start;
      }

      .x-axis-hover line,
      .y-axis-hover line,
      .y-slice-axis-hover line {
        stroke: black;
      }

      .x-axis-hover rect,
      .y-axis-hover rect,
      .y-slice-axis-hover rect {
        fill: white;
      }

      .axis {
        font-size: 11px;
      }

      .axis path.domain {
        fill: none;
      }

      .axis .tick line {
        stroke: #ddd;
      }

      .axis.slice {
        opacity: 0;
      }

      .axis.slice .tick line {
        stroke-dasharray: 2;
      }

      .small .axis text {
        display: none;
      }
      .small .axis .tick:first-of-type text {
        display: block;
      }
      .small .axis .tick:last-of-type text {
        display: block;
      }
      .medium .axis text {
        display: none;
      }
      .medium .axis .tick:nth-child(2n + 1) text {
        display: block;
      }
      .large .axis text {
        display: none;
      }
      .large .axis .tick:nth-child(2n + 1) text {
        display: block;
      }
    </style>
  </template>

  
</dom-module><dom-module id="tf-histogram-loader">
  <template>
    <tf-card-heading tag="[[tag]]" run="[[run]]" display-name="[[tagMetadata.displayName]]" description="[[tagMetadata.description]]" color="[[_runColor]]"></tf-card-heading>
    
    <vz-histogram-timeseries id="chart" time-property="[[timeProperty]]" mode="[[histogramMode]]" color-scale="[[_colorScaleFunction]]"></vz-histogram-timeseries>
    <div style="display: flex; flex-direction: row;">
      <paper-icon-button selected$="[[_expanded]]" icon="fullscreen" on-tap="_toggleExpanded"></paper-icon-button>
    </div>
    <style>
      :host {
        display: flex;
        flex-direction: column;
        width: 330px;
        height: 235px;
        margin-right: 10px;
        margin-bottom: 15px;
      }
      :host([_expanded]) {
        width: 700px;
        height: 500px;
      }

      vz-histogram-timeseries {
        -moz-user-select: none;
        -webkit-user-select: none;
        will-change: transform;
      }

      paper-icon-button {
        color: #2196f3;
        border-radius: 100%;
        width: 32px;
        height: 32px;
        padding: 4px;
      }

      paper-icon-button[selected] {
        background: var(--tb-ui-light-accent);
      }

      tf-card-heading {
        margin-bottom: 10px;
        width: 90%;
      }
    </style>
  </template>
  
</dom-module><dom-module id="tf-histogram-dashboard">
  <template>
    <tf-dashboard-layout>
      <div slot="sidebar">
        <div class="sidebar-section">
          <tf-option-selector id="histogramModeSelector" name="Histogram mode" selected-id="{{_histogramMode}}">
            <paper-button id="overlay">overlay</paper-button>
            <paper-button id="offset">offset</paper-button>
          </tf-option-selector>
        </div>
        <div class="sidebar-section">
          <tf-option-selector id="timePropertySelector" name="Offset time axis" selected-id="{{_timeProperty}}">
            <paper-button id="step">step</paper-button>
            <paper-button id="relative">relative</paper-button>
            <paper-button id="wall_time">wall</paper-button>
          </tf-option-selector>
        </div>
        <div class="sidebar-section">
          <tf-runs-selector selected-runs="{{_selectedRuns}}">
          </tf-runs-selector>
        </div>
      </div>
      <div slot="center">
        <template is="dom-if" if="[[_dataNotFound]]">
          <div class="no-data-warning">
            <h3>No histogram data was found.</h3>
            <p>Probable causes:</p>
            <ul>
              <li>
                You haven’t written any histogram data to your event files.
              </li>
              <li>TensorBoard can’t find your event files.</li>
            </ul>

            <p>
              If you’re new to using TensorBoard, and want to find out how to
              add data and set up your event files, check out the
              <a href="https://github.com/tensorflow/tensorboard/blob/master/README.md">README</a>
              and perhaps the
              <a href="https://www.tensorflow.org/get_started/summaries_and_tensorboard">TensorBoard tutorial</a>.
            </p>

            <p>
              If you think TensorBoard is configured properly, please see
              <a href="https://github.com/tensorflow/tensorboard/blob/master/README.md#my-tensorboard-isnt-showing-any-data-whats-wrong">the section of the README devoted to missing data problems</a>
              and consider filing an issue on GitHub.
            </p>
          </div>
        </template>
        <template is="dom-if" if="[[!_dataNotFound]]">
          <tf-tag-filterer tag-filter="{{_tagFilter}}"></tf-tag-filterer>
          <template is="dom-repeat" items="[[_categories]]" as="category">
            <tf-category-paginated-view category="[[category]]" initial-opened="[[_shouldOpen(index)]]">
              <template>
                <tf-histogram-loader run="[[item.run]]" tag="[[item.tag]]" active="[[active]]" tag-metadata="[[_tagMetadata(_runToTagInfo, item.run, item.tag)]]" time-property="[[_timeProperty]]" histogram-mode="[[_histogramMode]]" request-manager="[[_requestManager]]"></tf-histogram-loader>
              </template>
            </tf-category-paginated-view>
          </template>
        </template>
      </div>
    </tf-dashboard-layout>

    <style include="dashboard-style"></style>
    <style>
      .no-data-warning {
        max-width: 540px;
        margin: 80px auto 0 auto;
      }
    </style>
  </template>

  
</dom-module><dom-module id="tf-text-loader">
  <template>
    <tf-card-heading run="[[run]]" tag="[[tag]]" color="[[_runColor]]">
    </tf-card-heading>
    <paper-material elevation="1" id="steps-container" class="container scrollbar" style="border-color: [[_runColor]]">
      <template is="dom-repeat" items="[[_texts]]">
        <paper-material elevation="1" class="step-container">
          step <span class="step-value">[[_formatStep(item.step)]]</span>
        </paper-material>
        <paper-material elevation="1" class="text">
          <tf-markdown-view html="[[item.text]]"></tf-markdown-view>
        </paper-material>
      </template>
    </paper-material>
    <style include="scrollbar-style"></style>
    <style>
      :host {
        display: flex;
        flex-direction: column;
        width: 100%;
        height: auto;
        margin-right: 10px;
        margin-bottom: 15px;
      }
      .scrollbar {
        will-change: transform;
      }
      #steps-container {
        border-radius: 3px;
        border: 2px solid /* color computed and set as inline style */;
        display: block;
        max-height: 500px;
        overflow: auto;
        padding: 10px;
      }
      .text {
        background-color: white;
        border-radius: 0 3px 3px 3px;
        padding: 5px;
        word-break: break-word;
      }
      .step-container {
        background-color: var(--tb-ui-light-accent);
        border-bottom: none;
        border-radius: 3px 3px 0 0;
        border: 1px solid #ccc;
        display: inline-block;
        font-size: 12px;
        font-style: italic;
        margin-left: -1px; /* to correct for border */
        padding: 3px;
      }
      .step-container:not(:first-child) {
        margin-top: 15px;
      }

      tf-card-heading {
        margin-bottom: 10px;
      }
    </style>
  </template>
  
</dom-module><dom-module id="tf-text-dashboard">
  <template>
    <tf-dashboard-layout>
      <div class="sidebar" slot="sidebar">
        <div class="sidebar-section">
          <tf-runs-selector selected-runs="{{_selectedRuns}}">
          </tf-runs-selector>
        </div>
      </div>
      <div class="center" slot="center">
        <template is="dom-if" if="[[_dataNotFound]]">
          <div class="no-data-warning">
            <h3>No text data was found.</h3>
            <p>Probable causes:</p>
            <ul>
              <li>You haven’t written any text data to your event files.</li>
              <li>TensorBoard can’t find your event files.</li>
            </ul>

            <p>
              If you’re new to using TensorBoard, and want to find out how to
              add data and set up your event files, check out the
              <a href="https://github.com/tensorflow/tensorboard/blob/master/README.md">README</a>
              and perhaps the
              <a href="https://www.tensorflow.org/get_started/summaries_and_tensorboard">TensorBoard tutorial</a>.
            </p>

            <p>
              If you think TensorBoard is configured properly, please see
              <a href="https://github.com/tensorflow/tensorboard/blob/master/README.md#my-tensorboard-isnt-showing-any-data-whats-wrong">the section of the README devoted to missing data problems</a>
              and consider filing an issue on GitHub.
            </p>
          </div>
        </template>
        <template is="dom-if" if="[[!_dataNotFound]]">
          <tf-tag-filterer tag-filter="{{_tagFilter}}"></tf-tag-filterer>
          <template is="dom-repeat" items="[[_categories]]" as="category">
            <tf-category-paginated-view category="[[category]]" initial-opened="[[_shouldOpen(index)]]">
              <template>
                <tf-text-loader active="[[active]]" tag="[[item.tag]]" run="[[item.run]]" request-manager="[[_requestManager]]"></tf-text-loader>
              </template>
            </tf-category-paginated-view>
          </template>
        </template>
      </div>
    </tf-dashboard-layout>
    <style include="dashboard-style"></style>
    <style>
      .no-data-warning {
        max-width: 540px;
        margin: 80px auto 0 auto;
      }
    </style>
  </template>
  
</dom-module><dom-module id="tf-pr-curve-card">
  <template>
    <tf-card-heading tag="[[tag]]" display-name="[[tagMetadata.displayName]]" description="[[tagMetadata.description]]"></tf-card-heading>

    <tf-line-chart-data-loader x-components-creation-method="[[_xComponentsCreationMethod]]" y-value-accessor="[[_yValueAccessor]]" tooltip-columns="[[_tooltipColumns]]" color-scale="[[_colorScaleFunction]]" default-x-range="[[_defaultXRange]]" default-y-range="[[_defaultYRange]]" smoothing-enabled="[[_smoothingEnabled]]" request-manager="[[requestManager]]" data-to-load="[[runs]]" data-series="[[runs]]" load-key="[[tag]]" get-data-load-url="[[_dataUrl]]" load-data-callback="[[_createProcessDataFunction()]]" active="[[active]]"></tf-line-chart-data-loader>

    <div id="buttons-row">
      <paper-icon-button selected$="[[_expanded]]" icon="fullscreen" on-tap="_toggleExpanded"></paper-icon-button>
      <paper-icon-button icon="settings-overscan" on-tap="_resetDomain" title="Reset axes to [0, 1]."></paper-icon-button>
    </div>

    <div id="step-legend">
      <template is="dom-repeat" items="[[_runsWithStepAvailable]]" as="run">
        <div class="legend-row">
          <div class="color-box" style="background: [[_computeRunColor(run)]];"></div>
          [[run]] is at
          <span class="step-label-text">
            step [[_computeCurrentStepForRun(_runToPrCurveEntry, run)]] </span><br>
          <span class="wall-time-label-text">
            ([[_computeCurrentWallTimeForRun(_runToPrCurveEntry, run)]])
          </span>
        </div>
      </template>
    </div>

    <style>
      :host {
        display: flex;
        flex-direction: column;
        width: 500px;
        margin-right: 10px;
        margin-bottom: 25px;
      }
      :host([_expanded]) {
        width: 100%;
      }
      tf-line-chart-data-loader {
        height: 300px;
        position: relative;
      }
      :host([_expanded]) tf-line-chart-data-loader {
        height: 600px;
      }
      #buttons-row {
        display: flex;
        flex-direction: row;
      }
      #buttons-row paper-icon-button {
        color: #2196f3;
        border-radius: 100%;
        width: 32px;
        height: 32px;
        padding: 4px;
      }
      #buttons-row paper-icon-button[selected] {
        background: var(--tb-ui-light-accent);
      }
      #step-legend {
        box-sizing: border-box;
        font-size: 0.8em;
        max-height: 200px;
        overflow-y: auto;
        padding: 0 0 0 10px;
        width: 100%;
      }
      .legend-row {
        margin: 5px 0 5px 0;
        width: 100%;
      }
      .color-box {
        display: inline-block;
        border-radius: 1px;
        width: 10px;
        height: 10px;
      }
      .step-label-text {
        font-weight: bold;
      }
      .wall-time-label-text {
        color: #888;
        font-size: 0.8em;
      }
    </style>
  </template>
  
</dom-module><dom-module id="tf-pr-curve-steps-selector">
  <template>
    <template is="dom-repeat" items="[[_runsWithSliders]]" as="run">
      <div class="run-widget">
        <div class="run-display-container">
          <div class="run-color-box" style="background:[[_computeColorForRun(run)]];"></div>
          <div class="run-text">
            [[run]]
          </div>
        </div>
        <div class="step-display-container">
          [[_computeTimeTextForRun(runToAvailableTimeEntries, _runToStepIndex,
          run, timeDisplayType)]]
        </div>
        <paper-slider data-run$="[[run]]" step="1" type="number" min="0" max="[[_computeMaxStepIndexForRun(runToAvailableTimeEntries, run)]]" value="[[_getStep(_runToStepIndex, run)]]" on-immediate-value-changed="_sliderValueChanged"></paper-slider>
      </div>
    </template>
    <style>
      .run-widget {
        margin: 10px 0 0 0;
      }
      paper-slider {
        margin: -8px 0 0 -15px;
        width: 100%;
      }
      .step-display-container {
        font-size: 0.9em;
        margin: 0 15px 0 0;
      }
      .run-text {
        display: inline-block;
      }
      .run-color-box {
        width: 12px;
        height: 12px;
        border-radius: 3px;
        display: inline-block;
      }
    </style>
  </template>
  
</dom-module><dom-module id="tf-pr-curve-dashboard">
  <template>
    <tf-dashboard-layout>
      <div class="sidebar" slot="sidebar">
        <div class="sidebar-section">
          <tf-option-selector id="time-type-selector" name="Time Display Type" selected-id="{{_timeDisplayType}}">
            <paper-button id="step">step</paper-button><paper-button id="relative">relative</paper-button><paper-button id="wall_time">wall</paper-button>
          </tf-option-selector>
        </div>
        <template is="dom-if" if="[[_runToAvailableTimeEntries]]">
          <div class="sidebar-section" id="steps-selector-container">
            <tf-pr-curve-steps-selector runs="[[_relevantSelectedRuns]]" run-to-step="{{_runToStep}}" run-to-available-time-entries="[[_runToAvailableTimeEntries]]" time-display-type="[[_timeDisplayType]]"></tf-pr-curve-steps-selector>
          </div>
        </template>
        <div class="sidebar-section">
          <tf-runs-selector selected-runs="{{_selectedRuns}}">
          </tf-runs-selector>
        </div>
      </div>
      <div class="center" slot="center">
        <template is="dom-if" if="[[_dataNotFound]]">
          <div class="no-data-warning">
            <h3>No precision–recall curve data was found.</h3>
            <p>Probable causes:</p>
            <ul>
              <li>
                You haven’t written any precision–recall data to your event
                files.
              </li>
              <li>
                TensorBoard can’t find your event files.
              </li>
            </ul>
            <p>
              If you’re new to using TensorBoard, and want to find out how to
              add data and set up your event files, check out the
              <a href="https://github.com/tensorflow/tensorboard/blob/master/README.md">README</a>
              and perhaps the
              <a href="https://www.tensorflow.org/get_started/summaries_and_tensorboard">TensorBoard tutorial</a>.
            </p>

            <p>
              If you think TensorBoard is configured properly, please see
              <a href="https://github.com/tensorflow/tensorboard/blob/master/README.md#my-tensorboard-isnt-showing-any-data-whats-wrong">the section of the README devoted to missing data problems</a>
              and consider filing an issue on GitHub.
            </p>
          </div>
        </template>
        <template is="dom-if" if="[[!_dataNotFound]]">
          <tf-tag-filterer tag-filter="{{_tagFilter}}"></tf-tag-filterer>
          <template is="dom-repeat" items="[[_categories]]" as="category">
            <tf-category-paginated-view category="[[category]]" initial-opened="[[_shouldOpen(index)]]" get-category-item-key="[[_getCategoryItemKey]]">
              <template>
                <tf-pr-curve-card active="[[active]]" runs="[[item.runs]]" tag="[[item.tag]]" tag-metadata="[[_tagMetadata(_runToTagInfo, item.runs, item.tag)]]" request-manager="[[_requestManager]]" run-to-step-cap="[[_runToStep]]" on-data-change="[[_createDataChangeCallback(item.tag)]]"></tf-pr-curve-card>
              </template>
            </tf-category-paginated-view>
          </template>
        </template>
      </div>
    </tf-dashboard-layout>

    <style include="dashboard-style"></style>
    <style>
      .no-data-warning {
        max-width: 540px;
        margin: 80px auto 0 auto;
      }
      /** Do not let the steps selector occlude the run selector. */
      #steps-selector-container {
        max-height: 40%;
        overflow-y: auto;
      }
    </style>
  </template>

  
</dom-module><dom-module id="tf-profile-redirect-dashboard">
  <template>
    <div class="message">
      <h3>The profile plugin has moved.</h3>
      <p>
        Please install the new version of the profile plugin from PyPI by
        running the following command from the machine running TensorBoard:
      </p>
      <textarea id="commandTextarea" readonly rows="1" on-blur="_removeCopiedMessage">
[[_installCommand]]</textarea>
      <div id="copyContainer">
        <span id="copiedMessage"></span>
        <paper-button raised on-tap="_copyInstallCommand">Copy to clipboard</paper-button>
      </div>
    </div>

    <style>
      .message {
        margin: 80px auto 0 auto;
        max-width: 540px;
      }
      #commandTextarea {
        margin-top: 1ex;
        padding: 1ex 1em;
        resize: vertical;
        width: 100%;
      }
      #copyContainer {
        display: flex;
      }
      #copiedMessage {
        align-self: center;
        flex-grow: 1;
        font-style: italic;
        padding-right: 1em;
        text-align: right;
      }
    </style>
  </template>

  
</dom-module><dom-module id="tf-plugin-dialog">
  <template>
    
    <template is="dom-if" if="[[_open]]">
      <div id="dashboard-backdrop"></div>
    </template>
    <paper-dialog id="dialog" modal opened="{{_open}}" with-backdrop="[[_useNativeBackdrop]]">
      <h2 id="dialog-title">[[_title]]</h2>
      <div class="custom-message">[[_customMessage]]</div>
    </paper-dialog>
    <style>
      /** We rely on a separate `_hidden` property instead of directly making use
          of the `_open` attribute because this CSS specification may strangely
          affect other elements throughout TensorBoard. See #899. */
      #dashboard-backdrop {
        background: rgba(0, 0, 0, 0.6);
        width: 100%;
        height: 100%;
      }

      #dialog-title {
        padding-bottom: 15px;
      }

      .custom-message {
        margin-top: 0;
        margin-bottom: 15px;
      }
    </style>
  </template>
  
</dom-module><dom-module id="tf-beholder-video">
  <template>
    <div id="container">
      <img id="video" src$="[[_imageURL]]">
    </div>

    <style>
      img {
        image-rendering: pixelated;
        margin-right: 10px;
      }
    </style>
  </template>

  
</dom-module><dom-module id="tf-beholder-info">
  <template>
    <template is="dom-repeat" items="[[_items]]">
      <div class="section-info" style$="height: [[item.height]]px">
        <ul>
          <li>[[item.name]]</li>
          <li>shape: [[item.shape]]</li>
          <li>range: [ [[item.min]], [[item.max]] ]</li>
          <li>mean: [[item.mean]]</li>
        </ul>
      </div>
    </template>

    <style>
      .section-info {
        margin: 0 0 5px 0;
      }
      .section-info ul {
        list-style-type: none;
        margin: 0;
        padding-left: 10px;
      }
    </style>
  </template>

  
</dom-module><dom-module id="tf-beholder-dashboard">
  <template>
    <tf-plugin-dialog id="initialDialog"></tf-plugin-dialog>
    <tf-dashboard-layout>
      <div class="sidebar" slot="sidebar">
        <template is="dom-if" if="[[_controls_disabled]]">
          <div class="sidebar-section">
            <p class="controls-disabled-message">
              Controls disabled: directory is not writeable.
            </p>
            <p class="disclaimer">
              Beholder requires write access to the log directory in order to
              communicate visualization changes to the <code>Beholder</code>
              instance in your model.
            </p>
          </div>
        </template>
        <div class="sidebar-section">
          <h3>Values</h3>
          <paper-radio-group id="valuesSelector" selected="{{_values}}">
            <paper-radio-button name="trainable_variables" disabled="[[_controls_disabled]]">
              <pre>tf.trainable_variables()</pre>
            </paper-radio-button>
            <paper-radio-button id="option-arrays" name="arrays" disabled="[[_controls_disabled]]">
              <pre>b.update(arrays=[NP_ARRAYS])</pre>
            </paper-radio-button>
            <paper-radio-button id="option-frames" name="frames" disabled="[[_controls_disabled]]">
              <pre>b.update(frame=NP_ARRAY)</pre>
            </paper-radio-button>
          </paper-radio-group>

          <template is="dom-if" if="[[_valuesNotFrame(_values)]]">
            <paper-checkbox checked="{{_showAll}}" disabled="[[_controls_disabled]]">Show all data <i>(can be resource intensive)</i></paper-checkbox>
          </template>
        </div>

        <template is="dom-if" if="[[_valuesNotFrame(_values)]]">
          <div class="sidebar-section">
            <h3>Mode</h3>
            <paper-radio-group id="modeSelector" selected="{{_mode}}">
              <paper-radio-button name="current" disabled="[[_controls_disabled]]">
                current values
              </paper-radio-button>
              <paper-radio-button name="variance" disabled="[[_controls_disabled]]">
                variance over train steps
              </paper-radio-button>
            </paper-radio-group>
            <template is="dom-if" if="[[_varianceSelected(_mode)]]">
              <h4>Variance timesteps: {{_windowSize}}</h4>
              <paper-slider id="windowSlider" value="{{_windowSize}}" type="number" step="1" min="2" max="20" pin="true" disabled="[[_controls_disabled]]">
              </paper-slider>
            </template>
          </div>

          <div class="sidebar-section">
            <h3>Image scaling</h3>
            <paper-radio-group id="scalingSelector" selected="{{_scaling}}">
              <paper-radio-button id="option-layer" name="layer" disabled="[[_controls_disabled]]">
                per section
              </paper-radio-button>
              <paper-tooltip for="option-layer" position="right">
                Black is the lowest value in that section, white is that largest
                value in that section.
              </paper-tooltip>

              <paper-radio-button id="option-network" name="network" disabled="[[_controls_disabled]]">
                all sections
              </paper-radio-button>
              <paper-tooltip for="option-network" position="right">
                Black is the smallest value in all sections, white is the
                largest value in all sections.
              </paper-tooltip>
            </paper-radio-group>

            <div id="colormap-selection">
              <div id="colormap-selection-label">Colormap:</div>
              <paper-dropdown-menu no-label-float selected-item-label="{{_colormap}}" disabled="[[_controls_disabled]]">
                <paper-listbox slot="dropdown-content" selected="0">
                  <paper-item>magma</paper-item>
                  <paper-item>inferno</paper-item>
                  <paper-item>plasma</paper-item>
                  <paper-item>viridis</paper-item>
                  <paper-item>grayscale</paper-item>
                </paper-listbox>
              </paper-dropdown-menu>
            </div>
          </div>
        </template>

        <div class="sidebar-section">
          <h3>Updates per second: {{_FPS}}</h3>
          <paper-slider id="FPSSlider" value="{{_FPS}}" type="number" step="1" min="0" max="30" pin="true" disabled="[[_controls_disabled]]">
          </paper-slider>
        </div>

        <div class="sidebar-section">
          <div>
            <paper-button class="x-button" id="record_button" on-tap="_toggleRecord" disabled="[[_controls_disabled]]">
              [[_recordText]]
            </paper-button>
          </div>
        </div>

        <div class="sidebar-section">
          <p class="disclaimer">
            Note: Beholder currently only works well on local file systems.
          </p>
        </div>
      </div>
      <div class="center" slot="center">
        <template is="dom-if" if="[[!_is_active]]">
          <div class="no-data-warning">
            <h3>No Beholder data was found.</h3>

            <p>Probable causes:</p>
            <ul>
              <li>Your script isn't running.</li>
              <li>You aren't calling <code>beholder.update()</code>.</li>
            </ul>

            <p>
              To use Beholder, import and instantiate the
              <code>Beholder</code> class, and call its
              <code>update</code> method with a <code>Session</code> argument
              after every train step:
            </p>

            <pre>from tensorboard.plugins.beholder import Beholder
beholder = Beholder(LOG_DIRECTORY)

# inside train loop
beholder.update(
  session=sess,
  arrays=list_of_np_arrays,  # optional argument
  frame=two_dimensional_np_array,  # optional argument
)</pre>
            <p>
              If using <code>tf.train.MonitoredSession</code>, you can use
              <code>BeholderHook</code>:
            </p>

            <pre>from tensorboard.plugins.beholder import BeholderHook
beholder_hook = BeholderHook(LOG_DIRECTORY)
with MonitoredSession(..., hooks=[beholder_hook]) as sess:
  sess.run(train_op)</pre>

            <p>
              If you think everything is set up properly, please see
              <a href="https://github.com/tensorflow/tensorboard/blob/master/tensorboard/plugins/beholder/README.md">the README</a>
              for more information and consider filing an issue on GitHub.
            </p>

            <p class="disclaimer">
              Note: Beholder currently only works well on local file systems.
            </p>
          </div>
        </template>

        <template is="dom-if" if="[[_is_active]]">
          <tf-beholder-video id="video" fps="[[_FPS]]"></tf-beholder-video>

          <template is="dom-if" if="[[_valuesNotFrame(_values)]]">
            <tf-beholder-info id="info" fps="[[_FPS]]"> </tf-beholder-info>
          </template>
        </template>
      </div>
    </tf-dashboard-layout>
    <style include="dashboard-style"></style>
    <style>
      .center {
        height: 100%;
        display: flex;
        padding: 0;
      }

      .no-data-warning {
        max-width: 540px;
        margin: 80px auto 0;
      }

      paper-checkbox {
        display: block;
        padding: 4px;
      }

      paper-radio-button {
        display: flex;
        padding: 5px;

        --paper-radio-button-radio-container: {
          flex-grow: 0;
          flex-shrink: 0;
        }

        --paper-radio-button-label: {
          font-size: 13px;
          overflow: hidden;
          text-overflow: ellipsis;
        }
      }

      paper-radio-group {
        margin-top: 5px;
        width: 100%;
      }

      paper-slider {
        --paper-slider-active-color: var(--tb-orange-strong);
        --paper-slider-knob-color: var(--tb-orange-strong);
        --paper-slider-knob-start-border-color: var(--tb-orange-strong);
        --paper-slider-knob-start-color: var(--tb-orange-strong);
        --paper-slider-markers-color: var(--tb-orange-strong);
        --paper-slider-pin-color: var(--tb-orange-strong);
        --paper-slider-pin-start-color: var(--tb-orange-strong);
        flex-grow: 2;
      }

      pre {
        display: inline;
      }

      paper-button#record_button {
        color: #d32f2f;
      }

      paper-button#record_button.is-recording {
        background: #d32f2f;
        color: white;
      }

      .sidebar-section.beholder-dashboard:last-child {
        flex-grow: 0;
      }

      #colormap-selection {
        display: flex;
        margin-top: 5px;
      }

      #colormap-selection-label {
        margin-top: 13px;
      }

      #colormap-selection paper-dropdown-menu {
        margin-left: 10px;
        --paper-input-container-focus-color: var(--tb-orange-strong);
        width: 105px;
      }

      h4 {
        font-size: 14px;
        font-weight: normal;
        margin: 5px 0;
      }

      p.disclaimer {
        color: #999;
        font-style: italic;
      }

      p.controls-disabled-message {
        color: #c00;
        font-weight: bold;
      }

      .sidebar {
        font-size: 14px;
      }
    </style>
  </template>
  
</dom-module><dom-module id="vaadin-split-layout">
  <template>
    <style>
      :host {
        display: flex;
        overflow: hidden !important;
        transform: translateZ(0);
      }

      :host([vertical]) {
        flex-direction: column;
      }

      :host ::slotted(*) {
        flex: 1 1 auto;
        overflow: auto;
      }

      :host > #splitter {
        flex: none;
        position: relative;
        z-index: 1;
        overflow: visible;
        min-width: 8px;
        min-height: 8px;
        background: var(--divider-color, #ccc);
        fill: var(--primary-background-color, #fff);
        @apply --vaadin-split-layout-splitter;
      }

      :host(:not([vertical])) > #splitter {
        cursor: ew-resize;
      }

      :host([vertical]) > #splitter {
        cursor: ns-resize;
      }

      #handle,
      #splitter ::slotted([slot=handle]) {
        position: absolute;
        top: 50%;
        left: 50%;
        transform: translate(-50%, -50%);
      }

      :host([vertical]) > #splitter #handle {
        transform: translate(-50%, -50%) rotate(90deg);
      }
    </style>
    <slot id="primary" name="primary"></slot>
    <div id="splitter" on-track="_onHandleTrack" on-down="_preventDefault">
      <slot name="handle">
        <svg id="handle" width="40" height="40">
          <rect x="19" y="8" width="2" height="24"></rect>
        </svg>
      </slot>
    </div>
    <slot id="secondary" name="secondary"></slot>
  </template>

  
</dom-module><dom-module id="tf-hparams-query-pane">
  <template>
    <div class="pane">
      <vaadin-split-layout vertical>
        <vaadin-split-layout vertical id="hyperparameters-metrics-statuses">
          <vaadin-split-layout vertical id="hyperparameters-metrics">
            <div class="section hyperparameters">
              <div class="section-title">Hyperparameters</div>
              <template is="dom-repeat" items="{{_hparams}}" as="hparam">
                <div class="hparam">
                  <paper-checkbox checked="{{hparam.displayed}}" class="hparam-checkbox">
                    [[_hparamName(hparam.info)]]
                  </paper-checkbox>
                  
                  
                  <template is="dom-if" if="[[hparam.filter.domainDiscrete]]">
                    <template is="dom-repeat" items="[[hparam.filter.domainDiscrete]]">
                      <paper-checkbox checked="{{item.checked}}" class="discrete-value-checkbox" on-change="_queryServer">
                        [[_prettyPrint(item.value)]]
                      </paper-checkbox>
                    </template>
                  </template>
                  
                  <template is="dom-if" if="[[hparam.filter.interval]]">
                    <paper-input label="Min" value="{{hparam.filter.interval.min.value}}" allowed_pattern="[0-9.e\-]" on-value-changed="_queryServer" error-message="Invalid input" invalid="[[hparam.filter.interval.min.invalid]]" placeholder="-infinity">
                    </paper-input>
                    <paper-input label="Max" value="{{hparam.filter.interval.max.value}}" allowed_pattern="[0-9.e\-]" on-value-changed="_queryServer" error-message="Invalid input" invalid="[[hparam.filter.interval.max.invalid]]" placeholder="+infinity">
                    </paper-input>
                  </template>
                  
                  <template is="dom-if" if="[[hparam.filter.regexp]]">
                    <paper-input label="Regular expression" value="{{hparam.filter.regexp}}" on-value-changed="_queryServer">
                    </paper-input>
                  </template>
                </div>
              </template>
            </div>
            <div class="section metrics">
              <div class="section-title">Metrics</div>
              <template is="dom-repeat" items="{{_metrics}}" as="metric">
                <div class="metric">
                  
                  <paper-checkbox checked="{{metric.displayed}}" class="metric-checkbox">
                    [[_metricName(metric.info)]]
                  </paper-checkbox>
                  <div class="inline-element">
                    <paper-input label="Min" value="{{metric.filter.interval.min.value}}" allowed-pattern="[0-9.e\-]" on-value-changed="_queryServer" error-message="Invalid input" invalid="{{metric.filter.interval.min.invalid}}" placeholder="-infinity">
                    </paper-input>
                  </div>
                  <div class="inline-element">
                    <paper-input label="Max" allowed-pattern="[0-9.e\-]" value="{{metric.filter.interval.max.value}}" on-value-changed="_queryServer" error-message="Invalid input" invalid="{{metric.filter.interval.max.invalid}}" placeholder="+infinity">
                    </paper-input>
                  </div>
                </div>
              </template>
            </div>
          </vaadin-split-layout>
          <div class="section status">
            <div class="section-title">Status</div>
            <template is="dom-repeat" items="[[_statuses]]" as="status">
              <paper-checkbox checked="{{status.allowed}}" on-change="_queryServer">
                [[status.displayName]]
              </paper-checkbox>
            </template>
          </div>
        </vaadin-split-layout>
        <vaadin-split-layout vertical id="sorting-paging">
          <div class="section sorting">
            <div class="section-title">Sorting</div>
            <paper-dropdown-menu label="Sort by" on-selected-item-changed="_queryServer" horizontal-align="left">
              <paper-listbox class="dropdown-content" slot="dropdown-content" selected="{{_sortByIndex}}" on-selected-item-changed="_queryServer">
                <template is="dom-repeat" items="[[_hparams]]" as="hparam">
                  <paper-item>
                    [[_hparamName(hparam.info)]]
                  </paper-item>
                </template>
                <template is="dom-repeat" items="[[_metrics]]" as="metric">
                  <paper-item>
                    [[_metricName(metric.info)]]
                  </paper-item>
                </template>
              </paper-listbox>
            </paper-dropdown-menu>
            <paper-dropdown-menu label="Direction" on-selected-item-changed="_queryServer" horizontal-align="left">
              <paper-listbox class="dropdown-content" slot="dropdown-content" selected="{{_sortDirection}}">
                <paper-item>Ascending</paper-item>
                <paper-item>Descending</paper-item>
              </paper-listbox>
            </paper-dropdown-menu>
          </div>
          <vaadin-split-layout vertical id="paging-download">
            <div class="section paging">
              <div class="section-title">Paging</div>
              <div>
                Number of matching session groups:
                [[_totalSessionGroupsCountStr]]
              </div>
              <div class="inline-element page-number-input">
                <paper-input label="Page #" value="{{_pageNumberInput.value}}" allowed-pattern="[0-9]" error-message="Invalid input" invalid="[[_pageNumberInput.invalid]]" on-value-changed="_queryServer">
                  <div slot="suffix" class="page-suffix">
                    / [[_pageCountStr]]
                  </div>
                </paper-input>
              </div>
              <div class="inline-element page-size-input">
                <paper-input label="Max # of session groups per page:" value="{{_pageSizeInput.value}}" allowed-pattern="[0-9]" error-message="Invalid input" invalid="[[_pageSizeInput.invalid]]" on-value-changed="_queryServer">
                </paper-input>
              </div>
            </div>
            <div class="section download">
              <template is="dom-if" if="[[_sessionGroupsRequest]]">
                Download data as
                <span>
                  <a id="csvLink" download="hparams_table.csv" href="[[_csvUrl(_sessionGroupsRequest, configuration)]]">CSV</a>
                  <a id="jsonLink" download="hparams_table.json" href="[[_jsonUrl(_sessionGroupsRequest, configuration)]]">JSON</a>
                  <a id="latexLink" download="hparams_table.tex" href="[[_latexUrl(_sessionGroupsRequest, configuration)]]">LaTeX</a>
                </span>
              </template>
            </div>
          </vaadin-split-layout>
        </vaadin-split-layout>
      </vaadin-split-layout>
    </div>
    <style>
      .pane {
        display: flex;
        flex-direction: column;
        height: 100%;
      }
      .section {
        margin: 5px 10px 5px 10px;
        overflow-y: auto;
      }
      .section-title {
        display: block;
        font-weight: bold;
        text-decoration: underline;
        margin-bottom: 7px;
      }
      #hyperparameters-metrics-statuses {
        flex-basis: 70%;
        flex-shrink: 1;
        flex-grow: 1;
      }
      #hyperparameters-metrics {
        flex-basis: 90%;
        flex-shrink: 1;
        flex-grow: 1;
      }
      .hyperparameters {
        flex-basis: auto;
        flex-shrink: 1;
        flex-grow: 1;
      }
      .metrics {
        flex-basis: auto;
        flex-shrink: 1;
        flex-grow: 1;
      }
      .statuses {
        flex-basis: auto;
        flex-shrink: 0;
        flex-grow: 0;
      }
      #sorting-paging {
        flex-basis: 30%;
        flex-shrink: 0;
        flex-grow: 0;
      }
      #paging-download {
        flex-basis: 90%;
        flex-shrink: 1;
        flex-grow: 1;
      }
      .sorting {
        flex-basis: auto;
        flex-shrink: 0;
        flex-grow: 0;
      }
      .paging {
        flex-basis: auto;
        flex-shrink: 0;
        flex-grow: 0;
      }
      .download {
        flex-basis: auto;
        flex-shrink: 0;
        flex-grow: 0;
      }
      .discrete-value-checkbox,
      .metric-checkbox,
      .hparam-checkbox {
        display: block;
      }
      .discrete-value-checkbox {
        margin-left: 20px;
      }
      .hparam,
      .metric {
        display: block;
      }
      .inline-element {
        display: inline-block;
        width: 40%;
        margin-left: 10px;
      }
      .page-number-input {
        width: 20%;
      }
      .page-size-input {
        width: 60%;
      }
      vaadin-split-layout {
        height: 100%;
      }
      paper-listbox {
        max-height: 15em;
      }
      .page-suffix {
        white-space: nowrap;
      }
    </style>
  </template>
  
</dom-module><dom-module id="iron-pages">

  <template>
    <style>
      :host {
        display: block;
      }

      :host > ::slotted(:not(slot):not(.iron-selected)) {
        display: none !important;
      }
    </style>

    <slot></slot>
  </template>

  
</dom-module><dom-module id="paper-header-panel">
  <template>
    <style>
      :host {
        @apply --layout-vertical;
        position: relative;
        height: 100%;
        @apply --paper-header-panel;
      }

      #mainContainer {
        @apply --layout-flex;
        position: relative;
        overflow-y: auto;
        overflow-x: hidden;
        -webkit-overflow-scrolling: touch;
      }

      #mainPanel {
        @apply --layout-vertical;
        @apply --layout-flex;
        position: relative;
        min-height: 0;
        @apply --paper-header-panel-body;
      }

      #mainContainer {
        @apply --paper-header-panel-container;
      }

      /*
       * mode: scroll
       */
      :host([mode=scroll]) #mainContainer {
        @apply --paper-header-panel-scroll-container;
        overflow: visible;
      }

      :host([mode=scroll]) {
        overflow-y: auto;
        overflow-x: hidden;
        -webkit-overflow-scrolling: touch;
      }

      /*
       * mode: cover
       */
      :host([mode=cover]) #mainContainer {
        @apply --paper-header-panel-cover-container;
        position: absolute;
        top: 0;
        right: 0;
        bottom: 0;
        left: 0;
      }

      :host([mode=cover]) #mainPanel {
        position: static;
      }

      /*
       * mode: standard
       */
      :host([mode=standard]) #mainContainer {
        @apply --paper-header-panel-standard-container;
      }

      /*
       * mode: seamed
       */
      :host([mode=seamed]) #mainContainer {
        @apply --paper-header-panel-seamed-container;
      }


      /*
       * mode: waterfall
       */
      :host([mode=waterfall]) #mainContainer {
        @apply --paper-header-panel-waterfall-container;
      }

      /*
       * mode: waterfall-tall
       */
      :host([mode=waterfall-tall]) #mainContainer {
        @apply --paper-header-panel-waterfall-tall-container;
      }

      #dropShadow {
        transition: opacity 0.5s;
        height: 6px;
        box-shadow: inset 0px 5px 6px -3px rgba(0, 0, 0, 0.4);
        @apply --paper-header-panel-shadow;
        position: absolute;
        top: 0;
        left: 0;
        right: 0;
        opacity: 0;
        pointer-events: none;
      }

      #dropShadow.has-shadow {
        opacity: 1;
      }

      #mainContainer > ::slotted(.fit) {
        @apply --layout-fit;
      }

    </style>

    <slot id="headerSlot" name="header"></slot>

    <div id="mainPanel">
      <div id="mainContainer" class$="[[_computeMainContainerClass(mode)]]">
        <slot></slot>
      </div>
      <div id="dropShadow"></div>
    </div>
  </template>

  
</dom-module><dom-module id="paper-tab">
  <template>
    <style>
      :host {
        @apply --layout-inline;
        @apply --layout-center;
        @apply --layout-center-justified;
        @apply --layout-flex-auto;

        position: relative;
        padding: 0 12px;
        overflow: hidden;
        cursor: pointer;
        vertical-align: middle;

        @apply --paper-font-common-base;
        @apply --paper-tab;
      }

      :host(:focus) {
        outline: none;
      }

      :host([link]) {
        padding: 0;
      }

      .tab-content {
        height: 100%;
        transform: translateZ(0);
          -webkit-transform: translateZ(0);
        transition: opacity 0.1s cubic-bezier(0.4, 0.0, 1, 1);
        @apply --layout-horizontal;
        @apply --layout-center-center;
        @apply --layout-flex-auto;
        @apply --paper-tab-content;
      }

      :host(:not(.iron-selected)) > .tab-content {
        opacity: 0.8;

        @apply --paper-tab-content-unselected;
      }

      :host(:focus) .tab-content {
        opacity: 1;
        font-weight: 700;
      }

      paper-ripple {
        color: var(--paper-tab-ink, var(--paper-yellow-a100));
      }

      .tab-content > ::slotted(a) {
        @apply --layout-flex-auto;

        height: 100%;
      }
    </style>

    <div class="tab-content">
      <slot></slot>
    </div>
  </template>

  
</dom-module><iron-iconset-svg name="paper-tabs" size="24">
<svg><defs>
<g id="chevron-left"><path d="M15.41 7.41L14 6l-6 6 6 6 1.41-1.41L10.83 12z" /></g>
<g id="chevron-right"><path d="M10 6L8.59 7.41 13.17 12l-4.58 4.59L10 18l6-6z" /></g>
</defs></svg>
</iron-iconset-svg><dom-module id="paper-tabs">
  <template>
    <style>
      :host {
        @apply --layout;
        @apply --layout-center;

        height: 48px;
        font-size: 14px;
        font-weight: 500;
        overflow: hidden;
        -moz-user-select: none;
        -ms-user-select: none;
        -webkit-user-select: none;
        user-select: none;

        /* NOTE: Both values are needed, since some phones require the value to be `transparent`. */
        -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
        -webkit-tap-highlight-color: transparent;

        @apply --paper-tabs;
      }

      :host(:dir(rtl)) {
        @apply --layout-horizontal-reverse;
      }

      #tabsContainer {
        position: relative;
        height: 100%;
        white-space: nowrap;
        overflow: hidden;
        @apply --layout-flex-auto;
        @apply --paper-tabs-container;
      }

      #tabsContent {
        height: 100%;
        -moz-flex-basis: auto;
        -ms-flex-basis: auto;
        flex-basis: auto;
        @apply --paper-tabs-content;
      }

      #tabsContent.scrollable {
        position: absolute;
        white-space: nowrap;
      }

      #tabsContent:not(.scrollable),
      #tabsContent.scrollable.fit-container {
        @apply --layout-horizontal;
      }

      #tabsContent.scrollable.fit-container {
        min-width: 100%;
      }

      #tabsContent.scrollable.fit-container > ::slotted(*) {
        /* IE - prevent tabs from compressing when they should scroll. */
        -ms-flex: 1 0 auto;
        -webkit-flex: 1 0 auto;
        flex: 1 0 auto;
      }

      .hidden {
        display: none;
      }

      .not-visible {
        opacity: 0;
        cursor: default;
      }

      paper-icon-button {
        width: 48px;
        height: 48px;
        padding: 12px;
        margin: 0 4px;
      }

      #selectionBar {
        position: absolute;
        height: 0;
        bottom: 0;
        left: 0;
        right: 0;
        border-bottom: 2px solid var(--paper-tabs-selection-bar-color, var(--paper-yellow-a100));
          -webkit-transform: scale(0);
        transform: scale(0);
          -webkit-transform-origin: left center;
        transform-origin: left center;
          transition: -webkit-transform;
        transition: transform;

        @apply --paper-tabs-selection-bar;
      }

      #selectionBar.align-bottom {
        top: 0;
        bottom: auto;
      }

      #selectionBar.expand {
        transition-duration: 0.15s;
        transition-timing-function: cubic-bezier(0.4, 0.0, 1, 1);
      }

      #selectionBar.contract {
        transition-duration: 0.18s;
        transition-timing-function: cubic-bezier(0.0, 0.0, 0.2, 1);
      }

      #tabsContent > ::slotted(:not(#selectionBar)) {
        height: 100%;
      }
    </style>

    <paper-icon-button icon="paper-tabs:chevron-left" class$="[[_computeScrollButtonClass(_leftHidden, scrollable, hideScrollButtons)]]" on-up="_onScrollButtonUp" on-down="_onLeftScrollButtonDown" tabindex="-1"></paper-icon-button>

    <div id="tabsContainer" on-track="_scroll" on-down="_down">
      <div id="tabsContent" class$="[[_computeTabsContentClass(scrollable, fitContainer)]]">
        <div id="selectionBar" class$="[[_computeSelectionBarClass(noBar, alignBottom)]]" on-transitionend="_onBarTransitionEnd"></div>
        <slot></slot>
      </div>
    </div>

    <paper-icon-button icon="paper-tabs:chevron-right" class$="[[_computeScrollButtonClass(_rightHidden, scrollable, hideScrollButtons)]]" on-up="_onScrollButtonUp" on-down="_onRightScrollButtonDown" tabindex="-1"></paper-icon-button>

  </template>

  
</dom-module><dom-module id="paper-toolbar">
  <template>
    <style>
      :host {
        --calculated-paper-toolbar-height: var(--paper-toolbar-height, 64px);
        --calculated-paper-toolbar-sm-height: var(--paper-toolbar-sm-height, 56px);
        display: block;
        position: relative;
        box-sizing: border-box;
        -moz-box-sizing: border-box;
        height: var(--calculated-paper-toolbar-height);
        background: var(--paper-toolbar-background, var(--primary-color));
        color: var(--paper-toolbar-color, var(--dark-theme-text-color));
        @apply --paper-toolbar;
      }

      :host(.animate) {
        transition: var(--paper-toolbar-transition, height 0.18s ease-in);
      }

      :host(.medium-tall) {
        height: calc(var(--calculated-paper-toolbar-height) * 2);
        @apply --paper-toolbar-medium;
      }

      :host(.tall) {
        height: calc(var(--calculated-paper-toolbar-height) * 3);
        @apply --paper-toolbar-tall;
      }

      .toolbar-tools {
        position: relative;
        height: var(--calculated-paper-toolbar-height);
        padding: 0 16px;
        pointer-events: none;
        @apply --layout-horizontal;
        @apply --layout-center;
        @apply --paper-toolbar-content;
      }

      /*
       * TODO: Where should media query breakpoints live so they can be shared between elements?
       */

      @media (max-width: 600px) {
        :host {
          height: var(--calculated-paper-toolbar-sm-height);
        }

        :host(.medium-tall) {
          height: calc(var(--calculated-paper-toolbar-sm-height) * 2);
        }

        :host(.tall) {
          height: calc(var(--calculated-paper-toolbar-sm-height) * 3);
        }

        .toolbar-tools {
          height: var(--calculated-paper-toolbar-sm-height);
        }
      }

      #topBar {
        position: relative;
      }

      /* middle bar */
      #middleBar {
        position: absolute;
        top: 0;
        right: 0;
        left: 0;
      }

      :host(.tall) #middleBar,
      :host(.medium-tall) #middleBar {
        -webkit-transform: translateY(100%);
        transform: translateY(100%);
      }

      /* bottom bar */
      #bottomBar {
        position: absolute;
        right: 0;
        bottom: 0;
        left: 0;
      }

      /*
       * make elements (e.g. buttons) respond to mouse/touch events
       *
       * `.toolbar-tools` disables touch events so multiple toolbars can stack and not
       * absorb events. All children must have pointer events re-enabled to work as
       * expected.
       */
      .toolbar-tools > ::slotted(*:not([disabled])) {
        pointer-events: auto;
      }

      .toolbar-tools > ::slotted(.title) {
        @apply --paper-font-common-base;
        white-space: nowrap;
        overflow: hidden;
        text-overflow: ellipsis;
        font-size: 20px;
        font-weight: 400;
        line-height: 1;
        pointer-events: none;
        @apply --layout-flex;
      }

      .toolbar-tools > ::slotted(.title) {
        margin-left: 56px;
      }

      .toolbar-tools > ::slotted(paper-icon-button + .title) {
        margin-left: 0;
      }

      /**
       * The --paper-toolbar-title mixin is applied here instead of above to
       * fix the issue with margin-left being ignored due to css ordering.
       */
      .toolbar-tools > ::slotted(.title) {
        @apply --paper-toolbar-title;
      }

      .toolbar-tools > ::slotted(paper-icon-button[icon=menu]) {
        margin-right: 24px;
      }

      .toolbar-tools > ::slotted(.fit) {
        position: absolute;
        top: auto;
        right: 0;
        bottom: 0;
        left: 0;
        width: auto;
        margin: 0;
      }

      /* TODO(noms): Until we have a better solution for classes that don't use
       * /deep/ create our own.
       */
      .start-justified {
        @apply --layout-start-justified;
      }

      .center-justified {
        @apply --layout-center-justified;
      }

      .end-justified {
        @apply --layout-end-justified;
      }

      .around-justified {
        @apply --layout-around-justified;
      }

      .justified {
        @apply --layout-justified;
      }
    </style>

    <div id="topBar" class$="toolbar-tools [[_computeBarExtraClasses(justify)]]">
      <slot name="top"></slot>
    </div>

    <div id="middleBar" class$="toolbar-tools [[_computeBarExtraClasses(middleJustify)]]">
      <slot name="middle"></slot>
    </div>

    <div id="bottomBar" class$="toolbar-tools [[_computeBarExtraClasses(bottomJustify)]]">
      <slot name="bottom"></slot>
    </div>
  </template>

  
</dom-module><dom-module id="tf-hparams-scale-and-color-controls">
  <template>
    <div class="control-panel">
      
      <paper-dropdown-menu label="Color by" id="colorByDropDownMenu" horizontal-align="left">
        <paper-listbox class="dropdown-content" slot="dropdown-content" selected="{{options.colorByColumnIndex}}" id="colorByListBox">
          <template is="dom-repeat" items="[[options.columns]]" as="column" id="colorByColumnTemplate">
            <paper-item disabled="[[!_isNumericColumn(column.index)]]">
              [[column.name]]
            </paper-item>
          </template>
        </paper-listbox>
      </paper-dropdown-menu>

      
      <div class="columns-container">
        
        <template is="dom-repeat" items="{{options.columns}}" as="column">
          <template is="dom-if" if="[[_isNumericColumn(column.index)]]">
            <div class="column">
              <div class="column-title">
                [[column.name]]
              </div>
              <div>
                <paper-radio-group class="scale-radio-group" selected="{{column.scale}}">
                  <paper-radio-button name="LINEAR">
                    Linear
                  </paper-radio-button>
                  
                  <paper-radio-button id="logScaleButton_[[column.name]]" name="LOG" disabled="[[!_allowLogScale(column, sessionGroups.*)]]">
                    Logarithmic
                  </paper-radio-button>
                  <paper-radio-button name="QUANTILE">
                    Quantile
                  </paper-radio-button>
                </paper-radio-group>
              </div>
            </div>
          </template>
        </template>
      </div>
    </div>

    <style>
      :host {
        display: block;
      }
      .control-panel {
        overflow: auto;
      }
      .column {
        flex-grow: 1;
        flex-shrink: 1;
        margin-right: 5px;
        border: solid 1px darkgray;
        padding: 3px;
      }
      .column-title {
        /* Fit every title in one line so the radio boxes align vertically. */
        white-space: nowrap;
        text-decoration: underline;
      }
      .columns-container {
        display: flex;
        flex-direction: row;
      }
      .scale-radio-group paper-radio-button {
        padding: 2px;
        display: block;
      }
      paper-listbox {
        max-height: 15em;
      }
    </style>
  </template>

  
</dom-module><dom-module id="vaadin-grid-active-item-themability-styles">
  <template>
    <style>
      vaadin-grid-table .vaadin-grid-row[active] .vaadin-grid-cell:not([detailscell]) ::slotted(vaadin-grid-cell-content) {
        @apply(--vaadin-grid-body-row-active-cell);
      }
    </style>
  </template>
</dom-module><dom-module id="vaadin-grid-table-table-scroll-styles">
  <template>
    <style>
      #table {
        position: relative;
        overflow: auto;
        -webkit-overflow-scrolling: touch;
        z-index: -2;
      }

      vaadin-grid-table[ios] #table {
        transform: none;
      }

      vaadin-grid-table[fixed-sections] #table {
        transform: none;
      }
    </style>
  </template>
</dom-module><dom-module id="vaadin-grid-table-scroll-styles">
  <template>
    <style>
      vaadin-grid-table {
        transform: translateZ(0);
      }

      vaadin-grid-table-header {
        position: absolute;
        top: 0;
        width: 100%;
      }

      vaadin-grid-table-footer {
        position: absolute;
        bottom: 0;
        width: 100%;
      }

      vaadin-grid-table-body {
        z-index: -1;
      }

      vaadin-grid-table[fixed-sections] {
        /* Any value other than ‘none’ for the transform results in the creation of both a stacking context and
        a containing block. The object acts as a containing block for fixed positioned descendants. */
        transform: translateZ(0);
        overflow: hidden;
      }

      vaadin-grid-table[fixed-sections] vaadin-grid-table-header,
      vaadin-grid-table[fixed-sections] vaadin-grid-table-footer {
        position: fixed;
      }

      vaadin-grid-table[fixed-sections] vaadin-grid-table-body#items {
        position: fixed;
        width: 100%;
        will-change: transform;
      }
    </style>
  </template>
</dom-module><dom-module id="vaadin-grid-table-cell"></dom-module><dom-module id="vaadin-grid-table-header-cell"></dom-module><dom-module id="vaadin-grid-table-footer-cell"></dom-module><dom-module id="vaadin-grid-sizer-cell"></dom-module><dom-module id="vaadin-grid-sizer">
  <template>
    <style>
      :host {
        display: flex;
        visibility: hidden;
      }

      .cell {
        display: block;
        flex-shrink: 0;
        line-height: 0;
        font-size: 1px;
        margin-top: -1em;
      }

      .cell[hidden] {
        display: none;
      }
    </style>

    <template is="dom-repeat" items="[[_columns]]" as="column">
      <vaadin-grid-sizer-cell class="cell" column="[[column]]">&nbsp;</vaadin-grid-sizer-cell>
    </template>

  </template>
  
</dom-module><dom-module id="vaadin-grid-table-outer-scroller">
  <template>
    <style>
      :host {
        display: block;
        height: 100%;
        width: 100%;
        position: absolute;
        top: 0;
        box-sizing: border-box;
        overflow: auto;
      }

      :host([passthrough]) {
        pointer-events: none;
      }

      :host([ios]) {
        pointer-events: all;
        z-index: -3;
      }

      :host([ios][scrolling]) {
        z-index: 0;
      }
    </style>

    <slot></slot>

  </template>
  
</dom-module><dom-module id="vaadin-grid-table-focus-trap">
  <template>
    <style>
     :host {
       position: absolute;
       z-index: -3;
       height: 0;
       overflow: hidden;
     }

     :host(:focus),
     .primary:focus,
     ::slotted(.primary:focus),
     .secondary:focus,
     ::slotted(.secondary:focus) {
       outline: none;
     }
    </style>

    
    <div class="primary" tabindex="0" role="gridcell" on-focus="_onBaitFocus" on-blur="_onBaitBlur"><div aria-hidden="true">&nbsp;</div></div>
    <div class="secondary" tabindex="-1" role="gridcell" on-focus="_onBaitFocus" on-blur="_onBaitBlur"><div aria-hidden="true">&nbsp;</div></div>

    <slot></slot>
  </template>
  
</dom-module><dom-module id="vaadin-grid-table-row"></dom-module><dom-module id="vaadin-grid-table-header-row"></dom-module><dom-module id="vaadin-grid-row-details-styles">
  <template>
    <style>
      [detailscell] {
        position: absolute;
        bottom: 0;
        left: 0;
        width: 100%;
      }
    </style>
  </template>
</dom-module><dom-module id="vaadin-grid-row-details-themability-styles">
  <template>
    <style>
      .vaadin-grid-cell[detailscell] ::slotted(vaadin-grid-cell-content) {
        background: #fff;
        @apply(--vaadin-grid-body-row-details-cell);
      }
    </style>
  </template>
</dom-module><dom-module id="vaadin-grid-data-provider-themability-styles">
  <template>
    <style>

      /* Anim */
      @keyframes vaadin-grid-spin-360 {
        100% {
          transform: rotate(360deg);
        }
      }
      @-webkit-keyframes vaadin-grid-spin-360 {
        100% {
          -webkit-transform: rotate(360deg);
          transform: rotate(360deg);
        }
      }

      #spinner {
        border: 2px solid var(--primary-color, #03A9F4);
        border-radius: 50%;
        border-right-color: transparent;
        border-top-color: transparent;
        content: "";
        height: 16px;
        left: 50%;
        margin-left: -8px;
        margin-top: -8px;
        position: absolute;
        top: 50%;
        width: 16px;
        pointer-events: none;
        opacity: 0;
        @apply(--vaadin-grid-loading-spinner);
      }

      :host([loading]) #spinner {
        opacity: 1;
        -webkit-animation: vaadin-grid-spin-360 400ms linear infinite;
        animation: vaadin-grid-spin-360 400ms linear infinite;
      }

      :host([loading]) #items {
        opacity: 0.5;
      }

    </style>
  </template>
</dom-module><dom-module id="vaadin-grid-selection-themability-styles">
  <template>
    <style>
      vaadin-grid-table .vaadin-grid-row[selected] .vaadin-grid-cell:not([detailscell]) ::slotted(vaadin-grid-cell-content) {
        background-color: var(--paper-grey-100, rgb(243, 243, 243));
        @apply(--vaadin-grid-body-row-selected-cell);
      }
    </style>
  </template>
</dom-module><dom-module id="vaadin-grid-navigation-themability-styles">
  <template>
    <style>
      :host(:focus),
      #table:focus {
        outline: none;
      }

      :host([navigating]:not([interacting])) [focused] > .vaadin-grid-row[focused] > [focused] ::slotted(vaadin-grid-cell-content) {
        box-shadow: inset 0 0 0 3px rgba(0, 0, 0, 0.3);
        @apply(--vaadin-grid-focused-cell);
      }
    </style>
  </template>
</dom-module><dom-module id="vaadin-grid-column-reordering-themability-styles">
  <template>
    <style>
      vaadin-grid-table[reordering] .vaadin-grid-cell {
        background: #000;
      }

      :host([reordering]) .vaadin-grid-cell[reorder-status="dragging"] {
        background: var(--primary-color, #000);
      }

      vaadin-grid-table[reordering] .vaadin-grid-cell:not([detailscell]) ::slotted(vaadin-grid-cell-content) {
        transition: opacity 300ms;
        transform: translateZ(0);
        opacity: 0.8;
      }

      #scroller .vaadin-grid-cell[reorder-status="allowed"] ::slotted(vaadin-grid-cell-content) {
        opacity: 1;
      }

      #scroller .vaadin-grid-cell[reorder-status="dragging"] {
        background: var(--primary-color, #000);
      }

      #scroller .vaadin-grid-cell[reorder-status="dragging"] ::slotted(vaadin-grid-cell-content) {
        opacity: 0.95;
      }
    </style>
  </template>
</dom-module><dom-module id="vaadin-grid-table-styles">
  <template>
    <style>

      @keyframes appear {
        to {
          opacity: 1;
        }
      }

      vaadin-grid-table {
        display: block;
        position: relative;
        animation: 1ms appear;
      }

      @media only screen and (-webkit-max-device-pixel-ratio: 1) {
        :host {
          will-change: transform;
        }
      }

      #items {
        position: relative;
      }

      #items {
        border-top: 0 solid transparent;
        border-bottom: 0 solid transparent;
      }

      #items > .vaadin-grid-row {
        box-sizing: border-box;
        margin: 0;
        position: absolute;
      }

      vaadin-grid-table-body {
        display: block;
      }

      vaadin-grid-table-header .vaadin-grid-cell,
      vaadin-grid-table-footer .vaadin-grid-cell {
        top: 0;
      }

      .vaadin-grid-cell {
        padding: 0;
        flex-shrink: 0;
        flex-grow: 1;
        box-sizing: border-box;
        display: flex;
      }

      .vaadin-grid-cell:not([detailscell]) {
        position: relative;
      }

      .vaadin-grid-cell ::slotted(vaadin-grid-cell-content) {
         width: 100%;
         display: inline-flex;
         justify-content: center;
         flex-direction: column;
         white-space: nowrap;
         overflow: hidden;
      }

      .vaadin-grid-column-resize-handle {
        position: absolute;
        right: 0;
        height: 100%;
        cursor: col-resize;
        z-index: 1;
      }

      .vaadin-grid-column-resize-handle::before {
        position: absolute;
        content: "";
        height: 100%;
        width: 35px;
        transform: translateX(-50%);
      }

      [lastcolumn] .vaadin-grid-column-resize-handle::before,
      [last-frozen] .vaadin-grid-column-resize-handle::before {
        width: 18px;
        transform: translateX(-100%);
      }

      vaadin-grid-table[column-reordering-allowed] #header,
      vaadin-grid-table[column-resizing] {
        -ms-user-select: none;
        -moz-user-select: none;
        -webkit-user-select: none;
        user-select: none;
      }

      vaadin-grid-table[column-resizing] {
        cursor: col-resize;
      }

      .vaadin-grid-row:not([hidden]) {
        display: flex;
        width: 100%;
      }

      [frozen] {
        z-index: 2;
      }

      [hidden] {
        display: none;
      }

      vaadin-grid-table[no-content-pointer-events] .vaadin-grid-cell ::slotted(vaadin-grid-cell-content) {
        pointer-events: none;
      }
    </style>
  </template>
</dom-module><dom-module id="vaadin-grid-table-table-styles">
  <template>
    <style>
      :host([ios][column-resizing]) #outerscroller {
        overflow: hidden;
      }

      #fixedsizer,
      #outersizer {
        border-top: 0 solid transparent;
        border-bottom: 0 solid transparent;
      }

      #table {
        height: 100%;
        width: 100%;
        display: block;
        overflow: auto;
        box-sizing: border-box;
      }

      #table[overflow-hidden],
      #outerscroller[overflow-hidden] {
        overflow: hidden;
      }

      vaadin-grid-sizer {
        position: relative;
        width: 100%;
      }

      #sizerwrapper {
        position: absolute;
        width: 100%;
        z-index: -100;
      }

      #reorderghost {
        visibility: hidden;
        position: fixed;
        opacity: 0.5;
        pointer-events: none;
      }

      ::slotted(vaadin-grid-column),
      ::slotted(vaadin-grid-selection-column),
      ::slotted(vaadin-grid-column-group) {
        display: none;
      }

    </style>
  </template>
</dom-module><dom-module id="vaadin-grid-table-themability-styles">
  <template>
    <style>

      /* Default borders */
      vaadin-grid-table-header .vaadin-grid-row:last-child .vaadin-grid-cell ::slotted(vaadin-grid-cell-content) {
        border-bottom: 1px solid var(--divider-color, rgba(0, 0, 0, 0.08));
      }

      vaadin-grid-table-footer .vaadin-grid-row:first-child .vaadin-grid-cell ::slotted(vaadin-grid-cell-content) {
        border-top: 1px solid var(--divider-color, rgba(0, 0, 0, 0.08));
      }

      vaadin-grid-table-body .vaadin-grid-row:not([lastrow]) .vaadin-grid-cell ::slotted(vaadin-grid-cell-content) {
        border-bottom: 1px solid var(--divider-color, rgba(0, 0, 0, 0.08));
      }

      [last-frozen] ::slotted(vaadin-grid-cell-content) {
        border-right: 1px solid var(--divider-color, rgba(0, 0, 0, 0.08));
      }

      /* Column resize handle */

      .vaadin-grid-column-resize-handle {
        border-right: 1px solid var(--divider-color, rgba(0, 0, 0, 0.08));
        @apply(--vaadin-grid-column-resize-handle);
      }

      /* Cells */
      vaadin-grid-table .vaadin-grid-row .vaadin-grid-cell:not([detailscell]) ::slotted(vaadin-grid-cell-content) {
        background: #fff;
        text-align: left;
        padding: 8px;
        box-sizing: border-box;
        @apply(--vaadin-grid-cell);
      }

      vaadin-grid-table-header .vaadin-grid-row .vaadin-grid-cell:not([detailscell]) ::slotted(vaadin-grid-cell-content) {
        font-weight: 500;
        @apply(--vaadin-grid-header-cell);
      }

      vaadin-grid-table-footer .vaadin-grid-row .vaadin-grid-cell:not([detailscell]) ::slotted(vaadin-grid-cell-content) {
        font-weight: 500;
        @apply(--vaadin-grid-footer-cell);
      }

      vaadin-grid-table-body .vaadin-grid-row .vaadin-grid-cell:not([detailscell]) ::slotted(vaadin-grid-cell-content) {
        @apply(--vaadin-grid-body-cell);
      }

      vaadin-grid-table-body [odd] .vaadin-grid-cell:not([detailscell]) ::slotted(vaadin-grid-cell-content) {
        @apply(--vaadin-grid-body-row-odd-cell);
      }

      vaadin-grid-table .vaadin-grid-row .vaadin-grid-cell:not([detailscell])[last-frozen] ::slotted(vaadin-grid-cell-content) {
        @apply(--vaadin-grid-cell-last-frozen);
      }

      vaadin-grid-table:not([scrolling]) vaadin-grid-table-body .vaadin-grid-row:hover .vaadin-grid-cell ::slotted(vaadin-grid-cell-content) {
        @apply(--vaadin-grid-body-row-hover-cell);
      }

      vaadin-grid-table .vaadin-grid-row .vaadin-grid-cell.vaadin-grid-cell[lastcolumn] ::slotted(vaadin-grid-cell-content) {
        border-right: none;
      }

    </style>
  </template>
</dom-module><dom-module id="vaadin-grid-table">
  <template>
      <style include="vaadin-grid-table-table-scroll-styles"></style>
      <style include="vaadin-grid-table-table-styles"></style>

      <style include="vaadin-grid-data-provider-themability-styles"></style>

      <div id="spinner"></div>
      <div id="table" tabindex="-1" overflow-hidden$="[[_hideTableOverflow(scrollbarWidth, safari)]]">
        <div id="sizerwrapper">
          <vaadin-grid-sizer id="fixedsizer" top="[[_estScrollHeight]]" column-tree="[[columnTree]]"></vaadin-grid-sizer>
        </div>
        <slot name="header"></slot>
        <slot name="items"></slot>
        <slot name="footer"></slot>
      </div>

      <div id="reorderghost"></div>
      <vaadin-grid-table-outer-scroller id="outerscroller" scroll-target="[[scrollTarget]]" overflow-hidden$="[[_hideOuterScroller(scrollbarWidth, safari)]]" ios$="[[ios]]" scrolling$="[[scrolling]]">
      <vaadin-grid-sizer id="outersizer" top="[[_estScrollHeight]]" column-tree="[[columnTree]]"></vaadin-grid-sizer>
    </vaadin-grid-table-outer-scroller>
    <slot></slot>
    <slot name="footerFocusTrap"></slot>
  </template>
</dom-module><dom-module id="vaadin-grid-column">
  
</dom-module><dom-module id="vaadin-grid">
  <template>
    <style>
      :host {
        display: block;
        height: 400px;
        background: var(--primary-background-color, #fff);
        box-sizing: border-box;
        border: 1px solid var(--divider-color, rgba(0, 0, 0, 0.08));
      }

      :host(:focus) {
        -webkit-tap-highlight-color: transparent;
      }

      :host(:focus) {
        outline: none;
      }

      #scroller {
        height: 100%;
        width: 100%;
      }
    </style>

    <style include="vaadin-grid-table-scroll-styles"></style>
    <style include="vaadin-grid-row-details-styles"></style>
    <style include="vaadin-grid-table-styles"></style>

    <style include="vaadin-grid-table-themability-styles"></style>
    <style include="vaadin-grid-selection-themability-styles"></style>
    <style include="vaadin-grid-navigation-themability-styles"></style>
    <style include="vaadin-grid-active-item-themability-styles"></style>
    <style include="vaadin-grid-row-details-themability-styles"></style>
    <style include="vaadin-grid-column-reordering-themability-styles"></style>

    <vaadin-grid-table id="scroller" loading$="[[_loading]]" bind-data="[[_bindData]]" size="[[size]]" column-tree="[[_columnTree]]" row-details-template="[[_rowDetailsTemplate]]" column-reordering-allowed="[[columnReorderingAllowed]]">
      <vaadin-grid-table-header id="header" slot="header" target="[[_getContentTarget()]]" column-tree="[[_columnTree]]"></vaadin-grid-table-header>
      <vaadin-grid-table-body id="items" slot="items"></vaadin-grid-table-body>
      <vaadin-grid-table-footer id="footer" slot="footer" target="[[_getContentTarget()]]" column-tree="[[_columnTree]]"></vaadin-grid-table-footer>

      
      <slot name="footerFocusTrap"></slot>

      
      
      <slot></slot>

      <vaadin-grid-table-focus-trap id="footerFocusTrap" slot="footerFocusTrap" on-focus-gained="_onFooterFocus" on-focus-lost="_onFocusout">
      </vaadin-grid-table-focus-trap>
    </vaadin-grid-table>
  </template>
</dom-module><dom-module id="tf-hparams-session-group-details">
  <template>
    <template is="dom-if" if="[[!sessionGroup]]">
      <div>
        <h3>No session group selected</h3>
        <p>Please select a session group to see its metric-graphs here.</p>
      </div>
    </template>
    <template is="dom-if" if="[[!_haveMetrics(visibleSchema.*)]]">
      <div>
        <h3>No metrics are enabled</h3>
        <p>Please enable some metrics to see content here.</p>
      </div>
    </template>
    <div class="layout horizontal wrap session-group-details">
      <template is="dom-if" if="[[_haveMetricsAndSessionGroup(visibleSchema.*,
                                                  sessionGroup)]]">
        <template is="dom-repeat" items="[[visibleSchema.metricInfos]]" as="metricInfo">
          
          <tf-scalar-card class="scalar-card" color-scale="[[_colorScale]]" data-to-load="[[_computeSeriesForSessionGroupMetric(sessionGroup,
                          metricInfo)]]" tag="[[metricInfo.name.tag]]" tag-metadata="[[_computeTagMetadata(metricInfo)]]" x-type="[[_xType]]" multi-experiments="[[_noMultiExperiments]]" request-data="[[_requestData]]" active>
          </tf-scalar-card>
        </template>
      </template>
    </div>
    
    <style include="iron-flex">
      :host {
        display: block;
      }
    </style>
  </template>
  
</dom-module><dom-module id="tf-hparams-table-view">
  <template>
    <vaadin-grid class="session-group-table" id="sessionGroupsTable" column-reordering-allowed items="[[sessionGroups]]">
      <vaadin-grid-column flex-grow="0" width="10em" resizable>
        <template class="header">
          <div class="table-header table-cell">Trial ID</div>
        </template>
        <template>
          <div class="table-cell">[[item.name]]</div>
        </template>
      </vaadin-grid-column>
      <template is="dom-if" if="[[enableShowMetrics]]">
        <vaadin-grid-column flex-grow="0" width="5em" resizable>
          <template class="header">
            <div class="table-header table-cell">Show Metrics</div>
          </template>
          <template>
            <paper-checkbox class="table-cell" checked="{{expanded}}">
            </paper-checkbox>
          </template>
        </vaadin-grid-column>
      </template>
      <template is="dom-repeat" items="[[visibleSchema.hparamInfos]]" as="hparamInfo" index-as="hparamIndex">
        <vaadin-grid-column flex-grow="2" width="10em" resizable>
          <template class="header">
            <div class="table-header table-cell">
              [[_hparamName(hparamInfo)]]
            </div>
          </template>
          <template>
            <div class="table-cell">
              [[_sessionGroupHParam(item, hparamInfo.name)]]
            </div>
          </template>
        </vaadin-grid-column>
      </template>
      <template is="dom-repeat" items="{{visibleSchema.metricInfos}}" as="metricInfo" index-as="metricIndex">
        <vaadin-grid-column flex-grow="2" width="10em" resizable>
          <template class="header">
            <div class="table-header table-cell">
              [[_metricName(metricInfo)]]
            </div>
          </template>
          <template>
            <div class="table-cell">
              [[_sessionGroupMetric(item, metricInfo.name)]]
            </div>
          </template>
        </vaadin-grid-column>
      </template>
      <template class="row-details">
        <tf-hparams-session-group-details backend="[[backend]]" experiment-name="[[experimentName]]" session-group="[[item]]" visible-schema="[[visibleSchema]]" class="session-group-details">
        </tf-hparams-session-group-details>
      </template>
    </vaadin-grid>

    <style>
      :host {
        display: block;
      }
      .table-cell {
        white-space: nowrap;
        text-overflow: ellipsis;
        overflow: hidden;
      }
      .table-header {
        /* line-break overflowing column headers */
        white-space: normal;
        overflow-wrap: break-word;
      }
      .session-group-table {
        height: 100%;
      }
      .session-group-details {
        height: 360px;
        overflow-y: auto;
      }
    </style>
  </template>

  
</dom-module><dom-module id="tf-hparams-session-group-values">
  <template>
    
    <template is="dom-if" if="[[_propertiesArePopulated(visibleSchema, sessionGroup)]]">
      
      <tf-hparams-table-view visible-schema="[[visibleSchema]]" session-groups="[[_singletonSessionGroups(sessionGroup)]]">
      </tf-hparams-table-view>
    </template>
    <template is="dom-if" if="[[!_propertiesArePopulated(visibleSchema, sessionGroup)]]">
      <div>
        Click or hover over a session group to display its values here.
      </div>
    </template>

    <style>
      :host {
        display: block;
      }
    </style>
  </template>
  
</dom-module><dom-module id="tf-hparams-parallel-coords-plot">
  <template>
    <div id="container">
      <svg id="svg"></svg>
    </div>
    <style>
      :host {
        display: block;
      }
      svg {
        font: 10px sans-serif;
      }

      .background path {
        fill: none;
        stroke: #ddd;
        shape-rendering: crispEdges;
      }

      .foreground path {
        fill: none;
        stroke-opacity: 0.7;
        stroke-width: 1;
      }

      /* Will be set on foreground paths that are not "contained" in the current
         axes brushes. If no brushes are set, no path will have this class. */
      .foreground .invisible-path {
        display: none;
      }

      /* Style for the path closest to the mouse pointer (typically will become
      the selected path when the user clicks). */
      .foreground .peaked-path {
        stroke-width: 3;
      }

      /* The currently selected path class. We use !important to override the
         inline style that sets the regular color of a path. */
      .foreground .selected-path {
        stroke-width: 3 !important;
        stroke: #0f0 !important;
      }

      #container {
        height: 100%;
        width: 100%;
      }

      svg {
        width: 100%;
        height: 100%;
      }

      .axis text {
        text-shadow: 0 1px 0 #fff, 1px 0 0 #fff, 0 -1px 0 #fff, -1px 0 0 #fff;
        fill: #000;
        cursor: move;
      }
    </style>
  </template>

  
  
  
  
</dom-module><dom-module id="tf-hparams-parallel-coords-view">
  <template>
    
    <div class="pane">
      <vaadin-split-layout vertical>
        
        <tf-hparams-scale-and-color-controls id="controls" class="section" configuration="[[configuration]]" session-groups="[[sessionGroups]]" options="{{_options}}">
        </tf-hparams-scale-and-color-controls>
        <vaadin-split-layout vertical>
          
          <tf-hparams-parallel-coords-plot id="plot" class="section" session-groups="[[sessionGroups]]" selected-session-group="{{_selectedGroup}}" closest-session-group="{{_closestGroup}}" options="[[_options]]">
          </tf-hparams-parallel-coords-plot>
          <vaadin-split-layout vertical>
            <tf-hparams-session-group-values id="values" class="section" visible-schema="[[configuration.visibleSchema]]" session-group="[[_closestOrSelected(
                             _closestGroup, _selectedGroup)]]">
            </tf-hparams-session-group-values>
            <tf-hparams-session-group-details id="details" class="section" backend="[[backend]]" experiment-name="[[experimentName]]" session-group="[[_selectedGroup]]" visible-schema="[[configuration.visibleSchema]]">
            </tf-hparams-session-group-details>
          </vaadin-split-layout>
        </vaadin-split-layout>
      </vaadin-split-layout>
    </div>

    <style>
      .pane {
        display: flex;
        flex-direction: column;
        height: 100%;
      }
      .section {
        margin: 10px;
      }
      #controls {
        flex-grow: 0;
        flex-shrink: 0;
        flex-basis: auto;
        height: auto;
        overflow-y: auto;
        max-height: fit-content;
      }
      #plot {
        flex-grow: 1;
        flex-shrink: 1;
        flex-basis: auto;
        height: 100%;
        overflow-y: auto;
      }
      #values {
        flex-grow: 0;
        flex-shrink: 0;
        flex-basis: auto;
        height: 95px;
        overflow-y: auto;
        max-height: fit-content;
      }
      #details {
        flex-grow: 0;
        flex-shrink: 1;
        flex-basis: auto;
        height: auto;
        overflow-y: auto;
        max-height: fit-content;
      }
      vaadin-split-layout {
        height: 100%;
      }
    </style>
  </template>

  
</dom-module><dom-module id="tf-hparams-scatter-plot-matrix-plot">
  <template>
    <div id="container">
      <svg id="svg"></svg>
    </div>

    <style>
      :host {
        display: block;
      }
      svg {
        font: 10px sans-serif;
      }

      /* The closest data point marker to the mouse pointer. We use !important
         to override the inline style that sets the regular style of a marker.
      */
      .closest-marker {
        r: 6 !important;
      }

      /* The currently selected data point marker. We use !important to
         override the inline style that sets the regular style of a marker. */
      .selected-marker {
        r: 6 !important;
        fill: #0f0 !important;
      }
    </style>
  </template>

  
</dom-module><dom-module id="tf-hparams-scatter-plot-matrix-view">
  <template>
    <div class="pane">
      <vaadin-split-layout vertical>
        
        <tf-hparams-scale-and-color-controls class="section" id="controls" configuration="[[configuration]]" session-groups="[[sessionGroups]]" options="{{_options}}">
        </tf-hparams-scale-and-color-controls>
        <vaadin-split-layout vertical>
          
          <tf-hparams-scatter-plot-matrix-plot class="section" id="plot" visible-schema="[[configuration.visibleSchema]]" session-groups="[[sessionGroups]]" selected-session-group="{{_selectedGroup}}" closest-session-group="{{_closestGroup}}" options="[[_options]]">
          </tf-hparams-scatter-plot-matrix-plot>
          <vaadin-split-layout vertical>
            <tf-hparams-session-group-values class="section" id="values" visible-schema="[[configuration.visibleSchema]]" session-group="[[_closestOrSelected(
                                 _closestGroup, _selectedGroup)]]">
            </tf-hparams-session-group-values>
            
            <tf-hparams-session-group-details class="section" id="details" backend="[[backend]]" experiment-name="[[experimentName]]" session-group="[[_selectedGroup]]" visible-schema="[[configuration.visibleSchema]]">
            </tf-hparams-session-group-details>
          </vaadin-split-layout>
        </vaadin-split-layout>
      </vaadin-split-layout>
    </div>
    <style>
      .pane {
        display: flex;
        flex-direction: column;
        height: 100%;
      }
      .section {
        margin: 10px;
      }
      #controls {
        flex-grow: 0;
        flex-shrink: 0;
        flex-basis: auto;
        height: auto;
        overflow-y: auto;
        max-height: fit-content;
      }
      #plot {
        flex-grow: 1;
        flex-shrink: 1;
        flex-basis: auto;
        height: auto;
        overflow-y: auto;
        max-height: fit-content;
      }
      #values {
        flex-grow: 0;
        flex-shrink: 0;
        flex-basis: auto;
        height: 95px;
        overflow-y: auto;
        max-height: fit-content;
      }
      #details {
        flex-grow: 0;
        flex-shrink: 1;
        flex-basis: auto;
        height: auto;
        overflow-y: auto;
        max-height: fit-content;
      }
      vaadin-split-layout {
        height: 100%;
      }
    </style>
  </template>

  
</dom-module><dom-module id="tf-hparams-sessions-pane">
  <template>
    <paper-header-panel>
      <paper-toolbar slot="header" class="tab-bar">
        <paper-tabs selected="{{_selectedTab}}" slot="top">
          
          <paper-tab view-id="table-view">
            TABLE VIEW
          </paper-tab>
          <paper-tab view-id="parallel-coords-view">
            PARALLEL COORDINATES VIEW
          </paper-tab>
          <paper-tab view-id="scatter-plot-matrix-view">
            SCATTER PLOT MATRIX VIEW
          </paper-tab>
          <div class="help-and-feedback">
            <template is="dom-if" if="[[bugReportUrl]]">
              <a href$="[[bugReportUrl]]" target="_blank" rel="noopener noreferrer">
                <paper-button id="bug-report" raised title="Send a bug report or feature request">
                  Bug Report / Feature Request
                </paper-button>
              </a>
            </template>
            <template is="dom-if" if="[[helpUrl]]">
              <a href$="[[helpUrl]]" target="_blank" rel="noopener noreferrer">
                <paper-icon-button icon="help-outline" title="View documentation">
                </paper-icon-button>
              </a>
            </template>
          </div>
        </paper-tabs>
      </paper-toolbar>
      <iron-pages selected="[[_selectedTab]]" class="fit tab-view">
        <div id="0" class="tab">
          <tf-hparams-table-view backend="[[backend]]" experiment-name="[[experimentName]]" visible-schema="[[configuration.visibleSchema]]" session-groups="[[sessionGroups]]" enable-show-metrics>
          </tf-hparams-table-view>
        </div>
        <div id="1" class="tab">
          <tf-hparams-parallel-coords-view backend="[[backend]]" experiment-name="[[experimentName]]" configuration="[[configuration]]" session-groups="[[sessionGroups]]">
          </tf-hparams-parallel-coords-view>
        </div>
        <div id="2" class="tab">
          <tf-hparams-scatter-plot-matrix-view backend="[[backend]]" experiment-name="[[experimentName]]" configuration="[[configuration]]" session-groups="[[sessionGroups]]">
          </tf-hparams-scatter-plot-matrix-view>
        </div>
      </iron-pages>
    </paper-header-panel>

    <style>
      .tab-view {
        height: 100%;
      }
      .tab-bar {
        overflow-y: auto;
        color: white;
        background-color: var(
          --tb-toolbar-background-color,
          var(--tb-orange-strong)
        );
      }
      .tab {
        height: 100%;
      }
      paper-tabs {
        flex-grow: 1;
        width: 100%;
        height: 100%;
        --paper-tabs-selection-bar-color: white;
        --paper-tabs-content: {
          -webkit-font-smoothing: antialiased;
        }
      }
      tf-hparams-table-view {
        width: 100%;
        height: 100%;
      }
      .help-and-feedback {
        display: inline-flex; /* Ensure that icons stay aligned */
        justify-content: flex-end;
        align-items: center;
        text-align: right;
        color: white;
      }
      #bug-report {
        border: solid black;
        background: red;
        white-space: normal;
        word-break: break-words;
        font-size: 12px;
        max-width: 150px;
        text-align: left;
      }
      .help-and-feedback a {
        color: white;
        text-decoration: none;
      }
    </style>
  </template>

  
</dom-module><dom-module id="tf-hparams-main">
  <template>
    <vaadin-split-layout>
      <div class="sidebar" slot="sidebar">
        <tf-hparams-query-pane id="query-pane" backend="[[backend]]" experiment-name="[[experimentName]]" configuration="{{_configuration}}" session-groups="{{_sessionGroups}}">
        </tf-hparams-query-pane>
      </div>
      <div class="center" slot="center">
        <tf-hparams-sessions-pane id="sessions-pane" backend="[[backend]]" help-url="[[helpUrl]]" bug-report-url="[[bugReportUrl]]" experiment-name="[[experimentName]]" configuration="[[_configuration]]" session-groups="[[_sessionGroups]]">
        </tf-hparams-sessions-pane>
      </div>
    </vaadin-split-layout>
    <tf-hparams-google-analytics-tracker id="tracker" tracking-id="[[trackingId]]" name="tf_hparams">
    </tf-hparams-google-analytics-tracker>

    <style>
      vaadin-split-layout {
        width: 100%;
      }

      .sidebar {
        width: 20%;
        height: 100%;
        overflow: auto;
        flex-grow: 0;
        flex-shrink: 0;
        min-width: 10%;
      }

      .center {
        height: 100%;
        overflow-y: auto;
        flex-grow: 1;
        flex-shrink: 1;
        width: 80%;
      }

      :host {
        display: flex;
        flex-direction: row;
        height: 100%;
        width: 100%;
      }
    </style>
  </template>

  
</dom-module><dom-module id="tf-hparams-dashboard">
  <template>
    
    <tf-hparams-main id="hparams-main" backend="[[_backend]]" experiment-name="">
    </tf-hparams-main>
  </template>
  
</dom-module><dom-module id="tf-mesh-loader">
  <template>
    <tf-card-heading color="[[_runColor]]" class="tf-mesh-loader-header">
      <template is="dom-if" if="[[_hasMultipleSamples(ofSamples)]]">
        <div>sample: [[_getSampleText(sample)]] of [[ofSamples]]</div>
      </template>
      <template is="dom-if" if="[[_hasAtLeastOneStep(_steps)]]">
        <div class="heading-row">
          <div class="heading-label">
            step
            <span style="font-weight: bold">[[toLocaleString_(_stepValue)]]</span>
          </div>
          <div class="heading-label heading-right">
            <template is="dom-if" if="[[_currentWallTime]]">
              [[_currentWallTime]]
            </template>
          </div>
          <div class="label right">
            <paper-spinner-lite active hidden$="[[!_isMeshLoading]]">
            </paper-spinner-lite>
          </div>
        </div>
      </template>
      <template is="dom-if" if="[[_hasMultipleSteps(_steps)]]">
        <div>
          <paper-slider id="steps" immediate-value="{{_stepIndex}}" max="[[_getMaxStepIndex(_steps)]]" max-markers="[[_getMaxStepIndex(_steps)]]" snaps step="1" value="{{_stepIndex}}"></paper-slider>
        </div>
      </template>
    </tf-card-heading>
    <style>
      paper-slider {
        width: 100%;
        margin-left: 1px;
        margin-right: 1px;
      }
      .tf-mesh-loader-header {
        display: block;
        height: 105px;
      }
      [hidden] {
        display: none;
      }
    </style>
  </template>
  
</dom-module><dom-module id="mesh-dashboard">
  <template>
    <tf-dashboard-layout>
      <div slot="sidebar" class="all-controls">
        <div class="sidebar-section view-control">
          <h3 class="title">Point of view</h3>
          <div>
            <paper-radio-group id="view-radio-group" selected="{{_selectedView}}">
              <paper-radio-button id="all-radio-button" name="all">
                Display all points
              </paper-radio-button>
              <paper-tooltip animation-delay="0" for="all-radio-button" position="right" offset="0">
                Zoom and center camera to display all points at once. Note, that
                some points could be too far (i.e. too small) to be visible.
              </paper-tooltip>
              <paper-radio-button id="user-radio-button" name="user">
                Current view
              </paper-radio-button>
              <paper-tooltip animation-delay="0" for="user-radio-button" position="right" offset="0">
                Keep current camera position and zoom level.
              </paper-tooltip>
              <paper-radio-button id="share-radio-button" name="share">
                Share viewpoint
              </paper-radio-button>
              <paper-tooltip animation-delay="0" for="share-radio-button" position="right" offset="0">
                Share viewpoint among all cameras.
              </paper-tooltip>
            </paper-radio-group>
          </div>
        </div>
        <div class="sidebar-section runs-selector">
          <tf-runs-selector selected-runs="{{_selectedRuns}}">
          </tf-runs-selector>
        </div>
      </div>
      <div slot="center">
        <template is="dom-if" if="[[_dataNotFound]]">
          <div class="no-data-warning">
            <h3>No point cloud data was found.</h3>
            <p>Probable causes:</p>
            <ul>
              <li>
                You haven’t written any point cloud data to your event files.
              </li>
              <li>TensorBoard can’t find your event files.</li>
            </ul>

            <p>
              If you’re new to using TensorBoard, and want to find out how to
              add data and set up your event files, check out the
              <a href="https://github.com/tensorflow/tensorboard/blob/master/README.md">README</a>
              and perhaps the
              <a href="https://www.tensorflow.org/get_started/summaries_and_tensorboard">TensorBoard tutorial</a>.
            </p>

            <p>
              If you think TensorBoard is configured properly, please see
              <a href="https://github.com/tensorflow/tensorboard/blob/master/README.md#my-tensorboard-isnt-showing-any-data-whats-wrong">the section of the README devoted to missing data problems</a>
              and consider filing an issue on GitHub.
            </p>
          </div>
        </template>
        <template is="dom-if" if="[[!_dataNotFound]]">
          <tf-tag-filterer tag-filter="{{_tagFilter}}"></tf-tag-filterer>
          <template is="dom-repeat" items="[[_categories]]" as="category">
            <tf-category-paginated-view category="[[category]]" initial-opened="[[_shouldOpen(index)]]">
              <template>
                <tf-mesh-loader active="[[active]]" selected-view="[[_selectedView]]" run="[[item.run]]" tag="[[item.tag]]" sample="[[item.sample]]" of-samples="[[item.ofSamples]]" request-manager="[[_requestManager]]" class="tf-mesh-loader-container" on-camera-position-change="_onCameraPositionChanged">
                </tf-mesh-loader>
              </template>
            </tf-category-paginated-view>
          </template>
        </template>
      </div>
    </tf-dashboard-layout>

    <style include="dashboard-style"></style>
    <style>
      .no-data-warning {
        max-width: 540px;
        margin: 80px auto 0 auto;
      }
      paper-radio-button {
        display: block;
        padding: 5px;
      }
      .sidebar-section h3.title {
        color: var(--paper-grey-800);
        margin: 0;
        font-weight: normal;
        font-size: 14px;
        margin-bottom: 5px;
      }

      .runs-selector {
        flex-grow: 1;
      }

      tf-runs-selector {
        display: flex;
      }

      .view-control {
        display: block !important;
      }

      .view-control h3.title {
        padding-top: 16px;
        padding-bottom: 16px;
      }

      .allcontrols .view-control paper-radio-group {
        margin-top: 5px;
      }
      /* Layout must be horizontal, i.e. items arranged in a row. If items cannot fit in a row,
       * they should be moved to next line. All items must be square at all times. Minimum size of
       * the item is 480px. This means that maximum size of the item must be 480px + 479px = 959px.
       * */
      .horizontal {
        display: flex;
        flex-direction: row;
        flex-wrap: wrap;
      }
      tf-mesh-loader {
        width: 480px;
        flex-basis: 480px;
        flex-grow: 1;
        display: block;
      }
    </style>
  </template>

  
</dom-module><dom-module id="tf-tensorboard">
  <template>
    <paper-dialog with-backdrop id="settings">
      <h2>Settings</h2>
      <paper-checkbox id="auto-reload-checkbox" checked="{{autoReloadEnabled}}">
        Reload data every <span>[[autoReloadIntervalSecs]]</span>s.
      </paper-checkbox>
      <paper-input id="paginationLimitInput" label="Pagination limit" always-float-label type="number" min="1" step="1" on-change="_paginationLimitChanged" on-value-changed="_paginationLimitValidate"></paper-input>
    </paper-dialog>
    <paper-header-panel>
      <paper-toolbar id="toolbar" slot="header" class="header">
        <div id="toolbar-content" slot="top">
          <template is="dom-if" if="[[!_homePath]]">
            <div class="toolbar-title">[[brand]]</div>
          </template>
          <template is="dom-if" if="[[_homePath]]">
            <a href="[[_homePath]]" rel="noopener noreferrer" class="toolbar-title">[[brand]]</a>
          </template>
          <template is="dom-if" if="[[_activeDashboardsNotLoaded]]">
            <span class="toolbar-message">
              Loading active dashboards…
            </span>
          </template>
          <template is="dom-if" if="[[_activeDashboardsLoaded]]">
            <paper-tabs noink scrollable selected="{{_selectedDashboard}}" attr-for-selected="data-dashboard">
              <template is="dom-repeat" items="[[_dashboardData]]" as="dashboardDatum">
                <template is="dom-if" if="[[_isDashboardActive(disabledDashboards, _activeDashboards, dashboardDatum)]]">
                  <paper-tab data-dashboard$="[[dashboardDatum.plugin]]" title="[[dashboardDatum.tabName]]">
                    [[dashboardDatum.tabName]]
                  </paper-tab>
                </template>
              </template>
            </paper-tabs>
            <template is="dom-if" if="[[_inactiveDashboardsExist(_dashboardData, disabledDashboards, _activeDashboards)]]">
              <paper-dropdown-menu label="INACTIVE" no-label-float noink style="margin-left: 12px">
                <paper-listbox id="inactive-dashboards-menu" slot="dropdown-content" selected="{{_selectedDashboard}}" attr-for-selected="data-dashboard">
                  <template is="dom-repeat" items="[[_dashboardData]]" as="dashboardDatum">
                    <template is="dom-if" if="[[_isDashboardInactive(disabledDashboards, _activeDashboards, dashboardDatum)]]" restamp>
                      <paper-item data-dashboard$="[[dashboardDatum.plugin]]">[[dashboardDatum.tabName]]</paper-item>
                    </template>
                  </template>
                </paper-listbox>
              </paper-dropdown-menu>
            </template>
          </template>
          <div class="global-actions">
            <slot name="injected-header-items"></slot>
            <paper-icon-button id="reload-button" class$="[[_getDataRefreshingClass(_refreshing)]]" disabled$="[[_isReloadDisabled]]" icon="refresh" on-tap="reload" title$="Last updated: [[_lastReloadTimeShort]]"></paper-icon-button>
            <paper-icon-button icon="settings" on-tap="openSettings" id="settings-button"></paper-icon-button>
            <a href="https://github.com/tensorflow/tensorboard/blob/master/README.md" rel="noopener noreferrer" tabindex="-1" target="_blank">
              <paper-icon-button icon="help-outline"></paper-icon-button>
            </a>
          </div>
        </div>
      </paper-toolbar>

      <div id="content-pane" class="fit">
        <slot name="injected-overview"></slot>
        <div id="content">
          <template is="dom-if" if="[[_activeDashboardsFailedToLoad]]">
            <div class="warning-message">
              <h3>Failed to load the set of active dashboards.</h3>
              <p>
                This can occur if the TensorBoard backend is no longer running.
                Perhaps this page is cached?
              </p>

              <p>
                If you think that you’ve fixed the problem, click the reload
                button in the top-right.
                <template is="dom-if" if="[[autoReloadEnabled]]">
                  We’ll try to reload every
                  [[autoReloadIntervalSecs]]&nbsp;seconds as well.
                </template>
              </p>

              <p>
                <i>Last reload: [[_lastReloadTime]]</i>
                <template is="dom-if" if="[[_dataLocation]]">
                  </template></p><p>
                    <i>Log directory:
                      <span id="data_location">[[_dataLocation]]</span></i>
                  </p>
                
              <p></p>
            </div>
          </template>
          <template is="dom-if" if="[[_showNoDashboardsMessage]]">
            <div class="warning-message">
              <h3>No dashboards are active for the current data set.</h3>
              <p>Probable causes:</p>
              <ul>
                <li>You haven’t written any data to your event files.</li>
                <li>TensorBoard can’t find your event files.</li>
              </ul>

              If you’re new to using TensorBoard, and want to find out how to
              add data and set up your event files, check out the
              <a href="https://github.com/tensorflow/tensorboard/blob/master/README.md">README</a>
              and perhaps the
              <a href="https://www.tensorflow.org/get_started/summaries_and_tensorboard">TensorBoard tutorial</a>.
              <p>
                If you think TensorBoard is configured properly, please see
                <a href="https://github.com/tensorflow/tensorboard/blob/master/README.md#my-tensorboard-isnt-showing-any-data-whats-wrong">the section of the README devoted to missing data problems</a>
                and consider filing an issue on GitHub.
              </p>

              <p>
                <i>Last reload: [[_lastReloadTime]]</i>
                <template is="dom-if" if="[[_dataLocation]]">
                  </template></p><p>
                    <i>Data location:
                      <span id="data_location">[[_dataLocation]]</span></i>
                  </p>
                
              <p></p>
            </div>
          </template>
          <template is="dom-if" if="[[_showNoSuchDashboardMessage]]">
            <div class="warning-message">
              <h3>
                There’s no dashboard by the name of
                “<tt>[[_selectedDashboard]]</tt>.”
              </h3>
              <template is="dom-if" if="[[_activeDashboardsLoaded]]">
                <p>You can select a dashboard from the list above.</p></template>

              <p>
                <i>Last reload: [[_lastReloadTime]]</i>
                <template is="dom-if" if="[[_dataLocation]]">
                  </template></p><p>
                    <i>Data location:
                      <span id="data_location">[[_dataLocation]]</span></i>
                  </p>
                
              <p></p>
            </div>
          </template>
          <template is="dom-repeat" id="dashboards-template" items="[[_dashboardData]]" as="dashboardDatum" on-dom-change="_onTemplateChanged">
            <div class="dashboard-container" data-dashboard$="[[dashboardDatum.plugin]]" data-selected$="[[_selectedStatus(_selectedDashboard, dashboardDatum.plugin)]]">
              
            </div>
          </template>
        </div>
      </div>
    </paper-header-panel>

    <style>
      :host {
        height: 100%;
        display: block;
        background-color: var(--paper-grey-100);
      }

      #toolbar {
        background-color: var(
          --tb-toolbar-background-color,
          var(--tb-orange-strong)
        );
        -webkit-font-smoothing: antialiased;
      }

      .toolbar-title {
        font-size: 20px;
        margin-left: 6px;
        /* Increase clickable area for case where title is an anchor. */
        padding: 4px;
        text-rendering: optimizeLegibility;
        letter-spacing: -0.025em;
        font-weight: 500;
        display: var(--tb-toolbar-title-display, block);
      }

      a.toolbar-title {
        /* Override default anchor color. */
        color: inherit;
        /* Override default anchor text-decoration. */
        text-decoration: none;
      }

      .toolbar-message {
        opacity: 0.7;
        -webkit-font-smoothing: antialiased;
        font-size: 14px;
        font-weight: 500;
      }

      paper-tabs {
        flex-grow: 1;
        width: 100%;
        height: 100%;
        --paper-tabs-selection-bar-color: white;
        --paper-tabs-content: {
          -webkit-font-smoothing: antialiased;
          text-transform: uppercase;
        }
      }

      paper-dropdown-menu {
        --paper-input-container-color: rgba(255, 255, 255, 0.8);
        --paper-input-container-focus-color: white;
        --paper-input-container-input-color: white;
        --paper-dropdown-menu-icon: {
          color: white;
        }
        --paper-dropdown-menu-input: {
          -webkit-font-smoothing: antialiased;
          font-size: 14px;
          font-weight: 500;
        }
        --paper-input-container-label: {
          -webkit-font-smoothing: antialiased;
          font-size: 14px;
          font-weight: 500;
        }
      }

      paper-dropdown-menu paper-item {
        -webkit-font-smoothing: antialiased;
        font-size: 14px;
        font-weight: 500;
        text-transform: uppercase;
      }

      #inactive-dashboards-menu {
        --paper-listbox-background-color: var(
          --tb-toolbar-background-color,
          var(--tb-orange-strong)
        );
        --paper-listbox-color: white;
      }

      .global-actions {
        display: inline-flex; /* Ensure that icons stay aligned */
        justify-content: flex-end;
        align-items: center;
        text-align: right;
        color: white;
      }

      .global-actions a {
        color: white;
      }

      #toolbar-content {
        width: 100%;
        height: 100%;
        display: flex;
        flex-direction: row;
        justify-content: space-between;
        align-items: center;
      }

      #content-pane {
        align-items: stretch;
        display: flex;
        flex-direction: column;
        height: 100%;
        justify-content: stretch;
        width: 100%;
      }

      #content {
        flex: 1 1;
        overflow: hidden;
      }

      .dashboard-container {
        height: 100%;
      }

      /* Hide unselected dashboards. We still display them within a container
         of height 0 since Plottable produces degenerate charts when charts are
         reloaded while not displayed. */
      .dashboard-container:not([data-selected]) {
        max-height: 0;
        overflow: hidden;
        position: relative;
        /** We further make containers invisible. Some elements may anchor to
            the viewport instead of the container, in which case setting the max
            height here to 0 will not hide them. */
        visibility: hidden;
      }

      .dashboard-container iframe {
        border: none;
        height: 100%;
        width: 100%;
      }

      .warning-message {
        max-width: 540px;
        margin: 80px auto 0 auto;
      }

      [disabled] {
        opacity: 0.2;
        color: white;
      }

      #reload-button.refreshing {
        animation: rotate 2s linear infinite;
      }

      @keyframes rotate {
        0% {
          transform: rotate(0deg);
        }
        50% {
          transform: rotate(180deg);
        }
        100% {
          transform: rotate(360deg);
        }
      }
    </style>
  </template>
  
  
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5H1jCNkosMq20DStOahu6Tl25xsb5RqciLQK1kSpeRs15JKSgo+2DBNpTgyY1mugTZwLBQyFZ2LYikcEqfUfXzD5bqRfbmJc7cYYTstDGs2DiLeG4oBCqhtfubuK8OpzZGwftSZqHgjNcMqO0bGJkQTvYWwXWjfQkKZ/6Gt0O9Ma9RrPA7FkHm4ogchaY4T0BfhuQpl0SlqxIwD6dfNlAQepRTVGp5sm+1YGJbv55UKec+VpxVrICAWlg8rr/IVfIahPZWyD4cFFDlIMc+CTZ15JKxmYxJL5x33PQTi4/jNDXsEHs6OL1DQlR6YioBK1LayaotNggHdb6wZHpOYgdxN2h7EuKiQ2Cu82lamU02Q63JmZzS29vUgECR0IeX+G5RNlpnEnO7QNnchXLXsAOlQQHHeTBg7EsUtguvOiQEKbkgjf0n6GjHfqwIC4SWja8GiY+QtaysAIH+Xtc/S34rotjyJiIgZU5ikRm+iLHHqKCu1qwRWEv3fudKN0MuGkIb7vVjGeHHxCp9OWJ6ErT2plncvoXMmdytfNnJjFy1gw9xNMkd0saBFfI4o1358aFbq/Y7HG+0KmQY85AZYiQxA0RN7R7GoFWI0woIEO6jdfg5/lv1W9L8MdgGrzibDAjUzPbmi3IYPDcUi4SpawuXitn7HSA2yOtc0ts4mgYWjYsiSiVXBuGBQjXZXxxpS2Jq6yBdvXRk6hLpa/aV6B4YBjv08cEdkBW/TjBgnZNauhzxqZs3IZtaqmJYIwCdm2CuAwGScMv6WjknojNJSYEDVznSdIe4CUSKBCkndAmwd2jkRJS/wOiqKUozXfWEQvrk2GMFeh/k3cHmd+e5nwHpxKCSAEShab0a9gp/nOaf2S/o/xG9ll8TwiBm+JxaYSCbbEJObCxpFX4W0prjI5tAu+5849d5//w4G4tCb/Zm21f/T+Nbt3FsPz5tFFX9NlIbH+MUDEgQNPWNDZJoT5NdbIxox4IqtKPpOXydp7MulwVpi68NL3QjJdbr8VparAvCppfbCLx6mT+zMWP3/nLtb88S5po6i/tPz5fgrJign1I+C8ng+NvE7413p9rF168tNQhevfryFZSZJG3V0igtyMl6O9ysaVvgGqGS8vU4x6h4YtDo7tnP42xk5KyqVHRYYBXqWC0NOfkNTdnG6U3N+VkdtbnteAMzOxO65jaGRsbmNknmTiQDx9VYHY0ZGa1N+ST0xqbRVsbs1uw+hYOOkQjG1NTQ3NbbR0zW5Pf7ATySWpUAhnss/zDmi+ftaPolRu2TY+xLj7oy3F1rQgy9SGLmmrRS//lS2yb2xGz9qqistCVV0fiKdba0at0F5p/aiJS2moXkBRu+nbeQdPeSo9s6wkCN9L3MS9ieyHZcj2+9pNhhq58fh6l8yaHGNjLty5eiJdnZuh+NoqrBqvTz2Orv9swifLM8rOdS0p795yfyM/+IJ+ZrP3pVDKTYxpEhh8pOKo1y1L5Ha/zu0tFqbapOo5zFQVfP9S1p9gfZN4cTnie0LXRlfQ8qXwDaslp5pouMkwwuPsMOiE4aBZsOflG+ED4727GZgRNYxN9XVNjqrEZDCT0H52X7Qe9I/6E9zqfNz6qoQo7hPeaXz69V0QVtoQnI+7F0SO60X6TR2fG42gRPegI/N26X+yk+swhIP7btCIQRHWqtgyiJvtGpxYmsjNiZl/SSme/zt4Ji/uYe943oR5EfcllEZB2JjQoNCrSJzHSlR0ZfWs2gLH4Y3HYJ6Hd5x+6VMLVuBHe9WdPeF70sb1S6GFnUulRZzPjmYVAUOK4MXxDn61Pw5dKkhZ+SWJaEr0OQcdji2X+J/qSrn6ayTNrXK+e/51eTOBfc/d+4AuOj/SLTLiYk5FdGavnesQG1Hbfun/wIcp+umecGndz8Pmu/55jhWYX+XVxtsnULo8PN60YzgdNTK5k3ltIvMyZ3AAkf+lj/tJ/txSbX4a/APGYQkhYo4f8GZW4W9QBzMuFp9hX/bT43ghFd/nQxpC+T08fTX56yqdsHZrCmE1KDwtJSF6Kiz+44xkW1xdC9fcLpfWF0kDxtXdsSt3AG95nRWffzaXmLyFkYf0c3xov9MD9o/Po6sQzfuNk2yPGnpfTh58ktDOyE5tANdr8BVvFmjaoYk1lgw6b1+OBR5THfQ94Fx/8+pMaQh1UQ6ifwL0tQ7dm6M75BLKHm4+LQ5CXLRthbUwO33/58Fbd+Zq4GF0TpJCtdsrY6DQxgiXDv0ihT/A8P5cl7t3QuqBkyjQ1KTn3SXBi15Uk3FBBuF2KtIOuspLQaEZA2iKuQyBSJ5M4IjfcFcW5wfM5x+3gjWm7m5JfjmeNTykE/wmZd3no/oT7OI/gcnfKl+2fAYtdlacfU3kzjfOs1Tw9Dtic3BCSj8idAS1FWxWaDccRf9abIzQWp+/BxieuUAY4Fvs7MjriF3Ix8B/aoRRWwiT+2bfdReP76Bm04DfrWNneH9EMik9onGfaNlh0Le5++w/2ZydnfaE8OpE1Vawp0HL9y3Hc3o87gtUlOQUNrM/I29SN5u915eUZwlWyP5KdgzJtdaceGU/Xayq0jHL7rYg1jM/+QN5ab07+HAGdqByHgdsPegDH6nrUXeIA2teCTYJ/A45V8+hSlwlYwl2LgL3B127ta6hQQSejTE5FibMPfNr/6oc0nqOV9RXdiNwYw3YNWTseODkgBdYpsPNZbubQi/z2yPXYgYs7lzpH5DLsHv9+jP02v/J9dXKHGkUNC4hh0kGVWt851nI32nLbW34r7WccHf7nJBTdL39QUjEWHBhliem7iam4kUWM/VI0VWzF54bYrLdoykuh+WAdCb8fK+PiuvyukOrm4/sF1q+vzZfqCbVf7xJpP3caZmzmzhkItfsJtZYTYXYHu3UTaa7vAeS93ec+XGNz99/tivYf+A04luzXg78fz4tu/j75QCEzPykmujAvEVEH65Jr02lyNKefQ3Wlql8fGbLOE13d/MS/sdu3fjfXnfSd/UYPV1NLqlVBTPHgBw12eq/mS/JGElUMPfh2af/CphSmRNIYyekID2g8pnsxAz2DA4ljCfdZB9+sVmxGZE4l7UQVpGQkZAV7WpVSbP0mUzwbI2/umf9Uy0ktmcz+nVCXeYHBoIApdmVs5dfK0KN0MJ1jTb6V4v/+/3HuzUc7UyWlF4qOqYxBmdbeY2f3SIyyvkk0sHaD1eUgfqzRr9041pagnaRgtyT7OrL5i/+YoaCf4SxIlV5R5Dt26/HgsqTGbs3dJ4aWex4fg/DfFl2iB9MrRP+IHiyt2Aep97kfaNXLixA3Hh26BIdHZxoA79hwtwI4nlQAYsD6fAIv+xngqaASOJ5U3m0CvGMTiNs1dj2akplvk56fBM2U/vL+cpAoe/yAT243YP7wGJyNaa6b7M3ugJ5P5WQ7dz8v22AbItuZVvnAMfHeq3to+9sSKQdBtNMoyeD/R+mZme4Ohm42QDoitPNSevRecBYPdQnwwH4mKP7a2KvjnFt4VvzV6NrT2feIeRej4luQNtDocKUHY8xXMX60zvv+tDdcxzH7vnNIrQxuvcTdXMW4RdPdopNboUOSldQOFsf+X5cbbg+my7ABD0s8EaHpN++9V9z60pDUQyvXZ0zppZZHJ/eBk/D6wSNbB68k/HmVn7v8eR/qM8ydUV1FbwpipiUQvRYo3KSLfnG5AgnTQhyxZxLgCbOhu8G3e3y4m0gWxN2lq3Ze91rqXmKC9bGdjZMjvcEp3KHP9s1xfntFf+1DsIwqjmDUx+amJRsHUa/e+yz75Vsdoy+61DBxST+uNIZxF/YMj0Rn33TB5gyz+yK93DxKyKk4NuCBLZLZBDYiqmG4XvkGaaiTjRiGRrC3nlDZWN95kTQz4KQQi6bXidRmn02HhHsPXftVUw8Zq2PFQ3ei90GytP9z2iNCwEeeYYw9tWygNcxf7xxFBsbZA4HOnkG2QU4iZFhlT2Dv3SvRihZgE2D3CgGfQC8atsGlPWTfDXTy8S8lM1A2ASxOXEz88yar7JnAPu63nJfifq1kn1sVUvizxdmUfWc7q7+3Pq8/lp57B0io0K83MgPXKFSEDbjFl1xhlNSesZcn8F9wV1LuxpQT417qJp6jpvWBxfE/69JjN4KT+CgLgFtk7wRHtA69k9v61ph2h1pkELdhQCugMnrhH2W378pNmskrbMJbXIxjXrXKAVg8rkshfVe2kbzh2JT34fbNoY/9F9iGgW4OVn7GOhru2gd0rjhYxDqWfOkeyZj2PNvkvYG1p7v5evkagdicLkd/d7+bDv60TMsdmz3moqr+17qvcYDCAIWSOMrqnhy+y+6bauvSTuJiE1bh54v8tvhfe6mEf/fWE3aApGjZ9n5TiEqYWF97szYhxCBsdr5efn0LACd9+U1E7I/x/ndm/gy//TFjEV7YHj1bxoitPPcR2FT9cueJm5uemMURt70jqnhIHiQOhV88Ni8+YlkJXoFePnjPuVeD1wZfz6LXu5evKsrntqvjfi68andpd30zh/vZrg52fE2Av9cEYotXNTB/ZtZv2N+wfz+N+XNVQz73hlLXSu/Eq6FrQ69Gb19b6VYC82Eh1t3nBSYJ6hey9CROkMwC7QbbbiyWmTAIRel6hyVJthF20FL+GGGgiABNXYNsoqtHNtFQJ5vo6ZJNwKLDmR6Is1zBZI48KhZ/P/H+5uGHB5f2zz08dPPwq4mXNeI9/2GqVvCeNKrr2i51ILOS2mHH4K9mnrbrgfv7HtgEyYS74nsuj1dxfHnw89259ac93zyDgjycA1KDOL+ojwNpJqQ26eGDmsSU9LxCZpZv1ehEaH1hSV5hflFl1MBQJbWoNL+o9Byl9sGgd1VOXn6RRlp8TNjkXerSXVro5MfQh3eoz+9SQx/CEWFLnBef3f33FfI58uflZXdkVc6r5KhU/HC4LwiZfOKr4hOr26tgNDje0+rE9O3t4Bt9v31oYfoLhFfRs2LzPCzNM1z61G4r3Q1zuxDW0+xMVCRl+rUKPVz7zPPyEZtSlBwTn+NhY0d3SAscjHSuUAqzEjEVD5FMsPaxL5O7pvIiSo5mnekFuDkHJT1SNInkRyJF65EmFD78Ow0Gr+0qOi8T78x2n+m8N1tb115fXu3lauPkFpAZTYsOzHB1drbyaWA0lu8XbK27KFBXdciu8pBAI1Go5fwha4GmB33OJmMpxmN9zmV9zuSJoUGj8dvO4DkgQDKzTohOiwsOb4rpCFBOpuoaKSm08wV1pmRlXyhiaHnvs/JMlbI92pxNMQX7U4pOoHU4egIb30YCh4WrNVsLPJzAEY9FF+vzNvsutDETaXQd4n7l8Do86ZxA1eAlM10985qMQgM3bTVDQ4Ib5INKA+/V2qsgShuhXKhZOl8ZGlZZuVRdQ8lU1TdUVdE3pZBNg4zIID2c7jjuyFhg+I/7xy4IH/tlPWINXZV+ifuxkQEkGTsVgq6uWh+1uSKzsCr5bEiwv7dDbuRZEBgJs2Z4H7XTUSCeONrnWx+fVnglJpjOVCqTKqCwEgszW5PO9J3QV9E6PSbjnwFuIKwGu0XkhkFUn5CA5DmlUpoJMUJswgu8vSulNNZMbWgLcIvA4LRa4/w9P8f1Z+0w4FiQf59gbM40MSecMjcm6poagzbaUC5WEtxGq8Jn6RnKycgaHrHEHsoBpLKXMCFfEh4tDBxU70v3htT6BxuNLt4eqqm9O1zXifVxC7OycAkBLytvtzBLC7fQAKCVIfEqRUYWAcHOXhQPbzdnp2Df1e/efpwemno10dNQecXVzZzMFSWyraZhaMKISRWvjAnQIiGN33b7lu0RFVXWy1GmwPljo/uF75+3VgyMFcsEs5BTumOytJzw4Do1jEgggs2RjdXr2V2fbuJS3lK0OTQUSJorJwl3Xhst8HMoaCrZqh4ArVMDptOGyYd8CQ52mRutevc4Gv85c7D0mLlq8Lbo96oojSX65avg5sS44Ef21kk24Fhbi2vbiUWpz3PTYxGI27KeX9mcuj3f16Ij5q0fuZsoeZJo21VqlWXattAzZtV6wklh6GHSMTVvZ3uSooFLR6ZVppVT4oS5tauXVQ9mGyy8RH7nXiKazdkyWeNXq2s32971k109Apxco5z0vgiV7PSMvghnpDHHkdlN9EP2Lc6c8zXMbIrmoFGDgfrMabWUHkIm4cHjkUCE7mGo62ahdG3dNyl7V9LIwTOhsaByfO9vzmKbxkT8SnFezqvExFdZ5ZFrkwmMkkgXhebE2IdM89C2M4nWl6VNhjYVWczrdPPQgjXkb6pukZTTVJ6U1xQekTXeAaQow6+zX7e79I1No4xN9EmzWqZNsXU3CYaR3KUETWNjGzamzLVRHA8bFhT7Tw9XEMtm2t35ALnkU3NqsnxLtfq0t4zXqma7V5yNZZpukk6XlOOz+oEUJfT9tdxQEf3iHJfY0sHRrNHXx/Fb2Ma03mh2iGlsAhuiZsC3UTi2ibOklBdpIbQXCXKXN8c3Crv9Mvg7PeEwcNtIYb9vIK/GGT7Xy51TcFttsGsGXE784Jd7+TODRbS96R4K85voRlYrd05RDc25QNpQ0aLGUOcRMeZ7bkdGx/YbvpPoF87WjN5YekbPQCPzbgwts1dHoM+eniUcc2NfRGQsqKh84BkuryqrGuTjFy6E3QEy7slxrQl0L+8EemtSnMC0vC5RTp54WkFOR89HRw9Uae/Ck4q9JFOCTrWJnTCSK+MITyr31LzkrHLCSR4EG8XizVwYaGHFXA54BswA91eIN3NOQ4tr53ICg2agXTrO3C4JLS1dzAA3/2lgaSeZ22Wh1fcX+yBE7YyC73dikrljPqb84eJNX/8l4EzHeMbE+AXSY3yOe0RHB/rGRCFucEp0u6DpUIbwoYwm2HW95UX9rtguhJbQ/1cOQu3KLj9cx5W2inAPCfGH9P0pcPwQB9Ke354yH1IH759/xH5TGAfFH/kf9j9/uY2zzjffL8UPdLbCchm/u208JBNICr4x6JplVXLYYt+xiWz5qAhfL2/9ue45ZqDncpXLT/vzmYz0uG4oObvzx+8NN+eHRuJI8oBbZa8+R1MFHn98IlP+bIbNpDKnhJbvVeKqEqcWh9wmuIa+YTTXfRvYon2xpqWopLaltqKmpqO46GJ7bR9PCy0Kre1poUdgLgDPY5z+j4KHpud5z7rbdQGcMaE/7lIX+7bmwDtJuXzjnJ1w6SI5PTcjLuZKRV5qezri04u1jqIlauiR9EhkOHr0yIXiLA9eb0P9EBozP47eVMEsbW2sUnzyaf15ebOs7tG1Y8XJqiQDNSp0tNm0jdgkdZ+LgXNEaqRaulYK5VJsQV5dTEhKulakGjW4kpaaVkkTP6S65UKUp/wdbp1rdRhxkJT32gVrqroaLknM7MSYljpG7uUG5Nyp/54tvADh0sIYa582i6MGRrpEFWWiuab6KRJcy7vdejvud/wYOrmrQ3UPZzdAdj4bsl16trMHJwj9C8BBaPxKa5K4nayl8ATWSLdXfZuqajai9urlaXVLWl1S43gaFezUNHGc2viWGFVqfMapNI6ZqJrQkGIdrVlpSR2gMlMI5Rq69DmzV4hdMrcHAWfs9BAoTZU2Z769bOXxZc3VFkp4xWibBOFYwgTorrQA9CHSRO6XW+RWuU1ulzugk+IJJrd2XG6lfjva1JwnrQ15Fhg+vshoU78zxce0UticGgUkldh2f/wL0iv1vW3a8KS1TM8CWeMproOsp/4470mj6lkw1MdTho+p9Irw0VTODQiyMjyVlWFRVoYll3JHw5maAiAoawJL1qzs8owCRFg7UwQYHKuvA6APmyGAR8X+5eSiA+FGlKvISqHXVEyywqAtG9PLQDYOESOUrdi5bKecB7mT9W/92UnbzKds/CivQ1ggaPNaTYebto+Dm7It2LtszSNuSJ/mqPEUqaYzG67KzmDhcq440LVTrjHdCbAH3C3KLoZujDGxdgHfzSH/3ziKTf8HIG18azVlTW7R07J2d0c5mZEt3MkFd2eAu7W3sVJe7p0CX/6/fltthFVFKkqjtj7zaoWWRHyaxBAL0BcngJzxrUs1ANWoinudxTTyo7X3vEkF7WDJOkHMB/f2PmpRAYPiGEZh1PFXRQ6uOCwmCQHcLjO1QlaXT8roV1cmYLFRH/qIMoDdb6ZdyDqrc40JgDyupesAej3axsPANaHW0d+K3v6VKQO4dWcnBYyNfnCmBlndj15UYmvdLQVZXYXCAbDvSi53l78mgAvp6tvmI7ycB8vFRn4rC7Z0d8UzgaupqRsZLwDkzv5TIUDPRtu4pZzR/x9ttS/uo2IB5q++zRLVtCeAC/F3TemP0Fvzeym4EC8U3sW+Oa/B+37nEQDoFmu8ZrzdTlxV63fOfcsBAMDIT4LbAYC5ZvPd/8f+n1vebbmzALigAAAQwHHeYgTAdW6gdaFbBSKcajPz+Ekgi2VtdCuFUcG/XvOq0KvaX/LtBzg0FzbxQEo8IZXZxItGvw3ZH5eQQ0tmykBTWTCTZmJNLIkKSSU0YkCCXm33OCStrZMrQacrTnHJSMkVWjMprt2WUOdV1jUFdIKyYhLzf/dFofSrNUJPXZ0h23k0yS4yQ7itdzJmqjhwsrzqj+7MMqlnKY2qS+yyhGbcFLoA6XqJo95gFYoY6USEG+HNc6lmNUzcTbHsuFSqhFJgWYx5103ZxjzZymZTZ8QGj8RAxo2ShcMjb9pOU86KrQLkSLnRmOFGDjONFpx1CXp+s6dvOVx4h3IVL7nbxFUagep8f8S7NVocxKxEfnWDR6/hXkQ87T9Z9YNLZnCf9Dlmsfx8zbHCJMebeqYquSWXCc/YpjXvmnpUiazbSnKTQegpCAFh2s9hSjah52vufYbz9A+ryVFgrtCbZYzt0mfeGYLrgbJalzUNMqomgVWMVFks67y0EFM46+Y3I3DNNWVxTUwuiOvSaiYFqW2Ab7tDuU1RShGhKY6YnJTioazeKCeihEYwu6wmG9tUK49HpautZqJ1h+zsKPQcWAqIKVEnqsSSmJtqnhheK9M0WhgtmepO47uVyu7QWpqtDIeIjQmvctt4GOq3VGnMpi5Rs9OaD+OCoIJ9ijAlxEZ3q8K2cSvUZp3SmC0KHW3jbeojAD4qtIcFXFQPgB+g0B3g59viFAADqeUBeIDyWIQYj2NR/GIqMalKLI7FOYHJ8JDbG+VnZwxJhEixogQLFIRKghIFiqMigSxCBQ3lf2Jj4XzJMV2HhIZtGOJsxPx3x1+U6Iz5JTk2Ivg0hJqUYJ7IBqMJo7HA0wrlnUoclChnBYvwhxO5lcrUnXqV0epC08uiW50qEoH8CHRHjrfInPkG3P3JiRAlkIUK83VE+Guys6hlxhiJAQu2q5B9cEhhYPBIf8/JTwAA", 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", 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0UTVEIniStK2KkQNSWnadTS44MC0ZcShqn/tNbjSpzhJPWqmVtdIahqMy4JIurfISwFqdDknlN/ZkJWaJOfPmu0hCIViLEiCXeDo1fx31S8zs39RXB2funyM/h/4qJvzk1FE1AlLSBoRckNzDUC2Vie0A3dVydpP2+nlQlXWLmsM6kpxf2qNAnzxXE9I8zbaSdNBbUmBp9qN4v7SdxKEPSQw7OPKYqmGDHU1G25UrXOzQT8UESQ8gg7ZI7KplJNU1l8Ei8eqUd9TGu/Hq7MDh3qFLtVYpmTYB1RVu8jGGn+HIKixbp7pl08klbTB60qEpikY9sjLo9EaglvU96Bz3EvfEcLRjtkf+1rba2RopIJY4orU7uObY3Y9SlGnOR8UZRdWYYRWrEpkS6FgkfdvN4lxqew4RQe5mP6W+wnEkhvXYbXxIZd2qOGV+DxkBE9TT6D3p7Zq2VzJUAseOmluQLiocOOkAKDVWaXR015dYYhhgYrCcD6ml8QGDF/GOWXf4pN3DFIOvh2olo92vyup7stXa0MvaZcUB90LXMpfiqT0HdGwvKlR7KYetkJMa2zJ/eArTc+pPLwuQVoRD7jxII8nWDhElbFkfTa6MUhEyZ1g5yvyhBBlM5pzxUDZLT/37WD/EMrpTwTbt4IhlG7ZotGO0+OI4aZ8Go5MwWJcz21FJpnoSIFY/ccFOPVCPGOEsSkbF/PQIj5jGOrVbVnfwRTqNq6nNasQEQSYH7oLXLiW31UgXpk8J44vqKc0GKe+BUadt3wUGAkJHB7zFtQeGQxccdSieReslSPWf21E+PxPTi3gAyczhLgUOemFaBT28Nb0szb2eFfH0QV74/FTUWTUSxGVUnmvtGSxEF+eUJztVfeYpr5KlNEbYbqQ/w8UBwJAH0HamgKFli1D0/cDgt1aKRsyIiBkajPOqYfpIH2OmzjUXl2DjQBcnYNPzP23nDgeJTLHZjvA3F6eZS+qL+hBHp2CFSjh9r5hEHI5LWf8yYeu+fiXuj2dEpxTy4BSscCAS7mcaFpIjOC3TN15a8XSiiv1d5nw1J3XvVVdSdCXBLyTQ7X6UP791na9WoVPtyKsOfHdGN4V1cnOIkpCaGROZGQLYjJzIqtrI6Io6olJUVVV4RG1VQhxWw8BWVUnfSk9FQ88uQ89O+1zrjciqGmx1dYKSoDTFEzCaH0DKetZ6qpr6NipKhta6Pz/vyl71JjjIgm2Mo3dThbeljyxJqar/UU1W/8NoQ8MMt3tOGvIR8uHF1YdJ9djPRUuCiuyQgv0MsjKrmh7gbl1DWxUY4ZuThDq3YelD8tGWvyGgLy1vY6imbeEGLDOpHO7hsJgJURqr/sUfdMIUeQNS3akvcKdANyLxpG6ttwWvR5SKf+OeDogqLHnnfDSKVObzyPWhfkSuc08aOBScttLk84hUfq/nmw8c5a+vGV3LuuaOM8ilM73oei1b0HiQOciMcqb1WLAxXFpjymrKekl9Wf4AqqVC9FkHJbMIdHx1e6qd9XNg5NrO3gaSJT+J0MR/vZGQBOTgYj7stSKQKXNVXJDY31kSRgv+kF24ebC3cBUl1sW4FzlLSg6fQRYu7hOXE+KiplBr8IY64U2sYRs6DULU6B833EN1i/QIcale8TkFiZTnqB9vXuxNN79muxhd9Q6O2QgmRCCZ0QQ4jJKJnpTGv+v9oztv+iwCmx2v+PFGND0YOBw/gIUOqldmXGhMUkLkg/zYNCQBblntjGSPaFO1s10v9n8avPBleHDEAF69UZnZrVe/kugkTRzewS6vfEa97f0yw/q4iTWYFOobntaUn1ZEjgpQEKYF4W/NhTyv0Yd/jpcJkcNPVxmmV74yjU6gZf4ppenXaNNMkCyKPbn0FNpK3wWEJHdmZqV0gZyeMm+7ch9v5jGOnDmQHhB2JdRa3fDkj3rk9+T/0vjk3+XHvmFPPPktb7jXunTtve7t2X9N52URmJf7Oyv7Z1pSZER6MpvtCdeYGG/3KOJiemfEJYDQapiYQomOZSg1yG6ZmOa28yKIOPdT7qhorWaMNs35ggvr6G+bbL/MCjzP6ah2FK4FBKZTczOTe4A20vPvgNDZoDTV5MABkCq/LlcqJyNXIgcCLfEZqKciVoFpdFmDesA2F/EViFPTiYjRMXd1dPV1vB/kbEJBSleHOp5MjjQ3lda6OCtr5fRIdt1PHpfS7wyfXh9tfyRcLaIbr62anLqFf8lc4Y96mu5478FlTWmhS/jNdM+EJUwrPupOtsbzm0OtfvTfGH6QXPh6JozbrrScJ8y+FHD8bb/cUsVwggG/0BH0/sMvm6XGSpkVD6YkrWxDSTe7c+a4a9+DZXxyDkByItiaGDTEM0iu4ZkhD7Yc//CjBmoBQ54/eN/fc9+rwOayDzexK2zg39L2rRpGqZi8+EBartu5PKFZIS7+CS6kU6AePnpik+v3e5Sop2jiixQhuoqm7aMozvXeY41V+Vld4yx/kg4Gj7aU1KWcbDU7WvOfnqsuEoFnZqNFInyV/QiTCN5nZPE7+yH+3MbPICCNKvWjWSgsBAw5vWhq2g8k+yJtInsAQ44pGvpsA+o93SqOOAdyC3tVlhAy8kBF0JFz/cg210yPCDdP7xAzQPhtibvbpfSGstK9AlbQCoPe0sXS0v49hEVCL/h99hL428m9tBO/9ybefFp41hNCTIjwS8uEGDhR0dy1v7/a8ZixbckIvvJrm+BY0FLoZabrhRNHyKcHKnr6S5R8k83m9Hp++unpHLzlzRK4Kve1aWvrXsVreu2rvX03aKXVzpKkaavJDXQ1abeQV46TqbL0dQ1N9DUkmZmnCHqM41HHOAfqm/OfSS4K9QUz6i84iIkjPTGOf2D+8nwat/5Dv8AorzwlLqI8PxllC1r42qlofn899IsIf8EvSmxaRteT6WtFppluQF5yPm0H5ff4GW+Z9FRNX92p9/Tqd9zSLDHteYSAniPjaOrOBVJNenBsCSkb+ECP+P3O8mhOA355PmvXK+xFQG9UdVZuclGwu1FWoJPdy0Drcvdm9umtLDQhcfhBb1RfTnlOadBspjeo+OCnAYPvBgx+qug+qFaSF+EVYN6xnP1wEkFWlp++LWp8WFre0Fj0DuL/n6Bpnjw7SK47206egNWRk90XG/Don2Vs6T8vNHH1M59b7+U8Qm7CD/ByunG6sXvzNnHWWfcvykeYXlt9MTG7sG530/7m2uuFlxMrYDsd9PbtGZvUMz1npmGdh1sFsoXnUwTWI9iKfiQBd/t2N2CJ3TkBQNsVAKhoMpkPe40M8E5YoCEH5vQGfmL1womKR09WGi7PPrg0Ww9iYdkZ2RlwuuJtMcK7BBcPd0eHRbeWvGxLqAXqTpuelRfB0ybaRol6j5A1YOD7aIqKLv+cyWvA+jXRIN+IR2kxVXfYwMsK5FBsP9eu9rxyT8EF1wEWKFLu8fHLl9BdNE9SX8V1atZO69a2Rae/AqkIm3Q/WqMmGlOf6gEbtwUaPjhT5PVtLjaj4+Mb2sYmWofkGMekvJGec49yn4M576+9691P3cKFKYCFOpyXW+zQ432hn13YruyxGLJW4SftnAZi9gSosc+zc3/mKQ38O58cPrVzBK2+gC5uGYi3jtUUlPUwiB3He414ZJexpXEx0ybG5sFWfT2p3v0h5UmdzDJd5raIsZWDNWGLtFUPFrz/di73Drkl4PxquSd/fvrZZ8lGXN/CzTetITYrO97M7UHP010ByufOzPHYpntF73Xz6mKzJ4BDpzMwa9AAFfGC8hK8HdzdjWMteNXRFMACBfQo4pRhrmuuLiHGQS7OwNEm3nnrpJRAxg9haRk5nUd4rqtuIqKZvwlQvnr5B2uUXmrtq2WzUtE/4/MG9JI3MrBXiNP4+SaMrN1rFFFvsPC1xfjPUEa8HQkh9rGB9Paj1MH9rQ65PE6KOLC3iWNig/KfOSOIf67GfMXB/xbjSAusAv+Zivx/LyekzbMK/KXg2qreJCZVzg+31MwnxlW/AdlVth8r13oHPYLpKIB5W4vz80h43r/Ki+1Cd+ZPEWt0MjDa6fXRBZNgznu4PjDY4xZG61IHmLcUGnc/6sA2/98uWmrBVHSlfireILkspnAaLnllZAl0yOQBRbwNFWycUi36BPjPnrnroX1MokJHw0srermzqWv9SQZVLczV2s7TQEneUZ5Dql5P298gdeshJWLYxKPCkkJjGu9CcAzVhWM+MtgR0kixrIiMLHdQljK7YnBIZnzxIGkQC4Jhzs6hE41tz2Cgh/Lo+7NpxUJ2ijgZPv5e6b/95JiCeNKxKPEnoFBy6JYEhb1NPIvVS5gVLZzFXiHexaRzmaX/ciZ8f/ghfs6K04Vn9lfGr4zXLjxWnLNwwMzSf2XopPCKLdM8jwJjFru5w92zthddj52/PgJiWQHZJkReneNqucKR4OdKFmhZfzz8ZTp1+vM40vFwuUWQlzAud2JkrcqwymhjFIV/JQvyTJmAfZoJ+N8jMli6+a02nbZ7Oy9OjMvxEsgCzcvQNflhOnX6y8jjtpWHAqDxha6IOhen/EC4Jl3l3GUNHSfzEqSk81WCWsLtppJfvWrmXhZQW18D0xx9jJlqUfd4qETYpvvTGjZhQgafkY7vUHZbzg6fnD83/fwUmXuVspTJ2T2FyWx/XN5I63GlfZcnZbwABbhCdX+BCMQCRztfWViCQw9/PXj2dGGquds5NMjdMTw3BIe+XQgKHz2KIifGisKjY8Nj4hPt80dG3cvSssJj0zIL/foHivwzsiOJWbmuZS8G7IqSSZGx4qigIMJUf8Bqf6DHZNCww4C/h2e/370LcLb1uFDWjNsYJkShUiFYQa5XTjpYulI6hJZG+RykMR2lHn1G/U4FpdSmJxRdBX6lWNsa9NC6x7ObirfiPV5RkIjWrAJipbelUZLDuFT7jW430zx3aruBLP+dWNsq+uHFbfTKK0pIdV5CcgnBWvGmZowMl2aNkoelWBhXndFR1ROaeFVbxTsimlGyJ++R1dl1bBUN410Aq2cyj4Fgrmu8HNb/FBQ/6+U2lBQVPp7scCM/ncwvqS/PTLMx1zUxd44L8vR3jjM3NdWx+x/ZUHweXynDVll8vqGu8Cy+qrSEvaLoHKF2hGqkOhSmMkQ1GhlsNVAdHxpUHW03AMeeZr27emF+4UH27lV+DS7CscGKGiJCJQz29eFx0bnJMZJWx7VMAq8YXKAme2iDobSQOUis+FFx0aW3wWgO0UEYcqC8klL2moKzDfUF3Ozpp1v3V+2kexy5NREhQQ9kAgQgV0ofw5o6ReFmsSCgmx4UKCEGsqIq6pK28SC4wVlCKgHc+R6HjHwnh7Q8R6e0TAfHzMz/R0RV7oiJq6iK3lTVcFFVgYvp+i7ZLkXVReHZ4YXVHBxf5HPkoS7PwOXKcRc7RYG/knfv3BUrJFSl43JD3RycnHQ0mlJIwJpxX1dVSZL/2s4dMVXBS4XWZYHBcfn+DjcVjWWPZVwkOdUGE6MqQ5yLrmlIyN9RlhDyzEcMgX0Ilhl4M+GIsHgAlN3P9x6WyuamilfjCKYxdelayjMUwLyFGJzuJu7f7Azd/00mwJAhbe73yGi98ZCQDKdbc06kWRV7kLBOmvpCN67+ssKdiYb3eYVGr2+FWXvjgbbCpuhRX17+o54q1WJqr/CT3soyjK2lp66OOcFOw8bSU+eehStiCXerwfFiqIVPZw+9NYerF8HVNtjDhIKULL/oHJh51tpY/NDFQ1Y9rUcyfuzXrqbD6OMBXsyWwi8tV29iyJ05reyzGtnKR/C9emz+z7jmwVBZRVNJwzdQWUN5mq+koaJpVdXq8iy6yqyPuGeZl0zafNLtoDNDavVsevD9iqSi/0QqwislY/PVKvGcPNHOrWoRVqaAFlPHzQgeHU/j2wGzsc9NTgyZ92j6Owff3tA+2s01vBgVMGTKY54jycVZ/kHFWbEAz1AMxkd3qW0DLcUhD0YUhVTNH5H0YtWKR5o77rVdMbmcxxJzRcTZw0xbWMXicZp+3D2j+FFdc3snw7e8oLHMgYltldFbMUuqftSQXbjauLCk5+wa6OCY4HqvE0/61PbIq0lbQFtao1HfcXILdrzmsE2NTzMz/O++vx1b6H/bI0u5UOXIwbo3kuCqo+5oKoIW9sqoY40/cSYqOg2EFI/8pM01K/L++5oUE/suzH8pJt19ezQ8vNzf80ZfYuKbdGXr7ohI4w7+W0XvRR7GPw5TtsFGMh1ljFc2TS19Wp1Y1R8WkrPSBXKThptdmxZiMZHcSEhC/WAVfhwwcr7SgX+X40luZpeU04PtXvblyhQF/Vsipjjo8ahkBNuMvMzoEu2zdNNvX1KOr35j19ah76bT5CCXUPWWAnKE0fTiaUsooh6RfbKHelRxKnikgFYzB6HsX2YCM9SJNMSz/ssqC65lv/Nj4Uuu0I9mQUnmpKcdU7ILs0tj7zoyKjbVtwuWfCX+/94Sk+OeeUvayNj1EzzGdv7teY9ia8sHYh77OauEECO/xb50l7aRsh7yiJmnvfG/C9dZ/Dohrnh2tKNkLj62dEZufNYms+xIHPOJEGIm8Nu+p8/NrM2ozWX8xuIBPD5hBkHeJqaBPg2BAV2BAap6N+TkBfjlZa3lFAQEZRVBaGPeJTDa9FKrpVTDqUtFAkFYZ/8Es3NeFmJnz1y5JX8NWDfZHZVMQ1FpxC7v+5bDQJfB6aBkGo4ipXRGgI3lECSecFA2DkWl+lJs7ayfQ+JVByVjIiqHQqlwsZ6cv50bQmqFs+U44MppDNzcTU08Xcx4DVxdzc3c3AzhNC+SmMQXyc4XlQTMXu2zkGVMRpKQZChztf9XC2d/bUXwp8PQfa0Tld52Z9mL/U77/zx1HsPQEtwS1xLSQoc5xfXFKYaLvdSFx62CPIJ59CAkLC4MpFsS5LPlYWTu2FLT9tHnTzzqJLnygwU8/RycnM0XSAv1Ko2WUQhjE8Su9mzSeT5JKQc+HL7Q0N3ZbHdZEjBzlLlngRRjHMHL0cV8kbT46iml1KdRxEuEMJrPJpQ6HKgfYi0ferUX0wUKZdmNSQn59VCcXJ7XmJCUXY8UFGB74qPRra19CVHoNsCNDMfvAs3LzhHOtYW1fhg7f0xp0M7SWQZwjxBIXYwyO7JVVStzSjUsmRgQXJwSE1AVgrF9mM76elklVQVpdTVVlTrguZFrzMYI54dKhxF8Y1L8vItyoxKqKrOFflz46uUOuVPolSssPMdp696WUtcIvnMXKp40pVaZpg1GBBC/j6+vaLz42KnYn7Uv2klOxfL31Tv42mf5hISqKoiDwupi0rdVxYRF7oqF4k2b8OHmOv/1LY/vTvvDtz33k7HzTYwLDSgrDo/Lr0Cl7/5RIvvag6xIq8BC80t3JHU0NCUl7urIy6qow/BDX2R/8eCH/sKLD/7dvqB0/Tw9bdbyF/8BbA7/B8ACfv5RXi4s/FISFuuq3TPy/xld+aEw/7/MKFgNXAXhBB65quZ3mSNchXEMzjWjNmffEVy77YdFatbn0cri6o3s6doSBL/VEMh8d5R8AGqv7OXj7Ca4f4GMTaYvg+n0kAfMWAUnGXGbq5WubLETFC6/WIYgF1AL8uFFVasHdK2u0/W6QTfiIbHA5gvygmuF4A/RnOkT3Way54spBkO81cmv3dOk0ApZXJgOiMn5By5dK/gTcmD6RLyZLGPEyQCumQpztelDxEyeZUacZg8r6CL+WCHfqB23tFDzijCT7c2n2ArJAFtxcrcVQzi4EqKJ6RNUM9nAiJOvVkoJxLqiODYDRAJrtvBsknxFNpJyCU2M1VykPk3yDYNVohi3tQoHgVZp6OZCZDnjOkFDskq4nijcegh9PuvE5JKvQ50g+jd1bdeqZ1sdixjTOjCv6diLvkJ1nJdYR4410sawNoJ+zUjOShINpFgzFZyEmXM6HtqB8mpjAeduDMj/r/PZkuf+D7hl/haureDS0u+otzR8X3bZVuu5csq/2umBcedopEW8gHvF349HyjOhk4pHKvnj+bNyaIF8NLGTbM6MmQDumb/1qAmimZ+PBplWJmXh6LqSU9bIxFQDfVc/Ooi7qETTFD9xKpmamfyj8OaZZEJ1OAXGegvRs0KesdMf8OOk8lvar+fGgFrQByzkYE7awz8Q118VeWAeDX/e4QBmRp+xoE2+m7GMlofvvD/oaG9B4fka2H8Se6HrFqJLTH+wgH5KKNfdqN0JIDb2+w0mUSZMl/ar/dZgRnxZN8SfnmUeAnHhOHQfyB3h9xrb8lTF+40BiaA+G/tMsBstPAFki2qMQUS/5tcNCQScSy8XOQVfxRiEALnJ2ldCUowtWCR/bx1fBoCnf+puD8A7of3bP+v8X7X5l3kBy6EAAiwuW1oFsPzgUpZe2gHEfVPfVyJbQ+Pfi3eaa0BJkhD7gF+HchvzHPsXu7Bjn49F5zN8RXCq42hEmtMpgjdPSik1+KfGC9Rhhjki5BGFOh7P2jY+iolml5qNctvK6etiatjdxQ1O45ZT/UftEOE3bqvtRDIu1wZeAkdbRF7T2U5EBo1LadSY/qpN8LQwvoPx7dm69oGa7qbVQwQGv8Rx1KnFcVKDBsR6FEuP0M8z0krZRXo2dGVIpgrQZACxNubaIRKbn2lHsmgpZjlM0jyXZTtIjE+SYmfuBjMu9EnBAg0J7SSxU5jouEQW2Q64r7UQXTsi1rKzExqu1A+X6jlOatkw28nEF02OfXiKPKIS2pJjVzEZbbh3ISyxJ723GZchu0mFSaVLr173282DSdX1rOok5z8RfYdVfiCXH0hnhKAmyqsd8skpPf7wplGBVT7cT4/7Y57eJEJrEuNezyksAufPrcCIunfeVFXdJ2HqpdFkoZ1KqYlMukBm/Ja/KOWwUGBVA2qzUUANCRoyyqbbUiKXJc7FH9nO5zSHfEcye4+oFvHDWEbe54KHMapyEvmBaIey6DQ17eZMJ5Ccps8yPphAoIU0opigjziaqITS2h0IHAZOJg0ztFwK+wTcwrzSCwDrvIzxyBPmHfMMp0JDJH6JwCfgTFgBHAL+5BPz43dD42VNaMz8iDuhIZIjIvBGKEtHgB+QcNKFyZXG6iChNoVd1bfMfWhI0wE4A1wvQzAyX4bCzEmN3qWmgWUYfPyXYZ1S1F1muqjkufMQgMCRPQfeuAniJ9At5KbEXeAZLuxWVeHGxnWjrMG7WLQ1mFfOaLe47BD4atnWdUN2Z13y3lDgbc+HC6sgr7+IwIsjc7PHX98u5CYJ6wVv6hZ2bQUWvb15EHej0/q5rrPikXjbcGDnOhsJmr8hO7LRvBu4V/sNalTIU6RBh6Jrts38i4GTzfts3QMAAAA=", 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", "headers": [["content-type", "font/woff2"]], "ok": true, "status": 200, "status_text": ""}, "https://localhost:6006/index.js": {"data": "var CLOSURE_NO_DEPS = true;
// Copyright 2006 The Closure Library Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//      http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS-IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

/**
 * @fileoverview Bootstrap for the Google JS Library (Closure).
 *
 * In uncompiled mode base.js will attempt to load Closure's deps file, unless
 * the global <code>CLOSURE_NO_DEPS</code> is set to true.  This allows projects
 * to include their own deps file(s) from different locations.
 *
 * Avoid including base.js more than once. This is strictly discouraged and not
 * supported. goog.require(...) won't work properly in that case.
 *
 * @provideGoog
 */


/**
 * @define {boolean} Overridden to true by the compiler.
 */
var COMPILED = false;


/**
 * Base namespace for the Closure library.  Checks to see goog is already
 * defined in the current scope before assigning to prevent clobbering if
 * base.js is loaded more than once.
 *
 * @const
 */
var goog = goog || {};

/**
 * Reference to the global object.
 * https://www.ecma-international.org/ecma-262/9.0/index.html#sec-global-object
 *
 * More info on this implementation here:
 * https://docs.google.com/document/d/1NAeW4Wk7I7FV0Y2tcUFvQdGMc89k2vdgSXInw8_nvCI/edit
 *
 * @const
 * @suppress {undefinedVars} self won't be referenced unless `this` is falsy.
 * @type {!Global}
 */
goog.global =
    // Check `this` first for backwards compatibility.
    // Valid unless running as an ES module or in a function wrapper called
    //   without setting `this` properly.
    // Note that base.js can't usefully be imported as an ES module, but it may
    // be compiled into bundles that are loadable as ES modules.
    this ||
    // https://developer.mozilla.org/en-US/docs/Web/API/Window/self
    // For in-page browser environments and workers.
    self;


/**
 * A hook for overriding the define values in uncompiled mode.
 *
 * In uncompiled mode, `CLOSURE_UNCOMPILED_DEFINES` may be defined before
 * loading base.js.  If a key is defined in `CLOSURE_UNCOMPILED_DEFINES`,
 * `goog.define` will use the value instead of the default value.  This
 * allows flags to be overwritten without compilation (this is normally
 * accomplished with the compiler's "define" flag).
 *
 * Example:
 * <pre>
 *   var CLOSURE_UNCOMPILED_DEFINES = {'goog.DEBUG': false};
 * </pre>
 *
 * @type {Object<string, (string|number|boolean)>|undefined}
 */
goog.global.CLOSURE_UNCOMPILED_DEFINES;


/**
 * A hook for overriding the define values in uncompiled or compiled mode,
 * like CLOSURE_UNCOMPILED_DEFINES but effective in compiled code.  In
 * uncompiled code CLOSURE_UNCOMPILED_DEFINES takes precedence.
 *
 * Also unlike CLOSURE_UNCOMPILED_DEFINES the values must be number, boolean or
 * string literals or the compiler will emit an error.
 *
 * While any @define value may be set, only those set with goog.define will be
 * effective for uncompiled code.
 *
 * Example:
 * <pre>
 *   var CLOSURE_DEFINES = {'goog.DEBUG': false} ;
 * </pre>
 *
 * @type {Object<string, (string|number|boolean)>|undefined}
 */
goog.global.CLOSURE_DEFINES;


/**
 * Returns true if the specified value is not undefined.
 *
 * @param {?} val Variable to test.
 * @return {boolean} Whether variable is defined.
 * @deprecated Use `val !== undefined` instead.
 */
goog.isDef = function(val) {
  // void 0 always evaluates to undefined and hence we do not need to depend on
  // the definition of the global variable named 'undefined'.
  return val !== void 0;
};

/**
 * Returns true if the specified value is a string.
 * @param {?} val Variable to test.
 * @return {boolean} Whether variable is a string.
 * @deprecated Use `typeof val === 'string'` instead.
 */
goog.isString = function(val) {
  return typeof val == 'string';
};


/**
 * Returns true if the specified value is a boolean.
 * @param {?} val Variable to test.
 * @return {boolean} Whether variable is boolean.
 * @deprecated Use `typeof val === 'boolean'` instead.
 */
goog.isBoolean = function(val) {
  return typeof val == 'boolean';
};


/**
 * Returns true if the specified value is a number.
 * @param {?} val Variable to test.
 * @return {boolean} Whether variable is a number.
 * @deprecated Use `typeof val === 'number'` instead.
 */
goog.isNumber = function(val) {
  return typeof val == 'number';
};


/**
 * Builds an object structure for the provided namespace path, ensuring that
 * names that already exist are not overwritten. For example:
 * "a.b.c" -> a = {};a.b={};a.b.c={};
 * Used by goog.provide and goog.exportSymbol.
 * @param {string} name name of the object that this file defines.
 * @param {*=} opt_object the object to expose at the end of the path.
 * @param {Object=} opt_objectToExportTo The object to add the path to; default
 *     is `goog.global`.
 * @private
 */
goog.exportPath_ = function(name, opt_object, opt_objectToExportTo) {
  var parts = name.split('.');
  var cur = opt_objectToExportTo || goog.global;

  // Internet Explorer exhibits strange behavior when throwing errors from
  // methods externed in this manner.  See the testExportSymbolExceptions in
  // base_test.html for an example.
  if (!(parts[0] in cur) && typeof cur.execScript != 'undefined') {
    cur.execScript('var ' + parts[0]);
  }

  for (var part; parts.length && (part = parts.shift());) {
    if (!parts.length && opt_object !== undefined) {
      // last part and we have an object; use it
      cur[part] = opt_object;
    } else if (cur[part] && cur[part] !== Object.prototype[part]) {
      cur = cur[part];
    } else {
      cur = cur[part] = {};
    }
  }
};


/**
 * Defines a named value. In uncompiled mode, the value is retrieved from
 * CLOSURE_DEFINES or CLOSURE_UNCOMPILED_DEFINES if the object is defined and
 * has the property specified, and otherwise used the defined defaultValue.
 * When compiled the default can be overridden using the compiler options or the
 * value set in the CLOSURE_DEFINES object. Returns the defined value so that it
 * can be used safely in modules. Note that the value type MUST be either
 * boolean, number, or string.
 *
 * @param {string} name The distinguished name to provide.
 * @param {T} defaultValue
 * @return {T} The defined value.
 * @template T
 */
goog.define = function(name, defaultValue) {
  var value = defaultValue;
  if (!COMPILED) {
    var uncompiledDefines = goog.global.CLOSURE_UNCOMPILED_DEFINES;
    var defines = goog.global.CLOSURE_DEFINES;
    if (uncompiledDefines &&
        // Anti DOM-clobbering runtime check (b/37736576).
        /** @type {?} */ (uncompiledDefines).nodeType === undefined &&
        Object.prototype.hasOwnProperty.call(uncompiledDefines, name)) {
      value = uncompiledDefines[name];
    } else if (
        defines &&
        // Anti DOM-clobbering runtime check (b/37736576).
        /** @type {?} */ (defines).nodeType === undefined &&
        Object.prototype.hasOwnProperty.call(defines, name)) {
      value = defines[name];
    }
  }
  return value;
};


/**
 * @define {number} Integer year indicating the set of browser features that are
 * guaranteed to be present.  This is defined to include exactly features that
 * work correctly on all "modern" browsers that are stable on January 1 of the
 * specified year.  For example,
 * ```js
 * if (goog.FEATURESET_YEAR >= 2019) {
 *   // use APIs known to be available on all major stable browsers Jan 1, 2019
 * } else {
 *   // polyfill for older browsers
 * }
 * ```
 * This is intended to be the primary define for removing
 * unnecessary browser compatibility code (such as ponyfills and workarounds),
 * and should inform the default value for most other defines:
 * ```js
 * const ASSUME_NATIVE_PROMISE =
 *     goog.define('ASSUME_NATIVE_PROMISE', goog.FEATURESET_YEAR >= 2016);
 * ```
 *
 * The default assumption is that IE9 is the lowest supported browser, which was
 * first available Jan 1, 2012.
 *
 * TODO(user): Reference more thorough documentation when it's available.
 */
goog.FEATURESET_YEAR = goog.define('goog.FEATURESET_YEAR', 2012);


/**
 * @define {boolean} DEBUG is provided as a convenience so that debugging code
 * that should not be included in a production. It can be easily stripped
 * by specifying --define goog.DEBUG=false to the Closure Compiler aka
 * JSCompiler. For example, most toString() methods should be declared inside an
 * "if (goog.DEBUG)" conditional because they are generally used for debugging
 * purposes and it is difficult for the JSCompiler to statically determine
 * whether they are used.
 */
goog.DEBUG = goog.define('goog.DEBUG', true);


/**
 * @define {string} LOCALE defines the locale being used for compilation. It is
 * used to select locale specific data to be compiled in js binary. BUILD rule
 * can specify this value by "--define goog.LOCALE=<locale_name>" as a compiler
 * option.
 *
 * Take into account that the locale code format is important. You should use
 * the canonical Unicode format with hyphen as a delimiter. Language must be
 * lowercase, Language Script - Capitalized, Region - UPPERCASE.
 * There are few examples: pt-BR, en, en-US, sr-Latin-BO, zh-Hans-CN.
 *
 * See more info about locale codes here:
 * http://www.unicode.org/reports/tr35/#Unicode_Language_and_Locale_Identifiers
 *
 * For language codes you should use values defined by ISO 693-1. See it here
 * http://www.w3.org/WAI/ER/IG/ert/iso639.htm. There is only one exception from
 * this rule: the Hebrew language. For legacy reasons the old code (iw) should
 * be used instead of the new code (he).
 *
 */
goog.LOCALE = goog.define('goog.LOCALE', 'en');  // default to en


/**
 * @define {boolean} Whether this code is running on trusted sites.
 *
 * On untrusted sites, several native functions can be defined or overridden by
 * external libraries like Prototype, Datejs, and JQuery and setting this flag
 * to false forces closure to use its own implementations when possible.
 *
 * If your JavaScript can be loaded by a third party site and you are wary about
 * relying on non-standard implementations, specify
 * "--define goog.TRUSTED_SITE=false" to the compiler.
 */
goog.TRUSTED_SITE = goog.define('goog.TRUSTED_SITE', true);


/**
 * @define {boolean} Whether a project is expected to be running in strict mode.
 *
 * This define can be used to trigger alternate implementations compatible with
 * running in EcmaScript Strict mode or warn about unavailable functionality.
 * @see https://goo.gl/PudQ4y
 *
 */
goog.STRICT_MODE_COMPATIBLE = goog.define('goog.STRICT_MODE_COMPATIBLE', false);


/**
 * @define {boolean} Whether code that calls {@link goog.setTestOnly} should
 *     be disallowed in the compilation unit.
 */
goog.DISALLOW_TEST_ONLY_CODE =
    goog.define('goog.DISALLOW_TEST_ONLY_CODE', COMPILED && !goog.DEBUG);


/**
 * @define {boolean} Whether to use a Chrome app CSP-compliant method for
 *     loading scripts via goog.require. @see appendScriptSrcNode_.
 */
goog.ENABLE_CHROME_APP_SAFE_SCRIPT_LOADING =
    goog.define('goog.ENABLE_CHROME_APP_SAFE_SCRIPT_LOADING', false);


/**
 * Defines a namespace in Closure.
 *
 * A namespace may only be defined once in a codebase. It may be defined using
 * goog.provide() or goog.module().
 *
 * The presence of one or more goog.provide() calls in a file indicates
 * that the file defines the given objects/namespaces.
 * Provided symbols must not be null or undefined.
 *
 * In addition, goog.provide() creates the object stubs for a namespace
 * (for example, goog.provide("goog.foo.bar") will create the object
 * goog.foo.bar if it does not already exist).
 *
 * Build tools also scan for provide/require/module statements
 * to discern dependencies, build dependency files (see deps.js), etc.
 *
 * @see goog.require
 * @see goog.module
 * @param {string} name Namespace provided by this file in the form
 *     "goog.package.part".
 */
goog.provide = function(name) {
  if (goog.isInModuleLoader_()) {
    throw new Error('goog.provide cannot be used within a module.');
  }
  if (!COMPILED) {
    // Ensure that the same namespace isn't provided twice.
    // A goog.module/goog.provide maps a goog.require to a specific file
    if (goog.isProvided_(name)) {
      throw new Error('Namespace "' + name + '" already declared.');
    }
  }

  goog.constructNamespace_(name);
};


/**
 * @param {string} name Namespace provided by this file in the form
 *     "goog.package.part".
 * @param {Object=} opt_obj The object to embed in the namespace.
 * @private
 */
goog.constructNamespace_ = function(name, opt_obj) {
  if (!COMPILED) {
    delete goog.implicitNamespaces_[name];

    var namespace = name;
    while ((namespace = namespace.substring(0, namespace.lastIndexOf('.')))) {
      if (goog.getObjectByName(namespace)) {
        break;
      }
      goog.implicitNamespaces_[namespace] = true;
    }
  }

  goog.exportPath_(name, opt_obj);
};


/**
 * Returns CSP nonce, if set for any script tag.
 * @param {?Window=} opt_window The window context used to retrieve the nonce.
 *     Defaults to global context.
 * @return {string} CSP nonce or empty string if no nonce is present.
 */
goog.getScriptNonce = function(opt_window) {
  if (opt_window && opt_window != goog.global) {
    return goog.getScriptNonce_(opt_window.document);
  }
  if (goog.cspNonce_ === null) {
    goog.cspNonce_ = goog.getScriptNonce_(goog.global.document);
  }
  return goog.cspNonce_;
};


/**
 * According to the CSP3 spec a nonce must be a valid base64 string.
 * @see https://www.w3.org/TR/CSP3/#grammardef-base64-value
 * @private @const
 */
goog.NONCE_PATTERN_ = /^[\w+/_-]+[=]{0,2}$/;


/**
 * @private {?string}
 */
goog.cspNonce_ = null;


/**
 * Returns CSP nonce, if set for any script tag.
 * @param {!Document} doc
 * @return {string} CSP nonce or empty string if no nonce is present.
 * @private
 */
goog.getScriptNonce_ = function(doc) {
  var script = doc.querySelector && doc.querySelector('script[nonce]');
  if (script) {
    // Try to get the nonce from the IDL property first, because browsers that
    // implement additional nonce protection features (currently only Chrome) to
    // prevent nonce stealing via CSS do not expose the nonce via attributes.
    // See https://github.com/whatwg/html/issues/2369
    var nonce = script['nonce'] || script.getAttribute('nonce');
    if (nonce && goog.NONCE_PATTERN_.test(nonce)) {
      return nonce;
    }
  }
  return '';
};


/**
 * Module identifier validation regexp.
 * Note: This is a conservative check, it is very possible to be more lenient,
 *   the primary exclusion here is "/" and "\" and a leading ".", these
 *   restrictions are intended to leave the door open for using goog.require
 *   with relative file paths rather than module identifiers.
 * @private
 */
goog.VALID_MODULE_RE_ = /^[a-zA-Z_$][a-zA-Z0-9._$]*$/;


/**
 * Defines a module in Closure.
 *
 * Marks that this file must be loaded as a module and claims the namespace.
 *
 * A namespace may only be defined once in a codebase. It may be defined using
 * goog.provide() or goog.module().
 *
 * goog.module() has three requirements:
 * - goog.module may not be used in the same file as goog.provide.
 * - goog.module must be the first statement in the file.
 * - only one goog.module is allowed per file.
 *
 * When a goog.module annotated file is loaded, it is enclosed in
 * a strict function closure. This means that:
 * - any variables declared in a goog.module file are private to the file
 * (not global), though the compiler is expected to inline the module.
 * - The code must obey all the rules of "strict" JavaScript.
 * - the file will be marked as "use strict"
 *
 * NOTE: unlike goog.provide, goog.module does not declare any symbols by
 * itself. If declared symbols are desired, use
 * goog.module.declareLegacyNamespace().
 *
 *
 * See the public goog.module proposal: http://goo.gl/Va1hin
 *
 * @param {string} name Namespace provided by this file in the form
 *     "goog.package.part", is expected but not required.
 * @return {void}
 */
goog.module = function(name) {
  if (typeof name !== 'string' || !name ||
      name.search(goog.VALID_MODULE_RE_) == -1) {
    throw new Error('Invalid module identifier');
  }
  if (!goog.isInGoogModuleLoader_()) {
    throw new Error(
        'Module ' + name + ' has been loaded incorrectly. Note, ' +
        'modules cannot be loaded as normal scripts. They require some kind of ' +
        'pre-processing step. You\'re likely trying to load a module via a ' +
        'script tag or as a part of a concatenated bundle without rewriting the ' +
        'module. For more info see: ' +
        'https://github.com/google/closure-library/wiki/goog.module:-an-ES6-module-like-alternative-to-goog.provide.');
  }
  if (goog.moduleLoaderState_.moduleName) {
    throw new Error('goog.module may only be called once per module.');
  }

  // Store the module name for the loader.
  goog.moduleLoaderState_.moduleName = name;
  if (!COMPILED) {
    // Ensure that the same namespace isn't provided twice.
    // A goog.module/goog.provide maps a goog.require to a specific file
    if (goog.isProvided_(name)) {
      throw new Error('Namespace "' + name + '" already declared.');
    }
    delete goog.implicitNamespaces_[name];
  }
};


/**
 * @param {string} name The module identifier.
 * @return {?} The module exports for an already loaded module or null.
 *
 * Note: This is not an alternative to goog.require, it does not
 * indicate a hard dependency, instead it is used to indicate
 * an optional dependency or to access the exports of a module
 * that has already been loaded.
 * @suppress {missingProvide}
 */
goog.module.get = function(name) {
  return goog.module.getInternal_(name);
};


/**
 * @param {string} name The module identifier.
 * @return {?} The module exports for an already loaded module or null.
 * @private
 */
goog.module.getInternal_ = function(name) {
  if (!COMPILED) {
    if (name in goog.loadedModules_) {
      return goog.loadedModules_[name].exports;
    } else if (!goog.implicitNamespaces_[name]) {
      var ns = goog.getObjectByName(name);
      return ns != null ? ns : null;
    }
  }
  return null;
};


/**
 * Types of modules the debug loader can load.
 * @enum {string}
 */
goog.ModuleType = {
  ES6: 'es6',
  GOOG: 'goog'
};


/**
 * @private {?{
 *   moduleName: (string|undefined),
 *   declareLegacyNamespace:boolean,
 *   type: ?goog.ModuleType
 * }}
 */
goog.moduleLoaderState_ = null;


/**
 * @private
 * @return {boolean} Whether a goog.module or an es6 module is currently being
 *     initialized.
 */
goog.isInModuleLoader_ = function() {
  return goog.isInGoogModuleLoader_() || goog.isInEs6ModuleLoader_();
};


/**
 * @private
 * @return {boolean} Whether a goog.module is currently being initialized.
 */
goog.isInGoogModuleLoader_ = function() {
  return !!goog.moduleLoaderState_ &&
      goog.moduleLoaderState_.type == goog.ModuleType.GOOG;
};


/**
 * @private
 * @return {boolean} Whether an es6 module is currently being initialized.
 */
goog.isInEs6ModuleLoader_ = function() {
  var inLoader = !!goog.moduleLoaderState_ &&
      goog.moduleLoaderState_.type == goog.ModuleType.ES6;

  if (inLoader) {
    return true;
  }

  var jscomp = goog.global['$jscomp'];

  if (jscomp) {
    // jscomp may not have getCurrentModulePath if this is a compiled bundle
    // that has some of the runtime, but not all of it. This can happen if
    // optimizations are turned on so the unused runtime is removed but renaming
    // and Closure pass are off (so $jscomp is still named $jscomp and the
    // goog.provide/require calls still exist).
    if (typeof jscomp.getCurrentModulePath != 'function') {
      return false;
    }

    // Bundled ES6 module.
    return !!jscomp.getCurrentModulePath();
  }

  return false;
};


/**
 * Provide the module's exports as a globally accessible object under the
 * module's declared name.  This is intended to ease migration to goog.module
 * for files that have existing usages.
 * @suppress {missingProvide}
 */
goog.module.declareLegacyNamespace = function() {
  if (!COMPILED && !goog.isInGoogModuleLoader_()) {
    throw new Error(
        'goog.module.declareLegacyNamespace must be called from ' +
        'within a goog.module');
  }
  if (!COMPILED && !goog.moduleLoaderState_.moduleName) {
    throw new Error(
        'goog.module must be called prior to ' +
        'goog.module.declareLegacyNamespace.');
  }
  goog.moduleLoaderState_.declareLegacyNamespace = true;
};


/**
 * Associates an ES6 module with a Closure module ID so that is available via
 * goog.require. The associated ID  acts like a goog.module ID - it does not
 * create any global names, it is merely available via goog.require /
 * goog.module.get / goog.forwardDeclare / goog.requireType. goog.require and
 * goog.module.get will return the entire module as if it was import *'d. This
 * allows Closure files to reference ES6 modules for the sake of migration.
 *
 * @param {string} namespace
 * @suppress {missingProvide}
 */
goog.declareModuleId = function(namespace) {
  if (!COMPILED) {
    if (!goog.isInEs6ModuleLoader_()) {
      throw new Error(
          'goog.declareModuleId may only be called from ' +
          'within an ES6 module');
    }
    if (goog.moduleLoaderState_ && goog.moduleLoaderState_.moduleName) {
      throw new Error(
          'goog.declareModuleId may only be called once per module.');
    }
    if (namespace in goog.loadedModules_) {
      throw new Error(
          'Module with namespace "' + namespace + '" already exists.');
    }
  }
  if (goog.moduleLoaderState_) {
    // Not bundled - debug loading.
    goog.moduleLoaderState_.moduleName = namespace;
  } else {
    // Bundled - not debug loading, no module loader state.
    var jscomp = goog.global['$jscomp'];
    if (!jscomp || typeof jscomp.getCurrentModulePath != 'function') {
      throw new Error(
          'Module with namespace "' + namespace +
          '" has been loaded incorrectly.');
    }
    var exports = jscomp.require(jscomp.getCurrentModulePath());
    goog.loadedModules_[namespace] = {
      exports: exports,
      type: goog.ModuleType.ES6,
      moduleId: namespace
    };
  }
};


/**
 * Marks that the current file should only be used for testing, and never for
 * live code in production.
 *
 * In the case of unit tests, the message may optionally be an exact namespace
 * for the test (e.g. 'goog.stringTest'). The linter will then ignore the extra
 * provide (if not explicitly defined in the code).
 *
 * @param {string=} opt_message Optional message to add to the error that's
 *     raised when used in production code.
 */
goog.setTestOnly = function(opt_message) {
  if (goog.DISALLOW_TEST_ONLY_CODE) {
    opt_message = opt_message || '';
    throw new Error(
        'Importing test-only code into non-debug environment' +
        (opt_message ? ': ' + opt_message : '.'));
  }
};


/**
 * Forward declares a symbol. This is an indication to the compiler that the
 * symbol may be used in the source yet is not required and may not be provided
 * in compilation.
 *
 * The most common usage of forward declaration is code that takes a type as a
 * function parameter but does not need to require it. By forward declaring
 * instead of requiring, no hard dependency is made, and (if not required
 * elsewhere) the namespace may never be required and thus, not be pulled
 * into the JavaScript binary. If it is required elsewhere, it will be type
 * checked as normal.
 *
 * Before using goog.forwardDeclare, please read the documentation at
 * https://github.com/google/closure-compiler/wiki/Bad-Type-Annotation to
 * understand the options and tradeoffs when working with forward declarations.
 *
 * @param {string} name The namespace to forward declare in the form of
 *     "goog.package.part".
 */
goog.forwardDeclare = function(name) {};


/**
 * Forward declare type information. Used to assign types to goog.global
 * referenced object that would otherwise result in unknown type references
 * and thus block property disambiguation.
 */
goog.forwardDeclare('Document');
goog.forwardDeclare('HTMLScriptElement');
goog.forwardDeclare('XMLHttpRequest');


if (!COMPILED) {
  /**
   * Check if the given name has been goog.provided. This will return false for
   * names that are available only as implicit namespaces.
   * @param {string} name name of the object to look for.
   * @return {boolean} Whether the name has been provided.
   * @private
   */
  goog.isProvided_ = function(name) {
    return (name in goog.loadedModules_) ||
        (!goog.implicitNamespaces_[name] && goog.getObjectByName(name) != null);
  };

  /**
   * Namespaces implicitly defined by goog.provide. For example,
   * goog.provide('goog.events.Event') implicitly declares that 'goog' and
   * 'goog.events' must be namespaces.
   *
   * @type {!Object<string, (boolean|undefined)>}
   * @private
   */
  goog.implicitNamespaces_ = {'goog.module': true};

  // NOTE: We add goog.module as an implicit namespace as goog.module is defined
  // here and because the existing module package has not been moved yet out of
  // the goog.module namespace. This satisifies both the debug loader and
  // ahead-of-time dependency management.
}


/**
 * Returns an object based on its fully qualified external name.  The object
 * is not found if null or undefined.  If you are using a compilation pass that
 * renames property names beware that using this function will not find renamed
 * properties.
 *
 * @param {string} name The fully qualified name.
 * @param {Object=} opt_obj The object within which to look; default is
 *     |goog.global|.
 * @return {?} The value (object or primitive) or, if not found, null.
 */
goog.getObjectByName = function(name, opt_obj) {
  var parts = name.split('.');
  var cur = opt_obj || goog.global;
  for (var i = 0; i < parts.length; i++) {
    cur = cur[parts[i]];
    if (cur == null) {
      return null;
    }
  }
  return cur;
};


/**
 * Globalizes a whole namespace, such as goog or goog.lang.
 *
 * @param {!Object} obj The namespace to globalize.
 * @param {Object=} opt_global The object to add the properties to.
 * @deprecated Properties may be explicitly exported to the global scope, but
 *     this should no longer be done in bulk.
 */
goog.globalize = function(obj, opt_global) {
  var global = opt_global || goog.global;
  for (var x in obj) {
    global[x] = obj[x];
  }
};


/**
 * Adds a dependency from a file to the files it requires.
 * @param {string} relPath The path to the js file.
 * @param {!Array<string>} provides An array of strings with
 *     the names of the objects this file provides.
 * @param {!Array<string>} requires An array of strings with
 *     the names of the objects this file requires.
 * @param {boolean|!Object<string>=} opt_loadFlags Parameters indicating
 *     how the file must be loaded.  The boolean 'true' is equivalent
 *     to {'module': 'goog'} for backwards-compatibility.  Valid properties
 *     and values include {'module': 'goog'} and {'lang': 'es6'}.
 */
goog.addDependency = function(relPath, provides, requires, opt_loadFlags) {
  if (!COMPILED && goog.DEPENDENCIES_ENABLED) {
    goog.debugLoader_.addDependency(relPath, provides, requires, opt_loadFlags);
  }
};




// NOTE(nnaze): The debug DOM loader was included in base.js as an original way
// to do "debug-mode" development.  The dependency system can sometimes be
// confusing, as can the debug DOM loader's asynchronous nature.
//
// With the DOM loader, a call to goog.require() is not blocking -- the script
// will not load until some point after the current script.  If a namespace is
// needed at runtime, it needs to be defined in a previous script, or loaded via
// require() with its registered dependencies.
//
// User-defined namespaces may need their own deps file. For a reference on
// creating a deps file, see:
// Externally: https://developers.google.com/closure/library/docs/depswriter
//
// Because of legacy clients, the DOM loader can't be easily removed from
// base.js.  Work was done to make it disableable or replaceable for
// different environments (DOM-less JavaScript interpreters like Rhino or V8,
// for example). See bootstrap/ for more information.


/**
 * @define {boolean} Whether to enable the debug loader.
 *
 * If enabled, a call to goog.require() will attempt to load the namespace by
 * appending a script tag to the DOM (if the namespace has been registered).
 *
 * If disabled, goog.require() will simply assert that the namespace has been
 * provided (and depend on the fact that some outside tool correctly ordered
 * the script).
 */
goog.ENABLE_DEBUG_LOADER = goog.define('goog.ENABLE_DEBUG_LOADER', true);


/**
 * @param {string} msg
 * @private
 */
goog.logToConsole_ = function(msg) {
  if (goog.global.console) {
    goog.global.console['error'](msg);
  }
};


/**
 * Implements a system for the dynamic resolution of dependencies that works in
 * parallel with the BUILD system.
 *
 * Note that all calls to goog.require will be stripped by the compiler.
 *
 * @see goog.provide
 * @param {string} namespace Namespace (as was given in goog.provide,
 *     goog.module, or goog.declareModuleId) in the form
 *     "goog.package.part".
 * @return {?} If called within a goog.module or ES6 module file, the associated
 *     namespace or module otherwise null.
 */
goog.require = function(namespace) {
  if (!COMPILED) {
    // Might need to lazy load on old IE.
    if (goog.ENABLE_DEBUG_LOADER) {
      goog.debugLoader_.requested(namespace);
    }

    // If the object already exists we do not need to do anything.
    if (goog.isProvided_(namespace)) {
      if (goog.isInModuleLoader_()) {
        return goog.module.getInternal_(namespace);
      }
    } else if (goog.ENABLE_DEBUG_LOADER) {
      var moduleLoaderState = goog.moduleLoaderState_;
      goog.moduleLoaderState_ = null;
      try {
        goog.debugLoader_.load_(namespace);
      } finally {
        goog.moduleLoaderState_ = moduleLoaderState;
      }
    }

    return null;
  }
};


/**
 * Requires a symbol for its type information. This is an indication to the
 * compiler that the symbol may appear in type annotations, yet it is not
 * referenced at runtime.
 *
 * When called within a goog.module or ES6 module file, the return value may be
 * assigned to or destructured into a variable, but it may not be otherwise used
 * in code outside of a type annotation.
 *
 * Note that all calls to goog.requireType will be stripped by the compiler.
 *
 * @param {string} namespace Namespace (as was given in goog.provide,
 *     goog.module, or goog.declareModuleId) in the form
 *     "goog.package.part".
 * @return {?}
 */
goog.requireType = function(namespace) {
  // Return an empty object so that single-level destructuring of the return
  // value doesn't crash at runtime when using the debug loader. Multi-level
  // destructuring isn't supported.
  return {};
};


/**
 * Path for included scripts.
 * @type {string}
 */
goog.basePath = '';


/**
 * A hook for overriding the base path.
 * @type {string|undefined}
 */
goog.global.CLOSURE_BASE_PATH;


/**
 * Whether to attempt to load Closure's deps file. By default, when uncompiled,
 * deps files will attempt to be loaded.
 * @type {boolean|undefined}
 */
goog.global.CLOSURE_NO_DEPS;


/**
 * A function to import a single script. This is meant to be overridden when
 * Closure is being run in non-HTML contexts, such as web workers. It's defined
 * in the global scope so that it can be set before base.js is loaded, which
 * allows deps.js to be imported properly.
 *
 * The first parameter the script source, which is a relative URI. The second,
 * optional parameter is the script contents, in the event the script needed
 * transformation. It should return true if the script was imported, false
 * otherwise.
 * @type {(function(string, string=): boolean)|undefined}
 */
goog.global.CLOSURE_IMPORT_SCRIPT;


/**
 * Null function used for default values of callbacks, etc.
 * @return {void} Nothing.
 */
goog.nullFunction = function() {};


/**
 * When defining a class Foo with an abstract method bar(), you can do:
 * Foo.prototype.bar = goog.abstractMethod
 *
 * Now if a subclass of Foo fails to override bar(), an error will be thrown
 * when bar() is invoked.
 *
 * @type {!Function}
 * @throws {Error} when invoked to indicate the method should be overridden.
 * @deprecated Use "@abstract" annotation instead of goog.abstractMethod in new
 *     code. See
 *     https://github.com/google/closure-compiler/wiki/@abstract-classes-and-methods
 */
goog.abstractMethod = function() {
  throw new Error('unimplemented abstract method');
};


/**
 * Adds a `getInstance` static method that always returns the same
 * instance object.
 * @param {!Function} ctor The constructor for the class to add the static
 *     method to.
 * @suppress {missingProperties} 'instance_' isn't a property on 'Function'
 *     but we don't have a better type to use here.
 */
goog.addSingletonGetter = function(ctor) {
  // instance_ is immediately set to prevent issues with sealed constructors
  // such as are encountered when a constructor is returned as the export object
  // of a goog.module in unoptimized code.
  // Delcare type to avoid conformance violations that ctor.instance_ is unknown
  /** @type {undefined|!Object} @suppress {underscore} */
  ctor.instance_ = undefined;
  ctor.getInstance = function() {
    if (ctor.instance_) {
      return ctor.instance_;
    }
    if (goog.DEBUG) {
      // NOTE: JSCompiler can't optimize away Array#push.
      goog.instantiatedSingletons_[goog.instantiatedSingletons_.length] = ctor;
    }
    // Cast to avoid conformance violations that ctor.instance_ is unknown
    return /** @type {!Object|undefined} */ (ctor.instance_) = new ctor;
  };
};


/**
 * All singleton classes that have been instantiated, for testing. Don't read
 * it directly, use the `goog.testing.singleton` module. The compiler
 * removes this variable if unused.
 * @type {!Array<!Function>}
 * @private
 */
goog.instantiatedSingletons_ = [];


/**
 * @define {boolean} Whether to load goog.modules using `eval` when using
 * the debug loader.  This provides a better debugging experience as the
 * source is unmodified and can be edited using Chrome Workspaces or similar.
 * However in some environments the use of `eval` is banned
 * so we provide an alternative.
 */
goog.LOAD_MODULE_USING_EVAL = goog.define('goog.LOAD_MODULE_USING_EVAL', true);


/**
 * @define {boolean} Whether the exports of goog.modules should be sealed when
 * possible.
 */
goog.SEAL_MODULE_EXPORTS = goog.define('goog.SEAL_MODULE_EXPORTS', goog.DEBUG);


/**
 * The registry of initialized modules:
 * The module identifier or path to module exports map.
 * @private @const {!Object<string, {exports:?,type:string,moduleId:string}>}
 */
goog.loadedModules_ = {};


/**
 * True if the debug loader enabled and used.
 * @const {boolean}
 */
goog.DEPENDENCIES_ENABLED = !COMPILED && goog.ENABLE_DEBUG_LOADER;


/**
 * @define {string} How to decide whether to transpile.  Valid values
 * are 'always', 'never', and 'detect'.  The default ('detect') is to
 * use feature detection to determine which language levels need
 * transpilation.
 */
// NOTE(sdh): we could expand this to accept a language level to bypass
// detection: e.g. goog.TRANSPILE == 'es5' would transpile ES6 files but
// would leave ES3 and ES5 files alone.
goog.TRANSPILE = goog.define('goog.TRANSPILE', 'detect');

/**
 * @define {boolean} If true assume that ES modules have already been
 * transpiled by the jscompiler (in the same way that transpile.js would
 * transpile them - to jscomp modules). Useful only for servers that wish to use
 * the debug loader and transpile server side. Thus this is only respected if
 * goog.TRANSPILE is "never".
 */
goog.ASSUME_ES_MODULES_TRANSPILED =
    goog.define('goog.ASSUME_ES_MODULES_TRANSPILED', false);


/**
 * @define {string} If a file needs to be transpiled what the output language
 * should be. By default this is the highest language level this file detects
 * the current environment supports. Generally this flag should not be set, but
 * it could be useful to override. Example: If the current environment supports
 * ES6 then by default ES7+ files will be transpiled to ES6, unless this is
 * overridden.
 *
 * Valid values include: es3, es5, es6, es7, and es8. Anything not recognized
 * is treated as es3.
 *
 * Note that setting this value does not force transpilation. Just if
 * transpilation occurs this will be the output. So this is most useful when
 * goog.TRANSPILE is set to 'always' and then forcing the language level to be
 * something lower than what the environment detects.
 */
goog.TRANSPILE_TO_LANGUAGE = goog.define('goog.TRANSPILE_TO_LANGUAGE', '');


/**
 * @define {string} Path to the transpiler.  Executing the script at this
 * path (relative to base.js) should define a function $jscomp.transpile.
 */
goog.TRANSPILER = goog.define('goog.TRANSPILER', 'transpile.js');


/**
 * @package {?boolean}
 * Visible for testing.
 */
goog.hasBadLetScoping = null;


/**
 * @return {boolean}
 * @package Visible for testing.
 */
goog.useSafari10Workaround = function() {
  if (goog.hasBadLetScoping == null) {
    var hasBadLetScoping;
    try {
      hasBadLetScoping = !eval(
          '"use strict";' +
          'let x = 1; function f() { return typeof x; };' +
          'f() == "number";');
    } catch (e) {
      // Assume that ES6 syntax isn't supported.
      hasBadLetScoping = false;
    }
    goog.hasBadLetScoping = hasBadLetScoping;
  }
  return goog.hasBadLetScoping;
};


/**
 * @param {string} moduleDef
 * @return {string}
 * @package Visible for testing.
 */
goog.workaroundSafari10EvalBug = function(moduleDef) {
  return '(function(){' + moduleDef +
      '\n' +  // Terminate any trailing single line comment.
      ';' +   // Terminate any trailing expression.
      '})();\n';
};


/**
 * @param {function(?):?|string} moduleDef The module definition.
 */
goog.loadModule = function(moduleDef) {
  // NOTE: we allow function definitions to be either in the from
  // of a string to eval (which keeps the original source intact) or
  // in a eval forbidden environment (CSP) we allow a function definition
  // which in its body must call `goog.module`, and return the exports
  // of the module.
  var previousState = goog.moduleLoaderState_;
  try {
    goog.moduleLoaderState_ = {
      moduleName: '',
      declareLegacyNamespace: false,
      type: goog.ModuleType.GOOG
    };
    var exports;
    if (goog.isFunction(moduleDef)) {
      exports = moduleDef.call(undefined, {});
    } else if (typeof moduleDef === 'string') {
      if (goog.useSafari10Workaround()) {
        moduleDef = goog.workaroundSafari10EvalBug(moduleDef);
      }

      exports = goog.loadModuleFromSource_.call(undefined, moduleDef);
    } else {
      throw new Error('Invalid module definition');
    }

    var moduleName = goog.moduleLoaderState_.moduleName;
    if (typeof moduleName === 'string' && moduleName) {
      // Don't seal legacy namespaces as they may be used as a parent of
      // another namespace
      if (goog.moduleLoaderState_.declareLegacyNamespace) {
        goog.constructNamespace_(moduleName, exports);
      } else if (
          goog.SEAL_MODULE_EXPORTS && Object.seal &&
          typeof exports == 'object' && exports != null) {
        Object.seal(exports);
      }

      var data = {
        exports: exports,
        type: goog.ModuleType.GOOG,
        moduleId: goog.moduleLoaderState_.moduleName
      };
      goog.loadedModules_[moduleName] = data;
    } else {
      throw new Error('Invalid module name \"' + moduleName + '\"');
    }
  } finally {
    goog.moduleLoaderState_ = previousState;
  }
};


/**
 * @private @const
 */
goog.loadModuleFromSource_ = /** @type {function(string):?} */ (function() {
  // NOTE: we avoid declaring parameters or local variables here to avoid
  // masking globals or leaking values into the module definition.
  'use strict';
  var exports = {};
  eval(arguments[0]);
  return exports;
});


/**
 * Normalize a file path by removing redundant ".." and extraneous "." file
 * path components.
 * @param {string} path
 * @return {string}
 * @private
 */
goog.normalizePath_ = function(path) {
  var components = path.split('/');
  var i = 0;
  while (i < components.length) {
    if (components[i] == '.') {
      components.splice(i, 1);
    } else if (
        i && components[i] == '..' && components[i - 1] &&
        components[i - 1] != '..') {
      components.splice(--i, 2);
    } else {
      i++;
    }
  }
  return components.join('/');
};


/**
 * Provides a hook for loading a file when using Closure's goog.require() API
 * with goog.modules.  In particular this hook is provided to support Node.js.
 *
 * @type {(function(string):string)|undefined}
 */
goog.global.CLOSURE_LOAD_FILE_SYNC;


/**
 * Loads file by synchronous XHR. Should not be used in production environments.
 * @param {string} src Source URL.
 * @return {?string} File contents, or null if load failed.
 * @private
 */
goog.loadFileSync_ = function(src) {
  if (goog.global.CLOSURE_LOAD_FILE_SYNC) {
    return goog.global.CLOSURE_LOAD_FILE_SYNC(src);
  } else {
    try {
      /** @type {XMLHttpRequest} */
      var xhr = new goog.global['XMLHttpRequest']();
      xhr.open('get', src, false);
      xhr.send();
      // NOTE: Successful http: requests have a status of 200, but successful
      // file: requests may have a status of zero.  Any other status, or a
      // thrown exception (particularly in case of file: requests) indicates
      // some sort of error, which we treat as a missing or unavailable file.
      return xhr.status == 0 || xhr.status == 200 ? xhr.responseText : null;
    } catch (err) {
      // No need to rethrow or log, since errors should show up on their own.
      return null;
    }
  }
};


/**
 * Lazily retrieves the transpiler and applies it to the source.
 * @param {string} code JS code.
 * @param {string} path Path to the code.
 * @param {string} target Language level output.
 * @return {string} The transpiled code.
 * @private
 */
goog.transpile_ = function(code, path, target) {
  var jscomp = goog.global['$jscomp'];
  if (!jscomp) {
    goog.global['$jscomp'] = jscomp = {};
  }
  var transpile = jscomp.transpile;
  if (!transpile) {
    var transpilerPath = goog.basePath + goog.TRANSPILER;
    var transpilerCode = goog.loadFileSync_(transpilerPath);
    if (transpilerCode) {
      // This must be executed synchronously, since by the time we know we
      // need it, we're about to load and write the ES6 code synchronously,
      // so a normal script-tag load will be too slow. Wrapped in a function
      // so that code is eval'd in the global scope.
      (function() {
        (0, eval)(transpilerCode + '\n//# sourceURL=' + transpilerPath);
      }).call(goog.global);
      // Even though the transpiler is optional, if $gwtExport is found, it's
      // a sign the transpiler was loaded and the $jscomp.transpile *should*
      // be there.
      if (goog.global['$gwtExport'] && goog.global['$gwtExport']['$jscomp'] &&
          !goog.global['$gwtExport']['$jscomp']['transpile']) {
        throw new Error(
            'The transpiler did not properly export the "transpile" ' +
            'method. $gwtExport: ' + JSON.stringify(goog.global['$gwtExport']));
      }
      // transpile.js only exports a single $jscomp function, transpile. We
      // grab just that and add it to the existing definition of $jscomp which
      // contains the polyfills.
      goog.global['$jscomp'].transpile =
          goog.global['$gwtExport']['$jscomp']['transpile'];
      jscomp = goog.global['$jscomp'];
      transpile = jscomp.transpile;
    }
  }
  if (!transpile) {
    // The transpiler is an optional component.  If it's not available then
    // replace it with a pass-through function that simply logs.
    var suffix = ' requires transpilation but no transpiler was found.';
    transpile = jscomp.transpile = function(code, path) {
      // TODO(sdh): figure out some way to get this error to show up
      // in test results, noting that the failure may occur in many
      // different ways, including in loadModule() before the test
      // runner even comes up.
      goog.logToConsole_(path + suffix);
      return code;
    };
  }
  // Note: any transpilation errors/warnings will be logged to the console.
  return transpile(code, path, target);
};

//==============================================================================
// Language Enhancements
//==============================================================================


/**
 * This is a "fixed" version of the typeof operator.  It differs from the typeof
 * operator in such a way that null returns 'null' and arrays return 'array'.
 * @param {?} value The value to get the type of.
 * @return {string} The name of the type.
 */
goog.typeOf = function(value) {
  var s = typeof value;
  if (s == 'object') {
    if (value) {
      // Check these first, so we can avoid calling Object.prototype.toString if
      // possible.
      //
      // IE improperly marshals typeof across execution contexts, but a
      // cross-context object will still return false for "instanceof Object".
      if (value instanceof Array) {
        return 'array';
      } else if (value instanceof Object) {
        return s;
      }

      // HACK: In order to use an Object prototype method on the arbitrary
      //   value, the compiler requires the value be cast to type Object,
      //   even though the ECMA spec explicitly allows it.
      var className = Object.prototype.toString.call(
          /** @type {!Object} */ (value));
      // In Firefox 3.6, attempting to access iframe window objects' length
      // property throws an NS_ERROR_FAILURE, so we need to special-case it
      // here.
      if (className == '[object Window]') {
        return 'object';
      }

      // We cannot always use constructor == Array or instanceof Array because
      // different frames have different Array objects. In IE6, if the iframe
      // where the array was created is destroyed, the array loses its
      // prototype. Then dereferencing val.splice here throws an exception, so
      // we can't use goog.isFunction. Calling typeof directly returns 'unknown'
      // so that will work. In this case, this function will return false and
      // most array functions will still work because the array is still
      // array-like (supports length and []) even though it has lost its
      // prototype.
      // Mark Miller noticed that Object.prototype.toString
      // allows access to the unforgeable [[Class]] property.
      //  15.2.4.2 Object.prototype.toString ( )
      //  When the toString method is called, the following steps are taken:
      //      1. Get the [[Class]] property of this object.
      //      2. Compute a string value by concatenating the three strings
      //         "[object ", Result(1), and "]".
      //      3. Return Result(2).
      // and this behavior survives the destruction of the execution context.
      if ((className == '[object Array]' ||
           // In IE all non value types are wrapped as objects across window
           // boundaries (not iframe though) so we have to do object detection
           // for this edge case.
           typeof value.length == 'number' &&
               typeof value.splice != 'undefined' &&
               typeof value.propertyIsEnumerable != 'undefined' &&
               !value.propertyIsEnumerable('splice')

               )) {
        return 'array';
      }
      // HACK: There is still an array case that fails.
      //     function ArrayImpostor() {}
      //     ArrayImpostor.prototype = [];
      //     var impostor = new ArrayImpostor;
      // this can be fixed by getting rid of the fast path
      // (value instanceof Array) and solely relying on
      // (value && Object.prototype.toString.vall(value) === '[object Array]')
      // but that would require many more function calls and is not warranted
      // unless closure code is receiving objects from untrusted sources.

      // IE in cross-window calls does not correctly marshal the function type
      // (it appears just as an object) so we cannot use just typeof val ==
      // 'function'. However, if the object has a call property, it is a
      // function.
      if ((className == '[object Function]' ||
           typeof value.call != 'undefined' &&
               typeof value.propertyIsEnumerable != 'undefined' &&
               !value.propertyIsEnumerable('call'))) {
        return 'function';
      }

    } else {
      return 'null';
    }

  } else if (s == 'function' && typeof value.call == 'undefined') {
    // In Safari typeof nodeList returns 'function', and on Firefox typeof
    // behaves similarly for HTML{Applet,Embed,Object}, Elements and RegExps. We
    // would like to return object for those and we can detect an invalid
    // function by making sure that the function object has a call method.
    return 'object';
  }
  return s;
};


/**
 * Returns true if the specified value is null.
 * @param {?} val Variable to test.
 * @return {boolean} Whether variable is null.
 * @deprecated Use `val === null` instead.
 */
goog.isNull = function(val) {
  return val === null;
};


/**
 * Returns true if the specified value is defined and not null.
 * @param {?} val Variable to test.
 * @return {boolean} Whether variable is defined and not null.
 * @deprecated Use `val != null` instead.
 */
goog.isDefAndNotNull = function(val) {
  // Note that undefined == null.
  return val != null;
};


/**
 * Returns true if the specified value is an array.
 * @param {?} val Variable to test.
 * @return {boolean} Whether variable is an array.
 */
goog.isArray = function(val) {
  return goog.typeOf(val) == 'array';
};


/**
 * Returns true if the object looks like an array. To qualify as array like
 * the value needs to be either a NodeList or an object with a Number length
 * property. Note that for this function neither strings nor functions are
 * considered "array-like".
 *
 * @param {?} val Variable to test.
 * @return {boolean} Whether variable is an array.
 */
goog.isArrayLike = function(val) {
  var type = goog.typeOf(val);
  // We do not use goog.isObject here in order to exclude function values.
  return type == 'array' || type == 'object' && typeof val.length == 'number';
};


/**
 * Returns true if the object looks like a Date. To qualify as Date-like the
 * value needs to be an object and have a getFullYear() function.
 * @param {?} val Variable to test.
 * @return {boolean} Whether variable is a like a Date.
 */
goog.isDateLike = function(val) {
  return goog.isObject(val) && typeof val.getFullYear == 'function';
};


/**
 * Returns true if the specified value is a function.
 * @param {?} val Variable to test.
 * @return {boolean} Whether variable is a function.
 */
goog.isFunction = function(val) {
  return goog.typeOf(val) == 'function';
};


/**
 * Returns true if the specified value is an object.  This includes arrays and
 * functions.
 * @param {?} val Variable to test.
 * @return {boolean} Whether variable is an object.
 */
goog.isObject = function(val) {
  var type = typeof val;
  return type == 'object' && val != null || type == 'function';
  // return Object(val) === val also works, but is slower, especially if val is
  // not an object.
};


/**
 * Gets a unique ID for an object. This mutates the object so that further calls
 * with the same object as a parameter returns the same value. The unique ID is
 * guaranteed to be unique across the current session amongst objects that are
 * passed into `getUid`. There is no guarantee that the ID is unique or
 * consistent across sessions. It is unsafe to generate unique ID for function
 * prototypes.
 *
 * @param {Object} obj The object to get the unique ID for.
 * @return {number} The unique ID for the object.
 */
goog.getUid = function(obj) {
  // TODO(arv): Make the type stricter, do not accept null.

  // In Opera window.hasOwnProperty exists but always returns false so we avoid
  // using it. As a consequence the unique ID generated for BaseClass.prototype
  // and SubClass.prototype will be the same.
  // TODO(b/141512323): UUIDs are broken for ctors with class-side inheritance.
  return obj[goog.UID_PROPERTY_] ||
      (obj[goog.UID_PROPERTY_] = ++goog.uidCounter_);
};


/**
 * Whether the given object is already assigned a unique ID.
 *
 * This does not modify the object.
 *
 * @param {!Object} obj The object to check.
 * @return {boolean} Whether there is an assigned unique id for the object.
 */
goog.hasUid = function(obj) {
  return !!obj[goog.UID_PROPERTY_];
};


/**
 * Removes the unique ID from an object. This is useful if the object was
 * previously mutated using `goog.getUid` in which case the mutation is
 * undone.
 * @param {Object} obj The object to remove the unique ID field from.
 */
goog.removeUid = function(obj) {
  // TODO(arv): Make the type stricter, do not accept null.

  // In IE, DOM nodes are not instances of Object and throw an exception if we
  // try to delete.  Instead we try to use removeAttribute.
  if (obj !== null && 'removeAttribute' in obj) {
    obj.removeAttribute(goog.UID_PROPERTY_);
  }

  try {
    delete obj[goog.UID_PROPERTY_];
  } catch (ex) {
  }
};


/**
 * Name for unique ID property. Initialized in a way to help avoid collisions
 * with other closure JavaScript on the same page.
 * @type {string}
 * @private
 */
goog.UID_PROPERTY_ = 'closure_uid_' + ((Math.random() * 1e9) >>> 0);


/**
 * Counter for UID.
 * @type {number}
 * @private
 */
goog.uidCounter_ = 0;


/**
 * Adds a hash code field to an object. The hash code is unique for the
 * given object.
 * @param {Object} obj The object to get the hash code for.
 * @return {number} The hash code for the object.
 * @deprecated Use goog.getUid instead.
 */
goog.getHashCode = goog.getUid;


/**
 * Removes the hash code field from an object.
 * @param {Object} obj The object to remove the field from.
 * @deprecated Use goog.removeUid instead.
 */
goog.removeHashCode = goog.removeUid;


/**
 * Clones a value. The input may be an Object, Array, or basic type. Objects and
 * arrays will be cloned recursively.
 *
 * WARNINGS:
 * <code>goog.cloneObject</code> does not detect reference loops. Objects that
 * refer to themselves will cause infinite recursion.
 *
 * <code>goog.cloneObject</code> is unaware of unique identifiers, and copies
 * UIDs created by <code>getUid</code> into cloned results.
 *
 * @param {*} obj The value to clone.
 * @return {*} A clone of the input value.
 * @deprecated goog.cloneObject is unsafe. Prefer the goog.object methods.
 */
goog.cloneObject = function(obj) {
  var type = goog.typeOf(obj);
  if (type == 'object' || type == 'array') {
    if (typeof obj.clone === 'function') {
      return obj.clone();
    }
    var clone = type == 'array' ? [] : {};
    for (var key in obj) {
      clone[key] = goog.cloneObject(obj[key]);
    }
    return clone;
  }

  return obj;
};


/**
 * A native implementation of goog.bind.
 * @param {?function(this:T, ...)} fn A function to partially apply.
 * @param {T} selfObj Specifies the object which this should point to when the
 *     function is run.
 * @param {...*} var_args Additional arguments that are partially applied to the
 *     function.
 * @return {!Function} A partially-applied form of the function goog.bind() was
 *     invoked as a method of.
 * @template T
 * @private
 */
goog.bindNative_ = function(fn, selfObj, var_args) {
  return /** @type {!Function} */ (fn.call.apply(fn.bind, arguments));
};


/**
 * A pure-JS implementation of goog.bind.
 * @param {?function(this:T, ...)} fn A function to partially apply.
 * @param {T} selfObj Specifies the object which this should point to when the
 *     function is run.
 * @param {...*} var_args Additional arguments that are partially applied to the
 *     function.
 * @return {!Function} A partially-applied form of the function goog.bind() was
 *     invoked as a method of.
 * @template T
 * @private
 */
goog.bindJs_ = function(fn, selfObj, var_args) {
  if (!fn) {
    throw new Error();
  }

  if (arguments.length > 2) {
    var boundArgs = Array.prototype.slice.call(arguments, 2);
    return function() {
      // Prepend the bound arguments to the current arguments.
      var newArgs = Array.prototype.slice.call(arguments);
      Array.prototype.unshift.apply(newArgs, boundArgs);
      return fn.apply(selfObj, newArgs);
    };

  } else {
    return function() {
      return fn.apply(selfObj, arguments);
    };
  }
};


/**
 * Partially applies this function to a particular 'this object' and zero or
 * more arguments. The result is a new function with some arguments of the first
 * function pre-filled and the value of this 'pre-specified'.
 *
 * Remaining arguments specified at call-time are appended to the pre-specified
 * ones.
 *
 * Also see: {@link #partial}.
 *
 * Usage:
 * <pre>var barMethBound = goog.bind(myFunction, myObj, 'arg1', 'arg2');
 * barMethBound('arg3', 'arg4');</pre>
 *
 * @param {?function(this:T, ...)} fn A function to partially apply.
 * @param {T} selfObj Specifies the object which this should point to when the
 *     function is run.
 * @param {...*} var_args Additional arguments that are partially applied to the
 *     function.
 * @return {!Function} A partially-applied form of the function goog.bind() was
 *     invoked as a method of.
 * @template T
 * @suppress {deprecated} See above.
 */
goog.bind = function(fn, selfObj, var_args) {
  // TODO(nicksantos): narrow the type signature.
  if (Function.prototype.bind &&
      // NOTE(nicksantos): Somebody pulled base.js into the default Chrome
      // extension environment. This means that for Chrome extensions, they get
      // the implementation of Function.prototype.bind that calls goog.bind
      // instead of the native one. Even worse, we don't want to introduce a
      // circular dependency between goog.bind and Function.prototype.bind, so
      // we have to hack this to make sure it works correctly.
      Function.prototype.bind.toString().indexOf('native code') != -1) {
    goog.bind = goog.bindNative_;
  } else {
    goog.bind = goog.bindJs_;
  }
  return goog.bind.apply(null, arguments);
};


/**
 * Like goog.bind(), except that a 'this object' is not required. Useful when
 * the target function is already bound.
 *
 * Usage:
 * var g = goog.partial(f, arg1, arg2);
 * g(arg3, arg4);
 *
 * @param {Function} fn A function to partially apply.
 * @param {...*} var_args Additional arguments that are partially applied to fn.
 * @return {!Function} A partially-applied form of the function goog.partial()
 *     was invoked as a method of.
 */
goog.partial = function(fn, var_args) {
  var args = Array.prototype.slice.call(arguments, 1);
  return function() {
    // Clone the array (with slice()) and append additional arguments
    // to the existing arguments.
    var newArgs = args.slice();
    newArgs.push.apply(newArgs, arguments);
    return fn.apply(/** @type {?} */ (this), newArgs);
  };
};


/**
 * Copies all the members of a source object to a target object. This method
 * does not work on all browsers for all objects that contain keys such as
 * toString or hasOwnProperty. Use goog.object.extend for this purpose.
 *
 * NOTE: Some have advocated for the use of goog.mixin to setup classes
 * with multiple inheritence (traits, mixins, etc).  However, as it simply
 * uses "for in", this is not compatible with ES6 classes whose methods are
 * non-enumerable.  Changing this, would break cases where non-enumerable
 * properties are not expected.
 *
 * @param {Object} target Target.
 * @param {Object} source Source.
 * @deprecated Prefer Object.assign
 */
goog.mixin = function(target, source) {
  for (var x in source) {
    target[x] = source[x];
  }

  // For IE7 or lower, the for-in-loop does not contain any properties that are
  // not enumerable on the prototype object (for example, isPrototypeOf from
  // Object.prototype) but also it will not include 'replace' on objects that
  // extend String and change 'replace' (not that it is common for anyone to
  // extend anything except Object).
};


/**
 * @return {number} An integer value representing the number of milliseconds
 *     between midnight, January 1, 1970 and the current time.
 * @deprecated Use Date.now
 */
goog.now = (goog.TRUSTED_SITE && Date.now) || (function() {
             // Unary plus operator converts its operand to a number which in
             // the case of
             // a date is done by calling getTime().
             return +new Date();
           });


/**
 * Evals JavaScript in the global scope.  In IE this uses execScript, other
 * browsers use goog.global.eval. If goog.global.eval does not evaluate in the
 * global scope (for example, in Safari), appends a script tag instead.
 * Throws an exception if neither execScript or eval is defined.
 * @param {string} script JavaScript string.
 */
goog.globalEval = function(script) {
  if (goog.global.execScript) {
    goog.global.execScript(script, 'JavaScript');
  } else if (goog.global.eval) {
    // Test to see if eval works
    if (goog.evalWorksForGlobals_ == null) {
      try {
        goog.global.eval('var _evalTest_ = 1;');
      } catch (ignore) {
      }
      if (typeof goog.global['_evalTest_'] != 'undefined') {
        try {
          delete goog.global['_evalTest_'];
        } catch (ignore) {
          // Microsoft edge fails the deletion above in strict mode.
        }
        goog.evalWorksForGlobals_ = true;
      } else {
        goog.evalWorksForGlobals_ = false;
      }
    }

    if (goog.evalWorksForGlobals_) {
      goog.global.eval(script);
    } else {
      /** @type {!Document} */
      var doc = goog.global.document;
      var scriptElt =
          /** @type {!HTMLScriptElement} */ (doc.createElement('script'));
      scriptElt.type = 'text/javascript';
      scriptElt.defer = false;
      // Note(user): can't use .innerHTML since "t('<test>')" will fail and
      // .text doesn't work in Safari 2.  Therefore we append a text node.
      scriptElt.appendChild(doc.createTextNode(script));
      doc.head.appendChild(scriptElt);
      doc.head.removeChild(scriptElt);
    }
  } else {
    throw new Error('goog.globalEval not available');
  }
};


/**
 * Indicates whether or not we can call 'eval' directly to eval code in the
 * global scope. Set to a Boolean by the first call to goog.globalEval (which
 * empirically tests whether eval works for globals). @see goog.globalEval
 * @type {?boolean}
 * @private
 */
goog.evalWorksForGlobals_ = null;


/**
 * Optional map of CSS class names to obfuscated names used with
 * goog.getCssName().
 * @private {!Object<string, string>|undefined}
 * @see goog.setCssNameMapping
 */
goog.cssNameMapping_;


/**
 * Optional obfuscation style for CSS class names. Should be set to either
 * 'BY_WHOLE' or 'BY_PART' if defined.
 * @type {string|undefined}
 * @private
 * @see goog.setCssNameMapping
 */
goog.cssNameMappingStyle_;



/**
 * A hook for modifying the default behavior goog.getCssName. The function
 * if present, will receive the standard output of the goog.getCssName as
 * its input.
 *
 * @type {(function(string):string)|undefined}
 */
goog.global.CLOSURE_CSS_NAME_MAP_FN;


/**
 * Handles strings that are intended to be used as CSS class names.
 *
 * This function works in tandem with @see goog.setCssNameMapping.
 *
 * Without any mapping set, the arguments are simple joined with a hyphen and
 * passed through unaltered.
 *
 * When there is a mapping, there are two possible styles in which these
 * mappings are used. In the BY_PART style, each part (i.e. in between hyphens)
 * of the passed in css name is rewritten according to the map. In the BY_WHOLE
 * style, the full css name is looked up in the map directly. If a rewrite is
 * not specified by the map, the compiler will output a warning.
 *
 * When the mapping is passed to the compiler, it will replace calls to
 * goog.getCssName with the strings from the mapping, e.g.
 *     var x = goog.getCssName('foo');
 *     var y = goog.getCssName(this.baseClass, 'active');
 *  becomes:
 *     var x = 'foo';
 *     var y = this.baseClass + '-active';
 *
 * If one argument is passed it will be processed, if two are passed only the
 * modifier will be processed, as it is assumed the first argument was generated
 * as a result of calling goog.getCssName.
 *
 * @param {string} className The class name.
 * @param {string=} opt_modifier A modifier to be appended to the class name.
 * @return {string} The class name or the concatenation of the class name and
 *     the modifier.
 */
goog.getCssName = function(className, opt_modifier) {
  // String() is used for compatibility with compiled soy where the passed
  // className can be non-string objects.
  if (String(className).charAt(0) == '.') {
    throw new Error(
        'className passed in goog.getCssName must not start with ".".' +
        ' You passed: ' + className);
  }

  var getMapping = function(cssName) {
    return goog.cssNameMapping_[cssName] || cssName;
  };

  var renameByParts = function(cssName) {
    // Remap all the parts individually.
    var parts = cssName.split('-');
    var mapped = [];
    for (var i = 0; i < parts.length; i++) {
      mapped.push(getMapping(parts[i]));
    }
    return mapped.join('-');
  };

  var rename;
  if (goog.cssNameMapping_) {
    rename =
        goog.cssNameMappingStyle_ == 'BY_WHOLE' ? getMapping : renameByParts;
  } else {
    rename = function(a) {
      return a;
    };
  }

  var result =
      opt_modifier ? className + '-' + rename(opt_modifier) : rename(className);

  // The special CLOSURE_CSS_NAME_MAP_FN allows users to specify further
  // processing of the class name.
  if (goog.global.CLOSURE_CSS_NAME_MAP_FN) {
    return goog.global.CLOSURE_CSS_NAME_MAP_FN(result);
  }

  return result;
};


/**
 * Sets the map to check when returning a value from goog.getCssName(). Example:
 * <pre>
 * goog.setCssNameMapping({
 *   "goog": "a",
 *   "disabled": "b",
 * });
 *
 * var x = goog.getCssName('goog');
 * // The following evaluates to: "a a-b".
 * goog.getCssName('goog') + ' ' + goog.getCssName(x, 'disabled')
 * </pre>
 * When declared as a map of string literals to string literals, the JSCompiler
 * will replace all calls to goog.getCssName() using the supplied map if the
 * --process_closure_primitives flag is set.
 *
 * @param {!Object} mapping A map of strings to strings where keys are possible
 *     arguments to goog.getCssName() and values are the corresponding values
 *     that should be returned.
 * @param {string=} opt_style The style of css name mapping. There are two valid
 *     options: 'BY_PART', and 'BY_WHOLE'.
 * @see goog.getCssName for a description.
 */
goog.setCssNameMapping = function(mapping, opt_style) {
  goog.cssNameMapping_ = mapping;
  goog.cssNameMappingStyle_ = opt_style;
};


/**
 * To use CSS renaming in compiled mode, one of the input files should have a
 * call to goog.setCssNameMapping() with an object literal that the JSCompiler
 * can extract and use to replace all calls to goog.getCssName(). In uncompiled
 * mode, JavaScript code should be loaded before this base.js file that declares
 * a global variable, CLOSURE_CSS_NAME_MAPPING, which is used below. This is
 * to ensure that the mapping is loaded before any calls to goog.getCssName()
 * are made in uncompiled mode.
 *
 * A hook for overriding the CSS name mapping.
 * @type {!Object<string, string>|undefined}
 */
goog.global.CLOSURE_CSS_NAME_MAPPING;


if (!COMPILED && goog.global.CLOSURE_CSS_NAME_MAPPING) {
  // This does not call goog.setCssNameMapping() because the JSCompiler
  // requires that goog.setCssNameMapping() be called with an object literal.
  goog.cssNameMapping_ = goog.global.CLOSURE_CSS_NAME_MAPPING;
}


/**
 * Gets a localized message.
 *
 * This function is a compiler primitive. If you give the compiler a localized
 * message bundle, it will replace the string at compile-time with a localized
 * version, and expand goog.getMsg call to a concatenated string.
 *
 * Messages must be initialized in the form:
 * <code>
 * var MSG_NAME = goog.getMsg('Hello {$placeholder}', {'placeholder': 'world'});
 * </code>
 *
 * This function produces a string which should be treated as plain text. Use
 * {@link goog.html.SafeHtmlFormatter} in conjunction with goog.getMsg to
 * produce SafeHtml.
 *
 * @param {string} str Translatable string, places holders in the form {$foo}.
 * @param {Object<string, string>=} opt_values Maps place holder name to value.
 * @param {{html: boolean}=} opt_options Options:
 *     html: Escape '<' in str to '&lt;'. Used by Closure Templates where the
 *     generated code size and performance is critical which is why {@link
 *     goog.html.SafeHtmlFormatter} is not used. The value must be literal true
 *     or false.
 * @return {string} message with placeholders filled.
 */
goog.getMsg = function(str, opt_values, opt_options) {
  if (opt_options && opt_options.html) {
    // Note that '&' is not replaced because the translation can contain HTML
    // entities.
    str = str.replace(/</g, '&lt;');
  }
  if (opt_values) {
    str = str.replace(/\{\$([^}]+)}/g, function(match, key) {
      return (opt_values != null && key in opt_values) ? opt_values[key] :
                                                         match;
    });
  }
  return str;
};


/**
 * Gets a localized message. If the message does not have a translation, gives a
 * fallback message.
 *
 * This is useful when introducing a new message that has not yet been
 * translated into all languages.
 *
 * This function is a compiler primitive. Must be used in the form:
 * <code>var x = goog.getMsgWithFallback(MSG_A, MSG_B);</code>
 * where MSG_A and MSG_B were initialized with goog.getMsg.
 *
 * @param {string} a The preferred message.
 * @param {string} b The fallback message.
 * @return {string} The best translated message.
 */
goog.getMsgWithFallback = function(a, b) {
  return a;
};


/**
 * Exposes an unobfuscated global namespace path for the given object.
 * Note that fields of the exported object *will* be obfuscated, unless they are
 * exported in turn via this function or goog.exportProperty.
 *
 * Also handy for making public items that are defined in anonymous closures.
 *
 * ex. goog.exportSymbol('public.path.Foo', Foo);
 *
 * ex. goog.exportSymbol('public.path.Foo.staticFunction', Foo.staticFunction);
 *     public.path.Foo.staticFunction();
 *
 * ex. goog.exportSymbol('public.path.Foo.prototype.myMethod',
 *                       Foo.prototype.myMethod);
 *     new public.path.Foo().myMethod();
 *
 * @param {string} publicPath Unobfuscated name to export.
 * @param {*} object Object the name should point to.
 * @param {Object=} opt_objectToExportTo The object to add the path to; default
 *     is goog.global.
 */
goog.exportSymbol = function(publicPath, object, opt_objectToExportTo) {
  goog.exportPath_(publicPath, object, opt_objectToExportTo);
};


/**
 * Exports a property unobfuscated into the object's namespace.
 * ex. goog.exportProperty(Foo, 'staticFunction', Foo.staticFunction);
 * ex. goog.exportProperty(Foo.prototype, 'myMethod', Foo.prototype.myMethod);
 * @param {Object} object Object whose static property is being exported.
 * @param {string} publicName Unobfuscated name to export.
 * @param {*} symbol Object the name should point to.
 */
goog.exportProperty = function(object, publicName, symbol) {
  object[publicName] = symbol;
};


/**
 * Inherit the prototype methods from one constructor into another.
 *
 * Usage:
 * <pre>
 * function ParentClass(a, b) { }
 * ParentClass.prototype.foo = function(a) { };
 *
 * function ChildClass(a, b, c) {
 *   ChildClass.base(this, 'constructor', a, b);
 * }
 * goog.inherits(ChildClass, ParentClass);
 *
 * var child = new ChildClass('a', 'b', 'see');
 * child.foo(); // This works.
 * </pre>
 *
 * @param {!Function} childCtor Child class.
 * @param {!Function} parentCtor Parent class.
 * @suppress {strictMissingProperties} superClass_ and base is not defined on
 *    Function.
 */
goog.inherits = function(childCtor, parentCtor) {
  /** @constructor */
  function tempCtor() {}
  tempCtor.prototype = parentCtor.prototype;
  childCtor.superClass_ = parentCtor.prototype;
  childCtor.prototype = new tempCtor();
  /** @override */
  childCtor.prototype.constructor = childCtor;

  /**
   * Calls superclass constructor/method.
   *
   * This function is only available if you use goog.inherits to
   * express inheritance relationships between classes.
   *
   * NOTE: This is a replacement for goog.base and for superClass_
   * property defined in childCtor.
   *
   * @param {!Object} me Should always be "this".
   * @param {string} methodName The method name to call. Calling
   *     superclass constructor can be done with the special string
   *     'constructor'.
   * @param {...*} var_args The arguments to pass to superclass
   *     method/constructor.
   * @return {*} The return value of the superclass method/constructor.
   */
  childCtor.base = function(me, methodName, var_args) {
    // Copying using loop to avoid deop due to passing arguments object to
    // function. This is faster in many JS engines as of late 2014.
    var args = new Array(arguments.length - 2);
    for (var i = 2; i < arguments.length; i++) {
      args[i - 2] = arguments[i];
    }
    return parentCtor.prototype[methodName].apply(me, args);
  };
};


/**
 * Call up to the superclass.
 *
 * If this is called from a constructor, then this calls the superclass
 * constructor with arguments 1-N.
 *
 * If this is called from a prototype method, then you must pass the name of the
 * method as the second argument to this function. If you do not, you will get a
 * runtime error. This calls the superclass' method with arguments 2-N.
 *
 * This function only works if you use goog.inherits to express inheritance
 * relationships between your classes.
 *
 * This function is a compiler primitive. At compile-time, the compiler will do
 * macro expansion to remove a lot of the extra overhead that this function
 * introduces. The compiler will also enforce a lot of the assumptions that this
 * function makes, and treat it as a compiler error if you break them.
 *
 * @param {!Object} me Should always be "this".
 * @param {*=} opt_methodName The method name if calling a super method.
 * @param {...*} var_args The rest of the arguments.
 * @return {*} The return value of the superclass method.
 * @suppress {es5Strict} This method can not be used in strict mode, but
 *     all Closure Library consumers must depend on this file.
 * @deprecated goog.base is not strict mode compatible.  Prefer the static
 *     "base" method added to the constructor by goog.inherits
 *     or ES6 classes and the "super" keyword.
 */
goog.base = function(me, opt_methodName, var_args) {
  var caller = arguments.callee.caller;

  if (goog.STRICT_MODE_COMPATIBLE || (goog.DEBUG && !caller)) {
    throw new Error(
        'arguments.caller not defined.  goog.base() cannot be used ' +
        'with strict mode code. See ' +
        'http://www.ecma-international.org/ecma-262/5.1/#sec-C');
  }

  if (typeof caller.superClass_ !== 'undefined') {
    // Copying using loop to avoid deop due to passing arguments object to
    // function. This is faster in many JS engines as of late 2014.
    var ctorArgs = new Array(arguments.length - 1);
    for (var i = 1; i < arguments.length; i++) {
      ctorArgs[i - 1] = arguments[i];
    }
    // This is a constructor. Call the superclass constructor.
    return /** @type {!Function} */ (caller.superClass_)
        .constructor.apply(me, ctorArgs);
  }

  if (typeof opt_methodName != 'string' && typeof opt_methodName != 'symbol') {
    throw new Error(
        'method names provided to goog.base must be a string or a symbol');
  }

  // Copying using loop to avoid deop due to passing arguments object to
  // function. This is faster in many JS engines as of late 2014.
  var args = new Array(arguments.length - 2);
  for (var i = 2; i < arguments.length; i++) {
    args[i - 2] = arguments[i];
  }
  var foundCaller = false;
  for (var proto = me.constructor.prototype; proto;
       proto = Object.getPrototypeOf(proto)) {
    if (proto[opt_methodName] === caller) {
      foundCaller = true;
    } else if (foundCaller) {
      return proto[opt_methodName].apply(me, args);
    }
  }

  // If we did not find the caller in the prototype chain, then one of two
  // things happened:
  // 1) The caller is an instance method.
  // 2) This method was not called by the right caller.
  if (me[opt_methodName] === caller) {
    return me.constructor.prototype[opt_methodName].apply(me, args);
  } else {
    throw new Error(
        'goog.base called from a method of one name ' +
        'to a method of a different name');
  }
};


/**
 * Allow for aliasing within scope functions.  This function exists for
 * uncompiled code - in compiled code the calls will be inlined and the aliases
 * applied.  In uncompiled code the function is simply run since the aliases as
 * written are valid JavaScript.
 *
 *
 * @param {function()} fn Function to call.  This function can contain aliases
 *     to namespaces (e.g. "var dom = goog.dom") or classes
 *     (e.g. "var Timer = goog.Timer").
 */
goog.scope = function(fn) {
  if (goog.isInModuleLoader_()) {
    throw new Error('goog.scope is not supported within a module.');
  }
  fn.call(goog.global);
};


/*
 * To support uncompiled, strict mode bundles that use eval to divide source
 * like so:
 *    eval('someSource;//# sourceUrl sourcefile.js');
 * We need to export the globally defined symbols "goog" and "COMPILED".
 * Exporting "goog" breaks the compiler optimizations, so we required that
 * be defined externally.
 * NOTE: We don't use goog.exportSymbol here because we don't want to trigger
 * extern generation when that compiler option is enabled.
 */
if (!COMPILED) {
  goog.global['COMPILED'] = COMPILED;
}


//==============================================================================
// goog.defineClass implementation
//==============================================================================


/**
 * Creates a restricted form of a Closure "class":
 *   - from the compiler's perspective, the instance returned from the
 *     constructor is sealed (no new properties may be added).  This enables
 *     better checks.
 *   - the compiler will rewrite this definition to a form that is optimal
 *     for type checking and optimization (initially this will be a more
 *     traditional form).
 *
 * @param {Function} superClass The superclass, Object or null.
 * @param {goog.defineClass.ClassDescriptor} def
 *     An object literal describing
 *     the class.  It may have the following properties:
 *     "constructor": the constructor function
 *     "statics": an object literal containing methods to add to the constructor
 *        as "static" methods or a function that will receive the constructor
 *        function as its only parameter to which static properties can
 *        be added.
 *     all other properties are added to the prototype.
 * @return {!Function} The class constructor.
 * @deprecated Use ES6 class syntax instead.
 */
goog.defineClass = function(superClass, def) {
  // TODO(johnlenz): consider making the superClass an optional parameter.
  var constructor = def.constructor;
  var statics = def.statics;
  // Wrap the constructor prior to setting up the prototype and static methods.
  if (!constructor || constructor == Object.prototype.constructor) {
    constructor = function() {
      throw new Error(
          'cannot instantiate an interface (no constructor defined).');
    };
  }

  var cls = goog.defineClass.createSealingConstructor_(constructor, superClass);
  if (superClass) {
    goog.inherits(cls, superClass);
  }

  // Remove all the properties that should not be copied to the prototype.
  delete def.constructor;
  delete def.statics;

  goog.defineClass.applyProperties_(cls.prototype, def);
  if (statics != null) {
    if (statics instanceof Function) {
      statics(cls);
    } else {
      goog.defineClass.applyProperties_(cls, statics);
    }
  }

  return cls;
};


/**
 * @typedef {{
 *   constructor: (!Function|undefined),
 *   statics: (Object|undefined|function(Function):void)
 * }}
 */
goog.defineClass.ClassDescriptor;


/**
 * @define {boolean} Whether the instances returned by goog.defineClass should
 *     be sealed when possible.
 *
 * When sealing is disabled the constructor function will not be wrapped by
 * goog.defineClass, making it incompatible with ES6 class methods.
 */
goog.defineClass.SEAL_CLASS_INSTANCES =
    goog.define('goog.defineClass.SEAL_CLASS_INSTANCES', goog.DEBUG);


/**
 * If goog.defineClass.SEAL_CLASS_INSTANCES is enabled and Object.seal is
 * defined, this function will wrap the constructor in a function that seals the
 * results of the provided constructor function.
 *
 * @param {!Function} ctr The constructor whose results maybe be sealed.
 * @param {Function} superClass The superclass constructor.
 * @return {!Function} The replacement constructor.
 * @private
 */
goog.defineClass.createSealingConstructor_ = function(ctr, superClass) {
  if (!goog.defineClass.SEAL_CLASS_INSTANCES) {
    // Do now wrap the constructor when sealing is disabled. Angular code
    // depends on this for injection to work properly.
    return ctr;
  }

  // Compute whether the constructor is sealable at definition time, rather
  // than when the instance is being constructed.
  var superclassSealable = !goog.defineClass.isUnsealable_(superClass);

  /**
   * @this {Object}
   * @return {?}
   */
  var wrappedCtr = function() {
    // Don't seal an instance of a subclass when it calls the constructor of
    // its super class as there is most likely still setup to do.
    var instance = ctr.apply(this, arguments) || this;
    instance[goog.UID_PROPERTY_] = instance[goog.UID_PROPERTY_];

    if (this.constructor === wrappedCtr && superclassSealable &&
        Object.seal instanceof Function) {
      Object.seal(instance);
    }
    return instance;
  };

  return wrappedCtr;
};


/**
 * @param {Function} ctr The constructor to test.
 * @return {boolean} Whether the constructor has been tagged as unsealable
 *     using goog.tagUnsealableClass.
 * @private
 */
goog.defineClass.isUnsealable_ = function(ctr) {
  return ctr && ctr.prototype &&
      ctr.prototype[goog.UNSEALABLE_CONSTRUCTOR_PROPERTY_];
};


// TODO(johnlenz): share these values with the goog.object
/**
 * The names of the fields that are defined on Object.prototype.
 * @type {!Array<string>}
 * @private
 * @const
 */
goog.defineClass.OBJECT_PROTOTYPE_FIELDS_ = [
  'constructor', 'hasOwnProperty', 'isPrototypeOf', 'propertyIsEnumerable',
  'toLocaleString', 'toString', 'valueOf'
];


// TODO(johnlenz): share this function with the goog.object
/**
 * @param {!Object} target The object to add properties to.
 * @param {!Object} source The object to copy properties from.
 * @private
 */
goog.defineClass.applyProperties_ = function(target, source) {
  // TODO(johnlenz): update this to support ES5 getters/setters

  var key;
  for (key in source) {
    if (Object.prototype.hasOwnProperty.call(source, key)) {
      target[key] = source[key];
    }
  }

  // For IE the for-in-loop does not contain any properties that are not
  // enumerable on the prototype object (for example isPrototypeOf from
  // Object.prototype) and it will also not include 'replace' on objects that
  // extend String and change 'replace' (not that it is common for anyone to
  // extend anything except Object).
  for (var i = 0; i < goog.defineClass.OBJECT_PROTOTYPE_FIELDS_.length; i++) {
    key = goog.defineClass.OBJECT_PROTOTYPE_FIELDS_[i];
    if (Object.prototype.hasOwnProperty.call(source, key)) {
      target[key] = source[key];
    }
  }
};


/**
 * Sealing classes breaks the older idiom of assigning properties on the
 * prototype rather than in the constructor. As such, goog.defineClass
 * must not seal subclasses of these old-style classes until they are fixed.
 * Until then, this marks a class as "broken", instructing defineClass
 * not to seal subclasses.
 * @param {!Function} ctr The legacy constructor to tag as unsealable.
 */
goog.tagUnsealableClass = function(ctr) {
  if (!COMPILED && goog.defineClass.SEAL_CLASS_INSTANCES) {
    ctr.prototype[goog.UNSEALABLE_CONSTRUCTOR_PROPERTY_] = true;
  }
};


/**
 * Name for unsealable tag property.
 * @const @private {string}
 */
goog.UNSEALABLE_CONSTRUCTOR_PROPERTY_ = 'goog_defineClass_legacy_unsealable';


// There's a bug in the compiler where without collapse properties the
// Closure namespace defines do not guard code correctly. To help reduce code
// size also check for !COMPILED even though it redundant until this is fixed.
if (!COMPILED && goog.DEPENDENCIES_ENABLED) {

  /**
   * Tries to detect whether is in the context of an HTML document.
   * @return {boolean} True if it looks like HTML document.
   * @private
   */
  goog.inHtmlDocument_ = function() {
    /** @type {!Document} */
    var doc = goog.global.document;
    return doc != null && 'write' in doc;  // XULDocument misses write.
  };


  /**
   * We'd like to check for if the document readyState is 'loading'; however
   * there are bugs on IE 10 and below where the readyState being anything other
   * than 'complete' is not reliable.
   * @return {boolean}
   * @private
   */
  goog.isDocumentLoading_ = function() {
    // attachEvent is available on IE 6 thru 10 only, and thus can be used to
    // detect those browsers.
    /** @type {!HTMLDocument} */
    var doc = goog.global.document;
    return doc.attachEvent ? doc.readyState != 'complete' :
                             doc.readyState == 'loading';
  };


  /**
   * Tries to detect the base path of base.js script that bootstraps Closure.
   * @private
   */
  goog.findBasePath_ = function() {
    if (goog.global.CLOSURE_BASE_PATH != undefined &&
        // Anti DOM-clobbering runtime check (b/37736576).
        typeof goog.global.CLOSURE_BASE_PATH === 'string') {
      goog.basePath = goog.global.CLOSURE_BASE_PATH;
      return;
    } else if (!goog.inHtmlDocument_()) {
      return;
    }
    /** @type {!Document} */
    var doc = goog.global.document;
    // If we have a currentScript available, use it exclusively.
    var currentScript = doc.currentScript;
    if (currentScript) {
      var scripts = [currentScript];
    } else {
      var scripts = doc.getElementsByTagName('SCRIPT');
    }
    // Search backwards since the current script is in almost all cases the one
    // that has base.js.
    for (var i = scripts.length - 1; i >= 0; --i) {
      var script = /** @type {!HTMLScriptElement} */ (scripts[i]);
      var src = script.src;
      var qmark = src.lastIndexOf('?');
      var l = qmark == -1 ? src.length : qmark;
      if (src.substr(l - 7, 7) == 'base.js') {
        goog.basePath = src.substr(0, l - 7);
        return;
      }
    }
  };

  goog.findBasePath_();

  /** @struct @constructor @final */
  goog.Transpiler = function() {
    /** @private {?Object<string, boolean>} */
    this.requiresTranspilation_ = null;
    /** @private {string} */
    this.transpilationTarget_ = goog.TRANSPILE_TO_LANGUAGE;
  };


  /**
   * Returns a newly created map from language mode string to a boolean
   * indicating whether transpilation should be done for that mode as well as
   * the highest level language that this environment supports.
   *
   * Guaranteed invariant:
   * For any two modes, l1 and l2 where l2 is a newer mode than l1,
   * `map[l1] == true` implies that `map[l2] == true`.
   *
   * Note this method is extracted and used elsewhere, so it cannot rely on
   * anything external (it should easily be able to be transformed into a
   * standalone, top level function).
   *
   * @private
   * @return {{
   *   target: string,
   *   map: !Object<string, boolean>
   * }}
   */
  goog.Transpiler.prototype.createRequiresTranspilation_ = function() {
    var transpilationTarget = 'es3';
    var /** !Object<string, boolean> */ requiresTranspilation = {'es3': false};
    var transpilationRequiredForAllLaterModes = false;

    /**
     * Adds an entry to requiresTranspliation for the given language mode.
     *
     * IMPORTANT: Calls must be made in order from oldest to newest language
     * mode.
     * @param {string} modeName
     * @param {function(): boolean} isSupported Returns true if the JS engine
     *     supports the given mode.
     */
    function addNewerLanguageTranspilationCheck(modeName, isSupported) {
      if (transpilationRequiredForAllLaterModes) {
        requiresTranspilation[modeName] = true;
      } else if (isSupported()) {
        transpilationTarget = modeName;
        requiresTranspilation[modeName] = false;
      } else {
        requiresTranspilation[modeName] = true;
        transpilationRequiredForAllLaterModes = true;
      }
    }

    /**
     * Does the given code evaluate without syntax errors and return a truthy
     * result?
     */
    function /** boolean */ evalCheck(/** string */ code) {
      try {
        return !!eval(code);
      } catch (ignored) {
        return false;
      }
    }

    var userAgent = goog.global.navigator && goog.global.navigator.userAgent ?
        goog.global.navigator.userAgent :
        '';

    // Identify ES3-only browsers by their incorrect treatment of commas.
    addNewerLanguageTranspilationCheck('es5', function() {
      return evalCheck('[1,].length==1');
    });
    addNewerLanguageTranspilationCheck('es6', function() {
      // Edge has a non-deterministic (i.e., not reproducible) bug with ES6:
      // https://github.com/Microsoft/ChakraCore/issues/1496.
      var re = /Edge\/(\d+)(\.\d)*/i;
      var edgeUserAgent = userAgent.match(re);
      if (edgeUserAgent) {
        // The Reflect.construct test below is flaky on Edge. It can sometimes
        // pass or fail on 40 15.15063, so just exit early for Edge and treat
        // it as ES5. Until we're on a more up to date version just always use
        // ES5. See https://github.com/Microsoft/ChakraCore/issues/3217.
        return false;
      }
      // Test es6: [FF50 (?), Edge 14 (?), Chrome 50]
      //   (a) default params (specifically shadowing locals),
      //   (b) destructuring, (c) block-scoped functions,
      //   (d) for-of (const), (e) new.target/Reflect.construct
      var es6fullTest =
          'class X{constructor(){if(new.target!=String)throw 1;this.x=42}}' +
          'let q=Reflect.construct(X,[],String);if(q.x!=42||!(q instanceof ' +
          'String))throw 1;for(const a of[2,3]){if(a==2)continue;function ' +
          'f(z={a}){let a=0;return z.a}{function f(){return 0;}}return f()' +
          '==3}';

      return evalCheck('(()=>{"use strict";' + es6fullTest + '})()');
    });
    // ** and **= are the only new features in 'es7'
    addNewerLanguageTranspilationCheck('es7', function() {
      return evalCheck('2 ** 2 == 4');
    });
    // async functions are the only new features in 'es8'
    addNewerLanguageTranspilationCheck('es8', function() {
      return evalCheck('async () => 1, true');
    });
    addNewerLanguageTranspilationCheck('es9', function() {
      return evalCheck('({...rest} = {}), true');
    });
    addNewerLanguageTranspilationCheck('es_next', function() {
      return false;  // assume it always need to transpile
    });
    return {target: transpilationTarget, map: requiresTranspilation};
  };


  /**
   * Determines whether the given language needs to be transpiled.
   * @param {string} lang
   * @param {string|undefined} module
   * @return {boolean}
   */
  goog.Transpiler.prototype.needsTranspile = function(lang, module) {
    if (goog.TRANSPILE == 'always') {
      return true;
    } else if (goog.TRANSPILE == 'never') {
      return false;
    } else if (!this.requiresTranspilation_) {
      var obj = this.createRequiresTranspilation_();
      this.requiresTranspilation_ = obj.map;
      this.transpilationTarget_ = this.transpilationTarget_ || obj.target;
    }
    if (lang in this.requiresTranspilation_) {
      if (this.requiresTranspilation_[lang]) {
        return true;
      } else if (
          goog.inHtmlDocument_() && module == 'es6' &&
          !('noModule' in goog.global.document.createElement('script'))) {
        return true;
      } else {
        return false;
      }
    } else {
      throw new Error('Unknown language mode: ' + lang);
    }
  };


  /**
   * Lazily retrieves the transpiler and applies it to the source.
   * @param {string} code JS code.
   * @param {string} path Path to the code.
   * @return {string} The transpiled code.
   */
  goog.Transpiler.prototype.transpile = function(code, path) {
    // TODO(johnplaisted): We should delete goog.transpile_ and just have this
    // function. But there's some compile error atm where goog.global is being
    // stripped incorrectly without this.
    return goog.transpile_(code, path, this.transpilationTarget_);
  };


  /** @private @final {!goog.Transpiler} */
  goog.transpiler_ = new goog.Transpiler();

  /**
   * Rewrites closing script tags in input to avoid ending an enclosing script
   * tag.
   *
   * @param {string} str
   * @return {string}
   * @private
   */
  goog.protectScriptTag_ = function(str) {
    return str.replace(/<\/(SCRIPT)/ig, '\\x3c/$1');
  };


  /**
   * A debug loader is responsible for downloading and executing javascript
   * files in an unbundled, uncompiled environment.
   *
   * This can be custimized via the setDependencyFactory method, or by
   * CLOSURE_IMPORT_SCRIPT/CLOSURE_LOAD_FILE_SYNC.
   *
   * @struct @constructor @final @private
   */
  goog.DebugLoader_ = function() {
    /** @private @const {!Object<string, !goog.Dependency>} */
    this.dependencies_ = {};
    /** @private @const {!Object<string, string>} */
    this.idToPath_ = {};
    /** @private @const {!Object<string, boolean>} */
    this.written_ = {};
    /** @private @const {!Array<!goog.Dependency>} */
    this.loadingDeps_ = [];
    /** @private {!Array<!goog.Dependency>} */
    this.depsToLoad_ = [];
    /** @private {boolean} */
    this.paused_ = false;
    /** @private {!goog.DependencyFactory} */
    this.factory_ = new goog.DependencyFactory(goog.transpiler_);
    /** @private @const {!Object<string, !Function>} */
    this.deferredCallbacks_ = {};
    /** @private @const {!Array<string>} */
    this.deferredQueue_ = [];
  };

  /**
   * @param {!Array<string>} namespaces
   * @param {function(): undefined} callback Function to call once all the
   *     namespaces have loaded.
   */
  goog.DebugLoader_.prototype.bootstrap = function(namespaces, callback) {
    var cb = callback;
    function resolve() {
      if (cb) {
        goog.global.setTimeout(cb, 0);
        cb = null;
      }
    }

    if (!namespaces.length) {
      resolve();
      return;
    }

    var deps = [];
    for (var i = 0; i < namespaces.length; i++) {
      var path = this.getPathFromDeps_(namespaces[i]);
      if (!path) {
        throw new Error('Unregonized namespace: ' + namespaces[i]);
      }
      deps.push(this.dependencies_[path]);
    }

    var require = goog.require;
    var loaded = 0;
    for (var i = 0; i < namespaces.length; i++) {
      require(namespaces[i]);
      deps[i].onLoad(function() {
        if (++loaded == namespaces.length) {
          resolve();
        }
      });
    }
  };


  /**
   * Loads the Closure Dependency file.
   *
   * Exposed a public function so CLOSURE_NO_DEPS can be set to false, base
   * loaded, setDependencyFactory called, and then this called. i.e. allows
   * custom loading of the deps file.
   */
  goog.DebugLoader_.prototype.loadClosureDeps = function() {
    // Circumvent addDependency, which would try to transpile deps.js if
    // transpile is set to always.
    var relPath = 'deps.js';
    this.depsToLoad_.push(this.factory_.createDependency(
        goog.normalizePath_(goog.basePath + relPath), relPath, [], [], {},
        false));
    this.loadDeps_();
  };


  /**
   * Notifies the debug loader when a dependency has been requested.
   *
   * @param {string} absPathOrId Path of the dependency or goog id.
   * @param {boolean=} opt_force
   */
  goog.DebugLoader_.prototype.requested = function(absPathOrId, opt_force) {
    var path = this.getPathFromDeps_(absPathOrId);
    if (path &&
        (opt_force || this.areDepsLoaded_(this.dependencies_[path].requires))) {
      var callback = this.deferredCallbacks_[path];
      if (callback) {
        delete this.deferredCallbacks_[path];
        callback();
      }
    }
  };


  /**
   * Sets the dependency factory, which can be used to create custom
   * goog.Dependency implementations to control how dependencies are loaded.
   *
   * @param {!goog.DependencyFactory} factory
   */
  goog.DebugLoader_.prototype.setDependencyFactory = function(factory) {
    this.factory_ = factory;
  };


  /**
   * Travserses the dependency graph and queues the given dependency, and all of
   * its transitive dependencies, for loading and then starts loading if not
   * paused.
   *
   * @param {string} namespace
   * @private
   */
  goog.DebugLoader_.prototype.load_ = function(namespace) {
    if (!this.getPathFromDeps_(namespace)) {
      var errorMessage = 'goog.require could not find: ' + namespace;

      goog.logToConsole_(errorMessage);
      throw Error(errorMessage);
    } else {
      var loader = this;

      var deps = [];

      /** @param {string} namespace */
      var visit = function(namespace) {
        var path = loader.getPathFromDeps_(namespace);

        if (!path) {
          throw new Error('Bad dependency path or symbol: ' + namespace);
        }

        if (loader.written_[path]) {
          return;
        }

        loader.written_[path] = true;

        var dep = loader.dependencies_[path];
        for (var i = 0; i < dep.requires.length; i++) {
          if (!goog.isProvided_(dep.requires[i])) {
            visit(dep.requires[i]);
          }
        }

        deps.push(dep);
      };

      visit(namespace);

      var wasLoading = !!this.depsToLoad_.length;
      this.depsToLoad_ = this.depsToLoad_.concat(deps);

      if (!this.paused_ && !wasLoading) {
        this.loadDeps_();
      }
    }
  };


  /**
   * Loads any queued dependencies until they are all loaded or paused.
   *
   * @private
   */
  goog.DebugLoader_.prototype.loadDeps_ = function() {
    var loader = this;
    var paused = this.paused_;

    while (this.depsToLoad_.length && !paused) {
      (function() {
        var loadCallDone = false;
        var dep = loader.depsToLoad_.shift();

        var loaded = false;
        loader.loading_(dep);

        var controller = {
          pause: function() {
            if (loadCallDone) {
              throw new Error('Cannot call pause after the call to load.');
            } else {
              paused = true;
            }
          },
          resume: function() {
            if (loadCallDone) {
              loader.resume_();
            } else {
              // Some dep called pause and then resume in the same load call.
              // Just keep running this same loop.
              paused = false;
            }
          },
          loaded: function() {
            if (loaded) {
              throw new Error('Double call to loaded.');
            }

            loaded = true;
            loader.loaded_(dep);
          },
          pending: function() {
            // Defensive copy.
            var pending = [];
            for (var i = 0; i < loader.loadingDeps_.length; i++) {
              pending.push(loader.loadingDeps_[i]);
            }
            return pending;
          },
          /**
           * @param {goog.ModuleType} type
           */
          setModuleState: function(type) {
            goog.moduleLoaderState_ = {
              type: type,
              moduleName: '',
              declareLegacyNamespace: false
            };
          },
          /** @type {function(string, string, string=)} */
          registerEs6ModuleExports: function(
              path, exports, opt_closureNamespace) {
            if (opt_closureNamespace) {
              goog.loadedModules_[opt_closureNamespace] = {
                exports: exports,
                type: goog.ModuleType.ES6,
                moduleId: opt_closureNamespace || ''
              };
            }
          },
          /** @type {function(string, ?)} */
          registerGoogModuleExports: function(moduleId, exports) {
            goog.loadedModules_[moduleId] = {
              exports: exports,
              type: goog.ModuleType.GOOG,
              moduleId: moduleId
            };
          },
          clearModuleState: function() {
            goog.moduleLoaderState_ = null;
          },
          defer: function(callback) {
            if (loadCallDone) {
              throw new Error(
                  'Cannot register with defer after the call to load.');
            }
            loader.defer_(dep, callback);
          },
          areDepsLoaded: function() {
            return loader.areDepsLoaded_(dep.requires);
          }
        };

        try {
          dep.load(controller);
        } finally {
          loadCallDone = true;
        }
      })();
    }

    if (paused) {
      this.pause_();
    }
  };


  /** @private */
  goog.DebugLoader_.prototype.pause_ = function() {
    this.paused_ = true;
  };


  /** @private */
  goog.DebugLoader_.prototype.resume_ = function() {
    if (this.paused_) {
      this.paused_ = false;
      this.loadDeps_();
    }
  };


  /**
   * Marks the given dependency as loading (load has been called but it has not
   * yet marked itself as finished). Useful for dependencies that want to know
   * what else is loading. Example: goog.modules cannot eval if there are
   * loading dependencies.
   *
   * @param {!goog.Dependency} dep
   * @private
   */
  goog.DebugLoader_.prototype.loading_ = function(dep) {
    this.loadingDeps_.push(dep);
  };


  /**
   * Marks the given dependency as having finished loading and being available
   * for require.
   *
   * @param {!goog.Dependency} dep
   * @private
   */
  goog.DebugLoader_.prototype.loaded_ = function(dep) {
    for (var i = 0; i < this.loadingDeps_.length; i++) {
      if (this.loadingDeps_[i] == dep) {
        this.loadingDeps_.splice(i, 1);
        break;
      }
    }

    for (var i = 0; i < this.deferredQueue_.length; i++) {
      if (this.deferredQueue_[i] == dep.path) {
        this.deferredQueue_.splice(i, 1);
        break;
      }
    }

    if (this.loadingDeps_.length == this.deferredQueue_.length &&
        !this.depsToLoad_.length) {
      // Something has asked to load these, but they may not be directly
      // required again later, so load them now that we know we're done loading
      // everything else. e.g. a goog module entry point.
      while (this.deferredQueue_.length) {
        this.requested(this.deferredQueue_.shift(), true);
      }
    }

    dep.loaded();
  };


  /**
   * @param {!Array<string>} pathsOrIds
   * @return {boolean}
   * @private
   */
  goog.DebugLoader_.prototype.areDepsLoaded_ = function(pathsOrIds) {
    for (var i = 0; i < pathsOrIds.length; i++) {
      var path = this.getPathFromDeps_(pathsOrIds[i]);
      if (!path ||
          (!(path in this.deferredCallbacks_) &&
           !goog.isProvided_(pathsOrIds[i]))) {
        return false;
      }
    }

    return true;
  };


  /**
   * @param {string} absPathOrId
   * @return {?string}
   * @private
   */
  goog.DebugLoader_.prototype.getPathFromDeps_ = function(absPathOrId) {
    if (absPathOrId in this.idToPath_) {
      return this.idToPath_[absPathOrId];
    } else if (absPathOrId in this.dependencies_) {
      return absPathOrId;
    } else {
      return null;
    }
  };


  /**
   * @param {!goog.Dependency} dependency
   * @param {!Function} callback
   * @private
   */
  goog.DebugLoader_.prototype.defer_ = function(dependency, callback) {
    this.deferredCallbacks_[dependency.path] = callback;
    this.deferredQueue_.push(dependency.path);
  };


  /**
   * Interface for goog.Dependency implementations to have some control over
   * loading of dependencies.
   *
   * @record
   */
  goog.LoadController = function() {};


  /**
   * Tells the controller to halt loading of more dependencies.
   */
  goog.LoadController.prototype.pause = function() {};


  /**
   * Tells the controller to resume loading of more dependencies if paused.
   */
  goog.LoadController.prototype.resume = function() {};


  /**
   * Tells the controller that this dependency has finished loading.
   *
   * This causes this to be removed from pending() and any load callbacks to
   * fire.
   */
  goog.LoadController.prototype.loaded = function() {};


  /**
   * List of dependencies on which load has been called but which have not
   * called loaded on their controller. This includes the current dependency.
   *
   * @return {!Array<!goog.Dependency>}
   */
  goog.LoadController.prototype.pending = function() {};


  /**
   * Registers an object as an ES6 module's exports so that goog.modules may
   * require it by path.
   *
   * @param {string} path Full path of the module.
   * @param {?} exports
   * @param {string=} opt_closureNamespace Closure namespace to associate with
   *     this module.
   */
  goog.LoadController.prototype.registerEs6ModuleExports = function(
      path, exports, opt_closureNamespace) {};


  /**
   * Sets the current module state.
   *
   * @param {goog.ModuleType} type Type of module.
   */
  goog.LoadController.prototype.setModuleState = function(type) {};


  /**
   * Clears the current module state.
   */
  goog.LoadController.prototype.clearModuleState = function() {};


  /**
   * Registers a callback to call once the dependency is actually requested
   * via goog.require + all of the immediate dependencies have been loaded or
   * all other files have been loaded. Allows for lazy loading until
   * require'd without pausing dependency loading, which is needed on old IE.
   *
   * @param {!Function} callback
   */
  goog.LoadController.prototype.defer = function(callback) {};


  /**
   * @return {boolean}
   */
  goog.LoadController.prototype.areDepsLoaded = function() {};


  /**
   * Basic super class for all dependencies Closure Library can load.
   *
   * This default implementation is designed to load untranspiled, non-module
   * scripts in a web broswer.
   *
   * For transpiled non-goog.module files {@see goog.TranspiledDependency}.
   * For goog.modules see {@see goog.GoogModuleDependency}.
   * For untranspiled ES6 modules {@see goog.Es6ModuleDependency}.
   *
   * @param {string} path Absolute path of this script.
   * @param {string} relativePath Path of this script relative to goog.basePath.
   * @param {!Array<string>} provides goog.provided or goog.module symbols
   *     in this file.
   * @param {!Array<string>} requires goog symbols or relative paths to Closure
   *     this depends on.
   * @param {!Object<string, string>} loadFlags
   * @struct @constructor
   */
  goog.Dependency = function(
      path, relativePath, provides, requires, loadFlags) {
    /** @const */
    this.path = path;
    /** @const */
    this.relativePath = relativePath;
    /** @const */
    this.provides = provides;
    /** @const */
    this.requires = requires;
    /** @const */
    this.loadFlags = loadFlags;
    /** @private {boolean} */
    this.loaded_ = false;
    /** @private {!Array<function()>} */
    this.loadCallbacks_ = [];
  };


  /**
   * @return {string} The pathname part of this dependency's path if it is a
   *     URI.
   */
  goog.Dependency.prototype.getPathName = function() {
    var pathName = this.path;
    var protocolIndex = pathName.indexOf('://');
    if (protocolIndex >= 0) {
      pathName = pathName.substring(protocolIndex + 3);
      var slashIndex = pathName.indexOf('/');
      if (slashIndex >= 0) {
        pathName = pathName.substring(slashIndex + 1);
      }
    }
    return pathName;
  };


  /**
   * @param {function()} callback Callback to fire as soon as this has loaded.
   * @final
   */
  goog.Dependency.prototype.onLoad = function(callback) {
    if (this.loaded_) {
      callback();
    } else {
      this.loadCallbacks_.push(callback);
    }
  };


  /**
   * Marks this dependency as loaded and fires any callbacks registered with
   * onLoad.
   * @final
   */
  goog.Dependency.prototype.loaded = function() {
    this.loaded_ = true;
    var callbacks = this.loadCallbacks_;
    this.loadCallbacks_ = [];
    for (var i = 0; i < callbacks.length; i++) {
      callbacks[i]();
    }
  };


  /**
   * Whether or not document.written / appended script tags should be deferred.
   *
   * @private {boolean}
   */
  goog.Dependency.defer_ = false;


  /**
   * Map of script ready / state change callbacks. Old IE cannot handle putting
   * these properties on goog.global.
   *
   * @private @const {!Object<string, function(?):undefined>}
   */
  goog.Dependency.callbackMap_ = {};


  /**
   * @param {function(...?):?} callback
   * @return {string}
   * @private
   */
  goog.Dependency.registerCallback_ = function(callback) {
    var key = Math.random().toString(32);
    goog.Dependency.callbackMap_[key] = callback;
    return key;
  };


  /**
   * @param {string} key
   * @private
   */
  goog.Dependency.unregisterCallback_ = function(key) {
    delete goog.Dependency.callbackMap_[key];
  };


  /**
   * @param {string} key
   * @param {...?} var_args
   * @private
   * @suppress {unusedPrivateMembers}
   */
  goog.Dependency.callback_ = function(key, var_args) {
    if (key in goog.Dependency.callbackMap_) {
      var callback = goog.Dependency.callbackMap_[key];
      var args = [];
      for (var i = 1; i < arguments.length; i++) {
        args.push(arguments[i]);
      }
      callback.apply(undefined, args);
    } else {
      var errorMessage = 'Callback key ' + key +
          ' does not exist (was base.js loaded more than once?).';
      throw Error(errorMessage);
    }
  };


  /**
   * Starts loading this dependency. This dependency can pause loading if it
   * needs to and resume it later via the controller interface.
   *
   * When this is loaded it should call controller.loaded(). Note that this will
   * end up calling the loaded method of this dependency; there is no need to
   * call it explicitly.
   *
   * @param {!goog.LoadController} controller
   */
  goog.Dependency.prototype.load = function(controller) {
    if (goog.global.CLOSURE_IMPORT_SCRIPT) {
      if (goog.global.CLOSURE_IMPORT_SCRIPT(this.path)) {
        controller.loaded();
      } else {
        controller.pause();
      }
      return;
    }

    if (!goog.inHtmlDocument_()) {
      goog.logToConsole_(
          'Cannot use default debug loader outside of HTML documents.');
      if (this.relativePath == 'deps.js') {
        // Some old code is relying on base.js auto loading deps.js failing with
        // no error before later setting CLOSURE_IMPORT_SCRIPT.
        // CLOSURE_IMPORT_SCRIPT should be set *before* base.js is loaded, or
        // CLOSURE_NO_DEPS set to true.
        goog.logToConsole_(
            'Consider setting CLOSURE_IMPORT_SCRIPT before loading base.js, ' +
            'or setting CLOSURE_NO_DEPS to true.');
        controller.loaded();
      } else {
        controller.pause();
      }
      return;
    }

    /** @type {!HTMLDocument} */
    var doc = goog.global.document;

    // If the user tries to require a new symbol after document load,
    // something has gone terribly wrong. Doing a document.write would
    // wipe out the page. This does not apply to the CSP-compliant method
    // of writing script tags.
    if (doc.readyState == 'complete' &&
        !goog.ENABLE_CHROME_APP_SAFE_SCRIPT_LOADING) {
      // Certain test frameworks load base.js multiple times, which tries
      // to write deps.js each time. If that happens, just fail silently.
      // These frameworks wipe the page between each load of base.js, so this
      // is OK.
      var isDeps = /\bdeps.js$/.test(this.path);
      if (isDeps) {
        controller.loaded();
        return;
      } else {
        throw Error('Cannot write "' + this.path + '" after document load');
      }
    }

    if (!goog.ENABLE_CHROME_APP_SAFE_SCRIPT_LOADING &&
        goog.isDocumentLoading_()) {
      var key = goog.Dependency.registerCallback_(function(script) {
        if (!goog.DebugLoader_.IS_OLD_IE_ || script.readyState == 'complete') {
          goog.Dependency.unregisterCallback_(key);
          controller.loaded();
        }
      });
      var nonceAttr = !goog.DebugLoader_.IS_OLD_IE_ && goog.getScriptNonce() ?
          ' nonce="' + goog.getScriptNonce() + '"' :
          '';
      var event =
          goog.DebugLoader_.IS_OLD_IE_ ? 'onreadystatechange' : 'onload';
      var defer = goog.Dependency.defer_ ? 'defer' : '';
      var script = '<script src="' + this.path + '" ' + event +
          '="goog.Dependency.callback_(\'' + key +
          '\', this)" type="text/javascript" ' + defer + nonceAttr + '><' +
          '/script>';
      doc.write(
          goog.TRUSTED_TYPES_POLICY_ ?
              goog.TRUSTED_TYPES_POLICY_.createHTML(script) :
              script);
    } else {
      var scriptEl =
          /** @type {!HTMLScriptElement} */ (doc.createElement('script'));
      scriptEl.defer = goog.Dependency.defer_;
      scriptEl.async = false;
      scriptEl.type = 'text/javascript';

      // If CSP nonces are used, propagate them to dynamically created scripts.
      // This is necessary to allow nonce-based CSPs without 'strict-dynamic'.
      var nonce = goog.getScriptNonce();
      if (nonce) {
        scriptEl.setAttribute('nonce', nonce);
      }

      if (goog.DebugLoader_.IS_OLD_IE_) {
        // Execution order is not guaranteed on old IE, halt loading and write
        // these scripts one at a time, after each loads.
        controller.pause();
        scriptEl.onreadystatechange = function() {
          if (scriptEl.readyState == 'loaded' ||
              scriptEl.readyState == 'complete') {
            controller.loaded();
            controller.resume();
          }
        };
      } else {
        scriptEl.onload = function() {
          scriptEl.onload = null;
          controller.loaded();
        };
      }

      scriptEl.src = goog.TRUSTED_TYPES_POLICY_ ?
          goog.TRUSTED_TYPES_POLICY_.createScriptURL(this.path) :
          this.path;
      doc.head.appendChild(scriptEl);
    }
  };


  /**
   * @param {string} path Absolute path of this script.
   * @param {string} relativePath Path of this script relative to goog.basePath.
   * @param {!Array<string>} provides Should be an empty array.
   *     TODO(johnplaisted) add support for adding closure namespaces to ES6
   *     modules for interop purposes.
   * @param {!Array<string>} requires goog symbols or relative paths to Closure
   *     this depends on.
   * @param {!Object<string, string>} loadFlags
   * @struct @constructor
   * @extends {goog.Dependency}
   */
  goog.Es6ModuleDependency = function(
      path, relativePath, provides, requires, loadFlags) {
    goog.Es6ModuleDependency.base(
        this, 'constructor', path, relativePath, provides, requires, loadFlags);
  };
  goog.inherits(goog.Es6ModuleDependency, goog.Dependency);


  /** @override */
  goog.Es6ModuleDependency.prototype.load = function(controller) {
    if (goog.global.CLOSURE_IMPORT_SCRIPT) {
      if (goog.global.CLOSURE_IMPORT_SCRIPT(this.path)) {
        controller.loaded();
      } else {
        controller.pause();
      }
      return;
    }

    if (!goog.inHtmlDocument_()) {
      goog.logToConsole_(
          'Cannot use default debug loader outside of HTML documents.');
      controller.pause();
      return;
    }

    /** @type {!HTMLDocument} */
    var doc = goog.global.document;

    var dep = this;

    // TODO(johnplaisted): Does document.writing really speed up anything? Any
    // difference between this and just waiting for interactive mode and then
    // appending?
    function write(src, contents) {
      if (contents) {
        var script = '<script type="module" crossorigin>' + contents + '</' +
            'script>';
        doc.write(
            goog.TRUSTED_TYPES_POLICY_ ?
                goog.TRUSTED_TYPES_POLICY_.createHTML(script) :
                script);
      } else {
        var script = '<script type="module" crossorigin src="' + src + '"></' +
            'script>';
        doc.write(
            goog.TRUSTED_TYPES_POLICY_ ?
                goog.TRUSTED_TYPES_POLICY_.createHTML(script) :
                script);
      }
    }

    function append(src, contents) {
      var scriptEl =
          /** @type {!HTMLScriptElement} */ (doc.createElement('script'));
      scriptEl.defer = true;
      scriptEl.async = false;
      scriptEl.type = 'module';
      scriptEl.setAttribute('crossorigin', true);

      // If CSP nonces are used, propagate them to dynamically created scripts.
      // This is necessary to allow nonce-based CSPs without 'strict-dynamic'.
      var nonce = goog.getScriptNonce();
      if (nonce) {
        scriptEl.setAttribute('nonce', nonce);
      }

      if (contents) {
        scriptEl.textContent = goog.TRUSTED_TYPES_POLICY_ ?
            goog.TRUSTED_TYPES_POLICY_.createScript(contents) :
            contents;
      } else {
        scriptEl.src = goog.TRUSTED_TYPES_POLICY_ ?
            goog.TRUSTED_TYPES_POLICY_.createScriptURL(src) :
            src;
      }

      doc.head.appendChild(scriptEl);
    }

    var create;

    if (goog.isDocumentLoading_()) {
      create = write;
      // We can ONLY call document.write if we are guaranteed that any
      // non-module script tags document.written after this are deferred.
      // Small optimization, in theory document.writing is faster.
      goog.Dependency.defer_ = true;
    } else {
      create = append;
    }

    // Write 4 separate tags here:
    // 1) Sets the module state at the correct time (just before execution).
    // 2) A src node for this, which just hopefully lets the browser load it a
    //    little early (no need to parse #3).
    // 3) Import the module and register it.
    // 4) Clear the module state at the correct time. Guaranteed to run even
    //    if there is an error in the module (#3 will not run if there is an
    //    error in the module).
    var beforeKey = goog.Dependency.registerCallback_(function() {
      goog.Dependency.unregisterCallback_(beforeKey);
      controller.setModuleState(goog.ModuleType.ES6);
    });
    create(undefined, 'goog.Dependency.callback_("' + beforeKey + '")');

    // TODO(johnplaisted): Does this really speed up anything?
    create(this.path, undefined);

    var registerKey = goog.Dependency.registerCallback_(function(exports) {
      goog.Dependency.unregisterCallback_(registerKey);
      controller.registerEs6ModuleExports(
          dep.path, exports, goog.moduleLoaderState_.moduleName);
    });
    create(
        undefined,
        'import * as m from "' + this.path + '"; goog.Dependency.callback_("' +
            registerKey + '", m)');

    var afterKey = goog.Dependency.registerCallback_(function() {
      goog.Dependency.unregisterCallback_(afterKey);
      controller.clearModuleState();
      controller.loaded();
    });
    create(undefined, 'goog.Dependency.callback_("' + afterKey + '")');
  };


  /**
   * Superclass of any dependency that needs to be loaded into memory,
   * transformed, and then eval'd (goog.modules and transpiled files).
   *
   * @param {string} path Absolute path of this script.
   * @param {string} relativePath Path of this script relative to goog.basePath.
   * @param {!Array<string>} provides goog.provided or goog.module symbols
   *     in this file.
   * @param {!Array<string>} requires goog symbols or relative paths to Closure
   *     this depends on.
   * @param {!Object<string, string>} loadFlags
   * @struct @constructor @abstract
   * @extends {goog.Dependency}
   */
  goog.TransformedDependency = function(
      path, relativePath, provides, requires, loadFlags) {
    goog.TransformedDependency.base(
        this, 'constructor', path, relativePath, provides, requires, loadFlags);
    /** @private {?string} */
    this.contents_ = null;

    /**
     * Whether to lazily make the synchronous XHR (when goog.require'd) or make
     * the synchronous XHR when initially loading. On FireFox 61 there is a bug
     * where an ES6 module cannot make a synchronous XHR (rather, it can, but if
     * it does then no other ES6 modules will load after).
     *
     * tl;dr we lazy load due to bugs on older browsers and eager load due to
     * bugs on newer ones.
     *
     * https://bugzilla.mozilla.org/show_bug.cgi?id=1477090
     *
     * @private @const {boolean}
     */
    this.lazyFetch_ = !goog.inHtmlDocument_() ||
        !('noModule' in goog.global.document.createElement('script'));
  };
  goog.inherits(goog.TransformedDependency, goog.Dependency);


  /** @override */
  goog.TransformedDependency.prototype.load = function(controller) {
    var dep = this;

    function fetch() {
      dep.contents_ = goog.loadFileSync_(dep.path);

      if (dep.contents_) {
        dep.contents_ = dep.transform(dep.contents_);
        if (dep.contents_) {
          dep.contents_ += '\n//# sourceURL=' + dep.path;
        }
      }
    }

    if (goog.global.CLOSURE_IMPORT_SCRIPT) {
      fetch();
      if (this.contents_ &&
          goog.global.CLOSURE_IMPORT_SCRIPT('', this.contents_)) {
        this.contents_ = null;
        controller.loaded();
      } else {
        controller.pause();
      }
      return;
    }


    var isEs6 = this.loadFlags['module'] == goog.ModuleType.ES6;

    if (!this.lazyFetch_) {
      fetch();
    }

    function load() {
      if (dep.lazyFetch_) {
        fetch();
      }

      if (!dep.contents_) {
        // loadFileSync_ or transform are responsible. Assume they logged an
        // error.
        return;
      }

      if (isEs6) {
        controller.setModuleState(goog.ModuleType.ES6);
      }

      var namespace;

      try {
        var contents = dep.contents_;
        dep.contents_ = null;
        goog.globalEval(contents);
        if (isEs6) {
          namespace = goog.moduleLoaderState_.moduleName;
        }
      } finally {
        if (isEs6) {
          controller.clearModuleState();
        }
      }

      if (isEs6) {
        // Due to circular dependencies this may not be available for require
        // right now.
        goog.global['$jscomp']['require']['ensure'](
            [dep.getPathName()], function() {
              controller.registerEs6ModuleExports(
                  dep.path,
                  goog.global['$jscomp']['require'](dep.getPathName()),
                  namespace);
            });
      }

      controller.loaded();
    }

    // Do not fetch now; in FireFox 47 the synchronous XHR doesn't block all
    // events. If we fetched now and then document.write'd the contents the
    // document.write would be an eval and would execute too soon! Instead write
    // a script tag to fetch and eval synchronously at the correct time.
    function fetchInOwnScriptThenLoad() {
      /** @type {!HTMLDocument} */
      var doc = goog.global.document;

      var key = goog.Dependency.registerCallback_(function() {
        goog.Dependency.unregisterCallback_(key);
        load();
      });

      var script = '<script type="text/javascript">' +
          goog.protectScriptTag_('goog.Dependency.callback_("' + key + '");') +
          '</' +
          'script>';
      doc.write(
          goog.TRUSTED_TYPES_POLICY_ ?
              goog.TRUSTED_TYPES_POLICY_.createHTML(script) :
              script);
    }

    // If one thing is pending it is this.
    var anythingElsePending = controller.pending().length > 1;

    // If anything else is loading we need to lazy load due to bugs in old IE.
    // Specifically script tags with src and script tags with contents could
    // execute out of order if document.write is used, so we cannot use
    // document.write. Do not pause here; it breaks old IE as well.
    var useOldIeWorkAround =
        anythingElsePending && goog.DebugLoader_.IS_OLD_IE_;

    // Additionally if we are meant to defer scripts but the page is still
    // loading (e.g. an ES6 module is loading) then also defer. Or if we are
    // meant to defer and anything else is pending then defer (those may be
    // scripts that did not need transformation and are just script tags with
    // defer set to true, and we need to evaluate after that deferred script).
    var needsAsyncLoading = goog.Dependency.defer_ &&
        (anythingElsePending || goog.isDocumentLoading_());

    if (useOldIeWorkAround || needsAsyncLoading) {
      // Note that we only defer when we have to rather than 100% of the time.
      // Always defering would work, but then in theory the order of
      // goog.require calls would then matter. We want to enforce that most of
      // the time the order of the require calls does not matter.
      controller.defer(function() {
        load();
      });
      return;
    }
    // TODO(johnplaisted): Externs are missing onreadystatechange for
    // HTMLDocument.
    /** @type {?} */
    var doc = goog.global.document;

    var isInternetExplorer =
        goog.inHtmlDocument_() && 'ActiveXObject' in goog.global;

    // Don't delay in any version of IE. There's bug around this that will
    // cause out of order script execution. This means that on older IE ES6
    // modules will load too early (while the document is still loading + the
    // dom is not available). The other option is to load too late (when the
    // document is complete and the onload even will never fire). This seems
    // to be the lesser of two evils as scripts already act like the former.
    if (isEs6 && goog.inHtmlDocument_() && goog.isDocumentLoading_() &&
        !isInternetExplorer) {
      goog.Dependency.defer_ = true;
      // Transpiled ES6 modules still need to load like regular ES6 modules,
      // aka only after the document is interactive.
      controller.pause();
      var oldCallback = doc.onreadystatechange;
      doc.onreadystatechange = function() {
        if (doc.readyState == 'interactive') {
          doc.onreadystatechange = oldCallback;
          load();
          controller.resume();
        }
        if (goog.isFunction(oldCallback)) {
          oldCallback.apply(undefined, arguments);
        }
      };
    } else {
      // Always eval on old IE.
      if (goog.DebugLoader_.IS_OLD_IE_ || !goog.inHtmlDocument_() ||
          !goog.isDocumentLoading_()) {
        load();
      } else {
        fetchInOwnScriptThenLoad();
      }
    }
  };


  /**
   * @param {string} contents
   * @return {string}
   * @abstract
   */
  goog.TransformedDependency.prototype.transform = function(contents) {};


  /**
   * Any non-goog.module dependency which needs to be transpiled before eval.
   *
   * @param {string} path Absolute path of this script.
   * @param {string} relativePath Path of this script relative to goog.basePath.
   * @param {!Array<string>} provides goog.provided or goog.module symbols
   *     in this file.
   * @param {!Array<string>} requires goog symbols or relative paths to Closure
   *     this depends on.
   * @param {!Object<string, string>} loadFlags
   * @param {!goog.Transpiler} transpiler
   * @struct @constructor
   * @extends {goog.TransformedDependency}
   */
  goog.TranspiledDependency = function(
      path, relativePath, provides, requires, loadFlags, transpiler) {
    goog.TranspiledDependency.base(
        this, 'constructor', path, relativePath, provides, requires, loadFlags);
    /** @protected @const*/
    this.transpiler = transpiler;
  };
  goog.inherits(goog.TranspiledDependency, goog.TransformedDependency);


  /** @override */
  goog.TranspiledDependency.prototype.transform = function(contents) {
    // Transpile with the pathname so that ES6 modules are domain agnostic.
    return this.transpiler.transpile(contents, this.getPathName());
  };


  /**
   * An ES6 module dependency that was transpiled to a jscomp module outside
   * of the debug loader, e.g. server side.
   *
   * @param {string} path Absolute path of this script.
   * @param {string} relativePath Path of this script relative to goog.basePath.
   * @param {!Array<string>} provides goog.provided or goog.module symbols
   *     in this file.
   * @param {!Array<string>} requires goog symbols or relative paths to Closure
   *     this depends on.
   * @param {!Object<string, string>} loadFlags
   * @struct @constructor
   * @extends {goog.TransformedDependency}
   */
  goog.PreTranspiledEs6ModuleDependency = function(
      path, relativePath, provides, requires, loadFlags) {
    goog.PreTranspiledEs6ModuleDependency.base(
        this, 'constructor', path, relativePath, provides, requires, loadFlags);
  };
  goog.inherits(
      goog.PreTranspiledEs6ModuleDependency, goog.TransformedDependency);


  /** @override */
  goog.PreTranspiledEs6ModuleDependency.prototype.transform = function(
      contents) {
    return contents;
  };


  /**
   * A goog.module, transpiled or not. Will always perform some minimal
   * transformation even when not transpiled to wrap in a goog.loadModule
   * statement.
   *
   * @param {string} path Absolute path of this script.
   * @param {string} relativePath Path of this script relative to goog.basePath.
   * @param {!Array<string>} provides goog.provided or goog.module symbols
   *     in this file.
   * @param {!Array<string>} requires goog symbols or relative paths to Closure
   *     this depends on.
   * @param {!Object<string, string>} loadFlags
   * @param {boolean} needsTranspile
   * @param {!goog.Transpiler} transpiler
   * @struct @constructor
   * @extends {goog.TransformedDependency}
   */
  goog.GoogModuleDependency = function(
      path, relativePath, provides, requires, loadFlags, needsTranspile,
      transpiler) {
    goog.GoogModuleDependency.base(
        this, 'constructor', path, relativePath, provides, requires, loadFlags);
    /** @private @const */
    this.needsTranspile_ = needsTranspile;
    /** @private @const */
    this.transpiler_ = transpiler;
  };
  goog.inherits(goog.GoogModuleDependency, goog.TransformedDependency);


  /** @override */
  goog.GoogModuleDependency.prototype.transform = function(contents) {
    if (this.needsTranspile_) {
      contents = this.transpiler_.transpile(contents, this.getPathName());
    }

    if (!goog.LOAD_MODULE_USING_EVAL || goog.global.JSON === undefined) {
      return '' +
          'goog.loadModule(function(exports) {' +
          '"use strict";' + contents +
          '\n' +  // terminate any trailing single line comment.
          ';return exports' +
          '});' +
          '\n//# sourceURL=' + this.path + '\n';
    } else {
      return '' +
          'goog.loadModule(' +
          goog.global.JSON.stringify(
              contents + '\n//# sourceURL=' + this.path + '\n') +
          ');';
    }
  };


  /**
   * Whether the browser is IE9 or earlier, which needs special handling
   * for deferred modules.
   * @const @private {boolean}
   */
  goog.DebugLoader_.IS_OLD_IE_ = !!(
      !goog.global.atob && goog.global.document && goog.global.document['all']);


  /**
   * @param {string} relPath
   * @param {!Array<string>|undefined} provides
   * @param {!Array<string>} requires
   * @param {boolean|!Object<string>=} opt_loadFlags
   * @see goog.addDependency
   */
  goog.DebugLoader_.prototype.addDependency = function(
      relPath, provides, requires, opt_loadFlags) {
    provides = provides || [];
    relPath = relPath.replace(/\\/g, '/');
    var path = goog.normalizePath_(goog.basePath + relPath);
    if (!opt_loadFlags || typeof opt_loadFlags === 'boolean') {
      opt_loadFlags = opt_loadFlags ? {'module': goog.ModuleType.GOOG} : {};
    }
    var dep = this.factory_.createDependency(
        path, relPath, provides, requires, opt_loadFlags,
        goog.transpiler_.needsTranspile(
            opt_loadFlags['lang'] || 'es3', opt_loadFlags['module']));
    this.dependencies_[path] = dep;
    for (var i = 0; i < provides.length; i++) {
      this.idToPath_[provides[i]] = path;
    }
    this.idToPath_[relPath] = path;
  };


  /**
   * Creates goog.Dependency instances for the debug loader to load.
   *
   * Should be overridden to have the debug loader use custom subclasses of
   * goog.Dependency.
   *
   * @param {!goog.Transpiler} transpiler
   * @struct @constructor
   */
  goog.DependencyFactory = function(transpiler) {
    /** @protected @const */
    this.transpiler = transpiler;
  };


  /**
   * @param {string} path Absolute path of the file.
   * @param {string} relativePath Path relative to closure’s base.js.
   * @param {!Array<string>} provides Array of provided goog.provide/module ids.
   * @param {!Array<string>} requires Array of required goog.provide/module /
   *     relative ES6 module paths.
   * @param {!Object<string, string>} loadFlags
   * @param {boolean} needsTranspile True if the file needs to be transpiled
   *     per the goog.Transpiler.
   * @return {!goog.Dependency}
   */
  goog.DependencyFactory.prototype.createDependency = function(
      path, relativePath, provides, requires, loadFlags, needsTranspile) {

    if (loadFlags['module'] == goog.ModuleType.GOOG) {
      return new goog.GoogModuleDependency(
          path, relativePath, provides, requires, loadFlags, needsTranspile,
          this.transpiler);
    } else if (needsTranspile) {
      return new goog.TranspiledDependency(
          path, relativePath, provides, requires, loadFlags, this.transpiler);
    } else {
      if (loadFlags['module'] == goog.ModuleType.ES6) {
        if (goog.TRANSPILE == 'never' && goog.ASSUME_ES_MODULES_TRANSPILED) {
          return new goog.PreTranspiledEs6ModuleDependency(
              path, relativePath, provides, requires, loadFlags);
        } else {
          return new goog.Es6ModuleDependency(
              path, relativePath, provides, requires, loadFlags);
        }
      } else {
        return new goog.Dependency(
            path, relativePath, provides, requires, loadFlags);
      }
    }
  };


  /** @private @const */
  goog.debugLoader_ = new goog.DebugLoader_();


  /**
   * Loads the Closure Dependency file.
   *
   * Exposed a public function so CLOSURE_NO_DEPS can be set to false, base
   * loaded, setDependencyFactory called, and then this called. i.e. allows
   * custom loading of the deps file.
   */
  goog.loadClosureDeps = function() {
    goog.debugLoader_.loadClosureDeps();
  };


  /**
   * Sets the dependency factory, which can be used to create custom
   * goog.Dependency implementations to control how dependencies are loaded.
   *
   * Note: if you wish to call this function and provide your own implemnetation
   * it is a wise idea to set CLOSURE_NO_DEPS to true, otherwise the dependency
   * file and all of its goog.addDependency calls will use the default factory.
   * You can call goog.loadClosureDeps to load the Closure dependency file
   * later, after your factory is injected.
   *
   * @param {!goog.DependencyFactory} factory
   */
  goog.setDependencyFactory = function(factory) {
    goog.debugLoader_.setDependencyFactory(factory);
  };


  if (!goog.global.CLOSURE_NO_DEPS) {
    goog.debugLoader_.loadClosureDeps();
  }


  /**
   * Bootstraps the given namespaces and calls the callback once they are
   * available either via goog.require. This is a replacement for using
   * `goog.require` to bootstrap Closure JavaScript. Previously a `goog.require`
   * in an HTML file would guarantee that the require'd namespace was available
   * in the next immediate script tag. With ES6 modules this no longer a
   * guarantee.
   *
   * @param {!Array<string>} namespaces
   * @param {function(): ?} callback Function to call once all the namespaces
   *     have loaded. Always called asynchronously.
   */
  goog.bootstrap = function(namespaces, callback) {
    goog.debugLoader_.bootstrap(namespaces, callback);
  };
}


/**
 * @define {string} Trusted Types policy name. If non-empty then Closure will
 * use Trusted Types.
 */
goog.TRUSTED_TYPES_POLICY_NAME =
    goog.define('goog.TRUSTED_TYPES_POLICY_NAME', '');


/**
 * Returns the parameter.
 * @param {string} s
 * @return {string}
 * @private
 */
goog.identity_ = function(s) {
  return s;
};


/**
 * Creates Trusted Types policy if Trusted Types are supported by the browser.
 * The policy just blesses any string as a Trusted Type. It is not visibility
 * restricted because anyone can also call TrustedTypes.createPolicy directly.
 * However, the allowed names should be restricted by a HTTP header and the
 * reference to the created policy should be visibility restricted.
 * @param {string} name
 * @return {?TrustedTypePolicy}
 */
goog.createTrustedTypesPolicy = function(name) {
  var policy = null;
  // TODO(koto): Remove window.TrustedTypes variant when the newer API ships.
  var policyFactory = goog.global.trustedTypes || goog.global.TrustedTypes;
  if (!policyFactory || !policyFactory.createPolicy) {
    return policy;
  }
  // TrustedTypes.createPolicy throws if called with a name that is already
  // registered, even in report-only mode. Until the API changes, catch the
  // error not to break the applications functionally. In such case, the code
  // will fall back to using regular Safe Types.
  // TODO(koto): Remove catching once createPolicy API stops throwing.
  try {
    policy = policyFactory.createPolicy(name, {
      createHTML: goog.identity_,
      createScript: goog.identity_,
      createScriptURL: goog.identity_,
      createURL: goog.identity_
    });
  } catch (e) {
    goog.logToConsole_(e.message);
  }
  return policy;
};


/** @private @const {?TrustedTypePolicy} */
goog.TRUSTED_TYPES_POLICY_ = goog.TRUSTED_TYPES_POLICY_NAME ?
    goog.createTrustedTypesPolicy(goog.TRUSTED_TYPES_POLICY_NAME + '#base') :
    null;

// Copyright 2019 The Closure Library Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//      http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS-IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//
// This file has been auto-generated by GenJsDeps, please do not edit.

// Disable Clang formatter for this file.
// See http://goo.gl/SdiwZH
// clang-format off

goog.addDependency('collections/sets.js', ['goog.collections.sets'], ['goog.labs.collections.iterables'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('collections/sets_test.js', ['goog.collections.setsTest'], ['goog.collections.sets', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('../../third_party/closure/goog/mochikit/async/deferred.js', ['goog.async.Deferred', 'goog.async.Deferred.AlreadyCalledError', 'goog.async.Deferred.CanceledError'], ['goog.Promise', 'goog.Thenable', 'goog.array', 'goog.asserts', 'goog.debug.Error'], {});
goog.addDependency('../../third_party/closure/goog/mochikit/async/deferred_async_test.js', ['goog.async.deferredAsyncTest'], ['goog.async.Deferred', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('../../third_party/closure/goog/mochikit/async/deferred_test.js', ['goog.async.deferredTest'], ['goog.Promise', 'goog.Thenable', 'goog.async.Deferred', 'goog.testing.MockClock', 'goog.testing.PropertyReplacer', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('../../third_party/closure/goog/mochikit/async/deferredlist.js', ['goog.async.DeferredList'], ['goog.async.Deferred'], {});
goog.addDependency('../../third_party/closure/goog/mochikit/async/deferredlist_test.js', ['goog.async.deferredListTest'], ['goog.array', 'goog.async.Deferred', 'goog.async.DeferredList', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('proto/proto.js', ['goog.proto'], ['goog.proto.Serializer'], {});
goog.addDependency('proto/serializer.js', ['goog.proto.Serializer'], ['goog.json.Serializer', 'goog.string'], {});
goog.addDependency('proto/serializer_test.js', ['goog.protoTest'], ['goog.proto', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('a11y/aria/announcer.js', ['goog.a11y.aria.Announcer'], ['goog.Disposable', 'goog.Timer', 'goog.a11y.aria', 'goog.a11y.aria.LivePriority', 'goog.a11y.aria.State', 'goog.dom', 'goog.dom.TagName', 'goog.object'], {});
goog.addDependency('a11y/aria/announcer_test.js', ['goog.a11y.aria.AnnouncerTest'], ['goog.a11y.aria', 'goog.a11y.aria.Announcer', 'goog.a11y.aria.LivePriority', 'goog.a11y.aria.State', 'goog.array', 'goog.dom', 'goog.dom.TagName', 'goog.dom.iframe', 'goog.testing.MockClock', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('a11y/aria/aria.js', ['goog.a11y.aria'], ['goog.a11y.aria.Role', 'goog.a11y.aria.State', 'goog.a11y.aria.datatables', 'goog.array', 'goog.asserts', 'goog.dom', 'goog.dom.TagName', 'goog.object', 'goog.string'], {});
goog.addDependency('a11y/aria/aria_test.js', ['goog.a11y.ariaTest'], ['goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.a11y.aria.State', 'goog.dom', 'goog.dom.TagName', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('a11y/aria/attributes.js', ['goog.a11y.aria.AutoCompleteValues', 'goog.a11y.aria.CheckedValues', 'goog.a11y.aria.DropEffectValues', 'goog.a11y.aria.ExpandedValues', 'goog.a11y.aria.GrabbedValues', 'goog.a11y.aria.InvalidValues', 'goog.a11y.aria.LivePriority', 'goog.a11y.aria.OrientationValues', 'goog.a11y.aria.PressedValues', 'goog.a11y.aria.RelevantValues', 'goog.a11y.aria.SelectedValues', 'goog.a11y.aria.SortValues', 'goog.a11y.aria.State'], [], {});
goog.addDependency('a11y/aria/datatables.js', ['goog.a11y.aria.datatables'], ['goog.a11y.aria.State', 'goog.object'], {});
goog.addDependency('a11y/aria/roles.js', ['goog.a11y.aria.Role'], [], {});
goog.addDependency('array/array.js', ['goog.array'], ['goog.asserts'], {});
goog.addDependency('array/array_test.js', ['goog.arrayTest'], ['goog.array', 'goog.dom', 'goog.dom.TagName', 'goog.testing.PropertyReplacer', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es7', 'module': 'goog'});
goog.addDependency('asserts/asserts.js', ['goog.asserts', 'goog.asserts.AssertionError'], ['goog.debug.Error', 'goog.dom.NodeType'], {});
goog.addDependency('asserts/asserts_test.js', ['goog.assertsTest'], ['goog.asserts', 'goog.asserts.AssertionError', 'goog.dom', 'goog.dom.TagName', 'goog.reflect', 'goog.string', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('async/animationdelay.js', ['goog.async.AnimationDelay'], ['goog.Disposable', 'goog.events', 'goog.functions'], {});
goog.addDependency('async/animationdelay_test.js', ['goog.async.AnimationDelayTest'], ['goog.Promise', 'goog.Timer', 'goog.async.AnimationDelay', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('async/conditionaldelay.js', ['goog.async.ConditionalDelay'], ['goog.Disposable', 'goog.async.Delay'], {});
goog.addDependency('async/conditionaldelay_test.js', ['goog.async.ConditionalDelayTest'], ['goog.async.ConditionalDelay', 'goog.testing.MockClock', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('async/debouncer.js', ['goog.async.Debouncer'], ['goog.Disposable', 'goog.Timer'], {});
goog.addDependency('async/debouncer_test.js', ['goog.async.DebouncerTest'], ['goog.array', 'goog.async.Debouncer', 'goog.testing.MockClock', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('async/delay.js', ['goog.Delay', 'goog.async.Delay'], ['goog.Disposable', 'goog.Timer'], {});
goog.addDependency('async/delay_test.js', ['goog.async.DelayTest'], ['goog.async.Delay', 'goog.testing.MockClock', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('async/freelist.js', ['goog.async.FreeList'], [], {'lang': 'es6'});
goog.addDependency('async/freelist_test.js', ['goog.async.FreeListTest'], ['goog.async.FreeList', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('async/nexttick.js', ['goog.async.nextTick', 'goog.async.throwException'], ['goog.debug.entryPointRegistry', 'goog.dom', 'goog.dom.TagName', 'goog.dom.safe', 'goog.functions', 'goog.html.SafeHtml', 'goog.html.TrustedResourceUrl', 'goog.labs.userAgent.browser', 'goog.labs.userAgent.engine', 'goog.string.Const'], {});
goog.addDependency('async/nexttick_test.js', ['goog.async.nextTickTest'], ['goog.Promise', 'goog.async.nextTick', 'goog.debug.ErrorHandler', 'goog.debug.entryPointRegistry', 'goog.dom', 'goog.dom.TagName', 'goog.labs.userAgent.browser', 'goog.testing.MockClock', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('async/run.js', ['goog.async.run'], ['goog.async.WorkQueue', 'goog.async.nextTick', 'goog.async.throwException'], {});
goog.addDependency('async/run_next_tick_test.js', ['goog.async.runNextTickTest'], ['goog.async.run', 'goog.testing.MockClock', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('async/run_test.js', ['goog.async.runTest'], ['goog.async.run', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('async/throttle.js', ['goog.Throttle', 'goog.async.Throttle'], ['goog.Disposable', 'goog.Timer'], {});
goog.addDependency('async/throttle_test.js', ['goog.async.ThrottleTest'], ['goog.async.Throttle', 'goog.testing.MockClock', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('async/workqueue.js', ['goog.async.WorkItem', 'goog.async.WorkQueue'], ['goog.asserts', 'goog.async.FreeList'], {});
goog.addDependency('async/workqueue_test.js', ['goog.async.WorkQueueTest'], ['goog.async.WorkQueue', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('base.js', ['goog'], [], {});
goog.addDependency('base_module_test.js', ['goog.baseModuleTest'], ['goog.Timer', 'goog.test_module', 'goog.testing.PropertyReplacer', 'goog.testing.jsunit', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('base_test.js', ['goog.baseTest'], ['goog.Promise', 'goog.Timer', 'goog.Uri', 'goog.dom', 'goog.dom.TagName', 'goog.object', 'goog.test_module', 'goog.testing.PropertyReplacer', 'goog.testing.jsunit', 'goog.testing.recordFunction', 'goog.userAgent'], {'lang': 'es6'});
goog.addDependency('color/alpha.js', ['goog.color.alpha'], ['goog.color'], {});
goog.addDependency('color/alpha_test.js', ['goog.color.alphaTest'], ['goog.array', 'goog.color.alpha', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('color/color.js', ['goog.color', 'goog.color.Hsl', 'goog.color.Hsv', 'goog.color.Rgb'], ['goog.color.names', 'goog.math'], {});
goog.addDependency('color/color_test.js', ['goog.colorTest'], ['goog.array', 'goog.color', 'goog.color.names', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('color/names.js', ['goog.color.names'], [], {});
goog.addDependency('crypt/aes.js', ['goog.crypt.Aes'], ['goog.asserts', 'goog.crypt.BlockCipher'], {});
goog.addDependency('crypt/aes_test.js', ['goog.crypt.AesTest'], ['goog.crypt', 'goog.crypt.Aes', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('crypt/arc4.js', ['goog.crypt.Arc4'], ['goog.asserts'], {});
goog.addDependency('crypt/arc4_test.js', ['goog.crypt.Arc4Test'], ['goog.array', 'goog.crypt.Arc4', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('crypt/base64.js', ['goog.crypt.base64'], ['goog.asserts', 'goog.crypt', 'goog.string', 'goog.userAgent', 'goog.userAgent.product'], {'lang': 'es5'});
goog.addDependency('crypt/base64_test.js', ['goog.crypt.base64Test'], ['goog.crypt', 'goog.crypt.base64', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('crypt/basen.js', ['goog.crypt.baseN'], [], {'lang': 'es6'});
goog.addDependency('crypt/basen_test.js', ['goog.crypt.baseNTest'], ['goog.crypt.baseN', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('crypt/blobhasher.js', ['goog.crypt.BlobHasher', 'goog.crypt.BlobHasher.EventType'], ['goog.asserts', 'goog.events.EventTarget', 'goog.fs', 'goog.log'], {});
goog.addDependency('crypt/blobhasher_test.js', ['goog.crypt.BlobHasherTest'], ['goog.crypt', 'goog.crypt.BlobHasher', 'goog.crypt.Md5', 'goog.events', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('crypt/blockcipher.js', ['goog.crypt.BlockCipher'], [], {});
goog.addDependency('crypt/bytestring_perf.js', ['goog.crypt.byteArrayToStringPerf'], ['goog.array', 'goog.dom', 'goog.testing.PerformanceTable'], {});
goog.addDependency('crypt/cbc.js', ['goog.crypt.Cbc'], ['goog.array', 'goog.asserts', 'goog.crypt', 'goog.crypt.BlockCipher'], {});
goog.addDependency('crypt/cbc_test.js', ['goog.crypt.CbcTest'], ['goog.crypt', 'goog.crypt.Aes', 'goog.crypt.Cbc', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('crypt/crypt.js', ['goog.crypt'], ['goog.array', 'goog.asserts'], {});
goog.addDependency('crypt/crypt_test.js', ['goog.cryptTest'], ['goog.crypt', 'goog.string', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('crypt/ctr.js', ['goog.crypt.Ctr'], ['goog.array', 'goog.asserts', 'goog.crypt'], {});
goog.addDependency('crypt/ctr_test.js', ['goog.crypt.CtrTest'], ['goog.crypt', 'goog.crypt.Aes', 'goog.crypt.Ctr', 'goog.testing.jsunit'], {'lang': 'es6'});
goog.addDependency('crypt/hash.js', ['goog.crypt.Hash'], [], {});
goog.addDependency('crypt/hash32.js', ['goog.crypt.hash32'], ['goog.crypt'], {});
goog.addDependency('crypt/hash32_test.js', ['goog.crypt.hash32Test'], ['goog.crypt.hash32', 'goog.testing.TestCase', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('crypt/hashtester.js', ['goog.crypt.hashTester'], ['goog.array', 'goog.crypt', 'goog.dom', 'goog.dom.TagName', 'goog.reflect', 'goog.testing.PerformanceTable', 'goog.testing.PseudoRandom', 'goog.testing.asserts'], {});
goog.addDependency('crypt/hmac.js', ['goog.crypt.Hmac'], ['goog.crypt.Hash'], {});
goog.addDependency('crypt/hmac_test.js', ['goog.crypt.HmacTest'], ['goog.crypt.Hmac', 'goog.crypt.Sha1', 'goog.crypt.hashTester', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('crypt/md5.js', ['goog.crypt.Md5'], ['goog.crypt.Hash'], {});
goog.addDependency('crypt/md5_test.js', ['goog.crypt.Md5Test'], ['goog.crypt', 'goog.crypt.Md5', 'goog.crypt.hashTester', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('crypt/pbkdf2.js', ['goog.crypt.pbkdf2'], ['goog.array', 'goog.asserts', 'goog.crypt', 'goog.crypt.Hmac', 'goog.crypt.Sha1'], {});
goog.addDependency('crypt/pbkdf2_test.js', ['goog.crypt.pbkdf2Test'], ['goog.crypt', 'goog.crypt.pbkdf2', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('crypt/sha1.js', ['goog.crypt.Sha1'], ['goog.crypt.Hash'], {});
goog.addDependency('crypt/sha1_test.js', ['goog.crypt.Sha1Test'], ['goog.crypt', 'goog.crypt.Sha1', 'goog.crypt.hashTester', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('crypt/sha2.js', ['goog.crypt.Sha2'], ['goog.array', 'goog.asserts', 'goog.crypt.Hash'], {});
goog.addDependency('crypt/sha224.js', ['goog.crypt.Sha224'], ['goog.crypt.Sha2'], {});
goog.addDependency('crypt/sha224_test.js', ['goog.crypt.Sha224Test'], ['goog.crypt', 'goog.crypt.Sha224', 'goog.crypt.hashTester', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('crypt/sha256.js', ['goog.crypt.Sha256'], ['goog.crypt.Sha2'], {});
goog.addDependency('crypt/sha256_test.js', ['goog.crypt.Sha256Test'], ['goog.crypt', 'goog.crypt.Sha256', 'goog.crypt.hashTester', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('crypt/sha2_64bit.js', ['goog.crypt.Sha2_64bit'], ['goog.array', 'goog.asserts', 'goog.crypt.Hash', 'goog.math.Long'], {});
goog.addDependency('crypt/sha2_64bit_test.js', ['goog.crypt.Sha2_64bit_test'], ['goog.array', 'goog.crypt', 'goog.crypt.Sha384', 'goog.crypt.Sha512', 'goog.crypt.Sha512_256', 'goog.crypt.hashTester', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('crypt/sha384.js', ['goog.crypt.Sha384'], ['goog.crypt.Sha2_64bit'], {});
goog.addDependency('crypt/sha512.js', ['goog.crypt.Sha512'], ['goog.crypt.Sha2_64bit'], {});
goog.addDependency('crypt/sha512_256.js', ['goog.crypt.Sha512_256'], ['goog.crypt.Sha2_64bit'], {});
goog.addDependency('cssom/cssom.js', ['goog.cssom', 'goog.cssom.CssRuleType'], ['goog.array', 'goog.dom', 'goog.dom.TagName'], {});
goog.addDependency('cssom/cssom_test.js', ['goog.cssomTest'], ['goog.array', 'goog.cssom', 'goog.cssom.CssRuleType', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('cssom/iframe/style.js', ['goog.cssom.iframe.style'], ['goog.asserts', 'goog.cssom', 'goog.dom', 'goog.dom.NodeType', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.string', 'goog.style', 'goog.userAgent'], {});
goog.addDependency('cssom/iframe/style_test.js', ['goog.cssom.iframe.styleTest'], ['goog.cssom', 'goog.cssom.iframe.style', 'goog.dom', 'goog.dom.DomHelper', 'goog.dom.TagName', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('datasource/datamanager.js', ['goog.ds.DataManager'], ['goog.ds.BasicNodeList', 'goog.ds.DataNode', 'goog.ds.Expr', 'goog.object', 'goog.string', 'goog.structs', 'goog.structs.Map'], {});
goog.addDependency('datasource/datasource.js', ['goog.ds.BaseDataNode', 'goog.ds.BasicNodeList', 'goog.ds.DataNode', 'goog.ds.DataNodeList', 'goog.ds.EmptyNodeList', 'goog.ds.LoadState', 'goog.ds.SortedNodeList', 'goog.ds.Util', 'goog.ds.logger'], ['goog.array', 'goog.log'], {});
goog.addDependency('datasource/datasource_test.js', ['goog.ds.JsDataSourceTest'], ['goog.dom.xml', 'goog.ds.DataManager', 'goog.ds.JsDataSource', 'goog.ds.SortedNodeList', 'goog.ds.XmlDataSource', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('datasource/expr.js', ['goog.ds.Expr'], ['goog.ds.BasicNodeList', 'goog.ds.EmptyNodeList', 'goog.string'], {});
goog.addDependency('datasource/expr_test.js', ['goog.ds.ExprTest'], ['goog.ds.DataManager', 'goog.ds.Expr', 'goog.ds.JsDataSource', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('datasource/fastdatanode.js', ['goog.ds.AbstractFastDataNode', 'goog.ds.FastDataNode', 'goog.ds.FastListNode', 'goog.ds.PrimitiveFastDataNode'], ['goog.ds.DataManager', 'goog.ds.DataNodeList', 'goog.ds.EmptyNodeList', 'goog.string'], {});
goog.addDependency('datasource/fastdatanode_test.js', ['goog.ds.FastDataNodeTest'], ['goog.array', 'goog.ds.DataManager', 'goog.ds.Expr', 'goog.ds.FastDataNode', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('datasource/jsdatasource.js', ['goog.ds.JsDataSource', 'goog.ds.JsPropertyDataSource'], ['goog.ds.BaseDataNode', 'goog.ds.BasicNodeList', 'goog.ds.DataManager', 'goog.ds.DataNode', 'goog.ds.EmptyNodeList', 'goog.ds.LoadState'], {});
goog.addDependency('datasource/jsondatasource.js', ['goog.ds.JsonDataSource'], ['goog.dom', 'goog.dom.TagName', 'goog.dom.safe', 'goog.ds.DataManager', 'goog.ds.JsDataSource', 'goog.ds.LoadState', 'goog.ds.logger', 'goog.log'], {});
goog.addDependency('datasource/jsxmlhttpdatasource.js', ['goog.ds.JsXmlHttpDataSource'], ['goog.Uri', 'goog.ds.DataManager', 'goog.ds.FastDataNode', 'goog.ds.LoadState', 'goog.ds.logger', 'goog.events', 'goog.log', 'goog.net.EventType', 'goog.net.XhrIo'], {});
goog.addDependency('datasource/jsxmlhttpdatasource_test.js', ['goog.ds.JsXmlHttpDataSourceTest'], ['goog.ds.JsXmlHttpDataSource', 'goog.testing.TestQueue', 'goog.testing.net.XhrIo', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('datasource/xmldatasource.js', ['goog.ds.XmlDataSource', 'goog.ds.XmlHttpDataSource'], ['goog.Uri', 'goog.dom.NodeType', 'goog.dom.xml', 'goog.ds.BasicNodeList', 'goog.ds.DataManager', 'goog.ds.DataNode', 'goog.ds.LoadState', 'goog.ds.logger', 'goog.log', 'goog.net.XhrIo', 'goog.string'], {});
goog.addDependency('date/date.js', ['goog.date', 'goog.date.Date', 'goog.date.DateTime', 'goog.date.Interval', 'goog.date.month', 'goog.date.weekDay'], ['goog.asserts', 'goog.date.DateLike', 'goog.i18n.DateTimeSymbols', 'goog.string'], {});
goog.addDependency('date/date_test.js', ['goog.dateTest'], ['goog.array', 'goog.date', 'goog.date.Date', 'goog.date.DateTime', 'goog.date.Interval', 'goog.date.month', 'goog.date.weekDay', 'goog.i18n.DateTimeSymbols', 'goog.testing.ExpectedFailures', 'goog.testing.testSuite', 'goog.userAgent', 'goog.userAgent.platform', 'goog.userAgent.product', 'goog.userAgent.product.isVersion'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('date/datelike.js', ['goog.date.DateLike'], [], {});
goog.addDependency('date/daterange.js', ['goog.date.DateRange', 'goog.date.DateRange.Iterator', 'goog.date.DateRange.StandardDateRangeKeys'], ['goog.date.Date', 'goog.date.Interval', 'goog.iter.Iterator', 'goog.iter.StopIteration'], {});
goog.addDependency('date/daterange_test.js', ['goog.date.DateRangeTest'], ['goog.date.Date', 'goog.date.DateRange', 'goog.date.Interval', 'goog.i18n.DateTimeSymbols', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('date/duration.js', ['goog.date.duration'], ['goog.i18n.DateTimeFormat', 'goog.i18n.MessageFormat'], {});
goog.addDependency('date/duration_test.js', ['goog.date.durationTest'], ['goog.date.duration', 'goog.i18n.DateTimeFormat', 'goog.i18n.DateTimeSymbols', 'goog.i18n.DateTimeSymbols_bn', 'goog.i18n.DateTimeSymbols_en', 'goog.i18n.DateTimeSymbols_fa', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('date/relative.js', ['goog.date.relative', 'goog.date.relative.TimeDeltaFormatter', 'goog.date.relative.Unit'], ['goog.i18n.DateTimeFormat', 'goog.i18n.DateTimePatterns', 'goog.i18n.RelativeDateTimeFormat'], {});
goog.addDependency('date/relative_test.js', ['goog.date.relativeTest'], ['goog.date.relativeCommonTests'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('date/relativecommontests.js', ['goog.date.relativeCommonTests'], ['goog.date.DateTime', 'goog.date.relative', 'goog.i18n.DateTimeFormat', 'goog.i18n.DateTimePatterns_ar', 'goog.i18n.DateTimePatterns_bn', 'goog.i18n.DateTimePatterns_es', 'goog.i18n.DateTimePatterns_fa', 'goog.i18n.DateTimePatterns_fr', 'goog.i18n.DateTimePatterns_no', 'goog.i18n.DateTimeSymbols_ar', 'goog.i18n.DateTimeSymbols_bn', 'goog.i18n.DateTimeSymbols_es', 'goog.i18n.DateTimeSymbols_fa', 'goog.i18n.DateTimeSymbols_fr', 'goog.i18n.DateTimeSymbols_no', 'goog.i18n.NumberFormatSymbols_bn', 'goog.i18n.NumberFormatSymbols_en', 'goog.i18n.NumberFormatSymbols_fa', 'goog.i18n.NumberFormatSymbols_no', 'goog.i18n.relativeDateTimeSymbols', 'goog.testing.PropertyReplacer', 'goog.testing.jsunit'], {'lang': 'es6'});
goog.addDependency('date/utcdatetime.js', ['goog.date.UtcDateTime'], ['goog.date', 'goog.date.Date', 'goog.date.DateTime', 'goog.date.Interval'], {});
goog.addDependency('date/utcdatetime_test.js', ['goog.date.UtcDateTimeTest'], ['goog.date.Interval', 'goog.date.UtcDateTime', 'goog.date.month', 'goog.date.weekDay', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('db/cursor.js', ['goog.db.Cursor'], ['goog.async.Deferred', 'goog.db.Error', 'goog.db.KeyRange', 'goog.debug', 'goog.events.EventTarget'], {});
goog.addDependency('db/db.js', ['goog.db', 'goog.db.BlockedCallback', 'goog.db.UpgradeNeededCallback'], ['goog.asserts', 'goog.async.Deferred', 'goog.db.Error', 'goog.db.IndexedDb', 'goog.db.Transaction'], {});
goog.addDependency('db/db_test.js', ['goog.dbTest'], ['goog.Promise', 'goog.array', 'goog.db', 'goog.db.Cursor', 'goog.db.Error', 'goog.db.IndexedDb', 'goog.db.KeyRange', 'goog.db.Transaction', 'goog.events', 'goog.testing.PropertyReplacer', 'goog.testing.TestCase', 'goog.testing.asserts', 'goog.testing.testSuite', 'goog.userAgent.product'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('db/error.js', ['goog.db.DomErrorLike', 'goog.db.Error', 'goog.db.Error.ErrorCode', 'goog.db.Error.ErrorName', 'goog.db.Error.VersionChangeBlockedError'], ['goog.asserts', 'goog.debug.Error'], {});
goog.addDependency('db/index.js', ['goog.db.Index'], ['goog.async.Deferred', 'goog.db.Cursor', 'goog.db.Error', 'goog.db.KeyRange', 'goog.debug'], {});
goog.addDependency('db/indexeddb.js', ['goog.db.IndexedDb'], ['goog.db.Error', 'goog.db.ObjectStore', 'goog.db.Transaction', 'goog.events.Event', 'goog.events.EventHandler', 'goog.events.EventTarget'], {'lang': 'es6'});
goog.addDependency('db/keyrange.js', ['goog.db.KeyRange'], [], {});
goog.addDependency('db/objectstore.js', ['goog.db.ObjectStore'], ['goog.async.Deferred', 'goog.db.Cursor', 'goog.db.Error', 'goog.db.Index', 'goog.db.KeyRange', 'goog.debug'], {});
goog.addDependency('db/transaction.js', ['goog.db.Transaction', 'goog.db.Transaction.TransactionMode'], ['goog.async.Deferred', 'goog.db.Error', 'goog.db.ObjectStore', 'goog.events', 'goog.events.EventHandler', 'goog.events.EventTarget'], {});
goog.addDependency('debug/console.js', ['goog.debug.Console'], ['goog.debug.LogManager', 'goog.debug.Logger', 'goog.debug.TextFormatter'], {});
goog.addDependency('debug/console_test.js', ['goog.debug.ConsoleTest'], ['goog.debug.Console', 'goog.debug.LogRecord', 'goog.debug.Logger', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('debug/debug.js', ['goog.debug'], ['goog.array', 'goog.debug.errorcontext', 'goog.userAgent'], {});
goog.addDependency('debug/debug_test.js', ['goog.debugTest'], ['goog.debug', 'goog.debug.errorcontext', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('debug/debugwindow.js', ['goog.debug.DebugWindow'], ['goog.debug.HtmlFormatter', 'goog.debug.LogManager', 'goog.debug.Logger', 'goog.dom.safe', 'goog.html.SafeHtml', 'goog.html.SafeStyleSheet', 'goog.string.Const', 'goog.structs.CircularBuffer', 'goog.userAgent'], {});
goog.addDependency('debug/debugwindow_test.js', ['goog.debug.DebugWindowTest'], ['goog.debug.DebugWindow', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('debug/devcss/devcss.js', ['goog.debug.DevCss', 'goog.debug.DevCss.UserAgent'], ['goog.asserts', 'goog.cssom', 'goog.dom.classlist', 'goog.events', 'goog.events.EventType', 'goog.string', 'goog.userAgent'], {});
goog.addDependency('debug/devcss/devcss_test.js', ['goog.debug.DevCssTest'], ['goog.debug.DevCss', 'goog.style', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('debug/devcss/devcssrunner.js', ['goog.debug.devCssRunner'], ['goog.debug.DevCss'], {});
goog.addDependency('debug/divconsole.js', ['goog.debug.DivConsole'], ['goog.debug.HtmlFormatter', 'goog.debug.LogManager', 'goog.dom.DomHelper', 'goog.dom.TagName', 'goog.dom.safe', 'goog.html.SafeHtml', 'goog.html.SafeStyleSheet', 'goog.string.Const', 'goog.style'], {});
goog.addDependency('debug/enhanceerror_test.js', ['goog.debugEnhanceErrorTest'], ['goog.debug', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('debug/entrypointregistry.js', ['goog.debug.EntryPointMonitor', 'goog.debug.entryPointRegistry'], ['goog.asserts'], {});
goog.addDependency('debug/entrypointregistry_test.js', ['goog.debug.entryPointRegistryTest'], ['goog.debug.ErrorHandler', 'goog.debug.entryPointRegistry', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('debug/error.js', ['goog.debug.Error'], [], {'lang': 'es6'});
goog.addDependency('debug/error_test.js', ['goog.debug.ErrorTest'], ['goog.debug.Error', 'goog.testing.ExpectedFailures', 'goog.testing.testSuite', 'goog.userAgent', 'goog.userAgent.product'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('debug/errorcontext.js', ['goog.debug.errorcontext'], [], {});
goog.addDependency('debug/errorcontext_test.js', ['goog.debug.errorcontextTest'], ['goog.debug.errorcontext', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('debug/errorhandler.js', ['goog.debug.ErrorHandler', 'goog.debug.ErrorHandler.ProtectedFunctionError'], ['goog.Disposable', 'goog.asserts', 'goog.debug', 'goog.debug.EntryPointMonitor', 'goog.debug.Error', 'goog.debug.Trace'], {'lang': 'es6'});
goog.addDependency('debug/errorhandler_async_test.js', ['goog.debug.ErrorHandlerAsyncTest'], ['goog.Promise', 'goog.debug.ErrorHandler', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es8', 'module': 'goog'});
goog.addDependency('debug/errorhandler_test.js', ['goog.debug.ErrorHandlerTest'], ['goog.debug.ErrorHandler', 'goog.testing.MockControl', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('debug/errorhandlerweakdep.js', ['goog.debug.errorHandlerWeakDep'], [], {});
goog.addDependency('debug/errorreporter.js', ['goog.debug.ErrorReporter', 'goog.debug.ErrorReporter.ExceptionEvent'], ['goog.asserts', 'goog.debug', 'goog.debug.Error', 'goog.debug.ErrorHandler', 'goog.debug.entryPointRegistry', 'goog.debug.errorcontext', 'goog.events', 'goog.events.Event', 'goog.events.EventTarget', 'goog.log', 'goog.net.XhrIo', 'goog.object', 'goog.string', 'goog.uri.utils', 'goog.userAgent'], {});
goog.addDependency('debug/errorreporter_test.js', ['goog.debug.ErrorReporterTest'], ['goog.debug.Error', 'goog.debug.ErrorReporter', 'goog.debug.errorcontext', 'goog.events', 'goog.functions', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite', 'goog.userAgent', 'goog.userAgent.product'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('debug/fancywindow.js', ['goog.debug.FancyWindow'], ['goog.array', 'goog.asserts', 'goog.debug.DebugWindow', 'goog.debug.LogManager', 'goog.debug.Logger', 'goog.dom.DomHelper', 'goog.dom.TagName', 'goog.dom.safe', 'goog.html.SafeHtml', 'goog.html.SafeStyleSheet', 'goog.object', 'goog.string', 'goog.string.Const', 'goog.userAgent'], {});
goog.addDependency('debug/formatter.js', ['goog.debug.Formatter', 'goog.debug.HtmlFormatter', 'goog.debug.TextFormatter'], ['goog.debug', 'goog.debug.Logger', 'goog.debug.RelativeTimeProvider', 'goog.html.SafeHtml', 'goog.html.SafeUrl', 'goog.html.uncheckedconversions', 'goog.string.Const'], {});
goog.addDependency('debug/formatter_test.js', ['goog.debug.FormatterTest'], ['goog.debug.HtmlFormatter', 'goog.debug.LogRecord', 'goog.debug.Logger', 'goog.html.SafeHtml', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('debug/fpsdisplay.js', ['goog.debug.FpsDisplay'], ['goog.asserts', 'goog.async.AnimationDelay', 'goog.dom', 'goog.dom.TagName', 'goog.ui.Component'], {});
goog.addDependency('debug/fpsdisplay_test.js', ['goog.debug.FpsDisplayTest'], ['goog.Timer', 'goog.debug.FpsDisplay', 'goog.testing.TestCase', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('debug/logbuffer.js', ['goog.debug.LogBuffer'], ['goog.asserts', 'goog.debug.LogRecord'], {});
goog.addDependency('debug/logbuffer_test.js', ['goog.debug.LogBufferTest'], ['goog.debug.LogBuffer', 'goog.debug.Logger', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('debug/logger.js', ['goog.debug.LogManager', 'goog.debug.Loggable', 'goog.debug.Logger', 'goog.debug.Logger.Level'], ['goog.array', 'goog.asserts', 'goog.debug', 'goog.debug.LogBuffer', 'goog.debug.LogRecord'], {});
goog.addDependency('debug/logger_test.js', ['goog.debug.LoggerTest'], ['goog.debug.LogManager', 'goog.debug.Logger', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('debug/logrecord.js', ['goog.debug.LogRecord'], [], {});
goog.addDependency('debug/logrecordserializer.js', ['goog.debug.logRecordSerializer'], ['goog.debug.LogRecord', 'goog.debug.Logger', 'goog.json', 'goog.object'], {});
goog.addDependency('debug/logrecordserializer_test.js', ['goog.debug.logRecordSerializerTest'], ['goog.debug.LogRecord', 'goog.debug.Logger', 'goog.debug.logRecordSerializer', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('debug/relativetimeprovider.js', ['goog.debug.RelativeTimeProvider'], [], {});
goog.addDependency('debug/tracer.js', ['goog.debug.StopTraceDetail', 'goog.debug.Trace'], ['goog.array', 'goog.asserts', 'goog.debug.Logger', 'goog.iter', 'goog.log', 'goog.structs.Map', 'goog.structs.SimplePool'], {});
goog.addDependency('debug/tracer_test.js', ['goog.debug.TraceTest'], ['goog.array', 'goog.debug.Trace', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('defineclass_test.js', ['goog.defineClassTest'], ['goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('delegate/delegateregistry.js', ['goog.delegate.DelegateRegistry'], ['goog.array', 'goog.asserts', 'goog.debug'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('delegate/delegateregistry_test.js', ['goog.delegate.DelegateRegistryTest'], ['goog.array', 'goog.delegate.DelegateRegistry', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('delegate/delegates.js', ['goog.delegate.delegates'], [], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('delegate/delegates_test.js', ['goog.delegate.delegatesTest'], ['goog.delegate.delegates', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('disposable/disposable.js', ['goog.Disposable', 'goog.dispose', 'goog.disposeAll'], ['goog.disposable.IDisposable'], {});
goog.addDependency('disposable/disposable_test.js', ['goog.DisposableTest'], ['goog.Disposable', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('disposable/idisposable.js', ['goog.disposable.IDisposable'], [], {});
goog.addDependency('dom/abstractmultirange.js', ['goog.dom.AbstractMultiRange'], ['goog.array', 'goog.dom', 'goog.dom.AbstractRange', 'goog.dom.TextRange'], {});
goog.addDependency('dom/abstractrange.js', ['goog.dom.AbstractRange', 'goog.dom.RangeIterator', 'goog.dom.RangeType'], ['goog.dom', 'goog.dom.NodeType', 'goog.dom.SavedCaretRange', 'goog.dom.TagIterator', 'goog.userAgent'], {});
goog.addDependency('dom/abstractrange_test.js', ['goog.dom.AbstractRangeTest'], ['goog.dom', 'goog.dom.AbstractRange', 'goog.dom.Range', 'goog.dom.TagName', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/animationframe/animationframe.js', ['goog.dom.animationFrame', 'goog.dom.animationFrame.Spec', 'goog.dom.animationFrame.State'], ['goog.dom.animationFrame.polyfill'], {});
goog.addDependency('dom/animationframe/animationframe_test.js', ['goog.dom.AnimationFrameTest'], ['goog.dom.animationFrame', 'goog.testing.MockClock', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/animationframe/polyfill.js', ['goog.dom.animationFrame.polyfill'], [], {'lang': 'es6'});
goog.addDependency('dom/annotate.js', ['goog.dom.annotate', 'goog.dom.annotate.AnnotateFn'], ['goog.array', 'goog.asserts', 'goog.dom', 'goog.dom.NodeType', 'goog.dom.TagName', 'goog.dom.safe', 'goog.html.SafeHtml', 'goog.object'], {});
goog.addDependency('dom/annotate_test.js', ['goog.dom.annotateTest'], ['goog.dom', 'goog.dom.TagName', 'goog.dom.annotate', 'goog.html.SafeHtml', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/asserts.js', ['goog.dom.asserts'], ['goog.asserts'], {});
goog.addDependency('dom/asserts_test.js', ['goog.dom.assertsTest'], ['goog.dom.asserts', 'goog.testing.PropertyReplacer', 'goog.testing.StrictMock', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/attr.js', ['goog.dom.Attr'], [], {});
goog.addDependency('dom/browserfeature.js', ['goog.dom.BrowserFeature'], ['goog.userAgent'], {});
goog.addDependency('dom/browserfeature_test.js', ['goog.dom.BrowserFeatureTest'], ['goog.dom', 'goog.dom.BrowserFeature', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/browserrange/abstractrange.js', ['goog.dom.browserrange.AbstractRange'], ['goog.array', 'goog.asserts', 'goog.dom', 'goog.dom.NodeType', 'goog.dom.RangeEndpoint', 'goog.dom.TagName', 'goog.dom.TextRangeIterator', 'goog.iter', 'goog.math.Coordinate', 'goog.string', 'goog.string.StringBuffer', 'goog.userAgent'], {});
goog.addDependency('dom/browserrange/browserrange.js', ['goog.dom.browserrange', 'goog.dom.browserrange.Error'], ['goog.dom', 'goog.dom.BrowserFeature', 'goog.dom.NodeType', 'goog.dom.browserrange.GeckoRange', 'goog.dom.browserrange.IeRange', 'goog.dom.browserrange.OperaRange', 'goog.dom.browserrange.W3cRange', 'goog.dom.browserrange.WebKitRange', 'goog.userAgent'], {});
goog.addDependency('dom/browserrange/browserrange_test.js', ['goog.dom.browserrangeTest'], ['goog.dom', 'goog.dom.NodeType', 'goog.dom.Range', 'goog.dom.RangeEndpoint', 'goog.dom.TagName', 'goog.dom.browserrange', 'goog.html.testing', 'goog.testing.dom', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/browserrange/geckorange.js', ['goog.dom.browserrange.GeckoRange'], ['goog.dom.browserrange.W3cRange'], {});
goog.addDependency('dom/browserrange/ierange.js', ['goog.dom.browserrange.IeRange'], ['goog.array', 'goog.dom', 'goog.dom.NodeType', 'goog.dom.RangeEndpoint', 'goog.dom.TagName', 'goog.dom.browserrange.AbstractRange', 'goog.dom.safe', 'goog.html.uncheckedconversions', 'goog.log', 'goog.string'], {});
goog.addDependency('dom/browserrange/operarange.js', ['goog.dom.browserrange.OperaRange'], ['goog.dom.browserrange.W3cRange'], {});
goog.addDependency('dom/browserrange/w3crange.js', ['goog.dom.browserrange.W3cRange'], ['goog.array', 'goog.dom', 'goog.dom.NodeType', 'goog.dom.RangeEndpoint', 'goog.dom.TagName', 'goog.dom.browserrange.AbstractRange', 'goog.string', 'goog.userAgent'], {});
goog.addDependency('dom/browserrange/webkitrange.js', ['goog.dom.browserrange.WebKitRange'], ['goog.dom.RangeEndpoint', 'goog.dom.browserrange.W3cRange', 'goog.userAgent'], {});
goog.addDependency('dom/bufferedviewportsizemonitor.js', ['goog.dom.BufferedViewportSizeMonitor'], ['goog.asserts', 'goog.async.Delay', 'goog.events', 'goog.events.EventTarget', 'goog.events.EventType'], {});
goog.addDependency('dom/bufferedviewportsizemonitor_test.js', ['goog.dom.BufferedViewportSizeMonitorTest'], ['goog.dom.BufferedViewportSizeMonitor', 'goog.dom.ViewportSizeMonitor', 'goog.events', 'goog.events.EventType', 'goog.math.Size', 'goog.testing.MockClock', 'goog.testing.events', 'goog.testing.events.Event', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/classes.js', ['goog.dom.classes'], ['goog.array'], {});
goog.addDependency('dom/classes_test.js', ['goog.dom.classes_test'], ['goog.dom', 'goog.dom.TagName', 'goog.dom.classes', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/classlist.js', ['goog.dom.classlist'], ['goog.array'], {});
goog.addDependency('dom/classlist_test.js', ['goog.dom.classlist_test'], ['goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.testing.ExpectedFailures', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/controlrange.js', ['goog.dom.ControlRange', 'goog.dom.ControlRangeIterator'], ['goog.array', 'goog.dom', 'goog.dom.AbstractMultiRange', 'goog.dom.AbstractRange', 'goog.dom.RangeIterator', 'goog.dom.RangeType', 'goog.dom.SavedRange', 'goog.dom.TagWalkType', 'goog.dom.TextRange', 'goog.iter.StopIteration', 'goog.userAgent'], {});
goog.addDependency('dom/controlrange_test.js', ['goog.dom.ControlRangeTest'], ['goog.dom', 'goog.dom.ControlRange', 'goog.dom.RangeType', 'goog.dom.TagName', 'goog.dom.TextRange', 'goog.testing.dom', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/dataset.js', ['goog.dom.dataset'], ['goog.labs.userAgent.browser', 'goog.string', 'goog.userAgent.product'], {});
goog.addDependency('dom/dataset_test.js', ['goog.dom.datasetTest'], ['goog.dom', 'goog.dom.dataset', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/dom.js', ['goog.dom', 'goog.dom.Appendable', 'goog.dom.DomHelper'], ['goog.array', 'goog.asserts', 'goog.dom.BrowserFeature', 'goog.dom.NodeType', 'goog.dom.TagName', 'goog.dom.safe', 'goog.html.SafeHtml', 'goog.html.uncheckedconversions', 'goog.math.Coordinate', 'goog.math.Size', 'goog.object', 'goog.string', 'goog.string.Unicode', 'goog.userAgent'], {});
goog.addDependency('dom/dom_compile_test.js', ['goog.dom.DomCompileTest'], ['goog.dom', 'goog.dom.TagName', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/dom_test.js', ['goog.dom.dom_test'], ['goog.array', 'goog.asserts', 'goog.dom', 'goog.dom.BrowserFeature', 'goog.dom.DomHelper', 'goog.dom.InputType', 'goog.dom.NodeType', 'goog.dom.TagName', 'goog.functions', 'goog.html.SafeUrl', 'goog.html.testing', 'goog.object', 'goog.string.Const', 'goog.string.Unicode', 'goog.testing.PropertyReplacer', 'goog.testing.asserts', 'goog.testing.testSuite', 'goog.userAgent', 'goog.userAgent.product.isVersion'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/fontsizemonitor.js', ['goog.dom.FontSizeMonitor', 'goog.dom.FontSizeMonitor.EventType'], ['goog.dom', 'goog.dom.TagName', 'goog.events', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.userAgent'], {});
goog.addDependency('dom/fontsizemonitor_test.js', ['goog.dom.FontSizeMonitorTest'], ['goog.dom', 'goog.dom.FontSizeMonitor', 'goog.dom.TagName', 'goog.events', 'goog.events.Event', 'goog.testing.PropertyReplacer', 'goog.testing.events', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/forms.js', ['goog.dom.forms'], ['goog.dom.InputType', 'goog.dom.TagName', 'goog.dom.safe', 'goog.structs.Map', 'goog.window'], {});
goog.addDependency('dom/forms_test.js', ['goog.dom.formsTest'], ['goog.dom', 'goog.dom.forms', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/fullscreen.js', ['goog.dom.fullscreen', 'goog.dom.fullscreen.EventType'], ['goog.dom'], {});
goog.addDependency('dom/fullscreen_test.js', ['goog.dom.fullscreen_test'], ['goog.dom.DomHelper', 'goog.dom.fullscreen', 'goog.testing.PropertyReplacer', 'goog.testing.asserts', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/htmlelement.js', ['goog.dom.HtmlElement'], [], {});
goog.addDependency('dom/iframe.js', ['goog.dom.iframe'], ['goog.dom', 'goog.dom.TagName', 'goog.dom.safe', 'goog.html.SafeHtml', 'goog.html.SafeStyle', 'goog.html.TrustedResourceUrl', 'goog.string.Const', 'goog.userAgent'], {});
goog.addDependency('dom/iframe_test.js', ['goog.dom.iframeTest'], ['goog.dom', 'goog.dom.iframe', 'goog.html.SafeHtml', 'goog.html.SafeStyle', 'goog.string.Const', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/inputtype.js', ['goog.dom.InputType'], [], {});
goog.addDependency('dom/inputtype_test.js', ['goog.dom.InputTypeTest'], ['goog.dom.InputType', 'goog.object', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/iter.js', ['goog.dom.iter.AncestorIterator', 'goog.dom.iter.ChildIterator', 'goog.dom.iter.SiblingIterator'], ['goog.iter.Iterator', 'goog.iter.StopIteration'], {});
goog.addDependency('dom/iter_test.js', ['goog.dom.iterTest'], ['goog.dom', 'goog.dom.NodeType', 'goog.dom.iter.AncestorIterator', 'goog.dom.iter.ChildIterator', 'goog.dom.iter.SiblingIterator', 'goog.testing.dom', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/multirange.js', ['goog.dom.MultiRange', 'goog.dom.MultiRangeIterator'], ['goog.array', 'goog.dom', 'goog.dom.AbstractMultiRange', 'goog.dom.AbstractRange', 'goog.dom.RangeIterator', 'goog.dom.RangeType', 'goog.dom.SavedRange', 'goog.dom.TextRange', 'goog.iter', 'goog.iter.StopIteration', 'goog.log'], {});
goog.addDependency('dom/multirange_test.js', ['goog.dom.MultiRangeTest'], ['goog.dom', 'goog.dom.MultiRange', 'goog.dom.Range', 'goog.iter', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/nodeiterator.js', ['goog.dom.NodeIterator'], ['goog.dom.TagIterator'], {});
goog.addDependency('dom/nodeiterator_test.js', ['goog.dom.NodeIteratorTest'], ['goog.dom', 'goog.dom.NodeIterator', 'goog.testing.dom', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/nodeoffset.js', ['goog.dom.NodeOffset'], ['goog.Disposable', 'goog.dom.TagName'], {});
goog.addDependency('dom/nodeoffset_test.js', ['goog.dom.NodeOffsetTest'], ['goog.dom', 'goog.dom.NodeOffset', 'goog.dom.NodeType', 'goog.dom.TagName', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/nodetype.js', ['goog.dom.NodeType'], [], {});
goog.addDependency('dom/pattern/abstractpattern.js', ['goog.dom.pattern.AbstractPattern'], ['goog.dom.TagWalkType', 'goog.dom.pattern.MatchType'], {});
goog.addDependency('dom/pattern/allchildren.js', ['goog.dom.pattern.AllChildren'], ['goog.dom.pattern.AbstractPattern', 'goog.dom.pattern.MatchType'], {});
goog.addDependency('dom/pattern/callback/callback.js', ['goog.dom.pattern.callback'], ['goog.dom', 'goog.dom.TagWalkType', 'goog.iter'], {});
goog.addDependency('dom/pattern/callback/counter.js', ['goog.dom.pattern.callback.Counter'], [], {});
goog.addDependency('dom/pattern/callback/test.js', ['goog.dom.pattern.callback.Test'], ['goog.iter.StopIteration'], {});
goog.addDependency('dom/pattern/childmatches.js', ['goog.dom.pattern.ChildMatches'], ['goog.dom.pattern.AllChildren', 'goog.dom.pattern.MatchType'], {});
goog.addDependency('dom/pattern/endtag.js', ['goog.dom.pattern.EndTag'], ['goog.dom.TagWalkType', 'goog.dom.pattern.Tag'], {});
goog.addDependency('dom/pattern/fulltag.js', ['goog.dom.pattern.FullTag'], ['goog.dom.pattern.MatchType', 'goog.dom.pattern.StartTag', 'goog.dom.pattern.Tag'], {});
goog.addDependency('dom/pattern/matcher.js', ['goog.dom.pattern.Matcher'], ['goog.dom.TagIterator', 'goog.dom.pattern.MatchType', 'goog.iter'], {});
goog.addDependency('dom/pattern/matcher_test.js', ['goog.dom.pattern.matcherTest'], ['goog.dom', 'goog.dom.TagName', 'goog.dom.pattern.EndTag', 'goog.dom.pattern.FullTag', 'goog.dom.pattern.Matcher', 'goog.dom.pattern.Repeat', 'goog.dom.pattern.Sequence', 'goog.dom.pattern.StartTag', 'goog.dom.pattern.callback.Counter', 'goog.dom.pattern.callback.Test', 'goog.iter.StopIteration', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/pattern/nodetype.js', ['goog.dom.pattern.NodeType'], ['goog.dom.pattern.AbstractPattern', 'goog.dom.pattern.MatchType'], {});
goog.addDependency('dom/pattern/pattern.js', ['goog.dom.pattern', 'goog.dom.pattern.MatchType'], [], {});
goog.addDependency('dom/pattern/pattern_test.js', ['goog.dom.patternTest'], ['goog.dom', 'goog.dom.NodeType', 'goog.dom.TagWalkType', 'goog.dom.pattern.AllChildren', 'goog.dom.pattern.ChildMatches', 'goog.dom.pattern.EndTag', 'goog.dom.pattern.FullTag', 'goog.dom.pattern.MatchType', 'goog.dom.pattern.NodeType', 'goog.dom.pattern.Repeat', 'goog.dom.pattern.Sequence', 'goog.dom.pattern.StartTag', 'goog.dom.pattern.Text', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/pattern/repeat.js', ['goog.dom.pattern.Repeat'], ['goog.dom.NodeType', 'goog.dom.pattern.AbstractPattern', 'goog.dom.pattern.MatchType'], {});
goog.addDependency('dom/pattern/sequence.js', ['goog.dom.pattern.Sequence'], ['goog.dom.NodeType', 'goog.dom.pattern', 'goog.dom.pattern.AbstractPattern', 'goog.dom.pattern.MatchType'], {});
goog.addDependency('dom/pattern/starttag.js', ['goog.dom.pattern.StartTag'], ['goog.dom.TagWalkType', 'goog.dom.pattern.Tag'], {});
goog.addDependency('dom/pattern/tag.js', ['goog.dom.pattern.Tag'], ['goog.dom.pattern', 'goog.dom.pattern.AbstractPattern', 'goog.dom.pattern.MatchType', 'goog.object'], {});
goog.addDependency('dom/pattern/text.js', ['goog.dom.pattern.Text'], ['goog.dom.NodeType', 'goog.dom.pattern', 'goog.dom.pattern.AbstractPattern', 'goog.dom.pattern.MatchType'], {});
goog.addDependency('dom/range.js', ['goog.dom.Range'], ['goog.dom', 'goog.dom.AbstractRange', 'goog.dom.BrowserFeature', 'goog.dom.ControlRange', 'goog.dom.MultiRange', 'goog.dom.NodeType', 'goog.dom.TextRange'], {});
goog.addDependency('dom/range_test.js', ['goog.dom.RangeTest'], ['goog.dom', 'goog.dom.NodeType', 'goog.dom.Range', 'goog.dom.RangeType', 'goog.dom.TagName', 'goog.dom.TextRange', 'goog.dom.browserrange', 'goog.testing.dom', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/rangeendpoint.js', ['goog.dom.RangeEndpoint'], [], {});
goog.addDependency('dom/safe.js', ['goog.dom.safe', 'goog.dom.safe.InsertAdjacentHtmlPosition'], ['goog.asserts', 'goog.dom.asserts', 'goog.functions', 'goog.html.SafeHtml', 'goog.html.SafeScript', 'goog.html.SafeStyle', 'goog.html.SafeUrl', 'goog.html.TrustedResourceUrl', 'goog.html.uncheckedconversions', 'goog.string.Const', 'goog.string.internal'], {});
goog.addDependency('dom/safe_test.js', ['goog.dom.safeTest'], ['goog.asserts', 'goog.dom', 'goog.dom.TagName', 'goog.dom.safe', 'goog.dom.safe.InsertAdjacentHtmlPosition', 'goog.html.SafeHtml', 'goog.html.SafeScript', 'goog.html.SafeStyle', 'goog.html.SafeUrl', 'goog.html.TrustedResourceUrl', 'goog.html.testing', 'goog.string', 'goog.string.Const', 'goog.testing', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/savedcaretrange.js', ['goog.dom.SavedCaretRange'], ['goog.array', 'goog.dom', 'goog.dom.SavedRange', 'goog.dom.TagName', 'goog.string'], {});
goog.addDependency('dom/savedcaretrange_test.js', ['goog.dom.SavedCaretRangeTest'], ['goog.dom', 'goog.dom.Range', 'goog.dom.SavedCaretRange', 'goog.testing.dom', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/savedrange.js', ['goog.dom.SavedRange'], ['goog.Disposable', 'goog.log'], {});
goog.addDependency('dom/savedrange_test.js', ['goog.dom.SavedRangeTest'], ['goog.dom', 'goog.dom.Range', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/selection.js', ['goog.dom.selection'], ['goog.dom.InputType', 'goog.string', 'goog.userAgent'], {});
goog.addDependency('dom/selection_test.js', ['goog.dom.selectionTest'], ['goog.dom', 'goog.dom.InputType', 'goog.dom.TagName', 'goog.dom.selection', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/tagiterator.js', ['goog.dom.TagIterator', 'goog.dom.TagWalkType'], ['goog.dom', 'goog.dom.NodeType', 'goog.iter.Iterator', 'goog.iter.StopIteration'], {});
goog.addDependency('dom/tagiterator_test.js', ['goog.dom.TagIteratorTest'], ['goog.dom', 'goog.dom.TagIterator', 'goog.dom.TagName', 'goog.dom.TagWalkType', 'goog.iter', 'goog.iter.StopIteration', 'goog.testing.dom', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/tagname.js', ['goog.dom.TagName'], ['goog.dom.HtmlElement'], {});
goog.addDependency('dom/tagname_test.js', ['goog.dom.TagNameTest'], ['goog.dom.TagName', 'goog.object', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/tags.js', ['goog.dom.tags'], ['goog.object'], {});
goog.addDependency('dom/tags_test.js', ['goog.dom.tagsTest'], ['goog.dom.tags', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/textassert.js', ['goog.dom.textAssert'], ['goog.asserts', 'goog.dom', 'goog.dom.TagName'], {});
goog.addDependency('dom/textassert_test.js', ['goog.dom.textassert_test'], ['goog.dom.textAssert', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/textrange.js', ['goog.dom.TextRange'], ['goog.array', 'goog.dom', 'goog.dom.AbstractRange', 'goog.dom.RangeType', 'goog.dom.SavedRange', 'goog.dom.TagName', 'goog.dom.TextRangeIterator', 'goog.dom.browserrange', 'goog.string', 'goog.userAgent'], {});
goog.addDependency('dom/textrange_test.js', ['goog.dom.TextRangeTest'], ['goog.dom', 'goog.dom.ControlRange', 'goog.dom.Range', 'goog.dom.TextRange', 'goog.math.Coordinate', 'goog.style', 'goog.testing.ExpectedFailures', 'goog.testing.testSuite', 'goog.userAgent', 'goog.userAgent.product'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/textrangeiterator.js', ['goog.dom.TextRangeIterator'], ['goog.array', 'goog.dom', 'goog.dom.NodeType', 'goog.dom.RangeIterator', 'goog.dom.TagName', 'goog.iter.StopIteration'], {});
goog.addDependency('dom/textrangeiterator_test.js', ['goog.dom.TextRangeIteratorTest'], ['goog.dom', 'goog.dom.TagName', 'goog.dom.TextRangeIterator', 'goog.iter.StopIteration', 'goog.testing.dom', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/uri.js', ['goog.dom.uri'], ['goog.dom', 'goog.dom.TagName', 'goog.dom.safe', 'goog.html.uncheckedconversions', 'goog.string.Const'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/uri_test.js', ['goog.dom.uriTest'], ['goog.dom.uri', 'goog.testing.testSuite', 'goog.userAgent.product'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/vendor.js', ['goog.dom.vendor'], ['goog.string', 'goog.userAgent'], {});
goog.addDependency('dom/vendor_test.js', ['goog.dom.vendorTest'], ['goog.array', 'goog.dom.vendor', 'goog.labs.userAgent.util', 'goog.testing.MockUserAgent', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite', 'goog.userAgent', 'goog.userAgentTestUtil'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/viewportsizemonitor.js', ['goog.dom.ViewportSizeMonitor'], ['goog.dom', 'goog.events', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.math.Size'], {});
goog.addDependency('dom/viewportsizemonitor_test.js', ['goog.dom.ViewportSizeMonitorTest'], ['goog.dom.ViewportSizeMonitor', 'goog.events', 'goog.events.Event', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.math.Size', 'goog.testing.MockClock', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('dom/xml.js', ['goog.dom.xml'], ['goog.dom', 'goog.dom.NodeType', 'goog.dom.safe', 'goog.html.legacyconversions', 'goog.userAgent'], {});
goog.addDependency('dom/xml_test.js', ['goog.dom.xmlTest'], ['goog.dom', 'goog.dom.TagName', 'goog.dom.xml', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/browserfeature.js', ['goog.editor.BrowserFeature'], ['goog.editor.defines', 'goog.labs.userAgent.browser', 'goog.userAgent', 'goog.userAgent.product', 'goog.userAgent.product.isVersion'], {});
goog.addDependency('editor/browserfeature_test.js', ['goog.editor.BrowserFeatureTest'], ['goog.dom', 'goog.dom.Range', 'goog.dom.TagName', 'goog.editor.BrowserFeature', 'goog.testing.ExpectedFailures', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/clicktoeditwrapper.js', ['goog.editor.ClickToEditWrapper'], ['goog.Disposable', 'goog.dom', 'goog.dom.Range', 'goog.dom.TagName', 'goog.editor.BrowserFeature', 'goog.editor.Command', 'goog.editor.Field', 'goog.editor.range', 'goog.events.BrowserEvent', 'goog.events.EventHandler', 'goog.events.EventType'], {});
goog.addDependency('editor/clicktoeditwrapper_test.js', ['goog.editor.ClickToEditWrapperTest'], ['goog.dom', 'goog.dom.Range', 'goog.dom.TagName', 'goog.editor.ClickToEditWrapper', 'goog.editor.SeamlessField', 'goog.testing.MockClock', 'goog.testing.events', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/command.js', ['goog.editor.Command'], [], {});
goog.addDependency('editor/contenteditablefield.js', ['goog.editor.ContentEditableField'], ['goog.asserts', 'goog.editor.Field', 'goog.log'], {});
goog.addDependency('editor/contenteditablefield_test.js', ['goog.editor.ContentEditableFieldTest'], ['goog.dom', 'goog.editor.ContentEditableField', 'goog.html.SafeHtml', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/defines.js', ['goog.editor.defines'], [], {});
goog.addDependency('editor/field.js', ['goog.editor.Field', 'goog.editor.Field.EventType'], ['goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.array', 'goog.asserts', 'goog.async.Delay', 'goog.dom', 'goog.dom.Range', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.dom.safe', 'goog.editor.BrowserFeature', 'goog.editor.Command', 'goog.editor.PluginImpl', 'goog.editor.icontent', 'goog.editor.icontent.FieldFormatInfo', 'goog.editor.icontent.FieldStyleInfo', 'goog.editor.node', 'goog.editor.range', 'goog.events', 'goog.events.EventHandler', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.functions', 'goog.html.SafeHtml', 'goog.html.SafeStyleSheet', 'goog.log', 'goog.log.Level', 'goog.string', 'goog.string.Unicode', 'goog.style', 'goog.userAgent', 'goog.userAgent.product'], {});
goog.addDependency('editor/field_test.js', ['goog.editor.field_test'], ['goog.array', 'goog.dom', 'goog.dom.Range', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.editor.BrowserFeature', 'goog.editor.Field', 'goog.editor.Plugin', 'goog.editor.range', 'goog.events', 'goog.events.BrowserEvent', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.functions', 'goog.html.SafeHtml', 'goog.testing.LooseMock', 'goog.testing.MockClock', 'goog.testing.dom', 'goog.testing.events', 'goog.testing.events.Event', 'goog.testing.recordFunction', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/focus.js', ['goog.editor.focus'], ['goog.dom.selection'], {});
goog.addDependency('editor/focus_test.js', ['goog.editor.focusTest'], ['goog.dom.selection', 'goog.editor.BrowserFeature', 'goog.editor.focus', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/icontent.js', ['goog.editor.icontent', 'goog.editor.icontent.FieldFormatInfo', 'goog.editor.icontent.FieldStyleInfo'], ['goog.dom', 'goog.editor.BrowserFeature', 'goog.style', 'goog.userAgent'], {});
goog.addDependency('editor/icontent_test.js', ['goog.editor.icontentTest'], ['goog.dom', 'goog.dom.TagName', 'goog.editor.BrowserFeature', 'goog.editor.icontent', 'goog.editor.icontent.FieldFormatInfo', 'goog.editor.icontent.FieldStyleInfo', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/link.js', ['goog.editor.Link'], ['goog.array', 'goog.dom', 'goog.dom.NodeType', 'goog.dom.Range', 'goog.dom.TagName', 'goog.editor.BrowserFeature', 'goog.editor.Command', 'goog.editor.Field', 'goog.editor.node', 'goog.editor.range', 'goog.string', 'goog.string.Unicode', 'goog.uri.utils', 'goog.uri.utils.ComponentIndex'], {});
goog.addDependency('editor/link_test.js', ['goog.editor.LinkTest'], ['goog.dom', 'goog.dom.NodeType', 'goog.dom.Range', 'goog.dom.TagName', 'goog.editor.Link', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/node.js', ['goog.editor.node'], ['goog.dom', 'goog.dom.NodeType', 'goog.dom.TagName', 'goog.dom.iter.ChildIterator', 'goog.dom.iter.SiblingIterator', 'goog.iter', 'goog.object', 'goog.string', 'goog.string.Unicode', 'goog.userAgent'], {});
goog.addDependency('editor/node_test.js', ['goog.editor.nodeTest'], ['goog.array', 'goog.dom', 'goog.dom.NodeType', 'goog.dom.TagName', 'goog.editor.node', 'goog.style', 'goog.testing.ExpectedFailures', 'goog.testing.dom', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/plugin.js', ['goog.editor.Plugin'], ['goog.editor.Field', 'goog.editor.PluginImpl'], {});
goog.addDependency('editor/plugin_impl.js', ['goog.editor.PluginImpl'], ['goog.events.EventTarget', 'goog.functions', 'goog.log', 'goog.object', 'goog.reflect', 'goog.userAgent'], {});
goog.addDependency('editor/plugin_test.js', ['goog.editor.PluginTest'], ['goog.editor.Field', 'goog.editor.Plugin', 'goog.functions', 'goog.testing.StrictMock', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/plugins/abstractbubbleplugin.js', ['goog.editor.plugins.AbstractBubblePlugin'], ['goog.array', 'goog.dom', 'goog.dom.NodeType', 'goog.dom.Range', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.editor.Plugin', 'goog.editor.style', 'goog.events', 'goog.events.EventHandler', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.events.actionEventWrapper', 'goog.functions', 'goog.string.Unicode', 'goog.ui.Component', 'goog.ui.editor.Bubble', 'goog.userAgent'], {});
goog.addDependency('editor/plugins/abstractbubbleplugin_test.js', ['goog.editor.plugins.AbstractBubblePluginTest'], ['goog.dom', 'goog.dom.TagName', 'goog.editor.plugins.AbstractBubblePlugin', 'goog.events.BrowserEvent', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.functions', 'goog.style', 'goog.testing.editor.FieldMock', 'goog.testing.editor.TestHelper', 'goog.testing.events', 'goog.testing.events.Event', 'goog.testing.testSuite', 'goog.ui.editor.Bubble', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/plugins/abstractdialogplugin.js', ['goog.editor.plugins.AbstractDialogPlugin', 'goog.editor.plugins.AbstractDialogPlugin.EventType'], ['goog.dom', 'goog.dom.Range', 'goog.editor.Field', 'goog.editor.Plugin', 'goog.editor.range', 'goog.events', 'goog.ui.editor.AbstractDialog'], {});
goog.addDependency('editor/plugins/abstractdialogplugin_test.js', ['goog.editor.plugins.AbstractDialogPluginTest'], ['goog.dom', 'goog.dom.SavedRange', 'goog.dom.TagName', 'goog.editor.Field', 'goog.editor.plugins.AbstractDialogPlugin', 'goog.events.Event', 'goog.events.EventHandler', 'goog.functions', 'goog.html.SafeHtml', 'goog.testing.MockClock', 'goog.testing.MockControl', 'goog.testing.PropertyReplacer', 'goog.testing.editor.FieldMock', 'goog.testing.editor.TestHelper', 'goog.testing.events', 'goog.testing.mockmatchers.ArgumentMatcher', 'goog.testing.testSuite', 'goog.ui.editor.AbstractDialog', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/plugins/abstracttabhandler.js', ['goog.editor.plugins.AbstractTabHandler'], ['goog.editor.Plugin', 'goog.events.KeyCodes', 'goog.userAgent'], {});
goog.addDependency('editor/plugins/abstracttabhandler_test.js', ['goog.editor.plugins.AbstractTabHandlerTest'], ['goog.editor.Field', 'goog.editor.plugins.AbstractTabHandler', 'goog.events.BrowserEvent', 'goog.events.KeyCodes', 'goog.testing.StrictMock', 'goog.testing.editor.FieldMock', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/plugins/basictextformatter.js', ['goog.editor.plugins.BasicTextFormatter', 'goog.editor.plugins.BasicTextFormatter.COMMAND'], ['goog.array', 'goog.dom', 'goog.dom.NodeType', 'goog.dom.Range', 'goog.dom.TagName', 'goog.editor.BrowserFeature', 'goog.editor.Command', 'goog.editor.Link', 'goog.editor.Plugin', 'goog.editor.node', 'goog.editor.range', 'goog.editor.style', 'goog.iter', 'goog.iter.StopIteration', 'goog.log', 'goog.object', 'goog.string', 'goog.string.Unicode', 'goog.style', 'goog.ui.editor.messages', 'goog.userAgent'], {});
goog.addDependency('editor/plugins/basictextformatter_test.js', ['goog.editor.plugins.BasicTextFormatterTest'], ['goog.array', 'goog.dom', 'goog.dom.Range', 'goog.dom.TagName', 'goog.editor.BrowserFeature', 'goog.editor.Command', 'goog.editor.Field', 'goog.editor.Plugin', 'goog.editor.plugins.BasicTextFormatter', 'goog.html.SafeHtml', 'goog.object', 'goog.style', 'goog.testing.ExpectedFailures', 'goog.testing.LooseMock', 'goog.testing.PropertyReplacer', 'goog.testing.editor.FieldMock', 'goog.testing.editor.TestHelper', 'goog.testing.mockmatchers', 'goog.testing.testSuite', 'goog.userAgent', 'goog.userAgent.product'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/plugins/blockquote.js', ['goog.editor.plugins.Blockquote'], ['goog.dom', 'goog.dom.NodeType', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.editor.BrowserFeature', 'goog.editor.Command', 'goog.editor.Plugin', 'goog.editor.node', 'goog.functions', 'goog.log'], {});
goog.addDependency('editor/plugins/blockquote_test.js', ['goog.editor.plugins.BlockquoteTest'], ['goog.dom', 'goog.dom.Range', 'goog.dom.TagName', 'goog.editor.BrowserFeature', 'goog.editor.plugins.Blockquote', 'goog.testing.editor.FieldMock', 'goog.testing.editor.TestHelper', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/plugins/emoticons.js', ['goog.editor.plugins.Emoticons'], ['goog.dom.TagName', 'goog.editor.Plugin', 'goog.editor.range', 'goog.functions', 'goog.ui.emoji.Emoji', 'goog.userAgent'], {});
goog.addDependency('editor/plugins/emoticons_test.js', ['goog.editor.plugins.EmoticonsTest'], ['goog.Uri', 'goog.array', 'goog.dom', 'goog.dom.TagName', 'goog.editor.Field', 'goog.editor.plugins.Emoticons', 'goog.testing.testSuite', 'goog.ui.emoji.Emoji', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/plugins/enterhandler.js', ['goog.editor.plugins.EnterHandler'], ['goog.dom', 'goog.dom.NodeOffset', 'goog.dom.NodeType', 'goog.dom.Range', 'goog.dom.TagName', 'goog.editor.BrowserFeature', 'goog.editor.Plugin', 'goog.editor.node', 'goog.editor.plugins.Blockquote', 'goog.editor.range', 'goog.editor.style', 'goog.events.KeyCodes', 'goog.functions', 'goog.object', 'goog.string', 'goog.userAgent'], {});
goog.addDependency('editor/plugins/enterhandler_test.js', ['goog.editor.plugins.EnterHandlerTest'], ['goog.dom', 'goog.dom.NodeType', 'goog.dom.Range', 'goog.dom.TagName', 'goog.editor.BrowserFeature', 'goog.editor.Field', 'goog.editor.Plugin', 'goog.editor.plugins.Blockquote', 'goog.editor.plugins.EnterHandler', 'goog.editor.range', 'goog.events', 'goog.events.KeyCodes', 'goog.html.testing', 'goog.testing.ExpectedFailures', 'goog.testing.MockClock', 'goog.testing.dom', 'goog.testing.editor.TestHelper', 'goog.testing.events', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/plugins/firststrong.js', ['goog.editor.plugins.FirstStrong'], ['goog.dom.NodeType', 'goog.dom.TagIterator', 'goog.dom.TagName', 'goog.editor.Command', 'goog.editor.Field', 'goog.editor.Plugin', 'goog.editor.node', 'goog.editor.range', 'goog.i18n.bidi', 'goog.i18n.uChar', 'goog.iter', 'goog.userAgent'], {});
goog.addDependency('editor/plugins/firststrong_test.js', ['goog.editor.plugins.FirstStrongTest'], ['goog.dom.Range', 'goog.editor.Command', 'goog.editor.Field', 'goog.editor.plugins.FirstStrong', 'goog.editor.range', 'goog.events.KeyCodes', 'goog.html.testing', 'goog.testing.MockClock', 'goog.testing.editor.TestHelper', 'goog.testing.events', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/plugins/headerformatter.js', ['goog.editor.plugins.HeaderFormatter'], ['goog.editor.Command', 'goog.editor.Plugin', 'goog.userAgent'], {});
goog.addDependency('editor/plugins/headerformatter_test.js', ['goog.editor.plugins.HeaderFormatterTest'], ['goog.dom', 'goog.editor.Command', 'goog.editor.plugins.BasicTextFormatter', 'goog.editor.plugins.HeaderFormatter', 'goog.events.BrowserEvent', 'goog.testing.LooseMock', 'goog.testing.editor.FieldMock', 'goog.testing.editor.TestHelper', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/plugins/linkbubble.js', ['goog.editor.plugins.LinkBubble', 'goog.editor.plugins.LinkBubble.Action'], ['goog.array', 'goog.dom', 'goog.dom.Range', 'goog.dom.TagName', 'goog.editor.Command', 'goog.editor.Link', 'goog.editor.plugins.AbstractBubblePlugin', 'goog.functions', 'goog.string', 'goog.style', 'goog.ui.editor.messages', 'goog.uri.utils', 'goog.window'], {});
goog.addDependency('editor/plugins/linkbubble_test.js', ['goog.editor.plugins.LinkBubbleTest'], ['goog.dom', 'goog.dom.Range', 'goog.dom.TagName', 'goog.editor.Command', 'goog.editor.Link', 'goog.editor.plugins.LinkBubble', 'goog.events.BrowserEvent', 'goog.events.Event', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.string', 'goog.style', 'goog.testing.FunctionMock', 'goog.testing.PropertyReplacer', 'goog.testing.editor.FieldMock', 'goog.testing.editor.TestHelper', 'goog.testing.events', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/plugins/linkdialogplugin.js', ['goog.editor.plugins.LinkDialogPlugin'], ['goog.array', 'goog.dom', 'goog.editor.Command', 'goog.editor.plugins.AbstractDialogPlugin', 'goog.events.EventHandler', 'goog.functions', 'goog.ui.editor.AbstractDialog', 'goog.ui.editor.LinkDialog', 'goog.uri.utils'], {});
goog.addDependency('editor/plugins/linkdialogplugin_test.js', ['goog.ui.editor.plugins.LinkDialogTest'], ['goog.dom', 'goog.dom.DomHelper', 'goog.dom.NodeType', 'goog.dom.TagName', 'goog.editor.BrowserFeature', 'goog.editor.Command', 'goog.editor.Field', 'goog.editor.Link', 'goog.editor.plugins.LinkDialogPlugin', 'goog.html.SafeHtml', 'goog.string', 'goog.string.Unicode', 'goog.testing.MockControl', 'goog.testing.editor.FieldMock', 'goog.testing.editor.TestHelper', 'goog.testing.editor.dom', 'goog.testing.events', 'goog.testing.mockmatchers', 'goog.testing.testSuite', 'goog.ui.editor.AbstractDialog', 'goog.ui.editor.LinkDialog', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/plugins/linkshortcutplugin.js', ['goog.editor.plugins.LinkShortcutPlugin'], ['goog.editor.Command', 'goog.editor.Plugin'], {});
goog.addDependency('editor/plugins/linkshortcutplugin_test.js', ['goog.editor.plugins.LinkShortcutPluginTest'], ['goog.dom', 'goog.dom.TagName', 'goog.editor.Field', 'goog.editor.plugins.BasicTextFormatter', 'goog.editor.plugins.LinkBubble', 'goog.editor.plugins.LinkShortcutPlugin', 'goog.events.KeyCodes', 'goog.testing.PropertyReplacer', 'goog.testing.dom', 'goog.testing.events', 'goog.testing.testSuite', 'goog.userAgent.product'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/plugins/listtabhandler.js', ['goog.editor.plugins.ListTabHandler'], ['goog.dom', 'goog.dom.TagName', 'goog.editor.Command', 'goog.editor.plugins.AbstractTabHandler', 'goog.iter'], {});
goog.addDependency('editor/plugins/listtabhandler_test.js', ['goog.editor.plugins.ListTabHandlerTest'], ['goog.dom', 'goog.editor.Command', 'goog.editor.plugins.ListTabHandler', 'goog.events.BrowserEvent', 'goog.events.KeyCodes', 'goog.functions', 'goog.testing.StrictMock', 'goog.testing.editor.FieldMock', 'goog.testing.editor.TestHelper', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/plugins/loremipsum.js', ['goog.editor.plugins.LoremIpsum'], ['goog.asserts', 'goog.dom', 'goog.editor.Command', 'goog.editor.Field', 'goog.editor.Plugin', 'goog.editor.node', 'goog.functions', 'goog.html.SafeHtml', 'goog.userAgent'], {});
goog.addDependency('editor/plugins/loremipsum_test.js', ['goog.editor.plugins.LoremIpsumTest'], ['goog.dom', 'goog.editor.Command', 'goog.editor.Field', 'goog.editor.plugins.LoremIpsum', 'goog.html.SafeHtml', 'goog.string.Unicode', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/plugins/removeformatting.js', ['goog.editor.plugins.RemoveFormatting'], ['goog.dom', 'goog.dom.NodeType', 'goog.dom.Range', 'goog.dom.TagName', 'goog.editor.BrowserFeature', 'goog.editor.Plugin', 'goog.editor.node', 'goog.editor.range', 'goog.string', 'goog.userAgent'], {});
goog.addDependency('editor/plugins/removeformatting_test.js', ['goog.editor.plugins.RemoveFormattingTest'], ['goog.dom', 'goog.dom.Range', 'goog.dom.TagName', 'goog.editor.BrowserFeature', 'goog.editor.plugins.RemoveFormatting', 'goog.string', 'goog.testing.ExpectedFailures', 'goog.testing.dom', 'goog.testing.editor.FieldMock', 'goog.testing.editor.TestHelper', 'goog.testing.testSuite', 'goog.userAgent', 'goog.userAgent.product'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/plugins/spacestabhandler.js', ['goog.editor.plugins.SpacesTabHandler'], ['goog.dom.TagName', 'goog.editor.plugins.AbstractTabHandler', 'goog.editor.range'], {});
goog.addDependency('editor/plugins/spacestabhandler_test.js', ['goog.editor.plugins.SpacesTabHandlerTest'], ['goog.dom', 'goog.dom.Range', 'goog.editor.plugins.SpacesTabHandler', 'goog.events.BrowserEvent', 'goog.events.KeyCodes', 'goog.functions', 'goog.testing.StrictMock', 'goog.testing.editor.FieldMock', 'goog.testing.editor.TestHelper', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/plugins/tableeditor.js', ['goog.editor.plugins.TableEditor'], ['goog.array', 'goog.dom', 'goog.dom.Range', 'goog.dom.TagName', 'goog.editor.Plugin', 'goog.editor.Table', 'goog.editor.node', 'goog.editor.range', 'goog.object', 'goog.userAgent'], {});
goog.addDependency('editor/plugins/tableeditor_test.js', ['goog.editor.plugins.TableEditorTest'], ['goog.dom', 'goog.dom.Range', 'goog.dom.TagName', 'goog.editor.plugins.TableEditor', 'goog.object', 'goog.string', 'goog.testing.ExpectedFailures', 'goog.testing.TestCase', 'goog.testing.editor.FieldMock', 'goog.testing.editor.TestHelper', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/plugins/tagonenterhandler.js', ['goog.editor.plugins.TagOnEnterHandler'], ['goog.dom', 'goog.dom.NodeType', 'goog.dom.Range', 'goog.dom.TagName', 'goog.editor.Command', 'goog.editor.node', 'goog.editor.plugins.EnterHandler', 'goog.editor.range', 'goog.editor.style', 'goog.events.KeyCodes', 'goog.functions', 'goog.string.Unicode', 'goog.style', 'goog.userAgent'], {});
goog.addDependency('editor/plugins/tagonenterhandler_test.js', ['goog.editor.plugins.TagOnEnterHandlerTest'], ['goog.dom', 'goog.dom.NodeType', 'goog.dom.Range', 'goog.dom.TagName', 'goog.editor.BrowserFeature', 'goog.editor.Field', 'goog.editor.Plugin', 'goog.editor.plugins.TagOnEnterHandler', 'goog.events.KeyCodes', 'goog.html.SafeHtml', 'goog.string.Unicode', 'goog.testing.dom', 'goog.testing.editor.TestHelper', 'goog.testing.events', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/plugins/undoredo.js', ['goog.editor.plugins.UndoRedo'], ['goog.dom', 'goog.dom.NodeOffset', 'goog.dom.Range', 'goog.editor.BrowserFeature', 'goog.editor.Command', 'goog.editor.Field', 'goog.editor.Plugin', 'goog.editor.node', 'goog.editor.plugins.UndoRedoManager', 'goog.editor.plugins.UndoRedoState', 'goog.events', 'goog.events.EventHandler', 'goog.log', 'goog.object'], {});
goog.addDependency('editor/plugins/undoredo_test.js', ['goog.editor.plugins.UndoRedoTest'], ['goog.array', 'goog.dom', 'goog.dom.browserrange', 'goog.editor.Field', 'goog.editor.plugins.LoremIpsum', 'goog.editor.plugins.UndoRedo', 'goog.events', 'goog.functions', 'goog.html.SafeHtml', 'goog.testing.MockClock', 'goog.testing.PropertyReplacer', 'goog.testing.StrictMock', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/plugins/undoredomanager.js', ['goog.editor.plugins.UndoRedoManager', 'goog.editor.plugins.UndoRedoManager.EventType'], ['goog.editor.plugins.UndoRedoState', 'goog.events', 'goog.events.EventTarget'], {});
goog.addDependency('editor/plugins/undoredomanager_test.js', ['goog.editor.plugins.UndoRedoManagerTest'], ['goog.editor.plugins.UndoRedoManager', 'goog.editor.plugins.UndoRedoState', 'goog.events', 'goog.testing.StrictMock', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/plugins/undoredostate.js', ['goog.editor.plugins.UndoRedoState'], ['goog.events.EventTarget'], {});
goog.addDependency('editor/plugins/undoredostate_test.js', ['goog.editor.plugins.UndoRedoStateTest'], ['goog.editor.plugins.UndoRedoState', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/range.js', ['goog.editor.range', 'goog.editor.range.Point'], ['goog.array', 'goog.dom', 'goog.dom.NodeType', 'goog.dom.Range', 'goog.dom.RangeEndpoint', 'goog.dom.SavedCaretRange', 'goog.editor.node', 'goog.editor.style', 'goog.iter', 'goog.userAgent'], {});
goog.addDependency('editor/range_test.js', ['goog.editor.rangeTest'], ['goog.dom', 'goog.dom.Range', 'goog.dom.TagName', 'goog.editor.range', 'goog.editor.range.Point', 'goog.string', 'goog.testing.dom', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/seamlessfield.js', ['goog.editor.SeamlessField'], ['goog.cssom.iframe.style', 'goog.dom', 'goog.dom.Range', 'goog.dom.TagName', 'goog.dom.safe', 'goog.editor.BrowserFeature', 'goog.editor.Field', 'goog.editor.icontent', 'goog.editor.icontent.FieldFormatInfo', 'goog.editor.icontent.FieldStyleInfo', 'goog.editor.node', 'goog.events', 'goog.events.EventType', 'goog.html.SafeHtml', 'goog.log', 'goog.style'], {});
goog.addDependency('editor/seamlessfield_test.js', ['goog.editor.seamlessfield_test'], ['goog.dom', 'goog.dom.DomHelper', 'goog.dom.Range', 'goog.dom.TagName', 'goog.editor.BrowserFeature', 'goog.editor.Field', 'goog.editor.SeamlessField', 'goog.events', 'goog.functions', 'goog.html.SafeHtml', 'goog.style', 'goog.testing.MockClock', 'goog.testing.MockRange', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/style.js', ['goog.editor.style'], ['goog.array', 'goog.asserts', 'goog.dom', 'goog.dom.NodeType', 'goog.dom.TagName', 'goog.editor.BrowserFeature', 'goog.events.EventHandler', 'goog.events.EventType', 'goog.object', 'goog.style', 'goog.userAgent'], {});
goog.addDependency('editor/style_test.js', ['goog.editor.styleTest'], ['goog.dom', 'goog.dom.TagName', 'goog.editor.BrowserFeature', 'goog.editor.style', 'goog.events.EventHandler', 'goog.events.EventType', 'goog.style', 'goog.testing.LooseMock', 'goog.testing.mockmatchers', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('editor/table.js', ['goog.editor.Table', 'goog.editor.TableCell', 'goog.editor.TableRow'], ['goog.asserts', 'goog.dom', 'goog.dom.DomHelper', 'goog.dom.NodeType', 'goog.dom.TagName', 'goog.log', 'goog.string.Unicode', 'goog.style'], {});
goog.addDependency('editor/table_test.js', ['goog.editor.TableTest'], ['goog.dom', 'goog.dom.TagName', 'goog.editor.Table', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('events/actioneventwrapper.js', ['goog.events.actionEventWrapper'], ['goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.dom', 'goog.events', 'goog.events.EventHandler', 'goog.events.EventType', 'goog.events.EventWrapper', 'goog.events.KeyCodes'], {});
goog.addDependency('events/actioneventwrapper_test.js', ['goog.events.actionEventWrapperTest'], ['goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.events', 'goog.events.EventHandler', 'goog.events.KeyCodes', 'goog.events.actionEventWrapper', 'goog.testing.events', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('events/actionhandler.js', ['goog.events.ActionEvent', 'goog.events.ActionHandler', 'goog.events.ActionHandler.EventType', 'goog.events.BeforeActionEvent'], ['goog.events', 'goog.events.BrowserEvent', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.userAgent'], {});
goog.addDependency('events/actionhandler_test.js', ['goog.events.ActionHandlerTest'], ['goog.dom', 'goog.events', 'goog.events.ActionHandler', 'goog.testing.events', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('events/browserevent.js', ['goog.events.BrowserEvent', 'goog.events.BrowserEvent.MouseButton', 'goog.events.BrowserEvent.PointerType'], ['goog.debug', 'goog.events.BrowserFeature', 'goog.events.Event', 'goog.events.EventType', 'goog.reflect', 'goog.userAgent'], {});
goog.addDependency('events/browserevent_test.js', ['goog.events.BrowserEventTest'], ['goog.events.BrowserEvent', 'goog.events.BrowserFeature', 'goog.math.Coordinate', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('events/browserfeature.js', ['goog.events.BrowserFeature'], ['goog.userAgent'], {});
goog.addDependency('events/event.js', ['goog.events.Event', 'goog.events.EventLike'], ['goog.Disposable', 'goog.events.EventId'], {});
goog.addDependency('events/event_test.js', ['goog.events.EventTest'], ['goog.events.Event', 'goog.events.EventId', 'goog.events.EventTarget', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('events/eventhandler.js', ['goog.events.EventHandler'], ['goog.Disposable', 'goog.events', 'goog.object'], {});
goog.addDependency('events/eventhandler_test.js', ['goog.events.EventHandlerTest'], ['goog.events', 'goog.events.EventHandler', 'goog.events.EventTarget', 'goog.testing.PropertyReplacer', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('events/eventid.js', ['goog.events.EventId'], [], {});
goog.addDependency('events/events.js', ['goog.events', 'goog.events.CaptureSimulationMode', 'goog.events.Key', 'goog.events.ListenableType'], ['goog.asserts', 'goog.debug.entryPointRegistry', 'goog.events.BrowserEvent', 'goog.events.BrowserFeature', 'goog.events.Listenable', 'goog.events.ListenerMap'], {});
goog.addDependency('events/events_test.js', ['goog.eventsTest'], ['goog.asserts.AssertionError', 'goog.debug.EntryPointMonitor', 'goog.debug.ErrorHandler', 'goog.debug.entryPointRegistry', 'goog.dom', 'goog.dom.TagName', 'goog.events', 'goog.events.BrowserFeature', 'goog.events.CaptureSimulationMode', 'goog.events.Event', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.events.Listener', 'goog.functions', 'goog.testing.PropertyReplacer', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('events/eventtarget.js', ['goog.events.EventTarget'], ['goog.Disposable', 'goog.asserts', 'goog.events', 'goog.events.Event', 'goog.events.Listenable', 'goog.events.ListenerMap', 'goog.object'], {});
goog.addDependency('events/eventtarget_test.js', ['goog.events.EventTargetTest'], ['goog.events.EventTarget', 'goog.events.Listenable', 'goog.events.eventTargetTester', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('events/eventtarget_via_googevents_test.js', ['goog.events.EventTargetGoogEventsTest'], ['goog.events', 'goog.events.EventTarget', 'goog.events.eventTargetTester', 'goog.testing', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('events/eventtarget_via_w3cinterface_test.js', ['goog.events.EventTargetW3CTest'], ['goog.events.EventTarget', 'goog.events.eventTargetTester', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('events/eventtargettester.js', ['goog.events.eventTargetTester'], ['goog.array', 'goog.events.Event', 'goog.events.EventTarget', 'goog.events.Listenable', 'goog.testing.asserts', 'goog.testing.recordFunction'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('events/eventtype.js', ['goog.events.EventType', 'goog.events.MouseAsMouseEventType', 'goog.events.MouseEvents', 'goog.events.PointerAsMouseEventType', 'goog.events.PointerAsTouchEventType', 'goog.events.PointerFallbackEventType', 'goog.events.PointerTouchFallbackEventType'], ['goog.events.BrowserFeature', 'goog.userAgent'], {});
goog.addDependency('events/eventtype_test.js', ['goog.events.EventTypeTest'], ['goog.events.BrowserFeature', 'goog.events.EventType', 'goog.events.PointerFallbackEventType', 'goog.events.PointerTouchFallbackEventType', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('events/eventwrapper.js', ['goog.events.EventWrapper'], [], {});
goog.addDependency('events/filedrophandler.js', ['goog.events.FileDropHandler', 'goog.events.FileDropHandler.EventType'], ['goog.array', 'goog.dom', 'goog.events.BrowserEvent', 'goog.events.EventHandler', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.log', 'goog.log.Level'], {});
goog.addDependency('events/filedrophandler_test.js', ['goog.events.FileDropHandlerTest'], ['goog.events', 'goog.events.BrowserEvent', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.events.FileDropHandler', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('events/focushandler.js', ['goog.events.FocusHandler', 'goog.events.FocusHandler.EventType'], ['goog.events', 'goog.events.BrowserEvent', 'goog.events.EventTarget', 'goog.userAgent'], {});
goog.addDependency('events/imehandler.js', ['goog.events.ImeHandler', 'goog.events.ImeHandler.Event', 'goog.events.ImeHandler.EventType'], ['goog.events.Event', 'goog.events.EventHandler', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.userAgent'], {});
goog.addDependency('events/imehandler_test.js', ['goog.events.ImeHandlerTest'], ['goog.array', 'goog.dom', 'goog.events', 'goog.events.ImeHandler', 'goog.events.KeyCodes', 'goog.object', 'goog.string', 'goog.testing.PropertyReplacer', 'goog.testing.events', 'goog.testing.events.Event', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('events/inputhandler.js', ['goog.events.InputHandler', 'goog.events.InputHandler.EventType'], ['goog.Timer', 'goog.dom.TagName', 'goog.events.BrowserEvent', 'goog.events.EventHandler', 'goog.events.EventTarget', 'goog.events.KeyCodes', 'goog.userAgent'], {});
goog.addDependency('events/inputhandler_test.js', ['goog.events.InputHandlerTest'], ['goog.dom', 'goog.events.EventHandler', 'goog.events.EventType', 'goog.events.InputHandler', 'goog.events.KeyCodes', 'goog.testing.events', 'goog.testing.events.Event', 'goog.testing.recordFunction', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('events/keycodes.js', ['goog.events.KeyCodes'], ['goog.userAgent'], {});
goog.addDependency('events/keycodes_test.js', ['goog.events.KeyCodesTest'], ['goog.events.BrowserEvent', 'goog.events.KeyCodes', 'goog.object', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('events/keyhandler.js', ['goog.events.KeyEvent', 'goog.events.KeyHandler', 'goog.events.KeyHandler.EventType'], ['goog.events', 'goog.events.BrowserEvent', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.userAgent'], {});
goog.addDependency('events/keyhandler_test.js', ['goog.events.KeyEventTest'], ['goog.dom', 'goog.dom.TagName', 'goog.events', 'goog.events.BrowserEvent', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.events.KeyHandler', 'goog.testing.events', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('events/keynames.js', ['goog.events.KeyNames'], [], {});
goog.addDependency('events/keys.js', ['goog.events.Keys'], [], {'lang': 'es5'});
goog.addDependency('events/listenable.js', ['goog.events.Listenable', 'goog.events.ListenableKey'], ['goog.events.EventId'], {});
goog.addDependency('events/listenable_test.js', ['goog.events.ListenableTest'], ['goog.events.Listenable', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('events/listener.js', ['goog.events.Listener'], ['goog.events.ListenableKey'], {});
goog.addDependency('events/listenermap.js', ['goog.events.ListenerMap'], ['goog.array', 'goog.events.Listener', 'goog.object'], {});
goog.addDependency('events/listenermap_test.js', ['goog.events.ListenerMapTest'], ['goog.dispose', 'goog.events', 'goog.events.EventId', 'goog.events.EventTarget', 'goog.events.ListenerMap', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('events/mousewheelhandler.js', ['goog.events.MouseWheelEvent', 'goog.events.MouseWheelHandler', 'goog.events.MouseWheelHandler.EventType'], ['goog.dom', 'goog.events', 'goog.events.BrowserEvent', 'goog.events.EventTarget', 'goog.math', 'goog.style', 'goog.userAgent'], {});
goog.addDependency('events/mousewheelhandler_test.js', ['goog.events.MouseWheelHandlerTest'], ['goog.dom', 'goog.events', 'goog.events.BrowserEvent', 'goog.events.MouseWheelEvent', 'goog.events.MouseWheelHandler', 'goog.functions', 'goog.string', 'goog.testing.PropertyReplacer', 'goog.testing.events', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('events/onlinehandler.js', ['goog.events.OnlineHandler', 'goog.events.OnlineHandler.EventType'], ['goog.Timer', 'goog.events.BrowserFeature', 'goog.events.EventHandler', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.net.NetworkStatusMonitor'], {});
goog.addDependency('events/onlinelistener_test.js', ['goog.events.OnlineHandlerTest'], ['goog.events', 'goog.events.BrowserFeature', 'goog.events.Event', 'goog.events.EventHandler', 'goog.events.OnlineHandler', 'goog.net.NetworkStatusMonitor', 'goog.testing.MockClock', 'goog.testing.PropertyReplacer', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('events/pastehandler.js', ['goog.events.PasteHandler', 'goog.events.PasteHandler.EventType', 'goog.events.PasteHandler.State'], ['goog.Timer', 'goog.async.ConditionalDelay', 'goog.events.BrowserEvent', 'goog.events.EventHandler', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.log', 'goog.userAgent'], {});
goog.addDependency('events/pastehandler_test.js', ['goog.events.PasteHandlerTest'], ['goog.dom', 'goog.events', 'goog.events.BrowserEvent', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.events.PasteHandler', 'goog.testing.MockClock', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('events/wheelevent.js', ['goog.events.WheelEvent'], ['goog.asserts', 'goog.events.BrowserEvent'], {});
goog.addDependency('events/wheelhandler.js', ['goog.events.WheelHandler'], ['goog.dom', 'goog.events', 'goog.events.EventTarget', 'goog.events.WheelEvent', 'goog.style', 'goog.userAgent', 'goog.userAgent.product', 'goog.userAgent.product.isVersion'], {});
goog.addDependency('events/wheelhandler_test.js', ['goog.events.WheelHandlerTest'], ['goog.dom', 'goog.events', 'goog.events.BrowserEvent', 'goog.events.WheelEvent', 'goog.events.WheelHandler', 'goog.string', 'goog.testing.PropertyReplacer', 'goog.testing.events', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('format/emailaddress.js', ['goog.format.EmailAddress'], ['goog.string'], {});
goog.addDependency('format/emailaddress_test.js', ['goog.format.EmailAddressTest'], ['goog.array', 'goog.format.EmailAddress', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('format/format.js', ['goog.format'], ['goog.i18n.GraphemeBreak', 'goog.string', 'goog.userAgent'], {});
goog.addDependency('format/format_test.js', ['goog.formatTest'], ['goog.dom', 'goog.dom.TagName', 'goog.format', 'goog.string', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('format/htmlprettyprinter.js', ['goog.format.HtmlPrettyPrinter', 'goog.format.HtmlPrettyPrinter.Buffer'], ['goog.dom.TagName', 'goog.object', 'goog.string.StringBuffer'], {});
goog.addDependency('format/htmlprettyprinter_test.js', ['goog.format.HtmlPrettyPrinterTest'], ['goog.format.HtmlPrettyPrinter', 'goog.testing.MockClock', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('format/internationalizedemailaddress.js', ['goog.format.InternationalizedEmailAddress'], ['goog.format.EmailAddress', 'goog.string'], {});
goog.addDependency('format/internationalizedemailaddress_test.js', ['goog.format.InternationalizedEmailAddressTest'], ['goog.array', 'goog.format.InternationalizedEmailAddress', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('format/jsonprettyprinter.js', ['goog.format.JsonPrettyPrinter', 'goog.format.JsonPrettyPrinter.SafeHtmlDelimiters', 'goog.format.JsonPrettyPrinter.TextDelimiters'], ['goog.html.SafeHtml', 'goog.json', 'goog.json.Serializer', 'goog.string', 'goog.string.format'], {});
goog.addDependency('format/jsonprettyprinter_test.js', ['goog.format.JsonPrettyPrinterTest'], ['goog.format.JsonPrettyPrinter', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('fs/entry.js', ['goog.fs.DirectoryEntry', 'goog.fs.DirectoryEntry.Behavior', 'goog.fs.Entry', 'goog.fs.FileEntry'], [], {});
goog.addDependency('fs/entryimpl.js', ['goog.fs.DirectoryEntryImpl', 'goog.fs.EntryImpl', 'goog.fs.FileEntryImpl'], ['goog.array', 'goog.async.Deferred', 'goog.fs.DirectoryEntry', 'goog.fs.Entry', 'goog.fs.Error', 'goog.fs.FileEntry', 'goog.fs.FileWriter', 'goog.functions', 'goog.string'], {});
goog.addDependency('fs/error.js', ['goog.fs.DOMErrorLike', 'goog.fs.Error', 'goog.fs.Error.ErrorCode'], ['goog.asserts', 'goog.debug.Error', 'goog.object', 'goog.string'], {});
goog.addDependency('fs/filereader.js', ['goog.fs.FileReader', 'goog.fs.FileReader.EventType', 'goog.fs.FileReader.ReadyState'], ['goog.async.Deferred', 'goog.events.EventTarget', 'goog.fs.Error', 'goog.fs.ProgressEvent'], {});
goog.addDependency('fs/filesaver.js', ['goog.fs.FileSaver', 'goog.fs.FileSaver.EventType', 'goog.fs.FileSaver.ReadyState'], ['goog.events.EventTarget', 'goog.fs.Error', 'goog.fs.ProgressEvent'], {});
goog.addDependency('fs/filesystem.js', ['goog.fs.FileSystem'], [], {});
goog.addDependency('fs/filesystemimpl.js', ['goog.fs.FileSystemImpl'], ['goog.fs.DirectoryEntryImpl', 'goog.fs.FileSystem'], {});
goog.addDependency('fs/filewriter.js', ['goog.fs.FileWriter'], ['goog.fs.Error', 'goog.fs.FileSaver'], {});
goog.addDependency('fs/fs.js', ['goog.fs'], ['goog.array', 'goog.async.Deferred', 'goog.fs.Error', 'goog.fs.FileReader', 'goog.fs.FileSystemImpl', 'goog.fs.url', 'goog.userAgent'], {});
goog.addDependency('fs/fs_test.js', ['goog.fsTest'], ['goog.Promise', 'goog.array', 'goog.dom', 'goog.dom.TagName', 'goog.events', 'goog.fs', 'goog.fs.DirectoryEntry', 'goog.fs.Error', 'goog.fs.FileReader', 'goog.fs.FileSaver', 'goog.string', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('fs/progressevent.js', ['goog.fs.ProgressEvent'], ['goog.events.Event'], {});
goog.addDependency('fs/url.js', ['goog.fs.url'], [], {'lang': 'es6'});
goog.addDependency('fs/url_test.js', ['goog.urlTest'], ['goog.fs.url', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('functions/functions.js', ['goog.functions'], [], {'lang': 'es6'});
goog.addDependency('functions/functions_test.js', ['goog.functionsTest'], ['goog.array', 'goog.functions', 'goog.testing.MockClock', 'goog.testing.PropertyReplacer', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('fx/abstractdragdrop.js', ['goog.fx.AbstractDragDrop', 'goog.fx.AbstractDragDrop.EventType', 'goog.fx.DragDropEvent', 'goog.fx.DragDropItem'], ['goog.array', 'goog.asserts', 'goog.dom', 'goog.dom.classlist', 'goog.events', 'goog.events.Event', 'goog.events.EventHandler', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.fx.Dragger', 'goog.math.Box', 'goog.math.Coordinate', 'goog.style'], {});
goog.addDependency('fx/abstractdragdrop_test.js', ['goog.fx.AbstractDragDropTest'], ['goog.array', 'goog.dom', 'goog.dom.TagName', 'goog.events', 'goog.events.EventType', 'goog.functions', 'goog.fx.AbstractDragDrop', 'goog.fx.DragDropItem', 'goog.math.Box', 'goog.math.Coordinate', 'goog.style', 'goog.testing.events', 'goog.testing.events.Event', 'goog.testing.jsunit'], {'lang': 'es6'});
goog.addDependency('fx/anim/anim.js', ['goog.fx.anim', 'goog.fx.anim.Animated'], ['goog.async.AnimationDelay', 'goog.async.Delay', 'goog.object'], {});
goog.addDependency('fx/anim/anim_test.js', ['goog.fx.animTest'], ['goog.async.AnimationDelay', 'goog.async.Delay', 'goog.events', 'goog.functions', 'goog.fx.Animation', 'goog.fx.anim', 'goog.object', 'goog.testing.MockClock', 'goog.testing.PropertyReplacer', 'goog.testing.recordFunction', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('fx/animation.js', ['goog.fx.Animation', 'goog.fx.Animation.EventType', 'goog.fx.Animation.State', 'goog.fx.AnimationEvent'], ['goog.array', 'goog.asserts', 'goog.events.Event', 'goog.fx.Transition', 'goog.fx.TransitionBase', 'goog.fx.anim', 'goog.fx.anim.Animated'], {});
goog.addDependency('fx/animation_test.js', ['goog.fx.AnimationTest'], ['goog.events', 'goog.fx.Animation', 'goog.testing.MockClock', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('fx/animationqueue.js', ['goog.fx.AnimationParallelQueue', 'goog.fx.AnimationQueue', 'goog.fx.AnimationSerialQueue'], ['goog.array', 'goog.asserts', 'goog.events', 'goog.fx.Animation', 'goog.fx.Transition', 'goog.fx.TransitionBase'], {});
goog.addDependency('fx/animationqueue_test.js', ['goog.fx.AnimationQueueTest'], ['goog.events', 'goog.fx.Animation', 'goog.fx.AnimationParallelQueue', 'goog.fx.AnimationSerialQueue', 'goog.fx.Transition', 'goog.fx.anim', 'goog.testing.MockClock', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('fx/css3/fx.js', ['goog.fx.css3'], ['goog.fx.css3.Transition'], {});
goog.addDependency('fx/css3/transition.js', ['goog.fx.css3.Transition'], ['goog.Timer', 'goog.asserts', 'goog.fx.TransitionBase', 'goog.style', 'goog.style.transition'], {});
goog.addDependency('fx/css3/transition_test.js', ['goog.fx.css3.TransitionTest'], ['goog.dispose', 'goog.dom', 'goog.dom.TagName', 'goog.events', 'goog.fx.Transition', 'goog.fx.css3.Transition', 'goog.style.transition', 'goog.testing.MockClock', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('fx/cssspriteanimation.js', ['goog.fx.CssSpriteAnimation'], ['goog.fx.Animation'], {});
goog.addDependency('fx/cssspriteanimation_test.js', ['goog.fx.CssSpriteAnimationTest'], ['goog.fx.CssSpriteAnimation', 'goog.math.Box', 'goog.math.Size', 'goog.testing.MockClock', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('fx/dom.js', ['goog.fx.dom', 'goog.fx.dom.BgColorTransform', 'goog.fx.dom.ColorTransform', 'goog.fx.dom.Fade', 'goog.fx.dom.FadeIn', 'goog.fx.dom.FadeInAndShow', 'goog.fx.dom.FadeOut', 'goog.fx.dom.FadeOutAndHide', 'goog.fx.dom.PredefinedEffect', 'goog.fx.dom.Resize', 'goog.fx.dom.ResizeHeight', 'goog.fx.dom.ResizeWidth', 'goog.fx.dom.Scroll', 'goog.fx.dom.Slide', 'goog.fx.dom.SlideFrom', 'goog.fx.dom.Swipe'], ['goog.color', 'goog.events', 'goog.fx.Animation', 'goog.fx.Transition', 'goog.style', 'goog.style.bidi'], {});
goog.addDependency('fx/dragdrop.js', ['goog.fx.DragDrop'], ['goog.fx.AbstractDragDrop', 'goog.fx.DragDropItem'], {});
goog.addDependency('fx/dragdropgroup.js', ['goog.fx.DragDropGroup'], ['goog.dom', 'goog.fx.AbstractDragDrop', 'goog.fx.DragDropItem'], {});
goog.addDependency('fx/dragdropgroup_test.js', ['goog.fx.DragDropGroupTest'], ['goog.events', 'goog.fx.DragDropGroup', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('fx/dragger.js', ['goog.fx.DragEvent', 'goog.fx.Dragger', 'goog.fx.Dragger.EventType'], ['goog.dom', 'goog.dom.TagName', 'goog.events', 'goog.events.Event', 'goog.events.EventHandler', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.math.Coordinate', 'goog.math.Rect', 'goog.style', 'goog.style.bidi', 'goog.userAgent'], {});
goog.addDependency('fx/dragger_test.js', ['goog.fx.DraggerTest'], ['goog.dom', 'goog.dom.TagName', 'goog.events', 'goog.events.BrowserEvent', 'goog.events.Event', 'goog.events.EventType', 'goog.fx.Dragger', 'goog.math.Rect', 'goog.style.bidi', 'goog.testing.StrictMock', 'goog.testing.events', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('fx/draglistgroup.js', ['goog.fx.DragListDirection', 'goog.fx.DragListGroup', 'goog.fx.DragListGroup.EventType', 'goog.fx.DragListGroupEvent', 'goog.fx.DragListPermission'], ['goog.array', 'goog.asserts', 'goog.dom', 'goog.dom.classlist', 'goog.events', 'goog.events.Event', 'goog.events.EventHandler', 'goog.events.EventId', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.fx.Dragger', 'goog.math.Coordinate', 'goog.string', 'goog.style'], {});
goog.addDependency('fx/draglistgroup_test.js', ['goog.fx.DragListGroupTest'], ['goog.array', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events', 'goog.events.BrowserEvent', 'goog.events.BrowserFeature', 'goog.events.Event', 'goog.events.EventType', 'goog.fx.DragEvent', 'goog.fx.DragListDirection', 'goog.fx.DragListGroup', 'goog.fx.DragListPermission', 'goog.fx.Dragger', 'goog.math.Coordinate', 'goog.object', 'goog.style', 'goog.testing.events', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('fx/dragscrollsupport.js', ['goog.fx.DragScrollSupport'], ['goog.Disposable', 'goog.Timer', 'goog.dom', 'goog.events.EventHandler', 'goog.events.EventType', 'goog.math.Coordinate', 'goog.style'], {});
goog.addDependency('fx/dragscrollsupport_test.js', ['goog.fx.DragScrollSupportTest'], ['goog.fx.DragScrollSupport', 'goog.math.Coordinate', 'goog.math.Rect', 'goog.testing.MockClock', 'goog.testing.events', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('fx/easing.js', ['goog.fx.easing'], [], {});
goog.addDependency('fx/easing_test.js', ['goog.fx.easingTest'], ['goog.fx.easing', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('fx/fx.js', ['goog.fx'], ['goog.asserts', 'goog.fx.Animation', 'goog.fx.Animation.EventType', 'goog.fx.Animation.State', 'goog.fx.AnimationEvent', 'goog.fx.Transition.EventType', 'goog.fx.easing'], {});
goog.addDependency('fx/fx_test.js', ['goog.fxTest'], ['goog.fx.Animation', 'goog.object', 'goog.testing.MockClock', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('fx/transition.js', ['goog.fx.Transition', 'goog.fx.Transition.EventType'], [], {});
goog.addDependency('fx/transitionbase.js', ['goog.fx.TransitionBase', 'goog.fx.TransitionBase.State'], ['goog.events.EventTarget', 'goog.fx.Transition'], {});
goog.addDependency('goog.js', [], [], {'lang': 'es6', 'module': 'es6'});
goog.addDependency('graphics/abstractgraphics.js', ['goog.graphics.AbstractGraphics'], ['goog.dom', 'goog.graphics.AffineTransform', 'goog.graphics.Element', 'goog.graphics.EllipseElement', 'goog.graphics.Fill', 'goog.graphics.Font', 'goog.graphics.GroupElement', 'goog.graphics.Path', 'goog.graphics.PathElement', 'goog.graphics.RectElement', 'goog.graphics.Stroke', 'goog.graphics.StrokeAndFillElement', 'goog.graphics.TextElement', 'goog.math.Coordinate', 'goog.math.Size', 'goog.style', 'goog.ui.Component'], {});
goog.addDependency('graphics/affinetransform.js', ['goog.graphics.AffineTransform'], [], {'lang': 'es6'});
goog.addDependency('graphics/affinetransform_test.js', ['goog.graphics.AffineTransformTest'], ['goog.array', 'goog.graphics', 'goog.graphics.AffineTransform', 'goog.math', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('graphics/canvaselement.js', ['goog.graphics.CanvasEllipseElement', 'goog.graphics.CanvasGroupElement', 'goog.graphics.CanvasImageElement', 'goog.graphics.CanvasPathElement', 'goog.graphics.CanvasRectElement', 'goog.graphics.CanvasTextElement'], ['goog.array', 'goog.dom', 'goog.dom.TagName', 'goog.dom.safe', 'goog.graphics.EllipseElement', 'goog.graphics.Font', 'goog.graphics.GroupElement', 'goog.graphics.ImageElement', 'goog.graphics.Path', 'goog.graphics.PathElement', 'goog.graphics.RectElement', 'goog.graphics.TextElement', 'goog.html.SafeHtml', 'goog.html.uncheckedconversions', 'goog.math', 'goog.string', 'goog.string.Const'], {});
goog.addDependency('graphics/canvasgraphics.js', ['goog.graphics.CanvasGraphics'], ['goog.dom.TagName', 'goog.events.EventType', 'goog.graphics.AbstractGraphics', 'goog.graphics.CanvasEllipseElement', 'goog.graphics.CanvasGroupElement', 'goog.graphics.CanvasImageElement', 'goog.graphics.CanvasPathElement', 'goog.graphics.CanvasRectElement', 'goog.graphics.CanvasTextElement', 'goog.graphics.Font', 'goog.graphics.SolidFill', 'goog.math.Size', 'goog.style'], {});
goog.addDependency('graphics/canvasgraphics_test.js', ['goog.graphics.CanvasGraphicsTest'], ['goog.dom', 'goog.graphics.CanvasGraphics', 'goog.graphics.SolidFill', 'goog.graphics.Stroke', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('graphics/element.js', ['goog.graphics.Element'], ['goog.asserts', 'goog.events', 'goog.events.EventTarget', 'goog.events.Listenable', 'goog.graphics.AffineTransform', 'goog.math'], {});
goog.addDependency('graphics/ellipseelement.js', ['goog.graphics.EllipseElement'], ['goog.graphics.StrokeAndFillElement'], {});
goog.addDependency('graphics/ext/coordinates.js', ['goog.graphics.ext.coordinates'], ['goog.string'], {});
goog.addDependency('graphics/ext/coordinates_test.js', ['goog.graphics.ext.coordinatesTest'], ['goog.graphics', 'goog.graphics.ext.coordinates', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('graphics/ext/element.js', ['goog.graphics.ext.Element'], ['goog.events.EventTarget', 'goog.functions', 'goog.graphics.ext.coordinates'], {});
goog.addDependency('graphics/ext/element_test.js', ['goog.graphics.ext.ElementTest'], ['goog.graphics', 'goog.graphics.ext', 'goog.testing.StrictMock', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('graphics/ext/ellipse.js', ['goog.graphics.ext.Ellipse'], ['goog.graphics.ext.StrokeAndFillElement'], {});
goog.addDependency('graphics/ext/ext.js', ['goog.graphics.ext'], ['goog.graphics.ext.Ellipse', 'goog.graphics.ext.Graphics', 'goog.graphics.ext.Group', 'goog.graphics.ext.Image', 'goog.graphics.ext.Rectangle', 'goog.graphics.ext.Shape', 'goog.graphics.ext.coordinates'], {});
goog.addDependency('graphics/ext/graphics.js', ['goog.graphics.ext.Graphics'], ['goog.events', 'goog.events.EventType', 'goog.graphics', 'goog.graphics.ext.Group'], {});
goog.addDependency('graphics/ext/group.js', ['goog.graphics.ext.Group'], ['goog.array', 'goog.graphics.ext.Element'], {});
goog.addDependency('graphics/ext/image.js', ['goog.graphics.ext.Image'], ['goog.graphics.ext.Element'], {});
goog.addDependency('graphics/ext/path.js', ['goog.graphics.ext.Path'], ['goog.graphics.AffineTransform', 'goog.graphics.Path', 'goog.math.Rect'], {});
goog.addDependency('graphics/ext/path_test.js', ['goog.graphics.ext.PathTest'], ['goog.graphics', 'goog.graphics.ext.Path', 'goog.math.Rect', 'goog.testing.graphics', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('graphics/ext/rectangle.js', ['goog.graphics.ext.Rectangle'], ['goog.graphics.ext.StrokeAndFillElement'], {});
goog.addDependency('graphics/ext/shape.js', ['goog.graphics.ext.Shape'], ['goog.graphics.ext.StrokeAndFillElement'], {});
goog.addDependency('graphics/ext/strokeandfillelement.js', ['goog.graphics.ext.StrokeAndFillElement'], ['goog.graphics.ext.Element'], {});
goog.addDependency('graphics/fill.js', ['goog.graphics.Fill'], [], {});
goog.addDependency('graphics/font.js', ['goog.graphics.Font'], [], {});
goog.addDependency('graphics/graphics.js', ['goog.graphics'], ['goog.dom', 'goog.graphics.CanvasGraphics', 'goog.graphics.SvgGraphics', 'goog.graphics.VmlGraphics', 'goog.userAgent'], {});
goog.addDependency('graphics/groupelement.js', ['goog.graphics.GroupElement'], ['goog.graphics.Element'], {});
goog.addDependency('graphics/imageelement.js', ['goog.graphics.ImageElement'], ['goog.graphics.Element'], {});
goog.addDependency('graphics/lineargradient.js', ['goog.graphics.LinearGradient'], ['goog.asserts', 'goog.graphics.Fill'], {});
goog.addDependency('graphics/path.js', ['goog.graphics.Path', 'goog.graphics.Path.Segment'], ['goog.array', 'goog.graphics.AffineTransform', 'goog.math'], {});
goog.addDependency('graphics/path_test.js', ['goog.graphics.PathTest'], ['goog.array', 'goog.graphics.AffineTransform', 'goog.graphics.Path', 'goog.testing.graphics', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('graphics/pathelement.js', ['goog.graphics.PathElement'], ['goog.graphics.StrokeAndFillElement'], {});
goog.addDependency('graphics/paths.js', ['goog.graphics.paths'], ['goog.graphics.Path', 'goog.math.Coordinate'], {});
goog.addDependency('graphics/paths_test.js', ['goog.graphics.pathsTest'], ['goog.dom', 'goog.graphics', 'goog.graphics.paths', 'goog.math.Coordinate', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('graphics/rectelement.js', ['goog.graphics.RectElement'], ['goog.graphics.StrokeAndFillElement'], {});
goog.addDependency('graphics/solidfill.js', ['goog.graphics.SolidFill'], ['goog.graphics.Fill'], {});
goog.addDependency('graphics/solidfill_test.js', ['goog.graphics.SolidFillTest'], ['goog.graphics.SolidFill', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('graphics/stroke.js', ['goog.graphics.Stroke'], [], {});
goog.addDependency('graphics/strokeandfillelement.js', ['goog.graphics.StrokeAndFillElement'], ['goog.graphics.Element'], {});
goog.addDependency('graphics/svgelement.js', ['goog.graphics.SvgEllipseElement', 'goog.graphics.SvgGroupElement', 'goog.graphics.SvgImageElement', 'goog.graphics.SvgPathElement', 'goog.graphics.SvgRectElement', 'goog.graphics.SvgTextElement'], ['goog.dom', 'goog.graphics.EllipseElement', 'goog.graphics.GroupElement', 'goog.graphics.ImageElement', 'goog.graphics.PathElement', 'goog.graphics.RectElement', 'goog.graphics.TextElement'], {});
goog.addDependency('graphics/svggraphics.js', ['goog.graphics.SvgGraphics'], ['goog.Timer', 'goog.dom', 'goog.events.EventHandler', 'goog.events.EventType', 'goog.graphics.AbstractGraphics', 'goog.graphics.Font', 'goog.graphics.LinearGradient', 'goog.graphics.Path', 'goog.graphics.SolidFill', 'goog.graphics.Stroke', 'goog.graphics.SvgEllipseElement', 'goog.graphics.SvgGroupElement', 'goog.graphics.SvgImageElement', 'goog.graphics.SvgPathElement', 'goog.graphics.SvgRectElement', 'goog.graphics.SvgTextElement', 'goog.math', 'goog.math.Size', 'goog.style', 'goog.userAgent'], {});
goog.addDependency('graphics/svggraphics_test.js', ['goog.graphics.SvgGraphicsTest'], ['goog.dom', 'goog.graphics.AffineTransform', 'goog.graphics.SolidFill', 'goog.graphics.SvgGraphics', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('graphics/textelement.js', ['goog.graphics.TextElement'], ['goog.graphics.StrokeAndFillElement'], {});
goog.addDependency('graphics/vmlelement.js', ['goog.graphics.VmlEllipseElement', 'goog.graphics.VmlGroupElement', 'goog.graphics.VmlImageElement', 'goog.graphics.VmlPathElement', 'goog.graphics.VmlRectElement', 'goog.graphics.VmlTextElement'], ['goog.dom', 'goog.graphics.EllipseElement', 'goog.graphics.GroupElement', 'goog.graphics.ImageElement', 'goog.graphics.PathElement', 'goog.graphics.RectElement', 'goog.graphics.TextElement'], {});
goog.addDependency('graphics/vmlgraphics.js', ['goog.graphics.VmlGraphics'], ['goog.array', 'goog.dom.TagName', 'goog.dom.safe', 'goog.events', 'goog.events.EventHandler', 'goog.events.EventType', 'goog.graphics.AbstractGraphics', 'goog.graphics.Font', 'goog.graphics.LinearGradient', 'goog.graphics.Path', 'goog.graphics.SolidFill', 'goog.graphics.VmlEllipseElement', 'goog.graphics.VmlGroupElement', 'goog.graphics.VmlImageElement', 'goog.graphics.VmlPathElement', 'goog.graphics.VmlRectElement', 'goog.graphics.VmlTextElement', 'goog.html.uncheckedconversions', 'goog.math', 'goog.math.Size', 'goog.reflect', 'goog.string', 'goog.string.Const', 'goog.style', 'goog.userAgent'], {});
goog.addDependency('history/event.js', ['goog.history.Event'], ['goog.events.Event', 'goog.history.EventType'], {});
goog.addDependency('history/eventtype.js', ['goog.history.EventType'], [], {});
goog.addDependency('history/history.js', ['goog.History', 'goog.History.Event', 'goog.History.EventType'], ['goog.Timer', 'goog.asserts', 'goog.dom', 'goog.dom.InputType', 'goog.dom.safe', 'goog.events.Event', 'goog.events.EventHandler', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.history.Event', 'goog.history.EventType', 'goog.html.SafeHtml', 'goog.html.TrustedResourceUrl', 'goog.html.uncheckedconversions', 'goog.labs.userAgent.device', 'goog.memoize', 'goog.string', 'goog.string.Const', 'goog.userAgent'], {});
goog.addDependency('history/history_test.js', ['goog.HistoryTest'], ['goog.History', 'goog.dispose', 'goog.dom', 'goog.html.TrustedResourceUrl', 'goog.string.Const', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('history/html5history.js', ['goog.history.Html5History', 'goog.history.Html5History.TokenTransformer'], ['goog.asserts', 'goog.events', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.history.Event'], {});
goog.addDependency('history/html5history_test.js', ['goog.history.Html5HistoryTest'], ['goog.Timer', 'goog.events', 'goog.events.EventType', 'goog.history.EventType', 'goog.history.Html5History', 'goog.testing.MockControl', 'goog.testing.mockmatchers', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('html/cssspecificity.js', ['goog.html.CssSpecificity'], ['goog.userAgent', 'goog.userAgent.product'], {'module': 'goog'});
goog.addDependency('html/cssspecificity_test.js', ['goog.html.CssSpecificityTest'], ['goog.html.CssSpecificity', 'goog.testing.testSuite', 'goog.userAgent', 'goog.userAgent.product'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('html/flash.js', ['goog.html.flash'], ['goog.asserts', 'goog.html.SafeHtml'], {});
goog.addDependency('html/flash_test.js', ['goog.html.flashTest'], ['goog.html.SafeHtml', 'goog.html.TrustedResourceUrl', 'goog.html.flash', 'goog.string.Const', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('html/legacyconversions.js', ['goog.html.legacyconversions'], ['goog.html.SafeHtml', 'goog.html.SafeScript', 'goog.html.SafeStyle', 'goog.html.SafeStyleSheet', 'goog.html.SafeUrl', 'goog.html.TrustedResourceUrl'], {});
goog.addDependency('html/legacyconversions_test.js', ['goog.html.legacyconversionsTest'], ['goog.html.SafeHtml', 'goog.html.SafeScript', 'goog.html.SafeStyle', 'goog.html.SafeStyleSheet', 'goog.html.SafeUrl', 'goog.html.TrustedResourceUrl', 'goog.html.legacyconversions', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('html/safehtml.js', ['goog.html.SafeHtml'], ['goog.array', 'goog.asserts', 'goog.dom.TagName', 'goog.dom.tags', 'goog.html.SafeScript', 'goog.html.SafeStyle', 'goog.html.SafeStyleSheet', 'goog.html.SafeUrl', 'goog.html.TrustedResourceUrl', 'goog.html.trustedtypes', 'goog.i18n.bidi.Dir', 'goog.i18n.bidi.DirectionalString', 'goog.labs.userAgent.browser', 'goog.object', 'goog.string.Const', 'goog.string.TypedString', 'goog.string.internal'], {});
goog.addDependency('html/safehtml_test.js', ['goog.html.safeHtmlTest'], ['goog.html.SafeHtml', 'goog.html.SafeScript', 'goog.html.SafeStyle', 'goog.html.SafeStyleSheet', 'goog.html.SafeUrl', 'goog.html.TrustedResourceUrl', 'goog.html.testing', 'goog.html.trustedtypes', 'goog.i18n.bidi.Dir', 'goog.labs.userAgent.browser', 'goog.object', 'goog.string.Const', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('html/safehtmlformatter.js', ['goog.html.SafeHtmlFormatter'], ['goog.asserts', 'goog.dom.tags', 'goog.html.SafeHtml', 'goog.string'], {});
goog.addDependency('html/safehtmlformatter_test.js', ['goog.html.safeHtmlFormatterTest'], ['goog.html.SafeHtml', 'goog.html.SafeHtmlFormatter', 'goog.html.SafeUrl', 'goog.string', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('html/safescript.js', ['goog.html.SafeScript'], ['goog.asserts', 'goog.html.trustedtypes', 'goog.string.Const', 'goog.string.TypedString'], {});
goog.addDependency('html/safescript_test.js', ['goog.html.safeScriptTest'], ['goog.html.SafeScript', 'goog.html.trustedtypes', 'goog.object', 'goog.string.Const', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('html/safestyle.js', ['goog.html.SafeStyle'], ['goog.array', 'goog.asserts', 'goog.html.SafeUrl', 'goog.string.Const', 'goog.string.TypedString', 'goog.string.internal'], {'lang': 'es5'});
goog.addDependency('html/safestyle_test.js', ['goog.html.safeStyleTest'], ['goog.html.SafeStyle', 'goog.html.SafeUrl', 'goog.object', 'goog.string.Const', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('html/safestylesheet.js', ['goog.html.SafeStyleSheet'], ['goog.array', 'goog.asserts', 'goog.html.SafeStyle', 'goog.object', 'goog.string.Const', 'goog.string.TypedString', 'goog.string.internal'], {});
goog.addDependency('html/safestylesheet_test.js', ['goog.html.safeStyleSheetTest'], ['goog.html.SafeStyle', 'goog.html.SafeStyleSheet', 'goog.object', 'goog.string.Const', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('html/safeurl.js', ['goog.html.SafeUrl'], ['goog.asserts', 'goog.fs.url', 'goog.html.TrustedResourceUrl', 'goog.i18n.bidi.Dir', 'goog.i18n.bidi.DirectionalString', 'goog.string.Const', 'goog.string.TypedString', 'goog.string.internal'], {});
goog.addDependency('html/safeurl_test.js', ['goog.html.safeUrlTest'], ['goog.html.SafeUrl', 'goog.html.TrustedResourceUrl', 'goog.html.safeUrlTestVectors', 'goog.i18n.bidi.Dir', 'goog.object', 'goog.string.Const', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('html/safeurl_test_vectors.js', ['goog.html.safeUrlTestVectors'], [], {});
goog.addDependency('html/sanitizer/attributewhitelist.js', ['goog.html.sanitizer.AttributeSanitizedWhitelist', 'goog.html.sanitizer.AttributeWhitelist'], [], {});
goog.addDependency('html/sanitizer/csspropertysanitizer.js', ['goog.html.sanitizer.CssPropertySanitizer'], ['goog.asserts', 'goog.html.SafeUrl', 'goog.object', 'goog.string'], {'module': 'goog'});
goog.addDependency('html/sanitizer/csspropertysanitizer_test.js', ['goog.html.sanitizer.CssPropertySanitizerTest'], ['goog.functions', 'goog.html.SafeUrl', 'goog.html.sanitizer.CssPropertySanitizer', 'goog.html.sanitizer.noclobber', 'goog.testing.testSuite', 'goog.userAgent.product'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('html/sanitizer/csssanitizer.js', ['goog.html.sanitizer.CssSanitizer'], ['goog.array', 'goog.dom', 'goog.dom.TagName', 'goog.dom.safe', 'goog.html.CssSpecificity', 'goog.html.SafeStyle', 'goog.html.SafeStyleSheet', 'goog.html.SafeUrl', 'goog.html.sanitizer.CssPropertySanitizer', 'goog.html.sanitizer.noclobber', 'goog.html.uncheckedconversions', 'goog.object', 'goog.string', 'goog.string.Const', 'goog.userAgent', 'goog.userAgent.product'], {});
goog.addDependency('html/sanitizer/csssanitizer_test.js', ['goog.html.CssSanitizerTest'], ['goog.array', 'goog.html.SafeStyle', 'goog.html.SafeStyleSheet', 'goog.html.SafeUrl', 'goog.html.sanitizer.CssSanitizer', 'goog.html.testing', 'goog.string', 'goog.testing.dom', 'goog.testing.testSuite', 'goog.userAgent', 'goog.userAgent.product', 'goog.userAgent.product.isVersion'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('html/sanitizer/elementweakmap.js', ['goog.html.sanitizer.ElementWeakMap'], ['goog.html.sanitizer.noclobber'], {'module': 'goog'});
goog.addDependency('html/sanitizer/elementweakmap_test.js', ['goog.html.sanitizer.ElementWeakMapTest'], ['goog.html.sanitizer.ElementWeakMap', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('html/sanitizer/html_test_vectors.js', ['goog.html.htmlTestVectors'], [], {'lang': 'es5'});
goog.addDependency('html/sanitizer/htmlsanitizer.js', ['goog.html.sanitizer.HtmlSanitizer', 'goog.html.sanitizer.HtmlSanitizer.Builder', 'goog.html.sanitizer.HtmlSanitizerAttributePolicy', 'goog.html.sanitizer.HtmlSanitizerPolicy', 'goog.html.sanitizer.HtmlSanitizerPolicyContext', 'goog.html.sanitizer.HtmlSanitizerPolicyHints', 'goog.html.sanitizer.HtmlSanitizerUrlPolicy'], ['goog.array', 'goog.asserts', 'goog.dom', 'goog.dom.TagName', 'goog.functions', 'goog.html.SafeHtml', 'goog.html.SafeStyle', 'goog.html.SafeStyleSheet', 'goog.html.SafeUrl', 'goog.html.sanitizer.AttributeSanitizedWhitelist', 'goog.html.sanitizer.AttributeWhitelist', 'goog.html.sanitizer.CssSanitizer', 'goog.html.sanitizer.SafeDomTreeProcessor', 'goog.html.sanitizer.TagBlacklist', 'goog.html.sanitizer.TagWhitelist', 'goog.html.sanitizer.noclobber', 'goog.html.uncheckedconversions', 'goog.object', 'goog.string', 'goog.string.Const'], {'lang': 'es5'});
goog.addDependency('html/sanitizer/htmlsanitizer_test.js', ['goog.html.HtmlSanitizerTest'], ['goog.array', 'goog.dom', 'goog.functions', 'goog.html.SafeHtml', 'goog.html.SafeUrl', 'goog.html.sanitizer.HtmlSanitizer', 'goog.html.sanitizer.HtmlSanitizer.Builder', 'goog.html.sanitizer.TagWhitelist', 'goog.html.testing', 'goog.object', 'goog.string.Const', 'goog.testing.dom', 'goog.testing.testSuite', 'goog.userAgent', 'goog.userAgent.product'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('html/sanitizer/htmlsanitizer_unified_test.js', ['goog.html.HtmlSanitizerUnifiedTest'], ['goog.html.SafeHtml', 'goog.html.htmlTestVectors', 'goog.html.sanitizer.HtmlSanitizer.Builder', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('html/sanitizer/noclobber.js', ['goog.html.sanitizer.noclobber'], ['goog.asserts', 'goog.dom.NodeType', 'goog.userAgent.product'], {'lang': 'es5', 'module': 'goog'});
goog.addDependency('html/sanitizer/noclobber_test.js', ['goog.html.sanitizer.noclobberTest'], ['goog.dom.NodeType', 'goog.html.sanitizer.noclobber', 'goog.testing.PropertyReplacer', 'goog.testing.dom', 'goog.testing.testSuite', 'goog.userAgent.product'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('html/sanitizer/safedomtreeprocessor.js', ['goog.html.sanitizer.SafeDomTreeProcessor'], ['goog.dom', 'goog.dom.NodeType', 'goog.dom.TagName', 'goog.dom.safe', 'goog.html.sanitizer.ElementWeakMap', 'goog.html.sanitizer.noclobber', 'goog.html.uncheckedconversions', 'goog.log', 'goog.string.Const', 'goog.userAgent'], {'module': 'goog'});
goog.addDependency('html/sanitizer/safedomtreeprocessor_test.js', ['goog.html.sanitizer.SafeDomTreeProcessorTest'], ['goog.html.sanitizer.SafeDomTreeProcessor', 'goog.html.sanitizer.noclobber', 'goog.testing.dom', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('html/sanitizer/tagblacklist.js', ['goog.html.sanitizer.TagBlacklist'], [], {});
goog.addDependency('html/sanitizer/tagwhitelist.js', ['goog.html.sanitizer.TagWhitelist'], [], {});
goog.addDependency('html/sanitizer/unsafe.js', ['goog.html.sanitizer.unsafe'], ['goog.asserts', 'goog.html.sanitizer.HtmlSanitizer.Builder', 'goog.string', 'goog.string.Const'], {});
goog.addDependency('html/sanitizer/unsafe_test.js', ['goog.html.UnsafeTest'], ['goog.html.SafeHtml', 'goog.html.sanitizer.AttributeWhitelist', 'goog.html.sanitizer.HtmlSanitizer', 'goog.html.sanitizer.TagWhitelist', 'goog.html.sanitizer.unsafe', 'goog.string.Const', 'goog.testing.dom', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('html/silverlight.js', ['goog.html.silverlight'], ['goog.html.SafeHtml', 'goog.html.TrustedResourceUrl', 'goog.html.flash', 'goog.string.Const'], {});
goog.addDependency('html/silverlight_test.js', ['goog.html.silverlightTest'], ['goog.html.SafeHtml', 'goog.html.TrustedResourceUrl', 'goog.html.silverlight', 'goog.string.Const', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('html/testing.js', ['goog.html.testing'], ['goog.html.SafeHtml', 'goog.html.SafeScript', 'goog.html.SafeStyle', 'goog.html.SafeStyleSheet', 'goog.html.SafeUrl', 'goog.html.TrustedResourceUrl', 'goog.testing.mockmatchers.ArgumentMatcher'], {});
goog.addDependency('html/textextractor.js', ['goog.html.textExtractor'], ['goog.array', 'goog.dom.TagName', 'goog.html.sanitizer.HtmlSanitizer', 'goog.object', 'goog.userAgent'], {});
goog.addDependency('html/textextractor_test.js', ['goog.html.textExtractorTest'], ['goog.html.textExtractor', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('html/trustedresourceurl.js', ['goog.html.TrustedResourceUrl'], ['goog.asserts', 'goog.html.trustedtypes', 'goog.i18n.bidi.Dir', 'goog.i18n.bidi.DirectionalString', 'goog.string.Const', 'goog.string.TypedString'], {});
goog.addDependency('html/trustedresourceurl_test.js', ['goog.html.trustedResourceUrlTest'], ['goog.html.TrustedResourceUrl', 'goog.html.trustedtypes', 'goog.i18n.bidi.Dir', 'goog.object', 'goog.string.Const', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('html/trustedtypes.js', ['goog.html.trustedtypes'], [], {});
goog.addDependency('html/uncheckedconversions.js', ['goog.html.uncheckedconversions'], ['goog.asserts', 'goog.html.SafeHtml', 'goog.html.SafeScript', 'goog.html.SafeStyle', 'goog.html.SafeStyleSheet', 'goog.html.SafeUrl', 'goog.html.TrustedResourceUrl', 'goog.string.Const', 'goog.string.internal'], {});
goog.addDependency('html/uncheckedconversions_test.js', ['goog.html.uncheckedconversionsTest'], ['goog.html.SafeHtml', 'goog.html.SafeScript', 'goog.html.SafeStyle', 'goog.html.SafeStyleSheet', 'goog.html.SafeUrl', 'goog.html.TrustedResourceUrl', 'goog.html.uncheckedconversions', 'goog.i18n.bidi.Dir', 'goog.string.Const', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('html/utils.js', ['goog.html.utils'], ['goog.string'], {});
goog.addDependency('html/utils_test.js', ['goog.html.UtilsTest'], ['goog.array', 'goog.dom.TagName', 'goog.html.utils', 'goog.object', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('i18n/bidi.js', ['goog.i18n.bidi', 'goog.i18n.bidi.Dir', 'goog.i18n.bidi.DirectionalString', 'goog.i18n.bidi.Format'], [], {'lang': 'es6'});
goog.addDependency('i18n/bidi_test.js', ['goog.i18n.bidiTest'], ['goog.i18n.bidi', 'goog.i18n.bidi.Dir', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('i18n/bidiformatter.js', ['goog.i18n.BidiFormatter'], ['goog.html.SafeHtml', 'goog.i18n.bidi', 'goog.i18n.bidi.Dir', 'goog.i18n.bidi.Format'], {});
goog.addDependency('i18n/bidiformatter_test.js', ['goog.i18n.BidiFormatterTest'], ['goog.html.SafeHtml', 'goog.html.testing', 'goog.i18n.BidiFormatter', 'goog.i18n.bidi.Dir', 'goog.i18n.bidi.Format', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('i18n/charlistdecompressor.js', ['goog.i18n.CharListDecompressor'], ['goog.array', 'goog.i18n.uChar'], {});
goog.addDependency('i18n/charlistdecompressor_test.js', ['goog.i18n.CharListDecompressorTest'], ['goog.i18n.CharListDecompressor', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('i18n/charpickerdata.js', ['goog.i18n.CharPickerData'], [], {});
goog.addDependency('i18n/collation.js', ['goog.i18n.collation'], [], {'lang': 'es6'});
goog.addDependency('i18n/collation_test.js', ['goog.i18n.collationTest'], ['goog.i18n.collation', 'goog.testing.ExpectedFailures', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('i18n/compactnumberformatsymbols.js', ['goog.i18n.CompactNumberFormatSymbols', 'goog.i18n.CompactNumberFormatSymbols_af', 'goog.i18n.CompactNumberFormatSymbols_am', 'goog.i18n.CompactNumberFormatSymbols_ar', 'goog.i18n.CompactNumberFormatSymbols_ar_DZ', 'goog.i18n.CompactNumberFormatSymbols_ar_EG', 'goog.i18n.CompactNumberFormatSymbols_az', 'goog.i18n.CompactNumberFormatSymbols_be', 'goog.i18n.CompactNumberFormatSymbols_bg', 'goog.i18n.CompactNumberFormatSymbols_bn', 'goog.i18n.CompactNumberFormatSymbols_br', 'goog.i18n.CompactNumberFormatSymbols_bs', 'goog.i18n.CompactNumberFormatSymbols_ca', 'goog.i18n.CompactNumberFormatSymbols_chr', 'goog.i18n.CompactNumberFormatSymbols_cs', 'goog.i18n.CompactNumberFormatSymbols_cy', 'goog.i18n.CompactNumberFormatSymbols_da', 'goog.i18n.CompactNumberFormatSymbols_de', 'goog.i18n.CompactNumberFormatSymbols_de_AT', 'goog.i18n.CompactNumberFormatSymbols_de_CH', 'goog.i18n.CompactNumberFormatSymbols_el', 'goog.i18n.CompactNumberFormatSymbols_en', 'goog.i18n.CompactNumberFormatSymbols_en_AU', 'goog.i18n.CompactNumberFormatSymbols_en_CA', 'goog.i18n.CompactNumberFormatSymbols_en_GB', 'goog.i18n.CompactNumberFormatSymbols_en_IE', 'goog.i18n.CompactNumberFormatSymbols_en_IN', 'goog.i18n.CompactNumberFormatSymbols_en_SG', 'goog.i18n.CompactNumberFormatSymbols_en_US', 'goog.i18n.CompactNumberFormatSymbols_en_ZA', 'goog.i18n.CompactNumberFormatSymbols_es', 'goog.i18n.CompactNumberFormatSymbols_es_419', 'goog.i18n.CompactNumberFormatSymbols_es_ES', 'goog.i18n.CompactNumberFormatSymbols_es_MX', 'goog.i18n.CompactNumberFormatSymbols_es_US', 'goog.i18n.CompactNumberFormatSymbols_et', 'goog.i18n.CompactNumberFormatSymbols_eu', 'goog.i18n.CompactNumberFormatSymbols_fa', 'goog.i18n.CompactNumberFormatSymbols_fi', 'goog.i18n.CompactNumberFormatSymbols_fil', 'goog.i18n.CompactNumberFormatSymbols_fr', 'goog.i18n.CompactNumberFormatSymbols_fr_CA', 'goog.i18n.CompactNumberFormatSymbols_ga', 'goog.i18n.CompactNumberFormatSymbols_gl', 'goog.i18n.CompactNumberFormatSymbols_gsw', 'goog.i18n.CompactNumberFormatSymbols_gu', 'goog.i18n.CompactNumberFormatSymbols_haw', 'goog.i18n.CompactNumberFormatSymbols_he', 'goog.i18n.CompactNumberFormatSymbols_hi', 'goog.i18n.CompactNumberFormatSymbols_hr', 'goog.i18n.CompactNumberFormatSymbols_hu', 'goog.i18n.CompactNumberFormatSymbols_hy', 'goog.i18n.CompactNumberFormatSymbols_id', 'goog.i18n.CompactNumberFormatSymbols_in', 'goog.i18n.CompactNumberFormatSymbols_is', 'goog.i18n.CompactNumberFormatSymbols_it', 'goog.i18n.CompactNumberFormatSymbols_iw', 'goog.i18n.CompactNumberFormatSymbols_ja', 'goog.i18n.CompactNumberFormatSymbols_ka', 'goog.i18n.CompactNumberFormatSymbols_kk', 'goog.i18n.CompactNumberFormatSymbols_km', 'goog.i18n.CompactNumberFormatSymbols_kn', 'goog.i18n.CompactNumberFormatSymbols_ko', 'goog.i18n.CompactNumberFormatSymbols_ky', 'goog.i18n.CompactNumberFormatSymbols_ln', 'goog.i18n.CompactNumberFormatSymbols_lo', 'goog.i18n.CompactNumberFormatSymbols_lt', 'goog.i18n.CompactNumberFormatSymbols_lv', 'goog.i18n.CompactNumberFormatSymbols_mk', 'goog.i18n.CompactNumberFormatSymbols_ml', 'goog.i18n.CompactNumberFormatSymbols_mn', 'goog.i18n.CompactNumberFormatSymbols_mo', 'goog.i18n.CompactNumberFormatSymbols_mr', 'goog.i18n.CompactNumberFormatSymbols_ms', 'goog.i18n.CompactNumberFormatSymbols_mt', 'goog.i18n.CompactNumberFormatSymbols_my', 'goog.i18n.CompactNumberFormatSymbols_nb', 'goog.i18n.CompactNumberFormatSymbols_ne', 'goog.i18n.CompactNumberFormatSymbols_nl', 'goog.i18n.CompactNumberFormatSymbols_no', 'goog.i18n.CompactNumberFormatSymbols_no_NO', 'goog.i18n.CompactNumberFormatSymbols_or', 'goog.i18n.CompactNumberFormatSymbols_pa', 'goog.i18n.CompactNumberFormatSymbols_pl', 'goog.i18n.CompactNumberFormatSymbols_pt', 'goog.i18n.CompactNumberFormatSymbols_pt_BR', 'goog.i18n.CompactNumberFormatSymbols_pt_PT', 'goog.i18n.CompactNumberFormatSymbols_ro', 'goog.i18n.CompactNumberFormatSymbols_ru', 'goog.i18n.CompactNumberFormatSymbols_sh', 'goog.i18n.CompactNumberFormatSymbols_si', 'goog.i18n.CompactNumberFormatSymbols_sk', 'goog.i18n.CompactNumberFormatSymbols_sl', 'goog.i18n.CompactNumberFormatSymbols_sq', 'goog.i18n.CompactNumberFormatSymbols_sr', 'goog.i18n.CompactNumberFormatSymbols_sr_Latn', 'goog.i18n.CompactNumberFormatSymbols_sv', 'goog.i18n.CompactNumberFormatSymbols_sw', 'goog.i18n.CompactNumberFormatSymbols_ta', 'goog.i18n.CompactNumberFormatSymbols_te', 'goog.i18n.CompactNumberFormatSymbols_th', 'goog.i18n.CompactNumberFormatSymbols_tl', 'goog.i18n.CompactNumberFormatSymbols_tr', 'goog.i18n.CompactNumberFormatSymbols_uk', 'goog.i18n.CompactNumberFormatSymbols_ur', 'goog.i18n.CompactNumberFormatSymbols_uz', 'goog.i18n.CompactNumberFormatSymbols_vi', 'goog.i18n.CompactNumberFormatSymbols_zh', 'goog.i18n.CompactNumberFormatSymbols_zh_CN', 'goog.i18n.CompactNumberFormatSymbols_zh_HK', 'goog.i18n.CompactNumberFormatSymbols_zh_TW', 'goog.i18n.CompactNumberFormatSymbols_zu'], [], {});
goog.addDependency('i18n/compactnumberformatsymbolsext.js', ['goog.i18n.CompactNumberFormatSymbolsExt', 'goog.i18n.CompactNumberFormatSymbols_af_NA', 'goog.i18n.CompactNumberFormatSymbols_af_ZA', 'goog.i18n.CompactNumberFormatSymbols_agq', 'goog.i18n.CompactNumberFormatSymbols_agq_CM', 'goog.i18n.CompactNumberFormatSymbols_ak', 'goog.i18n.CompactNumberFormatSymbols_ak_GH', 'goog.i18n.CompactNumberFormatSymbols_am_ET', 'goog.i18n.CompactNumberFormatSymbols_ar_001', 'goog.i18n.CompactNumberFormatSymbols_ar_AE', 'goog.i18n.CompactNumberFormatSymbols_ar_BH', 'goog.i18n.CompactNumberFormatSymbols_ar_DJ', 'goog.i18n.CompactNumberFormatSymbols_ar_EH', 'goog.i18n.CompactNumberFormatSymbols_ar_ER', 'goog.i18n.CompactNumberFormatSymbols_ar_IL', 'goog.i18n.CompactNumberFormatSymbols_ar_IQ', 'goog.i18n.CompactNumberFormatSymbols_ar_JO', 'goog.i18n.CompactNumberFormatSymbols_ar_KM', 'goog.i18n.CompactNumberFormatSymbols_ar_KW', 'goog.i18n.CompactNumberFormatSymbols_ar_LB', 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'goog.i18n.CompactNumberFormatSymbols_en_GU', 'goog.i18n.CompactNumberFormatSymbols_en_GY', 'goog.i18n.CompactNumberFormatSymbols_en_HK', 'goog.i18n.CompactNumberFormatSymbols_en_IL', 'goog.i18n.CompactNumberFormatSymbols_en_IM', 'goog.i18n.CompactNumberFormatSymbols_en_IO', 'goog.i18n.CompactNumberFormatSymbols_en_JE', 'goog.i18n.CompactNumberFormatSymbols_en_JM', 'goog.i18n.CompactNumberFormatSymbols_en_KE', 'goog.i18n.CompactNumberFormatSymbols_en_KI', 'goog.i18n.CompactNumberFormatSymbols_en_KN', 'goog.i18n.CompactNumberFormatSymbols_en_KY', 'goog.i18n.CompactNumberFormatSymbols_en_LC', 'goog.i18n.CompactNumberFormatSymbols_en_LR', 'goog.i18n.CompactNumberFormatSymbols_en_LS', 'goog.i18n.CompactNumberFormatSymbols_en_MG', 'goog.i18n.CompactNumberFormatSymbols_en_MH', 'goog.i18n.CompactNumberFormatSymbols_en_MO', 'goog.i18n.CompactNumberFormatSymbols_en_MP', 'goog.i18n.CompactNumberFormatSymbols_en_MS', 'goog.i18n.CompactNumberFormatSymbols_en_MT', 'goog.i18n.CompactNumberFormatSymbols_en_MU', 'goog.i18n.CompactNumberFormatSymbols_en_MW', 'goog.i18n.CompactNumberFormatSymbols_en_MY', 'goog.i18n.CompactNumberFormatSymbols_en_NA', 'goog.i18n.CompactNumberFormatSymbols_en_NF', 'goog.i18n.CompactNumberFormatSymbols_en_NG', 'goog.i18n.CompactNumberFormatSymbols_en_NL', 'goog.i18n.CompactNumberFormatSymbols_en_NR', 'goog.i18n.CompactNumberFormatSymbols_en_NU', 'goog.i18n.CompactNumberFormatSymbols_en_NZ', 'goog.i18n.CompactNumberFormatSymbols_en_PG', 'goog.i18n.CompactNumberFormatSymbols_en_PH', 'goog.i18n.CompactNumberFormatSymbols_en_PK', 'goog.i18n.CompactNumberFormatSymbols_en_PN', 'goog.i18n.CompactNumberFormatSymbols_en_PR', 'goog.i18n.CompactNumberFormatSymbols_en_PW', 'goog.i18n.CompactNumberFormatSymbols_en_RW', 'goog.i18n.CompactNumberFormatSymbols_en_SB', 'goog.i18n.CompactNumberFormatSymbols_en_SC', 'goog.i18n.CompactNumberFormatSymbols_en_SD', 'goog.i18n.CompactNumberFormatSymbols_en_SE', 'goog.i18n.CompactNumberFormatSymbols_en_SH', 'goog.i18n.CompactNumberFormatSymbols_en_SI', 'goog.i18n.CompactNumberFormatSymbols_en_SL', 'goog.i18n.CompactNumberFormatSymbols_en_SS', 'goog.i18n.CompactNumberFormatSymbols_en_SX', 'goog.i18n.CompactNumberFormatSymbols_en_SZ', 'goog.i18n.CompactNumberFormatSymbols_en_TC', 'goog.i18n.CompactNumberFormatSymbols_en_TK', 'goog.i18n.CompactNumberFormatSymbols_en_TO', 'goog.i18n.CompactNumberFormatSymbols_en_TT', 'goog.i18n.CompactNumberFormatSymbols_en_TV', 'goog.i18n.CompactNumberFormatSymbols_en_TZ', 'goog.i18n.CompactNumberFormatSymbols_en_UG', 'goog.i18n.CompactNumberFormatSymbols_en_UM', 'goog.i18n.CompactNumberFormatSymbols_en_US_POSIX', 'goog.i18n.CompactNumberFormatSymbols_en_VC', 'goog.i18n.CompactNumberFormatSymbols_en_VG', 'goog.i18n.CompactNumberFormatSymbols_en_VI', 'goog.i18n.CompactNumberFormatSymbols_en_VU', 'goog.i18n.CompactNumberFormatSymbols_en_WS', 'goog.i18n.CompactNumberFormatSymbols_en_XA', 'goog.i18n.CompactNumberFormatSymbols_en_ZM', 'goog.i18n.CompactNumberFormatSymbols_en_ZW', 'goog.i18n.CompactNumberFormatSymbols_eo', 'goog.i18n.CompactNumberFormatSymbols_eo_001', 'goog.i18n.CompactNumberFormatSymbols_es_AR', 'goog.i18n.CompactNumberFormatSymbols_es_BO', 'goog.i18n.CompactNumberFormatSymbols_es_BR', 'goog.i18n.CompactNumberFormatSymbols_es_BZ', 'goog.i18n.CompactNumberFormatSymbols_es_CL', 'goog.i18n.CompactNumberFormatSymbols_es_CO', 'goog.i18n.CompactNumberFormatSymbols_es_CR', 'goog.i18n.CompactNumberFormatSymbols_es_CU', 'goog.i18n.CompactNumberFormatSymbols_es_DO', 'goog.i18n.CompactNumberFormatSymbols_es_EA', 'goog.i18n.CompactNumberFormatSymbols_es_EC', 'goog.i18n.CompactNumberFormatSymbols_es_GQ', 'goog.i18n.CompactNumberFormatSymbols_es_GT', 'goog.i18n.CompactNumberFormatSymbols_es_HN', 'goog.i18n.CompactNumberFormatSymbols_es_IC', 'goog.i18n.CompactNumberFormatSymbols_es_NI', 'goog.i18n.CompactNumberFormatSymbols_es_PA', 'goog.i18n.CompactNumberFormatSymbols_es_PE', 'goog.i18n.CompactNumberFormatSymbols_es_PH', 'goog.i18n.CompactNumberFormatSymbols_es_PR', 'goog.i18n.CompactNumberFormatSymbols_es_PY', 'goog.i18n.CompactNumberFormatSymbols_es_SV', 'goog.i18n.CompactNumberFormatSymbols_es_UY', 'goog.i18n.CompactNumberFormatSymbols_es_VE', 'goog.i18n.CompactNumberFormatSymbols_et_EE', 'goog.i18n.CompactNumberFormatSymbols_eu_ES', 'goog.i18n.CompactNumberFormatSymbols_ewo', 'goog.i18n.CompactNumberFormatSymbols_ewo_CM', 'goog.i18n.CompactNumberFormatSymbols_fa_AF', 'goog.i18n.CompactNumberFormatSymbols_fa_IR', 'goog.i18n.CompactNumberFormatSymbols_ff', 'goog.i18n.CompactNumberFormatSymbols_ff_Latn', 'goog.i18n.CompactNumberFormatSymbols_ff_Latn_BF', 'goog.i18n.CompactNumberFormatSymbols_ff_Latn_CM', 'goog.i18n.CompactNumberFormatSymbols_ff_Latn_GH', 'goog.i18n.CompactNumberFormatSymbols_ff_Latn_GM', 'goog.i18n.CompactNumberFormatSymbols_ff_Latn_GN', 'goog.i18n.CompactNumberFormatSymbols_ff_Latn_GW', 'goog.i18n.CompactNumberFormatSymbols_ff_Latn_LR', 'goog.i18n.CompactNumberFormatSymbols_ff_Latn_MR', 'goog.i18n.CompactNumberFormatSymbols_ff_Latn_NE', 'goog.i18n.CompactNumberFormatSymbols_ff_Latn_NG', 'goog.i18n.CompactNumberFormatSymbols_ff_Latn_SL', 'goog.i18n.CompactNumberFormatSymbols_ff_Latn_SN', 'goog.i18n.CompactNumberFormatSymbols_fi_FI', 'goog.i18n.CompactNumberFormatSymbols_fil_PH', 'goog.i18n.CompactNumberFormatSymbols_fo', 'goog.i18n.CompactNumberFormatSymbols_fo_DK', 'goog.i18n.CompactNumberFormatSymbols_fo_FO', 'goog.i18n.CompactNumberFormatSymbols_fr_BE', 'goog.i18n.CompactNumberFormatSymbols_fr_BF', 'goog.i18n.CompactNumberFormatSymbols_fr_BI', 'goog.i18n.CompactNumberFormatSymbols_fr_BJ', 'goog.i18n.CompactNumberFormatSymbols_fr_BL', 'goog.i18n.CompactNumberFormatSymbols_fr_CD', 'goog.i18n.CompactNumberFormatSymbols_fr_CF', 'goog.i18n.CompactNumberFormatSymbols_fr_CG', 'goog.i18n.CompactNumberFormatSymbols_fr_CH', 'goog.i18n.CompactNumberFormatSymbols_fr_CI', 'goog.i18n.CompactNumberFormatSymbols_fr_CM', 'goog.i18n.CompactNumberFormatSymbols_fr_DJ', 'goog.i18n.CompactNumberFormatSymbols_fr_DZ', 'goog.i18n.CompactNumberFormatSymbols_fr_FR', 'goog.i18n.CompactNumberFormatSymbols_fr_GA', 'goog.i18n.CompactNumberFormatSymbols_fr_GF', 'goog.i18n.CompactNumberFormatSymbols_fr_GN', 'goog.i18n.CompactNumberFormatSymbols_fr_GP', 'goog.i18n.CompactNumberFormatSymbols_fr_GQ', 'goog.i18n.CompactNumberFormatSymbols_fr_HT', 'goog.i18n.CompactNumberFormatSymbols_fr_KM', 'goog.i18n.CompactNumberFormatSymbols_fr_LU', 'goog.i18n.CompactNumberFormatSymbols_fr_MA', 'goog.i18n.CompactNumberFormatSymbols_fr_MC', 'goog.i18n.CompactNumberFormatSymbols_fr_MF', 'goog.i18n.CompactNumberFormatSymbols_fr_MG', 'goog.i18n.CompactNumberFormatSymbols_fr_ML', 'goog.i18n.CompactNumberFormatSymbols_fr_MQ', 'goog.i18n.CompactNumberFormatSymbols_fr_MR', 'goog.i18n.CompactNumberFormatSymbols_fr_MU', 'goog.i18n.CompactNumberFormatSymbols_fr_NC', 'goog.i18n.CompactNumberFormatSymbols_fr_NE', 'goog.i18n.CompactNumberFormatSymbols_fr_PF', 'goog.i18n.CompactNumberFormatSymbols_fr_PM', 'goog.i18n.CompactNumberFormatSymbols_fr_RE', 'goog.i18n.CompactNumberFormatSymbols_fr_RW', 'goog.i18n.CompactNumberFormatSymbols_fr_SC', 'goog.i18n.CompactNumberFormatSymbols_fr_SN', 'goog.i18n.CompactNumberFormatSymbols_fr_SY', 'goog.i18n.CompactNumberFormatSymbols_fr_TD', 'goog.i18n.CompactNumberFormatSymbols_fr_TG', 'goog.i18n.CompactNumberFormatSymbols_fr_TN', 'goog.i18n.CompactNumberFormatSymbols_fr_VU', 'goog.i18n.CompactNumberFormatSymbols_fr_WF', 'goog.i18n.CompactNumberFormatSymbols_fr_YT', 'goog.i18n.CompactNumberFormatSymbols_fur', 'goog.i18n.CompactNumberFormatSymbols_fur_IT', 'goog.i18n.CompactNumberFormatSymbols_fy', 'goog.i18n.CompactNumberFormatSymbols_fy_NL', 'goog.i18n.CompactNumberFormatSymbols_ga_IE', 'goog.i18n.CompactNumberFormatSymbols_gd', 'goog.i18n.CompactNumberFormatSymbols_gd_GB', 'goog.i18n.CompactNumberFormatSymbols_gl_ES', 'goog.i18n.CompactNumberFormatSymbols_gsw_CH', 'goog.i18n.CompactNumberFormatSymbols_gsw_FR', 'goog.i18n.CompactNumberFormatSymbols_gsw_LI', 'goog.i18n.CompactNumberFormatSymbols_gu_IN', 'goog.i18n.CompactNumberFormatSymbols_guz', 'goog.i18n.CompactNumberFormatSymbols_guz_KE', 'goog.i18n.CompactNumberFormatSymbols_gv', 'goog.i18n.CompactNumberFormatSymbols_gv_IM', 'goog.i18n.CompactNumberFormatSymbols_ha', 'goog.i18n.CompactNumberFormatSymbols_ha_GH', 'goog.i18n.CompactNumberFormatSymbols_ha_NE', 'goog.i18n.CompactNumberFormatSymbols_ha_NG', 'goog.i18n.CompactNumberFormatSymbols_haw_US', 'goog.i18n.CompactNumberFormatSymbols_he_IL', 'goog.i18n.CompactNumberFormatSymbols_hi_IN', 'goog.i18n.CompactNumberFormatSymbols_hr_BA', 'goog.i18n.CompactNumberFormatSymbols_hr_HR', 'goog.i18n.CompactNumberFormatSymbols_hsb', 'goog.i18n.CompactNumberFormatSymbols_hsb_DE', 'goog.i18n.CompactNumberFormatSymbols_hu_HU', 'goog.i18n.CompactNumberFormatSymbols_hy_AM', 'goog.i18n.CompactNumberFormatSymbols_ia', 'goog.i18n.CompactNumberFormatSymbols_ia_001', 'goog.i18n.CompactNumberFormatSymbols_id_ID', 'goog.i18n.CompactNumberFormatSymbols_ig', 'goog.i18n.CompactNumberFormatSymbols_ig_NG', 'goog.i18n.CompactNumberFormatSymbols_ii', 'goog.i18n.CompactNumberFormatSymbols_ii_CN', 'goog.i18n.CompactNumberFormatSymbols_is_IS', 'goog.i18n.CompactNumberFormatSymbols_it_CH', 'goog.i18n.CompactNumberFormatSymbols_it_IT', 'goog.i18n.CompactNumberFormatSymbols_it_SM', 'goog.i18n.CompactNumberFormatSymbols_it_VA', 'goog.i18n.CompactNumberFormatSymbols_ja_JP', 'goog.i18n.CompactNumberFormatSymbols_jgo', 'goog.i18n.CompactNumberFormatSymbols_jgo_CM', 'goog.i18n.CompactNumberFormatSymbols_jmc', 'goog.i18n.CompactNumberFormatSymbols_jmc_TZ', 'goog.i18n.CompactNumberFormatSymbols_jv', 'goog.i18n.CompactNumberFormatSymbols_jv_ID', 'goog.i18n.CompactNumberFormatSymbols_ka_GE', 'goog.i18n.CompactNumberFormatSymbols_kab', 'goog.i18n.CompactNumberFormatSymbols_kab_DZ', 'goog.i18n.CompactNumberFormatSymbols_kam', 'goog.i18n.CompactNumberFormatSymbols_kam_KE', 'goog.i18n.CompactNumberFormatSymbols_kde', 'goog.i18n.CompactNumberFormatSymbols_kde_TZ', 'goog.i18n.CompactNumberFormatSymbols_kea', 'goog.i18n.CompactNumberFormatSymbols_kea_CV', 'goog.i18n.CompactNumberFormatSymbols_khq', 'goog.i18n.CompactNumberFormatSymbols_khq_ML', 'goog.i18n.CompactNumberFormatSymbols_ki', 'goog.i18n.CompactNumberFormatSymbols_ki_KE', 'goog.i18n.CompactNumberFormatSymbols_kk_KZ', 'goog.i18n.CompactNumberFormatSymbols_kkj', 'goog.i18n.CompactNumberFormatSymbols_kkj_CM', 'goog.i18n.CompactNumberFormatSymbols_kl', 'goog.i18n.CompactNumberFormatSymbols_kl_GL', 'goog.i18n.CompactNumberFormatSymbols_kln', 'goog.i18n.CompactNumberFormatSymbols_kln_KE', 'goog.i18n.CompactNumberFormatSymbols_km_KH', 'goog.i18n.CompactNumberFormatSymbols_kn_IN', 'goog.i18n.CompactNumberFormatSymbols_ko_KP', 'goog.i18n.CompactNumberFormatSymbols_ko_KR', 'goog.i18n.CompactNumberFormatSymbols_kok', 'goog.i18n.CompactNumberFormatSymbols_kok_IN', 'goog.i18n.CompactNumberFormatSymbols_ks', 'goog.i18n.CompactNumberFormatSymbols_ks_IN', 'goog.i18n.CompactNumberFormatSymbols_ksb', 'goog.i18n.CompactNumberFormatSymbols_ksb_TZ', 'goog.i18n.CompactNumberFormatSymbols_ksf', 'goog.i18n.CompactNumberFormatSymbols_ksf_CM', 'goog.i18n.CompactNumberFormatSymbols_ksh', 'goog.i18n.CompactNumberFormatSymbols_ksh_DE', 'goog.i18n.CompactNumberFormatSymbols_ku', 'goog.i18n.CompactNumberFormatSymbols_ku_TR', 'goog.i18n.CompactNumberFormatSymbols_kw', 'goog.i18n.CompactNumberFormatSymbols_kw_GB', 'goog.i18n.CompactNumberFormatSymbols_ky_KG', 'goog.i18n.CompactNumberFormatSymbols_lag', 'goog.i18n.CompactNumberFormatSymbols_lag_TZ', 'goog.i18n.CompactNumberFormatSymbols_lb', 'goog.i18n.CompactNumberFormatSymbols_lb_LU', 'goog.i18n.CompactNumberFormatSymbols_lg', 'goog.i18n.CompactNumberFormatSymbols_lg_UG', 'goog.i18n.CompactNumberFormatSymbols_lkt', 'goog.i18n.CompactNumberFormatSymbols_lkt_US', 'goog.i18n.CompactNumberFormatSymbols_ln_AO', 'goog.i18n.CompactNumberFormatSymbols_ln_CD', 'goog.i18n.CompactNumberFormatSymbols_ln_CF', 'goog.i18n.CompactNumberFormatSymbols_ln_CG', 'goog.i18n.CompactNumberFormatSymbols_lo_LA', 'goog.i18n.CompactNumberFormatSymbols_lrc', 'goog.i18n.CompactNumberFormatSymbols_lrc_IQ', 'goog.i18n.CompactNumberFormatSymbols_lrc_IR', 'goog.i18n.CompactNumberFormatSymbols_lt_LT', 'goog.i18n.CompactNumberFormatSymbols_lu', 'goog.i18n.CompactNumberFormatSymbols_lu_CD', 'goog.i18n.CompactNumberFormatSymbols_luo', 'goog.i18n.CompactNumberFormatSymbols_luo_KE', 'goog.i18n.CompactNumberFormatSymbols_luy', 'goog.i18n.CompactNumberFormatSymbols_luy_KE', 'goog.i18n.CompactNumberFormatSymbols_lv_LV', 'goog.i18n.CompactNumberFormatSymbols_mas', 'goog.i18n.CompactNumberFormatSymbols_mas_KE', 'goog.i18n.CompactNumberFormatSymbols_mas_TZ', 'goog.i18n.CompactNumberFormatSymbols_mer', 'goog.i18n.CompactNumberFormatSymbols_mer_KE', 'goog.i18n.CompactNumberFormatSymbols_mfe', 'goog.i18n.CompactNumberFormatSymbols_mfe_MU', 'goog.i18n.CompactNumberFormatSymbols_mg', 'goog.i18n.CompactNumberFormatSymbols_mg_MG', 'goog.i18n.CompactNumberFormatSymbols_mgh', 'goog.i18n.CompactNumberFormatSymbols_mgh_MZ', 'goog.i18n.CompactNumberFormatSymbols_mgo', 'goog.i18n.CompactNumberFormatSymbols_mgo_CM', 'goog.i18n.CompactNumberFormatSymbols_mi', 'goog.i18n.CompactNumberFormatSymbols_mi_NZ', 'goog.i18n.CompactNumberFormatSymbols_mk_MK', 'goog.i18n.CompactNumberFormatSymbols_ml_IN', 'goog.i18n.CompactNumberFormatSymbols_mn_MN', 'goog.i18n.CompactNumberFormatSymbols_mr_IN', 'goog.i18n.CompactNumberFormatSymbols_ms_BN', 'goog.i18n.CompactNumberFormatSymbols_ms_MY', 'goog.i18n.CompactNumberFormatSymbols_ms_SG', 'goog.i18n.CompactNumberFormatSymbols_mt_MT', 'goog.i18n.CompactNumberFormatSymbols_mua', 'goog.i18n.CompactNumberFormatSymbols_mua_CM', 'goog.i18n.CompactNumberFormatSymbols_my_MM', 'goog.i18n.CompactNumberFormatSymbols_mzn', 'goog.i18n.CompactNumberFormatSymbols_mzn_IR', 'goog.i18n.CompactNumberFormatSymbols_naq', 'goog.i18n.CompactNumberFormatSymbols_naq_NA', 'goog.i18n.CompactNumberFormatSymbols_nb_NO', 'goog.i18n.CompactNumberFormatSymbols_nb_SJ', 'goog.i18n.CompactNumberFormatSymbols_nd', 'goog.i18n.CompactNumberFormatSymbols_nd_ZW', 'goog.i18n.CompactNumberFormatSymbols_nds', 'goog.i18n.CompactNumberFormatSymbols_nds_DE', 'goog.i18n.CompactNumberFormatSymbols_nds_NL', 'goog.i18n.CompactNumberFormatSymbols_ne_IN', 'goog.i18n.CompactNumberFormatSymbols_ne_NP', 'goog.i18n.CompactNumberFormatSymbols_nl_AW', 'goog.i18n.CompactNumberFormatSymbols_nl_BE', 'goog.i18n.CompactNumberFormatSymbols_nl_BQ', 'goog.i18n.CompactNumberFormatSymbols_nl_CW', 'goog.i18n.CompactNumberFormatSymbols_nl_NL', 'goog.i18n.CompactNumberFormatSymbols_nl_SR', 'goog.i18n.CompactNumberFormatSymbols_nl_SX', 'goog.i18n.CompactNumberFormatSymbols_nmg', 'goog.i18n.CompactNumberFormatSymbols_nmg_CM', 'goog.i18n.CompactNumberFormatSymbols_nn', 'goog.i18n.CompactNumberFormatSymbols_nn_NO', 'goog.i18n.CompactNumberFormatSymbols_nnh', 'goog.i18n.CompactNumberFormatSymbols_nnh_CM', 'goog.i18n.CompactNumberFormatSymbols_nus', 'goog.i18n.CompactNumberFormatSymbols_nus_SS', 'goog.i18n.CompactNumberFormatSymbols_nyn', 'goog.i18n.CompactNumberFormatSymbols_nyn_UG', 'goog.i18n.CompactNumberFormatSymbols_om', 'goog.i18n.CompactNumberFormatSymbols_om_ET', 'goog.i18n.CompactNumberFormatSymbols_om_KE', 'goog.i18n.CompactNumberFormatSymbols_or_IN', 'goog.i18n.CompactNumberFormatSymbols_os', 'goog.i18n.CompactNumberFormatSymbols_os_GE', 'goog.i18n.CompactNumberFormatSymbols_os_RU', 'goog.i18n.CompactNumberFormatSymbols_pa_Arab', 'goog.i18n.CompactNumberFormatSymbols_pa_Arab_PK', 'goog.i18n.CompactNumberFormatSymbols_pa_Guru', 'goog.i18n.CompactNumberFormatSymbols_pa_Guru_IN', 'goog.i18n.CompactNumberFormatSymbols_pl_PL', 'goog.i18n.CompactNumberFormatSymbols_ps', 'goog.i18n.CompactNumberFormatSymbols_ps_AF', 'goog.i18n.CompactNumberFormatSymbols_ps_PK', 'goog.i18n.CompactNumberFormatSymbols_pt_AO', 'goog.i18n.CompactNumberFormatSymbols_pt_CH', 'goog.i18n.CompactNumberFormatSymbols_pt_CV', 'goog.i18n.CompactNumberFormatSymbols_pt_GQ', 'goog.i18n.CompactNumberFormatSymbols_pt_GW', 'goog.i18n.CompactNumberFormatSymbols_pt_LU', 'goog.i18n.CompactNumberFormatSymbols_pt_MO', 'goog.i18n.CompactNumberFormatSymbols_pt_MZ', 'goog.i18n.CompactNumberFormatSymbols_pt_ST', 'goog.i18n.CompactNumberFormatSymbols_pt_TL', 'goog.i18n.CompactNumberFormatSymbols_qu', 'goog.i18n.CompactNumberFormatSymbols_qu_BO', 'goog.i18n.CompactNumberFormatSymbols_qu_EC', 'goog.i18n.CompactNumberFormatSymbols_qu_PE', 'goog.i18n.CompactNumberFormatSymbols_rm', 'goog.i18n.CompactNumberFormatSymbols_rm_CH', 'goog.i18n.CompactNumberFormatSymbols_rn', 'goog.i18n.CompactNumberFormatSymbols_rn_BI', 'goog.i18n.CompactNumberFormatSymbols_ro_MD', 'goog.i18n.CompactNumberFormatSymbols_ro_RO', 'goog.i18n.CompactNumberFormatSymbols_rof', 'goog.i18n.CompactNumberFormatSymbols_rof_TZ', 'goog.i18n.CompactNumberFormatSymbols_ru_BY', 'goog.i18n.CompactNumberFormatSymbols_ru_KG', 'goog.i18n.CompactNumberFormatSymbols_ru_KZ', 'goog.i18n.CompactNumberFormatSymbols_ru_MD', 'goog.i18n.CompactNumberFormatSymbols_ru_RU', 'goog.i18n.CompactNumberFormatSymbols_ru_UA', 'goog.i18n.CompactNumberFormatSymbols_rw', 'goog.i18n.CompactNumberFormatSymbols_rw_RW', 'goog.i18n.CompactNumberFormatSymbols_rwk', 'goog.i18n.CompactNumberFormatSymbols_rwk_TZ', 'goog.i18n.CompactNumberFormatSymbols_sah', 'goog.i18n.CompactNumberFormatSymbols_sah_RU', 'goog.i18n.CompactNumberFormatSymbols_saq', 'goog.i18n.CompactNumberFormatSymbols_saq_KE', 'goog.i18n.CompactNumberFormatSymbols_sbp', 'goog.i18n.CompactNumberFormatSymbols_sbp_TZ', 'goog.i18n.CompactNumberFormatSymbols_sd', 'goog.i18n.CompactNumberFormatSymbols_sd_PK', 'goog.i18n.CompactNumberFormatSymbols_se', 'goog.i18n.CompactNumberFormatSymbols_se_FI', 'goog.i18n.CompactNumberFormatSymbols_se_NO', 'goog.i18n.CompactNumberFormatSymbols_se_SE', 'goog.i18n.CompactNumberFormatSymbols_seh', 'goog.i18n.CompactNumberFormatSymbols_seh_MZ', 'goog.i18n.CompactNumberFormatSymbols_ses', 'goog.i18n.CompactNumberFormatSymbols_ses_ML', 'goog.i18n.CompactNumberFormatSymbols_sg', 'goog.i18n.CompactNumberFormatSymbols_sg_CF', 'goog.i18n.CompactNumberFormatSymbols_shi', 'goog.i18n.CompactNumberFormatSymbols_shi_Latn', 'goog.i18n.CompactNumberFormatSymbols_shi_Latn_MA', 'goog.i18n.CompactNumberFormatSymbols_shi_Tfng', 'goog.i18n.CompactNumberFormatSymbols_shi_Tfng_MA', 'goog.i18n.CompactNumberFormatSymbols_si_LK', 'goog.i18n.CompactNumberFormatSymbols_sk_SK', 'goog.i18n.CompactNumberFormatSymbols_sl_SI', 'goog.i18n.CompactNumberFormatSymbols_smn', 'goog.i18n.CompactNumberFormatSymbols_smn_FI', 'goog.i18n.CompactNumberFormatSymbols_sn', 'goog.i18n.CompactNumberFormatSymbols_sn_ZW', 'goog.i18n.CompactNumberFormatSymbols_so', 'goog.i18n.CompactNumberFormatSymbols_so_DJ', 'goog.i18n.CompactNumberFormatSymbols_so_ET', 'goog.i18n.CompactNumberFormatSymbols_so_KE', 'goog.i18n.CompactNumberFormatSymbols_so_SO', 'goog.i18n.CompactNumberFormatSymbols_sq_AL', 'goog.i18n.CompactNumberFormatSymbols_sq_MK', 'goog.i18n.CompactNumberFormatSymbols_sq_XK', 'goog.i18n.CompactNumberFormatSymbols_sr_Cyrl', 'goog.i18n.CompactNumberFormatSymbols_sr_Cyrl_BA', 'goog.i18n.CompactNumberFormatSymbols_sr_Cyrl_ME', 'goog.i18n.CompactNumberFormatSymbols_sr_Cyrl_RS', 'goog.i18n.CompactNumberFormatSymbols_sr_Cyrl_XK', 'goog.i18n.CompactNumberFormatSymbols_sr_Latn_BA', 'goog.i18n.CompactNumberFormatSymbols_sr_Latn_ME', 'goog.i18n.CompactNumberFormatSymbols_sr_Latn_RS', 'goog.i18n.CompactNumberFormatSymbols_sr_Latn_XK', 'goog.i18n.CompactNumberFormatSymbols_sv_AX', 'goog.i18n.CompactNumberFormatSymbols_sv_FI', 'goog.i18n.CompactNumberFormatSymbols_sv_SE', 'goog.i18n.CompactNumberFormatSymbols_sw_CD', 'goog.i18n.CompactNumberFormatSymbols_sw_KE', 'goog.i18n.CompactNumberFormatSymbols_sw_TZ', 'goog.i18n.CompactNumberFormatSymbols_sw_UG', 'goog.i18n.CompactNumberFormatSymbols_ta_IN', 'goog.i18n.CompactNumberFormatSymbols_ta_LK', 'goog.i18n.CompactNumberFormatSymbols_ta_MY', 'goog.i18n.CompactNumberFormatSymbols_ta_SG', 'goog.i18n.CompactNumberFormatSymbols_te_IN', 'goog.i18n.CompactNumberFormatSymbols_teo', 'goog.i18n.CompactNumberFormatSymbols_teo_KE', 'goog.i18n.CompactNumberFormatSymbols_teo_UG', 'goog.i18n.CompactNumberFormatSymbols_tg', 'goog.i18n.CompactNumberFormatSymbols_tg_TJ', 'goog.i18n.CompactNumberFormatSymbols_th_TH', 'goog.i18n.CompactNumberFormatSymbols_ti', 'goog.i18n.CompactNumberFormatSymbols_ti_ER', 'goog.i18n.CompactNumberFormatSymbols_ti_ET', 'goog.i18n.CompactNumberFormatSymbols_tk', 'goog.i18n.CompactNumberFormatSymbols_tk_TM', 'goog.i18n.CompactNumberFormatSymbols_to', 'goog.i18n.CompactNumberFormatSymbols_to_TO', 'goog.i18n.CompactNumberFormatSymbols_tr_CY', 'goog.i18n.CompactNumberFormatSymbols_tr_TR', 'goog.i18n.CompactNumberFormatSymbols_tt', 'goog.i18n.CompactNumberFormatSymbols_tt_RU', 'goog.i18n.CompactNumberFormatSymbols_twq', 'goog.i18n.CompactNumberFormatSymbols_twq_NE', 'goog.i18n.CompactNumberFormatSymbols_tzm', 'goog.i18n.CompactNumberFormatSymbols_tzm_MA', 'goog.i18n.CompactNumberFormatSymbols_ug', 'goog.i18n.CompactNumberFormatSymbols_ug_CN', 'goog.i18n.CompactNumberFormatSymbols_uk_UA', 'goog.i18n.CompactNumberFormatSymbols_ur_IN', 'goog.i18n.CompactNumberFormatSymbols_ur_PK', 'goog.i18n.CompactNumberFormatSymbols_uz_Arab', 'goog.i18n.CompactNumberFormatSymbols_uz_Arab_AF', 'goog.i18n.CompactNumberFormatSymbols_uz_Cyrl', 'goog.i18n.CompactNumberFormatSymbols_uz_Cyrl_UZ', 'goog.i18n.CompactNumberFormatSymbols_uz_Latn', 'goog.i18n.CompactNumberFormatSymbols_uz_Latn_UZ', 'goog.i18n.CompactNumberFormatSymbols_vai', 'goog.i18n.CompactNumberFormatSymbols_vai_Latn', 'goog.i18n.CompactNumberFormatSymbols_vai_Latn_LR', 'goog.i18n.CompactNumberFormatSymbols_vai_Vaii', 'goog.i18n.CompactNumberFormatSymbols_vai_Vaii_LR', 'goog.i18n.CompactNumberFormatSymbols_vi_VN', 'goog.i18n.CompactNumberFormatSymbols_vun', 'goog.i18n.CompactNumberFormatSymbols_vun_TZ', 'goog.i18n.CompactNumberFormatSymbols_wae', 'goog.i18n.CompactNumberFormatSymbols_wae_CH', 'goog.i18n.CompactNumberFormatSymbols_wo', 'goog.i18n.CompactNumberFormatSymbols_wo_SN', 'goog.i18n.CompactNumberFormatSymbols_xh', 'goog.i18n.CompactNumberFormatSymbols_xh_ZA', 'goog.i18n.CompactNumberFormatSymbols_xog', 'goog.i18n.CompactNumberFormatSymbols_xog_UG', 'goog.i18n.CompactNumberFormatSymbols_yav', 'goog.i18n.CompactNumberFormatSymbols_yav_CM', 'goog.i18n.CompactNumberFormatSymbols_yi', 'goog.i18n.CompactNumberFormatSymbols_yi_001', 'goog.i18n.CompactNumberFormatSymbols_yo', 'goog.i18n.CompactNumberFormatSymbols_yo_BJ', 'goog.i18n.CompactNumberFormatSymbols_yo_NG', 'goog.i18n.CompactNumberFormatSymbols_yue', 'goog.i18n.CompactNumberFormatSymbols_yue_Hans', 'goog.i18n.CompactNumberFormatSymbols_yue_Hans_CN', 'goog.i18n.CompactNumberFormatSymbols_yue_Hant', 'goog.i18n.CompactNumberFormatSymbols_yue_Hant_HK', 'goog.i18n.CompactNumberFormatSymbols_zgh', 'goog.i18n.CompactNumberFormatSymbols_zgh_MA', 'goog.i18n.CompactNumberFormatSymbols_zh_Hans', 'goog.i18n.CompactNumberFormatSymbols_zh_Hans_CN', 'goog.i18n.CompactNumberFormatSymbols_zh_Hans_HK', 'goog.i18n.CompactNumberFormatSymbols_zh_Hans_MO', 'goog.i18n.CompactNumberFormatSymbols_zh_Hans_SG', 'goog.i18n.CompactNumberFormatSymbols_zh_Hant', 'goog.i18n.CompactNumberFormatSymbols_zh_Hant_HK', 'goog.i18n.CompactNumberFormatSymbols_zh_Hant_MO', 'goog.i18n.CompactNumberFormatSymbols_zh_Hant_TW', 'goog.i18n.CompactNumberFormatSymbols_zu_ZA'], ['goog.i18n.CompactNumberFormatSymbols'], {});
goog.addDependency('i18n/currency.js', ['goog.i18n.currency', 'goog.i18n.currency.CurrencyInfo', 'goog.i18n.currency.CurrencyInfoTier2'], [], {'lang': 'es6'});
goog.addDependency('i18n/currency_test.js', ['goog.i18n.currencyTest'], ['goog.i18n.NumberFormat', 'goog.i18n.currency', 'goog.i18n.currency.CurrencyInfo', 'goog.object', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('i18n/currencycodemap.js', ['goog.i18n.currencyCodeMap', 'goog.i18n.currencyCodeMapTier2'], [], {});
goog.addDependency('i18n/dateintervalformat.js', ['goog.i18n.DateIntervalFormat'], ['goog.array', 'goog.asserts', 'goog.date.DateLike', 'goog.date.DateRange', 'goog.date.DateTime', 'goog.date.Interval', 'goog.i18n.DateTimeFormat', 'goog.i18n.DateTimeSymbols', 'goog.i18n.DateTimeSymbolsType', 'goog.i18n.TimeZone', 'goog.i18n.dateIntervalSymbols', 'goog.object'], {'lang': 'es5', 'module': 'goog'});
goog.addDependency('i18n/dateintervalformat_test.js', ['goog.i18n.DateIntervalFormatTest'], ['goog.date.Date', 'goog.date.DateRange', 'goog.date.DateTime', 'goog.date.Interval', 'goog.i18n.DateIntervalFormat', 'goog.i18n.DateTimeFormat', 'goog.i18n.DateTimeSymbols_ar_EG', 'goog.i18n.DateTimeSymbols_en', 'goog.i18n.DateTimeSymbols_fr_CA', 'goog.i18n.DateTimeSymbols_gl', 'goog.i18n.DateTimeSymbols_hi', 'goog.i18n.DateTimeSymbols_zh', 'goog.i18n.TimeZone', 'goog.i18n.dateIntervalPatterns', 'goog.i18n.dateIntervalSymbols', 'goog.object', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('i18n/dateintervalpatterns.js', ['goog.i18n.dateIntervalPatterns'], ['goog.i18n.dateIntervalSymbols'], {'module': 'goog'});
goog.addDependency('i18n/dateintervalpatternsext.js', ['goog.i18n.dateIntervalPatternsExt'], ['goog.i18n.dateIntervalPatterns'], {'module': 'goog'});
goog.addDependency('i18n/dateintervalsymbols.js', ['goog.i18n.dateIntervalSymbols'], [], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('i18n/dateintervalsymbolsext.js', ['goog.i18n.dateIntervalSymbolsExt'], ['goog.i18n.dateIntervalSymbols'], {'module': 'goog'});
goog.addDependency('i18n/datetimeformat.js', ['goog.i18n.DateTimeFormat', 'goog.i18n.DateTimeFormat.Format'], ['goog.asserts', 'goog.date', 'goog.i18n.DateTimeSymbols', 'goog.i18n.TimeZone', 'goog.string'], {});
goog.addDependency('i18n/datetimeformat_test.js', ['goog.i18n.DateTimeFormatTest'], ['goog.date.Date', 'goog.date.DateTime', 'goog.i18n.DateTimeFormat', 'goog.i18n.DateTimePatterns', 'goog.i18n.DateTimePatterns_ar_EG', 'goog.i18n.DateTimePatterns_bg', 'goog.i18n.DateTimePatterns_de', 'goog.i18n.DateTimePatterns_en', 'goog.i18n.DateTimePatterns_en_XA', 'goog.i18n.DateTimePatterns_fa', 'goog.i18n.DateTimePatterns_fr', 'goog.i18n.DateTimePatterns_ja', 'goog.i18n.DateTimePatterns_sv', 'goog.i18n.DateTimePatterns_zh_HK', 'goog.i18n.DateTimePatterns_zh_Hant_TW', 'goog.i18n.DateTimeSymbols', 'goog.i18n.DateTimeSymbols_ar_AE', 'goog.i18n.DateTimeSymbols_ar_EG', 'goog.i18n.DateTimeSymbols_ar_SA', 'goog.i18n.DateTimeSymbols_bn_BD', 'goog.i18n.DateTimeSymbols_de', 'goog.i18n.DateTimeSymbols_en', 'goog.i18n.DateTimeSymbols_en_GB', 'goog.i18n.DateTimeSymbols_en_IE', 'goog.i18n.DateTimeSymbols_en_IN', 'goog.i18n.DateTimeSymbols_en_US', 'goog.i18n.DateTimeSymbols_fa', 'goog.i18n.DateTimeSymbols_fr', 'goog.i18n.DateTimeSymbols_fr_DJ', 'goog.i18n.DateTimeSymbols_he_IL', 'goog.i18n.DateTimeSymbols_ja', 'goog.i18n.DateTimeSymbols_ro_RO', 'goog.i18n.DateTimeSymbols_sv', 'goog.i18n.TimeZone', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('i18n/datetimeparse.js', ['goog.i18n.DateTimeParse'], ['goog.asserts', 'goog.date', 'goog.i18n.DateTimeFormat', 'goog.i18n.DateTimeSymbols'], {});
goog.addDependency('i18n/datetimeparse_test.js', ['goog.i18n.DateTimeParseTest'], ['goog.date.Date', 'goog.i18n.DateTimeFormat', 'goog.i18n.DateTimeParse', 'goog.i18n.DateTimeSymbols', 'goog.i18n.DateTimeSymbols_en', 'goog.i18n.DateTimeSymbols_fa', 'goog.i18n.DateTimeSymbols_fr', 'goog.i18n.DateTimeSymbols_pl', 'goog.i18n.DateTimeSymbols_zh', 'goog.testing.ExpectedFailures', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('i18n/datetimepatterns.js', ['goog.i18n.DateTimePatterns', 'goog.i18n.DateTimePatterns_af', 'goog.i18n.DateTimePatterns_am', 'goog.i18n.DateTimePatterns_ar', 'goog.i18n.DateTimePatterns_ar_DZ', 'goog.i18n.DateTimePatterns_ar_EG', 'goog.i18n.DateTimePatterns_az', 'goog.i18n.DateTimePatterns_be', 'goog.i18n.DateTimePatterns_bg', 'goog.i18n.DateTimePatterns_bn', 'goog.i18n.DateTimePatterns_br', 'goog.i18n.DateTimePatterns_bs', 'goog.i18n.DateTimePatterns_ca', 'goog.i18n.DateTimePatterns_chr', 'goog.i18n.DateTimePatterns_cs', 'goog.i18n.DateTimePatterns_cy', 'goog.i18n.DateTimePatterns_da', 'goog.i18n.DateTimePatterns_de', 'goog.i18n.DateTimePatterns_de_AT', 'goog.i18n.DateTimePatterns_de_CH', 'goog.i18n.DateTimePatterns_el', 'goog.i18n.DateTimePatterns_en', 'goog.i18n.DateTimePatterns_en_AU', 'goog.i18n.DateTimePatterns_en_CA', 'goog.i18n.DateTimePatterns_en_GB', 'goog.i18n.DateTimePatterns_en_IE', 'goog.i18n.DateTimePatterns_en_IN', 'goog.i18n.DateTimePatterns_en_SG', 'goog.i18n.DateTimePatterns_en_US', 'goog.i18n.DateTimePatterns_en_ZA', 'goog.i18n.DateTimePatterns_es', 'goog.i18n.DateTimePatterns_es_419', 'goog.i18n.DateTimePatterns_es_ES', 'goog.i18n.DateTimePatterns_es_MX', 'goog.i18n.DateTimePatterns_es_US', 'goog.i18n.DateTimePatterns_et', 'goog.i18n.DateTimePatterns_eu', 'goog.i18n.DateTimePatterns_fa', 'goog.i18n.DateTimePatterns_fi', 'goog.i18n.DateTimePatterns_fil', 'goog.i18n.DateTimePatterns_fr', 'goog.i18n.DateTimePatterns_fr_CA', 'goog.i18n.DateTimePatterns_ga', 'goog.i18n.DateTimePatterns_gl', 'goog.i18n.DateTimePatterns_gsw', 'goog.i18n.DateTimePatterns_gu', 'goog.i18n.DateTimePatterns_haw', 'goog.i18n.DateTimePatterns_he', 'goog.i18n.DateTimePatterns_hi', 'goog.i18n.DateTimePatterns_hr', 'goog.i18n.DateTimePatterns_hu', 'goog.i18n.DateTimePatterns_hy', 'goog.i18n.DateTimePatterns_id', 'goog.i18n.DateTimePatterns_in', 'goog.i18n.DateTimePatterns_is', 'goog.i18n.DateTimePatterns_it', 'goog.i18n.DateTimePatterns_iw', 'goog.i18n.DateTimePatterns_ja', 'goog.i18n.DateTimePatterns_ka', 'goog.i18n.DateTimePatterns_kk', 'goog.i18n.DateTimePatterns_km', 'goog.i18n.DateTimePatterns_kn', 'goog.i18n.DateTimePatterns_ko', 'goog.i18n.DateTimePatterns_ky', 'goog.i18n.DateTimePatterns_ln', 'goog.i18n.DateTimePatterns_lo', 'goog.i18n.DateTimePatterns_lt', 'goog.i18n.DateTimePatterns_lv', 'goog.i18n.DateTimePatterns_mk', 'goog.i18n.DateTimePatterns_ml', 'goog.i18n.DateTimePatterns_mn', 'goog.i18n.DateTimePatterns_mo', 'goog.i18n.DateTimePatterns_mr', 'goog.i18n.DateTimePatterns_ms', 'goog.i18n.DateTimePatterns_mt', 'goog.i18n.DateTimePatterns_my', 'goog.i18n.DateTimePatterns_nb', 'goog.i18n.DateTimePatterns_ne', 'goog.i18n.DateTimePatterns_nl', 'goog.i18n.DateTimePatterns_no', 'goog.i18n.DateTimePatterns_no_NO', 'goog.i18n.DateTimePatterns_or', 'goog.i18n.DateTimePatterns_pa', 'goog.i18n.DateTimePatterns_pl', 'goog.i18n.DateTimePatterns_pt', 'goog.i18n.DateTimePatterns_pt_BR', 'goog.i18n.DateTimePatterns_pt_PT', 'goog.i18n.DateTimePatterns_ro', 'goog.i18n.DateTimePatterns_ru', 'goog.i18n.DateTimePatterns_sh', 'goog.i18n.DateTimePatterns_si', 'goog.i18n.DateTimePatterns_sk', 'goog.i18n.DateTimePatterns_sl', 'goog.i18n.DateTimePatterns_sq', 'goog.i18n.DateTimePatterns_sr', 'goog.i18n.DateTimePatterns_sr_Latn', 'goog.i18n.DateTimePatterns_sv', 'goog.i18n.DateTimePatterns_sw', 'goog.i18n.DateTimePatterns_ta', 'goog.i18n.DateTimePatterns_te', 'goog.i18n.DateTimePatterns_th', 'goog.i18n.DateTimePatterns_tl', 'goog.i18n.DateTimePatterns_tr', 'goog.i18n.DateTimePatterns_uk', 'goog.i18n.DateTimePatterns_ur', 'goog.i18n.DateTimePatterns_uz', 'goog.i18n.DateTimePatterns_vi', 'goog.i18n.DateTimePatterns_zh', 'goog.i18n.DateTimePatterns_zh_CN', 'goog.i18n.DateTimePatterns_zh_HK', 'goog.i18n.DateTimePatterns_zh_TW', 'goog.i18n.DateTimePatterns_zu'], [], {});
goog.addDependency('i18n/datetimepatternsext.js', ['goog.i18n.DateTimePatternsExt', 'goog.i18n.DateTimePatterns_af_NA', 'goog.i18n.DateTimePatterns_af_ZA', 'goog.i18n.DateTimePatterns_agq', 'goog.i18n.DateTimePatterns_agq_CM', 'goog.i18n.DateTimePatterns_ak', 'goog.i18n.DateTimePatterns_ak_GH', 'goog.i18n.DateTimePatterns_am_ET', 'goog.i18n.DateTimePatterns_ar_001', 'goog.i18n.DateTimePatterns_ar_AE', 'goog.i18n.DateTimePatterns_ar_BH', 'goog.i18n.DateTimePatterns_ar_DJ', 'goog.i18n.DateTimePatterns_ar_EH', 'goog.i18n.DateTimePatterns_ar_ER', 'goog.i18n.DateTimePatterns_ar_IL', 'goog.i18n.DateTimePatterns_ar_IQ', 'goog.i18n.DateTimePatterns_ar_JO', 'goog.i18n.DateTimePatterns_ar_KM', 'goog.i18n.DateTimePatterns_ar_KW', 'goog.i18n.DateTimePatterns_ar_LB', 'goog.i18n.DateTimePatterns_ar_LY', 'goog.i18n.DateTimePatterns_ar_MA', 'goog.i18n.DateTimePatterns_ar_MR', 'goog.i18n.DateTimePatterns_ar_OM', 'goog.i18n.DateTimePatterns_ar_PS', 'goog.i18n.DateTimePatterns_ar_QA', 'goog.i18n.DateTimePatterns_ar_SA', 'goog.i18n.DateTimePatterns_ar_SD', 'goog.i18n.DateTimePatterns_ar_SO', 'goog.i18n.DateTimePatterns_ar_SS', 'goog.i18n.DateTimePatterns_ar_SY', 'goog.i18n.DateTimePatterns_ar_TD', 'goog.i18n.DateTimePatterns_ar_TN', 'goog.i18n.DateTimePatterns_ar_XB', 'goog.i18n.DateTimePatterns_ar_YE', 'goog.i18n.DateTimePatterns_as', 'goog.i18n.DateTimePatterns_as_IN', 'goog.i18n.DateTimePatterns_asa', 'goog.i18n.DateTimePatterns_asa_TZ', 'goog.i18n.DateTimePatterns_ast', 'goog.i18n.DateTimePatterns_ast_ES', 'goog.i18n.DateTimePatterns_az_Cyrl', 'goog.i18n.DateTimePatterns_az_Cyrl_AZ', 'goog.i18n.DateTimePatterns_az_Latn', 'goog.i18n.DateTimePatterns_az_Latn_AZ', 'goog.i18n.DateTimePatterns_bas', 'goog.i18n.DateTimePatterns_bas_CM', 'goog.i18n.DateTimePatterns_be_BY', 'goog.i18n.DateTimePatterns_bem', 'goog.i18n.DateTimePatterns_bem_ZM', 'goog.i18n.DateTimePatterns_bez', 'goog.i18n.DateTimePatterns_bez_TZ', 'goog.i18n.DateTimePatterns_bg_BG', 'goog.i18n.DateTimePatterns_bm', 'goog.i18n.DateTimePatterns_bm_ML', 'goog.i18n.DateTimePatterns_bn_BD', 'goog.i18n.DateTimePatterns_bn_IN', 'goog.i18n.DateTimePatterns_bo', 'goog.i18n.DateTimePatterns_bo_CN', 'goog.i18n.DateTimePatterns_bo_IN', 'goog.i18n.DateTimePatterns_br_FR', 'goog.i18n.DateTimePatterns_brx', 'goog.i18n.DateTimePatterns_brx_IN', 'goog.i18n.DateTimePatterns_bs_Cyrl', 'goog.i18n.DateTimePatterns_bs_Cyrl_BA', 'goog.i18n.DateTimePatterns_bs_Latn', 'goog.i18n.DateTimePatterns_bs_Latn_BA', 'goog.i18n.DateTimePatterns_ca_AD', 'goog.i18n.DateTimePatterns_ca_ES', 'goog.i18n.DateTimePatterns_ca_FR', 'goog.i18n.DateTimePatterns_ca_IT', 'goog.i18n.DateTimePatterns_ccp', 'goog.i18n.DateTimePatterns_ccp_BD', 'goog.i18n.DateTimePatterns_ccp_IN', 'goog.i18n.DateTimePatterns_ce', 'goog.i18n.DateTimePatterns_ce_RU', 'goog.i18n.DateTimePatterns_ceb', 'goog.i18n.DateTimePatterns_ceb_PH', 'goog.i18n.DateTimePatterns_cgg', 'goog.i18n.DateTimePatterns_cgg_UG', 'goog.i18n.DateTimePatterns_chr_US', 'goog.i18n.DateTimePatterns_ckb', 'goog.i18n.DateTimePatterns_ckb_IQ', 'goog.i18n.DateTimePatterns_ckb_IR', 'goog.i18n.DateTimePatterns_cs_CZ', 'goog.i18n.DateTimePatterns_cy_GB', 'goog.i18n.DateTimePatterns_da_DK', 'goog.i18n.DateTimePatterns_da_GL', 'goog.i18n.DateTimePatterns_dav', 'goog.i18n.DateTimePatterns_dav_KE', 'goog.i18n.DateTimePatterns_de_BE', 'goog.i18n.DateTimePatterns_de_DE', 'goog.i18n.DateTimePatterns_de_IT', 'goog.i18n.DateTimePatterns_de_LI', 'goog.i18n.DateTimePatterns_de_LU', 'goog.i18n.DateTimePatterns_dje', 'goog.i18n.DateTimePatterns_dje_NE', 'goog.i18n.DateTimePatterns_dsb', 'goog.i18n.DateTimePatterns_dsb_DE', 'goog.i18n.DateTimePatterns_dua', 'goog.i18n.DateTimePatterns_dua_CM', 'goog.i18n.DateTimePatterns_dyo', 'goog.i18n.DateTimePatterns_dyo_SN', 'goog.i18n.DateTimePatterns_dz', 'goog.i18n.DateTimePatterns_dz_BT', 'goog.i18n.DateTimePatterns_ebu', 'goog.i18n.DateTimePatterns_ebu_KE', 'goog.i18n.DateTimePatterns_ee', 'goog.i18n.DateTimePatterns_ee_GH', 'goog.i18n.DateTimePatterns_ee_TG', 'goog.i18n.DateTimePatterns_el_CY', 'goog.i18n.DateTimePatterns_el_GR', 'goog.i18n.DateTimePatterns_en_001', 'goog.i18n.DateTimePatterns_en_150', 'goog.i18n.DateTimePatterns_en_AE', 'goog.i18n.DateTimePatterns_en_AG', 'goog.i18n.DateTimePatterns_en_AI', 'goog.i18n.DateTimePatterns_en_AS', 'goog.i18n.DateTimePatterns_en_AT', 'goog.i18n.DateTimePatterns_en_BB', 'goog.i18n.DateTimePatterns_en_BE', 'goog.i18n.DateTimePatterns_en_BI', 'goog.i18n.DateTimePatterns_en_BM', 'goog.i18n.DateTimePatterns_en_BS', 'goog.i18n.DateTimePatterns_en_BW', 'goog.i18n.DateTimePatterns_en_BZ', 'goog.i18n.DateTimePatterns_en_CC', 'goog.i18n.DateTimePatterns_en_CH', 'goog.i18n.DateTimePatterns_en_CK', 'goog.i18n.DateTimePatterns_en_CM', 'goog.i18n.DateTimePatterns_en_CX', 'goog.i18n.DateTimePatterns_en_CY', 'goog.i18n.DateTimePatterns_en_DE', 'goog.i18n.DateTimePatterns_en_DG', 'goog.i18n.DateTimePatterns_en_DK', 'goog.i18n.DateTimePatterns_en_DM', 'goog.i18n.DateTimePatterns_en_ER', 'goog.i18n.DateTimePatterns_en_FI', 'goog.i18n.DateTimePatterns_en_FJ', 'goog.i18n.DateTimePatterns_en_FK', 'goog.i18n.DateTimePatterns_en_FM', 'goog.i18n.DateTimePatterns_en_GD', 'goog.i18n.DateTimePatterns_en_GG', 'goog.i18n.DateTimePatterns_en_GH', 'goog.i18n.DateTimePatterns_en_GI', 'goog.i18n.DateTimePatterns_en_GM', 'goog.i18n.DateTimePatterns_en_GU', 'goog.i18n.DateTimePatterns_en_GY', 'goog.i18n.DateTimePatterns_en_HK', 'goog.i18n.DateTimePatterns_en_IL', 'goog.i18n.DateTimePatterns_en_IM', 'goog.i18n.DateTimePatterns_en_IO', 'goog.i18n.DateTimePatterns_en_JE', 'goog.i18n.DateTimePatterns_en_JM', 'goog.i18n.DateTimePatterns_en_KE', 'goog.i18n.DateTimePatterns_en_KI', 'goog.i18n.DateTimePatterns_en_KN', 'goog.i18n.DateTimePatterns_en_KY', 'goog.i18n.DateTimePatterns_en_LC', 'goog.i18n.DateTimePatterns_en_LR', 'goog.i18n.DateTimePatterns_en_LS', 'goog.i18n.DateTimePatterns_en_MG', 'goog.i18n.DateTimePatterns_en_MH', 'goog.i18n.DateTimePatterns_en_MO', 'goog.i18n.DateTimePatterns_en_MP', 'goog.i18n.DateTimePatterns_en_MS', 'goog.i18n.DateTimePatterns_en_MT', 'goog.i18n.DateTimePatterns_en_MU', 'goog.i18n.DateTimePatterns_en_MW', 'goog.i18n.DateTimePatterns_en_MY', 'goog.i18n.DateTimePatterns_en_NA', 'goog.i18n.DateTimePatterns_en_NF', 'goog.i18n.DateTimePatterns_en_NG', 'goog.i18n.DateTimePatterns_en_NL', 'goog.i18n.DateTimePatterns_en_NR', 'goog.i18n.DateTimePatterns_en_NU', 'goog.i18n.DateTimePatterns_en_NZ', 'goog.i18n.DateTimePatterns_en_PG', 'goog.i18n.DateTimePatterns_en_PH', 'goog.i18n.DateTimePatterns_en_PK', 'goog.i18n.DateTimePatterns_en_PN', 'goog.i18n.DateTimePatterns_en_PR', 'goog.i18n.DateTimePatterns_en_PW', 'goog.i18n.DateTimePatterns_en_RW', 'goog.i18n.DateTimePatterns_en_SB', 'goog.i18n.DateTimePatterns_en_SC', 'goog.i18n.DateTimePatterns_en_SD', 'goog.i18n.DateTimePatterns_en_SE', 'goog.i18n.DateTimePatterns_en_SH', 'goog.i18n.DateTimePatterns_en_SI', 'goog.i18n.DateTimePatterns_en_SL', 'goog.i18n.DateTimePatterns_en_SS', 'goog.i18n.DateTimePatterns_en_SX', 'goog.i18n.DateTimePatterns_en_SZ', 'goog.i18n.DateTimePatterns_en_TC', 'goog.i18n.DateTimePatterns_en_TK', 'goog.i18n.DateTimePatterns_en_TO', 'goog.i18n.DateTimePatterns_en_TT', 'goog.i18n.DateTimePatterns_en_TV', 'goog.i18n.DateTimePatterns_en_TZ', 'goog.i18n.DateTimePatterns_en_UG', 'goog.i18n.DateTimePatterns_en_UM', 'goog.i18n.DateTimePatterns_en_US_POSIX', 'goog.i18n.DateTimePatterns_en_VC', 'goog.i18n.DateTimePatterns_en_VG', 'goog.i18n.DateTimePatterns_en_VI', 'goog.i18n.DateTimePatterns_en_VU', 'goog.i18n.DateTimePatterns_en_WS', 'goog.i18n.DateTimePatterns_en_XA', 'goog.i18n.DateTimePatterns_en_ZM', 'goog.i18n.DateTimePatterns_en_ZW', 'goog.i18n.DateTimePatterns_eo', 'goog.i18n.DateTimePatterns_eo_001', 'goog.i18n.DateTimePatterns_es_AR', 'goog.i18n.DateTimePatterns_es_BO', 'goog.i18n.DateTimePatterns_es_BR', 'goog.i18n.DateTimePatterns_es_BZ', 'goog.i18n.DateTimePatterns_es_CL', 'goog.i18n.DateTimePatterns_es_CO', 'goog.i18n.DateTimePatterns_es_CR', 'goog.i18n.DateTimePatterns_es_CU', 'goog.i18n.DateTimePatterns_es_DO', 'goog.i18n.DateTimePatterns_es_EA', 'goog.i18n.DateTimePatterns_es_EC', 'goog.i18n.DateTimePatterns_es_GQ', 'goog.i18n.DateTimePatterns_es_GT', 'goog.i18n.DateTimePatterns_es_HN', 'goog.i18n.DateTimePatterns_es_IC', 'goog.i18n.DateTimePatterns_es_NI', 'goog.i18n.DateTimePatterns_es_PA', 'goog.i18n.DateTimePatterns_es_PE', 'goog.i18n.DateTimePatterns_es_PH', 'goog.i18n.DateTimePatterns_es_PR', 'goog.i18n.DateTimePatterns_es_PY', 'goog.i18n.DateTimePatterns_es_SV', 'goog.i18n.DateTimePatterns_es_UY', 'goog.i18n.DateTimePatterns_es_VE', 'goog.i18n.DateTimePatterns_et_EE', 'goog.i18n.DateTimePatterns_eu_ES', 'goog.i18n.DateTimePatterns_ewo', 'goog.i18n.DateTimePatterns_ewo_CM', 'goog.i18n.DateTimePatterns_fa_AF', 'goog.i18n.DateTimePatterns_fa_IR', 'goog.i18n.DateTimePatterns_ff', 'goog.i18n.DateTimePatterns_ff_Latn', 'goog.i18n.DateTimePatterns_ff_Latn_BF', 'goog.i18n.DateTimePatterns_ff_Latn_CM', 'goog.i18n.DateTimePatterns_ff_Latn_GH', 'goog.i18n.DateTimePatterns_ff_Latn_GM', 'goog.i18n.DateTimePatterns_ff_Latn_GN', 'goog.i18n.DateTimePatterns_ff_Latn_GW', 'goog.i18n.DateTimePatterns_ff_Latn_LR', 'goog.i18n.DateTimePatterns_ff_Latn_MR', 'goog.i18n.DateTimePatterns_ff_Latn_NE', 'goog.i18n.DateTimePatterns_ff_Latn_NG', 'goog.i18n.DateTimePatterns_ff_Latn_SL', 'goog.i18n.DateTimePatterns_ff_Latn_SN', 'goog.i18n.DateTimePatterns_fi_FI', 'goog.i18n.DateTimePatterns_fil_PH', 'goog.i18n.DateTimePatterns_fo', 'goog.i18n.DateTimePatterns_fo_DK', 'goog.i18n.DateTimePatterns_fo_FO', 'goog.i18n.DateTimePatterns_fr_BE', 'goog.i18n.DateTimePatterns_fr_BF', 'goog.i18n.DateTimePatterns_fr_BI', 'goog.i18n.DateTimePatterns_fr_BJ', 'goog.i18n.DateTimePatterns_fr_BL', 'goog.i18n.DateTimePatterns_fr_CD', 'goog.i18n.DateTimePatterns_fr_CF', 'goog.i18n.DateTimePatterns_fr_CG', 'goog.i18n.DateTimePatterns_fr_CH', 'goog.i18n.DateTimePatterns_fr_CI', 'goog.i18n.DateTimePatterns_fr_CM', 'goog.i18n.DateTimePatterns_fr_DJ', 'goog.i18n.DateTimePatterns_fr_DZ', 'goog.i18n.DateTimePatterns_fr_FR', 'goog.i18n.DateTimePatterns_fr_GA', 'goog.i18n.DateTimePatterns_fr_GF', 'goog.i18n.DateTimePatterns_fr_GN', 'goog.i18n.DateTimePatterns_fr_GP', 'goog.i18n.DateTimePatterns_fr_GQ', 'goog.i18n.DateTimePatterns_fr_HT', 'goog.i18n.DateTimePatterns_fr_KM', 'goog.i18n.DateTimePatterns_fr_LU', 'goog.i18n.DateTimePatterns_fr_MA', 'goog.i18n.DateTimePatterns_fr_MC', 'goog.i18n.DateTimePatterns_fr_MF', 'goog.i18n.DateTimePatterns_fr_MG', 'goog.i18n.DateTimePatterns_fr_ML', 'goog.i18n.DateTimePatterns_fr_MQ', 'goog.i18n.DateTimePatterns_fr_MR', 'goog.i18n.DateTimePatterns_fr_MU', 'goog.i18n.DateTimePatterns_fr_NC', 'goog.i18n.DateTimePatterns_fr_NE', 'goog.i18n.DateTimePatterns_fr_PF', 'goog.i18n.DateTimePatterns_fr_PM', 'goog.i18n.DateTimePatterns_fr_RE', 'goog.i18n.DateTimePatterns_fr_RW', 'goog.i18n.DateTimePatterns_fr_SC', 'goog.i18n.DateTimePatterns_fr_SN', 'goog.i18n.DateTimePatterns_fr_SY', 'goog.i18n.DateTimePatterns_fr_TD', 'goog.i18n.DateTimePatterns_fr_TG', 'goog.i18n.DateTimePatterns_fr_TN', 'goog.i18n.DateTimePatterns_fr_VU', 'goog.i18n.DateTimePatterns_fr_WF', 'goog.i18n.DateTimePatterns_fr_YT', 'goog.i18n.DateTimePatterns_fur', 'goog.i18n.DateTimePatterns_fur_IT', 'goog.i18n.DateTimePatterns_fy', 'goog.i18n.DateTimePatterns_fy_NL', 'goog.i18n.DateTimePatterns_ga_IE', 'goog.i18n.DateTimePatterns_gd', 'goog.i18n.DateTimePatterns_gd_GB', 'goog.i18n.DateTimePatterns_gl_ES', 'goog.i18n.DateTimePatterns_gsw_CH', 'goog.i18n.DateTimePatterns_gsw_FR', 'goog.i18n.DateTimePatterns_gsw_LI', 'goog.i18n.DateTimePatterns_gu_IN', 'goog.i18n.DateTimePatterns_guz', 'goog.i18n.DateTimePatterns_guz_KE', 'goog.i18n.DateTimePatterns_gv', 'goog.i18n.DateTimePatterns_gv_IM', 'goog.i18n.DateTimePatterns_ha', 'goog.i18n.DateTimePatterns_ha_GH', 'goog.i18n.DateTimePatterns_ha_NE', 'goog.i18n.DateTimePatterns_ha_NG', 'goog.i18n.DateTimePatterns_haw_US', 'goog.i18n.DateTimePatterns_he_IL', 'goog.i18n.DateTimePatterns_hi_IN', 'goog.i18n.DateTimePatterns_hr_BA', 'goog.i18n.DateTimePatterns_hr_HR', 'goog.i18n.DateTimePatterns_hsb', 'goog.i18n.DateTimePatterns_hsb_DE', 'goog.i18n.DateTimePatterns_hu_HU', 'goog.i18n.DateTimePatterns_hy_AM', 'goog.i18n.DateTimePatterns_ia', 'goog.i18n.DateTimePatterns_ia_001', 'goog.i18n.DateTimePatterns_id_ID', 'goog.i18n.DateTimePatterns_ig', 'goog.i18n.DateTimePatterns_ig_NG', 'goog.i18n.DateTimePatterns_ii', 'goog.i18n.DateTimePatterns_ii_CN', 'goog.i18n.DateTimePatterns_is_IS', 'goog.i18n.DateTimePatterns_it_CH', 'goog.i18n.DateTimePatterns_it_IT', 'goog.i18n.DateTimePatterns_it_SM', 'goog.i18n.DateTimePatterns_it_VA', 'goog.i18n.DateTimePatterns_ja_JP', 'goog.i18n.DateTimePatterns_jgo', 'goog.i18n.DateTimePatterns_jgo_CM', 'goog.i18n.DateTimePatterns_jmc', 'goog.i18n.DateTimePatterns_jmc_TZ', 'goog.i18n.DateTimePatterns_jv', 'goog.i18n.DateTimePatterns_jv_ID', 'goog.i18n.DateTimePatterns_ka_GE', 'goog.i18n.DateTimePatterns_kab', 'goog.i18n.DateTimePatterns_kab_DZ', 'goog.i18n.DateTimePatterns_kam', 'goog.i18n.DateTimePatterns_kam_KE', 'goog.i18n.DateTimePatterns_kde', 'goog.i18n.DateTimePatterns_kde_TZ', 'goog.i18n.DateTimePatterns_kea', 'goog.i18n.DateTimePatterns_kea_CV', 'goog.i18n.DateTimePatterns_khq', 'goog.i18n.DateTimePatterns_khq_ML', 'goog.i18n.DateTimePatterns_ki', 'goog.i18n.DateTimePatterns_ki_KE', 'goog.i18n.DateTimePatterns_kk_KZ', 'goog.i18n.DateTimePatterns_kkj', 'goog.i18n.DateTimePatterns_kkj_CM', 'goog.i18n.DateTimePatterns_kl', 'goog.i18n.DateTimePatterns_kl_GL', 'goog.i18n.DateTimePatterns_kln', 'goog.i18n.DateTimePatterns_kln_KE', 'goog.i18n.DateTimePatterns_km_KH', 'goog.i18n.DateTimePatterns_kn_IN', 'goog.i18n.DateTimePatterns_ko_KP', 'goog.i18n.DateTimePatterns_ko_KR', 'goog.i18n.DateTimePatterns_kok', 'goog.i18n.DateTimePatterns_kok_IN', 'goog.i18n.DateTimePatterns_ks', 'goog.i18n.DateTimePatterns_ks_IN', 'goog.i18n.DateTimePatterns_ksb', 'goog.i18n.DateTimePatterns_ksb_TZ', 'goog.i18n.DateTimePatterns_ksf', 'goog.i18n.DateTimePatterns_ksf_CM', 'goog.i18n.DateTimePatterns_ksh', 'goog.i18n.DateTimePatterns_ksh_DE', 'goog.i18n.DateTimePatterns_ku', 'goog.i18n.DateTimePatterns_ku_TR', 'goog.i18n.DateTimePatterns_kw', 'goog.i18n.DateTimePatterns_kw_GB', 'goog.i18n.DateTimePatterns_ky_KG', 'goog.i18n.DateTimePatterns_lag', 'goog.i18n.DateTimePatterns_lag_TZ', 'goog.i18n.DateTimePatterns_lb', 'goog.i18n.DateTimePatterns_lb_LU', 'goog.i18n.DateTimePatterns_lg', 'goog.i18n.DateTimePatterns_lg_UG', 'goog.i18n.DateTimePatterns_lkt', 'goog.i18n.DateTimePatterns_lkt_US', 'goog.i18n.DateTimePatterns_ln_AO', 'goog.i18n.DateTimePatterns_ln_CD', 'goog.i18n.DateTimePatterns_ln_CF', 'goog.i18n.DateTimePatterns_ln_CG', 'goog.i18n.DateTimePatterns_lo_LA', 'goog.i18n.DateTimePatterns_lrc', 'goog.i18n.DateTimePatterns_lrc_IQ', 'goog.i18n.DateTimePatterns_lrc_IR', 'goog.i18n.DateTimePatterns_lt_LT', 'goog.i18n.DateTimePatterns_lu', 'goog.i18n.DateTimePatterns_lu_CD', 'goog.i18n.DateTimePatterns_luo', 'goog.i18n.DateTimePatterns_luo_KE', 'goog.i18n.DateTimePatterns_luy', 'goog.i18n.DateTimePatterns_luy_KE', 'goog.i18n.DateTimePatterns_lv_LV', 'goog.i18n.DateTimePatterns_mas', 'goog.i18n.DateTimePatterns_mas_KE', 'goog.i18n.DateTimePatterns_mas_TZ', 'goog.i18n.DateTimePatterns_mer', 'goog.i18n.DateTimePatterns_mer_KE', 'goog.i18n.DateTimePatterns_mfe', 'goog.i18n.DateTimePatterns_mfe_MU', 'goog.i18n.DateTimePatterns_mg', 'goog.i18n.DateTimePatterns_mg_MG', 'goog.i18n.DateTimePatterns_mgh', 'goog.i18n.DateTimePatterns_mgh_MZ', 'goog.i18n.DateTimePatterns_mgo', 'goog.i18n.DateTimePatterns_mgo_CM', 'goog.i18n.DateTimePatterns_mi', 'goog.i18n.DateTimePatterns_mi_NZ', 'goog.i18n.DateTimePatterns_mk_MK', 'goog.i18n.DateTimePatterns_ml_IN', 'goog.i18n.DateTimePatterns_mn_MN', 'goog.i18n.DateTimePatterns_mr_IN', 'goog.i18n.DateTimePatterns_ms_BN', 'goog.i18n.DateTimePatterns_ms_MY', 'goog.i18n.DateTimePatterns_ms_SG', 'goog.i18n.DateTimePatterns_mt_MT', 'goog.i18n.DateTimePatterns_mua', 'goog.i18n.DateTimePatterns_mua_CM', 'goog.i18n.DateTimePatterns_my_MM', 'goog.i18n.DateTimePatterns_mzn', 'goog.i18n.DateTimePatterns_mzn_IR', 'goog.i18n.DateTimePatterns_naq', 'goog.i18n.DateTimePatterns_naq_NA', 'goog.i18n.DateTimePatterns_nb_NO', 'goog.i18n.DateTimePatterns_nb_SJ', 'goog.i18n.DateTimePatterns_nd', 'goog.i18n.DateTimePatterns_nd_ZW', 'goog.i18n.DateTimePatterns_nds', 'goog.i18n.DateTimePatterns_nds_DE', 'goog.i18n.DateTimePatterns_nds_NL', 'goog.i18n.DateTimePatterns_ne_IN', 'goog.i18n.DateTimePatterns_ne_NP', 'goog.i18n.DateTimePatterns_nl_AW', 'goog.i18n.DateTimePatterns_nl_BE', 'goog.i18n.DateTimePatterns_nl_BQ', 'goog.i18n.DateTimePatterns_nl_CW', 'goog.i18n.DateTimePatterns_nl_NL', 'goog.i18n.DateTimePatterns_nl_SR', 'goog.i18n.DateTimePatterns_nl_SX', 'goog.i18n.DateTimePatterns_nmg', 'goog.i18n.DateTimePatterns_nmg_CM', 'goog.i18n.DateTimePatterns_nn', 'goog.i18n.DateTimePatterns_nn_NO', 'goog.i18n.DateTimePatterns_nnh', 'goog.i18n.DateTimePatterns_nnh_CM', 'goog.i18n.DateTimePatterns_nus', 'goog.i18n.DateTimePatterns_nus_SS', 'goog.i18n.DateTimePatterns_nyn', 'goog.i18n.DateTimePatterns_nyn_UG', 'goog.i18n.DateTimePatterns_om', 'goog.i18n.DateTimePatterns_om_ET', 'goog.i18n.DateTimePatterns_om_KE', 'goog.i18n.DateTimePatterns_or_IN', 'goog.i18n.DateTimePatterns_os', 'goog.i18n.DateTimePatterns_os_GE', 'goog.i18n.DateTimePatterns_os_RU', 'goog.i18n.DateTimePatterns_pa_Arab', 'goog.i18n.DateTimePatterns_pa_Arab_PK', 'goog.i18n.DateTimePatterns_pa_Guru', 'goog.i18n.DateTimePatterns_pa_Guru_IN', 'goog.i18n.DateTimePatterns_pl_PL', 'goog.i18n.DateTimePatterns_ps', 'goog.i18n.DateTimePatterns_ps_AF', 'goog.i18n.DateTimePatterns_ps_PK', 'goog.i18n.DateTimePatterns_pt_AO', 'goog.i18n.DateTimePatterns_pt_CH', 'goog.i18n.DateTimePatterns_pt_CV', 'goog.i18n.DateTimePatterns_pt_GQ', 'goog.i18n.DateTimePatterns_pt_GW', 'goog.i18n.DateTimePatterns_pt_LU', 'goog.i18n.DateTimePatterns_pt_MO', 'goog.i18n.DateTimePatterns_pt_MZ', 'goog.i18n.DateTimePatterns_pt_ST', 'goog.i18n.DateTimePatterns_pt_TL', 'goog.i18n.DateTimePatterns_qu', 'goog.i18n.DateTimePatterns_qu_BO', 'goog.i18n.DateTimePatterns_qu_EC', 'goog.i18n.DateTimePatterns_qu_PE', 'goog.i18n.DateTimePatterns_rm', 'goog.i18n.DateTimePatterns_rm_CH', 'goog.i18n.DateTimePatterns_rn', 'goog.i18n.DateTimePatterns_rn_BI', 'goog.i18n.DateTimePatterns_ro_MD', 'goog.i18n.DateTimePatterns_ro_RO', 'goog.i18n.DateTimePatterns_rof', 'goog.i18n.DateTimePatterns_rof_TZ', 'goog.i18n.DateTimePatterns_ru_BY', 'goog.i18n.DateTimePatterns_ru_KG', 'goog.i18n.DateTimePatterns_ru_KZ', 'goog.i18n.DateTimePatterns_ru_MD', 'goog.i18n.DateTimePatterns_ru_RU', 'goog.i18n.DateTimePatterns_ru_UA', 'goog.i18n.DateTimePatterns_rw', 'goog.i18n.DateTimePatterns_rw_RW', 'goog.i18n.DateTimePatterns_rwk', 'goog.i18n.DateTimePatterns_rwk_TZ', 'goog.i18n.DateTimePatterns_sah', 'goog.i18n.DateTimePatterns_sah_RU', 'goog.i18n.DateTimePatterns_saq', 'goog.i18n.DateTimePatterns_saq_KE', 'goog.i18n.DateTimePatterns_sbp', 'goog.i18n.DateTimePatterns_sbp_TZ', 'goog.i18n.DateTimePatterns_sd', 'goog.i18n.DateTimePatterns_sd_PK', 'goog.i18n.DateTimePatterns_se', 'goog.i18n.DateTimePatterns_se_FI', 'goog.i18n.DateTimePatterns_se_NO', 'goog.i18n.DateTimePatterns_se_SE', 'goog.i18n.DateTimePatterns_seh', 'goog.i18n.DateTimePatterns_seh_MZ', 'goog.i18n.DateTimePatterns_ses', 'goog.i18n.DateTimePatterns_ses_ML', 'goog.i18n.DateTimePatterns_sg', 'goog.i18n.DateTimePatterns_sg_CF', 'goog.i18n.DateTimePatterns_shi', 'goog.i18n.DateTimePatterns_shi_Latn', 'goog.i18n.DateTimePatterns_shi_Latn_MA', 'goog.i18n.DateTimePatterns_shi_Tfng', 'goog.i18n.DateTimePatterns_shi_Tfng_MA', 'goog.i18n.DateTimePatterns_si_LK', 'goog.i18n.DateTimePatterns_sk_SK', 'goog.i18n.DateTimePatterns_sl_SI', 'goog.i18n.DateTimePatterns_smn', 'goog.i18n.DateTimePatterns_smn_FI', 'goog.i18n.DateTimePatterns_sn', 'goog.i18n.DateTimePatterns_sn_ZW', 'goog.i18n.DateTimePatterns_so', 'goog.i18n.DateTimePatterns_so_DJ', 'goog.i18n.DateTimePatterns_so_ET', 'goog.i18n.DateTimePatterns_so_KE', 'goog.i18n.DateTimePatterns_so_SO', 'goog.i18n.DateTimePatterns_sq_AL', 'goog.i18n.DateTimePatterns_sq_MK', 'goog.i18n.DateTimePatterns_sq_XK', 'goog.i18n.DateTimePatterns_sr_Cyrl', 'goog.i18n.DateTimePatterns_sr_Cyrl_BA', 'goog.i18n.DateTimePatterns_sr_Cyrl_ME', 'goog.i18n.DateTimePatterns_sr_Cyrl_RS', 'goog.i18n.DateTimePatterns_sr_Cyrl_XK', 'goog.i18n.DateTimePatterns_sr_Latn_BA', 'goog.i18n.DateTimePatterns_sr_Latn_ME', 'goog.i18n.DateTimePatterns_sr_Latn_RS', 'goog.i18n.DateTimePatterns_sr_Latn_XK', 'goog.i18n.DateTimePatterns_sv_AX', 'goog.i18n.DateTimePatterns_sv_FI', 'goog.i18n.DateTimePatterns_sv_SE', 'goog.i18n.DateTimePatterns_sw_CD', 'goog.i18n.DateTimePatterns_sw_KE', 'goog.i18n.DateTimePatterns_sw_TZ', 'goog.i18n.DateTimePatterns_sw_UG', 'goog.i18n.DateTimePatterns_ta_IN', 'goog.i18n.DateTimePatterns_ta_LK', 'goog.i18n.DateTimePatterns_ta_MY', 'goog.i18n.DateTimePatterns_ta_SG', 'goog.i18n.DateTimePatterns_te_IN', 'goog.i18n.DateTimePatterns_teo', 'goog.i18n.DateTimePatterns_teo_KE', 'goog.i18n.DateTimePatterns_teo_UG', 'goog.i18n.DateTimePatterns_tg', 'goog.i18n.DateTimePatterns_tg_TJ', 'goog.i18n.DateTimePatterns_th_TH', 'goog.i18n.DateTimePatterns_ti', 'goog.i18n.DateTimePatterns_ti_ER', 'goog.i18n.DateTimePatterns_ti_ET', 'goog.i18n.DateTimePatterns_tk', 'goog.i18n.DateTimePatterns_tk_TM', 'goog.i18n.DateTimePatterns_to', 'goog.i18n.DateTimePatterns_to_TO', 'goog.i18n.DateTimePatterns_tr_CY', 'goog.i18n.DateTimePatterns_tr_TR', 'goog.i18n.DateTimePatterns_tt', 'goog.i18n.DateTimePatterns_tt_RU', 'goog.i18n.DateTimePatterns_twq', 'goog.i18n.DateTimePatterns_twq_NE', 'goog.i18n.DateTimePatterns_tzm', 'goog.i18n.DateTimePatterns_tzm_MA', 'goog.i18n.DateTimePatterns_ug', 'goog.i18n.DateTimePatterns_ug_CN', 'goog.i18n.DateTimePatterns_uk_UA', 'goog.i18n.DateTimePatterns_ur_IN', 'goog.i18n.DateTimePatterns_ur_PK', 'goog.i18n.DateTimePatterns_uz_Arab', 'goog.i18n.DateTimePatterns_uz_Arab_AF', 'goog.i18n.DateTimePatterns_uz_Cyrl', 'goog.i18n.DateTimePatterns_uz_Cyrl_UZ', 'goog.i18n.DateTimePatterns_uz_Latn', 'goog.i18n.DateTimePatterns_uz_Latn_UZ', 'goog.i18n.DateTimePatterns_vai', 'goog.i18n.DateTimePatterns_vai_Latn', 'goog.i18n.DateTimePatterns_vai_Latn_LR', 'goog.i18n.DateTimePatterns_vai_Vaii', 'goog.i18n.DateTimePatterns_vai_Vaii_LR', 'goog.i18n.DateTimePatterns_vi_VN', 'goog.i18n.DateTimePatterns_vun', 'goog.i18n.DateTimePatterns_vun_TZ', 'goog.i18n.DateTimePatterns_wae', 'goog.i18n.DateTimePatterns_wae_CH', 'goog.i18n.DateTimePatterns_wo', 'goog.i18n.DateTimePatterns_wo_SN', 'goog.i18n.DateTimePatterns_xh', 'goog.i18n.DateTimePatterns_xh_ZA', 'goog.i18n.DateTimePatterns_xog', 'goog.i18n.DateTimePatterns_xog_UG', 'goog.i18n.DateTimePatterns_yav', 'goog.i18n.DateTimePatterns_yav_CM', 'goog.i18n.DateTimePatterns_yi', 'goog.i18n.DateTimePatterns_yi_001', 'goog.i18n.DateTimePatterns_yo', 'goog.i18n.DateTimePatterns_yo_BJ', 'goog.i18n.DateTimePatterns_yo_NG', 'goog.i18n.DateTimePatterns_yue', 'goog.i18n.DateTimePatterns_yue_Hans', 'goog.i18n.DateTimePatterns_yue_Hans_CN', 'goog.i18n.DateTimePatterns_yue_Hant', 'goog.i18n.DateTimePatterns_yue_Hant_HK', 'goog.i18n.DateTimePatterns_zgh', 'goog.i18n.DateTimePatterns_zgh_MA', 'goog.i18n.DateTimePatterns_zh_Hans', 'goog.i18n.DateTimePatterns_zh_Hans_CN', 'goog.i18n.DateTimePatterns_zh_Hans_HK', 'goog.i18n.DateTimePatterns_zh_Hans_MO', 'goog.i18n.DateTimePatterns_zh_Hans_SG', 'goog.i18n.DateTimePatterns_zh_Hant', 'goog.i18n.DateTimePatterns_zh_Hant_HK', 'goog.i18n.DateTimePatterns_zh_Hant_MO', 'goog.i18n.DateTimePatterns_zh_Hant_TW', 'goog.i18n.DateTimePatterns_zu_ZA'], ['goog.i18n.DateTimePatterns'], {});
goog.addDependency('i18n/datetimesymbols.js', ['goog.i18n.DateTimeSymbols', 'goog.i18n.DateTimeSymbolsType', 'goog.i18n.DateTimeSymbols_af', 'goog.i18n.DateTimeSymbols_am', 'goog.i18n.DateTimeSymbols_ar', 'goog.i18n.DateTimeSymbols_ar_DZ', 'goog.i18n.DateTimeSymbols_ar_EG', 'goog.i18n.DateTimeSymbols_az', 'goog.i18n.DateTimeSymbols_be', 'goog.i18n.DateTimeSymbols_bg', 'goog.i18n.DateTimeSymbols_bn', 'goog.i18n.DateTimeSymbols_br', 'goog.i18n.DateTimeSymbols_bs', 'goog.i18n.DateTimeSymbols_ca', 'goog.i18n.DateTimeSymbols_chr', 'goog.i18n.DateTimeSymbols_cs', 'goog.i18n.DateTimeSymbols_cy', 'goog.i18n.DateTimeSymbols_da', 'goog.i18n.DateTimeSymbols_de', 'goog.i18n.DateTimeSymbols_de_AT', 'goog.i18n.DateTimeSymbols_de_CH', 'goog.i18n.DateTimeSymbols_el', 'goog.i18n.DateTimeSymbols_en', 'goog.i18n.DateTimeSymbols_en_AU', 'goog.i18n.DateTimeSymbols_en_CA', 'goog.i18n.DateTimeSymbols_en_GB', 'goog.i18n.DateTimeSymbols_en_IE', 'goog.i18n.DateTimeSymbols_en_IN', 'goog.i18n.DateTimeSymbols_en_ISO', 'goog.i18n.DateTimeSymbols_en_SG', 'goog.i18n.DateTimeSymbols_en_US', 'goog.i18n.DateTimeSymbols_en_ZA', 'goog.i18n.DateTimeSymbols_es', 'goog.i18n.DateTimeSymbols_es_419', 'goog.i18n.DateTimeSymbols_es_ES', 'goog.i18n.DateTimeSymbols_es_MX', 'goog.i18n.DateTimeSymbols_es_US', 'goog.i18n.DateTimeSymbols_et', 'goog.i18n.DateTimeSymbols_eu', 'goog.i18n.DateTimeSymbols_fa', 'goog.i18n.DateTimeSymbols_fi', 'goog.i18n.DateTimeSymbols_fil', 'goog.i18n.DateTimeSymbols_fr', 'goog.i18n.DateTimeSymbols_fr_CA', 'goog.i18n.DateTimeSymbols_ga', 'goog.i18n.DateTimeSymbols_gl', 'goog.i18n.DateTimeSymbols_gsw', 'goog.i18n.DateTimeSymbols_gu', 'goog.i18n.DateTimeSymbols_haw', 'goog.i18n.DateTimeSymbols_he', 'goog.i18n.DateTimeSymbols_hi', 'goog.i18n.DateTimeSymbols_hr', 'goog.i18n.DateTimeSymbols_hu', 'goog.i18n.DateTimeSymbols_hy', 'goog.i18n.DateTimeSymbols_id', 'goog.i18n.DateTimeSymbols_in', 'goog.i18n.DateTimeSymbols_is', 'goog.i18n.DateTimeSymbols_it', 'goog.i18n.DateTimeSymbols_iw', 'goog.i18n.DateTimeSymbols_ja', 'goog.i18n.DateTimeSymbols_ka', 'goog.i18n.DateTimeSymbols_kk', 'goog.i18n.DateTimeSymbols_km', 'goog.i18n.DateTimeSymbols_kn', 'goog.i18n.DateTimeSymbols_ko', 'goog.i18n.DateTimeSymbols_ky', 'goog.i18n.DateTimeSymbols_ln', 'goog.i18n.DateTimeSymbols_lo', 'goog.i18n.DateTimeSymbols_lt', 'goog.i18n.DateTimeSymbols_lv', 'goog.i18n.DateTimeSymbols_mk', 'goog.i18n.DateTimeSymbols_ml', 'goog.i18n.DateTimeSymbols_mn', 'goog.i18n.DateTimeSymbols_mo', 'goog.i18n.DateTimeSymbols_mr', 'goog.i18n.DateTimeSymbols_ms', 'goog.i18n.DateTimeSymbols_mt', 'goog.i18n.DateTimeSymbols_my', 'goog.i18n.DateTimeSymbols_nb', 'goog.i18n.DateTimeSymbols_ne', 'goog.i18n.DateTimeSymbols_nl', 'goog.i18n.DateTimeSymbols_no', 'goog.i18n.DateTimeSymbols_no_NO', 'goog.i18n.DateTimeSymbols_or', 'goog.i18n.DateTimeSymbols_pa', 'goog.i18n.DateTimeSymbols_pl', 'goog.i18n.DateTimeSymbols_pt', 'goog.i18n.DateTimeSymbols_pt_BR', 'goog.i18n.DateTimeSymbols_pt_PT', 'goog.i18n.DateTimeSymbols_ro', 'goog.i18n.DateTimeSymbols_ru', 'goog.i18n.DateTimeSymbols_sh', 'goog.i18n.DateTimeSymbols_si', 'goog.i18n.DateTimeSymbols_sk', 'goog.i18n.DateTimeSymbols_sl', 'goog.i18n.DateTimeSymbols_sq', 'goog.i18n.DateTimeSymbols_sr', 'goog.i18n.DateTimeSymbols_sr_Latn', 'goog.i18n.DateTimeSymbols_sv', 'goog.i18n.DateTimeSymbols_sw', 'goog.i18n.DateTimeSymbols_ta', 'goog.i18n.DateTimeSymbols_te', 'goog.i18n.DateTimeSymbols_th', 'goog.i18n.DateTimeSymbols_tl', 'goog.i18n.DateTimeSymbols_tr', 'goog.i18n.DateTimeSymbols_uk', 'goog.i18n.DateTimeSymbols_ur', 'goog.i18n.DateTimeSymbols_uz', 'goog.i18n.DateTimeSymbols_vi', 'goog.i18n.DateTimeSymbols_zh', 'goog.i18n.DateTimeSymbols_zh_CN', 'goog.i18n.DateTimeSymbols_zh_HK', 'goog.i18n.DateTimeSymbols_zh_TW', 'goog.i18n.DateTimeSymbols_zu'], [], {});
goog.addDependency('i18n/datetimesymbolsext.js', ['goog.i18n.DateTimeSymbolsExt', 'goog.i18n.DateTimeSymbols_af_NA', 'goog.i18n.DateTimeSymbols_af_ZA', 'goog.i18n.DateTimeSymbols_agq', 'goog.i18n.DateTimeSymbols_agq_CM', 'goog.i18n.DateTimeSymbols_ak', 'goog.i18n.DateTimeSymbols_ak_GH', 'goog.i18n.DateTimeSymbols_am_ET', 'goog.i18n.DateTimeSymbols_ar_001', 'goog.i18n.DateTimeSymbols_ar_AE', 'goog.i18n.DateTimeSymbols_ar_BH', 'goog.i18n.DateTimeSymbols_ar_DJ', 'goog.i18n.DateTimeSymbols_ar_EH', 'goog.i18n.DateTimeSymbols_ar_ER', 'goog.i18n.DateTimeSymbols_ar_IL', 'goog.i18n.DateTimeSymbols_ar_IQ', 'goog.i18n.DateTimeSymbols_ar_JO', 'goog.i18n.DateTimeSymbols_ar_KM', 'goog.i18n.DateTimeSymbols_ar_KW', 'goog.i18n.DateTimeSymbols_ar_LB', 'goog.i18n.DateTimeSymbols_ar_LY', 'goog.i18n.DateTimeSymbols_ar_MA', 'goog.i18n.DateTimeSymbols_ar_MR', 'goog.i18n.DateTimeSymbols_ar_OM', 'goog.i18n.DateTimeSymbols_ar_PS', 'goog.i18n.DateTimeSymbols_ar_QA', 'goog.i18n.DateTimeSymbols_ar_SA', 'goog.i18n.DateTimeSymbols_ar_SD', 'goog.i18n.DateTimeSymbols_ar_SO', 'goog.i18n.DateTimeSymbols_ar_SS', 'goog.i18n.DateTimeSymbols_ar_SY', 'goog.i18n.DateTimeSymbols_ar_TD', 'goog.i18n.DateTimeSymbols_ar_TN', 'goog.i18n.DateTimeSymbols_ar_XB', 'goog.i18n.DateTimeSymbols_ar_YE', 'goog.i18n.DateTimeSymbols_as', 'goog.i18n.DateTimeSymbols_as_IN', 'goog.i18n.DateTimeSymbols_asa', 'goog.i18n.DateTimeSymbols_asa_TZ', 'goog.i18n.DateTimeSymbols_ast', 'goog.i18n.DateTimeSymbols_ast_ES', 'goog.i18n.DateTimeSymbols_az_Cyrl', 'goog.i18n.DateTimeSymbols_az_Cyrl_AZ', 'goog.i18n.DateTimeSymbols_az_Latn', 'goog.i18n.DateTimeSymbols_az_Latn_AZ', 'goog.i18n.DateTimeSymbols_bas', 'goog.i18n.DateTimeSymbols_bas_CM', 'goog.i18n.DateTimeSymbols_be_BY', 'goog.i18n.DateTimeSymbols_bem', 'goog.i18n.DateTimeSymbols_bem_ZM', 'goog.i18n.DateTimeSymbols_bez', 'goog.i18n.DateTimeSymbols_bez_TZ', 'goog.i18n.DateTimeSymbols_bg_BG', 'goog.i18n.DateTimeSymbols_bm', 'goog.i18n.DateTimeSymbols_bm_ML', 'goog.i18n.DateTimeSymbols_bn_BD', 'goog.i18n.DateTimeSymbols_bn_IN', 'goog.i18n.DateTimeSymbols_bo', 'goog.i18n.DateTimeSymbols_bo_CN', 'goog.i18n.DateTimeSymbols_bo_IN', 'goog.i18n.DateTimeSymbols_br_FR', 'goog.i18n.DateTimeSymbols_brx', 'goog.i18n.DateTimeSymbols_brx_IN', 'goog.i18n.DateTimeSymbols_bs_Cyrl', 'goog.i18n.DateTimeSymbols_bs_Cyrl_BA', 'goog.i18n.DateTimeSymbols_bs_Latn', 'goog.i18n.DateTimeSymbols_bs_Latn_BA', 'goog.i18n.DateTimeSymbols_ca_AD', 'goog.i18n.DateTimeSymbols_ca_ES', 'goog.i18n.DateTimeSymbols_ca_FR', 'goog.i18n.DateTimeSymbols_ca_IT', 'goog.i18n.DateTimeSymbols_ccp', 'goog.i18n.DateTimeSymbols_ccp_BD', 'goog.i18n.DateTimeSymbols_ccp_IN', 'goog.i18n.DateTimeSymbols_ce', 'goog.i18n.DateTimeSymbols_ce_RU', 'goog.i18n.DateTimeSymbols_ceb', 'goog.i18n.DateTimeSymbols_ceb_PH', 'goog.i18n.DateTimeSymbols_cgg', 'goog.i18n.DateTimeSymbols_cgg_UG', 'goog.i18n.DateTimeSymbols_chr_US', 'goog.i18n.DateTimeSymbols_ckb', 'goog.i18n.DateTimeSymbols_ckb_IQ', 'goog.i18n.DateTimeSymbols_ckb_IR', 'goog.i18n.DateTimeSymbols_cs_CZ', 'goog.i18n.DateTimeSymbols_cy_GB', 'goog.i18n.DateTimeSymbols_da_DK', 'goog.i18n.DateTimeSymbols_da_GL', 'goog.i18n.DateTimeSymbols_dav', 'goog.i18n.DateTimeSymbols_dav_KE', 'goog.i18n.DateTimeSymbols_de_BE', 'goog.i18n.DateTimeSymbols_de_DE', 'goog.i18n.DateTimeSymbols_de_IT', 'goog.i18n.DateTimeSymbols_de_LI', 'goog.i18n.DateTimeSymbols_de_LU', 'goog.i18n.DateTimeSymbols_dje', 'goog.i18n.DateTimeSymbols_dje_NE', 'goog.i18n.DateTimeSymbols_dsb', 'goog.i18n.DateTimeSymbols_dsb_DE', 'goog.i18n.DateTimeSymbols_dua', 'goog.i18n.DateTimeSymbols_dua_CM', 'goog.i18n.DateTimeSymbols_dyo', 'goog.i18n.DateTimeSymbols_dyo_SN', 'goog.i18n.DateTimeSymbols_dz', 'goog.i18n.DateTimeSymbols_dz_BT', 'goog.i18n.DateTimeSymbols_ebu', 'goog.i18n.DateTimeSymbols_ebu_KE', 'goog.i18n.DateTimeSymbols_ee', 'goog.i18n.DateTimeSymbols_ee_GH', 'goog.i18n.DateTimeSymbols_ee_TG', 'goog.i18n.DateTimeSymbols_el_CY', 'goog.i18n.DateTimeSymbols_el_GR', 'goog.i18n.DateTimeSymbols_en_001', 'goog.i18n.DateTimeSymbols_en_150', 'goog.i18n.DateTimeSymbols_en_AE', 'goog.i18n.DateTimeSymbols_en_AG', 'goog.i18n.DateTimeSymbols_en_AI', 'goog.i18n.DateTimeSymbols_en_AS', 'goog.i18n.DateTimeSymbols_en_AT', 'goog.i18n.DateTimeSymbols_en_BB', 'goog.i18n.DateTimeSymbols_en_BE', 'goog.i18n.DateTimeSymbols_en_BI', 'goog.i18n.DateTimeSymbols_en_BM', 'goog.i18n.DateTimeSymbols_en_BS', 'goog.i18n.DateTimeSymbols_en_BW', 'goog.i18n.DateTimeSymbols_en_BZ', 'goog.i18n.DateTimeSymbols_en_CC', 'goog.i18n.DateTimeSymbols_en_CH', 'goog.i18n.DateTimeSymbols_en_CK', 'goog.i18n.DateTimeSymbols_en_CM', 'goog.i18n.DateTimeSymbols_en_CX', 'goog.i18n.DateTimeSymbols_en_CY', 'goog.i18n.DateTimeSymbols_en_DE', 'goog.i18n.DateTimeSymbols_en_DG', 'goog.i18n.DateTimeSymbols_en_DK', 'goog.i18n.DateTimeSymbols_en_DM', 'goog.i18n.DateTimeSymbols_en_ER', 'goog.i18n.DateTimeSymbols_en_FI', 'goog.i18n.DateTimeSymbols_en_FJ', 'goog.i18n.DateTimeSymbols_en_FK', 'goog.i18n.DateTimeSymbols_en_FM', 'goog.i18n.DateTimeSymbols_en_GD', 'goog.i18n.DateTimeSymbols_en_GG', 'goog.i18n.DateTimeSymbols_en_GH', 'goog.i18n.DateTimeSymbols_en_GI', 'goog.i18n.DateTimeSymbols_en_GM', 'goog.i18n.DateTimeSymbols_en_GU', 'goog.i18n.DateTimeSymbols_en_GY', 'goog.i18n.DateTimeSymbols_en_HK', 'goog.i18n.DateTimeSymbols_en_IL', 'goog.i18n.DateTimeSymbols_en_IM', 'goog.i18n.DateTimeSymbols_en_IO', 'goog.i18n.DateTimeSymbols_en_JE', 'goog.i18n.DateTimeSymbols_en_JM', 'goog.i18n.DateTimeSymbols_en_KE', 'goog.i18n.DateTimeSymbols_en_KI', 'goog.i18n.DateTimeSymbols_en_KN', 'goog.i18n.DateTimeSymbols_en_KY', 'goog.i18n.DateTimeSymbols_en_LC', 'goog.i18n.DateTimeSymbols_en_LR', 'goog.i18n.DateTimeSymbols_en_LS', 'goog.i18n.DateTimeSymbols_en_MG', 'goog.i18n.DateTimeSymbols_en_MH', 'goog.i18n.DateTimeSymbols_en_MO', 'goog.i18n.DateTimeSymbols_en_MP', 'goog.i18n.DateTimeSymbols_en_MS', 'goog.i18n.DateTimeSymbols_en_MT', 'goog.i18n.DateTimeSymbols_en_MU', 'goog.i18n.DateTimeSymbols_en_MW', 'goog.i18n.DateTimeSymbols_en_MY', 'goog.i18n.DateTimeSymbols_en_NA', 'goog.i18n.DateTimeSymbols_en_NF', 'goog.i18n.DateTimeSymbols_en_NG', 'goog.i18n.DateTimeSymbols_en_NL', 'goog.i18n.DateTimeSymbols_en_NR', 'goog.i18n.DateTimeSymbols_en_NU', 'goog.i18n.DateTimeSymbols_en_NZ', 'goog.i18n.DateTimeSymbols_en_PG', 'goog.i18n.DateTimeSymbols_en_PH', 'goog.i18n.DateTimeSymbols_en_PK', 'goog.i18n.DateTimeSymbols_en_PN', 'goog.i18n.DateTimeSymbols_en_PR', 'goog.i18n.DateTimeSymbols_en_PW', 'goog.i18n.DateTimeSymbols_en_RW', 'goog.i18n.DateTimeSymbols_en_SB', 'goog.i18n.DateTimeSymbols_en_SC', 'goog.i18n.DateTimeSymbols_en_SD', 'goog.i18n.DateTimeSymbols_en_SE', 'goog.i18n.DateTimeSymbols_en_SH', 'goog.i18n.DateTimeSymbols_en_SI', 'goog.i18n.DateTimeSymbols_en_SL', 'goog.i18n.DateTimeSymbols_en_SS', 'goog.i18n.DateTimeSymbols_en_SX', 'goog.i18n.DateTimeSymbols_en_SZ', 'goog.i18n.DateTimeSymbols_en_TC', 'goog.i18n.DateTimeSymbols_en_TK', 'goog.i18n.DateTimeSymbols_en_TO', 'goog.i18n.DateTimeSymbols_en_TT', 'goog.i18n.DateTimeSymbols_en_TV', 'goog.i18n.DateTimeSymbols_en_TZ', 'goog.i18n.DateTimeSymbols_en_UG', 'goog.i18n.DateTimeSymbols_en_UM', 'goog.i18n.DateTimeSymbols_en_US_POSIX', 'goog.i18n.DateTimeSymbols_en_VC', 'goog.i18n.DateTimeSymbols_en_VG', 'goog.i18n.DateTimeSymbols_en_VI', 'goog.i18n.DateTimeSymbols_en_VU', 'goog.i18n.DateTimeSymbols_en_WS', 'goog.i18n.DateTimeSymbols_en_XA', 'goog.i18n.DateTimeSymbols_en_ZM', 'goog.i18n.DateTimeSymbols_en_ZW', 'goog.i18n.DateTimeSymbols_eo', 'goog.i18n.DateTimeSymbols_eo_001', 'goog.i18n.DateTimeSymbols_es_AR', 'goog.i18n.DateTimeSymbols_es_BO', 'goog.i18n.DateTimeSymbols_es_BR', 'goog.i18n.DateTimeSymbols_es_BZ', 'goog.i18n.DateTimeSymbols_es_CL', 'goog.i18n.DateTimeSymbols_es_CO', 'goog.i18n.DateTimeSymbols_es_CR', 'goog.i18n.DateTimeSymbols_es_CU', 'goog.i18n.DateTimeSymbols_es_DO', 'goog.i18n.DateTimeSymbols_es_EA', 'goog.i18n.DateTimeSymbols_es_EC', 'goog.i18n.DateTimeSymbols_es_GQ', 'goog.i18n.DateTimeSymbols_es_GT', 'goog.i18n.DateTimeSymbols_es_HN', 'goog.i18n.DateTimeSymbols_es_IC', 'goog.i18n.DateTimeSymbols_es_NI', 'goog.i18n.DateTimeSymbols_es_PA', 'goog.i18n.DateTimeSymbols_es_PE', 'goog.i18n.DateTimeSymbols_es_PH', 'goog.i18n.DateTimeSymbols_es_PR', 'goog.i18n.DateTimeSymbols_es_PY', 'goog.i18n.DateTimeSymbols_es_SV', 'goog.i18n.DateTimeSymbols_es_UY', 'goog.i18n.DateTimeSymbols_es_VE', 'goog.i18n.DateTimeSymbols_et_EE', 'goog.i18n.DateTimeSymbols_eu_ES', 'goog.i18n.DateTimeSymbols_ewo', 'goog.i18n.DateTimeSymbols_ewo_CM', 'goog.i18n.DateTimeSymbols_fa_AF', 'goog.i18n.DateTimeSymbols_fa_IR', 'goog.i18n.DateTimeSymbols_ff', 'goog.i18n.DateTimeSymbols_ff_Latn', 'goog.i18n.DateTimeSymbols_ff_Latn_BF', 'goog.i18n.DateTimeSymbols_ff_Latn_CM', 'goog.i18n.DateTimeSymbols_ff_Latn_GH', 'goog.i18n.DateTimeSymbols_ff_Latn_GM', 'goog.i18n.DateTimeSymbols_ff_Latn_GN', 'goog.i18n.DateTimeSymbols_ff_Latn_GW', 'goog.i18n.DateTimeSymbols_ff_Latn_LR', 'goog.i18n.DateTimeSymbols_ff_Latn_MR', 'goog.i18n.DateTimeSymbols_ff_Latn_NE', 'goog.i18n.DateTimeSymbols_ff_Latn_NG', 'goog.i18n.DateTimeSymbols_ff_Latn_SL', 'goog.i18n.DateTimeSymbols_ff_Latn_SN', 'goog.i18n.DateTimeSymbols_fi_FI', 'goog.i18n.DateTimeSymbols_fil_PH', 'goog.i18n.DateTimeSymbols_fo', 'goog.i18n.DateTimeSymbols_fo_DK', 'goog.i18n.DateTimeSymbols_fo_FO', 'goog.i18n.DateTimeSymbols_fr_BE', 'goog.i18n.DateTimeSymbols_fr_BF', 'goog.i18n.DateTimeSymbols_fr_BI', 'goog.i18n.DateTimeSymbols_fr_BJ', 'goog.i18n.DateTimeSymbols_fr_BL', 'goog.i18n.DateTimeSymbols_fr_CD', 'goog.i18n.DateTimeSymbols_fr_CF', 'goog.i18n.DateTimeSymbols_fr_CG', 'goog.i18n.DateTimeSymbols_fr_CH', 'goog.i18n.DateTimeSymbols_fr_CI', 'goog.i18n.DateTimeSymbols_fr_CM', 'goog.i18n.DateTimeSymbols_fr_DJ', 'goog.i18n.DateTimeSymbols_fr_DZ', 'goog.i18n.DateTimeSymbols_fr_FR', 'goog.i18n.DateTimeSymbols_fr_GA', 'goog.i18n.DateTimeSymbols_fr_GF', 'goog.i18n.DateTimeSymbols_fr_GN', 'goog.i18n.DateTimeSymbols_fr_GP', 'goog.i18n.DateTimeSymbols_fr_GQ', 'goog.i18n.DateTimeSymbols_fr_HT', 'goog.i18n.DateTimeSymbols_fr_KM', 'goog.i18n.DateTimeSymbols_fr_LU', 'goog.i18n.DateTimeSymbols_fr_MA', 'goog.i18n.DateTimeSymbols_fr_MC', 'goog.i18n.DateTimeSymbols_fr_MF', 'goog.i18n.DateTimeSymbols_fr_MG', 'goog.i18n.DateTimeSymbols_fr_ML', 'goog.i18n.DateTimeSymbols_fr_MQ', 'goog.i18n.DateTimeSymbols_fr_MR', 'goog.i18n.DateTimeSymbols_fr_MU', 'goog.i18n.DateTimeSymbols_fr_NC', 'goog.i18n.DateTimeSymbols_fr_NE', 'goog.i18n.DateTimeSymbols_fr_PF', 'goog.i18n.DateTimeSymbols_fr_PM', 'goog.i18n.DateTimeSymbols_fr_RE', 'goog.i18n.DateTimeSymbols_fr_RW', 'goog.i18n.DateTimeSymbols_fr_SC', 'goog.i18n.DateTimeSymbols_fr_SN', 'goog.i18n.DateTimeSymbols_fr_SY', 'goog.i18n.DateTimeSymbols_fr_TD', 'goog.i18n.DateTimeSymbols_fr_TG', 'goog.i18n.DateTimeSymbols_fr_TN', 'goog.i18n.DateTimeSymbols_fr_VU', 'goog.i18n.DateTimeSymbols_fr_WF', 'goog.i18n.DateTimeSymbols_fr_YT', 'goog.i18n.DateTimeSymbols_fur', 'goog.i18n.DateTimeSymbols_fur_IT', 'goog.i18n.DateTimeSymbols_fy', 'goog.i18n.DateTimeSymbols_fy_NL', 'goog.i18n.DateTimeSymbols_ga_IE', 'goog.i18n.DateTimeSymbols_gd', 'goog.i18n.DateTimeSymbols_gd_GB', 'goog.i18n.DateTimeSymbols_gl_ES', 'goog.i18n.DateTimeSymbols_gsw_CH', 'goog.i18n.DateTimeSymbols_gsw_FR', 'goog.i18n.DateTimeSymbols_gsw_LI', 'goog.i18n.DateTimeSymbols_gu_IN', 'goog.i18n.DateTimeSymbols_guz', 'goog.i18n.DateTimeSymbols_guz_KE', 'goog.i18n.DateTimeSymbols_gv', 'goog.i18n.DateTimeSymbols_gv_IM', 'goog.i18n.DateTimeSymbols_ha', 'goog.i18n.DateTimeSymbols_ha_GH', 'goog.i18n.DateTimeSymbols_ha_NE', 'goog.i18n.DateTimeSymbols_ha_NG', 'goog.i18n.DateTimeSymbols_haw_US', 'goog.i18n.DateTimeSymbols_he_IL', 'goog.i18n.DateTimeSymbols_hi_IN', 'goog.i18n.DateTimeSymbols_hr_BA', 'goog.i18n.DateTimeSymbols_hr_HR', 'goog.i18n.DateTimeSymbols_hsb', 'goog.i18n.DateTimeSymbols_hsb_DE', 'goog.i18n.DateTimeSymbols_hu_HU', 'goog.i18n.DateTimeSymbols_hy_AM', 'goog.i18n.DateTimeSymbols_ia', 'goog.i18n.DateTimeSymbols_ia_001', 'goog.i18n.DateTimeSymbols_id_ID', 'goog.i18n.DateTimeSymbols_ig', 'goog.i18n.DateTimeSymbols_ig_NG', 'goog.i18n.DateTimeSymbols_ii', 'goog.i18n.DateTimeSymbols_ii_CN', 'goog.i18n.DateTimeSymbols_is_IS', 'goog.i18n.DateTimeSymbols_it_CH', 'goog.i18n.DateTimeSymbols_it_IT', 'goog.i18n.DateTimeSymbols_it_SM', 'goog.i18n.DateTimeSymbols_it_VA', 'goog.i18n.DateTimeSymbols_ja_JP', 'goog.i18n.DateTimeSymbols_jgo', 'goog.i18n.DateTimeSymbols_jgo_CM', 'goog.i18n.DateTimeSymbols_jmc', 'goog.i18n.DateTimeSymbols_jmc_TZ', 'goog.i18n.DateTimeSymbols_jv', 'goog.i18n.DateTimeSymbols_jv_ID', 'goog.i18n.DateTimeSymbols_ka_GE', 'goog.i18n.DateTimeSymbols_kab', 'goog.i18n.DateTimeSymbols_kab_DZ', 'goog.i18n.DateTimeSymbols_kam', 'goog.i18n.DateTimeSymbols_kam_KE', 'goog.i18n.DateTimeSymbols_kde', 'goog.i18n.DateTimeSymbols_kde_TZ', 'goog.i18n.DateTimeSymbols_kea', 'goog.i18n.DateTimeSymbols_kea_CV', 'goog.i18n.DateTimeSymbols_khq', 'goog.i18n.DateTimeSymbols_khq_ML', 'goog.i18n.DateTimeSymbols_ki', 'goog.i18n.DateTimeSymbols_ki_KE', 'goog.i18n.DateTimeSymbols_kk_KZ', 'goog.i18n.DateTimeSymbols_kkj', 'goog.i18n.DateTimeSymbols_kkj_CM', 'goog.i18n.DateTimeSymbols_kl', 'goog.i18n.DateTimeSymbols_kl_GL', 'goog.i18n.DateTimeSymbols_kln', 'goog.i18n.DateTimeSymbols_kln_KE', 'goog.i18n.DateTimeSymbols_km_KH', 'goog.i18n.DateTimeSymbols_kn_IN', 'goog.i18n.DateTimeSymbols_ko_KP', 'goog.i18n.DateTimeSymbols_ko_KR', 'goog.i18n.DateTimeSymbols_kok', 'goog.i18n.DateTimeSymbols_kok_IN', 'goog.i18n.DateTimeSymbols_ks', 'goog.i18n.DateTimeSymbols_ks_IN', 'goog.i18n.DateTimeSymbols_ksb', 'goog.i18n.DateTimeSymbols_ksb_TZ', 'goog.i18n.DateTimeSymbols_ksf', 'goog.i18n.DateTimeSymbols_ksf_CM', 'goog.i18n.DateTimeSymbols_ksh', 'goog.i18n.DateTimeSymbols_ksh_DE', 'goog.i18n.DateTimeSymbols_ku', 'goog.i18n.DateTimeSymbols_ku_TR', 'goog.i18n.DateTimeSymbols_kw', 'goog.i18n.DateTimeSymbols_kw_GB', 'goog.i18n.DateTimeSymbols_ky_KG', 'goog.i18n.DateTimeSymbols_lag', 'goog.i18n.DateTimeSymbols_lag_TZ', 'goog.i18n.DateTimeSymbols_lb', 'goog.i18n.DateTimeSymbols_lb_LU', 'goog.i18n.DateTimeSymbols_lg', 'goog.i18n.DateTimeSymbols_lg_UG', 'goog.i18n.DateTimeSymbols_lkt', 'goog.i18n.DateTimeSymbols_lkt_US', 'goog.i18n.DateTimeSymbols_ln_AO', 'goog.i18n.DateTimeSymbols_ln_CD', 'goog.i18n.DateTimeSymbols_ln_CF', 'goog.i18n.DateTimeSymbols_ln_CG', 'goog.i18n.DateTimeSymbols_lo_LA', 'goog.i18n.DateTimeSymbols_lrc', 'goog.i18n.DateTimeSymbols_lrc_IQ', 'goog.i18n.DateTimeSymbols_lrc_IR', 'goog.i18n.DateTimeSymbols_lt_LT', 'goog.i18n.DateTimeSymbols_lu', 'goog.i18n.DateTimeSymbols_lu_CD', 'goog.i18n.DateTimeSymbols_luo', 'goog.i18n.DateTimeSymbols_luo_KE', 'goog.i18n.DateTimeSymbols_luy', 'goog.i18n.DateTimeSymbols_luy_KE', 'goog.i18n.DateTimeSymbols_lv_LV', 'goog.i18n.DateTimeSymbols_mas', 'goog.i18n.DateTimeSymbols_mas_KE', 'goog.i18n.DateTimeSymbols_mas_TZ', 'goog.i18n.DateTimeSymbols_mer', 'goog.i18n.DateTimeSymbols_mer_KE', 'goog.i18n.DateTimeSymbols_mfe', 'goog.i18n.DateTimeSymbols_mfe_MU', 'goog.i18n.DateTimeSymbols_mg', 'goog.i18n.DateTimeSymbols_mg_MG', 'goog.i18n.DateTimeSymbols_mgh', 'goog.i18n.DateTimeSymbols_mgh_MZ', 'goog.i18n.DateTimeSymbols_mgo', 'goog.i18n.DateTimeSymbols_mgo_CM', 'goog.i18n.DateTimeSymbols_mi', 'goog.i18n.DateTimeSymbols_mi_NZ', 'goog.i18n.DateTimeSymbols_mk_MK', 'goog.i18n.DateTimeSymbols_ml_IN', 'goog.i18n.DateTimeSymbols_mn_MN', 'goog.i18n.DateTimeSymbols_mr_IN', 'goog.i18n.DateTimeSymbols_ms_BN', 'goog.i18n.DateTimeSymbols_ms_MY', 'goog.i18n.DateTimeSymbols_ms_SG', 'goog.i18n.DateTimeSymbols_mt_MT', 'goog.i18n.DateTimeSymbols_mua', 'goog.i18n.DateTimeSymbols_mua_CM', 'goog.i18n.DateTimeSymbols_my_MM', 'goog.i18n.DateTimeSymbols_mzn', 'goog.i18n.DateTimeSymbols_mzn_IR', 'goog.i18n.DateTimeSymbols_naq', 'goog.i18n.DateTimeSymbols_naq_NA', 'goog.i18n.DateTimeSymbols_nb_NO', 'goog.i18n.DateTimeSymbols_nb_SJ', 'goog.i18n.DateTimeSymbols_nd', 'goog.i18n.DateTimeSymbols_nd_ZW', 'goog.i18n.DateTimeSymbols_nds', 'goog.i18n.DateTimeSymbols_nds_DE', 'goog.i18n.DateTimeSymbols_nds_NL', 'goog.i18n.DateTimeSymbols_ne_IN', 'goog.i18n.DateTimeSymbols_ne_NP', 'goog.i18n.DateTimeSymbols_nl_AW', 'goog.i18n.DateTimeSymbols_nl_BE', 'goog.i18n.DateTimeSymbols_nl_BQ', 'goog.i18n.DateTimeSymbols_nl_CW', 'goog.i18n.DateTimeSymbols_nl_NL', 'goog.i18n.DateTimeSymbols_nl_SR', 'goog.i18n.DateTimeSymbols_nl_SX', 'goog.i18n.DateTimeSymbols_nmg', 'goog.i18n.DateTimeSymbols_nmg_CM', 'goog.i18n.DateTimeSymbols_nn', 'goog.i18n.DateTimeSymbols_nn_NO', 'goog.i18n.DateTimeSymbols_nnh', 'goog.i18n.DateTimeSymbols_nnh_CM', 'goog.i18n.DateTimeSymbols_nus', 'goog.i18n.DateTimeSymbols_nus_SS', 'goog.i18n.DateTimeSymbols_nyn', 'goog.i18n.DateTimeSymbols_nyn_UG', 'goog.i18n.DateTimeSymbols_om', 'goog.i18n.DateTimeSymbols_om_ET', 'goog.i18n.DateTimeSymbols_om_KE', 'goog.i18n.DateTimeSymbols_or_IN', 'goog.i18n.DateTimeSymbols_os', 'goog.i18n.DateTimeSymbols_os_GE', 'goog.i18n.DateTimeSymbols_os_RU', 'goog.i18n.DateTimeSymbols_pa_Arab', 'goog.i18n.DateTimeSymbols_pa_Arab_PK', 'goog.i18n.DateTimeSymbols_pa_Guru', 'goog.i18n.DateTimeSymbols_pa_Guru_IN', 'goog.i18n.DateTimeSymbols_pl_PL', 'goog.i18n.DateTimeSymbols_ps', 'goog.i18n.DateTimeSymbols_ps_AF', 'goog.i18n.DateTimeSymbols_ps_PK', 'goog.i18n.DateTimeSymbols_pt_AO', 'goog.i18n.DateTimeSymbols_pt_CH', 'goog.i18n.DateTimeSymbols_pt_CV', 'goog.i18n.DateTimeSymbols_pt_GQ', 'goog.i18n.DateTimeSymbols_pt_GW', 'goog.i18n.DateTimeSymbols_pt_LU', 'goog.i18n.DateTimeSymbols_pt_MO', 'goog.i18n.DateTimeSymbols_pt_MZ', 'goog.i18n.DateTimeSymbols_pt_ST', 'goog.i18n.DateTimeSymbols_pt_TL', 'goog.i18n.DateTimeSymbols_qu', 'goog.i18n.DateTimeSymbols_qu_BO', 'goog.i18n.DateTimeSymbols_qu_EC', 'goog.i18n.DateTimeSymbols_qu_PE', 'goog.i18n.DateTimeSymbols_rm', 'goog.i18n.DateTimeSymbols_rm_CH', 'goog.i18n.DateTimeSymbols_rn', 'goog.i18n.DateTimeSymbols_rn_BI', 'goog.i18n.DateTimeSymbols_ro_MD', 'goog.i18n.DateTimeSymbols_ro_RO', 'goog.i18n.DateTimeSymbols_rof', 'goog.i18n.DateTimeSymbols_rof_TZ', 'goog.i18n.DateTimeSymbols_ru_BY', 'goog.i18n.DateTimeSymbols_ru_KG', 'goog.i18n.DateTimeSymbols_ru_KZ', 'goog.i18n.DateTimeSymbols_ru_MD', 'goog.i18n.DateTimeSymbols_ru_RU', 'goog.i18n.DateTimeSymbols_ru_UA', 'goog.i18n.DateTimeSymbols_rw', 'goog.i18n.DateTimeSymbols_rw_RW', 'goog.i18n.DateTimeSymbols_rwk', 'goog.i18n.DateTimeSymbols_rwk_TZ', 'goog.i18n.DateTimeSymbols_sah', 'goog.i18n.DateTimeSymbols_sah_RU', 'goog.i18n.DateTimeSymbols_saq', 'goog.i18n.DateTimeSymbols_saq_KE', 'goog.i18n.DateTimeSymbols_sbp', 'goog.i18n.DateTimeSymbols_sbp_TZ', 'goog.i18n.DateTimeSymbols_sd', 'goog.i18n.DateTimeSymbols_sd_PK', 'goog.i18n.DateTimeSymbols_se', 'goog.i18n.DateTimeSymbols_se_FI', 'goog.i18n.DateTimeSymbols_se_NO', 'goog.i18n.DateTimeSymbols_se_SE', 'goog.i18n.DateTimeSymbols_seh', 'goog.i18n.DateTimeSymbols_seh_MZ', 'goog.i18n.DateTimeSymbols_ses', 'goog.i18n.DateTimeSymbols_ses_ML', 'goog.i18n.DateTimeSymbols_sg', 'goog.i18n.DateTimeSymbols_sg_CF', 'goog.i18n.DateTimeSymbols_shi', 'goog.i18n.DateTimeSymbols_shi_Latn', 'goog.i18n.DateTimeSymbols_shi_Latn_MA', 'goog.i18n.DateTimeSymbols_shi_Tfng', 'goog.i18n.DateTimeSymbols_shi_Tfng_MA', 'goog.i18n.DateTimeSymbols_si_LK', 'goog.i18n.DateTimeSymbols_sk_SK', 'goog.i18n.DateTimeSymbols_sl_SI', 'goog.i18n.DateTimeSymbols_smn', 'goog.i18n.DateTimeSymbols_smn_FI', 'goog.i18n.DateTimeSymbols_sn', 'goog.i18n.DateTimeSymbols_sn_ZW', 'goog.i18n.DateTimeSymbols_so', 'goog.i18n.DateTimeSymbols_so_DJ', 'goog.i18n.DateTimeSymbols_so_ET', 'goog.i18n.DateTimeSymbols_so_KE', 'goog.i18n.DateTimeSymbols_so_SO', 'goog.i18n.DateTimeSymbols_sq_AL', 'goog.i18n.DateTimeSymbols_sq_MK', 'goog.i18n.DateTimeSymbols_sq_XK', 'goog.i18n.DateTimeSymbols_sr_Cyrl', 'goog.i18n.DateTimeSymbols_sr_Cyrl_BA', 'goog.i18n.DateTimeSymbols_sr_Cyrl_ME', 'goog.i18n.DateTimeSymbols_sr_Cyrl_RS', 'goog.i18n.DateTimeSymbols_sr_Cyrl_XK', 'goog.i18n.DateTimeSymbols_sr_Latn_BA', 'goog.i18n.DateTimeSymbols_sr_Latn_ME', 'goog.i18n.DateTimeSymbols_sr_Latn_RS', 'goog.i18n.DateTimeSymbols_sr_Latn_XK', 'goog.i18n.DateTimeSymbols_sv_AX', 'goog.i18n.DateTimeSymbols_sv_FI', 'goog.i18n.DateTimeSymbols_sv_SE', 'goog.i18n.DateTimeSymbols_sw_CD', 'goog.i18n.DateTimeSymbols_sw_KE', 'goog.i18n.DateTimeSymbols_sw_TZ', 'goog.i18n.DateTimeSymbols_sw_UG', 'goog.i18n.DateTimeSymbols_ta_IN', 'goog.i18n.DateTimeSymbols_ta_LK', 'goog.i18n.DateTimeSymbols_ta_MY', 'goog.i18n.DateTimeSymbols_ta_SG', 'goog.i18n.DateTimeSymbols_te_IN', 'goog.i18n.DateTimeSymbols_teo', 'goog.i18n.DateTimeSymbols_teo_KE', 'goog.i18n.DateTimeSymbols_teo_UG', 'goog.i18n.DateTimeSymbols_tg', 'goog.i18n.DateTimeSymbols_tg_TJ', 'goog.i18n.DateTimeSymbols_th_TH', 'goog.i18n.DateTimeSymbols_ti', 'goog.i18n.DateTimeSymbols_ti_ER', 'goog.i18n.DateTimeSymbols_ti_ET', 'goog.i18n.DateTimeSymbols_tk', 'goog.i18n.DateTimeSymbols_tk_TM', 'goog.i18n.DateTimeSymbols_to', 'goog.i18n.DateTimeSymbols_to_TO', 'goog.i18n.DateTimeSymbols_tr_CY', 'goog.i18n.DateTimeSymbols_tr_TR', 'goog.i18n.DateTimeSymbols_tt', 'goog.i18n.DateTimeSymbols_tt_RU', 'goog.i18n.DateTimeSymbols_twq', 'goog.i18n.DateTimeSymbols_twq_NE', 'goog.i18n.DateTimeSymbols_tzm', 'goog.i18n.DateTimeSymbols_tzm_MA', 'goog.i18n.DateTimeSymbols_ug', 'goog.i18n.DateTimeSymbols_ug_CN', 'goog.i18n.DateTimeSymbols_uk_UA', 'goog.i18n.DateTimeSymbols_ur_IN', 'goog.i18n.DateTimeSymbols_ur_PK', 'goog.i18n.DateTimeSymbols_uz_Arab', 'goog.i18n.DateTimeSymbols_uz_Arab_AF', 'goog.i18n.DateTimeSymbols_uz_Cyrl', 'goog.i18n.DateTimeSymbols_uz_Cyrl_UZ', 'goog.i18n.DateTimeSymbols_uz_Latn', 'goog.i18n.DateTimeSymbols_uz_Latn_UZ', 'goog.i18n.DateTimeSymbols_vai', 'goog.i18n.DateTimeSymbols_vai_Latn', 'goog.i18n.DateTimeSymbols_vai_Latn_LR', 'goog.i18n.DateTimeSymbols_vai_Vaii', 'goog.i18n.DateTimeSymbols_vai_Vaii_LR', 'goog.i18n.DateTimeSymbols_vi_VN', 'goog.i18n.DateTimeSymbols_vun', 'goog.i18n.DateTimeSymbols_vun_TZ', 'goog.i18n.DateTimeSymbols_wae', 'goog.i18n.DateTimeSymbols_wae_CH', 'goog.i18n.DateTimeSymbols_wo', 'goog.i18n.DateTimeSymbols_wo_SN', 'goog.i18n.DateTimeSymbols_xh', 'goog.i18n.DateTimeSymbols_xh_ZA', 'goog.i18n.DateTimeSymbols_xog', 'goog.i18n.DateTimeSymbols_xog_UG', 'goog.i18n.DateTimeSymbols_yav', 'goog.i18n.DateTimeSymbols_yav_CM', 'goog.i18n.DateTimeSymbols_yi', 'goog.i18n.DateTimeSymbols_yi_001', 'goog.i18n.DateTimeSymbols_yo', 'goog.i18n.DateTimeSymbols_yo_BJ', 'goog.i18n.DateTimeSymbols_yo_NG', 'goog.i18n.DateTimeSymbols_yue', 'goog.i18n.DateTimeSymbols_yue_Hans', 'goog.i18n.DateTimeSymbols_yue_Hans_CN', 'goog.i18n.DateTimeSymbols_yue_Hant', 'goog.i18n.DateTimeSymbols_yue_Hant_HK', 'goog.i18n.DateTimeSymbols_zgh', 'goog.i18n.DateTimeSymbols_zgh_MA', 'goog.i18n.DateTimeSymbols_zh_Hans', 'goog.i18n.DateTimeSymbols_zh_Hans_CN', 'goog.i18n.DateTimeSymbols_zh_Hans_HK', 'goog.i18n.DateTimeSymbols_zh_Hans_MO', 'goog.i18n.DateTimeSymbols_zh_Hans_SG', 'goog.i18n.DateTimeSymbols_zh_Hant', 'goog.i18n.DateTimeSymbols_zh_Hant_HK', 'goog.i18n.DateTimeSymbols_zh_Hant_MO', 'goog.i18n.DateTimeSymbols_zh_Hant_TW', 'goog.i18n.DateTimeSymbols_zu_ZA'], ['goog.i18n.DateTimeSymbols'], {});
goog.addDependency('i18n/graphemebreak.js', ['goog.i18n.GraphemeBreak'], ['goog.asserts', 'goog.i18n.uChar', 'goog.structs.InversionMap'], {});
goog.addDependency('i18n/graphemebreak_test.js', ['goog.i18n.GraphemeBreakTest'], ['goog.i18n.GraphemeBreak', 'goog.i18n.uChar', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('i18n/localefeature.js', ['goog.i18n.LocaleFeature'], [], {'module': 'goog'});
goog.addDependency('i18n/localefeature_test.js', ['goog.i18n.LocaleFeatureTest'], ['goog.i18n.LocaleFeature', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('i18n/messageformat.js', ['goog.i18n.MessageFormat'], ['goog.array', 'goog.asserts', 'goog.i18n.CompactNumberFormatSymbols', 'goog.i18n.NumberFormat', 'goog.i18n.NumberFormatSymbols', 'goog.i18n.ordinalRules', 'goog.i18n.pluralRules'], {});
goog.addDependency('i18n/messageformat_test.js', ['goog.i18n.MessageFormatTest'], ['goog.i18n.MessageFormat', 'goog.i18n.NumberFormatSymbols_hr', 'goog.i18n.pluralRules', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('i18n/mime.js', ['goog.i18n.mime', 'goog.i18n.mime.encode'], ['goog.array', 'goog.i18n.uChar'], {});
goog.addDependency('i18n/mime_test.js', ['goog.i18n.mime.encodeTest'], ['goog.i18n.mime.encode', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('i18n/numberformat.js', ['goog.i18n.NumberFormat', 'goog.i18n.NumberFormat.CurrencyStyle', 'goog.i18n.NumberFormat.Format'], ['goog.asserts', 'goog.i18n.CompactNumberFormatSymbols', 'goog.i18n.NumberFormatSymbols', 'goog.i18n.NumberFormatSymbols_u_nu_latn', 'goog.i18n.currency', 'goog.math', 'goog.string'], {});
goog.addDependency('i18n/numberformat_test.js', ['goog.i18n.NumberFormatTest'], ['goog.i18n.CompactNumberFormatSymbols', 'goog.i18n.CompactNumberFormatSymbols_de', 'goog.i18n.CompactNumberFormatSymbols_en', 'goog.i18n.CompactNumberFormatSymbols_fr', 'goog.i18n.NumberFormat', 'goog.i18n.NumberFormatSymbols', 'goog.i18n.NumberFormatSymbols_ar_EG', 'goog.i18n.NumberFormatSymbols_ar_EG_u_nu_latn', 'goog.i18n.NumberFormatSymbols_de', 'goog.i18n.NumberFormatSymbols_en', 'goog.i18n.NumberFormatSymbols_en_AU', 'goog.i18n.NumberFormatSymbols_en_US', 'goog.i18n.NumberFormatSymbols_fi', 'goog.i18n.NumberFormatSymbols_fr', 'goog.i18n.NumberFormatSymbols_pl', 'goog.i18n.NumberFormatSymbols_ro', 'goog.i18n.NumberFormatSymbols_u_nu_latn', 'goog.string', 'goog.testing.ExpectedFailures', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite', 'goog.userAgent', 'goog.userAgent.product', 'goog.userAgent.product.isVersion'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('i18n/numberformatsymbols.js', ['goog.i18n.NumberFormatSymbols', 'goog.i18n.NumberFormatSymbols_af', 'goog.i18n.NumberFormatSymbols_am', 'goog.i18n.NumberFormatSymbols_ar', 'goog.i18n.NumberFormatSymbols_ar_DZ', 'goog.i18n.NumberFormatSymbols_ar_EG', 'goog.i18n.NumberFormatSymbols_ar_EG_u_nu_latn', 'goog.i18n.NumberFormatSymbols_az', 'goog.i18n.NumberFormatSymbols_be', 'goog.i18n.NumberFormatSymbols_bg', 'goog.i18n.NumberFormatSymbols_bn', 'goog.i18n.NumberFormatSymbols_bn_u_nu_latn', 'goog.i18n.NumberFormatSymbols_br', 'goog.i18n.NumberFormatSymbols_bs', 'goog.i18n.NumberFormatSymbols_ca', 'goog.i18n.NumberFormatSymbols_chr', 'goog.i18n.NumberFormatSymbols_cs', 'goog.i18n.NumberFormatSymbols_cy', 'goog.i18n.NumberFormatSymbols_da', 'goog.i18n.NumberFormatSymbols_de', 'goog.i18n.NumberFormatSymbols_de_AT', 'goog.i18n.NumberFormatSymbols_de_CH', 'goog.i18n.NumberFormatSymbols_el', 'goog.i18n.NumberFormatSymbols_en', 'goog.i18n.NumberFormatSymbols_en_AU', 'goog.i18n.NumberFormatSymbols_en_CA', 'goog.i18n.NumberFormatSymbols_en_GB', 'goog.i18n.NumberFormatSymbols_en_IE', 'goog.i18n.NumberFormatSymbols_en_IN', 'goog.i18n.NumberFormatSymbols_en_SG', 'goog.i18n.NumberFormatSymbols_en_US', 'goog.i18n.NumberFormatSymbols_en_ZA', 'goog.i18n.NumberFormatSymbols_es', 'goog.i18n.NumberFormatSymbols_es_419', 'goog.i18n.NumberFormatSymbols_es_ES', 'goog.i18n.NumberFormatSymbols_es_MX', 'goog.i18n.NumberFormatSymbols_es_US', 'goog.i18n.NumberFormatSymbols_et', 'goog.i18n.NumberFormatSymbols_eu', 'goog.i18n.NumberFormatSymbols_fa', 'goog.i18n.NumberFormatSymbols_fa_u_nu_latn', 'goog.i18n.NumberFormatSymbols_fi', 'goog.i18n.NumberFormatSymbols_fil', 'goog.i18n.NumberFormatSymbols_fr', 'goog.i18n.NumberFormatSymbols_fr_CA', 'goog.i18n.NumberFormatSymbols_ga', 'goog.i18n.NumberFormatSymbols_gl', 'goog.i18n.NumberFormatSymbols_gsw', 'goog.i18n.NumberFormatSymbols_gu', 'goog.i18n.NumberFormatSymbols_haw', 'goog.i18n.NumberFormatSymbols_he', 'goog.i18n.NumberFormatSymbols_hi', 'goog.i18n.NumberFormatSymbols_hr', 'goog.i18n.NumberFormatSymbols_hu', 'goog.i18n.NumberFormatSymbols_hy', 'goog.i18n.NumberFormatSymbols_id', 'goog.i18n.NumberFormatSymbols_in', 'goog.i18n.NumberFormatSymbols_is', 'goog.i18n.NumberFormatSymbols_it', 'goog.i18n.NumberFormatSymbols_iw', 'goog.i18n.NumberFormatSymbols_ja', 'goog.i18n.NumberFormatSymbols_ka', 'goog.i18n.NumberFormatSymbols_kk', 'goog.i18n.NumberFormatSymbols_km', 'goog.i18n.NumberFormatSymbols_kn', 'goog.i18n.NumberFormatSymbols_ko', 'goog.i18n.NumberFormatSymbols_ky', 'goog.i18n.NumberFormatSymbols_ln', 'goog.i18n.NumberFormatSymbols_lo', 'goog.i18n.NumberFormatSymbols_lt', 'goog.i18n.NumberFormatSymbols_lv', 'goog.i18n.NumberFormatSymbols_mk', 'goog.i18n.NumberFormatSymbols_ml', 'goog.i18n.NumberFormatSymbols_mn', 'goog.i18n.NumberFormatSymbols_mo', 'goog.i18n.NumberFormatSymbols_mr', 'goog.i18n.NumberFormatSymbols_mr_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ms', 'goog.i18n.NumberFormatSymbols_mt', 'goog.i18n.NumberFormatSymbols_my', 'goog.i18n.NumberFormatSymbols_my_u_nu_latn', 'goog.i18n.NumberFormatSymbols_nb', 'goog.i18n.NumberFormatSymbols_ne', 'goog.i18n.NumberFormatSymbols_ne_u_nu_latn', 'goog.i18n.NumberFormatSymbols_nl', 'goog.i18n.NumberFormatSymbols_no', 'goog.i18n.NumberFormatSymbols_no_NO', 'goog.i18n.NumberFormatSymbols_or', 'goog.i18n.NumberFormatSymbols_pa', 'goog.i18n.NumberFormatSymbols_pl', 'goog.i18n.NumberFormatSymbols_pt', 'goog.i18n.NumberFormatSymbols_pt_BR', 'goog.i18n.NumberFormatSymbols_pt_PT', 'goog.i18n.NumberFormatSymbols_ro', 'goog.i18n.NumberFormatSymbols_ru', 'goog.i18n.NumberFormatSymbols_sh', 'goog.i18n.NumberFormatSymbols_si', 'goog.i18n.NumberFormatSymbols_sk', 'goog.i18n.NumberFormatSymbols_sl', 'goog.i18n.NumberFormatSymbols_sq', 'goog.i18n.NumberFormatSymbols_sr', 'goog.i18n.NumberFormatSymbols_sr_Latn', 'goog.i18n.NumberFormatSymbols_sv', 'goog.i18n.NumberFormatSymbols_sw', 'goog.i18n.NumberFormatSymbols_ta', 'goog.i18n.NumberFormatSymbols_te', 'goog.i18n.NumberFormatSymbols_th', 'goog.i18n.NumberFormatSymbols_tl', 'goog.i18n.NumberFormatSymbols_tr', 'goog.i18n.NumberFormatSymbols_u_nu_latn', 'goog.i18n.NumberFormatSymbols_uk', 'goog.i18n.NumberFormatSymbols_ur', 'goog.i18n.NumberFormatSymbols_uz', 'goog.i18n.NumberFormatSymbols_vi', 'goog.i18n.NumberFormatSymbols_zh', 'goog.i18n.NumberFormatSymbols_zh_CN', 'goog.i18n.NumberFormatSymbols_zh_HK', 'goog.i18n.NumberFormatSymbols_zh_TW', 'goog.i18n.NumberFormatSymbols_zu'], [], {});
goog.addDependency('i18n/numberformatsymbolsext.js', ['goog.i18n.NumberFormatSymbolsExt', 'goog.i18n.NumberFormatSymbols_af_NA', 'goog.i18n.NumberFormatSymbols_af_ZA', 'goog.i18n.NumberFormatSymbols_agq', 'goog.i18n.NumberFormatSymbols_agq_CM', 'goog.i18n.NumberFormatSymbols_ak', 'goog.i18n.NumberFormatSymbols_ak_GH', 'goog.i18n.NumberFormatSymbols_am_ET', 'goog.i18n.NumberFormatSymbols_ar_001', 'goog.i18n.NumberFormatSymbols_ar_AE', 'goog.i18n.NumberFormatSymbols_ar_AE_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ar_BH', 'goog.i18n.NumberFormatSymbols_ar_BH_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ar_DJ', 'goog.i18n.NumberFormatSymbols_ar_DJ_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ar_EH', 'goog.i18n.NumberFormatSymbols_ar_ER', 'goog.i18n.NumberFormatSymbols_ar_ER_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ar_IL', 'goog.i18n.NumberFormatSymbols_ar_IL_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ar_IQ', 'goog.i18n.NumberFormatSymbols_ar_IQ_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ar_JO', 'goog.i18n.NumberFormatSymbols_ar_JO_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ar_KM', 'goog.i18n.NumberFormatSymbols_ar_KM_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ar_KW', 'goog.i18n.NumberFormatSymbols_ar_KW_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ar_LB', 'goog.i18n.NumberFormatSymbols_ar_LB_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ar_LY', 'goog.i18n.NumberFormatSymbols_ar_MA', 'goog.i18n.NumberFormatSymbols_ar_MR', 'goog.i18n.NumberFormatSymbols_ar_MR_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ar_OM', 'goog.i18n.NumberFormatSymbols_ar_OM_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ar_PS', 'goog.i18n.NumberFormatSymbols_ar_PS_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ar_QA', 'goog.i18n.NumberFormatSymbols_ar_QA_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ar_SA', 'goog.i18n.NumberFormatSymbols_ar_SA_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ar_SD', 'goog.i18n.NumberFormatSymbols_ar_SD_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ar_SO', 'goog.i18n.NumberFormatSymbols_ar_SO_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ar_SS', 'goog.i18n.NumberFormatSymbols_ar_SS_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ar_SY', 'goog.i18n.NumberFormatSymbols_ar_SY_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ar_TD', 'goog.i18n.NumberFormatSymbols_ar_TD_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ar_TN', 'goog.i18n.NumberFormatSymbols_ar_XB', 'goog.i18n.NumberFormatSymbols_ar_YE', 'goog.i18n.NumberFormatSymbols_ar_YE_u_nu_latn', 'goog.i18n.NumberFormatSymbols_as', 'goog.i18n.NumberFormatSymbols_as_IN', 'goog.i18n.NumberFormatSymbols_as_IN_u_nu_latn', 'goog.i18n.NumberFormatSymbols_as_u_nu_latn', 'goog.i18n.NumberFormatSymbols_asa', 'goog.i18n.NumberFormatSymbols_asa_TZ', 'goog.i18n.NumberFormatSymbols_ast', 'goog.i18n.NumberFormatSymbols_ast_ES', 'goog.i18n.NumberFormatSymbols_az_Cyrl', 'goog.i18n.NumberFormatSymbols_az_Cyrl_AZ', 'goog.i18n.NumberFormatSymbols_az_Latn', 'goog.i18n.NumberFormatSymbols_az_Latn_AZ', 'goog.i18n.NumberFormatSymbols_bas', 'goog.i18n.NumberFormatSymbols_bas_CM', 'goog.i18n.NumberFormatSymbols_be_BY', 'goog.i18n.NumberFormatSymbols_bem', 'goog.i18n.NumberFormatSymbols_bem_ZM', 'goog.i18n.NumberFormatSymbols_bez', 'goog.i18n.NumberFormatSymbols_bez_TZ', 'goog.i18n.NumberFormatSymbols_bg_BG', 'goog.i18n.NumberFormatSymbols_bm', 'goog.i18n.NumberFormatSymbols_bm_ML', 'goog.i18n.NumberFormatSymbols_bn_BD', 'goog.i18n.NumberFormatSymbols_bn_BD_u_nu_latn', 'goog.i18n.NumberFormatSymbols_bn_IN', 'goog.i18n.NumberFormatSymbols_bn_IN_u_nu_latn', 'goog.i18n.NumberFormatSymbols_bo', 'goog.i18n.NumberFormatSymbols_bo_CN', 'goog.i18n.NumberFormatSymbols_bo_IN', 'goog.i18n.NumberFormatSymbols_br_FR', 'goog.i18n.NumberFormatSymbols_brx', 'goog.i18n.NumberFormatSymbols_brx_IN', 'goog.i18n.NumberFormatSymbols_bs_Cyrl', 'goog.i18n.NumberFormatSymbols_bs_Cyrl_BA', 'goog.i18n.NumberFormatSymbols_bs_Latn', 'goog.i18n.NumberFormatSymbols_bs_Latn_BA', 'goog.i18n.NumberFormatSymbols_ca_AD', 'goog.i18n.NumberFormatSymbols_ca_ES', 'goog.i18n.NumberFormatSymbols_ca_FR', 'goog.i18n.NumberFormatSymbols_ca_IT', 'goog.i18n.NumberFormatSymbols_ccp', 'goog.i18n.NumberFormatSymbols_ccp_BD', 'goog.i18n.NumberFormatSymbols_ccp_BD_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ccp_IN', 'goog.i18n.NumberFormatSymbols_ccp_IN_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ccp_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ce', 'goog.i18n.NumberFormatSymbols_ce_RU', 'goog.i18n.NumberFormatSymbols_ceb', 'goog.i18n.NumberFormatSymbols_ceb_PH', 'goog.i18n.NumberFormatSymbols_cgg', 'goog.i18n.NumberFormatSymbols_cgg_UG', 'goog.i18n.NumberFormatSymbols_chr_US', 'goog.i18n.NumberFormatSymbols_ckb', 'goog.i18n.NumberFormatSymbols_ckb_IQ', 'goog.i18n.NumberFormatSymbols_ckb_IQ_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ckb_IR', 'goog.i18n.NumberFormatSymbols_ckb_IR_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ckb_u_nu_latn', 'goog.i18n.NumberFormatSymbols_cs_CZ', 'goog.i18n.NumberFormatSymbols_cy_GB', 'goog.i18n.NumberFormatSymbols_da_DK', 'goog.i18n.NumberFormatSymbols_da_GL', 'goog.i18n.NumberFormatSymbols_dav', 'goog.i18n.NumberFormatSymbols_dav_KE', 'goog.i18n.NumberFormatSymbols_de_BE', 'goog.i18n.NumberFormatSymbols_de_DE', 'goog.i18n.NumberFormatSymbols_de_IT', 'goog.i18n.NumberFormatSymbols_de_LI', 'goog.i18n.NumberFormatSymbols_de_LU', 'goog.i18n.NumberFormatSymbols_dje', 'goog.i18n.NumberFormatSymbols_dje_NE', 'goog.i18n.NumberFormatSymbols_dsb', 'goog.i18n.NumberFormatSymbols_dsb_DE', 'goog.i18n.NumberFormatSymbols_dua', 'goog.i18n.NumberFormatSymbols_dua_CM', 'goog.i18n.NumberFormatSymbols_dyo', 'goog.i18n.NumberFormatSymbols_dyo_SN', 'goog.i18n.NumberFormatSymbols_dz', 'goog.i18n.NumberFormatSymbols_dz_BT', 'goog.i18n.NumberFormatSymbols_dz_BT_u_nu_latn', 'goog.i18n.NumberFormatSymbols_dz_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ebu', 'goog.i18n.NumberFormatSymbols_ebu_KE', 'goog.i18n.NumberFormatSymbols_ee', 'goog.i18n.NumberFormatSymbols_ee_GH', 'goog.i18n.NumberFormatSymbols_ee_TG', 'goog.i18n.NumberFormatSymbols_el_CY', 'goog.i18n.NumberFormatSymbols_el_GR', 'goog.i18n.NumberFormatSymbols_en_001', 'goog.i18n.NumberFormatSymbols_en_150', 'goog.i18n.NumberFormatSymbols_en_AE', 'goog.i18n.NumberFormatSymbols_en_AG', 'goog.i18n.NumberFormatSymbols_en_AI', 'goog.i18n.NumberFormatSymbols_en_AS', 'goog.i18n.NumberFormatSymbols_en_AT', 'goog.i18n.NumberFormatSymbols_en_BB', 'goog.i18n.NumberFormatSymbols_en_BE', 'goog.i18n.NumberFormatSymbols_en_BI', 'goog.i18n.NumberFormatSymbols_en_BM', 'goog.i18n.NumberFormatSymbols_en_BS', 'goog.i18n.NumberFormatSymbols_en_BW', 'goog.i18n.NumberFormatSymbols_en_BZ', 'goog.i18n.NumberFormatSymbols_en_CC', 'goog.i18n.NumberFormatSymbols_en_CH', 'goog.i18n.NumberFormatSymbols_en_CK', 'goog.i18n.NumberFormatSymbols_en_CM', 'goog.i18n.NumberFormatSymbols_en_CX', 'goog.i18n.NumberFormatSymbols_en_CY', 'goog.i18n.NumberFormatSymbols_en_DE', 'goog.i18n.NumberFormatSymbols_en_DG', 'goog.i18n.NumberFormatSymbols_en_DK', 'goog.i18n.NumberFormatSymbols_en_DM', 'goog.i18n.NumberFormatSymbols_en_ER', 'goog.i18n.NumberFormatSymbols_en_FI', 'goog.i18n.NumberFormatSymbols_en_FJ', 'goog.i18n.NumberFormatSymbols_en_FK', 'goog.i18n.NumberFormatSymbols_en_FM', 'goog.i18n.NumberFormatSymbols_en_GD', 'goog.i18n.NumberFormatSymbols_en_GG', 'goog.i18n.NumberFormatSymbols_en_GH', 'goog.i18n.NumberFormatSymbols_en_GI', 'goog.i18n.NumberFormatSymbols_en_GM', 'goog.i18n.NumberFormatSymbols_en_GU', 'goog.i18n.NumberFormatSymbols_en_GY', 'goog.i18n.NumberFormatSymbols_en_HK', 'goog.i18n.NumberFormatSymbols_en_IL', 'goog.i18n.NumberFormatSymbols_en_IM', 'goog.i18n.NumberFormatSymbols_en_IO', 'goog.i18n.NumberFormatSymbols_en_JE', 'goog.i18n.NumberFormatSymbols_en_JM', 'goog.i18n.NumberFormatSymbols_en_KE', 'goog.i18n.NumberFormatSymbols_en_KI', 'goog.i18n.NumberFormatSymbols_en_KN', 'goog.i18n.NumberFormatSymbols_en_KY', 'goog.i18n.NumberFormatSymbols_en_LC', 'goog.i18n.NumberFormatSymbols_en_LR', 'goog.i18n.NumberFormatSymbols_en_LS', 'goog.i18n.NumberFormatSymbols_en_MG', 'goog.i18n.NumberFormatSymbols_en_MH', 'goog.i18n.NumberFormatSymbols_en_MO', 'goog.i18n.NumberFormatSymbols_en_MP', 'goog.i18n.NumberFormatSymbols_en_MS', 'goog.i18n.NumberFormatSymbols_en_MT', 'goog.i18n.NumberFormatSymbols_en_MU', 'goog.i18n.NumberFormatSymbols_en_MW', 'goog.i18n.NumberFormatSymbols_en_MY', 'goog.i18n.NumberFormatSymbols_en_NA', 'goog.i18n.NumberFormatSymbols_en_NF', 'goog.i18n.NumberFormatSymbols_en_NG', 'goog.i18n.NumberFormatSymbols_en_NL', 'goog.i18n.NumberFormatSymbols_en_NR', 'goog.i18n.NumberFormatSymbols_en_NU', 'goog.i18n.NumberFormatSymbols_en_NZ', 'goog.i18n.NumberFormatSymbols_en_PG', 'goog.i18n.NumberFormatSymbols_en_PH', 'goog.i18n.NumberFormatSymbols_en_PK', 'goog.i18n.NumberFormatSymbols_en_PN', 'goog.i18n.NumberFormatSymbols_en_PR', 'goog.i18n.NumberFormatSymbols_en_PW', 'goog.i18n.NumberFormatSymbols_en_RW', 'goog.i18n.NumberFormatSymbols_en_SB', 'goog.i18n.NumberFormatSymbols_en_SC', 'goog.i18n.NumberFormatSymbols_en_SD', 'goog.i18n.NumberFormatSymbols_en_SE', 'goog.i18n.NumberFormatSymbols_en_SH', 'goog.i18n.NumberFormatSymbols_en_SI', 'goog.i18n.NumberFormatSymbols_en_SL', 'goog.i18n.NumberFormatSymbols_en_SS', 'goog.i18n.NumberFormatSymbols_en_SX', 'goog.i18n.NumberFormatSymbols_en_SZ', 'goog.i18n.NumberFormatSymbols_en_TC', 'goog.i18n.NumberFormatSymbols_en_TK', 'goog.i18n.NumberFormatSymbols_en_TO', 'goog.i18n.NumberFormatSymbols_en_TT', 'goog.i18n.NumberFormatSymbols_en_TV', 'goog.i18n.NumberFormatSymbols_en_TZ', 'goog.i18n.NumberFormatSymbols_en_UG', 'goog.i18n.NumberFormatSymbols_en_UM', 'goog.i18n.NumberFormatSymbols_en_US_POSIX', 'goog.i18n.NumberFormatSymbols_en_VC', 'goog.i18n.NumberFormatSymbols_en_VG', 'goog.i18n.NumberFormatSymbols_en_VI', 'goog.i18n.NumberFormatSymbols_en_VU', 'goog.i18n.NumberFormatSymbols_en_WS', 'goog.i18n.NumberFormatSymbols_en_XA', 'goog.i18n.NumberFormatSymbols_en_ZM', 'goog.i18n.NumberFormatSymbols_en_ZW', 'goog.i18n.NumberFormatSymbols_eo', 'goog.i18n.NumberFormatSymbols_eo_001', 'goog.i18n.NumberFormatSymbols_es_AR', 'goog.i18n.NumberFormatSymbols_es_BO', 'goog.i18n.NumberFormatSymbols_es_BR', 'goog.i18n.NumberFormatSymbols_es_BZ', 'goog.i18n.NumberFormatSymbols_es_CL', 'goog.i18n.NumberFormatSymbols_es_CO', 'goog.i18n.NumberFormatSymbols_es_CR', 'goog.i18n.NumberFormatSymbols_es_CU', 'goog.i18n.NumberFormatSymbols_es_DO', 'goog.i18n.NumberFormatSymbols_es_EA', 'goog.i18n.NumberFormatSymbols_es_EC', 'goog.i18n.NumberFormatSymbols_es_GQ', 'goog.i18n.NumberFormatSymbols_es_GT', 'goog.i18n.NumberFormatSymbols_es_HN', 'goog.i18n.NumberFormatSymbols_es_IC', 'goog.i18n.NumberFormatSymbols_es_NI', 'goog.i18n.NumberFormatSymbols_es_PA', 'goog.i18n.NumberFormatSymbols_es_PE', 'goog.i18n.NumberFormatSymbols_es_PH', 'goog.i18n.NumberFormatSymbols_es_PR', 'goog.i18n.NumberFormatSymbols_es_PY', 'goog.i18n.NumberFormatSymbols_es_SV', 'goog.i18n.NumberFormatSymbols_es_UY', 'goog.i18n.NumberFormatSymbols_es_VE', 'goog.i18n.NumberFormatSymbols_et_EE', 'goog.i18n.NumberFormatSymbols_eu_ES', 'goog.i18n.NumberFormatSymbols_ewo', 'goog.i18n.NumberFormatSymbols_ewo_CM', 'goog.i18n.NumberFormatSymbols_fa_AF', 'goog.i18n.NumberFormatSymbols_fa_AF_u_nu_latn', 'goog.i18n.NumberFormatSymbols_fa_IR', 'goog.i18n.NumberFormatSymbols_fa_IR_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ff', 'goog.i18n.NumberFormatSymbols_ff_Latn', 'goog.i18n.NumberFormatSymbols_ff_Latn_BF', 'goog.i18n.NumberFormatSymbols_ff_Latn_CM', 'goog.i18n.NumberFormatSymbols_ff_Latn_GH', 'goog.i18n.NumberFormatSymbols_ff_Latn_GM', 'goog.i18n.NumberFormatSymbols_ff_Latn_GN', 'goog.i18n.NumberFormatSymbols_ff_Latn_GW', 'goog.i18n.NumberFormatSymbols_ff_Latn_LR', 'goog.i18n.NumberFormatSymbols_ff_Latn_MR', 'goog.i18n.NumberFormatSymbols_ff_Latn_NE', 'goog.i18n.NumberFormatSymbols_ff_Latn_NG', 'goog.i18n.NumberFormatSymbols_ff_Latn_SL', 'goog.i18n.NumberFormatSymbols_ff_Latn_SN', 'goog.i18n.NumberFormatSymbols_fi_FI', 'goog.i18n.NumberFormatSymbols_fil_PH', 'goog.i18n.NumberFormatSymbols_fo', 'goog.i18n.NumberFormatSymbols_fo_DK', 'goog.i18n.NumberFormatSymbols_fo_FO', 'goog.i18n.NumberFormatSymbols_fr_BE', 'goog.i18n.NumberFormatSymbols_fr_BF', 'goog.i18n.NumberFormatSymbols_fr_BI', 'goog.i18n.NumberFormatSymbols_fr_BJ', 'goog.i18n.NumberFormatSymbols_fr_BL', 'goog.i18n.NumberFormatSymbols_fr_CD', 'goog.i18n.NumberFormatSymbols_fr_CF', 'goog.i18n.NumberFormatSymbols_fr_CG', 'goog.i18n.NumberFormatSymbols_fr_CH', 'goog.i18n.NumberFormatSymbols_fr_CI', 'goog.i18n.NumberFormatSymbols_fr_CM', 'goog.i18n.NumberFormatSymbols_fr_DJ', 'goog.i18n.NumberFormatSymbols_fr_DZ', 'goog.i18n.NumberFormatSymbols_fr_FR', 'goog.i18n.NumberFormatSymbols_fr_GA', 'goog.i18n.NumberFormatSymbols_fr_GF', 'goog.i18n.NumberFormatSymbols_fr_GN', 'goog.i18n.NumberFormatSymbols_fr_GP', 'goog.i18n.NumberFormatSymbols_fr_GQ', 'goog.i18n.NumberFormatSymbols_fr_HT', 'goog.i18n.NumberFormatSymbols_fr_KM', 'goog.i18n.NumberFormatSymbols_fr_LU', 'goog.i18n.NumberFormatSymbols_fr_MA', 'goog.i18n.NumberFormatSymbols_fr_MC', 'goog.i18n.NumberFormatSymbols_fr_MF', 'goog.i18n.NumberFormatSymbols_fr_MG', 'goog.i18n.NumberFormatSymbols_fr_ML', 'goog.i18n.NumberFormatSymbols_fr_MQ', 'goog.i18n.NumberFormatSymbols_fr_MR', 'goog.i18n.NumberFormatSymbols_fr_MU', 'goog.i18n.NumberFormatSymbols_fr_NC', 'goog.i18n.NumberFormatSymbols_fr_NE', 'goog.i18n.NumberFormatSymbols_fr_PF', 'goog.i18n.NumberFormatSymbols_fr_PM', 'goog.i18n.NumberFormatSymbols_fr_RE', 'goog.i18n.NumberFormatSymbols_fr_RW', 'goog.i18n.NumberFormatSymbols_fr_SC', 'goog.i18n.NumberFormatSymbols_fr_SN', 'goog.i18n.NumberFormatSymbols_fr_SY', 'goog.i18n.NumberFormatSymbols_fr_TD', 'goog.i18n.NumberFormatSymbols_fr_TG', 'goog.i18n.NumberFormatSymbols_fr_TN', 'goog.i18n.NumberFormatSymbols_fr_VU', 'goog.i18n.NumberFormatSymbols_fr_WF', 'goog.i18n.NumberFormatSymbols_fr_YT', 'goog.i18n.NumberFormatSymbols_fur', 'goog.i18n.NumberFormatSymbols_fur_IT', 'goog.i18n.NumberFormatSymbols_fy', 'goog.i18n.NumberFormatSymbols_fy_NL', 'goog.i18n.NumberFormatSymbols_ga_IE', 'goog.i18n.NumberFormatSymbols_gd', 'goog.i18n.NumberFormatSymbols_gd_GB', 'goog.i18n.NumberFormatSymbols_gl_ES', 'goog.i18n.NumberFormatSymbols_gsw_CH', 'goog.i18n.NumberFormatSymbols_gsw_FR', 'goog.i18n.NumberFormatSymbols_gsw_LI', 'goog.i18n.NumberFormatSymbols_gu_IN', 'goog.i18n.NumberFormatSymbols_guz', 'goog.i18n.NumberFormatSymbols_guz_KE', 'goog.i18n.NumberFormatSymbols_gv', 'goog.i18n.NumberFormatSymbols_gv_IM', 'goog.i18n.NumberFormatSymbols_ha', 'goog.i18n.NumberFormatSymbols_ha_GH', 'goog.i18n.NumberFormatSymbols_ha_NE', 'goog.i18n.NumberFormatSymbols_ha_NG', 'goog.i18n.NumberFormatSymbols_haw_US', 'goog.i18n.NumberFormatSymbols_he_IL', 'goog.i18n.NumberFormatSymbols_hi_IN', 'goog.i18n.NumberFormatSymbols_hr_BA', 'goog.i18n.NumberFormatSymbols_hr_HR', 'goog.i18n.NumberFormatSymbols_hsb', 'goog.i18n.NumberFormatSymbols_hsb_DE', 'goog.i18n.NumberFormatSymbols_hu_HU', 'goog.i18n.NumberFormatSymbols_hy_AM', 'goog.i18n.NumberFormatSymbols_ia', 'goog.i18n.NumberFormatSymbols_ia_001', 'goog.i18n.NumberFormatSymbols_id_ID', 'goog.i18n.NumberFormatSymbols_ig', 'goog.i18n.NumberFormatSymbols_ig_NG', 'goog.i18n.NumberFormatSymbols_ii', 'goog.i18n.NumberFormatSymbols_ii_CN', 'goog.i18n.NumberFormatSymbols_is_IS', 'goog.i18n.NumberFormatSymbols_it_CH', 'goog.i18n.NumberFormatSymbols_it_IT', 'goog.i18n.NumberFormatSymbols_it_SM', 'goog.i18n.NumberFormatSymbols_it_VA', 'goog.i18n.NumberFormatSymbols_ja_JP', 'goog.i18n.NumberFormatSymbols_jgo', 'goog.i18n.NumberFormatSymbols_jgo_CM', 'goog.i18n.NumberFormatSymbols_jmc', 'goog.i18n.NumberFormatSymbols_jmc_TZ', 'goog.i18n.NumberFormatSymbols_jv', 'goog.i18n.NumberFormatSymbols_jv_ID', 'goog.i18n.NumberFormatSymbols_ka_GE', 'goog.i18n.NumberFormatSymbols_kab', 'goog.i18n.NumberFormatSymbols_kab_DZ', 'goog.i18n.NumberFormatSymbols_kam', 'goog.i18n.NumberFormatSymbols_kam_KE', 'goog.i18n.NumberFormatSymbols_kde', 'goog.i18n.NumberFormatSymbols_kde_TZ', 'goog.i18n.NumberFormatSymbols_kea', 'goog.i18n.NumberFormatSymbols_kea_CV', 'goog.i18n.NumberFormatSymbols_khq', 'goog.i18n.NumberFormatSymbols_khq_ML', 'goog.i18n.NumberFormatSymbols_ki', 'goog.i18n.NumberFormatSymbols_ki_KE', 'goog.i18n.NumberFormatSymbols_kk_KZ', 'goog.i18n.NumberFormatSymbols_kkj', 'goog.i18n.NumberFormatSymbols_kkj_CM', 'goog.i18n.NumberFormatSymbols_kl', 'goog.i18n.NumberFormatSymbols_kl_GL', 'goog.i18n.NumberFormatSymbols_kln', 'goog.i18n.NumberFormatSymbols_kln_KE', 'goog.i18n.NumberFormatSymbols_km_KH', 'goog.i18n.NumberFormatSymbols_kn_IN', 'goog.i18n.NumberFormatSymbols_ko_KP', 'goog.i18n.NumberFormatSymbols_ko_KR', 'goog.i18n.NumberFormatSymbols_kok', 'goog.i18n.NumberFormatSymbols_kok_IN', 'goog.i18n.NumberFormatSymbols_ks', 'goog.i18n.NumberFormatSymbols_ks_IN', 'goog.i18n.NumberFormatSymbols_ks_IN_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ks_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ksb', 'goog.i18n.NumberFormatSymbols_ksb_TZ', 'goog.i18n.NumberFormatSymbols_ksf', 'goog.i18n.NumberFormatSymbols_ksf_CM', 'goog.i18n.NumberFormatSymbols_ksh', 'goog.i18n.NumberFormatSymbols_ksh_DE', 'goog.i18n.NumberFormatSymbols_ku', 'goog.i18n.NumberFormatSymbols_ku_TR', 'goog.i18n.NumberFormatSymbols_kw', 'goog.i18n.NumberFormatSymbols_kw_GB', 'goog.i18n.NumberFormatSymbols_ky_KG', 'goog.i18n.NumberFormatSymbols_lag', 'goog.i18n.NumberFormatSymbols_lag_TZ', 'goog.i18n.NumberFormatSymbols_lb', 'goog.i18n.NumberFormatSymbols_lb_LU', 'goog.i18n.NumberFormatSymbols_lg', 'goog.i18n.NumberFormatSymbols_lg_UG', 'goog.i18n.NumberFormatSymbols_lkt', 'goog.i18n.NumberFormatSymbols_lkt_US', 'goog.i18n.NumberFormatSymbols_ln_AO', 'goog.i18n.NumberFormatSymbols_ln_CD', 'goog.i18n.NumberFormatSymbols_ln_CF', 'goog.i18n.NumberFormatSymbols_ln_CG', 'goog.i18n.NumberFormatSymbols_lo_LA', 'goog.i18n.NumberFormatSymbols_lrc', 'goog.i18n.NumberFormatSymbols_lrc_IQ', 'goog.i18n.NumberFormatSymbols_lrc_IQ_u_nu_latn', 'goog.i18n.NumberFormatSymbols_lrc_IR', 'goog.i18n.NumberFormatSymbols_lrc_IR_u_nu_latn', 'goog.i18n.NumberFormatSymbols_lrc_u_nu_latn', 'goog.i18n.NumberFormatSymbols_lt_LT', 'goog.i18n.NumberFormatSymbols_lu', 'goog.i18n.NumberFormatSymbols_lu_CD', 'goog.i18n.NumberFormatSymbols_luo', 'goog.i18n.NumberFormatSymbols_luo_KE', 'goog.i18n.NumberFormatSymbols_luy', 'goog.i18n.NumberFormatSymbols_luy_KE', 'goog.i18n.NumberFormatSymbols_lv_LV', 'goog.i18n.NumberFormatSymbols_mas', 'goog.i18n.NumberFormatSymbols_mas_KE', 'goog.i18n.NumberFormatSymbols_mas_TZ', 'goog.i18n.NumberFormatSymbols_mer', 'goog.i18n.NumberFormatSymbols_mer_KE', 'goog.i18n.NumberFormatSymbols_mfe', 'goog.i18n.NumberFormatSymbols_mfe_MU', 'goog.i18n.NumberFormatSymbols_mg', 'goog.i18n.NumberFormatSymbols_mg_MG', 'goog.i18n.NumberFormatSymbols_mgh', 'goog.i18n.NumberFormatSymbols_mgh_MZ', 'goog.i18n.NumberFormatSymbols_mgo', 'goog.i18n.NumberFormatSymbols_mgo_CM', 'goog.i18n.NumberFormatSymbols_mi', 'goog.i18n.NumberFormatSymbols_mi_NZ', 'goog.i18n.NumberFormatSymbols_mk_MK', 'goog.i18n.NumberFormatSymbols_ml_IN', 'goog.i18n.NumberFormatSymbols_mn_MN', 'goog.i18n.NumberFormatSymbols_mr_IN', 'goog.i18n.NumberFormatSymbols_mr_IN_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ms_BN', 'goog.i18n.NumberFormatSymbols_ms_MY', 'goog.i18n.NumberFormatSymbols_ms_SG', 'goog.i18n.NumberFormatSymbols_mt_MT', 'goog.i18n.NumberFormatSymbols_mua', 'goog.i18n.NumberFormatSymbols_mua_CM', 'goog.i18n.NumberFormatSymbols_my_MM', 'goog.i18n.NumberFormatSymbols_my_MM_u_nu_latn', 'goog.i18n.NumberFormatSymbols_mzn', 'goog.i18n.NumberFormatSymbols_mzn_IR', 'goog.i18n.NumberFormatSymbols_mzn_IR_u_nu_latn', 'goog.i18n.NumberFormatSymbols_mzn_u_nu_latn', 'goog.i18n.NumberFormatSymbols_naq', 'goog.i18n.NumberFormatSymbols_naq_NA', 'goog.i18n.NumberFormatSymbols_nb_NO', 'goog.i18n.NumberFormatSymbols_nb_SJ', 'goog.i18n.NumberFormatSymbols_nd', 'goog.i18n.NumberFormatSymbols_nd_ZW', 'goog.i18n.NumberFormatSymbols_nds', 'goog.i18n.NumberFormatSymbols_nds_DE', 'goog.i18n.NumberFormatSymbols_nds_NL', 'goog.i18n.NumberFormatSymbols_ne_IN', 'goog.i18n.NumberFormatSymbols_ne_IN_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ne_NP', 'goog.i18n.NumberFormatSymbols_ne_NP_u_nu_latn', 'goog.i18n.NumberFormatSymbols_nl_AW', 'goog.i18n.NumberFormatSymbols_nl_BE', 'goog.i18n.NumberFormatSymbols_nl_BQ', 'goog.i18n.NumberFormatSymbols_nl_CW', 'goog.i18n.NumberFormatSymbols_nl_NL', 'goog.i18n.NumberFormatSymbols_nl_SR', 'goog.i18n.NumberFormatSymbols_nl_SX', 'goog.i18n.NumberFormatSymbols_nmg', 'goog.i18n.NumberFormatSymbols_nmg_CM', 'goog.i18n.NumberFormatSymbols_nn', 'goog.i18n.NumberFormatSymbols_nn_NO', 'goog.i18n.NumberFormatSymbols_nnh', 'goog.i18n.NumberFormatSymbols_nnh_CM', 'goog.i18n.NumberFormatSymbols_nus', 'goog.i18n.NumberFormatSymbols_nus_SS', 'goog.i18n.NumberFormatSymbols_nyn', 'goog.i18n.NumberFormatSymbols_nyn_UG', 'goog.i18n.NumberFormatSymbols_om', 'goog.i18n.NumberFormatSymbols_om_ET', 'goog.i18n.NumberFormatSymbols_om_KE', 'goog.i18n.NumberFormatSymbols_or_IN', 'goog.i18n.NumberFormatSymbols_os', 'goog.i18n.NumberFormatSymbols_os_GE', 'goog.i18n.NumberFormatSymbols_os_RU', 'goog.i18n.NumberFormatSymbols_pa_Arab', 'goog.i18n.NumberFormatSymbols_pa_Arab_PK', 'goog.i18n.NumberFormatSymbols_pa_Arab_PK_u_nu_latn', 'goog.i18n.NumberFormatSymbols_pa_Arab_u_nu_latn', 'goog.i18n.NumberFormatSymbols_pa_Guru', 'goog.i18n.NumberFormatSymbols_pa_Guru_IN', 'goog.i18n.NumberFormatSymbols_pl_PL', 'goog.i18n.NumberFormatSymbols_ps', 'goog.i18n.NumberFormatSymbols_ps_AF', 'goog.i18n.NumberFormatSymbols_ps_AF_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ps_PK', 'goog.i18n.NumberFormatSymbols_ps_PK_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ps_u_nu_latn', 'goog.i18n.NumberFormatSymbols_pt_AO', 'goog.i18n.NumberFormatSymbols_pt_CH', 'goog.i18n.NumberFormatSymbols_pt_CV', 'goog.i18n.NumberFormatSymbols_pt_GQ', 'goog.i18n.NumberFormatSymbols_pt_GW', 'goog.i18n.NumberFormatSymbols_pt_LU', 'goog.i18n.NumberFormatSymbols_pt_MO', 'goog.i18n.NumberFormatSymbols_pt_MZ', 'goog.i18n.NumberFormatSymbols_pt_ST', 'goog.i18n.NumberFormatSymbols_pt_TL', 'goog.i18n.NumberFormatSymbols_qu', 'goog.i18n.NumberFormatSymbols_qu_BO', 'goog.i18n.NumberFormatSymbols_qu_EC', 'goog.i18n.NumberFormatSymbols_qu_PE', 'goog.i18n.NumberFormatSymbols_rm', 'goog.i18n.NumberFormatSymbols_rm_CH', 'goog.i18n.NumberFormatSymbols_rn', 'goog.i18n.NumberFormatSymbols_rn_BI', 'goog.i18n.NumberFormatSymbols_ro_MD', 'goog.i18n.NumberFormatSymbols_ro_RO', 'goog.i18n.NumberFormatSymbols_rof', 'goog.i18n.NumberFormatSymbols_rof_TZ', 'goog.i18n.NumberFormatSymbols_ru_BY', 'goog.i18n.NumberFormatSymbols_ru_KG', 'goog.i18n.NumberFormatSymbols_ru_KZ', 'goog.i18n.NumberFormatSymbols_ru_MD', 'goog.i18n.NumberFormatSymbols_ru_RU', 'goog.i18n.NumberFormatSymbols_ru_UA', 'goog.i18n.NumberFormatSymbols_rw', 'goog.i18n.NumberFormatSymbols_rw_RW', 'goog.i18n.NumberFormatSymbols_rwk', 'goog.i18n.NumberFormatSymbols_rwk_TZ', 'goog.i18n.NumberFormatSymbols_sah', 'goog.i18n.NumberFormatSymbols_sah_RU', 'goog.i18n.NumberFormatSymbols_saq', 'goog.i18n.NumberFormatSymbols_saq_KE', 'goog.i18n.NumberFormatSymbols_sbp', 'goog.i18n.NumberFormatSymbols_sbp_TZ', 'goog.i18n.NumberFormatSymbols_sd', 'goog.i18n.NumberFormatSymbols_sd_PK', 'goog.i18n.NumberFormatSymbols_sd_PK_u_nu_latn', 'goog.i18n.NumberFormatSymbols_sd_u_nu_latn', 'goog.i18n.NumberFormatSymbols_se', 'goog.i18n.NumberFormatSymbols_se_FI', 'goog.i18n.NumberFormatSymbols_se_NO', 'goog.i18n.NumberFormatSymbols_se_SE', 'goog.i18n.NumberFormatSymbols_seh', 'goog.i18n.NumberFormatSymbols_seh_MZ', 'goog.i18n.NumberFormatSymbols_ses', 'goog.i18n.NumberFormatSymbols_ses_ML', 'goog.i18n.NumberFormatSymbols_sg', 'goog.i18n.NumberFormatSymbols_sg_CF', 'goog.i18n.NumberFormatSymbols_shi', 'goog.i18n.NumberFormatSymbols_shi_Latn', 'goog.i18n.NumberFormatSymbols_shi_Latn_MA', 'goog.i18n.NumberFormatSymbols_shi_Tfng', 'goog.i18n.NumberFormatSymbols_shi_Tfng_MA', 'goog.i18n.NumberFormatSymbols_si_LK', 'goog.i18n.NumberFormatSymbols_sk_SK', 'goog.i18n.NumberFormatSymbols_sl_SI', 'goog.i18n.NumberFormatSymbols_smn', 'goog.i18n.NumberFormatSymbols_smn_FI', 'goog.i18n.NumberFormatSymbols_sn', 'goog.i18n.NumberFormatSymbols_sn_ZW', 'goog.i18n.NumberFormatSymbols_so', 'goog.i18n.NumberFormatSymbols_so_DJ', 'goog.i18n.NumberFormatSymbols_so_ET', 'goog.i18n.NumberFormatSymbols_so_KE', 'goog.i18n.NumberFormatSymbols_so_SO', 'goog.i18n.NumberFormatSymbols_sq_AL', 'goog.i18n.NumberFormatSymbols_sq_MK', 'goog.i18n.NumberFormatSymbols_sq_XK', 'goog.i18n.NumberFormatSymbols_sr_Cyrl', 'goog.i18n.NumberFormatSymbols_sr_Cyrl_BA', 'goog.i18n.NumberFormatSymbols_sr_Cyrl_ME', 'goog.i18n.NumberFormatSymbols_sr_Cyrl_RS', 'goog.i18n.NumberFormatSymbols_sr_Cyrl_XK', 'goog.i18n.NumberFormatSymbols_sr_Latn_BA', 'goog.i18n.NumberFormatSymbols_sr_Latn_ME', 'goog.i18n.NumberFormatSymbols_sr_Latn_RS', 'goog.i18n.NumberFormatSymbols_sr_Latn_XK', 'goog.i18n.NumberFormatSymbols_sv_AX', 'goog.i18n.NumberFormatSymbols_sv_FI', 'goog.i18n.NumberFormatSymbols_sv_SE', 'goog.i18n.NumberFormatSymbols_sw_CD', 'goog.i18n.NumberFormatSymbols_sw_KE', 'goog.i18n.NumberFormatSymbols_sw_TZ', 'goog.i18n.NumberFormatSymbols_sw_UG', 'goog.i18n.NumberFormatSymbols_ta_IN', 'goog.i18n.NumberFormatSymbols_ta_LK', 'goog.i18n.NumberFormatSymbols_ta_MY', 'goog.i18n.NumberFormatSymbols_ta_SG', 'goog.i18n.NumberFormatSymbols_te_IN', 'goog.i18n.NumberFormatSymbols_teo', 'goog.i18n.NumberFormatSymbols_teo_KE', 'goog.i18n.NumberFormatSymbols_teo_UG', 'goog.i18n.NumberFormatSymbols_tg', 'goog.i18n.NumberFormatSymbols_tg_TJ', 'goog.i18n.NumberFormatSymbols_th_TH', 'goog.i18n.NumberFormatSymbols_ti', 'goog.i18n.NumberFormatSymbols_ti_ER', 'goog.i18n.NumberFormatSymbols_ti_ET', 'goog.i18n.NumberFormatSymbols_tk', 'goog.i18n.NumberFormatSymbols_tk_TM', 'goog.i18n.NumberFormatSymbols_to', 'goog.i18n.NumberFormatSymbols_to_TO', 'goog.i18n.NumberFormatSymbols_tr_CY', 'goog.i18n.NumberFormatSymbols_tr_TR', 'goog.i18n.NumberFormatSymbols_tt', 'goog.i18n.NumberFormatSymbols_tt_RU', 'goog.i18n.NumberFormatSymbols_twq', 'goog.i18n.NumberFormatSymbols_twq_NE', 'goog.i18n.NumberFormatSymbols_tzm', 'goog.i18n.NumberFormatSymbols_tzm_MA', 'goog.i18n.NumberFormatSymbols_ug', 'goog.i18n.NumberFormatSymbols_ug_CN', 'goog.i18n.NumberFormatSymbols_uk_UA', 'goog.i18n.NumberFormatSymbols_ur_IN', 'goog.i18n.NumberFormatSymbols_ur_IN_u_nu_latn', 'goog.i18n.NumberFormatSymbols_ur_PK', 'goog.i18n.NumberFormatSymbols_uz_Arab', 'goog.i18n.NumberFormatSymbols_uz_Arab_AF', 'goog.i18n.NumberFormatSymbols_uz_Arab_AF_u_nu_latn', 'goog.i18n.NumberFormatSymbols_uz_Arab_u_nu_latn', 'goog.i18n.NumberFormatSymbols_uz_Cyrl', 'goog.i18n.NumberFormatSymbols_uz_Cyrl_UZ', 'goog.i18n.NumberFormatSymbols_uz_Latn', 'goog.i18n.NumberFormatSymbols_uz_Latn_UZ', 'goog.i18n.NumberFormatSymbols_vai', 'goog.i18n.NumberFormatSymbols_vai_Latn', 'goog.i18n.NumberFormatSymbols_vai_Latn_LR', 'goog.i18n.NumberFormatSymbols_vai_Vaii', 'goog.i18n.NumberFormatSymbols_vai_Vaii_LR', 'goog.i18n.NumberFormatSymbols_vi_VN', 'goog.i18n.NumberFormatSymbols_vun', 'goog.i18n.NumberFormatSymbols_vun_TZ', 'goog.i18n.NumberFormatSymbols_wae', 'goog.i18n.NumberFormatSymbols_wae_CH', 'goog.i18n.NumberFormatSymbols_wo', 'goog.i18n.NumberFormatSymbols_wo_SN', 'goog.i18n.NumberFormatSymbols_xh', 'goog.i18n.NumberFormatSymbols_xh_ZA', 'goog.i18n.NumberFormatSymbols_xog', 'goog.i18n.NumberFormatSymbols_xog_UG', 'goog.i18n.NumberFormatSymbols_yav', 'goog.i18n.NumberFormatSymbols_yav_CM', 'goog.i18n.NumberFormatSymbols_yi', 'goog.i18n.NumberFormatSymbols_yi_001', 'goog.i18n.NumberFormatSymbols_yo', 'goog.i18n.NumberFormatSymbols_yo_BJ', 'goog.i18n.NumberFormatSymbols_yo_NG', 'goog.i18n.NumberFormatSymbols_yue', 'goog.i18n.NumberFormatSymbols_yue_Hans', 'goog.i18n.NumberFormatSymbols_yue_Hans_CN', 'goog.i18n.NumberFormatSymbols_yue_Hant', 'goog.i18n.NumberFormatSymbols_yue_Hant_HK', 'goog.i18n.NumberFormatSymbols_zgh', 'goog.i18n.NumberFormatSymbols_zgh_MA', 'goog.i18n.NumberFormatSymbols_zh_Hans', 'goog.i18n.NumberFormatSymbols_zh_Hans_CN', 'goog.i18n.NumberFormatSymbols_zh_Hans_HK', 'goog.i18n.NumberFormatSymbols_zh_Hans_MO', 'goog.i18n.NumberFormatSymbols_zh_Hans_SG', 'goog.i18n.NumberFormatSymbols_zh_Hant', 'goog.i18n.NumberFormatSymbols_zh_Hant_HK', 'goog.i18n.NumberFormatSymbols_zh_Hant_MO', 'goog.i18n.NumberFormatSymbols_zh_Hant_TW', 'goog.i18n.NumberFormatSymbols_zu_ZA'], ['goog.i18n.NumberFormatSymbols', 'goog.i18n.NumberFormatSymbols_u_nu_latn'], {});
goog.addDependency('i18n/ordinalrules.js', ['goog.i18n.ordinalRules'], [], {'lang': 'es6'});
goog.addDependency('i18n/pluralrules.js', ['goog.i18n.pluralRules'], [], {'lang': 'es6'});
goog.addDependency('i18n/pluralrules_test.js', ['goog.i18n.pluralRulesTest'], ['goog.i18n.pluralRules', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('i18n/relativedatetimeformat.js', ['goog.i18n.RelativeDateTimeFormat'], ['goog.asserts', 'goog.i18n.LocaleFeature', 'goog.i18n.MessageFormat', 'goog.i18n.relativeDateTimeSymbols'], {'lang': 'es5', 'module': 'goog'});
goog.addDependency('i18n/relativedatetimeformat_test.js', ['goog.i18n.RelativeDateTimeFormatTest'], ['goog.i18n.LocaleFeature', 'goog.i18n.NumberFormatSymbols_ar_EG', 'goog.i18n.NumberFormatSymbols_en', 'goog.i18n.NumberFormatSymbols_es', 'goog.i18n.NumberFormatSymbols_fa', 'goog.i18n.RelativeDateTimeFormat', 'goog.i18n.relativeDateTimeSymbols', 'goog.i18n.relativeDateTimeSymbolsExt', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('i18n/relativedatetimesymbols.js', ['goog.i18n.relativeDateTimeSymbols'], [], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('i18n/relativedatetimesymbolsext.js', ['goog.i18n.relativeDateTimeSymbolsExt'], ['goog.i18n.relativeDateTimeSymbols'], {'lang': 'es5', 'module': 'goog'});
goog.addDependency('i18n/timezone.js', ['goog.i18n.TimeZone'], ['goog.array', 'goog.date.DateLike', 'goog.object', 'goog.string'], {});
goog.addDependency('i18n/timezone_test.js', ['goog.i18n.TimeZoneTest'], ['goog.i18n.TimeZone', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('i18n/uchar.js', ['goog.i18n.uChar'], [], {'lang': 'es6'});
goog.addDependency('i18n/uchar/localnamefetcher.js', ['goog.i18n.uChar.LocalNameFetcher'], ['goog.i18n.uChar.NameFetcher', 'goog.i18n.uCharNames', 'goog.log'], {});
goog.addDependency('i18n/uchar/localnamefetcher_test.js', ['goog.i18n.uChar.LocalNameFetcherTest'], ['goog.i18n.uChar.LocalNameFetcher', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('i18n/uchar/namefetcher.js', ['goog.i18n.uChar.NameFetcher'], [], {});
goog.addDependency('i18n/uchar/remotenamefetcher.js', ['goog.i18n.uChar.RemoteNameFetcher'], ['goog.Disposable', 'goog.Uri', 'goog.events', 'goog.i18n.uChar', 'goog.i18n.uChar.NameFetcher', 'goog.log', 'goog.net.EventType', 'goog.net.XhrIo'], {});
goog.addDependency('i18n/uchar/remotenamefetcher_test.js', ['goog.i18n.uChar.RemoteNameFetcherTest'], ['goog.i18n.uChar.RemoteNameFetcher', 'goog.net.XhrIo', 'goog.testing.net.XhrIo', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('i18n/uchar_test.js', ['goog.i18n.uCharTest'], ['goog.i18n.uChar', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('i18n/ucharnames.js', ['goog.i18n.uCharNames'], ['goog.i18n.uChar'], {});
goog.addDependency('i18n/ucharnames_test.js', ['goog.i18n.uCharNamesTest'], ['goog.i18n.uCharNames', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('iter/es6.js', ['goog.iter.es6'], ['goog.iter.Iterable', 'goog.iter.Iterator', 'goog.iter.StopIteration'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('iter/es6_test.js', ['goog.iter.es6Test'], ['goog.iter', 'goog.iter.es6', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('iter/iter.js', ['goog.iter', 'goog.iter.Iterable', 'goog.iter.Iterator', 'goog.iter.StopIteration'], ['goog.array', 'goog.asserts', 'goog.functions', 'goog.math'], {});
goog.addDependency('iter/iter_test.js', ['goog.iterTest'], ['goog.iter', 'goog.iter.Iterator', 'goog.iter.StopIteration', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('json/hybrid.js', ['goog.json.hybrid'], ['goog.asserts', 'goog.json'], {});
goog.addDependency('json/hybrid_test.js', ['goog.json.hybridTest'], ['goog.json', 'goog.json.hybrid', 'goog.testing.PropertyReplacer', 'goog.testing.recordFunction', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('json/json.js', ['goog.json', 'goog.json.Replacer', 'goog.json.Reviver', 'goog.json.Serializer'], [], {'lang': 'es6'});
goog.addDependency('json/json_perf.js', ['goog.jsonPerf'], ['goog.dom', 'goog.json', 'goog.math', 'goog.string', 'goog.testing.PerformanceTable', 'goog.testing.PropertyReplacer', 'goog.testing.jsunit'], {});
goog.addDependency('json/json_test.js', ['goog.jsonTest'], ['goog.functions', 'goog.json', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('json/jsonable.js', ['goog.json.Jsonable'], [], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('json/nativejsonprocessor.js', ['goog.json.NativeJsonProcessor'], ['goog.asserts', 'goog.json.Processor'], {});
goog.addDependency('json/processor.js', ['goog.json.Processor'], ['goog.string.Parser', 'goog.string.Stringifier'], {});
goog.addDependency('json/processor_test.js', ['goog.json.processorTest'], ['goog.json.NativeJsonProcessor', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/collections/iterables.js', ['goog.labs.collections.iterables'], [], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/collections/iterables_test.js', ['goog.labs.iterableTest'], ['goog.labs.collections.iterables', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/dom/pagevisibilitymonitor.js', ['goog.labs.dom.PageVisibilityEvent', 'goog.labs.dom.PageVisibilityMonitor', 'goog.labs.dom.PageVisibilityState'], ['goog.dom', 'goog.dom.vendor', 'goog.events', 'goog.events.Event', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.memoize'], {});
goog.addDependency('labs/dom/pagevisibilitymonitor_test.js', ['goog.labs.dom.PageVisibilityMonitorTest'], ['goog.events', 'goog.functions', 'goog.labs.dom.PageVisibilityMonitor', 'goog.testing.PropertyReplacer', 'goog.testing.events', 'goog.testing.events.Event', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/events/nondisposableeventtarget.js', ['goog.labs.events.NonDisposableEventTarget'], ['goog.array', 'goog.asserts', 'goog.events.Event', 'goog.events.Listenable', 'goog.events.ListenerMap', 'goog.object'], {});
goog.addDependency('labs/events/nondisposableeventtarget_test.js', ['goog.labs.events.NonDisposableEventTargetTest'], ['goog.events.Listenable', 'goog.events.eventTargetTester', 'goog.labs.events.NonDisposableEventTarget', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/events/nondisposableeventtarget_via_googevents_test.js', ['goog.labs.events.NonDisposableEventTargetGoogEventsTest'], ['goog.events', 'goog.events.eventTargetTester', 'goog.labs.events.NonDisposableEventTarget', 'goog.testing', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/events/touch.js', ['goog.labs.events.touch', 'goog.labs.events.touch.TouchData'], ['goog.array', 'goog.asserts', 'goog.events.EventType', 'goog.string'], {});
goog.addDependency('labs/events/touch_test.js', ['goog.labs.events.touchTest'], ['goog.labs.events.touch', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/format/csv.js', ['goog.labs.format.csv', 'goog.labs.format.csv.ParseError', 'goog.labs.format.csv.Token'], ['goog.array', 'goog.asserts', 'goog.debug.Error', 'goog.object', 'goog.string', 'goog.string.newlines'], {});
goog.addDependency('labs/format/csv_test.js', ['goog.labs.format.csvTest'], ['goog.labs.format.csv', 'goog.labs.format.csv.ParseError', 'goog.object', 'goog.testing.asserts', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/i18n/listformat.js', ['goog.labs.i18n.GenderInfo', 'goog.labs.i18n.GenderInfo.Gender', 'goog.labs.i18n.ListFormat'], ['goog.asserts', 'goog.labs.i18n.ListFormatSymbols'], {});
goog.addDependency('labs/i18n/listformat_test.js', ['goog.labs.i18n.ListFormatTest'], ['goog.labs.i18n.GenderInfo', 'goog.labs.i18n.ListFormat', 'goog.labs.i18n.ListFormatSymbols', 'goog.labs.i18n.ListFormatSymbols_el', 'goog.labs.i18n.ListFormatSymbols_en', 'goog.labs.i18n.ListFormatSymbols_fr', 'goog.labs.i18n.ListFormatSymbols_ml', 'goog.labs.i18n.ListFormatSymbols_zu', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/i18n/listsymbols.js', ['goog.labs.i18n.ListFormatSymbols', 'goog.labs.i18n.ListFormatSymbols_af', 'goog.labs.i18n.ListFormatSymbols_am', 'goog.labs.i18n.ListFormatSymbols_ar', 'goog.labs.i18n.ListFormatSymbols_ar_DZ', 'goog.labs.i18n.ListFormatSymbols_ar_EG', 'goog.labs.i18n.ListFormatSymbols_az', 'goog.labs.i18n.ListFormatSymbols_be', 'goog.labs.i18n.ListFormatSymbols_bg', 'goog.labs.i18n.ListFormatSymbols_bn', 'goog.labs.i18n.ListFormatSymbols_br', 'goog.labs.i18n.ListFormatSymbols_bs', 'goog.labs.i18n.ListFormatSymbols_ca', 'goog.labs.i18n.ListFormatSymbols_chr', 'goog.labs.i18n.ListFormatSymbols_cs', 'goog.labs.i18n.ListFormatSymbols_cy', 'goog.labs.i18n.ListFormatSymbols_da', 'goog.labs.i18n.ListFormatSymbols_de', 'goog.labs.i18n.ListFormatSymbols_de_AT', 'goog.labs.i18n.ListFormatSymbols_de_CH', 'goog.labs.i18n.ListFormatSymbols_el', 'goog.labs.i18n.ListFormatSymbols_en', 'goog.labs.i18n.ListFormatSymbols_en_AU', 'goog.labs.i18n.ListFormatSymbols_en_CA', 'goog.labs.i18n.ListFormatSymbols_en_GB', 'goog.labs.i18n.ListFormatSymbols_en_IE', 'goog.labs.i18n.ListFormatSymbols_en_IN', 'goog.labs.i18n.ListFormatSymbols_en_SG', 'goog.labs.i18n.ListFormatSymbols_en_US', 'goog.labs.i18n.ListFormatSymbols_en_ZA', 'goog.labs.i18n.ListFormatSymbols_es', 'goog.labs.i18n.ListFormatSymbols_es_419', 'goog.labs.i18n.ListFormatSymbols_es_ES', 'goog.labs.i18n.ListFormatSymbols_es_MX', 'goog.labs.i18n.ListFormatSymbols_es_US', 'goog.labs.i18n.ListFormatSymbols_et', 'goog.labs.i18n.ListFormatSymbols_eu', 'goog.labs.i18n.ListFormatSymbols_fa', 'goog.labs.i18n.ListFormatSymbols_fi', 'goog.labs.i18n.ListFormatSymbols_fil', 'goog.labs.i18n.ListFormatSymbols_fr', 'goog.labs.i18n.ListFormatSymbols_fr_CA', 'goog.labs.i18n.ListFormatSymbols_ga', 'goog.labs.i18n.ListFormatSymbols_gl', 'goog.labs.i18n.ListFormatSymbols_gsw', 'goog.labs.i18n.ListFormatSymbols_gu', 'goog.labs.i18n.ListFormatSymbols_haw', 'goog.labs.i18n.ListFormatSymbols_he', 'goog.labs.i18n.ListFormatSymbols_hi', 'goog.labs.i18n.ListFormatSymbols_hr', 'goog.labs.i18n.ListFormatSymbols_hu', 'goog.labs.i18n.ListFormatSymbols_hy', 'goog.labs.i18n.ListFormatSymbols_id', 'goog.labs.i18n.ListFormatSymbols_in', 'goog.labs.i18n.ListFormatSymbols_is', 'goog.labs.i18n.ListFormatSymbols_it', 'goog.labs.i18n.ListFormatSymbols_iw', 'goog.labs.i18n.ListFormatSymbols_ja', 'goog.labs.i18n.ListFormatSymbols_ka', 'goog.labs.i18n.ListFormatSymbols_kk', 'goog.labs.i18n.ListFormatSymbols_km', 'goog.labs.i18n.ListFormatSymbols_kn', 'goog.labs.i18n.ListFormatSymbols_ko', 'goog.labs.i18n.ListFormatSymbols_ky', 'goog.labs.i18n.ListFormatSymbols_ln', 'goog.labs.i18n.ListFormatSymbols_lo', 'goog.labs.i18n.ListFormatSymbols_lt', 'goog.labs.i18n.ListFormatSymbols_lv', 'goog.labs.i18n.ListFormatSymbols_mk', 'goog.labs.i18n.ListFormatSymbols_ml', 'goog.labs.i18n.ListFormatSymbols_mn', 'goog.labs.i18n.ListFormatSymbols_mo', 'goog.labs.i18n.ListFormatSymbols_mr', 'goog.labs.i18n.ListFormatSymbols_ms', 'goog.labs.i18n.ListFormatSymbols_mt', 'goog.labs.i18n.ListFormatSymbols_my', 'goog.labs.i18n.ListFormatSymbols_nb', 'goog.labs.i18n.ListFormatSymbols_ne', 'goog.labs.i18n.ListFormatSymbols_nl', 'goog.labs.i18n.ListFormatSymbols_no', 'goog.labs.i18n.ListFormatSymbols_no_NO', 'goog.labs.i18n.ListFormatSymbols_or', 'goog.labs.i18n.ListFormatSymbols_pa', 'goog.labs.i18n.ListFormatSymbols_pl', 'goog.labs.i18n.ListFormatSymbols_pt', 'goog.labs.i18n.ListFormatSymbols_pt_BR', 'goog.labs.i18n.ListFormatSymbols_pt_PT', 'goog.labs.i18n.ListFormatSymbols_ro', 'goog.labs.i18n.ListFormatSymbols_ru', 'goog.labs.i18n.ListFormatSymbols_sh', 'goog.labs.i18n.ListFormatSymbols_si', 'goog.labs.i18n.ListFormatSymbols_sk', 'goog.labs.i18n.ListFormatSymbols_sl', 'goog.labs.i18n.ListFormatSymbols_sq', 'goog.labs.i18n.ListFormatSymbols_sr', 'goog.labs.i18n.ListFormatSymbols_sr_Latn', 'goog.labs.i18n.ListFormatSymbols_sv', 'goog.labs.i18n.ListFormatSymbols_sw', 'goog.labs.i18n.ListFormatSymbols_ta', 'goog.labs.i18n.ListFormatSymbols_te', 'goog.labs.i18n.ListFormatSymbols_th', 'goog.labs.i18n.ListFormatSymbols_tl', 'goog.labs.i18n.ListFormatSymbols_tr', 'goog.labs.i18n.ListFormatSymbols_uk', 'goog.labs.i18n.ListFormatSymbols_ur', 'goog.labs.i18n.ListFormatSymbols_uz', 'goog.labs.i18n.ListFormatSymbols_vi', 'goog.labs.i18n.ListFormatSymbols_zh', 'goog.labs.i18n.ListFormatSymbols_zh_CN', 'goog.labs.i18n.ListFormatSymbols_zh_HK', 'goog.labs.i18n.ListFormatSymbols_zh_TW', 'goog.labs.i18n.ListFormatSymbols_zu'], [], {});
goog.addDependency('labs/i18n/listsymbolsext.js', ['goog.labs.i18n.ListFormatSymbolsExt', 'goog.labs.i18n.ListFormatSymbols_af_NA', 'goog.labs.i18n.ListFormatSymbols_af_ZA', 'goog.labs.i18n.ListFormatSymbols_agq', 'goog.labs.i18n.ListFormatSymbols_agq_CM', 'goog.labs.i18n.ListFormatSymbols_ak', 'goog.labs.i18n.ListFormatSymbols_ak_GH', 'goog.labs.i18n.ListFormatSymbols_am_ET', 'goog.labs.i18n.ListFormatSymbols_ar_001', 'goog.labs.i18n.ListFormatSymbols_ar_AE', 'goog.labs.i18n.ListFormatSymbols_ar_BH', 'goog.labs.i18n.ListFormatSymbols_ar_DJ', 'goog.labs.i18n.ListFormatSymbols_ar_EH', 'goog.labs.i18n.ListFormatSymbols_ar_ER', 'goog.labs.i18n.ListFormatSymbols_ar_IL', 'goog.labs.i18n.ListFormatSymbols_ar_IQ', 'goog.labs.i18n.ListFormatSymbols_ar_JO', 'goog.labs.i18n.ListFormatSymbols_ar_KM', 'goog.labs.i18n.ListFormatSymbols_ar_KW', 'goog.labs.i18n.ListFormatSymbols_ar_LB', 'goog.labs.i18n.ListFormatSymbols_ar_LY', 'goog.labs.i18n.ListFormatSymbols_ar_MA', 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'goog.labs.i18n.ListFormatSymbols_ur_PK', 'goog.labs.i18n.ListFormatSymbols_uz_Arab', 'goog.labs.i18n.ListFormatSymbols_uz_Arab_AF', 'goog.labs.i18n.ListFormatSymbols_uz_Cyrl', 'goog.labs.i18n.ListFormatSymbols_uz_Cyrl_UZ', 'goog.labs.i18n.ListFormatSymbols_uz_Latn', 'goog.labs.i18n.ListFormatSymbols_uz_Latn_UZ', 'goog.labs.i18n.ListFormatSymbols_vai', 'goog.labs.i18n.ListFormatSymbols_vai_Latn', 'goog.labs.i18n.ListFormatSymbols_vai_Latn_LR', 'goog.labs.i18n.ListFormatSymbols_vai_Vaii', 'goog.labs.i18n.ListFormatSymbols_vai_Vaii_LR', 'goog.labs.i18n.ListFormatSymbols_vi_VN', 'goog.labs.i18n.ListFormatSymbols_vun', 'goog.labs.i18n.ListFormatSymbols_vun_TZ', 'goog.labs.i18n.ListFormatSymbols_wae', 'goog.labs.i18n.ListFormatSymbols_wae_CH', 'goog.labs.i18n.ListFormatSymbols_wo', 'goog.labs.i18n.ListFormatSymbols_wo_SN', 'goog.labs.i18n.ListFormatSymbols_xh', 'goog.labs.i18n.ListFormatSymbols_xh_ZA', 'goog.labs.i18n.ListFormatSymbols_xog', 'goog.labs.i18n.ListFormatSymbols_xog_UG', 'goog.labs.i18n.ListFormatSymbols_yav', 'goog.labs.i18n.ListFormatSymbols_yav_CM', 'goog.labs.i18n.ListFormatSymbols_yi', 'goog.labs.i18n.ListFormatSymbols_yi_001', 'goog.labs.i18n.ListFormatSymbols_yo', 'goog.labs.i18n.ListFormatSymbols_yo_BJ', 'goog.labs.i18n.ListFormatSymbols_yo_NG', 'goog.labs.i18n.ListFormatSymbols_yue', 'goog.labs.i18n.ListFormatSymbols_yue_Hans', 'goog.labs.i18n.ListFormatSymbols_yue_Hans_CN', 'goog.labs.i18n.ListFormatSymbols_yue_Hant', 'goog.labs.i18n.ListFormatSymbols_yue_Hant_HK', 'goog.labs.i18n.ListFormatSymbols_zgh', 'goog.labs.i18n.ListFormatSymbols_zgh_MA', 'goog.labs.i18n.ListFormatSymbols_zh_Hans', 'goog.labs.i18n.ListFormatSymbols_zh_Hans_CN', 'goog.labs.i18n.ListFormatSymbols_zh_Hans_HK', 'goog.labs.i18n.ListFormatSymbols_zh_Hans_MO', 'goog.labs.i18n.ListFormatSymbols_zh_Hans_SG', 'goog.labs.i18n.ListFormatSymbols_zh_Hant', 'goog.labs.i18n.ListFormatSymbols_zh_Hant_HK', 'goog.labs.i18n.ListFormatSymbols_zh_Hant_MO', 'goog.labs.i18n.ListFormatSymbols_zh_Hant_TW', 'goog.labs.i18n.ListFormatSymbols_zu_ZA'], ['goog.labs.i18n.ListFormatSymbols'], {});
goog.addDependency('labs/mock/mock.js', ['goog.labs.mock', 'goog.labs.mock.TimeoutError', 'goog.labs.mock.VerificationError'], ['goog.array', 'goog.asserts', 'goog.debug', 'goog.debug.Error', 'goog.functions', 'goog.labs.mock.timeout', 'goog.labs.mock.timeout.TimeoutMode', 'goog.labs.mock.verification', 'goog.labs.mock.verification.BaseVerificationMode', 'goog.labs.mock.verification.VerificationMode', 'goog.object'], {'lang': 'es6'});
goog.addDependency('labs/mock/mock_test.js', ['goog.labs.mockTest'], ['goog.array', 'goog.labs.mock', 'goog.labs.mock.TimeoutError', 'goog.labs.mock.VerificationError', 'goog.labs.mock.timeout', 'goog.labs.mock.verification', 'goog.labs.testing.AnythingMatcher', 'goog.labs.testing.GreaterThanMatcher', 'goog.string', 'goog.testing.jsunit'], {'lang': 'es8'});
goog.addDependency('labs/mock/timeoutmode.js', ['goog.labs.mock.timeout', 'goog.labs.mock.timeout.TimeoutMode'], [], {'lang': 'es6'});
goog.addDependency('labs/mock/verificationmode.js', ['goog.labs.mock.verification', 'goog.labs.mock.verification.BaseVerificationMode', 'goog.labs.mock.verification.VerificationMode'], [], {'lang': 'es6'});
goog.addDependency('labs/mock/verificationmode_test.js', ['goog.labs.mock.VerificationModeTest'], ['goog.labs.mock.verification', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/net/image.js', ['goog.labs.net.image'], ['goog.Promise', 'goog.dom.safe', 'goog.events.EventHandler', 'goog.events.EventType', 'goog.html.SafeUrl', 'goog.net.EventType', 'goog.userAgent'], {});
goog.addDependency('labs/net/image_test.js', ['goog.labs.net.imageTest'], ['goog.labs.net.image', 'goog.string', 'goog.testing.TestCase', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/net/webchannel.js', ['goog.net.WebChannel'], ['goog.events', 'goog.events.Event', 'goog.events.Listenable', 'goog.net.XmlHttpFactory'], {});
goog.addDependency('labs/net/webchannel/basetestchannel.js', ['goog.labs.net.webChannel.BaseTestChannel'], ['goog.labs.net.webChannel.Channel', 'goog.labs.net.webChannel.ChannelRequest', 'goog.labs.net.webChannel.WebChannelDebug', 'goog.labs.net.webChannel.requestStats', 'goog.net.WebChannel'], {});
goog.addDependency('labs/net/webchannel/channel.js', ['goog.labs.net.webChannel.Channel'], [], {'lang': 'es6'});
goog.addDependency('labs/net/webchannel/channelrequest.js', ['goog.labs.net.webChannel.ChannelRequest'], ['goog.Timer', 'goog.async.Throttle', 'goog.events.EventHandler', 'goog.labs.net.webChannel.Channel', 'goog.labs.net.webChannel.WebChannelDebug', 'goog.labs.net.webChannel.environment', 'goog.labs.net.webChannel.requestStats', 'goog.net.ErrorCode', 'goog.net.EventType', 'goog.net.WebChannel', 'goog.net.XmlHttp', 'goog.object', 'goog.string', 'goog.userAgent'], {});
goog.addDependency('labs/net/webchannel/channelrequest_test.js', ['goog.labs.net.webChannel.channelRequestTest'], ['goog.Uri', 'goog.functions', 'goog.labs.net.webChannel.ChannelRequest', 'goog.labs.net.webChannel.WebChannelDebug', 'goog.labs.net.webChannel.requestStats', 'goog.labs.net.webChannel.requestStats.ServerReachability', 'goog.testing.MockClock', 'goog.testing.PropertyReplacer', 'goog.testing.net.XhrIo', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/net/webchannel/connectionstate.js', ['goog.labs.net.webChannel.ConnectionState'], [], {});
goog.addDependency('labs/net/webchannel/environment.js', ['goog.labs.net.webChannel.environment'], ['goog.userAgent'], {'module': 'goog'});
goog.addDependency('labs/net/webchannel/environment_test.js', ['goog.labs.net.webChannel.EnvironmentTest'], ['goog.labs.net.webChannel.environment', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/net/webchannel/forwardchannelrequestpool.js', ['goog.labs.net.webChannel.ForwardChannelRequestPool'], ['goog.array', 'goog.labs.net.webChannel.ChannelRequest', 'goog.labs.net.webChannel.Wire', 'goog.string', 'goog.structs.Set'], {'module': 'goog'});
goog.addDependency('labs/net/webchannel/forwardchannelrequestpool_test.js', ['goog.labs.net.webChannel.ForwardChannelRequestPoolTest'], ['goog.labs.net.webChannel.ChannelRequest', 'goog.labs.net.webChannel.ForwardChannelRequestPool', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/net/webchannel/netutils.js', ['goog.labs.net.webChannel.netUtils'], ['goog.Uri', 'goog.labs.net.webChannel.WebChannelDebug'], {});
goog.addDependency('labs/net/webchannel/requeststats.js', ['goog.labs.net.webChannel.requestStats', 'goog.labs.net.webChannel.requestStats.Event', 'goog.labs.net.webChannel.requestStats.ServerReachability', 'goog.labs.net.webChannel.requestStats.ServerReachabilityEvent', 'goog.labs.net.webChannel.requestStats.Stat', 'goog.labs.net.webChannel.requestStats.StatEvent', 'goog.labs.net.webChannel.requestStats.TimingEvent'], ['goog.events.Event', 'goog.events.EventTarget'], {});
goog.addDependency('labs/net/webchannel/webchannelbase.js', ['goog.labs.net.webChannel.WebChannelBase'], ['goog.Uri', 'goog.array', 'goog.asserts', 'goog.async.run', 'goog.json', 'goog.labs.net.webChannel.BaseTestChannel', 'goog.labs.net.webChannel.Channel', 'goog.labs.net.webChannel.ChannelRequest', 'goog.labs.net.webChannel.ConnectionState', 'goog.labs.net.webChannel.ForwardChannelRequestPool', 'goog.labs.net.webChannel.WebChannelDebug', 'goog.labs.net.webChannel.Wire', 'goog.labs.net.webChannel.WireV8', 'goog.labs.net.webChannel.netUtils', 'goog.labs.net.webChannel.requestStats', 'goog.net.WebChannel', 'goog.net.XhrIo', 'goog.net.XmlHttpFactory', 'goog.net.rpc.HttpCors', 'goog.object', 'goog.string', 'goog.structs'], {});
goog.addDependency('labs/net/webchannel/webchannelbase_test.js', ['goog.labs.net.webChannel.webChannelBaseTest'], ['goog.Timer', 'goog.array', 'goog.dom', 'goog.functions', 'goog.json', 'goog.labs.net.webChannel.ChannelRequest', 'goog.labs.net.webChannel.ForwardChannelRequestPool', 'goog.labs.net.webChannel.WebChannelBase', 'goog.labs.net.webChannel.WebChannelBaseTransport', 'goog.labs.net.webChannel.WebChannelDebug', 'goog.labs.net.webChannel.Wire', 'goog.labs.net.webChannel.netUtils', 'goog.labs.net.webChannel.requestStats', 'goog.labs.net.webChannel.requestStats.Stat', 'goog.structs.Map', 'goog.testing.MockClock', 'goog.testing.PropertyReplacer', 'goog.testing.asserts', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/net/webchannel/webchannelbasetransport.js', ['goog.labs.net.webChannel.WebChannelBaseTransport'], ['goog.asserts', 'goog.events.EventTarget', 'goog.json', 'goog.labs.net.webChannel.ChannelRequest', 'goog.labs.net.webChannel.WebChannelBase', 'goog.labs.net.webChannel.Wire', 'goog.log', 'goog.net.WebChannel', 'goog.net.WebChannelTransport', 'goog.object', 'goog.string', 'goog.string.path'], {});
goog.addDependency('labs/net/webchannel/webchannelbasetransport_test.js', ['goog.labs.net.webChannel.webChannelBaseTransportTest'], ['goog.events', 'goog.functions', 'goog.json', 'goog.labs.net.webChannel.ChannelRequest', 'goog.labs.net.webChannel.WebChannelBase', 'goog.labs.net.webChannel.WebChannelBaseTransport', 'goog.labs.net.webChannel.Wire', 'goog.net.WebChannel', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/net/webchannel/webchanneldebug.js', ['goog.labs.net.webChannel.WebChannelDebug'], ['goog.json', 'goog.log'], {});
goog.addDependency('labs/net/webchannel/wire.js', ['goog.labs.net.webChannel.Wire'], [], {'lang': 'es6'});
goog.addDependency('labs/net/webchannel/wirev8.js', ['goog.labs.net.webChannel.WireV8'], ['goog.asserts', 'goog.json', 'goog.json.NativeJsonProcessor', 'goog.labs.net.webChannel.Wire', 'goog.structs'], {});
goog.addDependency('labs/net/webchannel/wirev8_test.js', ['goog.labs.net.webChannel.WireV8Test'], ['goog.labs.net.webChannel.WireV8', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/net/webchanneltransport.js', ['goog.net.WebChannelTransport'], [], {});
goog.addDependency('labs/net/webchanneltransportfactory.js', ['goog.net.createWebChannelTransport'], ['goog.functions', 'goog.labs.net.webChannel.WebChannelBaseTransport'], {});
goog.addDependency('labs/net/xhr.js', ['goog.labs.net.xhr', 'goog.labs.net.xhr.Error', 'goog.labs.net.xhr.HttpError', 'goog.labs.net.xhr.Options', 'goog.labs.net.xhr.PostData', 'goog.labs.net.xhr.ResponseType', 'goog.labs.net.xhr.TimeoutError'], ['goog.Promise', 'goog.asserts', 'goog.debug.Error', 'goog.net.HttpStatus', 'goog.net.XmlHttp', 'goog.object', 'goog.string', 'goog.uri.utils', 'goog.userAgent'], {});
goog.addDependency('labs/net/xhr_test.js', ['goog.labs.net.xhrTest'], ['goog.Promise', 'goog.events', 'goog.events.EventType', 'goog.labs.net.xhr', 'goog.net.WrapperXmlHttpFactory', 'goog.net.XhrLike', 'goog.net.XmlHttp', 'goog.testing.MockClock', 'goog.testing.TestCase', 'goog.testing.jsunit', 'goog.userAgent'], {'lang': 'es6'});
goog.addDependency('labs/pubsub/broadcastpubsub.js', ['goog.labs.pubsub.BroadcastPubSub'], ['goog.Disposable', 'goog.Timer', 'goog.array', 'goog.async.run', 'goog.events.EventHandler', 'goog.events.EventType', 'goog.log', 'goog.math', 'goog.pubsub.PubSub', 'goog.storage.Storage', 'goog.storage.mechanism.HTML5LocalStorage', 'goog.string', 'goog.userAgent'], {});
goog.addDependency('labs/pubsub/broadcastpubsub_test.js', ['goog.labs.pubsub.BroadcastPubSubTest'], ['goog.array', 'goog.debug.Logger', 'goog.json', 'goog.labs.pubsub.BroadcastPubSub', 'goog.storage.Storage', 'goog.structs.Map', 'goog.testing.MockClock', 'goog.testing.MockControl', 'goog.testing.events', 'goog.testing.events.Event', 'goog.testing.mockmatchers', 'goog.testing.mockmatchers.ArgumentMatcher', 'goog.testing.recordFunction', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/storage/boundedcollectablestorage.js', ['goog.labs.storage.BoundedCollectableStorage'], ['goog.array', 'goog.asserts', 'goog.iter', 'goog.storage.CollectableStorage', 'goog.storage.ErrorCode', 'goog.storage.ExpiringStorage'], {});
goog.addDependency('labs/storage/boundedcollectablestorage_test.js', ['goog.labs.storage.BoundedCollectableStorageTest'], ['goog.labs.storage.BoundedCollectableStorage', 'goog.storage.collectableStorageTester', 'goog.storage.storageTester', 'goog.testing.MockClock', 'goog.testing.storage.FakeMechanism', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/structs/multimap.js', ['goog.labs.structs.Multimap'], ['goog.array', 'goog.object'], {'lang': 'es6'});
goog.addDependency('labs/structs/multimap_test.js', ['goog.labs.structs.MultimapTest'], ['goog.labs.structs.Multimap', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/style/pixeldensitymonitor.js', ['goog.labs.style.PixelDensityMonitor', 'goog.labs.style.PixelDensityMonitor.Density', 'goog.labs.style.PixelDensityMonitor.EventType'], ['goog.events', 'goog.events.EventTarget'], {});
goog.addDependency('labs/style/pixeldensitymonitor_test.js', ['goog.labs.style.PixelDensityMonitorTest'], ['goog.array', 'goog.dom.DomHelper', 'goog.events', 'goog.labs.style.PixelDensityMonitor', 'goog.testing.MockControl', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/testing/assertthat.js', ['goog.labs.testing.MatcherError', 'goog.labs.testing.assertThat'], ['goog.debug.Error'], {});
goog.addDependency('labs/testing/assertthat_test.js', ['goog.labs.testing.assertThatTest'], ['goog.labs.testing.MatcherError', 'goog.labs.testing.assertThat', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/testing/decoratormatcher.js', ['goog.labs.testing.AnythingMatcher'], ['goog.labs.testing.Matcher'], {});
goog.addDependency('labs/testing/decoratormatcher_test.js', ['goog.labs.testing.decoratorMatcherTest'], ['goog.labs.testing.AnythingMatcher', 'goog.labs.testing.GreaterThanMatcher', 'goog.labs.testing.MatcherError', 'goog.labs.testing.assertThat', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/testing/dictionarymatcher.js', ['goog.labs.testing.HasEntriesMatcher', 'goog.labs.testing.HasEntryMatcher', 'goog.labs.testing.HasKeyMatcher', 'goog.labs.testing.HasValueMatcher'], ['goog.asserts', 'goog.labs.testing.Matcher', 'goog.object'], {});
goog.addDependency('labs/testing/dictionarymatcher_test.js', ['goog.labs.testing.dictionaryMatcherTest'], ['goog.labs.testing.HasEntryMatcher', 'goog.labs.testing.MatcherError', 'goog.labs.testing.assertThat', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/testing/environment.js', ['goog.labs.testing.Environment'], ['goog.Thenable', 'goog.array', 'goog.asserts', 'goog.debug.Console', 'goog.testing.MockClock', 'goog.testing.MockControl', 'goog.testing.PropertyReplacer', 'goog.testing.TestCase', 'goog.testing.jsunit'], {'lang': 'es6'});
goog.addDependency('labs/testing/environment_test.js', ['goog.labs.testing.environmentTest'], ['goog.asserts', 'goog.labs.testing.Environment', 'goog.testing.MockControl', 'goog.testing.PropertyReplacer', 'goog.testing.TestCase', 'goog.testing.testSuite'], {'lang': 'es8', 'module': 'goog'});
goog.addDependency('labs/testing/environment_usage_test.js', ['goog.labs.testing.environmentUsageTest'], ['goog.labs.testing.Environment', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/testing/json_fuzzing.js', ['goog.labs.testing.JsonFuzzing'], ['goog.string', 'goog.testing.PseudoRandom'], {});
goog.addDependency('labs/testing/json_fuzzing_test.js', ['goog.labs.testing.JsonFuzzingTest'], ['goog.json', 'goog.labs.testing.JsonFuzzing', 'goog.testing.asserts', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/testing/logicmatcher.js', ['goog.labs.testing.AllOfMatcher', 'goog.labs.testing.AnyOfMatcher', 'goog.labs.testing.IsNotMatcher', 'goog.labs.testing.logicMatchers'], ['goog.array', 'goog.labs.testing.Matcher'], {});
goog.addDependency('labs/testing/logicmatcher_test.js', ['goog.labs.testing.logicMatcherTest'], ['goog.labs.testing.AllOfMatcher', 'goog.labs.testing.GreaterThanMatcher', 'goog.labs.testing.MatcherError', 'goog.labs.testing.assertThat', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/testing/matcher.js', ['goog.labs.testing.Matcher'], [], {'lang': 'es6'});
goog.addDependency('labs/testing/numbermatcher.js', ['goog.labs.testing.AnyNumberMatcher', 'goog.labs.testing.CloseToMatcher', 'goog.labs.testing.EqualToMatcher', 'goog.labs.testing.GreaterThanEqualToMatcher', 'goog.labs.testing.GreaterThanMatcher', 'goog.labs.testing.LessThanEqualToMatcher', 'goog.labs.testing.LessThanMatcher'], ['goog.asserts', 'goog.labs.testing.Matcher'], {});
goog.addDependency('labs/testing/numbermatcher_test.js', ['goog.labs.testing.numberMatcherTest'], ['goog.labs.testing.LessThanMatcher', 'goog.labs.testing.MatcherError', 'goog.labs.testing.assertThat', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/testing/objectmatcher.js', ['goog.labs.testing.AnyObjectMatcher', 'goog.labs.testing.HasPropertyMatcher', 'goog.labs.testing.InstanceOfMatcher', 'goog.labs.testing.IsNullMatcher', 'goog.labs.testing.IsNullOrUndefinedMatcher', 'goog.labs.testing.IsUndefinedMatcher', 'goog.labs.testing.ObjectEqualsMatcher'], ['goog.labs.testing.Matcher'], {});
goog.addDependency('labs/testing/objectmatcher_test.js', ['goog.labs.testing.objectMatcherTest'], ['goog.labs.testing.MatcherError', 'goog.labs.testing.ObjectEqualsMatcher', 'goog.labs.testing.assertThat', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/testing/stringmatcher.js', ['goog.labs.testing.AnyStringMatcher', 'goog.labs.testing.ContainsStringMatcher', 'goog.labs.testing.EndsWithMatcher', 'goog.labs.testing.EqualToIgnoringWhitespaceMatcher', 'goog.labs.testing.EqualsMatcher', 'goog.labs.testing.RegexMatcher', 'goog.labs.testing.StartsWithMatcher', 'goog.labs.testing.StringContainsInOrderMatcher'], ['goog.asserts', 'goog.labs.testing.Matcher', 'goog.string'], {});
goog.addDependency('labs/testing/stringmatcher_test.js', ['goog.labs.testing.stringMatcherTest'], ['goog.labs.testing.MatcherError', 'goog.labs.testing.StringContainsInOrderMatcher', 'goog.labs.testing.assertThat', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/useragent/browser.js', ['goog.labs.userAgent.browser'], ['goog.array', 'goog.labs.userAgent.util', 'goog.object', 'goog.string.internal'], {});
goog.addDependency('labs/useragent/browser_test.js', ['goog.labs.userAgent.browserTest'], ['goog.labs.userAgent.browser', 'goog.labs.userAgent.testAgents', 'goog.labs.userAgent.util', 'goog.object', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/useragent/device.js', ['goog.labs.userAgent.device'], ['goog.labs.userAgent.util'], {});
goog.addDependency('labs/useragent/device_test.js', ['goog.labs.userAgent.deviceTest'], ['goog.labs.userAgent.device', 'goog.labs.userAgent.testAgents', 'goog.labs.userAgent.util', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/useragent/engine.js', ['goog.labs.userAgent.engine'], ['goog.array', 'goog.labs.userAgent.util', 'goog.string'], {});
goog.addDependency('labs/useragent/engine_test.js', ['goog.labs.userAgent.engineTest'], ['goog.labs.userAgent.engine', 'goog.labs.userAgent.testAgents', 'goog.labs.userAgent.util', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/useragent/extra.js', ['goog.labs.userAgent.extra'], ['goog.labs.userAgent.browser', 'goog.labs.userAgent.platform'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/useragent/extra_test.js', ['goog.labs.userAgent.extraTest'], ['goog.labs.userAgent.browser', 'goog.labs.userAgent.extra', 'goog.labs.userAgent.testAgents', 'goog.labs.userAgent.util', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/useragent/platform.js', ['goog.labs.userAgent.platform'], ['goog.labs.userAgent.util', 'goog.string'], {});
goog.addDependency('labs/useragent/platform_test.js', ['goog.labs.userAgent.platformTest'], ['goog.labs.userAgent.platform', 'goog.labs.userAgent.testAgents', 'goog.labs.userAgent.util', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/useragent/test_agents.js', ['goog.labs.userAgent.testAgents'], [], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/useragent/util.js', ['goog.labs.userAgent.util'], ['goog.string.internal'], {});
goog.addDependency('labs/useragent/util_test.js', ['goog.labs.userAgent.utilTest'], ['goog.functions', 'goog.labs.userAgent.testAgents', 'goog.labs.userAgent.util', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('labs/useragent/verifier.js', ['goog.labs.useragent.verifier'], [], {'lang': 'es6'});
goog.addDependency('labs/useragent/verifier_test.js', ['goog.labs.useragent.verifierTest'], ['goog.labs.userAgent.browser', 'goog.labs.useragent.verifier', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('loader/abstractmodulemanager.js', ['goog.loader.AbstractModuleManager', 'goog.loader.AbstractModuleManager.CallbackType', 'goog.loader.AbstractModuleManager.FailureType'], ['goog.module.AbstractModuleLoader', 'goog.module.ModuleInfo', 'goog.module.ModuleLoadCallback'], {});
goog.addDependency('loader/activemodulemanager.js', ['goog.loader.activeModuleManager'], ['goog.asserts', 'goog.loader.AbstractModuleManager'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('locale/countries.js', ['goog.locale.countries'], [], {});
goog.addDependency('locale/countrylanguagenames_test.js', ['goog.locale.countryLanguageNamesTest'], ['goog.locale', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('locale/defaultlocalenameconstants.js', ['goog.locale.defaultLocaleNameConstants'], [], {});
goog.addDependency('locale/genericfontnames.js', ['goog.locale.genericFontNames'], [], {});
goog.addDependency('locale/genericfontnames_test.js', ['goog.locale.genericFontNamesTest'], ['goog.locale.genericFontNames', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('locale/genericfontnamesdata.js', ['goog.locale.genericFontNamesData'], [], {});
goog.addDependency('locale/locale.js', ['goog.locale'], ['goog.locale.nativeNameConstants'], {});
goog.addDependency('locale/nativenameconstants.js', ['goog.locale.nativeNameConstants'], [], {});
goog.addDependency('locale/scriptToLanguages.js', ['goog.locale.scriptToLanguages'], ['goog.locale'], {});
goog.addDependency('locale/timezonedetection.js', ['goog.locale.timeZoneDetection'], ['goog.locale.TimeZoneFingerprint'], {});
goog.addDependency('locale/timezonedetection_test.js', ['goog.locale.timeZoneDetectionTest'], ['goog.locale.timeZoneDetection', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('locale/timezonefingerprint.js', ['goog.locale.TimeZoneFingerprint'], [], {});
goog.addDependency('locale/timezonelist.js', ['goog.locale.TimeZoneList', 'goog.locale.getTimeZoneAllLongNames', 'goog.locale.getTimeZoneSelectedLongNames', 'goog.locale.getTimeZoneSelectedShortNames'], ['goog.locale'], {});
goog.addDependency('locale/timezonelist_test.js', ['goog.locale.TimeZoneListTest'], ['goog.locale', 'goog.locale.TimeZoneList', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('log/log.js', ['goog.log', 'goog.log.Level', 'goog.log.LogRecord', 'goog.log.Logger'], ['goog.debug', 'goog.debug.LogManager', 'goog.debug.LogRecord', 'goog.debug.Logger'], {});
goog.addDependency('log/log_test.js', ['goog.logTest'], ['goog.debug.LogManager', 'goog.log', 'goog.log.Level', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('math/affinetransform.js', ['goog.math.AffineTransform'], [], {'lang': 'es6'});
goog.addDependency('math/affinetransform_test.js', ['goog.math.AffineTransformTest'], ['goog.array', 'goog.math', 'goog.math.AffineTransform', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('math/bezier.js', ['goog.math.Bezier'], ['goog.math', 'goog.math.Coordinate'], {});
goog.addDependency('math/bezier_test.js', ['goog.math.BezierTest'], ['goog.math', 'goog.math.Bezier', 'goog.math.Coordinate', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('math/box.js', ['goog.math.Box'], ['goog.asserts', 'goog.math.Coordinate'], {});
goog.addDependency('math/box_test.js', ['goog.math.BoxTest'], ['goog.math.Box', 'goog.math.Coordinate', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('math/coordinate.js', ['goog.math.Coordinate'], ['goog.math'], {});
goog.addDependency('math/coordinate3.js', ['goog.math.Coordinate3'], [], {'lang': 'es6'});
goog.addDependency('math/coordinate3_test.js', ['goog.math.Coordinate3Test'], ['goog.math.Coordinate3', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('math/coordinate_test.js', ['goog.math.CoordinateTest'], ['goog.math.Coordinate', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('math/exponentialbackoff.js', ['goog.math.ExponentialBackoff'], ['goog.asserts'], {});
goog.addDependency('math/exponentialbackoff_test.js', ['goog.math.ExponentialBackoffTest'], ['goog.math.ExponentialBackoff', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('math/integer.js', ['goog.math.Integer'], ['goog.reflect'], {});
goog.addDependency('math/integer_test.js', ['goog.math.IntegerTest'], ['goog.math.Integer', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('math/interpolator/interpolator1.js', ['goog.math.interpolator.Interpolator1'], [], {});
goog.addDependency('math/interpolator/linear1.js', ['goog.math.interpolator.Linear1'], ['goog.array', 'goog.asserts', 'goog.math', 'goog.math.interpolator.Interpolator1'], {});
goog.addDependency('math/interpolator/linear1_test.js', ['goog.math.interpolator.Linear1Test'], ['goog.math.interpolator.Linear1', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('math/interpolator/pchip1.js', ['goog.math.interpolator.Pchip1'], ['goog.math', 'goog.math.interpolator.Spline1'], {});
goog.addDependency('math/interpolator/pchip1_test.js', ['goog.math.interpolator.Pchip1Test'], ['goog.math.interpolator.Pchip1', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('math/interpolator/spline1.js', ['goog.math.interpolator.Spline1'], ['goog.array', 'goog.asserts', 'goog.math', 'goog.math.interpolator.Interpolator1', 'goog.math.tdma'], {});
goog.addDependency('math/interpolator/spline1_test.js', ['goog.math.interpolator.Spline1Test'], ['goog.math.interpolator.Spline1', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('math/irect.js', ['goog.math.IRect'], [], {});
goog.addDependency('math/line.js', ['goog.math.Line'], ['goog.math', 'goog.math.Coordinate'], {});
goog.addDependency('math/line_test.js', ['goog.math.LineTest'], ['goog.math.Coordinate', 'goog.math.Line', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('math/long.js', ['goog.math.Long'], ['goog.asserts', 'goog.reflect'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('math/long_test.js', ['goog.math.LongTest'], ['goog.asserts', 'goog.math.Long', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('math/math.js', ['goog.math'], ['goog.array', 'goog.asserts'], {});
goog.addDependency('math/math_test.js', ['goog.mathTest'], ['goog.math', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('math/matrix.js', ['goog.math.Matrix'], ['goog.array', 'goog.asserts', 'goog.math', 'goog.math.Size', 'goog.string'], {});
goog.addDependency('math/matrix_test.js', ['goog.math.MatrixTest'], ['goog.math.Matrix', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('math/path.js', ['goog.math.Path', 'goog.math.Path.Segment'], ['goog.array', 'goog.math', 'goog.math.AffineTransform'], {});
goog.addDependency('math/path_test.js', ['goog.math.PathTest'], ['goog.array', 'goog.math.AffineTransform', 'goog.math.Path', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('math/paths.js', ['goog.math.paths'], ['goog.math.Coordinate', 'goog.math.Path'], {});
goog.addDependency('math/paths_test.js', ['goog.math.pathsTest'], ['goog.math.Coordinate', 'goog.math.paths', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('math/range.js', ['goog.math.Range'], ['goog.asserts'], {});
goog.addDependency('math/range_test.js', ['goog.math.RangeTest'], ['goog.math.Range', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('math/rangeset.js', ['goog.math.RangeSet'], ['goog.array', 'goog.iter.Iterator', 'goog.iter.StopIteration', 'goog.math.Range'], {});
goog.addDependency('math/rangeset_test.js', ['goog.math.RangeSetTest'], ['goog.iter', 'goog.math.Range', 'goog.math.RangeSet', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('math/rect.js', ['goog.math.Rect'], ['goog.asserts', 'goog.math.Box', 'goog.math.Coordinate', 'goog.math.IRect', 'goog.math.Size'], {});
goog.addDependency('math/rect_test.js', ['goog.math.RectTest'], ['goog.math.Box', 'goog.math.Coordinate', 'goog.math.Rect', 'goog.math.Size', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('math/size.js', ['goog.math.Size'], [], {'lang': 'es6'});
goog.addDependency('math/size_test.js', ['goog.math.SizeTest'], ['goog.math.Size', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('math/tdma.js', ['goog.math.tdma'], [], {'lang': 'es6'});
goog.addDependency('math/tdma_test.js', ['goog.math.tdmaTest'], ['goog.math.tdma', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('math/vec2.js', ['goog.math.Vec2'], ['goog.math', 'goog.math.Coordinate'], {'lang': 'es6'});
goog.addDependency('math/vec2_test.js', ['goog.math.Vec2Test'], ['goog.math.Vec2', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('math/vec3.js', ['goog.math.Vec3'], ['goog.math', 'goog.math.Coordinate3'], {});
goog.addDependency('math/vec3_test.js', ['goog.math.Vec3Test'], ['goog.math.Coordinate3', 'goog.math.Vec3', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('memoize/memoize.js', ['goog.memoize'], [], {'lang': 'es6'});
goog.addDependency('memoize/memoize_test.js', ['goog.memoizeTest'], ['goog.memoize', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('messaging/abstractchannel.js', ['goog.messaging.AbstractChannel'], ['goog.Disposable', 'goog.json', 'goog.log', 'goog.messaging.MessageChannel'], {});
goog.addDependency('messaging/abstractchannel_test.js', ['goog.messaging.AbstractChannelTest'], ['goog.messaging.AbstractChannel', 'goog.testing.MockControl', 'goog.testing.async.MockControl', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('messaging/bufferedchannel.js', ['goog.messaging.BufferedChannel'], ['goog.Disposable', 'goog.Timer', 'goog.events', 'goog.log', 'goog.messaging.MessageChannel', 'goog.messaging.MultiChannel'], {});
goog.addDependency('messaging/bufferedchannel_test.js', ['goog.messaging.BufferedChannelTest'], ['goog.debug.Console', 'goog.dom', 'goog.dom.TagName', 'goog.log', 'goog.log.Level', 'goog.messaging.BufferedChannel', 'goog.testing.MockClock', 'goog.testing.MockControl', 'goog.testing.async.MockControl', 'goog.testing.messaging.MockMessageChannel', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('messaging/deferredchannel.js', ['goog.messaging.DeferredChannel'], ['goog.Disposable', 'goog.messaging.MessageChannel'], {});
goog.addDependency('messaging/deferredchannel_test.js', ['goog.messaging.DeferredChannelTest'], ['goog.async.Deferred', 'goog.messaging.DeferredChannel', 'goog.testing.MockControl', 'goog.testing.async.MockControl', 'goog.testing.messaging.MockMessageChannel', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('messaging/loggerclient.js', ['goog.messaging.LoggerClient'], ['goog.Disposable', 'goog.debug', 'goog.debug.LogManager', 'goog.debug.Logger'], {});
goog.addDependency('messaging/loggerclient_test.js', ['goog.messaging.LoggerClientTest'], ['goog.debug', 'goog.debug.Logger', 'goog.messaging.LoggerClient', 'goog.testing.MockControl', 'goog.testing.messaging.MockMessageChannel', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('messaging/loggerserver.js', ['goog.messaging.LoggerServer'], ['goog.Disposable', 'goog.log', 'goog.log.Level'], {});
goog.addDependency('messaging/loggerserver_test.js', ['goog.messaging.LoggerServerTest'], ['goog.debug.LogManager', 'goog.debug.Logger', 'goog.log', 'goog.log.Level', 'goog.messaging.LoggerServer', 'goog.testing.MockControl', 'goog.testing.PropertyReplacer', 'goog.testing.messaging.MockMessageChannel', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('messaging/messagechannel.js', ['goog.messaging.MessageChannel'], [], {});
goog.addDependency('messaging/messaging.js', ['goog.messaging'], [], {});
goog.addDependency('messaging/messaging_test.js', ['goog.testing.messaging.MockMessageChannelTest'], ['goog.messaging', 'goog.testing.MockControl', 'goog.testing.messaging.MockMessageChannel', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('messaging/multichannel.js', ['goog.messaging.MultiChannel', 'goog.messaging.MultiChannel.VirtualChannel'], ['goog.Disposable', 'goog.log', 'goog.messaging.MessageChannel', 'goog.object'], {});
goog.addDependency('messaging/multichannel_test.js', ['goog.messaging.MultiChannelTest'], ['goog.messaging.MultiChannel', 'goog.testing.MockControl', 'goog.testing.messaging.MockMessageChannel', 'goog.testing.mockmatchers.IgnoreArgument', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('messaging/portcaller.js', ['goog.messaging.PortCaller'], ['goog.Disposable', 'goog.async.Deferred', 'goog.messaging.DeferredChannel', 'goog.messaging.PortChannel', 'goog.messaging.PortNetwork', 'goog.object'], {});
goog.addDependency('messaging/portcaller_test.js', ['goog.messaging.PortCallerTest'], ['goog.events.EventTarget', 'goog.messaging.PortCaller', 'goog.messaging.PortNetwork', 'goog.testing.MockControl', 'goog.testing.messaging.MockMessageChannel', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('messaging/portchannel.js', ['goog.messaging.PortChannel'], ['goog.Timer', 'goog.array', 'goog.async.Deferred', 'goog.debug', 'goog.events', 'goog.events.EventType', 'goog.json', 'goog.log', 'goog.messaging.AbstractChannel', 'goog.messaging.DeferredChannel', 'goog.object', 'goog.string', 'goog.userAgent'], {});
goog.addDependency('messaging/portchannel_test.js', ['goog.messaging.PortChannelTest'], ['goog.Promise', 'goog.Timer', 'goog.dom', 'goog.dom.TagName', 'goog.events', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.json', 'goog.messaging.PortChannel', 'goog.testing.MockControl', 'goog.testing.TestCase', 'goog.testing.messaging.MockMessageEvent', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('messaging/portnetwork.js', ['goog.messaging.PortNetwork'], [], {});
goog.addDependency('messaging/portnetwork_test.js', ['goog.messaging.PortNetworkTest'], ['goog.Promise', 'goog.Timer', 'goog.labs.userAgent.browser', 'goog.messaging.PortChannel', 'goog.messaging.PortOperator', 'goog.testing.TestCase', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('messaging/portoperator.js', ['goog.messaging.PortOperator'], ['goog.Disposable', 'goog.asserts', 'goog.log', 'goog.messaging.PortChannel', 'goog.messaging.PortNetwork', 'goog.object'], {});
goog.addDependency('messaging/portoperator_test.js', ['goog.messaging.PortOperatorTest'], ['goog.messaging.PortNetwork', 'goog.messaging.PortOperator', 'goog.testing.MockControl', 'goog.testing.PropertyReplacer', 'goog.testing.messaging.MockMessageChannel', 'goog.testing.messaging.MockMessagePort', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('messaging/respondingchannel.js', ['goog.messaging.RespondingChannel'], ['goog.Disposable', 'goog.Promise', 'goog.log', 'goog.messaging.MultiChannel'], {});
goog.addDependency('messaging/respondingchannel_test.js', ['goog.messaging.RespondingChannelTest'], ['goog.Promise', 'goog.messaging.RespondingChannel', 'goog.testing.MockControl', 'goog.testing.PropertyReplacer', 'goog.testing.messaging.MockMessageChannel', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('messaging/testdata/portchannel_worker.js', ['goog.messaging.testdata.portchannel_worker'], ['goog.messaging.PortChannel'], {});
goog.addDependency('messaging/testdata/portnetwork_worker1.js', ['goog.messaging.testdata.portnetwork_worker1'], ['goog.messaging.PortCaller', 'goog.messaging.PortChannel'], {});
goog.addDependency('messaging/testdata/portnetwork_worker2.js', ['goog.messaging.testdata.portnetwork_worker2'], ['goog.messaging.PortCaller', 'goog.messaging.PortChannel'], {});
goog.addDependency('module/abstractmoduleloader.js', ['goog.module.AbstractModuleLoader'], ['goog.module', 'goog.module.ModuleInfo'], {});
goog.addDependency('module/basemodule.js', ['goog.module.BaseModule'], ['goog.Disposable', 'goog.module'], {});
goog.addDependency('module/loader.js', ['goog.module.Loader'], ['goog.Timer', 'goog.array', 'goog.asserts', 'goog.dom', 'goog.dom.TagName', 'goog.dom.safe', 'goog.html.legacyconversions', 'goog.module', 'goog.object'], {});
goog.addDependency('module/module.js', ['goog.module'], [], {});
goog.addDependency('module/moduleinfo.js', ['goog.module.ModuleInfo'], ['goog.Disposable', 'goog.async.throwException', 'goog.functions', 'goog.html.TrustedResourceUrl', 'goog.module', 'goog.module.BaseModule', 'goog.module.ModuleLoadCallback'], {});
goog.addDependency('module/moduleinfo_test.js', ['goog.module.ModuleInfoTest'], ['goog.module.BaseModule', 'goog.module.ModuleInfo', 'goog.testing.MockClock', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('module/moduleloadcallback.js', ['goog.module.ModuleLoadCallback'], ['goog.debug.entryPointRegistry', 'goog.module'], {});
goog.addDependency('module/moduleloadcallback_test.js', ['goog.module.ModuleLoadCallbackTest'], ['goog.debug.ErrorHandler', 'goog.debug.entryPointRegistry', 'goog.functions', 'goog.module.ModuleLoadCallback', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('module/moduleloader.js', ['goog.module.ModuleLoader'], ['goog.Timer', 'goog.array', 'goog.asserts', 'goog.dom', 'goog.dom.safe', 'goog.events', 'goog.events.Event', 'goog.events.EventHandler', 'goog.events.EventId', 'goog.events.EventTarget', 'goog.functions', 'goog.html.TrustedResourceUrl', 'goog.labs.userAgent.browser', 'goog.log', 'goog.module.AbstractModuleLoader', 'goog.net.BulkLoader', 'goog.net.EventType', 'goog.net.jsloader', 'goog.userAgent', 'goog.userAgent.product'], {'lang': 'es6'});
goog.addDependency('module/moduleloader_test.js', ['goog.module.ModuleLoaderTest'], ['goog.Promise', 'goog.array', 'goog.dom', 'goog.dom.TagName', 'goog.events', 'goog.functions', 'goog.html.TrustedResourceUrl', 'goog.loader.activeModuleManager', 'goog.module.ModuleLoader', 'goog.module.ModuleManager', 'goog.net.BulkLoader', 'goog.net.XmlHttp', 'goog.object', 'goog.string.Const', 'goog.testing.PropertyReplacer', 'goog.testing.TestCase', 'goog.testing.events.EventObserver', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('module/modulemanager.js', ['goog.module.ModuleManager', 'goog.module.ModuleManager.CallbackType', 'goog.module.ModuleManager.FailureType'], ['goog.array', 'goog.asserts', 'goog.async.Deferred', 'goog.debug.Trace', 'goog.disposable.IDisposable', 'goog.disposeAll', 'goog.loader.AbstractModuleManager', 'goog.loader.activeModuleManager', 'goog.log', 'goog.module', 'goog.module.ModuleInfo', 'goog.module.ModuleLoadCallback', 'goog.object'], {'lang': 'es6'});
goog.addDependency('module/modulemanager_test.js', ['goog.module.ModuleManagerTest'], ['goog.array', 'goog.functions', 'goog.module.BaseModule', 'goog.module.ModuleManager', 'goog.testing', 'goog.testing.MockClock', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('module/testdata/modA_1.js', ['goog.module.testdata.modA_1'], [], {});
goog.addDependency('module/testdata/modA_2.js', ['goog.module.testdata.modA_2'], ['goog.module.ModuleManager'], {});
goog.addDependency('module/testdata/modB_1.js', ['goog.module.testdata.modB_1'], ['goog.module.ModuleManager'], {});
goog.addDependency('net/browserchannel.js', ['goog.net.BrowserChannel', 'goog.net.BrowserChannel.Error', 'goog.net.BrowserChannel.Event', 'goog.net.BrowserChannel.Handler', 'goog.net.BrowserChannel.LogSaver', 'goog.net.BrowserChannel.QueuedMap', 'goog.net.BrowserChannel.ServerReachability', 'goog.net.BrowserChannel.ServerReachabilityEvent', 'goog.net.BrowserChannel.Stat', 'goog.net.BrowserChannel.StatEvent', 'goog.net.BrowserChannel.State', 'goog.net.BrowserChannel.TimingEvent'], ['goog.Uri', 'goog.array', 'goog.asserts', 'goog.debug.TextFormatter', 'goog.events.Event', 'goog.events.EventTarget', 'goog.json', 'goog.json.NativeJsonProcessor', 'goog.log', 'goog.net.BrowserTestChannel', 'goog.net.ChannelDebug', 'goog.net.ChannelRequest', 'goog.net.XhrIo', 'goog.net.tmpnetwork', 'goog.object', 'goog.string', 'goog.structs', 'goog.structs.CircularBuffer'], {});
goog.addDependency('net/browserchannel_test.js', ['goog.net.BrowserChannelTest'], ['goog.Timer', 'goog.array', 'goog.dom', 'goog.functions', 'goog.json', 'goog.net.BrowserChannel', 'goog.net.ChannelDebug', 'goog.net.ChannelRequest', 'goog.net.tmpnetwork', 'goog.structs.Map', 'goog.testing.MockClock', 'goog.testing.PropertyReplacer', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('net/browsertestchannel.js', ['goog.net.BrowserTestChannel'], ['goog.json.NativeJsonProcessor', 'goog.net.ChannelRequest', 'goog.net.ChannelRequest.Error', 'goog.net.tmpnetwork', 'goog.string.Parser'], {});
goog.addDependency('net/bulkloader.js', ['goog.net.BulkLoader'], ['goog.events.Event', 'goog.events.EventHandler', 'goog.events.EventTarget', 'goog.log', 'goog.net.BulkLoaderHelper', 'goog.net.EventType', 'goog.net.XhrIo'], {});
goog.addDependency('net/bulkloader_test.js', ['goog.net.BulkLoaderTest'], ['goog.events.Event', 'goog.events.EventHandler', 'goog.net.BulkLoader', 'goog.net.EventType', 'goog.testing.MockClock', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('net/bulkloaderhelper.js', ['goog.net.BulkLoaderHelper'], ['goog.Disposable'], {});
goog.addDependency('net/channeldebug.js', ['goog.net.ChannelDebug'], ['goog.json', 'goog.log'], {'lang': 'es6'});
goog.addDependency('net/channelrequest.js', ['goog.net.ChannelRequest', 'goog.net.ChannelRequest.Error'], ['goog.Timer', 'goog.async.Throttle', 'goog.dom.TagName', 'goog.dom.safe', 'goog.events.EventHandler', 'goog.html.SafeUrl', 'goog.html.uncheckedconversions', 'goog.net.ErrorCode', 'goog.net.EventType', 'goog.net.XmlHttp', 'goog.object', 'goog.string', 'goog.string.Const', 'goog.userAgent'], {});
goog.addDependency('net/channelrequest_test.js', ['goog.net.ChannelRequestTest'], ['goog.Uri', 'goog.functions', 'goog.net.BrowserChannel', 'goog.net.ChannelDebug', 'goog.net.ChannelRequest', 'goog.testing.MockClock', 'goog.testing.PropertyReplacer', 'goog.testing.net.XhrIo', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('net/cookies.js', ['goog.net.Cookies', 'goog.net.cookies'], ['goog.asserts', 'goog.string'], {'lang': 'es5'});
goog.addDependency('net/cookies_test.js', ['goog.net.cookiesTest'], ['goog.array', 'goog.net.Cookies', 'goog.net.cookies', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('net/corsxmlhttpfactory.js', ['goog.net.CorsXmlHttpFactory', 'goog.net.IeCorsXhrAdapter'], ['goog.net.HttpStatus', 'goog.net.XhrLike', 'goog.net.XmlHttp', 'goog.net.XmlHttpFactory'], {});
goog.addDependency('net/corsxmlhttpfactory_test.js', ['goog.net.CorsXmlHttpFactoryTest'], ['goog.net.CorsXmlHttpFactory', 'goog.net.IeCorsXhrAdapter', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('net/crossdomainrpc.js', ['goog.net.CrossDomainRpc'], ['goog.Uri', 'goog.dom', 'goog.dom.TagName', 'goog.dom.safe', 'goog.events', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.html.SafeHtml', 'goog.log', 'goog.net.EventType', 'goog.net.HttpStatus', 'goog.string', 'goog.userAgent'], {});
goog.addDependency('net/crossdomainrpc_test.js', ['goog.net.CrossDomainRpcTest'], ['goog.Promise', 'goog.log', 'goog.net.CrossDomainRpc', 'goog.testing.TestCase', 'goog.testing.testSuite', 'goog.userAgent', 'goog.userAgent.product'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('net/errorcode.js', ['goog.net.ErrorCode'], [], {});
goog.addDependency('net/eventtype.js', ['goog.net.EventType'], [], {});
goog.addDependency('net/fetchxmlhttpfactory.js', ['goog.net.FetchXmlHttp', 'goog.net.FetchXmlHttpFactory'], ['goog.asserts', 'goog.events.EventTarget', 'goog.functions', 'goog.log', 'goog.net.XhrLike', 'goog.net.XmlHttpFactory'], {'lang': 'es5'});
goog.addDependency('net/fetchxmlhttpfactory_test.js', ['goog.net.FetchXmlHttpFactoryTest'], ['goog.net.FetchXmlHttp', 'goog.net.FetchXmlHttpFactory', 'goog.testing.MockControl', 'goog.testing.recordFunction', 'goog.testing.testSuite', 'goog.userAgent.product', 'goog.userAgent.product.isVersion'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('net/filedownloader.js', ['goog.net.FileDownloader', 'goog.net.FileDownloader.Error'], ['goog.Disposable', 'goog.asserts', 'goog.async.Deferred', 'goog.crypt.hash32', 'goog.debug.Error', 'goog.events', 'goog.events.EventHandler', 'goog.fs', 'goog.fs.DirectoryEntry', 'goog.fs.Error', 'goog.fs.FileSaver', 'goog.net.EventType', 'goog.net.XhrIo', 'goog.net.XhrIoPool', 'goog.object'], {});
goog.addDependency('net/filedownloader_test.js', ['goog.net.FileDownloaderTest'], ['goog.fs.Error', 'goog.net.ErrorCode', 'goog.net.FileDownloader', 'goog.net.XhrIo', 'goog.testing.PropertyReplacer', 'goog.testing.TestCase', 'goog.testing.fs', 'goog.testing.fs.FileSystem', 'goog.testing.net.XhrIoPool', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('net/httpstatus.js', ['goog.net.HttpStatus'], [], {});
goog.addDependency('net/httpstatusname.js', ['goog.net.HttpStatusName'], [], {});
goog.addDependency('net/iframeio.js', ['goog.net.IframeIo', 'goog.net.IframeIo.IncrementalDataEvent'], ['goog.Timer', 'goog.Uri', 'goog.array', 'goog.asserts', 'goog.debug.HtmlFormatter', 'goog.dom', 'goog.dom.InputType', 'goog.dom.TagName', 'goog.dom.safe', 'goog.events', 'goog.events.Event', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.html.SafeUrl', 'goog.html.legacyconversions', 'goog.html.uncheckedconversions', 'goog.json', 'goog.log', 'goog.log.Level', 'goog.net.ErrorCode', 'goog.net.EventType', 'goog.reflect', 'goog.string', 'goog.string.Const', 'goog.structs', 'goog.userAgent'], {});
goog.addDependency('net/iframeio_test.js', ['goog.net.IframeIoTest'], ['goog.debug', 'goog.debug.DivConsole', 'goog.debug.LogManager', 'goog.dom', 'goog.dom.TagName', 'goog.events', 'goog.events.EventType', 'goog.log', 'goog.log.Level', 'goog.net.IframeIo', 'goog.testing.events', 'goog.testing.events.Event', 'goog.testing.jsunit', 'goog.userAgent'], {'lang': 'es6'});
goog.addDependency('net/iframeloadmonitor.js', ['goog.net.IframeLoadMonitor'], ['goog.dom', 'goog.events', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.userAgent'], {});
goog.addDependency('net/iframeloadmonitor_test.js', ['goog.net.IframeLoadMonitorTest'], ['goog.Promise', 'goog.Timer', 'goog.dom', 'goog.dom.TagName', 'goog.events', 'goog.net.IframeLoadMonitor', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('net/imageloader.js', ['goog.net.ImageLoader'], ['goog.array', 'goog.dom', 'goog.dom.TagName', 'goog.events.EventHandler', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.net.EventType', 'goog.object', 'goog.userAgent'], {});
goog.addDependency('net/imageloader_test.js', ['goog.net.ImageLoaderTest'], ['goog.Promise', 'goog.Timer', 'goog.array', 'goog.dispose', 'goog.events', 'goog.events.Event', 'goog.events.EventType', 'goog.net.EventType', 'goog.net.ImageLoader', 'goog.object', 'goog.string', 'goog.testing.TestCase', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('net/ipaddress.js', ['goog.net.IpAddress', 'goog.net.Ipv4Address', 'goog.net.Ipv6Address'], ['goog.array', 'goog.math.Integer', 'goog.object', 'goog.string'], {});
goog.addDependency('net/ipaddress_test.js', ['goog.net.IpAddressTest'], ['goog.array', 'goog.math.Integer', 'goog.net.IpAddress', 'goog.net.Ipv4Address', 'goog.net.Ipv6Address', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('net/jsloader.js', ['goog.net.jsloader', 'goog.net.jsloader.Error', 'goog.net.jsloader.ErrorCode', 'goog.net.jsloader.Options'], ['goog.array', 'goog.async.Deferred', 'goog.debug.Error', 'goog.dom', 'goog.dom.TagName', 'goog.dom.safe', 'goog.html.TrustedResourceUrl', 'goog.object'], {});
goog.addDependency('net/jsloader_test.js', ['goog.net.jsloaderTest'], ['goog.array', 'goog.dom', 'goog.dom.TagName', 'goog.html.TrustedResourceUrl', 'goog.net.jsloader', 'goog.net.jsloader.ErrorCode', 'goog.string.Const', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('net/jsonp.js', ['goog.net.Jsonp'], ['goog.html.TrustedResourceUrl', 'goog.net.jsloader', 'goog.object'], {});
goog.addDependency('net/jsonp_test.js', ['goog.net.JsonpTest'], ['goog.html.TrustedResourceUrl', 'goog.net.Jsonp', 'goog.string.Const', 'goog.testing.PropertyReplacer', 'goog.testing.recordFunction', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('net/mockiframeio.js', ['goog.net.MockIFrameIo'], ['goog.events.EventTarget', 'goog.net.ErrorCode', 'goog.net.EventType', 'goog.net.IframeIo'], {});
goog.addDependency('net/multiiframeloadmonitor.js', ['goog.net.MultiIframeLoadMonitor'], ['goog.events', 'goog.net.IframeLoadMonitor'], {});
goog.addDependency('net/multiiframeloadmonitor_test.js', ['goog.net.MultiIframeLoadMonitorTest'], ['goog.Promise', 'goog.Timer', 'goog.dom', 'goog.dom.TagName', 'goog.net.MultiIframeLoadMonitor', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('net/networkstatusmonitor.js', ['goog.net.NetworkStatusMonitor'], ['goog.events.Listenable'], {});
goog.addDependency('net/networktester.js', ['goog.net.NetworkTester'], ['goog.Timer', 'goog.Uri', 'goog.dom.safe', 'goog.log'], {});
goog.addDependency('net/networktester_test.js', ['goog.net.NetworkTesterTest'], ['goog.Uri', 'goog.net.NetworkTester', 'goog.testing.MockClock', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('net/rpc/httpcors.js', ['goog.net.rpc.HttpCors'], ['goog.Uri', 'goog.object', 'goog.string', 'goog.uri.utils'], {'module': 'goog'});
goog.addDependency('net/rpc/httpcors_test.js', ['goog.net.rpc.HttpCorsTest'], ['goog.Uri', 'goog.net.rpc.HttpCors', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('net/streams/base64pbstreamparser.js', ['goog.net.streams.Base64PbStreamParser'], ['goog.asserts', 'goog.net.streams.Base64StreamDecoder', 'goog.net.streams.PbStreamParser', 'goog.net.streams.StreamParser'], {'module': 'goog'});
goog.addDependency('net/streams/base64pbstreamparser_test.js', ['goog.net.streams.Base64PbStreamParserTest'], ['goog.crypt.base64', 'goog.net.streams.Base64PbStreamParser', 'goog.object', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('net/streams/base64streamdecoder.js', ['goog.net.streams.Base64StreamDecoder'], ['goog.asserts', 'goog.crypt.base64'], {});
goog.addDependency('net/streams/base64streamdecoder_test.js', ['goog.net.streams.Base64StreamDecoderTest'], ['goog.net.streams.Base64StreamDecoder', 'goog.testing.asserts', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('net/streams/jsonstreamparser.js', ['goog.net.streams.JsonStreamParser', 'goog.net.streams.JsonStreamParser.Options'], ['goog.asserts', 'goog.net.streams.StreamParser', 'goog.net.streams.utils'], {});
goog.addDependency('net/streams/jsonstreamparser_test.js', ['goog.net.streams.JsonStreamParserTest'], ['goog.array', 'goog.json', 'goog.labs.testing.JsonFuzzing', 'goog.net.streams.JsonStreamParser', 'goog.testing.asserts', 'goog.testing.testSuite', 'goog.uri.utils'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('net/streams/nodereadablestream.js', ['goog.net.streams.NodeReadableStream'], [], {});
goog.addDependency('net/streams/pbjsonstreamparser.js', ['goog.net.streams.PbJsonStreamParser'], ['goog.asserts', 'goog.net.streams.JsonStreamParser', 'goog.net.streams.StreamParser', 'goog.net.streams.utils'], {'module': 'goog'});
goog.addDependency('net/streams/pbjsonstreamparser_test.js', ['goog.net.streams.PbJsonStreamParserTest'], ['goog.net.streams.PbJsonStreamParser', 'goog.object', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('net/streams/pbstreamparser.js', ['goog.net.streams.PbStreamParser'], ['goog.asserts', 'goog.net.streams.StreamParser'], {});
goog.addDependency('net/streams/pbstreamparser_test.js', ['goog.net.streams.PbStreamParserTest'], ['goog.net.streams.PbStreamParser', 'goog.object', 'goog.testing.asserts', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('net/streams/streamfactory.js', ['goog.net.streams.createXhrNodeReadableStream'], ['goog.asserts', 'goog.net.streams.XhrNodeReadableStream', 'goog.net.streams.XhrStreamReader'], {});
goog.addDependency('net/streams/streamparser.js', ['goog.net.streams.StreamParser'], [], {});
goog.addDependency('net/streams/utils.js', ['goog.net.streams.utils'], [], {'module': 'goog'});
goog.addDependency('net/streams/xhrnodereadablestream.js', ['goog.net.streams.XhrNodeReadableStream'], ['goog.array', 'goog.log', 'goog.net.streams.NodeReadableStream', 'goog.net.streams.XhrStreamReader'], {});
goog.addDependency('net/streams/xhrnodereadablestream_test.js', ['goog.net.streams.XhrNodeReadableStreamTest'], ['goog.net.streams.NodeReadableStream', 'goog.net.streams.XhrNodeReadableStream', 'goog.net.streams.XhrStreamReader', 'goog.testing.PropertyReplacer', 'goog.testing.asserts', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('net/streams/xhrstreamreader.js', ['goog.net.streams.XhrStreamReader'], ['goog.events.EventHandler', 'goog.log', 'goog.net.ErrorCode', 'goog.net.EventType', 'goog.net.HttpStatus', 'goog.net.XhrIo', 'goog.net.XmlHttp', 'goog.net.streams.Base64PbStreamParser', 'goog.net.streams.JsonStreamParser', 'goog.net.streams.PbJsonStreamParser', 'goog.net.streams.PbStreamParser', 'goog.string', 'goog.userAgent'], {});
goog.addDependency('net/streams/xhrstreamreader_test.js', ['goog.net.streams.XhrStreamReaderTest'], ['goog.net.ErrorCode', 'goog.net.HttpStatus', 'goog.net.XhrIo', 'goog.net.XmlHttp', 'goog.net.streams.Base64PbStreamParser', 'goog.net.streams.JsonStreamParser', 'goog.net.streams.PbJsonStreamParser', 'goog.net.streams.PbStreamParser', 'goog.net.streams.XhrStreamReader', 'goog.object', 'goog.testing.net.XhrIo', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('net/testdata/jsloader_test1.js', ['goog.net.testdata.jsloader_test1'], [], {});
goog.addDependency('net/testdata/jsloader_test2.js', ['goog.net.testdata.jsloader_test2'], [], {});
goog.addDependency('net/testdata/jsloader_test3.js', ['goog.net.testdata.jsloader_test3'], [], {});
goog.addDependency('net/testdata/jsloader_test4.js', ['goog.net.testdata.jsloader_test4'], [], {});
goog.addDependency('net/tmpnetwork.js', ['goog.net.tmpnetwork'], ['goog.Uri', 'goog.dom.safe', 'goog.net.ChannelDebug'], {});
goog.addDependency('net/websocket.js', ['goog.net.WebSocket', 'goog.net.WebSocket.ErrorEvent', 'goog.net.WebSocket.EventType', 'goog.net.WebSocket.MessageEvent'], ['goog.Timer', 'goog.asserts', 'goog.debug.entryPointRegistry', 'goog.events', 'goog.events.Event', 'goog.events.EventTarget', 'goog.log'], {'lang': 'es5'});
goog.addDependency('net/websocket_test.js', ['goog.net.WebSocketTest'], ['goog.debug.EntryPointMonitor', 'goog.debug.ErrorHandler', 'goog.debug.entryPointRegistry', 'goog.events', 'goog.functions', 'goog.net.WebSocket', 'goog.testing.MockClock', 'goog.testing.PropertyReplacer', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('net/wrapperxmlhttpfactory.js', ['goog.net.WrapperXmlHttpFactory'], ['goog.net.XhrLike', 'goog.net.XmlHttpFactory'], {});
goog.addDependency('net/xhrio.js', ['goog.net.XhrIo', 'goog.net.XhrIo.ResponseType'], ['goog.Timer', 'goog.array', 'goog.asserts', 'goog.debug.entryPointRegistry', 'goog.events.EventTarget', 'goog.json.hybrid', 'goog.log', 'goog.net.ErrorCode', 'goog.net.EventType', 'goog.net.HttpStatus', 'goog.net.XmlHttp', 'goog.object', 'goog.string', 'goog.structs', 'goog.structs.Map', 'goog.uri.utils', 'goog.userAgent'], {});
goog.addDependency('net/xhrio_test.js', ['goog.net.XhrIoTest'], ['goog.Uri', 'goog.debug.EntryPointMonitor', 'goog.debug.ErrorHandler', 'goog.debug.entryPointRegistry', 'goog.events', 'goog.functions', 'goog.net.EventType', 'goog.net.WrapperXmlHttpFactory', 'goog.net.XhrIo', 'goog.net.XmlHttp', 'goog.object', 'goog.string', 'goog.testing.MockClock', 'goog.testing.PropertyReplacer', 'goog.testing.jsunit', 'goog.testing.net.XhrIo', 'goog.testing.recordFunction', 'goog.userAgent.product'], {'lang': 'es6'});
goog.addDependency('net/xhriopool.js', ['goog.net.XhrIoPool'], ['goog.net.XhrIo', 'goog.structs.PriorityPool'], {});
goog.addDependency('net/xhriopool_test.js', ['goog.net.XhrIoPoolTest'], ['goog.net.XhrIoPool', 'goog.structs.Map', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('net/xhrlike.js', ['goog.net.XhrLike'], [], {});
goog.addDependency('net/xhrmanager.js', ['goog.net.XhrManager', 'goog.net.XhrManager.Event', 'goog.net.XhrManager.Request'], ['goog.events', 'goog.events.Event', 'goog.events.EventHandler', 'goog.events.EventTarget', 'goog.net.ErrorCode', 'goog.net.EventType', 'goog.net.XhrIo', 'goog.net.XhrIoPool', 'goog.structs.Map'], {});
goog.addDependency('net/xhrmanager_test.js', ['goog.net.XhrManagerTest'], ['goog.events', 'goog.net.EventType', 'goog.net.XhrIo', 'goog.net.XhrManager', 'goog.testing.net.XhrIoPool', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('net/xmlhttp.js', ['goog.net.DefaultXmlHttpFactory', 'goog.net.XmlHttp', 'goog.net.XmlHttp.OptionType', 'goog.net.XmlHttp.ReadyState', 'goog.net.XmlHttpDefines'], ['goog.asserts', 'goog.net.WrapperXmlHttpFactory', 'goog.net.XmlHttpFactory'], {});
goog.addDependency('net/xmlhttpfactory.js', ['goog.net.XmlHttpFactory'], ['goog.net.XhrLike'], {});
goog.addDependency('net/xpc/crosspagechannel.js', ['goog.net.xpc.CrossPageChannel'], ['goog.Uri', 'goog.async.Deferred', 'goog.async.Delay', 'goog.dispose', 'goog.dom', 'goog.dom.TagName', 'goog.dom.safe', 'goog.events', 'goog.events.EventHandler', 'goog.events.EventType', 'goog.html.legacyconversions', 'goog.json', 'goog.log', 'goog.messaging.AbstractChannel', 'goog.net.xpc', 'goog.net.xpc.CfgFields', 'goog.net.xpc.ChannelStates', 'goog.net.xpc.CrossPageChannelRole', 'goog.net.xpc.DirectTransport', 'goog.net.xpc.NativeMessagingTransport', 'goog.net.xpc.TransportTypes', 'goog.net.xpc.UriCfgFields', 'goog.string', 'goog.uri.utils', 'goog.userAgent'], {});
goog.addDependency('net/xpc/crosspagechannel_test.js', ['goog.net.xpc.CrossPageChannelTest'], ['goog.Disposable', 'goog.Promise', 'goog.Timer', 'goog.Uri', 'goog.dom', 'goog.dom.TagName', 'goog.labs.userAgent.browser', 'goog.log', 'goog.log.Level', 'goog.net.xpc', 'goog.net.xpc.CfgFields', 'goog.net.xpc.CrossPageChannel', 'goog.net.xpc.CrossPageChannelRole', 'goog.net.xpc.TransportTypes', 'goog.object', 'goog.testing.PropertyReplacer', 'goog.testing.TestCase', 'goog.testing.jsunit'], {'lang': 'es8'});
goog.addDependency('net/xpc/crosspagechannelrole.js', ['goog.net.xpc.CrossPageChannelRole'], [], {});
goog.addDependency('net/xpc/directtransport.js', ['goog.net.xpc.DirectTransport'], ['goog.Timer', 'goog.async.Deferred', 'goog.events.EventHandler', 'goog.log', 'goog.net.xpc', 'goog.net.xpc.CfgFields', 'goog.net.xpc.CrossPageChannelRole', 'goog.net.xpc.Transport', 'goog.net.xpc.TransportTypes', 'goog.object'], {});
goog.addDependency('net/xpc/directtransport_test.js', ['goog.net.xpc.DirectTransportTest'], ['goog.Promise', 'goog.dom', 'goog.dom.TagName', 'goog.labs.userAgent.browser', 'goog.log', 'goog.log.Level', 'goog.net.xpc', 'goog.net.xpc.CfgFields', 'goog.net.xpc.CrossPageChannel', 'goog.net.xpc.CrossPageChannelRole', 'goog.net.xpc.TransportTypes', 'goog.testing.TestCase', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('net/xpc/iframepollingtransport.js', ['goog.net.xpc.IframePollingTransport', 'goog.net.xpc.IframePollingTransport.Receiver', 'goog.net.xpc.IframePollingTransport.Sender'], ['goog.array', 'goog.dom', 'goog.dom.TagName', 'goog.dom.safe', 'goog.log', 'goog.log.Level', 'goog.net.xpc', 'goog.net.xpc.CfgFields', 'goog.net.xpc.CrossPageChannelRole', 'goog.net.xpc.Transport', 'goog.net.xpc.TransportTypes', 'goog.userAgent'], {});
goog.addDependency('net/xpc/iframepollingtransport_test.js', ['goog.net.xpc.IframePollingTransportTest'], ['goog.Timer', 'goog.dom', 'goog.dom.TagName', 'goog.functions', 'goog.net.xpc.CfgFields', 'goog.net.xpc.CrossPageChannel', 'goog.net.xpc.CrossPageChannelRole', 'goog.net.xpc.IframePollingTransport', 'goog.object', 'goog.testing.MockClock', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('net/xpc/nativemessagingtransport.js', ['goog.net.xpc.NativeMessagingTransport'], ['goog.Timer', 'goog.asserts', 'goog.async.Deferred', 'goog.events', 'goog.events.EventHandler', 'goog.log', 'goog.net.xpc', 'goog.net.xpc.CrossPageChannelRole', 'goog.net.xpc.Transport', 'goog.net.xpc.TransportTypes'], {});
goog.addDependency('net/xpc/nativemessagingtransport_test.js', ['goog.net.xpc.NativeMessagingTransportTest'], ['goog.dom', 'goog.events', 'goog.net.xpc', 'goog.net.xpc.CfgFields', 'goog.net.xpc.CrossPageChannel', 'goog.net.xpc.CrossPageChannelRole', 'goog.net.xpc.NativeMessagingTransport', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('net/xpc/relay.js', ['goog.net.xpc.relay'], [], {'lang': 'es6'});
goog.addDependency('net/xpc/transport.js', ['goog.net.xpc.Transport'], ['goog.Disposable', 'goog.dom', 'goog.net.xpc.TransportNames'], {});
goog.addDependency('net/xpc/xpc.js', ['goog.net.xpc', 'goog.net.xpc.CfgFields', 'goog.net.xpc.ChannelStates', 'goog.net.xpc.TransportNames', 'goog.net.xpc.TransportTypes', 'goog.net.xpc.UriCfgFields'], ['goog.log'], {});
goog.addDependency('object/object.js', ['goog.object'], [], {'lang': 'es6'});
goog.addDependency('object/object_test.js', ['goog.objectTest'], ['goog.array', 'goog.functions', 'goog.object', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('positioning/absoluteposition.js', ['goog.positioning.AbsolutePosition'], ['goog.math.Coordinate', 'goog.positioning', 'goog.positioning.AbstractPosition'], {});
goog.addDependency('positioning/abstractposition.js', ['goog.positioning.AbstractPosition'], [], {});
goog.addDependency('positioning/anchoredposition.js', ['goog.positioning.AnchoredPosition'], ['goog.positioning', 'goog.positioning.AbstractPosition'], {});
goog.addDependency('positioning/anchoredposition_test.js', ['goog.positioning.AnchoredPositionTest'], ['goog.dom', 'goog.positioning.AnchoredPosition', 'goog.positioning.Corner', 'goog.positioning.Overflow', 'goog.style', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('positioning/anchoredviewportposition.js', ['goog.positioning.AnchoredViewportPosition'], ['goog.positioning', 'goog.positioning.AnchoredPosition', 'goog.positioning.Overflow', 'goog.positioning.OverflowStatus'], {});
goog.addDependency('positioning/anchoredviewportposition_test.js', ['goog.positioning.AnchoredViewportPositionTest'], ['goog.dom', 'goog.math.Box', 'goog.positioning.AnchoredViewportPosition', 'goog.positioning.Corner', 'goog.positioning.OverflowStatus', 'goog.style', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('positioning/clientposition.js', ['goog.positioning.ClientPosition'], ['goog.asserts', 'goog.dom', 'goog.math.Coordinate', 'goog.positioning', 'goog.positioning.AbstractPosition', 'goog.style'], {});
goog.addDependency('positioning/clientposition_test.js', ['goog.positioning.clientPositionTest'], ['goog.dom', 'goog.dom.TagName', 'goog.positioning.ClientPosition', 'goog.positioning.Corner', 'goog.style', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('positioning/menuanchoredposition.js', ['goog.positioning.MenuAnchoredPosition'], ['goog.positioning.AnchoredViewportPosition', 'goog.positioning.Overflow'], {});
goog.addDependency('positioning/menuanchoredposition_test.js', ['goog.positioning.MenuAnchoredPositionTest'], ['goog.dom', 'goog.dom.TagName', 'goog.positioning.Corner', 'goog.positioning.MenuAnchoredPosition', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('positioning/positioning.js', ['goog.positioning', 'goog.positioning.Corner', 'goog.positioning.CornerBit', 'goog.positioning.Overflow', 'goog.positioning.OverflowStatus'], ['goog.asserts', 'goog.dom', 'goog.dom.TagName', 'goog.math.Coordinate', 'goog.math.Rect', 'goog.math.Size', 'goog.style', 'goog.style.bidi'], {});
goog.addDependency('positioning/positioning_test.js', ['goog.positioningTest'], ['goog.dom', 'goog.dom.DomHelper', 'goog.dom.TagName', 'goog.labs.userAgent.browser', 'goog.math.Box', 'goog.math.Coordinate', 'goog.math.Size', 'goog.positioning', 'goog.positioning.Corner', 'goog.positioning.Overflow', 'goog.positioning.OverflowStatus', 'goog.style', 'goog.testing.ExpectedFailures', 'goog.testing.testSuite', 'goog.userAgent', 'goog.userAgent.product'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('positioning/viewportclientposition.js', ['goog.positioning.ViewportClientPosition'], ['goog.dom', 'goog.math.Coordinate', 'goog.positioning', 'goog.positioning.ClientPosition', 'goog.positioning.Overflow', 'goog.positioning.OverflowStatus', 'goog.style'], {});
goog.addDependency('positioning/viewportclientposition_test.js', ['goog.positioning.ViewportClientPositionTest'], ['goog.dom', 'goog.positioning.Corner', 'goog.positioning.Overflow', 'goog.positioning.ViewportClientPosition', 'goog.style', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('positioning/viewportposition.js', ['goog.positioning.ViewportPosition'], ['goog.math.Coordinate', 'goog.positioning', 'goog.positioning.AbstractPosition', 'goog.positioning.Corner', 'goog.style'], {});
goog.addDependency('promise/nativeresolver.js', ['goog.promise.NativeResolver'], [], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('promise/nativeresolver_test.js', ['goog.promise.nativeResolverTest'], ['goog.promise.NativeResolver', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('promise/promise.js', ['goog.Promise'], ['goog.Thenable', 'goog.asserts', 'goog.async.FreeList', 'goog.async.run', 'goog.async.throwException', 'goog.debug.Error', 'goog.promise.Resolver'], {});
goog.addDependency('promise/promise_test.js', ['goog.PromiseTest'], ['goog.Promise', 'goog.Thenable', 'goog.Timer', 'goog.functions', 'goog.testing.MockClock', 'goog.testing.PropertyReplacer', 'goog.testing.TestCase', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es8', 'module': 'goog'});
goog.addDependency('promise/resolver.js', ['goog.promise.Resolver'], [], {});
goog.addDependency('promise/testsuiteadapter.js', ['goog.promise.testSuiteAdapter'], ['goog.Promise'], {});
goog.addDependency('promise/thenable.js', ['goog.Thenable'], [], {});
goog.addDependency('proto2/descriptor.js', ['goog.proto2.Descriptor', 'goog.proto2.Metadata'], ['goog.array', 'goog.asserts', 'goog.object', 'goog.string'], {});
goog.addDependency('proto2/descriptor_test.js', ['goog.proto2.DescriptorTest'], ['goog.proto2.Descriptor', 'goog.proto2.Message', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('proto2/fielddescriptor.js', ['goog.proto2.FieldDescriptor'], ['goog.asserts', 'goog.string'], {});
goog.addDependency('proto2/fielddescriptor_test.js', ['goog.proto2.FieldDescriptorTest'], ['goog.proto2.FieldDescriptor', 'goog.proto2.Message', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('proto2/lazydeserializer.js', ['goog.proto2.LazyDeserializer'], ['goog.asserts', 'goog.proto2.Message', 'goog.proto2.Serializer'], {});
goog.addDependency('proto2/message.js', ['goog.proto2.Message'], ['goog.asserts', 'goog.proto2.Descriptor', 'goog.proto2.FieldDescriptor'], {});
goog.addDependency('proto2/message_test.js', ['goog.proto2.MessageTest'], ['goog.testing.testSuite', 'proto2.TestAllTypes', 'proto2.TestAllTypes.NestedEnum', 'proto2.TestAllTypes.NestedMessage', 'proto2.TestAllTypes.OptionalGroup', 'proto2.TestAllTypes.RepeatedGroup'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('proto2/objectserializer.js', ['goog.proto2.ObjectSerializer'], ['goog.asserts', 'goog.proto2.FieldDescriptor', 'goog.proto2.Serializer', 'goog.string'], {});
goog.addDependency('proto2/objectserializer_test.js', ['goog.proto2.ObjectSerializerTest'], ['goog.proto2.ObjectSerializer', 'goog.proto2.Serializer', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite', 'proto2.TestAllTypes'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('proto2/package_test.pb.js', ['someprotopackage.TestPackageTypes'], ['goog.proto2.Message', 'proto2.TestAllTypes'], {'lang': 'es6'});
goog.addDependency('proto2/pbliteserializer.js', ['goog.proto2.PbLiteSerializer'], ['goog.asserts', 'goog.proto2.FieldDescriptor', 'goog.proto2.LazyDeserializer', 'goog.proto2.Serializer'], {});
goog.addDependency('proto2/pbliteserializer_test.js', ['goog.proto2.PbLiteSerializerTest'], ['goog.proto2.PbLiteSerializer', 'goog.testing.testSuite', 'proto2.TestAllTypes'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('proto2/proto_test.js', ['goog.proto2.messageTest'], ['goog.proto2.FieldDescriptor', 'goog.testing.testSuite', 'proto2.TestAllTypes', 'proto2.TestDefaultParent', 'someprotopackage.TestPackageTypes'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('proto2/serializer.js', ['goog.proto2.Serializer'], ['goog.asserts', 'goog.proto2.FieldDescriptor', 'goog.proto2.Message'], {});
goog.addDependency('proto2/test.pb.js', ['proto2.TestAllTypes', 'proto2.TestAllTypes.NestedEnum', 'proto2.TestAllTypes.NestedMessage', 'proto2.TestAllTypes.OptionalGroup', 'proto2.TestAllTypes.RepeatedGroup', 'proto2.TestDefaultChild', 'proto2.TestDefaultParent'], ['goog.proto2.Message'], {});
goog.addDependency('proto2/textformatserializer.js', ['goog.proto2.TextFormatSerializer'], ['goog.array', 'goog.asserts', 'goog.math', 'goog.object', 'goog.proto2.FieldDescriptor', 'goog.proto2.Message', 'goog.proto2.Serializer', 'goog.string'], {});
goog.addDependency('proto2/textformatserializer_test.js', ['goog.proto2.TextFormatSerializerTest'], ['goog.proto2.ObjectSerializer', 'goog.proto2.TextFormatSerializer', 'goog.testing.testSuite', 'proto2.TestAllTypes'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('proto2/util.js', ['goog.proto2.Util'], ['goog.asserts'], {});
goog.addDependency('pubsub/pubsub.js', ['goog.pubsub.PubSub'], ['goog.Disposable', 'goog.array', 'goog.async.run'], {});
goog.addDependency('pubsub/pubsub_test.js', ['goog.pubsub.PubSubTest'], ['goog.array', 'goog.pubsub.PubSub', 'goog.testing.MockClock', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('pubsub/topicid.js', ['goog.pubsub.TopicId'], [], {});
goog.addDependency('pubsub/typedpubsub.js', ['goog.pubsub.TypedPubSub'], ['goog.Disposable', 'goog.pubsub.PubSub'], {});
goog.addDependency('pubsub/typedpubsub_test.js', ['goog.pubsub.TypedPubSubTest'], ['goog.array', 'goog.pubsub.TopicId', 'goog.pubsub.TypedPubSub', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('reflect/reflect.js', ['goog.reflect'], [], {'lang': 'es6'});
goog.addDependency('reflect/reflect_test.js', ['goog.reflectTest'], ['goog.object', 'goog.reflect', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('result/chain_test.js', ['goog.result.chainTest'], ['goog.Timer', 'goog.result', 'goog.testing.MockClock', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('result/combine_test.js', ['goog.result.combineTest'], ['goog.Timer', 'goog.array', 'goog.result', 'goog.testing.MockClock', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('result/deferredadaptor.js', ['goog.result.DeferredAdaptor'], ['goog.async.Deferred', 'goog.result', 'goog.result.Result'], {});
goog.addDependency('result/deferredadaptor_test.js', ['goog.result.DeferredAdaptorTest'], ['goog.async.Deferred', 'goog.result', 'goog.result.DeferredAdaptor', 'goog.result.SimpleResult', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('result/dependentresult.js', ['goog.result.DependentResult'], ['goog.result.Result'], {});
goog.addDependency('result/result_interface.js', ['goog.result.Result'], ['goog.Thenable'], {});
goog.addDependency('result/resultutil.js', ['goog.result'], ['goog.array', 'goog.result.DependentResult', 'goog.result.Result', 'goog.result.SimpleResult'], {});
goog.addDependency('result/resultutil_test.js', ['goog.resultTest'], ['goog.result', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('result/simpleresult.js', ['goog.result.SimpleResult', 'goog.result.SimpleResult.StateError'], ['goog.Promise', 'goog.Thenable', 'goog.debug.Error', 'goog.result.Result'], {});
goog.addDependency('result/simpleresult_test.js', ['goog.result.SimpleResultTest'], ['goog.Promise', 'goog.Thenable', 'goog.Timer', 'goog.result', 'goog.testing.MockClock', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('result/transform_test.js', ['goog.result.transformTest'], ['goog.Timer', 'goog.result', 'goog.result.SimpleResult', 'goog.testing.MockClock', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('result/wait_test.js', ['goog.result.waitTest'], ['goog.Timer', 'goog.result', 'goog.result.SimpleResult', 'goog.testing.MockClock', 'goog.testing.PropertyReplacer', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('soy/data.js', ['goog.soy.data.SanitizedContent', 'goog.soy.data.SanitizedContentKind', 'goog.soy.data.SanitizedCss', 'goog.soy.data.SanitizedHtml', 'goog.soy.data.SanitizedHtmlAttribute', 'goog.soy.data.SanitizedJs', 'goog.soy.data.SanitizedTrustedResourceUri', 'goog.soy.data.SanitizedUri'], ['goog.Uri', 'goog.asserts', 'goog.html.SafeHtml', 'goog.html.SafeScript', 'goog.html.SafeStyle', 'goog.html.SafeStyleSheet', 'goog.html.SafeUrl', 'goog.html.TrustedResourceUrl', 'goog.html.uncheckedconversions', 'goog.i18n.bidi.Dir', 'goog.string.Const'], {});
goog.addDependency('soy/data_test.js', ['goog.soy.dataTest'], ['goog.html.SafeHtml', 'goog.html.SafeStyleSheet', 'goog.html.SafeUrl', 'goog.html.TrustedResourceUrl', 'goog.soy.testHelper', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('soy/renderer.js', ['goog.soy.InjectedDataSupplier', 'goog.soy.Renderer'], ['goog.asserts', 'goog.dom', 'goog.soy', 'goog.soy.data.SanitizedContent', 'goog.soy.data.SanitizedContentKind'], {});
goog.addDependency('soy/renderer_test.js', ['goog.soy.RendererTest'], ['goog.dom', 'goog.dom.NodeType', 'goog.dom.TagName', 'goog.html.SafeHtml', 'goog.i18n.bidi.Dir', 'goog.soy.Renderer', 'goog.soy.data.SanitizedContentKind', 'goog.soy.testHelper', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('soy/soy.js', ['goog.soy'], ['goog.asserts', 'goog.dom', 'goog.dom.NodeType', 'goog.dom.TagName', 'goog.dom.safe', 'goog.html.SafeHtml', 'goog.soy.data.SanitizedContent'], {});
goog.addDependency('soy/soy_test.js', ['goog.soyTest'], ['goog.dom', 'goog.dom.NodeType', 'goog.dom.TagName', 'goog.functions', 'goog.soy', 'goog.soy.testHelper', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('soy/soy_testhelper.js', ['goog.soy.testHelper'], ['goog.dom', 'goog.dom.TagName', 'goog.i18n.bidi.Dir', 'goog.soy.data.SanitizedContent', 'goog.soy.data.SanitizedContentKind', 'goog.soy.data.SanitizedCss', 'goog.soy.data.SanitizedTrustedResourceUri', 'goog.string', 'goog.userAgent'], {'lang': 'es6'});
goog.addDependency('spell/spellcheck.js', ['goog.spell.SpellCheck', 'goog.spell.SpellCheck.WordChangedEvent'], ['goog.Timer', 'goog.events.Event', 'goog.events.EventTarget', 'goog.structs.Set'], {});
goog.addDependency('spell/spellcheck_test.js', ['goog.spell.SpellCheckTest'], ['goog.spell.SpellCheck', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('stats/basicstat.js', ['goog.stats.BasicStat'], ['goog.asserts', 'goog.log', 'goog.string.format', 'goog.structs.CircularBuffer'], {});
goog.addDependency('stats/basicstat_test.js', ['goog.stats.BasicStatTest'], ['goog.array', 'goog.stats.BasicStat', 'goog.string.format', 'goog.testing.PseudoRandom', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('storage/collectablestorage.js', ['goog.storage.CollectableStorage'], ['goog.array', 'goog.iter', 'goog.storage.ErrorCode', 'goog.storage.ExpiringStorage', 'goog.storage.RichStorage'], {});
goog.addDependency('storage/collectablestorage_test.js', ['goog.storage.CollectableStorageTest'], ['goog.storage.CollectableStorage', 'goog.storage.collectableStorageTester', 'goog.storage.storageTester', 'goog.testing.MockClock', 'goog.testing.storage.FakeMechanism', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('storage/collectablestoragetester.js', ['goog.storage.collectableStorageTester'], ['goog.testing.asserts'], {});
goog.addDependency('storage/encryptedstorage.js', ['goog.storage.EncryptedStorage'], ['goog.crypt', 'goog.crypt.Arc4', 'goog.crypt.Sha1', 'goog.crypt.base64', 'goog.json', 'goog.json.Serializer', 'goog.storage.CollectableStorage', 'goog.storage.ErrorCode', 'goog.storage.RichStorage'], {});
goog.addDependency('storage/encryptedstorage_test.js', ['goog.storage.EncryptedStorageTest'], ['goog.json', 'goog.storage.EncryptedStorage', 'goog.storage.ErrorCode', 'goog.storage.RichStorage', 'goog.storage.collectableStorageTester', 'goog.storage.storageTester', 'goog.testing.MockClock', 'goog.testing.PseudoRandom', 'goog.testing.storage.FakeMechanism', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('storage/errorcode.js', ['goog.storage.ErrorCode'], [], {});
goog.addDependency('storage/expiringstorage.js', ['goog.storage.ExpiringStorage'], ['goog.storage.RichStorage'], {});
goog.addDependency('storage/expiringstorage_test.js', ['goog.storage.ExpiringStorageTest'], ['goog.storage.ExpiringStorage', 'goog.storage.storageTester', 'goog.testing.MockClock', 'goog.testing.storage.FakeMechanism', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('storage/mechanism/errorcode.js', ['goog.storage.mechanism.ErrorCode'], [], {});
goog.addDependency('storage/mechanism/errorhandlingmechanism.js', ['goog.storage.mechanism.ErrorHandlingMechanism'], ['goog.storage.mechanism.Mechanism'], {});
goog.addDependency('storage/mechanism/errorhandlingmechanism_test.js', ['goog.storage.mechanism.ErrorHandlingMechanismTest'], ['goog.storage.mechanism.ErrorHandlingMechanism', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('storage/mechanism/html5localstorage.js', ['goog.storage.mechanism.HTML5LocalStorage'], ['goog.storage.mechanism.HTML5WebStorage'], {});
goog.addDependency('storage/mechanism/html5localstorage_test.js', ['goog.storage.mechanism.HTML5LocalStorageTest'], ['goog.storage.mechanism.HTML5LocalStorage', 'goog.storage.mechanism.mechanismSeparationTester', 'goog.storage.mechanism.mechanismSharingTester', 'goog.storage.mechanism.mechanismTestDefinition', 'goog.testing.testSuite', 'goog.userAgent', 'goog.userAgent.product'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('storage/mechanism/html5sessionstorage.js', ['goog.storage.mechanism.HTML5SessionStorage'], ['goog.storage.mechanism.HTML5WebStorage'], {});
goog.addDependency('storage/mechanism/html5sessionstorage_test.js', ['goog.storage.mechanism.HTML5SessionStorageTest'], ['goog.storage.mechanism.HTML5SessionStorage', 'goog.storage.mechanism.mechanismSeparationTester', 'goog.storage.mechanism.mechanismSharingTester', 'goog.storage.mechanism.mechanismTestDefinition', 'goog.testing.testSuite', 'goog.userAgent', 'goog.userAgent.product'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('storage/mechanism/html5webstorage.js', ['goog.storage.mechanism.HTML5WebStorage'], ['goog.asserts', 'goog.iter.Iterator', 'goog.iter.StopIteration', 'goog.storage.mechanism.ErrorCode', 'goog.storage.mechanism.IterableMechanism'], {});
goog.addDependency('storage/mechanism/html5webstorage_test.js', ['goog.storage.mechanism.HTML5WebStorageTest'], ['goog.storage.mechanism.ErrorCode', 'goog.storage.mechanism.HTML5WebStorage', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('storage/mechanism/ieuserdata.js', ['goog.storage.mechanism.IEUserData'], ['goog.asserts', 'goog.iter.Iterator', 'goog.iter.StopIteration', 'goog.storage.mechanism.ErrorCode', 'goog.storage.mechanism.IterableMechanism', 'goog.structs.Map', 'goog.userAgent'], {});
goog.addDependency('storage/mechanism/ieuserdata_test.js', ['goog.storage.mechanism.IEUserDataTest'], ['goog.storage.mechanism.IEUserData', 'goog.storage.mechanism.mechanismSeparationTester', 'goog.storage.mechanism.mechanismSharingTester', 'goog.storage.mechanism.mechanismTestDefinition', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('storage/mechanism/iterablemechanism.js', ['goog.storage.mechanism.IterableMechanism'], ['goog.array', 'goog.asserts', 'goog.iter', 'goog.storage.mechanism.Mechanism'], {});
goog.addDependency('storage/mechanism/iterablemechanismtester.js', ['goog.storage.mechanism.iterableMechanismTester'], ['goog.iter', 'goog.iter.StopIteration', 'goog.testing.asserts'], {});
goog.addDependency('storage/mechanism/mechanism.js', ['goog.storage.mechanism.Mechanism'], [], {});
goog.addDependency('storage/mechanism/mechanismfactory.js', ['goog.storage.mechanism.mechanismfactory'], ['goog.storage.mechanism.HTML5LocalStorage', 'goog.storage.mechanism.HTML5SessionStorage', 'goog.storage.mechanism.IEUserData', 'goog.storage.mechanism.PrefixedMechanism'], {});
goog.addDependency('storage/mechanism/mechanismfactory_test.js', ['goog.storage.mechanism.mechanismfactoryTest'], ['goog.storage.mechanism.mechanismfactory', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('storage/mechanism/mechanismseparationtester.js', ['goog.storage.mechanism.mechanismSeparationTester'], ['goog.iter.StopIteration', 'goog.storage.mechanism.mechanismTestDefinition', 'goog.testing.asserts'], {});
goog.addDependency('storage/mechanism/mechanismsharingtester.js', ['goog.storage.mechanism.mechanismSharingTester'], ['goog.iter.StopIteration', 'goog.storage.mechanism.mechanismTestDefinition', 'goog.testing.asserts'], {});
goog.addDependency('storage/mechanism/mechanismtestdefinition.js', ['goog.storage.mechanism.mechanismTestDefinition'], [], {});
goog.addDependency('storage/mechanism/mechanismtester.js', ['goog.storage.mechanism.mechanismTester'], ['goog.storage.mechanism.ErrorCode', 'goog.testing.asserts', 'goog.userAgent', 'goog.userAgent.product', 'goog.userAgent.product.isVersion'], {});
goog.addDependency('storage/mechanism/prefixedmechanism.js', ['goog.storage.mechanism.PrefixedMechanism'], ['goog.iter.Iterator', 'goog.storage.mechanism.IterableMechanism'], {});
goog.addDependency('storage/mechanism/prefixedmechanism_test.js', ['goog.storage.mechanism.PrefixedMechanismTest'], ['goog.storage.mechanism.HTML5LocalStorage', 'goog.storage.mechanism.PrefixedMechanism', 'goog.storage.mechanism.mechanismSeparationTester', 'goog.storage.mechanism.mechanismSharingTester', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('storage/richstorage.js', ['goog.storage.RichStorage', 'goog.storage.RichStorage.Wrapper'], ['goog.storage.ErrorCode', 'goog.storage.Storage'], {});
goog.addDependency('storage/richstorage_test.js', ['goog.storage.RichStorageTest'], ['goog.storage.ErrorCode', 'goog.storage.RichStorage', 'goog.storage.storageTester', 'goog.testing.storage.FakeMechanism', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('storage/storage.js', ['goog.storage.Storage'], ['goog.json', 'goog.storage.ErrorCode'], {});
goog.addDependency('storage/storage_test.js', ['goog.storage.storage_test'], ['goog.functions', 'goog.storage.ErrorCode', 'goog.storage.Storage', 'goog.storage.storageTester', 'goog.testing.asserts', 'goog.testing.storage.FakeMechanism', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('storage/storagetester.js', ['goog.storage.storageTester'], ['goog.storage.Storage', 'goog.structs.Map', 'goog.testing.asserts'], {});
goog.addDependency('streams/defines.js', ['goog.streams.defines'], [], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('streams/full.js', ['goog.streams.full'], ['goog.streams.defines', 'goog.streams.fullImpl', 'goog.streams.fullNativeImpl', 'goog.streams.fullTypes'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('streams/full_impl.js', ['goog.streams.fullImpl'], ['goog.asserts', 'goog.promise.NativeResolver', 'goog.streams.fullTypes', 'goog.streams.liteImpl'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('streams/full_impl_test.js', ['goog.streams.fullImplTest'], ['goog.streams.fullImpl', 'goog.streams.fullTestCases', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('streams/full_native_impl.js', ['goog.streams.fullNativeImpl'], ['goog.streams.fullTypes', 'goog.streams.liteNativeImpl'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('streams/full_native_impl_test.js', ['goog.streams.fullNativeImplTest'], ['goog.streams.fullNativeImpl', 'goog.streams.fullTestCases', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('streams/full_test_cases.js', ['goog.streams.fullTestCases'], ['goog.streams.fullTypes', 'goog.streams.liteTestCases', 'goog.testing.recordFunction'], {'lang': 'es9', 'module': 'goog'});
goog.addDependency('streams/full_types.js', ['goog.streams.fullTypes'], ['goog.streams.liteTypes'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('streams/lite.js', ['goog.streams.lite'], ['goog.streams.defines', 'goog.streams.liteImpl', 'goog.streams.liteNativeImpl', 'goog.streams.liteTypes'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('streams/lite_impl.js', ['goog.streams.liteImpl'], ['goog.asserts', 'goog.promise.NativeResolver', 'goog.streams.liteTypes'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('streams/lite_impl_test.js', ['goog.streams.liteImplTest'], ['goog.streams.liteImpl', 'goog.streams.liteTestCases', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('streams/lite_native_impl.js', ['goog.streams.liteNativeImpl'], ['goog.streams.liteTypes'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('streams/lite_native_impl_test.js', ['goog.streams.liteNativeImplTest'], ['goog.streams.liteNativeImpl', 'goog.streams.liteTestCases', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('streams/lite_test_cases.js', ['goog.streams.liteTestCases'], ['goog.streams.liteTypes', 'goog.testing.jsunit'], {'lang': 'es8', 'module': 'goog'});
goog.addDependency('streams/lite_types.js', ['goog.streams.liteTypes'], [], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('string/const.js', ['goog.string.Const'], ['goog.asserts', 'goog.string.TypedString'], {});
goog.addDependency('string/const_test.js', ['goog.string.constTest'], ['goog.string.Const', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('string/internal.js', ['goog.string.internal'], [], {'lang': 'es6'});
goog.addDependency('string/linkify.js', ['goog.string.linkify'], ['goog.html.SafeHtml', 'goog.string'], {});
goog.addDependency('string/linkify_test.js', ['goog.string.linkifyTest'], ['goog.dom', 'goog.dom.TagName', 'goog.dom.safe', 'goog.html.SafeHtml', 'goog.string', 'goog.string.linkify', 'goog.testing.dom', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('string/newlines.js', ['goog.string.newlines', 'goog.string.newlines.Line'], ['goog.array'], {});
goog.addDependency('string/newlines_test.js', ['goog.string.newlinesTest'], ['goog.string.newlines', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('string/parser.js', ['goog.string.Parser'], [], {});
goog.addDependency('string/path.js', ['goog.string.path'], ['goog.array', 'goog.string'], {});
goog.addDependency('string/path_test.js', ['goog.string.pathTest'], ['goog.string.path', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('string/string.js', ['goog.string', 'goog.string.Unicode'], ['goog.dom.safe', 'goog.html.uncheckedconversions', 'goog.string.Const', 'goog.string.internal'], {});
goog.addDependency('string/string_test.js', ['goog.stringTest'], ['goog.dom', 'goog.dom.TagName', 'goog.functions', 'goog.object', 'goog.string', 'goog.string.Unicode', 'goog.testing.MockControl', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('string/stringbuffer.js', ['goog.string.StringBuffer'], [], {'lang': 'es6'});
goog.addDependency('string/stringbuffer_test.js', ['goog.string.StringBufferTest'], ['goog.string.StringBuffer', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('string/stringformat.js', ['goog.string.format'], ['goog.string'], {});
goog.addDependency('string/stringformat_test.js', ['goog.string.formatTest'], ['goog.string.format', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('string/stringifier.js', ['goog.string.Stringifier'], [], {});
goog.addDependency('string/typedstring.js', ['goog.string.TypedString'], [], {});
goog.addDependency('structs/avltree.js', ['goog.structs.AvlTree'], ['goog.asserts', 'goog.structs.Collection'], {'module': 'goog'});
goog.addDependency('structs/avltree_test.js', ['goog.structs.AvlTreeTest'], ['goog.array', 'goog.structs.AvlTree', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('structs/circularbuffer.js', ['goog.structs.CircularBuffer'], [], {'lang': 'es6'});
goog.addDependency('structs/circularbuffer_test.js', ['goog.structs.CircularBufferTest'], ['goog.structs.CircularBuffer', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('structs/collection.js', ['goog.structs.Collection'], [], {});
goog.addDependency('structs/collection_test.js', ['goog.structs.CollectionTest'], ['goog.structs.AvlTree', 'goog.structs.Set', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('structs/heap.js', ['goog.structs.Heap'], ['goog.array', 'goog.object', 'goog.structs.Node'], {});
goog.addDependency('structs/heap_test.js', ['goog.structs.HeapTest'], ['goog.structs', 'goog.structs.Heap', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('structs/inversionmap.js', ['goog.structs.InversionMap'], ['goog.array', 'goog.asserts'], {});
goog.addDependency('structs/inversionmap_test.js', ['goog.structs.InversionMapTest'], ['goog.structs.InversionMap', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('structs/linkedmap.js', ['goog.structs.LinkedMap'], ['goog.structs.Map'], {});
goog.addDependency('structs/linkedmap_test.js', ['goog.structs.LinkedMapTest'], ['goog.structs.LinkedMap', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('structs/map.js', ['goog.structs.Map'], ['goog.iter.Iterator', 'goog.iter.StopIteration'], {});
goog.addDependency('structs/map_test.js', ['goog.structs.MapTest'], ['goog.iter', 'goog.structs', 'goog.structs.Map', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('structs/node.js', ['goog.structs.Node'], [], {});
goog.addDependency('structs/pool.js', ['goog.structs.Pool'], ['goog.Disposable', 'goog.structs.Queue', 'goog.structs.Set'], {});
goog.addDependency('structs/pool_test.js', ['goog.structs.PoolTest'], ['goog.structs.Pool', 'goog.testing.MockClock', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('structs/prioritypool.js', ['goog.structs.PriorityPool'], ['goog.structs.Pool', 'goog.structs.PriorityQueue'], {});
goog.addDependency('structs/prioritypool_test.js', ['goog.structs.PriorityPoolTest'], ['goog.structs.PriorityPool', 'goog.testing.MockClock', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('structs/priorityqueue.js', ['goog.structs.PriorityQueue'], ['goog.structs.Heap'], {});
goog.addDependency('structs/priorityqueue_test.js', ['goog.structs.PriorityQueueTest'], ['goog.structs', 'goog.structs.PriorityQueue', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('structs/quadtree.js', ['goog.structs.QuadTree', 'goog.structs.QuadTree.Node', 'goog.structs.QuadTree.Point'], ['goog.math.Coordinate'], {});
goog.addDependency('structs/quadtree_test.js', ['goog.structs.QuadTreeTest'], ['goog.structs', 'goog.structs.QuadTree', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('structs/queue.js', ['goog.structs.Queue'], ['goog.array'], {});
goog.addDependency('structs/queue_test.js', ['goog.structs.QueueTest'], ['goog.structs.Queue', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('structs/set.js', ['goog.structs.Set'], ['goog.structs', 'goog.structs.Collection', 'goog.structs.Map'], {});
goog.addDependency('structs/set_test.js', ['goog.structs.SetTest'], ['goog.iter', 'goog.structs', 'goog.structs.Set', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('structs/simplepool.js', ['goog.structs.SimplePool'], ['goog.Disposable'], {});
goog.addDependency('structs/stringset.js', ['goog.structs.StringSet'], ['goog.asserts', 'goog.iter'], {});
goog.addDependency('structs/stringset_test.js', ['goog.structs.StringSetTest'], ['goog.array', 'goog.iter', 'goog.structs.StringSet', 'goog.testing.asserts', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('structs/structs.js', ['goog.structs'], ['goog.array', 'goog.object'], {});
goog.addDependency('structs/structs_test.js', ['goog.structsTest'], ['goog.array', 'goog.dom', 'goog.dom.TagName', 'goog.structs', 'goog.structs.Map', 'goog.structs.Set', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('structs/treenode.js', ['goog.structs.TreeNode'], ['goog.array', 'goog.asserts', 'goog.structs.Node'], {});
goog.addDependency('structs/treenode_test.js', ['goog.structs.TreeNodeTest'], ['goog.structs.TreeNode', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('structs/trie.js', ['goog.structs.Trie'], ['goog.object', 'goog.structs'], {});
goog.addDependency('structs/trie_test.js', ['goog.structs.TrieTest'], ['goog.object', 'goog.structs', 'goog.structs.Trie', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('style/bidi.js', ['goog.style.bidi'], ['goog.dom', 'goog.style', 'goog.userAgent', 'goog.userAgent.platform', 'goog.userAgent.product', 'goog.userAgent.product.isVersion'], {});
goog.addDependency('style/bidi_test.js', ['goog.style.bidiTest'], ['goog.dom', 'goog.style', 'goog.style.bidi', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('style/cursor.js', ['goog.style.cursor'], ['goog.userAgent'], {});
goog.addDependency('style/cursor_test.js', ['goog.style.cursorTest'], ['goog.style.cursor', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('style/style.js', ['goog.style'], ['goog.array', 'goog.asserts', 'goog.dom', 'goog.dom.NodeType', 'goog.dom.TagName', 'goog.dom.vendor', 'goog.html.SafeStyleSheet', 'goog.math.Box', 'goog.math.Coordinate', 'goog.math.Rect', 'goog.math.Size', 'goog.object', 'goog.reflect', 'goog.string', 'goog.userAgent'], {});
goog.addDependency('style/style_document_scroll_test.js', ['goog.style.style_document_scroll_test'], ['goog.dom', 'goog.style', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('style/style_test.js', ['goog.style_test'], ['goog.array', 'goog.color', 'goog.dom', 'goog.dom.TagName', 'goog.events.BrowserEvent', 'goog.html.testing', 'goog.labs.userAgent.util', 'goog.math.Box', 'goog.math.Coordinate', 'goog.math.Rect', 'goog.math.Size', 'goog.object', 'goog.style', 'goog.testing.ExpectedFailures', 'goog.testing.MockUserAgent', 'goog.testing.asserts', 'goog.testing.testSuite', 'goog.userAgent', 'goog.userAgentTestUtil', 'goog.userAgentTestUtil.UserAgents'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('style/style_webkit_scrollbars_test.js', ['goog.style.webkitScrollbarsTest'], ['goog.asserts', 'goog.style', 'goog.styleScrollbarTester', 'goog.testing.ExpectedFailures', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('style/stylescrollbartester.js', ['goog.styleScrollbarTester'], ['goog.dom', 'goog.dom.TagName', 'goog.style', 'goog.testing.asserts'], {});
goog.addDependency('style/transform.js', ['goog.style.transform'], ['goog.functions', 'goog.math.Coordinate', 'goog.math.Coordinate3', 'goog.style', 'goog.userAgent', 'goog.userAgent.product.isVersion'], {});
goog.addDependency('style/transform_test.js', ['goog.style.transformTest'], ['goog.dom', 'goog.dom.TagName', 'goog.style', 'goog.style.transform', 'goog.testing.testSuite', 'goog.userAgent', 'goog.userAgent.product.isVersion'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('style/transition.js', ['goog.style.transition', 'goog.style.transition.Css3Property'], ['goog.array', 'goog.asserts', 'goog.dom', 'goog.dom.TagName', 'goog.dom.safe', 'goog.dom.vendor', 'goog.functions', 'goog.html.SafeHtml', 'goog.style', 'goog.userAgent'], {});
goog.addDependency('style/transition_test.js', ['goog.style.transitionTest'], ['goog.style', 'goog.style.transition', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('test_module.js', ['goog.test_module'], ['goog.test_module_dep'], {'module': 'goog'});
goog.addDependency('test_module_dep.js', ['goog.test_module_dep'], [], {'module': 'goog'});
goog.addDependency('testing/assertionfailure.js', ['goog.testing.safe.assertionFailure'], ['goog.asserts', 'goog.testing.asserts'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/asserts.js', ['goog.testing.asserts'], ['goog.testing.JsUnitException'], {});
goog.addDependency('testing/asserts_test.js', ['goog.testing.assertsTest'], ['goog.Promise', 'goog.array', 'goog.async.Deferred', 'goog.dom', 'goog.iter.Iterator', 'goog.iter.StopIteration', 'goog.structs.Map', 'goog.structs.Set', 'goog.testing.TestCase', 'goog.testing.asserts', 'goog.testing.testSuite', 'goog.userAgent', 'goog.userAgent.product'], {'lang': 'es8', 'module': 'goog'});
goog.addDependency('testing/async/mockcontrol.js', ['goog.testing.async.MockControl'], ['goog.asserts', 'goog.async.Deferred', 'goog.debug', 'goog.testing.MockControl', 'goog.testing.asserts', 'goog.testing.mockmatchers.IgnoreArgument'], {});
goog.addDependency('testing/async/mockcontrol_test.js', ['goog.testing.async.MockControlTest'], ['goog.async.Deferred', 'goog.testing.MockControl', 'goog.testing.asserts', 'goog.testing.async.MockControl', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/asynctestcase.js', ['goog.testing.AsyncTestCase', 'goog.testing.AsyncTestCase.ControlBreakingException'], ['goog.asserts', 'goog.testing.TestCase', 'goog.testing.asserts'], {});
goog.addDependency('testing/asynctestcase_async_test.js', ['goog.testing.AsyncTestCaseAsyncTest'], ['goog.testing.AsyncTestCase', 'goog.testing.TestCase', 'goog.testing.jsunit'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/asynctestcase_noasync_test.js', ['goog.testing.AsyncTestCaseSyncTest'], ['goog.testing.AsyncTestCase', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/asynctestcase_test.js', ['goog.testing.AsyncTestCaseTest'], ['goog.debug.Error', 'goog.testing.AsyncTestCase', 'goog.testing.asserts', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/benchmark.js', ['goog.testing.benchmark'], ['goog.dom', 'goog.dom.TagName', 'goog.testing.PerformanceTable', 'goog.testing.PerformanceTimer', 'goog.testing.TestCase'], {});
goog.addDependency('testing/continuationtestcase.js', ['goog.testing.ContinuationTestCase', 'goog.testing.ContinuationTestCase.ContinuationTest', 'goog.testing.ContinuationTestCase.Step'], ['goog.array', 'goog.events.EventHandler', 'goog.events.EventTarget', 'goog.testing.TestCase', 'goog.testing.asserts'], {});
goog.addDependency('testing/continuationtestcase_test.js', ['goog.testing.ContinuationTestCaseTest'], ['goog.events', 'goog.events.EventTarget', 'goog.testing.ContinuationTestCase', 'goog.testing.MockClock', 'goog.testing.PropertyReplacer', 'goog.testing.TestCase', 'goog.testing.jsunit'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/deferredtestcase.js', ['goog.testing.DeferredTestCase'], ['goog.async.Deferred', 'goog.testing.AsyncTestCase', 'goog.testing.TestCase'], {});
goog.addDependency('testing/deferredtestcase_test.js', ['goog.testing.DeferredTestCaseTest'], ['goog.async.Deferred', 'goog.testing.DeferredTestCase', 'goog.testing.TestCase', 'goog.testing.TestRunner', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/dom.js', ['goog.testing.dom'], ['goog.array', 'goog.asserts', 'goog.dom', 'goog.dom.AbstractRange', 'goog.dom.InputType', 'goog.dom.NodeIterator', 'goog.dom.NodeType', 'goog.dom.TagIterator', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.iter', 'goog.object', 'goog.string', 'goog.style', 'goog.testing.asserts', 'goog.userAgent'], {});
goog.addDependency('testing/dom_test.js', ['goog.testing.domTest'], ['goog.dom', 'goog.dom.TagName', 'goog.testing.dom', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/editor/dom.js', ['goog.testing.editor.dom'], ['goog.dom.AbstractRange', 'goog.dom.NodeType', 'goog.dom.TagIterator', 'goog.dom.TagWalkType', 'goog.iter', 'goog.string', 'goog.testing.asserts'], {});
goog.addDependency('testing/editor/dom_test.js', ['goog.testing.editor.domTest'], ['goog.dom', 'goog.dom.TagName', 'goog.functions', 'goog.testing.editor.dom', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/editor/fieldmock.js', ['goog.testing.editor.FieldMock'], ['goog.dom', 'goog.dom.Range', 'goog.editor.Field', 'goog.testing.LooseMock', 'goog.testing.mockmatchers'], {});
goog.addDependency('testing/editor/testhelper.js', ['goog.testing.editor.TestHelper'], ['goog.Disposable', 'goog.dom', 'goog.dom.Range', 'goog.editor.BrowserFeature', 'goog.editor.node', 'goog.editor.plugins.AbstractBubblePlugin', 'goog.testing.dom'], {});
goog.addDependency('testing/editor/testhelper_test.js', ['goog.testing.editor.TestHelperTest'], ['goog.dom', 'goog.dom.TagName', 'goog.editor.node', 'goog.testing.editor.TestHelper', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/events/eventobserver.js', ['goog.testing.events.EventObserver'], ['goog.array', 'goog.events.Event'], {});
goog.addDependency('testing/events/eventobserver_test.js', ['goog.testing.events.EventObserverTest'], ['goog.array', 'goog.events', 'goog.events.Event', 'goog.events.EventTarget', 'goog.testing.events.EventObserver', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/events/events.js', ['goog.testing.events', 'goog.testing.events.Event'], ['goog.Disposable', 'goog.asserts', 'goog.dom.NodeType', 'goog.events', 'goog.events.BrowserEvent', 'goog.events.BrowserFeature', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.object', 'goog.style', 'goog.userAgent'], {});
goog.addDependency('testing/events/events_test.js', ['goog.testing.eventsTest'], ['goog.array', 'goog.dom', 'goog.dom.InputType', 'goog.dom.TagName', 'goog.events', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.math.Coordinate', 'goog.string', 'goog.style', 'goog.testing.PropertyReplacer', 'goog.testing.events', 'goog.testing.recordFunction', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/events/matchers.js', ['goog.testing.events.EventMatcher'], ['goog.events.Event', 'goog.testing.mockmatchers.ArgumentMatcher'], {});
goog.addDependency('testing/events/matchers_test.js', ['goog.testing.events.EventMatcherTest'], ['goog.events.Event', 'goog.testing.events.EventMatcher', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/events/onlinehandler.js', ['goog.testing.events.OnlineHandler'], ['goog.events.EventTarget', 'goog.net.NetworkStatusMonitor'], {});
goog.addDependency('testing/events/onlinehandler_test.js', ['goog.testing.events.OnlineHandlerTest'], ['goog.events', 'goog.net.NetworkStatusMonitor', 'goog.testing.events.EventObserver', 'goog.testing.events.OnlineHandler', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/expectedfailures.js', ['goog.testing.ExpectedFailures'], ['goog.asserts', 'goog.debug.DivConsole', 'goog.dom', 'goog.dom.TagName', 'goog.events', 'goog.events.EventType', 'goog.log', 'goog.style', 'goog.testing.JsUnitException', 'goog.testing.TestCase', 'goog.testing.asserts'], {});
goog.addDependency('testing/expectedfailures_test.js', ['goog.testing.ExpectedFailuresTest'], ['goog.debug.Logger', 'goog.testing.ExpectedFailures', 'goog.testing.JsUnitException', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/fs/blob.js', ['goog.testing.fs.Blob'], ['goog.crypt', 'goog.crypt.base64'], {});
goog.addDependency('testing/fs/blob_test.js', ['goog.testing.fs.BlobTest'], ['goog.dom', 'goog.testing.fs.Blob', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/fs/directoryentry_test.js', ['goog.testing.fs.DirectoryEntryTest'], ['goog.array', 'goog.fs.DirectoryEntry', 'goog.fs.Error', 'goog.testing.MockClock', 'goog.testing.TestCase', 'goog.testing.fs.FileSystem', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/fs/entry.js', ['goog.testing.fs.DirectoryEntry', 'goog.testing.fs.Entry', 'goog.testing.fs.FileEntry'], ['goog.Timer', 'goog.array', 'goog.asserts', 'goog.async.Deferred', 'goog.fs.DirectoryEntry', 'goog.fs.DirectoryEntryImpl', 'goog.fs.Entry', 'goog.fs.Error', 'goog.fs.FileEntry', 'goog.functions', 'goog.object', 'goog.string', 'goog.testing.fs.File', 'goog.testing.fs.FileWriter'], {});
goog.addDependency('testing/fs/entry_test.js', ['goog.testing.fs.EntryTest'], ['goog.fs.DirectoryEntry', 'goog.fs.Error', 'goog.testing.MockClock', 'goog.testing.TestCase', 'goog.testing.fs.FileSystem', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/fs/file.js', ['goog.testing.fs.File'], ['goog.testing.fs.Blob'], {});
goog.addDependency('testing/fs/fileentry_test.js', ['goog.testing.fs.FileEntryTest'], ['goog.testing.MockClock', 'goog.testing.fs.FileEntry', 'goog.testing.fs.FileSystem', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/fs/filereader.js', ['goog.testing.fs.FileReader'], ['goog.Timer', 'goog.events.EventTarget', 'goog.fs.Error', 'goog.fs.FileReader', 'goog.testing.fs.Blob', 'goog.testing.fs.ProgressEvent'], {});
goog.addDependency('testing/fs/filereader_test.js', ['goog.testing.fs.FileReaderTest'], ['goog.Promise', 'goog.array', 'goog.events', 'goog.fs.Error', 'goog.fs.FileReader', 'goog.object', 'goog.testing.events.EventObserver', 'goog.testing.fs.FileReader', 'goog.testing.fs.FileSystem', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/fs/filesystem.js', ['goog.testing.fs.FileSystem'], ['goog.fs.FileSystem', 'goog.testing.fs.DirectoryEntry'], {});
goog.addDependency('testing/fs/filewriter.js', ['goog.testing.fs.FileWriter'], ['goog.Timer', 'goog.events.EventTarget', 'goog.fs.Error', 'goog.fs.FileSaver', 'goog.string', 'goog.testing.fs.Blob', 'goog.testing.fs.File', 'goog.testing.fs.ProgressEvent'], {});
goog.addDependency('testing/fs/filewriter_test.js', ['goog.testing.fs.FileWriterTest'], ['goog.Promise', 'goog.array', 'goog.events', 'goog.fs.Error', 'goog.fs.FileSaver', 'goog.object', 'goog.testing.MockClock', 'goog.testing.events.EventObserver', 'goog.testing.fs.Blob', 'goog.testing.fs.FileSystem', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/fs/fs.js', ['goog.testing.fs'], ['goog.Timer', 'goog.array', 'goog.async.Deferred', 'goog.fs', 'goog.testing.PropertyReplacer', 'goog.testing.fs.Blob', 'goog.testing.fs.FileSystem'], {});
goog.addDependency('testing/fs/fs_test.js', ['goog.testing.fsTest'], ['goog.testing.fs', 'goog.testing.fs.Blob', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/fs/integration_test.js', ['goog.testing.fs.integrationTest'], ['goog.Promise', 'goog.events', 'goog.fs', 'goog.fs.DirectoryEntry', 'goog.fs.Error', 'goog.fs.FileSaver', 'goog.testing.PropertyReplacer', 'goog.testing.fs', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/fs/progressevent.js', ['goog.testing.fs.ProgressEvent'], ['goog.events.Event'], {});
goog.addDependency('testing/functionmock.js', ['goog.testing', 'goog.testing.FunctionMock', 'goog.testing.GlobalFunctionMock', 'goog.testing.MethodMock'], ['goog.object', 'goog.testing.LooseMock', 'goog.testing.Mock', 'goog.testing.PropertyReplacer', 'goog.testing.StrictMock'], {});
goog.addDependency('testing/functionmock_test.js', ['goog.testing.FunctionMockTest'], ['goog.array', 'goog.string', 'goog.testing', 'goog.testing.FunctionMock', 'goog.testing.Mock', 'goog.testing.StrictMock', 'goog.testing.asserts', 'goog.testing.mockmatchers', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/graphics.js', ['goog.testing.graphics'], ['goog.graphics.Path', 'goog.testing.asserts'], {});
goog.addDependency('testing/i18n/asserts.js', ['goog.testing.i18n.asserts'], ['goog.testing.jsunit'], {'lang': 'es6'});
goog.addDependency('testing/i18n/asserts_test.js', ['goog.testing.i18n.assertsTest'], ['goog.testing.ExpectedFailures', 'goog.testing.i18n.asserts', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/jstdasyncwrapper.js', ['goog.testing.JsTdAsyncWrapper'], ['goog.Promise'], {});
goog.addDependency('testing/jstdtestcaseadapter.js', ['goog.testing.JsTdTestCaseAdapter'], ['goog.async.run', 'goog.functions', 'goog.testing.JsTdAsyncWrapper', 'goog.testing.TestCase', 'goog.testing.jsunit'], {});
goog.addDependency('testing/jsunit.js', ['goog.testing.jsunit'], ['goog.dom.TagName', 'goog.testing.TestCase', 'goog.testing.TestRunner', 'goog.userAgent'], {});
goog.addDependency('testing/jsunitexception.js', ['goog.testing.JsUnitException'], ['goog.testing.stacktrace'], {});
goog.addDependency('testing/loosemock.js', ['goog.testing.LooseExpectationCollection', 'goog.testing.LooseMock'], ['goog.array', 'goog.asserts', 'goog.structs.Map', 'goog.structs.Set', 'goog.testing.Mock'], {});
goog.addDependency('testing/loosemock_test.js', ['goog.testing.LooseMockTest'], ['goog.testing.LooseMock', 'goog.testing.mockmatchers', 'goog.testing.testSuite'], {'lang': 'es8', 'module': 'goog'});
goog.addDependency('testing/messaging/mockmessagechannel.js', ['goog.testing.messaging.MockMessageChannel'], ['goog.messaging.AbstractChannel', 'goog.testing.MockControl', 'goog.testing.asserts'], {});
goog.addDependency('testing/messaging/mockmessageevent.js', ['goog.testing.messaging.MockMessageEvent'], ['goog.events.BrowserEvent', 'goog.events.EventType', 'goog.testing.events.Event'], {});
goog.addDependency('testing/messaging/mockmessageport.js', ['goog.testing.messaging.MockMessagePort'], ['goog.events.EventTarget', 'goog.testing.MockControl'], {});
goog.addDependency('testing/messaging/mockportnetwork.js', ['goog.testing.messaging.MockPortNetwork'], ['goog.messaging.PortNetwork', 'goog.testing.messaging.MockMessageChannel'], {});
goog.addDependency('testing/mock.js', ['goog.testing.Mock', 'goog.testing.MockExpectation'], ['goog.Promise', 'goog.array', 'goog.asserts', 'goog.object', 'goog.promise.Resolver', 'goog.testing.JsUnitException', 'goog.testing.MockInterface', 'goog.testing.mockmatchers'], {});
goog.addDependency('testing/mock_test.js', ['goog.testing.MockTest'], ['goog.array', 'goog.testing', 'goog.testing.Mock', 'goog.testing.MockControl', 'goog.testing.MockExpectation', 'goog.testing.testSuite'], {'lang': 'es8', 'module': 'goog'});
goog.addDependency('testing/mockclassfactory.js', ['goog.testing.MockClassFactory', 'goog.testing.MockClassRecord'], ['goog.array', 'goog.object', 'goog.testing.LooseMock', 'goog.testing.StrictMock', 'goog.testing.TestCase', 'goog.testing.mockmatchers'], {});
goog.addDependency('testing/mockclassfactory_test.js', ['fake.BaseClass', 'fake.ChildClass', 'goog.testing.MockClassFactoryTest'], ['goog.testing', 'goog.testing.MockClassFactory', 'goog.testing.jsunit'], {'lang': 'es6'});
goog.addDependency('testing/mockclock.js', ['goog.testing.MockClock'], ['goog.Disposable', 'goog.Promise', 'goog.Thenable', 'goog.async.run', 'goog.testing.PropertyReplacer', 'goog.testing.events', 'goog.testing.events.Event'], {});
goog.addDependency('testing/mockclock_test.js', ['goog.testing.MockClockTest'], ['goog.Promise', 'goog.Timer', 'goog.events', 'goog.functions', 'goog.testing.MockClock', 'goog.testing.PropertyReplacer', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/mockcontrol.js', ['goog.testing.MockControl'], ['goog.Promise', 'goog.array', 'goog.testing', 'goog.testing.LooseMock', 'goog.testing.StrictMock'], {});
goog.addDependency('testing/mockcontrol_test.js', ['goog.testing.MockControlTest'], ['goog.testing.Mock', 'goog.testing.MockControl', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/mockinterface.js', ['goog.testing.MockInterface'], ['goog.Promise'], {});
goog.addDependency('testing/mockmatchers.js', ['goog.testing.mockmatchers', 'goog.testing.mockmatchers.ArgumentMatcher', 'goog.testing.mockmatchers.IgnoreArgument', 'goog.testing.mockmatchers.InstanceOf', 'goog.testing.mockmatchers.ObjectEquals', 'goog.testing.mockmatchers.RegexpMatch', 'goog.testing.mockmatchers.SaveArgument', 'goog.testing.mockmatchers.TypeOf'], ['goog.array', 'goog.dom', 'goog.testing.asserts'], {});
goog.addDependency('testing/mockmatchers_test.js', ['goog.testing.mockmatchersTest'], ['goog.dom', 'goog.dom.TagName', 'goog.testing.jsunit', 'goog.testing.mockmatchers', 'goog.testing.mockmatchers.ArgumentMatcher'], {'lang': 'es6'});
goog.addDependency('testing/mockrandom.js', ['goog.testing.MockRandom'], ['goog.Disposable'], {});
goog.addDependency('testing/mockrandom_test.js', ['goog.testing.MockRandomTest'], ['goog.testing.MockRandom', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/mockrange.js', ['goog.testing.MockRange'], ['goog.dom.AbstractRange', 'goog.testing.LooseMock'], {});
goog.addDependency('testing/mockrange_test.js', ['goog.testing.MockRangeTest'], ['goog.testing.MockRange', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/mockstorage.js', ['goog.testing.MockStorage'], ['goog.structs.Map'], {});
goog.addDependency('testing/mockstorage_test.js', ['goog.testing.MockStorageTest'], ['goog.testing.MockStorage', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/mockuseragent.js', ['goog.testing.MockUserAgent'], ['goog.Disposable', 'goog.labs.userAgent.util', 'goog.testing.PropertyReplacer', 'goog.userAgent'], {});
goog.addDependency('testing/mockuseragent_test.js', ['goog.testing.MockUserAgentTest'], ['goog.dispose', 'goog.testing.MockUserAgent', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/multitestrunner.js', ['goog.testing.MultiTestRunner', 'goog.testing.MultiTestRunner.TestFrame'], ['goog.Timer', 'goog.array', 'goog.asserts', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events.EventHandler', 'goog.functions', 'goog.object', 'goog.string', 'goog.testing.TestCase', 'goog.ui.Component', 'goog.ui.ServerChart', 'goog.ui.TableSorter'], {});
goog.addDependency('testing/multitestrunner_test.js', ['goog.testing.MultiTestRunnerTest'], ['goog.Promise', 'goog.array', 'goog.events', 'goog.testing.MockControl', 'goog.testing.MultiTestRunner', 'goog.testing.PropertyReplacer', 'goog.testing.TestCase', 'goog.testing.asserts', 'goog.testing.events', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/net/mockiframeio.js', ['goog.testing.net.MockIFrameIo'], ['goog.events.EventTarget', 'goog.net.ErrorCode', 'goog.net.EventType', 'goog.net.IframeIo', 'goog.testing.TestQueue'], {});
goog.addDependency('testing/net/xhrio.js', ['goog.testing.net.XhrIo'], ['goog.Uri', 'goog.array', 'goog.dom.xml', 'goog.events', 'goog.net.ErrorCode', 'goog.net.EventType', 'goog.net.HttpStatus', 'goog.net.XhrIo', 'goog.net.XmlHttp', 'goog.object', 'goog.structs', 'goog.structs.Map', 'goog.testing.TestQueue', 'goog.uri.utils'], {});
goog.addDependency('testing/net/xhrio_test.js', ['goog.testing.net.XhrIoTest'], ['goog.dom.xml', 'goog.events', 'goog.events.Event', 'goog.net.ErrorCode', 'goog.net.EventType', 'goog.net.XmlHttp', 'goog.object', 'goog.testing.MockControl', 'goog.testing.asserts', 'goog.testing.mockmatchers.InstanceOf', 'goog.testing.net.XhrIo', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/net/xhriopool.js', ['goog.testing.net.XhrIoPool'], ['goog.net.XhrIoPool', 'goog.testing.net.XhrIo'], {});
goog.addDependency('testing/objectpropertystring.js', ['goog.testing.ObjectPropertyString'], [], {});
goog.addDependency('testing/parallel_closure_test_suite.js', ['goog.testing.parallelClosureTestSuite'], ['goog.Promise', 'goog.asserts', 'goog.events', 'goog.json', 'goog.testing.MultiTestRunner', 'goog.testing.TestCase', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/parallel_closure_test_suite_test.js', ['goog.testing.parallelClosureTestSuiteTest'], ['goog.dom', 'goog.testing.MockControl', 'goog.testing.MultiTestRunner', 'goog.testing.PropertyReplacer', 'goog.testing.TestCase', 'goog.testing.mockmatchers', 'goog.testing.mockmatchers.ArgumentMatcher', 'goog.testing.parallelClosureTestSuite', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/performancetable.js', ['goog.testing.PerformanceTable'], ['goog.asserts', 'goog.dom', 'goog.dom.TagName', 'goog.testing.PerformanceTimer'], {});
goog.addDependency('testing/performancetimer.js', ['goog.testing.PerformanceTimer', 'goog.testing.PerformanceTimer.Task'], ['goog.array', 'goog.async.Deferred', 'goog.math'], {'lang': 'es6'});
goog.addDependency('testing/performancetimer_test.js', ['goog.testing.PerformanceTimerTest'], ['goog.async.Deferred', 'goog.dom', 'goog.math', 'goog.testing.MockClock', 'goog.testing.PerformanceTimer', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/propertyreplacer.js', ['goog.testing.PropertyReplacer'], ['goog.asserts', 'goog.userAgent'], {});
goog.addDependency('testing/propertyreplacer_test.js', ['goog.testing.PropertyReplacerTest'], ['goog.dom', 'goog.dom.TagName', 'goog.testing.PropertyReplacer', 'goog.testing.asserts', 'goog.testing.testSuite', 'goog.userAgent.product', 'goog.userAgent.product.isVersion'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/proto2/proto2.js', ['goog.testing.proto2'], ['goog.proto2.Message', 'goog.proto2.ObjectSerializer', 'goog.testing.asserts'], {});
goog.addDependency('testing/proto2/proto2_test.js', ['goog.testing.proto2Test'], ['goog.testing.proto2', 'goog.testing.testSuite', 'proto2.TestAllTypes'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/pseudorandom.js', ['goog.testing.PseudoRandom'], ['goog.Disposable'], {});
goog.addDependency('testing/pseudorandom_test.js', ['goog.testing.PseudoRandomTest'], ['goog.testing.PseudoRandom', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/recordfunction.js', ['goog.testing.FunctionCall', 'goog.testing.recordConstructor', 'goog.testing.recordFunction'], ['goog.Promise', 'goog.promise.Resolver', 'goog.testing.asserts'], {});
goog.addDependency('testing/recordfunction_test.js', ['goog.testing.recordFunctionTest'], ['goog.functions', 'goog.testing.PropertyReplacer', 'goog.testing.recordConstructor', 'goog.testing.recordFunction', 'goog.testing.testSuite'], {'lang': 'es8', 'module': 'goog'});
goog.addDependency('testing/shardingtestcase.js', ['goog.testing.ShardingTestCase'], ['goog.asserts', 'goog.testing.TestCase'], {});
goog.addDependency('testing/shardingtestcase_test.js', ['goog.testing.ShardingTestCaseTest'], ['goog.testing.ShardingTestCase', 'goog.testing.TestCase', 'goog.testing.asserts', 'goog.testing.jsunit'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/singleton.js', ['goog.testing.singleton'], [], {'lang': 'es6'});
goog.addDependency('testing/singleton_test.js', ['goog.testing.singletonTest'], ['goog.testing.asserts', 'goog.testing.singleton', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/stacktrace.js', ['goog.testing.stacktrace', 'goog.testing.stacktrace.Frame'], [], {'lang': 'es6'});
goog.addDependency('testing/stacktrace_test.js', ['goog.testing.stacktraceTest'], ['goog.functions', 'goog.string', 'goog.testing.ExpectedFailures', 'goog.testing.PropertyReplacer', 'goog.testing.StrictMock', 'goog.testing.asserts', 'goog.testing.stacktrace', 'goog.testing.stacktrace.Frame', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/storage/fakemechanism.js', ['goog.testing.storage.FakeMechanism'], ['goog.storage.mechanism.IterableMechanism', 'goog.structs.Map'], {});
goog.addDependency('testing/strictmock.js', ['goog.testing.StrictMock'], ['goog.array', 'goog.asserts', 'goog.structs.Set', 'goog.testing.Mock'], {});
goog.addDependency('testing/strictmock_test.js', ['goog.testing.StrictMockTest'], ['goog.testing.StrictMock', 'goog.testing.testSuite'], {'lang': 'es8', 'module': 'goog'});
goog.addDependency('testing/style/layoutasserts.js', ['goog.testing.style.layoutasserts'], ['goog.style', 'goog.testing.asserts', 'goog.testing.style'], {});
goog.addDependency('testing/style/layoutasserts_test.js', ['goog.testing.style.layoutassertsTest'], ['goog.dom', 'goog.dom.TagName', 'goog.style', 'goog.testing.style.layoutasserts', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/style/style.js', ['goog.testing.style'], ['goog.dom', 'goog.math.Rect', 'goog.style'], {});
goog.addDependency('testing/style/style_test.js', ['goog.testing.styleTest'], ['goog.dom', 'goog.dom.TagName', 'goog.style', 'goog.testing.style', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/testcase.js', ['goog.testing.TestCase', 'goog.testing.TestCase.Error', 'goog.testing.TestCase.Order', 'goog.testing.TestCase.Result', 'goog.testing.TestCase.Test'], ['goog.Promise', 'goog.Thenable', 'goog.array', 'goog.asserts', 'goog.dom', 'goog.dom.TagName', 'goog.object', 'goog.testing.JsUnitException', 'goog.testing.asserts'], {});
goog.addDependency('testing/testcase_test.js', ['goog.testing.TestCaseTest'], ['goog.Promise', 'goog.Timer', 'goog.functions', 'goog.string', 'goog.testing.ExpectedFailures', 'goog.testing.FunctionMock', 'goog.testing.JsUnitException', 'goog.testing.MethodMock', 'goog.testing.MockRandom', 'goog.testing.PropertyReplacer', 'goog.testing.TestCase', 'goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es8', 'module': 'goog'});
goog.addDependency('testing/testqueue.js', ['goog.testing.TestQueue'], [], {});
goog.addDependency('testing/testrunner.js', ['goog.testing.TestRunner'], ['goog.dom', 'goog.dom.TagName', 'goog.dom.safe', 'goog.json', 'goog.testing.TestCase', 'goog.userAgent'], {});
goog.addDependency('testing/testrunner_test.js', ['goog.testing.TestRunnerTest'], ['goog.testing.TestCase', 'goog.testing.TestRunner', 'goog.testing.asserts', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/testsuite.js', ['goog.testing.testSuite'], ['goog.labs.testing.Environment', 'goog.testing.TestCase'], {});
goog.addDependency('testing/testsuite_test.js', ['goog.testing.testSuiteTest'], ['goog.testing.TestCase', 'goog.testing.asserts', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/ui/rendererasserts.js', ['goog.testing.ui.rendererasserts'], ['goog.testing.asserts', 'goog.ui.ControlRenderer'], {});
goog.addDependency('testing/ui/rendererasserts_test.js', ['goog.testing.ui.rendererassertsTest'], ['goog.testing.asserts', 'goog.testing.testSuite', 'goog.testing.ui.rendererasserts', 'goog.ui.ControlRenderer'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('testing/ui/rendererharness.js', ['goog.testing.ui.RendererHarness'], ['goog.Disposable', 'goog.dom.NodeType', 'goog.testing.asserts', 'goog.testing.dom', 'goog.ui.Control', 'goog.ui.ControlRenderer'], {});
goog.addDependency('testing/ui/style.js', ['goog.testing.ui.style'], ['goog.array', 'goog.asserts', 'goog.dom', 'goog.dom.classlist', 'goog.testing.asserts'], {});
goog.addDependency('testing/ui/style_test.js', ['goog.testing.ui.styleTest'], ['goog.dom', 'goog.testing.testSuite', 'goog.testing.ui.style'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('timer/timer.js', ['goog.Timer'], ['goog.Promise', 'goog.events.EventTarget'], {});
goog.addDependency('timer/timer_test.js', ['goog.TimerTest'], ['goog.Promise', 'goog.Timer', 'goog.events', 'goog.testing.MockClock', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('tweak/entries.js', ['goog.tweak.BaseEntry', 'goog.tweak.BasePrimitiveSetting', 'goog.tweak.BaseSetting', 'goog.tweak.BooleanGroup', 'goog.tweak.BooleanInGroupSetting', 'goog.tweak.BooleanSetting', 'goog.tweak.ButtonAction', 'goog.tweak.NumericSetting', 'goog.tweak.StringSetting'], ['goog.array', 'goog.asserts', 'goog.log', 'goog.object'], {});
goog.addDependency('tweak/entries_test.js', ['goog.tweak.BaseEntryTest'], ['goog.testing.MockControl', 'goog.testing.testSuite', 'goog.tweak.testhelpers'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('tweak/registry.js', ['goog.tweak.Registry'], ['goog.array', 'goog.asserts', 'goog.log', 'goog.string', 'goog.tweak.BasePrimitiveSetting', 'goog.tweak.BaseSetting', 'goog.tweak.BooleanSetting', 'goog.tweak.NumericSetting', 'goog.tweak.StringSetting', 'goog.uri.utils'], {});
goog.addDependency('tweak/registry_test.js', ['goog.tweak.RegistryTest'], ['goog.asserts.AssertionError', 'goog.testing.testSuite', 'goog.tweak', 'goog.tweak.testhelpers'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('tweak/testhelpers.js', ['goog.tweak.testhelpers'], ['goog.tweak', 'goog.tweak.BooleanGroup', 'goog.tweak.BooleanInGroupSetting', 'goog.tweak.BooleanSetting', 'goog.tweak.ButtonAction', 'goog.tweak.NumericSetting', 'goog.tweak.Registry', 'goog.tweak.StringSetting'], {});
goog.addDependency('tweak/tweak.js', ['goog.tweak', 'goog.tweak.ConfigParams'], ['goog.asserts', 'goog.tweak.BaseSetting', 'goog.tweak.BooleanGroup', 'goog.tweak.BooleanInGroupSetting', 'goog.tweak.BooleanSetting', 'goog.tweak.ButtonAction', 'goog.tweak.NumericSetting', 'goog.tweak.Registry', 'goog.tweak.StringSetting'], {});
goog.addDependency('tweak/tweakui.js', ['goog.tweak.EntriesPanel', 'goog.tweak.TweakUi'], ['goog.array', 'goog.asserts', 'goog.dom', 'goog.dom.TagName', 'goog.dom.safe', 'goog.html.SafeHtml', 'goog.html.SafeStyleSheet', 'goog.object', 'goog.string.Const', 'goog.style', 'goog.tweak', 'goog.tweak.BaseEntry', 'goog.tweak.BooleanGroup', 'goog.tweak.BooleanInGroupSetting', 'goog.tweak.BooleanSetting', 'goog.tweak.ButtonAction', 'goog.tweak.NumericSetting', 'goog.tweak.StringSetting', 'goog.ui.Zippy', 'goog.userAgent'], {});
goog.addDependency('tweak/tweakui_test.js', ['goog.tweak.TweakUiTest'], ['goog.dom', 'goog.dom.TagName', 'goog.string', 'goog.testing.testSuite', 'goog.tweak', 'goog.tweak.TweakUi', 'goog.tweak.testhelpers'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/abstractspellchecker.js', ['goog.ui.AbstractSpellChecker', 'goog.ui.AbstractSpellChecker.AsyncResult'], ['goog.a11y.aria', 'goog.array', 'goog.asserts', 'goog.dom', 'goog.dom.InputType', 'goog.dom.NodeType', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.dom.selection', 'goog.events', 'goog.events.Event', 'goog.events.EventType', 'goog.math.Coordinate', 'goog.spell.SpellCheck', 'goog.structs.Set', 'goog.style', 'goog.ui.Component', 'goog.ui.MenuItem', 'goog.ui.MenuSeparator', 'goog.ui.PopupMenu'], {});
goog.addDependency('ui/ac/ac.js', ['goog.ui.ac'], ['goog.ui.ac.ArrayMatcher', 'goog.ui.ac.AutoComplete', 'goog.ui.ac.InputHandler', 'goog.ui.ac.Renderer'], {});
goog.addDependency('ui/ac/ac_test.js', ['goog.ui.acTest'], ['goog.asserts', 'goog.dom', 'goog.dom.NodeType', 'goog.dom.classlist', 'goog.dom.selection', 'goog.events', 'goog.events.BrowserEvent', 'goog.events.Event', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.style', 'goog.testing.MockClock', 'goog.testing.events', 'goog.testing.testSuite', 'goog.ui.ac', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/ac/arraymatcher.js', ['goog.ui.ac.ArrayMatcher'], ['goog.string'], {});
goog.addDependency('ui/ac/arraymatcher_test.js', ['goog.ui.ac.ArrayMatcherTest'], ['goog.testing.testSuite', 'goog.ui.ac.ArrayMatcher'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/ac/autocomplete.js', ['goog.ui.ac.AutoComplete', 'goog.ui.ac.AutoComplete.EventType'], ['goog.array', 'goog.asserts', 'goog.events', 'goog.events.EventTarget', 'goog.object', 'goog.ui.ac.RenderOptions'], {});
goog.addDependency('ui/ac/autocomplete_test.js', ['goog.ui.ac.AutoCompleteTest'], ['goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.dom', 'goog.dom.InputType', 'goog.dom.TagName', 'goog.events.EventHandler', 'goog.events.EventTarget', 'goog.string', 'goog.testing.MockControl', 'goog.testing.events', 'goog.testing.mockmatchers', 'goog.testing.testSuite', 'goog.ui.ac.AutoComplete', 'goog.ui.ac.InputHandler', 'goog.ui.ac.RenderOptions', 'goog.ui.ac.Renderer'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/ac/cachingmatcher.js', ['goog.ui.ac.CachingMatcher'], ['goog.array', 'goog.async.Throttle', 'goog.ui.ac.ArrayMatcher', 'goog.ui.ac.RenderOptions'], {});
goog.addDependency('ui/ac/cachingmatcher_test.js', ['goog.ui.ac.CachingMatcherTest'], ['goog.testing.MockControl', 'goog.testing.mockmatchers', 'goog.testing.testSuite', 'goog.ui.ac.CachingMatcher'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/ac/inputhandler.js', ['goog.ui.ac.InputHandler'], ['goog.Disposable', 'goog.Timer', 'goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.a11y.aria.State', 'goog.dom', 'goog.dom.selection', 'goog.events.EventHandler', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.events.KeyHandler', 'goog.string', 'goog.userAgent', 'goog.userAgent.product'], {});
goog.addDependency('ui/ac/inputhandler_test.js', ['goog.ui.ac.InputHandlerTest'], ['goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.a11y.aria.State', 'goog.dom', 'goog.dom.TagName', 'goog.dom.selection', 'goog.events.BrowserEvent', 'goog.events.Event', 'goog.events.EventTarget', 'goog.events.KeyCodes', 'goog.events.KeyHandler', 'goog.functions', 'goog.object', 'goog.testing.MockClock', 'goog.testing.testSuite', 'goog.ui.ac.InputHandler', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/ac/remote.js', ['goog.ui.ac.Remote'], ['goog.ui.ac.AutoComplete', 'goog.ui.ac.InputHandler', 'goog.ui.ac.RemoteArrayMatcher', 'goog.ui.ac.Renderer'], {});
goog.addDependency('ui/ac/remotearraymatcher.js', ['goog.ui.ac.RemoteArrayMatcher'], ['goog.Disposable', 'goog.Uri', 'goog.events', 'goog.net.EventType', 'goog.net.XhrIo'], {});
goog.addDependency('ui/ac/remotearraymatcher_test.js', ['goog.ui.ac.RemoteArrayMatcherTest'], ['goog.net.XhrIo', 'goog.testing.MockControl', 'goog.testing.net.XhrIo', 'goog.testing.testSuite', 'goog.ui.ac.RemoteArrayMatcher'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/ac/renderer.js', ['goog.ui.ac.Renderer', 'goog.ui.ac.Renderer.CustomRenderer'], ['goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.a11y.aria.State', 'goog.array', 'goog.asserts', 'goog.dispose', 'goog.dom', 'goog.dom.NodeType', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.fx.dom.FadeInAndShow', 'goog.fx.dom.FadeOutAndHide', 'goog.positioning', 'goog.positioning.Corner', 'goog.positioning.Overflow', 'goog.string', 'goog.style', 'goog.ui.IdGenerator', 'goog.ui.ac.AutoComplete'], {'lang': 'es6'});
goog.addDependency('ui/ac/renderer_test.js', ['goog.ui.ac.RendererTest'], ['goog.a11y.aria', 'goog.a11y.aria.State', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events', 'goog.fx.dom.FadeInAndShow', 'goog.fx.dom.FadeOutAndHide', 'goog.string', 'goog.style', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite', 'goog.ui.ac.AutoComplete', 'goog.ui.ac.Renderer'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/ac/renderoptions.js', ['goog.ui.ac.RenderOptions'], [], {});
goog.addDependency('ui/ac/richinputhandler.js', ['goog.ui.ac.RichInputHandler'], ['goog.ui.ac.InputHandler'], {});
goog.addDependency('ui/ac/richremote.js', ['goog.ui.ac.RichRemote'], ['goog.ui.ac.AutoComplete', 'goog.ui.ac.Remote', 'goog.ui.ac.Renderer', 'goog.ui.ac.RichInputHandler', 'goog.ui.ac.RichRemoteArrayMatcher'], {});
goog.addDependency('ui/ac/richremotearraymatcher.js', ['goog.ui.ac.RichRemoteArrayMatcher'], ['goog.dom', 'goog.ui.ac.RemoteArrayMatcher'], {});
goog.addDependency('ui/ac/richremotearraymatcher_test.js', ['goog.ui.ac.RichRemoteArrayMatcherTest'], ['goog.net.XhrIo', 'goog.testing.MockControl', 'goog.testing.net.XhrIo', 'goog.testing.testSuite', 'goog.ui.ac.RichRemoteArrayMatcher'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/activitymonitor.js', ['goog.ui.ActivityMonitor'], ['goog.array', 'goog.asserts', 'goog.dom', 'goog.events.EventHandler', 'goog.events.EventTarget', 'goog.events.EventType'], {});
goog.addDependency('ui/activitymonitor_test.js', ['goog.ui.ActivityMonitorTest'], ['goog.dom', 'goog.events', 'goog.events.Event', 'goog.events.EventType', 'goog.testing.MockClock', 'goog.testing.PropertyReplacer', 'goog.testing.events', 'goog.testing.recordFunction', 'goog.testing.testSuite', 'goog.ui.ActivityMonitor'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/advancedtooltip.js', ['goog.ui.AdvancedTooltip'], ['goog.events', 'goog.events.EventType', 'goog.math.Box', 'goog.math.Coordinate', 'goog.style', 'goog.ui.Tooltip', 'goog.userAgent'], {});
goog.addDependency('ui/advancedtooltip_test.js', ['goog.ui.AdvancedTooltipTest'], ['goog.dom', 'goog.dom.TagName', 'goog.events.Event', 'goog.events.EventType', 'goog.math.Box', 'goog.math.Coordinate', 'goog.style', 'goog.testing.MockClock', 'goog.testing.events', 'goog.testing.testSuite', 'goog.ui.AdvancedTooltip', 'goog.ui.Tooltip', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/animatedzippy.js', ['goog.ui.AnimatedZippy'], ['goog.a11y.aria.Role', 'goog.dom', 'goog.dom.TagName', 'goog.events', 'goog.fx.Animation', 'goog.fx.Transition', 'goog.fx.easing', 'goog.ui.Zippy', 'goog.ui.ZippyEvent'], {});
goog.addDependency('ui/animatedzippy_test.js', ['goog.ui.AnimatedZippyTest'], ['goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.a11y.aria.State', 'goog.asserts', 'goog.dom', 'goog.events', 'goog.functions', 'goog.fx.Animation', 'goog.fx.Transition', 'goog.testing.PropertyReplacer', 'goog.testing.asserts', 'goog.testing.testSuite', 'goog.ui.AnimatedZippy', 'goog.ui.Zippy'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/attachablemenu.js', ['goog.ui.AttachableMenu'], ['goog.a11y.aria', 'goog.a11y.aria.State', 'goog.array', 'goog.asserts', 'goog.dom', 'goog.dom.classlist', 'goog.events.Event', 'goog.events.KeyCodes', 'goog.string', 'goog.style', 'goog.ui.ItemEvent', 'goog.ui.MenuBase', 'goog.ui.PopupBase', 'goog.userAgent'], {});
goog.addDependency('ui/bidiinput.js', ['goog.ui.BidiInput'], ['goog.dom', 'goog.dom.InputType', 'goog.dom.TagName', 'goog.events', 'goog.events.InputHandler', 'goog.i18n.bidi', 'goog.ui.Component'], {});
goog.addDependency('ui/bidiinput_test.js', ['goog.ui.BidiInputTest'], ['goog.dom', 'goog.testing.testSuite', 'goog.ui.BidiInput'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/bubble.js', ['goog.ui.Bubble'], ['goog.Timer', 'goog.dom.safe', 'goog.events', 'goog.events.EventType', 'goog.html.SafeHtml', 'goog.math.Box', 'goog.positioning', 'goog.positioning.AbsolutePosition', 'goog.positioning.AnchoredPosition', 'goog.positioning.Corner', 'goog.positioning.CornerBit', 'goog.string.Const', 'goog.style', 'goog.ui.Component', 'goog.ui.Popup'], {});
goog.addDependency('ui/button.js', ['goog.ui.Button', 'goog.ui.Button.Side'], ['goog.events.EventType', 'goog.events.KeyCodes', 'goog.events.KeyHandler', 'goog.ui.ButtonRenderer', 'goog.ui.ButtonSide', 'goog.ui.Component', 'goog.ui.Control', 'goog.ui.NativeButtonRenderer', 'goog.ui.registry'], {});
goog.addDependency('ui/button_test.js', ['goog.ui.ButtonTest'], ['goog.dom', 'goog.dom.classlist', 'goog.events', 'goog.events.Event', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.events.KeyHandler', 'goog.testing.events', 'goog.testing.testSuite', 'goog.ui.Button', 'goog.ui.ButtonRenderer', 'goog.ui.ButtonSide', 'goog.ui.Component', 'goog.ui.NativeButtonRenderer'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/buttonrenderer.js', ['goog.ui.ButtonRenderer'], ['goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.a11y.aria.State', 'goog.asserts', 'goog.ui.ButtonSide', 'goog.ui.Component', 'goog.ui.ControlRenderer'], {});
goog.addDependency('ui/buttonrenderer_test.js', ['goog.ui.ButtonRendererTest'], ['goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.a11y.aria.State', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.testing.ExpectedFailures', 'goog.testing.testSuite', 'goog.testing.ui.rendererasserts', 'goog.ui.Button', 'goog.ui.ButtonRenderer', 'goog.ui.ButtonSide', 'goog.ui.Component', 'goog.ui.ControlRenderer'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/buttonside.js', ['goog.ui.ButtonSide'], [], {});
goog.addDependency('ui/charcounter.js', ['goog.ui.CharCounter', 'goog.ui.CharCounter.Display'], ['goog.dom', 'goog.events', 'goog.events.EventTarget', 'goog.events.InputHandler'], {});
goog.addDependency('ui/charcounter_test.js', ['goog.ui.CharCounterTest'], ['goog.dom', 'goog.testing.asserts', 'goog.testing.testSuite', 'goog.ui.CharCounter', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/charpicker.js', ['goog.ui.CharPicker'], ['goog.a11y.aria', 'goog.a11y.aria.State', 'goog.array', 'goog.asserts', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events', 'goog.events.Event', 'goog.events.EventHandler', 'goog.events.EventType', 'goog.events.InputHandler', 'goog.events.KeyCodes', 'goog.events.KeyHandler', 'goog.i18n.CharListDecompressor', 'goog.i18n.CharPickerData', 'goog.i18n.uChar', 'goog.i18n.uChar.NameFetcher', 'goog.structs.Set', 'goog.style', 'goog.ui.Button', 'goog.ui.Component', 'goog.ui.ContainerScroller', 'goog.ui.FlatButtonRenderer', 'goog.ui.HoverCard', 'goog.ui.LabelInput', 'goog.ui.Menu', 'goog.ui.MenuButton', 'goog.ui.MenuItem', 'goog.ui.Tooltip'], {});
goog.addDependency('ui/charpicker_test.js', ['goog.ui.CharPickerTest'], ['goog.a11y.aria', 'goog.a11y.aria.State', 'goog.dispose', 'goog.dom', 'goog.events.Event', 'goog.events.EventType', 'goog.i18n.CharPickerData', 'goog.i18n.uChar.NameFetcher', 'goog.testing.MockControl', 'goog.testing.events', 'goog.testing.mockmatchers', 'goog.testing.testSuite', 'goog.ui.CharPicker', 'goog.ui.FlatButtonRenderer'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/checkbox.js', ['goog.ui.Checkbox', 'goog.ui.Checkbox.State'], ['goog.a11y.aria', 'goog.a11y.aria.State', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.string', 'goog.ui.CheckboxRenderer', 'goog.ui.Component', 'goog.ui.Control', 'goog.ui.registry'], {});
goog.addDependency('ui/checkbox_test.js', ['goog.ui.CheckboxTest'], ['goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.a11y.aria.State', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events', 'goog.events.KeyCodes', 'goog.testing.events', 'goog.testing.testSuite', 'goog.ui.Checkbox', 'goog.ui.CheckboxRenderer', 'goog.ui.Component', 'goog.ui.ControlRenderer', 'goog.ui.decorate'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/checkboxmenuitem.js', ['goog.ui.CheckBoxMenuItem'], ['goog.ui.MenuItem', 'goog.ui.registry'], {});
goog.addDependency('ui/checkboxrenderer.js', ['goog.ui.CheckboxRenderer'], ['goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.a11y.aria.State', 'goog.array', 'goog.asserts', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.object', 'goog.ui.ControlRenderer'], {});
goog.addDependency('ui/colormenubutton.js', ['goog.ui.ColorMenuButton'], ['goog.array', 'goog.object', 'goog.ui.ColorMenuButtonRenderer', 'goog.ui.ColorPalette', 'goog.ui.Component', 'goog.ui.Menu', 'goog.ui.MenuButton', 'goog.ui.registry'], {});
goog.addDependency('ui/colormenubuttonrenderer.js', ['goog.ui.ColorMenuButtonRenderer'], ['goog.asserts', 'goog.color', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.ui.MenuButtonRenderer', 'goog.userAgent'], {});
goog.addDependency('ui/colormenubuttonrenderer_test.js', ['goog.ui.ColorMenuButtonTest'], ['goog.dom', 'goog.dom.TagName', 'goog.testing.testSuite', 'goog.testing.ui.RendererHarness', 'goog.testing.ui.rendererasserts', 'goog.ui.ColorMenuButton', 'goog.ui.ColorMenuButtonRenderer', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/colorpalette.js', ['goog.ui.ColorPalette'], ['goog.array', 'goog.color', 'goog.dom.TagName', 'goog.style', 'goog.ui.Palette', 'goog.ui.PaletteRenderer'], {});
goog.addDependency('ui/colorpalette_test.js', ['goog.ui.ColorPaletteTest'], ['goog.color', 'goog.dom.TagName', 'goog.testing.testSuite', 'goog.ui.ColorPalette'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/colorpicker.js', ['goog.ui.ColorPicker', 'goog.ui.ColorPicker.EventType'], ['goog.ui.ColorPalette', 'goog.ui.Component'], {});
goog.addDependency('ui/combobox.js', ['goog.ui.ComboBox', 'goog.ui.ComboBoxItem'], ['goog.Timer', 'goog.asserts', 'goog.dom', 'goog.dom.InputType', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events.EventType', 'goog.events.InputHandler', 'goog.events.KeyCodes', 'goog.events.KeyHandler', 'goog.log', 'goog.positioning.Corner', 'goog.positioning.MenuAnchoredPosition', 'goog.string', 'goog.style', 'goog.ui.Component', 'goog.ui.ItemEvent', 'goog.ui.LabelInput', 'goog.ui.Menu', 'goog.ui.MenuItem', 'goog.ui.MenuSeparator', 'goog.ui.registry', 'goog.userAgent'], {});
goog.addDependency('ui/combobox_test.js', ['goog.ui.ComboBoxTest'], ['goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events.KeyCodes', 'goog.testing.MockClock', 'goog.testing.events', 'goog.testing.testSuite', 'goog.ui.ComboBox', 'goog.ui.ComboBoxItem', 'goog.ui.Component', 'goog.ui.ControlRenderer', 'goog.ui.LabelInput', 'goog.ui.Menu', 'goog.ui.MenuItem'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/component.js', ['goog.ui.Component', 'goog.ui.Component.Error', 'goog.ui.Component.EventType', 'goog.ui.Component.State'], ['goog.array', 'goog.asserts', 'goog.dom', 'goog.dom.NodeType', 'goog.dom.TagName', 'goog.events.EventHandler', 'goog.events.EventTarget', 'goog.object', 'goog.style', 'goog.ui.IdGenerator'], {});
goog.addDependency('ui/component_test.js', ['goog.ui.ComponentTest'], ['goog.dom', 'goog.dom.DomHelper', 'goog.dom.NodeType', 'goog.dom.TagName', 'goog.events.EventTarget', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite', 'goog.ui.Component'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/componentutil.js', ['goog.ui.ComponentUtil'], ['goog.events.MouseAsMouseEventType', 'goog.events.MouseEvents', 'goog.events.PointerAsMouseEventType'], {});
goog.addDependency('ui/componentutil_test.js', ['goog.ui.ComponentUtilTest'], ['goog.events.MouseAsMouseEventType', 'goog.events.PointerAsMouseEventType', 'goog.testing.testSuite', 'goog.ui.Component', 'goog.ui.ComponentUtil'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/container.js', ['goog.ui.Container', 'goog.ui.Container.EventType', 'goog.ui.Container.Orientation'], ['goog.a11y.aria', 'goog.a11y.aria.State', 'goog.asserts', 'goog.dom', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.events.KeyHandler', 'goog.object', 'goog.style', 'goog.ui.Component', 'goog.ui.ComponentUtil', 'goog.ui.ContainerRenderer', 'goog.ui.Control'], {});
goog.addDependency('ui/container_test.js', ['goog.ui.ContainerTest'], ['goog.a11y.aria', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events', 'goog.events.BrowserEvent', 'goog.events.Event', 'goog.events.KeyCodes', 'goog.events.KeyEvent', 'goog.events.PointerFallbackEventType', 'goog.testing.events', 'goog.testing.events.Event', 'goog.testing.testSuite', 'goog.ui.Component', 'goog.ui.Container', 'goog.ui.Control'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/containerrenderer.js', ['goog.ui.ContainerRenderer'], ['goog.a11y.aria', 'goog.array', 'goog.asserts', 'goog.dom.NodeType', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.string', 'goog.style', 'goog.ui.registry', 'goog.userAgent'], {});
goog.addDependency('ui/containerrenderer_test.js', ['goog.ui.ContainerRendererTest'], ['goog.dom', 'goog.dom.TagName', 'goog.style', 'goog.testing.ExpectedFailures', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite', 'goog.testing.ui.rendererasserts', 'goog.ui.Container', 'goog.ui.ContainerRenderer', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/containerscroller.js', ['goog.ui.ContainerScroller'], ['goog.Disposable', 'goog.Timer', 'goog.events.EventHandler', 'goog.style', 'goog.ui.Component', 'goog.ui.Container'], {});
goog.addDependency('ui/containerscroller_test.js', ['goog.ui.ContainerScrollerTest'], ['goog.dom', 'goog.testing.MockClock', 'goog.testing.events', 'goog.testing.testSuite', 'goog.ui.Container', 'goog.ui.ContainerScroller'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/control.js', ['goog.ui.Control'], ['goog.Disposable', 'goog.array', 'goog.dom', 'goog.events.BrowserEvent', 'goog.events.Event', 'goog.events.EventHandler', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.events.KeyHandler', 'goog.string', 'goog.ui.Component', 'goog.ui.ComponentUtil', 'goog.ui.ControlContent', 'goog.ui.ControlRenderer', 'goog.ui.registry', 'goog.userAgent'], {});
goog.addDependency('ui/control_test.js', ['goog.ui.ControlTest'], ['goog.a11y.aria', 'goog.a11y.aria.State', 'goog.array', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events', 'goog.events.BrowserEvent', 'goog.events.KeyCodes', 'goog.events.PointerFallbackEventType', 'goog.html.testing', 'goog.object', 'goog.string', 'goog.style', 'goog.testing.ExpectedFailures', 'goog.testing.events', 'goog.testing.events.Event', 'goog.testing.testSuite', 'goog.ui.Component', 'goog.ui.Control', 'goog.ui.ControlRenderer', 'goog.ui.registry', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/controlcontent.js', ['goog.ui.ControlContent'], [], {});
goog.addDependency('ui/controlrenderer.js', ['goog.ui.ControlRenderer'], ['goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.a11y.aria.State', 'goog.array', 'goog.asserts', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.object', 'goog.string', 'goog.style', 'goog.ui.Component', 'goog.ui.ControlContent', 'goog.userAgent'], {});
goog.addDependency('ui/controlrenderer_test.js', ['goog.ui.ControlRendererTest'], ['goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.a11y.aria.State', 'goog.dom', 'goog.dom.NodeType', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.object', 'goog.style', 'goog.testing.ExpectedFailures', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite', 'goog.ui.Component', 'goog.ui.Control', 'goog.ui.ControlRenderer', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/cookieeditor.js', ['goog.ui.CookieEditor'], ['goog.asserts', 'goog.dom', 'goog.dom.TagName', 'goog.events.EventType', 'goog.net.cookies', 'goog.string', 'goog.style', 'goog.ui.Component'], {});
goog.addDependency('ui/cookieeditor_test.js', ['goog.ui.CookieEditorTest'], ['goog.dom', 'goog.events.Event', 'goog.events.EventType', 'goog.net.cookies', 'goog.testing.events', 'goog.testing.testSuite', 'goog.ui.CookieEditor'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/css3buttonrenderer.js', ['goog.ui.Css3ButtonRenderer'], ['goog.asserts', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.ui.Button', 'goog.ui.ButtonRenderer', 'goog.ui.Component', 'goog.ui.INLINE_BLOCK_CLASSNAME', 'goog.ui.registry'], {});
goog.addDependency('ui/css3menubuttonrenderer.js', ['goog.ui.Css3MenuButtonRenderer'], ['goog.dom', 'goog.dom.TagName', 'goog.ui.INLINE_BLOCK_CLASSNAME', 'goog.ui.MenuButton', 'goog.ui.MenuButtonRenderer', 'goog.ui.registry'], {});
goog.addDependency('ui/cssnames.js', ['goog.ui.INLINE_BLOCK_CLASSNAME'], [], {});
goog.addDependency('ui/custombutton.js', ['goog.ui.CustomButton'], ['goog.ui.Button', 'goog.ui.CustomButtonRenderer', 'goog.ui.registry'], {});
goog.addDependency('ui/custombuttonrenderer.js', ['goog.ui.CustomButtonRenderer'], ['goog.a11y.aria.Role', 'goog.asserts', 'goog.dom.NodeType', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.string', 'goog.ui.ButtonRenderer', 'goog.ui.INLINE_BLOCK_CLASSNAME'], {});
goog.addDependency('ui/customcolorpalette.js', ['goog.ui.CustomColorPalette'], ['goog.color', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.ui.ColorPalette', 'goog.ui.Component'], {});
goog.addDependency('ui/customcolorpalette_test.js', ['goog.ui.CustomColorPaletteTest'], ['goog.dom.TagName', 'goog.dom.classlist', 'goog.testing.testSuite', 'goog.ui.CustomColorPalette'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/datepicker.js', ['goog.ui.DatePicker', 'goog.ui.DatePicker.Events', 'goog.ui.DatePickerEvent'], ['goog.a11y.aria', 'goog.asserts', 'goog.date.Date', 'goog.date.DateRange', 'goog.date.Interval', 'goog.dom', 'goog.dom.NodeType', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events.Event', 'goog.events.EventType', 'goog.events.KeyHandler', 'goog.i18n.DateTimeFormat', 'goog.i18n.DateTimePatterns', 'goog.i18n.DateTimeSymbols', 'goog.style', 'goog.ui.Component', 'goog.ui.DefaultDatePickerRenderer', 'goog.ui.IdGenerator'], {});
goog.addDependency('ui/datepicker_test.js', ['goog.ui.DatePickerTest'], ['goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.date.Date', 'goog.date.DateRange', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events', 'goog.events.KeyCodes', 'goog.i18n.DateTimeSymbols', 'goog.i18n.DateTimeSymbols_en_US', 'goog.i18n.DateTimeSymbols_zh_HK', 'goog.style', 'goog.testing.events', 'goog.testing.recordFunction', 'goog.testing.testSuite', 'goog.ui.DatePicker'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/datepickerrenderer.js', ['goog.ui.DatePickerRenderer'], [], {});
goog.addDependency('ui/decorate.js', ['goog.ui.decorate'], ['goog.ui.registry'], {});
goog.addDependency('ui/decorate_test.js', ['goog.ui.decorateTest'], ['goog.testing.testSuite', 'goog.ui.decorate', 'goog.ui.registry'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/defaultdatepickerrenderer.js', ['goog.ui.DefaultDatePickerRenderer'], ['goog.dom', 'goog.dom.TagName', 'goog.ui.DatePickerRenderer'], {});
goog.addDependency('ui/dialog.js', ['goog.ui.Dialog', 'goog.ui.Dialog.ButtonSet', 'goog.ui.Dialog.ButtonSet.DefaultButtons', 'goog.ui.Dialog.DefaultButtonCaptions', 'goog.ui.Dialog.DefaultButtonKeys', 'goog.ui.Dialog.Event', 'goog.ui.Dialog.EventType'], ['goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.a11y.aria.State', 'goog.asserts', 'goog.dom', 'goog.dom.NodeType', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.dom.safe', 'goog.events', 'goog.events.Event', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.events.Keys', 'goog.fx.Dragger', 'goog.html.SafeHtml', 'goog.math.Rect', 'goog.string', 'goog.structs.Map', 'goog.style', 'goog.ui.ModalPopup'], {});
goog.addDependency('ui/dialog_test.js', ['goog.ui.DialogTest'], ['goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.a11y.aria.State', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.fx.css3', 'goog.html.SafeHtml', 'goog.html.testing', 'goog.style', 'goog.testing.MockClock', 'goog.testing.events', 'goog.testing.recordFunction', 'goog.testing.testSuite', 'goog.ui.Dialog', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/dimensionpicker.js', ['goog.ui.DimensionPicker'], ['goog.events.BrowserEvent.PointerType', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.math.Size', 'goog.ui.Component', 'goog.ui.ComponentUtil', 'goog.ui.Control', 'goog.ui.DimensionPickerRenderer', 'goog.ui.registry'], {});
goog.addDependency('ui/dimensionpicker_test.js', ['goog.ui.DimensionPickerTest'], ['goog.dom', 'goog.dom.TagName', 'goog.events.BrowserEvent', 'goog.events.KeyCodes', 'goog.math.Size', 'goog.testing.testSuite', 'goog.testing.ui.rendererasserts', 'goog.ui.DimensionPicker', 'goog.ui.DimensionPickerRenderer'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/dimensionpickerrenderer.js', ['goog.ui.DimensionPickerRenderer'], ['goog.a11y.aria.Announcer', 'goog.a11y.aria.LivePriority', 'goog.dom', 'goog.dom.TagName', 'goog.i18n.bidi', 'goog.style', 'goog.ui.ControlRenderer', 'goog.userAgent'], {});
goog.addDependency('ui/dimensionpickerrenderer_test.js', ['goog.ui.DimensionPickerRendererTest'], ['goog.a11y.aria.LivePriority', 'goog.array', 'goog.testing.recordFunction', 'goog.testing.testSuite', 'goog.ui.DimensionPicker', 'goog.ui.DimensionPickerRenderer'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/dragdropdetector.js', ['goog.ui.DragDropDetector', 'goog.ui.DragDropDetector.EventType', 'goog.ui.DragDropDetector.ImageDropEvent', 'goog.ui.DragDropDetector.LinkDropEvent'], ['goog.dom', 'goog.dom.InputType', 'goog.dom.TagName', 'goog.events.Event', 'goog.events.EventHandler', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.math.Coordinate', 'goog.string', 'goog.style', 'goog.userAgent'], {});
goog.addDependency('ui/drilldownrow.js', ['goog.ui.DrilldownRow'], ['goog.asserts', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.dom.safe', 'goog.html.SafeHtml', 'goog.string.Unicode', 'goog.ui.Component'], {});
goog.addDependency('ui/drilldownrow_test.js', ['goog.ui.DrilldownRowTest'], ['goog.dom', 'goog.dom.TagName', 'goog.html.SafeHtml', 'goog.testing.testSuite', 'goog.ui.DrilldownRow'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/editor/abstractdialog.js', ['goog.ui.editor.AbstractDialog', 'goog.ui.editor.AbstractDialog.Builder', 'goog.ui.editor.AbstractDialog.EventType'], ['goog.asserts', 'goog.dom', 'goog.dom.classlist', 'goog.events.EventTarget', 'goog.string', 'goog.ui.Dialog', 'goog.ui.PopupBase'], {});
goog.addDependency('ui/editor/abstractdialog_test.js', ['goog.ui.editor.AbstractDialogTest'], ['goog.dom', 'goog.dom.DomHelper', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events.Event', 'goog.events.EventHandler', 'goog.events.KeyCodes', 'goog.testing.MockControl', 'goog.testing.events', 'goog.testing.mockmatchers.ArgumentMatcher', 'goog.testing.testSuite', 'goog.ui.editor.AbstractDialog', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/editor/bubble.js', ['goog.ui.editor.Bubble'], ['goog.asserts', 'goog.dom', 'goog.dom.TagName', 'goog.dom.ViewportSizeMonitor', 'goog.dom.classlist', 'goog.editor.style', 'goog.events.EventHandler', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.functions', 'goog.log', 'goog.math.Box', 'goog.object', 'goog.positioning', 'goog.positioning.Corner', 'goog.positioning.Overflow', 'goog.positioning.OverflowStatus', 'goog.string', 'goog.style', 'goog.ui.Component', 'goog.ui.PopupBase', 'goog.userAgent'], {});
goog.addDependency('ui/editor/bubble_test.js', ['goog.ui.editor.BubbleTest'], ['goog.dom', 'goog.dom.TagName', 'goog.events', 'goog.events.EventType', 'goog.positioning.Corner', 'goog.positioning.OverflowStatus', 'goog.string', 'goog.style', 'goog.testing.editor.TestHelper', 'goog.testing.events', 'goog.testing.testSuite', 'goog.ui.Component', 'goog.ui.editor.Bubble', 'goog.userAgent.product'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/editor/defaulttoolbar.js', ['goog.ui.editor.ButtonDescriptor', 'goog.ui.editor.DefaultToolbar'], ['goog.asserts', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.editor.Command', 'goog.style', 'goog.ui.editor.ToolbarFactory', 'goog.ui.editor.messages', 'goog.userAgent'], {});
goog.addDependency('ui/editor/linkdialog.js', ['goog.ui.editor.LinkDialog', 'goog.ui.editor.LinkDialog.BeforeTestLinkEvent', 'goog.ui.editor.LinkDialog.EventType', 'goog.ui.editor.LinkDialog.OkEvent'], ['goog.a11y.aria', 'goog.a11y.aria.State', 'goog.dom', 'goog.dom.InputType', 'goog.dom.TagName', 'goog.dom.safe', 'goog.editor.BrowserFeature', 'goog.editor.Link', 'goog.editor.focus', 'goog.editor.node', 'goog.events.Event', 'goog.events.EventHandler', 'goog.events.InputHandler', 'goog.html.SafeHtml', 'goog.html.SafeHtmlFormatter', 'goog.string', 'goog.string.Unicode', 'goog.style', 'goog.ui.Button', 'goog.ui.Component', 'goog.ui.LinkButtonRenderer', 'goog.ui.editor.AbstractDialog', 'goog.ui.editor.TabPane', 'goog.ui.editor.messages', 'goog.userAgent', 'goog.window'], {});
goog.addDependency('ui/editor/linkdialog_test.js', ['goog.ui.editor.LinkDialogTest'], ['goog.dom', 'goog.dom.DomHelper', 'goog.dom.TagName', 'goog.editor.BrowserFeature', 'goog.editor.Link', 'goog.events', 'goog.events.EventHandler', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.style', 'goog.testing.MockControl', 'goog.testing.PropertyReplacer', 'goog.testing.dom', 'goog.testing.events', 'goog.testing.events.Event', 'goog.testing.mockmatchers', 'goog.testing.mockmatchers.ArgumentMatcher', 'goog.testing.testSuite', 'goog.ui.editor.AbstractDialog', 'goog.ui.editor.LinkDialog', 'goog.ui.editor.messages', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/editor/messages.js', ['goog.ui.editor.messages'], ['goog.html.SafeHtmlFormatter'], {});
goog.addDependency('ui/editor/tabpane.js', ['goog.ui.editor.TabPane'], ['goog.asserts', 'goog.dom', 'goog.dom.InputType', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events.EventHandler', 'goog.events.EventType', 'goog.style', 'goog.ui.Component', 'goog.ui.Control', 'goog.ui.Tab', 'goog.ui.TabBar'], {});
goog.addDependency('ui/editor/toolbarcontroller.js', ['goog.ui.editor.ToolbarController'], ['goog.editor.Field', 'goog.events.EventHandler', 'goog.events.EventTarget', 'goog.ui.Component'], {});
goog.addDependency('ui/editor/toolbarfactory.js', ['goog.ui.editor.ToolbarFactory'], ['goog.array', 'goog.dom', 'goog.dom.TagName', 'goog.string', 'goog.string.Unicode', 'goog.style', 'goog.ui.Component', 'goog.ui.Container', 'goog.ui.Option', 'goog.ui.Toolbar', 'goog.ui.ToolbarButton', 'goog.ui.ToolbarColorMenuButton', 'goog.ui.ToolbarMenuButton', 'goog.ui.ToolbarRenderer', 'goog.ui.ToolbarSelect', 'goog.userAgent'], {});
goog.addDependency('ui/editor/toolbarfactory_test.js', ['goog.ui.editor.ToolbarFactoryTest'], ['goog.dom', 'goog.testing.ExpectedFailures', 'goog.testing.editor.TestHelper', 'goog.testing.testSuite', 'goog.ui.editor.ToolbarFactory', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/emoji/emoji.js', ['goog.ui.emoji.Emoji'], [], {});
goog.addDependency('ui/emoji/emojipalette.js', ['goog.ui.emoji.EmojiPalette'], ['goog.events.EventType', 'goog.net.ImageLoader', 'goog.ui.Palette', 'goog.ui.emoji.Emoji', 'goog.ui.emoji.EmojiPaletteRenderer'], {});
goog.addDependency('ui/emoji/emojipaletterenderer.js', ['goog.ui.emoji.EmojiPaletteRenderer'], ['goog.a11y.aria', 'goog.asserts', 'goog.dom.NodeType', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.style', 'goog.ui.PaletteRenderer', 'goog.ui.emoji.Emoji'], {});
goog.addDependency('ui/emoji/emojipicker.js', ['goog.ui.emoji.EmojiPicker'], ['goog.dom.TagName', 'goog.style', 'goog.ui.Component', 'goog.ui.TabPane', 'goog.ui.emoji.Emoji', 'goog.ui.emoji.EmojiPalette', 'goog.ui.emoji.EmojiPaletteRenderer', 'goog.ui.emoji.ProgressiveEmojiPaletteRenderer'], {});
goog.addDependency('ui/emoji/emojipicker_test.js', ['goog.ui.emoji.EmojiPickerTest'], ['goog.dom.TagName', 'goog.dom.classlist', 'goog.events.EventHandler', 'goog.style', 'goog.testing.events', 'goog.testing.testSuite', 'goog.ui.Component', 'goog.ui.emoji.Emoji', 'goog.ui.emoji.EmojiPicker', 'goog.ui.emoji.SpriteInfo'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/emoji/fast_nonprogressive_emojipicker_test.js', ['goog.ui.emoji.FastNonProgressiveEmojiPickerTest'], ['goog.Promise', 'goog.dom.classlist', 'goog.events', 'goog.events.EventType', 'goog.net.EventType', 'goog.style', 'goog.testing.TestCase', 'goog.testing.testSuite', 'goog.ui.emoji.Emoji', 'goog.ui.emoji.EmojiPicker', 'goog.ui.emoji.SpriteInfo'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/emoji/fast_progressive_emojipicker_test.js', ['goog.ui.emoji.FastProgressiveEmojiPickerTest'], ['goog.Promise', 'goog.dom.classlist', 'goog.events', 'goog.events.EventType', 'goog.net.EventType', 'goog.style', 'goog.testing.testSuite', 'goog.ui.emoji.Emoji', 'goog.ui.emoji.EmojiPicker', 'goog.ui.emoji.SpriteInfo'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/emoji/popupemojipicker.js', ['goog.ui.emoji.PopupEmojiPicker'], ['goog.events.EventType', 'goog.positioning.AnchoredPosition', 'goog.positioning.Corner', 'goog.ui.Component', 'goog.ui.Popup', 'goog.ui.emoji.EmojiPicker'], {});
goog.addDependency('ui/emoji/popupemojipicker_test.js', ['goog.ui.emoji.PopupEmojiPickerTest'], ['goog.dom', 'goog.testing.testSuite', 'goog.ui.emoji.PopupEmojiPicker'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/emoji/progressiveemojipaletterenderer.js', ['goog.ui.emoji.ProgressiveEmojiPaletteRenderer'], ['goog.dom.TagName', 'goog.style', 'goog.ui.emoji.EmojiPaletteRenderer'], {});
goog.addDependency('ui/emoji/spriteinfo.js', ['goog.ui.emoji.SpriteInfo'], [], {'lang': 'es6'});
goog.addDependency('ui/emoji/spriteinfo_test.js', ['goog.ui.emoji.SpriteInfoTest'], ['goog.testing.testSuite', 'goog.ui.emoji.SpriteInfo'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/filteredmenu.js', ['goog.ui.FilteredMenu'], ['goog.a11y.aria', 'goog.a11y.aria.AutoCompleteValues', 'goog.a11y.aria.State', 'goog.dom', 'goog.dom.InputType', 'goog.dom.TagName', 'goog.events', 'goog.events.EventType', 'goog.events.InputHandler', 'goog.events.KeyCodes', 'goog.string', 'goog.style', 'goog.ui.Component', 'goog.ui.FilterObservingMenuItem', 'goog.ui.Menu', 'goog.ui.MenuItem', 'goog.userAgent'], {});
goog.addDependency('ui/filteredmenu_test.js', ['goog.ui.FilteredMenuTest'], ['goog.a11y.aria', 'goog.a11y.aria.AutoCompleteValues', 'goog.a11y.aria.State', 'goog.dom', 'goog.dom.TagName', 'goog.events', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.math.Rect', 'goog.style', 'goog.testing.events', 'goog.testing.testSuite', 'goog.ui.FilteredMenu', 'goog.ui.MenuItem'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/filterobservingmenuitem.js', ['goog.ui.FilterObservingMenuItem'], ['goog.ui.FilterObservingMenuItemRenderer', 'goog.ui.MenuItem', 'goog.ui.registry'], {});
goog.addDependency('ui/filterobservingmenuitemrenderer.js', ['goog.ui.FilterObservingMenuItemRenderer'], ['goog.ui.MenuItemRenderer'], {});
goog.addDependency('ui/flatbuttonrenderer.js', ['goog.ui.FlatButtonRenderer'], ['goog.a11y.aria.Role', 'goog.asserts', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.ui.Button', 'goog.ui.ButtonRenderer', 'goog.ui.INLINE_BLOCK_CLASSNAME', 'goog.ui.registry'], {});
goog.addDependency('ui/flatmenubuttonrenderer.js', ['goog.ui.FlatMenuButtonRenderer'], ['goog.dom', 'goog.dom.TagName', 'goog.style', 'goog.ui.FlatButtonRenderer', 'goog.ui.INLINE_BLOCK_CLASSNAME', 'goog.ui.Menu', 'goog.ui.MenuButton', 'goog.ui.MenuRenderer', 'goog.ui.registry'], {});
goog.addDependency('ui/formpost.js', ['goog.ui.FormPost'], ['goog.array', 'goog.dom.InputType', 'goog.dom.TagName', 'goog.dom.safe', 'goog.html.SafeHtml', 'goog.ui.Component'], {});
goog.addDependency('ui/formpost_test.js', ['goog.ui.FormPostTest'], ['goog.array', 'goog.dom', 'goog.dom.TagName', 'goog.object', 'goog.testing.testSuite', 'goog.ui.FormPost', 'goog.userAgent.product', 'goog.userAgent.product.isVersion'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/gauge.js', ['goog.ui.Gauge', 'goog.ui.GaugeColoredRange'], ['goog.a11y.aria', 'goog.asserts', 'goog.dom.TagName', 'goog.events', 'goog.fx.Animation', 'goog.fx.Transition', 'goog.fx.easing', 'goog.graphics', 'goog.graphics.Font', 'goog.graphics.Path', 'goog.graphics.SolidFill', 'goog.math', 'goog.ui.Component', 'goog.ui.GaugeTheme'], {});
goog.addDependency('ui/gaugetheme.js', ['goog.ui.GaugeTheme'], ['goog.graphics.LinearGradient', 'goog.graphics.SolidFill', 'goog.graphics.Stroke'], {});
goog.addDependency('ui/hovercard.js', ['goog.ui.HoverCard', 'goog.ui.HoverCard.EventType', 'goog.ui.HoverCard.TriggerEvent'], ['goog.array', 'goog.dom', 'goog.events', 'goog.events.Event', 'goog.events.EventType', 'goog.ui.AdvancedTooltip', 'goog.ui.PopupBase', 'goog.ui.Tooltip'], {});
goog.addDependency('ui/hovercard_test.js', ['goog.ui.HoverCardTest'], ['goog.dom', 'goog.events', 'goog.math.Coordinate', 'goog.style', 'goog.testing.MockClock', 'goog.testing.events', 'goog.testing.events.Event', 'goog.testing.testSuite', 'goog.ui.HoverCard'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/hsvapalette.js', ['goog.ui.HsvaPalette'], ['goog.array', 'goog.color.alpha', 'goog.dom.TagName', 'goog.events', 'goog.events.EventType', 'goog.style', 'goog.ui.Component', 'goog.ui.HsvPalette'], {});
goog.addDependency('ui/hsvapalette_test.js', ['goog.ui.HsvaPaletteTest'], ['goog.color.alpha', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events.Event', 'goog.math.Coordinate', 'goog.style', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite', 'goog.ui.HsvaPalette', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/hsvpalette.js', ['goog.ui.HsvPalette'], ['goog.color', 'goog.dom.InputType', 'goog.dom.TagName', 'goog.events', 'goog.events.EventType', 'goog.events.InputHandler', 'goog.style', 'goog.style.bidi', 'goog.ui.Component', 'goog.userAgent'], {});
goog.addDependency('ui/hsvpalette_test.js', ['goog.ui.HsvPaletteTest'], ['goog.color', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events', 'goog.events.Event', 'goog.math.Coordinate', 'goog.style', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite', 'goog.ui.Component', 'goog.ui.HsvPalette', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/idgenerator.js', ['goog.ui.IdGenerator'], [], {});
goog.addDependency('ui/idletimer.js', ['goog.ui.IdleTimer'], ['goog.Timer', 'goog.events', 'goog.events.EventTarget', 'goog.structs.Set', 'goog.ui.ActivityMonitor'], {});
goog.addDependency('ui/idletimer_test.js', ['goog.ui.IdleTimerTest'], ['goog.events', 'goog.testing.MockClock', 'goog.testing.testSuite', 'goog.ui.IdleTimer', 'goog.ui.MockActivityMonitor'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/iframemask.js', ['goog.ui.IframeMask'], ['goog.Disposable', 'goog.Timer', 'goog.dom', 'goog.dom.iframe', 'goog.events.EventHandler', 'goog.structs.Pool', 'goog.style'], {});
goog.addDependency('ui/iframemask_test.js', ['goog.ui.IframeMaskTest'], ['goog.dom', 'goog.dom.TagName', 'goog.dom.iframe', 'goog.structs.Pool', 'goog.style', 'goog.testing.MockClock', 'goog.testing.StrictMock', 'goog.testing.testSuite', 'goog.ui.IframeMask', 'goog.ui.Popup', 'goog.ui.PopupBase', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/imagelessbuttonrenderer.js', ['goog.ui.ImagelessButtonRenderer'], ['goog.dom.TagName', 'goog.dom.classlist', 'goog.ui.Button', 'goog.ui.Component', 'goog.ui.CustomButtonRenderer', 'goog.ui.INLINE_BLOCK_CLASSNAME', 'goog.ui.registry'], {});
goog.addDependency('ui/imagelessmenubuttonrenderer.js', ['goog.ui.ImagelessMenuButtonRenderer'], ['goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.ui.INLINE_BLOCK_CLASSNAME', 'goog.ui.MenuButton', 'goog.ui.MenuButtonRenderer', 'goog.ui.registry'], {});
goog.addDependency('ui/inputdatepicker.js', ['goog.ui.InputDatePicker'], ['goog.date.DateTime', 'goog.dom', 'goog.dom.InputType', 'goog.dom.TagName', 'goog.i18n.DateTimeParse', 'goog.string', 'goog.ui.Component', 'goog.ui.DatePicker', 'goog.ui.LabelInput', 'goog.ui.PopupBase', 'goog.ui.PopupDatePicker'], {});
goog.addDependency('ui/inputdatepicker_test.js', ['goog.ui.InputDatePickerTest'], ['goog.dom', 'goog.i18n.DateTimeFormat', 'goog.i18n.DateTimeParse', 'goog.testing.testSuite', 'goog.ui.InputDatePicker'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/itemevent.js', ['goog.ui.ItemEvent'], ['goog.events.Event'], {});
goog.addDependency('ui/keyboardeventdata.js', ['goog.ui.KeyboardEventData'], ['goog.asserts', 'goog.events.BrowserEvent'], {'lang': 'es6'});
goog.addDependency('ui/keyboardshortcuthandler.js', ['goog.ui.KeyboardShortcutEvent', 'goog.ui.KeyboardShortcutHandler', 'goog.ui.KeyboardShortcutHandler.EventType', 'goog.ui.KeyboardShortcutHandler.Modifiers'], ['goog.array', 'goog.asserts', 'goog.dom.TagName', 'goog.events', 'goog.events.Event', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.events.KeyNames', 'goog.events.Keys', 'goog.object', 'goog.ui.KeyboardEventData', 'goog.ui.SyntheticKeyboardEvent', 'goog.userAgent'], {});
goog.addDependency('ui/keyboardshortcuthandler_test.js', ['goog.ui.KeyboardShortcutHandlerTest'], ['goog.dom', 'goog.events', 'goog.events.BrowserEvent', 'goog.events.KeyCodes', 'goog.testing.MockClock', 'goog.testing.PropertyReplacer', 'goog.testing.StrictMock', 'goog.testing.events', 'goog.testing.testSuite', 'goog.ui.KeyboardShortcutHandler', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/labelinput.js', ['goog.ui.LabelInput'], ['goog.Timer', 'goog.a11y.aria', 'goog.a11y.aria.State', 'goog.asserts', 'goog.dom', 'goog.dom.InputType', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events.EventHandler', 'goog.events.EventType', 'goog.ui.Component', 'goog.userAgent'], {});
goog.addDependency('ui/labelinput_test.js', ['goog.ui.LabelInputTest'], ['goog.a11y.aria', 'goog.a11y.aria.State', 'goog.dom', 'goog.dom.classlist', 'goog.events.EventType', 'goog.testing.MockClock', 'goog.testing.events', 'goog.testing.events.Event', 'goog.testing.testSuite', 'goog.ui.LabelInput', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/linkbuttonrenderer.js', ['goog.ui.LinkButtonRenderer'], ['goog.ui.Button', 'goog.ui.FlatButtonRenderer', 'goog.ui.registry'], {});
goog.addDependency('ui/media/flashobject.js', ['goog.ui.media.FlashObject', 'goog.ui.media.FlashObject.ScriptAccessLevel', 'goog.ui.media.FlashObject.Wmodes'], ['goog.asserts', 'goog.dom.TagName', 'goog.dom.safe', 'goog.events.Event', 'goog.events.EventHandler', 'goog.events.EventType', 'goog.html.TrustedResourceUrl', 'goog.html.flash', 'goog.log', 'goog.object', 'goog.string', 'goog.structs.Map', 'goog.style', 'goog.ui.Component', 'goog.userAgent', 'goog.userAgent.flash'], {});
goog.addDependency('ui/media/flashobject_test.js', ['goog.ui.media.FlashObjectTest'], ['goog.dom', 'goog.dom.DomHelper', 'goog.dom.TagName', 'goog.events', 'goog.events.Event', 'goog.events.EventType', 'goog.html.testing', 'goog.testing.MockControl', 'goog.testing.events', 'goog.testing.testSuite', 'goog.ui.media.FlashObject', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/media/flickr.js', ['goog.ui.media.FlickrSet', 'goog.ui.media.FlickrSetModel'], ['goog.html.TrustedResourceUrl', 'goog.string.Const', 'goog.ui.media.FlashObject', 'goog.ui.media.Media', 'goog.ui.media.MediaModel', 'goog.ui.media.MediaRenderer'], {});
goog.addDependency('ui/media/flickr_test.js', ['goog.ui.media.FlickrSetTest'], ['goog.dom', 'goog.dom.TagName', 'goog.html.testing', 'goog.testing.testSuite', 'goog.ui.media.FlashObject', 'goog.ui.media.FlickrSet', 'goog.ui.media.FlickrSetModel', 'goog.ui.media.Media'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/media/googlevideo.js', ['goog.ui.media.GoogleVideo', 'goog.ui.media.GoogleVideoModel'], ['goog.html.TrustedResourceUrl', 'goog.string', 'goog.string.Const', 'goog.ui.media.FlashObject', 'goog.ui.media.Media', 'goog.ui.media.MediaModel', 'goog.ui.media.MediaRenderer'], {});
goog.addDependency('ui/media/googlevideo_test.js', ['goog.ui.media.GoogleVideoTest'], ['goog.dom', 'goog.dom.TagName', 'goog.testing.testSuite', 'goog.ui.media.FlashObject', 'goog.ui.media.GoogleVideo', 'goog.ui.media.GoogleVideoModel', 'goog.ui.media.Media'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/media/media.js', ['goog.ui.media.Media', 'goog.ui.media.MediaRenderer'], ['goog.asserts', 'goog.dom.TagName', 'goog.style', 'goog.ui.Component', 'goog.ui.Control', 'goog.ui.ControlRenderer'], {});
goog.addDependency('ui/media/media_test.js', ['goog.ui.media.MediaTest'], ['goog.dom', 'goog.dom.TagName', 'goog.html.testing', 'goog.math.Size', 'goog.testing.testSuite', 'goog.ui.ControlRenderer', 'goog.ui.media.Media', 'goog.ui.media.MediaModel', 'goog.ui.media.MediaRenderer'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/media/mediamodel.js', ['goog.ui.media.MediaModel', 'goog.ui.media.MediaModel.Category', 'goog.ui.media.MediaModel.Credit', 'goog.ui.media.MediaModel.Credit.Role', 'goog.ui.media.MediaModel.Credit.Scheme', 'goog.ui.media.MediaModel.Medium', 'goog.ui.media.MediaModel.MimeType', 'goog.ui.media.MediaModel.Player', 'goog.ui.media.MediaModel.SubTitle', 'goog.ui.media.MediaModel.Thumbnail'], ['goog.array', 'goog.html.TrustedResourceUrl'], {});
goog.addDependency('ui/media/mediamodel_test.js', ['goog.ui.media.MediaModelTest'], ['goog.testing.testSuite', 'goog.ui.media.MediaModel'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/media/mp3.js', ['goog.ui.media.Mp3'], ['goog.string', 'goog.ui.media.FlashObject', 'goog.ui.media.Media', 'goog.ui.media.MediaRenderer'], {});
goog.addDependency('ui/media/mp3_test.js', ['goog.ui.media.Mp3Test'], ['goog.dom', 'goog.dom.TagName', 'goog.html.testing', 'goog.testing.testSuite', 'goog.ui.media.FlashObject', 'goog.ui.media.Media', 'goog.ui.media.MediaModel', 'goog.ui.media.Mp3'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/media/photo.js', ['goog.ui.media.Photo'], ['goog.dom.TagName', 'goog.ui.media.Media', 'goog.ui.media.MediaRenderer'], {});
goog.addDependency('ui/media/photo_test.js', ['goog.ui.media.PhotoTest'], ['goog.dom', 'goog.dom.TagName', 'goog.html.testing', 'goog.testing.testSuite', 'goog.ui.media.MediaModel', 'goog.ui.media.Photo'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/media/picasa.js', ['goog.ui.media.PicasaAlbum', 'goog.ui.media.PicasaAlbumModel'], ['goog.html.TrustedResourceUrl', 'goog.string.Const', 'goog.ui.media.FlashObject', 'goog.ui.media.Media', 'goog.ui.media.MediaModel', 'goog.ui.media.MediaRenderer'], {});
goog.addDependency('ui/media/picasa_test.js', ['goog.ui.media.PicasaTest'], ['goog.dom', 'goog.dom.TagName', 'goog.testing.testSuite', 'goog.ui.media.FlashObject', 'goog.ui.media.Media', 'goog.ui.media.PicasaAlbum', 'goog.ui.media.PicasaAlbumModel'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/media/vimeo.js', ['goog.ui.media.Vimeo', 'goog.ui.media.VimeoModel'], ['goog.html.TrustedResourceUrl', 'goog.string', 'goog.string.Const', 'goog.ui.media.FlashObject', 'goog.ui.media.Media', 'goog.ui.media.MediaModel', 'goog.ui.media.MediaRenderer'], {});
goog.addDependency('ui/media/vimeo_test.js', ['goog.ui.media.VimeoTest'], ['goog.dom', 'goog.dom.TagName', 'goog.testing.testSuite', 'goog.ui.media.FlashObject', 'goog.ui.media.Media', 'goog.ui.media.Vimeo', 'goog.ui.media.VimeoModel'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/media/youtube.js', ['goog.ui.media.Youtube', 'goog.ui.media.YoutubeModel'], ['goog.dom.TagName', 'goog.html.TrustedResourceUrl', 'goog.string', 'goog.string.Const', 'goog.ui.Component', 'goog.ui.media.FlashObject', 'goog.ui.media.Media', 'goog.ui.media.MediaModel', 'goog.ui.media.MediaRenderer'], {});
goog.addDependency('ui/media/youtube_test.js', ['goog.ui.media.YoutubeTest'], ['goog.dom', 'goog.dom.TagName', 'goog.testing.testSuite', 'goog.ui.media.FlashObject', 'goog.ui.media.Youtube', 'goog.ui.media.YoutubeModel'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/menu.js', ['goog.ui.Menu', 'goog.ui.Menu.EventType'], ['goog.dom.TagName', 'goog.math.Coordinate', 'goog.string', 'goog.style', 'goog.ui.Component.EventType', 'goog.ui.Component.State', 'goog.ui.Container', 'goog.ui.Container.Orientation', 'goog.ui.MenuHeader', 'goog.ui.MenuItem', 'goog.ui.MenuRenderer', 'goog.ui.MenuSeparator'], {});
goog.addDependency('ui/menu_test.js', ['goog.ui.MenuTest'], ['goog.dom', 'goog.events', 'goog.math.Coordinate', 'goog.testing.events', 'goog.testing.testSuite', 'goog.ui.Component', 'goog.ui.Menu'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/menubar.js', ['goog.ui.menuBar'], ['goog.ui.Container', 'goog.ui.MenuBarRenderer'], {});
goog.addDependency('ui/menubardecorator.js', ['goog.ui.menuBarDecorator'], ['goog.ui.MenuBarRenderer', 'goog.ui.menuBar', 'goog.ui.registry'], {});
goog.addDependency('ui/menubarrenderer.js', ['goog.ui.MenuBarRenderer'], ['goog.a11y.aria.Role', 'goog.ui.Container', 'goog.ui.ContainerRenderer'], {});
goog.addDependency('ui/menubase.js', ['goog.ui.MenuBase'], ['goog.events.EventHandler', 'goog.events.EventType', 'goog.events.KeyHandler', 'goog.ui.Popup'], {});
goog.addDependency('ui/menubutton.js', ['goog.ui.MenuButton'], ['goog.Timer', 'goog.a11y.aria', 'goog.a11y.aria.State', 'goog.asserts', 'goog.dom', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.events.KeyHandler', 'goog.math.Box', 'goog.math.Coordinate', 'goog.math.Rect', 'goog.positioning', 'goog.positioning.Corner', 'goog.positioning.MenuAnchoredPosition', 'goog.positioning.Overflow', 'goog.style', 'goog.ui.Button', 'goog.ui.Component', 'goog.ui.IdGenerator', 'goog.ui.Menu', 'goog.ui.MenuButtonRenderer', 'goog.ui.MenuItem', 'goog.ui.MenuRenderer', 'goog.ui.SubMenu', 'goog.ui.registry', 'goog.userAgent', 'goog.userAgent.product'], {'lang': 'es6'});
goog.addDependency('ui/menubutton_test.js', ['goog.ui.MenuButtonTest'], ['goog.Timer', 'goog.a11y.aria', 'goog.a11y.aria.State', 'goog.dom', 'goog.dom.TagName', 'goog.events', 'goog.events.Event', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.events.KeyHandler', 'goog.positioning', 'goog.positioning.Corner', 'goog.positioning.MenuAnchoredPosition', 'goog.positioning.Overflow', 'goog.style', 'goog.testing.ExpectedFailures', 'goog.testing.PropertyReplacer', 'goog.testing.events', 'goog.testing.recordFunction', 'goog.testing.testSuite', 'goog.ui.Component', 'goog.ui.Menu', 'goog.ui.MenuButton', 'goog.ui.MenuItem', 'goog.ui.SubMenu', 'goog.userAgent', 'goog.userAgent.product', 'goog.userAgent.product.isVersion'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/menubuttonrenderer.js', ['goog.ui.MenuButtonRenderer'], ['goog.dom', 'goog.dom.TagName', 'goog.style', 'goog.ui.CustomButtonRenderer', 'goog.ui.INLINE_BLOCK_CLASSNAME', 'goog.ui.Menu', 'goog.ui.MenuRenderer'], {});
goog.addDependency('ui/menubuttonrenderer_test.js', ['goog.ui.MenuButtonRendererTest'], ['goog.a11y.aria', 'goog.a11y.aria.State', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.testing.testSuite', 'goog.testing.ui.rendererasserts', 'goog.ui.MenuButton', 'goog.ui.MenuButtonRenderer', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/menuheader.js', ['goog.ui.MenuHeader'], ['goog.ui.Component', 'goog.ui.Control', 'goog.ui.MenuHeaderRenderer', 'goog.ui.registry'], {});
goog.addDependency('ui/menuheaderrenderer.js', ['goog.ui.MenuHeaderRenderer'], ['goog.ui.ControlRenderer'], {});
goog.addDependency('ui/menuitem.js', ['goog.ui.MenuItem'], ['goog.a11y.aria.Role', 'goog.array', 'goog.dom', 'goog.dom.classlist', 'goog.math.Coordinate', 'goog.string', 'goog.ui.Component', 'goog.ui.Control', 'goog.ui.MenuItemRenderer', 'goog.ui.registry'], {});
goog.addDependency('ui/menuitem_test.js', ['goog.ui.MenuItemTest'], ['goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.array', 'goog.dom', 'goog.dom.NodeType', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events.KeyCodes', 'goog.html.testing', 'goog.math.Coordinate', 'goog.testing.events', 'goog.testing.recordFunction', 'goog.testing.testSuite', 'goog.ui.Component', 'goog.ui.MenuItem', 'goog.ui.MenuItemRenderer'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/menuitemrenderer.js', ['goog.ui.MenuItemRenderer'], ['goog.a11y.aria.Role', 'goog.asserts', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.ui.Component', 'goog.ui.ControlRenderer'], {});
goog.addDependency('ui/menuitemrenderer_test.js', ['goog.ui.MenuItemRendererTest'], ['goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.a11y.aria.State', 'goog.dom', 'goog.dom.classlist', 'goog.testing.testSuite', 'goog.testing.ui.rendererasserts', 'goog.ui.Component', 'goog.ui.MenuItem', 'goog.ui.MenuItemRenderer'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/menurenderer.js', ['goog.ui.MenuRenderer'], ['goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.a11y.aria.State', 'goog.asserts', 'goog.dom', 'goog.dom.TagName', 'goog.ui.ContainerRenderer', 'goog.ui.Separator'], {});
goog.addDependency('ui/menuseparator.js', ['goog.ui.MenuSeparator'], ['goog.ui.MenuSeparatorRenderer', 'goog.ui.Separator', 'goog.ui.registry'], {});
goog.addDependency('ui/menuseparatorrenderer.js', ['goog.ui.MenuSeparatorRenderer'], ['goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.ui.ControlRenderer'], {});
goog.addDependency('ui/menuseparatorrenderer_test.js', ['goog.ui.MenuSeparatorRendererTest'], ['goog.dom', 'goog.testing.testSuite', 'goog.ui.MenuSeparator', 'goog.ui.MenuSeparatorRenderer'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/mockactivitymonitor.js', ['goog.ui.MockActivityMonitor'], ['goog.events.EventType', 'goog.ui.ActivityMonitor'], {});
goog.addDependency('ui/mockactivitymonitor_test.js', ['goog.ui.MockActivityMonitorTest'], ['goog.events', 'goog.functions', 'goog.testing.recordFunction', 'goog.testing.testSuite', 'goog.ui.ActivityMonitor', 'goog.ui.MockActivityMonitor'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/modalariavisibilityhelper.js', ['goog.ui.ModalAriaVisibilityHelper'], ['goog.a11y.aria', 'goog.a11y.aria.State'], {});
goog.addDependency('ui/modalariavisibilityhelper_test.js', ['goog.ui.ModalAriaVisibilityHelperTest'], ['goog.a11y.aria', 'goog.a11y.aria.State', 'goog.dom', 'goog.string', 'goog.testing.testSuite', 'goog.ui.ModalAriaVisibilityHelper'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/modalpopup.js', ['goog.ui.ModalPopup'], ['goog.Timer', 'goog.asserts', 'goog.dom', 'goog.dom.TagName', 'goog.dom.animationFrame', 'goog.dom.classlist', 'goog.dom.iframe', 'goog.events', 'goog.events.EventType', 'goog.events.FocusHandler', 'goog.fx.Transition', 'goog.string', 'goog.style', 'goog.ui.Component', 'goog.ui.ModalAriaVisibilityHelper', 'goog.ui.PopupBase', 'goog.userAgent'], {});
goog.addDependency('ui/modalpopup_test.js', ['goog.ui.ModalPopupTest'], ['goog.a11y.aria', 'goog.a11y.aria.State', 'goog.dispose', 'goog.dom', 'goog.dom.TagName', 'goog.events', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.fx.Transition', 'goog.fx.css3', 'goog.string', 'goog.style', 'goog.testing.MockClock', 'goog.testing.events', 'goog.testing.testSuite', 'goog.ui.ModalPopup', 'goog.ui.PopupBase'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/nativebuttonrenderer.js', ['goog.ui.NativeButtonRenderer'], ['goog.asserts', 'goog.dom.InputType', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events.EventType', 'goog.ui.ButtonRenderer', 'goog.ui.Component'], {});
goog.addDependency('ui/nativebuttonrenderer_test.js', ['goog.ui.NativeButtonRendererTest'], ['goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events', 'goog.testing.ExpectedFailures', 'goog.testing.events', 'goog.testing.testSuite', 'goog.testing.ui.rendererasserts', 'goog.ui.Button', 'goog.ui.Component', 'goog.ui.NativeButtonRenderer', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/option.js', ['goog.ui.Option'], ['goog.ui.Component', 'goog.ui.MenuItem', 'goog.ui.registry'], {});
goog.addDependency('ui/palette.js', ['goog.ui.Palette'], ['goog.array', 'goog.dom', 'goog.events', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.math.Size', 'goog.ui.Component', 'goog.ui.Control', 'goog.ui.PaletteRenderer', 'goog.ui.SelectionModel'], {});
goog.addDependency('ui/palette_test.js', ['goog.ui.PaletteTest'], ['goog.a11y.aria', 'goog.dom', 'goog.events', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.events.KeyEvent', 'goog.testing.events.Event', 'goog.testing.recordFunction', 'goog.testing.testSuite', 'goog.ui.Component', 'goog.ui.Container', 'goog.ui.Palette'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/paletterenderer.js', ['goog.ui.PaletteRenderer'], ['goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.a11y.aria.State', 'goog.array', 'goog.asserts', 'goog.dom', 'goog.dom.NodeIterator', 'goog.dom.NodeType', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.dom.dataset', 'goog.iter', 'goog.style', 'goog.ui.ControlRenderer', 'goog.userAgent'], {});
goog.addDependency('ui/paletterenderer_test.js', ['goog.ui.PaletteRendererTest'], ['goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.a11y.aria.State', 'goog.array', 'goog.dom', 'goog.dom.TagName', 'goog.html.testing', 'goog.testing.testSuite', 'goog.ui.Palette', 'goog.ui.PaletteRenderer'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/plaintextspellchecker.js', ['goog.ui.PlainTextSpellChecker'], ['goog.Timer', 'goog.a11y.aria', 'goog.asserts', 'goog.dom', 'goog.dom.TagName', 'goog.events.EventHandler', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.events.KeyHandler', 'goog.spell.SpellCheck', 'goog.style', 'goog.ui.AbstractSpellChecker', 'goog.ui.Component', 'goog.userAgent'], {});
goog.addDependency('ui/plaintextspellchecker_test.js', ['goog.ui.PlainTextSpellCheckerTest'], ['goog.Timer', 'goog.dom', 'goog.events.KeyCodes', 'goog.spell.SpellCheck', 'goog.testing.events', 'goog.testing.testSuite', 'goog.ui.AbstractSpellChecker', 'goog.ui.PlainTextSpellChecker'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/popup.js', ['goog.ui.Popup'], ['goog.math.Box', 'goog.positioning.AbstractPosition', 'goog.positioning.Corner', 'goog.style', 'goog.ui.PopupBase'], {});
goog.addDependency('ui/popup_test.js', ['goog.ui.PopupTest'], ['goog.positioning.AnchoredPosition', 'goog.positioning.Corner', 'goog.style', 'goog.testing.testSuite', 'goog.ui.Popup', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/popupbase.js', ['goog.ui.PopupBase', 'goog.ui.PopupBase.EventType', 'goog.ui.PopupBase.Type'], ['goog.Timer', 'goog.array', 'goog.dom', 'goog.dom.TagName', 'goog.events', 'goog.events.EventHandler', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.fx.Transition', 'goog.style', 'goog.userAgent'], {});
goog.addDependency('ui/popupbase_test.js', ['goog.ui.PopupBaseTest'], ['goog.dom', 'goog.dom.TagName', 'goog.events', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.fx.Transition', 'goog.fx.css3', 'goog.testing.MockClock', 'goog.testing.events', 'goog.testing.events.Event', 'goog.testing.testSuite', 'goog.ui.PopupBase'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/popupcolorpicker.js', ['goog.ui.PopupColorPicker'], ['goog.asserts', 'goog.dom.classlist', 'goog.events.EventType', 'goog.positioning.AnchoredPosition', 'goog.positioning.Corner', 'goog.ui.ColorPicker', 'goog.ui.Component', 'goog.ui.Popup'], {});
goog.addDependency('ui/popupcolorpicker_test.js', ['goog.ui.PopupColorPickerTest'], ['goog.dom', 'goog.events', 'goog.testing.events', 'goog.testing.testSuite', 'goog.ui.ColorPicker', 'goog.ui.PopupColorPicker'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/popupdatepicker.js', ['goog.ui.PopupDatePicker'], ['goog.events.EventType', 'goog.positioning.AnchoredViewportPosition', 'goog.positioning.Corner', 'goog.style', 'goog.ui.Component', 'goog.ui.DatePicker', 'goog.ui.Popup', 'goog.ui.PopupBase'], {});
goog.addDependency('ui/popupdatepicker_test.js', ['goog.ui.PopupDatePickerTest'], ['goog.date.Date', 'goog.dom', 'goog.dom.TagName', 'goog.events', 'goog.style', 'goog.testing.MockControl', 'goog.testing.recordFunction', 'goog.testing.testSuite', 'goog.ui.DatePicker', 'goog.ui.PopupBase', 'goog.ui.PopupDatePicker'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/popupmenu.js', ['goog.ui.PopupMenu'], ['goog.events', 'goog.events.BrowserEvent', 'goog.events.BrowserEvent.MouseButton', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.positioning.AnchoredViewportPosition', 'goog.positioning.Corner', 'goog.positioning.MenuAnchoredPosition', 'goog.positioning.Overflow', 'goog.positioning.ViewportClientPosition', 'goog.structs.Map', 'goog.style', 'goog.ui.Component', 'goog.ui.Menu', 'goog.ui.PopupBase'], {});
goog.addDependency('ui/popupmenu_test.js', ['goog.ui.PopupMenuTest'], ['goog.dom', 'goog.events.BrowserEvent', 'goog.events.EventHandler', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.math.Box', 'goog.math.Coordinate', 'goog.positioning.Corner', 'goog.style', 'goog.testing.events', 'goog.testing.testSuite', 'goog.ui.Menu', 'goog.ui.MenuItem', 'goog.ui.PopupMenu'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/progressbar.js', ['goog.ui.ProgressBar', 'goog.ui.ProgressBar.Orientation'], ['goog.a11y.aria', 'goog.asserts', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events', 'goog.events.EventType', 'goog.ui.Component', 'goog.ui.RangeModel', 'goog.userAgent'], {});
goog.addDependency('ui/prompt.js', ['goog.ui.Prompt'], ['goog.Timer', 'goog.dom', 'goog.dom.InputType', 'goog.dom.TagName', 'goog.events', 'goog.events.EventType', 'goog.functions', 'goog.html.SafeHtml', 'goog.ui.Component', 'goog.ui.Dialog', 'goog.userAgent'], {});
goog.addDependency('ui/prompt_test.js', ['goog.ui.PromptTest'], ['goog.dom.selection', 'goog.events.InputHandler', 'goog.events.KeyCodes', 'goog.functions', 'goog.string', 'goog.testing.events', 'goog.testing.testSuite', 'goog.ui.BidiInput', 'goog.ui.Dialog', 'goog.ui.Prompt', 'goog.userAgent', 'goog.userAgent.product'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/rangemodel.js', ['goog.ui.RangeModel'], ['goog.events.EventTarget', 'goog.ui.Component'], {});
goog.addDependency('ui/rangemodel_test.js', ['goog.ui.RangeModelTest'], ['goog.testing.testSuite', 'goog.ui.RangeModel'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/ratings.js', ['goog.ui.Ratings', 'goog.ui.Ratings.EventType'], ['goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.a11y.aria.State', 'goog.asserts', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events.EventType', 'goog.ui.Component'], {});
goog.addDependency('ui/registry.js', ['goog.ui.registry'], ['goog.asserts', 'goog.dom.classlist'], {});
goog.addDependency('ui/registry_test.js', ['goog.ui.registryTest'], ['goog.object', 'goog.testing.testSuite', 'goog.ui.registry'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/richtextspellchecker.js', ['goog.ui.RichTextSpellChecker'], ['goog.Timer', 'goog.asserts', 'goog.dom', 'goog.dom.NodeType', 'goog.dom.Range', 'goog.events.EventHandler', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.events.KeyHandler', 'goog.math.Coordinate', 'goog.spell.SpellCheck', 'goog.string.StringBuffer', 'goog.style', 'goog.ui.AbstractSpellChecker', 'goog.ui.Component', 'goog.ui.PopupMenu'], {});
goog.addDependency('ui/richtextspellchecker_test.js', ['goog.ui.RichTextSpellCheckerTest'], ['goog.dom.Range', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events.KeyCodes', 'goog.object', 'goog.spell.SpellCheck', 'goog.testing.MockClock', 'goog.testing.events', 'goog.testing.testSuite', 'goog.ui.RichTextSpellChecker'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/roundedpanel.js', ['goog.ui.BaseRoundedPanel', 'goog.ui.CssRoundedPanel', 'goog.ui.GraphicsRoundedPanel', 'goog.ui.RoundedPanel', 'goog.ui.RoundedPanel.Corner'], ['goog.asserts', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.graphics', 'goog.graphics.Path', 'goog.graphics.SolidFill', 'goog.graphics.Stroke', 'goog.math', 'goog.math.Coordinate', 'goog.style', 'goog.ui.Component', 'goog.userAgent'], {});
goog.addDependency('ui/roundedpanel_test.js', ['goog.ui.RoundedPanelTest'], ['goog.testing.testSuite', 'goog.ui.CssRoundedPanel', 'goog.ui.GraphicsRoundedPanel', 'goog.ui.RoundedPanel', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/roundedtabrenderer.js', ['goog.ui.RoundedTabRenderer'], ['goog.dom', 'goog.dom.TagName', 'goog.ui.Tab', 'goog.ui.TabBar', 'goog.ui.TabRenderer', 'goog.ui.registry'], {});
goog.addDependency('ui/scrollfloater.js', ['goog.ui.ScrollFloater', 'goog.ui.ScrollFloater.EventType'], ['goog.array', 'goog.asserts', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events.EventType', 'goog.style', 'goog.ui.Component', 'goog.userAgent'], {});
goog.addDependency('ui/scrollfloater_test.js', ['goog.ui.ScrollFloaterTest'], ['goog.dom', 'goog.events', 'goog.style', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite', 'goog.ui.ScrollFloater'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/select.js', ['goog.ui.Select'], ['goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.a11y.aria.State', 'goog.array', 'goog.events.EventType', 'goog.ui.Component', 'goog.ui.IdGenerator', 'goog.ui.MenuButton', 'goog.ui.MenuItem', 'goog.ui.MenuRenderer', 'goog.ui.SelectionModel', 'goog.ui.registry'], {});
goog.addDependency('ui/select_test.js', ['goog.ui.SelectTest'], ['goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.a11y.aria.State', 'goog.dom', 'goog.events', 'goog.testing.recordFunction', 'goog.testing.testSuite', 'goog.ui.Component', 'goog.ui.CustomButtonRenderer', 'goog.ui.Menu', 'goog.ui.MenuItem', 'goog.ui.Select', 'goog.ui.Separator'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/selectionmenubutton.js', ['goog.ui.SelectionMenuButton', 'goog.ui.SelectionMenuButton.SelectionState'], ['goog.dom.InputType', 'goog.dom.TagName', 'goog.events.EventType', 'goog.style', 'goog.ui.Component', 'goog.ui.MenuButton', 'goog.ui.MenuItem', 'goog.ui.registry'], {});
goog.addDependency('ui/selectionmenubutton_test.js', ['goog.ui.SelectionMenuButtonTest'], ['goog.dom', 'goog.events', 'goog.testing.events', 'goog.testing.testSuite', 'goog.ui.Component', 'goog.ui.SelectionMenuButton'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/selectionmodel.js', ['goog.ui.SelectionModel'], ['goog.array', 'goog.events.EventTarget', 'goog.events.EventType'], {});
goog.addDependency('ui/selectionmodel_test.js', ['goog.ui.SelectionModelTest'], ['goog.array', 'goog.testing.recordFunction', 'goog.testing.testSuite', 'goog.ui.SelectionModel'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/separator.js', ['goog.ui.Separator'], ['goog.a11y.aria', 'goog.asserts', 'goog.ui.Component', 'goog.ui.Control', 'goog.ui.MenuSeparatorRenderer', 'goog.ui.registry'], {});
goog.addDependency('ui/serverchart.js', ['goog.ui.ServerChart', 'goog.ui.ServerChart.AxisDisplayType', 'goog.ui.ServerChart.ChartType', 'goog.ui.ServerChart.EncodingType', 'goog.ui.ServerChart.Event', 'goog.ui.ServerChart.LegendPosition', 'goog.ui.ServerChart.MaximumValue', 'goog.ui.ServerChart.MultiAxisAlignment', 'goog.ui.ServerChart.MultiAxisType', 'goog.ui.ServerChart.UriParam', 'goog.ui.ServerChart.UriTooLongEvent'], ['goog.Uri', 'goog.array', 'goog.asserts', 'goog.dom.TagName', 'goog.dom.safe', 'goog.events.Event', 'goog.string', 'goog.ui.Component'], {});
goog.addDependency('ui/serverchart_test.js', ['goog.ui.ServerChartTest'], ['goog.Uri', 'goog.events', 'goog.testing.testSuite', 'goog.ui.ServerChart'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/slider.js', ['goog.ui.Slider', 'goog.ui.Slider.Orientation'], ['goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.dom', 'goog.dom.TagName', 'goog.ui.SliderBase'], {});
goog.addDependency('ui/sliderbase.js', ['goog.ui.SliderBase', 'goog.ui.SliderBase.AnimationFactory', 'goog.ui.SliderBase.Orientation'], ['goog.Timer', 'goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.a11y.aria.State', 'goog.array', 'goog.asserts', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.events.KeyHandler', 'goog.events.MouseWheelHandler', 'goog.functions', 'goog.fx.AnimationParallelQueue', 'goog.fx.Dragger', 'goog.fx.Transition', 'goog.fx.dom.ResizeHeight', 'goog.fx.dom.ResizeWidth', 'goog.fx.dom.Slide', 'goog.math', 'goog.math.Coordinate', 'goog.style', 'goog.style.bidi', 'goog.ui.Component', 'goog.ui.RangeModel'], {});
goog.addDependency('ui/sliderbase_test.js', ['goog.ui.SliderBaseTest'], ['goog.a11y.aria', 'goog.a11y.aria.State', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.fx.Animation', 'goog.math.Coordinate', 'goog.style', 'goog.style.bidi', 'goog.testing.MockClock', 'goog.testing.MockControl', 'goog.testing.events', 'goog.testing.mockmatchers', 'goog.testing.recordFunction', 'goog.testing.testSuite', 'goog.ui.Component', 'goog.ui.SliderBase', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/splitpane.js', ['goog.ui.SplitPane', 'goog.ui.SplitPane.Orientation'], ['goog.asserts', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events.EventType', 'goog.fx.Dragger', 'goog.math.Rect', 'goog.math.Size', 'goog.style', 'goog.ui.Component', 'goog.userAgent'], {});
goog.addDependency('ui/splitpane_test.js', ['goog.ui.SplitPaneTest'], ['goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events', 'goog.math.Size', 'goog.style', 'goog.testing.events', 'goog.testing.recordFunction', 'goog.testing.testSuite', 'goog.ui.Component', 'goog.ui.SplitPane'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/style/app/buttonrenderer.js', ['goog.ui.style.app.ButtonRenderer'], ['goog.dom.TagName', 'goog.dom.classlist', 'goog.ui.Button', 'goog.ui.CustomButtonRenderer', 'goog.ui.INLINE_BLOCK_CLASSNAME', 'goog.ui.registry'], {});
goog.addDependency('ui/style/app/buttonrenderer_test.js', ['goog.ui.style.app.ButtonRendererTest'], ['goog.dom', 'goog.testing.testSuite', 'goog.testing.ui.style', 'goog.ui.Button', 'goog.ui.Component', 'goog.ui.style.app.ButtonRenderer', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/style/app/menubuttonrenderer.js', ['goog.ui.style.app.MenuButtonRenderer'], ['goog.a11y.aria.Role', 'goog.array', 'goog.dom', 'goog.dom.TagName', 'goog.style', 'goog.ui.Menu', 'goog.ui.MenuRenderer', 'goog.ui.style.app.ButtonRenderer'], {});
goog.addDependency('ui/style/app/menubuttonrenderer_test.js', ['goog.ui.style.app.MenuButtonRendererTest'], ['goog.dom', 'goog.testing.testSuite', 'goog.testing.ui.style', 'goog.ui.Component', 'goog.ui.MenuButton', 'goog.ui.style.app.MenuButtonRenderer'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/style/app/primaryactionbuttonrenderer.js', ['goog.ui.style.app.PrimaryActionButtonRenderer'], ['goog.ui.Button', 'goog.ui.registry', 'goog.ui.style.app.ButtonRenderer'], {});
goog.addDependency('ui/style/app/primaryactionbuttonrenderer_test.js', ['goog.ui.style.app.PrimaryActionButtonRendererTest'], ['goog.dom', 'goog.testing.testSuite', 'goog.testing.ui.style', 'goog.ui.Button', 'goog.ui.Component', 'goog.ui.style.app.PrimaryActionButtonRenderer'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/submenu.js', ['goog.ui.SubMenu'], ['goog.Timer', 'goog.asserts', 'goog.dom', 'goog.dom.classlist', 'goog.events.KeyCodes', 'goog.positioning.AnchoredViewportPosition', 'goog.positioning.Corner', 'goog.style', 'goog.ui.Component', 'goog.ui.Menu', 'goog.ui.MenuItem', 'goog.ui.SubMenuRenderer', 'goog.ui.registry'], {});
goog.addDependency('ui/submenu_test.js', ['goog.ui.SubMenuTest'], ['goog.a11y.aria', 'goog.a11y.aria.State', 'goog.dom', 'goog.dom.classlist', 'goog.events', 'goog.events.Event', 'goog.events.KeyCodes', 'goog.events.KeyHandler', 'goog.functions', 'goog.positioning', 'goog.positioning.Overflow', 'goog.style', 'goog.testing.MockClock', 'goog.testing.events', 'goog.testing.testSuite', 'goog.ui.Component', 'goog.ui.Menu', 'goog.ui.MenuItem', 'goog.ui.SubMenu', 'goog.ui.SubMenuRenderer'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/submenurenderer.js', ['goog.ui.SubMenuRenderer'], ['goog.a11y.aria', 'goog.a11y.aria.State', 'goog.asserts', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.style', 'goog.ui.Menu', 'goog.ui.MenuItemRenderer'], {});
goog.addDependency('ui/synthetickeyboardevent.js', ['goog.ui.SyntheticKeyboardEvent'], ['goog.events.Event', 'goog.ui.KeyboardEventData'], {});
goog.addDependency('ui/tab.js', ['goog.ui.Tab'], ['goog.ui.Component', 'goog.ui.Control', 'goog.ui.TabRenderer', 'goog.ui.registry'], {});
goog.addDependency('ui/tab_test.js', ['goog.ui.TabTest'], ['goog.dom', 'goog.testing.testSuite', 'goog.ui.Component', 'goog.ui.Tab', 'goog.ui.TabRenderer'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/tabbar.js', ['goog.ui.TabBar', 'goog.ui.TabBar.Location'], ['goog.ui.Component.EventType', 'goog.ui.Container', 'goog.ui.Container.Orientation', 'goog.ui.Tab', 'goog.ui.TabBarRenderer', 'goog.ui.registry'], {});
goog.addDependency('ui/tabbar_test.js', ['goog.ui.TabBarTest'], ['goog.dom', 'goog.events', 'goog.events.Event', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.testing.testSuite', 'goog.ui.Component', 'goog.ui.Container', 'goog.ui.Tab', 'goog.ui.TabBar', 'goog.ui.TabBarRenderer'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/tabbarrenderer.js', ['goog.ui.TabBarRenderer'], ['goog.a11y.aria.Role', 'goog.object', 'goog.ui.ContainerRenderer'], {});
goog.addDependency('ui/tabbarrenderer_test.js', ['goog.ui.TabBarRendererTest'], ['goog.a11y.aria.Role', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.testing.testSuite', 'goog.testing.ui.rendererasserts', 'goog.ui.Container', 'goog.ui.TabBar', 'goog.ui.TabBarRenderer'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/tablesorter.js', ['goog.ui.TableSorter', 'goog.ui.TableSorter.EventType'], ['goog.array', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events.EventType', 'goog.functions', 'goog.ui.Component'], {});
goog.addDependency('ui/tablesorter_test.js', ['goog.ui.TableSorterTest'], ['goog.array', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.testing.events', 'goog.testing.testSuite', 'goog.ui.TableSorter'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/tabpane.js', ['goog.ui.TabPane', 'goog.ui.TabPane.Events', 'goog.ui.TabPane.TabLocation', 'goog.ui.TabPane.TabPage', 'goog.ui.TabPaneEvent'], ['goog.asserts', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events', 'goog.events.Event', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.html.SafeStyleSheet', 'goog.style'], {});
goog.addDependency('ui/tabpane_test.js', ['goog.ui.TabPaneTest'], ['goog.dom', 'goog.testing.testSuite', 'goog.ui.TabPane'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/tabrenderer.js', ['goog.ui.TabRenderer'], ['goog.a11y.aria.Role', 'goog.ui.Component', 'goog.ui.ControlRenderer'], {});
goog.addDependency('ui/tabrenderer_test.js', ['goog.ui.TabRendererTest'], ['goog.a11y.aria.Role', 'goog.dom', 'goog.dom.classlist', 'goog.testing.dom', 'goog.testing.testSuite', 'goog.testing.ui.rendererasserts', 'goog.ui.Tab', 'goog.ui.TabRenderer'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/textarea.js', ['goog.ui.Textarea', 'goog.ui.Textarea.EventType'], ['goog.asserts', 'goog.dom', 'goog.dom.classlist', 'goog.events.EventType', 'goog.style', 'goog.ui.Control', 'goog.ui.TextareaRenderer', 'goog.userAgent'], {});
goog.addDependency('ui/textarea_test.js', ['goog.ui.TextareaTest'], ['goog.dom', 'goog.dom.classlist', 'goog.events', 'goog.style', 'goog.testing.ExpectedFailures', 'goog.testing.events.EventObserver', 'goog.testing.testSuite', 'goog.ui.Textarea', 'goog.ui.TextareaRenderer', 'goog.userAgent', 'goog.userAgent.product'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/textarearenderer.js', ['goog.ui.TextareaRenderer'], ['goog.dom.TagName', 'goog.ui.Component', 'goog.ui.ControlRenderer'], {});
goog.addDependency('ui/togglebutton.js', ['goog.ui.ToggleButton'], ['goog.ui.Button', 'goog.ui.Component', 'goog.ui.CustomButtonRenderer', 'goog.ui.registry'], {});
goog.addDependency('ui/toolbar.js', ['goog.ui.Toolbar'], ['goog.ui.Container', 'goog.ui.ToolbarRenderer'], {});
goog.addDependency('ui/toolbar_test.js', ['goog.ui.ToolbarTest'], ['goog.a11y.aria', 'goog.dom', 'goog.events.EventType', 'goog.testing.events', 'goog.testing.events.Event', 'goog.testing.testSuite', 'goog.ui.Toolbar', 'goog.ui.ToolbarMenuButton'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/toolbarbutton.js', ['goog.ui.ToolbarButton'], ['goog.ui.Button', 'goog.ui.ToolbarButtonRenderer', 'goog.ui.registry'], {});
goog.addDependency('ui/toolbarbuttonrenderer.js', ['goog.ui.ToolbarButtonRenderer'], ['goog.ui.CustomButtonRenderer'], {});
goog.addDependency('ui/toolbarcolormenubutton.js', ['goog.ui.ToolbarColorMenuButton'], ['goog.ui.ColorMenuButton', 'goog.ui.ToolbarColorMenuButtonRenderer', 'goog.ui.registry'], {});
goog.addDependency('ui/toolbarcolormenubuttonrenderer.js', ['goog.ui.ToolbarColorMenuButtonRenderer'], ['goog.asserts', 'goog.dom.classlist', 'goog.ui.ColorMenuButtonRenderer', 'goog.ui.MenuButtonRenderer', 'goog.ui.ToolbarMenuButtonRenderer'], {});
goog.addDependency('ui/toolbarcolormenubuttonrenderer_test.js', ['goog.ui.ToolbarColorMenuButtonRendererTest'], ['goog.dom', 'goog.testing.testSuite', 'goog.testing.ui.RendererHarness', 'goog.testing.ui.rendererasserts', 'goog.ui.ToolbarColorMenuButton', 'goog.ui.ToolbarColorMenuButtonRenderer'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/toolbarmenubutton.js', ['goog.ui.ToolbarMenuButton'], ['goog.ui.MenuButton', 'goog.ui.ToolbarMenuButtonRenderer', 'goog.ui.registry'], {});
goog.addDependency('ui/toolbarmenubuttonrenderer.js', ['goog.ui.ToolbarMenuButtonRenderer'], ['goog.ui.MenuButtonRenderer'], {});
goog.addDependency('ui/toolbarrenderer.js', ['goog.ui.ToolbarRenderer'], ['goog.a11y.aria.Role', 'goog.dom.TagName', 'goog.ui.Container', 'goog.ui.ContainerRenderer', 'goog.ui.Separator', 'goog.ui.ToolbarSeparatorRenderer'], {});
goog.addDependency('ui/toolbarselect.js', ['goog.ui.ToolbarSelect'], ['goog.ui.Select', 'goog.ui.ToolbarMenuButtonRenderer', 'goog.ui.registry'], {});
goog.addDependency('ui/toolbarseparator.js', ['goog.ui.ToolbarSeparator'], ['goog.ui.Separator', 'goog.ui.ToolbarSeparatorRenderer', 'goog.ui.registry'], {});
goog.addDependency('ui/toolbarseparatorrenderer.js', ['goog.ui.ToolbarSeparatorRenderer'], ['goog.asserts', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.ui.INLINE_BLOCK_CLASSNAME', 'goog.ui.MenuSeparatorRenderer'], {});
goog.addDependency('ui/toolbarseparatorrenderer_test.js', ['goog.ui.ToolbarSeparatorRendererTest'], ['goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.testing.testSuite', 'goog.ui.Component', 'goog.ui.INLINE_BLOCK_CLASSNAME', 'goog.ui.ToolbarSeparator', 'goog.ui.ToolbarSeparatorRenderer'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/toolbartogglebutton.js', ['goog.ui.ToolbarToggleButton'], ['goog.ui.ToggleButton', 'goog.ui.ToolbarButtonRenderer', 'goog.ui.registry'], {});
goog.addDependency('ui/tooltip.js', ['goog.ui.Tooltip', 'goog.ui.Tooltip.CursorTooltipPosition', 'goog.ui.Tooltip.ElementTooltipPosition', 'goog.ui.Tooltip.State'], ['goog.Timer', 'goog.array', 'goog.asserts', 'goog.dom', 'goog.dom.TagName', 'goog.dom.safe', 'goog.events', 'goog.events.EventType', 'goog.events.FocusHandler', 'goog.math.Box', 'goog.math.Coordinate', 'goog.positioning', 'goog.positioning.AnchoredPosition', 'goog.positioning.Corner', 'goog.positioning.Overflow', 'goog.positioning.OverflowStatus', 'goog.positioning.ViewportPosition', 'goog.structs.Set', 'goog.style', 'goog.ui.Popup', 'goog.ui.PopupBase'], {});
goog.addDependency('ui/tooltip_test.js', ['goog.ui.TooltipTest'], ['goog.dom', 'goog.dom.TagName', 'goog.events.Event', 'goog.events.EventHandler', 'goog.events.EventType', 'goog.events.FocusHandler', 'goog.html.testing', 'goog.math.Coordinate', 'goog.positioning.AbsolutePosition', 'goog.style', 'goog.testing.MockClock', 'goog.testing.TestQueue', 'goog.testing.events', 'goog.testing.testSuite', 'goog.ui.PopupBase', 'goog.ui.Tooltip', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/tree/basenode.js', ['goog.ui.tree.BaseNode', 'goog.ui.tree.BaseNode.EventType'], ['goog.Timer', 'goog.a11y.aria', 'goog.a11y.aria.State', 'goog.asserts', 'goog.dom.safe', 'goog.events.Event', 'goog.events.KeyCodes', 'goog.html.SafeHtml', 'goog.html.SafeStyle', 'goog.string', 'goog.string.StringBuffer', 'goog.style', 'goog.ui.Component'], {});
goog.addDependency('ui/tree/basenode_test.js', ['goog.ui.tree.BaseNodeTest'], ['goog.a11y.aria', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.html.testing', 'goog.testing.testSuite', 'goog.ui.Component', 'goog.ui.tree.BaseNode', 'goog.ui.tree.TreeControl', 'goog.ui.tree.TreeNode'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/tree/treecontrol.js', ['goog.ui.tree.TreeControl'], ['goog.a11y.aria', 'goog.asserts', 'goog.dom.classlist', 'goog.events.EventType', 'goog.events.FocusHandler', 'goog.events.KeyHandler', 'goog.html.SafeHtml', 'goog.log', 'goog.ui.tree.BaseNode', 'goog.ui.tree.TreeNode', 'goog.ui.tree.TypeAhead', 'goog.userAgent'], {});
goog.addDependency('ui/tree/treecontrol_test.js', ['goog.ui.tree.TreeControlTest'], ['goog.dom', 'goog.testing.testSuite', 'goog.ui.tree.TreeControl'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/tree/treenode.js', ['goog.ui.tree.TreeNode'], ['goog.ui.tree.BaseNode'], {});
goog.addDependency('ui/tree/typeahead.js', ['goog.ui.tree.TypeAhead', 'goog.ui.tree.TypeAhead.Offset'], ['goog.array', 'goog.events.KeyCodes', 'goog.string', 'goog.structs.Trie'], {});
goog.addDependency('ui/tree/typeahead_test.js', ['goog.ui.tree.TypeAheadTest'], ['goog.dom', 'goog.events.KeyCodes', 'goog.testing.testSuite', 'goog.ui.tree.TreeControl', 'goog.ui.tree.TypeAhead'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/tristatemenuitem.js', ['goog.ui.TriStateMenuItem', 'goog.ui.TriStateMenuItem.State'], ['goog.dom.classlist', 'goog.ui.Component', 'goog.ui.MenuItem', 'goog.ui.TriStateMenuItemRenderer', 'goog.ui.registry'], {});
goog.addDependency('ui/tristatemenuitemrenderer.js', ['goog.ui.TriStateMenuItemRenderer'], ['goog.asserts', 'goog.dom.classlist', 'goog.ui.MenuItemRenderer'], {});
goog.addDependency('ui/twothumbslider.js', ['goog.ui.TwoThumbSlider'], ['goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.dom', 'goog.dom.TagName', 'goog.ui.SliderBase'], {});
goog.addDependency('ui/twothumbslider_test.js', ['goog.ui.TwoThumbSliderTest'], ['goog.testing.testSuite', 'goog.ui.SliderBase', 'goog.ui.TwoThumbSlider'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('ui/zippy.js', ['goog.ui.Zippy', 'goog.ui.Zippy.Events', 'goog.ui.ZippyEvent'], ['goog.a11y.aria', 'goog.a11y.aria.Role', 'goog.a11y.aria.State', 'goog.dom', 'goog.dom.classlist', 'goog.events.Event', 'goog.events.EventHandler', 'goog.events.EventTarget', 'goog.events.EventType', 'goog.events.KeyCodes', 'goog.events.KeyHandler', 'goog.style'], {});
goog.addDependency('ui/zippy_test.js', ['goog.ui.ZippyTest'], ['goog.a11y.aria', 'goog.dom', 'goog.dom.TagName', 'goog.dom.classlist', 'goog.events', 'goog.events.KeyCodes', 'goog.object', 'goog.testing.events', 'goog.testing.testSuite', 'goog.ui.Zippy'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('uri/uri.js', ['goog.Uri', 'goog.Uri.QueryData'], ['goog.array', 'goog.asserts', 'goog.string', 'goog.structs', 'goog.structs.Map', 'goog.uri.utils', 'goog.uri.utils.ComponentIndex', 'goog.uri.utils.StandardQueryParam'], {});
goog.addDependency('uri/uri_test.js', ['goog.UriTest'], ['goog.Uri', 'goog.testing.testSuite'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('uri/utils.js', ['goog.uri.utils', 'goog.uri.utils.ComponentIndex', 'goog.uri.utils.QueryArray', 'goog.uri.utils.QueryValue', 'goog.uri.utils.StandardQueryParam'], ['goog.array', 'goog.asserts', 'goog.string'], {});
goog.addDependency('uri/utils_test.js', ['goog.uri.utilsTest'], ['goog.functions', 'goog.string', 'goog.testing.testSuite', 'goog.uri.utils'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('useragent/adobereader.js', ['goog.userAgent.adobeReader'], ['goog.string', 'goog.userAgent'], {'module': 'goog'});
goog.addDependency('useragent/adobereader_test.js', ['goog.userAgent.adobeReaderTest'], ['goog.testing.testSuite', 'goog.userAgent.adobeReader'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('useragent/flash.js', ['goog.userAgent.flash'], ['goog.string'], {});
goog.addDependency('useragent/flash_test.js', ['goog.userAgent.flashTest'], ['goog.testing.testSuite', 'goog.userAgent.flash'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('useragent/iphoto.js', ['goog.userAgent.iphoto'], ['goog.string', 'goog.userAgent'], {});
goog.addDependency('useragent/jscript.js', ['goog.userAgent.jscript'], ['goog.string'], {});
goog.addDependency('useragent/jscript_test.js', ['goog.userAgent.jscriptTest'], ['goog.testing.testSuite', 'goog.userAgent.jscript'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('useragent/keyboard.js', ['goog.userAgent.keyboard'], ['goog.labs.userAgent.platform'], {});
goog.addDependency('useragent/keyboard_test.js', ['goog.userAgent.keyboardTest'], ['goog.labs.userAgent.testAgents', 'goog.labs.userAgent.util', 'goog.testing.MockUserAgent', 'goog.testing.testSuite', 'goog.userAgent.keyboard', 'goog.userAgentTestUtil'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('useragent/platform.js', ['goog.userAgent.platform'], ['goog.string', 'goog.userAgent'], {});
goog.addDependency('useragent/platform_test.js', ['goog.userAgent.platformTest'], ['goog.testing.MockUserAgent', 'goog.testing.testSuite', 'goog.userAgent', 'goog.userAgent.platform', 'goog.userAgentTestUtil'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('useragent/product.js', ['goog.userAgent.product'], ['goog.labs.userAgent.browser', 'goog.labs.userAgent.platform', 'goog.userAgent'], {});
goog.addDependency('useragent/product_isversion.js', ['goog.userAgent.product.isVersion'], ['goog.labs.userAgent.platform', 'goog.string', 'goog.userAgent', 'goog.userAgent.product'], {});
goog.addDependency('useragent/product_test.js', ['goog.userAgent.productTest'], ['goog.array', 'goog.labs.userAgent.testAgents', 'goog.labs.userAgent.util', 'goog.testing.MockUserAgent', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite', 'goog.userAgent', 'goog.userAgent.product', 'goog.userAgent.product.isVersion', 'goog.userAgentTestUtil'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('useragent/useragent.js', ['goog.userAgent'], ['goog.labs.userAgent.browser', 'goog.labs.userAgent.engine', 'goog.labs.userAgent.platform', 'goog.labs.userAgent.util', 'goog.reflect', 'goog.string'], {});
goog.addDependency('useragent/useragent_quirks_test.js', ['goog.userAgentQuirksTest'], ['goog.testing.testSuite', 'goog.userAgent'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('useragent/useragent_test.js', ['goog.userAgentTest'], ['goog.array', 'goog.labs.userAgent.platform', 'goog.labs.userAgent.testAgents', 'goog.labs.userAgent.util', 'goog.testing.PropertyReplacer', 'goog.testing.testSuite', 'goog.userAgent', 'goog.userAgentTestUtil'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('useragent/useragenttestutil.js', ['goog.userAgentTestUtil', 'goog.userAgentTestUtil.UserAgents'], ['goog.labs.userAgent.browser', 'goog.labs.userAgent.engine', 'goog.labs.userAgent.platform', 'goog.object', 'goog.userAgent', 'goog.userAgent.keyboard', 'goog.userAgent.platform', 'goog.userAgent.product', 'goog.userAgent.product.isVersion'], {});
goog.addDependency('vec/float32array.js', ['goog.vec.Float32Array'], [], {'lang': 'es6'});
goog.addDependency('vec/float32array_test.js', ['goog.vec.Float32ArrayTest'], ['goog.testing.testSuite', 'goog.vec.Float32Array'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('vec/float64array.js', ['goog.vec.Float64Array'], [], {'lang': 'es6'});
goog.addDependency('vec/float64array_test.js', ['goog.vec.Float64ArrayTest'], ['goog.testing.testSuite', 'goog.vec.Float64Array'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('vec/mat3.js', ['goog.vec.Mat3'], ['goog.vec'], {});
goog.addDependency('vec/mat3_test.js', ['goog.vec.Mat3Test'], ['goog.testing.testSuite', 'goog.vec.Mat3'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('vec/mat3d.js', ['goog.vec.mat3d', 'goog.vec.mat3d.Type'], ['goog.vec', 'goog.vec.vec3d.Type'], {});
goog.addDependency('vec/mat3d_test.js', ['goog.vec.mat3dTest'], ['goog.testing.testSuite', 'goog.vec.mat3d'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('vec/mat3f.js', ['goog.vec.mat3f', 'goog.vec.mat3f.Type'], ['goog.vec', 'goog.vec.vec3f.Type'], {});
goog.addDependency('vec/mat3f_test.js', ['goog.vec.mat3fTest'], ['goog.testing.testSuite', 'goog.vec.mat3f'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('vec/mat4.js', ['goog.vec.Mat4'], ['goog.vec', 'goog.vec.Vec3', 'goog.vec.Vec4'], {});
goog.addDependency('vec/mat4_test.js', ['goog.vec.Mat4Test'], ['goog.testing.testSuite', 'goog.vec.Mat4', 'goog.vec.Vec3', 'goog.vec.Vec4'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('vec/mat4d.js', ['goog.vec.mat4d', 'goog.vec.mat4d.Type'], ['goog.vec', 'goog.vec.Quaternion', 'goog.vec.vec3d', 'goog.vec.vec4d'], {});
goog.addDependency('vec/mat4d_test.js', ['goog.vec.mat4dTest'], ['goog.testing.testSuite', 'goog.vec.Quaternion', 'goog.vec.mat4d', 'goog.vec.vec3d', 'goog.vec.vec4d'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('vec/mat4f.js', ['goog.vec.mat4f', 'goog.vec.mat4f.Type'], ['goog.vec', 'goog.vec.Quaternion', 'goog.vec.vec3f', 'goog.vec.vec4f'], {});
goog.addDependency('vec/mat4f_test.js', ['goog.vec.mat4fTest'], ['goog.testing.testSuite', 'goog.vec.Quaternion', 'goog.vec.mat4f', 'goog.vec.vec3f', 'goog.vec.vec4f'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('vec/quaternion.js', ['goog.vec.Quaternion', 'goog.vec.Quaternion.AnyType'], ['goog.vec', 'goog.vec.Vec3', 'goog.vec.Vec4'], {});
goog.addDependency('vec/quaternion_test.js', ['goog.vec.QuaternionTest'], ['goog.testing.testSuite', 'goog.vec.Mat3', 'goog.vec.Mat4', 'goog.vec.Quaternion', 'goog.vec.Vec3', 'goog.vec.vec3f'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('vec/ray.js', ['goog.vec.Ray'], ['goog.vec.Vec3'], {});
goog.addDependency('vec/ray_test.js', ['goog.vec.RayTest'], ['goog.testing.testSuite', 'goog.vec.Ray'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('vec/vec.js', ['goog.vec', 'goog.vec.AnyType', 'goog.vec.ArrayType', 'goog.vec.Float32', 'goog.vec.Float64', 'goog.vec.Number'], ['goog.vec.Float32Array', 'goog.vec.Float64Array'], {});
goog.addDependency('vec/vec2.js', ['goog.vec.Vec2'], ['goog.vec'], {});
goog.addDependency('vec/vec2_test.js', ['goog.vec.Vec2Test'], ['goog.testing.testSuite', 'goog.vec.Vec2'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('vec/vec2d.js', ['goog.vec.vec2d', 'goog.vec.vec2d.Type'], ['goog.vec'], {});
goog.addDependency('vec/vec2d_test.js', ['goog.vec.vec2dTest'], ['goog.testing.testSuite', 'goog.vec.vec2d'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('vec/vec2f.js', ['goog.vec.vec2f', 'goog.vec.vec2f.Type'], ['goog.vec'], {});
goog.addDependency('vec/vec2f_test.js', ['goog.vec.vec2fTest'], ['goog.testing.testSuite', 'goog.vec.vec2f'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('vec/vec3.js', ['goog.vec.Vec3'], ['goog.vec'], {});
goog.addDependency('vec/vec3_test.js', ['goog.vec.Vec3Test'], ['goog.testing.testSuite', 'goog.vec.Vec3'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('vec/vec3d.js', ['goog.vec.vec3d', 'goog.vec.vec3d.Type'], ['goog.vec'], {});
goog.addDependency('vec/vec3d_test.js', ['goog.vec.vec3dTest'], ['goog.testing.testSuite', 'goog.vec.vec3d'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('vec/vec3f.js', ['goog.vec.vec3f', 'goog.vec.vec3f.Type'], ['goog.vec'], {});
goog.addDependency('vec/vec3f_test.js', ['goog.vec.vec3fTest'], ['goog.testing.testSuite', 'goog.vec.vec3f'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('vec/vec4.js', ['goog.vec.Vec4'], ['goog.vec'], {});
goog.addDependency('vec/vec4_test.js', ['goog.vec.Vec4Test'], ['goog.testing.testSuite', 'goog.vec.Vec4'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('vec/vec4d.js', ['goog.vec.vec4d', 'goog.vec.vec4d.Type'], ['goog.vec'], {});
goog.addDependency('vec/vec4d_test.js', ['goog.vec.vec4dTest'], ['goog.testing.testSuite', 'goog.vec.vec4d'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('vec/vec4f.js', ['goog.vec.vec4f', 'goog.vec.vec4f.Type'], ['goog.vec'], {});
goog.addDependency('vec/vec4f_test.js', ['goog.vec.vec4fTest'], ['goog.testing.testSuite', 'goog.vec.vec4f'], {'lang': 'es6', 'module': 'goog'});
goog.addDependency('webgl/webgl.js', ['goog.webgl'], [], {});
goog.addDependency('window/window.js', ['goog.window'], ['goog.dom', 'goog.dom.TagName', 'goog.dom.safe', 'goog.html.SafeUrl', 'goog.html.uncheckedconversions', 'goog.labs.userAgent.platform', 'goog.string', 'goog.string.Const', 'goog.userAgent'], {});
goog.addDependency('window/window_test.js', ['goog.windowTest'], ['goog.Promise', 'goog.dom', 'goog.dom.TagName', 'goog.events', 'goog.functions', 'goog.html.SafeUrl', 'goog.labs.userAgent.browser', 'goog.labs.userAgent.engine', 'goog.labs.userAgent.platform', 'goog.string', 'goog.testing.PropertyReplacer', 'goog.testing.TestCase', 'goog.testing.testSuite', 'goog.window'], {'lang': 'es6', 'module': 'goog'});

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// Copyright 2014 Google Inc. All rights reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
//     You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
//     See the License for the specific language governing permissions and
// limitations under the License.

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/**
@license @nocompile
Copyright (c) 2018 The Polymer Project Authors. All rights reserved.
This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
Code distributed by Google as part of the polymer project is also
subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
*/
(function(){/*

 Copyright (c) 2016 The Polymer Project Authors. All rights reserved.
 This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
 The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
 The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
 Code distributed by Google as part of the polymer project is also
 subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
*/
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(function(){/*

Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
Code distributed by Google as part of the polymer project is also
subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
*/
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(function() {
  'use strict';

  const userPolymer = window.Polymer;

  /**
   * @namespace Polymer
   * @summary Polymer is a lightweight library built on top of the web
   *   standards-based Web Components API's, and makes it easy to build your
   *   own custom HTML elements.
   * @param {!PolymerInit} info Prototype for the custom element. It must contain
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   * @return {!Object} Returns a custom element class for the given provided
   *   prototype `info` object. The name of the element if given by `info.is`.
   */
  window.Polymer = function(info) {
    return window.Polymer._polymerFn(info);
  };

  // support user settings on the Polymer object
  if (userPolymer) {
    Object.assign(Polymer, userPolymer);
  }

  // To be plugged by legacy implementation if loaded
  /* eslint-disable valid-jsdoc */
  /**
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   *   prototype `info` object. The name of the element if given by `info.is`.
   */
  window.Polymer._polymerFn = function(info) { // eslint-disable-line no-unused-vars
    throw new Error('Load polymer.html to use the Polymer() function.');
  };
  /* eslint-enable */

  window.Polymer.version = '2.7.0';

  /* eslint-disable no-unused-vars */
  /*
  When using Closure Compiler, JSCompiler_renameProperty(property, object) is replaced by the munged name for object[property]
  We cannot alias this function, so we have to use a small shim that has the same behavior when not compiling.
  */
  window.JSCompiler_renameProperty = function(prop, obj) {
    return prop;
  };
  /* eslint-enable */

})();



  (function() {
    'use strict';

    let CSS_URL_RX = /(url\()([^)]*)(\))/g;
    let ABS_URL = /(^\/)|(^#)|(^[\w-\d]*:)/;
    let workingURL;
    let resolveDoc;
    /**
     * Resolves the given URL against the provided `baseUri'.
     * 
     * Note that this function performs no resolution for URLs that start
     * with `/` (absolute URLs) or `#` (hash identifiers).  For general purpose
     * URL resolution, use `window.URL`.
     *
     * @memberof Polymer.ResolveUrl
     * @param {string} url Input URL to resolve
     * @param {?string=} baseURI Base URI to resolve the URL against
     * @return {string} resolved URL
     */
    function resolveUrl(url, baseURI) {
      if (url && ABS_URL.test(url)) {
        return url;
      }
      // Lazy feature detection.
      if (workingURL === undefined) {
        workingURL = false;
        try {
          const u = new URL('b', 'http://a');
          u.pathname = 'c%20d';
          workingURL = (u.href === 'http://a/c%20d');
        } catch (e) {
          // silently fail
        }
      }
      if (!baseURI) {
        baseURI = document.baseURI || window.location.href;
      }
      if (workingURL) {
        return (new URL(url, baseURI)).href;
      }
      // Fallback to creating an anchor into a disconnected document.
      if (!resolveDoc) {
        resolveDoc = document.implementation.createHTMLDocument('temp');
        resolveDoc.base = resolveDoc.createElement('base');
        resolveDoc.head.appendChild(resolveDoc.base);
        resolveDoc.anchor = resolveDoc.createElement('a');
        resolveDoc.body.appendChild(resolveDoc.anchor);
      }
      resolveDoc.base.href = baseURI;
      resolveDoc.anchor.href = url;
      return resolveDoc.anchor.href || url;

    }

    /**
     * Resolves any relative URL's in the given CSS text against the provided
     * `ownerDocument`'s `baseURI`.
     *
     * @memberof Polymer.ResolveUrl
     * @param {string} cssText CSS text to process
     * @param {string} baseURI Base URI to resolve the URL against
     * @return {string} Processed CSS text with resolved URL's
     */
    function resolveCss(cssText, baseURI) {
      return cssText.replace(CSS_URL_RX, function(m, pre, url, post) {
        return pre + '\'' +
          resolveUrl(url.replace(/["']/g, ''), baseURI) +
          '\'' + post;
      });
    }

    /**
     * Returns a path from a given `url`. The path includes the trailing
     * `/` from the url.
     *
     * @memberof Polymer.ResolveUrl
     * @param {string} url Input URL to transform
     * @return {string} resolved path
     */
    function pathFromUrl(url) {
      return url.substring(0, url.lastIndexOf('/') + 1);
    }

    /**
     * Module with utilities for resolving relative URL's.
     *
     * @namespace
     * @memberof Polymer
     * @summary Module with utilities for resolving relative URL's.
     */
    Polymer.ResolveUrl = {
      resolveCss: resolveCss,
      resolveUrl: resolveUrl,
      pathFromUrl: pathFromUrl
    };

  })();



/** @suppress {deprecated} */
(function() {
  'use strict';

  /**
   * Sets the global, legacy settings.
   *
   * @deprecated
   * @namespace
   * @memberof Polymer
   */
  Polymer.Settings = Polymer.Settings || {};

  Polymer.Settings.useShadow = !(window.ShadyDOM);
  Polymer.Settings.useNativeCSSProperties =
    Boolean(!window.ShadyCSS || window.ShadyCSS.nativeCss);
  Polymer.Settings.useNativeCustomElements =
    !(window.customElements.polyfillWrapFlushCallback);


  /**
   * Globally settable property that is automatically assigned to
   * `Polymer.ElementMixin` instances, useful for binding in templates to
   * make URL's relative to an application's root.  Defaults to the main
   * document URL, but can be overridden by users.  It may be useful to set
   * `Polymer.rootPath` to provide a stable application mount path when
   * using client side routing.
   *
   * @memberof Polymer
   */
  Polymer.rootPath = Polymer.rootPath ||
    Polymer.ResolveUrl.pathFromUrl(document.baseURI || window.location.href);

  /**
   * Sets the global rootPath property used by `Polymer.ElementMixin` and
   * available via `Polymer.rootPath`.
   *
   * @memberof Polymer
   * @param {string} path The new root path
   * @return {void}
   */
  Polymer.setRootPath = function(path) {
    Polymer.rootPath = path;
  };

  /**
   * A global callback used to sanitize any value before inserting it into the DOM. The callback signature is:
   *
   *     Polymer = {
   *       sanitizeDOMValue: function(value, name, type, node) { ... }
   *     }
   *
   * Where:
   *
   * `value` is the value to sanitize.
   * `name` is the name of an attribute or property (for example, href).
   * `type` indicates where the value is being inserted: one of property, attribute, or text.
   * `node` is the node where the value is being inserted.
   *
   * @type {(function(*,string,string,Node):*)|undefined}
   * @memberof Polymer
   */
  Polymer.sanitizeDOMValue = Polymer.sanitizeDOMValue || null;

  /**
   * Sets the global sanitizeDOMValue available via `Polymer.sanitizeDOMValue`.
   *
   * @memberof Polymer
   * @param {(function(*,string,string,Node):*)|undefined} newSanitizeDOMValue the global sanitizeDOMValue callback
   * @return {void}
   */
  Polymer.setSanitizeDOMValue = function(newSanitizeDOMValue) {
    Polymer.sanitizeDOMValue = newSanitizeDOMValue;
  };

  /**
   * Globally settable property to make Polymer Gestures use passive TouchEvent listeners when recognizing gestures.
   * When set to `true`, gestures made from touch will not be able to prevent scrolling, allowing for smoother
   * scrolling performance.
   * Defaults to `false` for backwards compatibility.
   *
   * @memberof Polymer
   */
  Polymer.passiveTouchGestures = Polymer.passiveTouchGestures || false;

  /**
   * Sets `passiveTouchGestures` globally for all elements using Polymer Gestures.
   *
   * @memberof Polymer
   * @param {boolean} usePassive enable or disable passive touch gestures globally
   * @return {void}
   */
  Polymer.setPassiveTouchGestures = function(usePassive) {
    Polymer.passiveTouchGestures = usePassive;
  };

  Polymer.legacyOptimizations = Polymer.legacyOptimizations ||
      window.PolymerSettings && window.PolymerSettings.legacyOptimizations || false;

  /**
   * Sets `legacyOptimizations` globally for all elements. Enables
   * optimizations when only legacy Polymer() style elements are used.
   *
   * @memberof Polymer
   * @param {boolean} useLegacyOptimizations enable or disable legacy optimizations globally.
   * @return {void}
   */
  Polymer.setLegacyOptimizations = function(useLegacyOptimizations) {
    Polymer.legacyOptimizations = useLegacyOptimizations;
  };
})();



(function() {

  'use strict';

  // unique global id for deduping mixins.
  let dedupeId = 0;

  /**
   * @constructor
   * @extends {Function}
   * @private
   */
  function MixinFunction(){}
  /** @type {(WeakMap | undefined)} */
  MixinFunction.prototype.__mixinApplications;
  /** @type {(Object | undefined)} */
  MixinFunction.prototype.__mixinSet;

  /* eslint-disable valid-jsdoc */
  /**
   * Wraps an ES6 class expression mixin such that the mixin is only applied
   * if it has not already been applied its base argument. Also memoizes mixin
   * applications.
   *
   * @memberof Polymer
   * @template T
   * @param {T} mixin ES6 class expression mixin to wrap
   * @return {T}
   * @suppress {invalidCasts}
   */
  Polymer.dedupingMixin = function(mixin) {
    let mixinApplications = /** @type {!MixinFunction} */(mixin).__mixinApplications;
    if (!mixinApplications) {
      mixinApplications = new WeakMap();
      /** @type {!MixinFunction} */(mixin).__mixinApplications = mixinApplications;
    }
    // maintain a unique id for each mixin
    let mixinDedupeId = dedupeId++;
    function dedupingMixin(base) {
      let baseSet = /** @type {!MixinFunction} */(base).__mixinSet;
      if (baseSet && baseSet[mixinDedupeId]) {
        return base;
      }
      let map = mixinApplications;
      let extended = map.get(base);
      if (!extended) {
        extended = /** @type {!Function} */(mixin)(base);
        map.set(base, extended);
      }
      // copy inherited mixin set from the extended class, or the base class
      // NOTE: we avoid use of Set here because some browser (IE11)
      // cannot extend a base Set via the constructor.
      let mixinSet = Object.create(/** @type {!MixinFunction} */(extended).__mixinSet || baseSet || null);
      mixinSet[mixinDedupeId] = true;
      /** @type {!MixinFunction} */(extended).__mixinSet = mixinSet;
      return extended;
    }

    return /** @type {T} */ (dedupingMixin);
  };
  /* eslint-enable valid-jsdoc */

})();



(function() {
  'use strict';

  const MODULE_STYLE_LINK_SELECTOR = 'link[rel=import][type~=css]';
  const INCLUDE_ATTR = 'include';
  const SHADY_UNSCOPED_ATTR = 'shady-unscoped';

  function importModule(moduleId) {
    const /** Polymer.DomModule */ PolymerDomModule = customElements.get('dom-module');
    if (!PolymerDomModule) {
      return null;
    }
    return PolymerDomModule.import(moduleId);
  }

  function styleForImport(importDoc) {
    // NOTE: polyfill affordance.
    // under the HTMLImports polyfill, there will be no 'body',
    // but the import pseudo-doc can be used directly.
    let container = importDoc.body ? importDoc.body : importDoc;
    const importCss = Polymer.ResolveUrl.resolveCss(container.textContent,
      importDoc.baseURI);
    const style = document.createElement('style');
    style.textContent = importCss;
    return style;
  }

  /** @typedef {{assetpath: string}} */
  let templateWithAssetPath; // eslint-disable-line no-unused-vars

  /**
   * Module with utilities for collection CSS text from `<templates>`, external
   * stylesheets, and `dom-module`s.
   *
   * @namespace
   * @memberof Polymer
   * @summary Module with utilities for collection CSS text from various sources.
   */
  const StyleGather = {

    /**
     * Returns a list of <style> elements in a space-separated list of `dom-module`s.
     *
     * @memberof Polymer.StyleGather
     * @param {string} moduleIds List of dom-module id's within which to
     * search for css.
     * @return {!Array<!HTMLStyleElement>} Array of contained <style> elements
     * @this {StyleGather}
     */
     stylesFromModules(moduleIds) {
      const modules = moduleIds.trim().split(/\s+/);
      const styles = [];
      for (let i=0; i < modules.length; i++) {
        styles.push(...this.stylesFromModule(modules[i]));
      }
      return styles;
    },

    /**
     * Returns a list of <style> elements in a given `dom-module`.
     * Styles in a `dom-module` can come either from `<style>`s within the
     * first `<template>`, or else from one or more
     * `<link rel="import" type="css">` links outside the template.
     *
     * @memberof Polymer.StyleGather
     * @param {string} moduleId dom-module id to gather styles from
     * @return {!Array<!HTMLStyleElement>} Array of contained styles.
     * @this {StyleGather}
     */
    stylesFromModule(moduleId) {
      const m = importModule(moduleId);

      if (!m) {
        console.warn('Could not find style data in module named', moduleId);
        return [];
      }

      if (m._styles === undefined) {
        const styles = [];
        // module imports: <link rel="import" type="css">
        styles.push(...this._stylesFromModuleImports(m));
        // include css from the first template in the module
        const template = m.querySelector('template');
        if (template) {
          styles.push(...this.stylesFromTemplate(template,
            /** @type {templateWithAssetPath} */(m).assetpath));
        }

        m._styles = styles;
      }

      return m._styles;
    },

    /**
     * Returns the `<style>` elements within a given template.
     *
     * @memberof Polymer.StyleGather
     * @param {!HTMLTemplateElement} template Template to gather styles from
     * @param {string} baseURI baseURI for style content
     * @return {!Array<!HTMLStyleElement>} Array of styles
     * @this {StyleGather}
     */
    stylesFromTemplate(template, baseURI) {
      if (!template._styles) {
        const styles = [];
        // if element is a template, get content from its .content
        const e$ = template.content.querySelectorAll('style');
        for (let i=0; i < e$.length; i++) {
          let e = e$[i];
          // support style sharing by allowing styles to "include"
          // other dom-modules that contain styling
          let include = e.getAttribute(INCLUDE_ATTR);
          if (include) {
            styles.push(...this.stylesFromModules(include).filter(function(item, index, self) {
              return self.indexOf(item) === index;
            }));
          }
          if (baseURI) {
            e.textContent = Polymer.ResolveUrl.resolveCss(e.textContent, baseURI);
          }
          styles.push(e);
        }
        template._styles = styles;
      }
      return template._styles;
    },

    /**
     * Returns a list of <style> elements  from stylesheets loaded via `<link rel="import" type="css">` links within the specified `dom-module`.
     *
     * @memberof Polymer.StyleGather
     * @param {string} moduleId Id of `dom-module` to gather CSS from
     * @return {!Array<!HTMLStyleElement>} Array of contained styles.
     * @this {StyleGather}
     */
     stylesFromModuleImports(moduleId) {
      let m = importModule(moduleId);
      return m ? this._stylesFromModuleImports(m) : [];
    },

    /**
     * @memberof Polymer.StyleGather
     * @this {StyleGather}
     * @param {!HTMLElement} module dom-module element that could contain `<link rel="import" type="css">` styles
     * @return {!Array<!HTMLStyleElement>} Array of contained styles
     */
    _stylesFromModuleImports(module) {
      const styles = [];
      const p$ = module.querySelectorAll(MODULE_STYLE_LINK_SELECTOR);
      for (let i=0; i < p$.length; i++) {
        let p = p$[i];
        if (p.import) {
          const importDoc = p.import;
          const unscoped = p.hasAttribute(SHADY_UNSCOPED_ATTR);
          if (unscoped && !importDoc._unscopedStyle) {
            const style = styleForImport(importDoc);
            style.setAttribute(SHADY_UNSCOPED_ATTR, '');
            importDoc._unscopedStyle = style;
          } else if (!importDoc._style) {
            importDoc._style = styleForImport(importDoc);
          }
          styles.push(unscoped ? importDoc._unscopedStyle : importDoc._style);
        }
      }
      return styles;
    },

    /**
     *
     * Returns CSS text of styles in a space-separated list of `dom-module`s.
     * Note: This method is deprecated, use `stylesFromModules` instead.
     *
     * @deprecated
     * @memberof Polymer.StyleGather
     * @param {string} moduleIds List of dom-module id's within which to
     * search for css.
     * @return {string} Concatenated CSS content from specified `dom-module`s
     * @this {StyleGather}
     */
     cssFromModules(moduleIds) {
      let modules = moduleIds.trim().split(/\s+/);
      let cssText = '';
      for (let i=0; i < modules.length; i++) {
        cssText += this.cssFromModule(modules[i]);
      }
      return cssText;
    },

    /**
     * Returns CSS text of styles in a given `dom-module`.  CSS in a `dom-module`
     * can come either from `<style>`s within the first `<template>`, or else
     * from one or more `<link rel="import" type="css">` links outside the
     * template.
     *
     * Any `<styles>` processed are removed from their original location.
     * Note: This method is deprecated, use `styleFromModule` instead.
     *
     * @deprecated
     * @memberof Polymer.StyleGather
     * @param {string} moduleId dom-module id to gather styles from
     * @return {string} Concatenated CSS content from specified `dom-module`
     * @this {StyleGather}
     */
    cssFromModule(moduleId) {
      let m = importModule(moduleId);
      if (m && m._cssText === undefined) {
        // module imports: <link rel="import" type="css">
        let cssText = this._cssFromModuleImports(m);
        // include css from the first template in the module
        let t = m.querySelector('template');
        if (t) {
          cssText += this.cssFromTemplate(t,
            /** @type {templateWithAssetPath} */(m).assetpath);
        }
        m._cssText = cssText || null;
      }
      if (!m) {
        console.warn('Could not find style data in module named', moduleId);
      }
      return m && m._cssText || '';
    },

    /**
     * Returns CSS text of `<styles>` within a given template.
     *
     * Any `<styles>` processed are removed from their original location.
     * Note: This method is deprecated, use `styleFromTemplate` instead.
     *
     * @deprecated
     * @memberof Polymer.StyleGather
     * @param {!HTMLTemplateElement} template Template to gather styles from
     * @param {string} baseURI Base URI to resolve the URL against
     * @return {string} Concatenated CSS content from specified template
     * @this {StyleGather}
     */
    cssFromTemplate(template, baseURI) {
      let cssText = '';
      const e$ = this.stylesFromTemplate(template, baseURI);
      // if element is a template, get content from its .content
      for (let i=0; i < e$.length; i++) {
        let e = e$[i];
        if (e.parentNode) {
          e.parentNode.removeChild(e);
        }
        cssText += e.textContent;
      }
      return cssText;
    },

    /**
     * Returns CSS text from stylesheets loaded via `<link rel="import" type="css">`
     * links within the specified `dom-module`.
     *
     * Note: This method is deprecated, use `stylesFromModuleImports` instead.
     *
     * @deprecated
     *
     * @memberof Polymer.StyleGather
     * @param {string} moduleId Id of `dom-module` to gather CSS from
     * @return {string} Concatenated CSS content from links in specified `dom-module`
     * @this {StyleGather}
     */
    cssFromModuleImports(moduleId) {
      let m = importModule(moduleId);
      return m ? this._cssFromModuleImports(m) : '';
    },

    /**
     * @deprecated
     * @memberof Polymer.StyleGather
     * @this {StyleGather}
     * @param {!HTMLElement} module dom-module element that could contain `<link rel="import" type="css">` styles
     * @return {string} Concatenated CSS content from links in the dom-module
     */
     _cssFromModuleImports(module) {
      let cssText = '';
      let styles = this._stylesFromModuleImports(module);
      for (let i=0; i < styles.length; i++) {
        cssText += styles[i].textContent;
      }
      return cssText;
    }
  };

  Polymer.StyleGather = StyleGather;
})();


(function() {
  'use strict';

  let modules = {};
  let lcModules = {};
  function setModule(id, module) {
    // store id separate from lowercased id so that
    // in all cases mixedCase id will stored distinctly
    // and lowercase version is a fallback
    modules[id] = lcModules[id.toLowerCase()] = module;
  }
  function findModule(id) {
    return modules[id] || lcModules[id.toLowerCase()];
  }

  function styleOutsideTemplateCheck(inst) {
    if (inst.querySelector('style')) {
      console.warn('dom-module %s has style outside template', inst.id);
    }
  }

  /**
   * The `dom-module` element registers the dom it contains to the name given
   * by the module's id attribute. It provides a unified database of dom
   * accessible via its static `import` API.
   *
   * A key use case of `dom-module` is for providing custom element `<template>`s
   * via HTML imports that are parsed by the native HTML parser, that can be
   * relocated during a bundling pass and still looked up by `id`.
   *
   * Example:
   *
   *     <dom-module id="foo">
   *       <img src="stuff.png">
   *     </dom-module>
   *
   * Then in code in some other location that cannot access the dom-module above
   *
   *     let img = customElements.get('dom-module').import('foo', 'img');
   *
   * @customElement
   * @extends HTMLElement
   * @memberof Polymer
   * @summary Custom element that provides a registry of relocatable DOM content
   *   by `id` that is agnostic to bundling.
   * @unrestricted
   */
  class DomModule extends HTMLElement {

    static get observedAttributes() { return ['id']; }

    /**
     * Retrieves the element specified by the css `selector` in the module
     * registered by `id`. For example, this.import('foo', 'img');
     * @param {string} id The id of the dom-module in which to search.
     * @param {string=} selector The css selector by which to find the element.
     * @return {Element} Returns the element which matches `selector` in the
     * module registered at the specified `id`.
     */
    static import(id, selector) {
      if (id) {
        let m = findModule(id);
        if (m && selector) {
          return m.querySelector(selector);
        }
        return m;
      }
      return null;
    }

    /* eslint-disable no-unused-vars */
    /**
     * @param {string} name Name of attribute.
     * @param {?string} old Old value of attribute.
     * @param {?string} value Current value of attribute.
     * @param {?string} namespace Attribute namespace.
     * @return {void}
     */
    attributeChangedCallback(name, old, value, namespace) {
      if (old !== value) {
        this.register();
      }
    }
    /* eslint-enable no-unused-args */

    /**
     * The absolute URL of the original location of this `dom-module`.
     *
     * This value will differ from this element's `ownerDocument` in the
     * following ways:
     * - Takes into account any `assetpath` attribute added during bundling
     *   to indicate the original location relative to the bundled location
     * - Uses the HTMLImports polyfill's `importForElement` API to ensure
     *   the path is relative to the import document's location since
     *   `ownerDocument` is not currently polyfilled
     */
    get assetpath() {
      // Don't override existing assetpath.
      if (!this.__assetpath) {
        // note: assetpath set via an attribute must be relative to this
        // element's location; accomodate polyfilled HTMLImports
        const owner = window.HTMLImports && HTMLImports.importForElement ?
          HTMLImports.importForElement(this) || document : this.ownerDocument;
        const url = Polymer.ResolveUrl.resolveUrl(
          this.getAttribute('assetpath') || '', owner.baseURI);
        this.__assetpath = Polymer.ResolveUrl.pathFromUrl(url);
      }
      return this.__assetpath;
    }

    /**
     * Registers the dom-module at a given id. This method should only be called
     * when a dom-module is imperatively created. For
     * example, `document.createElement('dom-module').register('foo')`.
     * @param {string=} id The id at which to register the dom-module.
     * @return {void}
     */
    register(id) {
      id = id || this.id;
      if (id) {
        // Under strictTemplatePolicy, reject and null out any re-registered
        // dom-module since it is ambiguous whether first-in or last-in is trusted 
        if (Polymer.strictTemplatePolicy && findModule(id) !== undefined) {
          setModule(id, null);
          throw new Error(`strictTemplatePolicy: dom-module ${id} re-registered`);
        }
        this.id = id;
        setModule(id, this);
        styleOutsideTemplateCheck(this);
      }
    }
  }

  DomModule.prototype['modules'] = modules;

  customElements.define('dom-module', DomModule);

  /** @const */
  Polymer.DomModule = DomModule;

})();


(function() {
  'use strict';

  /**
   * Module with utilities for manipulating structured data path strings.
   *
   * @namespace
   * @memberof Polymer
   * @summary Module with utilities for manipulating structured data path strings.
   */
  const Path = {

    /**
     * Returns true if the given string is a structured data path (has dots).
     *
     * Example:
     *
     * ```
     * Polymer.Path.isPath('foo.bar.baz') // true
     * Polymer.Path.isPath('foo')         // false
     * ```
     *
     * @memberof Polymer.Path
     * @param {string} path Path string
     * @return {boolean} True if the string contained one or more dots
     */
    isPath: function(path) {
      return path.indexOf('.') >= 0;
    },

    /**
     * Returns the root property name for the given path.
     *
     * Example:
     *
     * ```
     * Polymer.Path.root('foo.bar.baz') // 'foo'
     * Polymer.Path.root('foo')         // 'foo'
     * ```
     *
     * @memberof Polymer.Path
     * @param {string} path Path string
     * @return {string} Root property name
     */
    root: function(path) {
      let dotIndex = path.indexOf('.');
      if (dotIndex === -1) {
        return path;
      }
      return path.slice(0, dotIndex);
    },

    /**
     * Given `base` is `foo.bar`, `foo` is an ancestor, `foo.bar` is not
     * Returns true if the given path is an ancestor of the base path.
     *
     * Example:
     *
     * ```
     * Polymer.Path.isAncestor('foo.bar', 'foo')         // true
     * Polymer.Path.isAncestor('foo.bar', 'foo.bar')     // false
     * Polymer.Path.isAncestor('foo.bar', 'foo.bar.baz') // false
     * ```
     *
     * @memberof Polymer.Path
     * @param {string} base Path string to test against.
     * @param {string} path Path string to test.
     * @return {boolean} True if `path` is an ancestor of `base`.
     */
    isAncestor: function(base, path) {
      //     base.startsWith(path + '.');
      return base.indexOf(path + '.') === 0;
    },

    /**
     * Given `base` is `foo.bar`, `foo.bar.baz` is an descendant
     *
     * Example:
     *
     * ```
     * Polymer.Path.isDescendant('foo.bar', 'foo.bar.baz') // true
     * Polymer.Path.isDescendant('foo.bar', 'foo.bar')     // false
     * Polymer.Path.isDescendant('foo.bar', 'foo')         // false
     * ```
     *
     * @memberof Polymer.Path
     * @param {string} base Path string to test against.
     * @param {string} path Path string to test.
     * @return {boolean} True if `path` is a descendant of `base`.
     */
    isDescendant: function(base, path) {
      //     path.startsWith(base + '.');
      return path.indexOf(base + '.') === 0;
    },

    /**
     * Replaces a previous base path with a new base path, preserving the
     * remainder of the path.
     *
     * User must ensure `path` has a prefix of `base`.
     *
     * Example:
     *
     * ```
     * Polymer.Path.translate('foo.bar', 'zot', 'foo.bar.baz') // 'zot.baz'
     * ```
     *
     * @memberof Polymer.Path
     * @param {string} base Current base string to remove
     * @param {string} newBase New base string to replace with
     * @param {string} path Path to translate
     * @return {string} Translated string
     */
    translate: function(base, newBase, path) {
      return newBase + path.slice(base.length);
    },

    /**
     * @param {string} base Path string to test against
     * @param {string} path Path string to test
     * @return {boolean} True if `path` is equal to `base`
     * @this {Path}
     */
    matches: function(base, path) {
      return (base === path) ||
             this.isAncestor(base, path) ||
             this.isDescendant(base, path);
    },

    /**
     * Converts array-based paths to flattened path.  String-based paths
     * are returned as-is.
     *
     * Example:
     *
     * ```
     * Polymer.Path.normalize(['foo.bar', 0, 'baz'])  // 'foo.bar.0.baz'
     * Polymer.Path.normalize('foo.bar.0.baz')        // 'foo.bar.0.baz'
     * ```
     *
     * @memberof Polymer.Path
     * @param {string | !Array<string|number>} path Input path
     * @return {string} Flattened path
     */
    normalize: function(path) {
      if (Array.isArray(path)) {
        let parts = [];
        for (let i=0; i<path.length; i++) {
          let args = path[i].toString().split('.');
          for (let j=0; j<args.length; j++) {
            parts.push(args[j]);
          }
        }
        return parts.join('.');
      } else {
        return path;
      }
    },

    /**
     * Splits a path into an array of property names. Accepts either arrays
     * of path parts or strings.
     *
     * Example:
     *
     * ```
     * Polymer.Path.split(['foo.bar', 0, 'baz'])  // ['foo', 'bar', '0', 'baz']
     * Polymer.Path.split('foo.bar.0.baz')        // ['foo', 'bar', '0', 'baz']
     * ```
     *
     * @memberof Polymer.Path
     * @param {string | !Array<string|number>} path Input path
     * @return {!Array<string>} Array of path parts
     * @this {Path}
     * @suppress {checkTypes}
     */
    split: function(path) {
      if (Array.isArray(path)) {
        return this.normalize(path).split('.');
      }
      return path.toString().split('.');
    },

    /**
     * Reads a value from a path.  If any sub-property in the path is `undefined`,
     * this method returns `undefined` (will never throw.
     *
     * @memberof Polymer.Path
     * @param {Object} root Object from which to dereference path from
     * @param {string | !Array<string|number>} path Path to read
     * @param {Object=} info If an object is provided to `info`, the normalized
     *  (flattened) path will be set to `info.path`.
     * @return {*} Value at path, or `undefined` if the path could not be
     *  fully dereferenced.
     * @this {Path}
     */
    get: function(root, path, info) {
      let prop = root;
      let parts = this.split(path);
      // Loop over path parts[0..n-1] and dereference
      for (let i=0; i<parts.length; i++) {
        if (!prop) {
          return;
        }
        let part = parts[i];
        prop = prop[part];
      }
      if (info) {
        info.path = parts.join('.');
      }
      return prop;
    },

    /**
     * Sets a value to a path.  If any sub-property in the path is `undefined`,
     * this method will no-op.
     *
     * @memberof Polymer.Path
     * @param {Object} root Object from which to dereference path from
     * @param {string | !Array<string|number>} path Path to set
     * @param {*} value Value to set to path
     * @return {string | undefined} The normalized version of the input path
     * @this {Path}
     */
    set: function(root, path, value) {
      let prop = root;
      let parts = this.split(path);
      let last = parts[parts.length-1];
      if (parts.length > 1) {
        // Loop over path parts[0..n-2] and dereference
        for (let i=0; i<parts.length-1; i++) {
          let part = parts[i];
          prop = prop[part];
          if (!prop) {
            return;
          }
        }
        // Set value to object at end of path
        prop[last] = value;
      } else {
        // Simple property set
        prop[path] = value;
      }
      return parts.join('.');
    }

  };

  /**
   * Returns true if the given string is a structured data path (has dots).
   *
   * This function is deprecated.  Use `Polymer.Path.isPath` instead.
   *
   * Example:
   *
   * ```
   * Polymer.Path.isDeep('foo.bar.baz') // true
   * Polymer.Path.isDeep('foo')         // false
   * ```
   *
   * @deprecated
   * @memberof Polymer.Path
   * @param {string} path Path string
   * @return {boolean} True if the string contained one or more dots
   */
  Path.isDeep = Path.isPath;

  Polymer.Path = Path;

})();


(function() {
  'use strict';

  const caseMap = {};
  const DASH_TO_CAMEL = /-[a-z]/g;
  const CAMEL_TO_DASH = /([A-Z])/g;

  /**
   * Module with utilities for converting between "dash-case" and "camelCase"
   * identifiers.
   *
   * @namespace
   * @memberof Polymer
   * @summary Module that provides utilities for converting between "dash-case"
   *   and "camelCase".
   */
  const CaseMap = {

    /**
     * Converts "dash-case" identifier (e.g. `foo-bar-baz`) to "camelCase"
     * (e.g. `fooBarBaz`).
     *
     * @memberof Polymer.CaseMap
     * @param {string} dash Dash-case identifier
     * @return {string} Camel-case representation of the identifier
     */
    dashToCamelCase(dash) {
      return caseMap[dash] || (
        caseMap[dash] = dash.indexOf('-') < 0 ? dash : dash.replace(DASH_TO_CAMEL,
          (m) => m[1].toUpperCase()
        )
      );
    },

    /**
     * Converts "camelCase" identifier (e.g. `fooBarBaz`) to "dash-case"
     * (e.g. `foo-bar-baz`).
     *
     * @memberof Polymer.CaseMap
     * @param {string} camel Camel-case identifier
     * @return {string} Dash-case representation of the identifier
     */
    camelToDashCase(camel) {
      return caseMap[camel] || (
        caseMap[camel] = camel.replace(CAMEL_TO_DASH, '-$1').toLowerCase()
      );
    }

  };

  Polymer.CaseMap = CaseMap;
})();


(function() {

  'use strict';

  // Microtask implemented using Mutation Observer
  let microtaskCurrHandle = 0;
  let microtaskLastHandle = 0;
  let microtaskCallbacks = [];
  let microtaskNodeContent = 0;
  let microtaskNode = document.createTextNode('');
  new window.MutationObserver(microtaskFlush).observe(microtaskNode, {characterData: true});

  function microtaskFlush() {
    const len = microtaskCallbacks.length;
    for (let i = 0; i < len; i++) {
      let cb = microtaskCallbacks[i];
      if (cb) {
        try {
          cb();
        } catch (e) {
          setTimeout(() => { throw e; });
        }
      }
    }
    microtaskCallbacks.splice(0, len);
    microtaskLastHandle += len;
  }

  /**
   * Module that provides a number of strategies for enqueuing asynchronous
   * tasks.  Each sub-module provides a standard `run(fn)` interface that returns a
   * handle, and a `cancel(handle)` interface for canceling async tasks before
   * they run.
   *
   * @namespace
   * @memberof Polymer
   * @summary Module that provides a number of strategies for enqueuing asynchronous
   * tasks.
   */
  Polymer.Async = {

    /**
     * Async interface wrapper around `setTimeout`.
     *
     * @namespace
     * @memberof Polymer.Async
     * @summary Async interface wrapper around `setTimeout`.
     */
    timeOut: {
      /**
       * Returns a sub-module with the async interface providing the provided
       * delay.
       *
       * @memberof Polymer.Async.timeOut
       * @param {number=} delay Time to wait before calling callbacks in ms
       * @return {!AsyncInterface} An async timeout interface
       */
      after(delay) {
        return {
          run(fn) { return window.setTimeout(fn, delay); },
          cancel(handle) {
            window.clearTimeout(handle);
          }
        };
      },
      /**
       * Enqueues a function called in the next task.
       *
       * @memberof Polymer.Async.timeOut
       * @param {!Function} fn Callback to run
       * @param {number=} delay Delay in milliseconds
       * @return {number} Handle used for canceling task
       */
      run(fn, delay) {
        return window.setTimeout(fn, delay);
      },
      /**
       * Cancels a previously enqueued `timeOut` callback.
       *
       * @memberof Polymer.Async.timeOut
       * @param {number} handle Handle returned from `run` of callback to cancel
       * @return {void}
       */
      cancel(handle) {
        window.clearTimeout(handle);
      }
    },

    /**
     * Async interface wrapper around `requestAnimationFrame`.
     *
     * @namespace
     * @memberof Polymer.Async
     * @summary Async interface wrapper around `requestAnimationFrame`.
     */
    animationFrame: {
      /**
       * Enqueues a function called at `requestAnimationFrame` timing.
       *
       * @memberof Polymer.Async.animationFrame
       * @param {function(number):void} fn Callback to run
       * @return {number} Handle used for canceling task
       */
      run(fn) {
        return window.requestAnimationFrame(fn);
      },
      /**
       * Cancels a previously enqueued `animationFrame` callback.
       *
       * @memberof Polymer.Async.animationFrame
       * @param {number} handle Handle returned from `run` of callback to cancel
       * @return {void}
       */
      cancel(handle) {
        window.cancelAnimationFrame(handle);
      }
    },

    /**
     * Async interface wrapper around `requestIdleCallback`.  Falls back to
     * `setTimeout` on browsers that do not support `requestIdleCallback`.
     *
     * @namespace
     * @memberof Polymer.Async
     * @summary Async interface wrapper around `requestIdleCallback`.
     */
    idlePeriod: {
      /**
       * Enqueues a function called at `requestIdleCallback` timing.
       *
       * @memberof Polymer.Async.idlePeriod
       * @param {function(!IdleDeadline):void} fn Callback to run
       * @return {number} Handle used for canceling task
       */
      run(fn) {
        return window.requestIdleCallback ?
          window.requestIdleCallback(fn) :
          window.setTimeout(fn, 16);
      },
      /**
       * Cancels a previously enqueued `idlePeriod` callback.
       *
       * @memberof Polymer.Async.idlePeriod
       * @param {number} handle Handle returned from `run` of callback to cancel
       * @return {void}
       */
      cancel(handle) {
        window.cancelIdleCallback ?
          window.cancelIdleCallback(handle) :
          window.clearTimeout(handle);
      }
    },

    /**
     * Async interface for enqueuing callbacks that run at microtask timing.
     *
     * Note that microtask timing is achieved via a single `MutationObserver`,
     * and thus callbacks enqueued with this API will all run in a single
     * batch, and not interleaved with other microtasks such as promises.
     * Promises are avoided as an implementation choice for the time being
     * due to Safari bugs that cause Promises to lack microtask guarantees.
     *
     * @namespace
     * @memberof Polymer.Async
     * @summary Async interface for enqueuing callbacks that run at microtask
     *   timing.
     */
    microTask: {

      /**
       * Enqueues a function called at microtask timing.
       *
       * @memberof Polymer.Async.microTask
       * @param {!Function=} callback Callback to run
       * @return {number} Handle used for canceling task
       */
      run(callback) {
        microtaskNode.textContent = microtaskNodeContent++;
        microtaskCallbacks.push(callback);
        return microtaskCurrHandle++;
      },

      /**
       * Cancels a previously enqueued `microTask` callback.
       *
       * @memberof Polymer.Async.microTask
       * @param {number} handle Handle returned from `run` of callback to cancel
       * @return {void}
       */
      cancel(handle) {
        const idx = handle - microtaskLastHandle;
        if (idx >= 0) {
          if (!microtaskCallbacks[idx]) {
            throw new Error('invalid async handle: ' + handle);
          }
          microtaskCallbacks[idx] = null;
        }
      }

    }
  };

})();


  (function () {

    'use strict';

    /** @const {!AsyncInterface} */
    const microtask = Polymer.Async.microTask;

    /**
     * Element class mixin that provides basic meta-programming for creating one
     * or more property accessors (getter/setter pair) that enqueue an async
     * (batched) `_propertiesChanged` callback.
     *
     * For basic usage of this mixin, call `MyClass.createProperties(props)`
     * once at class definition time to create property accessors for properties
     * named in props, implement `_propertiesChanged` to react as desired to
     * property changes, and implement `static get observedAttributes()` and
     * include lowercase versions of any property names that should be set from
     * attributes. Last, call `this._enableProperties()` in the element's
     * `connectedCallback` to enable the accessors.
     *
     * @mixinFunction
     * @polymer
     * @memberof Polymer
     * @summary Element class mixin for reacting to property changes from
     *   generated property accessors.
     */
    Polymer.PropertiesChanged = Polymer.dedupingMixin(superClass => {

      /**
       * @polymer
       * @mixinClass
       * @extends {superClass}
       * @implements {Polymer_PropertiesChanged}
       * @unrestricted
       */
      class PropertiesChanged extends superClass {

        /**
         * Creates property accessors for the given property names.
         * @param {!Object} props Object whose keys are names of accessors.
         * @return {void}
         * @protected
         */
        static createProperties(props) {
          const proto = this.prototype;
          for (let prop in props) {
            // don't stomp an existing accessor
            if (!(prop in proto)) {
              proto._createPropertyAccessor(prop);
            }
          }
        }

        /**
         * Returns an attribute name that corresponds to the given property.
         * The attribute name is the lowercased property name. Override to
         * customize this mapping.
         * @param {string} property Property to convert
         * @return {string} Attribute name corresponding to the given property.
         *
         * @protected
         */
        static attributeNameForProperty(property) {
          return property.toLowerCase();
        }

        /**
         * Override point to provide a type to which to deserialize a value to
         * a given property.
         * @param {string} name Name of property
         *
         * @protected
         */
        static typeForProperty(name) { } //eslint-disable-line no-unused-vars

        /**
         * Creates a setter/getter pair for the named property with its own
         * local storage.  The getter returns the value in the local storage,
         * and the setter calls `_setProperty`, which updates the local storage
         * for the property and enqueues a `_propertiesChanged` callback.
         *
         * This method may be called on a prototype or an instance.  Calling
         * this method may overwrite a property value that already exists on
         * the prototype/instance by creating the accessor.
         *
         * @param {string} property Name of the property
         * @param {boolean=} readOnly When true, no setter is created; the
         *   protected `_setProperty` function must be used to set the property
         * @return {void}
         * @protected
         */
        _createPropertyAccessor(property, readOnly) {
          this._addPropertyToAttributeMap(property);
          if (!this.hasOwnProperty('__dataHasAccessor')) {
            this.__dataHasAccessor = Object.assign({}, this.__dataHasAccessor);
          }
          if (!this.__dataHasAccessor[property]) {
            this.__dataHasAccessor[property] = true;
            this._definePropertyAccessor(property, readOnly);
          }
        }

        /**
         * Adds the given `property` to a map matching attribute names
         * to property names, using `attributeNameForProperty`. This map is
         * used when deserializing attribute values to properties.
         *
         * @param {string} property Name of the property
         */
        _addPropertyToAttributeMap(property) {
          if (!this.hasOwnProperty('__dataAttributes')) {
            this.__dataAttributes = Object.assign({}, this.__dataAttributes);
          }
          if (!this.__dataAttributes[property]) {
            const attr = this.constructor.attributeNameForProperty(property);
            this.__dataAttributes[attr] = property;
          }
        }

        /**
         * Defines a property accessor for the given property.
         * @param {string} property Name of the property
         * @param {boolean=} readOnly When true, no setter is created
         * @return {void}
         */
         _definePropertyAccessor(property, readOnly) {
          Object.defineProperty(this, property, {
            /* eslint-disable valid-jsdoc */
            /** @this {PropertiesChanged} */
            get() {
              return this._getProperty(property);
            },
            /** @this {PropertiesChanged} */
            set: readOnly ? function () {} : function (value) {
              this._setProperty(property, value);
            }
            /* eslint-enable */
          });
        }

        constructor() {
          super();
          this.__dataEnabled = false;
          this.__dataReady = false;
          this.__dataInvalid = false;
          this.__data = {};
          this.__dataPending = null;
          this.__dataOld = null;
          this.__dataInstanceProps = null;
          this.__serializing = false;
          this._initializeProperties();
        }

        /**
         * Lifecycle callback called when properties are enabled via
         * `_enableProperties`.
         *
         * Users may override this function to implement behavior that is
         * dependent on the element having its property data initialized, e.g.
         * from defaults (initialized from `constructor`, `_initializeProperties`),
         * `attributeChangedCallback`, or values propagated from host e.g. via
         * bindings.  `super.ready()` must be called to ensure the data system
         * becomes enabled.
         *
         * @return {void}
         * @public
         */
        ready() {
          this.__dataReady = true;
          this._flushProperties();
        }

        /**
         * Initializes the local storage for property accessors.
         *
         * Provided as an override point for performing any setup work prior
         * to initializing the property accessor system.
         *
         * @return {void}
         * @protected
         */
        _initializeProperties() {
          // Capture instance properties; these will be set into accessors
          // during first flush. Don't set them here, since we want
          // these to overwrite defaults/constructor assignments
          for (let p in this.__dataHasAccessor) {
            if (this.hasOwnProperty(p)) {
              this.__dataInstanceProps = this.__dataInstanceProps || {};
              this.__dataInstanceProps[p] = this[p];
              delete this[p];
            }
          }
        }

        /**
         * Called at ready time with bag of instance properties that overwrote
         * accessors when the element upgraded.
         *
         * The default implementation sets these properties back into the
         * setter at ready time.  This method is provided as an override
         * point for customizing or providing more efficient initialization.
         *
         * @param {Object} props Bag of property values that were overwritten
         *   when creating property accessors.
         * @return {void}
         * @protected
         */
        _initializeInstanceProperties(props) {
          Object.assign(this, props);
        }

        /**
         * Updates the local storage for a property (via `_setPendingProperty`)
         * and enqueues a `_proeprtiesChanged` callback.
         *
         * @param {string} property Name of the property
         * @param {*} value Value to set
         * @return {void}
         * @protected
         */
        _setProperty(property, value) {
          if (this._setPendingProperty(property, value)) {
            this._invalidateProperties();
          }
        }

        /**
         * Returns the value for the given property.
         * @param {string} property Name of property
         * @return {*} Value for the given property
         * @protected
         */
        _getProperty(property) {
          return this.__data[property];
        }

        /* eslint-disable no-unused-vars */
        /**
         * Updates the local storage for a property, records the previous value,
         * and adds it to the set of "pending changes" that will be passed to the
         * `_propertiesChanged` callback.  This method does not enqueue the
         * `_propertiesChanged` callback.
         *
         * @param {string} property Name of the property
         * @param {*} value Value to set
         * @param {boolean=} ext Not used here; affordance for closure
         * @return {boolean} Returns true if the property changed
         * @protected
         */
        _setPendingProperty(property, value, ext) {
          let old = this.__data[property];
          let changed = this._shouldPropertyChange(property, value, old);
          if (changed) {
            if (!this.__dataPending) {
              this.__dataPending = {};
              this.__dataOld = {};
            }
            // Ensure old is captured from the last turn
            if (this.__dataOld && !(property in this.__dataOld)) {
              this.__dataOld[property] = old;
            }
            this.__data[property] = value;
            this.__dataPending[property] = value;
          }
          return changed;
        }
        /* eslint-enable */

        /**
         * Marks the properties as invalid, and enqueues an async
         * `_propertiesChanged` callback.
         *
         * @return {void}
         * @protected
         */
        _invalidateProperties() {
          if (!this.__dataInvalid && this.__dataReady) {
            this.__dataInvalid = true;
            microtask.run(() => {
              if (this.__dataInvalid) {
                this.__dataInvalid = false;
                this._flushProperties();
              }
            });
          }
        }

        /**
         * Call to enable property accessor processing. Before this method is
         * called accessor values will be set but side effects are
         * queued. When called, any pending side effects occur immediately.
         * For elements, generally `connectedCallback` is a normal spot to do so.
         * It is safe to call this method multiple times as it only turns on
         * property accessors once.
         *
         * @return {void}
         * @protected
         */
        _enableProperties() {
          if (!this.__dataEnabled) {
            this.__dataEnabled = true;
            if (this.__dataInstanceProps) {
              this._initializeInstanceProperties(this.__dataInstanceProps);
              this.__dataInstanceProps = null;
            }
            this.ready();
          }
        }

        /**
         * Calls the `_propertiesChanged` callback with the current set of
         * pending changes (and old values recorded when pending changes were
         * set), and resets the pending set of changes. Generally, this method
         * should not be called in user code.
         *
         * @return {void}
         * @protected
         */
        _flushProperties() {
          const props = this.__data;
          const changedProps = this.__dataPending;
          const old = this.__dataOld;
          if (this._shouldPropertiesChange(props, changedProps, old)) {
            this.__dataPending = null;
            this.__dataOld = null;
            this._propertiesChanged(props, changedProps, old);
          }
        }

        /**
         * Called in `_flushProperties` to determine if `_propertiesChanged`
         * should be called. The default implementation returns true if
         * properties are pending. Override to customize when
         * `_propertiesChanged` is called.
         * @param {!Object} currentProps Bag of all current accessor values
         * @param {!Object} changedProps Bag of properties changed since the last
         *   call to `_propertiesChanged`
         * @param {!Object} oldProps Bag of previous values for each property
         *   in `changedProps`
         * @return {boolean} true if changedProps is truthy
         */
        _shouldPropertiesChange(currentProps, changedProps, oldProps) { // eslint-disable-line no-unused-vars
          return Boolean(changedProps);
        }

        /**
         * Callback called when any properties with accessors created via
         * `_createPropertyAccessor` have been set.
         *
         * @param {!Object} currentProps Bag of all current accessor values
         * @param {!Object} changedProps Bag of properties changed since the last
         *   call to `_propertiesChanged`
         * @param {!Object} oldProps Bag of previous values for each property
         *   in `changedProps`
         * @return {void}
         * @protected
         */
        _propertiesChanged(currentProps, changedProps, oldProps) { // eslint-disable-line no-unused-vars
        }

        /**
         * Method called to determine whether a property value should be
         * considered as a change and cause the `_propertiesChanged` callback
         * to be enqueued.
         *
         * The default implementation returns `true` if a strict equality
         * check fails. The method always returns false for `NaN`.
         *
         * Override this method to e.g. provide stricter checking for
         * Objects/Arrays when using immutable patterns.
         *
         * @param {string} property Property name
         * @param {*} value New property value
         * @param {*} old Previous property value
         * @return {boolean} Whether the property should be considered a change
         *   and enqueue a `_proeprtiesChanged` callback
         * @protected
         */
        _shouldPropertyChange(property, value, old) {
          return (
            // Strict equality check
            (old !== value &&
              // This ensures (old==NaN, value==NaN) always returns false
              (old === old || value === value))
          );
        }

        /**
         * Implements native Custom Elements `attributeChangedCallback` to
         * set an attribute value to a property via `_attributeToProperty`.
         *
         * @param {string} name Name of attribute that changed
         * @param {?string} old Old attribute value
         * @param {?string} value New attribute value
         * @param {?string} namespace Attribute namespace.
         * @return {void}
         * @suppress {missingProperties} Super may or may not implement the callback
         */
        attributeChangedCallback(name, old, value, namespace) {
          if (old !== value) {
            this._attributeToProperty(name, value);
          }
          if (super.attributeChangedCallback) {
            super.attributeChangedCallback(name, old, value, namespace);
          }
        }

        /**
         * Deserializes an attribute to its associated property.
         *
         * This method calls the `_deserializeValue` method to convert the string to
         * a typed value.
         *
         * @param {string} attribute Name of attribute to deserialize.
         * @param {?string} value of the attribute.
         * @param {*=} type type to deserialize to, defaults to the value
         * returned from `typeForProperty`
         * @return {void}
         */
        _attributeToProperty(attribute, value, type) {
          if (!this.__serializing) {
            const map = this.__dataAttributes;
            const property = map && map[attribute] || attribute;
            this[property] = this._deserializeValue(value, type ||
              this.constructor.typeForProperty(property));
          }
        }

        /**
         * Serializes a property to its associated attribute.
         *
         * @suppress {invalidCasts} Closure can't figure out `this` is an element.
         *
         * @param {string} property Property name to reflect.
         * @param {string=} attribute Attribute name to reflect to.
         * @param {*=} value Property value to refect.
         * @return {void}
         */
        _propertyToAttribute(property, attribute, value) {
          this.__serializing = true;
          value = (arguments.length < 3) ? this[property] : value;
          this._valueToNodeAttribute(/** @type {!HTMLElement} */(this), value,
            attribute || this.constructor.attributeNameForProperty(property));
          this.__serializing = false;
        }

        /**
         * Sets a typed value to an HTML attribute on a node.
         *
         * This method calls the `_serializeValue` method to convert the typed
         * value to a string.  If the `_serializeValue` method returns `undefined`,
         * the attribute will be removed (this is the default for boolean
         * type `false`).
         *
         * @param {Element} node Element to set attribute to.
         * @param {*} value Value to serialize.
         * @param {string} attribute Attribute name to serialize to.
         * @return {void}
         */
        _valueToNodeAttribute(node, value, attribute) {
          const str = this._serializeValue(value);
          if (str === undefined) {
            node.removeAttribute(attribute);
          } else {
            node.setAttribute(attribute, str);
          }
        }

        /**
         * Converts a typed JavaScript value to a string.
         *
         * This method is called when setting JS property values to
         * HTML attributes.  Users may override this method to provide
         * serialization for custom types.
         *
         * @param {*} value Property value to serialize.
         * @return {string | undefined} String serialized from the provided
         * property  value.
         */
        _serializeValue(value) {
          switch (typeof value) {
            case 'boolean':
              return value ? '' : undefined;
            default:
              return value != null ? value.toString() : undefined;
          }
        }

        /**
         * Converts a string to a typed JavaScript value.
         *
         * This method is called when reading HTML attribute values to
         * JS properties.  Users may override this method to provide
         * deserialization for custom `type`s. Types for `Boolean`, `String`,
         * and `Number` convert attributes to the expected types.
         *
         * @param {?string} value Value to deserialize.
         * @param {*=} type Type to deserialize the string to.
         * @return {*} Typed value deserialized from the provided string.
         */
        _deserializeValue(value, type) {
          switch (type) {
            case Boolean:
              return (value !== null);
            case Number:
              return Number(value);
            default:
              return value;
          }
        }

      }

      return PropertiesChanged;
    });


  })();



(function() {

  'use strict';

  let caseMap = Polymer.CaseMap;

  // Save map of native properties; this forms a blacklist or properties
  // that won't have their values "saved" by `saveAccessorValue`, since
  // reading from an HTMLElement accessor from the context of a prototype throws
  const nativeProperties = {};
  let proto = HTMLElement.prototype;
  while (proto) {
    let props = Object.getOwnPropertyNames(proto);
    for (let i=0; i<props.length; i++) {
      nativeProperties[props[i]] = true;
    }
    proto = Object.getPrototypeOf(proto);
  }

  /**
   * Used to save the value of a property that will be overridden with
   * an accessor. If the `model` is a prototype, the values will be saved
   * in `__dataProto`, and it's up to the user (or downstream mixin) to
   * decide how/when to set these values back into the accessors.
   * If `model` is already an instance (it has a `__data` property), then
   * the value will be set as a pending property, meaning the user should
   * call `_invalidateProperties` or `_flushProperties` to take effect
   *
   * @param {Object} model Prototype or instance
   * @param {string} property Name of property
   * @return {void}
   * @private
   */
  function saveAccessorValue(model, property) {
    // Don't read/store value for any native properties since they could throw
    if (!nativeProperties[property]) {
      let value = model[property];
      if (value !== undefined) {
        if (model.__data) {
          // Adding accessor to instance; update the property
          // It is the user's responsibility to call _flushProperties
          model._setPendingProperty(property, value);
        } else {
          // Adding accessor to proto; save proto's value for instance-time use
          if (!model.__dataProto) {
            model.__dataProto = {};
          } else if (!model.hasOwnProperty(JSCompiler_renameProperty('__dataProto', model))) {
            model.__dataProto = Object.create(model.__dataProto);
          }
          model.__dataProto[property] = value;
        }
      }
    }
  }

  /**
   * Element class mixin that provides basic meta-programming for creating one
   * or more property accessors (getter/setter pair) that enqueue an async
   * (batched) `_propertiesChanged` callback.
   *
   * For basic usage of this mixin:
   * 
   * -   Declare attributes to observe via the standard `static get observedAttributes()`. Use
   *     `dash-case` attribute names to represent `camelCase` property names. 
   * -   Implement the `_propertiesChanged` callback on the class.
   * -   Call `MyClass.createPropertiesForAttributes()` **once** on the class to generate 
   *     property accessors for each observed attribute. This must be called before the first 
   *     instance is created, for example, by calling it before calling `customElements.define`.
   *     It can also be called lazily from the element's `constructor`, as long as it's guarded so
   *     that the call is only made once, when the first instance is created.
   * -   Call `this._enableProperties()` in the element's `connectedCallback` to enable 
   *     the accessors.
   *
   * Any `observedAttributes` will automatically be
   * deserialized via `attributeChangedCallback` and set to the associated
   * property using `dash-case`-to-`camelCase` convention.
   *
   * @mixinFunction
   * @polymer
   * @appliesMixin Polymer.PropertiesChanged
   * @memberof Polymer
   * @summary Element class mixin for reacting to property changes from
   *   generated property accessors.
   */
  Polymer.PropertyAccessors = Polymer.dedupingMixin(superClass => {

    /**
     * @constructor
     * @extends {superClass}
     * @implements {Polymer_PropertiesChanged}
     * @unrestricted
     * @private
     */
     const base = Polymer.PropertiesChanged(superClass);

    /**
     * @polymer
     * @mixinClass
     * @implements {Polymer_PropertyAccessors}
     * @extends {base}
     * @unrestricted
     */
    class PropertyAccessors extends base {

      /**
       * Generates property accessors for all attributes in the standard
       * static `observedAttributes` array.
       *
       * Attribute names are mapped to property names using the `dash-case` to
       * `camelCase` convention
       *
       * @return {void}
       */
      static createPropertiesForAttributes() {
        let a$ = this.observedAttributes;
        for (let i=0; i < a$.length; i++) {
          this.prototype._createPropertyAccessor(caseMap.dashToCamelCase(a$[i]));
        }
      }

      /**
       * Returns an attribute name that corresponds to the given property.
       * By default, converts camel to dash case, e.g. `fooBar` to `foo-bar`.
       * @param {string} property Property to convert
       * @return {string} Attribute name corresponding to the given property.
       *
       * @protected
       */
      static attributeNameForProperty(property) {
        return caseMap.camelToDashCase(property);
      }

      /**
       * Overrides PropertiesChanged implementation to initialize values for
       * accessors created for values that already existed on the element
       * prototype.
       *
       * @return {void}
       * @protected
       */
      _initializeProperties() {
        if (this.__dataProto) {
          this._initializeProtoProperties(this.__dataProto);
          this.__dataProto = null;
        }
        super._initializeProperties();
      }

      /**
       * Called at instance time with bag of properties that were overwritten
       * by accessors on the prototype when accessors were created.
       *
       * The default implementation sets these properties back into the
       * setter at instance time.  This method is provided as an override
       * point for customizing or providing more efficient initialization.
       *
       * @param {Object} props Bag of property values that were overwritten
       *   when creating property accessors.
       * @return {void}
       * @protected
       */
      _initializeProtoProperties(props) {
        for (let p in props) {
          this._setProperty(p, props[p]);
        }
      }

      /**
       * Ensures the element has the given attribute. If it does not,
       * assigns the given value to the attribute.
       *
       * @suppress {invalidCasts} Closure can't figure out `this` is infact an element
       *
       * @param {string} attribute Name of attribute to ensure is set.
       * @param {string} value of the attribute.
       * @return {void}
       */
      _ensureAttribute(attribute, value) {
        const el = /** @type {!HTMLElement} */(this);
        if (!el.hasAttribute(attribute)) {
          this._valueToNodeAttribute(el, value, attribute);
        }
      }

      /**
       * Overrides PropertiesChanged implemention to serialize objects as JSON.
       *
       * @param {*} value Property value to serialize.
       * @return {string | undefined} String serialized from the provided property value.
       */
      _serializeValue(value) {
        /* eslint-disable no-fallthrough */
        switch (typeof value) {
          case 'object':
            if (value instanceof Date) {
              return value.toString();
            } else if (value) {
              try {
                return JSON.stringify(value);
              } catch(x) {
                return '';
              }
            }

          default:
            return super._serializeValue(value);
        }
      }

      /**
       * Converts a string to a typed JavaScript value.
       *
       * This method is called by Polymer when reading HTML attribute values to
       * JS properties.  Users may override this method on Polymer element
       * prototypes to provide deserialization for custom `type`s.  Note,
       * the `type` argument is the value of the `type` field provided in the
       * `properties` configuration object for a given property, and is
       * by convention the constructor for the type to deserialize.
       *
       *
       * @param {?string} value Attribute value to deserialize.
       * @param {*=} type Type to deserialize the string to.
       * @return {*} Typed value deserialized from the provided string.
       */
      _deserializeValue(value, type) {
        /**
         * @type {*}
         */
        let outValue;
        switch (type) {
          case Object:
            try {
              outValue = JSON.parse(/** @type {string} */(value));
            } catch(x) {
              // allow non-JSON literals like Strings and Numbers
              outValue = value;
            }
            break;
          case Array:
            try {
              outValue = JSON.parse(/** @type {string} */(value));
            } catch(x) {
              outValue = null;
              console.warn(`Polymer::Attributes: couldn't decode Array as JSON: ${value}`);
            }
            break;
          case Date:
            outValue = isNaN(value) ? String(value) : Number(value);
            outValue = new Date(outValue);
            break;
          default:
            outValue = super._deserializeValue(value, type);
            break;
        }
        return outValue;
      }
      /* eslint-enable no-fallthrough */

      /**
       * Overrides PropertiesChanged implementation to save existing prototype
       * property value so that it can be reset.
       * @param {string} property Name of the property
       * @param {boolean=} readOnly When true, no setter is created
       *
       * When calling on a prototype, any overwritten values are saved in
       * `__dataProto`, and it is up to the subclasser to decide how/when
       * to set those properties back into the accessor.  When calling on an
       * instance, the overwritten value is set via `_setPendingProperty`,
       * and the user should call `_invalidateProperties` or `_flushProperties`
       * for the values to take effect.
       * @protected
       * @return {void}
       */
      _definePropertyAccessor(property, readOnly) {
        saveAccessorValue(this, property);
        super._definePropertyAccessor(property, readOnly);
      }

      /**
       * Returns true if this library created an accessor for the given property.
       *
       * @param {string} property Property name
       * @return {boolean} True if an accessor was created
       */
      _hasAccessor(property) {
        return this.__dataHasAccessor && this.__dataHasAccessor[property];
      }

      /**
       * Returns true if the specified property has a pending change.
       *
       * @param {string} prop Property name
       * @return {boolean} True if property has a pending change
       * @protected
       */
      _isPropertyPending(prop) {
        return Boolean(this.__dataPending && (prop in this.__dataPending));
      }

    }

    return PropertyAccessors;

  });

})();


(function() {

  'use strict';

  const walker = document.createTreeWalker(document, NodeFilter.SHOW_ALL,
      null, false);

  // 1.x backwards-compatible auto-wrapper for template type extensions
  // This is a clear layering violation and gives favored-nation status to
  // dom-if and dom-repeat templates.  This is a conceit we're choosing to keep
  // a.) to ease 1.x backwards-compatibility due to loss of `is`, and
  // b.) to maintain if/repeat capability in parser-constrained elements
  //     (e.g. table, select) in lieu of native CE type extensions without
  //     massive new invention in this space (e.g. directive system)
  const templateExtensions = {
    'dom-if': true,
    'dom-repeat': true
  };
  function wrapTemplateExtension(node) {
    let is = node.getAttribute('is');
    if (is && templateExtensions[is]) {
      let t = node;
      t.removeAttribute('is');
      node = t.ownerDocument.createElement(is);
      t.parentNode.replaceChild(node, t);
      node.appendChild(t);
      while(t.attributes.length) {
        node.setAttribute(t.attributes[0].name, t.attributes[0].value);
        t.removeAttribute(t.attributes[0].name);
      }
    }
    return node;
  }

  function findTemplateNode(root, nodeInfo) {
    // recursively ascend tree until we hit root
    let parent = nodeInfo.parentInfo && findTemplateNode(root, nodeInfo.parentInfo);
    // unwind the stack, returning the indexed node at each level
    if (parent) {
      // note: marginally faster than indexing via childNodes
      // (http://jsperf.com/childnodes-lookup)
      walker.currentNode = parent;
      for (let n=walker.firstChild(), i=0; n; n=walker.nextSibling()) {
        if (nodeInfo.parentIndex === i++) {
          return n;
        }
      }
    } else {
      return root;
    }
  }

  // construct `$` map (from id annotations)
  function applyIdToMap(inst, map, node, nodeInfo) {
    if (nodeInfo.id) {
      map[nodeInfo.id] = node;
    }
  }

  // install event listeners (from event annotations)
  function applyEventListener(inst, node, nodeInfo) {
    if (nodeInfo.events && nodeInfo.events.length) {
      for (let j=0, e$=nodeInfo.events, e; (j<e$.length) && (e=e$[j]); j++) {
        inst._addMethodEventListenerToNode(node, e.name, e.value, inst);
      }
    }
  }

  // push configuration references at configure time
  function applyTemplateContent(inst, node, nodeInfo) {
    if (nodeInfo.templateInfo) {
      node._templateInfo = nodeInfo.templateInfo;
    }
  }

  function createNodeEventHandler(context, eventName, methodName) {
    // Instances can optionally have a _methodHost which allows redirecting where
    // to find methods. Currently used by `templatize`.
    context = context._methodHost || context;
    let handler = function(e) {
      if (context[methodName]) {
        context[methodName](e, e.detail);
      } else {
        console.warn('listener method `' + methodName + '` not defined');
      }
    };
    return handler;
  }

  /**
   * Element mixin that provides basic template parsing and stamping, including
   * the following template-related features for stamped templates:
   *
   * - Declarative event listeners (`on-eventname="listener"`)
   * - Map of node id's to stamped node instances (`this.$.id`)
   * - Nested template content caching/removal and re-installation (performance
   *   optimization)
   *
   * @mixinFunction
   * @polymer
   * @memberof Polymer
   * @summary Element class mixin that provides basic template parsing and stamping
   */
  Polymer.TemplateStamp = Polymer.dedupingMixin(superClass => {

    /**
     * @polymer
     * @mixinClass
     * @implements {Polymer_TemplateStamp}
     */
    class TemplateStamp extends superClass {

      /**
       * Scans a template to produce template metadata.
       *
       * Template-specific metadata are stored in the object returned, and node-
       * specific metadata are stored in objects in its flattened `nodeInfoList`
       * array.  Only nodes in the template that were parsed as nodes of
       * interest contain an object in `nodeInfoList`.  Each `nodeInfo` object
       * contains an `index` (`childNodes` index in parent) and optionally
       * `parent`, which points to node info of its parent (including its index).
       *
       * The template metadata object returned from this method has the following
       * structure (many fields optional):
       *
       * ```js
       *   {
       *     // Flattened list of node metadata (for nodes that generated metadata)
       *     nodeInfoList: [
       *       {
       *         // `id` attribute for any nodes with id's for generating `$` map
       *         id: {string},
       *         // `on-event="handler"` metadata
       *         events: [
       *           {
       *             name: {string},   // event name
       *             value: {string},  // handler method name
       *           }, ...
       *         ],
       *         // Notes when the template contained a `<slot>` for shady DOM
       *         // optimization purposes
       *         hasInsertionPoint: {boolean},
       *         // For nested `<template>`` nodes, nested template metadata
       *         templateInfo: {object}, // nested template metadata
       *         // Metadata to allow efficient retrieval of instanced node
       *         // corresponding to this metadata
       *         parentInfo: {number},   // reference to parent nodeInfo>
       *         parentIndex: {number},  // index in parent's `childNodes` collection
       *         infoIndex: {number},    // index of this `nodeInfo` in `templateInfo.nodeInfoList`
       *       },
       *       ...
       *     ],
       *     // When true, the template had the `strip-whitespace` attribute
       *     // or was nested in a template with that setting
       *     stripWhitespace: {boolean},
       *     // For nested templates, nested template content is moved into
       *     // a document fragment stored here; this is an optimization to
       *     // avoid the cost of nested template cloning
       *     content: {DocumentFragment}
       *   }
       * ```
       *
       * This method kicks off a recursive treewalk as follows:
       *
       * ```
       *    _parseTemplate <---------------------+
       *      _parseTemplateContent              |
       *        _parseTemplateNode  <------------|--+
       *          _parseTemplateNestedTemplate --+  |
       *          _parseTemplateChildNodes ---------+
       *          _parseTemplateNodeAttributes
       *            _parseTemplateNodeAttribute
       *
       * ```
       *
       * These methods may be overridden to add custom metadata about templates
       * to either `templateInfo` or `nodeInfo`.
       *
       * Note that this method may be destructive to the template, in that
       * e.g. event annotations may be removed after being noted in the
       * template metadata.
       *
       * @param {!HTMLTemplateElement} template Template to parse
       * @param {TemplateInfo=} outerTemplateInfo Template metadata from the outer
       *   template, for parsing nested templates
       * @return {!TemplateInfo} Parsed template metadata
       */
      static _parseTemplate(template, outerTemplateInfo) {
        // since a template may be re-used, memo-ize metadata
        if (!template._templateInfo) {
          let templateInfo = template._templateInfo = {};
          templateInfo.nodeInfoList = [];
          templateInfo.stripWhiteSpace = Polymer.legacyOptimizations ||
            (outerTemplateInfo && outerTemplateInfo.stripWhiteSpace) ||
            template.hasAttribute('strip-whitespace');
          this._parseTemplateContent(template, templateInfo, {parent: null});
        }
        return template._templateInfo;
      }

      static _parseTemplateContent(template, templateInfo, nodeInfo) {
        return this._parseTemplateNode(template.content, templateInfo, nodeInfo);
      }

      /**
       * Parses template node and adds template and node metadata based on
       * the current node, and its `childNodes` and `attributes`.
       *
       * This method may be overridden to add custom node or template specific
       * metadata based on this node.
       *
       * @param {Node} node Node to parse
       * @param {!TemplateInfo} templateInfo Template metadata for current template
       * @param {!NodeInfo} nodeInfo Node metadata for current template.
       * @return {boolean} `true` if the visited node added node-specific
       *   metadata to `nodeInfo`
       */
      static _parseTemplateNode(node, templateInfo, nodeInfo) {
        let noted;
        let element = /** @type {Element} */(node);
        if (element.localName == 'template' && !element.hasAttribute('preserve-content')) {
          noted = this._parseTemplateNestedTemplate(element, templateInfo, nodeInfo) || noted;
        } else if (element.localName === 'slot') {
          // For ShadyDom optimization, indicating there is an insertion point
          templateInfo.hasInsertionPoint = true;
        }
        walker.currentNode = element;
        if (walker.firstChild()) {
          noted = this._parseTemplateChildNodes(element, templateInfo, nodeInfo) || noted;
        }
        if (element.hasAttributes && element.hasAttributes()) {
          noted = this._parseTemplateNodeAttributes(element, templateInfo, nodeInfo) || noted;
        }
        return noted;
      }

      /**
       * Parses template child nodes for the given root node.
       *
       * This method also wraps whitelisted legacy template extensions
       * (`is="dom-if"` and `is="dom-repeat"`) with their equivalent element
       * wrappers, collapses text nodes, and strips whitespace from the template
       * if the `templateInfo.stripWhitespace` setting was provided.
       *
       * @param {Node} root Root node whose `childNodes` will be parsed
       * @param {!TemplateInfo} templateInfo Template metadata for current template
       * @param {!NodeInfo} nodeInfo Node metadata for current template.
       * @return {void}
       */
      static _parseTemplateChildNodes(root, templateInfo, nodeInfo) {
        if (root.localName === 'script' || root.localName === 'style') {
          return;
        }
        walker.currentNode = root;
        for (let node=walker.firstChild(), parentIndex=0, next; node; node=next) {
          // Wrap templates
          if (node.localName == 'template') {
            node = wrapTemplateExtension(node);
          }
          // collapse adjacent textNodes: fixes an IE issue that can cause
          // text nodes to be inexplicably split =(
          // note that root.normalize() should work but does not so we do this
          // manually.
          walker.currentNode = node;
          next = walker.nextSibling();
          if (node.nodeType === Node.TEXT_NODE) {
            let /** Node */ n = next;
            while (n && (n.nodeType === Node.TEXT_NODE)) {
              node.textContent += n.textContent;
              next = walker.nextSibling();
              root.removeChild(n);
              n = next;
            }
            // optionally strip whitespace
            if (templateInfo.stripWhiteSpace && !node.textContent.trim()) {
              root.removeChild(node);
              continue;
            }
          }
          let childInfo = { parentIndex, parentInfo: nodeInfo };
          if (this._parseTemplateNode(node, templateInfo, childInfo)) {
            childInfo.infoIndex = templateInfo.nodeInfoList.push(/** @type {!NodeInfo} */(childInfo)) - 1;
          }
          // Increment if not removed
          walker.currentNode = node;
          if (walker.parentNode()) {
            parentIndex++;
          }
        }
      }

      /**
       * Parses template content for the given nested `<template>`.
       *
       * Nested template info is stored as `templateInfo` in the current node's
       * `nodeInfo`. `template.content` is removed and stored in `templateInfo`.
       * It will then be the responsibility of the host to set it back to the
       * template and for users stamping nested templates to use the
       * `_contentForTemplate` method to retrieve the content for this template
       * (an optimization to avoid the cost of cloning nested template content).
       *
       * @param {HTMLTemplateElement} node Node to parse (a <template>)
       * @param {TemplateInfo} outerTemplateInfo Template metadata for current template
       *   that includes the template `node`
       * @param {!NodeInfo} nodeInfo Node metadata for current template.
       * @return {boolean} `true` if the visited node added node-specific
       *   metadata to `nodeInfo`
       */
      static _parseTemplateNestedTemplate(node, outerTemplateInfo, nodeInfo) {
        let templateInfo = this._parseTemplate(node, outerTemplateInfo);
        let content = templateInfo.content =
          node.content.ownerDocument.createDocumentFragment();
        content.appendChild(node.content);
        nodeInfo.templateInfo = templateInfo;
        return true;
      }

      /**
       * Parses template node attributes and adds node metadata to `nodeInfo`
       * for nodes of interest.
       *
       * @param {Element} node Node to parse
       * @param {TemplateInfo} templateInfo Template metadata for current template
       * @param {NodeInfo} nodeInfo Node metadata for current template.
       * @return {boolean} `true` if the visited node added node-specific
       *   metadata to `nodeInfo`
       */
      static _parseTemplateNodeAttributes(node, templateInfo, nodeInfo) {
        // Make copy of original attribute list, since the order may change
        // as attributes are added and removed
        let noted = false;
        let attrs = Array.from(node.attributes);
        for (let i=attrs.length-1, a; (a=attrs[i]); i--) {
          noted = this._parseTemplateNodeAttribute(node, templateInfo, nodeInfo, a.name, a.value) || noted;
        }
        return noted;
      }

      /**
       * Parses a single template node attribute and adds node metadata to
       * `nodeInfo` for attributes of interest.
       *
       * This implementation adds metadata for `on-event="handler"` attributes
       * and `id` attributes.
       *
       * @param {Element} node Node to parse
       * @param {!TemplateInfo} templateInfo Template metadata for current template
       * @param {!NodeInfo} nodeInfo Node metadata for current template.
       * @param {string} name Attribute name
       * @param {string} value Attribute value
       * @return {boolean} `true` if the visited node added node-specific
       *   metadata to `nodeInfo`
       */
      static _parseTemplateNodeAttribute(node, templateInfo, nodeInfo, name, value) {
        // events (on-*)
        if (name.slice(0, 3) === 'on-') {
          node.removeAttribute(name);
          nodeInfo.events = nodeInfo.events || [];
          nodeInfo.events.push({
            name: name.slice(3),
            value
          });
          return true;
        }
        // static id
        else if (name === 'id') {
          nodeInfo.id = value;
          return true;
        }
        return false;
      }

      /**
       * Returns the `content` document fragment for a given template.
       *
       * For nested templates, Polymer performs an optimization to cache nested
       * template content to avoid the cost of cloning deeply nested templates.
       * This method retrieves the cached content for a given template.
       *
       * @param {HTMLTemplateElement} template Template to retrieve `content` for
       * @return {DocumentFragment} Content fragment
       */
      static _contentForTemplate(template) {
        let templateInfo = /** @type {HTMLTemplateElementWithInfo} */ (template)._templateInfo;
        return (templateInfo && templateInfo.content) || template.content;
      }

      /**
       * Clones the provided template content and returns a document fragment
       * containing the cloned dom.
       *
       * The template is parsed (once and memoized) using this library's
       * template parsing features, and provides the following value-added
       * features:
       * * Adds declarative event listeners for `on-event="handler"` attributes
       * * Generates an "id map" for all nodes with id's under `$` on returned
       *   document fragment
       * * Passes template info including `content` back to templates as
       *   `_templateInfo` (a performance optimization to avoid deep template
       *   cloning)
       *
       * Note that the memoized template parsing process is destructive to the
       * template: attributes for bindings and declarative event listeners are
       * removed after being noted in notes, and any nested `<template>.content`
       * is removed and stored in notes as well.
       *
       * @param {!HTMLTemplateElement} template Template to stamp
       * @return {!StampedTemplate} Cloned template content
       */
      _stampTemplate(template) {
        // Polyfill support: bootstrap the template if it has not already been
        if (template && !template.content &&
            window.HTMLTemplateElement && HTMLTemplateElement.decorate) {
          HTMLTemplateElement.decorate(template);
        }
        let templateInfo = this.constructor._parseTemplate(template);
        let nodeInfo = templateInfo.nodeInfoList;
        let content = templateInfo.content || template.content;
        let dom = /** @type {DocumentFragment} */ (document.importNode(content, true));
        // NOTE: ShadyDom optimization indicating there is an insertion point
        dom.__noInsertionPoint = !templateInfo.hasInsertionPoint;
        let nodes = dom.nodeList = new Array(nodeInfo.length);
        dom.$ = {};
        for (let i=0, l=nodeInfo.length, info; (i<l) && (info=nodeInfo[i]); i++) {
          let node = nodes[i] = findTemplateNode(dom, info);
          applyIdToMap(this, dom.$, node, info);
          applyTemplateContent(this, node, info);
          applyEventListener(this, node, info);
        }
        dom = /** @type {!StampedTemplate} */(dom); // eslint-disable-line no-self-assign
        return dom;
      }

      /**
       * Adds an event listener by method name for the event provided.
       *
       * This method generates a handler function that looks up the method
       * name at handling time.
       *
       * @param {!Node} node Node to add listener on
       * @param {string} eventName Name of event
       * @param {string} methodName Name of method
       * @param {*=} context Context the method will be called on (defaults
       *   to `node`)
       * @return {Function} Generated handler function
       */
      _addMethodEventListenerToNode(node, eventName, methodName, context) {
        context = context || node;
        let handler = createNodeEventHandler(context, eventName, methodName);
        this._addEventListenerToNode(node, eventName, handler);
        return handler;
      }

      /**
       * Override point for adding custom or simulated event handling.
       *
       * @param {!Node} node Node to add event listener to
       * @param {string} eventName Name of event
       * @param {function(!Event):void} handler Listener function to add
       * @return {void}
       */
      _addEventListenerToNode(node, eventName, handler) {
        node.addEventListener(eventName, handler);
      }

      /**
       * Override point for adding custom or simulated event handling.
       *
       * @param {!Node} node Node to remove event listener from
       * @param {string} eventName Name of event
       * @param {function(!Event):void} handler Listener function to remove
       * @return {void}
       */
      _removeEventListenerFromNode(node, eventName, handler) {
        node.removeEventListener(eventName, handler);
      }

    }

    return TemplateStamp;

  });

})();


(function() {

  'use strict';

  /** @const {Object} */
  const CaseMap = Polymer.CaseMap;

  // Monotonically increasing unique ID used for de-duping effects triggered
  // from multiple properties in the same turn
  let dedupeId = 0;

  /**
   * Property effect types; effects are stored on the prototype using these keys
   * @enum {string}
   */
  const TYPES = {
    COMPUTE: '__computeEffects',
    REFLECT: '__reflectEffects',
    NOTIFY: '__notifyEffects',
    PROPAGATE: '__propagateEffects',
    OBSERVE: '__observeEffects',
    READ_ONLY: '__readOnly'
  };

  /** @const {RegExp} */
  const capitalAttributeRegex = /[A-Z]/;

  /**
   * @typedef {{
   * name: (string | undefined),
   * structured: (boolean | undefined),
   * wildcard: (boolean | undefined)
   * }}
   */
  let DataTrigger; //eslint-disable-line no-unused-vars

  /**
   * @typedef {{
   * info: ?,
   * trigger: (!DataTrigger | undefined),
   * fn: (!Function | undefined)
   * }}
   */
  let DataEffect; //eslint-disable-line no-unused-vars

  let PropertyEffectsType; //eslint-disable-line no-unused-vars

  /**
   * Ensures that the model has an own-property map of effects for the given type.
   * The model may be a prototype or an instance.
   *
   * Property effects are stored as arrays of effects by property in a map,
   * by named type on the model. e.g.
   *
   *   __computeEffects: {
   *     foo: [ ... ],
   *     bar: [ ... ]
   *   }
   *
   * If the model does not yet have an effect map for the type, one is created
   * and returned.  If it does, but it is not an own property (i.e. the
   * prototype had effects), the the map is deeply cloned and the copy is
   * set on the model and returned, ready for new effects to be added.
   *
   * @param {Object} model Prototype or instance
   * @param {string} type Property effect type
   * @return {Object} The own-property map of effects for the given type
   * @private
   */
  function ensureOwnEffectMap(model, type) {
    let effects = model[type];
    if (!effects) {
      effects = model[type] = {};
    } else if (!model.hasOwnProperty(type)) {
      effects = model[type] = Object.create(model[type]);
      for (let p in effects) {
        let protoFx = effects[p];
        let instFx = effects[p] = Array(protoFx.length);
        for (let i=0; i<protoFx.length; i++) {
          instFx[i] = protoFx[i];
        }
      }
    }
    return effects;
  }

  // -- effects ----------------------------------------------

  /**
   * Runs all effects of a given type for the given set of property changes
   * on an instance.
   *
   * @param {!PropertyEffectsType} inst The instance with effects to run
   * @param {Object} effects Object map of property-to-Array of effects
   * @param {Object} props Bag of current property changes
   * @param {Object=} oldProps Bag of previous values for changed properties
   * @param {boolean=} hasPaths True with `props` contains one or more paths
   * @param {*=} extraArgs Additional metadata to pass to effect function
   * @return {boolean} True if an effect ran for this property
   * @private
   */
  function runEffects(inst, effects, props, oldProps, hasPaths, extraArgs) {
    if (effects) {
      let ran = false;
      let id = dedupeId++;
      for (let prop in props) {
        if (runEffectsForProperty(inst, effects, id, prop, props, oldProps, hasPaths, extraArgs)) {
          ran = true;
        }
      }
      return ran;
    }
    return false;
  }

  /**
   * Runs a list of effects for a given property.
   *
   * @param {!PropertyEffectsType} inst The instance with effects to run
   * @param {Object} effects Object map of property-to-Array of effects
   * @param {number} dedupeId Counter used for de-duping effects
   * @param {string} prop Name of changed property
   * @param {*} props Changed properties
   * @param {*} oldProps Old properties
   * @param {boolean=} hasPaths True with `props` contains one or more paths
   * @param {*=} extraArgs Additional metadata to pass to effect function
   * @return {boolean} True if an effect ran for this property
   * @private
   */
  function runEffectsForProperty(inst, effects, dedupeId, prop, props, oldProps, hasPaths, extraArgs) {
    let ran = false;
    let rootProperty = hasPaths ? Polymer.Path.root(prop) : prop;
    let fxs = effects[rootProperty];
    if (fxs) {
      for (let i=0, l=fxs.length, fx; (i<l) && (fx=fxs[i]); i++) {
        if ((!fx.info || fx.info.lastRun !== dedupeId) &&
            (!hasPaths || pathMatchesTrigger(prop, fx.trigger))) {
          if (fx.info) {
            fx.info.lastRun = dedupeId;
          }
          fx.fn(inst, prop, props, oldProps, fx.info, hasPaths, extraArgs);
          ran = true;
        }
      }
    }
    return ran;
  }

  /**
   * Determines whether a property/path that has changed matches the trigger
   * criteria for an effect.  A trigger is a descriptor with the following
   * structure, which matches the descriptors returned from `parseArg`.
   * e.g. for `foo.bar.*`:
   * ```
   * trigger: {
   *   name: 'a.b',
   *   structured: true,
   *   wildcard: true
   * }
   * ```
   * If no trigger is given, the path is deemed to match.
   *
   * @param {string} path Path or property that changed
   * @param {DataTrigger} trigger Descriptor
   * @return {boolean} Whether the path matched the trigger
   */
  function pathMatchesTrigger(path, trigger) {
    if (trigger) {
      let triggerPath = trigger.name;
      return (triggerPath == path) ||
        (trigger.structured && Polymer.Path.isAncestor(triggerPath, path)) ||
        (trigger.wildcard && Polymer.Path.isDescendant(triggerPath, path));
    } else {
      return true;
    }
  }

  /**
   * Implements the "observer" effect.
   *
   * Calls the method with `info.methodName` on the instance, passing the
   * new and old values.
   *
   * @param {!PropertyEffectsType} inst The instance the effect will be run on
   * @param {string} property Name of property
   * @param {Object} props Bag of current property changes
   * @param {Object} oldProps Bag of previous values for changed properties
   * @param {?} info Effect metadata
   * @return {void}
   * @private
   */
  function runObserverEffect(inst, property, props, oldProps, info) {
    let fn = typeof info.method === "string" ? inst[info.method] : info.method;
    let changedProp = info.property;
    if (fn) {
      fn.call(inst, inst.__data[changedProp], oldProps[changedProp]);
    } else if (!info.dynamicFn) {
      console.warn('observer method `' + info.method + '` not defined');
    }
  }

  /**
   * Runs "notify" effects for a set of changed properties.
   *
   * This method differs from the generic `runEffects` method in that it
   * will dispatch path notification events in the case that the property
   * changed was a path and the root property for that path didn't have a
   * "notify" effect.  This is to maintain 1.0 behavior that did not require
   * `notify: true` to ensure object sub-property notifications were
   * sent.
   *
   * @param {!PropertyEffectsType} inst The instance with effects to run
   * @param {Object} notifyProps Bag of properties to notify
   * @param {Object} props Bag of current property changes
   * @param {Object} oldProps Bag of previous values for changed properties
   * @param {boolean} hasPaths True with `props` contains one or more paths
   * @return {void}
   * @private
   */
  function runNotifyEffects(inst, notifyProps, props, oldProps, hasPaths) {
    // Notify
    let fxs = inst[TYPES.NOTIFY];
    let notified;
    let id = dedupeId++;
    // Try normal notify effects; if none, fall back to try path notification
    for (let prop in notifyProps) {
      if (notifyProps[prop]) {
        if (fxs && runEffectsForProperty(inst, fxs, id, prop, props, oldProps, hasPaths)) {
          notified = true;
        } else if (hasPaths && notifyPath(inst, prop, props)) {
          notified = true;
        }
      }
    }
    // Flush host if we actually notified and host was batching
    // And the host has already initialized clients; this prevents
    // an issue with a host observing data changes before clients are ready.
    let host;
    if (notified && (host = inst.__dataHost) && host._invalidateProperties) {
      host._invalidateProperties();
    }
  }

  /**
   * Dispatches {property}-changed events with path information in the detail
   * object to indicate a sub-path of the property was changed.
   *
   * @param {!PropertyEffectsType} inst The element from which to fire the event
   * @param {string} path The path that was changed
   * @param {Object} props Bag of current property changes
   * @return {boolean} Returns true if the path was notified
   * @private
   */
  function notifyPath(inst, path, props) {
    let rootProperty = Polymer.Path.root(path);
    if (rootProperty !== path) {
      let eventName = Polymer.CaseMap.camelToDashCase(rootProperty) + '-changed';
      dispatchNotifyEvent(inst, eventName, props[path], path);
      return true;
    }
    return false;
  }

  /**
   * Dispatches {property}-changed events to indicate a property (or path)
   * changed.
   *
   * @param {!PropertyEffectsType} inst The element from which to fire the event
   * @param {string} eventName The name of the event to send ('{property}-changed')
   * @param {*} value The value of the changed property
   * @param {string | null | undefined} path If a sub-path of this property changed, the path
   *   that changed (optional).
   * @return {void}
   * @private
   * @suppress {invalidCasts}
   */
  function dispatchNotifyEvent(inst, eventName, value, path) {
    let detail = {
      value: value,
      queueProperty: true
    };
    if (path) {
      detail.path = path;
    }
    /** @type {!HTMLElement} */(inst).dispatchEvent(new CustomEvent(eventName, { detail }));
  }

  /**
   * Implements the "notify" effect.
   *
   * Dispatches a non-bubbling event named `info.eventName` on the instance
   * with a detail object containing the new `value`.
   *
   * @param {!PropertyEffectsType} inst The instance the effect will be run on
   * @param {string} property Name of property
   * @param {Object} props Bag of current property changes
   * @param {Object} oldProps Bag of previous values for changed properties
   * @param {?} info Effect metadata
   * @param {boolean} hasPaths True with `props` contains one or more paths
   * @return {void}
   * @private
   */
  function runNotifyEffect(inst, property, props, oldProps, info, hasPaths) {
    let rootProperty = hasPaths ? Polymer.Path.root(property) : property;
    let path = rootProperty != property ? property : null;
    let value = path ? Polymer.Path.get(inst, path) : inst.__data[property];
    if (path && value === undefined) {
      value = props[property];  // specifically for .splices
    }
    dispatchNotifyEvent(inst, info.eventName, value, path);
  }

  /**
   * Handler function for 2-way notification events. Receives context
   * information captured in the `addNotifyListener` closure from the
   * `__notifyListeners` metadata.
   *
   * Sets the value of the notified property to the host property or path.  If
   * the event contained path information, translate that path to the host
   * scope's name for that path first.
   *
   * @param {CustomEvent} event Notification event (e.g. '<property>-changed')
   * @param {!PropertyEffectsType} inst Host element instance handling the notification event
   * @param {string} fromProp Child element property that was bound
   * @param {string} toPath Host property/path that was bound
   * @param {boolean} negate Whether the binding was negated
   * @return {void}
   * @private
   */
  function handleNotification(event, inst, fromProp, toPath, negate) {
    let value;
    let detail = /** @type {Object} */(event.detail);
    let fromPath = detail && detail.path;
    if (fromPath) {
      toPath = Polymer.Path.translate(fromProp, toPath, fromPath);
      value = detail && detail.value;
    } else {
      value = event.currentTarget[fromProp];
    }
    value = negate ? !value : value;
    if (!inst[TYPES.READ_ONLY] || !inst[TYPES.READ_ONLY][toPath]) {
      if (inst._setPendingPropertyOrPath(toPath, value, true, Boolean(fromPath))
        && (!detail || !detail.queueProperty)) {
        inst._invalidateProperties();
      }
    }
  }

  /**
   * Implements the "reflect" effect.
   *
   * Sets the attribute named `info.attrName` to the given property value.
   *
   * @param {!PropertyEffectsType} inst The instance the effect will be run on
   * @param {string} property Name of property
   * @param {Object} props Bag of current property changes
   * @param {Object} oldProps Bag of previous values for changed properties
   * @param {?} info Effect metadata
   * @return {void}
   * @private
   */
  function runReflectEffect(inst, property, props, oldProps, info) {
    let value = inst.__data[property];
    if (Polymer.sanitizeDOMValue) {
      value = Polymer.sanitizeDOMValue(value, info.attrName, 'attribute', /** @type {Node} */(inst));
    }
    inst._propertyToAttribute(property, info.attrName, value);
  }

  /**
   * Runs "computed" effects for a set of changed properties.
   *
   * This method differs from the generic `runEffects` method in that it
   * continues to run computed effects based on the output of each pass until
   * there are no more newly computed properties.  This ensures that all
   * properties that will be computed by the initial set of changes are
   * computed before other effects (binding propagation, observers, and notify)
   * run.
   *
   * @param {!PropertyEffectsType} inst The instance the effect will be run on
   * @param {!Object} changedProps Bag of changed properties
   * @param {!Object} oldProps Bag of previous values for changed properties
   * @param {boolean} hasPaths True with `props` contains one or more paths
   * @return {void}
   * @private
   */
  function runComputedEffects(inst, changedProps, oldProps, hasPaths) {
    let computeEffects = inst[TYPES.COMPUTE];
    if (computeEffects) {
      let inputProps = changedProps;
      while (runEffects(inst, computeEffects, inputProps, oldProps, hasPaths)) {
        Object.assign(oldProps, inst.__dataOld);
        Object.assign(changedProps, inst.__dataPending);
        inputProps = inst.__dataPending;
        inst.__dataPending = null;
      }
    }
  }

  /**
   * Implements the "computed property" effect by running the method with the
   * values of the arguments specified in the `info` object and setting the
   * return value to the computed property specified.
   *
   * @param {!PropertyEffectsType} inst The instance the effect will be run on
   * @param {string} property Name of property
   * @param {Object} props Bag of current property changes
   * @param {Object} oldProps Bag of previous values for changed properties
   * @param {?} info Effect metadata
   * @return {void}
   * @private
   */
  function runComputedEffect(inst, property, props, oldProps, info) {
    let result = runMethodEffect(inst, property, props, oldProps, info);
    let computedProp = info.methodInfo;
    if (inst.__dataHasAccessor && inst.__dataHasAccessor[computedProp]) {
      inst._setPendingProperty(computedProp, result, true);
    } else {
      inst[computedProp] = result;
    }
  }

  /**
   * Computes path changes based on path links set up using the `linkPaths`
   * API.
   *
   * @param {!PropertyEffectsType} inst The instance whose props are changing
   * @param {string | !Array<(string|number)>} path Path that has changed
   * @param {*} value Value of changed path
   * @return {void}
   * @private
   */
  function computeLinkedPaths(inst, path, value) {
    let links = inst.__dataLinkedPaths;
    if (links) {
      let link;
      for (let a in links) {
        let b = links[a];
        if (Polymer.Path.isDescendant(a, path)) {
          link = Polymer.Path.translate(a, b, path);
          inst._setPendingPropertyOrPath(link, value, true, true);
        } else if (Polymer.Path.isDescendant(b, path)) {
          link = Polymer.Path.translate(b, a, path);
          inst._setPendingPropertyOrPath(link, value, true, true);
        }
      }
    }
  }

  // -- bindings ----------------------------------------------

  /**
   * Adds binding metadata to the current `nodeInfo`, and binding effects
   * for all part dependencies to `templateInfo`.
   *
   * @param {Function} constructor Class that `_parseTemplate` is currently
   *   running on
   * @param {TemplateInfo} templateInfo Template metadata for current template
   * @param {NodeInfo} nodeInfo Node metadata for current template node
   * @param {string} kind Binding kind, either 'property', 'attribute', or 'text'
   * @param {string} target Target property name
   * @param {!Array<!BindingPart>} parts Array of binding part metadata
   * @param {string=} literal Literal text surrounding binding parts (specified
   *   only for 'property' bindings, since these must be initialized as part
   *   of boot-up)
   * @return {void}
   * @private
   */
  function addBinding(constructor, templateInfo, nodeInfo, kind, target, parts, literal) {
    // Create binding metadata and add to nodeInfo
    nodeInfo.bindings = nodeInfo.bindings || [];
    let /** Binding */ binding = { kind, target, parts, literal, isCompound: (parts.length !== 1) };
    nodeInfo.bindings.push(binding);
    // Add listener info to binding metadata
    if (shouldAddListener(binding)) {
      let {event, negate} = binding.parts[0];
      binding.listenerEvent = event || (CaseMap.camelToDashCase(target) + '-changed');
      binding.listenerNegate = negate;
    }
    // Add "propagate" property effects to templateInfo
    let index = templateInfo.nodeInfoList.length;
    for (let i=0; i<binding.parts.length; i++) {
      let part = binding.parts[i];
      part.compoundIndex = i;
      addEffectForBindingPart(constructor, templateInfo, binding, part, index);
    }
  }

  /**
   * Adds property effects to the given `templateInfo` for the given binding
   * part.
   *
   * @param {Function} constructor Class that `_parseTemplate` is currently
   *   running on
   * @param {TemplateInfo} templateInfo Template metadata for current template
   * @param {!Binding} binding Binding metadata
   * @param {!BindingPart} part Binding part metadata
   * @param {number} index Index into `nodeInfoList` for this node
   * @return {void}
   */
  function addEffectForBindingPart(constructor, templateInfo, binding, part, index) {
    if (!part.literal) {
      if (binding.kind === 'attribute' && binding.target[0] === '-') {
        console.warn('Cannot set attribute ' + binding.target +
          ' because "-" is not a valid attribute starting character');
      } else {
        let dependencies = part.dependencies;
        let info = { index, binding, part, evaluator: constructor };
        for (let j=0; j<dependencies.length; j++) {
          let trigger = dependencies[j];
          if (typeof trigger == 'string') {
            trigger = parseArg(trigger);
            trigger.wildcard = true;
          }
          constructor._addTemplatePropertyEffect(templateInfo, trigger.rootProperty, {
            fn: runBindingEffect,
            info, trigger
          });
        }
      }
    }
  }

  /**
   * Implements the "binding" (property/path binding) effect.
   *
   * Note that binding syntax is overridable via `_parseBindings` and
   * `_evaluateBinding`.  This method will call `_evaluateBinding` for any
   * non-literal parts returned from `_parseBindings`.  However,
   * there is no support for _path_ bindings via custom binding parts,
   * as this is specific to Polymer's path binding syntax.
   *
   * @param {!PropertyEffectsType} inst The instance the effect will be run on
   * @param {string} path Name of property
   * @param {Object} props Bag of current property changes
   * @param {Object} oldProps Bag of previous values for changed properties
   * @param {?} info Effect metadata
   * @param {boolean} hasPaths True with `props` contains one or more paths
   * @param {Array} nodeList List of nodes associated with `nodeInfoList` template
   *   metadata
   * @return {void}
   * @private
   */
  function runBindingEffect(inst, path, props, oldProps, info, hasPaths, nodeList) {
    let node = nodeList[info.index];
    let binding = info.binding;
    let part = info.part;
    // Subpath notification: transform path and set to client
    // e.g.: foo="{{obj.sub}}", path: 'obj.sub.prop', set 'foo.prop'=obj.sub.prop
    if (hasPaths && part.source && (path.length > part.source.length) &&
        (binding.kind == 'property') && !binding.isCompound &&
        node.__isPropertyEffectsClient &&
        node.__dataHasAccessor && node.__dataHasAccessor[binding.target]) {
      let value = props[path];
      path = Polymer.Path.translate(part.source, binding.target, path);
      if (node._setPendingPropertyOrPath(path, value, false, true)) {
        inst._enqueueClient(node);
      }
    } else {
      let value = info.evaluator._evaluateBinding(inst, part, path, props, oldProps, hasPaths);
      // Propagate value to child
      applyBindingValue(inst, node, binding, part, value);
    }
  }

  /**
   * Sets the value for an "binding" (binding) effect to a node,
   * either as a property or attribute.
   *
   * @param {!PropertyEffectsType} inst The instance owning the binding effect
   * @param {Node} node Target node for binding
   * @param {!Binding} binding Binding metadata
   * @param {!BindingPart} part Binding part metadata
   * @param {*} value Value to set
   * @return {void}
   * @private
   */
  function applyBindingValue(inst, node, binding, part, value) {
    value = computeBindingValue(node, value, binding, part);
    if (Polymer.sanitizeDOMValue) {
      value = Polymer.sanitizeDOMValue(value, binding.target, binding.kind, node);
    }
    if (binding.kind == 'attribute') {
      // Attribute binding
      inst._valueToNodeAttribute(/** @type {Element} */(node), value, binding.target);
    } else {
      // Property binding
      let prop = binding.target;
      if (node.__isPropertyEffectsClient &&
          node.__dataHasAccessor && node.__dataHasAccessor[prop]) {
        if (!node[TYPES.READ_ONLY] || !node[TYPES.READ_ONLY][prop]) {
          if (node._setPendingProperty(prop, value)) {
            inst._enqueueClient(node);
          }
        }
      } else  {
        inst._setUnmanagedPropertyToNode(node, prop, value);
      }
    }
  }

  /**
   * Transforms an "binding" effect value based on compound & negation
   * effect metadata, as well as handling for special-case properties
   *
   * @param {Node} node Node the value will be set to
   * @param {*} value Value to set
   * @param {!Binding} binding Binding metadata
   * @param {!BindingPart} part Binding part metadata
   * @return {*} Transformed value to set
   * @private
   */
  function computeBindingValue(node, value, binding, part) {
    if (binding.isCompound) {
      let storage = node.__dataCompoundStorage[binding.target];
      storage[part.compoundIndex] = value;
      value = storage.join('');
    }
    if (binding.kind !== 'attribute') {
      // Some browsers serialize `undefined` to `"undefined"`
      if (binding.target === 'textContent' ||
          (binding.target === 'value' &&
            (node.localName === 'input' || node.localName === 'textarea'))) {
        value = value == undefined ? '' : value;
      }
    }
    return value;
  }

  /**
   * Returns true if a binding's metadata meets all the requirements to allow
   * 2-way binding, and therefore a `<property>-changed` event listener should be
   * added:
   * - used curly braces
   * - is a property (not attribute) binding
   * - is not a textContent binding
   * - is not compound
   *
   * @param {!Binding} binding Binding metadata
   * @return {boolean} True if 2-way listener should be added
   * @private
   */
  function shouldAddListener(binding) {
    return Boolean(binding.target) &&
           binding.kind != 'attribute' &&
           binding.kind != 'text' &&
           !binding.isCompound &&
           binding.parts[0].mode === '{';
  }

  /**
   * Setup compound binding storage structures, notify listeners, and dataHost
   * references onto the bound nodeList.
   *
   * @param {!PropertyEffectsType} inst Instance that bas been previously bound
   * @param {TemplateInfo} templateInfo Template metadata
   * @return {void}
   * @private
   */
  function setupBindings(inst, templateInfo) {
    // Setup compound storage, dataHost, and notify listeners
    let {nodeList, nodeInfoList} = templateInfo;
    if (nodeInfoList.length) {
      for (let i=0; i < nodeInfoList.length; i++) {
        let info = nodeInfoList[i];
        let node = nodeList[i];
        let bindings = info.bindings;
        if (bindings) {
          for (let i=0; i<bindings.length; i++) {
            let binding = bindings[i];
            setupCompoundStorage(node, binding);
            addNotifyListener(node, inst, binding);
          }
        }
        node.__dataHost = inst;
      }
    }
  }

  /**
   * Initializes `__dataCompoundStorage` local storage on a bound node with
   * initial literal data for compound bindings, and sets the joined
   * literal parts to the bound property.
   *
   * When changes to compound parts occur, they are first set into the compound
   * storage array for that property, and then the array is joined to result in
   * the final value set to the property/attribute.
   *
   * @param {Node} node Bound node to initialize
   * @param {Binding} binding Binding metadata
   * @return {void}
   * @private
   */
  function setupCompoundStorage(node, binding) {
    if (binding.isCompound) {
      // Create compound storage map
      let storage = node.__dataCompoundStorage ||
        (node.__dataCompoundStorage = {});
      let parts = binding.parts;
      // Copy literals from parts into storage for this binding
      let literals = new Array(parts.length);
      for (let j=0; j<parts.length; j++) {
        literals[j] = parts[j].literal;
      }
      let target = binding.target;
      storage[target] = literals;
      // Configure properties with their literal parts
      if (binding.literal && binding.kind == 'property') {
        node[target] = binding.literal;
      }
    }
  }

  /**
   * Adds a 2-way binding notification event listener to the node specified
   *
   * @param {Object} node Child element to add listener to
   * @param {!PropertyEffectsType} inst Host element instance to handle notification event
   * @param {Binding} binding Binding metadata
   * @return {void}
   * @private
   */
  function addNotifyListener(node, inst, binding) {
    if (binding.listenerEvent) {
      let part = binding.parts[0];
      node.addEventListener(binding.listenerEvent, function(e) {
        handleNotification(e, inst, binding.target, part.source, part.negate);
      });
    }
  }

  // -- for method-based effects (complexObserver & computed) --------------

  /**
   * Adds property effects for each argument in the method signature (and
   * optionally, for the method name if `dynamic` is true) that calls the
   * provided effect function.
   *
   * @param {Element | Object} model Prototype or instance
   * @param {!MethodSignature} sig Method signature metadata
   * @param {string} type Type of property effect to add
   * @param {Function} effectFn Function to run when arguments change
   * @param {*=} methodInfo Effect-specific information to be included in
   *   method effect metadata
   * @param {boolean|Object=} dynamicFn Boolean or object map indicating whether
   *   method names should be included as a dependency to the effect. Note,
   *   defaults to true if the signature is static (sig.static is true).
   * @return {void}
   * @private
   */
  function createMethodEffect(model, sig, type, effectFn, methodInfo, dynamicFn) {
    dynamicFn = sig.static || (dynamicFn &&
      (typeof dynamicFn !== 'object' || dynamicFn[sig.methodName]));
    let info = {
      methodName: sig.methodName,
      args: sig.args,
      methodInfo,
      dynamicFn
    };
    for (let i=0, arg; (i<sig.args.length) && (arg=sig.args[i]); i++) {
      if (!arg.literal) {
        model._addPropertyEffect(arg.rootProperty, type, {
          fn: effectFn, info: info, trigger: arg
        });
      }
    }
    if (dynamicFn) {
      model._addPropertyEffect(sig.methodName, type, {
        fn: effectFn, info: info
      });
    }
  }

  /**
   * Calls a method with arguments marshaled from properties on the instance
   * based on the method signature contained in the effect metadata.
   *
   * Multi-property observers, computed properties, and inline computing
   * functions call this function to invoke the method, then use the return
   * value accordingly.
   *
   * @param {!PropertyEffectsType} inst The instance the effect will be run on
   * @param {string} property Name of property
   * @param {Object} props Bag of current property changes
   * @param {Object} oldProps Bag of previous values for changed properties
   * @param {?} info Effect metadata
   * @return {*} Returns the return value from the method invocation
   * @private
   */
  function runMethodEffect(inst, property, props, oldProps, info) {
    // Instances can optionally have a _methodHost which allows redirecting where
    // to find methods. Currently used by `templatize`.
    let context = inst._methodHost || inst;
    let fn = context[info.methodName];
    if (fn) {
      let args = inst._marshalArgs(info.args, property, props);
      return fn.apply(context, args);
    } else if (!info.dynamicFn) {
      console.warn('method `' + info.methodName + '` not defined');
    }
  }

  const emptyArray = [];

  // Regular expressions used for binding
  const IDENT  = '(?:' + '[a-zA-Z_$][\\w.:$\\-*]*' + ')';
  const NUMBER = '(?:' + '[-+]?[0-9]*\\.?[0-9]+(?:[eE][-+]?[0-9]+)?' + ')';
  const SQUOTE_STRING = '(?:' + '\'(?:[^\'\\\\]|\\\\.)*\'' + ')';
  const DQUOTE_STRING = '(?:' + '"(?:[^"\\\\]|\\\\.)*"' + ')';
  const STRING = '(?:' + SQUOTE_STRING + '|' + DQUOTE_STRING + ')';
  const ARGUMENT = '(?:(' + IDENT + '|' + NUMBER + '|' +  STRING + ')\\s*' + ')';
  const ARGUMENTS = '(?:' + ARGUMENT + '(?:,\\s*' + ARGUMENT + ')*' + ')';
  const ARGUMENT_LIST = '(?:' + '\\(\\s*' +
                                '(?:' + ARGUMENTS + '?' + ')' +
                              '\\)\\s*' + ')';
  const BINDING = '(' + IDENT + '\\s*' + ARGUMENT_LIST + '?' + ')'; // Group 3
  const OPEN_BRACKET = '(\\[\\[|{{)' + '\\s*';
  const CLOSE_BRACKET = '(?:]]|}})';
  const NEGATE = '(?:(!)\\s*)?'; // Group 2
  const EXPRESSION = OPEN_BRACKET + NEGATE + BINDING + CLOSE_BRACKET;
  const bindingRegex = new RegExp(EXPRESSION, "g");

  /**
   * Create a string from binding parts of all the literal parts
   *
   * @param {!Array<BindingPart>} parts All parts to stringify
   * @return {string} String made from the literal parts
   */
  function literalFromParts(parts) {
    let s = '';
    for (let i=0; i<parts.length; i++) {
      let literal = parts[i].literal;
      s += literal || '';
    }
    return s;
  }

  /**
   * Parses an expression string for a method signature, and returns a metadata
   * describing the method in terms of `methodName`, `static` (whether all the
   * arguments are literals), and an array of `args`
   *
   * @param {string} expression The expression to parse
   * @return {?MethodSignature} The method metadata object if a method expression was
   *   found, otherwise `undefined`
   * @private
   */
  function parseMethod(expression) {
    // tries to match valid javascript property names
    let m = expression.match(/([^\s]+?)\(([\s\S]*)\)/);
    if (m) {
      let methodName = m[1];
      let sig = { methodName, static: true, args: emptyArray };
      if (m[2].trim()) {
        // replace escaped commas with comma entity, split on un-escaped commas
        let args = m[2].replace(/\\,/g, '&comma;').split(',');
        return parseArgs(args, sig);
      } else {
        return sig;
      }
    }
    return null;
  }

  /**
   * Parses an array of arguments and sets the `args` property of the supplied
   * signature metadata object. Sets the `static` property to false if any
   * argument is a non-literal.
   *
   * @param {!Array<string>} argList Array of argument names
   * @param {!MethodSignature} sig Method signature metadata object
   * @return {!MethodSignature} The updated signature metadata object
   * @private
   */
  function parseArgs(argList, sig) {
    sig.args = argList.map(function(rawArg) {
      let arg = parseArg(rawArg);
      if (!arg.literal) {
        sig.static = false;
      }
      return arg;
    }, this);
    return sig;
  }

  /**
   * Parses an individual argument, and returns an argument metadata object
   * with the following fields:
   *
   *   {
   *     value: 'prop',        // property/path or literal value
   *     literal: false,       // whether argument is a literal
   *     structured: false,    // whether the property is a path
   *     rootProperty: 'prop', // the root property of the path
   *     wildcard: false       // whether the argument was a wildcard '.*' path
   *   }
   *
   * @param {string} rawArg The string value of the argument
   * @return {!MethodArg} Argument metadata object
   * @private
   */
  function parseArg(rawArg) {
    // clean up whitespace
    let arg = rawArg.trim()
      // replace comma entity with comma
      .replace(/&comma;/g, ',')
      // repair extra escape sequences; note only commas strictly need
      // escaping, but we allow any other char to be escaped since its
      // likely users will do this
      .replace(/\\(.)/g, '\$1')
      ;
    // basic argument descriptor
    let a = {
      name: arg,
      value: '',
      literal: false
    };
    // detect literal value (must be String or Number)
    let fc = arg[0];
    if (fc === '-') {
      fc = arg[1];
    }
    if (fc >= '0' && fc <= '9') {
      fc = '#';
    }
    switch(fc) {
      case "'":
      case '"':
        a.value = arg.slice(1, -1);
        a.literal = true;
        break;
      case '#':
        a.value = Number(arg);
        a.literal = true;
        break;
    }
    // if not literal, look for structured path
    if (!a.literal) {
      a.rootProperty = Polymer.Path.root(arg);
      // detect structured path (has dots)
      a.structured = Polymer.Path.isPath(arg);
      if (a.structured) {
        a.wildcard = (arg.slice(-2) == '.*');
        if (a.wildcard) {
          a.name = arg.slice(0, -2);
        }
      }
    }
    return a;
  }

  // data api

  /**
   * Sends array splice notifications (`.splices` and `.length`)
   *
   * Note: this implementation only accepts normalized paths
   *
   * @param {!PropertyEffectsType} inst Instance to send notifications to
   * @param {Array} array The array the mutations occurred on
   * @param {string} path The path to the array that was mutated
   * @param {Array} splices Array of splice records
   * @return {void}
   * @private
   */
  function notifySplices(inst, array, path, splices) {
    let splicesPath = path + '.splices';
    inst.notifyPath(splicesPath, { indexSplices: splices });
    inst.notifyPath(path + '.length', array.length);
    // Null here to allow potentially large splice records to be GC'ed.
    inst.__data[splicesPath] = {indexSplices: null};
  }

  /**
   * Creates a splice record and sends an array splice notification for
   * the described mutation
   *
   * Note: this implementation only accepts normalized paths
   *
   * @param {!PropertyEffectsType} inst Instance to send notifications to
   * @param {Array} array The array the mutations occurred on
   * @param {string} path The path to the array that was mutated
   * @param {number} index Index at which the array mutation occurred
   * @param {number} addedCount Number of added items
   * @param {Array} removed Array of removed items
   * @return {void}
   * @private
   */
  function notifySplice(inst, array, path, index, addedCount, removed) {
    notifySplices(inst, array, path, [{
      index: index,
      addedCount: addedCount,
      removed: removed,
      object: array,
      type: 'splice'
    }]);
  }

  /**
   * Returns an upper-cased version of the string.
   *
   * @param {string} name String to uppercase
   * @return {string} Uppercased string
   * @private
   */
  function upper(name) {
    return name[0].toUpperCase() + name.substring(1);
  }

  /**
   * Element class mixin that provides meta-programming for Polymer's template
   * binding and data observation (collectively, "property effects") system.
   *
   * This mixin uses provides the following key static methods for adding
   * property effects to an element class:
   * - `addPropertyEffect`
   * - `createPropertyObserver`
   * - `createMethodObserver`
   * - `createNotifyingProperty`
   * - `createReadOnlyProperty`
   * - `createReflectedProperty`
   * - `createComputedProperty`
   * - `bindTemplate`
   *
   * Each method creates one or more property accessors, along with metadata
   * used by this mixin's implementation of `_propertiesChanged` to perform
   * the property effects.
   *
   * Underscored versions of the above methods also exist on the element
   * prototype for adding property effects on instances at runtime.
   *
   * Note that this mixin overrides several `PropertyAccessors` methods, in
   * many cases to maintain guarantees provided by the Polymer 1.x features;
   * notably it changes property accessors to be synchronous by default
   * whereas the default when using `PropertyAccessors` standalone is to be
   * async by default.
   *
   * @mixinFunction
   * @polymer
   * @appliesMixin Polymer.TemplateStamp
   * @appliesMixin Polymer.PropertyAccessors
   * @memberof Polymer
   * @summary Element class mixin that provides meta-programming for Polymer's
   * template binding and data observation system.
   */
  Polymer.PropertyEffects = Polymer.dedupingMixin(superClass => {

    /**
     * @constructor
     * @extends {superClass}
     * @implements {Polymer_PropertyAccessors}
     * @implements {Polymer_TemplateStamp}
     * @unrestricted
     * @private
     */
    const propertyEffectsBase = Polymer.TemplateStamp(Polymer.PropertyAccessors(superClass));

    /**
     * @polymer
     * @mixinClass
     * @implements {Polymer_PropertyEffects}
     * @extends {propertyEffectsBase}
     * @unrestricted
     */
    class PropertyEffects extends propertyEffectsBase {

      constructor() {
        super();
        /** @type {boolean} */
        // Used to identify users of this mixin, ala instanceof
        this.__isPropertyEffectsClient = true;
        /** @type {number} */
        // NOTE: used to track re-entrant calls to `_flushProperties`
        // path changes dirty check against `__dataTemp` only during one "turn"
        // and are cleared when `__dataCounter` returns to 0.
        this.__dataCounter = 0;
        /** @type {boolean} */
        this.__dataClientsReady;
        /** @type {Array} */
        this.__dataPendingClients;
        /** @type {Object} */
        this.__dataToNotify;
        /** @type {Object} */
        this.__dataLinkedPaths;
        /** @type {boolean} */
        this.__dataHasPaths;
        /** @type {Object} */
        this.__dataCompoundStorage;
        /** @type {Polymer_PropertyEffects} */
        this.__dataHost;
        /** @type {!Object} */
        this.__dataTemp;
        /** @type {boolean} */
        this.__dataClientsInitialized;
        /** @type {!Object} */
        this.__data;
        /** @type {!Object} */
        this.__dataPending;
        /** @type {!Object} */
        this.__dataOld;
        /** @type {Object} */
        this.__computeEffects;
        /** @type {Object} */
        this.__reflectEffects;
        /** @type {Object} */
        this.__notifyEffects;
        /** @type {Object} */
        this.__propagateEffects;
        /** @type {Object} */
        this.__observeEffects;
        /** @type {Object} */
        this.__readOnly;
        /** @type {!TemplateInfo} */
        this.__templateInfo;
      }

      get PROPERTY_EFFECT_TYPES() {
        return TYPES;
      }

      /**
       * @return {void}
       */
      _initializeProperties() {
        super._initializeProperties();
        hostStack.registerHost(this);
        this.__dataClientsReady = false;
        this.__dataPendingClients = null;
        this.__dataToNotify = null;
        this.__dataLinkedPaths = null;
        this.__dataHasPaths = false;
        // May be set on instance prior to upgrade
        this.__dataCompoundStorage = this.__dataCompoundStorage || null;
        this.__dataHost = this.__dataHost || null;
        this.__dataTemp = {};
        this.__dataClientsInitialized = false;
      }

      /**
       * Overrides `Polymer.PropertyAccessors` implementation to provide a
       * more efficient implementation of initializing properties from
       * the prototype on the instance.
       *
       * @override
       * @param {Object} props Properties to initialize on the prototype
       * @return {void}
       */
      _initializeProtoProperties(props) {
        this.__data = Object.create(props);
        this.__dataPending = Object.create(props);
        this.__dataOld = {};
      }

      /**
       * Overrides `Polymer.PropertyAccessors` implementation to avoid setting
       * `_setProperty`'s `shouldNotify: true`.
       *
       * @override
       * @param {Object} props Properties to initialize on the instance
       * @return {void}
       */
      _initializeInstanceProperties(props) {
        let readOnly = this[TYPES.READ_ONLY];
        for (let prop in props) {
          if (!readOnly || !readOnly[prop]) {
            this.__dataPending = this.__dataPending || {};
            this.__dataOld = this.__dataOld || {};
            this.__data[prop] = this.__dataPending[prop] = props[prop];
          }
        }
      }

      // Prototype setup ----------------------------------------

      /**
       * Equivalent to static `addPropertyEffect` API but can be called on
       * an instance to add effects at runtime.  See that method for
       * full API docs.
       *
       * @param {string} property Property that should trigger the effect
       * @param {string} type Effect type, from this.PROPERTY_EFFECT_TYPES
       * @param {Object=} effect Effect metadata object
       * @return {void}
       * @protected
       */
      _addPropertyEffect(property, type, effect) {
        this._createPropertyAccessor(property, type == TYPES.READ_ONLY);
        // effects are accumulated into arrays per property based on type
        let effects = ensureOwnEffectMap(this, type)[property];
        if (!effects) {
          effects = this[type][property] = [];
        }
        effects.push(effect);
      }

      /**
       * Removes the given property effect.
       *
       * @param {string} property Property the effect was associated with
       * @param {string} type Effect type, from this.PROPERTY_EFFECT_TYPES
       * @param {Object=} effect Effect metadata object to remove
       * @return {void}
       */
      _removePropertyEffect(property, type, effect) {
        let effects = ensureOwnEffectMap(this, type)[property];
        let idx = effects.indexOf(effect);
        if (idx >= 0) {
          effects.splice(idx, 1);
        }
      }

      /**
       * Returns whether the current prototype/instance has a property effect
       * of a certain type.
       *
       * @param {string} property Property name
       * @param {string=} type Effect type, from this.PROPERTY_EFFECT_TYPES
       * @return {boolean} True if the prototype/instance has an effect of this type
       * @protected
       */
      _hasPropertyEffect(property, type) {
        let effects = this[type];
        return Boolean(effects && effects[property]);
      }

      /**
       * Returns whether the current prototype/instance has a "read only"
       * accessor for the given property.
       *
       * @param {string} property Property name
       * @return {boolean} True if the prototype/instance has an effect of this type
       * @protected
       */
      _hasReadOnlyEffect(property) {
        return this._hasPropertyEffect(property, TYPES.READ_ONLY);
      }

      /**
       * Returns whether the current prototype/instance has a "notify"
       * property effect for the given property.
       *
       * @param {string} property Property name
       * @return {boolean} True if the prototype/instance has an effect of this type
       * @protected
       */
      _hasNotifyEffect(property) {
        return this._hasPropertyEffect(property, TYPES.NOTIFY);
      }

      /**
       * Returns whether the current prototype/instance has a "reflect to attribute"
       * property effect for the given property.
       *
       * @param {string} property Property name
       * @return {boolean} True if the prototype/instance has an effect of this type
       * @protected
       */
      _hasReflectEffect(property) {
        return this._hasPropertyEffect(property, TYPES.REFLECT);
      }

      /**
       * Returns whether the current prototype/instance has a "computed"
       * property effect for the given property.
       *
       * @param {string} property Property name
       * @return {boolean} True if the prototype/instance has an effect of this type
       * @protected
       */
      _hasComputedEffect(property) {
        return this._hasPropertyEffect(property, TYPES.COMPUTE);
      }

      // Runtime ----------------------------------------

      /**
       * Sets a pending property or path.  If the root property of the path in
       * question had no accessor, the path is set, otherwise it is enqueued
       * via `_setPendingProperty`.
       *
       * This function isolates relatively expensive functionality necessary
       * for the public API (`set`, `setProperties`, `notifyPath`, and property
       * change listeners via {{...}} bindings), such that it is only done
       * when paths enter the system, and not at every propagation step.  It
       * also sets a `__dataHasPaths` flag on the instance which is used to
       * fast-path slower path-matching code in the property effects host paths.
       *
       * `path` can be a path string or array of path parts as accepted by the
       * public API.
       *
       * @param {string | !Array<number|string>} path Path to set
       * @param {*} value Value to set
       * @param {boolean=} shouldNotify Set to true if this change should
       *  cause a property notification event dispatch
       * @param {boolean=} isPathNotification If the path being set is a path
       *   notification of an already changed value, as opposed to a request
       *   to set and notify the change.  In the latter `false` case, a dirty
       *   check is performed and then the value is set to the path before
       *   enqueuing the pending property change.
       * @return {boolean} Returns true if the property/path was enqueued in
       *   the pending changes bag.
       * @protected
       */
      _setPendingPropertyOrPath(path, value, shouldNotify, isPathNotification) {
        if (isPathNotification ||
            Polymer.Path.root(Array.isArray(path) ? path[0] : path) !== path) {
          // Dirty check changes being set to a path against the actual object,
          // since this is the entry point for paths into the system; from here
          // the only dirty checks are against the `__dataTemp` cache to prevent
          // duplicate work in the same turn only. Note, if this was a notification
          // of a change already set to a path (isPathNotification: true),
          // we always let the change through and skip the `set` since it was
          // already dirty checked at the point of entry and the underlying
          // object has already been updated
          if (!isPathNotification) {
            let old = Polymer.Path.get(this, path);
            path = /** @type {string} */ (Polymer.Path.set(this, path, value));
            // Use property-accessor's simpler dirty check
            if (!path || !super._shouldPropertyChange(path, value, old)) {
              return false;
            }
          }
          this.__dataHasPaths = true;
          if (this._setPendingProperty(/**@type{string}*/(path), value, shouldNotify)) {
            computeLinkedPaths(this, path, value);
            return true;
          }
        } else {
          if (this.__dataHasAccessor && this.__dataHasAccessor[path]) {
            return this._setPendingProperty(/**@type{string}*/(path), value, shouldNotify);
          } else {
            this[path] = value;
          }
        }
        return false;
      }

      /**
       * Applies a value to a non-Polymer element/node's property.
       *
       * The implementation makes a best-effort at binding interop:
       * Some native element properties have side-effects when
       * re-setting the same value (e.g. setting `<input>.value` resets the
       * cursor position), so we do a dirty-check before setting the value.
       * However, for better interop with non-Polymer custom elements that
       * accept objects, we explicitly re-set object changes coming from the
       * Polymer world (which may include deep object changes without the
       * top reference changing), erring on the side of providing more
       * information.
       *
       * Users may override this method to provide alternate approaches.
       *
       * @param {!Node} node The node to set a property on
       * @param {string} prop The property to set
       * @param {*} value The value to set
       * @return {void}
       * @protected
       */
      _setUnmanagedPropertyToNode(node, prop, value) {
        // It is a judgment call that resetting primitives is
        // "bad" and resettings objects is also "good"; alternatively we could
        // implement a whitelist of tag & property values that should never
        // be reset (e.g. <input>.value && <select>.value)
        if (value !== node[prop] || typeof value == 'object') {
          node[prop] = value;
        }
      }

      /**
       * Overrides the `PropertiesChanged` implementation to introduce special
       * dirty check logic depending on the property & value being set:
       *
       * 1. Any value set to a path (e.g. 'obj.prop': 42 or 'obj.prop': {...})
       *    Stored in `__dataTemp`, dirty checked against `__dataTemp`
       * 2. Object set to simple property (e.g. 'prop': {...})
       *    Stored in `__dataTemp` and `__data`, dirty checked against
       *    `__dataTemp` by default implementation of `_shouldPropertyChange`
       * 3. Primitive value set to simple property (e.g. 'prop': 42)
       *    Stored in `__data`, dirty checked against `__data`
       *
       * The dirty-check is important to prevent cycles due to two-way
       * notification, but paths and objects are only dirty checked against any
       * previous value set during this turn via a "temporary cache" that is
       * cleared when the last `_propertiesChanged` exits. This is so:
       * a. any cached array paths (e.g. 'array.3.prop') may be invalidated
       *    due to array mutations like shift/unshift/splice; this is fine
       *    since path changes are dirty-checked at user entry points like `set`
       * b. dirty-checking for objects only lasts one turn to allow the user
       *    to mutate the object in-place and re-set it with the same identity
       *    and have all sub-properties re-propagated in a subsequent turn.
       *
       * The temp cache is not necessarily sufficient to prevent invalid array
       * paths, since a splice can happen during the same turn (with pathological
       * user code); we could introduce a "fixup" for temporarily cached array
       * paths if needed: https://github.com/Polymer/polymer/issues/4227
       *
       * @override
       * @param {string} property Name of the property
       * @param {*} value Value to set
       * @param {boolean=} shouldNotify True if property should fire notification
       *   event (applies only for `notify: true` properties)
       * @return {boolean} Returns true if the property changed
       */
      _setPendingProperty(property, value, shouldNotify) {
        let isPath = this.__dataHasPaths && Polymer.Path.isPath(property);
        let prevProps = isPath ? this.__dataTemp : this.__data;
        if (this._shouldPropertyChange(property, value, prevProps[property])) {
          if (!this.__dataPending) {
            this.__dataPending = {};
            this.__dataOld = {};
          }
          // Ensure old is captured from the last turn
          if (!(property in this.__dataOld)) {
            this.__dataOld[property] = this.__data[property];
          }
          // Paths are stored in temporary cache (cleared at end of turn),
          // which is used for dirty-checking, all others stored in __data
          if (isPath) {
            this.__dataTemp[property] = value;
          } else {
            this.__data[property] = value;
          }
          // All changes go into pending property bag, passed to _propertiesChanged
          this.__dataPending[property] = value;
          // Track properties that should notify separately
          if (isPath || (this[TYPES.NOTIFY] && this[TYPES.NOTIFY][property])) {
            this.__dataToNotify = this.__dataToNotify || {};
            this.__dataToNotify[property] = shouldNotify;
          }
          return true;
        }
        return false;
      }

      /**
       * Overrides base implementation to ensure all accessors set `shouldNotify`
       * to true, for per-property notification tracking.
       *
       * @override
       * @param {string} property Name of the property
       * @param {*} value Value to set
       * @return {void}
       */
      _setProperty(property, value) {
        if (this._setPendingProperty(property, value, true)) {
          this._invalidateProperties();
        }
      }

      /**
       * Overrides `PropertyAccessor`'s default async queuing of
       * `_propertiesChanged`: if `__dataReady` is false (has not yet been
       * manually flushed), the function no-ops; otherwise flushes
       * `_propertiesChanged` synchronously.
       *
       * @override
       * @return {void}
       */
      _invalidateProperties() {
        if (this.__dataReady) {
          this._flushProperties();
        }
      }

      /**
       * Enqueues the given client on a list of pending clients, whose
       * pending property changes can later be flushed via a call to
       * `_flushClients`.
       *
       * @param {Object} client PropertyEffects client to enqueue
       * @return {void}
       * @protected
       */
      _enqueueClient(client) {
        this.__dataPendingClients = this.__dataPendingClients || [];
        if (client !== this) {
          this.__dataPendingClients.push(client);
        }
      }

      /**
       * Overrides superclass implementation.
       *
       * @return {void}
       * @protected
       */
      _flushProperties() {
        this.__dataCounter++;
        super._flushProperties();
        this.__dataCounter--;
      }

      /**
       * Flushes any clients previously enqueued via `_enqueueClient`, causing
       * their `_flushProperties` method to run.
       *
       * @return {void}
       * @protected
       */
      _flushClients() {
        if (!this.__dataClientsReady) {
          this.__dataClientsReady = true;
          this._readyClients();
          // Override point where accessors are turned on; importantly,
          // this is after clients have fully readied, providing a guarantee
          // that any property effects occur only after all clients are ready.
          this.__dataReady = true;
        } else {
          this.__enableOrFlushClients();
        }
      }

      // NOTE: We ensure clients either enable or flush as appropriate. This
      // handles two corner cases:
      // (1) clients flush properly when connected/enabled before the host
      // enables; e.g.
      //   (a) Templatize stamps with no properties and does not flush and
      //   (b) the instance is inserted into dom and
      //   (c) then the instance flushes.
      // (2) clients enable properly when not connected/enabled when the host
      // flushes; e.g.
      //   (a) a template is runtime stamped and not yet connected/enabled
      //   (b) a host sets a property, causing stamped dom to flush
      //   (c) the stamped dom enables.
      __enableOrFlushClients() {
        let clients = this.__dataPendingClients;
        if (clients) {
          this.__dataPendingClients = null;
          for (let i=0; i < clients.length; i++) {
            let client = clients[i];
            if (!client.__dataEnabled) {
              client._enableProperties();
            } else if (client.__dataPending) {
              client._flushProperties();
            }
          }
        }
      }

      /**
       * Perform any initial setup on client dom. Called before the first
       * `_flushProperties` call on client dom and before any element
       * observers are called.
       *
       * @return {void}
       * @protected
       */
      _readyClients() {
        this.__enableOrFlushClients();
      }

      /**
       * Sets a bag of property changes to this instance, and
       * synchronously processes all effects of the properties as a batch.
       *
       * Property names must be simple properties, not paths.  Batched
       * path propagation is not supported.
       *
       * @param {Object} props Bag of one or more key-value pairs whose key is
       *   a property and value is the new value to set for that property.
       * @param {boolean=} setReadOnly When true, any private values set in
       *   `props` will be set. By default, `setProperties` will not set
       *   `readOnly: true` root properties.
       * @return {void}
       * @public
       */
      setProperties(props, setReadOnly) {
        for (let path in props) {
          if (setReadOnly || !this[TYPES.READ_ONLY] || !this[TYPES.READ_ONLY][path]) {
            //TODO(kschaaf): explicitly disallow paths in setProperty?
            // wildcard observers currently only pass the first changed path
            // in the `info` object, and you could do some odd things batching
            // paths, e.g. {'foo.bar': {...}, 'foo': null}
            this._setPendingPropertyOrPath(path, props[path], true);
          }
        }
        this._invalidateProperties();
      }

      /**
       * Overrides `PropertyAccessors` so that property accessor
       * side effects are not enabled until after client dom is fully ready.
       * Also calls `_flushClients` callback to ensure client dom is enabled
       * that was not enabled as a result of flushing properties.
       *
       * @override
       * @return {void}
       */
      ready() {
        // It is important that `super.ready()` is not called here as it
        // immediately turns on accessors. Instead, we wait until `readyClients`
        // to enable accessors to provide a guarantee that clients are ready
        // before processing any accessors side effects.
        this._flushProperties();
        // If no data was pending, `_flushProperties` will not `flushClients`
        // so ensure this is done.
        if (!this.__dataClientsReady) {
          this._flushClients();
        }
        // Before ready, client notifications do not trigger _flushProperties.
        // Therefore a flush is necessary here if data has been set.
        if (this.__dataPending) {
          this._flushProperties();
        }
      }

      /**
       * Implements `PropertyAccessors`'s properties changed callback.
       *
       * Runs each class of effects for the batch of changed properties in
       * a specific order (compute, propagate, reflect, observe, notify).
       *
       * @param {!Object} currentProps Bag of all current accessor values
       * @param {!Object} changedProps Bag of properties changed since the last
       *   call to `_propertiesChanged`
       * @param {!Object} oldProps Bag of previous values for each property
       *   in `changedProps`
       * @return {void}
       */
      _propertiesChanged(currentProps, changedProps, oldProps) {
        // ----------------------------
        // let c = Object.getOwnPropertyNames(changedProps || {});
        // window.debug && console.group(this.localName + '#' + this.id + ': ' + c);
        // if (window.debug) { debugger; }
        // ----------------------------
        let hasPaths = this.__dataHasPaths;
        this.__dataHasPaths = false;
        // Compute properties
        runComputedEffects(this, changedProps, oldProps, hasPaths);
        // Clear notify properties prior to possible reentry (propagate, observe),
        // but after computing effects have a chance to add to them
        let notifyProps = this.__dataToNotify;
        this.__dataToNotify = null;
        // Propagate properties to clients
        this._propagatePropertyChanges(changedProps, oldProps, hasPaths);
        // Flush clients
        this._flushClients();
        // Reflect properties
        runEffects(this, this[TYPES.REFLECT], changedProps, oldProps, hasPaths);
        // Observe properties
        runEffects(this, this[TYPES.OBSERVE], changedProps, oldProps, hasPaths);
        // Notify properties to host
        if (notifyProps) {
          runNotifyEffects(this, notifyProps, changedProps, oldProps, hasPaths);
        }
        // Clear temporary cache at end of turn
        if (this.__dataCounter == 1) {
          this.__dataTemp = {};
        }
        // ----------------------------
        // window.debug && console.groupEnd(this.localName + '#' + this.id + ': ' + c);
        // ----------------------------
      }

      /**
       * Called to propagate any property changes to stamped template nodes
       * managed by this element.
       *
       * @param {Object} changedProps Bag of changed properties
       * @param {Object} oldProps Bag of previous values for changed properties
       * @param {boolean} hasPaths True with `props` contains one or more paths
       * @return {void}
       * @protected
       */
      _propagatePropertyChanges(changedProps, oldProps, hasPaths) {
        if (this[TYPES.PROPAGATE]) {
          runEffects(this, this[TYPES.PROPAGATE], changedProps, oldProps, hasPaths);
        }
        let templateInfo = this.__templateInfo;
        while (templateInfo) {
          runEffects(this, templateInfo.propertyEffects, changedProps, oldProps,
            hasPaths, templateInfo.nodeList);
          templateInfo = templateInfo.nextTemplateInfo;
        }
      }

      /**
       * Aliases one data path as another, such that path notifications from one
       * are routed to the other.
       *
       * @param {string | !Array<string|number>} to Target path to link.
       * @param {string | !Array<string|number>} from Source path to link.
       * @return {void}
       * @public
       */
      linkPaths(to, from) {
        to = Polymer.Path.normalize(to);
        from = Polymer.Path.normalize(from);
        this.__dataLinkedPaths = this.__dataLinkedPaths || {};
        this.__dataLinkedPaths[to] = from;
      }

      /**
       * Removes a data path alias previously established with `_linkPaths`.
       *
       * Note, the path to unlink should be the target (`to`) used when
       * linking the paths.
       *
       * @param {string | !Array<string|number>} path Target path to unlink.
       * @return {void}
       * @public
       */
      unlinkPaths(path) {
        path = Polymer.Path.normalize(path);
        if (this.__dataLinkedPaths) {
          delete this.__dataLinkedPaths[path];
        }
      }

      /**
       * Notify that an array has changed.
       *
       * Example:
       *
       *     this.items = [ {name: 'Jim'}, {name: 'Todd'}, {name: 'Bill'} ];
       *     ...
       *     this.items.splice(1, 1, {name: 'Sam'});
       *     this.items.push({name: 'Bob'});
       *     this.notifySplices('items', [
       *       { index: 1, removed: [{name: 'Todd'}], addedCount: 1, object: this.items, type: 'splice' },
       *       { index: 3, removed: [], addedCount: 1, object: this.items, type: 'splice'}
       *     ]);
       *
       * @param {string} path Path that should be notified.
       * @param {Array} splices Array of splice records indicating ordered
       *   changes that occurred to the array. Each record should have the
       *   following fields:
       *    * index: index at which the change occurred
       *    * removed: array of items that were removed from this index
       *    * addedCount: number of new items added at this index
       *    * object: a reference to the array in question
       *    * type: the string literal 'splice'
       *
       *   Note that splice records _must_ be normalized such that they are
       *   reported in index order (raw results from `Object.observe` are not
       *   ordered and must be normalized/merged before notifying).
       * @return {void}
       * @public
      */
      notifySplices(path, splices) {
        let info = {path: ''};
        let array = /** @type {Array} */(Polymer.Path.get(this, path, info));
        notifySplices(this, array, info.path, splices);
      }

      /**
       * Convenience method for reading a value from a path.
       *
       * Note, if any part in the path is undefined, this method returns
       * `undefined` (this method does not throw when dereferencing undefined
       * paths).
       *
       * @param {(string|!Array<(string|number)>)} path Path to the value
       *   to read.  The path may be specified as a string (e.g. `foo.bar.baz`)
       *   or an array of path parts (e.g. `['foo.bar', 'baz']`).  Note that
       *   bracketed expressions are not supported; string-based path parts
       *   *must* be separated by dots.  Note that when dereferencing array
       *   indices, the index may be used as a dotted part directly
       *   (e.g. `users.12.name` or `['users', 12, 'name']`).
       * @param {Object=} root Root object from which the path is evaluated.
       * @return {*} Value at the path, or `undefined` if any part of the path
       *   is undefined.
       * @public
       */
      get(path, root) {
        return Polymer.Path.get(root || this, path);
      }

      /**
       * Convenience method for setting a value to a path and notifying any
       * elements bound to the same path.
       *
       * Note, if any part in the path except for the last is undefined,
       * this method does nothing (this method does not throw when
       * dereferencing undefined paths).
       *
       * @param {(string|!Array<(string|number)>)} path Path to the value
       *   to write.  The path may be specified as a string (e.g. `'foo.bar.baz'`)
       *   or an array of path parts (e.g. `['foo.bar', 'baz']`).  Note that
       *   bracketed expressions are not supported; string-based path parts
       *   *must* be separated by dots.  Note that when dereferencing array
       *   indices, the index may be used as a dotted part directly
       *   (e.g. `'users.12.name'` or `['users', 12, 'name']`).
       * @param {*} value Value to set at the specified path.
       * @param {Object=} root Root object from which the path is evaluated.
       *   When specified, no notification will occur.
       * @return {void}
       * @public
      */
      set(path, value, root) {
        if (root) {
          Polymer.Path.set(root, path, value);
        } else {
          if (!this[TYPES.READ_ONLY] || !this[TYPES.READ_ONLY][/** @type {string} */(path)]) {
            if (this._setPendingPropertyOrPath(path, value, true)) {
              this._invalidateProperties();
            }
          }
        }
      }

      /**
       * Adds items onto the end of the array at the path specified.
       *
       * The arguments after `path` and return value match that of
       * `Array.prototype.push`.
       *
       * This method notifies other paths to the same array that a
       * splice occurred to the array.
       *
       * @param {string | !Array<string|number>} path Path to array.
       * @param {...*} items Items to push onto array
       * @return {number} New length of the array.
       * @public
       */
      push(path, ...items) {
        let info = {path: ''};
        let array = /** @type {Array}*/(Polymer.Path.get(this, path, info));
        let len = array.length;
        let ret = array.push(...items);
        if (items.length) {
          notifySplice(this, array, info.path, len, items.length, []);
        }
        return ret;
      }

      /**
       * Removes an item from the end of array at the path specified.
       *
       * The arguments after `path` and return value match that of
       * `Array.prototype.pop`.
       *
       * This method notifies other paths to the same array that a
       * splice occurred to the array.
       *
       * @param {string | !Array<string|number>} path Path to array.
       * @return {*} Item that was removed.
       * @public
       */
      pop(path) {
        let info = {path: ''};
        let array = /** @type {Array} */(Polymer.Path.get(this, path, info));
        let hadLength = Boolean(array.length);
        let ret = array.pop();
        if (hadLength) {
          notifySplice(this, array, info.path, array.length, 0, [ret]);
        }
        return ret;
      }

      /**
       * Starting from the start index specified, removes 0 or more items
       * from the array and inserts 0 or more new items in their place.
       *
       * The arguments after `path` and return value match that of
       * `Array.prototype.splice`.
       *
       * This method notifies other paths to the same array that a
       * splice occurred to the array.
       *
       * @param {string | !Array<string|number>} path Path to array.
       * @param {number} start Index from which to start removing/inserting.
       * @param {number} deleteCount Number of items to remove.
       * @param {...*} items Items to insert into array.
       * @return {Array} Array of removed items.
       * @public
       */
      splice(path, start, deleteCount, ...items) {
        let info = {path : ''};
        let array = /** @type {Array} */(Polymer.Path.get(this, path, info));
        // Normalize fancy native splice handling of crazy start values
        if (start < 0) {
          start = array.length - Math.floor(-start);
        } else if (start) {
          start = Math.floor(start);
        }
        // array.splice does different things based on the number of arguments
        // you pass in. Therefore, array.splice(0) and array.splice(0, undefined)
        // do different things. In the former, the whole array is cleared. In the
        // latter, no items are removed.
        // This means that we need to detect whether 1. one of the arguments
        // is actually passed in and then 2. determine how many arguments
        // we should pass on to the native array.splice
        //
        let ret;
        // Omit any additional arguments if they were not passed in
        if (arguments.length === 2) {
          ret = array.splice(start);
        // Either start was undefined and the others were defined, but in this
        // case we can safely pass on all arguments
        //
        // Note: this includes the case where none of the arguments were passed in,
        // e.g. this.splice('array'). However, if both start and deleteCount
        // are undefined, array.splice will not modify the array (as expected)
        } else {
          ret = array.splice(start, deleteCount, ...items);
        }
        // At the end, check whether any items were passed in (e.g. insertions)
        // or if the return array contains items (e.g. deletions).
        // Only notify if items were added or deleted.
        if (items.length || ret.length) {
          notifySplice(this, array, info.path, start, items.length, ret);
        }
        return ret;
      }

      /**
       * Removes an item from the beginning of array at the path specified.
       *
       * The arguments after `path` and return value match that of
       * `Array.prototype.pop`.
       *
       * This method notifies other paths to the same array that a
       * splice occurred to the array.
       *
       * @param {string | !Array<string|number>} path Path to array.
       * @return {*} Item that was removed.
       * @public
       */
      shift(path) {
        let info = {path: ''};
        let array = /** @type {Array} */(Polymer.Path.get(this, path, info));
        let hadLength = Boolean(array.length);
        let ret = array.shift();
        if (hadLength) {
          notifySplice(this, array, info.path, 0, 0, [ret]);
        }
        return ret;
      }

      /**
       * Adds items onto the beginning of the array at the path specified.
       *
       * The arguments after `path` and return value match that of
       * `Array.prototype.push`.
       *
       * This method notifies other paths to the same array that a
       * splice occurred to the array.
       *
       * @param {string | !Array<string|number>} path Path to array.
       * @param {...*} items Items to insert info array
       * @return {number} New length of the array.
       * @public
       */
      unshift(path, ...items) {
        let info = {path: ''};
        let array = /** @type {Array} */(Polymer.Path.get(this, path, info));
        let ret = array.unshift(...items);
        if (items.length) {
          notifySplice(this, array, info.path, 0, items.length, []);
        }
        return ret;
      }

      /**
       * Notify that a path has changed.
       *
       * Example:
       *
       *     this.item.user.name = 'Bob';
       *     this.notifyPath('item.user.name');
       *
       * @param {string} path Path that should be notified.
       * @param {*=} value Value at the path (optional).
       * @return {void}
       * @public
      */
      notifyPath(path, value) {
        /** @type {string} */
        let propPath;
        if (arguments.length == 1) {
          // Get value if not supplied
          let info = {path: ''};
          value = Polymer.Path.get(this, path, info);
          propPath = info.path;
        } else if (Array.isArray(path)) {
          // Normalize path if needed
          propPath = Polymer.Path.normalize(path);
        } else {
          propPath = /** @type{string} */(path);
        }
        if (this._setPendingPropertyOrPath(propPath, value, true, true)) {
          this._invalidateProperties();
        }
      }

      /**
       * Equivalent to static `createReadOnlyProperty` API but can be called on
       * an instance to add effects at runtime.  See that method for
       * full API docs.
       *
       * @param {string} property Property name
       * @param {boolean=} protectedSetter Creates a custom protected setter
       *   when `true`.
       * @return {void}
       * @protected
       */
      _createReadOnlyProperty(property, protectedSetter) {
        this._addPropertyEffect(property, TYPES.READ_ONLY);
        if (protectedSetter) {
          this['_set' + upper(property)] = /** @this {PropertyEffects} */function(value) {
            this._setProperty(property, value);
          };
        }
      }

      /**
       * Equivalent to static `createPropertyObserver` API but can be called on
       * an instance to add effects at runtime.  See that method for
       * full API docs.
       *
       * @param {string} property Property name
       * @param {string|function(*,*)} method Function or name of observer method to call
       * @param {boolean=} dynamicFn Whether the method name should be included as
       *   a dependency to the effect.
       * @return {void}
       * @protected
       */
      _createPropertyObserver(property, method, dynamicFn) {
        let info = { property, method, dynamicFn: Boolean(dynamicFn) };
        this._addPropertyEffect(property, TYPES.OBSERVE, {
          fn: runObserverEffect, info, trigger: {name: property}
        });
        if (dynamicFn) {
          this._addPropertyEffect(/** @type {string} */(method), TYPES.OBSERVE, {
            fn: runObserverEffect, info, trigger: {name: method}
          });
        }
      }

      /**
       * Equivalent to static `createMethodObserver` API but can be called on
       * an instance to add effects at runtime.  See that method for
       * full API docs.
       *
       * @param {string} expression Method expression
       * @param {boolean|Object=} dynamicFn Boolean or object map indicating
       *   whether method names should be included as a dependency to the effect.
       * @return {void}
       * @protected
       */
      _createMethodObserver(expression, dynamicFn) {
        let sig = parseMethod(expression);
        if (!sig) {
          throw new Error("Malformed observer expression '" + expression + "'");
        }
        createMethodEffect(this, sig, TYPES.OBSERVE, runMethodEffect, null, dynamicFn);
      }

      /**
       * Equivalent to static `createNotifyingProperty` API but can be called on
       * an instance to add effects at runtime.  See that method for
       * full API docs.
       *
       * @param {string} property Property name
       * @return {void}
       * @protected
       */
      _createNotifyingProperty(property) {
        this._addPropertyEffect(property, TYPES.NOTIFY, {
          fn: runNotifyEffect,
          info: {
            eventName: CaseMap.camelToDashCase(property) + '-changed',
            property: property
          }
        });
      }

      /**
       * Equivalent to static `createReflectedProperty` API but can be called on
       * an instance to add effects at runtime.  See that method for
       * full API docs.
       *
       * @param {string} property Property name
       * @return {void}
       * @protected
       */
      _createReflectedProperty(property) {
        let attr = this.constructor.attributeNameForProperty(property);
        if (attr[0] === '-') {
          console.warn('Property ' + property + ' cannot be reflected to attribute ' +
            attr + ' because "-" is not a valid starting attribute name. Use a lowercase first letter for the property instead.');
        } else {
          this._addPropertyEffect(property, TYPES.REFLECT, {
            fn: runReflectEffect,
            info: {
              attrName: attr
            }
          });
        }
      }

      /**
       * Equivalent to static `createComputedProperty` API but can be called on
       * an instance to add effects at runtime.  See that method for
       * full API docs.
       *
       * @param {string} property Name of computed property to set
       * @param {string} expression Method expression
       * @param {boolean|Object=} dynamicFn Boolean or object map indicating
       *   whether method names should be included as a dependency to the effect.
       * @return {void}
       * @protected
       */
      _createComputedProperty(property, expression, dynamicFn) {
        let sig = parseMethod(expression);
        if (!sig) {
          throw new Error("Malformed computed expression '" + expression + "'");
        }
        createMethodEffect(this, sig, TYPES.COMPUTE, runComputedEffect, property, dynamicFn);
      }

      /**
       * Gather the argument values for a method specified in the provided array
       * of argument metadata.
       *
       * The `path` and `value` arguments are used to fill in wildcard descriptor
       * when the method is being called as a result of a path notification.
       *
       * @param {!Array<!MethodArg>} args Array of argument metadata
       * @param {string} path Property/path name that triggered the method effect
       * @param {Object} props Bag of current property changes
       * @return {Array<*>} Array of argument values
       * @private
       */
      _marshalArgs(args, path, props) {
        const data = this.__data;
        let values = [];
        for (let i=0, l=args.length; i<l; i++) {
          let arg = args[i];
          let name = arg.name;
          let v;
          if (arg.literal) {
            v = arg.value;
          } else {
            if (arg.structured) {
              v = Polymer.Path.get(data, name);
              // when data is not stored e.g. `splices`
              if (v === undefined) {
                v = props[name];
              }
            } else {
              v = data[name];
            }
          }
          if (arg.wildcard) {
            // Only send the actual path changed info if the change that
            // caused the observer to run matched the wildcard
            let baseChanged = (name.indexOf(path + '.') === 0);
            let matches = (path.indexOf(name) === 0 && !baseChanged);
            values[i] = {
              path: matches ? path : name,
              value: matches ? props[path] : v,
              base: v
            };
          } else {
            values[i] = v;
          }
        }
        return values;
      }

      // -- static class methods ------------

      /**
       * Ensures an accessor exists for the specified property, and adds
       * to a list of "property effects" that will run when the accessor for
       * the specified property is set.  Effects are grouped by "type", which
       * roughly corresponds to a phase in effect processing.  The effect
       * metadata should be in the following form:
       *
       *     {
       *       fn: effectFunction, // Reference to function to call to perform effect
       *       info: { ... }       // Effect metadata passed to function
       *       trigger: {          // Optional triggering metadata; if not provided
       *         name: string      // the property is treated as a wildcard
       *         structured: boolean
       *         wildcard: boolean
       *       }
       *     }
       *
       * Effects are called from `_propertiesChanged` in the following order by
       * type:
       *
       * 1. COMPUTE
       * 2. PROPAGATE
       * 3. REFLECT
       * 4. OBSERVE
       * 5. NOTIFY
       *
       * Effect functions are called with the following signature:
       *
       *     effectFunction(inst, path, props, oldProps, info, hasPaths)
       *
       * @param {string} property Property that should trigger the effect
       * @param {string} type Effect type, from this.PROPERTY_EFFECT_TYPES
       * @param {Object=} effect Effect metadata object
       * @return {void}
       * @protected
       */
      static addPropertyEffect(property, type, effect) {
        this.prototype._addPropertyEffect(property, type, effect);
      }

      /**
       * Creates a single-property observer for the given property.
       *
       * @param {string} property Property name
       * @param {string|function(*,*)} method Function or name of observer method to call
       * @param {boolean=} dynamicFn Whether the method name should be included as
       *   a dependency to the effect.
       * @return {void}
       * @protected
       */
      static createPropertyObserver(property, method, dynamicFn) {
        this.prototype._createPropertyObserver(property, method, dynamicFn);
      }

      /**
       * Creates a multi-property "method observer" based on the provided
       * expression, which should be a string in the form of a normal JavaScript
       * function signature: `'methodName(arg1, [..., argn])'`.  Each argument
       * should correspond to a property or path in the context of this
       * prototype (or instance), or may be a literal string or number.
       *
       * @param {string} expression Method expression
       * @param {boolean|Object=} dynamicFn Boolean or object map indicating
       * @return {void}
       *   whether method names should be included as a dependency to the effect.
       * @protected
       */
      static createMethodObserver(expression, dynamicFn) {
        this.prototype._createMethodObserver(expression, dynamicFn);
      }

      /**
       * Causes the setter for the given property to dispatch `<property>-changed`
       * events to notify of changes to the property.
       *
       * @param {string} property Property name
       * @return {void}
       * @protected
       */
      static createNotifyingProperty(property) {
        this.prototype._createNotifyingProperty(property);
      }

      /**
       * Creates a read-only accessor for the given property.
       *
       * To set the property, use the protected `_setProperty` API.
       * To create a custom protected setter (e.g. `_setMyProp()` for
       * property `myProp`), pass `true` for `protectedSetter`.
       *
       * Note, if the property will have other property effects, this method
       * should be called first, before adding other effects.
       *
       * @param {string} property Property name
       * @param {boolean=} protectedSetter Creates a custom protected setter
       *   when `true`.
       * @return {void}
       * @protected
       */
      static createReadOnlyProperty(property, protectedSetter) {
        this.prototype._createReadOnlyProperty(property, protectedSetter);
      }

      /**
       * Causes the setter for the given property to reflect the property value
       * to a (dash-cased) attribute of the same name.
       *
       * @param {string} property Property name
       * @return {void}
       * @protected
       */
      static createReflectedProperty(property) {
        this.prototype._createReflectedProperty(property);
      }

      /**
       * Creates a computed property whose value is set to the result of the
       * method described by the given `expression` each time one or more
       * arguments to the method changes.  The expression should be a string
       * in the form of a normal JavaScript function signature:
       * `'methodName(arg1, [..., argn])'`
       *
       * @param {string} property Name of computed property to set
       * @param {string} expression Method expression
       * @param {boolean|Object=} dynamicFn Boolean or object map indicating whether
       *   method names should be included as a dependency to the effect.
       * @return {void}
       * @protected
       */
      static createComputedProperty(property, expression, dynamicFn) {
        this.prototype._createComputedProperty(property, expression, dynamicFn);
      }

      /**
       * Parses the provided template to ensure binding effects are created
       * for them, and then ensures property accessors are created for any
       * dependent properties in the template.  Binding effects for bound
       * templates are stored in a linked list on the instance so that
       * templates can be efficiently stamped and unstamped.
       *
       * @param {!HTMLTemplateElement} template Template containing binding
       *   bindings
       * @return {!TemplateInfo} Template metadata object
       * @protected
       */
      static bindTemplate(template) {
        return this.prototype._bindTemplate(template);
      }

      // -- binding ----------------------------------------------

      /**
       * Equivalent to static `bindTemplate` API but can be called on
       * an instance to add effects at runtime.  See that method for
       * full API docs.
       *
       * This method may be called on the prototype (for prototypical template
       * binding, to avoid creating accessors every instance) once per prototype,
       * and will be called with `runtimeBinding: true` by `_stampTemplate` to
       * create and link an instance of the template metadata associated with a
       * particular stamping.
       *
       * @param {!HTMLTemplateElement} template Template containing binding
       *   bindings
       * @param {boolean=} instanceBinding When false (default), performs
       *   "prototypical" binding of the template and overwrites any previously
       *   bound template for the class. When true (as passed from
       *   `_stampTemplate`), the template info is instanced and linked into
       *   the list of bound templates.
       * @return {!TemplateInfo} Template metadata object; for `runtimeBinding`,
       *   this is an instance of the prototypical template info
       * @protected
       */
      _bindTemplate(template, instanceBinding) {
        let templateInfo = this.constructor._parseTemplate(template);
        let wasPreBound = this.__templateInfo == templateInfo;
        // Optimization: since this is called twice for proto-bound templates,
        // don't attempt to recreate accessors if this template was pre-bound
        if (!wasPreBound) {
          for (let prop in templateInfo.propertyEffects) {
            this._createPropertyAccessor(prop);
          }
        }
        if (instanceBinding) {
          // For instance-time binding, create instance of template metadata
          // and link into list of templates if necessary
          templateInfo = /** @type {!TemplateInfo} */(Object.create(templateInfo));
          templateInfo.wasPreBound = wasPreBound;
          if (!wasPreBound && this.__templateInfo) {
            let last = this.__templateInfoLast || this.__templateInfo;
            this.__templateInfoLast = last.nextTemplateInfo = templateInfo;
            templateInfo.previousTemplateInfo = last;
            return templateInfo;
          }
        }
        return this.__templateInfo = templateInfo;
      }

      /**
       * Adds a property effect to the given template metadata, which is run
       * at the "propagate" stage of `_propertiesChanged` when the template
       * has been bound to the element via `_bindTemplate`.
       *
       * The `effect` object should match the format in `_addPropertyEffect`.
       *
       * @param {Object} templateInfo Template metadata to add effect to
       * @param {string} prop Property that should trigger the effect
       * @param {Object=} effect Effect metadata object
       * @return {void}
       * @protected
       */
      static _addTemplatePropertyEffect(templateInfo, prop, effect) {
        let hostProps = templateInfo.hostProps = templateInfo.hostProps || {};
        hostProps[prop] = true;
        let effects = templateInfo.propertyEffects = templateInfo.propertyEffects || {};
        let propEffects = effects[prop] = effects[prop] || [];
        propEffects.push(effect);
      }

      /**
       * Stamps the provided template and performs instance-time setup for
       * Polymer template features, including data bindings, declarative event
       * listeners, and the `this.$` map of `id`'s to nodes.  A document fragment
       * is returned containing the stamped DOM, ready for insertion into the
       * DOM.
       *
       * This method may be called more than once; however note that due to
       * `shadycss` polyfill limitations, only styles from templates prepared
       * using `ShadyCSS.prepareTemplate` will be correctly polyfilled (scoped
       * to the shadow root and support CSS custom properties), and note that
       * `ShadyCSS.prepareTemplate` may only be called once per element. As such,
       * any styles required by in runtime-stamped templates must be included
       * in the main element template.
       *
       * @param {!HTMLTemplateElement} template Template to stamp
       * @return {!StampedTemplate} Cloned template content
       * @override
       * @protected
       */
      _stampTemplate(template) {
        // Ensures that created dom is `_enqueueClient`'d to this element so
        // that it can be flushed on next call to `_flushProperties`
        hostStack.beginHosting(this);
        let dom = super._stampTemplate(template);
        hostStack.endHosting(this);
        let templateInfo = /** @type {!TemplateInfo} */(this._bindTemplate(template, true));
        // Add template-instance-specific data to instanced templateInfo
        templateInfo.nodeList = dom.nodeList;
        // Capture child nodes to allow unstamping of non-prototypical templates
        if (!templateInfo.wasPreBound) {
          let nodes = templateInfo.childNodes = [];
          for (let n=dom.firstChild; n; n=n.nextSibling) {
            nodes.push(n);
          }
        }
        dom.templateInfo = templateInfo;
        // Setup compound storage, 2-way listeners, and dataHost for bindings
        setupBindings(this, templateInfo);
        // Flush properties into template nodes if already booted
        if (this.__dataReady) {
          runEffects(this, templateInfo.propertyEffects, this.__data, null,
            false, templateInfo.nodeList);
        }
        return dom;
      }

      /**
       * Removes and unbinds the nodes previously contained in the provided
       * DocumentFragment returned from `_stampTemplate`.
       *
       * @param {!StampedTemplate} dom DocumentFragment previously returned
       *   from `_stampTemplate` associated with the nodes to be removed
       * @return {void}
       * @protected
       */
      _removeBoundDom(dom) {
        // Unlink template info
        let templateInfo = dom.templateInfo;
        if (templateInfo.previousTemplateInfo) {
          templateInfo.previousTemplateInfo.nextTemplateInfo =
            templateInfo.nextTemplateInfo;
        }
        if (templateInfo.nextTemplateInfo) {
          templateInfo.nextTemplateInfo.previousTemplateInfo =
            templateInfo.previousTemplateInfo;
        }
        if (this.__templateInfoLast == templateInfo) {
          this.__templateInfoLast = templateInfo.previousTemplateInfo;
        }
        templateInfo.previousTemplateInfo = templateInfo.nextTemplateInfo = null;
        // Remove stamped nodes
        let nodes = templateInfo.childNodes;
        for (let i=0; i<nodes.length; i++) {
          let node = nodes[i];
          node.parentNode.removeChild(node);
        }
      }

      /**
       * Overrides default `TemplateStamp` implementation to add support for
       * parsing bindings from `TextNode`'s' `textContent`.  A `bindings`
       * array is added to `nodeInfo` and populated with binding metadata
       * with information capturing the binding target, and a `parts` array
       * with one or more metadata objects capturing the source(s) of the
       * binding.
       *
       * @override
       * @param {Node} node Node to parse
       * @param {TemplateInfo} templateInfo Template metadata for current template
       * @param {NodeInfo} nodeInfo Node metadata for current template node
       * @return {boolean} `true` if the visited node added node-specific
       *   metadata to `nodeInfo`
       * @protected
       * @suppress {missingProperties} Interfaces in closure do not inherit statics, but classes do
       */
      static _parseTemplateNode(node, templateInfo, nodeInfo) {
        let noted = super._parseTemplateNode(node, templateInfo, nodeInfo);
        if (node.nodeType === Node.TEXT_NODE) {
          let parts = this._parseBindings(node.textContent, templateInfo);
          if (parts) {
            // Initialize the textContent with any literal parts
            // NOTE: default to a space here so the textNode remains; some browsers
            // (IE) omit an empty textNode following cloneNode/importNode.
            node.textContent = literalFromParts(parts) || ' ';
            addBinding(this, templateInfo, nodeInfo, 'text', 'textContent', parts);
            noted = true;
          }
        }
        return noted;
      }

      /**
       * Overrides default `TemplateStamp` implementation to add support for
       * parsing bindings from attributes.  A `bindings`
       * array is added to `nodeInfo` and populated with binding metadata
       * with information capturing the binding target, and a `parts` array
       * with one or more metadata objects capturing the source(s) of the
       * binding.
       *
       * @override
       * @param {Element} node Node to parse
       * @param {TemplateInfo} templateInfo Template metadata for current template
       * @param {NodeInfo} nodeInfo Node metadata for current template node
       * @param {string} name Attribute name
       * @param {string} value Attribute value
       * @return {boolean} `true` if the visited node added node-specific
       *   metadata to `nodeInfo`
       * @protected
       * @suppress {missingProperties} Interfaces in closure do not inherit statics, but classes do
       */
      static _parseTemplateNodeAttribute(node, templateInfo, nodeInfo, name, value) {
        let parts = this._parseBindings(value, templateInfo);
        if (parts) {
          // Attribute or property
          let origName = name;
          let kind = 'property';
          // The only way we see a capital letter here is if the attr has
          // a capital letter in it per spec. In this case, to make sure
          // this binding works, we go ahead and make the binding to the attribute.
          if (capitalAttributeRegex.test(name)) {
            kind = 'attribute';
          } else if (name[name.length-1] == '$') {
            name = name.slice(0, -1);
            kind = 'attribute';
          }
          // Initialize attribute bindings with any literal parts
          let literal = literalFromParts(parts);
          if (literal && kind == 'attribute') {
            // Ensure a ShadyCSS template scoped style is not removed
            // when a class$ binding's initial literal value is set.
            if (name == 'class' && node.hasAttribute('class')) {
              literal += ' ' + node.getAttribute(name);
            }
            node.setAttribute(name, literal);
          }
          // Clear attribute before removing, since IE won't allow removing
          // `value` attribute if it previously had a value (can't
          // unconditionally set '' before removing since attributes with `$`
          // can't be set using setAttribute)
          if (node.localName === 'input' && origName === 'value') {
            node.setAttribute(origName, '');
          }
          // Remove annotation
          node.removeAttribute(origName);
          // Case hackery: attributes are lower-case, but bind targets
          // (properties) are case sensitive. Gambit is to map dash-case to
          // camel-case: `foo-bar` becomes `fooBar`.
          // Attribute bindings are excepted.
          if (kind === 'property') {
            name = Polymer.CaseMap.dashToCamelCase(name);
          }
          addBinding(this, templateInfo, nodeInfo, kind, name, parts, literal);
          return true;
        } else {
          return super._parseTemplateNodeAttribute(node, templateInfo, nodeInfo, name, value);
        }
      }

      /**
       * Overrides default `TemplateStamp` implementation to add support for
       * binding the properties that a nested template depends on to the template
       * as `_host_<property>`.
       *
       * @override
       * @param {Node} node Node to parse
       * @param {TemplateInfo} templateInfo Template metadata for current template
       * @param {NodeInfo} nodeInfo Node metadata for current template node
       * @return {boolean} `true` if the visited node added node-specific
       *   metadata to `nodeInfo`
       * @protected
       * @suppress {missingProperties} Interfaces in closure do not inherit statics, but classes do
       */
      static _parseTemplateNestedTemplate(node, templateInfo, nodeInfo) {
        let noted = super._parseTemplateNestedTemplate(node, templateInfo, nodeInfo);
        // Merge host props into outer template and add bindings
        let hostProps = nodeInfo.templateInfo.hostProps;
        let mode = '{';
        for (let source in hostProps) {
          let parts = [{ mode, source, dependencies: [source] }];
          addBinding(this, templateInfo, nodeInfo, 'property', '_host_' + source, parts);
        }
        return noted;
      }

      /**
       * Called to parse text in a template (either attribute values or
       * textContent) into binding metadata.
       *
       * Any overrides of this method should return an array of binding part
       * metadata  representing one or more bindings found in the provided text
       * and any "literal" text in between.  Any non-literal parts will be passed
       * to `_evaluateBinding` when any dependencies change.  The only required
       * fields of each "part" in the returned array are as follows:
       *
       * - `dependencies` - Array containing trigger metadata for each property
       *   that should trigger the binding to update
       * - `literal` - String containing text if the part represents a literal;
       *   in this case no `dependencies` are needed
       *
       * Additional metadata for use by `_evaluateBinding` may be provided in
       * each part object as needed.
       *
       * The default implementation handles the following types of bindings
       * (one or more may be intermixed with literal strings):
       * - Property binding: `[[prop]]`
       * - Path binding: `[[object.prop]]`
       * - Negated property or path bindings: `[[!prop]]` or `[[!object.prop]]`
       * - Two-way property or path bindings (supports negation):
       *   `{{prop}}`, `{{object.prop}}`, `{{!prop}}` or `{{!object.prop}}`
       * - Inline computed method (supports negation):
       *   `[[compute(a, 'literal', b)]]`, `[[!compute(a, 'literal', b)]]`
       *
       * The default implementation uses a regular expression for best
       * performance. However, the regular expression uses a white-list of
       * allowed characters in a data-binding, which causes problems for
       * data-bindings that do use characters not in this white-list.
       *
       * Instead of updating the white-list with all allowed characters,
       * there is a StrictBindingParser (see lib/mixins/strict-binding-parser)
       * that uses a state machine instead. This state machine is able to handle
       * all characters. However, it is slightly less performant, therefore we
       * extracted it into a separate optional mixin.
       *
       * @param {string} text Text to parse from attribute or textContent
       * @param {Object} templateInfo Current template metadata
       * @return {Array<!BindingPart>} Array of binding part metadata
       * @protected
       */
      static _parseBindings(text, templateInfo) {
        let parts = [];
        let lastIndex = 0;
        let m;
        // Example: "literal1{{prop}}literal2[[!compute(foo,bar)]]final"
        // Regex matches:
        //        Iteration 1:  Iteration 2:
        // m[1]: '{{'          '[['
        // m[2]: ''            '!'
        // m[3]: 'prop'        'compute(foo,bar)'
        while ((m = bindingRegex.exec(text)) !== null) {
          // Add literal part
          if (m.index > lastIndex) {
            parts.push({literal: text.slice(lastIndex, m.index)});
          }
          // Add binding part
          let mode = m[1][0];
          let negate = Boolean(m[2]);
          let source = m[3].trim();
          let customEvent = false, notifyEvent = '', colon = -1;
          if (mode == '{' && (colon = source.indexOf('::')) > 0) {
            notifyEvent = source.substring(colon + 2);
            source = source.substring(0, colon);
            customEvent = true;
          }
          let signature = parseMethod(source);
          let dependencies = [];
          if (signature) {
            // Inline computed function
            let {args, methodName} = signature;
            for (let i=0; i<args.length; i++) {
              let arg = args[i];
              if (!arg.literal) {
                dependencies.push(arg);
              }
            }
            let dynamicFns = templateInfo.dynamicFns;
            if (dynamicFns && dynamicFns[methodName] || signature.static) {
              dependencies.push(methodName);
              signature.dynamicFn = true;
            }
          } else {
            // Property or path
            dependencies.push(source);
          }
          parts.push({
            source, mode, negate, customEvent, signature, dependencies,
            event: notifyEvent
          });
          lastIndex = bindingRegex.lastIndex;
        }
        // Add a final literal part
        if (lastIndex && lastIndex < text.length) {
          let literal = text.substring(lastIndex);
          if (literal) {
            parts.push({
              literal: literal
            });
          }
        }
        if (parts.length) {
          return parts;
        } else {
          return null;
        }
      }

      /**
       * Called to evaluate a previously parsed binding part based on a set of
       * one or more changed dependencies.
       *
       * @param {this} inst Element that should be used as scope for
       *   binding dependencies
       * @param {BindingPart} part Binding part metadata
       * @param {string} path Property/path that triggered this effect
       * @param {Object} props Bag of current property changes
       * @param {Object} oldProps Bag of previous values for changed properties
       * @param {boolean} hasPaths True with `props` contains one or more paths
       * @return {*} Value the binding part evaluated to
       * @protected
       */
      static _evaluateBinding(inst, part, path, props, oldProps, hasPaths) {
        let value;
        if (part.signature) {
          value = runMethodEffect(inst, path, props, oldProps, part.signature);
        } else if (path != part.source) {
          value = Polymer.Path.get(inst, part.source);
        } else {
          if (hasPaths && Polymer.Path.isPath(path)) {
            value = Polymer.Path.get(inst, path);
          } else {
            value = inst.__data[path];
          }
        }
        if (part.negate) {
          value = !value;
        }
        return value;
      }

    }

    // make a typing for closure :P
    PropertyEffectsType = PropertyEffects;

    return PropertyEffects;
  });

  /**
   * Helper api for enqueuing client dom created by a host element.
   *
   * By default elements are flushed via `_flushProperties` when
   * `connectedCallback` is called. Elements attach their client dom to
   * themselves at `ready` time which results from this first flush.
   * This provides an ordering guarantee that the client dom an element
   * creates is flushed before the element itself (i.e. client `ready`
   * fires before host `ready`).
   *
   * However, if `_flushProperties` is called *before* an element is connected,
   * as for example `Templatize` does, this ordering guarantee cannot be
   * satisfied because no elements are connected. (Note: Bound elements that
   * receive data do become enqueued clients and are properly ordered but
   * unbound elements are not.)
   *
   * To maintain the desired "client before host" ordering guarantee for this
   * case we rely on the "host stack. Client nodes registers themselves with
   * the creating host element when created. This ensures that all client dom
   * is readied in the proper order, maintaining the desired guarantee.
   *
   * @private
   */
  let hostStack = {

    stack: [],

    /**
     * @param {*} inst Instance to add to hostStack
     * @return {void}
     * @this {hostStack}
     */
    registerHost(inst) {
      if (this.stack.length) {
        let host = this.stack[this.stack.length-1];
        host._enqueueClient(inst);
      }
    },

    /**
     * @param {*} inst Instance to begin hosting
     * @return {void}
     * @this {hostStack}
     */
    beginHosting(inst) {
      this.stack.push(inst);
    },

    /**
     * @param {*} inst Instance to end hosting
     * @return {void}
     * @this {hostStack}
     */
    endHosting(inst) {
      let stackLen = this.stack.length;
      if (stackLen && this.stack[stackLen-1] == inst) {
        this.stack.pop();
      }
    }

  };

})();


(function() {
  'use strict';

  /**
   * Provides basic tracking of element definitions (registrations) and
   * instance counts.
   *
   * @namespace
   * @summary Provides basic tracking of element definitions (registrations) and
   * instance counts.
   */
  Polymer.telemetry = {
    /**
     * Total number of Polymer element instances created.
     * @type {number}
     */
    instanceCount: 0,
    /**
     * Array of Polymer element classes that have been finalized.
     * @type {Array<Polymer.Element>}
     */
    registrations: [],
    /**
     * @param {!PolymerElementConstructor} prototype Element prototype to log
     * @this {this}
     * @private
     */
    _regLog: function(prototype) {
      console.log('[' + prototype.is + ']: registered');
    },
    /**
     * Registers a class prototype for telemetry purposes.
     * @param {HTMLElement} prototype Element prototype to register
     * @this {this}
     * @protected
     */
    register: function(prototype) {
      this.registrations.push(prototype);
      Polymer.log && this._regLog(prototype);
    },
    /**
     * Logs all elements registered with an `is` to the console.
     * @public
     * @this {this}
     */
    dumpRegistrations: function() {
      this.registrations.forEach(this._regLog);
    }
  };

})();


(function() {
  'use strict';

  /**
   * Creates a copy of `props` with each property normalized such that
   * upgraded it is an object with at least a type property { type: Type}.
   *
   * @param {Object} props Properties to normalize
   * @return {Object} Copy of input `props` with normalized properties that
   * are in the form {type: Type}
   * @private
   */
  function normalizeProperties(props) {
    const output = {};
    for (let p in props) {
      const o = props[p];
      output[p] = (typeof o === 'function') ? {type: o} : o;
    }
    return output;
  }

  /**
   * Mixin that provides a minimal starting point to using the PropertiesChanged
   * mixin by providing a mechanism to declare properties in a static
   * getter (e.g. static get properties() { return { foo: String } }). Changes
   * are reported via the `_propertiesChanged` method.
   *
   * This mixin provides no specific support for rendering. Users are expected
   * to create a ShadowRoot and put content into it and update it in whatever
   * way makes sense. This can be done in reaction to properties changing by
   * implementing `_propertiesChanged`.
   *
   * @mixinFunction
   * @polymer
   * @appliesMixin Polymer.PropertiesChanged
   * @memberof Polymer
   * @summary Mixin that provides a minimal starting point for using
   * the PropertiesChanged mixin by providing a declarative `properties` object.
   */
   Polymer.PropertiesMixin = Polymer.dedupingMixin(superClass => {

    /**
     * @constructor
     * @extends {superClass}
     * @implements {Polymer_PropertiesChanged}
     * @private
     */
    const base = Polymer.PropertiesChanged(superClass);

    /**
     * Returns the super class constructor for the given class, if it is an
     * instance of the PropertiesMixin.
     *
     * @param {!PropertiesMixinConstructor} constructor PropertiesMixin constructor
     * @return {PropertiesMixinConstructor} Super class constructor
     */
    function superPropertiesClass(constructor) {
      const superCtor = Object.getPrototypeOf(constructor);

      // Note, the `PropertiesMixin` class below only refers to the class
      // generated by this call to the mixin; the instanceof test only works
      // because the mixin is deduped and guaranteed only to apply once, hence
      // all constructors in a proto chain will see the same `PropertiesMixin`
      return (superCtor.prototype instanceof PropertiesMixin) ?
        /** @type {PropertiesMixinConstructor} */ (superCtor) : null;
    }

    /**
     * Returns a memoized version of the `properties` object for the
     * given class. Properties not in object format are converted to at
     * least {type}.
     *
     * @param {PropertiesMixinConstructor} constructor PropertiesMixin constructor
     * @return {Object} Memoized properties object
     */
    function ownProperties(constructor) {
      if (!constructor.hasOwnProperty(JSCompiler_renameProperty('__ownProperties', constructor))) {
        let props = null;

        if (constructor.hasOwnProperty(JSCompiler_renameProperty('properties', constructor))) {
          const properties = constructor.properties;
          
          if (properties) {
            props = normalizeProperties(properties);
          }
        }

        constructor.__ownProperties = props;
      }
      return constructor.__ownProperties;
    }

    /**
     * @polymer
     * @mixinClass
     * @extends {base}
     * @implements {Polymer_PropertiesMixin}
     * @unrestricted
     */
    class PropertiesMixin extends base {

      /**
       * Implements standard custom elements getter to observes the attributes
       * listed in `properties`.
       * @suppress {missingProperties} Interfaces in closure do not inherit statics, but classes do
       */
      static get observedAttributes() {
        if (!this.hasOwnProperty('__observedAttributes')) {
          Polymer.telemetry.register(this.prototype);
          const props = this._properties;
          this.__observedAttributes = props ? Object.keys(props).map(p => this.attributeNameForProperty(p)) : [];
        }
        return this.__observedAttributes;
      }

      /**
       * Finalizes an element definition, including ensuring any super classes
       * are also finalized. This includes ensuring property
       * accessors exist on the element prototype. This method calls
       * `_finalizeClass` to finalize each constructor in the prototype chain.
       * @return {void}
       */
      static finalize() {
        if (!this.hasOwnProperty(JSCompiler_renameProperty('__finalized', this))) {
          const superCtor = superPropertiesClass(/** @type {PropertiesMixinConstructor} */(this));
          if (superCtor) {
            superCtor.finalize();
          }
          this.__finalized = true;
          this._finalizeClass();
        }
      }

      /**
       * Finalize an element class. This includes ensuring property
       * accessors exist on the element prototype. This method is called by
       * `finalize` and finalizes the class constructor.
       *
       * @protected
       */
      static _finalizeClass() {
        const props = ownProperties(/** @type {PropertiesMixinConstructor} */(this));
        if (props) {
          this.createProperties(props);
        }
      }

      /**
       * Returns a memoized version of all properties, including those inherited
       * from super classes. Properties not in object format are converted to
       * at least {type}.
       *
       * @return {Object} Object containing properties for this class
       * @protected
       */
      static get _properties() {
        if (!this.hasOwnProperty(
          JSCompiler_renameProperty('__properties', this))) {
          const superCtor = superPropertiesClass(/** @type {PropertiesMixinConstructor} */(this));
          this.__properties = Object.assign({},
            superCtor && superCtor._properties,
            ownProperties(/** @type {PropertiesMixinConstructor} */(this)));
        }
        return this.__properties;
      }

      /**
       * Overrides `PropertiesChanged` method to return type specified in the
       * static `properties` object for the given property.
       * @param {string} name Name of property
       * @return {*} Type to which to deserialize attribute
       *
       * @protected
       */
      static typeForProperty(name) {
        const info = this._properties[name];
        return info && info.type;
      }

      /**
       * Overrides `PropertiesChanged` method and adds a call to
       * `finalize` which lazily configures the element's property accessors.
       * @override
       * @return {void}
       */
      _initializeProperties() {
        Polymer.telemetry.instanceCount++;
        this.constructor.finalize();
        super._initializeProperties();
      }

      /**
       * Called when the element is added to a document.
       * Calls `_enableProperties` to turn on property system from
       * `PropertiesChanged`.
       * @suppress {missingProperties} Super may or may not implement the callback
       * @return {void}
       */
      connectedCallback() {
        if (super.connectedCallback) {
          super.connectedCallback();
        }
        this._enableProperties();
      }

      /**
       * Called when the element is removed from a document
       * @suppress {missingProperties} Super may or may not implement the callback
       * @return {void}
       */
      disconnectedCallback() {
        if (super.disconnectedCallback) {
          super.disconnectedCallback();
        }
      }

    }

    return PropertiesMixin;

  });

})();



(function() {
  'use strict';

  const builtCSS = window.ShadyCSS && window.ShadyCSS['cssBuild'];

  /**
   * Element class mixin that provides the core API for Polymer's meta-programming
   * features including template stamping, data-binding, attribute deserialization,
   * and property change observation.
   *
   * Subclassers may provide the following static getters to return metadata
   * used to configure Polymer's features for the class:
   *
   * - `static get is()`: When the template is provided via a `dom-module`,
   *   users should return the `dom-module` id from a static `is` getter.  If
   *   no template is needed or the template is provided directly via the
   *   `template` getter, there is no need to define `is` for the element.
   *
   * - `static get template()`: Users may provide the template directly (as
   *   opposed to via `dom-module`) by implementing a static `template` getter.
   *   The getter may return an `HTMLTemplateElement` or a string, which will
   *   automatically be parsed into a template.
   *
   * - `static get properties()`: Should return an object describing
   *   property-related metadata used by Polymer features (key: property name
   *   value: object containing property metadata). Valid keys in per-property
   *   metadata include:
   *   - `type` (String|Number|Object|Array|...): Used by
   *     `attributeChangedCallback` to determine how string-based attributes
   *     are deserialized to JavaScript property values.
   *   - `notify` (boolean): Causes a change in the property to fire a
   *     non-bubbling event called `<property>-changed`. Elements that have
   *     enabled two-way binding to the property use this event to observe changes.
   *   - `readOnly` (boolean): Creates a getter for the property, but no setter.
   *     To set a read-only property, use the private setter method
   *     `_setProperty(property, value)`.
   *   - `observer` (string): Observer method name that will be called when
   *     the property changes. The arguments of the method are
   *     `(value, previousValue)`.
   *   - `computed` (string): String describing method and dependent properties
   *     for computing the value of this property (e.g. `'computeFoo(bar, zot)'`).
   *     Computed properties are read-only by default and can only be changed
   *     via the return value of the computing method.
   *
   * - `static get observers()`: Array of strings describing multi-property
   *   observer methods and their dependent properties (e.g.
   *   `'observeABC(a, b, c)'`).
   *
   * The base class provides default implementations for the following standard
   * custom element lifecycle callbacks; users may override these, but should
   * call the super method to ensure
   * - `constructor`: Run when the element is created or upgraded
   * - `connectedCallback`: Run each time the element is connected to the
   *   document
   * - `disconnectedCallback`: Run each time the element is disconnected from
   *   the document
   * - `attributeChangedCallback`: Run each time an attribute in
   *   `observedAttributes` is set or removed (note: this element's default
   *   `observedAttributes` implementation will automatically return an array
   *   of dash-cased attributes based on `properties`)
   *
   * @mixinFunction
   * @polymer
   * @appliesMixin Polymer.PropertyEffects
   * @appliesMixin Polymer.PropertiesMixin
   * @memberof Polymer
   * @property rootPath {string} Set to the value of `Polymer.rootPath`,
   *   which defaults to the main document path
   * @property importPath {string} Set to the value of the class's static
   *   `importPath` property, which defaults to the path of this element's
   *   `dom-module` (when `is` is used), but can be overridden for other
   *   import strategies.
   * @summary Element class mixin that provides the core API for Polymer's
   * meta-programming features.
   */
  Polymer.ElementMixin = Polymer.dedupingMixin(base => {

    /**
     * @constructor
     * @extends {base}
     * @implements {Polymer_PropertyEffects}
     * @implements {Polymer_PropertiesMixin}
     * @private
     */
    const polymerElementBase = Polymer.PropertiesMixin(Polymer.PropertyEffects(base));

    /**
     * Returns a list of properties with default values.
     * This list is created as an optimization since it is a subset of
     * the list returned from `_properties`.
     * This list is used in `_initializeProperties` to set property defaults.
     *
     * @param {PolymerElementConstructor} constructor Element class
     * @return {PolymerElementProperties} Flattened properties for this class
     *   that have default values
     * @private
     */
    function propertyDefaults(constructor) {
      if (!constructor.hasOwnProperty(
        JSCompiler_renameProperty('__propertyDefaults', constructor))) {
        constructor.__propertyDefaults = null;
        let props = constructor._properties;
        for (let p in props) {
          let info = props[p];
          if ('value' in info) {
            constructor.__propertyDefaults = constructor.__propertyDefaults || {};
            constructor.__propertyDefaults[p] = info;
          }
        }
      }
      return constructor.__propertyDefaults;
    }

    /**
     * Returns a memoized version of the `observers` array.
     * @param {PolymerElementConstructor} constructor Element class
     * @return {Array} Array containing own observers for the given class
     * @protected
     */
    function ownObservers(constructor) {
      if (!constructor.hasOwnProperty(
        JSCompiler_renameProperty('__ownObservers', constructor))) {
          constructor.__ownObservers =
          constructor.hasOwnProperty(JSCompiler_renameProperty('observers', constructor)) ?
          /** @type {PolymerElementConstructor} */ (constructor).observers : null;
      }
      return constructor.__ownObservers;
    }

    /**
     * Creates effects for a property.
     *
     * Note, once a property has been set to
     * `readOnly`, `computed`, `reflectToAttribute`, or `notify`
     * these values may not be changed. For example, a subclass cannot
     * alter these settings. However, additional `observers` may be added
     * by subclasses.
     *
     * The info object should contain property metadata as follows:
     *
     * * `type`: {function} type to which an attribute matching the property
     * is deserialized. Note the property is camel-cased from a dash-cased
     * attribute. For example, 'foo-bar' attribute is deserialized to a
     * property named 'fooBar'.
     *
     * * `readOnly`: {boolean} creates a readOnly property and
     * makes a private setter for the private of the form '_setFoo' for a
     * property 'foo',
     *
     * * `computed`: {string} creates a computed property. A computed property
     * is also automatically set to `readOnly: true`. The value is calculated
     * by running a method and arguments parsed from the given string. For
     * example 'compute(foo)' will compute a given property when the
     * 'foo' property changes by executing the 'compute' method. This method
     * must return the computed value.
     *
     * * `reflectToAttribute`: {boolean} If true, the property value is reflected
     * to an attribute of the same name. Note, the attribute is dash-cased
     * so a property named 'fooBar' is reflected as 'foo-bar'.
     *
     * * `notify`: {boolean} sends a non-bubbling notification event when
     * the property changes. For example, a property named 'foo' sends an
     * event named 'foo-changed' with `event.detail` set to the value of
     * the property.
     *
     * * observer: {string} name of a method that runs when the property
     * changes. The arguments of the method are (value, previousValue).
     *
     * Note: Users may want control over modifying property
     * effects via subclassing. For example, a user might want to make a
     * reflectToAttribute property not do so in a subclass. We've chosen to
     * disable this because it leads to additional complication.
     * For example, a readOnly effect generates a special setter. If a subclass
     * disables the effect, the setter would fail unexpectedly.
     * Based on feedback, we may want to try to make effects more malleable
     * and/or provide an advanced api for manipulating them.
     * Also consider adding warnings when an effect cannot be changed.
     *
     * @param {!PolymerElement} proto Element class prototype to add accessors
     *   and effects to
     * @param {string} name Name of the property.
     * @param {Object} info Info object from which to create property effects.
     * Supported keys:
     * @param {Object} allProps Flattened map of all properties defined in this
     *   element (including inherited properties)
     * @return {void}
     * @private
     */
    function createPropertyFromConfig(proto, name, info, allProps) {
      // computed forces readOnly...
      if (info.computed) {
        info.readOnly = true;
      }
      // Note, since all computed properties are readOnly, this prevents
      // adding additional computed property effects (which leads to a confusing
      // setup where multiple triggers for setting a property)
      // While we do have `hasComputedEffect` this is set on the property's
      // dependencies rather than itself.
      if (info.computed && !proto._hasReadOnlyEffect(name)) {
        proto._createComputedProperty(name, info.computed, allProps);
      }
      if (info.readOnly && !proto._hasReadOnlyEffect(name)) {
        proto._createReadOnlyProperty(name, !info.computed);
      }
      if (info.reflectToAttribute && !proto._hasReflectEffect(name)) {
        proto._createReflectedProperty(name);
      }
      if (info.notify && !proto._hasNotifyEffect(name)) {
        proto._createNotifyingProperty(name);
      }
      // always add observer
      if (info.observer) {
        proto._createPropertyObserver(name, info.observer, allProps[info.observer]);
      }
      // always create the mapping from attribute back to property for deserialization.
      proto._addPropertyToAttributeMap(name);
    }

    /**
     * Process all style elements in the element template. Styles with the
     * `include` attribute are processed such that any styles in
     * the associated "style modules" are included in the element template.
     * @param {PolymerElementConstructor} klass Element class
     * @param {!HTMLTemplateElement} template Template to process
     * @param {string} is Name of element
     * @param {string} baseURI Base URI for element
     * @private
     */
    function processElementStyles(klass, template, is, baseURI) {
      if (!builtCSS) {
        const templateStyles = template.content.querySelectorAll('style');
        const stylesWithImports = Polymer.StyleGather.stylesFromTemplate(template);
        // insert styles from <link rel="import" type="css"> at the top of the template
        const linkedStyles = Polymer.StyleGather.stylesFromModuleImports(is);
        const firstTemplateChild = template.content.firstElementChild;
        for (let idx = 0; idx < linkedStyles.length; idx++) {
          let s = linkedStyles[idx];
          s.textContent = klass._processStyleText(s.textContent, baseURI);
          template.content.insertBefore(s, firstTemplateChild);
        }
        // keep track of the last "concrete" style in the template we have encountered
        let templateStyleIndex = 0;
        // ensure all gathered styles are actually in this template.
        for (let i = 0; i < stylesWithImports.length; i++) {
          let s = stylesWithImports[i];
          let templateStyle = templateStyles[templateStyleIndex];
          // if the style is not in this template, it's been "included" and
          // we put a clone of it in the template before the style that included it
          if (templateStyle !== s) {
            s = s.cloneNode(true);
            templateStyle.parentNode.insertBefore(s, templateStyle);
          } else {
            templateStyleIndex++;
          }
          s.textContent = klass._processStyleText(s.textContent, baseURI);
        }
      }
      if (window.ShadyCSS) {
        window.ShadyCSS.prepareTemplate(template, is);
      }
    }

    /**
     * Look up template from dom-module for element
     *
     * @param {!string} is Element name to look up
     * @return {!HTMLTemplateElement} Template found in dom module, or
     *   undefined if not found
     * @protected
     */
    function getTemplateFromDomModule(is) {
      let template = null;
      if (is && Polymer.DomModule) {
        template = Polymer.DomModule.import(is, 'template');
        // Under strictTemplatePolicy, require any element with an `is`
        // specified to have a dom-module
        if (Polymer.strictTemplatePolicy && !template) {
          throw new Error(`strictTemplatePolicy: expecting dom-module or null template for ${is}`);
        }
      }
      return template;
    }

  /**
     * @polymer
     * @mixinClass
     * @unrestricted
     * @implements {Polymer_ElementMixin}
     */
    class PolymerElement extends polymerElementBase {

      /**
       * Override of PropertiesMixin _finalizeClass to create observers and
       * find the template.
       * @return {void}
       * @protected
       * @override
       * @suppress {missingProperties} Interfaces in closure do not inherit statics, but classes do
       */
      static _finalizeClass() {
        super._finalizeClass();
        const observers = ownObservers(this);
        if (observers) {
          this.createObservers(observers, this._properties);
        }
        this._prepareTemplate();
      }

      static _prepareTemplate() {
        // note: create "working" template that is finalized at instance time
        let template = /** @type {PolymerElementConstructor} */ (this).template;
        if (template) {
          if (typeof template === 'string') {
            let t = document.createElement('template');
            t.innerHTML = template;
            template = t;
          } else if (!Polymer.legacyOptimizations) {
             template = template.cloneNode(true);
          }
        }

        this.prototype._template = template;
      }

      /**
       * Override of PropertiesChanged createProperties to create accessors
       * and property effects for all of the properties.
       * @return {void}
       * @protected
       * @override
       */
      static createProperties(props) {
        for (let p in props) {
          createPropertyFromConfig(this.prototype, p, props[p], props);
        }
      }

      /**
       * Creates observers for the given `observers` array.
       * Leverages `PropertyEffects` to create observers.
       * @param {Object} observers Array of observer descriptors for
       *   this class
       * @param {Object} dynamicFns Object containing keys for any properties
       *   that are functions and should trigger the effect when the function
       *   reference is changed
       * @return {void}
       * @protected
       */
      static createObservers(observers, dynamicFns) {
        const proto = this.prototype;
        for (let i=0; i < observers.length; i++) {
          proto._createMethodObserver(observers[i], dynamicFns);
        }
      }

      /**
       * Returns the template that will be stamped into this element's shadow root.
       *
       * If a `static get is()` getter is defined, the default implementation
       * will return the first `<template>` in a `dom-module` whose `id`
       * matches this element's `is`.
       *
       * Users may override this getter to return an arbitrary template
       * (in which case the `is` getter is unnecessary). The template returned
       * may be either an `HTMLTemplateElement` or a string that will be
       * automatically parsed into a template.
       *
       * Note that when subclassing, if the super class overrode the default
       * implementation and the subclass would like to provide an alternate
       * template via a `dom-module`, it should override this getter and
       * return `Polymer.DomModule.import(this.is, 'template')`.
       *
       * If a subclass would like to modify the super class template, it should
       * clone it rather than modify it in place.  If the getter does expensive
       * work such as cloning/modifying a template, it should memoize the
       * template for maximum performance:
       *
       *   let memoizedTemplate;
       *   class MySubClass extends MySuperClass {
       *     static get template() {
       *       if (!memoizedTemplate) {
       *         memoizedTemplate = MySuperClass.template.cloneNode(true);
       *         let subContent = document.createElement('div');
       *         subContent.textContent = 'This came from MySubClass';
       *         memoizedTemplate.content.appendChild(subContent);
       *       }
       *       return memoizedTemplate;
       *     }
       *   }
       *
       * @return {HTMLTemplateElement|string} Template to be stamped
       */
      static get template() {
        // Explanation of template-related properties:
        // - constructor.template (this getter): the template for the class.
        //     This can come from the prototype (for legacy elements), from a
        //     dom-module, or from the super class's template (or can be overridden
        //     altogether by the user)
        // - constructor._template: memoized version of constructor.template
        // - prototype._template: working template for the element, which will be
        //     parsed and modified in place. It is a cloned version of
        //     constructor.template, saved in _finalizeClass(). Note that before
        //     this getter is called, for legacy elements this could be from a
        //     _template field on the info object passed to Polymer(), a behavior,
        //     or set in registered(); once the static getter runs, a clone of it
        //     will overwrite it on the prototype as the working template.
        if (!this.hasOwnProperty(JSCompiler_renameProperty('_template', this))) {
          this._template =
            // If user has put template on prototype (e.g. in legacy via registered
            // callback or info object), prefer that first
            this.prototype.hasOwnProperty(JSCompiler_renameProperty('_template', this.prototype)) ?
            this.prototype._template :
            // Look in dom-module associated with this element's is
            (getTemplateFromDomModule(/** @type {PolymerElementConstructor}*/ (this).is) ||
            // Next look for superclass template (call the super impl this
            // way so that `this` points to the superclass)
            Object.getPrototypeOf(/** @type {PolymerElementConstructor}*/ (this).prototype).constructor.template);
        }
        return this._template;
      }

      /**
       * Set the template.
       *
       * @param {!HTMLTemplateElement|string} value Template to set.
       */
      static set template(value) {
        this._template = value;
      }

      /**
       * Path matching the url from which the element was imported.
       *
       * This path is used to resolve url's in template style cssText.
       * The `importPath` property is also set on element instances and can be
       * used to create bindings relative to the import path.
       *
       * For elements defined in ES modules, users should implement
       * `static get importMeta() { return import.meta; }`, and the default
       * implementation of `importPath` will  return `import.meta.url`'s path.
       * For elements defined in HTML imports, this getter will return the path
       * to the document containing a `dom-module` element matching this
       * element's static `is` property.
       *
       * Note, this path should contain a trailing `/`.
       *
       * @return {string} The import path for this element class
       * @suppress {missingProperties}
       */
      static get importPath() {
        if (!this.hasOwnProperty(JSCompiler_renameProperty('_importPath', this))) {
          const meta = this.importMeta;
          if (meta) {
            this._importPath = Polymer.ResolveUrl.pathFromUrl(meta.url);
          } else {
            const module = Polymer.DomModule && Polymer.DomModule.import(/** @type {PolymerElementConstructor} */ (this).is);
            this._importPath = (module && module.assetpath) ||
              Object.getPrototypeOf(/** @type {PolymerElementConstructor}*/ (this).prototype).constructor.importPath;
          }
        }
        return this._importPath;
      }

      constructor() {
        super();
        /** @type {HTMLTemplateElement} */
        this._template;
        /** @type {string} */
        this._importPath;
        /** @type {string} */
        this.rootPath;
        /** @type {string} */
        this.importPath;
        /** @type {StampedTemplate | HTMLElement | ShadowRoot} */
        this.root;
        /** @type {!Object<string, !Element>} */
        this.$;
      }

      /**
       * Overrides the default `Polymer.PropertyAccessors` to ensure class
       * metaprogramming related to property accessors and effects has
       * completed (calls `finalize`).
       *
       * It also initializes any property defaults provided via `value` in
       * `properties` metadata.
       *
       * @return {void}
       * @override
       * @suppress {invalidCasts}
       */
      _initializeProperties() {
        this.constructor.finalize();
        // note: finalize template when we have access to `localName` to
        // avoid dependence on `is` for polyfilling styling.
        this.constructor._finalizeTemplate(/** @type {!HTMLElement} */(this).localName);
        super._initializeProperties();
        // set path defaults
        this.rootPath = Polymer.rootPath;
        this.importPath = this.constructor.importPath;
        // apply property defaults...
        let p$ = propertyDefaults(this.constructor);
        if (!p$) {
          return;
        }
        for (let p in p$) {
          let info = p$[p];
          // Don't set default value if there is already an own property, which
          // happens when a `properties` property with default but no effects had
          // a property set (e.g. bound) by its host before upgrade
          if (!this.hasOwnProperty(p)) {
            let value = typeof info.value == 'function' ?
              info.value.call(this) :
              info.value;
            // Set via `_setProperty` if there is an accessor, to enable
            // initializing readOnly property defaults
            if (this._hasAccessor(p)) {
              this._setPendingProperty(p, value, true);
            } else {
              this[p] = value;
            }
          }
        }
      }

      /**
       * Gather style text for a style element in the template.
       *
       * @param {string} cssText Text containing styling to process
       * @param {string} baseURI Base URI to rebase CSS paths against
       * @return {string} The processed CSS text
       * @protected
       */
      static _processStyleText(cssText, baseURI) {
        return Polymer.ResolveUrl.resolveCss(cssText, baseURI);
      }

      /**
      * Configures an element `proto` to function with a given `template`.
      * The element name `is` and extends `ext` must be specified for ShadyCSS
      * style scoping.
      *
      * @param {string} is Tag name (or type extension name) for this element
      * @return {void}
      * @protected
      */
      static _finalizeTemplate(is) {
        /** @const {HTMLTemplateElement} */
        const template = this.prototype._template;
        if (template && !template.__polymerFinalized) {
          template.__polymerFinalized = true;
          const importPath = this.importPath;
          const baseURI = importPath ? Polymer.ResolveUrl.resolveUrl(importPath) : '';
          // e.g. support `include="module-name"`, and ShadyCSS
          processElementStyles(this, template, is, baseURI);
          this.prototype._bindTemplate(template);
        }
      }

      /**
       * Provides a default implementation of the standard Custom Elements
       * `connectedCallback`.
       *
       * The default implementation enables the property effects system and
       * flushes any pending properties, and updates shimmed CSS properties
       * when using the ShadyCSS scoping/custom properties polyfill.
       *
       * @suppress {missingProperties, invalidCasts} Super may or may not implement the callback
       * @return {void}
       */
      connectedCallback() {
        if (window.ShadyCSS && this._template) {
          window.ShadyCSS.styleElement(/** @type {!HTMLElement} */(this));
        }
        super.connectedCallback();
      }

      /**
       * Stamps the element template.
       *
       * @return {void}
       * @override
       */
      ready() {
        if (this._template) {
          this.root = this._stampTemplate(this._template);
          this.$ = this.root.$;
        }
        super.ready();
      }

      /**
       * Implements `PropertyEffects`'s `_readyClients` call. Attaches
       * element dom by calling `_attachDom` with the dom stamped from the
       * element's template via `_stampTemplate`. Note that this allows
       * client dom to be attached to the element prior to any observers
       * running.
       *
       * @return {void}
       * @override
       */
      _readyClients() {
        if (this._template) {
          this.root = this._attachDom(/** @type {StampedTemplate} */(this.root));
        }
        // The super._readyClients here sets the clients initialized flag.
        // We must wait to do this until after client dom is created/attached
        // so that this flag can be checked to prevent notifications fired
        // during this process from being handled before clients are ready.
        super._readyClients();
      }


      /**
       * Attaches an element's stamped dom to itself. By default,
       * this method creates a `shadowRoot` and adds the dom to it.
       * However, this method may be overridden to allow an element
       * to put its dom in another location.
       *
       * @throws {Error}
       * @suppress {missingReturn}
       * @param {StampedTemplate} dom to attach to the element.
       * @return {ShadowRoot} node to which the dom has been attached.
       */
      _attachDom(dom) {
        if (this.attachShadow) {
          if (dom) {
            if (!this.shadowRoot) {
              this.attachShadow({mode: 'open'});
            }
            this.shadowRoot.appendChild(dom);
            return this.shadowRoot;
          }
          return null;
        } else {
          throw new Error('ShadowDOM not available. ' +
            // TODO(sorvell): move to compile-time conditional when supported
          'Polymer.Element can create dom as children instead of in ' +
          'ShadowDOM by setting `this.root = this;\` before \`ready\`.');
        }
      }

      /**
       * When using the ShadyCSS scoping and custom property shim, causes all
       * shimmed styles in this element (and its subtree) to be updated
       * based on current custom property values.
       *
       * The optional parameter overrides inline custom property styles with an
       * object of properties where the keys are CSS properties, and the values
       * are strings.
       *
       * Example: `this.updateStyles({'--color': 'blue'})`
       *
       * These properties are retained unless a value of `null` is set.
       *
       * Note: This function does not support updating CSS mixins.
       * You can not dynamically change the value of an `@apply`.
       *
       * @param {Object=} properties Bag of custom property key/values to
       *   apply to this element.
       * @return {void}
       * @suppress {invalidCasts}
       */
      updateStyles(properties) {
        if (window.ShadyCSS) {
          window.ShadyCSS.styleSubtree(/** @type {!HTMLElement} */(this), properties);
        }
      }

      /**
       * Rewrites a given URL relative to a base URL. The base URL defaults to
       * the original location of the document containing the `dom-module` for
       * this element. This method will return the same URL before and after
       * bundling.
       *
       * Note that this function performs no resolution for URLs that start
       * with `/` (absolute URLs) or `#` (hash identifiers).  For general purpose
       * URL resolution, use `window.URL`.
       *
       * @param {string} url URL to resolve.
       * @param {string=} base Optional base URL to resolve against, defaults
       * to the element's `importPath`
       * @return {string} Rewritten URL relative to base
       */
      resolveUrl(url, base) {
        if (!base && this.importPath) {
          base = Polymer.ResolveUrl.resolveUrl(this.importPath);
        }
        return Polymer.ResolveUrl.resolveUrl(url, base);
      }

      /**
       * Overrides `PropertyAccessors` to add map of dynamic functions on
       * template info, for consumption by `PropertyEffects` template binding
       * code. This map determines which method templates should have accessors
       * created for them.
       *
       * @override
       * @suppress {missingProperties} Interfaces in closure do not inherit statics, but classes do
       */
      static _parseTemplateContent(template, templateInfo, nodeInfo) {
        templateInfo.dynamicFns = templateInfo.dynamicFns || this._properties;
        return super._parseTemplateContent(template, templateInfo, nodeInfo);
      }

    }

    return PolymerElement;
  });

  /**
   * When using the ShadyCSS scoping and custom property shim, causes all
   * shimmed `styles` (via `custom-style`) in the document (and its subtree)
   * to be updated based on current custom property values.
   *
   * The optional parameter overrides inline custom property styles with an
   * object of properties where the keys are CSS properties, and the values
   * are strings.
   *
   * Example: `Polymer.updateStyles({'--color': 'blue'})`
   *
   * These properties are retained unless a value of `null` is set.
   *
   * @param {Object=} props Bag of custom property key/values to
   *   apply to the document.
   * @return {void}
   */
  Polymer.updateStyles = function(props) {
    if (window.ShadyCSS) {
      window.ShadyCSS.styleDocument(props);
    }
  };

})();


(function() {
  'use strict';

  /**
   * @summary Collapse multiple callbacks into one invocation after a timer.
   * @memberof Polymer
   */
  class Debouncer {
    constructor() {
      this._asyncModule = null;
      this._callback = null;
      this._timer = null;
    }
    /**
     * Sets the scheduler; that is, a module with the Async interface,
     * a callback and optional arguments to be passed to the run function
     * from the async module.
     *
     * @param {!AsyncInterface} asyncModule Object with Async interface.
     * @param {function()} callback Callback to run.
     * @return {void}
     */
    setConfig(asyncModule, callback) {
      this._asyncModule = asyncModule;
      this._callback = callback;
      this._timer = this._asyncModule.run(() => {
        this._timer = null;
        this._callback();
      });
    }
    /**
     * Cancels an active debouncer and returns a reference to itself.
     *
     * @return {void}
     */
    cancel() {
      if (this.isActive()) {
        this._asyncModule.cancel(this._timer);
        this._timer = null;
      }
    }
    /**
     * Flushes an active debouncer and returns a reference to itself.
     *
     * @return {void}
     */
    flush() {
      if (this.isActive()) {
        this.cancel();
        this._callback();
      }
    }
    /**
     * Returns true if the debouncer is active.
     *
     * @return {boolean} True if active.
     */
    isActive() {
      return this._timer != null;
    }
    /**
     * Creates a debouncer if no debouncer is passed as a parameter
     * or it cancels an active debouncer otherwise. The following
     * example shows how a debouncer can be called multiple times within a
     * microtask and "debounced" such that the provided callback function is
     * called once. Add this method to a custom element:
     *
     * _debounceWork() {
     *   this._debounceJob = Polymer.Debouncer.debounce(this._debounceJob,
     *       Polymer.Async.microTask, () => {
     *     this._doWork();
     *   });
     * }
     *
     * If the `_debounceWork` method is called multiple times within the same
     * microtask, the `_doWork` function will be called only once at the next
     * microtask checkpoint.
     *
     * Note: In testing it is often convenient to avoid asynchrony. To accomplish
     * this with a debouncer, you can use `Polymer.enqueueDebouncer` and
     * `Polymer.flush`. For example, extend the above example by adding
     * `Polymer.enqueueDebouncer(this._debounceJob)` at the end of the
     * `_debounceWork` method. Then in a test, call `Polymer.flush` to ensure
     * the debouncer has completed.
     *
     * @param {Debouncer?} debouncer Debouncer object.
     * @param {!AsyncInterface} asyncModule Object with Async interface
     * @param {function()} callback Callback to run.
     * @return {!Debouncer} Returns a debouncer object.
     */
    static debounce(debouncer, asyncModule, callback) {
      if (debouncer instanceof Debouncer) {
        debouncer.cancel();
      } else {
        debouncer = new Debouncer();
      }
      debouncer.setConfig(asyncModule, callback);
      return debouncer;
    }
  }

  /** @const */
  Polymer.Debouncer = Debouncer;
})();


(function() {

  'use strict';

  // detect native touch action support
  let HAS_NATIVE_TA = typeof document.head.style.touchAction === 'string';
  let GESTURE_KEY = '__polymerGestures';
  let HANDLED_OBJ = '__polymerGesturesHandled';
  let TOUCH_ACTION = '__polymerGesturesTouchAction';
  // radius for tap and track
  let TAP_DISTANCE = 25;
  let TRACK_DISTANCE = 5;
  // number of last N track positions to keep
  let TRACK_LENGTH = 2;

  // Disabling "mouse" handlers for 2500ms is enough
  let MOUSE_TIMEOUT = 2500;
  let MOUSE_EVENTS = ['mousedown', 'mousemove', 'mouseup', 'click'];
  // an array of bitmask values for mapping MouseEvent.which to MouseEvent.buttons
  let MOUSE_WHICH_TO_BUTTONS = [0, 1, 4, 2];
  let MOUSE_HAS_BUTTONS = (function() {
    try {
      return new MouseEvent('test', {buttons: 1}).buttons === 1;
    } catch (e) {
      return false;
    }
  })();

  /**
   * @param {string} name Possible mouse event name
   * @return {boolean} true if mouse event, false if not
   */
  function isMouseEvent(name) {
    return MOUSE_EVENTS.indexOf(name) > -1;
  }

  /* eslint no-empty: ["error", { "allowEmptyCatch": true }] */
  // check for passive event listeners
  let SUPPORTS_PASSIVE = false;
  (function() {
    try {
      let opts = Object.defineProperty({}, 'passive', {get() {SUPPORTS_PASSIVE = true;}});
      window.addEventListener('test', null, opts);
      window.removeEventListener('test', null, opts);
    } catch(e) {}
  })();

  /**
   * Generate settings for event listeners, dependant on `Polymer.passiveTouchGestures`
   *
   * @param {string} eventName Event name to determine if `{passive}` option is needed
   * @return {{passive: boolean} | undefined} Options to use for addEventListener and removeEventListener
   */
  function PASSIVE_TOUCH(eventName) {
    if (isMouseEvent(eventName) || eventName === 'touchend') {
      return;
    }
    if (HAS_NATIVE_TA && SUPPORTS_PASSIVE && Polymer.passiveTouchGestures) {
      return {passive: true};
    } else {
      return;
    }
  }

  // Check for touch-only devices
  let IS_TOUCH_ONLY = navigator.userAgent.match(/iP(?:[oa]d|hone)|Android/);

  let GestureRecognizer = function(){}; // eslint-disable-line no-unused-vars
  /** @type {function(): void} */
  GestureRecognizer.prototype.reset;
  /** @type {function(MouseEvent): void | undefined} */
  GestureRecognizer.prototype.mousedown;
  /** @type {(function(MouseEvent): void | undefined)} */
  GestureRecognizer.prototype.mousemove;
  /** @type {(function(MouseEvent): void | undefined)} */
  GestureRecognizer.prototype.mouseup;
  /** @type {(function(TouchEvent): void | undefined)} */
  GestureRecognizer.prototype.touchstart;
  /** @type {(function(TouchEvent): void | undefined)} */
  GestureRecognizer.prototype.touchmove;
  /** @type {(function(TouchEvent): void | undefined)} */
  GestureRecognizer.prototype.touchend;
  /** @type {(function(MouseEvent): void | undefined)} */
  GestureRecognizer.prototype.click;

  // keep track of any labels hit by the mouseCanceller
  /** @type {!Array<!HTMLLabelElement>} */
  const clickedLabels = [];

  /** @type {!Object<boolean>} */
  const labellable = {
    'button': true,
    'input': true,
    'keygen': true,
    'meter': true,
    'output': true,
    'textarea': true,
    'progress': true,
    'select': true
  };

  // Defined at https://html.spec.whatwg.org/multipage/form-control-infrastructure.html#enabling-and-disabling-form-controls:-the-disabled-attribute
  /** @type {!Object<boolean>} */
  const canBeDisabled = {
    'button': true,
    'command': true,
    'fieldset': true,
    'input': true,
    'keygen': true,
    'optgroup': true,
    'option': true,
    'select': true,
    'textarea': true
  };

  /**
   * @param {HTMLElement} el Element to check labelling status
   * @return {boolean} element can have labels
   */
  function canBeLabelled(el) {
    return labellable[el.localName] || false;
  }

  /**
   * @param {HTMLElement} el Element that may be labelled.
   * @return {!Array<!HTMLLabelElement>} Relevant label for `el`
   */
  function matchingLabels(el) {
    let labels = Array.from(/** @type {HTMLInputElement} */(el).labels || []);
    // IE doesn't have `labels` and Safari doesn't populate `labels`
    // if element is in a shadowroot.
    // In this instance, finding the non-ancestor labels is enough,
    // as the mouseCancellor code will handle ancstor labels
    if (!labels.length) {
      labels = [];
      let root = el.getRootNode();
      // if there is an id on `el`, check for all labels with a matching `for` attribute
      if (el.id) {
        let matching = root.querySelectorAll(`label[for = ${el.id}]`);
        for (let i = 0; i < matching.length; i++) {
          labels.push(/** @type {!HTMLLabelElement} */(matching[i]));
        }
      }
    }
    return labels;
  }

  // touch will make synthetic mouse events
  // `preventDefault` on touchend will cancel them,
  // but this breaks `<input>` focus and link clicks
  // disable mouse handlers for MOUSE_TIMEOUT ms after
  // a touchend to ignore synthetic mouse events
  let mouseCanceller = function(mouseEvent) {
    // Check for sourceCapabilities, used to distinguish synthetic events
    // if mouseEvent did not come from a device that fires touch events,
    // it was made by a real mouse and should be counted
    // http://wicg.github.io/InputDeviceCapabilities/#dom-inputdevicecapabilities-firestouchevents
    let sc = mouseEvent.sourceCapabilities;
    if (sc && !sc.firesTouchEvents) {
      return;
    }
    // skip synthetic mouse events
    mouseEvent[HANDLED_OBJ] = {skip: true};
    // disable "ghost clicks"
    if (mouseEvent.type === 'click') {
      let clickFromLabel = false;
      let path = mouseEvent.composedPath && mouseEvent.composedPath();
      if (path) {
        for (let i = 0; i < path.length; i++) {
          if (path[i].nodeType === Node.ELEMENT_NODE) {
            if (path[i].localName === 'label') {
              clickedLabels.push(path[i]);
            } else if (canBeLabelled(path[i])) {
              let ownerLabels = matchingLabels(path[i]);
              // check if one of the clicked labels is labelling this element
              for (let j = 0; j < ownerLabels.length; j++) {
                clickFromLabel = clickFromLabel || clickedLabels.indexOf(ownerLabels[j]) > -1;
              }
            }
          }
          if (path[i] === POINTERSTATE.mouse.target) {
            return;
          }
        }
      }
      // if one of the clicked labels was labelling the target element,
      // this is not a ghost click
      if (clickFromLabel) {
        return;
      }
      mouseEvent.preventDefault();
      mouseEvent.stopPropagation();
    }
  };

  /**
   * @param {boolean=} setup True to add, false to remove.
   * @return {void}
   */
  function setupTeardownMouseCanceller(setup) {
    let events = IS_TOUCH_ONLY ? ['click'] : MOUSE_EVENTS;
    for (let i = 0, en; i < events.length; i++) {
      en = events[i];
      if (setup) {
        // reset clickLabels array
        clickedLabels.length = 0;
        document.addEventListener(en, mouseCanceller, true);
      } else {
        document.removeEventListener(en, mouseCanceller, true);
      }
    }
  }

  function ignoreMouse(e) {
    if (!POINTERSTATE.mouse.mouseIgnoreJob) {
      setupTeardownMouseCanceller(true);
    }
    let unset = function() {
      setupTeardownMouseCanceller();
      POINTERSTATE.mouse.target = null;
      POINTERSTATE.mouse.mouseIgnoreJob = null;
    };
    POINTERSTATE.mouse.target = e.composedPath()[0];
    POINTERSTATE.mouse.mouseIgnoreJob = Polymer.Debouncer.debounce(
          POINTERSTATE.mouse.mouseIgnoreJob
        , Polymer.Async.timeOut.after(MOUSE_TIMEOUT)
        , unset);
  }

  /**
   * @param {MouseEvent} ev event to test for left mouse button down
   * @return {boolean} has left mouse button down
   */
  function hasLeftMouseButton(ev) {
    let type = ev.type;
    // exit early if the event is not a mouse event
    if (!isMouseEvent(type)) {
      return false;
    }
    // ev.button is not reliable for mousemove (0 is overloaded as both left button and no buttons)
    // instead we use ev.buttons (bitmask of buttons) or fall back to ev.which (deprecated, 0 for no buttons, 1 for left button)
    if (type === 'mousemove') {
      // allow undefined for testing events
      let buttons = ev.buttons === undefined ? 1 : ev.buttons;
      if ((ev instanceof window.MouseEvent) && !MOUSE_HAS_BUTTONS) {
        buttons = MOUSE_WHICH_TO_BUTTONS[ev.which] || 0;
      }
      // buttons is a bitmask, check that the left button bit is set (1)
      return Boolean(buttons & 1);
    } else {
      // allow undefined for testing events
      let button = ev.button === undefined ? 0 : ev.button;
      // ev.button is 0 in mousedown/mouseup/click for left button activation
      return button === 0;
    }
  }

  function isSyntheticClick(ev) {
    if (ev.type === 'click') {
      // ev.detail is 0 for HTMLElement.click in most browsers
      if (ev.detail === 0) {
        return true;
      }
      // in the worst case, check that the x/y position of the click is within
      // the bounding box of the target of the event
      // Thanks IE 10 >:(
      let t = Gestures._findOriginalTarget(ev);
      // make sure the target of the event is an element so we can use getBoundingClientRect,
      // if not, just assume it is a synthetic click
      if (!t.nodeType || /** @type {Element} */(t).nodeType !== Node.ELEMENT_NODE) {
        return true;
      }
      let bcr = /** @type {Element} */(t).getBoundingClientRect();
      // use page x/y to account for scrolling
      let x = ev.pageX, y = ev.pageY;
      // ev is a synthetic click if the position is outside the bounding box of the target
      return !((x >= bcr.left && x <= bcr.right) && (y >= bcr.top && y <= bcr.bottom));
    }
    return false;
  }

  let POINTERSTATE = {
    mouse: {
      target: null,
      mouseIgnoreJob: null
    },
    touch: {
      x: 0,
      y: 0,
      id: -1,
      scrollDecided: false
    }
  };

  function firstTouchAction(ev) {
    let ta = 'auto';
    let path = ev.composedPath && ev.composedPath();
    if (path) {
      for (let i = 0, n; i < path.length; i++) {
        n = path[i];
        if (n[TOUCH_ACTION]) {
          ta = n[TOUCH_ACTION];
          break;
        }
      }
    }
    return ta;
  }

  function trackDocument(stateObj, movefn, upfn) {
    stateObj.movefn = movefn;
    stateObj.upfn = upfn;
    document.addEventListener('mousemove', movefn);
    document.addEventListener('mouseup', upfn);
  }

  function untrackDocument(stateObj) {
    document.removeEventListener('mousemove', stateObj.movefn);
    document.removeEventListener('mouseup', stateObj.upfn);
    stateObj.movefn = null;
    stateObj.upfn = null;
  }

  // use a document-wide touchend listener to start the ghost-click prevention mechanism
  // Use passive event listeners, if supported, to not affect scrolling performance
  document.addEventListener('touchend', ignoreMouse, SUPPORTS_PASSIVE ? {passive: true} : false);

  /**
   * Module for adding listeners to a node for the following normalized
   * cross-platform "gesture" events:
   * - `down` - mouse or touch went down
   * - `up` - mouse or touch went up
   * - `tap` - mouse click or finger tap
   * - `track` - mouse drag or touch move
   *
   * @namespace
   * @memberof Polymer
   * @summary Module for adding cross-platform gesture event listeners.
   */
  const Gestures = {
    gestures: {},
    recognizers: [],

    /**
     * Finds the element rendered on the screen at the provided coordinates.
     *
     * Similar to `document.elementFromPoint`, but pierces through
     * shadow roots.
     *
     * @memberof Polymer.Gestures
     * @param {number} x Horizontal pixel coordinate
     * @param {number} y Vertical pixel coordinate
     * @return {Element} Returns the deepest shadowRoot inclusive element
     * found at the screen position given.
     */
    deepTargetFind: function(x, y) {
      let node = document.elementFromPoint(x, y);
      let next = node;
      // this code path is only taken when native ShadowDOM is used
      // if there is a shadowroot, it may have a node at x/y
      // if there is not a shadowroot, exit the loop
      while (next && next.shadowRoot && !window.ShadyDOM) {
        // if there is a node at x/y in the shadowroot, look deeper
        let oldNext = next;
        next = next.shadowRoot.elementFromPoint(x, y);
        // on Safari, elementFromPoint may return the shadowRoot host
        if (oldNext === next) {
          break;
        }
        if (next) {
          node = next;
        }
      }
      return node;
    },
    /**
     * a cheaper check than ev.composedPath()[0];
     *
     * @private
     * @param {Event} ev Event.
     * @return {EventTarget} Returns the event target.
     */
    _findOriginalTarget: function(ev) {
      // shadowdom
      if (ev.composedPath) {
        const targets = /** @type {!Array<!EventTarget>} */(ev.composedPath());
        // It shouldn't be, but sometimes targets is empty (window on Safari).
        return targets.length > 0 ? targets[0] : ev.target;
      }
      // shadydom
      return ev.target;
    },

    /**
     * @private
     * @param {Event} ev Event.
     * @return {void}
     */
    _handleNative: function(ev) {
      let handled;
      let type = ev.type;
      let node = ev.currentTarget;
      let gobj = node[GESTURE_KEY];
      if (!gobj) {
        return;
      }
      let gs = gobj[type];
      if (!gs) {
        return;
      }
      if (!ev[HANDLED_OBJ]) {
        ev[HANDLED_OBJ] = {};
        if (type.slice(0, 5) === 'touch') {
          ev = /** @type {TouchEvent} */(ev); // eslint-disable-line no-self-assign
          let t = ev.changedTouches[0];
          if (type === 'touchstart') {
            // only handle the first finger
            if (ev.touches.length === 1) {
              POINTERSTATE.touch.id = t.identifier;
            }
          }
          if (POINTERSTATE.touch.id !== t.identifier) {
            return;
          }
          if (!HAS_NATIVE_TA) {
            if (type === 'touchstart' || type === 'touchmove') {
              Gestures._handleTouchAction(ev);
            }
          }
        }
      }
      handled = ev[HANDLED_OBJ];
      // used to ignore synthetic mouse events
      if (handled.skip) {
        return;
      }
      // reset recognizer state
      for (let i = 0, r; i < Gestures.recognizers.length; i++) {
        r = Gestures.recognizers[i];
        if (gs[r.name] && !handled[r.name]) {
          if (r.flow && r.flow.start.indexOf(ev.type) > -1 && r.reset) {
            r.reset();
          }
        }
      }
      // enforce gesture recognizer order
      for (let i = 0, r; i < Gestures.recognizers.length; i++) {
        r = Gestures.recognizers[i];
        if (gs[r.name] && !handled[r.name]) {
          handled[r.name] = true;
          r[type](ev);
        }
      }
    },

    /**
     * @private
     * @param {TouchEvent} ev Event.
     * @return {void}
     */
    _handleTouchAction: function(ev) {
      let t = ev.changedTouches[0];
      let type = ev.type;
      if (type === 'touchstart') {
        POINTERSTATE.touch.x = t.clientX;
        POINTERSTATE.touch.y = t.clientY;
        POINTERSTATE.touch.scrollDecided = false;
      } else if (type === 'touchmove') {
        if (POINTERSTATE.touch.scrollDecided) {
          return;
        }
        POINTERSTATE.touch.scrollDecided = true;
        let ta = firstTouchAction(ev);
        let prevent = false;
        let dx = Math.abs(POINTERSTATE.touch.x - t.clientX);
        let dy = Math.abs(POINTERSTATE.touch.y - t.clientY);
        if (!ev.cancelable) {
          // scrolling is happening
        } else if (ta === 'none') {
          prevent = true;
        } else if (ta === 'pan-x') {
          prevent = dy > dx;
        } else if (ta === 'pan-y') {
          prevent = dx > dy;
        }
        if (prevent) {
          ev.preventDefault();
        } else {
          Gestures.prevent('track');
        }
      }
    },

    /**
     * Adds an event listener to a node for the given gesture type.
     *
     * @memberof Polymer.Gestures
     * @param {!Node} node Node to add listener on
     * @param {string} evType Gesture type: `down`, `up`, `track`, or `tap`
     * @param {!function(!Event):void} handler Event listener function to call
     * @return {boolean} Returns true if a gesture event listener was added.
     * @this {Gestures}
     */
    addListener: function(node, evType, handler) {
      if (this.gestures[evType]) {
        this._add(node, evType, handler);
        return true;
      }
      return false;
    },

    /**
     * Removes an event listener from a node for the given gesture type.
     *
     * @memberof Polymer.Gestures
     * @param {!Node} node Node to remove listener from
     * @param {string} evType Gesture type: `down`, `up`, `track`, or `tap`
     * @param {!function(!Event):void} handler Event listener function previously passed to
     *  `addListener`.
     * @return {boolean} Returns true if a gesture event listener was removed.
     * @this {Gestures}
     */
    removeListener: function(node, evType, handler) {
      if (this.gestures[evType]) {
        this._remove(node, evType, handler);
        return true;
      }
      return false;
    },

    /**
     * automate the event listeners for the native events
     *
     * @private
     * @param {!HTMLElement} node Node on which to add the event.
     * @param {string} evType Event type to add.
     * @param {function(!Event)} handler Event handler function.
     * @return {void}
     * @this {Gestures}
     */
    _add: function(node, evType, handler) {
      let recognizer = this.gestures[evType];
      let deps = recognizer.deps;
      let name = recognizer.name;
      let gobj = node[GESTURE_KEY];
      if (!gobj) {
        node[GESTURE_KEY] = gobj = {};
      }
      for (let i = 0, dep, gd; i < deps.length; i++) {
        dep = deps[i];
        // don't add mouse handlers on iOS because they cause gray selection overlays
        if (IS_TOUCH_ONLY && isMouseEvent(dep) && dep !== 'click') {
          continue;
        }
        gd = gobj[dep];
        if (!gd) {
          gobj[dep] = gd = {_count: 0};
        }
        if (gd._count === 0) {
          node.addEventListener(dep, this._handleNative, PASSIVE_TOUCH(dep));
        }
        gd[name] = (gd[name] || 0) + 1;
        gd._count = (gd._count || 0) + 1;
      }
      node.addEventListener(evType, handler);
      if (recognizer.touchAction) {
        this.setTouchAction(node, recognizer.touchAction);
      }
    },

    /**
     * automate event listener removal for native events
     *
     * @private
     * @param {!HTMLElement} node Node on which to remove the event.
     * @param {string} evType Event type to remove.
     * @param {function(Event?)} handler Event handler function.
     * @return {void}
     * @this {Gestures}
     */
    _remove: function(node, evType, handler) {
      let recognizer = this.gestures[evType];
      let deps = recognizer.deps;
      let name = recognizer.name;
      let gobj = node[GESTURE_KEY];
      if (gobj) {
        for (let i = 0, dep, gd; i < deps.length; i++) {
          dep = deps[i];
          gd = gobj[dep];
          if (gd && gd[name]) {
            gd[name] = (gd[name] || 1) - 1;
            gd._count = (gd._count || 1) - 1;
            if (gd._count === 0) {
              node.removeEventListener(dep, this._handleNative, PASSIVE_TOUCH(dep));
            }
          }
        }
      }
      node.removeEventListener(evType, handler);
    },

    /**
     * Registers a new gesture event recognizer for adding new custom
     * gesture event types.
     *
     * @memberof Polymer.Gestures
     * @param {!GestureRecognizer} recog Gesture recognizer descriptor
     * @return {void}
     * @this {Gestures}
     */
    register: function(recog) {
      this.recognizers.push(recog);
      for (let i = 0; i < recog.emits.length; i++) {
        this.gestures[recog.emits[i]] = recog;
      }
    },

    /**
     * @private
     * @param {string} evName Event name.
     * @return {Object} Returns the gesture for the given event name.
     * @this {Gestures}
     */
    _findRecognizerByEvent: function(evName) {
      for (let i = 0, r; i < this.recognizers.length; i++) {
        r = this.recognizers[i];
        for (let j = 0, n; j < r.emits.length; j++) {
          n = r.emits[j];
          if (n === evName) {
            return r;
          }
        }
      }
      return null;
    },

    /**
     * Sets scrolling direction on node.
     *
     * This value is checked on first move, thus it should be called prior to
     * adding event listeners.
     *
     * @memberof Polymer.Gestures
     * @param {!Element} node Node to set touch action setting on
     * @param {string} value Touch action value
     * @return {void}
     */
    setTouchAction: function(node, value) {
      if (HAS_NATIVE_TA) {
        // NOTE: add touchAction async so that events can be added in
        // custom element constructors. Otherwise we run afoul of custom
        // elements restriction against settings attributes (style) in the
        // constructor.
        Polymer.Async.microTask.run(() => {
          node.style.touchAction = value;
        });
      }
      node[TOUCH_ACTION] = value;
    },

    /**
     * Dispatches an event on the `target` element of `type` with the given
     * `detail`.
     * @private
     * @param {!EventTarget} target The element on which to fire an event.
     * @param {string} type The type of event to fire.
     * @param {!Object=} detail The detail object to populate on the event.
     * @return {void}
     */
    _fire: function(target, type, detail) {
      let ev = new Event(type, { bubbles: true, cancelable: true, composed: true });
      ev.detail = detail;
      target.dispatchEvent(ev);
      // forward `preventDefault` in a clean way
      if (ev.defaultPrevented) {
        let preventer = detail.preventer || detail.sourceEvent;
        if (preventer && preventer.preventDefault) {
          preventer.preventDefault();
        }
      }
    },

    /**
     * Prevents the dispatch and default action of the given event name.
     *
     * @memberof Polymer.Gestures
     * @param {string} evName Event name.
     * @return {void}
     * @this {Gestures}
     */
    prevent: function(evName) {
      let recognizer = this._findRecognizerByEvent(evName);
      if (recognizer.info) {
        recognizer.info.prevent = true;
      }
    },

    /**
     * Reset the 2500ms timeout on processing mouse input after detecting touch input.
     *
     * Touch inputs create synthesized mouse inputs anywhere from 0 to 2000ms after the touch.
     * This method should only be called during testing with simulated touch inputs.
     * Calling this method in production may cause duplicate taps or other Gestures.
     *
     * @memberof Polymer.Gestures
     * @return {void}
     */
    resetMouseCanceller: function() {
      if (POINTERSTATE.mouse.mouseIgnoreJob) {
        POINTERSTATE.mouse.mouseIgnoreJob.flush();
      }
    }
  };

  /* eslint-disable valid-jsdoc */

  Gestures.register({
    name: 'downup',
    deps: ['mousedown', 'touchstart', 'touchend'],
    flow: {
      start: ['mousedown', 'touchstart'],
      end: ['mouseup', 'touchend']
    },
    emits: ['down', 'up'],

    info: {
      movefn: null,
      upfn: null
    },

    /**
     * @this {GestureRecognizer}
     * @return {void}
     */
    reset: function() {
      untrackDocument(this.info);
    },

    /**
     * @this {GestureRecognizer}
     * @param {MouseEvent} e
     * @return {void}
     */
    mousedown: function(e) {
      if (!hasLeftMouseButton(e)) {
        return;
      }
      let t = Gestures._findOriginalTarget(e);
      let self = this;
      let movefn = function movefn(e) {
        if (!hasLeftMouseButton(e)) {
          self._fire('up', t, e);
          untrackDocument(self.info);
        }
      };
      let upfn = function upfn(e) {
        if (hasLeftMouseButton(e)) {
          self._fire('up', t, e);
        }
        untrackDocument(self.info);
      };
      trackDocument(this.info, movefn, upfn);
      this._fire('down', t, e);
    },
    /**
     * @this {GestureRecognizer}
     * @param {TouchEvent} e
     * @return {void}
     */
    touchstart: function(e) {
      this._fire('down', Gestures._findOriginalTarget(e), e.changedTouches[0], e);
    },
    /**
     * @this {GestureRecognizer}
     * @param {TouchEvent} e
     * @return {void}
     */
    touchend: function(e) {
      this._fire('up', Gestures._findOriginalTarget(e), e.changedTouches[0], e);
    },
    /**
     * @param {string} type
     * @param {!EventTarget} target
     * @param {Event} event
     * @param {Function} preventer
     * @return {void}
     */
    _fire: function(type, target, event, preventer) {
      Gestures._fire(target, type, {
        x: event.clientX,
        y: event.clientY,
        sourceEvent: event,
        preventer: preventer,
        prevent: function(e) {
          return Gestures.prevent(e);
        }
      });
    }
  });

  Gestures.register({
    name: 'track',
    touchAction: 'none',
    deps: ['mousedown', 'touchstart', 'touchmove', 'touchend'],
    flow: {
      start: ['mousedown', 'touchstart'],
      end: ['mouseup', 'touchend']
    },
    emits: ['track'],

    info: {
      x: 0,
      y: 0,
      state: 'start',
      started: false,
      moves: [],
      /** @this {GestureRecognizer} */
      addMove: function(move) {
        if (this.moves.length > TRACK_LENGTH) {
          this.moves.shift();
        }
        this.moves.push(move);
      },
      movefn: null,
      upfn: null,
      prevent: false
    },

    /**
     * @this {GestureRecognizer}
     * @return {void}
     */
    reset: function() {
      this.info.state = 'start';
      this.info.started = false;
      this.info.moves = [];
      this.info.x = 0;
      this.info.y = 0;
      this.info.prevent = false;
      untrackDocument(this.info);
    },

    /**
     * @this {GestureRecognizer}
     * @param {number} x
     * @param {number} y
     * @return {boolean}
     */
    hasMovedEnough: function(x, y) {
      if (this.info.prevent) {
        return false;
      }
      if (this.info.started) {
        return true;
      }
      let dx = Math.abs(this.info.x - x);
      let dy = Math.abs(this.info.y - y);
      return (dx >= TRACK_DISTANCE || dy >= TRACK_DISTANCE);
    },
    /**
     * @this {GestureRecognizer}
     * @param {MouseEvent} e
     * @return {void}
     */
    mousedown: function(e) {
      if (!hasLeftMouseButton(e)) {
        return;
      }
      let t = Gestures._findOriginalTarget(e);
      let self = this;
      let movefn = function movefn(e) {
        let x = e.clientX, y = e.clientY;
        if (self.hasMovedEnough(x, y)) {
          // first move is 'start', subsequent moves are 'move', mouseup is 'end'
          self.info.state = self.info.started ? (e.type === 'mouseup' ? 'end' : 'track') : 'start';
          if (self.info.state === 'start') {
            // if and only if tracking, always prevent tap
            Gestures.prevent('tap');
          }
          self.info.addMove({x: x, y: y});
          if (!hasLeftMouseButton(e)) {
            // always _fire "end"
            self.info.state = 'end';
            untrackDocument(self.info);
          }
          self._fire(t, e);
          self.info.started = true;
        }
      };
      let upfn = function upfn(e) {
        if (self.info.started) {
          movefn(e);
        }

        // remove the temporary listeners
        untrackDocument(self.info);
      };
      // add temporary document listeners as mouse retargets
      trackDocument(this.info, movefn, upfn);
      this.info.x = e.clientX;
      this.info.y = e.clientY;
    },
    /**
     * @this {GestureRecognizer}
     * @param {TouchEvent} e
     * @return {void}
     */
    touchstart: function(e) {
      let ct = e.changedTouches[0];
      this.info.x = ct.clientX;
      this.info.y = ct.clientY;
    },
    /**
     * @this {GestureRecognizer}
     * @param {TouchEvent} e
     * @return {void}
     */
    touchmove: function(e) {
      let t = Gestures._findOriginalTarget(e);
      let ct = e.changedTouches[0];
      let x = ct.clientX, y = ct.clientY;
      if (this.hasMovedEnough(x, y)) {
        if (this.info.state === 'start') {
          // if and only if tracking, always prevent tap
          Gestures.prevent('tap');
        }
        this.info.addMove({x: x, y: y});
        this._fire(t, ct);
        this.info.state = 'track';
        this.info.started = true;
      }
    },
    /**
     * @this {GestureRecognizer}
     * @param {TouchEvent} e
     * @return {void}
     */
    touchend: function(e) {
      let t = Gestures._findOriginalTarget(e);
      let ct = e.changedTouches[0];
      // only trackend if track was started and not aborted
      if (this.info.started) {
        // reset started state on up
        this.info.state = 'end';
        this.info.addMove({x: ct.clientX, y: ct.clientY});
        this._fire(t, ct, e);
      }
    },

    /**
     * @this {GestureRecognizer}
     * @param {!EventTarget} target
     * @param {Touch} touch
     * @return {void}
     */
    _fire: function(target, touch) {
      let secondlast = this.info.moves[this.info.moves.length - 2];
      let lastmove = this.info.moves[this.info.moves.length - 1];
      let dx = lastmove.x - this.info.x;
      let dy = lastmove.y - this.info.y;
      let ddx, ddy = 0;
      if (secondlast) {
        ddx = lastmove.x - secondlast.x;
        ddy = lastmove.y - secondlast.y;
      }
      Gestures._fire(target, 'track', {
        state: this.info.state,
        x: touch.clientX,
        y: touch.clientY,
        dx: dx,
        dy: dy,
        ddx: ddx,
        ddy: ddy,
        sourceEvent: touch,
        hover: function() {
          return Gestures.deepTargetFind(touch.clientX, touch.clientY);
        }
      });
    }

  });

  Gestures.register({
    name: 'tap',
    deps: ['mousedown', 'click', 'touchstart', 'touchend'],
    flow: {
      start: ['mousedown', 'touchstart'],
      end: ['click', 'touchend']
    },
    emits: ['tap'],
    info: {
      x: NaN,
      y: NaN,
      prevent: false
    },
    /**
     * @this {GestureRecognizer}
     * @return {void}
     */
    reset: function() {
      this.info.x = NaN;
      this.info.y = NaN;
      this.info.prevent = false;
    },
    /**
     * @this {GestureRecognizer}
     * @param {MouseEvent} e
     * @return {void}
     */
    save: function(e) {
      this.info.x = e.clientX;
      this.info.y = e.clientY;
    },
    /**
     * @this {GestureRecognizer}
     * @param {MouseEvent} e
     * @return {void}
     */
    mousedown: function(e) {
      if (hasLeftMouseButton(e)) {
        this.save(e);
      }
    },
    /**
     * @this {GestureRecognizer}
     * @param {MouseEvent} e
     * @return {void}
     */
    click: function(e) {
      if (hasLeftMouseButton(e)) {
        this.forward(e);
      }
    },
    /**
     * @this {GestureRecognizer}
     * @param {TouchEvent} e
     * @return {void}
     */
    touchstart: function(e) {
      this.save(e.changedTouches[0], e);
    },
    /**
     * @this {GestureRecognizer}
     * @param {TouchEvent} e
     * @return {void}
     */
    touchend: function(e) {
      this.forward(e.changedTouches[0], e);
    },
    /**
     * @this {GestureRecognizer}
     * @param {Event | Touch} e
     * @param {Event=} preventer
     * @return {void}
     */
    forward: function(e, preventer) {
      let dx = Math.abs(e.clientX - this.info.x);
      let dy = Math.abs(e.clientY - this.info.y);
      // find original target from `preventer` for TouchEvents, or `e` for MouseEvents
      let t = Gestures._findOriginalTarget(/** @type {Event} */(preventer || e));
      if (!t || (canBeDisabled[/** @type {!HTMLElement} */(t).localName] && t.hasAttribute('disabled'))) {
        return;
      }
      // dx,dy can be NaN if `click` has been simulated and there was no `down` for `start`
      if (isNaN(dx) || isNaN(dy) || (dx <= TAP_DISTANCE && dy <= TAP_DISTANCE) || isSyntheticClick(e)) {
        // prevent taps from being generated if an event has canceled them
        if (!this.info.prevent) {
          Gestures._fire(t, 'tap', {
            x: e.clientX,
            y: e.clientY,
            sourceEvent: e,
            preventer: preventer
          });
        }
      }
    }
  });

  /* eslint-enable valid-jsdoc */

  /** @deprecated */
  Gestures.findOriginalTarget = Gestures._findOriginalTarget;

  /** @deprecated */
  Gestures.add = Gestures.addListener;

  /** @deprecated */
  Gestures.remove = Gestures.removeListener;

  Polymer.Gestures = Gestures;

})();


(function() {

  'use strict';

  /**
   * @const {Polymer.Gestures}
   */
  const gestures = Polymer.Gestures;

  /**
   * Element class mixin that provides API for adding Polymer's cross-platform
   * gesture events to nodes.
   *
   * The API is designed to be compatible with override points implemented
   * in `Polymer.TemplateStamp` such that declarative event listeners in
   * templates will support gesture events when this mixin is applied along with
   * `Polymer.TemplateStamp`.
   *
   * @mixinFunction
   * @polymer
   * @memberof Polymer
   * @summary Element class mixin that provides API for adding Polymer's cross-platform
   * gesture events to nodes
   */
  Polymer.GestureEventListeners = Polymer.dedupingMixin(superClass => {

    /**
     * @polymer
     * @mixinClass
     * @implements {Polymer_GestureEventListeners}
     */
    class GestureEventListeners extends superClass {

      /**
       * Add the event listener to the node if it is a gestures event.
       *
       * @param {!Node} node Node to add event listener to
       * @param {string} eventName Name of event
       * @param {function(!Event):void} handler Listener function to add
       * @return {void}
       */
      _addEventListenerToNode(node, eventName, handler) {
        if (!gestures.addListener(node, eventName, handler)) {
          super._addEventListenerToNode(node, eventName, handler);
        }
      }

      /**
       * Remove the event listener to the node if it is a gestures event.
       *
       * @param {!Node} node Node to remove event listener from
       * @param {string} eventName Name of event
       * @param {function(!Event):void} handler Listener function to remove
       * @return {void}
       */
      _removeEventListenerFromNode(node, eventName, handler) {
        if (!gestures.removeListener(node, eventName, handler)) {
          super._removeEventListenerFromNode(node, eventName, handler);
        }
      }

    }

    return GestureEventListeners;

  });

})();


  (function() {
    'use strict';

    const HOST_DIR = /:host\(:dir\((ltr|rtl)\)\)/g;
    const HOST_DIR_REPLACMENT = ':host([dir="$1"])';

    const EL_DIR = /([\s\w-#\.\[\]\*]*):dir\((ltr|rtl)\)/g;
    const EL_DIR_REPLACMENT = ':host([dir="$2"]) $1';

    const DIR_CHECK = /:dir\((?:ltr|rtl)\)/;
    
    const SHIM_SHADOW = Boolean(window['ShadyDOM'] && window['ShadyDOM']['inUse']);

    /**
     * @type {!Array<!Polymer_DirMixin>}
     */
    const DIR_INSTANCES = [];

    /** @type {MutationObserver} */
    let observer = null;

    let DOCUMENT_DIR = '';

    function getRTL() {
      DOCUMENT_DIR = document.documentElement.getAttribute('dir');
    }

    /**
     * @param {!Polymer_DirMixin} instance Instance to set RTL status on
     */
    function setRTL(instance) {
      if (!instance.__autoDirOptOut) {
        const el = /** @type {!HTMLElement} */(instance);
        el.setAttribute('dir', DOCUMENT_DIR);
      }
    }

    function updateDirection() {
      getRTL();
      DOCUMENT_DIR = document.documentElement.getAttribute('dir');
      for (let i = 0; i < DIR_INSTANCES.length; i++) {
        setRTL(DIR_INSTANCES[i]);
      }
    }

    function takeRecords() {
      if (observer && observer.takeRecords().length) {
        updateDirection();
      }
    }

    /**
     * Element class mixin that allows elements to use the `:dir` CSS Selector to have
     * text direction specific styling.
     *
     * With this mixin, any stylesheet provided in the template will transform `:dir` into
     * `:host([dir])` and sync direction with the page via the element's `dir` attribute.
     *
     * Elements can opt out of the global page text direction by setting the `dir` attribute
     * directly in `ready()` or in HTML.
     *
     * Caveats:
     * - Applications must set `<html dir="ltr">` or `<html dir="rtl">` to sync direction
     * - Automatic left-to-right or right-to-left styling is sync'd with the `<html>` element only.
     * - Changing `dir` at runtime is supported.
     * - Opting out of the global direction styling is permanent
     *
     * @mixinFunction
     * @polymer
     * @appliesMixin Polymer.PropertyAccessors
     * @memberof Polymer
     */
    Polymer.DirMixin = Polymer.dedupingMixin((base) => {

      if (!SHIM_SHADOW) {
        if (!observer) {
          getRTL();
          observer = new MutationObserver(updateDirection);
          observer.observe(document.documentElement, {attributes: true, attributeFilter: ['dir']});
        }
      }

      /**
       * @constructor
       * @extends {base}
       * @implements {Polymer_PropertyAccessors}
       * @private
       */
      const elementBase = Polymer.PropertyAccessors(base);

      /**
       * @polymer
       * @mixinClass
       * @implements {Polymer_DirMixin}
       */
      class Dir extends elementBase {

        /**
         * @override
         * @suppress {missingProperties} Interfaces in closure do not inherit statics, but classes do
         */
        static _processStyleText(cssText, baseURI) {
          cssText = super._processStyleText(cssText, baseURI);
          if (!SHIM_SHADOW && DIR_CHECK.test(cssText)) {
            cssText = this._replaceDirInCssText(cssText);
            this.__activateDir = true;
          }
          return cssText;
        }

        /**
         * Replace `:dir` in the given CSS text
         *
         * @param {string} text CSS text to replace DIR
         * @return {string} Modified CSS
         */
        static _replaceDirInCssText(text) {
          let replacedText = text;
          replacedText = replacedText.replace(HOST_DIR, HOST_DIR_REPLACMENT);
          replacedText = replacedText.replace(EL_DIR, EL_DIR_REPLACMENT);
          return replacedText;
        }

        constructor() {
          super();
          /** @type {boolean} */
          this.__autoDirOptOut = false;
        }

        /**
         * @suppress {invalidCasts} Closure doesn't understand that `this` is an HTMLElement
         * @return {void}
         */
        ready() {
          super.ready();
          this.__autoDirOptOut = /** @type {!HTMLElement} */(this).hasAttribute('dir');
        }

        /**
         * @suppress {missingProperties} If it exists on elementBase, it can be super'd
         * @return {void}
         */
        connectedCallback() {
          if (elementBase.prototype.connectedCallback) {
            super.connectedCallback();
          }
          if (this.constructor.__activateDir) {
            takeRecords();
            DIR_INSTANCES.push(this);
            setRTL(this);
          }
        }

        /**
         * @suppress {missingProperties} If it exists on elementBase, it can be super'd
         * @return {void}
         */
        disconnectedCallback() {
          if (elementBase.prototype.disconnectedCallback) {
            super.disconnectedCallback();
          }
          if (this.constructor.__activateDir) {
            const idx = DIR_INSTANCES.indexOf(this);
            if (idx > -1) {
              DIR_INSTANCES.splice(idx, 1);
            }
          }
        }
      }

      Dir.__activateDir = false;

      return Dir;
    });
  })();



(function() {

  'use strict';

  // run a callback when HTMLImports are ready or immediately if
  // this api is not available.
  function whenImportsReady(cb) {
    if (window.HTMLImports) {
      HTMLImports.whenReady(cb);
    } else {
      cb();
    }
  }

  /**
   * Convenience method for importing an HTML document imperatively.
   *
   * This method creates a new `<link rel="import">` element with
   * the provided URL and appends it to the document to start loading.
   * In the `onload` callback, the `import` property of the `link`
   * element will contain the imported document contents.
   *
   * @memberof Polymer
   * @param {string} href URL to document to load.
   * @param {?function(!Event):void=} onload Callback to notify when an import successfully
   *   loaded.
   * @param {?function(!ErrorEvent):void=} onerror Callback to notify when an import
   *   unsuccessfully loaded.
   * @param {boolean=} optAsync True if the import should be loaded `async`.
   *   Defaults to `false`.
   * @return {!HTMLLinkElement} The link element for the URL to be loaded.
   */
  Polymer.importHref = function(href, onload, onerror, optAsync) {
    let link = /** @type {HTMLLinkElement} */
      (document.head.querySelector('link[href="' + href + '"][import-href]'));
    if (!link) {
      link = /** @type {HTMLLinkElement} */ (document.createElement('link'));
      link.rel = 'import';
      link.href = href;
      link.setAttribute('import-href', '');
    }
    // always ensure link has `async` attribute if user specified one,
    // even if it was previously not async. This is considered less confusing.
    if (optAsync) {
      link.setAttribute('async', '');
    }
    // NOTE: the link may now be in 3 states: (1) pending insertion,
    // (2) inflight, (3) already loaded. In each case, we need to add
    // event listeners to process callbacks.
    let cleanup = function() {
      link.removeEventListener('load', loadListener);
      link.removeEventListener('error', errorListener);
    };
    let loadListener = function(event) {
      cleanup();
      // In case of a successful load, cache the load event on the link so
      // that it can be used to short-circuit this method in the future when
      // it is called with the same href param.
      link.__dynamicImportLoaded = true;
      if (onload) {
        whenImportsReady(() => {
          onload(event);
        });
      }
    };
    let errorListener = function(event) {
      cleanup();
      // In case of an error, remove the link from the document so that it
      // will be automatically created again the next time `importHref` is
      // called.
      if (link.parentNode) {
        link.parentNode.removeChild(link);
      }
      if (onerror) {
        whenImportsReady(() => {
          onerror(event);
        });
      }
    };
    link.addEventListener('load', loadListener);
    link.addEventListener('error', errorListener);
    if (link.parentNode == null) {
      document.head.appendChild(link);
    // if the link already loaded, dispatch a fake load event
    // so that listeners are called and get a proper event argument.
    } else if (link.__dynamicImportLoaded) {
      link.dispatchEvent(new Event('load'));
    }
    return link;
  };

})();


(function() {

  'use strict';

  let scheduled = false;
  let beforeRenderQueue = [];
  let afterRenderQueue = [];

  function schedule() {
    scheduled = true;
    // before next render
    requestAnimationFrame(function() {
      scheduled = false;
      flushQueue(beforeRenderQueue);
      // after the render
      setTimeout(function() {
        runQueue(afterRenderQueue);
      });
    });
  }

  function flushQueue(queue) {
    while (queue.length) {
      callMethod(queue.shift());
    }
  }

  function runQueue(queue) {
    for (let i=0, l=queue.length; i < l; i++) {
      callMethod(queue.shift());
    }
  }

  function callMethod(info) {
    const context = info[0];
    const callback = info[1];
    const args = info[2];
    try {
      callback.apply(context, args);
    } catch(e) {
      setTimeout(() => {
        throw e;
      });
    }
  }

  function flush() {
    while (beforeRenderQueue.length || afterRenderQueue.length) {
      flushQueue(beforeRenderQueue);
      flushQueue(afterRenderQueue);
    }
    scheduled = false;
  }

  /**
   * Module for scheduling flushable pre-render and post-render tasks.
   *
   * @namespace
   * @memberof Polymer
   * @summary Module for scheduling flushable pre-render and post-render tasks.
   */
  Polymer.RenderStatus = {

    /**
     * Enqueues a callback which will be run before the next render, at
     * `requestAnimationFrame` timing.
     *
     * This method is useful for enqueuing work that requires DOM measurement,
     * since measurement may not be reliable in custom element callbacks before
     * the first render, as well as for batching measurement tasks in general.
     *
     * Tasks in this queue may be flushed by calling `Polymer.RenderStatus.flush()`.
     *
     * @memberof Polymer.RenderStatus
     * @param {*} context Context object the callback function will be bound to
     * @param {function(...*):void} callback Callback function
     * @param {!Array=} args An array of arguments to call the callback function with
     * @return {void}
     */
    beforeNextRender: function(context, callback, args) {
      if (!scheduled) {
        schedule();
      }
      beforeRenderQueue.push([context, callback, args]);
    },

    /**
     * Enqueues a callback which will be run after the next render, equivalent
     * to one task (`setTimeout`) after the next `requestAnimationFrame`.
     *
     * This method is useful for tuning the first-render performance of an
     * element or application by deferring non-critical work until after the
     * first paint.  Typical non-render-critical work may include adding UI
     * event listeners and aria attributes.
     *
     * @memberof Polymer.RenderStatus
     * @param {*} context Context object the callback function will be bound to
     * @param {function(...*):void} callback Callback function
     * @param {!Array=} args An array of arguments to call the callback function with
     * @return {void}
     */
    afterNextRender: function(context, callback, args) {
      if (!scheduled) {
        schedule();
      }
      afterRenderQueue.push([context, callback, args]);
    },

    /**
     * Flushes all `beforeNextRender` tasks, followed by all `afterNextRender`
     * tasks.
     *
     * @memberof Polymer.RenderStatus
     * @return {void}
     */
    flush: flush

  };

})();


(function() {
  'use strict';

  // unresolved

  function resolve() {
    document.body.removeAttribute('unresolved');
  }

  if (window.WebComponents) {
    window.addEventListener('WebComponentsReady', resolve);
  } else {
    if (document.readyState === 'interactive' || document.readyState === 'complete') {
      resolve();
    } else {
      window.addEventListener('DOMContentLoaded', resolve);
    }
  }

})();


(function() {

  'use strict';

  function newSplice(index, removed, addedCount) {
    return {
      index: index,
      removed: removed,
      addedCount: addedCount
    };
  }

  const EDIT_LEAVE = 0;
  const EDIT_UPDATE = 1;
  const EDIT_ADD = 2;
  const EDIT_DELETE = 3;

  // Note: This function is *based* on the computation of the Levenshtein
  // "edit" distance. The one change is that "updates" are treated as two
  // edits - not one. With Array splices, an update is really a delete
  // followed by an add. By retaining this, we optimize for "keeping" the
  // maximum array items in the original array. For example:
  //
  //   'xxxx123' -> '123yyyy'
  //
  // With 1-edit updates, the shortest path would be just to update all seven
  // characters. With 2-edit updates, we delete 4, leave 3, and add 4. This
  // leaves the substring '123' intact.
  function calcEditDistances(current, currentStart, currentEnd,
                              old, oldStart, oldEnd) {
    // "Deletion" columns
    let rowCount = oldEnd - oldStart + 1;
    let columnCount = currentEnd - currentStart + 1;
    let distances = new Array(rowCount);

    // "Addition" rows. Initialize null column.
    for (let i = 0; i < rowCount; i++) {
      distances[i] = new Array(columnCount);
      distances[i][0] = i;
    }

    // Initialize null row
    for (let j = 0; j < columnCount; j++)
      distances[0][j] = j;

    for (let i = 1; i < rowCount; i++) {
      for (let j = 1; j < columnCount; j++) {
        if (equals(current[currentStart + j - 1], old[oldStart + i - 1]))
          distances[i][j] = distances[i - 1][j - 1];
        else {
          let north = distances[i - 1][j] + 1;
          let west = distances[i][j - 1] + 1;
          distances[i][j] = north < west ? north : west;
        }
      }
    }

    return distances;
  }

  // This starts at the final weight, and walks "backward" by finding
  // the minimum previous weight recursively until the origin of the weight
  // matrix.
  function spliceOperationsFromEditDistances(distances) {
    let i = distances.length - 1;
    let j = distances[0].length - 1;
    let current = distances[i][j];
    let edits = [];
    while (i > 0 || j > 0) {
      if (i == 0) {
        edits.push(EDIT_ADD);
        j--;
        continue;
      }
      if (j == 0) {
        edits.push(EDIT_DELETE);
        i--;
        continue;
      }
      let northWest = distances[i - 1][j - 1];
      let west = distances[i - 1][j];
      let north = distances[i][j - 1];

      let min;
      if (west < north)
        min = west < northWest ? west : northWest;
      else
        min = north < northWest ? north : northWest;

      if (min == northWest) {
        if (northWest == current) {
          edits.push(EDIT_LEAVE);
        } else {
          edits.push(EDIT_UPDATE);
          current = northWest;
        }
        i--;
        j--;
      } else if (min == west) {
        edits.push(EDIT_DELETE);
        i--;
        current = west;
      } else {
        edits.push(EDIT_ADD);
        j--;
        current = north;
      }
    }

    edits.reverse();
    return edits;
  }

  /**
   * Splice Projection functions:
   *
   * A splice map is a representation of how a previous array of items
   * was transformed into a new array of items. Conceptually it is a list of
   * tuples of
   *
   *   <index, removed, addedCount>
   *
   * which are kept in ascending index order of. The tuple represents that at
   * the |index|, |removed| sequence of items were removed, and counting forward
   * from |index|, |addedCount| items were added.
   */

  /**
   * Lacking individual splice mutation information, the minimal set of
   * splices can be synthesized given the previous state and final state of an
   * array. The basic approach is to calculate the edit distance matrix and
   * choose the shortest path through it.
   *
   * Complexity: O(l * p)
   *   l: The length of the current array
   *   p: The length of the old array
   *
   * @param {!Array} current The current "changed" array for which to
   * calculate splices.
   * @param {number} currentStart Starting index in the `current` array for
   * which splices are calculated.
   * @param {number} currentEnd Ending index in the `current` array for
   * which splices are calculated.
   * @param {!Array} old The original "unchanged" array to compare `current`
   * against to determine splices.
   * @param {number} oldStart Starting index in the `old` array for
   * which splices are calculated.
   * @param {number} oldEnd Ending index in the `old` array for
   * which splices are calculated.
   * @return {!Array} Returns an array of splice record objects. Each of these
   * contains: `index` the location where the splice occurred; `removed`
   * the array of removed items from this location; `addedCount` the number
   * of items added at this location.
   */
  function calcSplices(current, currentStart, currentEnd,
                        old, oldStart, oldEnd) {
    let prefixCount = 0;
    let suffixCount = 0;
    let splice;

    let minLength = Math.min(currentEnd - currentStart, oldEnd - oldStart);
    if (currentStart == 0 && oldStart == 0)
      prefixCount = sharedPrefix(current, old, minLength);

    if (currentEnd == current.length && oldEnd == old.length)
      suffixCount = sharedSuffix(current, old, minLength - prefixCount);

    currentStart += prefixCount;
    oldStart += prefixCount;
    currentEnd -= suffixCount;
    oldEnd -= suffixCount;

    if (currentEnd - currentStart == 0 && oldEnd - oldStart == 0)
      return [];

    if (currentStart == currentEnd) {
      splice = newSplice(currentStart, [], 0);
      while (oldStart < oldEnd)
        splice.removed.push(old[oldStart++]);

      return [ splice ];
    } else if (oldStart == oldEnd)
      return [ newSplice(currentStart, [], currentEnd - currentStart) ];

    let ops = spliceOperationsFromEditDistances(
        calcEditDistances(current, currentStart, currentEnd,
                               old, oldStart, oldEnd));

    splice = undefined;
    let splices = [];
    let index = currentStart;
    let oldIndex = oldStart;
    for (let i = 0; i < ops.length; i++) {
      switch(ops[i]) {
        case EDIT_LEAVE:
          if (splice) {
            splices.push(splice);
            splice = undefined;
          }

          index++;
          oldIndex++;
          break;
        case EDIT_UPDATE:
          if (!splice)
            splice = newSplice(index, [], 0);

          splice.addedCount++;
          index++;

          splice.removed.push(old[oldIndex]);
          oldIndex++;
          break;
        case EDIT_ADD:
          if (!splice)
            splice = newSplice(index, [], 0);

          splice.addedCount++;
          index++;
          break;
        case EDIT_DELETE:
          if (!splice)
            splice = newSplice(index, [], 0);

          splice.removed.push(old[oldIndex]);
          oldIndex++;
          break;
      }
    }

    if (splice) {
      splices.push(splice);
    }
    return splices;
  }

  function sharedPrefix(current, old, searchLength) {
    for (let i = 0; i < searchLength; i++)
      if (!equals(current[i], old[i]))
        return i;
    return searchLength;
  }

  function sharedSuffix(current, old, searchLength) {
    let index1 = current.length;
    let index2 = old.length;
    let count = 0;
    while (count < searchLength && equals(current[--index1], old[--index2]))
      count++;

    return count;
  }

  function calculateSplices(current, previous) {
    return calcSplices(current, 0, current.length, previous, 0,
                            previous.length);
  }

  function equals(currentValue, previousValue) {
    return currentValue === previousValue;
  }

  /**
   * @namespace
   * @memberof Polymer
   * @summary Module that provides utilities for diffing arrays.
   */
  Polymer.ArraySplice = {
    /**
     * Returns an array of splice records indicating the minimum edits required
     * to transform the `previous` array into the `current` array.
     *
     * Splice records are ordered by index and contain the following fields:
     * - `index`: index where edit started
     * - `removed`: array of removed items from this index
     * - `addedCount`: number of items added at this index
     *
     * This function is based on the Levenshtein "minimum edit distance"
     * algorithm. Note that updates are treated as removal followed by addition.
     *
     * The worst-case time complexity of this algorithm is `O(l * p)`
     *   l: The length of the current array
     *   p: The length of the previous array
     *
     * However, the worst-case complexity is reduced by an `O(n)` optimization
     * to detect any shared prefix & suffix between the two arrays and only
     * perform the more expensive minimum edit distance calculation over the
     * non-shared portions of the arrays.
     *
     * @function
     * @memberof Polymer.ArraySplice
     * @param {!Array} current The "changed" array for which splices will be
     * calculated.
     * @param {!Array} previous The "unchanged" original array to compare
     * `current` against to determine the splices.
     * @return {!Array} Returns an array of splice record objects. Each of these
     * contains: `index` the location where the splice occurred; `removed`
     * the array of removed items from this location; `addedCount` the number
     * of items added at this location.
     */
    calculateSplices
  };

})();


(function() {
  'use strict';

  /**
   * Returns true if `node` is a slot element
   * @param {Node} node Node to test.
   * @return {boolean} Returns true if the given `node` is a slot
   * @private
   */
  function isSlot(node) {
    return (node.localName === 'slot');
  }

  /**
   * Class that listens for changes (additions or removals) to
   * "flattened nodes" on a given `node`. The list of flattened nodes consists
   * of a node's children and, for any children that are `<slot>` elements,
   * the expanded flattened list of `assignedNodes`.
   * For example, if the observed node has children `<a></a><slot></slot><b></b>`
   * and the `<slot>` has one `<div>` assigned to it, then the flattened
   * nodes list is `<a></a><div></div><b></b>`. If the `<slot>` has other
   * `<slot>` elements assigned to it, these are flattened as well.
   *
   * The provided `callback` is called whenever any change to this list
   * of flattened nodes occurs, where an addition or removal of a node is
   * considered a change. The `callback` is called with one argument, an object
   * containing an array of any `addedNodes` and `removedNodes`.
   *
   * Note: the callback is called asynchronous to any changes
   * at a microtask checkpoint. This is because observation is performed using
   * `MutationObserver` and the `<slot>` element's `slotchange` event which
   * are asynchronous.
   *
   * An example:
   * ```js
   * class TestSelfObserve extends Polymer.Element {
   *   static get is() { return 'test-self-observe';}
   *   connectedCallback() {
   *     super.connectedCallback();
   *     this._observer = new Polymer.FlattenedNodesObserver(this, (info) => {
   *       this.info = info;
   *     });
   *   }
   *   disconnectedCallback() {
   *     super.disconnectedCallback();
   *     this._observer.disconnect();
   *   }
   * }
   * customElements.define(TestSelfObserve.is, TestSelfObserve);
   * ```
   *
   * @memberof Polymer
   * @summary Class that listens for changes (additions or removals) to
   * "flattened nodes" on a given `node`.
   */
  class FlattenedNodesObserver {

    /**
     * Returns the list of flattened nodes for the given `node`.
     * This list consists of a node's children and, for any children
     * that are `<slot>` elements, the expanded flattened list of `assignedNodes`.
     * For example, if the observed node has children `<a></a><slot></slot><b></b>`
     * and the `<slot>` has one `<div>` assigned to it, then the flattened
     * nodes list is `<a></a><div></div><b></b>`. If the `<slot>` has other
     * `<slot>` elements assigned to it, these are flattened as well.
     *
     * @param {HTMLElement|HTMLSlotElement} node The node for which to return the list of flattened nodes.
     * @return {Array} The list of flattened nodes for the given `node`.
    */
    static getFlattenedNodes(node) {
      if (isSlot(node)) {
        node = /** @type {HTMLSlotElement} */(node); // eslint-disable-line no-self-assign
        return node.assignedNodes({flatten: true});
      } else {
        return Array.from(node.childNodes).map((node) => {
          if (isSlot(node)) {
            node = /** @type {HTMLSlotElement} */(node); // eslint-disable-line no-self-assign
            return node.assignedNodes({flatten: true});
          } else {
            return [node];
          }
        }).reduce((a, b) => a.concat(b), []);
      }
    }

    /**
     * @param {Element} target Node on which to listen for changes.
     * @param {?function(!Element, { target: !Element, addedNodes: !Array<!Element>, removedNodes: !Array<!Element> }):void} callback Function called when there are additions
     * or removals from the target's list of flattened nodes.
    */
    constructor(target, callback) {
      /**
       * @type {MutationObserver}
       * @private
       */
      this._shadyChildrenObserver = null;
      /**
       * @type {MutationObserver}
       * @private
       */
      this._nativeChildrenObserver = null;
      this._connected = false;
      /**
       * @type {Element}
       * @private
       */
      this._target = target;
      this.callback = callback;
      this._effectiveNodes = [];
      this._observer = null;
      this._scheduled = false;
      /**
       * @type {function()}
       * @private
       */
      this._boundSchedule = () => {
        this._schedule();
      };
      this.connect();
      this._schedule();
    }

    /**
     * Activates an observer. This method is automatically called when
     * a `FlattenedNodesObserver` is created. It should only be called to
     * re-activate an observer that has been deactivated via the `disconnect` method.
     *
     * @return {void}
     */
    connect() {
      if (isSlot(this._target)) {
        this._listenSlots([this._target]);
      } else if (this._target.children) {
        this._listenSlots(this._target.children);
        if (window.ShadyDOM) {
          this._shadyChildrenObserver =
            ShadyDOM.observeChildren(this._target, (mutations) => {
              this._processMutations(mutations);
            });
        } else {
          this._nativeChildrenObserver =
            new MutationObserver((mutations) => {
              this._processMutations(mutations);
            });
          this._nativeChildrenObserver.observe(this._target, {childList: true});
        }
      }
      this._connected = true;
    }

    /**
     * Deactivates the flattened nodes observer. After calling this method
     * the observer callback will not be called when changes to flattened nodes
     * occur. The `connect` method may be subsequently called to reactivate
     * the observer.
     *
     * @return {void}
     */
    disconnect() {
      if (isSlot(this._target)) {
        this._unlistenSlots([this._target]);
      } else if (this._target.children) {
        this._unlistenSlots(this._target.children);
        if (window.ShadyDOM && this._shadyChildrenObserver) {
          ShadyDOM.unobserveChildren(this._shadyChildrenObserver);
          this._shadyChildrenObserver = null;
        } else if (this._nativeChildrenObserver) {
          this._nativeChildrenObserver.disconnect();
          this._nativeChildrenObserver = null;
        }
      }
      this._connected = false;
    }

    /**
     * @return {void}
     * @private
     */
    _schedule() {
      if (!this._scheduled) {
        this._scheduled = true;
        Polymer.Async.microTask.run(() => this.flush());
      }
    }

    /**
     * @param {Array<MutationRecord>} mutations Mutations signaled by the mutation observer
     * @return {void}
     * @private
     */
    _processMutations(mutations) {
      this._processSlotMutations(mutations);
      this.flush();
    }

    /**
     * @param {Array<MutationRecord>} mutations Mutations signaled by the mutation observer
     * @return {void}
     * @private
     */
    _processSlotMutations(mutations) {
      if (mutations) {
        for (let i=0; i < mutations.length; i++) {
          let mutation = mutations[i];
          if (mutation.addedNodes) {
            this._listenSlots(mutation.addedNodes);
          }
          if (mutation.removedNodes) {
            this._unlistenSlots(mutation.removedNodes);
          }
        }
      }
    }

    /**
     * Flushes the observer causing any pending changes to be immediately
     * delivered the observer callback. By default these changes are delivered
     * asynchronously at the next microtask checkpoint.
     *
     * @return {boolean} Returns true if any pending changes caused the observer
     * callback to run.
     */
    flush() {
      if (!this._connected) {
        return false;
      }
      if (window.ShadyDOM) {
        ShadyDOM.flush();
      }
      if (this._nativeChildrenObserver) {
        this._processSlotMutations(this._nativeChildrenObserver.takeRecords());
      } else if (this._shadyChildrenObserver) {
        this._processSlotMutations(this._shadyChildrenObserver.takeRecords());
      }
      this._scheduled = false;
      let info = {
        target: this._target,
        addedNodes: [],
        removedNodes: []
      };
      let newNodes = this.constructor.getFlattenedNodes(this._target);
      let splices = Polymer.ArraySplice.calculateSplices(newNodes,
        this._effectiveNodes);
      // process removals
      for (let i=0, s; (i<splices.length) && (s=splices[i]); i++) {
        for (let j=0, n; (j < s.removed.length) && (n=s.removed[j]); j++) {
          info.removedNodes.push(n);
        }
      }
      // process adds
      for (let i=0, s; (i<splices.length) && (s=splices[i]); i++) {
        for (let j=s.index; j < s.index + s.addedCount; j++) {
          info.addedNodes.push(newNodes[j]);
        }
      }
      // update cache
      this._effectiveNodes = newNodes;
      let didFlush = false;
      if (info.addedNodes.length || info.removedNodes.length) {
        didFlush = true;
        this.callback.call(this._target, info);
      }
      return didFlush;
    }

    /**
     * @param {!Array<Element|Node>|!NodeList<Node>} nodeList Nodes that could change
     * @return {void}
     * @private
     */
    _listenSlots(nodeList) {
      for (let i=0; i < nodeList.length; i++) {
        let n = nodeList[i];
        if (isSlot(n)) {
          n.addEventListener('slotchange', this._boundSchedule);
        }
      }
    }

    /**
     * @param {!Array<Element|Node>|!NodeList<Node>} nodeList Nodes that could change
     * @return {void}
     * @private
     */
    _unlistenSlots(nodeList) {
      for (let i=0; i < nodeList.length; i++) {
        let n = nodeList[i];
        if (isSlot(n)) {
          n.removeEventListener('slotchange', this._boundSchedule);
        }
      }
    }

  }

  Polymer.FlattenedNodesObserver = FlattenedNodesObserver;

})();


(function() {
  'use strict';

  let debouncerQueue = [];

  /**
   * Adds a `Polymer.Debouncer` to a list of globally flushable tasks.
   *
   * @memberof Polymer
   * @param {!Polymer.Debouncer} debouncer Debouncer to enqueue
   * @return {void}
   */
  Polymer.enqueueDebouncer = function(debouncer) {
    debouncerQueue.push(debouncer);
  };

  function flushDebouncers() {
    const didFlush = Boolean(debouncerQueue.length);
    while (debouncerQueue.length) {
      try {
        debouncerQueue.shift().flush();
      } catch(e) {
        setTimeout(() => {
          throw e;
        });
      }
    }
    return didFlush;
  }

  /**
   * Forces several classes of asynchronously queued tasks to flush:
   * - Debouncers added via `enqueueDebouncer`
   * - ShadyDOM distribution
   *
   * @memberof Polymer
   * @return {void}
   */
  Polymer.flush = function() {
    let shadyDOM, debouncers;
    do {
      shadyDOM = window.ShadyDOM && ShadyDOM.flush();
      if (window.ShadyCSS && window.ShadyCSS.ScopingShim) {
        window.ShadyCSS.ScopingShim.flush();
      }
      debouncers = flushDebouncers();
    } while (shadyDOM || debouncers);
  };

})();


(function() {
  'use strict';

  const p = Element.prototype;
  /**
   * @const {function(this:Node, string): boolean}
   */
  const normalizedMatchesSelector = p.matches || p.matchesSelector ||
    p.mozMatchesSelector || p.msMatchesSelector ||
    p.oMatchesSelector || p.webkitMatchesSelector;

  /**
   * Cross-platform `element.matches` shim.
   *
   * @function matchesSelector
   * @memberof Polymer.dom
   * @param {!Node} node Node to check selector against
   * @param {string} selector Selector to match
   * @return {boolean} True if node matched selector
   */
  const matchesSelector = function(node, selector) {
    return normalizedMatchesSelector.call(node, selector);
  };

  /**
   * Node API wrapper class returned from `Polymer.dom.(target)` when
   * `target` is a `Node`.
   *
   * @memberof Polymer
   */
  class DomApi {

    /**
     * @param {Node} node Node for which to create a Polymer.dom helper object.
     */
    constructor(node) {
      this.node = node;
    }

    /**
     * Returns an instance of `Polymer.FlattenedNodesObserver` that
     * listens for node changes on this element.
     *
     * @param {function(!Element, { target: !Element, addedNodes: !Array<!Element>, removedNodes: !Array<!Element> }):void} callback Called when direct or distributed children
     *   of this element changes
     * @return {!Polymer.FlattenedNodesObserver} Observer instance
     */
    observeNodes(callback) {
      return new Polymer.FlattenedNodesObserver(this.node, callback);
    }

    /**
     * Disconnects an observer previously created via `observeNodes`
     *
     * @param {!Polymer.FlattenedNodesObserver} observerHandle Observer instance
     *   to disconnect.
     * @return {void}
     */
    unobserveNodes(observerHandle) {
      observerHandle.disconnect();
    }

    /**
     * Provided as a backwards-compatible API only.  This method does nothing.
     * @return {void}
     */
    notifyObserver() {}

    /**
     * Returns true if the provided node is contained with this element's
     * light-DOM children or shadow root, including any nested shadow roots
     * of children therein.
     *
     * @param {Node} node Node to test
     * @return {boolean} Returns true if the given `node` is contained within
     *   this element's light or shadow DOM.
     */
    deepContains(node) {
      if (this.node.contains(node)) {
        return true;
      }
      let n = node;
      let doc = node.ownerDocument;
      // walk from node to `this` or `document`
      while (n && n !== doc && n !== this.node) {
        // use logical parentnode, or native ShadowRoot host
        n = n.parentNode || n.host;
      }
      return n === this.node;
    }

    /**
     * Returns the root node of this node.  Equivalent to `getRoodNode()`.
     *
     * @return {Node} Top most element in the dom tree in which the node
     * exists. If the node is connected to a document this is either a
     * shadowRoot or the document; otherwise, it may be the node
     * itself or a node or document fragment containing it.
     */
    getOwnerRoot() {
      return this.node.getRootNode();
    }

    /**
     * For slot elements, returns the nodes assigned to the slot; otherwise
     * an empty array. It is equivalent to `<slot>.addignedNodes({flatten:true})`.
     *
     * @return {!Array<!Node>} Array of assigned nodes
     */
    getDistributedNodes() {
      return (this.node.localName === 'slot') ?
        this.node.assignedNodes({flatten: true}) :
        [];
    }

    /**
     * Returns an array of all slots this element was distributed to.
     *
     * @return {!Array<!HTMLSlotElement>} Description
     */
    getDestinationInsertionPoints() {
      let ip$ = [];
      let n = this.node.assignedSlot;
      while (n) {
        ip$.push(n);
        n = n.assignedSlot;
      }
      return ip$;
    }

    /**
     * Calls `importNode` on the `ownerDocument` for this node.
     *
     * @param {!Node} node Node to import
     * @param {boolean} deep True if the node should be cloned deeply during
     *   import
     * @return {Node} Clone of given node imported to this owner document
     */
    importNode(node, deep) {
      let doc = this.node instanceof Document ? this.node :
        this.node.ownerDocument;
      return doc.importNode(node, deep);
    }

    /**
     * @return {!Array<!Node>} Returns a flattened list of all child nodes and
     * nodes assigned to child slots.
     */
    getEffectiveChildNodes() {
      return Polymer.FlattenedNodesObserver.getFlattenedNodes(this.node);
    }

    /**
     * Returns a filtered list of flattened child elements for this element based
     * on the given selector.
     *
     * @param {string} selector Selector to filter nodes against
     * @return {!Array<!HTMLElement>} List of flattened child elements
     */
    queryDistributedElements(selector) {
      let c$ = this.getEffectiveChildNodes();
      let list = [];
      for (let i=0, l=c$.length, c; (i<l) && (c=c$[i]); i++) {
        if ((c.nodeType === Node.ELEMENT_NODE) &&
            matchesSelector(c, selector)) {
          list.push(c);
        }
      }
      return list;
    }

    /**
     * For shadow roots, returns the currently focused element within this
     * shadow root.
     *
     * @return {Node|undefined} Currently focused element
     */
    get activeElement() {
      let node = this.node;
      return node._activeElement !== undefined ? node._activeElement : node.activeElement;
    }
  }

  function forwardMethods(proto, methods) {
    for (let i=0; i < methods.length; i++) {
      let method = methods[i];
      /* eslint-disable valid-jsdoc */
      proto[method] = /** @this {DomApi} */ function() {
        return this.node[method].apply(this.node, arguments);
      };
      /* eslint-enable */
    }
  }

  function forwardReadOnlyProperties(proto, properties) {
    for (let i=0; i < properties.length; i++) {
      let name = properties[i];
      Object.defineProperty(proto, name, {
        get: function() {
          const domApi = /** @type {DomApi} */(this);
          return domApi.node[name];
        },
        configurable: true
      });
    }
  }

  function forwardProperties(proto, properties) {
    for (let i=0; i < properties.length; i++) {
      let name = properties[i];
      Object.defineProperty(proto, name, {
        get: function() {
          const domApi = /** @type {DomApi} */(this);
          return domApi.node[name];
        },
        set: function(value) {
          /** @type {DomApi} */ (this).node[name] = value;
        },
        configurable: true
      });
    }
  }

  forwardMethods(DomApi.prototype, [
    'cloneNode', 'appendChild', 'insertBefore', 'removeChild',
    'replaceChild', 'setAttribute', 'removeAttribute',
    'querySelector', 'querySelectorAll'
  ]);

  forwardReadOnlyProperties(DomApi.prototype, [
    'parentNode', 'firstChild', 'lastChild',
    'nextSibling', 'previousSibling', 'firstElementChild',
    'lastElementChild', 'nextElementSibling', 'previousElementSibling',
    'childNodes', 'children', 'classList'
  ]);

  forwardProperties(DomApi.prototype, [
    'textContent', 'innerHTML'
  ]);


  /**
   * Event API wrapper class returned from `Polymer.dom.(target)` when
   * `target` is an `Event`.
   */
  class EventApi {
    constructor(event) {
      this.event = event;
    }

    /**
     * Returns the first node on the `composedPath` of this event.
     *
     * @return {!EventTarget} The node this event was dispatched to
     */
    get rootTarget() {
      return this.event.composedPath()[0];
    }

    /**
     * Returns the local (re-targeted) target for this event.
     *
     * @return {!EventTarget} The local (re-targeted) target for this event.
     */
    get localTarget() {
      return this.event.target;
    }

    /**
     * Returns the `composedPath` for this event.
     * @return {!Array<!EventTarget>} The nodes this event propagated through
     */
    get path() {
      return this.event.composedPath();
    }
  }

  Polymer.DomApi = DomApi;

  /**
   * @function
   * @param {boolean=} deep
   * @return {!Node}
   */
  Polymer.DomApi.prototype.cloneNode;
  /**
   * @function
   * @param {!Node} node
   * @return {!Node}
   */
  Polymer.DomApi.prototype.appendChild;
  /**
   * @function
   * @param {!Node} newChild
   * @param {Node} refChild
   * @return {!Node}
   */
  Polymer.DomApi.prototype.insertBefore;
  /**
   * @function
   * @param {!Node} node
   * @return {!Node}
   */
  Polymer.DomApi.prototype.removeChild;
  /**
   * @function
   * @param {!Node} oldChild
   * @param {!Node} newChild
   * @return {!Node}
   */
  Polymer.DomApi.prototype.replaceChild;
  /**
   * @function
   * @param {string} name
   * @param {string} value
   * @return {void}
   */
  Polymer.DomApi.prototype.setAttribute;
  /**
   * @function
   * @param {string} name
   * @return {void}
   */
  Polymer.DomApi.prototype.removeAttribute;
  /**
   * @function
   * @param {string} selector
   * @return {?Element}
   */
  Polymer.DomApi.prototype.querySelector;
  /**
   * @function
   * @param {string} selector
   * @return {!NodeList<!Element>}
   */
  Polymer.DomApi.prototype.querySelectorAll;

  /**
   * Legacy DOM and Event manipulation API wrapper factory used to abstract
   * differences between native Shadow DOM and "Shady DOM" when polyfilling on
   * older browsers.
   *
   * Note that in Polymer 2.x use of `Polymer.dom` is no longer required and
   * in the majority of cases simply facades directly to the standard native
   * API.
   *
   * @namespace
   * @summary Legacy DOM and Event manipulation API wrapper factory used to
   * abstract differences between native Shadow DOM and "Shady DOM."
   * @memberof Polymer
   * @param {(Node|Event)=} obj Node or event to operate on
   * @return {!DomApi|!EventApi} Wrapper providing either node API or event API
   */
  Polymer.dom = function(obj) {
    obj = obj || document;
    if (!obj.__domApi) {
      let helper;
      if (obj instanceof Event) {
        helper = new EventApi(obj);
      } else {
        helper = new DomApi(obj);
      }
      obj.__domApi = helper;
    }
    return obj.__domApi;
  };

  Polymer.dom.matchesSelector = matchesSelector;

  /**
   * Forces several classes of asynchronously queued tasks to flush:
   * - Debouncers added via `Polymer.enqueueDebouncer`
   * - ShadyDOM distribution
   *
   * This method facades to `Polymer.flush`.
   *
   * @memberof Polymer.dom
   */
  Polymer.dom.flush = Polymer.flush;

  /**
   * Adds a `Polymer.Debouncer` to a list of globally flushable tasks.
   *
   * This method facades to `Polymer.enqueueDebouncer`.
   *
   * @memberof Polymer.dom
   * @param {!Polymer.Debouncer} debouncer Debouncer to enqueue
   */
  Polymer.dom.addDebouncer = Polymer.enqueueDebouncer;
})();


(function() {

  'use strict';

  let styleInterface = window.ShadyCSS;

  /**
   * Element class mixin that provides Polymer's "legacy" API intended to be
   * backward-compatible to the greatest extent possible with the API
   * found on the Polymer 1.x `Polymer.Base` prototype applied to all elements
   * defined using the `Polymer({...})` function.
   *
   * @mixinFunction
   * @polymer
   * @appliesMixin Polymer.ElementMixin
   * @appliesMixin Polymer.GestureEventListeners
   * @property isAttached {boolean} Set to `true` in this element's
   *   `connectedCallback` and `false` in `disconnectedCallback`
   * @memberof Polymer
   * @summary Element class mixin that provides Polymer's "legacy" API
   */
  Polymer.LegacyElementMixin = Polymer.dedupingMixin((base) => {

    /**
     * @constructor
     * @extends {base}
     * @implements {Polymer_ElementMixin}
     * @implements {Polymer_GestureEventListeners}
     * @implements {Polymer_DirMixin}
     * @private
     */
    const legacyElementBase = Polymer.DirMixin(Polymer.GestureEventListeners(Polymer.ElementMixin(base)));

    /**
     * Map of simple names to touch action names
     * @dict
     */
    const DIRECTION_MAP = {
      'x': 'pan-x',
      'y': 'pan-y',
      'none': 'none',
      'all': 'auto'
    };

    /**
     * @polymer
     * @mixinClass
     * @extends {legacyElementBase}
     * @implements {Polymer_LegacyElementMixin}
     * @unrestricted
     */
    class LegacyElement extends legacyElementBase {

      constructor() {
        super();
        /** @type {boolean} */
        this.isAttached;
        /** @type {WeakMap<!Element, !Object<string, !Function>>} */
        this.__boundListeners;
        /** @type {Object<string, Function>} */
        this._debouncers;
      }

      /**
       * Forwards `importMeta` from the prototype (i.e. from the info object
       * passed to `Polymer({...})`) to the static API.
       *
       * @return {!Object} The `import.meta` object set on the prototype
       * @suppress {missingProperties} `this` is always in the instance in
       *  closure for some reason even in a static method, rather than the class
       */
      static get importMeta() {
        return this.prototype.importMeta;
      }

      /**
       * Legacy callback called during the `constructor`, for overriding
       * by the user.
       * @return {void}
       */
      created() {}

      /**
       * Provides an implementation of `connectedCallback`
       * which adds Polymer legacy API's `attached` method.
       * @return {void}
       * @override
       */
      connectedCallback() {
        super.connectedCallback();
        this.isAttached = true;
        this.attached();
      }

      /**
       * Legacy callback called during `connectedCallback`, for overriding
       * by the user.
       * @return {void}
       */
      attached() {}

      /**
       * Provides an implementation of `disconnectedCallback`
       * which adds Polymer legacy API's `detached` method.
       * @return {void}
       * @override
       */
      disconnectedCallback() {
        super.disconnectedCallback();
        this.isAttached = false;
        this.detached();
      }

      /**
       * Legacy callback called during `disconnectedCallback`, for overriding
       * by the user.
       * @return {void}
       */
      detached() {}

      /**
       * Provides an override implementation of `attributeChangedCallback`
       * which adds the Polymer legacy API's `attributeChanged` method.
       * @param {string} name Name of attribute.
       * @param {?string} old Old value of attribute.
       * @param {?string} value Current value of attribute.
       * @param {?string} namespace Attribute namespace.
       * @return {void}
       * @override
       */
      attributeChangedCallback(name, old, value, namespace) {
        if (old !== value) {
          super.attributeChangedCallback(name, old, value, namespace);
          this.attributeChanged(name, old, value);
        }
      }

      /**
       * Legacy callback called during `attributeChangedChallback`, for overriding
       * by the user.
       * @param {string} name Name of attribute.
       * @param {?string} old Old value of attribute.
       * @param {?string} value Current value of attribute.
       * @return {void}
       */
      attributeChanged(name, old, value) {} // eslint-disable-line no-unused-vars

      /**
       * Overrides the default `Polymer.PropertyEffects` implementation to
       * add support for class initialization via the `_registered` callback.
       * This is called only when the first instance of the element is created.
       *
       * @return {void}
       * @override
       * @suppress {invalidCasts}
       */
      _initializeProperties() {
        let proto = Object.getPrototypeOf(this);
        if (!proto.hasOwnProperty('__hasRegisterFinished')) {
          this._registered();
          // backstop in case the `_registered` implementation does not set this
          proto.__hasRegisterFinished = true;
        }
        super._initializeProperties();
        this.root = /** @type {HTMLElement} */(this);
        this.created();
        // Ensure listeners are applied immediately so that they are
        // added before declarative event listeners. This allows an element to
        // decorate itself via an event prior to any declarative listeners
        // seeing the event. Note, this ensures compatibility with 1.x ordering.
        this._applyListeners();
      }

      /**
       * Called automatically when an element is initializing.
       * Users may override this method to perform class registration time
       * work. The implementation should ensure the work is performed
       * only once for the class.
       * @protected
       * @return {void}
       */
      _registered() {}

      /**
       * Overrides the default `Polymer.PropertyEffects` implementation to
       * add support for installing `hostAttributes` and `listeners`.
       *
       * @return {void}
       * @override
       */
      ready() {
        this._ensureAttributes();
        super.ready();
      }

      /**
       * Ensures an element has required attributes. Called when the element
       * is being readied via `ready`. Users should override to set the
       * element's required attributes. The implementation should be sure
       * to check and not override existing attributes added by
       * the user of the element. Typically, setting attributes should be left
       * to the element user and not done here; reasonable exceptions include
       * setting aria roles and focusability.
       * @protected
       * @return {void}
       */
      _ensureAttributes() {}

      /**
       * Adds element event listeners. Called when the element
       * is being readied via `ready`. Users should override to
       * add any required element event listeners.
       * In performance critical elements, the work done here should be kept
       * to a minimum since it is done before the element is rendered. In
       * these elements, consider adding listeners asynchronously so as not to
       * block render.
       * @protected
       * @return {void}
       */
      _applyListeners() {}

      /**
       * Converts a typed JavaScript value to a string.
       *
       * Note this method is provided as backward-compatible legacy API
       * only.  It is not directly called by any Polymer features. To customize
       * how properties are serialized to attributes for attribute bindings and
       * `reflectToAttribute: true` properties as well as this method, override
       * the `_serializeValue` method provided by `Polymer.PropertyAccessors`.
       *
       * @param {*} value Value to deserialize
       * @return {string | undefined} Serialized value
       */
      serialize(value) {
        return this._serializeValue(value);
      }

      /**
       * Converts a string to a typed JavaScript value.
       *
       * Note this method is provided as backward-compatible legacy API
       * only.  It is not directly called by any Polymer features.  To customize
       * how attributes are deserialized to properties for in
       * `attributeChangedCallback`, override `_deserializeValue` method
       * provided by `Polymer.PropertyAccessors`.
       *
       * @param {string} value String to deserialize
       * @param {*} type Type to deserialize the string to
       * @return {*} Returns the deserialized value in the `type` given.
       */
      deserialize(value, type) {
        return this._deserializeValue(value, type);
      }

      /**
       * Serializes a property to its associated attribute.
       *
       * Note this method is provided as backward-compatible legacy API
       * only.  It is not directly called by any Polymer features.
       *
       * @param {string} property Property name to reflect.
       * @param {string=} attribute Attribute name to reflect.
       * @param {*=} value Property value to reflect.
       * @return {void}
       */
      reflectPropertyToAttribute(property, attribute, value) {
        this._propertyToAttribute(property, attribute, value);
      }

      /**
       * Sets a typed value to an HTML attribute on a node.
       *
       * Note this method is provided as backward-compatible legacy API
       * only.  It is not directly called by any Polymer features.
       *
       * @param {*} value Value to serialize.
       * @param {string} attribute Attribute name to serialize to.
       * @param {Element} node Element to set attribute to.
       * @return {void}
       */
      serializeValueToAttribute(value, attribute, node) {
        this._valueToNodeAttribute(/** @type {Element} */ (node || this), value, attribute);
      }

      /**
       * Copies own properties (including accessor descriptors) from a source
       * object to a target object.
       *
       * @param {Object} prototype Target object to copy properties to.
       * @param {Object} api Source object to copy properties from.
       * @return {Object} prototype object that was passed as first argument.
       */
      extend(prototype, api) {
        if (!(prototype && api)) {
          return prototype || api;
        }
        let n$ = Object.getOwnPropertyNames(api);
        for (let i=0, n; (i<n$.length) && (n=n$[i]); i++) {
          let pd = Object.getOwnPropertyDescriptor(api, n);
          if (pd) {
            Object.defineProperty(prototype, n, pd);
          }
        }
        return prototype;
      }

      /**
       * Copies props from a source object to a target object.
       *
       * Note, this method uses a simple `for...in` strategy for enumerating
       * properties.  To ensure only `ownProperties` are copied from source
       * to target and that accessor implementations are copied, use `extend`.
       *
       * @param {!Object} target Target object to copy properties to.
       * @param {!Object} source Source object to copy properties from.
       * @return {!Object} Target object that was passed as first argument.
       */
      mixin(target, source) {
        for (let i in source) {
          target[i] = source[i];
        }
        return target;
      }

      /**
       * Sets the prototype of an object.
       *
       * Note this method is provided as backward-compatible legacy API
       * only.  It is not directly called by any Polymer features.
       * @param {Object} object The object on which to set the prototype.
       * @param {Object} prototype The prototype that will be set on the given
       * `object`.
       * @return {Object} Returns the given `object` with its prototype set
       * to the given `prototype` object.
       */
      chainObject(object, prototype) {
        if (object && prototype && object !== prototype) {
          object.__proto__ = prototype;
        }
        return object;
      }

      /* **** Begin Template **** */

      /**
       * Calls `importNode` on the `content` of the `template` specified and
       * returns a document fragment containing the imported content.
       *
       * @param {HTMLTemplateElement} template HTML template element to instance.
       * @return {!DocumentFragment} Document fragment containing the imported
       *   template content.
      */
      instanceTemplate(template) {
        let content = this.constructor._contentForTemplate(template);
        let dom = /** @type {!DocumentFragment} */
          (document.importNode(content, true));
        return dom;
      }

      /* **** Begin Events **** */



      /**
       * Dispatches a custom event with an optional detail value.
       *
       * @param {string} type Name of event type.
       * @param {*=} detail Detail value containing event-specific
       *   payload.
       * @param {{ bubbles: (boolean|undefined), cancelable: (boolean|undefined), composed: (boolean|undefined) }=}
       *  options Object specifying options.  These may include:
       *  `bubbles` (boolean, defaults to `true`),
       *  `cancelable` (boolean, defaults to false), and
       *  `node` on which to fire the event (HTMLElement, defaults to `this`).
       * @return {!Event} The new event that was fired.
       */
      fire(type, detail, options) {
        options = options || {};
        detail = (detail === null || detail === undefined) ? {} : detail;
        let event = new Event(type, {
          bubbles: options.bubbles === undefined ? true : options.bubbles,
          cancelable: Boolean(options.cancelable),
          composed: options.composed === undefined ? true: options.composed
        });
        event.detail = detail;
        let node = options.node || this;
        node.dispatchEvent(event);
        return event;
      }

      /**
       * Convenience method to add an event listener on a given element,
       * late bound to a named method on this element.
       *
       * @param {Element} node Element to add event listener to.
       * @param {string} eventName Name of event to listen for.
       * @param {string} methodName Name of handler method on `this` to call.
       * @return {void}
       */
      listen(node, eventName, methodName) {
        node = /** @type {!Element} */ (node || this);
        let hbl = this.__boundListeners ||
          (this.__boundListeners = new WeakMap());
        let bl = hbl.get(node);
        if (!bl) {
          bl = {};
          hbl.set(node, bl);
        }
        let key = eventName + methodName;
        if (!bl[key]) {
          bl[key] = this._addMethodEventListenerToNode(
            node, eventName, methodName, this);
        }
      }

      /**
       * Convenience method to remove an event listener from a given element,
       * late bound to a named method on this element.
       *
       * @param {Element} node Element to remove event listener from.
       * @param {string} eventName Name of event to stop listening to.
       * @param {string} methodName Name of handler method on `this` to not call
       anymore.
       * @return {void}
       */
      unlisten(node, eventName, methodName) {
        node = /** @type {!Element} */ (node || this);
        let bl = this.__boundListeners && this.__boundListeners.get(node);
        let key = eventName + methodName;
        let handler = bl && bl[key];
        if (handler) {
          this._removeEventListenerFromNode(node, eventName, handler);
          bl[key] = null;
        }
      }

      /**
       * Override scrolling behavior to all direction, one direction, or none.
       *
       * Valid scroll directions:
       *   - 'all': scroll in any direction
       *   - 'x': scroll only in the 'x' direction
       *   - 'y': scroll only in the 'y' direction
       *   - 'none': disable scrolling for this node
       *
       * @param {string=} direction Direction to allow scrolling
       * Defaults to `all`.
       * @param {Element=} node Element to apply scroll direction setting.
       * Defaults to `this`.
       * @return {void}
       */
      setScrollDirection(direction, node) {
        Polymer.Gestures.setTouchAction(/** @type {Element} */ (node || this), DIRECTION_MAP[direction] || 'auto');
      }
      /* **** End Events **** */

      /**
       * Convenience method to run `querySelector` on this local DOM scope.
       *
       * This function calls `Polymer.dom(this.root).querySelector(slctr)`.
       *
       * @param {string} slctr Selector to run on this local DOM scope
       * @return {Element} Element found by the selector, or null if not found.
       */
      $$(slctr) {
        return this.root.querySelector(slctr);
      }

      /**
       * Return the element whose local dom within which this element
       * is contained. This is a shorthand for
       * `this.getRootNode().host`.
       * @this {Element}
       */
      get domHost() {
        let root = this.getRootNode();
        return (root instanceof DocumentFragment) ? /** @type {ShadowRoot} */ (root).host : root;
      }

      /**
       * Force this element to distribute its children to its local dom.
       * This should not be necessary as of Polymer 2.0.2 and is provided only
       * for backwards compatibility.
       * @return {void}
       */
      distributeContent() {
        if (window.ShadyDOM && this.shadowRoot) {
          ShadyDOM.flush();
        }
      }

      /**
       * Returns a list of nodes that are the effective childNodes. The effective
       * childNodes list is the same as the element's childNodes except that
       * any `<content>` elements are replaced with the list of nodes distributed
       * to the `<content>`, the result of its `getDistributedNodes` method.
       * @return {!Array<!Node>} List of effective child nodes.
       * @suppress {invalidCasts} LegacyElementMixin must be applied to an HTMLElement
       */
      getEffectiveChildNodes() {
        const thisEl = /** @type {Element} */ (this);
        const domApi = /** @type {Polymer.DomApi} */(Polymer.dom(thisEl));
        return domApi.getEffectiveChildNodes();
      }

      /**
       * Returns a list of nodes distributed within this element that match
       * `selector`. These can be dom children or elements distributed to
       * children that are insertion points.
       * @param {string} selector Selector to run.
       * @return {!Array<!Node>} List of distributed elements that match selector.
       * @suppress {invalidCasts} LegacyElementMixin must be applied to an HTMLElement
       */
      queryDistributedElements(selector) {
        const thisEl = /** @type {Element} */ (this);
        const domApi = /** @type {Polymer.DomApi} */(Polymer.dom(thisEl));
        return domApi.queryDistributedElements(selector);
      }

      /**
       * Returns a list of elements that are the effective children. The effective
       * children list is the same as the element's children except that
       * any `<content>` elements are replaced with the list of elements
       * distributed to the `<content>`.
       *
       * @return {!Array<!Node>} List of effective children.
       */
      getEffectiveChildren() {
        let list = this.getEffectiveChildNodes();
        return list.filter(function(/** @type {!Node} */ n) {
          return (n.nodeType === Node.ELEMENT_NODE);
        });
      }

      /**
       * Returns a string of text content that is the concatenation of the
       * text content's of the element's effective childNodes (the elements
       * returned by <a href="#getEffectiveChildNodes>getEffectiveChildNodes</a>.
       *
       * @return {string} List of effective children.
       */
      getEffectiveTextContent() {
        let cn = this.getEffectiveChildNodes();
        let tc = [];
        for (let i=0, c; (c = cn[i]); i++) {
          if (c.nodeType !== Node.COMMENT_NODE) {
            tc.push(c.textContent);
          }
        }
        return tc.join('');
      }

      /**
       * Returns the first effective childNode within this element that
       * match `selector`. These can be dom child nodes or elements distributed
       * to children that are insertion points.
       * @param {string} selector Selector to run.
       * @return {Node} First effective child node that matches selector.
       */
      queryEffectiveChildren(selector) {
        let e$ = this.queryDistributedElements(selector);
        return e$ && e$[0];
      }

      /**
       * Returns a list of effective childNodes within this element that
       * match `selector`. These can be dom child nodes or elements distributed
       * to children that are insertion points.
       * @param {string} selector Selector to run.
       * @return {!Array<!Node>} List of effective child nodes that match selector.
       */
      queryAllEffectiveChildren(selector) {
        return this.queryDistributedElements(selector);
      }

      /**
       * Returns a list of nodes distributed to this element's `<slot>`.
       *
       * If this element contains more than one `<slot>` in its local DOM,
       * an optional selector may be passed to choose the desired content.
       *
       * @param {string=} slctr CSS selector to choose the desired
       *   `<slot>`.  Defaults to `content`.
       * @return {!Array<!Node>} List of distributed nodes for the `<slot>`.
       */
      getContentChildNodes(slctr) {
        let content = this.root.querySelector(slctr || 'slot');
        return content ? /** @type {Polymer.DomApi} */(Polymer.dom(content)).getDistributedNodes() : [];
      }

      /**
       * Returns a list of element children distributed to this element's
       * `<slot>`.
       *
       * If this element contains more than one `<slot>` in its
       * local DOM, an optional selector may be passed to choose the desired
       * content.  This method differs from `getContentChildNodes` in that only
       * elements are returned.
       *
       * @param {string=} slctr CSS selector to choose the desired
       *   `<content>`.  Defaults to `content`.
       * @return {!Array<!HTMLElement>} List of distributed nodes for the
       *   `<slot>`.
       * @suppress {invalidCasts}
       */
      getContentChildren(slctr) {
        let children = /** @type {!Array<!HTMLElement>} */(this.getContentChildNodes(slctr).filter(function(n) {
          return (n.nodeType === Node.ELEMENT_NODE);
        }));
        return children;
      }

      /**
       * Checks whether an element is in this element's light DOM tree.
       *
       * @param {?Node} node The element to be checked.
       * @return {boolean} true if node is in this element's light DOM tree.
       * @suppress {invalidCasts} LegacyElementMixin must be applied to an HTMLElement
       */
      isLightDescendant(node) {
        const thisNode = /** @type {Node} */ (this);
        return thisNode !== node && thisNode.contains(node) &&
          thisNode.getRootNode() === node.getRootNode();
      }

      /**
       * Checks whether an element is in this element's local DOM tree.
       *
       * @param {!Element} node The element to be checked.
       * @return {boolean} true if node is in this element's local DOM tree.
       */
      isLocalDescendant(node) {
        return this.root === node.getRootNode();
      }

      /**
       * No-op for backwards compatibility. This should now be handled by
       * ShadyCss library.
       * @param  {*} container Unused
       * @param  {*} shouldObserve Unused
       * @return {void}
       */
      scopeSubtree(container, shouldObserve) { // eslint-disable-line no-unused-vars
      }

      /**
       * Returns the computed style value for the given property.
       * @param {string} property The css property name.
       * @return {string} Returns the computed css property value for the given
       * `property`.
       * @suppress {invalidCasts} LegacyElementMixin must be applied to an HTMLElement
       */
      getComputedStyleValue(property) {
        return styleInterface.getComputedStyleValue(/** @type {!Element} */(this), property);
      }

      // debounce

      /**
       * Call `debounce` to collapse multiple requests for a named task into
       * one invocation which is made after the wait time has elapsed with
       * no new request.  If no wait time is given, the callback will be called
       * at microtask timing (guaranteed before paint).
       *
       *     debouncedClickAction(e) {
       *       // will not call `processClick` more than once per 100ms
       *       this.debounce('click', function() {
       *        this.processClick();
       *       } 100);
       *     }
       *
       * @param {string} jobName String to identify the debounce job.
       * @param {function():void} callback Function that is called (with `this`
       *   context) when the wait time elapses.
       * @param {number} wait Optional wait time in milliseconds (ms) after the
       *   last signal that must elapse before invoking `callback`
       * @return {!Object} Returns a debouncer object on which exists the
       * following methods: `isActive()` returns true if the debouncer is
       * active; `cancel()` cancels the debouncer if it is active;
       * `flush()` immediately invokes the debounced callback if the debouncer
       * is active.
       */
      debounce(jobName, callback, wait) {
        this._debouncers = this._debouncers || {};
        return this._debouncers[jobName] = Polymer.Debouncer.debounce(
              this._debouncers[jobName]
            , wait > 0 ? Polymer.Async.timeOut.after(wait) : Polymer.Async.microTask
            , callback.bind(this));
      }

      /**
       * Returns whether a named debouncer is active.
       *
       * @param {string} jobName The name of the debouncer started with `debounce`
       * @return {boolean} Whether the debouncer is active (has not yet fired).
       */
      isDebouncerActive(jobName) {
        this._debouncers = this._debouncers || {};
        let debouncer = this._debouncers[jobName];
        return !!(debouncer && debouncer.isActive());
      }

      /**
       * Immediately calls the debouncer `callback` and inactivates it.
       *
       * @param {string} jobName The name of the debouncer started with `debounce`
       * @return {void}
       */
      flushDebouncer(jobName) {
        this._debouncers = this._debouncers || {};
        let debouncer = this._debouncers[jobName];
        if (debouncer) {
          debouncer.flush();
        }
      }

      /**
       * Cancels an active debouncer.  The `callback` will not be called.
       *
       * @param {string} jobName The name of the debouncer started with `debounce`
       * @return {void}
       */
      cancelDebouncer(jobName) {
        this._debouncers = this._debouncers || {};
        let debouncer = this._debouncers[jobName];
        if (debouncer) {
          debouncer.cancel();
        }
      }

      /**
       * Runs a callback function asynchronously.
       *
       * By default (if no waitTime is specified), async callbacks are run at
       * microtask timing, which will occur before paint.
       *
       * @param {!Function} callback The callback function to run, bound to `this`.
       * @param {number=} waitTime Time to wait before calling the
       *   `callback`.  If unspecified or 0, the callback will be run at microtask
       *   timing (before paint).
       * @return {number} Handle that may be used to cancel the async job.
       */
      async(callback, waitTime) {
        return waitTime > 0 ? Polymer.Async.timeOut.run(callback.bind(this), waitTime) :
            ~Polymer.Async.microTask.run(callback.bind(this));
      }

      /**
       * Cancels an async operation started with `async`.
       *
       * @param {number} handle Handle returned from original `async` call to
       *   cancel.
       * @return {void}
       */
      cancelAsync(handle) {
        handle < 0 ? Polymer.Async.microTask.cancel(~handle) :
            Polymer.Async.timeOut.cancel(handle);
      }

      // other

      /**
       * Convenience method for creating an element and configuring it.
       *
       * @param {string} tag HTML element tag to create.
       * @param {Object=} props Object of properties to configure on the
       *    instance.
       * @return {!Element} Newly created and configured element.
       */
      create(tag, props) {
        let elt = document.createElement(tag);
        if (props) {
          if (elt.setProperties) {
            elt.setProperties(props);
          } else {
            for (let n in props) {
              elt[n] = props[n];
            }
          }
        }
        return elt;
      }

      /**
       * Convenience method for importing an HTML document imperatively.
       *
       * This method creates a new `<link rel="import">` element with
       * the provided URL and appends it to the document to start loading.
       * In the `onload` callback, the `import` property of the `link`
       * element will contain the imported document contents.
       *
       * @param {string} href URL to document to load.
       * @param {?function(!Event):void=} onload Callback to notify when an import successfully
       *   loaded.
       * @param {?function(!ErrorEvent):void=} onerror Callback to notify when an import
       *   unsuccessfully loaded.
       * @param {boolean=} optAsync True if the import should be loaded `async`.
       *   Defaults to `false`.
       * @return {!HTMLLinkElement} The link element for the URL to be loaded.
       */
      importHref(href, onload, onerror, optAsync) { // eslint-disable-line no-unused-vars
        let loadFn = onload ? onload.bind(this) : null;
        let errorFn = onerror ? onerror.bind(this) : null;
        return Polymer.importHref(href, loadFn, errorFn, optAsync);
      }

      /**
       * Polyfill for Element.prototype.matches, which is sometimes still
       * prefixed.
       *
       * @param {string} selector Selector to test.
       * @param {!Element=} node Element to test the selector against.
       * @return {boolean} Whether the element matches the selector.
       */
      elementMatches(selector, node) {
        return Polymer.dom.matchesSelector(/** @type {!Element} */ (node || this), selector);
      }

      /**
       * Toggles an HTML attribute on or off.
       *
       * @param {string} name HTML attribute name
       * @param {boolean=} bool Boolean to force the attribute on or off.
       *    When unspecified, the state of the attribute will be reversed.
       * @param {Element=} node Node to target.  Defaults to `this`.
       * @return {void}
       */
      toggleAttribute(name, bool, node) {
        node = /** @type {Element} */ (node || this);
        if (arguments.length == 1) {
          bool = !node.hasAttribute(name);
        }
        if (bool) {
          node.setAttribute(name, '');
        } else {
          node.removeAttribute(name);
        }
      }


      /**
       * Toggles a CSS class on or off.
       *
       * @param {string} name CSS class name
       * @param {boolean=} bool Boolean to force the class on or off.
       *    When unspecified, the state of the class will be reversed.
       * @param {Element=} node Node to target.  Defaults to `this`.
       * @return {void}
       */
      toggleClass(name, bool, node) {
        node = /** @type {Element} */ (node || this);
        if (arguments.length == 1) {
          bool = !node.classList.contains(name);
        }
        if (bool) {
          node.classList.add(name);
        } else {
          node.classList.remove(name);
        }
      }

      /**
       * Cross-platform helper for setting an element's CSS `transform` property.
       *
       * @param {string} transformText Transform setting.
       * @param {Element=} node Element to apply the transform to.
       * Defaults to `this`
       * @return {void}
       */
      transform(transformText, node) {
        node = /** @type {Element} */ (node || this);
        node.style.webkitTransform = transformText;
        node.style.transform = transformText;
      }

      /**
       * Cross-platform helper for setting an element's CSS `translate3d`
       * property.
       *
       * @param {number} x X offset.
       * @param {number} y Y offset.
       * @param {number} z Z offset.
       * @param {Element=} node Element to apply the transform to.
       * Defaults to `this`.
       * @return {void}
       */
      translate3d(x, y, z, node) {
        node = /** @type {Element} */ (node || this);
        this.transform('translate3d(' + x + ',' + y + ',' + z + ')', node);
      }

      /**
       * Removes an item from an array, if it exists.
       *
       * If the array is specified by path, a change notification is
       * generated, so that observers, data bindings and computed
       * properties watching that path can update.
       *
       * If the array is passed directly, **no change
       * notification is generated**.
       *
       * @param {string | !Array<number|string>} arrayOrPath Path to array from which to remove the item
       *   (or the array itself).
       * @param {*} item Item to remove.
       * @return {Array} Array containing item removed.
       */
      arrayDelete(arrayOrPath, item) {
        let index;
        if (Array.isArray(arrayOrPath)) {
          index = arrayOrPath.indexOf(item);
          if (index >= 0) {
            return arrayOrPath.splice(index, 1);
          }
        } else {
          let arr = Polymer.Path.get(this, arrayOrPath);
          index = arr.indexOf(item);
          if (index >= 0) {
            return this.splice(arrayOrPath, index, 1);
          }
        }
        return null;
      }

      // logging

      /**
       * Facades `console.log`/`warn`/`error` as override point.
       *
       * @param {string} level One of 'log', 'warn', 'error'
       * @param {Array} args Array of strings or objects to log
       * @return {void}
       */
      _logger(level, args) {
        // accept ['foo', 'bar'] and [['foo', 'bar']]
        if (Array.isArray(args) && args.length === 1 && Array.isArray(args[0])) {
          args = args[0];
        }
        switch(level) {
          case 'log':
          case 'warn':
          case 'error':
            console[level](...args);
        }
      }

      /**
       * Facades `console.log` as an override point.
       *
       * @param {...*} args Array of strings or objects to log
       * @return {void}
       */
      _log(...args) {
        this._logger('log', args);
      }

      /**
       * Facades `console.warn` as an override point.
       *
       * @param {...*} args Array of strings or objects to log
       * @return {void}
       */
      _warn(...args) {
        this._logger('warn', args);
      }

      /**
       * Facades `console.error` as an override point.
       *
       * @param {...*} args Array of strings or objects to log
       * @return {void}
       */
      _error(...args) {
        this._logger('error', args);
      }

      /**
       * Formats a message using the element type an a method name.
       *
       * @param {string} methodName Method name to associate with message
       * @param {...*} args Array of strings or objects to log
       * @return {Array} Array with formatting information for `console`
       *   logging.
       */
      _logf(methodName, ...args) {
        return ['[%s::%s]', this.is, methodName, ...args];
      }

    }

    LegacyElement.prototype.is = '';

    return LegacyElement;

  });

})();



  (function() {

    'use strict';

    const lifecycleProps = {
      attached: true,
      detached: true,
      ready: true,
      created: true,
      beforeRegister: true,
      registered: true,
      attributeChanged: true,
      listeners: true,
      hostAttributes: true
    };

    const excludeOnInfo = {
      attached: true,
      detached: true,
      ready: true,
      created: true,
      beforeRegister: true,
      registered: true,
      attributeChanged: true,
      behaviors: true,
      _noAccessors: true
    };

    const excludeOnBehaviors = Object.assign({
      listeners: true,
      hostAttributes: true,
      properties: true,
      observers: true,
    }, excludeOnInfo);

    function copyProperties(source, target, excludeProps) {
      const noAccessors = source._noAccessors;
      for (let p in source) {
        if (!(p in excludeProps)) {
          if (noAccessors) {
            target[p] = source[p];
          } else {
            let pd = Object.getOwnPropertyDescriptor(source, p);
            if (pd) {
              // ensure property is configurable so that a later behavior can
              // re-configure it.
              pd.configurable = true;
              Object.defineProperty(target, p, pd);
            }
          }
        }
      }
    }

    /**
     * Applies a "legacy" behavior or array of behaviors to the provided class.
     *
     * Note: this method will automatically also apply the `Polymer.LegacyElementMixin`
     * to ensure that any legacy behaviors can rely on legacy Polymer API on
     * the underlying element.
     *
     * @template T
     * @param {!Object|!Array<!Object>} behaviors Behavior object or array of behaviors.
     * @param {function(new:T)} klass Element class.
     * @return {function(new:T)} Returns a new Element class extended by the
     * passed in `behaviors` and also by `Polymer.LegacyElementMixin`.
     * @memberof Polymer
     * @suppress {invalidCasts, checkTypes}
     */
    function mixinBehaviors(behaviors, klass) {
      return GenerateClassFromInfo({}, Polymer.LegacyElementMixin(klass), behaviors);
    }

    // NOTE:
    // 1.x
    // Behaviors were mixed in *in reverse order* and de-duped on the fly.
    // The rule was that behavior properties were copied onto the element
    // prototype if and only if the property did not already exist.
    // Given: Polymer{ behaviors: [A, B, C, A, B]}, property copy order was:
    // (1), B, (2), A, (3) C. This means prototype properties win over
    // B properties win over A win over C. This mirrors what would happen
    // with inheritance if element extended B extended A extended C.
    //
    // Again given, Polymer{ behaviors: [A, B, C, A, B]}, the resulting
    // `behaviors` array was [C, A, B].
    // Behavior lifecycle methods were called in behavior array order
    // followed by the element, e.g. (1) C.created, (2) A.created,
    // (3) B.created, (4) element.created. There was no support for
    // super, and "super-behavior" methods were callable only by name).
    //
    // 2.x
    // Behaviors are made into proper mixins which live in the
    // element's prototype chain. Behaviors are placed in the element prototype
    // eldest to youngest and de-duped youngest to oldest:
    // So, first [A, B, C, A, B] becomes [C, A, B] then,
    // the element prototype becomes (oldest) (1) Polymer.Element, (2) class(C),
    // (3) class(A), (4) class(B), (5) class(Polymer({...})).
    // Result:
    // This means element properties win over B properties win over A win
    // over C. (same as 1.x)
    // If lifecycle is called (super then me), order is
    // (1) C.created, (2) A.created, (3) B.created, (4) element.created
    // (again same as 1.x)
    function applyBehaviors(proto, behaviors, lifecycle) {
      for (let i=0; i<behaviors.length; i++) {
        applyInfo(proto, behaviors[i], lifecycle, excludeOnBehaviors);
      }
    }

    function applyInfo(proto, info, lifecycle, excludeProps) {
      copyProperties(info, proto, excludeProps);
      for (let p in lifecycleProps) {
        if (info[p]) {
          lifecycle[p] = lifecycle[p] || [];
          lifecycle[p].push(info[p]);
        }
      }
    }

    /**
     * @param {Array} behaviors List of behaviors to flatten.
     * @param {Array=} list Target list to flatten behaviors into.
     * @param {Array=} exclude List of behaviors to exclude from the list.
     * @return {!Array} Returns the list of flattened behaviors.
     */
    function flattenBehaviors(behaviors, list, exclude) {
      list = list || [];
      for (let i=behaviors.length-1; i >= 0; i--) {
        let b = behaviors[i];
        if (b) {
          if (Array.isArray(b)) {
            flattenBehaviors(b, list);
          } else {
            // dedup
            if (list.indexOf(b) < 0 && (!exclude || exclude.indexOf(b) < 0)) {
              list.unshift(b);
            }
          }
        } else {
          console.warn('behavior is null, check for missing or 404 import');
        }
      }
      return list;
    }

    /* Note about construction and extension of legacy classes.
      [Changed in Q4 2018 to optimize performance.]

      When calling `Polymer` or `mixinBehaviors`, the generated class below is
      made. The list of behaviors was previously made into one generated class per
      behavior, but this is no longer the case as behaviors are now called
      manually. Note, there may *still* be multiple generated classes in the
      element's prototype chain if extension is used with `mixinBehaviors`.

      The generated class is directly tied to the info object and behaviors
      used to create it. That list of behaviors is filtered so it's only the
      behaviors not active on the superclass. In order to call through to the
      entire list of lifecycle methods, it's important to call `super`.

      The element's `properties` and `observers` are controlled via the finalization
      mechanism provided by `PropertiesMixin`. `Properties` and `observers` are
      collected by manually traversing the prototype chain and merging.

      To limit changes, the `_registered` method is called via `_initializeProperties`
      and not `_finalizeClass`.
    */
    /**
     * @param {!PolymerInit} info Polymer info object
     * @param {function(new:HTMLElement)} Base base class to extend with info object
     * @param {Object} behaviors behaviors to copy into the element
     * @return {function(new:HTMLElement)} Generated class
     * @suppress {checkTypes}
     * @private
     */
    function GenerateClassFromInfo(info, Base, behaviors) {

      // manages behavior and lifecycle processing (filled in after class definition)
      let behaviorList;
      const lifecycle = {};

      /** @private */
      class PolymerGenerated extends Base {

        // explicitly not calling super._finalizeClass
        static _finalizeClass() {
          // if calling via a subclass that hasn't been generated, pass through to super
          if (!this.hasOwnProperty(window.JSCompiler_renameProperty('generatedFrom', this))) {
            super._finalizeClass();
          } else {
            // interleave properties and observers per behavior and `info`
            if (behaviorList) {
              for (let i=0, b; i < behaviorList.length; i++) {
                b = behaviorList[i];
                if (b.properties) {
                  this.createProperties(b.properties);
                }
                if (b.observers) {
                  this.createObservers(b.observers, b.properties);
                }
              }
            }
            if (info.properties) {
              this.createProperties(info.properties);
            }
            if (info.observers) {
              this.createObservers(info.observers, info.properties);
            }
            // make sure to prepare the element template
            this._prepareTemplate();
          }
        }

        static get properties() {
          const properties = {};
          if (behaviorList) {
            for (let i=0; i < behaviorList.length; i++) {
              Object.assign(properties, behaviorList[i].properties);
            }
          }
          Object.assign(properties, info.properties);
          return properties;
        }

        static get observers() {
          let observers = [];
          if (behaviorList) {
            for (let i=0, b; i < behaviorList.length; i++) {
              b = behaviorList[i];
              if (b.observers) {
                observers = observers.concat(b.observers);
              }
            }
          }
          if (info.observers) {
            observers = observers.concat(info.observers);
          }
          return observers;
        }

        /**
         * @return {void}
         */
        created() {
          super.created();
          const list = lifecycle.created;
          if (list) {
            for (let i=0; i < list.length; i++) {
              list[i].call(this);
            }
          }
        }

        /**
         * @return {void}
         */
        _registered() {
          /* NOTE: `beforeRegister` is called here for bc, but the behavior
            is different than in 1.x. In 1.0, the method was called *after*
            mixing prototypes together but *before* processing of meta-objects.
            However, dynamic effects can still be set here and can be done either
            in `beforeRegister` or `registered`. It is no longer possible to set
            `is` in `beforeRegister` as you could in 1.x.
          */
          // only proceed if the generated class' prototype has not been registered.
          const generatedProto = PolymerGenerated.prototype;
          if (!generatedProto.hasOwnProperty('__hasRegisterFinished')) {
            generatedProto.__hasRegisterFinished = true;
            // ensure superclass is registered first.
            super._registered();
            // copy properties onto the generated class lazily if we're optimizing,
            if (Polymer.legacyOptimizations) {
              copyPropertiesToProto(generatedProto);
            }
            // make sure legacy lifecycle is called on the *element*'s prototype
            // and not the generated class prototype; if the element has been
            // extended, these are *not* the same.
            const proto = Object.getPrototypeOf(this);
            let list = lifecycle.beforeRegister;
            if (list) {
              for (let i=0; i < list.length; i++) {
                list[i].call(proto);
              }
            }
            list = lifecycle.registered;
            if (list) {
              for (let i=0; i < list.length; i++) {
                list[i].call(proto);
              }
            }
          }
        }

        /**
         * @return {void}
         */
        _applyListeners() {
          super._applyListeners();
          const list = lifecycle.listeners;
          if (list) {
            for (let i=0; i < list.length; i++) {
              const listeners = list[i];
              if (listeners) {
                for (let l in listeners) {
                  this._addMethodEventListenerToNode(this, l, listeners[l]);
                }
              }
            }
          }
        }

        // note: exception to "super then me" rule;
        // do work before calling super so that super attributes
        // only apply if not already set.
        /**
         * @return {void}
         */
        _ensureAttributes() {
          const list = lifecycle.hostAttributes;
          if (list) {
            for (let i=list.length-1; i >= 0; i--) {
              const hostAttributes = list[i];
              for (let a in hostAttributes) {
                  this._ensureAttribute(a, hostAttributes[a]);
                }
            }
          }
          super._ensureAttributes();
        }

        /**
         * @return {void}
         */
        ready() {
          super.ready();
          let list = lifecycle.ready;
          if (list) {
            for (let i=0; i < list.length; i++) {
              list[i].call(this);
            }
          }
        }

        /**
         * @return {void}
         */
        attached() {
          super.attached();
          let list = lifecycle.attached;
          if (list) {
            for (let i=0; i < list.length; i++) {
              list[i].call(this);
            }
          }
        }

        /**
         * @return {void}
         */
        detached() {
          super.detached();
          let list = lifecycle.detached;
          if (list) {
            for (let i=0; i < list.length; i++) {
              list[i].call(this);
            }
          }
        }

        /**
         * Implements native Custom Elements `attributeChangedCallback` to
         * set an attribute value to a property via `_attributeToProperty`.
         *
         * @param {string} name Name of attribute that changed
         * @param {?string} old Old attribute value
         * @param {?string} value New attribute value
         * @return {void}
         */
        attributeChanged(name, old, value) {
          super.attributeChanged();
          let list = lifecycle.attributeChanged;
          if (list) {
            for (let i=0; i < list.length; i++) {
              list[i].call(this, name, old, value);
            }
          }
        }
      }

      // apply behaviors, note actual copying is done lazily at first instance creation
      if (behaviors) {
        // NOTE: ensure the behavior is extending a class with
        // legacy element api. This is necessary since behaviors expect to be able
        // to access 1.x legacy api.
        if (!Array.isArray(behaviors)) {
          behaviors = [behaviors];
        }
        let superBehaviors = Base.prototype.behaviors;
        // get flattened, deduped list of behaviors *not* already on super class
        behaviorList = flattenBehaviors(behaviors, null, superBehaviors);
        PolymerGenerated.prototype.behaviors = superBehaviors ?
          superBehaviors.concat(behaviors) : behaviorList;
      }

      const copyPropertiesToProto = (proto) => {
        if (behaviorList) {
          applyBehaviors(proto, behaviorList, lifecycle);
        }
        applyInfo(proto, info, lifecycle, excludeOnInfo);
      };

      // copy properties if we're not optimizing
      if (!Polymer.legacyOptimizations) {
        copyPropertiesToProto(PolymerGenerated.prototype);
      }

      PolymerGenerated.generatedFrom = info;

      return PolymerGenerated;
    }

    /**
     * Generates a class that extends `Polymer.LegacyElement` based on the
     * provided info object.  Metadata objects on the `info` object
     * (`properties`, `observers`, `listeners`, `behaviors`, `is`) are used
     * for Polymer's meta-programming systems, and any functions are copied
     * to the generated class.
     *
     * Valid "metadata" values are as follows:
     *
     * `is`: String providing the tag name to register the element under. In
     * addition, if a `dom-module` with the same id exists, the first template
     * in that `dom-module` will be stamped into the shadow root of this element,
     * with support for declarative event listeners (`on-...`), Polymer data
     * bindings (`[[...]]` and `{{...}}`), and id-based node finding into
     * `this.$`.
     *
     * `properties`: Object describing property-related metadata used by Polymer
     * features (key: property names, value: object containing property metadata).
     * Valid keys in per-property metadata include:
     * - `type` (String|Number|Object|Array|...): Used by
     *   `attributeChangedCallback` to determine how string-based attributes
     *   are deserialized to JavaScript property values.
     * - `notify` (boolean): Causes a change in the property to fire a
     *   non-bubbling event called `<property>-changed`. Elements that have
     *   enabled two-way binding to the property use this event to observe changes.
     * - `readOnly` (boolean): Creates a getter for the property, but no setter.
     *   To set a read-only property, use the private setter method
     *   `_setProperty(property, value)`.
     * - `observer` (string): Observer method name that will be called when
     *   the property changes. The arguments of the method are
     *   `(value, previousValue)`.
     * - `computed` (string): String describing method and dependent properties
     *   for computing the value of this property (e.g. `'computeFoo(bar, zot)'`).
     *   Computed properties are read-only by default and can only be changed
     *   via the return value of the computing method.
     *
     * `observers`: Array of strings describing multi-property observer methods
     *  and their dependent properties (e.g. `'observeABC(a, b, c)'`).
     *
     * `listeners`: Object describing event listeners to be added to each
     *  instance of this element (key: event name, value: method name).
     *
     * `behaviors`: Array of additional `info` objects containing metadata
     * and callbacks in the same format as the `info` object here which are
     * merged into this element.
     *
     * `hostAttributes`: Object listing attributes to be applied to the host
     *  once created (key: attribute name, value: attribute value).  Values
     *  are serialized based on the type of the value.  Host attributes should
     *  generally be limited to attributes such as `tabIndex` and `aria-...`.
     *  Attributes in `hostAttributes` are only applied if a user-supplied
     *  attribute is not already present (attributes in markup override
     *  `hostAttributes`).
     *
     * In addition, the following Polymer-specific callbacks may be provided:
     * - `registered`: called after first instance of this element,
     * - `created`: called during `constructor`
     * - `attached`: called during `connectedCallback`
     * - `detached`: called during `disconnectedCallback`
     * - `ready`: called before first `attached`, after all properties of
     *   this element have been propagated to its template and all observers
     *   have run
     *
     * @param {!PolymerInit} info Object containing Polymer metadata and functions
     *   to become class methods.
     * @template T
     * @param {function(T):T} mixin Optional mixin to apply to legacy base class
     *   before extending with Polymer metaprogramming.
     * @return {function(new:HTMLElement)} Generated class
     * @memberof Polymer
     */
    Polymer.Class = function(info, mixin) {
      if (!info) {
        console.warn('Polymer.Class requires `info` argument');
      }
      let klass = mixin ? mixin(Polymer.LegacyElementMixin(HTMLElement)) :
          Polymer.LegacyElementMixin(HTMLElement);
      klass = GenerateClassFromInfo(info, klass, info.behaviors);
      if (info._enableDisableUpgrade) {
        klass = Polymer.DisableUpgradeMixin(klass);
      }
      // decorate klass with registration info
      klass.is = klass.prototype.is = info.is;
      return klass;
    };

    Polymer.mixinBehaviors = mixinBehaviors;

  })();




  (function() {
    'use strict';

    /**
     * Legacy class factory and registration helper for defining Polymer
     * elements.
     *
     * This method is equivalent to
     * `customElements.define(info.is, Polymer.Class(info));`
     *
     * See `Polymer.Class` for details on valid legacy metadata format for `info`.
     *
     * @global
     * @override
     * @function Polymer
     * @param {!PolymerInit} info Object containing Polymer metadata and functions
     *   to become class methods.
     * @return {function(new: HTMLElement)} Generated class
     * @suppress {duplicate, invalidCasts, checkTypes}
     */
    window.Polymer._polymerFn = function(info) {
      // if input is a `class` (aka a function with a prototype), use the prototype
      // remember that the `constructor` will never be called
      let klass;
      if (typeof info === 'function') {
        klass = info;
      } else {
        klass = Polymer.Class(info);
      }
      customElements.define(klass.is, /** @type {!HTMLElement} */(klass));
      return klass;
    };

  })();



(function() {
  'use strict';

  // Common implementation for mixin & behavior
  function mutablePropertyChange(inst, property, value, old, mutableData) {
    let isObject;
    if (mutableData) {
      isObject = (typeof value === 'object' && value !== null);
      // Pull `old` for Objects from temp cache, but treat `null` as a primitive
      if (isObject) {
        old = inst.__dataTemp[property];
      }
    }
    // Strict equality check, but return false for NaN===NaN
    let shouldChange = (old !== value && (old === old || value === value));
    // Objects are stored in temporary cache (cleared at end of
    // turn), which is used for dirty-checking
    if (isObject && shouldChange) {
      inst.__dataTemp[property] = value;
    }
    return shouldChange;
  }

  /**
   * Element class mixin to skip strict dirty-checking for objects and arrays
   * (always consider them to be "dirty"), for use on elements utilizing
   * `Polymer.PropertyEffects`
   *
   * By default, `Polymer.PropertyEffects` performs strict dirty checking on
   * objects, which means that any deep modifications to an object or array will
   * not be propagated unless "immutable" data patterns are used (i.e. all object
   * references from the root to the mutation were changed).
   *
   * Polymer also provides a proprietary data mutation and path notification API
   * (e.g. `notifyPath`, `set`, and array mutation API's) that allow efficient
   * mutation and notification of deep changes in an object graph to all elements
   * bound to the same object graph.
   *
   * In cases where neither immutable patterns nor the data mutation API can be
   * used, applying this mixin will cause Polymer to skip dirty checking for
   * objects and arrays (always consider them to be "dirty").  This allows a
   * user to make a deep modification to a bound object graph, and then either
   * simply re-set the object (e.g. `this.items = this.items`) or call `notifyPath`
   * (e.g. `this.notifyPath('items')`) to update the tree.  Note that all
   * elements that wish to be updated based on deep mutations must apply this
   * mixin or otherwise skip strict dirty checking for objects/arrays.
   * Specifically, any elements in the binding tree between the source of a
   * mutation and the consumption of it must apply this mixin or enable the
   * `Polymer.OptionalMutableData` mixin.
   *
   * In order to make the dirty check strategy configurable, see
   * `Polymer.OptionalMutableData`.
   *
   * Note, the performance characteristics of propagating large object graphs
   * will be worse as opposed to using strict dirty checking with immutable
   * patterns or Polymer's path notification API.
   *
   * @mixinFunction
   * @polymer
   * @memberof Polymer
   * @summary Element class mixin to skip strict dirty-checking for objects
   *   and arrays
   */
  Polymer.MutableData = Polymer.dedupingMixin(superClass => {

    /**
     * @polymer
     * @mixinClass
     * @implements {Polymer_MutableData}
     */
    class MutableData extends superClass {
      /**
       * Overrides `Polymer.PropertyEffects` to provide option for skipping
       * strict equality checking for Objects and Arrays.
       *
       * This method pulls the value to dirty check against from the `__dataTemp`
       * cache (rather than the normal `__data` cache) for Objects.  Since the temp
       * cache is cleared at the end of a turn, this implementation allows
       * side-effects of deep object changes to be processed by re-setting the
       * same object (using the temp cache as an in-turn backstop to prevent
       * cycles due to 2-way notification).
       *
       * @param {string} property Property name
       * @param {*} value New property value
       * @param {*} old Previous property value
       * @return {boolean} Whether the property should be considered a change
       * @protected
       */
      _shouldPropertyChange(property, value, old) {
        return mutablePropertyChange(this, property, value, old, true);
      }

    }

    return MutableData;

  });


  /**
   * Element class mixin to add the optional ability to skip strict
   * dirty-checking for objects and arrays (always consider them to be
   * "dirty") by setting a `mutable-data` attribute on an element instance.
   *
   * By default, `Polymer.PropertyEffects` performs strict dirty checking on
   * objects, which means that any deep modifications to an object or array will
   * not be propagated unless "immutable" data patterns are used (i.e. all object
   * references from the root to the mutation were changed).
   *
   * Polymer also provides a proprietary data mutation and path notification API
   * (e.g. `notifyPath`, `set`, and array mutation API's) that allow efficient
   * mutation and notification of deep changes in an object graph to all elements
   * bound to the same object graph.
   *
   * In cases where neither immutable patterns nor the data mutation API can be
   * used, applying this mixin will allow Polymer to skip dirty checking for
   * objects and arrays (always consider them to be "dirty").  This allows a
   * user to make a deep modification to a bound object graph, and then either
   * simply re-set the object (e.g. `this.items = this.items`) or call `notifyPath`
   * (e.g. `this.notifyPath('items')`) to update the tree.  Note that all
   * elements that wish to be updated based on deep mutations must apply this
   * mixin or otherwise skip strict dirty checking for objects/arrays.
   * Specifically, any elements in the binding tree between the source of a
   * mutation and the consumption of it must enable this mixin or apply the
   * `Polymer.MutableData` mixin.
   *
   * While this mixin adds the ability to forgo Object/Array dirty checking,
   * the `mutableData` flag defaults to false and must be set on the instance.
   *
   * Note, the performance characteristics of propagating large object graphs
   * will be worse by relying on `mutableData: true` as opposed to using
   * strict dirty checking with immutable patterns or Polymer's path notification
   * API.
   *
   * @mixinFunction
   * @polymer
   * @memberof Polymer
   * @summary Element class mixin to optionally skip strict dirty-checking
   *   for objects and arrays
   */
  Polymer.OptionalMutableData = Polymer.dedupingMixin(superClass => {

    /**
     * @mixinClass
     * @polymer
     * @implements {Polymer_OptionalMutableData}
     */
    class OptionalMutableData extends superClass {

      static get properties() {
        return {
          /**
           * Instance-level flag for configuring the dirty-checking strategy
           * for this element.  When true, Objects and Arrays will skip dirty
           * checking, otherwise strict equality checking will be used.
           */
          mutableData: Boolean
        };
      }

      /**
       * Overrides `Polymer.PropertyEffects` to provide option for skipping
       * strict equality checking for Objects and Arrays.
       *
       * When `this.mutableData` is true on this instance, this method
       * pulls the value to dirty check against from the `__dataTemp` cache
       * (rather than the normal `__data` cache) for Objects.  Since the temp
       * cache is cleared at the end of a turn, this implementation allows
       * side-effects of deep object changes to be processed by re-setting the
       * same object (using the temp cache as an in-turn backstop to prevent
       * cycles due to 2-way notification).
       *
       * @param {string} property Property name
       * @param {*} value New property value
       * @param {*} old Previous property value
       * @return {boolean} Whether the property should be considered a change
       * @protected
       */
      _shouldPropertyChange(property, value, old) {
        return mutablePropertyChange(this, property, value, old, this.mutableData);
      }
    }

    return OptionalMutableData;

  });

  // Export for use by legacy behavior
  Polymer.MutableData._mutablePropertyChange = mutablePropertyChange;

})();


  (function() {
    'use strict';

    // Base class for HTMLTemplateElement extension that has property effects
    // machinery for propagating host properties to children. This is an ES5
    // class only because Babel (incorrectly) requires super() in the class
    // constructor even though no `this` is used and it returns an instance.
    let newInstance = null;

    /**
     * @constructor
     * @extends {HTMLTemplateElement}
     * @private
     */
    function HTMLTemplateElementExtension() { return newInstance; }
    HTMLTemplateElementExtension.prototype = Object.create(HTMLTemplateElement.prototype, {
      constructor: {
        value: HTMLTemplateElementExtension,
        writable: true
      }
    });

    /**
     * @constructor
     * @implements {Polymer_PropertyEffects}
     * @extends {HTMLTemplateElementExtension}
     * @private
     */
    const DataTemplate = Polymer.PropertyEffects(HTMLTemplateElementExtension);

    /**
     * @constructor
     * @implements {Polymer_MutableData}
     * @extends {DataTemplate}
     * @private
     */
    const MutableDataTemplate = Polymer.MutableData(DataTemplate);

    // Applies a DataTemplate subclass to a <template> instance
    function upgradeTemplate(template, constructor) {
      newInstance = template;
      Object.setPrototypeOf(template, constructor.prototype);
      new constructor();
      newInstance = null;
    }

    /**
     * Base class for TemplateInstance.
     * @constructor
     * @implements {Polymer_PropertyEffects}
     * @private
     */
    const base = Polymer.PropertyEffects(class {});

    /**
     * @polymer
     * @customElement
     * @appliesMixin Polymer.PropertyEffects
     * @unrestricted
     */
    class TemplateInstanceBase extends base {
      constructor(props) {
        super();
        this._configureProperties(props);
        this.root = this._stampTemplate(this.__dataHost);
        // Save list of stamped children
        let children = this.children = [];
        for (let n = this.root.firstChild; n; n=n.nextSibling) {
          children.push(n);
          n.__templatizeInstance = this;
        }
        if (this.__templatizeOwner &&
          this.__templatizeOwner.__hideTemplateChildren__) {
          this._showHideChildren(true);
        }
        // Flush props only when props are passed if instance props exist
        // or when there isn't instance props.
        let options = this.__templatizeOptions;
        if ((props && options.instanceProps) || !options.instanceProps) {
          this._enableProperties();
        }
      }
      /**
       * Configure the given `props` by calling `_setPendingProperty`. Also
       * sets any properties stored in `__hostProps`.
       * @private
       * @param {Object} props Object of property name-value pairs to set.
       * @return {void}
       */
      _configureProperties(props) {
        let options = this.__templatizeOptions;
        if (options.forwardHostProp) {
          for (let hprop in this.__hostProps) {
            this._setPendingProperty(hprop, this.__dataHost['_host_' + hprop]);
          }
        }
        // Any instance props passed in the constructor will overwrite host props;
        // normally this would be a user error but we don't specifically filter them
        for (let iprop in props) {
          this._setPendingProperty(iprop, props[iprop]);
        }
      }
      /**
       * Forwards a host property to this instance.  This method should be
       * called on instances from the `options.forwardHostProp` callback
       * to propagate changes of host properties to each instance.
       *
       * Note this method enqueues the change, which are flushed as a batch.
       *
       * @param {string} prop Property or path name
       * @param {*} value Value of the property to forward
       * @return {void}
       */
      forwardHostProp(prop, value) {
        if (this._setPendingPropertyOrPath(prop, value, false, true)) {
          this.__dataHost._enqueueClient(this);
        }
      }

      /**
       * Override point for adding custom or simulated event handling.
       *
       * @param {!Node} node Node to add event listener to
       * @param {string} eventName Name of event
       * @param {function(!Event):void} handler Listener function to add
       * @return {void}
       */
      _addEventListenerToNode(node, eventName, handler) {
        if (this._methodHost && this.__templatizeOptions.parentModel) {
          // If this instance should be considered a parent model, decorate
          // events this template instance as `model`
          this._methodHost._addEventListenerToNode(node, eventName, (e) => {
            e.model = this;
            handler(e);
          });
        } else {
          // Otherwise delegate to the template's host (which could be)
          // another template instance
          let templateHost = this.__dataHost.__dataHost;
          if (templateHost) {
            templateHost._addEventListenerToNode(node, eventName, handler);
          }
        }
      }
      /**
       * Shows or hides the template instance top level child elements. For
       * text nodes, `textContent` is removed while "hidden" and replaced when
       * "shown."
       * @param {boolean} hide Set to true to hide the children;
       * set to false to show them.
       * @return {void}
       * @protected
       */
      _showHideChildren(hide) {
        let c = this.children;
        for (let i=0; i<c.length; i++) {
          let n = c[i];
          // Ignore non-changes
          if (Boolean(hide) != Boolean(n.__hideTemplateChildren__)) {
            if (n.nodeType === Node.TEXT_NODE) {
              if (hide) {
                n.__polymerTextContent__ = n.textContent;
                n.textContent = '';
              } else {
                n.textContent = n.__polymerTextContent__;
              }
            // remove and replace slot
            } else if (n.localName === 'slot') {
              if (hide) {
                n.__polymerReplaced__ = document.createComment('hidden-slot');
                n.parentNode.replaceChild(n.__polymerReplaced__, n);
              } else {
                const replace = n.__polymerReplaced__;
                if (replace) {
                  replace.parentNode.replaceChild(n, replace);
                }
              }
            }

            else if (n.style) {
              if (hide) {
                n.__polymerDisplay__ = n.style.display;
                n.style.display = 'none';
              } else {
                n.style.display = n.__polymerDisplay__;
              }
            }
          }
          n.__hideTemplateChildren__ = hide;
          if (n._showHideChildren) {
            n._showHideChildren(hide);
          }
        }
      }
      /**
       * Overrides default property-effects implementation to intercept
       * textContent bindings while children are "hidden" and cache in
       * private storage for later retrieval.
       *
       * @param {!Node} node The node to set a property on
       * @param {string} prop The property to set
       * @param {*} value The value to set
       * @return {void}
       * @protected
       */
      _setUnmanagedPropertyToNode(node, prop, value) {
        if (node.__hideTemplateChildren__ &&
            node.nodeType == Node.TEXT_NODE && prop == 'textContent') {
          node.__polymerTextContent__ = value;
        } else {
          super._setUnmanagedPropertyToNode(node, prop, value);
        }
      }
      /**
       * Find the parent model of this template instance.  The parent model
       * is either another templatize instance that had option `parentModel: true`,
       * or else the host element.
       *
       * @return {!Polymer_PropertyEffects} The parent model of this instance
       */
      get parentModel() {
        let model = this.__parentModel;
        if (!model) {
          let options;
          model = this;
          do {
            // A template instance's `__dataHost` is a <template>
            // `model.__dataHost.__dataHost` is the template's host
            model = model.__dataHost.__dataHost;
          } while ((options = model.__templatizeOptions) && !options.parentModel);
          this.__parentModel = model;
        }
        return model;
      }

      /**
       * Stub of HTMLElement's `dispatchEvent`, so that effects that may
       * dispatch events safely no-op.
       *
       * @param {Event} event Event to dispatch
       * @return {boolean} Always true.
       */
       dispatchEvent(event) { // eslint-disable-line no-unused-vars
         return true;
      }
    }

    /** @type {!DataTemplate} */
    TemplateInstanceBase.prototype.__dataHost;
    /** @type {!TemplatizeOptions} */
    TemplateInstanceBase.prototype.__templatizeOptions;
    /** @type {!Polymer_PropertyEffects} */
    TemplateInstanceBase.prototype._methodHost;
    /** @type {!Object} */
    TemplateInstanceBase.prototype.__templatizeOwner;
    /** @type {!Object} */
    TemplateInstanceBase.prototype.__hostProps;

    /**
     * @constructor
     * @extends {TemplateInstanceBase}
     * @implements {Polymer_MutableData}
     * @private
     */
    const MutableTemplateInstanceBase = Polymer.MutableData(TemplateInstanceBase);

    function findMethodHost(template) {
      // Technically this should be the owner of the outermost template.
      // In shadow dom, this is always getRootNode().host, but we can
      // approximate this via cooperation with our dataHost always setting
      // `_methodHost` as long as there were bindings (or id's) on this
      // instance causing it to get a dataHost.
      let templateHost = template.__dataHost;
      return templateHost && templateHost._methodHost || templateHost;
    }

    /* eslint-disable valid-jsdoc */
    /**
     * @suppress {missingProperties} class.prototype is not defined for some reason
     */
    function createTemplatizerClass(template, templateInfo, options) {
      // Anonymous class created by the templatize
      let base = options.mutableData ?
        MutableTemplateInstanceBase : TemplateInstanceBase;
      // Affordance for global mixins onto TemplatizeInstance
      if (Polymer.Templatize.mixin) {
        base = Polymer.Templatize.mixin(base);
      }
      /**
       * @constructor
       * @extends {base}
       * @private
       */
      let klass = class extends base { };
      klass.prototype.__templatizeOptions = options;
      klass.prototype._bindTemplate(template);
      addNotifyEffects(klass, template, templateInfo, options);
      return klass;
    }

    /**
     * @suppress {missingProperties} class.prototype is not defined for some reason
     */
    function addPropagateEffects(template, templateInfo, options) {
      let userForwardHostProp = options.forwardHostProp;
      if (userForwardHostProp) {
        // Provide data API and property effects on memoized template class
        let klass = templateInfo.templatizeTemplateClass;
        if (!klass) {
          let base = options.mutableData ? MutableDataTemplate : DataTemplate;
          /** @private */
          klass = templateInfo.templatizeTemplateClass =
            class TemplatizedTemplate extends base {};
          // Add template - >instances effects
          // and host <- template effects
          let hostProps = templateInfo.hostProps;
          for (let prop in hostProps) {
            klass.prototype._addPropertyEffect('_host_' + prop,
              klass.prototype.PROPERTY_EFFECT_TYPES.PROPAGATE,
              {fn: createForwardHostPropEffect(prop, userForwardHostProp)});
            klass.prototype._createNotifyingProperty('_host_' + prop);
          }
        }
        upgradeTemplate(template, klass);
        // Mix any pre-bound data into __data; no need to flush this to
        // instances since they pull from the template at instance-time
        if (template.__dataProto) {
          // Note, generally `__dataProto` could be chained, but it's guaranteed
          // to not be since this is a vanilla template we just added effects to
          Object.assign(template.__data, template.__dataProto);
        }
        // Clear any pending data for performance
        template.__dataTemp = {};
        template.__dataPending = null;
        template.__dataOld = null;
        template._enableProperties();
      }
    }
    /* eslint-enable valid-jsdoc */

    function createForwardHostPropEffect(hostProp, userForwardHostProp) {
      return function forwardHostProp(template, prop, props) {
        userForwardHostProp.call(template.__templatizeOwner,
          prop.substring('_host_'.length), props[prop]);
      };
    }

    function addNotifyEffects(klass, template, templateInfo, options) {
      let hostProps = templateInfo.hostProps || {};
      for (let iprop in options.instanceProps) {
        delete hostProps[iprop];
        let userNotifyInstanceProp = options.notifyInstanceProp;
        if (userNotifyInstanceProp) {
          klass.prototype._addPropertyEffect(iprop,
            klass.prototype.PROPERTY_EFFECT_TYPES.NOTIFY,
            {fn: createNotifyInstancePropEffect(iprop, userNotifyInstanceProp)});
        }
      }
      if (options.forwardHostProp && template.__dataHost) {
        for (let hprop in hostProps) {
          klass.prototype._addPropertyEffect(hprop,
            klass.prototype.PROPERTY_EFFECT_TYPES.NOTIFY,
            {fn: createNotifyHostPropEffect()});
        }
      }
    }

    function createNotifyInstancePropEffect(instProp, userNotifyInstanceProp) {
      return function notifyInstanceProp(inst, prop, props) {
        userNotifyInstanceProp.call(inst.__templatizeOwner,
          inst, prop, props[prop]);
      };
    }

    function createNotifyHostPropEffect() {
      return function notifyHostProp(inst, prop, props) {
        inst.__dataHost._setPendingPropertyOrPath('_host_' + prop, props[prop], true, true);
      };
    }

    /**
     * Module for preparing and stamping instances of templates that utilize
     * Polymer's data-binding and declarative event listener features.
     *
     * Example:
     *
     *     // Get a template from somewhere, e.g. light DOM
     *     let template = this.querySelector('template');
     *     // Prepare the template
     *     let TemplateClass = Polymer.Templatize.templatize(template);
     *     // Instance the template with an initial data model
     *     let instance = new TemplateClass({myProp: 'initial'});
     *     // Insert the instance's DOM somewhere, e.g. element's shadow DOM
     *     this.shadowRoot.appendChild(instance.root);
     *     // Changing a property on the instance will propagate to bindings
     *     // in the template
     *     instance.myProp = 'new value';
     *
     * The `options` dictionary passed to `templatize` allows for customizing
     * features of the generated template class, including how outer-scope host
     * properties should be forwarded into template instances, how any instance
     * properties added into the template's scope should be notified out to
     * the host, and whether the instance should be decorated as a "parent model"
     * of any event handlers.
     *
     *     // Customize property forwarding and event model decoration
     *     let TemplateClass = Polymer.Templatize.templatize(template, this, {
     *       parentModel: true,
     *       forwardHostProp(property, value) {...},
     *       instanceProps: {...},
     *       notifyInstanceProp(instance, property, value) {...},
     *     });
     *
     * @namespace
     * @memberof Polymer
     * @summary Module for preparing and stamping instances of templates
     *   utilizing Polymer templating features.
     */
    Polymer.Templatize = {

      /**
       * Returns an anonymous `Polymer.PropertyEffects` class bound to the
       * `<template>` provided.  Instancing the class will result in the
       * template being stamped into a document fragment stored as the instance's
       * `root` property, after which it can be appended to the DOM.
       *
       * Templates may utilize all Polymer data-binding features as well as
       * declarative event listeners.  Event listeners and inline computing
       * functions in the template will be called on the host of the template.
       *
       * The constructor returned takes a single argument dictionary of initial
       * property values to propagate into template bindings.  Additionally
       * host properties can be forwarded in, and instance properties can be
       * notified out by providing optional callbacks in the `options` dictionary.
       *
       * Valid configuration in `options` are as follows:
       *
       * - `forwardHostProp(property, value)`: Called when a property referenced
       *   in the template changed on the template's host. As this library does
       *   not retain references to templates instanced by the user, it is the
       *   templatize owner's responsibility to forward host property changes into
       *   user-stamped instances.  The `instance.forwardHostProp(property, value)`
       *    method on the generated class should be called to forward host
       *   properties into the template to prevent unnecessary property-changed
       *   notifications. Any properties referenced in the template that are not
       *   defined in `instanceProps` will be notified up to the template's host
       *   automatically.
       * - `instanceProps`: Dictionary of property names that will be added
       *   to the instance by the templatize owner.  These properties shadow any
       *   host properties, and changes within the template to these properties
       *   will result in `notifyInstanceProp` being called.
       * - `mutableData`: When `true`, the generated class will skip strict
       *   dirty-checking for objects and arrays (always consider them to be
       *   "dirty").
       * - `notifyInstanceProp(instance, property, value)`: Called when
       *   an instance property changes.  Users may choose to call `notifyPath`
       *   on e.g. the owner to notify the change.
       * - `parentModel`: When `true`, events handled by declarative event listeners
       *   (`on-event="handler"`) will be decorated with a `model` property pointing
       *   to the template instance that stamped it.  It will also be returned
       *   from `instance.parentModel` in cases where template instance nesting
       *   causes an inner model to shadow an outer model.
       *
       * All callbacks are called bound to the `owner`. Any context
       * needed for the callbacks (such as references to `instances` stamped)
       * should be stored on the `owner` such that they can be retrieved via
       * `this`.
       *
       * When `options.forwardHostProp` is declared as an option, any properties
       * referenced in the template will be automatically forwarded from the host of
       * the `<template>` to instances, with the exception of any properties listed in
       * the `options.instanceProps` object.  `instanceProps` are assumed to be
       * managed by the owner of the instances, either passed into the constructor
       * or set after the fact.  Note, any properties passed into the constructor will
       * always be set to the instance (regardless of whether they would normally
       * be forwarded from the host).
       *
       * Note that `templatize()` can be run only once for a given `<template>`.
       * Further calls will result in an error. Also, there is a special
       * behavior if the template was duplicated through a mechanism such as
       * `<dom-repeat>` or `<test-fixture>`. In this case, all calls to
       * `templatize()` return the same class for all duplicates of a template.
       * The class returned from `templatize()` is generated only once using
       * the `options` from the first call. This means that any `options`
       * provided to subsequent calls will be ignored. Therefore, it is very
       * important not to close over any variables inside the callbacks. Also,
       * arrow functions must be avoided because they bind the outer `this`.
       * Inside the callbacks, any contextual information can be accessed
       * through `this`, which points to the `owner`.
       *
       * @memberof Polymer.Templatize
       * @param {!HTMLTemplateElement} template Template to templatize
       * @param {Polymer_PropertyEffects=} owner Owner of the template instances;
       *   any optional callbacks will be bound to this owner.
       * @param {Object=} options Options dictionary (see summary for details)
       * @return {function(new:TemplateInstanceBase)} Generated class bound to the template
       *   provided
       * @suppress {invalidCasts}
       */
      templatize(template, owner, options) {
        // Under strictTemplatePolicy, the templatized element must be owned
        // by a (trusted) Polymer element, indicated by existence of _methodHost;
        // e.g. for dom-if & dom-repeat in main document, _methodHost is null
        if (Polymer.strictTemplatePolicy && !findMethodHost(template)) {
          throw new Error('strictTemplatePolicy: template owner not trusted');
        }
        options = /** @type {!TemplatizeOptions} */(options || {});
        if (template.__templatizeOwner) {
          throw new Error('A <template> can only be templatized once');
        }
        template.__templatizeOwner = owner;
        const ctor = owner ? owner.constructor : TemplateInstanceBase;
        let templateInfo = ctor._parseTemplate(template);
        // Get memoized base class for the prototypical template, which
        // includes property effects for binding template & forwarding
        let baseClass = templateInfo.templatizeInstanceClass;
        if (!baseClass) {
          baseClass = createTemplatizerClass(template, templateInfo, options);
          templateInfo.templatizeInstanceClass = baseClass;
        }
        // Host property forwarding must be installed onto template instance
        addPropagateEffects(template, templateInfo, options);
        // Subclass base class and add reference for this specific template
        /** @private */
        let klass = class TemplateInstance extends baseClass {};
        klass.prototype._methodHost = findMethodHost(template);
        klass.prototype.__dataHost = template;
        klass.prototype.__templatizeOwner = owner;
        klass.prototype.__hostProps = templateInfo.hostProps;
        klass = /** @type {function(new:TemplateInstanceBase)} */(klass); //eslint-disable-line no-self-assign
        return klass;
      },

      /**
       * Returns the template "model" associated with a given element, which
       * serves as the binding scope for the template instance the element is
       * contained in. A template model is an instance of
       * `TemplateInstanceBase`, and should be used to manipulate data
       * associated with this template instance.
       *
       * Example:
       *
       *   let model = modelForElement(el);
       *   if (model.index < 10) {
       *     model.set('item.checked', true);
       *   }
       *
       * @memberof Polymer.Templatize
       * @param {HTMLTemplateElement} template The model will be returned for
       *   elements stamped from this template
       * @param {Node=} node Node for which to return a template model.
       * @return {TemplateInstanceBase} Template instance representing the
       *   binding scope for the element
       */
      modelForElement(template, node) {
        let model;
        while (node) {
          // An element with a __templatizeInstance marks the top boundary
          // of a scope; walk up until we find one, and then ensure that
          // its __dataHost matches `this`, meaning this dom-repeat stamped it
          if ((model = node.__templatizeInstance)) {
            // Found an element stamped by another template; keep walking up
            // from its __dataHost
            if (model.__dataHost != template) {
              node = model.__dataHost;
            } else {
              return model;
            }
          } else {
            // Still in a template scope, keep going up until
            // a __templatizeInstance is found
            node = node.parentNode;
          }
        }
        return null;
      }
    };

    Polymer.TemplateInstanceBase = TemplateInstanceBase;

  })();



  (function() {
    'use strict';

    let TemplateInstanceBase = Polymer.TemplateInstanceBase; // eslint-disable-line

    /**
     * @typedef {{
     *   _templatizerTemplate: HTMLTemplateElement,
     *   _parentModel: boolean,
     *   _instanceProps: Object,
     *   _forwardHostPropV2: Function,
     *   _notifyInstancePropV2: Function,
     *   ctor: TemplateInstanceBase
     * }}
     */
    let TemplatizerUser; // eslint-disable-line

    /**
     * The `Polymer.Templatizer` behavior adds methods to generate instances of
     * templates that are each managed by an anonymous `Polymer.PropertyEffects`
     * instance where data-bindings in the stamped template content are bound to
     * accessors on itself.
     *
     * This behavior is provided in Polymer 2.x as a hybrid-element convenience
     * only.  For non-hybrid usage, the `Polymer.Templatize` library
     * should be used instead.
     *
     * Example:
     *
     *     // Get a template from somewhere, e.g. light DOM
     *     let template = this.querySelector('template');
     *     // Prepare the template
     *     this.templatize(template);
     *     // Instance the template with an initial data model
     *     let instance = this.stamp({myProp: 'initial'});
     *     // Insert the instance's DOM somewhere, e.g. light DOM
     *     Polymer.dom(this).appendChild(instance.root);
     *     // Changing a property on the instance will propagate to bindings
     *     // in the template
     *     instance.myProp = 'new value';
     *
     * Users of `Templatizer` may need to implement the following abstract
     * API's to determine how properties and paths from the host should be
     * forwarded into to instances:
     *
     *     _forwardHostPropV2: function(prop, value)
     *
     * Likewise, users may implement these additional abstract API's to determine
     * how instance-specific properties that change on the instance should be
     * forwarded out to the host, if necessary.
     *
     *     _notifyInstancePropV2: function(inst, prop, value)
     *
     * In order to determine which properties are instance-specific and require
     * custom notification via `_notifyInstanceProp`, define an `_instanceProps`
     * object containing keys for each instance prop, for example:
     *
     *     _instanceProps: {
     *       item: true,
     *       index: true
     *     }
     *
     * Any properties used in the template that are not defined in _instanceProp
     * will be forwarded out to the Templatize `owner` automatically.
     *
     * Users may also implement the following abstract function to show or
     * hide any DOM generated using `stamp`:
     *
     *     _showHideChildren: function(shouldHide)
     *
     * Note that some callbacks are suffixed with `V2` in the Polymer 2.x behavior
     * as the implementations will need to differ from the callbacks required
     * by the 1.x Templatizer API due to changes in the `TemplateInstance` API
     * between versions 1.x and 2.x.
     *
     * @polymerBehavior
     */
    Polymer.Templatizer = {

      /**
       * Generates an anonymous `TemplateInstance` class (stored as `this.ctor`)
       * for the provided template.  This method should be called once per
       * template to prepare an element for stamping the template, followed
       * by `stamp` to create new instances of the template.
       *
       * @param {!HTMLTemplateElement} template Template to prepare
       * @param {boolean=} mutableData When `true`, the generated class will skip
       *   strict dirty-checking for objects and arrays (always consider them to
       *   be "dirty"). Defaults to false.
       * @return {void}
       * @this {TemplatizerUser}
       */
      templatize(template, mutableData) {
        this._templatizerTemplate = template;
        this.ctor = Polymer.Templatize.templatize(template, this, {
          mutableData: Boolean(mutableData),
          parentModel: this._parentModel,
          instanceProps: this._instanceProps,
          forwardHostProp: this._forwardHostPropV2,
          notifyInstanceProp: this._notifyInstancePropV2
        });
      },

      /**
       * Creates an instance of the template prepared by `templatize`.  The object
       * returned is an instance of the anonymous class generated by `templatize`
       * whose `root` property is a document fragment containing newly cloned
       * template content, and which has property accessors corresponding to
       * properties referenced in template bindings.
       *
       * @param {Object=} model Object containing initial property values to
       *   populate into the template bindings.
       * @return {TemplateInstanceBase} Returns the created instance of
       * the template prepared by `templatize`.
       * @this {TemplatizerUser}
       */
      stamp(model) {
        return new this.ctor(model);
      },

      /**
       * Returns the template "model" (`TemplateInstance`) associated with
       * a given element, which serves as the binding scope for the template
       * instance the element is contained in.  A template model should be used
       * to manipulate data associated with this template instance.
       *
       * @param {HTMLElement} el Element for which to return a template model.
       * @return {TemplateInstanceBase} Model representing the binding scope for
       *   the element.
       * @this {TemplatizerUser}
       */
      modelForElement(el) {
        return Polymer.Templatize.modelForElement(this._templatizerTemplate, el);
      }
    };

  })();



  (function() {
    'use strict';

    /**
     * @constructor
     * @extends {HTMLElement}
     * @implements {Polymer_PropertyEffects}
     * @implements {Polymer_OptionalMutableData}
     * @implements {Polymer_GestureEventListeners}
     * @private
     */
    const domBindBase =
      Polymer.GestureEventListeners(
        Polymer.OptionalMutableData(
          Polymer.PropertyEffects(HTMLElement)));

    /**
     * Custom element to allow using Polymer's template features (data binding,
     * declarative event listeners, etc.) in the main document without defining
     * a new custom element.
     *
     * `<template>` tags utilizing bindings may be wrapped with the `<dom-bind>`
     * element, which will immediately stamp the wrapped template into the main
     * document and bind elements to the `dom-bind` element itself as the
     * binding scope.
     *
     * @polymer
     * @customElement
     * @appliesMixin Polymer.PropertyEffects
     * @appliesMixin Polymer.OptionalMutableData
     * @appliesMixin Polymer.GestureEventListeners
     * @extends {domBindBase}
     * @memberof Polymer
     * @summary Custom element to allow using Polymer's template features (data
     *   binding, declarative event listeners, etc.) in the main document.
     */
    class DomBind extends domBindBase {

      static get observedAttributes() { return ['mutable-data']; }

      constructor() {
        super();
        if (Polymer.strictTemplatePolicy) {
          throw new Error(`strictTemplatePolicy: dom-bind not allowed`);
        }
        this.root = null;
        this.$ = null;
        this.__children = null;
      }

      /** @return {void} */
      attributeChangedCallback() {
        // assumes only one observed attribute
        this.mutableData = true;
      }

      /** @return {void} */
      connectedCallback() {
        this.style.display = 'none';
        this.render();
      }

      /** @return {void} */
      disconnectedCallback() {
        this.__removeChildren();
      }

      __insertChildren() {
        this.parentNode.insertBefore(this.root, this);
      }

      __removeChildren() {
        if (this.__children) {
          for (let i=0; i<this.__children.length; i++) {
            this.root.appendChild(this.__children[i]);
          }
        }
      }

      /**
       * Forces the element to render its content. This is typically only
       * necessary to call if HTMLImports with the async attribute are used.
       * @return {void}
       */
      render() {
        let template;
        if (!this.__children) {
          template = /** @type {HTMLTemplateElement} */(template || this.querySelector('template'));
          if (!template) {
            // Wait until childList changes and template should be there by then
            let observer = new MutationObserver(() => {
              template = /** @type {HTMLTemplateElement} */(this.querySelector('template'));
              if (template) {
                observer.disconnect();
                this.render();
              } else {
                throw new Error('dom-bind requires a <template> child');
              }
            });
            observer.observe(this, {childList: true});
            return;
          }
          this.root = this._stampTemplate(template);
          this.$ = this.root.$;
          this.__children = [];
          for (let n=this.root.firstChild; n; n=n.nextSibling) {
            this.__children[this.__children.length] = n;
          }
          this._enableProperties();
        }
        this.__insertChildren();
        this.dispatchEvent(new CustomEvent('dom-change', {
          bubbles: true,
          composed: true
        }));
      }

    }

    customElements.define('dom-bind', DomBind);

    /** @const */
    Polymer.DomBind = DomBind;

  })();



  (function() {
    'use strict';

    /**
     * Class representing a static string value which can be used to filter
     * strings by asseting that they have been created via this class. The
     * `value` property returns the string passed to the constructor.
     */
    class LiteralString {
      constructor(string) {
        /** @type {string} */
        this.value = string.toString();
      }
      /**
       * @return {string} LiteralString string value
       */
      toString() {
        return this.value;
      }
    }

    /**
     * @param {*} value Object to stringify into HTML
     * @return {string} HTML stringified form of `obj`
     */
    function literalValue(value) {
      if (value instanceof LiteralString) {
        return /** @type {!LiteralString} */(value).value;
      } else {
        throw new Error(`non-literal value passed to Polymer.htmlLiteral: ${value}`);
      }
    }

    /**
     * @param {*} value Object to stringify into HTML
     * @return {string} HTML stringified form of `obj`
     */
    function htmlValue(value) {
      if (value instanceof HTMLTemplateElement) {
        return /** @type {!HTMLTemplateElement } */(value).innerHTML;
      } else if (value instanceof LiteralString) {
        return literalValue(value);
      } else {
        throw new Error(`non-template value passed to Polymer.html: ${value}`);
      }
    }

    /**
     * A template literal tag that creates an HTML <template> element from the
     * contents of the string.
     *
     * This allows you to write a Polymer Template in JavaScript.
     *
     * Templates can be composed by interpolating `HTMLTemplateElement`s in
     * expressions in the JavaScript template literal. The nested template's
     * `innerHTML` is included in the containing template.  The only other
     * values allowed in expressions are those returned from `Polymer.htmlLiteral`
     * which ensures only literal values from JS source ever reach the HTML, to
     * guard against XSS risks.
     *
     * All other values are disallowed in expressions to help prevent XSS
     * attacks; however, `Polymer.htmlLiteral` can be used to compose static
     * string values into templates. This is useful to compose strings into
     * places that do not accept html, like the css text of a `style`
     * element.
     *
     * Example:
     *
     *     static get template() {
     *       return Polymer.html`
     *         <style>:host{ content:"..." }</style>
     *         <div class="shadowed">${this.partialTemplate}</div>
     *         ${super.template}
     *       `;
     *     }
     *     static get partialTemplate() { return Polymer.html`<span>Partial!</span>`; }
     *
     * @memberof Polymer
     * @param {!ITemplateArray} strings Constant parts of tagged template literal
     * @param {...*} values Variable parts of tagged template literal
     * @return {!HTMLTemplateElement} Constructed HTMLTemplateElement
     */
    Polymer.html = function html(strings, ...values) {
      const template = /** @type {!HTMLTemplateElement} */(document.createElement('template'));
      template.innerHTML = values.reduce((acc, v, idx) =>
          acc + htmlValue(v) + strings[idx + 1], strings[0]);
      return template;
    };

    /**
     * An html literal tag that can be used with `Polymer.html` to compose.
     * a literal string.
     *
     * Example:
     *
     *     static get template() {
     *       return Polymer.html`
     *         <style>
     *           :host { display: block; }
     *           ${styleTemplate}
     *         </style>
     *         <div class="shadowed">${staticValue}</div>
     *         ${super.template}
     *       `;
     *     }
     *     static get styleTemplate() { return Polymer.htmlLiteral`.shadowed { background: gray; }`; }
     *
     * @memberof Polymer
     * @param {!ITemplateArray} strings Constant parts of tagged template literal
     * @param {...*} values Variable parts of tagged template literal
     * @return {!LiteralString} Constructed literal string
     */
    Polymer.htmlLiteral = function(strings, ...values) {
      return new LiteralString(values.reduce((acc, v, idx) =>
          acc + literalValue(v) + strings[idx + 1], strings[0]));
    };
  })();


(function() {
  'use strict';

  /**
   * Base class that provides the core API for Polymer's meta-programming
   * features including template stamping, data-binding, attribute deserialization,
   * and property change observation.
   *
   * @customElement
   * @memberof Polymer
   * @constructor
   * @implements {Polymer_ElementMixin}
   * @extends {HTMLElement}
   * @appliesMixin Polymer.ElementMixin
   * @summary Custom element base class that provides the core API for Polymer's
   *   key meta-programming features including template stamping, data-binding,
   *   attribute deserialization, and property change observation
   */
  Polymer.Element = Polymer.ElementMixin(HTMLElement);

  // NOTE: this is here for modulizer to export `html` for the module version of this file
  Polymer.html = Polymer.html;
})();


(function() {
  'use strict';

  let TemplateInstanceBase = Polymer.TemplateInstanceBase; // eslint-disable-line

  /**
   * @constructor
   * @implements {Polymer_OptionalMutableData}
   * @extends {Polymer.Element}
   * @private
   */
  const domRepeatBase = Polymer.OptionalMutableData(Polymer.Element);

  /**
   * The `<dom-repeat>` element will automatically stamp and binds one instance
   * of template content to each object in a user-provided array.
   * `dom-repeat` accepts an `items` property, and one instance of the template
   * is stamped for each item into the DOM at the location of the `dom-repeat`
   * element.  The `item` property will be set on each instance's binding
   * scope, thus templates should bind to sub-properties of `item`.
   *
   * Example:
   *
   * ```html
   * <dom-module id="employee-list">
   *
   *   <template>
   *
   *     <div> Employee list: </div>
   *     <dom-repeat items="{{employees}}">
   *       <template>
   *         <div>First name: <span>{{item.first}}</span></div>
   *         <div>Last name: <span>{{item.last}}</span></div>
   *       </template>
   *     </dom-repeat>
   *
   *   </template>
   *
   * </dom-module>
   * ```
   *
   * With the following custom element definition:
   *
   * ```js
   * class EmployeeList extends Polymer.Element {
   *   static get is() { return 'employee-list'; }
   *   static get properties() {
   *     return {
   *       employees: {
   *         value() {
   *           return [
   *             {first: 'Bob', last: 'Smith'},
   *             {first: 'Sally', last: 'Johnson'},
   *             ...
   *           ];
   *         }
   *       }
   *     };
   *   }
   * }
   * ```
   *
   * Notifications for changes to items sub-properties will be forwarded to template
   * instances, which will update via the normal structured data notification system.
   *
   * Mutations to the `items` array itself should be made using the Array
   * mutation API's on `Polymer.Base` (`push`, `pop`, `splice`, `shift`,
   * `unshift`), and template instances will be kept in sync with the data in the
   * array.
   *
   * Events caught by event handlers within the `dom-repeat` template will be
   * decorated with a `model` property, which represents the binding scope for
   * each template instance.  The model is an instance of Polymer.Base, and should
   * be used to manipulate data on the instance, for example
   * `event.model.set('item.checked', true);`.
   *
   * Alternatively, the model for a template instance for an element stamped by
   * a `dom-repeat` can be obtained using the `modelForElement` API on the
   * `dom-repeat` that stamped it, for example
   * `this.$.domRepeat.modelForElement(event.target).set('item.checked', true);`.
   * This may be useful for manipulating instance data of event targets obtained
   * by event handlers on parents of the `dom-repeat` (event delegation).
   *
   * A view-specific filter/sort may be applied to each `dom-repeat` by supplying a
   * `filter` and/or `sort` property.  This may be a string that names a function on
   * the host, or a function may be assigned to the property directly.  The functions
   * should implemented following the standard `Array` filter/sort API.
   *
   * In order to re-run the filter or sort functions based on changes to sub-fields
   * of `items`, the `observe` property may be set as a space-separated list of
   * `item` sub-fields that should cause a re-filter/sort when modified.  If
   * the filter or sort function depends on properties not contained in `items`,
   * the user should observe changes to those properties and call `render` to update
   * the view based on the dependency change.
   *
   * For example, for an `dom-repeat` with a filter of the following:
   *
   * ```js
   * isEngineer(item) {
   *   return item.type == 'engineer' || item.manager.type == 'engineer';
   * }
   * ```
   *
   * Then the `observe` property should be configured as follows:
   *
   * ```html
   * <dom-repeat items="{{employees}}" filter="isEngineer" observe="type manager.type">
   * ```
   *
   * @customElement
   * @polymer
   * @memberof Polymer
   * @extends {domRepeatBase}
   * @appliesMixin Polymer.OptionalMutableData
   * @summary Custom element for stamping instance of a template bound to
   *   items in an array.
   */
  class DomRepeat extends domRepeatBase {

    // Not needed to find template; can be removed once the analyzer
    // can find the tag name from customElements.define call
    static get is() { return 'dom-repeat'; }

    static get template() { return null; }

    static get properties() {

      /**
       * Fired whenever DOM is added or removed by this template (by
       * default, rendering occurs lazily).  To force immediate rendering, call
       * `render`.
       *
       * @event dom-change
       */
      return {

        /**
         * An array containing items determining how many instances of the template
         * to stamp and that that each template instance should bind to.
         */
        items: {
          type: Array
        },

        /**
         * The name of the variable to add to the binding scope for the array
         * element associated with a given template instance.
         */
        as: {
          type: String,
          value: 'item'
        },

        /**
         * The name of the variable to add to the binding scope with the index
         * of the instance in the sorted and filtered list of rendered items.
         * Note, for the index in the `this.items` array, use the value of the
         * `itemsIndexAs` property.
         */
        indexAs: {
          type: String,
          value: 'index'
        },

        /**
         * The name of the variable to add to the binding scope with the index
         * of the instance in the `this.items` array. Note, for the index of
         * this instance in the sorted and filtered list of rendered items,
         * use the value of the `indexAs` property.
         */
        itemsIndexAs: {
          type: String,
          value: 'itemsIndex'
        },

        /**
         * A function that should determine the sort order of the items.  This
         * property should either be provided as a string, indicating a method
         * name on the element's host, or else be an actual function.  The
         * function should match the sort function passed to `Array.sort`.
         * Using a sort function has no effect on the underlying `items` array.
         */
        sort: {
          type: Function,
          observer: '__sortChanged'
        },

        /**
         * A function that can be used to filter items out of the view.  This
         * property should either be provided as a string, indicating a method
         * name on the element's host, or else be an actual function.  The
         * function should match the sort function passed to `Array.filter`.
         * Using a filter function has no effect on the underlying `items` array.
         */
        filter: {
          type: Function,
          observer: '__filterChanged'
        },

        /**
         * When using a `filter` or `sort` function, the `observe` property
         * should be set to a space-separated list of the names of item
         * sub-fields that should trigger a re-sort or re-filter when changed.
         * These should generally be fields of `item` that the sort or filter
         * function depends on.
         */
        observe: {
          type: String,
          observer: '__observeChanged'
        },

        /**
         * When using a `filter` or `sort` function, the `delay` property
         * determines a debounce time in ms after a change to observed item
         * properties that must pass before the filter or sort is re-run.
         * This is useful in rate-limiting shuffling of the view when
         * item changes may be frequent.
         */
        delay: Number,

        /**
         * Count of currently rendered items after `filter` (if any) has been applied.
         * If "chunking mode" is enabled, `renderedItemCount` is updated each time a
         * set of template instances is rendered.
         *
         */
        renderedItemCount: {
          type: Number,
          notify: true,
          readOnly: true
        },

        /**
         * Defines an initial count of template instances to render after setting
         * the `items` array, before the next paint, and puts the `dom-repeat`
         * into "chunking mode".  The remaining items will be created and rendered
         * incrementally at each animation frame therof until all instances have
         * been rendered.
         */
        initialCount: {
          type: Number,
          observer: '__initializeChunking'
        },

        /**
         * When `initialCount` is used, this property defines a frame rate (in
         * fps) to target by throttling the number of instances rendered each
         * frame to not exceed the budget for the target frame rate.  The
         * framerate is effectively the number of `requestAnimationFrame`s that
         * it tries to allow to actually fire in a given second. It does this
         * by measuring the time between `rAF`s and continuously adjusting the
         * number of items created each `rAF` to maintain the target framerate.
         * Setting this to a higher number allows lower latency and higher
         * throughput for event handlers and other tasks, but results in a
         * longer time for the remaining items to complete rendering.
         */
        targetFramerate: {
          type: Number,
          value: 20
        },

        _targetFrameTime: {
          type: Number,
          computed: '__computeFrameTime(targetFramerate)'
        }

      };

    }

    static get observers() {
      return [ '__itemsChanged(items.*)' ];
    }

    constructor() {
      super();
      this.__instances = [];
      this.__limit = Infinity;
      this.__pool = [];
      this.__renderDebouncer = null;
      this.__itemsIdxToInstIdx = {};
      this.__chunkCount = null;
      this.__lastChunkTime = null;
      this.__sortFn = null;
      this.__filterFn = null;
      this.__observePaths = null;
      this.__ctor = null;
      this.__isDetached = true;
      this.template = null;
    }

    /**
     * @return {void}
     */
    disconnectedCallback() {
      super.disconnectedCallback();
      this.__isDetached = true;
      for (let i=0; i<this.__instances.length; i++) {
        this.__detachInstance(i);
      }
    }

    /**
     * @return {void}
     */
    connectedCallback() {
      super.connectedCallback();
      this.style.display = 'none';
      // only perform attachment if the element was previously detached.
      if (this.__isDetached) {
        this.__isDetached = false;
        let parent = this.parentNode;
        for (let i=0; i<this.__instances.length; i++) {
          this.__attachInstance(i, parent);
        }
      }
    }

    __ensureTemplatized() {
      // Templatizing (generating the instance constructor) needs to wait
      // until ready, since won't have its template content handed back to
      // it until then
      if (!this.__ctor) {
        let template = this.template = /** @type {HTMLTemplateElement} */(this.querySelector('template'));
        if (!template) {
          // // Wait until childList changes and template should be there by then
          let observer = new MutationObserver(() => {
            if (this.querySelector('template')) {
              observer.disconnect();
              this.__render();
            } else {
              throw new Error('dom-repeat requires a <template> child');
            }
          });
          observer.observe(this, {childList: true});
          return false;
        }
        // Template instance props that should be excluded from forwarding
        let instanceProps = {};
        instanceProps[this.as] = true;
        instanceProps[this.indexAs] = true;
        instanceProps[this.itemsIndexAs] = true;
        this.__ctor = Polymer.Templatize.templatize(template, this, {
          mutableData: this.mutableData,
          parentModel: true,
          instanceProps: instanceProps,
          /**
           * @this {this}
           * @param {string} prop Property to set
           * @param {*} value Value to set property to
           */
          forwardHostProp: function(prop, value) {
            let i$ = this.__instances;
            for (let i=0, inst; (i<i$.length) && (inst=i$[i]); i++) {
              inst.forwardHostProp(prop, value);
            }
          },
          /**
           * @this {this}
           * @param {Object} inst Instance to notify
           * @param {string} prop Property to notify
           * @param {*} value Value to notify
           */
          notifyInstanceProp: function(inst, prop, value) {
            if (Polymer.Path.matches(this.as, prop)) {
              let idx = inst[this.itemsIndexAs];
              if (prop == this.as) {
                this.items[idx] = value;
              }
              let path = Polymer.Path.translate(this.as, 'items.' + idx, prop);
              this.notifyPath(path, value);
            }
          }
        });
      }
      return true;
    }

    __getMethodHost() {
      // Technically this should be the owner of the outermost template.
      // In shadow dom, this is always getRootNode().host, but we can
      // approximate this via cooperation with our dataHost always setting
      // `_methodHost` as long as there were bindings (or id's) on this
      // instance causing it to get a dataHost.
      return this.__dataHost._methodHost || this.__dataHost;
    }

    __functionFromPropertyValue(functionOrMethodName) {
      if (typeof functionOrMethodName === 'string') {
        let methodName = functionOrMethodName;
        let obj = this.__getMethodHost();
        return function() { return obj[methodName].apply(obj, arguments); };
      }

      return functionOrMethodName;
    }

    __sortChanged(sort) {
      this.__sortFn = this.__functionFromPropertyValue(sort);
      if (this.items) { this.__debounceRender(this.__render); }
    }

    __filterChanged(filter) {
      this.__filterFn = this.__functionFromPropertyValue(filter);
      if (this.items) { this.__debounceRender(this.__render); }
    }

    __computeFrameTime(rate) {
      return Math.ceil(1000/rate);
    }

    __initializeChunking() {
      if (this.initialCount) {
        this.__limit = this.initialCount;
        this.__chunkCount = this.initialCount;
        this.__lastChunkTime = performance.now();
      }
    }

    __tryRenderChunk() {
      // Debounced so that multiple calls through `_render` between animation
      // frames only queue one new rAF (e.g. array mutation & chunked render)
      if (this.items && this.__limit < this.items.length) {
        this.__debounceRender(this.__requestRenderChunk);
      }
    }

    __requestRenderChunk() {
      requestAnimationFrame(()=>this.__renderChunk());
    }

    __renderChunk() {
      // Simple auto chunkSize throttling algorithm based on feedback loop:
      // measure actual time between frames and scale chunk count by ratio
      // of target/actual frame time
      let currChunkTime = performance.now();
      let ratio = this._targetFrameTime / (currChunkTime - this.__lastChunkTime);
      this.__chunkCount = Math.round(this.__chunkCount * ratio) || 1;
      this.__limit += this.__chunkCount;
      this.__lastChunkTime = currChunkTime;
      this.__debounceRender(this.__render);
    }

    __observeChanged() {
      this.__observePaths = this.observe &&
        this.observe.replace('.*', '.').split(' ');
    }

    __itemsChanged(change) {
      if (this.items && !Array.isArray(this.items)) {
        console.warn('dom-repeat expected array for `items`, found', this.items);
      }
      // If path was to an item (e.g. 'items.3' or 'items.3.foo'), forward the
      // path to that instance synchronously (returns false for non-item paths)
      if (!this.__handleItemPath(change.path, change.value)) {
        // Otherwise, the array was reset ('items') or spliced ('items.splices'),
        // so queue a full refresh
        this.__initializeChunking();
        this.__debounceRender(this.__render);
      }
    }

    __handleObservedPaths(path) {
      // Handle cases where path changes should cause a re-sort/filter
      if (this.__sortFn || this.__filterFn) {
        if (!path) {
          // Always re-render if the item itself changed
          this.__debounceRender(this.__render, this.delay);
        } else if (this.__observePaths) {
          // Otherwise, re-render if the path changed matches an observed path
          let paths = this.__observePaths;
          for (let i=0; i<paths.length; i++) {
            if (path.indexOf(paths[i]) === 0) {
              this.__debounceRender(this.__render, this.delay);
            }
          }
        }
      }
    }

    /**
     * @param {function(this:DomRepeat)} fn Function to debounce.
     * @param {number=} delay Delay in ms to debounce by.
     */
    __debounceRender(fn, delay = 0) {
      this.__renderDebouncer = Polymer.Debouncer.debounce(
            this.__renderDebouncer
          , delay > 0 ? Polymer.Async.timeOut.after(delay) : Polymer.Async.microTask
          , fn.bind(this));
      Polymer.enqueueDebouncer(this.__renderDebouncer);
    }

    /**
     * Forces the element to render its content. Normally rendering is
     * asynchronous to a provoking change. This is done for efficiency so
     * that multiple changes trigger only a single render. The render method
     * should be called if, for example, template rendering is required to
     * validate application state.
     * @return {void}
     */
    render() {
      // Queue this repeater, then flush all in order
      this.__debounceRender(this.__render);
      Polymer.flush();
    }

    __render() {
      if (!this.__ensureTemplatized()) {
        // No template found yet
        return;
      }
      this.__applyFullRefresh();
      // Reset the pool
      // TODO(kschaaf): Reuse pool across turns and nested templates
      // Now that objects/arrays are re-evaluated when set, we can safely
      // reuse pooled instances across turns, however we still need to decide
      // semantics regarding how long to hold, how many to hold, etc.
      this.__pool.length = 0;
      // Set rendered item count
      this._setRenderedItemCount(this.__instances.length);
      // Notify users
      this.dispatchEvent(new CustomEvent('dom-change', {
        bubbles: true,
        composed: true
      }));
      // Check to see if we need to render more items
      this.__tryRenderChunk();
    }

    __applyFullRefresh() {
      let items = this.items || [];
      let isntIdxToItemsIdx = new Array(items.length);
      for (let i=0; i<items.length; i++) {
        isntIdxToItemsIdx[i] = i;
      }
      // Apply user filter
      if (this.__filterFn) {
        isntIdxToItemsIdx = isntIdxToItemsIdx.filter((i, idx, array) =>
          this.__filterFn(items[i], idx, array));
      }
      // Apply user sort
      if (this.__sortFn) {
        isntIdxToItemsIdx.sort((a, b) => this.__sortFn(items[a], items[b]));
      }
      // items->inst map kept for item path forwarding
      const itemsIdxToInstIdx = this.__itemsIdxToInstIdx = {};
      let instIdx = 0;
      // Generate instances and assign items
      const limit = Math.min(isntIdxToItemsIdx.length, this.__limit);
      for (; instIdx<limit; instIdx++) {
        let inst = this.__instances[instIdx];
        let itemIdx = isntIdxToItemsIdx[instIdx];
        let item = items[itemIdx];
        itemsIdxToInstIdx[itemIdx] = instIdx;
        if (inst) {
          inst._setPendingProperty(this.as, item);
          inst._setPendingProperty(this.indexAs, instIdx);
          inst._setPendingProperty(this.itemsIndexAs, itemIdx);
          inst._flushProperties();
        } else {
          this.__insertInstance(item, instIdx, itemIdx);
        }
      }
      // Remove any extra instances from previous state
      for (let i=this.__instances.length-1; i>=instIdx; i--) {
        this.__detachAndRemoveInstance(i);
      }
    }

    __detachInstance(idx) {
      let inst = this.__instances[idx];
      for (let i=0; i<inst.children.length; i++) {
        let el = inst.children[i];
        inst.root.appendChild(el);
      }
      return inst;
    }

    __attachInstance(idx, parent) {
      let inst = this.__instances[idx];
      parent.insertBefore(inst.root, this);
    }

    __detachAndRemoveInstance(idx) {
      let inst = this.__detachInstance(idx);
      if (inst) {
        this.__pool.push(inst);
      }
      this.__instances.splice(idx, 1);
    }

    __stampInstance(item, instIdx, itemIdx) {
      let model = {};
      model[this.as] = item;
      model[this.indexAs] = instIdx;
      model[this.itemsIndexAs] = itemIdx;
      return new this.__ctor(model);
    }

    __insertInstance(item, instIdx, itemIdx) {
      let inst = this.__pool.pop();
      if (inst) {
        // TODO(kschaaf): If the pool is shared across turns, hostProps
        // need to be re-set to reused instances in addition to item
        inst._setPendingProperty(this.as, item);
        inst._setPendingProperty(this.indexAs, instIdx);
        inst._setPendingProperty(this.itemsIndexAs, itemIdx);
        inst._flushProperties();
      } else {
        inst = this.__stampInstance(item, instIdx, itemIdx);
      }
      let beforeRow = this.__instances[instIdx + 1];
      let beforeNode = beforeRow ? beforeRow.children[0] : this;
      this.parentNode.insertBefore(inst.root, beforeNode);
      this.__instances[instIdx] = inst;
      return inst;
    }

    // Implements extension point from Templatize mixin
    /**
     * Shows or hides the template instance top level child elements. For
     * text nodes, `textContent` is removed while "hidden" and replaced when
     * "shown."
     * @param {boolean} hidden Set to true to hide the children;
     * set to false to show them.
     * @return {void}
     * @protected
     */
    _showHideChildren(hidden) {
      for (let i=0; i<this.__instances.length; i++) {
        this.__instances[i]._showHideChildren(hidden);
      }
    }

    // Called as a side effect of a host items.<key>.<path> path change,
    // responsible for notifying item.<path> changes to inst for key
    __handleItemPath(path, value) {
      let itemsPath = path.slice(6); // 'items.'.length == 6
      let dot = itemsPath.indexOf('.');
      let itemsIdx = dot < 0 ? itemsPath : itemsPath.substring(0, dot);
      // If path was index into array...
      if (itemsIdx == parseInt(itemsIdx, 10)) {
        let itemSubPath = dot < 0 ? '' : itemsPath.substring(dot+1);
        // If the path is observed, it will trigger a full refresh
        this.__handleObservedPaths(itemSubPath);
        // Note, even if a rull refresh is triggered, always do the path
        // notification because unless mutableData is used for dom-repeat
        // and all elements in the instance subtree, a full refresh may
        // not trigger the proper update.
        let instIdx = this.__itemsIdxToInstIdx[itemsIdx];
        let inst = this.__instances[instIdx];
        if (inst) {
          let itemPath = this.as + (itemSubPath ? '.' + itemSubPath : '');
          // This is effectively `notifyPath`, but avoids some of the overhead
          // of the public API
          inst._setPendingPropertyOrPath(itemPath, value, false, true);
          inst._flushProperties();
        }
        return true;
      }
    }

    /**
     * Returns the item associated with a given element stamped by
     * this `dom-repeat`.
     *
     * Note, to modify sub-properties of the item,
     * `modelForElement(el).set('item.<sub-prop>', value)`
     * should be used.
     *
     * @param {!HTMLElement} el Element for which to return the item.
     * @return {*} Item associated with the element.
     */
    itemForElement(el) {
      let instance = this.modelForElement(el);
      return instance && instance[this.as];
    }

    /**
     * Returns the inst index for a given element stamped by this `dom-repeat`.
     * If `sort` is provided, the index will reflect the sorted order (rather
     * than the original array order).
     *
     * @param {!HTMLElement} el Element for which to return the index.
     * @return {?number} Row index associated with the element (note this may
     *   not correspond to the array index if a user `sort` is applied).
     */
    indexForElement(el) {
      let instance = this.modelForElement(el);
      return instance && instance[this.indexAs];
    }

    /**
     * Returns the template "model" associated with a given element, which
     * serves as the binding scope for the template instance the element is
     * contained in. A template model is an instance of `Polymer.Base`, and
     * should be used to manipulate data associated with this template instance.
     *
     * Example:
     *
     *   let model = modelForElement(el);
     *   if (model.index < 10) {
     *     model.set('item.checked', true);
     *   }
     *
     * @param {!HTMLElement} el Element for which to return a template model.
     * @return {TemplateInstanceBase} Model representing the binding scope for
     *   the element.
     */
    modelForElement(el) {
      return Polymer.Templatize.modelForElement(this.template, el);
    }

  }

  customElements.define(DomRepeat.is, DomRepeat);

  /** @const */
  Polymer.DomRepeat = DomRepeat;

})();




(function() {
  'use strict';

  /**
   * The `<dom-if>` element will stamp a light-dom `<template>` child when
   * the `if` property becomes truthy, and the template can use Polymer
   * data-binding and declarative event features when used in the context of
   * a Polymer element's template.
   *
   * When `if` becomes falsy, the stamped content is hidden but not
   * removed from dom. When `if` subsequently becomes truthy again, the content
   * is simply re-shown. This approach is used due to its favorable performance
   * characteristics: the expense of creating template content is paid only
   * once and lazily.
   *
   * Set the `restamp` property to true to force the stamped content to be
   * created / destroyed when the `if` condition changes.
   *
   * @customElement
   * @polymer
   * @extends Polymer.Element
   * @memberof Polymer
   * @summary Custom element that conditionally stamps and hides or removes
   *   template content based on a boolean flag.
   */
  class DomIf extends Polymer.Element {

    // Not needed to find template; can be removed once the analyzer
    // can find the tag name from customElements.define call
    static get is() { return 'dom-if'; }

    static get template() { return null; }

    static get properties() {

      return {

        /**
         * Fired whenever DOM is added or removed/hidden by this template (by
         * default, rendering occurs lazily).  To force immediate rendering, call
         * `render`.
         *
         * @event dom-change
         */

        /**
         * A boolean indicating whether this template should stamp.
         */
        if: {
          type: Boolean,
          observer: '__debounceRender'
        },

        /**
         * When true, elements will be removed from DOM and discarded when `if`
         * becomes false and re-created and added back to the DOM when `if`
         * becomes true.  By default, stamped elements will be hidden but left
         * in the DOM when `if` becomes false, which is generally results
         * in better performance.
         */
        restamp: {
          type: Boolean,
          observer: '__debounceRender'
        }

      };

    }

    constructor() {
      super();
      this.__renderDebouncer = null;
      this.__invalidProps = null;
      this.__instance = null;
      this._lastIf = false;
      this.__ctor = null;
    }

    __debounceRender() {
      // Render is async for 2 reasons:
      // 1. To eliminate dom creation trashing if user code thrashes `if` in the
      //    same turn. This was more common in 1.x where a compound computed
      //    property could result in the result changing multiple times, but is
      //    mitigated to a large extent by batched property processing in 2.x.
      // 2. To avoid double object propagation when a bag including values bound
      //    to the `if` property as well as one or more hostProps could enqueue
      //    the <dom-if> to flush before the <template>'s host property
      //    forwarding. In that scenario creating an instance would result in
      //    the host props being set once, and then the enqueued changes on the
      //    template would set properties a second time, potentially causing an
      //    object to be set to an instance more than once.  Creating the
      //    instance async from flushing data ensures this doesn't happen. If
      //    we wanted a sync option in the future, simply having <dom-if> flush
      //    (or clear) its template's pending host properties before creating
      //    the instance would also avoid the problem.
      this.__renderDebouncer = Polymer.Debouncer.debounce(
            this.__renderDebouncer
          , Polymer.Async.microTask
          , () => this.__render());
      Polymer.enqueueDebouncer(this.__renderDebouncer);
    }

    /**
     * @return {void}
     */
    disconnectedCallback() {
      super.disconnectedCallback();
      if (!this.parentNode ||
          (this.parentNode.nodeType == Node.DOCUMENT_FRAGMENT_NODE &&
           !this.parentNode.host)) {
        this.__teardownInstance();
      }
    }

    /**
     * @return {void}
     */
    connectedCallback() {
      super.connectedCallback();
      this.style.display = 'none';
      if (this.if) {
        this.__debounceRender();
      }
    }

    /**
     * Forces the element to render its content. Normally rendering is
     * asynchronous to a provoking change. This is done for efficiency so
     * that multiple changes trigger only a single render. The render method
     * should be called if, for example, template rendering is required to
     * validate application state.
     * @return {void}
     */
    render() {
      Polymer.flush();
    }

    __render() {
      if (this.if) {
        if (!this.__ensureInstance()) {
          // No template found yet
          return;
        }
        this._showHideChildren();
      } else if (this.restamp) {
        this.__teardownInstance();
      }
      if (!this.restamp && this.__instance) {
        this._showHideChildren();
      }
      if (this.if != this._lastIf) {
        this.dispatchEvent(new CustomEvent('dom-change', {
          bubbles: true,
          composed: true
        }));
        this._lastIf = this.if;
      }
    }

    __ensureInstance() {
      let parentNode = this.parentNode;
      // Guard against element being detached while render was queued
      if (parentNode) {
        if (!this.__ctor) {
          let template = /** @type {HTMLTemplateElement} */(this.querySelector('template'));
          if (!template) {
            // Wait until childList changes and template should be there by then
            let observer = new MutationObserver(() => {
              if (this.querySelector('template')) {
                observer.disconnect();
                this.__render();
              } else {
                throw new Error('dom-if requires a <template> child');
              }
            });
            observer.observe(this, {childList: true});
            return false;
          }
          this.__ctor = Polymer.Templatize.templatize(template, this, {
            // dom-if templatizer instances require `mutable: true`, as
            // `__syncHostProperties` relies on that behavior to sync objects
            mutableData: true,
            /**
             * @param {string} prop Property to forward
             * @param {*} value Value of property
             * @this {this}
             */
            forwardHostProp: function(prop, value) {
              if (this.__instance) {
                if (this.if) {
                  this.__instance.forwardHostProp(prop, value);
                } else {
                  // If we have an instance but are squelching host property
                  // forwarding due to if being false, note the invalidated
                  // properties so `__syncHostProperties` can sync them the next
                  // time `if` becomes true
                  this.__invalidProps = this.__invalidProps || Object.create(null);
                  this.__invalidProps[Polymer.Path.root(prop)] = true;
                }
              }
            }
          });
        }
        if (!this.__instance) {
          this.__instance = new this.__ctor();
          parentNode.insertBefore(this.__instance.root, this);
        } else {
          this.__syncHostProperties();
          let c$ = this.__instance.children;
          if (c$ && c$.length) {
            // Detect case where dom-if was re-attached in new position
            let lastChild = this.previousSibling;
            if (lastChild !== c$[c$.length-1]) {
              for (let i=0, n; (i<c$.length) && (n=c$[i]); i++) {
                parentNode.insertBefore(n, this);
              }
            }
          }
        }
      }
      return true;
    }

    __syncHostProperties() {
      let props = this.__invalidProps;
      if (props) {
        for (let prop in props) {
          this.__instance._setPendingProperty(prop, this.__dataHost[prop]);
        }
        this.__invalidProps = null;
        this.__instance._flushProperties();
      }
    }

    __teardownInstance() {
      if (this.__instance) {
        let c$ = this.__instance.children;
        if (c$ && c$.length) {
          // use first child parent, for case when dom-if may have been detached
          let parent = c$[0].parentNode;
          // Instance children may be disconnected from parents when dom-if
          // detaches if a tree was innerHTML'ed
          if (parent) {
            for (let i=0, n; (i<c$.length) && (n=c$[i]); i++) {
              parent.removeChild(n);
            }
          }
        }
        this.__instance = null;
        this.__invalidProps = null;
      }
    }

    /**
     * Shows or hides the template instance top level child elements. For
     * text nodes, `textContent` is removed while "hidden" and replaced when
     * "shown."
     * @return {void}
     * @protected
     */
    _showHideChildren() {
      let hidden = this.__hideTemplateChildren__ || !this.if;
      if (this.__instance) {
        this.__instance._showHideChildren(hidden);
      }
    }

  }

  customElements.define(DomIf.is, DomIf);

  /** @const */
  Polymer.DomIf = DomIf;

})();


(function() {
  'use strict';

  /**
   * Element mixin for recording dynamic associations between item paths in a
   * master `items` array and a `selected` array such that path changes to the
   * master array (at the host) element or elsewhere via data-binding) are
   * correctly propagated to items in the selected array and vice-versa.
   *
   * The `items` property accepts an array of user data, and via the
   * `select(item)` and `deselect(item)` API, updates the `selected` property
   * which may be bound to other parts of the application, and any changes to
   * sub-fields of `selected` item(s) will be kept in sync with items in the
   * `items` array.  When `multi` is false, `selected` is a property
   * representing the last selected item.  When `multi` is true, `selected`
   * is an array of multiply selected items.
   *
   * @polymer
   * @mixinFunction
   * @appliesMixin Polymer.ElementMixin
   * @memberof Polymer
   * @summary Element mixin for recording dynamic associations between item paths in a
   * master `items` array and a `selected` array
   */
  let ArraySelectorMixin = Polymer.dedupingMixin(superClass => {

    /**
     * @constructor
     * @extends {superClass}
     * @implements {Polymer_ElementMixin}
     * @private
     */
    let elementBase = Polymer.ElementMixin(superClass);

    /**
     * @polymer
     * @mixinClass
     * @implements {Polymer_ArraySelectorMixin}
     * @unrestricted
     */
    class ArraySelectorMixin extends elementBase {

      static get properties() {

        return {

          /**
           * An array containing items from which selection will be made.
           */
          items: {
            type: Array,
          },

          /**
           * When `true`, multiple items may be selected at once (in this case,
           * `selected` is an array of currently selected items).  When `false`,
           * only one item may be selected at a time.
           */
          multi: {
            type: Boolean,
            value: false,
          },

          /**
           * When `multi` is true, this is an array that contains any selected.
           * When `multi` is false, this is the currently selected item, or `null`
           * if no item is selected.
           * @type {?(Object|Array<!Object>)}
           */
          selected: {
            type: Object,
            notify: true
          },

          /**
           * When `multi` is false, this is the currently selected item, or `null`
           * if no item is selected.
           * @type {?Object}
           */
          selectedItem: {
            type: Object,
            notify: true
          },

          /**
           * When `true`, calling `select` on an item that is already selected
           * will deselect the item.
           */
          toggle: {
            type: Boolean,
            value: false
          }

        };
      }

      static get observers() {
        return ['__updateSelection(multi, items.*)'];
      }

      constructor() {
        super();
        this.__lastItems = null;
        this.__lastMulti = null;
        this.__selectedMap = null;
      }

      __updateSelection(multi, itemsInfo) {
        let path = itemsInfo.path;
        if (path == 'items') {
          // Case 1 - items array changed, so diff against previous array and
          // deselect any removed items and adjust selected indices
          let newItems = itemsInfo.base || [];
          let lastItems = this.__lastItems;
          let lastMulti = this.__lastMulti;
          if (multi !== lastMulti) {
            this.clearSelection();
          }
          if (lastItems) {
            let splices = Polymer.ArraySplice.calculateSplices(newItems, lastItems);
            this.__applySplices(splices);
          }
          this.__lastItems = newItems;
          this.__lastMulti = multi;
        } else if (itemsInfo.path == 'items.splices') {
          // Case 2 - got specific splice information describing the array mutation:
          // deselect any removed items and adjust selected indices
          this.__applySplices(itemsInfo.value.indexSplices);
        } else {
          // Case 3 - an array element was changed, so deselect the previous
          // item for that index if it was previously selected
          let part = path.slice('items.'.length);
          let idx = parseInt(part, 10);
          if ((part.indexOf('.') < 0) && part == idx) {
            this.__deselectChangedIdx(idx);
          }
        }
      }

      __applySplices(splices) {
        let selected = this.__selectedMap;
        // Adjust selected indices and mark removals
        for (let i=0; i<splices.length; i++) {
          let s = splices[i];
          selected.forEach((idx, item) => {
            if (idx < s.index) {
              // no change
            } else if (idx >= s.index + s.removed.length) {
              // adjust index
              selected.set(item, idx + s.addedCount - s.removed.length);
            } else {
              // remove index
              selected.set(item, -1);
            }
          });
          for (let j=0; j<s.addedCount; j++) {
            let idx = s.index + j;
            if (selected.has(this.items[idx])) {
              selected.set(this.items[idx], idx);
            }
          }
        }
        // Update linked paths
        this.__updateLinks();
        // Remove selected items that were removed from the items array
        let sidx = 0;
        selected.forEach((idx, item) => {
          if (idx < 0) {
            if (this.multi) {
              this.splice('selected', sidx, 1);
            } else {
              this.selected = this.selectedItem = null;
            }
            selected.delete(item);
          } else {
            sidx++;
          }
        });
      }

      __updateLinks() {
        this.__dataLinkedPaths = {};
        if (this.multi) {
          let sidx = 0;
          this.__selectedMap.forEach(idx => {
            if (idx >= 0) {
              this.linkPaths('items.' + idx, 'selected.' + sidx++);
            }
          });
        } else {
          this.__selectedMap.forEach(idx => {
            this.linkPaths('selected', 'items.' + idx);
            this.linkPaths('selectedItem', 'items.' + idx);
          });
        }
      }

      /**
       * Clears the selection state.
       * @return {void}
       */
      clearSelection() {
        // Unbind previous selection
        this.__dataLinkedPaths = {};
        // The selected map stores 3 pieces of information:
        // key: items array object
        // value: items array index
        // order: selected array index
        this.__selectedMap = new Map();
        // Initialize selection
        this.selected = this.multi ? [] : null;
        this.selectedItem = null;
      }

      /**
       * Returns whether the item is currently selected.
       *
       * @param {*} item Item from `items` array to test
       * @return {boolean} Whether the item is selected
       */
      isSelected(item) {
        return this.__selectedMap.has(item);
      }

      /**
       * Returns whether the item is currently selected.
       *
       * @param {number} idx Index from `items` array to test
       * @return {boolean} Whether the item is selected
       */
      isIndexSelected(idx) {
        return this.isSelected(this.items[idx]);
      }

      __deselectChangedIdx(idx) {
        let sidx = this.__selectedIndexForItemIndex(idx);
        if (sidx >= 0) {
          let i = 0;
          this.__selectedMap.forEach((idx, item) => {
            if (sidx == i++) {
              this.deselect(item);
            }
          });
        }
      }

      __selectedIndexForItemIndex(idx) {
        let selected = this.__dataLinkedPaths['items.' + idx];
        if (selected) {
          return parseInt(selected.slice('selected.'.length), 10);
        }
      }

      /**
       * Deselects the given item if it is already selected.
       *
       * @param {*} item Item from `items` array to deselect
       * @return {void}
       */
      deselect(item) {
        let idx = this.__selectedMap.get(item);
        if (idx >= 0) {
          this.__selectedMap.delete(item);
          let sidx;
          if (this.multi) {
            sidx = this.__selectedIndexForItemIndex(idx);
          }
          this.__updateLinks();
          if (this.multi) {
            this.splice('selected', sidx, 1);
          } else {
            this.selected = this.selectedItem = null;
          }
        }
      }

      /**
       * Deselects the given index if it is already selected.
       *
       * @param {number} idx Index from `items` array to deselect
       * @return {void}
       */
      deselectIndex(idx) {
        this.deselect(this.items[idx]);
      }

      /**
       * Selects the given item.  When `toggle` is true, this will automatically
       * deselect the item if already selected.
       *
       * @param {*} item Item from `items` array to select
       * @return {void}
       */
      select(item) {
        this.selectIndex(this.items.indexOf(item));
      }

      /**
       * Selects the given index.  When `toggle` is true, this will automatically
       * deselect the item if already selected.
       *
       * @param {number} idx Index from `items` array to select
       * @return {void}
       */
      selectIndex(idx) {
        let item = this.items[idx];
        if (!this.isSelected(item)) {
          if (!this.multi) {
            this.__selectedMap.clear();
          }
          this.__selectedMap.set(item, idx);
          this.__updateLinks();
          if (this.multi) {
            this.push('selected', item);
          } else {
            this.selected = this.selectedItem = item;
          }
        } else if (this.toggle) {
          this.deselectIndex(idx);
        }
      }

    }

    return ArraySelectorMixin;

  });

  // export mixin
  Polymer.ArraySelectorMixin = ArraySelectorMixin;

  /**
   * @constructor
   * @extends {Polymer.Element}
   * @implements {Polymer_ArraySelectorMixin}
   * @private
   */
  let baseArraySelector = ArraySelectorMixin(Polymer.Element);

  /**
   * Element implementing the `Polymer.ArraySelector` mixin, which records
   * dynamic associations between item paths in a master `items` array and a
   * `selected` array such that path changes to the master array (at the host)
   * element or elsewhere via data-binding) are correctly propagated to items
   * in the selected array and vice-versa.
   *
   * The `items` property accepts an array of user data, and via the
   * `select(item)` and `deselect(item)` API, updates the `selected` property
   * which may be bound to other parts of the application, and any changes to
   * sub-fields of `selected` item(s) will be kept in sync with items in the
   * `items` array.  When `multi` is false, `selected` is a property
   * representing the last selected item.  When `multi` is true, `selected`
   * is an array of multiply selected items.
   *
   * Example:
   *
   * ```html
   * <dom-module id="employee-list">
   *
   *   <template>
   *
   *     <div> Employee list: </div>
   *     <dom-repeat id="employeeList" items="{{employees}}">
   *       <template>
   *         <div>First name: <span>{{item.first}}</span></div>
   *           <div>Last name: <span>{{item.last}}</span></div>
   *           <button on-click="toggleSelection">Select</button>
   *       </template>
   *     </dom-repeat>
   *
   *     <array-selector id="selector" items="{{employees}}" selected="{{selected}}" multi toggle></array-selector>
   *
   *     <div> Selected employees: </div>
   *     <dom-repeat items="{{selected}}">
   *       <template>
   *         <div>First name: <span>{{item.first}}</span></div>
   *         <div>Last name: <span>{{item.last}}</span></div>
   *       </template>
   *     </dom-repeat>
   *
   *   </template>
   *
   * </dom-module>
   * ```
   *
   * ```js
   *class EmployeeList extends Polymer.Element {
   *  static get is() { return 'employee-list'; }
   *  static get properties() {
   *    return {
   *      employees: {
   *        value() {
   *          return [
   *            {first: 'Bob', last: 'Smith'},
   *            {first: 'Sally', last: 'Johnson'},
   *            ...
   *          ];
   *        }
   *      }
   *    };
   *  }
   *  toggleSelection(e) {
   *    let item = this.$.employeeList.itemForElement(e.target);
   *    this.$.selector.select(item);
   *  }
   *}
   * ```
   *
   * @polymer
   * @customElement
   * @extends {baseArraySelector}
   * @appliesMixin Polymer.ArraySelectorMixin
   * @memberof Polymer
   * @summary Custom element that links paths between an input `items` array and
   *   an output `selected` item or array based on calls to its selection API.
   */
  class ArraySelector extends baseArraySelector {
    // Not needed to find template; can be removed once the analyzer
    // can find the tag name from customElements.define call
    static get is() { return 'array-selector'; }
  }
  customElements.define(ArraySelector.is, ArraySelector);

  /** @const */
  Polymer.ArraySelector = ArraySelector;

})();


(function(){/*

Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
Code distributed by Google as part of the polymer project is also
subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
*/
'use strict';var c=null,f=window.HTMLImports&&window.HTMLImports.whenReady||null,g;function h(a){requestAnimationFrame(function(){f?f(a):(c||(c=new Promise(function(a){g=a}),"complete"===document.readyState?g():document.addEventListener("readystatechange",function(){"complete"===document.readyState&&g()})),c.then(function(){a&&a()}))})};var k=null,l=null;function m(){this.customStyles=[];this.enqueued=!1;h(function(){window.ShadyCSS.flushCustomStyles&&window.ShadyCSS.flushCustomStyles()})}function n(a){!a.enqueued&&l&&(a.enqueued=!0,h(l))}m.prototype.c=function(a){a.__seenByShadyCSS||(a.__seenByShadyCSS=!0,this.customStyles.push(a),n(this))};m.prototype.b=function(a){if(a.__shadyCSSCachedStyle)return a.__shadyCSSCachedStyle;var b;a.getStyle?b=a.getStyle():b=a;return b};
m.prototype.a=function(){for(var a=this.customStyles,b=0;b<a.length;b++){var d=a[b];if(!d.__shadyCSSCachedStyle){var e=this.b(d);e&&(e=e.__appliedElement||e,k&&k(e),d.__shadyCSSCachedStyle=e)}}return a};m.prototype.addCustomStyle=m.prototype.c;m.prototype.getStyleForCustomStyle=m.prototype.b;m.prototype.processStyles=m.prototype.a;
Object.defineProperties(m.prototype,{transformCallback:{get:function(){return k},set:function(a){k=a}},validateCallback:{get:function(){return l},set:function(a){var b=!1;l||(b=!0);l=a;b&&n(this)}}});function p(a,b){for(var d in b)null===d?a.style.removeProperty(d):a.style.setProperty(d,b[d])};var q=!(window.ShadyDOM&&window.ShadyDOM.inUse),r;function t(a){r=a&&a.shimcssproperties?!1:q||!(navigator.userAgent.match(/AppleWebKit\/601|Edge\/15/)||!window.CSS||!CSS.supports||!CSS.supports("box-shadow","0 0 0 var(--foo)"))}var u;window.ShadyCSS&&void 0!==window.ShadyCSS.cssBuild&&(u=window.ShadyCSS.cssBuild);var v=!(!window.ShadyCSS||!window.ShadyCSS.disableRuntime);
window.ShadyCSS&&void 0!==window.ShadyCSS.nativeCss?r=window.ShadyCSS.nativeCss:window.ShadyCSS?(t(window.ShadyCSS),window.ShadyCSS=void 0):t(window.WebComponents&&window.WebComponents.flags);var w=r,x=u;var y=new m;window.ShadyCSS||(window.ShadyCSS={prepareTemplate:function(){},prepareTemplateDom:function(){},prepareTemplateStyles:function(){},styleSubtree:function(a,b){y.a();p(a,b)},styleElement:function(){y.a()},styleDocument:function(a){y.a();p(document.body,a)},getComputedStyleValue:function(a,b){return(a=window.getComputedStyle(a).getPropertyValue(b))?a.trim():""},flushCustomStyles:function(){},nativeCss:w,nativeShadow:q,cssBuild:x,disableRuntime:v});window.ShadyCSS.CustomStyleInterface=y;}).call(this);




(function() {
  'use strict';

  const attr = 'include';

  const CustomStyleInterface = window.ShadyCSS.CustomStyleInterface;

  /**
   * Custom element for defining styles in the main document that can take
   * advantage of [shady DOM](https://github.com/webcomponents/shadycss) shims
   * for style encapsulation, custom properties, and custom mixins.
   *
   * - Document styles defined in a `<custom-style>` are shimmed to ensure they
   *   do not leak into local DOM when running on browsers without native
   *   Shadow DOM.
   * - Custom properties can be defined in a `<custom-style>`. Use the `html` selector
   *   to define custom properties that apply to all custom elements.
   * - Custom mixins can be defined in a `<custom-style>`, if you import the optional
   *   [apply shim](https://github.com/webcomponents/shadycss#about-applyshim)
   *   (`shadycss/apply-shim.html`).
   *
   * To use:
   *
   * - Import `custom-style.html`.
   * - Place a `<custom-style>` element in the main document, wrapping an inline `<style>` tag that
   *   contains the CSS rules you want to shim.
   *
   * For example:
   *
   * ```html
   * <!-- import apply shim--only required if using mixins -->
   * <link rel="import" href="bower_components/shadycss/apply-shim.html">
   * <!-- import custom-style element -->
   * <link rel="import" href="bower_components/polymer/lib/elements/custom-style.html">
   *
   * <custom-style>
   *   <style>
   *     html {
   *       --custom-color: blue;
   *       --custom-mixin: {
   *         font-weight: bold;
   *         color: red;
   *       };
   *     }
   *   </style>
   * </custom-style>
   * ```
   *
   * @customElement
   * @extends HTMLElement
   * @memberof Polymer
   * @summary Custom element for defining styles in the main document that can
   *   take advantage of Polymer's style scoping and custom properties shims.
   */
  class CustomStyle extends HTMLElement {
    constructor() {
      super();
      this._style = null;
      CustomStyleInterface.addCustomStyle(this);
    }
    /**
     * Returns the light-DOM `<style>` child this element wraps.  Upon first
     * call any style modules referenced via the `include` attribute will be
     * concatenated to this element's `<style>`.
     *
     * @return {HTMLStyleElement} This element's light-DOM `<style>`
     */
    getStyle() {
      if (this._style) {
        return this._style;
      }
      const style = /** @type {HTMLStyleElement} */(this.querySelector('style'));
      if (!style) {
        return null;
      }
      this._style = style;
      const include = style.getAttribute(attr);
      if (include) {
        style.removeAttribute(attr);
        style.textContent = Polymer.StyleGather.cssFromModules(include) + style.textContent;
      }
      /*
      HTML Imports styling the main document are deprecated in Chrome
      https://crbug.com/523952

      If this element is not in the main document, then it must be in an HTML Import document.
      In that case, move the custom style to the main document.

      The ordering of `<custom-style>` should stay the same as when loaded by HTML Imports, but there may be odd
      cases of ordering w.r.t the main document styles.
      */
      if (this.ownerDocument !== window.document) {
        window.document.head.appendChild(this);
      }
      return this._style;
    }
  }

  window.customElements.define('custom-style', CustomStyle);

  /** @const */
  Polymer.CustomStyle = CustomStyle;
})();


(function() {
  'use strict';

  let mutablePropertyChange;
  /** @suppress {missingProperties} */
  (() => {
    mutablePropertyChange = Polymer.MutableData._mutablePropertyChange;
  })();

  /**
   * Legacy element behavior to skip strict dirty-checking for objects and arrays,
   * (always consider them to be "dirty") for use on legacy API Polymer elements.
   *
   * By default, `Polymer.PropertyEffects` performs strict dirty checking on
   * objects, which means that any deep modifications to an object or array will
   * not be propagated unless "immutable" data patterns are used (i.e. all object
   * references from the root to the mutation were changed).
   *
   * Polymer also provides a proprietary data mutation and path notification API
   * (e.g. `notifyPath`, `set`, and array mutation API's) that allow efficient
   * mutation and notification of deep changes in an object graph to all elements
   * bound to the same object graph.
   *
   * In cases where neither immutable patterns nor the data mutation API can be
   * used, applying this mixin will cause Polymer to skip dirty checking for
   * objects and arrays (always consider them to be "dirty").  This allows a
   * user to make a deep modification to a bound object graph, and then either
   * simply re-set the object (e.g. `this.items = this.items`) or call `notifyPath`
   * (e.g. `this.notifyPath('items')`) to update the tree.  Note that all
   * elements that wish to be updated based on deep mutations must apply this
   * mixin or otherwise skip strict dirty checking for objects/arrays.
   * Specifically, any elements in the binding tree between the source of a
   * mutation and the consumption of it must apply this behavior or enable the
   * `Polymer.OptionalMutableDataBehavior`.
   *
   * In order to make the dirty check strategy configurable, see
   * `Polymer.OptionalMutableDataBehavior`.
   *
   * Note, the performance characteristics of propagating large object graphs
   * will be worse as opposed to using strict dirty checking with immutable
   * patterns or Polymer's path notification API.
   *
   * @polymerBehavior
   * @memberof Polymer
   * @summary Behavior to skip strict dirty-checking for objects and
   *   arrays
   */
  Polymer.MutableDataBehavior = {

    /**
     * Overrides `Polymer.PropertyEffects` to provide option for skipping
     * strict equality checking for Objects and Arrays.
     *
     * This method pulls the value to dirty check against from the `__dataTemp`
     * cache (rather than the normal `__data` cache) for Objects.  Since the temp
     * cache is cleared at the end of a turn, this implementation allows
     * side-effects of deep object changes to be processed by re-setting the
     * same object (using the temp cache as an in-turn backstop to prevent
     * cycles due to 2-way notification).
     *
     * @param {string} property Property name
     * @param {*} value New property value
     * @param {*} old Previous property value
     * @return {boolean} Whether the property should be considered a change
     * @protected
     */
    _shouldPropertyChange(property, value, old) {
      return mutablePropertyChange(this, property, value, old, true);
    }
  };

  /**
   * Legacy element behavior to add the optional ability to skip strict
   * dirty-checking for objects and arrays (always consider them to be
   * "dirty") by setting a `mutable-data` attribute on an element instance.
   *
   * By default, `Polymer.PropertyEffects` performs strict dirty checking on
   * objects, which means that any deep modifications to an object or array will
   * not be propagated unless "immutable" data patterns are used (i.e. all object
   * references from the root to the mutation were changed).
   *
   * Polymer also provides a proprietary data mutation and path notification API
   * (e.g. `notifyPath`, `set`, and array mutation API's) that allow efficient
   * mutation and notification of deep changes in an object graph to all elements
   * bound to the same object graph.
   *
   * In cases where neither immutable patterns nor the data mutation API can be
   * used, applying this mixin will allow Polymer to skip dirty checking for
   * objects and arrays (always consider them to be "dirty").  This allows a
   * user to make a deep modification to a bound object graph, and then either
   * simply re-set the object (e.g. `this.items = this.items`) or call `notifyPath`
   * (e.g. `this.notifyPath('items')`) to update the tree.  Note that all
   * elements that wish to be updated based on deep mutations must apply this
   * mixin or otherwise skip strict dirty checking for objects/arrays.
   * Specifically, any elements in the binding tree between the source of a
   * mutation and the consumption of it must enable this behavior or apply the
   * `Polymer.OptionalMutableDataBehavior`.
   *
   * While this behavior adds the ability to forgo Object/Array dirty checking,
   * the `mutableData` flag defaults to false and must be set on the instance.
   *
   * Note, the performance characteristics of propagating large object graphs
   * will be worse by relying on `mutableData: true` as opposed to using
   * strict dirty checking with immutable patterns or Polymer's path notification
   * API.
   *
   * @polymerBehavior
   * @memberof Polymer
   * @summary Behavior to optionally skip strict dirty-checking for objects and
   *   arrays
   */
  Polymer.OptionalMutableDataBehavior = {

    properties: {
      /**
       * Instance-level flag for configuring the dirty-checking strategy
       * for this element.  When true, Objects and Arrays will skip dirty
       * checking, otherwise strict equality checking will be used.
       */
      mutableData: Boolean
    },

    /**
     * Overrides `Polymer.PropertyEffects` to skip strict equality checking
     * for Objects and Arrays.
     *
     * Pulls the value to dirty check against from the `__dataTemp` cache
     * (rather than the normal `__data` cache) for Objects.  Since the temp
     * cache is cleared at the end of a turn, this implementation allows
     * side-effects of deep object changes to be processed by re-setting the
     * same object (using the temp cache as an in-turn backstop to prevent
     * cycles due to 2-way notification).
     *
     * @param {string} property Property name
     * @param {*} value New property value
     * @param {*} old Previous property value
     * @return {boolean} Whether the property should be considered a change
     * @this {this}
     * @protected
     */
    _shouldPropertyChange(property, value, old) {
      return mutablePropertyChange(this, property, value, old, this.mutableData);
    }
  };

})();



  // bc
  Polymer.Base = Polymer.LegacyElementMixin(HTMLElement).prototype;

  // NOTE: this is here for modulizer to export `html` for the module version of this file
  Polymer.html = Polymer.html;

//# sourceURL=build://iron-flex-layout/iron-flex-layout.html.js
(function(){var b=document.createElement("style");b.textContent="[hidden] { display: none !important; }";document.head.appendChild(b)})();

//# sourceURL=build://iron-a11y-keys-behavior/iron-a11y-keys-behavior.html.js
(function(){function b(x,C){var G="";if(x)if(x=x.toLowerCase()," "===x||A.test(x))G="space";else if(y.test(x))G="esc";else if(1==x.length){if(!C||q.test(x))G=x}else G=w.test(x)?x.replace("arrow",""):"multiply"==x?"*":x;return G}function d(x){var C="";x&&(x in p?C=p[x]:u.test(x)?(x=parseInt(x.replace("U+","0x"),16),C=String.fromCharCode(x).toLowerCase()):C=x.toLowerCase());return C}function f(x){var C="";Number(x)&&(C=65<=x&&90>=x?String.fromCharCode(32+x):112<=x&&123>=x?"f"+(x-112+1):48<=x&&57>=x?
String(x-48):96<=x&&105>=x?String(x-96):m[x]);return C}function h(x,C){return x.key?b(x.key,C):x.detail&&x.detail.key?b(x.detail.key,C):d(x.keyIdentifier)||f(x.keyCode)||""}function k(x,C){return h(C,x.hasModifiers)===x.key&&(!x.hasModifiers||!!C.shiftKey===!!x.shiftKey&&!!C.ctrlKey===!!x.ctrlKey&&!!C.altKey===!!x.altKey&&!!C.metaKey===!!x.metaKey)}function r(x){return 1===x.length?{combo:x,key:x,event:"keydown"}:x.split("+").reduce(function(C,G){var D=G.split(":");G=D[0];D=D[1];G in n?(C[n[G]]=!0,
C.hasModifiers=!0):(C.key=G,C.event=D||"keydown");return C},{combo:x.split(":").shift()})}function l(x){return x.trim().split(" ").map(function(C){return r(C)})}var p={"U+0008":"backspace","U+0009":"tab","U+001B":"esc","U+0020":"space","U+007F":"del"},m={8:"backspace",9:"tab",13:"enter",27:"esc",33:"pageup",34:"pagedown",35:"end",36:"home",32:"space",37:"left",38:"up",39:"right",40:"down",46:"del",106:"*"},n={shift:"shiftKey",ctrl:"ctrlKey",alt:"altKey",meta:"metaKey"},q=/[a-z0-9*]/,u=/U\+/,w=/^arrow/,
A=/^space(bar)?/,y=/^escape$/;Polymer.IronA11yKeysBehavior={properties:{keyEventTarget:{type:Object,value:function(){return this}},stopKeyboardEventPropagation:{type:Boolean,value:!1},_boundKeyHandlers:{type:Array,value:function(){return[]}},_imperativeKeyBindings:{type:Object,value:function(){return{}}}},observers:["_resetKeyEventListeners(keyEventTarget, _boundKeyHandlers)"],keyBindings:{},registered:function(){this._prepKeyBindings()},attached:function(){this._listenKeyEventListeners()},detached:function(){this._unlistenKeyEventListeners()},
addOwnKeyBinding:function(x,C){this._imperativeKeyBindings[x]=C;this._prepKeyBindings();this._resetKeyEventListeners()},removeOwnKeyBindings:function(){this._imperativeKeyBindings={};this._prepKeyBindings();this._resetKeyEventListeners()},keyboardEventMatchesKeys:function(x,C){C=l(C);for(var G=0;G<C.length;++G)if(k(C[G],x))return!0;return!1},_collectKeyBindings:function(){var x=this.behaviors.map(function(C){return C.keyBindings});-1===x.indexOf(this.keyBindings)&&x.push(this.keyBindings);return x},
_prepKeyBindings:function(){this._keyBindings={};this._collectKeyBindings().forEach(function(G){for(var D in G)this._addKeyBinding(D,G[D])},this);for(var x in this._imperativeKeyBindings)this._addKeyBinding(x,this._imperativeKeyBindings[x]);for(var C in this._keyBindings)this._keyBindings[C].sort(function(G,D){G=G[0].hasModifiers;return G===D[0].hasModifiers?0:G?-1:1})},_addKeyBinding:function(x,C){l(x).forEach(function(G){this._keyBindings[G.event]=this._keyBindings[G.event]||[];this._keyBindings[G.event].push([G,
C])},this)},_resetKeyEventListeners:function(){this._unlistenKeyEventListeners();this.isAttached&&this._listenKeyEventListeners()},_listenKeyEventListeners:function(){this.keyEventTarget&&Object.keys(this._keyBindings).forEach(function(x){var C=this._onKeyBindingEvent.bind(this,this._keyBindings[x]);this._boundKeyHandlers.push([this.keyEventTarget,x,C]);this.keyEventTarget.addEventListener(x,C)},this)},_unlistenKeyEventListeners:function(){for(var x,C,G;this._boundKeyHandlers.length;)x=this._boundKeyHandlers.pop(),
C=x[0],G=x[1],x=x[2],C.removeEventListener(G,x)},_onKeyBindingEvent:function(x,C){this.stopKeyboardEventPropagation&&C.stopPropagation();if(!C.defaultPrevented)for(var G=0;G<x.length;G++){var D=x[G][0],B=x[G][1];if(k(D,C)&&(this._triggerKeyHandler(D,B,C),C.defaultPrevented))break}},_triggerKeyHandler:function(x,C,G){var D=Object.create(x);D.keyboardEvent=G;x=new CustomEvent(x.event,{detail:D,cancelable:!0});this[C].call(this,x);x.defaultPrevented&&G.preventDefault()}}})();

//# sourceURL=build://iron-behaviors/iron-control-state.html.js
Polymer.IronControlState={properties:{focused:{type:Boolean,value:!1,notify:!0,readOnly:!0,reflectToAttribute:!0},disabled:{type:Boolean,value:!1,notify:!0,observer:"_disabledChanged",reflectToAttribute:!0},_oldTabIndex:{type:String},_boundFocusBlurHandler:{type:Function,value:function(){return this._focusBlurHandler.bind(this)}},__handleEventRetargeting:{type:Boolean,value:function(){return!this.shadowRoot&&!Polymer.Element}}},observers:["_changedControlState(focused, disabled)"],ready:function(){this.addEventListener("focus",
this._boundFocusBlurHandler,!0);this.addEventListener("blur",this._boundFocusBlurHandler,!0)},_focusBlurHandler:function(b){if(Polymer.Element)this._setFocused("focus"===b.type);else if(b.target===this)this._setFocused("focus"===b.type);else if(this.__handleEventRetargeting){var d=Polymer.dom(b).localTarget;this.isLightDescendant(d)||this.fire(b.type,{sourceEvent:b},{node:this,bubbles:b.bubbles,cancelable:b.cancelable})}},_disabledChanged:function(b){this.setAttribute("aria-disabled",b?"true":"false");
this.style.pointerEvents=b?"none":"";b?(this._oldTabIndex=this.getAttribute("tabindex"),this._setFocused(!1),this.tabIndex=-1,this.blur()):void 0!==this._oldTabIndex&&(null===this._oldTabIndex?this.removeAttribute("tabindex"):this.setAttribute("tabindex",this._oldTabIndex))},_changedControlState:function(){this._controlStateChanged&&this._controlStateChanged()}};

//# sourceURL=build://iron-behaviors/iron-button-state.html.js
Polymer.IronButtonStateImpl={properties:{pressed:{type:Boolean,readOnly:!0,value:!1,reflectToAttribute:!0,observer:"_pressedChanged"},toggles:{type:Boolean,value:!1,reflectToAttribute:!0},active:{type:Boolean,value:!1,notify:!0,reflectToAttribute:!0},pointerDown:{type:Boolean,readOnly:!0,value:!1},receivedFocusFromKeyboard:{type:Boolean,readOnly:!0},ariaActiveAttribute:{type:String,value:"aria-pressed",observer:"_ariaActiveAttributeChanged"}},listeners:{down:"_downHandler",up:"_upHandler",tap:"_tapHandler"},
observers:["_focusChanged(focused)","_activeChanged(active, ariaActiveAttribute)"],keyBindings:{"enter:keydown":"_asyncClick","space:keydown":"_spaceKeyDownHandler","space:keyup":"_spaceKeyUpHandler"},_mouseEventRe:/^mouse/,_tapHandler:function(){this.toggles?this._userActivate(!this.active):this.active=!1},_focusChanged:function(b){this._detectKeyboardFocus(b);b||this._setPressed(!1)},_detectKeyboardFocus:function(b){this._setReceivedFocusFromKeyboard(!this.pointerDown&&b)},_userActivate:function(b){this.active!==
b&&(this.active=b,this.fire("change"))},_downHandler:function(){this._setPointerDown(!0);this._setPressed(!0);this._setReceivedFocusFromKeyboard(!1)},_upHandler:function(){this._setPointerDown(!1);this._setPressed(!1)},_spaceKeyDownHandler:function(b){b=b.detail.keyboardEvent;var d=Polymer.dom(b).localTarget;this.isLightDescendant(d)||(b.preventDefault(),b.stopImmediatePropagation(),this._setPressed(!0))},_spaceKeyUpHandler:function(b){b=Polymer.dom(b.detail.keyboardEvent).localTarget;this.isLightDescendant(b)||
(this.pressed&&this._asyncClick(),this._setPressed(!1))},_asyncClick:function(){this.async(function(){this.click()},1)},_pressedChanged:function(){this._changedButtonState()},_ariaActiveAttributeChanged:function(b,d){d&&d!=b&&this.hasAttribute(d)&&this.removeAttribute(d)},_activeChanged:function(b){this.toggles?this.setAttribute(this.ariaActiveAttribute,b?"true":"false"):this.removeAttribute(this.ariaActiveAttribute);this._changedButtonState()},_controlStateChanged:function(){this.disabled?this._setPressed(!1):
this._changedButtonState()},_changedButtonState:function(){this._buttonStateChanged&&this._buttonStateChanged()}};Polymer.IronButtonState=[Polymer.IronA11yKeysBehavior,Polymer.IronButtonStateImpl];

//# sourceURL=build://paper-behaviors/paper-ripple-behavior.html.js
Polymer.PaperRippleBehavior={properties:{noink:{type:Boolean,observer:"_noinkChanged"},_rippleContainer:{type:Object}},_buttonStateChanged:function(){this.focused&&this.ensureRipple()},_downHandler:function(b){Polymer.IronButtonStateImpl._downHandler.call(this,b);this.pressed&&this.ensureRipple(b)},ensureRipple:function(b){if(!this.hasRipple()){this._ripple=this._createRipple();this._ripple.noink=this.noink;var d=this._rippleContainer||this.root;d&&Polymer.dom(d).appendChild(this._ripple);if(b){d=
Polymer.dom(this._rippleContainer||this);var f=Polymer.dom(b).rootTarget;d.deepContains(f)&&this._ripple.uiDownAction(b)}}},getRipple:function(){this.ensureRipple();return this._ripple},hasRipple:function(){return!!this._ripple},_createRipple:function(){return document.createElement("paper-ripple")},_noinkChanged:function(b){this.hasRipple()&&(this._ripple.noink=b)}};

//# sourceURL=build://paper-behaviors/paper-button-behavior.html.js
Polymer.PaperButtonBehaviorImpl={properties:{elevation:{type:Number,reflectToAttribute:!0,readOnly:!0}},observers:["_calculateElevation(focused, disabled, active, pressed, receivedFocusFromKeyboard)","_computeKeyboardClass(receivedFocusFromKeyboard)"],hostAttributes:{role:"button",tabindex:"0",animated:!0},_calculateElevation:function(){var b=1;this.disabled?b=0:this.active||this.pressed?b=4:this.receivedFocusFromKeyboard&&(b=3);this._setElevation(b)},_computeKeyboardClass:function(b){this.toggleClass("keyboard-focus",
b)},_spaceKeyDownHandler:function(b){Polymer.IronButtonStateImpl._spaceKeyDownHandler.call(this,b);this.hasRipple()&&1>this.getRipple().ripples.length&&this._ripple.uiDownAction()},_spaceKeyUpHandler:function(b){Polymer.IronButtonStateImpl._spaceKeyUpHandler.call(this,b);this.hasRipple()&&this._ripple.uiUpAction()}};Polymer.PaperButtonBehavior=[Polymer.IronButtonState,Polymer.IronControlState,Polymer.PaperRippleBehavior,Polymer.PaperButtonBehaviorImpl];

//# sourceURL=build://iron-meta/iron-meta.html.js
(function(){Polymer.IronMeta=function(d){Polymer.IronMeta[" "](d);this.type=d&&d.type||"default";this.key=d&&d.key;d&&"value"in d&&(this.value=d.value)};Polymer.IronMeta[" "]=function(){};Polymer.IronMeta.types={};Polymer.IronMeta.prototype={get value(){var d=this.type,f=this.key;if(d&&f)return Polymer.IronMeta.types[d]&&Polymer.IronMeta.types[d][f]},set value(d){var f=this.type,h=this.key;f&&h&&(f=Polymer.IronMeta.types[f]=Polymer.IronMeta.types[f]||{},null==d?delete f[h]:f[h]=d)},get list(){if(this.type){var d=
Polymer.IronMeta.types[this.type];return d?Object.keys(d).map(function(f){return b[this.type][f]},this):[]}},byKey:function(d){this.key=d;return this.value}};var b=Polymer.IronMeta.types;Polymer({is:"iron-meta",properties:{type:{type:String,value:"default"},key:{type:String},value:{type:String,notify:!0},self:{type:Boolean,observer:"_selfChanged"},__meta:{type:Boolean,computed:"__computeMeta(type, key, value)"}},hostAttributes:{hidden:!0},__computeMeta:function(d,f,h){d=new Polymer.IronMeta({type:d,
key:f});void 0!==h&&h!==d.value?d.value=h:this.value!==d.value&&(this.value=d.value);return d},get list(){return this.__meta&&this.__meta.list},_selfChanged:function(d){d&&(this.value=this)},byKey:function(d){return(new Polymer.IronMeta({type:this.type,key:d})).value}})})();

//# sourceURL=build://iron-validatable-behavior/iron-validatable-behavior.html.js
Polymer.IronValidatableBehaviorMeta=null;
Polymer.IronValidatableBehavior={properties:{validator:{type:String},invalid:{notify:!0,reflectToAttribute:!0,type:Boolean,value:!1,observer:"_invalidChanged"}},registered:function(){Polymer.IronValidatableBehaviorMeta=new Polymer.IronMeta({type:"validator"})},_invalidChanged:function(){this.invalid?this.setAttribute("aria-invalid","true"):this.removeAttribute("aria-invalid")},get _validator(){return Polymer.IronValidatableBehaviorMeta&&Polymer.IronValidatableBehaviorMeta.byKey(this.validator)},hasValidator:function(){return null!=
this._validator},validate:function(b){this.invalid=void 0===b&&void 0!==this.value?!this._getValidity(this.value):!this._getValidity(b);return!this.invalid},_getValidity:function(b){return this.hasValidator()?this._validator.validate(b):!0}};

//# sourceURL=build://iron-form-element-behavior/iron-form-element-behavior.html.js
Polymer.IronFormElementBehavior={properties:{name:{type:String},value:{notify:!0,type:String},required:{type:Boolean,value:!1},_parentForm:{type:Object}},attached:function(){Polymer.Element||this.fire("iron-form-element-register")},detached:function(){!Polymer.Element&&this._parentForm&&this._parentForm.fire("iron-form-element-unregister",{target:this})}};

//# sourceURL=build://iron-checked-element-behavior/iron-checked-element-behavior.html.js
Polymer.IronCheckedElementBehaviorImpl={properties:{checked:{type:Boolean,value:!1,reflectToAttribute:!0,notify:!0,observer:"_checkedChanged"},toggles:{type:Boolean,value:!0,reflectToAttribute:!0},value:{type:String,value:"on",observer:"_valueChanged"}},observers:["_requiredChanged(required)"],created:function(){this._hasIronCheckedElementBehavior=!0},_getValidity:function(){return this.disabled||!this.required||this.checked},_requiredChanged:function(){this.required?this.setAttribute("aria-required",
"true"):this.removeAttribute("aria-required")},_checkedChanged:function(){this.active=this.checked;this.fire("iron-change")},_valueChanged:function(){if(void 0===this.value||null===this.value)this.value="on"}};Polymer.IronCheckedElementBehavior=[Polymer.IronFormElementBehavior,Polymer.IronValidatableBehavior,Polymer.IronCheckedElementBehaviorImpl];

//# sourceURL=build://paper-behaviors/paper-inky-focus-behavior.html.js
Polymer.PaperInkyFocusBehaviorImpl={observers:["_focusedChanged(receivedFocusFromKeyboard)"],_focusedChanged:function(b){b&&this.ensureRipple();this.hasRipple()&&(this._ripple.holdDown=b)},_createRipple:function(){var b=Polymer.PaperRippleBehavior._createRipple();b.id="ink";b.setAttribute("center","");b.classList.add("circle");return b}};Polymer.PaperInkyFocusBehavior=[Polymer.IronButtonState,Polymer.IronControlState,Polymer.PaperRippleBehavior,Polymer.PaperInkyFocusBehaviorImpl];

//# sourceURL=build://paper-behaviors/paper-checked-element-behavior.html.js
Polymer.PaperCheckedElementBehaviorImpl={_checkedChanged:function(){Polymer.IronCheckedElementBehaviorImpl._checkedChanged.call(this);this.hasRipple()&&(this.checked?this._ripple.setAttribute("checked",""):this._ripple.removeAttribute("checked"))},_buttonStateChanged:function(){Polymer.PaperRippleBehavior._buttonStateChanged.call(this);!this.disabled&&this.isAttached&&(this.checked=this.active)}};
Polymer.PaperCheckedElementBehavior=[Polymer.PaperInkyFocusBehavior,Polymer.IronCheckedElementBehavior,Polymer.PaperCheckedElementBehaviorImpl];

//# sourceURL=build://paper-input/paper-input-behavior.html.js
Polymer.PaperInputHelper={};Polymer.PaperInputHelper.NextLabelID=1;Polymer.PaperInputHelper.NextAddonID=1;Polymer.PaperInputHelper.NextInputID=1;
Polymer.PaperInputBehaviorImpl={properties:{label:{type:String},value:{notify:!0,type:String},disabled:{type:Boolean,value:!1},invalid:{type:Boolean,value:!1,notify:!0},allowedPattern:{type:String},type:{type:String},list:{type:String},pattern:{type:String},required:{type:Boolean,value:!1},errorMessage:{type:String},charCounter:{type:Boolean,value:!1},noLabelFloat:{type:Boolean,value:!1},alwaysFloatLabel:{type:Boolean,value:!1},autoValidate:{type:Boolean,value:!1},validator:{type:String},autocomplete:{type:String,
value:"off"},autofocus:{type:Boolean,observer:"_autofocusChanged"},inputmode:{type:String},minlength:{type:Number},maxlength:{type:Number},min:{type:String},max:{type:String},step:{type:String},name:{type:String},placeholder:{type:String,value:""},readonly:{type:Boolean,value:!1},size:{type:Number},autocapitalize:{type:String,value:"none"},autocorrect:{type:String,value:"off"},autosave:{type:String},results:{type:Number},accept:{type:String},multiple:{type:Boolean},_ariaDescribedBy:{type:String,value:""},
_ariaLabelledBy:{type:String,value:""},_inputId:{type:String,value:""}},listeners:{"addon-attached":"_onAddonAttached"},keyBindings:{"shift+tab:keydown":"_onShiftTabDown"},hostAttributes:{tabindex:0},get inputElement(){this.$||(this.$={});this.$.input||(this._generateInputId(),this.$.input=this.$$("#"+this._inputId));return this.$.input},get _focusableElement(){return this.inputElement},created:function(){this._typesThatHaveText="date datetime datetime-local month time week file".split(" ")},attached:function(){this._updateAriaLabelledBy();
!Polymer.Element&&this.inputElement&&-1!==this._typesThatHaveText.indexOf(this.inputElement.type)&&(this.alwaysFloatLabel=!0)},_appendStringWithSpace:function(b,d){return b?b+" "+d:d},_onAddonAttached:function(b){b=Polymer.dom(b).rootTarget;if(b.id)this._ariaDescribedBy=this._appendStringWithSpace(this._ariaDescribedBy,b.id);else{var d="paper-input-add-on-"+Polymer.PaperInputHelper.NextAddonID++;b.id=d;this._ariaDescribedBy=this._appendStringWithSpace(this._ariaDescribedBy,d)}},validate:function(){return this.inputElement.validate()},
_focusBlurHandler:function(b){Polymer.IronControlState._focusBlurHandler.call(this,b);this.focused&&!this._shiftTabPressed&&this._focusableElement&&this._focusableElement.focus()},_onShiftTabDown:function(){var b=this.getAttribute("tabindex");this._shiftTabPressed=!0;this.setAttribute("tabindex","-1");this.async(function(){this.setAttribute("tabindex",b);this._shiftTabPressed=!1},1)},_handleAutoValidate:function(){this.autoValidate&&this.validate()},updateValueAndPreserveCaret:function(b){try{var d=
this.inputElement.selectionStart;this.value=b;this.inputElement.selectionStart=d;this.inputElement.selectionEnd=d}catch(f){this.value=b}},_computeAlwaysFloatLabel:function(b,d){return d||b},_updateAriaLabelledBy:function(){var b=Polymer.dom(this.root).querySelector("label");if(b){if(b.id)var d=b.id;else d="paper-input-label-"+Polymer.PaperInputHelper.NextLabelID++,b.id=d;this._ariaLabelledBy=d}else this._ariaLabelledBy=""},_generateInputId:function(){this._inputId&&""!==this._inputId||(this._inputId=
"input-"+Polymer.PaperInputHelper.NextInputID++)},_onChange:function(b){this.shadowRoot&&this.fire(b.type,{sourceEvent:b},{node:this,bubbles:b.bubbles,cancelable:b.cancelable})},_autofocusChanged:function(){if(this.autofocus&&this._focusableElement){var b=document.activeElement;b instanceof HTMLElement&&b!==document.body&&b!==document.documentElement||this._focusableElement.focus()}}};Polymer.PaperInputBehavior=[Polymer.IronControlState,Polymer.IronA11yKeysBehavior,Polymer.PaperInputBehaviorImpl];

//# sourceURL=build://paper-input/paper-input-addon-behavior.html.js
Polymer.PaperInputAddonBehavior={attached:function(){this.fire("addon-attached")},update:function(){}};

//# sourceURL=build://iron-fit-behavior/iron-fit-behavior.html.js
Polymer.IronFitBehavior={properties:{sizingTarget:{type:Object,value:function(){return this}},fitInto:{type:Object,value:window},noOverlap:{type:Boolean},positionTarget:{type:Element},horizontalAlign:{type:String},verticalAlign:{type:String},dynamicAlign:{type:Boolean},horizontalOffset:{type:Number,value:0,notify:!0},verticalOffset:{type:Number,value:0,notify:!0},autoFitOnAttach:{type:Boolean,value:!1},_fitInfo:{type:Object}},get _fitWidth(){return this.fitInto===window?this.fitInto.innerWidth:this.fitInto.getBoundingClientRect().width},
get _fitHeight(){return this.fitInto===window?this.fitInto.innerHeight:this.fitInto.getBoundingClientRect().height},get _fitLeft(){return this.fitInto===window?0:this.fitInto.getBoundingClientRect().left},get _fitTop(){return this.fitInto===window?0:this.fitInto.getBoundingClientRect().top},get _defaultPositionTarget(){var b=Polymer.dom(this).parentNode;b&&b.nodeType===Node.DOCUMENT_FRAGMENT_NODE&&(b=b.host);return b},get _localeHorizontalAlign(){if(this._isRTL){if("right"===this.horizontalAlign)return"left";
if("left"===this.horizontalAlign)return"right"}return this.horizontalAlign},get __shouldPosition(){return(this.horizontalAlign||this.verticalAlign)&&this.positionTarget},attached:function(){"undefined"===typeof this._isRTL&&(this._isRTL="rtl"==window.getComputedStyle(this).direction);this.positionTarget=this.positionTarget||this._defaultPositionTarget;this.autoFitOnAttach&&("none"===window.getComputedStyle(this).display?setTimeout(function(){this.fit()}.bind(this)):(window.ShadyDOM&&ShadyDOM.flush(),
this.fit()))},detached:function(){this.__deferredFit&&(clearTimeout(this.__deferredFit),this.__deferredFit=null)},fit:function(){this.position();this.constrain();this.center()},_discoverInfo:function(){if(!this._fitInfo){var b=window.getComputedStyle(this),d=window.getComputedStyle(this.sizingTarget);this._fitInfo={inlineStyle:{top:this.style.top||"",left:this.style.left||"",position:this.style.position||""},sizerInlineStyle:{maxWidth:this.sizingTarget.style.maxWidth||"",maxHeight:this.sizingTarget.style.maxHeight||
"",boxSizing:this.sizingTarget.style.boxSizing||""},positionedBy:{vertically:"auto"!==b.top?"top":"auto"!==b.bottom?"bottom":null,horizontally:"auto"!==b.left?"left":"auto"!==b.right?"right":null},sizedBy:{height:"none"!==d.maxHeight,width:"none"!==d.maxWidth,minWidth:parseInt(d.minWidth,10)||0,minHeight:parseInt(d.minHeight,10)||0},margin:{top:parseInt(b.marginTop,10)||0,right:parseInt(b.marginRight,10)||0,bottom:parseInt(b.marginBottom,10)||0,left:parseInt(b.marginLeft,10)||0}}}},resetFit:function(){var b=
this._fitInfo||{},d;for(d in b.sizerInlineStyle)this.sizingTarget.style[d]=b.sizerInlineStyle[d];for(d in b.inlineStyle)this.style[d]=b.inlineStyle[d];this._fitInfo=null},refit:function(){var b=this.sizingTarget.scrollLeft,d=this.sizingTarget.scrollTop;this.resetFit();this.fit();this.sizingTarget.scrollLeft=b;this.sizingTarget.scrollTop=d},position:function(){if(this.__shouldPosition){this._discoverInfo();this.style.position="fixed";this.sizingTarget.style.boxSizing="border-box";this.style.left="0px";
this.style.top="0px";var b=this.getBoundingClientRect(),d=this.__getNormalizedRect(this.positionTarget),f=this.__getNormalizedRect(this.fitInto),h=this._fitInfo.margin,k=this.__getPosition(this._localeHorizontalAlign,this.verticalAlign,{width:b.width+h.left+h.right,height:b.height+h.top+h.bottom},b,d,f);d=k.left+h.left;k=k.top+h.top;var r=Math.min(f.right-h.right,d+b.width),l=Math.min(f.bottom-h.bottom,k+b.height);d=Math.max(f.left+h.left,Math.min(d,r-this._fitInfo.sizedBy.minWidth));k=Math.max(f.top+
h.top,Math.min(k,l-this._fitInfo.sizedBy.minHeight));this.sizingTarget.style.maxWidth=Math.max(r-d,this._fitInfo.sizedBy.minWidth)+"px";this.sizingTarget.style.maxHeight=Math.max(l-k,this._fitInfo.sizedBy.minHeight)+"px";this.style.left=d-b.left+"px";this.style.top=k-b.top+"px"}},constrain:function(){if(!this.__shouldPosition){this._discoverInfo();var b=this._fitInfo;b.positionedBy.vertically||(this.style.position="fixed",this.style.top="0px");b.positionedBy.horizontally||(this.style.position="fixed",
this.style.left="0px");this.sizingTarget.style.boxSizing="border-box";var d=this.getBoundingClientRect();b.sizedBy.height||this.__sizeDimension(d,b.positionedBy.vertically,"top","bottom","Height");b.sizedBy.width||this.__sizeDimension(d,b.positionedBy.horizontally,"left","right","Width")}},_sizeDimension:function(b,d,f,h,k){this.__sizeDimension(b,d,f,h,k)},__sizeDimension:function(b,d,f,h,k){var r=this._fitInfo,l=this.__getNormalizedRect(this.fitInto);l="Width"===k?l.width:l.height;d=d===h;var p=
"offset"+k;this.sizingTarget.style["max"+k]=l-r.margin[d?f:h]-(d?l-b[h]:b[f])-(this[p]-this.sizingTarget[p])+"px"},center:function(){if(!this.__shouldPosition){this._discoverInfo();var b=this._fitInfo.positionedBy;if(!b.vertically||!b.horizontally){this.style.position="fixed";b.vertically||(this.style.top="0px");b.horizontally||(this.style.left="0px");var d=this.getBoundingClientRect(),f=this.__getNormalizedRect(this.fitInto);b.vertically||(this.style.top=f.top-d.top+(f.height-d.height)/2+"px");b.horizontally||
(this.style.left=f.left-d.left+(f.width-d.width)/2+"px")}}},__getNormalizedRect:function(b){return b===document.documentElement||b===window?{top:0,left:0,width:window.innerWidth,height:window.innerHeight,right:window.innerWidth,bottom:window.innerHeight}:b.getBoundingClientRect()},__getOffscreenArea:function(b,d,f){return Math.abs(Math.min(0,b.top)+Math.min(0,f.bottom-(b.top+d.height)))*d.width+Math.abs(Math.min(0,b.left)+Math.min(0,f.right-(b.left+d.width)))*d.height},__getPosition:function(b,d,
f,h,k,r){var l=[{verticalAlign:"top",horizontalAlign:"left",top:k.top+this.verticalOffset,left:k.left+this.horizontalOffset},{verticalAlign:"top",horizontalAlign:"right",top:k.top+this.verticalOffset,left:k.right-f.width-this.horizontalOffset},{verticalAlign:"bottom",horizontalAlign:"left",top:k.bottom-f.height-this.verticalOffset,left:k.left+this.horizontalOffset},{verticalAlign:"bottom",horizontalAlign:"right",top:k.bottom-f.height-this.verticalOffset,left:k.right-f.width-this.horizontalOffset}];
if(this.noOverlap){for(var p=0,m=l.length;p<m;p++){var n={},q;for(q in l[p])n[q]=l[p][q];l.push(n)}l[0].top=l[1].top+=k.height;l[2].top=l[3].top-=k.height;l[4].left=l[6].left+=k.width;l[5].left=l[7].left-=k.width}d="auto"===d?null:d;b="auto"===b?null:b;b&&"center"!==b||(l.push({verticalAlign:"top",horizontalAlign:"center",top:k.top+this.verticalOffset+(this.noOverlap?k.height:0),left:k.left-h.width/2+k.width/2+this.horizontalOffset}),l.push({verticalAlign:"bottom",horizontalAlign:"center",top:k.bottom-
f.height-this.verticalOffset-(this.noOverlap?k.height:0),left:k.left-h.width/2+k.width/2+this.horizontalOffset}));d&&"middle"!==d||(l.push({verticalAlign:"middle",horizontalAlign:"left",top:k.top-h.height/2+k.height/2+this.verticalOffset,left:k.left+this.horizontalOffset+(this.noOverlap?k.width:0)}),l.push({verticalAlign:"middle",horizontalAlign:"right",top:k.top-h.height/2+k.height/2+this.verticalOffset,left:k.right-f.width-this.horizontalOffset-(this.noOverlap?k.width:0)}));"middle"===d&&"center"===
b&&l.push({verticalAlign:"middle",horizontalAlign:"center",top:k.top-h.height/2+k.height/2+this.verticalOffset,left:k.left-h.width/2+k.width/2+this.horizontalOffset});for(p=0;p<l.length;p++){h=l[p];k=h.verticalAlign===d;m=h.horizontalAlign===b;if(!this.dynamicAlign&&!this.noOverlap&&k&&m){var u=h;break}n=(!d||k)&&(!b||m);if(this.dynamicAlign||n){h.offscreenArea=this.__getOffscreenArea(h,f,r);if(0===h.offscreenArea&&n){u=h;break}u=u||h;n=h.offscreenArea-u.offscreenArea;if(0>n||0===n&&(k||m))u=h}}return u}};

//# sourceURL=build://iron-resizable-behavior/iron-resizable-behavior.html.js
Polymer.IronResizableBehavior={properties:{_parentResizable:{type:Object,observer:"_parentResizableChanged"},_notifyingDescendant:{type:Boolean,value:!1}},listeners:{"iron-request-resize-notifications":"_onIronRequestResizeNotifications"},created:function(){this._interestedResizables=[];this._boundNotifyResize=this.notifyResize.bind(this)},attached:function(){this._requestResizeNotifications()},detached:function(){this._parentResizable?this._parentResizable.stopResizeNotificationsFor(this):window.removeEventListener("resize",
this._boundNotifyResize);this._parentResizable=null},notifyResize:function(){this.isAttached&&(this._interestedResizables.forEach(function(b){this.resizerShouldNotify(b)&&this._notifyDescendant(b)},this),this._fireResize())},assignParentResizable:function(b){this._parentResizable=b},stopResizeNotificationsFor:function(b){var d=this._interestedResizables.indexOf(b);-1<d&&(this._interestedResizables.splice(d,1),this.unlisten(b,"iron-resize","_onDescendantIronResize"))},resizerShouldNotify:function(){return!0},
_onDescendantIronResize:function(b){this._notifyingDescendant?b.stopPropagation():Polymer.Settings.useShadow||this._fireResize()},_fireResize:function(){this.fire("iron-resize",null,{node:this,bubbles:!1})},_onIronRequestResizeNotifications:function(b){var d=Polymer.dom(b).rootTarget;d!==this&&(-1===this._interestedResizables.indexOf(d)&&(this._interestedResizables.push(d),this.listen(d,"iron-resize","_onDescendantIronResize")),d.assignParentResizable(this),this._notifyDescendant(d),b.stopPropagation())},
_parentResizableChanged:function(b){b&&window.removeEventListener("resize",this._boundNotifyResize)},_notifyDescendant:function(b){this.isAttached&&(this._notifyingDescendant=!0,b.notifyResize(),this._notifyingDescendant=!1)},_requestResizeNotifications:function(){if(this.isAttached)if("loading"===document.readyState){var b=this._requestResizeNotifications.bind(this);document.addEventListener("readystatechange",function f(){document.removeEventListener("readystatechange",f);b()})}else this.fire("iron-request-resize-notifications",
null,{node:this,bubbles:!0,cancelable:!0}),this._parentResizable||(window.addEventListener("resize",this._boundNotifyResize),this.notifyResize())}};

//# sourceURL=build://iron-overlay-behavior/iron-overlay-manager.html.js
Polymer.IronOverlayManagerClass=function(){this._overlays=[];this._minimumZ=101;this._backdropElement=null;Polymer.Gestures.add(document.documentElement,"tap",function(){});document.addEventListener("tap",this._onCaptureClick.bind(this),!0);document.addEventListener("focus",this._onCaptureFocus.bind(this),!0);document.addEventListener("keydown",this._onCaptureKeyDown.bind(this),!0)};
Polymer.IronOverlayManagerClass.prototype={constructor:Polymer.IronOverlayManagerClass,get backdropElement(){this._backdropElement||(this._backdropElement=document.createElement("iron-overlay-backdrop"));return this._backdropElement},get deepActiveElement(){var b=document.activeElement;b&&!1!==b instanceof Element||(b=document.body);for(;b.root&&Polymer.dom(b.root).activeElement;)b=Polymer.dom(b.root).activeElement;return b},_bringOverlayAtIndexToFront:function(b){var d=this._overlays[b];if(d){var f=
this._overlays.length-1,h=this._overlays[f];h&&this._shouldBeBehindOverlay(d,h)&&f--;if(!(b>=f)){h=Math.max(this.currentOverlayZ(),this._minimumZ);for(this._getZ(d)<=h&&this._applyOverlayZ(d,h);b<f;)this._overlays[b]=this._overlays[b+1],b++;this._overlays[f]=d}}},addOrRemoveOverlay:function(b){b.opened?this.addOverlay(b):this.removeOverlay(b)},addOverlay:function(b){var d=this._overlays.indexOf(b);if(0<=d)this._bringOverlayAtIndexToFront(d);else{d=this._overlays.length;var f=this._overlays[d-1],h=
Math.max(this._getZ(f),this._minimumZ),k=this._getZ(b);f&&this._shouldBeBehindOverlay(b,f)&&(this._applyOverlayZ(f,h),d--,h=Math.max(this._getZ(this._overlays[d-1]),this._minimumZ));k<=h&&this._applyOverlayZ(b,h);this._overlays.splice(d,0,b)}this.trackBackdrop()},removeOverlay:function(b){b=this._overlays.indexOf(b);-1!==b&&(this._overlays.splice(b,1),this.trackBackdrop())},currentOverlay:function(){return this._overlays[this._overlays.length-1]},currentOverlayZ:function(){return this._getZ(this.currentOverlay())},
ensureMinimumZ:function(b){this._minimumZ=Math.max(this._minimumZ,b)},focusOverlay:function(){var b=this.currentOverlay();b&&b._applyFocus()},trackBackdrop:function(){var b=this._overlayWithBackdrop();if(b||this._backdropElement)this.backdropElement.style.zIndex=this._getZ(b)-1,this.backdropElement.opened=!!b,this.backdropElement.prepare()},getBackdrops:function(){for(var b=[],d=0;d<this._overlays.length;d++)this._overlays[d].withBackdrop&&b.push(this._overlays[d]);return b},backdropZ:function(){return this._getZ(this._overlayWithBackdrop())-
1},_overlayWithBackdrop:function(){for(var b=this._overlays.length-1;0<=b;b--)if(this._overlays[b].withBackdrop)return this._overlays[b]},_getZ:function(b){var d=this._minimumZ;b&&(b=Number(b.style.zIndex||window.getComputedStyle(b).zIndex),b===b&&(d=b));return d},_setZ:function(b,d){b.style.zIndex=d},_applyOverlayZ:function(b,d){this._setZ(b,d+2)},_overlayInPath:function(b){b=b||[];for(var d=0;d<b.length;d++)if(b[d]._manager===this)return b[d]},_onCaptureClick:function(b){var d=this._overlays.length-
1;if(-1!==d)for(var f=Polymer.dom(b).path,h;(h=this._overlays[d])&&this._overlayInPath(f)!==h;)if(h._onCaptureClick(b),h.allowClickThrough)d--;else break},_onCaptureFocus:function(b){var d=this.currentOverlay();d&&d._onCaptureFocus(b)},_onCaptureKeyDown:function(b){var d=this.currentOverlay();d&&(Polymer.IronA11yKeysBehavior.keyboardEventMatchesKeys(b,"esc")?d._onCaptureEsc(b):Polymer.IronA11yKeysBehavior.keyboardEventMatchesKeys(b,"tab")&&d._onCaptureTab(b))},_shouldBeBehindOverlay:function(b,d){return!b.alwaysOnTop&&
d.alwaysOnTop}};Polymer.IronOverlayManager=new Polymer.IronOverlayManagerClass;

//# sourceURL=build://iron-overlay-behavior/iron-scroll-manager.html.js
(function(){var b=0,d=0,f=null,h=[],k=["wheel","mousewheel","DOMMouseScroll","touchstart","touchmove"];Polymer.IronScrollManager={get currentLockingElement(){return this._lockingElements[this._lockingElements.length-1]},elementIsScrollLocked:function(r){var l=this.currentLockingElement;if(void 0===l)return!1;if(this._hasCachedLockedElement(r))return!0;if(this._hasCachedUnlockedElement(r))return!1;(l=!!l&&l!==r&&!this._composedTreeContains(l,r))?this._lockedElementCache.push(r):this._unlockedElementCache.push(r);
return l},pushScrollLock:function(r){0<=this._lockingElements.indexOf(r)||(0===this._lockingElements.length&&this._lockScrollInteractions(),this._lockingElements.push(r),this._lockedElementCache=[],this._unlockedElementCache=[])},removeScrollLock:function(r){r=this._lockingElements.indexOf(r);-1!==r&&(this._lockingElements.splice(r,1),this._lockedElementCache=[],this._unlockedElementCache=[],0===this._lockingElements.length&&this._unlockScrollInteractions())},_lockingElements:[],_lockedElementCache:null,
_unlockedElementCache:null,_hasCachedLockedElement:function(r){return-1<this._lockedElementCache.indexOf(r)},_hasCachedUnlockedElement:function(r){return-1<this._unlockedElementCache.indexOf(r)},_composedTreeContains:function(r,l){var p,m;if(r.contains(l))return!0;r=Polymer.dom(r).querySelectorAll("content,slot");for(p=0;p<r.length;++p){var n=Polymer.dom(r[p]).getDistributedNodes();for(m=0;m<n.length;++m)if(n[m].nodeType===Node.ELEMENT_NODE&&this._composedTreeContains(n[m],l))return!0}return!1},_scrollInteractionHandler:function(r){r.cancelable&&
this._shouldPreventScrolling(r)&&r.preventDefault();r.targetTouches&&(r=r.targetTouches[0],b=r.pageX,d=r.pageY)},_lockScrollInteractions:function(){this._boundScrollHandler=this._boundScrollHandler||this._scrollInteractionHandler.bind(this);for(var r=0,l=k.length;r<l;r++)document.addEventListener(k[r],this._boundScrollHandler,{capture:!0,passive:!1})},_unlockScrollInteractions:function(){for(var r=0,l=k.length;r<l;r++)document.removeEventListener(k[r],this._boundScrollHandler,{capture:!0,passive:!1})},
_shouldPreventScrolling:function(r){var l=Polymer.dom(r).rootTarget;"touchmove"!==r.type&&f!==l&&(f=l,h=this._getScrollableNodes(Polymer.dom(r).path));if(!h.length)return!0;if("touchstart"===r.type)return!1;r=this._getScrollInfo(r);return!this._getScrollingNode(h,r.deltaX,r.deltaY)},_getScrollableNodes:function(r){for(var l=[],p=r.indexOf(this.currentLockingElement),m=0;m<=p;m++)if(r[m].nodeType===Node.ELEMENT_NODE){var n=r[m],q=n.style;"scroll"!==q.overflow&&"auto"!==q.overflow&&(q=window.getComputedStyle(n));
"scroll"!==q.overflow&&"auto"!==q.overflow||l.push(n)}return l},_getScrollingNode:function(r,l,p){if(l||p)for(var m=Math.abs(p)>=Math.abs(l),n=0;n<r.length;n++){var q=r[n];if(m?0>p?0<q.scrollTop:q.scrollTop<q.scrollHeight-q.clientHeight:0>l?0<q.scrollLeft:q.scrollLeft<q.scrollWidth-q.clientWidth)return q}},_getScrollInfo:function(r){var l={deltaX:r.deltaX,deltaY:r.deltaY};"deltaX"in r||("wheelDeltaX"in r&&"wheelDeltaY"in r?(l.deltaX=-r.wheelDeltaX,l.deltaY=-r.wheelDeltaY):"wheelDelta"in r?(l.deltaX=
0,l.deltaY=-r.wheelDelta):"axis"in r?(l.deltaX=1===r.axis?r.detail:0,l.deltaY=2===r.axis?r.detail:0):r.targetTouches&&(r=r.targetTouches[0],l.deltaX=b-r.pageX,l.deltaY=d-r.pageY));return l}}})();

//# sourceURL=build://iron-overlay-behavior/iron-focusables-helper.html.js
(function(){var b=Element.prototype,d=b.matches||b.matchesSelector||b.mozMatchesSelector||b.msMatchesSelector||b.oMatchesSelector||b.webkitMatchesSelector;Polymer.IronFocusablesHelper={getTabbableNodes:function(f){var h=[];return this._collectTabbableNodes(f,h)?this._sortByTabIndex(h):h},isFocusable:function(f){return d.call(f,"input, select, textarea, button, object")?d.call(f,":not([disabled])"):d.call(f,"a[href], area[href], iframe, [tabindex], [contentEditable]")},isTabbable:function(f){return this.isFocusable(f)&&
d.call(f,':not([tabindex\x3d"-1"])')&&this._isVisible(f)},_normalizedTabIndex:function(f){return this.isFocusable(f)?(f=f.getAttribute("tabindex")||0,Number(f)):-1},_collectTabbableNodes:function(f,h){if(f.nodeType!==Node.ELEMENT_NODE||!this._isVisible(f))return!1;var k=this._normalizedTabIndex(f),r=0<k;0<=k&&h.push(f);f="content"===f.localName||"slot"===f.localName?Polymer.dom(f).getDistributedNodes():Polymer.dom(f.root||f).children;for(k=0;k<f.length;k++)r=this._collectTabbableNodes(f[k],h)||r;
return r},_isVisible:function(f){var h=f.style;return"hidden"!==h.visibility&&"none"!==h.display?(h=window.getComputedStyle(f),"hidden"!==h.visibility&&"none"!==h.display):!1},_sortByTabIndex:function(f){var h=f.length;if(2>h)return f;var k=Math.ceil(h/2);h=this._sortByTabIndex(f.slice(0,k));f=this._sortByTabIndex(f.slice(k));return this._mergeSortByTabIndex(h,f)},_mergeSortByTabIndex:function(f,h){for(var k=[];0<f.length&&0<h.length;)this._hasLowerTabOrder(f[0],h[0])?k.push(h.shift()):k.push(f.shift());
return k.concat(f,h)},_hasLowerTabOrder:function(f,h){f=Math.max(f.tabIndex,0);h=Math.max(h.tabIndex,0);return 0===f||0===h?h>f:f>h}}})();

//# sourceURL=build://iron-overlay-behavior/iron-overlay-behavior.html.js
(function(){Polymer.IronOverlayBehaviorImpl={properties:{opened:{observer:"_openedChanged",type:Boolean,value:!1,notify:!0},canceled:{observer:"_canceledChanged",readOnly:!0,type:Boolean,value:!1},withBackdrop:{observer:"_withBackdropChanged",type:Boolean},noAutoFocus:{type:Boolean,value:!1},noCancelOnEscKey:{type:Boolean,value:!1},noCancelOnOutsideClick:{type:Boolean,value:!1},closingReason:{type:Object},restoreFocusOnClose:{type:Boolean,value:!1},allowClickThrough:{type:Boolean},alwaysOnTop:{type:Boolean},
scrollAction:{type:String},_manager:{type:Object,value:Polymer.IronOverlayManager},_focusedChild:{type:Object}},listeners:{"iron-resize":"_onIronResize"},observers:["__updateScrollObservers(isAttached, opened, scrollAction)"],get backdropElement(){return this._manager.backdropElement},get _focusNode(){return this._focusedChild||Polymer.dom(this).querySelector("[autofocus]")||this},get _focusableNodes(){return Polymer.IronFocusablesHelper.getTabbableNodes(this)},ready:function(){this.__shouldRemoveTabIndex=
this.__isAnimating=!1;this.__firstFocusableNode=this.__lastFocusableNode=null;this.__rafs={};this.__scrollTop=this.__scrollLeft=this.__restoreFocusNode=null;this.__onCaptureScroll=this.__onCaptureScroll.bind(this);this.__rootNodes=null;this._ensureSetup()},attached:function(){this.opened&&this._openedChanged(this.opened);this._observer=Polymer.dom(this).observeNodes(this._onNodesChange)},detached:function(){Polymer.dom(this).unobserveNodes(this._observer);this._observer=null;for(var b in this.__rafs)null!==
this.__rafs[b]&&cancelAnimationFrame(this.__rafs[b]);this.__rafs={};this._manager.removeOverlay(this);this.__isAnimating&&(this.opened?this._finishRenderOpened():(this._applyFocus(),this._finishRenderClosed()))},toggle:function(){this._setCanceled(!1);this.opened=!this.opened},open:function(){this._setCanceled(!1);this.opened=!0},close:function(){this._setCanceled(!1);this.opened=!1},cancel:function(b){this.fire("iron-overlay-canceled",b,{cancelable:!0}).defaultPrevented||(this._setCanceled(!0),this.opened=
!1)},invalidateTabbables:function(){this.__firstFocusableNode=this.__lastFocusableNode=null},_ensureSetup:function(){this._overlaySetup||(this._overlaySetup=!0,this.style.outline="none",this.style.display="none")},_openedChanged:function(b){b?this.removeAttribute("aria-hidden"):this.setAttribute("aria-hidden","true");this.isAttached&&(this.__isAnimating=!0,this.__deraf("__openedChanged",this.__openedChanged))},_canceledChanged:function(){this.closingReason=this.closingReason||{};this.closingReason.canceled=
this.canceled},_withBackdropChanged:function(){this.withBackdrop&&!this.hasAttribute("tabindex")?(this.setAttribute("tabindex","-1"),this.__shouldRemoveTabIndex=!0):this.__shouldRemoveTabIndex&&(this.removeAttribute("tabindex"),this.__shouldRemoveTabIndex=!1);this.opened&&this.isAttached&&this._manager.trackBackdrop()},_prepareRenderOpened:function(){this.__restoreFocusNode=this._manager.deepActiveElement;this._preparePositioning();this.refit();this._finishPositioning();this.noAutoFocus&&document.activeElement===
this._focusNode&&(this._focusNode.blur(),this.__restoreFocusNode.focus())},_renderOpened:function(){this._finishRenderOpened()},_renderClosed:function(){this._finishRenderClosed()},_finishRenderOpened:function(){this.notifyResize();this.__isAnimating=!1;this.fire("iron-overlay-opened")},_finishRenderClosed:function(){this.style.display="none";this.style.zIndex="";this.notifyResize();this.__isAnimating=!1;this.fire("iron-overlay-closed",this.closingReason)},_preparePositioning:function(){this.style.transition=
this.style.webkitTransition="none";this.style.transform=this.style.webkitTransform="none";this.style.display=""},_finishPositioning:function(){this.style.display="none";this.scrollTop=this.scrollTop;this.style.transition=this.style.webkitTransition="";this.style.transform=this.style.webkitTransform="";this.style.display="";this.scrollTop=this.scrollTop},_applyFocus:function(){if(this.opened)this.noAutoFocus||this._focusNode.focus();else{this._focusNode.blur();this._focusedChild=null;if(this.restoreFocusOnClose&&
this.__restoreFocusNode){var b=this._manager.deepActiveElement;(b===document.body||Polymer.dom(this).deepContains(b))&&this.__restoreFocusNode.focus()}this.__restoreFocusNode=null;(b=this._manager.currentOverlay())&&this!==b&&b._applyFocus()}},_onCaptureClick:function(b){this.noCancelOnOutsideClick||this.cancel(b)},_onCaptureFocus:function(b){if(this.withBackdrop){var d=Polymer.dom(b).path;-1===d.indexOf(this)?(b.stopPropagation(),this._applyFocus()):this._focusedChild=d[0]}},_onCaptureEsc:function(b){this.noCancelOnEscKey||
this.cancel(b)},_onCaptureTab:function(b){if(this.withBackdrop){this.__ensureFirstLastFocusables();var d=b.shiftKey,f=d?this.__firstFocusableNode:this.__lastFocusableNode;d=d?this.__lastFocusableNode:this.__firstFocusableNode;if(f===d)f=!0;else{var h=this._manager.deepActiveElement;f=h===f||h===this}f&&(b.preventDefault(),this._focusedChild=d,this._applyFocus())}},_onIronResize:function(){this.opened&&!this.__isAnimating&&this.__deraf("refit",this.refit)},_onNodesChange:function(){this.opened&&!this.__isAnimating&&
(this.invalidateTabbables(),this.notifyResize())},__ensureFirstLastFocusables:function(){if(!this.__firstFocusableNode||!this.__lastFocusableNode){var b=this._focusableNodes;this.__firstFocusableNode=b[0];this.__lastFocusableNode=b[b.length-1]}},__openedChanged:function(){this.opened?(this._prepareRenderOpened(),this._manager.addOverlay(this),this._applyFocus(),this._renderOpened()):(this._manager.removeOverlay(this),this._applyFocus(),this._renderClosed())},__deraf:function(b,d){var f=this.__rafs;
null!==f[b]&&cancelAnimationFrame(f[b]);f[b]=requestAnimationFrame(function(){f[b]=null;d.call(this)}.bind(this))},__updateScrollObservers:function(b,d,f){b&&d&&this.__isValidScrollAction(f)?("lock"===f&&(this.__saveScrollPosition(),Polymer.IronScrollManager.pushScrollLock(this)),this.__addScrollListeners()):(Polymer.IronScrollManager.removeScrollLock(this),this.__removeScrollListeners())},__addScrollListeners:function(){if(!this.__rootNodes){this.__rootNodes=[];if(Polymer.Settings.useShadow)for(var b=
this;b;)b.nodeType===Node.DOCUMENT_FRAGMENT_NODE&&b.host&&this.__rootNodes.push(b),b=b.host||b.assignedSlot||b.parentNode;this.__rootNodes.push(document)}this.__rootNodes.forEach(function(d){d.addEventListener("scroll",this.__onCaptureScroll,{capture:!0,passive:!0})},this)},__removeScrollListeners:function(){this.__rootNodes&&this.__rootNodes.forEach(function(b){b.removeEventListener("scroll",this.__onCaptureScroll,{capture:!0,passive:!0})},this);this.isAttached||(this.__rootNodes=null)},__isValidScrollAction:function(b){return"lock"===
b||"refit"===b||"cancel"===b},__onCaptureScroll:function(b){if(!(this.__isAnimating||0<=Polymer.dom(b).path.indexOf(this)))switch(this.scrollAction){case "lock":this.__restoreScrollPosition();break;case "refit":this.__deraf("refit",this.refit);break;case "cancel":this.cancel(b)}},__saveScrollPosition:function(){document.scrollingElement?(this.__scrollTop=document.scrollingElement.scrollTop,this.__scrollLeft=document.scrollingElement.scrollLeft):(this.__scrollTop=Math.max(document.documentElement.scrollTop,
document.body.scrollTop),this.__scrollLeft=Math.max(document.documentElement.scrollLeft,document.body.scrollLeft))},__restoreScrollPosition:function(){document.scrollingElement?(document.scrollingElement.scrollTop=this.__scrollTop,document.scrollingElement.scrollLeft=this.__scrollLeft):(document.documentElement.scrollTop=document.body.scrollTop=this.__scrollTop,document.documentElement.scrollLeft=document.body.scrollLeft=this.__scrollLeft)}};Polymer.IronOverlayBehavior=[Polymer.IronFitBehavior,Polymer.IronResizableBehavior,
Polymer.IronOverlayBehaviorImpl]})();

//# sourceURL=build://neon-animation/neon-animatable-behavior.html.js
Polymer.NeonAnimatableBehavior={properties:{animationConfig:{type:Object},entryAnimation:{observer:"_entryAnimationChanged",type:String},exitAnimation:{observer:"_exitAnimationChanged",type:String}},_entryAnimationChanged:function(){this.animationConfig=this.animationConfig||{};this.animationConfig.entry=[{name:this.entryAnimation,node:this}]},_exitAnimationChanged:function(){this.animationConfig=this.animationConfig||{};this.animationConfig.exit=[{name:this.exitAnimation,node:this}]},_copyProperties:function(b,
d){for(var f in d)b[f]=d[f]},_cloneConfig:function(b){var d={isClone:!0};this._copyProperties(d,b);return d},_getAnimationConfigRecursive:function(b,d,f){if(this.animationConfig)if(this.animationConfig.value&&"function"===typeof this.animationConfig.value)this._warn(this._logf("playAnimation","Please put 'animationConfig' inside of your components 'properties' object instead of outside of it."));else{var h=b?this.animationConfig[b]:this.animationConfig;Array.isArray(h)||(h=[h]);if(h)for(var k,r=0;k=
h[r];r++)if(k.animatable)k.animatable._getAnimationConfigRecursive(k.type||b,d,f);else if(k.id){var l=d[k.id];l?(l.isClone||(d[k.id]=this._cloneConfig(l),l=d[k.id]),this._copyProperties(l,k)):d[k.id]=k}else f.push(k)}},getAnimationConfig:function(b){var d={},f=[];this._getAnimationConfigRecursive(b,d,f);for(var h in d)f.push(d[h]);return f}};

//# sourceURL=build://neon-animation/neon-animation-runner-behavior.html.js
Polymer.NeonAnimationRunnerBehaviorImpl={_configureAnimations:function(b){var d=[],f=[];if(0<b.length)for(var h,k=0;h=b[k];k++){var r=document.createElement(h.name);if(r.isNeonAnimation){var l=null;r.configure||(r.configure=function(){return null});l=r.configure(h);f.push({result:l,config:h})}else console.warn(this.is+":",h.name,"not found!")}for(b=0;b<f.length;b++){l=f[b].result;h=f[b].config;try{"function"!=typeof l.cancel&&(l=document.timeline.play(l))}catch(p){l=null,console.warn("Couldnt play",
"(",h.name,").",p)}l&&d.push({neonAnimation:r,config:h,animation:l})}return d},_shouldComplete:function(b){for(var d=!0,f=0;f<b.length;f++)if("finished"!=b[f].animation.playState){d=!1;break}return d},_complete:function(b){for(var d=0;d<b.length;d++)b[d].neonAnimation.complete(b[d].config);for(d=0;d<b.length;d++)b[d].animation.cancel()},playAnimation:function(b,d){var f=this.getAnimationConfig(b);if(f){this._active=this._active||{};this._active[b]&&(this._complete(this._active[b]),delete this._active[b]);
var h=this._configureAnimations(f);if(0==h.length)this.fire("neon-animation-finish",d,{bubbles:!1});else for(this._active[b]=h,f=0;f<h.length;f++)h[f].animation.onfinish=function(){this._shouldComplete(h)&&(this._complete(h),delete this._active[b],this.fire("neon-animation-finish",d,{bubbles:!1}))}.bind(this)}},cancelAnimation:function(){for(var b in this._active){var d=this._active[b],f;for(f in d)d[f].animation.cancel()}this._active={}}};
Polymer.NeonAnimationRunnerBehavior=[Polymer.NeonAnimatableBehavior,Polymer.NeonAnimationRunnerBehaviorImpl];

//# sourceURL=build://iron-dropdown/iron-dropdown-scroll-manager.html.js
(function(){Polymer.IronDropdownScrollManager=Polymer.IronScrollManager})();

//# sourceURL=build://neon-animation/neon-animation-behavior.html.js
Polymer.NeonAnimationBehavior={properties:{animationTiming:{type:Object,value:function(){return{duration:500,easing:"cubic-bezier(0.4, 0, 0.2, 1)",fill:"both"}}}},isNeonAnimation:!0,created:function(){document.body.animate||console.warn("No web animations detected. This element will not function without a web animations polyfill.")},timingFromConfig:function(b){if(b.timing)for(var d in b.timing)this.animationTiming[d]=b.timing[d];return this.animationTiming},setPrefixedProperty:function(b,d,f){for(var h=
{transform:["webkitTransform"],transformOrigin:["mozTransformOrigin","webkitTransformOrigin"]}[d],k,r=0;k=h[r];r++)b.style[k]=f;b.style[d]=f},complete:function(){}};

//# sourceURL=build://neon-animation/animations/fade-in-animation.html.js
Polymer({is:"fade-in-animation",behaviors:[Polymer.NeonAnimationBehavior],configure:function(b){return this._effect=new KeyframeEffect(b.node,[{opacity:"0"},{opacity:"1"}],this.timingFromConfig(b))}});

//# sourceURL=build://neon-animation/animations/fade-out-animation.html.js
Polymer({is:"fade-out-animation",behaviors:[Polymer.NeonAnimationBehavior],configure:function(b){return this._effect=new KeyframeEffect(b.node,[{opacity:"1"},{opacity:"0"}],this.timingFromConfig(b))}});

//# sourceURL=build://paper-menu-button/paper-menu-button-animations.html.js
Polymer({is:"paper-menu-grow-height-animation",behaviors:[Polymer.NeonAnimationBehavior],configure:function(b){var d=b.node,f=d.getBoundingClientRect().height;return this._effect=new KeyframeEffect(d,[{height:f/2+"px"},{height:f+"px"}],this.timingFromConfig(b))}});Polymer({is:"paper-menu-grow-width-animation",behaviors:[Polymer.NeonAnimationBehavior],configure:function(b){var d=b.node,f=d.getBoundingClientRect().width;return this._effect=new KeyframeEffect(d,[{width:f/2+"px"},{width:f+"px"}],this.timingFromConfig(b))}});
Polymer({is:"paper-menu-shrink-width-animation",behaviors:[Polymer.NeonAnimationBehavior],configure:function(b){var d=b.node,f=d.getBoundingClientRect().width;return this._effect=new KeyframeEffect(d,[{width:f+"px"},{width:f-f/20+"px"}],this.timingFromConfig(b))}});
Polymer({is:"paper-menu-shrink-height-animation",behaviors:[Polymer.NeonAnimationBehavior],configure:function(b){var d=b.node,f=d.getBoundingClientRect().height;this.setPrefixedProperty(d,"transformOrigin","0 0");return this._effect=new KeyframeEffect(d,[{height:f+"px",transform:"translateY(0)"},{height:f/2+"px",transform:"translateY(-20px)"}],this.timingFromConfig(b))}});

//# sourceURL=build://iron-iconset-svg/iron-iconset-svg.html.js
Polymer({is:"iron-iconset-svg",properties:{name:{type:String,observer:"_nameChanged"},size:{type:Number,value:24},rtlMirroring:{type:Boolean,value:!1},useGlobalRtlAttribute:{type:Boolean,value:!1}},created:function(){this._meta=new Polymer.IronMeta({type:"iconset",key:null,value:null})},attached:function(){this.style.display="none"},getIconNames:function(){this._icons=this._createIconMap();return Object.keys(this._icons).map(function(b){return this.name+":"+b},this)},applyIcon:function(b,d){this.removeIcon(b);
if(d=this._cloneIcon(d,this.rtlMirroring&&this._targetIsRTL(b))){var f=Polymer.dom(b.root||b);f.insertBefore(d,f.childNodes[0]);return b._svgIcon=d}return null},removeIcon:function(b){b._svgIcon&&(Polymer.dom(b.root||b).removeChild(b._svgIcon),b._svgIcon=null)},_targetIsRTL:function(b){null==this.__targetIsRTL&&(this.useGlobalRtlAttribute?this.__targetIsRTL="rtl"===(document.body&&document.body.hasAttribute("dir")?document.body:document.documentElement).getAttribute("dir"):(b&&b.nodeType!==Node.ELEMENT_NODE&&
(b=b.host),this.__targetIsRTL=b&&"rtl"===window.getComputedStyle(b).direction));return this.__targetIsRTL},_nameChanged:function(){this._meta.value=null;this._meta.key=this.name;this._meta.value=this;this.async(function(){this.fire("iron-iconset-added",this,{node:window})})},_createIconMap:function(){var b=Object.create(null);Polymer.dom(this).querySelectorAll("[id]").forEach(function(d){b[d.id]=d});return b},_cloneIcon:function(b,d){this._icons=this._icons||this._createIconMap();return this._prepareSvgClone(this._icons[b],
this.size,d)},_prepareSvgClone:function(b,d,f){if(b){b=b.cloneNode(!0);var h=document.createElementNS("http://www.w3.org/2000/svg","svg");d=b.getAttribute("viewBox")||"0 0 "+d+" "+d;var k="pointer-events: none; display: block; width: 100%; height: 100%;";f&&b.hasAttribute("mirror-in-rtl")&&(k+="-webkit-transform:scale(-1,1);transform:scale(-1,1);transform-origin:center;");h.setAttribute("viewBox",d);h.setAttribute("preserveAspectRatio","xMidYMid meet");h.setAttribute("focusable","false");h.style.cssText=
k;h.appendChild(b).removeAttribute("id");return h}return null}});

//# sourceURL=build://iron-selector/iron-selection.html.js
Polymer.IronSelection=function(b){this.selection=[];this.selectCallback=b};
Polymer.IronSelection.prototype={get:function(){return this.multi?this.selection.slice():this.selection[0]},clear:function(b){this.selection.slice().forEach(function(d){(!b||0>b.indexOf(d))&&this.setItemSelected(d,!1)},this)},isSelected:function(b){return 0<=this.selection.indexOf(b)},setItemSelected:function(b,d){if(null!=b&&d!==this.isSelected(b)){if(d)this.selection.push(b);else{var f=this.selection.indexOf(b);0<=f&&this.selection.splice(f,1)}this.selectCallback&&this.selectCallback(b,d)}},select:function(b){this.multi?
this.toggle(b):this.get()!==b&&(this.setItemSelected(this.get(),!1),this.setItemSelected(b,!0))},toggle:function(b){this.setItemSelected(b,!this.isSelected(b))}};

//# sourceURL=build://iron-selector/iron-selectable.html.js
Polymer.IronSelectableBehavior={properties:{attrForSelected:{type:String,value:null},selected:{type:String,notify:!0},selectedItem:{type:Object,readOnly:!0,notify:!0},activateEvent:{type:String,value:"tap",observer:"_activateEventChanged"},selectable:String,selectedClass:{type:String,value:"iron-selected"},selectedAttribute:{type:String,value:null},fallbackSelection:{type:String,value:null},items:{type:Array,readOnly:!0,notify:!0,value:function(){return[]}},_excludedLocalNames:{type:Object,value:function(){return{template:1,
"dom-bind":1,"dom-if":1,"dom-repeat":1}}}},observers:["_updateAttrForSelected(attrForSelected)","_updateSelected(selected)","_checkFallback(fallbackSelection)"],created:function(){this._bindFilterItem=this._filterItem.bind(this);this._selection=new Polymer.IronSelection(this._applySelection.bind(this))},attached:function(){this._observer=this._observeItems(this);this._addListener(this.activateEvent)},detached:function(){this._observer&&Polymer.dom(this).unobserveNodes(this._observer);this._removeListener(this.activateEvent)},
indexOf:function(b){return this.items?this.items.indexOf(b):-1},select:function(b){this.selected=b},selectPrevious:function(){var b=this.items.length;b=(Number(this._valueToIndex(this.selected))-1+b)%b;this.selected=this._indexToValue(b)},selectNext:function(){var b=(Number(this._valueToIndex(this.selected))+1)%this.items.length;this.selected=this._indexToValue(b)},selectIndex:function(b){this.select(this._indexToValue(b))},forceSynchronousItemUpdate:function(){this._observer&&"function"===typeof this._observer.flush?
this._observer.flush():this._updateItems()},get _shouldUpdateSelection(){return null!=this.selected},_checkFallback:function(){this._updateSelected()},_addListener:function(b){this.listen(this,b,"_activateHandler")},_removeListener:function(b){this.unlisten(this,b,"_activateHandler")},_activateEventChanged:function(b,d){this._removeListener(d);this._addListener(b)},_updateItems:function(){var b=Polymer.dom(this).queryDistributedElements(this.selectable||"*");b=Array.prototype.filter.call(b,this._bindFilterItem);
this._setItems(b)},_updateAttrForSelected:function(){this.selectedItem&&(this.selected=this._valueForItem(this.selectedItem))},_updateSelected:function(){this._selectSelected(this.selected)},_selectSelected:function(){if(this.items){var b=this._valueToItem(this.selected);b?this._selection.select(b):this._selection.clear();this.fallbackSelection&&this.items.length&&void 0===this._selection.get()&&(this.selected=this.fallbackSelection)}},_filterItem:function(b){return!this._excludedLocalNames[b.localName]},
_valueToItem:function(b){return null==b?null:this.items[this._valueToIndex(b)]},_valueToIndex:function(b){if(this.attrForSelected)for(var d=0,f;f=this.items[d];d++){if(this._valueForItem(f)==b)return d}else return Number(b)},_indexToValue:function(b){if(this.attrForSelected){if(b=this.items[b])return this._valueForItem(b)}else return b},_valueForItem:function(b){if(!b)return null;if(!this.attrForSelected)return b=this.indexOf(b),-1===b?null:b;var d=b[Polymer.CaseMap.dashToCamelCase(this.attrForSelected)];
return void 0!=d?d:b.getAttribute(this.attrForSelected)},_applySelection:function(b,d){this.selectedClass&&this.toggleClass(this.selectedClass,d,b);this.selectedAttribute&&this.toggleAttribute(this.selectedAttribute,d,b);this._selectionChange();this.fire("iron-"+(d?"select":"deselect"),{item:b})},_selectionChange:function(){this._setSelectedItem(this._selection.get())},_observeItems:function(b){return Polymer.dom(b).observeNodes(function(d){this._updateItems();this._updateSelected();this.fire("iron-items-changed",
d,{bubbles:!1,cancelable:!1})})},_activateHandler:function(b){b=b.target;for(var d=this.items;b&&b!=this;){var f=d.indexOf(b);if(0<=f){d=this._indexToValue(f);this._itemActivate(d,b);break}b=b.parentNode}},_itemActivate:function(b,d){this.fire("iron-activate",{selected:b,item:d},{cancelable:!0}).defaultPrevented||this.select(b)}};

//# sourceURL=build://iron-selector/iron-multi-selectable.html.js
Polymer.IronMultiSelectableBehaviorImpl={properties:{multi:{type:Boolean,value:!1,observer:"multiChanged"},selectedValues:{type:Array,notify:!0,value:function(){return[]}},selectedItems:{type:Array,readOnly:!0,notify:!0,value:function(){return[]}}},observers:["_updateSelected(selectedValues.splices)"],select:function(b){this.multi?this._toggleSelected(b):this.selected=b},multiChanged:function(b){this._selection.multi=b;this._updateSelected()},get _shouldUpdateSelection(){return null!=this.selected||
null!=this.selectedValues&&this.selectedValues.length},_updateAttrForSelected:function(){this.multi?this.selectedItems&&0<this.selectedItems.length&&(this.selectedValues=this.selectedItems.map(function(b){return this._indexToValue(this.indexOf(b))},this).filter(function(b){return null!=b},this)):Polymer.IronSelectableBehavior._updateAttrForSelected.apply(this)},_updateSelected:function(){this.multi?this._selectMulti(this.selectedValues):this._selectSelected(this.selected)},_selectMulti:function(b){b=
b||[];b=(this._valuesToItems(b)||[]).filter(function(f){return null!==f&&void 0!==f});this._selection.clear(b);for(var d=0;d<b.length;d++)this._selection.setItemSelected(b[d],!0);this.fallbackSelection&&!this._selection.get().length&&this._valueToItem(this.fallbackSelection)&&this.select(this.fallbackSelection)},_selectionChange:function(){var b=this._selection.get();this.multi?(this._setSelectedItems(b),this._setSelectedItem(b.length?b[0]:null)):null!==b&&void 0!==b?(this._setSelectedItems([b]),
this._setSelectedItem(b)):(this._setSelectedItems([]),this._setSelectedItem(null))},_toggleSelected:function(b){var d=this.selectedValues.indexOf(b);0>d?this.push("selectedValues",b):this.splice("selectedValues",d,1)},_valuesToItems:function(b){return null==b?null:b.map(function(d){return this._valueToItem(d)},this)}};Polymer.IronMultiSelectableBehavior=[Polymer.IronSelectableBehavior,Polymer.IronMultiSelectableBehaviorImpl];

//# sourceURL=build://iron-menu-behavior/iron-menu-behavior.html.js
Polymer.IronMenuBehaviorImpl={properties:{focusedItem:{observer:"_focusedItemChanged",readOnly:!0,type:Object},attrForItemTitle:{type:String},disabled:{type:Boolean,value:!1,observer:"_disabledChanged"}},_MODIFIER_KEYS:"Alt AltGraph CapsLock Control Fn FnLock Hyper Meta NumLock OS ScrollLock Shift Super Symbol SymbolLock".split(" "),_SEARCH_RESET_TIMEOUT_MS:1E3,_previousTabIndex:0,hostAttributes:{role:"menu"},observers:["_updateMultiselectable(multi)"],listeners:{focus:"_onFocus",keydown:"_onKeydown",
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b=d.length;for(var f=0,h;h=this.items[f];f++)if(!h.hasAttribute("disabled")){var k=this.attrForItemTitle||"textContent";k=(h[k]||h.getAttribute(k)||"").trim();if(!(k.length<b)&&k.slice(0,b).toLocaleLowerCase()==d){this._setFocusedItem(h);break}}this._searchText=d;this.debounce("_clearSearchText",this._clearSearchText,this._SEARCH_RESET_TIMEOUT_MS)}},_clearSearchText:function(){this._searchText=""},_focusPrevious:function(){for(var b=this.items.length,d=Number(this.indexOf(this.focusedItem)),f=1;f<
b+1;f++){var h=this.items[(d-f+b)%b];if(!h.hasAttribute("disabled")){var k=Polymer.dom(h).getOwnerRoot()||document;this._setFocusedItem(h);if(Polymer.dom(k).activeElement==h)break}}},_focusNext:function(){for(var b=this.items.length,d=Number(this.indexOf(this.focusedItem)),f=1;f<b+1;f++){var h=this.items[(d+f)%b];if(!h.hasAttribute("disabled")){var k=Polymer.dom(h).getOwnerRoot()||document;this._setFocusedItem(h);if(Polymer.dom(k).activeElement==h)break}}},_applySelection:function(b,d){d?b.setAttribute("aria-selected",
"true"):b.removeAttribute("aria-selected");Polymer.IronSelectableBehavior._applySelection.apply(this,arguments)},_focusedItemChanged:function(b,d){d&&d.setAttribute("tabindex","-1");!b||b.hasAttribute("disabled")||this.disabled||(b.setAttribute("tabindex","0"),b.focus())},_onIronItemsChanged:function(b){b.detail.addedNodes.length&&this._resetTabindices()},_onShiftTabDown:function(){var b=this.getAttribute("tabindex");Polymer.IronMenuBehaviorImpl._shiftTabPressed=!0;this._setFocusedItem(null);this.setAttribute("tabindex",
"-1");this.async(function(){this.setAttribute("tabindex",b);Polymer.IronMenuBehaviorImpl._shiftTabPressed=!1},1)},_onFocus:function(b){!Polymer.IronMenuBehaviorImpl._shiftTabPressed&&(b=Polymer.dom(b).rootTarget,b===this||"undefined"===typeof b.tabIndex||this.isLightDescendant(b))&&(this._defaultFocusAsync=this.async(function(){var d=this.multi?this.selectedItems&&this.selectedItems[0]:this.selectedItem;this._setFocusedItem(null);d?this._setFocusedItem(d):this.items[0]&&this._focusNext()}))},_onUpKey:function(b){this._focusPrevious();
b.detail.keyboardEvent.preventDefault()},_onDownKey:function(b){this._focusNext();b.detail.keyboardEvent.preventDefault()},_onEscKey:function(){var b=this.focusedItem;b&&b.blur()},_onKeydown:function(b){this.keyboardEventMatchesKeys(b,"up down esc")||this._focusWithKeyboardEvent(b);b.stopPropagation()},_activateHandler:function(b){Polymer.IronSelectableBehavior._activateHandler.call(this,b);b.stopPropagation()},_disabledChanged:function(b){b?(this._previousTabIndex=this.hasAttribute("tabindex")?this.tabIndex:
0,this.removeAttribute("tabindex")):this.hasAttribute("tabindex")||this.setAttribute("tabindex",this._previousTabIndex)}};Polymer.IronMenuBehaviorImpl._shiftTabPressed=!1;Polymer.IronMenuBehavior=[Polymer.IronMultiSelectableBehavior,Polymer.IronA11yKeysBehavior,Polymer.IronMenuBehaviorImpl];

//# sourceURL=build://paper-item/paper-item-behavior.html.js
Polymer.PaperItemBehaviorImpl={hostAttributes:{role:"option",tabindex:"0"}};Polymer.PaperItemBehavior=[Polymer.IronButtonState,Polymer.IronControlState,Polymer.PaperItemBehaviorImpl];

/*

 Lodash <https://lodash.com/>
 Copyright JS Foundation and other contributors <https://js.foundation/>
 Released under MIT license <https://lodash.com/license>
 Based on Underscore.js 1.8.3 <http://underscorejs.org/LICENSE>
 Copyright Jeremy Ashkenas, DocumentCloud and Investigative Reporters & Editors
*/
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function basePullAll(array,values,iteratee,comparator){var indexOf=comparator?baseIndexOfWith:baseIndexOf,index=-1,length=values.length,seen=array;if(array===values)values=copyArray(values);if(iteratee)seen=arrayMap(array,baseUnary(iteratee));while(++index<length){var fromIndex=0,value=values[index],computed=iteratee?iteratee(value):value;while((fromIndex=indexOf(seen,computed,fromIndex,comparator))>-1){if(seen!==array)splice.call(seen,fromIndex,1);splice.call(array,fromIndex,1)}}return array}function basePullAt(array,
indexes){var length=array?indexes.length:0,lastIndex=length-1;while(length--){var index=indexes[length];if(length==lastIndex||index!==previous){var previous=index;if(isIndex(index))splice.call(array,index,1);else baseUnset(array,index)}}return array}function baseRandom(lower,upper){return lower+nativeFloor(nativeRandom()*(upper-lower+1))}function baseRange(start,end,step,fromRight){var index=-1,length=nativeMax(nativeCeil((end-start)/(step||1)),0),result=Array(length);while(length--){result[fromRight?
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function baseSet(object,path,value,customizer){if(!isObject(object))return object;path=castPath(path,object);var index=-1,length=path.length,lastIndex=length-1,nested=object;while(nested!=null&&++index<length){var key=toKey(path[index]),newValue=value;if(index!=lastIndex){var objValue=nested[key];newValue=customizer?customizer(objValue,key,nested):undefined;if(newValue===undefined)newValue=isObject(objValue)?objValue:isIndex(path[index+1])?[]:{}}assignValue(nested,key,newValue);nested=nested[key]}return object}
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retHighest?computed<=value:computed<value;if(setLow)low=mid+1;else high=mid}return nativeMin(high,MAX_ARRAY_INDEX)}function baseSortedUniq(array,iteratee){var index=-1,length=array.length,resIndex=0,result=[];while(++index<length){var value=array[index],computed=iteratee?iteratee(value):value;if(!index||!eq(computed,seen)){var seen=computed;result[resIndex++]=value===0?0:value}}return result}function baseToNumber(value){if(typeof value=="number")return value;if(isSymbol(value))return NAN;return+value}
function baseToString(value){if(typeof value=="string")return value;if(isArray(value))return arrayMap(value,baseToString)+"";if(isSymbol(value))return symbolToString?symbolToString.call(value):"";var result=value+"";return result=="0"&&1/value==-INFINITY?"-0":result}function baseUniq(array,iteratee,comparator){var index=-1,includes=arrayIncludes,length=array.length,isCommon=true,result=[],seen=result;if(comparator){isCommon=false;includes=arrayIncludesWith}else if(length>=LARGE_ARRAY_SIZE){var set=
iteratee?null:createSet(array);if(set)return setToArray(set);isCommon=false;includes=cacheHas;seen=new SetCache}else seen=iteratee?[]:result;outer:while(++index<length){var value=array[index],computed=iteratee?iteratee(value):value;value=comparator||value!==0?value:0;if(isCommon&&computed===computed){var seenIndex=seen.length;while(seenIndex--)if(seen[seenIndex]===computed)continue outer;if(iteratee)seen.push(computed);result.push(value)}else if(!includes(seen,computed,comparator)){if(seen!==result)seen.push(computed);
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value:[]}function castFunction(value){return typeof value=="function"?value:identity}function castPath(value,object){if(isArray(value))return value;return isKey(value,object)?[value]:stringToPath(toString(value))}var castRest=baseRest;function castSlice(array,start,end){var length=array.length;end=end===undefined?length:end;return!start&&end>=length?array:baseSlice(array,start,end)}var clearTimeout=ctxClearTimeout||function(id){return root.clearTimeout(id)};function cloneBuffer(buffer,isDeep){if(isDeep)return buffer.slice();
var length=buffer.length,result=allocUnsafe?allocUnsafe(length):new buffer.constructor(length);buffer.copy(result);return result}function cloneArrayBuffer(arrayBuffer){var result=new arrayBuffer.constructor(arrayBuffer.byteLength);(new Uint8Array(result)).set(new Uint8Array(arrayBuffer));return result}function cloneDataView(dataView,isDeep){var buffer=isDeep?cloneArrayBuffer(dataView.buffer):dataView.buffer;return new dataView.constructor(buffer,dataView.byteOffset,dataView.byteLength)}function cloneRegExp(regexp){var result=
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null,valIsReflexive=value===value,valIsSymbol=isSymbol(value);var othIsDefined=other!==undefined,othIsNull=other===null,othIsReflexive=other===other,othIsSymbol=isSymbol(other);if(!othIsNull&&!othIsSymbol&&!valIsSymbol&&value>other||valIsSymbol&&othIsDefined&&othIsReflexive&&!othIsNull&&!othIsSymbol||valIsNull&&othIsDefined&&othIsReflexive||!valIsDefined&&othIsReflexive||!valIsReflexive)return 1;if(!valIsNull&&!valIsSymbol&&!othIsSymbol&&value<other||othIsSymbol&&valIsDefined&&valIsReflexive&&!valIsNull&&
!valIsSymbol||othIsNull&&valIsDefined&&valIsReflexive||!othIsDefined&&valIsReflexive||!othIsReflexive)return-1}return 0}function compareMultiple(object,other,orders){var index=-1,objCriteria=object.criteria,othCriteria=other.criteria,length=objCriteria.length,ordersLength=orders.length;while(++index<length){var result=compareAscending(objCriteria[index],othCriteria[index]);if(result){if(index>=ordersLength)return result;var order=orders[index];return result*(order=="desc"?-1:1)}}return object.index-
other.index}function composeArgs(args,partials,holders,isCurried){var argsIndex=-1,argsLength=args.length,holdersLength=holders.length,leftIndex=-1,leftLength=partials.length,rangeLength=nativeMax(argsLength-holdersLength,0),result=Array(leftLength+rangeLength),isUncurried=!isCurried;while(++leftIndex<leftLength)result[leftIndex]=partials[leftIndex];while(++argsIndex<holdersLength)if(isUncurried||argsIndex<argsLength)result[holders[argsIndex]]=args[argsIndex];while(rangeLength--)result[leftIndex++]=
args[argsIndex++];return result}function composeArgsRight(args,partials,holders,isCurried){var argsIndex=-1,argsLength=args.length,holdersIndex=-1,holdersLength=holders.length,rightIndex=-1,rightLength=partials.length,rangeLength=nativeMax(argsLength-holdersLength,0),result=Array(rangeLength+rightLength),isUncurried=!isCurried;while(++argsIndex<rangeLength)result[argsIndex]=args[argsIndex];var offset=argsIndex;while(++rightIndex<rightLength)result[offset+rightIndex]=partials[rightIndex];while(++holdersIndex<
holdersLength)if(isUncurried||argsIndex<argsLength)result[offset+holders[holdersIndex]]=args[argsIndex++];return result}function copyArray(source,array){var index=-1,length=source.length;array||(array=Array(length));while(++index<length)array[index]=source[index];return array}function copyObject(source,props,object,customizer){var isNew=!object;object||(object={});var index=-1,length=props.length;while(++index<length){var key=props[index];var newValue=customizer?customizer(object[key],source[key],
key,object,source):undefined;if(newValue===undefined)newValue=source[key];if(isNew)baseAssignValue(object,key,newValue);else assignValue(object,key,newValue)}return object}function copySymbols(source,object){return copyObject(source,getSymbols(source),object)}function copySymbolsIn(source,object){return copyObject(source,getSymbolsIn(source),object)}function createAggregator(setter,initializer){return function(collection,iteratee){var func=isArray(collection)?arrayAggregator:baseAggregator,accumulator=
initializer?initializer():{};return func(collection,setter,getIteratee(iteratee,2),accumulator)}}function createAssigner(assigner){return baseRest(function(object,sources){var index=-1,length=sources.length,customizer=length>1?sources[length-1]:undefined,guard=length>2?sources[2]:undefined;customizer=assigner.length>3&&typeof customizer=="function"?(length--,customizer):undefined;if(guard&&isIterateeCall(sources[0],sources[1],guard)){customizer=length<3?undefined:customizer;length=1}object=Object(object);
while(++index<length){var source=sources[index];if(source)assigner(object,source,index,customizer)}return object})}function createBaseEach(eachFunc,fromRight){return function(collection,iteratee){if(collection==null)return collection;if(!isArrayLike(collection))return eachFunc(collection,iteratee);var length=collection.length,index=fromRight?length:-1,iterable=Object(collection);while(fromRight?index--:++index<length)if(iteratee(iterable[index],index,iterable)===false)break;return collection}}function createBaseFor(fromRight){return function(object,
iteratee,keysFunc){var index=-1,iterable=Object(object),props=keysFunc(object),length=props.length;while(length--){var key=props[fromRight?length:++index];if(iteratee(iterable[key],key,iterable)===false)break}return object}}function createBind(func,bitmask,thisArg){var isBind=bitmask&WRAP_BIND_FLAG,Ctor=createCtor(func);function wrapper(){var fn=this&&this!==root&&this instanceof wrapper?Ctor:func;return fn.apply(isBind?thisArg:this,arguments)}return wrapper}function createCaseFirst(methodName){return function(string){string=
toString(string);var strSymbols=hasUnicode(string)?stringToArray(string):undefined;var chr=strSymbols?strSymbols[0]:string.charAt(0);var trailing=strSymbols?castSlice(strSymbols,1).join(""):string.slice(1);return chr[methodName]()+trailing}}function createCompounder(callback){return function(string){return arrayReduce(words(deburr(string).replace(reApos,"")),callback,"")}}function createCtor(Ctor){return function(){var args=arguments;switch(args.length){case 0:return new Ctor;case 1:return new Ctor(args[0]);
case 2:return new Ctor(args[0],args[1]);case 3:return new Ctor(args[0],args[1],args[2]);case 4:return new Ctor(args[0],args[1],args[2],args[3]);case 5:return new Ctor(args[0],args[1],args[2],args[3],args[4]);case 6:return new Ctor(args[0],args[1],args[2],args[3],args[4],args[5]);case 7:return new Ctor(args[0],args[1],args[2],args[3],args[4],args[5],args[6])}var thisBinding=baseCreate(Ctor.prototype),result=Ctor.apply(thisBinding,args);return isObject(result)?result:thisBinding}}function createCurry(func,
bitmask,arity){var Ctor=createCtor(func);function wrapper(){var length=arguments.length,args=Array(length),index=length,placeholder=getHolder(wrapper);while(index--)args[index]=arguments[index];var holders=length<3&&args[0]!==placeholder&&args[length-1]!==placeholder?[]:replaceHolders(args,placeholder);length-=holders.length;if(length<arity)return createRecurry(func,bitmask,createHybrid,wrapper.placeholder,undefined,args,holders,undefined,undefined,arity-length);var fn=this&&this!==root&&this instanceof
wrapper?Ctor:func;return apply(fn,this,args)}return wrapper}function createFind(findIndexFunc){return function(collection,predicate,fromIndex){var iterable=Object(collection);if(!isArrayLike(collection)){var iteratee=getIteratee(predicate,3);collection=keys(collection);predicate=function(key){return iteratee(iterable[key],key,iterable)}}var index=findIndexFunc(collection,predicate,fromIndex);return index>-1?iterable[iteratee?collection[index]:index]:undefined}}function createFlow(fromRight){return flatRest(function(funcs){var length=
funcs.length,index=length,prereq=LodashWrapper.prototype.thru;if(fromRight)funcs.reverse();while(index--){var func=funcs[index];if(typeof func!="function")throw new TypeError(FUNC_ERROR_TEXT);if(prereq&&!wrapper&&getFuncName(func)=="wrapper")var wrapper=new LodashWrapper([],true)}index=wrapper?index:length;while(++index<length){func=funcs[index];var funcName=getFuncName(func),data=funcName=="wrapper"?getData(func):undefined;if(data&&isLaziable(data[0])&&data[1]==(WRAP_ARY_FLAG|WRAP_CURRY_FLAG|WRAP_PARTIAL_FLAG|
WRAP_REARG_FLAG)&&!data[4].length&&data[9]==1)wrapper=wrapper[getFuncName(data[0])].apply(wrapper,data[3]);else wrapper=func.length==1&&isLaziable(func)?wrapper[funcName]():wrapper.thru(func)}return function(){var args=arguments,value=args[0];if(wrapper&&args.length==1&&isArray(value))return wrapper.plant(value).value();var index=0,result=length?funcs[index].apply(this,args):value;while(++index<length)result=funcs[index].call(this,result);return result}})}function createHybrid(func,bitmask,thisArg,
partials,holders,partialsRight,holdersRight,argPos,ary,arity){var isAry=bitmask&WRAP_ARY_FLAG,isBind=bitmask&WRAP_BIND_FLAG,isBindKey=bitmask&WRAP_BIND_KEY_FLAG,isCurried=bitmask&(WRAP_CURRY_FLAG|WRAP_CURRY_RIGHT_FLAG),isFlip=bitmask&WRAP_FLIP_FLAG,Ctor=isBindKey?undefined:createCtor(func);function wrapper(){var length=arguments.length,args=Array(length),index=length;while(index--)args[index]=arguments[index];if(isCurried)var placeholder=getHolder(wrapper),holdersCount=countHolders(args,placeholder);
if(partials)args=composeArgs(args,partials,holders,isCurried);if(partialsRight)args=composeArgsRight(args,partialsRight,holdersRight,isCurried);length-=holdersCount;if(isCurried&&length<arity){var newHolders=replaceHolders(args,placeholder);return createRecurry(func,bitmask,createHybrid,wrapper.placeholder,thisArg,args,newHolders,argPos,ary,arity-length)}var thisBinding=isBind?thisArg:this,fn=isBindKey?thisBinding[func]:func;length=args.length;if(argPos)args=reorder(args,argPos);else if(isFlip&&length>
1)args.reverse();if(isAry&&ary<length)args.length=ary;if(this&&this!==root&&this instanceof wrapper)fn=Ctor||createCtor(fn);return fn.apply(thisBinding,args)}return wrapper}function createInverter(setter,toIteratee){return function(object,iteratee){return baseInverter(object,setter,toIteratee(iteratee),{})}}function createMathOperation(operator,defaultValue){return function(value,other){var result;if(value===undefined&&other===undefined)return defaultValue;if(value!==undefined)result=value;if(other!==
undefined){if(result===undefined)return other;if(typeof value=="string"||typeof other=="string"){value=baseToString(value);other=baseToString(other)}else{value=baseToNumber(value);other=baseToNumber(other)}result=operator(value,other)}return result}}function createOver(arrayFunc){return flatRest(function(iteratees){iteratees=arrayMap(iteratees,baseUnary(getIteratee()));return baseRest(function(args){var thisArg=this;return arrayFunc(iteratees,function(iteratee){return apply(iteratee,thisArg,args)})})})}
function createPadding(length,chars){chars=chars===undefined?" ":baseToString(chars);var charsLength=chars.length;if(charsLength<2)return charsLength?baseRepeat(chars,length):chars;var result=baseRepeat(chars,nativeCeil(length/stringSize(chars)));return hasUnicode(chars)?castSlice(stringToArray(result),0,length).join(""):result.slice(0,length)}function createPartial(func,bitmask,thisArg,partials){var isBind=bitmask&WRAP_BIND_FLAG,Ctor=createCtor(func);function wrapper(){var argsIndex=-1,argsLength=
arguments.length,leftIndex=-1,leftLength=partials.length,args=Array(leftLength+argsLength),fn=this&&this!==root&&this instanceof wrapper?Ctor:func;while(++leftIndex<leftLength)args[leftIndex]=partials[leftIndex];while(argsLength--)args[leftIndex++]=arguments[++argsIndex];return apply(fn,isBind?thisArg:this,args)}return wrapper}function createRange(fromRight){return function(start,end,step){if(step&&typeof step!="number"&&isIterateeCall(start,end,step))end=step=undefined;start=toFinite(start);if(end===
undefined){end=start;start=0}else end=toFinite(end);step=step===undefined?start<end?1:-1:toFinite(step);return baseRange(start,end,step,fromRight)}}function createRelationalOperation(operator){return function(value,other){if(!(typeof value=="string"&&typeof other=="string")){value=toNumber(value);other=toNumber(other)}return operator(value,other)}}function createRecurry(func,bitmask,wrapFunc,placeholder,thisArg,partials,holders,argPos,ary,arity){var isCurry=bitmask&WRAP_CURRY_FLAG,newHolders=isCurry?
holders:undefined,newHoldersRight=isCurry?undefined:holders,newPartials=isCurry?partials:undefined,newPartialsRight=isCurry?undefined:partials;bitmask|=isCurry?WRAP_PARTIAL_FLAG:WRAP_PARTIAL_RIGHT_FLAG;bitmask&=~(isCurry?WRAP_PARTIAL_RIGHT_FLAG:WRAP_PARTIAL_FLAG);if(!(bitmask&WRAP_CURRY_BOUND_FLAG))bitmask&=~(WRAP_BIND_FLAG|WRAP_BIND_KEY_FLAG);var newData=[func,bitmask,thisArg,newPartials,newHolders,newPartialsRight,newHoldersRight,argPos,ary,arity];var result=wrapFunc.apply(undefined,newData);if(isLaziable(func))setData(result,
newData);result.placeholder=placeholder;return setWrapToString(result,func,bitmask)}function createRound(methodName){var func=Math[methodName];return function(number,precision){number=toNumber(number);precision=precision==null?0:nativeMin(toInteger(precision),292);if(precision){var pair=(toString(number)+"e").split("e"),value=func(pair[0]+"e"+(+pair[1]+precision));pair=(toString(value)+"e").split("e");return+(pair[0]+"e"+(+pair[1]-precision))}return func(number)}}var createSet=!(Set&&1/setToArray(new Set([,
-0]))[1]==INFINITY)?noop:function(values){return new Set(values)};function createToPairs(keysFunc){return function(object){var tag=getTag(object);if(tag==mapTag)return mapToArray(object);if(tag==setTag)return setToPairs(object);return baseToPairs(object,keysFunc(object))}}function createWrap(func,bitmask,thisArg,partials,holders,argPos,ary,arity){var isBindKey=bitmask&WRAP_BIND_KEY_FLAG;if(!isBindKey&&typeof func!="function")throw new TypeError(FUNC_ERROR_TEXT);var length=partials?partials.length:
0;if(!length){bitmask&=~(WRAP_PARTIAL_FLAG|WRAP_PARTIAL_RIGHT_FLAG);partials=holders=undefined}ary=ary===undefined?ary:nativeMax(toInteger(ary),0);arity=arity===undefined?arity:toInteger(arity);length-=holders?holders.length:0;if(bitmask&WRAP_PARTIAL_RIGHT_FLAG){var partialsRight=partials,holdersRight=holders;partials=holders=undefined}var data=isBindKey?undefined:getData(func);var newData=[func,bitmask,thisArg,partials,holders,partialsRight,holdersRight,argPos,ary,arity];if(data)mergeData(newData,
data);func=newData[0];bitmask=newData[1];thisArg=newData[2];partials=newData[3];holders=newData[4];arity=newData[9]=newData[9]===undefined?isBindKey?0:func.length:nativeMax(newData[9]-length,0);if(!arity&&bitmask&(WRAP_CURRY_FLAG|WRAP_CURRY_RIGHT_FLAG))bitmask&=~(WRAP_CURRY_FLAG|WRAP_CURRY_RIGHT_FLAG);if(!bitmask||bitmask==WRAP_BIND_FLAG)var result=createBind(func,bitmask,thisArg);else if(bitmask==WRAP_CURRY_FLAG||bitmask==WRAP_CURRY_RIGHT_FLAG)result=createCurry(func,bitmask,arity);else if((bitmask==
WRAP_PARTIAL_FLAG||bitmask==(WRAP_BIND_FLAG|WRAP_PARTIAL_FLAG))&&!holders.length)result=createPartial(func,bitmask,thisArg,partials);else result=createHybrid.apply(undefined,newData);var setter=data?baseSetData:setData;return setWrapToString(setter(result,newData),func,bitmask)}function customDefaultsAssignIn(objValue,srcValue,key,object){if(objValue===undefined||eq(objValue,objectProto[key])&&!hasOwnProperty.call(object,key))return srcValue;return objValue}function customDefaultsMerge(objValue,srcValue,
key,object,source,stack){if(isObject(objValue)&&isObject(srcValue)){stack.set(srcValue,objValue);baseMerge(objValue,srcValue,undefined,customDefaultsMerge,stack);stack["delete"](srcValue)}return objValue}function customOmitClone(value){return isPlainObject(value)?undefined:value}function equalArrays(array,other,bitmask,customizer,equalFunc,stack){var isPartial=bitmask&COMPARE_PARTIAL_FLAG,arrLength=array.length,othLength=other.length;if(arrLength!=othLength&&!(isPartial&&othLength>arrLength))return false;
var stacked=stack.get(array);if(stacked&&stack.get(other))return stacked==other;var index=-1,result=true,seen=bitmask&COMPARE_UNORDERED_FLAG?new SetCache:undefined;stack.set(array,other);stack.set(other,array);while(++index<arrLength){var arrValue=array[index],othValue=other[index];if(customizer)var compared=isPartial?customizer(othValue,arrValue,index,other,array,stack):customizer(arrValue,othValue,index,array,other,stack);if(compared!==undefined){if(compared)continue;result=false;break}if(seen){if(!arraySome(other,
function(othValue,othIndex){if(!cacheHas(seen,othIndex)&&(arrValue===othValue||equalFunc(arrValue,othValue,bitmask,customizer,stack)))return seen.push(othIndex)})){result=false;break}}else if(!(arrValue===othValue||equalFunc(arrValue,othValue,bitmask,customizer,stack))){result=false;break}}stack["delete"](array);stack["delete"](other);return result}function equalByTag(object,other,tag,bitmask,customizer,equalFunc,stack){switch(tag){case dataViewTag:if(object.byteLength!=other.byteLength||object.byteOffset!=
other.byteOffset)return false;object=object.buffer;other=other.buffer;case arrayBufferTag:if(object.byteLength!=other.byteLength||!equalFunc(new Uint8Array(object),new Uint8Array(other)))return false;return true;case boolTag:case dateTag:case numberTag:return eq(+object,+other);case errorTag:return object.name==other.name&&object.message==other.message;case regexpTag:case stringTag:return object==other+"";case mapTag:var convert=mapToArray;case setTag:var isPartial=bitmask&COMPARE_PARTIAL_FLAG;convert||
(convert=setToArray);if(object.size!=other.size&&!isPartial)return false;var stacked=stack.get(object);if(stacked)return stacked==other;bitmask|=COMPARE_UNORDERED_FLAG;stack.set(object,other);var result=equalArrays(convert(object),convert(other),bitmask,customizer,equalFunc,stack);stack["delete"](object);return result;case symbolTag:if(symbolValueOf)return symbolValueOf.call(object)==symbolValueOf.call(other)}return false}function equalObjects(object,other,bitmask,customizer,equalFunc,stack){var isPartial=
bitmask&COMPARE_PARTIAL_FLAG,objProps=getAllKeys(object),objLength=objProps.length,othProps=getAllKeys(other),othLength=othProps.length;if(objLength!=othLength&&!isPartial)return false;var index=objLength;while(index--){var key=objProps[index];if(!(isPartial?key in other:hasOwnProperty.call(other,key)))return false}var stacked=stack.get(object);if(stacked&&stack.get(other))return stacked==other;var result=true;stack.set(object,other);stack.set(other,object);var skipCtor=isPartial;while(++index<objLength){key=
objProps[index];var objValue=object[key],othValue=other[key];if(customizer)var compared=isPartial?customizer(othValue,objValue,key,other,object,stack):customizer(objValue,othValue,key,object,other,stack);if(!(compared===undefined?objValue===othValue||equalFunc(objValue,othValue,bitmask,customizer,stack):compared)){result=false;break}skipCtor||(skipCtor=key=="constructor")}if(result&&!skipCtor){var objCtor=object.constructor,othCtor=other.constructor;if(objCtor!=othCtor&&("constructor"in object&&"constructor"in
other)&&!(typeof objCtor=="function"&&objCtor instanceof objCtor&&typeof othCtor=="function"&&othCtor instanceof othCtor))result=false}stack["delete"](object);stack["delete"](other);return result}function flatRest(func){return setToString(overRest(func,undefined,flatten),func+"")}function getAllKeys(object){return baseGetAllKeys(object,keys,getSymbols)}function getAllKeysIn(object){return baseGetAllKeys(object,keysIn,getSymbolsIn)}var getData=!metaMap?noop:function(func){return metaMap.get(func)};
function getFuncName(func){var result=func.name+"",array=realNames[result],length=hasOwnProperty.call(realNames,result)?array.length:0;while(length--){var data=array[length],otherFunc=data.func;if(otherFunc==null||otherFunc==func)return data.name}return result}function getHolder(func){var object=hasOwnProperty.call(lodash,"placeholder")?lodash:func;return object.placeholder}function getIteratee(){var result=lodash.iteratee||iteratee;result=result===iteratee?baseIteratee:result;return arguments.length?
result(arguments[0],arguments[1]):result}function getMapData(map,key){var data=map.__data__;return isKeyable(key)?data[typeof key=="string"?"string":"hash"]:data.map}function getMatchData(object){var result=keys(object),length=result.length;while(length--){var key=result[length],value=object[key];result[length]=[key,value,isStrictComparable(value)]}return result}function getNative(object,key){var value=getValue(object,key);return baseIsNative(value)?value:undefined}function getRawTag(value){var isOwn=
hasOwnProperty.call(value,symToStringTag),tag=value[symToStringTag];try{value[symToStringTag]=undefined;var unmasked=true}catch(e){}var result=nativeObjectToString.call(value);if(unmasked)if(isOwn)value[symToStringTag]=tag;else delete value[symToStringTag];return result}var getSymbols=!nativeGetSymbols?stubArray:function(object){if(object==null)return[];object=Object(object);return arrayFilter(nativeGetSymbols(object),function(symbol){return propertyIsEnumerable.call(object,symbol)})};var getSymbolsIn=
!nativeGetSymbols?stubArray:function(object){var result=[];while(object){arrayPush(result,getSymbols(object));object=getPrototype(object)}return result};var getTag=baseGetTag;if(DataView&&getTag(new DataView(new ArrayBuffer(1)))!=dataViewTag||Map&&getTag(new Map)!=mapTag||Promise&&getTag(Promise.resolve())!=promiseTag||Set&&getTag(new Set)!=setTag||WeakMap&&getTag(new WeakMap)!=weakMapTag)getTag=function(value){var result=baseGetTag(value),Ctor=result==objectTag?value.constructor:undefined,ctorString=
Ctor?toSource(Ctor):"";if(ctorString)switch(ctorString){case dataViewCtorString:return dataViewTag;case mapCtorString:return mapTag;case promiseCtorString:return promiseTag;case setCtorString:return setTag;case weakMapCtorString:return weakMapTag}return result};function getView(start,end,transforms){var index=-1,length=transforms.length;while(++index<length){var data=transforms[index],size=data.size;switch(data.type){case "drop":start+=size;break;case "dropRight":end-=size;break;case "take":end=nativeMin(end,
start+size);break;case "takeRight":start=nativeMax(start,end-size);break}}return{"start":start,"end":end}}function getWrapDetails(source){var match=source.match(reWrapDetails);return match?match[1].split(reSplitDetails):[]}function hasPath(object,path,hasFunc){path=castPath(path,object);var index=-1,length=path.length,result=false;while(++index<length){var key=toKey(path[index]);if(!(result=object!=null&&hasFunc(object,key)))break;object=object[key]}if(result||++index!=length)return result;length=
object==null?0:object.length;return!!length&&isLength(length)&&isIndex(key,length)&&(isArray(object)||isArguments(object))}function initCloneArray(array){var length=array.length,result=new array.constructor(length);if(length&&typeof array[0]=="string"&&hasOwnProperty.call(array,"index")){result.index=array.index;result.input=array.input}return result}function initCloneObject(object){return typeof object.constructor=="function"&&!isPrototype(object)?baseCreate(getPrototype(object)):{}}function initCloneByTag(object,
tag,isDeep){var Ctor=object.constructor;switch(tag){case arrayBufferTag:return cloneArrayBuffer(object);case boolTag:case dateTag:return new Ctor(+object);case dataViewTag:return cloneDataView(object,isDeep);case float32Tag:case float64Tag:case int8Tag:case int16Tag:case int32Tag:case uint8Tag:case uint8ClampedTag:case uint16Tag:case uint32Tag:return cloneTypedArray(object,isDeep);case mapTag:return new Ctor;case numberTag:case stringTag:return new Ctor(object);case regexpTag:return cloneRegExp(object);
case setTag:return new Ctor;case symbolTag:return cloneSymbol(object)}}function insertWrapDetails(source,details){var length=details.length;if(!length)return source;var lastIndex=length-1;details[lastIndex]=(length>1?"\x26 ":"")+details[lastIndex];details=details.join(length>2?", ":" ");return source.replace(reWrapComment,"{\n/* [wrapped with "+details+"] */\n")}function isFlattenable(value){return isArray(value)||isArguments(value)||!!(spreadableSymbol&&value&&value[spreadableSymbol])}function isIndex(value,
length){var type=typeof value;length=length==null?MAX_SAFE_INTEGER:length;return!!length&&(type=="number"||type!="symbol"&&reIsUint.test(value))&&(value>-1&&value%1==0&&value<length)}function isIterateeCall(value,index,object){if(!isObject(object))return false;var type=typeof index;if(type=="number"?isArrayLike(object)&&isIndex(index,object.length):type=="string"&&index in object)return eq(object[index],value);return false}function isKey(value,object){if(isArray(value))return false;var type=typeof value;
if(type=="number"||type=="symbol"||type=="boolean"||value==null||isSymbol(value))return true;return reIsPlainProp.test(value)||!reIsDeepProp.test(value)||object!=null&&value in Object(object)}function isKeyable(value){var type=typeof value;return type=="string"||type=="number"||type=="symbol"||type=="boolean"?value!=="__proto__":value===null}function isLaziable(func){var funcName=getFuncName(func),other=lodash[funcName];if(typeof other!="function"||!(funcName in LazyWrapper.prototype))return false;
if(func===other)return true;var data=getData(other);return!!data&&func===data[0]}function isMasked(func){return!!maskSrcKey&&maskSrcKey in func}var isMaskable=coreJsData?isFunction:stubFalse;function isPrototype(value){var Ctor=value&&value.constructor,proto=typeof Ctor=="function"&&Ctor.prototype||objectProto;return value===proto}function isStrictComparable(value){return value===value&&!isObject(value)}function matchesStrictComparable(key,srcValue){return function(object){if(object==null)return false;
return object[key]===srcValue&&(srcValue!==undefined||key in Object(object))}}function memoizeCapped(func){var result=memoize(func,function(key){if(cache.size===MAX_MEMOIZE_SIZE)cache.clear();return key});var cache=result.cache;return result}function mergeData(data,source){var bitmask=data[1],srcBitmask=source[1],newBitmask=bitmask|srcBitmask,isCommon=newBitmask<(WRAP_BIND_FLAG|WRAP_BIND_KEY_FLAG|WRAP_ARY_FLAG);var isCombo=srcBitmask==WRAP_ARY_FLAG&&bitmask==WRAP_CURRY_FLAG||srcBitmask==WRAP_ARY_FLAG&&
bitmask==WRAP_REARG_FLAG&&data[7].length<=source[8]||srcBitmask==(WRAP_ARY_FLAG|WRAP_REARG_FLAG)&&source[7].length<=source[8]&&bitmask==WRAP_CURRY_FLAG;if(!(isCommon||isCombo))return data;if(srcBitmask&WRAP_BIND_FLAG){data[2]=source[2];newBitmask|=bitmask&WRAP_BIND_FLAG?0:WRAP_CURRY_BOUND_FLAG}var value=source[3];if(value){var partials=data[3];data[3]=partials?composeArgs(partials,value,source[4]):value;data[4]=partials?replaceHolders(data[3],PLACEHOLDER):source[4]}value=source[5];if(value){partials=
data[5];data[5]=partials?composeArgsRight(partials,value,source[6]):value;data[6]=partials?replaceHolders(data[5],PLACEHOLDER):source[6]}value=source[7];if(value)data[7]=value;if(srcBitmask&WRAP_ARY_FLAG)data[8]=data[8]==null?source[8]:nativeMin(data[8],source[8]);if(data[9]==null)data[9]=source[9];data[0]=source[0];data[1]=newBitmask;return data}function nativeKeysIn(object){var result=[];if(object!=null)for(var key in Object(object))result.push(key);return result}function objectToString(value){return nativeObjectToString.call(value)}
function overRest(func,start,transform){start=nativeMax(start===undefined?func.length-1:start,0);return function(){var args=arguments,index=-1,length=nativeMax(args.length-start,0),array=Array(length);while(++index<length)array[index]=args[start+index];index=-1;var otherArgs=Array(start+1);while(++index<start)otherArgs[index]=args[index];otherArgs[start]=transform(array);return apply(func,this,otherArgs)}}function parent(object,path){return path.length<2?object:baseGet(object,baseSlice(path,0,-1))}
function reorder(array,indexes){var arrLength=array.length,length=nativeMin(indexes.length,arrLength),oldArray=copyArray(array);while(length--){var index=indexes[length];array[length]=isIndex(index,arrLength)?oldArray[index]:undefined}return array}var setData=shortOut(baseSetData);var setTimeout=ctxSetTimeout||function(func,wait){return root.setTimeout(func,wait)};var setToString=shortOut(baseSetToString);function setWrapToString(wrapper,reference,bitmask){var source=reference+"";return setToString(wrapper,
insertWrapDetails(source,updateWrapDetails(getWrapDetails(source),bitmask)))}function shortOut(func){var count=0,lastCalled=0;return function(){var stamp=nativeNow(),remaining=HOT_SPAN-(stamp-lastCalled);lastCalled=stamp;if(remaining>0){if(++count>=HOT_COUNT)return arguments[0]}else count=0;return func.apply(undefined,arguments)}}function shuffleSelf(array,size){var index=-1,length=array.length,lastIndex=length-1;size=size===undefined?length:size;while(++index<size){var rand=baseRandom(index,lastIndex),
value=array[rand];array[rand]=array[index];array[index]=value}array.length=size;return array}var stringToPath=memoizeCapped(function(string){var result=[];if(string.charCodeAt(0)===46)result.push("");string.replace(rePropName,function(match,number,quote,subString){result.push(quote?subString.replace(reEscapeChar,"$1"):number||match)});return result});function toKey(value){if(typeof value=="string"||isSymbol(value))return value;var result=value+"";return result=="0"&&1/value==-INFINITY?"-0":result}
function toSource(func){if(func!=null){try{return funcToString.call(func)}catch(e){}try{return func+""}catch(e$0){}}return""}function updateWrapDetails(details,bitmask){arrayEach(wrapFlags,function(pair){var value="_."+pair[0];if(bitmask&pair[1]&&!arrayIncludes(details,value))details.push(value)});return details.sort()}function wrapperClone(wrapper){if(wrapper instanceof LazyWrapper)return wrapper.clone();var result=new LodashWrapper(wrapper.__wrapped__,wrapper.__chain__);result.__actions__=copyArray(wrapper.__actions__);
result.__index__=wrapper.__index__;result.__values__=wrapper.__values__;return result}function chunk(array,size,guard){if(guard?isIterateeCall(array,size,guard):size===undefined)size=1;else size=nativeMax(toInteger(size),0);var length=array==null?0:array.length;if(!length||size<1)return[];var index=0,resIndex=0,result=Array(nativeCeil(length/size));while(index<length)result[resIndex++]=baseSlice(array,index,index+=size);return result}function compact(array){var index=-1,length=array==null?0:array.length,
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[]});function drop(array,n,guard){var length=array==null?0:array.length;if(!length)return[];n=guard||n===undefined?1:toInteger(n);return baseSlice(array,n<0?0:n,length)}function dropRight(array,n,guard){var length=array==null?0:array.length;if(!length)return[];n=guard||n===undefined?1:toInteger(n);n=length-n;return baseSlice(array,0,n<0?0:n)}function dropRightWhile(array,predicate){return array&&array.length?baseWhile(array,getIteratee(predicate,3),true,true):[]}function dropWhile(array,predicate){return array&&
array.length?baseWhile(array,getIteratee(predicate,3),true):[]}function fill(array,value,start,end){var length=array==null?0:array.length;if(!length)return[];if(start&&typeof start!="number"&&isIterateeCall(array,value,start)){start=0;end=length}return baseFill(array,value,start,end)}function findIndex(array,predicate,fromIndex){var length=array==null?0:array.length;if(!length)return-1;var index=fromIndex==null?0:toInteger(fromIndex);if(index<0)index=nativeMax(length+index,0);return baseFindIndex(array,
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return length?baseFlatten(array,INFINITY):[]}function flattenDepth(array,depth){var length=array==null?0:array.length;if(!length)return[];depth=depth===undefined?1:toInteger(depth);return baseFlatten(array,depth)}function fromPairs(pairs){var index=-1,length=pairs==null?0:pairs.length,result={};while(++index<length){var pair=pairs[index];result[pair[0]]=pair[1]}return result}function head(array){return array&&array.length?array[0]:undefined}function indexOf(array,value,fromIndex){var length=array==
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[]});function join(array,separator){return array==null?"":nativeJoin.call(array,separator)}function last(array){var length=array==null?0:array.length;return length?array[length-1]:undefined}function lastIndexOf(array,value,fromIndex){var length=array==null?0:array.length;if(!length)return-1;var index=length;if(fromIndex!==undefined){index=toInteger(fromIndex);index=index<0?nativeMax(length+index,0):nativeMin(index,length-1)}return value===value?strictLastIndexOf(array,value,index):baseFindIndex(array,
baseIsNaN,index,true)}function nth(array,n){return array&&array.length?baseNth(array,toInteger(n)):undefined}var pull=baseRest(pullAll);function pullAll(array,values){return array&&array.length&&values&&values.length?basePullAll(array,values):array}function pullAllBy(array,values,iteratee){return array&&array.length&&values&&values.length?basePullAll(array,values,getIteratee(iteratee,2)):array}function pullAllWith(array,values,comparator){return array&&array.length&&values&&values.length?basePullAll(array,
values,undefined,comparator):array}var pullAt=flatRest(function(array,indexes){var length=array==null?0:array.length,result=baseAt(array,indexes);basePullAt(array,arrayMap(indexes,function(index){return isIndex(index,length)?+index:index}).sort(compareAscending));return result});function remove(array,predicate){var result=[];if(!(array&&array.length))return result;var index=-1,indexes=[],length=array.length;predicate=getIteratee(predicate,3);while(++index<length){var value=array[index];if(predicate(value,
index,array)){result.push(value);indexes.push(index)}}basePullAt(array,indexes);return result}function reverse(array){return array==null?array:nativeReverse.call(array)}function slice(array,start,end){var length=array==null?0:array.length;if(!length)return[];if(end&&typeof end!="number"&&isIterateeCall(array,start,end)){start=0;end=length}else{start=start==null?0:toInteger(start);end=end===undefined?length:toInteger(end)}return baseSlice(array,start,end)}function sortedIndex(array,value){return baseSortedIndex(array,
value)}function sortedIndexBy(array,value,iteratee){return baseSortedIndexBy(array,value,getIteratee(iteratee,2))}function sortedIndexOf(array,value){var length=array==null?0:array.length;if(length){var index=baseSortedIndex(array,value);if(index<length&&eq(array[index],value))return index}return-1}function sortedLastIndex(array,value){return baseSortedIndex(array,value,true)}function sortedLastIndexBy(array,value,iteratee){return baseSortedIndexBy(array,value,getIteratee(iteratee,2),true)}function sortedLastIndexOf(array,
value){var length=array==null?0:array.length;if(length){var index=baseSortedIndex(array,value,true)-1;if(eq(array[index],value))return index}return-1}function sortedUniq(array){return array&&array.length?baseSortedUniq(array):[]}function sortedUniqBy(array,iteratee){return array&&array.length?baseSortedUniq(array,getIteratee(iteratee,2)):[]}function tail(array){var length=array==null?0:array.length;return length?baseSlice(array,1,length):[]}function take(array,n,guard){if(!(array&&array.length))return[];
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1,isArrayLikeObject,true))});var unionBy=baseRest(function(arrays){var iteratee=last(arrays);if(isArrayLikeObject(iteratee))iteratee=undefined;return baseUniq(baseFlatten(arrays,1,isArrayLikeObject,true),getIteratee(iteratee,2))});var unionWith=baseRest(function(arrays){var comparator=last(arrays);comparator=typeof comparator=="function"?comparator:undefined;return baseUniq(baseFlatten(arrays,1,isArrayLikeObject,true),undefined,comparator)});function uniq(array){return array&&array.length?baseUniq(array):
[]}function uniqBy(array,iteratee){return array&&array.length?baseUniq(array,getIteratee(iteratee,2)):[]}function uniqWith(array,comparator){comparator=typeof comparator=="function"?comparator:undefined;return array&&array.length?baseUniq(array,undefined,comparator):[]}function unzip(array){if(!(array&&array.length))return[];var length=0;array=arrayFilter(array,function(group){if(isArrayLikeObject(group)){length=nativeMax(group.length,length);return true}});return baseTimes(length,function(index){return arrayMap(array,
baseProperty(index))})}function unzipWith(array,iteratee){if(!(array&&array.length))return[];var result=unzip(array);if(iteratee==null)return result;return arrayMap(result,function(group){return apply(iteratee,undefined,group)})}var without=baseRest(function(array,values){return isArrayLikeObject(array)?baseDifference(array,values):[]});var xor=baseRest(function(arrays){return baseXor(arrayFilter(arrays,isArrayLikeObject))});var xorBy=baseRest(function(arrays){var iteratee=last(arrays);if(isArrayLikeObject(iteratee))iteratee=
undefined;return baseXor(arrayFilter(arrays,isArrayLikeObject),getIteratee(iteratee,2))});var xorWith=baseRest(function(arrays){var comparator=last(arrays);comparator=typeof comparator=="function"?comparator:undefined;return baseXor(arrayFilter(arrays,isArrayLikeObject),undefined,comparator)});var zip=baseRest(unzip);function zipObject(props,values){return baseZipObject(props||[],values||[],assignValue)}function zipObjectDeep(props,values){return baseZipObject(props||[],values||[],baseSet)}var zipWith=
baseRest(function(arrays){var length=arrays.length,iteratee=length>1?arrays[length-1]:undefined;iteratee=typeof iteratee=="function"?(arrays.pop(),iteratee):undefined;return unzipWith(arrays,iteratee)});function chain(value){var result=lodash(value);result.__chain__=true;return result}function tap(value,interceptor){interceptor(value);return value}function thru(value,interceptor){return interceptor(value)}var wrapperAt=flatRest(function(paths){var length=paths.length,start=length?paths[0]:0,value=
this.__wrapped__,interceptor=function(object){return baseAt(object,paths)};if(length>1||this.__actions__.length||!(value instanceof LazyWrapper)||!isIndex(start))return this.thru(interceptor);value=value.slice(start,+start+(length?1:0));value.__actions__.push({"func":thru,"args":[interceptor],"thisArg":undefined});return(new LodashWrapper(value,this.__chain__)).thru(function(array){if(length&&!array.length)array.push(undefined);return array})});function wrapperChain(){return chain(this)}function wrapperCommit(){return new LodashWrapper(this.value(),
this.__chain__)}function wrapperNext(){if(this.__values__===undefined)this.__values__=toArray(this.value());var done=this.__index__>=this.__values__.length,value=done?undefined:this.__values__[this.__index__++];return{"done":done,"value":value}}function wrapperToIterator(){return this}function wrapperPlant(value){var result,parent=this;while(parent instanceof baseLodash){var clone=wrapperClone(parent);clone.__index__=0;clone.__values__=undefined;if(result)previous.__wrapped__=clone;else result=clone;
var previous=clone;parent=parent.__wrapped__}previous.__wrapped__=value;return result}function wrapperReverse(){var value=this.__wrapped__;if(value instanceof LazyWrapper){var wrapped=value;if(this.__actions__.length)wrapped=new LazyWrapper(this);wrapped=wrapped.reverse();wrapped.__actions__.push({"func":thru,"args":[reverse],"thisArg":undefined});return new LodashWrapper(wrapped,this.__chain__)}return this.thru(reverse)}function wrapperValue(){return baseWrapperValue(this.__wrapped__,this.__actions__)}
var countBy=createAggregator(function(result,value,key){if(hasOwnProperty.call(result,key))++result[key];else baseAssignValue(result,key,1)});function every(collection,predicate,guard){var func=isArray(collection)?arrayEvery:baseEvery;if(guard&&isIterateeCall(collection,predicate,guard))predicate=undefined;return func(collection,getIteratee(predicate,3))}function filter(collection,predicate){var func=isArray(collection)?arrayFilter:baseFilter;return func(collection,getIteratee(predicate,3))}var find=
createFind(findIndex);var findLast=createFind(findLastIndex);function flatMap(collection,iteratee){return baseFlatten(map(collection,iteratee),1)}function flatMapDeep(collection,iteratee){return baseFlatten(map(collection,iteratee),INFINITY)}function flatMapDepth(collection,iteratee,depth){depth=depth===undefined?1:toInteger(depth);return baseFlatten(map(collection,iteratee),depth)}function forEach(collection,iteratee){var func=isArray(collection)?arrayEach:baseEach;return func(collection,getIteratee(iteratee,
3))}function forEachRight(collection,iteratee){var func=isArray(collection)?arrayEachRight:baseEachRight;return func(collection,getIteratee(iteratee,3))}var groupBy=createAggregator(function(result,value,key){if(hasOwnProperty.call(result,key))result[key].push(value);else baseAssignValue(result,key,[value])});function includes(collection,value,fromIndex,guard){collection=isArrayLike(collection)?collection:values(collection);fromIndex=fromIndex&&!guard?toInteger(fromIndex):0;var length=collection.length;
if(fromIndex<0)fromIndex=nativeMax(length+fromIndex,0);return isString(collection)?fromIndex<=length&&collection.indexOf(value,fromIndex)>-1:!!length&&baseIndexOf(collection,value,fromIndex)>-1}var invokeMap=baseRest(function(collection,path,args){var index=-1,isFunc=typeof path=="function",result=isArrayLike(collection)?Array(collection.length):[];baseEach(collection,function(value){result[++index]=isFunc?apply(path,value,args):baseInvoke(value,path,args)});return result});var keyBy=createAggregator(function(result,
value,key){baseAssignValue(result,key,value)});function map(collection,iteratee){var func=isArray(collection)?arrayMap:baseMap;return func(collection,getIteratee(iteratee,3))}function orderBy(collection,iteratees,orders,guard){if(collection==null)return[];if(!isArray(iteratees))iteratees=iteratees==null?[]:[iteratees];orders=guard?undefined:orders;if(!isArray(orders))orders=orders==null?[]:[orders];return baseOrderBy(collection,iteratees,orders)}var partition=createAggregator(function(result,value,
key){result[key?0:1].push(value)},function(){return[[],[]]});function reduce(collection,iteratee,accumulator){var func=isArray(collection)?arrayReduce:baseReduce,initAccum=arguments.length<3;return func(collection,getIteratee(iteratee,4),accumulator,initAccum,baseEach)}function reduceRight(collection,iteratee,accumulator){var func=isArray(collection)?arrayReduceRight:baseReduce,initAccum=arguments.length<3;return func(collection,getIteratee(iteratee,4),accumulator,initAccum,baseEachRight)}function reject(collection,
predicate){var func=isArray(collection)?arrayFilter:baseFilter;return func(collection,negate(getIteratee(predicate,3)))}function sample(collection){var func=isArray(collection)?arraySample:baseSample;return func(collection)}function sampleSize(collection,n,guard){if(guard?isIterateeCall(collection,n,guard):n===undefined)n=1;else n=toInteger(n);var func=isArray(collection)?arraySampleSize:baseSampleSize;return func(collection,n)}function shuffle(collection){var func=isArray(collection)?arrayShuffle:
baseShuffle;return func(collection)}function size(collection){if(collection==null)return 0;if(isArrayLike(collection))return isString(collection)?stringSize(collection):collection.length;var tag=getTag(collection);if(tag==mapTag||tag==setTag)return collection.size;return baseKeys(collection).length}function some(collection,predicate,guard){var func=isArray(collection)?arraySome:baseSome;if(guard&&isIterateeCall(collection,predicate,guard))predicate=undefined;return func(collection,getIteratee(predicate,
3))}var sortBy=baseRest(function(collection,iteratees){if(collection==null)return[];var length=iteratees.length;if(length>1&&isIterateeCall(collection,iteratees[0],iteratees[1]))iteratees=[];else if(length>2&&isIterateeCall(iteratees[0],iteratees[1],iteratees[2]))iteratees=[iteratees[0]];return baseOrderBy(collection,baseFlatten(iteratees,1),[])});var now=ctxNow||function(){return root.Date.now()};function after(n,func){if(typeof func!="function")throw new TypeError(FUNC_ERROR_TEXT);n=toInteger(n);
return function(){if(--n<1)return func.apply(this,arguments)}}function ary(func,n,guard){n=guard?undefined:n;n=func&&n==null?func.length:n;return createWrap(func,WRAP_ARY_FLAG,undefined,undefined,undefined,undefined,n)}function before(n,func){var result;if(typeof func!="function")throw new TypeError(FUNC_ERROR_TEXT);n=toInteger(n);return function(){if(--n>0)result=func.apply(this,arguments);if(n<=1)func=undefined;return result}}var bind=baseRest(function(func,thisArg,partials){var bitmask=WRAP_BIND_FLAG;
if(partials.length){var holders=replaceHolders(partials,getHolder(bind));bitmask|=WRAP_PARTIAL_FLAG}return createWrap(func,bitmask,thisArg,partials,holders)});var bindKey=baseRest(function(object,key,partials){var bitmask=WRAP_BIND_FLAG|WRAP_BIND_KEY_FLAG;if(partials.length){var holders=replaceHolders(partials,getHolder(bindKey));bitmask|=WRAP_PARTIAL_FLAG}return createWrap(key,bitmask,object,partials,holders)});function curry(func,arity,guard){arity=guard?undefined:arity;var result=createWrap(func,
WRAP_CURRY_FLAG,undefined,undefined,undefined,undefined,undefined,arity);result.placeholder=curry.placeholder;return result}function curryRight(func,arity,guard){arity=guard?undefined:arity;var result=createWrap(func,WRAP_CURRY_RIGHT_FLAG,undefined,undefined,undefined,undefined,undefined,arity);result.placeholder=curryRight.placeholder;return result}function debounce(func,wait,options){var lastArgs,lastThis,maxWait,result,timerId,lastCallTime,lastInvokeTime=0,leading=false,maxing=false,trailing=true;
if(typeof func!="function")throw new TypeError(FUNC_ERROR_TEXT);wait=toNumber(wait)||0;if(isObject(options)){leading=!!options.leading;maxing="maxWait"in options;maxWait=maxing?nativeMax(toNumber(options.maxWait)||0,wait):maxWait;trailing="trailing"in options?!!options.trailing:trailing}function invokeFunc(time){var args=lastArgs,thisArg=lastThis;lastArgs=lastThis=undefined;lastInvokeTime=time;result=func.apply(thisArg,args);return result}function leadingEdge(time){lastInvokeTime=time;timerId=setTimeout(timerExpired,
wait);return leading?invokeFunc(time):result}function remainingWait(time){var timeSinceLastCall=time-lastCallTime,timeSinceLastInvoke=time-lastInvokeTime,timeWaiting=wait-timeSinceLastCall;return maxing?nativeMin(timeWaiting,maxWait-timeSinceLastInvoke):timeWaiting}function shouldInvoke(time){var timeSinceLastCall=time-lastCallTime,timeSinceLastInvoke=time-lastInvokeTime;return lastCallTime===undefined||timeSinceLastCall>=wait||timeSinceLastCall<0||maxing&&timeSinceLastInvoke>=maxWait}function timerExpired(){var time=
now();if(shouldInvoke(time))return trailingEdge(time);timerId=setTimeout(timerExpired,remainingWait(time))}function trailingEdge(time){timerId=undefined;if(trailing&&lastArgs)return invokeFunc(time);lastArgs=lastThis=undefined;return result}function cancel(){if(timerId!==undefined)clearTimeout(timerId);lastInvokeTime=0;lastArgs=lastCallTime=lastThis=timerId=undefined}function flush(){return timerId===undefined?result:trailingEdge(now())}function debounced(){var time=now(),isInvoking=shouldInvoke(time);
lastArgs=arguments;lastThis=this;lastCallTime=time;if(isInvoking){if(timerId===undefined)return leadingEdge(lastCallTime);if(maxing){timerId=setTimeout(timerExpired,wait);return invokeFunc(lastCallTime)}}if(timerId===undefined)timerId=setTimeout(timerExpired,wait);return result}debounced.cancel=cancel;debounced.flush=flush;return debounced}var defer=baseRest(function(func,args){return baseDelay(func,1,args)});var delay=baseRest(function(func,wait,args){return baseDelay(func,toNumber(wait)||0,args)});
function flip(func){return createWrap(func,WRAP_FLIP_FLAG)}function memoize(func,resolver){if(typeof func!="function"||resolver!=null&&typeof resolver!="function")throw new TypeError(FUNC_ERROR_TEXT);var memoized=function(){var args=arguments,key=resolver?resolver.apply(this,args):args[0],cache=memoized.cache;if(cache.has(key))return cache.get(key);var result=func.apply(this,args);memoized.cache=cache.set(key,result)||cache;return result};memoized.cache=new (memoize.Cache||MapCache);return memoized}
memoize.Cache=MapCache;function negate(predicate){if(typeof predicate!="function")throw new TypeError(FUNC_ERROR_TEXT);return function(){var args=arguments;switch(args.length){case 0:return!predicate.call(this);case 1:return!predicate.call(this,args[0]);case 2:return!predicate.call(this,args[0],args[1]);case 3:return!predicate.call(this,args[0],args[1],args[2])}return!predicate.apply(this,args)}}function once(func){return before(2,func)}var overArgs=castRest(function(func,transforms){transforms=transforms.length==
1&&isArray(transforms[0])?arrayMap(transforms[0],baseUnary(getIteratee())):arrayMap(baseFlatten(transforms,1),baseUnary(getIteratee()));var funcsLength=transforms.length;return baseRest(function(args){var index=-1,length=nativeMin(args.length,funcsLength);while(++index<length)args[index]=transforms[index].call(this,args[index]);return apply(func,this,args)})});var partial=baseRest(function(func,partials){var holders=replaceHolders(partials,getHolder(partial));return createWrap(func,WRAP_PARTIAL_FLAG,
undefined,partials,holders)});var partialRight=baseRest(function(func,partials){var holders=replaceHolders(partials,getHolder(partialRight));return createWrap(func,WRAP_PARTIAL_RIGHT_FLAG,undefined,partials,holders)});var rearg=flatRest(function(func,indexes){return createWrap(func,WRAP_REARG_FLAG,undefined,undefined,undefined,indexes)});function rest(func,start){if(typeof func!="function")throw new TypeError(FUNC_ERROR_TEXT);start=start===undefined?start:toInteger(start);return baseRest(func,start)}
function spread(func,start){if(typeof func!="function")throw new TypeError(FUNC_ERROR_TEXT);start=start==null?0:nativeMax(toInteger(start),0);return baseRest(function(args){var array=args[start],otherArgs=castSlice(args,0,start);if(array)arrayPush(otherArgs,array);return apply(func,this,otherArgs)})}function throttle(func,wait,options){var leading=true,trailing=true;if(typeof func!="function")throw new TypeError(FUNC_ERROR_TEXT);if(isObject(options)){leading="leading"in options?!!options.leading:
leading;trailing="trailing"in options?!!options.trailing:trailing}return debounce(func,wait,{"leading":leading,"maxWait":wait,"trailing":trailing})}function unary(func){return ary(func,1)}function wrap(value,wrapper){return partial(castFunction(wrapper),value)}function castArray(){if(!arguments.length)return[];var value=arguments[0];return isArray(value)?value:[value]}function clone(value){return baseClone(value,CLONE_SYMBOLS_FLAG)}function cloneWith(value,customizer){customizer=typeof customizer==
"function"?customizer:undefined;return baseClone(value,CLONE_SYMBOLS_FLAG,customizer)}function cloneDeep(value){return baseClone(value,CLONE_DEEP_FLAG|CLONE_SYMBOLS_FLAG)}function cloneDeepWith(value,customizer){customizer=typeof customizer=="function"?customizer:undefined;return baseClone(value,CLONE_DEEP_FLAG|CLONE_SYMBOLS_FLAG,customizer)}function conformsTo(object,source){return source==null||baseConformsTo(object,source,keys(source))}function eq(value,other){return value===other||value!==value&&
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var _=runInContext();if(typeof define=="function"&&typeof define.amd=="object"&&define.amd){root._=_;define(function(){return _})}else if(freeModule){(freeModule.exports=_)._=_;freeExports._=_}else root._=_}).call(this);
//# sourceURL=build://vz-sorting/sorting.js
var ac;
(function(b){function d(k,r){let l;(function(m){m[m.NATURAL=0]="NATURAL";m[m.REAL=1]="REAL";m[m.EXPONENT_SIGN=2]="EXPONENT_SIGN";m[m.EXPONENT=3]="EXPONENT"})(l||(l={}));let p=l.NATURAL;for(;r<k.length;r++)if(p===l.NATURAL)if("."===k[r])p=l.REAL;else if("e"===k[r]||"E"===k[r])p=l.EXPONENT_SIGN;else{if(!f(k[r]))break}else if(p===l.REAL)if("e"===k[r]||"E"===k[r])p=l.EXPONENT_SIGN;else{if(!f(k[r]))break}else if(p===l.EXPONENT_SIGN)if(f(k[r])||"+"===k[r]||"-"===k[r])p=l.EXPONENT;else break;else if(p===l.EXPONENT&&
!f(k[r]))break;return r}function f(k){return"0"<=k&&"9">=k}function h(k){return"/"===k||"_"===k||f(k)}b.compareTagNames=function(k,r){let l=0,p=0;for(;;){if(l===k.length)return p===r.length?0:-1;if(p===r.length)return 1;if(f(k[l])&&f(r[p])){var m=l,n=p;l=d(k,l+1);p=d(r,p+1);m=parseFloat(k.slice(m,l));n=parseFloat(r.slice(n,p));if(m<n)return-1;if(m>n)return 1}else{if(h(k[l])){if(!h(r[p]))return-1}else{if(h(r[p]))return 1;if(k[l]<r[p])return-1;if(k[l]>r[p])return 1}l++;p++}}}})(ac||(ac={}));

//# sourceURL=build://tf-backend/requestManager.js
var dc;
(function(b){function d(q,u,w,A){const y=new XMLHttpRequest;y.open(q,u);w&&(y.withCredentials=w);A&&y.setRequestHeader("Content-Type",A);return y}function f(q){const u=new m;if(!q)return u.methodType=p.GET,u;u.methodType=p.POST;u.body=h(q);return u}function h(q){const u=new FormData;for(let w in q)w&&u.append(w,q[w]);return u}class k extends Error{constructor(){super(...arguments);this.name="RequestCancellationError"}}b.RequestCancellationError=k;class r extends Error{constructor(q){super(q);this.name=
"InvalidRequestOptionsError";Object.setPrototypeOf(this,r.prototype)}}b.InvalidRequestOptionsError=r;class l extends Error{constructor(q,u){super();this.message=`RequestNetworkError: ${q.status} at ${u}`;this.name="RequestNetworkError";this.req=q;this.url=u}}b.RequestNetworkError=l;let p;(function(q){q.GET="GET";q.POST="POST"})(p=b.HttpMethodType||(b.HttpMethodType={}));class m{validate(){if(this.methodType===p.GET&&this.body)throw new r("body must be missing for a GET request.");}}b.RequestOptions=
m;class n{constructor(q=1E3,u=3){this._queue=[];this._nActiveRequests=0;this._nSimultaneousRequests=q;this._maxRetries=u}request(q,u){u=f(u);return this.requestWithOptions(q,u)}requestWithOptions(q,u){u.validate();return(new Promise((w,A)=>{this._queue.push({resolve:w,reject:A});this.launchRequests()})).then(()=>this.promiseWithRetries(q,this._maxRetries,u)).then(w=>{this._nActiveRequests--;this.launchRequests();return w},w=>{"RequestNetworkError"===w.name&&(this._nActiveRequests--,this.launchRequests());
return Promise.reject(w)})}fetch(q,u){return(new Promise((w,A)=>{this._queue.push({resolve:w,reject:A});this.launchRequests()})).then(()=>{let w=1;return new Promise(A=>{const y=()=>{fetch(q,u).then(x=>{!x.ok&&this._maxRetries>w?(w++,y()):(A(x),this._nActiveRequests--,this.launchRequests())})};y()})})}clearQueue(){for(;0<this._queue.length;)this._queue.pop().reject(new k("Request cancelled by clearQueue"))}activeRequests(){return this._nActiveRequests}outstandingRequests(){return this._nActiveRequests+
this._queue.length}launchRequests(){for(;this._nActiveRequests<this._nSimultaneousRequests&&0<this._queue.length;)this._nActiveRequests++,this._queue.pop().resolve()}promiseWithRetries(q,u,w){return this._promiseFromUrl(q,w).then(A=>A,A=>0<u?this.promiseWithRetries(q,u-1,w):Promise.reject(A))}_promiseFromUrl(q,u){return new Promise((w,A)=>{const y=d(u.methodType,q,u.withCredentials,u.contentType);y.onload=function(){200===y.status?w(JSON.parse(y.responseText)):A(new l(y,q))};y.onerror=function(){A(new l(y,
q))};u.body?y.send(u.body):y.send()})}}b.RequestManager=n})(dc||(dc={}));

//# sourceURL=build://tf-backend/urlPathHelpers.js
(function(b){function d(f){return encodeURIComponent(f).replace(/\(/g,"%28").replace(/\)/g,"%29")}b.addParams=function(f,h){var k=Object.keys(h).sort().filter(l=>void 0!==h[l]);if(!k.length)return f;const r=-1!==f.indexOf("?")?"\x26":"?";k=[].concat(...k.map(l=>{const p=h[l];return(Array.isArray(p)?p:[p]).map(m=>`${l}=${d(m)}`)})).join("\x26");return f+r+k}})(dc||(dc={}));

//# sourceURL=build://tf-backend/router.js
(function(b){function d(r="data",l=new URLSearchParams(window.location.search)){"/"===r[r.length-1]&&(r=r.slice(0,r.length-1));return{environment:()=>f(r,"/environment"),experiments:()=>f(r,"/experiments"),pluginRoute:(p,m,n)=>f(r+"/plugin",`/${p}${m}`,n),pluginsListing:()=>f(r,"/plugins_listing",h({["experimentalPlugin"]:l.getAll("experimentalPlugin")})),runs:()=>f(r,"/runs"),runsForExperiment:p=>f(r,"/experiment_runs",h({experiment:String(p)}))}}function f(r,l,p=new URLSearchParams){r+=l;String(p)&&
(l=l.includes("?")?"\x26":"?",r+=l+String(p));return r}function h(r={}){const l=Object.keys(r).sort().filter(m=>r[m]),p=new URLSearchParams;l.forEach(m=>{const n=r[m];(Array.isArray(n)?n:[n]).forEach(q=>p.append(m,q))});return p}let k=d();b.createRouter=d;b.getRouter=function(){return k};b.setRouter=function(r){if(null==r)throw Error("Router required, but got: "+r);k=r};b.createSearchParam=h})(dc||(dc={}));

//# sourceURL=build://tf-backend/baseStore.js
(function(b){class d{constructor(h){this.listener=h}}b.ListenKey=d;class f{constructor(){this.requestManager=new b.RequestManager(1);this._listeners=new Set;this.initialized=!1}refresh(){return this.load().then(()=>{this.initialized=!0})}addListener(h){h=new d(h);this._listeners.add(h);return h}removeListenerByKey(h){this._listeners.delete(h)}emitChange(){this._listeners.forEach(h=>{try{h.listener()}catch(k){}})}}b.BaseStore=f})(dc||(dc={}));

//# sourceURL=build://tf-backend/environmentStore.js
(function(b){class d extends b.BaseStore{load(){const f=b.getRouter().environment();return this.requestManager.request(f).then(h=>{const k={dataLocation:h.data_location,windowTitle:h.window_title};void 0!==h.experiment_name&&(k.experimentName=h.experiment_name);void 0!==h.experiment_description&&(k.experimentDescription=h.experiment_description);void 0!==h.creation_time&&(k.creationTime=h.creation_time);_.isEqual(this.environment,k)||(this.environment=k,this.emitChange())})}getDataLocation(){return this.environment?
this.environment.dataLocation:""}getWindowTitle(){return this.environment?this.environment.windowTitle:""}getExperimentName(){return this.environment?this.environment.experimentName:""}getExperimentDescription(){return this.environment?this.environment.experimentDescription:""}getCreationTime(){return this.environment?this.environment.creationTime:null}}b.EnvironmentStore=d;b.environmentStore=new d})(dc||(dc={}));

//# sourceURL=build://tf-backend/experimentsStore.js
(function(b){class d extends b.BaseStore{constructor(){super(...arguments);this._experiments=[]}load(){const f=b.getRouter().experiments();return this.requestManager.request(f).then(h=>{_.isEqual(this._experiments,h)||(this._experiments=h,this.emitChange())})}getExperiments(){return this._experiments.slice()}}b.ExperimentsStore=d;b.experimentsStore=new d})(dc||(dc={}));

//# sourceURL=build://tf-backend/runsStore.js
(function(b){class d extends b.BaseStore{constructor(){super(...arguments);this._runs=[]}load(){const f=b.getRouter().runs();return this.requestManager.request(f).then(h=>{_.isEqual(this._runs,h)||(this._runs=h,this.emitChange())})}getRuns(){return this._runs.slice()}}b.RunsStore=d;b.runsStore=new d})(dc||(dc={}));

//# sourceURL=build://tf-backend/backend.js
(function(b){b.TYPES=[];b.getRunsNamed=function(d){return _.keys(d).sort(ac.compareTagNames)};b.getTags=function(d){return _.union.apply(null,_.values(d)).sort(ac.compareTagNames)};b.filterTags=function(d,f){let h=[];f.forEach(k=>h=h.concat(d[k]));return _.uniq(h).sort(ac.compareTagNames)}})(dc||(dc={}));

//# sourceURL=build://tf-backend/canceller.js
(function(b){class d{constructor(){this.cancellationCount=0}cancellable(f){const h=this.cancellationCount;return k=>f({value:k,cancelled:this.cancellationCount!==h})}cancelAll(){this.cancellationCount++}}b.Canceller=d})(dc||(dc={}));

//# sourceURL=build://tf-categorization-utils/categorizationUtils.js
var kc;
(function(b){function d(m,n){const q=(()=>{try{return new RegExp(n)}catch(u){return null}})();return{name:n,metadata:{type:p.SEARCH_RESULTS,validRegex:!!q,universalRegex:".*"===n},items:q?m.filter(u=>u.match(q)):[]}}function f(m,n="/"){const q=[],u={};m.forEach(w=>{var A=w.indexOf(n);A=0<=A?w.slice(0,A):w;if(!u[A]){const y={name:A,metadata:{type:p.PREFIX_GROUP},items:[]};u[A]=y;q.push(y)}u[A].items.push(w)});return q}function h(m,n=""){n=[d(m,n)];m=f(m);return[].concat(n,m)}function k(m,n,q){const u=
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//# sourceURL=build://tf-globals/globals.js
var xc;(function(b){let d=!1;b.setUseHash=function(h){d=h};b.useHash=function(){return d};let f="";b.setFakeHash=function(h){f=h};b.getFakeHash=function(){return f}})(xc||(xc={}));

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(function(b){Polymer({is:"tf-globals",_template:null,tf_globals:b})})(xc||(xc={}));

//# sourceURL=build://tf-storage/listeners.js
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(function(b){class d{constructor(k){this.listener=k}}b.ListenKey=d;const f=new Set,h=new Set;window.addEventListener("hashchange",()=>{f.forEach(k=>k.listener())});window.addEventListener("storage",()=>{h.forEach(k=>k.listener())});b.addHashListener=function(k){k=new d(k);f.add(k);return k};b.addStorageListener=function(k){k=new d(k);h.add(k);return k};b.fireStorageChanged=function(){h.forEach(k=>k.listener())};b.removeHashListenerByKey=function(k){f.delete(k)};b.removeStorageListenerByKey=function(k){h.delete(k)}})(bd||
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//# sourceURL=build://tf-storage/storage.js
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//# sourceURL=build://tf-dashboard-common/array-update-helper.js
var cd;(function(b){b.ArrayUpdateHelper={updateArrayProp(d,f,h){let k=this.get(d);if(!Array.isArray(f))throw RangeError(`Expected new value to '${d}' to be an array.`);Array.isArray(k)||(k=[],this.set(d,k));const r=new Set(f.map((m,n)=>h(m,n)));let l=0,p=0;for(;l<k.length&&p<f.length;)r.has(h(k[l],l))?(h(k[l],l)==h(f[p],p)?this.set(`${d}.${l}`,f[p]):this.splice(d,l,0,f[p]),p++,l++):this.splice(d,l,1);l<k.length&&this.splice(d,l);p<f.length&&this.push(d,...f.slice(p))}}})(cd||(cd={}));

//# sourceURL=build://tf-paginated-view/tf-dom-repeat.html.js
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//# sourceURL=build://tf-paginated-view/paginatedViewStore.js
var sd;
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//# sourceURL=build://tf-tensorboard/registry.js
var pe;
(function(b){(function(d){d.NOT_LOADED="NOT_LOADED";d.LOADED="LOADED";d.FAILED="FAILED"})(b.ActiveDashboardsLoadState||(b.ActiveDashboardsLoadState={}));b.dashboardRegistry={};b.registerDashboard=function(){var d={plugin:"beholder",elementName:"tf-beholder-dashboard",shouldRemoveDom:!0};if(!d.plugin)throw Error("Dashboard.plugin must be present");if(!d.elementName)throw Error("Dashboard.elementName must be present");if(d.plugin in b.dashboardRegistry)throw Error(`Plugin already registered: ${d.plugin}`);d.tabName||
(d.tabName=d.plugin);b.dashboardRegistry[d.plugin]=d}})(pe||(pe={}));

//# sourceURL=build://tf-utils/utils.js
var rf;
(function(b){function d(f,h,k){return 1===f?h:k}b.aggregateTagInfo=function(f,h){let k=void 0;const r={};Object.keys(f).forEach(p=>{const m=f[p];void 0===k&&(k=m.displayName);k!==m.displayName&&(k=null);void 0===r[m.description]&&(r[m.description]=[]);r[m.description].push(p)});h=null!=k?k:h;const l=(()=>{const p=Object.keys(r);return 0===p.length?"":1===p.length?p[0]:`${"\x3cp\x3e\x3cstrong\x3eMultiple descriptions:\x3c/strong\x3e\x3c/p\x3e"}<ul>${p.map(m=>{const n=r[m].map(u=>`<code>${u.replace(/</g,"\x26lt;").replace(/>/g,
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//# sourceURL=build://paper-spinner/paper-spinner-behavior.html.js
Polymer.PaperSpinnerBehavior={properties:{active:{type:Boolean,value:!1,reflectToAttribute:!0,observer:"__activeChanged"},alt:{type:String,value:"loading",observer:"__altChanged"},__coolingDown:{type:Boolean,value:!1}},__computeContainerClasses:function(b,d){return[b||d?"active":"",d?"cooldown":""].join(" ")},__activeChanged:function(b,d){this.__setAriaHidden(!b);this.__coolingDown=!b&&d},__altChanged:function(b){"loading"===b?this.alt=this.getAttribute("aria-label")||b:(this.__setAriaHidden(""===
b),this.setAttribute("aria-label",b))},__setAriaHidden:function(b){b?this.setAttribute("aria-hidden","true"):this.removeAttribute("aria-hidden")},__reset:function(){this.__coolingDown=this.active=!1}};

//# sourceURL=build://tf-dashboard-common/data-loader-behavior.js
(function(b){let d;(function(f){f[f.LOADING=0]="LOADING";f[f.LOADED=1]="LOADED"})(d||(d={}));b.DataLoaderBehavior={properties:{active:{type:Boolean,observer:"_loadDataIfActive"},loadKey:{type:String,value:""},dataToLoad:{type:Array,value:()=>[]},getDataLoadName:{type:Function,value:()=>f=>String(f)},loadDataCallback:Function,requestData:{type:Function,value:function(){return f=>this.requestManager.request(this.getDataLoadUrl(f))}},getDataLoadUrl:Function,dataLoading:{type:Boolean,readOnly:!0,reflectToAttribute:!0,
value:!1},_dataLoadState:{type:Object,value:()=>new Map},_canceller:{type:Object,value:()=>new dc.Canceller},_loadDataAsync:{type:Number,value:null}},observers:["_dataToLoadChanged(isAttached, dataToLoad.*)"],onLoadFinish(){},reload(){this._dataLoadState.clear();this._loadData()},reset(){null!=this._loadDataAsync&&(this.cancelAsync(this._loadDataAsync),this._loadDataAsync=null);this._canceller&&this._canceller.cancelAll();this._dataLoadState&&this._dataLoadState.clear();this.isAttached&&this._loadData()},
_dataToLoadChanged(){this.isAttached&&this._loadData()},created(){this._loadData=_.throttle(this._loadDataImpl,100,{leading:!0,trailing:!0})},detached(){null!=this._loadDataAsync&&(this.cancelAsync(this._loadDataAsync),this._loadDataAsync=null)},_loadDataIfActive(){this.active&&this._loadData()},_loadDataImpl(){this.active&&(this.cancelAsync(this._loadDataAsync),this._loadDataAsync=this.async(this._canceller.cancellable(f=>{if(!f.cancelled)return this._setDataLoading(!0),f=this.dataToLoad.filter(h=>
{h=this.getDataLoadName(h);return!this._dataLoadState.has(h)}).map(h=>{const k=this.getDataLoadName(h);this._dataLoadState.set(k,d.LOADING);return this.requestData(h).then(this._canceller.cancellable(r=>{r.cancelled||(this._dataLoadState.set(k,d.LOADED),this.loadDataCallback(this,h,r.value));return k}))}),Promise.all(f).then(this._canceller.cancellable(h=>{if(!h.cancelled){const k=new Set(h.value);if(this.dataToLoad.some(r=>k.has(this.getDataLoadName(r))))this.onLoadFinish()}Array.from(this._dataLoadState.values()).some(k=>
k===d.LOADING)||this._setDataLoading(!1)}),()=>{}).then(this._canceller.cancellable(({cancelled:h})=>{h||(this._loadDataAsync=null)}))})))}}})(cd||(cd={}));

//# sourceURL=build://tf-imports/plottable.js
/*
 MIT
 MIT
 @fileoverview Implements the Signature API to help in comparing when two
 Plottable objects have "changed".

 Memoization in Plottable is complicated by mutable scales and datasets. We cannot simply
 reference compare two e.g. scales since it may have internally mutated. To resolve this,
 we write a recursive Signature interface that holds an immutable snapshot of whatever
 state the scale/data was in at the time. Then on memoized function invocation we sign the
 new inputs and compare the signatures to decide if we should recompute.

 We must hand-write a signature for each custom class we wish to support.
 MIT

 @fileoverview manually add d3-selection-multi to d3 default bundle. Most of this code is
 copied from d3-selection-multi@1.0.0.
 See https://github.com/d3/d3-selection-multi/issues/11 for why we have to do this
 MIT
 @fileoverview Implements a convenient thunk function to handle the common case
 of creating a memoized function that takes its inputs from mutable class properties.
 MIT
 @fileoverview Implements a function memoizer using the Signature API.
 Plottable 3.7.0 (https://github.com/palantir/plottable)
 Copyright 2014-2017 Palantir Technologies
 Licensed under MIT (https://github.com/palantir/plottable/blob/master/LICENSE)
 is-plain-object <https://github.com/jonschlinkert/is-plain-object>

 Copyright (c) 2014-2017, Jon Schlinkert.
 Released under the MIT License.
 isobject <https://github.com/jonschlinkert/isobject>

 Copyright (c) 2014-2017, Jon Schlinkert.
 Released under the MIT License.
*/
(function(b,d){"object"===typeof exports&&"object"===typeof module?module.exports=d(require("d3")):"function"===typeof define&&define.amd?define(["d3"],d):"object"===typeof exports?exports.Plottable=d(require("d3")):b.Plottable=d(b.d3)})(this,function(b){return function(d){function f(k){if(h[k])return h[k].exports;var r=h[k]={i:k,l:!1,exports:{}};d[k].call(r.exports,r,r.exports,f);r.l=!0;return r.exports}var h={};f.m=d;f.c=h;f.i=function(k){return k};f.d=function(k,r,l){f.o(k,r)||Object.defineProperty(k,
r,{configurable:!1,enumerable:!0,get:l})};f.n=function(k){var r=k&&k.__esModule?function(){return k["default"]}:function(){return k};f.d(r,"a",r);return r};f.o=function(k,r){return Object.prototype.hasOwnProperty.call(k,r)};f.p="";return f(f.s=140)}([function(d,f,h){function k(r){for(var l in r)f.hasOwnProperty(l)||(f[l]=r[l])}d=h(107);f.Array=d;d=h(110);f.Color=d;d=h(55);f.DOM=d;d=h(56);f.Math=d;d=h(113);f.Object=d;d=h(57);f.RTree=d;d=h(115);f.Stacking=d;d=h(35);f.Window=d;k(h(108));k(h(109));k(h(12));
k(h(111));k(h(112));k(h(58));k(h(116))},function(d){d.exports=b},function(d,f,h){function k(G,D,B){var H=D.accessor;D=D.scale;if(null==D)return[];var K=G.data();null!=B&&(K=K.filter(function(L,J){return B(L,J,G)}));K=K.map(function(L,J){return H(L,J,G)});return D.extentOfValues(K)}var r=this&&this.__extends||function(G,D){function B(){this.constructor=G}for(var H in D)D.hasOwnProperty(H)&&(G[H]=D[H]);G.prototype=null===D?Object.create(D):(B.prototype=D.prototype,new B)},l=h(1),p=h(7);d=h(4);var m=
h(18),n=h(6),q=h(9),u=h(20),w=h(0),A=h(12),y=h(10),x=h(51),C=h(52);f.Renderer=y.makeEnum(["svg","canvas"]);h=function(G){function D(){var B=G.call(this)||this;B._dataChanged=!1;B._attrExtents={};B._animate=!1;B._animators={};B._propertyExtents={};B._resetEntityStore=function(){B._cachedEntityStore=void 0};B._overflowHidden=!0;B.addClass("plot");B._datasetToDrawer=new w.Map;B._attrBindings=l.map();B._includedValuesProvider=function(K,L){return B._includedValuesForScale(K,L)};B._renderCallback=function(){return B.render()};
B._onDatasetUpdateCallback=function(){return B._onDatasetUpdate()};B._propertyBindings=l.map();var H=(new p.Easing).maxTotalDuration(D._ANIMATION_MAX_DURATION);B.animator(x.Animator.MAIN,H);B.animator(x.Animator.RESET,new p.Null);B._deferredResetEntityStore=w.Window.debounce(C.DeferredRenderer.DEFERRED_RENDERING_DELAY,B._resetEntityStore);return B}r(D,G);D.getTotalDrawTime=function(B,H){return H.reduce(function(K,L){return K+L.animator.totalTime(B.length)},0)};D.applyDrawSteps=function(B,H){return B.map(function(K){var L=
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!0),this.datasets().forEach(function(H){return B._createNodesForDataset(H)}))};D.prototype._appendCanvasNode=function(){var B=this.element().select(".plot-canvas-container");B.empty()&&(B=this.element().append("div").classed("plot-canvas-container",!0),B.node().appendChild(this._canvas.node()))};D.prototype.setBounds=function(B,H,K,L){G.prototype.setBounds.call(this,B,H,K,L);this._resetEntityStore();null!=this._canvas&&(this._bufferCanvas&&!this._bufferCanvasValid&&(this._bufferCanvas.attr("width",
this._canvas.attr("width")),this._bufferCanvas.attr("height",this._canvas.attr("height")),(K=this._bufferCanvas.node().getContext("2d"))&&K.drawImage(this._canvas.node(),0,0),this._bufferCanvasValid=!0),K=null!=window.devicePixelRatio?window.devicePixelRatio:1,this._canvas.attr("width",B*K),this._canvas.attr("height",H*K),L=this._canvas.node().getContext("2d"))&&(L.setTransform(K,0,0,K,0,0),this._bufferCanvas&&L.drawImage(this._bufferCanvas.node(),0,0,B,H))};D.prototype.destroy=function(){var B=this;
G.prototype.destroy.call(this);this._scales().forEach(function(H){return H.offUpdate(B._renderCallback)});this.datasets([])};D.prototype._createNodesForDataset=function(B){B=this._datasetToDrawer.get(B);"svg"===this.renderer()?B.useSVG(this._renderArea):B.useCanvas(this._canvas);return B};D.prototype._createDrawer=function(){return new n.ProxyDrawer(function(){return new q.SVGDrawer("path","")},function(B){return new m.CanvasDrawer(B,function(){})})};D.prototype._getAnimator=function(B){return this._animateOnNextRender()?
this._animators[B]||new p.Null:new p.Null};D.prototype._onDatasetUpdate=function(){this._updateExtents();this._dataChanged=!0;this._resetEntityStore();this.renderLowPriority()};D.prototype.attr=function(B,H,K){if(null==H)return this._attrBindings.get(B);this._bindAttr(B,H,K);this.render();return this};D.prototype._bindProperty=function(B,H,K,L){var J=this._propertyBindings.get(B);J=null!=J?J.scale:null;this._propertyBindings.set(B,{accessor:"function"===typeof H?H:function(){return H},scale:K,postScale:L});
null!=J&&this._uninstallScaleForKey(J,B);null!=K&&this._installScaleForKey(K,B);this._clearAttrToProjectorCache()};D.prototype._bindAttr=function(B,H,K){var L=this._attrBindings.get(B);L=null!=L?L.scale:null;this._attrBindings.set(B,{accessor:"function"===typeof H?H:function(){return H},scale:K});null!=L&&this._uninstallScaleForKey(L,B);null!=K&&this._installScaleForKey(K,B);this._clearAttrToProjectorCache()};D.prototype._clearAttrToProjectorCache=function(){delete this._cachedAttrToProjector};D.prototype._getAttrToProjector=
function(){null==this._cachedAttrToProjector&&(this._cachedAttrToProjector=this._generateAttrToProjector());return w.Object.assign({},this._cachedAttrToProjector)};D.prototype._generateAttrToProjector=function(){var B={};this._attrBindings.each(function(K,L){B[L]=D._scaledAccessor(K)});var H=this._propertyProjectors();Object.keys(H).forEach(function(K){null==B[K]&&(B[K]=H[K])});return B};D.prototype.renderImmediately=function(){G.prototype.renderImmediately.call(this);this._isAnchored&&(this._paint(),
this._dataChanged=!1);return this};D.prototype.renderLowPriority=function(){this._renderCallback()};D.prototype.animated=function(B){if(null==B)return this._animate;this._animate=B;return this};D.prototype.detach=function(){G.prototype.detach.call(this);this._updateExtents();return this};D.prototype._scales=function(){var B=[];this._attrBindings.each(function(H){H=H.scale;null!=H&&-1===B.indexOf(H)&&B.push(H)});this._propertyBindings.each(function(H){H=H.scale;null!=H&&-1===B.indexOf(H)&&B.push(H)});
return B};D.prototype._updateExtents=function(){var B=this;this._resetEntityStore();this._scales().forEach(function(H){return H.addIncludedValuesProvider(B._includedValuesProvider)})};D.prototype._filterForProperty=function(){return null};D.prototype.getExtentsForAttr=function(B){var H=this;null==this._attrExtents[B]&&(this._attrExtents[B]=u.memThunk(function(){return H.datasets()},function(){return H._attrBindings.get(B)},function(K,L){return null==L||null==L.accessor?null:K.map(function(J){return k(J,
L,null)})}));return this._attrExtents[B]()};D.prototype.getExtentsForProperty=function(B){var H=this;null==this._propertyExtents[B]&&(this._propertyExtents[B]=u.memThunk(function(){return H.datasets()},function(){return H._propertyBindings.get(B)},function(){return H._filterForProperty(B)},function(K,L,J){return null==L||null==L.accessor?null:K.map(function(O){return k(O,L,J)})}));return this._propertyExtents[B]()};D.prototype._includedValuesForScale=function(B,H){var K=this;if(!this._isAnchored&&
!H)return[];var L=[];this._attrBindings.each(function(J,O){J.scale===B&&(J=K.getExtentsForAttr(O),null!=J&&(L=L.concat(l.merge(J))))});this._propertyBindings.each(function(J,O){J.scale===B&&(J=K.getExtentsForProperty(O),null!=J&&(L=L.concat(l.merge(J))))});return L};D.prototype.animator=function(B,H){if(void 0===H)return this._animators[B];this._animators[B]=H;return this};D.prototype.renderer=function(){return null==this._canvas?"svg":"canvas"};D.prototype.addDataset=function(B){this._addDataset(B);
this._onDatasetUpdate()};D.prototype._addDataset=function(B){this._removeDataset(B);var H=this._createDrawer(B);this._datasetToDrawer.set(B,H);this._isSetup&&this._createNodesForDataset(B);B.onUpdate(this._onDatasetUpdateCallback);return this};D.prototype.removeDataset=function(B){this._removeDataset(B);this._onDatasetUpdate()};D.prototype._removeDataset=function(B){if(-1===this.datasets().indexOf(B))return this;this._removeDatasetNodes(B);B.offUpdate(this._onDatasetUpdateCallback);this._datasetToDrawer.delete(B);
return this};D.prototype._removeDatasetNodes=function(B){this._datasetToDrawer.get(B).remove()};D.prototype.datasets=function(B){var H=this,K=[];this._datasetToDrawer.forEach(function(L,J){return K.push(J)});if(null==B)return K;K.forEach(function(L){return H._removeDataset(L)});B.forEach(function(L){return H._addDataset(L)});this._onDatasetUpdate();return this};D.prototype._generateDrawSteps=function(){return[{attrToProjector:this._getAttrToProjector(),animator:new p.Null}]};D.prototype._additionalPaint=
function(){};D.prototype._buildLightweightPlotEntities=function(B){var H=this,K=[];B.forEach(function(L,J){var O=H._datasetToDrawer.get(L),S=0;L.data().forEach(function(N,R){var X=H._pixelPoint(N,R,L);w.Math.isNaN(X.x)||w.Math.isNaN(X.y)||(K.push({datum:N,get position(){return H._pixelPoint.call(H,N,R,L)},index:R,dataset:L,datasetIndex:J,component:H,drawer:O,validDatumIndex:S}),S++)})});return K};D.prototype._getDataToDraw=function(){var B=new w.Map;this.datasets().forEach(function(H){return B.set(H,
H.data())});return B};D.prototype._paint=function(){var B=this;delete this._cachedAttrToProjector;var H=this._generateDrawSteps(),K=this._getDataToDraw(),L=this.datasets().map(function(O){return B._datasetToDrawer.get(O)});if("canvas"===this.renderer()){var J=this._canvas.node();J.getContext("2d").clearRect(0,0,J.clientWidth,J.clientHeight);this._bufferCanvasValid=!1}this.datasets().forEach(function(O,S){var N=D.applyDrawSteps(H,O);L[S].draw(K.get(O),N)});J=this.datasets().map(function(O){return D.getTotalDrawTime(K.get(O),
H)});J=w.Math.max(J,0);this._additionalPaint(J)};D.prototype.selections=function(B){var H=this;void 0===B&&(B=this.datasets());if("canvas"===this.renderer())return l.selectAll();var K=[];B.forEach(function(L){L=H._datasetToDrawer.get(L);null!=L&&(L=L.getVisualPrimitives(),K.push.apply(K,L))});return l.selectAll(K)};D.prototype.entities=function(B){var H=this;return this._getEntityStore(B).entities().map(function(K){return H._lightweightPlotEntityToPlotEntity(K)})};D.prototype._getEntityStore=function(B){function H(J){return K._entityBounds(J)}
var K=this;if(void 0!==B){var L=new w.EntityStore;L.addAll(this._buildLightweightPlotEntities(B),H,this._localOriginBounds());return L}void 0===this._cachedEntityStore&&(L=new w.EntityStore,L.addAll(this._buildLightweightPlotEntities(this.datasets()),H,this._localOriginBounds()),this._cachedEntityStore=L);return this._cachedEntityStore};D.prototype._localOriginBounds=function(){return{topLeft:{x:0,y:0},bottomRight:{x:this.width(),y:this.height()}}};D.prototype._entityBounds=function(B){B=this._pixelPoint(B.datum,
B.index,B.dataset);return{x:B.x,y:B.y,width:0,height:0}};D.prototype._lightweightPlotEntityToPlotEntity=function(B){return{bounds:this._entityBounds(B),component:B.component,dataset:B.dataset,datasetIndex:B.datasetIndex,datum:B.datum,index:B.index,position:B.position,selection:l.select(B.drawer.getVisualPrimitives()[B.validDatumIndex])}};D.prototype.entitiesAt=function(){throw Error("plots must implement entitiesAt");};D.prototype.entityNearest=function(B){B=this._getEntityStore().entityNearest(B);
return void 0===B?void 0:this._lightweightPlotEntityToPlotEntity(B)};D.prototype.entitiesIn=function(B,H){return this.entitiesInBounds(null==H?{x:B.topLeft.x,y:B.topLeft.y,width:B.bottomRight.x-B.topLeft.x,height:B.bottomRight.y-B.topLeft.y}:{x:B.min,y:H.min,width:B.max-B.min,height:H.max-H.min})};D.prototype.entitiesInBounds=function(B){var H=this;if(B=this._getEntityStore().entitiesInBounds(B))return B.map(function(K){return H._lightweightPlotEntityToPlotEntity(K)})};D.prototype.entitiesInXBounds=
function(B){var H=this;if(B=this._getEntityStore().entitiesInXBounds(B))return B.map(function(K){return H._lightweightPlotEntityToPlotEntity(K)})};D.prototype.entitiesInYBounds=function(B){var H=this;if(B=this._getEntityStore().entitiesInYBounds(B))return B.map(function(K){return H._lightweightPlotEntityToPlotEntity(K)})};D.prototype._uninstallScaleForKey=function(B){B.offUpdate(this._renderCallback);B.offUpdate(this._deferredResetEntityStore);B.removeIncludedValuesProvider(this._includedValuesProvider)};
D.prototype._installScaleForKey=function(B){B.onUpdate(this._renderCallback);B.onUpdate(this._deferredResetEntityStore);B.addIncludedValuesProvider(this._includedValuesProvider)};D.prototype._propertyProjectors=function(){return{}};D._scaledAccessor=function(B){var H=B.scale,K=B.accessor,L=B.postScale,J=null==H?K:function(O,S,N){return H.scale(K(O,S,N))};return null==L?J:function(O,S,N){return L(J(O,S,N),O,S,N)}};D.prototype._pixelPoint=function(){return{x:0,y:0}};D.prototype._animateOnNextRender=
function(){return this._animate&&this._dataChanged};return D}(d.Component);h._ANIMATION_MAX_DURATION=600;f.Plot=h},function(d,f,h){function k(p){for(var m in p)f.hasOwnProperty(m)||(f[m]=p[m])}d=h(105);f.TickGenerators=d;k(h(54));k(h(101));k(h(102));k(h(103));k(h(104));k(h(106));var r=h(54),l=h(11);f.isTransformable=function(p){return p instanceof l.QuantitativeScale||p instanceof r.Category}},function(d,f,h){var k=h(1),r=h(30),l=h(0),p=h(12);d=h(10);f.XAlignment=d.makeEnum(["left","center","right"]);
f.YAlignment=d.makeEnum(["top","center","bottom"]);d=function(){function m(){this._overflowHidden=!1;this._origin={x:0,y:0};this._xAlignment="left";this._yAlignment="top";this._isAnchored=this._isSetup=!1;this._cssClasses=new l.Set;this._destroyed=!1;this._onAnchorCallbacks=new l.CallbackSet;this._onDetachCallbacks=new l.CallbackSet;this._cssClasses.add("component")}m.prototype.anchor=function(n){n=p.coerceExternalD3(n);if(this._destroyed)throw Error("Can't reuse destroy()-ed Components!");this.isRoot()&&
(this._rootElement=n,this._rootElement.classed("plottable",!0));null!=this._element?n.node().appendChild(this._element.node()):(this._element=n.append("div"),this._setup());this._isAnchored=!0;this._onAnchorCallbacks.callCallbacks(this);return this};m.prototype.onAnchor=function(n){this._isAnchored&&n(this);this._onAnchorCallbacks.add(n)};m.prototype.offAnchor=function(n){this._onAnchorCallbacks.delete(n)};m.prototype._setup=function(){var n=this;this._isSetup||(this._cssClasses.forEach(function(q){n._element.classed(q,
!0)}),this._cssClasses=new l.Set,this._backgroundContainer=this._element.append("svg").classed("background-container",!0),this._content=this._element.append("svg").classed("content",!0),this._foregroundContainer=this._element.append("svg").classed("foreground-container",!0),this._overflowHidden?this._content.classed("component-overflow-hidden",!0):this._content.classed("component-overflow-visible",!0),this._isSetup=!0)};m.prototype.requestedSpace=function(){return{minWidth:0,minHeight:0}};m.prototype.computeLayout=
function(n,q,u){if(null==n||null==q||null==u){if(null==this._element)throw Error("anchor() must be called before computeLayout()");if(null!=this._rootElement)n={x:0,y:0},u=this._rootElement.node(),q=l.DOM.elementWidth(u),u=l.DOM.elementHeight(u);else throw Error("null arguments cannot be passed to computeLayout() on a non-root, unanchored node");}var w=this._sizeFromOffer(q,u),A=w.height;w=w.width;this.setBounds(w,A,n.x+(q-w)*m._xAlignToProportion[this._xAlignment],n.y+(u-A)*m._yAlignToProportion[this._yAlignment]);
return this};m.prototype.setBounds=function(n,q,u,w){void 0===u&&(u=0);void 0===w&&(w=0);this._width=n;this._height=q;this._origin={x:u,y:w};null!=this._element&&this._element.styles({left:u+"px",height:q+"px",top:w+"px",width:n+"px"});null!=this._resizeHandler&&this._resizeHandler({width:n,height:q})};m.prototype._sizeFromOffer=function(n,q){var u=this.requestedSpace(n,q);return{width:this.fixedWidth()?Math.min(n,u.minWidth):n,height:this.fixedHeight()?Math.min(q,u.minHeight):q}};m.prototype.render=
function(){this._isAnchored&&this._isSetup&&0<=this.width()&&0<=this.height()&&r.registerToRender(this);return this};m.prototype.renderLowPriority=function(){this.render()};m.prototype._scheduleComputeLayout=function(){this._isAnchored&&this._isSetup&&r.registerToComputeLayoutAndRender(this)};m.prototype.onResize=function(n){this._resizeHandler=n;return this};m.prototype.renderImmediately=function(){return this};m.prototype.redraw=function(){this._isAnchored&&this._isSetup&&(this.isRoot()?this._scheduleComputeLayout():
this.parent().redraw());return this};m.prototype.invalidateCache=function(){};m.prototype.renderTo=function(n){this.detach();if(null!=n){n="string"===typeof n?k.select(n):n instanceof Element?k.select(n):p.coerceExternalD3(n);if(!n.node()||null==n.node().nodeName)throw Error("Plottable requires a valid Element to renderTo");if("svg"===n.node().nodeName)throw Error("Plottable 3.x and later can only renderTo an HTML component; pass a div instead!");this.anchor(n)}if(null==this._element)throw Error("If a Component has never been rendered before, then renderTo must be given a node to render to, or a d3.Selection, or a selector string");
r.registerToComputeLayoutAndRender(this);r.flush()};m.prototype.xAlignment=function(n){if(null==n)return this._xAlignment;n=n.toLowerCase();if(null==m._xAlignToProportion[n])throw Error("Unsupported alignment: "+n);this._xAlignment=n;this.redraw();return this};m.prototype.yAlignment=function(n){if(null==n)return this._yAlignment;n=n.toLowerCase();if(null==m._yAlignToProportion[n])throw Error("Unsupported alignment: "+n);this._yAlignment=n;this.redraw();return this};m.prototype.hasClass=function(n){return null==
n?!1:null==this._element?this._cssClasses.has(n):this._element.classed(n)};m.prototype.addClass=function(n){null!=n&&(null==this._element?this._cssClasses.add(n):this._element.classed(n,!0))};m.prototype.removeClass=function(n){null!=n&&(null==this._element?this._cssClasses.delete(n):this._element.classed(n,!1))};m.prototype.fixedWidth=function(){return!1};m.prototype.fixedHeight=function(){return!1};m.prototype.detach=function(){this.parent(null);this._isAnchored&&this._element.remove();this._isAnchored=
!1;this._onDetachCallbacks.callCallbacks(this);return this};m.prototype.onDetach=function(n){this._onDetachCallbacks.add(n)};m.prototype.offDetach=function(n){this._onDetachCallbacks.delete(n)};m.prototype.parent=function(n){if(void 0===n)return this._parent;if(null!==n&&!n.has(this))throw Error("Passed invalid parent");this._parent=n;return this};m.prototype.bounds=function(){var n=this.origin();return{topLeft:n,bottomRight:{x:n.x+this.width(),y:n.y+this.height()}}};m.prototype.destroy=function(){this._destroyed=
!0;this.detach()};m.prototype.width=function(){return this._width};m.prototype.height=function(){return this._height};m.prototype.origin=function(){return{x:this._origin.x,y:this._origin.y}};m.prototype.originToRoot=function(){for(var n=this.origin(),q=this.parent();null!=q;){var u=q.origin();n.x+=u.x;n.y+=u.y;q=q.parent()}return n};m.prototype.root=function(){for(var n=this;!n.isRoot();)n=n.parent();return n};m.prototype.isRoot=function(){return null==this.parent()};m.prototype.foreground=function(){return this._foregroundContainer};
m.prototype.content=function(){return this._content};m.prototype.element=function(){return this._element};m.prototype.rootElement=function(){return this.root()._rootElement};m.prototype.background=function(){return this._backgroundContainer};return m}();d._xAlignToProportion={left:0,center:.5,right:1};d._yAlignToProportion={top:0,center:.5,bottom:1};f.Component=d},function(d,f,h){function k(r){for(var l in r)f.hasOwnProperty(l)||(f[l]=r[l])}k(h(59));k(h(62));k(h(133));k(h(21));k(h(64));k(h(66))},
function(d,f){d=function(){function h(k,r){this._svgDrawerFactory=k;this._canvasDrawerFactory=r}h.prototype.useSVG=function(k){null!=this._currentDrawer&&this._currentDrawer.remove();var r=this._svgDrawerFactory();r.attachTo(k);this._currentDrawer=r};h.prototype.useCanvas=function(k){null!=this._currentDrawer&&this._currentDrawer.remove();this._currentDrawer=this._canvasDrawerFactory(k.node().getContext("2d"))};h.prototype.getDrawer=function(){return this._currentDrawer};h.prototype.remove=function(){null!=
this._currentDrawer&&this._currentDrawer.remove()};h.prototype.draw=function(k,r){this._currentDrawer.draw(k,r)};h.prototype.getVisualPrimitives=function(){return this._currentDrawer.getVisualPrimitives()};h.prototype.getVisualPrimitiveAtIndex=function(k){return this._currentDrawer.getVisualPrimitiveAtIndex(k)};return h}();f.ProxyDrawer=d},function(d,f,h){function k(r){for(var l in r)f.hasOwnProperty(l)||(f[l]=r[l])}k(h(70));k(h(71))},function(d,f,h){function k(p){void 0===p&&(p=3);r(p);return function(m){return m.toFixed(p)}}
function r(p){if(0>p||20<p)throw new RangeError("Formatter precision must be between 0 and 20");if(p!==Math.floor(p))throw new RangeError("Formatter precision must be an integer");}var l=h(1);f.currency=function(p,m,n){void 0===p&&(p=2);void 0===m&&(m="$");void 0===n&&(n=!0);var q=k(p);return function(u){var w=q(Math.abs(u));""!==w&&(w=n?m+w:w+m,0>u&&(w="-"+w));return w}};f.fixed=k;f.general=function(){var p;void 0===p&&(p=3);r(p);return function(m){if("number"===typeof m){var n=Math.pow(10,p);return String(Math.round(m*
n)/n)}return String(m)}};f.identity=function(){return function(p){return String(p)}};f.percentage=function(p){void 0===p&&(p=0);var m=k(p);return function(n){var q=n.toString();q=Math.pow(10,q.length-(q.indexOf(".")+1));return m(parseInt((100*n*q).toString(),10)/q)+"%"}};f.siSuffix=function(p){void 0===p&&(p=3);r(p);return function(m){return l.format("."+p+"s")(m)}};f.shortScale=function(p){void 0===p&&(p=3);r(p);var m=l.format("."+p+"e"),n=l.format("."+p+"f"),q=Math.pow(10,18),u=Math.pow(10,-p);
return function(w){var A=Math.abs(w);if((A<u||A>=q)&&0!==A)return m(w);for(var y=-1;A>=Math.pow(1E3,y+2)&&4>y;)y++;A=-1===y?n(w):n(w/Math.pow(1E3,y+1))+"KMBTQ"[y];if(0<w&&"1000"===A.substr(0,4)||0>w&&"-1000"===A.substr(0,5))4>y?(y++,A=n(w/Math.pow(1E3,y+1))+"KMBTQ"[y]):A=m(w);return A}};f.multiTime=function(){var p=[{specifier:".%L",predicate:function(m){return 0!==m.getMilliseconds()}},{specifier:":%S",predicate:function(m){return 0!==m.getSeconds()}},{specifier:"%I:%M",predicate:function(m){return 0!==
m.getMinutes()}},{specifier:"%I %p",predicate:function(m){return 0!==m.getHours()}},{specifier:"%a %d",predicate:function(m){return 0!==m.getDay()&&1!==m.getDate()}},{specifier:"%b %d",predicate:function(m){return 1!==m.getDate()}},{specifier:"%b",predicate:function(m){return 0!==m.getMonth()}}];return function(m){var n=p.filter(function(q){return q.predicate(m)});return l.timeFormat(0<n.length?n[0].specifier:"%Y")(m)}};f.time=function(p){return l.timeFormat(p)}},function(d,f,h){var k=h(1),r=h(0);
d=function(){function l(p,m){this._root=k.select(document.createElementNS("http://www.w3.org/2000/svg","g"));this._className=m;this._svgElementName=p}l.prototype.draw=function(p,m){var n=this;this._createAndDestroyDOMElements(p);var q=0;m.forEach(function(u){r.Window.setTimeout(function(){return n._drawStep(u)},q);q+=u.animator.totalTime(p.length)})};l.prototype.getVisualPrimitives=function(){null==this._cachedVisualPrimitivesNodes&&(this._cachedVisualPrimitivesNodes=this._selection.nodes());return this._cachedVisualPrimitivesNodes};
l.prototype.getVisualPrimitiveAtIndex=function(p){return this.getVisualPrimitives()[p]};l.prototype.remove=function(){this._root.remove()};l.prototype.attachTo=function(p){p.node().appendChild(this._root.node())};l.prototype.getRoot=function(){return this._root};l.prototype.selector=function(){return this._svgElementName};l.prototype._applyDefaultAttributes=function(){};l.prototype._createAndDestroyDOMElements=function(p){p=p.filter(function(m){return null!=m});p=this._root.selectAll(this.selector()).data(p);
this._selection=p.enter().append(this._svgElementName).merge(p);p.exit().remove();this._cachedVisualPrimitivesNodes=null;null!=this._className&&this._selection.classed(this._className,!0);this._applyDefaultAttributes(this._selection)};l.prototype._drawStep=function(p){var m=this;["fill","stroke"].forEach(function(n){null!=p.attrToAppliedProjector[n]&&m._selection.attr(n,p.attrToAppliedProjector[n])});p.animator.animate(this._selection,p.attrToAppliedProjector);null!=this._className&&this._selection.classed(this._className,
!0)};return l}();f.SVGDrawer=d},function(d,f){f.makeEnum=function(h){return h.reduce(function(k,r){k[r]=r;return k},{})}},function(d,f,h){var k=this&&this.__extends||function(m,n){function q(){this.constructor=m}for(var u in n)n.hasOwnProperty(u)&&(m[u]=n[u]);m.prototype=null===n?Object.create(n):(q.prototype=n.prototype,new q)},r=h(1),l=h(26),p=h(0);d=function(m){function n(){var q=m.call(this)||this;q._tickGenerator=function(u){return u.defaultTicks()};q._padProportion=.05;q._snappingDomainEnabled=
!0;q._paddingExceptionsProviders=new p.Set;return q}k(n,m);n.prototype.autoDomain=function(){this._domainMax=this._domainMin=null;m.prototype.autoDomain.call(this)};n.prototype._autoDomainIfAutomaticMode=function(){if(null!=this._domainMin&&null!=this._domainMax)this._setDomain([this._domainMin,this._domainMax]);else{var q=this._getExtent();null!=this._domainMin?(q=q[1],this._domainMin>=q&&(q=this._expandSingleValueDomain([this._domainMin,this._domainMin])[1]),this._setDomain([this._domainMin,q])):
null!=this._domainMax?(q=q[0],this._domainMax<=q&&(q=this._expandSingleValueDomain([this._domainMax,this._domainMax])[0]),this._setDomain([q,this._domainMax])):m.prototype._autoDomainIfAutomaticMode.call(this)}};n.prototype._getUnboundedExtent=function(q){void 0===q&&(q=!1);q=this._getAllIncludedValues(q);var u=this._defaultExtent();0!==q.length&&(q=[p.Math.min(q,u[0]),p.Math.max(q,u[1])],u=this._padDomain(q));return u};n.prototype._getExtent=function(){var q=this._getUnboundedExtent();null!=this._domainMin&&
(q[0]=this._domainMin);null!=this._domainMax&&(q[1]=this._domainMax);return q};n.prototype.addPaddingExceptionsProvider=function(q){this._paddingExceptionsProviders.add(q);this._autoDomainIfAutomaticMode()};n.prototype.removePaddingExceptionsProvider=function(q){this._paddingExceptionsProviders.delete(q);this._autoDomainIfAutomaticMode()};n.prototype.padProportion=function(q){if(null==q)return this._padProportion;if(0>q)throw Error("padProportion must be non-negative");this._padProportion=q;this._autoDomainIfAutomaticMode();
return this};n.prototype._padDomain=function(q){var u=this;if(q[0].valueOf()===q[1].valueOf())return this._expandSingleValueDomain(q);if(0===this._padProportion)return q;var w=this._padProportion/2,A=q[0],y=q[1],x=!1,C=!1;this._paddingExceptionsProviders.forEach(function(D){D(u).forEach(function(B){B.valueOf()===A.valueOf()&&(x=!0);B.valueOf()===y.valueOf()&&(C=!0)})});var G=this._backingScaleDomain();this._backingScaleDomain(q);q=x?A:this.invert(this.scale(A)-(this.scale(y)-this.scale(A))*w);w=C?
y:this.invert(this.scale(y)+(this.scale(y)-this.scale(A))*w);this._backingScaleDomain(G);return this._snappingDomainEnabled?this._niceDomain([q,w]):[q,w]};n.prototype.snappingDomainEnabled=function(q){null!=q&&(this._snappingDomainEnabled=q,this._autoDomainIfAutomaticMode())};n.prototype._expandSingleValueDomain=function(q){return q};n.prototype.invert=function(){throw Error("Subclasses should override invert");};n.prototype.domain=function(q){null!=q&&(this._domainMin=q[0],this._domainMax=q[1]);
return m.prototype.domain.call(this,q)};n.prototype.domainMin=function(q){if(null==q)return this.domain()[0];this._domainMin=q;this._autoDomainIfAutomaticMode();return this};n.prototype.domainMax=function(q){if(null==q)return this.domain()[1];this._domainMax=q;this._autoDomainIfAutomaticMode();return this};n.prototype.extentOfValues=function(q){q=r.extent(q.filter(function(u){return p.Math.isValidNumber(+u)}));return null==q[0]||null==q[1]?[]:q};n.prototype.zoom=function(q,u){var w=this;this.domain(this.range().map(function(A){return w.invert(l.zoomOut(A,
q,u))}))};n.prototype.pan=function(q){var u=this;this.domain(this.range().map(function(w){return u.invert(w+q)}))};n.prototype.scaleTransformation=function(){throw Error("Subclasses should override scaleTransformation");};n.prototype.invertedTransformation=function(){throw Error("Subclasses should override invertedTransformation");};n.prototype.getTransformationExtent=function(){throw Error("Subclasses should override getTransformationExtent");};n.prototype.getTransformationDomain=function(){throw Error("Subclasses should override getTransformationDomain");
};n.prototype.setTransformationDomain=function(){throw Error("Subclasses should override setTransformationDomain");};n.prototype._setDomain=function(q){function u(w){return p.Math.isNaN(w)||Infinity===w||-Infinity===w}u(q[0])||u(q[1])?p.Window.warn("Warning: QuantitativeScales cannot take NaN or Infinity as a domain value. Ignoring."):m.prototype._setDomain.call(this,q)};n.prototype.defaultTicks=function(){throw Error("Subclasses should override _getDefaultTicks");};n.prototype.ticks=function(){return this._tickGenerator(this)};
n.prototype._niceDomain=function(){throw Error("Subclasses should override _niceDomain");};n.prototype._defaultExtent=function(){throw Error("Subclasses should override _defaultExtent");};n.prototype.tickGenerator=function(){var q=Plottable.Scales.TickGenerators.integerTickGenerator();null!=q&&(this._tickGenerator=q)};return n}(h(17).Scale);d._DEFAULT_NUM_TICKS=10;f.QuantitativeScale=d},function(d,f,h){var k=h(1);f.coerceExternalD3=function(r){if(null==r.attrs){if(null==r.nodes){var l=[];r.each(function(){l.push(this)});
return k.selectAll(l)}return k.selectAll(r.nodes())}return r}},function(d,f,h){function k(r){for(var l in r)f.hasOwnProperty(l)||(f[l]=r[l])}k(h(83));k(h(84));k(h(85))},function(d,f,h){function k(r){for(var l in r)f.hasOwnProperty(l)||(f[l]=r[l])}k(h(44));k(h(45));k(h(46));k(h(18));k(h(6));k(h(33));k(h(34));k(h(47));k(h(9));k(h(48))},function(d,f){d=function(){function h(){var k=this;this._anchorCallback=function(r){return k._anchor(r)};this._enabled=!0}h.prototype.attachTo=function(k){this._disconnect();
this._componentAttachedTo=k;this._connect();return this};h.prototype.detachFrom=function(){this.detach()};h.prototype.detach=function(){this._disconnect();this._componentAttachedTo=null;return this};h.prototype.enabled=function(k){if(null==k)return this._enabled;(this._enabled=k)?this._connect():this._disconnect();return this};h.prototype._anchor=function(){this._isAnchored=!0};h.prototype._unanchor=function(){this._isAnchored=!1};h.prototype._translateToComponentSpace=function(k){var r=this._componentAttachedTo.originToRoot();
return{x:k.x-r.x,y:k.y-r.y}};h.prototype._isInsideComponent=function(k){return 0<=k.x&&0<=k.y&&k.x<=this._componentAttachedTo.width()&&k.y<=this._componentAttachedTo.height()};h.prototype._connect=function(){if(this.enabled()&&null!=this._componentAttachedTo&&!this._isAnchored)this._componentAttachedTo.onAnchor(this._anchorCallback)};h.prototype._disconnect=function(){this._isAnchored&&this._unanchor();null!=this._componentAttachedTo&&this._componentAttachedTo.offAnchor(this._anchorCallback)};return h}();
f.Interaction=d},function(d,f,h){var k=this&&this.__extends||function(n,q){function u(){this.constructor=n}for(var w in q)q.hasOwnProperty(w)&&(n[w]=q[w]);n.prototype=null===q?Object.create(q):(u.prototype=q.prototype,new u)},r=h(3),l=h(0),p=h(52),m=h(2);d=function(n){function q(){var u=n.call(this)||this;u._autoAdjustXScaleDomain=!1;u._autoAdjustYScaleDomain=!1;u._deferredRendering=!1;u._applyDeferredRenderingTransform=function(w,A,y,x){u._isAnchored&&(null!=u._renderArea&&u._renderArea.attr("transform",
"translate("+w+", "+A+") scale("+y+", "+x+")"),null!=u._canvas&&u._canvas.style("transform","translate("+w+"px, "+A+"px) scale("+y+", "+x+")"))};u.addClass("xy-plot");u._adjustYDomainOnChangeFromXCallback=function(){return u._adjustYDomainOnChangeFromX()};u._adjustXDomainOnChangeFromYCallback=function(){return u._adjustXDomainOnChangeFromY()};u._renderCallback=function(){if(u.deferredRendering()){var w=u.x()&&u.x().scale,A=u.y()&&u.y().scale;u._deferredRenderer.updateDomains(w,A)}else u.render()};
u._deferredRenderer=new p.DeferredRenderer(function(){return u.render()},u._applyDeferredRenderingTransform);return u}k(q,n);q.prototype.render=function(){this.deferredRendering()&&this._deferredRenderer.resetTransforms();return n.prototype.render.call(this)};q.prototype.deferredRendering=function(){return this._deferredRendering};q.prototype.x=function(u,w,A){if(null==u)return this._propertyBindings.get(q._X_KEY);this._bindProperty(q._X_KEY,u,w,A);u=this.width();null!=w&&null!=u&&w.range([0,u]);
this._autoAdjustYScaleDomain&&this._updateYExtentsAndAutodomain();this.render();return this};q.prototype.y=function(u,w,A){if(null==u)return this._propertyBindings.get(q._Y_KEY);this._bindProperty(q._Y_KEY,u,w,A);u=this.height();null!=w&&null!=u&&(w instanceof r.Category?w.range([0,u]):w.range([u,0]));this._autoAdjustXScaleDomain&&this._updateXExtentsAndAutodomain();this.render();return this};q.prototype._filterForProperty=function(u){return"x"===u&&this._autoAdjustXScaleDomain?this._makeFilterByProperty("y"):
"y"===u&&this._autoAdjustYScaleDomain?this._makeFilterByProperty("x"):null};q.prototype._makeFilterByProperty=function(u){u=this._propertyBindings.get(u);if(null!=u){var w=u.accessor,A=u.scale;if(null!=A)return function(y,x,C){var G=A.range();return l.Math.inRange(A.scale(w(y,x,C)),G[0],G[1])}}return null};q.prototype._uninstallScaleForKey=function(u,w){n.prototype._uninstallScaleForKey.call(this,u,w);u.offUpdate(w===q._X_KEY?this._adjustYDomainOnChangeFromXCallback:this._adjustXDomainOnChangeFromYCallback)};
q.prototype._installScaleForKey=function(u,w){n.prototype._installScaleForKey.call(this,u,w);u.onUpdate(w===q._X_KEY?this._adjustYDomainOnChangeFromXCallback:this._adjustXDomainOnChangeFromYCallback)};q.prototype.destroy=function(){n.prototype.destroy.call(this);this.x().scale&&this.x().scale.offUpdate(this._adjustYDomainOnChangeFromXCallback);this.y().scale&&this.y().scale.offUpdate(this._adjustXDomainOnChangeFromYCallback);return this};q.prototype.autorangeMode=function(u){if(null==u)return this._autoAdjustXScaleDomain?
"x":this._autoAdjustYScaleDomain?"y":"none";switch(u){case "x":this._autoAdjustXScaleDomain=!0;this._autoAdjustYScaleDomain=!1;this._adjustXDomainOnChangeFromY();break;case "y":this._autoAdjustXScaleDomain=!1;this._autoAdjustYScaleDomain=!0;this._adjustYDomainOnChangeFromX();break;case "none":this._autoAdjustYScaleDomain=this._autoAdjustXScaleDomain=!1;break;default:throw Error("Invalid scale name '"+u+"', must be 'x', 'y' or 'none'");}return this};q.prototype.computeLayout=function(u,w,A){n.prototype.computeLayout.call(this,
u,w,A);u=(u=this.x())&&u.scale;null!=u&&u.range([0,this.width()]);u=(u=this.y())&&u.scale;null!=u&&(u instanceof r.Category?u.range([0,this.height()]):u.range([this.height(),0]));return this};q.prototype._updateXExtentsAndAutodomain=function(){var u=this.x().scale;null!=u&&u.autoDomain()};q.prototype._updateYExtentsAndAutodomain=function(){var u=this.y().scale;null!=u&&u.autoDomain()};q.prototype.showAllData=function(){this._updateXExtentsAndAutodomain();this._updateYExtentsAndAutodomain();return this};
q.prototype._adjustYDomainOnChangeFromX=function(){this._projectorsReady()&&this._autoAdjustYScaleDomain&&this._updateYExtentsAndAutodomain()};q.prototype._adjustXDomainOnChangeFromY=function(){this._projectorsReady()&&this._autoAdjustXScaleDomain&&this._updateXExtentsAndAutodomain()};q.prototype._projectorsReady=function(){var u=this.x(),w=this.y();return null!=u&&null!=u.accessor&&null!=w&&null!=w.accessor};q.prototype._pixelPoint=function(u,w,A){var y=m.Plot._scaledAccessor(this.x()),x=m.Plot._scaledAccessor(this.y());
return{x:y(u,w,A),y:x(u,w,A)}};q.prototype._getDataToDraw=function(){function u(y,x,C){var G=m.Plot._scaledAccessor(w.x())(y,x,C);y=m.Plot._scaledAccessor(w.y())(y,x,C);return l.Math.isValidNumber(G)&&l.Math.isValidNumber(y)}var w=this,A=n.prototype._getDataToDraw.call(this);this.datasets().forEach(function(y){A.set(y,A.get(y).filter(function(x,C){return u(x,C,y)}))});return A};return q}(m.Plot);d._X_KEY="x";d._Y_KEY="y";f.XYPlot=d},function(d,f,h){var k=h(0);d=function(){function r(){this._autoDomainAutomatically=
!0;this._domainModificationInProgress=!1;this._updateId=0;this._callbacks=new k.CallbackSet;this._includedValuesProviders=new k.Set}r.prototype.extentOfValues=function(){return[]};r.prototype._getAllIncludedValues=function(l){var p=this;void 0===l&&(l=!1);var m=[];this._includedValuesProviders.forEach(function(n){n=n(p,l);m=m.concat(n)});return m};r.prototype._getExtent=function(){return[]};r.prototype.onUpdate=function(l){this._callbacks.add(l);return this};r.prototype.offUpdate=function(l){this._callbacks.delete(l);
return this};r.prototype._dispatchUpdate=function(){this._updateId++;this._callbacks.callCallbacks(this)};r.prototype.autoDomain=function(){this._autoDomainAutomatically=!0;this._setDomain(this._getExtent())};r.prototype._autoDomainIfAutomaticMode=function(){this._autoDomainAutomatically&&this.autoDomain()};r.prototype.scale=function(){throw Error("Subclasses should override scale");};r.prototype.ticks=function(){return this.domain()};r.prototype.domain=function(l){if(null==l)return this._getDomain();
this._autoDomainAutomatically=!1;this._setDomain(l);return this};r.prototype._getDomain=function(){throw Error("Subclasses should override _getDomain");};r.prototype._setDomain=function(l){this._domainModificationInProgress||(this._domainModificationInProgress=!0,this._backingScaleDomain(l),this._dispatchUpdate(),this._domainModificationInProgress=!1)};r.prototype._backingScaleDomain=function(){throw Error("Subclasses should override _backingDomain");};r.prototype.range=function(l){if(null==l)return this._getRange();
this._setRange(l);return this};r.prototype._getRange=function(){throw Error("Subclasses should override _getRange");};r.prototype._setRange=function(){throw Error("Subclasses should override _setRange");};r.prototype.addIncludedValuesProvider=function(l){this._includedValuesProviders.add(l);this._autoDomainIfAutomaticMode();return this};r.prototype.removeIncludedValuesProvider=function(l){this._includedValuesProviders.delete(l);this._autoDomainIfAutomaticMode()};r.prototype.updateId=function(){return this._updateId};
return r}();f.Scale=d},function(d,f,h){function k(q,u,w,A){for(var y={},x=0;x<u.length;x++){var C=u[x];q.hasOwnProperty(C)&&(y[C]=q[C](w,A))}return y}function r(q){return(null!=q["stroke-opacity"]?parseFloat(q["stroke-opacity"]):1)*(null!=q.opacity?parseFloat(q.opacity):1)}function l(q){return(null!=q["fill-opacity"]?parseFloat(q["fill-opacity"]):1)*(null!=q.opacity?parseFloat(q.opacity):1)}function p(q){return null!=q["stroke-width"]?parseFloat(q["stroke-width"]):1}function m(q,u){if(u.stroke){q.lineWidth=
p(u);var w=n.color(u.stroke);w.opacity*=r(u);q.strokeStyle=w.toString();q.stroke()}u.fill&&(w=n.color(u.fill),w.opacity*=l(u),q.fillStyle=w.toString(),q.fill())}var n=h(1);d=function(){function q(u,w){this._context=u;this._drawStep=w}q.prototype.getDrawStep=function(){return this._drawStep};q.prototype.draw=function(u,w){w=w[w.length-1].attrToAppliedProjector;this._context.save();this._drawStep(this._context,u,w);this._context.restore()};q.prototype.getVisualPrimitives=function(){return[]};q.prototype.getVisualPrimitiveAtIndex=
function(){return null};q.prototype.remove=function(){};return q}();f.CanvasDrawer=d;f.ContextStyleAttrs="fill-opacity fill opacity stroke-opacity stroke-width stroke".split(" ");f.resolveAttributesSubsetWithStyles=function(q,u,w,A){return k(q,f.ContextStyleAttrs.concat(u),w,A)};f.resolveAttributes=k;f.getStrokeWidth=p;f.renderArea=function(q,u,w,A){q.save();q.beginPath();u.context(q);u(w);q.lineJoin="round";m(q,A);q.restore()};f.renderLine=function(q,u,w,A){q.save();q.beginPath();u.context(q);u(w);
q.lineJoin="round";m(q,A);q.restore()};f.renderPathWithStyle=m},function(d,f,h){function k(r){for(var l in r)f.hasOwnProperty(l)||(f[l]=r[l])}k(h(50));k(h(27));k(h(51));k(h(93));k(h(53));k(h(94));k(h(95));k(h(96));k(h(97));k(h(98));k(h(99));k(h(100))},function(d,f,h){function k(r){for(var l in r)f.hasOwnProperty(l)||(f[l]=r[l])}k(h(92));k(h(91));d=h(49);f.sign=d.sign},function(d,f,h){function k(r){for(var l in r)f.hasOwnProperty(l)||(f[l]=r[l])}k(h(134));k(h(135));k(h(136));k(h(137))},function(d,
f,h){var k=this&&this.__extends||function(n,q){function u(){this.constructor=n}for(var w in q)q.hasOwnProperty(w)&&(n[w]=q[w]);n.prototype=null===q?Object.create(q):(u.prototype=q.prototype,new u)},r=h(1),l=h(5);d=h(4);var p=h(8),m=h(0);h=h(10);f.AxisOrientation=h.makeEnum(["bottom","left","right","top"]);h=function(n){function q(u,w){var A=n.call(this)||this;A._endTickLength=5;A._innerTickLength=5;A._tickLabelPadding=10;A._margin=15;A._showEndTickLabels=!1;A._annotationsEnabled=!1;A._annotationTierCount=
1;if(null==u||null==w)throw Error("Axis requires a scale and orientation");A._scale=u;A.orientation(w);A._setDefaultAlignment();A.addClass("axis");A.isHorizontal()?A.addClass("x-axis"):A.addClass("y-axis");A.formatter(p.identity());A._rescaleCallback=function(){return A._rescale()};A._scale.onUpdate(A._rescaleCallback);A._annotatedTicks=[];A._annotationFormatter=p.identity();return A}k(q,n);q.prototype.destroy=function(){n.prototype.destroy.call(this);this._scale.offUpdate(this._rescaleCallback)};
q.prototype.tickLabelDataOnElement=function(u){if(null!=u){for(var w;null!=u&&u.classList&&void 0===w;)u.classList.contains(q.TICK_LABEL_CLASS)?w=u:u=u.parentNode;return void 0===u?void 0:r.select(u).datum()}};q.prototype._computeWidth=function(){return this._maxLabelTickLength()};q.prototype._computeHeight=function(){return this._maxLabelTickLength()};q.prototype.requestedSpace=function(){var u=0,w=0;if(this.isHorizontal()){if(w=this._computeHeight()+this._margin,this.annotationsEnabled()){var A=
this._annotationMeasurer.measure().height+2*q._ANNOTATION_LABEL_PADDING;w+=A*this.annotationTierCount()}}else u=this._computeWidth()+this._margin,this.annotationsEnabled()&&(A=this._annotationMeasurer.measure().height+2*q._ANNOTATION_LABEL_PADDING,u+=A*this.annotationTierCount());return{minWidth:u,minHeight:w}};q.prototype.fixedHeight=function(){return this.isHorizontal()};q.prototype.fixedWidth=function(){return!this.isHorizontal()};q.prototype._rescale=function(){this.render()};q.prototype.computeLayout=
function(u,w,A){n.prototype.computeLayout.call(this,u,w,A);this.isHorizontal()?this._scale.range([0,this.width()]):this._scale.range([this.height(),0]);return this};q.prototype._sizeFromOffer=function(u,w){var A=this.requestedSpace(u,w);return this.isHorizontal()?{width:u,height:A.minHeight}:{height:w,width:A.minWidth}};q.prototype._setup=function(){n.prototype._setup.call(this);this._tickMarkContainer=this.content().append("g").classed(q.TICK_MARK_CLASS+"-container",!0);this._tickLabelContainer=
this.content().append("g").classed(q.TICK_LABEL_CLASS+"-container",!0);this._baseline=this.content().append("line").classed("baseline",!0);this._annotationContainer=this.content().append("g").classed("annotation-container",!0);this._annotationContainer.append("g").classed("annotation-line-container",!0);this._annotationContainer.append("g").classed("annotation-circle-container",!0);this._annotationContainer.append("g").classed("annotation-rect-container",!0);var u=this._annotationContainer.append("g").classed("annotation-label-container",
!0);u=new l.SvgContext(u.node());this._annotationMeasurer=new l.CacheMeasurer(u);this._annotationWriter=new l.Writer(this._annotationMeasurer,u)};q.prototype._getTickValues=function(){return[]};q.prototype.renderImmediately=function(){var u=this._getTickValues(),w=this._tickMarkContainer.selectAll("."+q.TICK_MARK_CLASS).data(u),A=w.enter().append("line").classed(q.TICK_MARK_CLASS,!0).merge(w);A.attrs(this._generateTickMarkAttrHash());r.select(A.nodes()[0]).classed(q.END_TICK_MARK_CLASS,!0).attrs(this._generateTickMarkAttrHash(!0));
r.select(A.nodes()[u.length-1]).classed(q.END_TICK_MARK_CLASS,!0).attrs(this._generateTickMarkAttrHash(!0));w.exit().remove();this._baseline.attrs(this._generateBaselineAttrHash());this.annotationsEnabled()?this._drawAnnotations():this._removeAnnotations();return this};q.prototype.annotatedTicks=function(){return this._annotatedTicks};q.prototype.annotationFormatter=function(u){if(null==u)return this._annotationFormatter;this._annotationFormatter=u;this.render();return this};q.prototype.annotationsEnabled=
function(){return this._annotationsEnabled};q.prototype.annotationTierCount=function(){return this._annotationTierCount};q.prototype._drawAnnotations=function(){function u(fa){switch(C.orientation()){case "bottom":case "right":return y(fa);case "top":case "left":return y(fa)-D.get(fa).height}}function w(fa){return L.has(fa)?"hidden":"visible"}function A(fa){return C._scale.scale(fa)}function y(fa){switch(C.orientation()){case "bottom":case "right":return K.get(fa)*H+O;case "top":case "left":return J-
O-K.get(fa)*H}}function x(fa,oa,Z){fa=fa.selectAll("."+Z).data(B);oa=fa.enter().append(oa).classed(Z,!0).merge(fa);fa.exit().remove();return oa}var C=this,G=q._ANNOTATION_LABEL_PADDING,D=new m.Map,B=this._annotatedTicksToRender();B.forEach(function(fa){var oa=C._annotationMeasurer.measure(C.annotationFormatter()(fa));D.set(fa,{width:oa.width+2*G,height:oa.height+2*G})});var H=this._annotationMeasurer.measure().height+2*G,K=this._annotationToTier(D),L=new m.Set,J=this.isHorizontal()?this.height():
this.width(),O=this._coreSize(),S=Math.min(this.annotationTierCount(),Math.floor((J-O)/H));K.forEach(function(fa,oa){(-1===fa||fa>=S)&&L.add(oa)});switch(this.orientation()){case "bottom":case "right":var N=0;break;case "top":N=this.height();break;case "left":N=this.width()}var R=this.isHorizontal();x(this._annotationContainer.select(".annotation-line-container"),"line",q.ANNOTATION_LINE_CLASS).attrs({x1:R?A:N,x2:R?A:y,y1:R?N:A,y2:R?y:A,visibility:w});x(this._annotationContainer.select(".annotation-circle-container"),
"circle",q.ANNOTATION_CIRCLE_CLASS).attrs({cx:R?A:N,cy:R?N:A,r:3});x(this._annotationContainer.select(".annotation-rect-container"),"rect",q.ANNOTATION_RECT_CLASS).attrs({x:R?A:u,y:R?u:A,width:R?function(fa){return D.get(fa).width}:function(fa){return D.get(fa).height},height:R?function(fa){return D.get(fa).height}:function(fa){return D.get(fa).width},visibility:w});var X=this._annotationWriter,aa=this.annotationFormatter();N=x(this._annotationContainer.select(".annotation-label-container"),"g",q.ANNOTATION_LABEL_CLASS);
N.selectAll(".text-container").remove();N.attrs({transform:function(fa){var oa=R?A(fa):u(fa);fa=R?u(fa):A(fa);return"translate("+oa+","+fa+")"},visibility:w}).each(function(fa){X.write(aa(fa),R?D.get(fa).width:D.get(fa).height,R?D.get(fa).height:D.get(fa).width,{xAlign:"center",yAlign:"center",textRotation:R?0:90},r.select(this).node())})};q.prototype._annotatedTicksToRender=function(){var u=this,w=this._scale.range();return m.Array.uniq(this.annotatedTicks().filter(function(A){return null==A?!1:
m.Math.inRange(u._scale.scale(A),w[0],w[1])}))};q.prototype._coreSize=function(){var u=this.isHorizontal()?this.height():this.width(),w=this.isHorizontal()?this._computeHeight():this._computeWidth();return Math.min(w,u)};q.prototype._annotationTierHeight=function(){return this._annotationMeasurer.measure().height+2*q._ANNOTATION_LABEL_PADDING};q.prototype._annotationToTier=function(u){var w=this,A=[[]],y=new m.Map,x=this.isHorizontal()?this.width():this.height();this._annotatedTicksToRender().forEach(function(C){var G=
w._scale.scale(C),D=u.get(C).width;if(0>G||G+D>x)y.set(C,-1);else{for(var B=function(K){return A[K].some(function(L){var J=w._scale.scale(L);L=u.get(L).width;return G+D>=J&&G<=J+L})},H=0;B(H);)H++,A.length===H&&A.push([]);A[H].push(C);y.set(C,H)}});return y};q.prototype._removeAnnotations=function(){this._annotationContainer.selectAll(".annotation-line").remove();this._annotationContainer.selectAll(".annotation-circle").remove();this._annotationContainer.selectAll(".annotation-rect").remove();this._annotationContainer.selectAll(".annotation-label").remove()};
q.prototype._generateBaselineAttrHash=function(){var u={x1:0,y1:0,x2:0,y2:0};switch(this._orientation){case "bottom":u.x2=this.width();break;case "top":u.x2=this.width();u.y1=this.height();u.y2=this.height();break;case "left":u.x1=this.width();u.x2=this.width();u.y2=this.height();break;case "right":u.y2=this.height()}return u};q.prototype._generateTickMarkAttrHash=function(u){function w(x){return A._scale.scale(x)}var A=this;void 0===u&&(u=!1);var y={x1:0,y1:0,x2:0,y2:0};this.isHorizontal()?(y.x1=
w,y.x2=w):(y.y1=w,y.y2=w);u=u?this._endTickLength:this._innerTickLength;switch(this._orientation){case "bottom":y.y2=u;break;case "top":y.y1=this.height();y.y2=this.height()-u;break;case "left":y.x1=this.width();y.x2=this.width()-u;break;case "right":y.x2=u}return y};q.prototype._setDefaultAlignment=function(){switch(this._orientation){case "bottom":this.yAlignment("top");break;case "top":this.yAlignment("bottom");break;case "left":this.xAlignment("right");break;case "right":this.xAlignment("left")}};
q.prototype.isHorizontal=function(){return"top"===this._orientation||"bottom"===this._orientation};q.prototype.getScale=function(){return this._scale};q.prototype.formatter=function(u){if(null==u)return this._formatter;this._formatter=u;this.redraw();return this};q.prototype.innerTickLength=function(){return this._innerTickLength};q.prototype.endTickLength=function(){return this._endTickLength};q.prototype._maxLabelTickLength=function(){return this.showEndTickLabels()?Math.max(this.innerTickLength(),
this.endTickLength()):this.innerTickLength()};q.prototype.tickLabelPadding=function(u){if(null==u)return this._tickLabelPadding;if(0>u)throw Error("tick label padding must be positive");this._tickLabelPadding=u;this.redraw();return this};q.prototype.margin=function(u){if(null==u)return this._margin;if(0>u)throw Error("margin size must be positive");this._margin=u;this.redraw();return this};q.prototype.orientation=function(u){if(null==u)return this._orientation;u=u.toLowerCase();if("top"!==u&&"bottom"!==
u&&"left"!==u&&"right"!==u)throw Error("unsupported orientation");this._orientation=u;this.redraw();return this};q.prototype.showEndTickLabels=function(){return this._showEndTickLabels};q.prototype._showAllTickMarks=function(){this._tickMarkContainer.selectAll("."+q.TICK_MARK_CLASS).each(function(){r.select(this).style("visibility","inherit")})};q.prototype._showAllTickLabels=function(){this._tickLabelContainer.selectAll("."+q.TICK_LABEL_CLASS).each(function(){r.select(this).style("visibility","inherit")})};
q.prototype._hideOverflowingTickLabels=function(){var u=this.element().node().getBoundingClientRect(),w=this._tickLabelContainer.selectAll("."+q.TICK_LABEL_CLASS);w.empty()||w.each(function(){m.DOM.clientRectInside(this.getBoundingClientRect(),u)||r.select(this).style("visibility","hidden")})};q.prototype._hideTickMarksWithoutLabel=function(){var u=this._tickMarkContainer.selectAll("."+q.TICK_MARK_CLASS),w=this._tickLabelContainer.selectAll("."+q.TICK_LABEL_CLASS).filter(function(){var A=r.select(this).style("visibility");
return"inherit"===A||"visible"===A}).data();u.each(function(A){-1===w.indexOf(A)&&r.select(this).style("visibility","hidden")})};q.prototype.invalidateCache=function(){n.prototype.invalidateCache.call(this);this._annotationMeasurer.reset()};return q}(d.Component);h.END_TICK_MARK_CLASS="end-tick-mark";h.TICK_MARK_CLASS="tick-mark";h.TICK_LABEL_CLASS="tick-label";h.ANNOTATION_LINE_CLASS="annotation-line";h.ANNOTATION_RECT_CLASS="annotation-rect";h.ANNOTATION_CIRCLE_CLASS="annotation-circle";h.ANNOTATION_LABEL_CLASS=
"annotation-label";h._ANNOTATION_LABEL_PADDING=4;f.Axis=h},function(d,f){f.SHOW_WARNINGS=!0;f.ADD_TITLE_ELEMENTS=!0},function(d,f,h){var k=h(0);d=function(){function r(){this._eventToProcessingFunction={};this._eventTarget=document;this._eventNameToCallbackSet={};this._connected=!1}r.prototype._hasNoCallbacks=function(){for(var l=Object.keys(this._eventNameToCallbackSet),p=0;p<l.length;p++)if(0!==this._eventNameToCallbackSet[l[p]].size)return!1;return!0};r.prototype._connect=function(){var l=this;
this._connected||(Object.keys(this._eventToProcessingFunction).forEach(function(p){l._eventTarget.addEventListener(p,l._eventToProcessingFunction[p])}),this._connected=!0)};r.prototype._disconnect=function(){var l=this;this._connected&&this._hasNoCallbacks()&&(Object.keys(this._eventToProcessingFunction).forEach(function(p){l._eventTarget.removeEventListener(p,l._eventToProcessingFunction[p])}),this._connected=!1)};r.prototype._addCallbackForEvent=function(l,p){null==this._eventNameToCallbackSet[l]&&
(this._eventNameToCallbackSet[l]=new k.CallbackSet);this._eventNameToCallbackSet[l].add(p);this._connect()};r.prototype._removeCallbackForEvent=function(l,p){null!=this._eventNameToCallbackSet[l]&&this._eventNameToCallbackSet[l].delete(p);this._disconnect()};r.prototype._callCallbacksForEvent=function(l){for(var p=[],m=1;m<arguments.length;m++)p[m-1]=arguments[m];m=this._eventNameToCallbackSet[l];null!=m&&m.callCallbacks.apply(m,p)};return r}();f.Dispatcher=d},function(d,f,h){function k(r){for(var l in r)f.hasOwnProperty(l)||
(f[l]=r[l])}k(h(87));k(h(88));k(h(40));k(h(89));k(h(90));d=h(26);f.zoomOut=d.zoomOut},function(d,f){function h(n,q,u){return u-(u-n)*q}function k(n,q,u){return(n*q-u)/(q-1)}function r(n,q,u,w){var A=1<q;u=A?w:u;if(null==u)return q;n=n.getTransformationDomain();return(A?Math.min:Math.max)(q,u/Math.abs(n[1]-n[0]))}function l(n,q,u,w,A){if(1>=q)return{centerPoint:u,zoomAmount:q};if(null==w&&null==A)return{centerPoint:u,zoomAmount:q};var y=p(n),x=m(n),C=x?Infinity:-Infinity;x=x?-Infinity:Infinity;w=null==
w?C:w;A=null==A?x:A;x=n.getTransformationDomain();C=x[0];x=x[1];A=n.scaleTransformation(A);x=n.scaleTransformation(x);var G=h(x,q,u);w=n.scaleTransformation(w);n=n.scaleTransformation(C);C=h(n,q,u);return Math.abs(G-C)>Math.abs(A-w)?(q=(A-w)/(x-n),1!==q?{centerPoint:k(x,q,A),zoomAmount:q}:{centerPoint:u,zoomAmount:q}):G>A!=y?{centerPoint:k(x,q,A),zoomAmount:q}:C<w!=y?{centerPoint:k(n,q,w),zoomAmount:q}:{centerPoint:u,zoomAmount:q}}function p(n){n=n.range();return n[1]<n[0]}function m(n){n=n.getTransformationDomain();
return n[1]<n[0]}f.zoomOut=h;f.constrainedZoom=function(n,q,u,w,A,y,x){q=r(n,q,w,A);return l(n,q,u,y,x)};f.constrainZoomExtents=r;f.constrainZoomValues=l;f.constrainedTranslation=function(n,q,u,w){var A=n.getTransformationDomain(),y=A[0],x=A[1];A=p(n);0<q!==A?(u=w,null!=u&&(y=n.scaleTransformation(x),n=n.scaleTransformation(u),q=(A?Math.max:Math.min)(y+q,n)-y)):null!=u&&(y=n.scaleTransformation(y),n=n.scaleTransformation(u),q=(A?Math.min:Math.max)(y+q,n)-y);return q}},function(d,f,h){function k(H,
K,L){var J=H.scale;if(J instanceof y.Category)L=J.rangeBand();else{var O=H.accessor;H=l.set(C.Array.flatten(K.map(function(S){return S.data().map(function(N,R){return O(N,R,S)}).filter(function(N){return null!=N}).map(function(N){return N.valueOf()})}))).values().map(function(S){return+S});H.sort(function(S,N){return S-N});H=H.map(function(S){return J.scale(S)});H=l.pairs(H);L=C.Math.min(H,function(S){return Math.abs(S[1]-S[0])},L*B._SINGLE_BAR_DIMENSION_RATIO);L*=B._BAR_THICKNESS_RATIO}return L}
var r=this&&this.__extends||function(H,K){function L(){this.constructor=H}for(var J in K)K.hasOwnProperty(J)&&(H[J]=K[J]);H.prototype=null===K?Object.create(K):(L.prototype=K.prototype,new L)},l=h(1),p=h(5),m=h(7),n=h(8),q=h(14),u=h(6),w=h(34),A=h(20),y=h(3),x=h(11),C=h(0);d=h(10);var G=h(19),D=h(2);h=h(16);f.BarOrientation=d.makeEnum(["vertical","horizontal"]);f.LabelsPosition=d.makeEnum(["start","middle","end","outside"]);f.BarAlignment=d.makeEnum(["start","middle","end"]);var B=function(H){function K(L){void 0===
L&&(L="vertical");var J=H.call(this)||this;J._labelFormatter=n.identity();J._labelsEnabled=!1;J._labelsPosition=f.LabelsPosition.end;J._hideBarsIfAnyAreTooWide=!0;J._barAlignment="middle";J._computeBarPixelThickness=A.memoize(k);J._fixedBarPixelThickness=!0;J.addClass("bar-plot");if("vertical"!==L&&"horizontal"!==L)throw Error(L+" is not a valid orientation for Plots.Bar");J._isVertical="vertical"===L;J.animator("baseline",new m.Null);J.attr("fill",(new y.Color).range()[0]);J.attr(K._BAR_THICKNESS_KEY,
function(){return J._barPixelThickness()});J._labelConfig=new C.Map;J._baselineValueProvider=function(){return[J.baselineValue()]};return J}r(K,H);K.prototype.computeLayout=function(L,J,O){H.prototype.computeLayout.call(this,L,J,O);this._updateExtents();return this};K.prototype.x=function(L,J){if(null==L)return H.prototype.x.call(this);null==J?H.prototype.x.call(this,L):H.prototype.x.call(this,L,J);this._updateThicknessAttr();this._updateLengthScale();return this};K.prototype.y=function(L,J){if(null==
L)return H.prototype.y.call(this);null==J?H.prototype.y.call(this,L):H.prototype.y.call(this,L,J);this._updateLengthScale();return this};K.prototype.length=function(){return this._isVertical?this.y():this.x()};K.prototype.position=function(){return this._isVertical?this.x():this.y()};K.prototype.barEnd=function(){return this._propertyBindings.get(K._BAR_END_KEY)};K.prototype.barAlignment=function(L){if(null==L)return this._barAlignment;this._barAlignment=L;this._clearAttrToProjectorCache();this.render();
return this};K.prototype.orientation=function(){return this._isVertical?"vertical":"horizontal"};K.prototype._createDrawer=function(){return new u.ProxyDrawer(function(){return new w.RectangleSVGDrawer(K._BAR_AREA_CLASS)},function(L){return new q.RectangleCanvasDrawer(L)})};K.prototype._setup=function(){H.prototype._setup.call(this);this._baseline=this._renderArea.append("line").classed("baseline",!0)};K.prototype.baselineValue=function(){if(null!=this._baselineValue)return this._baselineValue;if(!this._projectorsReady())return 0;
var L=this.length().scale;return L?L instanceof y.Time?new Date(0):0:0};K.prototype.addDataset=function(L){H.prototype.addDataset.call(this,L)};K.prototype._addDataset=function(L){H.prototype._addDataset.call(this,L);return this};K.prototype.removeDataset=function(L){H.prototype.removeDataset.call(this,L)};K.prototype._removeDataset=function(L){H.prototype._removeDataset.call(this,L);return this};K.prototype.datasets=function(L){if(null==L)return H.prototype.datasets.call(this);H.prototype.datasets.call(this,
L);return this};K.prototype.labelsEnabled=function(L,J){if(null==L)return this._labelsEnabled;this._labelsEnabled=L;null!=J&&(this._labelsPosition=J);this._clearAttrToProjectorCache();this.render();return this};K.prototype.labelFormatter=function(L){if(null==L)return this._labelFormatter;this._labelFormatter=L;this._clearAttrToProjectorCache();this.render();return this};K.prototype._createNodesForDataset=function(L){var J=H.prototype._createNodesForDataset.call(this,L),O=this._renderArea.append("g").classed(K._LABEL_AREA_CLASS,
!0),S=new p.SvgContext(O.node()),N=new p.CacheMeasurer(S);S=new p.Writer(N,S);this._labelConfig.set(L,{labelArea:O,measurer:N,writer:S});return J};K.prototype._removeDatasetNodes=function(L){H.prototype._removeDatasetNodes.call(this,L);var J=this._labelConfig.get(L);null!=J&&(J.labelArea.remove(),this._labelConfig.delete(L))};K.prototype.entityNearest=function(L){var J=this;return this._computeBarPixelThickness.doLocked(function(){function O(ba,ka,W,Ea){return J._pixelPointBar(fa(ba,ka,W),oa,Ea)}
var S=J._isVertical?L.x:L.y,N=J._isVertical?L.y:L.x,R=J.bounds(),X={min:0,max:R.bottomRight.x-R.topLeft.x},aa={min:0,max:R.bottomRight.y-R.topLeft.y},fa=D.Plot._scaledAccessor(J.length()),oa=J.length().scale.scale(J.baselineValue()),Z=Infinity,ca=Infinity,ja;J._getEntityStore().entities().forEach(function(ba){var ka=J._entityBounds(ba);if(C.DOM.intersectsBBox(X,aa,ka)){var W=0,Ea=0;if(!C.DOM.intersectsBBox(L.x,L.y,ka,.5)){Ea=O(ba.datum,ba.index,ba.dataset,ka);W=Math.abs(S-(J._isVertical?Ea.x:Ea.y));
var va=J._isVertical?ka.y:ka.x;ka=va+(J._isVertical?ka.height:ka.width);Ea=N>=va-.5&&N<=ka+.5?0:Math.abs(N-(J._isVertical?Ea.y:Ea.x))}if(W<Z||W===Z&&Ea<ca)ja=ba,Z=W,ca=Ea}});if(void 0!==ja)return J._lightweightPlotEntityToPlotEntity(ja)})};K.prototype.entitiesAt=function(L){return this._entitiesIntersecting(L.x,L.y)};K.prototype._entitiesIntersecting=function(L,J){var O=this,S=[];this._getEntityStore().entities().forEach(function(N){C.DOM.intersectsBBox(L,J,O._entityBounds(N))&&S.push(O._lightweightPlotEntityToPlotEntity(N))});
return S};K.prototype._updateLengthScale=function(){if(this._projectorsReady()){var L=this.length().scale;L instanceof x.QuantitativeScale&&(L.addPaddingExceptionsProvider(this._baselineValueProvider),L.addIncludedValuesProvider(this._baselineValueProvider))}};K.prototype.renderImmediately=function(){var L=this;this._barPixelThickness();return this._computeBarPixelThickness.doLocked(function(){return H.prototype.renderImmediately.call(L)})};K.prototype._additionalPaint=function(L){var J=this,O=this.length().scale.scale(this.baselineValue());
O={x1:this._isVertical?0:O,y1:this._isVertical?O:0,x2:this._isVertical?this.width():O,y2:this._isVertical?O:this.height()};this._getAnimator("baseline").animate(this._baseline,O);this.datasets().forEach(function(S){return J._labelConfig.get(S).labelArea.selectAll("g").remove()});this._labelsEnabled&&C.Window.setTimeout(function(){return J._drawLabels()},L)};K.prototype.getExtentsForProperty=function(L){var J=this,O=H.prototype.getExtentsForProperty.call(this,L);if("x"===L&&this._isVertical)L=this.x();
else{if("y"!==L||this._isVertical)return O;L=this.y()}if(!(L&&L.scale&&L.scale instanceof x.QuantitativeScale))return O;var S=L.scale,N=this._barPixelThickness();return O=O.map(function(R){return l.extent([S.invert(J._getPositionAttr(S.scale(R[0]),N)),S.invert(J._getPositionAttr(S.scale(R[0]),N)+N),S.invert(J._getPositionAttr(S.scale(R[1]),N)),S.invert(J._getPositionAttr(S.scale(R[1]),N)+N)])})};K.prototype._getPositionAttr=function(L,J){this._isVertical||(L-=J,J*=-1);switch(this._barAlignment){case "start":return L;
case "end":return L-J;default:return L-J/2}};K.prototype._drawLabels=function(){var L=this,J=this._getDataToDraw(),O=this._getAttrToProjector(),S=this.datasets().some(function(N){return J.get(N).some(function(R,X){return null==R?!1:L._drawLabel(R,X,N,O)})});this._hideBarsIfAnyAreTooWide&&S&&this.datasets().forEach(function(N){return L._labelConfig.get(N).labelArea.selectAll("g").remove()})};K.prototype._drawLabel=function(L,J,O,S){var N=this._labelConfig.get(O),R=N.labelArea,X=N.measurer;N=N.writer;
var aa=this.length().accessor,fa=aa(L,J,O);aa=this.length().scale;var oa=null!=aa?aa.scale(fa):fa,Z=null!=aa?aa.scale(this.baselineValue()):this.baselineValue(),ca={x:S.x(L,J,O),y:S.y(L,J,O)};aa={width:S.width(L,J,O),height:S.height(L,J,O)};fa=this._labelFormatter(fa,L,J,O);X=X.measure(fa);var ja=this._shouldShowLabelOnBar(ca,aa,X);ca=this._calculateLabelProperties(ca,aa,X,ja,this._isVertical?oa<=Z:oa<Z);oa=ca.containerDimensions;Z=ca.labelContainerOrigin;ca=ca.alignment;L=S.fill(L,J,O);R=this._createLabelContainer(R,
Z,ja,L);N.write(fa,oa.width,oa.height,{xAlign:ca.x,yAlign:ca.y},R.node());return this._isVertical?aa.width<X.width:aa.height<X.height};K.prototype._shouldShowLabelOnBar=function(L,J,O){if(this._labelsPosition===f.LabelsPosition.outside)return!1;L=this._isVertical?L.y:L.x;var S=this._isVertical?J.height:J.width;J=this._isVertical?this.height():this.width();O=this._isVertical?O.height:O.width;var N=L+S;N>J?S=J-L:0>L&&(S=N);return O+K._LABEL_MARGIN_INSIDE_BAR<=S};K.prototype._calculateLabelProperties=
function(L,J,O,S,N){function R(ka){switch(ka){case "topLeft":Z=X._isVertical?"top":"left";ja+=K._LABEL_MARGIN_INSIDE_BAR;ba+=K._LABEL_MARGIN_INSIDE_BAR;break;case "center":ba+=(fa+oa)/2;break;case "bottomRight":Z=X._isVertical?"bottom":"right",ja-=K._LABEL_MARGIN_INSIDE_BAR,ba+=ca-K._LABEL_MARGIN_INSIDE_BAR-oa}}var X=this,aa=this._isVertical?L.y:L.x,fa=this._isVertical?J.height:J.width,oa=this._isVertical?O.height:O.width,Z="center",ca=fa,ja=aa,ba=aa;if(S)switch(this._labelsPosition){case f.LabelsPosition.start:N?
R("bottomRight"):R("topLeft");break;case f.LabelsPosition.middle:R("center");break;case f.LabelsPosition.end:N?R("topLeft"):R("bottomRight")}else N?(Z=this._isVertical?"top":"left",ca=fa+K._LABEL_MARGIN_INSIDE_BAR+oa,ja-=K._LABEL_MARGIN_INSIDE_BAR+oa,ba-=K._LABEL_MARGIN_INSIDE_BAR+oa):(Z=this._isVertical?"bottom":"right",ca=fa+K._LABEL_MARGIN_INSIDE_BAR+oa,ba+=fa+K._LABEL_MARGIN_INSIDE_BAR);return{containerDimensions:{width:this._isVertical?J.width:ca,height:this._isVertical?ca:J.height},labelContainerOrigin:{x:this._isVertical?
L.x:ja,y:this._isVertical?ja:L.y},labelOrigin:{x:this._isVertical?L.x+J.width/2-O.width/2:ba,y:this._isVertical?ba:L.y+J.height/2-O.height/2},alignment:{x:this._isVertical?"center":Z,y:this._isVertical?Z:"center"}}};K.prototype._createLabelContainer=function(L,J,O,S){L=L.append("g").attr("transform","translate("+J.x+", "+J.y+")");O?(L.classed("on-bar-label",!0),O=1.6*C.Color.contrast("white",S)<C.Color.contrast("black",S),L.classed(O?"dark-label":"light-label",!0)):L.classed("off-bar-label",!0);return L};
K.prototype._generateDrawSteps=function(){var L=[];if(this._animateOnNextRender()){var J=this._getAttrToProjector(),O=this.length().scale.scale(this.baselineValue()),S=this._isVertical?"height":"width";J[this._isVertical?"y":"x"]=function(){return O};J[S]=function(){return 0};L.push({attrToProjector:J,animator:this._getAnimator(G.Animator.RESET)})}L.push({attrToProjector:this._getAttrToProjector(),animator:this._getAnimator(G.Animator.MAIN)});return L};K.prototype._generateAttrToProjector=function(){function L(ca,
ja,ba){return Math.abs(S-aa(ca,ja,ba))}var J=this,O=H.prototype._generateAttrToProjector.call(this),S=this.length().scale.scale(this.baselineValue()),N=this._isVertical?"y":"x",R=this._isVertical?"x":"y",X=D.Plot._scaledAccessor(this.position()),aa=D.Plot._scaledAccessor(this.length()),fa=O[K._BAR_THICKNESS_KEY],oa=O.gap,Z=null==oa?fa:function(ca,ja,ba){return fa(ca,ja,ba)-oa(ca,ja,ba)};O.width=this._isVertical?Z:L;O.height=this._isVertical?L:Z;O[N]=function(ca,ja,ba){ca=aa(ca,ja,ba);return ca>S?
S:ca};O[R]=function(ca,ja,ba){return J._getPositionAttr(X(ca,ja,ba),fa(ca,ja,ba))};return O};K.prototype._updateThicknessAttr=function(){var L=this,J=this.position(),O=this.barEnd();null!=J&&null!=O?(this._fixedBarPixelThickness=!1,this.attr(K._BAR_THICKNESS_KEY,function(S,N,R){var X=J.accessor(S,N,R);S=O.accessor(S,N,R);X=J.scale?J.scale.scale(X):X;S=O.scale?O.scale.scale(S):S;return Math.abs(S-X)})):(this._fixedBarPixelThickness=!0,this.attr(K._BAR_THICKNESS_KEY,function(){return L._barPixelThickness()}))};
K.prototype._barPixelThickness=function(){return this._fixedBarPixelThickness?this._projectorsReady()?this._computeBarPixelThickness(this.position(),this.datasets(),this._isVertical?this.width():this.height()):0:0};K.prototype.entities=function(L){void 0===L&&(L=this.datasets());return this._projectorsReady()?H.prototype.entities.call(this,L):[]};K.prototype._entityBounds=function(L){return this._pixelBounds(L.datum,L.index,L.dataset)};K.prototype._pixelBounds=function(L,J,O){var S=this._getAttrToProjector();
return{x:S.x(L,J,O),y:S.y(L,J,O),width:S.width(L,J,O),height:S.height(L,J,O)}};K.prototype._pixelPoint=function(L,J,O){var S=this._pixelBounds(L,J,O);L=(this._isVertical?D.Plot._scaledAccessor(this.y()):D.Plot._scaledAccessor(this.x()))(L,J,O);J=(this._isVertical?this.y().scale:this.x().scale).scale(this.baselineValue());return this._pixelPointBar(L,J,S)};K.prototype._pixelPointBar=function(L,J,O){if(this._isVertical){var S=O.x+O.width/2;L=L<=J?O.y:O.y+O.height}else S=L>=J?O.x+O.width:O.x,L=O.y+O.height/
2;return{x:S,y:L}};K.prototype._uninstallScaleForKey=function(L,J){H.prototype._uninstallScaleForKey.call(this,L,J)};K.prototype._getDataToDraw=function(){var L=this,J=new C.Map,O=this._getAttrToProjector(),S=this.width(),N=this.height();this.datasets().forEach(function(R){var X=R.data().map(function(aa,fa){return L._isDatumOnScreen(O,S,N,aa,fa,R)?aa:null});J.set(R,X)});return J};K.prototype._isDatumOnScreen=function(L,J,O,S,N,R){var X=L.x(S,N,R),aa=L.y(S,N,R),fa=L.width(S,N,R);L=L.height(S,N,R);
return C.Math.isValidNumber(X)&&C.Math.isValidNumber(aa)&&C.Math.isValidNumber(fa)&&C.Math.isValidNumber(L)?C.Math.boundsIntersects(X,aa,fa,L,J,O):!1};return K}(h.XYPlot);B._BAR_THICKNESS_RATIO=.95;B._SINGLE_BAR_DIMENSION_RATIO=.4;B._BAR_AREA_CLASS="bar-area";B._BAR_END_KEY="barEnd";B._BAR_THICKNESS_KEY="width";B._LABEL_AREA_CLASS="bar-label-text-area";B._LABEL_MARGIN_INSIDE_BAR=10;f.Bar=B},function(d,f,h){var k=this&&this.__extends||function(w,A){function y(){this.constructor=w}for(var x in A)A.hasOwnProperty(x)&&
(w[x]=A[x]);w.prototype=null===A?Object.create(A):(y.prototype=A.prototype,new y)},r=h(1),l=h(5),p=h(8),m=h(3),n=h(0);d=h(10);var q=h(22);f.TimeInterval=d.makeEnum("second minute hour day week month year".split(" "));f.TimeAxisOrientation=d.makeEnum(["top","bottom"]);f.TierLabelPosition=d.makeEnum(["between","center"]);h=function(w){function A(y,x){y=w.call(this,y,x)||this;y._maxTimeIntervalPrecision=null;y._tierLabelPositions=[];y.addClass("time-axis");y.tickLabelPadding(5);y.axisConfigurations(A._DEFAULT_TIME_AXIS_CONFIGURATIONS);
y.annotationFormatter(p.time("%a %b %d, %Y"));return y}k(A,w);A.prototype.tierLabelPositions=function(y){if(null==y)return this._tierLabelPositions;if(!y.every(function(x){return"between"===x.toLowerCase()||"center"===x.toLowerCase()}))throw Error("Unsupported position for tier labels");this._tierLabelPositions=y;this.redraw();return this};A.prototype.maxTimeIntervalPrecision=function(y){if(null==y)return this._maxTimeIntervalPrecision;this._maxTimeIntervalPrecision=y;this.redraw();return this};A.prototype.currentAxisConfiguration=
function(){return this._possibleTimeAxisConfigurations[this._mostPreciseConfigIndex]};A.prototype.axisConfigurations=function(y){if(null!=y){this._possibleTimeAxisConfigurations=y;this._numTiers=n.Math.max(this._possibleTimeAxisConfigurations.map(function(G){return G.length}),0);this._isAnchored&&this._setupDomElements();y=this.tierLabelPositions();for(var x=[],C=0;C<this._numTiers;C++)x.push(y[C]||"between");this.tierLabelPositions(x);this.redraw()}};A.prototype._getMostPreciseConfigurationIndex=
function(){var y=this,x=this._possibleTimeAxisConfigurations.length;this._possibleTimeAxisConfigurations.forEach(function(C,G){G<x&&C.every(function(D){return y._checkTimeAxisTierConfiguration(D)})&&(x=G)});x===this._possibleTimeAxisConfigurations.length&&(n.Window.warn("zoomed out too far: could not find suitable interval to display labels"),--x);return x};A.prototype.orientation=function(y){if(y&&("right"===y.toLowerCase()||"left"===y.toLowerCase()))throw Error(y+" is not a supported orientation for TimeAxis - only horizontal orientations are supported");
return w.prototype.orientation.call(this,y)};A.prototype._computeHeight=function(){var y=this._measurer.measure().height;this._tierHeights=[];for(var x=0;x<this._numTiers;x++)this._tierHeights.push(y+this.tickLabelPadding()+("between"===this._tierLabelPositions[x]?0:this._maxLabelTickLength()));return r.sum(this._tierHeights)};A.prototype._getIntervalLength=function(y){var x=this._scale.domain()[0];y=m.Time.timeIntervalToD3Time(y.interval).offset(x,y.step);return y>this._scale.domain()[1]?this.width():
Math.abs(this._scale.scale(y)-this._scale.scale(x))};A.prototype._maxWidthForInterval=function(y){return this._measurer.measure(y.formatter(A._LONG_DATE)).width};A.prototype._checkTimeAxisTierConfiguration=function(y){if(null!=this._maxTimeIntervalPrecision){var x=A._SORTED_TIME_INTERVAL_INDEX[this._maxTimeIntervalPrecision],C=A._SORTED_TIME_INTERVAL_INDEX[y.interval];if(null!=x&&null!=C&&C<x)return!1}x=this._maxWidthForInterval(y)+2*this.tickLabelPadding();return Math.min(this._getIntervalLength(y),
this.width())>=x};A.prototype._sizeFromOffer=function(y,x){var C=w.prototype._sizeFromOffer.call(this,y,x);y=this._tierHeights.reduce(function(G,D){return G+D>C.height?G:G+D});x=this.margin()+(this.annotationsEnabled()?this.annotationTierCount()*this._annotationTierHeight():0);C.height=Math.min(C.height,y+x);return C};A.prototype._setup=function(){w.prototype._setup.call(this);this._setupDomElements()};A.prototype._setupDomElements=function(){this.content().selectAll("."+A.TIME_AXIS_TIER_CLASS).remove();
this._tierLabelContainers=[];this._tierMarkContainers=[];this._tierBaselines=[];this._tickLabelContainer.remove();this._baseline.remove();for(var y=0;y<this._numTiers;++y){var x=this.content().append("g").classed(A.TIME_AXIS_TIER_CLASS,!0);this._tierLabelContainers.push(x.append("g").classed(q.Axis.TICK_LABEL_CLASS+"-container",!0));this._tierMarkContainers.push(x.append("g").classed(q.Axis.TICK_MARK_CLASS+"-container",!0));this._tierBaselines.push(x.append("line").classed("baseline",!0))}y=new l.SvgContext(this._tierLabelContainers[0].node());
this._measurer=new l.CacheMeasurer(y)};A.prototype._getTickIntervalValues=function(y){return this._scale.tickInterval(y.interval,y.step)};A.prototype._getTickValues=function(){var y=this;return this._possibleTimeAxisConfigurations[this._mostPreciseConfigIndex].reduce(function(x,C){return x.concat(y._getTickIntervalValues(C))},[])};A.prototype._cleanTiers=function(){for(var y=0;y<this._tierLabelContainers.length;y++)this._tierLabelContainers[y].selectAll("."+q.Axis.TICK_LABEL_CLASS).remove(),this._tierMarkContainers[y].selectAll("."+
q.Axis.TICK_MARK_CLASS).remove(),this._tierBaselines[y].style("visibility","hidden")};A.prototype._getTickValuesForConfiguration=function(y){y=this._scale.tickInterval(y.interval,y.step);var x=this._scale.domain(),C=y.map(function(G){return G.valueOf()});-1===C.indexOf(x[0].valueOf())&&y.unshift(x[0]);-1===C.indexOf(x[1].valueOf())&&y.push(x[1]);return y};A.prototype._renderTierLabels=function(y,x,C){var G=this,D=this._getTickValuesForConfiguration(x),B=[];"between"===this._tierLabelPositions[C]&&
1===x.step?D.map(function(O,S){S+1>=D.length||B.push(new Date((D[S+1].valueOf()-D[S].valueOf())/2+D[S].valueOf()))}):B=D;y=y.selectAll("."+q.Axis.TICK_LABEL_CLASS).data(B,function(O){return String(O.valueOf())});var H=y.enter().append("g").classed(q.Axis.TICK_LABEL_CLASS,!0);H.append("text");var K="center"===this._tierLabelPositions[C]||1===x.step?0:this.tickLabelPadding();var L="bottom"===this.orientation()?r.sum(this._tierHeights.slice(0,C+1))-this.tickLabelPadding():"center"===this._tierLabelPositions[C]?
this.height()-r.sum(this._tierHeights.slice(0,C))-this.tickLabelPadding()-this._maxLabelTickLength():this.height()-r.sum(this._tierHeights.slice(0,C))-this.tickLabelPadding();H=y.merge(H);var J=H.selectAll("text");0<J.size()&&J.attr("transform","translate("+K+","+L+")");y.exit().remove();H.attr("transform",function(O){return"translate("+G._scale.scale(O)+",0)"});C="center"===this._tierLabelPositions[C]||1===x.step?"middle":"start";H.selectAll("text").text(x.formatter).style("text-anchor",C)};A.prototype._renderTickMarks=
function(y,x){y=this._tierMarkContainers[x].selectAll("."+q.Axis.TICK_MARK_CLASS).data(y);var C=y.enter().append("line").classed(q.Axis.TICK_MARK_CLASS,!0).merge(y),G=this._generateTickMarkAttrHash(),D=this._tierHeights.slice(0,x).reduce(function(B,H){return B+H},0);"bottom"===this.orientation()?(G.y1=D,G.y2=D+("center"===this._tierLabelPositions[x]?this.innerTickLength():this._tierHeights[x])):(G.y1=this.height()-D,G.y2=this.height()-(D+("center"===this._tierLabelPositions[x]?this.innerTickLength():
this._tierHeights[x])));C.attrs(G);"bottom"===this.orientation()?(G.y1=D,G.y2=D+("center"===this._tierLabelPositions[x]?this.endTickLength():this._tierHeights[x])):(G.y1=this.height()-D,G.y2=this.height()-(D+("center"===this._tierLabelPositions[x]?this.endTickLength():this._tierHeights[x])));r.select(C.nodes()[0]).attrs(G);r.select(C.nodes()[C.size()-1]).attrs(G);r.select(C.nodes()[0]).classed(q.Axis.END_TICK_MARK_CLASS,!0);r.select(C.nodes()[C.size()-1]).classed(q.Axis.END_TICK_MARK_CLASS,!0);y.exit().remove()};
A.prototype._renderLabellessTickMarks=function(y){y=this._tickMarkContainer.selectAll("."+q.Axis.TICK_MARK_CLASS).data(y);var x=y.enter().append("line").classed(q.Axis.TICK_MARK_CLASS,!0).merge(y),C=this._generateTickMarkAttrHash();C.y2="bottom"===this.orientation()?this.tickLabelPadding():this.height()-this.tickLabelPadding();x.attrs(C);y.exit().remove()};A.prototype._generateLabellessTicks=function(){return 1>this._mostPreciseConfigIndex?[]:this._getTickIntervalValues(this._possibleTimeAxisConfigurations[this._mostPreciseConfigIndex-
1][0])};A.prototype.renderImmediately=function(){var y=this;this._mostPreciseConfigIndex=this._getMostPreciseConfigurationIndex();var x=this._possibleTimeAxisConfigurations[this._mostPreciseConfigIndex];this._cleanTiers();x.forEach(function(H,K){return y._renderTierLabels(y._tierLabelContainers[K],H,K)});for(var C=x.map(function(H){return y._getTickValuesForConfiguration(H)}),G=0,D=0;D<Math.max(x.length,1);++D){var B=this._generateBaselineAttrHash();B.y1+="bottom"===this.orientation()?G:-G;B.y2=B.y1;
this._tierBaselines[D].attrs(B).style("visibility","inherit");G+=this._tierHeights[D]}G=[];D=this._scale.domain();D=this._scale.scale(D[1])-this._scale.scale(D[0]);1.5*this._getIntervalLength(x[0])>=D&&(G=this._generateLabellessTicks());this._renderLabellessTickMarks(G);this._hideOverflowingTiers();for(D=0;D<x.length;++D)this._renderTickMarks(C[D],D),this._hideOverlappingAndCutOffLabels(D);this.annotationsEnabled()?this._drawAnnotations():this._removeAnnotations();return this};A.prototype._hideOverflowingTiers=
function(){var y=this,x=this.height(),C=0;this.content().selectAll("."+A.TIME_AXIS_TIER_CLASS).attr("visibility",function(G,D){C+=y._tierHeights[D];return C<=x?"inherit":"hidden"})};A.prototype._hideOverlappingAndCutOffLabels=function(y){function x(H){return Math.floor(G.left)<=Math.ceil(H.left)&&Math.floor(G.top)<=Math.ceil(H.top)&&Math.floor(H.right)<=Math.ceil(G.left+C.width())&&Math.floor(H.bottom)<=Math.ceil(G.top+C.height())}var C=this,G=this.element().node().getBoundingClientRect(),D=this._tierMarkContainers[y].selectAll("."+
q.Axis.TICK_MARK_CLASS).filter(function(){var H=r.select(this).style("visibility");return"visible"===H||"inherit"===H}).nodes().map(function(H){return H.getBoundingClientRect()}),B;this._tierLabelContainers[y].selectAll("."+q.Axis.TICK_LABEL_CLASS).filter(function(){var H=r.select(this).style("visibility");return"visible"===H||"inherit"===H}).each(function(H,K){H=this.getBoundingClientRect();var L=r.select(this),J=D[K],O=D[K+1];K=null!=B&&n.DOM.clientRectsOverlap(H,B);J=null!=J&&n.DOM.clientRectsOverlap(H,
J);O=null!=O&&n.DOM.clientRectsOverlap(H,O);!x(H)||K||J||O?L.style("visibility","hidden"):(B=H,L.style("visibility","inherit"))})};A.prototype.invalidateCache=function(){w.prototype.invalidateCache.call(this);this._measurer.reset()};return A}(q.Axis);h.TIME_AXIS_TIER_CLASS="time-axis-tier";h._SORTED_TIME_INTERVAL_INDEX=(u={},u[f.TimeInterval.second]=0,u[f.TimeInterval.minute]=1,u[f.TimeInterval.hour]=2,u[f.TimeInterval.day]=3,u[f.TimeInterval.week]=4,u[f.TimeInterval.month]=5,u[f.TimeInterval.year]=
6,u);h._DEFAULT_TIME_AXIS_CONFIGURATIONS=[[{interval:f.TimeInterval.second,step:1,formatter:p.time("%I:%M:%S %p")},{interval:f.TimeInterval.day,step:1,formatter:p.time("%B %e, %Y")}],[{interval:f.TimeInterval.second,step:5,formatter:p.time("%I:%M:%S %p")},{interval:f.TimeInterval.day,step:1,formatter:p.time("%B %e, %Y")}],[{interval:f.TimeInterval.second,step:10,formatter:p.time("%I:%M:%S %p")},{interval:f.TimeInterval.day,step:1,formatter:p.time("%B %e, %Y")}],[{interval:f.TimeInterval.second,step:15,
formatter:p.time("%I:%M:%S %p")},{interval:f.TimeInterval.day,step:1,formatter:p.time("%B %e, %Y")}],[{interval:f.TimeInterval.second,step:30,formatter:p.time("%I:%M:%S %p")},{interval:f.TimeInterval.day,step:1,formatter:p.time("%B %e, %Y")}],[{interval:f.TimeInterval.minute,step:1,formatter:p.time("%I:%M %p")},{interval:f.TimeInterval.day,step:1,formatter:p.time("%B %e, %Y")}],[{interval:f.TimeInterval.minute,step:5,formatter:p.time("%I:%M %p")},{interval:f.TimeInterval.day,step:1,formatter:p.time("%B %e, %Y")}],
[{interval:f.TimeInterval.minute,step:10,formatter:p.time("%I:%M %p")},{interval:f.TimeInterval.day,step:1,formatter:p.time("%B %e, %Y")}],[{interval:f.TimeInterval.minute,step:15,formatter:p.time("%I:%M %p")},{interval:f.TimeInterval.day,step:1,formatter:p.time("%B %e, %Y")}],[{interval:f.TimeInterval.minute,step:30,formatter:p.time("%I:%M %p")},{interval:f.TimeInterval.day,step:1,formatter:p.time("%B %e, %Y")}],[{interval:f.TimeInterval.hour,step:1,formatter:p.time("%I %p")},{interval:f.TimeInterval.day,
step:1,formatter:p.time("%B %e, %Y")}],[{interval:f.TimeInterval.hour,step:3,formatter:p.time("%I %p")},{interval:f.TimeInterval.day,step:1,formatter:p.time("%B %e, %Y")}],[{interval:f.TimeInterval.hour,step:6,formatter:p.time("%I %p")},{interval:f.TimeInterval.day,step:1,formatter:p.time("%B %e, %Y")}],[{interval:f.TimeInterval.hour,step:12,formatter:p.time("%I %p")},{interval:f.TimeInterval.day,step:1,formatter:p.time("%B %e, %Y")}],[{interval:f.TimeInterval.day,step:1,formatter:p.time("%a %e")},
{interval:f.TimeInterval.month,step:1,formatter:p.time("%B %Y")}],[{interval:f.TimeInterval.day,step:1,formatter:p.time("%e")},{interval:f.TimeInterval.month,step:1,formatter:p.time("%B %Y")}],[{interval:f.TimeInterval.month,step:1,formatter:p.time("%B")},{interval:f.TimeInterval.year,step:1,formatter:p.time("%Y")}],[{interval:f.TimeInterval.month,step:1,formatter:p.time("%b")},{interval:f.TimeInterval.year,step:1,formatter:p.time("%Y")}],[{interval:f.TimeInterval.month,step:3,formatter:p.time("%b")},
{interval:f.TimeInterval.year,step:1,formatter:p.time("%Y")}],[{interval:f.TimeInterval.month,step:6,formatter:p.time("%b")},{interval:f.TimeInterval.year,step:1,formatter:p.time("%Y")}],[{interval:f.TimeInterval.year,step:1,formatter:p.time("%Y")}],[{interval:f.TimeInterval.year,step:1,formatter:p.time("%y")}],[{interval:f.TimeInterval.year,step:5,formatter:p.time("%Y")}],[{interval:f.TimeInterval.year,step:25,formatter:p.time("%Y")}],[{interval:f.TimeInterval.year,step:50,formatter:p.time("%Y")}],
[{interval:f.TimeInterval.year,step:100,formatter:p.time("%Y")}],[{interval:f.TimeInterval.year,step:200,formatter:p.time("%Y")}],[{interval:f.TimeInterval.year,step:500,formatter:p.time("%Y")}],[{interval:f.TimeInterval.year,step:1E3,formatter:p.time("%Y")}]];h._LONG_DATE=new Date(9999,8,29,12,59,9999);f.Time=h;var u},function(d,f,h){var k=this&&this.__extends||function(l,p){function m(){this.constructor=l}for(var n in p)p.hasOwnProperty(n)&&(l[n]=p[n]);l.prototype=null===p?Object.create(p):(m.prototype=
p.prototype,new m)},r=h(12);d=function(l){function p(){var m=l.call(this)||this;m._detachCallback=function(n){return m.remove(n)};return m}k(p,l);p.prototype.anchor=function(m){var n=this;m=r.coerceExternalD3(m);l.prototype.anchor.call(this,m);this._forEach(function(q){return q.anchor(n.element())});return this};p.prototype.render=function(){this._forEach(function(m){return m.render()});return this};p.prototype.has=function(){throw Error("has() is not implemented on ComponentContainer");};p.prototype._adoptAndAnchor=
function(m){m.parent(this);m.onDetach(this._detachCallback);this._isAnchored&&m.anchor(this.element())};p.prototype.remove=function(m){this.has(m)&&(m.offDetach(this._detachCallback),this._remove(m),m.detach(),this.redraw());return this};p.prototype._remove=function(){};p.prototype._forEach=function(){throw Error("_forEach() is not implemented on ComponentContainer");};p.prototype.destroy=function(){l.prototype.destroy.call(this);this._forEach(function(m){return m.destroy()})};p.prototype.invalidateCache=
function(){this._forEach(function(m){return m.invalidateCache()})};return p}(h(4).Component);f.ComponentContainer=d},function(d,f,h){function k(A){n.add(A);m.add(A);r()}function r(){q||(q=!0,w.render())}var l=h(0);d=h(10);var p=h(39),m=new l.Set,n=new l.Set,q=!1,u=!1;f.Policy=d.makeEnum(["immediate","animationFrame","timeout"]);var w=new p.AnimationFrame;f.renderPolicy=function(A){if(null==A)return w;switch(A){case f.Policy.immediate:w=new p.Immediate;break;case f.Policy.animationFrame:w=new p.AnimationFrame;
break;case f.Policy.timeout:w=new p.Timeout;break;default:l.Window.warn("Unrecognized renderPolicy: "+A)}};f.registerToRender=function(A){u&&l.Window.warn("Registered to render while other components are flushing: request may be ignored");m.add(A);r()};f.registerToComputeLayoutAndRender=k;f.registerToComputeLayout=function(A){k(A)};f.flush=function(){if(q){n.forEach(function(y){return y.computeLayout()});m.forEach(function(y){return y.render()});u=!0;var A=new l.Set;m.forEach(function(y){try{y.renderImmediately()}catch(x){window.setTimeout(function(){throw x;
},0),A.add(y)}});n=new l.Set;m=A;u=q=!1}}},function(d,f,h){var k=h(1);f.circle=function(){return function(l){return k.symbol().type(k.symbolCircle).size(Math.PI*Math.pow(l/2,2))}};f.square=function(){return function(l){return k.symbol().type(k.symbolSquare).size(Math.pow(l,2))}};f.cross=function(){return function(l){return k.symbol().type(k.symbolCross).size(5/9*Math.pow(l,2))}};f.diamond=function(){return function(l){return k.symbol().type(k.symbolDiamond).size(Math.tan(Math.PI/6)*Math.pow(l,2)/
2)}};f.triangle=function(){return function(l){return k.symbol().type(k.symbolTriangle).size(Math.sqrt(3)*Math.pow(l/2,2))}};f.star=function(){return function(l){return k.symbol().type(k.symbolStar).size(.8908130915292852*Math.pow(l/2,2))}};var r=3*(1/Math.sqrt(12)/2+1);f.wye=function(){return function(l){return k.symbol().type(k.symbolWye).size(r*Math.pow(l/2.4,2))}}},function(d,f,h){var k=this&&this.__extends||function(n,q){function u(){this.constructor=n}for(var w in q)q.hasOwnProperty(w)&&(n[w]=
q[w]);n.prototype=null===q?Object.create(q):(u.prototype=q.prototype,new u)},r=h(25),l=h(0),p=h(12),m=h(37);d=function(n){function q(){var u=n.call(this)||this;u._detectionRadius=3;u._resizable=!1;u._movable=!1;u._hasCorners=!0;u.addClass("drag-box-layer");u._dragInteraction=new r.Drag;u._setUpCallbacks();u._dragInteraction.attachTo(u);u._dragStartCallbacks=new l.CallbackSet;u._dragCallbacks=new l.CallbackSet;u._dragEndCallbacks=new l.CallbackSet;return u}k(q,n);q.prototype._setUpCallbacks=function(){function u(H,
K){0===B&&H.x===K.x&&H.y===K.y&&y.boxVisible(!1);y._dragEndCallbacks.callCallbacks(y.bounds())}function w(H,K){switch(B){case 0:G.x=K.x;G.y=K.y;break;case 1:x.bottom?G.y=K.y:x.top&&(C.y=K.y);x.right?G.x=K.x:x.left&&(C.x=K.x);break;case 2:H=K.x-D.x;var L=K.y-D.y;C.x+=H;C.y+=L;G.x+=H;G.y+=L;D=K}y._setBounds({topLeft:C,bottomRight:G});y._xBoundsMode===m.PropertyMode.VALUE&&null!=y.xScale()&&y._setXExtent([y.xScale().invert(C.x),y.xScale().invert(G.x)]);y._yBoundsMode===m.PropertyMode.VALUE&&null!=y.yScale()&&
y._setYExtent([y.yScale().invert(C.y),y.yScale().invert(G.y)]);y.render();y._dragCallbacks.callCallbacks(y.bounds())}function A(H){x=y._getResizingEdges(H);var K=y.bounds();K=K.topLeft.x<=H.x&&H.x<=K.bottomRight.x&&K.topLeft.y<=H.y&&H.y<=K.bottomRight.y;y.boxVisible()&&(x.top||x.bottom||x.left||x.right)?B=1:y.boxVisible()&&y.movable()&&K?B=2:(B=0,y._setBounds({topLeft:H,bottomRight:H}),y._xBoundsMode===m.PropertyMode.VALUE&&null!=y.xScale()&&y._setXExtent([y.xScale().invert(H.x),y.xScale().invert(H.x)]),
y._yBoundsMode===m.PropertyMode.VALUE&&null!=y.yScale()&&y._setYExtent([y.yScale().invert(H.y),y.yScale().invert(H.y)]),y.render());y.boxVisible(!0);K=y.bounds();C={x:K.topLeft.x,y:K.topLeft.y};G={x:K.bottomRight.x,y:K.bottomRight.y};D=H;y._dragStartCallbacks.callCallbacks(K)}var y=this,x,C,G,D,B=0;this._dragInteraction.onDragStart(A);this._dragInteraction.onDrag(w);this._dragInteraction.onDragEnd(u);this._disconnectInteraction=function(){y._dragInteraction.offDragStart(A);y._dragInteraction.offDrag(w);
y._dragInteraction.offDragEnd(u);y._dragInteraction.detach()}};q.prototype._setup=function(){function u(){return w._box.append("line").styles({opacity:0,stroke:"pink","pointer-events":"visibleStroke"})}var w=this;n.prototype._setup.call(this);this._detectionEdgeT=u().classed("drag-edge-tb",!0);this._detectionEdgeB=u().classed("drag-edge-tb",!0);this._detectionEdgeL=u().classed("drag-edge-lr",!0);this._detectionEdgeR=u().classed("drag-edge-lr",!0);if(this._hasCorners){var A=function(){return w._box.append("circle").styles({opacity:0,
fill:"pink","pointer-events":"visibleFill"})};this._detectionCornerTL=A().classed("drag-corner-tl",!0);this._detectionCornerTR=A().classed("drag-corner-tr",!0);this._detectionCornerBL=A().classed("drag-corner-bl",!0);this._detectionCornerBR=A().classed("drag-corner-br",!0)}};q.prototype._getResizingEdges=function(u){var w={top:!1,bottom:!1,left:!1,right:!1};if(!this.resizable())return w;var A=this.bounds(),y=A.topLeft.y,x=A.bottomRight.y,C=A.topLeft.x;A=A.bottomRight.x;var G=this._detectionRadius;
C-G<=u.x&&u.x<=A+G&&(w.top=y-G<=u.y&&u.y<=y+G,w.bottom=x-G<=u.y&&u.y<=x+G);y-G<=u.y&&u.y<=x+G&&(w.left=C-G<=u.x&&u.x<=C+G,w.right=A-G<=u.x&&u.x<=A+G);return w};q.prototype.renderImmediately=function(){n.prototype.renderImmediately.call(this);if(this.boxVisible()){var u=this.bounds(),w=u.topLeft.y,A=u.bottomRight.y,y=u.topLeft.x;u=u.bottomRight.x;this._detectionEdgeT.attrs({x1:y,y1:w,x2:u,y2:w,"stroke-width":2*this._detectionRadius});this._detectionEdgeB.attrs({x1:y,y1:A,x2:u,y2:A,"stroke-width":2*
this._detectionRadius});this._detectionEdgeL.attrs({x1:y,y1:w,x2:y,y2:A,"stroke-width":2*this._detectionRadius});this._detectionEdgeR.attrs({x1:u,y1:w,x2:u,y2:A,"stroke-width":2*this._detectionRadius});this._hasCorners&&(this._detectionCornerTL.attrs({cx:y,cy:w,r:this._detectionRadius}),this._detectionCornerTR.attrs({cx:u,cy:w,r:this._detectionRadius}),this._detectionCornerBL.attrs({cx:y,cy:A,r:this._detectionRadius}),this._detectionCornerBR.attrs({cx:u,cy:A,r:this._detectionRadius}))}return this};
q.prototype.detectionRadius=function(){return this._detectionRadius};q.prototype.resizable=function(u){if(null==u)return this._resizable;this._resizable=u;this._setResizableClasses(u);return this};q.prototype._setResizableClasses=function(u){u&&this.enabled()?(this.addClass("x-resizable"),this.addClass("y-resizable")):(this.removeClass("x-resizable"),this.removeClass("y-resizable"))};q.prototype.movable=function(){return this._movable};q.prototype._setMovableClass=function(){this.movable()&&this.enabled()?
this.addClass("movable"):this.removeClass("movable")};q.prototype.onDragStart=function(u){this._dragStartCallbacks.add(u)};q.prototype.offDragStart=function(u){this._dragStartCallbacks.delete(u)};q.prototype.onDrag=function(u){this._dragCallbacks.add(u);return this};q.prototype.offDrag=function(u){this._dragCallbacks.delete(u)};q.prototype.onDragEnd=function(u){this._dragEndCallbacks.add(u)};q.prototype.offDragEnd=function(u){this._dragEndCallbacks.delete(u)};q.prototype.dragInteraction=function(){return this._dragInteraction};
q.prototype.enabled=function(u){if(null==u)return this._dragInteraction.enabled();this._dragInteraction.enabled(u);this._setResizableClasses(this.resizable());this._setMovableClass();return this};q.prototype.destroy=function(){var u=this;n.prototype.destroy.call(this);this._dragStartCallbacks.forEach(function(w){return u._dragCallbacks.delete(w)});this._dragCallbacks.forEach(function(w){return u._dragCallbacks.delete(w)});this._dragEndCallbacks.forEach(function(w){return u._dragEndCallbacks.delete(w)});
this._disconnectInteraction()};q.prototype.detach=function(){this._resetState();this._dragInteraction.detach();n.prototype.detach.call(this);return this};q.prototype.anchor=function(u){u=p.coerceExternalD3(u);this._dragInteraction.attachTo(this);n.prototype.anchor.call(this,u);return this};q.prototype._resetState=function(){this.bounds({topLeft:{x:0,y:0},bottomRight:{x:0,y:0}})};return q}(h(43).SelectionBoxLayer);f.DragBoxLayer=d},function(d,f,h){var k=this&&this.__extends||function(p,m){function n(){this.constructor=
p}for(var q in m)m.hasOwnProperty(q)&&(p[q]=m[q]);p.prototype=null===m?Object.create(m):(n.prototype=m.prototype,new n)},r=h(18);d=function(p){function m(){return p.call(this,"path","line")||this}k(m,p);m.prototype._applyDefaultAttributes=function(n){n.style("fill","none")};m.prototype.getVisualPrimitiveAtIndex=function(){return p.prototype.getVisualPrimitiveAtIndex.call(this,0)};return m}(h(9).SVGDrawer);f.LineSVGDrawer=d;var l=["opacity","stroke-opacity","stroke-width","stroke"];f.makeLineCanvasDrawStep=
function(p){return function(m,n,q){q=r.resolveAttributes(q,l,n[0],0);r.renderLine(m,p(),n[0],q)}}},function(d,f,h){var k=this&&this.__extends||function(p,m){function n(){this.constructor=p}for(var q in m)m.hasOwnProperty(q)&&(p[q]=m[q]);p.prototype=null===m?Object.create(m):(n.prototype=m.prototype,new n)},r=h(18);d=function(p){function m(n){void 0===n&&(n="");var q=p.call(this,"rect","")||this;q._rootClassName=n;q._root.classed(q._rootClassName,!0);return q}k(m,p);return m}(h(9).SVGDrawer);f.RectangleSVGDrawer=
d;var l=["x","y","width","height"];f.RectangleCanvasDrawStep=function(p,m,n){p.save();m.forEach(function(q,u){null!=q&&(q=r.resolveAttributesSubsetWithStyles(n,l,q,u),p.beginPath(),p.rect(q.x,q.y,q.width,q.height),r.renderPathWithStyle(p,q))});p.restore()};d=function(p){function m(n){return p.call(this,n,f.RectangleCanvasDrawStep)||this}k(m,p);return m}(r.CanvasDrawer);f.RectangleCanvasDrawer=d},function(d,f,h){function k(p){l.SHOW_WARNINGS&&console.warn(p)}function r(p,m){for(var n=[],q=2;q<arguments.length;q++)n[q-
2]=arguments[q];return 0===m?(p(n),-1):window.setTimeout(p,m,n)}var l=h(23);f.warn=k;f.setTimeout=r;f.debounce=function(p,m,n){function q(){m.apply(n,w)}var u=null,w=[];return function(){w=Array.prototype.slice.call(arguments);clearTimeout(u);u=r(q,p)}};f.deprecated=function(p,m,n){void 0===n&&(n="");k("Method "+p+" has been deprecated in version "+m+". Please refer to the release notes. "+n)}},function(d,f){d=function(){function h(k){this.ruler=null!=k.createRuler?k.createRuler():k}h.prototype.measure=
function(k){void 0===k&&(k=h.HEIGHT_TEXT);return this.ruler(k)};return h}();d.HEIGHT_TEXT="bdpql";f.AbstractMeasurer=d},function(d,f,h){function k(r){for(var l in r)f.hasOwnProperty(l)||(f[l]=r[l])}k(h(32));k(h(74));k(h(75));k(h(41));k(h(42));k(h(76));k(h(77));k(h(78));k(h(79));k(h(43));k(h(80));k(h(81));k(h(82))},function(d,f,h){var k=h(0);d=function(){function r(l,p){void 0===l&&(l=[]);void 0===p&&(p={});this._updateId=0;this._data=l;this._metadata=p;this._callbacks=new k.CallbackSet}r.prototype.onUpdate=
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f.Dataset=d},function(d,f,h){var k=h(0),r=h(30);d=function(){function l(){}l.prototype.render=function(){r.flush()};return l}();f.Immediate=d;d=function(){function l(){}l.prototype.render=function(){k.DOM.requestAnimationFramePolyfill(r.flush)};return l}();f.AnimationFrame=d;d=function(){function l(){this._timeoutMsec=k.DOM.SCREEN_REFRESH_RATE_MILLISECONDS}l.prototype.render=function(){setTimeout(r.flush,this._timeoutMsec)};return l}();f.Timeout=d},function(d,f,h){var k=this&&this.__extends||function(p,
m){function n(){this.constructor=p}for(var q in m)m.hasOwnProperty(q)&&(p[q]=m[q]);p.prototype=null===m?Object.create(m):(n.prototype=m.prototype,new n)},r=h(13),l=h(0);d=function(p){function m(){var n=null!==p&&p.apply(this,arguments)||this;n._keyPressCallbacks={};n._keyReleaseCallbacks={};n._mouseMoveCallback=function(){return!1};n._downedKeys=new l.Set;n._keyDownCallback=function(q,u){return n._handleKeyDownEvent(q,u)};n._keyUpCallback=function(q){return n._handleKeyUpEvent(q)};return n}k(m,p);
m.prototype._anchor=function(n){p.prototype._anchor.call(this,n);this._positionDispatcher=r.Mouse.getDispatcher(this._componentAttachedTo);this._positionDispatcher.onMouseMove(this._mouseMoveCallback);this._keyDispatcher=r.Key.getDispatcher();this._keyDispatcher.onKeyDown(this._keyDownCallback);this._keyDispatcher.onKeyUp(this._keyUpCallback)};m.prototype._unanchor=function(){p.prototype._unanchor.call(this);this._positionDispatcher.offMouseMove(this._mouseMoveCallback);this._positionDispatcher=null;
this._keyDispatcher.offKeyDown(this._keyDownCallback);this._keyDispatcher.offKeyUp(this._keyUpCallback);this._keyDispatcher=null};m.prototype._handleKeyDownEvent=function(n,q){var u=this._translateToComponentSpace(this._positionDispatcher.lastMousePosition());this._isInsideComponent(u)&&!q.repeat&&(this._keyPressCallbacks[n]&&this._keyPressCallbacks[n].callCallbacks(n),this._downedKeys.add(n))};m.prototype._handleKeyUpEvent=function(n){this._downedKeys.has(n)&&this._keyReleaseCallbacks[n]&&this._keyReleaseCallbacks[n].callCallbacks(n);
this._downedKeys.delete(n)};m.prototype.onKeyPress=function(n,q){this._keyPressCallbacks[n]||(this._keyPressCallbacks[n]=new l.CallbackSet);this._keyPressCallbacks[n].add(q);return this};m.prototype.offKeyPress=function(n,q){this._keyPressCallbacks[n].delete(q);0===this._keyPressCallbacks[n].size&&delete this._keyPressCallbacks[n];return this};m.prototype.onKeyRelease=function(n,q){this._keyReleaseCallbacks[n]||(this._keyReleaseCallbacks[n]=new l.CallbackSet);this._keyReleaseCallbacks[n].add(q);return this};
m.prototype.offKeyRelease=function(n,q){this._keyReleaseCallbacks[n].delete(q);0===this._keyReleaseCallbacks[n].size&&delete this._keyReleaseCallbacks[n];return this};return m}(h(15).Interaction);f.Key=d},function(d,f,h){var k=this&&this.__extends||function(l,p){function m(){this.constructor=l}for(var n in p)p.hasOwnProperty(n)&&(l[n]=p[n]);l.prototype=null===p?Object.create(p):(m.prototype=p.prototype,new m)},r=h(0);d=function(l){function p(m){void 0===m&&(m=[]);var n=l.call(this)||this;n._components=
[];n.addClass("component-group");m.forEach(function(q){return n.append(q)});return n}k(p,l);p.prototype._forEach=function(m){this.components().forEach(m)};p.prototype.has=function(m){return 0<=this._components.indexOf(m)};p.prototype.requestedSpace=function(m,n){var q=this._components.map(function(u){return u.requestedSpace(m,n)});return{minWidth:r.Math.max(q,function(u){return u.minWidth},0),minHeight:r.Math.max(q,function(u){return u.minHeight},0)}};p.prototype.computeLayout=function(m,n,q){var u=
this;l.prototype.computeLayout.call(this,m,n,q);this._forEach(function(w){w.computeLayout({x:0,y:0},u.width(),u.height())});return this};p.prototype._sizeFromOffer=function(m,n){return{width:m,height:n}};p.prototype.fixedWidth=function(){return this._components.every(function(m){return m.fixedWidth()})};p.prototype.fixedHeight=function(){return this._components.every(function(m){return m.fixedHeight()})};p.prototype.components=function(){return this._components.slice()};p.prototype.append=function(m){null==
m||this.has(m)||(m.detach(),this._components.push(m),this._adoptAndAnchor(m),this.redraw());return this};p.prototype._remove=function(m){m=this._components.indexOf(m);0<=m&&this._components.splice(m,1)};return p}(h(29).ComponentContainer);f.Group=d},function(d,f,h){var k=this&&this.__extends||function(l,p){function m(){this.constructor=l}for(var n in p)p.hasOwnProperty(n)&&(l[n]=p[n]);l.prototype=null===p?Object.create(p):(m.prototype=p.prototype,new m)};h(0);d=h(4);var r;(function(l){l[l.VALUE=0]=
"VALUE";l[l.PIXEL=1]="PIXEL"})(r||(r={}));d=function(l){function p(m){var n=l.call(this)||this;n._mode=r.VALUE;if(m!==p.ORIENTATION_VERTICAL&&m!==p.ORIENTATION_HORIZONTAL)throw Error(m+" is not a valid orientation for GuideLineLayer");n._orientation=m;n._overflowHidden=!0;n.addClass("guide-line-layer");n._isVertical()?n.addClass("vertical"):n.addClass("horizontal");n._scaleUpdateCallback=function(){n._syncPixelPositionAndValue();n.render()};return n}k(p,l);p.prototype._setup=function(){l.prototype._setup.call(this);
this._guideLine=this.content().append("line").classed("guide-line",!0)};p.prototype._sizeFromOffer=function(m,n){return{width:m,height:n}};p.prototype._isVertical=function(){return this._orientation===p.ORIENTATION_VERTICAL};p.prototype.fixedWidth=function(){return!0};p.prototype.fixedHeight=function(){return!0};p.prototype.computeLayout=function(m,n,q){l.prototype.computeLayout.call(this,m,n,q);null!=this.scale()&&(this._isVertical()?this.scale().range([0,this.width()]):this.scale().range([this.height(),
0]));return this};p.prototype.renderImmediately=function(){l.prototype.renderImmediately.call(this);this._syncPixelPositionAndValue();this._guideLine.attrs({x1:this._isVertical()?this.pixelPosition():0,y1:this._isVertical()?0:this.pixelPosition(),x2:this._isVertical()?this.pixelPosition():this.width(),y2:this._isVertical()?this.height():this.pixelPosition()});return this};p.prototype._syncPixelPositionAndValue=function(){null!=this.scale()&&(this._mode===r.VALUE&&null!=this.value()?this._pixelPosition=
this.scale().scale(this.value()):this._mode===r.PIXEL&&null!=this.pixelPosition()&&(this._value=this.scale().invert(this.pixelPosition())))};p.prototype._setPixelPositionWithoutChangingMode=function(m){this._pixelPosition=m;null!=this.scale()&&(this._value=this.scale().invert(this.pixelPosition()));this.render()};p.prototype.scale=function(m){if(null==m)return this._scale;var n=this._scale;null!=n&&n.offUpdate(this._scaleUpdateCallback);this._scale=m;this._scale.onUpdate(this._scaleUpdateCallback);
this._syncPixelPositionAndValue();this.redraw();return this};p.prototype.value=function(m){if(null==m)return this._value;this._value=m;this._mode=r.VALUE;this._syncPixelPositionAndValue();this.render();return this};p.prototype.pixelPosition=function(){return this._pixelPosition};p.prototype.destroy=function(){l.prototype.destroy.call(this);null!=this.scale()&&this.scale().offUpdate(this._scaleUpdateCallback)};return p}(d.Component);d.ORIENTATION_VERTICAL="vertical";d.ORIENTATION_HORIZONTAL="horizontal";
f.GuideLineLayer=d},function(d,f,h){var k=this&&this.__extends||function(p,m){function n(){this.constructor=p}for(var q in m)m.hasOwnProperty(q)&&(p[q]=m[q]);p.prototype=null===m?Object.create(m):(n.prototype=m.prototype,new n)},r=h(0);d=h(4);var l;(function(p){p[p.VALUE=0]="VALUE";p[p.PIXEL=1]="PIXEL"})(l=f.PropertyMode||(f.PropertyMode={}));d=function(p){function m(){var n=p.call(this)||this;n._boxVisible=!1;n._boxBounds={topLeft:{x:0,y:0},bottomRight:{x:0,y:0}};n._xBoundsMode=l.PIXEL;n._yBoundsMode=
l.PIXEL;n.addClass("selection-box-layer");n._adjustBoundsCallback=function(){n.render()};n._overflowHidden=!0;n._xExtent=[void 0,void 0];n._yExtent=[void 0,void 0];return n}k(m,p);m.prototype._setup=function(){p.prototype._setup.call(this);this._box=this.content().append("g").classed("selection-box",!0).remove();this._boxArea=this._box.append("rect").classed("selection-area",!0)};m.prototype._sizeFromOffer=function(n,q){return{width:n,height:q}};m.prototype.bounds=function(n){if(null==n)return this._getBounds();
this._setBounds(n);this._yBoundsMode=this._xBoundsMode=l.PIXEL;this.render();return this};m.prototype._setBounds=function(n){this._boxBounds={topLeft:{x:Math.min(n.topLeft.x,n.bottomRight.x),y:Math.min(n.topLeft.y,n.bottomRight.y)},bottomRight:{x:Math.max(n.topLeft.x,n.bottomRight.x),y:Math.max(n.topLeft.y,n.bottomRight.y)}}};m.prototype._getBounds=function(){return{topLeft:{x:this._xBoundsMode===l.PIXEL?this._boxBounds.topLeft.x:null==this._xScale?0:Math.min(this.xScale().scale(this.xExtent()[0]),
this.xScale().scale(this.xExtent()[1])),y:this._yBoundsMode===l.PIXEL?this._boxBounds.topLeft.y:null==this._yScale?0:Math.min(this.yScale().scale(this.yExtent()[0]),this.yScale().scale(this.yExtent()[1]))},bottomRight:{x:this._xBoundsMode===l.PIXEL?this._boxBounds.bottomRight.x:null==this._xScale?0:Math.max(this.xScale().scale(this.xExtent()[0]),this.xScale().scale(this.xExtent()[1])),y:this._yBoundsMode===l.PIXEL?this._boxBounds.bottomRight.y:null==this._yScale?0:Math.max(this.yScale().scale(this.yExtent()[0]),
this.yScale().scale(this.yExtent()[1]))}}};m.prototype.renderImmediately=function(){p.prototype.renderImmediately.call(this);if(this._boxVisible){var n=this.bounds(),q=n.topLeft.y,u=n.bottomRight.y,w=n.topLeft.x;n=n.bottomRight.x;if(!(r.Math.isValidNumber(q)&&r.Math.isValidNumber(u)&&r.Math.isValidNumber(w)&&r.Math.isValidNumber(n)))throw Error("bounds have not been properly set");this._boxArea.attrs({x:w,y:q,width:n-w,height:u-q});this.content().node().appendChild(this._box.node())}else this._box.remove();
return this};m.prototype.boxVisible=function(n){if(null==n)return this._boxVisible;this._boxVisible=n;this.render();return this};m.prototype.fixedWidth=function(){return!0};m.prototype.fixedHeight=function(){return!0};m.prototype.xScale=function(n){if(null==n)return this._xScale;null!=this._xScale&&this._xScale.offUpdate(this._adjustBoundsCallback);this._xScale=n;this._xBoundsMode=l.VALUE;this._xScale.onUpdate(this._adjustBoundsCallback);this.render();return this};m.prototype.yScale=function(n){if(null==
n)return this._yScale;null!=this._yScale&&this._yScale.offUpdate(this._adjustBoundsCallback);this._yScale=n;this._yBoundsMode=l.VALUE;this._yScale.onUpdate(this._adjustBoundsCallback);this.render();return this};m.prototype.xExtent=function(){return this._getXExtent()};m.prototype._getXExtent=function(){return this._xBoundsMode===l.VALUE?this._xExtent:null==this._xScale?[void 0,void 0]:[this._xScale.invert(this._boxBounds.topLeft.x),this._xScale.invert(this._boxBounds.bottomRight.x)]};m.prototype._setXExtent=
function(n){this._xExtent=n};m.prototype.yExtent=function(){return this._getYExtent()};m.prototype._getYExtent=function(){return this._yBoundsMode===l.VALUE?this._yExtent:null==this._yScale?[void 0,void 0]:[this._yScale.invert(this._boxBounds.topLeft.y),this._yScale.invert(this._boxBounds.bottomRight.y)]};m.prototype._setYExtent=function(n){this._yExtent=n};m.prototype.destroy=function(){p.prototype.destroy.call(this);null!=this._xScale&&this.xScale().offUpdate(this._adjustBoundsCallback);null!=this._yScale&&
this.yScale().offUpdate(this._adjustBoundsCallback)};return m}(d.Component);f.SelectionBoxLayer=d},function(d,f,h){var k=this&&this.__extends||function(r,l){function p(){this.constructor=r}for(var m in l)l.hasOwnProperty(m)&&(r[m]=l[m]);r.prototype=null===l?Object.create(l):(p.prototype=l.prototype,new p)};d=function(r){function l(){return r.call(this,"path","arc fill")||this}k(l,r);l.prototype._applyDefaultAttributes=function(p){p.style("stroke","none")};return l}(h(9).SVGDrawer);f.ArcSVGDrawer=
d},function(d,f,h){var k=this&&this.__extends||function(r,l){function p(){this.constructor=r}for(var m in l)l.hasOwnProperty(m)&&(r[m]=l[m]);r.prototype=null===l?Object.create(l):(p.prototype=l.prototype,new p)};d=function(r){function l(){return r.call(this,"path","arc outline")||this}k(l,r);l.prototype._applyDefaultAttributes=function(p){p.style("fill","none")};return l}(h(9).SVGDrawer);f.ArcOutlineSVGDrawer=d},function(d,f,h){var k=this&&this.__extends||function(p,m){function n(){this.constructor=
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n,q){q=r.resolveAttributes(q,l,n[0],0);r.renderArea(m,p(),n[0],q)}}},function(d,f,h){var k=this&&this.__extends||function(r,l){function p(){this.constructor=r}for(var m in l)l.hasOwnProperty(m)&&(r[m]=l[m]);r.prototype=null===l?Object.create(l):(p.prototype=l.prototype,new p)};d=function(r){function l(){return r.call(this,"line","")||this}k(l,r);return l}(h(9).SVGDrawer);f.SegmentSVGDrawer=d},function(d,f,h){function k(n,q,u,w,A){return 0<=u+A&&u-A<=n&&0<=w+A&&w-A<=q}function r(n,q,u){if(null==n)return!1;
for(var w=0;w<u.length;w++){var A=u[w];if(n[A]!=q[A])return!1}return!0}var l=this&&this.__extends||function(n,q){function u(){this.constructor=n}for(var w in q)q.hasOwnProperty(w)&&(n[w]=q[w]);n.prototype=null===q?Object.create(q):(u.prototype=q.prototype,new u)},p=h(86),m=h(18);d=function(n){function q(){return n.call(this,"path","symbol")||this}l(q,n);return q}(h(9).SVGDrawer);f.SymbolSVGDrawer=d;f.makeSymbolCanvasDrawStep=function(n,q,u){var w=this;return function(A,y,x){var C=A.canvas,G=C.clientWidth;
C=C.clientHeight;for(var D=new p.CanvasBuffer(0,0),B=q(),H=u(),K=null,L=null,J=null,O=0;O<y.length;O++){var S=y[O];if(null!=S){var N=m.resolveAttributesSubsetWithStyles(x,["x","y"],S,O),R=H(S,O,n);if(k(G,C,N.x,N.y,R)){var X=r(K,N,m.ContextStyleAttrs);S=B(S,O,w._dataset);X&&J==R&&L==S||(K=m.getStrokeWidth(N),K=R+K+1,(K>D.screenWidth||K>D.screenHeight)&&D.resize(K,K,!0),D.clear(),K=D.ctx,K.beginPath(),S(R).context(K)(null),K.closePath(),m.renderPathWithStyle(K,N),L=S,J=R,K=N);D.blitCenter(A,N.x,N.y)}}}}}},
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var q=this&&this.__extends||function(D,B){function H(){this.constructor=D}for(var K in B)B.hasOwnProperty(K)&&(D[K]=B[K]);D.prototype=null===B?Object.create(B):(H.prototype=B.prototype,new H)},u=h(128),w=h(38),A=h(17);f.sign=k;f.signScale=r;f.signDataset=l;f.signRef=p;f.signArray=m;f.signObj=n;var y=function(){function D(){}D.prototype.isDifferent=function(B){return B instanceof this.constructor?this.isSignatureDifferent(B):!0};return D}();f.Signature=y;var x=function(D){function B(H){var K=D.call(this)||
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H;return K}q(B,D);B.prototype.isSignatureDifferent=function(H){var K=Object.keys(this.obj),L=Object.keys(H.obj);if(K.length!==L.length)return!0;for(L=0;L<K.length;L++){var J=K[L];if(!H.obj.hasOwnProperty(J)||this.obj[J].isDifferent(H.obj[J]))return!0}return!1};return B}(y);f.ObjectSignature=G},function(d,f,h){var k=this&&this.__extends||function(y,x){function C(){this.constructor=y}for(var G in x)x.hasOwnProperty(G)&&(y[G]=x[G]);y.prototype=null===x?Object.create(x):(C.prototype=x.prototype,new C)},
r=h(1),l=h(3),p=h(0),m=h(14),n=h(46),q=h(6),u=h(33),w=h(19);d=h(53);var A=h(2);h=function(y){function x(){var C=y.call(this)||this;C.addClass("area-plot");C.y0(0);C.attr("fill-opacity",.25);C.attr("fill",(new l.Color).range()[0]);C._lineDrawers=new p.Map;return C}k(x,y);x.prototype.y=function(C,G){if(null==C)return y.prototype.y.call(this);null==G?y.prototype.y.call(this,C):y.prototype.y.call(this,C,G);null!=G&&(C=this.y0().accessor,null!=C&&this._bindProperty(x._Y0_KEY,C,G),this._updateYScale());
return this};x.prototype.y0=function(C){if(null==C)return this._propertyBindings.get(x._Y0_KEY);var G=this.y();this._bindProperty(x._Y0_KEY,C,G&&G.scale);this._updateYScale();this.render();return this};x.prototype._onDatasetUpdate=function(){y.prototype._onDatasetUpdate.call(this);this._updateYScale()};x.prototype._addDataset=function(C){var G=this;this._lineDrawers.set(C,new m.ProxyDrawer(function(){return new u.LineSVGDrawer},function(D){return new m.CanvasDrawer(D,u.makeLineCanvasDrawStep(function(){var B=
A.Plot._scaledAccessor(G.x()),H=A.Plot._scaledAccessor(G.y());return G._d3LineFactory(C,B,H)}))}));y.prototype._addDataset.call(this,C);return this};x.prototype._createNodesForDataset=function(C){y.prototype._createNodesForDataset.call(this,C);C=this._lineDrawers.get(C);"svg"===this.renderer()?C.useSVG(this._renderArea):C.useCanvas(this._canvas);return C};x.prototype._removeDatasetNodes=function(C){y.prototype._removeDatasetNodes.call(this,C);this._lineDrawers.get(C).remove()};x.prototype._additionalPaint=
function(){var C=this,G=this._generateLineDrawSteps(),D=this._getDataToDraw();this.datasets().forEach(function(B){var H=A.Plot.applyDrawSteps(G,B);C._lineDrawers.get(B).draw(D.get(B),H)})};x.prototype._generateLineDrawSteps=function(){var C=[];if(this._animateOnNextRender()){var G=this._generateLineAttrToProjector();G.d=this._constructLineProjector(A.Plot._scaledAccessor(this.x()),this._getResetYFunction());C.push({attrToProjector:G,animator:this._getAnimator(w.Animator.RESET)})}C.push({attrToProjector:this._generateLineAttrToProjector(),
animator:this._getAnimator(w.Animator.MAIN)});return C};x.prototype._generateLineAttrToProjector=function(){var C=this._getAttrToProjector();C.d=this._constructLineProjector(A.Plot._scaledAccessor(this.x()),A.Plot._scaledAccessor(this.y()));return C};x.prototype._createDrawer=function(C){var G=this;return new q.ProxyDrawer(function(){return new n.AreaSVGDrawer},function(D){return new m.CanvasDrawer(D,n.makeAreaCanvasDrawStep(function(){var B=A.Plot._scaledAccessor(G.x()),H=A.Plot._scaledAccessor(G.y()),
K=A.Plot._scaledAccessor(G.y0());return G._createAreaGenerator(B,H,K,G._createDefinedProjector(B,H),C)}))})};x.prototype._generateDrawSteps=function(){var C=[];if(this._animateOnNextRender()){var G=this._getAttrToProjector();G.d=this._constructAreaProjector(A.Plot._scaledAccessor(this.x()),this._getResetYFunction(),A.Plot._scaledAccessor(this.y0()));C.push({attrToProjector:G,animator:this._getAnimator(w.Animator.RESET)})}C.push({attrToProjector:this._getAttrToProjector(),animator:this._getAnimator(w.Animator.MAIN)});
return C};x.prototype._updateYScale=function(){var C=this.getExtentsForProperty("y0");C=p.Array.uniq(p.Array.flatten(C));var G=1===C.length?C[0]:null;C=(C=this.y())&&C.scale;null!=C&&(null!=this._constantBaselineValueProvider&&(C.removePaddingExceptionsProvider(this._constantBaselineValueProvider),this._constantBaselineValueProvider=null),null!=G&&(this._constantBaselineValueProvider=function(){return[G]},C.addPaddingExceptionsProvider(this._constantBaselineValueProvider)))};x.prototype._getResetYFunction=
function(){return A.Plot._scaledAccessor(this.y0())};x.prototype._propertyProjectors=function(){var C=y.prototype._propertyProjectors.call(this);C.d=this._constructAreaProjector(A.Plot._scaledAccessor(this.x()),A.Plot._scaledAccessor(this.y()),A.Plot._scaledAccessor(this.y0()));return C};x.prototype.selections=function(C){var G=this;void 0===C&&(C=this.datasets());if("canvas"===this.renderer())return r.selectAll();var D=y.prototype.selections.call(this,C).nodes();C.map(function(B){return G._lineDrawers.get(B)}).filter(function(B){return null!=
B}).forEach(function(B){return D.push.apply(D,B.getVisualPrimitives())});return r.selectAll(D)};x.prototype._constructAreaProjector=function(C,G,D){var B=this,H=this._createDefinedProjector(A.Plot._scaledAccessor(this.x()),A.Plot._scaledAccessor(this.y()));return function(K,L,J){return B._createAreaGenerator(C,G,D,H,J)(K)}};x.prototype._createDefinedProjector=function(C,G){return function(D,B,H){var K=C(D,B,H);D=G(D,B,H);return p.Math.isValidNumber(K)&&p.Math.isValidNumber(D)}};x.prototype._createAreaGenerator=
function(C,G,D,B,H){var K=this._getCurveFactory();return r.area().x(function(L,J){return C(L,J,H)}).y1(function(L,J){return G(L,J,H)}).y0(function(L,J){return D(L,J,H)}).curve(K).defined(function(L,J){return B(L,J,H)})};return x}(d.Line);h._Y0_KEY="y0";f.Area=h},function(d,f){(function(h){h.MAIN="main";h.RESET="reset"})(f.Animator||(f.Animator={}))},function(d,f){var h=function(){function k(){var r=this;this.translate=this.scale=0;this.cachedDomain=[null,null];this.lastSeenDomain=[null,null];this.updateDomain=
function(l){r.lastSeenDomain=l.getTransformationDomain();var p=l.scaleTransformation(r.cachedDomain[1])-l.scaleTransformation(r.cachedDomain[0]),m=l.scaleTransformation(r.lastSeenDomain[1])-l.scaleTransformation(r.lastSeenDomain[0]);r.scale=p/m||1;r.translate=l.scaleTransformation(r.cachedDomain[0])-l.scaleTransformation(r.lastSeenDomain[0])*r.scale||0}}k.prototype.reset=function(){this.scale=1;this.translate=0;this.cachedDomain=this.lastSeenDomain};k.prototype.setDomain=function(r){this.cachedDomain=
r.getTransformationDomain()};return k}();d=function(){function k(r,l){var p=this;this.renderCallback=r;this.applyTransformCallback=l;this.domainTransformX=new h;this.domainTransformY=new h;this.renderDeferred=function(){p.applyTransform();clearTimeout(p.timeoutToken);p.timeoutToken=setTimeout(function(){p.renderCallback()},k.DEFERRED_RENDERING_DELAY)}}k.prototype.setDomains=function(r,l){r&&this.domainTransformX.setDomain(r);l&&this.domainTransformY.setDomain(l);this.renderDeferred()};k.prototype.updateDomains=
function(r,l){r&&this.domainTransformX.updateDomain(r);l&&this.domainTransformY.updateDomain(l);this.renderDeferred()};k.prototype.resetTransforms=function(){this.domainTransformX.reset();this.domainTransformY.reset();this.applyTransform()};k.prototype.applyTransform=function(){this.applyTransformCallback(this.domainTransformX.translate,this.domainTransformY.translate,this.domainTransformX.scale,this.domainTransformY.scale)};return k}();d.DEFERRED_RENDERING_DELAY=200;f.DeferredRenderer=d},function(d,
f,h){var k=this&&this.__extends||function(C,G){function D(){this.constructor=C}for(var B in G)G.hasOwnProperty(B)&&(C[B]=G[B]);C.prototype=null===G?Object.create(G):(D.prototype=G.prototype,new D)},r=h(1),l=h(7),p=h(14),m=h(6),n=h(33),q=h(3),u=h(11),w=h(0);d=h(10);var A=h(19),y=h(2);h=h(16);var x={linear:r.curveLinear,linearClosed:r.curveLinearClosed,step:r.curveStep,stepBefore:r.curveStepBefore,stepAfter:r.curveStepAfter,basis:r.curveBasis,basisOpen:r.curveBasisOpen,basisClosed:r.curveBasisClosed,
bundle:r.curveBundle,cardinal:r.curveCardinal,cardinalOpen:r.curveCardinalOpen,cardinalClosed:r.curveCardinalClosed,monotone:r.curveMonotoneX};f.CurveName=d.makeEnum("linear linearClosed step stepBefore stepAfter basis basisOpen basisClosed bundle cardinal cardinalOpen cardinalClosed monotone".split(" "));h=function(C){function G(){var D=C.call(this)||this;D._curve="linear";D._autorangeSmooth=!1;D._croppedRenderingEnabled=!0;D._collapseDenseVerticalLinesEnabled=!1;D._downsamplingEnabled=!1;D.addClass("line-plot");
var B=new l.Easing;B.stepDuration(y.Plot._ANIMATION_MAX_DURATION);B.easingMode("expInOut");B.maxTotalDuration(y.Plot._ANIMATION_MAX_DURATION);D.animator(A.Animator.MAIN,B);D.attr("stroke",(new q.Color).range()[0]);D.attr("stroke-width","2px");return D}k(G,C);G.prototype.x=function(D,B,H){if(null==D)return C.prototype.x.call(this);C.prototype.x.call(this,D,B,H);this._setScaleSnapping();return this};G.prototype.y=function(D,B,H){if(null==D)return C.prototype.y.call(this);C.prototype.y.call(this,D,B,
H);this._setScaleSnapping();return this};G.prototype.autorangeMode=function(D){if(null==D)return C.prototype.autorangeMode.call(this);C.prototype.autorangeMode.call(this,D);this._setScaleSnapping();return this};G.prototype.autorangeSmooth=function(){return this._autorangeSmooth};G.prototype._setScaleSnapping=function(){"x"===this.autorangeMode()&&this.x()&&this.x().scale&&this.x().scale instanceof u.QuantitativeScale&&this.x().scale.snappingDomainEnabled(!this.autorangeSmooth());"y"===this.autorangeMode()&&
this.y()&&this.y().scale&&this.y().scale instanceof u.QuantitativeScale&&this.y().scale.snappingDomainEnabled(!this.autorangeSmooth())};G.prototype.curve=function(D){if(null==D)return this._curve;this._curve=D;this.render();return this};G.prototype.downsamplingEnabled=function(){return this._downsamplingEnabled};G.prototype.croppedRenderingEnabled=function(D){if(null==D)return this._croppedRenderingEnabled;this._croppedRenderingEnabled=D;this.render();return this};G.prototype.collapseDenseLinesEnabled=
function(D){if(null==D)return this._collapseDenseVerticalLinesEnabled;this._collapseDenseVerticalLinesEnabled=D;this.render();return this};G.prototype._createDrawer=function(D){var B=this;return new m.ProxyDrawer(function(){return new n.LineSVGDrawer},function(H){return new p.CanvasDrawer(H,n.makeLineCanvasDrawStep(function(){return B._d3LineFactory(D)}))})};G.prototype.getExtentsForProperty=function(D){var B=C.prototype.getExtentsForProperty.call(this,D);if(!this._autorangeSmooth||this.autorangeMode()!==
D||"x"!==this.autorangeMode()&&"y"!==this.autorangeMode())return B;D=this._getEdgeIntersectionPoints();var H="y"===this.autorangeMode()?D.left.concat(D.right).map(function(K){return K.y}):D.top.concat(D.bottom).map(function(K){return K.x});return B.map(function(K){return r.extent(r.merge([K,H]))})};G.prototype._getEdgeIntersectionPoints=function(){var D=this;if(!(this.y().scale instanceof u.QuantitativeScale&&this.x().scale instanceof u.QuantitativeScale))return{left:[],right:[],top:[],bottom:[]};
var B=this.y().scale,H=this.x().scale,K={left:[],right:[],top:[],bottom:[]},L=H.scale(H.domain()[0]),J=H.scale(H.domain()[1]),O=B.scale(B.domain()[0]),S=B.scale(B.domain()[1]);this.datasets().forEach(function(N){for(var R=N.data(),X,aa,fa,oa,Z,ca,ja,ba=1;ba<R.length;ba++)oa=ca||H.scale(D.x().accessor(R[ba-1],ba-1,N)),Z=ja||B.scale(D.y().accessor(R[ba-1],ba-1,N)),ca=H.scale(D.x().accessor(R[ba],ba,N)),ja=B.scale(D.y().accessor(R[ba],ba,N)),oa<L===L<=ca&&(X=L-oa,aa=ca-oa,fa=ja-Z,X=X*fa/aa,K.left.push({x:L,
y:B.invert(Z+X)})),oa<J===J<=ca&&(X=J-oa,aa=ca-oa,fa=ja-Z,X=X*fa/aa,K.right.push({x:J,y:B.invert(Z+X)})),Z<S===S<=ja&&(aa=ca-oa,X=S-Z,fa=ja-Z,X=X*aa/fa,K.top.push({x:H.invert(oa+X),y:S})),Z<O===O<=ja&&(aa=ca-oa,X=O-Z,fa=ja-Z,X=X*aa/fa,K.bottom.push({x:H.invert(oa+X),y:O}))});return K};G.prototype._getResetYFunction=function(){var D=this.y().scale.domain(),B=Math.max(D[0],D[1]);D=Math.min(D[0],D[1]);B=0>B&&B||0<D&&D||0;var H=this.y().scale.scale(B);return function(){return H}};G.prototype._generateDrawSteps=
function(){var D=[];if(this._animateOnNextRender()){var B=this._getAttrToProjector();B.d=this._constructLineProjector(y.Plot._scaledAccessor(this.x()),this._getResetYFunction());D.push({attrToProjector:B,animator:this._getAnimator(A.Animator.RESET)})}D.push({attrToProjector:this._getAttrToProjector(),animator:this._getAnimator(A.Animator.MAIN)});return D};G.prototype._generateAttrToProjector=function(){var D=C.prototype._generateAttrToProjector.call(this);Object.keys(D).forEach(function(B){if("d"!==
B){var H=D[B];D[B]=function(K,L,J){return 0<K.length?H(K[0],L,J):null}}});return D};G.prototype.entitiesAt=function(D){D=this.entityNearestByXThenY(D);return null!=D?[D]:[]};G.prototype.entityNearestByXThenY=function(D){var B=Infinity,H=Infinity,K,L=this.bounds();this.entities().forEach(function(J){if(w.Math.within(J.position,L)){var O=Math.abs(D.x-J.position.x),S=Math.abs(D.y-J.position.y);if(O<B||O===B&&S<H)K=J,B=O,H=S}});return K};G.prototype._propertyProjectors=function(){var D=C.prototype._propertyProjectors.call(this);
D.d=this._constructLineProjector(y.Plot._scaledAccessor(this.x()),y.Plot._scaledAccessor(this.y()));return D};G.prototype._constructLineProjector=function(D,B){var H=this;return function(K,L,J){return H._d3LineFactory(J,D,B)(K)}};G.prototype._d3LineFactory=function(D,B,H){function K(L,J,O){var S=B(L,J,O);L=H(L,J,O);return w.Math.isValidNumber(S)&&w.Math.isValidNumber(L)}void 0===B&&(B=y.Plot._scaledAccessor(this.x()));void 0===H&&(H=y.Plot._scaledAccessor(this.y()));return r.line().x(function(L,J){return B(L,
J,D)}).y(function(L,J){return H(L,J,D)}).curve(this._getCurveFactory()).defined(function(L,J){return K(L,J,D)})};G.prototype._getCurveFactory=function(){var D=this.curve();return"string"===typeof D?(D=x[D],null==D?x.linear:D):D};G.prototype._getDataToDraw=function(){var D=this,B=new w.Map;this.datasets().forEach(function(H){var K=H.data();if(D._croppedRenderingEnabled||D._downsamplingEnabled){var L=K.map(function(J,O){return O});D._croppedRenderingEnabled&&(L=D._filterCroppedRendering(H,L));D._downsamplingEnabled&&
(L=D._filterDownsampling(H,L));D._collapseDenseVerticalLinesEnabled&&(L=D._filterDenseLines(H,L));B.set(H,[L.map(function(J){return K[J]})])}else B.set(H,[K])});return B};G.prototype._filterCroppedRendering=function(D,B){function H(fa,oa){return w.Math.inRange(fa,0,K.width())&&w.Math.inRange(oa,0,K.height())}for(var K=this,L=y.Plot._scaledAccessor(this.x()),J=y.Plot._scaledAccessor(this.y()),O=D.data(),S=[],N=0;N<B.length;N++){var R=L(O[B[N]],B[N],D),X=J(O[B[N]],B[N],D);R=H(R,X);if(!R&&null!=B[N-
1]&&null!=O[B[N-1]]){X=L(O[B[N-1]],B[N-1],D);var aa=J(O[B[N-1]],B[N-1],D);R=R||H(X,aa)}R||null==B[N+1]||null==O[B[N+1]]||(X=L(O[B[N+1]],B[N+1],D),aa=J(O[B[N+1]],B[N+1],D),R=R||H(X,aa));R&&S.push(B[N])}return S};G.prototype._filterDownsampling=function(D,B){function H(ja,ba){var ka=L(K[B[ja]],B[ja],D),W=J(K[B[ja]],B[ja],D),Ea=L(K[B[ja+1]],B[ja+1],D);ja=J(K[B[ja+1]],B[ja+1],D);return Infinity===ba?Math.floor(ka)===Math.floor(Ea):Math.floor(ja)===Math.floor(W+(Ea-ka)*ba)}if(0===B.length)return[];for(var K=
D.data(),L=y.Plot._scaledAccessor(this.x()),J=y.Plot._scaledAccessor(this.y()),O=[B[0]],S=0;S<B.length-1;){var N=B[S],R=L(K[B[S]],B[S],D),X=J(K[B[S]],B[S],D),aa=L(K[B[S+1]],B[S+1],D),fa=J(K[B[S+1]],B[S+1],D);fa=Math.floor(R)===Math.floor(aa)?Infinity:(fa-X)/(aa-R);aa=B[S];X=Infinity===fa?X:R;R=aa;for(var oa=X,Z=!0;S<B.length-1&&(Z||H(S,fa));){S++;Z=!1;var ca=Infinity===fa?J(K[B[S]],B[S],D):L(K[B[S]],B[S],D);ca>oa&&(oa=ca,R=B[S]);ca<X&&(X=ca,aa=B[S])}fa=B[S];aa!==N&&O.push(aa);R!==aa&&R!==N&&O.push(R);
fa!==N&&fa!==aa&&fa!==R&&O.push(fa)}return O};G.prototype._filterDenseLines=function(D,B){if(0===B.length)return[];var H=D.data(),K=y.Plot._scaledAccessor(this.x()),L=y.Plot._scaledAccessor(this.y());return this._bucketByX(D,B,function(J){return K(H[J],J,D)},function(J){return L(H[J],J,D)})};G.prototype._bucketByX=function(D,B,H,K){var L=[];D=D.data();for(var J=null,O=0;O<=B.length;++O){var S=B[O];if(null!=D[S]){var N=Math.floor(H(S)),R=K(S);null==J?J=new w.Bucket(S,N,R):J.isInBucket(N)?J.addToBucket(R,
S):(L.push.apply(L,J.getUniqueIndices()),J=new w.Bucket(S,N,R))}}null!=J&&L.push.apply(L,J.getUniqueIndices());return L};return G}(h.XYPlot);f.Line=h},function(d,f,h){var k=this&&this.__extends||function(n,q){function u(){this.constructor=n}for(var w in q)q.hasOwnProperty(w)&&(n[w]=q[w]);n.prototype=null===q?Object.create(q):(u.prototype=q.prototype,new u)},r=h(1),l=h(26),p=h(0),m=[0,1];d=function(n){function q(){var u=n.call(this)||this;u._range=[0,1];u._d3Scale=r.scaleBand();u._d3Scale.range(m);
u._d3TransformationScale=r.scaleLinear();u._d3TransformationScale.domain(m);u._innerPadding=q._convertToPlottableInnerPadding();u._outerPadding=q._convertToPlottableOuterPadding();return u}k(q,n);q.prototype.cloneWithoutProviders=function(){var u=(new q).domain(this.domain()).range(this.range()).innerPadding(this.innerPadding()).outerPadding(this.outerPadding());u._d3TransformationScale.domain(this._d3TransformationScale.domain());return u};q.prototype.extentOfValues=function(u){return p.Array.uniq(u)};
q.prototype._getExtent=function(){return p.Array.uniq(this._getAllIncludedValues())};q.prototype.domain=function(u){return n.prototype.domain.call(this,u)};q.prototype.invertRange=function(){var u,w=this;void 0===u&&(u=this.range());var A=this._d3Scale.bandwidth(),y=this.invertedTransformation(u[0]),x=this.invertedTransformation(u[1]);u=this._d3Scale.domain();var C=u.map(function(G){return w._d3Scale(G)+A/2});y=r.bisect(C,y);x=r.bisect(C,x);return u.slice(y,x)};q.prototype.range=function(u){return n.prototype.range.call(this,
u)};q._convertToPlottableInnerPadding=function(){return 1/.7-1};q._convertToPlottableOuterPadding=function(){return.5/.7};q.prototype._setBands=function(){var u=1-1/(1+this.innerPadding()),w=this.outerPadding()/(1+this.innerPadding());this._d3Scale.paddingInner(u);this._d3Scale.paddingOuter(w)};q.prototype.rangeBand=function(){return this._rescaleBand(this._d3Scale.bandwidth())};q.prototype.stepWidth=function(){return this._rescaleBand(this._d3Scale.bandwidth()*(1+this.innerPadding()))};q.prototype.ticks=
function(){return this.domain()};q.prototype.innerPadding=function(u){if(null==u)return this._innerPadding;this._innerPadding=u;this.range(this.range());this._dispatchUpdate();return this};q.prototype.outerPadding=function(u){if(null==u)return this._outerPadding;this._outerPadding=u;this.range(this.range());this._dispatchUpdate();return this};q.prototype.scale=function(u){u=this._d3Scale(u)+this._d3Scale.bandwidth()/2;return this._d3TransformationScale(u)};q.prototype.zoom=function(u,w){var A=this;
this._d3TransformationScale.domain(this._d3TransformationScale.range().map(function(y){return A._d3TransformationScale.invert(l.zoomOut(y,u,w))}));this._dispatchUpdate()};q.prototype.pan=function(u){var w=this;this._d3TransformationScale.domain(this._d3TransformationScale.range().map(function(A){return w._d3TransformationScale.invert(A+u)}));this._dispatchUpdate()};q.prototype.scaleTransformation=function(u){return this._d3TransformationScale(u)};q.prototype.invertedTransformation=function(u){return this._d3TransformationScale.invert(u)};
q.prototype.getTransformationExtent=function(){return m};q.prototype.getTransformationDomain=function(){return this._d3TransformationScale.domain()};q.prototype.setTransformationDomain=function(u){this._d3TransformationScale.domain(u);this._dispatchUpdate()};q.prototype._getDomain=function(){return this._backingScaleDomain()};q.prototype._backingScaleDomain=function(u){if(null==u)return this._d3Scale.domain();this._d3Scale.domain(u);this._setBands();return this};q.prototype._getRange=function(){return this._range};
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quadInOut:d.easeQuadInOut,cubic:d.easeCubic,cubicIn:d.easeCubicIn,cubicOut:d.easeCubicOut,cubicInOut:d.easeCubicInOut,poly:d.easePoly,polyIn:d.easePolyIn,polyOut:d.easePolyOut,polyInOut:d.easePolyInOut,sin:d.easeSin,sinIn:d.easeSinIn,sinOut:d.easeSinOut,sinInOut:d.easeSinInOut,exp:d.easeExp,expIn:d.easeExpIn,expOut:d.easeExpOut,expInOut:d.easeExpInOut,circle:d.easeCircle,circleIn:d.easeCircleIn,circleOut:d.easeCircleOut,circleInOut:d.easeCircleInOut,bounce:d.easeBounce,bounceIn:d.easeBounceIn,bounceOut:d.easeBounceOut,
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h=function(){function l(){this._startDelay=l._DEFAULT_START_DELAY_MILLISECONDS;this._stepDuration=l._DEFAULT_STEP_DURATION_MILLISECONDS;this._stepDelay=l._DEFAULT_ITERATIVE_DELAY_MILLISECONDS;this._maxTotalDuration=l._DEFAULT_MAX_TOTAL_DURATION_MILLISECONDS;this._easingMode=l._DEFAULT_EASING_MODE}l.prototype.totalTime=function(p){var m=this._getAdjustedIterativeDelay(p);return this.startDelay()+m*Math.max(p-1,0)+this.stepDuration()};l.prototype.animate=function(p,m){var n=this;p=k.coerceExternalD3(p);
var q=p.size(),u=this._getAdjustedIterativeDelay(q);return p.transition().ease(this._getEaseFactory()).duration(this.stepDuration()).delay(function(w,A){return n.startDelay()+u*A}).attrs(m)};l.prototype.startDelay=function(p){if(null==p)return this._startDelay;this._startDelay=p;return this};l.prototype.stepDuration=function(p){if(null==p)return Math.min(this._stepDuration,this._maxTotalDuration);this._stepDuration=p;return this};l.prototype.stepDelay=function(){return this._stepDelay};l.prototype.maxTotalDuration=
function(p){if(null==p)return this._maxTotalDuration;this._maxTotalDuration=p;return this};l.prototype.easingMode=function(p){if(null==p)return this._easingMode;this._easingMode=p;return this};l.prototype._getEaseFactory=function(){var p=this.easingMode();return"string"===typeof p?(p=r[p],null==p?r.linear:p):p};l.prototype._getAdjustedIterativeDelay=function(p){var m=this.maxTotalDuration()-this.stepDuration();m=Math.max(m,0);p=m/Math.max(p-1,1);return Math.min(this.stepDelay(),p)};return l}();h._DEFAULT_START_DELAY_MILLISECONDS=
0;h._DEFAULT_STEP_DURATION_MILLISECONDS=300;h._DEFAULT_ITERATIVE_DELAY_MILLISECONDS=15;h._DEFAULT_MAX_TOTAL_DURATION_MILLISECONDS=Infinity;h._DEFAULT_EASING_MODE="expOut";f.Easing=h},function(d,f,h){var k=h(12);d=function(){function r(){}r.prototype.totalTime=function(){return 0};r.prototype.animate=function(l,p){l=k.coerceExternalD3(l);return l.attrs(p)};return r}();f.Null=d},function(d,f,h){var k=this&&this.__extends||function(q,u){function w(){this.constructor=q}for(var A in u)u.hasOwnProperty(A)&&
(q[A]=u[A]);q.prototype=null===u?Object.create(u):(w.prototype=u.prototype,new w)},r=h(1),l=h(5),p=h(4),m=h(0),n=h(22);d=function(q){function u(w,A){void 0===A&&(A="bottom");w=q.call(this,w,A)||this;w._tickLabelAngle=0;w._tickLabelShearAngle=0;w.addClass("category-axis");return w}k(u,q);Object.defineProperty(u.prototype,"_wrapper",{get:function(){var w=new l.Wrapper;null!=this._tickLabelMaxLines&&w.maxLines(this._tickLabelMaxLines);return w},enumerable:!0,configurable:!0});Object.defineProperty(u.prototype,
"_writer",{get:function(){return new l.Writer(this._measurer,this._typesetterContext,this._wrapper)},enumerable:!0,configurable:!0});u.prototype._setup=function(){q.prototype._setup.call(this);this._typesetterContext=new l.SvgContext(this._tickLabelContainer.node());this._measurer=new l.CacheMeasurer(this._typesetterContext)};u.prototype._rescale=function(){return this.redraw()};u.prototype.requestedSpace=function(w,A){var y=this.isHorizontal()?0:this._tickSpaceRequired()+this.margin(),x=this.isHorizontal()?
this._tickSpaceRequired()+this.margin():0;if(0===this._scale.domain().length)return{minWidth:0,minHeight:0};if(this.annotationsEnabled()){var C=this._annotationTierHeight()*this.annotationTierCount();this.isHorizontal()?x+=C:y+=C}w=this._measureTickLabels(w,A);return{minWidth:w.usedWidth+y,minHeight:w.usedHeight+x}};u.prototype._coreSize=function(){var w=this.isHorizontal()?this.height():this.width(),A=this.isHorizontal()?this.requestedSpace(this.width(),this.height()).minHeight:this.requestedSpace(this.width(),
this.height()).minWidth,y=this.margin()+this._annotationTierHeight();return Math.min(A-y,w)};u.prototype._getTickValues=function(){return this.getDownsampleInfo().domain};u.prototype._sizeFromOffer=function(w,A){return p.Component.prototype._sizeFromOffer.call(this,w,A)};u.prototype.getDownsampleInfo=function(w){var A;void 0===w&&(w=this._scale);void 0===A&&(A=w.invertRange());var y=Math.ceil(u._MINIMUM_WIDTH_PER_LABEL_PX*(0===this._tickLabelAngle?1:1/Math.cos(this._tickLabelShearAngle/180*Math.PI))/
w.stepWidth());return{domain:A.filter(function(x,C){return 0===C%y}),stepWidth:y*w.stepWidth()}};u.prototype.tickLabelAngle=function(){return this._tickLabelAngle;throw Error("Angle undefined not supported; only 0, 90, and -90 are valid values");};u.prototype.tickLabelShearAngle=function(){return this._tickLabelShearAngle};u.prototype.tickLabelMaxWidth=function(w){if(0===arguments.length)return this._tickLabelMaxWidth;this._tickLabelMaxWidth=w;this.redraw();return this};u.prototype.tickLabelMaxLines=
function(w){if(0===arguments.length)return this._tickLabelMaxLines;this._tickLabelMaxLines=w;this.redraw();return this};u.prototype._tickSpaceRequired=function(){return this._maxLabelTickLength()+this.tickLabelPadding()};u.prototype._drawTicks=function(w,A){var y=this;switch(this.tickLabelAngle()){case 0:var x={left:"right",right:"left",top:"center",bottom:"center"};var C={left:"center",right:"center",top:"bottom",bottom:"top"};break;case 90:x={left:"center",right:"center",top:"right",bottom:"left"};
C={left:"top",right:"bottom",top:"center",bottom:"center"};break;case -90:x={left:"center",right:"center",top:"left",bottom:"right"},C={left:"bottom",right:"top",top:"center",bottom:"center"}}A.each(function(G){var D=r.select(this),B=y.isHorizontal()?w:y.width()-y._tickSpaceRequired(),H=y.isHorizontal()?y.height()-y._tickSpaceRequired():w,K={xAlign:x[y.orientation()],yAlign:C[y.orientation()],textRotation:y.tickLabelAngle(),textShear:y.tickLabelShearAngle()};if(null!=y._tickLabelMaxWidth){if("left"===
y.orientation()&&B>y._tickLabelMaxWidth){var L=B-y._tickLabelMaxWidth;L=D.attr("transform")+" translate("+L+", 0)";D.attr("transform",L)}B=Math.min(B,y._tickLabelMaxWidth)}y._writer.write(y.formatter()(G),B,H,K,D.node())})};u.prototype._measureTickLabels=function(w,A){var y=this,x=this._scale.cloneWithoutProviders().range([0,this.isHorizontal()?w:A]),C=this.getDownsampleInfo(x);x=C.domain;C=C.stepWidth;var G=w-this._tickSpaceRequired();this.isHorizontal()&&(G=C,0!==this._tickLabelAngle&&(G=A-this._tickSpaceRequired()),
G=Math.max(G,0));var D=C;this.isHorizontal()&&(D=A-this._tickSpaceRequired(),0!==this._tickLabelAngle&&(D=w-this._tickSpaceRequired()),D=Math.max(D,0));null!=this._tickLabelMaxWidth&&(G=Math.min(G,this._tickLabelMaxWidth));A=x.map(function(B){return y._wrapper.wrap(y.formatter()(B),y._measurer,G,D)});w=this.isHorizontal()&&0===this._tickLabelAngle?r.sum:m.Math.max;x=this.isHorizontal()&&0===this._tickLabelAngle?m.Math.max:r.sum;w=w(A,function(B){return y._measurer.measure(B.wrappedText).width},0);
A=x(A,function(B){return y._measurer.measure(B.wrappedText).height},0);0!==this._tickLabelAngle&&(A=[A,w],w=A[0],A=A[1]);return{usedWidth:w,usedHeight:A}};u.prototype.renderImmediately=function(){var w=this;q.prototype.renderImmediately.call(this);var A=this._scale,y=this.getDownsampleInfo(A),x=y.domain,C=y=y.stepWidth;this.isHorizontal()&&null!=this._tickLabelMaxWidth&&(C=Math.min(C,this._tickLabelMaxWidth));x=this._tickLabelContainer.selectAll("."+n.Axis.TICK_LABEL_CLASS).data(x);var G=x.enter().append("g").classed(n.Axis.TICK_LABEL_CLASS,
!0).merge(x);x.exit().remove();G.attr("transform",function(D){var B=A.scale(D)-C/2;D=w.isHorizontal()?B:0;B=w.isHorizontal()?0:B;return"translate("+D+","+B+")"});G.text("");this._drawTicks(y,G);y="right"===this.orientation()?this._tickSpaceRequired():0;x="bottom"===this.orientation()?this._tickSpaceRequired():0;this._tickLabelContainer.attr("transform","translate("+y+","+x+")");this._showAllTickMarks();this._showAllTickLabels();this._hideTickMarksWithoutLabel();return this};u.prototype.computeLayout=
function(w,A,y){q.prototype.computeLayout.call(this,w,A,y);this.isHorizontal()||this._scale.range([0,this.height()]);return this};u.prototype.invalidateCache=function(){q.prototype.invalidateCache.call(this);this._measurer.reset()};return u}(n.Axis);d._MINIMUM_WIDTH_PER_LABEL_PX=15;f.Category=d},function(d,f,h){var k=this&&this.__extends||function(q,u){function w(){this.constructor=q}for(var A in u)u.hasOwnProperty(A)&&(q[A]=u[A]);q.prototype=null===u?Object.create(u):(w.prototype=u.prototype,new w)},
r=h(1),l=h(5),p=h(8),m=h(0),n=h(22);d=function(q){function u(w,A){w=q.call(this,w,A)||this;w._tickLabelPositioning="center";w._usesTextWidthApproximation=!1;w.formatter(p.general());return w}k(u,q);u.prototype._setup=function(){q.prototype._setup.call(this);var w=new l.SvgContext(this._tickLabelContainer.node(),n.Axis.TICK_LABEL_CLASS);this._measurer=new l.CacheMeasurer(w);this._wrapper=(new l.Wrapper).maxLines(1)};u.prototype._computeWidth=function(){var w=this._usesTextWidthApproximation?this._computeApproximateTextWidth():
this._computeExactTextWidth();return"center"===this._tickLabelPositioning?this._maxLabelTickLength()+this.tickLabelPadding()+w:Math.max(this._maxLabelTickLength(),this.tickLabelPadding()+w)};u.prototype._computeExactTextWidth=function(){var w=this,A=this._getTickValues().map(function(y){y=w.formatter()(y);return w._measurer.measure(y).width});return m.Math.max(A,0)};u.prototype._computeApproximateTextWidth=function(){var w=this,A=this._getTickValues(),y=this._measurer.measure("M").width;A=A.map(function(x){return w.formatter()(x).length*
y});return m.Math.max(A,0)};u.prototype._computeHeight=function(){var w=this._measurer.measure().height;return"center"===this._tickLabelPositioning?this._maxLabelTickLength()+this.tickLabelPadding()+w:Math.max(this._maxLabelTickLength(),this.tickLabelPadding()+w)};u.prototype._getTickValues=function(){var w=this._scale,A=w.domain(),y=A[0]<=A[1]?A[0]:A[1],x=A[0]>=A[1]?A[0]:A[1];return w.ticks().filter(function(C){return C>=y&&C<=x})};u.prototype._rescale=function(){if(this._isSetup){if(!this.isHorizontal()){var w=
this._computeWidth();if(w>this.width()||w<this.width()-this.margin()){this.redraw();return}}this.render()}};u.prototype.renderImmediately=function(){var w=this;q.prototype.renderImmediately.call(this);var A={x:0,y:0,dx:"0em",dy:"0.3em"},y=this._maxLabelTickLength(),x=this.tickLabelPadding(),C="middle",G=0,D=0,B=0,H=0;if(this.isHorizontal())switch(this._tickLabelPositioning){case "left":C="end";G=-x;H=x;break;case "center":H=y+x;break;case "right":C="start",H=G=x}else switch(this._tickLabelPositioning){case "top":A.dy=
"-0.3em";B=x;D=-x;break;case "center":B=y+x;break;case "bottom":A.dy="1em",D=B=x}y=this._generateTickMarkAttrHash();switch(this.orientation()){case "bottom":A.x=y.x1;A.dy="0.95em";D=y.y1+H;break;case "top":A.x=y.x1;A.dy="-.25em";D=y.y1-H;break;case "left":C="end";G=y.x1-B;A.y=y.y1;break;case "right":C="start",G=y.x1+B,A.y=y.y1}B=this._getTickValues();B=this._tickLabelContainer.selectAll("."+n.Axis.TICK_LABEL_CLASS).data(B);B.exit().remove();B.enter().append("text").classed(n.Axis.TICK_LABEL_CLASS,
!0).merge(B).style("text-anchor",C).style("visibility","inherit").attrs(A).text(function(K){return w.formatter()(K)});this._tickLabelContainer.attr("transform","translate("+G+", "+D+")");this._showAllTickMarks();this.showEndTickLabels()||this._hideEndTickLabels();this._hideOverflowingTickLabels();this._hideOverlappingTickLabels();"center"!==this._tickLabelPositioning&&this._hideTickMarksWithoutLabel();return this};u.prototype.tickLabelPosition=function(w){if(null==w)return this._tickLabelPositioning;
w=w.toLowerCase();if(this.isHorizontal()){if("left"!==w&&"center"!==w&&"right"!==w)throw Error(w+" is not a valid tick label position for a horizontal NumericAxis");}else if("top"!==w&&"center"!==w&&"bottom"!==w)throw Error(w+" is not a valid tick label position for a vertical NumericAxis");this._tickLabelPositioning=w;this.redraw();return this};u.prototype.usesTextWidthApproximation=function(){this._usesTextWidthApproximation=!0};u.prototype._hideEndTickLabels=function(){var w=this.element().node().getBoundingClientRect(),
A=this._tickLabelContainer.selectAll("."+n.Axis.TICK_LABEL_CLASS);if(0!==A.size()){var y=A.nodes()[0];m.DOM.clientRectInside(y.getBoundingClientRect(),w)||r.select(y).style("visibility","hidden");A=A.nodes()[A.size()-1];m.DOM.clientRectInside(A.getBoundingClientRect(),w)||r.select(A).style("visibility","hidden")}};u.prototype._hideOverlappingTickLabels=function(){for(var w=this._tickLabelContainer.selectAll("."+n.Axis.TICK_LABEL_CLASS).filter(function(){var x=r.select(this).style("visibility");return"inherit"===
x||"visible"===x}),A=w.nodes().map(function(x){return x.getBoundingClientRect()}),y=1;!this._hasOverlapWithInterval(y,A)&&y<A.length;)y+=1;w.each(function(x,C){x=r.select(this);0!==C%y&&x.style("visibility","hidden")})};u.prototype._hasOverlapWithInterval=function(w,A){var y="center"===this._tickLabelPositioning?this.tickLabelPadding():3*this.tickLabelPadding();A=A.map(function(C){return m.DOM.expandRect(C,y)});for(var x=0;x<A.length-w;x+=w)if(m.DOM.clientRectsOverlap(A[x],A[x+w]))return!1;return!0};
u.prototype.invalidateCache=function(){q.prototype.invalidateCache.call(this);this._measurer.reset()};return u}(n.Axis);f.Numeric=d},function(d,f,h){var k=this&&this.__extends||function(p,m){function n(){this.constructor=p}for(var q in m)m.hasOwnProperty(q)&&(p[q]=m[q]);p.prototype=null===m?Object.create(m):(n.prototype=m.prototype,new n)};d=h(42);var r=h(25),l=h(0);h=function(p){function m(n){function q(){x&&(x=!1,y._dragEndCallbacks.callCallbacks(y))}function u(C,G){x&&(y._setPixelPositionWithoutChangingMode(y._isVertical()?
G.x:G.y),y._dragCallbacks.callCallbacks(y))}function w(C){A(C)&&(x=!0,y._dragStartCallbacks.callCallbacks(y))}function A(C){return y._isVertical()&&y.pixelPosition()-y.detectionRadius()<=C.x&&C.x<=y.pixelPosition()+y.detectionRadius()||!y._isVertical()&&y.pixelPosition()-y.detectionRadius()<=C.y&&C.y<=y.pixelPosition()+y.detectionRadius()}var y=p.call(this,n)||this;y._detectionRadius=3;y._enabled=!0;y.addClass("drag-line-layer");y.addClass("enabled");y._dragInteraction=new r.Drag;y._dragInteraction.attachTo(y);
var x=!1;y._dragInteraction.onDragStart(w);y._dragInteraction.onDrag(u);y._dragInteraction.onDragEnd(q);y._disconnectInteraction=function(){y._dragInteraction.offDragStart(w);y._dragInteraction.offDrag(u);y._dragInteraction.offDragEnd(q);y._dragInteraction.detach()};y._dragStartCallbacks=new l.CallbackSet;y._dragCallbacks=new l.CallbackSet;y._dragEndCallbacks=new l.CallbackSet;return y}k(m,p);m.prototype._setup=function(){p.prototype._setup.call(this);this._detectionEdge=this.content().append("line").styles({opacity:0,
stroke:"pink","pointer-events":"visibleStroke"}).classed("drag-edge",!0)};m.prototype.renderImmediately=function(){p.prototype.renderImmediately.call(this);this._detectionEdge.attrs({x1:this._isVertical()?this.pixelPosition():0,y1:this._isVertical()?0:this.pixelPosition(),x2:this._isVertical()?this.pixelPosition():this.width(),y2:this._isVertical()?this.height():this.pixelPosition(),"stroke-width":2*this._detectionRadius});return this};m.prototype.detectionRadius=function(){return this._detectionRadius};
m.prototype.enabled=function(n){if(null==n)return this._enabled;(this._enabled=n)?this.addClass("enabled"):this.removeClass("enabled");this._dragInteraction.enabled(n);return this};m.prototype.onDragStart=function(n){this._dragStartCallbacks.add(n)};m.prototype.offDragStart=function(n){this._dragStartCallbacks.delete(n)};m.prototype.onDrag=function(n){this._dragCallbacks.add(n);return this};m.prototype.offDrag=function(n){this._dragCallbacks.delete(n)};m.prototype.onDragEnd=function(n){this._dragEndCallbacks.add(n)};
m.prototype.offDragEnd=function(n){this._dragEndCallbacks.delete(n)};m.prototype.destroy=function(){var n=this;p.prototype.destroy.call(this);this._dragStartCallbacks.forEach(function(q){return n._dragStartCallbacks.delete(q)});this._dragCallbacks.forEach(function(q){return n._dragCallbacks.delete(q)});this._dragEndCallbacks.forEach(function(q){return n._dragEndCallbacks.delete(q)});this._disconnectInteraction()};return m}(d.GuideLineLayer);f.DragLineLayer=h},function(d,f,h){function k(l,p,m){var n=
{};if(void 0!==m)for(var q=0;q<m.length;q++)n[m[q]]=m[q-1];return function(u){var w=l.scale(u);if(!p)return w;var A;u=void 0===n[u]?void 0:l.scale(n[u]);void 0!==u&&(A=u+(w-u)/2);return A}}var r=this&&this.__extends||function(l,p){function m(){this.constructor=l}for(var n in p)p.hasOwnProperty(n)&&(l[n]=p[n]);l.prototype=null===p?Object.create(p):(m.prototype=p.prototype,new m)};d=function(l){function p(m,n){var q=l.call(this)||this;q.addClass("gridlines");q._xScale=m;q._yScale=n;q._renderCallback=
function(){return q.render()};if(q._xScale)q._xScale.onUpdate(q._renderCallback);if(q._yScale)q._yScale.onUpdate(q._renderCallback);return q}r(p,l);p.prototype.betweenX=function(){return this._betweenX};p.prototype.betweenY=function(){return this._betweenY};p.prototype.destroy=function(){l.prototype.destroy.call(this);this._xScale&&this._xScale.offUpdate(this._renderCallback);this._yScale&&this._yScale.offUpdate(this._renderCallback);return this};p.prototype._setup=function(){l.prototype._setup.call(this);
this._xLinesContainer=this.content().append("g").classed("x-gridlines",!0);this._yLinesContainer=this.content().append("g").classed("y-gridlines",!0)};p.prototype.renderImmediately=function(){l.prototype.renderImmediately.call(this);this._redrawXLines();this._redrawYLines();return this};p.prototype.computeLayout=function(m,n,q){l.prototype.computeLayout.call(this,m,n,q);null!=this._xScale&&this._xScale.range([0,this.width()]);null!=this._yScale&&this._yScale.range([this.height(),0]);return this};
p.prototype._redrawXLines=function(){if(this._xScale){var m=this.betweenX(),n=this._xScale.ticks().slice(m?1:0);n=this._xLinesContainer.selectAll("line").data(n);n.enter().append("line").merge(n).attr("x1",k(this._xScale,m,this._xScale.ticks())).attr("y1",0).attr("x2",k(this._xScale,m,this._xScale.ticks())).attr("y2",this.height()).classed("betweenline",m).classed("zeroline",function(q){return 0===q});n.exit().remove()}};p.prototype._redrawYLines=function(){if(this._yScale){var m=this.betweenY(),
n=this._yScale.ticks().slice(m?1:0);n=this._yLinesContainer.selectAll("line").data(n);n.enter().append("line").merge(n).attr("x1",0).attr("y1",k(this._yScale,m,this._yScale.ticks())).attr("x2",this.width()).attr("y2",k(this._yScale,m,this._yScale.ticks())).classed("betweenline",m).classed("zeroline",function(q){return 0===q});n.exit().remove()}};return p}(h(4).Component);f.Gridlines=d},function(d,f,h){var k=this&&this.__extends||function(n,q){function u(){this.constructor=n}for(var w in q)q.hasOwnProperty(w)&&
(n[w]=q[w]);n.prototype=null===q?Object.create(q):(u.prototype=q.prototype,new u)},r=h(5),l=h(23),p=h(8),m=h(0);d=function(n){function q(u){var w=n.call(this)||this;w._textPadding=5;if(null==u)throw Error("InterpolatedColorLegend requires a interpolatedColorScale");w._scale=u;w._redrawCallback=function(){return w.redraw()};w._scale.onUpdate(w._redrawCallback);w._formatter=p.general();w._orientation="horizontal";w._expands=!1;w.addClass("legend");w.addClass("interpolated-color-legend");return w}k(q,
n);q.prototype.destroy=function(){n.prototype.destroy.call(this);this._scale.offUpdate(this._redrawCallback)};q.prototype.formatter=function(u){if(void 0===u)return this._formatter;this._formatter=u;this.redraw();return this};q.prototype.expands=function(){return this._expands};q._ensureOrientation=function(u){u=u.toLowerCase();if("horizontal"===u||"left"===u||"right"===u)return u;throw Error('"'+u+'" is not a valid orientation for InterpolatedColorLegend');};q.prototype.orientation=function(u){if(null==
u)return this._orientation;this._orientation=q._ensureOrientation(u);this.redraw();return this};q.prototype.fixedWidth=function(){return!this.expands()||this._isVertical()};q.prototype.fixedHeight=function(){return!this.expands()||!this._isVertical()};q.prototype._generateTicks=function(u){void 0===u&&(u=q._DEFAULT_NUM_SWATCHES);var w=this._scale.domain();if(1===u)return[w[0]];for(var A=(w[1]-w[0])/(u-1),y=[],x=0;x<u;x++)y.push(w[0]+A*x);return y};q.prototype._setup=function(){n.prototype._setup.call(this);
this._swatchContainer=this.content().append("g").classed("swatch-container",!0);this._swatchBoundingBox=this.content().append("rect").classed("swatch-bounding-box",!0);this._lowerLabel=this.content().append("g").classed(q.LEGEND_LABEL_CLASS,!0);this._upperLabel=this.content().append("g").classed(q.LEGEND_LABEL_CLASS,!0);var u=new r.SvgContext(this.content().node());this._measurer=new r.Measurer(u);this._wrapper=new r.Wrapper;this._writer=new r.Writer(this._measurer,u,this._wrapper)};q.prototype.requestedSpace=
function(){var u=this,w=this._measurer.measure().height,A=this._scale.domain().map(function(C){return u._measurer.measure(u._formatter(C)).width}),y=q._DEFAULT_NUM_SWATCHES;if(this._isVertical()){var x=m.Math.max(A,0);A=w+w+this._textPadding+x+this._textPadding;x=y*w}else x=w+w+w,A=this._textPadding+A[0]+y*w+A[1]+this._textPadding;return{minWidth:A,minHeight:x}};q.prototype._isVertical=function(){return"horizontal"!==this._orientation};q.prototype.renderImmediately=function(){var u=this;n.prototype.renderImmediately.call(this);
var w=this._scale.domain(),A=this._formatter(w[0]),y=this._measurer.measure(A).width,x=this._formatter(w[1]);w=this._measurer.measure(x).width;var C=this._measurer.measure().height,G=this._textPadding,D=0,B=0,H=0,K=0,L={xAlign:"center",yAlign:"center",textRotation:0},J={xAlign:"center",yAlign:"center",textRotation:0},O={x:0,y:0,width:0,height:0};if(this._isVertical()){var S=Math.floor(this.height());var N=Math.max(y,w);var R=(this.width()-N-2*this._textPadding)/2;w=Math.max(this.width()-R-2*G-N,0);
C=1;var X=function(fa,oa){return u.height()-(oa+1)};J.yAlign="top";B=0;L.yAlign="bottom";K=0;if("left"===this._orientation){var aa=function(){return G+N+G};J.xAlign="right";D=-(R+w+G);L.xAlign="right";H=-(R+w+G)}else aa=function(){return R},J.xAlign="left",D=R+w+G,L.xAlign="left",H=R+w+G;O.width=w;O.height=S*C}else R=Math.max(G,(this.height()-C)/2),S=Math.max(Math.floor(this.width()-4*G-y-w),0),w=1,C=Math.max(this.height()-2*R,0),aa=function(fa,oa){return Math.floor(y+2*G)+oa},X=function(){return R},
J.xAlign="right",D=-G,L.xAlign="left",H=G,O.y=R,O.width=S*w,O.height=C;O.x=aa(null,0);this._upperLabel.text("");this._writer.write(x,this.width(),this.height(),J,this._upperLabel.node());this._upperLabel.attr("transform","translate("+D+", "+B+")");this._lowerLabel.text("");this._writer.write(A,this.width(),this.height(),L,this._lowerLabel.node());this._lowerLabel.attr("transform","translate("+H+", "+K+")");this._swatchBoundingBox.attrs(O);A=this._generateTicks(S);A=this._swatchContainer.selectAll("rect.swatch").data(A);
x=A.enter().append("rect").classed("swatch",!0);D=A.merge(x);A.exit().remove();D.attrs({fill:function(fa){return u._scale.scale(fa)},width:w,height:C,x:aa,y:X,"shape-rendering":"crispEdges"});l.ADD_TITLE_ELEMENTS&&x.append("title").text(function(fa){return u._formatter(fa)});return this};return q}(h(4).Component);d._DEFAULT_NUM_SWATCHES=11;d.LEGEND_LABEL_CLASS="legend-label";f.InterpolatedColorLegend=d},function(d,f,h){var k=this&&this.__extends||function(l,p){function m(){this.constructor=l}for(var n in p)p.hasOwnProperty(n)&&
(l[n]=p[n]);l.prototype=null===p?Object.create(p):(m.prototype=p.prototype,new m)},r=h(5);d=function(l){function p(m,n){void 0===m&&(m="");void 0===n&&(n=0);var q=l.call(this)||this;q.addClass("label");q.text(m);q.angle(n);q.xAlignment("center").yAlignment("center");q._padding=0;return q}k(p,l);p.prototype.requestedSpace=function(){var m=this._measurer.measure(this._text),n=(0===this.angle()?m.width:m.height)+2*this.padding();m=(0===this.angle()?m.height:m.width)+2*this.padding();return{minWidth:n,
minHeight:m}};p.prototype._setup=function(){l.prototype._setup.call(this);this._textContainer=this.content().append("g");var m=new r.SvgContext(this._textContainer.node());this._measurer=new r.CacheMeasurer(m);this._wrapper=new r.Wrapper;this._writer=new r.Writer(this._measurer,m,this._wrapper);this.text(this._text)};p.prototype.text=function(m){if(null==m)return this._text;if("string"!==typeof m)throw Error("Label.text() only takes strings as input");this._text=m;this.redraw();return this};p.prototype.angle=
function(m){if(null==m)return this._angle;m%=360;180<m?m-=360:-180>m&&(m+=360);if(-90===m||0===m||90===m)this._angle=m;else throw Error(m+" is not a valid angle for Label");this.redraw();return this};p.prototype.padding=function(m){if(null==m)return this._padding;m=+m;if(0>m)throw Error(m+" is not a valid padding value. Cannot be less than 0.");this._padding=m;this.redraw();return this};p.prototype.fixedWidth=function(){return!0};p.prototype.fixedHeight=function(){return!0};p.prototype.renderImmediately=
function(){l.prototype.renderImmediately.call(this);this._textContainer.selectAll("g").remove();var m=this._measurer.measure(this._text),n=Math.max(Math.min((this.height()-m.height)/2,this.padding()),0);m=Math.max(Math.min((this.width()-m.width)/2,this.padding()),0);this._textContainer.attr("transform","translate("+m+","+n+")");m=this.width()-2*m;n=this.height()-2*n;var q={xAlign:this.xAlignment(),yAlign:this.yAlignment(),textRotation:this.angle()};this._writer.write(this._text,m,n,q);return this};
p.prototype.invalidateCache=function(){l.prototype.invalidateCache.call(this);this._measurer.reset()};return p}(h(4).Component);f.Label=d;h=function(l){function p(m,n){m=l.call(this,m,n)||this;m.addClass(p.TITLE_LABEL_CLASS);return m}k(p,l);return p}(d);h.TITLE_LABEL_CLASS="title-label";f.TitleLabel=h;d=function(l){function p(m,n){m=l.call(this,m,n)||this;m.addClass(p.AXIS_LABEL_CLASS);return m}k(p,l);return p}(d);d.AXIS_LABEL_CLASS="axis-label";f.AxisLabel=d},function(d,f,h){var k=this&&this.__extends||
function(A,y){function x(){this.constructor=A}for(var C in y)y.hasOwnProperty(C)&&(A[C]=y[C]);A.prototype=null===y?Object.create(y):(x.prototype=y.prototype,new x)},r=h(1),l=h(5),p=h(23),m=h(8),n=h(31),q=h(0);d=h(4);var u=function(){function A(y,x,C){void 0===y&&(y=[]);void 0===x&&(x=0);void 0===C&&(C=Infinity);this.columns=y;this.bottomPadding=x;this.maxWidth=C}A.prototype.addColumn=function(y){var x=y.width,C=this.getWidthAvailable();y.width=Math.min(C,x);this.columns.push(y)};A.prototype.getBounds=
function(y){for(var x=this.columns[y],C=0,G=0;G<y;G++)C+=this.columns[G].width;return{topLeft:{x:C,y:0},bottomRight:{x:C+x.width,y:x.height}}};A.prototype.getHeight=function(){return q.Math.max(this.columns.map(function(y){return y.height}),0)+this.bottomPadding};A.prototype.getWidth=function(){return Math.min(this.columns.reduce(function(y,x){return y+x.width},0),this.maxWidth)};A.prototype.getWidthAvailable=function(){var y=this.getWidth();return Math.max(this.maxWidth-y,0)};return A}(),w=function(){function A(y,
x,C,G){void 0===y&&(y=Infinity);void 0===x&&(x=Infinity);void 0===C&&(C=0);void 0===G&&(G=[]);this.maxWidth=y;this.maxHeight=x;this.padding=C;this.rows=G}A.prototype.addRow=function(y){y.maxWidth=this.maxWidth-2*this.padding;this.rows.push(y)};A.prototype.getColumnBounds=function(y,x){var C=this.getRowBounds(y);y=this.rows[y].getBounds(x);y.topLeft.x+=C.topLeft.x;y.bottomRight.x+=C.topLeft.x;y.topLeft.y+=C.topLeft.y;y.bottomRight.y+=C.topLeft.y;return y};A.prototype.getRowBounds=function(y){for(var x=
this.padding,C=this.padding,G=0;G<y;G++)C+=this.rows[G].getHeight();return{topLeft:{x,y:C},bottomRight:{x:x+this.rows[y].getWidth(),y:C+this.rows[y].getHeight()}}};A.prototype.getHeight=function(){return Math.min(this.rows.reduce(function(y,x){return y+x.getHeight()},0)+2*this.padding,this.maxHeight)};A.prototype.getWidth=function(){return Math.min(q.Math.max(this.rows.map(function(y){return y.getWidth()}),0)+2*this.padding,this.maxWidth)};return A}();d=function(A){function y(x){var C=A.call(this)||
this;C._padding=5;C._rowBottomPadding=3;C.addClass("legend");C.maxEntriesPerRow(1);if(null==x)throw Error("Legend requires a colorScale");C._colorScale=x;C._redrawCallback=function(){return C.redraw()};C._colorScale.onUpdate(C._redrawCallback);C._formatter=m.identity();C.maxLinesPerEntry(1);C.xAlignment("right").yAlignment("top");C.comparator(function(G,D){var B=C._colorScale.domain().slice().map(function(H){return C._formatter(H)});return B.indexOf(G)-B.indexOf(D)});C._symbolFactoryAccessor=function(){return n.circle()};
C._symbolOpacityAccessor=function(){return 1};return C}k(y,A);y.prototype._setup=function(){A.prototype._setup.call(this);var x=this.content().append("g").classed(y.LEGEND_ROW_CLASS,!0);x.append("g").classed(y.LEGEND_ENTRY_CLASS,!0).append("text");x=new l.SvgContext(x.node(),null,p.ADD_TITLE_ELEMENTS);this._measurer=new l.CacheMeasurer(x);this._wrapper=(new l.Wrapper).maxLines(this.maxLinesPerEntry());this._writer=new l.Writer(this._measurer,x,this._wrapper)};y.prototype.formatter=function(x){if(null==
x)return this._formatter;this._formatter=x;this.redraw();return this};y.prototype.maxEntriesPerRow=function(x){if(null==x)return this._maxEntriesPerRow;this._maxEntriesPerRow=x;this.redraw();return this};y.prototype.maxLinesPerEntry=function(x){if(null==x)return this._maxLinesPerEntry;this._maxLinesPerEntry=x;this.redraw();return this};y.prototype.maxWidth=function(x){if(null==x)return this._maxWidth;this._maxWidth=x;this.redraw();return this};y.prototype.comparator=function(x){null!=x&&(this._comparator=
x,this.redraw())};y.prototype.colorScale=function(x){return null!=x?(this._colorScale.offUpdate(this._redrawCallback),this._colorScale=x,this._colorScale.onUpdate(this._redrawCallback),this.redraw(),this):this._colorScale};y.prototype.destroy=function(){A.prototype.destroy.call(this);this._colorScale.offUpdate(this._redrawCallback)};y.prototype._buildLegendTable=function(x,C){var G=this,D=this._measurer.measure().height,B=new w(x,C,this._padding);x=this._colorScale.domain().slice().sort(function(K,
L){return G._comparator(G._formatter(K),G._formatter(L))});var H=new u;B.addRow(H);H.bottomPadding=this._rowBottomPadding;x.forEach(function(K){H.columns.length/2===G.maxEntriesPerRow()&&(H=new u,H.bottomPadding=G._rowBottomPadding,B.addRow(H));var L=H.getWidthAvailable(),J=G._formatter(K),O=G._measurer.measure(J).width;0>L-D-O&&1<H.columns.length&&(H=new u,H.bottomPadding=G._rowBottomPadding,B.addRow(H));H.addColumn({width:D,height:D,data:{name:K,type:"symbol"}});L=H.getWidthAvailable();L=Math.min(L,
O);G._wrapper.maxLines(G.maxLinesPerEntry());J=G._wrapper.wrap(J,G._measurer,L).noLines*D;H.addColumn({width:L,height:J,data:{name:K,type:"text"}})});return B};y.prototype.requestedSpace=function(x,C){x=this._buildLegendTable(q.Math.min([this.maxWidth(),x],x),C);return{minHeight:x.getHeight(),minWidth:x.getWidth()}};y.prototype.entitiesAt=function(x){var C=this;if(!this._isSetup)return[];var G=this._buildLegendTable(this.width(),this.height());return G.rows.reduce(function(D,B,H){if(0!==D.length)return D;
var K=G.getRowBounds(H);return q.Math.within(x,K)?B.columns.reduce(function(L,J,O){var S=G.getColumnBounds(H,O);if(q.Math.within(x,S)){L=C.content().selectAll("."+y.LEGEND_ROW_CLASS).nodes()[H];O=r.select(L).selectAll("."+y.LEGEND_ENTRY_CLASS).nodes()[Math.floor(O/2)];var N=r.select(O).select("."+y.LEGEND_SYMBOL_CLASS);S=q.DOM.getTranslateValues(r.select(L));N=q.DOM.getTranslateValues(N);return[{bounds:q.DOM.elementBBox(r.select(L)),datum:J.data.name,position:{x:S[0]+N[0],y:S[1]+N[1]},selection:r.select(O),
component:C}]}return L},D):D},[])};y.prototype.renderImmediately=function(){A.prototype.renderImmediately.call(this);var x=this._buildLegendTable(this.width(),this.height());this.content().selectAll("*").remove();var C=this.content().selectAll("g."+y.LEGEND_ROW_CLASS).data(x.rows),G=C.enter().append("g").classed(y.LEGEND_ROW_CLASS,!0).merge(C);C.exit().remove();G.attr("transform",function(B,H){B=x.getRowBounds(H);return"translate("+B.topLeft.x+", "+B.topLeft.y+")"});var D=this;G.each(function(B,H){for(var K=
[],L=0;L<B.columns.length;L+=2)K.push([B.columns[L],B.columns[L+1]]);B=r.select(this).selectAll("g."+y.LEGEND_ENTRY_CLASS).data(K);K=B.enter().append("g").classed(y.LEGEND_ENTRY_CLASS,!0).merge(B);K.append("path").attr("d",function(J){J=J[0];return D.symbol()(J.data.name,H)(.6*J.height)(null)}).attr("transform",function(J){J=J[0];return"translate("+(x.getColumnBounds(H,x.rows[H].columns.indexOf(J)).topLeft.x+J.width/2)+", "+J.height/2+")"}).attr("fill",function(J){return D._colorScale.scale(J[0].data.name)}).attr("opacity",
function(J){return D.symbolOpacity()(J[0].data.name,H)}).classed(y.LEGEND_SYMBOL_CLASS,!0);K.append("g").classed("text-container",!0).attr("transform",function(J){return"translate("+x.getColumnBounds(H,x.rows[H].columns.indexOf(J[1])).topLeft.x+", 0)"}).each(function(J){var O=r.select(this);J=J[1];D._writer.write(D._formatter(J.data.name),J.width,D.height(),{xAlign:"left",yAlign:"top",textRotation:0},O.node())});B.exit().remove()});return this};y.prototype.symbol=function(x){if(null==x)return this._symbolFactoryAccessor;
this._symbolFactoryAccessor=x;this.render();return this};y.prototype.symbolOpacity=function(){return this._symbolOpacityAccessor};y.prototype.fixedWidth=function(){return!0};y.prototype.fixedHeight=function(){return!0};y.prototype.invalidateCache=function(){this._measurer.reset()};return y}(d.Component);d.LEGEND_ROW_CLASS="legend-row";d.LEGEND_ENTRY_CLASS="legend-entry";d.LEGEND_SYMBOL_CLASS="legend-symbol";f.Legend=d},function(d,f,h){var k=this&&this.__extends||function(p,m){function n(){this.constructor=
p}for(var q in m)m.hasOwnProperty(q)&&(p[q]=m[q]);p.prototype=null===m?Object.create(m):(n.prototype=m.prototype,new n)},r=h(2),l=h(0);d=function(p){function m(){return null!==p&&p.apply(this,arguments)||this}k(m,p);m.prototype.entityNearest=function(n){var q,u=Infinity;this.components().forEach(function(w){w=w.entityNearest(n);if(null!=w){var A=l.Math.distanceSquared(w.position,n);A<=u&&(u=A,q=w)}});return q};m.prototype.append=function(n){if(null!=n&&!(n instanceof r.Plot))throw Error("Plot Group only accepts plots");
p.prototype.append.call(this,n);return this};return m}(h(41).Group);f.PlotGroup=d},function(d,f,h){var k=this&&this.__extends||function(p,m){function n(){this.constructor=p}for(var q in m)m.hasOwnProperty(q)&&(p[q]=m[q]);p.prototype=null===m?Object.create(m):(n.prototype=m.prototype,new n)},r=h(1),l=h(0);d=function(p){function m(n){void 0===n&&(n=[]);var q=p.call(this)||this;q._rowPadding=0;q._columnPadding=0;q._rows=[];q._rowWeights=[];q._columnWeights=[];q._nRows=0;q._nCols=0;q._calculatedLayout=
null;q.addClass("table");n.forEach(function(u,w){u.forEach(function(A,y){null!=A&&q.add(A,w,y)})});return q}k(m,p);m.prototype._forEach=function(n){for(var q=0;q<this._nRows;q++)for(var u=0;u<this._nCols;u++)null!=this._rows[q][u]&&n(this._rows[q][u])};m.prototype.has=function(n){for(var q=0;q<this._nRows;q++)for(var u=0;u<this._nCols;u++)if(this._rows[q][u]===n)return!0;return!1};m.prototype.componentAt=function(n,q){return 0>n||n>=this._nRows||0>q||q>=this._nCols?null:this._rows[n][q]};m.prototype.add=
function(n,q,u){if(null==n)throw Error("Cannot add null to a table cell");if(!this.has(n)){if(null!=(this._rows[q]&&this._rows[q][u]))throw Error("cell is occupied");n.detach();this._nRows=Math.max(q+1,this._nRows);this._nCols=Math.max(u+1,this._nCols);this._padTableToSize(this._nRows,this._nCols);this._rows[q][u]=n;this._adoptAndAnchor(n);this.redraw()}return this};m.prototype._remove=function(n){for(var q=0;q<this._nRows;q++)for(var u=0;u<this._nCols;u++)if(this._rows[q][u]===n){this._rows[q][u]=
null;return}};m.prototype._iterateLayout=function(n,q,u){void 0===u&&(u=!1);var w=this._rows,A=r.transpose(this._rows);n-=this._columnPadding*(this._nCols-1);q-=this._rowPadding*(this._nRows-1);w=m._calcComponentWeights(this._rowWeights,w,function(N){return null==N||N.fixedHeight()});A=m._calcComponentWeights(this._columnWeights,A,function(N){return null==N||N.fixedWidth()});var y=A.map(function(N){return 0===N?.5:N}),x=w.map(function(N){return 0===N?.5:N});y=m._calcProportionalSpace(y,n);var C=m._calcProportionalSpace(x,
q),G=l.Array.createFilledArray(0,this._nCols),D=l.Array.createFilledArray(0,this._nRows);x=0;for(var B,H,K;;){D=l.Array.add(D,C);y=l.Array.add(G,y);B=this._determineGuarantees(y,D,u);G=B.guaranteedWidths;D=B.guaranteedHeights;H=B.wantsWidthArr.some(function(N){return N});K=B.wantsHeightArr.some(function(N){return N});var L=O,J=S;var O=n-r.sum(B.guaranteedWidths);var S=q-r.sum(B.guaranteedHeights);y=void 0;H?(y=B.wantsWidthArr.map(function(N){return N?.1:0}),y=l.Array.add(y,A)):y=A;C=void 0;K?(C=B.wantsHeightArr.map(function(N){return N?
.1:0}),C=l.Array.add(C,w)):C=w;y=m._calcProportionalSpace(y,O);C=m._calcProportionalSpace(C,S);x++;J=0<S&&S!==J;if(!(0<O&&O!==L||J))break;if(5<x)break}O=n-r.sum(B.guaranteedWidths);S=q-r.sum(B.guaranteedHeights);y=m._calcProportionalSpace(A,O);C=m._calcProportionalSpace(w,S);return{colProportionalSpace:y,rowProportionalSpace:C,guaranteedWidths:B.guaranteedWidths,guaranteedHeights:B.guaranteedHeights,wantsWidth:H,wantsHeight:K}};m.prototype._determineGuarantees=function(n,q,u){void 0===u&&(u=!1);var w=
l.Array.createFilledArray(0,this._nCols),A=l.Array.createFilledArray(0,this._nRows),y=l.Array.createFilledArray(!1,this._nCols),x=l.Array.createFilledArray(!1,this._nRows);this._rows.forEach(function(C,G){C.forEach(function(D,B){D=null!=D?D.requestedSpace(n[B],q[G]):{minWidth:0,minHeight:0};w[B]=Math.max(w[B],u?Math.min(D.minWidth,n[B]):D.minWidth);A[G]=Math.max(A[G],u?Math.min(D.minHeight,q[G]):D.minHeight);var H=D.minWidth>n[B];y[B]=y[B]||H;B=D.minHeight>q[G];x[G]=x[G]||B})});return{guaranteedWidths:w,
guaranteedHeights:A,wantsWidthArr:y,wantsHeightArr:x}};m.prototype.requestedSpace=function(n,q){this._calculatedLayout=this._iterateLayout(n,q);return{minWidth:r.sum(this._calculatedLayout.guaranteedWidths),minHeight:r.sum(this._calculatedLayout.guaranteedHeights)}};m.prototype.computeLayout=function(n,q,u){var w=this;p.prototype.computeLayout.call(this,n,q,u);n=r.sum(this._calculatedLayout.guaranteedWidths);q=r.sum(this._calculatedLayout.guaranteedHeights);u=this._calculatedLayout;if(n>this.width()||
q>this.height())u=this._iterateLayout(this.width(),this.height(),!0);var A=0,y=l.Array.add(u.rowProportionalSpace,u.guaranteedHeights),x=l.Array.add(u.colProportionalSpace,u.guaranteedWidths);this._rows.forEach(function(C,G){var D=0;C.forEach(function(B,H){null!=B&&B.computeLayout({x:D,y:A},x[H],y[G]);D+=x[H]+w._columnPadding});A+=y[G]+w._rowPadding});return this};m.prototype.rowPadding=function(n){if(null==n)return this._rowPadding;if(!l.Math.isValidNumber(n)||0>n)throw Error("rowPadding must be a non-negative finite value");
this._rowPadding=n;this.redraw();return this};m.prototype.columnPadding=function(n){if(null==n)return this._columnPadding;if(!l.Math.isValidNumber(n)||0>n)throw Error("columnPadding must be a non-negative finite value");this._columnPadding=n;this.redraw();return this};m.prototype.rowWeight=function(n,q){if(null==q)return this._rowWeights[n];if(!l.Math.isValidNumber(q)||0>q)throw Error("rowWeight must be a non-negative finite value");this._rowWeights[n]=q;this.redraw();return this};m.prototype.columnWeight=
function(n,q){if(null==q)return this._columnWeights[n];if(!l.Math.isValidNumber(q)||0>q)throw Error("columnWeight must be a non-negative finite value");this._columnWeights[n]=q;this.redraw();return this};m.prototype.fixedWidth=function(){var n=r.transpose(this._rows);return m._fixedSpace(n,function(q){return null==q||q.fixedWidth()})};m.prototype.fixedHeight=function(){return m._fixedSpace(this._rows,function(n){return null==n||n.fixedHeight()})};m.prototype._padTableToSize=function(n,q){for(var u=
0;u<n;u++){void 0===this._rows[u]&&(this._rows[u]=[],this._rowWeights[u]=null);for(var w=0;w<q;w++)void 0===this._rows[u][w]&&(this._rows[u][w]=null)}for(w=0;w<q;w++)void 0===this._columnWeights[w]&&(this._columnWeights[w]=null)};m._calcComponentWeights=function(n,q,u){return n.map(function(w,A){return null!=w?w:q[A].map(u).reduce(function(y,x){return y&&x},!0)?0:1})};m._calcProportionalSpace=function(n,q){var u=r.sum(n);return 0===u?l.Array.createFilledArray(0,n.length):n.map(function(w){return q*
w/u})};m._fixedSpace=function(n,q){function u(w){return w.reduce(function(A,y){return A&&y},!0)}return u(n.map(function(w){return u(w.map(q))}))};return m}(h(29).ComponentContainer);f.Table=d},function(d,f,h){var k=this&&this.__extends||function(r,l){function p(){this.constructor=r}for(var m in l)l.hasOwnProperty(m)&&(r[m]=l[m]);r.prototype=null===l?Object.create(l):(p.prototype=l.prototype,new p)};d=function(r){function l(){var p=r.call(this)||this;p.addClass("x-drag-box-layer");p._hasCorners=!1;
return p}k(l,r);l.prototype.computeLayout=function(p,m,n){r.prototype.computeLayout.call(this,p,m,n);this._setBounds(this.bounds());return this};l.prototype._setBounds=function(p){r.prototype._setBounds.call(this,{topLeft:{x:p.topLeft.x,y:0},bottomRight:{x:p.bottomRight.x,y:this.height()}})};l.prototype._setResizableClasses=function(p){p&&this.enabled()?this.addClass("x-resizable"):this.removeClass("x-resizable")};l.prototype.yScale=function(p){if(null==p)return r.prototype.yScale.call(this);throw Error("yScales cannot be set on an XDragBoxLayer");
};l.prototype.yExtent=function(){return r.prototype.yExtent.call(this);throw Error("XDragBoxLayer has no yExtent");};return l}(h(32).DragBoxLayer);f.XDragBoxLayer=d},function(d,f,h){var k=this&&this.__extends||function(r,l){function p(){this.constructor=r}for(var m in l)l.hasOwnProperty(m)&&(r[m]=l[m]);r.prototype=null===l?Object.create(l):(p.prototype=l.prototype,new p)};d=function(r){function l(){var p=r.call(this)||this;p.addClass("y-drag-box-layer");p._hasCorners=!1;return p}k(l,r);l.prototype.computeLayout=
function(p,m,n){r.prototype.computeLayout.call(this,p,m,n);this._setBounds(this.bounds());return this};l.prototype._setBounds=function(p){r.prototype._setBounds.call(this,{topLeft:{x:0,y:p.topLeft.y},bottomRight:{x:this.width(),y:p.bottomRight.y}})};l.prototype._setResizableClasses=function(p){p&&this.enabled()?this.addClass("y-resizable"):this.removeClass("y-resizable")};l.prototype.xScale=function(p){if(null==p)return r.prototype.xScale.call(this);throw Error("xScales cannot be set on an YDragBoxLayer");
};l.prototype.xExtent=function(){return r.prototype.xExtent.call(this);throw Error("YDragBoxLayer has no xExtent");};return l}(h(32).DragBoxLayer);f.YDragBoxLayer=d},function(d,f,h){var k=this&&this.__extends||function(r,l){function p(){this.constructor=r}for(var m in l)l.hasOwnProperty(m)&&(r[m]=l[m]);r.prototype=null===l?Object.create(l):(p.prototype=l.prototype,new p)};d=function(r){function l(){var p=r.call(this)||this;p._eventToProcessingFunction[l._KEYDOWN_EVENT_NAME]=function(m){return p._processKeydown(m)};
p._eventToProcessingFunction[l._KEYUP_EVENT_NAME]=function(m){return p._processKeyup(m)};return p}k(l,r);l.getDispatcher=function(){var p=document[l._DISPATCHER_KEY];null==p&&(p=new l,document[l._DISPATCHER_KEY]=p);return p};l.prototype._processKeydown=function(p){this._callCallbacksForEvent(l._KEYDOWN_EVENT_NAME,p.keyCode,p)};l.prototype._processKeyup=function(p){this._callCallbacksForEvent(l._KEYUP_EVENT_NAME,p.keyCode,p)};l.prototype.onKeyDown=function(p){this._addCallbackForEvent(l._KEYDOWN_EVENT_NAME,
p)};l.prototype.offKeyDown=function(p){this._removeCallbackForEvent(l._KEYDOWN_EVENT_NAME,p)};l.prototype.onKeyUp=function(p){this._addCallbackForEvent(l._KEYUP_EVENT_NAME,p)};l.prototype.offKeyUp=function(p){this._removeCallbackForEvent(l._KEYUP_EVENT_NAME,p)};return l}(h(24).Dispatcher);d._DISPATCHER_KEY="__Plottable_Dispatcher_Key";d._KEYDOWN_EVENT_NAME="keydown";d._KEYUP_EVENT_NAME="keyup";f.Key=d},function(d,f,h){var k=this&&this.__extends||function(l,p){function m(){this.constructor=l}for(var n in p)p.hasOwnProperty(n)&&
(l[n]=p[n]);l.prototype=null===p?Object.create(p):(m.prototype=p.prototype,new m)},r=h(0);d=function(l){function p(m){function n(u){return q._measureAndDispatch(m,u,p._MOUSEMOVE_EVENT_NAME,"page")}var q=l.call(this)||this;q._lastMousePosition={x:-1,y:-1};q._translator=r.getTranslator(m);q._eventToProcessingFunction[p._MOUSEOVER_EVENT_NAME]=n;q._eventToProcessingFunction[p._MOUSEMOVE_EVENT_NAME]=n;q._eventToProcessingFunction[p._MOUSEOUT_EVENT_NAME]=n;q._eventToProcessingFunction[p._MOUSEDOWN_EVENT_NAME]=
function(u){return q._measureAndDispatch(m,u,p._MOUSEDOWN_EVENT_NAME)};q._eventToProcessingFunction[p._MOUSEUP_EVENT_NAME]=function(u){return q._measureAndDispatch(m,u,p._MOUSEUP_EVENT_NAME,"page")};q._eventToProcessingFunction[p._WHEEL_EVENT_NAME]=function(u){return q._measureAndDispatch(m,u,p._WHEEL_EVENT_NAME)};q._eventToProcessingFunction[p._DBLCLICK_EVENT_NAME]=function(u){return q._measureAndDispatch(m,u,p._DBLCLICK_EVENT_NAME)};return q}k(p,l);p.getDispatcher=function(m){var n=m.root().rootElement(),
q=n[p._DISPATCHER_KEY];null==q&&(q=new p(m),n[p._DISPATCHER_KEY]=q);return q};p.prototype.onMouseMove=function(m){this._addCallbackForEvent(p._MOUSEMOVE_EVENT_NAME,m)};p.prototype.offMouseMove=function(m){this._removeCallbackForEvent(p._MOUSEMOVE_EVENT_NAME,m)};p.prototype.onMouseDown=function(m){this._addCallbackForEvent(p._MOUSEDOWN_EVENT_NAME,m)};p.prototype.offMouseDown=function(m){this._removeCallbackForEvent(p._MOUSEDOWN_EVENT_NAME,m)};p.prototype.onMouseUp=function(m){this._addCallbackForEvent(p._MOUSEUP_EVENT_NAME,
m)};p.prototype.offMouseUp=function(m){this._removeCallbackForEvent(p._MOUSEUP_EVENT_NAME,m)};p.prototype.onWheel=function(m){this._addCallbackForEvent(p._WHEEL_EVENT_NAME,m);return this};p.prototype.offWheel=function(m){this._removeCallbackForEvent(p._WHEEL_EVENT_NAME,m)};p.prototype.onDblClick=function(m){this._addCallbackForEvent(p._DBLCLICK_EVENT_NAME,m)};p.prototype.offDblClick=function(m){this._removeCallbackForEvent(p._DBLCLICK_EVENT_NAME,m)};p.prototype._measureAndDispatch=function(m,n,q,
u){void 0===u&&(u="element");if("page"!==u&&"element"!==u)throw Error("Invalid scope '"+u+"', must be 'element' or 'page'");if("page"===u||this.eventInside(m,n))this._lastMousePosition=this._translator.computePosition(n.clientX,n.clientY),this._callCallbacksForEvent(q,this.lastMousePosition(),n)};p.prototype.eventInside=function(m,n){return r.Translator.isEventInside(m,n)};p.prototype.lastMousePosition=function(){return this._lastMousePosition};return p}(h(24).Dispatcher);d._DISPATCHER_KEY="__Plottable_Dispatcher_Mouse";
d._MOUSEOVER_EVENT_NAME="mouseover";d._MOUSEMOVE_EVENT_NAME="mousemove";d._MOUSEOUT_EVENT_NAME="mouseout";d._MOUSEDOWN_EVENT_NAME="mousedown";d._MOUSEUP_EVENT_NAME="mouseup";d._WHEEL_EVENT_NAME="wheel";d._DBLCLICK_EVENT_NAME="dblclick";f.Mouse=d},function(d,f,h){var k=this&&this.__extends||function(l,p){function m(){this.constructor=l}for(var n in p)p.hasOwnProperty(n)&&(l[n]=p[n]);l.prototype=null===p?Object.create(p):(m.prototype=p.prototype,new m)},r=h(0);d=function(l){function p(m){var n=l.call(this)||
this;n._translator=r.getTranslator(m);n._eventToProcessingFunction[p._TOUCHSTART_EVENT_NAME]=function(q){return n._measureAndDispatch(m,q,p._TOUCHSTART_EVENT_NAME,"page")};n._eventToProcessingFunction[p._TOUCHMOVE_EVENT_NAME]=function(q){return n._measureAndDispatch(m,q,p._TOUCHMOVE_EVENT_NAME,"page")};n._eventToProcessingFunction[p._TOUCHEND_EVENT_NAME]=function(q){return n._measureAndDispatch(m,q,p._TOUCHEND_EVENT_NAME,"page")};n._eventToProcessingFunction[p._TOUCHCANCEL_EVENT_NAME]=function(q){return n._measureAndDispatch(m,
q,p._TOUCHCANCEL_EVENT_NAME,"page")};return n}k(p,l);p.getDispatcher=function(m){var n=m.root().rootElement(),q=n[p._DISPATCHER_KEY];null==q&&(q=new p(m),n[p._DISPATCHER_KEY]=q);return q};p.prototype.onTouchStart=function(m){this._addCallbackForEvent(p._TOUCHSTART_EVENT_NAME,m)};p.prototype.offTouchStart=function(m){this._removeCallbackForEvent(p._TOUCHSTART_EVENT_NAME,m)};p.prototype.onTouchMove=function(m){this._addCallbackForEvent(p._TOUCHMOVE_EVENT_NAME,m)};p.prototype.offTouchMove=function(m){this._removeCallbackForEvent(p._TOUCHMOVE_EVENT_NAME,
m)};p.prototype.onTouchEnd=function(m){this._addCallbackForEvent(p._TOUCHEND_EVENT_NAME,m)};p.prototype.offTouchEnd=function(m){this._removeCallbackForEvent(p._TOUCHEND_EVENT_NAME,m)};p.prototype.onTouchCancel=function(m){this._addCallbackForEvent(p._TOUCHCANCEL_EVENT_NAME,m)};p.prototype.offTouchCancel=function(m){this._removeCallbackForEvent(p._TOUCHCANCEL_EVENT_NAME,m)};p.prototype._measureAndDispatch=function(m,n,q,u){void 0===u&&(u="element");if("page"!==u&&"element"!==u)throw Error("Invalid scope '"+
u+"', must be 'element' or 'page'");if("element"!==u||this.eventInside(m,n)){m=n.changedTouches;u={};for(var w=[],A=0;A<m.length;A++){var y=m[A],x=y.identifier;y=this._translator.computePosition(y.clientX,y.clientY);null!=y&&(u[x]=y,w.push(x))}0<w.length&&this._callCallbacksForEvent(q,w,u,n)}};p.prototype.eventInside=function(m,n){return r.Translator.isEventInside(m,n)};return p}(h(24).Dispatcher);d._DISPATCHER_KEY="__Plottable_Dispatcher_Touch";d._TOUCHSTART_EVENT_NAME="touchstart";d._TOUCHMOVE_EVENT_NAME=
"touchmove";d._TOUCHEND_EVENT_NAME="touchend";d._TOUCHCANCEL_EVENT_NAME="touchcancel";f.Touch=d},function(d,f){d=function(){function h(k,r,l){void 0===l&&(l=window.devicePixelRatio);this.screenWidth=k;this.screenHeight=r;this.devicePixelRatio=l;this.pixelWidth=k*l;this.pixelHeight=r*l;this.canvas=document.createElement("canvas");this.ctx=this.canvas.getContext("2d");h.sizePixels(this.ctx,k,r,l)}h.sizePixels=function(k,r,l,p){var m=k.canvas;m.width=r*p;m.height=l*p;m.style.width=r+"px";m.style.height=
l+"px";k.setTransform(1,0,0,1,0,0);k.scale(p,p)};h.prototype.blit=function(k,r,l){void 0===r&&(r=0);void 0===l&&(l=0);k.drawImage(this.canvas,r,l,this.screenWidth,this.screenHeight)};h.prototype.blitCenter=function(k,r,l){void 0===r&&(r=0);void 0===l&&(l=0);this.blit(k,Math.floor(r-this.screenWidth/2),Math.floor(l-this.screenHeight/2))};h.prototype.resize=function(k,r,l){void 0===l&&(l=!1);var p=this.devicePixelRatio;this.screenWidth=k;this.screenHeight=r;this.pixelWidth=k*p;this.pixelHeight=r*p;
h.sizePixels(this.ctx,k,r,p);l&&this.ctx.translate(k/2,k/2);return this};h.prototype.clear=function(k){var r=this.pixelWidth,l=this.pixelHeight,p=this.ctx;p.save();p.setTransform(1,0,0,1,0,0);null==k?p.clearRect(0,0,r,l):(p.fillStyle=k,p.fillRect(0,0,r,l));p.restore();return this};h.prototype.getImageData=function(){return this.ctx.getImageData(0,0,this.pixelWidth,this.pixelHeight)};return h}();f.CanvasBuffer=d},function(d,f,h){var k=this&&this.__extends||function(p,m){function n(){this.constructor=
p}for(var q in m)m.hasOwnProperty(q)&&(p[q]=m[q]);p.prototype=null===m?Object.create(m):(n.prototype=m.prototype,new n)},r=h(13),l=h(0);d=function(p){function m(){var n=null!==p&&p.apply(this,arguments)||this;n._clickedDown=!1;n._doubleClicking=!1;n._onClickCallbacks=new l.CallbackSet;n._onDoubleClickCallbacks=new l.CallbackSet;n._mouseDownCallback=function(q){return n._handleClickDown(q)};n._mouseUpCallback=function(q,u){return n._handleClickUp(q,u)};n._dblClickCallback=function(q,u){return n._handleDblClick(q,
u)};n._touchStartCallback=function(q,u){return n._handleClickDown(u[q[0]])};n._touchEndCallback=function(q,u,w){return n._handleClickUp(u[q[0]],w)};n._touchCancelCallback=function(){return n._clickedDown=!1};return n}k(m,p);m.prototype._anchor=function(n){p.prototype._anchor.call(this,n);this._mouseDispatcher=r.Mouse.getDispatcher(n);this._mouseDispatcher.onMouseDown(this._mouseDownCallback);this._mouseDispatcher.onMouseUp(this._mouseUpCallback);this._mouseDispatcher.onDblClick(this._dblClickCallback);
this._touchDispatcher=r.Touch.getDispatcher(n);this._touchDispatcher.onTouchStart(this._touchStartCallback);this._touchDispatcher.onTouchEnd(this._touchEndCallback);this._touchDispatcher.onTouchCancel(this._touchCancelCallback)};m.prototype._unanchor=function(){p.prototype._unanchor.call(this);this._mouseDispatcher.offMouseDown(this._mouseDownCallback);this._mouseDispatcher.offMouseUp(this._mouseUpCallback);this._mouseDispatcher.offDblClick(this._dblClickCallback);this._mouseDispatcher=null;this._touchDispatcher.offTouchStart(this._touchStartCallback);
this._touchDispatcher.offTouchEnd(this._touchEndCallback);this._touchDispatcher.offTouchCancel(this._touchCancelCallback);this._touchDispatcher=null};m.prototype._handleClickDown=function(n){n=this._translateToComponentSpace(n);this._isInsideComponent(n)&&(this._clickedDown=!0,this._clickedPoint=n)};m.prototype._handleClickUp=function(n,q){var u=this,w=this._translateToComponentSpace(n);this._clickedDown&&m._pointsEqual(w,this._clickedPoint)&&setTimeout(function(){u._doubleClicking||u._onClickCallbacks.callCallbacks(w,
q)},0);this._clickedDown=!1};m.prototype._handleDblClick=function(n,q){var u=this;n=this._translateToComponentSpace(n);this._doubleClicking=!0;this._onDoubleClickCallbacks.callCallbacks(n,q);setTimeout(function(){return u._doubleClicking=!1},0)};m._pointsEqual=function(n,q){return n.x===q.x&&n.y===q.y};m.prototype.onClick=function(n){this._onClickCallbacks.add(n);return this};m.prototype.offClick=function(n){this._onClickCallbacks.delete(n);return this};m.prototype.onDoubleClick=function(n){this._onDoubleClickCallbacks.add(n)};
m.prototype.offDoubleClick=function(n){this._onDoubleClickCallbacks.delete(n);return this};return m}(h(15).Interaction);f.Click=d},function(d,f,h){var k=this&&this.__extends||function(p,m){function n(){this.constructor=p}for(var q in m)m.hasOwnProperty(q)&&(p[q]=m[q]);p.prototype=null===m?Object.create(m):(n.prototype=m.prototype,new n)},r=h(13),l=h(0);d=function(p){function m(){var n=null!==p&&p.apply(this,arguments)||this;n._dragging=!1;n._constrainedToComponent=!0;n._mouseFilter=m._DEFAULT_MOUSE_FILTER;
n._dragStartCallbacks=new l.CallbackSet;n._dragCallbacks=new l.CallbackSet;n._dragEndCallbacks=new l.CallbackSet;n._mouseDownCallback=function(q,u){return n._startDrag(q,u)};n._mouseMoveCallback=function(q){return n._doDrag(q)};n._mouseUpCallback=function(q,u){return n._endDrag(q,u)};n._touchStartCallback=function(q,u,w){return n._startDrag(u[q[0]],w)};n._touchMoveCallback=function(q,u){return n._doDrag(u[q[0]])};n._touchEndCallback=function(q,u,w){return n._endDrag(u[q[0]],w)};return n}k(m,p);m.prototype._anchor=
function(n){p.prototype._anchor.call(this,n);this._mouseDispatcher=r.Mouse.getDispatcher(this._componentAttachedTo);this._mouseDispatcher.onMouseDown(this._mouseDownCallback);this._mouseDispatcher.onMouseMove(this._mouseMoveCallback);this._mouseDispatcher.onMouseUp(this._mouseUpCallback);this._touchDispatcher=r.Touch.getDispatcher(this._componentAttachedTo);this._touchDispatcher.onTouchStart(this._touchStartCallback);this._touchDispatcher.onTouchMove(this._touchMoveCallback);this._touchDispatcher.onTouchEnd(this._touchEndCallback)};
m.prototype._unanchor=function(){p.prototype._unanchor.call(this);this._mouseDispatcher.offMouseDown(this._mouseDownCallback);this._mouseDispatcher.offMouseMove(this._mouseMoveCallback);this._mouseDispatcher.offMouseUp(this._mouseUpCallback);this._mouseDispatcher=null;this._touchDispatcher.offTouchStart(this._touchStartCallback);this._touchDispatcher.offTouchMove(this._touchMoveCallback);this._touchDispatcher.offTouchEnd(this._touchEndCallback);this._touchDispatcher=null};m.prototype._translateAndConstrain=
function(n){n=this._translateToComponentSpace(n);return this._constrainedToComponent?{x:l.Math.clamp(n.x,0,this._componentAttachedTo.width()),y:l.Math.clamp(n.y,0,this._componentAttachedTo.height())}:n};m.prototype._startDrag=function(n,q){q instanceof MouseEvent&&!this._mouseFilter(q)||(n=this._translateToComponentSpace(n),this._isInsideComponent(n)&&(q.preventDefault(),this._dragging=!0,this._dragOrigin=n,this._dragStartCallbacks.callCallbacks(this._dragOrigin)))};m.prototype._doDrag=function(n){this._dragging&&
this._dragCallbacks.callCallbacks(this._dragOrigin,this._translateAndConstrain(n))};m.prototype._endDrag=function(n,q){q instanceof MouseEvent&&0!==q.button||!this._dragging||(this._dragging=!1,this._dragEndCallbacks.callCallbacks(this._dragOrigin,this._translateAndConstrain(n)))};m.prototype.constrainedToComponent=function(){this._constrainedToComponent=!1};m.prototype.mouseFilter=function(n){0!==arguments.length&&(this._mouseFilter=n)};m.prototype.onDragStart=function(n){this._dragStartCallbacks.add(n)};
m.prototype.offDragStart=function(n){this._dragStartCallbacks.delete(n)};m.prototype.onDrag=function(n){this._dragCallbacks.add(n);return this};m.prototype.offDrag=function(n){this._dragCallbacks.delete(n)};m.prototype.onDragEnd=function(n){this._dragEndCallbacks.add(n)};m.prototype.offDragEnd=function(n){this._dragEndCallbacks.delete(n)};return m}(h(15).Interaction);d._DEFAULT_MOUSE_FILTER=function(p){return 0===p.button};f.Drag=d},function(d,f,h){var k=this&&this.__extends||function(u,w){function A(){this.constructor=
u}for(var y in w)w.hasOwnProperty(y)&&(u[y]=w[y]);u.prototype=null===w?Object.create(w):(A.prototype=w.prototype,new A)},r=h(1),l=h(13),p=h(3),m=h(0),n=h(25);d=h(15);var q=h(26);h=function(u){function w(A,y){var x=u.call(this)||this;x._wheelFilter=function(){return!0};x._wheelCallback=function(C,G){return x._handleWheelEvent(C,G)};x._touchStartCallback=function(C,G){return x._handleTouchStart(C,G)};x._touchMoveCallback=function(C,G){return x._handlePinch(C,G)};x._touchEndCallback=function(C){return x._handleTouchEnd(C)};
x._touchCancelCallback=function(C){return x._handleTouchEnd(C)};x._panEndCallbacks=new m.CallbackSet;x._zoomEndCallbacks=new m.CallbackSet;x._panZoomUpdateCallbacks=new m.CallbackSet;x._xScales=new m.Set;x._yScales=new m.Set;x._dragInteraction=new n.Drag;x._setupDragInteraction();x._touchIds=r.map();x._minDomainExtents=new m.Map;x._maxDomainExtents=new m.Map;x._minDomainValues=new m.Map;x._maxDomainValues=new m.Map;null!=A&&x.addXScale(A);null!=y&&x.addYScale(y);return x}k(w,u);w.prototype.dragInteraction=
function(){return this._dragInteraction};w.prototype.wheelFilter=function(A){0!==arguments.length&&(this._wheelFilter=A)};w.prototype.pan=function(A){var y=this;this.xScales().forEach(function(x){x.pan(y._constrainedTranslation(x,A.x))});this.yScales().forEach(function(x){x.pan(y._constrainedTranslation(x,A.y))});this._panZoomUpdateCallbacks.callCallbacks()};w.prototype.zoom=function(A,y,x){var C=this;void 0===x&&(x=!0);if(null!=y){var G=y.x;var D=y.y;x&&(this.xScales().forEach(function(B){B=C._constrainedZoom(B,
A,G);G=B.centerPoint;A=B.zoomAmount}),this.yScales().forEach(function(B){B=C._constrainedZoom(B,A,D);D=B.centerPoint;A=B.zoomAmount}))}this.xScales().forEach(function(B){var H=B.range();B.zoom(A,null==G?(H[1]+H[0])/2:G)});this.yScales().forEach(function(B){var H=B.range();B.zoom(A,null==D?(H[1]+H[0])/2:D)});this._panZoomUpdateCallbacks.callCallbacks();return{zoomAmount:A,centerValue:{centerX:G,centerY:D}}};w.prototype._anchor=function(A){u.prototype._anchor.call(this,A);this._dragInteraction.attachTo(A);
this._mouseDispatcher=l.Mouse.getDispatcher(this._componentAttachedTo);this._mouseDispatcher.onWheel(this._wheelCallback);this._touchDispatcher=l.Touch.getDispatcher(this._componentAttachedTo);this._touchDispatcher.onTouchStart(this._touchStartCallback);this._touchDispatcher.onTouchMove(this._touchMoveCallback);this._touchDispatcher.onTouchEnd(this._touchEndCallback);this._touchDispatcher.onTouchCancel(this._touchCancelCallback)};w.prototype._unanchor=function(){u.prototype._unanchor.call(this);this._mouseDispatcher.offWheel(this._wheelCallback);
this._mouseDispatcher=null;this._touchDispatcher.offTouchStart(this._touchStartCallback);this._touchDispatcher.offTouchMove(this._touchMoveCallback);this._touchDispatcher.offTouchEnd(this._touchEndCallback);this._touchDispatcher.offTouchCancel(this._touchCancelCallback);this._touchDispatcher=null;this._dragInteraction.detach()};w.prototype._handleTouchStart=function(A,y){for(var x=0;x<A.length&&2>this._touchIds.size();x++){var C=A[x];this._touchIds.set(C.toString(),this._translateToComponentSpace(y[C]))}};
w.prototype._handlePinch=function(A,y){var x=this;if(!(2>this._touchIds.size())){var C=this._touchIds.values();if(this._isInsideComponent(this._translateToComponentSpace(C[0]))&&this._isInsideComponent(this._translateToComponentSpace(C[1]))){var G=w._pointDistance(C[0],C[1]);if(0!==G){A.forEach(function(L){x._touchIds.has(L.toString())&&x._touchIds.set(L.toString(),x._translateToComponentSpace(y[L]))});A=this._touchIds.values();var D=w._pointDistance(A[0],A[1]);if(0!==D){var B=G/D,H=A.map(function(L,
J){return{x:(L.x-C[J].x)/B,y:(L.y-C[J].y)/B}});G=w.centerPoint(C[0],C[1]);G=this.zoom(B,G);A=G.centerValue;var K=G.zoomAmount;G=A.centerX;A=A.centerY;D=C.map(function(L,J){return{x:H[J].x*K+L.x,y:H[J].y*K+L.y}});this.pan({x:G-(D[0].x+D[1].x)/2,y:A-(D[0].y+D[1].y)/2})}}}}};w.centerPoint=function(A,y){return{x:(Math.min(A.x,y.x)+Math.max(A.x,y.x))/2,y:(Math.max(A.y,y.y)+Math.min(A.y,y.y))/2}};w._pointDistance=function(A,y){return Math.sqrt(Math.pow(Math.max(A.x,y.x)-Math.min(A.x,y.x),2)+Math.pow(Math.max(A.y,
y.y)-Math.min(A.y,y.y),2))};w.prototype._handleTouchEnd=function(A){var y=this;A.forEach(function(x){y._touchIds.remove(x.toString())});0<this._touchIds.size()&&this._zoomEndCallbacks.callCallbacks()};w.prototype._handleWheelEvent=function(A,y){this._wheelFilter(y)&&(A=this._translateToComponentSpace(A),this._isInsideComponent(A)&&(y.preventDefault(),this.zoom(Math.pow(2,(0!==y.deltaY?y.deltaY:y.deltaX)*(y.deltaMode?w._PIXELS_PER_LINE:1)*.002),A),this._zoomEndCallbacks.callCallbacks()))};w.prototype._constrainedZoom=
function(A,y,x){return q.constrainedZoom(A,y,x,this.minDomainExtent(A),this.maxDomainExtent(A),this.minDomainValue(A),this.maxDomainValue(A))};w.prototype._constrainedTranslation=function(A,y){return q.constrainedTranslation(A,y,this.minDomainValue(A),this.maxDomainValue(A))};w.prototype._setupDragInteraction=function(){var A=this;this._dragInteraction.constrainedToComponent();var y;this._dragInteraction.onDragStart(function(){return y=null});this._dragInteraction.onDrag(function(x,C){2<=A._touchIds.size()||
(A.pan({x:(null==y?x.x:y.x)-C.x,y:(null==y?x.y:y.y)-C.y}),y=C)});this._dragInteraction.onDragEnd(function(){return A._panEndCallbacks.callCallbacks()})};w.prototype._nonLinearScaleWithExtents=function(A){return null!=this.minDomainExtent(A)&&null!=this.maxDomainExtent(A)&&!(A instanceof p.Linear)&&!(A instanceof p.Time)};w.prototype.xScales=function(){var A=[];this._xScales.forEach(function(y){A.push(y)});return A};w.prototype.yScales=function(){var A=[];this._yScales.forEach(function(y){A.push(y)});
return A};w.prototype.addXScale=function(A){this._xScales.add(A)};w.prototype.removeXScale=function(A){this._xScales.delete(A);this._minDomainExtents.delete(A);this._maxDomainExtents.delete(A);this._minDomainValues.delete(A);this._maxDomainValues.delete(A);return this};w.prototype.addYScale=function(A){this._yScales.add(A)};w.prototype.removeYScale=function(A){this._yScales.delete(A);this._minDomainExtents.delete(A);this._maxDomainExtents.delete(A);this._minDomainValues.delete(A);this._maxDomainValues.delete(A);
return this};w.prototype.minDomainExtent=function(A){return this._minDomainExtents.get(A)};w.prototype.maxDomainExtent=function(A){return this._maxDomainExtents.get(A)};w.prototype.minDomainValue=function(A,y){if(null==y)return this._minDomainValues.get(A);this._minDomainValues.set(A,y);return this};w.prototype.maxDomainValue=function(A,y){if(null==y)return this._maxDomainValues.get(A);this._maxDomainValues.set(A,y);return this};w.prototype.setMinMaxDomainValuesTo=function(A){this._minDomainValues.delete(A);
this._maxDomainValues.delete(A);var y=A.getTransformationDomain(),x=y[1];this.minDomainValue(A,y[0]);this.maxDomainValue(A,x);return this};w.prototype.onPanEnd=function(A){this._panEndCallbacks.add(A)};w.prototype.offPanEnd=function(A){this._panEndCallbacks.delete(A);return this};w.prototype.onZoomEnd=function(A){this._zoomEndCallbacks.add(A)};w.prototype.offZoomEnd=function(A){this._zoomEndCallbacks.delete(A);return this};w.prototype.onPanZoomUpdate=function(A){this._panZoomUpdateCallbacks.add(A);
return this};w.prototype.offPanZoomUpdate=function(A){this._panZoomUpdateCallbacks.delete(A);return this};return w}(d.Interaction);h._PIXELS_PER_LINE=120;f.PanZoom=h},function(d,f,h){var k=this&&this.__extends||function(p,m){function n(){this.constructor=p}for(var q in m)m.hasOwnProperty(q)&&(p[q]=m[q]);p.prototype=null===m?Object.create(m):(n.prototype=m.prototype,new n)},r=h(13),l=h(0);d=function(p){function m(){var n=null!==p&&p.apply(this,arguments)||this;n._overComponent=!1;n._pointerEnterCallbacks=
new l.CallbackSet;n._pointerMoveCallbacks=new l.CallbackSet;n._pointerExitCallbacks=new l.CallbackSet;n._mouseMoveCallback=function(q,u){return n._handleMouseEvent(q,u)};n._touchStartCallback=function(q,u,w){return n._handleTouchEvent(u[q[0]],w)};return n}k(m,p);m.prototype._anchor=function(n){p.prototype._anchor.call(this,n);this._mouseDispatcher=r.Mouse.getDispatcher(this._componentAttachedTo);this._mouseDispatcher.onMouseMove(this._mouseMoveCallback);this._touchDispatcher=r.Touch.getDispatcher(this._componentAttachedTo);
this._touchDispatcher.onTouchStart(this._touchStartCallback)};m.prototype._unanchor=function(){p.prototype._unanchor.call(this);this._mouseDispatcher.offMouseMove(this._mouseMoveCallback);this._mouseDispatcher=null;this._touchDispatcher.offTouchStart(this._touchStartCallback);this._touchDispatcher=null};m.prototype._handleMouseEvent=function(n,q){q=this._mouseDispatcher.eventInside(this._componentAttachedTo,q);this._handlePointerEvent(n,q)};m.prototype._handleTouchEvent=function(n,q){q=this._touchDispatcher.eventInside(this._componentAttachedTo,
q);this._handlePointerEvent(n,q)};m.prototype._handlePointerEvent=function(n,q){n=this._translateToComponentSpace(n);var u=this._isInsideComponent(n);u&&q?(this._overComponent||this._pointerEnterCallbacks.callCallbacks(n),this._pointerMoveCallbacks.callCallbacks(n)):this._overComponent&&this._pointerExitCallbacks.callCallbacks(n);this._overComponent=u&&q};m.prototype.onPointerEnter=function(n){this._pointerEnterCallbacks.add(n);return this};m.prototype.offPointerEnter=function(n){this._pointerEnterCallbacks.delete(n);
return this};m.prototype.onPointerMove=function(n){this._pointerMoveCallbacks.add(n)};m.prototype.offPointerMove=function(n){this._pointerMoveCallbacks.delete(n);return this};m.prototype.onPointerExit=function(n){this._pointerExitCallbacks.add(n)};m.prototype.offPointerExit=function(n){this._pointerExitCallbacks.delete(n);return this};return m}(h(15).Interaction);f.Pointer=d},function(d,f,h){var k=h(20);f.memThunk=function(){for(var r=[],l=0;l<arguments.length;l++)r[l]=arguments[l];var p=r.slice(0,
-1),m=k.memoize(r[r.length-1]);return function(){var n=this,q=p.map(function(u){return u.apply(n)});return m.apply(void 0,q)}}},function(d,f,h){var k=h(49);f.memoize=function(r){function l(){for(var u=[],w=0;w<arguments.length;w++)u[w]=arguments[w];if(n)return m;w=k.signArray(u);void 0===p||p.isDifferent(w)?(q&&console.log("cache miss! computing"),p=w,m=r.apply(this,u)):q&&console.log("cache hit!");return m}var p=void 0,m,n=!1,q=!1;l.doLocked=function(u){if(n)throw Error("Locking an already locked memoize function!");
n=!0;u=u.apply(this);n=!1;return u};l.logPerformance=function(u){void 0===u&&(u=!0);q=u;return this};return l}},function(d,f,h){var k=this&&this.__extends||function(n,q){function u(){this.constructor=n}for(var w in q)q.hasOwnProperty(w)&&(n[w]=q[w]);n.prototype=null===q?Object.create(q):(u.prototype=q.prototype,new u)},r=h(3),l=h(0),p=h(27),m=h(2);d=function(n){function q(u){void 0===u&&(u="vertical");u=n.call(this,u)||this;u._clusterOffsets=new l.Map;return u}k(q,n);q.prototype._generateAttrToProjector=
function(){function u(){return y.rangeBand()}var w=this,A=n.prototype._generateAttrToProjector.call(this),y=this._makeInnerScale();A.width=this._isVertical?u:A.width;A.height=this._isVertical?A.height:u;var x=A.x,C=A.y;A.x=this._isVertical?function(G,D,B){return x(G,D,B)+w._clusterOffsets.get(B)}:function(G,D,B){return x(G,D,B)};A.y=this._isVertical?function(G,D,B){return C(G,D,B)}:function(G,D,B){return C(G,D,B)+w._clusterOffsets.get(B)};return A};q.prototype._updateClusterPosition=function(){var u=
this,w=this._makeInnerScale();this.datasets().forEach(function(A,y){return u._clusterOffsets.set(A,w.scale(String(y))-w.rangeBand()/2)})};q.prototype._makeInnerScale=function(){var u=new r.Category;u.domain(this.datasets().map(function(A,y){return String(y)}));var w=m.Plot._scaledAccessor(this.attr(p.Bar._BAR_THICKNESS_KEY));u.range([0,w(null,0,null)]);return u};q.prototype._getDataToDraw=function(){this._updateClusterPosition();return n.prototype._getDataToDraw.call(this)};return q}(p.Bar);f.ClusteredBar=
d},function(d,f,h){var k=this&&this.__extends||function(C,G){function D(){this.constructor=C}for(var B in G)G.hasOwnProperty(B)&&(C[B]=G[B]);C.prototype=null===G?Object.create(G):(D.prototype=G.prototype,new D)},r=h(1),l=h(5),p=h(7),m=h(8),n=h(3),q=h(0),u=h(44),w=h(45),A=h(6),y=h(35),x=h(2);d=function(C){function G(){var D=C.call(this)||this;D._startAngle=0;D._endAngle=2*Math.PI;D._labelFormatter=m.identity();D._labelsEnabled=!1;D.innerRadius(0);D.outerRadius(function(){var B=D._pieCenter();return Math.min(Math.max(D.width()-
B.x,B.x),Math.max(D.height()-B.y,B.y))});D.addClass("pie-plot");D.attr("fill",function(B,H){return String(H)},new n.Color);D._strokeDrawers=new q.Map;return D}k(G,C);G.prototype._setup=function(){var D=this;C.prototype._setup.call(this);this._strokeDrawers.forEach(function(B){return B.attachTo(D._renderArea)})};G.prototype.computeLayout=function(D,B,H){C.prototype.computeLayout.call(this,D,B,H);D=this._pieCenter();this._renderArea.attr("transform","translate("+D.x+","+D.y+")");D=Math.min(Math.max(this.width()-
D.x,D.x),Math.max(this.height()-D.y,D.y));null!=this.innerRadius().scale&&this.innerRadius().scale.range([0,D]);null!=this.outerRadius().scale&&this.outerRadius().scale.range([0,D]);return this};G.prototype.addDataset=function(D){C.prototype.addDataset.call(this,D)};G.prototype._addDataset=function(D){if(1===this.datasets().length)return q.Window.warn("Only one dataset is supported in Pie plots"),this;this._updatePieAngles();var B=new w.ArcOutlineSVGDrawer;this._isSetup&&B.attachTo(this._renderArea);
this._strokeDrawers.set(D,B);C.prototype._addDataset.call(this,D);return this};G.prototype.removeDataset=function(D){C.prototype.removeDataset.call(this,D)};G.prototype._removeDatasetNodes=function(D){C.prototype._removeDatasetNodes.call(this,D);this._strokeDrawers.get(D).remove()};G.prototype._removeDataset=function(D){C.prototype._removeDataset.call(this,D);this._startAngles=[];this._endAngles=[];return this};G.prototype.selections=function(D){var B=this;void 0===D&&(D=this.datasets());var H=C.prototype.selections.call(this,
D).nodes();D.forEach(function(K){K=B._strokeDrawers.get(K);null!=K&&H.push.apply(H,K.getVisualPrimitives())});return r.selectAll(H)};G.prototype._onDatasetUpdate=function(){C.prototype._onDatasetUpdate.call(this);this._updatePieAngles();this.render()};G.prototype._createDrawer=function(){return new A.ProxyDrawer(function(){return new u.ArcSVGDrawer},function(){y.warn("canvas renderer is not supported on Pie Plot!");return null})};G.prototype.entities=function(D){var B=this;void 0===D&&(D=this.datasets());
return C.prototype.entities.call(this,D).map(function(H){H.position.x+=B.width()/2;H.position.y+=B.height()/2;var K=r.select(B._strokeDrawers.get(H.dataset).getVisualPrimitiveAtIndex(H.index));H.strokeSelection=K;return H})};G.prototype.sectorValue=function(){return this._propertyBindings.get(G._SECTOR_VALUE_KEY)};G.prototype.innerRadius=function(D,B){if(null==D)return this._propertyBindings.get(G._INNER_RADIUS_KEY);this._bindProperty(G._INNER_RADIUS_KEY,D,B);this.render();return this};G.prototype.outerRadius=
function(D,B){if(null==D)return this._propertyBindings.get(G._OUTER_RADIUS_KEY);this._bindProperty(G._OUTER_RADIUS_KEY,D,B);this.render();return this};G.prototype.startAngle=function(D){if(null==D)return this._startAngle;this._startAngle=D;this._updatePieAngles();this.render();return this};G.prototype.endAngle=function(D){if(null==D)return this._endAngle;this._endAngle=D;this._updatePieAngles();this.render();return this};G.prototype.labelsEnabled=function(D){if(null==D)return this._labelsEnabled;
this._labelsEnabled=D;this.render();return this};G.prototype.labelFormatter=function(D){if(null==D)return this._labelFormatter;this._labelFormatter=D;this.render();return this};G.prototype.entitiesAt=function(D){var B=this.width()/2,H=this.height()/2;D=this._sliceIndexForPoint({x:D.x-B,y:D.y-H});return null==D?[]:[this.entities()[D]]};G.prototype._propertyProjectors=function(){var D=this,B=C.prototype._propertyProjectors.call(this),H=x.Plot._scaledAccessor(this.innerRadius()),K=x.Plot._scaledAccessor(this.outerRadius());
B.d=function(L,J,O){return r.arc().innerRadius(H(L,J,O)).outerRadius(K(L,J,O)).startAngle(D._startAngles[J]).endAngle(D._endAngles[J])(L,J)};return B};G.prototype._updatePieAngles=function(){if(null!=this.sectorValue()&&0!==this.datasets().length){var D=x.Plot._scaledAccessor(this.sectorValue()),B=this.datasets()[0],H=this._getDataToDraw().get(B);H=r.pie().sort(null).startAngle(this._startAngle).endAngle(this._endAngle).value(function(K,L){return D(K,L,B)})(H);this._startAngles=H.map(function(K){return K.startAngle});
this._endAngles=H.map(function(K){return K.endAngle})}};G.prototype._pieCenter=function(){var D=this._startAngle<this._endAngle?this._startAngle:this._endAngle,B=this._startAngle<this._endAngle?this._endAngle:this._startAngle,H=Math.sin(D);D=Math.cos(D);var K=Math.sin(B);B=Math.cos(B);var L;if(0<=H&&0<=K)if(0<=D&&0<=B){var J=D;var O=L=0;var S=K}else 0>D&&0>B?(J=0,L=-B,O=0,S=H):0<=D&&0>B?(J=D,L=-B,O=0,S=H):0>D&&0<=B&&(O=L=J=1,S=Math.max(H,K));else 0<=H&&0>K?0<=D&&0<=B?(J=Math.max(D,B),S=O=L=1):0>D&&
0>B?(J=0,L=1,O=-K,S=H):0<=D&&0>B?(J=D,L=1,O=-K,S=1):0>D&&0<=B&&(J=B,O=L=1,S=H):0>H&&0<=K?0<=D&&0<=B?(J=1,L=0,O=-H,S=K):0>D&&0>B?(J=1,L=Math.max(-D,-B),S=O=1):0<=D&&0>B?(J=1,L=-B,O=-H,S=1):0>D&&0<=B&&(J=1,L=-D,O=1,S=K):0>H&&0>K&&(0<=D&&0<=B?(J=B,L=0,O=-H,S=0):0>D&&0>B?(J=0,L=-D,O=-K,S=0):0<=D&&0>B?(L=J=1,O=Math.max(D,-B),S=1):0>D&&0<=B&&(J=B,L=-D,O=1,S=0));return{x:0==O+S?0:O/(O+S)*this.width(),y:0==J+L?0:J/(J+L)*this.height()}};G.prototype._getDataToDraw=function(){var D=C.prototype._getDataToDraw.call(this);
if(0===this.datasets().length)return D;var B=x.Plot._scaledAccessor(this.sectorValue()),H=this.datasets()[0],K=D.get(H).filter(function(L,J){return G._isValidData(B(L,J,H))});D.set(H,K);return D};G._isValidData=function(D){return q.Math.isValidNumber(D)&&0<=D};G.prototype._pixelPoint=function(D,B,H){var K=x.Plot._scaledAccessor(this.sectorValue());if(!G._isValidData(K(D,B,H)))return{x:NaN,y:NaN};var L=x.Plot._scaledAccessor(this.innerRadius())(D,B,H);D=x.Plot._scaledAccessor(this.outerRadius())(D,
B,H);L=(L+D)/2;D=r.pie().sort(null).value(function(J,O){J=K(J,O,H);return G._isValidData(J)?J:0}).startAngle(this._startAngle).endAngle(this._endAngle)(H.data());B=(D[B].startAngle+D[B].endAngle)/2;return{x:L*Math.sin(B),y:-L*Math.cos(B)}};G.prototype._additionalPaint=function(D){var B=this;this._renderArea.select(".label-area").remove();this._labelsEnabled&&q.Window.setTimeout(function(){return B._drawLabels()},D);var H=this._generateStrokeDrawSteps(),K=this._getDataToDraw();this.datasets().forEach(function(L){var J=
x.Plot.applyDrawSteps(H,L);B._strokeDrawers.get(L).draw(K.get(L),J)})};G.prototype._generateStrokeDrawSteps=function(){return[{attrToProjector:this._getAttrToProjector(),animator:new p.Null}]};G.prototype._sliceIndexForPoint=function(D){var B=Math.sqrt(Math.pow(D.x,2)+Math.pow(D.y,2)),H=Math.acos(-D.y/B);0>D.x&&(H=2*Math.PI-H);for(D=0;D<this._startAngles.length;D++)if(this._startAngles[D]<H&&this._endAngles[D]>H){var K=D;break}if(void 0!==K){D=this.datasets()[0];var L=D.data()[K];H=this.innerRadius().accessor(L,
K,D);D=this.outerRadius().accessor(L,K,D);if(B>H&&B<D)return K}return null};G.prototype._drawLabels=function(){var D=this,B=this._getAttrToProjector(),H=this._renderArea.append("g").classed("label-area",!0),K=new l.SvgContext(H.node()),L=new l.CacheMeasurer(K),J=new l.Writer(L,K),O=this.datasets()[0];this._getDataToDraw().get(O).forEach(function(S,N){var R=D.sectorValue().accessor(S,N,O);if(q.Math.isValidNumber(R)){R=D._labelFormatter(R,S,N,O);var X=L.measure(R),aa=(D._endAngles[N]+D._startAngles[N])/
2,fa=D.outerRadius().accessor(S,N,O);D.outerRadius().scale&&(fa=D.outerRadius().scale.scale(fa));var oa=D.innerRadius().accessor(S,N,O);D.innerRadius().scale&&(oa=D.innerRadius().scale.scale(oa));oa=(fa+oa)/2;fa=Math.sin(aa)*oa-X.width/2;oa=-Math.cos(aa)*oa-X.height/2;var Z=[{x:fa,y:oa},{x:fa,y:oa+X.height},{x:fa+X.width,y:oa},{x:fa+X.width,y:oa+X.height}];(aa=Z.every(function(ca){return Math.abs(ca.x)<=D.width()/2&&Math.abs(ca.y)<=D.height()/2}))&&(aa=Z.map(function(ca){return D._sliceIndexForPoint(ca)}).every(function(ca){return ca===
N}));S=B.fill(S,N,O);S=1.6*q.Color.contrast("white",S)<q.Color.contrast("black",S);fa=H.append("g").attr("transform","translate("+fa+","+oa+")");fa.classed(S?"dark-label":"light-label",!0);fa.style("visibility",aa?"inherit":"hidden");J.write(R,X.width,X.height,{xAlign:"center",yAlign:"center"},fa.node())}})};return G}(x.Plot);d._INNER_RADIUS_KEY="inner-radius";d._OUTER_RADIUS_KEY="outer-radius";d._SECTOR_VALUE_KEY="sector-value";f.Pie=d},function(d,f,h){var k=this&&this.__extends||function(y,x){function C(){this.constructor=
y}for(var G in x)x.hasOwnProperty(G)&&(y[G]=x[G]);y.prototype=null===x?Object.create(x):(C.prototype=x.prototype,new C)},r=h(1),l=h(5),p=h(7),m=h(14),n=h(6),q=h(34),u=h(3),w=h(0),A=h(2);d=function(y){function x(){var C=y.call(this)||this;C._labelsEnabled=!1;C._label=null;C.animator("rectangles",new p.Null);C.addClass("rectangle-plot");C.attr("fill",(new u.Color).range()[0]);return C}k(x,y);x.prototype._createDrawer=function(){return new n.ProxyDrawer(function(){return new q.RectangleSVGDrawer},function(C){return new m.RectangleCanvasDrawer(C)})};
x.prototype._generateAttrToProjector=function(){var C=this,G=y.prototype._generateAttrToProjector.call(this),D=A.Plot._scaledAccessor(this.x()),B=G[x._X2_KEY],H=A.Plot._scaledAccessor(this.y()),K=G[x._Y2_KEY],L=this.x().scale,J=this.y().scale;null!=B?(G.width=function(O,S,N){return Math.abs(B(O,S,N)-D(O,S,N))},G.x=function(O,S,N){return Math.min(B(O,S,N),D(O,S,N))}):(G.width=function(){return C._rectangleWidth(L)},G.x=function(O,S,N){return D(O,S,N)-.5*G.width(O,S,N)});null!=K?(G.height=function(O,
S,N){return Math.abs(K(O,S,N)-H(O,S,N))},G.y=function(O,S,N){return Math.max(K(O,S,N),H(O,S,N))-G.height(O,S,N)}):(G.height=function(){return C._rectangleWidth(J)},G.y=function(O,S,N){return H(O,S,N)-.5*G.height(O,S,N)});delete G[x._X2_KEY];delete G[x._Y2_KEY];return G};x.prototype._generateDrawSteps=function(){return[{attrToProjector:this._getAttrToProjector(),animator:this._getAnimator("rectangles")}]};x.prototype._filterForProperty=function(C){return"x2"===C?y.prototype._filterForProperty.call(this,
"x"):"y2"===C?y.prototype._filterForProperty.call(this,"y"):y.prototype._filterForProperty.call(this,C)};x.prototype.x=function(C,G,D){if(null==C)return y.prototype.x.call(this);null==G?y.prototype.x.call(this,C):y.prototype.x.call(this,C,G,D);null!=G&&(D=(C=this.x2())&&C.accessor,null!=D&&this._bindProperty(x._X2_KEY,D,G,C.postScale));G instanceof u.Category&&G.innerPadding(0).outerPadding(0);return this};x.prototype.x2=function(){return this._propertyBindings.get(x._X2_KEY)};x.prototype.y=function(C,
G,D){if(null==C)return y.prototype.y.call(this);null==G?y.prototype.y.call(this,C):y.prototype.y.call(this,C,G,D);null!=G&&(D=(C=this.y2())&&C.accessor,null!=D&&this._bindProperty(x._Y2_KEY,D,G,C.postScale));G instanceof u.Category&&G.innerPadding(0).outerPadding(0);return this};x.prototype.y2=function(){return this._propertyBindings.get(x._Y2_KEY)};x.prototype.entitiesAt=function(C){var G=this._getAttrToProjector();return this.entities().filter(function(D){var B=D.datum,H=D.index,K=D.dataset;D=G.x(B,
H,K);var L=G.y(B,H,K),J=G.width(B,H,K);B=G.height(B,H,K);return D<=C.x&&C.x<=D+J&&L<=C.y&&C.y<=L+B})};x.prototype._entityBounds=function(C){return this._entityBBox(C.datum,C.index,C.dataset,this._getAttrToProjector())};x.prototype._entityBBox=function(C,G,D,B){return{x:B.x(C,G,D),y:B.y(C,G,D),width:B.width(C,G,D),height:B.height(C,G,D)}};x.prototype.label=function(C){if(null==C)return this._label;this._label=C;this.render();return this};x.prototype.labelsEnabled=function(C){if(null==C)return this._labelsEnabled;
this._labelsEnabled=C;this.render();return this};x.prototype._propertyProjectors=function(){var C=y.prototype._propertyProjectors.call(this);null!=this.x2()&&(C.x2=A.Plot._scaledAccessor(this.x2()));null!=this.y2()&&(C.y2=A.Plot._scaledAccessor(this.y2()));return C};x.prototype._pixelPoint=function(C,G,D){var B=this._getAttrToProjector(),H=B.x(C,G,D),K=B.y(C,G,D),L=B.width(C,G,D);C=B.height(C,G,D);return{x:H+L/2,y:K+C/2}};x.prototype._rectangleWidth=function(C){if(C instanceof u.Category)return C.rangeBand();
var G=C===this.x().scale?this.x().accessor:this.y().accessor,D=r.set(w.Array.flatten(this.datasets().map(function(K){return K.data().map(function(L,J){return G(L,J,K).valueOf()})}))).values().map(function(K){return+K}),B=w.Math.min(D,0);D=w.Math.max(D,0);var H=C.scale(B);return(C.scale(D)-H)/Math.abs(D-B)};x.prototype._getDataToDraw=function(){var C=new w.Map,G=this._getAttrToProjector();this.datasets().forEach(function(D){var B=D.data().map(function(H,K){return w.Math.isValidNumber(G.x(H,K,D))&&
w.Math.isValidNumber(G.y(H,K,D))&&w.Math.isValidNumber(G.width(H,K,D))&&w.Math.isValidNumber(G.height(H,K,D))?H:null});C.set(D,B)});return C};x.prototype._additionalPaint=function(C){var G=this;this._renderArea.selectAll(".label-area").remove();this._labelsEnabled&&null!=this.label()&&w.Window.setTimeout(function(){return G._drawLabels()},C)};x.prototype._drawLabels=function(){var C=this,G=this._getDataToDraw();this.datasets().forEach(function(D,B){return C._drawLabel(G,D,B)})};x.prototype._drawLabel=
function(C,G,D){var B=this,H=this._getAttrToProjector(),K=this._renderArea.append("g").classed("label-area",!0),L=new l.SvgContext(K.node()),J=new l.CacheMeasurer(L),O=new l.Writer(J,L);L=this.x().scale.range();var S=this.y().scale.range(),N=Math.min.apply(null,L),R=Math.max.apply(null,L),X=Math.min.apply(null,S),aa=Math.max.apply(null,S);C.get(G).forEach(function(fa,oa){if(null!=fa){var Z=""+B.label()(fa,oa,G),ca=J.measure(Z),ja=H.x(fa,oa,G),ba=H.y(fa,oa,G),ka=H.width(fa,oa,G),W=H.height(fa,oa,G);
ca.height<=W&&ca.width<=ka&&(W=(W-ca.height)/2,ja+=(ka-ca.width)/2,ba+=W,ka={min:ja,max:ja+ca.width},W={min:ba,max:ba+ca.height},ka.min<N||ka.max>R||W.min<X||W.max>aa||B._overlayLabel(ka,W,oa,D,C)||(fa=H.fill(fa,oa,G),fa=1.6*w.Color.contrast("white",fa)<w.Color.contrast("black",fa),ja=K.append("g").attr("transform","translate("+ja+","+ba+")"),ja.classed(fa?"dark-label":"light-label",!0),O.write(Z,ca.width,ca.height,{xAlign:"center",yAlign:"center"},ja.node())))}})};x.prototype._overlayLabel=function(C,
G,D,B,H){for(var K=this._getAttrToProjector(),L=this.datasets(),J=B;J<L.length;J++)for(var O=L[J],S=H.get(O),N=J===B?D+1:0;N<S.length;N++)if(w.DOM.intersectsBBox(C,G,this._entityBBox(S[N],N,O,K)))return!0;return!1};return x}(h(16).XYPlot);d._X2_KEY="x2";d._Y2_KEY="y2";f.Rectangle=d},function(d,f,h){var k=this&&this.__extends||function(y,x){function C(){this.constructor=y}for(var G in x)x.hasOwnProperty(G)&&(y[G]=x[G]);y.prototype=null===x?Object.create(x):(C.prototype=x.prototype,new C)},r=h(31),
l=h(6),p=h(48),m=h(7),n=h(14),q=h(3),u=h(0),w=h(19),A=h(2);d=function(y){function x(){var C=y.call(this)||this;C.addClass("scatter-plot");var G=new m.Easing;G.startDelay(5);G.stepDuration(250);G.maxTotalDuration(A.Plot._ANIMATION_MAX_DURATION);C.animator(w.Animator.MAIN,G);C.attr("opacity",.6);C.attr("fill",(new q.Color).range()[0]);C.size(6);var D=r.circle();C.symbol(function(){return D});return C}k(x,y);x.prototype._buildLightweightPlotEntities=function(C){var G=this;return y.prototype._buildLightweightPlotEntities.call(this,
C).map(function(D){var B=A.Plot._scaledAccessor(G.size())(D.datum,D.index,D.dataset);D.diameter=B;return D})};x.prototype._createDrawer=function(C){var G=this;return new l.ProxyDrawer(function(){return new p.SymbolSVGDrawer},function(D){return new n.CanvasDrawer(D,p.makeSymbolCanvasDrawStep(C,function(){return A.Plot._scaledAccessor(G.symbol())},function(){return A.Plot._scaledAccessor(G.size())}))})};x.prototype.size=function(C,G){if(null==C)return this._propertyBindings.get(x._SIZE_KEY);this._bindProperty(x._SIZE_KEY,
C,G);this.render();return this};x.prototype.symbol=function(C){if(null==C)return this._propertyBindings.get(x._SYMBOL_KEY);this._propertyBindings.set(x._SYMBOL_KEY,{accessor:C});this.render();return this};x.prototype._generateDrawSteps=function(){var C=[];if(this._animateOnNextRender()){var G=this._getAttrToProjector(),D=A.Plot._scaledAccessor(this.symbol());G.d=function(B,H,K){return D(B,H,K)(0)(null)};C.push({attrToProjector:G,animator:this._getAnimator(w.Animator.RESET)})}C.push({attrToProjector:this._getAttrToProjector(),
animator:this._getAnimator(w.Animator.MAIN)});return C};x.prototype._propertyProjectors=function(){var C=y.prototype._propertyProjectors.call(this),G=A.Plot._scaledAccessor(this.x()),D=A.Plot._scaledAccessor(this.y());C.x=G;C.y=D;C.transform=function(B,H,K){return"translate("+G(B,H,K)+","+D(B,H,K)+")"};C.d=this._constructSymbolGenerator();return C};x.prototype._constructSymbolGenerator=function(){var C=A.Plot._scaledAccessor(this.symbol()),G=A.Plot._scaledAccessor(this.size());return function(D,B,
H){return C(D,B,H)(G(D,B,H))(null)}};x.prototype._entityBounds=function(C){return{x:C.position.x-C.diameter/2,y:C.position.y-C.diameter/2,width:C.diameter,height:C.diameter}};x.prototype._entityVisibleOnPlot=function(C,G){var D={min:G.topLeft.x,max:G.bottomRight.x};G={min:G.topLeft.y,max:G.bottomRight.y};C=this._entityBounds(C);return u.DOM.intersectsBBox(D,G,C)};x.prototype.entitiesAt=function(C){var G=A.Plot._scaledAccessor(this.x()),D=A.Plot._scaledAccessor(this.y()),B=A.Plot._scaledAccessor(this.size());
return this.entities().filter(function(H){var K=H.datum,L=H.index,J=H.dataset;H=G(K,L,J);var O=D(K,L,J);K=B(K,L,J);return H-K/2<=C.x&&C.x<=H+K/2&&O-K/2<=C.y&&C.y<=O+K/2})};return x}(h(16).XYPlot);d._SIZE_KEY="size";d._SYMBOL_KEY="symbol";f.Scatter=d},function(d,f,h){var k=this&&this.__extends||function(u,w){function A(){this.constructor=u}for(var y in w)w.hasOwnProperty(y)&&(u[y]=w[y]);u.prototype=null===w?Object.create(w):(A.prototype=w.prototype,new A)},r=h(7),l=h(6),p=h(47),m=h(3),n=h(35),q=h(2);
d=function(u){function w(){var A=u.call(this)||this;A.addClass("segment-plot");A.attr("stroke",(new m.Color).range()[0]);A.attr("stroke-width","2px");return A}k(w,u);w.prototype._createDrawer=function(){return new l.ProxyDrawer(function(){return new p.SegmentSVGDrawer},function(){n.warn("canvas renderer is not supported on Segment Plot!");return null})};w.prototype._generateDrawSteps=function(){return[{attrToProjector:this._getAttrToProjector(),animator:new r.Null}]};w.prototype._filterForProperty=
function(A){return"x2"===A?u.prototype._filterForProperty.call(this,"x"):"y2"===A?u.prototype._filterForProperty.call(this,"y"):u.prototype._filterForProperty.call(this,A)};w.prototype.x=function(A,y){if(null==A)return u.prototype.x.call(this);null==y?u.prototype.x.call(this,A):(u.prototype.x.call(this,A,y),A=(A=this.x2())&&A.accessor,null!=A&&this._bindProperty(w._X2_KEY,A,y));return this};w.prototype.x2=function(){return this._propertyBindings.get(w._X2_KEY)};w.prototype.y=function(A,y){if(null==
A)return u.prototype.y.call(this);null==y?u.prototype.y.call(this,A):(u.prototype.y.call(this,A,y),A=(A=this.y2())&&A.accessor,null!=A&&this._bindProperty(w._Y2_KEY,A,y));return this};w.prototype.y2=function(){return this._propertyBindings.get(w._Y2_KEY)};w.prototype._propertyProjectors=function(){var A=u.prototype._propertyProjectors.call(this);A.x1=q.Plot._scaledAccessor(this.x());A.x2=null==this.x2()?q.Plot._scaledAccessor(this.x()):q.Plot._scaledAccessor(this.x2());A.y1=q.Plot._scaledAccessor(this.y());
A.y2=null==this.y2()?q.Plot._scaledAccessor(this.y()):q.Plot._scaledAccessor(this.y2());return A};w.prototype.entitiesAt=function(A){A=this.entityNearest(A);return null!=A?[A]:[]};w.prototype.entitiesIn=function(A,y){if(null==y){var x={min:A.topLeft.x,max:A.bottomRight.x};A={min:A.topLeft.y,max:A.bottomRight.y}}else x=A,A=y;return this._entitiesIntersecting(x,A)};w.prototype._entitiesIntersecting=function(A,y){var x=this,C=[],G=this._getAttrToProjector();this.entities().forEach(function(D){x._lineIntersectsBox(D,
A,y,G)&&C.push(D)});return C};w.prototype._lineIntersectsBox=function(A,y,x,C){var G=this,D=C.x1(A.datum,A.index,A.dataset),B=C.x2(A.datum,A.index,A.dataset),H=C.y1(A.datum,A.index,A.dataset);A=C.y2(A.datum,A.index,A.dataset);if(y.min<=D&&D<=y.max&&x.min<=H&&H<=x.max||y.min<=B&&B<=y.max&&x.min<=A&&A<=x.max)return!0;var K={x:D,y:H},L={x:B,y:A},J=[{x:y.min,y:x.min},{x:y.min,y:x.max},{x:y.max,y:x.max},{x:y.max,y:x.min}];return 0<J.filter(function(O,S){return 0!==S?G._lineIntersectsSegment(K,L,O,J[S-
1])&&G._lineIntersectsSegment(O,J[S-1],K,L):!1}).length};w.prototype._lineIntersectsSegment=function(A,y,x,C){function G(D,B,H){return(B.x-D.x)*(H.y-B.y)-(B.y-D.y)*(H.x-B.x)}return 0>G(A,y,x)*G(A,y,C)};return w}(h(16).XYPlot);d._X2_KEY="x2";d._Y2_KEY="y2";f.Segment=d},function(d,f,h){var k=this&&this.__extends||function(q,u){function w(){this.constructor=q}for(var A in u)u.hasOwnProperty(A)&&(q[A]=u[A]);q.prototype=null===u?Object.create(u):(w.prototype=u.prototype,new w)},r=h(1),l=h(7),p=h(20),m=
h(0);d=h(50);var n=h(2);h=function(q){function u(){var w=q.call(this)||this;w._stackingResult=p.memThunk(function(){return w.datasets()},function(){return w.x().accessor},function(){return w.y().accessor},function(){return w._stackingOrder},function(A,y,x,C){return m.Stacking.stack(A,y,x,C)});w._stackedExtent=p.memThunk(w._stackingResult,function(){return w.x().accessor},function(){return w._filterForProperty("y")},function(A,y,x){return m.Stacking.stackedExtent(A,y,x)});w._baselineValue=0;w._stackingOrder=
"bottomup";w.addClass("stacked-area-plot");w.attr("fill-opacity",1);w._baselineValueProvider=function(){return[w._baselineValue]};w.croppedRenderingEnabled(!1);return w}k(u,q);u.prototype.croppedRenderingEnabled=function(w){return null==w?q.prototype.croppedRenderingEnabled.call(this):w?(m.Window.warn("Warning: Stacked Area Plot does not support cropped rendering."),this):q.prototype.croppedRenderingEnabled.call(this,w)};u.prototype._getAnimator=function(){return new l.Null};u.prototype._setup=function(){q.prototype._setup.call(this);
this._baseline=this._renderArea.append("line").classed("baseline",!0)};u.prototype.x=function(w,A){if(null==w)return q.prototype.x.call(this);null==A?q.prototype.x.call(this,w):q.prototype.x.call(this,w,A);this._checkSameDomain();return this};u.prototype.y=function(w,A){if(null==w)return q.prototype.y.call(this);null==A?q.prototype.y.call(this,w):q.prototype.y.call(this,w,A);this._checkSameDomain();return this};u.prototype.stackingOrder=function(w){if(null==w)return this._stackingOrder;this._stackingOrder=
w;this._onDatasetUpdate();return this};u.prototype.downsamplingEnabled=function(){return q.prototype.downsamplingEnabled.call(this)};u.prototype._additionalPaint=function(){var w=this.y().scale.scale(this._baselineValue);w={x1:0,y1:w,x2:this.width(),y2:w};this._getAnimator("baseline").animate(this._baseline,w)};u.prototype._updateYScale=function(){var w=this.y();w=w&&w.scale;null!=w&&(w.addPaddingExceptionsProvider(this._baselineValueProvider),w.addIncludedValuesProvider(this._baselineValueProvider))};
u.prototype._onDatasetUpdate=function(){this._checkSameDomain();q.prototype._onDatasetUpdate.call(this);return this};u.prototype.getExtentsForProperty=function(w){return"y"===w?[this._stackedExtent()]:q.prototype.getExtentsForProperty.call(this,w)};u.prototype._checkSameDomain=function(){if(this._projectorsReady()){var w=this.datasets(),A=this.x().accessor,y=w.map(function(C){return r.set(C.data().map(function(G,D){return A(G,D,C).toString()})).values()}),x=u._domainKeys(w,A);y.some(function(C){return C.length!==
x.length})&&m.Window.warn("the domains across the datasets are not the same. Plot may produce unintended behavior.")}};u._domainKeys=function(w,A){var y=r.set();w.forEach(function(x){x.data().forEach(function(C,G){y.add(A(C,G,x))})});return y.values()};u.prototype._propertyProjectors=function(){function w(D,B,H){return m.Stacking.normalizeKey(C(D,B,H))}var A=this,y=q.prototype._propertyProjectors.call(this),x=this.y().accessor,C=this.x().accessor,G=this._stackingResult();y.d=this._constructAreaProjector(n.Plot._scaledAccessor(this.x()),
function(D,B,H){return A.y().scale.scale(+x(D,B,H)+G.get(H).get(w(D,B,H)).offset)},function(D,B,H){return A.y().scale.scale(G.get(H).get(w(D,B,H)).offset)});return y};u.prototype._pixelPoint=function(w,A,y){var x=q.prototype._pixelPoint.call(this,w,A,y),C=this.x().accessor(w,A,y);w=this.y().accessor(w,A,y);y=this.y().scale.scale(+w+this._stackingResult().get(y).get(m.Stacking.normalizeKey(C)).offset);return{x:x.x,y}};return u}(d.Area);f.StackedArea=h},function(d,f,h){var k=this&&this.__extends||function(u,
w){function A(){this.constructor=u}for(var y in w)w.hasOwnProperty(y)&&(u[y]=w[y]);u.prototype=null===w?Object.create(w):(A.prototype=w.prototype,new A)},r=h(5),l=h(8),p=h(20),m=h(0),n=h(27),q=h(2);d=function(u){function w(A){void 0===A&&(A="vertical");var y=u.call(this,A)||this;y._extremaFormatter=l.identity();y._stackingResult=p.memThunk(function(){return y.datasets()},function(){return y.position().accessor},function(){return y.length().accessor},function(){return y._stackingOrder},function(x,
C,G,D){return m.Stacking.stack(x,C,G,D)});y._stackedExtent=p.memThunk(y._stackingResult,function(){return y.position().accessor},function(){return y._filterForProperty(y._isVertical?"y":"x")},function(x,C,G){return m.Stacking.stackedExtent(x,C,G)});y.addClass("stacked-bar-plot");y._stackingOrder="bottomup";return y}k(w,u);w.prototype.stackingOrder=function(A){if(null==A)return this._stackingOrder;this._stackingOrder=A;this._onDatasetUpdate();return this};w.prototype.extremaFormatter=function(A){if(0===
arguments.length)return this._extremaFormatter;this._extremaFormatter=A;this.render();return this};w.prototype._setup=function(){u.prototype._setup.call(this);this._labelArea=this._renderArea.append("g").classed(n.Bar._LABEL_AREA_CLASS,!0);var A=new r.SvgContext(this._labelArea.node());this._measurer=new r.CacheMeasurer(A);this._writer=new r.Writer(this._measurer,A)};w.prototype._drawLabels=function(){function A(L,J){var O=x._generateAttrToProjector(),S=x.width(),N=x.height();L.forEach(function(R){if(R.extent!==
C){var X=x.extremaFormatter()(R.extent),aa=x._measurer.measure(X),fa=R.stackedDatum,oa=fa.originalDatum,Z=fa.originalIndex;fa=fa.originalDataset;x._isDatumOnScreen(O,S,N,oa,Z,fa)&&(oa=q.Plot._scaledAccessor(x.attr(n.Bar._BAR_THICKNESS_KEY))(oa,Z,fa),Z=D.scale(R.extent),R=x._getPositionAttr(G.scale(R.axisValue),oa)+oa/2,R=J(x._isVertical?{x:R,y:Z}:{x:Z,y:R},aa,oa),X=y(X,{topLeft:R,bottomRight:{x:R.x+aa.width,y:R.y+aa.height}},oa),K.push(X))}})}function y(L,J,O){var S=J.topLeft,N=S.x,R=S.y;S=J.bottomRight.x-
J.topLeft.x;J=J.bottomRight.y-J.topLeft.y;O=x._isVertical?S>O:J>O;O||(N=x._labelArea.append("g").attr("transform","translate("+N+", "+R+")"),N.classed("stacked-bar-label",!0),x._writer.write(L,S,J,{xAlign:"center",yAlign:"center"},N.node()));return O}var x=this;u.prototype._drawLabels.call(this);this._labelArea.selectAll("g").remove();var C=+this.baselineValue(),G=this.position().scale,D=this.length().scale,B=m.Stacking.stackedExtents(this._stackingResult()),H=B.minimumExtents,K=[];A(B.maximumExtents,
function(L,J){var O=x._isVertical?J.width:J.height;J=x._isVertical?J.height:J.width;return{x:x._isVertical?L.x-O/2:L.x+w._EXTREMA_LABEL_MARGIN_FROM_BAR,y:x._isVertical?L.y-J:L.y-O/2}});A(H,function(L,J){var O=x._isVertical?J.width:J.height;J=x._isVertical?J.height:J.width;return{x:x._isVertical?L.x-O/2:L.x-J,y:x._isVertical?L.y+w._EXTREMA_LABEL_MARGIN_FROM_BAR:L.y-O/2}});K.some(function(L){return L})&&this._labelArea.selectAll("g").remove()};w.prototype._generateAttrToProjector=function(){function A(S,
N,R){return 0>+L(S,N,R)?C(S,N,R):x(S,N,R)}function y(S,N,R){return Math.abs(x(S,N,R)-C(S,N,R))}function x(S,N,R){return K.scale(+L(S,N,R)+O.get(R).get(G(S,N,R)).offset)}function C(S,N,R){return K.scale(O.get(R).get(G(S,N,R)).offset)}function G(S,N,R){return m.Stacking.normalizeKey(J(S,N,R))}var D=this,B=u.prototype._generateAttrToProjector.call(this),H=this._isVertical?"y":"x",K=this.length().scale,L=this.length().accessor,J=this.position().accessor,O=this._stackingResult();B[this._isVertical?"height":
"width"]=y;B[H]=function(S,N,R){return D._isVertical?A(S,N,R):A(S,N,R)-y(S,N,R)};return B};w.prototype.getExtentsForProperty=function(A){return A===(this._isVertical?"y":"x")?[this._stackedExtent()]:u.prototype.getExtentsForProperty.call(this,A)};w.prototype.invalidateCache=function(){u.prototype.invalidateCache.call(this);this._measurer.reset()};return w}(n.Bar);d._EXTREMA_LABEL_MARGIN_FROM_BAR=5;f.StackedBar=d},function(d,f,h){var k=this&&this.__extends||function(p,m){function n(){this.constructor=
p}for(var q in m)m.hasOwnProperty(q)&&(p[q]=m[q]);p.prototype=null===m?Object.create(m):(n.prototype=m.prototype,new n)},r=h(0);d=h(27);var l=h(2);h=function(p){function m(){var n=p.call(this)||this;n._connectorsEnabled=!1;n.addClass("waterfall-plot");return n}k(m,p);m.prototype.connectorsEnabled=function(n){if(null==n)return this._connectorsEnabled;this._connectorsEnabled=n;return this};m.prototype.total=function(n){if(null==n)return this._propertyBindings.get(m._TOTAL_KEY);this._bindProperty(m._TOTAL_KEY,
n,null);return this};m.prototype._additionalPaint=function(n){var q=this;this._connectorArea.selectAll("line").remove();this._connectorsEnabled&&r.Window.setTimeout(function(){return q._drawConnectors()},n)};m.prototype._createNodesForDataset=function(n){n=p.prototype._createNodesForDataset.call(this,n);this._connectorArea=this._renderArea.append("g").classed(m._CONNECTOR_AREA_CLASS,!0);return n};m.prototype.getExtentsForProperty=function(n){return"y"===n?[this._extent]:p.prototype.getExtentsForProperty.call(this,
n)};m.prototype._generateAttrToProjector=function(){var n=this,q=p.prototype._generateAttrToProjector.call(this),u=this.y().scale,w=l.Plot._scaledAccessor(this.total());null==this.attr("y")&&(q.y=function(A,y,x){var C=n.y().accessor(A,y,x);if(w(A,y,x))return Math.min(u.scale(C),u.scale(0));A=n._subtotals[y];if(0===y)return 0>C?u.scale(A-C):u.scale(A);y=n._subtotals[y-1];return A>y?u.scale(A):u.scale(y)});null==this.attr("height")&&(q.height=function(A,y,x){var C=w(A,y,x);A=n.y().accessor(A,y,x);if(C)return Math.abs(u.scale(A)-
u.scale(0));C=n._subtotals[y];if(0===y)return Math.abs(u.scale(C)-u.scale(C-A));y=n._subtotals[y-1];return Math.abs(u.scale(C)-u.scale(y))});q["class"]=function(A,y,x){var C="";null!=n.attr("class")&&(C=n.attr("class").accessor(A,y,x)+" ");if(w(A,y,x))return C+m._BAR_TOTAL_CLASS;A=n.y().accessor(A,y,x);return C+(0<A?m._BAR_GROWTH_CLASS:m._BAR_DECLINE_CLASS)};return q};m.prototype._onDatasetUpdate=function(){this._updateSubtotals();p.prototype._onDatasetUpdate.call(this);return this};m.prototype._calculateSubtotalsAndExtent=
function(n){var q=this,u=Number.MAX_VALUE,w=Number.MIN_VALUE,A=0,y=!1;n.data().forEach(function(x,C){var G=q.y().accessor(x,C,n);(x=q.total().accessor(x,C,n))&&0!==C||(A+=G);q._subtotals.push(A);A<u&&(u=A);A>w&&(w=A);x&&(G<u&&(u=G),G>w&&(w=G));if(!y&&x){C=G-A;for(G=0;G<q._subtotals.length;G++)q._subtotals[G]+=C;y=!0;A+=C;u+=C;w+=C}});this._extent=[u,w]};m.prototype._drawConnectors=function(){for(var n=this._getAttrToProjector(),q=this.datasets()[0],u=1;u<q.data().length;u++){var w=u-1,A=q.data()[u],
y=q.data()[w];y=n.x(y,w,q);var x=n.x(A,u,q)+n.width(A,u,q),C=n.y(A,u,q);if(0<this._subtotals[u]&&this._subtotals[u]>this._subtotals[w]||0>this._subtotals[u]&&this._subtotals[u]>=this._subtotals[w])C=n.y(A,u,q)+n.height(A,u,q);this._connectorArea.append("line").classed(m._CONNECTOR_CLASS,!0).attr("x1",y).attr("x2",x).attr("y1",C).attr("y2",C)}};m.prototype._updateSubtotals=function(){var n=this.datasets();0<n.length&&(n=n[n.length-1],this._subtotals=[],this._calculateSubtotalsAndExtent(n))};return m}(d.Bar);
h._BAR_DECLINE_CLASS="waterfall-decline";h._BAR_GROWTH_CLASS="waterfall-growth";h._BAR_TOTAL_CLASS="waterfall-total";h._CONNECTOR_CLASS="connector";h._CONNECTOR_AREA_CLASS="connector-area";h._TOTAL_KEY="total";f.Waterfall=h},function(d,f,h){var k=this&&this.__extends||function(p,m){function n(){this.constructor=p}for(var q in m)m.hasOwnProperty(q)&&(p[q]=m[q]);p.prototype=null===m?Object.create(m):(n.prototype=m.prototype,new n)},r=h(1),l=h(0);d=function(p){function m(n){var q=p.call(this)||this;
switch(n){case null:case void 0:null==m._plottableColorCache&&(m._plottableColorCache=m._getPlottableColors());n=r.scaleOrdinal().range(m._plottableColorCache);break;case "Category10":case "category10":case "10":n=r.scaleOrdinal(r.schemeCategory10);break;case "Category20":case "category20":case "20":n=r.scaleOrdinal(r.schemeCategory20);break;case "Category20b":case "category20b":case "20b":n=r.scaleOrdinal(r.schemeCategory20b);break;case "Category20c":case "category20c":case "20c":n=r.scaleOrdinal(r.schemeCategory20c);
break;default:throw Error("Unsupported ColorScale type");}q._d3Scale=n;return q}k(m,p);m.prototype.extentOfValues=function(n){return l.Array.uniq(n)};m.prototype._getExtent=function(){return l.Array.uniq(this._getAllIncludedValues())};m.invalidateColorCache=function(){m._plottableColorCache=null};m._getPlottableColors=function(){for(var n=[],q=r.select("body").append("plottable-color-tester"),u=l.Color.colorTest(q,""),w=0,A=l.Color.colorTest(q,"plottable-colors-0");null!=A&&w<this._MAXIMUM_COLORS_FROM_CSS&&
(A!==u||A!==n[n.length-1]);)n.push(A),w++,A=l.Color.colorTest(q,"plottable-colors-"+w);q.remove();return n};m.prototype.scale=function(n){var q=this._d3Scale(n);n=this.domain().indexOf(n);n=Math.floor(n/this.range().length);return l.Color.lightenColor(q,Math.log(n*m._LOOP_LIGHTEN_FACTOR+1))};m.prototype._getDomain=function(){return this._backingScaleDomain()};m.prototype._backingScaleDomain=function(n){if(null==n)return this._d3Scale.domain();this._d3Scale.domain(n);return this};m.prototype._getRange=
function(){return this._d3Scale.range()};m.prototype._setRange=function(n){this._d3Scale.range(n)};return m}(h(17).Scale);d._LOOP_LIGHTEN_FACTOR=1.6;d._MAXIMUM_COLORS_FROM_CSS=256;f.Color=d},function(d,f,h){var k=this&&this.__extends||function(p,m){function n(){this.constructor=p}for(var q in m)m.hasOwnProperty(q)&&(p[q]=m[q]);p.prototype=null===m?Object.create(m):(n.prototype=m.prototype,new n)},r=h(1),l=h(0);d=function(p){function m(n){void 0===n&&(n="linear");var q=p.call(this)||this;switch(n){case "linear":q._colorScale=
r.scaleLinear();break;case "log":q._colorScale=r.scaleLog();break;case "sqrt":q._colorScale=r.scaleSqrt();break;case "pow":q._colorScale=r.scalePow()}if(null==q._colorScale)throw Error("unknown QuantitativeScale scale type "+n);q.range(m.REDS);return q}k(m,p);m.prototype.extentOfValues=function(n){n=r.extent(n);return null==n[0]||null==n[1]?[]:n};m.prototype._d3InterpolatedScale=function(){return this._colorScale.range([0,1]).interpolate(this._interpolateColors())};m.prototype._interpolateColors=
function(){var n=this._colorRange;if(2>n.length)throw Error("Color scale arrays must have at least two elements.");return function(){return function(q){q=Math.max(0,Math.min(1,q));q*=n.length-1;var u=Math.floor(q),w=q-u;return r.interpolateLab(n[u],n[Math.ceil(q)])(w)}}};m.prototype._resetScale=function(){this._d3Scale=this._d3InterpolatedScale();this._autoDomainIfAutomaticMode();this._dispatchUpdate()};m.prototype.autoDomain=function(){var n=this._getAllIncludedValues();0<n.length&&this._setDomain([l.Math.min(n,
0),l.Math.max(n,0)])};m.prototype.scale=function(n){return this._d3Scale(n)};m.prototype._getDomain=function(){return this._backingScaleDomain()};m.prototype._backingScaleDomain=function(n){if(null==n)return this._d3Scale.domain();this._d3Scale.domain(n);return this};m.prototype._getRange=function(){return this._colorRange};m.prototype._setRange=function(n){this._colorRange=n;this._resetScale()};return m}(h(17).Scale);d.REDS="#FFFFFF #FFF6E1 #FEF4C0 #FED976 #FEB24C #FD8D3C #FC4E2A #E31A1C #B10026".split(" ");
d.BLUES="#FFFFFF #CCFFFF #A5FFFD #85F7FB #6ED3EF #55A7E0 #417FD0 #2545D3 #0B02E1".split(" ");d.POSNEG="#0B02E1 #2545D3 #417FD0 #55A7E0 #6ED3EF #85F7FB #A5FFFD #CCFFFF #FFFFFF #FFF6E1 #FEF4C0 #FED976 #FEB24C #FD8D3C #FC4E2A #E31A1C #B10026".split(" ");f.InterpolatedColor=d},function(d,f,h){var k=this&&this.__extends||function(l,p){function m(){this.constructor=l}for(var n in p)p.hasOwnProperty(n)&&(l[n]=p[n]);l.prototype=null===p?Object.create(p):(m.prototype=p.prototype,new m)},r=h(1);d=function(l){function p(){var m=
l.call(this)||this;m._d3Scale=r.scaleLinear();return m}k(p,l);p.prototype._defaultExtent=function(){return[0,1]};p.prototype._expandSingleValueDomain=function(m){return m[0]===m[1]?[m[0]-1,m[1]+1]:m};p.prototype.scale=function(m){return this._d3Scale(m)};p.prototype.scaleTransformation=function(m){return this.scale(m)};p.prototype.invertedTransformation=function(m){return this.invert(m)};p.prototype.getTransformationExtent=function(){return this._getUnboundedExtent(!0)};p.prototype.getTransformationDomain=
function(){return this.domain()};p.prototype.setTransformationDomain=function(m){this.domain(m)};p.prototype._getDomain=function(){return this._backingScaleDomain()};p.prototype._backingScaleDomain=function(m){if(null==m)return this._d3Scale.domain();this._d3Scale.domain(m);return this};p.prototype._getRange=function(){return this._d3Scale.range()};p.prototype._setRange=function(m){this._d3Scale.range(m)};p.prototype.invert=function(m){return this._d3Scale.invert(m)};p.prototype.defaultTicks=function(){return this._d3Scale.ticks()};
p.prototype._niceDomain=function(m,n){return this._d3Scale.copy().domain(m).nice(n).domain()};return p}(h(11).QuantitativeScale);f.Linear=d},function(d,f,h){var k=this&&this.__extends||function(m,n){function q(){this.constructor=m}for(var u in n)n.hasOwnProperty(u)&&(m[u]=n[u]);m.prototype=null===n?Object.create(n):(q.prototype=n.prototype,new q)},r=h(1),l=h(0),p=h(3);d=function(m){function n(q){void 0===q&&(q=10);var u=m.call(this)||this;u._d3Scale=r.scaleLinear();u._base=q;u._pivot=u._base;u._setDomain(u._defaultExtent());
if(1>=q)throw Error("ModifiedLogScale: The base must be \x3e 1");return u}k(n,m);n.prototype._adjustedLog=function(q){var u=0>q?-1:1;q*=u;q<this._pivot&&(q+=(this._pivot-q)/this._pivot);q=Math.log(q)/Math.log(this._base);return q*u};n.prototype._invertedAdjustedLog=function(q){var u=0>q?-1:1;q=Math.pow(this._base,q*u);q<this._pivot&&(q=this._pivot*(q-1)/(this._pivot-1));return q*u};n.prototype.scale=function(q){return this._d3Scale(this._adjustedLog(q))};n.prototype.invert=function(q){return this._invertedAdjustedLog(this._d3Scale.invert(q))};
n.prototype.scaleTransformation=function(q){return this.scale(q)};n.prototype.invertedTransformation=function(q){return this.invert(q)};n.prototype.getTransformationExtent=function(){return this._getUnboundedExtent(!0)};n.prototype.getTransformationDomain=function(){return this.domain()};n.prototype.setTransformationDomain=function(q){this.domain(q)};n.prototype._getDomain=function(){return this._untransformedDomain};n.prototype._setDomain=function(q){this._untransformedDomain=q;m.prototype._setDomain.call(this,
[this._adjustedLog(q[0]),this._adjustedLog(q[1])])};n.prototype._backingScaleDomain=function(q){if(null==q)return this._d3Scale.domain();this._d3Scale.domain(q);return this};n.prototype.ticks=function(){function q(G,D,B){return[G,D,B].sort(function(H,K){return H-K})[1]}var u=l.Math.min(this._untransformedDomain,0),w=l.Math.max(this._untransformedDomain,0),A=q(u,w,-this._pivot),y=q(u,w,this._pivot);A=this._logTicks(-A,-u).map(function(G){return-G}).reverse();y=this._logTicks(y,w);var x=Math.max(u,
-this._pivot),C=Math.min(w,this._pivot);x=r.scaleLinear().domain([x,C]).ticks(this._howManyTicks(x,C));A=A.concat(x).concat(y);1>=A.length&&(A=r.scaleLinear().domain([u,w]).ticks());return A};n.prototype._logTicks=function(q,u){var w=this,A=this._howManyTicks(q,u);if(0===A)return[];var y=Math.floor(Math.log(q)/Math.log(this._base)),x=Math.ceil(Math.log(u)/Math.log(this._base));A=r.range(x,y,-Math.ceil((x-y)/A));y=r.range(this._base,1,-(this._base-1)).map(Math.floor);var C=l.Array.uniq(y);A=A.map(function(G){return C.map(function(D){return Math.pow(w._base,
G-1)*D})});return l.Array.flatten(A).filter(function(G){return q<=G&&G<=u}).sort(function(G,D){return G-D})};n.prototype._howManyTicks=function(q,u){var w=this._adjustedLog(l.Math.min(this._untransformedDomain,0)),A=this._adjustedLog(l.Math.max(this._untransformedDomain,0));return Math.ceil((this._adjustedLog(u)-this._adjustedLog(q))/(A-w)*p.ModifiedLog._DEFAULT_NUM_TICKS)};n.prototype._niceDomain=function(q){return q};n.prototype._defaultExtent=function(){return[0,this._base]};n.prototype._expandSingleValueDomain=
function(q){return q[0]===q[1]?(q=q[0],0<q?[q/this._base,q*this._base]:0===q?[-this._base,this._base]:[q*this._base,q/this._base]):q};n.prototype._getRange=function(){return this._d3Scale.range()};n.prototype._setRange=function(q){this._d3Scale.range(q)};n.prototype.defaultTicks=function(){return this._d3Scale.ticks()};return n}(h(11).QuantitativeScale);f.ModifiedLog=d},function(d,f,h){var k=h(0);f.intervalTickGenerator=function(r){if(0>=r)throw Error("interval must be positive number");return function(l){l=
l.domain();var p=Math.min(l[0],l[1]);l=Math.max(l[0],l[1]);var m=Math.ceil(p/r)*r;p=0===p%r?[]:[p];var n=k.Math.range(0,Math.floor((l-m)/r)+1).map(function(q){return m+q*r});return p.concat(n).concat(0===l%r?[]:[l])}};f.integerTickGenerator=function(){return function(r){var l=r.defaultTicks();return l.filter(function(p,m){return 0===p%1||0===m||m===l.length-1})}}},function(d,f,h){var k=this&&this.__extends||function(p,m){function n(){this.constructor=p}for(var q in m)m.hasOwnProperty(q)&&(p[q]=m[q]);
p.prototype=null===m?Object.create(m):(n.prototype=m.prototype,new n)},r=h(1),l=h(28);d=function(p){function m(){var n=p.call(this)||this;n._d3Scale=r.scaleTime();n.autoDomain();return n}k(m,p);m.prototype.tickInterval=function(n,q){void 0===q&&(q=1);var u=r.scaleTime();m.timeIntervalToD3Time(n).every(q);u.domain(this.domain());u.range(this.range());return u.ticks()};m.prototype._setDomain=function(n){if(n[1]<n[0])throw Error("Scale.Time domain values must be in chronological order");return p.prototype._setDomain.call(this,
n)};m.prototype._defaultExtent=function(){return[new Date("1970-01-01"),new Date("1970-01-02")]};m.prototype._expandSingleValueDomain=function(n){var q=n[0].getTime(),u=n[1].getTime();return q===u?(n=new Date(q),n.setDate(n.getDate()-1),u=new Date(u),u.setDate(u.getDate()+1),[n,u]):n};m.prototype.scale=function(n){return this._d3Scale(n)};m.prototype.scaleTransformation=function(n){return this.scale(new Date(n))};m.prototype.invertedTransformation=function(n){return this.invert(n).getTime()};m.prototype.getTransformationExtent=
function(){var n=this._getUnboundedExtent(!0);return[n[0].valueOf(),n[1].valueOf()]};m.prototype.getTransformationDomain=function(){var n=this.domain();return[n[0].valueOf(),n[1].valueOf()]};m.prototype.setTransformationDomain=function(n){this.domain([new Date(n[0]),new Date(n[1])])};m.prototype._getDomain=function(){return this._backingScaleDomain()};m.prototype._backingScaleDomain=function(n){if(null==n)return this._d3Scale.domain();this._d3Scale.domain(n);return this};m.prototype._getRange=function(){return this._d3Scale.range()};
m.prototype._setRange=function(n){this._d3Scale.range(n)};m.prototype.invert=function(n){return this._d3Scale.invert(n)};m.prototype.defaultTicks=function(){return this._d3Scale.ticks()};m.prototype._niceDomain=function(n){return this._d3Scale.copy().domain(n).nice().domain()};m.timeIntervalToD3Time=function(n){switch(n){case l.TimeInterval.second:return r.timeSecond;case l.TimeInterval.minute:return r.timeMinute;case l.TimeInterval.hour:return r.timeHour;case l.TimeInterval.day:return r.timeDay;
case l.TimeInterval.week:return r.timeWeek;case l.TimeInterval.month:return r.timeMonth;case l.TimeInterval.year:return r.timeYear;default:throw Error("TimeInterval specified does not exist: "+n);}};return m}(h(11).QuantitativeScale);f.Time=d},function(d,f,h){var k=h(1),r=Array;f.add=function(l,p){if(l.length!==p.length)throw Error("attempted to add arrays of unequal length");return l.map(function(m,n){return l[n]+p[n]})};f.uniq=function(l){var p=k.set(),m=[];l.forEach(function(n){p.has(String(n))||
(p.add(String(n)),m.push(n))});return m};f.flatten=function(l){return r.prototype.concat.apply([],l)};f.createFilledArray=function(l,p){for(var m=[],n=0;n<p;n++)m[n]="function"===typeof l?l(n):l;return m}},function(d,f){d=function(){function h(k,r,l){this.maxIndex=this.minIndex=this.exitIndex=this.entryIndex=k;this.bucketValue=r;this.maxValue=this.minValue=l}h.prototype.isInBucket=function(k){return k==this.bucketValue};h.prototype.addToBucket=function(k,r){k<this.minValue&&(this.minValue=k,this.minIndex=
r);k>this.maxValue&&(this.maxValue=k,this.maxIndex=r);this.exitIndex=r};h.prototype.getUniqueIndices=function(){var k=[this.entryIndex,this.maxIndex,this.minIndex,this.exitIndex];return k.filter(function(r,l){return 0==l||r!=k[l-1]})};return h}();f.Bucket=d},function(d,f,h){var k=this&&this.__extends||function(r,l){function p(){this.constructor=r}for(var m in l)l.hasOwnProperty(m)&&(r[m]=l[m]);r.prototype=null===l?Object.create(l):(p.prototype=l.prototype,new p)};d=function(r){function l(){return null!==
r&&r.apply(this,arguments)||this}k(l,r);l.prototype.callCallbacks=function(){for(var p=this,m=[],n=0;n<arguments.length;n++)m[n]=arguments[n];this.forEach(function(q){q.apply(p,m)});return this};return l}(h(58).Set);f.CallbackSet=d},function(d,f,h){function k(p){function m(u){u/=255;return.03928>=u?u/12.92:l.pow((u+.055)/1.055,2.4)}var n=r.rgb(p);p=m(n.r);var q=m(n.g);n=m(n.b);return.2126*p+.7152*q+.0722*n}var r=h(1),l=Math;f.contrast=function(p,m){p=k(p)+.05;m=k(m)+.05;return p>m?p/m:m/p};f.lightenColor=
function(p,m){return r.color(p).brighter(m).rgb().toString()};f.colorTest=function(p,m){p.classed(m,!0);var n=p.style("background-color");if("transparent"===n)return null;n=/\((.+)\)/.exec(n);if(!n)return null;n=n[1].split(",").map(function(q){q=+q;var u=q.toString(16);return 16>q?"0"+u:u});if(4===n.length&&"00"===n[3])return null;n="#"+n.join("");p.classed(m,!1);return n}},function(d,f,h){var k=h(1),r=h(57);d=function(){function l(){this._entities=[];this._rtree=new r.RTree;this._tree=k.quadtree().x(function(p){return Math.floor(p.position.x)}).y(function(p){return Math.floor(p.position.y)})}
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5;f.SingleLineWrapper=d},function(d,f,h){var k=h(21),r={textRotation:0,textShear:0,xAlign:"left",yAlign:"top"};d=function(){function l(p,m,n){this._measurer=p;this._penFactory=m;this._wrapper=n}l.prototype.measurer=function(p){this._measurer=p;return this};l.prototype.wrapper=function(p){this._wrapper=p;return this};l.prototype.penFactory=function(p){this._penFactory=p;return this};l.prototype.write=function(p,m,n,q,u){void 0===q&&(q={});q=k.Methods.defaults({},r,q);if(-1===l.SupportedRotation.indexOf(q.textRotation))throw Error("unsupported rotation - "+
q.textRotation+". Supported rotations are "+l.SupportedRotation.join(", "));if(null!=q.textShear&&-80>q.textShear||80<q.textShear)throw Error("unsupported shear angle - "+q.textShear+". Must be between -80 and 80");var w=45<Math.abs(Math.abs(q.textRotation)-90),A=w?m:n,y=w?n:m,x=q.textShear,C=x*Math.PI/180;w=this._measurer.measure().height;var G=w*Math.tan(C);A=A/Math.cos(C)-Math.abs(G);var D=y*Math.cos(C);y=k.StringMethods.combineWhitespace(p);y=(this._wrapper?this._wrapper.wrap(y,this._measurer,
A,D).wrappedText:y).split("\n");C=l.XOffsetFactor[q.xAlign]*A*Math.sin(C)-l.YOffsetFactor[q.yAlign]*(D-y.length*w);x=q.textRotation+x;switch(q.textRotation){case 90:m=[m+C,0];break;case -90:m=[-C,n];break;case 180:m=[m,n+C];break;default:m=[0,-C]}p=this._penFactory.createPen(p,{translate:m,rotate:x},u);this.writeLines(y,p,A,w,G,q.xAlign);null!=p.destroy&&p.destroy()};l.prototype.writeLines=function(p,m,n,q,u,w){p.forEach(function(A,y){m.write(A,l.AnchorConverter[w],(0<u?(y+1)*u:y*u)+n*l.XOffsetFactor[w],
(y+1)*q)})};return l}();d.SupportedRotation=[-90,0,180,90];d.AnchorConverter={center:"middle",left:"start",right:"end"};d.XOffsetFactor={center:.5,left:0,right:1};d.YOffsetFactor={bottom:1,center:.5,top:0};f.Writer=d},function(d,f,h){function k(r){for(var l in r)f.hasOwnProperty(l)||(f[l]=r[l])}h(69);d=h(7);f.Animators=d;d=h(67);f.Axes=d;d=h(37);f.Components=d;d=h(23);f.Configs=d;d=h(8);f.Formatters=d;d=h(30);f.RenderController=d;d=h(39);f.RenderPolicies=d;d=h(31);f.SymbolFactories=d;d=h(13);f.Dispatchers=
d;d=h(14);f.Drawers=d;d=h(25);f.Interactions=d;d=h(19);f.Plots=d;d=h(3);f.Scales=d;d=h(0);f.Utils=d;k(h(22));d=h(28);f.TimeInterval=d.TimeInterval;k(h(4));k(h(29));k(h(38));d=h(68);f.version=d.version;k(h(24));k(h(6));k(h(15));k(h(40));k(h(16));k(h(2));k(h(11));k(h(17))}])});

//# sourceURL=build://vz-chart-helpers/plottable-interactions.js
var sf;
(function(b){function d(m){const n=[];for(;m&&m instanceof HTMLElement;)if(n.push(m),m.assignedSlot)m=m.assignedSlot;else if(m.parentElement)m=m.parentElement;else{const q=m.parentNode;m=q instanceof DocumentFragment?q.host:q!==m?q:null}return n}function f(m){var n=d(m);m=h;let q=null;for(const w of n){n=Plottable.Utils.DOM.getElementTransform(w);if(null!=n){var u=w.clientWidth/2;const A=w.clientHeight/2;m=Plottable.Utils.Math.multiplyTranslate(m,[u,A]);m=Plottable.Utils.Math.multiplyMatrix(m,Plottable.Utils.Math.invertMatrix(n));
m=Plottable.Utils.Math.multiplyTranslate(m,[-u,-A])}n=w.scrollLeft;u=w.scrollTop;if(null===q||w===q)n-=w.offsetLeft+w.clientLeft,u-=w.offsetTop+w.clientTop,q=w.offsetParent;m=Plottable.Utils.Math.multiplyTranslate(m,[n,u])}return m}const h=[1,0,0,1,0,0];class k extends Plottable.Utils.Translator{computePosition(m,n){m={x:m,y:n};n=f(this._rootElement);return null==n?m:Plottable.Utils.Math.applyTransform(n,m)}}class r extends Plottable.Dispatchers.Mouse{constructor(m){super(m);this._eventTarget=m.root().rootElement().node();
this._translator=new k(m.root().rootElement().node())}static getDispatcher(m){const n=m.root().rootElement();let q=n[r._DISPATCHER_KEY];q||(q=new r(m),n[r._DISPATCHER_KEY]=q);return q}}class l extends Plottable.Dispatchers.Touch{constructor(m){super(m);this._eventTarget=m.root().rootElement().node();this._translator=new k(m.root().rootElement().node())}static getDispatcher(m){const n=m.root().rootElement();let q=n[l._DISPATCHER_KEY];q||(q=new l(m),n[l._DISPATCHER_KEY]=q);return q}}Plottable.Interaction.prototype._isInsideComponent=
function(m){return 0<=m.x&&0<=m.y&&m.x<this._componentAttachedTo.width()&&m.y<this._componentAttachedTo.height()};class p extends Plottable.Interactions.Pointer{_anchor(){this._isAnchored=!0;this._mouseDispatcher=r.getDispatcher(this._componentAttachedTo);this._mouseDispatcher.onMouseMove(this._mouseMoveCallback);this._touchDispatcher=l.getDispatcher(this._componentAttachedTo);this._touchDispatcher.onTouchStart(this._touchStartCallback)}}b.PointerInteraction=p})(sf||(sf={}));

//# sourceURL=build://vz-chart-helpers/vz-chart-helpers.js
(function(b){function d(){let r=new Plottable.Scales.Linear;r.tickGenerator();let l=new Plottable.Axes.Numeric(r,"bottom");l.formatter(b.stepFormatter);return{scale:r,axis:l,accessor:p=>p.step}}function f(){let r=new Plottable.Scales.Time;return{scale:r,axis:new Plottable.Axes.Time(r,"bottom"),accessor:l=>l.wall_time}}function h(){let r=new Plottable.Scales.Linear;return{scale:r,axis:new Plottable.Axes.Numeric(r,"bottom"),accessor:b.relativeAccessor}}b.SYMBOLS_LIST=[{character:"\u25fc",method:Plottable.SymbolFactories.square},
{character:"\u25c6",method:Plottable.SymbolFactories.diamond},{character:"\u25b2",method:Plottable.SymbolFactories.triangle},{character:"\u2605",method:Plottable.SymbolFactories.star},{character:"\u271a",method:Plottable.SymbolFactories.cross}];let k;(function(r){r.STEP="step";r.RELATIVE="relative";r.WALL_TIME="wall_time"})(k=b.XType||(b.XType={}));b.Y_TOOLTIP_FORMATTER_PRECISION=4;b.STEP_FORMATTER_PRECISION=4;b.Y_AXIS_FORMATTER_PRECISION=3;b.TOOLTIP_Y_PIXEL_OFFSET=20;b.TOOLTIP_CIRCLE_SIZE=4;b.NAN_SYMBOL_SIZE=
6;b.multiscaleFormatter=function(r){return l=>{let p=Math.abs(l);1E-15>p&&(p=0);return(1E4<=p?d3.format("."+r+"~e"):0<p&&.01>p?d3.format("."+r+"~e"):d3.format("."+r+"~g"))(l)}};b.computeDomain=function(r,l){r=r.filter(n=>isFinite(n));if(0===r.length)return[-.1,1.1];l?(r=_.sortBy(r),l=d3.quantile(r,.05),r=d3.quantile(r,.95)):(l=d3.min(r),r=d3.max(r));let p,m=r-l;p=0===m?1.1*Math.abs(l)+1.1:.2*m;l=[0<=l&&l<m?-.1*r:l-p,r+p];return l=d3.scaleLinear().domain(l).nice().domain()};b.accessorize=function(r){return l=>
l[r]};b.stepFormatter=d3.format(`.${b.STEP_FORMATTER_PRECISION}~s`);b.stepX=d;b.timeFormatter=Plottable.Formatters.time("%a %b %e, %H:%M:%S");b.wallX=f;b.relativeAccessor=(r,l,p)=>{if(null!=r.relative)return r.relative;l=p.data();return(+r.wall_time-(0<l.length?+l[0].wall_time:0))/36E5};b.relativeFormatter=r=>{let l="",p=Math.floor(r/24);r-=24*p;p&&(l+=p+"d ");let m=Math.floor(r);r=60*(r-m);if(m||p)l+=m+"h ";let n=Math.floor(r);r=60*(r-n);if(n||m||p)l+=n+"m ";return l+Math.floor(r)+"s"};b.relativeX=
h;b.getXComponents=function(r){switch(r){case k.STEP:return d();case k.WALL_TIME:return f();case k.RELATIVE:return h();default:throw Error("invalid xType: "+r);}}})(sf||(sf={}));

//# sourceURL=build://vz-line-chart/dragZoomInteraction.js
var tg;
(function(b){class d extends Plottable.Components.SelectionBoxLayer{constructor(f,h,k){super();this.easeFn=d3.easeCubicInOut;this._animationTime=750;this.xScale(f);this.yScale(h);this._dragInteraction=new Plottable.Interactions.Drag;this._doubleClickInteraction=new Plottable.Interactions.Click;this.setupCallbacks();this.unzoomMethod=k;this.onDetach(()=>{this._doubleClickInteraction.detachFrom();this._dragInteraction.detachFrom()});this.onAnchor(()=>{this._doubleClickInteraction.attachTo(this);this._dragInteraction.attachTo(this)})}interactionStart(f){this.onStart=
f}interactionEnd(f){this.onEnd=f}dragInteraction(){return this._dragInteraction}setupCallbacks(){let f=!1;this._dragInteraction.onDragStart(h=>{this.bounds({topLeft:h,bottomRight:h});this.onStart()});this._dragInteraction.onDrag((h,k)=>{this.bounds({topLeft:h,bottomRight:k});this.boxVisible(!0);f=!0});this._dragInteraction.onDragEnd((h,k)=>{this.boxVisible(!1);this.bounds({topLeft:h,bottomRight:k});if(f)this.zoom();else this.onEnd();f=!1});this._doubleClickInteraction.onDoubleClick(this.unzoom.bind(this))}animationTime(f){if(null==
f)return this._animationTime;if(0>f)throw Error("animationTime cannot be negative");this._animationTime=f;return this}ease(f){if("function"!==typeof f)throw Error("ease function must be a function");0===f(0)&&1===f(1)||Plottable.Utils.Window.warn("Easing function does not maintain invariant f(0)\x3d\x3d0 \x26\x26 f(1)\x3d\x3d1. Bad behavior may result.");this.easeFn=f;return this}zoom(){let f=this.xExtent()[0].valueOf(),h=this.xExtent()[1].valueOf(),k=this.yExtent()[1].valueOf(),r=this.yExtent()[0].valueOf();
f!==h&&k!==r&&this.interpolateZoom(f,h,k,r)}unzoom(){var f=this.xScale();f._domainMin=null;f._domainMax=null;f=f._getExtent();this.xScale().domain(f);this.unzoomMethod()}isZooming(f){this._dragInteraction.enabled(!f);this._doubleClickInteraction.enabled(!f)}interpolateZoom(f,h,k,r){let l=this.xScale().domain()[0].valueOf(),p=this.xScale().domain()[1].valueOf(),m=this.yScale().domain()[0].valueOf(),n=this.yScale().domain()[1].valueOf(),q=this.easeFn,u=(y,x,C)=>d3.interpolateNumber(y,x)(q(C));this.isZooming(!0);
let w=Date.now(),A=()=>{var y=Date.now()-w;y=0===this._animationTime?1:Math.min(1,y/this._animationTime);let x=u(l,f,y),C=u(p,h,y),G=u(m,k,y),D=u(n,r,y);this.xScale().domain([x,C]);this.yScale().domain([G,D]);1>y?Plottable.Utils.DOM.requestAnimationFramePolyfill(A):(this.onEnd(),this.isZooming(!1))};A()}}b.DragZoomLayer=d})(tg||(tg={}));

//# sourceURL=build://vz-line-chart2/panZoomDragLayer.js
var ug;
(function(b){let d;(function(h){h[h.NONE=0]="NONE";h[h.DRAG_ZOOMING=1]="DRAG_ZOOMING";h[h.PANNING=2]="PANNING"})(d||(d={}));class f extends Plottable.Components.Group{constructor(h,k,r){super();this.state=d.NONE;this.panStartCallback=new Plottable.Utils.CallbackSet;this.panEndCallback=new Plottable.Utils.CallbackSet;this.panZoom=new Plottable.Interactions.PanZoom(h,k);this.panZoom.dragInteraction().mouseFilter(p=>f.isPanKey(p)&&0===p.button);this.panZoom.wheelFilter(this.canScrollZoom);this.dragZoomLayer=new tg.DragZoomLayer(h,
k,r);this.dragZoomLayer.dragInteraction().mouseFilter(p=>!f.isPanKey(p)&&0===p.button);this.append(this.dragZoomLayer);const l=this.onWheel.bind(this);this.onAnchor(()=>{this._mouseDispatcher=Plottable.Dispatchers.Mouse.getDispatcher(this);this._mouseDispatcher.onWheel(l);this.panZoom.attachTo(this)});this.onDetach(()=>{this.panZoom.detachFrom();this._mouseDispatcher&&(this._mouseDispatcher.offWheel(l),this._mouseDispatcher=null)});this.panZoom.dragInteraction().onDragStart(()=>{this.state==d.NONE&&
this.setState(d.PANNING)});this.panZoom.dragInteraction().onDragEnd(()=>{this.state==d.PANNING&&this.setState(d.NONE)});this.dragZoomLayer.dragInteraction().onDragStart(()=>{this.state==d.NONE&&this.setState(d.DRAG_ZOOMING)});this.dragZoomLayer.dragInteraction().onDragEnd(()=>{this.state==d.DRAG_ZOOMING&&this.setState(d.NONE)})}onWheel(h,k){if(!this.canScrollZoom(k)&&(h=this.element(),h.select(".help").empty())){var r=h.append("div").classed("help",!0);r.append("span").text("Alt + Scroll to Zoom");
r.on("animationend",()=>void r.remove())}}static isPanKey(h){return!!h.altKey||!!h.shiftKey}canScrollZoom(h){return h.altKey}setState(h){if(this.state!=h){var k=this.state;this.state=h;this.root().removeClass(this.stateClassName(k));this.root().addClass(this.stateClassName(h));k==d.PANNING&&this.panEndCallback.callCallbacks();h==d.PANNING&&this.panStartCallback.callCallbacks()}}stateClassName(h){switch(h){case d.PANNING:return"panning";case d.DRAG_ZOOMING:return"drag-zooming";default:return""}}onPanStart(h){this.panStartCallback.add(h)}onPanEnd(h){this.panEndCallback.add(h)}onScrollZoom(h){this.panZoom.onZoomEnd(h)}onDragZoomStart(h){this.dragZoomLayer.interactionStart(h)}onDragZoomEnd(h){this.dragZoomLayer.interactionEnd(h)}}
b.PanZoomDragLayer=f})(ug||(ug={}));

//# sourceURL=build://tf-card-heading/util.js
var Hh;(function(b){function d(f){if(!f)return null;let h=f.match(/^#([0-9a-f]{1,2})([0-9a-f]{1,2})([0-9a-f]{1,2})$/);if(!h)return null;if(4==f.length)for(f=1;3>=f;f++)h[f]+=h[f];return[parseInt(h[1],16),parseInt(h[2],16),parseInt(h[3],16)]}b.formatDate=function(f){return f?f.toString().replace(/GMT-\d+ \(([^)]+)\)/,"$1"):""};b.pickTextColor=function(f){return(f=d(f))?125<Math.round((299*f[0]+587*f[1]+114*f[2])/1E3)?"inherit":"#eee":"inherit"}})(Hh||(Hh={}));

//# sourceURL=build://iron-range-behavior/iron-range-behavior.html.js
Polymer.IronRangeBehavior={properties:{value:{type:Number,value:0,notify:!0,reflectToAttribute:!0},min:{type:Number,value:0,notify:!0},max:{type:Number,value:100,notify:!0},step:{type:Number,value:1,notify:!0},ratio:{type:Number,value:0,readOnly:!0,notify:!0}},observers:["_update(value, min, max, step)"],_calcRatio:function(b){return(this._clampValue(b)-this.min)/(this.max-this.min)},_clampValue:function(b){return Math.min(this.max,Math.max(this.min,this._calcStep(b)))},_calcStep:function(b){b=parseFloat(b);
if(!this.step)return b;b=Math.round((b-this.min)/this.step);return 1>this.step?b/(1/this.step)+this.min:b*this.step+this.min},_validateValue:function(){var b=this._clampValue(this.value);this.value=this.oldValue=isNaN(b)?this.oldValue:b;return this.value!==b},_update:function(){this._validateValue();this._setRatio(100*this._calcRatio(this.value))}};

//# sourceURL=build://tf-custom-scalar-dashboard/tf-custom-scalar-helpers.js
var Ih;
(function(b){class d{constructor(h,k,r,l,p){this.run=h;this.tag=k;this.name=r;this.scalarData=l;this.symbol=p}getName(){return this.name}setData(h){this.scalarData=h}getData(){return this.scalarData}getRun(){return this.run}getTag(){return this.tag}getSymbol(){return this.symbol}}b.DataSeries=d;b.generateDataSeriesName=function(h,k){return`${k} (${h})`};class f{constructor(h){this.runBasedColorScale=h}scale(h){return this.runBasedColorScale.scale(this.parseRunName(h))}parseRunName(h){return(h=h.match(/\((.*)\)$/))?
h[1]:""}}b.DataSeriesColorScale=f})(Ih||(Ih={}));

(function(f){if(typeof exports==="object"&&typeof module!=="undefined")module.exports=f();else if(typeof define==="function"&&define.amd)define([],f);else{var g;if(typeof window!=="undefined")g=window;else if(typeof global!=="undefined")g=global;else if(typeof self!=="undefined")g=self;else g=this;g.graphlib=f()}})(function(){var define,module,exports;return function e(t,n,r){function s(o,u){if(!n[o]){if(!t[o]){var a=typeof require=="function"&&require;if(!u&&a)return a(o,!0);if(i)return i(o,!0);
var f=new Error("Cannot find module '"+o+"'");throw f.code="MODULE_NOT_FOUND",f;}var l=n[o]={exports:{}};t[o][0].call(l.exports,function(e){var n=t[o][1][e];return s(n?n:e)},l,l.exports,e,t,n,r)}return n[o].exports}var i=typeof require=="function"&&require;for(var o=0;o<r.length;o++)s(r[o]);return s}({1:[function(require,module,exports){var lib=require("./lib");module.exports={Graph:lib.Graph,json:require("./lib/json"),alg:require("./lib/alg"),version:lib.version}},{"./lib":17,"./lib/alg":8,"./lib/json":18}],
2:[function(require,module,exports){var _=require("../lodash");module.exports=components;function components(g){var visited={},cmpts=[],cmpt;function dfs(v){if(_.has(visited,v))return;visited[v]=true;cmpt.push(v);_.each(g.successors(v),dfs);_.each(g.predecessors(v),dfs)}_.each(g.nodes(),function(v){cmpt=[];dfs(v);if(cmpt.length)cmpts.push(cmpt)});return cmpts}},{"../lodash":19}],3:[function(require,module,exports){var _=require("../lodash");module.exports=dfs;function dfs(g,vs,order){if(!_.isArray(vs))vs=
[vs];var navigation=(g.isDirected()?g.successors:g.neighbors).bind(g);var acc=[],visited={};_.each(vs,function(v){if(!g.hasNode(v))throw new Error("Graph does not have node: "+v);doDfs(g,v,order==="post",visited,navigation,acc)});return acc}function doDfs(g,v,postorder,visited,navigation,acc){if(!_.has(visited,v)){visited[v]=true;if(!postorder)acc.push(v);_.each(navigation(v),function(w){doDfs(g,w,postorder,visited,navigation,acc)});if(postorder)acc.push(v)}}},{"../lodash":19}],4:[function(require,
module,exports){var dijkstra=require("./dijkstra"),_=require("../lodash");module.exports=dijkstraAll;function dijkstraAll(g,weightFunc,edgeFunc){return _.transform(g.nodes(),function(acc,v){acc[v]=dijkstra(g,v,weightFunc,edgeFunc)},{})}},{"../lodash":19,"./dijkstra":5}],5:[function(require,module,exports){var _=require("../lodash"),PriorityQueue=require("../data/priority-queue");module.exports=dijkstra;var DEFAULT_WEIGHT_FUNC=_.constant(1);function dijkstra(g,source,weightFn,edgeFn){return runDijkstra(g,
String(source),weightFn||DEFAULT_WEIGHT_FUNC,edgeFn||function(v){return g.outEdges(v)})}function runDijkstra(g,source,weightFn,edgeFn){var results={},pq=new PriorityQueue,v,vEntry;var updateNeighbors=function(edge){var w=edge.v!==v?edge.v:edge.w,wEntry=results[w],weight=weightFn(edge),distance=vEntry.distance+weight;if(weight<0)throw new Error("dijkstra does not allow negative edge weights. "+"Bad edge: "+edge+" Weight: "+weight);if(distance<wEntry.distance){wEntry.distance=distance;wEntry.predecessor=
v;pq.decrease(w,distance)}};g.nodes().forEach(function(v){var distance=v===source?0:Number.POSITIVE_INFINITY;results[v]={distance:distance};pq.add(v,distance)});while(pq.size()>0){v=pq.removeMin();vEntry=results[v];if(vEntry.distance===Number.POSITIVE_INFINITY)break;edgeFn(v).forEach(updateNeighbors)}return results}},{"../data/priority-queue":15,"../lodash":19}],6:[function(require,module,exports){var _=require("../lodash"),tarjan=require("./tarjan");module.exports=findCycles;function findCycles(g){return _.filter(tarjan(g),
function(cmpt){return cmpt.length>1||cmpt.length===1&&g.hasEdge(cmpt[0],cmpt[0])})}},{"../lodash":19,"./tarjan":13}],7:[function(require,module,exports){var _=require("../lodash");module.exports=floydWarshall;var DEFAULT_WEIGHT_FUNC=_.constant(1);function floydWarshall(g,weightFn,edgeFn){return runFloydWarshall(g,weightFn||DEFAULT_WEIGHT_FUNC,edgeFn||function(v){return g.outEdges(v)})}function runFloydWarshall(g,weightFn,edgeFn){var results={},nodes=g.nodes();nodes.forEach(function(v){results[v]=
{};results[v][v]={distance:0};nodes.forEach(function(w){if(v!==w)results[v][w]={distance:Number.POSITIVE_INFINITY}});edgeFn(v).forEach(function(edge){var w=edge.v===v?edge.w:edge.v,d=weightFn(edge);results[v][w]={distance:d,predecessor:v}})});nodes.forEach(function(k){var rowK=results[k];nodes.forEach(function(i){var rowI=results[i];nodes.forEach(function(j){var ik=rowI[k];var kj=rowK[j];var ij=rowI[j];var altDistance=ik.distance+kj.distance;if(altDistance<ij.distance){ij.distance=altDistance;ij.predecessor=
kj.predecessor}})})});return results}},{"../lodash":19}],8:[function(require,module,exports){module.exports={components:require("./components"),dijkstra:require("./dijkstra"),dijkstraAll:require("./dijkstra-all"),findCycles:require("./find-cycles"),floydWarshall:require("./floyd-warshall"),isAcyclic:require("./is-acyclic"),postorder:require("./postorder"),preorder:require("./preorder"),prim:require("./prim"),tarjan:require("./tarjan"),topsort:require("./topsort")}},{"./components":2,"./dijkstra":5,
"./dijkstra-all":4,"./find-cycles":6,"./floyd-warshall":7,"./is-acyclic":9,"./postorder":10,"./preorder":11,"./prim":12,"./tarjan":13,"./topsort":14}],9:[function(require,module,exports){var topsort=require("./topsort");module.exports=isAcyclic;function isAcyclic(g){try{topsort(g)}catch(e){if(e instanceof topsort.CycleException)return false;throw e;}return true}},{"./topsort":14}],10:[function(require,module,exports){var dfs=require("./dfs");module.exports=postorder;function postorder(g,vs){return dfs(g,
vs,"post")}},{"./dfs":3}],11:[function(require,module,exports){var dfs=require("./dfs");module.exports=preorder;function preorder(g,vs){return dfs(g,vs,"pre")}},{"./dfs":3}],12:[function(require,module,exports){var _=require("../lodash"),Graph=require("../graph"),PriorityQueue=require("../data/priority-queue");module.exports=prim;function prim(g,weightFunc){var result=new Graph,parents={},pq=new PriorityQueue,v;function updateNeighbors(edge){var w=edge.v===v?edge.w:edge.v,pri=pq.priority(w);if(pri!==
undefined){var edgeWeight=weightFunc(edge);if(edgeWeight<pri){parents[w]=v;pq.decrease(w,edgeWeight)}}}if(g.nodeCount()===0)return result;_.each(g.nodes(),function(v){pq.add(v,Number.POSITIVE_INFINITY);result.setNode(v)});pq.decrease(g.nodes()[0],0);var init=false;while(pq.size()>0){v=pq.removeMin();if(_.has(parents,v))result.setEdge(v,parents[v]);else if(init)throw new Error("Input graph is not connected: "+g);else init=true;g.nodeEdges(v).forEach(updateNeighbors)}return result}},{"../data/priority-queue":15,
"../graph":16,"../lodash":19}],13:[function(require,module,exports){var _=require("../lodash");module.exports=tarjan;function tarjan(g){var index=0,stack=[],visited={},results=[];function dfs(v){var entry=visited[v]={onStack:true,lowlink:index,index:index++};stack.push(v);g.successors(v).forEach(function(w){if(!_.has(visited,w)){dfs(w);entry.lowlink=Math.min(entry.lowlink,visited[w].lowlink)}else if(visited[w].onStack)entry.lowlink=Math.min(entry.lowlink,visited[w].index)});if(entry.lowlink===entry.index){var cmpt=
[],w;do{w=stack.pop();visited[w].onStack=false;cmpt.push(w)}while(v!==w);results.push(cmpt)}}g.nodes().forEach(function(v){if(!_.has(visited,v))dfs(v)});return results}},{"../lodash":19}],14:[function(require,module,exports){var _=require("../lodash");module.exports=topsort;topsort.CycleException=CycleException;function topsort(g){var visited={},stack={},results=[];function visit(node){if(_.has(stack,node))throw new CycleException;if(!_.has(visited,node)){stack[node]=true;visited[node]=true;_.each(g.predecessors(node),
visit);delete stack[node];results.push(node)}}_.each(g.sinks(),visit);if(_.size(visited)!==g.nodeCount())throw new CycleException;return results}function CycleException(){}},{"../lodash":19}],15:[function(require,module,exports){var _=require("../lodash");module.exports=PriorityQueue;function PriorityQueue(){this._arr=[];this._keyIndices={}}PriorityQueue.prototype.size=function(){return this._arr.length};PriorityQueue.prototype.keys=function(){return this._arr.map(function(x){return x.key})};PriorityQueue.prototype.has=
function(key){return _.has(this._keyIndices,key)};PriorityQueue.prototype.priority=function(key){var index=this._keyIndices[key];if(index!==undefined)return this._arr[index].priority};PriorityQueue.prototype.min=function(){if(this.size()===0)throw new Error("Queue underflow");return this._arr[0].key};PriorityQueue.prototype.add=function(key,priority){var keyIndices=this._keyIndices;key=String(key);if(!_.has(keyIndices,key)){var arr=this._arr;var index=arr.length;keyIndices[key]=index;arr.push({key:key,
priority:priority});this._decrease(index);return true}return false};PriorityQueue.prototype.removeMin=function(){this._swap(0,this._arr.length-1);var min=this._arr.pop();delete this._keyIndices[min.key];this._heapify(0);return min.key};PriorityQueue.prototype.decrease=function(key,priority){var index=this._keyIndices[key];if(priority>this._arr[index].priority)throw new Error("New priority is greater than current priority. "+"Key: "+key+" Old: "+this._arr[index].priority+" New: "+priority);this._arr[index].priority=
priority;this._decrease(index)};PriorityQueue.prototype._heapify=function(i){var arr=this._arr;var l=2*i,r=l+1,largest=i;if(l<arr.length){largest=arr[l].priority<arr[largest].priority?l:largest;if(r<arr.length)largest=arr[r].priority<arr[largest].priority?r:largest;if(largest!==i){this._swap(i,largest);this._heapify(largest)}}};PriorityQueue.prototype._decrease=function(index){var arr=this._arr;var priority=arr[index].priority;var parent;while(index!==0){parent=index>>1;if(arr[parent].priority<priority)break;
this._swap(index,parent);index=parent}};PriorityQueue.prototype._swap=function(i,j){var arr=this._arr;var keyIndices=this._keyIndices;var origArrI=arr[i];var origArrJ=arr[j];arr[i]=origArrJ;arr[j]=origArrI;keyIndices[origArrJ.key]=i;keyIndices[origArrI.key]=j}},{"../lodash":19}],16:[function(require,module,exports){var _=require("./lodash");module.exports=Graph;var DEFAULT_EDGE_NAME="\x00",GRAPH_NODE="\x00",EDGE_KEY_DELIM="\u0001";function Graph(opts){this._isDirected=_.has(opts,"directed")?opts.directed:
true;this._isMultigraph=_.has(opts,"multigraph")?opts.multigraph:false;this._isCompound=_.has(opts,"compound")?opts.compound:false;this._label=undefined;this._defaultNodeLabelFn=_.constant(undefined);this._defaultEdgeLabelFn=_.constant(undefined);this._nodes={};if(this._isCompound){this._parent={};this._children={};this._children[GRAPH_NODE]={}}this._in={};this._preds={};this._out={};this._sucs={};this._edgeObjs={};this._edgeLabels={}}Graph.prototype._nodeCount=0;Graph.prototype._edgeCount=0;Graph.prototype.isDirected=
function(){return this._isDirected};Graph.prototype.isMultigraph=function(){return this._isMultigraph};Graph.prototype.isCompound=function(){return this._isCompound};Graph.prototype.setGraph=function(label){this._label=label;return this};Graph.prototype.graph=function(){return this._label};Graph.prototype.setDefaultNodeLabel=function(newDefault){if(!_.isFunction(newDefault))newDefault=_.constant(newDefault);this._defaultNodeLabelFn=newDefault;return this};Graph.prototype.nodeCount=function(){return this._nodeCount};
Graph.prototype.nodes=function(){return _.keys(this._nodes)};Graph.prototype.sources=function(){var self=this;return _.filter(this.nodes(),function(v){return _.isEmpty(self._in[v])})};Graph.prototype.sinks=function(){var self=this;return _.filter(this.nodes(),function(v){return _.isEmpty(self._out[v])})};Graph.prototype.setNodes=function(vs,value){var args=arguments;var self=this;_.each(vs,function(v){if(args.length>1)self.setNode(v,value);else self.setNode(v)});return this};Graph.prototype.setNode=
function(v,value){if(_.has(this._nodes,v)){if(arguments.length>1)this._nodes[v]=value;return this}this._nodes[v]=arguments.length>1?value:this._defaultNodeLabelFn(v);if(this._isCompound){this._parent[v]=GRAPH_NODE;this._children[v]={};this._children[GRAPH_NODE][v]=true}this._in[v]={};this._preds[v]={};this._out[v]={};this._sucs[v]={};++this._nodeCount;return this};Graph.prototype.node=function(v){return this._nodes[v]};Graph.prototype.hasNode=function(v){return _.has(this._nodes,v)};Graph.prototype.removeNode=
function(v){var self=this;if(_.has(this._nodes,v)){var removeEdge=function(e){self.removeEdge(self._edgeObjs[e])};delete this._nodes[v];if(this._isCompound){this._removeFromParentsChildList(v);delete this._parent[v];_.each(this.children(v),function(child){self.setParent(child)});delete this._children[v]}_.each(_.keys(this._in[v]),removeEdge);delete this._in[v];delete this._preds[v];_.each(_.keys(this._out[v]),removeEdge);delete this._out[v];delete this._sucs[v];--this._nodeCount}return this};Graph.prototype.setParent=
function(v,parent){if(!this._isCompound)throw new Error("Cannot set parent in a non-compound graph");if(_.isUndefined(parent))parent=GRAPH_NODE;else{parent+="";for(var ancestor=parent;!_.isUndefined(ancestor);ancestor=this.parent(ancestor))if(ancestor===v)throw new Error("Setting "+parent+" as parent of "+v+" would create a cycle");this.setNode(parent)}this.setNode(v);this._removeFromParentsChildList(v);this._parent[v]=parent;this._children[parent][v]=true;return this};Graph.prototype._removeFromParentsChildList=
function(v){delete this._children[this._parent[v]][v]};Graph.prototype.parent=function(v){if(this._isCompound){var parent=this._parent[v];if(parent!==GRAPH_NODE)return parent}};Graph.prototype.children=function(v){if(_.isUndefined(v))v=GRAPH_NODE;if(this._isCompound){var children=this._children[v];if(children)return _.keys(children)}else if(v===GRAPH_NODE)return this.nodes();else if(this.hasNode(v))return[]};Graph.prototype.predecessors=function(v){var predsV=this._preds[v];if(predsV)return _.keys(predsV)};
Graph.prototype.successors=function(v){var sucsV=this._sucs[v];if(sucsV)return _.keys(sucsV)};Graph.prototype.neighbors=function(v){var preds=this.predecessors(v);if(preds)return _.union(preds,this.successors(v))};Graph.prototype.isLeaf=function(v){var neighbors;if(this.isDirected())neighbors=this.successors(v);else neighbors=this.neighbors(v);return neighbors.length===0};Graph.prototype.filterNodes=function(filter){var copy=new this.constructor({directed:this._isDirected,multigraph:this._isMultigraph,
compound:this._isCompound});copy.setGraph(this.graph());var self=this;_.each(this._nodes,function(value,v){if(filter(v))copy.setNode(v,value)});_.each(this._edgeObjs,function(e){if(copy.hasNode(e.v)&&copy.hasNode(e.w))copy.setEdge(e,self.edge(e))});var parents={};function findParent(v){var parent=self.parent(v);if(parent===undefined||copy.hasNode(parent)){parents[v]=parent;return parent}else if(parent in parents)return parents[parent];else return findParent(parent)}if(this._isCompound)_.each(copy.nodes(),
function(v){copy.setParent(v,findParent(v))});return copy};Graph.prototype.setDefaultEdgeLabel=function(newDefault){if(!_.isFunction(newDefault))newDefault=_.constant(newDefault);this._defaultEdgeLabelFn=newDefault;return this};Graph.prototype.edgeCount=function(){return this._edgeCount};Graph.prototype.edges=function(){return _.values(this._edgeObjs)};Graph.prototype.setPath=function(vs,value){var self=this,args=arguments;_.reduce(vs,function(v,w){if(args.length>1)self.setEdge(v,w,value);else self.setEdge(v,
w);return w});return this};Graph.prototype.setEdge=function(){var v,w,name,value,valueSpecified=false,arg0=arguments[0];if(typeof arg0==="object"&&arg0!==null&&"v"in arg0){v=arg0.v;w=arg0.w;name=arg0.name;if(arguments.length===2){value=arguments[1];valueSpecified=true}}else{v=arg0;w=arguments[1];name=arguments[3];if(arguments.length>2){value=arguments[2];valueSpecified=true}}v=""+v;w=""+w;if(!_.isUndefined(name))name=""+name;var e=edgeArgsToId(this._isDirected,v,w,name);if(_.has(this._edgeLabels,
e)){if(valueSpecified)this._edgeLabels[e]=value;return this}if(!_.isUndefined(name)&&!this._isMultigraph)throw new Error("Cannot set a named edge when isMultigraph \x3d false");this.setNode(v);this.setNode(w);this._edgeLabels[e]=valueSpecified?value:this._defaultEdgeLabelFn(v,w,name);var edgeObj=edgeArgsToObj(this._isDirected,v,w,name);v=edgeObj.v;w=edgeObj.w;Object.freeze(edgeObj);this._edgeObjs[e]=edgeObj;incrementOrInitEntry(this._preds[w],v);incrementOrInitEntry(this._sucs[v],w);this._in[w][e]=
edgeObj;this._out[v][e]=edgeObj;this._edgeCount++;return this};Graph.prototype.edge=function(v,w,name){var e=arguments.length===1?edgeObjToId(this._isDirected,arguments[0]):edgeArgsToId(this._isDirected,v,w,name);return this._edgeLabels[e]};Graph.prototype.hasEdge=function(v,w,name){var e=arguments.length===1?edgeObjToId(this._isDirected,arguments[0]):edgeArgsToId(this._isDirected,v,w,name);return _.has(this._edgeLabels,e)};Graph.prototype.removeEdge=function(v,w,name){var e=arguments.length===1?
edgeObjToId(this._isDirected,arguments[0]):edgeArgsToId(this._isDirected,v,w,name),edge=this._edgeObjs[e];if(edge){v=edge.v;w=edge.w;delete this._edgeLabels[e];delete this._edgeObjs[e];decrementOrRemoveEntry(this._preds[w],v);decrementOrRemoveEntry(this._sucs[v],w);delete this._in[w][e];delete this._out[v][e];this._edgeCount--}return this};Graph.prototype.inEdges=function(v,u){var inV=this._in[v];if(inV){var edges=_.values(inV);if(!u)return edges;return _.filter(edges,function(edge){return edge.v===
u})}};Graph.prototype.outEdges=function(v,w){var outV=this._out[v];if(outV){var edges=_.values(outV);if(!w)return edges;return _.filter(edges,function(edge){return edge.w===w})}};Graph.prototype.nodeEdges=function(v,w){var inEdges=this.inEdges(v,w);if(inEdges)return inEdges.concat(this.outEdges(v,w))};function incrementOrInitEntry(map,k){if(map[k])map[k]++;else map[k]=1}function decrementOrRemoveEntry(map,k){if(!--map[k])delete map[k]}function edgeArgsToId(isDirected,v_,w_,name){var v=""+v_;var w=
""+w_;if(!isDirected&&v>w){var tmp=v;v=w;w=tmp}return v+EDGE_KEY_DELIM+w+EDGE_KEY_DELIM+(_.isUndefined(name)?DEFAULT_EDGE_NAME:name)}function edgeArgsToObj(isDirected,v_,w_,name){var v=""+v_;var w=""+w_;if(!isDirected&&v>w){var tmp=v;v=w;w=tmp}var edgeObj={v:v,w:w};if(name)edgeObj.name=name;return edgeObj}function edgeObjToId(isDirected,edgeObj){return edgeArgsToId(isDirected,edgeObj.v,edgeObj.w,edgeObj.name)}},{"./lodash":19}],17:[function(require,module,exports){module.exports={Graph:require("./graph"),
version:require("./version")}},{"./graph":16,"./version":20}],18:[function(require,module,exports){var _=require("./lodash"),Graph=require("./graph");module.exports={write:write,read:read};function write(g){var json={options:{directed:g.isDirected(),multigraph:g.isMultigraph(),compound:g.isCompound()},nodes:writeNodes(g),edges:writeEdges(g)};if(!_.isUndefined(g.graph()))json.value=_.clone(g.graph());return json}function writeNodes(g){return _.map(g.nodes(),function(v){var nodeValue=g.node(v),parent=
g.parent(v),node={v:v};if(!_.isUndefined(nodeValue))node.value=nodeValue;if(!_.isUndefined(parent))node.parent=parent;return node})}function writeEdges(g){return _.map(g.edges(),function(e){var edgeValue=g.edge(e),edge={v:e.v,w:e.w};if(!_.isUndefined(e.name))edge.name=e.name;if(!_.isUndefined(edgeValue))edge.value=edgeValue;return edge})}function read(json){var g=(new Graph(json.options)).setGraph(json.value);_.each(json.nodes,function(entry){g.setNode(entry.v,entry.value);if(entry.parent)g.setParent(entry.v,
entry.parent)});_.each(json.edges,function(entry){g.setEdge({v:entry.v,w:entry.w,name:entry.name},entry.value)});return g}},{"./graph":16,"./lodash":19}],19:[function(require,module,exports){var lodash;if(typeof require==="function")try{lodash=require("lodash")}catch(e){}if(!lodash)lodash=window._;module.exports=lodash},{"lodash":undefined}],20:[function(require,module,exports){module.exports="2.1.5"},{}]},{},[1])(1)});
(function(f){if(typeof exports==="object"&&typeof module!=="undefined")module.exports=f();else if(typeof define==="function"&&define.amd)define([],f);else{var g;if(typeof window!=="undefined")g=window;else if(typeof global!=="undefined")g=global;else if(typeof self!=="undefined")g=self;else g=this;g.dagre=f()}})(function(){var define,module,exports;return function e(t,n,r){function s(o,u){if(!n[o]){if(!t[o]){var a=typeof require=="function"&&require;if(!u&&a)return a(o,!0);if(i)return i(o,!0);var f=
new Error("Cannot find module '"+o+"'");throw f.code="MODULE_NOT_FOUND",f;}var l=n[o]={exports:{}};t[o][0].call(l.exports,function(e){var n=t[o][1][e];return s(n?n:e)},l,l.exports,e,t,n,r)}return n[o].exports}var i=typeof require=="function"&&require;for(var o=0;o<r.length;o++)s(r[o]);return s}({1:[function(require,module,exports){module.exports={graphlib:require("./lib/graphlib"),layout:require("./lib/layout"),debug:require("./lib/debug"),util:{time:require("./lib/util").time,notime:require("./lib/util").notime},
version:require("./lib/version")}},{"./lib/debug":6,"./lib/graphlib":7,"./lib/layout":9,"./lib/util":29,"./lib/version":30}],2:[function(require,module,exports){var _=require("./lodash"),greedyFAS=require("./greedy-fas");module.exports={run:run,undo:undo};function run(g){var fas=g.graph().acyclicer==="greedy"?greedyFAS(g,weightFn(g)):dfsFAS(g);_.forEach(fas,function(e){var label=g.edge(e);g.removeEdge(e);label.forwardName=e.name;label.reversed=true;g.setEdge(e.w,e.v,label,_.uniqueId("rev"))});function weightFn(g){return function(e){return g.edge(e).weight}}
}function dfsFAS(g){var fas=[],stack={},visited={};function dfs(v){if(_.has(visited,v))return;visited[v]=true;stack[v]=true;_.forEach(g.outEdges(v),function(e){if(_.has(stack,e.w))fas.push(e);else dfs(e.w)});delete stack[v]}_.forEach(g.nodes(),dfs);return fas}function undo(g){_.forEach(g.edges(),function(e){var label=g.edge(e);if(label.reversed){g.removeEdge(e);var forwardName=label.forwardName;delete label.reversed;delete label.forwardName;g.setEdge(e.w,e.v,label,forwardName)}})}},{"./greedy-fas":8,
"./lodash":10}],3:[function(require,module,exports){var _=require("./lodash"),util=require("./util");module.exports=addBorderSegments;function addBorderSegments(g){function dfs(v){var children=g.children(v),node=g.node(v);if(children.length)_.forEach(children,dfs);if(_.has(node,"minRank")){node.borderLeft=[];node.borderRight=[];for(var rank=node.minRank,maxRank=node.maxRank+1;rank<maxRank;++rank){addBorderNode(g,"borderLeft","_bl",v,node,rank);addBorderNode(g,"borderRight","_br",v,node,rank)}}}_.forEach(g.children(),
dfs)}function addBorderNode(g,prop,prefix,sg,sgNode,rank){var label={width:0,height:0,rank:rank,borderType:prop},prev=sgNode[prop][rank-1],curr=util.addDummyNode(g,"border",label,prefix);sgNode[prop][rank]=curr;g.setParent(curr,sg);if(prev)g.setEdge(prev,curr,{weight:1})}},{"./lodash":10,"./util":29}],4:[function(require,module,exports){var _=require("./lodash");module.exports={adjust:adjust,undo:undo};function adjust(g){var rankDir=g.graph().rankdir.toLowerCase();if(rankDir==="lr"||rankDir==="rl")swapWidthHeight(g)}
function undo(g){var rankDir=g.graph().rankdir.toLowerCase();if(rankDir==="bt"||rankDir==="rl")reverseY(g);if(rankDir==="lr"||rankDir==="rl"){swapXY(g);swapWidthHeight(g)}}function swapWidthHeight(g){_.forEach(g.nodes(),function(v){swapWidthHeightOne(g.node(v))});_.forEach(g.edges(),function(e){swapWidthHeightOne(g.edge(e))})}function swapWidthHeightOne(attrs){var w=attrs.width;attrs.width=attrs.height;attrs.height=w}function reverseY(g){_.forEach(g.nodes(),function(v){reverseYOne(g.node(v))});_.forEach(g.edges(),
function(e){var edge=g.edge(e);_.forEach(edge.points,reverseYOne);if(_.has(edge,"y"))reverseYOne(edge)})}function reverseYOne(attrs){attrs.y=-attrs.y}function swapXY(g){_.forEach(g.nodes(),function(v){swapXYOne(g.node(v))});_.forEach(g.edges(),function(e){var edge=g.edge(e);_.forEach(edge.points,swapXYOne);if(_.has(edge,"x"))swapXYOne(edge)})}function swapXYOne(attrs){var x=attrs.x;attrs.x=attrs.y;attrs.y=x}},{"./lodash":10}],5:[function(require,module,exports){module.exports=List;function List(){var sentinel=
{};sentinel._next=sentinel._prev=sentinel;this._sentinel=sentinel}List.prototype.dequeue=function(){var sentinel=this._sentinel,entry=sentinel._prev;if(entry!==sentinel){unlink(entry);return entry}};List.prototype.enqueue=function(entry){var sentinel=this._sentinel;if(entry._prev&&entry._next)unlink(entry);entry._next=sentinel._next;sentinel._next._prev=entry;sentinel._next=entry;entry._prev=sentinel};List.prototype.toString=function(){var strs=[],sentinel=this._sentinel,curr=sentinel._prev;while(curr!==
sentinel){strs.push(JSON.stringify(curr,filterOutLinks));curr=curr._prev}return"["+strs.join(", ")+"]"};function unlink(entry){entry._prev._next=entry._next;entry._next._prev=entry._prev;delete entry._next;delete entry._prev}function filterOutLinks(k,v){if(k!=="_next"&&k!=="_prev")return v}},{}],6:[function(require,module,exports){var _=require("./lodash"),util=require("./util"),Graph=require("./graphlib").Graph;module.exports={debugOrdering:debugOrdering};function debugOrdering(g){var layerMatrix=
util.buildLayerMatrix(g);var h=(new Graph({compound:true,multigraph:true})).setGraph({});_.forEach(g.nodes(),function(v){h.setNode(v,{label:v});h.setParent(v,"layer"+g.node(v).rank)});_.forEach(g.edges(),function(e){h.setEdge(e.v,e.w,{},e.name)});_.forEach(layerMatrix,function(layer,i){var layerV="layer"+i;h.setNode(layerV,{rank:"same"});_.reduce(layer,function(u,v){h.setEdge(u,v,{style:"invis"});return v})});return h}},{"./graphlib":7,"./lodash":10,"./util":29}],7:[function(require,module,exports){var graphlib;
if(typeof require==="function")try{graphlib=require("graphlib")}catch(e){}if(!graphlib)graphlib=window.graphlib;module.exports=graphlib},{"graphlib":undefined}],8:[function(require,module,exports){var _=require("./lodash"),Graph=require("./graphlib").Graph,List=require("./data/list");module.exports=greedyFAS;var DEFAULT_WEIGHT_FN=_.constant(1);function greedyFAS(g,weightFn){if(g.nodeCount()<=1)return[];var state=buildState(g,weightFn||DEFAULT_WEIGHT_FN);var results=doGreedyFAS(state.graph,state.buckets,
state.zeroIdx);return _.flatten(_.map(results,function(e){return g.outEdges(e.v,e.w)}),true)}function doGreedyFAS(g,buckets,zeroIdx){var results=[],sources=buckets[buckets.length-1],sinks=buckets[0];var entry;while(g.nodeCount()){while(entry=sinks.dequeue())removeNode(g,buckets,zeroIdx,entry);while(entry=sources.dequeue())removeNode(g,buckets,zeroIdx,entry);if(g.nodeCount())for(var i=buckets.length-2;i>0;--i){entry=buckets[i].dequeue();if(entry){results=results.concat(removeNode(g,buckets,zeroIdx,
entry,true));break}}}return results}function removeNode(g,buckets,zeroIdx,entry,collectPredecessors){var results=collectPredecessors?[]:undefined;_.forEach(g.inEdges(entry.v),function(edge){var weight=g.edge(edge),uEntry=g.node(edge.v);if(collectPredecessors)results.push({v:edge.v,w:edge.w});uEntry.out-=weight;assignBucket(buckets,zeroIdx,uEntry)});_.forEach(g.outEdges(entry.v),function(edge){var weight=g.edge(edge),w=edge.w,wEntry=g.node(w);wEntry["in"]-=weight;assignBucket(buckets,zeroIdx,wEntry)});
g.removeNode(entry.v);return results}function buildState(g,weightFn){var fasGraph=new Graph,maxIn=0,maxOut=0;_.forEach(g.nodes(),function(v){fasGraph.setNode(v,{v:v,"in":0,out:0})});_.forEach(g.edges(),function(e){var prevWeight=fasGraph.edge(e.v,e.w)||0,weight=weightFn(e),edgeWeight=prevWeight+weight;fasGraph.setEdge(e.v,e.w,edgeWeight);maxOut=Math.max(maxOut,fasGraph.node(e.v).out+=weight);maxIn=Math.max(maxIn,fasGraph.node(e.w)["in"]+=weight)});var buckets=_.range(maxOut+maxIn+3).map(function(){return new List});
var zeroIdx=maxIn+1;_.forEach(fasGraph.nodes(),function(v){assignBucket(buckets,zeroIdx,fasGraph.node(v))});return{graph:fasGraph,buckets:buckets,zeroIdx:zeroIdx}}function assignBucket(buckets,zeroIdx,entry){if(!entry.out)buckets[0].enqueue(entry);else if(!entry["in"])buckets[buckets.length-1].enqueue(entry);else buckets[entry.out-entry["in"]+zeroIdx].enqueue(entry)}},{"./data/list":5,"./graphlib":7,"./lodash":10}],9:[function(require,module,exports){var _=require("./lodash"),acyclic=require("./acyclic"),
normalize=require("./normalize"),rank=require("./rank"),normalizeRanks=require("./util").normalizeRanks,parentDummyChains=require("./parent-dummy-chains"),removeEmptyRanks=require("./util").removeEmptyRanks,nestingGraph=require("./nesting-graph"),addBorderSegments=require("./add-border-segments"),coordinateSystem=require("./coordinate-system"),order=require("./order"),position=require("./position"),util=require("./util"),Graph=require("./graphlib").Graph;module.exports=layout;function layout(g,opts){var time=
opts&&opts.debugTiming?util.time:util.notime;time("layout",function(){var layoutGraph=time("  buildLayoutGraph",function(){return buildLayoutGraph(g)});time("  runLayout",function(){runLayout(layoutGraph,time)});time("  updateInputGraph",function(){updateInputGraph(g,layoutGraph)})})}function runLayout(g,time){time("    makeSpaceForEdgeLabels",function(){makeSpaceForEdgeLabels(g)});time("    removeSelfEdges",function(){removeSelfEdges(g)});time("    acyclic",function(){acyclic.run(g)});time("    nestingGraph.run",
function(){nestingGraph.run(g)});time("    rank",function(){rank(util.asNonCompoundGraph(g))});time("    injectEdgeLabelProxies",function(){injectEdgeLabelProxies(g)});time("    removeEmptyRanks",function(){removeEmptyRanks(g)});time("    nestingGraph.cleanup",function(){nestingGraph.cleanup(g)});time("    normalizeRanks",function(){normalizeRanks(g)});time("    assignRankMinMax",function(){assignRankMinMax(g)});time("    removeEdgeLabelProxies",function(){removeEdgeLabelProxies(g)});time("    normalize.run",
function(){normalize.run(g)});time("    parentDummyChains",function(){parentDummyChains(g)});time("    addBorderSegments",function(){addBorderSegments(g)});time("    order",function(){order(g)});time("    insertSelfEdges",function(){insertSelfEdges(g)});time("    adjustCoordinateSystem",function(){coordinateSystem.adjust(g)});time("    position",function(){position(g)});time("    positionSelfEdges",function(){positionSelfEdges(g)});time("    removeBorderNodes",function(){removeBorderNodes(g)});time("    normalize.undo",
function(){normalize.undo(g)});time("    fixupEdgeLabelCoords",function(){fixupEdgeLabelCoords(g)});time("    undoCoordinateSystem",function(){coordinateSystem.undo(g)});time("    translateGraph",function(){translateGraph(g)});time("    assignNodeIntersects",function(){assignNodeIntersects(g)});time("    reversePoints",function(){reversePointsForReversedEdges(g)});time("    acyclic.undo",function(){acyclic.undo(g)})}function updateInputGraph(inputGraph,layoutGraph){_.forEach(inputGraph.nodes(),function(v){var inputLabel=
inputGraph.node(v),layoutLabel=layoutGraph.node(v);if(inputLabel){inputLabel.x=layoutLabel.x;inputLabel.y=layoutLabel.y;if(layoutGraph.children(v).length){inputLabel.width=layoutLabel.width;inputLabel.height=layoutLabel.height}}});_.forEach(inputGraph.edges(),function(e){var inputLabel=inputGraph.edge(e),layoutLabel=layoutGraph.edge(e);inputLabel.points=layoutLabel.points;if(_.has(layoutLabel,"x")){inputLabel.x=layoutLabel.x;inputLabel.y=layoutLabel.y}});inputGraph.graph().width=layoutGraph.graph().width;
inputGraph.graph().height=layoutGraph.graph().height}var graphNumAttrs=["nodesep","edgesep","ranksep","marginx","marginy"],graphDefaults={ranksep:50,edgesep:20,nodesep:50,rankdir:"tb"},graphAttrs=["acyclicer","ranker","rankdir","align"],nodeNumAttrs=["width","height"],nodeDefaults={width:0,height:0},edgeNumAttrs=["minlen","weight","width","height","labeloffset"],edgeDefaults={minlen:1,weight:1,width:0,height:0,labeloffset:10,labelpos:"r"},edgeAttrs=["labelpos"];function buildLayoutGraph(inputGraph){var g=
new Graph({multigraph:true,compound:true}),graph=canonicalize(inputGraph.graph());g.setGraph(_.merge({},graphDefaults,selectNumberAttrs(graph,graphNumAttrs),_.pick(graph,graphAttrs)));_.forEach(inputGraph.nodes(),function(v){var node=canonicalize(inputGraph.node(v));g.setNode(v,_.defaults(selectNumberAttrs(node,nodeNumAttrs),nodeDefaults));g.setParent(v,inputGraph.parent(v))});_.forEach(inputGraph.edges(),function(e){var edge=canonicalize(inputGraph.edge(e));g.setEdge(e,_.merge({},edgeDefaults,selectNumberAttrs(edge,
edgeNumAttrs),_.pick(edge,edgeAttrs)))});return g}function makeSpaceForEdgeLabels(g){var graph=g.graph();graph.ranksep/=2;_.forEach(g.edges(),function(e){var edge=g.edge(e);edge.minlen*=2;if(edge.labelpos.toLowerCase()!=="c")if(graph.rankdir==="TB"||graph.rankdir==="BT")edge.width+=edge.labeloffset;else edge.height+=edge.labeloffset})}function injectEdgeLabelProxies(g){_.forEach(g.edges(),function(e){var edge=g.edge(e);if(edge.width&&edge.height){var v=g.node(e.v),w=g.node(e.w),label={rank:(w.rank-
v.rank)/2+v.rank,e:e};util.addDummyNode(g,"edge-proxy",label,"_ep")}})}function assignRankMinMax(g){var maxRank=0;_.forEach(g.nodes(),function(v){var node=g.node(v);if(node.borderTop){node.minRank=g.node(node.borderTop).rank;node.maxRank=g.node(node.borderBottom).rank;maxRank=_.max(maxRank,node.maxRank)}});g.graph().maxRank=maxRank}function removeEdgeLabelProxies(g){_.forEach(g.nodes(),function(v){var node=g.node(v);if(node.dummy==="edge-proxy"){g.edge(node.e).labelRank=node.rank;g.removeNode(v)}})}
function translateGraph(g){var minX=Number.POSITIVE_INFINITY,maxX=0,minY=Number.POSITIVE_INFINITY,maxY=0,graphLabel=g.graph(),marginX=graphLabel.marginx||0,marginY=graphLabel.marginy||0;function getExtremes(attrs){var x=attrs.x,y=attrs.y,w=attrs.width,h=attrs.height;minX=Math.min(minX,x-w/2);maxX=Math.max(maxX,x+w/2);minY=Math.min(minY,y-h/2);maxY=Math.max(maxY,y+h/2)}_.forEach(g.nodes(),function(v){getExtremes(g.node(v))});_.forEach(g.edges(),function(e){var edge=g.edge(e);if(_.has(edge,"x"))getExtremes(edge)});
minX-=marginX;minY-=marginY;_.forEach(g.nodes(),function(v){var node=g.node(v);node.x-=minX;node.y-=minY});_.forEach(g.edges(),function(e){var edge=g.edge(e);_.forEach(edge.points,function(p){p.x-=minX;p.y-=minY});if(_.has(edge,"x"))edge.x-=minX;if(_.has(edge,"y"))edge.y-=minY});graphLabel.width=maxX-minX+marginX;graphLabel.height=maxY-minY+marginY}function assignNodeIntersects(g){_.forEach(g.edges(),function(e){var edge=g.edge(e),nodeV=g.node(e.v),nodeW=g.node(e.w),p1,p2;if(!edge.points){edge.points=
[];p1=nodeW;p2=nodeV}else{p1=edge.points[0];p2=edge.points[edge.points.length-1]}edge.points.unshift(util.intersectRect(nodeV,p1));edge.points.push(util.intersectRect(nodeW,p2))})}function fixupEdgeLabelCoords(g){_.forEach(g.edges(),function(e){var edge=g.edge(e);if(_.has(edge,"x")){if(edge.labelpos==="l"||edge.labelpos==="r")edge.width-=edge.labeloffset;switch(edge.labelpos){case "l":edge.x-=edge.width/2+edge.labeloffset;break;case "r":edge.x+=edge.width/2+edge.labeloffset;break}}})}function reversePointsForReversedEdges(g){_.forEach(g.edges(),
function(e){var edge=g.edge(e);if(edge.reversed)edge.points.reverse()})}function removeBorderNodes(g){_.forEach(g.nodes(),function(v){if(g.children(v).length){var node=g.node(v),t=g.node(node.borderTop),b=g.node(node.borderBottom),l=g.node(_.last(node.borderLeft)),r=g.node(_.last(node.borderRight));node.width=Math.abs(r.x-l.x);node.height=Math.abs(b.y-t.y);node.x=l.x+node.width/2;node.y=t.y+node.height/2}});_.forEach(g.nodes(),function(v){if(g.node(v).dummy==="border")g.removeNode(v)})}function removeSelfEdges(g){_.forEach(g.edges(),
function(e){if(e.v===e.w){var node=g.node(e.v);if(!node.selfEdges)node.selfEdges=[];node.selfEdges.push({e:e,label:g.edge(e)});g.removeEdge(e)}})}function insertSelfEdges(g){var layers=util.buildLayerMatrix(g);_.forEach(layers,function(layer){var orderShift=0;_.forEach(layer,function(v,i){var node=g.node(v);node.order=i+orderShift;_.forEach(node.selfEdges,function(selfEdge){util.addDummyNode(g,"selfedge",{width:selfEdge.label.width,height:selfEdge.label.height,rank:node.rank,order:i+ ++orderShift,
e:selfEdge.e,label:selfEdge.label},"_se")});delete node.selfEdges})})}function positionSelfEdges(g){_.forEach(g.nodes(),function(v){var node=g.node(v);if(node.dummy==="selfedge"){var selfNode=g.node(node.e.v),x=selfNode.x+selfNode.width/2,y=selfNode.y,dx=node.x-x,dy=selfNode.height/2;g.setEdge(node.e,node.label);g.removeNode(v);node.label.points=[{x:x+2*dx/3,y:y-dy},{x:x+5*dx/6,y:y-dy},{x:x+dx,y:y},{x:x+5*dx/6,y:y+dy},{x:x+2*dx/3,y:y+dy}];node.label.x=node.x;node.label.y=node.y}})}function selectNumberAttrs(obj,
attrs){return _.mapValues(_.pick(obj,attrs),Number)}function canonicalize(attrs){var newAttrs={};_.forEach(attrs,function(v,k){newAttrs[k.toLowerCase()]=v});return newAttrs}},{"./acyclic":2,"./add-border-segments":3,"./coordinate-system":4,"./graphlib":7,"./lodash":10,"./nesting-graph":11,"./normalize":12,"./order":17,"./parent-dummy-chains":22,"./position":24,"./rank":26,"./util":29}],10:[function(require,module,exports){var lodash;if(typeof require==="function")try{lodash=require("lodash")}catch(e){}if(!lodash)lodash=
window._;module.exports=lodash},{"lodash":undefined}],11:[function(require,module,exports){var _=require("./lodash"),util=require("./util");module.exports={run:run,cleanup:cleanup};function run(g){var root=util.addDummyNode(g,"root",{},"_root");var depths=treeDepths(g);var height=_.max(_.values(depths))-1;var nodeSep=2*height+1;g.graph().nestingRoot=root;_.forEach(g.edges(),function(e){g.edge(e).minlen*=nodeSep});var weight=sumWeights(g)+1;_.forEach(g.children(),function(child){dfs(g,root,nodeSep,
weight,height,depths,child)});g.graph().nodeRankFactor=nodeSep}function dfs(g,root,nodeSep,weight,height,depths,v){var children=g.children(v);if(!children.length){if(v!==root)g.setEdge(root,v,{weight:0,minlen:nodeSep});return}var top=util.addBorderNode(g,"_bt"),bottom=util.addBorderNode(g,"_bb"),label=g.node(v);g.setParent(top,v);label.borderTop=top;g.setParent(bottom,v);label.borderBottom=bottom;_.forEach(children,function(child){dfs(g,root,nodeSep,weight,height,depths,child);var childNode=g.node(child),
childTop=childNode.borderTop?childNode.borderTop:child,childBottom=childNode.borderBottom?childNode.borderBottom:child,thisWeight=childNode.borderTop?weight:2*weight,minlen=childTop!==childBottom?1:height-depths[v]+1;g.setEdge(top,childTop,{weight:thisWeight,minlen:minlen,nestingEdge:true});g.setEdge(childBottom,bottom,{weight:thisWeight,minlen:minlen,nestingEdge:true})});if(!g.parent(v))g.setEdge(root,top,{weight:0,minlen:height+depths[v]})}function treeDepths(g){var depths={};function dfs(v,depth){var children=
g.children(v);if(children&&children.length)_.forEach(children,function(child){dfs(child,depth+1)});depths[v]=depth}_.forEach(g.children(),function(v){dfs(v,1)});return depths}function sumWeights(g){return _.reduce(g.edges(),function(acc,e){return acc+g.edge(e).weight},0)}function cleanup(g){var graphLabel=g.graph();g.removeNode(graphLabel.nestingRoot);delete graphLabel.nestingRoot;_.forEach(g.edges(),function(e){var edge=g.edge(e);if(edge.nestingEdge)g.removeEdge(e)})}},{"./lodash":10,"./util":29}],
12:[function(require,module,exports){var _=require("./lodash"),util=require("./util");module.exports={run:run,undo:undo};function run(g){g.graph().dummyChains=[];_.forEach(g.edges(),function(edge){normalizeEdge(g,edge)})}function normalizeEdge(g,e){var v=e.v,vRank=g.node(v).rank,w=e.w,wRank=g.node(w).rank,name=e.name,edgeLabel=g.edge(e),labelRank=edgeLabel.labelRank;if(wRank===vRank+1)return;g.removeEdge(e);var dummy,attrs,i;for(i=0,++vRank;vRank<wRank;++i,++vRank){edgeLabel.points=[];attrs={width:0,
height:0,edgeLabel:edgeLabel,edgeObj:e,rank:vRank};dummy=util.addDummyNode(g,"edge",attrs,"_d");if(vRank===labelRank){attrs.width=edgeLabel.width;attrs.height=edgeLabel.height;attrs.dummy="edge-label";attrs.labelpos=edgeLabel.labelpos}g.setEdge(v,dummy,{weight:edgeLabel.weight},name);if(i===0)g.graph().dummyChains.push(dummy);v=dummy}g.setEdge(v,w,{weight:edgeLabel.weight},name)}function undo(g){_.forEach(g.graph().dummyChains,function(v){var node=g.node(v),origLabel=node.edgeLabel,w;g.setEdge(node.edgeObj,
origLabel);while(node.dummy){w=g.successors(v)[0];g.removeNode(v);origLabel.points.push({x:node.x,y:node.y});if(node.dummy==="edge-label"){origLabel.x=node.x;origLabel.y=node.y;origLabel.width=node.width;origLabel.height=node.height}v=w;node=g.node(v)}})}},{"./lodash":10,"./util":29}],13:[function(require,module,exports){var _=require("../lodash");module.exports=addSubgraphConstraints;function addSubgraphConstraints(g,cg,vs){var prev={},rootPrev;_.forEach(vs,function(v){var child=g.parent(v),parent,
prevChild;while(child){parent=g.parent(child);if(parent){prevChild=prev[parent];prev[parent]=child}else{prevChild=rootPrev;rootPrev=child}if(prevChild&&prevChild!==child){cg.setEdge(prevChild,child);return}child=parent}})}},{"../lodash":10}],14:[function(require,module,exports){var _=require("../lodash");module.exports=barycenter;function barycenter(g,movable){return _.map(movable,function(v){var inV=g.inEdges(v);if(!inV.length)return{v:v};else{var result=_.reduce(inV,function(acc,e){var edge=g.edge(e),
nodeU=g.node(e.v);return{sum:acc.sum+edge.weight*nodeU.order,weight:acc.weight+edge.weight}},{sum:0,weight:0});return{v:v,barycenter:result.sum/result.weight,weight:result.weight}}})}},{"../lodash":10}],15:[function(require,module,exports){var _=require("../lodash"),Graph=require("../graphlib").Graph;module.exports=buildLayerGraph;function buildLayerGraph(g,rank,relationship){var root=createRootNode(g),result=(new Graph({compound:true})).setGraph({root:root}).setDefaultNodeLabel(function(v){return g.node(v)});
_.forEach(g.nodes(),function(v){var node=g.node(v),parent=g.parent(v);if(node.rank===rank||node.minRank<=rank&&rank<=node.maxRank){result.setNode(v);result.setParent(v,parent||root);_.forEach(g[relationship](v),function(e){var u=e.v===v?e.w:e.v,edge=result.edge(u,v),weight=!_.isUndefined(edge)?edge.weight:0;result.setEdge(u,v,{weight:g.edge(e).weight+weight})});if(_.has(node,"minRank"))result.setNode(v,{borderLeft:node.borderLeft[rank],borderRight:node.borderRight[rank]})}});return result}function createRootNode(g){var v;
while(g.hasNode(v=_.uniqueId("_root")));return v}},{"../graphlib":7,"../lodash":10}],16:[function(require,module,exports){var _=require("../lodash");module.exports=crossCount;function crossCount(g,layering){var cc=0;for(var i=1;i<layering.length;++i)cc+=twoLayerCrossCount(g,layering[i-1],layering[i]);return cc}function twoLayerCrossCount(g,northLayer,southLayer){var southPos=_.zipObject(southLayer,_.map(southLayer,function(v,i){return i}));var southEntries=_.flatten(_.map(northLayer,function(v){return _.chain(g.outEdges(v)).map(function(e){return{pos:southPos[e.w],
weight:g.edge(e).weight}}).sortBy("pos").value()}),true);var firstIndex=1;while(firstIndex<southLayer.length)firstIndex<<=1;var treeSize=2*firstIndex-1;firstIndex-=1;var tree=_.map(new Array(treeSize),function(){return 0});var cc=0;_.forEach(southEntries.forEach(function(entry){var index=entry.pos+firstIndex;tree[index]+=entry.weight;var weightSum=0;while(index>0){if(index%2)weightSum+=tree[index+1];index=index-1>>1;tree[index]+=entry.weight}cc+=entry.weight*weightSum}));return cc}},{"../lodash":10}],
17:[function(require,module,exports){var _=require("../lodash"),initOrder=require("./init-order"),crossCount=require("./cross-count"),sortSubgraph=require("./sort-subgraph"),buildLayerGraph=require("./build-layer-graph"),addSubgraphConstraints=require("./add-subgraph-constraints"),Graph=require("../graphlib").Graph,util=require("../util");module.exports=order;function order(g){var maxRank=util.maxRank(g),downLayerGraphs=buildLayerGraphs(g,_.range(1,maxRank+1),"inEdges"),upLayerGraphs=buildLayerGraphs(g,
_.range(maxRank-1,-1,-1),"outEdges");var layering=initOrder(g);assignOrder(g,layering);var bestCC=Number.POSITIVE_INFINITY,best;for(var i=0,lastBest=0;lastBest<4;++i,++lastBest){sweepLayerGraphs(i%2?downLayerGraphs:upLayerGraphs,i%4>=2);layering=util.buildLayerMatrix(g);var cc=crossCount(g,layering);if(cc<bestCC){lastBest=0;best=_.cloneDeep(layering);bestCC=cc}}assignOrder(g,best)}function buildLayerGraphs(g,ranks,relationship){return _.map(ranks,function(rank){return buildLayerGraph(g,rank,relationship)})}
function sweepLayerGraphs(layerGraphs,biasRight){var cg=new Graph;_.forEach(layerGraphs,function(lg){var root=lg.graph().root;var sorted=sortSubgraph(lg,root,cg,biasRight);_.forEach(sorted.vs,function(v,i){lg.node(v).order=i});addSubgraphConstraints(lg,cg,sorted.vs)})}function assignOrder(g,layering){_.forEach(layering,function(layer){_.forEach(layer,function(v,i){g.node(v).order=i})})}},{"../graphlib":7,"../lodash":10,"../util":29,"./add-subgraph-constraints":13,"./build-layer-graph":15,"./cross-count":16,
"./init-order":18,"./sort-subgraph":20}],18:[function(require,module,exports){var _=require("../lodash");module.exports=initOrder;function initOrder(g){var visited={},simpleNodes=_.filter(g.nodes(),function(v){return!g.children(v).length}),maxRank=_.max(_.map(simpleNodes,function(v){return g.node(v).rank})),layers=_.map(_.range(maxRank+1),function(){return[]});function dfs(v){if(_.has(visited,v))return;visited[v]=true;var node=g.node(v);layers[node.rank].push(v);_.forEach(g.successors(v),dfs)}var orderedVs=
_.sortBy(simpleNodes,function(v){return g.node(v).rank});_.forEach(orderedVs,dfs);return layers}},{"../lodash":10}],19:[function(require,module,exports){var _=require("../lodash");module.exports=resolveConflicts;function resolveConflicts(entries,cg){var mappedEntries={};_.forEach(entries,function(entry,i){var tmp=mappedEntries[entry.v]={indegree:0,"in":[],out:[],vs:[entry.v],i:i};if(!_.isUndefined(entry.barycenter)){tmp.barycenter=entry.barycenter;tmp.weight=entry.weight}});_.forEach(cg.edges(),function(e){var entryV=
mappedEntries[e.v],entryW=mappedEntries[e.w];if(!_.isUndefined(entryV)&&!_.isUndefined(entryW)){entryW.indegree++;entryV.out.push(mappedEntries[e.w])}});var sourceSet=_.filter(mappedEntries,function(entry){return!entry.indegree});return doResolveConflicts(sourceSet)}function doResolveConflicts(sourceSet){var entries=[];function handleIn(vEntry){return function(uEntry){if(uEntry.merged)return;if(_.isUndefined(uEntry.barycenter)||_.isUndefined(vEntry.barycenter)||uEntry.barycenter>=vEntry.barycenter)mergeEntries(vEntry,
uEntry)}}function handleOut(vEntry){return function(wEntry){wEntry["in"].push(vEntry);if(--wEntry.indegree===0)sourceSet.push(wEntry)}}while(sourceSet.length){var entry=sourceSet.pop();entries.push(entry);_.forEach(entry["in"].reverse(),handleIn(entry));_.forEach(entry.out,handleOut(entry))}return _.chain(entries).filter(function(entry){return!entry.merged}).map(function(entry){return _.pick(entry,["vs","i","barycenter","weight"])}).value()}function mergeEntries(target,source){var sum=0,weight=0;
if(target.weight){sum+=target.barycenter*target.weight;weight+=target.weight}if(source.weight){sum+=source.barycenter*source.weight;weight+=source.weight}target.vs=source.vs.concat(target.vs);target.barycenter=sum/weight;target.weight=weight;target.i=Math.min(source.i,target.i);source.merged=true}},{"../lodash":10}],20:[function(require,module,exports){var _=require("../lodash"),barycenter=require("./barycenter"),resolveConflicts=require("./resolve-conflicts"),sort=require("./sort");module.exports=
sortSubgraph;function sortSubgraph(g,v,cg,biasRight){var movable=g.children(v),node=g.node(v),bl=node?node.borderLeft:undefined,br=node?node.borderRight:undefined,subgraphs={};if(bl)movable=_.filter(movable,function(w){return w!==bl&&w!==br});var barycenters=barycenter(g,movable);_.forEach(barycenters,function(entry){if(g.children(entry.v).length){var subgraphResult=sortSubgraph(g,entry.v,cg,biasRight);subgraphs[entry.v]=subgraphResult;if(_.has(subgraphResult,"barycenter"))mergeBarycenters(entry,
subgraphResult)}});var entries=resolveConflicts(barycenters,cg);expandSubgraphs(entries,subgraphs);var result=sort(entries,biasRight);if(bl){result.vs=_.flatten([bl,result.vs,br],true);if(g.predecessors(bl).length){var blPred=g.node(g.predecessors(bl)[0]),brPred=g.node(g.predecessors(br)[0]);if(!_.has(result,"barycenter")){result.barycenter=0;result.weight=0}result.barycenter=(result.barycenter*result.weight+blPred.order+brPred.order)/(result.weight+2);result.weight+=2}}return result}function expandSubgraphs(entries,
subgraphs){_.forEach(entries,function(entry){entry.vs=_.flatten(entry.vs.map(function(v){if(subgraphs[v])return subgraphs[v].vs;return v}),true)})}function mergeBarycenters(target,other){if(!_.isUndefined(target.barycenter)){target.barycenter=(target.barycenter*target.weight+other.barycenter*other.weight)/(target.weight+other.weight);target.weight+=other.weight}else{target.barycenter=other.barycenter;target.weight=other.weight}}},{"../lodash":10,"./barycenter":14,"./resolve-conflicts":19,"./sort":21}],
21:[function(require,module,exports){var _=require("../lodash"),util=require("../util");module.exports=sort;function sort(entries,biasRight){var parts=util.partition(entries,function(entry){return _.has(entry,"barycenter")});var sortable=parts.lhs,unsortable=_.sortBy(parts.rhs,function(entry){return-entry.i}),vs=[],sum=0,weight=0,vsIndex=0;sortable.sort(compareWithBias(!!biasRight));vsIndex=consumeUnsortable(vs,unsortable,vsIndex);_.forEach(sortable,function(entry){vsIndex+=entry.vs.length;vs.push(entry.vs);
sum+=entry.barycenter*entry.weight;weight+=entry.weight;vsIndex=consumeUnsortable(vs,unsortable,vsIndex)});var result={vs:_.flatten(vs,true)};if(weight){result.barycenter=sum/weight;result.weight=weight}return result}function consumeUnsortable(vs,unsortable,index){var last;while(unsortable.length&&(last=_.last(unsortable)).i<=index){unsortable.pop();vs.push(last.vs);index++}return index}function compareWithBias(bias){return function(entryV,entryW){if(entryV.barycenter<entryW.barycenter)return-1;else if(entryV.barycenter>
entryW.barycenter)return 1;return!bias?entryV.i-entryW.i:entryW.i-entryV.i}}},{"../lodash":10,"../util":29}],22:[function(require,module,exports){var _=require("./lodash");module.exports=parentDummyChains;function parentDummyChains(g){var postorderNums=postorder(g);_.forEach(g.graph().dummyChains,function(v){var node=g.node(v),edgeObj=node.edgeObj,pathData=findPath(g,postorderNums,edgeObj.v,edgeObj.w),path=pathData.path,lca=pathData.lca,pathIdx=0,pathV=path[pathIdx],ascending=true;while(v!==edgeObj.w){node=
g.node(v);if(ascending){while((pathV=path[pathIdx])!==lca&&g.node(pathV).maxRank<node.rank)pathIdx++;if(pathV===lca)ascending=false}if(!ascending){while(pathIdx<path.length-1&&g.node(pathV=path[pathIdx+1]).minRank<=node.rank)pathIdx++;pathV=path[pathIdx]}g.setParent(v,pathV);v=g.successors(v)[0]}})}function findPath(g,postorderNums,v,w){var vPath=[],wPath=[],low=Math.min(postorderNums[v].low,postorderNums[w].low),lim=Math.max(postorderNums[v].lim,postorderNums[w].lim),parent,lca;parent=v;do{parent=
g.parent(parent);vPath.push(parent)}while(parent&&(postorderNums[parent].low>low||lim>postorderNums[parent].lim));lca=parent;parent=w;while((parent=g.parent(parent))!==lca)wPath.push(parent);return{path:vPath.concat(wPath.reverse()),lca:lca}}function postorder(g){var result={},lim=0;function dfs(v){var low=lim;_.forEach(g.children(v),dfs);result[v]={low:low,lim:lim++}}_.forEach(g.children(),dfs);return result}},{"./lodash":10}],23:[function(require,module,exports){var _=require("../lodash"),Graph=
require("../graphlib").Graph,util=require("../util");module.exports={positionX:positionX,findType1Conflicts:findType1Conflicts,findType2Conflicts:findType2Conflicts,addConflict:addConflict,hasConflict:hasConflict,verticalAlignment:verticalAlignment,horizontalCompaction:horizontalCompaction,alignCoordinates:alignCoordinates,findSmallestWidthAlignment:findSmallestWidthAlignment,balance:balance};function findType1Conflicts(g,layering){var conflicts={};function visitLayer(prevLayer,layer){var k0=0,scanPos=
0,prevLayerLength=prevLayer.length,lastNode=_.last(layer);_.forEach(layer,function(v,i){var w=findOtherInnerSegmentNode(g,v),k1=w?g.node(w).order:prevLayerLength;if(w||v===lastNode){_.forEach(layer.slice(scanPos,i+1),function(scanNode){_.forEach(g.predecessors(scanNode),function(u){var uLabel=g.node(u),uPos=uLabel.order;if((uPos<k0||k1<uPos)&&!(uLabel.dummy&&g.node(scanNode).dummy))addConflict(conflicts,u,scanNode)})});scanPos=i+1;k0=k1}});return layer}_.reduce(layering,visitLayer);return conflicts}
function findType2Conflicts(g,layering){var conflicts={};function scan(south,southPos,southEnd,prevNorthBorder,nextNorthBorder){var v;_.forEach(_.range(southPos,southEnd),function(i){v=south[i];if(g.node(v).dummy)_.forEach(g.predecessors(v),function(u){var uNode=g.node(u);if(uNode.dummy&&(uNode.order<prevNorthBorder||uNode.order>nextNorthBorder))addConflict(conflicts,u,v)})})}function visitLayer(north,south){var prevNorthPos=-1,nextNorthPos,southPos=0;_.forEach(south,function(v,southLookahead){if(g.node(v).dummy===
"border"){var predecessors=g.predecessors(v);if(predecessors.length){nextNorthPos=g.node(predecessors[0]).order;scan(south,southPos,southLookahead,prevNorthPos,nextNorthPos);southPos=southLookahead;prevNorthPos=nextNorthPos}}scan(south,southPos,south.length,nextNorthPos,north.length)});return south}_.reduce(layering,visitLayer);return conflicts}function findOtherInnerSegmentNode(g,v){if(g.node(v).dummy)return _.find(g.predecessors(v),function(u){return g.node(u).dummy})}function addConflict(conflicts,
v,w){if(v>w){var tmp=v;v=w;w=tmp}var conflictsV=conflicts[v];if(!conflictsV)conflicts[v]=conflictsV={};conflictsV[w]=true}function hasConflict(conflicts,v,w){if(v>w){var tmp=v;v=w;w=tmp}return _.has(conflicts[v],w)}function verticalAlignment(g,layering,conflicts,neighborFn){var root={},align={},pos={};_.forEach(layering,function(layer){_.forEach(layer,function(v,order){root[v]=v;align[v]=v;pos[v]=order})});_.forEach(layering,function(layer){var prevIdx=-1;_.forEach(layer,function(v){var ws=neighborFn(v);
if(ws.length){ws=_.sortBy(ws,function(w){return pos[w]});var mp=(ws.length-1)/2;for(var i=Math.floor(mp),il=Math.ceil(mp);i<=il;++i){var w=ws[i];if(align[v]===v&&prevIdx<pos[w]&&!hasConflict(conflicts,v,w)){align[w]=v;align[v]=root[v]=root[w];prevIdx=pos[w]}}}})});return{root:root,align:align}}function horizontalCompaction(g,layering,root,align,reverseSep){var xs={},blockG=buildBlockGraph(g,layering,root,reverseSep),borderType=reverseSep?"borderLeft":"borderRight";function iterate(setXsFunc,nextNodesFunc){var stack=
blockG.nodes();var elem=stack.pop();var visited={};while(elem){if(visited[elem])setXsFunc(elem);else{visited[elem]=true;stack.push(elem);stack=stack.concat(nextNodesFunc(elem))}elem=stack.pop()}}function pass1(elem){xs[elem]=blockG.inEdges(elem).reduce(function(acc,e){return Math.max(acc,xs[e.v]+blockG.edge(e))},0)}function pass2(elem){var min=blockG.outEdges(elem).reduce(function(acc,e){return Math.min(acc,xs[e.w]-blockG.edge(e))},Number.POSITIVE_INFINITY);var node=g.node(elem);if(min!==Number.POSITIVE_INFINITY&&
node.borderType!==borderType)xs[elem]=Math.max(xs[elem],min)}iterate(pass1,_.bind(blockG.predecessors,blockG));iterate(pass2,_.bind(blockG.successors,blockG));_.forEach(align,function(v){xs[v]=xs[root[v]]});return xs}function buildBlockGraph(g,layering,root,reverseSep){var blockGraph=new Graph,graphLabel=g.graph(),sepFn=sep(graphLabel.nodesep,graphLabel.edgesep,reverseSep);_.forEach(layering,function(layer){var u;_.forEach(layer,function(v){var vRoot=root[v];blockGraph.setNode(vRoot);if(u){var uRoot=
root[u],prevMax=blockGraph.edge(uRoot,vRoot);blockGraph.setEdge(uRoot,vRoot,Math.max(sepFn(g,v,u),prevMax||0))}u=v})});return blockGraph}function findSmallestWidthAlignment(g,xss){return _.minBy(_.values(xss),function(xs){var max=Number.NEGATIVE_INFINITY;var min=Number.POSITIVE_INFINITY;_.forIn(xs,function(x,v){var halfWidth=width(g,v)/2;max=Math.max(x+halfWidth,max);min=Math.min(x-halfWidth,min)});return max-min})}function alignCoordinates(xss,alignTo){var alignToVals=_.values(alignTo),alignToMin=
_.min(alignToVals),alignToMax=_.max(alignToVals);_.forEach(["u","d"],function(vert){_.forEach(["l","r"],function(horiz){var alignment=vert+horiz,xs=xss[alignment],delta;if(xs===alignTo)return;var xsVals=_.values(xs);delta=horiz==="l"?alignToMin-_.min(xsVals):alignToMax-_.max(xsVals);if(delta)xss[alignment]=_.mapValues(xs,function(x){return x+delta})})})}function balance(xss,align){return _.mapValues(xss.ul,function(ignore,v){if(align)return xss[align.toLowerCase()][v];else{var xs=_.sortBy(_.map(xss,
v));return(xs[1]+xs[2])/2}})}function positionX(g){var layering=util.buildLayerMatrix(g),conflicts=_.merge(findType1Conflicts(g,layering),findType2Conflicts(g,layering));var xss={},adjustedLayering;_.forEach(["u","d"],function(vert){adjustedLayering=vert==="u"?layering:_.values(layering).reverse();_.forEach(["l","r"],function(horiz){if(horiz==="r")adjustedLayering=_.map(adjustedLayering,function(inner){return _.values(inner).reverse()});var neighborFn=_.bind(vert==="u"?g.predecessors:g.successors,
g);var align=verticalAlignment(g,adjustedLayering,conflicts,neighborFn);var xs=horizontalCompaction(g,adjustedLayering,align.root,align.align,horiz==="r");if(horiz==="r")xs=_.mapValues(xs,function(x){return-x});xss[vert+horiz]=xs})});var smallestWidth=findSmallestWidthAlignment(g,xss);alignCoordinates(xss,smallestWidth);return balance(xss,g.graph().align)}function sep(nodeSep,edgeSep,reverseSep){return function(g,v,w){var vLabel=g.node(v),wLabel=g.node(w),sum=0,delta;sum+=vLabel.width/2;if(_.has(vLabel,
"labelpos"))switch(vLabel.labelpos.toLowerCase()){case "l":delta=-vLabel.width/2;break;case "r":delta=vLabel.width/2;break}if(delta)sum+=reverseSep?delta:-delta;delta=0;sum+=(vLabel.dummy?edgeSep:nodeSep)/2;sum+=(wLabel.dummy?edgeSep:nodeSep)/2;sum+=wLabel.width/2;if(_.has(wLabel,"labelpos"))switch(wLabel.labelpos.toLowerCase()){case "l":delta=wLabel.width/2;break;case "r":delta=-wLabel.width/2;break}if(delta)sum+=reverseSep?delta:-delta;delta=0;return sum}}function width(g,v){return g.node(v).width}
},{"../graphlib":7,"../lodash":10,"../util":29}],24:[function(require,module,exports){var _=require("../lodash"),util=require("../util"),positionX=require("./bk").positionX;module.exports=position;function position(g){g=util.asNonCompoundGraph(g);positionY(g);_.forEach(positionX(g),function(x,v){g.node(v).x=x})}function positionY(g){var layering=util.buildLayerMatrix(g),rankSep=g.graph().ranksep,prevY=0;_.forEach(layering,function(layer){var maxHeight=_.max(_.map(layer,function(v){return g.node(v).height}));
_.forEach(layer,function(v){g.node(v).y=prevY+maxHeight/2});prevY+=maxHeight+rankSep})}},{"../lodash":10,"../util":29,"./bk":23}],25:[function(require,module,exports){var _=require("../lodash"),Graph=require("../graphlib").Graph,slack=require("./util").slack;module.exports=feasibleTree;function feasibleTree(g){var t=new Graph({directed:false});var start=g.nodes()[0],size=g.nodeCount();t.setNode(start,{});var edge,delta;while(tightTree(t,g)<size){edge=findMinSlackEdge(t,g);delta=t.hasNode(edge.v)?
slack(g,edge):-slack(g,edge);shiftRanks(t,g,delta)}return t}function tightTree(t,g){function dfs(v){_.forEach(g.nodeEdges(v),function(e){var edgeV=e.v,w=v===edgeV?e.w:edgeV;if(!t.hasNode(w)&&!slack(g,e)){t.setNode(w,{});t.setEdge(v,w,{});dfs(w)}})}_.forEach(t.nodes(),dfs);return t.nodeCount()}function findMinSlackEdge(t,g){return _.minBy(g.edges(),function(e){if(t.hasNode(e.v)!==t.hasNode(e.w))return slack(g,e)})}function shiftRanks(t,g,delta){_.forEach(t.nodes(),function(v){g.node(v).rank+=delta})}
},{"../graphlib":7,"../lodash":10,"./util":28}],26:[function(require,module,exports){var rankUtil=require("./util"),longestPath=rankUtil.longestPath,feasibleTree=require("./feasible-tree"),networkSimplex=require("./network-simplex");module.exports=rank;function rank(g){switch(g.graph().ranker){case "network-simplex":networkSimplexRanker(g);break;case "tight-tree":tightTreeRanker(g);break;case "longest-path":longestPathRanker(g);break;default:networkSimplexRanker(g)}}var longestPathRanker=longestPath;
function tightTreeRanker(g){longestPath(g);feasibleTree(g)}function networkSimplexRanker(g){networkSimplex(g)}},{"./feasible-tree":25,"./network-simplex":27,"./util":28}],27:[function(require,module,exports){var _=require("../lodash"),feasibleTree=require("./feasible-tree"),slack=require("./util").slack,initRank=require("./util").longestPath,preorder=require("../graphlib").alg.preorder,postorder=require("../graphlib").alg.postorder,simplify=require("../util").simplify;module.exports=networkSimplex;
networkSimplex.initLowLimValues=initLowLimValues;networkSimplex.initCutValues=initCutValues;networkSimplex.calcCutValue=calcCutValue;networkSimplex.leaveEdge=leaveEdge;networkSimplex.enterEdge=enterEdge;networkSimplex.exchangeEdges=exchangeEdges;function networkSimplex(g){g=simplify(g);initRank(g);var t=feasibleTree(g);initLowLimValues(t);initCutValues(t,g);var e,f;while(e=leaveEdge(t)){f=enterEdge(t,g,e);exchangeEdges(t,g,e,f)}}function initCutValues(t,g){var vs=postorder(t,t.nodes());vs=vs.slice(0,
vs.length-1);_.forEach(vs,function(v){assignCutValue(t,g,v)})}function assignCutValue(t,g,child){var childLab=t.node(child),parent=childLab.parent;t.edge(child,parent).cutvalue=calcCutValue(t,g,child)}function calcCutValue(t,g,child){var childLab=t.node(child),parent=childLab.parent,childIsTail=true,graphEdge=g.edge(child,parent),cutValue=0;if(!graphEdge){childIsTail=false;graphEdge=g.edge(parent,child)}cutValue=graphEdge.weight;_.forEach(g.nodeEdges(child),function(e){var isOutEdge=e.v===child,other=
isOutEdge?e.w:e.v;if(other!==parent){var pointsToHead=isOutEdge===childIsTail,otherWeight=g.edge(e).weight;cutValue+=pointsToHead?otherWeight:-otherWeight;if(isTreeEdge(t,child,other)){var otherCutValue=t.edge(child,other).cutvalue;cutValue+=pointsToHead?-otherCutValue:otherCutValue}}});return cutValue}function initLowLimValues(tree,root){if(arguments.length<2)root=tree.nodes()[0];dfsAssignLowLim(tree,{},1,root)}function dfsAssignLowLim(tree,visited,nextLim,v,parent){var low=nextLim,label=tree.node(v);
visited[v]=true;_.forEach(tree.neighbors(v),function(w){if(!_.has(visited,w))nextLim=dfsAssignLowLim(tree,visited,nextLim,w,v)});label.low=low;label.lim=nextLim++;if(parent)label.parent=parent;else delete label.parent;return nextLim}function leaveEdge(tree){return _.find(tree.edges(),function(e){return tree.edge(e).cutvalue<0})}function enterEdge(t,g,edge){var v=edge.v,w=edge.w;if(!g.hasEdge(v,w)){v=edge.w;w=edge.v}var vLabel=t.node(v),wLabel=t.node(w),tailLabel=vLabel,flip=false;if(vLabel.lim>wLabel.lim){tailLabel=
wLabel;flip=true}var candidates=_.filter(g.edges(),function(edge){return flip===isDescendant(t,t.node(edge.v),tailLabel)&&flip!==isDescendant(t,t.node(edge.w),tailLabel)});return _.minBy(candidates,function(edge){return slack(g,edge)})}function exchangeEdges(t,g,e,f){var v=e.v,w=e.w;t.removeEdge(v,w);t.setEdge(f.v,f.w,{});initLowLimValues(t);initCutValues(t,g);updateRanks(t,g)}function updateRanks(t,g){var root=_.find(t.nodes(),function(v){return!g.node(v).parent}),vs=preorder(t,root);vs=vs.slice(1);
_.forEach(vs,function(v){var parent=t.node(v).parent,edge=g.edge(v,parent),flipped=false;if(!edge){edge=g.edge(parent,v);flipped=true}g.node(v).rank=g.node(parent).rank+(flipped?edge.minlen:-edge.minlen)})}function isTreeEdge(tree,u,v){return tree.hasEdge(u,v)}function isDescendant(tree,vLabel,rootLabel){return rootLabel.low<=vLabel.lim&&vLabel.lim<=rootLabel.lim}},{"../graphlib":7,"../lodash":10,"../util":29,"./feasible-tree":25,"./util":28}],28:[function(require,module,exports){var _=require("../lodash");
module.exports={longestPath:longestPath,slack:slack};function longestPath(g){var visited={};function dfs(v){var label=g.node(v);if(_.has(visited,v))return label.rank;visited[v]=true;var rank=_.minBy(_.map(g.outEdges(v),function(e){return dfs(e.w)-g.edge(e).minlen}));if(rank===Number.POSITIVE_INFINITY||rank===undefined||rank===null)rank=0;return label.rank=rank}_.forEach(g.sources(),dfs)}function slack(g,e){return g.node(e.w).rank-g.node(e.v).rank-g.edge(e).minlen}},{"../lodash":10}],29:[function(require,
module,exports){var _=require("./lodash"),Graph=require("./graphlib").Graph;module.exports={addDummyNode:addDummyNode,simplify:simplify,asNonCompoundGraph:asNonCompoundGraph,successorWeights:successorWeights,predecessorWeights:predecessorWeights,intersectRect:intersectRect,buildLayerMatrix:buildLayerMatrix,normalizeRanks:normalizeRanks,removeEmptyRanks:removeEmptyRanks,addBorderNode:addBorderNode,maxRank:maxRank,partition:partition,time:time,notime:notime};function addDummyNode(g,type,attrs,name){var v;
do v=_.uniqueId(name);while(g.hasNode(v));attrs.dummy=type;g.setNode(v,attrs);return v}function simplify(g){var simplified=(new Graph).setGraph(g.graph());_.forEach(g.nodes(),function(v){simplified.setNode(v,g.node(v))});_.forEach(g.edges(),function(e){var simpleLabel=simplified.edge(e.v,e.w)||{weight:0,minlen:1},label=g.edge(e);simplified.setEdge(e.v,e.w,{weight:simpleLabel.weight+label.weight,minlen:Math.max(simpleLabel.minlen,label.minlen)})});return simplified}function asNonCompoundGraph(g){var simplified=
(new Graph({multigraph:g.isMultigraph()})).setGraph(g.graph());_.forEach(g.nodes(),function(v){if(!g.children(v).length)simplified.setNode(v,g.node(v))});_.forEach(g.edges(),function(e){simplified.setEdge(e,g.edge(e))});return simplified}function successorWeights(g){var weightMap=_.map(g.nodes(),function(v){var sucs={};_.forEach(g.outEdges(v),function(e){sucs[e.w]=(sucs[e.w]||0)+g.edge(e).weight});return sucs});return _.zipObject(g.nodes(),weightMap)}function predecessorWeights(g){var weightMap=_.map(g.nodes(),
function(v){var preds={};_.forEach(g.inEdges(v),function(e){preds[e.v]=(preds[e.v]||0)+g.edge(e).weight});return preds});return _.zipObject(g.nodes(),weightMap)}function intersectRect(rect,point){var x=rect.x;var y=rect.y;var dx=point.x-x;var dy=point.y-y;var w=rect.width/2;var h=rect.height/2;if(!dx&&!dy)throw new Error("Not possible to find intersection inside of the rectangle");var sx,sy;if(Math.abs(dy)*w>Math.abs(dx)*h){if(dy<0)h=-h;sx=h*dx/dy;sy=h}else{if(dx<0)w=-w;sx=w;sy=w*dy/dx}return{x:x+
sx,y:y+sy}}function buildLayerMatrix(g){var layering=_.map(_.range(maxRank(g)+1),function(){return[]});_.forEach(g.nodes(),function(v){var node=g.node(v),rank=node.rank;if(!_.isUndefined(rank))layering[rank][node.order]=v});return layering}function normalizeRanks(g){var min=_.minBy(_.map(g.nodes(),function(v){return g.node(v).rank}));_.forEach(g.nodes(),function(v){var node=g.node(v);if(_.has(node,"rank"))node.rank-=min})}function removeEmptyRanks(g){var offset=_.minBy(_.map(g.nodes(),function(v){return g.node(v).rank}));
var layers=[];_.forEach(g.nodes(),function(v){var rank=g.node(v).rank-offset;if(!layers[rank])layers[rank]=[];layers[rank].push(v)});var delta=0,nodeRankFactor=g.graph().nodeRankFactor;_.forEach(layers,function(vs,i){if(_.isUndefined(vs)&&i%nodeRankFactor!==0)--delta;else if(delta)_.forEach(vs,function(v){g.node(v).rank+=delta})})}function addBorderNode(g,prefix,rank,order){var node={width:0,height:0};if(arguments.length>=4){node.rank=rank;node.order=order}return addDummyNode(g,"border",node,prefix)}
function maxRank(g){return _.max(_.map(g.nodes(),function(v){var rank=g.node(v).rank;if(!_.isUndefined(rank))return rank}))}function partition(collection,fn){var result={lhs:[],rhs:[]};_.forEach(collection,function(value){if(fn(value))result.lhs.push(value);else result.rhs.push(value)});return result}function time(name,fn){var start=_.now();try{return fn()}finally{console.log(name+" time: "+(_.now()-start)+"ms")}}function notime(name,fn){return fn()}},{"./graphlib":7,"./lodash":10}],30:[function(require,
module,exports){module.exports="0.8.2"},{}]},{},[1])(1)});
//# sourceURL=build://tf-graph-common/annotation.js
var tf;
(function(b){(function(d){(function(f){(function(h){function k(q){return(d.render.AnnotationType[q]||"").toLowerCase()||null}function r(q,u){u.annotationType===d.render.AnnotationType.SUMMARY?f.selectOrCreateChild(q,"use").attr("class","summary").attr("xlink:href","#summary-icon").attr("cursor","pointer"):(q=f.node.buildShape(q,u,f.Class.Annotation.NODE),f.selectOrCreateChild(q,"title").text(u.node.name))}function l(q,u){let w=u.node.name.split("/");return p(q,w[w.length-1],u,null)}function p(q,u,
w,A){let y=f.Class.Annotation.LABEL;A&&(y+=" "+A);q=q.append("text").attr("class",y).attr("dy",".35em").attr("text-anchor",w.isIn?"end":"start").text(u);return b.graph.scene.node.enforceLabelWidth(q,-1)}function m(q,u,w,A){q.on("mouseover",y=>{A.fire("annotation-highlight",{name:y.node.name,hostName:u.node.name})}).on("mouseout",y=>{A.fire("annotation-unhighlight",{name:y.node.name,hostName:u.node.name})}).on("click",y=>{d3.event.stopPropagation();A.fire("annotation-select",{name:y.node.name,hostName:u.node.name})});
if(w.annotationType!==d.render.AnnotationType.SUMMARY&&w.annotationType!==d.render.AnnotationType.CONSTANT)q.on("contextmenu",f.contextmenu.getMenu(A,f.node.getContextMenu(w.node,A)))}function n(q,u,w,A){let y=d.layout.computeCXPositionOfNodeShape(u);w.renderNodeInfo&&w.annotationType!==d.render.AnnotationType.ELLIPSIS&&f.node.stylize(q,w.renderNodeInfo,A,f.Class.Annotation.NODE);w.annotationType===d.render.AnnotationType.SUMMARY&&(w.width+=10);q.select("text."+f.Class.Annotation.LABEL).transition().attr("x",
y+w.dx+(w.isIn?-1:1)*(w.width/2+w.labelOffset)).attr("y",u.y+w.dy);q.select("use.summary").transition().attr("x",y+w.dx-3).attr("y",u.y+w.dy-6);f.positionEllipse(q.select("."+f.Class.Annotation.NODE+" ellipse"),y+w.dx,u.y+w.dy,w.width,w.height);f.positionRect(q.select("."+f.Class.Annotation.NODE+" rect"),y+w.dx,u.y+w.dy,w.width,w.height);f.positionRect(q.select("."+f.Class.Annotation.NODE+" use"),y+w.dx,u.y+w.dy,w.width,w.height);q.select("path."+f.Class.Annotation.EDGE).transition().attr("d",x=>
{x=x.points.map(C=>({x:C.dx+y,y:C.dy+u.y}));return f.edge.interpolate(x)})}h.buildGroup=function(q,u,w,A){q=q.selectAll(function(){return this.childNodes}).data(u.list,y=>y.node.name);q.enter().append("g").attr("data-name",y=>y.node.name).each(function(y){let x=d3.select(this);A.addAnnotationGroup(y,w,x);let C=f.Class.Annotation.EDGE,G=y.renderMetaedgeInfo&&y.renderMetaedgeInfo.metaedge;G&&!G.numRegularEdges&&(C+=" "+f.Class.Annotation.CONTROL_EDGE);G&&G.numRefEdges&&(C+=" "+f.Class.Edge.REF_LINE);
f.edge.appendEdge(x,y,A,C);y.annotationType!==d.render.AnnotationType.ELLIPSIS?(l(x,y),r(x,y)):p(x,y.node.name,y,f.Class.Annotation.ELLIPSIS)}).merge(q).attr("class",y=>f.Class.Annotation.GROUP+" "+k(y.annotationType)+" "+f.node.nodeClass(y)).each(function(y){let x=d3.select(this);n(x,w,y,A);y.annotationType!==d.render.AnnotationType.ELLIPSIS&&m(x,w,y,A)});q.exit().each(function(y){let x=d3.select(this);A.removeAnnotationGroup(y,w,x)}).remove();return q}})(f.annotation||(f.annotation={}))})(d.scene||
(d.scene={}))})(b.graph||(b.graph={}))})(tf||(tf={}));

//# sourceURL=build://tf-graph-common/colors.js
(function(b){b.COLORS=[{name:"Google Blue",color:"#4184f3",active:"#3a53c5",disabled:"#cad8fc"},{name:"Google Red",color:"#db4437",active:"#8f2a0c",disabled:"#e8c6c1"},{name:"Google Yellow",color:"#f4b400",active:"#db9200",disabled:"#f7e8b0"},{name:"Google Green",color:"#0f9d58",active:"#488046",disabled:"#c2e1cc"},{name:"Purple",color:"#aa46bb",active:"#5c1398",disabled:"#d7bce6"},{name:"Teal",color:"#00abc0",active:"#47828e",disabled:"#c2eaf2"},{name:"Deep Orange",color:"#ff6f42",active:"#ca4a06",
disabled:"#f2cbba"},{name:"Lime",color:"#9d9c23",active:"#7f771d",disabled:"#f1f4c2"},{name:"Indigo",color:"#5b6abf",active:"#3e47a9",disabled:"#c5c8e8"},{name:"Pink",color:"#ef6191",active:"#ca1c60",disabled:"#e9b9ce"},{name:"Deep Teal",color:"#00786a",active:"#2b4f43",disabled:"#bededa"},{name:"Deep Pink",color:"#c1175a",active:"#75084f",disabled:"#de8cae"},{name:"Gray",color:"#9E9E9E",active:"#424242",disabled:"F5F5F5"}].reduce((d,f)=>{d[f.name]=f;return d},{});b.OP_GROUP_COLORS=[{color:"Google Red",
groups:"gen_legacy_ops legacy_ops legacy_flogs_input legacy_image_input legacy_input_example_input legacy_sequence_input legacy_seti_input_input".split(" ")},{color:"Deep Orange",groups:["constant_ops"]},{color:"Indigo",groups:["state_ops"]},{color:"Purple",groups:["nn_ops","nn"]},{color:"Google Green",groups:["math_ops"]},{color:"Lime",groups:["array_ops"]},{color:"Teal",groups:["control_flow_ops","data_flow_ops"]},{color:"Pink",groups:["summary_ops"]},{color:"Deep Pink",groups:["io_ops"]}].reduce((d,
f)=>{f.groups.forEach(function(h){d[h]=f.color});return d},{})})(tf||(tf={}));

//# sourceURL=build://tf-graph-common/common.js
(function(b){(function(d){(function(f){f.OP_GRAPH="op_graph";f.CONCEPTUAL_GRAPH="conceptual_graph";f.PROFILE="profile"})(d.SelectionType||(d.SelectionType={}))})(b.graph||(b.graph={}))})(tf||(tf={}));

//# sourceURL=build://tf-graph-common/contextmenu.js
(function(b){(function(d){(function(f){(function(h){function k(r){let l=0,p=0;for(;r&&0<=r.offsetLeft&&0<=r.offsetTop;)l+=r.offsetLeft-r.scrollLeft,p+=r.offsetTop-r.scrollTop,r=r.offsetParent;return{left:l,top:p}}h.getMenu=function(r,l){const p=r.getContextMenu(),m=d3.select(r.getContextMenu());return function(n,q){function u(y){y&&y.composedPath().includes(p)||(m.style("display","none"),document.body.removeEventListener("mousedown",u,{capture:!0}))}let w=d3.event;const A=k(r);m.style("display","block").style("left",
w.clientX-A.left+1+"px").style("top",w.clientY-A.top+1+"px");w.preventDefault();w.stopPropagation();document.body.addEventListener("mousedown",u,{capture:!0});m.html("");m.append("ul").selectAll("li").data(l).enter().append("li").on("click",y=>{y.action(this,n,q);u()}).html(function(y){return y.title(n)})}}})(f.contextmenu||(f.contextmenu={}))})(d.scene||(d.scene={}))})(b.graph||(b.graph={}))})(tf||(tf={}));

//# sourceURL=build://tf-graph-common/edge.js
(function(b){(function(d){(function(f){(function(h){function k(y){return y.v+d.EDGE_KEY_DELIM+y.w}function r(y,x){x=x.getNodeByName(y.v);if(null==x.outputShapes||_.isEmpty(x.outputShapes))return null;y=x.outputShapes[y.outputTensorKey];return null==y?null:0===y.length?"scalar":y.map(C=>-1===C?"?":C).join("\u00d7")}function l(y,x){return x.edgeLabelFunction?x.edgeLabelFunction(y,x):1<y.baseEdgeList.length?y.baseEdgeList.length+" tensors":r(y.baseEdgeList[0],x)}function p(y,x,C){const G=document.createElementNS(b.graph.scene.SVG_NAMESPACE,
"path");for(let D=1;D<y.length;D++)if(G.setAttribute("d",C(y.slice(0,D))),G.getTotalLength()>x)return D-1;return y.length-1}function m(y,x,C){var G=d3.line().x(K=>K.x).y(K=>K.y),D=d3.select(document.createElementNS("http://www.w3.org/2000/svg","path")).attr("d",G(y)),B=+x.attr("markerWidth"),H=x.attr("viewBox").split(" ").map(Number);H=H[2]-H[0];x=+x.attr("refX");D=D.node();if(C)return B*=1-x/H,C=D.getPointAtLength(B),G=p(y,B,G),y[G-1]={x:C.x,y:C.y},y.slice(G-1);C=1-x/H;B=D.getTotalLength()-B*C;C=
D.getPointAtLength(B);G=p(y,B,G);y[G]={x:C.x,y:C.y};return y.slice(0,G+1)}function n(y,x,C,G){G=G||f.Class.Edge.LINE;x.label&&x.label.structural&&(G+=" "+f.Class.Edge.STRUCTURAL);x.label&&x.label.metaedge&&x.label.metaedge.numRefEdges&&(G+=" "+f.Class.Edge.REFERENCE_EDGE);C.handleEdgeSelected&&(G+=" "+f.Class.Edge.SELECTABLE);let D="path_"+k(x);if(C.renderHierarchy.edgeWidthFunction)var B=C.renderHierarchy.edgeWidthFunction(x,G);else B=1,null!=x.label&&null!=x.label.metaedge&&(B=x.label.metaedge.totalSize),
B=C.renderHierarchy.edgeWidthSizedBasedScale(B);G=y.append("path").attr("id",D).attr("class",G).style("stroke-width",B+"px");x.label&&x.label.metaedge&&(x.label.metaedge.numRefEdges?(B=`reference-arrowhead-${A(B)}`,G.style("marker-start",`url(#${B})`),x.label.startMarkerId=B):(B=`dataflow-arrowhead-${A(B)}`,G.style("marker-end",`url(#${B})`),x.label.endMarkerId=B));null!=x.label&&null!=x.label.metaedge&&(x=l(x.label.metaedge,C.renderHierarchy),null!=x&&y.append("text").append("textPath").attr("xlink:href",
"#"+D).attr("startOffset","50%").attr("text-anchor","middle").attr("dominant-baseline","central").text(x))}function q(y,x,C,G,D){G=C.label;let B=G.adjoiningMetaedge,H=G.points;y=y.shadowRoot;C.label.startMarkerId&&(H=m(H,d3.select(y.querySelector("#"+C.label.startMarkerId)),!0));C.label.endMarkerId&&(H=m(H,d3.select(y.querySelector("#"+C.label.endMarkerId)),!1));if(!B)return d3.interpolate(D,h.interpolate(H));let K=B.edgeGroup.node().firstChild,L=G.metaedge.inbound;return function(){let J=K.getPointAtLength(L?
K.getTotalLength():0).matrixTransform(K.getCTM()).matrixTransform(x.getCTM().inverse()),O=L?0:H.length-1;H[O].x=J.x;H[O].y=J.y;return h.interpolate(H)}}function u(y,x){d3.select(x).select("path."+f.Class.Edge.LINE).transition().attrTween("d",function(C,G,D){return q(y,this,C,G,D)})}function w(y,x){y.classed("faded",x.label.isFadedOut);x=x.label.metaedge;y.select("path."+f.Class.Edge.LINE).classed("control-dep",x&&!x.numRegularEdges)}h.MIN_EDGE_WIDTH=.75;h.MAX_EDGE_WIDTH=12;h.EDGE_WIDTH_SIZE_BASED_SCALE=
d3.scalePow().exponent(.3).domain([1,5E6]).range([h.MIN_EDGE_WIDTH,h.MAX_EDGE_WIDTH]).clamp(!0);let A=d3.scaleQuantize().domain([h.MIN_EDGE_WIDTH,h.MAX_EDGE_WIDTH]).range(["small","medium","large","xlarge"]);h.getEdgeKey=k;h.buildGroup=function(y,x,C){let G=[];G=_.reduce(x.edges(),(D,B)=>{let H=x.edge(B);D.push({v:B.v,w:B.w,label:H});return D},G);y=f.selectOrCreateChild(y,"g",f.Class.Edge.CONTAINER).selectAll(function(){return this.childNodes}).data(G,k);y.enter().append("g").attr("class",f.Class.Edge.GROUP).attr("data-edge",
k).each(function(D){let B=d3.select(this);D.label.edgeGroup=B;C._edgeGroupIndex[k(D)]=B;if(C.handleEdgeSelected)B.on("click",H=>{d3.event.stopPropagation();C.fire("edge-select",{edgeData:H,edgeGroup:B})});n(B,D,C)}).merge(y).each(function(){u(C,this)}).each(function(D){w(d3.select(this),D,C)});y.exit().each(D=>{delete C._edgeGroupIndex[k(D)]}).remove();return y};h.getLabelForBaseEdge=r;h.getLabelForEdge=l;h.appendEdge=n;h.interpolate=d3.line().curve(d3.curveBasis).x(y=>y.x).y(y=>y.y)})(f.edge||(f.edge=
{}))})(d.scene||(d.scene={}))})(b.graph||(b.graph={}))})(tf||(tf={}));

//# sourceURL=build://tf-graph-common/externs.js

//# sourceURL=build://tf-graph-common/graph.js
(function(b){(function(d){function f(J,O,S,N,R){return(S?S+"/":"")+(J+("undefined"!==typeof N&&"undefined"!==typeof R?"["+N+"-"+R+"]":"#")+O)}function h(J){if(!J)return null;for(let O=0;O<J.length;O++){let {key:S,value:N}=J[O];if("_output_shapes"===S){if(!N.list.shape)break;let R=N.list.shape.map(X=>X.unknown_rank?null:null==X.dim||1===X.dim.length&&null==X.dim[0].size?[]:X.dim.map(aa=>aa.size));J.splice(O,1);return R}}return null}function k(J){if(!J)return null;for(let O=0;O<J.length;O++)if("_XlaCluster"===
J[O].key)return J[O].value.s||null;return null}function r(J){let O=[];_.each(J,S=>{let N="^"===S[0];N&&(S=S.substring(1));let R=S,X="0",aa=S.match(/(.*):(\w+:\d+)$/);if(aa)R=aa[1],X=aa[2];else if(aa=S.match(/(.*):(\d+)$/))R=aa[1],X=aa[2];0!==O.length&&R===O[O.length-1].name||O.push({name:R,outputTensorKey:X,isControlDependency:N})});return O}function l(J,O,S,N,R,X){O!==S.name&&J.edges.push({v:O,w:S.name,outputTensorKey:N.outputTensorKey,isControlDependency:N.isControlDependency,isReferenceEdge:!0===
R.refEdges[S.op+" "+X]})}function p(J,O,S){S=S||{};let N=new graphlib.Graph(S);N.setGraph({name:J,rankdir:S.rankdir||"BT",type:O});return N}function m(J){return function(O){for(let S=0;S<J.length;S++){let N=new RegExp(J[S]);if("string"===typeof O.op&&O.op.match(N))return!0}return!1}}function n(J){let O=J.split(d.NAMESPACE_DELIM);return J+d.NAMESPACE_DELIM+"("+O[O.length-1]+")"}function q(J,O){let S={},N={};J.sort();for(let R=0;R<J.length-1;++R){let X=J[R];_.each(w(X).slice(0,-1),aa=>{N[aa]=!0});for(let aa=
R+1;aa<J.length;++aa){let fa=J[aa];if(_.startsWith(fa,X)){if(fa.length>X.length&&fa.charAt(X.length)===d.NAMESPACE_DELIM){S[X]=n(X);break}}else break}}_.each(O,R=>{R in N&&(S[R]=n(R))});return S}function u(J){let O=J.nodes().map(function(S){return J.neighbors(S).length});O.sort();return O}function w(J,O){let S=[],N=J.indexOf(d.NAMESPACE_DELIM);for(;0<=N;)S.push(J.substring(0,N)),N=J.indexOf(d.NAMESPACE_DELIM,N+1);O&&(O=O[J])&&S.push(O);S.push(J);return S}d.NAMESPACE_DELIM="/";d.ROOT_NAME="__root__";
d.FUNCTION_LIBRARY_NODE_PREFIX="__function_library__";d.LARGE_ATTRS_KEY="_too_large_attrs";d.LIMIT_ATTR_SIZE=1024;d.EDGE_KEY_DELIM="--";let A;(function(J){J[J.FULL=0]="FULL";J[J.EMBEDDED=1]="EMBEDDED";J[J.META=2]="META";J[J.SERIES=3]="SERIES";J[J.CORE=4]="CORE";J[J.SHADOW=5]="SHADOW";J[J.BRIDGE=6]="BRIDGE";J[J.EDGE=7]="EDGE"})(A=d.GraphType||(d.GraphType={}));let y;(function(J){J[J.META=0]="META";J[J.OP=1]="OP";J[J.SERIES=2]="SERIES";J[J.BRIDGE=3]="BRIDGE";J[J.ELLIPSIS=4]="ELLIPSIS"})(y=d.NodeType||
(d.NodeType={}));let x;(function(J){J[J.INCLUDE=0]="INCLUDE";J[J.EXCLUDE=1]="EXCLUDE";J[J.UNSPECIFIED=2]="UNSPECIFIED"})(x=d.InclusionType||(d.InclusionType={}));(function(J){J[J.GROUP=0]="GROUP";J[J.UNGROUP=1]="UNGROUP"})(d.SeriesGroupingType||(d.SeriesGroupingType={}));class C{constructor(){this.nodes={};this.edges=[]}}d.SlimGraph=C;class G{constructor(J){this.type=y.ELLIPSIS;this.isGroupNode=!1;this.cardinality=1;this.stats=this.parentNode=null;this.setNumMoreNodes(J);this.include=x.UNSPECIFIED}setNumMoreNodes(J){this.numMoreNodes=
J;this.name="... "+J+" more"}}d.EllipsisNodeImpl=G;class D{constructor(J){this.op=J.op;this.name=J.name;this.device=J.device;this.attr=J.attr;this.inputs=r(J.input);this.outputShapes=h(J.attr);this.xlaCluster=k(J.attr);this.compatible=!1;this.type=y.OP;this.isGroupNode=!1;this.cardinality=1;this.inEmbeddings=[];this.outEmbeddings=[];this.parentNode=null;this.include=x.UNSPECIFIED;this.owningSeries=null}}d.OpNodeImpl=D;d.createMetanode=function(J,O={}){return new H(J,O)};d.joinStatsInfoWithGraph=function(J,
O,S){_.each(J.nodes,N=>{N.stats=null});_.each(O.dev_stats,N=>{S&&!S[N.device]||_.each(N.node_stats,R=>{let X=R.node_name in J.nodes?R.node_name:n(R.node_name);if(X in J.nodes){var aa=0;R.memory&&_.each(R.memory,oa=>{oa.total_bytes&&(0<oa.total_bytes?aa+=Number(oa.total_bytes):console.log("ignoring negative memory allocation for "+X))});var fa=null;R.output&&(fa=_.map(R.output,oa=>_.map(oa.tensor_description.shape.dim,Z=>Number(Z.size))));J.nodes[X].device=N.device;null==J.nodes[X].stats&&(J.nodes[X].stats=
new B(fa));J.nodes[X].stats.addBytesAllocation(aa);R.all_end_rel_micros&&(0<R.all_end_rel_micros?J.nodes[X].stats.addExecutionTime(R.all_start_micros,R.all_start_micros+R.all_end_rel_micros):console.log("ignoring negative runtime for "+X))}})})};class B{constructor(J){this.totalBytes=0;this.outputSize=J}addExecutionTime(J,O){this.startTime=null!=this.startTime?Math.min(this.startTime,J):J;this.endTime=null!=this.endTime?Math.max(this.endTime,O):O}addBytesAllocation(J){this.totalBytes=null!=this.totalBytes?
Math.max(this.totalBytes,J):J}combine(J){null!=J.totalBytes&&(this.totalBytes+=J.totalBytes);null!=J.getTotalMicros()&&this.addExecutionTime(J.startTime,J.endTime)}getTotalMicros(){return null==this.startTime||null==this.endTime?null:this.endTime-this.startTime}}d.NodeStats=B;class H{constructor(J,O={}){this.name=J;this.type=y.META;this.depth=1;this.isGroupNode=!0;this.cardinality=0;this.metagraph=p(J,A.META,O);this.bridgegraph=null;this.opHistogram={};this.deviceHistogram={};this.xlaClusterHistogram=
{};this.compatibilityHistogram={compatible:0,incompatible:0};this.parentNode=this.templateId=null;this.hasNonControlEdges=!1;this.include=x.UNSPECIFIED;this.associatedFunction=""}getFirstChild(){return this.metagraph.node(this.metagraph.nodes()[0])}getRootOp(){let J=this.name.split("/");return this.metagraph.node(this.name+"/("+J[J.length-1]+")")}leaves(){let J=[],O=[this],S;for(;O.length;){let N=O.shift();N.isGroupNode?(S=N.metagraph,_.each(S.nodes(),R=>O.push(S.node(R)))):J.push(N.name)}return J}}
d.MetanodeImpl=H;d.createMetaedge=function(J,O){return new K(J,O)};class K{constructor(J,O){this.v=J;this.w=O;this.baseEdgeList=[];this.inbound=null;this.totalSize=this.numRefEdges=this.numControlEdges=this.numRegularEdges=0}addBaseEdge(J,O){this.baseEdgeList.push(J);J.isControlDependency?this.numControlEdges+=1:this.numRegularEdges+=1;J.isReferenceEdge&&(this.numRefEdges+=1);this.totalSize+=K.computeSizeOfEdge(J,O);O.maxMetaEdgeSize=Math.max(O.maxMetaEdgeSize,this.totalSize)}static computeSizeOfEdge(J,
O){let S=O.node(J.v);if(!S.outputShapes)return 1;O.hasShapeInfo=!0;J=Object.keys(S.outputShapes).map(N=>S.outputShapes[N]).map(N=>null==N?1:N.reduce((R,X)=>{-1===X&&(X=1);return R*X},1));return _.sum(J)}}d.MetaedgeImpl=K;d.createSeriesNode=function(J,O,S,N,R,X){return new L(J,O,S,N,R,X)};d.getSeriesNodeName=f;class L{constructor(J,O,S,N,R,X){this.name=R||f(J,O,S);this.type=y.SERIES;this.hasLoop=!1;this.prefix=J;this.suffix=O;this.clusterId=N;this.ids=[];this.parent=S;this.isGroupNode=!0;this.cardinality=
0;this.metagraph=p(R,A.SERIES,X);this.parentNode=this.bridgegraph=null;this.deviceHistogram={};this.xlaClusterHistogram={};this.compatibilityHistogram={compatible:0,incompatible:0};this.hasNonControlEdges=!1;this.include=x.UNSPECIFIED}}d.DefaultBuildParams={enableEmbedding:!0,inEmbeddingTypes:["Const"],outEmbeddingTypes:["^[a-zA-Z]+Summary$"],refEdges:{"Assign 0":!0,"AssignAdd 0":!0,"AssignSub 0":!0,"assign 0":!0,"assign_add 0":!0,"assign_sub 0":!0,"count_up_to 0":!0,"ScatterAdd 0":!0,"ScatterSub 0":!0,
"ScatterUpdate 0":!0,"scatter_add 0":!0,"scatter_sub 0":!0,"scatter_update 0":!0}};d.build=function(J,O,S){let N={},R={},X={},aa=m(O.inEmbeddingTypes),fa=m(O.outEmbeddingTypes),oa=[],Z=J.node,ca=Array(Z.length);return b.graph.util.runAsyncTask("Normalizing names",30,()=>{let ja=Array(Z.length),ba=0;const ka=Ea=>{let va=new D(Ea);if(aa(va))return oa.push(va.name),N[va.name]=va;if(fa(va))return oa.push(va.name),R[va.name]=va,_.each(va.inputs,ya=>{ya=ya.name;X[ya]=X[ya]||[];X[ya].push(va)}),va;ja[ba]=
va;ca[ba]=va.name;ba++;return va};_.each(Z,ka);const W=Ea=>{const va=d.FUNCTION_LIBRARY_NODE_PREFIX+Ea.signature.name;ka({name:va,input:[],device:"",op:"",attr:[]});if(Ea.signature.input_arg){let xa=0;var ya=Sa=>{ka({name:va+d.NAMESPACE_DELIM+Sa.name,input:[],device:"",op:"input_arg",attr:[{key:"T",value:{type:Sa.type}}]}).functionInputIndex=xa;xa++};Ea.signature.input_arg.name?ya(Ea.signature.input_arg):_.each(Ea.signature.input_arg,ya)}let Aa=0;const Fa={};Ea.signature.output_arg&&(ya=xa=>{Fa[va+
d.NAMESPACE_DELIM+xa.name]=Aa;Aa++},Ea.signature.output_arg.name?ya(Ea.signature.output_arg):_.each(Ea.signature.output_arg,ya));_.each(Ea.node_def,xa=>{xa.name=va+"/"+xa.name;"string"===typeof xa.input&&(xa.input=[xa.input]);const Sa=ka(xa);_.isNumber(Fa[xa.name])&&(Sa.functionOutputIndex=Fa[xa.name]);_.each(Sa.inputs,Xa=>{Xa.name=va+d.NAMESPACE_DELIM+Xa.name})})};J.library&&J.library.function&&_.each(J.library.function,W);ja.splice(ba);ca.splice(ba);return ja},S).then(ja=>b.graph.util.runAsyncTask("Building the data structure",
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ba[W.name]||W.name});return ka},S))};d.createGraph=p;d.getStrictName=n;d.hasSimilarDegreeSequence=function(J,O){J=u(J);O=u(O);for(let S=0;S<J.length;S++)if(J[S]!==O[S])return!1;return!0};d.getHierarchicalPath=w;d.getIncludeNodeButtonString=function(J){return J===b.graph.InclusionType.EXCLUDE?"Add to main graph":"Remove from main graph"};d.getGroupSeriesNodeButtonString=function(J){return J===b.graph.SeriesGroupingType.GROUP?"Ungroup this series of nodes":"Group this series of nodes"};d.toggleNodeSeriesGroup=
function(J,O){J[O]=O in J&&J[O]!==b.graph.SeriesGroupingType.GROUP?b.graph.SeriesGroupingType.GROUP:b.graph.SeriesGroupingType.UNGROUP}})(b.graph||(b.graph={}))})(tf||(tf={}));

//# sourceURL=build://tf-graph-common/hierarchy.js
(function(b){(function(d){(function(f){function h(w,A,y,x){A=y?w.inEdges(A.name):w.outEdges(A.name);_.each(A,C=>{C=w.edge(C);(C.numRegularEdges?x.regular:x.control).push(C)})}function k(w,A){const y={};_.each(A.nodes,x=>{let C=d.getHierarchicalPath(x.name),G=w.root;G.depth=Math.max(C.length,G.depth);y[x.op]||(y[x.op]=[]);y[x.op].push(x);for(let B=0;B<C.length;B++){G.depth=Math.max(G.depth,C.length-B);G.cardinality+=x.cardinality;G.opHistogram[x.op]=(G.opHistogram[x.op]||0)+1;null!=x.device&&(G.deviceHistogram[x.device]=
(G.deviceHistogram[x.device]||0)+1);null!=x.xlaCluster&&(G.xlaClusterHistogram[x.xlaCluster]=(G.xlaClusterHistogram[x.xlaCluster]||0)+1);x.compatible?G.compatibilityHistogram.compatible=(G.compatibilityHistogram.compatible||0)+1:G.compatibilityHistogram.incompatible=(G.compatibilityHistogram.incompatible||0)+1;_.each(x.inEmbeddings,K=>{K.compatible?G.compatibilityHistogram.compatible=(G.compatibilityHistogram.compatible||0)+1:G.compatibilityHistogram.incompatible=(G.compatibilityHistogram.incompatible||
0)+1});_.each(x.outEmbeddings,K=>{K.compatible?G.compatibilityHistogram.compatible=(G.compatibilityHistogram.compatible||0)+1:G.compatibilityHistogram.incompatible=(G.compatibilityHistogram.incompatible||0)+1});if(B===C.length-1)break;var D=C[B];let H=w.node(D);H||(H=d.createMetanode(D,w.graphOptions),H.parentNode=G,w.setNode(D,H),G.metagraph.setNode(D,H),0===D.indexOf(b.graph.FUNCTION_LIBRARY_NODE_PREFIX)&&G.name===b.graph.ROOT_NAME&&(D=D.substring(b.graph.FUNCTION_LIBRARY_NODE_PREFIX.length),y[D]||
(y[D]=[]),w.libraryFunctions[D]={node:H,usages:y[D]},H.associatedFunction=D));G=H}w.setNode(x.name,x);x.parentNode=G;G.metagraph.setNode(x.name,x);_.each(x.inEmbeddings,function(B){w.setNode(B.name,B);B.parentNode=x});_.each(x.outEmbeddings,function(B){w.setNode(B.name,B);B.parentNode=x})})}function r(w,A){let y=w.getNodeMap(),x=[],C=[],G=(D,B)=>{let H=0;for(;D;)B[H++]=D.name,D=D.parentNode;return H-1};_.each(A.edges,D=>{var B=G(A.nodes[D.v],x),H=G(A.nodes[D.w],C);if(-1!==B&&-1!==H){for(;x[B]===C[H];)if(B--,
H--,0>B||0>H)throw Error("No difference found between ancestor paths.");var K=y[x[B+1]];B=x[B];H=C[H];var L=K.metagraph.edge(B,H);L||(L=d.createMetaedge(B,H),K.metagraph.setEdge(B,H,L));K.hasNonControlEdges||D.isControlDependency||(K.hasNonControlEdges=!0);L.addBaseEdge(D,w)}})}function l(w,A,y,x,C,G){let D=w.metagraph;_.each(D.nodes(),B=>{B=D.node(B);B.type===b.graph.NodeType.META&&l(B,A,y,x,C,G)});w=p(D);w=(G?n:m)(w,D,A.graphOptions);_.each(w,function(B,H){let K=B.metagraph.nodes();_.each(K,L=>
{L=D.node(L);L.owningSeries||(L.owningSeries=H)});K.length<x&&!(B.name in C)&&(C[B.name]=b.graph.SeriesGroupingType.UNGROUP);B.name in C&&C[B.name]===b.graph.SeriesGroupingType.UNGROUP||(A.setNode(H,B),D.setNode(H,B),_.each(K,L=>{let J=D.node(L);B.metagraph.setNode(L,J);B.parentNode=J.parentNode;B.cardinality++;null!=J.device&&(B.deviceHistogram[J.device]=(B.deviceHistogram[J.device]||0)+1);null!=J.xlaCluster&&(B.xlaClusterHistogram[J.xlaCluster]=(B.xlaClusterHistogram[J.xlaCluster]||0)+1);J.compatible?
B.compatibilityHistogram.compatible=(B.compatibilityHistogram.compatible||0)+1:B.compatibilityHistogram.incompatible=(B.compatibilityHistogram.incompatible||0)+1;_.each(J.inEmbeddings,O=>{O.compatible?B.compatibilityHistogram.compatible=(B.compatibilityHistogram.compatible||0)+1:B.compatibilityHistogram.incompatible=(B.compatibilityHistogram.incompatible||0)+1});_.each(J.outEmbeddings,O=>{O.compatible?B.compatibilityHistogram.compatible=(B.compatibilityHistogram.compatible||0)+1:B.compatibilityHistogram.incompatible=
(B.compatibilityHistogram.incompatible||0)+1});J.parentNode=B;y[L]=H;D.removeNode(L)}))})}function p(w){return _.reduce(w.nodes(),(A,y)=>{y=w.node(y);if(y.type===d.NodeType.META)return A;let x=y.op;x&&(A[x]=A[x]||[],A[x].push(y.name));return A},{})}function m(w,A,y){let x={};_.each(w,function(C,G){if(!(1>=C.length)){var D={};_.each(C,function(B){var H="*"===B.charAt(B.length-1),K=B.split("/"),L=K[K.length-1];K=K.slice(0,K.length-1).join("/");var J=L.match(/^(\D*)_(\d+)$/);let O="";J?(L=J[1],J=J[2]):
(L=H?L.substr(0,L.length-1):L,J=0,O=H?"*":"");H=d.getSeriesNodeName(L,O,K);D[H]=D[H]||[];B=d.createSeriesNode(L,O,K,+J,B,y);D[H].push(B)});_.each(D,function(B){if(!(2>B.length)){B.sort(function(K,L){return+K.clusterId-+L.clusterId});var H=[B[0]];for(let K=1;K<B.length;K++){let L=B[K];L.clusterId===H[H.length-1].clusterId+1?H.push(L):(q(H,x,+G,A,y),H=[L])}q(H,x,+G,A,y)}})}});return x}function n(w,A,y){let x={};_.each(w,function(C,G){if(!(1>=C.length)){var D={},B={};_.each(C,function(K){let L="*"===
K.charAt(K.length-1);var J=K.split("/");let O=J[J.length-1];J=J.slice(0,J.length-1).join("/");const S=/(\d+)/g;var N;let R,X,aa,fa=0;for(;N=S.exec(O);)++fa,R=O.slice(0,N.index),X=N[0],N=O.slice(N.index+N[0].length),aa=d.getSeriesNodeName(R,N,J),D[aa]=D[aa],D[aa]||(D[aa]=d.createSeriesNode(R,N,J,+X,K,y)),D[aa].ids.push(X),B[K]=B[K]||[],B[K].push([aa,X]);1>fa&&(R=L?O.substr(0,O.length-1):O,X=0,N=L?"*":"",aa=d.getSeriesNodeName(R,N,J),D[aa]=D[aa],D[aa]||(D[aa]=d.createSeriesNode(R,N,J,+X,K,y)),D[aa].ids.push(X),
B[K]=B[K]||[],B[K].push([aa,X]))});var H={};_.each(B,function(K,L){K.sort(function(S,N){return D[N[0]].ids.length-D[S[0]].ids.length});var J=K[0][0];K=K[0][1];H[J]=H[J]||[];const O=L.split("/");L=d.createSeriesNode(D[J].prefix,D[J].suffix,O.slice(0,O.length-1).join("/"),+K,L,y);H[J].push(L)});_.each(H,function(K){if(!(2>K.length)){K.sort(function(J,O){return+J.clusterId-+O.clusterId});var L=[K[0]];for(let J=1;J<K.length;J++){let O=K[J];O.clusterId===L[L.length-1].clusterId+1?L.push(O):(q(L,x,+G,A,
y),L=[O])}q(L,x,+G,A,y)}})}});return x}function q(w,A,y,x,C){if(1<w.length){let G=d.getSeriesNodeName(w[0].prefix,w[0].suffix,w[0].parent,w[0].clusterId,w[w.length-1].clusterId),D=d.createSeriesNode(w[0].prefix,w[0].suffix,w[0].parent,y,G,C);_.each(w,function(B){D.ids.push(B.clusterId);D.metagraph.setNode(B.name,x.node(B.name))});A[G]=D}}class u{constructor(w){this.hasShapeInfo=!1;this.maxMetaEdgeSize=1;this.graphOptions=w||{};this.graphOptions.compound=!0;this.root=d.createMetanode(d.ROOT_NAME,this.graphOptions);
this.libraryFunctions={};this.xlaClusters=this.devices=this.templates=null;this.index={};this.index[d.ROOT_NAME]=this.root;this.orderings={}}getNodeMap(){return this.index}node(w){return this.index[w]}setNode(w,A){this.index[w]=A}getBridgegraph(w){var A=this.index[w];if(!A)throw Error("Could not find node in hierarchy: "+w);if(!("metagraph"in A))return null;if(A.bridgegraph)return A.bridgegraph;let y=A.bridgegraph=d.createGraph("BRIDGEGRAPH",d.GraphType.BRIDGE,this.graphOptions);if(!(A.parentNode&&
"metagraph"in A.parentNode))return y;var x=A.parentNode;A=x.metagraph;x=this.getBridgegraph(x.name);_.each([A,x],C=>{C.edges().filter(G=>G.v===w||G.w===w).forEach(G=>{let D=G.w===w,B=C.edge(G);_.each(B.baseEdgeList,H=>{let [K,L]=D?[H.w,G.v]:[H.v,G.w];var J=this.getChildName(w,K);J={v:D?L:J,w:D?J:L};let O=y.edge(J);O||(O=d.createMetaedge(J.v,J.w),O.inbound=D,y.setEdge(J.v,J.w,O));O.addBaseEdge(H,this)})})});return y}getChildName(w,A){let y=this.index[A];for(;y;){if(y.parentNode&&y.parentNode.name===
w)return y.name;y=y.parentNode}throw Error("Could not find immediate child for descendant: "+A);}getPredecessors(w){let A=this.index[w];if(!A)throw Error("Could not find node with name: "+w);let y=this.getOneWayEdges(A,!0);A.isGroupNode||_.each(A.inEmbeddings,x=>{_.each(A.inputs,C=>{if(C.name===x.name){let G=new d.MetaedgeImpl(x.name,w);G.addBaseEdge({isControlDependency:C.isControlDependency,outputTensorKey:C.outputTensorKey,isReferenceEdge:!1,v:x.name,w},this);y.regular.push(G)}})});return y}getSuccessors(w){let A=
this.index[w];if(!A)throw Error("Could not find node with name: "+w);let y=this.getOneWayEdges(A,!1);A.isGroupNode||_.each(A.outEmbeddings,x=>{_.each(x.inputs,C=>{if(C.name===w){let G=new d.MetaedgeImpl(w,x.name);G.addBaseEdge({isControlDependency:C.isControlDependency,outputTensorKey:C.outputTensorKey,isReferenceEdge:!1,v:w,w:x.name},this);y.regular.push(G)}})});return y}getOneWayEdges(w,A){let y={control:[],regular:[]};if(!w.parentNode||!w.parentNode.isGroupNode)return y;var x=w.parentNode;let C=
x.metagraph;x=this.getBridgegraph(x.name);h(C,w,A,y);h(x,w,A,y);return y}getTopologicalOrdering(w){var A=this.index[w];if(!A)throw Error("Could not find node with name: "+w);if(!A.isGroupNode)return null;if(w in this.orderings)return this.orderings[w];let y={},x={},C=A.metagraph;_.each(C.edges(),D=>{C.edge(D).numRegularEdges&&(D.v in y||(y[D.v]=[]),y[D.v].push(D.w),x[D.w]=!0)});let G=_.difference(_.keys(y),_.keys(x));w=this.orderings[w]={};for(A=0;G.length;){let D=G.shift();w[D]=A++;_.each(y[D],B=>
G.push(B));delete y[D]}return w}getTemplateIndex(){let w=d3.keys(this.templates),A=d3.scaleOrdinal().domain(w).range(d3.range(0,w.length));return y=>A(y)}}f.DefaultHierarchyParams={verifyTemplate:!0,seriesNodeMinSize:5,seriesMap:{},rankDirection:"BT",useGeneralizedSeriesPatterns:!1};f.build=function(w,A,y){let x=new u({rankdir:A.rankDirection}),C={};return b.graph.util.runAsyncTask("Adding nodes",20,()=>{let G={},D={};_.each(w.nodes,B=>{B.device&&(G[B.device]=!0);B.xlaCluster&&(D[B.xlaCluster]=!0)});
x.devices=_.keys(G);x.xlaClusters=_.keys(D);k(x,w)},y).then(()=>b.graph.util.runAsyncTask("Detect series",20,()=>{0<A.seriesNodeMinSize&&l(x.root,x,C,A.seriesNodeMinSize,A.seriesMap,A.useGeneralizedSeriesPatterns)},y)).then(()=>b.graph.util.runAsyncTask("Adding edges",30,()=>{r(x,w,C)},y)).then(()=>b.graph.util.runAsyncTask("Finding similar subgraphs",30,()=>{x.templates=d.template.detect(x,A.verifyTemplate)},y)).then(()=>x)};f.joinAndAggregateStats=function(w){let A={},y={};_.each(w.root.leaves(),
x=>{x=w.node(x);null!=x.device&&(A[x.device]=!0);null!=x.xlaCluster&&(y[x.xlaCluster]=!0)});w.devices=_.keys(A);w.xlaClusters=_.keys(y);_.each(w.getNodeMap(),x=>{x.isGroupNode&&(x.stats=new d.NodeStats(null),x.deviceHistogram={})});_.each(w.root.leaves(),x=>{let C=x=w.node(x);for(;null!=C.parentNode;){if(null!=x.device){var G=C.parentNode.deviceHistogram;G[x.device]=(G[x.device]||0)+1}null!=x.xlaCluster&&(G=C.parentNode.xlaClusterHistogram,G[x.xlaCluster]=(G[x.xlaCluster]||0)+1);null!=x.stats&&C.parentNode.stats.combine(x.stats);
C=C.parentNode}})};f.getIncompatibleOps=function(w,A){let y=[],x={};_.each(w.root.leaves(),C=>{C=w.node(C);if(C.type==d.NodeType.OP){if(!C.compatible)if(C.owningSeries)if(A&&A.seriesMap[C.owningSeries]===b.graph.SeriesGroupingType.UNGROUP)y.push(C);else{if(!x[C.owningSeries]){let G=w.node(C.owningSeries);G&&(x[C.owningSeries]=G,y.push(G))}}else y.push(C);_.each(C.inEmbeddings,G=>{G.compatible||y.push(G)});_.each(C.outEmbeddings,G=>{G.compatible||y.push(G)})}});return y}})(d.hierarchy||(d.hierarchy=
{}))})(b.graph||(b.graph={}))})(tf||(tf={}));

//# sourceURL=build://tf-graph-common/layout.js
(function(b){(function(d){(function(f){function h(x){x.node.isGroupNode&&r(x);x.node.type===d.NodeType.META?p(x):x.node.type===d.NodeType.SERIES&&m(x)}function k(x){x.inboxWidth=0<x.inAnnotations.list.length?f.PARAMS.annotations.inboxWidth:0;x.outboxWidth=0<x.outAnnotations.list.length?f.PARAMS.annotations.outboxWidth:0;x.coreBox.width=x.width;x.coreBox.height=x.height;x.width=Math.max(x.coreBox.width+x.inboxWidth+x.outboxWidth,3*x.displayName.length)}function r(x){let C=x.coreGraph.nodes().map(G=>
x.coreGraph.node(G)).concat(x.isolatedInExtract,x.isolatedOutExtract,x.libraryFunctionsExtract);_.each(C,G=>{switch(G.node.type){case d.NodeType.OP:_.extend(G,f.PARAMS.nodeSize.op);break;case d.NodeType.BRIDGE:_.extend(G,f.PARAMS.nodeSize.bridge);break;case d.NodeType.META:G.expanded?h(G):(_.extend(G,f.PARAMS.nodeSize.meta),G.height=f.PARAMS.nodeSize.meta.height(G.node.cardinality));break;case d.NodeType.SERIES:G.expanded?(_.extend(G,f.PARAMS.nodeSize.series.expanded),h(G)):_.extend(G,G.node.hasNonControlEdges?
f.PARAMS.nodeSize.series.vertical:f.PARAMS.nodeSize.series.horizontal);break;default:throw Error("Unrecognized node type: "+G.node.type);}G.expanded||k(G);n(G)})}function l(x,C){_.extend(x.graph(),{nodesep:C.nodeSep,ranksep:C.rankSep,edgesep:C.edgeSep});let G=[],D=[];_.each(x.nodes(),J=>{x.node(J).node.type===d.NodeType.BRIDGE?G.push(J):D.push(J)});if(!D.length)return{width:0,height:0};dagre.layout(x);let B=Infinity,H=Infinity,K=-Infinity,L=-Infinity;_.each(D,J=>{J=x.node(J);var O=.5*J.width,S=J.x-
O;O=J.x+O;B=S<B?S:B;K=O>K?O:K;O=.5*J.height;S=J.y-O;J=J.y+O;H=S<H?S:H;L=J>L?J:L});_.each(x.edges(),J=>{J=x.edge(J);if(!J.structural){var O=x.node(J.metaedge.v),S=x.node(J.metaedge.w);if(3===J.points.length&&A(J.points)){if(null!=O){var N=O.expanded?O.x:u(O);J.points[0].x=N}null!=S&&(N=S.expanded?S.x:u(S),J.points[2].x=N);J.points=[J.points[0],J.points[1]]}N=J.points[J.points.length-2];null!=S&&(J.points[J.points.length-1]=y(N,S));S=J.points[1];null!=O&&(J.points[0]=y(S,O));_.each(J.points,R=>{B=R.x<
B?R.x:B;K=R.x>K?R.x:K;H=R.y<H?R.y:H;L=R.y>L?R.y:L})}});_.each(x.nodes(),J=>{J=x.node(J);J.x-=B;J.y-=H});_.each(x.edges(),J=>{_.each(x.edge(J).points,O=>{O.x-=B;O.y-=H})});return{width:K-B,height:L-H}}function p(x){let C=f.PARAMS.subscene.meta;_.extend(x,C);_.extend(x.coreBox,l(x.coreGraph,f.PARAMS.graph.meta));var G=x.isolatedInExtract.length?_.max(x.isolatedInExtract,B=>B.width).width:null;x.inExtractBox.width=null!=G?G:0;x.inExtractBox.height=_.reduce(x.isolatedInExtract,(B,H,K)=>{K=0<K?C.extractYOffset:
0;H.x=0;H.y=B+K+H.height/2;return B+K+H.height},0);G=x.isolatedOutExtract.length?_.max(x.isolatedOutExtract,B=>B.width).width:null;x.outExtractBox.width=null!=G?G:0;x.outExtractBox.height=_.reduce(x.isolatedOutExtract,(B,H,K)=>{K=0<K?C.extractYOffset:0;H.x=0;H.y=B+K+H.height/2;return B+K+H.height},0);G=x.libraryFunctionsExtract.length?_.max(x.libraryFunctionsExtract,B=>B.width).width:null;x.libraryFunctionsBox.width=null!=G?G:0;x.libraryFunctionsBox.height=_.reduce(x.libraryFunctionsExtract,(B,H,
K)=>{K=0<K?C.extractYOffset:0;H.x=0;H.y=B+K+H.height/2;return B+K+H.height},0);G=0;0<x.isolatedInExtract.length&&G++;0<x.isolatedOutExtract.length&&G++;0<x.libraryFunctionsExtract.length&&G++;0<x.coreGraph.nodeCount()&&G++;let D=f.PARAMS.subscene.meta.extractXOffset;G=1>=G?0:G*D;x.coreBox.width+=Math.max(f.MIN_AUX_WIDTH,x.inExtractBox.width+x.outExtractBox.width)+G+x.libraryFunctionsBox.width+G;x.coreBox.height=C.labelHeight+Math.max(x.inExtractBox.height,x.coreBox.height,x.libraryFunctionsBox.height,
x.outExtractBox.height);x.width=x.coreBox.width+C.paddingLeft+C.paddingRight;x.height=x.paddingTop+x.coreBox.height+x.paddingBottom}function m(x){let C=x.coreGraph,G=f.PARAMS.subscene.series;_.extend(x,G);_.extend(x.coreBox,l(x.coreGraph,f.PARAMS.graph.series));_.each(C.nodes(),D=>{C.node(D).excluded=!1});x.width=x.coreBox.width+G.paddingLeft+G.paddingRight;x.height=x.coreBox.height+G.paddingTop+G.paddingBottom}function n(x){if(!x.expanded){var C=x.inAnnotations.list,G=x.outAnnotations.list;_.each(C,
O=>q(O));_.each(G,O=>q(O));var D=f.PARAMS.annotations,B=_.reduce(C,(O,S,N)=>{N=0<N?D.yOffset:0;S.dx=-(x.coreBox.width+S.width)/2-D.xOffset;S.dy=O+N+S.height/2;return O+N+S.height},0);_.each(C,O=>{O.dy-=B/2;O.labelOffset=D.labelOffset});var H=_.reduce(G,(O,S,N)=>{N=0<N?D.yOffset:0;S.dx=(x.coreBox.width+S.width)/2+D.xOffset;S.dy=O+N+S.height/2;return O+N+S.height},0);_.each(G,O=>{O.dy-=H/2;O.labelOffset=D.labelOffset});var K=Math.min(x.height/2-x.radius,B/2);K=0>K?0:K;var L=d3.scaleLinear().domain([0,
C.length-1]).range([-K,K]);_.each(C,(O,S)=>{O.points=[{dx:O.dx+O.width/2,dy:O.dy},{dx:-x.coreBox.width/2,dy:1<C.length?L(S):0}]});K=Math.min(x.height/2-x.radius,H/2);K=0>K?0:K;var J=d3.scaleLinear().domain([0,G.length-1]).range([-K,K]);_.each(G,(O,S)=>{O.points=[{dx:x.coreBox.width/2,dy:1<G.length?J(S):0},{dx:O.dx-O.width/2,dy:O.dy}]});x.height=Math.max(x.height,B,H)}}function q(x){switch(x.annotationType){case d.render.AnnotationType.CONSTANT:_.extend(x,f.PARAMS.constant.size);break;case d.render.AnnotationType.SHORTCUT:if(x.node.type===
d.NodeType.OP)_.extend(x,f.PARAMS.shortcutSize.op);else if(x.node.type===d.NodeType.META)_.extend(x,f.PARAMS.shortcutSize.meta);else if(x.node.type===d.NodeType.SERIES)_.extend(x,f.PARAMS.shortcutSize.series);else throw Error("Invalid node type: "+x.node.type);break;case d.render.AnnotationType.SUMMARY:_.extend(x,f.PARAMS.constant.size)}}function u(x){return x.expanded?x.x:x.x-x.width/2+(x.inAnnotations.list.length?x.inboxWidth:0)+x.coreBox.width/2}function w(x,C){return 180*Math.atan((C.y-x.y)/(C.x-
x.x))/Math.PI}function A(x){let C=w(x[0],x[1]);for(let G=1;G<x.length-1;G++){let D=w(x[G],x[G+1]);if(1<Math.abs(D-C))return!1;C=D}return!0}function y(x,C){let G=C.expanded?C.x:u(C),D=C.y;var B=x.x-G;x=x.y-D;let H=C.expanded?C.width:C.coreBox.width,K=C.expanded?C.height:C.coreBox.height;Math.abs(x)*H/2>Math.abs(B)*K/2?(0>x&&(K=-K),C=0===x?0:K/2*B/x,B=K/2):(0>B&&(H=-H),C=H/2,B=0===B?0:H/2*x/B);return{x:G+C,y:D+B}}f.PARAMS={animation:{duration:250},graph:{meta:{nodeSep:5,rankSep:25,edgeSep:5},series:{nodeSep:5,
rankSep:25,edgeSep:5},padding:{paddingTop:40,paddingLeft:20}},subscene:{meta:{paddingTop:10,paddingBottom:10,paddingLeft:10,paddingRight:10,labelHeight:20,extractXOffset:15,extractYOffset:20},series:{paddingTop:10,paddingBottom:10,paddingLeft:10,paddingRight:10,labelHeight:10}},nodeSize:{meta:{radius:5,width:60,maxLabelWidth:52,height:d3.scaleLinear().domain([1,200]).range([15,60]).clamp(!0),expandButtonRadius:3},op:{width:15,height:6,radius:3,labelOffset:-8,maxLabelWidth:30},series:{expanded:{radius:10,
labelOffset:0},vertical:{width:16,height:13,labelOffset:-13},horizontal:{width:24,height:8,radius:10,labelOffset:-10}},bridge:{width:20,height:20,radius:2,labelOffset:0}},shortcutSize:{op:{width:10,height:4},meta:{width:12,height:4,radius:1},series:{width:14,height:4}},annotations:{inboxWidth:50,outboxWidth:50,xOffset:10,yOffset:3,labelOffset:2,maxLabelWidth:120},constant:{size:{width:4,height:4}},series:{maxStackCount:3,parallelStackOffsetRatio:.2,towerStackOffsetRatio:.5},minimap:{size:150}};f.MIN_AUX_WIDTH=
140;f.layoutScene=h;f.computeCXPositionOfNodeShape=u})(d.layout||(d.layout={}))})(b.graph||(b.graph={}))})(tf||(tf={}));

//# sourceURL=build://tf-graph-common/loader.js
var Jh=this&&this.__awaiter||function(b,d,f,h){return new (f||(f=Promise))(function(k,r){function l(n){try{m(h.next(n))}catch(q){r(q)}}function p(n){try{m(h["throw"](n))}catch(q){r(q)}}function m(n){n.done?k(n.value):(new f(function(q){q(n.value)})).then(l,p)}m((h=h.apply(b,d||[])).next())})};
(function(b){(function(d){(function(f){f.fetchAndConstructHierarchicalGraph=function(h,k,r,l=new d.op.TpuCompatibilityProvider,p=d.hierarchy.DefaultHierarchyParams){const m=d.util.getSubtaskTracker(h,20,"Graph"),n=d.util.getSubtaskTracker(h,50,"Namespace hierarchy");return d.parser.fetchAndParseGraphData(k,r,d.util.getSubtaskTracker(h,30,"Data")).then(function(q){if(!q.node)throw Error("The graph is empty. This can happen when TensorFlow could not trace any graph. Please refer to https://github.com/tensorflow/tensorboard/issues/1961 for more information.");
return d.build(q,d.DefaultBuildParams,m)},()=>{throw Error("Malformed GraphDef. This can sometimes be caused by a bad network connection or difficulty reconciling multiple GraphDefs; for the latter case, please refer to https://github.com/tensorflow/tensorboard/issues/1929.");}).then(q=>Jh(this,void 0,void 0,function*(){d.op.checkOpsForCompatibility(q,l);const u=yield d.hierarchy.build(q,p,n);return{graph:q,graphHierarchy:u}})).catch(q=>{h.reportError(`Graph visualization failed.\n\n${q}`,q);throw q;
})}})(d.loader||(d.loader={}))})(b.graph||(b.graph={}))})(tf||(tf={}));

//# sourceURL=build://tf-graph-common/node.js
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f.Class.Annotation.GROUP).each(W=>{ja.removeAnnotationGroup(W,ba)})}).remove();return Z};h.getContextMenu=m;h.canBeInSeries=n;h.getSeriesName=q;h.getGroupSettingLabel=w;h.enforceLabelWidth=y;let fa=null;h.buildShape=G;h.nodeClass=D;let oa;(function(Z){Z[Z.STRUCTURE=0]="STRUCTURE";Z[Z.DEVICE=1]="DEVICE";Z[Z.XLA_CLUSTER=2]="XLA_CLUSTER";Z[Z.COMPUTE_TIME=3]="COMPUTE_TIME";Z[Z.MEMORY=4]="MEMORY";Z[Z.OP_COMPATIBILITY=5]="OP_COMPATIBILITY"})(oa=h.ColorBy||(h.ColorBy={}));h.removeGradientDefinitions=function(Z){d3.select(Z).select("defs#_graph-gradients").remove()};
h.getFillForNode=K;h.stylize=L;h.getStrokeForFill=J;h.updateInputTrace=function(Z,ca,ja,ba){const ka=d3.select(Z);ka.selectAll(".input-highlight").classed("input-highlight",!1);ka.selectAll(".non-input").classed("non-input",!1);ka.selectAll(".input-parent").classed("input-parent",!1);ka.selectAll(".input-child").classed("input-child",!1);ka.selectAll(".input-edge-highlight").classed("input-edge-highlight",!1);ka.selectAll(".non-input-edge-highlight").classed("non-input-edge-highlight",!1);ka.selectAll(".input-highlight-selected").classed("input-highlight-selected",
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(b.graph={}))})(tf||(tf={}));

//# sourceURL=build://tf-graph-common/op.js
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f.TpuCompatibilityProvider=h;f.checkOpsForCompatibility=function(k,r){if(null===r)throw Error("Compatibility provider required, but got: "+r);_.each(k.nodes,l=>{l.compatible=r.opValid(l);_.each(l.inEmbeddings,p=>{p.compatible=r.opValid(p)});_.each(l.outEmbeddings,p=>{p.compatible=r.opValid(p)})})}})(d.op||(d.op={}))})(b.graph||(b.graph={}))})(tf||(tf={}));

//# sourceURL=build://tf-graph-common/parser.js
(function(b){(function(d){(function(f){function h(u){if("true"===u)return!0;if("false"===u)return!1;if('"'===u[0])return u.substring(1,u.length-1);let w=parseFloat(u);return isNaN(w)?u:w}function k(u){return new Promise((w,A)=>{fetch(u).then(y=>{y.ok?y.arrayBuffer().then(w,A):y.text().then(A,A)})})}function r(u,w,A=1E6,y="\n"){return new Promise(function(x,C){function G(D,B,H){var K=H>=u.byteLength;B=B.split(y);B[0]=D+B[0];const L=K?"":B.pop();for(let J of B)try{w(J)}catch(O){C(O);return}K?x(!0):
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B[B.length-1]){case "{":B=B.substring(0,B.length-2).trim();let H={};C.push(D);G.push(B);y(D,B,H,G);D=H;break;case "}":D=C.pop();G.pop();break;default:B=A(B),y(D,B.name,B.value,G.concat(B.name))}}).then(function(){return x})}f.fetchPbTxt=k;f.fetchAndParseMetadata=function(u,w){return b.graph.util.runTask(()=>null==u?Promise.resolve(null):k(u),w).then(A=>b.graph.util.runAsyncPromiseTask("Parsing metadata.pbtxt",60,()=>null!=A?p(A):Promise.resolve(null),w))};f.fetchAndParseGraphData=function(u,w,A){return b.graph.util.runAsyncPromiseTask("Reading graph pbtxt",
40,()=>w?new Promise(function(y,x){let C=new FileReader;C.onload=()=>y(C.result);C.onerror=()=>x(C.error);C.readAsArrayBuffer(w)}):k(u),A).then(y=>b.graph.util.runAsyncPromiseTask("Parsing graph.pbtxt",60,()=>l(y),A))};f.streamParse=r;const n={"library.function":!0,"library.function.node_def":!0,"library.function.node_def.input":!0,"library.function.node_def.attr":!0,"library.function.node_def.attr.value.list.b":!0,"library.function.node_def.attr.value.list.f":!0,"library.function.node_def.attr.value.list.func":!0,
"library.function.node_def.attr.value.list.i":!0,"library.function.node_def.attr.value.list.s":!0,"library.function.node_def.attr.value.list.shape":!0,"library.function.node_def.attr.value.list.shape.dim":!0,"library.function.node_def.attr.value.list.tensor":!0,"library.function.node_def.attr.value.list.type":!0,"library.function.node_def.attr.value.shape.dim":!0,"library.function.node_def.attr.value.tensor.string_val":!0,"library.function.node_def.attr.value.tensor.tensor_shape.dim":!0,"library.function.signature.input_arg":!0,
"library.function.signature.output_arg":!0,"library.versions":!0,node:!0,"node.input":!0,"node.attr":!0,"node.attr.value.list.b":!0,"node.attr.value.list.f":!0,"node.attr.value.list.func":!0,"node.attr.value.list.i":!0,"node.attr.value.list.s":!0,"node.attr.value.list.shape":!0,"node.attr.value.list.shape.dim":!0,"node.attr.value.list.tensor":!0,"node.attr.value.list.type":!0,"node.attr.value.shape.dim":!0,"node.attr.value.tensor.string_val":!0,"node.attr.value.tensor.tensor_shape.dim":!0},q={"step_stats.dev_stats":!0,
"step_stats.dev_stats.node_stats":!0,"step_stats.dev_stats.node_stats.output":!0,"step_stats.dev_stats.node_stats.memory":!0,"step_stats.dev_stats.node_stats.output.tensor_description.shape.dim":!0};f.parseGraphPbTxt=l;f.parseStatsPbTxt=p})(d.parser||(d.parser={}))})(b.graph||(b.graph={}))})(tf||(tf={}));

//# sourceURL=build://tf-graph-common/proto.js

//# sourceURL=build://tf-graph-common/render.js
(function(b){(function(d){(function(f){function h(N,R,X,aa,fa){R=new H(R,X,aa,fa,!0);N.inAnnotations.push(R)}function k(N,R,X,aa,fa){R=new H(R,X,aa,fa,!1);N.outAnnotations.push(R)}function r(N,R){_.each(N.nodes(),X=>{X=N.node(X);X.expanded=1<R;if(0<R)switch(X.node.type){case d.NodeType.META:case d.NodeType.SERIES:l(X,R-1)}})}function l(N,R){N.coreGraph&&r(N.coreGraph,R)}function p(N,R,X){let aa=N.node(R),fa=N.node(X),oa=N.edge(R,X);if(aa.node.include!==d.InclusionType.INCLUDE&&fa.node.include!==d.InclusionType.INCLUDE||
aa.node.include===d.InclusionType.EXCLUDE||fa.node.include===d.InclusionType.EXCLUDE)k(aa,fa.node,fa,oa,K.SHORTCUT),h(fa,aa.node,aa,oa,K.SHORTCUT),N.removeEdge(R,X)}function m(N,R,X){let aa=N.coreGraph,fa=aa.node(R);fa.isOutExtract=!0;_.each(aa.predecessors(R),oa=>{p(aa,oa,R)});(G.detachAllEdgesForHighDegree||X)&&_.each(aa.successors(R),oa=>{p(aa,R,oa)});0===aa.neighbors(R).length&&(fa.node.include=d.InclusionType.EXCLUDE,N.isolatedOutExtract.push(fa),aa.removeNode(R))}function n(N,R,X){let aa=N.coreGraph,
fa=aa.node(R);fa.isInExtract=!0;_.each(aa.successors(R),oa=>{p(aa,R,oa)});(G.detachAllEdgesForHighDegree||X)&&_.each(aa.predecessors(R),oa=>{p(aa,oa,R)});0===aa.neighbors(R).length&&(fa.node.include=d.InclusionType.EXCLUDE,N.isolatedInExtract.push(fa),aa.removeNode(R))}function q(N,R){if(N.type===d.NodeType.OP)for(var X=0;X<R.length;X++){if(N.op===R[X])return!0}else if(N.type===d.NodeType.META&&(N=N.getRootOp()))for(X=0;X<R.length;X++)if(N.op===R[X])return!0;return!1}function u(N){let R=N.coreGraph;
_.each(R.nodes(),X=>{R.node(X).node.include!==d.InclusionType.EXCLUDE||X.startsWith(b.graph.FUNCTION_LIBRARY_NODE_PREFIX)||(N.coreGraph.outEdges(X).length>N.coreGraph.inEdges(X).length?m(N,X,!0):n(N,X,!0))})}function w(N){let R=N.coreGraph;_.each(R.nodes(),X=>{let aa=R.node(X);aa.node.include===d.InclusionType.UNSPECIFIED&&q(aa.node,G.outExtractTypes)&&m(N,X)})}function A(N){let R=N.coreGraph;_.each(R.nodes(),X=>{let aa=R.node(X);aa.node.include===d.InclusionType.UNSPECIFIED&&q(aa.node,G.inExtractTypes)&&
n(N,X)})}function y(N){let R=N.coreGraph,X={},aa={},fa=0;_.each(R.nodes(),ka=>{if(R.node(ka).node.include===d.InclusionType.UNSPECIFIED){var W=_.reduce(R.predecessors(ka),(va,ya)=>{ya=R.edge(ya,ka).metaedge;return va+(ya.numRegularEdges?1:0)},0);0===W&&0<R.predecessors(ka).length&&(W=R.predecessors(ka).length);var Ea=_.reduce(R.successors(ka),(va,ya)=>{ya=R.edge(ka,ya).metaedge;return va+(ya.numRegularEdges?1:0)},0);0===Ea&&0<R.successors(ka).length&&(Ea=R.successors(ka).length);X[ka]=W;aa[ka]=Ea;
fa++}});if(!(fa<G.minNodeCountForExtraction)){var oa=G.minDegreeForExtraction-1,Z=Math.round(.75*fa),ca=Math.round(.25*fa),ja=Object.keys(X).sort((ka,W)=>X[ka]-X[W]),ba=X[ja[Z]];ba=ba+ba-X[ja[ca]];ba=Math.max(ba,oa);for(let ka=fa-1;X[ja[ka]]>ba;ka--)n(N,ja[ka]);ja=Object.keys(aa).sort((ka,W)=>aa[ka]-aa[W]);Z=aa[ja[Z]];ca=Z+4*(Z-aa[ja[ca]]);ca=Math.max(ca,oa);for(oa=fa-1;aa[ja[oa]]>ca;oa--)(Z=R.node(ja[oa]))&&!Z.isInExtract&&m(N,ja[oa])}}function x(N){let R=N.coreGraph,X={};_.each(R.edges(),aa=>{R.edge(aa).metaedge.numRegularEdges||
((X[aa.v]=X[aa.v]||[]).push(aa),(X[aa.w]=X[aa.w]||[]).push(aa))});_.each(X,aa=>{aa.length>G.maxControlDegree&&_.each(aa,fa=>p(R,fa.v,fa.w))})}function C(N){u(N);G.outExtractTypes&&w(N);G.inExtractTypes&&A(N);y(N);G.maxControlDegree&&x(N);let R=N.coreGraph;_.each(R.nodes(),X=>{let aa=R.node(X);var fa=R.neighbors(X).length;if(aa.node.include===d.InclusionType.UNSPECIFIED&&0===fa){fa=0<aa.outAnnotations.list.length;let oa=0<aa.inAnnotations.list.length;aa.isInExtract?(N.isolatedInExtract.push(aa),aa.node.include=
d.InclusionType.EXCLUDE,R.removeNode(X)):aa.isOutExtract?(N.isolatedOutExtract.push(aa),aa.node.include=d.InclusionType.EXCLUDE,R.removeNode(X)):G.extractIsolatedNodesWithAnnotationsOnOneSide&&(fa&&!oa?(aa.isInExtract=!0,N.isolatedInExtract.push(aa),aa.node.include=d.InclusionType.EXCLUDE,R.removeNode(X)):oa&&!fa&&(aa.isOutExtract=!0,N.isolatedOutExtract.push(aa),aa.node.include=d.InclusionType.EXCLUDE,R.removeNode(X)))}})}f.OpNodeColors={DEFAULT_FILL:"#ffffff",DEFAULT_STROKE:"#b2b2b2",COMPATIBLE:"#0f9d58",
INCOMPATIBLE:"#db4437"};f.MetanodeColors={DEFAULT_FILL:"#d9d9d9",DEFAULT_STROKE:"#a6a6a6",SATURATION:.6,LIGHTNESS:.85,EXPANDED_COLOR:"#f0f0f0",HUES:[220,100,180,40,20,340,260,300,140,60],STRUCTURE_PALETTE(N,R){var X=f.MetanodeColors.HUES;N=X[N%X.length];X=Math.sin(N*Math.PI/360);return d3.hsl(N,.01*(R?30:90-60*X),.01*(R?95:80)).toString()},DEVICE_PALETTE(N){return f.MetanodeColors.STRUCTURE_PALETTE(N)},XLA_CLUSTER_PALETTE(N){return f.MetanodeColors.STRUCTURE_PALETTE(N)},UNKNOWN:"#eee",GRADIENT_OUTLINE:"#888"};
f.SeriesNodeColors={DEFAULT_FILL:"white",DEFAULT_STROKE:"#b2b2b2"};const G={enableExtraction:!0,minNodeCountForExtraction:15,minDegreeForExtraction:5,maxControlDegree:4,maxBridgePathDegree:4,outExtractTypes:["NoOp"],inExtractTypes:[],detachAllEdgesForHighDegree:!0,extractIsolatedNodesWithAnnotationsOnOneSide:!0,enableBridgegraph:!0,minMaxColors:["#fff5f0","#fb6a4a"],maxAnnotations:5},D=new RegExp("^(?:"+b.graph.FUNCTION_LIBRARY_NODE_PREFIX+")?(\\w+)_[a-z0-9]{8}(?:_\\d+)?$");class B{constructor(N,
R){this.hierarchy=N;this.displayingStats=R;this.index={};this.renderedOpNames=[];this.computeScales();this.hasSubhierarchy={};this.root=new S(N.root,N.graphOptions);this.index[N.root.name]=this.root;this.renderedOpNames.push(N.root.name);this.buildSubhierarchy(N.root.name);this.root.expanded=!0;this.traceInputs=!1}computeScales(){this.deviceColorMap=d3.scaleOrdinal().domain(this.hierarchy.devices).range(_.map(d3.range(this.hierarchy.devices.length),f.MetanodeColors.DEVICE_PALETTE));this.xlaClusterColorMap=
d3.scaleOrdinal().domain(this.hierarchy.xlaClusters).range(_.map(d3.range(this.hierarchy.xlaClusters.length),f.MetanodeColors.XLA_CLUSTER_PALETTE));let N=this.hierarchy.root.metagraph;var R=d3.max(N.nodes(),X=>{X=N.node(X);if(null!=X.stats)return X.stats.totalBytes});this.memoryUsageScale=d3.scaleLinear().domain([0,R]).range(G.minMaxColors);R=d3.max(N.nodes(),X=>{X=N.node(X);if(null!=X.stats)return X.stats.getTotalMicros()});this.computeTimeScale=d3.scaleLinear().domain([0,R]).range(G.minMaxColors);
this.edgeWidthSizedBasedScale=this.hierarchy.hasShapeInfo?d.scene.edge.EDGE_WIDTH_SIZE_BASED_SCALE:d3.scaleLinear().domain([1,this.hierarchy.maxMetaEdgeSize]).range([d.scene.edge.MIN_EDGE_WIDTH,d.scene.edge.MAX_EDGE_WIDTH])}getRenderNodeByName(N){return this.index[N]}getNodeByName(N){return this.hierarchy.node(N)}colorHistogram(N,R){if(0<Object.keys(N).length){const X=_.sum(Object.keys(N).map(aa=>N[aa]));return Object.keys(N).map(aa=>({color:R(aa),proportion:N[aa]/X}))}console.info("no pairs found!");
return null}getOrCreateRenderNodeByName(N){if(!N)return null;if(N in this.index)return this.index[N];var R=this.hierarchy.node(N);if(!R)return null;let X=R.isGroupNode?new S(R,this.hierarchy.graphOptions):new J(R);this.index[N]=X;this.renderedOpNames.push(N);R.stats&&(X.memoryColor=this.memoryUsageScale(R.stats.totalBytes),X.computeTimeColor=this.computeTimeScale(R.stats.getTotalMicros()));X.isFadedOut=this.displayingStats&&!b.graph.util.hasDisplayableNodeStats(R.stats);var aa=null,fa=null,oa=null;
if(R.isGroupNode){aa=R.deviceHistogram;fa=R.xlaClusterHistogram;var Z=R.compatibilityHistogram.compatible;R=R.compatibilityHistogram.incompatible;if(0!=Z||0!=R)oa=Z/(Z+R)}else(Z=X.node.device)&&(aa={[Z]:1}),(Z=X.node.xlaCluster)&&(fa={[Z]:1}),X.node.type===d.NodeType.OP&&(oa=X.node.compatible?1:0);aa&&(X.deviceColors=this.colorHistogram(aa,this.deviceColorMap));fa&&(X.xlaClusterColors=this.colorHistogram(fa,this.xlaClusterColorMap));null!=oa&&(X.compatibilityColors=[{color:b.graph.render.OpNodeColors.COMPATIBLE,
proportion:oa},{color:b.graph.render.OpNodeColors.INCOMPATIBLE,proportion:1-oa}]);return this.index[N]}getNearestVisibleAncestor(N){var R=d.getHierarchicalPath(N);let X=0,aa=null;for(;X<R.length&&(N=R[X],aa=this.getRenderNodeByName(N),aa.expanded);X++);return X==R.length-2&&(R=R[X+1],aa.inAnnotations.nodeNames[R]||aa.outAnnotations.nodeNames[R])?R:N}setDepth(N){l(this.root,+N)}isNodeAuxiliary(N){let R=this.getRenderNodeByName(N.node.parentNode.name),X=_.find(R.isolatedInExtract,aa=>aa.node.name===
N.node.name);if(X)return!0;X=_.find(R.isolatedOutExtract,aa=>aa.node.name===N.node.name);return!!X}getNamesOfRenderedOps(){return this.renderedOpNames}cloneAndAddFunctionOpNode(N,R,X,aa){var fa=X.name.replace(R,aa);let oa=N.metagraph.node(fa);if(oa)return oa;oa=new d.OpNodeImpl({name:fa,input:[],device:X.device,op:X.op,attr:_.cloneDeep(X.attr)});oa.cardinality=X.cardinality;oa.include=X.include;oa.outputShapes=_.cloneDeep(X.outputShapes);oa.xlaCluster=X.xlaCluster;oa.functionInputIndex=X.functionInputIndex;
oa.functionOutputIndex=X.functionOutputIndex;oa.inputs=X.inputs.map(Z=>{const ca=_.clone(Z);ca.name=Z.name.replace(R,aa);return ca});oa.parentNode=N;N.metagraph.setNode(oa.name,oa);this.hierarchy.setNode(oa.name,oa);fa=Z=>this.cloneAndAddFunctionOpNode(N,R,Z,aa);oa.inEmbeddings=X.inEmbeddings.map(fa);oa.outEmbeddings=X.outEmbeddings.map(fa);return oa}cloneFunctionLibraryMetanode(N,R,X,aa,fa){const oa={};N=this.cloneFunctionLibraryMetanodeHelper(N,R,X,aa,fa,oa);_.isEmpty(oa)||this.patchEdgesFromFunctionOutputs(R,
oa);return N}cloneFunctionLibraryMetanodeHelper(N,R,X,aa,fa,oa){const Z=b.graph.createMetanode(X.name.replace(aa,fa));Z.depth=X.depth;Z.cardinality=X.cardinality;Z.templateId=X.templateId;Z.opHistogram=_.clone(X.opHistogram);Z.deviceHistogram=_.clone(X.deviceHistogram);Z.xlaClusterHistogram=_.clone(X.xlaClusterHistogram);Z.hasNonControlEdges=X.hasNonControlEdges;Z.include=X.include;Z.nodeAttributes=_.clone(X.nodeAttributes);Z.associatedFunction=X.associatedFunction;_.each(X.metagraph.nodes(),ca=>
{ca=X.metagraph.node(ca);switch(ca.type){case d.NodeType.META:ca=this.cloneFunctionLibraryMetanodeHelper(N,R,ca,aa,fa,oa);ca.parentNode=Z;Z.metagraph.setNode(ca.name,ca);this.hierarchy.setNode(ca.name,ca);break;case d.NodeType.OP:ca=this.cloneAndAddFunctionOpNode(Z,aa,ca,fa);_.isNumber(ca.functionInputIndex)&&this.patchEdgesIntoFunctionInputs(R,ca);_.isNumber(ca.functionOutputIndex)&&(oa[ca.functionOutputIndex]=ca);break;default:console.warn(ca.name+" is oddly neither a metanode nor an opnode.")}});
this.cloneLibraryMetanodeEdges(X,Z,aa,fa);return Z}cloneLibraryMetanodeEdges(N,R,X,aa){_.each(N.metagraph.edges(),fa=>{fa=N.metagraph.edge(fa);const oa=fa.v.replace(X,aa),Z=fa.w.replace(X,aa),ca=new d.MetaedgeImpl(oa,Z);ca.inbound=fa.inbound;ca.numRegularEdges=fa.numRegularEdges;ca.numControlEdges=fa.numControlEdges;ca.numRefEdges=fa.numRefEdges;ca.totalSize=fa.totalSize;fa.baseEdgeList&&(ca.baseEdgeList=fa.baseEdgeList.map(ja=>{const ba=_.clone(ja);ba.v=ja.v.replace(X,aa);ba.w=ja.w.replace(X,aa);
return ba}));R.metagraph.node(Z)?R.metagraph.setEdge(oa,Z,ca):R.metagraph.setEdge(Z,oa,ca)})}patchEdgesIntoFunctionInputs(N,R){let X=Math.min(R.functionInputIndex,N.inputs.length-1);for(var aa=_.clone(N.inputs[X]);aa.isControlDependency;)X++,aa=N.inputs[X];R.inputs.push(aa);aa=this.hierarchy.getPredecessors(N.name);let fa,oa=0;_.each(aa.regular,Z=>{oa+=Z.numRegularEdges;if(oa>X)return fa=Z,!1});_.each(fa.baseEdgeList,Z=>{Z.w===N.name&&(Z.w=R.name);Z.v===N.name&&(Z.v=R.name)})}patchEdgesFromFunctionOutputs(N,
R){const X=this.hierarchy.getSuccessors(N.name);_.each(X.regular,aa=>{_.each(aa.baseEdgeList,fa=>{const oa=this.hierarchy.node(fa.w);_.each(oa.inputs,Z=>{Z.name===N.name&&(Z.name=R[Z.outputTensorKey].name,Z.outputTensorKey=fa.outputTensorKey)})});_.each(aa.baseEdgeList,fa=>{fa.v=R[fa.outputTensorKey].name;fa.outputTensorKey="0"})})}buildSubhierarchy(N){if(!(N in this.hasSubhierarchy)){this.hasSubhierarchy[N]=!0;var R=this.index[N];if(R.node.type===d.NodeType.META||R.node.type===d.NodeType.SERIES){var X=
R.node.metagraph,aa=R.coreGraph,fa=[],oa=[];_.isEmpty(this.hierarchy.libraryFunctions)||(_.each(X.nodes(),ya=>{const Aa=X.node(ya),Fa=this.hierarchy.libraryFunctions[Aa.op];Fa&&0!==ya.indexOf(b.graph.FUNCTION_LIBRARY_NODE_PREFIX)&&(ya=this.cloneFunctionLibraryMetanode(X,Aa,Fa.node,Fa.node.name,Aa.name),fa.push(Aa),oa.push(ya))}),_.each(oa,(ya,Aa)=>{Aa=fa[Aa];ya.parentNode=Aa.parentNode;X.setNode(Aa.name,ya);this.hierarchy.setNode(Aa.name,ya)}));_.each(X.nodes(),ya=>{let Aa=this.getOrCreateRenderNodeByName(ya),
Fa=Aa.node;aa.setNode(ya,Aa);Fa.isGroupNode||(_.each(Fa.inEmbeddings,xa=>{let Sa=new O(null),Xa=new J(xa);h(Aa,xa,Xa,Sa,K.CONSTANT);this.index[xa.name]=Xa}),_.each(Fa.outEmbeddings,xa=>{let Sa=new O(null),Xa=new J(xa);k(Aa,xa,Xa,Sa,K.SUMMARY);this.index[xa.name]=Xa}))});_.each(X.edges(),ya=>{var Aa=X.edge(ya);Aa=new O(Aa);Aa.isFadedOut=this.index[ya.v].isFadedOut||this.index[ya.w].isFadedOut;aa.setEdge(ya.v,ya.w,Aa)});G.enableExtraction&&R.node.type===d.NodeType.META&&C(R);_.isEmpty(this.hierarchy.libraryFunctions)||
this.buildSubhierarchiesForNeededFunctions(X);N===b.graph.ROOT_NAME&&_.forOwn(this.hierarchy.libraryFunctions,ya=>{ya=ya.node;const Aa=this.getOrCreateRenderNodeByName(ya.name);R.libraryFunctionsExtract.push(Aa);Aa.node.include=d.InclusionType.EXCLUDE;aa.removeNode(ya.name)});var Z=R.node.parentNode;if(Z){var ca=this.index[Z.name],ja=(ya,...Aa)=>Aa.concat([ya?"IN":"OUT"]).join("~~"),ba=this.hierarchy.getBridgegraph(N),ka={},W={},Ea={};_.each(ba.edges(),ya=>{let Aa=!!X.node(ya.w),Fa=Aa?ya.v:ya.w;ba.edge(ya).numRegularEdges?
Aa?W[Fa]=(W[Fa]||0)+1:ka[Fa]=(ka[Fa]||0)+1:Ea[Fa]=(Ea[Fa]||0)+1});var va=this.hierarchy.getNodeMap();_.each(ba.edges(),ya=>{var Aa=ba.edge(ya);let Fa=!!X.node(ya.w),[xa,Sa]=Fa?[ya.w,ya.v]:[ya.v,ya.w];var Xa=this.index[xa],ub=this.index[Sa],Bb=ub?ub.node:va[Sa],qb=!Aa.numRegularEdges&&Ea[Sa]>G.maxControlDegree,[,zb]=Fa?[R.inAnnotations,Xa.inAnnotations]:[R.outAnnotations,Xa.outAnnotations];let vb=(Fa?W:ka)[Sa]>G.maxBridgePathDegree;ya=null;var Gb=!1;G.enableBridgegraph&&!vb&&!qb&&Xa.isInCore()&&(Gb=
Ob=>ca.coreGraph.edge(Fa?{v:Ob,w:N}:{v:N,w:Ob}),(ya=Gb(Sa))||(ya=Gb(ja(Fa,Sa,Z.name))),Gb=!!ya);Xa=!1;if(ya&&!Aa.numRegularEdges){Xa=ya;for(qb=ca.node;Xa.adjoiningMetaedge;)Xa=Xa.adjoiningMetaedge,qb=qb.parentNode;qb=this.hierarchy.getTopologicalOrdering(qb.name);Xa=Xa.metaedge;Xa=qb[Xa.v]>qb[Xa.w]}Gb&&!Xa?(Bb=ja(Fa,N),ub=ja(Fa,Sa,N),zb=aa.node(ub),zb||(Gb=aa.node(Bb),Gb||(Gb=new J({name:Bb,type:d.NodeType.BRIDGE,isGroupNode:!1,cardinality:0,parentNode:null,stats:null,include:d.InclusionType.UNSPECIFIED,
inbound:Fa,nodeAttributes:{}}),this.index[Bb]=Gb,aa.setNode(Bb,Gb)),zb=new J({name:ub,type:d.NodeType.BRIDGE,isGroupNode:!1,cardinality:1,parentNode:null,stats:null,include:d.InclusionType.UNSPECIFIED,inbound:Fa,nodeAttributes:{}}),this.index[ub]=zb,aa.setNode(ub,zb),aa.setParent(ub,Bb),Gb.node.cardinality++),Aa=new O(Aa),Aa.adjoiningMetaedge=ya,Fa?aa.setEdge(ub,xa,Aa):aa.setEdge(xa,ub,Aa)):zb.push(new H(Bb,ub,new O(Aa),K.SHORTCUT,Fa))});_.each([!0,!1],ya=>{let Aa=ja(ya,N),Fa=aa.node(Aa);Fa&&_.each(aa.nodes(),
xa=>{if(aa.node(xa).node.type!==d.NodeType.BRIDGE&&(ya?!aa.predecessors(xa).length:!aa.successors(xa).length)){var Sa=ja(ya,N,"STRUCTURAL_TARGET"),Xa=aa.node(Sa);Xa||(Xa=new J({name:Sa,type:d.NodeType.BRIDGE,isGroupNode:!1,cardinality:1,parentNode:null,stats:null,include:d.InclusionType.UNSPECIFIED,inbound:ya,nodeAttributes:{}}),Xa.structural=!0,this.index[Sa]=Xa,aa.setNode(Sa,Xa),Fa.node.cardinality++,aa.setParent(Sa,Aa));Xa=new O(null);Xa.structural=!0;Xa.weight--;ya?aa.setEdge(Sa,xa,Xa):aa.setEdge(xa,
Sa,Xa)}})})}}}}buildSubhierarchiesForNeededFunctions(N){_.each(N.edges(),R=>{R=N.edge(R);R=new O(R);_.forEach(R.metaedge.baseEdgeList,X=>{var aa=X.v.split(b.graph.NAMESPACE_DELIM);for(var fa=aa.length;0<=fa;fa--){X=aa.slice(0,fa);const oa=this.hierarchy.node(X.join(b.graph.NAMESPACE_DELIM));if(oa){if(oa.type===d.NodeType.OP&&this.hierarchy.libraryFunctions[oa.op])for(aa=1;aa<X.length;aa++)(fa=X.slice(0,aa).join(b.graph.NAMESPACE_DELIM))&&this.buildSubhierarchy(fa);break}}})})}}f.RenderGraphInfo=B;
class H{constructor(N,R,X,aa,fa){this.node=N;this.renderNodeInfo=R;this.renderMetaedgeInfo=X;this.annotationType=aa;this.height=this.width=this.dy=this.dx=0;X&&X.metaedge&&(this.v=X.metaedge.v,this.w=X.metaedge.w);this.isIn=fa;this.points=[]}}f.Annotation=H;let K;(function(N){N[N.SHORTCUT=0]="SHORTCUT";N[N.CONSTANT=1]="CONSTANT";N[N.SUMMARY=2]="SUMMARY";N[N.ELLIPSIS=3]="ELLIPSIS"})(K=f.AnnotationType||(f.AnnotationType={}));class L{constructor(){this.list=[];this.nodeNames={}}push(N){if(!(N.node.name in
this.nodeNames))if(this.nodeNames[N.node.name]=!0,this.list.length<G.maxAnnotations)this.list.push(N);else{var R=this.list[this.list.length-1];R.annotationType===K.ELLIPSIS?(N=R.node,N.setNumMoreNodes(++N.numMoreNodes)):(R=new b.graph.EllipsisNodeImpl(1),this.list.push(new H(R,new J(R),null,K.ELLIPSIS,N.isIn)))}}}f.AnnotationList=L;class J{constructor(N){this.node=N;this.expanded=!1;this.inAnnotations=new L;this.outAnnotations=new L;this.outboxWidth=this.inboxWidth=this.height=this.width=this.y=this.x=
0;this.structural=this.excluded=!1;this.paddingBottom=this.paddingRight=this.paddingLeft=this.paddingTop=this.labelHeight=this.radius=this.labelOffset=0;this.isOutExtract=this.isInExtract=!1;this.coreBox={width:0,height:0};this.isFadedOut=!1;this.displayName=N.name.substring(N.name.lastIndexOf(b.graph.NAMESPACE_DELIM)+1);N.type===d.NodeType.META&&N.associatedFunction&&((N=this.displayName.match(D))?this.displayName=N[1]:_.startsWith(this.displayName,b.graph.FUNCTION_LIBRARY_NODE_PREFIX)&&(this.displayName=
this.displayName.substring(b.graph.FUNCTION_LIBRARY_NODE_PREFIX.length)))}isInCore(){return!this.isInExtract&&!this.isOutExtract&&!this.isLibraryFunction}}f.RenderNodeInfo=J;class O{constructor(N){this.metaedge=N;this.adjoiningMetaedge=null;this.structural=!1;this.weight=1;this.isFadedOut=!1}}f.RenderMetaedgeInfo=O;class S extends J{constructor(N,R){super(N);N=N.metagraph.graph();R.compound=!0;this.coreGraph=d.createGraph(N.name,d.GraphType.CORE,R);this.inExtractBox={width:0,height:0};this.outExtractBox=
{width:0,height:0};this.libraryFunctionsBox={width:0,height:0};this.isolatedInExtract=[];this.isolatedOutExtract=[];this.libraryFunctionsExtract=[]}}f.RenderGroupNodeInfo=S;f.makeInExtract=n;f.mapIndexToHue=function(N){return 1+579.2561679725*N%358};f.expandUntilNodeIsShown=function(N,R){var X=document.getElementById("scene");R=R.split("/");var aa=R[R.length-1].match(/(.*):\w+/);2===aa.length&&(R[R.length-1]=aa[1]);aa=R[0];let fa=N.getRenderNodeByName(aa);for(let oa=1;oa<R.length&&fa.node.type!==
b.graph.NodeType.OP;oa++)N.buildSubhierarchy(aa),fa.expanded=!0,X.setNodeExpanded(fa),aa+="/"+R[oa],fa=N.getRenderNodeByName(aa);return fa.node.name}})(d.render||(d.render={}))})(b.graph||(b.graph={}))})(tf||(tf={}));

//# sourceURL=build://tf-graph-common/scene.js
(function(b){(function(d){(function(f){function h(q,u,w,A){var y=k(q,u,w);if(!y.empty())return y;u=document.createElementNS("http://www.w3.org/2000/svg",u);if(w instanceof Array)for(y=0;y<w.length;y++)u.classList.add(w[y]);else u.classList.add(w);A?q.node().insertBefore(u,A):q.node().appendChild(u);return d3.select(u).datum(q.datum())}function k(q,u,w){q=q.node().childNodes;for(let A=0;A<q.length;A++){let y=q[A];if(y.tagName===u)if(w instanceof Array){let x=!0;for(let C=0;C<w.length;C++)x=x&&y.classList.contains(w[C]);
if(x)return d3.select(y)}else if(!w||y.classList.contains(w))return d3.select(y)}return d3.select(null)}function r(q,u){let w=u.node.type===d.NodeType.SERIES?0:d.layout.PARAMS.subscene.meta.labelHeight;l(k(q,"g",f.Class.Scene.CORE),0,w);var A=0<u.isolatedInExtract.length,y=0<u.isolatedOutExtract.length;let x=0<u.libraryFunctionsExtract.length,C=d.layout.PARAMS.subscene.meta.extractXOffset,G=0;A&&(G+=u.outExtractBox.width);y&&(G+=u.outExtractBox.width);A&&(A=u.coreBox.width,A=G<d.layout.MIN_AUX_WIDTH?
A-d.layout.MIN_AUX_WIDTH+u.inExtractBox.width/2:A-u.inExtractBox.width/2-u.outExtractBox.width-(y?C:0),A=A-u.libraryFunctionsBox.width-(x?C:0),l(k(q,"g",f.Class.Scene.INEXTRACT),A,w));y&&(y=u.coreBox.width,y=G<d.layout.MIN_AUX_WIDTH?y-d.layout.MIN_AUX_WIDTH+u.outExtractBox.width/2:y-u.outExtractBox.width/2,y=y-u.libraryFunctionsBox.width-(x?C:0),l(k(q,"g",f.Class.Scene.OUTEXTRACT),y,w));x&&(u=u.coreBox.width-u.libraryFunctionsBox.width/2,l(k(q,"g",f.Class.Scene.FUNCTION_LIBRARY),u,w))}function l(q,
u,w){null!=q.attr("transform")&&(q=q.transition("position"));q.attr("transform","translate("+u+","+w+")")}function p(q,u){return u?q.toFixed(0):1<=Math.abs(q)?q.toFixed(1):q.toExponential(1)}function m(q,u,w,A){let y="Device: "+q.device_name+"\n";y+="dtype: "+q.dtype+"\n";let x="(scalar)";0<q.shape.length&&(x="("+q.shape.join(",")+")");y=y+("\nshape: "+x+"\n\n#(elements): ")+(u+"\n");q=[];for(u=0;u<w.length;u++)0<w[u]&&q.push("#("+f.healthPillEntries[u].label+"): "+w[u]);y+=q.join(", ")+"\n\n";A.max>=
A.min&&(y+="min: "+A.min+", max: "+A.max+"\n",y+="mean: "+A.mean+", stddev: "+A.stddev);return y}function n(q,u,w,A,y=60,x=10,C=0,G){d3.select(q.parentNode).selectAll(".health-pill").remove();if(u){var D=u.value,B=D.slice(2,8),H=B[0],K=B[1],L=B[5],J=D[1],O={min:D[8],max:D[9],mean:D[10],stddev:Math.sqrt(D[11])};null==y&&(y=60);null==x&&(x=10);null==C&&(C=0);null!=w&&w.node.type===b.graph.NodeType.OP&&(y/=2,x/=2);D=document.createElementNS(f.SVG_NAMESPACE,"g");D.classList.add("health-pill");var S=document.createElementNS(f.SVG_NAMESPACE,
"defs");D.appendChild(S);var N=document.createElementNS(f.SVG_NAMESPACE,"linearGradient");A="health-pill-gradient-"+A;N.setAttribute("id",A);var R=0,X="0%";for(let fa=0;fa<B.length;fa++)if(B[fa]){R+=B[fa];var aa=document.createElementNS(f.SVG_NAMESPACE,"stop");aa.setAttribute("offset",X);aa.setAttribute("stop-color",f.healthPillEntries[fa].background_color);N.appendChild(aa);X=document.createElementNS(f.SVG_NAMESPACE,"stop");aa=100*R/J+"%";X.setAttribute("offset",aa);X.setAttribute("stop-color",f.healthPillEntries[fa].background_color);
N.appendChild(X);X=aa}S.appendChild(N);S=document.createElementNS(f.SVG_NAMESPACE,"rect");S.setAttribute("fill","url(#"+A+")");S.setAttribute("width",String(y));S.setAttribute("height",String(x));S.setAttribute("y",String(C));D.appendChild(S);S=document.createElementNS(f.SVG_NAMESPACE,"title");S.textContent=m(u,J,B,O);D.appendChild(S);u=!1;if(null!=w&&(S=w.x-y/2,x=w.y-x-w.height/2-2,0>w.labelOffset&&(x+=w.labelOffset),D.setAttribute("transform","translate("+S+", "+x+")"),(B[2]||B[3]||B[4])&&(w=w.node.attr)&&
w.length))for(B=0;B<w.length;B++)if("T"===w[B].key){u=(w=w[B].value.type)&&/^DT_(BOOL|INT|UINT)/.test(w);break}w=document.createElementNS(f.SVG_NAMESPACE,"text");if(Number.isFinite(O.min)&&Number.isFinite(O.max)){if(B=p(O.min,u),O=p(O.max,u),w.textContent=1<J?B+" ~ "+O:B,0<H||0<K||0<L)w.textContent+=" (",J=[],0<H&&J.push(`NaN\u00d7${H}`),0<K&&J.push(`-\u221e\u00d7${K}`),0<L&&J.push(`+\u221e\u00d7${L}`),w.textContent+=J.join("; ")+")"}else w.textContent="(No finite elements)";w.classList.add("health-pill-stats");
null==G&&(G=y/2);w.setAttribute("x",String(G));w.setAttribute("y",String(C-2));D.appendChild(w);Polymer.dom(q.parentNode).appendChild(D)}}f.SVG_NAMESPACE="http://www.w3.org/2000/svg";f.Class={Node:{CONTAINER:"nodes",GROUP:"node",SHAPE:"nodeshape",COLOR_TARGET:"nodecolortarget",LABEL:"nodelabel",BUTTON_CONTAINER:"buttoncontainer",BUTTON_CIRCLE:"buttoncircle",EXPAND_BUTTON:"expandbutton",COLLAPSE_BUTTON:"collapsebutton"},Edge:{CONTAINER:"edges",GROUP:"edge",LINE:"edgeline",REFERENCE_EDGE:"referenceedge",
REF_LINE:"refline",SELECTABLE:"selectableedge",SELECTED:"selectededge",STRUCTURAL:"structural"},Annotation:{OUTBOX:"out-annotations",INBOX:"in-annotations",GROUP:"annotation",NODE:"annotation-node",EDGE:"annotation-edge",CONTROL_EDGE:"annotation-control-edge",LABEL:"annotation-label",ELLIPSIS:"annotation-ellipsis"},Scene:{GROUP:"scene",CORE:"core",FUNCTION_LIBRARY:"function-library",INEXTRACT:"in-extract",OUTEXTRACT:"out-extract"},Subscene:{GROUP:"subscene"},OPNODE:"op",METANODE:"meta",SERIESNODE:"series",
BRIDGENODE:"bridge",ELLIPSISNODE:"ellipsis"};f.healthPillEntries=[{background_color:"#CC2F2C",label:"NaN"},{background_color:"#FF8D00",label:"-\u221e"},{background_color:"#EAEAEA",label:"-"},{background_color:"#A5A5A5",label:"0"},{background_color:"#262626",label:"+"},{background_color:"#003ED4",label:"+\u221e"}];f.fit=function(q,u,w,A){var y=q.getBoundingClientRect();let x=null;try{if(x=u.getBBox(),0===x.width)return}catch(C){return}u=d.layout.PARAMS.graph;y=d3.zoomIdentity.scale(.9*Math.min(y.width/
x.width,y.height/x.height,2)).translate(u.padding.paddingLeft,u.padding.paddingTop);d3.select(q).transition().duration(500).call(w.transform,y).on("end.fitted",()=>{w.on("end.fitted",null);A()})};f.panToNode=function(q,u,w,A){w=d3.select(u).select(`[data-name="${q}"]`).node();if(!w)return console.warn(`panToNode() failed for node name "${q}"`),!1;var y=w.getBBox(),x=w.getScreenCTM();q=u.createSVGPoint();w=u.createSVGPoint();q.x=y.x;q.y=y.y;w.x=y.x+y.width;w.y=y.y+y.height;q=q.matrixTransform(x);w=
w.matrixTransform(x);x=(G,D,B,H)=>!(G>B&&D<H);y=u.getBoundingClientRect();const C=y.top+y.height-150;return x(q.x,w.x,y.left,y.left+y.width-320)||x(q.y,w.y,y.top,C)?(x=y.left+y.width/2-(q.x+w.x)/2,q=y.top+y.height/2-(q.y+w.y)/2,w=d3.zoomTransform(u),d3.select(u).transition().duration(500).call(A.translateBy,x/w.k,q/w.k),!0):!1};f.selectOrCreateChild=h;f.selectChild=k;f.buildGroup=function(q,u,w,A){A=A||f.Class.Scene.GROUP;let y=k(q,"g",A).empty();q=h(q,"g",A);A=h(q,"g",f.Class.Scene.CORE);let x=_.reduce(u.coreGraph.nodes(),
(C,G)=>{G=u.coreGraph.node(G);G.excluded||C.push(G);return C},[]);u.node.type===d.NodeType.SERIES&&x.reverse();f.edge.buildGroup(A,u.coreGraph,w);f.node.buildGroup(A,x,w);0<u.isolatedInExtract.length?(A=h(q,"g",f.Class.Scene.INEXTRACT),f.node.buildGroup(A,u.isolatedInExtract,w)):k(q,"g",f.Class.Scene.INEXTRACT).remove();0<u.isolatedOutExtract.length?(A=h(q,"g",f.Class.Scene.OUTEXTRACT),f.node.buildGroup(A,u.isolatedOutExtract,w)):k(q,"g",f.Class.Scene.OUTEXTRACT).remove();0<u.libraryFunctionsExtract.length?
(A=h(q,"g",f.Class.Scene.FUNCTION_LIBRARY),f.node.buildGroup(A,u.libraryFunctionsExtract,w)):k(q,"g",f.Class.Scene.FUNCTION_LIBRARY).remove();r(q,u);y&&q.attr("opacity",0).transition().attr("opacity",1);return q};f.addGraphClickListener=function(q,u){d3.select(q).on("click",()=>{u.fire("graph-select")})};f.translate=l;f.positionRect=function(q,u,w,A,y){q.transition().attr("x",u-A/2).attr("y",w-y/2).attr("width",A).attr("height",y)};f.positionTriangle=function(q,u,w,A,y){y/=2;A/=2;u=[[u,w-y],[u+A,
w+y],[u-A,w+y]];q.transition().attr("points",u.map(x=>x.join(",")).join(" "))};f.positionButton=function(q,u){let w=d.layout.computeCXPositionOfNodeShape(u)+(u.expanded?u.width:u.coreBox.width)/2-6,A=u.y-(u.expanded?u.height:u.coreBox.height)/2+6;u.node.type!==d.NodeType.SERIES||u.expanded||(w+=10,A-=2);u="translate("+w+","+A+")";q.selectAll("path").transition().attr("transform",u);q.select("circle").transition().attr({cx:w,cy:A,r:d.layout.PARAMS.nodeSize.meta.expandButtonRadius})};f.positionEllipse=
function(q,u,w,A,y){q.transition().attr("cx",u).attr("cy",w).attr("rx",A/2).attr("ry",y/2)};f.humanizeHealthPillStat=p;f.addHealthPill=n;f.addHealthPills=function(q,u,w){if(u){var A=1;d3.select(q).selectAll("g.nodeshape").each(function(y){const x=u[y.node.name];n(this,x?x[w]:null,y,A++)})}}})(d.scene||(d.scene={}))})(b.graph||(b.graph={}))})(tf||(tf={}));

//# sourceURL=build://tf-graph-common/template.js
(function(b){(function(d){(function(f){function h(n){let q=_.map({depth:n.depth,"|V|":n.metagraph.nodes().length,"|E|":n.metagraph.edges().length},function(u,w){return w+"\x3d"+u}).join(" ");n=_.map(n.opHistogram,function(u,w){return w+"\x3d"+u}).join(",");return q+" [ops] "+n}function k(n){const q=n.getNodeMap();let u=Object.keys(q).reduce((w,A)=>{const y=q[A];if(y.type!==d.NodeType.META)return w;A=A.split("/").length-1;let x=h(y),C=w[x]||{nodes:[],level:A};w[x]=C;C.nodes.push(y);C.level>A&&(C.level=
A);return w},{});return Object.keys(u).map(w=>[w,u[w]]).filter(([,w])=>{w=w.nodes;if(1<w.length)return!0;w=w[0];return w.type===d.NodeType.META&&w.associatedFunction}).sort(([,w])=>w.nodes[0].depth)}function r(n,q){return _.reduce(n,function(u,w){let A=w[0],y=[];w[1].nodes.forEach(function(x){for(let C=0;C<y.length;C++)if(!q||p(y[C].metanode.metagraph,x.metagraph)){x.templateId=y[C].metanode.templateId;y[C].members.push(x.name);return}x.templateId=A+"["+y.length+"]";y.push({metanode:x,members:[x.name]})});
y.forEach(function(x){u[x.metanode.templateId]={level:w[1].level,nodes:x.members}});return u},{})}function l(n,q,u){return _.sortBy(n,[w=>q.node(w).op,w=>q.node(w).templateId,w=>q.neighbors(w).length,w=>q.predecessors(w).length,w=>q.successors(w).length,w=>w.substr(u.length)])}function p(n,q){function u(H,K){let L=H.substr(w.length),J=K.substr(A.length);if(y[L]^x[J])return console.warn("different visit pattern","["+w+"]",L,"["+A+"]",J),!0;y[L]||(y[L]=x[J]=!0,C.push({n1:H,n2:K}));return!1}if(!b.graph.hasSimilarDegreeSequence(n,
q))return!1;let w=n.graph().name,A=q.graph().name,y={},x={},C=[];var G=n.sources(),D=q.sources();if(G.length!==D.length)return console.log("different source length"),!1;G=l(G,n,w);D=l(D,q,A);for(var B=0;B<G.length;B++)if(u(G[B],D[B]))return!1;for(;0<C.length;){D=C.pop();if(!m(n.node(D.n1),q.node(D.n2)))return!1;G=n.successors(D.n1);D=q.successors(D.n2);if(G.length!==D.length)return console.log("# of successors mismatch",G,D),!1;G=l(G,n,w);D=l(D,q,A);for(B=0;B<G.length;B++)if(u(G[B],D[B]))return!1}return!0}
function m(n,q){if(n.type===d.NodeType.META)return n.templateId&&q.templateId&&n.templateId===q.templateId;if(n.type===d.NodeType.OP&&q.type===d.NodeType.OP)return n.op===q.op;if(n.type===d.NodeType.SERIES&&q.type===d.NodeType.SERIES){let u=n.metagraph.nodeCount();return u===q.metagraph.nodeCount()&&(0===u||n.metagraph.node(n.metagraph.nodes()[0]).op===q.metagraph.node(q.metagraph.nodes()[0]).op)}return!1}f.detect=function(n,q){n=k(n);let u=r(n,q);return Object.keys(u).sort(w=>u[w].level).reduce((w,
A)=>{w[A]=u[A];return w},{})}})(d.template||(d.template={}))})(b.graph||(b.graph={}))})(tf||(tf={}));

//# sourceURL=build://tf-graph-common/util.js
(function(b){(function(d){(function(f){f.time=function(h,k){let r=Date.now();k=k();console.log(h,":",Date.now()-r,"ms");return k};f.getTracker=function(h){return{setMessage:function(k){h.set("progress",{value:h.progress.value,msg:k})},updateProgress:function(k){h.set("progress",{value:h.progress.value+k,msg:h.progress.msg})},reportError:function(k,r){console.error(r.stack);h.set("progress",{value:h.progress.value,msg:k,error:!0})}}};f.getSubtaskTracker=function(h,k,r){return{setMessage:function(l){h.setMessage(r+
": "+l)},updateProgress:function(l){h.updateProgress(l*k/100)},reportError:function(l,p){h.reportError(r+": "+l,p)}}};f.runTask=function(h,k){k.setMessage("Reading metadata pbtxt");try{let r=b.graph.util.time("Reading metadata pbtxt",h);k.updateProgress(40);return r}catch(r){k.reportError("Failed Reading metadata pbtxt",r)}};f.runAsyncTask=function(h,k,r,l){return new Promise(p=>{l.setMessage(h);setTimeout(function(){try{let m=b.graph.util.time(h,r);l.updateProgress(k);p(m)}catch(m){l.reportError("Failed "+
h,m)}},20)})};f.runAsyncPromiseTask=function(h,k,r,l){return new Promise((p,m)=>{function n(q){l.reportError("Failed "+h,q);m(q)}l.setMessage(h);setTimeout(function(){try{let q=Date.now();r().then(function(u){console.log(h,":",Date.now()-q,"ms");l.updateProgress(k);p(u)}).catch(n)}catch(q){n(q)}},20)})};f.escapeQuerySelector=function(h){return h.replace(/([:.\[\],/\\\(\)])/g,"\\$1")};f.MEMORY_UNITS=[{symbol:"B"},{symbol:"KB",numUnits:1024},{symbol:"MB",numUnits:1024},{symbol:"GB",numUnits:1024},{symbol:"TB",
numUnits:1024},{symbol:"PB",numUnits:1024}];f.TIME_UNITS=[{symbol:"\u00b5s"},{symbol:"ms",numUnits:1E3},{symbol:"s",numUnits:1E3},{symbol:"min",numUnits:60},{symbol:"hr",numUnits:60},{symbol:"days",numUnits:24}];f.convertUnitsToHumanReadable=function(h,k,r=0){return r+1<k.length&&h>=k[r+1].numUnits?b.graph.util.convertUnitsToHumanReadable(h/k[r+1].numUnits,k,r+1):Number(h.toPrecision(3))+" "+k[r].symbol};f.hasDisplayableNodeStats=function(h){return h&&(0<h.totalBytes||0<h.getTotalMicros()||h.outputSize)?
!0:!1};f.removeCommonPrefix=function(h){if(2>h.length)return h;let k=0,r=0,l=_.min(_.map(h,p=>p.length));for(;;){k++;let p=_.map(h,m=>m.substring(0,k));if(p.every((m,n)=>0===n?!0:m===p[n-1])){if(k>=l)return h;r=k}else break}return _.map(h,p=>p.substring(r))};f.computeHumanFriendlyTime=function(h){h=+new Date-+new Date(h/1E3);return 3E4>h?"just now":6E4>h?Math.floor(h/1E3)+" seconds ago":12E4>h?"a minute ago":36E5>h?Math.floor(h/6E4)+" minutes ago":1==Math.floor(h/36E5)?"an hour ago":864E5>h?Math.floor(h/
36E5)+" hours ago":1728E5>h?"yesterday":Math.floor(h/864E5)+" days ago"}})(d.util||(d.util={}))})(b.graph||(b.graph={}))})(tf||(tf={}));

//# sourceURL=build://tf-graph-common/minimap.js
(function(b){(function(d){class f{constructor(h,k,r,l,p,m){this.svg=h;this.labelPadding=m;this.zoomG=k;this.mainZoom=r;this.maxWandH=p;h=d3.select(l.shadowRoot);let n=h.select("svg"),q=n.select("rect");this.viewpointCoord={x:0,y:0};k=d3.drag().subject(Object).on("drag",()=>{this.viewpointCoord.x=d3.event.x;this.viewpointCoord.y=d3.event.y;this.updateViewpoint()});q.datum(this.viewpointCoord).call(k);n.on("click",()=>{if(!d3.event.defaultPrevented){var u=Number(q.attr("width")),w=Number(q.attr("height")),
A=d3.mouse(n.node());this.viewpointCoord.x=A[0]-u/2;this.viewpointCoord.y=A[1]-w/2;this.updateViewpoint()}});this.viewpoint=q.node();this.minimapSvg=n.node();this.minimap=l;this.canvas=h.select("canvas.first").node();this.canvasBuffer=h.select("canvas.second").node();this.downloadCanvas=h.select("canvas.download").node();d3.select(this.downloadCanvas).style("display","none");this.update()}updateViewpoint(){d3.select(this.viewpoint).attr("x",this.viewpointCoord.x).attr("y",this.viewpointCoord.y);let h=
-this.viewpointCoord.x*this.scaleMain/this.scaleMinimap,k=-this.viewpointCoord.y*this.scaleMain/this.scaleMinimap;d3.select(this.svg).call(this.mainZoom.transform,d3.zoomIdentity.translate(h,k).scale(this.scaleMain))}update(){let h=null;try{if(h=this.zoomG.getBBox(),0===h.width)return}catch(u){return}var k=d3.select("#graphdownload");this.download=k.node();k.on("click",()=>{URL.revokeObjectURL(this.download.href);var u=this.downloadCanvas.toDataURL("image/png");const w=u.slice(0,u.indexOf(","));if(w.endsWith(";base64")){var A=
atob(u.slice(u.indexOf(",")+1));u=(new Uint8Array(A.length)).map((y,x)=>A.charCodeAt(x));this.download.href=URL.createObjectURL(new Blob([u],{type:"image/png"}))}else console.warn(`non-base64 data URL (${w}); cannot use blob download`),this.download.href=u});k=d3.select(this.svg);var r="",l=this.svg;l=(l.getRootNode?l.getRootNode():this.svg.parentNode).styleSheets;for(var p=0;p<l.length;p++)try{var m=l[p].cssRules||l[p].rules;if(null!=m)for(let u=0;u<m.length;u++)r+=m[u].cssText.replace(/ ?tf-[\w-]+ ?/g,
"")+"\n"}catch(u){if("SecurityError"!==u.name)throw u;}m=k.append("style");m.text(r);r=d3.select(this.zoomG);l=r.attr("transform");r.attr("transform",null);h.height+=h.y;h.width+=h.x;h.height+=2*this.labelPadding;h.width+=2*this.labelPadding;k.attr("width",h.width).attr("height",h.height);this.scaleMinimap=this.maxWandH/Math.max(h.width,h.height);this.minimapSize={width:h.width*this.scaleMinimap,height:h.height*this.scaleMinimap};d3.select(this.minimapSvg).attr(this.minimapSize);d3.select(this.canvasBuffer).attr(this.minimapSize);
p=d3.select(this.downloadCanvas);p.style("width",h.width);p.style("height",h.height);p.attr("width",3*h.width);p.attr("height",3*h.height);null!=this.translate&&null!=this.zoom&&requestAnimationFrame(()=>this.zoom());let n=(new XMLSerializer).serializeToString(this.svg);m.remove();k.attr("width",null).attr("height",null);r.attr("transform",l);let q=new Image;q.onload=()=>{var u=this.canvasBuffer.getContext("2d");u.clearRect(0,0,this.canvasBuffer.width,this.canvasBuffer.height);u.drawImage(q,0,0,this.minimapSize.width,
this.minimapSize.height);requestAnimationFrame(()=>{d3.select(this.canvasBuffer).style("display",null);d3.select(this.canvas).style("display","none");[this.canvas,this.canvasBuffer]=[this.canvasBuffer,this.canvas]});u=this.downloadCanvas.getContext("2d");u.clearRect(0,0,this.downloadCanvas.width,this.downloadCanvas.height);u.drawImage(q,0,0,this.downloadCanvas.width,this.downloadCanvas.height)};q.onerror=()=>{q.src=URL.createObjectURL(new Blob([n],{type:"image/svg+xml;charset\x3dutf-8"}))};q.src=
"data:image/svg+xml;charset\x3dutf-8,"+encodeURIComponent(n)}zoom(h){if(null!=this.scaleMinimap){h&&(this.translate=[h.x,h.y],this.scaleMain=h.k);var k=this.svg.getBoundingClientRect(),r=d3.select(this.viewpoint);this.viewpointCoord.x=-this.translate[0]*this.scaleMinimap/this.scaleMain;this.viewpointCoord.y=-this.translate[1]*this.scaleMinimap/this.scaleMain;h=k.width*this.scaleMinimap/this.scaleMain;k=k.height*this.scaleMinimap/this.scaleMain;r.attr("x",this.viewpointCoord.x).attr("y",this.viewpointCoord.y).attr("width",
h).attr("height",k);r=this.minimapSize.width;var l=this.minimapSize.height,p=this.viewpointCoord.x,m=this.viewpointCoord.y;.8>(Math.min(Math.max(0,p+h),r)-Math.min(Math.max(0,p),r))*(Math.min(Math.max(0,m+k),l)-Math.min(Math.max(0,m),l))/(r*l)?this.minimap.classList.remove("hidden"):this.minimap.classList.add("hidden")}}}d.Minimap=f})(b.scene||(b.scene={}))})(tf||(tf={}));

//# sourceURL=build://tf-graph-loader/tf-graph-loader.js
(function(b){(function(d){(function(){Polymer({is:"tf-graph-loader",_template:null,properties:{datasets:Array,selectedData:{type:Number,value:0},selectedFile:Object,compatibilityProvider:{type:Object,value:()=>new b.graph.op.TpuCompatibilityProvider},overridingHierarchyParams:{type:Object,value:()=>({})},progress:{type:Object,notify:!0},outGraphHierarchy:{type:Object,readOnly:!0,notify:!0},outGraph:{type:Object,readOnly:!0,notify:!0},outHierarchyParams:{type:Object,readOnly:!0,notify:!0}},observers:["_loadData(datasets, selectedData, overridingHierarchyParams, compatibilityProvider)",
"_loadFile(selectedFile, overridingHierarchyParams, compatibilityProvider)"],_loadData(){this.debounce("load",()=>{const f=this.datasets[this.selectedData];f&&this._parseAndConstructHierarchicalGraph(f.path)})},_parseAndConstructHierarchicalGraph(f,h){const k=this.overridingHierarchyParams,r=this.compatibilityProvider;this.progress={value:0,msg:""};const l=b.graph.util.getTracker(this),p=Object.assign({},b.graph.hierarchy.DefaultHierarchyParams,k);b.graph.loader.fetchAndConstructHierarchicalGraph(l,
f,h,r,p).then(({graph:m,graphHierarchy:n})=>{this._setOutHierarchyParams(p);this._setOutGraph(m);this._setOutGraphHierarchy(n)})},_loadFile(f){if(f){f=f.target;var h=f.files[0];h&&(f.value="",this._parseAndConstructHierarchicalGraph(null,h))}}})})(d.loader||(d.loader={}))})(b.graph||(b.graph={}))})(tf||(tf={}));

//# sourceURL=build://tf-debugger-dashboard/health-pills.js
var Kh;
(function(b){function d(k,r){if(null==k)throw Error(`Missing refValue for condition (${r}).`);}function f(k){return null==k||0==k.length||1!==k[0]}const h={INF_OR_NAN:{description:"Contains +/-\u221e or NaN",predicate:k=>0<k[2]||0<k[3]||0<k[7]},INF:{description:"Contains +/-\u221e",predicate:k=>0<k[3]||0<k[7]},NAN:{description:"Contains NaN",predicate:k=>0<k[2]},MAX_GT:{description:"Max \x3e",predicate:(k,r)=>{d(r,"MAX_GT");return k[9]>r}},MAX_LT:{description:"Max \x3c",predicate:(k,r)=>{d(r,"MAX_LT");
return k[9]<r}},MIN_GT:{description:"Min \x3e",predicate:(k,r)=>{d(r,"MIN_GT");return k[8]>r}},MIN_LT:{description:"Min \x3c",predicate:(k,r)=>{d(r,"MIN_LT");return k[8]<r}},MEAN_GT:{description:"Mean \x3e",predicate:(k,r)=>{d(r,"MEAN_GT");return k[10]>r}},MEAN_LT:{description:"Mean \x3c",predicate:(k,r)=>{d(r,"MEAN_LT");return k[10]<r}},RANGE_GT:{description:"Max - Min \x3e",predicate:(k,r)=>{d(r,"RANGE_GT");return k[9]-k[8]>r}},RANGE_LT:{description:"Max - Min \x3c",predicate:(k,r)=>{d(r,"RANGE_LT");
return k[9]-k[8]<r}},STDDEV_GT:{description:"Standard deviation \x3e",predicate:(k,r)=>{d(r,"STDDEV_GT");return Math.sqrt(k[11])>r}},STDDEV_LT:{description:"Standard deviation \x3c",predicate:(k,r)=>{d(r,"STDDEV_LT");return Math.sqrt(k[11])<r}}};b.tensorConditionDescription2Key=function(k){for(const r in h)if(h.hasOwnProperty(r)&&h[r].description===k)return r;return null};b.checkHealthPillAgainstTensorConditionKey=function(k,r,l){if(f(r))return!1;k=h[k].predicate;return k(r,l)}})(Kh||(Kh={}));

//# sourceURL=build://tensor-widget/tensor_widget_binary.js
function Lh(b){return null!==b.match(/^int[0-9]+$/)||null!==b.match(/^uint[0-9]+$/)}function Mh(b){return null!==b.match(/^float[0-9]+$/)||null!==b.match(/^bfloat[0-9]+$/)}function Yi(b){return"bool"===b.toLowerCase()||"boolean"===b.toLowerCase()}function Zi(b){return"str"===b.toLowerCase()||"string"===b.toLowerCase()}
class $i{constructor(b){this.isShown=!1;this.blurHideFunction=null;this.dropdown=document.createElement("div");this.dropdown.classList.add("tensor-widget-dim-dropdown");this.dropdown.style.position="fixed";this.dropdown.style.display="none";b.appendChild(this.dropdown)}show(b,d,f){f.forEach(k=>{const r=document.createElement("div");r.classList.add("tensor-widget-dim-dropdown-menu-item");r.textContent=k.caption;this.dropdown.appendChild(r);k.disabled?r.classList.add("tensor-widget-dim-dropdown-menu-item-disabled"):
(r.addEventListener("click",l=>{l.stopPropagation();this.dropdown.click();if(null!==k.onClick)k.onClick(l);this.hide()}),r.addEventListener("mouseenter",l=>{if(null!==k.onHover)k.onHover(l);r.classList.add("tensor-widget-dim-dropdown-menu-item-active")}),r.addEventListener("mouseleave",()=>{r.classList.remove("tensor-widget-dim-dropdown-menu-item-active");if(null!==k.onHover){var l=[];for(let p=0;p<r.children.length;++p){const m=r.children[p];m.classList.contains("tensor-widget-dim-dropdown")&&l.push(m)}l.forEach(p=>
r.removeChild(p))}}))});this.dropdown.style.display="block";this.dropdown.style.top=b+"px";this.dropdown.style.left=d+"px";f=this.dropdown.getBoundingClientRect();const h=f.left-d;this.dropdown.style.top=(b-(f.top-b)).toFixed(1)+"px";this.dropdown.style.left=(d-h).toFixed(1)+"px";this.isShown=!0;this.blurHideFunction=()=>{this.hide()};setTimeout(()=>window.addEventListener("click",this.blurHideFunction),50)}hide(){for(this.dropdown.style.display="none";this.dropdown.firstChild;)this.dropdown.removeChild(this.dropdown.firstChild);
this.isShown=!1;null!=this.blurHideFunction&&window.removeEventListener("click",this.blurHideFunction)}shown(){return this.isShown}}
class aj{constructor(b,d){this.config=b;this.parentElement=d;this.baseFlatMenu=new $i(this.parentElement);this.currentChoiceSelections={};this.config.items.forEach((f,h)=>{null!=f.options&&(this.currentChoiceSelections[h]=f.defaultSelection)})}show(b,d){const f=[];this.config.items.forEach((h,k)=>{const r={caption:h.caption,onClick:null,onHover:null};if(null!=h.options){const l=this.currentChoiceSelections[k];r.onHover=p=>{var m=p.target;const n=[];h.options.forEach((q,u)=>{n.push({caption:u===l?
q+" (\u2713)":q,onClick:()=>{l!==u&&(this.currentChoiceSelections[k]=u,h.callback(u))},onHover:null})});p=new $i(m);m=m.getBoundingClientRect();p.show(m.top,m.right,n)}}else r.onClick=h.callback;null==h.isEnabled||h.isEnabled()||(r.disabled=!0);f.push(r)});this.baseFlatMenu.show(b,d,f)}hide(){this.baseFlatMenu.hide()}shown(){return this.baseFlatMenu.shown()}}function bj(b){let d=1;b.forEach(f=>{d*=f});return d}function cj(b){return 0===b.length?"scalar":`[${b}]`}
function dj(b){const d={slicingDimsAndIndices:[],viewingDims:[],verticalRange:null,horizontalRange:null};var f=b.length;if(1===f)d.viewingDims=[0];else if(1<f){if(2<f)for(let h=0;h<f-2;++h)d.slicingDimsAndIndices.push({dim:h,index:0===b[h]?null:0});for(f=b.length-2;f<b.length;++f)d.viewingDims.push(f)}return d}
function ej(b,d){if(b.viewingDims[0]!==d.viewingDims[0]||b.viewingDims[1]!==d.viewingDims[1])return!1;d=b.slicingDimsAndIndices.map(f=>f.dim);d.sort();b=b.slicingDimsAndIndices.map(f=>f.dim);b.sort();return JSON.stringify(d)===JSON.stringify(b)}var fj;(function(b){b[b.UP=1]="UP";b[b.DOWN=2]="DOWN";b[b.LEFT=3]="LEFT";b[b.RIGHT=4]="RIGHT"})(fj||(fj={}));
class gj{constructor(b,d,f,h,k,r){this.shape=b;this.sliceDims=[];this.sliceIndices=[];if(0===bj(this.shape))throw Error("TensorElementSelection doesn't support tensor with zero elements.");for(b=0;b<d.slicingDimsAndIndices.length;++b){this.sliceDims.push(d.slicingDimsAndIndices[b].dim);const l=d.slicingDimsAndIndices[b].index;if(null===l)throw Error("Failed to create TensorElementSelection due to "+`undetermined slicing index at dimension ${b}`);this.sliceIndices.push(l)}this.rank=this.shape.length;
if(0<this.rank&&this.sliceDims.length>=this.rank)throw Error(`Expected sliceDims to have a length less than rank ${this.rank}, `+`but got length ${this.sliceDims.length}`);this.viewDims=[];for(d=0;d<this.rank;++d)-1===this.sliceDims.indexOf(d)&&this.viewDims.push(d);if(2<this.viewDims.length)throw Error("Only selections in 1D and 2D are supported.");this.rowStart=null==f?0:f;this.colStart=null==h?0:h;this.rowCount=null==k?1:k;this.colCount=null==r?1:r}getElementStatus(b){if(b.length!==this.rank)throw Error(`Expected indices to have a rank of ${this.rank}, `+
`but got ${b.length} ([${b}])`);for(var d=0;d<b.length;++d)if(-1!==this.sliceDims.indexOf(d)&&b[d]!==this.sliceIndices[this.sliceDims.indexOf(d)])return null;d=null;const f=this.rowStart+this.rowCount;var h=this.colStart+this.colCount;if(0===this.viewDims.length)0===b.length&&(d={topEdge:!0,bottomEdge:!0,leftEdge:!0,rightEdge:!0});else if(1===this.viewDims.length)h=this.viewDims[0],b[h]>=this.rowStart&&b[h]<f&&(d={topEdge:b[h]===this.rowStart,bottomEdge:b[h]===f-1,leftEdge:!0,rightEdge:!0});else if(2===
this.viewDims.length){const k=this.viewDims[0],r=this.viewDims[1];b[k]>=this.rowStart&&b[k]<f&&b[r]>=this.colStart&&b[r]<h&&(d={topEdge:b[k]===this.rowStart,bottomEdge:b[k]===f-1,leftEdge:b[r]===this.colStart,rightEdge:b[r]===h-1})}else throw Error(`Unexpected length of viewDims: ${this.viewDims}`);return d}move(b,d){let f=null;if(0===this.rank||1===this.rank&&(b===fj.LEFT||b===fj.RIGHT))return null;if(null===d.verticalRange||null===d.verticalRange[1])throw Error("Failed to move due to undetermined vertical range.");
b===fj.UP?0<this.rowStart&&(this.rowStart--,null!=d.verticalRange&&this.rowStart<d.verticalRange[0]&&(f=fj.UP)):b===fj.DOWN?null!=d.viewingDims&&null!=d.viewingDims[0]&&this.rowStart<this.shape[d.viewingDims[0]]-1&&(this.rowStart++,null!=d.verticalRange&&this.rowStart>=d.verticalRange[1]&&(f=fj.DOWN)):b===fj.LEFT?0<this.colStart&&(this.colStart--,null!=d.horizontalRange&&this.colStart<d.horizontalRange[0]&&(f=fj.LEFT)):b===fj.RIGHT&&null!=d.viewingDims&&null!=d.viewingDims[1]&&this.colStart<this.shape[d.viewingDims[1]]-
1&&(this.colStart++,null!=d.horizontalRange&&this.colStart>=d.horizontalRange[1]&&(f=fj.RIGHT));this.colCount=this.rowCount=1;return f}getRowStart(){return this.rowStart}getRowCount(){return this.rowCount}getColStart(){return this.colStart}getColCount(){return this.colCount}}
class hj{constructor(b,d,f=()=>{}){this.rootDiv=b;this.shape=d;this.onSlicingSpecChange=f;this.dimControls=[];this.dimInputs=[];this.commas=[];this.dropdowns=[];this.bracketDivs=[null,null];this.dimControlsListenerAttached=[];this.rank=this.shape.length;if(3>this.rank)throw Error("Dimension control is not applicable to tensor shapes less than "+`3D: received ${this.rank}D tensor shape: `+`${JSON.stringify(this.shape)}.`);this.createComponents();this.slicingSpec=dj(d)}createComponents(){for(;this.rootDiv.firstChild;)this.rootDiv.removeChild(this.rootDiv.firstChild);
this.dimControls=[];this.dimInputs=[];this.commas=[];this.dropdowns=[];this.dimControlsListenerAttached=[];this.bracketDivs[0]=document.createElement("div");this.bracketDivs[0].textContent="Slicing: [";this.bracketDivs[0].classList.add("tensor-widget-dim-brackets");this.rootDiv.appendChild(this.bracketDivs[0]);for(let d=0;d<this.rank;++d){var b=document.createElement("div");b.classList.add("tensor-widget-dim");b.title=`Dimension ${d}: size=${this.shape[d]}`;this.rootDiv.appendChild(b);this.dimControls.push(b);
this.dimControlsListenerAttached.push(!1);b=document.createElement("input");b.classList.add("tensor-widget-dim");b.style.display="none";this.rootDiv.appendChild(b);this.dimInputs.push(b);d<this.rank-1&&(b=document.createElement("div"),b.classList.add("tensor-widget-dim-comma"),b.textContent=",",this.rootDiv.appendChild(b),this.commas.push(b));b=document.createElement("div");b.classList.add("tensor-widget-dim-dropdown");b.style.display="none";this.rootDiv.appendChild(b);this.dropdowns.push(b)}this.bracketDivs[1]=
document.createElement("div");this.bracketDivs[1].textContent="]";this.bracketDivs[1].classList.add("tensor-widget-dim-brackets");this.rootDiv.appendChild(this.bracketDivs[1]);this.rootDiv.addEventListener("mouseleave",()=>{this.clearAllDropdowns()})}render(b){null!=b&&(this.slicingSpec=JSON.parse(JSON.stringify(b)));if(null===this.slicingSpec)throw Error("Slicing control rendering failed due to missing slicing spec.");const d=this.slicingSpec.slicingDimsAndIndices.map(f=>f.dim);b=this.slicingSpec.slicingDimsAndIndices.map(f=>
f.index);for(let f=0;f<this.rank;++f){const h=this.dimControls[f],k=this.dimInputs[f],r=this.dropdowns[f];if("none"!==k.style.display)continue;const l=this.shape[f];if(-1!==d.indexOf(f)){const p=b[d.indexOf(f)];h.textContent=String(p);k.classList.add("tensor-widget-dim");k.type="number";k.min="0";k.max=String(l-1);k.value=String(p);this.dimControlsListenerAttached[f]||(h.addEventListener("click",()=>{this.clearAllDropdowns();h.style.display="none";k.style.display="inline-block"}),k.addEventListener("change",
()=>{if(null===this.slicingSpec)throw Error("Slicing control change callback failed due to missing spec.");const m=parseInt(k.value,10);!isFinite(m)||0>m||m>=l||Math.floor(l)!=l?k.value=String(this.slicingSpec.slicingDimsAndIndices[d.indexOf(f)].index):(this.slicingSpec.slicingDimsAndIndices[d.indexOf(f)].index=m,h.textContent=String(m),this.onSlicingSpecChange(this.slicingSpec))}),k.addEventListener("blur",()=>{k.style.display="none";h.style.display="inline-block"}),this.dimControlsListenerAttached[f]=
!0)}else{if(this.slicingSpec.viewingDims[0]===f){if(null===this.slicingSpec.verticalRange)throw Error("Missing vertical range.");h.textContent=`\u2195 ${this.slicingSpec.verticalRange[0]}:`+`${this.slicingSpec.verticalRange[1]}`}else{if(null===this.slicingSpec.horizontalRange)throw Error("Missing horizontal range.");h.textContent=`\u2194 ${this.slicingSpec.horizontalRange[0]}:`+`${this.slicingSpec.horizontalRange[1]}`}h.classList.add("tensor-widget-dim");this.dimControlsListenerAttached[f]||(h.addEventListener("click",
()=>{const p=h.getBoundingClientRect();this.renderDropdownMenuItems(r,p.bottom,p.left,f)}),this.dimControlsListenerAttached[f]=!0)}}}renderDropdownMenuItems(b,d,f,h){if(null===this.slicingSpec)throw Error("Slicing control cannot render dropdown menu items due to missing slicing spec.");this.clearAllDropdowns();const k=this.slicingSpec.slicingDimsAndIndices.map(r=>r.dim);for(let r=0;r<this.rank;++r){if(-1===k.indexOf(r))continue;if(h===this.slicingSpec.viewingDims[1]&&r<=this.slicingSpec.viewingDims[0]||
h==this.slicingSpec.viewingDims[0]&&r>=this.slicingSpec.viewingDims[1])continue;const l=document.createElement("div");l.classList.add("tensor-widget-dim-dropdown-menu-item");l.textContent=`Swap with dimension ${r}`;b.appendChild(l);l.addEventListener("mouseenter",()=>{l.classList.add("tensor-widget-dim-dropdown-menu-item-active");this.dimControls[r].classList.add("tensor-widget-dim-highlighted")});l.addEventListener("mouseleave",()=>{l.classList.remove("tensor-widget-dim-dropdown-menu-item-active");
this.dimControls[r].classList.remove("tensor-widget-dim-highlighted")});const p=this.slicingSpec.viewingDims[0]===h;l.addEventListener("click",()=>{if(null===this.slicingSpec)throw Error("Dimension swapping failed due to missing slicing spec");const m=k.indexOf(r);this.slicingSpec.viewingDims[p?0:1]=r;this.slicingSpec.slicingDimsAndIndices[m]={dim:h,index:0};this.slicingSpec.verticalRange=null;this.slicingSpec.horizontalRange=null;if(this.onSlicingSpecChange)this.onSlicingSpecChange(this.slicingSpec)})}b.addEventListener("mouseleave",
()=>{b.style.display="none"});if(b.firstChild){b.style.position="fixed";b.style.top=`${d}px`;b.style.left=`${f}px`;b.style.display="block";const r=b.getBoundingClientRect(),l=r.left-f;b.style.top=(d-(r.top-d)).toFixed(1)+"px";b.style.left=(f-l).toFixed(1)+"px"}}setSlicingSpec(b){this.slicingSpec=JSON.parse(JSON.stringify(b));if(null===this.slicingSpec)throw Error("Cannot set slicing spec to null.");this.render(this.slicingSpec)}clearAllDropdowns(){this.dropdowns.forEach(b=>{if(null!=b){for(;b.firstChild;)b.removeChild(b.firstChild);
b.style.display="none"}})}}function Qk(b){return 20>=b.length?b:b.slice(0,10)+"..."+b.slice(b.length-7,b.length)}function Rk(b,d){if(isNaN(b))return"NaN";if(-Infinity===b)return"-\u221e";if(Infinity===b)return"+\u221e";if(null==f){var f=Math.abs(b);f=1E3>f&&.01<=f||0===f?"fixed":"exponential"}return null==f||"fixed"===f?d?`${b}`:b.toFixed(2):b.toExponential(2)}function Sk(b,d=!0){return b?d?"T":"True":d?"F":"False"}function Tk(b,d=4){return null===d||b.length<=d?b:b.slice(0,d-1)+"\u2026"}
class Uk{constructor(b){this.config=b;if(!isFinite(b.min))throw Error(`min value (${b.min}) is not finite`);if(!isFinite(b.max))throw Error(`max value (${b.max}) is not finite`);if(b.max<b.min)throw Error(`max (${b.max}) is < min (${b.min})`);}render(b,d){if(this.config.min!==this.config.max){var f=b.getContext("2d");if(null!=f){var h=b.width/100,k=b.height,r=.6*k;for(let l=0;100>l;++l){const p=h*l,m=.2*k,[n,q,u]=this.getRGB(l/100*(this.config.max-this.config.min)+this.config.min);f.beginPath();f.fillStyle=
`rgba(${n}, ${q}, ${u}, 1)`;f.fillRect(p,m,h,r);f.stroke()}null!=d&&d>=this.config.min&&d<=this.config.max&&(b=(d-this.config.min)/(this.config.max-this.config.min)*b.width,f.beginPath(),f.fillStyle="rgba(0, 0, 0, 1)",f.moveTo(b,.2*k),f.lineTo(b-4,0),f.lineTo(b+4,0),f.fill(),f.beginPath(),f.moveTo(b,.8*k),f.lineTo(b-4,k),f.lineTo(b+4,k),f.fill())}}}}
class Vk extends Uk{getRGB(b){if(isNaN(b))return[255,0,0];if(!isFinite(b))return 0<b?[0,0,255]:[255,127.5,0];b=this.config.min===this.config.max?.5:(b-this.config.min)/(this.config.max-this.config.min);b=Math.max(Math.min(b,1),0);return[255*b,255*b,255*b]}}
class Wk extends Uk{getRGB(b){if(isNaN(b))return[63.75,63.75,63.75];if(!isFinite(b))return 0>b?[127.5,127.5,127.5]:[191.25,191.25,191.25];let d=0,f=0,h=0;b=this.config.min===this.config.max?.5:(b-this.config.min)/(this.config.max-this.config.min);b=Math.max(Math.min(b,1),0);.35>=b?(f=b/.35,h=1):.35<b&&.65>=b?(d=(b-.35)/(.65-.35),f=1,h=(.65-b)/(.65-.35)):.65<b&&(d=1,f=(1-b)/.35);return[255*d,255*f,255*h]}}
Jh=function(b,d,f,h){function k(r){return r instanceof f?r:new f(function(l){l(r)})}return new (f||(f=Promise))(function(r,l){function p(q){try{n(h.next(q))}catch(u){l(u)}}function m(q){try{n(h["throw"](q))}catch(u){l(u)}}function n(q){q.done?r(q.value):k(q.value).then(p,m)}n((h=h.apply(b,d||[])).next())})};var Xk;(function(b){b[b.TEXT=1]="TEXT";b[b.IMAGE=2]="IMAGE"})(Xk||(Xk={}));const Yk={Grayscale:Vk,Jet:Wk};
class Zk{constructor(b,d,f){this.rootElement=b;this.tensorView=d;this.baseRulerTick=this.topRuler=this.valueSection=this.slicingSpecRoot=this.menuThumb=this.infoSubsection=this.headerSection=null;this.topRulerTicks=[];this.leftRulerTicks=[];this.valueRows=[];this.valueDivs=[];this.slicingControl=this.valueTooltip=null;this.colsCutoff=this.rowsCutoff=!1;this.menu=this.menuConfig=this.selection=null;this.colorMapName="Grayscale";this.colorMap=null;this.showIndicesOnTicks=!1;this.imageCellSize=16;this.minImageCellSize=
4;this.maxImageCellSize=40;this.zoomStepRatio=1.2;this.numericSummary=null;this.options=f||{};this.slicingSpec=dj(this.tensorView.spec.shape);this.rank=this.tensorView.spec.shape.length;this.valueRenderMode=Xk.TEXT}render(){return Jh(this,void 0,void 0,function*(){this.rootElement.classList.add("tensor-widget");this.renderHeader();if(!(Lh(this.tensorView.spec.dtype)||Mh(this.tensorView.spec.dtype)||Yi(this.tensorView.spec.dtype)||Zi(this.tensorView.spec.dtype)))throw Error(`Rendering dtype ${this.tensorView.spec.dtype} is not supported yet.`);
yield this.renderValues()})}renderHeader(){null==this.headerSection&&(this.headerSection=document.createElement("div"),this.headerSection.classList.add("tensor-widget-header"),this.rootElement.appendChild(this.headerSection),this.createMenu());this.renderInfo()}renderInfo(){if(null===this.headerSection)throw Error("Rendering tensor info failed due to mising header section");null==this.infoSubsection&&(this.infoSubsection=document.createElement("div"),this.infoSubsection.classList.add("tensor-widget-info"),
this.headerSection.appendChild(this.infoSubsection));for(;this.infoSubsection.firstChild;)this.infoSubsection.removeChild(this.infoSubsection.firstChild);this.renderName();this.renderDType();this.renderShape()}renderName(){if(null==this.infoSubsection)throw Error("Rendering tensor name failed due to missing info subsection.");if(null!=this.options.name&&0!==this.options.name.length){var b=document.createElement("div");b.classList.add("tensor-widget-tensor-name");b.textContent=Qk(this.options.name);
b.title=this.options.name;this.infoSubsection.appendChild(b)}}renderDType(){if(null==this.infoSubsection)throw Error("Rendering tensor dtype failed due to missing info subsection.");const b=document.createElement("div");b.classList.add("tensor-widget-dtype");var d=document.createElement("span");d.classList.add("tensor-widget-dtype-label");d.textContent="dtype:";b.appendChild(d);d=document.createElement("span");d.textContent=this.tensorView.spec.dtype;b.appendChild(d);this.infoSubsection.appendChild(b)}renderShape(){if(null==
this.infoSubsection)throw Error("Rendering tensor shape failed due to missing info subsection.");const b=document.createElement("div");b.classList.add("tensor-widget-shape");var d=document.createElement("div");d.classList.add("tensor-widget-shape-label");d.textContent="shape:";b.appendChild(d);d=document.createElement("div");d.classList.add("tensor-widget-shape-value");d.textContent=cj(this.tensorView.spec.shape);b.appendChild(d);this.infoSubsection.appendChild(b)}createMenu(){this.menuConfig={items:[]};
if(Mh(this.tensorView.spec.dtype)||Lh(this.tensorView.spec.dtype)||Yi(this.tensorView.spec.dtype))this.menuConfig.items.push({caption:"Select display mode...",options:["Text","Image"],defaultSelection:0,callback:b=>{0===b?(this.valueRenderMode=Xk.TEXT,this.renderValues()):(this.valueRenderMode=Xk.IMAGE,this.tensorView.getNumericSummary().then(d=>{this.numericSummary=d;this.renderValues()}))}}),this.menuConfig.items.push({caption:"Select color map...",options:Object.keys(Yk),defaultSelection:0,callback:b=>
{this.colorMapName=Object.keys(Yk)[b];this.renderValues()},isEnabled:()=>this.valueRenderMode===Xk.IMAGE}),this.menuConfig.items.push({caption:"Zoom in (Image mode)",callback:()=>{this.zoomInOneStepAndRenderValues()},isEnabled:()=>this.valueRenderMode===Xk.IMAGE}),this.menuConfig.items.push({caption:"Zoom out (Image mode)",callback:()=>{this.zoomOutOneStepAndRenderValues()},isEnabled:()=>this.valueRenderMode===Xk.IMAGE});null!==this.menuConfig&&0<this.menuConfig.items.length&&(this.menu=new aj(this.menuConfig,
this.headerSection),this.renderMenuThumb())}zoomInOneStepAndRenderValues(){this.imageCellSize*this.zoomStepRatio<=this.maxImageCellSize&&(this.imageCellSize*=this.zoomStepRatio,this.renderValues())}zoomOutOneStepAndRenderValues(){this.imageCellSize/this.zoomStepRatio>=this.minImageCellSize&&(this.imageCellSize/=this.zoomStepRatio,this.renderValues())}renderMenuThumb(){if(null==this.headerSection)throw Error("Rendering menu thumb failed due to missing header section.");this.menuThumb=document.createElement("div");
this.menuThumb.textContent="\u22ee";this.menuThumb.classList.add("tensor-widget-menu-thumb");this.headerSection.appendChild(this.menuThumb);this.menuThumb.addEventListener("click",()=>{if(null!==this.menu)if(this.menu.shown())this.menu.hide();else{const b=this.menuThumb.getBoundingClientRect();this.menu.show(b.bottom,b.left)}})}renderValues(){return Jh(this,void 0,void 0,function*(){2<this.rank&&null===this.slicingSpecRoot&&(this.slicingSpecRoot=document.createElement("div"),this.slicingSpecRoot.classList.add("tensor-widget-slicing-group"),
this.rootElement.appendChild(this.slicingSpecRoot));null==this.valueSection&&(this.valueSection=document.createElement("div"),this.valueSection.classList.add("tensor-widget-value-section"),this.rootElement.appendChild(this.valueSection),this.valueSection.addEventListener("wheel",b=>Jh(this,void 0,void 0,function*(){let d=!1;null==this.options.wheelZoomKey||"ctrl"===this.options.wheelZoomKey?d=b.ctrlKey:"alt"===this.options.wheelZoomKey?d=b.altKey:"shift"===this.options.wheelZoomKey&&(d=b.shiftKey);
d&&this.valueRenderMode===Xk.IMAGE?(b.stopPropagation(),b.preventDefault(),0<b.deltaY?this.zoomOutOneStepAndRenderValues():this.zoomInOneStepAndRenderValues()):null!=this.selection&&(b.stopPropagation(),b.preventDefault(),this.hideValueTooltip(),yield this.scrollUpOrDown(0<b.deltaY?fj.DOWN:fj.UP))})),this.valueSection.tabIndex=1024,this.valueSection.addEventListener("keydown",b=>{var d=[38,40,37,39];if(null!=this.selection&&-1!==d.indexOf(b.keyCode)){b.stopPropagation();b.preventDefault();this.hideValueTooltip();
let f=d=null;38===b.keyCode?f=fj.UP:40===b.keyCode?f=fj.DOWN:37===b.keyCode?f=fj.LEFT:39===b.keyCode&&(f=fj.RIGHT);null!==f&&(d=this.selection.move(f,this.slicingSpec));null===d?this.renderSelection():d===fj.UP||d===fj.DOWN?this.scrollUpOrDown(d):(d===fj.LEFT||d===fj.RIGHT)&&this.scrollLeftOrRight(d)}}));this.clearValueSection();this.createTopRuler();this.createLeftRuler();this.createValueDivs();yield this.renderRulersAndValueDivs();2<this.rank&&(this.slicingControl=new hj(this.slicingSpecRoot,this.tensorView.spec.shape,
b=>Jh(this,void 0,void 0,function*(){ej(this.slicingSpec,b)?(this.slicingSpec=JSON.parse(JSON.stringify(b)),yield this.renderRulersAndValueDivs()):(this.slicingSpec=JSON.parse(JSON.stringify(b)),yield this.render())})),this.slicingControl.render(this.slicingSpec))})}clearValueSection(){if(null!==this.valueSection){for(;this.valueSection.firstChild;)this.valueSection.removeChild(this.valueSection.firstChild);this.topRuler=null;this.valueRows=[]}}createTopRuler(){if(null===this.valueSection)throw Error("Failed to create top ruler due to missing value section.");
null==this.topRuler&&(this.topRuler=document.createElement("div"),this.topRuler.classList.add("tenesor-widget-top-ruler"),this.topRuler.style.whiteSpace="nowrap",this.valueSection.appendChild(this.topRuler),this.topRulerTicks=[],this.topRuler.addEventListener("wheel",f=>Jh(this,void 0,void 0,function*(){null!=this.selection&&(f.stopPropagation(),f.preventDefault(),this.hideValueTooltip(),yield this.scrollLeftOrRight(0<f.deltaY?fj.RIGHT:fj.LEFT))})));for(;this.topRuler.firstChild;)this.topRuler.removeChild(this.topRuler.firstChild);
this.baseRulerTick=document.createElement("div");this.baseRulerTick.classList.add("tensor-widget-top-ruler-tick");this.topRuler.appendChild(this.baseRulerTick);2<=this.rank&&(this.slicingSpec.horizontalRange=[0,null]);let b;b=1>=this.rank?1:this.tensorView.spec.shape[this.slicingSpec.viewingDims[1]];const d=this.rootElement.getBoundingClientRect().right;this.colsCutoff=!1;for(let f=0;f<b;++f){const h=document.createElement("div");h.classList.add("tensor-widget-top-ruler-tick");this.valueRenderMode===
Xk.IMAGE&&(h.style.width=`${this.imageCellSize}px`);this.topRuler.appendChild(h);this.topRulerTicks.push(h);if(h.getBoundingClientRect().right>=d){if(2<=this.rank){if(null===this.slicingSpec.horizontalRange)throw Error(`Missing horizontal range for ${this.rank}D tensor.`);this.slicingSpec.horizontalRange[1]=f+1;this.colsCutoff=!0}break}}if(!this.colsCutoff&&2<=this.rank){if(null===this.slicingSpec.horizontalRange)throw Error(`Missing horizontal range for ${this.rank}D tensor.`);this.slicingSpec.horizontalRange[1]=
b}}createLeftRuler(){if(null===this.valueSection)throw Error("Failed to create left ruler due to missing value section.");this.valueRows=[];this.leftRulerTicks=[];1<=this.rank&&(this.slicingSpec.verticalRange=[0,null]);let b;b=0===this.rank?1:this.tensorView.spec.shape[this.slicingSpec.viewingDims[0]];this.rowsCutoff=!1;const d=this.rootElement.getBoundingClientRect().bottom;for(let f=0;f<b;++f){const h=document.createElement("div");h.classList.add("tensor-widget-value-row");this.valueRenderMode===
Xk.IMAGE&&(h.style.height=`${this.imageCellSize}px`,h.style.lineHeight=`${this.imageCellSize}px`);this.valueSection.appendChild(h);this.valueRows.push(h);const k=document.createElement("div");k.classList.add("tensor-widget-top-ruler-tick");this.valueRenderMode===Xk.IMAGE&&(k.style.height=`${this.imageCellSize}px`,k.style.lineHeight=`${this.imageCellSize}px`);h.appendChild(k);this.leftRulerTicks.push(k);if(k.getBoundingClientRect().bottom>=d){if(1<=this.rank){if(null===this.slicingSpec.verticalRange)throw Error(`Missing vertical range for ${this.rank}D tensor.`);
this.slicingSpec.verticalRange[1]=f+1;this.rowsCutoff=!0}break}}if(!this.rowsCutoff&&1<=this.rank){if(null===this.slicingSpec.verticalRange)throw Error(`Missing vertical range for ${this.rank}D tensor.`);this.slicingSpec.verticalRange[1]=b}}createValueDivs(){if(null===this.valueRows)throw Error("Value rows are unexpectedly uninitialized.");this.valueDivs=[];const b=this.topRulerTicks.length,d=this.valueRows.length;for(let f=0;f<d;++f){this.valueDivs[f]=[];for(let h=0;h<b;++h){const k=document.createElement("div");
k.classList.add("tensor-widget-value-div");this.valueRenderMode===Xk.IMAGE&&(k.style.width=`${this.imageCellSize}px`,k.style.height=`${this.imageCellSize}px`,k.style.lineHeight=`${this.imageCellSize}px`);this.valueRows[f].appendChild(k);this.valueDivs[f].push(k);k.addEventListener("click",()=>{this.selection=new gj(this.tensorView.spec.shape,this.slicingSpec,null==this.slicingSpec.verticalRange||null==this.slicingSpec.verticalRange[0]?0:this.slicingSpec.verticalRange[0]+f,null==this.slicingSpec.horizontalRange||
null==this.slicingSpec.horizontalRange[0]?0:this.slicingSpec.horizontalRange[0]+h,1,1);this.renderSelection()});k.addEventListener("mouseenter",()=>{const r=k.getAttribute("detailed-value");if(null!==r){var l=this.rootElement.getBoundingClientRect(),p=k.getBoundingClientRect(),m=p.bottom-p.top,n=p.right-p.left,q=this.calculateIndices(f,h);this.drawValueTooltip(q,r,p.top-l.top+.8*m,p.left-l.left+.75*n)}});k.addEventListener("mouseleave",()=>{this.hideValueTooltip()})}}}renderTopRuler(){if(2<=this.rank){const b=
this.tensorView.spec.shape[this.slicingSpec.viewingDims[1]];for(let d=0;d<this.topRulerTicks.length;++d){if(null===this.slicingSpec.horizontalRange)throw Error(`Missing horizontal range for ${this.rank}D tensor.`);const f=this.slicingSpec.horizontalRange[0]+d;this.showIndicesOnTicks&&(this.topRulerTicks[d].textContent=f<b?`${f}`:"")}}}renderLeftRuler(){if(1<=this.rank){const b=this.tensorView.spec.shape[this.slicingSpec.viewingDims[0]];for(let d=0;d<this.leftRulerTicks.length;++d){if(null===this.slicingSpec.verticalRange)throw Error(`Missing vertcial range for ${this.rank}D tensor.`);
const f=this.slicingSpec.verticalRange[0]+d;this.showIndicesOnTicks&&(this.leftRulerTicks[d].textContent=f<b?`${f}`:"")}}}renderValueDivs(){return Jh(this,void 0,void 0,function*(){const b=this.valueDivs.length,d=this.valueDivs[0].length;let f=yield this.tensorView.view(this.slicingSpec);0===this.rank?f=[[f]]:1===this.rank&&(f=f.map(p=>[p]));const h=this.getValueClass(),k=this.valueRenderMode;if(k===Xk.IMAGE){if(null==this.numericSummary)throw Error("Failed to render image representation of tensor due to missing numeric summary");
var r=this.numericSummary.minimum,l=this.numericSummary.maximum;if(null==r||null==l)throw Error("Failed to render image representation of tensor due to missing minimum or maximum values in numeric summary");r={min:r,max:l};this.colorMap=this.colorMapName in Yk?new Yk[this.colorMapName](r):new Vk(r)}for(r=0;r<b;++r)for(l=0;l<d;++l){const p=this.valueDivs[r][l];if(r<f.length&&l<f[r].length){const m=f[r][l];if(k===Xk.IMAGE){const [n,q,u]=this.colorMap.getRGB(m);p.style.backgroundColor=`rgb(${n}, ${q}, ${u})`}else"numeric"===
h?p.textContent=Rk(m,Lh(this.tensorView.spec.dtype)):"boolean"===h?p.textContent=Sk(m):"string"===h&&(p.textContent=Tk(m));p.setAttribute("detailed-value",this.getDetailedValueTooltipString(m))}else p.textContent="",p.setAttribute("detailed-value","")}this.renderSelection()})}getDetailedValueTooltipString(b){return"boolean"===this.getValueClass()?Sk(b,!1):"string"===this.getValueClass()?`Length-${b.length} string: "${Tk(b,500)}"`:String(b)}renderSelection(){if(null!=this.selection){var b=this.valueDivs.length,
d=this.valueDivs[0].length;for(let h=0;h<b;++h)for(let k=0;k<d;++k){const r=this.valueDivs[h][k];r.classList.remove("tensor-widget-value-div-selection");r.classList.remove("tensor-widget-value-div-selection-top");r.classList.remove("tensor-widget-value-div-selection-bottom");r.classList.remove("tensor-widget-value-div-selection-left");r.classList.remove("tensor-widget-value-div-selection-right");var f=this.calculateIndices(h,k);f=this.selection.getElementStatus(f);null!==f&&(r.classList.add("tensor-widget-value-div-selection"),
f.topEdge&&r.classList.add("tensor-widget-value-div-selection-top"),f.bottomEdge&&r.classList.add("tensor-widget-value-div-selection-bottom"),f.leftEdge&&r.classList.add("tensor-widget-value-div-selection-left"),f.rightEdge&&r.classList.add("tensor-widget-value-div-selection-right"))}}}calculateIndices(b,d){const f=[],h=this.slicingSpec.slicingDimsAndIndices.map(r=>r.dim),k=this.slicingSpec.slicingDimsAndIndices.map(r=>r.index);for(let r=0;r<this.rank;++r)if(-1!==h.indexOf(r)){const l=k[h.indexOf(r)];
if(null===l)throw Error("Failed to calculate indices: "+`Undetermined index at dimension ${r}`);f.push(l)}else if(r===this.slicingSpec.viewingDims[0]){if(null===this.slicingSpec.verticalRange||null===this.slicingSpec.verticalRange[0])throw Error("Failed to calculate indices due to undertermined vertical range.");f.push(this.slicingSpec.verticalRange[0]+b)}else if(r===this.slicingSpec.viewingDims[1]){if(null===this.slicingSpec.horizontalRange||null===this.slicingSpec.horizontalRange[0])throw Error("Failed to calculate indices due to undertermined vertical range.");
f.push(this.slicingSpec.horizontalRange[0]+d)}return f}drawValueTooltip(b,d,f,h){null===this.valueTooltip&&(this.valueTooltip=document.createElement("div"),this.valueTooltip.classList.add("tensor-widget-value-tooltip"),this.rootElement.appendChild(this.valueTooltip));for(;this.valueTooltip.firstChild;)this.valueTooltip.removeChild(this.valueTooltip.firstChild);const k=document.createElement("div");k.classList.add("tensor-widget-value-tooltip-indices");k.textContent=`Indices: ${JSON.stringify(b)}`;
this.valueTooltip.appendChild(k);b=document.createElement("div");b.classList.add("tensor-widget-value-tooltip-value");b.textContent=d;this.valueTooltip.appendChild(b);this.valueTooltip.style.top=`${f}px`;this.valueTooltip.style.left=`${h}px`;this.valueTooltip.style.display="block";this.valueRenderMode==Xk.IMAGE&&null!=this.colorMap&&(f=document.createElement("canvas"),f.classList.add("tensor-widget-value-tooltip-colorbar"),this.valueTooltip.appendChild(f),this.colorMap.render(f,parseFloat(d)))}hideValueTooltip(){null!=
this.valueTooltip&&(this.valueTooltip.style.display="none")}renderRulersAndValueDivs(){return Jh(this,void 0,void 0,function*(){null!=this.slicingControl&&this.slicingControl.setSlicingSpec(this.slicingSpec);this.calculateShowIndicesOnRulerTicks();this.renderTopRuler();this.renderLeftRuler();yield this.renderValueDivs()})}calculateShowIndicesOnRulerTicks(){if(2<=this.rank){var b=this.topRulerTicks[0].getBoundingClientRect();this.showIndicesOnTicks=b.right-b.left>9*Math.ceil(Math.log(this.tensorView.spec.shape[this.slicingSpec.viewingDims[0]])/
Math.LN10)}else 1===this.rank?(b=this.leftRulerTicks[0].getBoundingClientRect(),this.showIndicesOnTicks=16<b.bottom-b.top):this.showIndicesOnTicks=!1}scrollHorizontally(b){return Jh(this,void 0,void 0,function*(){if(!(1>=this.rank)){if(null===this.slicingSpec.horizontalRange)throw Error(`Missing horizontal range for ${this.rank}D tensor.`);var d=this.tensorView.spec.shape[this.slicingSpec.viewingDims[1]];if(0>b||b>=d)throw Error(`Index out of bound: ${b} is outside [0, ${d}])`);this.slicingSpec.horizontalRange[0]=
b;this.slicingSpec.horizontalRange[1]=b+this.topRulerTicks.length;d=this.tensorView.spec.shape[this.slicingSpec.viewingDims[1]];this.slicingSpec.horizontalRange[1]>d&&(this.slicingSpec.horizontalRange[1]=d);yield this.renderRulersAndValueDivs()}})}scrollVertically(b){return Jh(this,void 0,void 0,function*(){if(0!==this.rank){if(null===this.slicingSpec.verticalRange)throw Error(`Missing vertical range for ${this.rank}D tensor.`);if(null===this.valueRows)throw Error("Vertical scrolling failed due to missing value rows.");
var d=this.tensorView.spec.shape[this.slicingSpec.viewingDims[0]];if(0>b||b>=d)throw Error(`Index out of bound: ${b} is outside [0, ${d}])`);this.slicingSpec.verticalRange[0]=b;this.slicingSpec.verticalRange[1]=b+this.valueRows.length;d=this.tensorView.spec.shape[this.slicingSpec.viewingDims[0]];this.slicingSpec.verticalRange[1]>d&&(this.slicingSpec.verticalRange[1]=d);yield this.renderRulersAndValueDivs()}})}scrollUpOrDown(b){return Jh(this,void 0,void 0,function*(){if(0!==this.rank&&this.rowsCutoff){if(null===
this.slicingSpec.verticalRange)throw Error(`Missing vertical range for ${this.rank}D tensor.`);if(null===this.valueRows)throw Error("Vertical scrolling failed due to missing value rows.");var d=this.slicingSpec.verticalRange[0];b===fj.DOWN?d<this.tensorView.spec.shape[this.slicingSpec.viewingDims[0]]-(this.valueRows.length-1)&&(yield this.scrollVertically(d+1)):0<=d-1&&(yield this.scrollVertically(d-1))}})}scrollLeftOrRight(b){return Jh(this,void 0,void 0,function*(){if(!(1>=this.rank)&&this.colsCutoff){if(null===
this.slicingSpec.horizontalRange)throw Error("Horizontal scrolling failed due to missing horizontal range.");var d=this.slicingSpec.horizontalRange[0];b===fj.RIGHT?d<this.tensorView.spec.shape[this.slicingSpec.viewingDims[1]]-(this.topRulerTicks.length-1)&&(yield this.scrollHorizontally(d+1)):0<=d-1&&(yield this.scrollHorizontally(d-1))}})}navigateToIndices(){return Jh(this,void 0,void 0,function*(){throw Error("navigateToIndices() is not implemented yet.");})}getValueClass(){const b=this.tensorView.spec.dtype;
return Lh(b)||Mh(b)?"numeric":Yi(b)?"boolean":"string"}}var $k=Object.freeze({__proto__:null,tensorWidget:function(b,d,f){return new Zk(b,d,f)},VERSION:"0.0.0"});window.tensor_widget=$k;

//# sourceURL=build://iron-scroll-target-behavior/iron-scroll-target-behavior.html.js
Polymer.IronScrollTargetBehavior={properties:{scrollTarget:{type:HTMLElement,value:function(){return this._defaultScrollTarget}}},observers:["_scrollTargetChanged(scrollTarget, isAttached)"],_shouldHaveListener:!0,_scrollTargetChanged:function(b,d){this._oldScrollTarget&&(this._toggleScrollListener(!1,this._oldScrollTarget),this._oldScrollTarget=null);d&&("document"===b?this.scrollTarget=this._doc:"string"===typeof b?this.scrollTarget=(d=this.domHost)&&d.$?d.$[b]:Polymer.dom(this.ownerDocument).querySelector("#"+
b):this._isValidScrollTarget()&&(this._oldScrollTarget=b,this._toggleScrollListener(this._shouldHaveListener,b)))},_scrollHandler:function(){},get _defaultScrollTarget(){return this._doc},get _doc(){return this.ownerDocument.documentElement},get _scrollTop(){return this._isValidScrollTarget()?this.scrollTarget===this._doc?window.pageYOffset:this.scrollTarget.scrollTop:0},get _scrollLeft(){return this._isValidScrollTarget()?this.scrollTarget===this._doc?window.pageXOffset:this.scrollTarget.scrollLeft:
0},set _scrollTop(b){this.scrollTarget===this._doc?window.scrollTo(window.pageXOffset,b):this._isValidScrollTarget()&&(this.scrollTarget.scrollTop=b)},set _scrollLeft(b){this.scrollTarget===this._doc?window.scrollTo(b,window.pageYOffset):this._isValidScrollTarget()&&(this.scrollTarget.scrollLeft=b)},scroll:function(b,d){this.scrollTarget===this._doc?window.scrollTo(b,d):this._isValidScrollTarget()&&(this.scrollTarget.scrollLeft=b,this.scrollTarget.scrollTop=d)},get _scrollTargetWidth(){return this._isValidScrollTarget()?
this.scrollTarget===this._doc?window.innerWidth:this.scrollTarget.offsetWidth:0},get _scrollTargetHeight(){return this._isValidScrollTarget()?this.scrollTarget===this._doc?window.innerHeight:this.scrollTarget.offsetHeight:0},_isValidScrollTarget:function(){return this.scrollTarget instanceof HTMLElement},_toggleScrollListener:function(b,d){d=d===this._doc?window:d;b?this._boundScrollHandler||(this._boundScrollHandler=this._scrollHandler.bind(this),d.addEventListener("scroll",this._boundScrollHandler)):
this._boundScrollHandler&&(d.removeEventListener("scroll",this._boundScrollHandler),this._boundScrollHandler=null)},toggleScrollListener:function(b){this._shouldHaveListener=b;this._toggleScrollListener(b,this.scrollTarget)}};

//# sourceURL=build://iron-menu-behavior/iron-menubar-behavior.html.js
Polymer.IronMenubarBehaviorImpl={hostAttributes:{role:"menubar"},keyBindings:{left:"_onLeftKey",right:"_onRightKey"},_onUpKey:function(b){this.focusedItem.click();b.detail.keyboardEvent.preventDefault()},_onDownKey:function(b){this.focusedItem.click();b.detail.keyboardEvent.preventDefault()},get _isRTL(){return"rtl"===window.getComputedStyle(this).direction},_onLeftKey:function(b){this._isRTL?this._focusNext():this._focusPrevious();b.detail.keyboardEvent.preventDefault()},_onRightKey:function(b){this._isRTL?
this._focusPrevious():this._focusNext();b.detail.keyboardEvent.preventDefault()},_onKeydown:function(b){this.keyboardEventMatchesKeys(b,"up down left right esc")||this._focusWithKeyboardEvent(b)}};Polymer.IronMenubarBehavior=[Polymer.IronMenuBehavior,Polymer.IronMenubarBehaviorImpl];

//# sourceURL=build://tf-graph-loader/tf-graph-dashboard-loader.js
Jh=this&&this.__awaiter||function(b,d,f,h){return new (f||(f=Promise))(function(k,r){function l(n){try{m(h.next(n))}catch(q){r(q)}}function p(n){try{m(h["throw"](n))}catch(q){r(q)}}function m(n){n.done?k(n.value):(new f(function(q){q(n.value)})).then(l,p)}m((h=h.apply(b,d||[])).next())})};
(function(b){(function(d){(function(){Polymer({is:"tf-graph-dashboard-loader",_template:null,properties:{datasets:Array,progress:{type:Object,notify:!0},selection:Object,selectedFile:Object,compatibilityProvider:{type:Object,value:()=>new b.graph.op.TpuCompatibilityProvider},hierarchyParams:{type:Object,value:()=>b.graph.hierarchy.DefaultHierarchyParams},outGraphHierarchy:{type:Object,readOnly:!0,notify:!0},outGraph:{type:Object,readOnly:!0,notify:!0},outStats:{type:Object,readOnly:!0,notify:!0},
_graphRunTag:Object},observers:["_selectionChanged(selection, compatibilityProvider)","_selectedFileChanged(selectedFile, compatibilityProvider)"],_selectionChanged(){this.debounce("selectionchange",()=>{this._load(this.selection)})},_load:function(f){const h=f.run,k=f.tag;f=f.type;switch(f){case b.graph.SelectionType.OP_GRAPH:case b.graph.SelectionType.CONCEPTUAL_GRAPH:this._setOutStats(null);var r=new URLSearchParams;r.set("run",h);r.set("conceptual",String(f===b.graph.SelectionType.CONCEPTUAL_GRAPH));
k&&r.set("tag",k);f=dc.getRouter().pluginRoute("graphs","/graph",r);return this._fetchAndConstructHierarchicalGraph(f).then(()=>{this._graphRunTag={run:h,tag:k}});case b.graph.SelectionType.PROFILE:{({tags:f}=this.datasets.find(({name:m})=>m===h));const l=f.find(m=>m.tag===k).opGraph?k:null;console.assert(f.find(m=>m.tag===l),`Required tag (${l}) is missing.`);f=this._graphRunTag&&this._graphRunTag.run===h&&this._graphRunTag.tag===l?Promise.resolve():this._load({run:h,tag:l,type:b.graph.SelectionType.OP_GRAPH});
r=new URLSearchParams;r.set("tag",k);r.set("run",h);const p=dc.getRouter().pluginRoute("graphs","/run_metadata",r);return f.then(()=>this._readAndParseMetadata(p))}default:return Promise.reject(Error(`Unknown selection type: ${f}`))}},_readAndParseMetadata:function(f){this.set("progress",{value:0,msg:""});b.graph.parser.fetchAndParseMetadata(f,b.graph.util.getTracker(this)).then(h=>{this._setOutStats(h)})},_fetchAndConstructHierarchicalGraph:function(f,h){return Jh(this,void 0,void 0,function*(){this.set("progress",
{value:0,msg:""});return b.graph.loader.fetchAndConstructHierarchicalGraph(b.graph.util.getTracker(this),f,h,this.compatibilityProvider,this.hierarchyParams).then(({graph:k,graphHierarchy:r})=>{this._setOutGraph(k);this._setOutGraphHierarchy(r)})})},_selectedFileChanged:function(f){if(f){f=f.target;var h=f.files[0];h&&(f.value="",this._fetchAndConstructHierarchicalGraph(null,h))}}})})(d.loader||(d.loader={}))})(b.graph||(b.graph={}))})(tf||(tf={}));

//# sourceURL=build://tf-histogram-dashboard/histogramCore.js
var el;
(function(b){function d(h){const [k,r,l]=h;return{wall_time:k,step:r,min:d3.min(l.map(([p])=>p)),max:d3.max(l.map(([,p])=>p)),buckets:l.map(([p,m,n])=>({left:p,right:m,count:n}))}}function f(h,k,r,l=30){r===k&&(r=1.1*k+1,k=k/1.1-1);const p=(r-k)/l;let m=0;return d3.range(k,r,p).map(n=>{const q=n+p;let u=0;for(;m<h.buckets.length;){const A=Math.min(r,h.buckets[m].right);var w=Math.max(k,h.buckets[m].left);const y=Math.min(A,q)-Math.max(w,n);w=y/(A-w)*h.buckets[m].count;u+=0<y?w:0;if(A>q)break;m++}return{x:n,
dx:p,y:u}})}b.backendToIntermediate=d;b.intermediateToD3=f;b.backendToVz=function(h){h=h.map(d);const k=d3.min(h,l=>l.min),r=d3.max(h,l=>l.max);return h.map(l=>({wall_time:l.wall_time,step:l.step,bins:f(l,k,r)}))}})(el||(el={}));

//# sourceURL=build://tf-hparams-utils/tf-hparams-utils.html.js
(function(b){(function(d){(function(f){function h(B){return""!==B.displayName&&void 0!==B.displayName?B.displayName:B.name}function k(B){if(""!==B.displayName&&void 0!==B.displayName)return B.displayName;let H=B.name.group;B=B.name.tag;void 0===H&&(H="");void 0===B&&(B="");return""===H?B:H+"."+B}function r(B){return B.hparamColumns.length}function l(B){return B.metricColumns.length}function p(B,H){return B[H]}function m(B,H){return B.find(K=>_.isEqual(K.name,H))}function n(B,H,K){return H.hparams[B.hparamColumns[K].hparamInfo.name]}
function q(B,H,K){B=m(H.metricValues,B.metricColumns[K].metricInfo.name);return void 0===B?void 0:B.value}function u(B,H,K){return K<B.hparamColumns.length?n(B,H,K):q(B,H,K-B.hparamColumns.length)}function w(B){return B.hparamInfos.length}function A(B){return B.metricInfos.length}function y(B,H,K){return H.hparams[B.hparamInfos[K].name]}function x(B,H,K){B=m(H.metricValues,B.metricInfos[K].name);return void 0===B?void 0:B.value}function C(B,H,K){return K<B.hparamInfos.length?y(B,H,K):x(B,H,K-B.hparamInfos.length)}
function G(B){return _.isNumber(B)?B.toPrecision(5):void 0===B?"":B.toString()}function D(B,H){return B*B+H*H}f.hparamName=h;f.metricName=k;f.schemaColumnName=function(B,H){return H<B.hparamColumns.length?h(B.hparamColumns[H].hparamInfo):k(B.metricColumns[H-B.hparamColumns.length].metricInfo)};f.numHParams=r;f.numMetrics=l;f.numColumns=function(B){return r(B)+l(B)};f.hparamValueByName=p;f.metricValueByName=m;f.hparamValueByIndex=n;f.metricValueByIndex=q;f.columnValueByIndex=u;f.numericColumnExtent=
function(B,H,K){return d3.extent(H,L=>u(B,L,K))};f.getAbsoluteColumnIndex=function(B,H,K){if(K<H.hparamInfos.length)B=B.hparamColumns.findIndex(L=>L.hparamInfo.name===H.hparamInfos[K].name);else{const L=H.metricInfos[K-H.hparamInfos.length].name;B=B.hparamColumns.length+B.metricColumns.findIndex(J=>J.metricInfo.name===L)}console.assert(-1!==B);return B};f.schemaVisibleColumnName=function(B,H){return H<B.hparamInfos.length?h(B.hparamInfos[H]):k(B.metricInfos[H-B.hparamInfos.length])};f.numVisibleHParams=
w;f.numVisibleMetrics=A;f.numVisibleColumns=function(B){return w(B)+A(B)};f.visibleNumericColumnExtent=function(B,H,K){return d3.extent(H,L=>C(B,L,K))};f.prettyPrintHParamValueByName=function(B,H){return G(p(B,H))};f.prettyPrintMetricValueByName=function(B,H){return G(m(B,H))};f.sessionGroupWithName=function(B,H){return B.find(K=>K.name===H)};f.hparamValueByVisibleIndex=y;f.metricValueByVisibleIndex=x;f.columnValueByVisibleIndex=C;f.prettyPrint=G;f.l2NormSquared=D;f.euclideanDist=function(B,H,K,L){return Math.sqrt(D(B-
K,H-L))};f.pointToRectangleDist=function(B,H,K,L,J,O){if(B<K&&H<L)return f.euclideanDist(B,H,K,L);if(K<=B&&B<J&&H<L)return L-H;if(J<=B&&H<L)return f.euclideanDist(B,H,J,L);if(B<K&&L<=H&&H<O)return K-B;if(K<=B&&B<J&&L<=H&&H<O)return 0;if(J<=B&&L<=H&&H<O)return B-J;if(B<K&&O<=H)return f.euclideanDist(B,H,K,O);if(K<=B&&B<J&&O<=H)return H-O;if(J<=B&&O<=H)return f.euclideanDist(B,H,J,O);throw"Point (x,y) must be in one of the regions defined above.";};f.translateStr=function(B,H){return void 0===H?"translate("+
B+")":"translate("+B+","+H+")"};f.rotateStr=function(B,H){let K="rotate(90";void 0!==B&&void 0!==H&&(K=K+","+B+","+H);return K+")"};f.isNullOrUndefined=function(B){return null===B||void 0===B};f.quadTreeVisitPointsInRect=function(B,H,K,L,J,O){B.visit((S,N,R,X,aa)=>{if(void 0===S.length){do N=B.x()(S.data),R=B.y()(S.data),H<=N&&N<L&&K<=R&&R<J&&O(S.data);while(S=S.next);return!0}return N>=L||X<=H||R>=J||aa<=K})};f.quadTreeVisitPointsInDisk=function(B,H,K,L,J){B.visit((O,S,N,R,X)=>{if(void 0===O.length){do S=
B.x()(O.data),N=B.y()(O.data),S=f.euclideanDist(H,K,S,N),S<=L&&J(O.data,S);while(O=O.next);return!0}return f.pointToRectangleDist(H,K,S,N,R,X)>L})};f.filterSet=function(B,H){const K=new Set;B.forEach(L=>{H(L)&&K.add(L)});return K};f.setArrayObservably=function(B,H){const K=B.get("sessionGroups",B);Array.isArray(K)?B.splice.apply(B,["sessionGroups",0,K.length].concat(H)):B.set("sessionGroups",H)};f.hashOfString=function(B){let H=0;for(let K=0;K<B.length;++K)H=31*H+B.charCodeAt(K)&4294967295;return H+
Math.pow(2,31)}})(d.utils||(d.utils={}))})(b.hparams||(b.hparams={}))})(tf||(tf={}));

//# sourceURL=build://vaadin-grid/vaadin-grid-cell-click-behavior.html.js
window.vaadin=window.vaadin||{};vaadin.elements=vaadin.elements||{};vaadin.elements.grid=vaadin.elements.grid||{};
vaadin.elements.grid.CellClickBehavior={listeners:{click:"_onClick"},attached:function(){this._cellContentFocusHandler=function(b){b.target!==this._cellContent&&this.fire("cell-content-focus",{cell:this})}.bind(this);this.addEventListener("focus",this._cellContentFocusHandler,!0)},detached:function(){this.removeEventListener("focus",this._cellContentFocusHandler,!0)},_onClick:function(b){"vaadin-grid-sorter"!==this.localName&&this.fire("cell-focus",{cell:this});if(this._cellClick){var d=Polymer.dom(b).localTarget;
d.getDistributedNodes&&(d=Polymer.dom(d).getDistributedNodes()[0]);var f=Polymer.dom(b).path;f=Array.prototype.slice.call(f,0,f.indexOf(d)+1);d.contains(this.target&&this.target.root.activeElement||document.activeElement)||f.some(this._isFocusable)||this._cellClick(b)}},_isFocusable:function(b){var d=Polymer.dom(b).parentNode;d=-1!==Array.prototype.indexOf.call(Polymer.dom(d).querySelectorAll("[tabindex], button, input, select, textarea, object, iframe, label, a[href], area[href]"),b);return!b.disabled&&
d}};

//# sourceURL=build://vaadin-grid/vaadin-grid-focusable-cell-container-behavior.html.js
window.vaadin=window.vaadin||{};vaadin.elements=vaadin.elements||{};vaadin.elements.grid=vaadin.elements.grid||{};
vaadin.elements.grid.FocusableCellContainerBehavior={properties:{focused:{type:Boolean,reflectToAttribute:!0},_focusedRow:Object,_focusedRowIndex:Number,_focusedCell:Object,_focusedCellIndex:Number,_lastFocusedCell:Object},observers:["_announceFocusedCell(_focusedCell, focused)","_dispatchEvents(_focusedCell, focused)","_focusedCellChanged(_focusedRowIndex, _focusedCellIndex)"],_announceFocusedCell:function(b,d){void 0!==b&&void 0!==d&&this.domHost.navigating&&d&&(d=Polymer.Element?b._cellContent.getAttribute("slot"):
b._cellContent.id,"vaadin-grid-table-body"!==this.is||b.hasAttribute("detailscell")||(b=Array.prototype.indexOf.call(Polymer.dom(b.parentElement).querySelectorAll(".vaadin-grid-cell"),b),d=this.domHost.$.header.lastElementChild.children[b]._cellContent.id+" "+d),this.domHost.$.footerFocusTrap.activeTarget=d)},_dispatchEvents:function(b,d){void 0!==b&&void 0!==d&&(this._lastFocusedCell&&(this._lastFocusedCell._cellContent.dispatchEvent(new CustomEvent("cell-focusout")),this._lastFocusedCell=void 0),
d&&(b._cellContent.dispatchEvent(new CustomEvent("cell-focusin")),this._lastFocusedCell=b))},_focusedCellChanged:function(b,d){void 0!==b&&void 0!==d&&Array.prototype.forEach.call(Polymer.dom(this).children,function(f,h){f.focused=h===b;f.focused&&(this._focusedRow=f,this._focusedCellIndex=Math.min(d,f.children.length-1),this._focusedCell=f.children[this._focusedCellIndex]);f.cells.forEach(function(k,r){k.focused=r===this._focusedCellIndex}.bind(this))}.bind(this))},focusLeft:function(){if(!this._focusedCell.hasAttribute("detailscell")){var b=
this._visibleCellIndexes();0<b.length&&(this._focusedCellIndex=b[Math.max(0,b.indexOf(this._focusedCellIndex)-1)])}},focusDown:function(){this._focusedRowIndex=Math.min(this._focusedRowIndex+1,this.children.length-1)},_visibleCellIndexes:function(){var b=[];if(this._focusedRow&&this._focusedRow.children){for(var d=this._focusedRow.children,f=0;f<d.length;f++)d[f].hidden||d[f]===this._focusedRow._rowDetailsCell||b.push(f);b.sort(function(h,k){return d[h].column._order<d[k].column._order?-1:1})}return b},
focusPageDown:function(){this._focusedRowIndex=Math.min(this._focusedRowIndex+10,this.children.length-1)},focusPageUp:function(){this._focusedRowIndex=Math.max(0,this._focusedRowIndex-10)},focusRight:function(){if(!this._focusedCell.hasAttribute("detailscell")){var b=this._visibleCellIndexes();0<b.length&&(this._focusedCellIndex=b[Math.min(b.indexOf(this._focusedCellIndex)+1,b.length-1)])}},focusUp:function(){this._focusedRowIndex=Math.max(0,this._focusedRowIndex-1)},focusHome:function(){if(!this._focusedCell.hasAttribute("detailscell")){var b=
this._visibleCellIndexes();0<b.length&&(this._focusedCellIndex=b[0])}},focusEnd:function(){if(!this._focusedCell.hasAttribute("detailscell")){var b=this._visibleCellIndexes();0<b.length&&(this._focusedCellIndex=b[b.length-1])}},focusFirst:function(){this._focusedRowIndex=0;this.focusHome()},focusLast:function(){this._focusedRowIndex=this.children.length-1;this.focusEnd()}};

//# sourceURL=build://vaadin-grid/vaadin-grid-table-header-footer.html.js
(function(){var b={properties:{columnTree:Array,target:Object,_rows:Array},observers:["_columnTreeChanged(columnTree, target)","_rowsChanged(_rows)"],_columnTreeChanged:function(d,f){if(void 0!==d&&void 0!==f){this._rows&&this._rows.forEach(function(l){Polymer.dom(l).innerHTML=""});for(var h=[],k=0;k<d.length;k++){var r=this._createRow();r.target=f;r._isColumnRow=k==d.length-1;r.columns=d[k];h.push(r)}this._rows="vaadin-grid-table-header"===this.localName?h:h.reverse()}},_rowsChanged:function(d){Polymer.dom(this).innerHTML=
"";d.forEach(function(f){Polymer.dom(this).appendChild(f)}.bind(this))}};Polymer({is:"vaadin-grid-table-header",behaviors:[b,vaadin.elements.grid.FocusableCellContainerBehavior],_createRow:function(){return document.createElement("vaadin-grid-table-header-row")}});Polymer({is:"vaadin-grid-table-body",behaviors:[vaadin.elements.grid.FocusableCellContainerBehavior],observers:["_announceFocusedRow(_focusedRow)"],_announceFocusedRow:function(d){this.fire("iron-announce",{text:"Row "+(d.index+1)+" of "+
this.domHost.size})},_moveFocusToDetailsCell:function(){this._focusedCell.focused=!1;this._focusedRow._rowDetailsCell.focused=!0;this._focusedCell=this._focusedRow._rowDetailsCell},_focusedRowHasDetailsCell:function(){return this._focusedRow&&this._focusedRow._rowDetailsCell&&this._focusedCell!==this._focusedRow._rowDetailsCell},focusDown:function(){this._focusedRowHasDetailsCell()?this._moveFocusToDetailsCell():this._focusedRowIndex=Math.min(this._focusedRowIndex+1,this.domHost.size-1)},focusUp:function(){this._focusedRow&&
this._focusedCell===this._focusedRow._rowDetailsCell?this._focusedCellChanged(this._focusedRowIndex,this._focusedCellIndex):(this._focusedRowIndex=Math.max(0,this._focusedRowIndex-1),this._focusedRowHasDetailsCell()&&this._moveFocusToDetailsCell())},focusLast:function(){this._focusedRowIndex=this.domHost.size-1;this.focusEnd()},_focusedCellChanged:function(d,f){void 0!==d&&void 0!==f&&Array.prototype.forEach.call(Polymer.dom(this).children,function(h){h.focused=h.index===d;h.index===d&&(this._focusedRow=
h,this._focusedCell=h.children[f]);h.iterateCells(function(k,r){k.focused=r===f})}.bind(this))}});Polymer({is:"vaadin-grid-table-footer",behaviors:[b,vaadin.elements.grid.FocusableCellContainerBehavior],_createRow:function(){return document.createElement("vaadin-grid-table-footer-row")}})})();

//# sourceURL=build://vaadin-grid/vaadin-grid-templatizer.html.js
window.vaadin=window.vaadin||{};vaadin.elements=vaadin.elements||{};vaadin.elements.grid=vaadin.elements.grid||{};vaadin.elements.grid.Templatizer=function(){};
vaadin.elements.grid.Templatizer=Polymer({is:"vaadin-grid-templatizer",behaviors:[Polymer.Templatizer],properties:{dataHost:Object,template:Object,_templateInstances:{type:Array,value:function(){return[]}},_parentPathValues:{value:function(){return{}}}},observers:["_templateInstancesChanged(_templateInstances.*, _parentPathValues.*)"],created:function(){this._parentModel=!0;this._instanceProps={expanded:!0,index:!0,item:!0,selected:!0}},createInstance:function(){this._ensureTemplatized();var b=this.stamp({});
this.addInstance(b);return b},addInstance:function(b){-1===this._templateInstances.indexOf(b)&&this.push("_templateInstances",b)},removeInstance:function(b){this.splice("_templateInstances",this._templateInstances.indexOf(b),1)},_ensureTemplatized:function(){this.template._templatized||(this.template._templatized=!0,this.templatize(this.template),this._parentProps=this._parentProps||{},Polymer.Element||Object.keys(this._parentProps).forEach(function(){},this))},_forwardInstanceProp:function(b,d,f){void 0!==
b["__"+d+"__"]&&b["__"+d+"__"]!==f&&this.fire("template-instance-changed",{prop:d,value:f,inst:b})},_forwardInstancePath:function(b,d,f){0!==d.indexOf("item.")||this._suppressItemChangeEvent||this.fire("item-changed",{item:b.item,path:d.substring(5),value:f})},_notifyInstancePropV2:function(b,d,f){this._forwardInstanceProp(b,d,f);this._forwardInstancePath(b,d,f)},_forwardParentProp:function(b,d){this._parentPathValues[b]=d;this._templateInstances.forEach(function(f){f.set(b,d)},this)},_forwardParentPath:function(b,
d){this.set(["_parentPathValues",b],d);this._templateInstances.forEach(function(f){f.notifyPath(b,d)},this)},_forwardHostPropV2:function(b,d){this._forwardParentProp(b,d);this._templateInstances&&this._templateInstances.forEach(function(f){f.notifyPath(b,d)},this)},_templateInstancesChanged:function(b){if("_templateInstances"===b.path){var d=0;var f=this._templateInstances.length}else if("_templateInstances.splices"===b.path)d=b.value.index,f=b.value.addedCount;else return;Object.keys(this._parentPathValues||
{}).forEach(function(h){for(var k=d;k<d+f;k++)this._templateInstances[k].set(h,this._parentPathValues[h])},this)}});

//# sourceURL=build://vaadin-grid/vaadin-grid-selection-behavior.html.js
window.vaadin=window.vaadin||{};vaadin.elements=vaadin.elements||{};vaadin.elements.grid=vaadin.elements.grid||{};
vaadin.elements.grid.SelectionBehavior={properties:{selectedItems:{type:Object,notify:!0,value:function(){return[]}}},observers:["_selectedItemsChanged(selectedItems.*)"],listeners:{"template-instance-changed":"_templateInstanceChangedSelection"},_templateInstanceChangedSelection:function(b){if("selected"===b.detail.prop){var d=b.detail.inst.item;(this._isSelected(d)?this.deselectItem:this.selectItem).bind(this)(d);this.fire("iron-announce",{text:(this._isSelected(d)?"Selected":"Deselected")+" Row "+
(b.detail.inst.index+1)+" of "+this.size});b.stopPropagation()}},_isSelected:function(b){return this.selectedItems&&-1<this.selectedItems.indexOf(b)},selectItem:function(b){b=this._takeItem(b);this._isSelected(b)||this.push("selectedItems",b)},deselectItem:function(b){b=this._takeItem(b);b=this.selectedItems.indexOf(b);-1<b&&this.splice("selectedItems",b,1)},_toggleItem:function(b){b=this._takeItem(b);-1===this.selectedItems.indexOf(b)?this.selectItem(b):this.deselectItem(b)},_takeItem:function(b){return"number"===
typeof b&&0<=b&&this.items&&this.items.length>b?this.items[b]:b},_selectedItemsChanged:function(b){!this.$.scroller._physicalItems||"selectedItems"!==b.path&&"selectedItems.splices"!==b.path||this.$.scroller._physicalItems.forEach(function(d){d.selected=this._isSelected(d.item)}.bind(this))}};

//# sourceURL=build://vaadin-grid/iron-list-behavior.html.js
window.vaadin=window.vaadin||{};vaadin.elements=vaadin.elements||{};vaadin.elements.grid=vaadin.elements.grid||{};
vaadin.elements.grid.IronListBehaviorImpl=function(){var b=navigator.userAgent.match(/iP(?:hone|ad;(?: U;)? CPU) OS (\d+)/),d=b&&8<=b[1];return{is:"iron-list",properties:{maxPhysicalCount:{type:Number,value:500},as:{type:String,value:"item"},indexAs:{type:String,value:"index"}},_ratio:.5,_scrollerPaddingTop:0,_scrollPosition:0,_physicalSize:0,_physicalAverage:0,_physicalAverageCount:0,_physicalTop:0,_virtualCount:0,_physicalIndexForKey:null,_estScrollHeight:0,_scrollHeight:0,_viewportHeight:0,_viewportWidth:0,
_physicalItems:null,_physicalSizes:null,_firstVisibleIndexVal:null,_lastVisibleIndexVal:null,_collection:null,_itemsRendered:!1,_lastPage:null,_maxPages:3,_itemsPerRow:1,_itemWidth:0,_rowHeight:0,get _physicalBottom(){return this._physicalTop+this._physicalSize},get _scrollBottom(){return this._scrollPosition+this._viewportHeight},get _virtualEnd(){return this._virtualStart+this._physicalCount-1},get _hiddenContentSize(){return this._physicalSize-this._viewportHeight},get _maxScrollTop(){return this._estScrollHeight-
this._viewportHeight+this._scrollerPaddingTop},_minVirtualStart:0,get _maxVirtualStart(){return Math.max(0,this._virtualCount-this._physicalCount)},_virtualStartVal:0,set _virtualStart(f){this._virtualStartVal=Math.min(this._maxVirtualStart,Math.max(this._minVirtualStart,f))},get _virtualStart(){return this._virtualStartVal||0},_physicalStartVal:0,set _physicalStart(f){this._physicalStartVal=f%this._physicalCount;0>this._physicalStartVal&&(this._physicalStartVal=this._physicalCount+this._physicalStartVal);
this._physicalEnd=(this._physicalStart+this._physicalCount-1)%this._physicalCount},get _physicalStart(){return this._physicalStartVal||0},_physicalCountVal:0,set _physicalCount(f){this._physicalCountVal=f;this._physicalEnd=(this._physicalStart+this._physicalCount-1)%this._physicalCount},get _physicalCount(){return this._physicalCountVal},_physicalEnd:0,get _optPhysicalSize(){return this._viewportHeight*this._maxPages},get _optPhysicalCount(){return this._estRowsInView*this._itemsPerRow*this._maxPages},
get _isVisible(){return this.scrollTarget&&!(!this.scrollTarget.offsetWidth&&!this.scrollTarget.offsetHeight)},get firstVisibleIndex(){if(null===this._firstVisibleIndexVal){var f=Math.floor(this._physicalTop+this._scrollerPaddingTop);this._firstVisibleIndexVal=this._iterateItems(function(h,k){f+=this._getPhysicalSizeIncrement(h);if(f>this._scrollPosition)return k})||0}return this._firstVisibleIndexVal},get lastVisibleIndex(){if(null===this._lastVisibleIndexVal){var f=this._physicalTop;this._iterateItems(function(h,
k){if(f<this._scrollBottom)this._lastVisibleIndexVal=k;else return!0;f+=this._getPhysicalSizeIncrement(h)})}return this._lastVisibleIndexVal},get _defaultScrollTarget(){return this},get _virtualRowCount(){return Math.ceil(this._virtualCount/this._itemsPerRow)},get _estRowsInView(){return Math.ceil(this._viewportHeight/this._rowHeight)},get _physicalRows(){return Math.ceil(this._physicalCount/this._itemsPerRow)},attached:function(){this.updateViewportBoundaries();this._render();this.listen(this,"iron-resize",
"_resizeHandler")},detached:function(){this._itemsRendered=!1;this.unlisten(this,"iron-resize","_resizeHandler")},updateViewportBoundaries:function(){this._scrollerPaddingTop=this.scrollTarget===this?0:parseInt(window.getComputedStyle(this)["padding-top"]||0,10);this._viewportHeight=this._scrollTargetHeight},_update:function(f,h){this._assignModels(f);this._updateMetrics(f);if(h)for(;h.length;)f=h.pop(),this._physicalTop-=this._getPhysicalSizeIncrement(f);this._positionItems();this._updateScrollerSize();
this._increasePoolIfNeeded()},_increasePoolIfNeeded:function(){if(0===this._viewportHeight)return!1;var f=this._physicalSizes.reduce(function(k,r){return k+(r||100)},0),h=f>this._viewportHeight;if(f>=this._optPhysicalSize&&h)return!1;f=Math.floor(this._physicalSize/this._viewportHeight);0===f?this._debounceTemplate(this._increasePool.bind(this,Math.round(.5*this._physicalCount))):this._lastPage!==f&&h?Polymer.dom.addDebouncer(this.debounce("_debounceTemplate",this._increasePool.bind(this,this._itemsPerRow),
16)):this._debounceTemplate(this._increasePool.bind(this,Math.ceil(this._viewportHeight/(this._physicalSize/this._physicalCount)*this._maxPages-this._physicalCount)||1));this._lastPage=f;return!0},_debounceTemplate:function(f){Polymer.dom.addDebouncer(this.debounce("_debounceTemplate",f))},_increasePool:function(f){var h=this._physicalCount;f=Math.min(this._physicalCount+f,this._virtualCount-this._virtualStart,Math.max(this.maxPhysicalCount,25))-h;0>=f||([].push.apply(this._physicalItems,this._createPool(f)),
[].push.apply(this._physicalSizes,Array(f)),this._physicalCount=h+f,this._update())},_render:function(){var f=0<this._virtualCount||0<this._physicalCount;this.isAttached&&!this._itemsRendered&&this._isVisible&&f&&(this._lastPage=0,this._update(),this._itemsRendered=!0)},_iterateItems:function(f,h){var k,r;if(2===arguments.length&&h)for(r=0;r<h.length;r++){var l=h[r];var p=this._computeVidx(l);if(null!=(k=f.call(this,l,p)))return k}else{l=this._physicalStart;for(p=this._virtualStart;l<this._physicalCount;l++,
p++)if(null!=(k=f.call(this,l,p)))return k;for(l=0;l<this._physicalStart;l++,p++)if(null!=(k=f.call(this,l,p)))return k}},_computeVidx:function(f){return f>=this._physicalStart?this._virtualStart+(f-this._physicalStart):this._virtualStart+(this._physicalCount-this._physicalStart)+f},_updateMetrics:function(f){this.scrolling&&Polymer.dom.flush();var h=0,k=0,r=this._physicalAverageCount,l=this._physicalAverage;this._iterateItems(function(p){k+=this._physicalSizes[p]||0;this._physicalSizes[p]=this._physicalItems[p].offsetHeight;
h+=this._physicalSizes[p];this._physicalAverageCount+=this._physicalSizes[p]?1:0},f);this._viewportHeight=this._scrollTargetHeight;this._physicalSize=this._physicalSize+h-k;this._physicalAverageCount!==r&&(this._physicalAverage=Math.round((l*r+h)/this._physicalAverageCount))},_positionItems:function(){this._adjustScrollPosition();var f=this._physicalTop;this._iterateItems(function(h){this._physicalItems[h].style.transform=this._getTranslate(0,f);f+=this._physicalSizes[h]})},_getPhysicalSizeIncrement:function(f){return this._physicalSizes[f]},
_shouldRenderNextRow:function(f){return f%this._itemsPerRow===this._itemsPerRow-1},_adjustScrollPosition:function(){var f=0===this._virtualStart?this._physicalTop:Math.min(this._scrollPosition+this._physicalTop,0);f&&(this._physicalTop-=f,d||0===this._physicalTop||this._resetScrollPosition(this._scrollTop-f))},_resetScrollPosition:function(f){this.scrollTarget&&(this._scrollPosition=this._scrollTop=f)},_updateScrollerSize:function(f){this._estScrollHeight=this._physicalBottom+Math.max(this._virtualCount-
this._physicalCount-this._virtualStart,0)*this._physicalAverage;if((f=(f=f||0===this._scrollHeight)||this._scrollPosition>=this._estScrollHeight-this._physicalSize)||Math.abs(this._estScrollHeight-this._scrollHeight)>=this._optPhysicalSize)this.$.items.style.height=this._estScrollHeight+"px",this._scrollHeight=this._estScrollHeight},scrollToIndex:function(f){Polymer.dom.flush();f=Math.min(Math.max(f,0),this._virtualCount-1);if(!this._isIndexRendered(f)||f>=this._maxVirtualStart)this._virtualStart=
f-1;this._assignModels();this._updateMetrics();this._physicalTop=Math.floor(this._virtualStart/this._itemsPerRow)*this._physicalAverage;for(var h=this._physicalStart,k=this._virtualStart,r=0,l=this._hiddenContentSize;k<f&&r<=l;)r+=this._getPhysicalSizeIncrement(h),h=(h+1)%this._physicalCount,k++;this._updateScrollerSize(!0);this._positionItems();this._resetScrollPosition(this._physicalTop+this._scrollerPaddingTop+r);this._increasePoolIfNeeded();this._lastVisibleIndexVal=this._firstVisibleIndexVal=
null},_resetAverage:function(){this._physicalAverageCount=this._physicalAverage=0},_resizeHandler:function(){Polymer.dom.addDebouncer(this.debounce("_debounceTemplate",function(){this.updateViewportBoundaries();this._render();this._itemsRendered&&this._physicalItems&&this._isVisible&&(this._resetAverage(),this.scrollToIndex(this.firstVisibleIndex))}.bind(this),1))},updateSizeForItem:function(f){f=this._physicalIndexForKey[f];null!=f&&(this._updateMetrics([f]),this._positionItems())},_isIndexRendered:function(f){return f>=
this._virtualStart&&f<=this._virtualEnd},_isIndexVisible:function(f){return f>=this.firstVisibleIndex&&f<=this.lastVisibleIndex}}}();vaadin.elements.grid.IronListBehavior=[Polymer.Templatizer,Polymer.IronScrollTargetBehavior,vaadin.elements.grid.IronListBehaviorImpl];

//# sourceURL=build://vaadin-grid/vaadin-grid-column.html.js
window.vaadin=window.vaadin||{};vaadin.elements=vaadin.elements||{};vaadin.elements.grid=vaadin.elements.grid||{};
vaadin.elements.grid.ColumnBaseBehavior={properties:{resizable:{type:Boolean,value:function(){if("vaadin-grid-column-group"!==this.localName){var b=Polymer.dom(this).parentNode;return b&&"vaadin-grid-column-group"===b.localName?b.resizable||!1:!1}}},headerTemplate:{type:Object},footerTemplate:{type:Object},frozen:{type:Boolean,notify:!0,value:!1},hidden:{type:Boolean,notify:!0},_lastFrozen:{type:Boolean,notify:!0,value:!1},_order:Number,_reorderStatus:Boolean},observers:["_footerTemplateChanged(footerTemplate)",
"_headerTemplateChanged(headerTemplate)","_lastFrozenChanged(_lastFrozen)"],created:function(){function b(d){0<=d.addedNodes.length&&(this.headerTemplate=this._prepareHeaderTemplate(),this.footerTemplate=this._prepareFooterTemplate(),this.template=this._prepareBodyTemplate())}this._templateObserver=Polymer.Element?new Polymer.FlattenedNodesObserver(this,b):Polymer.dom(this).observeNodes(b)},_prepareHeaderTemplate:function(){return this._prepareTemplatizer(this._findTemplate("template.header")||null,
{})},_prepareFooterTemplate:function(){return this._prepareTemplatizer(this._findTemplate("template.footer")||null,{})},_prepareBodyTemplate:function(){return this._prepareTemplatizer(this._findTemplate("template:not(.header):not(.footer)",{}))},_prepareTemplatizer:function(b,d){if(b&&!b.templatizer){var f=new vaadin.elements.grid.Templatizer;f.dataHost=this.dataHost;f._instanceProps=d||f._instanceProps;f.template=b;b.templatizer=f}return b},_selectFirstTemplate:function(b){return Array.prototype.filter.call(Polymer.dom(this).querySelectorAll(b),
function(d){return Polymer.dom(d).parentNode===this}.bind(this))[0]},_findTemplate:function(b){(b=this._selectFirstTemplate(b))&&this.dataHost&&(b._rootDataHost=this.dataHost._rootDataHost||this.dataHost);return b},_headerTemplateChanged:function(b){this.fire("property-changed",{path:"headerTemplate",value:b})},_footerTemplateChanged:function(b){this.fire("property-changed",{path:"footerTemplate",value:b})},_flexGrowChanged:function(b){this.fire("property-changed",{path:"flexGrow",value:b})},_widthChanged:function(b){this.fire("property-changed",
{path:"width",value:b})},_lastFrozenChanged:function(b){this.fire("property-changed",{path:"lastFrozen",value:b})}};
vaadin.elements.grid.ColumnBehaviorImpl={properties:{width:{type:String,value:"100px"},flexGrow:{type:Number,value:1},template:{type:Object}},observers:"_flexGrowChanged(flexGrow);_widthChanged(width);_templateChanged(template);_frozenChanged(frozen, isAttached);_hiddenChanged(hidden);_orderChanged(_order);_reorderStatusChanged(_reorderStatus);_resizableChanged(resizable)".split(";"),_frozenChanged:function(b,d){void 0!==b&&void 0!==d&&(void 0===this._oldFrozen&&!1===b||this.fire("property-changed",
{path:"frozen",value:b}),this._oldFrozen=b)},_templateChanged:function(b){b&&b.templatizer&&Polymer.dom(this.root).appendChild(b.templatizer);this.fire("property-changed",{path:"template",value:b},{bubbles:!1})},_hiddenChanged:function(b){this.fire("property-changed",{path:"hidden",value:b})},_orderChanged:function(b){this.fire("property-changed",{path:"order",value:b})},_reorderStatusChanged:function(b){this.fire("property-changed",{path:"reorderStatus",value:b})},_resizableChanged:function(b){this.fire("property-changed",
{path:"resizable",value:b})}};vaadin.elements.grid.ColumnBehavior=[vaadin.elements.grid.ColumnBaseBehavior,vaadin.elements.grid.ColumnBehaviorImpl];

//# sourceURL=build://vaadin-grid/vaadin-grid-array-data-provider-behavior.html.js
window.vaadin=window.vaadin||{};vaadin.elements=vaadin.elements||{};vaadin.elements.grid=vaadin.elements.grid||{};
vaadin.elements.grid.ArrayDataProviderBehavior={properties:{items:Array},observers:["_itemsChanged(items, items.*)"],_itemsChanged:function(b,d){void 0!==b&&void 0!==d&&(this.size=(b||[]).length,this.dataProvider=this.dataProvider||this._arrayDataProvider,this.clearCache())},_arrayDataProvider:function(b,d){var f=(this.items||[]).slice(0);this._checkPaths(this._filters,"filtering",f)&&(f=this._filter(f));this.size=f.length;b.sortOrders.length&&this._checkPaths(this._sorters,"sorting",f)&&(f=f.sort(this._multiSort.bind(this)));
var h=b.page*b.pageSize;d(f.slice(h,h+b.pageSize),f.length)},_checkPaths:function(b,d,f){if(!f.length)return!1;var h=!0,k;for(k in b){var r=b[k].path;if(r&&-1!==r.indexOf(".")){var l=r.replace(/\.[^\.]*$/,"");void 0===Polymer.Base.get(l,f[0])&&(console.warn('Path "'+r+'" used for '+d+" does not exist in all of the items, "+d+" is disabled."),h=!1)}}return h},_multiSort:function(b,d){return this._sorters.map(function(f){return"asc"===f.direction?this._compare(Polymer.Base.get(f.path,b),Polymer.Base.get(f.path,
d)):"desc"===f.direction?this._compare(Polymer.Base.get(f.path,d),Polymer.Base.get(f.path,b)):0},this).reduce(function(f,h){return f?f:h},0)},_normalizeEmptyValue:function(b){return 0<=[void 0,null].indexOf(b)?"":isNaN(b)?b.toString():b},_compare:function(b,d){b=this._normalizeEmptyValue(b);d=this._normalizeEmptyValue(d);return b<d?-1:b>d?1:0},_filter:function(b){return b.filter(function(d){return 0===this._filters.filter(function(f){return-1===this._normalizeEmptyValue(Polymer.Base.get(f.path,d)).toString().toLowerCase().indexOf(f.value.toString().toLowerCase())}.bind(this)).length},
this)}};

//# sourceURL=build://vaadin-grid/vaadin-grid-dynamic-columns-behavior.html.js
window.vaadin=window.vaadin||{};vaadin.elements=vaadin.elements||{};vaadin.elements.grid=vaadin.elements.grid||{};
vaadin.elements.grid.DynamicColumnsBehavior={ready:function(){this._addNodeObserver()},_hasColumnGroups:function(b){for(var d=0;d<b.length;d++)if("vaadin-grid-column-group"===b[d].localName)return!0;return!1},_getChildColumns:function(b){return Polymer.dom(b).queryDistributedElements("vaadin-grid-column, vaadin-grid-column-group, vaadin-grid-selection-column")},_flattenColumnGroups:function(b){return b.map(function(d){return"vaadin-grid-column-group"===d.localName?this._getChildColumns(d):[d]},this).reduce(function(d,
f){return d.concat(f)},[])},_getColumnTree:function(){for(var b=[],d=this.queryAllEffectiveChildren("vaadin-grid-column, vaadin-grid-column-group, vaadin-grid-selection-column");;){b.push(d);if(!this._hasColumnGroups(d))break;d=this._flattenColumnGroups(d)}return b},_updateColumnTree:function(){var b=this._getColumnTree();this._arrayEquals(b,this._columnTree)||(this._columnTree=b)},_addNodeObserver:function(){this._observer=Polymer.dom(this).observeNodes(function(b){function d(f){return f.nodeType===
Node.ELEMENT_NODE&&/^vaadin-grid-(column|selection)/i.test(f.localName)}(0<b.addedNodes.filter(d).length||0<b.removedNodes.filter(d).length)&&this._updateColumnTree();(Polymer.Settings.useNativeShadow||Polymer.Settings.useShadow)&&Polymer.dom(this).appendChild(this.$.footerFocusTrap);this.debounce("check-imports",this._checkImports,2E3)}.bind(this))},_arrayEquals:function(b,d){if(!b||!d||b.length!=d.length)return!1;for(var f=0,h=b.length;f<h;f++)if(b[f]instanceof Array&&d[f]instanceof Array){if(!this._arrayEquals(b[f],
d[f]))return!1}else if(b[f]!=d[f])return!1;return!0},_checkImports:function(){["vaadin-grid-column-group","vaadin-grid-sorter","vaadin-grid-filter","vaadin-grid-selection-column"].forEach(function(b){var d=Polymer.dom(this).querySelector(b);!d||(Polymer.isInstance?Polymer.isInstance(d):d instanceof Polymer.Element)||console.warn("Make sure you have imported the required module for \x3c"+b+"\x3e element.")},this)}};

//# sourceURL=build://vaadin-grid/vaadin-grid-sort-behavior.html.js
window.vaadin=window.vaadin||{};vaadin.elements=vaadin.elements||{};vaadin.elements.grid=vaadin.elements.grid||{};
vaadin.elements.grid.SortBehavior={properties:{multiSort:{type:Boolean,value:!1},_sorters:{type:Array,value:function(){return[]}},_previousSorters:{type:Array,value:function(){return[]}}},listeners:{"sorter-changed":"_onSorterChanged"},ready:function(){Polymer.Element&&!Polymer.Settings.useNativeShadow&&this.async(function(){var b=Polymer.dom(this).querySelectorAll("vaadin-grid-sorter");Array.prototype.forEach.call(b,function(d){d.fire&&d.fire("sorter-changed")})})},_onSorterChanged:function(b){var d=
b.target;this._removeArrayItem(this._sorters,d);d._order=null;this.multiSort?(d.direction&&this._sorters.unshift(d),this._sorters.forEach(function(f,h){f._order=1<this._sorters.length?h:null},this)):(this._sorters.forEach(function(f){f._order=null;f.direction=null}),d.direction&&(this._sorters=[d]));b.stopPropagation();this.dataProvider&&JSON.stringify(this._previousSorters)!==JSON.stringify(this._mapSorters())&&this.clearCache();this._previousSorters=this._mapSorters()},_mapSorters:function(){return this._sorters.map(function(b){return{path:b.path,
direction:b.direction}})},_removeArrayItem:function(b,d){d=b.indexOf(d);-1<d&&b.splice(d,1)}};

//# sourceURL=build://vaadin-grid/vaadin-grid-filter-behavior.html.js
window.vaadin=window.vaadin||{};vaadin.elements=vaadin.elements||{};vaadin.elements.grid=vaadin.elements.grid||{};vaadin.elements.grid.FilterBehavior={properties:{_filters:{type:Array,value:function(){return[]}}},listeners:{"filter-changed":"_filterChanged"},_filterChanged:function(b){-1===this._filters.indexOf(b.target)&&this._filters.push(b.target);b.stopPropagation();this.dataProvider&&this.clearCache()},_mapFilters:function(){return this._filters.map(function(b){return{path:b.path,value:b.value}})}};

//# sourceURL=build://tf-hparams-parallel-coords-plot/utils.html.js
(function(b){(function(d){(function(f){function h(k,r,l){function p(){if(0===k.length)return[1,2];const [m,n]=d3.extent(k);return m!==n?[m,n]:0<m?[.5*m,1.5*m]:0>m?[1.5*m,.5*m]:[-1,1]}if("LINEAR"===l)return d3.scaleLinear().domain(p()).range([r,0]);if("LOG"===l)return l=p(),0>=l[0]&&0<=l[1]?h(k,r,"LINEAR"):d3.scaleLog().domain(l).range([r,0]);if("QUANTILE"===l)return l=d3.range(20).map(m=>r-m*r/19),0===k.length&&(k=[1]),d3.scaleQuantile().domain(_.uniq(k)).range(l);if("NON_NUMERIC"===l)return d3.scalePoint().domain(_.uniq(k.sort())).range([r,
0]).padding(.1);throw RangeError("Unknown scale: "+l);}f.findClosestPath=function(k,r,l){function p(y,x,C,G){const D=y-C,B=x-G;C=m-C;G=n-G;const H=(D*C+B*G)/(D*D+B*B);return 0>=H?b.hparams.utils.l2NormSquared(C,G):1<=H?b.hparams.utils.l2NormSquared(y-m,x-n):b.hparams.utils.l2NormSquared(C-H*D,G-H*B)}if(2>r.length)return console.error("Less than two axes in parallel coordinates plot."),null;const m=l[0],n=l[1];if(m<=r[0]||m>=r[r.length-1])return null;const q=_.sortedIndex(r,m);console.assert(0<q);
console.assert(q<r.length);const u=q-1;let w=null,A=null;k.forEach(y=>{const x=p(y.controlPoints[u][0],y.controlPoints[u][1],y.controlPoints[q][0],y.controlPoints[q][1]);100<x||!(null===w||x<w)||(w=x,A=y)});return A};f.pointScaleInverseImage=function(k,r,l){return k.domain().filter(p=>{p=k(p);return r<=p&&p<=l})};f.quantileScaleInverseImage=function(k,r,l){const p=k.range(),m=p.filter(n=>r<=n&&n<=l).map(n=>{const q=k.invertExtent(n);return n===p[p.length-1]?[q[0],q[1]+1]:q});return 0==m.length?[0,
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//# sourceURL=build://tf-hparams-google-analytics-tracker/tf-hparams-google-analytics-tracker.html.js
(function(){Polymer({is:"tf-hparams-google-analytics-tracker",handleEvent:function(){}})})();

//# sourceURL=build://tf-hparams-backend/tf-hparams-backend.html.js
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//# sourceURL=build://tf-imports/three.js
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Na=0;6>Na;Na++)Ya[Na]=Ja||ma?ma?ha.image[Na].image:ha.image[Na]:F(ha.image[Na],!1,!0,t.maxCubemapSize);var cb=Ya[0],ia=I(cb)||t.isWebGL2,Ga=v.convert(ha.format),La=v.convert(ha.type),nb=U(Ga,La);ta(34067,ha,ia);if(Ja){for(Na=0;6>Na;Na++){var Va=Ya[Na].mipmaps;for(Ja=0;Ja<Va.length;Ja++){var ib=Va[Ja];1023!==ha.format&&1022!==ha.format?-1<e.getCompressedTextureFormats().indexOf(Ga)?e.compressedTexImage2D(34069+Na,Ja,nb,ib.width,ib.height,0,ib.data):console.warn("THREE.WebGLRenderer: Attempt to load unsupported compressed texture format in .setTextureCube()"):
e.texImage2D(34069+Na,Ja,nb,ib.width,ib.height,0,Ga,La,ib.data)}}za.__maxMipLevel=Va.length-1}else{Va=ha.mipmaps;for(Na=0;6>Na;Na++)if(ma)for(e.texImage2D(34069+Na,0,nb,Ya[Na].width,Ya[Na].height,0,Ga,La,Ya[Na].data),Ja=0;Ja<Va.length;Ja++)ib=Va[Ja],ib=ib.image[Na].image,e.texImage2D(34069+Na,Ja+1,nb,ib.width,ib.height,0,Ga,La,ib.data);else for(e.texImage2D(34069+Na,0,nb,Ga,La,Ya[Na]),Ja=0;Ja<Va.length;Ja++)ib=Va[Ja],e.texImage2D(34069+Na,Ja+1,nb,Ga,La,ib.image[Na]);za.__maxMipLevel=Va.length}P(ha,
ia)&&Q(34067,ha,cb.width,cb.height);za.__version=ha.version;if(ha.onUpdate)ha.onUpdate(ha)}else e.activeTexture(33984+ma),e.bindTexture(34067,za.__webglTexture)}}function na(ha,ma){e.activeTexture(33984+ma);e.bindTexture(34067,g.get(ha).__webglTexture)}function ta(ha,ma,za){za?(a.texParameteri(ha,10242,v.convert(ma.wrapS)),a.texParameteri(ha,10243,v.convert(ma.wrapT)),32879!==ha&&35866!==ha||a.texParameteri(ha,32882,v.convert(ma.wrapR)),a.texParameteri(ha,10240,v.convert(ma.magFilter)),a.texParameteri(ha,
10241,v.convert(ma.minFilter))):(a.texParameteri(ha,10242,33071),a.texParameteri(ha,10243,33071),32879!==ha&&35866!==ha||a.texParameteri(ha,32882,33071),1001===ma.wrapS&&1001===ma.wrapT||console.warn("THREE.WebGLRenderer: Texture is not power of two. Texture.wrapS and Texture.wrapT should be set to THREE.ClampToEdgeWrapping."),a.texParameteri(ha,10240,V(ma.magFilter)),a.texParameteri(ha,10241,V(ma.minFilter)),1003!==ma.minFilter&&1006!==ma.minFilter&&console.warn("THREE.WebGLRenderer: Texture is not power of two. Texture.minFilter should be set to THREE.NearestFilter or THREE.LinearFilter."));
!(za=c.get("EXT_texture_filter_anisotropic"))||1015===ma.type&&null===c.get("OES_texture_float_linear")||1016===ma.type&&null===(t.isWebGL2||c.get("OES_texture_half_float_linear"))||!(1<ma.anisotropy||g.get(ma).__currentAnisotropy)||(a.texParameterf(ha,za.TEXTURE_MAX_ANISOTROPY_EXT,Math.min(ma.anisotropy,t.getMaxAnisotropy())),g.get(ma).__currentAnisotropy=ma.anisotropy)}function Ba(ha,ma){void 0===ha.__webglInit&&(ha.__webglInit=!0,ma.addEventListener("dispose",ea),ha.__webglTexture=a.createTexture(),
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6402;if(1015===ma.type){if(!t.isWebGL2)throw Error("Float Depth Texture only supported in WebGL2.0");ia=36012}else t.isWebGL2&&(ia=33189);1026===ma.format&&6402===ia&&1012!==ma.type&&1014!==ma.type&&(console.warn("THREE.WebGLRenderer: Use UnsignedShortType or UnsignedIntType for DepthFormat DepthTexture."),ma.type=1012,cb=v.convert(ma.type));1027===ma.format&&(ia=34041,1020!==ma.type&&(console.warn("THREE.WebGLRenderer: Use UnsignedInt248Type for DepthStencilFormat DepthTexture."),ma.type=1020,cb=
v.convert(ma.type)));e.texImage2D(3553,0,ia,za.width,za.height,0,Na,cb,null)}else if(ma.isDataTexture)if(0<Ga.length&&Ya){for(var La=0,nb=Ga.length;La<nb;La++)Ja=Ga[La],e.texImage2D(3553,La,ia,Ja.width,Ja.height,0,Na,cb,Ja.data);ma.generateMipmaps=!1;ha.__maxMipLevel=Ga.length-1}else e.texImage2D(3553,0,ia,za.width,za.height,0,Na,cb,za.data),ha.__maxMipLevel=0;else if(ma.isCompressedTexture){La=0;for(nb=Ga.length;La<nb;La++)Ja=Ga[La],1023!==ma.format&&1022!==ma.format?-1<e.getCompressedTextureFormats().indexOf(Na)?
e.compressedTexImage2D(3553,La,ia,Ja.width,Ja.height,0,Ja.data):console.warn("THREE.WebGLRenderer: Attempt to load unsupported compressed texture format in .uploadTexture()"):e.texImage2D(3553,La,ia,Ja.width,Ja.height,0,Na,cb,Ja.data);ha.__maxMipLevel=Ga.length-1}else if(ma.isDataTexture2DArray)e.texImage3D(35866,0,ia,za.width,za.height,za.depth,0,Na,cb,za.data),ha.__maxMipLevel=0;else if(ma.isDataTexture3D)e.texImage3D(32879,0,ia,za.width,za.height,za.depth,0,Na,cb,za.data),ha.__maxMipLevel=0;else if(0<
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za,Ja,g.get(ma.texture).__webglTexture,0);a.bindFramebuffer(36160,null)}function Ca(ha,ma,za){a.bindRenderbuffer(36161,ha);if(ma.depthBuffer&&!ma.stencilBuffer)za?(za=Ma(ma),a.renderbufferStorageMultisample(36161,za,33189,ma.width,ma.height)):a.renderbufferStorage(36161,33189,ma.width,ma.height),a.framebufferRenderbuffer(36160,36096,36161,ha);else if(ma.depthBuffer&&ma.stencilBuffer)za?(za=Ma(ma),a.renderbufferStorageMultisample(36161,za,35056,ma.width,ma.height)):a.renderbufferStorage(36161,34041,
ma.width,ma.height),a.framebufferRenderbuffer(36160,33306,36161,ha);else{ha=v.convert(ma.texture.format);var Ja=v.convert(ma.texture.type);ha=U(ha,Ja);za?(za=Ma(ma),a.renderbufferStorageMultisample(36161,za,ha,ma.width,ma.height)):a.renderbufferStorage(36161,ha,ma.width,ma.height)}a.bindRenderbuffer(36161,null)}function Ha(ha,ma){if(ma&&ma.isWebGLRenderTargetCube)throw Error("Depth Texture with cube render targets is not supported");a.bindFramebuffer(36160,ha);if(!ma.depthTexture||!ma.depthTexture.isDepthTexture)throw Error("renderTarget.depthTexture must be an instance of THREE.DepthTexture");
g.get(ma.depthTexture).__webglTexture&&ma.depthTexture.image.width===ma.width&&ma.depthTexture.image.height===ma.height||(ma.depthTexture.image.width=ma.width,ma.depthTexture.image.height=ma.height,ma.depthTexture.needsUpdate=!0);qa(ma.depthTexture,0);ha=g.get(ma.depthTexture).__webglTexture;if(1026===ma.depthTexture.format)a.framebufferTexture2D(36160,36096,3553,ha,0);else if(1027===ma.depthTexture.format)a.framebufferTexture2D(36160,33306,3553,ha,0);else throw Error("Unknown depthTexture format");
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ha);a.bindFramebuffer(36160,null)}function Ma(ha){return t.isWebGL2&&ha.isWebGLMultisampleRenderTarget?Math.min(t.maxSamples,ha.samples):0}function db(ha){var ma=z.render.frame;tb.get(ha)!==ma&&(tb.set(ha,ma),ha.update())}var tb=new WeakMap,Ka,bb="undefined"!==typeof OffscreenCanvas,jb=0,Eb=!1,xb=!1;this.allocateTextureUnit=function(){var ha=jb;ha>=t.maxTextures&&console.warn("THREE.WebGLTextures: Trying to use "+ha+" texture units while this GPU supports only "+t.maxTextures);jb+=1;return ha};this.resetTextureUnits=
function(){jb=0};this.setTexture2D=qa;this.setTexture2DArray=function(ha,ma){var za=g.get(ha);0<ha.version&&za.__version!==ha.version?Ta(za,ha,ma):(e.activeTexture(33984+ma),e.bindTexture(35866,za.__webglTexture))};this.setTexture3D=function(ha,ma){var za=g.get(ha);0<ha.version&&za.__version!==ha.version?Ta(za,ha,ma):(e.activeTexture(33984+ma),e.bindTexture(32879,za.__webglTexture))};this.setTextureCube=ua;this.setTextureCubeDynamic=na;this.setupRenderTarget=function(ha){var ma=g.get(ha),za=g.get(ha.texture);
ha.addEventListener("dispose",da);za.__webglTexture=a.createTexture();z.memory.textures++;var Ja=!0===ha.isWebGLRenderTargetCube,Ya=!0===ha.isWebGLMultisampleRenderTarget,Na=I(ha)||t.isWebGL2;if(Ja)for(ma.__webglFramebuffer=[],Ya=0;6>Ya;Ya++)ma.__webglFramebuffer[Ya]=a.createFramebuffer();else if(ma.__webglFramebuffer=a.createFramebuffer(),Ya)if(t.isWebGL2){ma.__webglMultisampledFramebuffer=a.createFramebuffer();ma.__webglColorRenderbuffer=a.createRenderbuffer();a.bindRenderbuffer(36161,ma.__webglColorRenderbuffer);
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if(Ja){e.bindTexture(34067,za.__webglTexture);ta(34067,ha.texture,Na);for(Ya=0;6>Ya;Ya++)Ua(ma.__webglFramebuffer[Ya],ha,36064,34069+Ya);P(ha.texture,Na)&&Q(34067,ha.texture,ha.width,ha.height);e.bindTexture(34067,null)}else e.bindTexture(3553,za.__webglTexture),ta(3553,ha.texture,Na),Ua(ma.__webglFramebuffer,ha,36064,3553),P(ha.texture,Na)&&Q(3553,ha.texture,ha.width,ha.height),e.bindTexture(3553,null);ha.depthBuffer&&Da(ha)};this.updateRenderTargetMipmap=function(ha){var ma=ha.texture,za=I(ha)||
t.isWebGL2;if(P(ma,za)){za=ha.isWebGLRenderTargetCube?34067:3553;var Ja=g.get(ma).__webglTexture;e.bindTexture(za,Ja);Q(za,ma,ha.width,ha.height);e.bindTexture(za,null)}};this.updateMultisampleRenderTarget=function(ha){if(ha.isWebGLMultisampleRenderTarget)if(t.isWebGL2){var ma=g.get(ha);a.bindFramebuffer(36008,ma.__webglMultisampledFramebuffer);a.bindFramebuffer(36009,ma.__webglFramebuffer);ma=ha.width;var za=ha.height,Ja=16384;ha.depthBuffer&&(Ja|=256);ha.stencilBuffer&&(Ja|=1024);a.blitFramebuffer(0,
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g||35841===g||35842===g||35843===g)if(t=c.get("WEBGL_compressed_texture_pvrtc"),null!==t){if(35840===g)return t.COMPRESSED_RGB_PVRTC_4BPPV1_IMG;if(35841===g)return t.COMPRESSED_RGB_PVRTC_2BPPV1_IMG;if(35842===g)return t.COMPRESSED_RGBA_PVRTC_4BPPV1_IMG;if(35843===g)return t.COMPRESSED_RGBA_PVRTC_2BPPV1_IMG}if(36196===g&&(t=c.get("WEBGL_compressed_texture_etc1"),null!==t))return t.COMPRESSED_RGB_ETC1_WEBGL;if(37808===g||37809===g||37810===g||37811===g||37812===g||37813===g||37814===g||37815===g||37816===
g||37817===g||37818===g||37819===g||37820===g||37821===g)if(t=c.get("WEBGL_compressed_texture_astc"),null!==t)return g;if(103===g||104===g){if(e.isWebGL2){if(103===g)return 32775;if(104===g)return 32776}t=c.get("EXT_blend_minmax");if(null!==t){if(103===g)return t.MIN_EXT;if(104===g)return t.MAX_EXT}}if(1020===g){if(e.isWebGL2)return 34042;t=c.get("WEBGL_depth_texture");if(null!==t)return t.UNSIGNED_INT_24_8_WEBGL}return 0}}}function we(){A.call(this);this.type="Group"}function wf(a){vb.call(this);
this.cameras=a||[]}function Gj(a,c,e){Hj.setFromMatrixPosition(c.matrixWorld);Ij.setFromMatrixPosition(e.matrixWorld);var g=Hj.distanceTo(Ij),t=c.projectionMatrix.elements,v=e.projectionMatrix.elements,z=t[14]/(t[10]-1);e=t[14]/(t[10]+1);var E=(t[9]+1)/t[5],F=(t[9]-1)/t[5],I=(t[8]-1)/t[0],M=(v[8]+1)/v[0];t=z*I;v=z*M;M=g/(-I+M);I=M*-I;c.matrixWorld.decompose(a.position,a.quaternion,a.scale);a.translateX(I);a.translateZ(M);a.matrixWorld.compose(a.position,a.quaternion,a.scale);a.matrixWorldInverse.getInverse(a.matrixWorld);
c=z+M;z=e+M;a.projectionMatrix.makePerspective(t-I,v+(g-I),E*e/z*c,F*e/z*c,c,z)}function Sh(a){function c(){return null!==I&&!0===I.isPresenting}function e(){if(c()){var Ha=I.getEyeParameters("left");z=2*Ha.renderWidth*ea;E=Ha.renderHeight*ea;Ta=a.getPixelRatio();a.getSize(Ba);a.setDrawingBufferSize(z,E,1);ua.viewport.set(0,0,z/2,E);na.viewport.set(z/2,0,z/2,E);Ca.start();F.dispatchEvent({type:"sessionstart"})}else F.enabled&&a.setDrawingBufferSize(Ba.width,Ba.height,Ta),Ca.stop(),F.dispatchEvent({type:"sessionend"})}
function g(Ha){for(var Da=navigator.getGamepads&&navigator.getGamepads(),Ma=0,db=0,tb=Da.length;Ma<tb;Ma++){var Ka=Da[Ma];if(Ka&&("Daydream Controller"===Ka.id||"Gear VR Controller"===Ka.id||"Oculus Go Controller"===Ka.id||"OpenVR Gamepad"===Ka.id||Ka.id.startsWith("Oculus Touch")||Ka.id.startsWith("HTC Vive Focus")||Ka.id.startsWith("Spatial Controller"))){if(db===Ha)return Ka;db++}}}function t(){for(var Ha=0;Ha<Q.length;Ha++){var Da=Q[Ha],Ma=g(Ha);if(void 0!==Ma&&void 0!==Ma.pose){if(null===Ma.pose)break;
var db=Ma.pose;!1===db.hasPosition&&Da.position.set(.2,-.6,-.05);null!==db.position&&Da.position.fromArray(db.position);null!==db.orientation&&Da.quaternion.fromArray(db.orientation);Da.matrix.compose(Da.position,Da.quaternion,Da.scale);Da.matrix.premultiply(U);Da.matrix.decompose(Da.position,Da.quaternion,Da.scale);Da.matrixWorldNeedsUpdate=!0;Da.visible=!0;db="Daydream Controller"===Ma.id?0:1;void 0===Ua[Ha]&&(Ua[Ha]=!1);Ua[Ha]!==Ma.buttons[db].pressed&&(Ua[Ha]=Ma.buttons[db].pressed,!0===Ua[Ha]?
Da.dispatchEvent({type:"selectstart"}):(Da.dispatchEvent({type:"selectend"}),Da.dispatchEvent({type:"select"})))}else Da.visible=!1}}function v(Ha,Da){null!==Da&&4===Da.length&&Ha.set(Da[0]*z,Da[1]*E,Da[2]*z,Da[3]*E)}var z,E,F=this,I=null,M=null,P=null,Q=[],U=new q,V=new q,ea=1,da="local-floor";"undefined"!==typeof window&&"VRFrameData"in window&&(M=new window.VRFrameData,window.addEventListener("vrdisplaypresentchange",e,!1));var ra=new q,pa=new h,qa=new k,ua=new vb;ua.viewport=new p;ua.layers.enable(1);
var na=new vb;na.viewport=new p;na.layers.enable(2);var ta=new wf([ua,na]);ta.layers.enable(1);ta.layers.enable(2);var Ba=new f,Ta,Ua=[];this.enabled=!1;this.getController=function(Ha){var Da=Q[Ha];void 0===Da&&(Da=new we,Da.matrixAutoUpdate=!1,Da.visible=!1,Q[Ha]=Da);return Da};this.getDevice=function(){return I};this.setDevice=function(Ha){void 0!==Ha&&(I=Ha);Ca.setContext(Ha)};this.setFramebufferScaleFactor=function(Ha){ea=Ha};this.setReferenceSpaceType=function(Ha){da=Ha};this.setPoseTarget=function(Ha){void 0!==
Ha&&(P=Ha)};this.getCamera=function(Ha){var Da="local-floor"===da?1.6:0;if(!1===c())return Ha.position.set(0,Da,0),Ha.rotation.set(0,0,0),Ha;I.depthNear=Ha.near;I.depthFar=Ha.far;I.getFrameData(M);if("local-floor"===da){var Ma=I.stageParameters;Ma?U.fromArray(Ma.sittingToStandingTransform):U.makeTranslation(0,Da,0)}Da=M.pose;Ma=null!==P?P:Ha;Ma.matrix.copy(U);Ma.matrix.decompose(Ma.position,Ma.quaternion,Ma.scale);null!==Da.orientation&&(pa.fromArray(Da.orientation),Ma.quaternion.multiply(pa));null!==
Da.position&&(pa.setFromRotationMatrix(U),qa.fromArray(Da.position),qa.applyQuaternion(pa),Ma.position.add(qa));Ma.updateMatrixWorld();ua.near=Ha.near;na.near=Ha.near;ua.far=Ha.far;na.far=Ha.far;ua.matrixWorldInverse.fromArray(M.leftViewMatrix);na.matrixWorldInverse.fromArray(M.rightViewMatrix);V.getInverse(U);"local-floor"===da&&(ua.matrixWorldInverse.multiply(V),na.matrixWorldInverse.multiply(V));Ha=Ma.parent;null!==Ha&&(ra.getInverse(Ha.matrixWorld),ua.matrixWorldInverse.multiply(ra),na.matrixWorldInverse.multiply(ra));
ua.matrixWorld.getInverse(ua.matrixWorldInverse);na.matrixWorld.getInverse(na.matrixWorldInverse);ua.projectionMatrix.fromArray(M.leftProjectionMatrix);na.projectionMatrix.fromArray(M.rightProjectionMatrix);Gj(ta,ua,na);Ha=I.getLayers();Ha.length&&(Ha=Ha[0],v(ua.viewport,Ha.leftBounds),v(na.viewport,Ha.rightBounds));t();return ta};this.getStandingMatrix=function(){return U};this.isPresenting=c;var Ca=new ec;this.setAnimationLoop=function(Ha){Ca.setAnimationLoop(Ha);c()&&Ca.start()};this.submitFrame=
function(){c()&&I.submitFrame()};this.dispose=function(){"undefined"!==typeof window&&window.removeEventListener("vrdisplaypresentchange",e)};this.setFrameOfReferenceType=function(){console.warn("THREE.WebVRManager: setFrameOfReferenceType() has been deprecated.")}}function Jj(a,c){function e(){return null!==F&&null!==I}function g(qa){for(var ua=0;ua<Q.length;ua++)U[ua]===qa.inputSource&&Q[ua].dispatchEvent({type:qa.type})}function t(){a.setFramebuffer(null);a.setRenderTarget(a.getRenderTarget());
pa.stop();E.dispatchEvent({type:"sessionend"})}function v(qa){I=qa;pa.setContext(F);pa.start();E.dispatchEvent({type:"sessionstart"})}function z(qa,ua){null===ua?qa.matrixWorld.copy(qa.matrix):qa.matrixWorld.multiplyMatrices(ua.matrixWorld,qa.matrix);qa.matrixWorldInverse.getInverse(qa.matrixWorld)}var E=this,F=null,I=null,M="local-floor",P=null,Q=[],U=[],V=new vb;V.layers.enable(1);V.viewport=new p;var ea=new vb;ea.layers.enable(2);ea.viewport=new p;var da=new wf([V,ea]);da.layers.enable(1);da.layers.enable(2);
this.enabled=!1;this.getController=function(qa){var ua=Q[qa];void 0===ua&&(ua=new we,ua.matrixAutoUpdate=!1,ua.visible=!1,Q[qa]=ua);return ua};this.setFramebufferScaleFactor=function(){};this.setReferenceSpaceType=function(qa){M=qa};this.getSession=function(){return F};this.setSession=function(qa){F=qa;null!==F&&(F.addEventListener("select",g),F.addEventListener("selectstart",g),F.addEventListener("selectend",g),F.addEventListener("end",t),F.updateRenderState({baseLayer:new XRWebGLLayer(F,c)}),F.requestReferenceSpace(M).then(v),
U=F.inputSources,F.addEventListener("inputsourceschange",function(){U=F.inputSources;console.log(U);for(var ua=0;ua<Q.length;ua++)Q[ua].userData.inputSource=U[ua]}))};this.getCamera=function(qa){if(e()){var ua=qa.parent,na=da.cameras;z(da,ua);for(var ta=0;ta<na.length;ta++)z(na[ta],ua);qa.matrixWorld.copy(da.matrixWorld);qa=qa.children;ta=0;for(ua=qa.length;ta<ua;ta++)qa[ta].updateMatrixWorld(!0);Gj(da,V,ea);return da}return qa};this.isPresenting=e;var ra=null,pa=new ec;pa.setAnimationLoop(function(qa,
ua){P=ua.getViewerPose(I);if(null!==P){var na=P.views,ta=F.renderState.baseLayer;a.setFramebuffer(ta.framebuffer);for(var Ba=0;Ba<na.length;Ba++){var Ta=na[Ba],Ua=ta.getViewport(Ta),Ca=da.cameras[Ba];Ca.matrix.fromArray(Ta.transform.inverse.matrix).getInverse(Ca.matrix);Ca.projectionMatrix.fromArray(Ta.projectionMatrix);Ca.viewport.set(Ua.x,Ua.y,Ua.width,Ua.height);0===Ba&&da.matrix.copy(Ca.matrix)}}for(Ba=0;Ba<Q.length;Ba++){na=Q[Ba];if(ta=U[Ba])if(ta=ua.getPose(ta.targetRaySpace,I),null!==ta){na.matrix.fromArray(ta.transform.matrix);
na.matrix.decompose(na.position,na.rotation,na.scale);na.visible=!0;continue}na.visible=!1}ra&&ra(qa)});this.setAnimationLoop=function(qa){ra=qa};this.dispose=function(){};this.getStandingMatrix=function(){console.warn("THREE.WebXRManager: getStandingMatrix() is no longer needed.");return new q};this.getDevice=function(){console.warn("THREE.WebXRManager: getDevice() has been deprecated.")};this.setDevice=function(){console.warn("THREE.WebXRManager: setDevice() has been deprecated.")};this.setFrameOfReferenceType=
function(){console.warn("THREE.WebXRManager: setFrameOfReferenceType() has been deprecated.")};this.submitFrame=function(){}}function Th(a){var c;function e(){return null===ib?fc:1}function g(){Mb=new dd(Ra);gc=new Nb(Ra,Mb,a);gc.isWebGL2||(Mb.get("WEBGL_depth_texture"),Mb.get("OES_texture_float"),Mb.get("OES_texture_half_float"),Mb.get("OES_texture_half_float_linear"),Mb.get("OES_standard_derivatives"),Mb.get("OES_element_index_uint"),Mb.get("ANGLE_instanced_arrays"));Mb.get("OES_texture_float_linear");
Pc=new Fj(Ra,Mb,gc);yb=new pm(Ra,Mb,Pc,gc);yb.scissor(Tb.copy(xe).multiplyScalar(fc).floor());yb.viewport(Lb.copy(ye).multiplyScalar(fc).floor());zd=new ml(Ra);hc=new gm;Qc=new qm(Ra,Mb,yb,hc,gc,Pc,zd);yg=new Qd(Ra);Uh=new vd(Ra,yg,zd);ze=new pl(Uh,zd);Kj=new ol(Ra);Rd=new fm(ia,Mb,gc);zg=new jm;Ae=new om;Ad=new ud(ia,yb,ze,ma);Lj=new sa(Ra,Mb,zd,gc);Mj=new wg(Ra,Mb,zd,gc);zd.programs=Rd.programs;ia.capabilities=gc;ia.extensions=Mb;ia.properties=hc;ia.renderLists=zg;ia.state=yb;ia.info=zd}function t(T){T.preventDefault();
console.log("THREE.WebGLRenderer: Context Lost.");Ga=!0}function v(){console.log("THREE.WebGLRenderer: Context Restored.");Ga=!1;g()}function z(T){T=T.target;T.removeEventListener("dispose",z);E(T)}function E(T){F(T);hc.remove(T)}function F(T){var Y=hc.get(T).program;T.program=void 0;void 0!==Y&&Rd.releaseProgram(Y)}function I(T,Y){T.render(function(la){ia.renderBufferImmediate(la,Y)})}function M(T,Y,la){if(la&&la.isInstancedBufferGeometry&&!gc.isWebGL2&&null===Mb.get("ANGLE_instanced_arrays"))console.error("THREE.WebGLRenderer.setupVertexAttributes: using THREE.InstancedBufferGeometry but hardware does not support extension ANGLE_instanced_arrays.");
else{yb.initAttributes();var Ia=la.attributes;Y=Y.getAttributes();T=T.defaultAttributeValues;for(var Oa in Y){var ab=Y[Oa];if(0<=ab){var Pa=Ia[Oa];if(void 0!==Pa){var fb=Pa.normalized,Cb=Pa.itemSize,ob=yg.get(Pa);if(void 0!==ob){var $a=ob.buffer,Rc=ob.type;ob=ob.bytesPerElement;if(Pa.isInterleavedBufferAttribute){var zc=Pa.data,Be=zc.stride;Pa=Pa.offset;zc&&zc.isInstancedInterleavedBuffer?(yb.enableAttributeAndDivisor(ab,zc.meshPerAttribute),void 0===la.maxInstancedCount&&(la.maxInstancedCount=zc.meshPerAttribute*
zc.count)):yb.enableAttribute(ab);Ra.bindBuffer(34962,$a);Ra.vertexAttribPointer(ab,Cb,Rc,fb,Be*ob,Pa*ob)}else Pa.isInstancedBufferAttribute?(yb.enableAttributeAndDivisor(ab,Pa.meshPerAttribute),void 0===la.maxInstancedCount&&(la.maxInstancedCount=Pa.meshPerAttribute*Pa.count)):yb.enableAttribute(ab),Ra.bindBuffer(34962,$a),Ra.vertexAttribPointer(ab,Cb,Rc,fb,0,0)}}else if(void 0!==T&&(fb=T[Oa],void 0!==fb))switch(fb.length){case 2:Ra.vertexAttrib2fv(ab,fb);break;case 3:Ra.vertexAttrib3fv(ab,fb);break;
case 4:Ra.vertexAttrib4fv(ab,fb);break;default:Ra.vertexAttrib1fv(ab,fb)}}}yb.disableUnusedAttributes()}}function P(T,Y,la,Ia){if(!1!==T.visible){if(T.layers.test(Y.layers))if(T.isGroup)la=T.renderOrder;else if(T.isLOD)!0===T.autoUpdate&&T.update(Y);else if(T.isLight)cb.pushLight(T),T.castShadow&&cb.pushShadow(T);else if(T.isSprite){if(!T.frustumCulled||Vh.intersectsSprite(T)){Ia&&Bd.setFromMatrixPosition(T.matrixWorld).applyMatrix4(xf);var Oa=ze.update(T),ab=T.material;ab.visible&&Na.push(T,Oa,ab,
la,Bd.z,null)}}else if(T.isImmediateRenderObject)Ia&&Bd.setFromMatrixPosition(T.matrixWorld).applyMatrix4(xf),Na.push(T,null,T.material,la,Bd.z,null);else if(T.isMesh||T.isLine||T.isPoints)if(T.isSkinnedMesh&&T.skeleton.update(),!T.frustumCulled||Vh.intersectsObject(T))if(Ia&&Bd.setFromMatrixPosition(T.matrixWorld).applyMatrix4(xf),Oa=ze.update(T),ab=T.material,Array.isArray(ab))for(var Pa=Oa.groups,fb=0,Cb=Pa.length;fb<Cb;fb++){var ob=Pa[fb],$a=ab[ob.materialIndex];$a&&$a.visible&&Na.push(T,Oa,$a,
la,Bd.z,ob)}else ab.visible&&Na.push(T,Oa,ab,la,Bd.z,null);T=T.children;fb=0;for(Cb=T.length;fb<Cb;fb++)P(T[fb],Y,la,Ia)}}function Q(T,Y,la,Ia){for(var Oa=0,ab=T.length;Oa<ab;Oa++){var Pa=T[Oa],fb=Pa.object,Cb=Pa.geometry,ob=void 0===Ia?Pa.material:Ia;Pa=Pa.group;if(la.isArrayCamera){sb=la;for(var $a=la.cameras,Rc=0,zc=$a.length;Rc<zc;Rc++){var Be=$a[Rc];fb.layers.test(Be.layers)&&(yb.viewport(Lb.copy(Be.viewport)),cb.setupLights(Be),U(fb,Y,Be,Cb,ob,Pa))}}else sb=null,U(fb,Y,la,Cb,ob,Pa)}}function U(T,
Y,la,Ia,Oa,ab){T.onBeforeRender(ia,Y,la,Ia,Oa,ab);cb=Ae.get(Y,sb||la);T.modelViewMatrix.multiplyMatrices(la.matrixWorldInverse,T.matrixWorld);T.normalMatrix.getNormalMatrix(T.modelViewMatrix);T.isImmediateRenderObject?(yb.setMaterial(Oa),Ia=ea(la,Y.fog,Oa,T),eb=c=null,mb=!1,I(T,Ia)):ia.renderBufferDirect(la,Y.fog,Ia,Oa,T,ab);cb=Ae.get(Y,sb||la)}function V(T,Y,la){var Ia=hc.get(T),Oa=cb.state.lights,ab=Oa.state.version;la=Rd.getParameters(T,Oa.state,cb.state.shadowsArray,Y,Ac.numPlanes,Ac.numIntersection,
la);var Pa=Rd.getProgramCode(T,la),fb=Ia.program,Cb=!0;if(void 0===fb)T.addEventListener("dispose",z);else if(fb.code!==Pa)F(T);else{if(Ia.lightsStateVersion!==ab)Ia.lightsStateVersion=ab;else if(void 0!==la.shaderID)return;Cb=!1}Cb&&(la.shaderID?(Pa=Oc[la.shaderID],Ia.shader={name:T.type,uniforms:ub(Pa.uniforms),vertexShader:Pa.vertexShader,fragmentShader:Pa.fragmentShader}):Ia.shader={name:T.type,uniforms:T.uniforms,vertexShader:T.vertexShader,fragmentShader:T.fragmentShader},Pa=Rd.getProgramCode(T,
la),fb=Rd.acquireProgram(T,Ia.shader,la,Pa),Ia.program=fb,T.program=fb);la=fb.getAttributes();if(T.morphTargets)for(Pa=T.numSupportedMorphTargets=0;Pa<ia.maxMorphTargets;Pa++)0<=la["morphTarget"+Pa]&&T.numSupportedMorphTargets++;if(T.morphNormals)for(Pa=T.numSupportedMorphNormals=0;Pa<ia.maxMorphNormals;Pa++)0<=la["morphNormal"+Pa]&&T.numSupportedMorphNormals++;la=Ia.shader.uniforms;if(!T.isShaderMaterial&&!T.isRawShaderMaterial||!0===T.clipping)Ia.numClippingPlanes=Ac.numPlanes,Ia.numIntersection=
Ac.numIntersection,la.clippingPlanes=Ac.uniform;Ia.fog=Y;Ia.lightsStateVersion=ab;T.lights&&(la.ambientLightColor.value=Oa.state.ambient,la.lightProbe.value=Oa.state.probe,la.directionalLights.value=Oa.state.directional,la.spotLights.value=Oa.state.spot,la.rectAreaLights.value=Oa.state.rectArea,la.pointLights.value=Oa.state.point,la.hemisphereLights.value=Oa.state.hemi,la.directionalShadowMap.value=Oa.state.directionalShadowMap,la.directionalShadowMatrix.value=Oa.state.directionalShadowMatrix,la.spotShadowMap.value=
Oa.state.spotShadowMap,la.spotShadowMatrix.value=Oa.state.spotShadowMatrix,la.pointShadowMap.value=Oa.state.pointShadowMap,la.pointShadowMatrix.value=Oa.state.pointShadowMatrix);T=Ia.program.getUniforms();T=wd.seqWithValue(T.seq,la);Ia.uniformsList=T}function ea(T,Y,la,Ia){Qc.resetTextureUnits();var Oa=hc.get(la),ab=cb.state.lights;Ag&&(Wh||T!==pb)&&Ac.setState(la.clippingPlanes,la.clipIntersection,la.clipShadows,T,Oa,T===pb&&la.id===Qa);!1===la.needsUpdate&&(void 0===Oa.program?la.needsUpdate=!0:
la.fog&&Oa.fog!==Y?la.needsUpdate=!0:la.lights&&Oa.lightsStateVersion!==ab.state.version?la.needsUpdate=!0:void 0===Oa.numClippingPlanes||Oa.numClippingPlanes===Ac.numPlanes&&Oa.numIntersection===Ac.numIntersection||(la.needsUpdate=!0));la.needsUpdate&&(V(la,Y,Ia),la.needsUpdate=!1);var Pa=!1,fb=ab=!1,Cb=Oa.program,ob=Cb.getUniforms(),$a=Oa.shader.uniforms;yb.useProgram(Cb.program)&&(fb=ab=Pa=!0);la.id!==Qa&&(Qa=la.id,ab=!0);if(Pa||pb!==T){ob.setValue(Ra,"projectionMatrix",T.projectionMatrix);gc.logarithmicDepthBuffer&&
ob.setValue(Ra,"logDepthBufFC",2/(Math.log(T.far+1)/Math.LN2));pb!==T&&(pb=T,fb=ab=!0);if(la.isShaderMaterial||la.isMeshPhongMaterial||la.isMeshStandardMaterial||la.envMap)Pa=ob.map.cameraPosition,void 0!==Pa&&Pa.setValue(Ra,Bd.setFromMatrixPosition(T.matrixWorld));(la.isMeshPhongMaterial||la.isMeshLambertMaterial||la.isMeshBasicMaterial||la.isMeshStandardMaterial||la.isShaderMaterial||la.skinning)&&ob.setValue(Ra,"viewMatrix",T.matrixWorldInverse)}if(la.skinning&&(ob.setOptional(Ra,Ia,"bindMatrix"),
ob.setOptional(Ra,Ia,"bindMatrixInverse"),T=Ia.skeleton))if(Pa=T.bones,gc.floatVertexTextures){if(void 0===T.boneTexture){Pa=Math.sqrt(4*Pa.length);Pa=hb.ceilPowerOfTwo(Pa);Pa=Math.max(Pa,4);var Rc=new Float32Array(Pa*Pa*4);Rc.set(T.boneMatrices);var zc=new Ab(Rc,Pa,Pa,1023,1015);zc.needsUpdate=!0;T.boneMatrices=Rc;T.boneTexture=zc;T.boneTextureSize=Pa}ob.setValue(Ra,"boneTexture",T.boneTexture,Qc);ob.setValue(Ra,"boneTextureSize",T.boneTextureSize)}else ob.setOptional(Ra,T,"boneMatrices");ab&&(ob.setValue(Ra,
"toneMappingExposure",ia.toneMappingExposure),ob.setValue(Ra,"toneMappingWhitePoint",ia.toneMappingWhitePoint),la.lights&&tb($a,fb),Y&&la.fog&&na($a,Y),la.isMeshBasicMaterial?da($a,la):la.isMeshLambertMaterial?(da($a,la),ta($a,la)):la.isMeshPhongMaterial?(da($a,la),la.isMeshToonMaterial?Ta($a,la):Ba($a,la)):la.isMeshStandardMaterial?(da($a,la),la.isMeshPhysicalMaterial?Ca($a,la):Ua($a,la)):la.isMeshMatcapMaterial?(da($a,la),Ha($a,la)):la.isMeshDepthMaterial?(da($a,la),Da($a,la)):la.isMeshDistanceMaterial?
(da($a,la),Ma($a,la)):la.isMeshNormalMaterial?(da($a,la),db($a,la)):la.isLineBasicMaterial?(ra($a,la),la.isLineDashedMaterial&&pa($a,la)):la.isPointsMaterial?qa($a,la):la.isSpriteMaterial?ua($a,la):la.isShadowMaterial&&($a.color.value.copy(la.color),$a.opacity.value=la.opacity),void 0!==$a.ltc_1&&($a.ltc_1.value=Wa.LTC_1),void 0!==$a.ltc_2&&($a.ltc_2.value=Wa.LTC_2),wd.upload(Ra,Oa.uniformsList,$a,Qc));la.isShaderMaterial&&!0===la.uniformsNeedUpdate&&(wd.upload(Ra,Oa.uniformsList,$a,Qc),la.uniformsNeedUpdate=
!1);la.isSpriteMaterial&&ob.setValue(Ra,"center",Ia.center);ob.setValue(Ra,"modelViewMatrix",Ia.modelViewMatrix);ob.setValue(Ra,"normalMatrix",Ia.normalMatrix);ob.setValue(Ra,"modelMatrix",Ia.matrixWorld);return Cb}function da(T,Y){T.opacity.value=Y.opacity;Y.color&&T.diffuse.value.copy(Y.color);Y.emissive&&T.emissive.value.copy(Y.emissive).multiplyScalar(Y.emissiveIntensity);Y.map&&(T.map.value=Y.map);Y.alphaMap&&(T.alphaMap.value=Y.alphaMap);Y.specularMap&&(T.specularMap.value=Y.specularMap);Y.envMap&&
(T.envMap.value=Y.envMap,T.flipEnvMap.value=Y.envMap.isCubeTexture?-1:1,T.reflectivity.value=Y.reflectivity,T.refractionRatio.value=Y.refractionRatio,T.maxMipLevel.value=hc.get(Y.envMap).__maxMipLevel);Y.lightMap&&(T.lightMap.value=Y.lightMap,T.lightMapIntensity.value=Y.lightMapIntensity);Y.aoMap&&(T.aoMap.value=Y.aoMap,T.aoMapIntensity.value=Y.aoMapIntensity);if(Y.map)var la=Y.map;else Y.specularMap?la=Y.specularMap:Y.displacementMap?la=Y.displacementMap:Y.normalMap?la=Y.normalMap:Y.bumpMap?la=Y.bumpMap:
Y.roughnessMap?la=Y.roughnessMap:Y.metalnessMap?la=Y.metalnessMap:Y.alphaMap?la=Y.alphaMap:Y.emissiveMap&&(la=Y.emissiveMap);void 0!==la&&(la.isWebGLRenderTarget&&(la=la.texture),!0===la.matrixAutoUpdate&&la.updateMatrix(),T.uvTransform.value.copy(la.matrix))}function ra(T,Y){T.diffuse.value.copy(Y.color);T.opacity.value=Y.opacity}function pa(T,Y){T.dashSize.value=Y.dashSize;T.totalSize.value=Y.dashSize+Y.gapSize;T.scale.value=Y.scale}function qa(T,Y){T.diffuse.value.copy(Y.color);T.opacity.value=
Y.opacity;T.size.value=Y.size*fc;T.scale.value=.5*Bc;T.map.value=Y.map;null!==Y.map&&(!0===Y.map.matrixAutoUpdate&&Y.map.updateMatrix(),T.uvTransform.value.copy(Y.map.matrix))}function ua(T,Y){T.diffuse.value.copy(Y.color);T.opacity.value=Y.opacity;T.rotation.value=Y.rotation;T.map.value=Y.map;null!==Y.map&&(!0===Y.map.matrixAutoUpdate&&Y.map.updateMatrix(),T.uvTransform.value.copy(Y.map.matrix))}function na(T,Y){T.fogColor.value.copy(Y.color);Y.isFog?(T.fogNear.value=Y.near,T.fogFar.value=Y.far):
Y.isFogExp2&&(T.fogDensity.value=Y.density)}function ta(T,Y){Y.emissiveMap&&(T.emissiveMap.value=Y.emissiveMap)}function Ba(T,Y){T.specular.value.copy(Y.specular);T.shininess.value=Math.max(Y.shininess,1E-4);Y.emissiveMap&&(T.emissiveMap.value=Y.emissiveMap);Y.bumpMap&&(T.bumpMap.value=Y.bumpMap,T.bumpScale.value=Y.bumpScale,1===Y.side&&(T.bumpScale.value*=-1));Y.normalMap&&(T.normalMap.value=Y.normalMap,T.normalScale.value.copy(Y.normalScale),1===Y.side&&T.normalScale.value.negate());Y.displacementMap&&
(T.displacementMap.value=Y.displacementMap,T.displacementScale.value=Y.displacementScale,T.displacementBias.value=Y.displacementBias)}function Ta(T,Y){Ba(T,Y);Y.gradientMap&&(T.gradientMap.value=Y.gradientMap)}function Ua(T,Y){T.roughness.value=Y.roughness;T.metalness.value=Y.metalness;Y.roughnessMap&&(T.roughnessMap.value=Y.roughnessMap);Y.metalnessMap&&(T.metalnessMap.value=Y.metalnessMap);Y.emissiveMap&&(T.emissiveMap.value=Y.emissiveMap);Y.bumpMap&&(T.bumpMap.value=Y.bumpMap,T.bumpScale.value=
Y.bumpScale,1===Y.side&&(T.bumpScale.value*=-1));Y.normalMap&&(T.normalMap.value=Y.normalMap,T.normalScale.value.copy(Y.normalScale),1===Y.side&&T.normalScale.value.negate());Y.displacementMap&&(T.displacementMap.value=Y.displacementMap,T.displacementScale.value=Y.displacementScale,T.displacementBias.value=Y.displacementBias);Y.envMap&&(T.envMapIntensity.value=Y.envMapIntensity)}function Ca(T,Y){Ua(T,Y);T.reflectivity.value=Y.reflectivity;T.clearcoat.value=Y.clearcoat;T.clearcoatRoughness.value=Y.clearcoatRoughness;
Y.sheen&&T.sheen.value.copy(Y.sheen);Y.clearcoatNormalMap&&(T.clearcoatNormalScale.value.copy(Y.clearcoatNormalScale),T.clearcoatNormalMap.value=Y.clearcoatNormalMap,1===Y.side&&T.clearcoatNormalScale.value.negate());T.transparency.value=Y.transparency}function Ha(T,Y){Y.matcap&&(T.matcap.value=Y.matcap);Y.bumpMap&&(T.bumpMap.value=Y.bumpMap,T.bumpScale.value=Y.bumpScale,1===Y.side&&(T.bumpScale.value*=-1));Y.normalMap&&(T.normalMap.value=Y.normalMap,T.normalScale.value.copy(Y.normalScale),1===Y.side&&
T.normalScale.value.negate());Y.displacementMap&&(T.displacementMap.value=Y.displacementMap,T.displacementScale.value=Y.displacementScale,T.displacementBias.value=Y.displacementBias)}function Da(T,Y){Y.displacementMap&&(T.displacementMap.value=Y.displacementMap,T.displacementScale.value=Y.displacementScale,T.displacementBias.value=Y.displacementBias)}function Ma(T,Y){Y.displacementMap&&(T.displacementMap.value=Y.displacementMap,T.displacementScale.value=Y.displacementScale,T.displacementBias.value=
Y.displacementBias);T.referencePosition.value.copy(Y.referencePosition);T.nearDistance.value=Y.nearDistance;T.farDistance.value=Y.farDistance}function db(T,Y){Y.bumpMap&&(T.bumpMap.value=Y.bumpMap,T.bumpScale.value=Y.bumpScale,1===Y.side&&(T.bumpScale.value*=-1));Y.normalMap&&(T.normalMap.value=Y.normalMap,T.normalScale.value.copy(Y.normalScale),1===Y.side&&T.normalScale.value.negate());Y.displacementMap&&(T.displacementMap.value=Y.displacementMap,T.displacementScale.value=Y.displacementScale,T.displacementBias.value=
Y.displacementBias)}function tb(T,Y){T.ambientLightColor.needsUpdate=Y;T.lightProbe.needsUpdate=Y;T.directionalLights.needsUpdate=Y;T.pointLights.needsUpdate=Y;T.spotLights.needsUpdate=Y;T.rectAreaLights.needsUpdate=Y;T.hemisphereLights.needsUpdate=Y}a=a||{};var Ka=void 0!==a.canvas?a.canvas:document.createElementNS("http://www.w3.org/1999/xhtml","canvas"),bb=void 0!==a.context?a.context:null,jb=void 0!==a.alpha?a.alpha:!1,Eb=void 0!==a.depth?a.depth:!0,xb=void 0!==a.stencil?a.stencil:!0,ha=void 0!==
a.antialias?a.antialias:!1,ma=void 0!==a.premultipliedAlpha?a.premultipliedAlpha:!0,za=void 0!==a.preserveDrawingBuffer?a.preserveDrawingBuffer:!1,Ja=void 0!==a.powerPreference?a.powerPreference:"default",Ya=void 0!==a.failIfMajorPerformanceCaveat?a.failIfMajorPerformanceCaveat:!1,Na=null,cb=null;this.domElement=Ka;this.debug={checkShaderErrors:!0};this.sortObjects=this.autoClearStencil=this.autoClearDepth=this.autoClearColor=this.autoClear=!0;this.clippingPlanes=[];this.localClippingEnabled=!1;this.gammaFactor=
2;this.physicallyCorrectLights=this.gammaOutput=this.gammaInput=!1;this.toneMappingWhitePoint=this.toneMappingExposure=this.toneMapping=1;this.maxMorphTargets=8;this.maxMorphNormals=4;var ia=this,Ga=!1,La=null,nb=0,Va=0,ib=null,kb=null,Qa=-1;var eb=c=null;var mb=!1;var pb=null,sb=null,Lb=new p,Tb=new p,qc=null,Sc=Ka.width,Bc=Ka.height,fc=1,ye=new p(0,0,Sc,Bc),xe=new p(0,0,Sc,Bc),Xh=!1,Vh=new lc,Ac=new yc,Ag=!1,Wh=!1,xf=new q,Bd=new k;try{jb={alpha:jb,depth:Eb,stencil:xb,antialias:ha,premultipliedAlpha:ma,
preserveDrawingBuffer:za,powerPreference:Ja,failIfMajorPerformanceCaveat:Ya,xrCompatible:!0};Ka.addEventListener("webglcontextlost",t,!1);Ka.addEventListener("webglcontextrestored",v,!1);var Ra=bb||Ka.getContext("webgl",jb)||Ka.getContext("experimental-webgl",jb);if(null===Ra){if(null!==Ka.getContext("webgl"))throw Error("Error creating WebGL context with your selected attributes.");throw Error("Error creating WebGL context.");}void 0===Ra.getShaderPrecisionFormat&&(Ra.getShaderPrecisionFormat=function(){return{rangeMin:1,
rangeMax:1,precision:1}})}catch(T){throw console.error("THREE.WebGLRenderer: "+T.message),T;}var Mb,gc,yb,zd,hc,Qc,yg,Uh,ze,Rd,zg,Ae,Ad,Kj,Lj,Mj,Pc;g();var fd="undefined"!==typeof navigator&&"xr"in navigator&&"supportsSession"in navigator.xr?new Jj(ia,Ra):new Sh(ia);this.vr=fd;var Nj=new Ej(ia,ze,gc.maxTextureSize);this.shadowMap=Nj;this.getContext=function(){return Ra};this.getContextAttributes=function(){return Ra.getContextAttributes()};this.forceContextLoss=function(){var T=Mb.get("WEBGL_lose_context");
T&&T.loseContext()};this.forceContextRestore=function(){var T=Mb.get("WEBGL_lose_context");T&&T.restoreContext()};this.getPixelRatio=function(){return fc};this.setPixelRatio=function(){var T=window.devicePixelRatio;void 0!==T&&(fc=T,this.setSize(Sc,Bc,!1))};this.getSize=function(T){void 0===T&&(console.warn("WebGLRenderer: .getsize() now requires a Vector2 as an argument"),T=new f);return T.set(Sc,Bc)};this.setSize=function(T,Y,la){fd.isPresenting()?console.warn("THREE.WebGLRenderer: Can't change size while VR device is presenting."):
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T&&(console.warn("WebGLRenderer: .getCurrentViewport() now requires a Vector4 as an argument"),T=new p);return T.copy(Lb)};this.getViewport=function(T){return T.copy(ye)};this.setViewport=function(T,Y){(0).isVector4?ye.set((0).x,(0).y,(0).z,(0).w):ye.set(0,0,T,Y);yb.viewport(Lb.copy(ye).multiplyScalar(fc).floor())};this.getScissor=function(T){return T.copy(xe)};this.setScissor=function(T,Y,la,Ia){T.isVector4?xe.set(T.x,T.y,T.z,T.w):xe.set(T,Y,la,Ia);yb.scissor(Tb.copy(xe).multiplyScalar(fc).floor())};
this.getScissorTest=function(){return Xh};this.setScissorTest=function(T){yb.setScissorTest(Xh=T)};this.getClearColor=function(){return Ad.getClearColor()};this.setClearColor=function(){Ad.setClearColor.apply(Ad,arguments)};this.getClearAlpha=function(){return Ad.getClearAlpha()};this.setClearAlpha=function(){Ad.setClearAlpha.apply(Ad,arguments)};this.clear=function(T,Y,la){var Ia=0;if(void 0===T||T)Ia|=16384;if(void 0===Y||Y)Ia|=256;if(void 0===la||la)Ia|=1024;Ra.clear(Ia)};this.clearColor=function(){this.clear(!0,
!1,!1)};this.clearDepth=function(){this.clear(!1,!0,!1)};this.clearStencil=function(){this.clear(!1,!1,!0)};this.dispose=function(){Ka.removeEventListener("webglcontextlost",t,!1);Ka.removeEventListener("webglcontextrestored",v,!1);zg.dispose();Ae.dispose();hc.dispose();ze.dispose();fd.dispose();Bg.stop()};this.renderBufferImmediate=function(T,Y){yb.initAttributes();var la=hc.get(T);T.hasPositions&&!la.position&&(la.position=Ra.createBuffer());T.hasNormals&&!la.normal&&(la.normal=Ra.createBuffer());
T.hasUvs&&!la.uv&&(la.uv=Ra.createBuffer());T.hasColors&&!la.color&&(la.color=Ra.createBuffer());Y=Y.getAttributes();T.hasPositions&&(Ra.bindBuffer(34962,la.position),Ra.bufferData(34962,T.positionArray,35048),yb.enableAttribute(Y.position),Ra.vertexAttribPointer(Y.position,3,5126,!1,0,0));T.hasNormals&&(Ra.bindBuffer(34962,la.normal),Ra.bufferData(34962,T.normalArray,35048),yb.enableAttribute(Y.normal),Ra.vertexAttribPointer(Y.normal,3,5126,!1,0,0));T.hasUvs&&(Ra.bindBuffer(34962,la.uv),Ra.bufferData(34962,
T.uvArray,35048),yb.enableAttribute(Y.uv),Ra.vertexAttribPointer(Y.uv,2,5126,!1,0,0));T.hasColors&&(Ra.bindBuffer(34962,la.color),Ra.bufferData(34962,T.colorArray,35048),yb.enableAttribute(Y.color),Ra.vertexAttribPointer(Y.color,3,5126,!1,0,0));yb.disableUnusedAttributes();Ra.drawArrays(4,0,T.count);T.count=0};this.renderBufferDirect=function(T,Y,la,Ia,Oa,ab){yb.setMaterial(Ia,Oa.isMesh&&0>Oa.matrixWorld.determinant());var Pa=ea(T,Y,Ia,Oa),fb=!1;if(c!==la.id||eb!==Pa.id||mb!==(!0===Ia.wireframe))c=
la.id,eb=Pa.id,mb=!0===Ia.wireframe,fb=!0;Oa.morphTargetInfluences&&(Kj.update(Oa,la,Ia,Pa),fb=!0);var Cb=la.index,ob=la.attributes.position;Y=1;!0===Ia.wireframe&&(Cb=Uh.getWireframeAttribute(la),Y=2);T=Lj;if(null!==Cb){var $a=yg.get(Cb);T=Mj;T.setIndex($a)}fb&&(M(Ia,Pa,la),null!==Cb&&Ra.bindBuffer(34963,$a.buffer));$a=Infinity;null!==Cb?$a=Cb.count:void 0!==ob&&($a=ob.count);ob=la.drawRange.start*Y;Pa=null!==ab?ab.start*Y:0;Cb=Math.max(ob,Pa);ab=Math.max(0,Math.min($a,ob+la.drawRange.count*Y,Pa+
(null!==ab?ab.count*Y:Infinity))-1-Cb+1);if(0!==ab){if(Oa.isMesh)if(!0===Ia.wireframe)yb.setLineWidth(Ia.wireframeLinewidth*e()),T.setMode(1);else switch(Oa.drawMode){case 0:T.setMode(4);break;case 1:T.setMode(5);break;case 2:T.setMode(6)}else Oa.isLine?(Ia=Ia.linewidth,void 0===Ia&&(Ia=1),yb.setLineWidth(Ia*e()),Oa.isLineSegments?T.setMode(1):Oa.isLineLoop?T.setMode(2):T.setMode(3)):Oa.isPoints?T.setMode(0):Oa.isSprite&&T.setMode(4);la&&la.isInstancedBufferGeometry?0<la.maxInstancedCount&&T.renderInstances(la,
Cb,ab):T.render(Cb,ab)}};this.compile=function(T,Y){cb=Ae.get(T,Y);cb.init();T.traverse(function(la){la.isLight&&(cb.pushLight(la),la.castShadow&&cb.pushShadow(la))});cb.setupLights(Y);T.traverse(function(la){if(la.material)if(Array.isArray(la.material))for(var Ia=0;Ia<la.material.length;Ia++)V(la.material[Ia],T.fog,la);else V(la.material,T.fog,la)})};var Yh=null,Bg=new ec;Bg.setAnimationLoop(function(T){fd.isPresenting()||Yh&&Yh(T)});"undefined"!==typeof window&&Bg.setContext(window);this.setAnimationLoop=
function(T){Yh=T;fd.setAnimationLoop(T);Bg.start()};this.render=function(T,Y,la,Ia){if(void 0!==la){console.warn("THREE.WebGLRenderer.render(): the renderTarget argument has been removed. Use .setRenderTarget() instead.");var Oa=la}if(void 0!==Ia){console.warn("THREE.WebGLRenderer.render(): the forceClear argument has been removed. Use .clear() instead.");var ab=Ia}Y&&Y.isCamera?Ga||(eb=c=null,mb=!1,Qa=-1,pb=null,!0===T.autoUpdate&&T.updateMatrixWorld(),null===Y.parent&&Y.updateMatrixWorld(),fd.enabled&&
(Y=fd.getCamera(Y)),cb=Ae.get(T,Y),cb.init(),T.onBeforeRender(ia,T,Y,Oa||ib),xf.multiplyMatrices(Y.projectionMatrix,Y.matrixWorldInverse),Vh.setFromMatrix(xf),Wh=this.localClippingEnabled,Ag=Ac.init(this.clippingPlanes,Wh,Y),Na=zg.get(T,Y),Na.init(),P(T,Y,0,ia.sortObjects),!0===ia.sortObjects&&Na.sort(),Ag&&Ac.beginShadows(),Nj.render(cb.state.shadowsArray,T,Y),cb.setupLights(Y),Ag&&Ac.endShadows(),this.info.autoReset&&this.info.reset(),void 0!==Oa&&this.setRenderTarget(Oa),Ad.render(Na,T,Y,ab),la=
Na.opaque,Ia=Na.transparent,T.overrideMaterial?(Oa=T.overrideMaterial,la.length&&Q(la,T,Y,Oa),Ia.length&&Q(Ia,T,Y,Oa)):(la.length&&Q(la,T,Y),Ia.length&&Q(Ia,T,Y)),null!==ib&&(Qc.updateRenderTargetMipmap(ib),Qc.updateMultisampleRenderTarget(ib)),yb.buffers.depth.setTest(!0),yb.buffers.depth.setMask(!0),yb.buffers.color.setMask(!0),yb.setPolygonOffset(!1),fd.enabled&&fd.submitFrame(),cb=Na=null):console.error("THREE.WebGLRenderer.render: camera is not an instance of THREE.Camera.")};this.setFramebuffer=
function(T){La!==T&&Ra.bindFramebuffer(36160,T);La=T};this.getActiveCubeFace=function(){return nb};this.getActiveMipmapLevel=function(){return Va};this.getRenderTarget=function(){return ib};this.setRenderTarget=function(T,Y,la){ib=T;nb=Y;Va=la;T&&void 0===hc.get(T).__webglFramebuffer&&Qc.setupRenderTarget(T);var Ia=La,Oa=!1;T?(Ia=hc.get(T).__webglFramebuffer,T.isWebGLRenderTargetCube?(Ia=Ia[Y||0],Oa=!0):Ia=T.isWebGLMultisampleRenderTarget?hc.get(T).__webglMultisampledFramebuffer:Ia,Lb.copy(T.viewport),
Tb.copy(T.scissor),qc=T.scissorTest):(Lb.copy(ye).multiplyScalar(fc).floor(),Tb.copy(xe).multiplyScalar(fc).floor(),qc=Xh);kb!==Ia&&(Ra.bindFramebuffer(36160,Ia),kb=Ia);yb.viewport(Lb);yb.scissor(Tb);yb.setScissorTest(qc);Oa&&(T=hc.get(T.texture),Ra.framebufferTexture2D(36160,36064,34069+(Y||0),T.__webglTexture,la||0))};this.readRenderTargetPixels=function(T,Y,la,Ia,Oa,ab,Pa){if(T&&T.isWebGLRenderTarget){var fb=hc.get(T).__webglFramebuffer;T.isWebGLRenderTargetCube&&void 0!==Pa&&(fb=fb[Pa]);if(fb){Pa=
!1;fb!==kb&&(Ra.bindFramebuffer(36160,fb),Pa=!0);try{var Cb=T.texture,ob=Cb.format,$a=Cb.type;1023!==ob&&Pc.convert(ob)!==Ra.getParameter(35739)?console.error("THREE.WebGLRenderer.readRenderTargetPixels: renderTarget is not in RGBA or implementation defined format."):1009===$a||Pc.convert($a)===Ra.getParameter(35738)||1015===$a&&(gc.isWebGL2||Mb.get("OES_texture_float")||Mb.get("WEBGL_color_buffer_float"))||1016===$a&&(gc.isWebGL2?Mb.get("EXT_color_buffer_float"):Mb.get("EXT_color_buffer_half_float"))?
36053===Ra.checkFramebufferStatus(36160)?0<=Y&&Y<=T.width-Ia&&0<=la&&la<=T.height-Oa&&Ra.readPixels(Y,la,Ia,Oa,Pc.convert(ob),Pc.convert($a),ab):console.error("THREE.WebGLRenderer.readRenderTargetPixels: readPixels from renderTarget failed. Framebuffer not complete."):console.error("THREE.WebGLRenderer.readRenderTargetPixels: renderTarget is not in UnsignedByteType or implementation defined type.")}finally{Pa&&Ra.bindFramebuffer(36160,kb)}}}else console.error("THREE.WebGLRenderer.readRenderTargetPixels: renderTarget is not THREE.WebGLRenderTarget.")};
this.copyFramebufferToTexture=function(T,Y,la){var Ia=Y.image.width,Oa=Y.image.height,ab=Pc.convert(Y.format);Qc.setTexture2D(Y,0);Ra.copyTexImage2D(3553,la||0,ab,T.x,T.y,Ia,Oa,0)};this.copyTextureToTexture=function(T,Y,la,Ia){var Oa=Y.image.width,ab=Y.image.height,Pa=Pc.convert(la.format),fb=Pc.convert(la.type);Qc.setTexture2D(la,0);Y.isDataTexture?Ra.texSubImage2D(3553,Ia||0,T.x,T.y,Oa,ab,Pa,fb,Y.image.data):Ra.texSubImage2D(3553,Ia||0,T.x,T.y,Pa,fb,Y.image)};"undefined"!==typeof __THREE_DEVTOOLS__&&
__THREE_DEVTOOLS__.dispatchEvent(new CustomEvent("observe",{detail:this}))}function Cg(a,c){this.name="";this.color=new H(a);this.density=void 0!==c?c:2.5E-4}function Dg(a,c,e){this.name="";this.color=new H(a);this.near=void 0!==c?c:1;this.far=void 0!==e?e:1E3}function Sd(a,c){this.array=a;this.stride=c;this.count=void 0!==a?a.length/c:0;this.dynamic=!1;this.updateRange={offset:0,count:-1};this.version=0}function yf(a,c,e,g){this.data=a;this.itemSize=c;this.offset=e;this.normalized=!0===g}function Cd(a){S.call(this);
this.type="SpriteMaterial";this.color=new H(16777215);this.map=null;this.rotation=0;this.sizeAttenuation=!0;this.lights=!1;this.transparent=!0;this.setValues(a)}function zf(a){A.call(this);this.type="Sprite";if(void 0===Ce){Ce=new va;var c=new Sd(new Float32Array([-.5,-.5,0,0,0,.5,-.5,0,1,0,.5,.5,0,1,1,-.5,.5,0,0,1]),5);Ce.setIndex([0,1,2,0,2,3]);Ce.addAttribute("position",new yf(c,3,0,!1));Ce.addAttribute("uv",new yf(c,2,3,!1))}this.geometry=Ce;this.material=void 0!==a?a:new Cd;this.center=new f(.5,
.5)}function Eg(a,c,e,g,t,v){De.subVectors(a,e).addScalar(.5).multiply(g);void 0!==t?(Af.x=v*De.x-t*De.y,Af.y=t*De.x+v*De.y):Af.copy(De);a.copy(c);a.x+=Af.x;a.y+=Af.y;a.applyMatrix4(Oj)}function Bf(){A.call(this);this.type="LOD";Object.defineProperties(this,{levels:{enumerable:!0,value:[]}});this.autoUpdate=!0}function Cf(a,c){a&&a.isGeometry&&console.error("THREE.SkinnedMesh no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.");ya.call(this,a,c);this.type="SkinnedMesh";this.bindMode=
"attached";this.bindMatrix=new q;this.bindMatrixInverse=new q}function Fg(a,c){a=a||[];this.bones=a.slice(0);this.boneMatrices=new Float32Array(16*this.bones.length);if(void 0===c)this.calculateInverses();else if(this.bones.length===c.length)this.boneInverses=c.slice(0);else for(console.warn("THREE.Skeleton boneInverses is the wrong length."),this.boneInverses=[],a=0,c=this.bones.length;a<c;a++)this.boneInverses.push(new q)}function Zh(){A.call(this);this.type="Bone"}function Fb(a){S.call(this);this.type=
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!0}function Ef(a,c,e,g,t,v,z,E,F,I){I=void 0!==I?I:1026;if(1026!==I&&1027!==I)throw Error("DepthTexture format must be either THREE.DepthFormat or THREE.DepthStencilFormat");void 0===e&&1026===I&&(e=1012);void 0===e&&1027===I&&(e=1020);l.call(this,null,g,t,v,z,E,I,e,F);this.image={width:a,height:c};this.magFilter=void 0!==z?z:1003;this.minFilter=void 0!==E?E:1003;this.generateMipmaps=this.flipY=!1}function Ge(a){va.call(this);this.type="WireframeGeometry";var c=[],e,g,t,v=[0,0],z={},E=["a","b","c"];
if(a&&a.isGeometry){var F=a.faces;var I=0;for(g=F.length;I<g;I++){var M=F[I];for(e=0;3>e;e++){var P=M[E[e]];var Q=M[E[(e+1)%3]];v[0]=Math.min(P,Q);v[1]=Math.max(P,Q);P=v[0]+","+v[1];void 0===z[P]&&(z[P]={index1:v[0],index2:v[1]})}}for(P in z)I=z[P],E=a.vertices[I.index1],c.push(E.x,E.y,E.z),E=a.vertices[I.index2],c.push(E.x,E.y,E.z)}else if(a&&a.isBufferGeometry)if(E=new k,null!==a.index){F=a.attributes.position;M=a.index;var U=a.groups;0===U.length&&(U=[{start:0,count:M.count,materialIndex:0}]);
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c){return a.distance-c.distance}function xi(a,c,e,g){if(!1!==a.visible&&(a.raycast(c,e),!0===g)){a=a.children;g=0;for(var t=a.length;g<t;g++)xi(a[g],c,e,!0)}}function fk(a,c,e){this.radius=void 0!==a?a:1;this.phi=void 0!==c?c:0;this.theta=void 0!==e?e:0;return this}function gk(a,c,e){this.radius=void 0!==a?a:1;this.theta=void 0!==c?c:0;this.y=void 0!==e?e:0;return this}function yi(a,c){this.min=void 0!==a?a:new f(Infinity,Infinity);this.max=void 0!==c?c:new f(-Infinity,-Infinity)}function zi(a,c){this.start=
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c;a=new qb({defines:{GAMMA_OUTPUT:""},uniforms:{sh:{value:this.lightProbe.sh.coefficients},intensity:{value:this.lightProbe.intensity}},vertexShader:"varying vec3 vNormal;\nvoid main() {\n\tvNormal \x3d normalize( normalMatrix * normal );\n\tgl_Position \x3d projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\n}",fragmentShader:"#define RECIPROCAL_PI 0.318309886\nvec3 inverseTransformDirection( in vec3 normal, in mat4 matrix ) {\n\t// matrix is assumed to be orthogonal\n\treturn normalize( ( vec4( normal, 0.0 ) * matrix ).xyz );\n}\nvec3 linearToOutput( in vec3 a ) {\n\t#ifdef GAMMA_OUTPUT\n\t\treturn pow( a, vec3( 1.0 / float( GAMMA_FACTOR ) ) );\n\t#else\n\t\treturn a;\n\t#endif\n}\n// source: https://graphics.stanford.edu/papers/envmap/envmap.pdf\nvec3 shGetIrradianceAt( in vec3 normal, in vec3 shCoefficients[ 9 ] ) {\n\t// normal is assumed to have unit length\n\tfloat x \x3d normal.x, y \x3d normal.y, z \x3d normal.z;\n\t// band 0\n\tvec3 result \x3d shCoefficients[ 0 ] * 0.886227;\n\t// band 1\n\tresult +\x3d shCoefficients[ 1 ] * 2.0 * 0.511664 * y;\n\tresult +\x3d shCoefficients[ 2 ] * 2.0 * 0.511664 * z;\n\tresult +\x3d shCoefficients[ 3 ] * 2.0 * 0.511664 * x;\n\t// band 2\n\tresult +\x3d shCoefficients[ 4 ] * 2.0 * 0.429043 * x * y;\n\tresult +\x3d shCoefficients[ 5 ] * 2.0 * 0.429043 * y * z;\n\tresult +\x3d shCoefficients[ 6 ] * ( 0.743125 * z * z - 0.247708 );\n\tresult +\x3d shCoefficients[ 7 ] * 2.0 * 0.429043 * x * z;\n\tresult +\x3d shCoefficients[ 8 ] * 0.429043 * ( x * x - y * y );\n\treturn result;\n}\nuniform vec3 sh[ 9 ]; // sh coefficients\nuniform float intensity; // light probe intensity\nvarying vec3 vNormal;\nvoid main() {\n\tvec3 normal \x3d normalize( vNormal );\n\tvec3 worldNormal \x3d inverseTransformDirection( normal, viewMatrix );\n\tvec3 irradiance \x3d shGetIrradianceAt( worldNormal, sh );\n\tvec3 outgoingLight \x3d RECIPROCAL_PI * irradiance * intensity;\n\toutgoingLight \x3d linearToOutput( outgoingLight );\n\tgl_FragColor \x3d vec4( outgoingLight, 1.0 );\n}"});
c=new Dd(1,32,16);ya.call(this,c,a);this.onBeforeRender()}function fh(a,c,e,g){a=a||10;c=c||10;e=new H(void 0!==e?e:4473924);g=new H(void 0!==g?g:8947848);var t=c/2,v=a/c,z=a/2;a=[];for(var E=[],F=0,I=0,M=-z;F<=c;F++,M+=v){a.push(-z,0,M,z,0,M);a.push(M,0,-z,M,0,z);var P=F===t?e:g;P.toArray(E,I);I+=3;P.toArray(E,I);I+=3;P.toArray(E,I);I+=3;P.toArray(E,I);I+=3}c=new va;c.addAttribute("position",new ba(a,3));c.addAttribute("color",new ba(E,3));e=new Fb({vertexColors:2});Ib.call(this,c,e)}function gh(a,
c,e,g,t,v){a=a||10;c=c||16;e=e||8;g=g||64;t=new H(void 0!==t?t:4473924);v=new H(void 0!==v?v:8947848);var z=[],E=[],F;for(F=0;F<=c;F++){var I=F/c*2*Math.PI;var M=Math.sin(I)*a;I=Math.cos(I)*a;z.push(0,0,0);z.push(M,0,I);var P=F&1?t:v;E.push(P.r,P.g,P.b);E.push(P.r,P.g,P.b)}for(F=0;F<=e;F++){P=F&1?t:v;var Q=a-a/e*F;for(c=0;c<g;c++)I=c/g*2*Math.PI,M=Math.sin(I)*Q,I=Math.cos(I)*Q,z.push(M,0,I),E.push(P.r,P.g,P.b),I=(c+1)/g*2*Math.PI,M=Math.sin(I)*Q,I=Math.cos(I)*Q,z.push(M,0,I),E.push(P.r,P.g,P.b)}a=
new va;a.addAttribute("position",new ba(z,3));a.addAttribute("color",new ba(E,3));z=new Fb({vertexColors:2});Ib.call(this,a,z)}function ef(a,c,e,g){this.audio=a;this.range=c||1;this.divisionsInnerAngle=e||16;this.divisionsOuterAngle=g||2;a=new va;a.addAttribute("position",new R(new Float32Array(3*(3*(this.divisionsInnerAngle+2*this.divisionsOuterAngle)+3)),3));c=new Fb({color:65280});e=new Fb({color:16776960});Xb.call(this,a,[e,c]);this.update()}function fg(a,c,e,g){this.object=a;this.size=void 0!==
c?c:1;a=void 0!==e?e:16776960;g=void 0!==g?g:1;c=0;(e=this.object.geometry)&&e.isGeometry?c=e.faces.length:console.warn("THREE.FaceNormalsHelper: only THREE.Geometry is supported. Use THREE.VertexNormalsHelper, instead.");e=new va;c=new ba(6*c,3);e.addAttribute("position",c);Ib.call(this,e,new Fb({color:a,linewidth:g}));this.matrixAutoUpdate=!1;this.update()}function ff(a,c,e){A.call(this);this.light=a;this.light.updateMatrixWorld();this.matrix=a.matrixWorld;this.matrixAutoUpdate=!1;this.color=e;
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function ig(a,c,e){this.type="PlaneHelper";this.plane=a;this.size=void 0===c?1:c;a=void 0!==e?e:16776960;c=new va;c.addAttribute("position",new ba([1,-1,1,-1,1,1,-1,-1,1,1,1,1,-1,1,1,-1,-1,1,1,-1,1,1,1,1,0,0,1,0,0,0],3));c.computeBoundingSphere();Xb.call(this,c,new Fb({color:a}));c=new va;c.addAttribute("position",new ba([1,1,1,-1,1,1,-1,-1,1,1,1,1,-1,-1,1,1,-1,1],3));c.computeBoundingSphere();this.add(new ya(c,new N({color:a,opacity:.2,transparent:!0,depthWrite:!1})))}function kd(a,c,e,g,t,v){A.call(this);
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a);}},clone:function(){return new this.constructor(this.x,this.y)},copy:function(a){this.x=a.x;this.y=a.y;return this},add:function(a,c){if(void 0!==c)return console.warn("THREE.Vector2: .add() now only accepts one argument. Use .addVectors( a, b ) instead."),this.addVectors(a,c);this.x+=a.x;this.y+=a.y;return this},addScalar:function(a){this.x+=a;this.y+=a;return this},addVectors:function(a,c){this.x=a.x+c.x;this.y=a.y+c.y;return this},addScaledVector:function(a,c){this.x+=a.x*c;this.y+=a.y*c;return this},
sub:function(a,c){if(void 0!==c)return console.warn("THREE.Vector2: .sub() now only accepts one argument. Use .subVectors( a, b ) instead."),this.subVectors(a,c);this.x-=a.x;this.y-=a.y;return this},subScalar:function(a){this.x-=a;this.y-=a;return this},subVectors:function(a,c){this.x=a.x-c.x;this.y=a.y-c.y;return this},multiply:function(a){this.x*=a.x;this.y*=a.y;return this},multiplyScalar:function(a){this.x*=a;this.y*=a;return this},divide:function(a){this.x/=a.x;this.y/=a.y;return this},divideScalar:function(a){return this.multiplyScalar(1/
a)},applyMatrix3:function(a){var c=this.x,e=this.y;a=a.elements;this.x=a[0]*c+a[3]*e+a[6];this.y=a[1]*c+a[4]*e+a[7];return this},min:function(a){this.x=Math.min(this.x,a.x);this.y=Math.min(this.y,a.y);return this},max:function(a){this.x=Math.max(this.x,a.x);this.y=Math.max(this.y,a.y);return this},clamp:function(a,c){this.x=Math.max(a.x,Math.min(c.x,this.x));this.y=Math.max(a.y,Math.min(c.y,this.y));return this},clampScalar:function(a,c){this.x=Math.max(a,Math.min(c,this.x));this.y=Math.max(a,Math.min(c,
this.y));return this},clampLength:function(a,c){var e=this.length();return this.divideScalar(e||1).multiplyScalar(Math.max(a,Math.min(c,e)))},floor:function(){this.x=Math.floor(this.x);this.y=Math.floor(this.y);return this},ceil:function(){this.x=Math.ceil(this.x);this.y=Math.ceil(this.y);return this},round:function(){this.x=Math.round(this.x);this.y=Math.round(this.y);return this},roundToZero:function(){this.x=0>this.x?Math.ceil(this.x):Math.floor(this.x);this.y=0>this.y?Math.ceil(this.y):Math.floor(this.y);
return this},negate:function(){this.x=-this.x;this.y=-this.y;return this},dot:function(a){return this.x*a.x+this.y*a.y},cross:function(a){return this.x*a.y-this.y*a.x},lengthSq:function(){return this.x*this.x+this.y*this.y},length:function(){return Math.sqrt(this.x*this.x+this.y*this.y)},manhattanLength:function(){return Math.abs(this.x)+Math.abs(this.y)},normalize:function(){return this.divideScalar(this.length()||1)},angle:function(){var a=Math.atan2(this.y,this.x);0>a&&(a+=2*Math.PI);return a},
distanceTo:function(a){return Math.sqrt(this.distanceToSquared(a))},distanceToSquared:function(a){var c=this.x-a.x;a=this.y-a.y;return c*c+a*a},manhattanDistanceTo:function(a){return Math.abs(this.x-a.x)+Math.abs(this.y-a.y)},setLength:function(a){return this.normalize().multiplyScalar(a)},lerp:function(a,c){this.x+=(a.x-this.x)*c;this.y+=(a.y-this.y)*c;return this},lerpVectors:function(a,c,e){return this.subVectors(c,a).multiplyScalar(e).add(a)},equals:function(a){return a.x===this.x&&a.y===this.y},
fromArray:function(a,c){void 0===c&&(c=0);this.x=a[c];this.y=a[c+1];return this},toArray:function(a,c){void 0===a&&(a=[]);void 0===c&&(c=0);a[c]=this.x;a[c+1]=this.y;return a},fromBufferAttribute:function(a,c,e){void 0!==e&&console.warn("THREE.Vector2: offset has been removed from .fromBufferAttribute().");this.x=a.getX(c);this.y=a.getY(c);return this},rotateAround:function(a,c){var e=Math.cos(c);c=Math.sin(c);var g=this.x-a.x,t=this.y-a.y;this.x=g*e-t*c+a.x;this.y=g*c+t*e+a.y;return this}});Object.assign(h,
{slerp:function(a,c,e,g){return e.copy(a).slerp(c,g)},slerpFlat:function(a,c,e,g,t,v,z){var E=e[g+0],F=e[g+1],I=e[g+2];e=e[g+3];g=t[v+0];var M=t[v+1],P=t[v+2];t=t[v+3];if(e!==t||E!==g||F!==M||I!==P){v=1-z;var Q=E*g+F*M+I*P+e*t,U=0<=Q?1:-1,V=1-Q*Q;V>Number.EPSILON&&(V=Math.sqrt(V),Q=Math.atan2(V,Q*U),v=Math.sin(v*Q)/V,z=Math.sin(z*Q)/V);U*=z;E=E*v+g*U;F=F*v+M*U;I=I*v+P*U;e=e*v+t*U;v===1-z&&(z=1/Math.sqrt(E*E+F*F+I*I+e*e),E*=z,F*=z,I*=z,e*=z)}a[c]=E;a[c+1]=F;a[c+2]=I;a[c+3]=e}});Object.defineProperties(h.prototype,
{x:{get:function(){return this._x},set:function(a){this._x=a;this._onChangeCallback()}},y:{get:function(){return this._y},set:function(a){this._y=a;this._onChangeCallback()}},z:{get:function(){return this._z},set:function(a){this._z=a;this._onChangeCallback()}},w:{get:function(){return this._w},set:function(a){this._w=a;this._onChangeCallback()}}});Object.assign(h.prototype,{isQuaternion:!0,set:function(a,c,e,g){this._x=a;this._y=c;this._z=e;this._w=g;this._onChangeCallback();return this},clone:function(){return new this.constructor(this._x,
this._y,this._z,this._w)},copy:function(a){this._x=a.x;this._y=a.y;this._z=a.z;this._w=a.w;this._onChangeCallback();return this},setFromEuler:function(a,c){if(!a||!a.isEuler)throw Error("THREE.Quaternion: .setFromEuler() now expects an Euler rotation rather than a Vector3 and order.");var e=a._x,g=a._y,t=a._z;a=a.order;var v=Math.cos,z=Math.sin,E=v(e/2),F=v(g/2);v=v(t/2);e=z(e/2);g=z(g/2);t=z(t/2);"XYZ"===a?(this._x=e*F*v+E*g*t,this._y=E*g*v-e*F*t,this._z=E*F*t+e*g*v,this._w=E*F*v-e*g*t):"YXZ"===
a?(this._x=e*F*v+E*g*t,this._y=E*g*v-e*F*t,this._z=E*F*t-e*g*v,this._w=E*F*v+e*g*t):"ZXY"===a?(this._x=e*F*v-E*g*t,this._y=E*g*v+e*F*t,this._z=E*F*t+e*g*v,this._w=E*F*v-e*g*t):"ZYX"===a?(this._x=e*F*v-E*g*t,this._y=E*g*v+e*F*t,this._z=E*F*t-e*g*v,this._w=E*F*v+e*g*t):"YZX"===a?(this._x=e*F*v+E*g*t,this._y=E*g*v+e*F*t,this._z=E*F*t-e*g*v,this._w=E*F*v-e*g*t):"XZY"===a&&(this._x=e*F*v-E*g*t,this._y=E*g*v-e*F*t,this._z=E*F*t+e*g*v,this._w=E*F*v+e*g*t);!1!==c&&this._onChangeCallback();return this},setFromAxisAngle:function(a,
c){c/=2;var e=Math.sin(c);this._x=a.x*e;this._y=a.y*e;this._z=a.z*e;this._w=Math.cos(c);this._onChangeCallback();return this},setFromRotationMatrix:function(a){var c=a.elements,e=c[0];a=c[4];var g=c[8],t=c[1],v=c[5],z=c[9],E=c[2],F=c[6];c=c[10];var I=e+v+c;0<I?(e=.5/Math.sqrt(I+1),this._w=.25/e,this._x=(F-z)*e,this._y=(g-E)*e,this._z=(t-a)*e):e>v&&e>c?(e=2*Math.sqrt(1+e-v-c),this._w=(F-z)/e,this._x=.25*e,this._y=(a+t)/e,this._z=(g+E)/e):v>c?(e=2*Math.sqrt(1+v-e-c),this._w=(g-E)/e,this._x=(a+t)/e,
this._y=.25*e,this._z=(z+F)/e):(e=2*Math.sqrt(1+c-e-v),this._w=(t-a)/e,this._x=(g+E)/e,this._y=(z+F)/e,this._z=.25*e);this._onChangeCallback();return this},setFromUnitVectors:function(a,c){var e=a.dot(c)+1;1E-6>e?(e=0,Math.abs(a.x)>Math.abs(a.z)?(this._x=-a.y,this._y=a.x,this._z=0):(this._x=0,this._y=-a.z,this._z=a.y)):(this._x=a.y*c.z-a.z*c.y,this._y=a.z*c.x-a.x*c.z,this._z=a.x*c.y-a.y*c.x);this._w=e;return this.normalize()},angleTo:function(a){return 2*Math.acos(Math.abs(hb.clamp(this.dot(a),-1,
1)))},rotateTowards:function(a,c){var e=this.angleTo(a);if(0===e)return this;this.slerp(a,Math.min(1,c/e));return this},inverse:function(){return this.conjugate()},conjugate:function(){this._x*=-1;this._y*=-1;this._z*=-1;this._onChangeCallback();return this},dot:function(a){return this._x*a._x+this._y*a._y+this._z*a._z+this._w*a._w},lengthSq:function(){return this._x*this._x+this._y*this._y+this._z*this._z+this._w*this._w},length:function(){return Math.sqrt(this._x*this._x+this._y*this._y+this._z*
this._z+this._w*this._w)},normalize:function(){var a=this.length();0===a?(this._z=this._y=this._x=0,this._w=1):(a=1/a,this._x*=a,this._y*=a,this._z*=a,this._w*=a);this._onChangeCallback();return this},multiply:function(a,c){return void 0!==c?(console.warn("THREE.Quaternion: .multiply() now only accepts one argument. Use .multiplyQuaternions( a, b ) instead."),this.multiplyQuaternions(a,c)):this.multiplyQuaternions(this,a)},premultiply:function(a){return this.multiplyQuaternions(a,this)},multiplyQuaternions:function(a,
c){var e=a._x,g=a._y,t=a._z;a=a._w;var v=c._x,z=c._y,E=c._z;c=c._w;this._x=e*c+a*v+g*E-t*z;this._y=g*c+a*z+t*v-e*E;this._z=t*c+a*E+e*z-g*v;this._w=a*c-e*v-g*z-t*E;this._onChangeCallback();return this},slerp:function(a,c){if(0===c)return this;if(1===c)return this.copy(a);var e=this._x,g=this._y,t=this._z,v=this._w,z=v*a._w+e*a._x+g*a._y+t*a._z;0>z?(this._w=-a._w,this._x=-a._x,this._y=-a._y,this._z=-a._z,z=-z):this.copy(a);if(1<=z)return this._w=v,this._x=e,this._y=g,this._z=t,this;a=1-z*z;if(a<=Number.EPSILON)return z=
1-c,this._w=z*v+c*this._w,this._x=z*e+c*this._x,this._y=z*g+c*this._y,this._z=z*t+c*this._z,this.normalize(),this._onChangeCallback(),this;a=Math.sqrt(a);var E=Math.atan2(a,z);z=Math.sin((1-c)*E)/a;c=Math.sin(c*E)/a;this._w=v*z+this._w*c;this._x=e*z+this._x*c;this._y=g*z+this._y*c;this._z=t*z+this._z*c;this._onChangeCallback();return this},equals:function(a){return a._x===this._x&&a._y===this._y&&a._z===this._z&&a._w===this._w},fromArray:function(a,c){void 0===c&&(c=0);this._x=a[c];this._y=a[c+1];
this._z=a[c+2];this._w=a[c+3];this._onChangeCallback();return this},toArray:function(a,c){void 0===a&&(a=[]);void 0===c&&(c=0);a[c]=this._x;a[c+1]=this._y;a[c+2]=this._z;a[c+3]=this._w;return a},_onChange:function(a){this._onChangeCallback=a;return this},_onChangeCallback:function(){}});var Ci=new k,kk=new h;Object.assign(k.prototype,{isVector3:!0,set:function(a,c,e){this.x=a;this.y=c;this.z=e;return this},setScalar:function(a){this.z=this.y=this.x=a;return this},setX:function(a){this.x=a;return this},
setY:function(a){this.y=a;return this},setZ:function(a){this.z=a;return this},setComponent:function(a,c){switch(a){case 0:this.x=c;break;case 1:this.y=c;break;case 2:this.z=c;break;default:throw Error("index is out of range: "+a);}return this},getComponent:function(a){switch(a){case 0:return this.x;case 1:return this.y;case 2:return this.z;default:throw Error("index is out of range: "+a);}},clone:function(){return new this.constructor(this.x,this.y,this.z)},copy:function(a){this.x=a.x;this.y=a.y;
this.z=a.z;return this},add:function(a,c){if(void 0!==c)return console.warn("THREE.Vector3: .add() now only accepts one argument. Use .addVectors( a, b ) instead."),this.addVectors(a,c);this.x+=a.x;this.y+=a.y;this.z+=a.z;return this},addScalar:function(a){this.x+=a;this.y+=a;this.z+=a;return this},addVectors:function(a,c){this.x=a.x+c.x;this.y=a.y+c.y;this.z=a.z+c.z;return this},addScaledVector:function(a,c){this.x+=a.x*c;this.y+=a.y*c;this.z+=a.z*c;return this},sub:function(a,c){if(void 0!==c)return console.warn("THREE.Vector3: .sub() now only accepts one argument. Use .subVectors( a, b ) instead."),
this.subVectors(a,c);this.x-=a.x;this.y-=a.y;this.z-=a.z;return this},subScalar:function(a){this.x-=a;this.y-=a;this.z-=a;return this},subVectors:function(a,c){this.x=a.x-c.x;this.y=a.y-c.y;this.z=a.z-c.z;return this},multiply:function(a,c){if(void 0!==c)return console.warn("THREE.Vector3: .multiply() now only accepts one argument. Use .multiplyVectors( a, b ) instead."),this.multiplyVectors(a,c);this.x*=a.x;this.y*=a.y;this.z*=a.z;return this},multiplyScalar:function(a){this.x*=a;this.y*=a;this.z*=
a;return this},multiplyVectors:function(a,c){this.x=a.x*c.x;this.y=a.y*c.y;this.z=a.z*c.z;return this},applyEuler:function(a){a&&a.isEuler||console.error("THREE.Vector3: .applyEuler() now expects an Euler rotation rather than a Vector3 and order.");return this.applyQuaternion(kk.setFromEuler(a))},applyAxisAngle:function(a,c){return this.applyQuaternion(kk.setFromAxisAngle(a,c))},applyMatrix3:function(a){var c=this.x,e=this.y,g=this.z;a=a.elements;this.x=a[0]*c+a[3]*e+a[6]*g;this.y=a[1]*c+a[4]*e+a[7]*
g;this.z=a[2]*c+a[5]*e+a[8]*g;return this},applyMatrix4:function(a){var c=this.x,e=this.y,g=this.z;a=a.elements;var t=1/(a[3]*c+a[7]*e+a[11]*g+a[15]);this.x=(a[0]*c+a[4]*e+a[8]*g+a[12])*t;this.y=(a[1]*c+a[5]*e+a[9]*g+a[13])*t;this.z=(a[2]*c+a[6]*e+a[10]*g+a[14])*t;return this},applyQuaternion:function(a){var c=this.x,e=this.y,g=this.z,t=a.x,v=a.y,z=a.z;a=a.w;var E=a*c+v*g-z*e,F=a*e+z*c-t*g,I=a*g+t*e-v*c;c=-t*c-v*e-z*g;this.x=E*a+c*-t+F*-z-I*-v;this.y=F*a+c*-v+I*-t-E*-z;this.z=I*a+c*-z+E*-v-F*-t;return this},
project:function(a){return this.applyMatrix4(a.matrixWorldInverse).applyMatrix4(a.projectionMatrix)},unproject:function(a){return this.applyMatrix4(a.projectionMatrixInverse).applyMatrix4(a.matrixWorld)},transformDirection:function(a){var c=this.x,e=this.y,g=this.z;a=a.elements;this.x=a[0]*c+a[4]*e+a[8]*g;this.y=a[1]*c+a[5]*e+a[9]*g;this.z=a[2]*c+a[6]*e+a[10]*g;return this.normalize()},divide:function(a){this.x/=a.x;this.y/=a.y;this.z/=a.z;return this},divideScalar:function(a){return this.multiplyScalar(1/
a)},min:function(a){this.x=Math.min(this.x,a.x);this.y=Math.min(this.y,a.y);this.z=Math.min(this.z,a.z);return this},max:function(a){this.x=Math.max(this.x,a.x);this.y=Math.max(this.y,a.y);this.z=Math.max(this.z,a.z);return this},clamp:function(a,c){this.x=Math.max(a.x,Math.min(c.x,this.x));this.y=Math.max(a.y,Math.min(c.y,this.y));this.z=Math.max(a.z,Math.min(c.z,this.z));return this},clampScalar:function(a,c){this.x=Math.max(a,Math.min(c,this.x));this.y=Math.max(a,Math.min(c,this.y));this.z=Math.max(a,
Math.min(c,this.z));return this},clampLength:function(a,c){var e=this.length();return this.divideScalar(e||1).multiplyScalar(Math.max(a,Math.min(c,e)))},floor:function(){this.x=Math.floor(this.x);this.y=Math.floor(this.y);this.z=Math.floor(this.z);return this},ceil:function(){this.x=Math.ceil(this.x);this.y=Math.ceil(this.y);this.z=Math.ceil(this.z);return this},round:function(){this.x=Math.round(this.x);this.y=Math.round(this.y);this.z=Math.round(this.z);return this},roundToZero:function(){this.x=
0>this.x?Math.ceil(this.x):Math.floor(this.x);this.y=0>this.y?Math.ceil(this.y):Math.floor(this.y);this.z=0>this.z?Math.ceil(this.z):Math.floor(this.z);return this},negate:function(){this.x=-this.x;this.y=-this.y;this.z=-this.z;return this},dot:function(a){return this.x*a.x+this.y*a.y+this.z*a.z},lengthSq:function(){return this.x*this.x+this.y*this.y+this.z*this.z},length:function(){return Math.sqrt(this.x*this.x+this.y*this.y+this.z*this.z)},manhattanLength:function(){return Math.abs(this.x)+Math.abs(this.y)+
Math.abs(this.z)},normalize:function(){return this.divideScalar(this.length()||1)},setLength:function(a){return this.normalize().multiplyScalar(a)},lerp:function(a,c){this.x+=(a.x-this.x)*c;this.y+=(a.y-this.y)*c;this.z+=(a.z-this.z)*c;return this},lerpVectors:function(a,c,e){return this.subVectors(c,a).multiplyScalar(e).add(a)},cross:function(a,c){return void 0!==c?(console.warn("THREE.Vector3: .cross() now only accepts one argument. Use .crossVectors( a, b ) instead."),this.crossVectors(a,c)):this.crossVectors(this,
a)},crossVectors:function(a,c){var e=a.x,g=a.y;a=a.z;var t=c.x,v=c.y;c=c.z;this.x=g*c-a*v;this.y=a*t-e*c;this.z=e*v-g*t;return this},projectOnVector:function(a){var c=a.dot(this)/a.lengthSq();return this.copy(a).multiplyScalar(c)},projectOnPlane:function(a){Ci.copy(this).projectOnVector(a);return this.sub(Ci)},reflect:function(a){return this.sub(Ci.copy(a).multiplyScalar(2*this.dot(a)))},angleTo:function(a){return Math.acos(hb.clamp(this.dot(a)/Math.sqrt(this.lengthSq()*a.lengthSq()),-1,1))},distanceTo:function(a){return Math.sqrt(this.distanceToSquared(a))},
distanceToSquared:function(a){var c=this.x-a.x,e=this.y-a.y;a=this.z-a.z;return c*c+e*e+a*a},manhattanDistanceTo:function(a){return Math.abs(this.x-a.x)+Math.abs(this.y-a.y)+Math.abs(this.z-a.z)},setFromSpherical:function(a){return this.setFromSphericalCoords(a.radius,a.phi,a.theta)},setFromSphericalCoords:function(a,c,e){var g=Math.sin(c)*a;this.x=g*Math.sin(e);this.y=Math.cos(c)*a;this.z=g*Math.cos(e);return this},setFromCylindrical:function(a){return this.setFromCylindricalCoords(a.radius,a.theta,
a.y)},setFromCylindricalCoords:function(a,c,e){this.x=a*Math.sin(c);this.y=e;this.z=a*Math.cos(c);return this},setFromMatrixPosition:function(a){a=a.elements;this.x=a[12];this.y=a[13];this.z=a[14];return this},setFromMatrixScale:function(a){var c=this.setFromMatrixColumn(a,0).length(),e=this.setFromMatrixColumn(a,1).length();a=this.setFromMatrixColumn(a,2).length();this.x=c;this.y=e;this.z=a;return this},setFromMatrixColumn:function(a,c){return this.fromArray(a.elements,4*c)},equals:function(a){return a.x===
this.x&&a.y===this.y&&a.z===this.z},fromArray:function(a,c){void 0===c&&(c=0);this.x=a[c];this.y=a[c+1];this.z=a[c+2];return this},toArray:function(a,c){void 0===a&&(a=[]);void 0===c&&(c=0);a[c]=this.x;a[c+1]=this.y;a[c+2]=this.z;return a},fromBufferAttribute:function(a,c,e){void 0!==e&&console.warn("THREE.Vector3: offset has been removed from .fromBufferAttribute().");this.x=a.getX(c);this.y=a.getY(c);this.z=a.getZ(c);return this}});var ge=new k;Object.assign(r.prototype,{isMatrix3:!0,set:function(a,
c,e,g,t,v,z,E,F){var I=this.elements;I[0]=a;I[1]=g;I[2]=z;I[3]=c;I[4]=t;I[5]=E;I[6]=e;I[7]=v;I[8]=F;return this},identity:function(){this.set(1,0,0,0,1,0,0,0,1);return this},clone:function(){return(new this.constructor).fromArray(this.elements)},copy:function(a){var c=this.elements;a=a.elements;c[0]=a[0];c[1]=a[1];c[2]=a[2];c[3]=a[3];c[4]=a[4];c[5]=a[5];c[6]=a[6];c[7]=a[7];c[8]=a[8];return this},setFromMatrix4:function(a){a=a.elements;this.set(a[0],a[4],a[8],a[1],a[5],a[9],a[2],a[6],a[10]);return this},
applyToBufferAttribute:function(a){for(var c=0,e=a.count;c<e;c++)ge.x=a.getX(c),ge.y=a.getY(c),ge.z=a.getZ(c),ge.applyMatrix3(this),a.setXYZ(c,ge.x,ge.y,ge.z);return a},multiply:function(a){return this.multiplyMatrices(this,a)},premultiply:function(a){return this.multiplyMatrices(a,this)},multiplyMatrices:function(a,c){var e=a.elements,g=c.elements;c=this.elements;a=e[0];var t=e[3],v=e[6],z=e[1],E=e[4],F=e[7],I=e[2],M=e[5];e=e[8];var P=g[0],Q=g[3],U=g[6],V=g[1],ea=g[4],da=g[7],ra=g[2],pa=g[5];g=g[8];
c[0]=a*P+t*V+v*ra;c[3]=a*Q+t*ea+v*pa;c[6]=a*U+t*da+v*g;c[1]=z*P+E*V+F*ra;c[4]=z*Q+E*ea+F*pa;c[7]=z*U+E*da+F*g;c[2]=I*P+M*V+e*ra;c[5]=I*Q+M*ea+e*pa;c[8]=I*U+M*da+e*g;return this},multiplyScalar:function(a){var c=this.elements;c[0]*=a;c[3]*=a;c[6]*=a;c[1]*=a;c[4]*=a;c[7]*=a;c[2]*=a;c[5]*=a;c[8]*=a;return this},determinant:function(){var a=this.elements,c=a[0],e=a[1],g=a[2],t=a[3],v=a[4],z=a[5],E=a[6],F=a[7];a=a[8];return c*v*a-c*z*F-e*t*a+e*z*E+g*t*F-g*v*E},getInverse:function(a,c){a&&a.isMatrix4&&
console.error("THREE.Matrix3: .getInverse() no longer takes a Matrix4 argument.");var e=a.elements;a=this.elements;var g=e[0],t=e[1],v=e[2],z=e[3],E=e[4],F=e[5],I=e[6],M=e[7];e=e[8];var P=e*E-F*M,Q=F*I-e*z,U=M*z-E*I,V=g*P+t*Q+v*U;if(0===V){if(!0===c)throw Error("THREE.Matrix3: .getInverse() can't invert matrix, determinant is 0");console.warn("THREE.Matrix3: .getInverse() can't invert matrix, determinant is 0");return this.identity()}c=1/V;a[0]=P*c;a[1]=(v*M-e*t)*c;a[2]=(F*t-v*E)*c;a[3]=Q*c;a[4]=
(e*g-v*I)*c;a[5]=(v*z-F*g)*c;a[6]=U*c;a[7]=(t*I-M*g)*c;a[8]=(E*g-t*z)*c;return this},transpose:function(){var a=this.elements;var c=a[1];a[1]=a[3];a[3]=c;c=a[2];a[2]=a[6];a[6]=c;c=a[5];a[5]=a[7];a[7]=c;return this},getNormalMatrix:function(a){return this.setFromMatrix4(a).getInverse(this).transpose()},transposeIntoArray:function(a){var c=this.elements;a[0]=c[0];a[1]=c[3];a[2]=c[6];a[3]=c[1];a[4]=c[4];a[5]=c[7];a[6]=c[2];a[7]=c[5];a[8]=c[8];return this},setUvTransform:function(a,c,e,g,t,v,z){var E=
Math.cos(t);t=Math.sin(t);this.set(e*E,e*t,-e*(E*v+t*z)+v+a,-g*t,g*E,-g*(-t*v+E*z)+z+c,0,0,1)},scale:function(a,c){var e=this.elements;e[0]*=a;e[3]*=a;e[6]*=a;e[1]*=c;e[4]*=c;e[7]*=c;return this},rotate:function(a){var c=Math.cos(a);a=Math.sin(a);var e=this.elements,g=e[0],t=e[3],v=e[6],z=e[1],E=e[4],F=e[7];e[0]=c*g+a*z;e[3]=c*t+a*E;e[6]=c*v+a*F;e[1]=-a*g+c*z;e[4]=-a*t+c*E;e[7]=-a*v+c*F;return this},translate:function(a,c){var e=this.elements;e[0]+=a*e[2];e[3]+=a*e[5];e[6]+=a*e[8];e[1]+=c*e[2];e[4]+=
c*e[5];e[7]+=c*e[8];return this},equals:function(a){var c=this.elements;a=a.elements;for(var e=0;9>e;e++)if(c[e]!==a[e])return!1;return!0},fromArray:function(a,c){void 0===c&&(c=0);for(var e=0;9>e;e++)this.elements[e]=a[e+c];return this},toArray:function(a,c){void 0===a&&(a=[]);void 0===c&&(c=0);var e=this.elements;a[c]=e[0];a[c+1]=e[1];a[c+2]=e[2];a[c+3]=e[3];a[c+4]=e[4];a[c+5]=e[5];a[c+6]=e[6];a[c+7]=e[7];a[c+8]=e[8];return a}});var gf,Fd={getDataURL:function(a){if("undefined"==typeof HTMLCanvasElement)return a.src;
if(!(a instanceof HTMLCanvasElement)){void 0===gf&&(gf=document.createElementNS("http://www.w3.org/1999/xhtml","canvas"));gf.width=a.width;gf.height=a.height;var c=gf.getContext("2d");a instanceof ImageData?c.putImageData(a,0,0):c.drawImage(a,0,0,a.width,a.height);a=gf}return 2048<a.width||2048<a.height?a.toDataURL("image/jpeg",.6):a.toDataURL("image/png")}},hl=0;l.DEFAULT_IMAGE=void 0;l.DEFAULT_MAPPING=300;l.prototype=Object.assign(Object.create(d.prototype),{constructor:l,isTexture:!0,updateMatrix:function(){this.matrix.setUvTransform(this.offset.x,
this.offset.y,this.repeat.x,this.repeat.y,this.rotation,this.center.x,this.center.y)},clone:function(){return(new this.constructor).copy(this)},copy:function(a){this.name=a.name;this.image=a.image;this.mipmaps=a.mipmaps.slice(0);this.mapping=a.mapping;this.wrapS=a.wrapS;this.wrapT=a.wrapT;this.magFilter=a.magFilter;this.minFilter=a.minFilter;this.anisotropy=a.anisotropy;this.format=a.format;this.type=a.type;this.offset.copy(a.offset);this.repeat.copy(a.repeat);this.center.copy(a.center);this.rotation=
a.rotation;this.matrixAutoUpdate=a.matrixAutoUpdate;this.matrix.copy(a.matrix);this.generateMipmaps=a.generateMipmaps;this.premultiplyAlpha=a.premultiplyAlpha;this.flipY=a.flipY;this.unpackAlignment=a.unpackAlignment;this.encoding=a.encoding;return this},toJSON:function(a){var c=void 0===a||"string"===typeof a;if(!c&&void 0!==a.textures[this.uuid])return a.textures[this.uuid];var e={metadata:{version:4.5,type:"Texture",generator:"Texture.toJSON"},uuid:this.uuid,name:this.name,mapping:this.mapping,
repeat:[this.repeat.x,this.repeat.y],offset:[this.offset.x,this.offset.y],center:[this.center.x,this.center.y],rotation:this.rotation,wrap:[this.wrapS,this.wrapT],format:this.format,type:this.type,encoding:this.encoding,minFilter:this.minFilter,magFilter:this.magFilter,anisotropy:this.anisotropy,flipY:this.flipY,premultiplyAlpha:this.premultiplyAlpha,unpackAlignment:this.unpackAlignment};if(void 0!==this.image){var g=this.image;void 0===g.uuid&&(g.uuid=hb.generateUUID());if(!c&&void 0===a.images[g.uuid]){if(Array.isArray(g)){var t=
[];for(var v=0,z=g.length;v<z;v++)t.push(Fd.getDataURL(g[v]))}else t=Fd.getDataURL(g);a.images[g.uuid]={uuid:g.uuid,url:t}}e.image=g.uuid}c||(a.textures[this.uuid]=e);return e},dispose:function(){this.dispatchEvent({type:"dispose"})},transformUv:function(a){if(300!==this.mapping)return a;a.applyMatrix3(this.matrix);if(0>a.x||1<a.x)switch(this.wrapS){case 1E3:a.x-=Math.floor(a.x);break;case 1001:a.x=0>a.x?0:1;break;case 1002:a.x=1===Math.abs(Math.floor(a.x)%2)?Math.ceil(a.x)-a.x:a.x-Math.floor(a.x)}if(0>
a.y||1<a.y)switch(this.wrapT){case 1E3:a.y-=Math.floor(a.y);break;case 1001:a.y=0>a.y?0:1;break;case 1002:a.y=1===Math.abs(Math.floor(a.y)%2)?Math.ceil(a.y)-a.y:a.y-Math.floor(a.y)}this.flipY&&(a.y=1-a.y);return a}});Object.defineProperty(l.prototype,"needsUpdate",{set:function(a){!0===a&&this.version++}});Object.defineProperties(p.prototype,{width:{get:function(){return this.z},set:function(a){this.z=a}},height:{get:function(){return this.w},set:function(a){this.w=a}}});Object.assign(p.prototype,
{isVector4:!0,set:function(a,c,e,g){this.x=a;this.y=c;this.z=e;this.w=g;return this},setScalar:function(a){this.w=this.z=this.y=this.x=a;return this},setX:function(a){this.x=a;return this},setY:function(a){this.y=a;return this},setZ:function(a){this.z=a;return this},setW:function(a){this.w=a;return this},setComponent:function(a,c){switch(a){case 0:this.x=c;break;case 1:this.y=c;break;case 2:this.z=c;break;case 3:this.w=c;break;default:throw Error("index is out of range: "+a);}return this},getComponent:function(a){switch(a){case 0:return this.x;
case 1:return this.y;case 2:return this.z;case 3:return this.w;default:throw Error("index is out of range: "+a);}},clone:function(){return new this.constructor(this.x,this.y,this.z,this.w)},copy:function(a){this.x=a.x;this.y=a.y;this.z=a.z;this.w=void 0!==a.w?a.w:1;return this},add:function(a,c){if(void 0!==c)return console.warn("THREE.Vector4: .add() now only accepts one argument. Use .addVectors( a, b ) instead."),this.addVectors(a,c);this.x+=a.x;this.y+=a.y;this.z+=a.z;this.w+=a.w;return this},
addScalar:function(a){this.x+=a;this.y+=a;this.z+=a;this.w+=a;return this},addVectors:function(a,c){this.x=a.x+c.x;this.y=a.y+c.y;this.z=a.z+c.z;this.w=a.w+c.w;return this},addScaledVector:function(a,c){this.x+=a.x*c;this.y+=a.y*c;this.z+=a.z*c;this.w+=a.w*c;return this},sub:function(a,c){if(void 0!==c)return console.warn("THREE.Vector4: .sub() now only accepts one argument. Use .subVectors( a, b ) instead."),this.subVectors(a,c);this.x-=a.x;this.y-=a.y;this.z-=a.z;this.w-=a.w;return this},subScalar:function(a){this.x-=
a;this.y-=a;this.z-=a;this.w-=a;return this},subVectors:function(a,c){this.x=a.x-c.x;this.y=a.y-c.y;this.z=a.z-c.z;this.w=a.w-c.w;return this},multiplyScalar:function(a){this.x*=a;this.y*=a;this.z*=a;this.w*=a;return this},applyMatrix4:function(a){var c=this.x,e=this.y,g=this.z,t=this.w;a=a.elements;this.x=a[0]*c+a[4]*e+a[8]*g+a[12]*t;this.y=a[1]*c+a[5]*e+a[9]*g+a[13]*t;this.z=a[2]*c+a[6]*e+a[10]*g+a[14]*t;this.w=a[3]*c+a[7]*e+a[11]*g+a[15]*t;return this},divideScalar:function(a){return this.multiplyScalar(1/
a)},setAxisAngleFromQuaternion:function(a){this.w=2*Math.acos(a.w);var c=Math.sqrt(1-a.w*a.w);1E-4>c?(this.x=1,this.z=this.y=0):(this.x=a.x/c,this.y=a.y/c,this.z=a.z/c);return this},setAxisAngleFromRotationMatrix:function(a){a=a.elements;var c=a[0];var e=a[4];var g=a[8],t=a[1],v=a[5],z=a[9];var E=a[2];var F=a[6];var I=a[10];if(.01>Math.abs(e-t)&&.01>Math.abs(g-E)&&.01>Math.abs(z-F)){if(.1>Math.abs(e+t)&&.1>Math.abs(g+E)&&.1>Math.abs(z+F)&&.1>Math.abs(c+v+I-3))return this.set(1,0,0,0),this;a=Math.PI;
c=(c+1)/2;v=(v+1)/2;I=(I+1)/2;e=(e+t)/4;g=(g+E)/4;z=(z+F)/4;c>v&&c>I?.01>c?(F=0,e=E=.707106781):(F=Math.sqrt(c),E=e/F,e=g/F):v>I?.01>v?(F=.707106781,E=0,e=.707106781):(E=Math.sqrt(v),F=e/E,e=z/E):.01>I?(E=F=.707106781,e=0):(e=Math.sqrt(I),F=g/e,E=z/e);this.set(F,E,e,a);return this}a=Math.sqrt((F-z)*(F-z)+(g-E)*(g-E)+(t-e)*(t-e));.001>Math.abs(a)&&(a=1);this.x=(F-z)/a;this.y=(g-E)/a;this.z=(t-e)/a;this.w=Math.acos((c+v+I-1)/2);return this},min:function(a){this.x=Math.min(this.x,a.x);this.y=Math.min(this.y,
a.y);this.z=Math.min(this.z,a.z);this.w=Math.min(this.w,a.w);return this},max:function(a){this.x=Math.max(this.x,a.x);this.y=Math.max(this.y,a.y);this.z=Math.max(this.z,a.z);this.w=Math.max(this.w,a.w);return this},clamp:function(a,c){this.x=Math.max(a.x,Math.min(c.x,this.x));this.y=Math.max(a.y,Math.min(c.y,this.y));this.z=Math.max(a.z,Math.min(c.z,this.z));this.w=Math.max(a.w,Math.min(c.w,this.w));return this},clampScalar:function(a,c){this.x=Math.max(a,Math.min(c,this.x));this.y=Math.max(a,Math.min(c,
this.y));this.z=Math.max(a,Math.min(c,this.z));this.w=Math.max(a,Math.min(c,this.w));return this},clampLength:function(a,c){var e=this.length();return this.divideScalar(e||1).multiplyScalar(Math.max(a,Math.min(c,e)))},floor:function(){this.x=Math.floor(this.x);this.y=Math.floor(this.y);this.z=Math.floor(this.z);this.w=Math.floor(this.w);return this},ceil:function(){this.x=Math.ceil(this.x);this.y=Math.ceil(this.y);this.z=Math.ceil(this.z);this.w=Math.ceil(this.w);return this},round:function(){this.x=
Math.round(this.x);this.y=Math.round(this.y);this.z=Math.round(this.z);this.w=Math.round(this.w);return this},roundToZero:function(){this.x=0>this.x?Math.ceil(this.x):Math.floor(this.x);this.y=0>this.y?Math.ceil(this.y):Math.floor(this.y);this.z=0>this.z?Math.ceil(this.z):Math.floor(this.z);this.w=0>this.w?Math.ceil(this.w):Math.floor(this.w);return this},negate:function(){this.x=-this.x;this.y=-this.y;this.z=-this.z;this.w=-this.w;return this},dot:function(a){return this.x*a.x+this.y*a.y+this.z*
a.z+this.w*a.w},lengthSq:function(){return this.x*this.x+this.y*this.y+this.z*this.z+this.w*this.w},length:function(){return Math.sqrt(this.x*this.x+this.y*this.y+this.z*this.z+this.w*this.w)},manhattanLength:function(){return Math.abs(this.x)+Math.abs(this.y)+Math.abs(this.z)+Math.abs(this.w)},normalize:function(){return this.divideScalar(this.length()||1)},setLength:function(a){return this.normalize().multiplyScalar(a)},lerp:function(a,c){this.x+=(a.x-this.x)*c;this.y+=(a.y-this.y)*c;this.z+=(a.z-
this.z)*c;this.w+=(a.w-this.w)*c;return this},lerpVectors:function(a,c,e){return this.subVectors(c,a).multiplyScalar(e).add(a)},equals:function(a){return a.x===this.x&&a.y===this.y&&a.z===this.z&&a.w===this.w},fromArray:function(a,c){void 0===c&&(c=0);this.x=a[c];this.y=a[c+1];this.z=a[c+2];this.w=a[c+3];return this},toArray:function(a,c){void 0===a&&(a=[]);void 0===c&&(c=0);a[c]=this.x;a[c+1]=this.y;a[c+2]=this.z;a[c+3]=this.w;return a},fromBufferAttribute:function(a,c,e){void 0!==e&&console.warn("THREE.Vector4: offset has been removed from .fromBufferAttribute().");
this.x=a.getX(c);this.y=a.getY(c);this.z=a.getZ(c);this.w=a.getW(c);return this}});m.prototype=Object.assign(Object.create(d.prototype),{constructor:m,isWebGLRenderTarget:!0,setSize:function(a,c){if(this.width!==a||this.height!==c)this.width=a,this.height=c,this.texture.image.width=a,this.texture.image.height=c,this.dispose();this.viewport.set(0,0,a,c);this.scissor.set(0,0,a,c)},clone:function(){return(new this.constructor).copy(this)},copy:function(a){this.width=a.width;this.height=a.height;this.viewport.copy(a.viewport);
this.texture=a.texture.clone();this.depthBuffer=a.depthBuffer;this.stencilBuffer=a.stencilBuffer;this.depthTexture=a.depthTexture;return this},dispose:function(){this.dispatchEvent({type:"dispose"})}});n.prototype=Object.assign(Object.create(m.prototype),{constructor:n,isWebGLMultisampleRenderTarget:!0,copy:function(a){m.prototype.copy.call(this,a);this.samples=a.samples;return this}});var tc=new k,Rb=new q,Sm=new k(0,0,0),Tm=new k(1,1,1),Gd=new k,jh=new k,ic=new k;Object.assign(q.prototype,{isMatrix4:!0,
set:function(a,c,e,g,t,v,z,E,F,I,M,P,Q,U,V,ea){var da=this.elements;da[0]=a;da[4]=c;da[8]=e;da[12]=g;da[1]=t;da[5]=v;da[9]=z;da[13]=E;da[2]=F;da[6]=I;da[10]=M;da[14]=P;da[3]=Q;da[7]=U;da[11]=V;da[15]=ea;return this},identity:function(){this.set(1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1);return this},clone:function(){return(new q).fromArray(this.elements)},copy:function(a){var c=this.elements;a=a.elements;c[0]=a[0];c[1]=a[1];c[2]=a[2];c[3]=a[3];c[4]=a[4];c[5]=a[5];c[6]=a[6];c[7]=a[7];c[8]=a[8];c[9]=a[9];c[10]=
a[10];c[11]=a[11];c[12]=a[12];c[13]=a[13];c[14]=a[14];c[15]=a[15];return this},copyPosition:function(a){var c=this.elements;a=a.elements;c[12]=a[12];c[13]=a[13];c[14]=a[14];return this},extractBasis:function(a,c,e){a.setFromMatrixColumn(this,0);c.setFromMatrixColumn(this,1);e.setFromMatrixColumn(this,2);return this},makeBasis:function(a,c,e){this.set(a.x,c.x,e.x,0,a.y,c.y,e.y,0,a.z,c.z,e.z,0,0,0,0,1);return this},extractRotation:function(a){var c=this.elements,e=a.elements,g=1/tc.setFromMatrixColumn(a,
0).length(),t=1/tc.setFromMatrixColumn(a,1).length();a=1/tc.setFromMatrixColumn(a,2).length();c[0]=e[0]*g;c[1]=e[1]*g;c[2]=e[2]*g;c[3]=0;c[4]=e[4]*t;c[5]=e[5]*t;c[6]=e[6]*t;c[7]=0;c[8]=e[8]*a;c[9]=e[9]*a;c[10]=e[10]*a;c[11]=0;c[12]=0;c[13]=0;c[14]=0;c[15]=1;return this},makeRotationFromEuler:function(a){a&&a.isEuler||console.error("THREE.Matrix4: .makeRotationFromEuler() now expects a Euler rotation rather than a Vector3 and order.");var c=this.elements,e=a.x,g=a.y,t=a.z,v=Math.cos(e);e=Math.sin(e);
var z=Math.cos(g);g=Math.sin(g);var E=Math.cos(t);t=Math.sin(t);if("XYZ"===a.order){a=v*E;var F=v*t,I=e*E,M=e*t;c[0]=z*E;c[4]=-z*t;c[8]=g;c[1]=F+I*g;c[5]=a-M*g;c[9]=-e*z;c[2]=M-a*g;c[6]=I+F*g;c[10]=v*z}else"YXZ"===a.order?(a=z*E,F=z*t,I=g*E,M=g*t,c[0]=a+M*e,c[4]=I*e-F,c[8]=v*g,c[1]=v*t,c[5]=v*E,c[9]=-e,c[2]=F*e-I,c[6]=M+a*e,c[10]=v*z):"ZXY"===a.order?(a=z*E,F=z*t,I=g*E,M=g*t,c[0]=a-M*e,c[4]=-v*t,c[8]=I+F*e,c[1]=F+I*e,c[5]=v*E,c[9]=M-a*e,c[2]=-v*g,c[6]=e,c[10]=v*z):"ZYX"===a.order?(a=v*E,F=v*t,I=e*
E,M=e*t,c[0]=z*E,c[4]=I*g-F,c[8]=a*g+M,c[1]=z*t,c[5]=M*g+a,c[9]=F*g-I,c[2]=-g,c[6]=e*z,c[10]=v*z):"YZX"===a.order?(a=v*z,F=v*g,I=e*z,M=e*g,c[0]=z*E,c[4]=M-a*t,c[8]=I*t+F,c[1]=t,c[5]=v*E,c[9]=-e*E,c[2]=-g*E,c[6]=F*t+I,c[10]=a-M*t):"XZY"===a.order&&(a=v*z,F=v*g,I=e*z,M=e*g,c[0]=z*E,c[4]=-t,c[8]=g*E,c[1]=a*t+M,c[5]=v*E,c[9]=F*t-I,c[2]=I*t-F,c[6]=e*E,c[10]=M*t+a);c[3]=0;c[7]=0;c[11]=0;c[12]=0;c[13]=0;c[14]=0;c[15]=1;return this},makeRotationFromQuaternion:function(a){return this.compose(Sm,a,Tm)},lookAt:function(a,
c,e){var g=this.elements;ic.subVectors(a,c);0===ic.lengthSq()&&(ic.z=1);ic.normalize();Gd.crossVectors(e,ic);0===Gd.lengthSq()&&(1===Math.abs(e.z)?ic.x+=1E-4:ic.z+=1E-4,ic.normalize(),Gd.crossVectors(e,ic));Gd.normalize();jh.crossVectors(ic,Gd);g[0]=Gd.x;g[4]=jh.x;g[8]=ic.x;g[1]=Gd.y;g[5]=jh.y;g[9]=ic.y;g[2]=Gd.z;g[6]=jh.z;g[10]=ic.z;return this},multiply:function(a,c){return void 0!==c?(console.warn("THREE.Matrix4: .multiply() now only accepts one argument. Use .multiplyMatrices( a, b ) instead."),
this.multiplyMatrices(a,c)):this.multiplyMatrices(this,a)},premultiply:function(a){return this.multiplyMatrices(a,this)},multiplyMatrices:function(a,c){var e=a.elements,g=c.elements;c=this.elements;a=e[0];var t=e[4],v=e[8],z=e[12],E=e[1],F=e[5],I=e[9],M=e[13],P=e[2],Q=e[6],U=e[10],V=e[14],ea=e[3],da=e[7],ra=e[11];e=e[15];var pa=g[0],qa=g[4],ua=g[8],na=g[12],ta=g[1],Ba=g[5],Ta=g[9],Ua=g[13],Ca=g[2],Ha=g[6],Da=g[10],Ma=g[14],db=g[3],tb=g[7],Ka=g[11];g=g[15];c[0]=a*pa+t*ta+v*Ca+z*db;c[4]=a*qa+t*Ba+v*
Ha+z*tb;c[8]=a*ua+t*Ta+v*Da+z*Ka;c[12]=a*na+t*Ua+v*Ma+z*g;c[1]=E*pa+F*ta+I*Ca+M*db;c[5]=E*qa+F*Ba+I*Ha+M*tb;c[9]=E*ua+F*Ta+I*Da+M*Ka;c[13]=E*na+F*Ua+I*Ma+M*g;c[2]=P*pa+Q*ta+U*Ca+V*db;c[6]=P*qa+Q*Ba+U*Ha+V*tb;c[10]=P*ua+Q*Ta+U*Da+V*Ka;c[14]=P*na+Q*Ua+U*Ma+V*g;c[3]=ea*pa+da*ta+ra*Ca+e*db;c[7]=ea*qa+da*Ba+ra*Ha+e*tb;c[11]=ea*ua+da*Ta+ra*Da+e*Ka;c[15]=ea*na+da*Ua+ra*Ma+e*g;return this},multiplyScalar:function(a){var c=this.elements;c[0]*=a;c[4]*=a;c[8]*=a;c[12]*=a;c[1]*=a;c[5]*=a;c[9]*=a;c[13]*=a;c[2]*=
a;c[6]*=a;c[10]*=a;c[14]*=a;c[3]*=a;c[7]*=a;c[11]*=a;c[15]*=a;return this},applyToBufferAttribute:function(a){for(var c=0,e=a.count;c<e;c++)tc.x=a.getX(c),tc.y=a.getY(c),tc.z=a.getZ(c),tc.applyMatrix4(this),a.setXYZ(c,tc.x,tc.y,tc.z);return a},determinant:function(){var a=this.elements,c=a[0],e=a[4],g=a[8],t=a[12],v=a[1],z=a[5],E=a[9],F=a[13],I=a[2],M=a[6],P=a[10],Q=a[14];return a[3]*(+t*E*M-g*F*M-t*z*P+e*F*P+g*z*Q-e*E*Q)+a[7]*(+c*E*Q-c*F*P+t*v*P-g*v*Q+g*F*I-t*E*I)+a[11]*(+c*F*M-c*z*Q-t*v*M+e*v*Q+
t*z*I-e*F*I)+a[15]*(-g*z*I-c*E*M+c*z*P+g*v*M-e*v*P+e*E*I)},transpose:function(){var a=this.elements;var c=a[1];a[1]=a[4];a[4]=c;c=a[2];a[2]=a[8];a[8]=c;c=a[6];a[6]=a[9];a[9]=c;c=a[3];a[3]=a[12];a[12]=c;c=a[7];a[7]=a[13];a[13]=c;c=a[11];a[11]=a[14];a[14]=c;return this},setPosition:function(a,c,e){var g=this.elements;a.isVector3?(g[12]=a.x,g[13]=a.y,g[14]=a.z):(g[12]=a,g[13]=c,g[14]=e);return this},getInverse:function(a,c){var e=this.elements,g=a.elements;a=g[0];var t=g[1],v=g[2],z=g[3],E=g[4],F=g[5],
I=g[6],M=g[7],P=g[8],Q=g[9],U=g[10],V=g[11],ea=g[12],da=g[13],ra=g[14];g=g[15];var pa=Q*ra*M-da*U*M+da*I*V-F*ra*V-Q*I*g+F*U*g,qa=ea*U*M-P*ra*M-ea*I*V+E*ra*V+P*I*g-E*U*g,ua=P*da*M-ea*Q*M+ea*F*V-E*da*V-P*F*g+E*Q*g,na=ea*Q*I-P*da*I-ea*F*U+E*da*U+P*F*ra-E*Q*ra,ta=a*pa+t*qa+v*ua+z*na;if(0===ta){if(!0===c)throw Error("THREE.Matrix4: .getInverse() can't invert matrix, determinant is 0");console.warn("THREE.Matrix4: .getInverse() can't invert matrix, determinant is 0");return this.identity()}c=1/ta;e[0]=
pa*c;e[1]=(da*U*z-Q*ra*z-da*v*V+t*ra*V+Q*v*g-t*U*g)*c;e[2]=(F*ra*z-da*I*z+da*v*M-t*ra*M-F*v*g+t*I*g)*c;e[3]=(Q*I*z-F*U*z-Q*v*M+t*U*M+F*v*V-t*I*V)*c;e[4]=qa*c;e[5]=(P*ra*z-ea*U*z+ea*v*V-a*ra*V-P*v*g+a*U*g)*c;e[6]=(ea*I*z-E*ra*z-ea*v*M+a*ra*M+E*v*g-a*I*g)*c;e[7]=(E*U*z-P*I*z+P*v*M-a*U*M-E*v*V+a*I*V)*c;e[8]=ua*c;e[9]=(ea*Q*z-P*da*z-ea*t*V+a*da*V+P*t*g-a*Q*g)*c;e[10]=(E*da*z-ea*F*z+ea*t*M-a*da*M-E*t*g+a*F*g)*c;e[11]=(P*F*z-E*Q*z-P*t*M+a*Q*M+E*t*V-a*F*V)*c;e[12]=na*c;e[13]=(P*da*v-ea*Q*v+ea*t*U-a*da*U-
P*t*ra+a*Q*ra)*c;e[14]=(ea*F*v-E*da*v-ea*t*I+a*da*I+E*t*ra-a*F*ra)*c;e[15]=(E*Q*v-P*F*v+P*t*I-a*Q*I-E*t*U+a*F*U)*c;return this},scale:function(a){var c=this.elements,e=a.x,g=a.y;a=a.z;c[0]*=e;c[4]*=g;c[8]*=a;c[1]*=e;c[5]*=g;c[9]*=a;c[2]*=e;c[6]*=g;c[10]*=a;c[3]*=e;c[7]*=g;c[11]*=a;return this},getMaxScaleOnAxis:function(){var a=this.elements;return Math.sqrt(Math.max(a[0]*a[0]+a[1]*a[1]+a[2]*a[2],a[4]*a[4]+a[5]*a[5]+a[6]*a[6],a[8]*a[8]+a[9]*a[9]+a[10]*a[10]))},makeTranslation:function(a,c,e){this.set(1,
0,0,a,0,1,0,c,0,0,1,e,0,0,0,1);return this},makeRotationX:function(a){var c=Math.cos(a);a=Math.sin(a);this.set(1,0,0,0,0,c,-a,0,0,a,c,0,0,0,0,1);return this},makeRotationY:function(a){var c=Math.cos(a);a=Math.sin(a);this.set(c,0,a,0,0,1,0,0,-a,0,c,0,0,0,0,1);return this},makeRotationZ:function(a){var c=Math.cos(a);a=Math.sin(a);this.set(c,-a,0,0,a,c,0,0,0,0,1,0,0,0,0,1);return this},makeRotationAxis:function(a,c){var e=Math.cos(c);c=Math.sin(c);var g=1-e,t=a.x,v=a.y;a=a.z;var z=g*t,E=g*v;this.set(z*
t+e,z*v-c*a,z*a+c*v,0,z*v+c*a,E*v+e,E*a-c*t,0,z*a-c*v,E*a+c*t,g*a*a+e,0,0,0,0,1);return this},makeScale:function(a,c,e){this.set(a,0,0,0,0,c,0,0,0,0,e,0,0,0,0,1);return this},makeShear:function(a,c,e){this.set(1,c,e,0,a,1,e,0,a,c,1,0,0,0,0,1);return this},compose:function(a,c,e){var g=this.elements,t=c._x,v=c._y,z=c._z,E=c._w,F=t+t,I=v+v,M=z+z;c=t*F;var P=t*I;t*=M;var Q=v*I;v*=M;z*=M;F*=E;I*=E;E*=M;M=e.x;var U=e.y;e=e.z;g[0]=(1-(Q+z))*M;g[1]=(P+E)*M;g[2]=(t-I)*M;g[3]=0;g[4]=(P-E)*U;g[5]=(1-(c+z))*
U;g[6]=(v+F)*U;g[7]=0;g[8]=(t+I)*e;g[9]=(v-F)*e;g[10]=(1-(c+Q))*e;g[11]=0;g[12]=a.x;g[13]=a.y;g[14]=a.z;g[15]=1;return this},decompose:function(a,c,e){var g=this.elements,t=tc.set(g[0],g[1],g[2]).length(),v=tc.set(g[4],g[5],g[6]).length(),z=tc.set(g[8],g[9],g[10]).length();0>this.determinant()&&(t=-t);a.x=g[12];a.y=g[13];a.z=g[14];Rb.copy(this);a=1/t;g=1/v;var E=1/z;Rb.elements[0]*=a;Rb.elements[1]*=a;Rb.elements[2]*=a;Rb.elements[4]*=g;Rb.elements[5]*=g;Rb.elements[6]*=g;Rb.elements[8]*=E;Rb.elements[9]*=
E;Rb.elements[10]*=E;c.setFromRotationMatrix(Rb);e.x=t;e.y=v;e.z=z;return this},makePerspective:function(a,c,e,g,t,v){void 0===v&&console.warn("THREE.Matrix4: .makePerspective() has been redefined and has a new signature. Please check the docs.");var z=this.elements;z[0]=2*t/(c-a);z[4]=0;z[8]=(c+a)/(c-a);z[12]=0;z[1]=0;z[5]=2*t/(e-g);z[9]=(e+g)/(e-g);z[13]=0;z[2]=0;z[6]=0;z[10]=-(v+t)/(v-t);z[14]=-2*v*t/(v-t);z[3]=0;z[7]=0;z[11]=-1;z[15]=0;return this},makeOrthographic:function(a,c,e,g,t,v){var z=
this.elements,E=1/(c-a),F=1/(e-g),I=1/(v-t);z[0]=2*E;z[4]=0;z[8]=0;z[12]=-((c+a)*E);z[1]=0;z[5]=2*F;z[9]=0;z[13]=-((e+g)*F);z[2]=0;z[6]=0;z[10]=-2*I;z[14]=-((v+t)*I);z[3]=0;z[7]=0;z[11]=0;z[15]=1;return this},equals:function(a){var c=this.elements;a=a.elements;for(var e=0;16>e;e++)if(c[e]!==a[e])return!1;return!0},fromArray:function(a,c){void 0===c&&(c=0);for(var e=0;16>e;e++)this.elements[e]=a[e+c];return this},toArray:function(a,c){void 0===a&&(a=[]);void 0===c&&(c=0);var e=this.elements;a[c]=e[0];
a[c+1]=e[1];a[c+2]=e[2];a[c+3]=e[3];a[c+4]=e[4];a[c+5]=e[5];a[c+6]=e[6];a[c+7]=e[7];a[c+8]=e[8];a[c+9]=e[9];a[c+10]=e[10];a[c+11]=e[11];a[c+12]=e[12];a[c+13]=e[13];a[c+14]=e[14];a[c+15]=e[15];return a}});var lk=new q,mk=new h;u.RotationOrders="XYZ YZX ZXY XZY YXZ ZYX".split(" ");u.DefaultOrder="XYZ";Object.defineProperties(u.prototype,{x:{get:function(){return this._x},set:function(a){this._x=a;this._onChangeCallback()}},y:{get:function(){return this._y},set:function(a){this._y=a;this._onChangeCallback()}},
z:{get:function(){return this._z},set:function(a){this._z=a;this._onChangeCallback()}},order:{get:function(){return this._order},set:function(a){this._order=a;this._onChangeCallback()}}});Object.assign(u.prototype,{isEuler:!0,set:function(a,c,e,g){this._x=a;this._y=c;this._z=e;this._order=g||this._order;this._onChangeCallback();return this},clone:function(){return new this.constructor(this._x,this._y,this._z,this._order)},copy:function(a){this._x=a._x;this._y=a._y;this._z=a._z;this._order=a._order;
this._onChangeCallback();return this},setFromRotationMatrix:function(a,c,e){var g=hb.clamp,t=a.elements;a=t[0];var v=t[4],z=t[8],E=t[1],F=t[5],I=t[9],M=t[2],P=t[6];t=t[10];c=c||this._order;"XYZ"===c?(this._y=Math.asin(g(z,-1,1)),.9999999>Math.abs(z)?(this._x=Math.atan2(-I,t),this._z=Math.atan2(-v,a)):(this._x=Math.atan2(P,F),this._z=0)):"YXZ"===c?(this._x=Math.asin(-g(I,-1,1)),.9999999>Math.abs(I)?(this._y=Math.atan2(z,t),this._z=Math.atan2(E,F)):(this._y=Math.atan2(-M,a),this._z=0)):"ZXY"===c?(this._x=
Math.asin(g(P,-1,1)),.9999999>Math.abs(P)?(this._y=Math.atan2(-M,t),this._z=Math.atan2(-v,F)):(this._y=0,this._z=Math.atan2(E,a))):"ZYX"===c?(this._y=Math.asin(-g(M,-1,1)),.9999999>Math.abs(M)?(this._x=Math.atan2(P,t),this._z=Math.atan2(E,a)):(this._x=0,this._z=Math.atan2(-v,F))):"YZX"===c?(this._z=Math.asin(g(E,-1,1)),.9999999>Math.abs(E)?(this._x=Math.atan2(-I,F),this._y=Math.atan2(-M,a)):(this._x=0,this._y=Math.atan2(z,t))):"XZY"===c?(this._z=Math.asin(-g(v,-1,1)),.9999999>Math.abs(v)?(this._x=
Math.atan2(P,F),this._y=Math.atan2(z,a)):(this._x=Math.atan2(-I,t),this._y=0)):console.warn("THREE.Euler: .setFromRotationMatrix() given unsupported order: "+c);this._order=c;!1!==e&&this._onChangeCallback();return this},setFromQuaternion:function(a,c,e){lk.makeRotationFromQuaternion(a);return this.setFromRotationMatrix(lk,c,e)},setFromVector3:function(a,c){return this.set(a.x,a.y,a.z,c||this._order)},reorder:function(a){mk.setFromEuler(this);return this.setFromQuaternion(mk,a)},equals:function(a){return a._x===
this._x&&a._y===this._y&&a._z===this._z&&a._order===this._order},fromArray:function(a){this._x=a[0];this._y=a[1];this._z=a[2];void 0!==a[3]&&(this._order=a[3]);this._onChangeCallback();return this},toArray:function(a,c){void 0===a&&(a=[]);void 0===c&&(c=0);a[c]=this._x;a[c+1]=this._y;a[c+2]=this._z;a[c+3]=this._order;return a},toVector3:function(a){return a?a.set(this._x,this._y,this._z):new k(this._x,this._y,this._z)},_onChange:function(a){this._onChangeCallback=a;return this},_onChangeCallback:function(){}});
Object.assign(w.prototype,{set:function(a){this.mask=1<<a|0},enable:function(a){this.mask=this.mask|1<<a|0},enableAll:function(){this.mask=-1},toggle:function(a){this.mask^=1<<a|0},disable:function(a){this.mask&=~(1<<a|0)},disableAll:function(){this.mask=0},test:function(a){return 0!==(this.mask&a.mask)}});var il=0,nk=new k,hf=new h,ld=new q,kh=new k,lg=new k,Um=new k,Vm=new h,ok=new k(1,0,0),pk=new k(0,1,0),qk=new k(0,0,1),Wm={type:"added"},Xm={type:"removed"};A.DefaultUp=new k(0,1,0);A.DefaultMatrixAutoUpdate=
!0;A.prototype=Object.assign(Object.create(d.prototype),{constructor:A,isObject3D:!0,onBeforeRender:function(){},onAfterRender:function(){},applyMatrix:function(a){this.matrixAutoUpdate&&this.updateMatrix();this.matrix.premultiply(a);this.matrix.decompose(this.position,this.quaternion,this.scale)},applyQuaternion:function(a){this.quaternion.premultiply(a);return this},setRotationFromAxisAngle:function(a,c){this.quaternion.setFromAxisAngle(a,c)},setRotationFromEuler:function(a){this.quaternion.setFromEuler(a,
!0)},setRotationFromMatrix:function(a){this.quaternion.setFromRotationMatrix(a)},setRotationFromQuaternion:function(a){this.quaternion.copy(a)},rotateOnAxis:function(a,c){hf.setFromAxisAngle(a,c);this.quaternion.multiply(hf);return this},rotateOnWorldAxis:function(a,c){hf.setFromAxisAngle(a,c);this.quaternion.premultiply(hf);return this},rotateX:function(a){return this.rotateOnAxis(ok,a)},rotateY:function(a){return this.rotateOnAxis(pk,a)},rotateZ:function(a){return this.rotateOnAxis(qk,a)},translateOnAxis:function(a,
c){nk.copy(a).applyQuaternion(this.quaternion);this.position.add(nk.multiplyScalar(c));return this},translateX:function(a){return this.translateOnAxis(ok,a)},translateY:function(a){return this.translateOnAxis(pk,a)},translateZ:function(a){return this.translateOnAxis(qk,a)},localToWorld:function(a){return a.applyMatrix4(this.matrixWorld)},worldToLocal:function(a){return a.applyMatrix4(ld.getInverse(this.matrixWorld))},lookAt:function(a,c,e){a.isVector3?kh.copy(a):kh.set(a,c,e);a=this.parent;this.updateWorldMatrix(!0,
!1);lg.setFromMatrixPosition(this.matrixWorld);this.isCamera||this.isLight?ld.lookAt(lg,kh,this.up):ld.lookAt(kh,lg,this.up);this.quaternion.setFromRotationMatrix(ld);a&&(ld.extractRotation(a.matrixWorld),hf.setFromRotationMatrix(ld),this.quaternion.premultiply(hf.inverse()))},add:function(a){if(1<arguments.length){for(var c=0;c<arguments.length;c++)this.add(arguments[c]);return this}if(a===this)return console.error("THREE.Object3D.add: object can't be added as a child of itself.",a),this;a&&a.isObject3D?
(null!==a.parent&&a.parent.remove(a),a.parent=this,this.children.push(a),a.dispatchEvent(Wm)):console.error("THREE.Object3D.add: object not an instance of THREE.Object3D.",a);return this},remove:function(a){if(1<arguments.length){for(var c=0;c<arguments.length;c++)this.remove(arguments[c]);return this}c=this.children.indexOf(a);-1!==c&&(a.parent=null,this.children.splice(c,1),a.dispatchEvent(Xm));return this},attach:function(a){this.updateWorldMatrix(!0,!1);ld.getInverse(this.matrixWorld);null!==
a.parent&&(a.parent.updateWorldMatrix(!0,!1),ld.multiply(a.parent.matrixWorld));a.applyMatrix(ld);a.updateWorldMatrix(!1,!1);this.add(a);return this},getObjectById:function(a){return this.getObjectByProperty("id",a)},getObjectByName:function(a){return this.getObjectByProperty("name",a)},getObjectByProperty:function(a,c){if(this[a]===c)return this;for(var e=0,g=this.children.length;e<g;e++){var t=this.children[e].getObjectByProperty(a,c);if(void 0!==t)return t}},getWorldPosition:function(a){void 0===
a&&(console.warn("THREE.Object3D: .getWorldPosition() target is now required"),a=new k);this.updateMatrixWorld(!0);return a.setFromMatrixPosition(this.matrixWorld)},getWorldQuaternion:function(a){void 0===a&&(console.warn("THREE.Object3D: .getWorldQuaternion() target is now required"),a=new h);this.updateMatrixWorld(!0);this.matrixWorld.decompose(lg,a,Um);return a},getWorldScale:function(a){void 0===a&&(console.warn("THREE.Object3D: .getWorldScale() target is now required"),a=new k);this.updateMatrixWorld(!0);
this.matrixWorld.decompose(lg,Vm,a);return a},getWorldDirection:function(a){void 0===a&&(console.warn("THREE.Object3D: .getWorldDirection() target is now required"),a=new k);this.updateMatrixWorld(!0);var c=this.matrixWorld.elements;return a.set(c[8],c[9],c[10]).normalize()},raycast:function(){},traverse:function(a){a(this);for(var c=this.children,e=0,g=c.length;e<g;e++)c[e].traverse(a)},traverseVisible:function(a){if(!1!==this.visible){a(this);for(var c=this.children,e=0,g=c.length;e<g;e++)c[e].traverseVisible(a)}},
traverseAncestors:function(a){var c=this.parent;null!==c&&(a(c),c.traverseAncestors(a))},updateMatrix:function(){this.matrix.compose(this.position,this.quaternion,this.scale);this.matrixWorldNeedsUpdate=!0},updateMatrixWorld:function(a){this.matrixAutoUpdate&&this.updateMatrix();if(this.matrixWorldNeedsUpdate||a)null===this.parent?this.matrixWorld.copy(this.matrix):this.matrixWorld.multiplyMatrices(this.parent.matrixWorld,this.matrix),this.matrixWorldNeedsUpdate=!1,a=!0;for(var c=this.children,e=
0,g=c.length;e<g;e++)c[e].updateMatrixWorld(a)},updateWorldMatrix:function(a,c){var e=this.parent;!0===a&&null!==e&&e.updateWorldMatrix(!0,!1);this.matrixAutoUpdate&&this.updateMatrix();null===this.parent?this.matrixWorld.copy(this.matrix):this.matrixWorld.multiplyMatrices(this.parent.matrixWorld,this.matrix);if(!0===c)for(a=this.children,c=0,e=a.length;c<e;c++)a[c].updateWorldMatrix(!1,!0)},toJSON:function(a){function c(M,P){void 0===M[P.uuid]&&(M[P.uuid]=P.toJSON(a));return P.uuid}function e(M){var P=
[],Q;for(Q in M){var U=M[Q];delete U.metadata;P.push(U)}return P}var g=void 0===a||"string"===typeof a,t={};g&&(a={geometries:{},materials:{},textures:{},images:{},shapes:{}},t.metadata={version:4.5,type:"Object",generator:"Object3D.toJSON"});var v={};v.uuid=this.uuid;v.type=this.type;""!==this.name&&(v.name=this.name);!0===this.castShadow&&(v.castShadow=!0);!0===this.receiveShadow&&(v.receiveShadow=!0);!1===this.visible&&(v.visible=!1);!1===this.frustumCulled&&(v.frustumCulled=!1);0!==this.renderOrder&&
(v.renderOrder=this.renderOrder);"{}"!==JSON.stringify(this.userData)&&(v.userData=this.userData);v.layers=this.layers.mask;v.matrix=this.matrix.toArray();!1===this.matrixAutoUpdate&&(v.matrixAutoUpdate=!1);this.isMesh&&0!==this.drawMode&&(v.drawMode=this.drawMode);if(this.isMesh||this.isLine||this.isPoints){v.geometry=c(a.geometries,this.geometry);var z=this.geometry.parameters;if(void 0!==z&&void 0!==z.shapes)if(z=z.shapes,Array.isArray(z))for(var E=0,F=z.length;E<F;E++)c(a.shapes,z[E]);else c(a.shapes,
z)}if(void 0!==this.material)if(Array.isArray(this.material)){z=[];E=0;for(F=this.material.length;E<F;E++)z.push(c(a.materials,this.material[E]));v.material=z}else v.material=c(a.materials,this.material);if(0<this.children.length)for(v.children=[],E=0;E<this.children.length;E++)v.children.push(this.children[E].toJSON(a).object);if(g){g=e(a.geometries);E=e(a.materials);F=e(a.textures);var I=e(a.images);z=e(a.shapes);0<g.length&&(t.geometries=g);0<E.length&&(t.materials=E);0<F.length&&(t.textures=F);
0<I.length&&(t.images=I);0<z.length&&(t.shapes=z)}t.object=v;return t},clone:function(a){return(new this.constructor).copy(this,a)},copy:function(a,c){void 0===c&&(c=!0);this.name=a.name;this.up.copy(a.up);this.position.copy(a.position);this.quaternion.copy(a.quaternion);this.scale.copy(a.scale);this.matrix.copy(a.matrix);this.matrixWorld.copy(a.matrixWorld);this.matrixAutoUpdate=a.matrixAutoUpdate;this.matrixWorldNeedsUpdate=a.matrixWorldNeedsUpdate;this.layers.mask=a.layers.mask;this.visible=a.visible;
this.castShadow=a.castShadow;this.receiveShadow=a.receiveShadow;this.frustumCulled=a.frustumCulled;this.renderOrder=a.renderOrder;this.userData=JSON.parse(JSON.stringify(a.userData));if(!0===c)for(c=0;c<a.children.length;c++)this.add(a.children[c].clone());return this}});y.prototype=Object.assign(Object.create(A.prototype),{constructor:y,isScene:!0,copy:function(a,c){A.prototype.copy.call(this,a,c);null!==a.background&&(this.background=a.background.clone());null!==a.fog&&(this.fog=a.fog.clone());
null!==a.overrideMaterial&&(this.overrideMaterial=a.overrideMaterial.clone());this.autoUpdate=a.autoUpdate;this.matrixAutoUpdate=a.matrixAutoUpdate;return this},toJSON:function(a){var c=A.prototype.toJSON.call(this,a);null!==this.background&&(c.object.background=this.background.toJSON(a));null!==this.fog&&(c.object.fog=this.fog.toJSON());return c},dispose:function(){this.dispatchEvent({type:"dispose"})}});var md=[new k,new k,new k,new k,new k,new k,new k,new k],Yc=new k,jf=new k,kf=new k,lf=new k,
Hd=new k,Id=new k,he=new k,mg=new k,lh=new k,mh=new k,Md=new k;Object.assign(x.prototype,{isBox3:!0,set:function(a,c){this.min.copy(a);this.max.copy(c);return this},setFromArray:function(a){for(var c=Infinity,e=Infinity,g=Infinity,t=-Infinity,v=-Infinity,z=-Infinity,E=0,F=a.length;E<F;E+=3){var I=a[E],M=a[E+1],P=a[E+2];I<c&&(c=I);M<e&&(e=M);P<g&&(g=P);I>t&&(t=I);M>v&&(v=M);P>z&&(z=P)}this.min.set(c,e,g);this.max.set(t,v,z);return this},setFromBufferAttribute:function(a){for(var c=Infinity,e=Infinity,
g=Infinity,t=-Infinity,v=-Infinity,z=-Infinity,E=0,F=a.count;E<F;E++){var I=a.getX(E),M=a.getY(E),P=a.getZ(E);I<c&&(c=I);M<e&&(e=M);P<g&&(g=P);I>t&&(t=I);M>v&&(v=M);P>z&&(z=P)}this.min.set(c,e,g);this.max.set(t,v,z);return this},setFromPoints:function(a){this.makeEmpty();for(var c=0,e=a.length;c<e;c++)this.expandByPoint(a[c]);return this},setFromCenterAndSize:function(a,c){c=Yc.copy(c).multiplyScalar(.5);this.min.copy(a).sub(c);this.max.copy(a).add(c);return this},setFromObject:function(a){this.makeEmpty();
return this.expandByObject(a)},clone:function(){return(new this.constructor).copy(this)},copy:function(a){this.min.copy(a.min);this.max.copy(a.max);return this},makeEmpty:function(){this.min.x=this.min.y=this.min.z=Infinity;this.max.x=this.max.y=this.max.z=-Infinity;return this},isEmpty:function(){return this.max.x<this.min.x||this.max.y<this.min.y||this.max.z<this.min.z},getCenter:function(a){void 0===a&&(console.warn("THREE.Box3: .getCenter() target is now required"),a=new k);return this.isEmpty()?
a.set(0,0,0):a.addVectors(this.min,this.max).multiplyScalar(.5)},getSize:function(a){void 0===a&&(console.warn("THREE.Box3: .getSize() target is now required"),a=new k);return this.isEmpty()?a.set(0,0,0):a.subVectors(this.max,this.min)},expandByPoint:function(a){this.min.min(a);this.max.max(a);return this},expandByVector:function(a){this.min.sub(a);this.max.add(a);return this},expandByScalar:function(a){this.min.addScalar(-a);this.max.addScalar(a);return this},expandByObject:function(a){var c;a.updateWorldMatrix(!1,
!1);var e=a.geometry;if(void 0!==e)if(e.isGeometry){var g=e.vertices;e=0;for(c=g.length;e<c;e++)Yc.copy(g[e]),Yc.applyMatrix4(a.matrixWorld),this.expandByPoint(Yc)}else if(e.isBufferGeometry&&(g=e.attributes.position,void 0!==g))for(e=0,c=g.count;e<c;e++)Yc.fromBufferAttribute(g,e).applyMatrix4(a.matrixWorld),this.expandByPoint(Yc);a=a.children;e=0;for(c=a.length;e<c;e++)this.expandByObject(a[e]);return this},containsPoint:function(a){return a.x<this.min.x||a.x>this.max.x||a.y<this.min.y||a.y>this.max.y||
a.z<this.min.z||a.z>this.max.z?!1:!0},containsBox:function(a){return this.min.x<=a.min.x&&a.max.x<=this.max.x&&this.min.y<=a.min.y&&a.max.y<=this.max.y&&this.min.z<=a.min.z&&a.max.z<=this.max.z},getParameter:function(a,c){void 0===c&&(console.warn("THREE.Box3: .getParameter() target is now required"),c=new k);return c.set((a.x-this.min.x)/(this.max.x-this.min.x),(a.y-this.min.y)/(this.max.y-this.min.y),(a.z-this.min.z)/(this.max.z-this.min.z))},intersectsBox:function(a){return a.max.x<this.min.x||
a.min.x>this.max.x||a.max.y<this.min.y||a.min.y>this.max.y||a.max.z<this.min.z||a.min.z>this.max.z?!1:!0},intersectsSphere:function(a){this.clampPoint(a.center,Yc);return Yc.distanceToSquared(a.center)<=a.radius*a.radius},intersectsPlane:function(a){if(0<a.normal.x){var c=a.normal.x*this.min.x;var e=a.normal.x*this.max.x}else c=a.normal.x*this.max.x,e=a.normal.x*this.min.x;0<a.normal.y?(c+=a.normal.y*this.min.y,e+=a.normal.y*this.max.y):(c+=a.normal.y*this.max.y,e+=a.normal.y*this.min.y);0<a.normal.z?
(c+=a.normal.z*this.min.z,e+=a.normal.z*this.max.z):(c+=a.normal.z*this.max.z,e+=a.normal.z*this.min.z);return c<=-a.constant&&e>=-a.constant},intersectsTriangle:function(a){if(this.isEmpty())return!1;this.getCenter(mg);lh.subVectors(this.max,mg);jf.subVectors(a.a,mg);kf.subVectors(a.b,mg);lf.subVectors(a.c,mg);Hd.subVectors(kf,jf);Id.subVectors(lf,kf);he.subVectors(jf,lf);a=[0,-Hd.z,Hd.y,0,-Id.z,Id.y,0,-he.z,he.y,Hd.z,0,-Hd.x,Id.z,0,-Id.x,he.z,0,-he.x,-Hd.y,Hd.x,0,-Id.y,Id.x,0,-he.y,he.x,0];if(!C(a,
jf,kf,lf,lh))return!1;a=[1,0,0,0,1,0,0,0,1];if(!C(a,jf,kf,lf,lh))return!1;mh.crossVectors(Hd,Id);a=[mh.x,mh.y,mh.z];return C(a,jf,kf,lf,lh)},clampPoint:function(a,c){void 0===c&&(console.warn("THREE.Box3: .clampPoint() target is now required"),c=new k);return c.copy(a).clamp(this.min,this.max)},distanceToPoint:function(a){return Yc.copy(a).clamp(this.min,this.max).sub(a).length()},getBoundingSphere:function(a){void 0===a&&console.error("THREE.Box3: .getBoundingSphere() target is now required");this.getCenter(a.center);
a.radius=.5*this.getSize(Yc).length();return a},intersect:function(a){this.min.max(a.min);this.max.min(a.max);this.isEmpty()&&this.makeEmpty();return this},union:function(a){this.min.min(a.min);this.max.max(a.max);return this},applyMatrix4:function(a){if(this.isEmpty())return this;md[0].set(this.min.x,this.min.y,this.min.z).applyMatrix4(a);md[1].set(this.min.x,this.min.y,this.max.z).applyMatrix4(a);md[2].set(this.min.x,this.max.y,this.min.z).applyMatrix4(a);md[3].set(this.min.x,this.max.y,this.max.z).applyMatrix4(a);
md[4].set(this.max.x,this.min.y,this.min.z).applyMatrix4(a);md[5].set(this.max.x,this.min.y,this.max.z).applyMatrix4(a);md[6].set(this.max.x,this.max.y,this.min.z).applyMatrix4(a);md[7].set(this.max.x,this.max.y,this.max.z).applyMatrix4(a);this.setFromPoints(md);return this},translate:function(a){this.min.add(a);this.max.add(a);return this},equals:function(a){return a.min.equals(this.min)&&a.max.equals(this.max)}});var Ym=new x;Object.assign(G.prototype,{set:function(a,c){this.center.copy(a);this.radius=
c;return this},setFromPoints:function(a,c){var e=this.center;void 0!==c?e.copy(c):Ym.setFromPoints(a).getCenter(e);for(var g=c=0,t=a.length;g<t;g++)c=Math.max(c,e.distanceToSquared(a[g]));this.radius=Math.sqrt(c);return this},clone:function(){return(new this.constructor).copy(this)},copy:function(a){this.center.copy(a.center);this.radius=a.radius;return this},empty:function(){return 0>=this.radius},containsPoint:function(a){return a.distanceToSquared(this.center)<=this.radius*this.radius},distanceToPoint:function(a){return a.distanceTo(this.center)-
this.radius},intersectsSphere:function(a){var c=this.radius+a.radius;return a.center.distanceToSquared(this.center)<=c*c},intersectsBox:function(a){return a.intersectsSphere(this)},intersectsPlane:function(a){return Math.abs(a.distanceToPoint(this.center))<=this.radius},clampPoint:function(a,c){var e=this.center.distanceToSquared(a);void 0===c&&(console.warn("THREE.Sphere: .clampPoint() target is now required"),c=new k);c.copy(a);e>this.radius*this.radius&&(c.sub(this.center).normalize(),c.multiplyScalar(this.radius).add(this.center));
return c},getBoundingBox:function(a){void 0===a&&(console.warn("THREE.Sphere: .getBoundingBox() target is now required"),a=new x);a.set(this.center,this.center);a.expandByScalar(this.radius);return a},applyMatrix4:function(a){this.center.applyMatrix4(a);this.radius*=a.getMaxScaleOnAxis();return this},translate:function(a){this.center.add(a);return this},equals:function(a){return a.center.equals(this.center)&&a.radius===this.radius}});var nd=new k,Di=new k,nh=new k,Jd=new k,Ei=new k,oh=new k,Fi=new k;
Object.assign(D.prototype,{set:function(a,c){this.origin.copy(a);this.direction.copy(c);return this},clone:function(){return(new this.constructor).copy(this)},copy:function(a){this.origin.copy(a.origin);this.direction.copy(a.direction);return this},at:function(a,c){void 0===c&&(console.warn("THREE.Ray: .at() target is now required"),c=new k);return c.copy(this.direction).multiplyScalar(a).add(this.origin)},lookAt:function(a){this.direction.copy(a).sub(this.origin).normalize();return this},recast:function(a){this.origin.copy(this.at(a,
nd));return this},closestPointToPoint:function(a,c){void 0===c&&(console.warn("THREE.Ray: .closestPointToPoint() target is now required"),c=new k);c.subVectors(a,this.origin);a=c.dot(this.direction);return 0>a?c.copy(this.origin):c.copy(this.direction).multiplyScalar(a).add(this.origin)},distanceToPoint:function(a){return Math.sqrt(this.distanceSqToPoint(a))},distanceSqToPoint:function(a){var c=nd.subVectors(a,this.origin).dot(this.direction);if(0>c)return this.origin.distanceToSquared(a);nd.copy(this.direction).multiplyScalar(c).add(this.origin);
return nd.distanceToSquared(a)},distanceSqToSegment:function(a,c,e,g){Di.copy(a).add(c).multiplyScalar(.5);nh.copy(c).sub(a).normalize();Jd.copy(this.origin).sub(Di);var t=.5*a.distanceTo(c),v=-this.direction.dot(nh),z=Jd.dot(this.direction),E=-Jd.dot(nh),F=Jd.lengthSq(),I=Math.abs(1-v*v);if(0<I){a=v*E-z;c=v*z-E;var M=t*I;0<=a?c>=-M?c<=M?(t=1/I,a*=t,c*=t,v=a*(a+v*c+2*z)+c*(v*a+c+2*E)+F):(c=t,a=Math.max(0,-(v*c+z)),v=-a*a+c*(c+2*E)+F):(c=-t,a=Math.max(0,-(v*c+z)),v=-a*a+c*(c+2*E)+F):c<=-M?(a=Math.max(0,
-(-v*t+z)),c=0<a?-t:Math.min(Math.max(-t,-E),t),v=-a*a+c*(c+2*E)+F):c<=M?(a=0,c=Math.min(Math.max(-t,-E),t),v=c*(c+2*E)+F):(a=Math.max(0,-(v*t+z)),c=0<a?t:Math.min(Math.max(-t,-E),t),v=-a*a+c*(c+2*E)+F)}else c=0<v?-t:t,a=Math.max(0,-(v*c+z)),v=-a*a+c*(c+2*E)+F;e&&e.copy(this.direction).multiplyScalar(a).add(this.origin);g&&g.copy(nh).multiplyScalar(c).add(Di);return v},intersectSphere:function(a,c){nd.subVectors(a.center,this.origin);var e=nd.dot(this.direction),g=nd.dot(nd)-e*e;a=a.radius*a.radius;
if(g>a)return null;a=Math.sqrt(a-g);g=e-a;e+=a;return 0>g&&0>e?null:0>g?this.at(e,c):this.at(g,c)},intersectsSphere:function(a){return this.distanceSqToPoint(a.center)<=a.radius*a.radius},distanceToPlane:function(a){var c=a.normal.dot(this.direction);if(0===c)return 0===a.distanceToPoint(this.origin)?0:null;a=-(this.origin.dot(a.normal)+a.constant)/c;return 0<=a?a:null},intersectPlane:function(a,c){a=this.distanceToPlane(a);return null===a?null:this.at(a,c)},intersectsPlane:function(a){var c=a.distanceToPoint(this.origin);
return 0===c||0>a.normal.dot(this.direction)*c?!0:!1},intersectBox:function(a,c){var e=1/this.direction.x;var g=1/this.direction.y;var t=1/this.direction.z,v=this.origin;if(0<=e){var z=(a.min.x-v.x)*e;e*=a.max.x-v.x}else z=(a.max.x-v.x)*e,e*=a.min.x-v.x;if(0<=g){var E=(a.min.y-v.y)*g;g*=a.max.y-v.y}else E=(a.max.y-v.y)*g,g*=a.min.y-v.y;if(z>g||E>e)return null;if(E>z||z!==z)z=E;if(g<e||e!==e)e=g;0<=t?(E=(a.min.z-v.z)*t,a=(a.max.z-v.z)*t):(E=(a.max.z-v.z)*t,a=(a.min.z-v.z)*t);if(z>a||E>e)return null;
if(E>z||z!==z)z=E;if(a<e||e!==e)e=a;return 0>e?null:this.at(0<=z?z:e,c)},intersectsBox:function(a){return null!==this.intersectBox(a,nd)},intersectTriangle:function(a,c,e,g,t){Ei.subVectors(c,a);oh.subVectors(e,a);Fi.crossVectors(Ei,oh);c=this.direction.dot(Fi);if(0<c){if(g)return null;g=1}else if(0>c)g=-1,c=-c;else return null;Jd.subVectors(this.origin,a);a=g*this.direction.dot(oh.crossVectors(Jd,oh));if(0>a)return null;e=g*this.direction.dot(Ei.cross(Jd));if(0>e||a+e>c)return null;a=-g*Jd.dot(Fi);
return 0>a?null:this.at(a/c,t)},applyMatrix4:function(a){this.origin.applyMatrix4(a);this.direction.transformDirection(a);return this},equals:function(a){return a.origin.equals(this.origin)&&a.direction.equals(this.direction)}});var Kc=new k,od=new k,Gi=new k,pd=new k,mf=new k,nf=new k,rk=new k,Hi=new k,Ii=new k,Ji=new k;Object.assign(B,{getNormal:function(a,c,e,g){void 0===g&&(console.warn("THREE.Triangle: .getNormal() target is now required"),g=new k);g.subVectors(e,c);Kc.subVectors(a,c);g.cross(Kc);
a=g.lengthSq();return 0<a?g.multiplyScalar(1/Math.sqrt(a)):g.set(0,0,0)},getBarycoord:function(a,c,e,g,t){Kc.subVectors(g,c);od.subVectors(e,c);Gi.subVectors(a,c);a=Kc.dot(Kc);c=Kc.dot(od);e=Kc.dot(Gi);var v=od.dot(od);g=od.dot(Gi);var z=a*v-c*c;void 0===t&&(console.warn("THREE.Triangle: .getBarycoord() target is now required"),t=new k);if(0===z)return t.set(-2,-1,-1);z=1/z;v=(v*e-c*g)*z;a=(a*g-c*e)*z;return t.set(1-v-a,a,v)},containsPoint:function(a,c,e,g){B.getBarycoord(a,c,e,g,pd);return 0<=pd.x&&
0<=pd.y&&1>=pd.x+pd.y},getUV:function(a,c,e,g,t,v,z,E){this.getBarycoord(a,c,e,g,pd);E.set(0,0);E.addScaledVector(t,pd.x);E.addScaledVector(v,pd.y);E.addScaledVector(z,pd.z);return E},isFrontFacing:function(a,c,e,g){Kc.subVectors(e,c);od.subVectors(a,c);return 0>Kc.cross(od).dot(g)?!0:!1}});Object.assign(B.prototype,{set:function(a,c,e){this.a.copy(a);this.b.copy(c);this.c.copy(e);return this},setFromPointsAndIndices:function(a,c,e,g){this.a.copy(a[c]);this.b.copy(a[e]);this.c.copy(a[g]);return this},
clone:function(){return(new this.constructor).copy(this)},copy:function(a){this.a.copy(a.a);this.b.copy(a.b);this.c.copy(a.c);return this},getArea:function(){Kc.subVectors(this.c,this.b);od.subVectors(this.a,this.b);return.5*Kc.cross(od).length()},getMidpoint:function(a){void 0===a&&(console.warn("THREE.Triangle: .getMidpoint() target is now required"),a=new k);return a.addVectors(this.a,this.b).add(this.c).multiplyScalar(1/3)},getNormal:function(a){return B.getNormal(this.a,this.b,this.c,a)},getPlane:function(a){void 0===
a&&(console.warn("THREE.Triangle: .getPlane() target is now required"),a=new k);return a.setFromCoplanarPoints(this.a,this.b,this.c)},getBarycoord:function(a,c){return B.getBarycoord(a,this.a,this.b,this.c,c)},getUV:function(a,c,e,g,t){return B.getUV(a,this.a,this.b,this.c,c,e,g,t)},containsPoint:function(a){return B.containsPoint(a,this.a,this.b,this.c)},isFrontFacing:function(a){return B.isFrontFacing(this.a,this.b,this.c,a)},intersectsBox:function(a){return a.intersectsTriangle(this)},closestPointToPoint:function(a,
c){void 0===c&&(console.warn("THREE.Triangle: .closestPointToPoint() target is now required"),c=new k);var e=this.a,g=this.b,t=this.c;mf.subVectors(g,e);nf.subVectors(t,e);Hi.subVectors(a,e);var v=mf.dot(Hi),z=nf.dot(Hi);if(0>=v&&0>=z)return c.copy(e);Ii.subVectors(a,g);var E=mf.dot(Ii),F=nf.dot(Ii);if(0<=E&&F<=E)return c.copy(g);var I=v*F-E*z;if(0>=I&&0<=v&&0>=E)return g=v/(v-E),c.copy(e).addScaledVector(mf,g);Ji.subVectors(a,t);a=mf.dot(Ji);var M=nf.dot(Ji);if(0<=M&&a<=M)return c.copy(t);v=a*z-
v*M;if(0>=v&&0<=z&&0>=M)return I=z/(z-M),c.copy(e).addScaledVector(nf,I);z=E*M-a*F;if(0>=z&&0<=F-E&&0<=a-M)return rk.subVectors(t,g),I=(F-E)/(F-E+(a-M)),c.copy(g).addScaledVector(rk,I);t=1/(z+v+I);g=v*t;I*=t;return c.copy(e).addScaledVector(mf,g).addScaledVector(nf,I)},equals:function(a){return a.a.equals(this.a)&&a.b.equals(this.b)&&a.c.equals(this.c)}});var Zm={aliceblue:15792383,antiquewhite:16444375,aqua:65535,aquamarine:8388564,azure:15794175,beige:16119260,bisque:16770244,black:0,blanchedalmond:16772045,
blue:255,blueviolet:9055202,brown:10824234,burlywood:14596231,cadetblue:6266528,chartreuse:8388352,chocolate:13789470,coral:16744272,cornflowerblue:6591981,cornsilk:16775388,crimson:14423100,cyan:65535,darkblue:139,darkcyan:35723,darkgoldenrod:12092939,darkgray:11119017,darkgreen:25600,darkgrey:11119017,darkkhaki:12433259,darkmagenta:9109643,darkolivegreen:5597999,darkorange:16747520,darkorchid:10040012,darkred:9109504,darksalmon:15308410,darkseagreen:9419919,darkslateblue:4734347,darkslategray:3100495,
darkslategrey:3100495,darkturquoise:52945,darkviolet:9699539,deeppink:16716947,deepskyblue:49151,dimgray:6908265,dimgrey:6908265,dodgerblue:2003199,firebrick:11674146,floralwhite:16775920,forestgreen:2263842,fuchsia:16711935,gainsboro:14474460,ghostwhite:16316671,gold:16766720,goldenrod:14329120,gray:8421504,green:32768,greenyellow:11403055,grey:8421504,honeydew:15794160,hotpink:16738740,indianred:13458524,indigo:4915330,ivory:16777200,khaki:15787660,lavender:15132410,lavenderblush:16773365,lawngreen:8190976,
lemonchiffon:16775885,lightblue:11393254,lightcoral:15761536,lightcyan:14745599,lightgoldenrodyellow:16448210,lightgray:13882323,lightgreen:9498256,lightgrey:13882323,lightpink:16758465,lightsalmon:16752762,lightseagreen:2142890,lightskyblue:8900346,lightslategray:7833753,lightslategrey:7833753,lightsteelblue:11584734,lightyellow:16777184,lime:65280,limegreen:3329330,linen:16445670,magenta:16711935,maroon:8388608,mediumaquamarine:6737322,mediumblue:205,mediumorchid:12211667,mediumpurple:9662683,mediumseagreen:3978097,
mediumslateblue:8087790,mediumspringgreen:64154,mediumturquoise:4772300,mediumvioletred:13047173,midnightblue:1644912,mintcream:16121850,mistyrose:16770273,moccasin:16770229,navajowhite:16768685,navy:128,oldlace:16643558,olive:8421376,olivedrab:7048739,orange:16753920,orangered:16729344,orchid:14315734,palegoldenrod:15657130,palegreen:10025880,paleturquoise:11529966,palevioletred:14381203,papayawhip:16773077,peachpuff:16767673,peru:13468991,pink:16761035,plum:14524637,powderblue:11591910,purple:8388736,
rebeccapurple:6697881,red:16711680,rosybrown:12357519,royalblue:4286945,saddlebrown:9127187,salmon:16416882,sandybrown:16032864,seagreen:3050327,seashell:16774638,sienna:10506797,silver:12632256,skyblue:8900331,slateblue:6970061,slategray:7372944,slategrey:7372944,snow:16775930,springgreen:65407,steelblue:4620980,tan:13808780,teal:32896,thistle:14204888,tomato:16737095,turquoise:4251856,violet:15631086,wheat:16113331,white:16777215,whitesmoke:16119285,yellow:16776960,yellowgreen:10145074},oc={h:0,
s:0,l:0},ph={h:0,s:0,l:0};Object.assign(H.prototype,{isColor:!0,r:1,g:1,b:1,set:function(a){a&&a.isColor?this.copy(a):"number"===typeof a?this.setHex(a):"string"===typeof a&&this.setStyle(a);return this},setScalar:function(a){this.b=this.g=this.r=a;return this},setHex:function(a){a=Math.floor(a);this.r=(a>>16&255)/255;this.g=(a>>8&255)/255;this.b=(a&255)/255;return this},setRGB:function(a,c,e){this.r=a;this.g=c;this.b=e;return this},setHSL:function(a,c,e){a=hb.euclideanModulo(a,1);c=hb.clamp(c,0,
1);e=hb.clamp(e,0,1);0===c?this.r=this.g=this.b=e:(c=.5>=e?e*(1+c):e+c-e*c,e=2*e-c,this.r=K(e,c,a+1/3),this.g=K(e,c,a),this.b=K(e,c,a-1/3));return this},setStyle:function(a){function c(z){void 0!==z&&1>parseFloat(z)&&console.warn("THREE.Color: Alpha component of "+a+" will be ignored.")}var e;if(e=/^((?:rgb|hsl)a?)\(\s*([^\)]*)\)/.exec(a)){var g=e[2];switch(e[1]){case "rgb":case "rgba":if(e=/^(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*(,\s*([0-9]*\.?[0-9]+)\s*)?$/.exec(g))return this.r=Math.min(255,parseInt(e[1],
10))/255,this.g=Math.min(255,parseInt(e[2],10))/255,this.b=Math.min(255,parseInt(e[3],10))/255,c(e[5]),this;if(e=/^(\d+)%\s*,\s*(\d+)%\s*,\s*(\d+)%\s*(,\s*([0-9]*\.?[0-9]+)\s*)?$/.exec(g))return this.r=Math.min(100,parseInt(e[1],10))/100,this.g=Math.min(100,parseInt(e[2],10))/100,this.b=Math.min(100,parseInt(e[3],10))/100,c(e[5]),this;break;case "hsl":case "hsla":if(e=/^([0-9]*\.?[0-9]+)\s*,\s*(\d+)%\s*,\s*(\d+)%\s*(,\s*([0-9]*\.?[0-9]+)\s*)?$/.exec(g)){g=parseFloat(e[1])/360;var t=parseInt(e[2],
10)/100,v=parseInt(e[3],10)/100;c(e[5]);return this.setHSL(g,t,v)}}}else if(e=/^#([A-Fa-f0-9]+)$/.exec(a)){e=e[1];g=e.length;if(3===g)return this.r=parseInt(e.charAt(0)+e.charAt(0),16)/255,this.g=parseInt(e.charAt(1)+e.charAt(1),16)/255,this.b=parseInt(e.charAt(2)+e.charAt(2),16)/255,this;if(6===g)return this.r=parseInt(e.charAt(0)+e.charAt(1),16)/255,this.g=parseInt(e.charAt(2)+e.charAt(3),16)/255,this.b=parseInt(e.charAt(4)+e.charAt(5),16)/255,this}a&&0<a.length&&(e=Zm[a],void 0!==e?this.setHex(e):
console.warn("THREE.Color: Unknown color "+a));return this},clone:function(){return new this.constructor(this.r,this.g,this.b)},copy:function(a){this.r=a.r;this.g=a.g;this.b=a.b;return this},copyGammaToLinear:function(a,c){void 0===c&&(c=2);this.r=Math.pow(a.r,c);this.g=Math.pow(a.g,c);this.b=Math.pow(a.b,c);return this},copyLinearToGamma:function(a,c){void 0===c&&(c=2);c=0<c?1/c:1;this.r=Math.pow(a.r,c);this.g=Math.pow(a.g,c);this.b=Math.pow(a.b,c);return this},convertGammaToLinear:function(a){this.copyGammaToLinear(this,
a);return this},convertLinearToGamma:function(a){this.copyLinearToGamma(this,a);return this},copySRGBToLinear:function(a){this.r=L(a.r);this.g=L(a.g);this.b=L(a.b);return this},copyLinearToSRGB:function(a){this.r=J(a.r);this.g=J(a.g);this.b=J(a.b);return this},convertSRGBToLinear:function(){this.copySRGBToLinear(this);return this},convertLinearToSRGB:function(){this.copyLinearToSRGB(this);return this},getHex:function(){return 255*this.r<<16^255*this.g<<8^255*this.b<<0},getHexString:function(){return("000000"+
this.getHex().toString(16)).slice(-6)},getHSL:function(a){void 0===a&&(console.warn("THREE.Color: .getHSL() target is now required"),a={h:0,s:0,l:0});var c=this.r,e=this.g,g=this.b,t=Math.max(c,e,g),v=Math.min(c,e,g),z,E=(v+t)/2;if(v===t)v=z=0;else{var F=t-v;v=.5>=E?F/(t+v):F/(2-t-v);switch(t){case c:z=(e-g)/F+(e<g?6:0);break;case e:z=(g-c)/F+2;break;case g:z=(c-e)/F+4}z/=6}a.h=z;a.s=v;a.l=E;return a},getStyle:function(){return"rgb("+(255*this.r|0)+","+(255*this.g|0)+","+(255*this.b|0)+")"},offsetHSL:function(a,
c,e){this.getHSL(oc);oc.h+=a;oc.s+=c;oc.l+=e;this.setHSL(oc.h,oc.s,oc.l);return this},add:function(a){this.r+=a.r;this.g+=a.g;this.b+=a.b;return this},addColors:function(a,c){this.r=a.r+c.r;this.g=a.g+c.g;this.b=a.b+c.b;return this},addScalar:function(a){this.r+=a;this.g+=a;this.b+=a;return this},sub:function(a){this.r=Math.max(0,this.r-a.r);this.g=Math.max(0,this.g-a.g);this.b=Math.max(0,this.b-a.b);return this},multiply:function(a){this.r*=a.r;this.g*=a.g;this.b*=a.b;return this},multiplyScalar:function(a){this.r*=
a;this.g*=a;this.b*=a;return this},lerp:function(a,c){this.r+=(a.r-this.r)*c;this.g+=(a.g-this.g)*c;this.b+=(a.b-this.b)*c;return this},lerpHSL:function(a,c){this.getHSL(oc);a.getHSL(ph);a=hb.lerp(oc.h,ph.h,c);var e=hb.lerp(oc.s,ph.s,c);c=hb.lerp(oc.l,ph.l,c);this.setHSL(a,e,c);return this},equals:function(a){return a.r===this.r&&a.g===this.g&&a.b===this.b},fromArray:function(a,c){void 0===c&&(c=0);this.r=a[c];this.g=a[c+1];this.b=a[c+2];return this},toArray:function(a,c){void 0===a&&(a=[]);void 0===
c&&(c=0);a[c]=this.r;a[c+1]=this.g;a[c+2]=this.b;return a},toJSON:function(){return this.getHex()}});Object.assign(O.prototype,{clone:function(){return(new this.constructor).copy(this)},copy:function(a){this.a=a.a;this.b=a.b;this.c=a.c;this.normal.copy(a.normal);this.color.copy(a.color);this.materialIndex=a.materialIndex;for(var c=0,e=a.vertexNormals.length;c<e;c++)this.vertexNormals[c]=a.vertexNormals[c].clone();c=0;for(e=a.vertexColors.length;c<e;c++)this.vertexColors[c]=a.vertexColors[c].clone();
return this}});var jl=0;S.prototype=Object.assign(Object.create(d.prototype),{constructor:S,isMaterial:!0,onBeforeCompile:function(){},setValues:function(a){if(void 0!==a)for(var c in a){var e=a[c];if(void 0===e)console.warn("THREE.Material: '"+c+"' parameter is undefined.");else if("shading"===c)console.warn("THREE."+this.type+": .shading has been removed. Use the boolean .flatShading instead."),this.flatShading=1===e?!0:!1;else{var g=this[c];void 0===g?console.warn("THREE."+this.type+": '"+c+"' is not a property of this material."):
g&&g.isColor?g.set(e):g&&g.isVector3&&e&&e.isVector3?g.copy(e):this[c]=e}}},toJSON:function(a){function c(t){var v=[],z;for(z in t){var E=t[z];delete E.metadata;v.push(E)}return v}var e=void 0===a||"string"===typeof a;e&&(a={textures:{},images:{}});var g={metadata:{version:4.5,type:"Material",generator:"Material.toJSON"}};g.uuid=this.uuid;g.type=this.type;""!==this.name&&(g.name=this.name);this.color&&this.color.isColor&&(g.color=this.color.getHex());void 0!==this.roughness&&(g.roughness=this.roughness);
void 0!==this.metalness&&(g.metalness=this.metalness);this.emissive&&this.emissive.isColor&&(g.emissive=this.emissive.getHex());this.emissiveIntensity&&1!==this.emissiveIntensity&&(g.emissiveIntensity=this.emissiveIntensity);this.specular&&this.specular.isColor&&(g.specular=this.specular.getHex());void 0!==this.shininess&&(g.shininess=this.shininess);void 0!==this.clearcoat&&(g.clearcoat=this.clearcoat);void 0!==this.clearcoatRoughness&&(g.clearcoatRoughness=this.clearcoatRoughness);this.clearcoatNormalMap&&
this.clearcoatNormalMap.isTexture&&(g.clearcoatNormalMap=this.clearcoatNormalMap.toJSON(a).uuid,g.clearcoatNormalScale=this.clearcoatNormalScale.toArray());this.map&&this.map.isTexture&&(g.map=this.map.toJSON(a).uuid);this.matcap&&this.matcap.isTexture&&(g.matcap=this.matcap.toJSON(a).uuid);this.alphaMap&&this.alphaMap.isTexture&&(g.alphaMap=this.alphaMap.toJSON(a).uuid);this.lightMap&&this.lightMap.isTexture&&(g.lightMap=this.lightMap.toJSON(a).uuid);this.aoMap&&this.aoMap.isTexture&&(g.aoMap=this.aoMap.toJSON(a).uuid,
g.aoMapIntensity=this.aoMapIntensity);this.bumpMap&&this.bumpMap.isTexture&&(g.bumpMap=this.bumpMap.toJSON(a).uuid,g.bumpScale=this.bumpScale);this.normalMap&&this.normalMap.isTexture&&(g.normalMap=this.normalMap.toJSON(a).uuid,g.normalMapType=this.normalMapType,g.normalScale=this.normalScale.toArray());this.displacementMap&&this.displacementMap.isTexture&&(g.displacementMap=this.displacementMap.toJSON(a).uuid,g.displacementScale=this.displacementScale,g.displacementBias=this.displacementBias);this.roughnessMap&&
this.roughnessMap.isTexture&&(g.roughnessMap=this.roughnessMap.toJSON(a).uuid);this.metalnessMap&&this.metalnessMap.isTexture&&(g.metalnessMap=this.metalnessMap.toJSON(a).uuid);this.emissiveMap&&this.emissiveMap.isTexture&&(g.emissiveMap=this.emissiveMap.toJSON(a).uuid);this.specularMap&&this.specularMap.isTexture&&(g.specularMap=this.specularMap.toJSON(a).uuid);this.envMap&&this.envMap.isTexture&&(g.envMap=this.envMap.toJSON(a).uuid,g.reflectivity=this.reflectivity,g.refractionRatio=this.refractionRatio,
void 0!==this.combine&&(g.combine=this.combine),void 0!==this.envMapIntensity&&(g.envMapIntensity=this.envMapIntensity));this.gradientMap&&this.gradientMap.isTexture&&(g.gradientMap=this.gradientMap.toJSON(a).uuid);void 0!==this.size&&(g.size=this.size);void 0!==this.sizeAttenuation&&(g.sizeAttenuation=this.sizeAttenuation);1!==this.blending&&(g.blending=this.blending);!0===this.flatShading&&(g.flatShading=this.flatShading);0!==this.side&&(g.side=this.side);0!==this.vertexColors&&(g.vertexColors=
this.vertexColors);1>this.opacity&&(g.opacity=this.opacity);!0===this.transparent&&(g.transparent=this.transparent);g.depthFunc=this.depthFunc;g.depthTest=this.depthTest;g.depthWrite=this.depthWrite;g.stencilWrite=this.stencilWrite;g.stencilFunc=this.stencilFunc;g.stencilRef=this.stencilRef;g.stencilMask=this.stencilMask;g.stencilFail=this.stencilFail;g.stencilZFail=this.stencilZFail;g.stencilZPass=this.stencilZPass;this.rotation&&0!==this.rotation&&(g.rotation=this.rotation);!0===this.polygonOffset&&
(g.polygonOffset=!0);0!==this.polygonOffsetFactor&&(g.polygonOffsetFactor=this.polygonOffsetFactor);0!==this.polygonOffsetUnits&&(g.polygonOffsetUnits=this.polygonOffsetUnits);this.linewidth&&1!==this.linewidth&&(g.linewidth=this.linewidth);void 0!==this.dashSize&&(g.dashSize=this.dashSize);void 0!==this.gapSize&&(g.gapSize=this.gapSize);void 0!==this.scale&&(g.scale=this.scale);!0===this.dithering&&(g.dithering=!0);0<this.alphaTest&&(g.alphaTest=this.alphaTest);!0===this.premultipliedAlpha&&(g.premultipliedAlpha=
this.premultipliedAlpha);!0===this.wireframe&&(g.wireframe=this.wireframe);1<this.wireframeLinewidth&&(g.wireframeLinewidth=this.wireframeLinewidth);"round"!==this.wireframeLinecap&&(g.wireframeLinecap=this.wireframeLinecap);"round"!==this.wireframeLinejoin&&(g.wireframeLinejoin=this.wireframeLinejoin);!0===this.morphTargets&&(g.morphTargets=!0);!0===this.morphNormals&&(g.morphNormals=!0);!0===this.skinning&&(g.skinning=!0);!1===this.visible&&(g.visible=!1);!1===this.toneMapped&&(g.toneMapped=!1);
"{}"!==JSON.stringify(this.userData)&&(g.userData=this.userData);e&&(e=c(a.textures),a=c(a.images),0<e.length&&(g.textures=e),0<a.length&&(g.images=a));return g},clone:function(){return(new this.constructor).copy(this)},copy:function(a){this.name=a.name;this.fog=a.fog;this.lights=a.lights;this.blending=a.blending;this.side=a.side;this.flatShading=a.flatShading;this.vertexColors=a.vertexColors;this.opacity=a.opacity;this.transparent=a.transparent;this.blendSrc=a.blendSrc;this.blendDst=a.blendDst;this.blendEquation=
a.blendEquation;this.blendSrcAlpha=a.blendSrcAlpha;this.blendDstAlpha=a.blendDstAlpha;this.blendEquationAlpha=a.blendEquationAlpha;this.depthFunc=a.depthFunc;this.depthTest=a.depthTest;this.depthWrite=a.depthWrite;this.stencilWrite=a.stencilWrite;this.stencilFunc=a.stencilFunc;this.stencilRef=a.stencilRef;this.stencilMask=a.stencilMask;this.stencilFail=a.stencilFail;this.stencilZFail=a.stencilZFail;this.stencilZPass=a.stencilZPass;this.colorWrite=a.colorWrite;this.precision=a.precision;this.polygonOffset=
a.polygonOffset;this.polygonOffsetFactor=a.polygonOffsetFactor;this.polygonOffsetUnits=a.polygonOffsetUnits;this.dithering=a.dithering;this.alphaTest=a.alphaTest;this.premultipliedAlpha=a.premultipliedAlpha;this.visible=a.visible;this.toneMapped=a.toneMapped;this.userData=JSON.parse(JSON.stringify(a.userData));this.clipShadows=a.clipShadows;this.clipIntersection=a.clipIntersection;var c=a.clippingPlanes,e=null;if(null!==c){var g=c.length;e=Array(g);for(var t=0;t!==g;++t)e[t]=c[t].clone()}this.clippingPlanes=
e;this.shadowSide=a.shadowSide;return this},dispose:function(){this.dispatchEvent({type:"dispose"})}});N.prototype=Object.create(S.prototype);N.prototype.constructor=N;N.prototype.isMeshBasicMaterial=!0;N.prototype.copy=function(a){S.prototype.copy.call(this,a);this.color.copy(a.color);this.map=a.map;this.lightMap=a.lightMap;this.lightMapIntensity=a.lightMapIntensity;this.aoMap=a.aoMap;this.aoMapIntensity=a.aoMapIntensity;this.specularMap=a.specularMap;this.alphaMap=a.alphaMap;this.envMap=a.envMap;
this.combine=a.combine;this.reflectivity=a.reflectivity;this.refractionRatio=a.refractionRatio;this.wireframe=a.wireframe;this.wireframeLinewidth=a.wireframeLinewidth;this.wireframeLinecap=a.wireframeLinecap;this.wireframeLinejoin=a.wireframeLinejoin;this.skinning=a.skinning;this.morphTargets=a.morphTargets;return this};Object.defineProperty(R.prototype,"needsUpdate",{set:function(a){!0===a&&this.version++}});Object.assign(R.prototype,{isBufferAttribute:!0,onUploadCallback:function(){},setArray:function(a){if(Array.isArray(a))throw new TypeError("THREE.BufferAttribute: array should be a Typed Array.");
this.count=void 0!==a?a.length/this.itemSize:0;this.array=a;return this},setDynamic:function(a){this.dynamic=a;return this},copy:function(a){this.name=a.name;this.array=new a.array.constructor(a.array);this.itemSize=a.itemSize;this.count=a.count;this.normalized=a.normalized;this.dynamic=a.dynamic;return this},copyAt:function(a,c,e){a*=this.itemSize;e*=c.itemSize;for(var g=0,t=this.itemSize;g<t;g++)this.array[a+g]=c.array[e+g];return this},copyArray:function(a){this.array.set(a);return this},copyColorsArray:function(a){for(var c=
this.array,e=0,g=0,t=a.length;g<t;g++){var v=a[g];void 0===v&&(console.warn("THREE.BufferAttribute.copyColorsArray(): color is undefined",g),v=new H);c[e++]=v.r;c[e++]=v.g;c[e++]=v.b}return this},copyVector2sArray:function(a){for(var c=this.array,e=0,g=0,t=a.length;g<t;g++){var v=a[g];void 0===v&&(console.warn("THREE.BufferAttribute.copyVector2sArray(): vector is undefined",g),v=new f);c[e++]=v.x;c[e++]=v.y}return this},copyVector3sArray:function(a){for(var c=this.array,e=0,g=0,t=a.length;g<t;g++){var v=
a[g];void 0===v&&(console.warn("THREE.BufferAttribute.copyVector3sArray(): vector is undefined",g),v=new k);c[e++]=v.x;c[e++]=v.y;c[e++]=v.z}return this},copyVector4sArray:function(a){for(var c=this.array,e=0,g=0,t=a.length;g<t;g++){var v=a[g];void 0===v&&(console.warn("THREE.BufferAttribute.copyVector4sArray(): vector is undefined",g),v=new p);c[e++]=v.x;c[e++]=v.y;c[e++]=v.z;c[e++]=v.w}return this},set:function(a,c){void 0===c&&(c=0);this.array.set(a,c);return this},getX:function(a){return this.array[a*
this.itemSize]},setX:function(a,c){this.array[a*this.itemSize]=c;return this},getY:function(a){return this.array[a*this.itemSize+1]},setY:function(a,c){this.array[a*this.itemSize+1]=c;return this},getZ:function(a){return this.array[a*this.itemSize+2]},setZ:function(a,c){this.array[a*this.itemSize+2]=c;return this},getW:function(a){return this.array[a*this.itemSize+3]},setW:function(a,c){this.array[a*this.itemSize+3]=c;return this},setXY:function(a,c,e){a*=this.itemSize;this.array[a+0]=c;this.array[a+
1]=e;return this},setXYZ:function(a,c,e,g){a*=this.itemSize;this.array[a+0]=c;this.array[a+1]=e;this.array[a+2]=g;return this},setXYZW:function(a,c,e,g,t){a*=this.itemSize;this.array[a+0]=c;this.array[a+1]=e;this.array[a+2]=g;this.array[a+3]=t;return this},onUpload:function(a){this.onUploadCallback=a;return this},clone:function(){return(new this.constructor(this.array,this.itemSize)).copy(this)},toJSON:function(){return{itemSize:this.itemSize,type:this.array.constructor.name,array:Array.prototype.slice.call(this.array),
normalized:this.normalized}}});X.prototype=Object.create(R.prototype);X.prototype.constructor=X;aa.prototype=Object.create(R.prototype);aa.prototype.constructor=aa;fa.prototype=Object.create(R.prototype);fa.prototype.constructor=fa;oa.prototype=Object.create(R.prototype);oa.prototype.constructor=oa;Z.prototype=Object.create(R.prototype);Z.prototype.constructor=Z;ca.prototype=Object.create(R.prototype);ca.prototype.constructor=ca;ja.prototype=Object.create(R.prototype);ja.prototype.constructor=ja;
ba.prototype=Object.create(R.prototype);ba.prototype.constructor=ba;ka.prototype=Object.create(R.prototype);ka.prototype.constructor=ka;Object.assign(W.prototype,{computeGroups:function(a){var c=[],e=void 0;a=a.faces;for(var g=0;g<a.length;g++){var t=a[g];if(t.materialIndex!==e){e=t.materialIndex;void 0!==v&&(v.count=3*g-v.start,c.push(v));var v={start:3*g,materialIndex:e}}}void 0!==v&&(v.count=3*g-v.start,c.push(v));this.groups=c},fromGeometry:function(a){var c=a.faces,e=a.vertices,g=a.faceVertexUvs,
t=g[0]&&0<g[0].length,v=g[1]&&0<g[1].length,z=a.morphTargets,E=z.length;if(0<E){var F=[];for(var I=0;I<E;I++)F[I]={name:z[I].name,data:[]};this.morphTargets.position=F}var M=a.morphNormals,P=M.length;if(0<P){var Q=[];for(I=0;I<P;I++)Q[I]={name:M[I].name,data:[]};this.morphTargets.normal=Q}var U=a.skinIndices,V=a.skinWeights,ea=U.length===e.length,da=V.length===e.length;0<e.length&&0===c.length&&console.error("THREE.DirectGeometry: Faceless geometries are not supported.");for(I=0;I<c.length;I++){var ra=
c[I];this.vertices.push(e[ra.a],e[ra.b],e[ra.c]);var pa=ra.vertexNormals;3===pa.length?this.normals.push(pa[0],pa[1],pa[2]):(pa=ra.normal,this.normals.push(pa,pa,pa));pa=ra.vertexColors;3===pa.length?this.colors.push(pa[0],pa[1],pa[2]):(pa=ra.color,this.colors.push(pa,pa,pa));!0===t&&(pa=g[0][I],void 0!==pa?this.uvs.push(pa[0],pa[1],pa[2]):(console.warn("THREE.DirectGeometry.fromGeometry(): Undefined vertexUv ",I),this.uvs.push(new f,new f,new f)));!0===v&&(pa=g[1][I],void 0!==pa?this.uvs2.push(pa[0],
pa[1],pa[2]):(console.warn("THREE.DirectGeometry.fromGeometry(): Undefined vertexUv2 ",I),this.uvs2.push(new f,new f,new f)));for(pa=0;pa<E;pa++){var qa=z[pa].vertices;F[pa].data.push(qa[ra.a],qa[ra.b],qa[ra.c])}for(pa=0;pa<P;pa++)qa=M[pa].vertexNormals[I],Q[pa].data.push(qa.a,qa.b,qa.c);ea&&this.skinIndices.push(U[ra.a],U[ra.b],U[ra.c]);da&&this.skinWeights.push(V[ra.a],V[ra.b],V[ra.c])}this.computeGroups(a);this.verticesNeedUpdate=a.verticesNeedUpdate;this.normalsNeedUpdate=a.normalsNeedUpdate;
this.colorsNeedUpdate=a.colorsNeedUpdate;this.uvsNeedUpdate=a.uvsNeedUpdate;this.groupsNeedUpdate=a.groupsNeedUpdate;null!==a.boundingSphere&&(this.boundingSphere=a.boundingSphere.clone());null!==a.boundingBox&&(this.boundingBox=a.boundingBox.clone());return this}});var kl=1,Zc=new q,Ki=new A,qh=new k,ie=new x,Li=new x,Lc=new k;va.prototype=Object.assign(Object.create(d.prototype),{constructor:va,isBufferGeometry:!0,getIndex:function(){return this.index},setIndex:function(a){this.index=Array.isArray(a)?
new (65535<Ea(a)?ja:Z)(a,1):a},addAttribute:function(a,c,e){if(!(c&&c.isBufferAttribute||c&&c.isInterleavedBufferAttribute))return console.warn("THREE.BufferGeometry: .addAttribute() now expects ( name, attribute )."),this.addAttribute(a,new R(c,e));if("index"===a)return console.warn("THREE.BufferGeometry.addAttribute: Use .setIndex() for index attribute."),this.setIndex(c),this;this.attributes[a]=c;return this},getAttribute:function(a){return this.attributes[a]},removeAttribute:function(a){delete this.attributes[a];
return this},addGroup:function(a,c,e){this.groups.push({start:a,count:c,materialIndex:void 0!==e?e:0})},clearGroups:function(){this.groups=[]},setDrawRange:function(a,c){this.drawRange.start=a;this.drawRange.count=c},applyMatrix:function(a){var c=this.attributes.position;void 0!==c&&(a.applyToBufferAttribute(c),c.needsUpdate=!0);var e=this.attributes.normal;void 0!==e&&(c=(new r).getNormalMatrix(a),c.applyToBufferAttribute(e),e.needsUpdate=!0);e=this.attributes.tangent;void 0!==e&&(c=(new r).getNormalMatrix(a),
c.applyToBufferAttribute(e),e.needsUpdate=!0);null!==this.boundingBox&&this.computeBoundingBox();null!==this.boundingSphere&&this.computeBoundingSphere();return this},rotateX:function(a){Zc.makeRotationX(a);this.applyMatrix(Zc);return this},rotateY:function(a){Zc.makeRotationY(a);this.applyMatrix(Zc);return this},rotateZ:function(a){Zc.makeRotationZ(a);this.applyMatrix(Zc);return this},translate:function(a,c,e){Zc.makeTranslation(a,c,e);this.applyMatrix(Zc);return this},scale:function(a,c,e){Zc.makeScale(a,
c,e);this.applyMatrix(Zc);return this},lookAt:function(a){Ki.lookAt(a);Ki.updateMatrix();this.applyMatrix(Ki.matrix);return this},center:function(){this.computeBoundingBox();this.boundingBox.getCenter(qh).negate();this.translate(qh.x,qh.y,qh.z);return this},setFromObject:function(a){var c=a.geometry;if(a.isPoints||a.isLine){a=new ba(3*c.vertices.length,3);var e=new ba(3*c.colors.length,3);this.addAttribute("position",a.copyVector3sArray(c.vertices));this.addAttribute("color",e.copyColorsArray(c.colors));
c.lineDistances&&c.lineDistances.length===c.vertices.length&&(a=new ba(c.lineDistances.length,1),this.addAttribute("lineDistance",a.copyArray(c.lineDistances)));null!==c.boundingSphere&&(this.boundingSphere=c.boundingSphere.clone());null!==c.boundingBox&&(this.boundingBox=c.boundingBox.clone())}else a.isMesh&&c&&c.isGeometry&&this.fromGeometry(c);return this},setFromPoints:function(a){for(var c=[],e=0,g=a.length;e<g;e++){var t=a[e];c.push(t.x,t.y,t.z||0)}this.addAttribute("position",new ba(c,3));
return this},updateFromObject:function(a){var c=a.geometry;if(a.isMesh){var e=c.__directGeometry;!0===c.elementsNeedUpdate&&(e=void 0,c.elementsNeedUpdate=!1);if(void 0===e)return this.fromGeometry(c);e.verticesNeedUpdate=c.verticesNeedUpdate;e.normalsNeedUpdate=c.normalsNeedUpdate;e.colorsNeedUpdate=c.colorsNeedUpdate;e.uvsNeedUpdate=c.uvsNeedUpdate;e.groupsNeedUpdate=c.groupsNeedUpdate;c.verticesNeedUpdate=!1;c.normalsNeedUpdate=!1;c.colorsNeedUpdate=!1;c.uvsNeedUpdate=!1;c.groupsNeedUpdate=!1;
c=e}!0===c.verticesNeedUpdate&&(e=this.attributes.position,void 0!==e&&(e.copyVector3sArray(c.vertices),e.needsUpdate=!0),c.verticesNeedUpdate=!1);!0===c.normalsNeedUpdate&&(e=this.attributes.normal,void 0!==e&&(e.copyVector3sArray(c.normals),e.needsUpdate=!0),c.normalsNeedUpdate=!1);!0===c.colorsNeedUpdate&&(e=this.attributes.color,void 0!==e&&(e.copyColorsArray(c.colors),e.needsUpdate=!0),c.colorsNeedUpdate=!1);c.uvsNeedUpdate&&(e=this.attributes.uv,void 0!==e&&(e.copyVector2sArray(c.uvs),e.needsUpdate=
!0),c.uvsNeedUpdate=!1);c.lineDistancesNeedUpdate&&(e=this.attributes.lineDistance,void 0!==e&&(e.copyArray(c.lineDistances),e.needsUpdate=!0),c.lineDistancesNeedUpdate=!1);c.groupsNeedUpdate&&(c.computeGroups(a.geometry),this.groups=c.groups,c.groupsNeedUpdate=!1);return this},fromGeometry:function(a){a.__directGeometry=(new W).fromGeometry(a);return this.fromDirectGeometry(a.__directGeometry)},fromDirectGeometry:function(a){this.addAttribute("position",(new R(new Float32Array(3*a.vertices.length),
3)).copyVector3sArray(a.vertices));0<a.normals.length&&this.addAttribute("normal",(new R(new Float32Array(3*a.normals.length),3)).copyVector3sArray(a.normals));0<a.colors.length&&this.addAttribute("color",(new R(new Float32Array(3*a.colors.length),3)).copyColorsArray(a.colors));0<a.uvs.length&&this.addAttribute("uv",(new R(new Float32Array(2*a.uvs.length),2)).copyVector2sArray(a.uvs));0<a.uvs2.length&&this.addAttribute("uv2",(new R(new Float32Array(2*a.uvs2.length),2)).copyVector2sArray(a.uvs2));
this.groups=a.groups;for(var c in a.morphTargets){for(var e=[],g=a.morphTargets[c],t=0,v=g.length;t<v;t++){var z=g[t],E=new ba(3*z.data.length,3);E.name=z.name;e.push(E.copyVector3sArray(z.data))}this.morphAttributes[c]=e}0<a.skinIndices.length&&(c=new ba(4*a.skinIndices.length,4),this.addAttribute("skinIndex",c.copyVector4sArray(a.skinIndices)));0<a.skinWeights.length&&(c=new ba(4*a.skinWeights.length,4),this.addAttribute("skinWeight",c.copyVector4sArray(a.skinWeights)));null!==a.boundingSphere&&
(this.boundingSphere=a.boundingSphere.clone());null!==a.boundingBox&&(this.boundingBox=a.boundingBox.clone());return this},computeBoundingBox:function(){null===this.boundingBox&&(this.boundingBox=new x);var a=this.attributes.position,c=this.morphAttributes.position;if(void 0!==a){if(this.boundingBox.setFromBufferAttribute(a),c){a=0;for(var e=c.length;a<e;a++)ie.setFromBufferAttribute(c[a]),this.boundingBox.expandByPoint(ie.min),this.boundingBox.expandByPoint(ie.max)}}else this.boundingBox.makeEmpty();
(isNaN(this.boundingBox.min.x)||isNaN(this.boundingBox.min.y)||isNaN(this.boundingBox.min.z))&&console.error('THREE.BufferGeometry.computeBoundingBox: Computed min/max have NaN values. The "position" attribute is likely to have NaN values.',this)},computeBoundingSphere:function(){null===this.boundingSphere&&(this.boundingSphere=new G);var a=this.attributes.position,c=this.morphAttributes.position;if(a){var e=this.boundingSphere.center;ie.setFromBufferAttribute(a);if(c)for(var g=0,t=c.length;g<t;g++){var v=
c[g];Li.setFromBufferAttribute(v);ie.expandByPoint(Li.min);ie.expandByPoint(Li.max)}ie.getCenter(e);var z=0;g=0;for(t=a.count;g<t;g++)Lc.fromBufferAttribute(a,g),z=Math.max(z,e.distanceToSquared(Lc));if(c)for(g=0,t=c.length;g<t;g++){v=c[g];a=0;for(var E=v.count;a<E;a++)Lc.fromBufferAttribute(v,a),z=Math.max(z,e.distanceToSquared(Lc))}this.boundingSphere.radius=Math.sqrt(z);isNaN(this.boundingSphere.radius)&&console.error('THREE.BufferGeometry.computeBoundingSphere(): Computed radius is NaN. The "position" attribute is likely to have NaN values.',
this)}},computeFaceNormals:function(){},computeVertexNormals:function(){var a=this.index,c=this.attributes;if(c.position){var e=c.position.array;if(void 0===c.normal)this.addAttribute("normal",new R(new Float32Array(e.length),3));else for(var g=c.normal.array,t=0,v=g.length;t<v;t++)g[t]=0;g=c.normal.array;var z=new k,E=new k,F=new k,I=new k,M=new k;if(a){var P=a.array;t=0;for(v=a.count;t<v;t+=3){a=3*P[t+0];var Q=3*P[t+1];var U=3*P[t+2];z.fromArray(e,a);E.fromArray(e,Q);F.fromArray(e,U);I.subVectors(F,
E);M.subVectors(z,E);I.cross(M);g[a]+=I.x;g[a+1]+=I.y;g[a+2]+=I.z;g[Q]+=I.x;g[Q+1]+=I.y;g[Q+2]+=I.z;g[U]+=I.x;g[U+1]+=I.y;g[U+2]+=I.z}}else for(t=0,v=e.length;t<v;t+=9)z.fromArray(e,t),E.fromArray(e,t+3),F.fromArray(e,t+6),I.subVectors(F,E),M.subVectors(z,E),I.cross(M),g[t]=I.x,g[t+1]=I.y,g[t+2]=I.z,g[t+3]=I.x,g[t+4]=I.y,g[t+5]=I.z,g[t+6]=I.x,g[t+7]=I.y,g[t+8]=I.z;this.normalizeNormals();c.normal.needsUpdate=!0}},merge:function(a,c){if(a&&a.isBufferGeometry){void 0===c&&(c=0,console.warn("THREE.BufferGeometry.merge(): Overwriting original geometry, starting at offset\x3d0. Use BufferGeometryUtils.mergeBufferGeometries() for lossless merge."));
var e=this.attributes,g;for(g in e)if(void 0!==a.attributes[g]){var t=e[g].array,v=a.attributes[g],z=v.array,E=v.itemSize*c;v=Math.min(z.length,t.length-E);for(var F=0;F<v;F++,E++)t[E]=z[F]}return this}console.error("THREE.BufferGeometry.merge(): geometry not an instance of THREE.BufferGeometry.",a)},normalizeNormals:function(){for(var a=this.attributes.normal,c=0,e=a.count;c<e;c++)Lc.x=a.getX(c),Lc.y=a.getY(c),Lc.z=a.getZ(c),Lc.normalize(),a.setXYZ(c,Lc.x,Lc.y,Lc.z)},toNonIndexed:function(){function a(M,
P){var Q=M.array;M=M.itemSize;for(var U=new Q.constructor(P.length*M),V,ea=0,da=0,ra=P.length;da<ra;da++){V=P[da]*M;for(var pa=0;pa<M;pa++)U[ea++]=Q[V++]}return new R(U,M)}if(null===this.index)return console.warn("THREE.BufferGeometry.toNonIndexed(): Geometry is already non-indexed."),this;var c=new va,e=this.index.array,g=this.attributes,t;for(t in g){var v=g[t];v=a(v,e);c.addAttribute(t,v)}var z=this.morphAttributes;for(t in z){var E=[],F=z[t];g=0;for(var I=F.length;g<I;g++)v=F[g],v=a(v,e),E.push(v);
c.morphAttributes[t]=E}e=this.groups;g=0;for(t=e.length;g<t;g++)v=e[g],c.addGroup(v.start,v.count,v.materialIndex);return c},toJSON:function(){var a={metadata:{version:4.5,type:"BufferGeometry",generator:"BufferGeometry.toJSON"}};a.uuid=this.uuid;a.type=this.type;""!==this.name&&(a.name=this.name);0<Object.keys(this.userData).length&&(a.userData=this.userData);if(void 0!==this.parameters){var c=this.parameters;for(I in c)void 0!==c[I]&&(a[I]=c[I]);return a}a.data={attributes:{}};c=this.index;null!==
c&&(a.data.index={type:c.array.constructor.name,array:Array.prototype.slice.call(c.array)});var e=this.attributes;for(I in e){c=e[I];var g=c.toJSON();""!==c.name&&(g.name=c.name);a.data.attributes[I]=g}e={};var t=!1;for(I in this.morphAttributes){for(var v=this.morphAttributes[I],z=[],E=0,F=v.length;E<F;E++)c=v[E],g=c.toJSON(),""!==c.name&&(g.name=c.name),z.push(g);0<z.length&&(e[I]=z,t=!0)}t&&(a.data.morphAttributes=e);var I=this.groups;0<I.length&&(a.data.groups=JSON.parse(JSON.stringify(I)));I=
this.boundingSphere;null!==I&&(a.data.boundingSphere={center:I.center.toArray(),radius:I.radius});return a},clone:function(){return(new va).copy(this)},copy:function(a){var c;this.index=null;this.attributes={};this.morphAttributes={};this.groups=[];this.boundingSphere=this.boundingBox=null;this.name=a.name;var e=a.index;null!==e&&this.setIndex(e.clone());e=a.attributes;for(z in e)this.addAttribute(z,e[z].clone());var g=a.morphAttributes;for(z in g){var t=[],v=g[z];e=0;for(c=v.length;e<c;e++)t.push(v[e].clone());
this.morphAttributes[z]=t}var z=a.groups;e=0;for(c=z.length;e<c;e++)g=z[e],this.addGroup(g.start,g.count,g.materialIndex);z=a.boundingBox;null!==z&&(this.boundingBox=z.clone());z=a.boundingSphere;null!==z&&(this.boundingSphere=z.clone());this.drawRange.start=a.drawRange.start;this.drawRange.count=a.drawRange.count;this.userData=a.userData;return this},dispose:function(){this.dispatchEvent({type:"dispose"})}});var sk=new q,je=new D,Mi=new G,Nd=new k,Od=new k,Pd=new k,ij=new k,jj=new k,kj=new k,Nh=
new k,Oh=new k,Ph=new k,qe=new f,re=new f,se=new f,uf=new k,vg=new k;ya.prototype=Object.assign(Object.create(A.prototype),{constructor:ya,isMesh:!0,setDrawMode:function(a){this.drawMode=a},copy:function(a){A.prototype.copy.call(this,a);this.drawMode=a.drawMode;void 0!==a.morphTargetInfluences&&(this.morphTargetInfluences=a.morphTargetInfluences.slice());void 0!==a.morphTargetDictionary&&(this.morphTargetDictionary=Object.assign({},a.morphTargetDictionary));return this},updateMorphTargets:function(){var a=
this.geometry;if(a.isBufferGeometry){a=a.morphAttributes;var c=Object.keys(a);if(0<c.length){var e=a[c[0]];if(void 0!==e)for(this.morphTargetInfluences=[],this.morphTargetDictionary={},a=0,c=e.length;a<c;a++){var g=e[a].name||String(a);this.morphTargetInfluences.push(0);this.morphTargetDictionary[g]=a}}}else a=a.morphTargets,void 0!==a&&0<a.length&&console.error("THREE.Mesh.updateMorphTargets() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.")},raycast:function(a,c){var e=this.geometry,
g=this.material,t=this.matrixWorld;if(void 0!==g&&(null===e.boundingSphere&&e.computeBoundingSphere(),Mi.copy(e.boundingSphere),Mi.applyMatrix4(t),!1!==a.ray.intersectsSphere(Mi)&&(sk.getInverse(t),je.copy(a.ray).applyMatrix4(sk),null===e.boundingBox||!1!==je.intersectsBox(e.boundingBox))))if(e.isBufferGeometry){var v=e.index;t=e.attributes.position;var z=e.morphAttributes.position,E=e.attributes.uv,F=e.attributes.uv2,I=e.groups,M=e.drawRange,P,Q;if(null!==v)if(Array.isArray(g)){var U=0;for(P=I.length;U<
P;U++){var V=I[U];var ea=g[V.materialIndex];var da=Math.max(V.start,M.start);for(Q=e=Math.min(V.start+V.count,M.start+M.count);da<Q;da+=3){e=v.getX(da);var ra=v.getX(da+1);var pa=v.getX(da+2);if(e=Fa(this,ea,a,je,t,z,E,F,e,ra,pa))e.faceIndex=Math.floor(da/3),e.face.materialIndex=V.materialIndex,c.push(e)}}}else for(da=Math.max(0,M.start),e=Math.min(v.count,M.start+M.count),U=da,P=e;U<P;U+=3){if(e=v.getX(U),ra=v.getX(U+1),pa=v.getX(U+2),e=Fa(this,g,a,je,t,z,E,F,e,ra,pa))e.faceIndex=Math.floor(U/3),
c.push(e)}else if(void 0!==t)if(Array.isArray(g))for(U=0,P=I.length;U<P;U++)for(V=I[U],ea=g[V.materialIndex],da=Math.max(V.start,M.start),Q=e=Math.min(V.start+V.count,M.start+M.count);da<Q;da+=3){if(e=da,ra=da+1,pa=da+2,e=Fa(this,ea,a,je,t,z,E,F,e,ra,pa))e.faceIndex=Math.floor(da/3),e.face.materialIndex=V.materialIndex,c.push(e)}else for(da=Math.max(0,M.start),e=Math.min(t.count,M.start+M.count),U=da,P=e;U<P;U+=3)if(e=U,ra=U+1,pa=U+2,e=Fa(this,g,a,je,t,z,E,F,e,ra,pa))e.faceIndex=Math.floor(U/3),c.push(e)}else if(e.isGeometry)for(t=
Array.isArray(g),z=e.vertices,E=e.faces,e=e.faceVertexUvs[0],0<e.length&&(v=e),U=0,P=E.length;U<P;U++)if(V=E[U],e=t?g[V.materialIndex]:g,void 0!==e&&(F=z[V.a],I=z[V.b],M=z[V.c],e=Aa(this,e,a,je,F,I,M,uf)))v&&v[U]&&(ea=v[U],qe.copy(ea[0]),re.copy(ea[1]),se.copy(ea[2]),e.uv=B.getUV(uf,F,I,M,qe,re,se,new f)),e.face=V,e.faceIndex=U,c.push(e)},clone:function(){return(new this.constructor(this.geometry,this.material)).copy(this)}});var ll=0,$c=new q,Ni=new A,rh=new k;xa.prototype=Object.assign(Object.create(d.prototype),
{constructor:xa,isGeometry:!0,applyMatrix:function(a){for(var c=(new r).getNormalMatrix(a),e=0,g=this.vertices.length;e<g;e++)this.vertices[e].applyMatrix4(a);e=0;for(g=this.faces.length;e<g;e++){a=this.faces[e];a.normal.applyMatrix3(c).normalize();for(var t=0,v=a.vertexNormals.length;t<v;t++)a.vertexNormals[t].applyMatrix3(c).normalize()}null!==this.boundingBox&&this.computeBoundingBox();null!==this.boundingSphere&&this.computeBoundingSphere();this.normalsNeedUpdate=this.verticesNeedUpdate=!0;return this},
rotateX:function(a){$c.makeRotationX(a);this.applyMatrix($c);return this},rotateY:function(a){$c.makeRotationY(a);this.applyMatrix($c);return this},rotateZ:function(a){$c.makeRotationZ(a);this.applyMatrix($c);return this},translate:function(a,c,e){$c.makeTranslation(a,c,e);this.applyMatrix($c);return this},scale:function(a,c,e){$c.makeScale(a,c,e);this.applyMatrix($c);return this},lookAt:function(a){Ni.lookAt(a);Ni.updateMatrix();this.applyMatrix(Ni.matrix);return this},fromBufferGeometry:function(a){function c(U,
V,ea,da){var ra=void 0===E?[]:[e.colors[U].clone(),e.colors[V].clone(),e.colors[ea].clone()],pa=void 0===z?[]:[(new k).fromArray(z,3*U),(new k).fromArray(z,3*V),(new k).fromArray(z,3*ea)];da=new O(U,V,ea,pa,ra,da);e.faces.push(da);void 0!==F&&e.faceVertexUvs[0].push([(new f).fromArray(F,2*U),(new f).fromArray(F,2*V),(new f).fromArray(F,2*ea)]);void 0!==I&&e.faceVertexUvs[1].push([(new f).fromArray(I,2*U),(new f).fromArray(I,2*V),(new f).fromArray(I,2*ea)])}var e=this,g=null!==a.index?a.index.array:
void 0,t=a.attributes,v=t.position.array,z=void 0!==t.normal?t.normal.array:void 0,E=void 0!==t.color?t.color.array:void 0,F=void 0!==t.uv?t.uv.array:void 0,I=void 0!==t.uv2?t.uv2.array:void 0;void 0!==I&&(this.faceVertexUvs[1]=[]);for(t=0;t<v.length;t+=3)e.vertices.push((new k).fromArray(v,t)),void 0!==E&&e.colors.push((new H).fromArray(E,t));var M=a.groups;if(0<M.length)for(t=0;t<M.length;t++){v=M[t];var P=v.start,Q=P;for(P+=v.count;Q<P;Q+=3)void 0!==g?c(g[Q],g[Q+1],g[Q+2],v.materialIndex):c(Q,
Q+1,Q+2,v.materialIndex)}else if(void 0!==g)for(t=0;t<g.length;t+=3)c(g[t],g[t+1],g[t+2]);else for(t=0;t<v.length/3;t+=3)c(t,t+1,t+2);this.computeFaceNormals();null!==a.boundingBox&&(this.boundingBox=a.boundingBox.clone());null!==a.boundingSphere&&(this.boundingSphere=a.boundingSphere.clone());return this},center:function(){this.computeBoundingBox();this.boundingBox.getCenter(rh).negate();this.translate(rh.x,rh.y,rh.z);return this},normalize:function(){this.computeBoundingSphere();var a=this.boundingSphere.center,
c=this.boundingSphere.radius;c=0===c?1:1/c;var e=new q;e.set(c,0,0,-c*a.x,0,c,0,-c*a.y,0,0,c,-c*a.z,0,0,0,1);this.applyMatrix(e);return this},computeFaceNormals:function(){for(var a=new k,c=new k,e=0,g=this.faces.length;e<g;e++){var t=this.faces[e],v=this.vertices[t.a],z=this.vertices[t.b];a.subVectors(this.vertices[t.c],z);c.subVectors(v,z);a.cross(c);a.normalize();t.normal.copy(a)}},computeVertexNormals:function(a){void 0===a&&(a=!0);var c;var e=Array(this.vertices.length);var g=0;for(c=this.vertices.length;g<
c;g++)e[g]=new k;if(a){var t=new k,v=new k;a=0;for(g=this.faces.length;a<g;a++){c=this.faces[a];var z=this.vertices[c.a];var E=this.vertices[c.b];var F=this.vertices[c.c];t.subVectors(F,E);v.subVectors(z,E);t.cross(v);e[c.a].add(t);e[c.b].add(t);e[c.c].add(t)}}else for(this.computeFaceNormals(),a=0,g=this.faces.length;a<g;a++)c=this.faces[a],e[c.a].add(c.normal),e[c.b].add(c.normal),e[c.c].add(c.normal);g=0;for(c=this.vertices.length;g<c;g++)e[g].normalize();a=0;for(g=this.faces.length;a<g;a++)c=
this.faces[a],z=c.vertexNormals,3===z.length?(z[0].copy(e[c.a]),z[1].copy(e[c.b]),z[2].copy(e[c.c])):(z[0]=e[c.a].clone(),z[1]=e[c.b].clone(),z[2]=e[c.c].clone());0<this.faces.length&&(this.normalsNeedUpdate=!0)},computeFlatVertexNormals:function(){var a;this.computeFaceNormals();var c=0;for(a=this.faces.length;c<a;c++){var e=this.faces[c];var g=e.vertexNormals;3===g.length?(g[0].copy(e.normal),g[1].copy(e.normal),g[2].copy(e.normal)):(g[0]=e.normal.clone(),g[1]=e.normal.clone(),g[2]=e.normal.clone())}0<
this.faces.length&&(this.normalsNeedUpdate=!0)},computeMorphNormals:function(){var a,c;var e=0;for(c=this.faces.length;e<c;e++){var g=this.faces[e];g.__originalFaceNormal?g.__originalFaceNormal.copy(g.normal):g.__originalFaceNormal=g.normal.clone();g.__originalVertexNormals||(g.__originalVertexNormals=[]);var t=0;for(a=g.vertexNormals.length;t<a;t++)g.__originalVertexNormals[t]?g.__originalVertexNormals[t].copy(g.vertexNormals[t]):g.__originalVertexNormals[t]=g.vertexNormals[t].clone()}var v=new xa;
v.faces=this.faces;t=0;for(a=this.morphTargets.length;t<a;t++){if(!this.morphNormals[t]){this.morphNormals[t]={};this.morphNormals[t].faceNormals=[];this.morphNormals[t].vertexNormals=[];g=this.morphNormals[t].faceNormals;var z=this.morphNormals[t].vertexNormals;e=0;for(c=this.faces.length;e<c;e++){var E=new k;var F={a:new k,b:new k,c:new k};g.push(E);z.push(F)}}z=this.morphNormals[t];v.vertices=this.morphTargets[t].vertices;v.computeFaceNormals();v.computeVertexNormals();e=0;for(c=this.faces.length;e<
c;e++)g=this.faces[e],E=z.faceNormals[e],F=z.vertexNormals[e],E.copy(g.normal),F.a.copy(g.vertexNormals[0]),F.b.copy(g.vertexNormals[1]),F.c.copy(g.vertexNormals[2])}e=0;for(c=this.faces.length;e<c;e++)g=this.faces[e],g.normal=g.__originalFaceNormal,g.vertexNormals=g.__originalVertexNormals},computeBoundingBox:function(){null===this.boundingBox&&(this.boundingBox=new x);this.boundingBox.setFromPoints(this.vertices)},computeBoundingSphere:function(){null===this.boundingSphere&&(this.boundingSphere=
new G);this.boundingSphere.setFromPoints(this.vertices)},merge:function(a,c,e){if(a&&a.isGeometry){var g,t=this.vertices.length,v=this.vertices,z=a.vertices,E=this.faces,F=a.faces,I=this.colors,M=a.colors;void 0===e&&(e=0);void 0!==c&&(g=(new r).getNormalMatrix(c));for(var P=0,Q=z.length;P<Q;P++){var U=z[P].clone();void 0!==c&&U.applyMatrix4(c);v.push(U)}P=0;for(Q=M.length;P<Q;P++)I.push(M[P].clone());P=0;for(Q=F.length;P<Q;P++){z=F[P];var V=z.vertexNormals;M=z.vertexColors;I=new O(z.a+t,z.b+t,z.c+
t);I.normal.copy(z.normal);void 0!==g&&I.normal.applyMatrix3(g).normalize();c=0;for(v=V.length;c<v;c++)U=V[c].clone(),void 0!==g&&U.applyMatrix3(g).normalize(),I.vertexNormals.push(U);I.color.copy(z.color);c=0;for(v=M.length;c<v;c++)U=M[c],I.vertexColors.push(U.clone());I.materialIndex=z.materialIndex+e;E.push(I)}P=0;for(Q=a.faceVertexUvs.length;P<Q;P++)for(e=a.faceVertexUvs[P],void 0===this.faceVertexUvs[P]&&(this.faceVertexUvs[P]=[]),c=0,v=e.length;c<v;c++){g=e[c];t=[];E=0;for(F=g.length;E<F;E++)t.push(g[E].clone());
this.faceVertexUvs[P].push(t)}}else console.error("THREE.Geometry.merge(): geometry not an instance of THREE.Geometry.",a)},mergeMesh:function(a){a&&a.isMesh?(a.matrixAutoUpdate&&a.updateMatrix(),this.merge(a.geometry,a.matrix)):console.error("THREE.Geometry.mergeMesh(): mesh not an instance of THREE.Mesh.",a)},mergeVertices:function(){var a={},c=[],e=[],g=Math.pow(10,4),t;var v=0;for(t=this.vertices.length;v<t;v++){var z=this.vertices[v];z=Math.round(z.x*g)+"_"+Math.round(z.y*g)+"_"+Math.round(z.z*
g);void 0===a[z]?(a[z]=v,c.push(this.vertices[v]),e[v]=c.length-1):e[v]=e[a[z]]}a=[];v=0;for(t=this.faces.length;v<t;v++)for(g=this.faces[v],g.a=e[g.a],g.b=e[g.b],g.c=e[g.c],g=[g.a,g.b,g.c],z=0;3>z;z++)if(g[z]===g[(z+1)%3]){a.push(v);break}for(v=a.length-1;0<=v;v--)for(g=a[v],this.faces.splice(g,1),e=0,t=this.faceVertexUvs.length;e<t;e++)this.faceVertexUvs[e].splice(g,1);v=this.vertices.length-c.length;this.vertices=c;return v},setFromPoints:function(a){this.vertices=[];for(var c=0,e=a.length;c<e;c++){var g=
a[c];this.vertices.push(new k(g.x,g.y,g.z||0))}return this},sortFacesByMaterialIndex:function(){for(var a=this.faces,c=a.length,e=0;e<c;e++)a[e]._id=e;a.sort(function(F,I){return F.materialIndex-I.materialIndex});var g=this.faceVertexUvs[0],t=this.faceVertexUvs[1],v,z;g&&g.length===c&&(v=[]);t&&t.length===c&&(z=[]);for(e=0;e<c;e++){var E=a[e]._id;v&&v.push(g[E]);z&&z.push(t[E])}v&&(this.faceVertexUvs[0]=v);z&&(this.faceVertexUvs[1]=z)},toJSON:function(){function a(na,ta,Ba){return Ba?na|1<<ta:na&
~(1<<ta)}function c(na){var ta=na.x.toString()+na.y.toString()+na.z.toString();if(void 0!==I[ta])return I[ta];I[ta]=F.length/3;F.push(na.x,na.y,na.z);return I[ta]}function e(na){var ta=na.r.toString()+na.g.toString()+na.b.toString();if(void 0!==P[ta])return P[ta];P[ta]=M.length;M.push(na.getHex());return P[ta]}function g(na){var ta=na.x.toString()+na.y.toString();if(void 0!==U[ta])return U[ta];U[ta]=Q.length/2;Q.push(na.x,na.y);return U[ta]}var t={metadata:{version:4.5,type:"Geometry",generator:"Geometry.toJSON"}};
t.uuid=this.uuid;t.type=this.type;""!==this.name&&(t.name=this.name);if(void 0!==this.parameters){var v=this.parameters,z;for(z in v)void 0!==v[z]&&(t[z]=v[z]);return t}v=[];for(z=0;z<this.vertices.length;z++){var E=this.vertices[z];v.push(E.x,E.y,E.z)}E=[];var F=[],I={},M=[],P={},Q=[],U={};for(z=0;z<this.faces.length;z++){var V=this.faces[z],ea=void 0!==this.faceVertexUvs[0][z],da=0<V.normal.length(),ra=0<V.vertexNormals.length,pa=1!==V.color.r||1!==V.color.g||1!==V.color.b,qa=0<V.vertexColors.length,
ua=0;ua=a(ua,0,0);ua=a(ua,1,!0);ua=a(ua,2,!1);ua=a(ua,3,ea);ua=a(ua,4,da);ua=a(ua,5,ra);ua=a(ua,6,pa);ua=a(ua,7,qa);E.push(ua);E.push(V.a,V.b,V.c);E.push(V.materialIndex);ea&&(ea=this.faceVertexUvs[0][z],E.push(g(ea[0]),g(ea[1]),g(ea[2])));da&&E.push(c(V.normal));ra&&(da=V.vertexNormals,E.push(c(da[0]),c(da[1]),c(da[2])));pa&&E.push(e(V.color));qa&&(V=V.vertexColors,E.push(e(V[0]),e(V[1]),e(V[2])))}t.data={};t.data.vertices=v;t.data.normals=F;0<M.length&&(t.data.colors=M);0<Q.length&&(t.data.uvs=
[Q]);t.data.faces=E;return t},clone:function(){return(new xa).copy(this)},copy:function(a){var c,e,g;this.vertices=[];this.colors=[];this.faces=[];this.faceVertexUvs=[[]];this.morphTargets=[];this.morphNormals=[];this.skinWeights=[];this.skinIndices=[];this.lineDistances=[];this.boundingSphere=this.boundingBox=null;this.name=a.name;var t=a.vertices;var v=0;for(c=t.length;v<c;v++)this.vertices.push(t[v].clone());t=a.colors;v=0;for(c=t.length;v<c;v++)this.colors.push(t[v].clone());t=a.faces;v=0;for(c=
t.length;v<c;v++)this.faces.push(t[v].clone());v=0;for(c=a.faceVertexUvs.length;v<c;v++){var z=a.faceVertexUvs[v];void 0===this.faceVertexUvs[v]&&(this.faceVertexUvs[v]=[]);t=0;for(e=z.length;t<e;t++){var E=z[t],F=[];var I=0;for(g=E.length;I<g;I++)F.push(E[I].clone());this.faceVertexUvs[v].push(F)}}I=a.morphTargets;v=0;for(c=I.length;v<c;v++){g={};g.name=I[v].name;if(void 0!==I[v].vertices)for(g.vertices=[],t=0,e=I[v].vertices.length;t<e;t++)g.vertices.push(I[v].vertices[t].clone());if(void 0!==I[v].normals)for(g.normals=
[],t=0,e=I[v].normals.length;t<e;t++)g.normals.push(I[v].normals[t].clone());this.morphTargets.push(g)}I=a.morphNormals;v=0;for(c=I.length;v<c;v++){g={};if(void 0!==I[v].vertexNormals)for(g.vertexNormals=[],t=0,e=I[v].vertexNormals.length;t<e;t++)z=I[v].vertexNormals[t],E={},E.a=z.a.clone(),E.b=z.b.clone(),E.c=z.c.clone(),g.vertexNormals.push(E);if(void 0!==I[v].faceNormals)for(g.faceNormals=[],t=0,e=I[v].faceNormals.length;t<e;t++)g.faceNormals.push(I[v].faceNormals[t].clone());this.morphNormals.push(g)}t=
a.skinWeights;v=0;for(c=t.length;v<c;v++)this.skinWeights.push(t[v].clone());t=a.skinIndices;v=0;for(c=t.length;v<c;v++)this.skinIndices.push(t[v].clone());t=a.lineDistances;v=0;for(c=t.length;v<c;v++)this.lineDistances.push(t[v]);v=a.boundingBox;null!==v&&(this.boundingBox=v.clone());v=a.boundingSphere;null!==v&&(this.boundingSphere=v.clone());this.elementsNeedUpdate=a.elementsNeedUpdate;this.verticesNeedUpdate=a.verticesNeedUpdate;this.uvsNeedUpdate=a.uvsNeedUpdate;this.normalsNeedUpdate=a.normalsNeedUpdate;
this.colorsNeedUpdate=a.colorsNeedUpdate;this.lineDistancesNeedUpdate=a.lineDistancesNeedUpdate;this.groupsNeedUpdate=a.groupsNeedUpdate;return this},dispose:function(){this.dispatchEvent({type:"dispose"})}});Sa.prototype=Object.create(xa.prototype);Sa.prototype.constructor=Sa;Xa.prototype=Object.create(va.prototype);Xa.prototype.constructor=Xa;var $m={clone:ub,merge:Bb};qb.prototype=Object.create(S.prototype);qb.prototype.constructor=qb;qb.prototype.isShaderMaterial=!0;qb.prototype.copy=function(a){S.prototype.copy.call(this,
a);this.fragmentShader=a.fragmentShader;this.vertexShader=a.vertexShader;this.uniforms=ub(a.uniforms);this.defines=Object.assign({},a.defines);this.wireframe=a.wireframe;this.wireframeLinewidth=a.wireframeLinewidth;this.lights=a.lights;this.clipping=a.clipping;this.skinning=a.skinning;this.morphTargets=a.morphTargets;this.morphNormals=a.morphNormals;this.extensions=a.extensions;return this};qb.prototype.toJSON=function(a){var c=S.prototype.toJSON.call(this,a);c.uniforms={};for(var e in this.uniforms){var g=
this.uniforms[e].value;c.uniforms[e]=g&&g.isTexture?{type:"t",value:g.toJSON(a).uuid}:g&&g.isColor?{type:"c",value:g.getHex()}:g&&g.isVector2?{type:"v2",value:g.toArray()}:g&&g.isVector3?{type:"v3",value:g.toArray()}:g&&g.isVector4?{type:"v4",value:g.toArray()}:g&&g.isMatrix3?{type:"m3",value:g.toArray()}:g&&g.isMatrix4?{type:"m4",value:g.toArray()}:{value:g}}0<Object.keys(this.defines).length&&(c.defines=this.defines);c.vertexShader=this.vertexShader;c.fragmentShader=this.fragmentShader;a={};for(var t in this.extensions)!0===
this.extensions[t]&&(a[t]=!0);0<Object.keys(a).length&&(c.extensions=a);return c};zb.prototype=Object.assign(Object.create(A.prototype),{constructor:zb,isCamera:!0,copy:function(a,c){A.prototype.copy.call(this,a,c);this.matrixWorldInverse.copy(a.matrixWorldInverse);this.projectionMatrix.copy(a.projectionMatrix);this.projectionMatrixInverse.copy(a.projectionMatrixInverse);return this},getWorldDirection:function(a){void 0===a&&(console.warn("THREE.Camera: .getWorldDirection() target is now required"),
a=new k);this.updateMatrixWorld(!0);var c=this.matrixWorld.elements;return a.set(-c[8],-c[9],-c[10]).normalize()},updateMatrixWorld:function(a){A.prototype.updateMatrixWorld.call(this,a);this.matrixWorldInverse.getInverse(this.matrixWorld)},clone:function(){return(new this.constructor).copy(this)}});vb.prototype=Object.assign(Object.create(zb.prototype),{constructor:vb,isPerspectiveCamera:!0,copy:function(a,c){zb.prototype.copy.call(this,a,c);this.fov=a.fov;this.zoom=a.zoom;this.near=a.near;this.far=
a.far;this.focus=a.focus;this.aspect=a.aspect;this.view=null===a.view?null:Object.assign({},a.view);this.filmGauge=a.filmGauge;this.filmOffset=a.filmOffset;return this},setFocalLength:function(a){this.fov=2*hb.RAD2DEG*Math.atan(.5*this.getFilmHeight()/a);this.updateProjectionMatrix()},getFocalLength:function(){return.5*this.getFilmHeight()/Math.tan(.5*hb.DEG2RAD*this.fov)},getEffectiveFOV:function(){return 2*hb.RAD2DEG*Math.atan(Math.tan(.5*hb.DEG2RAD*this.fov)/this.zoom)},getFilmWidth:function(){return this.filmGauge*
Math.min(this.aspect,1)},getFilmHeight:function(){return this.filmGauge/Math.max(this.aspect,1)},setViewOffset:function(a,c,e,g,t,v){this.aspect=a/c;null===this.view&&(this.view={enabled:!0,fullWidth:1,fullHeight:1,offsetX:0,offsetY:0,width:1,height:1});this.view.enabled=!0;this.view.fullWidth=a;this.view.fullHeight=c;this.view.offsetX=e;this.view.offsetY=g;this.view.width=t;this.view.height=v;this.updateProjectionMatrix()},clearViewOffset:function(){null!==this.view&&(this.view.enabled=!1);this.updateProjectionMatrix()},
updateProjectionMatrix:function(){var a=this.near,c=a*Math.tan(.5*hb.DEG2RAD*this.fov)/this.zoom,e=2*c,g=this.aspect*e,t=-.5*g,v=this.view;if(null!==this.view&&this.view.enabled){var z=v.fullWidth,E=v.fullHeight;t+=v.offsetX*g/z;c-=v.offsetY*e/E;g*=v.width/z;e*=v.height/E}v=this.filmOffset;0!==v&&(t+=a*v/this.getFilmWidth());this.projectionMatrix.makePerspective(t,t+g,c,c-e,a,this.far);this.projectionMatrixInverse.getInverse(this.projectionMatrix)},toJSON:function(a){a=A.prototype.toJSON.call(this,
a);a.object.fov=this.fov;a.object.zoom=this.zoom;a.object.near=this.near;a.object.far=this.far;a.object.focus=this.focus;a.object.aspect=this.aspect;null!==this.view&&(a.object.view=Object.assign({},this.view));a.object.filmGauge=this.filmGauge;a.object.filmOffset=this.filmOffset;return a}});Gb.prototype=Object.create(A.prototype);Gb.prototype.constructor=Gb;Ob.prototype=Object.create(m.prototype);Ob.prototype.constructor=Ob;Ob.prototype.isWebGLRenderTargetCube=!0;Ob.prototype.fromEquirectangularTexture=
function(a,c){this.texture.type=c.type;this.texture.format=c.format;this.texture.encoding=c.encoding;var e=new y,g=new qb({type:"CubemapFromEquirect",uniforms:ub({tEquirect:{value:null}}),vertexShader:"varying vec3 vWorldDirection;\nvec3 transformDirection( in vec3 dir, in mat4 matrix ) {\n\treturn normalize( ( matrix * vec4( dir, 0.0 ) ).xyz );\n}\nvoid main() {\n\tvWorldDirection \x3d transformDirection( position, modelMatrix );\n\t#include \x3cbegin_vertex\x3e\n\t#include \x3cproject_vertex\x3e\n}",
fragmentShader:"uniform sampler2D tEquirect;\nvarying vec3 vWorldDirection;\n#define RECIPROCAL_PI 0.31830988618\n#define RECIPROCAL_PI2 0.15915494\nvoid main() {\n\tvec3 direction \x3d normalize( vWorldDirection );\n\tvec2 sampleUV;\n\tsampleUV.y \x3d asin( clamp( direction.y, - 1.0, 1.0 ) ) * RECIPROCAL_PI + 0.5;\n\tsampleUV.x \x3d atan( direction.z, direction.x ) * RECIPROCAL_PI2 + 0.5;\n\tgl_FragColor \x3d texture2D( tEquirect, sampleUV );\n}",side:1,blending:0});g.uniforms.tEquirect.value=c;
c=new ya(new Xa(5,5,5),g);e.add(c);g=new Gb(1,10,1);g.renderTarget=this;g.renderTarget.texture.name="CubeCameraTexture";g.update(a,e);c.geometry.dispose();c.material.dispose();return this};Ab.prototype=Object.create(l.prototype);Ab.prototype.constructor=Ab;Ab.prototype.isDataTexture=!0;var Oi=new k,an=new k,bn=new r;Object.assign(Hb.prototype,{isPlane:!0,set:function(a,c){this.normal.copy(a);this.constant=c;return this},setComponents:function(a,c,e,g){this.normal.set(a,c,e);this.constant=g;return this},
setFromNormalAndCoplanarPoint:function(a,c){this.normal.copy(a);this.constant=-c.dot(this.normal);return this},setFromCoplanarPoints:function(a,c,e){c=Oi.subVectors(e,c).cross(an.subVectors(a,c)).normalize();this.setFromNormalAndCoplanarPoint(c,a);return this},clone:function(){return(new this.constructor).copy(this)},copy:function(a){this.normal.copy(a.normal);this.constant=a.constant;return this},normalize:function(){var a=1/this.normal.length();this.normal.multiplyScalar(a);this.constant*=a;return this},
negate:function(){this.constant*=-1;this.normal.negate();return this},distanceToPoint:function(a){return this.normal.dot(a)+this.constant},distanceToSphere:function(a){return this.distanceToPoint(a.center)-a.radius},projectPoint:function(a,c){void 0===c&&(console.warn("THREE.Plane: .projectPoint() target is now required"),c=new k);return c.copy(this.normal).multiplyScalar(-this.distanceToPoint(a)).add(a)},intersectLine:function(a,c){void 0===c&&(console.warn("THREE.Plane: .intersectLine() target is now required"),
c=new k);var e=a.delta(Oi),g=this.normal.dot(e);if(0===g){if(0===this.distanceToPoint(a.start))return c.copy(a.start)}else if(g=-(a.start.dot(this.normal)+this.constant)/g,!(0>g||1<g))return c.copy(e).multiplyScalar(g).add(a.start)},intersectsLine:function(a){var c=this.distanceToPoint(a.start);a=this.distanceToPoint(a.end);return 0>c&&0<a||0>a&&0<c},intersectsBox:function(a){return a.intersectsPlane(this)},intersectsSphere:function(a){return a.intersectsPlane(this)},coplanarPoint:function(a){void 0===
a&&(console.warn("THREE.Plane: .coplanarPoint() target is now required"),a=new k);return a.copy(this.normal).multiplyScalar(-this.constant)},applyMatrix4:function(a,c){c=c||bn.getNormalMatrix(a);a=this.coplanarPoint(Oi).applyMatrix4(a);c=this.normal.applyMatrix3(c).normalize();this.constant=-a.dot(c);return this},translate:function(a){this.constant-=a.dot(this.normal);return this},equals:function(a){return a.normal.equals(this.normal)&&a.constant===this.constant}});var of=new G,sh=new k;Object.assign(lc.prototype,
{set:function(a,c,e,g,t,v){var z=this.planes;z[0].copy(a);z[1].copy(c);z[2].copy(e);z[3].copy(g);z[4].copy(t);z[5].copy(v);return this},clone:function(){return(new this.constructor).copy(this)},copy:function(a){for(var c=this.planes,e=0;6>e;e++)c[e].copy(a.planes[e]);return this},setFromMatrix:function(a){var c=this.planes,e=a.elements;a=e[0];var g=e[1],t=e[2],v=e[3],z=e[4],E=e[5],F=e[6],I=e[7],M=e[8],P=e[9],Q=e[10],U=e[11],V=e[12],ea=e[13],da=e[14];e=e[15];c[0].setComponents(v-a,I-z,U-M,e-V).normalize();
c[1].setComponents(v+a,I+z,U+M,e+V).normalize();c[2].setComponents(v+g,I+E,U+P,e+ea).normalize();c[3].setComponents(v-g,I-E,U-P,e-ea).normalize();c[4].setComponents(v-t,I-F,U-Q,e-da).normalize();c[5].setComponents(v+t,I+F,U+Q,e+da).normalize();return this},intersectsObject:function(a){var c=a.geometry;null===c.boundingSphere&&c.computeBoundingSphere();of.copy(c.boundingSphere).applyMatrix4(a.matrixWorld);return this.intersectsSphere(of)},intersectsSprite:function(a){of.center.set(0,0,0);of.radius=
.7071067811865476;of.applyMatrix4(a.matrixWorld);return this.intersectsSphere(of)},intersectsSphere:function(a){var c=this.planes,e=a.center;a=-a.radius;for(var g=0;6>g;g++)if(c[g].distanceToPoint(e)<a)return!1;return!0},intersectsBox:function(a){for(var c=this.planes,e=0;6>e;e++){var g=c[e];sh.x=0<g.normal.x?a.max.x:a.min.x;sh.y=0<g.normal.y?a.max.y:a.min.y;sh.z=0<g.normal.z?a.max.z:a.min.z;if(0>g.distanceToPoint(sh))return!1}return!0},containsPoint:function(a){for(var c=this.planes,e=0;6>e;e++)if(0>
c[e].distanceToPoint(a))return!1;return!0}});var rb={alphamap_fragment:"#ifdef USE_ALPHAMAP\n\tdiffuseColor.a *\x3d texture2D( alphaMap, vUv ).g;\n#endif",alphamap_pars_fragment:"#ifdef USE_ALPHAMAP\n\tuniform sampler2D alphaMap;\n#endif",alphatest_fragment:"#ifdef ALPHATEST\n\tif ( diffuseColor.a \x3c ALPHATEST ) discard;\n#endif",aomap_fragment:"#ifdef USE_AOMAP\n\tfloat ambientOcclusion \x3d ( texture2D( aoMap, vUv2 ).r - 1.0 ) * aoMapIntensity + 1.0;\n\treflectedLight.indirectDiffuse *\x3d ambientOcclusion;\n\t#if defined( USE_ENVMAP ) \x26\x26 defined( STANDARD )\n\t\tfloat dotNV \x3d saturate( dot( geometry.normal, geometry.viewDir ) );\n\t\treflectedLight.indirectSpecular *\x3d computeSpecularOcclusion( dotNV, ambientOcclusion, material.specularRoughness );\n\t#endif\n#endif",
aomap_pars_fragment:"#ifdef USE_AOMAP\n\tuniform sampler2D aoMap;\n\tuniform float aoMapIntensity;\n#endif",begin_vertex:"vec3 transformed \x3d vec3( position );",beginnormal_vertex:"vec3 objectNormal \x3d vec3( normal );\n#ifdef USE_TANGENT\n\tvec3 objectTangent \x3d vec3( tangent.xyz );\n#endif",bsdfs:"vec2 integrateSpecularBRDF( const in float dotNV, const in float roughness ) {\n\tconst vec4 c0 \x3d vec4( - 1, - 0.0275, - 0.572, 0.022 );\n\tconst vec4 c1 \x3d vec4( 1, 0.0425, 1.04, - 0.04 );\n\tvec4 r \x3d roughness * c0 + c1;\n\tfloat a004 \x3d min( r.x * r.x, exp2( - 9.28 * dotNV ) ) * r.x + r.y;\n\treturn vec2( -1.04, 1.04 ) * a004 + r.zw;\n}\nfloat punctualLightIntensityToIrradianceFactor( const in float lightDistance, const in float cutoffDistance, const in float decayExponent ) {\n#if defined ( PHYSICALLY_CORRECT_LIGHTS )\n\tfloat distanceFalloff \x3d 1.0 / max( pow( lightDistance, decayExponent ), 0.01 );\n\tif( cutoffDistance \x3e 0.0 ) {\n\t\tdistanceFalloff *\x3d pow2( saturate( 1.0 - pow4( lightDistance / cutoffDistance ) ) );\n\t}\n\treturn distanceFalloff;\n#else\n\tif( cutoffDistance \x3e 0.0 \x26\x26 decayExponent \x3e 0.0 ) {\n\t\treturn pow( saturate( -lightDistance / cutoffDistance + 1.0 ), decayExponent );\n\t}\n\treturn 1.0;\n#endif\n}\nvec3 BRDF_Diffuse_Lambert( const in vec3 diffuseColor ) {\n\treturn RECIPROCAL_PI * diffuseColor;\n}\nvec3 F_Schlick( const in vec3 specularColor, const in float dotLH ) {\n\tfloat fresnel \x3d exp2( ( -5.55473 * dotLH - 6.98316 ) * dotLH );\n\treturn ( 1.0 - specularColor ) * fresnel + specularColor;\n}\nvec3 F_Schlick_RoughnessDependent( const in vec3 F0, const in float dotNV, const in float roughness ) {\n\tfloat fresnel \x3d exp2( ( -5.55473 * dotNV - 6.98316 ) * dotNV );\n\tvec3 Fr \x3d max( vec3( 1.0 - roughness ), F0 ) - F0;\n\treturn Fr * fresnel + F0;\n}\nfloat G_GGX_Smith( const in float alpha, const in float dotNL, const in float dotNV ) {\n\tfloat a2 \x3d pow2( alpha );\n\tfloat gl \x3d dotNL + sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNL ) );\n\tfloat gv \x3d dotNV + sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNV ) );\n\treturn 1.0 / ( gl * gv );\n}\nfloat G_GGX_SmithCorrelated( const in float alpha, const in float dotNL, const in float dotNV ) {\n\tfloat a2 \x3d pow2( alpha );\n\tfloat gv \x3d dotNL * sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNV ) );\n\tfloat gl \x3d dotNV * sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNL ) );\n\treturn 0.5 / max( gv + gl, EPSILON );\n}\nfloat D_GGX( const in float alpha, const in float dotNH ) {\n\tfloat a2 \x3d pow2( alpha );\n\tfloat denom \x3d pow2( dotNH ) * ( a2 - 1.0 ) + 1.0;\n\treturn RECIPROCAL_PI * a2 / pow2( denom );\n}\nvec3 BRDF_Specular_GGX( const in IncidentLight incidentLight, const in vec3 viewDir, const in vec3 normal, const in vec3 specularColor, const in float roughness ) {\n\tfloat alpha \x3d pow2( roughness );\n\tvec3 halfDir \x3d normalize( incidentLight.direction + viewDir );\n\tfloat dotNL \x3d saturate( dot( normal, incidentLight.direction ) );\n\tfloat dotNV \x3d saturate( dot( normal, viewDir ) );\n\tfloat dotNH \x3d saturate( dot( normal, halfDir ) );\n\tfloat dotLH \x3d saturate( dot( incidentLight.direction, halfDir ) );\n\tvec3 F \x3d F_Schlick( specularColor, dotLH );\n\tfloat G \x3d G_GGX_SmithCorrelated( alpha, dotNL, dotNV );\n\tfloat D \x3d D_GGX( alpha, dotNH );\n\treturn F * ( G * D );\n}\nvec2 LTC_Uv( const in vec3 N, const in vec3 V, const in float roughness ) {\n\tconst float LUT_SIZE  \x3d 64.0;\n\tconst float LUT_SCALE \x3d ( LUT_SIZE - 1.0 ) / LUT_SIZE;\n\tconst float LUT_BIAS  \x3d 0.5 / LUT_SIZE;\n\tfloat dotNV \x3d saturate( dot( N, V ) );\n\tvec2 uv \x3d vec2( roughness, sqrt( 1.0 - dotNV ) );\n\tuv \x3d uv * LUT_SCALE + LUT_BIAS;\n\treturn uv;\n}\nfloat LTC_ClippedSphereFormFactor( const in vec3 f ) {\n\tfloat l \x3d length( f );\n\treturn max( ( l * l + f.z ) / ( l + 1.0 ), 0.0 );\n}\nvec3 LTC_EdgeVectorFormFactor( const in vec3 v1, const in vec3 v2 ) {\n\tfloat x \x3d dot( v1, v2 );\n\tfloat y \x3d abs( x );\n\tfloat a \x3d 0.8543985 + ( 0.4965155 + 0.0145206 * y ) * y;\n\tfloat b \x3d 3.4175940 + ( 4.1616724 + y ) * y;\n\tfloat v \x3d a / b;\n\tfloat theta_sintheta \x3d ( x \x3e 0.0 ) ? v : 0.5 * inversesqrt( max( 1.0 - x * x, 1e-7 ) ) - v;\n\treturn cross( v1, v2 ) * theta_sintheta;\n}\nvec3 LTC_Evaluate( const in vec3 N, const in vec3 V, const in vec3 P, const in mat3 mInv, const in vec3 rectCoords[ 4 ] ) {\n\tvec3 v1 \x3d rectCoords[ 1 ] - rectCoords[ 0 ];\n\tvec3 v2 \x3d rectCoords[ 3 ] - rectCoords[ 0 ];\n\tvec3 lightNormal \x3d cross( v1, v2 );\n\tif( dot( lightNormal, P - rectCoords[ 0 ] ) \x3c 0.0 ) return vec3( 0.0 );\n\tvec3 T1, T2;\n\tT1 \x3d normalize( V - N * dot( V, N ) );\n\tT2 \x3d - cross( N, T1 );\n\tmat3 mat \x3d mInv * transposeMat3( mat3( T1, T2, N ) );\n\tvec3 coords[ 4 ];\n\tcoords[ 0 ] \x3d mat * ( rectCoords[ 0 ] - P );\n\tcoords[ 1 ] \x3d mat * ( rectCoords[ 1 ] - P );\n\tcoords[ 2 ] \x3d mat * ( rectCoords[ 2 ] - P );\n\tcoords[ 3 ] \x3d mat * ( rectCoords[ 3 ] - P );\n\tcoords[ 0 ] \x3d normalize( coords[ 0 ] );\n\tcoords[ 1 ] \x3d normalize( coords[ 1 ] );\n\tcoords[ 2 ] \x3d normalize( coords[ 2 ] );\n\tcoords[ 3 ] \x3d normalize( coords[ 3 ] );\n\tvec3 vectorFormFactor \x3d vec3( 0.0 );\n\tvectorFormFactor +\x3d LTC_EdgeVectorFormFactor( coords[ 0 ], coords[ 1 ] );\n\tvectorFormFactor +\x3d LTC_EdgeVectorFormFactor( coords[ 1 ], coords[ 2 ] );\n\tvectorFormFactor +\x3d LTC_EdgeVectorFormFactor( coords[ 2 ], coords[ 3 ] );\n\tvectorFormFactor +\x3d LTC_EdgeVectorFormFactor( coords[ 3 ], coords[ 0 ] );\n\tfloat result \x3d LTC_ClippedSphereFormFactor( vectorFormFactor );\n\treturn vec3( result );\n}\nvec3 BRDF_Specular_GGX_Environment( const in vec3 viewDir, const in vec3 normal, const in vec3 specularColor, const in float roughness ) {\n\tfloat dotNV \x3d saturate( dot( normal, viewDir ) );\n\tvec2 brdf \x3d integrateSpecularBRDF( dotNV, roughness );\n\treturn specularColor * brdf.x + brdf.y;\n}\nvoid BRDF_Specular_Multiscattering_Environment( const in GeometricContext geometry, const in vec3 specularColor, const in float roughness, inout vec3 singleScatter, inout vec3 multiScatter ) {\n\tfloat dotNV \x3d saturate( dot( geometry.normal, geometry.viewDir ) );\n\tvec3 F \x3d F_Schlick_RoughnessDependent( specularColor, dotNV, roughness );\n\tvec2 brdf \x3d integrateSpecularBRDF( dotNV, roughness );\n\tvec3 FssEss \x3d F * brdf.x + brdf.y;\n\tfloat Ess \x3d brdf.x + brdf.y;\n\tfloat Ems \x3d 1.0 - Ess;\n\tvec3 Favg \x3d specularColor + ( 1.0 - specularColor ) * 0.047619;\tvec3 Fms \x3d FssEss * Favg / ( 1.0 - Ems * Favg );\n\tsingleScatter +\x3d FssEss;\n\tmultiScatter +\x3d Fms * Ems;\n}\nfloat G_BlinnPhong_Implicit( ) {\n\treturn 0.25;\n}\nfloat D_BlinnPhong( const in float shininess, const in float dotNH ) {\n\treturn RECIPROCAL_PI * ( shininess * 0.5 + 1.0 ) * pow( dotNH, shininess );\n}\nvec3 BRDF_Specular_BlinnPhong( const in IncidentLight incidentLight, const in GeometricContext geometry, const in vec3 specularColor, const in float shininess ) {\n\tvec3 halfDir \x3d normalize( incidentLight.direction + geometry.viewDir );\n\tfloat dotNH \x3d saturate( dot( geometry.normal, halfDir ) );\n\tfloat dotLH \x3d saturate( dot( incidentLight.direction, halfDir ) );\n\tvec3 F \x3d F_Schlick( specularColor, dotLH );\n\tfloat G \x3d G_BlinnPhong_Implicit( );\n\tfloat D \x3d D_BlinnPhong( shininess, dotNH );\n\treturn F * ( G * D );\n}\nfloat GGXRoughnessToBlinnExponent( const in float ggxRoughness ) {\n\treturn ( 2.0 / pow2( ggxRoughness + 0.0001 ) - 2.0 );\n}\nfloat BlinnExponentToGGXRoughness( const in float blinnExponent ) {\n\treturn sqrt( 2.0 / ( blinnExponent + 2.0 ) );\n}\n#if defined( USE_SHEEN )\nfloat D_Charlie(float roughness, float NoH) {\n\tfloat invAlpha  \x3d 1.0 / roughness;\n\tfloat cos2h \x3d NoH * NoH;\n\tfloat sin2h \x3d max(1.0 - cos2h, 0.0078125);\treturn (2.0 + invAlpha) * pow(sin2h, invAlpha * 0.5) / (2.0 * PI);\n}\nfloat V_Neubelt(float NoV, float NoL) {\n\treturn saturate(1.0 / (4.0 * (NoL + NoV - NoL * NoV)));\n}\nvec3 BRDF_Specular_Sheen( const in float roughness, const in vec3 L, const in GeometricContext geometry, vec3 specularColor ) {\n\tvec3 N \x3d geometry.normal;\n\tvec3 V \x3d geometry.viewDir;\n\tvec3 H \x3d normalize( V + L );\n\tfloat dotNH \x3d saturate( dot( N, H ) );\n\treturn specularColor * D_Charlie( roughness, dotNH ) * V_Neubelt( dot(N, V), dot(N, L) );\n}\n#endif",
bumpmap_pars_fragment:"#ifdef USE_BUMPMAP\n\tuniform sampler2D bumpMap;\n\tuniform float bumpScale;\n\tvec2 dHdxy_fwd() {\n\t\tvec2 dSTdx \x3d dFdx( vUv );\n\t\tvec2 dSTdy \x3d dFdy( vUv );\n\t\tfloat Hll \x3d bumpScale * texture2D( bumpMap, vUv ).x;\n\t\tfloat dBx \x3d bumpScale * texture2D( bumpMap, vUv + dSTdx ).x - Hll;\n\t\tfloat dBy \x3d bumpScale * texture2D( bumpMap, vUv + dSTdy ).x - Hll;\n\t\treturn vec2( dBx, dBy );\n\t}\n\tvec3 perturbNormalArb( vec3 surf_pos, vec3 surf_norm, vec2 dHdxy ) {\n\t\tvec3 vSigmaX \x3d vec3( dFdx( surf_pos.x ), dFdx( surf_pos.y ), dFdx( surf_pos.z ) );\n\t\tvec3 vSigmaY \x3d vec3( dFdy( surf_pos.x ), dFdy( surf_pos.y ), dFdy( surf_pos.z ) );\n\t\tvec3 vN \x3d surf_norm;\n\t\tvec3 R1 \x3d cross( vSigmaY, vN );\n\t\tvec3 R2 \x3d cross( vN, vSigmaX );\n\t\tfloat fDet \x3d dot( vSigmaX, R1 );\n\t\tfDet *\x3d ( float( gl_FrontFacing ) * 2.0 - 1.0 );\n\t\tvec3 vGrad \x3d sign( fDet ) * ( dHdxy.x * R1 + dHdxy.y * R2 );\n\t\treturn normalize( abs( fDet ) * surf_norm - vGrad );\n\t}\n#endif",
clipping_planes_fragment:"#if NUM_CLIPPING_PLANES \x3e 0\n\tvec4 plane;\n\t#pragma unroll_loop\n\tfor ( int i \x3d 0; i \x3c UNION_CLIPPING_PLANES; i ++ ) {\n\t\tplane \x3d clippingPlanes[ i ];\n\t\tif ( dot( vViewPosition, plane.xyz ) \x3e plane.w ) discard;\n\t}\n\t#if UNION_CLIPPING_PLANES \x3c NUM_CLIPPING_PLANES\n\t\tbool clipped \x3d true;\n\t\t#pragma unroll_loop\n\t\tfor ( int i \x3d UNION_CLIPPING_PLANES; i \x3c NUM_CLIPPING_PLANES; i ++ ) {\n\t\t\tplane \x3d clippingPlanes[ i ];\n\t\t\tclipped \x3d ( dot( vViewPosition, plane.xyz ) \x3e plane.w ) \x26\x26 clipped;\n\t\t}\n\t\tif ( clipped ) discard;\n\t#endif\n#endif",
clipping_planes_pars_fragment:"#if NUM_CLIPPING_PLANES \x3e 0\n\t#if ! defined( STANDARD ) \x26\x26 ! defined( PHONG ) \x26\x26 ! defined( MATCAP )\n\t\tvarying vec3 vViewPosition;\n\t#endif\n\tuniform vec4 clippingPlanes[ NUM_CLIPPING_PLANES ];\n#endif",clipping_planes_pars_vertex:"#if NUM_CLIPPING_PLANES \x3e 0 \x26\x26 ! defined( STANDARD ) \x26\x26 ! defined( PHONG ) \x26\x26 ! defined( MATCAP )\n\tvarying vec3 vViewPosition;\n#endif",clipping_planes_vertex:"#if NUM_CLIPPING_PLANES \x3e 0 \x26\x26 ! defined( STANDARD ) \x26\x26 ! defined( PHONG ) \x26\x26 ! defined( MATCAP )\n\tvViewPosition \x3d - mvPosition.xyz;\n#endif",
color_fragment:"#ifdef USE_COLOR\n\tdiffuseColor.rgb *\x3d vColor;\n#endif",color_pars_fragment:"#ifdef USE_COLOR\n\tvarying vec3 vColor;\n#endif",color_pars_vertex:"#ifdef USE_COLOR\n\tvarying vec3 vColor;\n#endif",color_vertex:"#ifdef USE_COLOR\n\tvColor.xyz \x3d color.xyz;\n#endif",common:"#define PI 3.14159265359\n#define PI2 6.28318530718\n#define PI_HALF 1.5707963267949\n#define RECIPROCAL_PI 0.31830988618\n#define RECIPROCAL_PI2 0.15915494\n#define LOG2 1.442695\n#define EPSILON 1e-6\n#define saturate(a) clamp( a, 0.0, 1.0 )\n#define whiteComplement(a) ( 1.0 - saturate( a ) )\nfloat pow2( const in float x ) { return x*x; }\nfloat pow3( const in float x ) { return x*x*x; }\nfloat pow4( const in float x ) { float x2 \x3d x*x; return x2*x2; }\nfloat average( const in vec3 color ) { return dot( color, vec3( 0.3333 ) ); }\nhighp float rand( const in vec2 uv ) {\n\tconst highp float a \x3d 12.9898, b \x3d 78.233, c \x3d 43758.5453;\n\thighp float dt \x3d dot( uv.xy, vec2( a,b ) ), sn \x3d mod( dt, PI );\n\treturn fract(sin(sn) * c);\n}\n#ifdef HIGH_PRECISION\n\tfloat precisionSafeLength( vec3 v ) { return length( v ); }\n#else\n\tfloat max3( vec3 v ) { return max( max( v.x, v.y ), v.z ); }\n\tfloat precisionSafeLength( vec3 v ) {\n\t\tfloat maxComponent \x3d max3( abs( v ) );\n\t\treturn length( v / maxComponent ) * maxComponent;\n\t}\n#endif\nstruct IncidentLight {\n\tvec3 color;\n\tvec3 direction;\n\tbool visible;\n};\nstruct ReflectedLight {\n\tvec3 directDiffuse;\n\tvec3 directSpecular;\n\tvec3 indirectDiffuse;\n\tvec3 indirectSpecular;\n};\nstruct GeometricContext {\n\tvec3 position;\n\tvec3 normal;\n\tvec3 viewDir;\n#ifdef CLEARCOAT\n\tvec3 clearcoatNormal;\n#endif\n};\nvec3 transformDirection( in vec3 dir, in mat4 matrix ) {\n\treturn normalize( ( matrix * vec4( dir, 0.0 ) ).xyz );\n}\nvec3 inverseTransformDirection( in vec3 dir, in mat4 matrix ) {\n\treturn normalize( ( vec4( dir, 0.0 ) * matrix ).xyz );\n}\nvec3 projectOnPlane(in vec3 point, in vec3 pointOnPlane, in vec3 planeNormal ) {\n\tfloat distance \x3d dot( planeNormal, point - pointOnPlane );\n\treturn - distance * planeNormal + point;\n}\nfloat sideOfPlane( in vec3 point, in vec3 pointOnPlane, in vec3 planeNormal ) {\n\treturn sign( dot( point - pointOnPlane, planeNormal ) );\n}\nvec3 linePlaneIntersect( in vec3 pointOnLine, in vec3 lineDirection, in vec3 pointOnPlane, in vec3 planeNormal ) {\n\treturn lineDirection * ( dot( planeNormal, pointOnPlane - pointOnLine ) / dot( planeNormal, lineDirection ) ) + pointOnLine;\n}\nmat3 transposeMat3( const in mat3 m ) {\n\tmat3 tmp;\n\ttmp[ 0 ] \x3d vec3( m[ 0 ].x, m[ 1 ].x, m[ 2 ].x );\n\ttmp[ 1 ] \x3d vec3( m[ 0 ].y, m[ 1 ].y, m[ 2 ].y );\n\ttmp[ 2 ] \x3d vec3( m[ 0 ].z, m[ 1 ].z, m[ 2 ].z );\n\treturn tmp;\n}\nfloat linearToRelativeLuminance( const in vec3 color ) {\n\tvec3 weights \x3d vec3( 0.2126, 0.7152, 0.0722 );\n\treturn dot( weights, color.rgb );\n}",
cube_uv_reflection_fragment:"#ifdef ENVMAP_TYPE_CUBE_UV\n#define cubeUV_textureSize (1024.0)\nint getFaceFromDirection(vec3 direction) {\n\tvec3 absDirection \x3d abs(direction);\n\tint face \x3d -1;\n\tif( absDirection.x \x3e absDirection.z ) {\n\t\tif(absDirection.x \x3e absDirection.y )\n\t\t\tface \x3d direction.x \x3e 0.0 ? 0 : 3;\n\t\telse\n\t\t\tface \x3d direction.y \x3e 0.0 ? 1 : 4;\n\t}\n\telse {\n\t\tif(absDirection.z \x3e absDirection.y )\n\t\t\tface \x3d direction.z \x3e 0.0 ? 2 : 5;\n\t\telse\n\t\t\tface \x3d direction.y \x3e 0.0 ? 1 : 4;\n\t}\n\treturn face;\n}\n#define cubeUV_maxLods1  (log2(cubeUV_textureSize*0.25) - 1.0)\n#define cubeUV_rangeClamp (exp2((6.0 - 1.0) * 2.0))\nvec2 MipLevelInfo( vec3 vec, float roughnessLevel, float roughness ) {\n\tfloat scale \x3d exp2(cubeUV_maxLods1 - roughnessLevel);\n\tfloat dxRoughness \x3d dFdx(roughness);\n\tfloat dyRoughness \x3d dFdy(roughness);\n\tvec3 dx \x3d dFdx( vec * scale * dxRoughness );\n\tvec3 dy \x3d dFdy( vec * scale * dyRoughness );\n\tfloat d \x3d max( dot( dx, dx ), dot( dy, dy ) );\n\td \x3d clamp(d, 1.0, cubeUV_rangeClamp);\n\tfloat mipLevel \x3d 0.5 * log2(d);\n\treturn vec2(floor(mipLevel), fract(mipLevel));\n}\n#define cubeUV_maxLods2 (log2(cubeUV_textureSize*0.25) - 2.0)\n#define cubeUV_rcpTextureSize (1.0 / cubeUV_textureSize)\nvec2 getCubeUV(vec3 direction, float roughnessLevel, float mipLevel) {\n\tmipLevel \x3d roughnessLevel \x3e cubeUV_maxLods2 - 3.0 ? 0.0 : mipLevel;\n\tfloat a \x3d 16.0 * cubeUV_rcpTextureSize;\n\tvec2 exp2_packed \x3d exp2( vec2( roughnessLevel, mipLevel ) );\n\tvec2 rcp_exp2_packed \x3d vec2( 1.0 ) / exp2_packed;\n\tfloat powScale \x3d exp2_packed.x * exp2_packed.y;\n\tfloat scale \x3d rcp_exp2_packed.x * rcp_exp2_packed.y * 0.25;\n\tfloat mipOffset \x3d 0.75*(1.0 - rcp_exp2_packed.y) * rcp_exp2_packed.x;\n\tbool bRes \x3d mipLevel \x3d\x3d 0.0;\n\tscale \x3d  bRes \x26\x26 (scale \x3c a) ? a : scale;\n\tvec3 r;\n\tvec2 offset;\n\tint face \x3d getFaceFromDirection(direction);\n\tfloat rcpPowScale \x3d 1.0 / powScale;\n\tif( face \x3d\x3d 0) {\n\t\tr \x3d vec3(direction.x, -direction.z, direction.y);\n\t\toffset \x3d vec2(0.0+mipOffset,0.75 * rcpPowScale);\n\t\toffset.y \x3d bRes \x26\x26 (offset.y \x3c 2.0*a) ? a : offset.y;\n\t}\n\telse if( face \x3d\x3d 1) {\n\t\tr \x3d vec3(direction.y, direction.x, direction.z);\n\t\toffset \x3d vec2(scale+mipOffset, 0.75 * rcpPowScale);\n\t\toffset.y \x3d bRes \x26\x26 (offset.y \x3c 2.0*a) ? a : offset.y;\n\t}\n\telse if( face \x3d\x3d 2) {\n\t\tr \x3d vec3(direction.z, direction.x, direction.y);\n\t\toffset \x3d vec2(2.0*scale+mipOffset, 0.75 * rcpPowScale);\n\t\toffset.y \x3d bRes \x26\x26 (offset.y \x3c 2.0*a) ? a : offset.y;\n\t}\n\telse if( face \x3d\x3d 3) {\n\t\tr \x3d vec3(direction.x, direction.z, direction.y);\n\t\toffset \x3d vec2(0.0+mipOffset,0.5 * rcpPowScale);\n\t\toffset.y \x3d bRes \x26\x26 (offset.y \x3c 2.0*a) ? 0.0 : offset.y;\n\t}\n\telse if( face \x3d\x3d 4) {\n\t\tr \x3d vec3(direction.y, direction.x, -direction.z);\n\t\toffset \x3d vec2(scale+mipOffset, 0.5 * rcpPowScale);\n\t\toffset.y \x3d bRes \x26\x26 (offset.y \x3c 2.0*a) ? 0.0 : offset.y;\n\t}\n\telse {\n\t\tr \x3d vec3(direction.z, -direction.x, direction.y);\n\t\toffset \x3d vec2(2.0*scale+mipOffset, 0.5 * rcpPowScale);\n\t\toffset.y \x3d bRes \x26\x26 (offset.y \x3c 2.0*a) ? 0.0 : offset.y;\n\t}\n\tr \x3d normalize(r);\n\tfloat texelOffset \x3d 0.5 * cubeUV_rcpTextureSize;\n\tvec2 s \x3d ( r.yz / abs( r.x ) + vec2( 1.0 ) ) * 0.5;\n\tvec2 base \x3d offset + vec2( texelOffset );\n\treturn base + s * ( scale - 2.0 * texelOffset );\n}\n#define cubeUV_maxLods3 (log2(cubeUV_textureSize*0.25) - 3.0)\nvec4 textureCubeUV( sampler2D envMap, vec3 reflectedDirection, float roughness ) {\n\tfloat roughnessVal \x3d roughness* cubeUV_maxLods3;\n\tfloat r1 \x3d floor(roughnessVal);\n\tfloat r2 \x3d r1 + 1.0;\n\tfloat t \x3d fract(roughnessVal);\n\tvec2 mipInfo \x3d MipLevelInfo(reflectedDirection, r1, roughness);\n\tfloat s \x3d mipInfo.y;\n\tfloat level0 \x3d mipInfo.x;\n\tfloat level1 \x3d level0 + 1.0;\n\tlevel1 \x3d level1 \x3e 5.0 ? 5.0 : level1;\n\tlevel0 +\x3d min( floor( s + 0.5 ), 5.0 );\n\tvec2 uv_10 \x3d getCubeUV(reflectedDirection, r1, level0);\n\tvec4 color10 \x3d envMapTexelToLinear(texture2D(envMap, uv_10));\n\tvec2 uv_20 \x3d getCubeUV(reflectedDirection, r2, level0);\n\tvec4 color20 \x3d envMapTexelToLinear(texture2D(envMap, uv_20));\n\tvec4 result \x3d mix(color10, color20, t);\n\treturn vec4(result.rgb, 1.0);\n}\n#endif",
defaultnormal_vertex:"vec3 transformedNormal \x3d normalMatrix * objectNormal;\n#ifdef FLIP_SIDED\n\ttransformedNormal \x3d - transformedNormal;\n#endif\n#ifdef USE_TANGENT\n\tvec3 transformedTangent \x3d normalMatrix * objectTangent;\n\t#ifdef FLIP_SIDED\n\t\ttransformedTangent \x3d - transformedTangent;\n\t#endif\n#endif",displacementmap_pars_vertex:"#ifdef USE_DISPLACEMENTMAP\n\tuniform sampler2D displacementMap;\n\tuniform float displacementScale;\n\tuniform float displacementBias;\n#endif",displacementmap_vertex:"#ifdef USE_DISPLACEMENTMAP\n\ttransformed +\x3d normalize( objectNormal ) * ( texture2D( displacementMap, uv ).x * displacementScale + displacementBias );\n#endif",
emissivemap_fragment:"#ifdef USE_EMISSIVEMAP\n\tvec4 emissiveColor \x3d texture2D( emissiveMap, vUv );\n\temissiveColor.rgb \x3d emissiveMapTexelToLinear( emissiveColor ).rgb;\n\ttotalEmissiveRadiance *\x3d emissiveColor.rgb;\n#endif",emissivemap_pars_fragment:"#ifdef USE_EMISSIVEMAP\n\tuniform sampler2D emissiveMap;\n#endif",encodings_fragment:"gl_FragColor \x3d linearToOutputTexel( gl_FragColor );",encodings_pars_fragment:"\nvec4 LinearToLinear( in vec4 value ) {\n\treturn value;\n}\nvec4 GammaToLinear( in vec4 value, in float gammaFactor ) {\n\treturn vec4( pow( value.rgb, vec3( gammaFactor ) ), value.a );\n}\nvec4 LinearToGamma( in vec4 value, in float gammaFactor ) {\n\treturn vec4( pow( value.rgb, vec3( 1.0 / gammaFactor ) ), value.a );\n}\nvec4 sRGBToLinear( in vec4 value ) {\n\treturn vec4( mix( pow( value.rgb * 0.9478672986 + vec3( 0.0521327014 ), vec3( 2.4 ) ), value.rgb * 0.0773993808, vec3( lessThanEqual( value.rgb, vec3( 0.04045 ) ) ) ), value.a );\n}\nvec4 LinearTosRGB( in vec4 value ) {\n\treturn vec4( mix( pow( value.rgb, vec3( 0.41666 ) ) * 1.055 - vec3( 0.055 ), value.rgb * 12.92, vec3( lessThanEqual( value.rgb, vec3( 0.0031308 ) ) ) ), value.a );\n}\nvec4 RGBEToLinear( in vec4 value ) {\n\treturn vec4( value.rgb * exp2( value.a * 255.0 - 128.0 ), 1.0 );\n}\nvec4 LinearToRGBE( in vec4 value ) {\n\tfloat maxComponent \x3d max( max( value.r, value.g ), value.b );\n\tfloat fExp \x3d clamp( ceil( log2( maxComponent ) ), -128.0, 127.0 );\n\treturn vec4( value.rgb / exp2( fExp ), ( fExp + 128.0 ) / 255.0 );\n}\nvec4 RGBMToLinear( in vec4 value, in float maxRange ) {\n\treturn vec4( value.rgb * value.a * maxRange, 1.0 );\n}\nvec4 LinearToRGBM( in vec4 value, in float maxRange ) {\n\tfloat maxRGB \x3d max( value.r, max( value.g, value.b ) );\n\tfloat M \x3d clamp( maxRGB / maxRange, 0.0, 1.0 );\n\tM \x3d ceil( M * 255.0 ) / 255.0;\n\treturn vec4( value.rgb / ( M * maxRange ), M );\n}\nvec4 RGBDToLinear( in vec4 value, in float maxRange ) {\n\treturn vec4( value.rgb * ( ( maxRange / 255.0 ) / value.a ), 1.0 );\n}\nvec4 LinearToRGBD( in vec4 value, in float maxRange ) {\n\tfloat maxRGB \x3d max( value.r, max( value.g, value.b ) );\n\tfloat D \x3d max( maxRange / maxRGB, 1.0 );\n\tD \x3d min( floor( D ) / 255.0, 1.0 );\n\treturn vec4( value.rgb * ( D * ( 255.0 / maxRange ) ), D );\n}\nconst mat3 cLogLuvM \x3d mat3( 0.2209, 0.3390, 0.4184, 0.1138, 0.6780, 0.7319, 0.0102, 0.1130, 0.2969 );\nvec4 LinearToLogLuv( in vec4 value )  {\n\tvec3 Xp_Y_XYZp \x3d cLogLuvM * value.rgb;\n\tXp_Y_XYZp \x3d max( Xp_Y_XYZp, vec3( 1e-6, 1e-6, 1e-6 ) );\n\tvec4 vResult;\n\tvResult.xy \x3d Xp_Y_XYZp.xy / Xp_Y_XYZp.z;\n\tfloat Le \x3d 2.0 * log2(Xp_Y_XYZp.y) + 127.0;\n\tvResult.w \x3d fract( Le );\n\tvResult.z \x3d ( Le - ( floor( vResult.w * 255.0 ) ) / 255.0 ) / 255.0;\n\treturn vResult;\n}\nconst mat3 cLogLuvInverseM \x3d mat3( 6.0014, -2.7008, -1.7996, -1.3320, 3.1029, -5.7721, 0.3008, -1.0882, 5.6268 );\nvec4 LogLuvToLinear( in vec4 value ) {\n\tfloat Le \x3d value.z * 255.0 + value.w;\n\tvec3 Xp_Y_XYZp;\n\tXp_Y_XYZp.y \x3d exp2( ( Le - 127.0 ) / 2.0 );\n\tXp_Y_XYZp.z \x3d Xp_Y_XYZp.y / value.y;\n\tXp_Y_XYZp.x \x3d value.x * Xp_Y_XYZp.z;\n\tvec3 vRGB \x3d cLogLuvInverseM * Xp_Y_XYZp.rgb;\n\treturn vec4( max( vRGB, 0.0 ), 1.0 );\n}",
envmap_fragment:"#ifdef USE_ENVMAP\n\t#ifdef ENV_WORLDPOS\n\t\tvec3 cameraToVertex \x3d normalize( vWorldPosition - cameraPosition );\n\t\tvec3 worldNormal \x3d inverseTransformDirection( normal, viewMatrix );\n\t\t#ifdef ENVMAP_MODE_REFLECTION\n\t\t\tvec3 reflectVec \x3d reflect( cameraToVertex, worldNormal );\n\t\t#else\n\t\t\tvec3 reflectVec \x3d refract( cameraToVertex, worldNormal, refractionRatio );\n\t\t#endif\n\t#else\n\t\tvec3 reflectVec \x3d vReflect;\n\t#endif\n\t#ifdef ENVMAP_TYPE_CUBE\n\t\tvec4 envColor \x3d textureCube( envMap, vec3( flipEnvMap * reflectVec.x, reflectVec.yz ) );\n\t#elif defined( ENVMAP_TYPE_EQUIREC )\n\t\tvec2 sampleUV;\n\t\treflectVec \x3d normalize( reflectVec );\n\t\tsampleUV.y \x3d asin( clamp( reflectVec.y, - 1.0, 1.0 ) ) * RECIPROCAL_PI + 0.5;\n\t\tsampleUV.x \x3d atan( reflectVec.z, reflectVec.x ) * RECIPROCAL_PI2 + 0.5;\n\t\tvec4 envColor \x3d texture2D( envMap, sampleUV );\n\t#elif defined( ENVMAP_TYPE_SPHERE )\n\t\treflectVec \x3d normalize( reflectVec );\n\t\tvec3 reflectView \x3d normalize( ( viewMatrix * vec4( reflectVec, 0.0 ) ).xyz + vec3( 0.0, 0.0, 1.0 ) );\n\t\tvec4 envColor \x3d texture2D( envMap, reflectView.xy * 0.5 + 0.5 );\n\t#else\n\t\tvec4 envColor \x3d vec4( 0.0 );\n\t#endif\n\tenvColor \x3d envMapTexelToLinear( envColor );\n\t#ifdef ENVMAP_BLENDING_MULTIPLY\n\t\toutgoingLight \x3d mix( outgoingLight, outgoingLight * envColor.xyz, specularStrength * reflectivity );\n\t#elif defined( ENVMAP_BLENDING_MIX )\n\t\toutgoingLight \x3d mix( outgoingLight, envColor.xyz, specularStrength * reflectivity );\n\t#elif defined( ENVMAP_BLENDING_ADD )\n\t\toutgoingLight +\x3d envColor.xyz * specularStrength * reflectivity;\n\t#endif\n#endif",
envmap_common_pars_fragment:"#ifdef USE_ENVMAP\n\tuniform float envMapIntensity;\n\tuniform float flipEnvMap;\n\tuniform int maxMipLevel;\n\t#ifdef ENVMAP_TYPE_CUBE\n\t\tuniform samplerCube envMap;\n\t#else\n\t\tuniform sampler2D envMap;\n\t#endif\n\t\n#endif",envmap_pars_fragment:"#ifdef USE_ENVMAP\n\tuniform float reflectivity;\n\t#if defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) || defined( PHONG )\n\t\t#define ENV_WORLDPOS\n\t#endif\n\t#ifdef ENV_WORLDPOS\n\t\tvarying vec3 vWorldPosition;\n\t\tuniform float refractionRatio;\n\t#else\n\t\tvarying vec3 vReflect;\n\t#endif\n#endif",
envmap_pars_vertex:"#ifdef USE_ENVMAP\n\t#if defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) ||defined( PHONG )\n\t\t#define ENV_WORLDPOS\n\t#endif\n\t#ifdef ENV_WORLDPOS\n\t\t\n\t\tvarying vec3 vWorldPosition;\n\t#else\n\t\tvarying vec3 vReflect;\n\t\tuniform float refractionRatio;\n\t#endif\n#endif",envmap_physical_pars_fragment:"#if defined( USE_ENVMAP )\n\t#ifdef ENVMAP_MODE_REFRACTION\n\t\tuniform float refractionRatio;\n\t#endif\n\tvec3 getLightProbeIndirectIrradiance( const in GeometricContext geometry, const in int maxMIPLevel ) {\n\t\tvec3 worldNormal \x3d inverseTransformDirection( geometry.normal, viewMatrix );\n\t\t#ifdef ENVMAP_TYPE_CUBE\n\t\t\tvec3 queryVec \x3d vec3( flipEnvMap * worldNormal.x, worldNormal.yz );\n\t\t\t#ifdef TEXTURE_LOD_EXT\n\t\t\t\tvec4 envMapColor \x3d textureCubeLodEXT( envMap, queryVec, float( maxMIPLevel ) );\n\t\t\t#else\n\t\t\t\tvec4 envMapColor \x3d textureCube( envMap, queryVec, float( maxMIPLevel ) );\n\t\t\t#endif\n\t\t\tenvMapColor.rgb \x3d envMapTexelToLinear( envMapColor ).rgb;\n\t\t#elif defined( ENVMAP_TYPE_CUBE_UV )\n\t\t\tvec3 queryVec \x3d vec3( flipEnvMap * worldNormal.x, worldNormal.yz );\n\t\t\tvec4 envMapColor \x3d textureCubeUV( envMap, queryVec, 1.0 );\n\t\t#else\n\t\t\tvec4 envMapColor \x3d vec4( 0.0 );\n\t\t#endif\n\t\treturn PI * envMapColor.rgb * envMapIntensity;\n\t}\n\tfloat getSpecularMIPLevel( const in float roughness, const in int maxMIPLevel ) {\n\t\tfloat maxMIPLevelScalar \x3d float( maxMIPLevel );\n\t\tfloat sigma \x3d PI * roughness * roughness / ( 1.0 + roughness );\n\t\tfloat desiredMIPLevel \x3d maxMIPLevelScalar + log2( sigma );\n\t\treturn clamp( desiredMIPLevel, 0.0, maxMIPLevelScalar );\n\t}\n\tvec3 getLightProbeIndirectRadiance( const in vec3 viewDir, const in vec3 normal, const in float roughness, const in int maxMIPLevel ) {\n\t\t#ifdef ENVMAP_MODE_REFLECTION\n\t\t  vec3 reflectVec \x3d reflect( -viewDir, normal );\n\t\t  reflectVec \x3d normalize( mix( reflectVec, normal, roughness * roughness) );\n\t\t#else\n\t\t  vec3 reflectVec \x3d refract( -viewDir, normal, refractionRatio );\n\t\t#endif\n\t\treflectVec \x3d inverseTransformDirection( reflectVec, viewMatrix );\n\t\tfloat specularMIPLevel \x3d getSpecularMIPLevel( roughness, maxMIPLevel );\n\t\t#ifdef ENVMAP_TYPE_CUBE\n\t\t\tvec3 queryReflectVec \x3d vec3( flipEnvMap * reflectVec.x, reflectVec.yz );\n\t\t\t#ifdef TEXTURE_LOD_EXT\n\t\t\t\tvec4 envMapColor \x3d textureCubeLodEXT( envMap, queryReflectVec, specularMIPLevel );\n\t\t\t#else\n\t\t\t\tvec4 envMapColor \x3d textureCube( envMap, queryReflectVec, specularMIPLevel );\n\t\t\t#endif\n\t\t\tenvMapColor.rgb \x3d envMapTexelToLinear( envMapColor ).rgb;\n\t\t#elif defined( ENVMAP_TYPE_CUBE_UV )\n\t\t\tvec3 queryReflectVec \x3d vec3( flipEnvMap * reflectVec.x, reflectVec.yz );\n\t\t\tvec4 envMapColor \x3d textureCubeUV( envMap, queryReflectVec, roughness );\n\t\t#elif defined( ENVMAP_TYPE_EQUIREC )\n\t\t\tvec2 sampleUV;\n\t\t\tsampleUV.y \x3d asin( clamp( reflectVec.y, - 1.0, 1.0 ) ) * RECIPROCAL_PI + 0.5;\n\t\t\tsampleUV.x \x3d atan( reflectVec.z, reflectVec.x ) * RECIPROCAL_PI2 + 0.5;\n\t\t\t#ifdef TEXTURE_LOD_EXT\n\t\t\t\tvec4 envMapColor \x3d texture2DLodEXT( envMap, sampleUV, specularMIPLevel );\n\t\t\t#else\n\t\t\t\tvec4 envMapColor \x3d texture2D( envMap, sampleUV, specularMIPLevel );\n\t\t\t#endif\n\t\t\tenvMapColor.rgb \x3d envMapTexelToLinear( envMapColor ).rgb;\n\t\t#elif defined( ENVMAP_TYPE_SPHERE )\n\t\t\tvec3 reflectView \x3d normalize( ( viewMatrix * vec4( reflectVec, 0.0 ) ).xyz + vec3( 0.0,0.0,1.0 ) );\n\t\t\t#ifdef TEXTURE_LOD_EXT\n\t\t\t\tvec4 envMapColor \x3d texture2DLodEXT( envMap, reflectView.xy * 0.5 + 0.5, specularMIPLevel );\n\t\t\t#else\n\t\t\t\tvec4 envMapColor \x3d texture2D( envMap, reflectView.xy * 0.5 + 0.5, specularMIPLevel );\n\t\t\t#endif\n\t\t\tenvMapColor.rgb \x3d envMapTexelToLinear( envMapColor ).rgb;\n\t\t#endif\n\t\treturn envMapColor.rgb * envMapIntensity;\n\t}\n#endif",
envmap_vertex:"#ifdef USE_ENVMAP\n\t#ifdef ENV_WORLDPOS\n\t\tvWorldPosition \x3d worldPosition.xyz;\n\t#else\n\t\tvec3 cameraToVertex \x3d normalize( worldPosition.xyz - cameraPosition );\n\t\tvec3 worldNormal \x3d inverseTransformDirection( transformedNormal, viewMatrix );\n\t\t#ifdef ENVMAP_MODE_REFLECTION\n\t\t\tvReflect \x3d reflect( cameraToVertex, worldNormal );\n\t\t#else\n\t\t\tvReflect \x3d refract( cameraToVertex, worldNormal, refractionRatio );\n\t\t#endif\n\t#endif\n#endif",fog_vertex:"#ifdef USE_FOG\n\tfogDepth \x3d -mvPosition.z;\n#endif",
fog_pars_vertex:"#ifdef USE_FOG\n\tvarying float fogDepth;\n#endif",fog_fragment:"#ifdef USE_FOG\n\t#ifdef FOG_EXP2\n\t\tfloat fogFactor \x3d 1.0 - exp( - fogDensity * fogDensity * fogDepth * fogDepth );\n\t#else\n\t\tfloat fogFactor \x3d smoothstep( fogNear, fogFar, fogDepth );\n\t#endif\n\tgl_FragColor.rgb \x3d mix( gl_FragColor.rgb, fogColor, fogFactor );\n#endif",fog_pars_fragment:"#ifdef USE_FOG\n\tuniform vec3 fogColor;\n\tvarying float fogDepth;\n\t#ifdef FOG_EXP2\n\t\tuniform float fogDensity;\n\t#else\n\t\tuniform float fogNear;\n\t\tuniform float fogFar;\n\t#endif\n#endif",
gradientmap_pars_fragment:"#ifdef TOON\n\tuniform sampler2D gradientMap;\n\tvec3 getGradientIrradiance( vec3 normal, vec3 lightDirection ) {\n\t\tfloat dotNL \x3d dot( normal, lightDirection );\n\t\tvec2 coord \x3d vec2( dotNL * 0.5 + 0.5, 0.0 );\n\t\t#ifdef USE_GRADIENTMAP\n\t\t\treturn texture2D( gradientMap, coord ).rgb;\n\t\t#else\n\t\t\treturn ( coord.x \x3c 0.7 ) ? vec3( 0.7 ) : vec3( 1.0 );\n\t\t#endif\n\t}\n#endif",lightmap_fragment:"#ifdef USE_LIGHTMAP\n\treflectedLight.indirectDiffuse +\x3d PI * texture2D( lightMap, vUv2 ).xyz * lightMapIntensity;\n#endif",
lightmap_pars_fragment:"#ifdef USE_LIGHTMAP\n\tuniform sampler2D lightMap;\n\tuniform float lightMapIntensity;\n#endif",lights_lambert_vertex:"vec3 diffuse \x3d vec3( 1.0 );\nGeometricContext geometry;\ngeometry.position \x3d mvPosition.xyz;\ngeometry.normal \x3d normalize( transformedNormal );\ngeometry.viewDir \x3d normalize( -mvPosition.xyz );\nGeometricContext backGeometry;\nbackGeometry.position \x3d geometry.position;\nbackGeometry.normal \x3d -geometry.normal;\nbackGeometry.viewDir \x3d geometry.viewDir;\nvLightFront \x3d vec3( 0.0 );\nvIndirectFront \x3d vec3( 0.0 );\n#ifdef DOUBLE_SIDED\n\tvLightBack \x3d vec3( 0.0 );\n\tvIndirectBack \x3d vec3( 0.0 );\n#endif\nIncidentLight directLight;\nfloat dotNL;\nvec3 directLightColor_Diffuse;\n#if NUM_POINT_LIGHTS \x3e 0\n\t#pragma unroll_loop\n\tfor ( int i \x3d 0; i \x3c NUM_POINT_LIGHTS; i ++ ) {\n\t\tgetPointDirectLightIrradiance( pointLights[ i ], geometry, directLight );\n\t\tdotNL \x3d dot( geometry.normal, directLight.direction );\n\t\tdirectLightColor_Diffuse \x3d PI * directLight.color;\n\t\tvLightFront +\x3d saturate( dotNL ) * directLightColor_Diffuse;\n\t\t#ifdef DOUBLE_SIDED\n\t\t\tvLightBack +\x3d saturate( -dotNL ) * directLightColor_Diffuse;\n\t\t#endif\n\t}\n#endif\n#if NUM_SPOT_LIGHTS \x3e 0\n\t#pragma unroll_loop\n\tfor ( int i \x3d 0; i \x3c NUM_SPOT_LIGHTS; i ++ ) {\n\t\tgetSpotDirectLightIrradiance( spotLights[ i ], geometry, directLight );\n\t\tdotNL \x3d dot( geometry.normal, directLight.direction );\n\t\tdirectLightColor_Diffuse \x3d PI * directLight.color;\n\t\tvLightFront +\x3d saturate( dotNL ) * directLightColor_Diffuse;\n\t\t#ifdef DOUBLE_SIDED\n\t\t\tvLightBack +\x3d saturate( -dotNL ) * directLightColor_Diffuse;\n\t\t#endif\n\t}\n#endif\n#if NUM_DIR_LIGHTS \x3e 0\n\t#pragma unroll_loop\n\tfor ( int i \x3d 0; i \x3c NUM_DIR_LIGHTS; i ++ ) {\n\t\tgetDirectionalDirectLightIrradiance( directionalLights[ i ], geometry, directLight );\n\t\tdotNL \x3d dot( geometry.normal, directLight.direction );\n\t\tdirectLightColor_Diffuse \x3d PI * directLight.color;\n\t\tvLightFront +\x3d saturate( dotNL ) * directLightColor_Diffuse;\n\t\t#ifdef DOUBLE_SIDED\n\t\t\tvLightBack +\x3d saturate( -dotNL ) * directLightColor_Diffuse;\n\t\t#endif\n\t}\n#endif\n#if NUM_HEMI_LIGHTS \x3e 0\n\t#pragma unroll_loop\n\tfor ( int i \x3d 0; i \x3c NUM_HEMI_LIGHTS; i ++ ) {\n\t\tvIndirectFront +\x3d getHemisphereLightIrradiance( hemisphereLights[ i ], geometry );\n\t\t#ifdef DOUBLE_SIDED\n\t\t\tvIndirectBack +\x3d getHemisphereLightIrradiance( hemisphereLights[ i ], backGeometry );\n\t\t#endif\n\t}\n#endif",
lights_pars_begin:"uniform vec3 ambientLightColor;\nuniform vec3 lightProbe[ 9 ];\nvec3 shGetIrradianceAt( in vec3 normal, in vec3 shCoefficients[ 9 ] ) {\n\tfloat x \x3d normal.x, y \x3d normal.y, z \x3d normal.z;\n\tvec3 result \x3d shCoefficients[ 0 ] * 0.886227;\n\tresult +\x3d shCoefficients[ 1 ] * 2.0 * 0.511664 * y;\n\tresult +\x3d shCoefficients[ 2 ] * 2.0 * 0.511664 * z;\n\tresult +\x3d shCoefficients[ 3 ] * 2.0 * 0.511664 * x;\n\tresult +\x3d shCoefficients[ 4 ] * 2.0 * 0.429043 * x * y;\n\tresult +\x3d shCoefficients[ 5 ] * 2.0 * 0.429043 * y * z;\n\tresult +\x3d shCoefficients[ 6 ] * ( 0.743125 * z * z - 0.247708 );\n\tresult +\x3d shCoefficients[ 7 ] * 2.0 * 0.429043 * x * z;\n\tresult +\x3d shCoefficients[ 8 ] * 0.429043 * ( x * x - y * y );\n\treturn result;\n}\nvec3 getLightProbeIrradiance( const in vec3 lightProbe[ 9 ], const in GeometricContext geometry ) {\n\tvec3 worldNormal \x3d inverseTransformDirection( geometry.normal, viewMatrix );\n\tvec3 irradiance \x3d shGetIrradianceAt( worldNormal, lightProbe );\n\treturn irradiance;\n}\nvec3 getAmbientLightIrradiance( const in vec3 ambientLightColor ) {\n\tvec3 irradiance \x3d ambientLightColor;\n\t#ifndef PHYSICALLY_CORRECT_LIGHTS\n\t\tirradiance *\x3d PI;\n\t#endif\n\treturn irradiance;\n}\n#if NUM_DIR_LIGHTS \x3e 0\n\tstruct DirectionalLight {\n\t\tvec3 direction;\n\t\tvec3 color;\n\t\tint shadow;\n\t\tfloat shadowBias;\n\t\tfloat shadowRadius;\n\t\tvec2 shadowMapSize;\n\t};\n\tuniform DirectionalLight directionalLights[ NUM_DIR_LIGHTS ];\n\tvoid getDirectionalDirectLightIrradiance( const in DirectionalLight directionalLight, const in GeometricContext geometry, out IncidentLight directLight ) {\n\t\tdirectLight.color \x3d directionalLight.color;\n\t\tdirectLight.direction \x3d directionalLight.direction;\n\t\tdirectLight.visible \x3d true;\n\t}\n#endif\n#if NUM_POINT_LIGHTS \x3e 0\n\tstruct PointLight {\n\t\tvec3 position;\n\t\tvec3 color;\n\t\tfloat distance;\n\t\tfloat decay;\n\t\tint shadow;\n\t\tfloat shadowBias;\n\t\tfloat shadowRadius;\n\t\tvec2 shadowMapSize;\n\t\tfloat shadowCameraNear;\n\t\tfloat shadowCameraFar;\n\t};\n\tuniform PointLight pointLights[ NUM_POINT_LIGHTS ];\n\tvoid getPointDirectLightIrradiance( const in PointLight pointLight, const in GeometricContext geometry, out IncidentLight directLight ) {\n\t\tvec3 lVector \x3d pointLight.position - geometry.position;\n\t\tdirectLight.direction \x3d normalize( lVector );\n\t\tfloat lightDistance \x3d length( lVector );\n\t\tdirectLight.color \x3d pointLight.color;\n\t\tdirectLight.color *\x3d punctualLightIntensityToIrradianceFactor( lightDistance, pointLight.distance, pointLight.decay );\n\t\tdirectLight.visible \x3d ( directLight.color !\x3d vec3( 0.0 ) );\n\t}\n#endif\n#if NUM_SPOT_LIGHTS \x3e 0\n\tstruct SpotLight {\n\t\tvec3 position;\n\t\tvec3 direction;\n\t\tvec3 color;\n\t\tfloat distance;\n\t\tfloat decay;\n\t\tfloat coneCos;\n\t\tfloat penumbraCos;\n\t\tint shadow;\n\t\tfloat shadowBias;\n\t\tfloat shadowRadius;\n\t\tvec2 shadowMapSize;\n\t};\n\tuniform SpotLight spotLights[ NUM_SPOT_LIGHTS ];\n\tvoid getSpotDirectLightIrradiance( const in SpotLight spotLight, const in GeometricContext geometry, out IncidentLight directLight  ) {\n\t\tvec3 lVector \x3d spotLight.position - geometry.position;\n\t\tdirectLight.direction \x3d normalize( lVector );\n\t\tfloat lightDistance \x3d length( lVector );\n\t\tfloat angleCos \x3d dot( directLight.direction, spotLight.direction );\n\t\tif ( angleCos \x3e spotLight.coneCos ) {\n\t\t\tfloat spotEffect \x3d smoothstep( spotLight.coneCos, spotLight.penumbraCos, angleCos );\n\t\t\tdirectLight.color \x3d spotLight.color;\n\t\t\tdirectLight.color *\x3d spotEffect * punctualLightIntensityToIrradianceFactor( lightDistance, spotLight.distance, spotLight.decay );\n\t\t\tdirectLight.visible \x3d true;\n\t\t} else {\n\t\t\tdirectLight.color \x3d vec3( 0.0 );\n\t\t\tdirectLight.visible \x3d false;\n\t\t}\n\t}\n#endif\n#if NUM_RECT_AREA_LIGHTS \x3e 0\n\tstruct RectAreaLight {\n\t\tvec3 color;\n\t\tvec3 position;\n\t\tvec3 halfWidth;\n\t\tvec3 halfHeight;\n\t};\n\tuniform sampler2D ltc_1;\tuniform sampler2D ltc_2;\n\tuniform RectAreaLight rectAreaLights[ NUM_RECT_AREA_LIGHTS ];\n#endif\n#if NUM_HEMI_LIGHTS \x3e 0\n\tstruct HemisphereLight {\n\t\tvec3 direction;\n\t\tvec3 skyColor;\n\t\tvec3 groundColor;\n\t};\n\tuniform HemisphereLight hemisphereLights[ NUM_HEMI_LIGHTS ];\n\tvec3 getHemisphereLightIrradiance( const in HemisphereLight hemiLight, const in GeometricContext geometry ) {\n\t\tfloat dotNL \x3d dot( geometry.normal, hemiLight.direction );\n\t\tfloat hemiDiffuseWeight \x3d 0.5 * dotNL + 0.5;\n\t\tvec3 irradiance \x3d mix( hemiLight.groundColor, hemiLight.skyColor, hemiDiffuseWeight );\n\t\t#ifndef PHYSICALLY_CORRECT_LIGHTS\n\t\t\tirradiance *\x3d PI;\n\t\t#endif\n\t\treturn irradiance;\n\t}\n#endif",
lights_phong_fragment:"BlinnPhongMaterial material;\nmaterial.diffuseColor \x3d diffuseColor.rgb;\nmaterial.specularColor \x3d specular;\nmaterial.specularShininess \x3d shininess;\nmaterial.specularStrength \x3d specularStrength;",lights_phong_pars_fragment:"varying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n#endif\nstruct BlinnPhongMaterial {\n\tvec3\tdiffuseColor;\n\tvec3\tspecularColor;\n\tfloat\tspecularShininess;\n\tfloat\tspecularStrength;\n};\nvoid RE_Direct_BlinnPhong( const in IncidentLight directLight, const in GeometricContext geometry, const in BlinnPhongMaterial material, inout ReflectedLight reflectedLight ) {\n\t#ifdef TOON\n\t\tvec3 irradiance \x3d getGradientIrradiance( geometry.normal, directLight.direction ) * directLight.color;\n\t#else\n\t\tfloat dotNL \x3d saturate( dot( geometry.normal, directLight.direction ) );\n\t\tvec3 irradiance \x3d dotNL * directLight.color;\n\t#endif\n\t#ifndef PHYSICALLY_CORRECT_LIGHTS\n\t\tirradiance *\x3d PI;\n\t#endif\n\treflectedLight.directDiffuse +\x3d irradiance * BRDF_Diffuse_Lambert( material.diffuseColor );\n\treflectedLight.directSpecular +\x3d irradiance * BRDF_Specular_BlinnPhong( directLight, geometry, material.specularColor, material.specularShininess ) * material.specularStrength;\n}\nvoid RE_IndirectDiffuse_BlinnPhong( const in vec3 irradiance, const in GeometricContext geometry, const in BlinnPhongMaterial material, inout ReflectedLight reflectedLight ) {\n\treflectedLight.indirectDiffuse +\x3d irradiance * BRDF_Diffuse_Lambert( material.diffuseColor );\n}\n#define RE_Direct\t\t\t\tRE_Direct_BlinnPhong\n#define RE_IndirectDiffuse\t\tRE_IndirectDiffuse_BlinnPhong\n#define Material_LightProbeLOD( material )\t(0)",
lights_physical_fragment:"PhysicalMaterial material;\nmaterial.diffuseColor \x3d diffuseColor.rgb * ( 1.0 - metalnessFactor );\nmaterial.specularRoughness \x3d clamp( roughnessFactor, 0.04, 1.0 );\n#ifdef REFLECTIVITY\n\tmaterial.specularColor \x3d mix( vec3( MAXIMUM_SPECULAR_COEFFICIENT * pow2( reflectivity ) ), diffuseColor.rgb, metalnessFactor );\n#else\n\tmaterial.specularColor \x3d mix( vec3( DEFAULT_SPECULAR_COEFFICIENT ), diffuseColor.rgb, metalnessFactor );\n#endif\n#ifdef CLEARCOAT\n\tmaterial.clearcoat \x3d saturate( clearcoat );\tmaterial.clearcoatRoughness \x3d clamp( clearcoatRoughness, 0.04, 1.0 );\n#endif\n#ifdef USE_SHEEN\n\tmaterial.sheenColor \x3d sheen;\n#endif",
lights_physical_pars_fragment:"struct PhysicalMaterial {\n\tvec3\tdiffuseColor;\n\tfloat\tspecularRoughness;\n\tvec3\tspecularColor;\n#ifdef CLEARCOAT\n\tfloat clearcoat;\n\tfloat clearcoatRoughness;\n#endif\n#ifdef USE_SHEEN\n\tvec3 sheenColor;\n#endif\n};\n#define MAXIMUM_SPECULAR_COEFFICIENT 0.16\n#define DEFAULT_SPECULAR_COEFFICIENT 0.04\nfloat clearcoatDHRApprox( const in float roughness, const in float dotNL ) {\n\treturn DEFAULT_SPECULAR_COEFFICIENT + ( 1.0 - DEFAULT_SPECULAR_COEFFICIENT ) * ( pow( 1.0 - dotNL, 5.0 ) * pow( 1.0 - roughness, 2.0 ) );\n}\n#if NUM_RECT_AREA_LIGHTS \x3e 0\n\tvoid RE_Direct_RectArea_Physical( const in RectAreaLight rectAreaLight, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\n\t\tvec3 normal \x3d geometry.normal;\n\t\tvec3 viewDir \x3d geometry.viewDir;\n\t\tvec3 position \x3d geometry.position;\n\t\tvec3 lightPos \x3d rectAreaLight.position;\n\t\tvec3 halfWidth \x3d rectAreaLight.halfWidth;\n\t\tvec3 halfHeight \x3d rectAreaLight.halfHeight;\n\t\tvec3 lightColor \x3d rectAreaLight.color;\n\t\tfloat roughness \x3d material.specularRoughness;\n\t\tvec3 rectCoords[ 4 ];\n\t\trectCoords[ 0 ] \x3d lightPos + halfWidth - halfHeight;\t\trectCoords[ 1 ] \x3d lightPos - halfWidth - halfHeight;\n\t\trectCoords[ 2 ] \x3d lightPos - halfWidth + halfHeight;\n\t\trectCoords[ 3 ] \x3d lightPos + halfWidth + halfHeight;\n\t\tvec2 uv \x3d LTC_Uv( normal, viewDir, roughness );\n\t\tvec4 t1 \x3d texture2D( ltc_1, uv );\n\t\tvec4 t2 \x3d texture2D( ltc_2, uv );\n\t\tmat3 mInv \x3d mat3(\n\t\t\tvec3( t1.x, 0, t1.y ),\n\t\t\tvec3(    0, 1,    0 ),\n\t\t\tvec3( t1.z, 0, t1.w )\n\t\t);\n\t\tvec3 fresnel \x3d ( material.specularColor * t2.x + ( vec3( 1.0 ) - material.specularColor ) * t2.y );\n\t\treflectedLight.directSpecular +\x3d lightColor * fresnel * LTC_Evaluate( normal, viewDir, position, mInv, rectCoords );\n\t\treflectedLight.directDiffuse +\x3d lightColor * material.diffuseColor * LTC_Evaluate( normal, viewDir, position, mat3( 1.0 ), rectCoords );\n\t}\n#endif\nvoid RE_Direct_Physical( const in IncidentLight directLight, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\n\tfloat dotNL \x3d saturate( dot( geometry.normal, directLight.direction ) );\n\tvec3 irradiance \x3d dotNL * directLight.color;\n\t#ifndef PHYSICALLY_CORRECT_LIGHTS\n\t\tirradiance *\x3d PI;\n\t#endif\n\t#ifdef CLEARCOAT\n\t\tfloat ccDotNL \x3d saturate( dot( geometry.clearcoatNormal, directLight.direction ) );\n\t\tvec3 ccIrradiance \x3d ccDotNL * directLight.color;\n\t\t#ifndef PHYSICALLY_CORRECT_LIGHTS\n\t\t\tccIrradiance *\x3d PI;\n\t\t#endif\n\t\tfloat clearcoatDHR \x3d material.clearcoat * clearcoatDHRApprox( material.clearcoatRoughness, ccDotNL );\n\t\treflectedLight.directSpecular +\x3d ccIrradiance * material.clearcoat * BRDF_Specular_GGX( directLight, geometry.viewDir, geometry.clearcoatNormal, vec3( DEFAULT_SPECULAR_COEFFICIENT ), material.clearcoatRoughness );\n\t#else\n\t\tfloat clearcoatDHR \x3d 0.0;\n\t#endif\n\t#ifdef USE_SHEEN\n\t\treflectedLight.directSpecular +\x3d ( 1.0 - clearcoatDHR ) * irradiance * BRDF_Specular_Sheen(\n\t\t\tmaterial.specularRoughness,\n\t\t\tdirectLight.direction,\n\t\t\tgeometry,\n\t\t\tmaterial.sheenColor\n\t\t);\n\t#else\n\t\treflectedLight.directSpecular +\x3d ( 1.0 - clearcoatDHR ) * irradiance * BRDF_Specular_GGX( directLight, geometry.viewDir, geometry.normal, material.specularColor, material.specularRoughness);\n\t#endif\n\treflectedLight.directDiffuse +\x3d ( 1.0 - clearcoatDHR ) * irradiance * BRDF_Diffuse_Lambert( material.diffuseColor );\n}\nvoid RE_IndirectDiffuse_Physical( const in vec3 irradiance, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\n\treflectedLight.indirectDiffuse +\x3d irradiance * BRDF_Diffuse_Lambert( material.diffuseColor );\n}\nvoid RE_IndirectSpecular_Physical( const in vec3 radiance, const in vec3 irradiance, const in vec3 clearcoatRadiance, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight) {\n\t#ifdef CLEARCOAT\n\t\tfloat ccDotNV \x3d saturate( dot( geometry.clearcoatNormal, geometry.viewDir ) );\n\t\treflectedLight.indirectSpecular +\x3d clearcoatRadiance * material.clearcoat * BRDF_Specular_GGX_Environment( geometry.viewDir, geometry.clearcoatNormal, vec3( DEFAULT_SPECULAR_COEFFICIENT ), material.clearcoatRoughness );\n\t\tfloat ccDotNL \x3d ccDotNV;\n\t\tfloat clearcoatDHR \x3d material.clearcoat * clearcoatDHRApprox( material.clearcoatRoughness, ccDotNL );\n\t#else\n\t\tfloat clearcoatDHR \x3d 0.0;\n\t#endif\n\tfloat clearcoatInv \x3d 1.0 - clearcoatDHR;\n\tvec3 singleScattering \x3d vec3( 0.0 );\n\tvec3 multiScattering \x3d vec3( 0.0 );\n\tvec3 cosineWeightedIrradiance \x3d irradiance * RECIPROCAL_PI;\n\tBRDF_Specular_Multiscattering_Environment( geometry, material.specularColor, material.specularRoughness, singleScattering, multiScattering );\n\tvec3 diffuse \x3d material.diffuseColor * ( 1.0 - ( singleScattering + multiScattering ) );\n\treflectedLight.indirectSpecular +\x3d clearcoatInv * radiance * singleScattering;\n\treflectedLight.indirectDiffuse +\x3d multiScattering * cosineWeightedIrradiance;\n\treflectedLight.indirectDiffuse +\x3d diffuse * cosineWeightedIrradiance;\n}\n#define RE_Direct\t\t\t\tRE_Direct_Physical\n#define RE_Direct_RectArea\t\tRE_Direct_RectArea_Physical\n#define RE_IndirectDiffuse\t\tRE_IndirectDiffuse_Physical\n#define RE_IndirectSpecular\t\tRE_IndirectSpecular_Physical\nfloat computeSpecularOcclusion( const in float dotNV, const in float ambientOcclusion, const in float roughness ) {\n\treturn saturate( pow( dotNV + ambientOcclusion, exp2( - 16.0 * roughness - 1.0 ) ) - 1.0 + ambientOcclusion );\n}",
lights_fragment_begin:"\nGeometricContext geometry;\ngeometry.position \x3d - vViewPosition;\ngeometry.normal \x3d normal;\ngeometry.viewDir \x3d normalize( vViewPosition );\n#ifdef CLEARCOAT\n\tgeometry.clearcoatNormal \x3d clearcoatNormal;\n#endif\nIncidentLight directLight;\n#if ( NUM_POINT_LIGHTS \x3e 0 ) \x26\x26 defined( RE_Direct )\n\tPointLight pointLight;\n\t#pragma unroll_loop\n\tfor ( int i \x3d 0; i \x3c NUM_POINT_LIGHTS; i ++ ) {\n\t\tpointLight \x3d pointLights[ i ];\n\t\tgetPointDirectLightIrradiance( pointLight, geometry, directLight );\n\t\t#if defined( USE_SHADOWMAP ) \x26\x26 ( UNROLLED_LOOP_INDEX \x3c NUM_POINT_LIGHT_SHADOWS )\n\t\tdirectLight.color *\x3d all( bvec2( pointLight.shadow, directLight.visible ) ) ? getPointShadow( pointShadowMap[ i ], pointLight.shadowMapSize, pointLight.shadowBias, pointLight.shadowRadius, vPointShadowCoord[ i ], pointLight.shadowCameraNear, pointLight.shadowCameraFar ) : 1.0;\n\t\t#endif\n\t\tRE_Direct( directLight, geometry, material, reflectedLight );\n\t}\n#endif\n#if ( NUM_SPOT_LIGHTS \x3e 0 ) \x26\x26 defined( RE_Direct )\n\tSpotLight spotLight;\n\t#pragma unroll_loop\n\tfor ( int i \x3d 0; i \x3c NUM_SPOT_LIGHTS; i ++ ) {\n\t\tspotLight \x3d spotLights[ i ];\n\t\tgetSpotDirectLightIrradiance( spotLight, geometry, directLight );\n\t\t#if defined( USE_SHADOWMAP ) \x26\x26 ( UNROLLED_LOOP_INDEX \x3c NUM_SPOT_LIGHT_SHADOWS )\n\t\tdirectLight.color *\x3d all( bvec2( spotLight.shadow, directLight.visible ) ) ? getShadow( spotShadowMap[ i ], spotLight.shadowMapSize, spotLight.shadowBias, spotLight.shadowRadius, vSpotShadowCoord[ i ] ) : 1.0;\n\t\t#endif\n\t\tRE_Direct( directLight, geometry, material, reflectedLight );\n\t}\n#endif\n#if ( NUM_DIR_LIGHTS \x3e 0 ) \x26\x26 defined( RE_Direct )\n\tDirectionalLight directionalLight;\n\t#pragma unroll_loop\n\tfor ( int i \x3d 0; i \x3c NUM_DIR_LIGHTS; i ++ ) {\n\t\tdirectionalLight \x3d directionalLights[ i ];\n\t\tgetDirectionalDirectLightIrradiance( directionalLight, geometry, directLight );\n\t\t#if defined( USE_SHADOWMAP ) \x26\x26 ( UNROLLED_LOOP_INDEX \x3c NUM_DIR_LIGHT_SHADOWS )\n\t\tdirectLight.color *\x3d all( bvec2( directionalLight.shadow, directLight.visible ) ) ? getShadow( directionalShadowMap[ i ], directionalLight.shadowMapSize, directionalLight.shadowBias, directionalLight.shadowRadius, vDirectionalShadowCoord[ i ] ) : 1.0;\n\t\t#endif\n\t\tRE_Direct( directLight, geometry, material, reflectedLight );\n\t}\n#endif\n#if ( NUM_RECT_AREA_LIGHTS \x3e 0 ) \x26\x26 defined( RE_Direct_RectArea )\n\tRectAreaLight rectAreaLight;\n\t#pragma unroll_loop\n\tfor ( int i \x3d 0; i \x3c NUM_RECT_AREA_LIGHTS; i ++ ) {\n\t\trectAreaLight \x3d rectAreaLights[ i ];\n\t\tRE_Direct_RectArea( rectAreaLight, geometry, material, reflectedLight );\n\t}\n#endif\n#if defined( RE_IndirectDiffuse )\n\tvec3 iblIrradiance \x3d vec3( 0.0 );\n\tvec3 irradiance \x3d getAmbientLightIrradiance( ambientLightColor );\n\tirradiance +\x3d getLightProbeIrradiance( lightProbe, geometry );\n\t#if ( NUM_HEMI_LIGHTS \x3e 0 )\n\t\t#pragma unroll_loop\n\t\tfor ( int i \x3d 0; i \x3c NUM_HEMI_LIGHTS; i ++ ) {\n\t\t\tirradiance +\x3d getHemisphereLightIrradiance( hemisphereLights[ i ], geometry );\n\t\t}\n\t#endif\n#endif\n#if defined( RE_IndirectSpecular )\n\tvec3 radiance \x3d vec3( 0.0 );\n\tvec3 clearcoatRadiance \x3d vec3( 0.0 );\n#endif",
lights_fragment_maps:"#if defined( RE_IndirectDiffuse )\n\t#ifdef USE_LIGHTMAP\n\t\tvec3 lightMapIrradiance \x3d texture2D( lightMap, vUv2 ).xyz * lightMapIntensity;\n\t\t#ifndef PHYSICALLY_CORRECT_LIGHTS\n\t\t\tlightMapIrradiance *\x3d PI;\n\t\t#endif\n\t\tirradiance +\x3d lightMapIrradiance;\n\t#endif\n\t#if defined( USE_ENVMAP ) \x26\x26 defined( STANDARD ) \x26\x26 defined( ENVMAP_TYPE_CUBE_UV )\n\t\tiblIrradiance +\x3d getLightProbeIndirectIrradiance( geometry, maxMipLevel );\n\t#endif\n#endif\n#if defined( USE_ENVMAP ) \x26\x26 defined( RE_IndirectSpecular )\n\tradiance +\x3d getLightProbeIndirectRadiance( geometry.viewDir, geometry.normal, material.specularRoughness, maxMipLevel );\n\t#ifdef CLEARCOAT\n\t\tclearcoatRadiance +\x3d getLightProbeIndirectRadiance( geometry.viewDir, geometry.clearcoatNormal, material.clearcoatRoughness, maxMipLevel );\n\t#endif\n#endif",
lights_fragment_end:"#if defined( RE_IndirectDiffuse )\n\tRE_IndirectDiffuse( irradiance, geometry, material, reflectedLight );\n#endif\n#if defined( RE_IndirectSpecular )\n\tRE_IndirectSpecular( radiance, iblIrradiance, clearcoatRadiance, geometry, material, reflectedLight );\n#endif",logdepthbuf_fragment:"#if defined( USE_LOGDEPTHBUF ) \x26\x26 defined( USE_LOGDEPTHBUF_EXT )\n\tgl_FragDepthEXT \x3d log2( vFragDepth ) * logDepthBufFC * 0.5;\n#endif",logdepthbuf_pars_fragment:"#if defined( USE_LOGDEPTHBUF ) \x26\x26 defined( USE_LOGDEPTHBUF_EXT )\n\tuniform float logDepthBufFC;\n\tvarying float vFragDepth;\n#endif",
logdepthbuf_pars_vertex:"#ifdef USE_LOGDEPTHBUF\n\t#ifdef USE_LOGDEPTHBUF_EXT\n\t\tvarying float vFragDepth;\n\t#else\n\t\tuniform float logDepthBufFC;\n\t#endif\n#endif",logdepthbuf_vertex:"#ifdef USE_LOGDEPTHBUF\n\t#ifdef USE_LOGDEPTHBUF_EXT\n\t\tvFragDepth \x3d 1.0 + gl_Position.w;\n\t#else\n\t\tgl_Position.z \x3d log2( max( EPSILON, gl_Position.w + 1.0 ) ) * logDepthBufFC - 1.0;\n\t\tgl_Position.z *\x3d gl_Position.w;\n\t#endif\n#endif",map_fragment:"#ifdef USE_MAP\n\tvec4 texelColor \x3d texture2D( map, vUv );\n\ttexelColor \x3d mapTexelToLinear( texelColor );\n\tdiffuseColor *\x3d texelColor;\n#endif",
map_pars_fragment:"#ifdef USE_MAP\n\tuniform sampler2D map;\n#endif",map_particle_fragment:"#ifdef USE_MAP\n\tvec2 uv \x3d ( uvTransform * vec3( gl_PointCoord.x, 1.0 - gl_PointCoord.y, 1 ) ).xy;\n\tvec4 mapTexel \x3d texture2D( map, uv );\n\tdiffuseColor *\x3d mapTexelToLinear( mapTexel );\n#endif",map_particle_pars_fragment:"#ifdef USE_MAP\n\tuniform mat3 uvTransform;\n\tuniform sampler2D map;\n#endif",metalnessmap_fragment:"float metalnessFactor \x3d metalness;\n#ifdef USE_METALNESSMAP\n\tvec4 texelMetalness \x3d texture2D( metalnessMap, vUv );\n\tmetalnessFactor *\x3d texelMetalness.b;\n#endif",
metalnessmap_pars_fragment:"#ifdef USE_METALNESSMAP\n\tuniform sampler2D metalnessMap;\n#endif",morphnormal_vertex:"#ifdef USE_MORPHNORMALS\n\tobjectNormal +\x3d ( morphNormal0 - normal ) * morphTargetInfluences[ 0 ];\n\tobjectNormal +\x3d ( morphNormal1 - normal ) * morphTargetInfluences[ 1 ];\n\tobjectNormal +\x3d ( morphNormal2 - normal ) * morphTargetInfluences[ 2 ];\n\tobjectNormal +\x3d ( morphNormal3 - normal ) * morphTargetInfluences[ 3 ];\n#endif",morphtarget_pars_vertex:"#ifdef USE_MORPHTARGETS\n\t#ifndef USE_MORPHNORMALS\n\tuniform float morphTargetInfluences[ 8 ];\n\t#else\n\tuniform float morphTargetInfluences[ 4 ];\n\t#endif\n#endif",
morphtarget_vertex:"#ifdef USE_MORPHTARGETS\n\ttransformed +\x3d ( morphTarget0 - position ) * morphTargetInfluences[ 0 ];\n\ttransformed +\x3d ( morphTarget1 - position ) * morphTargetInfluences[ 1 ];\n\ttransformed +\x3d ( morphTarget2 - position ) * morphTargetInfluences[ 2 ];\n\ttransformed +\x3d ( morphTarget3 - position ) * morphTargetInfluences[ 3 ];\n\t#ifndef USE_MORPHNORMALS\n\ttransformed +\x3d ( morphTarget4 - position ) * morphTargetInfluences[ 4 ];\n\ttransformed +\x3d ( morphTarget5 - position ) * morphTargetInfluences[ 5 ];\n\ttransformed +\x3d ( morphTarget6 - position ) * morphTargetInfluences[ 6 ];\n\ttransformed +\x3d ( morphTarget7 - position ) * morphTargetInfluences[ 7 ];\n\t#endif\n#endif",
normal_fragment_begin:"#ifdef FLAT_SHADED\n\tvec3 fdx \x3d vec3( dFdx( vViewPosition.x ), dFdx( vViewPosition.y ), dFdx( vViewPosition.z ) );\n\tvec3 fdy \x3d vec3( dFdy( vViewPosition.x ), dFdy( vViewPosition.y ), dFdy( vViewPosition.z ) );\n\tvec3 normal \x3d normalize( cross( fdx, fdy ) );\n#else\n\tvec3 normal \x3d normalize( vNormal );\n\t#ifdef DOUBLE_SIDED\n\t\tnormal \x3d normal * ( float( gl_FrontFacing ) * 2.0 - 1.0 );\n\t#endif\n\t#ifdef USE_TANGENT\n\t\tvec3 tangent \x3d normalize( vTangent );\n\t\tvec3 bitangent \x3d normalize( vBitangent );\n\t\t#ifdef DOUBLE_SIDED\n\t\t\ttangent \x3d tangent * ( float( gl_FrontFacing ) * 2.0 - 1.0 );\n\t\t\tbitangent \x3d bitangent * ( float( gl_FrontFacing ) * 2.0 - 1.0 );\n\t\t#endif\n\t#endif\n#endif\nvec3 geometryNormal \x3d normal;",
normal_fragment_maps:"#ifdef OBJECTSPACE_NORMALMAP\n\tnormal \x3d texture2D( normalMap, vUv ).xyz * 2.0 - 1.0;\n\t#ifdef FLIP_SIDED\n\t\tnormal \x3d - normal;\n\t#endif\n\t#ifdef DOUBLE_SIDED\n\t\tnormal \x3d normal * ( float( gl_FrontFacing ) * 2.0 - 1.0 );\n\t#endif\n\tnormal \x3d normalize( normalMatrix * normal );\n#elif defined( TANGENTSPACE_NORMALMAP )\n\t#ifdef USE_TANGENT\n\t\tmat3 vTBN \x3d mat3( tangent, bitangent, normal );\n\t\tvec3 mapN \x3d texture2D( normalMap, vUv ).xyz * 2.0 - 1.0;\n\t\tmapN.xy \x3d normalScale * mapN.xy;\n\t\tnormal \x3d normalize( vTBN * mapN );\n\t#else\n\t\tnormal \x3d perturbNormal2Arb( -vViewPosition, normal, normalScale, normalMap );\n\t#endif\n#elif defined( USE_BUMPMAP )\n\tnormal \x3d perturbNormalArb( -vViewPosition, normal, dHdxy_fwd() );\n#endif",
normalmap_pars_fragment:"#ifdef USE_NORMALMAP\n\tuniform sampler2D normalMap;\n\tuniform vec2 normalScale;\n#endif\n#ifdef OBJECTSPACE_NORMALMAP\n\tuniform mat3 normalMatrix;\n#endif\n#if ! defined ( USE_TANGENT ) \x26\x26 ( defined ( TANGENTSPACE_NORMALMAP ) || defined ( USE_CLEARCOAT_NORMALMAP ) )\n\tvec3 perturbNormal2Arb( vec3 eye_pos, vec3 surf_norm, vec2 normalScale, in sampler2D normalMap ) {\n\t\tvec3 q0 \x3d vec3( dFdx( eye_pos.x ), dFdx( eye_pos.y ), dFdx( eye_pos.z ) );\n\t\tvec3 q1 \x3d vec3( dFdy( eye_pos.x ), dFdy( eye_pos.y ), dFdy( eye_pos.z ) );\n\t\tvec2 st0 \x3d dFdx( vUv.st );\n\t\tvec2 st1 \x3d dFdy( vUv.st );\n\t\tfloat scale \x3d sign( st1.t * st0.s - st0.t * st1.s );\n\t\tvec3 S \x3d normalize( ( q0 * st1.t - q1 * st0.t ) * scale );\n\t\tvec3 T \x3d normalize( ( - q0 * st1.s + q1 * st0.s ) * scale );\n\t\tvec3 N \x3d normalize( surf_norm );\n\t\tvec3 mapN \x3d texture2D( normalMap, vUv ).xyz * 2.0 - 1.0;\n\t\tmapN.xy *\x3d normalScale;\n\t\t#ifdef DOUBLE_SIDED\n\t\t\tvec3 NfromST \x3d cross( S, T );\n\t\t\tif( dot( NfromST, N ) \x3e 0.0 ) {\n\t\t\t\tS *\x3d -1.0;\n\t\t\t\tT *\x3d -1.0;\n\t\t\t}\n\t\t#else\n\t\t\tmapN.xy *\x3d ( float( gl_FrontFacing ) * 2.0 - 1.0 );\n\t\t#endif\n\t\tmat3 tsn \x3d mat3( S, T, N );\n\t\treturn normalize( tsn * mapN );\n\t}\n#endif",
clearcoat_normal_fragment_begin:"#ifdef CLEARCOAT\n\tvec3 clearcoatNormal \x3d geometryNormal;\n#endif",clearcoat_normal_fragment_maps:"#ifdef USE_CLEARCOAT_NORMALMAP\n\t#ifdef USE_TANGENT\n\t\tmat3 vTBN \x3d mat3( tangent, bitangent, clearcoatNormal );\n\t\tvec3 mapN \x3d texture2D( normalMap, vUv ).xyz * 2.0 - 1.0;\n\t\tmapN.xy \x3d clearcoatNormalScale * mapN.xy;\n\t\tclearcoatNormal \x3d normalize( vTBN * mapN );\n\t#else\n\t\tclearcoatNormal \x3d perturbNormal2Arb( - vViewPosition, clearcoatNormal, clearcoatNormalScale, clearcoatNormalMap );\n\t#endif\n#endif",
clearcoat_normalmap_pars_fragment:"#ifdef USE_CLEARCOAT_NORMALMAP\n\tuniform sampler2D clearcoatNormalMap;\n\tuniform vec2 clearcoatNormalScale;\n#endif",packing:"vec3 packNormalToRGB( const in vec3 normal ) {\n\treturn normalize( normal ) * 0.5 + 0.5;\n}\nvec3 unpackRGBToNormal( const in vec3 rgb ) {\n\treturn 2.0 * rgb.xyz - 1.0;\n}\nconst float PackUpscale \x3d 256. / 255.;const float UnpackDownscale \x3d 255. / 256.;\nconst vec3 PackFactors \x3d vec3( 256. * 256. * 256., 256. * 256.,  256. );\nconst vec4 UnpackFactors \x3d UnpackDownscale / vec4( PackFactors, 1. );\nconst float ShiftRight8 \x3d 1. / 256.;\nvec4 packDepthToRGBA( const in float v ) {\n\tvec4 r \x3d vec4( fract( v * PackFactors ), v );\n\tr.yzw -\x3d r.xyz * ShiftRight8;\treturn r * PackUpscale;\n}\nfloat unpackRGBAToDepth( const in vec4 v ) {\n\treturn dot( v, UnpackFactors );\n}\nvec4 encodeHalfRGBA ( vec2 v ) {\n\tvec4 encoded \x3d vec4( 0.0 );\n\tconst vec2 offset \x3d vec2( 1.0 / 255.0, 0.0 );\n\tencoded.xy \x3d vec2( v.x, fract( v.x * 255.0 ) );\n\tencoded.xy \x3d encoded.xy - ( encoded.yy * offset );\n\tencoded.zw \x3d vec2( v.y, fract( v.y * 255.0 ) );\n\tencoded.zw \x3d encoded.zw - ( encoded.ww * offset );\n\treturn encoded;\n}\nvec2 decodeHalfRGBA( vec4 v ) {\n\treturn vec2( v.x + ( v.y / 255.0 ), v.z + ( v.w / 255.0 ) );\n}\nfloat viewZToOrthographicDepth( const in float viewZ, const in float near, const in float far ) {\n\treturn ( viewZ + near ) / ( near - far );\n}\nfloat orthographicDepthToViewZ( const in float linearClipZ, const in float near, const in float far ) {\n\treturn linearClipZ * ( near - far ) - near;\n}\nfloat viewZToPerspectiveDepth( const in float viewZ, const in float near, const in float far ) {\n\treturn (( near + viewZ ) * far ) / (( far - near ) * viewZ );\n}\nfloat perspectiveDepthToViewZ( const in float invClipZ, const in float near, const in float far ) {\n\treturn ( near * far ) / ( ( far - near ) * invClipZ - far );\n}",
premultiplied_alpha_fragment:"#ifdef PREMULTIPLIED_ALPHA\n\tgl_FragColor.rgb *\x3d gl_FragColor.a;\n#endif",project_vertex:"vec4 mvPosition \x3d modelViewMatrix * vec4( transformed, 1.0 );\ngl_Position \x3d projectionMatrix * mvPosition;",dithering_fragment:"#ifdef DITHERING\n\tgl_FragColor.rgb \x3d dithering( gl_FragColor.rgb );\n#endif",dithering_pars_fragment:"#ifdef DITHERING\n\tvec3 dithering( vec3 color ) {\n\t\tfloat grid_position \x3d rand( gl_FragCoord.xy );\n\t\tvec3 dither_shift_RGB \x3d vec3( 0.25 / 255.0, -0.25 / 255.0, 0.25 / 255.0 );\n\t\tdither_shift_RGB \x3d mix( 2.0 * dither_shift_RGB, -2.0 * dither_shift_RGB, grid_position );\n\t\treturn color + dither_shift_RGB;\n\t}\n#endif",
roughnessmap_fragment:"float roughnessFactor \x3d roughness;\n#ifdef USE_ROUGHNESSMAP\n\tvec4 texelRoughness \x3d texture2D( roughnessMap, vUv );\n\troughnessFactor *\x3d texelRoughness.g;\n#endif",roughnessmap_pars_fragment:"#ifdef USE_ROUGHNESSMAP\n\tuniform sampler2D roughnessMap;\n#endif",shadowmap_pars_fragment:"#ifdef USE_SHADOWMAP\n\t#if NUM_DIR_LIGHT_SHADOWS \x3e 0\n\t\tuniform sampler2D directionalShadowMap[ NUM_DIR_LIGHT_SHADOWS ];\n\t\tvarying vec4 vDirectionalShadowCoord[ NUM_DIR_LIGHT_SHADOWS ];\n\t#endif\n\t#if NUM_SPOT_LIGHT_SHADOWS \x3e 0\n\t\tuniform sampler2D spotShadowMap[ NUM_SPOT_LIGHT_SHADOWS ];\n\t\tvarying vec4 vSpotShadowCoord[ NUM_SPOT_LIGHT_SHADOWS ];\n\t#endif\n\t#if NUM_POINT_LIGHT_SHADOWS \x3e 0\n\t\tuniform sampler2D pointShadowMap[ NUM_POINT_LIGHT_SHADOWS ];\n\t\tvarying vec4 vPointShadowCoord[ NUM_POINT_LIGHT_SHADOWS ];\n\t#endif\n\tfloat texture2DCompare( sampler2D depths, vec2 uv, float compare ) {\n\t\treturn step( compare, unpackRGBAToDepth( texture2D( depths, uv ) ) );\n\t}\n\tvec2 texture2DDistribution( sampler2D shadow, vec2 uv ) {\n\t\treturn decodeHalfRGBA( texture2D( shadow, uv ) );\n\t}\n\tfloat VSMShadow (sampler2D shadow, vec2 uv, float compare ){\n\t\tfloat occlusion \x3d 1.0;\n\t\tvec2 distribution \x3d texture2DDistribution( shadow, uv );\n\t\tfloat hard_shadow \x3d step( compare , distribution.x );\n\t\tif (hard_shadow !\x3d 1.0 ) {\n\t\t\tfloat distance \x3d compare - distribution.x ;\n\t\t\tfloat variance \x3d max( 0.00000, distribution.y * distribution.y );\n\t\t\tfloat softness_probability \x3d variance / (variance + distance * distance );\t\t\tsoftness_probability \x3d clamp( ( softness_probability - 0.3 ) / ( 0.95 - 0.3 ), 0.0, 1.0 );\t\t\tocclusion \x3d clamp( max( hard_shadow, softness_probability ), 0.0, 1.0 );\n\t\t}\n\t\treturn occlusion;\n\t}\n\tfloat texture2DShadowLerp( sampler2D depths, vec2 size, vec2 uv, float compare ) {\n\t\tconst vec2 offset \x3d vec2( 0.0, 1.0 );\n\t\tvec2 texelSize \x3d vec2( 1.0 ) / size;\n\t\tvec2 centroidUV \x3d ( floor( uv * size - 0.5 ) + 0.5 ) * texelSize;\n\t\tfloat lb \x3d texture2DCompare( depths, centroidUV + texelSize * offset.xx, compare );\n\t\tfloat lt \x3d texture2DCompare( depths, centroidUV + texelSize * offset.xy, compare );\n\t\tfloat rb \x3d texture2DCompare( depths, centroidUV + texelSize * offset.yx, compare );\n\t\tfloat rt \x3d texture2DCompare( depths, centroidUV + texelSize * offset.yy, compare );\n\t\tvec2 f \x3d fract( uv * size + 0.5 );\n\t\tfloat a \x3d mix( lb, lt, f.y );\n\t\tfloat b \x3d mix( rb, rt, f.y );\n\t\tfloat c \x3d mix( a, b, f.x );\n\t\treturn c;\n\t}\n\tfloat getShadow( sampler2D shadowMap, vec2 shadowMapSize, float shadowBias, float shadowRadius, vec4 shadowCoord ) {\n\t\tfloat shadow \x3d 1.0;\n\t\tshadowCoord.xyz /\x3d shadowCoord.w;\n\t\tshadowCoord.z +\x3d shadowBias;\n\t\tbvec4 inFrustumVec \x3d bvec4 ( shadowCoord.x \x3e\x3d 0.0, shadowCoord.x \x3c\x3d 1.0, shadowCoord.y \x3e\x3d 0.0, shadowCoord.y \x3c\x3d 1.0 );\n\t\tbool inFrustum \x3d all( inFrustumVec );\n\t\tbvec2 frustumTestVec \x3d bvec2( inFrustum, shadowCoord.z \x3c\x3d 1.0 );\n\t\tbool frustumTest \x3d all( frustumTestVec );\n\t\tif ( frustumTest ) {\n\t\t#if defined( SHADOWMAP_TYPE_PCF )\n\t\t\tvec2 texelSize \x3d vec2( 1.0 ) / shadowMapSize;\n\t\t\tfloat dx0 \x3d - texelSize.x * shadowRadius;\n\t\t\tfloat dy0 \x3d - texelSize.y * shadowRadius;\n\t\t\tfloat dx1 \x3d + texelSize.x * shadowRadius;\n\t\t\tfloat dy1 \x3d + texelSize.y * shadowRadius;\n\t\t\tfloat dx2 \x3d dx0 / 2.0;\n\t\t\tfloat dy2 \x3d dy0 / 2.0;\n\t\t\tfloat dx3 \x3d dx1 / 2.0;\n\t\t\tfloat dy3 \x3d dy1 / 2.0;\n\t\t\tshadow \x3d (\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, dy0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, dy0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, dy2 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy2 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, dy2 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, 0.0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, 0.0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy, shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, 0.0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, 0.0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, dy3 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy3 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, dy3 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, dy1 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy1 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, dy1 ), shadowCoord.z )\n\t\t\t) * ( 1.0 / 17.0 );\n\t\t#elif defined( SHADOWMAP_TYPE_PCF_SOFT )\n\t\t\tvec2 texelSize \x3d vec2( 1.0 ) / shadowMapSize;\n\t\t\tfloat dx0 \x3d - texelSize.x * shadowRadius;\n\t\t\tfloat dy0 \x3d - texelSize.y * shadowRadius;\n\t\t\tfloat dx1 \x3d + texelSize.x * shadowRadius;\n\t\t\tfloat dy1 \x3d + texelSize.y * shadowRadius;\n\t\t\tshadow \x3d (\n\t\t\t\ttexture2DShadowLerp( shadowMap, shadowMapSize, shadowCoord.xy + vec2( dx0, dy0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DShadowLerp( shadowMap, shadowMapSize, shadowCoord.xy + vec2( 0.0, dy0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DShadowLerp( shadowMap, shadowMapSize, shadowCoord.xy + vec2( dx1, dy0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DShadowLerp( shadowMap, shadowMapSize, shadowCoord.xy + vec2( dx0, 0.0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DShadowLerp( shadowMap, shadowMapSize, shadowCoord.xy, shadowCoord.z ) +\n\t\t\t\ttexture2DShadowLerp( shadowMap, shadowMapSize, shadowCoord.xy + vec2( dx1, 0.0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DShadowLerp( shadowMap, shadowMapSize, shadowCoord.xy + vec2( dx0, dy1 ), shadowCoord.z ) +\n\t\t\t\ttexture2DShadowLerp( shadowMap, shadowMapSize, shadowCoord.xy + vec2( 0.0, dy1 ), shadowCoord.z ) +\n\t\t\t\ttexture2DShadowLerp( shadowMap, shadowMapSize, shadowCoord.xy + vec2( dx1, dy1 ), shadowCoord.z )\n\t\t\t) * ( 1.0 / 9.0 );\n\t\t#elif defined( SHADOWMAP_TYPE_VSM )\n\t\t\tshadow \x3d VSMShadow( shadowMap, shadowCoord.xy, shadowCoord.z );\n\t\t#else\n\t\t\tshadow \x3d texture2DCompare( shadowMap, shadowCoord.xy, shadowCoord.z );\n\t\t#endif\n\t\t}\n\t\treturn shadow;\n\t}\n\tvec2 cubeToUV( vec3 v, float texelSizeY ) {\n\t\tvec3 absV \x3d abs( v );\n\t\tfloat scaleToCube \x3d 1.0 / max( absV.x, max( absV.y, absV.z ) );\n\t\tabsV *\x3d scaleToCube;\n\t\tv *\x3d scaleToCube * ( 1.0 - 2.0 * texelSizeY );\n\t\tvec2 planar \x3d v.xy;\n\t\tfloat almostATexel \x3d 1.5 * texelSizeY;\n\t\tfloat almostOne \x3d 1.0 - almostATexel;\n\t\tif ( absV.z \x3e\x3d almostOne ) {\n\t\t\tif ( v.z \x3e 0.0 )\n\t\t\t\tplanar.x \x3d 4.0 - v.x;\n\t\t} else if ( absV.x \x3e\x3d almostOne ) {\n\t\t\tfloat signX \x3d sign( v.x );\n\t\t\tplanar.x \x3d v.z * signX + 2.0 * signX;\n\t\t} else if ( absV.y \x3e\x3d almostOne ) {\n\t\t\tfloat signY \x3d sign( v.y );\n\t\t\tplanar.x \x3d v.x + 2.0 * signY + 2.0;\n\t\t\tplanar.y \x3d v.z * signY - 2.0;\n\t\t}\n\t\treturn vec2( 0.125, 0.25 ) * planar + vec2( 0.375, 0.75 );\n\t}\n\tfloat getPointShadow( sampler2D shadowMap, vec2 shadowMapSize, float shadowBias, float shadowRadius, vec4 shadowCoord, float shadowCameraNear, float shadowCameraFar ) {\n\t\tvec2 texelSize \x3d vec2( 1.0 ) / ( shadowMapSize * vec2( 4.0, 2.0 ) );\n\t\tvec3 lightToPosition \x3d shadowCoord.xyz;\n\t\tfloat dp \x3d ( length( lightToPosition ) - shadowCameraNear ) / ( shadowCameraFar - shadowCameraNear );\t\tdp +\x3d shadowBias;\n\t\tvec3 bd3D \x3d normalize( lightToPosition );\n\t\t#if defined( SHADOWMAP_TYPE_PCF ) || defined( SHADOWMAP_TYPE_PCF_SOFT ) || defined( SHADOWMAP_TYPE_VSM )\n\t\t\tvec2 offset \x3d vec2( - 1, 1 ) * shadowRadius * texelSize.y;\n\t\t\treturn (\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xyy, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yyy, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xyx, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yyx, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xxy, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yxy, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xxx, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yxx, texelSize.y ), dp )\n\t\t\t) * ( 1.0 / 9.0 );\n\t\t#else\n\t\t\treturn texture2DCompare( shadowMap, cubeToUV( bd3D, texelSize.y ), dp );\n\t\t#endif\n\t}\n#endif",
shadowmap_pars_vertex:"#ifdef USE_SHADOWMAP\n\t#if NUM_DIR_LIGHT_SHADOWS \x3e 0\n\t\tuniform mat4 directionalShadowMatrix[ NUM_DIR_LIGHT_SHADOWS ];\n\t\tvarying vec4 vDirectionalShadowCoord[ NUM_DIR_LIGHT_SHADOWS ];\n\t#endif\n\t#if NUM_SPOT_LIGHT_SHADOWS \x3e 0\n\t\tuniform mat4 spotShadowMatrix[ NUM_SPOT_LIGHT_SHADOWS ];\n\t\tvarying vec4 vSpotShadowCoord[ NUM_SPOT_LIGHT_SHADOWS ];\n\t#endif\n\t#if NUM_POINT_LIGHT_SHADOWS \x3e 0\n\t\tuniform mat4 pointShadowMatrix[ NUM_POINT_LIGHT_SHADOWS ];\n\t\tvarying vec4 vPointShadowCoord[ NUM_POINT_LIGHT_SHADOWS ];\n\t#endif\n#endif",
shadowmap_vertex:"#ifdef USE_SHADOWMAP\n\t#if NUM_DIR_LIGHT_SHADOWS \x3e 0\n\t#pragma unroll_loop\n\tfor ( int i \x3d 0; i \x3c NUM_DIR_LIGHT_SHADOWS; i ++ ) {\n\t\tvDirectionalShadowCoord[ i ] \x3d directionalShadowMatrix[ i ] * worldPosition;\n\t}\n\t#endif\n\t#if NUM_SPOT_LIGHT_SHADOWS \x3e 0\n\t#pragma unroll_loop\n\tfor ( int i \x3d 0; i \x3c NUM_SPOT_LIGHT_SHADOWS; i ++ ) {\n\t\tvSpotShadowCoord[ i ] \x3d spotShadowMatrix[ i ] * worldPosition;\n\t}\n\t#endif\n\t#if NUM_POINT_LIGHT_SHADOWS \x3e 0\n\t#pragma unroll_loop\n\tfor ( int i \x3d 0; i \x3c NUM_POINT_LIGHT_SHADOWS; i ++ ) {\n\t\tvPointShadowCoord[ i ] \x3d pointShadowMatrix[ i ] * worldPosition;\n\t}\n\t#endif\n#endif",
shadowmask_pars_fragment:"float getShadowMask() {\n\tfloat shadow \x3d 1.0;\n\t#ifdef USE_SHADOWMAP\n\t#if NUM_DIR_LIGHT_SHADOWS \x3e 0\n\tDirectionalLight directionalLight;\n\t#pragma unroll_loop\n\tfor ( int i \x3d 0; i \x3c NUM_DIR_LIGHT_SHADOWS; i ++ ) {\n\t\tdirectionalLight \x3d directionalLights[ i ];\n\t\tshadow *\x3d bool( directionalLight.shadow ) ? getShadow( directionalShadowMap[ i ], directionalLight.shadowMapSize, directionalLight.shadowBias, directionalLight.shadowRadius, vDirectionalShadowCoord[ i ] ) : 1.0;\n\t}\n\t#endif\n\t#if NUM_SPOT_LIGHT_SHADOWS \x3e 0\n\tSpotLight spotLight;\n\t#pragma unroll_loop\n\tfor ( int i \x3d 0; i \x3c NUM_SPOT_LIGHT_SHADOWS; i ++ ) {\n\t\tspotLight \x3d spotLights[ i ];\n\t\tshadow *\x3d bool( spotLight.shadow ) ? getShadow( spotShadowMap[ i ], spotLight.shadowMapSize, spotLight.shadowBias, spotLight.shadowRadius, vSpotShadowCoord[ i ] ) : 1.0;\n\t}\n\t#endif\n\t#if NUM_POINT_LIGHT_SHADOWS \x3e 0\n\tPointLight pointLight;\n\t#pragma unroll_loop\n\tfor ( int i \x3d 0; i \x3c NUM_POINT_LIGHT_SHADOWS; i ++ ) {\n\t\tpointLight \x3d pointLights[ i ];\n\t\tshadow *\x3d bool( pointLight.shadow ) ? getPointShadow( pointShadowMap[ i ], pointLight.shadowMapSize, pointLight.shadowBias, pointLight.shadowRadius, vPointShadowCoord[ i ], pointLight.shadowCameraNear, pointLight.shadowCameraFar ) : 1.0;\n\t}\n\t#endif\n\t#endif\n\treturn shadow;\n}",
skinbase_vertex:"#ifdef USE_SKINNING\n\tmat4 boneMatX \x3d getBoneMatrix( skinIndex.x );\n\tmat4 boneMatY \x3d getBoneMatrix( skinIndex.y );\n\tmat4 boneMatZ \x3d getBoneMatrix( skinIndex.z );\n\tmat4 boneMatW \x3d getBoneMatrix( skinIndex.w );\n#endif",skinning_pars_vertex:"#ifdef USE_SKINNING\n\tuniform mat4 bindMatrix;\n\tuniform mat4 bindMatrixInverse;\n\t#ifdef BONE_TEXTURE\n\t\tuniform highp sampler2D boneTexture;\n\t\tuniform int boneTextureSize;\n\t\tmat4 getBoneMatrix( const in float i ) {\n\t\t\tfloat j \x3d i * 4.0;\n\t\t\tfloat x \x3d mod( j, float( boneTextureSize ) );\n\t\t\tfloat y \x3d floor( j / float( boneTextureSize ) );\n\t\t\tfloat dx \x3d 1.0 / float( boneTextureSize );\n\t\t\tfloat dy \x3d 1.0 / float( boneTextureSize );\n\t\t\ty \x3d dy * ( y + 0.5 );\n\t\t\tvec4 v1 \x3d texture2D( boneTexture, vec2( dx * ( x + 0.5 ), y ) );\n\t\t\tvec4 v2 \x3d texture2D( boneTexture, vec2( dx * ( x + 1.5 ), y ) );\n\t\t\tvec4 v3 \x3d texture2D( boneTexture, vec2( dx * ( x + 2.5 ), y ) );\n\t\t\tvec4 v4 \x3d texture2D( boneTexture, vec2( dx * ( x + 3.5 ), y ) );\n\t\t\tmat4 bone \x3d mat4( v1, v2, v3, v4 );\n\t\t\treturn bone;\n\t\t}\n\t#else\n\t\tuniform mat4 boneMatrices[ MAX_BONES ];\n\t\tmat4 getBoneMatrix( const in float i ) {\n\t\t\tmat4 bone \x3d boneMatrices[ int(i) ];\n\t\t\treturn bone;\n\t\t}\n\t#endif\n#endif",
skinning_vertex:"#ifdef USE_SKINNING\n\tvec4 skinVertex \x3d bindMatrix * vec4( transformed, 1.0 );\n\tvec4 skinned \x3d vec4( 0.0 );\n\tskinned +\x3d boneMatX * skinVertex * skinWeight.x;\n\tskinned +\x3d boneMatY * skinVertex * skinWeight.y;\n\tskinned +\x3d boneMatZ * skinVertex * skinWeight.z;\n\tskinned +\x3d boneMatW * skinVertex * skinWeight.w;\n\ttransformed \x3d ( bindMatrixInverse * skinned ).xyz;\n#endif",skinnormal_vertex:"#ifdef USE_SKINNING\n\tmat4 skinMatrix \x3d mat4( 0.0 );\n\tskinMatrix +\x3d skinWeight.x * boneMatX;\n\tskinMatrix +\x3d skinWeight.y * boneMatY;\n\tskinMatrix +\x3d skinWeight.z * boneMatZ;\n\tskinMatrix +\x3d skinWeight.w * boneMatW;\n\tskinMatrix  \x3d bindMatrixInverse * skinMatrix * bindMatrix;\n\tobjectNormal \x3d vec4( skinMatrix * vec4( objectNormal, 0.0 ) ).xyz;\n\t#ifdef USE_TANGENT\n\t\tobjectTangent \x3d vec4( skinMatrix * vec4( objectTangent, 0.0 ) ).xyz;\n\t#endif\n#endif",
specularmap_fragment:"float specularStrength;\n#ifdef USE_SPECULARMAP\n\tvec4 texelSpecular \x3d texture2D( specularMap, vUv );\n\tspecularStrength \x3d texelSpecular.r;\n#else\n\tspecularStrength \x3d 1.0;\n#endif",specularmap_pars_fragment:"#ifdef USE_SPECULARMAP\n\tuniform sampler2D specularMap;\n#endif",tonemapping_fragment:"#if defined( TONE_MAPPING )\n\tgl_FragColor.rgb \x3d toneMapping( gl_FragColor.rgb );\n#endif",tonemapping_pars_fragment:"#ifndef saturate\n\t#define saturate(a) clamp( a, 0.0, 1.0 )\n#endif\nuniform float toneMappingExposure;\nuniform float toneMappingWhitePoint;\nvec3 LinearToneMapping( vec3 color ) {\n\treturn toneMappingExposure * color;\n}\nvec3 ReinhardToneMapping( vec3 color ) {\n\tcolor *\x3d toneMappingExposure;\n\treturn saturate( color / ( vec3( 1.0 ) + color ) );\n}\n#define Uncharted2Helper( x ) max( ( ( x * ( 0.15 * x + 0.10 * 0.50 ) + 0.20 * 0.02 ) / ( x * ( 0.15 * x + 0.50 ) + 0.20 * 0.30 ) ) - 0.02 / 0.30, vec3( 0.0 ) )\nvec3 Uncharted2ToneMapping( vec3 color ) {\n\tcolor *\x3d toneMappingExposure;\n\treturn saturate( Uncharted2Helper( color ) / Uncharted2Helper( vec3( toneMappingWhitePoint ) ) );\n}\nvec3 OptimizedCineonToneMapping( vec3 color ) {\n\tcolor *\x3d toneMappingExposure;\n\tcolor \x3d max( vec3( 0.0 ), color - 0.004 );\n\treturn pow( ( color * ( 6.2 * color + 0.5 ) ) / ( color * ( 6.2 * color + 1.7 ) + 0.06 ), vec3( 2.2 ) );\n}\nvec3 ACESFilmicToneMapping( vec3 color ) {\n\tcolor *\x3d toneMappingExposure;\n\treturn saturate( ( color * ( 2.51 * color + 0.03 ) ) / ( color * ( 2.43 * color + 0.59 ) + 0.14 ) );\n}",
uv_pars_fragment:"#ifdef USE_UV\n\tvarying vec2 vUv;\n#endif",uv_pars_vertex:"#ifdef USE_UV\n\tvarying vec2 vUv;\n\tuniform mat3 uvTransform;\n#endif",uv_vertex:"#ifdef USE_UV\n\tvUv \x3d ( uvTransform * vec3( uv, 1 ) ).xy;\n#endif",uv2_pars_fragment:"#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\n\tvarying vec2 vUv2;\n#endif",uv2_pars_vertex:"#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\n\tattribute vec2 uv2;\n\tvarying vec2 vUv2;\n#endif",uv2_vertex:"#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\n\tvUv2 \x3d uv2;\n#endif",
worldpos_vertex:"#if defined( USE_ENVMAP ) || defined( DISTANCE ) || defined ( USE_SHADOWMAP )\n\tvec4 worldPosition \x3d modelMatrix * vec4( transformed, 1.0 );\n#endif",background_frag:"uniform sampler2D t2D;\nvarying vec2 vUv;\nvoid main() {\n\tvec4 texColor \x3d texture2D( t2D, vUv );\n\tgl_FragColor \x3d mapTexelToLinear( texColor );\n\t#include \x3ctonemapping_fragment\x3e\n\t#include \x3cencodings_fragment\x3e\n}",background_vert:"varying vec2 vUv;\nuniform mat3 uvTransform;\nvoid main() {\n\tvUv \x3d ( uvTransform * vec3( uv, 1 ) ).xy;\n\tgl_Position \x3d vec4( position.xy, 1.0, 1.0 );\n}",
cube_frag:"uniform samplerCube tCube;\nuniform float tFlip;\nuniform float opacity;\nvarying vec3 vWorldDirection;\nvoid main() {\n\tvec4 texColor \x3d textureCube( tCube, vec3( tFlip * vWorldDirection.x, vWorldDirection.yz ) );\n\tgl_FragColor \x3d mapTexelToLinear( texColor );\n\tgl_FragColor.a *\x3d opacity;\n\t#include \x3ctonemapping_fragment\x3e\n\t#include \x3cencodings_fragment\x3e\n}",cube_vert:"varying vec3 vWorldDirection;\n#include \x3ccommon\x3e\nvoid main() {\n\tvWorldDirection \x3d transformDirection( position, modelMatrix );\n\t#include \x3cbegin_vertex\x3e\n\t#include \x3cproject_vertex\x3e\n\tgl_Position.z \x3d gl_Position.w;\n}",
depth_frag:"#if DEPTH_PACKING \x3d\x3d 3200\n\tuniform float opacity;\n#endif\n#include \x3ccommon\x3e\n#include \x3cpacking\x3e\n#include \x3cuv_pars_fragment\x3e\n#include \x3cmap_pars_fragment\x3e\n#include \x3calphamap_pars_fragment\x3e\n#include \x3clogdepthbuf_pars_fragment\x3e\n#include \x3cclipping_planes_pars_fragment\x3e\nvoid main() {\n\t#include \x3cclipping_planes_fragment\x3e\n\tvec4 diffuseColor \x3d vec4( 1.0 );\n\t#if DEPTH_PACKING \x3d\x3d 3200\n\t\tdiffuseColor.a \x3d opacity;\n\t#endif\n\t#include \x3cmap_fragment\x3e\n\t#include \x3calphamap_fragment\x3e\n\t#include \x3calphatest_fragment\x3e\n\t#include \x3clogdepthbuf_fragment\x3e\n\t#if DEPTH_PACKING \x3d\x3d 3200\n\t\tgl_FragColor \x3d vec4( vec3( 1.0 - gl_FragCoord.z ), opacity );\n\t#elif DEPTH_PACKING \x3d\x3d 3201\n\t\tgl_FragColor \x3d packDepthToRGBA( gl_FragCoord.z );\n\t#endif\n}",
depth_vert:"#include \x3ccommon\x3e\n#include \x3cuv_pars_vertex\x3e\n#include \x3cdisplacementmap_pars_vertex\x3e\n#include \x3cmorphtarget_pars_vertex\x3e\n#include \x3cskinning_pars_vertex\x3e\n#include \x3clogdepthbuf_pars_vertex\x3e\n#include \x3cclipping_planes_pars_vertex\x3e\nvoid main() {\n\t#include \x3cuv_vertex\x3e\n\t#include \x3cskinbase_vertex\x3e\n\t#ifdef USE_DISPLACEMENTMAP\n\t\t#include \x3cbeginnormal_vertex\x3e\n\t\t#include \x3cmorphnormal_vertex\x3e\n\t\t#include \x3cskinnormal_vertex\x3e\n\t#endif\n\t#include \x3cbegin_vertex\x3e\n\t#include \x3cmorphtarget_vertex\x3e\n\t#include \x3cskinning_vertex\x3e\n\t#include \x3cdisplacementmap_vertex\x3e\n\t#include \x3cproject_vertex\x3e\n\t#include \x3clogdepthbuf_vertex\x3e\n\t#include \x3cclipping_planes_vertex\x3e\n}",
distanceRGBA_frag:"#define DISTANCE\nuniform vec3 referencePosition;\nuniform float nearDistance;\nuniform float farDistance;\nvarying vec3 vWorldPosition;\n#include \x3ccommon\x3e\n#include \x3cpacking\x3e\n#include \x3cuv_pars_fragment\x3e\n#include \x3cmap_pars_fragment\x3e\n#include \x3calphamap_pars_fragment\x3e\n#include \x3cclipping_planes_pars_fragment\x3e\nvoid main () {\n\t#include \x3cclipping_planes_fragment\x3e\n\tvec4 diffuseColor \x3d vec4( 1.0 );\n\t#include \x3cmap_fragment\x3e\n\t#include \x3calphamap_fragment\x3e\n\t#include \x3calphatest_fragment\x3e\n\tfloat dist \x3d length( vWorldPosition - referencePosition );\n\tdist \x3d ( dist - nearDistance ) / ( farDistance - nearDistance );\n\tdist \x3d saturate( dist );\n\tgl_FragColor \x3d packDepthToRGBA( dist );\n}",
distanceRGBA_vert:"#define DISTANCE\nvarying vec3 vWorldPosition;\n#include \x3ccommon\x3e\n#include \x3cuv_pars_vertex\x3e\n#include \x3cdisplacementmap_pars_vertex\x3e\n#include \x3cmorphtarget_pars_vertex\x3e\n#include \x3cskinning_pars_vertex\x3e\n#include \x3cclipping_planes_pars_vertex\x3e\nvoid main() {\n\t#include \x3cuv_vertex\x3e\n\t#include \x3cskinbase_vertex\x3e\n\t#ifdef USE_DISPLACEMENTMAP\n\t\t#include \x3cbeginnormal_vertex\x3e\n\t\t#include \x3cmorphnormal_vertex\x3e\n\t\t#include \x3cskinnormal_vertex\x3e\n\t#endif\n\t#include \x3cbegin_vertex\x3e\n\t#include \x3cmorphtarget_vertex\x3e\n\t#include \x3cskinning_vertex\x3e\n\t#include \x3cdisplacementmap_vertex\x3e\n\t#include \x3cproject_vertex\x3e\n\t#include \x3cworldpos_vertex\x3e\n\t#include \x3cclipping_planes_vertex\x3e\n\tvWorldPosition \x3d worldPosition.xyz;\n}",
equirect_frag:"uniform sampler2D tEquirect;\nvarying vec3 vWorldDirection;\n#include \x3ccommon\x3e\nvoid main() {\n\tvec3 direction \x3d normalize( vWorldDirection );\n\tvec2 sampleUV;\n\tsampleUV.y \x3d asin( clamp( direction.y, - 1.0, 1.0 ) ) * RECIPROCAL_PI + 0.5;\n\tsampleUV.x \x3d atan( direction.z, direction.x ) * RECIPROCAL_PI2 + 0.5;\n\tvec4 texColor \x3d texture2D( tEquirect, sampleUV );\n\tgl_FragColor \x3d mapTexelToLinear( texColor );\n\t#include \x3ctonemapping_fragment\x3e\n\t#include \x3cencodings_fragment\x3e\n}",
equirect_vert:"varying vec3 vWorldDirection;\n#include \x3ccommon\x3e\nvoid main() {\n\tvWorldDirection \x3d transformDirection( position, modelMatrix );\n\t#include \x3cbegin_vertex\x3e\n\t#include \x3cproject_vertex\x3e\n}",linedashed_frag:"uniform vec3 diffuse;\nuniform float opacity;\nuniform float dashSize;\nuniform float totalSize;\nvarying float vLineDistance;\n#include \x3ccommon\x3e\n#include \x3ccolor_pars_fragment\x3e\n#include \x3cfog_pars_fragment\x3e\n#include \x3clogdepthbuf_pars_fragment\x3e\n#include \x3cclipping_planes_pars_fragment\x3e\nvoid main() {\n\t#include \x3cclipping_planes_fragment\x3e\n\tif ( mod( vLineDistance, totalSize ) \x3e dashSize ) {\n\t\tdiscard;\n\t}\n\tvec3 outgoingLight \x3d vec3( 0.0 );\n\tvec4 diffuseColor \x3d vec4( diffuse, opacity );\n\t#include \x3clogdepthbuf_fragment\x3e\n\t#include \x3ccolor_fragment\x3e\n\toutgoingLight \x3d diffuseColor.rgb;\n\tgl_FragColor \x3d vec4( outgoingLight, diffuseColor.a );\n\t#include \x3cpremultiplied_alpha_fragment\x3e\n\t#include \x3ctonemapping_fragment\x3e\n\t#include \x3cencodings_fragment\x3e\n\t#include \x3cfog_fragment\x3e\n}",
linedashed_vert:"uniform float scale;\nattribute float lineDistance;\nvarying float vLineDistance;\n#include \x3ccommon\x3e\n#include \x3ccolor_pars_vertex\x3e\n#include \x3cfog_pars_vertex\x3e\n#include \x3clogdepthbuf_pars_vertex\x3e\n#include \x3cclipping_planes_pars_vertex\x3e\nvoid main() {\n\t#include \x3ccolor_vertex\x3e\n\tvLineDistance \x3d scale * lineDistance;\n\tvec4 mvPosition \x3d modelViewMatrix * vec4( position, 1.0 );\n\tgl_Position \x3d projectionMatrix * mvPosition;\n\t#include \x3clogdepthbuf_vertex\x3e\n\t#include \x3cclipping_planes_vertex\x3e\n\t#include \x3cfog_vertex\x3e\n}",
meshbasic_frag:"uniform vec3 diffuse;\nuniform float opacity;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n#endif\n#include \x3ccommon\x3e\n#include \x3ccolor_pars_fragment\x3e\n#include \x3cuv_pars_fragment\x3e\n#include \x3cuv2_pars_fragment\x3e\n#include \x3cmap_pars_fragment\x3e\n#include \x3calphamap_pars_fragment\x3e\n#include \x3caomap_pars_fragment\x3e\n#include \x3clightmap_pars_fragment\x3e\n#include \x3cenvmap_common_pars_fragment\x3e\n#include \x3cenvmap_pars_fragment\x3e\n#include \x3cfog_pars_fragment\x3e\n#include \x3cspecularmap_pars_fragment\x3e\n#include \x3clogdepthbuf_pars_fragment\x3e\n#include \x3cclipping_planes_pars_fragment\x3e\nvoid main() {\n\t#include \x3cclipping_planes_fragment\x3e\n\tvec4 diffuseColor \x3d vec4( diffuse, opacity );\n\t#include \x3clogdepthbuf_fragment\x3e\n\t#include \x3cmap_fragment\x3e\n\t#include \x3ccolor_fragment\x3e\n\t#include \x3calphamap_fragment\x3e\n\t#include \x3calphatest_fragment\x3e\n\t#include \x3cspecularmap_fragment\x3e\n\tReflectedLight reflectedLight \x3d ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\n\t#ifdef USE_LIGHTMAP\n\t\treflectedLight.indirectDiffuse +\x3d texture2D( lightMap, vUv2 ).xyz * lightMapIntensity;\n\t#else\n\t\treflectedLight.indirectDiffuse +\x3d vec3( 1.0 );\n\t#endif\n\t#include \x3caomap_fragment\x3e\n\treflectedLight.indirectDiffuse *\x3d diffuseColor.rgb;\n\tvec3 outgoingLight \x3d reflectedLight.indirectDiffuse;\n\t#include \x3cenvmap_fragment\x3e\n\tgl_FragColor \x3d vec4( outgoingLight, diffuseColor.a );\n\t#include \x3cpremultiplied_alpha_fragment\x3e\n\t#include \x3ctonemapping_fragment\x3e\n\t#include \x3cencodings_fragment\x3e\n\t#include \x3cfog_fragment\x3e\n}",
meshbasic_vert:"#include \x3ccommon\x3e\n#include \x3cuv_pars_vertex\x3e\n#include \x3cuv2_pars_vertex\x3e\n#include \x3cenvmap_pars_vertex\x3e\n#include \x3ccolor_pars_vertex\x3e\n#include \x3cfog_pars_vertex\x3e\n#include \x3cmorphtarget_pars_vertex\x3e\n#include \x3cskinning_pars_vertex\x3e\n#include \x3clogdepthbuf_pars_vertex\x3e\n#include \x3cclipping_planes_pars_vertex\x3e\nvoid main() {\n\t#include \x3cuv_vertex\x3e\n\t#include \x3cuv2_vertex\x3e\n\t#include \x3ccolor_vertex\x3e\n\t#include \x3cskinbase_vertex\x3e\n\t#ifdef USE_ENVMAP\n\t#include \x3cbeginnormal_vertex\x3e\n\t#include \x3cmorphnormal_vertex\x3e\n\t#include \x3cskinnormal_vertex\x3e\n\t#include \x3cdefaultnormal_vertex\x3e\n\t#endif\n\t#include \x3cbegin_vertex\x3e\n\t#include \x3cmorphtarget_vertex\x3e\n\t#include \x3cskinning_vertex\x3e\n\t#include \x3cproject_vertex\x3e\n\t#include \x3clogdepthbuf_vertex\x3e\n\t#include \x3cworldpos_vertex\x3e\n\t#include \x3cclipping_planes_vertex\x3e\n\t#include \x3cenvmap_vertex\x3e\n\t#include \x3cfog_vertex\x3e\n}",
meshlambert_frag:"uniform vec3 diffuse;\nuniform vec3 emissive;\nuniform float opacity;\nvarying vec3 vLightFront;\nvarying vec3 vIndirectFront;\n#ifdef DOUBLE_SIDED\n\tvarying vec3 vLightBack;\n\tvarying vec3 vIndirectBack;\n#endif\n#include \x3ccommon\x3e\n#include \x3cpacking\x3e\n#include \x3cdithering_pars_fragment\x3e\n#include \x3ccolor_pars_fragment\x3e\n#include \x3cuv_pars_fragment\x3e\n#include \x3cuv2_pars_fragment\x3e\n#include \x3cmap_pars_fragment\x3e\n#include \x3calphamap_pars_fragment\x3e\n#include \x3caomap_pars_fragment\x3e\n#include \x3clightmap_pars_fragment\x3e\n#include \x3cemissivemap_pars_fragment\x3e\n#include \x3cenvmap_common_pars_fragment\x3e\n#include \x3cenvmap_pars_fragment\x3e\n#include \x3cbsdfs\x3e\n#include \x3clights_pars_begin\x3e\n#include \x3cfog_pars_fragment\x3e\n#include \x3cshadowmap_pars_fragment\x3e\n#include \x3cshadowmask_pars_fragment\x3e\n#include \x3cspecularmap_pars_fragment\x3e\n#include \x3clogdepthbuf_pars_fragment\x3e\n#include \x3cclipping_planes_pars_fragment\x3e\nvoid main() {\n\t#include \x3cclipping_planes_fragment\x3e\n\tvec4 diffuseColor \x3d vec4( diffuse, opacity );\n\tReflectedLight reflectedLight \x3d ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\n\tvec3 totalEmissiveRadiance \x3d emissive;\n\t#include \x3clogdepthbuf_fragment\x3e\n\t#include \x3cmap_fragment\x3e\n\t#include \x3ccolor_fragment\x3e\n\t#include \x3calphamap_fragment\x3e\n\t#include \x3calphatest_fragment\x3e\n\t#include \x3cspecularmap_fragment\x3e\n\t#include \x3cemissivemap_fragment\x3e\n\treflectedLight.indirectDiffuse \x3d getAmbientLightIrradiance( ambientLightColor );\n\t#ifdef DOUBLE_SIDED\n\t\treflectedLight.indirectDiffuse +\x3d ( gl_FrontFacing ) ? vIndirectFront : vIndirectBack;\n\t#else\n\t\treflectedLight.indirectDiffuse +\x3d vIndirectFront;\n\t#endif\n\t#include \x3clightmap_fragment\x3e\n\treflectedLight.indirectDiffuse *\x3d BRDF_Diffuse_Lambert( diffuseColor.rgb );\n\t#ifdef DOUBLE_SIDED\n\t\treflectedLight.directDiffuse \x3d ( gl_FrontFacing ) ? vLightFront : vLightBack;\n\t#else\n\t\treflectedLight.directDiffuse \x3d vLightFront;\n\t#endif\n\treflectedLight.directDiffuse *\x3d BRDF_Diffuse_Lambert( diffuseColor.rgb ) * getShadowMask();\n\t#include \x3caomap_fragment\x3e\n\tvec3 outgoingLight \x3d reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + totalEmissiveRadiance;\n\t#include \x3cenvmap_fragment\x3e\n\tgl_FragColor \x3d vec4( outgoingLight, diffuseColor.a );\n\t#include \x3ctonemapping_fragment\x3e\n\t#include \x3cencodings_fragment\x3e\n\t#include \x3cfog_fragment\x3e\n\t#include \x3cpremultiplied_alpha_fragment\x3e\n\t#include \x3cdithering_fragment\x3e\n}",
meshlambert_vert:"#define LAMBERT\nvarying vec3 vLightFront;\nvarying vec3 vIndirectFront;\n#ifdef DOUBLE_SIDED\n\tvarying vec3 vLightBack;\n\tvarying vec3 vIndirectBack;\n#endif\n#include \x3ccommon\x3e\n#include \x3cuv_pars_vertex\x3e\n#include \x3cuv2_pars_vertex\x3e\n#include \x3cenvmap_pars_vertex\x3e\n#include \x3cbsdfs\x3e\n#include \x3clights_pars_begin\x3e\n#include \x3ccolor_pars_vertex\x3e\n#include \x3cfog_pars_vertex\x3e\n#include \x3cmorphtarget_pars_vertex\x3e\n#include \x3cskinning_pars_vertex\x3e\n#include \x3cshadowmap_pars_vertex\x3e\n#include \x3clogdepthbuf_pars_vertex\x3e\n#include \x3cclipping_planes_pars_vertex\x3e\nvoid main() {\n\t#include \x3cuv_vertex\x3e\n\t#include \x3cuv2_vertex\x3e\n\t#include \x3ccolor_vertex\x3e\n\t#include \x3cbeginnormal_vertex\x3e\n\t#include \x3cmorphnormal_vertex\x3e\n\t#include \x3cskinbase_vertex\x3e\n\t#include \x3cskinnormal_vertex\x3e\n\t#include \x3cdefaultnormal_vertex\x3e\n\t#include \x3cbegin_vertex\x3e\n\t#include \x3cmorphtarget_vertex\x3e\n\t#include \x3cskinning_vertex\x3e\n\t#include \x3cproject_vertex\x3e\n\t#include \x3clogdepthbuf_vertex\x3e\n\t#include \x3cclipping_planes_vertex\x3e\n\t#include \x3cworldpos_vertex\x3e\n\t#include \x3cenvmap_vertex\x3e\n\t#include \x3clights_lambert_vertex\x3e\n\t#include \x3cshadowmap_vertex\x3e\n\t#include \x3cfog_vertex\x3e\n}",
meshmatcap_frag:"#define MATCAP\nuniform vec3 diffuse;\nuniform float opacity;\nuniform sampler2D matcap;\nvarying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n#endif\n#include \x3ccommon\x3e\n#include \x3cuv_pars_fragment\x3e\n#include \x3cmap_pars_fragment\x3e\n#include \x3calphamap_pars_fragment\x3e\n#include \x3cfog_pars_fragment\x3e\n#include \x3cbumpmap_pars_fragment\x3e\n#include \x3cnormalmap_pars_fragment\x3e\n#include \x3clogdepthbuf_pars_fragment\x3e\n#include \x3cclipping_planes_pars_fragment\x3e\nvoid main() {\n\t#include \x3cclipping_planes_fragment\x3e\n\tvec4 diffuseColor \x3d vec4( diffuse, opacity );\n\t#include \x3clogdepthbuf_fragment\x3e\n\t#include \x3cmap_fragment\x3e\n\t#include \x3calphamap_fragment\x3e\n\t#include \x3calphatest_fragment\x3e\n\t#include \x3cnormal_fragment_begin\x3e\n\t#include \x3cnormal_fragment_maps\x3e\n\tvec3 viewDir \x3d normalize( vViewPosition );\n\tvec3 x \x3d normalize( vec3( viewDir.z, 0.0, - viewDir.x ) );\n\tvec3 y \x3d cross( viewDir, x );\n\tvec2 uv \x3d vec2( dot( x, normal ), dot( y, normal ) ) * 0.495 + 0.5;\n\t#ifdef USE_MATCAP\n\t\tvec4 matcapColor \x3d texture2D( matcap, uv );\n\t\tmatcapColor \x3d matcapTexelToLinear( matcapColor );\n\t#else\n\t\tvec4 matcapColor \x3d vec4( 1.0 );\n\t#endif\n\tvec3 outgoingLight \x3d diffuseColor.rgb * matcapColor.rgb;\n\tgl_FragColor \x3d vec4( outgoingLight, diffuseColor.a );\n\t#include \x3cpremultiplied_alpha_fragment\x3e\n\t#include \x3ctonemapping_fragment\x3e\n\t#include \x3cencodings_fragment\x3e\n\t#include \x3cfog_fragment\x3e\n}",
meshmatcap_vert:"#define MATCAP\nvarying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n#endif\n#include \x3ccommon\x3e\n#include \x3cuv_pars_vertex\x3e\n#include \x3cdisplacementmap_pars_vertex\x3e\n#include \x3cfog_pars_vertex\x3e\n#include \x3cmorphtarget_pars_vertex\x3e\n#include \x3cskinning_pars_vertex\x3e\n#include \x3clogdepthbuf_pars_vertex\x3e\n#include \x3cclipping_planes_pars_vertex\x3e\nvoid main() {\n\t#include \x3cuv_vertex\x3e\n\t#include \x3cbeginnormal_vertex\x3e\n\t#include \x3cmorphnormal_vertex\x3e\n\t#include \x3cskinbase_vertex\x3e\n\t#include \x3cskinnormal_vertex\x3e\n\t#include \x3cdefaultnormal_vertex\x3e\n\t#ifndef FLAT_SHADED\n\t\tvNormal \x3d normalize( transformedNormal );\n\t#endif\n\t#include \x3cbegin_vertex\x3e\n\t#include \x3cmorphtarget_vertex\x3e\n\t#include \x3cskinning_vertex\x3e\n\t#include \x3cdisplacementmap_vertex\x3e\n\t#include \x3cproject_vertex\x3e\n\t#include \x3clogdepthbuf_vertex\x3e\n\t#include \x3cclipping_planes_vertex\x3e\n\t#include \x3cfog_vertex\x3e\n\tvViewPosition \x3d - mvPosition.xyz;\n}",
meshphong_frag:"#define PHONG\nuniform vec3 diffuse;\nuniform vec3 emissive;\nuniform vec3 specular;\nuniform float shininess;\nuniform float opacity;\n#include \x3ccommon\x3e\n#include \x3cpacking\x3e\n#include \x3cdithering_pars_fragment\x3e\n#include \x3ccolor_pars_fragment\x3e\n#include \x3cuv_pars_fragment\x3e\n#include \x3cuv2_pars_fragment\x3e\n#include \x3cmap_pars_fragment\x3e\n#include \x3calphamap_pars_fragment\x3e\n#include \x3caomap_pars_fragment\x3e\n#include \x3clightmap_pars_fragment\x3e\n#include \x3cemissivemap_pars_fragment\x3e\n#include \x3cenvmap_common_pars_fragment\x3e\n#include \x3cenvmap_pars_fragment\x3e\n#include \x3cgradientmap_pars_fragment\x3e\n#include \x3cfog_pars_fragment\x3e\n#include \x3cbsdfs\x3e\n#include \x3clights_pars_begin\x3e\n#include \x3clights_phong_pars_fragment\x3e\n#include \x3cshadowmap_pars_fragment\x3e\n#include \x3cbumpmap_pars_fragment\x3e\n#include \x3cnormalmap_pars_fragment\x3e\n#include \x3cspecularmap_pars_fragment\x3e\n#include \x3clogdepthbuf_pars_fragment\x3e\n#include \x3cclipping_planes_pars_fragment\x3e\nvoid main() {\n\t#include \x3cclipping_planes_fragment\x3e\n\tvec4 diffuseColor \x3d vec4( diffuse, opacity );\n\tReflectedLight reflectedLight \x3d ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\n\tvec3 totalEmissiveRadiance \x3d emissive;\n\t#include \x3clogdepthbuf_fragment\x3e\n\t#include \x3cmap_fragment\x3e\n\t#include \x3ccolor_fragment\x3e\n\t#include \x3calphamap_fragment\x3e\n\t#include \x3calphatest_fragment\x3e\n\t#include \x3cspecularmap_fragment\x3e\n\t#include \x3cnormal_fragment_begin\x3e\n\t#include \x3cnormal_fragment_maps\x3e\n\t#include \x3cemissivemap_fragment\x3e\n\t#include \x3clights_phong_fragment\x3e\n\t#include \x3clights_fragment_begin\x3e\n\t#include \x3clights_fragment_maps\x3e\n\t#include \x3clights_fragment_end\x3e\n\t#include \x3caomap_fragment\x3e\n\tvec3 outgoingLight \x3d reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + reflectedLight.directSpecular + reflectedLight.indirectSpecular + totalEmissiveRadiance;\n\t#include \x3cenvmap_fragment\x3e\n\tgl_FragColor \x3d vec4( outgoingLight, diffuseColor.a );\n\t#include \x3ctonemapping_fragment\x3e\n\t#include \x3cencodings_fragment\x3e\n\t#include \x3cfog_fragment\x3e\n\t#include \x3cpremultiplied_alpha_fragment\x3e\n\t#include \x3cdithering_fragment\x3e\n}",
meshphong_vert:"#define PHONG\nvarying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n#endif\n#include \x3ccommon\x3e\n#include \x3cuv_pars_vertex\x3e\n#include \x3cuv2_pars_vertex\x3e\n#include \x3cdisplacementmap_pars_vertex\x3e\n#include \x3cenvmap_pars_vertex\x3e\n#include \x3ccolor_pars_vertex\x3e\n#include \x3cfog_pars_vertex\x3e\n#include \x3cmorphtarget_pars_vertex\x3e\n#include \x3cskinning_pars_vertex\x3e\n#include \x3cshadowmap_pars_vertex\x3e\n#include \x3clogdepthbuf_pars_vertex\x3e\n#include \x3cclipping_planes_pars_vertex\x3e\nvoid main() {\n\t#include \x3cuv_vertex\x3e\n\t#include \x3cuv2_vertex\x3e\n\t#include \x3ccolor_vertex\x3e\n\t#include \x3cbeginnormal_vertex\x3e\n\t#include \x3cmorphnormal_vertex\x3e\n\t#include \x3cskinbase_vertex\x3e\n\t#include \x3cskinnormal_vertex\x3e\n\t#include \x3cdefaultnormal_vertex\x3e\n#ifndef FLAT_SHADED\n\tvNormal \x3d normalize( transformedNormal );\n#endif\n\t#include \x3cbegin_vertex\x3e\n\t#include \x3cmorphtarget_vertex\x3e\n\t#include \x3cskinning_vertex\x3e\n\t#include \x3cdisplacementmap_vertex\x3e\n\t#include \x3cproject_vertex\x3e\n\t#include \x3clogdepthbuf_vertex\x3e\n\t#include \x3cclipping_planes_vertex\x3e\n\tvViewPosition \x3d - mvPosition.xyz;\n\t#include \x3cworldpos_vertex\x3e\n\t#include \x3cenvmap_vertex\x3e\n\t#include \x3cshadowmap_vertex\x3e\n\t#include \x3cfog_vertex\x3e\n}",
meshphysical_frag:"#define STANDARD\n#ifdef PHYSICAL\n\t#define REFLECTIVITY\n\t#define CLEARCOAT\n\t#define TRANSPARENCY\n#endif\nuniform vec3 diffuse;\nuniform vec3 emissive;\nuniform float roughness;\nuniform float metalness;\nuniform float opacity;\n#ifdef TRANSPARENCY\n\tuniform float transparency;\n#endif\n#ifdef REFLECTIVITY\n\tuniform float reflectivity;\n#endif\n#ifdef CLEARCOAT\n\tuniform float clearcoat;\n\tuniform float clearcoatRoughness;\n#endif\n#ifdef USE_SHEEN\n\tuniform vec3 sheen;\n#endif\nvarying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n\t#ifdef USE_TANGENT\n\t\tvarying vec3 vTangent;\n\t\tvarying vec3 vBitangent;\n\t#endif\n#endif\n#include \x3ccommon\x3e\n#include \x3cpacking\x3e\n#include \x3cdithering_pars_fragment\x3e\n#include \x3ccolor_pars_fragment\x3e\n#include \x3cuv_pars_fragment\x3e\n#include \x3cuv2_pars_fragment\x3e\n#include \x3cmap_pars_fragment\x3e\n#include \x3calphamap_pars_fragment\x3e\n#include \x3caomap_pars_fragment\x3e\n#include \x3clightmap_pars_fragment\x3e\n#include \x3cemissivemap_pars_fragment\x3e\n#include \x3cbsdfs\x3e\n#include \x3ccube_uv_reflection_fragment\x3e\n#include \x3cenvmap_common_pars_fragment\x3e\n#include \x3cenvmap_physical_pars_fragment\x3e\n#include \x3cfog_pars_fragment\x3e\n#include \x3clights_pars_begin\x3e\n#include \x3clights_physical_pars_fragment\x3e\n#include \x3cshadowmap_pars_fragment\x3e\n#include \x3cbumpmap_pars_fragment\x3e\n#include \x3cnormalmap_pars_fragment\x3e\n#include \x3cclearcoat_normalmap_pars_fragment\x3e\n#include \x3croughnessmap_pars_fragment\x3e\n#include \x3cmetalnessmap_pars_fragment\x3e\n#include \x3clogdepthbuf_pars_fragment\x3e\n#include \x3cclipping_planes_pars_fragment\x3e\nvoid main() {\n\t#include \x3cclipping_planes_fragment\x3e\n\tvec4 diffuseColor \x3d vec4( diffuse, opacity );\n\tReflectedLight reflectedLight \x3d ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\n\tvec3 totalEmissiveRadiance \x3d emissive;\n\t#include \x3clogdepthbuf_fragment\x3e\n\t#include \x3cmap_fragment\x3e\n\t#include \x3ccolor_fragment\x3e\n\t#include \x3calphamap_fragment\x3e\n\t#include \x3calphatest_fragment\x3e\n\t#include \x3croughnessmap_fragment\x3e\n\t#include \x3cmetalnessmap_fragment\x3e\n\t#include \x3cnormal_fragment_begin\x3e\n\t#include \x3cnormal_fragment_maps\x3e\n\t#include \x3cclearcoat_normal_fragment_begin\x3e\n\t#include \x3cclearcoat_normal_fragment_maps\x3e\n\t#include \x3cemissivemap_fragment\x3e\n\t#include \x3clights_physical_fragment\x3e\n\t#include \x3clights_fragment_begin\x3e\n\t#include \x3clights_fragment_maps\x3e\n\t#include \x3clights_fragment_end\x3e\n\t#include \x3caomap_fragment\x3e\n\tvec3 outgoingLight \x3d reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + reflectedLight.directSpecular + reflectedLight.indirectSpecular + totalEmissiveRadiance;\n\t#ifdef TRANSPARENCY\n\t\tdiffuseColor.a *\x3d saturate( 1. - transparency + linearToRelativeLuminance( reflectedLight.directSpecular + reflectedLight.indirectSpecular ) );\n\t#endif\n\tgl_FragColor \x3d vec4( outgoingLight, diffuseColor.a );\n\t#include \x3ctonemapping_fragment\x3e\n\t#include \x3cencodings_fragment\x3e\n\t#include \x3cfog_fragment\x3e\n\t#include \x3cpremultiplied_alpha_fragment\x3e\n\t#include \x3cdithering_fragment\x3e\n}",
meshphysical_vert:"#define STANDARD\nvarying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n\t#ifdef USE_TANGENT\n\t\tvarying vec3 vTangent;\n\t\tvarying vec3 vBitangent;\n\t#endif\n#endif\n#include \x3ccommon\x3e\n#include \x3cuv_pars_vertex\x3e\n#include \x3cuv2_pars_vertex\x3e\n#include \x3cdisplacementmap_pars_vertex\x3e\n#include \x3ccolor_pars_vertex\x3e\n#include \x3cfog_pars_vertex\x3e\n#include \x3cmorphtarget_pars_vertex\x3e\n#include \x3cskinning_pars_vertex\x3e\n#include \x3cshadowmap_pars_vertex\x3e\n#include \x3clogdepthbuf_pars_vertex\x3e\n#include \x3cclipping_planes_pars_vertex\x3e\nvoid main() {\n\t#include \x3cuv_vertex\x3e\n\t#include \x3cuv2_vertex\x3e\n\t#include \x3ccolor_vertex\x3e\n\t#include \x3cbeginnormal_vertex\x3e\n\t#include \x3cmorphnormal_vertex\x3e\n\t#include \x3cskinbase_vertex\x3e\n\t#include \x3cskinnormal_vertex\x3e\n\t#include \x3cdefaultnormal_vertex\x3e\n#ifndef FLAT_SHADED\n\tvNormal \x3d normalize( transformedNormal );\n\t#ifdef USE_TANGENT\n\t\tvTangent \x3d normalize( transformedTangent );\n\t\tvBitangent \x3d normalize( cross( vNormal, vTangent ) * tangent.w );\n\t#endif\n#endif\n\t#include \x3cbegin_vertex\x3e\n\t#include \x3cmorphtarget_vertex\x3e\n\t#include \x3cskinning_vertex\x3e\n\t#include \x3cdisplacementmap_vertex\x3e\n\t#include \x3cproject_vertex\x3e\n\t#include \x3clogdepthbuf_vertex\x3e\n\t#include \x3cclipping_planes_vertex\x3e\n\tvViewPosition \x3d - mvPosition.xyz;\n\t#include \x3cworldpos_vertex\x3e\n\t#include \x3cshadowmap_vertex\x3e\n\t#include \x3cfog_vertex\x3e\n}",
normal_frag:"#define NORMAL\nuniform float opacity;\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\n\tvarying vec3 vViewPosition;\n#endif\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n\t#ifdef USE_TANGENT\n\t\tvarying vec3 vTangent;\n\t\tvarying vec3 vBitangent;\n\t#endif\n#endif\n#include \x3cpacking\x3e\n#include \x3cuv_pars_fragment\x3e\n#include \x3cbumpmap_pars_fragment\x3e\n#include \x3cnormalmap_pars_fragment\x3e\n#include \x3clogdepthbuf_pars_fragment\x3e\n#include \x3cclipping_planes_pars_fragment\x3e\nvoid main() {\n\t#include \x3cclipping_planes_fragment\x3e\n\t#include \x3clogdepthbuf_fragment\x3e\n\t#include \x3cnormal_fragment_begin\x3e\n\t#include \x3cnormal_fragment_maps\x3e\n\tgl_FragColor \x3d vec4( packNormalToRGB( normal ), opacity );\n}",
normal_vert:"#define NORMAL\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\n\tvarying vec3 vViewPosition;\n#endif\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n\t#ifdef USE_TANGENT\n\t\tvarying vec3 vTangent;\n\t\tvarying vec3 vBitangent;\n\t#endif\n#endif\n#include \x3cuv_pars_vertex\x3e\n#include \x3cdisplacementmap_pars_vertex\x3e\n#include \x3cmorphtarget_pars_vertex\x3e\n#include \x3cskinning_pars_vertex\x3e\n#include \x3clogdepthbuf_pars_vertex\x3e\n#include \x3cclipping_planes_pars_vertex\x3e\nvoid main() {\n\t#include \x3cuv_vertex\x3e\n\t#include \x3cbeginnormal_vertex\x3e\n\t#include \x3cmorphnormal_vertex\x3e\n\t#include \x3cskinbase_vertex\x3e\n\t#include \x3cskinnormal_vertex\x3e\n\t#include \x3cdefaultnormal_vertex\x3e\n#ifndef FLAT_SHADED\n\tvNormal \x3d normalize( transformedNormal );\n\t#ifdef USE_TANGENT\n\t\tvTangent \x3d normalize( transformedTangent );\n\t\tvBitangent \x3d normalize( cross( vNormal, vTangent ) * tangent.w );\n\t#endif\n#endif\n\t#include \x3cbegin_vertex\x3e\n\t#include \x3cmorphtarget_vertex\x3e\n\t#include \x3cskinning_vertex\x3e\n\t#include \x3cdisplacementmap_vertex\x3e\n\t#include \x3cproject_vertex\x3e\n\t#include \x3clogdepthbuf_vertex\x3e\n\t#include \x3cclipping_planes_vertex\x3e\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\n\tvViewPosition \x3d - mvPosition.xyz;\n#endif\n}",
points_frag:"uniform vec3 diffuse;\nuniform float opacity;\n#include \x3ccommon\x3e\n#include \x3ccolor_pars_fragment\x3e\n#include \x3cmap_particle_pars_fragment\x3e\n#include \x3cfog_pars_fragment\x3e\n#include \x3clogdepthbuf_pars_fragment\x3e\n#include \x3cclipping_planes_pars_fragment\x3e\nvoid main() {\n\t#include \x3cclipping_planes_fragment\x3e\n\tvec3 outgoingLight \x3d vec3( 0.0 );\n\tvec4 diffuseColor \x3d vec4( diffuse, opacity );\n\t#include \x3clogdepthbuf_fragment\x3e\n\t#include \x3cmap_particle_fragment\x3e\n\t#include \x3ccolor_fragment\x3e\n\t#include \x3calphatest_fragment\x3e\n\toutgoingLight \x3d diffuseColor.rgb;\n\tgl_FragColor \x3d vec4( outgoingLight, diffuseColor.a );\n\t#include \x3cpremultiplied_alpha_fragment\x3e\n\t#include \x3ctonemapping_fragment\x3e\n\t#include \x3cencodings_fragment\x3e\n\t#include \x3cfog_fragment\x3e\n}",
points_vert:"uniform float size;\nuniform float scale;\n#include \x3ccommon\x3e\n#include \x3ccolor_pars_vertex\x3e\n#include \x3cfog_pars_vertex\x3e\n#include \x3cmorphtarget_pars_vertex\x3e\n#include \x3clogdepthbuf_pars_vertex\x3e\n#include \x3cclipping_planes_pars_vertex\x3e\nvoid main() {\n\t#include \x3ccolor_vertex\x3e\n\t#include \x3cbegin_vertex\x3e\n\t#include \x3cmorphtarget_vertex\x3e\n\t#include \x3cproject_vertex\x3e\n\tgl_PointSize \x3d size;\n\t#ifdef USE_SIZEATTENUATION\n\t\tbool isPerspective \x3d ( projectionMatrix[ 2 ][ 3 ] \x3d\x3d - 1.0 );\n\t\tif ( isPerspective ) gl_PointSize *\x3d ( scale / - mvPosition.z );\n\t#endif\n\t#include \x3clogdepthbuf_vertex\x3e\n\t#include \x3cclipping_planes_vertex\x3e\n\t#include \x3cworldpos_vertex\x3e\n\t#include \x3cfog_vertex\x3e\n}",
shadow_frag:"uniform vec3 color;\nuniform float opacity;\n#include \x3ccommon\x3e\n#include \x3cpacking\x3e\n#include \x3cfog_pars_fragment\x3e\n#include \x3cbsdfs\x3e\n#include \x3clights_pars_begin\x3e\n#include \x3cshadowmap_pars_fragment\x3e\n#include \x3cshadowmask_pars_fragment\x3e\nvoid main() {\n\tgl_FragColor \x3d vec4( color, opacity * ( 1.0 - getShadowMask() ) );\n\t#include \x3cfog_fragment\x3e\n}",shadow_vert:"#include \x3cfog_pars_vertex\x3e\n#include \x3cshadowmap_pars_vertex\x3e\nvoid main() {\n\t#include \x3cbegin_vertex\x3e\n\t#include \x3cproject_vertex\x3e\n\t#include \x3cworldpos_vertex\x3e\n\t#include \x3cshadowmap_vertex\x3e\n\t#include \x3cfog_vertex\x3e\n}",
sprite_frag:"uniform vec3 diffuse;\nuniform float opacity;\n#include \x3ccommon\x3e\n#include \x3cuv_pars_fragment\x3e\n#include \x3cmap_pars_fragment\x3e\n#include \x3cfog_pars_fragment\x3e\n#include \x3clogdepthbuf_pars_fragment\x3e\n#include \x3cclipping_planes_pars_fragment\x3e\nvoid main() {\n\t#include \x3cclipping_planes_fragment\x3e\n\tvec3 outgoingLight \x3d vec3( 0.0 );\n\tvec4 diffuseColor \x3d vec4( diffuse, opacity );\n\t#include \x3clogdepthbuf_fragment\x3e\n\t#include \x3cmap_fragment\x3e\n\t#include \x3calphatest_fragment\x3e\n\toutgoingLight \x3d diffuseColor.rgb;\n\tgl_FragColor \x3d vec4( outgoingLight, diffuseColor.a );\n\t#include \x3ctonemapping_fragment\x3e\n\t#include \x3cencodings_fragment\x3e\n\t#include \x3cfog_fragment\x3e\n}",
sprite_vert:"uniform float rotation;\nuniform vec2 center;\n#include \x3ccommon\x3e\n#include \x3cuv_pars_vertex\x3e\n#include \x3cfog_pars_vertex\x3e\n#include \x3clogdepthbuf_pars_vertex\x3e\n#include \x3cclipping_planes_pars_vertex\x3e\nvoid main() {\n\t#include \x3cuv_vertex\x3e\n\tvec4 mvPosition \x3d modelViewMatrix * vec4( 0.0, 0.0, 0.0, 1.0 );\n\tvec2 scale;\n\tscale.x \x3d length( vec3( modelMatrix[ 0 ].x, modelMatrix[ 0 ].y, modelMatrix[ 0 ].z ) );\n\tscale.y \x3d length( vec3( modelMatrix[ 1 ].x, modelMatrix[ 1 ].y, modelMatrix[ 1 ].z ) );\n\t#ifndef USE_SIZEATTENUATION\n\t\tbool isPerspective \x3d ( projectionMatrix[ 2 ][ 3 ] \x3d\x3d - 1.0 );\n\t\tif ( isPerspective ) scale *\x3d - mvPosition.z;\n\t#endif\n\tvec2 alignedPosition \x3d ( position.xy - ( center - vec2( 0.5 ) ) ) * scale;\n\tvec2 rotatedPosition;\n\trotatedPosition.x \x3d cos( rotation ) * alignedPosition.x - sin( rotation ) * alignedPosition.y;\n\trotatedPosition.y \x3d sin( rotation ) * alignedPosition.x + cos( rotation ) * alignedPosition.y;\n\tmvPosition.xy +\x3d rotatedPosition;\n\tgl_Position \x3d projectionMatrix * mvPosition;\n\t#include \x3clogdepthbuf_vertex\x3e\n\t#include \x3cclipping_planes_vertex\x3e\n\t#include \x3cfog_vertex\x3e\n}"},
Wa={common:{diffuse:{value:new H(15658734)},opacity:{value:1},map:{value:null},uvTransform:{value:new r},alphaMap:{value:null}},specularmap:{specularMap:{value:null}},envmap:{envMap:{value:null},flipEnvMap:{value:-1},reflectivity:{value:1},refractionRatio:{value:.98},maxMipLevel:{value:0}},aomap:{aoMap:{value:null},aoMapIntensity:{value:1}},lightmap:{lightMap:{value:null},lightMapIntensity:{value:1}},emissivemap:{emissiveMap:{value:null}},bumpmap:{bumpMap:{value:null},bumpScale:{value:1}},normalmap:{normalMap:{value:null},
normalScale:{value:new f(1,1)}},displacementmap:{displacementMap:{value:null},displacementScale:{value:1},displacementBias:{value:0}},roughnessmap:{roughnessMap:{value:null}},metalnessmap:{metalnessMap:{value:null}},gradientmap:{gradientMap:{value:null}},fog:{fogDensity:{value:2.5E-4},fogNear:{value:1},fogFar:{value:2E3},fogColor:{value:new H(16777215)}},lights:{ambientLightColor:{value:[]},lightProbe:{value:[]},directionalLights:{value:[],properties:{direction:{},color:{},shadow:{},shadowBias:{},
shadowRadius:{},shadowMapSize:{}}},directionalShadowMap:{value:[]},directionalShadowMatrix:{value:[]},spotLights:{value:[],properties:{color:{},position:{},direction:{},distance:{},coneCos:{},penumbraCos:{},decay:{},shadow:{},shadowBias:{},shadowRadius:{},shadowMapSize:{}}},spotShadowMap:{value:[]},spotShadowMatrix:{value:[]},pointLights:{value:[],properties:{color:{},position:{},decay:{},distance:{},shadow:{},shadowBias:{},shadowRadius:{},shadowMapSize:{},shadowCameraNear:{},shadowCameraFar:{}}},
pointShadowMap:{value:[]},pointShadowMatrix:{value:[]},hemisphereLights:{value:[],properties:{direction:{},skyColor:{},groundColor:{}}},rectAreaLights:{value:[],properties:{color:{},position:{},width:{},height:{}}}},points:{diffuse:{value:new H(15658734)},opacity:{value:1},size:{value:1},scale:{value:1},map:{value:null},uvTransform:{value:new r}},sprite:{diffuse:{value:new H(15658734)},opacity:{value:1},center:{value:new f(.5,.5)},rotation:{value:0},map:{value:null},uvTransform:{value:new r}}},Oc=
{basic:{uniforms:Bb([Wa.common,Wa.specularmap,Wa.envmap,Wa.aomap,Wa.lightmap,Wa.fog]),vertexShader:rb.meshbasic_vert,fragmentShader:rb.meshbasic_frag},lambert:{uniforms:Bb([Wa.common,Wa.specularmap,Wa.envmap,Wa.aomap,Wa.lightmap,Wa.emissivemap,Wa.fog,Wa.lights,{emissive:{value:new H(0)}}]),vertexShader:rb.meshlambert_vert,fragmentShader:rb.meshlambert_frag},phong:{uniforms:Bb([Wa.common,Wa.specularmap,Wa.envmap,Wa.aomap,Wa.lightmap,Wa.emissivemap,Wa.bumpmap,Wa.normalmap,Wa.displacementmap,Wa.gradientmap,
Wa.fog,Wa.lights,{emissive:{value:new H(0)},specular:{value:new H(1118481)},shininess:{value:30}}]),vertexShader:rb.meshphong_vert,fragmentShader:rb.meshphong_frag},standard:{uniforms:Bb([Wa.common,Wa.envmap,Wa.aomap,Wa.lightmap,Wa.emissivemap,Wa.bumpmap,Wa.normalmap,Wa.displacementmap,Wa.roughnessmap,Wa.metalnessmap,Wa.fog,Wa.lights,{emissive:{value:new H(0)},roughness:{value:.5},metalness:{value:.5},envMapIntensity:{value:1}}]),vertexShader:rb.meshphysical_vert,fragmentShader:rb.meshphysical_frag},
matcap:{uniforms:Bb([Wa.common,Wa.bumpmap,Wa.normalmap,Wa.displacementmap,Wa.fog,{matcap:{value:null}}]),vertexShader:rb.meshmatcap_vert,fragmentShader:rb.meshmatcap_frag},points:{uniforms:Bb([Wa.points,Wa.fog]),vertexShader:rb.points_vert,fragmentShader:rb.points_frag},dashed:{uniforms:Bb([Wa.common,Wa.fog,{scale:{value:1},dashSize:{value:1},totalSize:{value:2}}]),vertexShader:rb.linedashed_vert,fragmentShader:rb.linedashed_frag},depth:{uniforms:Bb([Wa.common,Wa.displacementmap]),vertexShader:rb.depth_vert,
fragmentShader:rb.depth_frag},normal:{uniforms:Bb([Wa.common,Wa.bumpmap,Wa.normalmap,Wa.displacementmap,{opacity:{value:1}}]),vertexShader:rb.normal_vert,fragmentShader:rb.normal_frag},sprite:{uniforms:Bb([Wa.sprite,Wa.fog]),vertexShader:rb.sprite_vert,fragmentShader:rb.sprite_frag},background:{uniforms:{uvTransform:{value:new r},t2D:{value:null}},vertexShader:rb.background_vert,fragmentShader:rb.background_frag},cube:{uniforms:{tCube:{value:null},tFlip:{value:-1},opacity:{value:1}},vertexShader:rb.cube_vert,
fragmentShader:rb.cube_frag},equirect:{uniforms:{tEquirect:{value:null}},vertexShader:rb.equirect_vert,fragmentShader:rb.equirect_frag},distanceRGBA:{uniforms:Bb([Wa.common,Wa.displacementmap,{referencePosition:{value:new k},nearDistance:{value:1},farDistance:{value:1E3}}]),vertexShader:rb.distanceRGBA_vert,fragmentShader:rb.distanceRGBA_frag},shadow:{uniforms:Bb([Wa.lights,Wa.fog,{color:{value:new H(0)},opacity:{value:1}}]),vertexShader:rb.shadow_vert,fragmentShader:rb.shadow_frag}};Oc.physical=
{uniforms:Bb([Oc.standard.uniforms,{transparency:{value:0},clearcoat:{value:0},clearcoatRoughness:{value:0},sheen:{value:new H(0)},clearcoatNormalScale:{value:new f(1,1)},clearcoatNormalMap:{value:null}}]),vertexShader:rb.meshphysical_vert,fragmentShader:rb.meshphysical_frag};td.prototype=Object.create(xa.prototype);td.prototype.constructor=td;Nc.prototype=Object.create(va.prototype);Nc.prototype.constructor=Nc;ed.prototype=Object.create(l.prototype);ed.prototype.constructor=ed;ed.prototype.isCubeTexture=
!0;Object.defineProperty(ed.prototype,"images",{get:function(){return this.image},set:function(a){this.image=a}});te.prototype=Object.create(l.prototype);te.prototype.constructor=te;te.prototype.isDataTexture2DArray=!0;ue.prototype=Object.create(l.prototype);ue.prototype.constructor=ue;ue.prototype.isDataTexture3D=!0;var rj=new l,zl=new te,Bl=new ue,sj=new ed,lj=[],nj=[],qj=new Float32Array(16),pj=new Float32Array(9),oj=new Float32Array(4);tj.prototype.updateCache=function(a){var c=this.cache;a instanceof
Float32Array&&c.length!==a.length&&(this.cache=new Float32Array(a.length));pc(c,a)};uj.prototype.setValue=function(a,c,e){for(var g=this.seq,t=0,v=g.length;t!==v;++t){var z=g[t];z.setValue(a,c[z.id],e)}};var Qh=/([\w\d_]+)(\])?(\[|\.)?/g;wd.prototype.setValue=function(a,c,e,g){c=this.map[c];void 0!==c&&c.setValue(a,e,g)};wd.prototype.setOptional=function(a,c,e){c=c[e];void 0!==c&&this.setValue(a,e,c)};wd.upload=function(a,c,e,g){for(var t=0,v=c.length;t!==v;++t){var z=c[t],E=e[z.id];!1!==E.needsUpdate&&
z.setValue(a,E.value,g)}};wd.seqWithValue=function(a,c){for(var e=[],g=0,t=a.length;g!==t;++g){var v=a[g];v.id in c&&e.push(v)}return e};var em=0,nm=0;xd.prototype=Object.create(S.prototype);xd.prototype.constructor=xd;xd.prototype.isMeshDepthMaterial=!0;xd.prototype.copy=function(a){S.prototype.copy.call(this,a);this.depthPacking=a.depthPacking;this.skinning=a.skinning;this.morphTargets=a.morphTargets;this.map=a.map;this.alphaMap=a.alphaMap;this.displacementMap=a.displacementMap;this.displacementScale=
a.displacementScale;this.displacementBias=a.displacementBias;this.wireframe=a.wireframe;this.wireframeLinewidth=a.wireframeLinewidth;return this};yd.prototype=Object.create(S.prototype);yd.prototype.constructor=yd;yd.prototype.isMeshDistanceMaterial=!0;yd.prototype.copy=function(a){S.prototype.copy.call(this,a);this.referencePosition.copy(a.referencePosition);this.nearDistance=a.nearDistance;this.farDistance=a.farDistance;this.skinning=a.skinning;this.morphTargets=a.morphTargets;this.map=a.map;this.alphaMap=
a.alphaMap;this.displacementMap=a.displacementMap;this.displacementScale=a.displacementScale;this.displacementBias=a.displacementBias;return this};we.prototype=Object.assign(Object.create(A.prototype),{constructor:we,isGroup:!0});wf.prototype=Object.assign(Object.create(vb.prototype),{constructor:wf,isArrayCamera:!0});var Hj=new k,Ij=new k;Object.assign(Sh.prototype,d.prototype);Object.assign(Jj.prototype,d.prototype);Object.assign(Cg.prototype,{isFogExp2:!0,clone:function(){return new Cg(this.color,
this.density)},toJSON:function(){return{type:"FogExp2",color:this.color.getHex(),density:this.density}}});Object.assign(Dg.prototype,{isFog:!0,clone:function(){return new Dg(this.color,this.near,this.far)},toJSON:function(){return{type:"Fog",color:this.color.getHex(),near:this.near,far:this.far}}});Object.defineProperty(Sd.prototype,"needsUpdate",{set:function(a){!0===a&&this.version++}});Object.assign(Sd.prototype,{isInterleavedBuffer:!0,onUploadCallback:function(){},setArray:function(a){if(Array.isArray(a))throw new TypeError("THREE.BufferAttribute: array should be a Typed Array.");
this.count=void 0!==a?a.length/this.stride:0;this.array=a;return this},setDynamic:function(a){this.dynamic=a;return this},copy:function(a){this.array=new a.array.constructor(a.array);this.count=a.count;this.stride=a.stride;this.dynamic=a.dynamic;return this},copyAt:function(a,c,e){a*=this.stride;e*=c.stride;for(var g=0,t=this.stride;g<t;g++)this.array[a+g]=c.array[e+g];return this},set:function(a,c){void 0===c&&(c=0);this.array.set(a,c);return this},clone:function(){return(new this.constructor).copy(this)},
onUpload:function(a){this.onUploadCallback=a;return this}});Object.defineProperties(yf.prototype,{count:{get:function(){return this.data.count}},array:{get:function(){return this.data.array}}});Object.assign(yf.prototype,{isInterleavedBufferAttribute:!0,setX:function(a,c){this.data.array[a*this.data.stride+this.offset]=c;return this},setY:function(a,c){this.data.array[a*this.data.stride+this.offset+1]=c;return this},setZ:function(a,c){this.data.array[a*this.data.stride+this.offset+2]=c;return this},
setW:function(a,c){this.data.array[a*this.data.stride+this.offset+3]=c;return this},getX:function(a){return this.data.array[a*this.data.stride+this.offset]},getY:function(a){return this.data.array[a*this.data.stride+this.offset+1]},getZ:function(a){return this.data.array[a*this.data.stride+this.offset+2]},getW:function(a){return this.data.array[a*this.data.stride+this.offset+3]},setXY:function(a,c,e){a=a*this.data.stride+this.offset;this.data.array[a+0]=c;this.data.array[a+1]=e;return this},setXYZ:function(a,
c,e,g){a=a*this.data.stride+this.offset;this.data.array[a+0]=c;this.data.array[a+1]=e;this.data.array[a+2]=g;return this},setXYZW:function(a,c,e,g,t){a=a*this.data.stride+this.offset;this.data.array[a+0]=c;this.data.array[a+1]=e;this.data.array[a+2]=g;this.data.array[a+3]=t;return this}});Cd.prototype=Object.create(S.prototype);Cd.prototype.constructor=Cd;Cd.prototype.isSpriteMaterial=!0;Cd.prototype.copy=function(a){S.prototype.copy.call(this,a);this.color.copy(a.color);this.map=a.map;this.rotation=
a.rotation;this.sizeAttenuation=a.sizeAttenuation;return this};var Ce,ng=new k,pf=new k,qf=new k,De=new f,Af=new f,Oj=new q,th=new k,og=new k,uh=new k,tk=new f,Pi=new f,uk=new f;zf.prototype=Object.assign(Object.create(A.prototype),{constructor:zf,isSprite:!0,raycast:function(a,c){null===a.camera&&console.error('THREE.Sprite: "Raycaster.camera" needs to be set in order to raycast against sprites.');pf.setFromMatrixScale(this.matrixWorld);Oj.copy(a.camera.matrixWorld);this.modelViewMatrix.multiplyMatrices(a.camera.matrixWorldInverse,
this.matrixWorld);qf.setFromMatrixPosition(this.modelViewMatrix);a.camera.isPerspectiveCamera&&!1===this.material.sizeAttenuation&&pf.multiplyScalar(-qf.z);var e=this.material.rotation;if(0!==e){var g=Math.cos(e);var t=Math.sin(e)}e=this.center;Eg(th.set(-.5,-.5,0),qf,e,pf,t,g);Eg(og.set(.5,-.5,0),qf,e,pf,t,g);Eg(uh.set(.5,.5,0),qf,e,pf,t,g);tk.set(0,0);Pi.set(1,0);uk.set(1,1);var v=a.ray.intersectTriangle(th,og,uh,!1,ng);if(null===v&&(Eg(og.set(-.5,.5,0),qf,e,pf,t,g),Pi.set(0,1),v=a.ray.intersectTriangle(th,
uh,og,!1,ng),null===v))return;t=a.ray.origin.distanceTo(ng);t<a.near||t>a.far||c.push({distance:t,point:ng.clone(),uv:B.getUV(ng,th,og,uh,tk,Pi,uk,new f),face:null,object:this})},clone:function(){return(new this.constructor(this.material)).copy(this)},copy:function(a){A.prototype.copy.call(this,a);void 0!==a.center&&this.center.copy(a.center);return this}});var vh=new k,vk=new k;Bf.prototype=Object.assign(Object.create(A.prototype),{constructor:Bf,isLOD:!0,copy:function(a){A.prototype.copy.call(this,
a,!1);a=a.levels;for(var c=0,e=a.length;c<e;c++){var g=a[c];this.addLevel(g.object.clone(),g.distance)}return this},addLevel:function(a,c){void 0===c&&(c=0);c=Math.abs(c);for(var e=this.levels,g=0;g<e.length&&!(c<e[g].distance);g++);e.splice(g,0,{distance:c,object:a});this.add(a);return this},getObjectForDistance:function(a){for(var c=this.levels,e=1,g=c.length;e<g&&!(a<c[e].distance);e++);return c[e-1].object},raycast:function(a,c){vh.setFromMatrixPosition(this.matrixWorld);this.getObjectForDistance(a.ray.origin.distanceTo(vh)).raycast(a,
c)},update:function(a){var c=this.levels;if(1<c.length){vh.setFromMatrixPosition(a.matrixWorld);vk.setFromMatrixPosition(this.matrixWorld);a=vh.distanceTo(vk);c[0].object.visible=!0;for(var e=1,g=c.length;e<g;e++)if(a>=c[e].distance)c[e-1].object.visible=!1,c[e].object.visible=!0;else break;for(;e<g;e++)c[e].object.visible=!1}},toJSON:function(a){a=A.prototype.toJSON.call(this,a);a.object.levels=[];for(var c=this.levels,e=0,g=c.length;e<g;e++){var t=c[e];a.object.levels.push({object:t.object.uuid,
distance:t.distance})}return a}});Cf.prototype=Object.assign(Object.create(ya.prototype),{constructor:Cf,isSkinnedMesh:!0,bind:function(a,c){this.skeleton=a;void 0===c&&(this.updateMatrixWorld(!0),this.skeleton.calculateInverses(),c=this.matrixWorld);this.bindMatrix.copy(c);this.bindMatrixInverse.getInverse(c)},pose:function(){this.skeleton.pose()},normalizeSkinWeights:function(){for(var a=new p,c=this.geometry.attributes.skinWeight,e=0,g=c.count;e<g;e++){a.x=c.getX(e);a.y=c.getY(e);a.z=c.getZ(e);
a.w=c.getW(e);var t=1/a.manhattanLength();Infinity!==t?a.multiplyScalar(t):a.set(1,0,0,0);c.setXYZW(e,a.x,a.y,a.z,a.w)}},updateMatrixWorld:function(a){ya.prototype.updateMatrixWorld.call(this,a);"attached"===this.bindMode?this.bindMatrixInverse.getInverse(this.matrixWorld):"detached"===this.bindMode?this.bindMatrixInverse.getInverse(this.bindMatrix):console.warn("THREE.SkinnedMesh: Unrecognized bindMode: "+this.bindMode)},clone:function(){return(new this.constructor(this.geometry,this.material)).copy(this)}});
var wk=new q,cn=new q;Object.assign(Fg.prototype,{calculateInverses:function(){this.boneInverses=[];for(var a=0,c=this.bones.length;a<c;a++){var e=new q;this.bones[a]&&e.getInverse(this.bones[a].matrixWorld);this.boneInverses.push(e)}},pose:function(){var a,c;var e=0;for(c=this.bones.length;e<c;e++)(a=this.bones[e])&&a.matrixWorld.getInverse(this.boneInverses[e]);e=0;for(c=this.bones.length;e<c;e++)if(a=this.bones[e])a.parent&&a.parent.isBone?(a.matrix.getInverse(a.parent.matrixWorld),a.matrix.multiply(a.matrixWorld)):
a.matrix.copy(a.matrixWorld),a.matrix.decompose(a.position,a.quaternion,a.scale)},update:function(){for(var a=this.bones,c=this.boneInverses,e=this.boneMatrices,g=this.boneTexture,t=0,v=a.length;t<v;t++)wk.multiplyMatrices(a[t]?a[t].matrixWorld:cn,c[t]),wk.toArray(e,16*t);void 0!==g&&(g.needsUpdate=!0)},clone:function(){return new Fg(this.bones,this.boneInverses)},getBoneByName:function(a){for(var c=0,e=this.bones.length;c<e;c++){var g=this.bones[c];if(g.name===a)return g}}});Zh.prototype=Object.assign(Object.create(A.prototype),
{constructor:Zh,isBone:!0});Fb.prototype=Object.create(S.prototype);Fb.prototype.constructor=Fb;Fb.prototype.isLineBasicMaterial=!0;Fb.prototype.copy=function(a){S.prototype.copy.call(this,a);this.color.copy(a.color);this.linewidth=a.linewidth;this.linecap=a.linecap;this.linejoin=a.linejoin;return this};var xk=new k,yk=new k,zk=new q,wh=new D,pg=new G;Xb.prototype=Object.assign(Object.create(A.prototype),{constructor:Xb,isLine:!0,computeLineDistances:function(){var a=this.geometry;if(a.isBufferGeometry)if(null===
a.index){for(var c=a.attributes.position,e=[0],g=1,t=c.count;g<t;g++)xk.fromBufferAttribute(c,g-1),yk.fromBufferAttribute(c,g),e[g]=e[g-1],e[g]+=xk.distanceTo(yk);a.addAttribute("lineDistance",new ba(e,1))}else console.warn("THREE.Line.computeLineDistances(): Computation only possible with non-indexed BufferGeometry.");else if(a.isGeometry)for(c=a.vertices,e=a.lineDistances,e[0]=0,g=1,t=c.length;g<t;g++)e[g]=e[g-1],e[g]+=c[g-1].distanceTo(c[g]);return this},raycast:function(a,c){var e=a.linePrecision,
g=this.geometry,t=this.matrixWorld;null===g.boundingSphere&&g.computeBoundingSphere();pg.copy(g.boundingSphere);pg.applyMatrix4(t);pg.radius+=e;if(!1!==a.ray.intersectsSphere(pg)){zk.getInverse(t);wh.copy(a.ray).applyMatrix4(zk);e/=(this.scale.x+this.scale.y+this.scale.z)/3;e*=e;var v=new k,z=new k;t=new k;var E=new k,F=this&&this.isLineSegments?2:1;if(g.isBufferGeometry){var I=g.index,M=g.attributes.position.array;if(null!==I){I=I.array;g=0;for(var P=I.length-1;g<P;g+=F){var Q=I[g+1];v.fromArray(M,
3*I[g]);z.fromArray(M,3*Q);Q=wh.distanceSqToSegment(v,z,E,t);Q>e||(E.applyMatrix4(this.matrixWorld),Q=a.ray.origin.distanceTo(E),Q<a.near||Q>a.far||c.push({distance:Q,point:t.clone().applyMatrix4(this.matrixWorld),index:g,face:null,faceIndex:null,object:this}))}}else for(g=0,P=M.length/3-1;g<P;g+=F)v.fromArray(M,3*g),z.fromArray(M,3*g+3),Q=wh.distanceSqToSegment(v,z,E,t),Q>e||(E.applyMatrix4(this.matrixWorld),Q=a.ray.origin.distanceTo(E),Q<a.near||Q>a.far||c.push({distance:Q,point:t.clone().applyMatrix4(this.matrixWorld),
index:g,face:null,faceIndex:null,object:this}))}else if(g.isGeometry)for(v=g.vertices,z=v.length,g=0;g<z-1;g+=F)Q=wh.distanceSqToSegment(v[g],v[g+1],E,t),Q>e||(E.applyMatrix4(this.matrixWorld),Q=a.ray.origin.distanceTo(E),Q<a.near||Q>a.far||c.push({distance:Q,point:t.clone().applyMatrix4(this.matrixWorld),index:g,face:null,faceIndex:null,object:this}))}},clone:function(){return(new this.constructor(this.geometry,this.material)).copy(this)}});var xh=new k,yh=new k;Ib.prototype=Object.assign(Object.create(Xb.prototype),
{constructor:Ib,isLineSegments:!0,computeLineDistances:function(){var a=this.geometry;if(a.isBufferGeometry)if(null===a.index){for(var c=a.attributes.position,e=[],g=0,t=c.count;g<t;g+=2)xh.fromBufferAttribute(c,g),yh.fromBufferAttribute(c,g+1),e[g]=0===g?0:e[g-1],e[g+1]=e[g]+xh.distanceTo(yh);a.addAttribute("lineDistance",new ba(e,1))}else console.warn("THREE.LineSegments.computeLineDistances(): Computation only possible with non-indexed BufferGeometry.");else if(a.isGeometry)for(c=a.vertices,e=
a.lineDistances,g=0,t=c.length;g<t;g+=2)xh.copy(c[g]),yh.copy(c[g+1]),e[g]=0===g?0:e[g-1],e[g+1]=e[g]+xh.distanceTo(yh);return this}});Gg.prototype=Object.assign(Object.create(Xb.prototype),{constructor:Gg,isLineLoop:!0});Cc.prototype=Object.create(S.prototype);Cc.prototype.constructor=Cc;Cc.prototype.isPointsMaterial=!0;Cc.prototype.copy=function(a){S.prototype.copy.call(this,a);this.color.copy(a.color);this.map=a.map;this.size=a.size;this.sizeAttenuation=a.sizeAttenuation;this.morphTargets=a.morphTargets;
return this};var Ak=new q,ai=new D,qg=new G,zh=new k;Ee.prototype=Object.assign(Object.create(A.prototype),{constructor:Ee,isPoints:!0,raycast:function(a,c){var e=this.geometry,g=this.matrixWorld,t=a.params.Points.threshold;null===e.boundingSphere&&e.computeBoundingSphere();qg.copy(e.boundingSphere);qg.applyMatrix4(g);qg.radius+=t;if(!1!==a.ray.intersectsSphere(qg))if(Ak.getInverse(g),ai.copy(a.ray).applyMatrix4(Ak),t/=(this.scale.x+this.scale.y+this.scale.z)/3,t*=t,e.isBufferGeometry){var v=e.index;
e=e.attributes.position.array;if(null!==v){var z=v.array;v=0;for(var E=z.length;v<E;v++){var F=z[v];zh.fromArray(e,3*F);$h(zh,F,t,g,a,c,this)}}else for(v=0,z=e.length/3;v<z;v++)zh.fromArray(e,3*v),$h(zh,v,t,g,a,c,this)}else for(e=e.vertices,v=0,z=e.length;v<z;v++)$h(e[v],v,t,g,a,c,this)},updateMorphTargets:function(){var a=this.geometry;if(a.isBufferGeometry){a=a.morphAttributes;var c=Object.keys(a);if(0<c.length){var e=a[c[0]];if(void 0!==e)for(this.morphTargetInfluences=[],this.morphTargetDictionary=
{},a=0,c=e.length;a<c;a++){var g=e[a].name||String(a);this.morphTargetInfluences.push(0);this.morphTargetDictionary[g]=a}}}else a=a.morphTargets,void 0!==a&&0<a.length&&console.error("THREE.Points.updateMorphTargets() does not support THREE.Geometry. Use THREE.BufferGeometry instead.")},clone:function(){return(new this.constructor(this.geometry,this.material)).copy(this)}});bi.prototype=Object.assign(Object.create(l.prototype),{constructor:bi,isVideoTexture:!0,update:function(){var a=this.image;a.readyState>=
a.HAVE_CURRENT_DATA&&(this.needsUpdate=!0)}});Fe.prototype=Object.create(l.prototype);Fe.prototype.constructor=Fe;Fe.prototype.isCompressedTexture=!0;Df.prototype=Object.create(l.prototype);Df.prototype.constructor=Df;Df.prototype.isCanvasTexture=!0;Ef.prototype=Object.create(l.prototype);Ef.prototype.constructor=Ef;Ef.prototype.isDepthTexture=!0;Ge.prototype=Object.create(va.prototype);Ge.prototype.constructor=Ge;Ff.prototype=Object.create(xa.prototype);Ff.prototype.constructor=Ff;He.prototype=Object.create(va.prototype);
He.prototype.constructor=He;Gf.prototype=Object.create(xa.prototype);Gf.prototype.constructor=Gf;mc.prototype=Object.create(va.prototype);mc.prototype.constructor=mc;Hf.prototype=Object.create(xa.prototype);Hf.prototype.constructor=Hf;Ie.prototype=Object.create(mc.prototype);Ie.prototype.constructor=Ie;If.prototype=Object.create(xa.prototype);If.prototype.constructor=If;Td.prototype=Object.create(mc.prototype);Td.prototype.constructor=Td;Jf.prototype=Object.create(xa.prototype);Jf.prototype.constructor=
Jf;Je.prototype=Object.create(mc.prototype);Je.prototype.constructor=Je;Kf.prototype=Object.create(xa.prototype);Kf.prototype.constructor=Kf;Ke.prototype=Object.create(mc.prototype);Ke.prototype.constructor=Ke;Lf.prototype=Object.create(xa.prototype);Lf.prototype.constructor=Lf;Ud.prototype=Object.create(va.prototype);Ud.prototype.constructor=Ud;Ud.prototype.toJSON=function(){var a=va.prototype.toJSON.call(this);a.path=this.parameters.path.toJSON();return a};Mf.prototype=Object.create(xa.prototype);
Mf.prototype.constructor=Mf;Le.prototype=Object.create(va.prototype);Le.prototype.constructor=Le;Nf.prototype=Object.create(xa.prototype);Nf.prototype.constructor=Nf;Me.prototype=Object.create(va.prototype);Me.prototype.constructor=Me;var dn={triangulate:function(a,c,e){e=e||2;var g=c&&c.length,t=g?c[0]*e:a.length,v=Pj(a,0,t,e,!0),z=[];if(!v||v.next===v.prev)return z;g&&(v=ym(a,c,v,e));if(a.length>80*e){var E=c=a[0];var F=g=a[1];for(var I=e;I<t;I+=e){var M=a[I];var P=a[I+1];M<E&&(E=M);P<F&&(F=P);
M>c&&(c=M);P>g&&(g=P)}M=Math.max(c-E,g-F);M=0!==M?1/M:0}Qf(v,z,e,E,F,M);return z}},gd={area:function(a){for(var c=a.length,e=0,g=c-1,t=0;t<c;g=t++)e+=a[g].x*a[t].y-a[t].x*a[g].y;return.5*e},isClockWise:function(a){return 0>gd.area(a)},triangulateShape:function(a,c){var e=[],g=[],t=[];Tj(a);Uj(e,a);var v=a.length;c.forEach(Tj);for(a=0;a<c.length;a++)g.push(v),v+=c[a].length,Uj(e,c[a]);c=dn.triangulate(e,g);for(a=0;a<c.length;a+=3)t.push(c.slice(a,a+3));return t}};Wd.prototype=Object.create(xa.prototype);
Wd.prototype.constructor=Wd;Wd.prototype.toJSON=function(){var a=xa.prototype.toJSON.call(this);return Vj(this.parameters.shapes,this.parameters.options,a)};Tc.prototype=Object.create(va.prototype);Tc.prototype.constructor=Tc;Tc.prototype.toJSON=function(){var a=va.prototype.toJSON.call(this);return Vj(this.parameters.shapes,this.parameters.options,a)};var Gm={generateTopUV:function(a,c,e,g,t){a=c[3*g];g=c[3*g+1];var v=c[3*t];t=c[3*t+1];return[new f(c[3*e],c[3*e+1]),new f(a,g),new f(v,t)]},generateSideWallUV:function(a,
c,e,g,t,v){a=c[3*e];var z=c[3*e+1];e=c[3*e+2];var E=c[3*g],F=c[3*g+1];g=c[3*g+2];var I=c[3*t],M=c[3*t+1];t=c[3*t+2];var P=c[3*v],Q=c[3*v+1];c=c[3*v+2];return.01>Math.abs(z-F)?[new f(a,1-e),new f(E,1-g),new f(I,1-t),new f(P,1-c)]:[new f(z,1-e),new f(F,1-g),new f(M,1-t),new f(Q,1-c)]}};Sf.prototype=Object.create(xa.prototype);Sf.prototype.constructor=Sf;Oe.prototype=Object.create(Tc.prototype);Oe.prototype.constructor=Oe;Tf.prototype=Object.create(xa.prototype);Tf.prototype.constructor=Tf;Dd.prototype=
Object.create(va.prototype);Dd.prototype.constructor=Dd;Uf.prototype=Object.create(xa.prototype);Uf.prototype.constructor=Uf;Pe.prototype=Object.create(va.prototype);Pe.prototype.constructor=Pe;Vf.prototype=Object.create(xa.prototype);Vf.prototype.constructor=Vf;Qe.prototype=Object.create(va.prototype);Qe.prototype.constructor=Qe;Xd.prototype=Object.create(xa.prototype);Xd.prototype.constructor=Xd;Xd.prototype.toJSON=function(){var a=xa.prototype.toJSON.call(this);return Wj(this.parameters.shapes,
a)};Yd.prototype=Object.create(va.prototype);Yd.prototype.constructor=Yd;Yd.prototype.toJSON=function(){var a=va.prototype.toJSON.call(this);return Wj(this.parameters.shapes,a)};Re.prototype=Object.create(va.prototype);Re.prototype.constructor=Re;Zd.prototype=Object.create(xa.prototype);Zd.prototype.constructor=Zd;hd.prototype=Object.create(va.prototype);hd.prototype.constructor=hd;Wf.prototype=Object.create(Zd.prototype);Wf.prototype.constructor=Wf;Xf.prototype=Object.create(hd.prototype);Xf.prototype.constructor=
Xf;Yf.prototype=Object.create(xa.prototype);Yf.prototype.constructor=Yf;Se.prototype=Object.create(va.prototype);Se.prototype.constructor=Se;var jc=Object.freeze({WireframeGeometry:Ge,ParametricGeometry:Ff,ParametricBufferGeometry:He,TetrahedronGeometry:Hf,TetrahedronBufferGeometry:Ie,OctahedronGeometry:If,OctahedronBufferGeometry:Td,IcosahedronGeometry:Jf,IcosahedronBufferGeometry:Je,DodecahedronGeometry:Kf,DodecahedronBufferGeometry:Ke,PolyhedronGeometry:Gf,PolyhedronBufferGeometry:mc,TubeGeometry:Lf,
TubeBufferGeometry:Ud,TorusKnotGeometry:Mf,TorusKnotBufferGeometry:Le,TorusGeometry:Nf,TorusBufferGeometry:Me,TextGeometry:Sf,TextBufferGeometry:Oe,SphereGeometry:Tf,SphereBufferGeometry:Dd,RingGeometry:Uf,RingBufferGeometry:Pe,PlaneGeometry:td,PlaneBufferGeometry:Nc,LatheGeometry:Vf,LatheBufferGeometry:Qe,ShapeGeometry:Xd,ShapeBufferGeometry:Yd,ExtrudeGeometry:Wd,ExtrudeBufferGeometry:Tc,EdgesGeometry:Re,ConeGeometry:Wf,ConeBufferGeometry:Xf,CylinderGeometry:Zd,CylinderBufferGeometry:hd,CircleGeometry:Yf,
CircleBufferGeometry:Se,BoxGeometry:Sa,BoxBufferGeometry:Xa});$d.prototype=Object.create(S.prototype);$d.prototype.constructor=$d;$d.prototype.isShadowMaterial=!0;$d.prototype.copy=function(a){S.prototype.copy.call(this,a);this.color.copy(a.color);return this};Te.prototype=Object.create(qb.prototype);Te.prototype.constructor=Te;Te.prototype.isRawShaderMaterial=!0;Uc.prototype=Object.create(S.prototype);Uc.prototype.constructor=Uc;Uc.prototype.isMeshStandardMaterial=!0;Uc.prototype.copy=function(a){S.prototype.copy.call(this,
a);this.defines={STANDARD:""};this.color.copy(a.color);this.roughness=a.roughness;this.metalness=a.metalness;this.map=a.map;this.lightMap=a.lightMap;this.lightMapIntensity=a.lightMapIntensity;this.aoMap=a.aoMap;this.aoMapIntensity=a.aoMapIntensity;this.emissive.copy(a.emissive);this.emissiveMap=a.emissiveMap;this.emissiveIntensity=a.emissiveIntensity;this.bumpMap=a.bumpMap;this.bumpScale=a.bumpScale;this.normalMap=a.normalMap;this.normalMapType=a.normalMapType;this.normalScale.copy(a.normalScale);
this.displacementMap=a.displacementMap;this.displacementScale=a.displacementScale;this.displacementBias=a.displacementBias;this.roughnessMap=a.roughnessMap;this.metalnessMap=a.metalnessMap;this.alphaMap=a.alphaMap;this.envMap=a.envMap;this.envMapIntensity=a.envMapIntensity;this.refractionRatio=a.refractionRatio;this.wireframe=a.wireframe;this.wireframeLinewidth=a.wireframeLinewidth;this.wireframeLinecap=a.wireframeLinecap;this.wireframeLinejoin=a.wireframeLinejoin;this.skinning=a.skinning;this.morphTargets=
a.morphTargets;this.morphNormals=a.morphNormals;return this};ae.prototype=Object.create(Uc.prototype);ae.prototype.constructor=ae;ae.prototype.isMeshPhysicalMaterial=!0;ae.prototype.copy=function(a){Uc.prototype.copy.call(this,a);this.defines={STANDARD:"",PHYSICAL:""};this.reflectivity=a.reflectivity;this.clearcoat=a.clearcoat;this.clearcoatRoughness=a.clearcoatRoughness;this.sheen=a.sheen?(this.sheen||new H).copy(a.sheen):null;this.clearcoatNormalMap=a.clearcoatNormalMap;this.clearcoatNormalScale.copy(a.clearcoatNormalScale);
this.transparency=a.transparency;return this};Dc.prototype=Object.create(S.prototype);Dc.prototype.constructor=Dc;Dc.prototype.isMeshPhongMaterial=!0;Dc.prototype.copy=function(a){S.prototype.copy.call(this,a);this.color.copy(a.color);this.specular.copy(a.specular);this.shininess=a.shininess;this.map=a.map;this.lightMap=a.lightMap;this.lightMapIntensity=a.lightMapIntensity;this.aoMap=a.aoMap;this.aoMapIntensity=a.aoMapIntensity;this.emissive.copy(a.emissive);this.emissiveMap=a.emissiveMap;this.emissiveIntensity=
a.emissiveIntensity;this.bumpMap=a.bumpMap;this.bumpScale=a.bumpScale;this.normalMap=a.normalMap;this.normalMapType=a.normalMapType;this.normalScale.copy(a.normalScale);this.displacementMap=a.displacementMap;this.displacementScale=a.displacementScale;this.displacementBias=a.displacementBias;this.specularMap=a.specularMap;this.alphaMap=a.alphaMap;this.envMap=a.envMap;this.combine=a.combine;this.reflectivity=a.reflectivity;this.refractionRatio=a.refractionRatio;this.wireframe=a.wireframe;this.wireframeLinewidth=
a.wireframeLinewidth;this.wireframeLinecap=a.wireframeLinecap;this.wireframeLinejoin=a.wireframeLinejoin;this.skinning=a.skinning;this.morphTargets=a.morphTargets;this.morphNormals=a.morphNormals;return this};be.prototype=Object.create(Dc.prototype);be.prototype.constructor=be;be.prototype.isMeshToonMaterial=!0;be.prototype.copy=function(a){Dc.prototype.copy.call(this,a);this.gradientMap=a.gradientMap;return this};ce.prototype=Object.create(S.prototype);ce.prototype.constructor=ce;ce.prototype.isMeshNormalMaterial=
!0;ce.prototype.copy=function(a){S.prototype.copy.call(this,a);this.bumpMap=a.bumpMap;this.bumpScale=a.bumpScale;this.normalMap=a.normalMap;this.normalMapType=a.normalMapType;this.normalScale.copy(a.normalScale);this.displacementMap=a.displacementMap;this.displacementScale=a.displacementScale;this.displacementBias=a.displacementBias;this.wireframe=a.wireframe;this.wireframeLinewidth=a.wireframeLinewidth;this.skinning=a.skinning;this.morphTargets=a.morphTargets;this.morphNormals=a.morphNormals;return this};
de.prototype=Object.create(S.prototype);de.prototype.constructor=de;de.prototype.isMeshLambertMaterial=!0;de.prototype.copy=function(a){S.prototype.copy.call(this,a);this.color.copy(a.color);this.map=a.map;this.lightMap=a.lightMap;this.lightMapIntensity=a.lightMapIntensity;this.aoMap=a.aoMap;this.aoMapIntensity=a.aoMapIntensity;this.emissive.copy(a.emissive);this.emissiveMap=a.emissiveMap;this.emissiveIntensity=a.emissiveIntensity;this.specularMap=a.specularMap;this.alphaMap=a.alphaMap;this.envMap=
a.envMap;this.combine=a.combine;this.reflectivity=a.reflectivity;this.refractionRatio=a.refractionRatio;this.wireframe=a.wireframe;this.wireframeLinewidth=a.wireframeLinewidth;this.wireframeLinecap=a.wireframeLinecap;this.wireframeLinejoin=a.wireframeLinejoin;this.skinning=a.skinning;this.morphTargets=a.morphTargets;this.morphNormals=a.morphNormals;return this};ee.prototype=Object.create(S.prototype);ee.prototype.constructor=ee;ee.prototype.isMeshMatcapMaterial=!0;ee.prototype.copy=function(a){S.prototype.copy.call(this,
a);this.defines={MATCAP:""};this.color.copy(a.color);this.matcap=a.matcap;this.map=a.map;this.bumpMap=a.bumpMap;this.bumpScale=a.bumpScale;this.normalMap=a.normalMap;this.normalMapType=a.normalMapType;this.normalScale.copy(a.normalScale);this.displacementMap=a.displacementMap;this.displacementScale=a.displacementScale;this.displacementBias=a.displacementBias;this.alphaMap=a.alphaMap;this.skinning=a.skinning;this.morphTargets=a.morphTargets;this.morphNormals=a.morphNormals;return this};fe.prototype=
Object.create(Fb.prototype);fe.prototype.constructor=fe;fe.prototype.isLineDashedMaterial=!0;fe.prototype.copy=function(a){Fb.prototype.copy.call(this,a);this.scale=a.scale;this.dashSize=a.dashSize;this.gapSize=a.gapSize;return this};var en=Object.freeze({ShadowMaterial:$d,SpriteMaterial:Cd,RawShaderMaterial:Te,ShaderMaterial:qb,PointsMaterial:Cc,MeshPhysicalMaterial:ae,MeshStandardMaterial:Uc,MeshPhongMaterial:Dc,MeshToonMaterial:be,MeshNormalMaterial:ce,MeshLambertMaterial:de,MeshDepthMaterial:xd,
MeshDistanceMaterial:yd,MeshBasicMaterial:N,MeshMatcapMaterial:ee,LineDashedMaterial:fe,LineBasicMaterial:Fb,Material:S}),Vb={arraySlice:function(a,c,e){return Vb.isTypedArray(a)?new a.constructor(a.subarray(c,void 0!==e?e:a.length)):a.slice(c,e)},convertArray:function(a,c,e){return!a||!e&&a.constructor===c?a:"number"===typeof c.BYTES_PER_ELEMENT?new c(a):Array.prototype.slice.call(a)},isTypedArray:function(a){return ArrayBuffer.isView(a)&&!(a instanceof DataView)},getKeyframeOrder:function(a){for(var c=
a.length,e=Array(c),g=0;g!==c;++g)e[g]=g;e.sort(function(t,v){return a[t]-a[v]});return e},sortedArray:function(a,c,e){for(var g=a.length,t=new a.constructor(g),v=0,z=0;z!==g;++v)for(var E=e[v]*c,F=0;F!==c;++F)t[z++]=a[E+F];return t},flattenJSON:function(a,c,e,g){for(var t=1,v=a[0];void 0!==v&&void 0===v[g];)v=a[t++];if(void 0!==v){var z=v[g];if(void 0!==z)if(Array.isArray(z)){do z=v[g],void 0!==z&&(c.push(v.time),e.push.apply(e,z)),v=a[t++];while(void 0!==v)}else if(void 0!==z.toArray){do z=v[g],
void 0!==z&&(c.push(v.time),z.toArray(e,e.length)),v=a[t++];while(void 0!==v)}else{do z=v[g],void 0!==z&&(c.push(v.time),e.push(z)),v=a[t++];while(void 0!==v)}}}};Object.assign(rc.prototype,{evaluate:function(a){var c=this.parameterPositions,e=this._cachedIndex,g=c[e],t=c[e-1];a:{b:{c:{d:if(!(a<g)){for(var v=e+2;;){if(void 0===g){if(a<t)break d;this._cachedIndex=e=c.length;return this.afterEnd_(e-1,a,t)}if(e===v)break;t=g;g=c[++e];if(a<g)break b}g=c.length;break c}if(a>=t)break a;else{v=c[1];a<v&&
(e=2,t=v);for(v=e-2;;){if(void 0===t)return this._cachedIndex=0,this.beforeStart_(0,a,g);if(e===v)break;g=t;t=c[--e-1];if(a>=t)break b}g=e;e=0}}for(;e<g;)t=e+g>>>1,a<c[t]?g=t:e=t+1;g=c[e];t=c[e-1];if(void 0===t)return this._cachedIndex=0,this.beforeStart_(0,a,g);if(void 0===g)return this._cachedIndex=e=c.length,this.afterEnd_(e-1,t,a)}this._cachedIndex=e;this.intervalChanged_(e,t,g)}return this.interpolate_(e,t,a,g)},settings:null,DefaultSettings_:{},getSettings_:function(){return this.settings||
this.DefaultSettings_},copySampleValue_:function(a){var c=this.resultBuffer,e=this.sampleValues,g=this.valueSize;a*=g;for(var t=0;t!==g;++t)c[t]=e[a+t];return c},interpolate_:function(){throw Error("call to abstract method");},intervalChanged_:function(){}});Object.assign(rc.prototype,{beforeStart_:rc.prototype.copySampleValue_,afterEnd_:rc.prototype.copySampleValue_});Hg.prototype=Object.assign(Object.create(rc.prototype),{constructor:Hg,DefaultSettings_:{endingStart:2400,endingEnd:2400},intervalChanged_:function(a,
c,e){var g=this.parameterPositions,t=a-2,v=a+1,z=g[t],E=g[v];if(void 0===z)switch(this.getSettings_().endingStart){case 2401:t=a;z=2*c-e;break;case 2402:t=g.length-2;z=c+g[t]-g[t+1];break;default:t=a,z=e}if(void 0===E)switch(this.getSettings_().endingEnd){case 2401:v=a;E=2*e-c;break;case 2402:v=1;E=e+g[1]-g[0];break;default:v=a-1,E=c}a=.5*(e-c);g=this.valueSize;this._weightPrev=a/(c-z);this._weightNext=a/(E-e);this._offsetPrev=t*g;this._offsetNext=v*g},interpolate_:function(a,c,e,g){var t=this.resultBuffer,
v=this.sampleValues,z=this.valueSize;a*=z;var E=a-z,F=this._offsetPrev,I=this._offsetNext,M=this._weightPrev,P=this._weightNext,Q=(e-c)/(g-c);e=Q*Q;g=e*Q;c=-M*g+2*M*e-M*Q;M=(1+M)*g+(-1.5-2*M)*e+(-.5+M)*Q+1;Q=(-1-P)*g+(1.5+P)*e+.5*Q;P=P*g-P*e;for(e=0;e!==z;++e)t[e]=c*v[F+e]+M*v[E+e]+Q*v[a+e]+P*v[I+e];return t}});Zf.prototype=Object.assign(Object.create(rc.prototype),{constructor:Zf,interpolate_:function(a,c,e,g){var t=this.resultBuffer,v=this.sampleValues,z=this.valueSize;a*=z;var E=a-z;c=(e-c)/(g-
c);e=1-c;for(g=0;g!==z;++g)t[g]=v[E+g]*e+v[a+g]*c;return t}});Ig.prototype=Object.assign(Object.create(rc.prototype),{constructor:Ig,interpolate_:function(a){return this.copySampleValue_(a-1)}});Object.assign(Zb,{toJSON:function(a){var c=a.constructor;if(void 0!==c.toJSON)c=c.toJSON(a);else{c={name:a.name,times:Vb.convertArray(a.times,Array),values:Vb.convertArray(a.values,Array)};var e=a.getInterpolation();e!==a.DefaultInterpolation&&(c.interpolation=e)}c.type=a.ValueTypeName;return c}});Object.assign(Zb.prototype,
{constructor:Zb,TimeBufferType:Float32Array,ValueBufferType:Float32Array,DefaultInterpolation:2301,InterpolantFactoryMethodDiscrete:function(a){return new Ig(this.times,this.values,this.getValueSize(),a)},InterpolantFactoryMethodLinear:function(a){return new Zf(this.times,this.values,this.getValueSize(),a)},InterpolantFactoryMethodSmooth:function(a){return new Hg(this.times,this.values,this.getValueSize(),a)},setInterpolation:function(a){switch(a){case 2300:var c=this.InterpolantFactoryMethodDiscrete;
break;case 2301:c=this.InterpolantFactoryMethodLinear;break;case 2302:c=this.InterpolantFactoryMethodSmooth}if(void 0===c){c="unsupported interpolation for "+this.ValueTypeName+" keyframe track named "+this.name;if(void 0===this.createInterpolant)if(a!==this.DefaultInterpolation)this.setInterpolation(this.DefaultInterpolation);else throw Error(c);console.warn("THREE.KeyframeTrack:",c);return this}this.createInterpolant=c;return this},getInterpolation:function(){switch(this.createInterpolant){case this.InterpolantFactoryMethodDiscrete:return 2300;
case this.InterpolantFactoryMethodLinear:return 2301;case this.InterpolantFactoryMethodSmooth:return 2302}},getValueSize:function(){return this.values.length/this.times.length},shift:function(a){if(0!==a)for(var c=this.times,e=0,g=c.length;e!==g;++e)c[e]+=a;return this},scale:function(a){if(1!==a)for(var c=this.times,e=0,g=c.length;e!==g;++e)c[e]*=a;return this},trim:function(a,c){for(var e=this.times,g=e.length,t=0,v=g-1;t!==g&&e[t]<a;)++t;for(;-1!==v&&e[v]>c;)--v;++v;if(0!==t||v!==g)t>=v&&(v=Math.max(v,
1),t=v-1),a=this.getValueSize(),this.times=Vb.arraySlice(e,t,v),this.values=Vb.arraySlice(this.values,t*a,v*a);return this},validate:function(){var a=!0,c=this.getValueSize();0!==c-Math.floor(c)&&(console.error("THREE.KeyframeTrack: Invalid value size in track.",this),a=!1);var e=this.times;c=this.values;var g=e.length;0===g&&(console.error("THREE.KeyframeTrack: Track is empty.",this),a=!1);for(var t=null,v=0;v!==g;v++){var z=e[v];if("number"===typeof z&&isNaN(z)){console.error("THREE.KeyframeTrack: Time is not a valid number.",
this,v,z);a=!1;break}if(null!==t&&t>z){console.error("THREE.KeyframeTrack: Out of order keys.",this,v,z,t);a=!1;break}t=z}if(void 0!==c&&Vb.isTypedArray(c))for(v=0,e=c.length;v!==e;++v)if(g=c[v],isNaN(g)){console.error("THREE.KeyframeTrack: Value is not a valid number.",this,v,g);a=!1;break}return a},optimize:function(){for(var a=this.times,c=this.values,e=this.getValueSize(),g=2302===this.getInterpolation(),t=1,v=a.length-1,z=1;z<v;++z){var E=!1,F=a[z];if(F!==a[z+1]&&(1!==z||F!==F[0]))if(g)E=!0;
else{var I=z*e,M=I-e,P=I+e;for(F=0;F!==e;++F){var Q=c[I+F];if(Q!==c[M+F]||Q!==c[P+F]){E=!0;break}}}if(E){if(z!==t)for(a[t]=a[z],E=z*e,I=t*e,F=0;F!==e;++F)c[I+F]=c[E+F];++t}}if(0<v){a[t]=a[v];E=v*e;I=t*e;for(F=0;F!==e;++F)c[I+F]=c[E+F];++t}t!==a.length&&(this.times=Vb.arraySlice(a,0,t),this.values=Vb.arraySlice(c,0,t*e));return this},clone:function(){var a=Vb.arraySlice(this.times,0),c=Vb.arraySlice(this.values,0);a=new this.constructor(this.name,a,c);a.createInterpolant=this.createInterpolant;return a}});
Jg.prototype=Object.assign(Object.create(Zb.prototype),{constructor:Jg,ValueTypeName:"bool",ValueBufferType:Array,DefaultInterpolation:2300,InterpolantFactoryMethodLinear:void 0,InterpolantFactoryMethodSmooth:void 0});Kg.prototype=Object.assign(Object.create(Zb.prototype),{constructor:Kg,ValueTypeName:"color"});Ue.prototype=Object.assign(Object.create(Zb.prototype),{constructor:Ue,ValueTypeName:"number"});Lg.prototype=Object.assign(Object.create(rc.prototype),{constructor:Lg,interpolate_:function(a,
c,e,g){var t=this.resultBuffer,v=this.sampleValues,z=this.valueSize;a*=z;c=(e-c)/(g-c);for(e=a+z;a!==e;a+=4)h.slerpFlat(t,0,v,a-z,v,a,c);return t}});$f.prototype=Object.assign(Object.create(Zb.prototype),{constructor:$f,ValueTypeName:"quaternion",DefaultInterpolation:2301,InterpolantFactoryMethodLinear:function(a){return new Lg(this.times,this.values,this.getValueSize(),a)},InterpolantFactoryMethodSmooth:void 0});Mg.prototype=Object.assign(Object.create(Zb.prototype),{constructor:Mg,ValueTypeName:"string",
ValueBufferType:Array,DefaultInterpolation:2300,InterpolantFactoryMethodLinear:void 0,InterpolantFactoryMethodSmooth:void 0});Ve.prototype=Object.assign(Object.create(Zb.prototype),{constructor:Ve,ValueTypeName:"vector"});Object.assign(vc,{parse:function(a){for(var c=[],e=a.tracks,g=1/(a.fps||1),t=0,v=e.length;t!==v;++t)c.push(Im(e[t]).scale(g));return new vc(a.name,a.duration,c)},toJSON:function(a){var c=[],e=a.tracks;a={name:a.name,duration:a.duration,tracks:c,uuid:a.uuid};for(var g=0,t=e.length;g!==
t;++g)c.push(Zb.toJSON(e[g]));return a},CreateFromMorphTargetSequence:function(a,c,e,g){for(var t=c.length,v=[],z=0;z<t;z++){var E=[],F=[];E.push((z+t-1)%t,z,(z+1)%t);F.push(0,1,0);var I=Vb.getKeyframeOrder(E);E=Vb.sortedArray(E,1,I);F=Vb.sortedArray(F,1,I);g||0!==E[0]||(E.push(t),F.push(F[0]));v.push((new Ue(".morphTargetInfluences["+c[z].name+"]",E,F)).scale(1/e))}return new vc(a,-1,v)},findByName:function(a,c){var e=a;Array.isArray(a)||(e=a.geometry&&a.geometry.animations||a.animations);for(a=
0;a<e.length;a++)if(e[a].name===c)return e[a];return null},CreateClipsFromMorphTargetSequences:function(a,c,e){for(var g={},t=/^([\w-]*?)([\d]+)$/,v=0,z=a.length;v<z;v++){var E=a[v],F=E.name.match(t);if(F&&1<F.length){var I=F[1];(F=g[I])||(g[I]=F=[]);F.push(E)}}a=[];for(I in g)a.push(vc.CreateFromMorphTargetSequence(I,g[I],c,e));return a},parseAnimation:function(a,c){function e(ea,da,ra,pa,qa){if(0!==ra.length){var ua=[],na=[];Vb.flattenJSON(ra,ua,na,pa);0!==ua.length&&qa.push(new ea(da,ua,na))}}
if(!a)return console.error("THREE.AnimationClip: No animation in JSONLoader data."),null;var g=[],t=a.name||"default",v=a.length||-1,z=a.fps||30;a=a.hierarchy||[];for(var E=0;E<a.length;E++){var F=a[E].keys;if(F&&0!==F.length)if(F[0].morphTargets){v={};for(var I=0;I<F.length;I++)if(F[I].morphTargets)for(var M=0;M<F[I].morphTargets.length;M++)v[F[I].morphTargets[M]]=-1;for(var P in v){var Q=[],U=[];for(M=0;M!==F[I].morphTargets.length;++M){var V=F[I];Q.push(V.time);U.push(V.morphTarget===P?1:0)}g.push(new Ue(".morphTargetInfluence["+
P+"]",Q,U))}v=v.length*(z||1)}else I=".bones["+c[E].name+"]",e(Ve,I+".position",F,"pos",g),e($f,I+".quaternion",F,"rot",g),e(Ve,I+".scale",F,"scl",g)}return 0===g.length?null:new vc(t,v,g)}});Object.assign(vc.prototype,{resetDuration:function(){for(var a=0,c=0,e=this.tracks.length;c!==e;++c){var g=this.tracks[c];a=Math.max(a,g.times[g.times.length-1])}this.duration=a;return this},trim:function(){for(var a=0;a<this.tracks.length;a++)this.tracks[a].trim(0,this.duration);return this},validate:function(){for(var a=
!0,c=0;c<this.tracks.length;c++)a=a&&this.tracks[c].validate();return a},optimize:function(){for(var a=0;a<this.tracks.length;a++)this.tracks[a].optimize();return this},clone:function(){for(var a=[],c=0;c<this.tracks.length;c++)a.push(this.tracks[c].clone());return new vc(this.name,this.duration,a)}});var ke={enabled:!1,files:{},add:function(a,c){!1!==this.enabled&&(this.files[a]=c)},get:function(a){if(!1!==this.enabled)return this.files[a]},remove:function(a){delete this.files[a]},clear:function(){this.files=
{}}},Xj=new ei;Object.assign(Db.prototype,{load:function(){},parse:function(){},setCrossOrigin:function(a){this.crossOrigin=a;return this},setPath:function(a){this.path=a;return this},setResourcePath:function(a){this.resourcePath=a;return this}});Db.Handlers={handlers:[],add:function(a,c){this.handlers.push(a,c)},get:function(a){for(var c=this.handlers,e=0,g=c.length;e<g;e+=2){var t=c[e+1];if(c[e].test(a))return t}return null}};var Mc={};wc.prototype=Object.assign(Object.create(Db.prototype),{constructor:wc,
load:function(a,c,e,g){void 0===a&&(a="");void 0!==this.path&&(a=this.path+a);a=this.manager.resolveURL(a);var t=this,v=ke.get(a);if(void 0!==v)return t.manager.itemStart(a),setTimeout(function(){c&&c(v);t.manager.itemEnd(a)},0),v;if(void 0!==Mc[a])Mc[a].push({onLoad:c,onProgress:e,onError:g});else{var z=a.match(/^data:(.*?)(;base64)?,(.*)$/);if(z){e=z[1];var E=!!z[2];z=z[3];z=decodeURIComponent(z);E&&(z=atob(z));try{var F=(this.responseType||"").toLowerCase();switch(F){case "arraybuffer":case "blob":var I=
new Uint8Array(z.length);for(E=0;E<z.length;E++)I[E]=z.charCodeAt(E);var M="blob"===F?new Blob([I.buffer],{type:e}):I.buffer;break;case "document":M=(new DOMParser).parseFromString(z,e);break;case "json":M=JSON.parse(z);break;default:M=z}setTimeout(function(){c&&c(M);t.manager.itemEnd(a)},0)}catch(Q){setTimeout(function(){g&&g(Q);t.manager.itemError(a);t.manager.itemEnd(a)},0)}}else{Mc[a]=[];Mc[a].push({onLoad:c,onProgress:e,onError:g});var P=new XMLHttpRequest;P.open("GET",a,!0);P.addEventListener("load",
function(Q){var U=this.response;ke.add(a,U);var V=Mc[a];delete Mc[a];if(200===this.status||0===this.status){0===this.status&&console.warn("THREE.FileLoader: HTTP Status 0 received.");for(var ea=0,da=V.length;ea<da;ea++){var ra=V[ea];if(ra.onLoad)ra.onLoad(U)}}else{ea=0;for(da=V.length;ea<da;ea++)if(ra=V[ea],ra.onError)ra.onError(Q);t.manager.itemError(a)}t.manager.itemEnd(a)},!1);P.addEventListener("progress",function(Q){for(var U=Mc[a],V=0,ea=U.length;V<ea;V++){var da=U[V];if(da.onProgress)da.onProgress(Q)}},
!1);P.addEventListener("error",function(Q){var U=Mc[a];delete Mc[a];for(var V=0,ea=U.length;V<ea;V++){var da=U[V];if(da.onError)da.onError(Q)}t.manager.itemError(a);t.manager.itemEnd(a)},!1);P.addEventListener("abort",function(Q){var U=Mc[a];delete Mc[a];for(var V=0,ea=U.length;V<ea;V++){var da=U[V];if(da.onError)da.onError(Q)}t.manager.itemError(a);t.manager.itemEnd(a)},!1);void 0!==this.responseType&&(P.responseType=this.responseType);void 0!==this.withCredentials&&(P.withCredentials=this.withCredentials);
P.overrideMimeType&&P.overrideMimeType(void 0!==this.mimeType?this.mimeType:"text/plain");for(E in this.requestHeader)P.setRequestHeader(E,this.requestHeader[E]);P.send(null)}t.manager.itemStart(a);return P}},setResponseType:function(a){this.responseType=a;return this},setWithCredentials:function(a){this.withCredentials=a;return this},setMimeType:function(a){this.mimeType=a;return this},setRequestHeader:function(a){this.requestHeader=a;return this}});fi.prototype=Object.assign(Object.create(Db.prototype),
{constructor:fi,load:function(a,c,e,g){var t=this,v=new wc(t.manager);v.setPath(t.path);v.load(a,function(z){c(t.parse(JSON.parse(z)))},e,g)},parse:function(a){for(var c=[],e=0;e<a.length;e++){var g=vc.parse(a[e]);c.push(g)}return c}});gi.prototype=Object.assign(Object.create(Db.prototype),{constructor:gi,load:function(a,c,e,g){function t(Q){F.load(a[Q],function(U){U=v._parser(U,!0);z[Q]={width:U.width,height:U.height,format:U.format,mipmaps:U.mipmaps};I+=1;6===I&&(1===U.mipmapCount&&(E.minFilter=
1006),E.format=U.format,E.needsUpdate=!0,c&&c(E))},e,g)}var v=this,z=[],E=new Fe;E.image=z;var F=new wc(this.manager);F.setPath(this.path);F.setResponseType("arraybuffer");if(Array.isArray(a))for(var I=0,M=0,P=a.length;M<P;++M)t(M);else F.load(a,function(Q){Q=v._parser(Q,!0);if(Q.isCubemap)for(var U=Q.mipmaps.length/Q.mipmapCount,V=0;V<U;V++){z[V]={mipmaps:[]};for(var ea=0;ea<Q.mipmapCount;ea++)z[V].mipmaps.push(Q.mipmaps[V*Q.mipmapCount+ea]),z[V].format=Q.format,z[V].width=Q.width,z[V].height=Q.height}else E.image.width=
Q.width,E.image.height=Q.height,E.mipmaps=Q.mipmaps;1===Q.mipmapCount&&(E.minFilter=1006);E.format=Q.format;E.needsUpdate=!0;c&&c(E)},e,g);return E}});Ng.prototype=Object.assign(Object.create(Db.prototype),{constructor:Ng,load:function(a,c,e,g){var t=this,v=new Ab,z=new wc(this.manager);z.setResponseType("arraybuffer");z.setPath(this.path);z.load(a,function(E){if(E=t._parser(E))void 0!==E.image?v.image=E.image:void 0!==E.data&&(v.image.width=E.width,v.image.height=E.height,v.image.data=E.data),v.wrapS=
void 0!==E.wrapS?E.wrapS:1001,v.wrapT=void 0!==E.wrapT?E.wrapT:1001,v.magFilter=void 0!==E.magFilter?E.magFilter:1006,v.minFilter=void 0!==E.minFilter?E.minFilter:1008,v.anisotropy=void 0!==E.anisotropy?E.anisotropy:1,void 0!==E.format&&(v.format=E.format),void 0!==E.type&&(v.type=E.type),void 0!==E.mipmaps&&(v.mipmaps=E.mipmaps),1===E.mipmapCount&&(v.minFilter=1006),v.needsUpdate=!0,c&&c(v,E)},e,g);return v}});We.prototype=Object.assign(Object.create(Db.prototype),{constructor:We,load:function(a,
c,e,g){function t(){F.removeEventListener("load",t,!1);F.removeEventListener("error",v,!1);ke.add(a,this);c&&c(this);z.manager.itemEnd(a)}function v(I){F.removeEventListener("load",t,!1);F.removeEventListener("error",v,!1);g&&g(I);z.manager.itemError(a);z.manager.itemEnd(a)}void 0!==this.path&&(a=this.path+a);a=this.manager.resolveURL(a);var z=this,E=ke.get(a);if(void 0!==E)return z.manager.itemStart(a),setTimeout(function(){c&&c(E);z.manager.itemEnd(a)},0),E;var F=document.createElementNS("http://www.w3.org/1999/xhtml",
"img");F.addEventListener("load",t,!1);F.addEventListener("error",v,!1);"data:"!==a.substr(0,5)&&void 0!==this.crossOrigin&&(F.crossOrigin=this.crossOrigin);z.manager.itemStart(a);F.src=a;return F}});Og.prototype=Object.assign(Object.create(Db.prototype),{constructor:Og,load:function(a,c,e,g){function t(F){z.load(a[F],function(I){v.images[F]=I;E++;6===E&&(v.needsUpdate=!0,c&&c(v))},void 0,g)}var v=new ed,z=new We(this.manager);z.setCrossOrigin(this.crossOrigin);z.setPath(this.path);var E=0;for(e=
0;e<a.length;++e)t(e);return v}});Pg.prototype=Object.assign(Object.create(Db.prototype),{constructor:Pg,load:function(a,c,e,g){var t=new l,v=new We(this.manager);v.setCrossOrigin(this.crossOrigin);v.setPath(this.path);v.load(a,function(z){t.image=z;z=0<a.search(/\.jpe?g($|\?)/i)||0===a.search(/^data:image\/jpeg/);t.format=z?1022:1023;t.needsUpdate=!0;void 0!==c&&c(t)},e,g);return t}});Object.assign(Za.prototype,{getPoint:function(){console.warn("THREE.Curve: .getPoint() not implemented.");return null},
getPointAt:function(a,c){a=this.getUtoTmapping(a);return this.getPoint(a,c)},getPoints:function(a){void 0===a&&(a=5);for(var c=[],e=0;e<=a;e++)c.push(this.getPoint(e/a));return c},getSpacedPoints:function(a){void 0===a&&(a=5);for(var c=[],e=0;e<=a;e++)c.push(this.getPointAt(e/a));return c},getLength:function(){var a=this.getLengths();return a[a.length-1]},getLengths:function(a){void 0===a&&(a=this.arcLengthDivisions);if(this.cacheArcLengths&&this.cacheArcLengths.length===a+1&&!this.needsUpdate)return this.cacheArcLengths;
this.needsUpdate=!1;var c=[],e=this.getPoint(0),g,t=0;c.push(0);for(g=1;g<=a;g++){var v=this.getPoint(g/a);t+=v.distanceTo(e);c.push(t);e=v}return this.cacheArcLengths=c},updateArcLengths:function(){this.needsUpdate=!0;this.getLengths()},getUtoTmapping:function(a,c){var e=this.getLengths(),g=e.length;c=c?c:a*e[g-1];for(var t=0,v=g-1,z;t<=v;)if(a=Math.floor(t+(v-t)/2),z=e[a]-c,0>z)t=a+1;else if(0<z)v=a-1;else{v=a;break}a=v;if(e[a]===c)return a/(g-1);t=e[a];return(a+(c-t)/(e[a+1]-t))/(g-1)},getTangent:function(a){var c=
a-1E-4;a+=1E-4;0>c&&(c=0);1<a&&(a=1);c=this.getPoint(c);return this.getPoint(a).clone().sub(c).normalize()},getTangentAt:function(a){a=this.getUtoTmapping(a);return this.getTangent(a)},computeFrenetFrames:function(a,c){var e=new k,g=[],t=[],v=[],z=new k,E=new q,F;for(F=0;F<=a;F++){var I=F/a;g[F]=this.getTangentAt(I);g[F].normalize()}t[0]=new k;v[0]=new k;F=Number.MAX_VALUE;I=Math.abs(g[0].x);var M=Math.abs(g[0].y),P=Math.abs(g[0].z);I<=F&&(F=I,e.set(1,0,0));M<=F&&(F=M,e.set(0,1,0));P<=F&&e.set(0,
0,1);z.crossVectors(g[0],e).normalize();t[0].crossVectors(g[0],z);v[0].crossVectors(g[0],t[0]);for(F=1;F<=a;F++)t[F]=t[F-1].clone(),v[F]=v[F-1].clone(),z.crossVectors(g[F-1],g[F]),z.length()>Number.EPSILON&&(z.normalize(),e=Math.acos(hb.clamp(g[F-1].dot(g[F]),-1,1)),t[F].applyMatrix4(E.makeRotationAxis(z,e))),v[F].crossVectors(g[F],t[F]);if(!0===c)for(e=Math.acos(hb.clamp(t[0].dot(t[a]),-1,1)),e/=a,0<g[0].dot(z.crossVectors(t[0],t[a]))&&(e=-e),F=1;F<=a;F++)t[F].applyMatrix4(E.makeRotationAxis(g[F],
e*F)),v[F].crossVectors(g[F],t[F]);return{tangents:g,normals:t,binormals:v}},clone:function(){return(new this.constructor).copy(this)},copy:function(a){this.arcLengthDivisions=a.arcLengthDivisions;return this},toJSON:function(){var a={metadata:{version:4.5,type:"Curve",generator:"Curve.toJSON"}};a.arcLengthDivisions=this.arcLengthDivisions;a.type=this.type;return a},fromJSON:function(a){this.arcLengthDivisions=a.arcLengthDivisions;return this}});sc.prototype=Object.create(Za.prototype);sc.prototype.constructor=
sc;sc.prototype.isEllipseCurve=!0;sc.prototype.getPoint=function(a,c){c=c||new f;for(var e=2*Math.PI,g=this.aEndAngle-this.aStartAngle,t=Math.abs(g)<Number.EPSILON;0>g;)g+=e;for(;g>e;)g-=e;g<Number.EPSILON&&(g=t?0:e);!0!==this.aClockwise||t||(g=g===e?-e:g-e);e=this.aStartAngle+a*g;a=this.aX+this.xRadius*Math.cos(e);var v=this.aY+this.yRadius*Math.sin(e);0!==this.aRotation&&(e=Math.cos(this.aRotation),g=Math.sin(this.aRotation),t=a-this.aX,v-=this.aY,a=t*e-v*g+this.aX,v=t*g+v*e+this.aY);return c.set(a,
v)};sc.prototype.copy=function(a){Za.prototype.copy.call(this,a);this.aX=a.aX;this.aY=a.aY;this.xRadius=a.xRadius;this.yRadius=a.yRadius;this.aStartAngle=a.aStartAngle;this.aEndAngle=a.aEndAngle;this.aClockwise=a.aClockwise;this.aRotation=a.aRotation;return this};sc.prototype.toJSON=function(){var a=Za.prototype.toJSON.call(this);a.aX=this.aX;a.aY=this.aY;a.xRadius=this.xRadius;a.yRadius=this.yRadius;a.aStartAngle=this.aStartAngle;a.aEndAngle=this.aEndAngle;a.aClockwise=this.aClockwise;a.aRotation=
this.aRotation;return a};sc.prototype.fromJSON=function(a){Za.prototype.fromJSON.call(this,a);this.aX=a.aX;this.aY=a.aY;this.xRadius=a.xRadius;this.yRadius=a.yRadius;this.aStartAngle=a.aStartAngle;this.aEndAngle=a.aEndAngle;this.aClockwise=a.aClockwise;this.aRotation=a.aRotation;return this};Xe.prototype=Object.create(sc.prototype);Xe.prototype.constructor=Xe;Xe.prototype.isArcCurve=!0;var Ah=new k,Qi=new hi,Ri=new hi,Si=new hi;bc.prototype=Object.create(Za.prototype);bc.prototype.constructor=bc;
bc.prototype.isCatmullRomCurve3=!0;bc.prototype.getPoint=function(a,c){c=c||new k;var e=this.points,g=e.length;a*=g-(this.closed?0:1);var t=Math.floor(a);a-=t;this.closed?t+=0<t?0:(Math.floor(Math.abs(t)/g)+1)*g:0===a&&t===g-1&&(t=g-2,a=1);if(this.closed||0<t)var v=e[(t-1)%g];else Ah.subVectors(e[0],e[1]).add(e[0]),v=Ah;var z=e[t%g];var E=e[(t+1)%g];this.closed||t+2<g?e=e[(t+2)%g]:(Ah.subVectors(e[g-1],e[g-2]).add(e[g-1]),e=Ah);if("centripetal"===this.curveType||"chordal"===this.curveType){var F=
"chordal"===this.curveType?.5:.25;g=Math.pow(v.distanceToSquared(z),F);t=Math.pow(z.distanceToSquared(E),F);F=Math.pow(E.distanceToSquared(e),F);1E-4>t&&(t=1);1E-4>g&&(g=t);1E-4>F&&(F=t);Qi.initNonuniformCatmullRom(v.x,z.x,E.x,e.x,g,t,F);Ri.initNonuniformCatmullRom(v.y,z.y,E.y,e.y,g,t,F);Si.initNonuniformCatmullRom(v.z,z.z,E.z,e.z,g,t,F)}else"catmullrom"===this.curveType&&(Qi.initCatmullRom(v.x,z.x,E.x,e.x,this.tension),Ri.initCatmullRom(v.y,z.y,E.y,e.y,this.tension),Si.initCatmullRom(v.z,z.z,E.z,
e.z,this.tension));c.set(Qi.calc(a),Ri.calc(a),Si.calc(a));return c};bc.prototype.copy=function(a){Za.prototype.copy.call(this,a);this.points=[];for(var c=0,e=a.points.length;c<e;c++)this.points.push(a.points[c].clone());this.closed=a.closed;this.curveType=a.curveType;this.tension=a.tension;return this};bc.prototype.toJSON=function(){var a=Za.prototype.toJSON.call(this);a.points=[];for(var c=0,e=this.points.length;c<e;c++)a.points.push(this.points[c].toArray());a.closed=this.closed;a.curveType=this.curveType;
a.tension=this.tension;return a};bc.prototype.fromJSON=function(a){Za.prototype.fromJSON.call(this,a);this.points=[];for(var c=0,e=a.points.length;c<e;c++){var g=a.points[c];this.points.push((new k).fromArray(g))}this.closed=a.closed;this.curveType=a.curveType;this.tension=a.tension;return this};Ec.prototype=Object.create(Za.prototype);Ec.prototype.constructor=Ec;Ec.prototype.isCubicBezierCurve=!0;Ec.prototype.getPoint=function(a,c){c=c||new f;var e=this.v0,g=this.v1,t=this.v2,v=this.v3;c.set(bg(a,
e.x,g.x,t.x,v.x),bg(a,e.y,g.y,t.y,v.y));return c};Ec.prototype.copy=function(a){Za.prototype.copy.call(this,a);this.v0.copy(a.v0);this.v1.copy(a.v1);this.v2.copy(a.v2);this.v3.copy(a.v3);return this};Ec.prototype.toJSON=function(){var a=Za.prototype.toJSON.call(this);a.v0=this.v0.toArray();a.v1=this.v1.toArray();a.v2=this.v2.toArray();a.v3=this.v3.toArray();return a};Ec.prototype.fromJSON=function(a){Za.prototype.fromJSON.call(this,a);this.v0.fromArray(a.v0);this.v1.fromArray(a.v1);this.v2.fromArray(a.v2);
this.v3.fromArray(a.v3);return this};Vc.prototype=Object.create(Za.prototype);Vc.prototype.constructor=Vc;Vc.prototype.isCubicBezierCurve3=!0;Vc.prototype.getPoint=function(a,c){c=c||new k;var e=this.v0,g=this.v1,t=this.v2,v=this.v3;c.set(bg(a,e.x,g.x,t.x,v.x),bg(a,e.y,g.y,t.y,v.y),bg(a,e.z,g.z,t.z,v.z));return c};Vc.prototype.copy=function(a){Za.prototype.copy.call(this,a);this.v0.copy(a.v0);this.v1.copy(a.v1);this.v2.copy(a.v2);this.v3.copy(a.v3);return this};Vc.prototype.toJSON=function(){var a=
Za.prototype.toJSON.call(this);a.v0=this.v0.toArray();a.v1=this.v1.toArray();a.v2=this.v2.toArray();a.v3=this.v3.toArray();return a};Vc.prototype.fromJSON=function(a){Za.prototype.fromJSON.call(this,a);this.v0.fromArray(a.v0);this.v1.fromArray(a.v1);this.v2.fromArray(a.v2);this.v3.fromArray(a.v3);return this};nc.prototype=Object.create(Za.prototype);nc.prototype.constructor=nc;nc.prototype.isLineCurve=!0;nc.prototype.getPoint=function(a,c){c=c||new f;1===a?c.copy(this.v2):(c.copy(this.v2).sub(this.v1),
c.multiplyScalar(a).add(this.v1));return c};nc.prototype.getPointAt=function(a,c){return this.getPoint(a,c)};nc.prototype.getTangent=function(){return this.v2.clone().sub(this.v1).normalize()};nc.prototype.copy=function(a){Za.prototype.copy.call(this,a);this.v1.copy(a.v1);this.v2.copy(a.v2);return this};nc.prototype.toJSON=function(){var a=Za.prototype.toJSON.call(this);a.v1=this.v1.toArray();a.v2=this.v2.toArray();return a};nc.prototype.fromJSON=function(a){Za.prototype.fromJSON.call(this,a);this.v1.fromArray(a.v1);
this.v2.fromArray(a.v2);return this};Fc.prototype=Object.create(Za.prototype);Fc.prototype.constructor=Fc;Fc.prototype.isLineCurve3=!0;Fc.prototype.getPoint=function(a,c){c=c||new k;1===a?c.copy(this.v2):(c.copy(this.v2).sub(this.v1),c.multiplyScalar(a).add(this.v1));return c};Fc.prototype.getPointAt=function(a,c){return this.getPoint(a,c)};Fc.prototype.copy=function(a){Za.prototype.copy.call(this,a);this.v1.copy(a.v1);this.v2.copy(a.v2);return this};Fc.prototype.toJSON=function(){var a=Za.prototype.toJSON.call(this);
a.v1=this.v1.toArray();a.v2=this.v2.toArray();return a};Fc.prototype.fromJSON=function(a){Za.prototype.fromJSON.call(this,a);this.v1.fromArray(a.v1);this.v2.fromArray(a.v2);return this};Gc.prototype=Object.create(Za.prototype);Gc.prototype.constructor=Gc;Gc.prototype.isQuadraticBezierCurve=!0;Gc.prototype.getPoint=function(a,c){c=c||new f;var e=this.v0,g=this.v1,t=this.v2;c.set(ag(a,e.x,g.x,t.x),ag(a,e.y,g.y,t.y));return c};Gc.prototype.copy=function(a){Za.prototype.copy.call(this,a);this.v0.copy(a.v0);
this.v1.copy(a.v1);this.v2.copy(a.v2);return this};Gc.prototype.toJSON=function(){var a=Za.prototype.toJSON.call(this);a.v0=this.v0.toArray();a.v1=this.v1.toArray();a.v2=this.v2.toArray();return a};Gc.prototype.fromJSON=function(a){Za.prototype.fromJSON.call(this,a);this.v0.fromArray(a.v0);this.v1.fromArray(a.v1);this.v2.fromArray(a.v2);return this};Wc.prototype=Object.create(Za.prototype);Wc.prototype.constructor=Wc;Wc.prototype.isQuadraticBezierCurve3=!0;Wc.prototype.getPoint=function(a,c){c=c||
new k;var e=this.v0,g=this.v1,t=this.v2;c.set(ag(a,e.x,g.x,t.x),ag(a,e.y,g.y,t.y),ag(a,e.z,g.z,t.z));return c};Wc.prototype.copy=function(a){Za.prototype.copy.call(this,a);this.v0.copy(a.v0);this.v1.copy(a.v1);this.v2.copy(a.v2);return this};Wc.prototype.toJSON=function(){var a=Za.prototype.toJSON.call(this);a.v0=this.v0.toArray();a.v1=this.v1.toArray();a.v2=this.v2.toArray();return a};Wc.prototype.fromJSON=function(a){Za.prototype.fromJSON.call(this,a);this.v0.fromArray(a.v0);this.v1.fromArray(a.v1);
this.v2.fromArray(a.v2);return this};Hc.prototype=Object.create(Za.prototype);Hc.prototype.constructor=Hc;Hc.prototype.isSplineCurve=!0;Hc.prototype.getPoint=function(a,c){c=c||new f;var e=this.points,g=(e.length-1)*a;a=Math.floor(g);g-=a;var t=e[0===a?a:a-1],v=e[a],z=e[a>e.length-2?e.length-1:a+1];e=e[a>e.length-3?e.length-1:a+2];c.set(Yj(g,t.x,v.x,z.x,e.x),Yj(g,t.y,v.y,z.y,e.y));return c};Hc.prototype.copy=function(a){Za.prototype.copy.call(this,a);this.points=[];for(var c=0,e=a.points.length;c<
e;c++)this.points.push(a.points[c].clone());return this};Hc.prototype.toJSON=function(){var a=Za.prototype.toJSON.call(this);a.points=[];for(var c=0,e=this.points.length;c<e;c++)a.points.push(this.points[c].toArray());return a};Hc.prototype.fromJSON=function(a){Za.prototype.fromJSON.call(this,a);this.points=[];for(var c=0,e=a.points.length;c<e;c++){var g=a.points[c];this.points.push((new f).fromArray(g))}return this};var Ti=Object.freeze({ArcCurve:Xe,CatmullRomCurve3:bc,CubicBezierCurve:Ec,CubicBezierCurve3:Vc,
EllipseCurve:sc,LineCurve:nc,LineCurve3:Fc,QuadraticBezierCurve:Gc,QuadraticBezierCurve3:Wc,SplineCurve:Hc});id.prototype=Object.assign(Object.create(Za.prototype),{constructor:id,add:function(a){this.curves.push(a)},closePath:function(){var a=this.curves[0].getPoint(0),c=this.curves[this.curves.length-1].getPoint(1);a.equals(c)||this.curves.push(new nc(c,a))},getPoint:function(a){var c=a*this.getLength(),e=this.getCurveLengths();for(a=0;a<e.length;){if(e[a]>=c)return c=e[a]-c,a=this.curves[a],e=
a.getLength(),a.getPointAt(0===e?0:1-c/e);a++}return null},getLength:function(){var a=this.getCurveLengths();return a[a.length-1]},updateArcLengths:function(){this.needsUpdate=!0;this.cacheLengths=null;this.getCurveLengths()},getCurveLengths:function(){if(this.cacheLengths&&this.cacheLengths.length===this.curves.length)return this.cacheLengths;for(var a=[],c=0,e=0,g=this.curves.length;e<g;e++)c+=this.curves[e].getLength(),a.push(c);return this.cacheLengths=a},getSpacedPoints:function(a){void 0===
a&&(a=40);for(var c=[],e=0;e<=a;e++)c.push(this.getPoint(e/a));this.autoClose&&c.push(c[0]);return c},getPoints:function(a){a=a||12;for(var c=[],e,g=0,t=this.curves;g<t.length;g++){var v=t[g];v=v.getPoints(v&&v.isEllipseCurve?2*a:v&&(v.isLineCurve||v.isLineCurve3)?1:v&&v.isSplineCurve?a*v.points.length:a);for(var z=0;z<v.length;z++){var E=v[z];e&&e.equals(E)||(c.push(E),e=E)}}this.autoClose&&1<c.length&&!c[c.length-1].equals(c[0])&&c.push(c[0]);return c},copy:function(a){Za.prototype.copy.call(this,
a);this.curves=[];for(var c=0,e=a.curves.length;c<e;c++)this.curves.push(a.curves[c].clone());this.autoClose=a.autoClose;return this},toJSON:function(){var a=Za.prototype.toJSON.call(this);a.autoClose=this.autoClose;a.curves=[];for(var c=0,e=this.curves.length;c<e;c++)a.curves.push(this.curves[c].toJSON());return a},fromJSON:function(a){Za.prototype.fromJSON.call(this,a);this.autoClose=a.autoClose;this.curves=[];for(var c=0,e=a.curves.length;c<e;c++){var g=a.curves[c];this.curves.push((new Ti[g.type]).fromJSON(g))}return this}});
Ic.prototype=Object.assign(Object.create(id.prototype),{constructor:Ic,setFromPoints:function(a){this.moveTo(a[0].x,a[0].y);for(var c=1,e=a.length;c<e;c++)this.lineTo(a[c].x,a[c].y)},moveTo:function(a,c){this.currentPoint.set(a,c)},lineTo:function(a,c){var e=new nc(this.currentPoint.clone(),new f(a,c));this.curves.push(e);this.currentPoint.set(a,c)},quadraticCurveTo:function(a,c,e,g){a=new Gc(this.currentPoint.clone(),new f(a,c),new f(e,g));this.curves.push(a);this.currentPoint.set(e,g)},bezierCurveTo:function(a,
c,e,g,t,v){a=new Ec(this.currentPoint.clone(),new f(a,c),new f(e,g),new f(t,v));this.curves.push(a);this.currentPoint.set(t,v)},splineThru:function(a){var c=[this.currentPoint.clone()].concat(a);c=new Hc(c);this.curves.push(c);this.currentPoint.copy(a[a.length-1])},arc:function(a,c,e,g,t,v){this.absarc(a+this.currentPoint.x,c+this.currentPoint.y,e,g,t,v)},absarc:function(a,c,e,g,t,v){this.absellipse(a,c,e,e,g,t,v)},ellipse:function(a,c,e,g,t,v,z,E){this.absellipse(a+this.currentPoint.x,c+this.currentPoint.y,
e,g,t,v,z,E)},absellipse:function(a,c,e,g,t,v,z,E){a=new sc(a,c,e,g,t,v,z,E);0<this.curves.length&&(c=a.getPoint(0),c.equals(this.currentPoint)||this.lineTo(c.x,c.y));this.curves.push(a);a=a.getPoint(1);this.currentPoint.copy(a)},copy:function(a){id.prototype.copy.call(this,a);this.currentPoint.copy(a.currentPoint);return this},toJSON:function(){var a=id.prototype.toJSON.call(this);a.currentPoint=this.currentPoint.toArray();return a},fromJSON:function(a){id.prototype.fromJSON.call(this,a);this.currentPoint.fromArray(a.currentPoint);
return this}});Ed.prototype=Object.assign(Object.create(Ic.prototype),{constructor:Ed,getPointsHoles:function(a){for(var c=[],e=0,g=this.holes.length;e<g;e++)c[e]=this.holes[e].getPoints(a);return c},extractPoints:function(a){return{shape:this.getPoints(a),holes:this.getPointsHoles(a)}},copy:function(a){Ic.prototype.copy.call(this,a);this.holes=[];for(var c=0,e=a.holes.length;c<e;c++)this.holes.push(a.holes[c].clone());return this},toJSON:function(){var a=Ic.prototype.toJSON.call(this);a.uuid=this.uuid;
a.holes=[];for(var c=0,e=this.holes.length;c<e;c++)a.holes.push(this.holes[c].toJSON());return a},fromJSON:function(a){Ic.prototype.fromJSON.call(this,a);this.uuid=a.uuid;this.holes=[];for(var c=0,e=a.holes.length;c<e;c++){var g=a.holes[c];this.holes.push((new Ic).fromJSON(g))}return this}});Kb.prototype=Object.assign(Object.create(A.prototype),{constructor:Kb,isLight:!0,copy:function(a){A.prototype.copy.call(this,a);this.color.copy(a.color);this.intensity=a.intensity;return this},toJSON:function(a){a=
A.prototype.toJSON.call(this,a);a.object.color=this.color.getHex();a.object.intensity=this.intensity;void 0!==this.groundColor&&(a.object.groundColor=this.groundColor.getHex());void 0!==this.distance&&(a.object.distance=this.distance);void 0!==this.angle&&(a.object.angle=this.angle);void 0!==this.decay&&(a.object.decay=this.decay);void 0!==this.penumbra&&(a.object.penumbra=this.penumbra);void 0!==this.shadow&&(a.object.shadow=this.shadow.toJSON());return a}});Qg.prototype=Object.assign(Object.create(Kb.prototype),
{constructor:Qg,isHemisphereLight:!0,copy:function(a){Kb.prototype.copy.call(this,a);this.groundColor.copy(a.groundColor);return this}});Object.assign(Xc.prototype,{_projScreenMatrix:new q,_lightPositionWorld:new k,_lookTarget:new k,getViewportCount:function(){return this._viewportCount},getFrustum:function(){return this._frustum},updateMatrices:function(a){var c=this.camera,e=this.matrix,g=this._projScreenMatrix,t=this._lookTarget,v=this._lightPositionWorld;v.setFromMatrixPosition(a.matrixWorld);
c.position.copy(v);t.setFromMatrixPosition(a.target.matrixWorld);c.lookAt(t);c.updateMatrixWorld();g.multiplyMatrices(c.projectionMatrix,c.matrixWorldInverse);this._frustum.setFromMatrix(g);e.set(.5,0,0,.5,0,.5,0,.5,0,0,.5,.5,0,0,0,1);e.multiply(c.projectionMatrix);e.multiply(c.matrixWorldInverse)},getViewport:function(a){return this._viewports[a]},getFrameExtents:function(){return this._frameExtents},copy:function(a){this.camera=a.camera.clone();this.bias=a.bias;this.radius=a.radius;this.mapSize.copy(a.mapSize);
return this},clone:function(){return(new this.constructor).copy(this)},toJSON:function(){var a={};0!==this.bias&&(a.bias=this.bias);1!==this.radius&&(a.radius=this.radius);if(512!==this.mapSize.x||512!==this.mapSize.y)a.mapSize=this.mapSize.toArray();a.camera=this.camera.toJSON(!1).object;delete a.camera.matrix;return a}});Rg.prototype=Object.assign(Object.create(Xc.prototype),{constructor:Rg,isSpotLightShadow:!0,updateMatrices:function(a,c,e){var g=this.camera,t=2*hb.RAD2DEG*a.angle,v=this.mapSize.width/
this.mapSize.height,z=a.distance||g.far;if(t!==g.fov||v!==g.aspect||z!==g.far)g.fov=t,g.aspect=v,g.far=z,g.updateProjectionMatrix();Xc.prototype.updateMatrices.call(this,a,c,e)}});Sg.prototype=Object.assign(Object.create(Kb.prototype),{constructor:Sg,isSpotLight:!0,copy:function(a){Kb.prototype.copy.call(this,a);this.distance=a.distance;this.angle=a.angle;this.penumbra=a.penumbra;this.decay=a.decay;this.target=a.target.clone();this.shadow=a.shadow.clone();return this}});ii.prototype=Object.assign(Object.create(Xc.prototype),
{constructor:ii,isPointLightShadow:!0,updateMatrices:function(a,c,e){c=this.camera;var g=this.matrix,t=this._lightPositionWorld,v=this._lookTarget,z=this._projScreenMatrix;t.setFromMatrixPosition(a.matrixWorld);c.position.copy(t);v.copy(c.position);v.add(this._cubeDirections[e]);c.up.copy(this._cubeUps[e]);c.lookAt(v);c.updateMatrixWorld();g.makeTranslation(-t.x,-t.y,-t.z);z.multiplyMatrices(c.projectionMatrix,c.matrixWorldInverse);this._frustum.setFromMatrix(z)}});Tg.prototype=Object.assign(Object.create(Kb.prototype),
{constructor:Tg,isPointLight:!0,copy:function(a){Kb.prototype.copy.call(this,a);this.distance=a.distance;this.decay=a.decay;this.shadow=a.shadow.clone();return this}});cg.prototype=Object.assign(Object.create(zb.prototype),{constructor:cg,isOrthographicCamera:!0,copy:function(a,c){zb.prototype.copy.call(this,a,c);this.left=a.left;this.right=a.right;this.top=a.top;this.bottom=a.bottom;this.near=a.near;this.far=a.far;this.zoom=a.zoom;this.view=null===a.view?null:Object.assign({},a.view);return this},
setViewOffset:function(a,c,e,g,t,v){null===this.view&&(this.view={enabled:!0,fullWidth:1,fullHeight:1,offsetX:0,offsetY:0,width:1,height:1});this.view.enabled=!0;this.view.fullWidth=a;this.view.fullHeight=c;this.view.offsetX=e;this.view.offsetY=g;this.view.width=t;this.view.height=v;this.updateProjectionMatrix()},clearViewOffset:function(){null!==this.view&&(this.view.enabled=!1);this.updateProjectionMatrix()},updateProjectionMatrix:function(){var a=(this.right-this.left)/(2*this.zoom),c=(this.top-
this.bottom)/(2*this.zoom),e=(this.right+this.left)/2,g=(this.top+this.bottom)/2,t=e-a;e+=a;a=g+c;c=g-c;if(null!==this.view&&this.view.enabled){e=this.zoom/(this.view.width/this.view.fullWidth);c=this.zoom/(this.view.height/this.view.fullHeight);var v=(this.right-this.left)/this.view.width;g=(this.top-this.bottom)/this.view.height;t+=this.view.offsetX/e*v;e=t+this.view.width/e*v;a-=this.view.offsetY/c*g;c=a-this.view.height/c*g}this.projectionMatrix.makeOrthographic(t,e,a,c,this.near,this.far);this.projectionMatrixInverse.getInverse(this.projectionMatrix)},
toJSON:function(a){a=A.prototype.toJSON.call(this,a);a.object.zoom=this.zoom;a.object.left=this.left;a.object.right=this.right;a.object.top=this.top;a.object.bottom=this.bottom;a.object.near=this.near;a.object.far=this.far;null!==this.view&&(a.object.view=Object.assign({},this.view));return a}});Ug.prototype=Object.assign(Object.create(Xc.prototype),{constructor:Ug,isDirectionalLightShadow:!0,updateMatrices:function(a,c,e){Xc.prototype.updateMatrices.call(this,a,c,e)}});Vg.prototype=Object.assign(Object.create(Kb.prototype),
{constructor:Vg,isDirectionalLight:!0,copy:function(a){Kb.prototype.copy.call(this,a);this.target=a.target.clone();this.shadow=a.shadow.clone();return this}});Wg.prototype=Object.assign(Object.create(Kb.prototype),{constructor:Wg,isAmbientLight:!0});Xg.prototype=Object.assign(Object.create(Kb.prototype),{constructor:Xg,isRectAreaLight:!0,copy:function(a){Kb.prototype.copy.call(this,a);this.width=a.width;this.height=a.height;return this},toJSON:function(a){a=Kb.prototype.toJSON.call(this,a);a.object.width=
this.width;a.object.height=this.height;return a}});Yg.prototype=Object.assign(Object.create(Db.prototype),{constructor:Yg,load:function(a,c,e,g){var t=this,v=new wc(t.manager);v.setPath(t.path);v.load(a,function(z){c(t.parse(JSON.parse(z)))},e,g)},parse:function(a){function c(E){void 0===e[E]&&console.warn("THREE.MaterialLoader: Undefined texture",E);return e[E]}var e=this.textures,g=new en[a.type];void 0!==a.uuid&&(g.uuid=a.uuid);void 0!==a.name&&(g.name=a.name);void 0!==a.color&&g.color.setHex(a.color);
void 0!==a.roughness&&(g.roughness=a.roughness);void 0!==a.metalness&&(g.metalness=a.metalness);void 0!==a.emissive&&g.emissive.setHex(a.emissive);void 0!==a.specular&&g.specular.setHex(a.specular);void 0!==a.shininess&&(g.shininess=a.shininess);void 0!==a.clearcoat&&(g.clearcoat=a.clearcoat);void 0!==a.clearcoatRoughness&&(g.clearcoatRoughness=a.clearcoatRoughness);void 0!==a.vertexColors&&(g.vertexColors=a.vertexColors);void 0!==a.fog&&(g.fog=a.fog);void 0!==a.flatShading&&(g.flatShading=a.flatShading);
void 0!==a.blending&&(g.blending=a.blending);void 0!==a.combine&&(g.combine=a.combine);void 0!==a.side&&(g.side=a.side);void 0!==a.opacity&&(g.opacity=a.opacity);void 0!==a.transparent&&(g.transparent=a.transparent);void 0!==a.alphaTest&&(g.alphaTest=a.alphaTest);void 0!==a.depthTest&&(g.depthTest=a.depthTest);void 0!==a.depthWrite&&(g.depthWrite=a.depthWrite);void 0!==a.colorWrite&&(g.colorWrite=a.colorWrite);void 0!==a.wireframe&&(g.wireframe=a.wireframe);void 0!==a.wireframeLinewidth&&(g.wireframeLinewidth=
a.wireframeLinewidth);void 0!==a.wireframeLinecap&&(g.wireframeLinecap=a.wireframeLinecap);void 0!==a.wireframeLinejoin&&(g.wireframeLinejoin=a.wireframeLinejoin);void 0!==a.rotation&&(g.rotation=a.rotation);1!==a.linewidth&&(g.linewidth=a.linewidth);void 0!==a.dashSize&&(g.dashSize=a.dashSize);void 0!==a.gapSize&&(g.gapSize=a.gapSize);void 0!==a.scale&&(g.scale=a.scale);void 0!==a.polygonOffset&&(g.polygonOffset=a.polygonOffset);void 0!==a.polygonOffsetFactor&&(g.polygonOffsetFactor=a.polygonOffsetFactor);
void 0!==a.polygonOffsetUnits&&(g.polygonOffsetUnits=a.polygonOffsetUnits);void 0!==a.skinning&&(g.skinning=a.skinning);void 0!==a.morphTargets&&(g.morphTargets=a.morphTargets);void 0!==a.morphNormals&&(g.morphNormals=a.morphNormals);void 0!==a.dithering&&(g.dithering=a.dithering);void 0!==a.visible&&(g.visible=a.visible);void 0!==a.toneMapped&&(g.toneMapped=a.toneMapped);void 0!==a.userData&&(g.userData=a.userData);if(void 0!==a.uniforms)for(var t in a.uniforms){var v=a.uniforms[t];g.uniforms[t]=
{};switch(v.type){case "t":g.uniforms[t].value=c(v.value);break;case "c":g.uniforms[t].value=(new H).setHex(v.value);break;case "v2":g.uniforms[t].value=(new f).fromArray(v.value);break;case "v3":g.uniforms[t].value=(new k).fromArray(v.value);break;case "v4":g.uniforms[t].value=(new p).fromArray(v.value);break;case "m3":g.uniforms[t].value=(new r).fromArray(v.value);case "m4":g.uniforms[t].value=(new q).fromArray(v.value);break;default:g.uniforms[t].value=v.value}}void 0!==a.defines&&(g.defines=a.defines);
void 0!==a.vertexShader&&(g.vertexShader=a.vertexShader);void 0!==a.fragmentShader&&(g.fragmentShader=a.fragmentShader);if(void 0!==a.extensions)for(var z in a.extensions)g.extensions[z]=a.extensions[z];void 0!==a.shading&&(g.flatShading=1===a.shading);void 0!==a.size&&(g.size=a.size);void 0!==a.sizeAttenuation&&(g.sizeAttenuation=a.sizeAttenuation);void 0!==a.map&&(g.map=c(a.map));void 0!==a.matcap&&(g.matcap=c(a.matcap));void 0!==a.alphaMap&&(g.alphaMap=c(a.alphaMap),g.transparent=!0);void 0!==
a.bumpMap&&(g.bumpMap=c(a.bumpMap));void 0!==a.bumpScale&&(g.bumpScale=a.bumpScale);void 0!==a.normalMap&&(g.normalMap=c(a.normalMap));void 0!==a.normalMapType&&(g.normalMapType=a.normalMapType);void 0!==a.normalScale&&(t=a.normalScale,!1===Array.isArray(t)&&(t=[t,t]),g.normalScale=(new f).fromArray(t));void 0!==a.displacementMap&&(g.displacementMap=c(a.displacementMap));void 0!==a.displacementScale&&(g.displacementScale=a.displacementScale);void 0!==a.displacementBias&&(g.displacementBias=a.displacementBias);
void 0!==a.roughnessMap&&(g.roughnessMap=c(a.roughnessMap));void 0!==a.metalnessMap&&(g.metalnessMap=c(a.metalnessMap));void 0!==a.emissiveMap&&(g.emissiveMap=c(a.emissiveMap));void 0!==a.emissiveIntensity&&(g.emissiveIntensity=a.emissiveIntensity);void 0!==a.specularMap&&(g.specularMap=c(a.specularMap));void 0!==a.envMap&&(g.envMap=c(a.envMap));void 0!==a.envMapIntensity&&(g.envMapIntensity=a.envMapIntensity);void 0!==a.reflectivity&&(g.reflectivity=a.reflectivity);void 0!==a.refractionRatio&&(g.refractionRatio=
a.refractionRatio);void 0!==a.lightMap&&(g.lightMap=c(a.lightMap));void 0!==a.lightMapIntensity&&(g.lightMapIntensity=a.lightMapIntensity);void 0!==a.aoMap&&(g.aoMap=c(a.aoMap));void 0!==a.aoMapIntensity&&(g.aoMapIntensity=a.aoMapIntensity);void 0!==a.gradientMap&&(g.gradientMap=c(a.gradientMap));void 0!==a.clearcoatNormalMap&&(g.clearcoatNormalMap=c(a.clearcoatNormalMap));void 0!==a.clearcoatNormalScale&&(g.clearcoatNormalScale=(new f).fromArray(a.clearcoatNormalScale));return g},setTextures:function(a){this.textures=
a;return this}});var Ui={decodeText:function(a){if("undefined"!==typeof TextDecoder)return(new TextDecoder).decode(a);for(var c="",e=0,g=a.length;e<g;e++)c+=String.fromCharCode(a[e]);try{return decodeURIComponent(escape(c))}catch(t){return c}},extractUrlBase:function(a){var c=a.lastIndexOf("/");return-1===c?"./":a.substr(0,c+1)}};Zg.prototype=Object.assign(Object.create(va.prototype),{constructor:Zg,isInstancedBufferGeometry:!0,copy:function(a){va.prototype.copy.call(this,a);this.maxInstancedCount=
a.maxInstancedCount;return this},clone:function(){return(new this.constructor).copy(this)},toJSON:function(){var a=va.prototype.toJSON.call(this);a.maxInstancedCount=this.maxInstancedCount;a.isInstancedBufferGeometry=!0;return a}});$g.prototype=Object.assign(Object.create(R.prototype),{constructor:$g,isInstancedBufferAttribute:!0,copy:function(a){R.prototype.copy.call(this,a);this.meshPerAttribute=a.meshPerAttribute;return this},toJSON:function(){var a=R.prototype.toJSON.call(this);a.meshPerAttribute=
this.meshPerAttribute;a.isInstancedBufferAttribute=!0;return a}});ah.prototype=Object.assign(Object.create(Db.prototype),{constructor:ah,load:function(a,c,e,g){var t=this,v=new wc(t.manager);v.setPath(t.path);v.load(a,function(z){c(t.parse(JSON.parse(z)))},e,g)},parse:function(a){var c=a.isInstancedBufferGeometry?new Zg:new va,e=a.data.index;if(void 0!==e){var g=new Vi[e.type](e.array);c.setIndex(new R(g,1))}e=a.data.attributes;for(var t in e){var v=e[t];g=new Vi[v.type](v.array);g=new (v.isInstancedBufferAttribute?
$g:R)(g,v.itemSize,v.normalized);void 0!==v.name&&(g.name=v.name);c.addAttribute(t,g)}var z=a.data.morphAttributes;if(z)for(t in z){var E=z[t],F=[];e=0;for(var I=E.length;e<I;e++)v=E[e],g=new Vi[v.type](v.array),g=new R(g,v.itemSize,v.normalized),void 0!==v.name&&(g.name=v.name),F.push(g);c.morphAttributes[t]=F}t=a.data.groups||a.data.drawcalls||a.data.offsets;if(void 0!==t)for(e=0,v=t.length;e!==v;++e)g=t[e],c.addGroup(g.start,g.count,g.materialIndex);e=a.data.boundingSphere;void 0!==e&&(t=new k,
void 0!==e.center&&t.fromArray(e.center),c.boundingSphere=new G(t,e.radius));a.name&&(c.name=a.name);a.userData&&(c.userData=a.userData);return c}});var Vi={Int8Array,Uint8Array,Uint8ClampedArray:"undefined"!==typeof Uint8ClampedArray?Uint8ClampedArray:Uint8Array,Int16Array,Uint16Array,Int32Array,Uint32Array,Float32Array,Float64Array};bh.prototype=Object.assign(Object.create(Db.prototype),{constructor:bh,load:function(a,c,e,g){var t=this,v=""===this.path?Ui.extractUrlBase(a):this.path;this.resourcePath=
this.resourcePath||v;v=new wc(t.manager);v.setPath(this.path);v.load(a,function(z){var E=null;try{E=JSON.parse(z)}catch(F){void 0!==g&&g(F);console.error("THREE:ObjectLoader: Can't parse "+a+".",F.message);return}z=E.metadata;void 0===z||void 0===z.type||"geometry"===z.type.toLowerCase()?console.error("THREE.ObjectLoader: Can't load "+a):t.parse(E,c)},e,g)},parse:function(a,c){var e=this.parseShape(a.shapes);e=this.parseGeometries(a.geometries,e);var g=this.parseImages(a.images,function(){void 0!==
c&&c(t)});g=this.parseTextures(a.textures,g);g=this.parseMaterials(a.materials,g);var t=this.parseObject(a.object,e,g);a.animations&&(t.animations=this.parseAnimations(a.animations));void 0!==a.images&&0!==a.images.length||void 0===c||c(t);return t},parseShape:function(a){var c={};if(void 0!==a)for(var e=0,g=a.length;e<g;e++){var t=(new Ed).fromJSON(a[e]);c[t.uuid]=t}return c},parseGeometries:function(a,c){var e={};if(void 0!==a)for(var g=new ah,t=0,v=a.length;t<v;t++){var z=a[t];switch(z.type){case "PlaneGeometry":case "PlaneBufferGeometry":var E=
new jc[z.type](z.width,z.height,z.widthSegments,z.heightSegments);break;case "BoxGeometry":case "BoxBufferGeometry":case "CubeGeometry":E=new jc[z.type](z.width,z.height,z.depth,z.widthSegments,z.heightSegments,z.depthSegments);break;case "CircleGeometry":case "CircleBufferGeometry":E=new jc[z.type](z.radius,z.segments,z.thetaStart,z.thetaLength);break;case "CylinderGeometry":case "CylinderBufferGeometry":E=new jc[z.type](z.radiusTop,z.radiusBottom,z.height,z.radialSegments,z.heightSegments,z.openEnded,
z.thetaStart,z.thetaLength);break;case "ConeGeometry":case "ConeBufferGeometry":E=new jc[z.type](z.radius,z.height,z.radialSegments,z.heightSegments,z.openEnded,z.thetaStart,z.thetaLength);break;case "SphereGeometry":case "SphereBufferGeometry":E=new jc[z.type](z.radius,z.widthSegments,z.heightSegments,z.phiStart,z.phiLength,z.thetaStart,z.thetaLength);break;case "DodecahedronGeometry":case "DodecahedronBufferGeometry":case "IcosahedronGeometry":case "IcosahedronBufferGeometry":case "OctahedronGeometry":case "OctahedronBufferGeometry":case "TetrahedronGeometry":case "TetrahedronBufferGeometry":E=
new jc[z.type](z.radius,z.detail);break;case "RingGeometry":case "RingBufferGeometry":E=new jc[z.type](z.innerRadius,z.outerRadius,z.thetaSegments,z.phiSegments,z.thetaStart,z.thetaLength);break;case "TorusGeometry":case "TorusBufferGeometry":E=new jc[z.type](z.radius,z.tube,z.radialSegments,z.tubularSegments,z.arc);break;case "TorusKnotGeometry":case "TorusKnotBufferGeometry":E=new jc[z.type](z.radius,z.tube,z.tubularSegments,z.radialSegments,z.p,z.q);break;case "TubeGeometry":case "TubeBufferGeometry":E=
new jc[z.type]((new Ti[z.path.type]).fromJSON(z.path),z.tubularSegments,z.radius,z.radialSegments,z.closed);break;case "LatheGeometry":case "LatheBufferGeometry":E=new jc[z.type](z.points,z.segments,z.phiStart,z.phiLength);break;case "PolyhedronGeometry":case "PolyhedronBufferGeometry":E=new jc[z.type](z.vertices,z.indices,z.radius,z.details);break;case "ShapeGeometry":case "ShapeBufferGeometry":E=[];for(var F=0,I=z.shapes.length;F<I;F++){var M=c[z.shapes[F]];E.push(M)}E=new jc[z.type](E,z.curveSegments);
break;case "ExtrudeGeometry":case "ExtrudeBufferGeometry":E=[];F=0;for(I=z.shapes.length;F<I;F++)M=c[z.shapes[F]],E.push(M);F=z.options.extrudePath;void 0!==F&&(z.options.extrudePath=(new Ti[F.type]).fromJSON(F));E=new jc[z.type](E,z.options);break;case "BufferGeometry":case "InstancedBufferGeometry":E=g.parse(z);break;case "Geometry":"THREE"in window&&"LegacyJSONLoader"in THREE?E=(new THREE.LegacyJSONLoader).parse(z,this.resourcePath).geometry:console.error('THREE.ObjectLoader: You have to import LegacyJSONLoader in order load geometry data of type "Geometry".');
break;default:console.warn('THREE.ObjectLoader: Unsupported geometry type "'+z.type+'"');continue}E.uuid=z.uuid;void 0!==z.name&&(E.name=z.name);!0===E.isBufferGeometry&&void 0!==z.userData&&(E.userData=z.userData);e[z.uuid]=E}return e},parseMaterials:function(a,c){var e={},g={};if(void 0!==a){var t=new Yg;t.setTextures(c);c=0;for(var v=a.length;c<v;c++){var z=a[c];if("MultiMaterial"===z.type){for(var E=[],F=0;F<z.materials.length;F++){var I=z.materials[F];void 0===e[I.uuid]&&(e[I.uuid]=t.parse(I));
E.push(e[I.uuid])}g[z.uuid]=E}else void 0===e[z.uuid]&&(e[z.uuid]=t.parse(z)),g[z.uuid]=e[z.uuid]}}return g},parseAnimations:function(a){for(var c=[],e=0;e<a.length;e++){var g=a[e],t=vc.parse(g);void 0!==g.uuid&&(t.uuid=g.uuid);c.push(t)}return c},parseImages:function(a,c){function e(Q){g.manager.itemStart(Q);return v.load(Q,function(){g.manager.itemEnd(Q)},void 0,function(){g.manager.itemError(Q);g.manager.itemEnd(Q)})}var g=this,t={};if(void 0!==a&&0<a.length){c=new ei(c);var v=new We(c);v.setCrossOrigin(this.crossOrigin);
c=0;for(var z=a.length;c<z;c++){var E=a[c],F=E.url;if(Array.isArray(F)){t[E.uuid]=[];for(var I=0,M=F.length;I<M;I++){var P=F[I];P=/^(\/\/)|([a-z]+:(\/\/)?)/i.test(P)?P:g.resourcePath+P;t[E.uuid].push(e(P))}}else P=/^(\/\/)|([a-z]+:(\/\/)?)/i.test(E.url)?E.url:g.resourcePath+E.url,t[E.uuid]=e(P)}}return t},parseTextures:function(a,c){function e(F,I){if("number"===typeof F)return F;console.warn("THREE.ObjectLoader.parseTexture: Constant should be in numeric form.",F);return I[F]}var g={};if(void 0!==
a)for(var t=0,v=a.length;t<v;t++){var z=a[t];void 0===z.image&&console.warn('THREE.ObjectLoader: No "image" specified for',z.uuid);void 0===c[z.image]&&console.warn("THREE.ObjectLoader: Undefined image",z.image);var E=Array.isArray(c[z.image])?new ed(c[z.image]):new l(c[z.image]);E.needsUpdate=!0;E.uuid=z.uuid;void 0!==z.name&&(E.name=z.name);void 0!==z.mapping&&(E.mapping=e(z.mapping,fn));void 0!==z.offset&&E.offset.fromArray(z.offset);void 0!==z.repeat&&E.repeat.fromArray(z.repeat);void 0!==z.center&&
E.center.fromArray(z.center);void 0!==z.rotation&&(E.rotation=z.rotation);void 0!==z.wrap&&(E.wrapS=e(z.wrap[0],Bk),E.wrapT=e(z.wrap[1],Bk));void 0!==z.format&&(E.format=z.format);void 0!==z.type&&(E.type=z.type);void 0!==z.encoding&&(E.encoding=z.encoding);void 0!==z.minFilter&&(E.minFilter=e(z.minFilter,Ck));void 0!==z.magFilter&&(E.magFilter=e(z.magFilter,Ck));void 0!==z.anisotropy&&(E.anisotropy=z.anisotropy);void 0!==z.flipY&&(E.flipY=z.flipY);void 0!==z.premultiplyAlpha&&(E.premultiplyAlpha=
z.premultiplyAlpha);void 0!==z.unpackAlignment&&(E.unpackAlignment=z.unpackAlignment);g[z.uuid]=E}return g},parseObject:function(a,c,e){function g(I){void 0===c[I]&&console.warn("THREE.ObjectLoader: Undefined geometry",I);return c[I]}function t(I){if(void 0!==I){if(Array.isArray(I)){for(var M=[],P=0,Q=I.length;P<Q;P++){var U=I[P];void 0===e[U]&&console.warn("THREE.ObjectLoader: Undefined material",U);M.push(e[U])}return M}void 0===e[I]&&console.warn("THREE.ObjectLoader: Undefined material",I);return e[I]}}
switch(a.type){case "Scene":var v=new y;void 0!==a.background&&Number.isInteger(a.background)&&(v.background=new H(a.background));void 0!==a.fog&&("Fog"===a.fog.type?v.fog=new Dg(a.fog.color,a.fog.near,a.fog.far):"FogExp2"===a.fog.type&&(v.fog=new Cg(a.fog.color,a.fog.density)));break;case "PerspectiveCamera":v=new vb(a.fov,a.aspect,a.near,a.far);void 0!==a.focus&&(v.focus=a.focus);void 0!==a.zoom&&(v.zoom=a.zoom);void 0!==a.filmGauge&&(v.filmGauge=a.filmGauge);void 0!==a.filmOffset&&(v.filmOffset=
a.filmOffset);void 0!==a.view&&(v.view=Object.assign({},a.view));break;case "OrthographicCamera":v=new cg(a.left,a.right,a.top,a.bottom,a.near,a.far);void 0!==a.zoom&&(v.zoom=a.zoom);void 0!==a.view&&(v.view=Object.assign({},a.view));break;case "AmbientLight":v=new Wg(a.color,a.intensity);break;case "DirectionalLight":v=new Vg(a.color,a.intensity);break;case "PointLight":v=new Tg(a.color,a.intensity,a.distance,a.decay);break;case "RectAreaLight":v=new Xg(a.color,a.intensity,a.width,a.height);break;
case "SpotLight":v=new Sg(a.color,a.intensity,a.distance,a.angle,a.penumbra,a.decay);break;case "HemisphereLight":v=new Qg(a.color,a.groundColor,a.intensity);break;case "SkinnedMesh":console.warn("THREE.ObjectLoader.parseObject() does not support SkinnedMesh yet.");case "Mesh":v=g(a.geometry);var z=t(a.material);v=v.bones&&0<v.bones.length?new Cf(v,z):new ya(v,z);void 0!==a.drawMode&&v.setDrawMode(a.drawMode);break;case "LOD":v=new Bf;break;case "Line":v=new Xb(g(a.geometry),t(a.material),a.mode);
break;case "LineLoop":v=new Gg(g(a.geometry),t(a.material));break;case "LineSegments":v=new Ib(g(a.geometry),t(a.material));break;case "PointCloud":case "Points":v=new Ee(g(a.geometry),t(a.material));break;case "Sprite":v=new zf(t(a.material));break;case "Group":v=new we;break;default:v=new A}v.uuid=a.uuid;void 0!==a.name&&(v.name=a.name);void 0!==a.matrix?(v.matrix.fromArray(a.matrix),void 0!==a.matrixAutoUpdate&&(v.matrixAutoUpdate=a.matrixAutoUpdate),v.matrixAutoUpdate&&v.matrix.decompose(v.position,
v.quaternion,v.scale)):(void 0!==a.position&&v.position.fromArray(a.position),void 0!==a.rotation&&v.rotation.fromArray(a.rotation),void 0!==a.quaternion&&v.quaternion.fromArray(a.quaternion),void 0!==a.scale&&v.scale.fromArray(a.scale));void 0!==a.castShadow&&(v.castShadow=a.castShadow);void 0!==a.receiveShadow&&(v.receiveShadow=a.receiveShadow);a.shadow&&(void 0!==a.shadow.bias&&(v.shadow.bias=a.shadow.bias),void 0!==a.shadow.radius&&(v.shadow.radius=a.shadow.radius),void 0!==a.shadow.mapSize&&
v.shadow.mapSize.fromArray(a.shadow.mapSize),void 0!==a.shadow.camera&&(v.shadow.camera=this.parseObject(a.shadow.camera)));void 0!==a.visible&&(v.visible=a.visible);void 0!==a.frustumCulled&&(v.frustumCulled=a.frustumCulled);void 0!==a.renderOrder&&(v.renderOrder=a.renderOrder);void 0!==a.userData&&(v.userData=a.userData);void 0!==a.layers&&(v.layers.mask=a.layers);if(void 0!==a.children){z=a.children;for(var E=0;E<z.length;E++)v.add(this.parseObject(z[E],c,e))}if("LOD"===a.type)for(a=a.levels,z=
0;z<a.length;z++){E=a[z];var F=v.getObjectByProperty("uuid",E.object);void 0!==F&&v.addLevel(F,E.distance)}return v}});var fn={UVMapping:300,CubeReflectionMapping:301,CubeRefractionMapping:302,EquirectangularReflectionMapping:303,EquirectangularRefractionMapping:304,SphericalReflectionMapping:305,CubeUVReflectionMapping:306,CubeUVRefractionMapping:307},Bk={RepeatWrapping:1E3,ClampToEdgeWrapping:1001,MirroredRepeatWrapping:1002},Ck={NearestFilter:1003,NearestMipmapNearestFilter:1004,NearestMipmapLinearFilter:1005,
LinearFilter:1006,LinearMipmapNearestFilter:1007,LinearMipmapLinearFilter:1008};ji.prototype=Object.assign(Object.create(Db.prototype),{constructor:ji,setOptions:function(a){this.options=a;return this},load:function(a,c,e,g){void 0===a&&(a="");void 0!==this.path&&(a=this.path+a);a=this.manager.resolveURL(a);var t=this,v=ke.get(a);if(void 0!==v)return t.manager.itemStart(a),setTimeout(function(){c&&c(v);t.manager.itemEnd(a)},0),v;fetch(a).then(function(z){return z.blob()}).then(function(z){return void 0===
t.options?createImageBitmap(z):createImageBitmap(z,t.options)}).then(function(z){ke.add(a,z);c&&c(z);t.manager.itemEnd(a)}).catch(function(z){g&&g(z);t.manager.itemError(a);t.manager.itemEnd(a)});t.manager.itemStart(a)}});Object.assign(ki.prototype,{moveTo:function(a,c){this.currentPath=new Ic;this.subPaths.push(this.currentPath);this.currentPath.moveTo(a,c)},lineTo:function(a,c){this.currentPath.lineTo(a,c)},quadraticCurveTo:function(a,c,e,g){this.currentPath.quadraticCurveTo(a,c,e,g)},bezierCurveTo:function(a,
c,e,g,t,v){this.currentPath.bezierCurveTo(a,c,e,g,t,v)},splineThru:function(a){this.currentPath.splineThru(a)},toShapes:function(a,c){function e(da){for(var ra=[],pa=0,qa=da.length;pa<qa;pa++){var ua=da[pa],na=new Ed;na.curves=ua.curves;ra.push(na)}return ra}function g(da,ra){for(var pa=ra.length,qa=!1,ua=pa-1,na=0;na<pa;ua=na++){var ta=ra[ua],Ba=ra[na],Ta=Ba.x-ta.x,Ua=Ba.y-ta.y;if(Math.abs(Ua)>Number.EPSILON){if(0>Ua&&(ta=ra[na],Ta=-Ta,Ba=ra[ua],Ua=-Ua),!(da.y<ta.y||da.y>Ba.y))if(da.y===ta.y){if(da.x===
ta.x)return!0}else{ua=Ua*(da.x-ta.x)-Ta*(da.y-ta.y);if(0===ua)return!0;0>ua||(qa=!qa)}}else if(da.y===ta.y&&(Ba.x<=da.x&&da.x<=ta.x||ta.x<=da.x&&da.x<=Ba.x))return!0}return qa}var t=gd.isClockWise,v=this.subPaths;if(0===v.length)return[];if(!0===c)return e(v);c=[];if(1===v.length){var z=v[0];var E=new Ed;E.curves=z.curves;c.push(E);return c}var F=!t(v[0].getPoints());F=a?!F:F;E=[];var I=[],M=[],P=0;I[P]=void 0;M[P]=[];for(var Q=0,U=v.length;Q<U;Q++){z=v[Q];var V=z.getPoints();var ea=t(V);(ea=a?!ea:
ea)?(!F&&I[P]&&P++,I[P]={s:new Ed,p:V},I[P].s.curves=z.curves,F&&P++,M[P]=[]):M[P].push({h:z,p:V[0]})}if(!I[0])return e(v);if(1<I.length){Q=!1;a=[];t=0;for(v=I.length;t<v;t++)E[t]=[];t=0;for(v=I.length;t<v;t++)for(z=M[t],ea=0;ea<z.length;ea++){F=z[ea];P=!0;for(V=0;V<I.length;V++)g(F.p,I[V].p)&&(t!==V&&a.push({froms:t,tos:V,hole:ea}),P?(P=!1,E[V].push(F)):Q=!0);P&&E[t].push(F)}0<a.length&&(Q||(M=E))}Q=0;for(t=I.length;Q<t;Q++)for(E=I[Q].s,c.push(E),a=M[Q],v=0,z=a.length;v<z;v++)E.holes.push(a[v].h);
return c}});Object.assign(li.prototype,{isFont:!0,generateShapes:function(a,c){void 0===c&&(c=100);var e=[];a=Qm(a,c,this.data);c=0;for(var g=a.length;c<g;c++)Array.prototype.push.apply(e,a[c].toShapes());return e}});mi.prototype=Object.assign(Object.create(Db.prototype),{constructor:mi,load:function(a,c,e,g){var t=this,v=new wc(this.manager);v.setPath(this.path);v.load(a,function(z){try{var E=JSON.parse(z)}catch(F){console.warn("THREE.FontLoader: typeface.js support is being deprecated. Use typeface.json instead."),
E=JSON.parse(z.substring(65,z.length-2))}z=t.parse(E);c&&c(z)},e,g)},parse:function(a){return new li(a)}});var Bh,ri={getContext:function(){void 0===Bh&&(Bh=new (window.AudioContext||window.webkitAudioContext));return Bh},setContext:function(a){Bh=a}};ch.prototype=Object.assign(Object.create(Db.prototype),{constructor:ch,load:function(a,c,e,g){var t=new wc(this.manager);t.setResponseType("arraybuffer");t.setPath(this.path);t.load(a,function(v){v=v.slice(0);ri.getContext().decodeAudioData(v,function(z){c(z)})},
e,g)}});Object.assign(dh.prototype,{isSphericalHarmonics3:!0,set:function(a){for(var c=0;9>c;c++)this.coefficients[c].copy(a[c]);return this},zero:function(){for(var a=0;9>a;a++)this.coefficients[a].set(0,0,0);return this},getAt:function(a,c){var e=a.x,g=a.y;a=a.z;var t=this.coefficients;c.copy(t[0]).multiplyScalar(.282095);c.addScale(t[1],.488603*g);c.addScale(t[2],.488603*a);c.addScale(t[3],.488603*e);c.addScale(t[4],1.092548*e*g);c.addScale(t[5],1.092548*g*a);c.addScale(t[6],.315392*(3*a*a-1));
c.addScale(t[7],1.092548*e*a);c.addScale(t[8],.546274*(e*e-g*g));return c},getIrradianceAt:function(a,c){var e=a.x,g=a.y;a=a.z;var t=this.coefficients;c.copy(t[0]).multiplyScalar(.886227);c.addScale(t[1],1.023328*g);c.addScale(t[2],1.023328*a);c.addScale(t[3],1.023328*e);c.addScale(t[4],.858086*e*g);c.addScale(t[5],.858086*g*a);c.addScale(t[6],.743125*a*a-.247708);c.addScale(t[7],.858086*e*a);c.addScale(t[8],.429043*(e*e-g*g));return c},add:function(a){for(var c=0;9>c;c++)this.coefficients[c].add(a.coefficients[c]);
return this},scale:function(a){for(var c=0;9>c;c++)this.coefficients[c].multiplyScalar(a);return this},lerp:function(a,c){for(var e=0;9>e;e++)this.coefficients[e].lerp(a.coefficients[e],c);return this},equals:function(a){for(var c=0;9>c;c++)if(!this.coefficients[c].equals(a.coefficients[c]))return!1;return!0},copy:function(a){return this.set(a.coefficients)},clone:function(){return(new this.constructor).copy(this)},fromArray:function(a,c){void 0===c&&(c=0);for(var e=this.coefficients,g=0;9>g;g++)e[g].fromArray(a,
c+3*g);return this},toArray:function(a,c){void 0===a&&(a=[]);void 0===c&&(c=0);for(var e=this.coefficients,g=0;9>g;g++)e[g].toArray(a,c+3*g);return a}});Object.assign(dh,{getBasisAt:function(a,c){var e=a.x,g=a.y;a=a.z;c[0]=.282095;c[1]=.488603*g;c[2]=.488603*a;c[3]=.488603*e;c[4]=1.092548*e*g;c[5]=1.092548*g*a;c[6]=.315392*(3*a*a-1);c[7]=1.092548*e*a;c[8]=.546274*(e*e-g*g)}});Jc.prototype=Object.assign(Object.create(Kb.prototype),{constructor:Jc,isLightProbe:!0,copy:function(a){Kb.prototype.copy.call(this,
a);this.sh.copy(a.sh);this.intensity=a.intensity;return this},toJSON:function(a){return Kb.prototype.toJSON.call(this,a)}});ni.prototype=Object.assign(Object.create(Jc.prototype),{constructor:ni,isHemisphereLightProbe:!0,copy:function(a){Jc.prototype.copy.call(this,a);return this},toJSON:function(a){return Jc.prototype.toJSON.call(this,a)}});oi.prototype=Object.assign(Object.create(Jc.prototype),{constructor:oi,isAmbientLightProbe:!0,copy:function(a){Jc.prototype.copy.call(this,a);return this},toJSON:function(a){return Jc.prototype.toJSON.call(this,
a)}});var Dk=new q,Ek=new q;Object.assign(Zj.prototype,{update:function(a){var c=this._cache;if(c.focus!==a.focus||c.fov!==a.fov||c.aspect!==a.aspect*this.aspect||c.near!==a.near||c.far!==a.far||c.zoom!==a.zoom||c.eyeSep!==this.eyeSep){c.focus=a.focus;c.fov=a.fov;c.aspect=a.aspect*this.aspect;c.near=a.near;c.far=a.far;c.zoom=a.zoom;c.eyeSep=this.eyeSep;var e=a.projectionMatrix.clone(),g=c.eyeSep/2,t=g*c.near/c.focus,v=c.near*Math.tan(hb.DEG2RAD*c.fov*.5)/c.zoom;Ek.elements[12]=-g;Dk.elements[12]=
g;g=-v*c.aspect+t;var z=v*c.aspect+t;e.elements[0]=2*c.near/(z-g);e.elements[8]=(z+g)/(z-g);this.cameraL.projectionMatrix.copy(e);g=-v*c.aspect-t;z=v*c.aspect-t;e.elements[0]=2*c.near/(z-g);e.elements[8]=(z+g)/(z-g);this.cameraR.projectionMatrix.copy(e)}this.cameraL.matrixWorld.copy(a.matrixWorld).multiply(Ek);this.cameraR.matrixWorld.copy(a.matrixWorld).multiply(Dk)}});Object.assign(pi.prototype,{start:function(){this.oldTime=this.startTime=("undefined"===typeof performance?Date:performance).now();
this.elapsedTime=0;this.running=!0},stop:function(){this.getElapsedTime();this.autoStart=this.running=!1},getElapsedTime:function(){this.getDelta();return this.elapsedTime},getDelta:function(){var a=0;if(this.autoStart&&!this.running)return this.start(),0;if(this.running){var c=("undefined"===typeof performance?Date:performance).now();a=(c-this.oldTime)/1E3;this.oldTime=c;this.elapsedTime+=a}return a}});var le=new k,Fk=new h,gn=new k,me=new k;qi.prototype=Object.assign(Object.create(A.prototype),
{constructor:qi,getInput:function(){return this.gain},removeFilter:function(){null!==this.filter&&(this.gain.disconnect(this.filter),this.filter.disconnect(this.context.destination),this.gain.connect(this.context.destination),this.filter=null);return this},getFilter:function(){return this.filter},setFilter:function(a){null!==this.filter?(this.gain.disconnect(this.filter),this.filter.disconnect(this.context.destination)):this.gain.disconnect(this.context.destination);this.filter=a;this.gain.connect(this.filter);
this.filter.connect(this.context.destination);return this},getMasterVolume:function(){return this.gain.gain.value},setMasterVolume:function(a){this.gain.gain.setTargetAtTime(a,this.context.currentTime,.01);return this},updateMatrixWorld:function(a){A.prototype.updateMatrixWorld.call(this,a);a=this.context.listener;var c=this.up;this.timeDelta=this._clock.getDelta();this.matrixWorld.decompose(le,Fk,gn);me.set(0,0,-1).applyQuaternion(Fk);if(a.positionX){var e=this.context.currentTime+this.timeDelta;
a.positionX.linearRampToValueAtTime(le.x,e);a.positionY.linearRampToValueAtTime(le.y,e);a.positionZ.linearRampToValueAtTime(le.z,e);a.forwardX.linearRampToValueAtTime(me.x,e);a.forwardY.linearRampToValueAtTime(me.y,e);a.forwardZ.linearRampToValueAtTime(me.z,e);a.upX.linearRampToValueAtTime(c.x,e);a.upY.linearRampToValueAtTime(c.y,e);a.upZ.linearRampToValueAtTime(c.z,e)}else a.setPosition(le.x,le.y,le.z),a.setOrientation(me.x,me.y,me.z,c.x,c.y,c.z)}});Ye.prototype=Object.assign(Object.create(A.prototype),
{constructor:Ye,getOutput:function(){return this.gain},setNodeSource:function(a){this.hasPlaybackControl=!1;this.sourceType="audioNode";this.source=a;this.connect();return this},setMediaElementSource:function(a){this.hasPlaybackControl=!1;this.sourceType="mediaNode";this.source=this.context.createMediaElementSource(a);this.connect();return this},setBuffer:function(a){this.buffer=a;this.sourceType="buffer";this.autoplay&&this.play();return this},play:function(){if(!0===this.isPlaying)console.warn("THREE.Audio: Audio is already playing.");
else if(!1===this.hasPlaybackControl)console.warn("THREE.Audio: this Audio has no playback control.");else{var a=this.context.createBufferSource();a.buffer=this.buffer;a.loop=this.loop;a.onended=this.onEnded.bind(this);this.startTime=this.context.currentTime;a.start(this.startTime,this.offset,this.duration);this.isPlaying=!0;this.source=a;this.setDetune(this.detune);this.setPlaybackRate(this.playbackRate);return this.connect()}},pause:function(){if(!1===this.hasPlaybackControl)console.warn("THREE.Audio: this Audio has no playback control.");
else return!0===this.isPlaying&&(this.source.stop(),this.source.onended=null,this.offset+=(this.context.currentTime-this.startTime)*this.playbackRate,this.isPlaying=!1),this},stop:function(){if(!1===this.hasPlaybackControl)console.warn("THREE.Audio: this Audio has no playback control.");else return this.source.stop(),this.source.onended=null,this.offset=0,this.isPlaying=!1,this},connect:function(){if(0<this.filters.length){this.source.connect(this.filters[0]);for(var a=1,c=this.filters.length;a<c;a++)this.filters[a-
1].connect(this.filters[a]);this.filters[this.filters.length-1].connect(this.getOutput())}else this.source.connect(this.getOutput());return this},disconnect:function(){if(0<this.filters.length){this.source.disconnect(this.filters[0]);for(var a=1,c=this.filters.length;a<c;a++)this.filters[a-1].disconnect(this.filters[a]);this.filters[this.filters.length-1].disconnect(this.getOutput())}else this.source.disconnect(this.getOutput());return this},getFilters:function(){return this.filters},setFilters:function(a){a||
(a=[]);!0===this.isPlaying?(this.disconnect(),this.filters=a,this.connect()):this.filters=a;return this},setDetune:function(a){this.detune=a;if(void 0!==this.source.detune)return!0===this.isPlaying&&this.source.detune.setTargetAtTime(this.detune,this.context.currentTime,.01),this},getDetune:function(){return this.detune},getFilter:function(){return this.getFilters()[0]},setFilter:function(a){return this.setFilters(a?[a]:[])},setPlaybackRate:function(a){if(!1===this.hasPlaybackControl)console.warn("THREE.Audio: this Audio has no playback control.");
else return this.playbackRate=a,!0===this.isPlaying&&this.source.playbackRate.setTargetAtTime(this.playbackRate,this.context.currentTime,.01),this},getPlaybackRate:function(){return this.playbackRate},onEnded:function(){this.isPlaying=!1},getLoop:function(){return!1===this.hasPlaybackControl?(console.warn("THREE.Audio: this Audio has no playback control."),!1):this.loop},setLoop:function(a){if(!1===this.hasPlaybackControl)console.warn("THREE.Audio: this Audio has no playback control.");else return this.loop=
a,!0===this.isPlaying&&(this.source.loop=this.loop),this},getVolume:function(){return this.gain.gain.value},setVolume:function(a){this.gain.gain.setTargetAtTime(a,this.context.currentTime,.01);return this}});var ne=new k,Gk=new h,hn=new k,oe=new k;si.prototype=Object.assign(Object.create(Ye.prototype),{constructor:si,getOutput:function(){return this.panner},getRefDistance:function(){return this.panner.refDistance},setRefDistance:function(a){this.panner.refDistance=a;return this},getRolloffFactor:function(){return this.panner.rolloffFactor},
setRolloffFactor:function(a){this.panner.rolloffFactor=a;return this},getDistanceModel:function(){return this.panner.distanceModel},setDistanceModel:function(a){this.panner.distanceModel=a;return this},getMaxDistance:function(){return this.panner.maxDistance},setMaxDistance:function(a){this.panner.maxDistance=a;return this},setDirectionalCone:function(a,c,e){this.panner.coneInnerAngle=a;this.panner.coneOuterAngle=c;this.panner.coneOuterGain=e;return this},updateMatrixWorld:function(a){A.prototype.updateMatrixWorld.call(this,
a);if(!0!==this.hasPlaybackControl||!1!==this.isPlaying)if(this.matrixWorld.decompose(ne,Gk,hn),oe.set(0,0,1).applyQuaternion(Gk),a=this.panner,a.positionX){var c=this.context.currentTime+this.listener.timeDelta;a.positionX.linearRampToValueAtTime(ne.x,c);a.positionY.linearRampToValueAtTime(ne.y,c);a.positionZ.linearRampToValueAtTime(ne.z,c);a.orientationX.linearRampToValueAtTime(oe.x,c);a.orientationY.linearRampToValueAtTime(oe.y,c);a.orientationZ.linearRampToValueAtTime(oe.z,c)}else a.setPosition(ne.x,
ne.y,ne.z),a.setOrientation(oe.x,oe.y,oe.z)}});Object.assign(ti.prototype,{getFrequencyData:function(){this.analyser.getByteFrequencyData(this.data);return this.data},getAverageFrequency:function(){for(var a=0,c=this.getFrequencyData(),e=0;e<c.length;e++)a+=c[e];return a/c.length}});Object.assign(ui.prototype,{accumulate:function(a,c){var e=this.buffer,g=this.valueSize;a=a*g+g;var t=this.cumulativeWeight;if(0===t){for(t=0;t!==g;++t)e[a+t]=e[t];t=c}else t+=c,this._mixBufferRegion(e,a,0,c/t,g);this.cumulativeWeight=
t},apply:function(a){var c=this.valueSize,e=this.buffer;a=a*c+c;var g=this.cumulativeWeight,t=this.binding;this.cumulativeWeight=0;1>g&&this._mixBufferRegion(e,a,3*c,1-g,c);g=c;for(var v=c+c;g!==v;++g)if(e[g]!==e[g+c]){t.setValue(e,a);break}},saveOriginalState:function(){var a=this.buffer,c=this.valueSize,e=3*c;this.binding.getValue(a,e);for(var g=c;g!==e;++g)a[g]=a[e+g%c];this.cumulativeWeight=0},restoreOriginalState:function(){this.binding.setValue(this.buffer,3*this.valueSize)},_select:function(a,
c,e,g,t){if(.5<=g)for(g=0;g!==t;++g)a[c+g]=a[e+g]},_slerp:function(a,c,e,g){h.slerpFlat(a,c,a,c,a,e,g)},_lerp:function(a,c,e,g,t){for(var v=1-g,z=0;z!==t;++z){var E=c+z;a[E]=a[E]*v+a[e+z]*g}}});var jn=/[\[\]\.:\/]/g,kn="[^"+"\\[\\]\\.:\\/".replace("\\.","")+"]",ln=/((?:WC+[\/:])*)/.source.replace("WC","[^\\[\\]\\.:\\/]"),mn=/(WCOD+)?/.source.replace("WCOD",kn),nn=/(?:\.(WC+)(?:\[(.+)\])?)?/.source.replace("WC","[^\\[\\]\\.:\\/]"),on=/\.(WC+)(?:\[(.+)\])?/.source.replace("WC","[^\\[\\]\\.:\\/]"),pn=
new RegExp("^"+ln+mn+nn+on+"$"),qn=["material","materials","bones"];Object.assign(ak.prototype,{getValue:function(a,c){this.bind();var e=this._bindings[this._targetGroup.nCachedObjects_];void 0!==e&&e.getValue(a,c)},setValue:function(a,c){for(var e=this._bindings,g=this._targetGroup.nCachedObjects_,t=e.length;g!==t;++g)e[g].setValue(a,c)},bind:function(){for(var a=this._bindings,c=this._targetGroup.nCachedObjects_,e=a.length;c!==e;++c)a[c].bind()},unbind:function(){for(var a=this._bindings,c=this._targetGroup.nCachedObjects_,
e=a.length;c!==e;++c)a[c].unbind()}});Object.assign(cc,{Composite:ak,create:function(a,c,e){return a&&a.isAnimationObjectGroup?new cc.Composite(a,c,e):new cc(a,c,e)},sanitizeNodeName:function(a){return a.replace(/\s/g,"_").replace(jn,"")},parseTrackName:function(a){var c=pn.exec(a);if(!c)throw Error("PropertyBinding: Cannot parse trackName: "+a);c={nodeName:c[2],objectName:c[3],objectIndex:c[4],propertyName:c[5],propertyIndex:c[6]};var e=c.nodeName&&c.nodeName.lastIndexOf(".");if(void 0!==e&&-1!==
e){var g=c.nodeName.substring(e+1);-1!==qn.indexOf(g)&&(c.nodeName=c.nodeName.substring(0,e),c.objectName=g)}if(null===c.propertyName||0===c.propertyName.length)throw Error("PropertyBinding: can not parse propertyName from trackName: "+a);return c},findNode:function(a,c){if(!c||""===c||"root"===c||"."===c||-1===c||c===a.name||c===a.uuid)return a;if(a.skeleton){var e=a.skeleton.getBoneByName(c);if(void 0!==e)return e}if(a.children){var g=function(t){for(var v=0;v<t.length;v++){var z=t[v];if(z.name===
c||z.uuid===c)return z;if(z=g(z.children))return z}return null};if(a=g(a.children))return a}return null}});Object.assign(cc.prototype,{_getValue_unavailable:function(){},_setValue_unavailable:function(){},BindingType:{Direct:0,EntireArray:1,ArrayElement:2,HasFromToArray:3},Versioning:{None:0,NeedsUpdate:1,MatrixWorldNeedsUpdate:2},GetterByBindingType:[function(a,c){a[c]=this.node[this.propertyName]},function(a,c){for(var e=this.resolvedProperty,g=0,t=e.length;g!==t;++g)a[c++]=e[g]},function(a,c){a[c]=
this.resolvedProperty[this.propertyIndex]},function(a,c){this.resolvedProperty.toArray(a,c)}],SetterByBindingTypeAndVersioning:[[function(a,c){this.targetObject[this.propertyName]=a[c]},function(a,c){this.targetObject[this.propertyName]=a[c];this.targetObject.needsUpdate=!0},function(a,c){this.targetObject[this.propertyName]=a[c];this.targetObject.matrixWorldNeedsUpdate=!0}],[function(a,c){for(var e=this.resolvedProperty,g=0,t=e.length;g!==t;++g)e[g]=a[c++]},function(a,c){for(var e=this.resolvedProperty,
g=0,t=e.length;g!==t;++g)e[g]=a[c++];this.targetObject.needsUpdate=!0},function(a,c){for(var e=this.resolvedProperty,g=0,t=e.length;g!==t;++g)e[g]=a[c++];this.targetObject.matrixWorldNeedsUpdate=!0}],[function(a,c){this.resolvedProperty[this.propertyIndex]=a[c]},function(a,c){this.resolvedProperty[this.propertyIndex]=a[c];this.targetObject.needsUpdate=!0},function(a,c){this.resolvedProperty[this.propertyIndex]=a[c];this.targetObject.matrixWorldNeedsUpdate=!0}],[function(a,c){this.resolvedProperty.fromArray(a,
c)},function(a,c){this.resolvedProperty.fromArray(a,c);this.targetObject.needsUpdate=!0},function(a,c){this.resolvedProperty.fromArray(a,c);this.targetObject.matrixWorldNeedsUpdate=!0}]],getValue:function(a,c){this.bind();this.getValue(a,c)},setValue:function(a,c){this.bind();this.setValue(a,c)},bind:function(){var a=this.node,c=this.parsedPath,e=c.objectName,g=c.propertyName,t=c.propertyIndex;a||(this.node=a=cc.findNode(this.rootNode,c.nodeName)||this.rootNode);this.getValue=this._getValue_unavailable;
this.setValue=this._setValue_unavailable;if(a){if(e){var v=c.objectIndex;switch(e){case "materials":if(!a.material){console.error("THREE.PropertyBinding: Can not bind to material as node does not have a material.",this);return}if(!a.material.materials){console.error("THREE.PropertyBinding: Can not bind to material.materials as node.material does not have a materials array.",this);return}a=a.material.materials;break;case "bones":if(!a.skeleton){console.error("THREE.PropertyBinding: Can not bind to bones as node does not have a skeleton.",
this);return}a=a.skeleton.bones;for(e=0;e<a.length;e++)if(a[e].name===v){v=e;break}break;default:if(void 0===a[e]){console.error("THREE.PropertyBinding: Can not bind to objectName of node undefined.",this);return}a=a[e]}if(void 0!==v){if(void 0===a[v]){console.error("THREE.PropertyBinding: Trying to bind to objectIndex of objectName, but is undefined.",this,a);return}a=a[v]}}v=a[g];if(void 0===v)console.error("THREE.PropertyBinding: Trying to update property for track: "+c.nodeName+"."+g+" but it wasn't found.",
a);else{c=this.Versioning.None;this.targetObject=a;void 0!==a.needsUpdate?c=this.Versioning.NeedsUpdate:void 0!==a.matrixWorldNeedsUpdate&&(c=this.Versioning.MatrixWorldNeedsUpdate);e=this.BindingType.Direct;if(void 0!==t){if("morphTargetInfluences"===g){if(!a.geometry){console.error("THREE.PropertyBinding: Can not bind to morphTargetInfluences because node does not have a geometry.",this);return}if(a.geometry.isBufferGeometry){if(!a.geometry.morphAttributes){console.error("THREE.PropertyBinding: Can not bind to morphTargetInfluences because node does not have a geometry.morphAttributes.",
this);return}for(e=0;e<this.node.geometry.morphAttributes.position.length;e++)if(a.geometry.morphAttributes.position[e].name===t){t=e;break}}else{if(!a.geometry.morphTargets){console.error("THREE.PropertyBinding: Can not bind to morphTargetInfluences because node does not have a geometry.morphTargets.",this);return}for(e=0;e<this.node.geometry.morphTargets.length;e++)if(a.geometry.morphTargets[e].name===t){t=e;break}}}e=this.BindingType.ArrayElement;this.resolvedProperty=v;this.propertyIndex=t}else void 0!==
v.fromArray&&void 0!==v.toArray?(e=this.BindingType.HasFromToArray,this.resolvedProperty=v):Array.isArray(v)?(e=this.BindingType.EntireArray,this.resolvedProperty=v):this.propertyName=g;this.getValue=this.GetterByBindingType[e];this.setValue=this.SetterByBindingTypeAndVersioning[e][c]}}else console.error("THREE.PropertyBinding: Trying to update node for track: "+this.path+" but it wasn't found.")},unbind:function(){this.node=null;this.getValue=this._getValue_unbound;this.setValue=this._setValue_unbound}});
Object.assign(cc.prototype,{_getValue_unbound:cc.prototype.getValue,_setValue_unbound:cc.prototype.setValue});Object.assign(bk.prototype,{isAnimationObjectGroup:!0,add:function(){for(var a=this._objects,c=a.length,e=this.nCachedObjects_,g=this._indicesByUUID,t=this._paths,v=this._parsedPaths,z=this._bindings,E=z.length,F=void 0,I=0,M=arguments.length;I!==M;++I){var P=arguments[I],Q=P.uuid,U=g[Q];if(void 0===U){U=c++;g[Q]=U;a.push(P);Q=0;for(var V=E;Q!==V;++Q)z[Q].push(new cc(P,t[Q],v[Q]))}else if(U<
e){F=a[U];var ea=--e;V=a[ea];g[V.uuid]=U;a[U]=V;g[Q]=ea;a[ea]=P;Q=0;for(V=E;Q!==V;++Q){var da=z[Q],ra=da[U];da[U]=da[ea];void 0===ra&&(ra=new cc(P,t[Q],v[Q]));da[ea]=ra}}else a[U]!==F&&console.error("THREE.AnimationObjectGroup: Different objects with the same UUID detected. Clean the caches or recreate your infrastructure when reloading scenes.")}this.nCachedObjects_=e},remove:function(){for(var a=this._objects,c=this.nCachedObjects_,e=this._indicesByUUID,g=this._bindings,t=g.length,v=0,z=arguments.length;v!==
z;++v){var E=arguments[v],F=E.uuid,I=e[F];if(void 0!==I&&I>=c){var M=c++,P=a[M];e[P.uuid]=I;a[I]=P;e[F]=M;a[M]=E;E=0;for(F=t;E!==F;++E){P=g[E];var Q=P[I];P[I]=P[M];P[M]=Q}}}this.nCachedObjects_=c},uncache:function(){for(var a=this._objects,c=a.length,e=this.nCachedObjects_,g=this._indicesByUUID,t=this._bindings,v=t.length,z=0,E=arguments.length;z!==E;++z){var F=arguments[z].uuid,I=g[F];if(void 0!==I)if(delete g[F],I<e){F=--e;var M=a[F],P=--c,Q=a[P];g[M.uuid]=I;a[I]=M;g[Q.uuid]=F;a[F]=Q;a.pop();M=
0;for(Q=v;M!==Q;++M){var U=t[M],V=U[P];U[I]=U[F];U[F]=V;U.pop()}}else for(P=--c,Q=a[P],g[Q.uuid]=I,a[I]=Q,a.pop(),M=0,Q=v;M!==Q;++M)U=t[M],U[I]=U[P],U.pop()}this.nCachedObjects_=e},subscribe_:function(a,c){var e=this._bindingsIndicesByPath,g=e[a],t=this._bindings;if(void 0!==g)return t[g];var v=this._paths,z=this._parsedPaths,E=this._objects,F=this.nCachedObjects_,I=Array(E.length);g=t.length;e[a]=g;v.push(a);z.push(c);t.push(I);e=F;for(g=E.length;e!==g;++e)I[e]=new cc(E[e],a,c);return I},unsubscribe_:function(a){var c=
this._bindingsIndicesByPath,e=c[a];if(void 0!==e){var g=this._paths,t=this._parsedPaths,v=this._bindings,z=v.length-1,E=v[z];c[a[z]]=e;v[e]=E;v.pop();t[e]=t[z];t.pop();g[e]=g[z];g.pop()}}});Object.assign(ck.prototype,{play:function(){this._mixer._activateAction(this);return this},stop:function(){this._mixer._deactivateAction(this);return this.reset()},reset:function(){this.paused=!1;this.enabled=!0;this.time=0;this._loopCount=-1;this._startTime=null;return this.stopFading().stopWarping()},isRunning:function(){return this.enabled&&
!this.paused&&0!==this.timeScale&&null===this._startTime&&this._mixer._isActiveAction(this)},isScheduled:function(){return this._mixer._isActiveAction(this)},startAt:function(a){this._startTime=a;return this},setLoop:function(a,c){this.loop=a;this.repetitions=c;return this},setEffectiveWeight:function(a){this.weight=a;this._effectiveWeight=this.enabled?a:0;return this.stopFading()},getEffectiveWeight:function(){return this._effectiveWeight},fadeIn:function(a){return this._scheduleFading(a,0,1)},fadeOut:function(a){return this._scheduleFading(a,
1,0)},crossFadeFrom:function(a,c,e){a.fadeOut(c);this.fadeIn(c);if(e){e=this._clip.duration;var g=a._clip.duration,t=e/g;a.warp(1,g/e,c);this.warp(t,1,c)}return this},crossFadeTo:function(a,c,e){return a.crossFadeFrom(this,c,e)},stopFading:function(){var a=this._weightInterpolant;null!==a&&(this._weightInterpolant=null,this._mixer._takeBackControlInterpolant(a));return this},setEffectiveTimeScale:function(a){this.timeScale=a;this._effectiveTimeScale=this.paused?0:a;return this.stopWarping()},getEffectiveTimeScale:function(){return this._effectiveTimeScale},
setDuration:function(a){this.timeScale=this._clip.duration/a;return this.stopWarping()},syncWith:function(a){this.time=a.time;this.timeScale=a.timeScale;return this.stopWarping()},halt:function(a){return this.warp(this._effectiveTimeScale,0,a)},warp:function(a,c,e){var g=this._mixer,t=g.time,v=this._timeScaleInterpolant,z=this.timeScale;null===v&&(this._timeScaleInterpolant=v=g._lendControlInterpolant());g=v.parameterPositions;v=v.sampleValues;g[0]=t;g[1]=t+e;v[0]=a/z;v[1]=c/z;return this},stopWarping:function(){var a=
this._timeScaleInterpolant;null!==a&&(this._timeScaleInterpolant=null,this._mixer._takeBackControlInterpolant(a));return this},getMixer:function(){return this._mixer},getClip:function(){return this._clip},getRoot:function(){return this._localRoot||this._mixer._root},_update:function(a,c,e,g){if(this.enabled){var t=this._startTime;if(null!==t){c=(a-t)*e;if(0>c||0===e)return;this._startTime=null;c*=e}c*=this._updateTimeScale(a);e=this._updateTime(c);a=this._updateWeight(a);if(0<a){c=this._interpolants;
t=this._propertyBindings;for(var v=0,z=c.length;v!==z;++v)c[v].evaluate(e),t[v].accumulate(g,a)}}else this._updateWeight(a)},_updateWeight:function(a){var c=0;if(this.enabled){c=this.weight;var e=this._weightInterpolant;if(null!==e){var g=e.evaluate(a)[0];c*=g;a>e.parameterPositions[1]&&(this.stopFading(),0===g&&(this.enabled=!1))}}return this._effectiveWeight=c},_updateTimeScale:function(a){var c=0;if(!this.paused){c=this.timeScale;var e=this._timeScaleInterpolant;if(null!==e){var g=e.evaluate(a)[0];
c*=g;a>e.parameterPositions[1]&&(this.stopWarping(),0===c?this.paused=!0:this.timeScale=c)}}return this._effectiveTimeScale=c},_updateTime:function(a){var c=this.time+a,e=this._clip.duration,g=this.loop,t=this._loopCount,v=2202===g;if(0===a)return-1===t?c:v&&1===(t&1)?e-c:c;if(2200===g)a:{if(-1===t&&(this._loopCount=0,this._setEndings(!0,!0,!1)),c>=e)c=e;else if(0>c)c=0;else{this.time=c;break a}this.clampWhenFinished?this.paused=!0:this.enabled=!1;this.time=c;this._mixer.dispatchEvent({type:"finished",
action:this,direction:0>a?-1:1})}else{-1===t&&(0<=a?(t=0,this._setEndings(!0,0===this.repetitions,v)):this._setEndings(0===this.repetitions,!0,v));if(c>=e||0>c){g=Math.floor(c/e);c-=e*g;t+=Math.abs(g);var z=this.repetitions-t;0>=z?(this.clampWhenFinished?this.paused=!0:this.enabled=!1,this.time=c=0<a?e:0,this._mixer.dispatchEvent({type:"finished",action:this,direction:0<a?1:-1})):(1===z?(a=0>a,this._setEndings(a,!a,v)):this._setEndings(!1,!1,v),this._loopCount=t,this.time=c,this._mixer.dispatchEvent({type:"loop",
action:this,loopDelta:g}))}else this.time=c;if(v&&1===(t&1))return e-c}return c},_setEndings:function(a,c,e){var g=this._interpolantSettings;e?(g.endingStart=2401,g.endingEnd=2401):(g.endingStart=a?this.zeroSlopeAtStart?2401:2400:2402,g.endingEnd=c?this.zeroSlopeAtEnd?2401:2400:2402)},_scheduleFading:function(a,c,e){var g=this._mixer,t=g.time,v=this._weightInterpolant;null===v&&(this._weightInterpolant=v=g._lendControlInterpolant());g=v.parameterPositions;v=v.sampleValues;g[0]=t;v[0]=c;g[1]=t+a;v[1]=
e;return this}});vi.prototype=Object.assign(Object.create(d.prototype),{constructor:vi,_bindAction:function(a,c){var e=a._localRoot||this._root,g=a._clip.tracks,t=g.length,v=a._propertyBindings;a=a._interpolants;var z=e.uuid,E=this._bindingsByRootAndName,F=E[z];void 0===F&&(F={},E[z]=F);for(E=0;E!==t;++E){var I=g[E],M=I.name,P=F[M];if(void 0===P){P=v[E];if(void 0!==P){null===P._cacheIndex&&(++P.referenceCount,this._addInactiveBinding(P,z,M));continue}P=new ui(cc.create(e,M,c&&c._propertyBindings[E].binding.parsedPath),
I.ValueTypeName,I.getValueSize());++P.referenceCount;this._addInactiveBinding(P,z,M)}v[E]=P;a[E].resultBuffer=P.buffer}},_activateAction:function(a){if(!this._isActiveAction(a)){if(null===a._cacheIndex){var c=(a._localRoot||this._root).uuid,e=a._clip.uuid,g=this._actionsByClip[e];this._bindAction(a,g&&g.knownActions[0]);this._addInactiveAction(a,e,c)}c=a._propertyBindings;e=0;for(g=c.length;e!==g;++e){var t=c[e];0===t.useCount++&&(this._lendBinding(t),t.saveOriginalState())}this._lendAction(a)}},
_deactivateAction:function(a){if(this._isActiveAction(a)){for(var c=a._propertyBindings,e=0,g=c.length;e!==g;++e){var t=c[e];0===--t.useCount&&(t.restoreOriginalState(),this._takeBackBinding(t))}this._takeBackAction(a)}},_initMemoryManager:function(){this._actions=[];this._nActiveActions=0;this._actionsByClip={};this._bindings=[];this._nActiveBindings=0;this._bindingsByRootAndName={};this._controlInterpolants=[];this._nActiveControlInterpolants=0;var a=this;this.stats={actions:{get total(){return a._actions.length},
get inUse(){return a._nActiveActions}},bindings:{get total(){return a._bindings.length},get inUse(){return a._nActiveBindings}},controlInterpolants:{get total(){return a._controlInterpolants.length},get inUse(){return a._nActiveControlInterpolants}}}},_isActiveAction:function(a){a=a._cacheIndex;return null!==a&&a<this._nActiveActions},_addInactiveAction:function(a,c,e){var g=this._actions,t=this._actionsByClip,v=t[c];void 0===v?(v={knownActions:[a],actionByRoot:{}},a._byClipCacheIndex=0,t[c]=v):(c=
v.knownActions,a._byClipCacheIndex=c.length,c.push(a));a._cacheIndex=g.length;g.push(a);v.actionByRoot[e]=a},_removeInactiveAction:function(a){var c=this._actions,e=c[c.length-1],g=a._cacheIndex;e._cacheIndex=g;c[g]=e;c.pop();a._cacheIndex=null;c=a._clip.uuid;e=this._actionsByClip;g=e[c];var t=g.knownActions,v=t[t.length-1],z=a._byClipCacheIndex;v._byClipCacheIndex=z;t[z]=v;t.pop();a._byClipCacheIndex=null;delete g.actionByRoot[(a._localRoot||this._root).uuid];0===t.length&&delete e[c];this._removeInactiveBindingsForAction(a)},
_removeInactiveBindingsForAction:function(a){a=a._propertyBindings;for(var c=0,e=a.length;c!==e;++c){var g=a[c];0===--g.referenceCount&&this._removeInactiveBinding(g)}},_lendAction:function(a){var c=this._actions,e=a._cacheIndex,g=this._nActiveActions++,t=c[g];a._cacheIndex=g;c[g]=a;t._cacheIndex=e;c[e]=t},_takeBackAction:function(a){var c=this._actions,e=a._cacheIndex,g=--this._nActiveActions,t=c[g];a._cacheIndex=g;c[g]=a;t._cacheIndex=e;c[e]=t},_addInactiveBinding:function(a,c,e){var g=this._bindingsByRootAndName,
t=g[c],v=this._bindings;void 0===t&&(t={},g[c]=t);t[e]=a;a._cacheIndex=v.length;v.push(a)},_removeInactiveBinding:function(a){var c=this._bindings,e=a.binding,g=e.rootNode.uuid;e=e.path;var t=this._bindingsByRootAndName,v=t[g],z=c[c.length-1];a=a._cacheIndex;z._cacheIndex=a;c[a]=z;c.pop();delete v[e];0===Object.keys(v).length&&delete t[g]},_lendBinding:function(a){var c=this._bindings,e=a._cacheIndex,g=this._nActiveBindings++,t=c[g];a._cacheIndex=g;c[g]=a;t._cacheIndex=e;c[e]=t},_takeBackBinding:function(a){var c=
this._bindings,e=a._cacheIndex,g=--this._nActiveBindings,t=c[g];a._cacheIndex=g;c[g]=a;t._cacheIndex=e;c[e]=t},_lendControlInterpolant:function(){var a=this._controlInterpolants,c=this._nActiveControlInterpolants++,e=a[c];void 0===e&&(e=new Zf(new Float32Array(2),new Float32Array(2),1,this._controlInterpolantsResultBuffer),e.__cacheIndex=c,a[c]=e);return e},_takeBackControlInterpolant:function(a){var c=this._controlInterpolants,e=a.__cacheIndex,g=--this._nActiveControlInterpolants,t=c[g];a.__cacheIndex=
g;c[g]=a;t.__cacheIndex=e;c[e]=t},_controlInterpolantsResultBuffer:new Float32Array(1),clipAction:function(a,c){var e=c||this._root,g=e.uuid;e="string"===typeof a?vc.findByName(e,a):a;a=null!==e?e.uuid:a;var t=this._actionsByClip[a],v=null;if(void 0!==t){v=t.actionByRoot[g];if(void 0!==v)return v;v=t.knownActions[0];null===e&&(e=v._clip)}if(null===e)return null;c=new ck(this,e,c);this._bindAction(c,v);this._addInactiveAction(c,a,g);return c},existingAction:function(a,c){var e=c||this._root;c=e.uuid;
e="string"===typeof a?vc.findByName(e,a):a;a=this._actionsByClip[e?e.uuid:a];return void 0!==a?a.actionByRoot[c]||null:null},stopAllAction:function(){for(var a=this._actions,c=this._nActiveActions,e=this._bindings,g=this._nActiveBindings,t=this._nActiveBindings=this._nActiveActions=0;t!==c;++t)a[t].reset();for(t=0;t!==g;++t)e[t].useCount=0;return this},update:function(a){a*=this.timeScale;for(var c=this._actions,e=this._nActiveActions,g=this.time+=a,t=Math.sign(a),v=this._accuIndex^=1,z=0;z!==e;++z)c[z]._update(g,
a,t,v);a=this._bindings;c=this._nActiveBindings;for(z=0;z!==c;++z)a[z].apply(v);return this},getRoot:function(){return this._root},uncacheClip:function(a){var c=this._actions;a=a.uuid;var e=this._actionsByClip,g=e[a];if(void 0!==g){g=g.knownActions;for(var t=0,v=g.length;t!==v;++t){var z=g[t];this._deactivateAction(z);var E=z._cacheIndex,F=c[c.length-1];z._cacheIndex=null;z._byClipCacheIndex=null;F._cacheIndex=E;c[E]=F;c.pop();this._removeInactiveBindingsForAction(z)}delete e[a]}},uncacheRoot:function(a){a=
a.uuid;var c=this._actionsByClip;for(g in c){var e=c[g].actionByRoot[a];void 0!==e&&(this._deactivateAction(e),this._removeInactiveAction(e))}var g=this._bindingsByRootAndName[a];if(void 0!==g)for(var t in g)a=g[t],a.restoreOriginalState(),this._removeInactiveBinding(a)},uncacheAction:function(a,c){a=this.existingAction(a,c);null!==a&&(this._deactivateAction(a),this._removeInactiveAction(a))}});eh.prototype.clone=function(){return new eh(void 0===this.value.clone?this.value:this.value.clone())};wi.prototype=
Object.assign(Object.create(Sd.prototype),{constructor:wi,isInstancedInterleavedBuffer:!0,copy:function(a){Sd.prototype.copy.call(this,a);this.meshPerAttribute=a.meshPerAttribute;return this}});Object.assign(dk.prototype,{linePrecision:1,set:function(a,c){this.ray.set(a,c)},setFromCamera:function(a,c){c&&c.isPerspectiveCamera?(this.ray.origin.setFromMatrixPosition(c.matrixWorld),this.ray.direction.set(a.x,a.y,.5).unproject(c).sub(this.ray.origin).normalize(),this.camera=c):c&&c.isOrthographicCamera?
(this.ray.origin.set(a.x,a.y,(c.near+c.far)/(c.near-c.far)).unproject(c),this.ray.direction.set(0,0,-1).transformDirection(c.matrixWorld),this.camera=c):console.error("THREE.Raycaster: Unsupported camera type.")},intersectObject:function(a,c,e){e=e||[];xi(a,this,e,c);e.sort(ek);return e},intersectObjects:function(a,c,e){e=e||[];if(!1===Array.isArray(a))return console.warn("THREE.Raycaster.intersectObjects: objects is not an Array."),e;for(var g=0,t=a.length;g<t;g++)xi(a[g],this,e,c);e.sort(ek);return e}});
Object.assign(fk.prototype,{set:function(a,c,e){this.radius=a;this.phi=c;this.theta=e;return this},clone:function(){return(new this.constructor).copy(this)},copy:function(a){this.radius=a.radius;this.phi=a.phi;this.theta=a.theta;return this},makeSafe:function(){this.phi=Math.max(1E-6,Math.min(Math.PI-1E-6,this.phi));return this},setFromVector3:function(a){return this.setFromCartesianCoords(a.x,a.y,a.z)},setFromCartesianCoords:function(a,c,e){this.radius=Math.sqrt(a*a+c*c+e*e);0===this.radius?this.phi=
this.theta=0:(this.theta=Math.atan2(a,e),this.phi=Math.acos(hb.clamp(c/this.radius,-1,1)));return this}});Object.assign(gk.prototype,{set:function(a,c,e){this.radius=a;this.theta=c;this.y=e;return this},clone:function(){return(new this.constructor).copy(this)},copy:function(a){this.radius=a.radius;this.theta=a.theta;this.y=a.y;return this},setFromVector3:function(a){return this.setFromCartesianCoords(a.x,a.y,a.z)},setFromCartesianCoords:function(a,c,e){this.radius=Math.sqrt(a*a+e*e);this.theta=Math.atan2(a,
e);this.y=c;return this}});var Hk=new f;Object.assign(yi.prototype,{set:function(a,c){this.min.copy(a);this.max.copy(c);return this},setFromPoints:function(a){this.makeEmpty();for(var c=0,e=a.length;c<e;c++)this.expandByPoint(a[c]);return this},setFromCenterAndSize:function(a,c){c=Hk.copy(c).multiplyScalar(.5);this.min.copy(a).sub(c);this.max.copy(a).add(c);return this},clone:function(){return(new this.constructor).copy(this)},copy:function(a){this.min.copy(a.min);this.max.copy(a.max);return this},
makeEmpty:function(){this.min.x=this.min.y=Infinity;this.max.x=this.max.y=-Infinity;return this},isEmpty:function(){return this.max.x<this.min.x||this.max.y<this.min.y},getCenter:function(a){void 0===a&&(console.warn("THREE.Box2: .getCenter() target is now required"),a=new f);return this.isEmpty()?a.set(0,0):a.addVectors(this.min,this.max).multiplyScalar(.5)},getSize:function(a){void 0===a&&(console.warn("THREE.Box2: .getSize() target is now required"),a=new f);return this.isEmpty()?a.set(0,0):a.subVectors(this.max,
this.min)},expandByPoint:function(a){this.min.min(a);this.max.max(a);return this},expandByVector:function(a){this.min.sub(a);this.max.add(a);return this},expandByScalar:function(a){this.min.addScalar(-a);this.max.addScalar(a);return this},containsPoint:function(a){return a.x<this.min.x||a.x>this.max.x||a.y<this.min.y||a.y>this.max.y?!1:!0},containsBox:function(a){return this.min.x<=a.min.x&&a.max.x<=this.max.x&&this.min.y<=a.min.y&&a.max.y<=this.max.y},getParameter:function(a,c){void 0===c&&(console.warn("THREE.Box2: .getParameter() target is now required"),
c=new f);return c.set((a.x-this.min.x)/(this.max.x-this.min.x),(a.y-this.min.y)/(this.max.y-this.min.y))},intersectsBox:function(a){return a.max.x<this.min.x||a.min.x>this.max.x||a.max.y<this.min.y||a.min.y>this.max.y?!1:!0},clampPoint:function(a,c){void 0===c&&(console.warn("THREE.Box2: .clampPoint() target is now required"),c=new f);return c.copy(a).clamp(this.min,this.max)},distanceToPoint:function(a){return Hk.copy(a).clamp(this.min,this.max).sub(a).length()},intersect:function(a){this.min.max(a.min);
this.max.min(a.max);return this},union:function(a){this.min.min(a.min);this.max.max(a.max);return this},translate:function(a){this.min.add(a);this.max.add(a);return this},equals:function(a){return a.min.equals(this.min)&&a.max.equals(this.max)}});var Ik=new k,Ch=new k;Object.assign(zi.prototype,{set:function(a,c){this.start.copy(a);this.end.copy(c);return this},clone:function(){return(new this.constructor).copy(this)},copy:function(a){this.start.copy(a.start);this.end.copy(a.end);return this},getCenter:function(a){void 0===
a&&(console.warn("THREE.Line3: .getCenter() target is now required"),a=new k);return a.addVectors(this.start,this.end).multiplyScalar(.5)},delta:function(a){void 0===a&&(console.warn("THREE.Line3: .delta() target is now required"),a=new k);return a.subVectors(this.end,this.start)},distanceSq:function(){return this.start.distanceToSquared(this.end)},distance:function(){return this.start.distanceTo(this.end)},at:function(a,c){void 0===c&&(console.warn("THREE.Line3: .at() target is now required"),c=
new k);return this.delta(c).multiplyScalar(a).add(this.start)},closestPointToPointParameter:function(a,c){Ik.subVectors(a,this.start);Ch.subVectors(this.end,this.start);a=Ch.dot(Ik)/Ch.dot(Ch);c&&(a=hb.clamp(a,0,1));return a},closestPointToPoint:function(a,c,e){a=this.closestPointToPointParameter(a,c);void 0===e&&(console.warn("THREE.Line3: .closestPointToPoint() target is now required"),e=new k);return this.delta(e).multiplyScalar(a).add(this.start)},applyMatrix4:function(a){this.start.applyMatrix4(a);
this.end.applyMatrix4(a);return this},equals:function(a){return a.start.equals(this.start)&&a.end.equals(this.end)}});dg.prototype=Object.create(A.prototype);dg.prototype.constructor=dg;dg.prototype.isImmediateRenderObject=!0;var ad=new k,qd=new k,Wi=new r,rn=["a","b","c"];eg.prototype=Object.create(Ib.prototype);eg.prototype.constructor=eg;eg.prototype.update=function(){this.object.updateMatrixWorld(!0);Wi.getNormalMatrix(this.object.matrixWorld);var a=this.object.matrixWorld,c=this.geometry.attributes.position,
e=this.object.geometry;if(e&&e.isGeometry)for(var g=e.vertices,t=e.faces,v=e=0,z=t.length;v<z;v++)for(var E=t[v],F=0,I=E.vertexNormals.length;F<I;F++){var M=E.vertexNormals[F];ad.copy(g[E[rn[F]]]).applyMatrix4(a);qd.copy(M).applyMatrix3(Wi).normalize().multiplyScalar(this.size).add(ad);c.setXYZ(e,ad.x,ad.y,ad.z);e+=1;c.setXYZ(e,qd.x,qd.y,qd.z);e+=1}else if(e&&e.isBufferGeometry)for(g=e.attributes.position,t=e.attributes.normal,F=e=0,I=g.count;F<I;F++)ad.set(g.getX(F),g.getY(F),g.getZ(F)).applyMatrix4(a),
qd.set(t.getX(F),t.getY(F),t.getZ(F)),qd.applyMatrix3(Wi).normalize().multiplyScalar(this.size).add(ad),c.setXYZ(e,ad.x,ad.y,ad.z),e+=1,c.setXYZ(e,qd.x,qd.y,qd.z),e+=1;c.needsUpdate=!0};var Jk=new k;Ze.prototype=Object.create(A.prototype);Ze.prototype.constructor=Ze;Ze.prototype.dispose=function(){this.cone.geometry.dispose();this.cone.material.dispose()};Ze.prototype.update=function(){this.light.updateMatrixWorld();var a=this.light.distance?this.light.distance:1E3,c=a*Math.tan(this.light.angle);
this.cone.scale.set(c,c,a);Jk.setFromMatrixPosition(this.light.target.matrixWorld);this.cone.lookAt(Jk);void 0!==this.color?this.cone.material.color.set(this.color):this.cone.material.color.copy(this.light.color)};var Kd=new k,Dh=new q,Xi=new q;$e.prototype=Object.create(Ib.prototype);$e.prototype.constructor=$e;$e.prototype.updateMatrixWorld=function(a){var c=this.bones,e=this.geometry,g=e.getAttribute("position");Xi.getInverse(this.root.matrixWorld);for(var t=0,v=0;t<c.length;t++){var z=c[t];z.parent&&
z.parent.isBone&&(Dh.multiplyMatrices(Xi,z.matrixWorld),Kd.setFromMatrixPosition(Dh),g.setXYZ(v,Kd.x,Kd.y,Kd.z),Dh.multiplyMatrices(Xi,z.parent.matrixWorld),Kd.setFromMatrixPosition(Dh),g.setXYZ(v+1,Kd.x,Kd.y,Kd.z),v+=2)}e.getAttribute("position").needsUpdate=!0;A.prototype.updateMatrixWorld.call(this,a)};af.prototype=Object.create(ya.prototype);af.prototype.constructor=af;af.prototype.dispose=function(){this.geometry.dispose();this.material.dispose()};af.prototype.update=function(){void 0!==this.color?
this.material.color.set(this.color):this.material.color.copy(this.light.color)};bf.prototype=Object.create(Xb.prototype);bf.prototype.constructor=bf;bf.prototype.update=function(){this.scale.set(.5*this.light.width,.5*this.light.height,1);if(void 0!==this.color)this.material.color.set(this.color),this.children[0].material.color.set(this.color);else{this.material.color.copy(this.light.color).multiplyScalar(this.light.intensity);var a=this.material.color,c=Math.max(a.r,a.g,a.b);1<c&&a.multiplyScalar(1/
c);this.children[0].material.color.copy(this.material.color)}};bf.prototype.dispose=function(){this.geometry.dispose();this.material.dispose();this.children[0].geometry.dispose();this.children[0].material.dispose()};var sn=new k,Kk=new H,Lk=new H;cf.prototype=Object.create(A.prototype);cf.prototype.constructor=cf;cf.prototype.dispose=function(){this.children[0].geometry.dispose();this.children[0].material.dispose()};cf.prototype.update=function(){var a=this.children[0];if(void 0!==this.color)this.material.color.set(this.color);
else{var c=a.geometry.getAttribute("color");Kk.copy(this.light.color);Lk.copy(this.light.groundColor);for(var e=0,g=c.count;e<g;e++){var t=e<g/2?Kk:Lk;c.setXYZ(e,t.r,t.g,t.b)}c.needsUpdate=!0}a.lookAt(sn.setFromMatrixPosition(this.light.matrixWorld).negate())};df.prototype=Object.create(ya.prototype);df.prototype.constructor=df;df.prototype.dispose=function(){this.geometry.dispose();this.material.dispose()};df.prototype.onBeforeRender=function(){this.position.copy(this.lightProbe.position);this.scale.set(1,
1,1).multiplyScalar(this.size);this.material.uniforms.intensity.value=this.lightProbe.intensity};fh.prototype=Object.assign(Object.create(Ib.prototype),{constructor:fh,copy:function(a){Ib.prototype.copy.call(this,a);this.geometry.copy(a.geometry);this.material.copy(a.material);return this},clone:function(){return(new this.constructor).copy(this)}});gh.prototype=Object.create(Ib.prototype);gh.prototype.constructor=gh;ef.prototype=Object.create(Xb.prototype);ef.prototype.constructor=ef;ef.prototype.update=
function(){function a(V,ea,da,ra){da=(ea-V)/da;U.setXYZ(F,0,0,0);I++;for(M=V;M<ea;M+=da)P=F+I,U.setXYZ(P,Math.sin(M)*e,0,Math.cos(M)*e),U.setXYZ(P+1,Math.sin(Math.min(M+da,ea))*e,0,Math.cos(Math.min(M+da,ea))*e),U.setXYZ(P+2,0,0,0),I+=3;Q.addGroup(F,I,ra);F+=I;I=0}var c=this.audio,e=this.range,g=this.divisionsInnerAngle,t=this.divisionsOuterAngle,v=hb.degToRad(c.panner.coneInnerAngle);c=hb.degToRad(c.panner.coneOuterAngle);var z=v/2,E=c/2,F=0,I=0,M,P,Q=this.geometry,U=Q.attributes.position;Q.clearGroups();
a(-E,-z,t,0);a(-z,z,g,1);a(z,E,t,0);U.needsUpdate=!0;v===c&&(this.material[0].visible=!1)};ef.prototype.dispose=function(){this.geometry.dispose();this.material[0].dispose();this.material[1].dispose()};var rg=new k,Eh=new k,Mk=new r;fg.prototype=Object.create(Ib.prototype);fg.prototype.constructor=fg;fg.prototype.update=function(){this.object.updateMatrixWorld(!0);Mk.getNormalMatrix(this.object.matrixWorld);var a=this.object.matrixWorld,c=this.geometry.attributes.position,e=this.object.geometry,g=
e.vertices;e=e.faces;for(var t=0,v=0,z=e.length;v<z;v++){var E=e[v],F=E.normal;rg.copy(g[E.a]).add(g[E.b]).add(g[E.c]).divideScalar(3).applyMatrix4(a);Eh.copy(F).applyMatrix3(Mk).normalize().multiplyScalar(this.size).add(rg);c.setXYZ(t,rg.x,rg.y,rg.z);t+=1;c.setXYZ(t,Eh.x,Eh.y,Eh.z);t+=1}c.needsUpdate=!0};var Nk=new k,Fh=new k,Ok=new k;ff.prototype=Object.create(A.prototype);ff.prototype.constructor=ff;ff.prototype.dispose=function(){this.lightPlane.geometry.dispose();this.lightPlane.material.dispose();
this.targetLine.geometry.dispose();this.targetLine.material.dispose()};ff.prototype.update=function(){Nk.setFromMatrixPosition(this.light.matrixWorld);Fh.setFromMatrixPosition(this.light.target.matrixWorld);Ok.subVectors(Fh,Nk);this.lightPlane.lookAt(Fh);void 0!==this.color?(this.lightPlane.material.color.set(this.color),this.targetLine.material.color.set(this.color)):(this.lightPlane.material.color.copy(this.light.color),this.targetLine.material.color.copy(this.light.color));this.targetLine.lookAt(Fh);
this.targetLine.scale.z=Ok.length()};var hh=new k,Pb=new zb;gg.prototype=Object.create(Ib.prototype);gg.prototype.constructor=gg;gg.prototype.update=function(){var a=this.geometry,c=this.pointMap;Pb.projectionMatrixInverse.copy(this.camera.projectionMatrixInverse);Qb("c",c,a,Pb,0,0,-1);Qb("t",c,a,Pb,0,0,1);Qb("n1",c,a,Pb,-1,-1,-1);Qb("n2",c,a,Pb,1,-1,-1);Qb("n3",c,a,Pb,-1,1,-1);Qb("n4",c,a,Pb,1,1,-1);Qb("f1",c,a,Pb,-1,-1,1);Qb("f2",c,a,Pb,1,-1,1);Qb("f3",c,a,Pb,-1,1,1);Qb("f4",c,a,Pb,1,1,1);Qb("u1",
c,a,Pb,.7,1.1,-1);Qb("u2",c,a,Pb,-.7,1.1,-1);Qb("u3",c,a,Pb,0,2,-1);Qb("cf1",c,a,Pb,-1,0,1);Qb("cf2",c,a,Pb,1,0,1);Qb("cf3",c,a,Pb,0,-1,1);Qb("cf4",c,a,Pb,0,1,1);Qb("cn1",c,a,Pb,-1,0,-1);Qb("cn2",c,a,Pb,1,0,-1);Qb("cn3",c,a,Pb,0,-1,-1);Qb("cn4",c,a,Pb,0,1,-1);a.getAttribute("position").needsUpdate=!0};var Gh=new x;jd.prototype=Object.create(Ib.prototype);jd.prototype.constructor=jd;jd.prototype.update=function(a){void 0!==a&&console.warn("THREE.BoxHelper: .update() has no longer arguments.");void 0!==
this.object&&Gh.setFromObject(this.object);if(!Gh.isEmpty()){a=Gh.min;var c=Gh.max,e=this.geometry.attributes.position,g=e.array;g[0]=c.x;g[1]=c.y;g[2]=c.z;g[3]=a.x;g[4]=c.y;g[5]=c.z;g[6]=a.x;g[7]=a.y;g[8]=c.z;g[9]=c.x;g[10]=a.y;g[11]=c.z;g[12]=c.x;g[13]=c.y;g[14]=a.z;g[15]=a.x;g[16]=c.y;g[17]=a.z;g[18]=a.x;g[19]=a.y;g[20]=a.z;g[21]=c.x;g[22]=a.y;g[23]=a.z;e.needsUpdate=!0;this.geometry.computeBoundingSphere()}};jd.prototype.setFromObject=function(a){this.object=a;this.update();return this};jd.prototype.copy=
function(a){Ib.prototype.copy.call(this,a);this.object=a.object;return this};jd.prototype.clone=function(){return(new this.constructor).copy(this)};hg.prototype=Object.create(Ib.prototype);hg.prototype.constructor=hg;hg.prototype.updateMatrixWorld=function(a){var c=this.box;c.isEmpty()||(c.getCenter(this.position),c.getSize(this.scale),this.scale.multiplyScalar(.5),A.prototype.updateMatrixWorld.call(this,a))};ig.prototype=Object.create(Xb.prototype);ig.prototype.constructor=ig;ig.prototype.updateMatrixWorld=
function(a){var c=-this.plane.constant;1E-8>Math.abs(c)&&(c=1E-8);this.scale.set(.5*this.size,.5*this.size,c);this.children[0].material.side=0>c?1:0;this.lookAt(this.plane.normal);A.prototype.updateMatrixWorld.call(this,a)};var Pk=new k,ih,Ai;kd.prototype=Object.create(A.prototype);kd.prototype.constructor=kd;kd.prototype.setDirection=function(a){.99999<a.y?this.quaternion.set(0,0,0,1):-.99999>a.y?this.quaternion.set(1,0,0,0):(Pk.set(a.z,0,-a.x).normalize(),this.quaternion.setFromAxisAngle(Pk,Math.acos(a.y)))};
kd.prototype.setLength=function(a,c,e){void 0===c&&(c=.2*a);void 0===e&&(e=.2*c);this.line.scale.set(1,Math.max(0,a-c),1);this.line.updateMatrix();this.cone.scale.set(e,c,e);this.cone.position.y=a;this.cone.updateMatrix()};kd.prototype.setColor=function(a){this.line.material.color.set(a);this.cone.material.color.set(a)};kd.prototype.copy=function(a){A.prototype.copy.call(this,a,!1);this.line.copy(a.line);this.cone.copy(a.cone);return this};kd.prototype.clone=function(){return(new this.constructor).copy(this)};
jg.prototype=Object.create(Ib.prototype);jg.prototype.constructor=jg;Za.create=function(a,c){console.log("THREE.Curve.create() has been deprecated");a.prototype=Object.create(Za.prototype);a.prototype.constructor=a;a.prototype.getPoint=c;return a};Object.assign(id.prototype,{createPointsGeometry:function(a){console.warn("THREE.CurvePath: .createPointsGeometry() has been removed. Use new THREE.Geometry().setFromPoints( points ) instead.");a=this.getPoints(a);return this.createGeometry(a)},createSpacedPointsGeometry:function(a){console.warn("THREE.CurvePath: .createSpacedPointsGeometry() has been removed. Use new THREE.Geometry().setFromPoints( points ) instead.");
a=this.getSpacedPoints(a);return this.createGeometry(a)},createGeometry:function(a){console.warn("THREE.CurvePath: .createGeometry() has been removed. Use new THREE.Geometry().setFromPoints( points ) instead.");for(var c=new xa,e=0,g=a.length;e<g;e++){var t=a[e];c.vertices.push(new k(t.x,t.y,t.z||0))}return c}});Object.assign(Ic.prototype,{fromPoints:function(a){console.warn("THREE.Path: .fromPoints() has been renamed to .setFromPoints().");this.setFromPoints(a)}});ik.prototype=Object.create(bc.prototype);
jk.prototype=Object.create(bc.prototype);Bi.prototype=Object.create(bc.prototype);Object.assign(Bi.prototype,{initFromArray:function(){console.error("THREE.Spline: .initFromArray() has been removed.")},getControlPointsArray:function(){console.error("THREE.Spline: .getControlPointsArray() has been removed.")},reparametrizeByArcLength:function(){console.error("THREE.Spline: .reparametrizeByArcLength() has been removed.")}});fh.prototype.setColors=function(){console.error("THREE.GridHelper: setColors() has been deprecated, pass them in the constructor instead.")};
$e.prototype.update=function(){console.error("THREE.SkeletonHelper: update() no longer needs to be called.")};Object.assign(Db.prototype,{extractUrlBase:function(a){console.warn("THREE.Loader: .extractUrlBase() has been deprecated. Use THREE.LoaderUtils.extractUrlBase() instead.");return Ui.extractUrlBase(a)}});Object.assign(bh.prototype,{setTexturePath:function(a){console.warn("THREE.ObjectLoader: .setTexturePath() has been renamed to .setResourcePath().");return this.setResourcePath(a)}});Object.assign(yi.prototype,
{center:function(a){console.warn("THREE.Box2: .center() has been renamed to .getCenter().");return this.getCenter(a)},empty:function(){console.warn("THREE.Box2: .empty() has been renamed to .isEmpty().");return this.isEmpty()},isIntersectionBox:function(a){console.warn("THREE.Box2: .isIntersectionBox() has been renamed to .intersectsBox().");return this.intersectsBox(a)},size:function(a){console.warn("THREE.Box2: .size() has been renamed to .getSize().");return this.getSize(a)}});Object.assign(x.prototype,
{center:function(a){console.warn("THREE.Box3: .center() has been renamed to .getCenter().");return this.getCenter(a)},empty:function(){console.warn("THREE.Box3: .empty() has been renamed to .isEmpty().");return this.isEmpty()},isIntersectionBox:function(a){console.warn("THREE.Box3: .isIntersectionBox() has been renamed to .intersectsBox().");return this.intersectsBox(a)},isIntersectionSphere:function(a){console.warn("THREE.Box3: .isIntersectionSphere() has been renamed to .intersectsSphere().");return this.intersectsSphere(a)},
size:function(a){console.warn("THREE.Box3: .size() has been renamed to .getSize().");return this.getSize(a)}});zi.prototype.center=function(a){console.warn("THREE.Line3: .center() has been renamed to .getCenter().");return this.getCenter(a)};Object.assign(hb,{random16:function(){console.warn("THREE.Math: .random16() has been deprecated. Use Math.random() instead.");return Math.random()},nearestPowerOfTwo:function(a){console.warn("THREE.Math: .nearestPowerOfTwo() has been renamed to .floorPowerOfTwo().");
return hb.floorPowerOfTwo(a)},nextPowerOfTwo:function(a){console.warn("THREE.Math: .nextPowerOfTwo() has been renamed to .ceilPowerOfTwo().");return hb.ceilPowerOfTwo(a)}});Object.assign(r.prototype,{flattenToArrayOffset:function(a,c){console.warn("THREE.Matrix3: .flattenToArrayOffset() has been deprecated. Use .toArray() instead.");return this.toArray(a,c)},multiplyVector3:function(a){console.warn("THREE.Matrix3: .multiplyVector3() has been removed. Use vector.applyMatrix3( matrix ) instead.");return a.applyMatrix3(this)},
multiplyVector3Array:function(){console.error("THREE.Matrix3: .multiplyVector3Array() has been removed.")},applyToBuffer:function(a){console.warn("THREE.Matrix3: .applyToBuffer() has been removed. Use matrix.applyToBufferAttribute( attribute ) instead.");return this.applyToBufferAttribute(a)},applyToVector3Array:function(){console.error("THREE.Matrix3: .applyToVector3Array() has been removed.")}});Object.assign(q.prototype,{extractPosition:function(a){console.warn("THREE.Matrix4: .extractPosition() has been renamed to .copyPosition().");
return this.copyPosition(a)},flattenToArrayOffset:function(a,c){console.warn("THREE.Matrix4: .flattenToArrayOffset() has been deprecated. Use .toArray() instead.");return this.toArray(a,c)},getPosition:function(){console.warn("THREE.Matrix4: .getPosition() has been removed. Use Vector3.setFromMatrixPosition( matrix ) instead.");return(new k).setFromMatrixColumn(this,3)},setRotationFromQuaternion:function(a){console.warn("THREE.Matrix4: .setRotationFromQuaternion() has been renamed to .makeRotationFromQuaternion().");
return this.makeRotationFromQuaternion(a)},multiplyToArray:function(){console.warn("THREE.Matrix4: .multiplyToArray() has been removed.")},multiplyVector3:function(a){console.warn("THREE.Matrix4: .multiplyVector3() has been removed. Use vector.applyMatrix4( matrix ) instead.");return a.applyMatrix4(this)},multiplyVector4:function(a){console.warn("THREE.Matrix4: .multiplyVector4() has been removed. Use vector.applyMatrix4( matrix ) instead.");return a.applyMatrix4(this)},multiplyVector3Array:function(){console.error("THREE.Matrix4: .multiplyVector3Array() has been removed.")},
rotateAxis:function(a){console.warn("THREE.Matrix4: .rotateAxis() has been removed. Use Vector3.transformDirection( matrix ) instead.");a.transformDirection(this)},crossVector:function(a){console.warn("THREE.Matrix4: .crossVector() has been removed. Use vector.applyMatrix4( matrix ) instead.");return a.applyMatrix4(this)},translate:function(){console.error("THREE.Matrix4: .translate() has been removed.")},rotateX:function(){console.error("THREE.Matrix4: .rotateX() has been removed.")},rotateY:function(){console.error("THREE.Matrix4: .rotateY() has been removed.")},
rotateZ:function(){console.error("THREE.Matrix4: .rotateZ() has been removed.")},rotateByAxis:function(){console.error("THREE.Matrix4: .rotateByAxis() has been removed.")},applyToBuffer:function(a){console.warn("THREE.Matrix4: .applyToBuffer() has been removed. Use matrix.applyToBufferAttribute( attribute ) instead.");return this.applyToBufferAttribute(a)},applyToVector3Array:function(){console.error("THREE.Matrix4: .applyToVector3Array() has been removed.")},makeFrustum:function(a,c,e,g,t,v){console.warn("THREE.Matrix4: .makeFrustum() has been removed. Use .makePerspective( left, right, top, bottom, near, far ) instead.");
return this.makePerspective(a,c,g,e,t,v)}});Hb.prototype.isIntersectionLine=function(a){console.warn("THREE.Plane: .isIntersectionLine() has been renamed to .intersectsLine().");return this.intersectsLine(a)};h.prototype.multiplyVector3=function(a){console.warn("THREE.Quaternion: .multiplyVector3() has been removed. Use is now vector.applyQuaternion( quaternion ) instead.");return a.applyQuaternion(this)};Object.assign(D.prototype,{isIntersectionBox:function(a){console.warn("THREE.Ray: .isIntersectionBox() has been renamed to .intersectsBox().");
return this.intersectsBox(a)},isIntersectionPlane:function(a){console.warn("THREE.Ray: .isIntersectionPlane() has been renamed to .intersectsPlane().");return this.intersectsPlane(a)},isIntersectionSphere:function(a){console.warn("THREE.Ray: .isIntersectionSphere() has been renamed to .intersectsSphere().");return this.intersectsSphere(a)}});Object.assign(B.prototype,{area:function(){console.warn("THREE.Triangle: .area() has been renamed to .getArea().");return this.getArea()},barycoordFromPoint:function(a,
c){console.warn("THREE.Triangle: .barycoordFromPoint() has been renamed to .getBarycoord().");return this.getBarycoord(a,c)},midpoint:function(a){console.warn("THREE.Triangle: .midpoint() has been renamed to .getMidpoint().");return this.getMidpoint(a)},normal:function(a){console.warn("THREE.Triangle: .normal() has been renamed to .getNormal().");return this.getNormal(a)},plane:function(a){console.warn("THREE.Triangle: .plane() has been renamed to .getPlane().");return this.getPlane(a)}});Object.assign(B,
{barycoordFromPoint:function(a,c,e,g,t){console.warn("THREE.Triangle: .barycoordFromPoint() has been renamed to .getBarycoord().");return B.getBarycoord(a,c,e,g,t)},normal:function(a,c,e,g){console.warn("THREE.Triangle: .normal() has been renamed to .getNormal().");return B.getNormal(a,c,e,g)}});Object.assign(Ed.prototype,{extractAllPoints:function(a){console.warn("THREE.Shape: .extractAllPoints() has been removed. Use .extractPoints() instead.");return this.extractPoints(a)},extrude:function(a){console.warn("THREE.Shape: .extrude() has been removed. Use ExtrudeGeometry() instead.");
return new Wd(this,a)},makeGeometry:function(a){console.warn("THREE.Shape: .makeGeometry() has been removed. Use ShapeGeometry() instead.");return new Xd(this,a)}});Object.assign(f.prototype,{fromAttribute:function(a,c,e){console.warn("THREE.Vector2: .fromAttribute() has been renamed to .fromBufferAttribute().");return this.fromBufferAttribute(a,c,e)},distanceToManhattan:function(a){console.warn("THREE.Vector2: .distanceToManhattan() has been renamed to .manhattanDistanceTo().");return this.manhattanDistanceTo(a)},
lengthManhattan:function(){console.warn("THREE.Vector2: .lengthManhattan() has been renamed to .manhattanLength().");return this.manhattanLength()}});Object.assign(k.prototype,{setEulerFromRotationMatrix:function(){console.error("THREE.Vector3: .setEulerFromRotationMatrix() has been removed. Use Euler.setFromRotationMatrix() instead.")},setEulerFromQuaternion:function(){console.error("THREE.Vector3: .setEulerFromQuaternion() has been removed. Use Euler.setFromQuaternion() instead.")},getPositionFromMatrix:function(a){console.warn("THREE.Vector3: .getPositionFromMatrix() has been renamed to .setFromMatrixPosition().");
return this.setFromMatrixPosition(a)},getScaleFromMatrix:function(a){console.warn("THREE.Vector3: .getScaleFromMatrix() has been renamed to .setFromMatrixScale().");return this.setFromMatrixScale(a)},getColumnFromMatrix:function(a,c){console.warn("THREE.Vector3: .getColumnFromMatrix() has been renamed to .setFromMatrixColumn().");return this.setFromMatrixColumn(c,a)},applyProjection:function(a){console.warn("THREE.Vector3: .applyProjection() has been removed. Use .applyMatrix4( m ) instead.");return this.applyMatrix4(a)},
fromAttribute:function(a,c,e){console.warn("THREE.Vector3: .fromAttribute() has been renamed to .fromBufferAttribute().");return this.fromBufferAttribute(a,c,e)},distanceToManhattan:function(a){console.warn("THREE.Vector3: .distanceToManhattan() has been renamed to .manhattanDistanceTo().");return this.manhattanDistanceTo(a)},lengthManhattan:function(){console.warn("THREE.Vector3: .lengthManhattan() has been renamed to .manhattanLength().");return this.manhattanLength()}});Object.assign(p.prototype,
{fromAttribute:function(a,c,e){console.warn("THREE.Vector4: .fromAttribute() has been renamed to .fromBufferAttribute().");return this.fromBufferAttribute(a,c,e)},lengthManhattan:function(){console.warn("THREE.Vector4: .lengthManhattan() has been renamed to .manhattanLength().");return this.manhattanLength()}});Object.assign(xa.prototype,{computeTangents:function(){console.error("THREE.Geometry: .computeTangents() has been removed.")},computeLineDistances:function(){console.error("THREE.Geometry: .computeLineDistances() has been removed. Use THREE.Line.computeLineDistances() instead.")}});
Object.assign(A.prototype,{getChildByName:function(a){console.warn("THREE.Object3D: .getChildByName() has been renamed to .getObjectByName().");return this.getObjectByName(a)},renderDepth:function(){console.warn("THREE.Object3D: .renderDepth has been removed. Use .renderOrder, instead.")},translate:function(a,c){console.warn("THREE.Object3D: .translate() has been removed. Use .translateOnAxis( axis, distance ) instead.");return this.translateOnAxis(c,a)},getWorldRotation:function(){console.error("THREE.Object3D: .getWorldRotation() has been removed. Use THREE.Object3D.getWorldQuaternion( target ) instead.")}});
Object.defineProperties(A.prototype,{eulerOrder:{get:function(){console.warn("THREE.Object3D: .eulerOrder is now .rotation.order.");return this.rotation.order},set:function(a){console.warn("THREE.Object3D: .eulerOrder is now .rotation.order.");this.rotation.order=a}},useQuaternion:{get:function(){console.warn("THREE.Object3D: .useQuaternion has been removed. The library now uses quaternions by default.")},set:function(){console.warn("THREE.Object3D: .useQuaternion has been removed. The library now uses quaternions by default.")}}});
Object.defineProperties(Bf.prototype,{objects:{get:function(){console.warn("THREE.LOD: .objects has been renamed to .levels.");return this.levels}}});Object.defineProperty(Fg.prototype,"useVertexTexture",{get:function(){console.warn("THREE.Skeleton: useVertexTexture has been removed.")},set:function(){console.warn("THREE.Skeleton: useVertexTexture has been removed.")}});Cf.prototype.initBones=function(){console.error("THREE.SkinnedMesh: initBones() has been removed.")};Object.defineProperty(Za.prototype,
"__arcLengthDivisions",{get:function(){console.warn("THREE.Curve: .__arcLengthDivisions is now .arcLengthDivisions.");return this.arcLengthDivisions},set:function(a){console.warn("THREE.Curve: .__arcLengthDivisions is now .arcLengthDivisions.");this.arcLengthDivisions=a}});vb.prototype.setLens=function(a,c){console.warn("THREE.PerspectiveCamera.setLens is deprecated. Use .setFocalLength and .filmGauge for a photographic setup.");void 0!==c&&(this.filmGauge=c);this.setFocalLength(a)};Object.defineProperties(Kb.prototype,
{onlyShadow:{set:function(){console.warn("THREE.Light: .onlyShadow has been removed.")}},shadowCameraFov:{set:function(a){console.warn("THREE.Light: .shadowCameraFov is now .shadow.camera.fov.");this.shadow.camera.fov=a}},shadowCameraLeft:{set:function(a){console.warn("THREE.Light: .shadowCameraLeft is now .shadow.camera.left.");this.shadow.camera.left=a}},shadowCameraRight:{set:function(a){console.warn("THREE.Light: .shadowCameraRight is now .shadow.camera.right.");this.shadow.camera.right=a}},shadowCameraTop:{set:function(a){console.warn("THREE.Light: .shadowCameraTop is now .shadow.camera.top.");
this.shadow.camera.top=a}},shadowCameraBottom:{set:function(a){console.warn("THREE.Light: .shadowCameraBottom is now .shadow.camera.bottom.");this.shadow.camera.bottom=a}},shadowCameraNear:{set:function(a){console.warn("THREE.Light: .shadowCameraNear is now .shadow.camera.near.");this.shadow.camera.near=a}},shadowCameraFar:{set:function(a){console.warn("THREE.Light: .shadowCameraFar is now .shadow.camera.far.");this.shadow.camera.far=a}},shadowCameraVisible:{set:function(){console.warn("THREE.Light: .shadowCameraVisible has been removed. Use new THREE.CameraHelper( light.shadow.camera ) instead.")}},
shadowBias:{set:function(a){console.warn("THREE.Light: .shadowBias is now .shadow.bias.");this.shadow.bias=a}},shadowDarkness:{set:function(){console.warn("THREE.Light: .shadowDarkness has been removed.")}},shadowMapWidth:{set:function(a){console.warn("THREE.Light: .shadowMapWidth is now .shadow.mapSize.width.");this.shadow.mapSize.width=a}},shadowMapHeight:{set:function(a){console.warn("THREE.Light: .shadowMapHeight is now .shadow.mapSize.height.");this.shadow.mapSize.height=a}}});Object.defineProperties(R.prototype,
{length:{get:function(){console.warn("THREE.BufferAttribute: .length has been deprecated. Use .count instead.");return this.array.length}},copyIndicesArray:function(){console.error("THREE.BufferAttribute: .copyIndicesArray() has been removed.")}});Object.assign(va.prototype,{addIndex:function(a){console.warn("THREE.BufferGeometry: .addIndex() has been renamed to .setIndex().");this.setIndex(a)},addDrawCall:function(a,c,e){void 0!==e&&console.warn("THREE.BufferGeometry: .addDrawCall() no longer supports indexOffset.");
console.warn("THREE.BufferGeometry: .addDrawCall() is now .addGroup().");this.addGroup(a,c)},clearDrawCalls:function(){console.warn("THREE.BufferGeometry: .clearDrawCalls() is now .clearGroups().");this.clearGroups()},computeTangents:function(){console.warn("THREE.BufferGeometry: .computeTangents() has been removed.")},computeOffsets:function(){console.warn("THREE.BufferGeometry: .computeOffsets() has been removed.")}});Object.defineProperties(va.prototype,{drawcalls:{get:function(){console.error("THREE.BufferGeometry: .drawcalls has been renamed to .groups.");
return this.groups}},offsets:{get:function(){console.warn("THREE.BufferGeometry: .offsets has been renamed to .groups.");return this.groups}}});Object.assign(Tc.prototype,{getArrays:function(){console.error("THREE.ExtrudeBufferGeometry: .getArrays() has been removed.")},addShapeList:function(){console.error("THREE.ExtrudeBufferGeometry: .addShapeList() has been removed.")},addShape:function(){console.error("THREE.ExtrudeBufferGeometry: .addShape() has been removed.")}});Object.defineProperties(eh.prototype,
{dynamic:{set:function(){console.warn("THREE.Uniform: .dynamic has been removed. Use object.onBeforeRender() instead.")}},onUpdate:{value:function(){console.warn("THREE.Uniform: .onUpdate() has been removed. Use object.onBeforeRender() instead.");return this}}});Object.defineProperties(S.prototype,{wrapAround:{get:function(){console.warn("THREE.Material: .wrapAround has been removed.")},set:function(){console.warn("THREE.Material: .wrapAround has been removed.")}},overdraw:{get:function(){console.warn("THREE.Material: .overdraw has been removed.")},
set:function(){console.warn("THREE.Material: .overdraw has been removed.")}},wrapRGB:{get:function(){console.warn("THREE.Material: .wrapRGB has been removed.");return new H}},shading:{get:function(){console.error("THREE."+this.type+": .shading has been removed. Use the boolean .flatShading instead.")},set:function(a){console.warn("THREE."+this.type+": .shading has been removed. Use the boolean .flatShading instead.");this.flatShading=1===a}}});Object.defineProperties(Dc.prototype,{metal:{get:function(){console.warn("THREE.MeshPhongMaterial: .metal has been removed. Use THREE.MeshStandardMaterial instead.");
return!1},set:function(){console.warn("THREE.MeshPhongMaterial: .metal has been removed. Use THREE.MeshStandardMaterial instead")}}});Object.defineProperties(qb.prototype,{derivatives:{get:function(){console.warn("THREE.ShaderMaterial: .derivatives has been moved to .extensions.derivatives.");return this.extensions.derivatives},set:function(a){console.warn("THREE. ShaderMaterial: .derivatives has been moved to .extensions.derivatives.");this.extensions.derivatives=a}}});Object.assign(Th.prototype,
{clearTarget:function(a,c,e,g){console.warn("THREE.WebGLRenderer: .clearTarget() has been deprecated. Use .setRenderTarget() and .clear() instead.");this.setRenderTarget(a);this.clear(c,e,g)},animate:function(a){console.warn("THREE.WebGLRenderer: .animate() is now .setAnimationLoop().");this.setAnimationLoop(a)},getCurrentRenderTarget:function(){console.warn("THREE.WebGLRenderer: .getCurrentRenderTarget() is now .getRenderTarget().");return this.getRenderTarget()},getMaxAnisotropy:function(){console.warn("THREE.WebGLRenderer: .getMaxAnisotropy() is now .capabilities.getMaxAnisotropy().");
return this.capabilities.getMaxAnisotropy()},getPrecision:function(){console.warn("THREE.WebGLRenderer: .getPrecision() is now .capabilities.precision.");return this.capabilities.precision},resetGLState:function(){console.warn("THREE.WebGLRenderer: .resetGLState() is now .state.reset().");return this.state.reset()},supportsFloatTextures:function(){console.warn("THREE.WebGLRenderer: .supportsFloatTextures() is now .extensions.get( 'OES_texture_float' ).");return this.extensions.get("OES_texture_float")},
supportsHalfFloatTextures:function(){console.warn("THREE.WebGLRenderer: .supportsHalfFloatTextures() is now .extensions.get( 'OES_texture_half_float' ).");return this.extensions.get("OES_texture_half_float")},supportsStandardDerivatives:function(){console.warn("THREE.WebGLRenderer: .supportsStandardDerivatives() is now .extensions.get( 'OES_standard_derivatives' ).");return this.extensions.get("OES_standard_derivatives")},supportsCompressedTextureS3TC:function(){console.warn("THREE.WebGLRenderer: .supportsCompressedTextureS3TC() is now .extensions.get( 'WEBGL_compressed_texture_s3tc' ).");
return this.extensions.get("WEBGL_compressed_texture_s3tc")},supportsCompressedTexturePVRTC:function(){console.warn("THREE.WebGLRenderer: .supportsCompressedTexturePVRTC() is now .extensions.get( 'WEBGL_compressed_texture_pvrtc' ).");return this.extensions.get("WEBGL_compressed_texture_pvrtc")},supportsBlendMinMax:function(){console.warn("THREE.WebGLRenderer: .supportsBlendMinMax() is now .extensions.get( 'EXT_blend_minmax' ).");return this.extensions.get("EXT_blend_minmax")},supportsVertexTextures:function(){console.warn("THREE.WebGLRenderer: .supportsVertexTextures() is now .capabilities.vertexTextures.");
return this.capabilities.vertexTextures},supportsInstancedArrays:function(){console.warn("THREE.WebGLRenderer: .supportsInstancedArrays() is now .extensions.get( 'ANGLE_instanced_arrays' ).");return this.extensions.get("ANGLE_instanced_arrays")},enableScissorTest:function(a){console.warn("THREE.WebGLRenderer: .enableScissorTest() is now .setScissorTest().");this.setScissorTest(a)},initMaterial:function(){console.warn("THREE.WebGLRenderer: .initMaterial() has been removed.")},addPrePlugin:function(){console.warn("THREE.WebGLRenderer: .addPrePlugin() has been removed.")},
addPostPlugin:function(){console.warn("THREE.WebGLRenderer: .addPostPlugin() has been removed.")},updateShadowMap:function(){console.warn("THREE.WebGLRenderer: .updateShadowMap() has been removed.")},setFaceCulling:function(){console.warn("THREE.WebGLRenderer: .setFaceCulling() has been removed.")},allocTextureUnit:function(){console.warn("THREE.WebGLRenderer: .allocTextureUnit() has been removed.")},setTexture:function(){console.warn("THREE.WebGLRenderer: .setTexture() has been removed.")},setTexture2D:function(){console.warn("THREE.WebGLRenderer: .setTexture2D() has been removed.")},
setTextureCube:function(){console.warn("THREE.WebGLRenderer: .setTextureCube() has been removed.")},getActiveMipMapLevel:function(){console.warn("THREE.WebGLRenderer: .getActiveMipMapLevel() is now .getActiveMipmapLevel().");return this.getActiveMipmapLevel()}});Object.defineProperties(Th.prototype,{shadowMapEnabled:{get:function(){return this.shadowMap.enabled},set:function(a){console.warn("THREE.WebGLRenderer: .shadowMapEnabled is now .shadowMap.enabled.");this.shadowMap.enabled=a}},shadowMapType:{get:function(){return this.shadowMap.type},
set:function(a){console.warn("THREE.WebGLRenderer: .shadowMapType is now .shadowMap.type.");this.shadowMap.type=a}},shadowMapCullFace:{get:function(){console.warn("THREE.WebGLRenderer: .shadowMapCullFace has been removed. Set Material.shadowSide instead.")},set:function(){console.warn("THREE.WebGLRenderer: .shadowMapCullFace has been removed. Set Material.shadowSide instead.")}},context:{get:function(){console.warn("THREE.WebGLRenderer: .context has been removed. Use .getContext() instead.");return this.getContext()}}});
Object.defineProperties(Ej.prototype,{cullFace:{get:function(){console.warn("THREE.WebGLRenderer: .shadowMap.cullFace has been removed. Set Material.shadowSide instead.")},set:function(){console.warn("THREE.WebGLRenderer: .shadowMap.cullFace has been removed. Set Material.shadowSide instead.")}},renderReverseSided:{get:function(){console.warn("THREE.WebGLRenderer: .shadowMap.renderReverseSided has been removed. Set Material.shadowSide instead.")},set:function(){console.warn("THREE.WebGLRenderer: .shadowMap.renderReverseSided has been removed. Set Material.shadowSide instead.")}},
renderSingleSided:{get:function(){console.warn("THREE.WebGLRenderer: .shadowMap.renderSingleSided has been removed. Set Material.shadowSide instead.")},set:function(){console.warn("THREE.WebGLRenderer: .shadowMap.renderSingleSided has been removed. Set Material.shadowSide instead.")}}});Object.defineProperties(Ob.prototype,{activeCubeFace:{set:function(){console.warn("THREE.WebGLRenderTargetCube: .activeCubeFace has been removed. It is now the second parameter of WebGLRenderer.setRenderTarget().")}},
activeMipMapLevel:{set:function(){console.warn("THREE.WebGLRenderTargetCube: .activeMipMapLevel has been removed. It is now the third parameter of WebGLRenderer.setRenderTarget().")}}});Object.defineProperties(m.prototype,{wrapS:{get:function(){console.warn("THREE.WebGLRenderTarget: .wrapS is now .texture.wrapS.");return this.texture.wrapS},set:function(a){console.warn("THREE.WebGLRenderTarget: .wrapS is now .texture.wrapS.");this.texture.wrapS=a}},wrapT:{get:function(){console.warn("THREE.WebGLRenderTarget: .wrapT is now .texture.wrapT.");
return this.texture.wrapT},set:function(a){console.warn("THREE.WebGLRenderTarget: .wrapT is now .texture.wrapT.");this.texture.wrapT=a}},magFilter:{get:function(){console.warn("THREE.WebGLRenderTarget: .magFilter is now .texture.magFilter.");return this.texture.magFilter},set:function(a){console.warn("THREE.WebGLRenderTarget: .magFilter is now .texture.magFilter.");this.texture.magFilter=a}},minFilter:{get:function(){console.warn("THREE.WebGLRenderTarget: .minFilter is now .texture.minFilter.");return this.texture.minFilter},
set:function(a){console.warn("THREE.WebGLRenderTarget: .minFilter is now .texture.minFilter.");this.texture.minFilter=a}},anisotropy:{get:function(){console.warn("THREE.WebGLRenderTarget: .anisotropy is now .texture.anisotropy.");return this.texture.anisotropy},set:function(a){console.warn("THREE.WebGLRenderTarget: .anisotropy is now .texture.anisotropy.");this.texture.anisotropy=a}},offset:{get:function(){console.warn("THREE.WebGLRenderTarget: .offset is now .texture.offset.");return this.texture.offset},
set:function(a){console.warn("THREE.WebGLRenderTarget: .offset is now .texture.offset.");this.texture.offset=a}},repeat:{get:function(){console.warn("THREE.WebGLRenderTarget: .repeat is now .texture.repeat.");return this.texture.repeat},set:function(a){console.warn("THREE.WebGLRenderTarget: .repeat is now .texture.repeat.");this.texture.repeat=a}},format:{get:function(){console.warn("THREE.WebGLRenderTarget: .format is now .texture.format.");return this.texture.format},set:function(a){console.warn("THREE.WebGLRenderTarget: .format is now .texture.format.");
this.texture.format=a}},type:{get:function(){console.warn("THREE.WebGLRenderTarget: .type is now .texture.type.");return this.texture.type},set:function(a){console.warn("THREE.WebGLRenderTarget: .type is now .texture.type.");this.texture.type=a}},generateMipmaps:{get:function(){console.warn("THREE.WebGLRenderTarget: .generateMipmaps is now .texture.generateMipmaps.");return this.texture.generateMipmaps},set:function(a){console.warn("THREE.WebGLRenderTarget: .generateMipmaps is now .texture.generateMipmaps.");
this.texture.generateMipmaps=a}}});Object.defineProperties(Sh.prototype,{standing:{set:function(){console.warn("THREE.WebVRManager: .standing has been removed.")}},userHeight:{set:function(){console.warn("THREE.WebVRManager: .userHeight has been removed.")}}});Ye.prototype.load=function(a){console.warn("THREE.Audio: .load has been deprecated. Use THREE.AudioLoader instead.");var c=this;(new ch).load(a,function(e){c.setBuffer(e)});return this};ti.prototype.getData=function(){console.warn("THREE.AudioAnalyser: .getData() is now .getFrequencyData().");
return this.getFrequencyData()};Gb.prototype.updateCubeMap=function(a,c){console.warn("THREE.CubeCamera: .updateCubeMap() is now .update().");return this.update(a,c)};Fd.crossOrigin=void 0;Fd.loadTexture=function(a,c,e,g){console.warn("THREE.ImageUtils.loadTexture has been deprecated. Use THREE.TextureLoader() instead.");var t=new Pg;t.setCrossOrigin(this.crossOrigin);a=t.load(a,e,void 0,g);c&&(a.mapping=c);return a};Fd.loadTextureCube=function(a,c,e,g){console.warn("THREE.ImageUtils.loadTextureCube has been deprecated. Use THREE.CubeTextureLoader() instead.");
var t=new Og;t.setCrossOrigin(this.crossOrigin);a=t.load(a,e,void 0,g);c&&(a.mapping=c);return a};Fd.loadCompressedTexture=function(){console.error("THREE.ImageUtils.loadCompressedTexture has been removed. Use THREE.DDSLoader instead.")};Fd.loadCompressedTextureCube=function(){console.error("THREE.ImageUtils.loadCompressedTextureCube has been removed. Use THREE.DDSLoader instead.")};b.ACESFilmicToneMapping=5;b.AddEquation=100;b.AddOperation=2;b.AdditiveBlending=2;b.AlphaFormat=1021;b.AlwaysDepth=
1;b.AlwaysStencilFunc=519;b.AmbientLight=Wg;b.AmbientLightProbe=oi;b.AnimationClip=vc;b.AnimationLoader=fi;b.AnimationMixer=vi;b.AnimationObjectGroup=bk;b.AnimationUtils=Vb;b.ArcCurve=Xe;b.ArrayCamera=wf;b.ArrowHelper=kd;b.Audio=Ye;b.AudioAnalyser=ti;b.AudioContext=ri;b.AudioListener=qi;b.AudioLoader=ch;b.AxesHelper=jg;b.AxisHelper=function(a){console.warn("THREE.AxisHelper has been renamed to THREE.AxesHelper.");return new jg(a)};b.BackSide=1;b.BasicDepthPacking=3200;b.BasicShadowMap=0;b.BinaryTextureLoader=
function(a){console.warn("THREE.BinaryTextureLoader has been renamed to THREE.DataTextureLoader.");return new Ng(a)};b.Bone=Zh;b.BooleanKeyframeTrack=Jg;b.BoundingBoxHelper=function(a,c){console.warn("THREE.BoundingBoxHelper has been deprecated. Creating a THREE.BoxHelper instead.");return new jd(a,c)};b.Box2=yi;b.Box3=x;b.Box3Helper=hg;b.BoxBufferGeometry=Xa;b.BoxGeometry=Sa;b.BoxHelper=jd;b.BufferAttribute=R;b.BufferGeometry=va;b.BufferGeometryLoader=ah;b.ByteType=1010;b.Cache=ke;b.Camera=zb;b.CameraHelper=
gg;b.CanvasRenderer=function(){console.error("THREE.CanvasRenderer has been removed")};b.CanvasTexture=Df;b.CatmullRomCurve3=bc;b.CineonToneMapping=4;b.CircleBufferGeometry=Se;b.CircleGeometry=Yf;b.ClampToEdgeWrapping=1001;b.Clock=pi;b.ClosedSplineCurve3=ik;b.Color=H;b.ColorKeyframeTrack=Kg;b.CompressedTexture=Fe;b.CompressedTextureLoader=gi;b.ConeBufferGeometry=Xf;b.ConeGeometry=Wf;b.CubeCamera=Gb;b.CubeGeometry=Sa;b.CubeReflectionMapping=301;b.CubeRefractionMapping=302;b.CubeTexture=ed;b.CubeTextureLoader=
Og;b.CubeUVReflectionMapping=306;b.CubeUVRefractionMapping=307;b.CubicBezierCurve=Ec;b.CubicBezierCurve3=Vc;b.CubicInterpolant=Hg;b.CullFaceBack=1;b.CullFaceFront=2;b.CullFaceFrontBack=3;b.CullFaceNone=0;b.Curve=Za;b.CurvePath=id;b.CustomBlending=5;b.CylinderBufferGeometry=hd;b.CylinderGeometry=Zd;b.Cylindrical=gk;b.DataTexture=Ab;b.DataTexture2DArray=te;b.DataTexture3D=ue;b.DataTextureLoader=Ng;b.DecrementStencilOp=7683;b.DecrementWrapStencilOp=34056;b.DefaultLoadingManager=Xj;b.DepthFormat=1026;
b.DepthStencilFormat=1027;b.DepthTexture=Ef;b.DirectionalLight=Vg;b.DirectionalLightHelper=ff;b.DirectionalLightShadow=Ug;b.DiscreteInterpolant=Ig;b.DodecahedronBufferGeometry=Ke;b.DodecahedronGeometry=Kf;b.DoubleSide=2;b.DstAlphaFactor=206;b.DstColorFactor=208;b.DynamicBufferAttribute=function(a,c){console.warn("THREE.DynamicBufferAttribute has been removed. Use new THREE.BufferAttribute().setDynamic( true ) instead.");return(new R(a,c)).setDynamic(!0)};b.EdgesGeometry=Re;b.EdgesHelper=function(a,
c){console.warn("THREE.EdgesHelper has been removed. Use THREE.EdgesGeometry instead.");return new Ib(new Re(a.geometry),new Fb({color:void 0!==c?c:16777215}))};b.EllipseCurve=sc;b.EqualDepth=4;b.EqualStencilFunc=514;b.EquirectangularReflectionMapping=303;b.EquirectangularRefractionMapping=304;b.Euler=u;b.EventDispatcher=d;b.ExtrudeBufferGeometry=Tc;b.ExtrudeGeometry=Wd;b.Face3=O;b.Face4=function(a,c,e,g,t,v,z){console.warn("THREE.Face4 has been removed. A THREE.Face3 will be created instead.");return new O(a,
c,e,t,v,z)};b.FaceColors=1;b.FaceNormalsHelper=fg;b.FileLoader=wc;b.FlatShading=1;b.Float32Attribute=function(a,c){console.warn("THREE.Float32Attribute has been removed. Use new THREE.Float32BufferAttribute() instead.");return new ba(a,c)};b.Float32BufferAttribute=ba;b.Float64Attribute=function(a,c){console.warn("THREE.Float64Attribute has been removed. Use new THREE.Float64BufferAttribute() instead.");return new ka(a,c)};b.Float64BufferAttribute=ka;b.FloatType=1015;b.Fog=Dg;b.FogExp2=Cg;b.Font=li;
b.FontLoader=mi;b.FrontFaceDirectionCCW=1;b.FrontFaceDirectionCW=0;b.FrontSide=0;b.Frustum=lc;b.GammaEncoding=3007;b.Geometry=xa;b.GeometryUtils={merge:function(a,c,e){console.warn("THREE.GeometryUtils: .merge() has been moved to Geometry. Use geometry.merge( geometry2, matrix, materialIndexOffset ) instead.");if(c.isMesh){c.matrixAutoUpdate&&c.updateMatrix();var g=c.matrix;c=c.geometry}a.merge(c,g,e)},center:function(a){console.warn("THREE.GeometryUtils: .center() has been moved to Geometry. Use geometry.center() instead.");
return a.center()}};b.GreaterDepth=6;b.GreaterEqualDepth=5;b.GreaterEqualStencilFunc=518;b.GreaterStencilFunc=516;b.GridHelper=fh;b.Group=we;b.HalfFloatType=1016;b.HemisphereLight=Qg;b.HemisphereLightHelper=cf;b.HemisphereLightProbe=ni;b.IcosahedronBufferGeometry=Je;b.IcosahedronGeometry=Jf;b.ImageBitmapLoader=ji;b.ImageLoader=We;b.ImageUtils=Fd;b.ImmediateRenderObject=dg;b.IncrementStencilOp=7682;b.IncrementWrapStencilOp=34055;b.InstancedBufferAttribute=$g;b.InstancedBufferGeometry=Zg;b.InstancedInterleavedBuffer=
wi;b.Int16Attribute=function(a,c){console.warn("THREE.Int16Attribute has been removed. Use new THREE.Int16BufferAttribute() instead.");return new oa(a,c)};b.Int16BufferAttribute=oa;b.Int32Attribute=function(a,c){console.warn("THREE.Int32Attribute has been removed. Use new THREE.Int32BufferAttribute() instead.");return new ca(a,c)};b.Int32BufferAttribute=ca;b.Int8Attribute=function(a,c){console.warn("THREE.Int8Attribute has been removed. Use new THREE.Int8BufferAttribute() instead.");return new X(a,
c)};b.Int8BufferAttribute=X;b.IntType=1013;b.InterleavedBuffer=Sd;b.InterleavedBufferAttribute=yf;b.Interpolant=rc;b.InterpolateDiscrete=2300;b.InterpolateLinear=2301;b.InterpolateSmooth=2302;b.InvertStencilOp=5386;b.JSONLoader=function(){console.error("THREE.JSONLoader has been removed.")};b.KeepStencilOp=7680;b.KeyframeTrack=Zb;b.LOD=Bf;b.LatheBufferGeometry=Qe;b.LatheGeometry=Vf;b.Layers=w;b.LensFlare=function(){console.error("THREE.LensFlare has been moved to /examples/js/objects/Lensflare.js")};
b.LessDepth=2;b.LessEqualDepth=3;b.LessEqualStencilFunc=515;b.LessStencilFunc=513;b.Light=Kb;b.LightProbe=Jc;b.LightProbeHelper=df;b.LightShadow=Xc;b.Line=Xb;b.Line3=zi;b.LineBasicMaterial=Fb;b.LineCurve=nc;b.LineCurve3=Fc;b.LineDashedMaterial=fe;b.LineLoop=Gg;b.LinePieces=1;b.LineSegments=Ib;b.LineStrip=0;b.LinearEncoding=3E3;b.LinearFilter=1006;b.LinearInterpolant=Zf;b.LinearMipMapLinearFilter=1008;b.LinearMipMapNearestFilter=1007;b.LinearMipmapLinearFilter=1008;b.LinearMipmapNearestFilter=1007;
b.LinearToneMapping=1;b.Loader=Db;b.LoaderUtils=Ui;b.LoadingManager=ei;b.LogLuvEncoding=3003;b.LoopOnce=2200;b.LoopPingPong=2202;b.LoopRepeat=2201;b.LuminanceAlphaFormat=1025;b.LuminanceFormat=1024;b.MOUSE={LEFT:0,MIDDLE:1,RIGHT:2,ROTATE:0,DOLLY:1,PAN:2};b.Material=S;b.MaterialLoader=Yg;b.Math=hb;b.Matrix3=r;b.Matrix4=q;b.MaxEquation=104;b.Mesh=ya;b.MeshBasicMaterial=N;b.MeshDepthMaterial=xd;b.MeshDistanceMaterial=yd;b.MeshFaceMaterial=function(a){console.warn("THREE.MeshFaceMaterial has been removed. Use an Array instead.");
return a};b.MeshLambertMaterial=de;b.MeshMatcapMaterial=ee;b.MeshNormalMaterial=ce;b.MeshPhongMaterial=Dc;b.MeshPhysicalMaterial=ae;b.MeshStandardMaterial=Uc;b.MeshToonMaterial=be;b.MinEquation=103;b.MirroredRepeatWrapping=1002;b.MixOperation=1;b.MultiMaterial=function(a){void 0===a&&(a=[]);console.warn("THREE.MultiMaterial has been removed. Use an Array instead.");a.isMultiMaterial=!0;a.materials=a;a.clone=function(){return a.slice()};return a};b.MultiplyBlending=4;b.MultiplyOperation=0;b.NearestFilter=
1003;b.NearestMipMapLinearFilter=1005;b.NearestMipMapNearestFilter=1004;b.NearestMipmapLinearFilter=1005;b.NearestMipmapNearestFilter=1004;b.NeverDepth=0;b.NeverStencilFunc=512;b.NoBlending=0;b.NoColors=0;b.NoToneMapping=0;b.NormalBlending=1;b.NotEqualDepth=7;b.NotEqualStencilFunc=517;b.NumberKeyframeTrack=Ue;b.Object3D=A;b.ObjectLoader=bh;b.ObjectSpaceNormalMap=1;b.OctahedronBufferGeometry=Td;b.OctahedronGeometry=If;b.OneFactor=201;b.OneMinusDstAlphaFactor=207;b.OneMinusDstColorFactor=209;b.OneMinusSrcAlphaFactor=
205;b.OneMinusSrcColorFactor=203;b.OrthographicCamera=cg;b.PCFShadowMap=1;b.PCFSoftShadowMap=2;b.ParametricBufferGeometry=He;b.ParametricGeometry=Ff;b.Particle=function(a){console.warn("THREE.Particle has been renamed to THREE.Sprite.");return new zf(a)};b.ParticleBasicMaterial=function(a){console.warn("THREE.ParticleBasicMaterial has been renamed to THREE.PointsMaterial.");return new Cc(a)};b.ParticleSystem=function(a,c){console.warn("THREE.ParticleSystem has been renamed to THREE.Points.");return new Ee(a,
c)};b.ParticleSystemMaterial=function(a){console.warn("THREE.ParticleSystemMaterial has been renamed to THREE.PointsMaterial.");return new Cc(a)};b.Path=Ic;b.PerspectiveCamera=vb;b.Plane=Hb;b.PlaneBufferGeometry=Nc;b.PlaneGeometry=td;b.PlaneHelper=ig;b.PointCloud=function(a,c){console.warn("THREE.PointCloud has been renamed to THREE.Points.");return new Ee(a,c)};b.PointCloudMaterial=function(a){console.warn("THREE.PointCloudMaterial has been renamed to THREE.PointsMaterial.");return new Cc(a)};b.PointLight=
Tg;b.PointLightHelper=af;b.Points=Ee;b.PointsMaterial=Cc;b.PolarGridHelper=gh;b.PolyhedronBufferGeometry=mc;b.PolyhedronGeometry=Gf;b.PositionalAudio=si;b.PositionalAudioHelper=ef;b.PropertyBinding=cc;b.PropertyMixer=ui;b.QuadraticBezierCurve=Gc;b.QuadraticBezierCurve3=Wc;b.Quaternion=h;b.QuaternionKeyframeTrack=$f;b.QuaternionLinearInterpolant=Lg;b.REVISION="108";b.RGBADepthPacking=3201;b.RGBAFormat=1023;b.RGBA_ASTC_10x10_Format=37819;b.RGBA_ASTC_10x5_Format=37816;b.RGBA_ASTC_10x6_Format=37817;b.RGBA_ASTC_10x8_Format=
37818;b.RGBA_ASTC_12x10_Format=37820;b.RGBA_ASTC_12x12_Format=37821;b.RGBA_ASTC_4x4_Format=37808;b.RGBA_ASTC_5x4_Format=37809;b.RGBA_ASTC_5x5_Format=37810;b.RGBA_ASTC_6x5_Format=37811;b.RGBA_ASTC_6x6_Format=37812;b.RGBA_ASTC_8x5_Format=37813;b.RGBA_ASTC_8x6_Format=37814;b.RGBA_ASTC_8x8_Format=37815;b.RGBA_PVRTC_2BPPV1_Format=35843;b.RGBA_PVRTC_4BPPV1_Format=35842;b.RGBA_S3TC_DXT1_Format=33777;b.RGBA_S3TC_DXT3_Format=33778;b.RGBA_S3TC_DXT5_Format=33779;b.RGBDEncoding=3006;b.RGBEEncoding=3002;b.RGBEFormat=
1023;b.RGBFormat=1022;b.RGBM16Encoding=3005;b.RGBM7Encoding=3004;b.RGB_ETC1_Format=36196;b.RGB_PVRTC_2BPPV1_Format=35841;b.RGB_PVRTC_4BPPV1_Format=35840;b.RGB_S3TC_DXT1_Format=33776;b.RawShaderMaterial=Te;b.Ray=D;b.Raycaster=dk;b.RectAreaLight=Xg;b.RectAreaLightHelper=bf;b.RedFormat=1028;b.ReinhardToneMapping=2;b.RepeatWrapping=1E3;b.ReplaceStencilOp=7681;b.ReverseSubtractEquation=102;b.RingBufferGeometry=Pe;b.RingGeometry=Uf;b.Scene=y;b.SceneUtils={createMultiMaterialObject:function(){console.error("THREE.SceneUtils has been moved to /examples/js/utils/SceneUtils.js")},
detach:function(){console.error("THREE.SceneUtils has been moved to /examples/js/utils/SceneUtils.js")},attach:function(){console.error("THREE.SceneUtils has been moved to /examples/js/utils/SceneUtils.js")}};b.ShaderChunk=rb;b.ShaderLib=Oc;b.ShaderMaterial=qb;b.ShadowMaterial=$d;b.Shape=Ed;b.ShapeBufferGeometry=Yd;b.ShapeGeometry=Xd;b.ShapePath=ki;b.ShapeUtils=gd;b.ShortType=1011;b.Skeleton=Fg;b.SkeletonHelper=$e;b.SkinnedMesh=Cf;b.SmoothShading=2;b.Sphere=G;b.SphereBufferGeometry=Dd;b.SphereGeometry=
Tf;b.Spherical=fk;b.SphericalHarmonics3=dh;b.SphericalReflectionMapping=305;b.Spline=Bi;b.SplineCurve=Hc;b.SplineCurve3=jk;b.SpotLight=Sg;b.SpotLightHelper=Ze;b.SpotLightShadow=Rg;b.Sprite=zf;b.SpriteMaterial=Cd;b.SrcAlphaFactor=204;b.SrcAlphaSaturateFactor=210;b.SrcColorFactor=202;b.StereoCamera=Zj;b.StringKeyframeTrack=Mg;b.SubtractEquation=101;b.SubtractiveBlending=3;b.TOUCH={ROTATE:0,PAN:1,DOLLY_PAN:2,DOLLY_ROTATE:3};b.TangentSpaceNormalMap=0;b.TetrahedronBufferGeometry=Ie;b.TetrahedronGeometry=
Hf;b.TextBufferGeometry=Oe;b.TextGeometry=Sf;b.Texture=l;b.TextureLoader=Pg;b.TorusBufferGeometry=Me;b.TorusGeometry=Nf;b.TorusKnotBufferGeometry=Le;b.TorusKnotGeometry=Mf;b.Triangle=B;b.TriangleFanDrawMode=2;b.TriangleStripDrawMode=1;b.TrianglesDrawMode=0;b.TubeBufferGeometry=Ud;b.TubeGeometry=Lf;b.UVMapping=300;b.Uint16Attribute=function(a,c){console.warn("THREE.Uint16Attribute has been removed. Use new THREE.Uint16BufferAttribute() instead.");return new Z(a,c)};b.Uint16BufferAttribute=Z;b.Uint32Attribute=
function(a,c){console.warn("THREE.Uint32Attribute has been removed. Use new THREE.Uint32BufferAttribute() instead.");return new ja(a,c)};b.Uint32BufferAttribute=ja;b.Uint8Attribute=function(a,c){console.warn("THREE.Uint8Attribute has been removed. Use new THREE.Uint8BufferAttribute() instead.");return new aa(a,c)};b.Uint8BufferAttribute=aa;b.Uint8ClampedAttribute=function(a,c){console.warn("THREE.Uint8ClampedAttribute has been removed. Use new THREE.Uint8ClampedBufferAttribute() instead.");return new fa(a,
c)};b.Uint8ClampedBufferAttribute=fa;b.Uncharted2ToneMapping=3;b.Uniform=eh;b.UniformsLib=Wa;b.UniformsUtils=$m;b.UnsignedByteType=1009;b.UnsignedInt248Type=1020;b.UnsignedIntType=1014;b.UnsignedShort4444Type=1017;b.UnsignedShort5551Type=1018;b.UnsignedShort565Type=1019;b.UnsignedShortType=1012;b.VSMShadowMap=3;b.Vector2=f;b.Vector3=k;b.Vector4=p;b.VectorKeyframeTrack=Ve;b.Vertex=function(a,c,e){console.warn("THREE.Vertex has been removed. Use THREE.Vector3 instead.");return new k(a,c,e)};b.VertexColors=
2;b.VertexNormalsHelper=eg;b.VideoTexture=bi;b.WebGLMultisampleRenderTarget=n;b.WebGLRenderTarget=m;b.WebGLRenderTargetCube=Ob;b.WebGLRenderer=Th;b.WebGLUtils=Fj;b.WireframeGeometry=Ge;b.WireframeHelper=function(a,c){console.warn("THREE.WireframeHelper has been removed. Use THREE.WireframeGeometry instead.");return new Ib(new Ge(a.geometry),new Fb({color:void 0!==c?c:16777215}))};b.WrapAroundEnding=2402;b.XHRLoader=function(a){console.warn("THREE.XHRLoader has been renamed to THREE.FileLoader.");
return new wc(a)};b.ZeroCurvatureEnding=2400;b.ZeroFactor=200;b.ZeroSlopeEnding=2401;b.ZeroStencilOp=0;b.sRGBEncoding=3001;Object.defineProperty(b,"__esModule",{value:!0})});

//# sourceURL=build://tf-imports/OrbitControls.js
THREE.OrbitControls=function(b,d){function f(){return 2*Math.PI/60/60*W.autoRotateSpeed}function h(){return Math.pow(.95,W.zoomSpeed)}function k(sa){Xa.theta-=sa}function r(sa){Xa.phi-=sa}function l(sa){W.object.isPerspectiveCamera?ub/=sa:W.object.isOrthographicCamera?(W.object.zoom=Math.max(W.minZoom,Math.min(W.maxZoom,W.object.zoom*sa)),W.object.updateProjectionMatrix(),qb=!0):(console.warn("WARNING: OrbitControls.js encountered an unknown camera type - dolly/zoom disabled."),W.enableZoom=!1)}function p(sa){W.object.isPerspectiveCamera?
ub*=sa:W.object.isOrthographicCamera?(W.object.zoom=Math.max(W.minZoom,Math.min(W.maxZoom,W.object.zoom/sa)),W.object.updateProjectionMatrix(),qb=!0):(console.warn("WARNING: OrbitControls.js encountered an unknown camera type - dolly/zoom disabled."),W.enableZoom=!1)}function m(sa){zb.set(sa.clientX,sa.clientY)}function n(sa){lc.set(sa.clientX,sa.clientY)}function q(sa){Ob.set(sa.clientX,sa.clientY)}function u(sa){vb.set(sa.clientX,sa.clientY);Gb.subVectors(vb,zb).multiplyScalar(W.rotateSpeed);sa=
W.domElement===document?W.domElement.body:W.domElement;k(2*Math.PI*Gb.x/sa.clientHeight);r(2*Math.PI*Gb.y/sa.clientHeight);zb.copy(vb);W.update()}function w(sa){ec.set(sa.clientX,sa.clientY);Qd.subVectors(ec,lc);0<Qd.y?l(h()):0>Qd.y&&p(h());lc.copy(ec);W.update()}function A(sa){Ab.set(sa.clientX,sa.clientY);Hb.subVectors(Ab,Ob).multiplyScalar(W.panSpeed);ud(Hb.x,Hb.y);Ob.copy(Ab);W.update()}function y(){}function x(sa){0>sa.deltaY?p(h()):0<sa.deltaY&&l(h());W.update()}function C(sa){var Nb=!1;switch(sa.keyCode){case W.keys.UP:ud(0,
W.keyPanSpeed);Nb=!0;break;case W.keys.BOTTOM:ud(0,-W.keyPanSpeed);Nb=!0;break;case W.keys.LEFT:ud(W.keyPanSpeed,0);Nb=!0;break;case W.keys.RIGHT:ud(-W.keyPanSpeed,0),Nb=!0}Nb&&(sa.preventDefault(),W.update())}function G(sa){1==sa.touches.length?zb.set(sa.touches[0].pageX,sa.touches[0].pageY):zb.set(.5*(sa.touches[0].pageX+sa.touches[1].pageX),.5*(sa.touches[0].pageY+sa.touches[1].pageY))}function D(sa){1==sa.touches.length?Ob.set(sa.touches[0].pageX,sa.touches[0].pageY):Ob.set(.5*(sa.touches[0].pageX+
sa.touches[1].pageX),.5*(sa.touches[0].pageY+sa.touches[1].pageY))}function B(sa){var Nb=sa.touches[0].pageX-sa.touches[1].pageX;sa=sa.touches[0].pageY-sa.touches[1].pageY;lc.set(0,Math.sqrt(Nb*Nb+sa*sa))}function H(sa){W.enableZoom&&B(sa);W.enablePan&&D(sa)}function K(sa){W.enableZoom&&B(sa);W.enableRotate&&G(sa)}function L(sa){1==sa.touches.length?vb.set(sa.touches[0].pageX,sa.touches[0].pageY):vb.set(.5*(sa.touches[0].pageX+sa.touches[1].pageX),.5*(sa.touches[0].pageY+sa.touches[1].pageY));Gb.subVectors(vb,
zb).multiplyScalar(W.rotateSpeed);sa=W.domElement===document?W.domElement.body:W.domElement;k(2*Math.PI*Gb.x/sa.clientHeight);r(2*Math.PI*Gb.y/sa.clientHeight);zb.copy(vb)}function J(sa){1==sa.touches.length?Ab.set(sa.touches[0].pageX,sa.touches[0].pageY):Ab.set(.5*(sa.touches[0].pageX+sa.touches[1].pageX),.5*(sa.touches[0].pageY+sa.touches[1].pageY));Hb.subVectors(Ab,Ob).multiplyScalar(W.panSpeed);ud(Hb.x,Hb.y);Ob.copy(Ab)}function O(sa){var Nb=sa.touches[0].pageX-sa.touches[1].pageX;sa=sa.touches[0].pageY-
sa.touches[1].pageY;ec.set(0,Math.sqrt(Nb*Nb+sa*sa));Qd.set(0,Math.pow(ec.y/lc.y,W.zoomSpeed));l(Qd.y);lc.copy(ec)}function S(sa){W.enableZoom&&O(sa);W.enablePan&&J(sa)}function N(sa){W.enableZoom&&O(sa);W.enableRotate&&L(sa)}function R(){}function X(sa){if(!1!==W.enabled){sa.preventDefault();W.domElement.focus?W.domElement.focus():window.focus();switch(sa.button){case 0:switch(W.mouseButtons.LEFT){case THREE.MOUSE.ROTATE:if(sa.ctrlKey||sa.metaKey||sa.shiftKey){if(!1===W.enablePan)return;q(sa);Fa=
Aa.PAN}else{if(!1===W.enableRotate)return;m(sa);Fa=Aa.ROTATE}break;case THREE.MOUSE.PAN:if(sa.ctrlKey||sa.metaKey||sa.shiftKey){if(!1===W.enableRotate)return;m(sa);Fa=Aa.ROTATE}else{if(!1===W.enablePan)return;q(sa);Fa=Aa.PAN}break;default:Fa=Aa.NONE}break;case 1:switch(W.mouseButtons.MIDDLE){case THREE.MOUSE.DOLLY:if(!1===W.enableZoom)return;n(sa);Fa=Aa.DOLLY;break;default:Fa=Aa.NONE}break;case 2:switch(W.mouseButtons.RIGHT){case THREE.MOUSE.ROTATE:if(!1===W.enableRotate)return;m(sa);Fa=Aa.ROTATE;
break;case THREE.MOUSE.PAN:if(!1===W.enablePan)return;q(sa);Fa=Aa.PAN;break;default:Fa=Aa.NONE}}Fa!==Aa.NONE&&(document.addEventListener("mousemove",aa,!1),document.addEventListener("mouseup",fa,!1),W.dispatchEvent(va))}}function aa(sa){if(!1!==W.enabled)switch(sa.preventDefault(),Fa){case Aa.ROTATE:if(!1===W.enableRotate)break;u(sa);break;case Aa.DOLLY:if(!1===W.enableZoom)break;w(sa);break;case Aa.PAN:!1!==W.enablePan&&A(sa)}}function fa(sa){!1!==W.enabled&&(y(sa),document.removeEventListener("mousemove",
aa,!1),document.removeEventListener("mouseup",fa,!1),W.dispatchEvent(ya),Fa=Aa.NONE)}function oa(sa){!1===W.enabled||!1===W.enableZoom||Fa!==Aa.NONE&&Fa!==Aa.ROTATE||(sa.preventDefault(),sa.stopPropagation(),W.dispatchEvent(va),x(sa),W.dispatchEvent(ya))}function Z(sa){!1!==W.enabled&&!1!==W.enableKeys&&!1!==W.enablePan&&C(sa)}function ca(sa){if(!1!==W.enabled){sa.preventDefault();switch(sa.touches.length){case 1:switch(W.touches.ONE){case THREE.TOUCH.ROTATE:if(!1===W.enableRotate)return;G(sa);Fa=
Aa.TOUCH_ROTATE;break;case THREE.TOUCH.PAN:if(!1===W.enablePan)return;D(sa);Fa=Aa.TOUCH_PAN;break;default:Fa=Aa.NONE}break;case 2:switch(W.touches.TWO){case THREE.TOUCH.DOLLY_PAN:if(!1===W.enableZoom&&!1===W.enablePan)return;H(sa);Fa=Aa.TOUCH_DOLLY_PAN;break;case THREE.TOUCH.DOLLY_ROTATE:if(!1===W.enableZoom&&!1===W.enableRotate)return;K(sa);Fa=Aa.TOUCH_DOLLY_ROTATE;break;default:Fa=Aa.NONE}break;default:Fa=Aa.NONE}Fa!==Aa.NONE&&W.dispatchEvent(va)}}function ja(sa){if(!1!==W.enabled)switch(sa.preventDefault(),
sa.stopPropagation(),Fa){case Aa.TOUCH_ROTATE:if(!1===W.enableRotate)break;L(sa);W.update();break;case Aa.TOUCH_PAN:if(!1===W.enablePan)break;J(sa);W.update();break;case Aa.TOUCH_DOLLY_PAN:if(!1===W.enableZoom&&!1===W.enablePan)break;S(sa);W.update();break;case Aa.TOUCH_DOLLY_ROTATE:if(!1===W.enableZoom&&!1===W.enableRotate)break;N(sa);W.update();break;default:Fa=Aa.NONE}}function ba(sa){!1!==W.enabled&&(R(sa),W.dispatchEvent(ya),Fa=Aa.NONE)}function ka(sa){!1!==W.enabled&&sa.preventDefault()}this.object=
b;this.domElement=void 0!==d?d:document;this.enabled=!0;this.target=new THREE.Vector3;this.minDistance=0;this.maxDistance=Infinity;this.minZoom=0;this.maxZoom=Infinity;this.minPolarAngle=0;this.maxPolarAngle=Math.PI;this.minAzimuthAngle=-Infinity;this.maxAzimuthAngle=Infinity;this.enableDamping=!1;this.dampingFactor=.05;this.enableZoom=!0;this.zoomSpeed=1;this.enableRotate=!0;this.rotateSpeed=1;this.enablePan=!0;this.panSpeed=1;this.screenSpacePanning=!1;this.keyPanSpeed=7;this.autoRotate=!1;this.autoRotateSpeed=
2;this.enableKeys=!0;this.keys={LEFT:37,UP:38,RIGHT:39,BOTTOM:40};this.mouseButtons={LEFT:THREE.MOUSE.ROTATE,MIDDLE:THREE.MOUSE.DOLLY,RIGHT:THREE.MOUSE.PAN};this.touches={ONE:THREE.TOUCH.ROTATE,TWO:THREE.TOUCH.DOLLY_PAN};this.target0=this.target.clone();this.position0=this.object.position.clone();this.zoom0=this.object.zoom;this.getPolarAngle=function(){return Sa.phi};this.getAzimuthalAngle=function(){return Sa.theta};this.saveState=function(){W.target0.copy(W.target);W.position0.copy(W.object.position);
W.zoom0=W.object.zoom};this.reset=function(){W.target.copy(W.target0);W.object.position.copy(W.position0);W.object.zoom=W.zoom0;W.object.updateProjectionMatrix();W.dispatchEvent(Ea);W.update();Fa=Aa.NONE};this.update=function(){var sa=new THREE.Vector3,Nb=(new THREE.Quaternion).setFromUnitVectors(b.up,new THREE.Vector3(0,1,0)),yc=Nb.clone().inverse(),dd=new THREE.Vector3,vd=new THREE.Quaternion;return function(){var wg=W.object.position;sa.copy(wg).sub(W.target);sa.applyQuaternion(Nb);Sa.setFromVector3(sa);
W.autoRotate&&Fa===Aa.NONE&&k(f());W.enableDamping?(Sa.theta+=Xa.theta*W.dampingFactor,Sa.phi+=Xa.phi*W.dampingFactor):(Sa.theta+=Xa.theta,Sa.phi+=Xa.phi);Sa.theta=Math.max(W.minAzimuthAngle,Math.min(W.maxAzimuthAngle,Sa.theta));Sa.phi=Math.max(W.minPolarAngle,Math.min(W.maxPolarAngle,Sa.phi));Sa.makeSafe();Sa.radius*=ub;Sa.radius=Math.max(W.minDistance,Math.min(W.maxDistance,Sa.radius));!0===W.enableDamping?W.target.addScaledVector(Bb,W.dampingFactor):W.target.add(Bb);sa.setFromSpherical(Sa);sa.applyQuaternion(yc);
wg.copy(W.target).add(sa);W.object.lookAt(W.target);!0===W.enableDamping?(Xa.theta*=1-W.dampingFactor,Xa.phi*=1-W.dampingFactor,Bb.multiplyScalar(1-W.dampingFactor)):(Xa.set(0,0,0),Bb.set(0,0,0));ub=1;return qb||dd.distanceToSquared(W.object.position)>xa||8*(1-vd.dot(W.object.quaternion))>xa?(W.dispatchEvent(Ea),dd.copy(W.object.position),vd.copy(W.object.quaternion),qb=!1,!0):!1}}();this.dispose=function(){W.domElement.removeEventListener("contextmenu",ka,!1);W.domElement.removeEventListener("mousedown",
X,!1);W.domElement.removeEventListener("wheel",oa,!1);W.domElement.removeEventListener("touchstart",ca,!1);W.domElement.removeEventListener("touchend",ba,!1);W.domElement.removeEventListener("touchmove",ja,!1);document.removeEventListener("mousemove",aa,!1);document.removeEventListener("mouseup",fa,!1);window.removeEventListener("keydown",Z,!1)};var W=this,Ea={type:"change"},va={type:"start"},ya={type:"end"},Aa={NONE:-1,ROTATE:0,DOLLY:1,PAN:2,TOUCH_ROTATE:3,TOUCH_PAN:4,TOUCH_DOLLY_PAN:5,TOUCH_DOLLY_ROTATE:6},
Fa=Aa.NONE,xa=1E-6,Sa=new THREE.Spherical,Xa=new THREE.Spherical,ub=1,Bb=new THREE.Vector3,qb=!1,zb=new THREE.Vector2,vb=new THREE.Vector2,Gb=new THREE.Vector2,Ob=new THREE.Vector2,Ab=new THREE.Vector2,Hb=new THREE.Vector2,lc=new THREE.Vector2,ec=new THREE.Vector2,Qd=new THREE.Vector2,td=function(){var sa=new THREE.Vector3;return function(Nb,yc){sa.setFromMatrixColumn(yc,0);sa.multiplyScalar(-Nb);Bb.add(sa)}}(),Nc=function(){var sa=new THREE.Vector3;return function(Nb,yc){!0===W.screenSpacePanning?
sa.setFromMatrixColumn(yc,1):(sa.setFromMatrixColumn(yc,0),sa.crossVectors(W.object.up,sa));sa.multiplyScalar(Nb);Bb.add(sa)}}(),ud=function(){var sa=new THREE.Vector3;return function(Nb,yc){var dd=W.domElement===document?W.domElement.body:W.domElement;if(W.object.isPerspectiveCamera){sa.copy(W.object.position).sub(W.target);var vd=sa.length();vd*=Math.tan(W.object.fov/2*Math.PI/180);td(2*Nb*vd/dd.clientHeight,W.object.matrix);Nc(2*yc*vd/dd.clientHeight,W.object.matrix)}else W.object.isOrthographicCamera?
(td(Nb*(W.object.right-W.object.left)/W.object.zoom/dd.clientWidth,W.object.matrix),Nc(yc*(W.object.top-W.object.bottom)/W.object.zoom/dd.clientHeight,W.object.matrix)):(console.warn("WARNING: OrbitControls.js encountered an unknown camera type - pan disabled."),W.enablePan=!1)}}();W.domElement.addEventListener("contextmenu",ka,!1);W.domElement.addEventListener("mousedown",X,!1);W.domElement.addEventListener("wheel",oa,!1);W.domElement.addEventListener("touchstart",ca,!1);W.domElement.addEventListener("touchend",
ba,!1);W.domElement.addEventListener("touchmove",ja,!1);window.addEventListener("keydown",Z,!1);this.update()};THREE.OrbitControls.prototype=Object.create(THREE.EventDispatcher.prototype);THREE.OrbitControls.prototype.constructor=THREE.OrbitControls;THREE.MapControls=function(b,d){THREE.OrbitControls.call(this,b,d);this.mouseButtons.LEFT=THREE.MOUSE.PAN;this.mouseButtons.RIGHT=THREE.MOUSE.ROTATE;this.touches.ONE=THREE.TOUCH.PAN;this.touches.TWO=THREE.TOUCH.DOLLY_ROTATE};
THREE.MapControls.prototype=Object.create(THREE.EventDispatcher.prototype);THREE.MapControls.prototype.constructor=THREE.MapControls;

//# sourceURL=build://tf-imports/array-buffer-data-provider.js
var fl;
(function(b){b.ErrorCodes={CANCELLED:1};const d={VERTEX:1,FACE:2,COLOR:3},f={VERTEX:"float32",FACE:"int32",COLOR:"uint8"};class h{constructor(k){this._requestManager=k;this._canceller=new dc.Canceller}reload(k,r,l){this._canceller.cancelAll();return this._fetchMetadata(k,r,l)}_fetchDataByStep(k,r,l,p,m,n){function q(u){let w=[];for(let A=0;A<u.length/3;A++){let y=[];for(let x=0;3>x;x++)y.push(u[3*A+x]);w.push(y)}return w}k=dc.getRouter().pluginRoute("mesh","/data",new URLSearchParams({tag:r,run:k,
content_type:l,sample:p,step:m}));r=this._canceller.cancellable(u=>{if(u.cancelled)return Promise.reject({code:b.ErrorCodes.CANCELLED,message:"Response was invalidated."});u=u.value;switch(l){case "VERTEX":n.vertices=q(new Float32Array(u));break;case "FACE":n.faces=q(new Int32Array(u));break;case "COLOR":n.colors=q(new Uint8Array(u))}return n});return this._requestManager.fetch(k,null,"arraybuffer",f[l]).then(u=>u.arrayBuffer()).then(r)}fetchData(k,r,l,p){let m=[],n=new Map;Object.keys(d).forEach(q=>
{k.components&1<<d[q]&&m.push(this._fetchDataByStep(r,l,q,p,k.step,n))});return Promise.all(m)}_fetchMetadata(k,r,l){this._canceller.cancelAll();k=dc.getRouter().pluginRoute("mesh","/meshes",new URLSearchParams({tag:r,run:k,sample:l}));r=this._canceller.cancellable(p=>p.cancelled?Promise.reject({code:b.ErrorCodes.CANCELLED,message:"Response was invalidated."}):p.value);return this._requestManager.fetch(k).then(p=>p.json()).then(r).then(this._processMetadata.bind(this))}_processMetadata(k){if(k){var r=
new Map;for(let p=0;p<k.length;p++){let m=k[p];r.has(m.step)||r.set(m.step,[]);r.get(m.step).push(m)}var l=[];r.forEach(p=>{p=this._createStepDatum(p[0]);l.push(p)});return l}}_createStepDatum(k){return{wall_time:new Date(1E3*k.wall_time),step:k.step,config:k.config,content_type:k.content_type,components:k.components}}}b.ArrayBufferDataProvider=h})(fl||(fl={}));

//# sourceURL=build://tf-imports/mesh-viewer.js
(function(b){class d extends THREE.EventDispatcher{constructor(f){super();this._lastMesh=null;this._clock=new THREE.Clock;this._canvasSize=null;this._runColor=f}_isObject(f){return"object"==typeof f&&null!=f&&!Array.isArray(f)}_applyDefaults(f,h){let k={};f=[f,h];for(h=0;h<f.length;h++){const r=f[h];for(let l in r){const p=l in k;this._isObject(r[l])?k[l]=this._applyDefaults(k[l]||{},r[l]):p||(k[l]=r[l])}}return k}_createWorld(f,h){this.isReady()||(this._scene=new THREE.Scene,this._camera=f=new THREE[f.camera.cls](f.camera.fov,
this._canvasSize.width/this._canvasSize.height,f.camera.near,f.camera.far),h=new THREE.OrbitControls(f,h),h.lookSpeed=.4,h.movementSpeed=20,h.noFly=!0,h.lookVertical=!0,h.constrainVertical=!0,h.verticalMin=1,h.verticalMax=2,h.addEventListener("change",this._onCameraPositionChange.bind(this)),this._cameraControls=h,this._renderer=new THREE.WebGLRenderer({antialias:!0}),this._renderer.setPixelRatio(),this._renderer.setSize(this._canvasSize.width,this._canvasSize.height),this._renderer.setClearColor(16777215,
1))}_clearScene(){for(;0<this._scene.children.length;)this._scene.remove(this._scene.children[0])}getRenderer(){return this._renderer}getCameraControls(){return this._cameraControls}isReady(){return!!this._camera&&!!this._cameraControls}getCameraPosition(){return{far:this._camera.far,position:this._camera.position.clone(),target:this._cameraControls.target.clone()}}setCanvasSize(f){this._canvasSize=f}draw(){this._animationFrameIndex&&cancelAnimationFrame(this._animationFrameIndex);this._camera.aspect=
this._canvasSize.width/this._canvasSize.height;this._camera.updateProjectionMatrix();this._renderer.setSize(this._canvasSize.width,this._canvasSize.height);const f=function(){var h=this._clock.getDelta();this._cameraControls.update(h);this._animationFrameIndex=requestAnimationFrame(f);this._renderer.render(this._scene,this._camera)}.bind(this);f()}updateScene(f,h){let k={};"config"in f&&f.config&&(k=JSON.parse(f.config));this.dispatchEvent({type:"beforeUpdateScene"});k=this._applyDefaults(k,{camera:{cls:"PerspectiveCamera",
fov:75,near:.1,far:1E3},lights:[{cls:"AmbientLight",color:"#ffffff",intensity:.75},{cls:"DirectionalLight",color:"#ffffff",intensity:.75,position:[0,-1,2]}]});this._createWorld(k,h);this._clearScene();this._createLights(this._scene,k);this._createGeometry(f,k);this.draw()}resetView(){if(this.isReady()){this._cameraControls.reset();if(!f&&this._lastMesh)var f=this._lastMesh;f&&(this._fitObjectToViewport(f),this._lastMesh=f);this._cameraControls.update()}}_createGeometry(f,h){f=f.mesh;f.vertices&&f.faces&&
f.faces.length?this._createMesh(f,h):this._createPointCloud(f,h)}_createPointCloud(f,h){var k=f.vertices;f=f.colors;let r={material:{cls:"PointsMaterial",size:.005}};f&&f.length==k.length?r.material.vertexColors=THREE.VertexColors:r.material.color=this._runColor;h=this._applyDefaults(h,r);var l=new THREE.Geometry;k.forEach(function(p){var m=new THREE.Vector3(p[0],p[1],p[2]);m.x=1*p[0];m.y=1*p[1];m.z=1*p[2];l.vertices.push(m)});f&&f.length==k.length&&f.forEach(function(p){p=new THREE.Color(p[0]/255,
p[1]/255,p[2]/255);l.colors.push(p)});k=new THREE[h.material.cls](h.material);k=new THREE.Points(l,k);this._scene.add(k);this._lastMesh=k}setCameraViewpoint(f,h,k){this._silent=!0;this._camera.far=h;this._camera.position.set(f.x,f.y,f.z);this._camera.lookAt(k.clone());this._camera.updateProjectionMatrix();this._cameraControls.target=k.clone();this._cameraControls.update();this._silent=!1}_onCameraPositionChange(f){this._silent||this.dispatchEvent({type:"cameraPositionChange",event:f})}_fitObjectToViewport(f){var h=
new THREE.Box3;h.setFromObject(f);f=h.center();var k=h.size();k=1.25*Math.abs(Math.max(k.x,k.y,k.z)/(2*Math.tan(Math.PI/180*this._camera.fov/2)));h=h.min.z;this.setCameraViewpoint({x:f.x,y:f.y,z:k},3*(0>h?-h+k:k-h),f)}_createMesh(f,h){var k=f.vertices;const r=f.faces,l=f.colors;f=this._applyDefaults(h,{material:{cls:"MeshStandardMaterial",color:"#a0a0a0",roughness:1,metalness:0}});let p=new THREE.Geometry;k.forEach(function(m){let n=new THREE.Vector3(m[0],m[1],m[2]);n.x=1*m[0];n.y=1*m[1];n.z=1*m[2];
p.vertices.push(n)});r.forEach(function(m){let n=new THREE.Face3(m[0],m[1],m[2]);if(l&&l.length){m=[l[m[0]],l[m[1]],l[m[2]]];for(let u=0;u<m.length;u++){var q=m[u];q=new THREE.Color(q[0]/255,q[1]/255,q[2]/255);n.vertexColors.push(q)}}p.faces.push(n)});l&&l.length&&(f.material=f.material||{},f.material.vertexColors=THREE.VertexColors);p.center();p.computeBoundingSphere();p.computeVertexNormals();k=new THREE[f.material.cls](f.material);k=new THREE.Mesh(p,k);k.castShadow=!0;k.receiveShadow=!0;this._scene.add(k);
this._lastMesh=k}_createLights(f,h){for(let k=0;k<h.lights.length;k++){const r=h.lights[k];let l=new THREE[r.cls](r.color,r.intensity);r.position&&l.position.set(r.position[0],r.position[1],r.position[2]);f.add(l)}}}b.MeshViewer=d})(fl||(fl={}));

//# sourceURL=build://tf-plugin-util/message.js
Jh=this&&this.__awaiter||function(b,d,f,h){return new (f||(f=Promise))(function(k,r){function l(n){try{m(h.next(n))}catch(q){r(q)}}function p(n){try{m(h["throw"](n))}catch(q){r(q)}}function m(n){n.done?k(n.value):(new f(function(q){q(n.value)})).then(l,p)}m((h=h.apply(b,d||[])).next())})};var gl;
(function(b){(function(d){(function(f){class h{constructor(k){this.port=k;this.id=0;this.responseWaits=new Map;this.listeners=new Map;this.port.addEventListener("message",r=>this.onMessage(r))}listen(k,r){this.listeners.set(k,r)}unlisten(k){this.listeners.delete(k)}onMessage(k){return Jh(this,void 0,void 0,function*(){var r=JSON.parse(k.data);const l=r.type,p=r.id,m=r.payload;var n=r.error;if(r.isReply){if(this.responseWaits.has(p)){var {resolve:q,reject:u}=this.responseWaits.get(p);this.responseWaits.delete(p);
n?u(Error(n)):q(m)}}else{n=r=null;if(this.listeners.has(l)){const w=this.listeners.get(l);try{r=yield w(m)}catch(A){n=A}}this.postMessage({["type"]:l,["id"]:p,["payload"]:r,["error"]:n,["isReply"]:!0})}})}postMessage(k){this.port.postMessage(JSON.stringify(k))}sendMessage(k){const r=this.id++;this.postMessage({type:"experimental.RunsChanged",id:r,payload:k,error:null,isReply:!1});return new Promise((l,p)=>{this.responseWaits.set(r,{resolve:l,reject:p})})}}f.IPC=h})(d.DO_NOT_USE_INTERNAL||(d.DO_NOT_USE_INTERNAL=
{}))})(b.lib||(b.lib={}))})(gl||(gl={}));

//# sourceURL=build://tf-plugin-util/plugin-host-ipc.js
(function(b){(function(d){function f(m,n){const q=new b.lib.DO_NOT_USE_INTERNAL.IPC(m);r.add(q);p.set(q,n);m.start();for(const [u,w]of l)m=h(w,q),q.listen(u,m)}function h(m,n){return q=>{var u=p.get(n);u=k.get(u)||null;return m(u,q)}}d.registerPluginIframe=function(m,n){k.set(m,{pluginName:n})};const k=new WeakMap,r=new Set,l=new Map,p=new Map;window.addEventListener("message",m=>{if("experimental.bootstrap"===m.data){var n=m.ports[0];n&&(m=m.source?m.source.frameElement:null)&&f(n,m)}});d.broadcast=
function(){var m=dc.runsStore.getRuns();for(var n of r)p.get(n).isConnected||(r.delete(n),p.delete(n));n=[...r].map(q=>q.sendMessage(m));return Promise.all(n)};d.listen=function(m,n){l.set(m,n);for(const q of r){const u=h(n,q);q.listen(m,u)}};d.unlisten=function(m){l.delete(m);for(const n of r)n.unlisten(m)}})(b.host||(b.host={}))})(gl||(gl={}));

//# sourceURL=build://tf-plugin-util/core-host-impl.js
gl.host.listen("experimental.GetURLPluginData",b=>{if(b){b=`p.${b.pluginName}.`;var d={};for(let f in bd.urlDict)f.startsWith(b)&&(d[f.substring(b.length)]=bd.urlDict[f]);return d}});

//# sourceURL=build://tf-plugin-util/runs-host-impl.js
gl.host.listen("experimental.GetRuns",()=>dc.runsStore.getRuns());dc.runsStore.addListener(()=>gl.host.broadcast());

//# sourceURL=build://paper-ripple/paper-ripple.html.js
(function(){function b(h){this.element=h;this.width=this.boundingRect.width;this.height=this.boundingRect.height;this.size=Math.max(this.width,this.height)}function d(h){this.element=h;this.color=window.getComputedStyle(h).color;this.wave=document.createElement("div");this.waveContainer=document.createElement("div");this.wave.style.backgroundColor=this.color;this.wave.classList.add("wave");this.waveContainer.classList.add("wave-container");Polymer.dom(this.waveContainer).appendChild(this.wave);this.resetInteractionState()}
var f={distance:function(h,k,r,l){h-=r;k-=l;return Math.sqrt(h*h+k*k)},now:window.performance&&window.performance.now?window.performance.now.bind(window.performance):Date.now};b.prototype={get boundingRect(){return this.element.getBoundingClientRect()},furthestCornerDistanceFrom:function(h,k){var r=f.distance(h,k,0,0),l=f.distance(h,k,this.width,0),p=f.distance(h,k,0,this.height);h=f.distance(h,k,this.width,this.height);return Math.max(r,l,p,h)}};d.MAX_RADIUS=300;d.prototype={get recenters(){return this.element.recenters},
get center(){return this.element.center},get mouseDownElapsed(){if(!this.mouseDownStart)return 0;var h=f.now()-this.mouseDownStart;this.mouseUpStart&&(h-=this.mouseUpElapsed);return h},get mouseUpElapsed(){return this.mouseUpStart?f.now()-this.mouseUpStart:0},get mouseDownElapsedSeconds(){return this.mouseDownElapsed/1E3},get mouseUpElapsedSeconds(){return this.mouseUpElapsed/1E3},get mouseInteractionSeconds(){return this.mouseDownElapsedSeconds+this.mouseUpElapsedSeconds},get initialOpacity(){return this.element.initialOpacity},
get opacityDecayVelocity(){return this.element.opacityDecayVelocity},get radius(){var h=1.1*Math.min(Math.sqrt(this.containerMetrics.width*this.containerMetrics.width+this.containerMetrics.height*this.containerMetrics.height),d.MAX_RADIUS)+5;return Math.abs(h*(1-Math.pow(80,-(this.mouseInteractionSeconds/(1.1-h/d.MAX_RADIUS*.2)))))},get opacity(){return this.mouseUpStart?Math.max(0,this.initialOpacity-this.mouseUpElapsedSeconds*this.opacityDecayVelocity):this.initialOpacity},get outerOpacity(){return Math.max(0,
Math.min(.3*this.mouseUpElapsedSeconds,this.opacity))},get isOpacityFullyDecayed(){return.01>this.opacity&&this.radius>=Math.min(this.maxRadius,d.MAX_RADIUS)},get isRestingAtMaxRadius(){return this.opacity>=this.initialOpacity&&this.radius>=Math.min(this.maxRadius,d.MAX_RADIUS)},get isAnimationComplete(){return this.mouseUpStart?this.isOpacityFullyDecayed:this.isRestingAtMaxRadius},get translationFraction(){return Math.min(1,this.radius/this.containerMetrics.size*2/Math.sqrt(2))},get xNow(){return this.xEnd?
this.xStart+this.translationFraction*(this.xEnd-this.xStart):this.xStart},get yNow(){return this.yEnd?this.yStart+this.translationFraction*(this.yEnd-this.yStart):this.yStart},get isMouseDown(){return this.mouseDownStart&&!this.mouseUpStart},resetInteractionState:function(){this.slideDistance=this.yEnd=this.xEnd=this.yStart=this.xStart=this.mouseUpStart=this.mouseDownStart=this.maxRadius=0;this.containerMetrics=new b(this.element)},draw:function(){this.wave.style.opacity=this.opacity;var h=this.radius/
(this.containerMetrics.size/2);var k=this.xNow-this.containerMetrics.width/2;var r=this.yNow-this.containerMetrics.height/2;this.waveContainer.style.webkitTransform="translate("+k+"px, "+r+"px)";this.waveContainer.style.transform="translate3d("+k+"px, "+r+"px, 0)";this.wave.style.webkitTransform="scale("+h+","+h+")";this.wave.style.transform="scale3d("+h+","+h+",1)"},downAction:function(h){var k=this.containerMetrics.width/2,r=this.containerMetrics.height/2;this.resetInteractionState();this.mouseDownStart=
f.now();this.center?(this.xStart=k,this.yStart=r,this.slideDistance=f.distance(this.xStart,this.yStart,this.xEnd,this.yEnd)):(this.xStart=h?h.detail.x-this.containerMetrics.boundingRect.left:this.containerMetrics.width/2,this.yStart=h?h.detail.y-this.containerMetrics.boundingRect.top:this.containerMetrics.height/2);this.recenters&&(this.xEnd=k,this.yEnd=r,this.slideDistance=f.distance(this.xStart,this.yStart,this.xEnd,this.yEnd));this.maxRadius=this.containerMetrics.furthestCornerDistanceFrom(this.xStart,
this.yStart);this.waveContainer.style.top=(this.containerMetrics.height-this.containerMetrics.size)/2+"px";this.waveContainer.style.left=(this.containerMetrics.width-this.containerMetrics.size)/2+"px";this.waveContainer.style.width=this.containerMetrics.size+"px";this.waveContainer.style.height=this.containerMetrics.size+"px"},upAction:function(){this.isMouseDown&&(this.mouseUpStart=f.now())},remove:function(){Polymer.dom(this.waveContainer.parentNode).removeChild(this.waveContainer)}};Polymer({is:"paper-ripple",
behaviors:[Polymer.IronA11yKeysBehavior],properties:{initialOpacity:{type:Number,value:.25},opacityDecayVelocity:{type:Number,value:.8},recenters:{type:Boolean,value:!1},center:{type:Boolean,value:!1},ripples:{type:Array,value:function(){return[]}},animating:{type:Boolean,readOnly:!0,reflectToAttribute:!0,value:!1},holdDown:{type:Boolean,value:!1,observer:"_holdDownChanged"},noink:{type:Boolean,value:!1},_animating:{type:Boolean},_boundAnimate:{type:Function,value:function(){return this.animate.bind(this)}}},
get target(){return this.keyEventTarget},keyBindings:{"enter:keydown":"_onEnterKeydown","space:keydown":"_onSpaceKeydown","space:keyup":"_onSpaceKeyup"},attached:function(){var h=this.keyEventTarget=11==this.parentNode.nodeType?Polymer.dom(this).getOwnerRoot().host:this.parentNode;this.listen(h,"up","uiUpAction");this.listen(h,"down","uiDownAction")},detached:function(){this.unlisten(this.keyEventTarget,"up","uiUpAction");this.unlisten(this.keyEventTarget,"down","uiDownAction");this.keyEventTarget=
null},get shouldKeepAnimating(){for(var h=0;h<this.ripples.length;++h)if(!this.ripples[h].isAnimationComplete)return!0;return!1},simulatedRipple:function(){this.downAction(null);this.async(function(){this.upAction()},1)},uiDownAction:function(h){this.noink||this.downAction(h)},downAction:function(h){this.holdDown&&0<this.ripples.length||(this.addRipple().downAction(h),this._animating||(this._animating=!0,this.animate()))},uiUpAction:function(h){this.noink||this.upAction(h)},upAction:function(h){this.holdDown||
(this.ripples.forEach(function(k){k.upAction(h)}),this._animating=!0,this.animate())},onAnimationComplete:function(){this._animating=!1;this.$.background.style.backgroundColor=null;this.fire("transitionend")},addRipple:function(){var h=new d(this);Polymer.dom(this.$.waves).appendChild(h.waveContainer);this.$.background.style.backgroundColor=h.color;this.ripples.push(h);this._setAnimating(!0);return h},removeRipple:function(h){var k=this.ripples.indexOf(h);0>k||(this.ripples.splice(k,1),h.remove(),
this.ripples.length||this._setAnimating(!1))},animate:function(){if(this._animating){var h;for(h=0;h<this.ripples.length;++h){var k=this.ripples[h];k.draw();this.$.background.style.opacity=k.outerOpacity;k.isOpacityFullyDecayed&&!k.isRestingAtMaxRadius&&this.removeRipple(k)}if(this.shouldKeepAnimating||0!==this.ripples.length)window.requestAnimationFrame(this._boundAnimate);else this.onAnimationComplete()}},_onEnterKeydown:function(){this.uiDownAction();this.async(this.uiUpAction,1)},_onSpaceKeydown:function(){this.uiDownAction()},
_onSpaceKeyup:function(){this.uiUpAction()},_holdDownChanged:function(h,k){void 0!==k&&(h?this.downAction():this.upAction())}})})();

//# sourceURL=build://paper-button/paper-button.html.js
Polymer({is:"paper-button",behaviors:[Polymer.PaperButtonBehavior],properties:{raised:{type:Boolean,reflectToAttribute:!0,value:!1,observer:"_calculateElevation"}},_calculateElevation:function(){this.raised?Polymer.PaperButtonBehaviorImpl._calculateElevation.apply(this):this._setElevation(0)}});

//# sourceURL=build://paper-checkbox/paper-checkbox.html.js
Polymer({is:"paper-checkbox",behaviors:[Polymer.PaperCheckedElementBehavior],hostAttributes:{role:"checkbox","aria-checked":!1,tabindex:0},properties:{ariaActiveAttribute:{type:String,value:"aria-checked"}},attached:function(){Polymer.RenderStatus.afterNextRender(this,function(){if("-1px"===this.getComputedStyleValue("--calculated-paper-checkbox-ink-size").trim()){var b=this.getComputedStyleValue("--calculated-paper-checkbox-size").trim(),d="px",f=b.match(/[A-Za-z]+$/);null!==f&&(d=f[0]);b=parseFloat(b);
f=8/3*b;"px"===d&&(f=Math.floor(f),f%2!==b%2&&f++);this.updateStyles({"--paper-checkbox-ink-size":f+d})}})},_computeCheckboxClass:function(b,d){var f="";b&&(f+="checked ");d&&(f+="invalid");return f},_computeCheckmarkClass:function(b){return b?"":"hidden"},_createRipple:function(){this._rippleContainer=this.$.checkboxContainer;return Polymer.PaperInkyFocusBehaviorImpl._createRipple.call(this)}});

//# sourceURL=build://iron-icon/iron-icon.html.js
Polymer({is:"iron-icon",properties:{icon:{type:String},theme:{type:String},src:{type:String},_meta:{value:Polymer.Base.create("iron-meta",{type:"iconset"})}},observers:["_updateIcon(_meta, isAttached)","_updateIcon(theme, isAttached)","_srcChanged(src, isAttached)","_iconChanged(icon, isAttached)"],_DEFAULT_ICONSET:"icons",_iconChanged:function(b){b=(b||"").split(":");this._iconName=b.pop();this._iconsetName=b.pop()||this._DEFAULT_ICONSET;this._updateIcon()},_srcChanged:function(){this._updateIcon()},
_usesIconset:function(){return this.icon||!this.src},_updateIcon:function(){this._usesIconset()?(this._img&&this._img.parentNode&&Polymer.dom(this.root).removeChild(this._img),""===this._iconName?this._iconset&&this._iconset.removeIcon(this):this._iconsetName&&this._meta&&((this._iconset=this._meta.byKey(this._iconsetName))?(this._iconset.applyIcon(this,this._iconName,this.theme),this.unlisten(window,"iron-iconset-added","_updateIcon")):this.listen(window,"iron-iconset-added","_updateIcon"))):(this._iconset&&
this._iconset.removeIcon(this),this._img||(this._img=document.createElement("img"),this._img.style.width="100%",this._img.style.height="100%",this._img.draggable=!1),this._img.src=this.src,Polymer.dom(this.root).appendChild(this._img))}});

//# sourceURL=build://iron-a11y-announcer/iron-a11y-announcer.html.js
(function(){Polymer.IronA11yAnnouncer=function(){};Polymer.IronA11yAnnouncer=Polymer({is:"iron-a11y-announcer",properties:{mode:{type:String,value:"polite"},_text:{type:String,value:""}},created:function(){Polymer.IronA11yAnnouncer.instance||(Polymer.IronA11yAnnouncer.instance=this);document.body.addEventListener("iron-announce",this._onIronAnnounce.bind(this))},announce:function(b){this._text="";this.async(function(){this._text=b},100)},_onIronAnnounce:function(b){b.detail&&b.detail.text&&this.announce(b.detail.text)}});
Polymer.IronA11yAnnouncer.instance=null;Polymer.IronA11yAnnouncer.requestAvailability=function(){Polymer.IronA11yAnnouncer.instance||(Polymer.IronA11yAnnouncer.instance=document.createElement("iron-a11y-announcer"));document.body.appendChild(Polymer.IronA11yAnnouncer.instance)}})();

//# sourceURL=build://iron-input/iron-input.html.js
Polymer({is:"iron-input",behaviors:[Polymer.IronValidatableBehavior],properties:{bindValue:{type:String,value:""},value:{type:String,computed:"_computeValue(bindValue)"},allowedPattern:{type:String},autoValidate:{type:Boolean,value:!1},_inputElement:Object},observers:["_bindValueChanged(bindValue, _inputElement)"],listeners:{input:"_onInput",keypress:"_onKeypress"},created:function(){Polymer.IronA11yAnnouncer.requestAvailability();this._previousValidInput="";this._patternAlreadyChecked=!1},attached:function(){this._observer=
Polymer.dom(this).observeNodes(function(){this._initSlottedInput()}.bind(this))},detached:function(){this._observer&&(Polymer.dom(this).unobserveNodes(this._observer),this._observer=null)},get inputElement(){return this._inputElement},_initSlottedInput:function(){this._inputElement=this.getEffectiveChildren()[0];this.inputElement&&this.inputElement.value&&(this.bindValue=this.inputElement.value);this.fire("iron-input-ready")},get _patternRegExp(){if(this.allowedPattern)var b=new RegExp(this.allowedPattern);
else switch(this.inputElement.type){case "number":b=/[0-9.,e-]/}return b},_bindValueChanged:function(b,d){d&&(void 0===b?d.value=null:b!==d.value&&(this.inputElement.value=b),this.autoValidate&&this.validate(),this.fire("bind-value-changed",{value:b}))},_onInput:function(){!this.allowedPattern||this._patternAlreadyChecked||this._checkPatternValidity()||(this._announceInvalidCharacter("Invalid string of characters not entered."),this.inputElement.value=this._previousValidInput);this.bindValue=this._previousValidInput=
this.inputElement.value;this._patternAlreadyChecked=!1},_isPrintable:function(b){var d=19==b.keyCode||20==b.keyCode||45==b.keyCode||46==b.keyCode||144==b.keyCode||145==b.keyCode||32<b.keyCode&&41>b.keyCode||111<b.keyCode&&124>b.keyCode;return!(8==b.keyCode||9==b.keyCode||13==b.keyCode||27==b.keyCode)&&!(0==b.charCode&&d)},_onKeypress:function(b){if(this.allowedPattern||"number"===this.inputElement.type){var d=this._patternRegExp;if(d&&!(b.metaKey||b.ctrlKey||b.altKey)){this._patternAlreadyChecked=
!0;var f=String.fromCharCode(b.charCode);this._isPrintable(b)&&!d.test(f)&&(b.preventDefault(),this._announceInvalidCharacter("Invalid character "+f+" not entered."))}}},_checkPatternValidity:function(){var b=this._patternRegExp;if(!b)return!0;for(var d=0;d<this.inputElement.value.length;d++)if(!b.test(this.inputElement.value[d]))return!1;return!0},validate:function(){if(!this.inputElement)return this.invalid=!1,!0;var b=this.inputElement.checkValidity();b&&(this.required&&""===this.bindValue?b=!1:
this.hasValidator()&&(b=Polymer.IronValidatableBehavior.validate.call(this,this.bindValue)));this.invalid=!b;this.fire("iron-input-validate");return b},_announceInvalidCharacter:function(b){this.fire("iron-announce",{text:b})},_computeValue:function(b){return b}});

//# sourceURL=build://paper-input/paper-input-char-counter.html.js
Polymer({is:"paper-input-char-counter",behaviors:[Polymer.PaperInputAddonBehavior],properties:{_charCounterStr:{type:String,value:"0"}},update:function(b){if(b.inputElement){b.value=b.value||"";var d=b.value.toString().length.toString();b.inputElement.hasAttribute("maxlength")&&(d+="/"+b.inputElement.getAttribute("maxlength"));this._charCounterStr=d}}});

//# sourceURL=build://paper-input/paper-input-container.html.js
Polymer({is:"paper-input-container",properties:{noLabelFloat:{type:Boolean,value:!1},alwaysFloatLabel:{type:Boolean,value:!1},attrForValue:{type:String,value:"bind-value"},autoValidate:{type:Boolean,value:!1},invalid:{observer:"_invalidChanged",type:Boolean,value:!1},focused:{readOnly:!0,type:Boolean,value:!1,notify:!0},_addons:{type:Array},_inputHasContent:{type:Boolean,value:!1},_inputSelector:{type:String,value:"input,iron-input,textarea,.paper-input-input"},_boundOnFocus:{type:Function,value:function(){return this._onFocus.bind(this)}},
_boundOnBlur:{type:Function,value:function(){return this._onBlur.bind(this)}},_boundOnInput:{type:Function,value:function(){return this._onInput.bind(this)}},_boundValueChanged:{type:Function,value:function(){return this._onValueChanged.bind(this)}}},listeners:{"addon-attached":"_onAddonAttached","iron-input-validate":"_onIronInputValidate"},get _valueChangedEvent(){return this.attrForValue+"-changed"},get _propertyForValue(){return Polymer.CaseMap.dashToCamelCase(this.attrForValue)},get _inputElement(){return Polymer.dom(this).querySelector(this._inputSelector)},
get _inputElementValue(){return this._inputElement[this._propertyForValue]||this._inputElement.value},ready:function(){this.__isFirstValueUpdate=!0;this._addons||(this._addons=[]);this.addEventListener("focus",this._boundOnFocus,!0);this.addEventListener("blur",this._boundOnBlur,!0)},attached:function(){this.attrForValue?this._inputElement.addEventListener(this._valueChangedEvent,this._boundValueChanged):this.addEventListener("input",this._onInput);this._inputElementValue&&""!=this._inputElementValue?
this._handleValueAndAutoValidate(this._inputElement):this._handleValue(this._inputElement)},_onAddonAttached:function(b){this._addons||(this._addons=[]);b=b.target;-1===this._addons.indexOf(b)&&(this._addons.push(b),this.isAttached&&this._handleValue(this._inputElement))},_onFocus:function(){this._setFocused(!0)},_onBlur:function(){this._setFocused(!1);this._handleValueAndAutoValidate(this._inputElement)},_onInput:function(b){this._handleValueAndAutoValidate(b.target)},_onValueChanged:function(b){var d=
b.target;if(this.__isFirstValueUpdate&&(this.__isFirstValueUpdate=!1,void 0===d.value||""===d.value))return;this._handleValueAndAutoValidate(b.target)},_handleValue:function(b){var d=this._inputElementValue;d||0===d||"number"===b.type&&!b.checkValidity()?this._inputHasContent=!0:this._inputHasContent=!1;this.updateAddons({inputElement:b,value:d,invalid:this.invalid})},_handleValueAndAutoValidate:function(b){this.autoValidate&&b&&(this.invalid=!(b.validate?b.validate(this._inputElementValue):b.checkValidity()));
this._handleValue(b)},_onIronInputValidate:function(){this.invalid=this._inputElement.invalid},_invalidChanged:function(){this._addons&&this.updateAddons({invalid:this.invalid})},updateAddons:function(b){for(var d,f=0;d=this._addons[f];f++)d.update(b)},_computeInputContentClass:function(b,d,f,h,k){var r="input-content";b?(k&&(r+=" label-is-hidden"),h&&(r+=" is-invalid")):(b=this.querySelector("label"),d||k?(r+=" label-is-floating",this.$.labelAndInputContainer.style.position="static",h?r+=" is-invalid":
f&&(r+=" label-is-highlighted")):(b&&(this.$.labelAndInputContainer.style.position="relative"),h&&(r+=" is-invalid")));f&&(r+=" focused");return r},_computeUnderlineClass:function(b,d){var f="underline";d?f+=" is-invalid":b&&(f+=" is-highlighted");return f},_computeAddOnContentClass:function(b,d){var f="add-on-content";d?f+=" is-invalid":b&&(f+=" is-highlighted");return f}});

//# sourceURL=build://paper-input/paper-input-error.html.js
Polymer({is:"paper-input-error",behaviors:[Polymer.PaperInputAddonBehavior],properties:{invalid:{readOnly:!0,reflectToAttribute:!0,type:Boolean}},update:function(b){this._setInvalid(b.invalid)}});

//# sourceURL=build://paper-input/paper-input.html.js
Polymer({is:"paper-input",behaviors:[Polymer.PaperInputBehavior,Polymer.IronFormElementBehavior],properties:{value:{type:String}},beforeRegister:function(){var b="function"==typeof document.createElement("iron-input")._initSlottedInput?"v1":"v0",d=Polymer.DomModule.import("paper-input","template");b=Polymer.DomModule.import("paper-input","template#"+b);(d=d.content.querySelector("#template-placeholder"))&&d.parentNode.replaceChild(b.content,d)},get _focusableElement(){return Polymer.Element?this.inputElement._inputElement:
this.inputElement},listeners:{"iron-input-ready":"_onIronInputReady"},_onIronInputReady:function(){this.$.nativeInput||(this.$.nativeInput=this.$$("input"));this.inputElement&&-1!==this._typesThatHaveText.indexOf(this.$.nativeInput.type)&&(this.alwaysFloatLabel=!0);this.inputElement.bindValue&&this.$.container._handleValueAndAutoValidate(this.inputElement)}});

//# sourceURL=build://iron-overlay-behavior/iron-overlay-backdrop.html.js
(function(){Polymer({is:"iron-overlay-backdrop",properties:{opened:{reflectToAttribute:!0,type:Boolean,value:!1,observer:"_openedChanged"}},listeners:{transitionend:"_onTransitionend"},created:function(){this.__openedRaf=null},attached:function(){this.opened&&this._openedChanged(this.opened)},prepare:function(){this.opened&&!this.parentNode&&Polymer.dom(document.body).appendChild(this)},open:function(){this.opened=!0},close:function(){this.opened=!1},complete:function(){this.opened||this.parentNode!==
document.body||Polymer.dom(this.parentNode).removeChild(this)},_onTransitionend:function(b){b&&b.target===this&&this.complete()},_openedChanged:function(b){b?this.prepare():(b=window.getComputedStyle(this),"0s"!==b.transitionDuration&&0!=b.opacity||this.complete());this.isAttached&&(this.__openedRaf&&(window.cancelAnimationFrame(this.__openedRaf),this.__openedRaf=null),this.scrollTop=this.scrollTop,this.__openedRaf=window.requestAnimationFrame(function(){this.__openedRaf=null;this.toggleClass("opened",
this.opened)}.bind(this)))}})})();

//# sourceURL=build://iron-dropdown/iron-dropdown.html.js
(function(){Polymer({is:"iron-dropdown",behaviors:[Polymer.IronControlState,Polymer.IronA11yKeysBehavior,Polymer.IronOverlayBehavior,Polymer.NeonAnimationRunnerBehavior],properties:{horizontalAlign:{type:String,value:"left",reflectToAttribute:!0},verticalAlign:{type:String,value:"top",reflectToAttribute:!0},openAnimationConfig:{type:Object},closeAnimationConfig:{type:Object},focusTarget:{type:Object},noAnimations:{type:Boolean,value:!1},allowOutsideScroll:{type:Boolean,value:!1,observer:"_allowOutsideScrollChanged"}},
listeners:{"neon-animation-finish":"_onNeonAnimationFinish"},observers:["_updateOverlayPosition(positionTarget, verticalAlign, horizontalAlign, verticalOffset, horizontalOffset)"],get containedElement(){for(var b=Polymer.dom(this.$.content).getDistributedNodes(),d=0,f=b.length;d<f;d++)if(b[d].nodeType===Node.ELEMENT_NODE)return b[d]},ready:function(){this.scrollAction||(this.scrollAction=this.allowOutsideScroll?"refit":"lock");this._readied=!0},attached:function(){this.sizingTarget&&this.sizingTarget!==
this||(this.sizingTarget=this.containedElement||this)},detached:function(){this.cancelAnimation()},_openedChanged:function(){this.opened&&this.disabled?this.cancel():(this.cancelAnimation(),this._updateAnimationConfig(),Polymer.IronOverlayBehaviorImpl._openedChanged.apply(this,arguments))},_renderOpened:function(){!this.noAnimations&&this.animationConfig.open?(this.$.contentWrapper.classList.add("animating"),this.playAnimation("open")):Polymer.IronOverlayBehaviorImpl._renderOpened.apply(this,arguments)},
_renderClosed:function(){!this.noAnimations&&this.animationConfig.close?(this.$.contentWrapper.classList.add("animating"),this.playAnimation("close")):Polymer.IronOverlayBehaviorImpl._renderClosed.apply(this,arguments)},_onNeonAnimationFinish:function(){this.$.contentWrapper.classList.remove("animating");this.opened?this._finishRenderOpened():this._finishRenderClosed()},_updateAnimationConfig:function(){for(var b=this.containedElement,d=[].concat(this.openAnimationConfig||[]).concat(this.closeAnimationConfig||
[]),f=0;f<d.length;f++)d[f].node=b;this.animationConfig={open:this.openAnimationConfig,close:this.closeAnimationConfig}},_updateOverlayPosition:function(){this.isAttached&&this.notifyResize()},_allowOutsideScrollChanged:function(b){this._readied&&(b?this.scrollAction&&"lock"!==this.scrollAction||(this.scrollAction="refit"):this.scrollAction="lock")},_applyFocus:function(){var b=this.focusTarget||this.containedElement;b&&this.opened&&!this.noAutoFocus?b.focus():Polymer.IronOverlayBehaviorImpl._applyFocus.apply(this,
arguments)}})})();

//# sourceURL=build://paper-menu-button/paper-menu-button.html.js
(function(){var b={ANIMATION_CUBIC_BEZIER:"cubic-bezier(.3,.95,.5,1)",MAX_ANIMATION_TIME_MS:400};Polymer.PaperMenuButton=function(){};Polymer.PaperMenuButton.prototype.registered=function(){};Polymer.PaperMenuButton.prototype.addOwnKeyBinding=function(){};Polymer.PaperMenuButton.prototype.removeOwnKeyBindings=function(){};Polymer.PaperMenuButton.prototype.keyboardEventMatchesKeys=function(){};Polymer.PaperMenuButton.prototype._collectKeyBindings=function(){};Polymer.PaperMenuButton.prototype._prepKeyBindings=
function(){};Polymer.PaperMenuButton.prototype._addKeyBinding=function(){};Polymer.PaperMenuButton.prototype._resetKeyEventListeners=function(){};Polymer.PaperMenuButton.prototype._listenKeyEventListeners=function(){};Polymer.PaperMenuButton.prototype._unlistenKeyEventListeners=function(){};Polymer.PaperMenuButton.prototype._onKeyBindingEvent=function(){};Polymer.PaperMenuButton.prototype._triggerKeyHandler=function(){};Polymer.PaperMenuButton.prototype._focusBlurHandler=function(d){if(Polymer.Element)this._setFocused("focus"===
d.type);else if(d.target===this)this._setFocused("focus"===d.type);else if(this.__handleEventRetargeting){var f=Polymer.dom(d).localTarget;this.isLightDescendant(f)||this.fire(d.type,{sourceEvent:d},{node:this,bubbles:d.bubbles,cancelable:d.cancelable})}};Polymer.PaperMenuButton.prototype._changedControlState=function(){this._controlStateChanged&&this._controlStateChanged()};Polymer.PaperMenuButton.prototype._setFocused=function(){};Polymer.PaperMenuButton=Polymer({is:"paper-menu-button",behaviors:[Polymer.IronA11yKeysBehavior,
Polymer.IronControlState],properties:{opened:{type:Boolean,value:!1,notify:!0,observer:"_openedChanged"},horizontalAlign:{type:String,value:"left",reflectToAttribute:!0},verticalAlign:{type:String,value:"top",reflectToAttribute:!0},dynamicAlign:{type:Boolean},horizontalOffset:{type:Number,value:0,notify:!0},verticalOffset:{type:Number,value:0,notify:!0},noOverlap:{type:Boolean},noAnimations:{type:Boolean,value:!1},ignoreSelect:{type:Boolean,value:!1},closeOnActivate:{type:Boolean,value:!1},openAnimationConfig:{type:Object,
value:function(){return[{name:"fade-in-animation",timing:{delay:100,duration:200}},{name:"paper-menu-grow-width-animation",timing:{delay:100,duration:150,easing:b.ANIMATION_CUBIC_BEZIER}},{name:"paper-menu-grow-height-animation",timing:{delay:100,duration:275,easing:b.ANIMATION_CUBIC_BEZIER}}]}},closeAnimationConfig:{type:Object,value:function(){return[{name:"fade-out-animation",timing:{duration:150}},{name:"paper-menu-shrink-width-animation",timing:{delay:100,duration:50,easing:b.ANIMATION_CUBIC_BEZIER}},
{name:"paper-menu-shrink-height-animation",timing:{duration:200,easing:"ease-in"}}]}},allowOutsideScroll:{type:Boolean,value:!1},restoreFocusOnClose:{type:Boolean,value:!0},_dropdownContent:{type:Object}},hostAttributes:{role:"group","aria-haspopup":"true"},listeners:{"iron-activate":"_onIronActivate","iron-select":"_onIronSelect"},get contentElement(){for(var d=Polymer.dom(this.$.content).getDistributedNodes(),f=0,h=d.length;f<h;f++)if(d[f].nodeType===Node.ELEMENT_NODE)return d[f]},toggle:function(){this.opened?
this.close():this.open()},open:function(){this.disabled||this.$.dropdown.open()},close:function(){this.$.dropdown.close()},_onIronSelect:function(){this.ignoreSelect||this.close()},_onIronActivate:function(){this.closeOnActivate&&this.close()},_openedChanged:function(d,f){d?(this._dropdownContent=this.contentElement,this.fire("paper-dropdown-open")):null!=f&&this.fire("paper-dropdown-close")},_disabledChanged:function(d){Polymer.IronControlState._disabledChanged.apply(this,arguments);d&&this.opened&&
this.close()},__onIronOverlayCanceled:function(d){var f=this.$.trigger;-1<Polymer.dom(d.detail).path.indexOf(f)&&d.preventDefault()}});Object.keys(b).forEach(function(d){Polymer.PaperMenuButton[d]=b[d]})})();

//# sourceURL=build://paper-dropdown-menu/paper-dropdown-menu.html.js
(function(){Polymer({is:"paper-dropdown-menu",behaviors:[Polymer.IronButtonState,Polymer.IronControlState,Polymer.IronFormElementBehavior,Polymer.IronValidatableBehavior],properties:{selectedItemLabel:{type:String,notify:!0,readOnly:!0},selectedItem:{type:Object,notify:!0,readOnly:!0},value:{type:String,notify:!0},label:{type:String},placeholder:{type:String},errorMessage:{type:String},opened:{type:Boolean,notify:!0,value:!1,observer:"_openedChanged"},allowOutsideScroll:{type:Boolean,value:!1},noLabelFloat:{type:Boolean,
value:!1,reflectToAttribute:!0},alwaysFloatLabel:{type:Boolean,value:!1},noAnimations:{type:Boolean,value:!1},horizontalAlign:{type:String,value:"right"},verticalAlign:{type:String,value:"top"},verticalOffset:Number,dynamicAlign:{type:Boolean},restoreFocusOnClose:{type:Boolean,value:!0}},listeners:{tap:"_onTap"},keyBindings:{"up down":"open",esc:"close"},hostAttributes:{role:"combobox","aria-autocomplete":"none","aria-haspopup":"true"},observers:["_selectedItemChanged(selectedItem)"],attached:function(){var b=
this.contentElement;b&&b.selectedItem&&this._setSelectedItem(b.selectedItem)},get contentElement(){for(var b=Polymer.dom(this.$.content).getDistributedNodes(),d=0,f=b.length;d<f;d++)if(b[d].nodeType===Node.ELEMENT_NODE)return b[d]},open:function(){this.$.menuButton.open()},close:function(){this.$.menuButton.close()},_onIronSelect:function(b){this._setSelectedItem(b.detail.item)},_onIronDeselect:function(){this._setSelectedItem(null)},_onTap:function(b){Polymer.Gestures.findOriginalTarget(b)===this&&
this.open()},_selectedItemChanged:function(b){this.value=b=b?b.label||b.getAttribute("label")||b.textContent.trim():"";this._setSelectedItemLabel(b)},_computeMenuVerticalOffset:function(b,d){return d?d:b?-4:8},_getValidity:function(){return this.disabled||!this.required||this.required&&!!this.value},_openedChanged:function(){var b=this.opened?"true":"false",d=this.contentElement;d&&d.setAttribute("aria-expanded",b)}})})();

//# sourceURL=build://paper-listbox/paper-listbox.html.js
(function(){Polymer({is:"paper-listbox",behaviors:[Polymer.IronMenuBehavior],hostAttributes:{role:"listbox"}})})();

//# sourceURL=build://paper-item/paper-item.html.js
Polymer({is:"paper-item",behaviors:[Polymer.PaperItemBehavior]});

//# sourceURL=build://tf-backend/tf-backend-polymer.js
(function(b){Polymer({is:"tf-backend",_template:null,tf_backend:b})})(dc||(dc={}));

//# sourceURL=build://tf-storage/tf-storage-polymer.js
(function(b){Polymer({is:"tf-storage",_template:null,tf_storage:b})})(bd||(bd={}));

//# sourceURL=build://tf-categorization-utils/tf-tag-filterer.html.js
Polymer({is:"tf-tag-filterer",properties:{tagFilter:{type:String,notify:!0,computed:"_computeTagFilter(_tagFilter)"},_tagFilter:{type:String,value:bd.getStringInitializer("tagFilter",{defaultValue:"",useLocalStorage:!1,polymerProperty:"_tagFilter"}),observer:"_tagFilterObserver"}},_tagFilterObserver:bd.getStringObserver("tagFilter",{defaultValue:"",useLocalStorage:!1,polymerProperty:"_tagFilter"}),_computeTagFilter(){return this._tagFilter}});

//# sourceURL=build://tf-dashboard-common/tf-dashboard-layout.html.js
Polymer({is:"tf-dashboard-layout"});

//# sourceURL=build://tf-dashboard-common/tf-option-selector.html.js
Polymer({is:"tf-option-selector",properties:{name:String,selectedId:{type:String,notify:!0,observer:"_selectedIdChanged"}},attached:function(){this.async(function(){this.getEffectiveChildren().forEach(function(b){this.listen(b,"tap","_selectTarget")}.bind(this))})},_selectTarget:function(b){this.selectedId=b.currentTarget.id},_selectedIdChanged:function(){var b=this.queryEffectiveChildren("#"+this.selectedId);b&&(this.getEffectiveChildren().forEach(function(d){d.classList.remove("selected")}),b.classList.add("selected"))}});

//# sourceURL=build://iron-collapse/iron-collapse.html.js
Polymer({is:"iron-collapse",behaviors:[Polymer.IronResizableBehavior],properties:{horizontal:{type:Boolean,value:!1,observer:"_horizontalChanged"},opened:{type:Boolean,value:!1,notify:!0,observer:"_openedChanged"},transitioning:{type:Boolean,notify:!0,readOnly:!0},noAnimation:{type:Boolean},_desiredSize:{type:String,value:""}},get dimension(){return this.horizontal?"width":"height"},get _dimensionMax(){return this.horizontal?"maxWidth":"maxHeight"},get _dimensionMaxCss(){return this.horizontal?"max-width":
"max-height"},hostAttributes:{role:"group","aria-hidden":"true"},listeners:{transitionend:"_onTransitionEnd"},toggle:function(){this.opened=!this.opened},show:function(){this.opened=!0},hide:function(){this.opened=!1},updateSize:function(b,d){b="auto"===b?"":b;d=d&&!this.noAnimation&&this.isAttached&&this._desiredSize!==b;this._desiredSize=b;this._updateTransition(!1);d&&(d=this._calcSize(),""===b&&(this.style[this._dimensionMax]="",b=this._calcSize()),this.style[this._dimensionMax]=d,this.scrollTop=
this.scrollTop,this._updateTransition(!0),d=b!==d);this.style[this._dimensionMax]=b;d||this._transitionEnd()},enableTransition:function(b){Polymer.Base._warn("`enableTransition()` is deprecated, use `noAnimation` instead.");this.noAnimation=!b},_updateTransition:function(b){this.style.transitionDuration=b&&!this.noAnimation?"":"0s"},_horizontalChanged:function(){this.style.transitionProperty=this._dimensionMaxCss;this.style["maxWidth"===this._dimensionMax?"maxHeight":"maxWidth"]="";this.updateSize(this.opened?
"auto":"0px",!1)},_openedChanged:function(){this.setAttribute("aria-hidden",!this.opened);this._setTransitioning(!0);this.toggleClass("iron-collapse-closed",!1);this.toggleClass("iron-collapse-opened",!1);this.updateSize(this.opened?"auto":"0px",!0);this.opened&&this.focus()},_transitionEnd:function(){this.style[this._dimensionMax]=this._desiredSize;this.toggleClass("iron-collapse-closed",!this.opened);this.toggleClass("iron-collapse-opened",this.opened);this._updateTransition(!1);this.notifyResize();
this._setTransitioning(!1)},_onTransitionEnd:function(b){Polymer.dom(b).rootTarget===this&&this._transitionEnd()},_calcSize:function(){return this.getBoundingClientRect()[this.dimension]+"px"}});

//# sourceURL=build://tf-paginated-view/tf-category-paginated-view.html.js
Polymer({is:"tf-category-paginated-view",properties:{category:Object,initialOpened:Boolean,opened:{type:Boolean,notify:!0,readOnly:!0},_contentActive:{type:Boolean,computed:"_computeContentActive(opened)"},disablePagination:{type:Boolean,value:!1},_count:{type:Number,computed:"_computeCount(category.items.*)"},_hasMultiple:{type:Boolean,computed:"_computeHasMultiple(_count)"},_paneRendered:{type:Boolean,computed:"_computePaneRendered(category)",observer:"_onPaneRenderedChanged"},_itemsRendered:{type:Boolean,
computed:"_computeItemsRendered(opened, _paneRendered)"},_isSearchResults:{type:Boolean,computed:"_computeIsSearchResults(category.metadata.type)"},_isInvalidSearchResults:{type:Boolean,computed:"_computeIsInvalidSearchResults(category.metadata)"},_isUniversalSearchQuery:{type:Boolean,computed:"_computeIsUniversalSearchQuery(category.metadata)"},getCategoryItemKey:{type:Function,value:()=>b=>JSON.stringify(b),observer:"_getCategoryItemKeyChanged"},_limit:{type:Number,value:12,observer:"_limitChanged"},
_activeIndex:{type:Number,value:0},_currentPage:{type:Number,computed:"_computeCurrentPage(_limit, _activeIndex)"},_pageCount:{type:Number,computed:"_computePageCount(category.items.*, _limit)"},_multiplePagesExist:{type:Boolean,computed:"_computeMultiplePagesExist(_pageCount, disablePagination)"},_hasPreviousPage:{type:Boolean,computed:"_computeHasPreviousPage(_currentPage)"},_hasNextPage:{type:Boolean,computed:"_computeHasNextPage(_currentPage, _pageCount)"},_inputWidth:{type:String,computed:"_computeInputWidth(_pageCount)",
observer:"_updateInputWidth"},_pageInputValue:{type:String,computed:"_computePageInputValue(_pageInputFocused, _pageInputRawValue, _currentPage)",observer:"_updatePageInputValue"},_pageInputRawValue:{type:String,value:""},_pageInputFocused:{type:Boolean,value:!1}},observers:["_clampActiveIndex(category.items.*)","_updateRenderedItems(_itemsRendered, category.items.*, _limit, _activeIndex, _pageCount, disablePagination)"],behaviors:[rd.TfDomRepeatBehavior],_computeCount(){return this.category.items.length},
_computeHasMultiple(){return 1<this._count},_togglePane(){this._setOpened(!this.opened)},_computeContentActive(){return this.opened},_onPaneRenderedChanged(b,d){b&&b!==d&&this.$.ifRendered.render()},_computePaneRendered(b){return!(b.metadata.type===kc.CategoryType.SEARCH_RESULTS&&""===b.name)},_computeItemsRendered(){return this._paneRendered&&this.opened},_computeIsSearchResults(b){return b===kc.CategoryType.SEARCH_RESULTS},_computeIsInvalidSearchResults(b){return b.type===kc.CategoryType.SEARCH_RESULTS&&
!b.validRegex},_computeIsUniversalSearchQuery(b){return b.type===kc.CategoryType.SEARCH_RESULTS&&b.universalRegex},_isCompositeSearch(){const b=this.category.metadata.type;return this.category.metadata.compositeSearch&&b===kc.CategoryType.SEARCH_RESULTS},ready(){this._setOpened(null==this.initialOpened?!0:this.initialOpened);this._limitListener=()=>{this.set("_limit",sd.getLimit())};sd.addLimitListener(this._limitListener);this._limitListener()},detached(){sd.removeLimitListener(this._limitListener)},
_updateRenderedItems(b,d,f,h,k,r){b&&(b=Math.floor(h/f),d=this.category.items||[],this.updateDom(r?d:d.slice(b*f,(b+1)*f),this.getCategoryItemKey))},_limitChanged(b){this.setCacheSize(2*b)},_getCategoryItemKeyChanged(){this.setGetItemKey(this.getCategoryItemKey)},_computeCurrentPage(b,d){return Math.floor(d/b)+1},_computePageCount(b,d){return this.category?Math.ceil(this.category.items.length/d):0},_computeMultiplePagesExist(b,d){return!d&&1<b},_computeHasPreviousPage(b){return 1<b},_computeHasNextPage(b,
d){return b<d},_computeInputWidth(b){return`calc(${b.toString().length}em + 20px)`},_setActiveIndex(b){const d=(this.category.items||[]).length-1;b>d&&(b=d);0>b&&(b=0);this.set("_activeIndex",b)},_clampActiveIndex(){this._setActiveIndex(this._activeIndex)},_performPreviousPage(){this._setActiveIndex(this._activeIndex-this._limit)},_performNextPage(){this._setActiveIndex(this._activeIndex+this._limit)},_computePageInputValue(b,d,f){return b?d:f.toString()},_handlePageInputEvent(b){this.set("_pageInputRawValue",
b.target.value);b=Number(b.target.value||NaN);isNaN(b)||this._setActiveIndex(this._limit*(Math.max(1,Math.min(b,this._pageCount))-1))},_handlePageChangeEvent(){this.set("_pageInputRawValue",this._currentPage.toString())},_handlePageFocusEvent(){this.set("_pageInputRawValue",this._pageInputValue);this.set("_pageInputFocused",!0)},_handlePageBlurEvent(){this.set("_pageInputFocused",!1)},_updatePageInputValue(b){const d=this.$$("#page-input input");d&&(d.value=b)},_updateInputWidth(){this.updateStyles({"--tf-category-paginated-view-page-input-width":this._inputWidth})}});

//# sourceURL=build://paper-dialog/paper-dialog.html.js
Polymer({is:"paper-dialog",behaviors:[Polymer.PaperDialogBehavior,Polymer.NeonAnimationRunnerBehavior],listeners:{"neon-animation-finish":"_onNeonAnimationFinish"},_renderOpened:function(){this.cancelAnimation();this.playAnimation("entry")},_renderClosed:function(){this.cancelAnimation();this.playAnimation("exit")},_onNeonAnimationFinish:function(){this.opened?this._finishRenderOpened():this._finishRenderClosed()}});

//# sourceURL=build://tf-color-scale/palettes.js
var Ld;
(function(b){b.palettes={googleStandard:"#db4437 #ff7043 #f4b400 #0f9d58 #00796b #00acc1 #4285f4 #5c6bc0 #ab47bc".split(" "),googleCool:"#9e9d24 #0f9d58 #00796b #00acc1 #4285f4 #5c6bc0 #607d8b".split(" "),googleWarm:"#795548 #ab47bc #f06292 #c2185b #db4437 #ff7043 #f4b400".split(" "),googleColorBlindAssist:"#ff7043 #00ACC1 #AB47BC #2A56C6 #0b8043 #F7CB4D #c0ca33 #5e35b1 #A52714".split(" "),tensorboardColorBlindAssist:"#ff7043 #0077bb #cc3311 #33bbee #ee3377 #009988 #bbbbbb".split(" "),colorBlindAssist1:"#4477aa #44aaaa #aaaa44 #aa7744 #aa4455 #aa4488".split(" "),colorBlindAssist2:"#88ccee #44aa99 #117733 #999933 #ddcc77 #cc6677 #882255 #aa4499".split(" "),
colorBlindAssist3:"#332288 #6699cc #88ccee #44aa99 #117733 #999933 #ddcc77 #cc6677 #aa4466 #882255 #661100 #aa4499".split(" "),colorBlindAssist4:"#4477aa #66ccee #228833 #ccbb44 #ee6677 #aa3377 #bbbbbb".split(" "),colorBlindAssist5:"#FF6DB6 #920000 #924900 #DBD100 #24FF24 #006DDB #490092".split(" "),mldash:"#E47EAD #F4640D #FAA300 #F5E636 #00A077 #0077B8 #00B7ED".split(" ")};b.standard=b.palettes.tensorboardColorBlindAssist})(Ld||(Ld={}));

//# sourceURL=build://tf-color-scale/colorScale.js
(function(b){function d(h,k){function r(){l.setDomain(k())}const l=new f;h.addListener(r);r();return p=>l.getColor(p)}class f{constructor(h=b.standard){this.palette=h;this.identifiers=d3.map()}setDomain(h){this.identifiers=d3.map();h.forEach((k,r)=>{this.identifiers.set(k,this.palette[r%this.palette.length])})}getColor(h){if(!this.identifiers.has(h))throw Error(`String ${h} was not in the domain.`);return this.identifiers.get(h)}}b.ColorScale=f;b.runsColorScale=d(dc.runsStore,()=>dc.runsStore.getRuns());
b.experimentsColorScale=d(dc.experimentsStore,()=>dc.experimentsStore.getExperiments().map(({name:h})=>h))})(Ld||(Ld={}));

//# sourceURL=build://paper-icon-button/paper-icon-button.html.js
Polymer({is:"paper-icon-button",hostAttributes:{role:"button",tabindex:"0"},behaviors:[Polymer.PaperInkyFocusBehavior],properties:{src:{type:String},icon:{type:String},alt:{type:String,observer:"_altChanged"}},_altChanged:function(b,d){var f=this.getAttribute("aria-label");f&&d!=f||this.setAttribute("aria-label",b)}});

//# sourceURL=build://tf-dashboard-common/tf-multi-checkbox.js
(function(){Polymer({is:"tf-multi-checkbox",properties:{names:{type:Array,value:()=>[]},coloring:{type:Object,value:{getColor:()=>""}},regex:{type:String,notify:!0,value:""},_regex:{type:Object,computed:"_makeRegex(regex)"},namesMatchingRegex:{type:Array,computed:"computeNamesMatchingRegex(names.*, _regex)"},selectionState:{type:Object,notify:!0,value:()=>({})},outSelected:{type:Array,notify:!0,computed:"computeOutSelected(namesMatchingRegex.*, selectionState.*)"},maxNamesToEnableByDefault:{type:Number,
value:40},_debouncedRegexChange:{type:Object,value:function(){var b=_.debounce(d=>{this.regex=d},150,{leading:!1});return function(){var d=this.$$("#names-regex").value;""==d?this.async(()=>{this.regex=d},30):b(d)}}}},observers:["_setIsolatorIcon(selectionState, names)"],_makeRegex:function(b){try{return new RegExp(b)}catch(d){return null}},_setIsolatorIcon:function(){var b=this.selectionState,d=_.filter(_.values(b)).length;Array.prototype.slice.call(this.root.querySelectorAll(".isolator")).forEach(function(f){f.icon=
1===d&&b[f.name]?"radio-button-checked":"radio-button-unchecked"})},computeNamesMatchingRegex:function(){const b=this._regex;return b?this.names.filter(d=>b.test(d)):this.names},computeOutSelected:function(){var b=this.selectionState,d=this.namesMatchingRegex.length<=this.maxNamesToEnableByDefault;return this.namesMatchingRegex.filter(f=>null==b[f]?d:b[f])},synchronizeColors:function(){this._setIsolatorIcon();this.root.querySelectorAll("paper-checkbox").forEach(b=>{const d=this.coloring.getColor(b.name);
b.updateStyles({"--paper-checkbox-checked-color":d,"--paper-checkbox-checked-ink-color":d,"--paper-checkbox-unchecked-color":d,"--paper-checkbox-unchecked-ink-color":d})});this.root.querySelectorAll(".isolator").forEach(b=>{const d=this.coloring.getColor(b.name);b.style.color=d});window.requestAnimationFrame(()=>{this.updateStyles()})},_isolateName:function(b){var d=Polymer.dom(b).localTarget.name,f={};this.names.forEach(function(h){f[h]=h==d});this.selectionState=f},_checkboxChange:function(b){b=
Polymer.dom(b).localTarget;const d=_.clone(this.selectionState);d[b.name]=b.checked;this.selectionState=d},_isChecked:function(b){return-1!=this.outSelected.indexOf(b)},toggleAll:function(){const b=this.namesMatchingRegex.some(f=>this.outSelected.includes(f)),d={};this.names.forEach(f=>{d[f]=!b});this.selectionState=d}})})(cd||(cd={}));

//# sourceURL=build://tf-runs-selector/tf-wbr-string.html.js
Polymer({is:"tf-wbr-string",properties:{value:String,_parts:{type:Array,computed:"_computeParts(value)"}},_computeParts(b){const d=[],f=/[/=_,-]/;for(null==b&&(b="");;){const h=b.search(f);if(-1===h){d.push(b);break}else d.push(b.slice(0,h+1)),b=b.slice(h+1)}return d}});

//# sourceURL=build://tf-runs-selector/tf-runs-selector.html.js
Polymer({is:"tf-runs-selector",properties:{runSelectionState:{type:Object,observer:"_storeRunSelectionState",value:bd.getObjectInitializer("runSelectionState",{defaultValue:{}})},regexInput:{type:String,value:bd.getStringInitializer("regexInput",{defaultValue:""}),observer:"_regexObserver"},selectedRuns:{type:Array,notify:!0},runs:Array,dataLocation:{type:String,notify:!0},_clippedDataLocation:{type:String,computed:"_getClippedDataLocation(dataLocation, _dataLocationClipLength)"},_dataLocationClipLength:{type:Number,
value:250,readOnly:!0},coloring:{type:Object,value:{getColor:Ld.runsColorScale}}},attached(){this._runStoreListener=dc.runsStore.addListener(()=>{this.set("runs",dc.runsStore.getRuns())});this.set("runs",dc.runsStore.getRuns());this._envStoreListener=dc.environmentStore.addListener(()=>{this.set("dataLocation",dc.environmentStore.getDataLocation())});this.set("dataLocation",dc.environmentStore.getDataLocation())},detached(){dc.runsStore.removeListenerByKey(this._runStoreListener);dc.environmentStore.removeListenerByKey(this._envStoreListener)},
_toggleAll:function(){this.$.multiCheckbox.toggleAll()},_getClippedDataLocation:function(b,d){if(void 0!==b&&!(b.length>d))return b},_openDataLocationDialog:function(b){b.preventDefault();this.$$("#data-location-dialog").open()},_shouldShowExpandDataLocationButton(b,d){return b&&b.length>d},_storeRunSelectionState:bd.getObjectObserver("runSelectionState",{defaultValue:{}}),_regexObserver:bd.getStringObserver("regexInput",{defaultValue:""})});

//# sourceURL=build://paper-spinner/paper-spinner-lite.html.js
Polymer({is:"paper-spinner-lite",behaviors:[Polymer.PaperSpinnerBehavior]});

//# sourceURL=build://vz-chart-helpers/vz-chart-tooltip.js
var sg;
(function(b){let d;(function(h){h.AUTO="auto";h.BOTTOM="bottom";h.RIGHT="right"})(d=b.TooltipPosition||(b.TooltipPosition={}));const f={boxShadow:"0 1px 4px rgba(0, 0, 0, .3)",opacity:0,position:"fixed",willChange:"transform",zIndex:5};Polymer({is:"vz-chart-tooltip",_template:null,properties:{contentComponentName:String,position:{type:String,value:d.AUTO},minDistFromEdge:{type:Number,value:15}},ready(){this._tunnel=this._raf=this._styleCache=null},attached(){this._tunnel=this._createTunnel();this._hideOnBlur=
()=>{document.hidden&&this.hide()};window.addEventListener("visibilitychange",this._hideOnBlur)},detached(){this.hide();this._removeTunnel(this._tunnel);this._tunnel=null;window.removeEventListener("visibilitychange",this._hideOnBlur)},content(){return this._tunnel.shadowRoot},hide(){window.cancelAnimationFrame(this._raf);this._styleCache=null;this._tunnel.style.opacity=0},updateAndPosition(h){window.cancelAnimationFrame(this._raf);this._raf=window.requestAnimationFrame(()=>{this.isAttached&&this._repositionImpl(h)})},
_repositionImpl(h){const k=this._tunnel;h=h.getBoundingClientRect();const r=k.getBoundingClientRect(),l=window.innerHeight,p=document.body.clientWidth,m=h.top,n=m+h.height,q=r.height+sf.TOOLTIP_Y_PIXEL_OFFSET;let u=null,w=Math.max(this.minDistFromEdge,h.left),A=null,y=m;this.position==d.RIGHT?w=h.right:(y=n+sf.TOOLTIP_Y_PIXEL_OFFSET,p<w+r.width+this.minDistFromEdge&&(w=null,A=this.minDistFromEdge));this.position==d.AUTO&&0<h.top-q&&l<h.top+h.height+q&&(y=null,u=l-m+sf.TOOLTIP_Y_PIXEL_OFFSET);h={contain:"content",
opacity:1,left:w?`${w}px`:null,right:A?`${A}px`:null,top:y?`${y}px`:null,bottom:u?`${u}px`:null};_.isEqual(this._styleCache,h)||(Object.assign(k.style,h),this._styleCache=h)},_createTunnel(){if(!this.contentComponentName)throw new RangeError("Require `contentComponentName` to be a name of a Polymer component");const h=document.createElement(this.contentComponentName);Object.assign(h.style,f);document.body.appendChild(h);return h},_removeTunnel(h){document.body.removeChild(h)}})})(sg||(sg={}));

//# sourceURL=build://vz-line-chart2/tf-scale.js
(function(b){class d extends Plottable.QuantitativeScale{constructor(){super(...arguments);this._ignoreOutlier=!1}setValueProviderForDomain(f){this._valueProviderForDomain=f}ignoreOutlier(f){return"boolean"==typeof f?(this._ignoreOutlier=f,this):this._ignoreOutlier}_getAllIncludedValues(){const f=this._valueProviderForDomain?this._valueProviderForDomain():[];return this.extentOfValues(f)}}b.TfScale=d})(ug||(ug={}));

//# sourceURL=build://vz-line-chart2/linear-scale.js
(function(b){class d extends Plottable.Scales.Linear{constructor(){super();this._ignoreOutlier=!1;this.padProportion(.2)}setValueProviderForDomain(f){this._valueProviderForDomain=f}_niceDomain(f,h){const [k,r]=f,l=r-k;f=0===l?1.1*Math.abs(k)+1.1:l*this.padProportion();return super._niceDomain([0<=k&&k<l?-.1*r:k-f,r+f],h)}_getUnboundedExtent(f){f=this._getAllIncludedValues(f);let h=this._defaultExtent();0!==f.length&&(f=[Plottable.Utils.Math.min(f,h[0]),Plottable.Utils.Math.max(f,h[1])],h=this._niceDomain(f));
return h}_getAllIncludedValues(){const f=this._valueProviderForDomain?this._valueProviderForDomain():[];return this.extentOfValues(f)}extentOfValues(f){var h=f=f.filter(k=>Plottable.Utils.Math.isValidNumber(k));if(this.ignoreOutlier()){h=f.sort((l,p)=>l-p);const k=d3.quantile(h,.05),r=d3.quantile(h,.95);h=f.filter(l=>l>=k&&l<=r)}f=d3.extent(h);return null==f[0]||null==f[1]?[]:f}ignoreOutlier(f){return"boolean"==typeof f?(this._ignoreOutlier=f,this):this._ignoreOutlier}}b.LinearScale=d})(ug||(ug={}));

//# sourceURL=build://vz-line-chart2/log-scale.js
(function(b){function d(k){return Math.log10(k)}function f(k){return Math.pow(10,k)}b.MIN_POSITIVE_VALUE=Math.pow(2,-1074);class h extends b.TfScale{constructor(){super();this._d3LogScale=d3.scaleLog();this.padProportion(.2)}scale(k){return 0>=k?NaN:this._d3LogScale(k)}invert(k){return this._d3LogScale.invert(k)}scaleTransformation(k){return this.scale(k)}invertedTransformation(k){return this.invert(k)}getTransformationDomain(){return this.domain()}_getDomain(){return this._untransformedDomain}_setDomain(k){this._untransformedDomain=
k;const [r,l]=k;super._setDomain([Math.max(b.MIN_POSITIVE_VALUE,r),l])}_niceDomain(k){const [r,l]=k;k=Math.max(d(b.MIN_POSITIVE_VALUE),d(r));const p=d(l);var m=p-k;m=m?m*this.padProportion():1;return[f(Math.max(d(b.MIN_POSITIVE_VALUE),k-m)),f(p+m)]}_getUnboundedExtent(k){k=this._getAllIncludedValues(k);let r=this._defaultExtent();0!==k.length&&(k=[Plottable.Utils.Math.min(k,r[0]),Plottable.Utils.Math.max(k,r[1])],r=this._niceDomain(k));return r}_getAllIncludedValues(){return super._getAllIncludedValues().map(k=>
0<k?k:b.MIN_POSITIVE_VALUE)}_defaultExtent(){return[1,10]}_backingScaleDomain(k){if(null==k)return this._d3LogScale.domain();this._d3LogScale.domain(k);return this}_getRange(){return this._d3LogScale.range()}_setRange(k){this._d3LogScale.range(k)}defaultTicks(){return this._d3LogScale.ticks()}ticks(){return this._d3LogScale.ticks()}extentOfValues(k){let r=k=k.filter(l=>Plottable.Utils.Math.isValidNumber(l)&&0<l);if(this.ignoreOutlier()){k=k.map(d).sort((m,n)=>m-n);const l=d3.quantile(k,.05),p=d3.quantile(k,
.95);r=k.filter(m=>m>=l&&m<=p).map(f)}k=d3.extent(r);return null==k[0]||null==k[1]?[]:k}}b.LogScale=h})(ug||(ug={}));

//# sourceURL=build://vz-line-chart2/line-chart.js
(function(b){let d;(function(k){k[k.TEXT=0]="TEXT";k[k.DOM=1]="DOM"})(d||(d={}));let f;(function(k){k.LOG="log";k.LINEAR="linear"})(f||(f={}));class h{constructor(k,r,l,p,m,n,q,u,w,A,y){this.dirtyDatasets=new Set;this.seriesNames=[];this.name2datasets={};this.colorScale=p;this.tooltip=m;this.datasets=[];this._ignoreYOutliers=!1;this.lastPointsDataset=new Plottable.Dataset;this.nanDataset=new Plottable.Dataset;this.yValueAccessor=r;this.symbolFunction=A;this._defaultXRange=u;this._defaultYRange=w;
this.tooltipColumns=n;this.buildChart(k,l,q,y)}buildChart(k,r,l,p){this.destroy();k=k();this.xAccessor=k.accessor;this.xScale=k.scale;this.xAxis=k.axis;this.xAxis.margin(0).tickLabelPadding(3);p&&this.xAxis.formatter(p);this.yScale=h.getYScaleFromType(r);this.yScale.setValueProviderForDomain(()=>this.getValuesForYAxisDomainCompute());this.yAxis=new Plottable.Axes.Numeric(this.yScale,"left");p=sf.multiscaleFormatter(sf.Y_AXIS_FORMATTER_PRECISION);this.yAxis.margin(0).tickLabelPadding(5).formatter(p);
this.yAxis.usesTextWidthApproximation();this.fillArea=l;p=new b.PanZoomDragLayer(this.xScale,this.yScale,()=>this.resetDomain());this.tooltipInteraction=this.createTooltipInteraction(p);this.tooltipPointsComponent=new Plottable.Component;l=this.buildPlot(this.xScale,this.yScale,l);this.gridlines=new Plottable.Components.Gridlines(this.xScale,this.yScale);k=null;r!==f.LOG&&(k=new Plottable.Components.GuideLineLayer("horizontal"),k.scale(this.yScale).value(0));r=new Plottable.Components.GuideLineLayer("vertical");
r.scale(this.xScale).value(0);this.center=new Plottable.Components.Group([this.gridlines,k,r,l,this.tooltipPointsComponent,p]);this.center.addClass("main");this.outer=new Plottable.Components.Table([[this.yAxis,this.center],[null,this.xAxis]])}buildPlot(k,r,l){l&&(this.marginAreaPlot=new Plottable.Plots.Area,this.marginAreaPlot.x(this.xAccessor,k),this.marginAreaPlot.y(l.higherAccessor,r),this.marginAreaPlot.y0(l.lowerAccessor),this.marginAreaPlot.attr("fill",(q,u,w)=>this.colorScale.scale(w.metadata().name)),
this.marginAreaPlot.attr("fill-opacity",.3),this.marginAreaPlot.attr("stroke-width",0));this.smoothedAccessor=q=>q.smoothed;l=new Plottable.Plots.Line;l.x(this.xAccessor,k);l.y(this.yValueAccessor,r);l.attr("stroke",(q,u,w)=>this.colorScale.scale(w.metadata().name));this.linePlot=l;this.setupTooltips(l);let p=new Plottable.Plots.Line;p.x(this.xAccessor,k);p.y(this.smoothedAccessor,r);p.attr("stroke",(q,u,w)=>this.colorScale.scale(w.metadata().name));this.smoothLinePlot=p;if(this.symbolFunction){var m=
new Plottable.Plots.Scatter;m.x(this.xAccessor,k);m.y(this.yValueAccessor,r);m.attr("fill",(q,u,w)=>this.colorScale.scale(w.metadata().name));m.attr("opacity",1);m.size(2*sf.TOOLTIP_CIRCLE_SIZE);m.symbol((q,u,w)=>this.symbolFunction(w.metadata().name));this.markersScatterPlot=m}m=new Plottable.Plots.Scatter;m.x(this.xAccessor,k);m.y(this.yValueAccessor,r);m.attr("fill",q=>this.colorScale.scale(q.name));m.attr("opacity",1);m.size(2*sf.TOOLTIP_CIRCLE_SIZE);m.datasets([this.lastPointsDataset]);this.scatterPlot=
m;let n=new Plottable.Plots.Scatter;n.x(this.xAccessor,k);n.y(q=>q.displayY,r);n.attr("fill",q=>this.colorScale.scale(q.name));n.attr("opacity",1);n.size(2*sf.NAN_SYMBOL_SIZE);n.datasets([this.nanDataset]);n.symbol(Plottable.SymbolFactories.triangle);this.nanDisplay=n;k=[n,m,p,l];this.marginAreaPlot&&k.push(this.marginAreaPlot);this.markersScatterPlot&&k.push(this.markersScatterPlot);return new Plottable.Components.Group(k)}ignoreYOutliers(k){k!==this._ignoreYOutliers&&(this._ignoreYOutliers=k,this.updateSpecialDatasets(),
this.yScale.ignoreOutlier(k),this.resetYDomain())}getValuesForYAxisDomainCompute(){const k=this.getAccessorsForComputingYRange();return _.flattenDeep(this.datasets.map(r=>k.map(l=>r.data().map(p=>l(p,-1,r))))).filter(isFinite)}updateSpecialDatasets(){const k=this.getYAxisAccessor();var r=this.datasets.map(l=>{let p=null,m=l.data().filter(n=>!isNaN(k(n,-1,l)));0<m.length&&(p=m[m.length-1],p.name=l.metadata().name,p.relative=sf.relativeAccessor(p,-1,l));return p}).filter(l=>null!=l);this.lastPointsDataset.data(r);
this.markersScatterPlot&&this.markersScatterPlot.datasets(this.datasets.map(this.createSampledDatasetForMarkers));r=_.flatten(this.datasets.map(l=>{let p=null,m=l.data(),n=0;for(;n<m.length&&null==p;)isNaN(k(m[n],-1,l))||(p=k(m[n],-1,l)),n++;null==p&&(p=0);let q=[];for(n=0;n<m.length;n++)isNaN(k(m[n],-1,l))?(m[n].name=l.metadata().name,m[n].displayY=p,m[n].relative=sf.relativeAccessor(m[n],-1,l),q.push(m[n])):p=k(m[n],-1,l);return q}));this.nanDataset.data(r)}resetDomain(){this.resetXDomain();this.resetYDomain()}resetXDomain(){if(null!=
this._defaultXRange)var k=this._defaultXRange;else k=this.xScale,k._domainMin=null,k._domainMax=null,k=k._getExtent();this.xScale.domain(k)}resetYDomain(){null!=this._defaultYRange?this.yScale.domain(this._defaultYRange):(this.yScale.autoDomain(),this.yScale.domain(this.yScale.domain()))}getAccessorsForComputingYRange(){const k=[this.getYAxisAccessor()];this.fillArea&&k.push(this.fillArea.lowerAccessor,this.fillArea.higherAccessor);return k}getYAxisAccessor(){return this.smoothingEnabled?this.smoothedAccessor:
this.yValueAccessor}createTooltipInteraction(k){const r=new sf.PointerInteraction,l=()=>{r.enabled(!1);this.hideTooltips()},p=()=>r.enabled(!0);k.onPanStart(l);k.onDragZoomStart(l);k.onPanEnd(p);k.onDragZoomEnd(p);k.onScrollZoom(()=>this.updateTooltipContent(this._lastMousePosition));r.onPointerMove(m=>{this._lastMousePosition=m;this.updateTooltipContent(m)});r.onPointerExit(()=>this.hideTooltips());return r}updateTooltipContent(k){this.linePlot&&(window.cancelAnimationFrame(this._tooltipUpdateAnimationFrame),
this._tooltipUpdateAnimationFrame=window.requestAnimationFrame(()=>{let r={x:k.x,y:k.y,datum:null,dataset:null},l=this.gridlines.content().node().getBBox();var p=this.linePlot.datasets().map(u=>this.findClosestPoint(r,u)).filter(Boolean);let m=Plottable.Utils.DOM.intersectsBBox,n=p.filter(u=>m(u.x,u.y,l)||isNaN(this.yValueAccessor(u.datum,0,u.dataset))),q=n.filter(u=>!isNaN(this.yValueAccessor(u.datum,0,u.dataset)));0!==p.length?(this.scatterPlot.attr("display","none"),p=this.tooltipPointsComponent.content().selectAll(".point").data(q,
u=>u.dataset.metadata().name),p.enter().append("circle").classed("point",!0),p.attr("r",sf.TOOLTIP_CIRCLE_SIZE).attr("cx",u=>u.x).attr("cy",u=>u.y).style("stroke","none").attr("fill",u=>this.colorScale.scale(u.dataset.metadata().name)),p.exit().remove(),this.drawTooltips(n,r,this.tooltipColumns)):this.hideTooltips()}))}hideTooltips(){window.cancelAnimationFrame(this._tooltipUpdateAnimationFrame);this.tooltip.hide();this.scatterPlot.attr("display","block");this.tooltipPointsComponent.content().selectAll(".point").remove()}setupTooltips(k){k.onDetach(()=>
{this.tooltipInteraction.detachFrom();this.tooltipInteraction.enabled(!1)});k.onAnchor(()=>{this.tooltipInteraction.attachTo(k);this.tooltipInteraction.enabled(!0)})}drawTooltips(k,r,l){if(k.length){var p=this.colorScale;l=[{title:"",static:!1,evalType:d.DOM,evaluate(y){d3.select(this).select("span").style("background-color",()=>p.scale(y.dataset.metadata().name));return""},enter(y){d3.select(this).append("span").classed("swatch",!0).style("background-color",()=>p.scale(y.dataset.metadata().name))}},
...l];var m=y=>Math.pow(y.x-r.x,2)+Math.pow(y.y-r.y,2),n=_.min(k.map(m)),q=this.smoothingEnabled?this.smoothedAccessor:this.yValueAccessor;k="ascending"===this.tooltipSortingMethod?_.sortBy(k,y=>q(y.datum,-1,y.dataset)):"descending"===this.tooltipSortingMethod?_.sortBy(k,y=>q(y.datum,-1,y.dataset)).reverse():"nearest"===this.tooltipSortingMethod?_.sortBy(k,m):k.slice(0).reverse();var u=this,w=d3.select(this.tooltip.content()).select("table"),A=w.select("thead").selectAll("th").data(l,y=>y.title);
A.enter().append("th").text(y=>y.title).nodes();A.exit().remove();k=w.select("tbody").selectAll("tr").data(k,y=>y.dataset.metadata().name);k.classed("distant",y=>{var x=y.dataset.data()[0],C=_.last(y.dataset.data());x=this.xScale.scale(this.xAccessor(x,0,y.dataset));C=this.xScale.scale(this.xAccessor(C,0,y.dataset));y=this.smoothingEnabled?y.datum.smoothed:this.yValueAccessor(y.datum,0,y.dataset);return r.x<x||r.x>C||isNaN(y)}).classed("closest",y=>m(y)===n).each(function(y){u.drawTooltipRow(this,
l,y)}).order();k.exit().remove();k.enter().append("tr").each(function(y){u.drawTooltipRow(this,l,y)}).nodes();this.tooltip.updateAndPosition(this.targetSVG.node())}else this.tooltip.hide()}drawTooltipRow(k,r,l){const p=this;k=d3.select(k).selectAll("td").data(r);k.each(function(m){m.static||p.drawTooltipColumn.call(p,this,m,l)});k.exit().remove();k.enter().append("td").each(function(m){m.enter&&m.enter.call(this,l);p.drawTooltipColumn.call(p,this,m,l)})}drawTooltipColumn(k,r,l){const p=this.smoothingEnabled;
r.evalType==d.DOM?r.evaluate.call(k,l,{smoothingEnabled:p}):d3.select(k).text(r.evaluate.call(k,l,{smoothingEnabled:p}))}findClosestPoint(k,r){const l=r.data().map((n,q)=>this.xScale.scale(this.xAccessor(n,q,r)));let p=_.sortedIndex(l,k.x);if(0==l.length)return null;p===l.length?--p:0!==p&&(p=Math.abs(l[p-1]-k.x)<Math.abs(l[p]-k.x)?p-1:p);k=r.data()[p];const m=this.smoothingEnabled?this.smoothedAccessor(k,p,r):this.yValueAccessor(k,p,r);return{x:l[p],y:this.yScale.scale(m),datum:k,dataset:r}}resmoothDataset(k){let r=
k.data();const l=this.smoothingWeight;let p=0<r.length?0:NaN,m=0;const n=r.map((u,w)=>this.yValueAccessor(u,w,k)),q=n.every(u=>u==n[0]);r.forEach((u,w)=>{w=n[w];q||!Number.isFinite(w)?u.smoothed=w:(p=p*l+(1-l)*w,m++,w=1,1!==l&&(w=1-Math.pow(l,m)),u.smoothed=p/w)})}getDataset(k){void 0===this.name2datasets[k]&&(this.name2datasets[k]=new Plottable.Dataset([],{name:k,meta:null}));return this.name2datasets[k]}static getYScaleFromType(k){if(k===f.LOG)return new b.LogScale;if(k===f.LINEAR)return new b.LinearScale;
throw Error("Unrecognized yScale type "+k);}setVisibleSeries(k){this.disableChanges();k=k.sort();k.reverse();this.seriesNames=k}disableChanges(){this.dirtyDatasets.size||(this.linePlot.datasets([]),this.smoothLinePlot&&this.smoothLinePlot.datasets([]),this.marginAreaPlot&&this.marginAreaPlot.datasets([]))}commitChanges(){this.datasets=this.seriesNames.map(k=>this.getDataset(k));[...this.dirtyDatasets].forEach(k=>{this.smoothingEnabled&&this.resmoothDataset(this.getDataset(k))});this.updateSpecialDatasets();
this.linePlot.datasets(this.datasets);this.smoothingEnabled&&this.smoothLinePlot.datasets(this.datasets);this.marginAreaPlot&&this.marginAreaPlot.datasets(this.datasets);this.measureBBoxAndMaybeInvalidateLayoutInRaf();this.dirtyDatasets.clear()}createSampledDatasetForMarkers(k){const r=k.data();if(20>=r.length)return k;const l=Math.ceil(r.length/20),p=Array(Math.floor(r.length/l));for(let m=0,n=0;m<p.length;m++,n+=l)p[m]=r[n];return new Plottable.Dataset(p,k.metadata())}setSeriesData(k,r){this.disableChanges();
this.getDataset(k).data(r);this.dirtyDatasets.add(k)}setSeriesMetadata(k,r){this.disableChanges();this.getDataset(k).metadata(Object.assign({},this.getDataset(k).metadata(),{meta:r}));this.dirtyDatasets.add(k)}smoothingUpdate(k){this.smoothingWeight=k;this.datasets.forEach(r=>this.resmoothDataset(r));this.smoothingEnabled||(this.linePlot.addClass("ghost"),this.scatterPlot.y(this.smoothedAccessor,this.yScale),this.smoothingEnabled=!0,this.smoothLinePlot.datasets(this.datasets));this.markersScatterPlot&&
this.markersScatterPlot.y(this.getYAxisAccessor(),this.yScale);this.updateSpecialDatasets()}smoothingDisable(){this.smoothingEnabled&&(this.linePlot.removeClass("ghost"),this.scatterPlot.y(this.yValueAccessor,this.yScale),this.smoothLinePlot.datasets([]),this.smoothingEnabled=!1,this.updateSpecialDatasets());this.markersScatterPlot&&this.markersScatterPlot.y(this.getYAxisAccessor(),this.yScale)}setTooltipSortingMethod(k){this.tooltipSortingMethod=k}renderTo(k){this.targetSVG=k;this.outer.renderTo(k);
null!=this._defaultXRange&&this.resetXDomain();null!=this._defaultYRange&&this.resetYDomain();this.measureBBoxAndMaybeInvalidateLayoutInRaf()}redraw(){window.cancelAnimationFrame(this._redrawRaf);this._redrawRaf=window.requestAnimationFrame(()=>{this.measureBBoxAndMaybeInvalidateLayout();this.outer.redraw()})}measureBBoxAndMaybeInvalidateLayoutInRaf(){window.cancelAnimationFrame(this._invalidateLayoutRaf);this._invalidateLayoutRaf=window.requestAnimationFrame(()=>{this.measureBBoxAndMaybeInvalidateLayout()})}measureBBoxAndMaybeInvalidateLayout(){if(this._lastDrawBBox){const k=
this._lastDrawBBox.width,{width:r}=this.targetSVG.node().getBoundingClientRect();0==k&&k<r&&this.outer.invalidateCache()}this._lastDrawBBox=this.targetSVG.node().getBoundingClientRect()}destroy(){window.cancelAnimationFrame(this._redrawRaf);window.cancelAnimationFrame(this._invalidateLayoutRaf);this.outer&&this.outer.destroy()}onAnchor(k){if(this.outer)this.outer.onAnchor(k)}}b.LineChart=h})(ug||(ug={}));

//# sourceURL=build://vz-line-chart2/vz-line-chart2.js
(function(b){const d=sf.multiscaleFormatter(sf.Y_TOOLTIP_FORMATTER_PRECISION),f=h=>isNaN(h)?"NaN":d(h);b.DEFAULT_TOOLTIP_COLUMNS=[{title:"Name",evaluate:h=>h.dataset.metadata().name},{title:"Smoothed",evaluate(h,k){return f(k.smoothingEnabled?h.datum.smoothed:h.datum.scalar)}},{title:"Value",evaluate:h=>f(h.datum.scalar)},{title:"Step",evaluate:h=>sf.stepFormatter(h.datum.step)},{title:"Time",evaluate:h=>sf.timeFormatter(h.datum.wall_time)},{title:"Relative",evaluate:h=>sf.relativeFormatter(sf.relativeAccessor(h.datum,
-1,h.dataset))}];Polymer({is:"vz-line-chart2",properties:{colorScale:{type:Object,value:function(){return(new Plottable.Scales.Color).range(d3.schemeCategory10)}},symbolFunction:Object,smoothingEnabled:{type:Boolean,notify:!0,value:!1},smoothingWeight:{type:Number,value:.6},xType:{type:String,value:""},xComponentsCreationMethod:{type:Object,value:""},xAxisFormatter:Object,yValueAccessor:{type:Object,value:()=>h=>h.scalar},tooltipColumns:{type:Array,value:()=>b.DEFAULT_TOOLTIP_COLUMNS},fillArea:Object,
defaultXRange:Array,defaultYRange:Array,yScaleType:{type:String,value:"linear"},ignoreYOutliers:{type:Boolean,value:!1},tooltipSortingMethod:{type:String,value:"default"},tooltipPosition:{type:String,value:sg.TooltipPosition.BOTTOM},_chart:Object,_visibleSeriesCache:{type:Array,value:()=>[]},_seriesDataCache:{type:Object,value:()=>({})},_seriesMetadataCache:{type:Object,value:()=>({})},_makeChartAsyncCallbackId:{type:Number,value:null}},observers:["_makeChart(xComponentsCreationMethod, xType, yValueAccessor, yScaleType, tooltipColumns, colorScale, isAttached)",
"_reloadFromCache(_chart, _visibleSeriesCache)","_smoothingChanged(smoothingEnabled, smoothingWeight, _chart)","_tooltipSortingMethodChanged(tooltipSortingMethod, _chart)","_outliersChanged(ignoreYOutliers, _chart)"],ready(){this.scopeSubtree(this.$.chartdiv,!0)},attached(){const h={capture:!0,passive:!0};this._listen(this,"mousedown",this._onMouseDown.bind(this),h);this._listen(this,"mouseup",this._onMouseUp.bind(this),h);this._listen(window,"keydown",this._onKeyDown.bind(this),h);this._listen(window,
"keyup",this._onKeyUp.bind(this),h)},detached(){this.cancelAsync(this._makeChartAsyncCallbackId);this._chart&&this._chart.destroy();this._listeners&&(this._listeners.forEach(({node:h,eventName:k,func:r,option:l})=>{h.removeEventListener(k,r,l)}),this._listeners.clear())},_listen(h,k,r,l={}){this._listeners||(this._listeners=new Set);this._listeners.add({node:h,eventName:k,func:r,option:l});h.addEventListener(k,r,l)},_onKeyDown(h){this.toggleClass("pankey",b.PanZoomDragLayer.isPanKey(h))},_onKeyUp(h){this.toggleClass("pankey",
b.PanZoomDragLayer.isPanKey(h))},_onMouseDown(){this.toggleClass("mousedown",!0)},_onMouseUp(){this.toggleClass("mousedown",!1)},setVisibleSeries:function(h){_.isEqual(this._visibleSeriesCache,h)||(this._visibleSeriesCache=h)},setSeriesData:function(h,k){this._seriesDataCache[h]=k;this._chart&&this._chart.setSeriesData(h,k)},setSeriesMetadata(h,k){this._seriesMetadataCache[h]=k;this._chart&&this._chart.setSeriesMetadata(h,k)},commitChanges(){this._chart&&this._chart.commitChanges()},resetDomain:function(){this._chart&&
this._chart.resetDomain()},redraw:function(){this._chart&&this._chart.redraw()},_makeChart:function(h,k,r,l,p,m){k||h?k&&(h=()=>sf.getXComponents(k)):h=sf.stepX;null!==this._makeChartAsyncCallbackId&&(this.cancelAsync(this._makeChartAsyncCallbackId),this._makeChartAsyncCallbackId=null);this._makeChartAsyncCallbackId=this.async(function(){this._makeChartAsyncCallbackId=null;if(h&&this.yValueAccessor&&this.tooltipColumns){var n=new b.LineChart(h,this.yValueAccessor,l,m,this.$.tooltip,this.tooltipColumns,
this.fillArea,this.defaultXRange,this.defaultYRange,this.symbolFunction,this.xAxisFormatter),q=d3.select(this.$.chartdiv);n.renderTo(q);this._chart&&this._chart.destroy();this._chart=n;this._chart.onAnchor(()=>this.fire("chart-attached"))}},350)},_reloadFromCache:function(){this._chart&&(this._visibleSeriesCache.forEach(h=>{this._chart.setSeriesData(h,this._seriesDataCache[h]||[])}),this._visibleSeriesCache.filter(h=>this._seriesMetadataCache[h]).forEach(h=>{this._chart.setSeriesMetadata(h,this._seriesMetadataCache[h])}),
this._chart.setVisibleSeries(this._visibleSeriesCache),this._chart.commitChanges())},_smoothingChanged:function(){this._chart&&(this.smoothingEnabled?this._chart.smoothingUpdate(this.smoothingWeight):this._chart.smoothingDisable())},_outliersChanged:function(){this._chart&&this._chart.ignoreYOutliers(this.ignoreYOutliers)},_tooltipSortingMethodChanged:function(){this._chart&&this._chart.setTooltipSortingMethod(this.tooltipSortingMethod)},getExporter(){return new b.LineChartExporter(this.$.chartdiv)}})})(ug||
(ug={}));

//# sourceURL=build://vz-line-chart2/vz-line-chart2.html.js
Polymer({is:"vz-line-chart-tooltip"});

//# sourceURL=build://vz-line-chart2/line-chart-exporter.js
(function(b){let d;(function(k){k.GROUP="G";k.DIV="DIV";k.SVG="SVG";k.TEXT="TEXT"})(d||(d={}));class f{constructor(k){this.uniqueId=0;this.root=k}exportAsString(){const k=this.convert(this.root);if(!k)return"";const r=this.createRootSvg();r.appendChild(k);return r.outerHTML}createUniqueId(){return`${"clip"}_${this.uniqueId++}`}getSize(){return this.root.getBoundingClientRect()}createRootSvg(){const k=document.createElement("svg"),r=this.getSize();k.setAttributeNS("svg","viewBox",`0 0 ${r.width} ${r.height}`);
k.setAttribute("xmlns","http://www.w3.org/2000/svg");return k}convert(k){let r=null;var l=k.nodeName.toUpperCase();if(k.nodeType!=Node.ELEMENT_NODE||l!=d.DIV&&l!=d.SVG)r=k.cloneNode();else{r=document.createElement(d.GROUP);var p=window.getComputedStyle(k),m=parseInt(p.left,10),n=parseInt(p.top,10);if(m||n)l=this.createUniqueId(),r.setAttribute("transform",`translate(${m}, ${n})`),r.setAttribute("clip-path",`url(#${l})`),n=parseInt(p.height,10),m=document.createElement("rect"),m.setAttribute("width",
String(parseInt(p.width,10))),m.setAttribute("height",String(n)),p=document.createElementNS("svg","clipPath"),p.id=l,p.appendChild(m),r.appendChild(p)}Array.from(k.childNodes).map(q=>this.convert(q)).filter(Boolean).forEach(q=>r.appendChild(q));return r.nodeName.toUpperCase()==d.GROUP&&!r.hasChildNodes()||this.shouldOmitNode(k)?null:this.stripClass(this.transferStyle(k,r))}stripClass(k){k.nodeType==Node.ELEMENT_NODE&&k.removeAttribute("class");return k}transferStyle(k,r){if(r.nodeType!=Node.ELEMENT_NODE)return r;
const l=r.nodeName.toUpperCase();k=window.getComputedStyle(k);l==d.TEXT&&Object.assign(r.style,{fontFamily:k.fontFamily,fontSize:k.fontSize,fontWeight:k.fontWeight});l!=d.GROUP&&(r.setAttribute("fill",k.fill),r.setAttribute("stroke",k.stroke),r.setAttribute("stroke-width",k.strokeWidth));"1"!=k.opacity&&r.setAttribute("opacity",k.opacity);return r}shouldOmitNode(){return!1}}b.PlottableExporter=f;class h extends f{shouldOmitNode(k){return k.nodeType==Node.ELEMENT_NODE?k.classList.contains("scatter-plot"):
!1}}b.LineChartExporter=h})(ug||(ug={}));

//# sourceURL=build://tf-line-chart-data-loader/tf-line-chart-data-loader.html.js
(function(){const b=[],d=function(){return _.throttle(function h(){if(0!=b.length){var k=b.shift();k.active&&(k.redraw(),k._maybeRenderedInBadState=!1);window.cancelAnimationFrame(0);window.requestAnimationFrame(h)}},100)}();Polymer({is:"tf-line-chart-data-loader",properties:{active:{type:Boolean,observer:"_fixBadStateWhenActive"},dataSeries:Array,requestManager:Object,logScaleActive:{type:Boolean,observer:"_logScaleChanged"},xComponentsCreationMethod:Object,xType:String,yValueAccessor:Object,fillArea:Object,
smoothingEnabled:Boolean,smoothingWeight:Number,tooltipColumns:Array,tooltipSortingMethod:String,tooltipPosition:String,ignoreYOutliers:Boolean,defaultXRange:Array,defaultYRange:Array,symbolFunction:Object,colorScale:{type:Object,value:()=>({scale:Ld.runsColorScale})},_resetDomainOnNextLoad:{type:Boolean,value:!0},_maybeRenderedInBadState:{type:Boolean,value:!1,reflectToAttribute:!0}},behaviors:[cd.DataLoaderBehavior],observers:["_dataSeriesChanged(dataSeries.*)","_loadKeyChanged(loadKey)"],onLoadFinish(){0<
this.dataToLoad.length&&this._resetDomainOnNextLoad&&(this._resetDomainOnNextLoad=!1,this.$.chart.resetDomain());this.redraw()},detached(){cancelAnimationFrame(this._redrawRaf)},exportAsSvgString(){return this.$.chart.getExporter().exportAsString()},resetDomain(){this.$.chart.resetDomain()},setSeriesData(f,h){this.$.chart.setSeriesData(f,h)},setSeriesMetadata(f,h){this.$.chart.setSeriesMetadata(f,h)},commitChanges(){this.$.chart.commitChanges()},redraw(){cancelAnimationFrame(this._redrawRaf);this._redrawRaf=
window.requestAnimationFrame(()=>{this.active?this.$.chart.redraw():this._maybeRenderedInBadState=!0})},_loadKeyChanged(){this.reset();this._resetDomainOnNextLoad=!0},_dataSeriesChanged(){this.$.chart.setVisibleSeries(this.dataSeries)},_logScaleChanged(f){this.$.chart.yScaleType=f?"log":"linear";this.redraw()},_fixBadStateWhenActive(){this.active&&this._maybeRenderedInBadState&&(b.push(this),d())},_onChartAttached(){this.active||(this._maybeRenderedInBadState=!0)}})})();

//# sourceURL=build://paper-dialog-scrollable/paper-dialog-scrollable.html.js
Polymer({is:"paper-dialog-scrollable",properties:{dialogElement:{type:Object}},get scrollTarget(){return this.$.scrollable},ready:function(){this._ensureTarget();this.classList.add("no-padding")},attached:function(){this._ensureTarget();requestAnimationFrame(this.updateScrollState.bind(this))},updateScrollState:function(){this.toggleClass("is-scrolled",0<this.scrollTarget.scrollTop);this.toggleClass("can-scroll",this.scrollTarget.offsetHeight<this.scrollTarget.scrollHeight);this.toggleClass("scrolled-to-bottom",
this.scrollTarget.scrollTop+this.scrollTarget.offsetHeight>=this.scrollTarget.scrollHeight)},_ensureTarget:function(){(this.dialogElement=this.dialogElement||this.parentElement)&&this.dialogElement.behaviors&&0<=this.dialogElement.behaviors.indexOf(Polymer.PaperDialogBehaviorImpl)?(this.dialogElement.sizingTarget=this.scrollTarget,this.scrollTarget.classList.remove("fit")):this.dialogElement&&this.scrollTarget.classList.add("fit")}});

//# sourceURL=build://tf-markdown-view/tf-markdown-view.html.js
Polymer({is:"tf-markdown-view",properties:{html:{type:String,value:""}},attached(){window.requestAnimationFrame(()=>{this.scopeSubtree(this.$.markdown,!0)})}});

//# sourceURL=build://tf-card-heading/tf-card-heading.html.js
Polymer({is:"tf-card-heading",properties:{displayName:{type:String,value:null},tag:{type:String,value:null},run:{type:String,value:null},description:{type:String,value:null},color:{type:String,value:null},_runBackground:{type:String,computed:"_computeRunBackground(color)",readOnly:!0,observer:"_updateHeadingStyle"},_runColor:{type:String,computed:"_computeRunColor(color)",readOnly:!0,observer:"_updateHeadingStyle"},_nameLabel:{type:String,computed:"_computeNameLabel(displayName, tag)"},_tagLabel:{type:String,
computed:"_computeTagLabel(displayName, tag)"}},_updateHeadingStyle(){this.updateStyles({"--tf-card-heading-background-color":this._runBackground,"--tf-card-heading-color":this._runColor})},_computeRunBackground(b){return b||"none"},_computeRunColor(b){return Hh.pickTextColor(b)},_computeNameLabel(b,d){return b||d||""},_computeTagLabel(b,d){return d&&d!==b?d:""},_toggleDescriptionDialog(b){this.$.descriptionDialog.positionTarget=b.target;this.$.descriptionDialog.toggle()}});

//# sourceURL=build://tf-dashboard-common/tf-downloader.html.js
Polymer({is:"tf-downloader",properties:{_run:{type:String,value:""},runs:Array,tag:String,urlFn:Function},_csvUrl(b,d,f){return d?dc.addParams(f(b,d),{format:"csv"}):""},_jsonUrl(b,d,f){return d?f(b,d):""},_csvName(b,d){return d?`run-${d}-tag-${b}.csv`:""},_jsonName(b,d){return d?`run-${d}-tag-${b}.json`:""}});

//# sourceURL=build://tf-scalar-dashboard/tf-scalar-card.html.js
Polymer({is:"tf-scalar-card",properties:{tag:String,dataToLoad:Array,xType:String,active:Boolean,ignoreYOutliers:Boolean,requestManager:Object,showDownLinks:Boolean,smoothingEnabled:Boolean,smoothingWeight:Number,tagMetadata:Object,colorScale:{type:Object,value:null},tooltipSortingMethod:String,_loadDataCallback:{type:Object,value:function(){return(b,d,f)=>{f=f.map(k=>({wall_time:new Date(1E3*k[0]),step:k[1],scalar:k[2]}));const h=this._getSeriesNameFromDatum(d);b.setSeriesMetadata(h,d);b.setSeriesData(h,
f);b.commitChanges()}},readOnly:!0},getDataLoadUrl:{type:Function,value:function(){return({tag:b,run:d})=>dc.getRouter().pluginRoute("scalars","/scalars",new URLSearchParams({tag:b,run:d}))}},_downloadUrlFn:{type:Function,value:function(){return(b,d)=>this.getDataLoadUrl({tag:b,run:d})}},requestData:Function,_getDataLoadName:{type:Function,value:function(){return b=>this._getSeriesNameFromDatum(b)}},_expanded:{type:Boolean,value:!1,reflectToAttribute:!0},_logScaleActive:Boolean,_tooltipColumns:{type:Array,
value:function(){const b=ug.DEFAULT_TOOLTIP_COLUMNS.slice(),d=b.findIndex(f=>"Name"==f.title);b.splice(d,1,{title:"Name",evaluate:f=>{f=f.dataset.metadata().meta;return this._getSeriesDisplayNameFromDatum(f)}});return b}}},reload(){this.$$("tf-line-chart-data-loader").reload()},redraw(){this.$$("tf-line-chart-data-loader").redraw()},_toggleExpanded(){this.set("_expanded",!this._expanded);this.redraw()},_toggleLogScale(){this.set("_logScaleActive",!this._logScaleActive)},_resetDomain(){const b=this.$$("tf-line-chart-data-loader");
b&&b.resetDomain()},_updateDownloadLink(){const b=this.$$("tf-line-chart-data-loader").exportAsSvgString();this.$$("#svgLink").href=`data:image/svg+xml;base64,${btoa(b)}`},_runsFromData(b){return b.map(d=>d.run)},_getDataSeries(){return this.dataToLoad.map(b=>this._getSeriesNameFromDatum(b))},_getSeriesNameFromDatum({run:b,experiment:d={name:"_default"}}){return JSON.stringify([d.name,b])},_getSeriesDisplayNameFromDatum(b){return b.run},_getColorScale(){return null!==this.colorScale?this.colorScale:
{scale:b=>{[,b]=JSON.parse(b);return Ld.runsColorScale(b)}}}});

//# sourceURL=build://paper-progress/paper-progress.html.js
Polymer({is:"paper-progress",behaviors:[Polymer.IronRangeBehavior],properties:{secondaryProgress:{type:Number,value:0},secondaryRatio:{type:Number,value:0,readOnly:!0},indeterminate:{type:Boolean,value:!1,observer:"_toggleIndeterminate"},disabled:{type:Boolean,value:!1,reflectToAttribute:!0,observer:"_disabledChanged"}},observers:["_progressChanged(secondaryProgress, value, min, max, indeterminate)"],hostAttributes:{role:"progressbar"},_toggleIndeterminate:function(b){this.toggleClass("indeterminate",
b,this.$.primaryProgress)},_transformProgress:function(b,d){b.style.transform=b.style.webkitTransform="scaleX("+d/100+")"},_mainRatioChanged:function(b){this._transformProgress(this.$.primaryProgress,b)},_progressChanged:function(b,d,f,h,k){b=this._clampValue(b);d=this._clampValue(d);var r=100*this._calcRatio(b),l=100*this._calcRatio(d);this._setSecondaryRatio(r);this._transformProgress(this.$.secondaryProgress,r);this._transformProgress(this.$.primaryProgress,l);this.secondaryProgress=b;k?this.removeAttribute("aria-valuenow"):
this.setAttribute("aria-valuenow",d);this.setAttribute("aria-valuemin",f);this.setAttribute("aria-valuemax",h)},_disabledChanged:function(b){this.setAttribute("aria-disabled",b?"true":"false")},_hideSecondaryProgress:function(b){return 0===b}});

//# sourceURL=build://paper-slider/paper-slider.html.js
Polymer({is:"paper-slider",behaviors:[Polymer.IronA11yKeysBehavior,Polymer.IronFormElementBehavior,Polymer.PaperInkyFocusBehavior,Polymer.IronRangeBehavior],properties:{snaps:{type:Boolean,value:!1,notify:!0},pin:{type:Boolean,value:!1,notify:!0},secondaryProgress:{type:Number,value:0,notify:!0,observer:"_secondaryProgressChanged"},editable:{type:Boolean,value:!1},immediateValue:{type:Number,value:0,readOnly:!0,notify:!0},maxMarkers:{type:Number,value:0,notify:!0},expand:{type:Boolean,value:!1,readOnly:!0},
ignoreBarTouch:{type:Boolean,value:!1},dragging:{type:Boolean,value:!1,readOnly:!0,notify:!0},transiting:{type:Boolean,value:!1,readOnly:!0},markers:{type:Array,readOnly:!0,value:function(){return[]}}},observers:["_updateKnob(value, min, max, snaps, step)","_valueChanged(value)","_immediateValueChanged(immediateValue)","_updateMarkers(maxMarkers, min, max, snaps)"],hostAttributes:{role:"slider",tabindex:0},keyBindings:{left:"_leftKey",right:"_rightKey","down pagedown home":"_decrementKey","up pageup end":"_incrementKey"},
ready:function(){this.ignoreBarTouch&&Polymer.Gestures.setTouchAction(this.$.sliderBar,"auto")},increment:function(){this.value=this._clampValue(this.value+this.step)},decrement:function(){this.value=this._clampValue(this.value-this.step)},_updateKnob:function(b,d,f){this.setAttribute("aria-valuemin",d);this.setAttribute("aria-valuemax",f);this.setAttribute("aria-valuenow",b);this._positionKnob(100*this._calcRatio(b))},_valueChanged:function(){this.fire("value-change",{composed:!0})},_immediateValueChanged:function(){this.dragging?
this.fire("immediate-value-change",{composed:!0}):this.value=this.immediateValue},_secondaryProgressChanged:function(){this.secondaryProgress=this._clampValue(this.secondaryProgress)},_expandKnob:function(){this._setExpand(!0)},_resetKnob:function(){this.cancelDebouncer("expandKnob");this._setExpand(!1)},_positionKnob:function(b){this._setImmediateValue(this._calcStep(this._calcKnobPosition(b)));this._setRatio(100*this._calcRatio(this.immediateValue));this.$.sliderKnob.style.left=this.ratio+"%";this.dragging&&
(this._knobstartx=this.ratio*this._w/100,this.translate3d(0,0,0,this.$.sliderKnob))},_calcKnobPosition:function(b){return(this.max-this.min)*b/100+this.min},_onTrack:function(b){b.stopPropagation();switch(b.detail.state){case "start":this._trackStart(b);break;case "track":this._trackX(b);break;case "end":this._trackEnd()}},_trackStart:function(){this._setTransiting(!1);this._w=this.$.sliderBar.offsetWidth;this._knobstartx=this._startx=this._x=this.ratio*this._w/100;this._minx=-this._startx;this._maxx=
this._w-this._startx;this.$.sliderKnob.classList.add("dragging");this._setDragging(!0)},_trackX:function(b){this.dragging||this._trackStart(b);this._x=this._startx+Math.min(this._maxx,Math.max(this._minx,b.detail.dx*(this._isRTL?-1:1)));this._setImmediateValue(this._calcStep(this._calcKnobPosition(this._x/this._w*100)));this.translate3d(this._calcRatio(this.immediateValue)*this._w-this._knobstartx+"px",0,0,this.$.sliderKnob)},_trackEnd:function(){var b=this.$.sliderKnob.style;this.$.sliderKnob.classList.remove("dragging");
this._setDragging(!1);this._resetKnob();this.value=this.immediateValue;b.transform=b.webkitTransform="";this.fire("change",{composed:!0})},_knobdown:function(b){this._expandKnob();b.preventDefault();this.focus()},_bartrack:function(b){this._allowBarEvent(b)&&this._onTrack(b)},_barclick:function(b){this._w=this.$.sliderBar.offsetWidth;var d=this.$.sliderBar.getBoundingClientRect();d=(b.detail.x-d.left)/this._w*100;this._isRTL&&(d=100-d);var f=this.ratio;this._setTransiting(!0);this._positionKnob(d);
f===this.ratio&&this._setTransiting(!1);this.async(function(){this.fire("change",{composed:!0})});b.preventDefault();this.focus()},_bardown:function(b){this._allowBarEvent(b)&&(this.debounce("expandKnob",this._expandKnob,60),this._barclick(b))},_knobTransitionEnd:function(b){b.target===this.$.sliderKnob&&this._setTransiting(!1)},_updateMarkers:function(b,d,f,h){h||this._setMarkers([]);d=Math.round((f-d)/this.step);d>b&&(d=b);if(0>d||!isFinite(d))d=0;this._setMarkers(Array(d))},_mergeClasses:function(b){return Object.keys(b).filter(function(d){return b[d]}).join(" ")},
_getClassNames:function(){return this._mergeClasses({disabled:this.disabled,pin:this.pin,snaps:this.snaps,ring:this.immediateValue<=this.min,expand:this.expand,dragging:this.dragging,transiting:this.transiting,editable:this.editable})},_allowBarEvent:function(b){return!this.ignoreBarTouch||b.detail.sourceEvent instanceof MouseEvent},get _isRTL(){void 0===this.__isRTL&&(this.__isRTL="rtl"===window.getComputedStyle(this).direction);return this.__isRTL},_leftKey:function(b){this._isRTL?this._incrementKey(b):
this._decrementKey(b)},_rightKey:function(b){this._isRTL?this._decrementKey(b):this._incrementKey(b)},_incrementKey:function(b){this.disabled||("end"===b.detail.key?this.value=this.max:this.increment(),this.fire("change"),b.preventDefault())},_decrementKey:function(b){this.disabled||("home"===b.detail.key?this.value=this.min:this.decrement(),this.fire("change"),b.preventDefault())},_changeValue:function(b){this.value=b.target.value;this.fire("change",{composed:!0})},_inputKeyDown:function(b){b.stopPropagation()},
_createRipple:function(){this._rippleContainer=this.$.sliderKnob;return Polymer.PaperInkyFocusBehaviorImpl._createRipple.call(this)},_focusedChanged:function(b){b&&this.ensureRipple();this.hasRipple()&&(this._ripple.style.display=b?"":"none",this._ripple.holdDown=b)}});

//# sourceURL=build://tf-scalar-dashboard/tf-smoothing-input.html.js
Polymer({is:"tf-smoothing-input",properties:{step:Number,max:Number,min:Number,weight:{type:Number,value:.6,notify:!0},_immediateWeightNumberForPaperSlider:{type:Number,notify:!0,observer:"_immediateWeightNumberForPaperSliderChanged"},_inputWeightStringForPaperInput:{type:String,notify:!0,observer:"_inputWeightStringForPaperInputChanged"}},_updateWeight:_.debounce(function(b){this.weight=b},250),_immediateWeightNumberForPaperSliderChanged:function(){this._inputWeightStringForPaperInput=this._immediateWeightNumberForPaperSlider.toString();
this._updateWeight.call(this,this._immediateWeightNumberForPaperSlider)},_inputWeightStringForPaperInputChanged:function(){0>+this._inputWeightStringForPaperInput?this._inputWeightStringForPaperInput="0":1<+this._inputWeightStringForPaperInput&&(this._inputWeightStringForPaperInput="1");var b=+this._inputWeightStringForPaperInput;isNaN(b)||this._updateWeight.call(this,b)}});

//# sourceURL=build://tf-scalar-dashboard/tf-scalar-dashboard.html.js
Polymer({is:"tf-scalar-dashboard",properties:{_showDownloadLinks:{type:Boolean,notify:!0,value:bd.getBooleanInitializer("_showDownloadLinks",{defaultValue:!1,useLocalStorage:!0}),observer:"_showDownloadLinksObserver"},_smoothingWeight:{type:Number,notify:!0,value:bd.getNumberInitializer("_smoothingWeight",{defaultValue:.6}),observer:"_smoothingWeightObserver"},_smoothingEnabled:{type:Boolean,computed:"_computeSmoothingEnabled(_smoothingWeight)"},_ignoreYOutliers:{type:Boolean,value:bd.getBooleanInitializer("_ignoreYOutliers",
{defaultValue:!0,useLocalStorage:!0}),observer:"_ignoreYOutliersObserver"},_xType:{type:String,value:sf.XType.STEP},_selectedRuns:{type:Array,value:()=>[]},_runToTagInfo:Object,_dataNotFound:Boolean,_tagFilter:{type:String,value:""},_categoriesDomReady:Boolean,_categories:{type:Array,value:()=>[]},_getCategoryItemKey:{type:Function,value:()=>b=>b.tag},_requestManager:{type:Object,value:()=>new dc.RequestManager(50)}},behaviors:[cd.ArrayUpdateHelper],observers:["_updateCategories(_runToTagInfo, _selectedRuns, _tagFilter, _categoriesDomReady)"],
_showDownloadLinksObserver:bd.getBooleanObserver("_showDownloadLinks",{defaultValue:!1,useLocalStorage:!0}),_smoothingWeightObserver:bd.getNumberObserver("_smoothingWeight",{defaultValue:.6}),_ignoreYOutliersObserver:bd.getBooleanObserver("_ignoreYOutliers",{defaultValue:!0,useLocalStorage:!0}),_computeSmoothingEnabled(b){return 0<b},_getCategoryKey(b){return b.metadata.type==kc.CategoryType.SEARCH_RESULTS?"":b.name},_shouldOpen(b){return 2>=b},ready(){this.reload()},reload(){this._fetchTags().then(()=>
{this._reloadCharts()})},_fetchTags(){const b=dc.getRouter().pluginRoute("scalars","/tags");return this._requestManager.request(b).then(d=>{if(!_.isEqual(d,this._runToTagInfo)){var f=_.mapValues(d,h=>Object.keys(h));f=dc.getTags(f);this.set("_dataNotFound",0===f.length);this.set("_runToTagInfo",d);this.async(()=>{this.set("_categoriesDomReady",!0)})}})},_reloadCharts(){this.root.querySelectorAll("tf-scalar-card").forEach(b=>{b.reload()})},_updateCategories(b,d,f){b=_.mapValues(b,h=>Object.keys(h));
d=kc.categorizeTags(b,d,f);d.forEach(h=>{h.items=h.items.map(k=>({tag:k.tag,series:k.runs.map(r=>({run:r,tag:k.tag}))}))});this.updateArrayProp("_categories",d,this._getCategoryKey)},_tagMetadata(b,d,f){const h=f.tag,k={};f.series.forEach(({run:p})=>{k[p]=d[p][h]});f=h.replace(/\/scalar_summary$/,"");let {description:r,displayName:l}=rf.aggregateTagInfo(k,f);b.metadata.type==kc.CategoryType.PREFIX_GROUP&&l.startsWith(b.name+"/")&&(l=l.slice(b.name.length+1));return{description:r,displayName:l}}});

//# sourceURL=build://tf-custom-scalar-dashboard/tf-custom-scalar-margin-chart-card.html.js
Polymer({is:"tf-custom-scalar-margin-chart-card",properties:{runs:Array,xType:String,active:{type:Boolean,value:!0,readOnly:!0},title:String,marginChartSeries:Array,ignoreYOutliers:Boolean,requestManager:Object,showDownloadLinks:Boolean,tagMetadata:Object,tooltipSortingMethod:String,_colorScale:{type:Object,value:new Ih.DataSeriesColorScale({scale:Ld.runsColorScale}),readOnly:!0},_tagFilter:{type:String,computed:"_computeTagFilter(marginChartSeries)"},_tagFilterInvalid:Boolean,_nameToDataSeries:{type:Object,
value:()=>({})},_seriesNames:{type:Object,computed:"_computeSeriesNames(_nameToDataSeries, runs)"},_expanded:{type:Boolean,value:!1,reflectToAttribute:!0},_logScaleActive:Boolean,_dataUrl:{type:Function,value:function(){return b=>{const d=this._tagFilter;return dc.addParams(dc.getRouter().pluginRoute("custom_scalars","/scalars"),{tag:d,run:b})}}},_runToNextAvailableSymbolIndex:{type:Object,value:{}},_matchesListOpened:{type:Boolean,value:!1},_titleDisplayString:{type:String,computed:"_computeTitleDisplayString(title)"},
_fillArea:{type:Object,readOnly:!0,value:{lowerAccessor:b=>b.lower,higherAccessor:b=>b.upper}},_tooltipColumns:{type:Array,value:function(){const b=sf.multiscaleFormatter(sf.Y_TOOLTIP_FORMATTER_PRECISION),d=f=>isNaN(f)?"NaN":b(f);return[{title:"Name",evaluate:f=>f.dataset.metadata().name},{title:"Value",evaluate:f=>d(f.datum.scalar)},{title:"Lower Margin",evaluate:f=>d(f.datum.lower)},{title:"Upper Margin",evaluate:f=>d(f.datum.upper)},{title:"Step",evaluate:f=>sf.stepFormatter(f.datum.step)},{title:"Time",
evaluate:f=>sf.timeFormatter(f.datum.wall_time)},{title:"Relative",evaluate:f=>sf.relativeFormatter(sf.relativeAccessor(f.datum,-1,f.dataset))}]}},_missingTags:{type:Array,value:[]},_missingTagsCollapsibleOpened:{type:Boolean,value:!1},_stepsMismatch:Object},observers:["_updateChart(_nameToDataSeries)","_refreshDataSeries(_tagFilter)"],reload(){this.$.loader.reload()},redraw(){this.$.loader.redraw()},_toggleExpanded(){this.set("_expanded",!this._expanded);this.redraw()},_toggleLogScale(){this.set("_logScaleActive",
!this._logScaleActive)},_resetDomain(){const b=this.$.loader;b&&b.resetDomain()},_csvUrl(b,d){if(!d)return"";b=this._downloadDataUrl(b,d);return dc.addParams(b,{format:"csv"})},_jsonUrl(b,d){if(!d)return"";b=this._downloadDataUrl(b,d);return dc.addParams(b,{format:"json"})},_downloadDataUrl(b,d){b=b[d];b={tag:b.getTag(),run:b.getRun()};return dc.addParams(dc.getRouter().pluginRoute("custom_scalars","/download_data"),b)},_createProcessDataFunction(b){return(d,f,h)=>{if(h.regex_valid){var k=_.clone(this._nameToDataSeries),
r=[];_.forEach(b,l=>{var p=!1,m=h.tag_to_events[l.value];const n=h.tag_to_events[l.lower],q=h.tag_to_events[l.upper];_.isUndefined(m)&&(r.push(l.value),p=!0);_.isUndefined(n)&&(r.push(l.lower),p=!0);_.isUndefined(q)&&(r.push(l.upper),p=!0);if(!p){var u=y=>y[1];if(p=this._findStepMismatch(l,m.map(u),n.map(u),q.map(u)))this.set("_stepsMismatch",p);else{var w=y=>y[2];p=m.map((y,x)=>({wall_time:new Date(1E3*y[0]),step:u(y),scalar:w(y),lower:w(n[x]),upper:w(q[x])}));m=Ih.generateDataSeriesName(f,l.value);
var A=k[m];A?A.setData(p):(l=this._createNewDataSeries(f,l.value,m,p),k[m]=l)}}});this.set("_nameToDataSeries",k);d=_.findIndex(this._missingTags,l=>l.run===f);if(r.length&&3!=r.length){const l={run:f,tags:r};0<=d?this.splice("_missingTags",d,1,l):this.push("_missingTags",l)}else 0<=d&&this.splice("_missingTags",d,1)}else this.set("_tagFilterInvalid",!0)}},_findStepMismatch(b,d,f,h){return _.isEqual(f,d)&&_.isEqual(h,d)?null:{seriesObject:b,valueSteps:d,lowerSteps:f,upperSteps:h}},_createNewDataSeries(b,
d,f,h){this._runToNextAvailableSymbolIndex[b]|=0;d=new Ih.DataSeries(b,d,f,h,sf.SYMBOLS_LIST[this._runToNextAvailableSymbolIndex[b]]);this._runToNextAvailableSymbolIndex[b]=(this._runToNextAvailableSymbolIndex[b]+1)%sf.SYMBOLS_LIST.length;return d},_updateChart(b){_.forOwn(b,d=>{this.$.loader.setSeriesData(d.getName(),d.getData())});this.$.loader.commitChanges()},_computeSeriesNames(){const b=new Set(this.runs);return Object.entries(this._nameToDataSeries).filter(([,d])=>b.has(d.run)).map(([d])=>
d)},_determineColor(b,d){return b.scale(d)},_refreshDataSeries(){this.set("_nameToDataSeries",{})},_createSymbolFunction(){return b=>this._nameToDataSeries[b].getSymbol().method()},_determineSymbol(b,d){return b[d].getSymbol().character},_computeTagFilter(b){return _.flatten(b.map(d=>[d.value,d.lower,d.upper])).map(d=>"("+this._escapeRegexCharacters(d)+")").join("|")},_escapeRegexCharacters(b){return b.replace(/[.*+?^${}()|[\]\\]/g,"\\$\x26")},_getToggleCollapsibleIcon(b){return b?"expand-less":"expand-more"},
_toggleMatchesOpen(){this.set("_matchesListOpened",!this._matchesListOpened)},_computeTitleDisplayString(b){return b||"untitled"},_separateWithCommas(b){return b.join(", ")},_toggleMissingTagsCollapsibleOpen(){this.set("_missingTagsCollapsibleOpened",!this._missingTagsCollapsibleOpened)},_matchListEntryColorUpdated(){const b=this.$$("#match-list-repeat");b&&this.root.querySelectorAll(".match-list-entry").forEach(d=>{const f=b.itemForElement(d);d.style.color=this._determineColor(this._colorScale,f)})}});

//# sourceURL=build://tf-custom-scalar-dashboard/tf-custom-scalar-multi-line-chart-card.html.js
Polymer({is:"tf-custom-scalar-multi-line-chart-card",properties:{runs:Array,xType:String,active:{type:Boolean,value:!0,readOnly:!0},title:String,tagRegexes:Array,ignoreYOutliers:Boolean,requestManager:Object,showDownloadLinks:Boolean,smoothingEnabled:Boolean,smoothingWeight:Number,tagMetadata:Object,tooltipSortingMethod:String,_colorScale:{type:Object,value:new Ih.DataSeriesColorScale({scale:Ld.runsColorScale}),readOnly:!0},_tagFilter:{type:String,computed:"_computeTagFilter(tagRegexes)"},_nameToDataSeries:{type:Object,
value:()=>({})},_seriesNames:{type:Object,computed:"_computeSeriesNames(_nameToDataSeries, runs)"},_expanded:{type:Boolean,value:!1,reflectToAttribute:!0},_logScaleActive:Boolean,_dataUrl:{type:Function,value:function(){return b=>{const d=this._tagFilter;return dc.addParams(dc.getRouter().pluginRoute("custom_scalars","/scalars"),{tag:d,run:b})}}},_runToNextAvailableSymbolIndex:{type:Object,value:{}},_matchesListOpened:{type:Boolean,value:!1},_titleDisplayString:{type:String,computed:"_computeTitleDisplayString(title)"}},
observers:["_updateChart(_nameToDataSeries)","_refreshDataSeries(_tagFilter)"],reload(){this.$.loader.reload()},redraw(){this.$.loader.redraw()},_toggleExpanded(){this.set("_expanded",!this._expanded);this.redraw()},_toggleLogScale(){this.set("_logScaleActive",!this._logScaleActive)},_resetDomain(){const b=this.$.loader;b&&b.resetDomain()},_csvUrl(b,d){if(!d)return"";b=this._downloadDataUrl(b,d);return dc.addParams(b,{format:"csv"})},_jsonUrl(b,d){if(!d)return"";b=this._downloadDataUrl(b,d);return dc.addParams(b,
{format:"json"})},_downloadDataUrl(b,d){b=b[d];b={tag:b.getTag(),run:b.getRun()};return dc.addParams(dc.getRouter().pluginRoute("custom_scalars","/download_data"),b)},_createProcessDataFunction(){return(b,d,f)=>{if(f.regex_valid){const h=_.clone(this._nameToDataSeries);_.forOwn(f.tag_to_events,(k,r)=>{const l=k.map(m=>({wall_time:new Date(1E3*m[0]),step:m[1],scalar:m[2]}));k=Ih.generateDataSeriesName(d,r);const p=h[k];p?p.setData(l):(_.isUndefined(this._runToNextAvailableSymbolIndex[d])&&(this._runToNextAvailableSymbolIndex[d]=
0),r=new Ih.DataSeries(d,r,k,l,sf.SYMBOLS_LIST[this._runToNextAvailableSymbolIndex[d]]),h[k]=r,this._runToNextAvailableSymbolIndex[d]=(this._runToNextAvailableSymbolIndex[d]+1)%sf.SYMBOLS_LIST.length)});this.set("_nameToDataSeries",h)}}},_updateChart(b){Object.entries(b).forEach(([d,f])=>{this.$.loader.setSeriesData(d,f.getData())});this.$.loader.commitChanges()},_computeSelectedRunsSet(b){const d={};_.forEach(b,f=>{d[f]=1});return d},_computeSeriesNames(){const b=new Set(this.runs);return Object.entries(this._nameToDataSeries).filter(([,
d])=>b.has(d.run)).map(([d])=>d)},_determineColor(b,d){return b.scale(d)},_refreshDataSeries(){this.set("_nameToDataSeries",{})},_createSymbolFunction(){return b=>this._nameToDataSeries[b].getSymbol().method()},_determineSymbol(b,d){return b[d].getSymbol().character},_computeTagFilter(b){return 1===b.length?b[0]:b.map(d=>"("+d+")").join("|")},_getToggleMatchesIcon(b){return b?"expand-less":"expand-more"},_toggleMatchesOpen(){this.set("_matchesListOpened",!this._matchesListOpened)},_computeTitleDisplayString(b){return b||
"untitled"},_matchListEntryColorUpdated(){const b=this.$$("#match-list-repeat");b&&this.root.querySelectorAll(".match-list-entry").forEach(d=>{const f=b.itemForElement(d);d.style.color=this._determineColor(this._colorScale,f)})}});

//# sourceURL=build://tf-custom-scalar-dashboard/tf-custom-scalar-dashboard.html.js
Polymer({is:"tf-custom-scalar-dashboard",properties:{_requestManager:{type:Object,value:()=>new dc.RequestManager(50)},_canceller:{type:Object,value:()=>new dc.Canceller},_selectedRuns:Array,_showDownloadLinks:{type:Boolean,notify:!0,value:bd.getBooleanInitializer("_showDownloadLinks",{defaultValue:!1,useLocalStorage:!0}),observer:"_showDownloadLinksObserver"},_smoothingEnabled:{type:Boolean,computed:"_computeSmoothingEnabled(_smoothingWeight)"},_smoothingWeight:{type:Number,notify:!0,value:bd.getNumberInitializer("_smoothingWeight",
{defaultValue:.6}),observer:"_smoothingWeightObserver"},_ignoreYOutliers:{type:Boolean,value:bd.getBooleanInitializer("_ignoreYOutliers",{defaultValue:!0,useLocalStorage:!0}),observer:"_ignoreYOutliersObserver"},_xType:{type:String,value:"step"},_layout:Object,_dataNotFound:Boolean,_categories:{type:Array,computed:"_makeCategories(_layout)"},_openedCategories:{type:Object},_active:{type:Boolean,value:!0,readOnly:!0}},ready(){this.reload()},reload(){const b=dc.getRouter().pluginsListing(),d=this._canceller.cancellable(f=>
{f.cancelled||(this.set("_dataNotFound",!f.value.custom_scalars),this._dataNotFound||this._retrieveLayoutAndData())});this._requestManager.request(b).then(d)},_reloadCharts(){this.root.querySelectorAll("tf-custom-scalar-margin-chart-card, tf-custom-scalar-multi-line-chart-card").forEach(b=>{b.reload()})},_retrieveLayoutAndData(){const b=dc.getRouter().pluginRoute("custom_scalars","/layout"),d=this._canceller.cancellable(f=>{f.cancelled||(this.set("_layout",f.value),this._dataNotFound||this._reloadCharts())});
this._requestManager.request(b).then(d)},_showDownloadLinksObserver:bd.getBooleanObserver("_showDownloadLinks",{defaultValue:!1,useLocalStorage:!0}),_smoothingWeightObserver:bd.getNumberObserver("_smoothingWeight",{defaultValue:.6}),_ignoreYOutliersObserver:bd.getBooleanObserver("_ignoreYOutliers",{defaultValue:!0,useLocalStorage:!0}),_computeSmoothingEnabled(b){return 0<b},_makeCategories(b){if(!b.category)return[];let d=!1;this._openedCategories||(d=!0,this._openedCategories={});return b.category.map(f=>
{d&&!f.closed&&(this._openedCategories[f.title]=!0);return{name:f.title,items:f.chart,metadata:{opened:!!this._openedCategories[f.title]}}})},_categoryOpenedToggled(b){b=b.target;b.opened?this._openedCategories[b.category.name]=!0:delete this._openedCategories[b.category.name]}});

//# sourceURL=build://tf-image-dashboard/tf-image-loader.html.js
Polymer({is:"tf-image-loader",properties:{run:String,tag:String,sample:Number,ofSamples:Number,tagMetadata:Object,_runColor:{type:String,computed:"_computeRunColor(run)"},actualSize:{type:Boolean,value:!1,reflectToAttribute:!0},brightnessAdjustment:{type:Number,value:.5},contrastPercentage:{type:Number,value:0},requestManager:Object,_metadataCanceller:{type:Object,value:()=>new dc.Canceller},_imageCanceller:{type:Object,value:()=>new dc.Canceller},_steps:{type:Array,value:[],notify:!0},_stepIndex:{type:Number,
notify:!0},_currentStep:{type:Object,computed:"_computeCurrentStep(_steps, _stepIndex)"},_hasAtLeastOneStep:{type:Boolean,computed:"_computeHasAtLeastOneStep(_steps)"},_hasMultipleSteps:{type:Boolean,computed:"_computeHasMultipleSteps(_steps)"},_stepValue:{type:Number,computed:"_computeStepValue(_currentStep)"},_currentWallTime:{type:String,computed:"_computeCurrentWallTime(_currentStep)"},_maxStepIndex:{type:Number,computed:"_computeMaxStepIndex(_steps)"},_sampleText:{type:String,computed:"_computeSampleText(sample)"},
_hasMultipleSamples:{type:Boolean,computed:"_computeHasMultipleSamples(ofSamples)"},_isImageLoading:{type:Boolean,value:!1}},observers:["reload(run, tag)","_updateImageUrl(_currentStep, brightnessAdjustment, contrastPercentage)"],_computeRunColor(b){return Ld.runsColorScale(b)},_computeHasAtLeastOneStep(b){return!!b&&0<b.length},_computeHasMultipleSteps(b){return!!b&&1<b.length},_computeCurrentStep(b,d){return b[d]||null},_computeStepValue(b){return b?b.step:0},_computeCurrentWallTime(b){return b?
Hh.formatDate(b.wall_time):""},_computeMaxStepIndex(b){return b.length-1},_computeSampleText(b){return`${b+1}`},_computeHasMultipleSamples(b){return 1<b},_getAriaExpanded(){return this.actualSize?"true":"false"},attached(){this._attached=!0;this.reload()},reload(){if(this._attached){this._metadataCanceller.cancelAll();var b=dc.addParams(dc.getRouter().pluginRoute("images","/images"),{tag:this.tag,run:this.run,sample:this.sample}),d=this._metadataCanceller.cancellable(f=>{f.cancelled||(f=f.value.map(this._createStepDatum.bind(this)),
this.set("_steps",f),this.set("_stepIndex",f.length-1))});this.requestManager.request(b).then(d)}},_createStepDatum(b){let d=dc.getRouter().pluginRoute("images","/individualImage");d=dc.addParams(d,{ts:b.wall_time});d+="\x26"+b.query;return{wall_time:new Date(1E3*b.wall_time),step:b.step,url:d}},_updateImageUrl(b,d,f){if(b){var h=new Image;this._imageCanceller.cancelAll();h.onload=h.onerror=this._imageCanceller.cancellable(k=>{k.cancelled||(k=this.$$("#main-image-container"),k.innerHTML="",Polymer.dom(k).appendChild(h),
this.set("_isImageLoading",!1))}).bind(this);h.style.filter=`contrast(${f}%) `;h.style.filter+=`brightness(${d})`;this.set("_isImageLoading",!0);h.src=b.url}},_handleTap(){this.set("actualSize",!this.actualSize)},_toLocaleString(b){return b.toLocaleString()}});

//# sourceURL=build://tf-image-dashboard/tf-image-dashboard.html.js
Polymer({is:"tf-image-dashboard",properties:{_selectedRuns:Array,_runToTagInfo:Object,_dataNotFound:Boolean,_actualSize:Boolean,_defaultBrightnessAdjustment:{type:Number,value:1,readOnly:!0},_defaultContrastPercentage:{type:Number,value:100,readOnly:!0},_brightnessAdjustment:{type:Number,value:1},_contrastPercentage:{type:Number,value:100},_tagFilter:String,_brightnessIsDefault:{type:Boolean,computed:"_computeBrightnessIsDefault(_brightnessAdjustment)"},_contrastIsDefault:{type:Boolean,computed:"_computeContrastIsDefault(_contrastPercentage)"},
_categoriesDomReady:Boolean,_categories:{type:Array,computed:"_makeCategories(_runToTagInfo, _selectedRuns, _tagFilter, _categoriesDomReady)"},_requestManager:{type:Object,value:()=>new dc.RequestManager}},ready(){this.reload()},reload(){this._fetchTags().then(()=>{this._reloadImages()})},_fetchTags(){const b=dc.getRouter().pluginRoute("images","/tags");return this._requestManager.request(b).then(d=>{if(!_.isEqual(d,this._runToTagInfo)){var f=_.mapValues(d,h=>Object.keys(h));f=dc.getTags(f);this.set("_dataNotFound",
0===f.length);this.set("_runToTagInfo",d);this.async(()=>{this.set("_categoriesDomReady",!0)})}})},_reloadImages(){this.root.querySelectorAll("tf-image-loader").forEach(b=>{b.reload()})},_shouldOpen(b){return 2>=b},_resetBrightness(){this._brightnessAdjustment=this._defaultBrightnessAdjustment},_resetContrast(){this._contrastPercentage=this._defaultContrastPercentage},_computeBrightnessIsDefault(b){return b===this._defaultBrightnessAdjustment},_computeContrastIsDefault(b){return b===this._defaultContrastPercentage},
_makeCategories(b,d,f){function h(r){const l=b[r.run][r.tag].samples;return _.range(l).map(p=>Object.assign({},r,{sample:p,ofSamples:l}))}const k=_.mapValues(b,r=>Object.keys(r));return kc.categorizeRunTagCombinations(k,d,f).map(r=>Object.assign({},r,{items:[].concat.apply([],r.items.map(h))}))},_tagMetadata(b,d,f){return b[d][f]}});

//# sourceURL=build://tf-audio-dashboard/tf-audio-loader.html.js
Polymer({is:"tf-audio-loader",properties:{run:String,tag:String,sample:Number,totalSamples:Number,tagMetadata:Object,_runColor:{type:String,computed:"_computeRunColor(run)"},requestManager:Object,_metadataCanceller:{type:Object,value:()=>new dc.Canceller},_steps:{type:Array,value:()=>[]},_stepIndex:Number,_hasAtLeastOneStep:{type:Boolean,computed:"_computeHasAtLeastOneStep(_steps)"},_hasMultipleSteps:{type:Boolean,computed:"_computeHasMultipleSteps(_steps)"},_currentDatum:{type:Object,computed:"_computeCurrentDatum(_steps, _stepIndex)"},
_maxStepIndex:{type:Number,computed:"_computeMaxStepIndex(_steps)"},_sampleText:{type:String,computed:"_computeSampleText(sample)"},_hasMultipleSamples:{type:Boolean,computed:"_computeHasMultipleSamples(totalSamples)"}},observers:["reload(run, tag)"],_computeRunColor(b){return Ld.runsColorScale(b)},_computeHasAtLeastOneStep(b){return!!b&&0<b.length},_computeHasMultipleSteps(b){return!!b&&1<b.length},_computeMaxStepIndex(b){return b.length-1},_computeCurrentDatum(b,d){return b[d]},_computeSampleText(b){return`${b+
1}`},_computeHasMultipleSamples(b){return 1<b},attached(){this._attached=!0;this.reload()},reload(){if(this._attached){this._metadataCanceller.cancelAll();var b=dc.getRouter().pluginRoute("audio","/audio",new URLSearchParams({tag:this.tag,run:this.run,sample:this.sample})),d=this._metadataCanceller.cancellable(f=>{f.cancelled||(f=f.value.map(this._createStepDatum.bind(this)),this.set("_steps",f),this.set("_stepIndex",f.length-1))});this.requestManager.request(b).then(d)}},_createStepDatum(b){var d=
new URLSearchParams(b.query);d.append("ts",b.wall_time);d=dc.getRouter().pluginRoute("audio","/individualAudio",d);return{wall_time:Hh.formatDate(new Date(1E3*b.wall_time)),step:b.step,label:b.label,contentType:b.contentType,url:d}}});

//# sourceURL=build://tf-audio-dashboard/tf-audio-dashboard.html.js
Polymer({is:"tf-audio-dashboard",properties:{_selectedRuns:Array,_runToTagInfo:Object,_dataNotFound:Boolean,_tagFilter:{type:String,value:""},_categories:{type:Array,computed:"_makeCategories(_runToTagInfo, _selectedRuns, _tagFilter)"},_requestManager:{type:Object,value:()=>new dc.RequestManager}},ready(){this.reload()},reload(){this._fetchTags().then(()=>{this._reloadAudio()})},_fetchTags(){const b=dc.getRouter().pluginRoute("audio","/tags");return this._requestManager.request(b).then(d=>{if(!_.isEqual(d,
this._runToTagInfo)){var f=_.mapValues(d,h=>Object.keys(h));f=dc.getTags(f);this.set("_dataNotFound",0===f.length);this.set("_runToTagInfo",d)}})},_reloadAudio(){this.root.querySelectorAll("tf-audio-loader").forEach(b=>{b.reload()})},_shouldOpen(b){return 2>=b},_makeCategories(b,d,f){function h(r){const l=b[r.run][r.tag].samples;return _.range(l).map(p=>Object.assign({},r,{sample:p,totalSamples:l}))}const k=_.mapValues(b,r=>Object.keys(r));return kc.categorizeRunTagCombinations(k,d,f).map(r=>Object.assign({},
r,{items:[].concat.apply([],r.items.map(h))}))},_tagMetadata(b,d,f){return b[d][f]}});

//# sourceURL=build://iron-autogrow-textarea/iron-autogrow-textarea.html.js
Polymer({is:"iron-autogrow-textarea",behaviors:[Polymer.IronValidatableBehavior,Polymer.IronControlState],properties:{value:{observer:"_valueChanged",type:String,notify:!0},bindValue:{observer:"_bindValueChanged",type:String,notify:!0},rows:{type:Number,value:1,observer:"_updateCached"},maxRows:{type:Number,value:0,observer:"_updateCached"},autocomplete:{type:String,value:"off"},autofocus:{type:Boolean,value:!1},inputmode:{type:String},placeholder:{type:String},readonly:{type:String},required:{type:Boolean},
minlength:{type:Number},maxlength:{type:Number},label:{type:String}},listeners:{input:"_onInput"},get textarea(){return this.$.textarea},get selectionStart(){return this.$.textarea.selectionStart},get selectionEnd(){return this.$.textarea.selectionEnd},set selectionStart(b){this.$.textarea.selectionStart=b},set selectionEnd(b){this.$.textarea.selectionEnd=b},attached:function(){navigator.userAgent.match(/iP(?:[oa]d|hone)/)&&(this.$.textarea.style.marginLeft="-3px")},validate:function(){var b=this.$.textarea.validity.valid;
b&&(this.required&&""===this.value?b=!1:this.hasValidator()&&(b=Polymer.IronValidatableBehavior.validate.call(this,this.value)));this.invalid=!b;this.fire("iron-input-validate");return b},_bindValueChanged:function(b){this.value=b},_valueChanged:function(b){var d=this.textarea;d&&(d.value!==b&&(d.value=b||0===b?b:""),this.bindValue=b,this.$.mirror.innerHTML=this._valueForMirror(),this.fire("bind-value-changed",{value:this.bindValue}))},_onInput:function(b){var d=Polymer.dom(b).path;this.value=d?d[0].value:
b.target.value},_constrain:function(b){b=b||[""];for(b=0<this.maxRows&&b.length>this.maxRows?b.slice(0,this.maxRows):b.slice(0);0<this.rows&&b.length<this.rows;)b.push("");return b.join("\x3cbr/\x3e")+"\x26#160;"},_valueForMirror:function(){var b=this.textarea;if(b)return this.tokens=b&&b.value?b.value.replace(/&/gm,"\x26amp;").replace(/"/gm,"\x26quot;").replace(/'/gm,"\x26#39;").replace(/</gm,"\x26lt;").replace(/>/gm,"\x26gt;").split("\n"):[""],this._constrain(this.tokens)},_updateCached:function(){this.$.mirror.innerHTML=
this._constrain(this.tokens)}});

//# sourceURL=build://paper-input/paper-textarea.html.js
Polymer({is:"paper-textarea",behaviors:[Polymer.PaperInputBehavior,Polymer.IronFormElementBehavior],properties:{_ariaLabelledBy:{observer:"_ariaLabelledByChanged",type:String},_ariaDescribedBy:{observer:"_ariaDescribedByChanged",type:String},value:{type:String},rows:{type:Number,value:1},maxRows:{type:Number,value:0}},get selectionStart(){return this.$.input.textarea.selectionStart},set selectionStart(b){this.$.input.textarea.selectionStart=b},get selectionEnd(){return this.$.input.textarea.selectionEnd},
set selectionEnd(b){this.$.input.textarea.selectionEnd=b},_ariaLabelledByChanged:function(b){this._focusableElement.setAttribute("aria-labelledby",b)},_ariaDescribedByChanged:function(b){this._focusableElement.setAttribute("aria-describedby",b)},get _focusableElement(){return this.inputElement.textarea}});

//# sourceURL=build://paper-toast/paper-toast.html.js
(function(){var b=null;Polymer({is:"paper-toast",behaviors:[Polymer.IronOverlayBehavior],properties:{fitInto:{type:Object,value:window,observer:"_onFitIntoChanged"},horizontalAlign:{type:String,value:"left"},verticalAlign:{type:String,value:"bottom"},duration:{type:Number,value:3E3},text:{type:String,value:""},noCancelOnOutsideClick:{type:Boolean,value:!0},noAutoFocus:{type:Boolean,value:!0}},listeners:{transitionend:"__onTransitionEnd"},get visible(){Polymer.Base._warn("`visible` is deprecated, use `opened` instead");
return this.opened},get _canAutoClose(){return 0<this.duration&&Infinity!==this.duration},created:function(){this._autoClose=null;Polymer.IronA11yAnnouncer.requestAvailability()},show:function(d){"string"==typeof d&&(d={text:d});for(var f in d)0===f.indexOf("_")?Polymer.Base._warn('The property "'+f+'" is private and was not set.'):f in this?this[f]=d[f]:Polymer.Base._warn('The property "'+f+'" is not valid.');this.open()},hide:function(){this.close()},__onTransitionEnd:function(d){d&&d.target===
this&&"opacity"===d.propertyName&&(this.opened?this._finishRenderOpened():this._finishRenderClosed())},_openedChanged:function(){null!==this._autoClose&&(this.cancelAsync(this._autoClose),this._autoClose=null);this.opened?(b&&b!==this&&b.close(),b=this,this.fire("iron-announce",{text:this.text}),this._canAutoClose&&(this._autoClose=this.async(this.close,this.duration))):b===this&&(b=null);Polymer.IronOverlayBehaviorImpl._openedChanged.apply(this,arguments)},_renderOpened:function(){this.classList.add("paper-toast-open")},
_renderClosed:function(){this.classList.remove("paper-toast-open")},_onFitIntoChanged:function(d){this.positionTarget=d}})})();

//# sourceURL=build://paper-toggle-button/paper-toggle-button.html.js
Polymer({is:"paper-toggle-button",behaviors:[Polymer.PaperCheckedElementBehavior],hostAttributes:{role:"button","aria-pressed":"false",tabindex:0},properties:{},listeners:{track:"_ontrack"},attached:function(){Polymer.RenderStatus.afterNextRender(this,function(){Polymer.Gestures.setTouchAction(this,"pan-y")})},_ontrack:function(b){b=b.detail;"start"===b.state?this._trackStart(b):"track"===b.state?this._trackMove(b):"end"===b.state&&this._trackEnd(b)},_trackStart:function(){this._width=this.$.toggleBar.offsetWidth/
2;this._trackChecked=this.checked;this.$.toggleButton.classList.add("dragging")},_trackMove:function(b){b=b.dx;this._x=Math.min(this._width,Math.max(0,this._trackChecked?this._width+b:b));this.translate3d(this._x+"px",0,0,this.$.toggleButton);this._userActivate(this._x>this._width/2)},_trackEnd:function(){this.$.toggleButton.classList.remove("dragging");this.transform("",this.$.toggleButton)},_createRipple:function(){this._rippleContainer=this.$.toggleButton;var b=Polymer.PaperRippleBehavior._createRipple();
b.id="ink";b.setAttribute("recenters","");b.classList.add("circle","toggle-ink");return b}});

//# sourceURL=build://tf-graph/tf-graph-minimap.html.js
Polymer({is:"tf-graph-minimap",init:function(b,d,f,h,k){return new tf.scene.Minimap(b,d,f,this,h,k)}});

//# sourceURL=build://tf-graph/tf-graph-scene.html.js
Polymer({is:"tf-graph-scene",properties:{renderHierarchy:Object,name:String,colorBy:String,traceInputs:Boolean,_hasRenderHierarchyBeenFitOnce:Boolean,_isAttached:Boolean,_zoom:Object,highlightedNode:{type:String,observer:"_highlightedNodeChanged"},selectedNode:{type:String,observer:"_selectedNodeChanged"},handleEdgeSelected:Object,_zoomed:{type:Boolean,observer:"_onZoomChanged",value:!1},_zoomStartCoords:{type:Object,value:null},_zoomTransform:{type:Object,value:null},_maxZoomDistanceForClick:{type:Number,
value:20},templateIndex:Function,minimap:Object,_nodeGroupIndex:{type:Object,value:function(){return{}}},_annotationGroupIndex:{type:Object,value:function(){return{}}},_edgeGroupIndex:{type:Object,value:function(){return{}}},maxMetanodeLabelLengthFontSize:{type:Number,value:9},minMetanodeLabelLengthFontSize:{type:Number,value:6},maxMetanodeLabelLengthLargeFont:{type:Number,value:11},maxMetanodeLabelLength:{type:Number,value:18},progress:Object,nodeContextMenuItems:Array,nodeNamesToHealthPills:Object,
healthPillStepIndex:Number},observers:["_colorByChanged(colorBy)","_renderHierarchyChanged(renderHierarchy)","_animateAndFit(_isAttached, renderHierarchy)","_updateHealthPills(nodeNamesToHealthPills, healthPillStepIndex)","_updateInputTrace(traceInputs, selectedNode)"],getNode:function(b){return this.renderHierarchy.getRenderNodeByName(b)},isNodeExpanded:function(b){return b.expanded},setNodeExpanded:function(){this._build(this.renderHierarchy);this._updateLabels(!this._zoomed)},panToNode(b){tf.graph.scene.panToNode(b,
this.$.svg,this.$.root,this._zoom)&&(this._zoomed=!0)},getGraphSvgRoot(){return this.$.svg},getContextMenu(){return this.$.contextMenu},_resetState:function(){this._nodeGroupIndex={};this._annotationGroupIndex={};this._edgeGroupIndex={};this._updateLabels(!1);d3.select(this.$.svg).select("#root").selectAll("*").remove();tf.graph.scene.node.removeGradientDefinitions(this.$.svg)},_build:function(b){this.templateIndex=b.hierarchy.getTemplateIndex();tf.graph.util.time("tf-graph-scene (layout):",function(){tf.graph.layout.layoutScene(b.root,
this)}.bind(this));tf.graph.util.time("tf-graph-scene (build scene):",function(){tf.graph.scene.buildGroup(d3.select(this.$.root),b.root,this);tf.graph.scene.addGraphClickListener(this.$.svg,this);this._updateInputTrace()}.bind(this));setTimeout(function(){this._updateHealthPills(this.nodeNamesToHealthPills,this.healthPillStepIndex);this.minimap.update()}.bind(this),tf.graph.layout.PARAMS.animation.duration)},ready:function(){this._zoom=d3.zoom().on("end",function(){this._zoomStartCoords&&(Math.sqrt(Math.pow(this._zoomStartCoords.x-
this._zoomTransform.x,2)+Math.pow(this._zoomStartCoords.y-this._zoomTransform.y,2))<this._maxZoomDistanceForClick?this._fireEnableClick():setTimeout(this._fireEnableClick.bind(this),50));this._zoomStartCoords=null}.bind(this)).on("zoom",function(){this._zoomTransform=d3.event.transform;this._zoomStartCoords||(this._zoomStartCoords=this._zoomTransform,this.fire("disable-click"));this._zoomed=!0;d3.select(this.$.root).attr("transform",d3.event.transform);this.minimap.zoom(d3.event.transform)}.bind(this));
d3.select(this.$.svg).call(this._zoom).on("dblclick.zoom",null);d3.select(window).on("resize",function(){this.minimap.zoom()}.bind(this));this.minimap=this.$.minimap.init(this.$.svg,this.$.root,this._zoom,tf.graph.layout.PARAMS.minimap.size,tf.graph.layout.PARAMS.subscene.meta.labelHeight)},attached:function(){this.set("_isAttached",!0)},detached:function(){this.set("_isAttached",!1)},_renderHierarchyChanged:function(b){this._hasRenderHierarchyBeenFitOnce=!1;this._resetState();this._build(b)},_animateAndFit:function(b){!this._hasRenderHierarchyBeenFitOnce&&
b&&setTimeout(this.fit.bind(this),tf.graph.layout.PARAMS.animation.duration)},_updateLabels:function(b){var d=this.$$(".title"),f=d.style,h=this.$$(".auxTitle"),k=h.style,r=this.$$(".functionLibraryTitle").style;const l=d3.select(this.$.svg);var p=l.select("."+tf.graph.scene.Class.Scene.GROUP+"\x3e."+tf.graph.scene.Class.Scene.CORE).node();if(b&&p&&this.progress&&100===this.progress.value){b=l.select("."+tf.graph.scene.Class.Scene.GROUP+"\x3e."+tf.graph.scene.Class.Scene.INEXTRACT).node()||l.select("."+
tf.graph.scene.Class.Scene.GROUP+"\x3e."+tf.graph.scene.Class.Scene.OUTEXTRACT).node();var m=p.getCTM().e;p=b?b.getCTM().e:null;f.display="inline";f.left=m+"px";null!==p&&p!==m?(k.display="inline",p=Math.max(m+d.getBoundingClientRect().width,p),k.left=p+"px"):k.display="none";d=(d=l.select("."+tf.graph.scene.Class.Scene.GROUP+"\x3e."+tf.graph.scene.Class.Scene.FUNCTION_LIBRARY).node())?d.getCTM().e:null;null!==d&&d!==p?(r.display="inline",d=Math.max(p+h.getBoundingClientRect().width,d),r.left=d+"px"):
r.display="none"}else f.display="none",k.display="none",r.display="none"},_colorByChanged:function(){null!=this.renderHierarchy&&(_.each(this._nodeGroupIndex,(b,d)=>{this._updateNodeState(d)}),this.minimap.update())},fit:function(){this._hasRenderHierarchyBeenFitOnce=!0;tf.graph.scene.fit(this.$.svg,this.$.root,this._zoom,function(){this._zoomed=!1}.bind(this))},isNodeSelected:function(b){return b===this.selectedNode},isNodeHighlighted:function(b){return b===this.highlightedNode},addAnnotationGroup:function(b,
d,f){b=b.node.name;this._annotationGroupIndex[b]=this._annotationGroupIndex[b]||{};this._annotationGroupIndex[b][d.node.name]=f},getAnnotationGroupsIndex:function(b){return this._annotationGroupIndex[b]},removeAnnotationGroup:function(b,d){delete this._annotationGroupIndex[b.node.name][d.node.name]},addNodeGroup:function(b,d){this._nodeGroupIndex[b]=d},getNodeGroup:function(b){return this._nodeGroupIndex[b]},removeNodeGroup:function(b){delete this._nodeGroupIndex[b]},addEdgeGroup:function(b,d){this._edgeGroupIndex[b]=
d},getEdgeGroup:function(b){return this._edgeGroupIndex[b]},_updateHealthPills:function(b,d){tf.graph.scene.addHealthPills(this.$.svg,b,d)},_updateNodeState:function(b){var d=this.getNode(b),f=this.getNodeGroup(b);f&&tf.graph.scene.node.stylize(f,d,this);d.node.type===tf.graph.NodeType.META&&d.node.associatedFunction&&!d.isLibraryFunction&&(f=d3.select("."+tf.graph.scene.Class.Scene.GROUP+"\x3e."+tf.graph.scene.Class.Scene.FUNCTION_LIBRARY+' g[data-name\x3d"'+(tf.graph.FUNCTION_LIBRARY_NODE_PREFIX+
d.node.associatedFunction)+'"]'),tf.graph.scene.node.stylize(f,d,this));_.each(this.getAnnotationGroupsIndex(b),h=>{tf.graph.scene.node.stylize(h,d,this,tf.graph.scene.Class.Annotation.NODE)})},_selectedNodeChanged:function(b,d){if(b!==d&&(d&&this._updateNodeState(d),b)){this.minimap.update();d=this.renderHierarchy.hierarchy.node(b);for(var f=[];null!=d.parentNode&&d.parentNode.name!=tf.graph.ROOT_NAME;)d=d.parentNode,f.push(d.name);var h;_.forEachRight(f,k=>{this.renderHierarchy.buildSubhierarchy(k);
k=this.renderHierarchy.getRenderNodeByName(k);k.node.isGroupNode&&!k.expanded&&(k.expanded=!0,h||(h=k))});h&&(this.setNodeExpanded(h),this._zoomed=!0);b&&this._updateNodeState(b);setTimeout(()=>{this.panToNode(b)},tf.graph.layout.PARAMS.animation.duration)}},_highlightedNodeChanged:function(b,d){b!==d&&(b&&this._updateNodeState(b),d&&this._updateNodeState(d))},_onZoomChanged:function(){this._updateLabels(!this._zoomed)},_fireEnableClick:function(){this.fire("enable-click")},_updateInputTrace:function(){tf.graph.scene.node.updateInputTrace(this.getGraphSvgRoot(),
this.renderHierarchy,this.selectedNode,this.traceInputs)}});

//# sourceURL=build://tf-graph/tf-graph.html.js
Polymer({is:"tf-graph",properties:{graphHierarchy:{type:Object,notify:!0,observer:"_graphChanged"},basicGraph:Object,stats:Object,devicesForStats:Object,hierarchyParams:Object,progress:{type:Object,notify:!0},title:String,selectedNode:{type:String,notify:!0},selectedEdge:{type:Object,notify:!0},_lastSelectedEdgeGroup:Object,highlightedNode:{type:String,notify:!0},colorBy:String,colorByParams:{type:Object,notify:!0,readOnly:!0},renderHierarchy:{type:Object,readOnly:!0,notify:!0},traceInputs:Boolean,
nodeContextMenuItems:Array,_renderDepth:{type:Number,value:1},_allowGraphSelect:{type:Boolean,value:!0},nodeNamesToHealthPills:Object,healthPillStepIndex:Number,edgeWidthFunction:{type:Object,value:""},handleNodeSelected:{type:Object,value:""},edgeLabelFunction:{type:Object,value:""},handleEdgeSelected:{type:Object,value:""}},observers:["_statsChanged(stats, devicesForStats)","_buildNewRenderHierarchy(graphHierarchy, edgeWidthFunction, handleNodeSelected, edgeLabelFunction, handleEdgeSelected)","_selectedNodeChanged(selectedNode)",
"_selectedEdgeChanged(selectedEdge)"],panToNode(b){this.$$("tf-graph-scene").panToNode(b)},_buildNewRenderHierarchy(b){b&&this._buildRenderHierarchy(b)},_statsChanged:function(b,d){this.graphHierarchy&&(b&&d&&(tf.graph.joinStatsInfoWithGraph(this.basicGraph,b,d),tf.graph.hierarchy.joinAndAggregateStats(this.graphHierarchy)),this._buildRenderHierarchy(this.graphHierarchy))},_buildRenderHierarchy:function(b){tf.graph.util.time("new tf.graph.render.Hierarchy",function(){function d(h){return{minValue:h.domain()[0],
maxValue:h.domain()[1],startColor:h.range()[0],endColor:h.range()[1]}}if(b.root.type===tf.graph.NodeType.META){var f=new tf.graph.render.RenderGraphInfo(b,!!this.stats);f.edgeLabelFunction=this.edgeLabelFunction;f.edgeWidthFunction=this.edgeWidthFunction;this._setColorByParams({compute_time:d(f.computeTimeScale),memory:d(f.memoryUsageScale),device:_.map(f.deviceColorMap.domain(),function(h){return{device:h,color:f.deviceColorMap(h)}}),xla_cluster:_.map(f.xlaClusterColorMap.domain(),function(h){return{xla_cluster:h,
color:f.xlaClusterColorMap(h)}})});this._setRenderHierarchy(f);this.async(function(){this.fire("rendered")})}}.bind(this))},_getVisible:function(b){return b?this.renderHierarchy.getNearestVisibleAncestor(b):b},listeners:{"graph-select":"_graphSelected","disable-click":"_disableClick","enable-click":"_enableClick","node-toggle-expand":"_nodeToggleExpand","node-select":"_nodeSelected","node-highlight":"_nodeHighlighted","node-unhighlight":"_nodeUnhighlighted","node-toggle-extract":"_nodeToggleExtract",
"node-toggle-seriesgroup":"_nodeToggleSeriesGroup","edge-select":"_edgeSelected","annotation-select":"_nodeSelected","annotation-highlight":"_nodeHighlighted","annotation-unhighlight":"_nodeUnhighlighted"},fit:function(){this.$.scene.fit()},_graphChanged:function(){this.fire("graph-select")},_graphSelected:function(){this._allowGraphSelect&&(this.set("selectedNode",null),this.set("selectedEdge",null));this._allowGraphSelect=!0},_disableClick:function(){this._allowGraphSelect=!1},_enableClick:function(){this._allowGraphSelect=
!0},_selectedNodeChanged(b){this.handleNodeSelected&&this.handleNodeSelected(b)},_selectedEdgeChanged(b){this._deselectPreviousEdge();b&&(this._lastSelectedEdgeGroup.classed(tf.graph.scene.Class.Edge.SELECTED,!0),this._updateMarkerOfSelectedEdge(b));this.handleEdgeSelected&&this.handleEdgeSelected(b)},_nodeSelected:function(b){this._allowGraphSelect&&this.set("selectedNode",b.detail.name);this._allowGraphSelect=!0},_edgeSelected(b){this._allowGraphSelect&&(this.set("_lastSelectedEdgeGroup",b.detail.edgeGroup),
this.set("selectedEdge",b.detail.edgeData));this._allowGraphSelect=!0},_nodeHighlighted:function(b){this.set("highlightedNode",b.detail.name)},_nodeUnhighlighted:function(){this.set("highlightedNode",null)},_nodeToggleExpand:function(b){this._nodeSelected(b);b=b.detail.name;var d=this.renderHierarchy.getRenderNodeByName(b);d.node.type!==tf.graph.NodeType.OP&&(this.renderHierarchy.buildSubhierarchy(b),d.expanded=!d.expanded,this.async(function(){this.$.scene.setNodeExpanded(d)},75))},_nodeToggleExtract:function(b){this.nodeToggleExtract(b.detail.name)},
nodeToggleExtract:function(b){b=this.renderHierarchy.getRenderNodeByName(b);b.node.include=b.node.include==tf.graph.InclusionType.INCLUDE?tf.graph.InclusionType.EXCLUDE:b.node.include==tf.graph.InclusionType.EXCLUDE?tf.graph.InclusionType.INCLUDE:this.renderHierarchy.isNodeAuxiliary(b)?tf.graph.InclusionType.INCLUDE:tf.graph.InclusionType.EXCLUDE;this._buildRenderHierarchy(this.graphHierarchy)},_nodeToggleSeriesGroup:function(b){this.nodeToggleSeriesGroup(b.detail.name)},nodeToggleSeriesGroup:function(b){tf.graph.toggleNodeSeriesGroup(this.hierarchyParams.seriesMap,
b);this.set("progress",{value:0,msg:""});tf.graph.hierarchy.build(this.basicGraph,this.hierarchyParams,tf.graph.util.getSubtaskTracker(tf.graph.util.getTracker(this),100,"Namespace hierarchy")).then(function(d){this.set("graphHierarchy",d);this._buildRenderHierarchy(this.graphHierarchy)}.bind(this))},_deselectPreviousEdge(){d3.select("."+tf.graph.scene.Class.Edge.SELECTED).classed(tf.graph.scene.Class.Edge.SELECTED,!1).each(b=>{if(b.label){const d=d3.select(this).selectAll("path.edgeline");b.label.startMarkerId&&
d.style("marker-start",`url(#${b.label.startMarkerId})`);b.label.endMarkerId&&d.style("marker-end",`url(#${b.label.endMarkerId})`)}})},_updateMarkerOfSelectedEdge(b){if(b.label){var d=b.label.startMarkerId||b.label.endMarkerId;if(d){const f=d.replace("dataflow-","selected-");let h=this.$$("#"+f);h||(d=this.$.scene.querySelector("#"+d),h=d.cloneNode(!0),h.setAttribute("id",f),h.classList.add("selected-arrowhead"),d.parentNode.appendChild(h));b=b.label.startMarkerId?"marker-start":"marker-end";this._lastSelectedEdgeGroup.selectAll("path.edgeline").style(b,
`url(#${f})`)}}},not:function(b){return!b}});

//# sourceURL=build://tf-debugger-dashboard/tf-debugger-continue-dialog.html.js
Polymer({is:"tf-debugger-continue-dialog",properties:{continueNum:{type:Number,value:5},sessionRunGo:Function,tensorConditionGo:Function,forceContinuationStop:Function,_continueButtonText:{type:String,value:"Continue..."},_continueButtonContinueText:{type:String,value:"Continue...",readonly:!0},_continueButtonStopText:{type:String,value:"Stop Continuation",readonly:!0},_selectedTensorCondition:String,_tensorConditionRefValue:{type:Number,value:0,notify:!0},_isRefValueInputHidden:{type:Boolean,value:!0,
notify:!0}},observers:["_onSelectedTensorConditionChanged(_selectedTensorCondition)"],notifyContinuationStop(){this.updateContinueButtonText(!1)},_openDialog(){this.$.continueDialog.open()},_closeDialog(){this.$.continueDialog.close()},_continueButtonCallback(){this._continueButtonText===this._continueButtonStopText?this.forceContinuationStop():this._openDialog()},updateContinueButtonText(b){this.set("_continueButtonText",b?this._continueButtonStopText:this._continueButtonContinueText)},_sessionRunGoButtonCallback(){0<
this.continueNum?(this.sessionRunGo(this.continueNum),this.updateContinueButtonText(!0),this._closeDialog()):this.set("continueNum",1)},_tensorContinueGoButtonCallback(){if(null!=this._selectedTensorCondition){var b=Kh.tensorConditionDescription2Key(this._selectedTensorCondition);null==b&&console.error("Invalid Tensor Condition name:"+this._selectedTensorCondition);var d=Number(this._tensorConditionRefValue);Number.isFinite(d)?(this.tensorConditionGo(b,d),this.updateContinueButtonText(!0),this._closeDialog()):
this.set("_tensorConditionRefValue",0)}},_onSelectedTensorConditionChanged(b){b=Kh.tensorConditionDescription2Key(b);this.set("_isRefValueInputHidden",-1!==["INF_OR_NAN","INF","NAN"].indexOf(b))}});

//# sourceURL=build://tf-debugger-dashboard/tf-debugger-initial-dialog.html.js
Polymer({is:"tf-debugger-initial-dialog",properties:{_title:{type:String,value:null},_customMessage:{type:String,value:null},_hasCustomMessage:{type:Boolean,computed:"_computeHasCustomMessage(_customMessage)"},_host:{type:String,value:null},_port:{type:String,value:null},_open:{type:Boolean},_hidden:{type:Boolean,computed:"_computeHidden(_open)",reflectToAttribute:!0}},openDialog(b,d){this.set("_title","Debugger is waiting for Session.run() connections...");this.set("_customMessage",null);this.$.dialog.open();
null!=b&&null!=d&&(this.set("_host",b),this.set("_port",d))},closeDialog(){this.$.dialog.close()},openDisabledDialog(){this.set("_title","Debugger is not enabled in this TensorBoard instance");this.set("_customMessage","To enable the debugger in TensorBoard, use the flag: --debugger_port \x3cport_number\x3e");this.$.dialog.open()},_computeHidden(b){return!b},_computeHasCustomMessage(b){return!_.isEmpty(b)}});

//# sourceURL=build://tf-debugger-dashboard/tf-debugger-resizer.html.js
Polymer({is:"tf-debugger-resizer",properties:{currentLength:{type:Number,notify:!0},minLength:Number,maxLength:Number,isHorizontal:{type:Boolean,value:!1,reflectToAttribute:!0},_resizerIdentifier:{type:Boolean,value:!0,readOnly:!0,reflectToAttribute:!0},_isVertical:{type:Boolean,computed:"_computeIsVertical(isHorizontal)",reflectToAttribute:!0,readOnly:!0},_dragStartPosition:Number,_dragStartLength:Number,_previousMouseMoveCallback:Object,_previousMouseUpCallback:Object},listeners:{mousedown:"_handleMouseDown"},
_handleMouseDown(b){b.preventDefault();this._endDrag();this._previousMouseMoveCallback=d=>{d.preventDefault();d=this._dragStartLength+(this._getPositionRelativeToViewport(d)-this._dragStartPosition);d=Math.max(d,this.minLength);d=Math.min(d,this.maxLength);this.set("currentLength",d)};this._previousMouseUpCallback=d=>{d.preventDefault();this._endDrag()};this.set("_dragStartPosition",this._getPositionRelativeToViewport(b));this.set("_dragStartLength",this.currentLength);window.addEventListener("mouseup",
this._previousMouseUpCallback,!1);window.addEventListener("mousemove",this._previousMouseMoveCallback,!1)},_getPositionRelativeToViewport(b){return this.isHorizontal?b.clientY:b.clientX},_endDrag(){window.removeEventListener("mousemove",this._previousMouseMoveCallback,!1);this._previousMouseMoveCallback=null;window.removeEventListener("mouseup",this._previousMouseUpCallback,!1);this._previousMouseUpCallback=null},_computeIsVertical(b){return!b}});

//# sourceURL=build://tf-debugger-dashboard/selection-tree-node.js
(function(b){b.NODE_NAME_SEPARATOR="/";b.DEVICE_NAME_PATTERN=/^\/job:[A-Za-z0-9_]+\/replica:[0-9_]+\/task:[0-9]+\/device:[A-Za-z0-9_]+:[0-9]+/;let d;(function(k){k[k.EMPTY=0]="EMPTY";k[k.CHECKED=1]="CHECKED";k[k.PARTIAL=2]="PARTIAL"})(d=b.CheckboxState||(b.CheckboxState={}));b.splitNodeName=function(k){let r=[];const l=k.match(b.DEVICE_NAME_PATTERN);null!=l&&(r.push(l[0]),"/"!==k[l[0].length]&&console.error('No slash ("/") after device name in node name:',k),k=k.slice(l[0].length+1));return r.concat(k.split(b.NODE_NAME_SEPARATOR))};
b.getCleanNodeName=function(k){let r=k;const l=k.match(b.DEVICE_NAME_PATTERN);null!=l?(r.length>l[0].length&&"/"!=r[l[0].length]&&console.error('No slash ("/") after device name in node name:',k),r=r.slice(l[0].length+1)):"/"===r[0]&&(r=r.slice(1));r.indexOf(")")===r.length-1&&(r=r.slice(0,r.indexOf("/(")));return r};b.sortAndBaseExpandDebugWatches=function(k){k.sort((l,p)=>l.node_name<p.node_name?-1:l.node_name>p.node_name?1:l.output_slot-p.output_slot);for(let l=0;l<k.length;++l){var r=k[l].node_name+
"/";let p=!1;for(let m=l+1;m<k.length;++m)if(0===k[m].node_name.indexOf(r)){p=!0;break}p&&(r=k[l].node_name.split("/"),k[l].node_name+="/("+r[r.length-1]+")")}};b.removeNodeNameBaseExpansion=function(k){return k.endsWith(")")?k.slice(0,k.lastIndexOf("/(")):k};b.assembleDeviceAndNodeNames=function(k){const r=[null,null];if(k[0].match(b.DEVICE_NAME_PATTERN)){let l=k[0];"/"===l[l.length-1]&&(l=l.slice(0,l.length-1));r[0]=l;r[1]=k.slice(1).join("/")}else r[1]=k.join("/");return r};let f;(function(k){k[k.NodeName=
0]="NodeName";k[k.OpType=1]="OpType"})(f=b.DebugWatchFilterMode||(b.DebugWatchFilterMode={}));b.filterDebugWatches=function(k,r,l){if(r===f.NodeName)return k.filter(p=>p.node_name.match(l));if(r===f.OpType)return k.filter(p=>p.op_type.match(l))};class h{constructor(k,r,l,p){this.debugWatchChange=r;this.debugWatch=p;this.name=k;this.debugWatch=p;this.checkboxState=d.EMPTY;this.parent=l;this.children={};this.checkbox=document.createElement("paper-checkbox");this.checkbox.addEventListener("change",()=>
{this._handleChange()},!1)}_handleChange(){if(this.avoidPropagation)this.debugWatch&&this.debugWatchChange(this.debugWatch,this.isCheckboxChecked());else if(this.debugWatch)this.setCheckboxState(this.isCheckboxChecked()?d.CHECKED:d.EMPTY,!0),this.isCheckboxChecked()?this.setNodesAboveToChecked():this.setNodesAboveToEmpty(),this.debugWatchChange(this.debugWatch,this.isCheckboxChecked());else if(this.setCheckboxState(this.isCheckboxChecked()?d.CHECKED:d.EMPTY,!0),this.isCheckboxChecked()){const r=_.values(this.children);
for(;r.length;){var k=r.pop();_.forEach(k.children,l=>r.push(l));k.setCheckboxState(d.CHECKED,!0)}this.setNodesAboveToChecked()}else{const r=_.values(this.children);for(;r.length;)k=r.pop(),_.forEach(k.children,l=>r.push(l)),k.setCheckboxState(d.EMPTY,!0);this.setNodesAboveToEmpty()}}isLeaf(){return!!this.debugWatch}setToAllCheckedExternally(){this.setCheckboxState(d.CHECKED);this._handleChange()}setCheckboxState(k,r){this.avoidPropagation=r;this.checkboxState=k;this.checkbox.classList.toggle("partial-checkbox",
k===d.PARTIAL);k===d.CHECKED?this.checkbox.setAttribute("checked","checked"):this.checkbox.removeAttribute("checked");this.avoidPropagation=!1}isCheckboxChecked(){return this.checkbox.hasAttribute("checked")}setNodesAboveToChecked(){let k=this.parent,r=!1;for(;k;)r?k.setCheckboxState(d.PARTIAL,!0):(r=-1!==_.findIndex(_.values(k.children),l=>l.checkboxState!==d.CHECKED),k.setCheckboxState(r?d.PARTIAL:d.CHECKED,!0)),k=k.parent}setNodesAboveToEmpty(){let k=this.parent,r=!1;for(;k;)r?k.setCheckboxState(d.PARTIAL,
!0):(r=-1!==_.findIndex(_.values(k.children),l=>l.checkboxState!==d.EMPTY),k.setCheckboxState(r?d.PARTIAL:d.EMPTY,!0)),k=k.parent}setLevelDom(k){this.levelDom=k}}b.SelectionTreeNode=h})(Kh||(Kh={}));

//# sourceURL=build://tf-debugger-dashboard/tf-op-selector.html.js
Polymer({is:"tf-op-selector",properties:{debugWatches:Array,debugWatchChange:Object,nodeClicked:Function,forceExpandAndCheckNodeName:{type:String,value:null},forceExpandNodeName:{type:String,value:null},_selectedDebugWatchMapping:{type:Object,value:()=>({})},_levelName2Container:{type:Object,value:null},_levelName2Node:{type:Object,value:null},_watchHierarchy:{type:Object,computed:"_computeWatchHierarchy(debugWatches, debugWatchChange, _filterMode, _filterInput)"},_filterMode:{type:String,value:"Node Name",
notify:!0},_filterInput:{type:String,value:"",notify:!0},_isLoading:{type:Boolean,value:!1},_highlightedLevelDom:{type:Object,value:null}},observers:["_renderHierarchyWithTimeout(_watchHierarchy, debugWatchChange)","_handleForceNodeExpandAndCheck(forceExpandAndCheckNodeName)","_handleForceNodeExpand(forceExpandNodeName)"],_computeWatchHierarchy(b,d,f,h){h=h.trim();let k=b;null!=f&&0<h.length&&(k=Kh.filterDebugWatches(b,Kh.DebugWatchFilterMode[f.replace(/\s/g,"")],new RegExp(h)));const r=new Kh.SelectionTreeNode("",
d);r.isRoot=!0;_.forEach(k,l=>{const p=Kh.splitNodeName(l.device_name+"/"+l.node_name);let m=r;_.forEach(p,(n,q)=>{q===p.length-1?(q=new Kh.SelectionTreeNode(n,d,m,l),m.children[n]=q):(m.children[n]||(m.children[n]=new Kh.SelectionTreeNode(n,d,m)),m=m.children[n])})});return r},_clearSelectorHierarchy(){const b=this.$$("#selector-hierarchy");for(;b.firstChild;)b.removeChild(b.firstChild)},_renderHierarchyWithTimeout(b,d,f,h){this._isLoading||(this.set("_isLoading",!0),this._clearSelectorHierarchy(),
setTimeout(()=>{this._renderHierarchy(b,d,f,h)},10))},_renderHierarchy(b,d){this.set("_levelName2Container",{});this.set("_levelName2Node",{});b=this._renderLevel(null,null,b,d);Polymer.dom(this.$$("#selector-hierarchy")).appendChild(b);this.set("_isLoading",!1)},_renderLevel(b,d,f,h){const k=document.createElement("div");null!=b&&k.setAttribute("level-name",b);let r;r=null==d?b:d+"/"+b;Polymer.dom(k).classList.add("level-container");const l=document.createElement("iron-collapse");if(b){this._levelName2Container[r]=
l;l.removeAttribute("opened");Polymer.dom(k).classList.add("indented-level-container");d=document.createElement("div");Polymer.dom(d).classList.add("level-title");const n=document.createElement("paper-icon-button");Polymer.dom(n).classList.add("node-expand-button");const q=()=>{n.setAttribute("icon",l.hasAttribute("opened")?"expand-less":"expand-more")};n.addEventListener("click",()=>{l.hasAttribute("opened")?l.removeAttribute("opened"):l.setAttribute("opened",!0);q()},!1);q();Polymer.dom(d).appendChild(n);
Polymer.dom(d).appendChild(f.checkbox);f.setLevelDom(d);const u=document.createElement("span");Polymer.dom(u).classList.add("level-title-text");u.textContent=b;Polymer.dom(d).appendChild(u);Polymer.dom(k).appendChild(d);(b.match(Kh.DEVICE_NAME_PATTERN)||1===Object.keys(f.children).length)&&l.setAttribute("opened",!0)}else l.setAttribute("opened",!0);const p=[],m=[];Polymer.dom(l).classList.add("content-container");_.forEach(f.children,(n,q)=>{const u=n.debugWatch;var w=r;null==r&&(w="");w+="/"+q;
this._levelName2Node[w]=n;null!=this._selectedDebugWatchMapping[w]&&(n.setCheckboxState(Kh.CheckboxState.CHECKED),n.setNodesAboveToChecked());if(u){w=document.createElement("div");Polymer.dom(w).classList.add("op-description");n.checkbox.addEventListener("change",y=>{this._handleLeafNodeSelected(h,u,y.target.checked)},!1);Polymer.dom(w).appendChild(n.checkbox);n.setLevelDom(w);var A=document.createElement("span");A.textContent="["+u.op_type+"]";A.setAttribute("class","op-type");Polymer.dom(w).appendChild(A);
A=document.createElement("span");A.textContent=q;A.setAttribute("class","op-title-leaf");A.addEventListener("click",()=>{const y=this._getDeviceAndNodeNames(q,k);this.nodeClicked(y[0],y[1])},!1);Polymer.dom(w).appendChild(A);m.push(w)}else n.checkbox.addEventListener("change",y=>{this._handleMetaNodeChange(n,h,y.target.checked)}),p.push(this._renderLevel(q,r,n,h))});b=n=>{Polymer.dom(l).appendChild(n)};_.forEach(m,b);_.forEach(p,b);Polymer.dom(k).appendChild(l);return k},_getLeafDebugWatches(b,d){b.debugWatch?
d.push(b.debugWatch):_.forEach(b.children,f=>{this._getLeafDebugWatches(f,d)})},_getDeviceAndNodeNames(b,d){for(b=[b];;){const f=d.getAttribute("level-name");if(null==f)break;else b.push(f);d=Polymer.dom(d).parentNode.parentNode}b.reverse();return Kh.assembleDeviceAndNodeNames(b)},_handleMetaNodeChange(b,d,f){let h=[];this._getLeafDebugWatches(b,h);_.forEach(h,k=>{this._handleLeafNodeSelected(d,k,f)})},_handleLeafNodeSelected(b,d,f){const h=d.device_name+"/"+d.node_name;f?this._selectedDebugWatchMapping[h]=
d:delete this._selectedDebugWatchMapping[h];b(d,f)},_handleForceNode(b,d){this.set("_filterInput","");setTimeout(()=>{if(null!=b&&null!=this._levelName2Container){var f=Kh.splitNodeName(b);for(let k=1;k<=f.length;++k){var h=f.slice(0,k).join("/");const r=this._levelName2Node[h];null!=r&&null!=r.levelDom&&r.levelDom.scrollIntoView({block:"center",behaviour:"smooth"});k<f.length?null!=this._levelName2Container[h]&&this._levelName2Container[h].setAttribute("opened",!0):(r.debugWatch||this._handleMetaNodeChange(r,
r.debugWatchChange,!0),d&&(r.setToAllCheckedExternally(),(h=r.debugWatch)&&null==this._selectedDebugWatchMapping[h.node_name]&&(this._selectedDebugWatchMapping[b]=h)),null!=this._highlightedLevelDom&&this._highlightedLevelDom.classList.remove("highlighted"),r.levelDom.classList.add("highlighted"),this.set("_highlightedLevelDom",r.levelDom))}}},20)},_handleForceNodeExpandAndCheck(b){this._handleForceNode(b,!0)},_handleForceNodeExpand(b){this._handleForceNode(b,!1)}});

//# sourceURL=build://tf-debugger-dashboard/tf-session-runs-view.html.js
Polymer({is:"tf-session-runs-view",properties:{latestSessionRun:Object,sessionRunKeyToDeviceNames:Object,soleActive:Boolean,nodeOrTensorClicked:Function,_runKey2Count:{type:Object,value:{}},_runKey2NumDevices:{type:Object,value:{}},_activeRunKey:String},observers:["renderLatest(latestSessionRun)","setSoleActiveStatus(soleActive)"],renderLatest(b){b=JSON.stringify(b);this._runKey2Count[b]=void 0===this._runKey2Count[b]?1:this._runKey2Count[b]+1;void 0===this._runKey2NumDevices[b]&&(this._runKey2NumDevices[b]=
0);this._activeRunKey=b;this._renderSessionRunTable()},updateNumDevices(b){null!=this._activeRunKey&&(this._runKey2NumDevices[this._activeRunKey]=b,this._renderSessionRunTable())},setSoleActiveStatus(){this._renderSessionRunTable()},_renderSessionRunTable(){this._clearTable();this._renderHeader();let b;for(const f in this._runKey2Count)if(this._runKey2Count.hasOwnProperty(f)){var d=JSON.parse(f);(d=this._renderRow(d,this._runKey2NumDevices[f],this._runKey2Count[f],this._activeRunKey===f,this.soleActive))&&
(b=d)}b&&(Polymer.dom(this.$$("#session-runs-table")).parentNode.parentNode.scrollTop=b.offsetTop)},_clearTable(){const b=this.$$("#session-runs-table");for(;b.firstChild;)b.removeChild(b.firstChild)},_renderHeader(){const b=document.createElement("tr"),d=document.createElement("th");d.textContent="Feeds";const f=document.createElement("th");f.textContent="Fetches";const h=document.createElement("th");h.textContent="Targets";const k=document.createElement("th");k.textContent="#(Devices)";const r=
document.createElement("th");r.textContent="Count";b.appendChild(d);b.appendChild(f);b.appendChild(h);b.appendChild(k);b.appendChild(r);Polymer.dom(this.$$("#session-runs-table")).appendChild(b)},_renderRow(b,d,f,h,k){const r=document.createElement("tr"),l=this._renderGraphElements(b.feeds),p=this._renderGraphElements(b.fetches);b=this._renderGraphElements(b.targets);const m=document.createElement("td");m.textContent=d;d=document.createElement("td");d.textContent=f;r.appendChild(l);r.appendChild(p);
r.appendChild(b);r.appendChild(m);r.appendChild(d);h&&(k?r.setAttribute("class","sole-active-session-run"):r.setAttribute("class","active-session-run"));Polymer.dom(this.$$("#session-runs-table")).appendChild(r);if(h)return r},_renderGraphElements(b){const d=document.createElement("td");_.forEach(b,f=>{const h=document.createElement("div");h.textContent=f;h.setAttribute("class","node-or-tensor-element");h.addEventListener("click",()=>{this.nodeOrTensorClicked(f)});d.appendChild(h)});return d}});

//# sourceURL=build://tf-debugger-dashboard/tf-source-code-view.html.js
Polymer({is:"tf-source-code-view",properties:{requestManager:{type:Object,value:null},focusNodeName:{type:String,value:null},_oldFocusNodeName:{type:String,value:null},debugWatches:{type:Array,value:[]},nodeClicked:{type:Function,value:null},continueToNode:{type:Function,value:null},_highlightedElements:{type:Array,value:[]},_filePathSelected:Number,_fullFilePaths:{type:Array,value:null},_shortFilePaths:{type:Array,value:null},_fileLines:{type:Array,value:null},_nodeName2DeviceName:{type:Object,value:null},
_nodeName2BaseExpandedNodeName:{type:Object,value:null},_nodeName2NodeElements:{type:Object,value:null},_nodeName2StackTopNodeElement:{type:Object,value:null},_setHightlightOriginNodeElement:{type:Object,value:null},_fullStackShown:{type:Boolean,value:!1},_fullStackNodeName:{type:String,value:null},_renderDelayMillis:{type:Number,value:50,readonly:!0}},observers:["_renderFile(_filePathSelected)","_focusOnNode(focusNodeName)"],render(b){null!=b&&this.set("_debugWatches",b);this._querySourceCodeEndPoint({mode:"paths"}).then(d=>
{this.set("_fullFilePaths",d.paths);const f=d.paths.map(h=>({id:h,name:this._shortenPath(h,d.paths)}));this.set("_shortFilePaths",f);0<f.length&&this.set("_filePathSelected",0)})},_shortenPath(b){b=b.replace(/\\/g,"/");b=b.split("/");return b[b.length-1]},_renderFile(b){if(null!=b){var d=this._shortFilePaths[b].id;this._querySourceCodeEndPoint({mode:"content",file_path:d}).then(f=>{const h=[],k=f.content[d],r=f.lineno_to_op_name_and_stack_pos;f={};for(var l in r)r.hasOwnProperty(l)&&(f[l]=r[l].length);
this._filterFileTracebacksByDebugWatches(r);for(l=0;l<k.length;++l){const m=l+1;h.push({lineno:m,numNodes:null!=r[m]?String(r[m].length)+"/"+String(f[m])+" \u25bc":"",text:this._htmlEscape(k[l])})}this.set("_fileLines",h);const p=this;setTimeout(()=>{const m={},n={};for(const u in r){if(!r.hasOwnProperty(u))continue;for(var q=p.$$("#source-line-nodes-"+u);q.firstChild;)q.removeChild(q.firstChild);const w=r[u];w.sort(function(A,y){return A[0]<y[0]?-1:A[0]>y[0]?1:0});for(let A=0;A<w.length;++A){const y=
w[A][0],x=w[A][1],C=document.createElement("div"),G=document.createElement("span");G.setAttribute("class","source-line-node-enttry");G.setAttribute("sourceLineno",u);G.textContent=y;G.addEventListener("tap",()=>{this.nodeClicked(this._nodeName2DeviceName[y],this._nodeName2BaseExpandedNodeName[y],!0)});const D=document.createElement("paper-icon-button");D.setAttribute("icon","filter-list");D.setAttribute("title","Show stack");D.addEventListener("tap",()=>{this._highlightNodeElements(y);this.set("_fullStackNodeName",
y);this.set("_fullStackShown",!0);this._populateFullStack(y,this._fullFilePaths[this._filePathSelected],Number(u))});const B=document.createElement("paper-icon-button");B.setAttribute("icon","forward");B.setAttribute("title","Continue to");B.addEventListener("tap",()=>{this.nodeClicked(this._nodeName2DeviceName[y],this._nodeName2BaseExpandedNodeName[y],!0);const H=this._nodeName2DeviceName[y],K=this._nodeName2BaseExpandedNodeName[y];this.set("_setHightlightOriginNodeElement",G);this.continueToNode(H,
K)});C.appendChild(D);C.appendChild(B);C.appendChild(G);q.appendChild(C);m.hasOwnProperty(y)||(m[y]=[]);m[y].push(G);n.hasOwnProperty(y)||(n[y]=[G,x]);x>n[y][1]&&(n[y]=[G,x])}q.setAttribute("hidden",!0);q=p.$$("#source-line-node-toggle-"+u);null==q.getAttribute("tapCallbackSet")&&(q.addEventListener("tap",()=>{p._toggleLineNodes(Number(u))}),q.setAttribute("tapCallbackSet",!0))}p.set("_nodeName2NodeElements",m);for(const u in n)n.hasOwnProperty(u)&&(n[u]=n[u][0]);p.set("_nodeName2StackTopNodeElement",
n)},this._renderDelayMillis)})}},_toggleLineNodes(b,d=!1){b=this.$$("#source-line-nodes-"+b);null==b.getAttribute("hidden")&&!0!==d?b.setAttribute("hidden",!0):b.removeAttribute("hidden")},_filterFileTracebacksByDebugWatches(b){const d=this.debugWatches.map(k=>Kh.removeNodeNameBaseExpansion(k.node_name)),f={},h={};for(const k of this.debugWatches){const r=Kh.removeNodeNameBaseExpansion(k.node_name);f[r]=k.device_name;h[r]=k.node_name}this.set("_nodeName2DeviceName",f);this.set("_nodeName2BaseExpandedNodeName",
h);for(const k in b)b.hasOwnProperty(k)&&(b[k]=b[k].filter(r=>_.includes(d,r[0])))},_querySourceCodeEndPoint(b){const d=dc.getRouter().pluginRoute("debugger","/source_code");b=dc.addParams(d,b);return this.requestManager.request(b)},_htmlEscape(b){return b.replace(/ /g,"\u00a0")},_focusOnNode(b){if(null!=b){var d=this._shortFilePaths[this._filePathSelected].id,f=this;this._querySourceCodeEndPoint({mode:"op_traceback",op_name:b}).then(h=>{const k=h.op_traceback[b];h=[];for(let l=0;l<k.length;++l){const p=
k[l][1];k[l][0]===d&&h.push(p)}for(var r of f._highlightedElements)r.classList.remove("highlighted-source-line");r=[];for(const l of h)h=this.$$("#source-line-"+l),r.push(h),h.classList.add("highlighted-source-line"),f._toggleLineNodes(l,!0);f.set("_highlightedElements",r);this._highlightNodeElements(b)})}},_highlightNodeElements(b){if(null!=this._oldFocusNodeName)for(const d of this._nodeName2NodeElements[this._oldFocusNodeName])d.style["font-weight"]="normal";for(const d of this._nodeName2NodeElements[b])d.style["font-weight"]=
"bold";null==this._setHightlightOriginNodeElement?this._nodeName2StackTopNodeElement[b].scrollIntoView({block:"center",behaviour:"smooth"}):this.set("_setHightlightOriginNodeElement",null);this.set("_oldFocusNodeName",b)},_populateFullStack(b,d,f){this._querySourceCodeEndPoint({mode:"op_traceback",op_name:b}).then(h=>{const k=this.$$("#full-stack-content");for(;k.firstChild;)k.removeChild(k.firstChild);for(const r of h.op_traceback[b]){const l=document.createElement("li"),p=r[0],m=Number(r[1]);l.textContent=
p+": "+String(m);_.includes(this._fullFilePaths,p)?(l.classList.add("stack-frame-clickable"),l.style.color="blue",l.style["text-decoration"]="underline",l.style.cursor="pointer",p===d&&m===f&&(l.style["font-weight"]="bold"),l.addEventListener("tap",()=>{this.set("_filePathSelected",this._fullFilePaths.indexOf(p));setTimeout(()=>{this._toggleLineNodes(m,!0);for(const n of this._nodeName2NodeElements[b])Number(n.getAttribute("sourceLineno"))===Number(m)&&(n.scrollIntoView({block:"center",behaviour:"smooth"}),
this.set("_setHightlightOriginNodeElement",l),this._highlightNodeElements(b),d===p&&f===m||this._populateFullStack(b,p,m))},2*this._renderDelayMillis)})):(l.classList.add("stack-frame-nonclickable"),l.style.color="#555");k.appendChild(l)}})},_closeFullStackDialog(){this.set("_fullStackShown",!1)}});

//# sourceURL=build://tf-debugger-dashboard/tf-tensor-data-summary.html.js
Polymer({is:"tf-tensor-data-summary",properties:{latestTensorData:Object,expandHandler:Object,continueToCallback:Function,highlightedNodeName:{type:String,value:null},tensorNameClicked:{type:Function,value:null},getHealthPill:Function,_healthPillsEnabled:{type:Boolean,value:!0,notify:!0},_watchKeys:{type:Array,value:[]},_watchKey2Data:{type:Object,value:{}},_watchKey2Count:{type:Object,value:{}},_watchKey2ExpandHandler:{type:Object,value:{}},_watchKey2ValueShort:{type:Object,value:{}},_watchKey2Row:{type:Object,
value:{}},_activeWatchKey:String,_healthPillWidth:{type:Number,value:200,readonly:!0},_healthPillHeight:{type:Number,value:32,readonly:!0}},observers:["_renderLatest(latestTensorData, expandHandler)","_highlight(highlightedNodeName)"],listeners:{"show-health-pills.change":"_showHealthPillsChanged"},ready(){this._renderHealthPillLegend()},enableHealthPills(){this.set("_healthPillsEnabled",!0);this._renderHealthPillLegend()},_showHealthPillsChanged(){this._healthPillsEnabled?this._renderHealthPillLegend():
this._clearHealthPillLegend();this._renderAll()},_renderAll(){this._clearTensorDataTable();for(const b of this._watchKeys)this._renderLatest(this._watchKey2Data[b],this._watchKey2ExpandHandler[b])},_tensorData2WatchKey(b){return b.deviceName+"/"+b.tensorName+":"+b.debugOp},_renderLatest(b,d){if(b){var f=this._tensorData2WatchKey(b),h=null;"Uninitialized"!==b.dtype&&"Unsupported"!==b.dtype&&(h=()=>d(b));var k=null!=b.value?JSON.stringify(b.value,(r,l)=>l.toFixed?Number(l.toFixed(3)):l):"(Click to view)";
this._watchKey2Data[f]=b;-1===this._watchKeys.indexOf(f)?(this._watchKeys.push(f),this._watchKey2Count[f]=1):this._watchKey2Count[f]+=1;this._watchKey2ExpandHandler[f]=h;this._watchKey2ValueShort[f]=k;this._activeWatchKey=f;this._removeActiveStatusFromAllRows();this._renderRow(f)}},_clearTensorDataTable(){for(const b in this._watchKey2Row)this._watchKey2Row.hasOwnProperty(b)&&(this._watchKey2Row[b].remove(),delete this._watchKey2Row[b])},_clearTensorDataRow(b){for(;b.firstChild;)b.removeChild(b.firstChild)},
_clearHealthPillLegend(){const b=this.$$("#health-pill-legend");for(;b.firstChild;)b.removeChild(b.firstChild)},_renderHealthPillLegend(){this._clearHealthPillLegend();const b=this.$$("#health-pill-legend");var d=document.createElement("div");d.textContent="Legend:";b.appendChild(d);d.style["margin-right"]="0.5em";d.style.display="inline-block";for(d=0;d<tf.graph.scene.healthPillEntries.length;++d){const f=tf.graph.scene.healthPillEntries[d],h=document.createElement("div");h.style.display="inline-block";
h.style["margin-right"]="0.25em";const k=document.createElement("span");k.textContent="\u25a0";k.style.color=f.background_color;const r=document.createElement("span");r.textContent=f.label;r.style.color=f.background_color;h.appendChild(k);h.appendChild(r);b.appendChild(h)}},_removeActiveStatusFromAllRows(){for(const b in this._watchKey2Row){if(!this._watchKey2Row.hasOwnProperty(b))continue;const d=this._watchKey2Row[b];Polymer.dom(d).classList.remove("active-tensor");Polymer.dom(d).classList.remove("highlighted")}},
_renderRow(b){let d,f=!1;null!=this._watchKey2Row[b]?(d=this._watchKey2Row[b],this._clearTensorDataRow(d),f=!1):(d=document.createElement("tr"),f=!0);const h=this._watchKey2Data[b].deviceName,k=this._watchKey2Data[b].maybeBaseExpandedNodeName,r=h+"/"+k;var l=this._watchKey2Count[b],p=this._watchKey2Data[b].tensorName,m=this._watchKey2Data[b].debugOp,n=this._watchKey2ValueShort[b];const q=this._watchKey2ExpandHandler[b],u=b===this._activeWatchKey,w=document.createElement("td");Polymer.dom(w).classList.add("tensor-name");
w.style["text-decoration"]="underline";w.style.cursor="pointer";w.textContent=p;w.addEventListener("tap",()=>{null!=this.tensorNameClicked&&this.tensorNameClicked(h,k)});const A=document.createElement("td");A.textContent=l;const y=this._watchKey2Data[b].dtype;l=document.createElement("td");const x=this._watchKey2Data[b].shape;l.textContent=y;const C=document.createElement("td");C.textContent=JSON.stringify(x);const G=document.createElement("td");G.textContent=n;Polymer.dom(G).classList.add("value-expansion-link");
null!=q&&(G.addEventListener("tap",q,!1),G.style["text-decoration"]="underline",G.style.cursor="pointer");n=null;n=this._healthPillsEnabled?this._renderHealthPill(p+":"+m,{device_name:h,node_name:k,dtype:y,shape:x,value:null},q):document.createElement("td");p=document.createElement("td");m=document.createElement("paper-icon-button");m.setAttribute("icon","forward");m.setAttribute("title","Continue to");m.addEventListener("click",()=>{this.continueToCallback(h,k)});p.appendChild(m);d.appendChild(w);
d.appendChild(A);d.appendChild(l);d.appendChild(C);d.appendChild(G);d.appendChild(n);d.appendChild(p);d.setAttribute("nodeNameWithDevice",r);u&&(Polymer.dom(d).classList.add("active-tensor"),Polymer.dom(d).classList.add("highlighted"));this._watchKey2Row[b]=d;f&&Polymer.dom(this.$$("#tensor-data-table tbody")).appendChild(d);d.scrollIntoView({block:"end",inline:"nearest",behaviour:"smooth"})},_renderHealthPill(b,d,f){const h=document.createElement("td");Polymer.dom(h).classList.add("health-pill");
null!=f&&h.addEventListener("tap",f,!1);f=document.createElementNS(tf.graph.scene.SVG_NAMESPACE,"svg");f.setAttribute("width",this._healthPillWidth);f.setAttribute("height",this._healthPillHeight);const k=document.createElementNS(tf.graph.scene.SVG_NAMESPACE,"g");f.appendChild(k);h.appendChild(f);const r="tdp/"+b;this.getHealthPill(b,d.device_name,d.node_name,l=>{null==l?(h.textContent="N/A",h.style.color="gray"):(d.value=l,tf.graph.scene.addHealthPill(k,d,null,r,this._healthPillWidth,this._healthPillHeight/
2,this._healthPillHeight/2,0))});return h},_highlight(b){Polymer.dom(this.$$("#tensor-data-table"));const d=[];for(const f in this._watchKey2Row){if(!this._watchKey2Row.hasOwnProperty(f))continue;const h=this._watchKey2Row[f];null!=h.getAttribute&&(h.getAttribute("nodeNameWithDevice")===b?d.push(h):Polymer.dom(h).classList.remove("highlighted"))}if(null!=b)for(b=0;b<d.length;++b)Polymer.dom(d[b]).classList.add("highlighted"),d[b].scrollIntoView({block:"end",inline:"nearest",behaviour:"smooth"})}});

//# sourceURL=build://tf-debugger-dashboard/tf-debugger-line-chart.html.js
Polymer({is:"tf-debugger-line-chart",properties:{data:{type:Object,value:null},_defaultSeriesName:{type:String,value:"__debugger_data__",readonly:!0},_lineChartXComponentsCreationMethod:{type:Object,readOnly:!0,value:()=>()=>{const b=new Plottable.Scales.Linear;return{scale:b,axis:new Plottable.Axes.Numeric(b,"bottom"),accessor:d=>d.step}}},_lineChartYValueAccessor:{type:Object,readOnly:!0,value:()=>b=>b.scalar},_lineChartTooltipColumns:{type:Array,readOnly:!0,value:()=>[{title:"Name",evaluate:b=>
"step\x3d"+b.datum.step+"; scalar\x3d "+b.datum.scalar}]},_lineChartSmoothingEnabled:{type:Boolean,value:!1,readOnly:!0}},observers:["render(data)"],render(b){if(null!=b){var d=this.$$("vz-line-chart2");d.setVisibleSeries([this._defaultSeriesName]);var f=[],h=b.x;b=b.y;for(let k=0;k<h.length;++k)f.push({step:h[k],scalar:b[k]});d.setSeriesData(this._defaultSeriesName,f);d.commitChanges()}}});

//# sourceURL=build://tf-debugger-dashboard/tf-tensor-value-view.html.js
Polymer({is:"tf-tensor-value-view",properties:{viewId:String,tensorName:String,debugOp:String,deviceName:String,maybeBaseExpandedNodeName:String,slicing:String,timeIndices:String,dtype:String,shape:Array,continueToButtonCallback:Object,closeButtonCallback:Object,tensorNameCallback:Object,tensorWidget:Object,getHealthPill:Function,_isTensorValueScalar:{type:Boolean,value:!1},_isTensorValueLineChart:{type:Boolean,value:!1},_isTensorValueImage:{type:Boolean,value:!1},_dataScalar:{type:Number,value:null},
_lineChartData:{type:Array,value:null},_dataImageSrc:{type:String,value:null},_requestManager:{type:Object,value:()=>new dc.RequestManager(10)}},observers:["_updateTimeIndicesToggle(timeIndices)"],renderTensorValue(){if(this.tensorName)if(null==this.slicing){this.set("_useTensorWidget",!0);const d={spec:{dtype:this.dtype,shape:this.shape},get:()=>{throw Error("tensorView.get() is not implemented yet.");},view:f=>{const h=this;return Wb(function*(){const k=h.shape.length,r=f.slicingDimsAndIndices.map(m=>
m.dim),l=f.slicingDimsAndIndices.map(m=>m.index);let p="[";for(let m=0;m<k;++m)-1!==r.indexOf(m)?p+=`${l[r.indexOf(m)]}`:f.viewingDims[0]===m?p+=`${f.verticalRange[0]}:${f.verticalRange[1]}`:f.viewingDims[1]===m&&(p+=`${f.horizontalRange[0]}:${f.horizontalRange[1]}`),m<k-1&&(p+=",");p+="]";return new Promise((m,n)=>{const q=h._getTensorDataURL({watch_key:h.tensorName+":"+h.debugOp,slicing:p,time_indices:h.timeIndices,mapping:"none"});h._requestManager.request(q).then(u=>{null==u.error?m(u.tensor_data[u.tensor_data.length-
1]):n(u.error)}).catch(u=>n(u))})})},getNumericSummary:()=>{const f=this;return Wb(function*(){return new Promise((h,k)=>{const r=f.tensorName+":"+f.debugOp;f.getHealthPill(r,f.deviceName,f.maybeBaseExpandedNodeName,l=>{null==l?k(`Failed to get health pill for watch key ${r}`):h({elementCount:l[1],minimum:l[8],maximum:l[9]})})})})}};setTimeout(()=>{null==this.tensorWidget&&(this.tensorWidget=tensor_widget.tensorWidget(this.$$("#tensor-widget"),d,{wheelZoomKey:"alt"}));this.tensorWidget.render()},
10)}else{this.set("_useTensorWidget",!1);var b=this._rankFromSlicing(this.slicing.trim());const d=this._isTimeIndicesSingleStep(this.timeIndices);let f=b;if(!d){if(1<b){this._showToast("History for tensors \x3e 1D is not yet supported.");return}f+=1}b=this._getTensorDataURL({watch_key:this.tensorName+":"+this.debugOp,slicing:this.slicing,time_indices:this.timeIndices,mapping:2<=f?"image/png":"none"});this._requestManager.request(b).then(h=>{this.$$("#debug-op").textContent=this._calculateDebugOpToDisplay();
if(null!=h.error)this._showToast(h.error.type+": "+h.error.message);else if(h=d?h.tensor_data[0]:h.tensor_data,0===f)this._setVisualizationType("scalar"),this.set("_dataScalar",h);else if(1===f){this._setVisualizationType("lineChart");let k={x:[],y:h};for(let r=0;r<h.length;++r)k.x.push(r+1);this.set("_lineChartData",k)}else 2<=f?(this._setVisualizationType("image"),this.set("_dataImageSrc","data:image/png;base64,"+h)):this._showToast("Visualization of rank-"+f+" tensors is not yet supported.")})}},
refresh(){this.tensorName.trim()&&this.renderTensorValue()},_getTensorDataURL(b){const d=dc.getRouter().pluginRoute("debugger","/tensor_data");return dc.addParams(d,b)},_rankFromSlicing(b){b.startsWith("[")&&(b=b.slice(1,b.length-1));if(0===b.length)return 0;{b=b.split(",");let d=b.length;for(const f of b)isNaN(Number(f))||d--;return d}},_setVisualizationType(b){"scalar"===b?(this.set("_isValueScalar",!0),this.set("_isValueLineChart",!1),this.set("_isValueImage",!1)):"lineChart"===b?(this.set("_isValueScalar",
!1),this.set("_isValueLineChart",!0),this.set("_isValueImage",!1)):"image"===b?(this.set("_isValueScalar",!1),this.set("_isValueLineChart",!1),this.set("_isValueImage",!0)):console.error("Invalid visualizationType:",b)},_timeIndicesToggleButtonCallback(){"full history"===Polymer.dom(this.$$("#time-indices-toggle-button")).textContent.toLowerCase()?this.set("timeIndices",":"):this.set("timeIndices","-1");this.renderTensorValue()},_updateTimeIndicesToggle(b){this._isTimeIndicesSingleStep(b)?Polymer.dom(this.$$("#time-indices-toggle-button")).textContent=
"Full History":Polymer.dom(this.$$("#time-indices-toggle-button")).textContent="Latest Time Point"},_isTimeIndicesSingleStep(b){b.startsWith("[")&&(b=b.slice(1,b.length-1));return!isNaN(Number(b))},_calculateDebugOpToDisplay(){return"DebugIdentity"===this.debugOp?"":this.debugOp},_showToast(b){this.$.tensorValueToast.setAttribute("text",b);this.$.tensorValueToast.open()}});

//# sourceURL=build://tf-debugger-dashboard/tf-tensor-value-multi-view.html.js
Polymer({is:"tf-tensor-value-multi-view",properties:{continueToCallback:Function,tensorNameClicked:Function,_tensorViewCounter:{type:Number,value:0},getHealthPill:Function},addView(b){const d=this.$$("#multi-tensor-view-container"),f=document.createElement("tf-tensor-value-view");f.setAttribute("class","debugger-tensor-view");f.viewId=b.viewId;f.tensorName=b.tensorName;f.debugOp=b.debugOp;f.deviceName=b.deviceName;f.maybeBaseExpandedNodeName=b.maybeBaseExpandedNodeName;f.dtype=b.dtype;f.shape=b.shape;
f.slicing=b.slicing;f.timeIndices=b.timeIndices;f.closeButtonCallback=this._createCloseButtonCallback(b.viewId);f.continueToButtonCallback=()=>{this.continueToCallback(b.deviceName,b.maybeBaseExpandedNodeName)};f.tensorNameCallback=()=>{this.tensorNameClicked(b.deviceName,b.maybeBaseExpandedNodeName)};f.getHealthPill=this.getHealthPill;d.appendChild(f);f.refresh()},getViews(){const b=[];_.forEach(this.root.querySelectorAll(".debugger-tensor-view"),d=>{b.push({viewId:d.viewId,tensorName:d.tensorName,
debugOp:d.debugOp,slicing:d.slicing,timeIndices:d.timeIndices})});return b},renderTensorValues(){_.forEach(this.root.querySelectorAll(".debugger-tensor-view"),b=>{b.renderTensorValue()})},_redrawViews(b){const d=this.$$("#multi-tensor-view-container");_.forEach(this.root.querySelectorAll(".debugger-tensor-view"),f=>{d.removeChild(f)});_.forEach(b,f=>{this.addView(f)})},_createCloseButtonCallback(b){return()=>{const d=[],f=this.root.querySelectorAll(".debugger-tensor-view");for(let h=0;h<f.length;++h){const k=
f[h];k.viewId!==b&&d.push({viewId:k.viewId,tensorName:k.tensorName,debugOp:k.debugOp,dtype:k.dtype,shape:k.shape,slicing:k.slicing,timeIndices:k.timeIndices})}this._redrawViews(d)}}});

//# sourceURL=build://tf-debugger-dashboard/tensor-shape-helper.js
(function(b){function d(f,h){return f<=h?"::":"::"+Math.ceil(f/h)}b.getDefaultSlicing=function(f){return 0===f.length?"":1===f.length?"["+d(f[0],1E3)+"]":2===f.length?"["+d(f[0],250)+", "+d(f[1],250)+"]":null};b.rankFromSlicing=function(f){f.startsWith("[")&&(f=f.slice(1,f.length-1));if(0===f.length)return 0;{f=f.split(",");let h=f.length;for(const k of f)isNaN(Number(k))||h--;return h}}})(Kh||(Kh={}));

//# sourceURL=build://tf-debugger-dashboard/tf-debugger-dashboard.html.js
const al=()=>window.innerHeight||document.documentElement.clientHeight||document.body.clientHeight,bl=()=>window.innerWidth||document.documentElement.clientWidth||document.body.clientWidth,cl=(al()-70)/2;
Polymer({is:"tf-debugger-dashboard",properties:{_topRightTabs:{type:Array,value:[{id:"tab-runtime-graphs",name:"Runtime Graphs"},{id:"tab-tensor-values",name:"Tensor Values"}],readonly:!0},_isTopRightRuntimeGraphsActive:{type:Boolean,value:!0},_isTopRightTensorValuesActive:{type:Boolean,value:!1},_topRightSelected:{type:String,value:"0",observer:"_topRightSelectedChanged"},_longPollCount:{type:Number,value:0},_stepButtonText:{type:String,value:"Step"},_continueButtonText:{type:String,value:"Continue..."},
_tensorViewIdCounter:{type:Number,value:0},isReloadDisabled:{type:Boolean,value:!0,readOnly:!0},alreadyStarted:{type:Boolean,value:!1},_currentSessionRunInfo:{type:String,value:null},_sessionRunTotalCounter:{type:Number,value:0},_sessionRunCounters:{type:Object,value:{}},_sessionRunKey2DeviceNames:{type:Object,value:{}},_activeSessionRunKey:{type:String,value:null},_activeSessionRunDevices:{type:Array,value:[]},_activeSessionRunNumDevices:{type:Number,value:-1},_activeRuntimeGraphDeviceName:{type:String,
value:null,notify:!0},_highlightNodeName:{type:String,value:null},_continueToType:{type:String,value:""},_continueToCounter:{type:Number,value:0},_continueStop:{type:Boolean,value:!1},_continueToTarget:{type:String,value:""},_continueToCounterTarget:{type:Number,value:-1},_forceExpandAndCheckNodeName:String,_forceExpandNodeName:String,_sourceFocusNodeName:String,_sourceCodeShown:{type:Boolean,value:!1,observer:"_showSourceCodeChanged"},_graphProgress:{type:Object},_requestManager:{type:Object,value:()=>
new dc.RequestManager(50)},_busy:{type:Boolean,value:!1},_leftPaneWidth:{type:Number,value:bd.getNumberInitializer("_leftPaneWidth",{defaultValue:450}),observer:"_leftPaneWidthObserver"},_minleftPaneWidth:{type:Number,value:450,readOnly:!0},_maxleftPaneWidth:{type:Number,computed:"_computeMaxleftPaneWidth(_windowWidth, _maxMainContentWidth, _resizerWidth)"},_maxMainContentWidth:{type:Number,value:350,readOnly:!0},_topRightQuadrantHeight:{type:Number,value:bd.getNumberInitializer("_topRightQuadrantHeight",
{defaultValue:cl}),observer:"_topRightQuadrantHeightObserver"},_minTopRightQuadrantHeight:{type:Number,value:200,readOnly:!0},_maxTopRightQuadrantHeight:{type:Number,computed:"_computeMaxTopRightQuadrantHeight(_windowHeight, _resizerWidth)"},_resizerWidth:{type:Number,value:30,readOnly:!0},_windowWidth:Number,_windowHeight:Number,_debugWatches:Array,_latestSessionRun:Object},observers:["_onActiveRuntimeGraphDeviceNameChange(_activeRuntimeGraphDeviceName)","_sizeDashboardRegions(_leftPaneWidth, _topRightQuadrantHeight, _windowWidth)",
"_graphProgressUpdated(_graphProgress)"],ready(){this._handleWindowResize();window.addEventListener("resize",()=>{this._handleWindowResize()},!1);this.reload()},long_poll(){const b={pos:++this._longPollCount};let d=dc.getRouter().pluginRoute("debugger","/comm");d=dc.addParams(d,b);this._requestManager.request(d).then(f=>{const h=f.type;f=f.data;if("meta"===h){var k=f.run_key,r=k[0].split(","),l=k[1].split(",");const m=k[2].split(",");var p=this._activeSessionRunKey;this.set("_activeSessionRunKey",
k);this.set("_latestSessionRun",{feeds:r,fetches:l,targets:m});this.set("_sessionRunSoleActive",!0);void 0===this._sessionRunKey2DeviceNames[k]?(this._sessionRunKey2DeviceNames[k]=[],this.set("_activeSessionRunDevices",[])):this.set("_activeSessionRunDevices",this._sessionRunKey2DeviceNames[k]);this._currentSessionRunInfo=r="Feeds: "+r+"; Fetches: "+l+"; Targets: "+m;this._sessionRunCounters.hasOwnProperty(r)?this._sessionRunCounters[r]+=1:this._sessionRunCounters[r]=1;this._sessionRunTotalCounter++;
this.$.initialDialog.closeDialog();this._continueToType&&_.isEqual(p,k)||(this._processGatedGrpcDebugOps(k,!1),this._announceNewSessionRun())}else"tensor"===h?(k=f.device_name,p=f.node_name,r=f.maybe_base_expanded_node_name,this._activeRuntimeGraphDeviceName!=k?this.set("_activeRuntimeGraphDeviceName",k):!this._continueToType&&this._isTopRightRuntimeGraphsActive&&(this._focusOnGraphNode(k,r),this.set("_forceExpandNodeName",k+"/"+r)),this.set("_sessionRunSoleActive",!1),l=p+":"+f.output_slot,this.set("_latestTensorData",
{deviceName:k,tensorName:l,nodeName:p,maybeBaseExpandedNodeName:r,debugOp:f.debug_op,dtype:f.dtype,shape:f.shape,value:f.values}),this._maybeUpdateTensorValueViews(l,f.debug_op),this.set("_busy",!1)):console.error("Invalid long-polling response type: ",h);null!=this._continueToType&&this._processContinueTo(h,f);this.long_poll()})},_processContinueTo(b,d){this._continueStop?this._clearContinueTo():"SessionRun"===this._continueToType?this._processContinueToSessionRun("meta"===b):"TensorCondition"===
this._continueToType?this._step():"op"===this._continueToType?this._processContinueToOp("meta"===b,d):null!=this._continueToType&&""!==this._continueToType&&console.error("Invalid _continueToType:",this._continueToType)},_processContinueToSessionRun(b){b&&this.set("_continueToCounter",this._continueToCounter+1);this._continueToCounter<this._continueToCounterTarget?this._step():this._clearContinueTo()},_processContinueToOp(b,d){b&&this._announceNewSessionRun();b=d.device_name;d=d.maybe_base_expanded_node_name;
const f=null==d?null:Kh.removeNodeNameBaseExpansion(d);b+"/"+d===this._continueToTarget||b+"/"+f===this._continueToTarget?(this._clearContinueTo(),this._sourceCodeShown&&this.set("_sourceFocusNodeName",f)):this._step()},_maybeUpdateTensorValueViews(b,d){const f=this.$$("#tensorValueMultiView");if(null!=f){var h=!1;_.forEach(f.getViews(),k=>{if(k.tensorName===b&&k.debugOp===d)return h=!0,!1});h&&f.renderTensorValues()}},reload(){if(!this.alreadyStarted){this.set("alreadyStarted",!0);var b=dc.getRouter().pluginRoute("debugger",
"/debugger_grpc_host_port");this._requestManager.request(b).then(d=>{0<d.port?(this.$.initialDialog.openDialog(d.host,d.port),this.long_poll()):this.$.initialDialog.openDisabledDialog()})}},_showSourceCodeChanged(){this._sourceCodeShown?(this.$$("#node-entries").style.height="40%",this.$.sourceCodeView.render()):this.$$("#node-entries").style.height="80%"},_showToast(b){this.$.toast.setAttribute("text",b);this.$.toast.open()},_announceNewSessionRun(){this._showToast("Session.run() #"+this._sessionRunTotalCounter+
" is starting.")},_displayGraph(b,d){b={run_key:JSON.stringify(b),device_name:d};b=dc.addParams("/data/plugin/debugger/debugger_graph",b);this.$.loader.datasets=[{name:"/debugger_graph",path:b}];this.$.loader.set("selectedDataset",0)},_processGatedGrpcDebugOps(b,d){d?console.log("Polling for first GraphDef for run key:",b):this.set("_activeRuntimeGraphDeviceName",null);var f={mode:"retrieve_all",run_key:JSON.stringify(b)};const h=dc.getRouter().pluginRoute("debugger","/gated_grpc");f=dc.addParams(h,
f);let k=[];this._requestManager.request(f).then(r=>{if(0==r.device_names.length)d||this._step(),this._processGatedGrpcDebugOps(b,!0);else{var l=null;for(const p in r.gated_grpc_tensors)if(r.gated_grpc_tensors.hasOwnProperty(p)){-1===this._sessionRunKey2DeviceNames[b].indexOf(p)&&(this._sessionRunKey2DeviceNames[b].push(p),this.$.sessionRunsView.updateNumDevices(this._sessionRunKey2DeviceNames[b].length));this.set("_activeSessionRunDevices",this._sessionRunKey2DeviceNames[b].slice());l=this._activeSessionRunDevices[this._activeSessionRunDevices.length-
1];const m=r.gated_grpc_tensors[p];for(let n=0;n<m.length;++n)k.push({device_name:p,node_name:m[n][0],op_type:m[n][1],output_slot:m[n][2],debug_op:m[n][3]})}null!=l&&(this.set("_activeRuntimeGraphDeviceName",l),r=Polymer.dom(this.$$("#active-runtime-graph-device-name")),null!=r&&r.setAttribute("selected",l));Kh.sortAndBaseExpandDebugWatches(k);this.set("_debugWatches",k);this.$.sourceCodeView.render(k)}})},_createDebugWatchChangeHandler(){return(b,d)=>{d=d?"break":"disable";this._requestBreakpointStateChange(Kh.getCleanNodeName(b.device_name+
"/"+b.node_name),b.output_slot,b.debug_op,d)}},_focusOnGraphNode(b,d){null!=b&&this._activeRuntimeGraphDeviceName!==b&&this.set("_activeRuntimeGraphDeviceName",b);this._setTopRightRuntimeGraphsToActive();const f=this.$$("#graph");if(f.selectedNode===d)f.panToNode(d);else{const h=f.get("renderHierarchy").hierarchy.getNodeMap();null==h[d]&&(d=Kh.removeNodeNameBaseExpansion(d));null!=h[d]&&f.set("selectedNode",d)}this.set("_highlightNodeName",b+"/"+d)},_createNodeClickedHandler(){return(b,d,f)=>{this._sourceCodeShown&&
!0!==f&&this.set("_sourceFocusNodeName",Kh.removeNodeNameBaseExpansion(d));this._focusOnGraphNode(b,d);this.set("_forceExpandNodeName",b+"/"+d)}},_createFeedFetchTargetClickedHandler(){return b=>{let d=b;-1!==d.indexOf(":")&&(d=d.slice(0,d.indexOf(":")));b=_.find(this._debugWatches,f=>f.node_name===d||0===f.node_name.indexOf(d)&&"("===f.node_name[d.length]);null==b?this._showToast("Node '"+d+"' is not in the runtime graph of the current Session.run or does not have a debug op attached."):this._focusOnGraphNode(b.device_name,
d)}},_createTensorDataExpandHandler(){return b=>{this._setTopRightTensorValuesToActive();setTimeout(()=>{this.$$("#tensorValueMultiView").addView({viewId:this._createTensorViewId(),deviceName:b.deviceName,tensorName:b.tensorName,nodeName:b.nodeName,maybeBaseExpandedNodeName:b.maybeBaseExpandedNodeName,debugOp:b.debugOp,dtype:b.dtype,shape:b.shape,slicing:Kh.getDefaultSlicing(b.shape),timeIndices:"-1"})},10)}},_createTensorViewId(){const b="debugger-tensor-view-"+this._tensorViewIdCounter;this._tensorViewIdCounter++;
return b},_createNodeContextMenuItems(){return[{title:()=>"Expand and highlight",action:b=>{const d=Kh.getCleanNodeName(b.node.name);b=this._activeRuntimeGraphDeviceName+"/"+b.node.name;this.set("_forceExpandNodeName",b);this.set("_highlightNodeName",b);this._sourceCodeShown&&this.set("_sourceFocusNodeName",Kh.removeNodeNameBaseExpansion(d))}},{title:()=>"Add breakpoint",action:b=>{const d=Kh.getCleanNodeName(b.node.name);this.set("_forceExpandAndCheckNodeName",this._activeRuntimeGraphDeviceName+
"/"+b.node.name);this._sourceCodeShown&&this.set("_sourceFocusNodeName",Kh.removeNodeNameBaseExpansion(d))}},{title:()=>"Continue to",action:b=>{-1!==["_Arg","_Retval"].indexOf(b.node.op)?this._showToast('Cannot continue to node "'+b.node.name+'", due to op type "'+b.node.op+'".'):this._continueToNode(this._activeRuntimeGraphDeviceName,b.node.name)}}]},_createGetHealthPill(){return(b,d,f,h)=>{var k={watch_key:b,time_indices:"-1",mapping:"health-pill"};const r=dc.getRouter().pluginRoute("debugger",
"/tensor_data");k=dc.addParams(r,k);this._requestManager.request(k).then(l=>{l=l.tensor_data[0];h(l);this._conditionalHealthPillStop(b,d,f,l)})}},_conditionalHealthPillStop(b,d,f,h){if("TensorCondition"===this._continueToType&&Kh.checkHealthPillAgainstTensorConditionKey(this._continueToTarget,h,this._continueToCounterTarget)){this.set("_continueStop",!0);h=Kh.removeNodeNameBaseExpansion(f);this._sourceCodeShown&&this.set("_sourceFocusNodeName",h);this._focusOnGraphNode(d,f);const k=d+"/"+f;this.set("_forceExpandNodeName",
k);setTimeout(()=>{this.set("_highlightNodeName",null);this.set("_highlightNodeName",k)},100);this._showToast('Tensor condition "'+this._continueToTarget+'" is met by watch key: "'+b+'".\nStopping continuation.')}},_continueToNode(b,d){const f=Kh.getCleanNodeName(d);b=b+"/"+d;this._requestBreakpointStateChange(f,0,"DebugIdentity","break");this.set("_forceExpandAndCheckNodeName",b);this._sourceCodeShown&&this.set("_sourceFocusNodeName",Kh.removeNodeNameBaseExpansion(f));this._setContinueTo("op",b);
this.$.continueDialog.updateContinueButtonText(!0);this._step()},_createContinueToNodeHandler(){return(b,d)=>{this._continueToNode(b,d)}},_onActiveRuntimeGraphDeviceNameChange(b){const d=Polymer.dom(this.$$("#runtime-graph-device-name"));if(0<this._activeSessionRunDevices.length){let f;f=b+(" (device "+(this._activeSessionRunDevices.indexOf(b)+1)+" of "+this._activeSessionRunDevices.length+")");this._isTopRightRuntimeGraphsActive&&null!=d&&(d.textContent=f)}else this._isTopRightRuntimeGraphsActive&&
null!=d&&(d.textContent="Waiting for device...");null!=b&&this._displayGraph(this._activeSessionRunKey,b)},_step(){if(null!=this._activeSessionRunKey){this.set("_busy",!0);var b={mode:"retrieve_device_names",run_key:JSON.stringify(this._activeSessionRunKey)},d=dc.getRouter().pluginRoute("debugger","/gated_grpc");b=dc.addParams(d,b);this._requestManager.request(b).then(f=>{let h=!1;for(let k=0;k<f.device_names.length;++k)if(-1===this._activeSessionRunDevices.indexOf(f.device_names[k])){h=!0;break}f=
dc.getRouter().pluginRoute("debugger","/ack");this._requestManager.request(f).then(()=>{h&&this._processGatedGrpcDebugOps(this._activeSessionRunKey,!1)})})}},_createSessionRunGo(){return b=>{this._setContinueTo("SessionRun",this._currentSessionRunInfo,b);this._step()}},_createTensorConditionGo(){return(b,d)=>{this._setContinueTo("TensorCondition",b,d);this.$.tensorDataSummary.enableHealthPills();this._step()}},_createForceContinuationStop(){return()=>{this._showToast('Continuation of type "'+this._continueToType+
'" was interrupted by user.');this.set("_continueStop",!0)}},_setContinueTo(b,d,f=-1){this._continueToType=b;this._continueToTarget=d;this._continueToCounterTarget=f;this._continueToCounter=0;this._continueStop=!1},_clearContinueTo(){this.$.continueDialog.notifyContinuationStop();this._continueToTarget=this._continueToType="";this._continueToCounterTarget=-1;this._continueToCounter=0;this._continueStop=!1;this.set("_busy",!1)},_createContinueToCallback(){return(b,d)=>{this._setContinueTo("op",b+"/"+
d);this._step();this._isTopRightRuntimeGraphsActive&&this._focusOnGraphNode(b,d);this.set("_forceExpandNodeName",b+"/"+d)}},_topRightSelectedChanged(b){b=this._topRightTabs[b].id;this.set("_isTopRightRuntimeGraphsActive","tab-runtime-graphs"===b);this.set("_isTopRightTensorValuesActive","tab-tensor-values"===b)},_setTopRightRuntimeGraphsToActive(){this.set("_topRightSelected","0");this.set("_isTopRightRuntimeGraphsActive",!0);this.set("_isTopRightTensorValuesActive",!1)},_setTopRightTensorValuesToActive(){this.set("_topRightSelected",
"1");this.set("_isTopRightRuntimeGraphsActive",!1);this.set("_isTopRightTensorValuesActive",!0)},_requestBreakpointStateChange(b,d,f,h){b={mode:"set_state",node_name:b,output_slot:d,debug_op:f,state:h};d=dc.getRouter().pluginRoute("debugger","/gated_grpc");b=dc.addParams(d,b);this.set("_busy",!0);this._requestManager.request(b).then(k=>{this.set("_busy",!1);console.log("Breakpoint set_state response: ",k)})},_graphProgressUpdated(b){const d=this.$$("#top-right-progress-bar");null==this._latestSessionRun?
(d.setAttribute("value",0),this.set("_busy",!1)):(d.setAttribute("value",b.value),this.set("_busy",100>b.value))},_handleWindowResize(){this.set("_windowWidth",bl());this.set("_windowHeight",al());this._sizeDashboardRegions(this._leftPaneWidth,this._topRightQuadrantHeight,this._windowWidth)},_computeMaxleftPaneWidth(b,d,f){return b-d-f},_computeMaxTopRightQuadrantHeight(b,d){return b-d-70},_sizeDashboardRegions(b,d,f){this.$$("#left-pane").style.width=b+"px";b=f-b-this._resizerWidth-8;this.$$("#center-content").style.width=
b+"px";b=d-this._resizerWidth;this.$$("#top-right-quadrant").style.height=b+"px";this.$$("#tensor-data").style.top=d+"px"},_leftPaneWidthObserver:bd.getNumberObserver("_leftPaneWidth",{defaultValue:450}),_topRightQuadrantHeightObserver:bd.getNumberObserver("_topRightQuadrantHeight",{defaultValue:cl})});

//# sourceURL=build://paper-material/paper-material.html.js
Polymer({is:"paper-material",properties:{elevation:{type:Number,reflectToAttribute:!0,value:1},animated:{type:Boolean,reflectToAttribute:!0,value:!1}}});

//# sourceURL=build://tf-graph-debugger-data-card/tf-graph-debugger-data-card.html.js
(function(){Polymer({is:"tf-graph-debugger-data-card",properties:{renderHierarchy:Object,debuggerNumericAlerts:{type:Array,notify:!0},nodeNamesToHealthPills:Object,healthPillStepIndex:{type:Number,notify:!0},specificHealthPillStep:{type:Number,value:0,notify:!0},selectedNode:{type:String,notify:!0},highlightedNode:{type:String,notify:!0},selectedNodeInclude:{type:Number,notify:!0},areHealthPillsLoading:Boolean,healthPillEntries:{type:Array,value:tf.graph.scene.healthPillEntries,readOnly:!0},healthPillValuesForSelectedNode:{type:Array,
computed:"_computeHealthPillForNode(nodeNamesToHealthPills, healthPillStepIndex, selectedNode, allStepsModeEnabled, areHealthPillsLoading)"},allStepsModeEnabled:{type:Boolean,notify:!0},_biggestStepEverSeen:{type:Number,computed:"_computeBiggestStepEverSeen(nodeNamesToHealthPills)"},_maxStepIndex:{type:Number,computed:"_computeMaxStepIndex(nodeNamesToHealthPills)"},_currentStepDisplayValue:{type:String,computed:"_computeCurrentStepDisplayValue(nodeNamesToHealthPills, healthPillStepIndex, allStepsModeEnabled, specificHealthPillStep, areHealthPillsLoading)"}},
observers:["_updateAlertsList(debuggerNumericAlerts)"],ready:function(){var b=document.getElementById("mainContainer"),d=document.querySelector("tf-dashboard-layout .scrollbar");b&&d&&(b.style.overflow="hidden",d.style.overflow="hidden")},_healthPillsAvailable:function(b,d){return b&&d},_computeTensorCountString:function(b,d){return b?b[d].toFixed(0):""},_computeHealthPillForNode:function(b,d,f,h,k){if(k||!f)return null;b=b[f];return b?(d=b[h?0:d])?d.value.slice(2,8):null:null},_computeCurrentStepDisplayValue:function(b,
d,f,h,k){if(f)return h.toFixed(0);if(k)return 0;for(let r in b)return b[r][d].step.toFixed(0);return 0},_computeBiggestStepEverSeen:function(b){for(let d in b)return b=b[d],Math.max(this._biggestStepEverSeen,b[b.length-1].step);return this._biggestStepEverSeen||0},_computeMaxStepIndex:function(b){for(let d in b)return b[d].length-1;return 0},_hasDebuggerNumericAlerts:function(b){return b&&b.length},_updateAlertsList:function(b){var d=this.$$("#numeric-alerts-body");if(d){d.innerText="";for(var f=
0;f<b.length;f++){var h=b[f],k=document.createElement("tr"),r=document.createElement("td");r.innerText=tf.graph.util.computeHumanFriendlyTime(h.first_timestamp);r.classList.add("first-offense-td");k.appendChild(r);r=document.createElement("td");r.classList.add("tensor-device-td");var l=document.createElement("div");l.classList.add("tensor-section-within-table");l.innerText=h.tensor_name;this._addOpExpansionListener(l,h.tensor_name);r.appendChild(l);l=document.createElement("div");l.classList.add("device-section-within-table");
l.innerText="("+h.device_name+")";r.appendChild(l);k.appendChild(r);r=document.createElement("div");r.classList.add("mini-health-pill");l=document.createElement("td");l.classList.add("mini-health-pill-td");l.appendChild(r);k.appendChild(l);h.neg_inf_event_count&&(l=document.createElement("div"),l.classList.add("negative-inf-mini-health-pill-section"),l.innerText=h.neg_inf_event_count,l.setAttribute("title",h.neg_inf_event_count+" events with -\u221e"),r.appendChild(l));h.pos_inf_event_count&&(l=document.createElement("div"),
l.classList.add("positive-inf-mini-health-pill-section"),l.innerText=h.pos_inf_event_count,l.setAttribute("title",h.pos_inf_event_count+" events with +\u221e"),r.appendChild(l));h.nan_event_count&&(l=document.createElement("div"),l.classList.add("nan-mini-health-pill-section"),l.innerText=h.nan_event_count,l.setAttribute("title",h.nan_event_count+" events with NaN"),r.appendChild(l));Polymer.dom(d).appendChild(k)}}},_addOpExpansionListener:function(b,d){b.addEventListener("click",()=>{var f=tf.graph.render.expandUntilNodeIsShown(this.renderHierarchy,
d),h,k=document.querySelector("tf-graph-info#graph-info");k&&(h=k.scrollHeight-k.scrollTop);var r=this.selectedNode;this.set("selectedNode",f);f=()=>{k.scrollTop=k.scrollHeight-h};k&&(r?f():window.setTimeout(f,20))})}})})();

//# sourceURL=build://iron-list/iron-list.html.js
(function(){var b=navigator.userAgent.match(/iP(?:hone|ad;(?: U;)? CPU) OS (\d+)/),d=b&&8<=b[1],f=null!=Polymer.flush,h=f?Polymer.Async.animationFrame:0,k=f?Polymer.Async.idlePeriod:1,r=f?Polymer.Async.microTask:2;Polymer.OptionalMutableDataBehavior||(Polymer.OptionalMutableDataBehavior={});Polymer({is:"iron-list",properties:{items:{type:Array},as:{type:String,value:"item"},indexAs:{type:String,value:"index"},selectedAs:{type:String,value:"selected"},grid:{type:Boolean,value:!1,reflectToAttribute:!0,
observer:"_gridChanged"},selectionEnabled:{type:Boolean,value:!1},selectedItem:{type:Object,notify:!0},selectedItems:{type:Object,notify:!0},multiSelection:{type:Boolean,value:!1},scrollOffset:{type:Number,value:0}},observers:["_itemsChanged(items.*)","_selectionEnabledChanged(selectionEnabled)","_multiSelectionChanged(multiSelection)","_setOverflow(scrollTarget, scrollOffset)"],behaviors:[Polymer.Templatizer,Polymer.IronResizableBehavior,Polymer.IronScrollTargetBehavior,Polymer.OptionalMutableDataBehavior],
_ratio:.5,_scrollerPaddingTop:0,_scrollPosition:0,_physicalSize:0,_physicalAverage:0,_physicalAverageCount:0,_physicalTop:0,_virtualCount:0,_estScrollHeight:0,_scrollHeight:0,_viewportHeight:0,_viewportWidth:0,_physicalItems:null,_physicalSizes:null,_firstVisibleIndexVal:null,_collection:null,_lastVisibleIndexVal:null,_maxPages:2,_focusedItem:null,_focusedVirtualIndex:-1,_focusedPhysicalIndex:-1,_offscreenFocusedItem:null,_focusBackfillItem:null,_itemsPerRow:1,_itemWidth:0,_rowHeight:0,_templateCost:0,
_parentModel:!0,get _physicalBottom(){return this._physicalTop+this._physicalSize},get _scrollBottom(){return this._scrollPosition+this._viewportHeight},get _virtualEnd(){return this._virtualStart+this._physicalCount-1},get _hiddenContentSize(){return(this.grid?this._physicalRows*this._rowHeight:this._physicalSize)-this._viewportHeight},get _itemsParent(){return Polymer.dom(Polymer.dom(this._userTemplate).parentNode)},get _maxScrollTop(){return this._estScrollHeight-this._viewportHeight+this._scrollOffset},
get _maxVirtualStart(){var l=this._convertIndexToCompleteRow(this._virtualCount);return Math.max(0,l-this._physicalCount)},set _virtualStart(l){l=this._clamp(l,0,this._maxVirtualStart);this.grid&&(l-=l%this._itemsPerRow);this._virtualStartVal=l},get _virtualStart(){return this._virtualStartVal||0},set _physicalStart(l){l%=this._physicalCount;0>l&&(l=this._physicalCount+l);this.grid&&(l-=l%this._itemsPerRow);this._physicalStartVal=l},get _physicalStart(){return this._physicalStartVal||0},get _physicalEnd(){return(this._physicalStart+
this._physicalCount-1)%this._physicalCount},set _physicalCount(l){this._physicalCountVal=l},get _physicalCount(){return this._physicalCountVal||0},get _optPhysicalSize(){return 0===this._viewportHeight?Infinity:this._viewportHeight*this._maxPages},get _isVisible(){return!(!this.offsetWidth&&!this.offsetHeight)},get firstVisibleIndex(){var l=this._firstVisibleIndexVal;if(null==l){var p=this._physicalTop+this._scrollOffset;this._firstVisibleIndexVal=l=this._iterateItems(function(m,n){p+=this._getPhysicalSizeIncrement(m);
if(p>this._scrollPosition)return this.grid?n-n%this._itemsPerRow:n;if(this.grid&&this._virtualCount-1===n)return n-n%this._itemsPerRow})||0}return l},get lastVisibleIndex(){var l=this._lastVisibleIndexVal;if(null==l){if(this.grid)l=Math.min(this._virtualCount,this.firstVisibleIndex+this._estRowsInView*this._itemsPerRow-1);else{var p=this._physicalTop+this._scrollOffset;this._iterateItems(function(m,n){p<this._scrollBottom&&(l=n);p+=this._getPhysicalSizeIncrement(m)})}this._lastVisibleIndexVal=l}return l},
get _defaultScrollTarget(){return this},get _virtualRowCount(){return Math.ceil(this._virtualCount/this._itemsPerRow)},get _estRowsInView(){return Math.ceil(this._viewportHeight/this._rowHeight)},get _physicalRows(){return Math.ceil(this._physicalCount/this._itemsPerRow)},get _scrollOffset(){return this._scrollerPaddingTop+this.scrollOffset},ready:function(){this.addEventListener("focus",this._didFocus.bind(this),!0)},attached:function(){this._debounce("_render",this._render,h);this.listen(this,"iron-resize",
"_resizeHandler");this.listen(this,"keydown","_keydownHandler")},detached:function(){this.unlisten(this,"iron-resize","_resizeHandler");this.unlisten(this,"keydown","_keydownHandler")},_setOverflow:function(l){this.style.webkitOverflowScrolling=l===this?"touch":"";this.style.overflowY=l===this?"auto":"";this._firstVisibleIndexVal=this._lastVisibleIndexVal=null;this._debounce("_render",this._render,h)},updateViewportBoundaries:function(){var l=window.getComputedStyle(this);this._scrollerPaddingTop=
this.scrollTarget===this?0:parseInt(l["padding-top"],10);this._isRTL="rtl"===l.direction;this._viewportWidth=this.$.items.offsetWidth;this._viewportHeight=this._scrollTargetHeight;this.grid&&this._updateGridMetrics()},_scrollHandler:function(){var l=Math.max(0,Math.min(this._maxScrollTop,this._scrollTop)),p=l-this._scrollPosition,m=0<=p;this._scrollPosition=l;this._lastVisibleIndexVal=this._firstVisibleIndexVal=null;Math.abs(p)>this._physicalSize&&0<this._physicalSize?(p-=this._scrollOffset,m=Math.round(p/
this._physicalAverage)*this._itemsPerRow,this._virtualStart+=m,this._physicalStart+=m,this._physicalTop=Math.floor(this._virtualStart/this._itemsPerRow)*this._physicalAverage,this._update()):0<this._physicalCount&&(l=this._getReusables(m),m?(this._physicalTop=l.physicalTop,this._virtualStart+=l.indexes.length,this._physicalStart+=l.indexes.length):(this._virtualStart-=l.indexes.length,this._physicalStart-=l.indexes.length),this._update(l.indexes,m?null:l.indexes),this._debounce("_increasePoolIfNeeded",
this._increasePoolIfNeeded.bind(this,0),r))},_getReusables:function(l){var p=[],m=this._hiddenContentSize*this._ratio,n=this._virtualStart,q=this._virtualEnd,u=this._physicalCount,w=this._physicalTop+this._scrollOffset;var A=this._physicalBottom+this._scrollOffset;var y=this._scrollTop,x=this._scrollBottom;if(l){var C=this._physicalStart;A=y-w}else C=this._physicalEnd,A-=x;for(;;){var G=this._getPhysicalSizeIncrement(C);A-=G;if(p.length>=u||A<=m)break;if(l){if(q+p.length+1>=this._virtualCount)break;
if(w+G>=y-this._scrollOffset)break;p.push(C);w+=G;C=(C+1)%u}else{if(0>=n-p.length)break;if(w+this._physicalSize-G<=x)break;p.push(C);w-=G;C=0===C?u-1:C-1}}return{indexes:p,physicalTop:w-this._scrollOffset}},_update:function(l,p){if(!(l&&0===l.length||0===this._physicalCount)){this._manageFocus();this._assignModels(l);this._updateMetrics(l);if(p)for(;p.length;)l=p.pop(),this._physicalTop-=this._getPhysicalSizeIncrement(l);this._positionItems();this._updateScrollerSize()}},_createPool:function(l){this._ensureTemplatized();
var p,m=Array(l);for(p=0;p<l;p++){var n=this.stamp(null);m[p]=n.root.querySelector("*");this._itemsParent.appendChild(n.root)}return m},_isClientFull:function(){return 0!=this._scrollBottom&&this._physicalBottom-1>=this._scrollBottom&&this._physicalTop<=this._scrollPosition},_increasePoolIfNeeded:function(l){l=this._clamp(this._physicalCount+l,3,this._virtualCount-this._virtualStart);l=this._convertIndexToCompleteRow(l);if(this.grid){var p=l%this._itemsPerRow;p&&l-p<=this._physicalCount&&(l+=this._itemsPerRow);
l-=p}l-=this._physicalCount;p=Math.round(.5*this._physicalCount);if(!(0>l)){if(0<l){p=window.performance.now();[].push.apply(this._physicalItems,this._createPool(l));for(var m=0;m<l;m++)this._physicalSizes.push(0);this._physicalCount+=l;this._physicalStart>this._physicalEnd&&this._isIndexRendered(this._focusedVirtualIndex)&&this._getPhysicalIndex(this._focusedVirtualIndex)<this._physicalEnd&&(this._physicalStart+=l);this._update();this._templateCost=(window.performance.now()-p)/l;p=Math.round(.5*
this._physicalCount)}this._virtualEnd>=this._virtualCount-1||0===p||(this._isClientFull()?this._physicalSize<this._optPhysicalSize&&this._debounce("_increasePoolIfNeeded",this._increasePoolIfNeeded.bind(this,this._clamp(Math.round(50/this._templateCost),1,p)),k):this._debounce("_increasePoolIfNeeded",this._increasePoolIfNeeded.bind(this,p),r))}},_render:function(){if(this.isAttached&&this._isVisible)if(0!==this._physicalCount){var l=this._getReusables(!0);this._physicalTop=l.physicalTop;this._virtualStart+=
l.indexes.length;this._physicalStart+=l.indexes.length;this._update(l.indexes);this._update();this._increasePoolIfNeeded(0)}else 0<this._virtualCount&&(this.updateViewportBoundaries(),this._increasePoolIfNeeded(3))},_ensureTemplatized:function(){if(!this.ctor){(this._userTemplate=this.queryEffectiveChildren("template"))||console.warn("iron-list requires a template to be provided in light-dom");var l={__key__:!0};l[this.as]=!0;l[this.indexAs]=!0;l[this.selectedAs]=!0;l.tabIndex=!0;this._instanceProps=
l;this.templatize(this._userTemplate,this.mutableData)}},_gridChanged:function(l,p){"undefined"!==typeof p&&(this.notifyResize(),Polymer.flush?Polymer.flush():Polymer.dom.flush(),l&&this._updateGridMetrics())},_itemsChanged:function(l){if("items"===l.path)this._physicalTop=this._virtualStart=0,this._virtualCount=this.items?this.items.length:0,this._collection=this.items&&Polymer.Collection?Polymer.Collection.get(this.items):null,this._physicalIndexForKey={},this._lastVisibleIndexVal=this._firstVisibleIndexVal=
null,this._physicalCount=this._physicalCount||0,this._physicalItems=this._physicalItems||[],this._physicalSizes=this._physicalSizes||[],this._physicalStart=0,this._scrollTop>this._scrollOffset&&this._resetScrollPosition(0),this._removeFocusedItem(),this._debounce("_render",this._render,h);else if("items.splices"===l.path){this._adjustVirtualIndex(l.value.indexSplices);this._virtualCount=this.items?this.items.length:0;if(l.value.indexSplices.some(function(m){return 0<m.addedCount||0<m.removed.length})){var p=
this._getActiveElement();this.contains(p)&&p.blur()}l=l.value.indexSplices.some(function(m){return m.index+m.addedCount>=this._virtualStart&&m.index<=this._virtualEnd},this);this._isClientFull()&&!l||this._debounce("_render",this._render,h)}else"items.length"!==l.path&&this._forwardItemPath(l.path,l.value)},_forwardItemPath:function(l,p){l=l.slice(6);var m=l.indexOf(".");-1===m&&(m=l.length);var n,q=this.modelForElement(this._offscreenFocusedItem);if(f){var u=parseInt(l.substring(0,m),10);if(n=this._isIndexRendered(u)){var w=
this._getPhysicalIndex(u);var A=this.modelForElement(this._physicalItems[w])}else q&&(A=q);if(!A||A[this.indexAs]!==u)return}else if(u=l.substring(0,m),q&&q.__key__===u)A=q;else if(w=this._physicalIndexForKey[u],A=this.modelForElement(this._physicalItems[w]),!A||A.__key__!==u)return;l=l.substring(m+1);l=this.as+(l?"."+l:"");f?A._setPendingPropertyOrPath(l,p,!1,!0):A.notifyPath(l,p,!0);A._flushProperties&&A._flushProperties(!0);n&&(this._updateMetrics([w]),this._positionItems(),this._updateScrollerSize())},
_adjustVirtualIndex:function(l){l.forEach(function(p){p.removed.forEach(this._removeItem,this);p.index<this._virtualStart&&(p=Math.max(p.addedCount-p.removed.length,p.index-this._virtualStart),this._virtualStart+=p,0<=this._focusedVirtualIndex&&(this._focusedVirtualIndex+=p))},this)},_removeItem:function(l){this.$.selector.deselect(l);this._focusedItem&&this.modelForElement(this._focusedItem)[this.as]===l&&this._removeFocusedItem()},_iterateItems:function(l,p){var m,n;if(2===arguments.length&&p)for(n=
0;n<p.length;n++){var q=p[n];var u=this._computeVidx(q);if(null!=(m=l.call(this,q,u)))return m}else{q=this._physicalStart;for(u=this._virtualStart;q<this._physicalCount;q++,u++)if(null!=(m=l.call(this,q,u)))return m;for(q=0;q<this._physicalStart;q++,u++)if(null!=(m=l.call(this,q,u)))return m}},_computeVidx:function(l){return l>=this._physicalStart?this._virtualStart+(l-this._physicalStart):this._virtualStart+(this._physicalCount-this._physicalStart)+l},_assignModels:function(l){this._iterateItems(function(p,
m){var n=this._physicalItems[p],q=this.items&&this.items[m];if(null!=q){var u=this.modelForElement(n);u.__key__=this._collection?this._collection.getKey(q):null;this._forwardProperty(u,this.as,q);this._forwardProperty(u,this.selectedAs,this.$.selector.isSelected(q));this._forwardProperty(u,this.indexAs,m);this._forwardProperty(u,"tabIndex",this._focusedVirtualIndex===m?0:-1);this._physicalIndexForKey[u.__key__]=p;u._flushProperties&&u._flushProperties(!0);n.removeAttribute("hidden")}else n.setAttribute("hidden",
"")},l)},_updateMetrics:function(l){Polymer.flush?Polymer.flush():Polymer.dom.flush();var p=0,m=0,n=this._physicalAverageCount,q=this._physicalAverage;this._iterateItems(function(u){m+=this._physicalSizes[u];this._physicalSizes[u]=this._physicalItems[u].offsetHeight;p+=this._physicalSizes[u];this._physicalAverageCount+=this._physicalSizes[u]?1:0},l);this.grid?(this._updateGridMetrics(),this._physicalSize=Math.ceil(this._physicalCount/this._itemsPerRow)*this._rowHeight):(m=1===this._itemsPerRow?m:
Math.ceil(this._physicalCount/this._itemsPerRow)*this._rowHeight,this._physicalSize=this._physicalSize+p-m,this._itemsPerRow=1);this._physicalAverageCount!==n&&(this._physicalAverage=Math.round((q*n+p)/this._physicalAverageCount))},_updateGridMetrics:function(){this._itemWidth=0<this._physicalCount?this._physicalItems[0].getBoundingClientRect().width:200;this._rowHeight=0<this._physicalCount?this._physicalItems[0].offsetHeight:200;this._itemsPerRow=this._itemWidth?Math.floor(this._viewportWidth/this._itemWidth):
this._itemsPerRow},_positionItems:function(){this._adjustScrollPosition();var l=this._physicalTop;if(this.grid){var p=(this._viewportWidth-this._itemsPerRow*this._itemWidth)/2;this._iterateItems(function(m,n){var q=Math.floor(n%this._itemsPerRow*this._itemWidth+p);this._isRTL&&(q*=-1);this.translate3d(q+"px",l+"px",0,this._physicalItems[m]);this._shouldRenderNextRow(n)&&(l+=this._rowHeight)})}else this._iterateItems(function(m){this.translate3d(0,l+"px",0,this._physicalItems[m]);l+=this._physicalSizes[m]})},
_getPhysicalSizeIncrement:function(l){return this.grid?this._computeVidx(l)%this._itemsPerRow!==this._itemsPerRow-1?0:this._rowHeight:this._physicalSizes[l]},_shouldRenderNextRow:function(l){return l%this._itemsPerRow===this._itemsPerRow-1},_adjustScrollPosition:function(){var l=0===this._virtualStart?this._physicalTop:Math.min(this._scrollPosition+this._physicalTop,0);if(0!==l){this._physicalTop-=l;var p=this._scrollTop;!d&&0<p&&this._resetScrollPosition(p-l)}},_resetScrollPosition:function(l){this.scrollTarget&&
0<=l&&(this._scrollPosition=this._scrollTop=l)},_updateScrollerSize:function(l){this._estScrollHeight=this.grid?this._virtualRowCount*this._rowHeight:this._physicalBottom+Math.max(this._virtualCount-this._physicalCount-this._virtualStart,0)*this._physicalAverage;if((l=(l=(l=l||0===this._scrollHeight)||this._scrollPosition>=this._estScrollHeight-this._physicalSize)||this.grid&&this.$.items.style.height<this._estScrollHeight)||Math.abs(this._estScrollHeight-this._scrollHeight)>=this._viewportHeight)this.$.items.style.height=
this._estScrollHeight+"px",this._scrollHeight=this._estScrollHeight},scrollToItem:function(l){return this.scrollToIndex(this.items.indexOf(l))},scrollToIndex:function(l){if(!("number"!==typeof l||0>l||l>this.items.length-1)&&(Polymer.flush?Polymer.flush():Polymer.dom.flush(),0!==this._physicalCount)){l=this._clamp(l,0,this._virtualCount-1);if(!this._isIndexRendered(l)||l>=this._maxVirtualStart)this._virtualStart=this.grid?l-2*this._itemsPerRow:l-1;this._manageFocus();this._assignModels();this._updateMetrics();
this._physicalTop=Math.floor(this._virtualStart/this._itemsPerRow)*this._physicalAverage;for(var p=this._physicalStart,m=this._virtualStart,n=0,q=this._hiddenContentSize;m<l&&n<=q;)n+=this._getPhysicalSizeIncrement(p),p=(p+1)%this._physicalCount,m++;this._updateScrollerSize(!0);this._positionItems();this._resetScrollPosition(this._physicalTop+this._scrollOffset+n);this._increasePoolIfNeeded(0);this._lastVisibleIndexVal=this._firstVisibleIndexVal=null}},_resetAverage:function(){this._physicalAverageCount=
this._physicalAverage=0},_resizeHandler:function(){this._debounce("_render",function(){this._lastVisibleIndexVal=this._firstVisibleIndexVal=null;this.updateViewportBoundaries();this._isVisible?(this.toggleScrollListener(!0),this._resetAverage(),this._render()):this.toggleScrollListener(!1)},h)},selectItem:function(l){return this.selectIndex(this.items.indexOf(l))},selectIndex:function(l){if(!(0>l||l>=this._virtualCount)){!this.multiSelection&&this.selectedItem&&this.clearSelection();if(this._isIndexRendered(l)){var p=
this.modelForElement(this._physicalItems[this._getPhysicalIndex(l)]);p&&(p[this.selectedAs]=!0);this.updateSizeForIndex(l)}this.$.selector.selectIndex?this.$.selector.selectIndex(l):this.$.selector.select(this.items[l])}},deselectItem:function(l){return this.deselectIndex(this.items.indexOf(l))},deselectIndex:function(l){0>l||l>=this._virtualCount||(this._isIndexRendered(l)&&(this.modelForElement(this._physicalItems[this._getPhysicalIndex(l)])[this.selectedAs]=!1,this.updateSizeForIndex(l)),this.$.selector.deselectIndex?
this.$.selector.deselectIndex(l):this.$.selector.deselect(this.items[l]))},toggleSelectionForItem:function(l){return this.toggleSelectionForIndex(this.items.indexOf(l))},toggleSelectionForIndex:function(l){(this.$.selector.isIndexSelected?this.$.selector.isIndexSelected(l):this.$.selector.isSelected(this.items[l]))?this.deselectIndex(l):this.selectIndex(l)},clearSelection:function(){this._iterateItems(function(l){this.modelForElement(this._physicalItems[l])[this.selectedAs]=!1});this.$.selector.clearSelection()},
_selectionEnabledChanged:function(l){(l?this.listen:this.unlisten).call(this,this,"tap","_selectionHandler")},_selectionHandler:function(l){var p=this.modelForElement(l.target);if(p){var m=Polymer.dom(l).path[0];l=this._getActiveElement();var n=this._physicalItems[this._getPhysicalIndex(p[this.indexAs])];if("input"!==m.localName&&"button"!==m.localName&&"select"!==m.localName){m=p.tabIndex;p.tabIndex=-100;var q=l?l.tabIndex:-1;p.tabIndex=m;l&&n!==l&&n.contains(l)&&-100!==q||this.toggleSelectionForItem(p[this.as])}}},
_multiSelectionChanged:function(l){this.clearSelection();this.$.selector.multi=l},updateSizeForItem:function(l){return this.updateSizeForIndex(this.items.indexOf(l))},updateSizeForIndex:function(l){if(!this._isIndexRendered(l))return null;this._updateMetrics([this._getPhysicalIndex(l)]);this._positionItems();return null},_manageFocus:function(){var l=this._focusedVirtualIndex;0<=l&&l<this._virtualCount?this._isIndexRendered(l)?this._restoreFocusedItem():this._createFocusBackfillItem():0<this._virtualCount&&
0<this._physicalCount&&(this._focusedPhysicalIndex=this._physicalStart,this._focusedVirtualIndex=this._virtualStart,this._focusedItem=this._physicalItems[this._physicalStart])},_convertIndexToCompleteRow:function(l){this._itemsPerRow=this._itemsPerRow||1;return this.grid?Math.ceil(l/this._itemsPerRow)*this._itemsPerRow:l},_isIndexRendered:function(l){return l>=this._virtualStart&&l<=this._virtualEnd},_isIndexVisible:function(l){return l>=this.firstVisibleIndex&&l<=this.lastVisibleIndex},_getPhysicalIndex:function(l){return f?
(this._physicalStart+(l-this._virtualStart))%this._physicalCount:this._physicalIndexForKey[this._collection.getKey(this.items[l])]},focusItem:function(l){this._focusPhysicalItem(l)},_focusPhysicalItem:function(l){if(!(0>l||l>=this._virtualCount)){this._restoreFocusedItem();this._isIndexRendered(l)||this.scrollToIndex(l);var p=this._physicalItems[this._getPhysicalIndex(l)],m=this.modelForElement(p),n;m.tabIndex=-100;-100===p.tabIndex&&(n=p);n||(n=Polymer.dom(p).querySelector('[tabindex\x3d"-100"]'));
m.tabIndex=0;this._focusedVirtualIndex=l;n&&n.focus()}},_removeFocusedItem:function(){this._offscreenFocusedItem&&this._itemsParent.removeChild(this._offscreenFocusedItem);this._focusedItem=this._focusBackfillItem=this._offscreenFocusedItem=null;this._focusedPhysicalIndex=this._focusedVirtualIndex=-1},_createFocusBackfillItem:function(){var l=this._focusedPhysicalIndex;if(!(this._offscreenFocusedItem||0>this._focusedVirtualIndex)){if(!this._focusBackfillItem){var p=this.stamp(null);this._focusBackfillItem=
p.root.querySelector("*");this._itemsParent.appendChild(p.root)}this._offscreenFocusedItem=this._physicalItems[l];this.modelForElement(this._offscreenFocusedItem).tabIndex=0;this._physicalItems[l]=this._focusBackfillItem;this._focusedPhysicalIndex=l;this.translate3d(0,"-10000px",0,this._offscreenFocusedItem)}},_restoreFocusedItem:function(){if(this._offscreenFocusedItem&&!(0>this._focusedVirtualIndex)){this._assignModels();var l=this._focusedPhysicalIndex=this._getPhysicalIndex(this._focusedVirtualIndex),
p=this._physicalItems[l];if(p){var m=this.modelForElement(p),n=this.modelForElement(this._offscreenFocusedItem);m[this.as]===n[this.as]?(this._focusBackfillItem=p,m.tabIndex=-1,this._physicalItems[l]=this._offscreenFocusedItem,this.translate3d(0,"-10000px",0,this._focusBackfillItem)):(this._removeFocusedItem(),this._focusBackfillItem=null);this._offscreenFocusedItem=null}}},_didFocus:function(l){l=this.modelForElement(l.target);var p=this.modelForElement(this._focusedItem),m=null!==this._offscreenFocusedItem,
n=this._focusedVirtualIndex;l&&(p===l?this._isIndexVisible(n)||this.scrollToIndex(n):(this._restoreFocusedItem(),p&&(p.tabIndex=-1),l.tabIndex=0,this._focusedVirtualIndex=n=l[this.indexAs],this._focusedPhysicalIndex=this._getPhysicalIndex(n),this._focusedItem=this._physicalItems[this._focusedPhysicalIndex],m&&!this._offscreenFocusedItem&&this._update()))},_keydownHandler:function(l){switch(l.keyCode){case 40:this._focusedVirtualIndex<this._virtualCount-1&&l.preventDefault();this._focusPhysicalItem(this._focusedVirtualIndex+
(this.grid?this._itemsPerRow:1));break;case 39:this.grid&&this._focusPhysicalItem(this._focusedVirtualIndex+(this._isRTL?-1:1));break;case 38:0<this._focusedVirtualIndex&&l.preventDefault();this._focusPhysicalItem(this._focusedVirtualIndex-(this.grid?this._itemsPerRow:1));break;case 37:this.grid&&this._focusPhysicalItem(this._focusedVirtualIndex+(this._isRTL?1:-1));break;case 13:this._focusPhysicalItem(this._focusedVirtualIndex),this.selectionEnabled&&this._selectionHandler(l)}},_clamp:function(l,
p,m){return Math.min(m,Math.max(p,l))},_debounce:function(l,p,m){f?(this._debouncers=this._debouncers||{},this._debouncers[l]=Polymer.Debouncer.debounce(this._debouncers[l],m,p.bind(this)),Polymer.enqueueDebouncer(this._debouncers[l])):Polymer.dom.addDebouncer(this.debounce(l,p))},_forwardProperty:function(l,p,m){f?l._setPendingProperty(p,m):l[p]=m},_forwardHostPropV2:function(l,p){(this._physicalItems||[]).concat([this._offscreenFocusedItem,this._focusBackfillItem]).forEach(function(m){m&&this.modelForElement(m).forwardHostProp(l,
p)},this)},_notifyInstancePropV2:function(l,p,m){Polymer.Path.matches(this.as,p)&&(l=l[this.indexAs],p==this.as&&(this.items[l]=m),this.notifyPath(Polymer.Path.translate(this.as,"items."+l,p),m))},_getStampedChildren:function(){return this._physicalItems},_forwardInstancePath:function(l,p,m){0===p.indexOf(this.as+".")&&this.notifyPath("items."+l.__key__+"."+p.slice(this.as.length+1),m)},_forwardParentPath:function(l,p){(this._physicalItems||[]).concat([this._offscreenFocusedItem,this._focusBackfillItem]).forEach(function(m){m&&
this.modelForElement(m).notifyPath(l,p,!0)},this)},_forwardParentProp:function(l,p){(this._physicalItems||[]).concat([this._offscreenFocusedItem,this._focusBackfillItem]).forEach(function(m){m&&(this.modelForElement(m)[l]=p)},this)},_getActiveElement:function(){var l=this._itemsParent.node.domHost;return Polymer.dom(l?l.root:document).activeElement}})})();

//# sourceURL=build://paper-item/paper-item-body.html.js
Polymer({is:"paper-item-body"});

//# sourceURL=build://tf-graph-common/tf-graph-icon.js
(function(b){(function(d){(function(f){let h;(function(k){k.CONST="CONST";k.META="META";k.OP="OP";k.SERIES="SERIES";k.SUMMARY="SUMMARY"})(h=f.GraphIconType||(f.GraphIconType={}));Polymer({is:"tf-graph-icon",properties:{type:String,vertical:{type:Boolean,value:!1},fillOverride:{type:String,value:null},strokeOverride:{type:String,value:null},height:{type:Number,value:20},faded:{type:Boolean,value:!1},_fill:{type:String,computed:"_computeFill(type, fillOverride)"},_stroke:{type:String,computed:"_computeStroke(type, strokeOverride)"}},
getSvgDefinableElement(){return this.$.svgDefs},_computeFill(k,r){if(null!=r)return r;switch(k){case h.META:return b.graph.render.MetanodeColors.DEFAULT_FILL;case h.SERIES:return b.graph.render.SeriesNodeColors.DEFAULT_FILL;default:return b.graph.render.OpNodeColors.DEFAULT_FILL}},_computeStroke(k,r){if(null!=r)return r;switch(k){case h.META:return b.graph.render.MetanodeColors.DEFAULT_STROKE;case h.SERIES:return b.graph.render.SeriesNodeColors.DEFAULT_STROKE;default:return b.graph.render.OpNodeColors.DEFAULT_STROKE}},
_isType(k,r){return k===r},_fadedClass:function(k,r){return k?"faded-"+r:""}})})(d.icon||(d.icon={}))})(b.graph||(b.graph={}))})(tf||(tf={}));

//# sourceURL=build://tf-graph-common/tf-node-icon.html.js
(function(){Polymer({is:"tf-node-icon",properties:{node:{type:Object,value:null},renderInfo:{type:Object,value:null},colorBy:{type:Object,value:"structural"},templateIndex:{type:Function,value:null},type:{type:String,value:null},vertical:{type:Boolean,value:!1},const:{type:Boolean,value:!1},summary:{type:Boolean,value:!1},fill:{type:String,value:null},height:{type:Number,value:20},_fillOverride:{type:String,computed:"_computeFillOverride(node, renderInfo, colorBy, templateIndex, fill)",observer:"_onFillOverrideChanged"}},
_computeFillOverride:function(b,d,f,h,k){return b&&d&&f&&h?(b=tf.graph.scene.node,b.getFillForNode(h,b.ColorBy[f.toUpperCase()],d,!1)):k},_getStrokeOverride:function(b){return b?tf.graph.scene.node.getStrokeForFill(b):null},_getType:function(b,d,f,h){const k=tf.graph.icon.GraphIconType;if(b)switch(b.type){case tf.graph.NodeType.OP:return b=b.op,"string"!==typeof b?k.OP:"Const"===b||f?k.CONST:b.endsWith("Summary")||d?k.SUMMARY:k.OP;case tf.graph.NodeType.META:return k.META;case tf.graph.NodeType.SERIES:return k.SERIES}return h},
_isVertical:function(b,d){return b?b.hasNonControlEdges:!!d},_getFaded:function(b){return b&&b.isFadedOut},_onFillOverrideChanged(b,d){const f=this.node,h=this.renderInfo,k=this.colorBy,r=this.templateIndex,l=tf.graph.scene.node;b!==d&&l.removeGradientDefinitions(this.$.icon.getSvgDefinableElement());f&&h&&k&&r&&l.getFillForNode(r,l.ColorBy[k.toUpperCase()],h,!1,this.$.icon.getSvgDefinableElement())}})})();

//# sourceURL=build://tf-graph-op-compat-card/tf-graph-op-compat-list-item.html.js
(function(){Polymer({is:"tf-graph-op-compat-list-item",properties:{cardNode:Object,itemNode:Object,edgeLabel:String,itemRenderInfo:Object,name:String,itemType:{type:String,observer:"_itemTypeChanged"},colorBy:String,colorByParams:Object,templateIndex:Function},_itemTypeChanged:function(){"subnode"!==this.itemType?this.$["list-item"].classList.add("clickable"):this.$["list-item"].classList.remove("clickable")},_nodeListener:function(b){this.fire("node-list-item-"+b.type,{nodeName:this.name,type:this.itemType})},
_fadedClass:function(b){return b&&b.isFadedOut?"faded":""}})})();

//# sourceURL=build://tf-graph-op-compat-card/tf-graph-op-compat-card.html.js
(function(){Polymer({is:"tf-graph-op-compat-card",properties:{graphHierarchy:Object,hierarchyParams:Object,renderHierarchy:Object,nodeTitle:String,_templateIndex:{type:Function,computed:"_getTemplateIndex(graphHierarchy)"},_incompatibleOpNodes:{type:Object,computed:"_getIncompatibleOpNodes(graphHierarchy, hierarchyParams)"},_expanded:{type:Boolean,value:!0},_opCompatScore:{type:Number,computed:"_computeOpCompatScore(graphHierarchy)"},_opCompatScoreLabel:{type:String,computed:"_getOpCompatScoreLabel(_opCompatScore)"},
_opCompatColor:{type:String,value:tf.graph.render.OpNodeColors.COMPATIBLE},_opIncompatColor:{type:String,value:tf.graph.render.OpNodeColors.INCOMPATIBLE},_totalIncompatOps:{type:Number,computed:"_getTotalIncompatibleOps(graphHierarchy)"}},_getTemplateIndex:function(b){return b.getTemplateIndex()},_getNode:function(b,d){return d.node(b)},_getPrintableHTMLNodeName:function(b){return(b||"").replace(/\//g,"\x3cwbr\x3e/")},_getRenderInfo:function(b){return this.renderHierarchy.getOrCreateRenderNodeByName(b)},
_toggleExpanded:function(){this._expanded=!this._expanded},_getToggleIcon:function(b){return b?"expand-less":"expand-more"},_resizeList:function(b){(b=document.querySelector(b))&&b.fire("iron-resize")},_getIncompatibleOpNodes:function(b,d){if(b&&b.root)return this.async(this._resizeList.bind(this,"#incompatibleOpsList")),tf.graph.hierarchy.getIncompatibleOps(b,d)},_computeOpCompatScore:function(b){if(b&&b.root){var d=b.root;b=d.compatibilityHistogram.compatible;d=d.compatibilityHistogram.incompatible;
return 0==b&&0==d?0:Math.floor(100*b/(b+d))/100}return 0},_getOpCompatScoreLabel:function(b){return d3.format(".0%")(b)},_getTotalIncompatibleOps:function(b){return b&&b.root?b.root.compatibilityHistogram.incompatible:0}})})();

//# sourceURL=build://tf-graph-info/tf-node-list-item.html.js
(function(){Polymer({is:"tf-node-list-item",properties:{cardNode:Object,itemNode:Object,edgeLabel:String,itemRenderInfo:Object,name:String,itemType:{type:String,observer:"_itemTypeChanged"},colorBy:String,colorByParams:Object,templateIndex:Function},_itemTypeChanged:function(){"subnode"!==this.itemType?this.$["list-item"].classList.add("clickable"):this.$["list-item"].classList.remove("clickable")},_nodeListener:function(b){this.fire("node-list-item-"+b.type,{cardNode:this.cardNode.name,nodeName:this.name,
type:this.itemType})},_fadedClass:function(b){return b&&b.isFadedOut?"faded":""}})})();

//# sourceURL=build://tf-graph-info/tf-node-info.html.js
(function(){Polymer({is:"tf-node-info",properties:{graphNodeName:String,graphHierarchy:Object,renderHierarchy:Object,colorBy:String,_templateIndex:{type:Function,computed:"_getTemplateIndex(graphHierarchy)"},_node:{type:Object,computed:"_getNode(graphNodeName, graphHierarchy)",observer:"_resetState"},_nodeStats:{type:Object,computed:"_getNodeStats(graphNodeName, graphHierarchy)",observer:"_resetState"},_hasDisplayableNodeStats:{type:Object,computed:"_getHasDisplayableNodeStats(_nodeStats)"},_nodeStatsFormattedBytes:{type:String,
computed:"_getNodeStatsFormattedBytes(_nodeStats)"},_nodeStatsFormattedComputeTime:{type:String,computed:"_getNodeStatsFormattedComputeTime(_nodeStats)"},_nodeStatsFormattedOutputSizes:{type:Array,computed:"_getNodeStatsFormattedOutputSizes(_nodeStats)"},nodeInclude:{type:Number,observer:"_nodeIncludeStateChanged"},_attributes:{type:Array,computed:"_getAttributes(_node)"},_device:{type:String,computed:"_getDevice(_node)"},_successors:{type:Object,computed:"_getSuccessors(_node, graphHierarchy)"},
_predecessors:{type:Object,computed:"_getPredecessors(_node, graphHierarchy)"},_functionUsages:{type:Array,computed:"_getFunctionUsages(_node, graphHierarchy)"},_subnodes:{type:Array,computed:"_getSubnodes(_node)"},_expanded:{type:Boolean,value:!0},_totalPredecessors:{type:Number,computed:"_getTotalPred(_predecessors)"},_totalSuccessors:{type:Number,computed:"_getTotalSucc(_successors)"},_openedControlPred:{type:Boolean,value:!1},_openedControlSucc:{type:Boolean,value:!1},_auxButtonText:String,_groupButtonText:String},
expandNode:function(){this.fire("_node.expand",this.node)},_getTemplateIndex:function(b){return b.getTemplateIndex()},_getNode:function(b,d){return d.node(b)},_getNodeStats:function(b,d){return(b=this._getNode(b,d))?b.stats:null},_getTotalMicros:function(b){return b?b.getTotalMicros():0},_getHasDisplayableNodeStats:function(b){return tf.graph.util.hasDisplayableNodeStats(b)},_getNodeStatsFormattedBytes:function(b){if(b&&b.totalBytes)return tf.graph.util.convertUnitsToHumanReadable(b.totalBytes,tf.graph.util.MEMORY_UNITS)},
_getNodeStatsFormattedComputeTime:function(b){if(b&&b.getTotalMicros())return tf.graph.util.convertUnitsToHumanReadable(b.getTotalMicros(),tf.graph.util.TIME_UNITS)},_getNodeStatsFormattedOutputSizes:function(b){if(b&&b.outputSize&&b.outputSize.length)return _.map(b.outputSize,function(d){return 0===d.length?"scalar":"["+d.join(", ")+"]"})},_getPrintableHTMLNodeName:function(b){return(b||"").replace(/\//g,"\x3cwbr\x3e/")},_getRenderInfo:function(b){return this.renderHierarchy.getOrCreateRenderNodeByName(b)},
_getAttributes:function(b){this.async(this._resizeList.bind(this,"#attributesList"));if(!b||!b.attr)return[];var d=[];_.each(b.attr,function(f){f.key===tf.graph.LARGE_ATTRS_KEY?d=d.concat(f.value.list.s.map(function(h){return{key:h,value:"Too large to show..."}})):d.push({key:f.key,value:JSON.stringify(f.value)})});return d},_getDevice:function(b){return b?b.device:null},_getSuccessors(b,d){this._refreshNodeItemList("inputsList");return b?this._convertEdgeListToEdgeInfoList(d.getSuccessors(b.name),
!1,b.isGroupNode):{regular:[],control:[]}},_getPredecessors(b,d){this._refreshNodeItemList("outputsList");return b?this._convertEdgeListToEdgeInfoList(d.getPredecessors(b.name),!0,b.isGroupNode):{regular:[],control:[]}},_getFunctionUsages(b,d){this._refreshNodeItemList("functionUsagesList");return b&&b.type===tf.graph.NodeType.META?(b=d.libraryFunctions[b.associatedFunction])?b.usages:[]:[]},_refreshNodeItemList(b){this.async(this._resizeList.bind(this,`#${b}`))},_convertEdgeListToEdgeInfoList:function(b,
d,f){var h=r=>_.map(r.baseEdgeList,l=>{var p=d?l.v:l.w;return{name:p,node:this._getNode(p,this.graphHierarchy),edgeLabel:tf.graph.scene.edge.getLabelForBaseEdge(l,this.renderHierarchy),renderInfo:this._getRenderInfo(p,this.renderHierarchy)}}),k=function(r){var l=[];_.each(r,p=>{var m=d?p.v:p.w;f&&1!=p.baseEdgeList.length?l.push({name:m,node:this._getNode(m,this.graphHierarchy),edgeLabel:tf.graph.scene.edge.getLabelForEdge(p,this.renderHierarchy),renderInfo:this._getRenderInfo(m,this.renderHierarchy)}):
l=l.concat(h(p))});return l}.bind(this);return{regular:k(b.regular),control:k(b.control)}},_getSubnodes:function(b){return b&&b.metagraph?b.metagraph.nodes():null},_getTotalPred:function(b){return b.regular.length+b.control.length},_getTotalSucc:function(b){return b.regular.length+b.control.length},_toggleControlPred:function(){this._openedControlPred=!this._openedControlPred},_toggleControlSucc:function(){this._openedControlSucc=!this._openedControlSucc},_toggleExpanded:function(){this._expanded=
!this._expanded},_getToggleIcon:function(b){return b?"expand-less":"expand-more"},_resetState:function(){this._openedControlSucc=this._openedControlPred=!1;this.set("_groupButtonText",tf.graph.scene.node.getGroupSettingLabel(this._node));this._node&&(Polymer.dom(this.$.nodetitle).innerHTML=this._getPrintableHTMLNodeName(this._node.name))},_resizeList:function(b){(b=document.querySelector(b))&&b.fire("iron-resize")},_toggleInclude:function(){this.fire("node-toggle-inclusion",{name:this.graphNodeName})},
_nodeIncludeStateChanged:function(b){this.set("_auxButtonText",tf.graph.getIncludeNodeButtonString(b))},_toggleGroup:function(){var b=tf.graph.scene.node.getSeriesName(this._node);this.fire("node-toggle-seriesgroup",{name:b})},_isLibraryFunction(b){return b&&b.name.startsWith(tf.graph.FUNCTION_LIBRARY_NODE_PREFIX)},_isInSeries:function(b){return tf.graph.scene.node.canBeInSeries(b)}})})();

//# sourceURL=build://tf-graph-info/tf-graph-info.html.js
(function(){Polymer({is:"tf-graph-info",properties:{title:String,graphHierarchy:Object,graph:Object,renderHierarchy:Object,nodeNamesToHealthPills:Object,healthPillStepIndex:{type:Number,notify:!0},colorBy:String,compatNodeTitle:String,selectedNode:{type:String,notify:!0},highlightedNode:{type:String,notify:!0},selectedNodeInclude:{type:Number,notify:!0},debuggerDataEnabled:Boolean},listeners:{"node-list-item-click":"_nodeListItemClicked","node-list-item-mouseover":"_nodeListItemMouseover","node-list-item-mouseout":"_nodeListItemMouseout"},
_nodeListItemClicked:function(b){this.selectedNode=b.detail.nodeName},_nodeListItemMouseover:function(b){this.highlightedNode=b.detail.nodeName},_nodeListItemMouseout:function(){this.highlightedNode=null},_healthPillsAvailable:function(b,d){return b&&d&&0<Object.keys(d).length},_equals:function(b,d){return b===d}})})();

//# sourceURL=build://tf-graph-board/tf-graph-board.html.js
Polymer({is:"tf-graph-board",properties:{graphHierarchy:Object,graph:Object,stats:Object,progress:Object,traceInputs:Boolean,colorBy:String,colorByParams:{type:Object,notify:!0},renderHierarchy:{type:Object,notify:!0},debuggerDataEnabled:Boolean,areHealthPillsLoading:Boolean,debuggerNumericAlerts:{type:Array,notify:!0},nodeNamesToHealthPills:Object,allStepsModeEnabled:{type:Boolean,notify:!0,value:!1},specificHealthPillStep:{type:Number,notify:!0,value:0},healthPillStepIndex:Number,selectedNode:{type:String,
notify:!0},compatNodeTitle:{type:String,value:"TPU Compatibility"},edgeWidthFunction:Object,_selectedNodeInclude:Number,_highlightedNode:String,handleNodeSelected:Object,edgeLabelFunction:Object,handleEdgeSelected:Object},observers:["_updateNodeInclude(selectedNode, renderHierarchy)"],fit:function(){this.$.graph.fit()},_isNotComplete:function(b){return 100>b.value},_getContainerClass:function(b){var d="container";b.error&&(d+=" error");this._isNotComplete(b)&&(d+=" loading");return d},_onNodeInclusionToggled(b){this.$.graph.nodeToggleExtract(b.detail.name)},
_onNodeSeriesGroupToggled(b){this.$.graph.nodeToggleSeriesGroup(b.detail.name)},_updateNodeInclude(){const b=this.renderHierarchy?this.renderHierarchy.getNodeByName(this.selectedNode):null;this._selectedNodeInclude=b?b.include:tf.graph.InclusionType.UNSPECIFIED}});

//# sourceURL=build://paper-radio-button/paper-radio-button.html.js
Polymer({is:"paper-radio-button",behaviors:[Polymer.PaperCheckedElementBehavior],hostAttributes:{role:"radio","aria-checked":!1,tabindex:0},properties:{ariaActiveAttribute:{type:String,value:"aria-checked"}},ready:function(){this._rippleContainer=this.$.radioContainer},attached:function(){Polymer.RenderStatus.afterNextRender(this,function(){if("-1px"===this.getComputedStyleValue("--calculated-paper-radio-button-ink-size").trim()){var b=parseFloat(this.getComputedStyleValue("--calculated-paper-radio-button-size").trim()),
d=Math.floor(3*b);d%2!==b%2&&d++;this.updateStyles({"--paper-radio-button-ink-size":d+"px"})}})}});

//# sourceURL=build://paper-radio-group/paper-radio-group.html.js
Polymer({is:"paper-radio-group",behaviors:[Polymer.IronMenubarBehavior],hostAttributes:{role:"radiogroup"},properties:{attrForSelected:{type:String,value:"name"},selectedAttribute:{type:String,value:"checked"},selectable:{type:String,value:"paper-radio-button"},allowEmptySelection:{type:Boolean,value:!1}},select:function(b){var d=this._valueToItem(b);if(!d||!d.hasAttribute("disabled")){if(this.selected){d=this._valueToItem(this.selected);if(this.selected==b)if(this.allowEmptySelection)b="";else{d&&
(d.checked=!0);return}d&&(d.checked=!1)}Polymer.IronSelectableBehavior.select.apply(this,[b]);this.fire("paper-radio-group-changed")}},_activateFocusedItem:function(){this._itemActivate(this._valueForItem(this.focusedItem),this.focusedItem)},_onUpKey:function(b){this._focusPrevious();b.preventDefault();this._activateFocusedItem()},_onDownKey:function(b){this._focusNext();b.preventDefault();this._activateFocusedItem()},_onLeftKey:function(b){Polymer.IronMenubarBehaviorImpl._onLeftKey.apply(this,arguments);
this._activateFocusedItem()},_onRightKey:function(b){Polymer.IronMenubarBehaviorImpl._onRightKey.apply(this,arguments);this._activateFocusedItem()}});

//# sourceURL=build://paper-tooltip/paper-tooltip.html.js
Polymer({is:"paper-tooltip",hostAttributes:{role:"tooltip",tabindex:-1},properties:{for:{type:String,observer:"_findTarget"},manualMode:{type:Boolean,value:!1,observer:"_manualModeChanged"},position:{type:String,value:"bottom"},fitToVisibleBounds:{type:Boolean,value:!1},offset:{type:Number,value:14},marginTop:{type:Number,value:14},animationDelay:{type:Number,value:500,observer:"_delayChange"},animationEntry:{type:String,value:""},animationExit:{type:String,value:""},animationConfig:{type:Object,
value:function(){return{entry:[{name:"fade-in-animation",node:this,timing:{delay:0}}],exit:[{name:"fade-out-animation",node:this}]}}},_showing:{type:Boolean,value:!1}},listeners:{webkitAnimationEnd:"_onAnimationEnd"},get target(){var b=Polymer.dom(this).parentNode,d=Polymer.dom(this).getOwnerRoot();return this.for?Polymer.dom(d).querySelector("#"+this.for):b.nodeType==Node.DOCUMENT_FRAGMENT_NODE?d.host:b},attached:function(){this._findTarget()},detached:function(){this.manualMode||this._removeListeners()},
playAnimation:function(b){"entry"===b?this.show():"exit"===b&&this.hide()},cancelAnimation:function(){this.$.tooltip.classList.add("cancel-animation")},show:function(){if(!this._showing){if(""===Polymer.dom(this).textContent.trim()){for(var b=!0,d=Polymer.dom(this).getEffectiveChildNodes(),f=0;f<d.length;f++)if(""!==d[f].textContent.trim()){b=!1;break}if(b)return}this._showing=!0;this.$.tooltip.classList.remove("hidden");this.$.tooltip.classList.remove("cancel-animation");this.$.tooltip.classList.remove(this._getAnimationType("exit"));
this.updatePosition();this._animationPlaying=!0;this.$.tooltip.classList.add(this._getAnimationType("entry"))}},hide:function(){this._showing&&(this._animationPlaying?(this._showing=!1,this._cancelAnimation()):(this._onAnimationFinish(),this._showing=!1,this._animationPlaying=!0))},updatePosition:function(){if(this._target&&this.offsetParent){var b=this.offset;14!=this.marginTop&&14==this.offset&&(b=this.marginTop);var d=this.offsetParent.getBoundingClientRect(),f=this._target.getBoundingClientRect(),
h=this.getBoundingClientRect(),k=(f.width-h.width)/2,r=(f.height-h.height)/2,l=f.left-d.left,p=f.top-d.top;switch(this.position){case "top":var m=l+k;var n=p-h.height-b;break;case "bottom":m=l+k;n=p+f.height+b;break;case "left":m=l-h.width-b;n=p+r;break;case "right":m=l+f.width+b,n=p+r}this.fitToVisibleBounds?(d.left+m+h.width>window.innerWidth?(this.style.right="0px",this.style.left="auto"):(this.style.left=Math.max(0,m)+"px",this.style.right="auto"),d.top+n+h.height>window.innerHeight?(this.style.bottom=
d.height+"px",this.style.top="auto"):(this.style.top=Math.max(-d.top,n)+"px",this.style.bottom="auto")):(this.style.left=m+"px",this.style.top=n+"px")}},_addListeners:function(){this._target&&(this.listen(this._target,"mouseenter","show"),this.listen(this._target,"focus","show"),this.listen(this._target,"mouseleave","hide"),this.listen(this._target,"blur","hide"),this.listen(this._target,"tap","hide"));this.listen(this.$.tooltip,"animationend","_onAnimationEnd");this.listen(this,"mouseenter","hide")},
_findTarget:function(){this.manualMode||this._removeListeners();this._target=this.target;this.manualMode||this._addListeners()},_delayChange:function(b){500!==b&&this.updateStyles({"--paper-tooltip-delay-in":b+"ms"})},_manualModeChanged:function(){this.manualMode?this._removeListeners():this._addListeners()},_cancelAnimation:function(){this.$.tooltip.classList.remove(this._getAnimationType("entry"));this.$.tooltip.classList.remove(this._getAnimationType("exit"));this.$.tooltip.classList.remove("cancel-animation");
this.$.tooltip.classList.add("hidden")},_onAnimationFinish:function(){this._showing&&(this.$.tooltip.classList.remove(this._getAnimationType("entry")),this.$.tooltip.classList.remove("cancel-animation"),this.$.tooltip.classList.add(this._getAnimationType("exit")))},_onAnimationEnd:function(){this._animationPlaying=!1;this._showing||(this.$.tooltip.classList.remove(this._getAnimationType("exit")),this.$.tooltip.classList.add("hidden"))},_getAnimationType:function(b){if("entry"===b&&""!==this.animationEntry)return this.animationEntry;
if("exit"===b&&""!==this.animationExit)return this.animationExit;if(this.animationConfig[b]&&"string"===typeof this.animationConfig[b][0].name){if(this.animationConfig[b][0].timing&&this.animationConfig[b][0].timing.delay&&0!==this.animationConfig[b][0].timing.delay){var d=this.animationConfig[b][0].timing.delay;"entry"===b?this.updateStyles({"--paper-tooltip-delay-in":d+"ms"}):"exit"===b&&this.updateStyles({"--paper-tooltip-delay-out":d+"ms"})}return this.animationConfig[b][0].name}},_removeListeners:function(){this._target&&
(this.unlisten(this._target,"mouseenter","show"),this.unlisten(this._target,"focus","show"),this.unlisten(this._target,"mouseleave","hide"),this.unlisten(this._target,"blur","hide"),this.unlisten(this._target,"tap","hide"));this.unlisten(this.$.tooltip,"animationend","_onAnimationEnd");this.unlisten(this,"mouseenter","hide")}});

//# sourceURL=build://tf-graph-node-search/tf-graph-node-search.html.js
Polymer({is:"tf-graph-node-search",properties:{renderHierarchy:Object,selectedNode:{type:String,notify:!0},_rawRegexInput:{type:String,value:""},_regexInput:{type:String,computed:"_computeRegexInput(renderHierarchy, _rawRegexInput)"},_previousRegexInput:{type:String,value:""},_searchTimeoutDelay:{type:Number,value:150,readOnly:!0},_searchPending:Boolean,_maxRegexResults:{type:Number,value:42},_regexMatches:Array},observers:["_regexInputChanged(_regexInput)"],_computeRegexInput(b,d){return d.trim()},
_regexInputChanged(){this._requestSearch()},_clearSearchResults(){this.set("_regexMatches",[])},_requestSearch(){this._searchPending||(this._regexInput===this._previousRegexInput?this._searchPending=!1:(this._searchPending=!0,this._executeSearch(),this.async(()=>{this._searchPending=!1;this._requestSearch()},this._searchTimeoutDelay)))},_executeSearch(){if(this._previousRegexInput=this._regexInput){try{var b=new RegExp(this._regexInput)}catch(f){this._clearSearchResults();return}var d=[];_.each(this.renderHierarchy.hierarchy.getNodeMap(),
(f,h)=>{if(d.length>=this._maxRegexResults)return!1;b.test(h)&&d.push(h)});this.set("_regexMatches",d)}else this._clearSearchResults()},_matchClicked(b){this.set("selectedNode",b.model.item)}});

//# sourceURL=build://tf-graph-controls/tf-graph-controls.js
(function(b){(function(d){(function(f){const h=/device:([^:]+:[0-9]+)$/,k=[{regex:h}],r=[];let l;(function(m){m.COMPUTE_TIME="compute_time";m.MEMORY="memory";m.STRUCTURE="structure";m.XLA_CLUSTER="xla_cluster";m.OP_COMPATIBILITY="op_compatibility"})(l=f.ColorBy||(f.ColorBy={}));const p=new Set([l.COMPUTE_TIME,l.MEMORY]);Polymer({is:"tf-graph-controls",properties:{stats:{value:null,type:Object,observer:"_statsChanged"},devicesForStats:{value:null,type:Object,notify:!0,readonly:!0},colorBy:{type:String,
value:l.STRUCTURE,notify:!0},colorByParams:{type:Object,notify:!0,readonly:!0},datasets:{type:Array,observer:"_datasetsChanged",value:()=>[]},renderHierarchy:{type:Object},selection:{type:Object,notify:!0,readOnly:!0,computed:"_computeSelection(datasets, _selectedRunIndex, _selectedTagIndex, _selectedGraphType)"},selectedFile:{type:Object,notify:!0},_selectedRunIndex:{type:Number,value:0,observer:"_selectedRunIndexChanged"},traceInputs:{type:Boolean,notify:!0,value:!1},_selectedTagIndex:{type:Number,
value:0,observer:"_selectedTagIndexChanged"},_selectedGraphType:{type:String,value:b.graph.SelectionType.OP_GRAPH},selectedNode:{type:String,notify:!0},_currentDevices:{type:Array,computed:"_getCurrentDevices(devicesForStats)"},_currentDeviceParams:{type:Array,computed:"_getCurrentDeviceParams(colorByParams)"},_currentXlaClusterParams:{type:Array,computed:"_getCurrentXlaClusterParams(colorByParams)"},_currentGradientParams:{type:Object,computed:"_getCurrentGradientParams(colorByParams, colorBy)"},
showSessionRunsDropdown:{type:Boolean,value:!0},showUploadButton:{type:Boolean,value:!0},healthPillsFeatureEnabled:Boolean,healthPillsToggledOn:{type:Boolean,notify:!0},_legendOpened:{type:Boolean,value:!0}},_xlaClustersProvided:function(m){return m&&m.hierarchy&&0<m.hierarchy.xlaClusters.length},_statsChanged:function(m){if(null!=m){var n={};_.each(m.dev_stats,function(q){var u=_.some(k,function(A){return A.regex.test(q.device)}),w=_.some(r,function(A){return A.regex.test(q.device)});u&&!w&&(n[q.device]=
!0)});this.set("devicesForStats",n)}},_getCurrentDevices:function(m){var n=this.stats;n=(n?n.dev_stats:[]).map(u=>u.device).filter(u=>k.some(w=>w.regex.test(u)));const q=b.graph.util.removeCommonPrefix(n);if(1==q.length){const u=q[0].match(h);u&&(q[0]=u[1])}return n.map((u,w)=>{let A=null;r.forEach(y=>{y.regex.test(u)&&(A=y.msg)});return{device:u,suffix:q[w],used:m[u],ignoredMsg:A}})},_deviceCheckboxClicked:function(m){m=m.target;const n=Object.assign({},this.devicesForStats),q=m.value;m.checked?
n[q]=!0:delete n[q];this.set("devicesForStats",n)},_numTags:function(m,n){return this._getTags(m,n).length},_getTags:function(m,n){return m&&m[n]?m[n].tags:[]},_fit:function(){this.fire("fit-tap")},_isGradientColoring:function(m,n){return p.has(n)&&null!=m},_equals:function(m,n){return m===n},_getCurrentDeviceParams:function(m){m=m.device.filter(u=>k.some(w=>w.regex.test(u.device)));const n=b.graph.util.removeCommonPrefix(m.map(u=>u.device));if(1==n.length){var q=n[0].match(h);q&&(n[0]=q[1])}return m.map((u,
w)=>({device:n[w],color:u.color}))},_getCurrentXlaClusterParams:function(m){return m.xla_cluster},_getCurrentGradientParams:function(m,n){if(this._isGradientColoring(this.stats,n)){m=m[n];var q=m.minValue,u=m.maxValue;n===l.MEMORY?(q=b.graph.util.convertUnitsToHumanReadable(q,b.graph.util.MEMORY_UNITS),u=b.graph.util.convertUnitsToHumanReadable(u,b.graph.util.MEMORY_UNITS)):n===l.COMPUTE_TIME&&(q=b.graph.util.convertUnitsToHumanReadable(q,b.graph.util.TIME_UNITS),u=b.graph.util.convertUnitsToHumanReadable(u,
b.graph.util.TIME_UNITS));return{minValue:q,maxValue:u,startColor:m.startColor,endColor:m.endColor}}},download:function(){this.$.graphdownload.click()},_updateFileInput:function(m){var n=m.target.files[0];if(n){n=n.name;var q=n.lastIndexOf(".");0<=q&&(n=n.substring(0,q));q=n.lastIndexOf("/");0<=q&&(n=n.substring(q+1));this._setDownloadFilename(n);this.set("selectedFile",m)}},_datasetsChanged:function(m,n){null!=n&&(this._selectedRunIndex=0)},_computeSelection:function(m,n,q,u){return m[n]&&m[n].tags[q]?
{run:m[n].name,tag:m[n].tags[q].tag,type:u}:null},_selectedRunIndexChanged:function(m){this.datasets&&(this.colorBy=l.STRUCTURE,this._selectedTagIndex=0,this._selectedGraphType=this._getDefaultSelectionType(),this.traceInputs=!1,this._setDownloadFilename(this.datasets[m]?this.datasets[m].name:""))},_selectedTagIndexChanged(){this._selectedGraphType=this._getDefaultSelectionType()},_getDefaultSelectionType(){const m=this.datasets,n=this._selectedRunIndex,q=this._selectedTagIndex;return m&&m[n]&&m[n].tags[q]&&
!m[n].tags[q].opGraph?m[n].tags[q].profile?b.graph.SelectionType.PROFILE:m[n].tags[q].conceptualGraph?b.graph.SelectionType.CONCEPTUAL_GRAPH:b.graph.SelectionType.OP_GRAPH:b.graph.SelectionType.OP_GRAPH},_getFile:function(){this.$$("#file").click()},_setDownloadFilename:function(m){this.$.graphdownload.setAttribute("download",m+".png")},_statsNotNull:function(m){return null!==m},_toggleLegendOpen(){this.set("_legendOpened",!this._legendOpened)},_getToggleText(m){return m?"Close legend.":"Expand legend."},
_getToggleLegendIcon(m){return m?"expand-more":"expand-less"},_getSelectionOpGraphDisabled(m,n,q){return!m[n]||!m[n].tags[q]||!m[n].tags[q].opGraph},_getSelectionProfileDisabled(m,n,q){return!m[n]||!m[n].tags[q]||!m[n].tags[q].profile},_getSelectionConceptualGraphDisabled(m,n,q){return!m[n]||!m[n].tags[q]||!m[n].tags[q].conceptualGraph}})})(d.controls||(d.controls={}))})(b.graph||(b.graph={}))})(tf||(tf={}));

//# sourceURL=build://tf-graph-dashboard/tf-graph-dashboard.html.js
Polymer({is:"tf-graph-dashboard",properties:{_datasets:{type:Array,value:()=>[]},_datasetsFetched:{type:Boolean,value:!1},_selectedDataset:{type:Number,value:0},_renderHierarchy:{type:Object,observer:"_renderHierarchyChanged"},_requestManager:{type:Object,value:()=>new dc.RequestManager},_canceller:{type:Object,value:()=>new dc.Canceller},_debuggerDataEnabled:Boolean,allStepsModeEnabled:Boolean,specificHealthPillStep:{type:Number,value:0},healthPillsToggledOn:{type:Boolean,value:!1,observer:"_healthPillsToggledOnChanged"},
selectedNode:{type:String,notify:!0},_isAttached:Boolean,_initialized:Boolean,_areHealthPillsLoading:Boolean,_debuggerNumericAlerts:{type:Array,value:[],notify:!0},_nodeNamesToHealthPills:{type:Object,value:{}},_healthPillStepIndex:Number,_healthPillRequestId:{type:Number,value:1},_healthPillStepRequestTimerId:Number,_healthPillStepRequestTimerDelay:{type:Number,value:500,readOnly:!0},runs:Array,run:{type:String,notify:!0,value:bd.getStringInitializer("run",{defaultValue:"",useLocalStorage:!1}),observer:"_runObserver"},
_selection:{type:Object},_compatibilityProvider:Object,_traceInputs:Boolean},listeners:{"node-toggle-expand":"_handleNodeToggleExpand"},observers:["_maybeFetchHealthPills(_debuggerDataEnabled, allStepsModeEnabled, specificHealthPillStep, _selectedNode)","_maybeInitializeDashboard(_isAttached)","_determineSelectedDataset(_datasetsFetched, _datasets, run)","_updateSelectedDatasetName(_datasetsFetched, _datasets, _selectedDataset)"],attached:function(){this.set("_isAttached",!0)},detached:function(){this.set("_isAttached",
!1)},reload:function(){this._debuggerDataEnabled||this._requestManager.request(dc.getRouter().pluginsListing()).then(this._canceller.cancellable(b=>{b.cancelled||b.value["debugger"]&&this.set("_debuggerDataEnabled",!0)}));this._maybeFetchHealthPills()},_fit:function(){this.$$("#graphboard").fit()},_runObserver:bd.getStringObserver("run",{defaultValue:"",polymerProperty:"run",useLocalStorage:!1}),_fetchDataset(){return this._requestManager.request(dc.getRouter().pluginRoute("graphs","/info"))},_fetchHealthPills(b,
d){b={node_names:JSON.stringify(b),run:"__debugger_data__"};void 0!==d&&(b.step=d);d=dc.getRouter().pluginRoute("debugger","/health_pills");return this._requestManager.request(d,b)},_fetchDebuggerNumericsAlerts(){return this._requestManager.request(dc.getRouter().pluginRoute("debugger","/numerics_alert_report"))},_graphUrl(b,d,f){return dc.getRouter().pluginRoute("graphs","/graph",new URLSearchParams({run:b,limit_attr_size:d,large_attrs_key:f}))},_shouldRequestHealthPills:function(){return this._debuggerDataEnabled&&
this.healthPillsToggledOn&&this._renderHierarchy&&this._datasetsState(this._datasetsFetched,this._datasets,"PRESENT")},_maybeInitializeDashboard:function(b){!this._initialized&&b&&(this.set("_compatibilityProvider",new tf.graph.op.TpuCompatibilityProvider),this._initialized=!0,this._fetchDataset().then(d=>{this._datasets=Object.keys(d).sort(ac.compareTagNames).map(f=>{const h=d[f];var k=Object.keys(h.tags).sort(ac.compareTagNames).map(r=>h.tags[r]).map(({tag:r,conceptual_graph:l,op_graph:p,profile:m})=>
({tag:r,displayName:r,conceptualGraph:l,opGraph:p,profile:m}));k=h.run_graph?[{tag:null,displayName:"Default",conceptualGraph:!1,opGraph:!0,profile:!1},...k]:k;return{name:f,tags:k}});this._datasetsFetched=!0}))},_determineSelectedDataset(b,d,f){f?(d=d.findIndex(h=>h.name===f),-1===d?b&&(b=this.$$("#error-dialog"),b.textContent=`No dataset named "${f}" could be found.`,b.open()):this.set("_selectedDataset",d)):this.set("_selectedDataset",0)},_updateSelectedDatasetName(b,d,f){b&&(d.length<=f||this.set("run",
d[f].name))},_requestHealthPills:function(){this.set("_areHealthPillsLoading",!0);var b=++this._healthPillRequestId;null!==this._healthPillStepRequestTimerId&&(window.clearTimeout(this._healthPillStepRequestTimerId),this._healthPillStepRequestTimerId=null);this.allStepsModeEnabled?this._healthPillStepRequestTimerId=setTimeout(function(){this._healthPillStepRequestTimerId=null;this._initiateNetworkRequestForHealthPills(b)}.bind(this),this._healthPillStepRequestTimerDelay):this._initiateNetworkRequestForHealthPills(b)},
_initiateNetworkRequestForHealthPills:function(b){if(this._healthPillRequestId===b){var d=this._fetchHealthPills(this._renderHierarchy.getNamesOfRenderedOps(),this.allStepsModeEnabled?this.specificHealthPillStep:void 0),f=this._fetchDebuggerNumericsAlerts();Promise.all([d,f]).then(function(h){var k=h[0];h=h[1];if(this.healthPillsToggledOn&&b===this._healthPillRequestId){for(var r in k){this.set("_healthPillStepIndex",k[r].length-1);break}this.set("_debuggerNumericAlerts",h);this.set("_nodeNamesToHealthPills",
k);this.set("_areHealthPillsLoading",!1);this.set("_healthPillStepRequestTimerId",null)}}.bind(this))}},_datasetsState:function(b,d,f){return b?d&&d.length?"PRESENT"===f:"EMPTY"===f:"NOT_LOADED"===f},_renderHierarchyChanged:function(){this.reload()},_handleNodeToggleExpand:function(){this._maybeFetchHealthPills()},_healthPillsToggledOnChanged:function(b){b?this.reload():this.set("_nodeNamesToHealthPills",{})},_maybeFetchHealthPills:function(){this._shouldRequestHealthPills()&&this._requestHealthPills()}});

//# sourceURL=build://vz-distribution-chart/vz-distribution-chart.js
var dl;
(function(b){class d{constructor(f,h){this.run2datasets={};this.colorScale=h;this.buildChart(f)}getDataset(f){void 0===this.run2datasets[f]&&(this.run2datasets[f]=new Plottable.Dataset([],{run:f}));return this.run2datasets[f]}buildChart(f){this.outer&&this.outer.destroy();f=sf.getXComponents(f);this.xAccessor=f.accessor;this.xScale=f.scale;this.xAxis=f.axis;this.xAxis.margin(0).tickLabelPadding(3);this.yScale=new Plottable.Scales.Linear;this.yAxis=new Plottable.Axes.Numeric(this.yScale,"left");f=
sf.multiscaleFormatter(sf.Y_AXIS_FORMATTER_PRECISION);this.yAxis.margin(0).tickLabelPadding(5).formatter(f);this.yAxis.usesTextWidthApproximation();f=this.buildPlot(this.xAccessor,this.xScale,this.yScale);this.gridlines=new Plottable.Components.Gridlines(this.xScale,this.yScale);this.center=new Plottable.Components.Group([this.gridlines,f]);this.outer=new Plottable.Components.Table([[this.yAxis,this.center],[null,this.xAxis]])}buildPlot(f,h,k){let r=[0,228,1587,3085,5E3,6915,8413,9772,1E4],l=_.range(r.length-
1).map(u=>(r[u+1]-r[u])/2500),p=r.map((u,w)=>A=>A[w][1]),m=p[4],n=_.range(p.length-1).map(u=>{let w=new Plottable.Plots.Area;w.x(f,h);let A=4<u?p[u]:p[u+1];w.y(4<u?p[u+1]:p[u],k);w.y0(A);w.attr("fill",(y,x,C)=>this.colorScale.scale(C.metadata().run));w.attr("stroke",(y,x,C)=>this.colorScale.scale(C.metadata().run));w.attr("stroke-weight",()=>"0.5px");w.attr("stroke-opacity",()=>l[u]);w.attr("fill-opacity",()=>l[u]);return w}),q=new Plottable.Plots.Line;q.x(f,h);q.y(m,k);q.attr("stroke",(u,w,A)=>this.colorScale.scale(A.run));
this.plots=n;return new Plottable.Components.Group(n)}setVisibleSeries(f){this.runs=f;let h=f.map(k=>this.getDataset(k));this.plots.forEach(k=>k.datasets(h))}setSeriesData(f,h){this.getDataset(f).data(h)}renderTo(f){this.targetSVG=f;this.outer.renderTo(f)}redraw(){this.outer.redraw()}destroy(){this.outer.destroy()}}b.DistributionChart=d;Polymer({is:"vz-distribution-chart",properties:{colorScale:{type:Object,value:function(){return(new Plottable.Scales.Color).range(d3.schemeCategory10)}},xType:{type:String,
value:"step"},_attached:Boolean,_chart:Object,_visibleSeriesCache:{type:Array,value:function(){return[]}},_seriesDataCache:{type:Object,value:function(){return{}}},_makeChartAsyncCallbackId:{type:Number,value:null}},observers:["_makeChart(xType, colorScale, _attached)","_reloadFromCache(_chart)"],setVisibleSeries:function(f){this._visibleSeriesCache=f;this._chart&&(this._chart.setVisibleSeries(f),this.redraw())},setSeriesData:function(f,h){this._seriesDataCache[f]=h;this._chart&&this._chart.setSeriesData(f,
h)},redraw:function(){this._chart.redraw()},ready:function(){this.scopeSubtree(this.$.chartdiv,!0)},_makeChart:function(f,h,k){null===this._makeChartAsyncCallbackId&&this.cancelAsync(this._makeChartAsyncCallbackId);this._makeChartAsyncCallbackId=this.async(function(){this._makeChartAsyncCallbackId=null;if(k){this._chart&&this._chart.destroy();var r=new d(f,h),l=d3.select(this.$.chartdiv);r.renderTo(l);this._chart=r}},350)},_reloadFromCache:function(){this._chart&&(this._chart.setVisibleSeries(this._visibleSeriesCache),
this._visibleSeriesCache.forEach(function(f){this._chart.setSeriesData(f,this._seriesDataCache[f]||[])}.bind(this)))},attached:function(){this._attached=!0},detached:function(){this._attached=!1}})})(dl||(dl={}));

//# sourceURL=build://tf-distribution-dashboard/tf-distribution-loader.html.js
Polymer({is:"tf-distribution-loader",properties:{run:String,tag:String,tagMetadata:Object,xType:String,dataToLoad:{type:Array,computed:"_computeDataToLoad(run, tag)"},getDataLoadName:{type:Function,value:()=>({run:b})=>b},getDataLoadUrl:{type:Function,value:()=>({tag:b,run:d})=>dc.addParams(dc.getRouter().pluginRoute("distributions","/distributions"),{tag:b,run:d})},loadDataCallback:{type:Function,value:function(){return(b,d,f)=>{b=f.map(h=>{const [k,r,l]=h;l.wall_time=new Date(1E3*k);l.step=r;return l});
d=this.getDataLoadName(d);this.$.chart.setSeriesData(d,b);this.$.chart.setVisibleSeries([d])}}},_colorScale:{type:Object,value:()=>({scale:Ld.runsColorScale}),readOnly:!0},_runColor:{type:String,computed:"_computeRunColor(run)"},_expanded:{type:Boolean,value:!1,reflectToAttribute:!0},requestManager:Object,_canceller:{type:Object,value:()=>new dc.Canceller}},observers:["reload(run, tag)"],behaviors:[cd.DataLoaderBehavior],_computeDataToLoad(b,d){return[{run:b,tag:d}]},_computeRunColor(b){return this._colorScale.scale(b)},
redraw(){this.$.chart.redraw()},_toggleExpanded(){this.set("_expanded",!this._expanded);this.redraw()}});

//# sourceURL=build://tf-distribution-dashboard/tf-distribution-dashboard.html.js
Polymer({is:"tf-distribution-dashboard",properties:{_xType:{type:String,value:"step"},_selectedRuns:Array,_runToTag:Object,_runToTagInfo:Object,_dataNotFound:Boolean,_tagFilter:String,_categoriesDomReady:Boolean,_categories:{type:Array,computed:"_makeCategories(_runToTag, _selectedRuns, _tagFilter, _categoriesDomReady)"},_requestManager:{type:Object,value:()=>new dc.RequestManager}},ready(){this.reload()},reload(){this._fetchTags().then(()=>{this._reloadDistributions()})},_fetchTags(){const b=dc.getRouter().pluginRoute("distributions",
"/tags");return this._requestManager.request(b).then(d=>{if(!_.isEqual(d,this._runToTagInfo)){var f=_.mapValues(d,k=>Object.keys(k)),h=dc.getTags(f);this.set("_dataNotFound",0===h.length);this.set("_runToTag",f);this.set("_runToTagInfo",d);this.async(()=>{this.set("_categoriesDomReady",!0)})}})},_reloadDistributions(){this.root.querySelectorAll("tf-distribution-loader").forEach(b=>{b.reload()})},_shouldOpen(b){return 2>=b},_makeCategories(b,d,f){return kc.categorizeRunTagCombinations(b,d,f)},_tagMetadata(b,
d,f){return b[d][f]}});

//# sourceURL=build://vz-histogram-timeseries/vz-histogram-timeseries.html.js
Polymer({is:"vz-histogram-timeseries",properties:{mode:{type:String,value:"offset"},timeProperty:{type:String,value:"step"},bins:{type:String,value:"bins"},x:{type:String,value:"x"},dx:{type:String,value:"dx"},y:{type:String,value:"y"},colorScale:{type:Object,value:function(){return d3.scaleOrdinal(d3.schemeCategory10)}},modeTransitionDuration:{type:Number,value:500},_attached:Boolean,_name:{type:String,value:null},_data:{type:Array,value:null}},observers:["redraw(timeProperty, _attached)","_modeRedraw(mode)"],
ready:function(){this.scopeSubtree(this.$.svg,!0)},attached:function(){this._attached=!0},detached:function(){this._attached=!1},setSeriesData:function(b,d){this._name=b;this._data=d;this.redraw()},redraw:function(){this._draw(0)},_modeRedraw:function(){this._draw(this.modeTransitionDuration)},_draw:function(b){if(this._attached&&this._data){if(void 0===b)throw Error("vz-histogram-timeseries _draw needs duration");if(0>=this._data.length)throw Error("Not enough steps in the data");if(!this._data[0].hasOwnProperty(this.bins))throw Error("No bins property of '"+
this.bins+"' in data");if(0>=this._data[0][this.bins].length)throw Error("Must have at least one bin in bins in data");if(!this._data[0][this.bins][0].hasOwnProperty(this.x))throw Error("No x property '"+this.x+"' on bins data");if(!this._data[0][this.bins][0].hasOwnProperty(this.dx))throw Error("No dx property '"+this.dx+"' on bins data");if(!this._data[0][this.bins][0].hasOwnProperty(this.y))throw Error("No y property '"+this.y+"' on bins data");var d=this.timeProperty,f=this.x,h=this.bins,k=this.dx,
r=this.y,l=this._data,p=this.mode,m=d3.hcl(this.colorScale(this._name)),n=d3.select(this.$.tooltip),q=function(xa){return xa[f]},u=function(xa){return xa[r]},w=function(xa){return xa[f]+xa[k]},A=function(xa){return xa[d]};"relative"===d&&(A=function(xa){return xa.wall_time-l[0].wall_time});var y=this.$.svg.getBoundingClientRect(),x=y.width,C=y.height,G=5;if("offset"===p){var D=C/2.5;G=D+5}else D=C-G-20;var B=x-24-60,H=C-G-20;d3.min(l,q);d3.max(l,w);var K=d3.format(".3n");y=d3.format(".0f");"wall_time"===
d?y=d3.timeFormat("%m/%d %X"):"relative"===d&&(y=function(xa){return d3.format(".1r")(xa/36E5)+"h"});var L=l.map(function(xa){return[d3.min(xa[h],q),d3.max(xa[h],w)]}),J=l.map(function(xa){return d3.extent(xa[h],u)}),O=d3.extent(l,A),S=("wall_time"===d?d3.scaleTime():d3.scaleLinear()).domain(O).range([0,"offset"===p?H:0]),N=d3.scaleLinear().domain([0,d3.max(l,function(xa,Sa){return J[Sa][1]})]).range([D,0]),R=d3.scaleLinear().domain(N.domain()).range([500,0]),X=d3.scaleLinear().domain([d3.min(l,function(xa,
Sa){return L[Sa][0]}),d3.max(l,function(xa,Sa){return L[Sa][1]})]).nice().range([0,B]),aa=d3.scaleLinear().domain(X.domain()).range([0,500]),fa=d3.scaleLinear().domain(d3.extent(l,A)).range([m.darker(),m.brighter()]).interpolate(d3.interpolateHcl);m=d3.axisBottom(X).ticks();var oa=d3.axisRight(S).ticks().tickFormat(y),Z=d3.axisRight(N).ticks().tickSize(B+5).tickFormat(K),ca=function(xa){return xa[f]+xa[k]/2},ja=d3.line().x(function(xa){return aa(ca(xa))}).y(function(xa){return R(xa[r])}),ba=function(xa){return"M"+
aa(ca(xa[0]))+","+R(0)+"L"+ja(xa).slice(1)+"L"+aa(ca(xa[xa.length-1]))+","+R(0)},ka=this.$.svg;y=d3.select(ka);b=y.transition().duration(b);y=y.select("g").classed("small",function(){return 0<B&&150>=B}).classed("medium",function(){return 150<B&&300>=B}).classed("large",function(){return 300<B});b=b.select("g").attr("transform","translate(24,"+G+")");var W=d3.bisector(w).left;O=y.select(".stage").on("mouseover",function(){va.style("opacity",1);ya.style("opacity",1);Aa.style("opacity",1);Fa.style("opacity",
1);n.style("opacity",1)}).on("mouseout",function(){va.style("opacity",0);ya.style("opacity",0);Aa.style("opacity",0);Fa.style("opacity",0);va.classed("hover-closest",!1);Ea.classed("outline-hover",!1);n.style("opacity",0)}).on("mousemove",function(){function xa(Ab){return Math.min(Ab[h].length-1,W(Ab[h],Xa))}var Sa=d3.mouse(this),Xa=X.invert(Sa[0]);S.invert(Sa[1]);var ub,Bb=Infinity,qb;va.attr("transform",function(Ab){var Hb=xa(Ab);qb=Ab;var lc=X(Ab[h][Hb][f]+Ab[h][Hb][k]/2);Hb=N(Ab[h][Hb][r]);var ec=
"offset"===p?S(A(Ab))-(D-Hb):Hb;ec=Math.abs(Sa[1]-ec);ec<Bb&&(Bb=ec,ub=Ab);return"translate("+lc+","+Hb+")"});va.select("text").text(function(Ab){var Hb=xa(Ab);return Ab[h][Hb][r]});va.classed("hover-closest",function(Ab){return Ab===ub});Ea.classed("outline-hover",function(Ab){return Ab===ub});var zb=xa(qb);ya.attr("transform",function(){return"translate("+X(qb[h][zb][f]+qb[h][zb][k]/2)+", "+H+")"}).select("text").text(function(){return K(qb[h][zb][f]+qb[h][zb][k]/2)});var vb=oa.tickFormat();Aa.attr("transform",
function(){return"translate("+B+", "+("offset"===p?S(A(ub)):0)+")"}).style("display","offset"===p?"":"none").select("text").text(function(){return vb(A(ub))});var Gb=Z.tickFormat();Fa.attr("transform",function(){return"translate("+B+", "+("offset"===p?0:N(ub[h][zb][r]))+")"}).style("display","offset"===p?"none":"").select("text").text(function(){return Gb(ub[h][zb][r])});var Ob=d3.mouse(ka);n.style("transform","translate("+(Ob[0]+15)+"px,"+(Ob[1]-15)+"px)").select("span").text("offset"===p?Gb(ub[h][zb][r]):
("step"===d?"step ":"")+vb(A(ub)))});O.select(".background").attr("transform","translate(-24,"+-G+")").attr("width",x).attr("height",C);C=O.selectAll(".histogram").data(l);C.exit().remove();x=C.enter().append("g").attr("class","histogram");C=x.merge(C).sort(function(xa,Sa){return A(xa)-A(Sa)});G=b.selectAll(".histogram").attr("transform",function(xa){return"translate(0, "+("offset"===p?S(A(xa))-D:0)+")"});x.append("line").attr("class","baseline");G.select(".baseline").style("stroke-opacity",function(){return"offset"===
p?.1:0}).attr("y1",D).attr("y2",D).attr("x2",B);x.append("path").attr("class","outline");var Ea=C.select(".outline").attr("vector-effect","non-scaling-stroke").attr("d",function(xa){return ba(xa[h])}).style("stroke-width",1);G.select(".outline").attr("transform","scale("+B/500+", "+D/500+")").style("stroke",function(xa){return"offset"===p?"white":fa(A(xa))}).style("fill-opacity",function(){return"offset"===p?1:0}).style("fill",function(xa){return fa(A(xa))});x=x.append("g").attr("class","hover").style("fill",
function(xa){return fa(A(xa))});var va=C.select(".hover");x.append("circle").attr("r",2);x.append("text").style("display","none").attr("dx",4);x=y.select(".x-axis-hover").selectAll(".label").data(["x"]);C=x.enter().append("g").attr("class","label");var ya=x.merge(C);C.append("rect").attr("x",-20).attr("y",6).attr("width",40).attr("height",14);C.append("line").attr("x1",0).attr("x2",0).attr("y1",0).attr("y2",6);C.append("text").attr("dy",18);x=y.select(".y-axis-hover").selectAll(".label").data(["y"]);
C=x.enter().append("g").attr("class","label");var Aa=x.merge(C);C.append("rect").attr("x",8).attr("y",-6).attr("width",40).attr("height",14);C.append("line").attr("x1",0).attr("x2",6).attr("y1",0).attr("y2",0);C.append("text").attr("dx",8).attr("dy",4);y=y.select(".y-slice-axis-hover").selectAll(".label").data(["y"]);x=y.enter().append("g").attr("class","label");var Fa=y.merge(x);x.append("rect").attr("x",8).attr("y",-6).attr("width",40).attr("height",14);x.append("line").attr("x1",0).attr("x2",6).attr("y1",
0).attr("y2",0);x.append("text").attr("dx",8).attr("dy",4);b.select(".y.axis.slice").style("opacity","offset"===p?0:1).attr("transform","translate(0, "+("offset"===p?-D:0)+")").call(Z);b.select(".x.axis").attr("transform","translate(0, "+H+")").call(m);b.select(".y.axis").style("opacity","offset"===p?1:0).attr("transform","translate("+B+", "+("offset"===p?0:H)+")").call(oa);b.selectAll(".tick text").attr("fill","#aaa");b.selectAll(".axis path.domain").attr("stroke","none")}}});

//# sourceURL=build://tf-histogram-dashboard/tf-histogram-loader.html.js
Polymer({is:"tf-histogram-loader",properties:{run:String,tag:String,dataToLoad:{type:Array,computed:"_computeDataToLoad(run, tag)"},getDataLoadName:{type:Function,value:()=>({run:b})=>b},getDataLoadUrl:{type:Function,value:()=>({tag:b,run:d})=>dc.addParams(dc.getRouter().pluginRoute("histograms","/histograms"),{tag:b,run:d})},loadDataCallback:{type:Function,value:function(){return(b,d,f)=>{b=el.backendToVz(f);d=this.getDataLoadName(d);this.$.chart.setSeriesData(d,b)}}},tagMetadata:Object,timeProperty:String,
histogramMode:String,_colorScaleFunction:{type:Object,value:()=>Ld.runsColorScale},_runColor:{type:String,computed:"_computeRunColor(run)"},_expanded:{type:Boolean,value:!1,reflectToAttribute:!0}},observers:["reload(run, tag, requestManager)"],behaviors:[cd.DataLoaderBehavior],_computeDataToLoad(b,d){return[{run:b,tag:d}]},_computeRunColor(b){return this._colorScaleFunction(b)},redraw(){this.$.chart.redraw()},_toggleExpanded(){this.set("_expanded",!this._expanded);this.redraw()}});

//# sourceURL=build://tf-histogram-dashboard/tf-histogram-dashboard.html.js
Polymer({is:"tf-histogram-dashboard",properties:{_histogramMode:{type:String,value:"offset"},_timeProperty:{type:String,value:"step"},_selectedRuns:Array,_runToTag:Object,_runToTagInfo:Object,_dataNotFound:Boolean,_tagFilter:String,_restamp:{type:Boolean,value:!1},_categoriesDomReady:Boolean,_categories:{type:Array,computed:"_makeCategories(_runToTag, _selectedRuns, _tagFilter, _categoriesDomReady)"},_requestManager:{type:Object,value:()=>new dc.RequestManager}},listeners:{"content-visibility-changed":"_redrawCategoryPane"},
_redrawCategoryPane(b,d){d&&b.target.querySelectorAll("tf-histogram-loader").forEach(f=>f.redraw())},ready(){this.reload()},reload(){this._fetchTags().then(()=>{this._reloadHistograms()})},_fetchTags(){const b=dc.getRouter().pluginRoute("histograms","/tags");return this._requestManager.request(b).then(d=>{if(!_.isEqual(d,this._runToTagInfo)){var f=_.mapValues(d,k=>Object.keys(k)),h=dc.getTags(f);this.set("_dataNotFound",0===h.length);this.set("_runToTag",f);this.set("_runToTagInfo",d);this.async(()=>
{this.set("_categoriesDomReady",!0)})}})},_reloadHistograms(){this.root.querySelectorAll("tf-histogram-loader").forEach(b=>{b.reload()})},_shouldOpen(b){return 2>=b},_makeCategories(b,d,f){return kc.categorizeRunTagCombinations(b,d,f)},_tagMetadata(b,d,f){return b[d][f]}});

//# sourceURL=build://tf-text-dashboard/tf-text-loader.html.js
Polymer({is:"tf-text-loader",properties:{run:String,tag:String,_runColor:{type:String,computed:"_computeRunColor(run)"},_texts:{type:Array,value:[]},requestManager:Object,_canceller:{type:Object,value:()=>new dc.Canceller}},_computeRunColor(b){return Ld.runsColorScale(b)},attached(){this._attached=!0;this.reload()},reload(){if(this._attached){this._canceller.cancelAll();var b=dc.addParams(dc.getRouter().pluginRoute("text","/text"),{tag:this.tag,run:this.run}),d=this._canceller.cancellable(f=>{f.cancelled||
(f=f.value.map(h=>({wall_time:new Date(1E3*h.wall_time),step:h.step,text:h.text})),this.set("_texts",f.slice().reverse()))});this.requestManager.request(b).then(d)}},_formatStep(b){return d3.format(",")(b)}});

//# sourceURL=build://tf-text-dashboard/tf-text-dashboard.html.js
Polymer({is:"tf-text-dashboard",properties:{_selectedRuns:Array,_runToTag:Object,_dataNotFound:Boolean,_tagFilter:String,_categoriesDomReady:Boolean,_categories:{type:Array,computed:"_makeCategories(_runToTag, _selectedRuns, _tagFilter, _categoriesDomReady)"},_requestManager:{type:Object,value:()=>new dc.RequestManager}},ready(){this.reload()},reload(){this._fetchTags().then(()=>{this._reloadTexts()})},_shouldOpen(b){return 2>=b},_fetchTags(){const b=dc.getRouter().pluginRoute("text","/tags");return this._requestManager.request(b).then(d=>
{if(!_.isEqual(d,this._runToTag)){var f=dc.getTags(d);this.set("_dataNotFound",0===f.length);this.set("_runToTag",d);this.async(()=>{this.set("_categoriesDomReady",!0)})}})},_reloadTexts(){this.root.querySelectorAll("tf-text-loader").forEach(b=>{b.reload()})},_makeCategories(b,d,f){return kc.categorizeRunTagCombinations(b,d,f)}});

//# sourceURL=build://tf-pr-curve-dashboard/tf-pr-curve-card.html.js
Polymer({is:"tf-pr-curve-card",properties:{runs:Array,tag:String,tagMetadata:Object,runToStepCap:Object,requestManager:Object,active:Boolean,_expanded:{type:Boolean,value:!1,reflectToAttribute:!0},_runToPrCurveEntry:{type:Object,value:()=>({})},_previousRunToPrCurveEntry:{type:Object,value:()=>({})},_runsWithStepAvailable:{type:Array,computed:"_computeRunsWithStepAvailable(runs, _runToPrCurveEntry)"},_setOfRelevantRuns:{type:Object,computed:"_computeSetOfRelevantRuns(_runsWithStepAvailable)"},_runToDataOverTime:Object,
onDataChange:Function,_colorScaleFunction:{type:Object,value:()=>({scale:Ld.runsColorScale})},_canceller:{type:Object,value:()=>new dc.Canceller},_attached:Boolean,_xComponentsCreationMethod:{type:Object,readOnly:!0,value:()=>()=>{const b=new Plottable.Scales.Linear;return{scale:b,axis:new Plottable.Axes.Numeric(b,"bottom"),accessor:d=>d.recall}}},_yValueAccessor:{type:Object,readOnly:!0,value:()=>b=>b.precision},_tooltipColumns:{type:Array,readOnly:!0,value:()=>{const b=sf.multiscaleFormatter(sf.Y_TOOLTIP_FORMATTER_PRECISION),
d=f=>isNaN(f)?"NaN":b(f);return[{title:"Run",evaluate:f=>f.dataset.metadata().name},{title:"Threshold",evaluate:f=>d(f.datum.thresholds)},{title:"Precision",evaluate:f=>d(f.datum.precision)},{title:"Recall",evaluate:f=>d(f.datum.recall)},{title:"TP",evaluate:f=>f.datum.true_positives},{title:"FP",evaluate:f=>f.datum.false_positives},{title:"TN",evaluate:f=>f.datum.true_negatives},{title:"FN",evaluate:f=>f.datum.false_negatives}]}},_seriesDataFields:{type:Array,value:"thresholds precision recall true_positives false_positives true_negatives false_negatives".split(" "),
readOnly:!0},_defaultXRange:{type:Array,value:[-.05,1.05],readOnly:!0},_defaultYRange:{type:Array,value:[-.05,1.05],readOnly:!0},_dataUrl:{type:Function,value:function(){return b=>{const d=this.tag;return dc.addParams(dc.getRouter().pluginRoute("pr_curves","/pr_curves"),{tag:d,run:b})}}},_smoothingEnabled:{type:Boolean,value:!1,readOnly:!0}},observers:["reload(runs, tag)","_setChartData(_runToPrCurveEntry, _previousRunToPrCurveEntry, _setOfRelevantRuns)","_updateRunToPrCurveEntry(_runToDataOverTime, runToStepCap)",
"_notifyDataChange(_runToDataOverTime)"],_createProcessDataFunction(){return(b,d,f)=>{this.set("_runToDataOverTime",Object.assign({},this._runToDataOverTime,f))}},_computeRunColor(b){return this._colorScaleFunction.scale(b)},attached(){this._attached=!0;this.reload()},reload(){this._attached&&(0===this.runs.length?this.set("_runToDataOverTime",{}):this.$$("tf-line-chart-data-loader").reload())},_setChartData(b,d,f){_.forOwn(b,(h,k)=>{const r=d[k];r&&b[k].step===r.step||(f[k]?this._updateSeriesDataForRun(k,
h):this._clearSeriesData(k))})},_updateSeriesDataForRun(b,d){var f=_.reduce(this._seriesDataFields,(k,r)=>{k[r]=d[r].slice().reverse();return k},{});const h=Array(f[this._seriesDataFields[0]].length);for(let k=0;k<h.length;k++)h[k]=_.mapValues(f,r=>r[k]);f=this.$$("tf-line-chart-data-loader");f.setSeriesData(b,h);f.commitChanges()},_clearSeriesData(b){const d=this.$$("tf-line-chart-data-loader");d.setSeriesData(b,[]);d.commitChanges()},_updateRunToPrCurveEntry(b,d){const f={};_.forOwn(b,(h,k)=>{h&&
h.length&&(f[k]=this._computeEntryClosestOrEqualToStepCap(d[k],h))});this.set("_previousRunToPrCurveEntry",this._runToPrCurveEntry);this.set("_runToPrCurveEntry",f)},_notifyDataChange(b){if(this.onDataChange)this.onDataChange(b)},_computeEntryClosestOrEqualToStepCap(b,d){b=Math.min(_.sortedIndex(d.map(f=>f.step),b),d.length-1);return d[b]},_computeRunsWithStepAvailable(b,d){return _.filter(b,f=>d[f]).sort()},_computeSetOfRelevantRuns(b){const d={};_.forEach(b,f=>{d[f]=!0});return d},_computeCurrentStepForRun(b,
d){return(b=b[d])?b.step:null},_computeCurrentWallTimeForRun(b,d){return(b=b[d])?(new Date(1E3*b.wall_time)).toString():null},_toggleExpanded(){this.set("_expanded",!this._expanded);this.redraw()},_resetDomain(){this.$$("tf-line-chart-data-loader").resetDomain()},redraw(){this.$$("tf-line-chart-data-loader").redraw()}});

//# sourceURL=build://tf-pr-curve-dashboard/tf-pr-curve-steps-selector.html.js
Polymer({is:"tf-pr-curve-steps-selector",properties:{runs:Array,runToAvailableTimeEntries:Object,runToStep:{type:Object,notify:!0,computed:"_computeRunToStep(runToAvailableTimeEntries, _runToStepIndex)"},timeDisplayType:String,_runToStepIndex:{type:Object,value:()=>({})},_runsWithSliders:{type:Array,computed:"_computeRunsWithSliders(runs, runToAvailableTimeEntries)"}},observers:["_updateStepsForNewRuns(runToAvailableTimeEntries)"],_computeColorForRun(b){return Ld.runsColorScale(b)},_computeTimeTextForRun(b,
d,f,h){d=d[f];if(!_.isNumber(d))return"";b=b[f];if(!b)return"";b=b[d][h];if("step"===h)return`step ${b}`;if("relative"===h)return 1>b?`${(1E3*b).toFixed(2)} ms`:`${b.toFixed(2)} s`;if("wall_time"===h)return(new Date(1E3*b)).toString();throw Error(`The display type of ${h} is not recognized.`);},_sliderValueChanged(b){const d=b.target.dataset.run,f=b.target.immediateValue,h=Object.assign({},this._runToStepIndex);isNaN(f)?delete h[d]:h[d]=b.target.immediateValue;this._runToStepIndex=h},_computeMaxStepIndexForRun(b,
d){return(b=b[d])&&b.length?b.length-1:0},_updateStepsForNewRuns(b){const d=Object.assign({},this._runToStepIndex);_.forOwn(b,(f,h)=>{_.isNumber(d[h])||(d[h]=f.length-1)});this._runToStepIndex=d},_getStep(b,d){return this._runToStepIndex?this._runToStepIndex[d]:0},_computeRunToStep(b,d){const f={};_.forOwn(d,(h,k)=>{const r=b[k];r&&(f[k]=r[h].step)});return f},_computeRunsWithSliders(b,d){return b.filter(f=>d[f])}});

//# sourceURL=build://tf-pr-curve-dashboard/tf-pr-curve-dashboard.html.js
Polymer({is:"tf-pr-curve-dashboard",properties:{_timeDisplayType:{type:String,value:"step"},_selectedRuns:{type:Array,value:()=>[]},_runToTagInfo:{type:Object,value:()=>({})},_runToAvailableTimeEntries:{type:Object,computed:"_computeRunToAvailableTimeEntries(_tagToRunToData)"},_tagToRunToData:{type:Object,value:()=>({})},_relevantSelectedRuns:{type:Array,computed:"_computeRelevantSelectedRuns(_selectedRuns, _runToTagInfo)"},_runToStep:{type:Object,notify:!0},_dataNotFound:Boolean,_tagFilter:String,
_categoriesDomReady:Boolean,_categories:{type:Array,computed:"_makeCategories(_runToTagInfo, _selectedRuns, _tagFilter, _categoriesDomReady)"},_getCategoryItemKey:{type:Function,value:()=>b=>b.tag},_requestManager:{type:Object,value:()=>new dc.RequestManager},_step:{type:Number,value:0,notify:!0}},ready(){this.reload()},reload(){Promise.all([this._fetchTags()]).then(()=>{this._reloadCards()})},_shouldOpen(b){return 2>=b},_fetchTags(){const b=dc.getRouter().pluginRoute("pr_curves","/tags");return this._requestManager.request(b).then(d=>
{if(!_.isEqual(d,this._runToTagInfo)){var f=_.mapValues(d,h=>_.keys(h));f=dc.getTags(f);this.set("_dataNotFound",0===f.length);this.set("_runToTagInfo",d);this.async(()=>{this.set("_categoriesDomReady",!0)})}})},_reloadCards(){_.forEach(this.root.querySelectorAll("tf-pr-curve-card"),b=>{b.reload()})},_makeCategories(b,d,f){b=_.mapValues(b,h=>Object.keys(h));return kc.categorizeTags(b,d,f)},_computeColorForRun(b){return Ld.runsColorScale(b)},_computeRelevantSelectedRuns(b,d){return b.filter(f=>d[f])},
_tagMetadata(b,d,f){const h={};d.forEach(k=>{h[k]=b[k][f]});d=f.replace(/\/pr_curves$/,"");return rf.aggregateTagInfo(h,d)},_createDataChangeCallback(b){return d=>{this.set("_tagToRunToData",Object.assign({},this._tagToRunToData,{[b]:d}))}},_computeRunToAvailableTimeEntries(b){const d={};for(var f of Object.entries(b)){const [h,k]=f;for(const r of Object.entries(k)){const [l]=r;if(null==d[l]||h<d[l])d[l]=h}}f={};for(const h of Object.entries(d)){const [k,r]=h;{const l=b[r][k];f[k]=l.map(p=>({step:p.step,
wall_time:p.wall_time,relative:p.wall_time-l[0].wall_time}))}}return f}});

//# sourceURL=build://tf-profile-redirect-dashboard/tf-profile-redirect-dashboard.html.js
(function(){Polymer({is:"tf-profile-redirect-dashboard",properties:{_installCommand:{type:String,readOnly:!0,value:"pip install -U tensorboard-plugin-profile"}},_copyInstallCommand(){const b=this;return Wb(function*(){const d=()=>Wb(function*(){b.$.commandTextarea.select();try{yield navigator.clipboard.writeText(b._installCommand)}catch(f){if(!document.execCommand("copy"))return Promise.reject()}});try{yield d(),b.$.copiedMessage.innerText="Copied."}catch(f){b.$.copiedMessage.innerText="Failed to copy to clipboard."}})},
_removeCopiedMessage(){this.$.copiedMessage.innerText=""}})})();

//# sourceURL=build://tf-tensorboard/plugin-dialog.html.js
Polymer({is:"tf-plugin-dialog",properties:{_title:{type:String,value:null},_customMessage:{type:String,value:null},_open:{type:Boolean},_hidden:{type:Boolean,computed:"_computeHidden(_open)",reflectToAttribute:!0},_useNativeBackdrop:{type:Boolean,value:!1,readOnly:!0}},openNoTensorFlowDialog(){this.openDialog("This plugin is disabled without TensorFlow",'To enable this plugin in TensorBoard, install TensorFlow with "pip install tensorflow" or equivalent.')},openOldTensorFlowDialog(b){this.openDialog("This plugin is disabled without TensorFlow "+
b,"To enable this plugin in TensorBoard, install TensorFlow "+b+' or greater with "pip install tensorflow" or equivalent.')},openDialog(b,d){this.set("_title",b);this.set("_customMessage",d);this.$.dialog.open()},closeDialog(){this.$.dialog.close()},_computeHidden(b){return!b}});

//# sourceURL=build://tf-beholder-dashboard/tf-beholder-video.html.js
(function(){const b=dc.getRouter().pluginRoute("beholder","/beholder-frame"),d=dc.getRouter().pluginRoute("beholder","/ping");Polymer({is:"tf-beholder-video",properties:{fps:{type:Number,value:10,observer:"_fpsChanged"},pingSleep:{type:Number,value:1E3},xhrTimeout:{type:Number,value:2500},_imageURL:{type:String,value:"data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs\x3d"},_xhr:Object,_timer:Number,_isDead:Boolean},attached(){this.set("_imageURL",b);this._ping()},detached(){this._clear();
this.set("_imageURL","data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs\x3d")},_ping(){this._clear();this._xhr=new XMLHttpRequest;this._xhr.open("GET",d,!0);this._xhr.timeout=this.xhrTimeout;this._xhr.onload=this._onPingLoad.bind(this);this._xhr.onerror=this._onPing.bind(this,!1,this.pingSleep);this._xhr.ontimeout=this._onPing.bind(this,!1,1);this._xhr.send(null)},_onPingLoad(){if(200==this._xhr.status){const f=JSON.parse(this._xhr.responseText);this._onPing("alive"==f.status,this.pingSleep)}else this._onPing(!1,
this.pingSleep)},_onPing(f,h){f&&this._isDead&&this.set("_imageURL",b+"?t\x3d"+(new Date).getTime());this._isDead=!f;this._timer=window.setTimeout(()=>this._ping(),h)},_clear(){this._timer&&(window.clearTimeout(this._timer),this._timer=null);this._xhr&&(this._xhr.readyState<XMLHttpRequest.DONE&&this._xhr.abort(),this._xhr=null)},_fpsChanged(f,h){0==f?this._clear():0==h&&this._ping()}})})();

//# sourceURL=build://tf-beholder-dashboard/tf-beholder-info.html.js
(function(){const b=dc.getRouter().pluginRoute("beholder","/section-info");Polymer({is:"tf-beholder-info",properties:{fps:{type:Number,value:10,observer:"_fpsChanged"},xhrTimeout:{type:Number,value:1E4},_items:{type:Array,value:()=>[{name:"Loading..."}]},_xhr:Object,_timer:Number},attached(){this._load()},detached(){this._clear()},_load(){this._clear();this._xhr=new XMLHttpRequest;this._xhr.open("GET",b,!0);this._xhr.timeout=this.xhrTimeout;this._xhr.onload=this._onLoad.bind(this);this._xhr.onerror=
this._retry.bind(this,this._getSleep());this._xhr.ontimeout=this._retry.bind(this,1);this._xhr.send(null)},_onLoad(){if(200==this._xhr.status){const d=JSON.parse(this._xhr.responseText);console.assert(Array.isArray(d),"Expected response to be in an array");this._items=d}this._retry(this._getSleep())},_retry(d){this._timer=window.setTimeout(this._load.bind(this),d)},_getSleep(){return 1E3/(0===this.fps?1:this.fps)},_clear(){this._timer&&(window.clearTimeout(this._timer),this._timer=null);this._xhr&&
(this._xhr.readyState<XMLHttpRequest.DONE&&this._xhr.abort(),this._xhr=null)},_fpsChanged(d,f){0==d?this._clear():0==f&&this._load()}})})();

//# sourceURL=build://tf-beholder-dashboard/tf-beholder-dashboard.html.js
(function(){Polymer({is:"tf-beholder-dashboard",properties:{_requestManager:{type:Object,value:()=>new dc.RequestManager(10,0)},_isAvailable:Boolean,_values:{type:String,value:"trainable_variables",observer:"_configChanged"},_mode:{type:String,value:"variance",observer:"_configChanged"},_scaling:{type:String,value:"layer",observer:"_configChanged"},_windowSize:{type:Number,value:15,observer:"_configChanged"},_previousFPS:{type:Number,value:30},_FPS:{type:Number,value:10,observer:"_configChanged"},
_recordText:{type:String,value:"start recording"},_isRecording:{type:Boolean,value:!1,observer:"_configChanged"},_showAll:{type:Boolean,value:!1,observer:"_configChanged"},_colormap:{type:String,value:"magma",observer:"_configChanged"},_is_active:{type:Boolean,value:!1,observer:"_configChanged"},_controls_disabled:{type:Boolean,value:!1,observer:"_configChanged"}},_valuesNotFrame(b){return"frames"!==b},_varianceSelected(b){return"variance"===b},_configChanged(){if(this._is_active&&!this._controls_disabled){var b=
[this._values,this._mode,this._scaling,this._windowSize,this._FPS,this._isRecording,this._showAll,this._colormap],d;for(d of b)if("undefined"===typeof d||""===d)return;b=dc.getRouter().pluginRoute("beholder","/change-config");this._requestManager.request(b,{values:this._values,mode:this._mode,scaling:this._scaling,window_size:this._windowSize,FPS:this._FPS,is_recording:this._isRecording,show_all:this._showAll,colormap:this._colormap})}},_toggleRecord(){"start recording"==this._recordText?(this.set("_recordText",
"stop recording"),this.set("_isRecording",!0)):(this.set("_recordText","start recording"),this.set("_isRecording",!1));this.$.record_button.classList.toggle("is-recording")},attached:function(){this._requestManager.request(dc.getRouter().pluginsListing()).then(b=>{"beholder"in b?(this.$.initialDialog.closeDialog(),this.set("_isAvailable",!0)):(this.$.initialDialog.openNoTensorFlowDialog(),this.set("_isAvailable",!1))})},ready(){this.reload()},reload(){if(this._isAvailable){const b=dc.getRouter().pluginRoute("beholder",
"/is-active");this._requestManager.request(b).then(d=>{this.set("_is_active",d.is_active);this.set("_controls_disabled",!d.is_config_writable)})}}});pe.registerDashboard()})();

//# sourceURL=build://vaadin-split-layout/vaadin-split-layout.html.js
Polymer({is:"vaadin-split-layout",behaviors:[Polymer.IronResizableBehavior],properties:{vertical:{type:Boolean,reflectToAttribute:!0,value:!1},_previousPrimaryPointerEvents:String,_previousSecondaryPointerEvents:String},attached:function(){this._observer=Polymer.dom(this).observeNodes(this._processChildren)},detached:function(){Polymer.dom(this).unobserveNodes(this._observer)},_processChildren:function(){this.getEffectiveChildren().filter(function(b){return b.classList.contains("splitter-handle")?
(Polymer.dom(b).setAttribute("slot","handle"),!1):!0}).forEach(function(b,d){0===d?(this._primaryChild=b,Polymer.dom(b).setAttribute("slot","primary")):1==d?(this._secondaryChild=b,Polymer.dom(b).setAttribute("slot","secondary")):Polymer.dom(b).removeAttribute("slot")}.bind(this))},_setFlexBasis:function(b,d,f){d=Math.max(0,Math.min(d,f));0===d&&(d=1E-6);b.style.flex="1 1 "+d+"px"},_onHandleTrack:function(b){if(this._primaryChild&&this._secondaryChild){var d=this.vertical?"height":"width";"start"===
b.detail.state?(this._startSize={container:this.getBoundingClientRect()[d]-this.$.splitter.getBoundingClientRect()[d],primary:this._primaryChild.getBoundingClientRect()[d],secondary:this._secondaryChild.getBoundingClientRect()[d]},this._previousPrimaryPointerEvents=this._primaryChild.style.pointerEvents,this._previousSecondaryPointerEvents=this._secondaryChild.style.pointerEvents,this._primaryChild.style.pointerEvents="none",this._secondaryChild.style.pointerEvents="none"):(d=this.vertical?b.detail.dy:
b.detail.dx,this._setFlexBasis(this._primaryChild,this._startSize.primary+d,this._startSize.container),this._setFlexBasis(this._secondaryChild,this._startSize.secondary-d,this._startSize.container),this.notifyResize(),"end"===b.detail.state&&(delete this._startSize,this._primaryChild.style.pointerEvents=this._previousPrimaryPointerEvents,this._secondaryChild.style.pointerEvents=this._previousSecondaryPointerEvents))}},_preventDefault:function(b){b.preventDefault()}});

//# sourceURL=build://tf-hparams-query-pane/tf-hparams-query-pane.html.js
Polymer({is:"tf-hparams-query-pane",properties:{backend:Object,experimentName:String,configuration:{type:Object,value:()=>({schema:{hparamColumns:[],metricColumns:[]},columnsVisibility:[],visibleSchema:{hparamInfos:[],metricInfos:[]}}),readOnly:!0,notify:!0},sessionGroups:{type:Array,value:()=>[],readOnly:!0,notify:!0},_experiment:Object,_hparams:Array,_metrics:Array,_statuses:{type:Array,value:()=>[{value:"STATUS_UNKNOWN",displayName:"Unknown",allowed:!0},{value:"STATUS_SUCCESS",displayName:"Success",
allowed:!0},{value:"STATUS_FAILURE",displayName:"Failure",allowed:!0},{value:"STATUS_RUNNING",displayName:"Running",allowed:!0}]},_getExperimentResolved:{type:Object,value:function(){return new Promise(b=>{this._resolveGetExperiment=b})}},_resolveGetExperiment:Function,_listSessionGroupsCanceller:{type:Object,value:()=>new dc.Canceller},_sortByIndex:Number,_sortDirection:Number,_pageSizeInput:{type:Object,value:{value:"100",invalid:!1}},_pageNumberInput:{type:Object,value:{value:"1",invalid:!1}},
_pageCountStr:{type:String,value:"?"},_totalSessionGroupsCountStr:String,_sessionGroupsRequest:Object},observers:["_computeExperimentAndRelatedProps(backend, experimentName)","_updateConfiguration(_hparams.*, _metrics.*)"],reload(){this._queryServer()},_csvUrl(b,d){return this._downloadDataUrl(b,d,"csv")},_jsonUrl(b,d){return this._downloadDataUrl(b,d,"json")},_latexUrl(b,d){return this._downloadDataUrl(b,d,"latex")},_downloadDataUrl(b,d,f){return this.backend.getDownloadUrl(f,b,d.columnsVisibility)},
_computeExperimentAndRelatedProps(){const b=tf.hparams.utils;b.isNullOrUndefined(this.backend)||b.isNullOrUndefined(this.experimentName)||this.backend.getExperiment({experimentName:this.experimentName}).then(d=>{_.isEqual(d,this._experiment)||(this.set("_experiment",d),this._computeHParams(),this._computeMetrics(),this._queryServer(),this._resolveGetExperiment())})},_computeHParams(){const b=[];this._experiment.hparamInfos.forEach((d,f)=>{const h={info:d,displayed:5>f,filter:{}};h.info.hasOwnProperty("domainDiscrete")?
(h.filter.domainDiscrete=[],h.info.domainDiscrete.forEach(k=>{h.filter.domainDiscrete.push({value:k,checked:!0})})):"DATA_TYPE_BOOL"===h.info.type?h.filter.domainDiscrete=[{value:!1,checked:!0},{value:!0,checked:!0}]:"DATA_TYPE_FLOAT64"===h.info.type?h.filter.interval={min:{value:"",invalid:!1},max:{value:"",invalid:!1}}:"DATA_TYPE_STRING"===h.info.type?h.filter.regexp="":console.warn("unknown hparam.info.type: %s",h.info.type);b.push(h)});this.set("_hparams",b)},_computeMetrics(){const b=[];this._experiment.metricInfos.forEach((d,
f)=>{b.push({info:d,filter:{interval:{min:{value:"",invalid:!1},max:{value:"",invalid:!1}}},displayed:5>f})});this.set("_metrics",b)},_computeSchema(){return this._hparams&&this._metrics?{hparamColumns:this._hparams.map(b=>({hparamInfo:b.info})),metricColumns:this._metrics.map(b=>({metricInfo:b.info}))}:{hparamColumns:[],metricColumns:[]}},_updateConfiguration(){this.debounce("_updateConfiguration",()=>{this._setConfiguration({schema:this._computeSchema(),columnsVisibility:this._computeColumnsVisibility(),
visibleSchema:this._computeVisibleSchema()})})},_computeColumnsVisibility(){return this._hparams&&this._metrics?this._hparams.map(b=>b.displayed).concat(this._metrics.map(b=>b.displayed)):[]},_computeVisibleSchema(){if(!this._hparams||!this._metrics)return{hparamInfos:[],metricInfos:[]};const b=this._hparams.filter(f=>f.displayed).map(f=>f.info),d=this._metrics.filter(f=>f.displayed).map(f=>f.info);return{hparamInfos:b,metricInfos:d}},_queryServer(){this.debounce("queryServer",()=>this._queryServerNoDebounce(),
100)},_queryServerNoDebounce(){return this._sendListSessionGroupsRequest().then(this._listSessionGroupsCanceller.cancellable(({value:b,cancelled:d})=>{d||(0<=b.totalSize?(this.set("_pageCountStr",String(Math.ceil(b.totalSize/+this._pageSizeInput.value))),this.set("_totalSessionGroupsCountStr",b.totalSize)):(this.set("_pageCountStr","?"),this.set("_totalSessionGroupsCountStr","Unknown")),tf.hparams.utils.setArrayObservably(this,b.sessionGroups))}))},_sendListSessionGroupsRequest(){const b=this._buildListSessionGroupsRequest();
if(null!==b)return this.set("_sessionGroupsRequest",b),this._listSessionGroupsCanceller.cancelAll(),this.backend.listSessionGroups(b)},_buildListSessionGroupsRequest(){function b(m){var n=f.get(m+".min.value");console.assert(void 0!==n);n=""===n?"-Infinity":+n;f.set(m+".min.invalid",isNaN(n));h=h&&!isNaN(n);var q=f.get(m+".max.value");console.assert(void 0!==q);q=""===q?"Infinity":+q;f.set(m+".max.invalid",isNaN(q));h=h&&!isNaN(q);return isNaN(n)||isNaN(q)?null:{minValue:n,maxValue:q}}function d(m){var n=
f.get(m+".value");console.assert(void 0!==n);n=+n;const q=Number.isInteger(n)&&0<n;f.set(m+".invalid",!q);h=h&&q;return q?n:null}const f=this;let h=!0;const k=this._statuses.filter(m=>m.allowed).map(m=>m.value);let r=[];this._hparams.forEach((m,n)=>{let q={hparam:m.info.name};if(m.filter.domainDiscrete)q.filterDiscrete=[],m.filter.domainDiscrete.forEach(u=>{u.checked&&q.filterDiscrete.push(u.value)});else if(m.filter.interval)q.filterInterval=b("_hparams."+n+".filter.interval");else if(m.filter.regexp)q.filterRegexp=
m.filter.regexp;else return console.error("hparam.filter with no domainDiscrete, interval or regexp properties set: %s",m),null;r.push(q)});this._metrics.forEach((m,n)=>{m={metric:m.info.name,filterInterval:b("_metrics."+n+".filter.interval")};r.push(m)});if(void 0!==this._sortByIndex&&void 0!==this._sortDirection){if(!(this._sortByIndex in r))return console.error("No column in colParams with index sortByIndex: %s",this._sortByIndex),null;r[this._sortByIndex].order=0===this._sortDirection?"ORDER_ASC":
"ORDER_DESC"}const l=d("_pageNumberInput"),p=d("_pageSizeInput");return h?{experimentName:this.experimentName,allowedStatuses:k,colParams:r,startIndex:p*(l-1),sliceSize:p}:null},_metricSortByIndex(b){return b+this._hparams.length},_hparamName:tf.hparams.utils.hparamName,_metricName:tf.hparams.utils.metricName,_prettyPrint:tf.hparams.utils.prettyPrint});

//# sourceURL=build://iron-pages/iron-pages.html.js
Polymer({is:"iron-pages",behaviors:[Polymer.IronResizableBehavior,Polymer.IronSelectableBehavior],properties:{activateEvent:{type:String,value:null}},observers:["_selectedPageChanged(selected)"],_selectedPageChanged:function(){this.async(this.notifyResize)}});

//# sourceURL=build://paper-header-panel/paper-header-panel.html.js
(function(){var b={scroll:!0},d={standard:2,waterfall:1,"waterfall-tall":1},f={"waterfall-tall":!0};Polymer({is:"paper-header-panel",properties:{mode:{type:String,value:"standard",observer:"_modeChanged",reflectToAttribute:!0},shadow:{type:Boolean,value:!1},tallClass:{type:String,value:"tall"},atTop:{type:Boolean,value:!0,notify:!0,readOnly:!0,reflectToAttribute:!0}},observers:["_computeDropShadowHidden(atTop, mode, shadow)"],attached:function(){this._addListener();this._keepScrollingState()},detached:function(){this._removeListener()},
ready:function(){this.scrollHandler=this._scroll.bind(this);console.warn(this.is,"is deprecated. Please use app-layout instead!")},get header(){return Polymer.dom(this.$.headerSlot).getDistributedNodes()[0]},get scroller(){return this._getScrollerForMode(this.mode)},get visibleShadow(){return this.$.dropShadow.classList.contains("has-shadow")},_computeDropShadowHidden:function(h,k){k=d[k];this.shadow?this.toggleClass("has-shadow",!0,this.$.dropShadow):2===k?this.toggleClass("has-shadow",!0,this.$.dropShadow):
1!==k||h?this.toggleClass("has-shadow",!1,this.$.dropShadow):this.toggleClass("has-shadow",!0,this.$.dropShadow)},_computeMainContainerClass:function(h){var k={};k.flex="cover"!==h;return Object.keys(k).filter(function(r){return k[r]}).join(" ")},_addListener:function(){this.scroller.addEventListener("scroll",this.scrollHandler)},_removeListener:function(){this.scroller.removeEventListener("scroll",this.scrollHandler)},_modeChanged:function(h,k){var r=this.header;r&&(f[k]&&!f[h]?(r.classList.remove(this.tallClass),
this.async(function(){r.classList.remove("animate")},200)):this.toggleClass("animate",f[h],r));this._keepScrollingState()},_keepScrollingState:function(){var h=this.scroller,k=this.header;this._setAtTop(0===h.scrollTop);k&&this.tallClass&&f[this.mode]&&this.toggleClass(this.tallClass,this.atTop||k.classList.contains(this.tallClass)&&h.scrollHeight<this.offsetHeight,k)},_scroll:function(){this._keepScrollingState();this.fire("content-scroll",{target:this.scroller},{bubbles:!1})},_getScrollerForMode:function(h){return b[h]?
this:this.$.mainContainer}})})();

//# sourceURL=build://paper-tabs/paper-tab.html.js
Polymer({is:"paper-tab",behaviors:[Polymer.IronControlState,Polymer.IronButtonState,Polymer.PaperRippleBehavior],properties:{link:{type:Boolean,value:!1,reflectToAttribute:!0}},hostAttributes:{role:"tab"},listeners:{down:"_updateNoink",tap:"_onTap"},attached:function(){this._updateNoink()},get _parentNoink(){var b=Polymer.dom(this).parentNode;return!!b&&!!b.noink},_updateNoink:function(){this.noink=!!this.noink||!!this._parentNoink},_onTap:function(b){if(this.link){var d=this.queryEffectiveChildren("a");
d&&b.target!==d&&d.click()}}});

//# sourceURL=build://paper-tabs/paper-tabs.html.js
Polymer({is:"paper-tabs",behaviors:[Polymer.IronResizableBehavior,Polymer.IronMenubarBehavior],properties:{noink:{type:Boolean,value:!1,observer:"_noinkChanged"},noBar:{type:Boolean,value:!1},noSlide:{type:Boolean,value:!1},scrollable:{type:Boolean,value:!1},fitContainer:{type:Boolean,value:!1},disableDrag:{type:Boolean,value:!1},hideScrollButtons:{type:Boolean,value:!1},alignBottom:{type:Boolean,value:!1},selectable:{type:String,value:"paper-tab"},autoselect:{type:Boolean,value:!1},autoselectDelay:{type:Number,
value:0},_step:{type:Number,value:10},_holdDelay:{type:Number,value:1},_leftHidden:{type:Boolean,value:!1},_rightHidden:{type:Boolean,value:!1},_previousTab:{type:Object}},hostAttributes:{role:"tablist"},listeners:{"iron-resize":"_onTabSizingChanged","iron-items-changed":"_onTabSizingChanged","iron-select":"_onIronSelect","iron-deselect":"_onIronDeselect"},keyBindings:{"left:keyup right:keyup":"_onArrowKeyup"},created:function(){this._holdJob=null;this._pendingActivationTimeout=this._pendingActivationItem=
void 0;this._bindDelayedActivationHandler=this._delayedActivationHandler.bind(this);this.addEventListener("blur",this._onBlurCapture.bind(this),!0)},ready:function(){this.setScrollDirection("y",this.$.tabsContainer)},detached:function(){this._cancelPendingActivation()},_noinkChanged:function(b){Polymer.dom(this).querySelectorAll("paper-tab").forEach(b?this._setNoinkAttribute:this._removeNoinkAttribute)},_setNoinkAttribute:function(b){b.setAttribute("noink","")},_removeNoinkAttribute:function(b){b.removeAttribute("noink")},
_computeScrollButtonClass:function(b,d,f){return!d||f?"hidden":b?"not-visible":""},_computeTabsContentClass:function(b,d){return b?"scrollable"+(d?" fit-container":""):" fit-container"},_computeSelectionBarClass:function(b,d){return b?"hidden":d?"align-bottom":""},_onTabSizingChanged:function(){this.debounce("_onTabSizingChanged",function(){this._scroll();this._tabChanged(this.selectedItem)},10)},_onIronSelect:function(b){this._tabChanged(b.detail.item,this._previousTab);this._previousTab=b.detail.item;
this.cancelDebouncer("tab-changed")},_onIronDeselect:function(){this.debounce("tab-changed",function(){this._tabChanged(null,this._previousTab);this._previousTab=null},1)},_activateHandler:function(){this._cancelPendingActivation();Polymer.IronMenuBehaviorImpl._activateHandler.apply(this,arguments)},_scheduleActivation:function(b,d){this._pendingActivationItem=b;this._pendingActivationTimeout=this.async(this._bindDelayedActivationHandler,d)},_delayedActivationHandler:function(){var b=this._pendingActivationItem;
this._pendingActivationTimeout=this._pendingActivationItem=void 0;b.fire(this.activateEvent,null,{bubbles:!0,cancelable:!0})},_cancelPendingActivation:function(){void 0!==this._pendingActivationTimeout&&(this.cancelAsync(this._pendingActivationTimeout),this._pendingActivationTimeout=this._pendingActivationItem=void 0)},_onArrowKeyup:function(){this.autoselect&&this._scheduleActivation(this.focusedItem,this.autoselectDelay)},_onBlurCapture:function(b){b.target===this._pendingActivationItem&&this._cancelPendingActivation()},
get _tabContainerScrollSize(){return Math.max(0,this.$.tabsContainer.scrollWidth-this.$.tabsContainer.offsetWidth)},_scroll:function(b,d){this.scrollable&&this._affectScroll(d&&-d.ddx||0)},_down:function(){this.async(function(){this._defaultFocusAsync&&(this.cancelAsync(this._defaultFocusAsync),this._defaultFocusAsync=null)},1)},_affectScroll:function(b){this.$.tabsContainer.scrollLeft+=b;b=this.$.tabsContainer.scrollLeft;this._leftHidden=0===b;this._rightHidden=b===this._tabContainerScrollSize},
_onLeftScrollButtonDown:function(){this._scrollToLeft();this._holdJob=setInterval(this._scrollToLeft.bind(this),this._holdDelay)},_onRightScrollButtonDown:function(){this._scrollToRight();this._holdJob=setInterval(this._scrollToRight.bind(this),this._holdDelay)},_onScrollButtonUp:function(){clearInterval(this._holdJob);this._holdJob=null},_scrollToLeft:function(){this._affectScroll(-this._step)},_scrollToRight:function(){this._affectScroll(this._step)},_tabChanged:function(b,d){if(b){var f=this.$.tabsContent.getBoundingClientRect(),
h=f.width,k=b.getBoundingClientRect();f=k.left-f.left;this._pos={width:this._calcPercent(k.width,h),left:this._calcPercent(f,h)};if(this.noSlide||null==d)this.$.selectionBar.classList.remove("expand"),this.$.selectionBar.classList.remove("contract"),this._positionBar(this._pos.width,this._pos.left);else{var r=d.getBoundingClientRect();d=this.items.indexOf(d);b=this.items.indexOf(b);this.$.selectionBar.classList.add("expand");b=d<b;this._isRTL&&(b=!b);b?this._positionBar(this._calcPercent(k.left+k.width-
r.left,h)-5,this._left):this._positionBar(this._calcPercent(r.left+r.width-k.left,h)-5,this._calcPercent(f,h)+5);this.scrollable&&this._scrollToSelectedIfNeeded(k.width,f)}}else this.$.selectionBar.classList.remove("expand"),this.$.selectionBar.classList.remove("contract"),this._positionBar(0,0)},_scrollToSelectedIfNeeded:function(b,d){d-=this.$.tabsContainer.scrollLeft;0>d?this.$.tabsContainer.scrollLeft+=d:(d+=b-this.$.tabsContainer.offsetWidth,0<d&&(this.$.tabsContainer.scrollLeft+=d))},_calcPercent:function(b,
d){return 100*b/d},_positionBar:function(b,d){b=b||0;d=d||0;this._width=b;this._left=d;this.transform("translateX("+d+"%) scaleX("+b/100+")",this.$.selectionBar)},_onBarTransitionEnd:function(){var b=this.$.selectionBar.classList;b.contains("expand")?(b.remove("expand"),b.add("contract"),this._positionBar(this._pos.width,this._pos.left)):b.contains("contract")&&b.remove("contract")}});

//# sourceURL=build://paper-toolbar/paper-toolbar.html.js
Polymer({is:"paper-toolbar",hostAttributes:{role:"toolbar"},properties:{bottomJustify:{type:String,value:""},justify:{type:String,value:""},middleJustify:{type:String,value:""}},ready:function(){console.warn(this.is,"is deprecated. Please use app-layout instead!")},attached:function(){this._observer=this._observe(this);this._updateAriaLabelledBy()},detached:function(){this._observer&&this._observer.disconnect()},_observe:function(b){var d=new MutationObserver(function(){this._updateAriaLabelledBy()}.bind(this));
d.observe(b,{childList:!0,subtree:!0});return d},_updateAriaLabelledBy:function(){Polymer.dom.flush();for(var b=[],d=Array.prototype.slice.call(Polymer.dom(this.root).querySelectorAll("slot")).concat(Array.prototype.slice.call(Polymer.dom(this.root).querySelectorAll("content"))),f,h=0;f=d[h];h++){f=Polymer.dom(f).getDistributedNodes();for(var k,r=0;k=f[r];r++)if(k.classList&&k.classList.contains("title"))if(k.id)b.push(k.id);else{var l="paper-toolbar-label-"+Math.floor(1E4*Math.random());k.id=l;b.push(l)}}0<
b.length&&this.setAttribute("aria-labelledby",b.join(" "))},_computeBarExtraClasses:function(b){return b?b+("justified"===b?"":"-justified"):""}});

//# sourceURL=build://tf-hparams-scale-and-color-controls/tf-hparams-scale-and-color-controls.html.js
Polymer({is:"tf-hparams-scale-and-color-controls",properties:{configuration:Object,sessionGroups:Array,options:{type:Object,notify:!0,value:null}},observers:["_configurationChanged(configuration.*)","_unselectDisabledLogScales(sessionGroups.*)"],_configurationChanged(){const b=this.configuration.visibleSchema,d=this.configuration.schema,f={columns:b.hparamInfos.map((h,k)=>({name:tf.hparams.utils.hparamName(h),index:k,absoluteIndex:tf.hparams.utils.getAbsoluteColumnIndex(d,b,k),scale:this._isNumericColumn(k)?
"LINEAR":"NON_NUMERIC"})).concat(b.metricInfos.map((h,k)=>{k+=b.hparamInfos.length;return{scale:"LINEAR",name:tf.hparams.utils.metricName(h),index:k,absoluteIndex:tf.hparams.utils.getAbsoluteColumnIndex(d,b,k)}})),minColor:"#0000FF",maxColor:"#FF0000",configuration:this.configuration};this.set("options",f);Polymer.dom.flush();this.set("options.colorByColumnIndex",this._defaultColorByColumnIndex())},_unselectDisabledLogScales(){null!==this.options&&this.options.columns.forEach(b=>{const d="options.columns."+
b.index;this._allowLogScale(b)||"LOG"!==b.scale||this.set(d+".scale","LINEAR")})},_allowLogScale(b){if(!this._isNumericColumn(b.index)||!this.sessionGroups)return!1;const [d,f]=tf.hparams.utils.visibleNumericColumnExtent(this.configuration.visibleSchema,this.sessionGroups,b.index);return 0<d||0>f},_isNumericColumn(b){return b>=this.configuration.visibleSchema.hparamInfos.length||"DATA_TYPE_FLOAT64"===this.configuration.visibleSchema.hparamInfos[b].type},_defaultColorByColumnIndex(){if(0<this.configuration.visibleSchema.metricInfos.length)return this.configuration.visibleSchema.hparamInfos.length;
const b=this.configuration.visibleSchema.hparamInfos.findIndex(d=>"DATA_TYPE_FLOAT64"===d.type);if(-1!==b)return b}});

//# sourceURL=build://vaadin-grid/vaadin-grid-active-item-behavior.html.js
window.vaadin=window.vaadin||{};vaadin.elements=vaadin.elements||{};vaadin.elements.grid=vaadin.elements.grid||{};
vaadin.elements.grid.ActiveItemBehavior={properties:{activeItem:{type:Object,notify:!0,value:null}},listeners:{"cell-activate":"_activateItem"},observers:["_activeItemChanged(activeItem)"],_activateItem:function(b){var d=b.detail.model.item;this.activeItem=this.activeItem!==d?d:null;b.stopImmediatePropagation()},_activeItemChanged:function(){this.$.scroller._physicalItems&&this.$.scroller._physicalItems.forEach(function(b){this._updateItem(b,b.item)}.bind(this))}};

//# sourceURL=build://vaadin-grid/vaadin-grid-table-scroll-behavior.html.js
window.vaadin=window.vaadin||{};vaadin.elements=vaadin.elements||{};vaadin.elements.grid=vaadin.elements.grid||{};
vaadin.elements.grid.TableScrollBehaviorImpl={properties:{_vidxOffset:{type:Number,value:0},ios:{type:Boolean,value:navigator.userAgent.match(/iP(?:hone|ad;(?: U;)? CPU) OS (\d+)/),reflectToAttribute:!0},fixedSections:{type:Boolean,reflectToAttribute:!0,computed:"_hasFixedSections(scrollbarWidth)"},_frozenCells:{type:Array,value:function(){return[]}},scrolling:{type:Boolean,reflectToAttribute:!0}},ready:function(){this.scrollTarget=this.$.table},attached:function(){this.listen(this.scrollTarget,"wheel",
"_onWheel")},detached:function(){this.unlisten(this.scrollTarget,"wheel","_onWheel")},scrollToScaledIndex:function(b){this._pendingScrollToScaledIndex=null;this.$.items.style.borderTopWidth||(this._pendingScrollToScaledIndex=b);b=Math.min(Math.max(b,0),this.size-1);this.$.table.scrollTop=b/this.size*this.$.table.scrollHeight;this._scrollHandler();this.scrollToIndex(b-this._vidxOffset);this._resetScrollPosition(this._scrollPosition);this._scrollHandler();this._vidxOffset+this.lastVisibleIndex===this.size-
1&&(this.$.table.scrollTop=this.$.table.scrollHeight-this.$.table.offsetHeight,this._scrollHandler())},_hasFixedSections:function(b){return navigator.userAgent.match(/Edge/)&&0===b},_onWheel:function(b){if(!b.ctrlKey&&!this._hasScrolledAncestor(b.target,b.deltaX,b.deltaY)){var d=this.$.table,f=b.deltaY;1===b.deltaMode&&(f*=grid.$.scroller._physicalAverage);var h=Math.abs(b.deltaX)+Math.abs(f);this._canScroll(d,b.deltaX,f)?(b.preventDefault(),d.scrollTop+=f,d.scrollLeft+=b.deltaX,this._scrollHandler(),
this._hasResidualMomentum=!0,this._ignoreNewWheel=this.debounce("ignore-new-wheel",function(){this._ignoreNewWheel=null},500)):this._hasResidualMomentum&&h<=this._previousMomentum||this._ignoreNewWheel?b.preventDefault():h>this._previousMomentum&&(this._hasResidualMomentum=!1);this._previousMomentum=h}},_hasScrolledAncestor:function(b,d,f){if(this._canScroll(b,d,f))return!0;if("vaadin-grid-cell-content"!==b.localName&&b!==this&&b.parentElement)return this._hasScrolledAncestor(b.parentElement,d,f)},
_canScroll:function(b,d,f){return 0<f&&b.scrollTop<b.scrollHeight-b.offsetHeight||0>f&&0<b.scrollTop||0<d&&b.scrollLeft<b.scrollWidth-b.offsetWidth||0>d&&0<b.scrollLeft},_scrollHandler:function(){var b=Math.max(0,Math.min(this._maxScrollTop,this._scrollTop)),d=b-this._scrollPosition,f=this._ratio,h=0,k=this._hiddenContentSize,r=f,l=[];this._scrollPosition=b;this._lastVisibleIndexVal=this._firstVisibleIndexVal=null;var p=this._scrollBottom;var m=this._physicalBottom;if(Math.abs(d)>this._physicalSize)this._physicalTop+=
d,h=Math.round(d/this._physicalAverage);else if(0>d){var n=b-this._physicalTop;l=this._virtualStart;var q=[];var u=this._physicalEnd;for(r=n/k;r<f&&h<this._physicalCount&&0<l-h&&m-this._getPhysicalSizeIncrement(u)>p;)n=this._getPhysicalSizeIncrement(u),r+=n/k,m-=n,q.push(u),h++,u=0===u?this._physicalCount-1:u-1;l=q;h=-h}else if(0<d){var w=this._virtualEnd,A=this._virtualCount-1;q=[];u=this._physicalStart;for(r=(m-p)/k;r<f&&h<this._physicalCount&&w+h<A&&this._physicalTop+this._getPhysicalSizeIncrement(u)<
b;)n=this._getPhysicalSizeIncrement(u),r+=n/k,this._physicalTop+=n,q.push(u),h++,u=(u+1)%this._physicalCount}this._virtualCount<this.size&&this._adjustVirtualIndexOffset(d);0===h?(m<p||this._physicalTop>b)&&this._increasePoolIfNeeded():(this._virtualStart+=h,this._physicalStart+=h,this._update(q,l));this._translateStationaryElements();this.hasAttribute("reordering")||(this.scrolling=!0);this.debounce("vaadin-grid-scrolling",function(){this.scrolling=!1;this._reorderRows()},100)},_adjustVirtualIndexOffset:function(b){if(1E4<
Math.abs(b))this._noScale?this._noScale=!1:(b=Math.round(this._scrollPosition/this._scrollHeight*1E3)/1E3,this._vidxOffset=Math.round(b*this.size-b*this._virtualCount),0===this._scrollTop&&this.scrollToIndex(0));else{b=this._vidxOffset||0;0===this._scrollTop?(this._vidxOffset=0,b!==this._vidxOffset&&this.scrollToIndex(0)):1E3>this.firstVisibleIndex&&0<this._vidxOffset&&(this._vidxOffset-=Math.min(this._vidxOffset,100),this.scrollToIndex(this.firstVisibleIndex+(b-this._vidxOffset)+1),this._noScale=
!0);var d=this.size-this._virtualCount;this._scrollTop>=this._maxScrollTop?(this._vidxOffset=d,b!==this._vidxOffset&&this.scrollToIndex(this._virtualCount)):this.firstVisibleIndex>this._virtualCount-1E3&&this._vidxOffset<d&&(this._vidxOffset+=Math.min(d-this._vidxOffset,100),this.scrollToIndex(this.firstVisibleIndex-(this._vidxOffset-b)),this._noScale=!0)}},_reorderRows:function(){var b=Polymer.dom(this.$.items),d=b.querySelectorAll(".vaadin-grid-row"),f=d.length-(d[0].index-(this._virtualStart+this._vidxOffset));
if(f<d.length/2)for(var h=0;h<f;h++)b.appendChild(d[h]);else for(;f<d.length;f++)b.insertBefore(d[f],d[0])},_frozenCellsChanged:function(){this.debounce("cache-elements",function(){Polymer.dom(this.domHost.root).querySelectorAll(".vaadin-grid-cell").forEach(function(b){b.style.transform=""});this._frozenCells=Array.prototype.slice.call(Polymer.dom(this.domHost.root).querySelectorAll("[frozen]"));this._translateStationaryElements()});this._updateLastFrozen()},_updateLastFrozen:function(){if(this.columnTree){var b=
this.columnTree[this.columnTree.length-1].slice(0);b.sort(function(f,h){return f._order-h._order});var d=b.reduce(function(f,h,k){h._lastFrozen=!1;return h.frozen&&!h.hidden?k:f},void 0);void 0!==d&&(b[d]._lastFrozen=!0)}},_translateStationaryElements:function(){this.fixedSections?(this.$.items.style.transform=this._getTranslate(-this._scrollLeft||0,-this._scrollTop||0),this.$.footer.style.transform=this.$.header.style.transform=this._getTranslate(-this._scrollLeft||0,0)):this.$.footer.style.transform=
this.$.header.style.transform=this._getTranslate(0,this._scrollTop);for(var b=this._getTranslate(this._scrollLeft,0),d=0;d<this._frozenCells.length;d++)this._frozenCells[d].style.transform=b},_getTranslate:function(b,d){return"translate("+b+"px,"+d+"px)"}};vaadin.elements.grid.TableScrollBehavior=[Polymer.IronScrollTargetBehavior,vaadin.elements.grid.TableScrollBehaviorImpl];

//# sourceURL=build://vaadin-grid/vaadin-grid-table-cell.html.js
(function(){var b={properties:{column:Object,expanded:Boolean,flexGrow:Number,colSpan:Number,focused:{type:Boolean,reflectToAttribute:!0},frozen:{type:Boolean,reflectToAttribute:!0},lastFrozen:{type:Boolean,reflectToAttribute:!0},hidden:{type:Boolean,reflectToAttribute:!0},instance:Object,index:Number,item:Object,selected:Boolean,template:Object,target:Object,width:String,order:Number,reorderStatus:{type:String,reflectToAttribute:!0},_childColumns:Array,_cellContent:Object,_insertionPoint:Object,
_templatizer:Object},observers:"_columnChanged(column);_cellAttached(column, isAttached);_expandedChanged(expanded, instance);_flexGrowChanged(flexGrow);_indexChanged(index, instance);_itemChanged(item, instance);_instanceChanged(instance, target);_selectedChanged(selected, instance);_toggleContent(isAttached, _cellContent, _insertionPoint);_toggleInstance(isAttached, _templatizer, instance);_widthChanged(width);_orderChanged(order);_visibleChildColumnsChanged(_visibleChildColumns);_childColumnsChanged(_childColumns)".split(";"),
ready:function(){this.classList.add("vaadin-grid-cell");!1===Polymer.Settings.useShadow&&(this.classList.add("style-scope"),this.classList.add("vaadin-grid"))},_columnChanged:function(d){this.flexGrow=d.flexGrow;this.frozen=d.frozen;this.lastFrozen=d._lastFrozen;this.headerTemplate=d.headerTemplate;this.footerTemplate=d.footerTemplate;this.template=d.template;this.width=d.width;this.hidden=d.hidden;this.resizable=d.resizable;this._childColumns=d._childColumns;this.order=d._order;d.colSpan&&(this.colSpan=
d.colSpan);this.listen(d,"property-changed","_columnPropChanged")},_cellAttached:function(d,f){void 0!==d&&void 0!==f&&(f?this.listen(d,"property-changed","_columnPropChanged"):this.async(function(){this.isAttached||this.unlisten(d,"property-changed","_columnPropChanged")}))},_columnPropChanged:function(d){d.target==this.column&&(this[d.detail.path]=d.detail.value)},_expandedChanged:function(d,f){void 0!==d&&void 0!==f&&(f.__expanded__=d,f.expanded=d)},_flexGrowChanged:function(d){this.style.flexGrow=
d},_indexChanged:function(d,f){void 0!==d&&void 0!==f&&(f.index=d)},_itemChanged:function(d,f){void 0!==d&&void 0!==f&&(f.item=d)},_selectedChanged:function(d,f){void 0!==d&&void 0!==f&&(f.__selected__=d,f.selected=d)},_childColumnsChanged:function(d){this.colSpan=d.length},_toggleContent:function(d,f,h){void 0!==d&&void 0!==f&&void 0!==h&&(d?(Polymer.dom(f).parentNode!==this.target&&Polymer.dom(this.target).appendChild(f),Polymer.dom(this).appendChild(h)):this.async(function(){this.isAttached||Polymer.dom(f).parentNode!==
this.target||Polymer.dom(this.target).removeChild(f)}))},_toggleInstance:function(d,f,h){void 0!==d&&void 0!==f&&void 0!==h&&(d?f.addInstance(h):f.removeInstance(h))},_widthChanged:function(d){this.style.width=d},_orderChanged:function(d){this.style.order=d},_templateChanged:function(d){this.instance=d.templatizer.createInstance();this._templatizer=d.templatizer},_instanceChanged:function(d,f){void 0!==d&&void 0!==f&&(this.style.height="",this._cellContent=this._cellContent||document.createElement("vaadin-grid-cell-content"),
d="vaadin-grid-cell-content-"+(vaadin.elements.grid._contentIndex=vaadin.elements.grid._contentIndex+1||0),this._cellContent.innerHTML="",Polymer.dom(this._cellContent).appendChild(this.instance.root),this._cellContent.setAttribute("id",d),Polymer.Element?(this._cellContent.setAttribute("slot",d),this._insertionPoint=this._insertionPoint||document.createElement("slot"),this._insertionPoint.setAttribute("name",d)):(this._insertionPoint=this._insertionPoint||document.createElement("content"),this._insertionPoint.setAttribute("select",
"#"+d)))}};Polymer({is:"vaadin-grid-table-cell",behaviors:[b,vaadin.elements.grid.CellClickBehavior],observers:["_templateChanged(template)"],_cellClick:function(d){d.defaultPrevented||this.fire("cell-activate",{model:this.instance})}});Polymer({is:"vaadin-grid-table-header-cell",properties:{headerTemplate:Object,resizable:Boolean,columnResizing:{type:Boolean,reflectToAttribute:!0}},behaviors:[b,vaadin.elements.grid.CellClickBehavior],observers:["_headerTemplateChanged(headerTemplate)","_isEmptyChanged(_isEmpty, isAttached)",
"_resizableChanged(resizable)"],listeners:{mousedown:"_cancelMouseDownOnResize",mousemove:"_enableDrag",mouseout:"_disableDrag",touchstart:"_onTouchStart",touchmove:"_onTouchMove",touchend:"_onTouchEnd",contextmenu:"_onContextMenu"},_onContextMenu:function(d){this._reorderGhost&&d.preventDefault()},_onTouchStart:function(d){d.target!==this._resizeHandle&&this.target.columnReorderingAllowed&&(this._startReorderTimeout=setTimeout(this._startReorder.bind(this,d),100))},_startReorder:function(d){this._reorderGhost=
this._getGhost();this._reorderGhost.style.visibility="visible";var f=new CustomEvent("dragstart",{bubbles:!0});this._cellContent.dispatchEvent(f);this._reorderXY={x:d.touches[0].clientX-this.getBoundingClientRect().left,y:d.touches[0].clientY-this.getBoundingClientRect().top};this._updateGhostPosition(d.touches[0].clientX,d.touches[0].clientY)},_onTouchMove:function(d){if(this._reorderGhost){d.preventDefault();var f=new CustomEvent("dragover",{bubbles:!0});f.clientX=d.touches[0].clientX;f.clientY=
d.touches[0].clientY;var h=this._contentFromPoint(f.clientX,f.clientY);h&&h.dispatchEvent(f);this._updateGhostPosition(d.touches[0].clientX,d.touches[0].clientY)}else clearTimeout(this._startReorderTimeout)},_updateGhostPosition:function(d,f){d-=this._reorderXY.x;f=f-this._reorderXY.y-50;var h=parseInt(this._reorderGhost.style.left||0),k=parseInt(this._reorderGhost.style.top||0),r=this._reorderGhost.getBoundingClientRect();this._reorderGhost.style.left=h-(r.left-d)+"px";this._reorderGhost.style.top=
k-(r.top-f)+"px"},_onTouchEnd:function(d){clearTimeout(this._startReorderTimeout);this._reorderGhost&&(d.preventDefault(),d=new CustomEvent("dragend",{bubbles:!0}),this.dispatchEvent(d),this._reorderGhost.style.visibility="hidden",this._reorderGhost=null)},_contentFromPoint:function(d,f){if(Polymer.Settings.useShadow){var h=this.target.$.scroller;h.toggleAttribute("no-content-pointer-events",!0);d=this.domHost.root.elementFromPoint(d,f);h.toggleAttribute("no-content-pointer-events",!1);if(d&&d.getContentChildren)return d.getContentChildren(Polymer.Element?
"slot":"content")[0]}else return document.elementFromPoint(d,f)},_getGhost:function(){var d=this.target.$.scroller.$.reorderghost;d.innerText=this._cellContent.innerText;var f=window.getComputedStyle(this._cellContent);"boxSizing display width height background alignItems padding border flex-direction overflow".split(" ").forEach(function(h){d.style[h]=f[h]},this);return d},_enableDrag:function(){this._cellContent.draggable=this.target.columnReorderingAllowed&&!window.getSelection().toString()},_disableDrag:function(){this._cellContent.draggable=
!1},_cancelMouseDownOnResize:function(d){d.target===this._resizeHandle&&d.preventDefault()},_resizableChanged:function(d){d?(this._resizeHandle=document.createElement("div"),this._resizeHandle.classList.add("vaadin-grid-column-resize-handle"),this.listen(this._resizeHandle,"track","_onTrack"),Polymer.dom(this).appendChild(this._resizeHandle)):this._resizeHandle&&(this.unlisten(this._resizeHandle,"track","_onTrack"),Polymer.dom(this).removeChild(this._resizeHandle))},_onTrack:function(d){this.columnResizing=
!0;var f=this.column;"vaadin-grid-column-group"===f.localName&&(f=Array.prototype.slice.call(f._childColumns,0).sort(function(r,l){return r._order-l._order}).filter(function(r){return!r.hidden}).pop());var h=this._getHeaderCellByColumn(f);if(h.offsetWidth){var k=window.getComputedStyle(h._cellContent);f.width=Math.max(10+parseInt(k.paddingLeft)+parseInt(k.paddingRight),h.offsetWidth+d.detail.x-h.getBoundingClientRect().right)+"px";f.flexGrow=0}Array.prototype.slice.call(Polymer.dom(this.parentElement.parentElement).querySelectorAll(".vaadin-grid-row:last-child .vaadin-grid-cell")).sort(function(r,
l){return r.column._order-l.column._order}).forEach(function(r,l,p){l<p.indexOf(h)&&(r.column.width=r.offsetWidth+"px",r.column.flexGrow=0)});this.columnResizing&&"end"===d.detail.state&&(this.columnResizing=!1);this.fire("column-resizing")},_getHeaderCellByColumn:function(d){return Array.prototype.filter.call(Polymer.dom(this.parentElement.parentElement).querySelectorAll(".vaadin-grid-row:last-child .vaadin-grid-cell"),function(f){return f.column===d})[0]},_headerTemplateChanged:function(d){void 0!==
d&&(null===d||!this._isColumnRow&&"vaadin-grid-column-group"!==this.column.localName?(this.instance={root:document.createElement("div")},this._isEmpty=!0):(this.instance=d.templatizer.createInstance(),this._templatizer=d.templatizer,this._isEmpty=!1))},_isEmptyChanged:function(d,f){f&&this.fire("cell-empty-changed")}});Polymer({is:"vaadin-grid-table-footer-cell",properties:{footerTemplate:Object},behaviors:[b,vaadin.elements.grid.CellClickBehavior],observers:["_footerTemplateChanged(footerTemplate)",
"_isEmptyChanged(_isEmpty, isAttached)"],_footerTemplateChanged:function(d){void 0!==d&&(null===d||!this._isColumnRow&&"vaadin-grid-column-group"!==this.column.localName?(this.instance={root:document.createElement("div")},this._isEmpty=!0):(this.instance=d.templatizer.createInstance(),this._templatizer=d.templatizer,this._isEmpty=!1))},_isEmptyChanged:function(d,f){f&&this.fire("cell-empty-changed")}});Polymer({is:"vaadin-grid-sizer-cell",behaviors:[b]})})();

//# sourceURL=build://vaadin-grid/vaadin-grid-sizer.html.js
Polymer({is:"vaadin-grid-sizer",properties:{columnTree:Array,top:Number,_columns:Array},observers:["_columnTreeChanged(columnTree)","_topChanged(top)"],_columnTreeChanged:function(b){this._columns=b[b.length-1]},_topChanged:function(b){this.style.top=b+"px"}});

//# sourceURL=build://vaadin-grid/vaadin-grid-table-outer-scroller.html.js
Polymer({is:"vaadin-grid-table-outer-scroller",properties:{scrollTarget:{type:Object,observer:"_scrollTargetChanged"},passthrough:{type:Boolean,reflectToAttribute:!0,value:!0}},listeners:{scroll:"_syncScrollTarget"},attached:function(){this.listen(this.domHost,"mousemove","_onMouseMove");this.style.webkitOverflowScrolling="touch"},detached:function(){this.unlisten(this.domHost,"mousemove","_onMouseMove")},_scrollTargetChanged:function(b,d){d&&this.unlisten(d,"scroll","_syncOuterScroller");this.listen(b,
"scroll","_syncOuterScroller")},_onMouseMove:function(b){this.passthrough=b.offsetY<=this.clientHeight&&b.offsetX<=this.clientWidth},_syncOuterScroller:function(){this._syncingScrollTarget||(this._syncingOuterScroller=!0,this.scrollTop=this.domHost._scrollTop,this.scrollLeft=this.domHost._scrollLeft);this._syncingScrollTarget=!1},_syncScrollTarget:function(){this._syncingOuterScroller||(this._syncingScrollTarget=!0,this.scrollTarget.scrollTop=this.scrollTop,this.scrollTarget.scrollLeft=this.scrollLeft,
this.domHost._scrollHandler());this._syncingOuterScroller=!1}});

//# sourceURL=build://vaadin-grid/vaadin-grid-table-focus-trap.html.js
Polymer({is:"vaadin-grid-table-focus-trap",hostAttributes:{role:"gridcell"},properties:{activeTarget:{type:String,observer:"_activeTargetChanged"}},ready:function(){this._primary=Polymer.dom(this.root).querySelector(".primary");this._secondary=Polymer.dom(this.root).querySelector(".secondary");if(Polymer.Settings.useNativeShadow||Polymer.Settings.useShadow)Polymer.dom(this).appendChild(this._secondary),Polymer.dom(this).appendChild(this._primary)},focus:function(){this._focused!==this._primary?this._primary.focus():
this._secondary.focus()},_onBaitFocus:function(b){this._focused=b.target;this._movingFocusInternally||(this.fire("focus-gained"),this._primary.tabIndex=-1)},_onBaitBlur:function(){this._movingFocusInternally||(this.fire("focus-lost"),this._primary.tabIndex=0)},_activeTargetChanged:function(b){this._movingFocusInternally=!0;this._focused===this._primary?(this._secondary.setAttribute("aria-labelledby",b),this._secondary.focus()):(this._primary.setAttribute("aria-labelledby",b),this._primary.focus());
this._movingFocusInternally=!1},_reannounce:function(){this._movingFocusInternally=!0;this._focused===this._primary?(this._secondary.setAttribute("aria-labelledby",this.activeTarget),this._secondary.focus()):(this._primary.setAttribute("aria-labelledby",this.activeTarget),this._primary.focus());this._movingFocusInternally=!1}});

//# sourceURL=build://vaadin-grid/vaadin-grid-table-row.html.js
(function(){var b={properties:{active:{type:Boolean,reflectToAttribute:!0,value:!1},columns:Array,index:Number,cells:{value:[]},target:Object,expanded:{value:!1},focused:{type:Boolean,reflectToAttribute:!0},item:Object,selected:{reflectToAttribute:!0},_rowDetailsCell:Object,rowDetailsTemplate:Object},observers:"_columnsChanged(columns, target);_indexChanged(index, cells);_itemChanged(item, cells);_itemChangedForDetails(item, _rowDetailsCell);_rowDetailsChanged(expanded, rowDetailsTemplate, target);_rowDetailsCellIndexChanged(_rowDetailsCell, index);_rowDetailsCellChanged(_rowDetailsCell, target);_selectedChanged(selected, cells);_selectedChangedForDetails(selected, _rowDetailsCell)".split(";"),
ready:function(){this.classList.add("vaadin-grid-row");!1===Polymer.Settings.useShadow&&(this.classList.add("style-scope"),this.classList.add("vaadin-grid"))},iterateCells:function(d){this.cells.forEach(d);this._rowDetailsCell&&d(this._rowDetailsCell)},_rowDetailsChanged:function(d,f,h){if(void 0!==d&&void 0!==f&&void 0!==h){if(d){var k=document.createElement("vaadin-grid-table-cell");k.setAttribute("detailscell",!0);k.frozen=!0;k.target=h;k.template=f;k.toggleAttribute("lastcolumn",!0);Polymer.dom(this.root).appendChild(k);
Polymer.dom.flush();this._rowDetailsCell=k}else this._rowDetailsCell&&(Polymer.dom(this.root).removeChild(this._rowDetailsCell),this._rowDetailsCell=null);this.iterateCells(function(r){r.expanded=d});this.target.$.scroller._frozenCellsChanged()}},_updateRowVisibility:function(){this.hidden=this.cells.every(function(d){return d._isEmpty})},_rowDetailsCellChanged:function(d,f){void 0!==d&&void 0!==f&&f.$.scroller._update()},_rowDetailsCellIndexChanged:function(d,f){void 0!==d&&void 0!==f&&(d?(d.index=
f,Polymer.dom.flush(),this.updateRowDetailsCellMetrics()):this.style.paddingBottom="")},updateRowDetailsCellMetrics:function(){this._rowDetailsCell&&(this.target&&this.target._observer&&this.target._observer.flush&&this.target._observer.flush(),this._rowDetailsCell.style.height="",this.style.paddingBottom=this._rowDetailsCell.style.height=this._rowDetailsCell.clientHeight+"px")},_columnsChanged:function(d,f){if(void 0!==d&&void 0!==f){Polymer.dom(this).innerHTML="";var h=[];d.forEach(function(k){var r=
"_"+this.is.replace(/-/g,"_")+"_cells";r=k[r]=k[r]||[];var l=r.filter(function(m){return!Polymer.dom(m).parentNode})[0];if(!l){l=this._createCell();var p=Array.prototype.some.call(this.target.querySelectorAll("dom-repeat"),function(m){return!m.restamp});(p=p||"vaadin-grid-table-header-row"===this.is||"vaadin-grid-table-footer-row"===this.is)||r.push(l)}l.index=this.index;l.target=this.target;l._isColumnRow=this._isColumnRow;l.column=k;l.expanded=this.expanded;Polymer.dom(this).appendChild(l);h.push(l)}.bind(this));
this.cells=h}},_indexChanged:function(d,f){void 0!==d&&void 0!==f&&f.forEach(function(h){h.index=d})},_itemChanged:function(d,f){void 0!==d&&void 0!==f&&f.forEach(function(h){h.item=d})},_itemChangedForDetails:function(d,f){void 0!==d&&void 0!==f&&f&&(f.item=d)},_selectedChanged:function(d,f){void 0!==d&&void 0!==f&&f.forEach(function(h){h.selected=d})},_selectedChangedForDetails:function(d,f){void 0!==d&&void 0!==f&&f&&(f.selected=d)},updateLastColumn:function(){this.cells.slice(0).sort(function(d,
f){return d.column._order-f.column._order}).forEach(function(d,f,h){d.toggleAttribute("lastcolumn",f===h.length-1)})}};Polymer({is:"vaadin-grid-table-row",behaviors:[b],_createCell:function(){return document.createElement("vaadin-grid-table-cell")}});Polymer({is:"vaadin-grid-table-header-row",behaviors:[b],observers:["_updateRowVisibility(columns)"],listeners:{"cell-empty-changed":"_updateRowVisibility"},_createCell:function(){return document.createElement("vaadin-grid-table-header-cell")}});Polymer({is:"vaadin-grid-table-footer-row",
behaviors:[b],observers:["_updateRowVisibility(columns)"],listeners:{"cell-empty-changed":"_updateRowVisibility"},_createCell:function(){return document.createElement("vaadin-grid-table-footer-cell")}})})();

//# sourceURL=build://vaadin-grid/vaadin-grid-row-details-behavior.html.js
window.vaadin=window.vaadin||{};vaadin.elements=vaadin.elements||{};vaadin.elements.grid=vaadin.elements.grid||{};
vaadin.elements.grid.RowDetailsBehavior={properties:{expandedItems:{type:Array,value:function(){return[]}}},listeners:{"template-instance-changed":"_templateInstanceChangedExpanded"},observers:["_expandedItemsChanged(expandedItems.*, dataProvider)","_rowDetailsTemplateChanged(_rowDetailsTemplate)"],_expandedItemsChanged:function(b,d){void 0!==b&&void 0!==d&&(this._flushItemsDebouncer(),this.$.scroller._physicalItems&&this.$.scroller._physicalItems.forEach(function(f){f.expanded=this._isExpanded(f.item)}.bind(this)))},
_rowDetailsTemplateChanged:function(b){var d=new vaadin.elements.grid.Templatizer;d.dataHost=this.dataHost;d._instanceProps={expanded:!0,index:!0,item:!0,selected:!0};Polymer.dom(this.root).appendChild(d);d.template=b;b.templatizer=d},_isExpanded:function(b){return this.expandedItems&&-1!==this.expandedItems.indexOf(b)},expandItem:function(b){this._isExpanded(b)||this.push("expandedItems",b)},collapseItem:function(b){this._isExpanded(b)&&this.splice("expandedItems",this.expandedItems.indexOf(b),1)},
_templateInstanceChangedExpanded:function(b){"expanded"===b.detail.prop&&(b.detail.value?this.expandItem(b.detail.inst.item):this.collapseItem(b.detail.inst.item),b.stopPropagation())}};

//# sourceURL=build://vaadin-grid/vaadin-grid-data-provider-behavior.html.js
window.vaadin=window.vaadin||{};vaadin.elements=vaadin.elements||{};vaadin.elements.grid=vaadin.elements.grid||{};
vaadin.elements.grid.DataProviderBehavior={listeners:{"item-changed":"_templateItemChanged"},properties:{pageSize:{type:Number,value:50,observer:"_pageSizeChanged"},dataProvider:{type:Object,notify:!0,observer:"_dataProviderChanged"},_loading:Boolean,_cache:{type:Object,value:function(){return{}}},_pendingRequests:{type:Object,value:function(){return{}}}},_templateItemChanged:function(b){var d=b.detail.item;Array.prototype.forEach.call(Polymer.dom(this.$.items).children,function(f){f.item===d&&f.iterateCells(function(h){h._templatizer._suppressItemChangeEvent=
!0;h.instance.notifyPath("item."+b.detail.path,b.detail.value);h._templatizer._suppressItemChangeEvent=!1})})},_getCachedItem:function(b){var d=this._getPageForIndex(b),f=this._cache&&this._cache[d];return f?f[b-d*this.pageSize]:null},_getItem:function(b,d){this._updateItem(d,this._getCachedItem(b));this._eagerlyLoadPages();var f=this._uncachedPagesForPhysicalItems();0<f.length&&(this._loading=!0,this.debounce("load",function(){f.forEach(function(h){this._loadPage(h)}.bind(this))},100))},_cachedPagesForPhysicalItems:function(){return this._pagesForPhysicalItems().filter(function(b){return void 0!==
this._cache&&void 0!==this._cache[b]}.bind(this))},_uncachedPagesForPhysicalItems:function(){return this._pagesForPhysicalItems().filter(function(b){return void 0!==this._cache&&void 0===this._cache[b]}.bind(this))},_eagerlyLoadPages:function(){var b=this._cachedPagesForPhysicalItems().slice(0);if(0<b.length){b.sort(function(f,h){return f>h});var d=Math.min(b[b.length-1]+1,Math.max(0,Math.floor(this.size/this.pageSize)-1));this._loadPage(Math.max(0,b[0]-1));this._loadPage(d)}},_pagesForPhysicalItems:function(){return[this._getPageForIndex(this.$.scroller.firstVisibleIndex+
this.$.scroller._vidxOffset)].concat(this.$.scroller._physicalItems.filter(function(b){return b.index}).map(function(b){return this._getPageForIndex(b.index)}.bind(this))).reduce(function(b,d){-1===b.indexOf(d)&&b.push(d);return b},[])},_updateItems:function(b,d){for(var f=0;f<this.pageSize;f++){var h=this.$.scroller._virtualIndexToItem[b*this.pageSize+f];h&&(this._updateItem(h,d[f]),this.debounce("update-heights",function(){this.$.scroller._updateMetrics();this.$.scroller._positionItems();this.$.scroller._updateScrollerSize()},
1))}},_loadPage:function(b,d){d=d||this._updateItems.bind(this);if(!this._cache[b]&&!this._pendingRequests[b]&&this.dataProvider){this._pendingRequests[b]=!0;var f={page:b,pageSize:this.pageSize,sortOrders:this._mapSorters(),filters:this._mapFilters()};this.dataProvider(f,function(h){this._cache[b]=h;delete this._pendingRequests[b];d(b,h);this._loading=0<this._pendingRequests.length;this.debounce("check-size",this._checkSize,2E3)}.bind(this))}},_getPageForIndex:function(b){return Math.floor(b/this.pageSize)},
clearCache:function(){this._cache={};this._pendingRequests={};this.$.scroller.hasData&&this.$.scroller._update();this._flushItemsDebouncer()},_flushItemsDebouncer:function(){this.flushDebouncer("load")},_pageSizeChanged:function(b,d){void 0!==d&&b!==d&&this.clearCache()},_checkSize:function(){void 0===this.size&&console.warn('The \x3cvaadin-grid\x3e needs a value for "size" property in order to display rows.')},_dataProviderChanged:function(b,d){void 0!==d&&this.clearCache();this.$.scroller.hasData||
(this._loading=!0,this._loadPage(0,function(){this.$.scroller.hasData=!0}.bind(this)))}};

//# sourceURL=build://vaadin-grid/vaadin-grid-keyboard-navigation-behavior.html.js
window.vaadin=window.vaadin||{};vaadin.elements=vaadin.elements||{};vaadin.elements.grid=vaadin.elements.grid||{};
vaadin.elements.grid.TableKeyboardBehaviorImpl={hostAttributes:{role:"application",tabindex:0},keyBindings:{"ctrl+home":"_onCtrlHome","ctrl+end":"_onCtrlEnd",down:"_onArrowDown",end:"_onEnd",enter:"_onEnter",esc:"_onEscape",f2:"_onF2",home:"_onHome",left:"_onArrowLeft",pagedown:"_onPageDown",pageup:"_onPageUp",right:"_onArrowRight",space:"_onSpace",tab:"_onTab",up:"_onArrowUp"},attached:function(){Polymer.IronA11yAnnouncer.requestAvailability()},properties:{_virtualFocus:{type:Object,observer:"_virtualFocusChanged"},
interacting:{type:Boolean,reflectToAttribute:!0,value:!1},navigating:{type:Boolean,reflectToAttribute:!0,value:!1}},listeners:{focus:"_onFocus","cell-focus":"_onCellFocus","cell-content-focus":"_onCellContentFocus"},ready:function(){document.addEventListener("keydown",function(b){9===b.keyCode&&(this._tabbed=!0);9===b.keyCode&&b.shiftKey&&(this._shiftTabbed=!0)}.bind(this),!0);document.addEventListener("keyup",function(b){9===b.keyCode&&(this._tabbed=!1);9===b.keyCode&&b.shiftKey&&(this._shiftTabbed=
!1)}.bind(this),!0)},_isFooterVisible:function(){return 0<this.$.footer._rows.filter(function(b){return!b.hidden}).length},_onFocus:function(){this._tabbed&&!this._shiftTabbed&&this._activateNavigation()},_activateNavigation:function(){this.$.footerFocusTrap.focus()},_onFocusout:function(){this.interacting=this.navigating=!1},_onFooterFocus:function(){this.navigating=!0;this.interacting=!1;this._virtualFocus=this._virtualFocus||(this._shiftTabbed?this._isFooterVisible()?this.$.footer:this.$.items:
this.$.header)},_virtualFocusChanged:function(b,d){d&&(d.focused=!1);b&&(b._focusedCellIndex=b._focusedCellIndex||0,b._focusedRowIndex=b._focusedRowIndex||0,b.focused=!0,b===this.$.items&&this._ensureVirtualFocusInViewport())},_onTab:function(b){if(!this.interacting&&this._virtualFocus)if(this.navigating)if(b.detail.keyboardEvent.shiftKey)switch(this._virtualFocus){case this.$.footer:this._virtualFocus=this.$.items;b.preventDefault();break;case this.$.items:this._virtualFocus=this.$.header;b.preventDefault();
break;case this.$.header:this.focus(),this._virtualFocus=null}else switch(this._virtualFocus){case this.$.header:this._virtualFocus=this.$.items;b.preventDefault();break;case this.$.items:this._isFooterVisible()?(this._virtualFocus=this.$.footer,b.preventDefault()):this.async(function(){this._virtualFocus=null},1);break;case this.$.footer:this._virtualFocus=null}else this._activateNavigation(),b.preventDefault()},_isAboveViewport:function(b){return this.firstVisibleIndex>b},_onArrowDown:function(b){this.interacting||
(b.preventDefault(),this.navigating=!0,this._virtualFocus.focusDown(),this._ensureVirtualFocusInViewport())},_scrollPageDown:function(){var b=this.$.header.getBoundingClientRect(),d=this.$.footer.getBoundingClientRect();this.$.scroller.$.table.scrollTop+=d.top-b.bottom;this.$.scroller._scrollHandler()},_onPageDown:function(b){this.interacting||(b.preventDefault(),this.navigating=!0,this._virtualFocus===this.$.items?(b=this.$.scroller.lastVisibleIndex,this._scrollPageDown(),this._virtualFocus._focusedRowIndex+=
this.$.scroller.lastVisibleIndex-b||this.$.scroller.lastVisibleIndex-this._virtualFocus._focusedRowIndex,this._ensureVirtualFocusInViewport()):this._virtualFocus.focusPageDown())},_scrollPageUp:function(){var b=this.$.header.getBoundingClientRect(),d=this.$.footer.getBoundingClientRect();this.$.scroller.$.table.scrollTop-=d.top-b.bottom;this.$.scroller._scrollHandler()},_onPageUp:function(b){this.interacting||(b.preventDefault(),this.navigating=!0,this._virtualFocus===this.$.items?(b=this.$.scroller.lastVisibleIndex,
this._scrollPageUp(),this._virtualFocus._focusedRowIndex-=b-this.$.scroller.lastVisibleIndex||this._virtualFocus._focusedRowIndex,this._ensureVirtualFocusInViewport()):this._virtualFocus.focusPageUp())},_onArrowUp:function(b){this.interacting||(b.preventDefault(),this.navigating=!0,this._virtualFocus.focusUp(),this._ensureVirtualFocusInViewport())},_onArrowRight:function(b){this.interacting||(b.preventDefault(),this.navigating=!0,this._virtualFocus.focusRight(),this._ensureVirtualFocusInViewport())},
_onArrowLeft:function(b){this.interacting||(b.preventDefault(),this.navigating=!0,this._virtualFocus.focusLeft(),this._ensureVirtualFocusInViewport())},_onHome:function(b){this.interacting||(b.preventDefault(),this.navigating=!0,this._virtualFocus.focusHome(),this._ensureVirtualFocusInViewport())},_onEnd:function(b){this.interacting||(b.preventDefault(),this.navigating=!0,this._virtualFocus.focusEnd(),this._ensureVirtualFocusInViewport())},_moveFocusToFocusTarget:function(){var b=this._virtualFocus._focusedCell._cellContent;
(b=b.querySelector("[focus-target]")||b.firstElementChild)&&b.focus()},_onEnter:function(b){this.interacting?"input"===b.detail.keyboardEvent.target.localName&&"text"===b.detail.keyboardEvent.target.type&&this.$.footerFocusTrap.focus():(b.preventDefault(),this._moveFocusToFocusTarget())},_onEscape:function(){this.interacting?this.$.footerFocusTrap.focus():this.navigating&&(this.navigating=!1)},_onF2:function(b){b.preventDefault();this.interacting?this.$.footerFocusTrap.focus():this._moveFocusToFocusTarget()},
_onCtrlHome:function(b){this.interacting||(b.preventDefault(),this.navigating=!0,this._virtualFocus.focusFirst(),this._ensureVirtualFocusInViewport())},_onCtrlEnd:function(b){this.interacting||(b.preventDefault(),this.navigating=!0,this._virtualFocus.focusLast(),this._ensureVirtualFocusInViewport())},_onSpace:function(b){if(!this.interacting){b.preventDefault();b=this._virtualFocus._focusedCell;var d=b.getContentChildren(Polymer.Element?"slot":"content")[0].firstElementChild;d?d.click():this.navigating&&
this.fire("cell-activate",{model:b.instance})}},_onCellContentFocus:function(b){this.interacting=!0;this._onCellFocus(b)},_onCellFocus:function(b){b=b.detail.cell;var d=b.parentElement,f=d.parentElement,h=Array.prototype.indexOf.call(Polymer.dom(f).children,d);f===this.$.items&&(h=d.index);f._focusedRowIndex=h;f._focusedCellIndex=Array.prototype.indexOf.call(Polymer.dom(d).children,b);this._virtualFocus=f;b.hasAttribute("detailscell")&&(f._focusedCellIndex=0,f._moveFocusToDetailsCell())},_ensureVirtualFocusInViewport:function(){var b=
this.$.scroller._vidxOffset+this.$.scroller._virtualStart,d=this._virtualFocus._focusedRowIndex;this._virtualFocus===this.$.items&&(d<b||d>b+this.$.scroller._physicalCount)&&(this.$.scroller.scrollToScaledIndex(d),this._virtualFocus._focusedCellChanged(d,this._virtualFocus._focusedCellIndex));this._ensureElementInViewport(this._virtualFocus._focusedCell)},_ensureElementInViewport:function(b){var d=b.getBoundingClientRect();if(this._virtualFocus===this.$.items){var f=this.$.footer.getBoundingClientRect().top,
h=this.$.header.getBoundingClientRect().bottom;d.bottom>f?this.$.scroller.$.table.scrollTop+=d.bottom-f:d.top<h&&(this.$.scroller.$.table.scrollTop+=d.top-h)}if(!b.hasAttribute("detailscell")){b=this.$.scroller.$.table.getBoundingClientRect().right;f=this.$.scroller.$.table.getBoundingClientRect().left;if(h=this._virtualFocus._focusedRow.querySelector("[last-frozen]"))f=h.getBoundingClientRect().right;d.right>b?this.$.scroller.$.table.scrollLeft+=d.right-b:d.left<f&&(this.$.scroller.$.table.scrollLeft+=
d.left-f)}}};vaadin.elements.grid.TableKeyboardBehavior=[vaadin.elements.grid.TableKeyboardBehaviorImpl,Polymer.IronA11yKeysBehavior];

//# sourceURL=build://vaadin-grid/vaadin-grid-column-reordering-behavior.html.js
window.vaadin=window.vaadin||{};vaadin.elements=vaadin.elements||{};vaadin.elements.grid=vaadin.elements.grid||{};vaadin.elements.grid.ColumnReorderingBehavior={properties:{columnReorderingAllowed:{type:Boolean,value:!1}}};
vaadin.elements.grid.TableColumnReorderingBehavior={properties:{_orderBaseScope:{type:Number,value:1E7}},listeners:{dragstart:"_onDragStart",dragover:"_onDragOver",dragend:"_onDragEnd"},observers:["_updateOrders(columnTree, columnTree.*)"],_updateOrders:function(b,d){void 0!==b&&void 0!==d&&b[0].forEach(function(f,h){f._order=(h+1)*this._orderBaseScope},this)},_onDragStart:function(b){if("vaadin-grid-cell-content"===b.target.localName){var d=this._getCellByCellContent(b.target);d&&(this.toggleAttribute("reordering",
!0),this._draggedColumn=d.column,this._setSiblingsReorderStatus(this._draggedColumn,"allowed"),this._draggedColumn._reorderStatus="dragging",b.dataTransfer&&(b.dataTransfer.setData("text",""),b.dataTransfer.effectAllowed="move"),this._autoScroller())}},_setSiblingsReorderStatus:function(b,d){Array.prototype.filter.call(Polymer.dom(Polymer.dom(b).parentNode).children,function(f){return/column/.test(f.localName)&&this._isSwapAllowed(f,b)},this).forEach(function(f){f._reorderStatus=d})},_onDragOver:function(b){if(this._draggedColumn){var d=
(Polymer.Element?b.composedPath():Polymer.dom(b).path).filter(function(f){return"vaadin-grid-cell-content"===f.localName})[0];d&&(b.preventDefault(),d=this._getCellByCellContent(d),(d=this._getTargetColumn(d,this._draggedColumn))&&this._isSwapAllowed(this._draggedColumn,d)&&this._isSwappableByPosition(d,b.clientX)&&this._swapColumnOrders(this._draggedColumn,d),this._lastDragClientX=b.clientX)}},_autoScroller:function(){if(this._lastDragClientX){var b=this._lastDragClientX-this.getBoundingClientRect().right+
50,d=this.getBoundingClientRect().left-this._lastDragClientX+50;0<b?this.$.table.scrollLeft+=b/10:0<d&&(this.$.table.scrollLeft-=d/10);this._scrollHandler()}this._draggedColumn&&this.async(this._autoScroller,10)},_onDragEnd:function(){this._draggedColumn&&(this.toggleAttribute("reordering",!1),this._draggedColumn._reorderStatus="",this._setSiblingsReorderStatus(this._draggedColumn,""),this._lastDragClientX=this._draggedColumn=null)},_isSwapAllowed:function(b,d){if(b&&d){var f=b.parentElement===d.parentElement,
h=b.frozen===d.frozen;return b!==d&&f&&h}},_isSwappableByPosition:function(b,d){var f=Array.prototype.filter.call(Polymer.dom(this.$.header).querySelectorAll(".vaadin-grid-cell"),function(k){return k.column===b})[0],h=this.$.header.querySelector("[reorder-status\x3ddragging]").getBoundingClientRect();return f.getBoundingClientRect().left>h.left?d>f.getBoundingClientRect().right-h.width:d<f.getBoundingClientRect().left+h.width},_getCellByCellContent:function(b){if(Polymer.Element)return b.assignedSlot.parentNode;
b=Polymer.dom(b).getDestinationInsertionPoints()[0];return Polymer.dom(b).parentNode},_swapColumnOrders:function(b,d){var f=b._order;b._order=d._order;d._order=f;this._updateLastFrozen();this._updateLastColumn()},_getTargetColumn:function(b,d){if(b&&d){for(var f=b.column;f.parentElement!==d.parentElement&&f!==this.target;)f=f.parentElement;return f.parentElement===d.parentElement?f:b.column}}};

//# sourceURL=build://vaadin-grid/vaadin-grid-table.html.js
Polymer({is:"vaadin-grid-table",behaviors:[vaadin.elements.grid.IronListBehavior,vaadin.elements.grid.TableScrollBehavior,vaadin.elements.grid.TableColumnReorderingBehavior,Polymer.Templatizer],properties:{size:Number,columnTree:Array,bindData:Function,rowDetailsTemplate:Object,columnReorderingAllowed:{type:Boolean,reflectToAttribute:!0},safari:{type:Boolean,value:/^((?!chrome|android).)*safari/i.test(navigator.userAgent)},scrollbarWidth:{type:Number,value:function(){var b=document.createElement("div");
b.style.width="100px";b.style.height="100px";b.style.overflow="scroll";b.style.position="absolute";b.style.top="-9999px";document.body.appendChild(b);var d=b.offsetWidth-b.clientWidth;document.body.removeChild(b);return d}},target:Object,hasData:Boolean},observers:["_columnTreeChanged(columnTree, _physicalItems, _physicalCountVal)","_sizeChanged(size, bindData, hasData)","_rowDetailsTemplateChanged(rowDetailsTemplate, _physicalItems, _physicalCountVal)"],listeners:{"property-changed":"_columnPropChanged",
animationend:"_onAnimationEnd","column-resizing":"_onColumnResize"},ready:function(){this.$=this.$||{};this.$.header=this.domHost.$.header;this.$.items=this.domHost.$.items;this.$.footer=this.domHost.$.footer},_onColumnResize:function(){this.toggleAttribute("column-resizing",this.$.header.querySelector("[column-resizing]"));this._gridResizeHandler()},_onAnimationEnd:function(b){/appear/.test(b.animationName)&&(this._render(),this._updateHeaderFooterMetrics(),b.stopPropagation())},_columnPropChanged:function(b){"headerTemplate"===
b.detail.path&&this.toggleAttribute("has-templates",!0,this.$.header);"footerTemplate"===b.detail.path&&this.toggleAttribute("has-templates",!0,this.$.footer);/frozen|hidden/.test(b.detail.path)&&this._frozenCellsChanged();"hidden"===b.detail.path&&this._gridResizeHandler()},_hideOuterScroller:function(b,d){return 0===b&&!d},_hideTableOverflow:function(b,d){return 0===b&&d},_rowDetailsTemplateChanged:function(b,d,f){void 0!==b&&d&&void 0!==f&&Array.prototype.forEach.call(d,function(h){h.rowDetailsTemplate=
b})},_columnTreeChanged:function(b,d,f){void 0!==b&&d&&void 0!==f&&(Polymer.RenderStatus.afterNextRender(this,this._update),this._frozenCellsChanged(),this._hasTemplatesChanged(b),Array.prototype.forEach.call(d,function(h){h.columns=b[b.length-1]}),this._gridResizeHandler(),Polymer.dom.flush(this),this._updateLastColumn())},_updateLastColumn:function(){Array.prototype.forEach.call(Polymer.dom(this.domHost.root).querySelectorAll(".vaadin-grid-row"),function(b){b.updateLastColumn()})},_updateHeaderFooterMetrics:function(){this._physicalSizes&&
Polymer.dom.flush();this._updateHeaderFooterMetricsSync();Polymer.RenderStatus.afterNextRender(this.$.header,function(){this._updateHeaderFooterMetricsSync();this._pendingScrollToScaledIndex&&this.scrollToScaledIndex(this._pendingScrollToScaledIndex)}.bind(this))},_updateHeaderFooterMetricsSync:function(){var b=this.$.header.clientHeight+"px",d=this.$.footer.clientHeight+"px";[this.$.outersizer,this.$.fixedsizer,this.$.items].forEach(function(f){f.style.borderTopWidth=b;f.style.borderBottomWidth=
d})},_hasTemplatesChanged:function(b){var d=!1,f=!1;b.forEach(function(h){return h.forEach(function(k){d=d||k.headerTemplate;f=f||k.footerTemplate})});this.toggleAttribute("has-templates",d,this.$.header);this.toggleAttribute("has-templates",f,this.$.footer)},_createPool:function(b){for(var d=Array(b),f=0;f<b;f++){var h=document.createElement("vaadin-grid-table-row");h.target=this.domHost;d[f]=h;h.setAttribute("hidden","");Polymer.dom(this.$.items).appendChild(h)}return d},_sizeChanged:function(b,
d,f){if(void 0!==b&&void 0!==d&&void 0!==f){var h=this._scrollTop,k=this.firstVisibleIndex+this._vidxOffset;this._virtualCount=Math.min(b,1E5);this._physicalIndexForKey={};this._lastVisibleIndexVal=this._firstVisibleIndexVal=null;this._vidxOffset=0;this._physicalItems||(this._physicalCount=Math.max(1,Math.min(25,this._virtualCount)),this._physicalItems=this._createPool(this._physicalCount),this._physicalSizes=Array(this._physicalCount));this._itemsRendered=!1;this._debounceTemplate(function(){this._render();
this._viewportHeight&&(this.scrollToScaledIndex(Math.min(k,this.size)),this._scrollTop=h,this._scrollHandler(),this.flushDebouncer("vaadin-grid-scrolling"))})}},_assignModels:function(b){this._virtualIndexToItem=this._virtualIndexToItem||{};this._iterateItems(function(d,f){d=this._physicalItems[d];d.index&&delete this._virtualIndexToItem[d.index];d.index=f+this._vidxOffset;this._virtualIndexToItem[d.index]=d;d.toggleAttribute("odd",d.index%2);d.toggleAttribute("lastrow",d.index===this.size-1);d.toggleAttribute("hidden",
d.index>=this.size);this.bindData(d.index,d)},b)},_gridResizeHandler:function(){this._updateHeaderFooterMetrics();this._physicalSizes&&(this._physicalItems.forEach(function(b){b.updateRowDetailsCellMetrics()}),this.debounce("vaadin-grid-resizing",function(){this._update()}.bind(this),1))}});

//# sourceURL=build://vaadin-grid/vaadin-grid-column.html-2.js
Polymer({is:"vaadin-grid-column",behaviors:[vaadin.elements.grid.ColumnBehavior]});

//# sourceURL=build://vaadin-grid/vaadin-grid.html.js
Polymer({is:"vaadin-grid",properties:{_columnTree:{type:Array,notify:!0},size:Number,_rowDetailsTemplate:Object,_bindData:{type:Object,value:function(){return this._getItem.bind(this)}}},behaviors:[Polymer.IronA11yKeysBehavior,Polymer.IronResizableBehavior,vaadin.elements.grid.ActiveItemBehavior,vaadin.elements.grid.RowDetailsBehavior,vaadin.elements.grid.DataProviderBehavior,vaadin.elements.grid.DynamicColumnsBehavior,vaadin.elements.grid.ArrayDataProviderBehavior,vaadin.elements.grid.SelectionBehavior,
vaadin.elements.grid.SortBehavior,vaadin.elements.grid.FilterBehavior,vaadin.elements.grid.ColumnReorderingBehavior,vaadin.elements.grid.TableKeyboardBehavior],listeners:{"property-changed":"_columnPropChanged","iron-resize":"_gridResizeHandler"},_updateItem:function(b,d){b.style.minHeight=d?"":this.$.scroller._physicalAverage+"px";b.item=d;b.selected=this._isSelected(d);b.expanded=this._isExpanded(d);b.active=null!==d&&d==this.activeItem;b.focused=b.index===this.$.items._focusedRowIndex},_getContentTarget:function(){return this},
ready:function(){this._updateColumnTree();this._rowDetailsTemplate=Polymer.dom(this).querySelector("template.row-details")||void 0;this.$.scroller.target=this;null===document.doctype&&console.warn('\x3cvaadin-grid\x3e requires the "standards mode" declaration. Please add \x3c!DOCTYPE html\x3e to the HTML document.')},_columnPropChanged:function(b){"_childColumns"===b.detail.path&&this._updateColumnTree();b.stopPropagation()},_gridResizeHandler:function(){this.$.scroller._gridResizeHandler()}});

//# sourceURL=build://tf-hparams-session-group-details/tf-hparams-session-group-details.html.js
Polymer({is:"tf-hparams-session-group-details",properties:{backend:Object,experimentName:String,visibleSchema:Object,sessionGroup:Object,_xType:{type:String,value:sf.XType.STEP},_noMultiExperiments:{type:Boolean,value:!1},_indexOfSession:Object,_sessionGroupNameHash:Number,_requestData:{type:Function,value:function(){return({tag:b,run:d})=>this.backend.listMetricEvals({experimentName:this.experimentName,sessionName:d,metricName:b})}},_colorScale:{type:Object,value:function(){return{scale:b=>{b=JSON.parse(b)[1];
b=this._indexOfSession.get(b);const d=Ld.standard;return d[(this._sessionGroupNameHash+b)%d.length]}}}}},behaviors:[Polymer.IronResizableBehavior],listeners:{"iron-resize":"redraw"},observers:["_sessionGroupChanged(sessionGroup.*)"],redraw(){Polymer.dom(this.root).querySelectorAll("tf-scalar-card").forEach(b=>b.redraw())},_sessionGroupChanged(){this.sessionGroup?(this._indexOfSession=new Map(this.sessionGroup.sessions.map((b,d)=>[b.name,d])),this._sessionGroupNameHash=tf.hparams.utils.hashOfString(this.sessionGroup.name)):
(this._indexOfSession=new Map,this._sessionGroupNameHash=0);Polymer.dom(this.root).querySelectorAll("tf-scalar-card").forEach(b=>{const d=b.get("tag");b.set("tag","");b.set("tag",d)})},_haveMetrics(){return this.visibleSchema&&Array.isArray(this.visibleSchema.metricInfos)&&0<this.visibleSchema.metricInfos.length},_haveMetricsAndSessionGroup(){return this.sessionGroup&&this._haveMetrics()},_computeSeriesForSessionGroupMetric(b,d){return null===b||null===d?[]:b.sessions.filter(f=>void 0!==tf.hparams.utils.metricValueByName(f.metricValues,
d.name)).map(f=>({tag:d.name,run:f.name}))},_computeTagMetadata(b){return{displayName:tf.hparams.utils.metricName(b),description:b.description||""}}});

//# sourceURL=build://tf-hparams-table-view/tf-hparams-table-view.html.js
Polymer({is:"tf-hparams-table-view",properties:{visibleSchema:Object,sessionGroups:Array,enableShowMetrics:Boolean,backend:Object,experimentName:String},observers:["_visibleSchemaOrSessionGroupsChanged(visibleSchema.*, sessionGroups.*)"],_visibleSchemaOrSessionGroupsChanged(){const b=this.$.sessionGroupsTable.get("expandedItems");this.$.sessionGroupsTable.set("expandedItems",[]);Polymer.dom.flush();const d=new Map;this.sessionGroups.forEach(f=>{d.set(f.name,f)});this.$.sessionGroupsTable.set("expandedItems",
b.map(f=>d.get(f.name)).filter(Boolean))},_hparamName:tf.hparams.utils.hparamName,_metricName:tf.hparams.utils.metricName,_sessionGroupHParam(b,d){return null!=b&&Object.prototype.hasOwnProperty.call(b.hparams,d)?tf.hparams.utils.prettyPrint(b.hparams[d]):""},_sessionGroupMetric(b,d){if(null==b)return null;for(let f=0;f<b.metricValues.length;++f){let h=b.metricValues[f];if(h.name.group===d.group&&h.name.tag==d.tag)return tf.hparams.utils.prettyPrint(h.value)}return""},_rowNumber(b){return b+1}});

//# sourceURL=build://tf-hparams-session-group-values/tf-hparams-session-group-values.html.js
Polymer({is:"tf-hparams-session-group-values",properties:{sessionGroup:{type:Object,value:null},visibleSchema:{type:Object,value:null}},_propertiesArePopulated:function(b,d){return void 0!==b&&null!==b&&void 0!==d&&null!==d},_singletonSessionGroups:function(b){return null===b||void 0===b?[]:[b]}});

//# sourceURL=build://tf-hparams-parallel-coords-plot/axes.js
(function(b){(function(d){(function(f){function h(q){return null!==q.sourceEvent}let k;(function(q){q.LINEAR="LINEAR";q.LOG="LOG";q.QUANTILE="QUANTILE";q.NON_NUMERIC="NON_NUMERIC"})(k=f.ScaleType||(f.ScaleType={}));class r{isPassing(){return!0}}class l{constructor(q,u,w,A){this._lower=q;this._upper=u;this._lowerOpen=w;this._upperOpen=A}isPassing(q){return this._before(this._lower,q,!this._lowerOpen)&&this._before(q,this._upper,!this._upperOpen)}_before(q,u,w){return w?q<=u:q<u}}class p{constructor(q){this._domainSet=
q}isPassing(q){return-1!==this._domainSet.findIndex(u=>u===q)}}class m{constructor(q,u,w,A){this._svgProps=q;this._schema=u;this._interactionManager=w;this._colIndex=A;this._isDisplayed=!1;this._scaleType=this._yScale=null;this.setBrushSelection(null)}colIndex(){return this._colIndex}yScale(){return this._yScale}scaleType(){return this._scaleType}brushSelection(){return this._brushSelection}isDisplayed(){return this._isDisplayed}setBrushSelection(q){this._brushSelection=q;this._brushFilter=this._buildBrushFilter(this.brushSelection(),
this.scaleType(),this.yScale())}setDomainAndScale(q,u){this._scaleType=u;this._yScale=b.hparams.parallel_coords_plot.createAxisScale(q.slice(),this._svgProps.height,this.scaleType());this._brushFilter=this._buildBrushFilter(this.brushSelection(),this.scaleType(),this.yScale())}brushFilter(){return this._brushFilter}updateDOM(q){var u=d3.axisLeft(this.yScale());this.scaleType()===k.QUANTILE&&(u=u.tickValues(this.yScale().quantiles()).tickFormat(d3.format("-.6g")));var w=d3.select(q);w.selectAll("g").remove();
w.append("g").classed("axis",!0).call(u).append("text").classed("axis-title",!0).style("cursor","move").style("text-anchor","middle").attr("y",-9).text(A=>b.hparams.utils.schemaColumnName(this._schema,A));w.call(d3.drag().on("start",()=>{q.setAttribute("is-dragging","");this._interactionManager.onDragStart(this.colIndex())}).on("drag",()=>this._interactionManager.onDrag(d3.event.x)).on("end",()=>{this._interactionManager.onDragEnd();q.removeAttribute("is-dragging")}));u=d3.brushY().extent([[-8,0],
[8,this._svgProps.height+1]]).on("start",()=>{h(d3.event)&&(q.setAttribute("is-brushing",""),this._interactionManager.onBrushChanged(this.colIndex()))}).on("brush",()=>{if(h(d3.event))this._interactionManager.onBrushChanged(this.colIndex())}).on("end",()=>{h(d3.event)&&(this._interactionManager.onBrushChanged(this.colIndex()),q.removeAttribute("is-brushing"))});w=d3.select(q).append("g").classed("brush",!0);w.call(u);u.move(w,this.brushSelection())}setDisplayed(q){this._isDisplayed=q}_buildBrushFilter(q,
u,w){if(null===q)return new r;if(null===u)return console.error("Scale type is null, but brushSelection isn't: ",q),new r;switch(u){case k.LINEAR:case k.LOG:{const [A,y]=b.hparams.parallel_coords_plot.continuousScaleInverseImage(w,q[0],q[1]);return new l(A,y,!1,!1)}case k.QUANTILE:{const [A,y]=b.hparams.parallel_coords_plot.quantileScaleInverseImage(w,q[0],q[1]);return new l(A,y,!1,!0)}case k.NON_NUMERIC:return new p(b.hparams.parallel_coords_plot.pointScaleInverseImage(w,q[0],q[1]))}console.error("Unknown scale type: ",
u);return new r}}f.Axis=m;class n{constructor(q,u,w){this._svgProps=q;this._schema=u;this._axes=this._createAxes(w);this._stationaryAxesPositions=d3.scalePoint().range([1,this._svgProps.width-1]).padding(.5);this._draggedAxis=null;this._svgProps.svgG.selectAll("g.axis-parent").remove();this._parentsSel=this._svgProps.svgG.selectAll(".axis-parent")}updateAxes(q,u){console.assert(!this.isAxisDragging());const w=new Set;q.columns.forEach(y=>{const x=y.absoluteIndex;let C=this._axes[x];C.setDisplayed(!0);
const G=u.map(D=>b.hparams.utils.columnValueByIndex(this._schema,D,x));C.setDomainAndScale(G,y.scale);w.add(x)});this._axes.forEach(y=>{w.has(y.colIndex())||y.setDisplayed(!1)});this._updateStationaryAxesPositions(w);this._parentsSel=this._parentsSel.data(Array.from(w),y=>y);this._parentsSel.exit().remove();this._parentsSel=this._parentsSel.enter().append("g").classed("axis-parent",!0).merge(this._parentsSel);const A=this;this._parentsSel.call(y=>this._updateAxesPositionsInDOM(y)).each(function(y){A._axes[y].updateDOM(this)})}mapVisibleAxes(q){return this._stationaryAxesPositions.domain().map(u=>
q(this.getAxisPosition(u),this._axes[u]))}allVisibleAxesSatisfy(q){return this._stationaryAxesPositions.domain().every(u=>q(this.getAxisPosition(u),this._axes[u]))}getAxisForColIndex(q){return this._axes[q]}dragStart(q){console.assert(!this.isAxisDragging());console.assert(this._axes[q].isDisplayed());this._draggedAxis=this._axes[q];this._draggedAxisPosition=this._stationaryAxesPositions(q)}drag(q){this._draggedAxisPosition=q=Math.min(Math.max(q,0),this._svgProps.width);q=this._stationaryAxesPositions.domain();
q.sort((u,w)=>this.getAxisPosition(u)-this.getAxisPosition(w));this._stationaryAxesPositions.domain(q);this._updateAxesPositionsInDOM(this._parentsSel)}dragEnd(){console.assert(this.isAxisDragging());this._draggedAxis=this._draggedAxisPosition=null;this._updateAxesPositionsInDOM(this._parentsSel.transition().duration(500))}isAxisDragging(){return null!==this._draggedAxis}getAxisPosition(q){return null!==this._draggedAxis&&this._draggedAxis.colIndex()===q?this._draggedAxisPosition:this._stationaryAxesPositions(q)}_updateStationaryAxesPositions(q){var u=
this._stationaryAxesPositions.domain().filter(w=>q.has(w));u=Array.from(new Set([...u,...Array.from(q)]));this._stationaryAxesPositions.domain(u)}_updateAxesPositionsInDOM(q){q.attr("transform",u=>b.hparams.utils.translateStr(this.getAxisPosition(u)))}_createAxes(q){return d3.range(b.hparams.utils.numColumns(this._schema)).map(u=>new m(this._svgProps,this._schema,q,u))}}f.AxesCollection=n})(d.parallel_coords_plot||(d.parallel_coords_plot={}))})(b.hparams||(b.hparams={}))})(tf||(tf={}));

//# sourceURL=build://tf-hparams-parallel-coords-plot/lines.js
(function(b){(function(d){(function(f){let h;(function(l){l[l.FOREGROUND=0]="FOREGROUND";l[l.BACKGROUND=1]="BACKGROUND"})(h=f.LineType||(f.LineType={}));class k{constructor(l){void 0===l&&(l=d3.selectAll(null));console.assert(1>=l.size());this._sessionGroupSel=l}sessionGroup(){return 1===this._sessionGroupSel.size()?this._sessionGroupSel.datum():null}isNull(){return null===this.sessionGroup()}selection(){return this._sessionGroupSel}equalsTo(l){return this.isNull()?l.isNull():l.isNull()?!1:l.sessionGroup().name==
this.sessionGroup().name}}f.SessionGroupHandle=k;class r{constructor(l,p,m){this._svgProps=l;this._schema=p;this._axesCollection=m;this._sessionGroups=[];this._svgProps.svgG.selectAll("g.background").remove();this._svgProps.svgG.selectAll("g.foreground").remove();this._bgPathsSel=this._svgProps.svgG.append("g").classed("background",!0).selectAll("path");this._fgPathsSel=this._svgProps.svgG.append("g").classed("foreground",!0).selectAll("path");this._updateVisibleFgPathsSel();this._peakedSessionGroupHandle=
new k;this._selectedSessionGroupHandle=new k;this._d3line=d3.line().curve(d3.curveLinear)}getSessionGroupHandle(l){return null===l||void 0===l?new k:new k(this._fgPathsSel.filter(p=>p.name===l.name))}hideBackgroundLines(){this._bgPathsSel.attr("visibility","hidden")}showBackgroundLines(){this._bgPathsSel.attr("visibility",null)}peakedSessionGroupHandle(){return this._peakedSessionGroupHandle}selectedSessionGroupHandle(){return this._selectedSessionGroupHandle}recomputeControlPoints(l,p=0){(l===h.FOREGROUND?
this._fgPathsSel:this._bgPathsSel).transition().duration(p).attr("d",m=>this._pathDAttribute(m));l===h.FOREGROUND&&window.setTimeout(()=>{const m=this;this._fgPathsSel.each(function(n){m._setControlPointsProperty(this,n)})})}recomputeForegroundLinesVisibility(){this._fgPathsSel.classed("invisible-path",l=>!this._axesCollection.allVisibleAxesSatisfy((p,m)=>m.brushFilter().isPassing(b.hparams.utils.columnValueByIndex(this._schema,l,m.colIndex()))));this._updateVisibleFgPathsSel()}setForegroundLinesColor(l,
p,m){l=this._createLineColorFunction(l,p,m);this._fgPathsSel.attr("stroke",l)}redraw(l,p,m,n){const q=this._peakedSessionGroupHandle.sessionGroup(),u=this._selectedSessionGroupHandle.sessionGroup();this._sessionGroups=l;this._fgPathsSel=this._recomputePathSelection(this._fgPathsSel);this._bgPathsSel=this._recomputePathSelection(this._bgPathsSel);this._peakedSessionGroupHandle=this.getSessionGroupHandle(q);this._selectedSessionGroupHandle=this.getSessionGroupHandle(u);this.recomputeControlPoints(h.FOREGROUND);
this.recomputeControlPoints(h.BACKGROUND);this.recomputeForegroundLinesVisibility();this.setForegroundLinesColor(p,m,n)}updatePeakedSessionGroup(l){this._peakedSessionGroupHandle.selection().classed("peaked-path",!1);this._peakedSessionGroupHandle=l;this._peakedSessionGroupHandle.selection().classed("peaked-path",!0)}clearPeakedSessionGroup(){this.updatePeakedSessionGroup(new k)}updateSelectedSessionGroup(l){this._selectedSessionGroupHandle.selection().classed("selected-path",!1);this._selectedSessionGroupHandle=
l;this._selectedSessionGroupHandle.selection().classed("selected-path",!0)}findClosestSessionGroup(l,p){const m=this._axesCollection.mapVisibleAxes(n=>n);l=b.hparams.parallel_coords_plot.findClosestPath(this._visibleFgPathsSel.nodes(),m,[l,p]);return null===l?new k:new k(d3.select(l))}_createLineColorFunction(l,p,m){if(null===l)return()=>"red";const n=d3.scaleLinear().domain(b.hparams.utils.numericColumnExtent(this._schema,this._sessionGroups,l)).range([p,m]).interpolate(d3.interpolateLab);return q=>
n(b.hparams.utils.columnValueByIndex(this._schema,q,l))}_recomputePathSelection(l){l=l.data(this._sessionGroups,p=>p.name);l.exit().remove();return l.enter().append("path").merge(l)}_setControlPointsProperty(l,p){l.controlPoints=this._computeControlPoints(p)}_computeControlPoints(l){return this._axesCollection.mapVisibleAxes((p,m)=>[p,m.yScale()(b.hparams.utils.columnValueByIndex(this._schema,l,m.colIndex()))])}_pathDAttribute(l){return this._d3line(this._computeControlPoints(l))}_updateVisibleFgPathsSel(){this._visibleFgPathsSel=
this._fgPathsSel.filter(":not(.invisible-path)")}}f.LinesCollection=r})(d.parallel_coords_plot||(d.parallel_coords_plot={}))})(b.hparams||(b.hparams={}))})(tf||(tf={}));

//# sourceURL=build://tf-hparams-parallel-coords-plot/interaction_manager.js
(function(b){(function(d){(function(f){class h{constructor(r,l){this.svg=d3.select(r);r=100*l+20;this.svg.attr("viewBox",`0 0 ${r} ${240}`);this.svg.attr("preserveAspectRatio","xMidYMid");this.svg.style("min-width",r+"px");this.svg.style("min-height","240px");this.width=r-10-10;this.height=200;this.svgG=this.svg.append("g").attr("transform",b.hparams.utils.translateStr(10,30))}}f.SVGProperties=h;class k{constructor(r,l,p,m){this._svgProps=r;this._schema=l;this._peakedSessionGroupChangedCB=p;this._selectedSessionGroupChangedCB=
m;this._axesCollection=new f.AxesCollection(r,l,this);this._linesCollection=new f.LinesCollection(r,l,this._axesCollection);this._svgProps.svg.on("click",()=>this.onClick()).on("mousemove mouseenter",()=>{const [n,q]=d3.mouse(this._svgProps.svgG.node());this.onMouseMoved(n,q)}).on("mouseleave",()=>this.onMouseLeave())}onDragStart(r){this._axesCollection.dragStart(r);this._linesCollection.hideBackgroundLines()}onDrag(r){this._axesCollection.drag(r);this._linesCollection.recomputeControlPoints(f.LineType.FOREGROUND)}onDragEnd(){this._axesCollection.dragEnd();
this._linesCollection.recomputeControlPoints(f.LineType.FOREGROUND,500);window.setTimeout(()=>{this._linesCollection.recomputeControlPoints(f.LineType.BACKGROUND);this._linesCollection.showBackgroundLines()},500)}onBrushChanged(r){this._axesCollection.getAxisForColIndex(r).setBrushSelection(d3.event.selection);this._linesCollection.recomputeForegroundLinesVisibility()}onMouseMoved(r,l){this._linesCollection.updatePeakedSessionGroup(this._linesCollection.findClosestSessionGroup(r,l));this._peakedSessionGroupChangedCB(this._linesCollection.peakedSessionGroupHandle().sessionGroup())}onMouseLeave(){this._linesCollection.peakedSessionGroupHandle().isNull()||
(this._linesCollection.clearPeakedSessionGroup(),this._peakedSessionGroupChangedCB(null))}onClick(){this._linesCollection.peakedSessionGroupHandle().sessionGroup()===this._linesCollection.selectedSessionGroupHandle().sessionGroup()?this._linesCollection.updateSelectedSessionGroup(new f.SessionGroupHandle):this._linesCollection.updateSelectedSessionGroup(this._linesCollection.peakedSessionGroupHandle());this._selectedSessionGroupChangedCB(this._linesCollection.selectedSessionGroupHandle().sessionGroup())}onOptionsOrSessionGroupsChanged(r,
l){this._axesCollection.updateAxes(r,l);const p=this._linesCollection.peakedSessionGroupHandle(),m=this._linesCollection.selectedSessionGroupHandle();this._linesCollection.redraw(l,void 0!==r.colorByColumnIndex?r.columns[r.colorByColumnIndex].absoluteIndex:null,r.minColor,r.maxColor);p.equalsTo(this._linesCollection.peakedSessionGroupHandle())||this._peakedSessionGroupChangedCB(this._linesCollection.peakedSessionGroupHandle().sessionGroup());m.equalsTo(this._linesCollection.selectedSessionGroupHandle())||
this._selectedSessionGroupChangedCB(this._linesCollection.selectedSessionGroupHandle().sessionGroup())}schema(){return this._schema}}f.InteractionManager=k})(d.parallel_coords_plot||(d.parallel_coords_plot={}))})(b.hparams||(b.hparams={}))})(tf||(tf={}));

//# sourceURL=build://tf-hparams-parallel-coords-plot/tf-hparams-parallel-coords-plot.html.js
Polymer({is:"tf-hparams-parallel-coords-plot",properties:{sessionGroups:Array,options:Object,selectedSessionGroup:{type:Object,value:null,readOnly:!0,notify:!0},closestSessionGroup:{type:Object,value:null,readOnly:!0,notify:!0},redrawCount:{type:Number,value:0},_validSessionGroups:Array,_interactionManager:Object},observers:["_optionsOrSessionGroupsChanged(options.*, sessionGroups.*)"],_optionsOrSessionGroupsChanged(){if(null!==this.options){var b=this.options.configuration;if(void 0===this._interactionManager||
!_.isEqual(this._interactionManager.schema(),b.schema)){d3.select(this.$.svg).selectAll("*").remove();const d=new tf.hparams.parallel_coords_plot.SVGProperties(this.$.svg,tf.hparams.utils.numColumns(b.schema));this.scopeSubtree(this.$.svg,!0);this._interactionManager=new tf.hparams.parallel_coords_plot.InteractionManager(d,b.schema,f=>this.closestSessionGroupChanged(f),f=>this.selectedSessionGroupChanged(f))}this._computeValidSessionGroups();this._interactionManager.onOptionsOrSessionGroupsChanged(this.options,
this._validSessionGroups);this.redrawCount++}},closestSessionGroupChanged(b){this._setClosestSessionGroup(b)},selectedSessionGroupChanged(b){this._setSelectedSessionGroup(b)},_computeValidSessionGroups(){const b=tf.hparams.utils;if(void 0===this.sessionGroups)this._validSessionGroups=void 0;else{var d=this.options.configuration.schema;this._validSessionGroups=this.sessionGroups.filter(f=>{for(let h=0;h<b.numColumns(d);++h)if(this.options.configuration.columnsVisibility[h]&&void 0===b.columnValueByIndex(d,
f,h))return!1;return!0})}}});

//# sourceURL=build://tf-hparams-parallel-coords-view/tf-hparams-parallel-coords-view.html.js
Polymer({is:"tf-hparams-parallel-coords-view",properties:{backend:Object,experimentName:String,configuration:Object,sessionGroups:Array},_closestOrSelected:function(b,d){return null!==b?b:d}});

//# sourceURL=build://tf-hparams-scatter-plot-matrix-plot/tf-hparams-scatter-plot-matrix-plot.html.js
Polymer({is:"tf-hparams-scatter-plot-matrix-plot",properties:{visibleSchema:Object,sessionGroups:Array,options:Object,selectedSessionGroup:{type:Object,value:null,readOnly:!0,notify:!0},closestSessionGroup:{type:Object,value:null,readOnly:!0,notify:!0},_container:{type:Object,value:null},_svg:{type:Object,value:null},width:{type:Number,value:0},height:{type:Number,value:0},_brushedCellIndex:{type:Object,value:null},_brushSelection:{type:Object,value:null}},observers:["_sessionGroupsChanged(sessionGroups.*)",
"_visibleSchemaChanged(visibleSchema.*)","_redraw(options.*)"],ready(){this._container=this.$.container;this._svg=d3.select(this.$.svg);this._redraw()},_sessionGroupsChanged(){null!==this.selectedSessionGroup&&this._setSelectedSessionGroup(tf.hparams.utils.sessionGroupWithName(this.sessionGroups,this.selectedSessionGroup.name)||null);this._redraw()},_visibleSchemaChanged(){this._brushSelection=this._brushedCellIndex=null;this._redraw()},_redraw(){this.debounce("_redraw",()=>{const b=tf.hparams.utils;
this.width=Math.max(150*b.numVisibleColumns(this.visibleSchema),1200);this.height=Math.max(112.5*b.numVisibleMetrics(this.visibleSchema),480);this._container.style.width=this.width+"px";this._container.style.height=this.height+"px";this._svg.attr("width",this.width).attr("height",this.height);this._svg.selectAll("g").remove();this._draw()},100)},_draw(){function b(ka){return"x-axis-clip-path-"+ka}function d(ka){return"x-label-clip-path-"+ka}function f(ka){return"y-axis-clip-path-"+ka}function h(ka){return"y-label-clip-path-"+
ka}function k(ka,W,Ea,va,ya){Ea=Math.floor(Ea/va);va=W.scale();if("QUANTILE"===ya){let Aa=va.quantiles();Aa=d3.range(0,Aa.length,Math.ceil(Aa.length/Ea)).map(Fa=>Aa[Fa]);W.tickValues(Aa).tickFormat(d3.format("-.2g"))}"LINEAR"!==ya&&"LOG"!==ya||W.ticks(Ea);ka.call(W);ka.selectAll(".domain").remove();ka.selectAll(".tick line").attr("stroke","#ddd")}function r(ka,W){return L[W](x._colValue(ka,W))}function l(ka,W){return J[W](x._metricValue(ka,W))}function p(ka,W){const Ea=[];X[ka][W].each(function(){Ea.push(this)});
return d3.quadtree().x(va=>d3.select(va).datum().x).y(va=>d3.select(va).datum().y).addAll(Ea)}function m(){let ka=new Set(R.nodes());w()||(ka=n(x._brushedCellIndex,x._brushSelection));d3.selectAll(Array.from(y.filterSet(ka,W=>!Z.has(W)))).attr("fill",N);d3.selectAll(Array.from(y.filterSet(Z,W=>!ka.has(W)))).attr("fill","#ddd");Z=ka}function n(ka,W){console.assert(null!==ka);console.assert(null!==W);const [Ea,va]=ka,ya=new Set;y.quadTreeVisitPointsInRect(fa[Ea][va],W[0][0],W[0][1],W[1][0],W[1][1],
Aa=>{d3.select(Aa).datum().sessionGroupMarkers.forEach(Fa=>{ya.add(Fa)})});return ya}function q(ka){const W=d3.brushSelection(ka);!u()&&null===W||u()&&ka===oa.node()&&_.isEqual(W,x._brushSelection)||(x._brushSelection=W,null!==W?(oa=d3.select(ka),x._brushedCellIndex=oa.datum()):(oa=null,x._brushedCellIndex=null),m())}function u(){return null!==x._brushedCellIndex&&null!==x._brushSelection}function w(){return!u()||x._brushSelection[0][0]===x._brushSelection[1][0]||x._brushSelection[0][1]===x._brushSelection[1][1]}
function A(ka,W,Ea,va,ya){let Aa=Infinity,Fa=null;y.quadTreeVisitPointsInDisk(fa[ka][W],Ea,va,ya,(xa,Sa)=>{Z.has(xa)&&Sa<Aa&&(xa=d3.select(xa).datum(),Aa=Sa,Fa=xa.sessionGroup)});return null===Fa?null:d3.selectAll(aa.get(Fa))}const y=tf.hparams.utils,x=this;if(this.sessionGroups&&0!=this.sessionGroups.length&&this.visibleSchema&&0!=this.visibleSchema.metricInfos.length){var C=d3.range(y.numVisibleColumns(x.visibleSchema)),G=d3.range(y.numVisibleMetrics(x.visibleSchema)),D=d3.scaleBand().domain(C).range([85,
this.width-1-5]).paddingInner(.1),B=d3.scaleBand().domain(G).range([this.height-1-5-50,5]).paddingInner(.1),H=D.bandwidth(),K=B.bandwidth(),L=C.map(ka=>x._cellScale(ka,[0,H-1])),J=G.map(ka=>x._cellScale(ka+y.numVisibleHParams(x.visibleSchema),[K-1,0])),O=this._svg.selectAll(".x-axis").data(C).enter().append("g").classed("x-axis",!0).attr("transform",ka=>y.translateStr(D(ka),0));O.append("clipPath").attr("id",b).append("rect").attr("x",-5).attr("y",0).attr("width",H+10).attr("height",x.height-25);
O.append("clipPath").attr("id",d).append("rect").attr("x",0).attr("y",x.height-25).attr("width",H).attr("height",25);O.append("g").attr("clip-path",ka=>"url(#"+b(ka)+")").each(function(ka){d3.select(this).call(k,d3.axisBottom(L[ka]).tickSize(x.height-50),H,40,x.options.columns[ka].scale)});O.append("g").classed("x-axis-label",!0).attr("clip-path",ka=>"url(#"+d(ka)+")").append("text").attr("text-anchor","middle").attr("x",H/2).attr("y",x.height-1-12.5).text(ka=>y.schemaVisibleColumnName(x.visibleSchema,
ka)).append("title").text(ka=>y.schemaVisibleColumnName(x.visibleSchema,ka));O=this._svg.selectAll(".y-axis").data(G).enter().append("g").classed("y-axis",!0).attr("transform",ka=>y.translateStr(x.width-1,B(ka)));O.append("clipPath").attr("id",f).append("rect").attr("x",-(x.width-40-1)).attr("y",-5).attr("width",x.width-40).attr("height",K+10);O.append("clipPath").attr("id",h).append("rect").attr("x",-(x.width-1)).attr("y",0).attr("width",40).attr("height",K);O.append("g").attr("clip-path",ka=>"url(#"+
f(ka)+")").each(function(ka){d3.select(this).call(k,d3.axisLeft(J[ka]).tickSize(x.width-80),K,20,x.options.columns[ka+y.numVisibleHParams(x.visibleSchema)].scale)});O.append("g").classed("y-axis-label",!0).attr("clip-path",ka=>"url(#"+h(ka)+")").append("text").attr("text-anchor","middle").attr("x",-(x.width-20-1)).attr("y",K/2).attr("transform",y.rotateStr(-(x.width-20-1),K/2)).text(ka=>y.metricName(x.visibleSchema.metricInfos[ka])).append("title").text(ka=>y.metricName(x.visibleSchema.metricInfos[ka]));
O=this._svg.selectAll(".cell").data(d3.cross(C,G)).enter().append("g").classed("cell",!0).attr("transform",([ka,W])=>y.translateStr(D(ka),B(W)));O.append("g").classed("frame",!0).append("rect").attr("x",-5).attr("y",-5).attr("width",H+10).attr("height",K+10).attr("stroke","#000").attr("fill","none").attr("shape-rendering","crispEdges");var S=null;void 0!==x.options.colorByColumnIndex&&(S=d3.scaleLinear().domain(this._colExtent(this.options.colorByColumnIndex)).range([this.options.minColor,this.options.maxColor]).interpolate(d3.interpolateLab));
var N=void 0===x.options.colorByColumnIndex?()=>"red":({sessionGroup:ka})=>S(this._colValue(ka,x.options.colorByColumnIndex)),[R,X,aa]=function(ka,W){const Ea=ka.selectAll(".data-marker").data(([ya,Aa])=>x.sessionGroups.filter(Fa=>void 0!==x._colValue(Fa,ya)&&void 0!==x._metricValue(Fa,Aa)).map(Fa=>({col:ya,metric:Aa,sessionGroup:Fa,x:r(Fa,ya),y:l(Fa,Aa),sessionGroupMarkers:null}))).enter().append("circle").classed("data-marker",!0).attr("cx",({x:ya})=>ya).attr("cy",({y:ya})=>ya).attr("r",2).attr("fill",
W),va=new Map;x.sessionGroups.forEach(ya=>{va.set(ya,[])});Ea.each(function(ya){va.get(ya.sessionGroup).push(this)});Ea.each(ya=>{const Aa=va.get(ya.sessionGroup);ya.sessionGroupMarkers=new Set(Aa)});ka=C.map(ya=>G.map(Aa=>Ea.filter(Fa=>Fa.col==ya&&Fa.metric==Aa)));return[Ea,ka,va]}(O.append("g"),N),fa=C.map(ka=>G.map(W=>p(ka,W))),oa=null;u()&&(oa=O.filter(ka=>_.isEqual(ka,x._brushedCellIndex)),console.assert(1==oa.size(),oa));var Z=new Set(R.nodes());m();var ca=d3.brush().extent([[-4,-4],[H-1+5-
1,K-1+5-1]]).on("start",function(){u()&&oa.node()!=this&&ca.move(oa,null);q(this)}).on("brush",function(){q(this)}).on("end",function(){q(this)});O.call(ca);u()&&ca.move(oa,x._brushSelection);var ja=null,ba=null;null!==this.selectedSessionGroup&&(ba=d3.selectAll(aa.get(this.selectedSessionGroup)).classed("selected-marker",!0));O.on("click",function(){var ka=ja===ba?null:ja;ka!==ba&&(null!==ba&&ba.classed("selected-marker",!1),ba=ka,null!==ba&&ba.classed("selected-marker",!0),ka=null===ba?null:ba.datum().sessionGroup,
x._setSelectedSessionGroup(ka))}).on("mousemove mouseenter",function([ka,W]){const [Ea,va]=d3.mouse(this);ka=A(ka,W,Ea,va,20);ja!==ka&&(null!==ja&&ja.classed("closest-marker",!1),ja=ka,null!==ja?(ja.classed("closest-marker",!0),x._setClosestSessionGroup(ja.datum().sessionGroup)):x._setClosestSessionGroup(null))}).on("mouseleave",function(){null!==ja&&(ja.classed("closest-marker",!1),ja=null,x._setClosestSessionGroup(null))});this._svg.selectAll("*").classed("tf-hparams-scatter-plot-matrix-plot",!0)}},
_cellScale(b,d){var f=this._colExtent(b);const h=d3.scaleLinear().domain(f).range(d);if("LINEAR"===this.options.columns[b].scale)return h;if("LOG"===this.options.columns[b].scale)return 0>=f[0]&&0<=f[1]?h:d3.scaleLog().domain(f).range(d);if("QUANTILE"===this.options.columns[b].scale){const k=(d[1]-d[0])/19;f=d3.range(20).map(r=>d[0]+k*r);return d3.scaleQuantile().domain(_.uniq(this.sessionGroups.map(r=>this._colValue(r,b)))).range(f)}if("NON_NUMERIC"===this.options.columns[b].scale)return d3.scalePoint().domain(_.uniq(this.sessionGroups.map(k=>
this._colValue(k,b)).sort())).range(d).padding(.1);throw"Unknown scale for column: "+b+". options: "+this.options;},_colValue(b,d){return tf.hparams.utils.columnValueByVisibleIndex(this.visibleSchema,b,d)},_metricValue(b,d){return tf.hparams.utils.metricValueByVisibleIndex(this.visibleSchema,b,d)},_colExtent(b){return tf.hparams.utils.visibleNumericColumnExtent(this.visibleSchema,this.sessionGroups,b)}});

//# sourceURL=build://tf-hparams-scatter-plot-matrix-view/tf-hparams-scatter-plot-matrix-view.html.js
Polymer({is:"tf-hparams-scatter-plot-matrix-view",properties:{backend:Object,experimentName:String,configuration:Object,sessionGroups:Array},_closestOrSelected:function(b,d){return null!==b?b:d}});

//# sourceURL=build://tf-hparams-sessions-pane/tf-hparams-sessions-pane.html.js
Polymer({is:"tf-hparams-sessions-pane",properties:{backend:Object,helpUrl:String,bugReportUrl:String,experimentName:String,configuration:Object,sessionGroups:Array,_selectedTab:{type:Number,value:0}}});

//# sourceURL=build://tf-hparams-main/tf-hparams-main.html.js
Polymer({is:"tf-hparams-main",properties:{backend:Object,experimentName:String,trackingId:String,helpUrl:String,bugReportUrl:String,_configuration:Object,_sessionGroups:Array,_throttledSendEventToGA:{type:Function,value:()=>_.throttle(function(){this._handleGAEvent({detail:{hitType:"event",eventCategory:"UserInteraction",eventLabel:"Experiment: "+this.experimentName}})},6E4,{leading:!0})}},listeners:{mousemove:"_sendEventToGA",tap:"_sendEventToGA","google-analytics-tracking":"_handleGAEvent"},attached(){this._handleGAEvent({detail:{hitType:"pageview"}})},
reload(){this.$["query-pane"].reload()},_sendEventToGA(){this._throttledSendEventToGA(this)},_handleGAEvent(b){this.$.tracker.handleEvent(b)}});

//# sourceURL=build://tf-hparams-dashboard/tf-hparams-dashboard.html.js
(function(){Polymer({is:"tf-hparams-dashboard",properties:{_backend:{type:Object,value:()=>new tf.hparams.Backend(dc.getRouter().pluginRoute("hparams",""),new dc.RequestManager,!!(window.TENSORBOARD_ENV||{}).IN_COLAB)}},reload(){this.$["hparams-main"].reload()}})})();

//# sourceURL=build://tf-mesh-dashboard/mesh-loader.js
(function(b){Polymer({is:"tf-mesh-loader",properties:{run:String,tag:String,sample:Number,ofSamples:Number,selectedView:{type:String,value:"all"},active:{type:Boolean,value:!1},requestManager:Object,_meshViewer:{type:Object},_dataProvider:{type:Object},_colorScaleFunction:{type:Object,value:()=>Ld.runsColorScale},_runColor:{type:String,computed:"_computeRunColor(run)"},_steps:{type:Array,value:()=>[],notify:!0},_stepIndex:{type:Number,notify:!0},_currentStep:{type:Object,computed:"_computeCurrentStep(_steps, _stepIndex)"},
_meshViewerAttached:{type:Boolean,value:!1},_cameraPositionInitialized:{type:Boolean,value:!1},_stepValue:{type:Number,computed:"_computeStepValue(_currentStep)"},_currentWallTime:{type:String,computed:"_computeCurrentWallTime(_currentStep)"},_isMeshLoading:{type:Boolean,value:!1}},observers:["reload(run, tag, active, _dataProvider, _meshViewer)","_updateScene(_currentStep.*, _meshViewer)","_debouncedFetchMesh(_currentStep)","_updateView(selectedView)"],_computeRunColor:function(d){return this._colorScaleFunction(d)},
attached:function(){this._dataProvider=new b.ArrayBufferDataProvider(this.requestManager);const d=new b.MeshViewer(this._runColor);d.addEventListener("beforeUpdateScene",this._updateCanvasSize.bind(this));d.addEventListener("cameraPositionChange",this._onCameraPositionChange.bind(this));this._meshViewer=d},reload:function(){this.active&&this._dataProvider&&(this.set("_isMeshLoading",!0),this._dataProvider.reload(this.run,this.tag,this.sample).then(d=>{d&&(this.set("_steps",d),this.set("_stepIndex",
d.length-1))}).catch(d=>{if(!d||!d.code||d.code!=b.ErrorCodes.CANCELLED)throw Error(d||"Response processing failed.");}))},_updateScene:function(){const d=this._currentStep;d&&d.mesh&&(this._meshViewer.updateScene(d,this),this._cameraPositionInitialized||(this._meshViewer.resetView(),this._cameraPositionInitialized=!0),this._meshViewerAttached||(this.root.appendChild(this._meshViewer.getRenderer().domElement),this._meshViewerAttached=!0))},_debouncedFetchMesh(){this.debounce("fetchMesh",()=>this._maybeFetchMesh(),
100)},_maybeFetchMesh(){const d=this;return Wb(function*(){const f=d._currentStep;if(f&&!f.mesh&&!f.meshFetching){f.meshFetching=!0;d._isMeshLoading=!0;try{const h=yield d._dataProvider.fetchData(f,d.run,d.tag,d.sample);f.mesh=h[0];d.notifyPath("_currentStep.mesh")}catch(h){if(!h||!h.code||h.code!=b.ErrorCodes.CANCELLED)throw h=h||"Response processing failed.",Error(h);}finally{d._isMeshLoading=!1,f.meshFetching=!1}}})},_onCameraPositionChange:function(){if(this._meshViewer.isReady()){var d=new CustomEvent("camera-position-change",
{detail:this._meshViewer.getCameraPosition()});this.dispatchEvent(d)}},setCameraViewpoint:function(d,f,h){this._meshViewer.setCameraViewpoint(d,f,h)},_updateCanvasSize:function(){const d=this.offsetWidth,f=this.$$(".tf-mesh-loader-header").offsetHeight;this._meshViewer.setCanvasSize({width:d,height:d-f})},redraw:function(){this._updateCanvasSize();this.isConnected&&this._meshViewer.draw()},_hasAtLeastOneStep:function(d){return!!d&&0<d.length},_hasMultipleSteps:function(d){return!!d&&1<d.length},_computeCurrentStep:function(d,
f){return d[f]||null},_computeStepValue:function(d){return d?d.step:0},_computeCurrentWallTime:function(d){return d?Hh.formatDate(d.wall_time):""},_getMaxStepIndex:function(d){return d.length-1},_getSampleText:function(d){return String(d+1)},_hasMultipleSamples:function(d){return 1<d},_updateView:function(d){this._meshViewer&&"all"==d&&this._meshViewer.resetView()},toLocaleString_:function(d){return d.toLocaleString()}})})(fl||(fl={}));

//# sourceURL=build://tf-mesh-dashboard/tf-mesh-dashboard.html.js
(function(){Polymer({is:"mesh-dashboard",properties:{_selectedRuns:Array,_runToTagInfo:Object,_dataNotFound:Boolean,_tagFilter:{type:String,value:".*"},_selectedView:{type:String,notify:!0,value:"all"},_categories:{type:Array,computed:"_makeCategories(_runToTagInfo, _selectedRuns, _tagFilter)"},_requestManager:{type:Object,value:()=>new dc.RequestManager}},ready(){window.addEventListener("resize",()=>{this._handleWindowResize()},!1);this.reload()},_getAllChildren(){return this.root.querySelectorAll("tf-mesh-loader")},
_onCameraPositionChanged(b){"share"==this._selectedView&&this._getAllChildren().forEach(d=>{b.target!=d&&d.setCameraViewpoint(b.detail.position,b.detail.far,b.detail.target)})},_shouldOpen(b){return 2>=b},reload(){this._fetchTags().then(this._reloadMeshes.bind(this))},_handleWindowResize(){this._getAllChildren().forEach(b=>{b.redraw()})},_fetchTags(){const b=dc.getRouter().pluginRoute("mesh","/tags");return this._requestManager.request(b).then(d=>{if(!_.isEqual(d,this._runToTagInfo)){var f=_.mapValues(d,
h=>Object.keys(h));f=dc.getTags(f);this.set("_dataNotFound",0===f.length);this.set("_runToTagInfo",d)}})},_reloadMeshes(){this._getAllChildren().forEach(b=>{b.reload()})},_makeCategories(b,d,f){function h(r){const l=b[r.run][r.tag].samples;return _.range(l).map(p=>Object.assign({},r,{sample:p,ofSamples:l}))}const k=_.mapValues(b,r=>Object.keys(r));return kc.categorizeRunTagCombinations(k,d,f).map(r=>Object.assign({},r,{items:[].concat.apply([],r.items.map(h))}))}})})();

//# sourceURL=build://tf-tensorboard/autoReloadBehavior.js
(function(b){function d(){return(new URLSearchParams(window.location.search)).has("_DisableAutoReload")}b.AUTORELOAD_LOCALSTORAGE_KEY="TF.TensorBoard.autoReloadEnabled";b.AutoReloadBehavior={properties:{autoReloadEnabled:{type:Boolean,observer:"_autoReloadObserver",value:()=>{var f=window.localStorage.getItem(b.AUTORELOAD_LOCALSTORAGE_KEY);return"true"===f||null==f}},_autoReloadId:{type:Number},_missedAutoReload:{type:Boolean,value:!1},_boundHandleVisibilityChange:{type:Object},autoReloadIntervalSecs:{type:Number,
value:30}},attached:function(){this._boundHandleVisibilityChange=this._handleVisibilityChange.bind(this);document.addEventListener("visibilitychange",this._boundHandleVisibilityChange)},detached:function(){window.clearTimeout(this._autoReloadId);document.removeEventListener("visibilitychange",this._boundHandleVisibilityChange)},_autoReloadObserver:function(f){window.localStorage.setItem(b.AUTORELOAD_LOCALSTORAGE_KEY,f);f&&!d()?this._autoReloadId=window.setTimeout(()=>this._doAutoReload(),1E3*this.autoReloadIntervalSecs):
window.clearTimeout(this._autoReloadId)},_doAutoReload:function(){this._isDocumentVisible()?this._doReload():this._missedAutoReload=!0;this._autoReloadId=window.setTimeout(()=>this._doAutoReload(),1E3*this.autoReloadIntervalSecs)},_doReload:function(){if(null==this.reload)throw Error("AutoReloadBehavior requires a reload method");this.reload()},_handleVisibilityChange:function(){this._isDocumentVisible()&&this._missedAutoReload&&(this._missedAutoReload=!1,this._doReload())},_isDocumentVisible:function(){return"visible"===
document.visibilityState}}})(pe||(pe={}));

//# sourceURL=build://tf-tensorboard/tf-tensorboard.html.js
const tn={getLocation(){return window.location}};
Polymer({is:"tf-tensorboard",behaviors:[pe.AutoReloadBehavior],properties:{brand:{type:String,value:"TensorBoard-X"},homePath:{type:String,value:""},_homePath:{type:String,computed:"_sanitizeHomePath(homePath)"},title:{type:String,observer:"_updateTitle"},router:{type:Object,observer:"_updateRouter"},demoDir:{type:String,value:null},useHash:{type:Boolean,value:!1},disabledDashboards:{type:String,value:""},_dashboardData:{type:Array,computed:"_computeDashboardData(_dashboardRegistry)"},_dashboardRegistry:{type:Object,
computed:"_computeDashboardRegistry(_pluginsListing)"},_pluginsListing:{type:Object,value:()=>({})},_activeDashboards:{type:Array,computed:"_computeActiveDashboard(_dashboardData, _pluginsListing)"},_activeDashboardsLoadState:{type:String,value:pe.ActiveDashboardsLoadState.NOT_LOADED},_activeDashboardsNotLoaded:{type:Boolean,computed:"_computeActiveDashboardsNotLoaded(_activeDashboardsLoadState)"},_activeDashboardsLoaded:{type:Boolean,computed:"_computeActiveDashboardsLoaded(_activeDashboardsLoadState)"},
_activeDashboardsFailedToLoad:{type:Boolean,computed:"_computeActiveDashboardsFailedToLoad(_activeDashboardsLoadState)"},_showNoDashboardsMessage:{type:Boolean,computed:"_computeShowNoDashboardsMessage(_activeDashboardsLoaded, _activeDashboards, _selectedDashboard)"},_showNoSuchDashboardMessage:{type:Boolean,computed:"_computeShowNoSuchDashboardMessage(_activeDashboardsLoaded, _dashboardRegistry, _selectedDashboard)"},_selectedDashboard:{type:String,value:bd.getString(bd.TAB)||null,observer:"_selectedDashboardChanged"},
_dashboardToMaybeRemove:String,_dashboardContainersStamped:{type:Object,value:()=>({})},_isReloadDisabled:{type:Boolean,value:!1},_lastReloadTime:{type:String,value:"not yet loaded"},_lastReloadTimeShort:{type:String,value:"Not yet loaded"},_dataLocation:{type:String,value:null},_requestManager:{type:Object,value:()=>new dc.RequestManager},_canceller:{type:Object,value:()=>new dc.Canceller},_refreshing:{type:Boolean,value:!1}},observers:["_updateSelectedDashboardFromActive(_selectedDashboard, _activeDashboards)",
"_ensureSelectedDashboardStamped(_dashboardRegistry, _dashboardContainersStamped, _activeDashboards, _selectedDashboard)"],_sanitizeHomePath(b){if(!b)return"";const d=tn.getLocation(),f=new URL(b,d.href),h="http:"===f.protocol||"https:"===f.protocol,k=f.origin===d.origin;if(!h)throw new RangeError(`Expect 'homePath' to be of http: or https:. ${b}`);if(!k)throw new RangeError(`Expect 'homePath' be a path or have the same origin. ${b} vs. ${d.origin}`);return h&&k?f.toString():""},_activeDashboardsUpdated(){},
_isDashboardActive(b,d,f){return 0<=(b||"").split(",").indexOf(f.plugin)||!(d||[]).includes(f.plugin)?!1:!0},_isDashboardInactive(b,d,f){return 0<=(b||"").split(",").indexOf(f.plugin)?!1:(d||[]).includes(f.plugin)?!1:!0},_inactiveDashboardsExist(b,d,f){if(!f)return!1;const h=new Set;b.forEach(k=>{h.add(k.plugin)});(d||"").split(",").forEach(k=>{h.delete(k.plugin)});f.forEach(k=>{h.delete(k)});return 0<h.size},_getDashboardFromIndex(b,d){return b[d]},_selectedStatus(b,d){return b===d},_selectedDashboardChanged(b){b=
b||"";bd.setString(bd.TAB,b);let d=window.location.pathname;d+=d.endsWith("/")?b:"/"+b;ga("set","page",d);ga("send","pageview")},_updateSelectedDashboardFromActive(b,d){d&&null==b&&(b=d[0]||null,null!=b&&(bd.setString(bd.TAB,b,{useLocationReplace:!0}),this._selectedDashboard=b))},_updateSelectedDashboardFromHash(){const b=bd.getString(bd.TAB);this.set("_selectedDashboard",b||null)},_ensureSelectedDashboardStamped(b,d,f,h){if(f&&h&&d[h]&&(d=this._dashboardToMaybeRemove,this._dashboardToMaybeRemove=
h,d&&d!=h&&b[d].removeDom&&(d=this.$$(`.dashboard-container[data-dashboard=${d}]`),d.firstChild&&d.firstChild.remove()),d=this.$$(`.dashboard-container[data-dashboard=${h}]`))){b=b[h];if(0===d.children.length)switch(f=b.loadingMechanism,f.type){case "CUSTOM_ELEMENT":h=document.createElement(f.elementName);h.id="dashboard";d.appendChild(h);break;case "IFRAME":this._renderPluginIframe(d,h,f);break;default:console.warn("Invariant violation:",f)}this.set("_isReloadDisabled",b.disableReload)}},_renderPluginIframe(b,
d){const f=document.createElement("iframe");f.id="dashboard";gl.host.registerPluginIframe(f,d);const h=new URL("data/plugin_entry.html",window.location.href);h.searchParams.set("name",d);f.setAttribute("src",h.toString());b.appendChild(f)},_selectedDashboardComponent(){return this.$$(`.dashboard-container[data-dashboard=${this._selectedDashboard}] #dashboard`)},ready(){xc.setUseHash(this.useHash);this._updateSelectedDashboardFromHash();window.addEventListener("hashchange",()=>{this._updateSelectedDashboardFromHash()},
!1);dc.environmentStore.addListener(()=>{this._dataLocation=dc.environmentStore.getDataLocation();const b=dc.environmentStore.getWindowTitle();b&&(window.document.title=b)});bd.migrateLegacyURLScheme();this._reloadData();this._lastReloadTime=(new Date).toString()},_computeActiveDashboard(){return this._dashboardData?this._dashboardData.map(b=>b.plugin).filter(b=>{b=this._pluginsListing[b];return"boolean"===typeof b?b:b&&b.enabled}):[]},_onTemplateChanged(){const b={};for(const d of this.root.querySelectorAll(".dashboard-container"))b[d.dataset.dashboard]=
!0;this._dashboardContainersStamped=b},_computeDashboardRegistry(b){const d={};for(const [h,k]of Object.entries(pe.dashboardRegistry))d[h]={plugin:k.plugin,loadingMechanism:{type:"CUSTOM_ELEMENT",elementName:k.elementName},tabName:k.tabName.toUpperCase(),disableReload:k.isReloadDisabled||!1,removeDom:k.removeDom||!1};if(null!=b)for(const [h,k]of Object.entries(b))if("boolean"!==typeof k){switch(k.loading_mechanism.type){case "NONE":null==d[h]&&console.warn("Plugin has no loading mechanism and no baked-in registry entry: %s",
h);continue;case "CUSTOM_ELEMENT":var f={type:"CUSTOM_ELEMENT",elementName:k.loading_mechanism.element_name};break;case "IFRAME":f={type:"IFRAME",modulePath:k.loading_mechanism.module_path};break;default:console.warn("Unknown loading mechanism for plugin %s: %s",h,k.loading_mechanism);continue}null==f&&console.error("Invariant violation: loadingMechanism is %s for %s",f,h);d[h]={plugin:h,loadingMechanism:f,tabName:k.tab_name.toUpperCase(),disableReload:k.disable_reload,removeDom:k.remove_dom}}f={};
for(const h of Object.keys(b))d[h]&&(f[h]=d[h]);Object.assign(f,d);return f},_computeDashboardData(b){return Object.values(b)},_fetchPluginsListing(){this._canceller.cancelAll();const b=this._canceller.cancellable(d=>{d.cancelled||(this._pluginsListing=d.value,this._activeDashboardsLoadState=pe.ActiveDashboardsLoadState.LOADED)});return this._requestManager.request(dc.getRouter().pluginsListing()).then(b,()=>{this._activeDashboardsLoadState===pe.ActiveDashboardsLoadState.NOT_LOADED?this._activeDashboardsLoadState=
pe.ActiveDashboardsLoadState.FAILED:console.warn("Failed to reload the set of active plugins; using old value.")})},_computeActiveDashboardsNotLoaded(b){return b===pe.ActiveDashboardsLoadState.NOT_LOADED},_computeActiveDashboardsLoaded(b){return b===pe.ActiveDashboardsLoadState.LOADED},_computeActiveDashboardsFailedToLoad(b){return b===pe.ActiveDashboardsLoadState.FAILED},_computeShowNoDashboardsMessage(b,d,f){return b&&0===d.length&&null==f},_computeShowNoSuchDashboardMessage(b,d,f){return b&&!!f&&
null==d[f]},_updateRouter(b){dc.setRouter(b)},_updateTitle(b){b&&this.set("brand",b)},reload(){this._isReloadDisabled||(this._reloadData().then(()=>{const b=this._selectedDashboardComponent();b&&b.reload&&b.reload()}),this._lastReloadTime=(new Date).toString())},_reloadData(){this._refreshing=!0;return Promise.all([this._fetchPluginsListing(),dc.environmentStore.refresh(),dc.runsStore.refresh(),dc.experimentsStore.refresh()]).then(()=>{this._lastReloadTimeShort=(new Date).toLocaleDateString(void 0,
{month:"long",day:"numeric",hour:"numeric",minute:"numeric",second:"numeric"})}).finally(()=>{this._refreshing=!1})},_getDataRefreshingClass(){return this._refreshing?"refreshing":""},openSettings(){this.$.settings.open();this.$.paginationLimitInput.value=sd.getLimit()},_paginationLimitValidate(b){b.target.validate()},_paginationLimitChanged(b){b=Number.parseInt(b.target.value,10);b===+b&&0<b&&sd.setLimit(b)}});
", "headers": [["content-type", "application/javascript; charset=utf-8"]], "ok": true, "status": 200, "status_text": ""}}} colab_type="code" id="NtL_P5-bYQ4e" outputId="b4706f95-0e5d-4a53-f190-205fef5fb14b"
# %tensorboard --logdir logs/fit
# + [markdown] colab_type="text" id="u3JEgXlaZOdK"
# #### Inference
# + colab={"base_uri": "https://localhost:8080/", "height": 104} colab_type="code" id="e-6U-BjvZSOV" outputId="83d59a29-853d-4dd0-fdd2-92aa988bf542"
# Apply model on test data and save predictions
predict_and_save(model, x_te, 'mlp')
# + [markdown] colab_type="text" id="pN8xG8C2nt-_"
# - We have applied MLP architecture on our data set.
#
# + [markdown] colab_type="text" id="t-FXh0_BrqX5"
# ## 4. Summary and Conclusion
# + colab={} colab_type="code" id="1mrO7kvvb8wg"
from prettytable import PrettyTable
x = PrettyTable()
x.field_names = ["Model", "train_auc","val_auc", 'Kaggle score']
# + colab={} colab_type="code" id="0HcNzB8qcQ7B"
x.add_row(["1. LogisticRegression", 0.54, 0.54, 0.53])
x.add_row(["2. SVM", 0.53, 0.52, 0.50])
x.add_row(["3. Decision Tree", 0.67, 0.63, 0.58])
x.add_row(["4. RF", 0.99, 0.67, 0.57])
x.add_row(["5. XgBoost", 0.96, 0.66, 0.57])
x.add_row(["6. LightGBM", '-', 0.63, 0.61])
x.add_row(["7. Voting Classifier", '-', '-', 0.58])
x.add_row(["8. Cascading Classifier", '-', '-', 0.56])
x.add_row(["9. DT - Feature Importance - DT", '-', '-', 0.59])
x.add_row(["10. LightGBM - Feature Importance - DT", '-', 0.63, 0.55])
x.add_row(["11. LightGBM - Feature Selection - SelectKBest", '-', 0.64, 0.61])
x.add_row(["12. LightGBM - Feature Extraction - PCA", '-', 0.56, 0.52])
x.add_row(["13. Deep Learning - MLP", 0.50, 0.50, 0.50])
# + colab={"base_uri": "https://localhost:8080/", "height": 302} colab_type="code" id="RLw9700aeQHF" outputId="b381ac0b-a5e4-46d1-b813-6e0ace1be4b5"
print(x)
# + [markdown] colab_type="text" id="kFpinVxgfo1R"
# <h3>Summary</h3>
# + [markdown] colab_type="text" id="EvUkeXoqGI2c"
# -> EDA
# - We have analyzed each and every feature from train and validation dataset with the help of different plots like barplot, PDF and CDF.
# - We have also analyzed missing values and their percentages in features.
#
# + [markdown] colab_type="text" id="8ccym_VNfq3N"
# -> Feature Engineering
# - From songs, songs_extra_info and members we have combined all information related to user and songs and extracted various features.
# - We have extracted features like membership days, information of year, month and day from registration and expiration dates.
# - We have also extracted groupby features for songs and users with respect to artist, lyricist, composer, language, age etc.
# - We have extracted features like song year, country code, registration code from isrc code for each and every song. Along with that we have extracted counts like artist count, genre count, lyricist count, composer count etc.
# + [markdown] colab_type="text" id="tzh6eo9ugpnG"
# -> Sampling
# - As data size is very large to fit our RAM so we have samplled 1.5M train points and 0.7 val points.
# - Our data is in chronological order.
#
# + [markdown] colab_type="text" id="CHCaepxMg_Ri"
# -> Pre-processing
# - We have transform all our numerical features using standardization.
# - For categorical features we have used label encoding.
# - As one hot encoding will result into large dimensionality which may result less performance.
# + [markdown] colab_type="text" id="B3kvksZ-qTdr"
# -> Models
# - We have applied various Machine learning algorithms like LR, SVM, DT, RF, GBDT (using XgBoost, LightGBM), Stacking classifier, Voting classifier.
# - We have done hyper parameter tuning to get best result.
# - We have also applied MLP architecture.
# - We can see that LightGBM gives better performance compare to other models.
# + [markdown] colab_type="text" id="ueZ-Y2zGrFrJ"
# -> Feature Extraction and Selection
# - We have selected best features based on DT feature importance and applied LighGBM model.
# - We also used sklearn's SelectKBest method to choose best features and applied LightGBM.
# - We have also tried PCA and find the maximum explaiend variance using CDF.
# - We can see that LightGBM with SelectKBest gives best perforamnce.
# + [markdown] colab_type="text" id="QHk6AT46rtWI"
# -> Future steps
# - Due to RAM limitations we have taken less amount of data.
# - If we use whole data we can get better results.
# - Deep learning requires more data to get good results.
# - By tweaking parameters on large data points we can achieve better results.
# - We can also think of more feature extraction.
| 5,597,289 |
/04.00-DS-with-Python.ipynb | 125f3d538489e21eea68a186bec5e028d47a994a | [] | no_license | WenchiLiu/panda-101 | https://github.com/WenchiLiu/panda-101 | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 19,462 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# 
# 
# <p style = "font-size:16px"><b> BY: Lakshmi Sindhu.P </b></p>
# # Introduction:
# ## Problem Statement:
# Mathematical equation solver using character and symbol recognition using image processing and Convolutional Neural Networks(CNN).
#
# The main objective of this project to develop a project which identifies the numbers, symbols and characters present in a Handwritten Wquation and solves the equation, and in the end gives the soluton to the equation.
#
# # PART - 1: Data Extraction
# ## Importing required libraries
import numpy as np
import cv2
from PIL import Image
from matplotlib import pyplot as plt
# %matplotlib inline
import os
from os import listdir
from os.path import isfile, join
import pandas as pd
def load_images_from_folder(folder):
train_data=[]
for filename in os.listdir(folder):
img = cv2.imread(os.path.join(folder,filename),cv2.IMREAD_GRAYSCALE)
img=~img
if img is not None:
ret,thresh=cv2.threshold(img,127,255,cv2.THRESH_BINARY)
ctrs,ret=cv2.findContours(thresh,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)
cnt=sorted(ctrs, key=lambda ctr: cv2.boundingRect(ctr)[0])
w=int(28)
h=int(28)
maxi=0
for c in cnt:
x,y,w,h=cv2.boundingRect(c)
maxi=max(w*h,maxi)
if maxi==w*h:
x_max=x
y_max=y
w_max=w
h_max=h
im_crop= thresh[y_max:y_max+h_max+10, x_max:x_max+w_max+10]
im_resize = cv2.resize(im_crop,(28,28))
im_resize=np.reshape(im_resize,(784,1))
train_data.append(im_resize)
return train_data
data = []
# +
# images under '0' folder
data=load_images_from_folder("D:\\INTERNSHIPS\\LGM\\Task-9\\extracted_images\\0\\")
len(data)
for i in range(0,len(data)):
data[i]=np.append(data[i],['0'])
print(len(data))
# +
# images under '1' folder
data1=load_images_from_folder("D:\\INTERNSHIPS\\LGM\\Task-9\\extracted_images\\1\\")
for i in range(0,len(data1)):
data1[i]=np.append(data1[i],['1'])
data=np.concatenate((data,data1))
print(len(data))
# +
# images under '2' folder
data2=load_images_from_folder("D:\\INTERNSHIPS\\LGM\\Task-9\\extracted_images\\2\\")
for i in range(0,len(data2)):
data2[i]=np.append(data2[i],['2'])
data=np.concatenate((data,data2))
print(len(data))
# +
# images under '3' folder
data3=load_images_from_folder("D:\\INTERNSHIPS\\LGM\\Task-9\\extracted_images\\3\\")
for i in range(0,len(data3)):
data3[i]=np.append(data3[i],['3'])
data=np.concatenate((data,data3))
print(len(data))
# +
# images under '4' folder
data4=load_images_from_folder("D:\\INTERNSHIPS\\LGM\\Task-9\\extracted_images\\4\\")
for i in range(0,len(data4)):
data4[i]=np.append(data4[i],['4'])
data=np.concatenate((data,data4))
print(len(data))
# +
# images under '5' folder
data5=load_images_from_folder("D:\\INTERNSHIPS\\LGM\\Task-9\\extracted_images\\5\\")
for i in range(0,len(data5)):
data5[i]=np.append(data5[i],['5'])
data=np.concatenate((data,data5))
print(len(data))
# +
# images under '6' folder
data6=load_images_from_folder("D:\\INTERNSHIPS\\LGM\\Task-9\\extracted_images\\6\\")
for i in range(0,len(data6)):
data6[i]=np.append(data6[i],['6'])
data=np.concatenate((data,data6))
print(len(data))
# +
# images under '7' folder
data7=load_images_from_folder("D:\\INTERNSHIPS\\LGM\\Task-9\\extracted_images\\7\\")
for i in range(0,len(data7)):
data7[i]=np.append(data7[i],['7'])
data=np.concatenate((data,data7))
print(len(data))
# +
# images under '8' folder
data8=load_images_from_folder("D:\\INTERNSHIPS\\LGM\\Task-9\\extracted_images\\8\\")
for i in range(0,len(data8)):
data8[i]=np.append(data8[i],['8'])
data=np.concatenate((data,data8))
print(len(data))
# +
# images under '9' folder
data9=load_images_from_folder("D:\\INTERNSHIPS\\LGM\\Task-9\\extracted_images\\9\\")
for i in range(0,len(data9)):
data9[i]=np.append(data9[i],['9'])
data=np.concatenate((data,data9))
print(len(data))
# +
#assigning images under '-' folder=10
data10=load_images_from_folder("D:\\INTERNSHIPS\\LGM\\Task-9\\extracted_images\\-\\")
for i in range(0,len(data10)):
data10[i]=np.append(data10[i],['10'])
data=np.concatenate((data,data10))
print(len(data))
# +
#assigning images under '+' folder = 11
data11=load_images_from_folder("D:\\INTERNSHIPS\\LGM\\Task-9\\extracted_images\\+\\")
for i in range(0,len(data11)):
data11[i]=np.append(data11[i],['11'])
data=np.concatenate((data,data11))
print(len(data))
# +
#assigning images under 'times' folder(multiplication) = 12
data12=load_images_from_folder("D:\\INTERNSHIPS\\LGM\\Task-9\\extracted_images\\times\\")
for i in range(0,len(data12)):
data12[i]=np.append(data12[i],['12'])
data=np.concatenate((data,data12))
print(len(data))
# -
df=pd.DataFrame(data,index=None)
df.to_csv('train_final.csv',index=False)
# <p style="font-size:22px"><b>Part-2, Model Training in notebook = <br><br>
# "LGM VIP-September 2021 TASK-8___Part-2(model training)"</b></p>
tury column
# **Try it yourself!**
# 1. Mutate a new column called `mass_rel`, which will calculate a planet's mass relative to earth's. Assuming the scale of these values, you can assume the mass of the earth to be 5.97. Try this using assign.
# 2. Mutate a new column called `orbital_yr` using `assign`. Using this new column, create a new column `orbital_weeks`, which converts `orbital_yr` to weeks. (**Hint**: There are 52 weeks in a year).
# 3. Mutate two columns using assign. The first will be called `x10`, which will multiply the `number` column by 10. The second will be `dist_mi`, which will convert `distance` from astronomical units to miles. Use the multiplier 93 for simplicity.
#Answer to 1
#Answer to 2
#Answer to 3
# ## Putting things together: transform and apply
# The `transform` and `apply` methods work on the specified elements of a Series or DataFrame. A transformation can return some transformed version of the full data to recombine. For such a transformation, the output is the same shape as the input.
#
# The ``apply()`` method lets you apply an arbitrary function to the group results.
# The function should take a ``DataFrame``, and return either a Pandas object (e.g., ``DataFrame``, ``Series``) or a scalar; the combine operation will be tailored to the type of output returned.
# Create a new column, 'mass_std' which normalizes the the mass of the planets
#Create a new column method_min which labels everything other than Radial Velocity, Transit, Imaging, and Microlensing as 'Other'
#check to see if things worked how you expected
# ## Groupby and summarize: group items by some desired similarity
# As with R, groupby alone doesn't do much; however, combined with summarizing functionality, it can be very powerful. Recall that summarizing functions reduce the dimensionality of the data down to one or more values. Let's see how this works in Python.
# +
#How many planets are there for each method_min type?
#We can also return a particular column
# -
#What is the mean distance for all of the planet systems (given by the `number` column)
# You can also group by multiple keys by passing in a list of keys:
# +
#group by multiple keys
# -
# ### The `agg` function
# You can use the `agg` function most aptly performs the `summarise` functionality. You can use this function to perform several operations on different rows/columns of the data. Using `agg`, you can pass in a list of the operations you want to do on the columns using a string, pandas, or numpy function as below:
#Find the max, median, and min of mass and distance
# You can also apply specific aggregation functions on specific columns. You can do this by using the column names as keys and the functions (as strings) as values.
# Find the total counts of the numbers column, the mean of the distance column
# **Try it yourself!!**
# 1. How many planets are there for each of the raw `method`s?
# 2. What is the mean and max orbital period for each method for each planet system (`number` variable)
# 3. **Bonus**: What are the maximum masses for planets with a distance of >1000 and <1000?
#Answer to 1
#Answer to 2
#Answer to 3
# ## Arrange: change the ordering of the data
#Sort data according to age
# +
#Sort data according to number, ascending
# +
#Sort data according to mass and distance
# +
#Sort data according to mass - ascending and distance - descending
# -
# **Try it yourself!**
# 1. Sort rows by `year` and `distance`
# 2. Sort rows by `orbital_weeks` ascending and `dist_mi` descending
#Answer 1
#Answer 2
# # Join: Put datasets together based on matching keys
#
# There are many ways to perform joins in python. One way is through the `merge` method. The `concat` method is extremely valuable when there are several dataframes to be joined. An astronomers csv file is provided which contains an astronomer corresponding to many years. This will be used during the join.
#load the astronomers dataset locally, called `astronomers.csv`
#Perform an inner join on the data using the 'year' column as the key
#Can also perform an outer join to see if anything was missed
# ## Plotting examples
# +
#create scatter plot using pandas
# +
#create pairplot using seaborn
# -
# # What have we learned?
# 1. General functions to get an understanding of the dataset
# 2. Select functions
# 3. Filter functions
# 4. Mutate functions
# - `Assign`
# 5. `Transform` and `Apply`
# 5. Groupby and summarise functions
# 6. Aggregation functions
# 7. Joining functions
# 8. Plotting functions
#
# Good luck with your data science endeavors using Python!
| 10,083 |
/hourly-data.ipynb | 23c6e8d1251ddd9d054a7215500b1197d68a2e45 | [
"MIT"
] | permissive | rmsare/cs229-project | https://github.com/rmsare/cs229-project | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 55,778 | # ---
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# name: python3
# ---
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
WEATHER_FILE = 'data/weather/los_angeles_weather_2000_2018.csv'
POLLUTANT_FILE = 'data/pm25/los_angeles_pm25_2016_2018.csv'
df = pd.read_csv(WEATHER_FILE)
df.head()
df['DATE'] = pd.to_datetime(df.DATE)
df['DATE'] = df['DATE'].dt.round('1h')
df = df.set_index(df.DATE)
variables = ['SLP', 'TMP', 'DEW']
for col in variables:
df[col][~(df[col].str.endswith(',5', ',1'))] = 'nan'
df[col] = df[col].str.replace(',5', '')
df[col] = df[col].str.replace(',1', '')
df[col] = df[col].astype(float)
df['DIR'] = df.WND.str[0:3].astype(float)
df['DIR'][df.DIR == 999] = np.nan
df['SPD'] = df.WND.str[8:12].astype(float)
df['SPD'][df.SPD == 9999] = np.nan
df['MONTH'] = df.index.month
df['HOUR'] = df.index.hour
df['DOW'] = df.index.dayofweek
df = df[['SLP', 'SPD', 'DIR', 'TMP', 'DEW', 'MONTH', 'HOUR', 'DOW']]
df.head()
df.to_csv(WEATHER_FILE.replace('.csv', '_cleaned.csv'))
im = plt.imshow(df.corr(), cmap='RdBu_r', vmin=-1, vmax=1)
cb = plt.colorbar(im)
ax = plt.gca()
ax.set_xticklabels(df.columns)
ax.set_yticklabels(df.columns)
df2 = pd.read_csv(POLLUTANT_FILE)
df2['date'] = df2['Date Local'] + ' ' + df2['Time Local']
df2 = df2.set_index(pd.to_datetime(df2.date))
df2.plot(y='Sample Measurement')
df2 = df2[['Sample Measurement']]
df2.columns = ['PM25']
df.tail()
df2.head()
df = df[~df.index.duplicated(keep='first')]
df2 = df2[~df2.index.duplicated(keep='first')]
df2 = df2[['PM25']]
df3 = pd.concat([df, df2], axis=1)
df3['DOW'] = df3.index.dayofweek
df3['HOUR'] = df3.index.hour
df3['MONTH'] = df3.index.month
df3.to_csv('data/los_angeles_weather_pm25.csv')
df3.head()
e=(15,5))
plt.bar(coun.keys(),coun.values())
plt.xlabel('Country',size=15)
plt.ylabel('No of jobs',size=15)
plt.title('Counrty wise posting',size=15)
plt.xticks(rotation=20)
plt.show()
# US had highest job postings
edu=dict(df.required_education.value_counts()[:6])
del edu['']
edu
plt.figure(figsize=(20,10))
plt.bar(edu.keys(),edu.values())
plt.xlabel('Education level',size=15)
plt.ylabel('No of jobs',size=15)
plt.title('No. of jobs with education level',size=15)
plt.xticks(rotation=20)
plt.show()
# most job require a degree level of Bachelor
# # top 10 titles posted which were non fraudulent
print(df[df.fraudulent==0].title.value_counts()[:10])
# # combine all text
df['text']=df['title']+df['company_profile']+df['description']+df['requirements']+df['benefits']
del df['title']
del df['company_profile']
del df['location']
del df['department']
del df['description']
del df['benefits']
del df['required_experience']
del df['required_education']
del df['function']
del df['country']
del df['industry']
del df['requirements']
df.head()
realjobs_text=df[df.fraudulent==0].text
fraudjobs_text= df[df.fraudulent==1].text
realjobs_text
# # create word cloud of words used in fraudulent and real jobs
STOPWORDS = spacy.lang.en.stop_words.STOP_WORDS
plt.figure(figsize=(16,14))
wc= WordCloud(min_font_size=3,max_words=1000,width=1600,height=800,stopwords=STOPWORDS).generate(str(" ".join(fraudjobs_text)))
plt.imshow(wc,interpolation='bilinear')
STOPWORDS = spacy.lang.en.stop_words.STOP_WORDS
plt.figure(figsize=(16,14))
wc= WordCloud(min_font_size=3,max_words=1000,width=1600,height=800,stopwords=STOPWORDS).generate(str(" ".join(realjobs_text)))
plt.imshow(wc,interpolation='bilinear')
# !pip install spacy && python -m spacy download en
# # tokenize, clean and remove stop words from text
# +
#create a list of punctuation then stop words
punc= string.punctuation
nlp=spacy.load("en_core_web_sm")
#this is already a trained model
stop_words= spacy.lang.en.stop_words.STOP_WORDS
parser= English()
#create tokenizer function
def spacy_tokenizer(sentence):
mytokens =parser(sentence)
#convert token to lower case
mytokens=[word.lemma_.lower().strip() if word.lemma_ != '-PRON-' else word.lower_ for word in mytokens]
#remove stop words
mytokens=[ word for word in mytokens if word not in stop_Words and word not in punctuation]
return mytokens
#custom transform using spacy
#cleaning text
class predictor(TransformerMixin):
def transform(self,X,**transform_params):
return(clean_text(text) for text in X)
def fit(self,X,y=None,**fit_params):
return self
def get_params(self,deep=True):
return ()
#remove spaces and convert lower case
def clean_text(text):
return text.strip().lower()
# -
df['text'] = df['text'].apply(clean_text)
cv = TfidfVectorizer(max_features = 100)
x = cv.fit_transform(df['text'])
df1=pd.DataFrame(x.toarray(),columns=cv.get_feature_names())
df.drop(['text'],axis=1,inplace=True)
main_df=pd.concat([df1,df],axis=1)
main_df.head()
df.head()
df1
Y= main_df.iloc[:,-1]
X=main_df.iloc[:,:-1]
#all columns in x only fraudulent col in y
# # split data into training and tesing to apply prediction model
x_train,xtest,ytrain,ytest=train_test_split(X,Y,test_size=0.3)
#70% training 30%testing
print(x_train.shape)
print(ytrain.shape)
# # random forest
#random forest
from sklearn.ensemble import RandomForestClassifier
rfc=RandomForestClassifier(n_jobs=3,oob_score=True,n_estimators=100,criterion='entropy')
model=rfc.fit(x_train,ytrain)
xtest
pred=rfc.predict(xtest)
score= accuracy_score(ytest,pred)
pred
# # accuracy score
print(score)
print(confusion_matrix(ytest,pred))
print(classification_report(ytest,pred))
# # Apply support vector machine
from sklearn import svm
from sklearn.metrics import confusion_matrix, classification_report, accuracy_score
from sklearn.preprocessing import StandardScaler,LabelEncoder
from sklearn.model_selection import train_test_split
clf= svm.SVC()
clf.fit(x_train,ytrain)
predy= clf.predict(xtest)
classification_report(ytest,predy)
# # accuracy score
print(accuracy_score(ytest,predy))
# not much difference from random forrest
print(confusion_matrix(ytest,predy))
xtest
predy
# predictied array
| 6,260 |
/.ipynb_checkpoints/ABC 047 圧縮 groups グルーピング-checkpoint.ipynb | 329ebeaa31b31445905445c90e4aff443ffd77cc | [] | no_license | yakeshiba/Competitive-programming | https://github.com/yakeshiba/Competitive-programming | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 1,221 | # ---
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# language: python
# name: python3
# ---
# +
#C
S = [1 if x=="W" else 0 for x in input()]
groups = []
tmp = 0
zeros = 0
for n in S:
if n == 1:
if zeros != 0:#直前まで０だったとき
groups.append(zeros)
zeros = 0#リセット
tmp += 1
elif n == 0:
if tmp != 0:#直前まで１だったとき
groups.append(tmp)
tmp = 0#リセット
zeros -= 1
#終端の処理
if tmp != 0:
groups.append(tmp)
if zeros != 0:
groups.append(zeros)
print(len(groups)-1)
| 717 |
/Figures/.ipynb_checkpoints/S01_AllCells_Supp-checkpoint.ipynb | c8519cb41242e612f9f33f58cde189db6dd9094c | [] | no_license | immunogenomics/FibroblastAtlas2022 | https://github.com/immunogenomics/FibroblastAtlas2022 | 3 | 0 | null | null | null | null | Jupyter Notebook | false | false | .r | 1,891,807 | # ---
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# language: R
# name: ir
# ---
suppressPackageStartupMessages({
source('../libs.R')
source('../utils.R')
source('../Figures/colors.R')
# source('../utils_mapping.R')
source('../utils_plotting.R')
})
# # Load
# ## All-cells
# +
meta_data <- readRDS('../../data/cache/cell_df_Mar30_2020.rds')
# -
# ## tissue-defined clusters
fib <- list()
fib$synovium <- readRDS('../../data/cache/synovium_fibroblasts_obj.rds')
fib$lung <- readRDS('../../data/cache/lung_fibroblasts_obj.rds')
fib$salivarygland <- readRDS('../../data/cache/salivarygland_fibroblasts_obj.rds')
fib$gut <- readRDS('../../data/cache/fibroblast_object_gut.rds')
fib$synovium$meta_data <- fib$synovium$meta_data %>%
dplyr::mutate(tissue_cell_type = case_when(
tissue_cell_type == 'NOTCH3+ perivascular' ~ 'Sublining',
TRUE ~ tissue_cell_type
))
effects <- fib %>% map('presto') %>% map('effects')
# ## Public datasets
# +
## Adult Human Cell Atlas
effects_ahca <- readRDS('../../data/AdultHumanCellAtlas/cache/presto_cluster_nested.rds')
effects_ahca$padj <- p.adjust(effects_ahca$pvalue, 'BH')
effects_ahca$contrast <- gsub('mesenchymal', 'fibroblast', effects_ahca$contrast)
effects_ahca$Cluster <- gsub('mesenchymal', 'fibroblast', effects_ahca$Cluster)
pb_ahca <- readRDS('../../data/AdultHumanCellAtlas/cache/pseudobulk.rds')
pb_ahca$meta_data$Tissue <- pb_ahca$meta_data$tissue
# +
effects_ts <- readRDS('../../data/tabula_sapiens/presto_cluster_nested.rds')
effects_ts$padj <- p.adjust(effects_ts$pvalue, 'BH')
# effects_ahca$contrast <- gsub('mesenchymal', 'fibroblast', effects_ahca$contrast)
# effects_ahca$Cluster <- gsub('mesenchymal', 'fibroblast', effects_ahca$Cluster)
pb_ts <- readRDS('../../data/tabula_sapiens/pb.rds')
pb_ts$exprs_norm <- normalizeData(pb_ts$counts_mat, 1e4, 'log')
pb_ts$meta_data$Tissue <- pb_ts$meta_data$Organ
pb_ts$meta_data$CellType <- pb_ts$meta_data$Compartment
# -
# # AHCA + TS Heatmap
# +
## Get all labels to assign colors
all_tissues <- c(
as.character(unique(pb_ts$meta_data$Tissue)),
unique(pb_ahca$meta_data$Tissue)
) %>% unique
all_clusters <- c(
as.character(unique(pb_ts$meta_data$CellType)),
unique(pb_ahca$meta_data$CellType)
) %>% unique
# colors <- c(tableau_color_pal()(10), 'grey')
# glue("\t{all_clusters} = \'{colors}\',") %>% writeLines
# colors <- tableau_color_pal('Tableau 20')(17)
# glue("\t{all_tissues} = \'{colors}\',") %>% writeLines
# writeLines(paste(glue("\'{all_tissues}\'"), collapse = ','))
# +
ht_opt(RESET = TRUE)
ht_opt("heatmap_column_names_gp" = gpar(fontsize = 6))
ht_opt("heatmap_row_names_gp" = gpar(fontsize = 6))
do_heat <- function(pb, .title='') {
## Synchronize names
pb$meta_data <- pb$meta_data %>% dplyr::mutate(CellType = case_when(
CellType %in% c('Endothelial', 'endothelial') ~ 'Endothelial',
CellType %in% c('Stromal', 'stromal', 'mesenchymal') ~ 'Stromal',
CellType %in% c('mural_muscle', 'muscle_mural') ~ 'Mural_Muscle',
CellType %in% c('Epithelial', 'epithelial') ~ 'Epithelial',
CellType %in% c('Immune', 'immune') ~ 'Immune'
))
## Plot the top variable genes in the matrix
genes <- apply(pb$exprs_norm, 1, sd) %>% sort(TRUE) %>% head(1000) %>% names()
X <- pb$exprs_norm[genes, ] %>% t %>% scale %>% t
colnames(X) <- with(pb$meta_data, paste(CellType, Tissue))
fig.size(6, 8)
res <- Heatmap(show_column_names = FALSE,
X, show_row_dend = FALSE, show_row_names = FALSE, use_raster = TRUE,
top_annotation = columnAnnotation(df = pb$meta_data[, c('CellType', 'Tissue')], col=palette_heatmap, show_annotation_name=FALSE),
name = 'Scaled\nlog\nCP10K', column_title = .title
)
res <- grid.grabExpr(draw(res, padding = unit(c(1, 1, 1, 15), "mm"), heatmap_legend_side = 'right')) #bottom, left, top, right paddings
# res <- grid.grabExpr(draw(res))
res <- wrap_elements(full = res)
return(res)
}
# -
(p1 <- do_heat(pb_ahca, 'Adult Human Cell Atlas'))
(p2 <- do_heat(pb_ts, 'Tabula Sapiens'))
# # ACHA + TS barplots
# +
nshared <- data.table(effects_ts)[
## marker is upregulated in all tissues for that cluster
, sum(padj < .05 & beta > 0), by = .(Cluster, feature)
][V1 > 1 & Cluster == 'Stromal'] %>% nrow
genes <- data.table(effects_ts)[
## marker is upregulated in all tissues for that cluster
, sum(padj < .05 & beta > 0), by = .(Cluster, feature)
][V1 == 1 & Cluster == 'Stromal', feature]
df_ts <- data.table(effects_ts)[
## marker is upregulated in all tissues for that cluster
padj < .05 & beta > 0 & feature %in% genes & Cluster == 'Stromal', .N, by = Tissue
] %>%
# rbind(tibble(Tissue = 'SHARED', N = nshared)) %>%
rbind(
data.table(effects_ts)[
## marker is upregulated in all tissues for that cluster
, sum(padj < .05 & beta > 0), by = .(Cluster, feature)
][V1 > 1 & Cluster == 'Stromal'][, .N, by = V1][, .(Tissue = sprintf('%d tissues', V1), N)][]
# ][V1 > 1 & Cluster == 'Stromal'][, .N, by = V1][, .(Tissue = sprintf('Shared by %d tissues', V1), N)][]
) %>%
dplyr::mutate(Study = 'Tabula Sapiens')
df_ts
# +
nshared <- data.table(effects_ahca)[
## marker is upregulated in all tissues for that cluster
, sum(padj < .05 & beta > 0), by = .(Cluster, feature)
][V1 > 1 & Cluster == 'fibroblast'] %>% nrow
genes <- data.table(effects_ahca)[
## marker is upregulated in all tissues for that cluster
, sum(padj < .05 & beta > 0), by = .(Cluster, feature)
][V1 == 1 & Cluster == 'fibroblast', feature]
df_ahca <- data.table(effects_ahca)[
## marker is upregulated in all tissues for that cluster
padj < .05 & beta > 0 & feature %in% genes & Cluster == 'fibroblast', .N, by = tissue
] %>%
# rbind(tibble(tissue = 'SHARED', N = nshared)) %>%
rbind(
data.table(effects_ahca)[
## marker is upregulated in all tissues for that cluster
, sum(padj < .05 & beta > 0), by = .(Cluster, feature)
][V1 > 1 & Cluster == 'fibroblast'][, .N, by = V1][, .(tissue = sprintf('%d tissues', V1), N)][]
# ][V1 > 1 & Cluster == 'fibroblast'][, .N, by = V1][, .(tissue = sprintf('Shared by %d tissues', V1), N)][]
) %>%
dplyr::mutate(Study = 'Adult Human Cell Atlas') %>%
dplyr::rename(Tissue = tissue)
df_ahca
# +
foo <- function(.df, .subtitle) {
ggplot(.df, aes(reorder(Tissue, grepl('^\\d', Tissue)), N, fill = grepl('^\\d', Tissue))) +
# ggplot(aes(reorder(Tissue, N), N, fill = grepl('Shared', Tissue))) +
geom_bar(stat = 'identity') +
theme_tufte(base_size = 14) + geom_rangeframe() +
labs(x = '', y = '# markers', fill = 'Category', subtitle = .subtitle) +
scale_fill_manual(values = c('grey', muted('red'))) +
# scale_fill_manual(values = c(muted('red'), 'grey', muted('blue'))) +
# labs(title = 'Adult Human Cell Atlas') +
# facet_wrap(~Study, scales = 'free_x') +
guides(fill = FALSE) +
theme(
axis.text.x = element_text(angle = 90, hjust = 1),
axis.title.y = element_text(hjust=0),
axis.title = element_text(size = 10)
) +
# facet_grid(~Study, scales='free', space='free') +
NULL
}
# p3 <- bind_rows(list(df_ahca, df_ts)) %>%
# bind_rows(list(df_ahca, df_mca, df_tm, df_ts)) %>%
# dplyr::mutate(Study = factor(Study, c('Adult Human Cell Atlas', 'Tabula Sapiens', 'Mouse Cell Atlas', 'Tabula Muris'))) %>%
# ggplot(aes(reorder(V1, V1), N, fill = label)) +
# p3
fig.size(4, 8)
(p3 <- foo(df_ahca, 'Adult Human Cell Atlas')) |
(p4 <- foo(df_ts, 'Tabula Sapiens'))
# -
# # Lung clinical stats
# +
# openxlsx::readWorkbook(fname, 2)
# +
fname <- '../../manuscript/tables/clinical_tables.xlsx'
df <- openxlsx::readWorkbook(fname, 2) %>%
# left_join(
# fread('../../data/clinical/clinical_lung_tlc_dlco.txt')[, LibraryID := paste0('BRI', BRI)][, BRI := NULL][, .(LibraryID, DiseaseState=Early, TLC, DLCO)],
# by = 'LibraryID'
# ) %>%
dplyr::mutate(
# DiseaseState=case_when(
# Disease.Stage == 0 ~ 'Late',
# DiseaseState == 1 ~ 'Early'
# ),
TLC = as.integer(TLC),
DLCO = as.integer(DLCO)
)
head(df)
set.seed(42L)
plt1 <- df %>%
subset(!is.na(TLC)) %>%
ggplot(aes(Disease.Stage, TLC)) +
geom_boxplot(outlier.shape = NA) +
geom_point(shape = 21, size = 2, position=position_jitter(height = 0, width = .1)) +
labs(y = 'TLC (%Predicted)', x = 'Lung Disease State', subtitle = glue("p={round(broom::tidy(t.test(TLC~Disease.Stage, df, alternative = 'greater'))$p.value, 3)}")) +
NULL
plt2 <- df %>%
subset(!is.na(DLCO)) %>%
ggplot(aes(Disease.Stage, DLCO)) +
geom_boxplot(outlier.shape = NA) +
geom_point(shape = 21, size = 2, position=position_jitter(height = 0, width = .1)) +
labs(y = 'DLCO (%Predicted)', x = 'Lung Disease State', subtitle = glue("p={round(broom::tidy(t.test(DLCO~Disease.Stage, df, alternative = 'greater'))$p.value, 3)}")) +
NULL
p_e <- wrap_elements(full = (plt1 | plt2))
fig.size(3, 5)
p_e
# -
# # percent mito (OLD)
# +
# fig.size(13, 6)
# p1 <- data.table(meta_data)[
# , pm_med := median(percent_mito), by = LibraryID
# ][] %>%
# ggplot(aes(100 * percent_mito, reorder(LibraryID, pm_med), fill = Tissue)) +
# geom_density_ridges2(aes(height = ..ndensity..), alpha = .6, color = NA) +
# geom_vline(xintercept = c(.2), linetype = 2) +
# facet_grid(Tissue~., scales = 'free', space = 'free') +
# theme(legend.position = 'bottom') +
# theme(
# axis.title.y = element_blank(),
# # axis.text.y = element_blank(),
# axis.ticks.y = element_blank()
# ) +
# guides(fill = FALSE) +
# labs(x = 'Percent mitochondrial reads') +
# NULL
# p1
# -
# # nGenes (OLD)
# +
# fig.size(13, 6)
# p2 <- data.table(meta_data)[
# , nGene_med := median(nGene), by = LibraryID
# ][] %>%
# ggplot(aes(nGene, reorder(LibraryID, nGene_med), fill = Tissue)) +
# geom_density_ridges2(aes(height = ..ndensity..), alpha = .6, color = NA) +
# scale_x_continuous(trans = 'log10') +
# geom_vline(xintercept = c(500, 1000), linetype = 2) +
# facet_grid(Tissue~., scales = 'free', space = 'free') +
# theme(legend.position = 'bottom') +
# theme(
# axis.title.y = element_blank(),
# axis.text.y = element_blank(),
# axis.ticks.y = element_blank()
# ) +
# labs(x = 'Number unique genes') +
# NULL
# p2
# -
# # singlets/doublets (OLD)
# +
# fig.size(13, 6)
# p3 <- data.table(meta_data)[
# !is.na(scDblFinder.class), sum(scDblFinder.class == 'doublet') / .N, by = .(LibraryID, Tissue)
# ][
# , stat_med := median(V1), by = LibraryID
# ][] %>%
# ggplot(aes(100 * V1, reorder(LibraryID, stat_med), fill = Tissue)) +
# geom_bar(stat = 'identity') +
# facet_grid(Tissue~., scales = 'free', space = 'free') +
# theme(legend.position = 'bottom') +
# labs(x = 'Percent Doublets') +
# theme(
# axis.title.y = element_blank(),
# axis.text.y = element_blank(),
# axis.ticks.y = element_blank()
# ) +
# guides(fill = FALSE) +
# NULL
# p3
# -
# # Barplots
dname <- '../../data/cache/all_obj_all/'
fields_load <- c('meta_data', 'clusters_df')
obj <- map(fields_load, function(name) {
readRDS(file.path(dname, paste0(name, '.rds')))
})
names(obj) <- fields_load
Clusters <- obj$clusters_df[, 1]
obj$meta_data$Lineage <- case_when(
Clusters %in% c('5', '6', '0') ~ 'Fibroblast',
Clusters %in% c('10') ~ 'Mural',
Clusters %in% c('12', '7', '8') ~ 'Epithelial',
Clusters %in% c('13') ~ 'Glial',
Clusters %in% c('3', '4') ~ 'Endothelial',
Clusters %in% c('9') ~ 'Plasma',
Clusters %in% c('1', '2', '11') ~ 'Leukocyte'
)
plt_df <- obj$meta_data %>%
dplyr::mutate(Type = case_when(
Lineage %in% c('Fibroblast', 'Endothelial', 'Mural') ~ 'Stromal',
TRUE ~ 'Non-Stromal'
)) %>%
with(table(Tissue, Lineage, Type)) %>%
data.table() %>%
subset(N > 0)
lineage_levels <- plt_df %>%
dplyr::select(Lineage, Type) %>%
unique() %>%
arrange(Type) %>%
with(Lineage)
# +
fig.size(3, 10)
p5 <- plt_df %>%
dplyr::mutate(Lineage = factor(Lineage, lineage_levels)) %>%
dplyr::mutate(Tissue = factor(Tissue, rev(c('Lung', 'SalivaryGland', 'Synovium', 'Gut')))) %>%
ggplot(aes(Tissue, N, fill = Type)) +
geom_bar(stat = 'identity') +
# geom_bar(stat = 'identity', position = position_fill()) +
# scale_fill_tableau('Tableau 10') +
# coord_flip() +
# guides(fill = FALSE) +
# labs(y = 'Ratio of cells') +
theme(
axis.title.y = element_blank(),
legend.position = 'bottom',
axis.text.x = element_text(angle = 90, hjust = 1),
strip.text = element_text(size = 8)
) +
facet_wrap(~Lineage, nrow = 1) +
scale_y_log10() +
scale_fill_tableau() +
labs(y = 'Number cells') +
NULL
pg <- ggplotGrob(p5)
for(i in which(grepl("^strip", pg$layout$name))){
pg$grobs[[i]]$layout$clip <- "off"
}
p5 <- wrap_elements(pg)
p5
# -
# # Tissue-defined corplot
# +
effects <- fib %>% map('presto') %>% map('effects')
all_genes <- effects %>%
map('feature') %>%
map(unique) %>%
reduce(intersect)
effects_all <- effects %>%
map(dplyr::select, -pvalue) %>% ## old p-values were one-tailed
map(dplyr::mutate, pvalue = 2 * exp(pnorm(abs(zscore), log.p = TRUE, lower.tail = FALSE))) %>%
bind_rows(.id = 'Tissue') %>%
subset(!Cluster %in% c('DOUBLET', 'LOWQC', 'UNKNOWN', 'NOTCH3+ perivascular')) %>%
subset(feature %in% all_genes) ## only keep genes in all datasets
# +
# genes_shared <- data.table(effects_all)[beta > 0.5 & pvalue < .01, .SD[1], by = .(feature, Tissue)][, .N, by = feature][N > 1, feature]
# length(genes_shared)
genes_shared <- data.table(effects_all)[beta > 0.5 & pvalue < .01, unique(feature)]
length(genes_shared)
plt_mat <- effects_all %>%
# subset(Cluster %in% markers_all$Cluster) %>%
subset(feature %in% genes_shared) %>%
dplyr::select(feature, Cluster, Tissue, zscore) %>%
tidyr::unite(Tissue_Cluster, Tissue, Cluster) %>%
tidyr::spread(Tissue_Cluster, zscore) %>%
tibble::column_to_rownames('feature') %>%
t() %>% scale %>% t %>% cor()
dim(plt_mat)
# hr <- -plt_mat %>% as.dist %>% hclust %>% as.dendrogram()
set.seed(10)
# hr <- plt_mat %>% pmax(0) %>% t %>% scale %>% t %>% dist %>% hclust %>% as.dendrogram()
hr <- plt_mat %>% pmax(0) %>% dist %>% hclust %>% as.dendrogram()
# hr <- plt_mat %>% scale %>% dist %>% hclust %>% as.dendrogram()
# hr <- plt_mat %>% dist %>% hclust %>% as.dendrogram()
.x <- dendextend::cutree(hr, 5)
gene_clusters <- LETTERS[.x]
names(gene_clusters) <- names(.x)
# LETTERS[dendextend::cutree(hr, 8)]
table(gene_clusters)
head(gene_clusters)
# +
fig.size(6, 8)
ht_opt(RESET = TRUE)
ht_opt("heatmap_column_names_gp" = gpar(fontsize = 10))
ht_opt("heatmap_row_names_gp" = gpar(fontsize = 10))
h0 <- Heatmap(
# show_row_names = FALSE,
matrix = plt_mat,
name = 'Pearson\nCorrelation',
# plt_mat %>% t %>% cor(),
row_labels = gsub('^(.*?)_(.*)', '\\2', rownames(plt_mat)),
column_labels = gsub('^(.*?)_(.*)', '\\2', rownames(plt_mat)),
colorRamp2(c(-1, 0, 1), c('white', 'white', muted('red'))),
# column_names_rot = 45,
show_row_dend = FALSE,
right_annotation = rowAnnotation(
df = data.frame(Tissue = gsub('^(.*?)_(.*)', '\\1', rownames(plt_mat))),
show_legend = TRUE,
show_annotation_name = FALSE,
col = palette_heatmap
# annotation_legend_param = list(legend_direction = 'horizon')
),
bottom_annotation = columnAnnotation(
df = data.frame(Tissue = gsub('^(.*?)_(.*)', '\\1', rownames(plt_mat))),
show_legend = FALSE,
show_annotation_name = FALSE,
# annotation_legend_param = list(legend_direction = 'horizontal'),
col = palette_heatmap
),
# column_split = 8,
# row_split = 8
# heatmap_legend_param = list(
# legend_direction="horizontal"
# # legend_side = 'bottom_right'
# # legend_side = c('bottom', 'right')
# ),
column_split = gene_clusters,
row_split = gene_clusters
)
# f0 <- grid.grabExpr(draw(h0, heatmap_legend_side = 'bottom'))
f0 <- grid.grabExpr(draw(h0))
fig.size(6, 8)
f0 <- grid.grabExpr(draw(h0, padding = unit(c(1, 1, 1, 13), "mm"), heatmap_legend_side = 'right')) #bottom, left, top, right paddings
wrap_elements(f0)
# -
# # Per-library tissue-defined cluster barplots
meta_data_fib <- readRDS('../../data/cache/obj_fibroblasts/meta_data.rds')
fig.size(6, 12)
f1 <- meta_data_fib %>%
with(table(LibraryID, Tissue, tissue_cluster)) %>%
prop.table(1) %>%
data.table() %>%
subset(N > 0) %>%
# dplyr::mutate(tissue_cluster = glue('({Tissue}) {tissue_cluster}')) %>%
ggplot(aes(LibraryID, N, fill = tissue_cluster)) +
geom_bar(stat = 'identity', position = position_fill()) +
scale_fill_manual(values = palette_global[unique(meta_data_fib$tissue_cluster)]) +
theme(axis.text.y= element_blank(), axis.ticks.y = element_blank()) +
# facet_grid(Tissue~., scales = 'free', space = 'free') +
facet_wrap(~Tissue, nrow=1, scales = 'free_y') +
coord_flip() +
theme(legend.position = 'bottom') +
labs(fill = 'Tissue-Defined Cluster', y = 'Cluster Frequency in Sample') +
guides(fill = guide_legend(override.aes = list(stroke = 1, alpha = 1, shape = 16, size = 2)), alpha = FALSE) +
NULL
f1
# +
fig.size(6, 12)
f1 <- meta_data_fib %>%
with(table(LibraryID, Tissue, tissue_cluster)) %>%
prop.table(1) %>%
data.table() %>%
subset(N > 0) %>%
# dplyr::mutate(tissue_cluster = glue('({Tissue}) {tissue_cluster}')) %>%
split(.$Tissue) %>%
imap(function(.SD, .tissue) {
ggplot(.SD, aes(LibraryID, N, fill = tissue_cluster)) +
geom_bar(stat = 'identity', position = position_fill()) +
scale_fill_manual(values = palette_global[unique(.SD$tissue_cluster)]) +
# scale_fill_manual(values = palette_global[unique(meta_data_fib$tissue_cluster)]) +
theme(axis.text.y= element_blank(), axis.ticks.y = element_blank()) +
# facet_grid(Tissue~., scales = 'free', space = 'free') +
# facet_wrap(~Tissue, nrow=1, scales = 'free_y') +
coord_flip() +
theme(
legend.position = 'bottom',
legend.box = 'vertical',
legend.direction = 'vertical'
) +
labs(fill = 'Tissue-Defined Cluster', y = 'Cluster Frequency in Sample') +
labs(title = .tissue) +
guides(fill = guide_legend(override.aes = list(stroke = 1, alpha = 1, shape = 16, size = 2)), alpha = 'none') +
NULL
}) %>%
purrr::reduce(`+`) +
plot_layout(nrow = 1)
f1
# -
# # Cartoon
# +
library(png)
g0 <- readPNG('../../manuscript/figures/cartoon_facs.png') %>%
rasterGrob(interpolate = TRUE)
fig.size(4, 6)
wrap_elements(g0)
# -
# # Panels
# +
fig.size(22, 12)
fig <- (
(
## row1
(p1 | p2)
) /
(
## row2
(wrap_elements(full = p3) | wrap_elements(full = p4) | p_e)
# (p_e | wrap_elements(full = g0)) + plot_layout(widths = c(1, 3))
# (
# (wrap_elements(full = p3) / wrap_elements(full = p4)) |
# wrap_elements(full = g0)
# # (wrap_elements(full = p3) | wrap_elements(full = p4) | wrap_elements(full = p5)) +
# # plot_layout(widths = c(1, 1, 1.5))
# )
) /
(
wrap_elements(full = g0)
) /
(
## row3
(p5 | wrap_elements(full = f0)) +
plot_layout(widths = c(1.5, 2))
) /
(
## row4
wrap_elements(full = f1)
)
) +
plot_layout(heights = c(1, .6, 1, 1.3, 1)) +
plot_annotation(tag_levels = 'a')
fig
# +
ggsave(
filename = '../../manuscript/figures/SuppFigure1.pdf',
plot = fig,
units = 'in', device = 'pdf',
height = 22, width = 12,
useDingbats = FALSE
)
# + active=""
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| 21,063 |
/DEA方法/SBM模型.ipynb | a228a826c0f85d7bacd66edebb3fd4899d917ded | [
"MIT"
] | permissive | lnsongxf/econometrics-6 | https://github.com/lnsongxf/econometrics-6 | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 3,017 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# # SBM-ML模型
#
# 参考文献：命令控制与可交易环境政策下的碳排放强度减排效应
#
# ## 原理
#
# 目标函数：$\omega^{t}=\max \sum_{k=1}^{K} a_{k}^{t}$
#
# s.t. 对第 1 个省区：
#
# $\sum_{k=1}^{K} z_{k}^{1, t} \times y_{k}^{t} \geqslant y_{1}^{t}+a_{1}^{t}$
#
# $\sum_{k=1}^{K} z_{k}^{1, t} \times u_{k}^{t}=u_{1}^{t} $
#
# $\sum_{k=1}^{K} z_{k}^{1, t} \times$
#
# $e_{k}^{t}=e_{1}^{t} $
#
# $\sum_{k=1}^{K} z_{k}^{1, t} \times x_{k, n}^{t} \leqslant x_{1, n}^{t}, n=1,2 $
#
# $z_{k}^{1, t} \geqslant 0, k=1, \cdots, K$
#
# ……
#
# 对第 K 个省区：
#
# $\sum_{k=1}^{K} z_{k}^{K . t} \times y_{k}^{t} \geqslant y_{K}^{t}+a_{K}^{t} $
#
# $\sum_{k=1}^{K} z_{k}^{K, t} \times u_{k}^{t}=u_{K}^{t} $
#
# $\sum_{k=1}^{K} z_{k}^{K . t} \times$
#
# $e_{k}^{t}=e_{K}^{t} $
#
# $\sum_{k=1}^{K} z_{k}^{K, t} \times x_{k, n}^{t} \leqslant x_{K, n}^{t}, n=1,2$
#
# $z_{k}^{K, t} \geqslant 0, k=1, \cdots, K$
#
# $a_{k}^{t}$ 为 $t$ 期第 $k$ 个省区的潜在产出增量, $\omega^{t}$ 为 $t$ 期最大潜在产出增量总和。非能源投入与合意产出的不等式约束代表这两个变量具有强可处置性，能源投入与非合意产出的等式约束代表它们是弱可处置的，决策单元的权重 $z$ 的非负性约束说明模型满足规模报酬不变假设。
# ## matlab 程序
#
# ``` matlab
# clc;clear;
# data = xlsread('C:\Users\Administrator\Desktop\数据.xlsx');
# x = data(2:4,:) ; y = data(5:6,:);
# t = 10; % 年份
# k = 12; % 省份
# LB = [-1000000*ones(k,1);zeros(k*k,1)]; UB = [];
#
# for i = 1:t
# f = -[ones(1,k),zeros(1,k*k)]'; % 目标函数
#
# for j = 1:k
# xx(:,j) = x(:,i+(j-1)*t);
# yy(:,j) = y(:,i+(j-1)*t);
# end
#
# Aeq = []; beq = []; A = []; b = [];
# for j = 1:k
# Aeq = [Aeq ; zeros(2,k*j),[xx(3,:);yy(2,:)],zeros(2,k*(k-j))];
# beq = [beq ; xx(3,j) ; yy(2,j)];
#
# A = [A; zeros(2,k*j),xx(1:2,:),zeros(2,k*(k-j))];
# a = zeros(1,k); a(1,j)=1;
# A = [A; a,zeros(1,k*(j-1)),-yy(1,:),zeros(1,k*(k-j))];
# b = [b; xx(1:2,j)];
# b = [b; -yy(1,j)];
# end
# w(:,i)=linprog(f,A,b,Aeq,beq,LB,UB);
# end
#
# ```
'city']=lb.fit_transform(df['city'])
return df,lb
def ileb(df,lb):
df['city']=lb.inverse_transform(df['city'])
return df
def imputevalues(df):
# print(len(df))
wantedcolumnlist=[]
columnlist=df.columns.tolist()
#print(len(columnlist))
for i in range(len(columnlist)):
columnname=columnlist[i]
nan_rows = df[columnname].isnull().sum()
# print(i,nan_rows)
nullcount=(nan_rows/len(df))*100
#print(nullcount)
if nullcount!=0:
df[columnname]=df[columnname].fillna(df[columnname].mean())
else:
pass
return df
def split(df):
features = df.drop(["total_cases"], axis=1).columns
df_train, df_val = train_test_split(df, test_size=0.20,random_state=42)
print("df_train shape",df_train.shape,"\ndf_val shape",df_val.shape)
return df_train,df_val,features
def testread():
test=pd.read_csv("dataset/dengue_features_test.csv")
return test
df=joins(train_df,label)
columns=convertlist()
columns
df=celsius(df,columns)
df=preprocess(df)
df=drop(df)
df=imputevalues(df)
df,lb=leb(df)
def is_chances(features):
coolest = []
warmest=[]
normal=[]
for observation in features['station_avg_temp_c']:
# print(observation)
if int(observation) > 28:
warmest.append(1)
else:
warmest.append(0)
for observation in features['station_avg_temp_c']:
if observation < 24:
coolest.append(1)
else:
coolest.append(0)
for observation in features['station_avg_temp_c']:
if int(observation) > 24 & int(observation) <=28:
normal.append(1)
else:
normal.append(0)
return warmest,coolest,normal
# +
warmest,coolest,normal = is_chances(df)
df['verywarm']=warmest
df['verycool']=coolest
df['normal']=normal
# -
df_train,df_val,features=split(df)
# +
test=testread()
test=celsius(test,columns)
test=preprocess(test)
test=drop(test)
test=imputevalues(test)
warmest,coolest,normal = is_chances(test)
test['verywarm']=warmest
test['verycool']=coolest
test['normal']=normal
#test,lbs=leb(test)
# -
Regressor = [
RandomForestRegressor(n_estimators=150,max_depth=18,min_weight_fraction_leaf=0.002),
LGBMRegressor(objective='regression',num_leaves=31, learning_rate=0.01,n_estimators=150),
XGBRegressor(max_depth=18,learning_rate=0.01,subsample=1,colsample_bylevel=1,colsample_bytree=1,n_estimators=200,min_child_weight=5,seed=25),
GradientBoostingRegressor()
]
# +
Accuracy=[]
Model=[]
for regressor in Regressor:
try:
test,lbs=leb(test)
fit = regressor.fit(df_train[features], df_train["total_cases"])
pred = fit.predict(df_val[features])
except Exception:
fit = regressor.fit(df_train[features], df_train["total_cases"])
pred = fit.predict(df_val[features])
score = regressor.score(df_val[features], df_val["total_cases"])
Model.append(regressor.__class__.__name__)
print('Accuracy of '+regressor.__class__.__name__+' is '+str(score))
print("************************************************************")
val_actual=np.array(df_val["total_cases"])
val_pred=np.array(pred)
error=(val_actual-val_pred)**2
error_mean=round(np.mean(error))
error_sq=math.sqrt(error_mean)
print(regressor.__class__.__name__+" RMSE is "+str(error_sq))
# predictions = fit.predict(test[features])
# p=predictions.astype('int')
# test['total_cases']=p
# test['city']=ileb(test,lbs)
# test[['city','year','weekofyear','total_cases']].to_csv("dataset/submission "+regressor.__class__.__name__+".csv" ,index=False)
# +
### save best model
gbr = GradientBoostingRegressor()
fit = gbr.fit(df_train[features], df_train["total_cases"])
pickle.dump(fit,open( "disease_mode.pickle", "wb" ) )
### save label encoder
pickle.dump(lb,open( "disease_label_encoder.pickle","wb"))
# -
correlation=df.corr()
plt.figure(figsize=(20,20))
flatui = ["#9b59b6", "#3498db", "#95a5a6", "#e74c3c", "#34495e", "#2ecc71"]
sns.heatmap(correlation>0.25 , vmax=1, square=True,annot=True,cmap=flatui)
plt.title('Correlation between different fearures')
sns.set(font_scale = 2.5)
(abs(correlation)
.total_cases
.drop('total_cases')
.sort_values()
.plot
.barh())
if(x2==x2b):
x=x2
x1=((10.0*x)-120)/1.1
print "La solucion de X2 es: ", x
i+=1
print "La solucion de X1 es: ",x1
a=x1
b=x2
plt.title("Sistema de ecuaciones")
plt.grid(True)
plt.axis([380,430,53,60])
plt.ylabel('x2')
plt.xlabel('x1')
plt.axhline(0, color="black")
plt.plot(equis1, funcion1, 'm-', linewidth=1.0)
plt.plot(equis1, funcion2, 'y-',linewidth=1.0)
plt.plot(405,56.55, 'co', label="Pto corte:\nx1=404.6512\nx2=56.5116")
plt.legend(loc = 'upper right', numpoints = 1)
plt.show()
print "Reemplazamos los valores de X1 y X2 y son cercanos a los valores reales: "
ecuacion1=-1.1*x1+10*x
print "El resultado para la primera ecuacion es %.2f" %ecuacion1
ecuacion2=-2*x1+17.4*x
print "El resultado para la segunda ecuacion es %.2f" %ecuacion2
print "\nb)\nEl sistema se satisface con dos valores para X2. Esto se apoya en que el determinante es proximo a cero, y el sistema\n y el sistema tienda a tener varias soluciones."
#determinante
matrizCoef=[[-1.1,10],[-2,17.4]]
det=matrizCoef[0][0]*matrizCoef[1][1]-matrizCoef[0][1]*matrizCoef[1][0]
print "\nc)\nEl determinante de la matriz de coeficientes es: %.2f" %det
#eliminacion de incognitas
aux=[]
sol=[120,174]
aux2=[]
for i in range(len(matrizCoef)):
aux.append([])
if(i==0):
for j in range(len(matrizCoef[0])):
aux[i].append(matrizCoef[1][0]*matrizCoef[i][j])
aux2.append(matrizCoef[1][0]*sol[i])
else:
for j in range(len(matrizCoef[1])):
aux[i].append(matrizCoef[0][0]*matrizCoef[i][j])
aux2.append(matrizCoef[0][0]*sol[i])
print "\nd) Por eliminacion de incognitas: "
var2=(aux2[0]-aux2[1])/(aux[0][1]-aux[1][1])
var1=(sol[0]-matrizCoef[0][1]*var2)/matrizCoef[0][0]
print " El valor de X1 es= %.4f" %var1
print " El valor de X2 es= %.4f" %var2
# -
# 9.6 Para el sistema de ecuaciones que sigue: 2*X2+5*X3=9; 2*X1+X2+X3=9; 3*X1+X2=10. a) Calcule el determinante. b) Use la regla de Cramer para encontrar cuál es el valor de las x. c) Sustituya el resultado en las ecuaciones originales para efectos de comprobación
# +
#a) Determinante
matriz=[[0,2,5],[2,1,1],[3,1,0]]
solucion=[9,9,10]
det=(matriz[0][0]*(matriz[1][1]*matriz[2][2]-matriz[1][2]*matriz[2][1]))-(matriz[0][1]*(matriz[1][0]*matriz[2][2]-matriz[1][2]*matriz[2][0]))+(matriz[0][2]*(matriz[1][0]*matriz[2][1]-matriz[1][1]*matriz[2][0]))
print "\na) El determinante de la matriz de coeficientes es= %d"%det
d1=(solucion[0]*(matriz[1][1]*matriz[2][2]-matriz[1][2]*matriz[2][1]))-(solucion[1]*(matriz[0][1]*matriz[2][2]-matriz[2][1]*matriz[0][2]))+(solucion[2]*(matriz[0][1]*matriz[1][2]-matriz[1][1]*matriz[0][2]))
d2=-1*(solucion[0]*(matriz[1][0]*matriz[2][2]-matriz[2][0]*matriz[1][2]))+(solucion[1]*(matriz[0][0]*matriz[2][2]-matriz[2][0]*matriz[0][2]))-(solucion[2]*(matriz[0][0]*matriz[1][2]-matriz[1][0]*matriz[0][2]))
d3=(solucion[0]*(matriz[1][0]*matriz[2][1]-matriz[2][0]*matriz[1][1]))-(solucion[1]*(matriz[0][0]*matriz[2][1]-matriz[2][0]*matriz[0][1]))+(solucion[2]*(matriz[0][0]*matriz[1][1]-matriz[1][0]*matriz[0][1]))
x1=d1/det
x2=d2/det
x3=d3/det
print "b)\n La solución para X1= %d"%x1
print " La solución para X2= %d"%x2
print " La solución para X3= %d"%x3
ecuacion1=2*x2+5*x3
ecuacion2=2*x1+x2+x3
ecuacion3=3*x1+x2
if(ecuacion1==solucion[0]):
print "c)\n El valor sustituido satisface la ecuacion #1. El resultado es=%d"%ecuacion1
if(ecuacion2==solucion[1]):
print " El valor sustituido satisface la ecuacion #2. El resultado es=%d"%ecuacion2
if(ecuacion3==solucion[2]):
print " El valor sustituido satisface la ecuacion #3. El resultado es=%d"%ecuacion3
# -
# 9.7 Dadas las ecuaciones: 0.5*X1–X2=–9.5; 1.02*X1–2*X2=–18.8. a) Resuelva en forma gráfica. b) Calcule el determinante. c) Con base en los incisos a) y b), ¿qué es de esperarse con respecto de la condición del sistema? d) Resuelva por medio de la eliminación de incógnitas. e) Resuelva otra vez, pero modifique ligeramente el elemento a11 a 0.52. Interprete sus resultados.
# +
#a) Gráfica
import math
import matplotlib.pyplot as plt
# %matplotlib inline
valores, funcion1, funcion2=[],[],[]
a,b,c=0,0,-40
i=-70
for i in range (-70,100):
x2=(0.5*i)+9.5
x2b=0.51*i+9.4
valores.append(i)
funcion1.append(x2)
funcion2.append(x2b)
while c<=1000:
fx1=0.02*a-19
fx2=(((1.0/1.02)-1.0)*b)-(9.4/1.02)
if(fx1==-18.8):
print "\na) La solucion para X1= %.0f"%a
punto1=a
if(fx2==-9.5):
print " La solucion para X2= %.1f"%b
punto2=b
a+=0.1
b+=0.1
c+=1
plt.title("Sistema de ecuaciones")
plt.grid(True)
plt.axis([-50,70,-80,80])
plt.ylabel('x2')
plt.xlabel('x1')
plt.plot(valores, funcion1, 'g-', linewidth=1.0)
plt.plot(valores, funcion2, 'y-', linewidth=1.0)
plt.plot(punto1,punto2, 'bo', label="Pto corte:\nx1=10\nx2=14.5")
plt.legend(loc = 'upper right', numpoints = 1)
plt.show()
#determinante
matrizCoef=[[0.5,-1],[1.02,-2]]
det=matrizCoef[0][0]*matrizCoef[1][1]-matrizCoef[0][1]*matrizCoef[1][0]
print "\nb) El determinante de la matriz de coeficientes es: %.2f" %det
print "\nc) Dado que el det se aproxima a 0, el sistema podria tener otras soluciones(asi sea que varien por unos decimales)."
#eliminacion incognitas
aux=[]
sol=[-9.5,-18.8]
aux2=[]
for i in range(len(matrizCoef)):
aux.append([])
if(i==0):
for j in range(len(matrizCoef[0])):
aux[i].append(matrizCoef[1][0]*matrizCoef[i][j])
aux2.append(matrizCoef[1][0]*sol[i])
else:
for j in range(len(matrizCoef[1])):
aux[i].append(matrizCoef[0][0]*matrizCoef[i][j])
aux2.append(matrizCoef[0][0]*sol[i])
print "\nd) Por eliminacion de incognitas: "
var2=(aux2[0]-aux2[1])/(aux[0][1]-aux[1][1])
var1=(sol[0]-matrizCoef[0][1]*var2)/matrizCoef[0][0]
print " El valor de X1 es= %.0f" %var1
print " El valor de X2 es= %.1f" %var2
# +
#numeral e) del 9.7
#a) Gráfica
import math
import matplotlib.pyplot as plt
# %matplotlib inline
valores, funcion1, funcion2=[],[],[]
a,b,c=-20,0,-40
i=-70
print "\ne) REPITIENDO TODO LO ANTERIOR PERO CAMBIANDO a11 por 0.52:\n"
for i in range (-70,100):
x2=(0.52*i)+9.5
x2b=0.51*i+9.4
valores.append(i)
funcion1.append(x2)
funcion2.append(x2b)
while c<=1000:
fx1=-0.02*a-19
fx2=(((1.04/1.02)-1.0)*b)-(9.776/1.02)
if(fx1==-18.8):
print "a) La solucion para X1= %d"%a
punto1=a
if(fx2==-9.5):
print " La solucion para X2= %.1f"%b
punto2=b
a+=1
b+=0.1
c+=1
plt.title("Sistema de ecuaciones")
plt.grid(True)
plt.axis([-50,70,-60,80])
plt.ylabel('x2')
plt.xlabel('x1')
plt.plot(valores, funcion1, 'm-', linewidth=1.0)
plt.plot(valores, funcion2, 'y-', linewidth=1.0)
plt.plot(punto1,punto2, '*', label="Pto corte:\nx1=-10\nx2=4.3")
plt.legend(loc = 'upper right', numpoints = 1)
plt.show()
#determinante
matrizCoef=[[0.52,-1],[1.02,-2]]
det=matrizCoef[0][0]*matrizCoef[1][1]-matrizCoef[0][1]*matrizCoef[1][0]
print "\nb) El determinante de la matriz de coeficientes es: %.2f" %det
print "\nc) Dado que el det se aproxima a 0, el sistema podria tener otras soluciones(asi sea que varien por unos decimales)."
#eliminacion incognitas
aux=[]
sol=[-9.5,-18.8]
aux2=[]
for i in range(len(matrizCoef)):
aux.append([])
if(i==0):
for j in range(len(matrizCoef[0])):
aux[i].append(matrizCoef[1][0]*matrizCoef[i][j])
aux2.append(matrizCoef[1][0]*sol[i])
else:
for j in range(len(matrizCoef[1])):
aux[i].append(matrizCoef[0][0]*matrizCoef[i][j])
aux2.append(matrizCoef[0][0]*sol[i])
print "\nd) Por eliminacion de incognitas: "
var2=(aux2[0]-aux2[1])/(aux[0][1]-aux[1][1])
var1=(sol[0]-matrizCoef[0][1]*var2)/matrizCoef[0][0]
print " El valor de X1 es= %.0f" %var1
print " El valor de X2 es= %.1f" %var2
# -
# 9.8 Dadas las ecuaciones siguientes: 10*X1+2*X2–X3=27; –3*X1–6*X2+2*X3=–61.5; X1+X2+5*X3=–21.5. a) Resuelva por eliminación de Gauss simple. Efectúe todos los pasos del cálculo. b) Sustituya los resultados en las ecuaciones originales a fin de comprobar sus respuestas.
# +
matrizCoef=[[10.0,2.0,-1.0],[-3.0,-6.0, 2.0],[1.0,1.0,5.0]]
sol=[27.0,-61.5,-21.5]
x,d,y,i,j,k=[],0.0,0.0,0,0,0
n=len(matrizCoef)
for m in range (len(matrizCoef)):
x.append(0*len(matrizCoef[0]))
print "a) Por metodo de Gauss simple:\n "
for i in range(0,n):
for j in range(i,n):
if(i==j):
d=matrizCoef[i][j]
for s in range (0,n):
matrizCoef[i][s]=((matrizCoef[i][s])/d)
sol[i]=((sol[i])/d)
else:
d=matrizCoef[j][i]
for s in range (0,n):
matrizCoef[j][s]=matrizCoef[j][s]-(d*matrizCoef[i][s])
sol[j]=sol[j]-(d*sol[i])
i=n-1
j=n-1
while(i>=0):
y=sol[i]
while(j>=i):
y=y-x[j]*matrizCoef[i][j]
j-=1
x[i]=y
i-=1
j=n-1
for i in range (0,n):
k=i+1
print "El valor de la incognita X%d es %.1f"%(k,x[i])
print "\nb) Sustituyendo los valores:\n "
ecuacionA=10*x[0]+2*x[1]-x[2]
ecuacionB=-3*x[0]-6*x[1]+2*x[2]
ecuacionC=x[0]+x[1]+5*x[2]
print "El resultado para la primera ecuacion es ",ecuacionA
print "El resultado para la segunda ecuacion es ",ecuacionB
print "El resultado para la tercera ecuacion es ",ecuacionC
# -
| 15,896 |
/SungKim/exam_11/cnn_08_mnist_layers.ipynb | ee90f45ab9f30e42af76ff96d7ec4b5b3e28548d | [] | no_license | lhs7091/PythonTensorExam | https://github.com/lhs7091/PythonTensorExam | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 17,677 | # ---
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# ---
# + [markdown] id="view-in-github" colab_type="text"
# <a href="https://colab.research.google.com/github/lhs7091/PythonTensorExam/blob/master/exam_11/cnn_08_mnist_layers.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# + id="4zuV47BXOSBa" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 1000} outputId="fcf89a7b-a230-421c-dfca-bbf5183df43d"
# %tensorflow_version 1.x
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
tf.set_random_seed(777)
mnist = input_data.read_data_sets("./mist_data", one_hot=True)
# parameters
learning_rate = 0.001
training_epochs = 15
batch_size = 100
class Model:
def __init__(self, sess, name):
self.sess = sess
self.name = name
self._build_net()
def _build_net(self):
with tf.variable_scope(self.name):
# for testing
self.training = tf.placeholder(tf.bool)
# input placeholders
self.X = tf.placeholder(tf.float32, [None, 784])
# image 28*28*1(black/white), input layer
X_image = tf.reshape(self.X, [-1,28,28,1])
self.Y = tf.placeholder(tf.float32, [None, 10])
# Convolutional Layer #1
conv1 = tf.layers.conv2d(inputs=X_image, filters=32, kernel_size=[3,3], padding='SAME', activation=tf.nn.relu)
# Pooling Layer 1
pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2,2], padding='SAME', strides=2)
dropout1 = tf.layers.dropout(inputs=pool1, rate=0.3, training=self.training)
# Convolutional Layer #2
conv2 = tf.layers.conv2d(inputs=dropout1, filters=64, kernel_size=[3,3], padding='SAME', activation=tf.nn.relu)
pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2,2], padding='SAME', strides=2)
dropout2 = tf.layers.dropout(inputs=pool2, rate=0.3, training=self.training)
# Convolutional Layer #3
conv3 = tf.layers.conv2d(inputs=dropout2, filters=128, kernel_size=[3,3], padding='SAME', activation=tf.nn.relu)
pool3 = tf.layers.max_pooling2d(inputs=conv3, pool_size=[2,2], padding='SAME', strides=2)
dropout3 = tf.layers.dropout(inputs=pool3, rate=0.3, training=self.training)
# Dense Layer with Relu
flat = tf.reshape(dropout3, [-1,4*4*128])
dense4 = tf.layers.dense(inputs=flat, units=625, activation=tf.nn.relu)
dropout4 = tf.layers.dropout(inputs=dense4, rate=0.5, training=self.training)
# Logits(no activation) Layer: L5 Final FC 625 inputs -> 10 outputs
self.logits = tf.layers.dense(inputs=dropout4, units=10)
# define cost/loss & optimizer
self.cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=self.logits, labels=self.Y), name='cost')
self.optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(self.cost)
correct_prediction = tf.equal(tf.argmax(self.logits, 1), tf.argmax(self.Y, 1))
self.accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32), name='accuracy')
def predict(self, x_test, training=False):
#return self.sess.run(self.logits, feed_dict={self.X: x_test, self.training:training})
return self.sess.run(tf.argmax(self.logits,1), feed_dict={self.X: x_test, self.training:training})
def get_accuracy(self, x_test, y_test, training=False):
return self.sess.run(self.accuracy, feed_dict={self.X: x_test, self.Y:y_test, self.training:training})
def train(self, x_data, y_data, training=True):
return self.sess.run([self.cost, self.optimizer], feed_dict={self.X:x_data, self.Y:y_data, self.training:training})
# initalize
sess = tf.Session()
m1 = Model(sess, "m1")
sess.run(tf.global_variables_initializer())
print('Learning Started!!')
# train my model
for epoch in range(training_epochs):
avg_cost = 0
total_batch = int(mnist.train.num_examples/batch_size)
for i in range(total_batch):
batch_xs, batch_ys = mnist.train.next_batch(batch_size)
c, _ = m1.train(batch_xs, batch_ys)
avg_cost = c / total_batch
print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost))
print("Learning Finished!!!!")
# Test model and check accuracy
print('Accuracy:', m1.get_accuracy(mnist.test.images, mnist.test.labels))
# + id="vbuNEZK8PQ3j" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 299} outputId="82d0c3ae-901a-4c1f-896b-e46c40b8a8b1"
import random
import matplotlib.pyplot as plt
# Get one and predict
r = random.randint(0, mnist.test.num_examples - 1)
print("Label: ", sess.run(tf.argmax(mnist.test.labels[r:r + 1], 1)))
print("Prediction: ", m1.predict(mnist.test.images[r:r + 1]))
plt.imshow(mnist.test.images[r:r + 1].reshape(28, 28), cmap="Greys", interpolation="nearest",)
plt.show()
| 5,206 |
/Exercise Docs.ipynb | 69fd71137a57ad85f2dba905d997c4c714fe9a35 | [] | no_license | mhbw/git_files | https://github.com/mhbw/git_files | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 245,540 | # ---
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# language: python
# name: python3
# ---
# ## TMC Exercise
#
# This is the scratch paper and quick codebase for the TMC exercise
# +
## import the packages and the flat file
import pandas as pd
from scipy import stats
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import importlib
importlib.reload(plt); importlib.reload(sns)
np.random.seed(123)
## quick glance at the training data
training_data = pd.read_csv("exercise_data.csv")
training_data.head()
# +
too_high = training_data[training_data["Response_Rate"]>100].index
# Delete these row indexes from dataFrame
training_data_rr = training_data.drop(too_high)
zero_attendees = training_data_rr[training_data_rr["Response_Rate"]==0].index
training_data_rr = training_data_rr.drop(zero_attendees)
training_data_rr.head()
# +
def ecdf(data):
n = len(data)
x = np.sort(data)
y = np.arange(1, n+1) / n
return x, y
# Specify array of percentiles: percentiles
percentiles = np.array([2.5, 25, 50, 75, 97.5])
# Compute percentiles: ptiles_vers
ptiles_pers = np.percentile(training_data_rr.Strongly_Agree, percentiles)
x_val, y_val = ecdf(training_data_rr.Strongly_Agree)
plt.plot(x_val, y_val,marker='.', linestyle='none')
plt.plot(ptiles_pers, percentiles/100, marker='D', color='red',
linestyle='none')
plt.xlabel('Over All Strongly Agree Percentage Rate')
plt.ylabel('Percentage Under the Curve')
plt.savefig('ecdf_agree_1.png', format='png', dpi=600)
plt.show()
# -
ptiles_pers
# +
## ploting out the groups
x_val, y_val = ecdf(training_data_rr.Strongly_Agree)
pocx_val, pocy_val = ecdf(training_data_rr.PoCI_Strongly_Agree)
nonx_val, nony_val = ecdf(training_data_rr.Non_PoCI_Strongly_Agree)
plt.plot(x_val, y_val,marker='.', linestyle='none')
plt.plot(pocx_val, pocy_val,marker='.', linestyle='none')
plt.plot(nonx_val, nony_val,marker='.', linestyle='none')
plt.plot(ptiles_pers, percentiles/100, marker='D', color='red',
linestyle='none')
plt.xlabel('Over All Strongly Agree Percentage Rate - Broken Out By Group')
plt.ylabel('Percentage Under the Curve')
plt.legend(['All', "PoC", "White"], loc='upper left')
plt.savefig('ecdf_agree_error.png', format='png', dpi=600)
plt.show()
# +
## ploting out the groups
pocptiles_pers = np.percentile(training_data_rr.PoCI_Strongly_Agree, percentiles)
pocx_val, pocy_val = ecdf(training_data_rr.PoCI_Strongly_Agree)
#plt.plot(x_val, y_val,marker='.', linestyle='none')
plt.plot(pocx_val, pocy_val,marker='.', linestyle='none')
#plt.plot(nonx_val, nony_val,marker='.', linestyle='none')
plt.plot(pocptiles_pers, percentiles/100, marker='D', color='red',
linestyle='none')
plt.xlabel('Over All Strongly Agree Percentage Rate - Broken Out By Group')
plt.ylabel('Percentage Under the Curve')
#plt.legend('APN', loc='upper left')
plt.savefig('ecdf_justpoc.png', format='png', dpi=600)
plt.show()
# +
## ploting out the groups
Non_pocptiles_pers = np.percentile(training_data_rr.Non_PoCI_Strongly_Agree, percentiles)
Non_pocx_val, Non_pocy_val = ecdf(training_data_rr.Non_PoCI_Strongly_Agree)
#plt.plot(x_val, y_val,marker='.', linestyle='none')
plt.plot(Non_pocx_val, Non_pocy_val,marker='.', linestyle='none')
#plt.plot(nonx_val, nony_val,marker='.', linestyle='none')
plt.plot(Non_pocptiles_pers, percentiles/100, marker='D', color='red',
linestyle='none')
plt.xlabel('Over All Strongly Agree Percentage Rate - Broken Out By Group')
plt.ylabel('Percentage Under the Curve')
#plt.legend('APN', loc='upper left')
plt.savefig('ecdf_Non_poc.png', format='png', dpi=600)
plt.show()
# -
print("Percentage of People of Color 'Strongly Agree' that they can implement the training tools: ", round(training_data_rr.PoCI_Strongly_Agree.mean(), 2))
print("Percentage Non-PoC: ", round(training_data_rr.Non_PoCI_Strongly_Agree.mean(), 2))
#training_data_rr.PoCI_Strongly_Agree.mean()
stats.ttest_ind(training_data_rr.PoCI_Strongly_Agree, training_data_rr.Non_PoCI_Strongly_Agree, equal_var = False)
# With a P-value that high we can't really make any inferences about the data with it. But Let's break it out by trainer racial demographics.
# +
Trainer_poc = training_data_rr[training_data_rr["TrainerPoCI"] != "Y"].index
# Delete these row indexes from dataFrame
training_data_poc = training_data_rr.drop(Trainer_poc)
Trainer_nonpoc = training_data_rr[training_data_rr["TrainerPoCI"] != "N"].index
# Delete these row indexes from dataFrame
training_data_npoc = training_data_rr.drop(Trainer_nonpoc)
print("Percentage of People of Color 'Strongly Agree' that they can implement the tools *when the trainer is white*: ", round(training_data_npoc.PoCI_Strongly_Agree.mean(), 2))
print("Percentage when the trainer is a Person of Color: ", round(training_data_poc.PoCI_Strongly_Agree.mean(), 2))
#training_data_rr.PoCI_Strongly_Agree.mean()
# -
# This is more interesting since it's a 5% difference. Meanwhile the average is less different for white attendees, who feel slightly more than 1% more confident with a white trainer.
# +
print("Percentage of white attendees 'Strongly Agree' that they can implement the tools *when the trainer is white*: ", round(training_data_npoc.Non_PoCI_Strongly_Agree.mean(), 2))
print("Percentage when the trainer is a Person of Color: ", round(training_data_poc.Non_PoCI_Strongly_Agree.mean(), 2))
#training_data_rr.PoCI_Strongly_Agree.mean()
# -
# Let's run a T-Test for both of these to see if they're significant.
stats.ttest_ind(training_data_npoc.PoCI_Strongly_Agree, training_data_poc.PoCI_Strongly_Agree)
stats.ttest_ind(training_data_npoc.Non_PoCI_Strongly_Agree, training_data_poc.Non_PoCI_Strongly_Agree)
## just looking at data for the white trainers for both groups
stats.ttest_ind(training_data_npoc.PoCI_Strongly_Agree, training_data_npoc.Non_PoCI_Strongly_Agree)
# Interestingly we again see a low test value and a high p-value, so we can't conclusively reject the null hypothesis, but that doesn't mean we should ignore the outcomes.
stats.ttest_ind(training_data_poc.PoCI_Strongly_Agree, training_data_poc.Non_PoCI_Strongly_Agree)
# Let's chart some of the results, for visual snap shots.
# +
sns.distplot(training_data_npoc.PoCI_Strongly_Agree)
sns.distplot(training_data_npoc.Non_PoCI_Strongly_Agree)
plt.xlabel('Percentage Confidence By Group - White Trainers')
plt.legend(['People of Color', "White"], loc='upper left')
plt.savefig('per_conf_whitetrain.png', format='png', dpi=600)
plt.show()
# -
training_data_npoc.head()
# +
sns.distplot(training_data_poc.PoCI_Strongly_Agree)
sns.distplot(training_data_poc.Non_PoCI_Strongly_Agree)
plt.xlabel('Percentage Confidence By Group - PoC Trainers')
plt.legend(['People of Color', "White"], loc='upper left')
plt.savefig('per_conf_poctrain.png', format='png', dpi=600)
plt.show()
# -
training_data_npoc.Non_PoCI_Strongly_Agree.mean()
# +
sns.distplot(training_data_npoc.PoCI_Strongly_Agree)
sns.distplot(training_data_npoc.Non_PoCI_Strongly_Agree)
plt.xlabel('Percentage Confidence By Group - White Trainers')
plt.legend(['People of Color', "White"], loc='upper left')
plt.savefig('per_conf_poctrain.png', format='png', dpi=600)
plt.show()
# +
sns.distplot(training_data_poc.PoCI_Strongly_Agree)
sns.distplot(training_data_npoc.PoCI_Strongly_Agree)
plt.xlabel('PoC Confidence - Layered')
plt.legend(['PoC Trainer', "White Trainer"], loc='upper left')
plt.savefig('poc_conf_poctrain.png', format='png', dpi=600)
plt.show()
# -
# # Analyzing the trainers who have longevity
# +
from datetime import datetime
from dateutil.relativedelta import relativedelta
two_yrs_ago = datetime.now() - relativedelta(years=2)
type(two_yrs_ago)
training_data_rr["Staff_Start_Date"] = pd.to_datetime(training_data_rr["Staff_Start_Date"])
# +
new_staff = training_data_rr[training_data_rr["Staff_Start_Date"]> two_yrs_ago].index
old_staff = training_data_rr[training_data_rr["Staff_Start_Date"]<= two_yrs_ago].index
# -
old_staff_data_rr = training_data_rr.drop(new_staff)
old_staff_data_rr.dropna(subset=["Staff_Start_Date"], inplace=True)
old_staff_data_rr.head()
new_staff_data_rr = training_data_rr.drop(old_staff)
new_staff_data_rr.dropna(subset=["Staff_Start_Date"], inplace=True)
new_staff_data_rr.head()
stats.ttest_ind(old_staff_data_rr.PoCI_Strongly_Agree, old_staff_data_rr.Non_PoCI_Strongly_Agree)
print(old_staff_data_rr.PoCI_Strongly_Agree.mean())
print(old_staff_data_rr.Non_PoCI_Strongly_Agree.mean())
stats.ttest_ind(new_staff_data_rr.PoCI_Strongly_Agree, new_staff_data_rr.Non_PoCI_Strongly_Agree)
print(new_staff_data_rr.PoCI_Strongly_Agree.mean())
print(new_staff_data_rr.Non_PoCI_Strongly_Agree.mean())
# +
sns.distplot(new_staff_data_rr.Strongly_Agree)
sns.distplot(old_staff_data_rr.Strongly_Agree)
plt.xlabel('Confidence - Layered')
plt.legend(['New Trainer', "Older Trainer"], loc='upper left')
plt.savefig('new_staff_conf_poctrain.png', format='png', dpi=600)
plt.show()
# -
| 9,179 |
/guesty_ci_co_log.ipynb | d4640bebabbfa0ebd7a3be5ffbe38a015da44d47 | [] | no_license | JorgeJuarezCasai/python-notes | https://github.com/JorgeJuarezCasai/python-notes | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 1,155 | # ---
# jupyter:
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# ---
# + [markdown] id="view-in-github" colab_type="text"
# <a href="https://colab.research.google.com/github/Vaycold/tensorflow_tutorial/blob/main/%2316.%EB%A7%9E%EC%B6%A4%ED%95%99%EC%8A%B5%20%EB%91%98%EB%9F%AC%EB%B3%B4%EA%B8%B0.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# + [markdown] id="UkAGMwIEjAIk"
# ## 붗꽃 분류문제
# + colab={"base_uri": "https://localhost:8080/"} id="JpYDg0zqiVcP" outputId="d41ca5c7-1fcc-49fd-d969-ffb0e787302e"
# 데이터셋 다운로드
import os
import tensorflow as tf
import matplotlib.pyplot as plt
train_dataset_url = "https://storage.googleapis.com/download.tensorflow.org/data/iris_training.csv"
train_dataset_fp = tf.keras.utils.get_file(fname=os.path.basename(train_dataset_url),
origin=train_dataset_url)
print("데이터셋이 복사된 위치: {}".format(train_dataset_fp))
# + colab={"base_uri": "https://localhost:8080/"} id="Nm1Yxg5CjI9c" outputId="c0beb196-c5b5-47b1-bbe3-c37642ca63b0"
# !head -n5 {train_dataset_fp}
'''
첫 번째 줄 = 헤더(정보를 가지고 있음) ; 120개 sample, 각 sample's feautre : 4, # of labels : 3
'''
column_names = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species']
feature_names = column_names[:-1]
label_name = column_names[-1]
print('\n')
print('Feature : ', feature_names)
print()
print('Labels : ', label_name)
# + id="-77dWpE_jjZp"
class_names = ['Iris setosa', 'Iris versicolor', 'Iris virginica']
# + id="eiMPMPIjkn4N"
# tf.data.Dataset 생성
'''
텐서플로의 데이터셋 api는 데이터를 적재할 때 발생하는 다양한 경우를 다룰 수 있음.
훈련에 필요한 형태로 데이터를 읽고 변환하는 API
데이터셋이 CSV파일이므로 적절한 형태로 데이터를 구분하기 위해 make_csv_dataset 함수를 사용
-> 훈련모델을 위한 데이터를 생성하므로, 초기값은(shuffle = True, shuffle_buffer_size = 10000)과
무한반복(num_epochs= None)으로 설정되어 있음.
'''
batch_size = 32
train_dataset = tf.data.experimental.make_csv_dataset(
train_dataset_fp, # csv 파일 인듯?
batch_size = batch_size,
column_names = column_names,
label_name = label_name,
num_epochs = 1
)
# + colab={"base_uri": "https://localhost:8080/"} id="oyoF1I06lYbL" outputId="33f23ff8-be4b-4a8e-9f8b-d1173942051d"
# make_csv_dataset 함수 : (features,label) 쌍으로 구성된 tf.data.Dataset을 반환
# features -> 딕셔너리 객체인 {'feature_name' : value} 로 주어짐
feature, labels = next(iter(train_dataset))
print(feature)
# + colab={"base_uri": "https://localhost:8080/", "height": 282} id="UJOYDzIWlZfc" outputId="e9fb7f27-136a-446f-ea7a-bf465c2ef46f"
plt.scatter(
feature['petal_length'],
feature['sepal_length'],
c = labels,
cmap = 'viridis'
)
plt.xlabel('Petal length', size = 12)
plt.ylabel('Sepal length', size = 12)
plt.show()
# + id="YCvSOow4mAfF"
# 모델 구축 단계를 단순화 하기 위해 특성 딕셔너리를 (batch_size, num_features) 의 형태를 가지는
# 단일 배열로 구성하는 함수 생성
def pack_features_vector(features, labels) :
'''특성들을 단일 배열로 묶자.'''
features = tf.stack(list(features.values()), axis=1)
return features, labels
# + id="mobnedxNmfbR"
# 그 후 각 (features, labels) 쌍의 특성을 훈련 데이터 세트에 쌓기 위해 tf.data.Dataset.map 사용
train_dataset = train_dataset.map(pack_features_vector)
# + colab={"base_uri": "https://localhost:8080/"} id="OM6y-frmmpNT" outputId="338af5fd-1e5b-4db5-d9d8-7227a25cba15"
# 데이터셋의 특성요소는 이제 형태가 (batch_size, num_features)인 배열
features, labels = next(iter(train_dataset))
print(features[:5])
# + id="ECwwFfe-mxmn"
# keras를 사용한 모델 생성
model = tf.keras.Sequential([
tf.keras.layers.Dense(10, activation = tf.nn.relu, input_shape = (4,)),
tf.keras.layers.Dense(10, activation = tf.nn.relu),
tf.keras.layers.Dense(3)
])
# + colab={"base_uri": "https://localhost:8080/"} id="12E7Z8MNnfSx" outputId="1026b89c-1175-45da-b9c3-2e93bfc3a351"
features
# + colab={"base_uri": "https://localhost:8080/"} id="mjQfN4ranlf_" outputId="4ea98c9c-2694-46c2-a057-48fddefdc938"
predictions = model(features)
predictions[:5]
# + colab={"base_uri": "https://localhost:8080/"} id="WGsXjpeKnvwp" outputId="2477fbd8-8dd7-4c5e-faab-b34c8d76c78e"
tf.nn.softmax(predictions[:5])
# + colab={"base_uri": "https://localhost:8080/"} id="PagPaRDpn1Jo" outputId="a052f89c-fcd9-4b16-a442-64d819757e01"
print(' 예측 : {}'.format(tf.argmax(predictions, axis=1)))
print(' 레이블 :{}'.format(labels))
# + colab={"base_uri": "https://localhost:8080/"} id="OqtszCUNoBpl" outputId="530bff26-bddb-4326-f256-48da55909bac"
# 모델 훈련
'''
[손실함수와 그래디언트 함수 정의]
- 모델의 손실을 계산해야함
- 모델의 예측이 원하는 레이블과 얼마나 일치하는 지 또한 모델이 잘 작동하는 지에 대한 척도로 사용
- 이 값을 최소화 및 최적화
'''
loss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits = True)
def loss(model,x,y) :
y_ = model(x)
return loss_object(y_true = y, y_pred = y_)
l = loss(model, features, labels)
print(' 손실 테스트 : {}'.format(l))
# + id="HKJT544womVD"
# 모델 최적화를 위해 그라디언트를 계산
def grad(model, inputs, targets) :
with tf.GradientTape() as tape :
loss_value = loss(model, inputs, targets)
return loss_value, tape.gradient(loss_value, model.trainable_variables)
# + id="9LjPadQDpAJU"
optimizer = tf.keras.optimizers.Adam(learning_rate=0.01)
# + colab={"base_uri": "https://localhost:8080/"} id="BZ44bVB7pIq4" outputId="3ce24672-a7ad-4c34-dc10-8a44e97c95a6"
loss_value, grads = grad(model, features, labels)
print('단계 : {}, 초기손실 : {}'.format(optimizer.iterations.numpy(),
loss_value.numpy()))
optimizer.apply_gradients(zip(grads, model.trainable_variables))
print('단계 : {}, 손실 : {}'.format(optimizer.iterations.numpy(),
loss(model, features, labels).numpy()))
# + id="vFw_HOVkpkH6" outputId="0e9c9ede-4b31-48cf-895a-9ff97362c421" colab={"base_uri": "https://localhost:8080/"}
# 도식화를 위해 결과를 저장
train_loss_results = []
train_accuracy_results = []
num_epochs = 201
for epoch in range(num_epochs) :
epoch_loss_avg = tf.keras.metrics.Mean()
epoch_accuracy = tf.keras.metrics.SparseCategoricalAccuracy()
for x,y in train_dataset :
loss_value, grads = grad(model, x, y)
optimizer.apply_gradients(zip(grads, model.trainable_variables))
epoch_loss_avg(loss_value) # 현재 배치의 손실
epoch_accuracy(y, model(x))
train_loss_results.append(epoch_loss_avg.result())
train_accuracy_results.append(epoch_accuracy.result())
if epoch % 50 == 0 :
print("epoch {:03d} : 손실 : {:.3f}, 정확도 : {:.3%}".format(epoch, epoch_loss_avg.result(),epoch_accuracy.result() ))
# + id="XG9n0PDsUUl0" outputId="30f93d19-27fc-4cf2-9bb8-4321202c78db" colab={"base_uri": "https://localhost:8080/", "height": 1000}
fig, axes = plt.subplots(2, sharex=True, figsize=(12, 8))
fig.suptitle('훈련 지표')
axes[0].set_ylabel("손실", fontsize=14)
axes[0].plot(train_loss_results)
axes[1].set_ylabel("정확도", fontsize=14)
axes[1].set_xlabel("에포크", fontsize=14)
axes[1].plot(train_accuracy_results)
plt.show()
# + id="-DeOYOMgUmz8"
lled-heatmap')
# -
# 마찬가지로, 많은 열이 서로 상관관계가 전혀 없다. 이는 주성분분석(PCA)와 같이 차원 축소 변환을 할 때 일정 수준의 상관관계가 필요할 수 있으므로 유용한 결과다. 음의 상관관계인 변수는 ps_ind_06_bin, ps_ind_07_bin, ps_ind_08_bin, ps_ind_09_bin 이다. <br>
# 흥미로운 점은 이전에 ps_car_03_cat, ps_car_05_cat이 결측값이나 null값이 많았지만 이 두 변수가 서로 강한 양의 상관관계를 보인다.
# ### 1.2.2. Mutual information plots
# mutual information은 target 변수와 그것을 계산하는 해당 변수간 상호 정보를 알 수 있어 유용하다. 분류 문제에서 sklearn의 mutual_info_classif 메소드를 통해 두 랜덤 변수간 종속성을 0(서로 독립)에서 높은 값(종속)의 범주로 측정할 수 있다. 따라서 변수가 target의 정보를 얼마나 포함하는지 알 수 있다.<br>
# sklearn의 mutual_info_classif는 k-nearest neighbors 거리의 엔트로피 추정에 기반한 비모수 방법에 의존한다.
mf = mutual_info_classif(train_float.values, train.target.values,n_neighbors=3,random_state=17)
print(mf)
# # 2. Feature inspection and filtering
# ## 2.1. Binary features inspection
# 데이터는 이진수 값만 있는 열도 있다. 이러한 이항 값이 포함된 모든 열을 저장하고 다음과 같이 이항값의 수직 plotly barplot을 생성한다.
# +
bin_col = [col for col in train.columns if '_bin' in col] ### _bin 들어간 열이름
zero_list = []
one_list = []
for col in bin_col:
zero_list.append((train[col]==0).sum()) ### 각 열의 값이 0인 데이터 개수
one_list.append((train[col]==1).sum()) ### 각 열의 값이 1인 데이터 개수
print(zero_list)
print(one_list)
# +
trace1 = go.Bar(x=bin_col, y=zero_list, name='Zero count')
trace2 = go.Bar(x=bin_col, y=one_list, name='One count')
data = [trace1, trace2]
layout = go.Layout(barmode='stack',title='Count of 1 and 0 in binary variables')
fig = go.Figure(data=data,layout=layout)
py.iplot(fig,filename='stacked-bar')
# -
# ps_ind_10_bin, ps_ind_11_bin, ps_ind_12_bin, ps_ind_13_bin 변수는 0이 거의 대부분이다. 이것은 변수가 target에 대한 정보를 많이 포함하고 있지 않는다는 것이기 때문에 변수가 유용한지 의문이 제기된다.
# ## 2.2. Categorical and Ordinal feature inspection
# _cat 접미사가 있는 범주형 변수를 살펴보자.....인데 코드가 없다.
# # 3. Feature importance ranking via learning models
# ## 3.1. Feature importance via Random Forest
# 이제 Random Forest에 train data를 적합하여 모델을 구현하고 학습이 끝나면 변수의 순위를 확인해보자. 이것은 앙상블 모델(약한 의사결정나무 학습기를 Bootstrap에 적용한 앙상블)을 사용하는 빠른 방법이고, 이 앙상블 모델은 유용한 변수 중요도를 얻는 데 파라미터를 많이 조정하지 않아도 되고 target 불균형에 꽤 강력하다. Random Forest를 호출해보자.
from sklearn.ensemble import RandomForestClassifier
rf = RandomForestClassifier(n_estimators=150,max_depth=8,min_samples_leaf=4,max_features=0.2,random_state=0)
rf.fit(train.drop(['id','target'],axis=1),train.target)
features = train.drop(['id','target'],axis=1).columns.values
print('-------학습 완료-------')
# ### 3.1.1. Plot.ly Scatter Plot of feature importances
# RandomForest를 학습시킨 후, 'featureimportances'를 호출해 변수 중요도 리스트를 얻고 Scatter plot을 그릴 수 있다. <br>
# 여기선 Scatter 명령을 호출하고 이전 Plotly plot에 따라 x와 y축을 정의해야한다. scatter plot에서 marker은 scatter point의 크기, 색상, 스케일을 정의한다.
# +
# Scatter plot
trace = go.Scatter(y=rf.feature_importances_,x=features,mode='markers',
marker=dict(sizemode='diameter',sizeref=1,size=13,color=rf.feature_importances_,colorscale='Portland',showscale=True),text=features)
data = [trace]
layout = go.Layout(autosize=True,title='Random Forest Feature Importance',hovermode='closest',
xaxis=dict(ticklen=5,showgrid=False,zeroline=False,showline=False),
yaxis=dict(title='Feature Importance',showgrid=False,zeroline=False,ticklen=5,gridwidth=2),showlegend=False)
fig = go.Figure(data=data, layout=layout)
py.iplot(fig,filename='scatter2010')
# -
# 또한 plotly barplots를 통해 중요도 순위가 매겨진 모든 변수의 목록을 정렬할 수 있다.
x,y = (list(x) for x in zip(*sorted(zip(rf.feature_importances_,features),reverse=False)))
trace2 = go.Bar(x=x,y=y,marker=dict(color=x,colorscale='Viridis',reversescale=True),
name='Random Forest Feature importance', orientation='h')
layout = dict(title='Barplot of Feature importances',width=900,height=2000,
yaxis=dict(showgrid=False,showline=False,showticklabels=True))
fig1 = go.Figure(data=[trace2])
fig1['layout'].update(layout)
py.iplot(fig1,filename='plots')
# ### 3.1.2. Decision Tree visualization
# 단순성을 위해 max_depth=3 인 의사결정나무를 적합하면, 의사결정 가지에서 3개의 수준만 볼 수 있다. skelarn의 'export_graphviz'에서 시각화를 그래프로 그려 시각화된 나무 이미지를 내보내고 가져온다.
# +
from sklearn import tree
from IPython.display import Image as PIamge
from subprocess import check_call
from PIL import Image, ImageDraw, ImageFont
import re
decision_tree = tree.DecisionTreeClassifier(max_depth=3)
decision_tree.fit(train.drop(['id','target'],axis=1),train.target)
# 학습된 모델을 .dot 파일로 내보내기
with open('tree1.dot','w') as f:
f = tree.export_graphviz(decision_tree,out_file=f,max_depth=4,impurity=False,
feature_names=train.drop(['id','target'],axis=1).columns.values,
class_names=['No','Yes'],rounded=True,filled=True)
# 웹 노트북에 표시할 수 있도록 .dot을 .png로 변환
check_call(['dot','-Tpng','tree1.dot','-o','tree1.png'],shell=True) ### 에러, shell 임의 추가
# PIL을 통해 차트에 주석 달기
img = Image.open('tree1.png')
draw = ImageDraw.Draw(img)
img.save('sample-out.png')
PImage('sample-out.png')
# -
# ## 3.2. Feature importance via Gradient Boosting model
# 단지 호기심으로 변수 중요도를 얻기 위해 또 다른 학습 방법을 시도해보자. 이번에는 Gradient Boosting 분류기를 사용해 train 데이터를 적합한다. Gradient Boosting은 전진선택법으로 진행되며, 각 단계에서 회귀 나무들이 손실함수의 gradient에 적합된다.
# +
from sklearn.ensemble import GradientBoostingClassifier
gb = GradientBoostingClassifier(n_estimators=100,max_depth=3,min_samples_leaf=4,max_features=0.2,random_state=0)
gb.fit(train.drop(['id','target'],axis=1), train.target)
geatures = train.drop(['id','target'],axis=1).columns.values
print('------학습완료------')
# +
# Scatter plot
trace = go.Scatter(y=gb.feature_importances_,x=features,mode='markers',
marker=dict(sizemode='diameter',sizeref=1,size=13,color=gb.feature_importances_,colorscale='Portland',showscale=True),text=features)
data = [trace]
layout = go.Layout(autosize=True,title='Gradient Boosting Machine Feature Importace',hovermode='closest',
xaxis=dict(ticklen=5,showgrid=False,zeroline=False,showline=False),
yaxis=dict(title='Feature Importance',showgrid=False,zeroline=False,ticklen=5,gridwidth=2),showlegend=False)
fig = go.Figure(data=data,layout=layout)
py.iplot(fig,filename='scatter2010')
# +
x, y = (list(x) for x in zip(*sorted(zip(gb.feature_importances_,features),reverse = False)))
trace2 = go.Bar(x=x ,y=y,marker=dict(color=x,colorscale = 'Viridis',reversescale = True),
name='Gradient Boosting Classifer Feature importance',orientation='h',)
layout = dict(title='Barplot of Feature importances',width = 900, height = 2000,
yaxis=dict(showgrid=False,showline=False,showticklabels=True))
fig1 = go.Figure(data=[trace2])
fig1['layout'].update(layout)
py.iplot(fig1,filename='plots')
# -
# Random Forest와 Gradient Boosting 학습 모델에서 모두 가장 중요한 변수는 ps_car_13이라는 것을 알 수 있다.
# # 2nd
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
# %matplotlib inline
import plotly.offline as py
py.init_notebook_mode(connected=True)
import plotly.graph_objs as go
import plotly.tools as tls
import warnings
from collections import Counter
from sklearn.feature_selection import mutual_info_classif
warnings.filterwarnings('ignore')
# pandas를 통해 training data를 불러오자.
train = pd.read_csv('../input/train.csv')
train.head()
# train 데이터셋 행,열 수 확인
rows = train.shape[0]
columns = train.shape[1]
print('The train dataset contains {0} rows and {1} columns'.format(rows,columns))
# # 1. Data Quality checks
# ## 1.1. Data checks
# ### 1.1.1. Null or missing values check
# train데이터셋에 null값이 있는지 확인해보자.
# 모든 열에 isnull을 확인하기 위해 any()를 두 번 적용
train.isnull().any().any()
# Null값 확인 결과 False를 반환하지만, -1값이 결측값을 나타내기 때문에 실제로 결측값이 없다고 볼 수는 없다. 따라서 Porto Seguro가 데이터의 모든 null값을 -1값으로 대체한 것을 알 수 있다. 이제 데이터에 실제 결측값이 있는지 확인해보자.<br>
# 다음과 같이 모든 -1값을 null로 대체하여 어떤 열이 -1값을 갖는지 확인할 수 있다.
train_copy = train
train_copy = train_copy.replace(-1,np.NaN)
# 다음으로, 데이터셋에서 결측값을 시각화하기 유용한 Missingno 패키지를 사용해 시각화한다.
import missingno as msno
# 열의 null과 결측값 확인
msno.matrix(df=train_copy.iloc[:,2:29],figsize=(20,14),color=(0.42,0.5,0.05))
# 시각화를 통해 결측값을 분명히 볼 수 있고, 빈 흰색 줄은 특정 열의 결측값을 나타낸다. 여기서 59개의 변수 중 7개의 변수가 null값이 있다. 이는 missingno matrix plot이 약 40개의 홀수 변수에만 적합할 수 있으므로 나머지 5개의 null값이 있는 열은 제외되었다. 모든 null값을 시각화하려면 데이터프레임을 자르거나 figsize를 조정한다.
# <br><br>
# 관측된 null값이 있는 7개 열은 ps_ind_05_cat, ps_reg_03, ps_car_03_cat, par_05_cat, ps_car_07_cat, ps_car_09_cat, ps_car_14다. 대부분 결측값이 있는 변수는 _cat 접미사가 붙는다. ps_reg_03, ps_car_03_cat, ps_car_05 변수는 null값이 많으므로 -1를 NaN값으로 대체하는 것은 좋지 않다.
# ### 1.1.2. Target variable inspection
# 일반적인 또 다른 표준 검사는 target 변수와 관련되고, 열 이름은 편히 'target'이라고 한다. 데이터를 target에 가장 잘 매핑하는 함수를 학습시키기 위해 주어진 데이터(id 제외)와 함께 지도학습 모델에 사용한다.
data = [go.Bar(x = train['target'].value_counts().index.values,
y = train['target'].value_counts().values,
text = 'Distribution of target variable')]
layout = go.Layout(title = 'Target variable distribution')
fig = go.Figure(data=data, layout=layout)
py.iplot(fig, filename='basic-bar')
# target변수가 너무 불균형하므로 유의해야한다.
# ### 1.1.3. Datatype check
# train 데이터셋이 데이터타입(integer/character/float) 구성을 확인한다. Collections 모듈의 Counter 메소드를 통해 고유한 데이터타입의 수를 얻는다.
Counter(train.dtypes.values)
# train 데이터셋은 59개 열로 이루어져있고, 변수는 integer과 float의 두 가지 데이터타입으로만 이뤄져있다.<br>
# 또한 제공된 데이터의 변수 이름엔 _bin, _cat, _reg와 같은 약어로 접미사가 있다. _bin은 이진변수, 나머지는 연속형이나 순서형을 나타낸다. float값(아마 연속형 변수)과 integer값(아마 이항, 범주형, 순서형 변수)을 살펴보며 단순화한다.
train_float = train.select_dtypes(include=['float64'])
train_int = train.select_dtypes(include=['int64'])
# ## 1.2. Plots
# ### 1.2.1. Correlation plots
# 변수간 선형관계와 몇 가지 정보를 얻기 위해 선형 상관관계도를 생성한다. 통계적 시각화 패키지 seaborn을 사용해 상관관계값을 heatmap으로 그린다. pandas 데이터프레임의 corr() 내장 메소드로 피어슨 상관관계를 편히 계산할 수 있고, seaborn의 상관관계도도 'heatmap'이라는 단어만 있으면 호출할 수 있다.
# #### Correlation of float features
colotmap = plt.cm.magma
plt.figure(figsize=(16,12))
plt.title('Pearson correlation of continuous features',y=1.05,size=15)
sns.heatmap(train_float.corr(),linewidths=0.1,vmax=0.1,square=True,cmap=colormap,linecolor='white',annot=True)
# 대부분의 변수가 서로 상관관계가 없는 것을 알 수 있다. 양의 선형 관계를 나타내는 변수 쌍은 (ps_reg_01,ps_reg_03), (ps_reg_02, ps_reg_03), (ps_car_12, ps_car_13), (ps_car_13, ps_car_15)이다.
# #### Correlations of integer features
# integer 데이터타입의 열에 대해 Plotly 라이브러리를 사용해 상호적으로 상관관계가 어떻게 나타나는지 heatmap을 생성해보자. 이전 Plotly plot처럼 go.Heatmap을 호출해 heatmap 객체를 얻는다. 서로 다른 세 축에 값을 제공해야하는데, x와 y축은 열 이름을, z축은 상관관계값을 준다.
# train_int = train_int.drop(['id','target'],axis=1)
# colormap = plt.cm.bone
# plt.figure(figsize=(21,16))
# plt.title('Pearson correlation of categorical features', y=1.0,size=15)
# sns.heatmap(train_cat.corr(), linewiedths=0.1,vmax=1.0,square=True,cmap=colormap,linecolor='white',annot=True)
data = [go.Heatmap(z = train_int.corr().values,
x = train_int.columns.values,
y = train_int.columns.values,
colorscale='Viridis',reversescale=False,opacity=1.0)]
layout = go.Layout(title = 'Pearson Correlation of Integer-type features',
xaxis = dict(ticks='',nticks=36), yaxis = dict(ticks=''),
width = 900, height = 700)
fig = go.Figure(data=data, layout=layout)
py.iplot(fig, filename='labelled-heatmap')
# 마찬가지로, 많은 열이 서로 상관관계가 전혀 없다. 이는 주성분분석(PCA)과 같이 차원 축소 변환을 할 때 일정 수준의 상관관계가 필요할 수 잇으므로 유용한 결과다. 음의 상관관계인 변수는 ps_ind_06_bin, ps_ind_07_bin, ps_ind_08_bin, ps_ind_09_bin 이다. ps_car_03_cat, ps_car_05_cat은 결측값이나 null값이 많지만 양의 상관관계를 보인다.
# ### 1.2.2. Mutual information plots
# mutual information은 target변수와 그것을 계산하는 변수간 상호적인 정보를 알 수 있어 유용하다. 분류 문제에서 sklearn의 mutual_info_classif 메소드로 두 랜덤 변수간 종속성을 0(독립)에서 높은 값(종속)의 범주로 측정할 수 있다. 따라서 변수가 target의 정보를 얼마나 포함하는지 알 수 있다.<br>
# sklearn의 mutual_info_classif는 k-nearest neighbors 거리의 엔트로피 추정에 기반한 비모수 방법에 의존한다.
mf = mutual_info_classif(train_float.values,train.target.values,n_neighbors=3,random_state=17)
print(mf)
# # 2. Feature inspection and filtering
# ## 2.1. Binary features inspection
# 데이터는 이진수 값만 있는 열도 있다. 이러한 이항 값이 포함된 모든 열을 저장하고 이항값의 수직 plotly barplot을 생성한다.
bin_col = [col for col in train.columns if '_bin' in col]
zero_list = []
one_list = []
for col in bin_col:
zero_list.append((train[col]==0).sum())
one_list.append((train[col]==1).sum())
# +
trace1 = go.Bar(x=bin_col, y=zero_list, name='Zero count')
trace2 = go.Bar(x=bin_col, y=one_list, name='One count')
data = [trace1, trace2]
layout = go.Layout(barmode='stack',title='Count of 1 and 0 in binary variables')
fig = go.Figure(data=data, layout=layout)
py.iplot(fig, filename='stacked-bar')
# -
# ps_ind_10_bin, ps_ind_11_bin, ps_ind_12_bin, ps_ind_13_bin 변수는 0이 거의 대부분이다. 변수가 target에 대한 정보를 많이 포함하고 있지 않다는 것이므로 변수가 유용한지에 대한 의문이 제기된다.
# ## 2.2. Categorical and Ordinal feature inspection
# _cat 접미사가 있는 범주형 변수를 살펴보자....
# # 3. Feature importance ranking via learning models
# ## 3.1. Feature importance via Random Forest
# 이제 Random Forest에 train data를 적합하여 모델을 구현하고 학습이 끝나면 변수의 순위를 확인해보자. 이건 앙상블 모델(약한 의사결정나무를 Bootstrap에 적용한 앙상블)을 사용하는 빠른 방법이고, 이 앙상블 모델은 유용한 변수 중요도를 얻는 데 파라미터를 많이 조정하지 않아도 되고 target 불균형에 꽤 강력하다. Random Forest를 호출해보자.
from sklearn.ensemble import RandomForestClassifier
rf = RandomForestClassifier(n_estimators=150,max_depth=8,min_samples_leaf=4,
max_features=0.2,random_state=0)
rf.fit(train.drop(['id','target'],axis=1),train.target)
features = train.drop(['id','target'],axis=1).columns.values
print('-------학습 완료--------')
# ### 3.1.1. Plot.ly Scatter Plot of feature importances
# Random Forest를 학습시킨 후, 'featrueimportances'를 호출해 변수 중요도 리스트를 얻어 Scatterplot을 그릴 수 있다. Scatter 명령을 호출하고 이전 Plotly plot과 같이 x와 y축을 정의해야한다. scatter plot에서 marker는 scatter point의 크기, 색상, 스케일을 정의한다.
# Scatter plot
trace = go.Scatter(y=rf.feature_importances_, x=features, mode='markers',
marker=dict(sizemode='diameter',sizeref=1,size=13,
color=rf.feature_importances_,colorscale='Portland',
showscale=True),text=features)
data = [trace]
layout = go.Layout(autosize=True,title='Random Forest Feature Importance',hovermode='closest',
xaxis=dict(ticklen=5,showgrid=False,zeroline=False,showline=False),
yaxis=dict(title='Feature Importance',showgrid=False,zeroline=False,ticklen=5,gridwidth=2),showlegend=False)
fig = go.Figure(data=data, layout=layout)
py.iplot(fig, filename='scatter2010')
# 또한 plotly barplots를 통해 중요도 순위가 매겨진 모든 변수의 목록을 정렬할 수 있다.
x, y = (list(x) for x in zip(*sorted(zip(rf.feature_importances_,features),reverse=False)))
trace2 = go.Bar(x=x,y=y,marker=dict(color=x,colorscale='Viridis',reversescale=True),
name='Random Forest Feature importance', orientation='h')
layout = dict(title='Barplot of Feature importances',width=900,height=2000,
yaxis=dict(showgrid=False,showline=False,showticklabels=True))
fig1 = go.Figure(data=[trace2])
fig1['layout'].update(layout)
py.iplot(fig1, filename='plots')
# ### 3.1.2. Decision Tree visualtization
# 단순성을 위해 max_depth=3인 의사결정나무를 적합하면, 의사결정 가지에서 3개의 수준만 볼 수 있다. sklearn의 'export_graphviz'에서 그래프로 시각화해 나무 이미지를 내보내고 가져온다.
# +
from sklearn import tree
from IPython.display import Image as PIame
from subprocess import check_call
from PIL import Image, ImageDraw, ImageFont
import re
decision_tree = tree.DecisionTreeClassifier(max_depth=3)
decision_tree.fit(train.drop(['id','target'],axis=1),train.target)
# 학습된 모델을 .dot 파일로 내보내기
with open('tree1.dot','w') as f:
f = tree.export_graphviz(decision_tree, out_file=f, max_depth=4, impurity=False,
feature_names=train.drop(['id','target'],axis=1).columns.values,
class_names=['No','Yes'], rounded=True, filled=True)
# 웹 노트북에 표시할 수 있도록 .dot을 .png로 변환
check_call(['dot','-Tpng','tree1.dot','-o','tree1.png'],shell=True)
# PIL을 통해 차트에 주석 달기
img = Image.open('tree1.png')
draw = ImageDraw.Draw(img)
img.save('sample-out.png')
PImage('sample-out.png')
# -
# ## 3.2. Feature importance via Gradient Boosting model
# 단지 호기심으로 변수 중요도를 얻기 위해 또 다른 학습 방법을 시도해보자. 이번에는 Gradient Boosting 분류기를 사용해 train 데이터를 적합한다. Gradient Boosting은 전진선택법으로 진행되며, 각 단계에서 회귀 나무들이 손실함수의 gradeint에 적합된다.
from sklearn.ensemble import GradientBoostingClassifier
gb = GradientBoostingClassifier(n_estimators=100,max_depth=3,min_samples_leaf=4,max_features=0.2,random_state=0)
gb.fit(train.drop(['id','target'],axis=1),train.target)
features = train.drop(['id','target'],axis=1).columns.values
print('------학습 완료------')
# Scatter plot
trace = go.Scatter(y = gb.feature_importances_, x = features, mode = 'markers',
marker = dict(sizemode='diameter',sizeref=1,size=13,
color=gb.feature_importances_,
colorscale='Portland',showscale=True),text=features)
data = [trace]
layout = go.Layout(autosize=True, title='Gradient Boosting Marchine Feature Importance',
hovermode='closest',xaxis=dict(ticklen=5,showgrid=False,zeroline=False,showline=False),
yaxis=dict(title='Feature Importance',showgrid=False,zeroline=False,ticklen=5,gridwidth=2),
showlegend=False)
fig = go.Figure(data=data, layout=layout)
py.iplot(fig, filename='scatter2010')
x, y = (list(x) for x in zip(*sorted(zip(gb.feature_importances_,features),reverse=False)))
trace2 = go.Bar(x=x,y=y,marker=dict(color=x,colorscale='Viridis',reversescale=True),
name='Gradient Boosting Classifier Feature importance',orientation='h')
layout = dict(title='Barplot of Feature importances',width=900,height=2000,
yaxis=dict(showgrid=False,showline=False,showticklabels=True))
fig1 = go.Figure(data=[trace2])
fig1['layout'].update(layout)
py.iplot(fig1, filename='plots')
# Random Forest 와 Gradient Boosting 학습 모델에서 모두 가장 중요한 변수는 ps_car_13이라는 것을 알 수 있다.
| 24,715 |
/AnimationPractice.ipynb | 94e021c095901c4500e6780c73e6c65b263af216 | [] | no_license | venom510/Applied-plotting-charting-and-data-representation-in-python-coursera | https://github.com/venom510/Applied-plotting-charting-and-data-representation-in-python-coursera | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 185,210 | # ---
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# display_name: Python 3
# language: python
# name: python3
# ---
import tensorflow as tf
tf.executing_eagerly()
x = tf.constant([[1, 2, 3], [4, 5, 6]])
m = tf.linalg.matrix_transpose(x)
print("{}".format(m))
tf.__version__
port declarative_base
from sqlalchemy import create_engine, Sequence
# +
Base = declarative_base()
class User(Base):
__tablename__ = 'users'
id = Column(Integer, Sequence('user_id_seq'), primary_key=True)
name = Column(String(50))
fullname = Column(String(50))
password = Column(String(12))
def __repr__(self):
return "<User(name='%s', fullname='%s', password='%s')>" % (
self.name, self.fullname, self.password)
class Address(Base):
__tablename__ = 'addresses'
id = Column(Integer, primary_key=True)
email_address = Column(String, nullable=False)
user_id = Column(Integer, ForeignKey('users.id'))
user = relationship("User", back_populates="addresses")
def __repr__(self):
return "<Address(email_address='%s')>" % self.email_address
User.addresses = relationship("Address", order_by=Address.id, back_populates="user", cascade='all, delete, delete-orphan')
# +
#create a many to many relationship
#there is a many to many relationship betweek blogpost and a keywords.. we need a association table called 'blog_keyword"
from sqlalchemy import Text
class PostKeyword(Base):
__tablename__ = 'postkeyword'
post_id = Column( ForeignKey('posts.id'), primary_key=True)
keyword_id = Column(ForeignKey('keywords.id'), primary_key=True)
class Post(Base):
__tablename__ = 'posts'
id = Column(Integer, primary_key=True)
head = Column(String,nullable=False)
body = Column(Text)
user_id = Column(Integer, ForeignKey('users.id'))
##many to many
keywords = relationship('Keyword', secondary = PostKeyword.__table__, back_populates='posts')
class Keyword(Base):
__tablename__ = 'keywords'
id = Column(Integer, primary_key=True)
key = Column(String, nullable=False, unique=True)
posts = relationship('Post', secondary=PostKeyword.__table__, back_populates='keywords')
Post.author = relationship(User, back_populates='posts')
User.posts = relationship(Post, back_populates = 'author', lazy='dynamic')
# -
engine = create_engine('postgres://postgres:robus@localhost:5432/qw')
Base.metadata.create_all(engine)
Session = sessionmaker(bind=engine)
session = Session()
query = session.query(User).filter(User.name=='Robus').one()
robus = User(name='robus', fullname='robus', password='asdsadsad')
session.add(robus)
session.commit()
robus in session
robus_post = Post(head='this is head', body='this is boy', author=robus)
session.add(robus_post)
session.commit()
session.close()
robus_post in session
session.rollback()
robus_post in session
rahul = User(name='rahul', fullname='rahul', password='asdsasd')
session = Session()
session.add(rahul)
session.commit()
post = Post(head="heas", body="this is body", author=rahul)
session.add(post)
session.commit()
post.keywords.append(Keyword(key='asd'))
post.keywords.append(Keyword(key='first'))
session.commit()
])
bottom_right.set_xticks([])
top_left.set_yticks([])
top_right.set_yticks([])
bottom_left.set_yticks([])
bottom_right.set_yticks([])
fig.suptitle('Number of bins : {0}'.format(bins))
plt.tight_layout()
fig = plt.figure()
a = animation.FuncAnimation(fig, update, interval=50)
| 3,727 |
/data/diversity/scripts/zee_diversity.ipynb | 10265c65e62b7a187cf17043c9dcf579fd544c8d | [] | no_license | QuentinMahieu/Project_soccer_2 | https://github.com/QuentinMahieu/Project_soccer_2 | 1 | 0 | null | 2021-01-10T07:40:53 | 2021-01-05T08:32:48 | Jupyter Notebook | Jupyter Notebook | false | false | .py | 64,416 | # ---
# jupyter:
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# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# +
#Server
import socket
server = socket.socket(socket.AF_INET,socket.SOCK_STREAM)
host = socket.gethostname()
port = 1234
server.bind((host,port))
server.listen(5)
print('Waiting for connection')
conn,addr = server.accept()
print("Recieved Connection request from client",)
conn.send("Request is accepted".encode())
while True:
msg = conn.recv(1024)
msg = msg.decode()
print("Client: ",msg)
if msg == 'bye':
break
msg = input("Server: ")
conn.send(msg.encode())
conn.close()
# +
#Client
import socket
usocket = socket.socket(family=socket.AF_INET, type=socket.SOCK_DGRAM)
serveraddr = ("127.0.0.1", 20001)
print('Connection request to {} is accepted by server'.format(serveraddr))
print("Enter 'bye' to exit")
while(True):
clientmsg = input("Client: ")
cmbytes = str.encode(clientmsg)
bs = 1024
usocket.sendto(cmbytes, serveraddr)
msgserver = usocket.recvfrom(bs)
msg = "Server: {}".format(msgserver[0].decode())
print(msg)
if msgserver[0].decode() == 'bye':
break
usocket.close()
If existing entity, add to test id list for entity
if entity_id in entity_id_to_ix.keys():
cur_ix = entity_id_to_ix[entity_id]
ix = max(list(ix_to_entity_id.keys())) + 1
cur_ix.append(ix)
entity_id_to_ix[entity_id] = cur_ix
ix_to_entity_id[ix] = entity_id
return ix, ix_to_entity_id, entity_id_to_ix
# Else, create new entry for entity
else:
# If no entry has been created
if len(ix_to_entity_id.keys()) == 0:
ix = -1
else:
ix = max(list(ix_to_entity_id.keys()))
ix_to_entity_id[ix + 1] = entity_id
entity_id_to_ix[entity_id] = [ix + 1]
return ix + 1, ix_to_entity_id, entity_id_to_ix
def get_mention_ids(df, return_ds=False):
df.reset_index(inplace=True, drop="Index")
ix_to_entity_id = {}
entity_id_to_ix = {}
prev_entities = []
mention_ids = []
for row_ix in df.index:
label = df["entity_ids"][row_ix]
if pd.isnull(label):
prev_entities = []
mention_ids.append("")
continue
else:
entities = label.split(" ")
indices = []
for entity in entities:
if entity in prev_entities:
ix, ix_to_entity_id, entity_id_to_ix = get_index(entity,
ix_to_entity_id,
entity_id_to_ix,
True)
indices.append(ix)
else:
ix, ix_to_entity_id, entity_id_to_ix = get_index(entity,
ix_to_entity_id,
entity_id_to_ix)
indices.append(ix)
mention_ids.append(replace_indices(indices))
prev_entities = entities
if return_ds:
return ix_to_entity_id, entity_id_to_ix
else:
df["mention_ids"] = pd.Series(mention_ids)
return df
#################################################
# ================ ACCURACY METRIC ==============
#################################################
def check_common_preds(y_pred, y, ix_to_label):
y_pred = set([ix_to_label[int(pred)] for pred in y_pred.split(" ")])
y = set(y.split(" "))
return len(y.intersection(y_pred)) > 0
def check_valid_record(df, y_pred, ix):
r1 = df["entity_ids"][ix]
r2 = y_pred[ix]
pos = df["pos"][ix]
prediction_limit = 3
# Check conditions for valid prediction record
if pd.isnull(r1) or pd.isnull(r2):
return False
if r1 == "" or r2 == "":
return False
if r1 is None or r2 is None:
return False
if r1 == "None" or r2 == "None":
return False
if "PRP" not in pos:
return False
if len(r2.split(" ")) > prediction_limit:
return False
else:
return True
def check_accuracy(y_pred, df):
"""
:param y_pred: pandas series for predicted mention_ids
:param df: The data frame with original entity and mention ids
:return: float: mean accuracy of the predictions
"""
y = df["entity_ids"].copy()
# build test_id to entity dictionaries and vice versa
ix_to_label, label_to_ix = get_mention_ids(df, True)
scores = []
for ix in y_pred.index:
if check_valid_record(df, y_pred, ix):
scores.append(1.0 if check_common_preds(y_pred[ix], y[ix], ix_to_label)
else 0.0)
return np.mean(scores)
# -
# ### Naive solution: directly selecting the previous antecedent
# +
def naive_resolver(df):
"""
:param df: The dataframe
:return:
"""
for ix in df[df.pos.str.contains("PRP")].pos.index:
if pd.notnull(df["mention_ids"][ix]) and "PRP" in df["pos"][ix]:
df.at[ix, "mention_ids"] = str(
get_closest_prev_antecedent(df["mention_ids"], ix))
return df
def get_closest_prev_antecedent(d, ix, threshold=100):
"""
:param d: dataframe for the data
:param ix: current row index
:param threshold: how far back should we check
:return:
"""
distance = 1
while distance < threshold:
if pd.isnull(d[ix - distance]):
distance += 1
continue
else:
# Splitting the columns with multiple labels
# with a space.
options = str(d[ix - distance]).split(" ")
# Randomly choose between the possible options
ch = choice(options)
return str(ch)
def replace_indices(indices):
return str(indices)[1:-1].replace(", ", " ")
# -
# ### Machine learning solution: learn a classifier to predict coreferent
def ret_last_mentions(d, ix, threshold=50):
"""
:param d: dataframe for the data
:param ix: current row index
:param threshold: how far back should we check
:return:
"""
lst = set()
distance = 0
while distance < threshold:
if (ix - distance) < 0 or pd.isnull(d["mention_ids"][ix - distance]):
distance += 1
continue
else:
# Splitting the columns with multiple labels
# with a space.
options = str(d["mention_ids"][ix - distance]).split(" ")
options_entity = str(d["entity_ids"][ix - distance]).split(" ")
for i in options:
for j in options_entity:
if (i == 'None'):
continue
else:
lst.add((i, j))
distance += 1
return list(lst)
def get_train_test(df):
lst_all = []
lst_matching = []
for ix in df[df.pos.str.contains("PRP")].pos.index:
if pd.notnull(df["mention_ids"][ix]) and "PRP" in df["pos"][ix]:
mention_id_options = df["mention_ids"].iloc[ix].split(" ")
entity_id_options = df["entity_ids"].iloc[ix].split(" ")
for i in mention_id_options:
for j in entity_id_options:
if (i == 'None'):
continue
else:
mention_id = int(i)
entity_id = j
for k in (ret_last_mentions(df[["mention_ids", "entity_ids"]], ix)):
other_mention_id = int(k[0])
if mention_id != other_mention_id and ([mention_id, other_mention_id] not in lst_all):
lst_all.append([mention_id, other_mention_id])
if mention_id != other_mention_id and entity_id == k[1] and ([mention_id, other_mention_id] not in lst_matching):
lst_matching.append([mention_id, other_mention_id])
alls = np.array(lst_all)
matching = np.array(lst_matching)
labels = np.zeros((alls.shape[0]))
for i in range(len(lst_all)):
if lst_all[i] in lst_matching:
labels[i] = 1
return alls, labels
def ret_last_mentions_no_entity(d, ix, threshold=50):
"""
:param d: dataframe for the data
:param ix: current row index
:param threshold: how far back should we check
:return:
"""
lst = set()
distance = 0
while distance < threshold:
if (ix - distance) < 0 or pd.isnull(d[ix - distance]):
distance += 1
continue
else:
# Splitting the columns with multiple labels
# with a space.
options = str(d[ix - distance]).split(" ")
for i in options:
if (i == 'None'):
continue
else:
lst.add(i)
distance += 1
return list(lst)
def get_train(df):
lst_all = []
for ix in df[df.pos.str.contains("PRP")].pos.index:
if pd.notnull(df["mention_ids"][ix]) and "PRP" in df["pos"][ix]:
mention_id_options = df["mention_ids"].iloc[ix].split(" ")
for i in mention_id_options:
if (i == 'None'):
continue
else:
mention_id = int(i)
for k in (ret_last_mentions_no_entity(df["mention_ids"], ix)):
other_mention_id = int(k)
if mention_id != other_mention_id and ([mention_id, other_mention_id] not in lst_all):
lst_all.append([mention_id, other_mention_id])
alls = np.array(lst_all)
return alls
# ### New resolver
def better_resolver(train_df, test_df):
sln = {}
X_train, Y_train = get_train_test(train_df)
X_test = get_train(test_df)
df = pd.DataFrame(columns=['mention_ids'])
logreg = LogisticRegression()
logreg.fit(X_train, Y_train)
Y_pred = logreg.predict_proba(X_test)[:,1]
for i in range(X_test.shape[0]):
if Y_pred[i] > .50:
val = X_test[i]
sln[val[0]] = str(val[1])
for ix in test_df[test_df.pos.str.contains("PRP")].pos.index:
if pd.notnull(test_df["mention_ids"][ix]) and "PRP" in test_df["pos"][ix]:
mention_id_options = test_df["mention_ids"].iloc[ix].split(" ")
for j in mention_id_options:
mention_id = int(j)
if (mention_id in sln.keys()):
df.at[ix, "mention_ids"] = sln[mention_id]
return df
# +
train_df = pd.read_csv("train.coref.data.txt", sep="\t")
dev_df = pd.read_csv("dev.coref.data.txt", sep="\t")
# This modifies the dataframe in place. If your function
# writes to a new file, you could read the data to pass it
# to check_accuracy in a data frame.
result_df = better_resolver(train_df, dev_df)["mention_ids"]
print("Accuracy of Model: {:.4f}".format(check_accuracy(result_df, dev_df)))
# -
# ### New text prediction to CSV
def better_resolver_2(train_df, test_df):
sln = {}
X_train, Y_train = get_train_test(train_df)
X_test = get_train(test_df)
logreg = LogisticRegression()
logreg.fit(X_train, Y_train)
Y_pred = logreg.predict_proba(X_test)[:,1]
for i in range(X_test.shape[0]):
if Y_pred[i] > .50:
val = X_test[i]
sln[val[0]] = str(val[1])
for ix in test_df[test_df.pos.str.contains("PRP")].pos.index:
if pd.notnull(test_df["mention_ids"][ix]) and "PRP" in test_df["pos"][ix]:
mention_id_options = test_df["mention_ids"].iloc[ix].split(" ")
for j in mention_id_options:
mention_id = int(j)
if (mention_id in sln.keys()):
test_df.at[ix, "mention_ids"] = sln[mention_id]
return test_df
# +
train_df = pd.read_csv("train.coref.data.txt", sep="\t")
dev_df = pd.read_csv("dev.coref.data.txt", sep="\t")
test_df = pd.read_csv("test.coref.data.txt", sep="\t")
# This modifies the dataframe in place. If your function
# writes to a new file, you could read the data to pass it
# to check_accuracy in a data frame.
result_df = better_resolver_2(train_df, test_df)
# -
result_df.to_csv("test_predictions.txt", sep='\t')
# ### Trying other machine learning models
# +
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC, LinearSVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB # Gaussian Naive Bays
from sklearn.linear_model import Perceptron
from sklearn.linear_model import SGDClassifier #stochastic gradient descent
from sklearn.tree import DecisionTreeClassifier
import xgboost as xgb
# +
def better_resolver(train_df, test_df):
sln = {}
X_train, Y_train = get_train_test(train_df)
X_test = get_train(test_df)
df = pd.DataFrame(columns=['mention_ids'])
logreg = LogisticRegression()
logreg.fit(X_train, Y_train)
Y_pred = logreg.predict_proba(X_test)[:,1]
for i in range(X_test.shape[0]):
if Y_pred[i] > .50:
val = X_test[i]
sln[val[0]] = str(val[1])
for ix in test_df[test_df.pos.str.contains("PRP")].pos.index:
if pd.notnull(test_df["mention_ids"][ix]) and "PRP" in test_df["pos"][ix]:
mention_id_options = test_df["mention_ids"].iloc[ix].split(" ")
for j in mention_id_options:
mention_id = int(j)
if (mention_id in sln.keys()):
df.at[ix, "mention_ids"] = sln[mention_id]
return df
result_df = better_resolver(train_df, dev_df)["mention_ids"]
print("Accuracy of Model: {:.4f}".format(check_accuracy(result_df, dev_df)))
# +
def better_resolver(train_df, test_df):
sln = {}
X_train, Y_train = get_train_test(train_df)
X_test = get_train(test_df)
df = pd.DataFrame(columns=['mention_ids'])
svc = SVC(probability=True)
svc.fit(X_train, Y_train)
Y_pred = svc.predict_proba(X_test)[:,1]
for i in range(X_test.shape[0]):
if Y_pred[i] > .50:
val = X_test[i]
sln[val[0]] = str(val[1])
for ix in test_df[test_df.pos.str.contains("PRP")].pos.index:
if pd.notnull(test_df["mention_ids"][ix]) and "PRP" in test_df["pos"][ix]:
mention_id_options = test_df["mention_ids"].iloc[ix].split(" ")
for j in mention_id_options:
mention_id = int(j)
if (mention_id in sln.keys()):
df.at[ix, "mention_ids"] = sln[mention_id]
return df
result_df = better_resolver(train_df, dev_df)["mention_ids"]
print("Accuracy of Model: {:.4f}".format(check_accuracy(result_df, dev_df)))
# +
def better_resolver(train_df, test_df):
sln = {}
X_train, Y_train = get_train_test(train_df)
X_test = get_train(test_df)
df = pd.DataFrame(columns=['mention_ids'])
knn = KNeighborsClassifier(n_neighbors = 3)
knn.fit(X_train, Y_train)
Y_pred = knn.predict_proba(X_test)[:,1]
for i in range(X_test.shape[0]):
if Y_pred[i] > .50:
val = X_test[i]
sln[val[0]] = str(val[1])
for ix in test_df[test_df.pos.str.contains("PRP")].pos.index:
if pd.notnull(test_df["mention_ids"][ix]) and "PRP" in test_df["pos"][ix]:
mention_id_options = test_df["mention_ids"].iloc[ix].split(" ")
for j in mention_id_options:
mention_id = int(j)
if (mention_id in sln.keys()):
df.at[ix, "mention_ids"] = sln[mention_id]
return df
result_df = better_resolver(train_df, dev_df)["mention_ids"]
print("Accuracy of Model: {:.4f}".format(check_accuracy(result_df, dev_df)))
# +
def better_resolver(train_df, test_df):
sln = {}
X_train, Y_train = get_train_test(train_df)
X_test = get_train(test_df)
df = pd.DataFrame(columns=['mention_ids'])
gradboost = xgb.XGBClassifier(n_estimators=1000)
gradboost.fit(X_train, Y_train)
Y_pred = gradboost.predict_proba(X_test)[:,1]
for i in range(X_test.shape[0]):
if Y_pred[i] > .50:
val = X_test[i]
sln[val[0]] = str(val[1])
for ix in test_df[test_df.pos.str.contains("PRP")].pos.index:
if pd.notnull(test_df["mention_ids"][ix]) and "PRP" in test_df["pos"][ix]:
mention_id_options = test_df["mention_ids"].iloc[ix].split(" ")
for j in mention_id_options:
mention_id = int(j)
if (mention_id in sln.keys()):
df.at[ix, "mention_ids"] = sln[mention_id]
return df
result_df = better_resolver(train_df, dev_df)["mention_ids"]
print("Accuracy of Model: {:.4f}".format(check_accuracy(result_df, dev_df)))
# +
def better_resolver(train_df, test_df):
sln = {}
X_train, Y_train = get_train_test(train_df)
X_test = get_train(test_df)
df = pd.DataFrame(columns=['mention_ids'])
random_forest = RandomForestClassifier(n_estimators=1000)
random_forest.fit(X_train, Y_train)
Y_pred = random_forest.predict_proba(X_test)[:,1]
for i in range(X_test.shape[0]):
if Y_pred[i] > .50:
val = X_test[i]
sln[val[0]] = str(val[1])
for ix in test_df[test_df.pos.str.contains("PRP")].pos.index:
if pd.notnull(test_df["mention_ids"][ix]) and "PRP" in test_df["pos"][ix]:
mention_id_options = test_df["mention_ids"].iloc[ix].split(" ")
for j in mention_id_options:
mention_id = int(j)
if (mention_id in sln.keys()):
df.at[ix, "mention_ids"] = sln[mention_id]
return df
result_df = better_resolver(train_df, dev_df)["mention_ids"]
print("Accuracy of Model: {:.4f}".format(check_accuracy(result_df, dev_df)))
# -
| 18,404 |
/Project/.ipynb_checkpoints/Intro to Pandas I-checkpoint.ipynb | 355c8801465fac0bf407f51c6105550102185ac8 | [] | no_license | todormanev956/love2late | https://github.com/todormanev956/love2late | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 308,429 | # ---
# jupyter:
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# extension: .py
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# language: python
# name: neale_myenv
# ---
# ## Plotting lat-lon and ZM
# +
### RBN: 18 Aug 2020: Set up panel plots that can selectively plot full field, obs., bias, and run differences. ###
import numpy as np
import xarray as xr
import pandas as pd
from metpy.interpolate import log_interpolate_1d
from metpy.units import units
import matplotlib.pyplot as mp
from matplotlib import cm
from mpl_toolkits.axes_grid1 import AxesGrid
from mpl_toolkits.basemap import Basemap as bmap
import matplotlib as mpl
import cartopy
import cartopy.crs as ccrs
import cartopy.util as cutil
import cartopy.feature as cfeature # Map features
from cartopy.mpl.geoaxes import GeoAxes
from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter
from sys import exit
# #%xmode Minimal ## Fortran-like error tracebacks (doesn't seem to work though)
# +
# Set up case/data variables
dir_root = '/glade/p/cgd/amp/people/hannay/amwg/climo/'
ldiff_ctrl = True # Difference compared to the control?
lp3d_if_read_in = True # Plot 3D zonal average if it is read in for another variable.
seas = np.array(['MAM']) # If 2 seasons does 2 columns, if 1 does successive rows.
var_name = 'PRECT'
## Cases to focus on
#case_desc = np.array(['C6','rC5','rUW','rMG1','rC5pm','rpfrac','rZMc'])
#case_desc = np.array(['C6','C5','rUW','rUW'])
#case_desc = np.array(['C6','rC5','rUW','rMG1','rC5p','rC5pm','rpfrac','rice'])
# TMQ #
#case_desc = np.array(['C6','C5','rUW','rUW'])
#PRECT
#case_desc = np.array(['C6','C5','rC5','rUW','rUWp','rMG1','rpfrac','rTMS','rZMc','rZMp','rice','rpremit','rM3'])
#SWCF
#case_desc = np.array(['C6','rC5','rUW','rMG1','rC5p','rC5pm','rpfrac','rice'])
#LWCF
#case_desc = np.array(['C6','rC5','rUW','rMG1','rUWp','rC5pm','rice','rZMc'])
#case_desc = np.array(['C6','rC5','rUW','rUWp','rMG1','rpfrac','rice','rC5pm','rZMc'])
#case_desc = np.array(['C6','rC5','rCE2i',])
#case_desc = np.array(['C6','rC5','rUW','rMG1'])
#AODVIS
#case_desc = np.array(['C6','rC5','rUW','rUWp','rC5pm','rC5pdust','rC5psalt','rTMS'])
##case_desc = np.array(['C6','rC5','rUW','rUWp'])
#SST cases
#case_desc = np.array(['C6','AMdsst','CE2','CE2sst','CE2dsst','CE2csst'])
#case_lname = np.array(['AMIP (monthly)','AMIP (daily)','Coupled','Coupled (monthly)','Coupled (daily)','Coupled (climo.)'])
# Limited SST cases
case_desc = np.array(['C6','CAMdsst','CE2','CE2sst','C5','CE1'])
case_lname = np.array(['AMIP (monthly SSTs)','AMIP (daily SSTs)','Coupled','Coupled (monthly SSTs)','CAM5','CESM1'])
lcase_lname = True
###################
nrow = 2 ; ncol = 3 # Rows and columns
# #of contour intervals
ncint = 20 ; ancint = 20
# Domain
lat_min = -30. ; lat_max = 30.
lon_min = 180-160. ; lon_max = 180-60.
lon_ave_w = 120. ; lon_ave_e = 160.
# -
# ## Variable, Case, Season and Plot Type Options
# <p style="color:violet;">Case and Variable Options, with Attributes
# +
# List of case info.
cases_df = cam_revert_list()
ncases = case_desc.size
# List of variable info.
pvars = {}
pvars['PRECT'] = ['Precipitation','mm/day','2d','x','x',
86400.*1000,2.,20.,-2.,2.,'terrain_r','PRGn']
pvars['LHFLX'] = ['Surface Latent Heat Flux','W/m^2','2d','x','x',
1,100.,300.,-100.,100.,'PuBuGn','PRGn']
pvars['SWCF'] = ['Short Wave Cloud Forcing','W/m^2','2d','x','x',
1.,-100,-10.,-50.,50.,'Purples','RdBu_r']
pvars['LWCF'] = ['Long Wave Cloud Forcing','W/m^2','2d','x','x',
1.,10.,85.,-30.,30.,'Reds','RdBu_r']
pvars['CLDTOT'] = ['Total Cloud','%','2d','x','x',
100.,0,100.,-30.,30.,'PuBuGn','PRGn']
pvars['CLDLOW'] = ['Total Cloud','%','2d','x','x',
100.,0,100.,-30.,30.,'PuBuGn','PRGn']
pvars['TMQ'] = ['Precipitable Water','mm','2d','x','x',
1,0.,60.,-8.,8.,'terrain_r','PRGn']
pvars['T800'] = ['800-mb Temperature','m/s','3d','T',800.,
1.,0,100.,-30.,30.,'PuBuGn','PRGn']
pvars['Z500'] = ['500-mb Geopotential Height','m','3d','Z3',500.,
1.,0,100.,-30.,30.,'PuBuGn','PRGn']
pvars['U200'] = ['200-mb Zonal Wind','m/s','3d','U',200.,
1.,-20,80.,-10.,10.,'PuBuGn','PRGn']
pvars['U850'] = ['850-mb Zonal Wind','m/s','3d','U',850.,
1.,-40,40.,-10.,10.,'PRGn','PRGn']
pvars['U10'] = ['10-m Wind Speed','m/s','2d','x','x',
1.,0,10.,-5.,5.,'PuBuGn','PRGn']
pvars['TAUX'] = ['Surface Zonal Stress','m/s','2d','x','x',
1.,-0.4,0.4,-0.1,0.1,'Purples','PRGn']
pvars['AODVIS'] = ['Aeorosol Optical Depth','m','2d','x','x',
1.,0.,1.5,-0.5,0.5,'terrain_r','PRGn']
## Prefix letters for figures.
fig_let = np.array(['(a) ','(b) ','(c) ','(d) ','(e) ','(f) ','(g) ','(h) ','(i) ','(j) ','(k) ','(l) ','(m) '])
# -
# ## Determine if 3D fields are required from the climo. files
# +
pvars_df = pd.DataFrame.from_dict(pvars, orient='index',columns=['long name','munits','dim_num','3d_var','pres_lev','mscale','cmin','cmax','acmin','acmax','cmap','acmap'])
print(pvars_df)
var_name_in = var_name # Could ultimately be different from var_name as 3D variable might be required.
### Do we need to find a p-level variable from
# list out keys and values separately
dim_var_file = pvars_df.loc[var_name]['dim_num']
l3d_var = True if '3d' in dim_var_file else False
#### Construct 2D pressure field from 3d history file field ##
if l3d_var:
var_name_in = pvars_df.loc[var_name]['3d_var'] # Which 3D variable is need for this field?
plev_val = pvars_df.loc[var_name]['pres_lev']
print('** ',var_name,' will be calculated from 3d variable ',var_name_in,' **')
else :
print('** ',var_name,' available on history file, no pressure interpolation required **')
lp3d_if_read_in = False # Cannot plot the 3D field
# -
# ## Main Plotting Loop
# +
# Check #case and plot spaces.
ncases = case_desc.size
nplots = nrow*ncol
if ncases > nplots:
print('')
print('*** nrow*ncol does not math ncases - exiting ***')
exit('nplot,ncase mi-match')
########## SET UP LAT LON PLOTS ##############
nseas = seas.size # Different for different seasons
if nseas==1:
axes_pad = 0.4 ; share_all=False ; cbar_location='right' ; cbar_mode='each' ; cbar_pad = 0.02 ; cbar_size = '2%'
else:
axes_pad = 0.1 ; share_all=False ; cbar_location='right' ; cbar_mode='edge' ; cbar_pad = 0.02 ; cbar_size = '2%'
# Grab/Process data
mscale = pvars_df.loc[var_name]['mscale']
# Set up graphics
#pproj = ccrs.Mollweide(central_longitude=0.0)
pproj = ccrs.PlateCarree(central_longitude=180.0)
#pproj = ccrs.EckertV(central_longitude=-180.)
axes_class = (GeoAxes,dict(map_projection=pproj))
#mp.rcParams["mpl_toolkits.legacy_colorbar"] = False
fig = mp.figure(figsize=(20, 13))
axgr = AxesGrid(fig, 111, axes_class=axes_class,
nrows_ncols=(nrow, ncol),
axes_pad=axes_pad,
share_all=share_all,
cbar_location=cbar_location,
cbar_mode=cbar_mode,
cbar_pad=cbar_pad,
cbar_size='2%',
label_mode='') # note the empty label_mode
# lat,lon Contouring choices #
#mp.close()
cmin = pvars_df.loc[var_name]['cmin'] ; cmax = pvars_df.loc[var_name]['cmax']
acmin = pvars_df.loc[var_name]['acmin']; acmax = pvars_df.loc[var_name]['acmax']
dcont = (cmax-cmin)/ncint ; adcont = (acmax-acmin)/ancint
plevels = np.arange(cmin,cmax+dcont,dcont,dtype=np.float)
aplevels = np.arange(acmin,acmax+adcont,adcont,dtype=np.float)
if var_name == 'PRECT':
plevels = np.array([1,2,3,4,5,8,10,12,15,20,25,30])
aplevels = 0.5*np.array([-15,-12,-8,-6,-4,-3,-2,-1,0,1,2,3,4,6,8,12,15])
if var_name == 'AODVIS':
plevels = np.array([0.05,0.1,0.15,0.2,0.3,0.4,0.6,0.8,1.0,1.5,2.0,2.5])
aplevels = np.array([-1.,-0.5,-0.2,-0.1,-0.05,-0.025,-0.01,-0.005,0.,0.005,0.01,0.025,0.05,0.1,0.2,0.5,1.])
############### SETUP ZONAL AVERAGE PLOTS ################
if lp3d_if_read_in:
fig_zm,axs_zm = mp.subplots(nrows=nrow, ncols=ncol,figsize=[16, 10])
# These array counters are just for the ax of the zm plots.
irow = np.zeros((nplots),dtype=int)
icol = np.zeros((nplots),dtype=int)
for ii in range(0,nrow): irow[ii*ncol:ii*ncol+ncol] = ii
for ij in range(0,nrow): icol[ij*ncol:ij*ncol+ncol] = np.arange(0,ncol,1)
#### PLOTTING ###
print('+++++ Plotting +++++ '+var_name)
print('# cases = ',ncases)
pvar_ctrl = None
###################################
#### LOOP Cases and Subfigures ####
###################################
for iseas, this_seas in enumerate(seas):
pvar3d_ctrl = None
pvar_ctrl = None
if nseas==1:
isub0 = 0 ; isub1 = ncases ; isub_step = 1
else :
isub0 = iseas ; isub1 = 2*ncases+iseas-nseas+2 ; isub_step = nseas
iplots = np.arange(isub0,isub1,isub_step)
print('++++ SEASON = ',seas[iseas])
for icase, ax in enumerate(axgr[isub0:isub1:isub_step]):
pvar = None # Reset
cdesc = case_desc[icase]
cname = cases_df.loc[cdesc]['run name']
mscale = pvars_df.loc[var_name]['mscale']
print(icase)
izm = irow[icase] ; jzm = icol[icase] # Paneling indices
if cdesc=='CE2':
file_in = dir_root+cname+'/yrs_1979-2005/'+cname+'_'+this_seas+'_climo.nc'
else :
file_in = dir_root+cname+'/0.9x1.25/'+cname+'_'+this_seas+'_climo.nc'
print('')
print('Case ',(icase+1),' of ',ncases,' -> '+cdesc+' - '+cname)
print('-File = '+file_in)
print('')
## Read in variable data ##
case_nc = xr.open_dataset(file_in,engine='netcdf4')
# Composite variables #
if var_name == 'PRECT':
pvar = case_nc['PRECC'].isel(time=0)+case_nc['PRECL'].isel(time=0)
# Interpolated from 3D variable #
if l3d_var:
print('++ Interpolating from 3D field ##')
pvar3d = case_nc[var_name_in].isel(time=0) # Read in 3D variable
# pvar3d = units.Quantity(case_nc[var_name_in])
pvar3d.squeeze()
print('')
pres_levu = plev_val * units.hPa # Set interpolation level (in hPa)
# Read in vars required for interpolation and 3D pressure values
ps,P0,hyam,hybm = case_nc['PS'].isel(time=0).squeeze(),case_nc['P0'],case_nc['hyam'],case_nc['hybm']
pres_lev = pvar3d.copy(deep=True) # Make meta copy from the 3d variable
pres_lev = hyam*P0 + hybm*ps # Change values to 3D pressure.
pres_lev = units.Quantity(pres_lev, 'Pa') # Attach metpy units
# Perform vertical interpolation
pvar = ps.copy(deep=True) # Copy off meta data from the 2D PS field.
pvar = log_interpolate_1d(pres_levu, pres_lev, pvar3d, axis=0) # Annoying as all the meta data is lost.
pvar = pvar.squeeze()
pvar = xr.DataArray(pvar, coords=[pvar3d.lat, pvar3d.lon], dims=["lat", "lon"])
# Save off mean/ctrl
if pvar3d_ctrl is None: pvar3d_ctrl = pvar3d
if icase > 0 and ldiff_ctrl:
pvar3d = np.subtract(pvar3d,pvar3d_ctrl)
pvar3d = pvar3d.interp(lat=pvar3d_ctrl.lat,lon=pvar3d_ctrl.lon) if cdesc == 'C5' else pvar3d
# Standard 2D variable read.
if pvar is None:
pvar = case_nc[var_name_in].isel(time=0).squeeze()
pvar = mscale*pvar # Scale to useful units.
lon = case_nc['lon']
lat = case_nc['lat']
## Retain control case data ##
if pvar_ctrl is None: pvar_ctrl = pvar
## Contouring (full or anom) ##
clevels = plevels
levs_4colbar = clevels
cmap = pvars_df.loc[var_name]['cmap']
## Modify if anom plot ##
if icase > 0 and ldiff_ctrl:
# I think CAM5 coords are slighly misalligned so have to interpolate to the pvar_ctrl (mostly CAM6/C6)
pvar = pvar.interp(lat=pvar_ctrl.lat,lon=pvar_ctrl.lon) if cdesc == 'C5' else pvar
pvar = np.subtract(pvar,pvar_ctrl)
levs_4colbar = aplevels
cmap = pvars_df.loc[var_name]['acmap']
clevels = levs_4colbar[abs(levs_4colbar) > 1.e-15] # Remove zero contour, but somtimes it can be very small as well.
## Plotting for each ax ##
# Domain, this seems flakey for cartopy ##
ax.set_extent([lon_min, lon_max, lat_min, lat_max], crs=pproj)
ax.coastlines(linewidth=1)
ax.gridlines()
ax.add_feature(cartopy.feature.LAND, zorder=0)
# ax.set_xticks(np.linspace(180., 180, 5), crs=pproj)
if jzm==0 and nseas==1: # Just the left hand panels.
ax.set_yticks(np.linspace(lat_min, lat_max, 5), crs=pproj)
lat_formatter = LatitudeFormatter()
ax.yaxis.set_major_formatter(lat_formatter)
## Add Cyclic Point ##
pvarc,lonc = cutil.add_cyclic_point(pvar,coord=lon)
## Translate to -180->+180 - maddening
ilon_pivot = int(lon.size/2)+1
pvarc = np.roll(pvarc, ilon_pivot, axis=1) # 144 points will roll you from 0-360 ->-180-180 data
## CONTOUR PLOT (normalize colorbar for non-linear intervals) ##
norm = cm.colors.BoundaryNorm(boundaries=levs_4colbar, ncolors=256)
pplot = ax.contourf(lonc, lat, pvarc,transform=pproj,cmap=cmap, extend='both',levels=clevels,norm=norm)
#### COLORBARS - Don't need one on every figure. #####
axgr.cbar_axes[icase].colorbar(pplot) # Plot individual color bars before line contour screws it up!
pplot = mpl.colorbar.ColorbarBase(axgr.cbar_axes[icase], ticks=levs_4colbar[::2], # Every other tick.
cmap=cmap,boundaries=levs_4colbar,orientation='vertical',norm=norm)
axgr.cbar_axes[icase].tick_params(labelsize=8)
if ldiff_ctrl: # Make sure smallest contour intervals are white
pplot = ax.contourf(lonc, lat, pvarc,levels=[-min(abs(clevels)),min(abs(clevels))], colors='w')
### Remove the colorbar entirely for these panels (just need two, 1 for full and 1 for anoms)
if icase in np.arange(2,ncases,1) and iseas==nseas-1:
pplot = mpl.colorbar.ColorbarBase(axgr.cbar_axes[icase]).remove()
#### LINE PLOT ####
pplot = ax.contour (lonc, lat, pvarc,transform=pproj
,levels=clevels,colors='black',linewidths=0.5)
#### ADD PLOT TEXT/TITLE ####
if icase==0 and nseas>1 :
bbox=dict(boxstyle = "square",facecolor = "white")
ax.text(-170., 40., seas[iseas],fontsize=20,fontweight='bold',bbox=bbox,zorder=10)
if iseas==0:
cdesc_fig = cdesc if not lcase_lname else case_lname[icase]
ax.text(lon_min+2, lat_min+3., fig_let[icase]+cdesc_fig,fontsize=20,fontweight='bold',backgroundcolor = 'white',zorder=10)
#### CONTOUR LABELING ####
ax.clabel(
pplot, # Typically best results when labelling line contours.
colors=['black'],
fontsize=8,
manual=False, # Automatic placement vs manual placement.
inline=False, # Cut the line where the label will be placed.
fmt=' {:.0f} '.format, # Labels as integers, with some extra space.
)
## ZM PLOTS ##
if lp3d_if_read_in:
pvar_zm = pvar3d.loc[:,lat_min:lat_max,lon_ave_w:lon_ave_e].mean(dim='lon') # Calc. zonal average
lat_p = lat.loc[lat_min:lat_max]
# print(axs_zm[izm,jzm].__dict__)
if izm==nrow-1:
axs_zm[izm,jzm].set_xlabel("Latitude", fontsize = 8)
if jzm==0:
axs_zm[izm,jzm].set_ylabel("Pressure (mb)", fontsize = 8)
axs_zm[izm,jzm].set_xticks(np.linspace(-90, 90, 7))
axs_zm[izm,jzm].set_title(fig_let[icase]+cdesc, x=0.72, y=0.02,fontweight ="bold",fontsize = 10,
horizontalalignment='left',backgroundcolor = 'white')
zm_plot = axs_zm[izm,jzm].contourf(lat_p, pvar_zm.lev, pvar_zm,levels=clevels,cmap=cmap,extend='both')
fig_zm.colorbar(zm_plot,ax=axs_zm[izm,jzm],ticks=levs_4colbar[::2],pad=0.01)
axs_zm[izm,jzm].tick_params(labelsize=8) # Change axis values font height.
# Contouring
zm_plot = axs_zm[izm,jzm].contourf(lat_p, pvar_zm.lev, pvar_zm,levels=[-1,1], colors='w')
zm_plot = axs_zm[izm,jzm].contour(lat_p, pvar_zm.lev, pvar_zm,colors='black',linewidths=1.0,levels=clevels)
axs_zm[izm,jzm].invert_yaxis()
axs_zm[izm,jzm].set_ylim([1000.,50.])
## Contour labaling ##
ax.clabel(
zm_plot, # Typically best results when labelling line contours.
colors=['black'],
fontsize=10,
manual=False, # Automatic placement vs manual placement.
inline=True, # Cut the line where the label will be placed.
fmt=' {:.0f} '.format) # Labels as integers, with some extra space.
# mp.close()
del(pvar,pvar_ctrl,lon)
# Output fig
if nseas==1:
mp.savefig('revert2_sst_freq_'+var_name+'_'+seas[0]+'.png', dpi=300, bbox_inches='tight')
else :
mp.savefig('revert2_sst_freq_'+var_name+'.png', dpi=300, bbox_inches='tight')
#if l3d_var:
# mp.savefig('CAM6_oview_zm_'+var_name_in+'_'+seas+'.png', dpi=300, bbox_inches='tight')
# -
# ## Control, revert and SST simulation names
# +
#####################################
# CAM6 Revert Experiments + others
#####################################
def cam_revert_list():
rl = {} # Revert List
# Releases
rl['C4'] = ['f40.1979_amip.track1.1deg.001']
rl['C5'] = ['30L_cam5301_FAMIP.001']
rl['C6'] = ['f.e20.FHIST.f09_f09.cesm2_1.001']
rl['CC4'] = ['b40.20th.track1.1deg.012']
rl['CE1'] = ['b.e11.B20TRC5CNBDRD.f09_g16.001']
rl['CE2'] = ['b.e21.BHIST.f09_g17.CMIP6-historical.001']
# Reverts
rl['rC5now'] = ['f.e20.FHIST.f09_f09.cesm2_1_cam5.001']
rl['rC5'] = ['f.e20.FHIST.f09_f09.cesm2_1_true-cam5.001']
rl['rC5t'] = ['f.e20.FHIST.f09_f09.cesm2_1_true-cam5_param_topo.001']
rl['rUWold'] = ['f.e20.FHIST.f09_f09.cesm2_1_uw.001']
rl['rGW'] = ['f.e20.FHIST.f09_f09.cesm2_1_iogw.001']
rl['rZMc'] = ['f.e20.FHIST.f09_f09.cesm2_1_capeten.001']
rl['rMG1'] = ['f.e20.FHIST.f09_f09.cesm2_1_mg1.001']
rl['rSB'] = ['f.e20.FHIST.f09_f09.cesm2_1_sb.002']
rl['rTMS'] = ['f.e20.FHIST.f09_f09.cesm2_1_tms.001']
rl['rCE2i'] = ['f.e20.FHIST.f09_f09.cesm2_1_revert125.001']
rl['rC5p'] = ['f.e20.FHIST.f09_f09.cesm2_1_revertcam5param.001']
rl['rC5pm'] = ['f.e20.FHIST.f09_f09.cesm2_1_revertcam5param.002']
rl['rZMp'] = ['f.e20.FHIST.f09_f09.cesm2_1_cam5_zmconv.001']
rl['rM3'] = ['f.e20.FHIST.f09_f09.cesm2_1_mam3.001']
rl['rUW'] = ['f.e20.FHIST.f09_f09.cesm2_1_uw.002']
rl['rUWp'] = ['f.e20.FHIST.f09_f09.cesm2_1_uw.003']
rl['rMG1ii'] = ['f.e20.FHIST.f09_f09.cesm2_1_mg1.002']
rl['rice'] = ['f.e20.FHIST.f09_f09.cesm2_1_ice-micro.001']
rl['rpfrac'] = ['f.e20.FHIST.f09_f09.cesm2_1_precip_frac_method.001']
rl['rpremit'] = ['f.e20.FHIST.f09_f09.cesm2_1_cld_premit.001']
rl['rC5psalt'] = ['f.e20.FHIST.f09_f09.cesm2_1_revertc5seasalt.001']
rl['rC5pdust'] = ['f.e20.FHIST.f09_f09.cesm2_1_revertc5dust.001']
rl['rL30'] = ['f.e20.FHIST.f09_f09.cesm2_1_L30.001']
# SST configs
rl['AMdsst'] = ['f.e20.FHIST.f09_f09.cesm2_1_reynolds_daily_sst.006']
rl['CE2dsst'] = ['f.e20.FHIST.f09_f09.cesm2_1_coupled-sst-amip_daily.001']
rl['CE2sst'] = ['f.e20.FHIST.f09_f09.cesm2_1_coupled-sst-amip.001']
rl['CE2csst'] = ['f.e20.FHIST.f09_f09.cesm2_1_coupled-sst-climo.001']
# Data frame
rl_df = pd.DataFrame.from_dict(rl, orient='index',columns=['run name'])
return rl_df
# -
| 20,410 |
/ML/Logistic_Regression.ipynb | 33455edc7c408dc0890dcf95e7881dc310d96262 | [] | no_license | 321HG/SyPy | https://github.com/321HG/SyPy | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 5,023,628 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 2
# language: python
# name: python2
# ---
import ctypes
from VisionException import * #
# # Describes Class Servo - Communicate, Write and read from Vision Servo-motors
# +
#des-iPython class Servo(object):
#ini-tab -> Inicio classe
# -
# # visionlib
# .
# doxygen-iPython
__visionlib = None #self-iPython __visionlib
# # Constructor Class
# Initializes the communication with the servos.
# doxygen-iPython
# +
#des-iPython def __init__(self,posTILT,posPAN):
#ini-tab -> Inicio construtor
print 'Start the Class Servo'
# Using shared memory from C++ functions
# Loads Vision library in the path /build/lib/libvision.so (Needs compilation and build)
ctypes.cdll.LoadLibrary('../build/lib/libvision.so')
# Calls lybrary that contains C++ functions
__visionlib = ctypes.CDLL('../build/lib/libvision.so')
# Defines return type (boolean)
__visionlib.initServo.restype = ctypes.c_bool
# Conects to Pan and tilt servos
if __visionlib.initServo(ctypes.c_int(posTILT), ctypes.c_int(posPAN)):
# Error treatament, if not connected to servos exit(0)
raise VisionException(4, '')
# Define return type of the method dxlReadByte (int)
__visionlib.dxlReadByte.restype = ctypes.c_int
# Define return type of the method dxlReadWord (int)
__visionlib.dxlReadWord.restype = ctypes.c_int
# Define initial torque with 50% to servo ID=20, parameter 34 seted with 512
__visionlib.dxlWriteWord(ctypes.c_int(20), ctypes.c_int(34), ctypes.c_int(512));
# Define initial torque with 50% to servo ID=19, parameter 34 seted with 512
__visionlib.dxlWriteWord(ctypes.c_int(19), ctypes.c_int(34), ctypes.c_int(512));
# +
#end-tab -> Fim construtor
# -
# # readByte
# Reads a byte from servo defined by ID.
# doxygen-iPython
#edes-iPython def readByte(self, ID, Pos):
def readByte(ID, Pos):
# Returns a byte from servo defined by ID in pos position
return __visionlib.dxlReadByte( ctypes.c_int(ID), ctypes.c_int(Pos))
# # readWord
# Reads a word from servo defined by ID.
# doxygen-iPython
#edes-iPython def readWord(self, ID, Pos):
def readWord(ID, Pos):
# Returns a word from servo defined by ID in pos position
return __visionlib.dxlReadWord( ctypes.c_int(ID), ctypes.c_int(Pos))
# # writeByte
# Writes a byte from servo defined by ID.
# doxygen-iPython
#edes-iPython def writeByte(self, ID, Pos, value):
def writeByte(ID, Pos, value):
# Writes a byte in servo ID, Position Pos, and the value to be written
__visionlib.dxlWriteByte( ctypes.c_int(ID), ctypes.c_int(Pos), ctypes.c_int(value))
# # writeWord
# Writes a word from servo defined by ID.
# doxygen-iPython
#edes-iPython def writeWord(self, ID, Pos, value):
def writeWord(ID, Pos, value):
# Writes a word in servo ID, Position Pos, and the value to be written
__visionlib.dxlWriteWord( ctypes.c_int(ID), ctypes.c_int(Pos), ctypes.c_int(value))
# +
#end-tab -> Fim classe
ons = logmodel.predict(xtest)
from sklearn.metrics import classification_report
print(classification_report(ytest, predictions))
from sklearn.metrics import confusion_matrix
confusion_matrix(ytest, predictions)
| 3,314 |
/frozen layer.ipynb | 1eff1570e509363efc89edfbde48b3c159fae1c2 | [
"MIT"
] | permissive | tianyining/Herring-2021-Hybrid-ERT-Inversion | https://github.com/tianyining/Herring-2021-Hybrid-ERT-Inversion | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 359,606 | # ---
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# ---
# +
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
def lorenz(x, y, z, s=10, r=28, b=2.667):
x_dot = s*(y - x)
y_dot = r*x - y - x*z
z_dot = x*y - b*z
return x_dot, y_dot, z_dot
dt = 0.01
stepCnt = 10000
# Need one more for the initial values
xs = np.empty((stepCnt + 1,))
ys = np.empty((stepCnt + 1,))
zs = np.empty((stepCnt + 1,))
# Setting initial values
xs[0], ys[0], zs[0] = (0.005, 00.25, 0.1)
# Stepping through "time".
for i in range(stepCnt):
# Derivatives of the X, Y, Z state
x_dot, y_dot, z_dot = lorenz(xs[i], ys[i], zs[i])
xs[i + 1] = xs[i] + (x_dot * dt)
ys[i + 1] = ys[i] + (y_dot * dt)
zs[i + 1] = zs[i] + (z_dot * dt)
fig = plt.figure()
ax = fig.gca(projection='3d')
ax.plot(xs, ys, zs, lw=0.5)
ax.set_xlabel("X Axis")
ax.set_ylabel("Y Axis")
ax.set_zlabel("Z Axis")
ax.set_title("Lorenz Attractor")
plt.show()
# +
# now plot two-dimensional cuts of the three-dimensional phase space
fig, ax = plt.subplots(1, 3, sharex=False, sharey=False, figsize=(17, 6))
# plot the x values vs the y values
ax[0].plot(x, y, color='r', alpha=0.7, linewidth=0.3)
ax[0].set_title('x-y phase plane', fontproperties=title_font)
# plot the x values vs the z values
ax[1].plot(x, z, color='m', alpha=0.7, linewidth=0.3)
ax[1].set_title('x-z phase plane', fontproperties=title_font)
# plot the y values vs the z values
ax[2].plot(y, z, color='b', alpha=0.7, linewidth=0.3)
ax[2].set_title('y-z phase plane', fontproperties=title_font)
fig.savefig('{}/lorenz-attractor-phase-plane.png'.format(save_folder), dpi=180, bbox_inches='tight')
plt.show()
# -
| 1,923 |
/ANN/DropOutANN.ipynb | d97786861ab38dc2ad8f56ad5eb5d8864844a81c | [] | no_license | Ardibid/DeepLearning | https://github.com/Ardibid/DeepLearning | 0 | 2 | null | null | null | null | Jupyter Notebook | false | false | .py | 132,551 | # ---
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# language: python
# name: python3
# ---
# # Drop out regularization
# based on: Udemy DEEP LEARNING A-Z™ Course
# Drop out is a solution for overfitting in ML.<br>
# It shows itself as high variance in k-fold cross validation. <br>
# It means some high accuracy and some low accuracy, result a high variance. <br>
#
# ## How it works:
# At each epoch, the NN removes some of the nodes from the calculations. It will break down the correlations between the nodes and data and see if it still works. If so, those nodes will be removed finally, or get zero weights.<br>
# To add it, we need to add it to be added to each layer.
#
# ### Setup
# To setup, we need to have these packages installed: <br>
# 1- Tensorflow<br>
# 2- Keras<br>
# 3- Theano | we will not work with this <br>
# ### A classification problem
# based on the multi-dimensional data
# +
import tensorflow as tf
# Theano is a mathematical library based on numpy, that can run on the GPU
#import theano
import keras
# we will need these for the ANN
from keras.models import Sequential
from keras.layers import Dense
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# will need this for data pre-processing
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
# will need this for splitting the data set
from sklearn.model_selection import train_test_split
# will need it for the scaling!
from sklearn.preprocessing import StandardScaler
# will need to calculate the loss
from sklearn.metrics import confusion_matrix
################################################################################
# will need it for the cross validation
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import cross_val_score
################################################################################
# will need it for the drop out
from keras.layers import Dropout
# -
# ## Data pre-processing
# Reading data from the CSV file and make it ready for the next steps.
# ### Imporet data
dataset = pd.read_csv('Churn_Modelling.csv')
# we only need features from 3 to 12 (upper bound is not inclusive)
X = dataset.iloc[:,3:13].values
y = dataset.iloc[:,13].values
print (X[0])
# ### Encoding categorical data
# There are two variables that are categorial, country and gender <br>
# So, we convert them into one integer values! So if we apply it to country, it will replcae France, Germany, and Spain with 0,1,2! Nice!
# +
# this will handle the country
labelEncoderX01 = LabelEncoder()
X[:,1] = labelEncoderX01.fit_transform(X[:,1])
# this will handle the gender
labelEncoderX02 = LabelEncoder()
X[:,2] = labelEncoderX02.fit_transform(X[:,2])
# -
# ### Dummy coding
# Now, we need to make some dummy coding! This will prevent the data to make this impression that categoprial values are actually impying any order. Spain is not higher or lower than France nor Germany.
# We don't ned to do it with gender, since it only has two variables.
# It basically make that 1,2,3 for countries into 3 different variables:
# France 0, 0, 1
# Germany 0, 1, 0
# Spain 1, 0, 0
# But we need to remove one of them, because we don't need the third one (if it is not German or French, then it is Spanish!)
oneHotEncoder = OneHotEncoder(categorical_features=[1])
X= oneHotEncoder.fit_transform(X).toarray()
X = X[:, 1:]
# ### Splitting the data into training and test
# Easy peasy!
XTrain, XTest, yTrain, yTest = train_test_split(X,y, test_size = 0.2, random_state = 0)
# ### Feature Scaling
# We must normalize the data, otherwise one feature is going to dominate the others!
sc = StandardScaler()
# we first need to find the scale
XTrain = sc.fit_transform(XTrain)
# then we apply the same scale to the test set!
XTest = sc.transform(XTest)
# # Cross Validation / K-Fold
# This is a function that makes the ANN architecture like the previous section, but based on the Scikit_learn
def buildClassifier():
classifier = Sequential()
classifier.add(Dense(units= 6, kernel_initializer= "uniform",activation = "relu",input_dim= 11))
# we start from 0.1 and then increase it by 0.1 to get out of overfitting,
# don't go beyond 0.5
classifier.add(Dropout(rate=0.2))
classifier.add(Dense(units= 6, kernel_initializer= "uniform",activation = "relu"))
classifier.add(Dropout(rate = 0.2))
classifier.add(Dense(units= 1, kernel_initializer= "uniform",activation = "sigmoid"))
classifier.compile(optimizer = 'adam', loss ='binary_crossentropy',metrics = ['accuracy'])
return classifier
# Do not add n_jobs = -1 if you use GPU, it will use only one CUDA core!
# %%time
classifier = KerasClassifier(build_fn = buildClassifier, batch_size= 500, epochs = 100)
# returns the 10 accuracies from the 10 fold cross validation
acuracies = cross_val_score(estimator = classifier,X = XTrain, y = yTrain, cv = 10)#, n_jobs = -1)
print(acuracies.mean())
print(acuracies.std())
# +
array([ 0.82875 , 0.82625 , 0.83250001, 0.82624999, 0.84875 ,
0.83000001, 0.85250001, 0.81874999, 0.85250001, 0.84125002])
113s
array([ 0.78625 , 0.79000002, 0.80000001, 0.78250003, 0.81625003,
0.81 , 0.78750002, 0.79624999, 0.79874998, 0.79500002])
33.1s
array([ 0.80624998, 0.79000002, 0.815 , 0.815 , 0.81625003,
0.81 , 0.83375001, 0.79374999, 0.81375003, 0.79500002])
77
0.796000009775
0.0101056905279
# -
| 5,658 |
/machine_learning/dashboard2/bikeavailability_dashboard2.ipynb | 21d2c3b070d8d11cef0799e62f630beaadf74c12 | [] | no_license | blawson1015/beth-culler | https://github.com/blawson1015/beth-culler | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 573,597 | # ---
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# ---
# + [markdown] slideshow={"slide_type": "-"}
# # Reservoir operation modelling
# In this Notebook we will see how we can simulate the operation of water supply reservoir system.
#
# <left><img src="../../util/images/Dam1.gif" width = "800px"><left>
#
# We consider a simple illustrative system where a reservoir is operated to supply water to a domestic consumption node, while ensuring a minimum environmental flow in the downstream river (also called “environmental compensation flow”) and maintaining the water level in the reservoir within prescribed limits. We use a mathematical model to link all the key variables that represent the reservoir dynamics (inflow, storage and outflows) and use model simulation/optimisation to determine the reservoir release scheduling that will best meet the water demand over a coming period of time, given the predicted (or assumed) scenario of future inflows.
# <left> <img src="../../util/images/system_representation_IO0.png" width = "600px"><left>
# ## Import libraries
# Before getting started, let's import some libraries that will be used throughout the Notebook:
# -
from bqplot import pyplot as plt
from bqplot import *
from bqplot.traits import *
import numpy as np
import ipywidgets as widgets
from Modules.Interactive_release_schedule import Interactive_release_single,Interactive_release_double, Interactive_Pareto_front
warnings.filterwarnings('ignore') # to ignore warning messages
# ## The reservoir model
#
# The mathematical model of the reservoir essentially consists of a water balance equation, where the storage (***s***) at a future time step (for example, at the beginning of the next week) is predicted from the storage at the current time (the beginning of the this week) by adding and subtracting the inflows and outflows that will occur during the temporal interval ahead:
#
# $s(t+1) = s(t) + I(t) – E(t) – env(t) - spill(t) – Qreg(t)$
#
# Where
#
# ***s(t)*** = reservoir storage at time-step t, in Vol (for example: ML)
#
# ***I(t)*** = reservoir inflows in the interval [t,t+1], in Vol/time (for example: ML/week).
#
# ***E(t)*** = evaporation from the reservoir surface area in the interval [t,t+1], in Vol/time (for example: ML/week).
#
# ***env(t)*** = environmental compensation flow in the interval [t,t+1], in Vol/time (for example: ML/week).
#
# ***spill(t)*** = outflow through spillways (if any) in the interval [t,t+1], in Vol/time (for example: ML/week).
#
# ***Qreg(t)*** = regulated reservoir release for water supply in the interval [t,t+1], in Vol/time (for example: ML/week).
# <left> <img src="../../util/images/system_representation_IO1.png" width = "600px"><left>
# #### Implementation of the reservoir simulation function
# Here we define a function that implements the reservoir simulation, that is, iteratively apply the mass balance equation and reconstruct the temporal evolution of the reservoir variables over the simulation period
# + code_folding=[]
def syst_sim(N,I,e,d,S0,Smax,env_min,Qreg):
# Declare output variables
S = [0]*(N+1) # reservoir storage in ML
spill = [0]*N # spillage in ML
env = [env_min]*N # environmental compensation flow
S[0] = S0 # initial storage
for t in range(N): # Loop for each time-step (week)
# If at week t the inflow (I) is lower than the minimum environmental compensation (env_min),
# then the environmental compensation (env) = inflow (I)
if env_min >= I[t] :
env[t] = I[t]
# If the minimum environmental compensation is higher than the water resource available (S + I - E)
# then the environmental compensation is equal to the higher value between 0 and the resource available
if env_min >= S[t] + I[t] - e[t]:
env[t] = max(0,S[t] + I[t] - e[t])
# If the demand is higher than the water resource available (S + I - E - env)
# then the release for water supply is equal to the higher value between 0 and the resource available
if d[t] >= S[t] + I[t] - e[t] - env[t]:
Qreg[t] = min(Qreg[t],max(0,S[t] + I[t] - e[t] - env[t]))
# The spillage is equal to the higher value between 0 and the resource available exceeding the reservoir capacity
spill[t] = max(0,S[t] + I[t] - Qreg[t] - env[t] - e[t] - Smax)
# The final storage (initial storage in the next step) is equal to the storage + inflow - outflows
S[t+1] = S[t] + I[t] - Qreg[t] - env[t]- e[t] - spill[t]
return S,env,spill,Qreg
# -
# #### Definition of inflow and demand scenarios
# Let's assume we want to look at the next 8 weeks the number of weeks (so ***N=8***), and assume we have forecasts of inflows and demand for this period.
# +
N = 8 # (weeks) length of the simulation period
I_fore = np.array([15,17,19,11,9,4,3,8]) # (ML/week) time series of inflow forecasts
T_fore = np.array([13,13,17,18,20,22,25,26]) # (degC) time series of temperature forecasts
d_fore = np.array([15]*N)*T_fore/15 # (ML/week) time series of demand forecasts, estimated as a function of temperature
# (this is onviously a very simplified approach, and one could use a much more sophisticated demand model)
# -
# Plot the inflow and demand forecasts:
# Axis characterisitcs
x_sc_1 = LinearScale();y_sc_1 = LinearScale(min=0,max=35)
x_ax_1 = Axis(label='week', scale=x_sc_1);y_ax_1 = Axis(label='ML/week', scale=y_sc_1, orientation='vertical')
# Bar plot
inflow_plot = plt.bar(np.arange(1,N+1),I_fore,colors=['blue'],stroke = 'lightgray',scales={'x': x_sc_1, 'y': y_sc_1},
labels = ['inflow'], display_legend = True)
#Figure characteristics
fig_1a = plt.Figure(marks = [inflow_plot],title = 'Inflow forecast for the next 8 weeks', axes=[x_ax_1, y_ax_1],
layout={'min_width': '1000px', 'max_height': '300px'}, legend_style = {'fill': 'white', 'opacity': 0.5})
widgets.VBox([fig_1a])
# Bar plot (we use the same axis as the weekly inflows figure)
demand_plot = plt.bar(np.arange(1,N+1),d_fore,colors=['gray'],stroke = 'lightgray',opacities = [1]*N,
labels = ['demand'], display_legend = True,
stroke_width = 1,scales={'x': x_sc_1, 'y': y_sc_1})
#Figure characteristics
fig_1b = plt.Figure(marks = [demand_plot],title = 'Demand forecast for the next 8 weeks', axes=[x_ax_1, y_ax_1],
layout={'min_width': '1000px', 'max_height': '300px'}, legend_style = {'fill': 'white', 'opacity': 1})
widgets.VBox([fig_1b])
# #### Definition of other input parameters
# Let's define other variables that are needed for the reservoir system simulation, such as the reservoir storage capacity, the environmental compensation flow, etc.
# +
### Constraints ###
s_max = 150 # (ML) Maximum storage (=reservoir capacity)
s_min = 0 # (ML) Minimum storage (set to zero for now)
env_min = 2 # (ML/week) # Environmental compensation flow
### Initial conditions ###
s_0 = 80 # (ML) # Storage volume at the beginning of the simulation period
e_fore = T_fore*0.1 # (ML/week) Time series of evaporation from the reservoir (this is a very simplified
# approach and could be replaced by a more realistic estimation approach)
# -
# ## Determining the release scheduling by trial and error (manual optimisation)
# Here we want to use the reservoir model to assist the reservoir operator in determining the best scheduling of regulated reservoir releases (Qreg) in response to a certain scenario of inflows. The goal is to minimise the deficit with respect to a prescribed water demand, that is, to minimise the objective function:
#
# $$TSD = \sum_{t=1}^{N} [ \ max( \ 0, \ d(t)-Qreg(t) \ ) \ ]^2 $$
#
# where N is the length of the simulation period that we are considering, and d(t) is the water demand for each time-interval in that period, and TSD stands for Total Squared Deficit. Notice that the function $max(0,...)$ enables us to only count the difference between demand d and release u when this is positive, that is, when the release u is smaller than the demand d, and a water shortage is indeed produced. Also, the squaring is a 'mathematical trick' to make sure that larger deficit amounts are given more weight than smaller ones. This translates the fact that small deficit amounts are easier to mitigate and hence more acceptable, while larger ones can cause disproportionately severe impacts and should be avoided as much as possible.
#
# #### Determining the optimal release scheduling via interactive visualisation
#
# Use the slider to set the release amount for each week in the simulation period, and in doing so try to minimise the Total Squared Deficit.
# Interactive release scheduling
fig_2a,fig_2b,release1,release2,release3,release4,release5,release6,release7,release8 = Interactive_release_single(
N,I_fore,e_fore,d_fore,s_0,s_max,env_min, demand_plot)
Box_layout = widgets.Layout(justify_content='center')
widgets.VBox([widgets.HBox(
[release1,release2,release3,release4,release5,release6,release7,release8],layout=Box_layout),fig_2b,fig_2a],layout=Box_layout)
# **Comment**: clearly it is not possible to fully meet the demand at all times in the simulation period. For example if we fully cover the demand for the first 7 weeks, we drawdown the reservoir to a point that we are forced to dramatically reduce the release in the last week, causing a very severe deficit. A more effective approach is to cause smaller deficits across all time steps, that is, tolerate some small deficits even when in principle we may fully cover the demand, in order to prevent more severe deficits in the later period. This type of approach is called ***hedging*** (see for example [You and Cai 2008](https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2006WR005481)).
#
# Btw, the minimum TSD value that can be achieved is **49, try to beat it!**
#
# ## From single to multi-objective
# Now let's assume that, besides minimising supply deficits, we are also interested in minimising the chances that the reservoir level go below a minimum threshold. This could be, for example, because the quality of the water deteriorates when levels are low, requiring more costly treatment. We measure how well this criterion is satisfied by the following objective function:
#
# $$MSV = \sum_{t=1}^{N} max ( \ rc - S(t) , \ 0)$$
#
# where, again, N is the length of the simulation period, S is the reservoir storage, and rc is the minimum reservoir storage threshold that should preferably not be transpassed (MSV stands for Minimum Storage Violation).
#
# For our case, let's set this threshold to 30 ML:
# Minimum storage threshold
ms = np.array([30]*(N+1)) # in ML
# Now use the slider to set the release amount for each week in the simulation period, in a way that jointly minimise the Total Squared Deficit and the Minimum Storage Violation.
fig_3a,fig_3b,release1,release2,release3,release4,release5,release6,release7,release8 = Interactive_release_double(
N,I_fore,e_fore,d_fore,s_0,s_max,ms,env_min, demand_plot)
Box_layout = widgets.Layout(justify_content='center')
widgets.VBox([widgets.HBox(
[release1,release2,release3,release4,release5,release6,release7,release8],layout=Box_layout),fig_3b,fig_3a],layout=Box_layout)
# ***Comment*** It is possible to find a release scheduling that produce no violation of the minimum storage threshold, although it will produce some supply deficit - the record is **305, can you beat it?**. However, one could also allow some violation of the storage threshold in order to reduce the deficits. The two objectives are conflicting: improving on one of them implies doing worse on the other.
#
# ### References
#
# Deb K. et al (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation, 6(2), 182-197, doi:10.1109/4235.996017.
#
# You J.Y. and Cai X. (2008) Hedging rule for reservoir operations: 1. A theoretical analysis, Water Resour. Res., 44, W01415, doi:10.1029/2006WR005481.
_week(day)
workingday = get_workingday(day)
cnt = 0
user_out.weekday = day_of_week
user_out.workingday = workingday
user_out.season = season
user_out.holiday = holiday
user_out.mnth = month
user_out.temp = temp
user_predictions.weekday = day_of_week
user_predictions.workingday = workingday
user_predictions.season_1 = season1
user_predictions.season_2 = season2
user_predictions.season_3 = season3
user_predictions.season_4 = season4
user_predictions.holiday_0 = holiday_0
user_predictions.holiday_1 = holiday_1
user_predictions.temp = temp
user_predictions.mnth_1 = month_1
user_predictions.mnth_2 = month_2
user_predictions.mnth_3 = month_3
user_predictions.mnth_4 = month_4
user_predictions.mnth_5 = month_5
user_predictions.mnth_6 = month_6
user_predictions.mnth_7 = month_7
user_predictions.mnth_8 = month_8
user_predictions.mnth_9 = month_9
user_predictions.mnth_10 = month_10
user_predictions.mnth_11 = month_11
user_predictions.mnth_12 = month_12
# Create dummy variables of the user dataset
hr = pd.get_dummies(user_predictions['hr'],prefix='hr',drop_first=False)
data1 = pd.concat([user_predictions,hr],axis=1)
data1.drop(['hr'], axis=1,inplace=True)
x4 = data1.drop('cnt',axis=1)
trainedModel = getTrainedModel()
predictions = trainedModel.predict(x4)
user_out.bikes_rented = predictions
user_out['mnth'] = user_out['mnth'].apply(lambda x: calendar.month_abbr[x])
wdi = {0:"Mon", 1:"Tue", 2:"Wed", 3:"Thu", 4:"Fri", 5:"Sat", 6:"Sun"}
user_out["weekday"].replace(wdi, inplace=True)
sdi = {"1":"spring", "2":"summer", "3":"fall", "4":"winter"}
user_out["season"].replace(sdi, inplace=True)
di = {1: "no", 0: "yes"}
user_out["workingday"].replace(di, inplace=True)
di2 = {1: "yes", 0: "no"}
user_out["holiday"].replace(di2, inplace=True)
print(user_out[['mnth', 'weekday', 'temp','season','workingday','holiday', 'hr', 'bikes_rented']])
# Demand per hour
# Negative numbers can be assumed to = 0 bikes rented for that hour
dt = day.strftime("%B %d, %Y")
plt.figure(figsize=(12,6))
plt.ylim(0, 700)
sns.barplot(data = user_out, x = 'hr', y = 'bikes_rented', palette = 'rainbow').set(title= dt)
# Predict the number of bikes rented during the hour of user's choice for each temperature in range 0.0 - 1.0 C, predicted in 0.2 increments
def get_hr_prediction(dy, hr):
hour = hr
day = dy
df = pd.DataFrame()
months = get_months(day)
month = months[0]
month_1 = months[1]
month_2 = months[2]
month_3 = months[3]
month_4 = months[4]
month_5 = months[5]
month_6 = months[6]
month_7 = months[7]
month_8 = months[8]
month_9 = months[9]
month_10 = months[10]
month_11 = months[11]
month_12 = months[12]
season = get_season(day)
seasons = set_seasons(season)
season1 = seasons[0]
season2 = seasons[1]
season3 = seasons[2]
season4 = seasons[3]
holiday = get_holiday(day)
holidays = set_holidays(holiday)
holiday_0 = holidays[0]
holiday_1 = holidays[1]
day_of_week = get_day_of_week(day)
workingday = get_workingday(day)
cnt = 0
user_out.weekday = day_of_week
user_out.workingday = workingday
user_out.season = season
user_out.holiday = holiday
user_out.mnth = month
user_predictions.weekday = day_of_week
user_predictions.workingday = workingday
user_predictions.season_1 = season1
user_predictions.season_2 = season2
user_predictions.season_3 = season3
user_predictions.season_4 = season4
user_predictions.holiday_0 = holiday_0
user_predictions.holiday_1 = holiday_1
user_predictions.mnth_1 = month_1
user_predictions.mnth_2 = month_2
user_predictions.mnth_3 = month_3
user_predictions.mnth_4 = month_4
user_predictions.mnth_5 = month_5
user_predictions.mnth_6 = month_6
user_predictions.mnth_7 = month_7
user_predictions.mnth_8 = month_8
user_predictions.mnth_9 = month_9
user_predictions.mnth_10 = month_10
user_predictions.mnth_11 = month_11
user_predictions.mnth_12 = month_12
# Create dummy variables of the user dataset
hourd = pd.get_dummies(user_predictions['hr'],prefix='hr',drop_first=False)
temp = 0.0
for t in range(0, 12, 2):
user_out.temp = temp
user_predictions.temp = temp
data1 = pd.concat([user_predictions,hourd],axis=1)
data1.drop(['hr'], axis=1,inplace=True)
x5 = data1.drop('cnt',axis=1)
trainedModel = getTrainedModel()
predictions = trainedModel.predict(x5)
user_out.bikes_rented = predictions
df = pd.concat([df, user_out])
temp = temp + 0.2
indexNames = df[df['hr'] != hour].index
df.drop(indexNames, inplace=True)
df['mnth'] = df['mnth'].apply(lambda x: calendar.month_abbr[x])
wdi = {0:"Mon", 1:"Tue", 2:"Wed", 3:"Thu", 4:"Fri", 5:"Sat", 6:"Sun"}
df["weekday"].replace(wdi, inplace=True)
print(df[['mnth','weekday','hr', 'temp', 'bikes_rented']])
dt = day.strftime("%B %d, %Y")
fig = plt.figure(figsize=(20,10))
ax = fig.add_subplot()
ax.plot(df.temp, df.bikes_rented)
ax.set_xlabel("temp in Celsius")
ax.set_ylabel("bikes rented")
ax.set_title("Hour: " + str(hour))
# +
# Make the dashboard interactive and allow users to query for wanted information
# Request a date from the user for the day they would like to check bike availability
from ipywidgets import DOMWidget, ValueWidget, register, interact, interactive, fixed, interact_manual, Layout
from ipywidgets.widgets.interaction import show_inline_matplotlib_plots
from IPython.display import display, clear_output
import ipywidgets as widgets
tom = tomorrow.date()
day_picked = widgets.DatePicker(
description='Pick a Date',
disabled=False,
value = tom
)
def f(daypicked):
print('Date Chosen: {}'.format(daypicked))
out1 = widgets.interactive_output(f, {'daypicked': day_picked})
widgets.HBox([widgets.VBox([day_picked]), out1])
# +
# Demand per hour on a day user has chosen with pre-supplied average temps
# Negative numbers can be assumed to = 0 bikes rented for that hour
from IPython.display import display
daypicked = day_picked.value
button1 = widgets.Button(description="View Predictions")
output1 = widgets.Output()
display(button1, output1)
def on_button_clicked1(b):
with output1:
clear_output()
daypicked = day_picked.value
get_day_prediction(daypicked)
show_inline_matplotlib_plots()
button1.on_click(on_button_clicked1)
# +
# Allow the user to pick an hour to adjust the temperature settings for
hr_picked = widgets.Dropdown(
options=['0','1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23'],
value='12',
description='Pick an Hour:',
disabled=False,
)
def h(hrpicked):
print('Hour Chosen: {}'.format(hrpicked))
out2 = widgets.interactive_output(h, {'hrpicked': hr_picked})
widgets.HBox([widgets.VBox([hr_picked]), out2])
# +
hrpicked = int(hr_picked.value)
print(hrpicked)
tmpvl = user_out["temp"].iloc[hrpicked]
print(tmpvl)
# +
# Allow the user to adjust the temperature to see how changes in temperature affect bike availability
temp_picked = widgets.FloatSlider(
value=tmpvl,
min=0.0,
max=1.0,
step=0.1,
description='Temp Celsius',
disabled=False,
continuous_update=False,
orientation='horizontal',
readout=True,
readout_format='.1f',
)
def t(tmppicked):
print('Temperature Chosen: {}'.format(tmppicked))
out3 = widgets.interactive_output(t, {'tmppicked': temp_picked})
widgets.HBox([widgets.VBox([temp_picked]), out3])
# +
# View how a specific temperature affects bike availability for every hour during day
button2 = widgets.Button(description="View Predictions")
output2 = widgets.Output()
display(button2, output2)
def on_button_clicked2(b):
with output2:
clear_output()
daypicked = day_picked.value
temppicked = temp_picked.value
get_tmp_prediction(daypicked, temppicked)
show_inline_matplotlib_plots()
button2.on_click(on_button_clicked2)
# +
# View how the bike availability at a specfic hour changes as the temperature changes
button3 = widgets.Button(description="View Predictions")
out3 = widgets.Output()
display(button3, out3)
def on_button_clicked3(b):
with out3:
clear_output()
hrpicked = int(hr_picked.value)
daypicked = day_picked.value
get_hr_prediction(daypicked, hrpicked)
show_inline_matplotlib_plots()
button3.on_click(on_button_clicked3)
| 21,190 |
/notebooks/Convolutional Neural Network from scratch via Numpy.ipynb | d0b1f04e273d993429e04b3aa4cfc1c6beb00201 | [] | no_license | mhuang74-data-science-courses/neural-network-101 | https://github.com/mhuang74-data-science-courses/neural-network-101 | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 1,491 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 2
# language: python
# name: python2
# ---
import cv2
import numpy as np
from matplotlib import pyplot as plt
img = cv2.imread('5.png',0)
plt.imshow(img,"gray")
plt.show()
kernel = np.ones((10,10),np.uint8)
eroded = cv2.erode(img,kernel,15)
dilated = cv2.dilate(img,kernel,15)
plt.subplot(131),plt.imshow(img,"gray"),plt.title("original")
plt.subplot(132),plt.imshow(eroded,"gray"),plt.title("eroded")
plt.subplot(133),plt.imshow(dilated,"gray"),plt.title("dilated")
plt.show()
img1 = cv2.imread('opening.png')
img2 = cv2.imread('closing.png')
kernel= np.ones((5,5),np.uint8)
opened_img = cv2.morphologyEx(img1,cv2.MORPH_OPEN,kernel)
closed_img = cv2.morphologyEx(img2,cv2.MORPH_CLOSE,kernel)
plt.subplot(121),plt.imshow(img1),plt.title("original")
plt.subplot(122),plt.imshow(opened_img),plt.title("Opened image")
plt.show()
plt.subplot(121),plt.imshow(img2),plt.title("original")
plt.subplot(122),plt.imshow(closed_img),plt.title("Closed image")
plt.show()
# boundary of the image - difference between opening and closing
kernel = np.ones((2,2),np.uint8)
gradient_img = cv2.morphologyEx(img,cv2.MORPH_GRADIENT,kernel)
plt.imshow(gradient_img,"gray"),plt.title("Boundary")
plt.show()
cv2.getStructuringElement(cv2.MORPH_RECT,(5,5))
cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(5,5))
cv2.getStructuringElement(cv2.MORPH_CROSS,(5,5))
cameraman = cv2.imread('cameraman.png',0)
laplacian_filter = cv2.Laplacian(cameraman,cv2.CV_64F)
sobel_x = cv2.Sobel(cameraman,cv2.CV_64F,1,0,ksize=1)
sobel_y = cv2.Sobel(cameraman,cv2.CV_64F,0,1,ksize=1)
plt.subplot(141),plt.imshow(cameraman,"gray"),plt.title("original")
plt.subplot(142),plt.imshow(laplacian_filter,"gray"),plt.title("laplacian")
plt.subplot(143),plt.imshow(sobel_x,"gray"),plt.title("sobel_x")
plt.subplot(144),plt.imshow(sobel_y,"gray"),plt.title("sobel_y")
plt.show()
plt.imshow(laplacian_filter,"gray")
plt.show()
', 'Hour Ending', 'windGenerationMWh']
df = df.drop('Hour Ending', axis=1)
df = df.set_index('date')
df.index = pd.to_datetime(df.index)
df = df.sort_index()
# Resample to average daily power generation
#df = df.resample('1D', how='mean')
# Append to main DataFrame
dfWind = pd.concat([dfWind, df])
df.head()
# -
# ## Write to CSV
dfWind.to_csv('../data/MISO_wind_hourly.csv')
| 2,538 |
/K-means_Clustering/Project/.ipynb_checkpoints/Project-checkpoint.ipynb | 212a638b70addaf2c892d6e57aa53b82c8f5d572 | [] | no_license | sambilkar/Spark-and-Python-for-Big-Data-with-PySpark | https://github.com/sambilkar/Spark-and-Python-for-Big-Data-with-PySpark | 2 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 10,377 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
btBCG = pd.read_csv('BCG.csv',
na_values='n.a')
btBCG
btBCG = btBCG.interpolate().fillna({})
btBCG
btDPT = pd.read_csv('DPT.csv',
na_values='n.a')
btDPT
btDPT = btDPT.interpolate().fillna({})
btDPT
btCampak = pd.read_csv('Campak.csv',
na_values='n.a')
btCampak
btCampak = btCampak.interpolate().fillna({})
btCampak
btPolio = pd.read_csv('Polio.csv',
na_values='n.a')
btPolio
btPolio = btPolio.interpolate().fillna({})
btPolio
# +
plt.figure('Persentasi balita terimunisasi 1995-2017', figsize = (15,8))
plt.subplot(221)
plt.bar(btBCG['Tahun'], btBCG['% Balita yang pernah mendapat imunisasi BCG'], color = 'r')
plt.title('BCG')
plt.xticks(btBCG['Tahun'], rotation = 90)
plt.subplot(222)
plt.bar(btCampak['Tahun'], btCampak['% Balita yang pernah mendapat imunisasi Campak'], color = 'g')
plt.title('Campak')
plt.xticks(btCampak['Tahun'], rotation = 90)
plt.subplot(223)
plt.bar(btDPT['Tahun'], btDPT['% Balita yang pernah mendapat imunisasi DPT'], color = 'y')
plt.title('DPT')
plt.xticks(btDPT['Tahun'], rotation = 90)
plt.subplot(224)
plt.bar(btPolio['Tahun'], btPolio['% Balita yang pernah mendapat imunisasi Polio'], color = 'b')
plt.title('Polio')
plt.xticks(btPolio['Tahun'], rotation = 90)
plt.tight_layout()
plt.show()
# +
plt.figure('Persentasi balita tak terimunisasi 1995-2017', figsize = (15,8))
plt.subplot(221)
plt.bar(btBCG['Tahun'], 100 - btBCG['% Balita yang pernah mendapat imunisasi BCG'], color = 'r')
plt.title('BCG')
plt.xticks(btBCG['Tahun'], rotation = 90)
plt.subplot(222)
plt.bar(btCampak['Tahun'], 100 - btCampak['% Balita yang pernah mendapat imunisasi Campak'], color = 'g')
plt.title('Campak')
plt.xticks(btCampak['Tahun'], rotation = 90)
plt.subplot(223)
plt.bar(btDPT['Tahun'], 100 - btDPT['% Balita yang pernah mendapat imunisasi DPT'], color = 'y')
plt.title('DPT')
plt.xticks(btDPT['Tahun'], rotation = 90)
plt.subplot(224)
plt.bar(btPolio['Tahun'], 100 - btPolio['% Balita yang pernah mendapat imunisasi Polio'], color = 'b')
plt.title('Polio')
plt.xticks(btPolio['Tahun'], rotation = 90)
plt.tight_layout()
plt.show()
# -
self.cmd_vel = v0
self.cmd_hdg = heading
self.vel_step = 0
self.hdg_step = 0
self.vel_delta = 0
self.hdg_delta = 0
def update(self):
vx = self.vel * cos(radians(90-self.hdg))
vy = self.vel * sin(radians(90-self.hdg))
self.x += vx
self.y += vy
if self.hdg_step > 0:
self.hdg_step -= 1
self.hdg += self.hdg_delta
if self.vel_step > 0:
self.vel_step -= 1
self.vel += self.vel_delta
return (self.x, self.y)
def set_commanded_heading(self, hdg_degrees, steps):
self.cmd_hdg = hdg_degrees
self.hdg_delta = angle_between(self.cmd_hdg,
self.hdg) / steps
if abs(self.hdg_delta) > 0:
self.hdg_step = steps
else:
self.hdg_step = 0
def set_commanded_speed(self, speed, steps):
self.cmd_vel = speed
self.vel_delta = (self.cmd_vel - self.vel) / steps
if abs(self.vel_delta) > 0:
self.vel_step = steps
else:
self.vel_step = 0
# -
# Now let's implement a simulated sensor with noise.
# +
from numpy.random import randn
class NoisySensor(object):
def __init__(self, std_noise=1.):
self.std = std_noise
def sense(self, pos):
"""Pass in actual position as tuple (x, y).
Returns position with noise added (x,y)"""
return (pos[0] + (randn() * self.std),
pos[1] + (randn() * self.std))
# -
# Now let's generate a track and plot it to test that everything is working. I'll put the data generation in a function so we can create paths of different lengths (why will be clear soon).
# +
import code.book_plots as bp
import numpy as np
import matplotlib.pyplot as plt
def generate_data(steady_count, std):
t = ManeuveringTarget(x0=0, y0=0, v0=0.3, heading=0)
xs, ys = [], []
for i in range(30):
x, y = t.update()
xs.append(x)
ys.append(y)
t.set_commanded_heading(310, 25)
t.set_commanded_speed(1, 15)
for i in range(steady_count):
x, y = t.update()
xs.append(x)
ys.append(y)
ns = NoisySensor(std)
pos = np.array(list(zip(xs, ys)))
zs = np.array([ns.sense(p) for p in pos])
return pos, zs
sensor_std = 2.
track, zs = generate_data(50, sensor_std)
plt.figure()
bp.plot_measurements(*zip(*zs), alpha=0.5)
plt.plot(*zip(*track), color='b', label='track')
plt.axis('equal')
plt.legend(loc=4)
bp.set_labels(title='Track vs Measurements', x='X', y='Y')
# -
# This large amount of noise allows us to see the effect of various design choices more easily.
#
# Now we can implement a Kalman filter to track this object. But let's make a simplification. The *x* and *y* coordinates are independent, so we can track each independently. In the remainder of this chapter we will only track the *x* coordinate to keep the code and matrices as small as possible.
#
# We start with a constant velocity filter.
# +
from filterpy.kalman import KalmanFilter
from filterpy.common import Q_discrete_white_noise
def make_cv_filter(dt, std):
cvfilter = KalmanFilter(dim_x = 2, dim_z=1)
cvfilter.x = np.array([0., 0.])
cvfilter.P *= 3
cvfilter.R *= std**2
cvfilter.F = np.array([[1, dt],
[0, 1]], dtype=float)
cvfilter.H = np.array([[1, 0]], dtype=float)
cvfilter.Q = Q_discrete_white_noise(dim=2, dt=dt, var=0.02)
return cvfilter
def initialize_filter(kf, std_R=None):
""" helper function - we will be reinitialing the filter
many times.
"""
kf.x.fill(0.)
kf.P = np.eye(kf.dim_x) * .1
if std_R is not None:
kf.R = np.eye(kf.dim_z) * std_R
# -
# Now we run it:
# +
sensor_std = 2.
dt = 0.1
# initialize filter
cvfilter = make_cv_filter(dt, sensor_std)
initialize_filter(cvfilter)
# run it
z_xs = zs[:, 0]
kxs, _, _, _ = cvfilter.batch_filter(z_xs)
# plot results
t = np.arange(0, len(z_xs) * dt, dt)
bp.plot_track(t, track[:, 0])
bp.plot_filter(t, kxs[:, 0], label='KF')
bp.set_labels(title='Track vs KF', x='time (sec)', y='X');
plt.legend(loc=4);
# -
# We can see from the plot that the Kalman filter was unable to track the change in heading. Recall from the **g-h Filter** chapter that this is because the filter is not modeling acceleration, hence it will always lag the input. The filter will eventually catch up with the signal if the signal enters a steady state. Let's look at that.
# +
# reinitialize filter
initialize_filter(cvfilter)
track2, zs2 = generate_data(150, sensor_std)
xs2 = track2[:, 0]
z_xs2 = zs2[:, 0]
t = np.arange(0, len(xs2) * dt, dt)
kxs2, _, _, _ = cvfilter.batch_filter(z_xs2)
bp.plot_track(t, xs2)
bp.plot_filter(t, kxs2[:, 0], label='KF')
plt.legend(loc=4)
bp.set_labels(title='Effects of Acceleration',
x='time (sec)', y='X')
# -
# The underlying problem is that our process model is correct for the steady state sections, but incorrect for when the object is maneuvering. We can try to account for this by increasing the size of Q, like so.
# +
# reinitialize filter
initialize_filter(cvfilter)
cvfilter.Q = Q_discrete_white_noise(dim=2, dt=dt, var=2.0)
# recompute track
kxs2, _, _, _ = cvfilter.batch_filter(z_xs2)
bp.plot_track(t, xs2)
bp.plot_filter(t, kxs2[:, 0], label='KF')
plt.legend(loc=4)
bp.set_labels(title='Large Q (var=2.0)', x='time (sec)', y='X')
# -
# We can see that the filter reacquired the track more quickly, but at the cost of a lot of noise in the output. Furthermore, many tracking situations could not tolerate the amount of lag shown between seconds 4 and 8. We could reduce it further at the cost of very noisy output, like so:
# +
# reinitialize filter
initialize_filter(cvfilter)
cvfilter.Q = Q_discrete_white_noise(dim=2, dt=dt, var=20.0)
# recompute track
cvfilter.x.fill(0.)
kxs2, _, _, _ = cvfilter.batch_filter(z_xs2)
bp.plot_track(t, xs2)
bp.plot_filter(t, kxs2[:, 0], label='KF')
plt.legend(loc=4)
bp.set_labels(title='Huge Q (var=20.0)', x='time (sec)', y='X')
# -
# Maneuvers imply acceleration, so let's implement a constant acceleration Kalman filter and see how it fairs with the same data.
# +
def make_ca_filter(dt, std):
cafilter = KalmanFilter(dim_x=3, dim_z=1)
cafilter.x = np.array([0., 0., 0.])
cafilter.P *= 3
cafilter.R *= std
cafilter.Q = Q_discrete_white_noise(dim=3, dt=dt, var=0.02)
cafilter.F = np.array([[1, dt, 0.5*dt*dt],
[0, 1, dt],
[0, 0, 1]])
cafilter.H = np.array([[1., 0, 0]])
return cafilter
def initialize_const_accel(f):
f.x = np.array([0., 0., 0.])
f.P = np.eye(3) * 3
# +
cafilter = make_ca_filter(dt, sensor_std)
initialize_const_accel(cafilter)
kxs2, _, _, _ = cafilter.batch_filter(z_xs2)
bp.plot_track(t, xs2)
bp.plot_filter(t, kxs2[:, 0], label='KF')
plt.legend(loc=4)
bp.set_labels(title='Constant Acceration Kalman Filter',
x='time (sec)', y='X')
# -
# The constant acceleration model is able to track the maneuver with no lag, but at the cost of very noisy output during the steady state behavior. The noisy output is due to the filter being unable to distinguish between the beginning of an maneuver and noise in the signal. Noise in the signal implies an acceleration, and so the acceleration term of the filter tracks it.
#
# It seems we cannot win. A constant velocity filter cannot react quickly when the target is accelerating, but a constant acceleration filter misinterprets noise during zero acceleration regimes as acceleration instead of nosie.
#
# Yet there is an important insight here that will lead us to a solution. When the target is not maneuvering (the acceleration is zero) the constant velocity filter performs optimally. When the target is maneuvering the constant acceleration filter performs well, as does the constant velocity filter with an artificially large process noise $\mathbf{Q}$. If we make a filter that adapts itself to the behavior of the tracked object we could have the best of both worlds.
# ## Detecting a Maneuver
# Before we discuss how to create an adaptive filter we have to ask "how do we detect a maneuver?" We cannot reasonably adapt a filter to respond to maneuvers if we do not know when a maneuver is happening.
#
# We have been defining *maneuver* as the time when the tracked object is accelerating, but in general we can say that the object is maneuvering with respect to the Kalman filter if its behavior is different than the process model being used by the filter.
#
# What is the mathematical consequence of a maneuvering object for the filter? The object will be behaving differently than predicted by the filter, so the residual will be large. Recall that the residual is the difference between the current prediction of the filter and the measurement.
# <img src="./figs/residual_chart.png">
# To confirm this, let's plot the residual for the filter during the maneuver. I will reduce the amount of noise in the data to make it easier to see the residual.
# +
from code.adaptive_internal import plot_track_and_residuals
def show_residual_chart():
dt = 0.1
sensor_std = 0.2
# initialize filter
cvfilter = make_cv_filter(dt, sensor_std)
initialize_filter(cvfilter)
pos2, zs2 = generate_data(150, sensor_std)
xs2 = pos2[:, 0]
z_xs2 = zs2[:, 0]
cvfilter.Q = Q_discrete_white_noise(dim=2, dt=dt, var=0.02)
xs, res = [], []
for z in z_xs2:
cvfilter.predict()
cvfilter.update([z])
xs.append(cvfilter.x[0])
res.append(cvfilter.y[0])
xs = np.asarray(xs)
plot_track_and_residuals(t, xs, z_xs2, res)
show_residual_chart()
# -
# On the left I have plotted the noisy measurements against the Kalman filter output. On the right I display the residuals computed by the filter - the difference between the measurement and the predictions made by the Kalman filter. Let me emphasize this to make this clear. The plot on the right is not merely the difference between the two lines in the left plot. The left plot shows the difference between the measurements and the final Kalman filter output, whereas the right plot shows us the difference between the measurements and the *predictions of the process model*.
#
# That may seem like a subtle distinction, but from the plots you see it is not. The amount of deviation in the left plot when the maneuver starts is small, but the deviation in the right plot tells a different story. If the tracked object was moving according to the process model the residual plot should bounce around 0.0. This is because the measurements will be obeying the equation
#
# $$\mathtt{measurement} = \mathtt{process\_model}(t) + \mathtt{noise}(t)$$
#
# Once the target starts maneuvering the predictions of the target behavior will not match the behavior as the equation will be
#
# $$\mathtt{measurement} = \mathtt{process\_model}(t) + \mathtt{maneuver\_delta}(t) + \mathtt{noise}(t)$$
#
# Therefore if the residuals diverge from a mean of 0.0 we know that a maneuver has commenced.
#
# We can see from the residual plot that we have our work cut out for us. We can clearly see the result of the maneuver in the residual plot, but the amount of noise in the signal obscures the start of the maneuver. This is our age old problem of extracting the signal from the noise.
# ## Adjustable Process Noise
#
# The first approach we will consider will use a lower order model and adjust the process noise based on whether a maneuver is occurring or not. When the residual gets "large" (for some reasonable definition of large) we will increase the process noise. This will cause the filter to favor the measurement over the process prediction and the filter will track the signal closely. When the residual is small we will then scale back the process noise.
#
# There are many ways of doing this in the literature, I will consider a couple of choices.
# ### Continuous Adjustment
#
# The first method (from Bar-Shalom [1]) normalizes the square of the residual using the following equation:
#
# $$ \epsilon = \mathbf{y^\mathsf{T}S}^{-1}\mathbf{y}$$
#
# where $\mathbf{y}$ is the residual and $\mathbf{S}$ is the measurement covariance, which has the equation
#
# $$\mathbf{S} = \mathbf{HPH^\mathsf{T}} + \mathbf{R}$$
#
# If the linear algebra used to compute this confuses you, recall that we can think of matrix inverses in terms of division, so $\epsilon = \mathbf{y^\mathsf{T}S}^{-1}\mathbf{y}$ can be thought of as computing
#
# $$\epsilon\approx\frac{\mathbf{y}^2}{\mathbf{S}}$$
#
# Both $\mathbf{y}$ and $\mathbf{S}$ are attributes of `filterpy.KalmanFilter` so implementation will be straightforward.
#
# Let's look at a plot of $\epsilon$ against time.
# +
from filterpy.common import dot3
from numpy.linalg import inv
sensor_std = 0.2
cvfilter= make_cv_filter(dt, sensor_std)
_, zs2 = generate_data(150, sensor_std)
epss = []
for z in zs2[:, 0]:
cvfilter.predict()
cvfilter.update([z])
y, S = cvfilter.y, cvfilter.S
eps = dot3(y.T, inv(cvfilter.S), y)
epss.append(eps)
plt.plot(t, epss)
bp.set_labels(title='Epsilon vs time',
x='time (sec)', y='$\epsilon$')
# -
# This plot should make clear the effect of normalizing the residual. Squaring the residual ensures that the signal is always greater than zero, and normalizing by the measurement covariance scales the signal so that we can distinguish when the residual is markedly changed relative to the measurement noise. The maneuver starts at t=3 seconds, and we can see that $\epsilon$ starts to increase rapidly not long after that.
#
# We will want to start scaling $\mathbf{Q}$ up once $\epsilon$ exceeds some limit, and back down once it again falls below that limit. We multiply $\mathbf{Q}$ by a scaling factor. Perhaps there is literature on choosing this factor analytically; I derive it experimentally. We can be somewhat more analytical about choosing the limit for $\epsilon$ (named $\epsilon_{max}$) - generally speaking once the residual is greater than 3 standard deviations or so we can assume the difference is due to a real change and not to noise. However, sensors are rarely truly Gaussian and so a larger number, such as 5-6 standard deviations is used in practice.
#
# I have implemented this algorithm using reasonable values for $\epsilon_{max}$ and the $\mathbf{Q}$ scaling factor. To make inspection of the result easier I have limited the plot to the first 10 seconds of simulation.
# +
from filterpy.common import dot3
from numpy.linalg import inv
# reinitialize filter
sensor_std = 0.2
cvfilter= make_cv_filter(dt, sensor_std)
_, zs2 = generate_data(180, sensor_std)
Q_scale_factor = 1000.
eps_max = 4.
xs, epss = [], []
count = 0
for i, z in zip(t, zs2[:, 0]):
cvfilter.predict()
cvfilter.update([z])
y, S = cvfilter.y, cvfilter.S
eps = dot3(y.T, inv(cvfilter.S), y)
epss.append(eps)
xs.append(cvfilter.x[0])
if eps > eps_max:
cvfilter.Q *= Q_scale_factor
count += 1
elif count > 0:
cvfilter.Q /= Q_scale_factor
count -= 1
bp.plot_measurements(t, z_xs2, lw=6, label='z')
bp.plot_filter(t, xs, label='filter')
plt.legend(loc=4)
bp.set_labels(title='epsilon=4', x='time (sec)', y='$\epsilon$')
# -
# The performance of this filter is markedly better than the constant velocity filter. The constant velocity filter took roughly 10 seconds to reacquire the signal after the start of the maneuver. The adaptive filter takes under a second to do the same.
# ### Continuous Adjustment - Standard Deviation Version
# Another, very similar method from Zarchan [2] sets the limit based on the standard deviation of the measurement error covariance. Here the equations are:
#
# $$ \begin{aligned}
# std &= \sqrt{\mathbf{HPH}^\mathsf{T} + \mathbf{R}} \\
# &= \sqrt{\mathbf{S}}
# \end{aligned}
# $$
#
# If the absolute value of the residual is more than some multiple of the standard deviation computed above we increase the process noise by a fixed amount, recompute Q, and continue.
# +
from numpy.linalg import inv
from math import sqrt
def zarchan_adaptive_filter(Q_scale_factor, std_scale,
std_title=False,
Q_title=False):
cvfilter = make_cv_filter(dt, std=0.2)
pos2, zs2 = generate_data(180-30, std=0.2)
xs2 = pos2[:,0]
z_xs2 = zs2[:,0]
# reinitialize filter
initialize_filter(cvfilter)
cvfilter.R = np.eye(1)*0.2
phi = 0.02
cvfilter.Q = Q_discrete_white_noise(dim=2, dt=dt, var=phi)
xs, ys = [], []
count = 0
for i,z in zip(t,z_xs2):
cvfilter.predict()
cvfilter.update([z])
y = cvfilter.y
S = cvfilter.S
std = sqrt(S)
xs.append(cvfilter.x)
ys.append(y)
if abs(y[0]) > std_scale*std:
phi += Q_scale_factor
cvfilter.Q = Q_discrete_white_noise(2, dt, phi)
count += 1
elif count > 0:
phi -= Q_scale_factor
cvfilter.Q = Q_discrete_white_noise(2, dt, phi)
count -= 1
xs = np.asarray(xs)
plt.subplot(121)
bp.plot_measurements(t, z_xs2, label='z')
bp.plot_filter(t, xs[:, 0])
bp.set_labels(x='time (sec)', y='$\epsilon$')
plt.legend(loc=2)
if std_title:
plt.title('position(std={})'.format(std_scale))
elif Q_title:
plt.title('position(Q scale={})'.format(Q_scale_factor))
else:
plt.title('position')
plt.subplot(122)
plt.plot(t, xs[:, 1])
plt.xlabel('time (sec)')
if std_title:
plt.title('velocity(std={})'.format(std_scale))
elif Q_title:
plt.title('velocity(Q scale={})'.format(Q_scale_factor))
else:
plt.title('velocity')
plt.show()
zarchan_adaptive_filter(1000, 2, std_title=True)
# -
# So I chose to use 1000 as the scaling factor for the noise, and 2 as the standard deviation limit. Why these numbers? Well, first, let's look at the difference between 2 and 3 standard deviations.
# **Two Standard Deviations**
zarchan_adaptive_filter(1000, 2, std_title=True)
# **Three Standard Deviations**
zarchan_adaptive_filter(1000, 3, std_title=True)
# We can see from the charts that the filter output for the position is very similar regardless of weather we use 2 standard deviations or three. But the computation of the velocity is a different matter. Let's explore this further. First, let's make the standard deviation very small.
zarchan_adaptive_filter(1000, .1, std_title=True)
zarchan_adaptive_filter(1000, 1, std_title=True)
# As the standard deviation limit gets smaller the computation of the velocity gets worse. Think about why this is so. If we start varying the filter so that it prefers the measurement over the prediction as soon as the residual deviates even slightly from the prediction we very quickly be giving almost all the weight towards the measurement. With no weight for the prediction we have no information from which to create the hidden variables. So, when the limit is 0.1 std you can see that the velocity is swamped by the noise in the measurement. On the other hand, because we are favoring the measurements so much the position follows the maneuver almost perfectly.
#
# Now let's look at the effect of various increments for the process noise. Here I have held the standard deviation limit to 2 std, and varied the increment from 1 to 10,000.
zarchan_adaptive_filter(1, 2, Q_title=True)
zarchan_adaptive_filter(10, 2, Q_title=True)
zarchan_adaptive_filter(100, 2, Q_title=True)
zarchan_adaptive_filter(1000, 2, Q_title=True)
zarchan_adaptive_filter(10000, 2, Q_title=True)
# Here we can see that the position estimate gets marginally better as the increment factor increases, but that the velocity estimate starts to create a large overshoot.
#
# It isn't possible for me to tell you which of these is 'correct'. You will need to test your filter's performance against real and simulated data, and choose the design that best matches the performance you need for each of the state variables.
# ## Fading Memory Filter
# Fading memory filters are not normally classified as an adaptive filter since they do not adapt to the the input, but they do provide good performance with maneuvering targets. They also have the benefit of having a very simple computational form for first, second, and third order kinematic filters (e.g. the filters we are using in this chapter). This simple form does not require the Ricatti equations to compute the gain of the Kalman filter, which drastically reduces the amount of computation. However, there is also a form that works with the standard Kalman filter. I will focus on the latter in this chapter since our focus is more on adaptive filters. Both forms of the fading memory filter are implemented in `FilterPy`.
#
# The Kalman filter is recursive, but it incorporates all of the previous measurements into the current computation of the filter gain. If the target behavior is consistent with the process model than this allows the Kalman filter to find the optimal estimate for every measurement. Consider a ball in flight - we can clearly estimate the position of the ball at time t better if we take into account all the previous measurement. If we only used some of the measurements we would be less certain about the current position, and thus more influenced by the noise in the measurement. If this is still not clear, consider the worst case. Suppose we forget all but the last measurement and estimates. We would then have no confidence in the position and trajectory of the ball, and would have little choice but to weight the current measurement heavily. If the measurement is noisy, the estimate is noisy. We see this effect every time a Kalman filter is initialized. The early estimates are noisy, but then they settle down as more measurements are acquired.
#
# However, if the target is maneuvering it is not always behaving like the process model predicts. In this case remembering all of the past measurements and estimates is a liability. We can see this in all of the charts above. The target initiates a turn, and the Kalman filter continues to project movement in a straight line. This is because the filter has built a history of the target's movement, and incorrectly 'feels' confident that the target is moving in a straight line at a given heading and velocity.
#
# The fading memory filter accounts for this problem by giving less weight to older measurements, and greater weight to the more recent measurements.
#
# There are many formulations for the fading memory filter; I use the one provided by Dan Simon in *Optimal State Estimation* [3]. I will not go through his derivation, but only provide the results.
#
# The Kalman filter equation for the covariances of the estimation error is
#
# $$ \bar{\mathbf P} = \mathbf{FPF}^\mathsf T + \mathbf Q $$
#
# We can force the filter to forget past measurements by multiplying a term $\alpha$
#
# $$ \tilde{\mathbf P} = \alpha^2\mathbf{FPF}^\mathsf T + \mathbf Q$$
#
# where $\alpha > 1.0$. If $\alpha == 1$ then we get the normal Kalman filter performance. $\alpha$ is an attribute of the `KalmanFilger` class; its value defaults to 1 so the filter acts like a Kalman filter unless $\alpha$ is assigned a value other than 1. There is no hard and fast rule for choosing $\alpha$, but it is typically very close to 1, such as 1.01. You will need to make many runs with either simulated or real data to determine a value that responds to maneuvers without causing the estimate to become too noisy due to overly weighting the noisy measurement.
#
# Why does this work? If we increase the estimate error covariance the filter becomes more uncertain about it's estimate, hence it gives more weight to the measurement.
#
# One caveat - if we use $\alpha$ than we are computing $\tilde{\mathbf P}$, not $\bar{\mathbf P}$. In other words, `KalmanFilter.P` *is not* equal to the covariance of the prior, so do not treat it as if it is.
#
# Let's filter our data using the fading memory filter and see the result. I will inject a lot of error into the system so that we can compare various approaches.
# +
pos2, zs2 = generate_data(70, std=1.2)
xs2 = pos2[:, 0]
z_xs2 = zs2[:, 0]
cvfilter = make_cv_filter(dt, std=1.2)
cvfilter.x.fill(0.)
cvfilter.Q = Q_discrete_white_noise(dim=2, dt=dt, var=0.02)
cvfilter.alpha = 1.00
xs, res = [], []
for z in z_xs2:
cvfilter.predict()
cvfilter.update([z])
xs.append(cvfilter.x[0])
res.append(cvfilter.y[0])
xs = np.asarray(xs)
plt.subplot(221)
bp.plot_measurements(t[0:100], z_xs2, label='z')
plt.plot(t[0:100], xs, label='filter')
plt.legend(loc=2)
plt.title('Standard Kalman Filter')
cvfilter.x.fill(0.)
cvfilter.Q = Q_discrete_white_noise(dim=2, dt=dt, var=20.)
cvfilter.alpha = 1.00
xs, res = [], []
for z in z_xs2:
cvfilter.predict()
cvfilter.update([z])
xs.append(cvfilter.x[0])
res.append(cvfilter.y[0])
xs = np.asarray(xs)
plt.figure(figsize=(9, 6))
plt.subplot(222)
bp.plot_measurements(t[0:100], z_xs2, label='z')
plt.plot(t[0:100], xs, label='filter')
plt.legend(loc=2)
plt.title('$\mathbf{Q}=20$')
cvfilter.x.fill(0.)
cvfilter.Q = Q_discrete_white_noise(dim=2, dt=dt, var=0.02)
cvfilter.alpha = 1.02
xs, res = [], []
for z in z_xs2:
cvfilter.predict()
cvfilter.update([z])
xs.append(cvfilter.x[0])
res.append(cvfilter.y[0])
xs = np.asarray(xs)
plt.subplot(223)
bp.plot_measurements(t[0:100], z_xs2, label='z')
plt.plot(t[0:100], xs, label='filter')
plt.legend(loc=2)
plt.title('Fading Memory ($\\alpha$ = 1.02)')
cvfilter.x.fill(0.)
cvfilter.Q = Q_discrete_white_noise(dim=2, dt=dt, var=0.02)
cvfilter.alpha = 1.05
xs, res = [], []
for z in z_xs2:
cvfilter.predict()
cvfilter.update([z])
xs.append(cvfilter.x[0])
res.append(cvfilter.y[0])
xs = np.asarray(xs)
plt.subplot(224)
bp.plot_measurements(t[0:100], z_xs2, label='z')
plt.plot(t[0:100], xs, label='filter')
plt.legend(loc=2)
plt.title('Fading Memory ($\\alpha$ = 1.05)');
# -
# The first plot shows the performance of the Kalman filter. The filter diverges when the maneuver starts and does not reacquire the signal until about 10 seconds. I then made the filter track the maneuver very quickly by making the process noise large, but this has the cost of making the filter estimate very noisy due to unduly weighting the noisy measurements. I then implemented a fading memory filter with $\alpha=1.02$. The filtered estimate is very smooth, but it does take a few seconds to converge when the target regains steady state behavior. However, the time to do so is considerably smaller than for the Kalman filter, and the amount of lag is much smaller - the estimate for the fading memory is much closer to the actual track than the Kalman filter's track is. Finally, I bumped up $\alpha$ to 1.05. Here we can see that the filter responds almost instantly to the maneuver, but that the estimate is not as straight during the steady state operation because the filter is forgetting the past measurements.
#
# This is quite good performance for such a small change in code! Note that there is no 'correct' choice here. You will need to design your filter based on your needs and the characteristics of the measurement noise, process noise, and maneuvering behavior of the target.
# ## Multiple Model Estimation
# The example I have been using in this chapter entails a target moving in a steady state, performing a maneuver, and then returning to a steady state. We have been thinking of this as two models - a constant velocity model, and a constant acceleration model. Whenever you can describe the system as obeying one of a finite set of models you can use *Multiple Model (MM) Estimation*. We use a bank of multiple filters, each using a different process to describe the system, and either switch between them or blend them based on the dynamics of the tracked object.
#
# As you might imagine this is a broad topic, and there are many ways of designing and implementing MM estimators. But consider a simple approach for the target we have been tracking in this chapter. One idea would be to simultaneously run a constant velocity and a constant acceleration filter, and to switch between their outputs when we detect a maneuver by inspecting the residuals. Even this choice gives us many options. Consider the dynamics of a turning object. For example, an automobile turns on a wheelbase - the front wheels turn, and the car pivots around the rear wheels. This is a nonlinear process, so for best results we would want to use some type of nonlinear filter (EKF, UKF, etc) to model the turns. On the other hand, a linear constant velocity filter would perform fine for the steady state portions of the travel. So our bank of filters might consist of a linear KF and an EKF filter for the turns. However, neither is particularly well suited for modeling behaviors such as accelerating and braking. So a highly performing MM estimator might contain a bank of many filters, each designed to perform best for a certain performance envelope of the tracked object.
#
# Of course, you do not need to base your filters on the order of the model. You can use different noise models, different adapters in each. For example, in the section above I showed many plots depicting the effects of changing parameters on the estimate of the velocity and position. Perhaps one setting works better for position, and a different setting for velocity. Put both in your bank of filters. You could then take the best estimates for the position from one filter, and the best estimate for the velocity from a different filter.
#
# ### A Two Filter Adaptive Filter
#
# I trust the idea of switching between filters to get the best performance is clear, but what mathematical foundations should we use to implement it? The problem that we face is trying to detect via noisy measurements when a change in regime should result in a change in model. What aspect of the Kalman filter measures how far the measurement deviates from the prediction? Yes, the *residual*.
#
# Let's say we have a first order (constant velocity) Kalman filter. As long as the target is not maneuvering the filter will track it's behavior closely, and roughly 68% of the measurements should fall within 1$\sigma$. Furthermore the residual should fluctuate around 0 because as many if the sensor is Gaussian an equal number of measurement should have positive error as have negative errors. If the residual grows and stays beyond predicted bounds then the target must not be performing as predicted by the state model. We saw this earlier in this chart where the residual switched from bouncing around 0 to suddenly jumping and staying above zero once the tracked object began maneuvering.
show_residual_chart()
# For this problem we saw that the constant velocity filter performed better the constant acceleration filter while the object was in steady state, and the opposite was true when the object is maneuvering. In the chart above that transition occurs at 4 seconds.
#
# So the algorithm is easy. Initialize both a constant velocity and constant acceleration filter and run them together in a predict/update loop. After every update examine the residual of the constant velocity filter. If it falls within theoretical bounds use the estimate from the constant velocity filter as the estimate, otherwise use the estimate from the constant acceleration filter.
# +
def run_filter_bank(threshold, show_zs=True):
dt = 0.1
cvfilter= make_cv_filter(dt, std=0.8)
cafilter = make_ca_filter(dt, std=0.8)
pos, zs = generate_data(120, std=0.8)
z_xs = zs[:, 0]
t = np.arange(0, len(z_xs) * dt, dt)
xs, res = [], []
for z in z_xs:
cvfilter.predict()
cafilter.predict()
cvfilter.update([z])
cafilter.update([z])
std = np.sqrt(cvfilter.R[0,0])
if abs(cvfilter.y[0]) < 2 * std:
xs.append(cvfilter.x[0])
else:
xs.append(cafilter.x[0])
res.append(cvfilter.y[0])
xs = np.asarray(xs)
if show_zs:
plot_track_and_residuals(t, xs, z_xs, res)
else:
plot_track_and_residuals(t, xs, None, res)
run_filter_bank(threshold=1.4)
# -
# Here the filter tracks the maneuver closely. While the target is not maneuvering our estimate is nearly noise free, and then once it does maneuver we quickly detect that and switch to the constant acceleration filter. However, it is not ideal. Here is the filter output plotted alone:
run_filter_bank(threshold=1.4, show_zs=False)
# You can see that the estimate jumps when the filter bank switches from one filter to the other. I would not use this algorithm in a production system. The next section gives a state of the art implementation of a filter bank that eliminates this problem.
# ## MMAE
#
# The core idea of using several filters to detect a maneuver is sound, but the estimate is jagged when we abruptly transition between the filters. Choosing one filter over the other flies in the face of this entire book, which uses probability to determine the *likelihood* of measurements and models. We don't choose *either* the measurement or prediction, depending on which is more likely, we choose a *blend* of the two in proportion to their likelihoods. We should do the same here. This approach is called the *Multiple Model Adaptive Estimator*, or MMAE.
#
# In the **Designing Kalman Filters** chapter we learned the *likelihood function*
#
# $$\mathcal{L} = \frac{1}{\sqrt{2\pi S}}\exp [-\frac{1}{2}\mathbf{y}^\mathsf{T}\mathbf{S}^{-1}\mathbf{y}]$$
#
# which tells us how likely a filter is to be performing optimally given the inputs. $\mathbf y$ is the residual and $\mathbf S$ is the system uncertainty (covariance in measurement space). This is just a Gaussian of the residual and the system uncertainty. A large residual will give a large uncertainty, and thus low likelihood that the measurement matches the filter's current state. We can use this to compute the probability that each filter is the best fit to the data. If we have N filters, we can compute the probability that filter i is correct in relation to the rest of the filters with
#
# $$p_k^i = \frac{\mathcal{L}_k^ip_{k-1}^i}{\sum\limits_{j=1}^N \mathcal{L}_k^jp_{k-1}^j}$$
#
# That looks messy, but it is straightforward. The numerator is just the likelihood from this time step multiplied by the probability that this filter was correct at the last time frame. We need all of the probabilities for the filter to sum to one, so we normalize by the probabilities for all of the other filters with the term in the denominator.
#
# That is a recursive definition, so we need to assign some initial probability for each filter. In the absence of better information, use $\frac{1}{N}$ for each. Then we can compute the estimated state as the sum of the state from each filter multiplied the the probability of that filter being correct.
#
# Here is a complete implementation:
# +
def run_filter_bank():
dt = 0.1
cvfilter = make_cv_filter(dt, std=0.2)
cafilter = make_ca_filter(dt, std=0.2)
_, zs = generate_data(120, std=0.2)
z_xs = zs[:, 0]
#z_xs = [i for i in range(120)]
t = np.arange(0, len(z_xs) * dt, dt)
xs, probs = [], []
pv, pa = 0.8, 0.2
pvsum, pasum = 0., 0.
for i, z in enumerate(z_xs):
cvfilter.predict()
cafilter.predict()
cvfilter.update([z])
cafilter.update([z])
cv_likelihood = cvfilter.likelihood * pv
ca_likelihood = cafilter.likelihood * pa
pv = (cv_likelihood) / (cv_likelihood + ca_likelihood)
pa = (ca_likelihood) / (cv_likelihood + ca_likelihood)
x = (pv * cvfilter.x[0]) + (pa*cafilter.x[0])
xs.append(x)
probs.append(pv / pa)
xs = np.asarray(xs)
plt.subplot(121)
plt.plot(t, xs)
plt.subplot(122)
plt.plot(t, xs)
plt.plot(t, z_xs)
return xs, probs
xs, probs = run_filter_bank()
# -
# I plot the filter's estimates alone on the left so you can see how smooth the result is. On the right I plot both the estimate and the measurements to prove that the filter is tracking the maneuver.
#
# Again I want to emphasize that this is nothing more than the Bayesian algorithm we have been using throughout the book. We have two (or more) measurements or estimate, each with an associated probability. We choose are estimate as a weighted combination of each of those values, where the weights are proportional to the probability of correctness. The computation of the probability at each step is
#
# $$\frac{\texttt{Prob(meas | state)} \times\texttt{prior}}{\texttt{normalization}}$$
#
# which is Bayes therom.
#
# For real world problems you are likely to need more than two filters in your bank. In my job I track objects using computer vision. I track hockey pucks. Pucks slide, they bounce and skitter, they roll, they ricochet, they are picked up and carried, and they are 'dribbled' quickly by the players. I track humans who are athletes, and their capacity for nonlinear behavior is nearly limitless. A two filter bank doesn't get very far in those circumstances. I need to model multiple process models, different assumptions for noise due to the computer vision detection, and so on. But you have the main idea.
#
# ### Limitations of the MMAE Filter
#
# The MMAE as I have presented it has a significant problem. Look at this chart of the ratio of the probability for the constant velocity vs constant acceleration filter.
plt.plot(t[0:len(probs)], probs)
plt.title('probability ratio p(cv)/p(ca)')
plt.xlabel('time (sec)');
# For the first three seconds, while the tracked object travels in a straight direction, the constant velocity filter become much more probable than the constant acceleration filter. Once the maneuver starts the probability quickly changes to to favor the constant acceleration model. However, the maneuver is completed by second six. You might expect that the probability for the constant velocity filter would once again become large, but instead it remains at zero.
#
# This happens because of the recursive computation of the probability:
#
# $$p_k = \frac{\mathcal{L}p_{k-1}}{\sum \text{probabilities}}$$
#
# Once the probability becomes very small it can never recover. The result is that the filter bank quickly converges on only the most probable filters. A robust scheme needs to monitor the probability of each filter and kill off the filters with very low probability and replace them with filters with greater likelihood of performing well. You can subdivide the existing filters into new filters that try to span the characteristics that make them perform well. In the worst case, if a filter has diverged you can reinitialize a filter's state so that it is closer to the current measurements.
# ## Interacting Multiple Models (IMM)
#
# Let's think about multiple models in another way. The scenario is as before - we wish to track a maneuvering target. We can design a set of Kalman filters which make different modeling assumptions. They can differ in terms of the filter order, or in the amount of noise in the process model. As each new measurement comes in each filter has a probability of being the correct model.
#
# This naive approach leads to combinatorial explosion. At step 1 we generate $N$ hypotheses, or 1 per filter. At step 2 we generate another $N$ hypotheses which then need to be combined with the prior $N$ hypotheses, which yields $N^2$ hypothesis. Many different schemes have been tried which either cull unlikely hypotheses or merge similar ones, but the algorithms still suffered from computational expense and/or poor performance. I will not cover these in this book, but prominent examples in the literature are the generalized pseudo Bayes (GPB) algorithms.
#
# The *Interacting Multiple Models* (IMM) algorithm was invented by Blom[5] to solve the combinatorial explosion problem of multiple models. A subsequent paper by Blom and Bar-Shalom is the most cited paper [6]. The idea is to have 1 filter for each possible mode of behavior of the system. At each epoch we we let the filters *interact* with each other. The more likely filters modify the estimates of the less likely filters so they more nearly represent the current state of the sytem. This blending is done probabilistically, so the unlikely filters also modify the likely filters, but by a much smaller amount.
#
# For example, suppose we have two modes: going straight, or turning. Each mode is represented by a Kalman filter, maybe a first order and second order filter. Now say the target it turning. The second order filter will produce a good estimate, and the first order filter will lag the signal. The likelihood function of each tells us which of the filters is most probable. The first order filter will have low likelihood, so we adjust its estimate greatly with the second order filter. The the second order filter is very likely, so its estimate will only be changed slightly by the first order Kalman filter.
#
# Now suppose the target stops turning. Because we have been revising the first order filter's estimate with the second order estimate it will not have been lagging the signal by very much. within just a few epochs it will be producing very good (high likelihood) estimates and be the most probable filter. It will then start contributing heavily to the estimate of the second order filter. Recall that a second order filter mistakes measurement noise for acceleration. This adjustment insures reduces this effect greatly.
# ### Mode Probabilities
#
# We define a set of modes for our system, $m$, and assume that the target is always in one of these modes. In the discussion above we have the modes straight and turn, so $m=\{\text{straight},\ \text{turn}\}$.
#
#
# We assign a probability that the target is in any given mode. This gives us a vector of *mode probabilities* with one probability for each possible mode. $m$ has two modes, so we will have a vector of two probabilities. If we think that there is a 70% chance that the target is going straight we can say
#
# $$\mu = \begin{bmatrix} 0.7 & 0.3\end{bmatrix}$$
#
# We get 0.3 for the turn because the probabilities must sum to one. $\mu$ is typically but not universally used as the symbol for the mode probabilities, so I will use it. Do not confuse it with the mean.
#
# In Python we can implement this as
mu = np.array([0.7, 0.3])
mu
# We can formalize it by saying that the prior probability that $m_i$ is correct (the maneuvering object is in mode $i$), given the prior measurements $Z$, is
#
# $$\mu_i = P(m_i|Z)$$
# ### Mode Transitions
#
# Next we have to consider that this is a maneuvering target. It will go straight, then turn, then go straight again. We can model the transition between these modes as a [*Markov chain*](https://en.wikipedia.org/wiki/Markov_chain), as in this illustration:
# +
import code.adaptive_internal as adaptive_internal
from code.book_plots import interactive_plot
adaptive_internal.plot_markov_chain()
# -
# This shows an example of two modes for a target, going straight and performing a turn. If the current mode of the target is straight, then we predict that there is a 97% chance of the target continuing straight, and a 3% chance of starting a turn. Once the target is turning, we then predict that there is a 95% chance of staying in the turn, and a 5% of returning to a straight path.
#
# The algorithm is not sensitive to the exact numbers, and you will typically use simulation or trials to choose appropriate values. However, these values are quite representative.
#
# We represent Markov chains with a [*transition probability matrix*](https://en.wikipedia.org/wiki/Stochastic_matrix), which we will call $\mathbf M$. For the Markov chain in the illustration we would write
#
# $$\mathbf M = \begin{bmatrix}.97 & .03\\.05 & .95\end{bmatrix}$$
#
# In other words $\mathbf M[i, j]$ is the probability of mode being $i$ given that the last mode was $j$. In this example the probability of the mode currently being straight $(i=0)$ given that the last mode was a turn $(j=1)$ is $\mathbf M[0,1] = 0.05$. In Python we'd write:
M = np.array([[.97, .03], [.05, .95]])
M
# This allows us to compute the new mode probabilities based on the probability of a transition. Let's compute the probability of the mode being straight after a transition. There are two ways for us to be moving straight. We could have been moving straight, and then continued straight, or we could have been turning, but then went straight. The former probability is calculated with $(0.7\times 0.97)$ and the latter with $(0.3\times 0.05)$. We are multiplying the mode probability with the relevant probability from the Markov Chain. The *total probability* is the sum of the two, or $(0.7)(0.97) + (0.3)(0.05) = 0.694$.
#
# Recall the [*total probability theorem*](https://en.wikipedia.org/wiki/Law_of_total_probability) from the second chapter. It states that the probability of several distinct events is
#
# $$P(A) = \sum P(A\mid B)\, P(B)$$
#
# Here $P(A\mid B)$ is the transition matrix $\mathbf M$ and $P(B)$ is $\mu$. We are using arrays and matrices, and so we take advantage of the fact that a vector times a matrix computes the sum of products:
#
# $$\begin{bmatrix}\mu_1 & \mu_2 \end{bmatrix}\begin{bmatrix}m_{11} & m_{12}\\m_{21} & m_{22}\end{bmatrix} = \begin{bmatrix}\mu_1 m_{11} + \mu_2 m_{21} & \mu_1 m_{12} + \mu_2 m_{22}\end{bmatrix}$$
#
# The IMM literature expresses this as
#
# $$\bar c_j = \sum\limits_{i=1}^{N} \mu_i M_{ij}$$
#
# We use NumPy's `dot` function to compute this for us:
cbar = np.dot(mu, M)
cbar
# ### Computing the Mode Probabilities
#
#
# We will compute the new mode probabilities using Bayes theorem. Recall that Bayes theorem states
#
# $$\text{posterior} = \frac{\text{prior} \cdot \text{likelihood}}{\text{normalization factor}}$$
#
# Here the prior is the total probability computation we performed in the last section. The Kalman filter computes the *likelihood*, which is the likelihood of the measurements given the current state of the filter. For review the equation is:
#
# $$
# \mathcal{L} = \frac{1}{\sqrt{2\pi \mathbf S}}\exp [-\frac{1}{2}\mathbf y^\mathsf T\mathbf S^{-1}\mathbf y]$$
#
# In mathematical notation the updated mode probability is:
#
# $$\mu_i = \| \mathcal{L}_i {\bar c}_{i}\|$$
#
# In words, for each Kalman filter (mode) we compute the mode probability as the probability of the current mode taking the possible transition into account times the likelihood that this is the correct mode. Then we normalize all of the probabilities so they sum to one.
#
# This is trivial to compute in Python. I'll introduce the variable `L` to store the likelihoods. Likelihoods are computed by the `KalmanFilter.update()` step, and in the code snippet below I just hard coded values for `L` since we haven't created the Kalman filters yet:
# L = [kf0.L, kf1.L] # get likelihoods from Kalman filters
L = [0.000134, 0.0000748]
mu = cbar * L
mu /= sum(mu) # normalize
mu
# Here you can see that the relatively strong likelihood for the straight filter pushed the probability for the straight mode from 70% to 80.2%.
# ## Mixing Probabilities
#
# At this point we could use the mode transitions to compute the probabilities for all possible choices. If $\mu = \begin{bmatrix} 0.63 & 0.27\end{bmatrix}$, then we can use the transition probability matrix to compute all possible outcomes. In other words, if the current mode is straight $(\mu=0.63)$, we can compute two new probabilities based on whether the target keeps moving straight, or turns. We do the same for the turning mode $(\mu=0.27)$. We will have gone from 2 mode probabilities to 4. At the next step 4 will turn into 8, and so on. It's computationally exact, but infeasible in practice. After only 30 epochs you'd require 8GB of memory to store the mode probabilities in double precision.
#
# We need a better, albeit approximate way. IMMs resolve this by computing *mixing probabilities*. The idea is simple. Let's say the first mode (straight) is currently very likely, and the second mode (turn) is unlikely. Instead of having the Kalman filter for the straight mode compute its state as the weighted average of all of the filters in the filter bank. Filters with high probability of matching the target's mode get weighted more than filters with lower probability. The result is that the information from the probable filters improve the accuracy of the filters that are improbable. This is the crux of the algorithm.
#
# What we need to do is very simple. Each Kalman filter performs the update step, computing a new mean and covariance. But then we compute a new mean and covariance for each filter as a weighted sum of these means and covariances according to *mixing probabilities* which we call $\omega$. Likely filters will be slightly adjusted by the unlikely filters, and the unlikely filters will be strongly adjusted by the likely ones. The literature calls these adjusted means and covariances either the *mixed conditions* or *mixed initial conditions*. I use the notation $\mathbf x^m_j$ for the mixed state, and $\mathbf P^m_j$ for the mixed covariance. The equations are:
#
# $$\begin{aligned}
# \mathbf x^m_j &= \sum_{i=1}^N \omega_{ij} \mathbf x_i \\
# \mathbf P^m_j &= \sum_{i=1}^N \omega_{ij}\left[(\mathbf x^i - \mathbf x^m_i) (\mathbf x^i - \mathbf x^m_i)^\mathsf T + \mathbf P_i\right]
# \end{aligned}$$
# Just think of the subscripts as indexes into arrays. Putting it in pseudo-Python we can write this as:
#
# ```python
# for j in N:
# x0[j] = sum_over_i(w[i,j] * x[i])
# P0[j] = sum_over_i(w[i, j] * (P[i] + np.outer(x[i] - x0[j])))
# ```
#
# Don't let the notation confuse what is a simple idea: incorporate estimates from the probable filters into the estimates of the improbable filters, ensuring all have a good estimate.
# How do we compute the mixing probabilities? Think about it, and try to give a reasonable answer before reading on. We have mode probabilities which describe the current probability of each mode, and then transition probabilities describing how likely we are to change modes. How do we compute the new probability?
#
# Bayes theorem, of course! Prior times the likelihood, normalized. The prior is the mode probability, and the likelihood comes from the Markov chain, which we store in the matrix $\mathbf M$.
#
# $$\boldsymbol\omega_{ij} = \| \mu_i \cdot \mathbf M_{ij}\|$$
#
# We can compute this as follows. I computed the update of $\mu$ and $\bar c$ out of order above (ou must compute $\bar c$ incorporating the transition probability matrix into $\mu$), so I'll need to correct that here:
# +
cbar = np.dot(mu, M) #compute total probability that target is in mode j
omega = np.zeros((2, 2))
for i in range(2):
for j in range(2):
omega[i, j] = (M[i, j] * mu[i]) / cbar[j]
omega
# -
# The Kalman filters need to perform the prediction step to compute the new prior. They use the mixed estimates:
#
# $$
# \begin{aligned}
# \bar{\mathbf x}_j &= \mathbf F_j\mathbf x^m_j\\
# \bar{\mathbf P}_j &= \mathbf F_j\mathbf P^m_j\mathbf F_j^\mathsf T + \mathbf Q_j
# \end{aligned}$$
# ### IMM Estimate
#
# Now we need a final state estimate from the bank of filters. How do we do that? Just weight the mixed estimate from each Kalman filter:
#
# $$\begin{aligned}
# \mathbf x &= \sum_{j=1}^N \mu_j{\bar{\mathbf x}}_j\\
# \mathbf P &= \sum_{j=1}^N \mu_j\left[(\bar{{\mathbf x}}_j - \bar{\mathbf x})({\bar{\mathbf x}}_j - \bar{\mathbf x})^\mathsf T + \bar{\mathbf P_j}\right]
# \end{aligned}$$
# ### Tracking Maneuvering Target with the IMM
#
# Let's work an example. Crassidis[4] is one of the few texts with a worked example, so I have chosen his example. He tracks a moving target for 600 seconds. The target starts off moving straight, and then a control input is injected starting at 400 seconds, causing the target to make a 90 degree turn. He uses two constant acceleration Kalman filters. One filter assumes no process noise, and the other assumes process noise with spectral density $10^{-3}\mathbf I$. He assumes very good initialization of the filters, setting $\mathbf P =10^{-12}$ for both filters. My implementation follows:
# +
import copy
from filterpy.kalman import IMMEstimator
from scipy.linalg import block_diag
N = 600
dt = 1.
track = adaptive_internal.turning_target(N)
# create noisy measurements
zs = np.zeros((N, 2))
r = 1
for i in range(N):
zs[i, 0] = track[i, 0] + randn()*r
zs[i, 1] = track[i, 2] + randn()*r
ca = KalmanFilter(6, 2)
dt2 = (dt**2)/2
F = np.array([[1, dt, dt2],
[0, 1, dt],
[0, 0, 1]])
ca.F = block_diag(F, F)
ca.x = np.array([[2000., 0, 0, 10000, -15, 0]]).T
ca.P *= 1.e-12
ca.R *= r**2
q = np.array([[.05, .125, 1/6],
[.125, 1/3, .5],
[1/6, .5, 1]])*1.e-3
ca.Q = block_diag(q, q)
ca.H = np.array([[1, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 0]])
# create identical filter, but with no process error
cano = copy.deepcopy(ca)
cano.Q *= 0
filters = [ca, cano]
M = np.array([[0.97, 0.03],
[0.03, 0.97]])
mu = np.array([0.5, 0.5])
bank = IMMEstimator(filters, mu, M)
xs, probs = [], []
cvxs, caxs = [], []
for i, z in enumerate(zs):
z = np.array([z]).T
bank.update(z)
xs.append(bank.x.copy())
cvxs.append(ca.x.copy())
caxs.append(cano.x.copy())
probs.append(bank.mu.copy())
xs = np.array(xs)
cvxs = np.array(cvxs)
caxs = np.array(caxs)
probs = np.array(probs)
plt.subplot(121)
plt.plot(xs[:, 0], xs[:, 3], 'k')
plt.scatter(zs[:, 0], zs[:, 1], marker='+')
plt.subplot(122)
plt.plot(probs[:, 0])
plt.plot(probs[:, 1])
plt.ylim(-1.5, 1.5)
plt.title('probability ratio p(cv)/p(ca)');
# -
# It is rather hard to see the performance of the filter, so let's look at the performance just as the turn starts. I've swapped the $x$ and $y$ axis to let us zoom in closely. In the chart below the turn starts at $Y=4000$. If you look very closely you can see that the estimate wavers slightly after the turn is initiated, but the filter tracks the measurement without lag and soon tracks smoothly.
plt.plot(xs[390:450, 3], xs[390:450, 0], 'k')
plt.scatter(zs[390:450, 1], zs[390:450, 0], marker='+', s=100);
plt.xlabel('Y'); plt.ylabel('X')
plt.gca().invert_xaxis()
plt.axis('equal')
# ## Summary
#
# This chapter contains some of the more challenging material in this book. However, it is the gateway to implementing realistic Kalman filters. If we are controlling a robot we know its process model, and it is easy to construct a Kalman filter for it. Far more commonly we are given a set of time series data and asked to make sense of it. The process model is largely unknown to us. We use the techniques in this chapter to *learn* (in a machine learning sense) how to parameterize our models. The models change over time as the target maneuver, so our filters must be adaptive.
#
# Finding an optimal answer involves combinatorial explosion, and is impractical in practice. The IMM algorithm has become the standard algorithm because of its good performance and computational tractability.
#
# A real filter bank usually involves more than two filters. It is common to have many filters. As the target's regime changes some filters become infinitesimally likely. Most adaptive filters implement an algorithm that kills off extremely unlikely filters and replaces them with filters that more closely match the current regime. This is highly specific to your problem space, and is usually very ad-hoc. You will need to devise schemes for killing and creating filters and test them against simulated or real data.
#
# Despite the complexity of the algorithms, I hope you recognize the underlying ideas are very simple. We use the same two tools that we learned in the second chapter: Bayes theorem and the total probability theorem. We incorporate new information using Bayes theorem, and compute the effect of the process models using the total probability theorem.
#
# For me, this chapter underscores the beauty of the Bayesian formulation of Kalman filters. I don't much care if you learn the details of the IMM algorithm. I do hope that you see that very simple probabilistic reasoning led to these results. The linear algebra equations of the Kalman filter that Dr. Kalman derived came from a different form of reasoning called *orthogonal projection*. It is beautiful in its own way, and I urge you to read his paper. But I'm not sure I find them intuitive to use, and it is not at all clear how to devise new, non-optimal filters such as the IMM using those techniques. In contrast, Bayes theorem lets us handle these problems with ease.
# ## References
#
# * [1] Bar-Shalom, Y., Xiao-Rong L., and Thiagalingam Kirubarajan. *Estimation with Applications to Tracking and Navigation*. New York: Wiley, p. 424, 2001.
#
#
# * [2] Zarchan, P., and Musoff, H., *Fundamentals of Kalman Filtering: A Practical Approach*. Reston, VA: American Institute of Aeronautics and Astronautics, 2000. Print.
#
#
# * [3] Simon, D., *Optimal State Estimation: Kalman, H and Nonlinear Approaches*. Hoboken, NJ: Wiley-Interscience, p. 208-212, 2006
#
#
# * [4] Crassidis, John L., and John L. Junkins. *Optimal estimation of dynamic systems*. CRC press, 2011.
#
#
# * [5] Blom, H.A.P., "An Efficient Filter for Abruptly Changing Systems", *Proceedings of 23rd Conference on Decision and Control*, Las Vegas, NV, Dec 1984.
#
#
# * [6] Blom, H.A.P and Bar-Shalom, Y., "The Interacting Multiple Model Algorithm for Systems with Markovian Switching Coefficients", *IEEE Transactions on Automatic Control*, Vol. AC-8, No. 8, Aug. 1998, pp. 780-783.
| 60,613 |
/Fifa19.ipynb | 1239f2788d59993df3aa651e2e9918bb5819f685 | [] | no_license | raybg/KNN | https://github.com/raybg/KNN | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 382,990 | # ---
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# name: python3
# ---
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from pandas import read_csv
filename = ("../input/housing.csv")
names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV']
boston = read_csv(filename, delim_whitespace=True, names=names)
print(boston.shape)
boston.head()
# Another way to access data:
# +
# from sklearn.datasets import load_boston
# boston_dataset = load_boston()
# print(boston_dataset.keys())
# boston = pd.DataFrame(boston_dataset.data, columns=boston_dataset.feature_names)
# boston['MEDV'] = boston_dataset.target
# -
# **Data Set Characteristics:**
#
# :Number of Instances: 506
#
# :Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.
#
# :Attribute Information (in order):
# - CRIM per capita crime rate by town
# - ZN proportion of residential land zoned for lots over 25,000 sq.ft.
# - INDUS proportion of non-retail business acres per town
# - CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)
# - NOX nitric oxides concentration (parts per 10 million)
# - RM average number of rooms per dwelling
# - AGE proportion of owner-occupied units built prior to 1940
# - DIS weighted distances to five Boston employment centres
# - RAD index of accessibility to radial highways
# - TAX full-value property-tax rate per $10,000
# - PTRATIO pupil-teacher ratio by town
# - B 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town
# - LSTAT % lower status of the population
# - MEDV Median value of owner-occupied homes in $1000's
#
boston.isnull().sum()
boston.corr()
plt.figure(figsize=(12,10))
sns.heatmap(boston.corr().round(2),cmap='coolwarm',annot=True)
corr = boston.corr()
corr['MEDV'].sort_values(ascending = False)
# +
fig, axes = plt.subplots(1, 2, figsize=(15, 5))
axes[0].scatter(x=boston['LSTAT'],y=boston['MEDV'],color='red')
axes[0].set_ylabel('MEDV')
axes[0].set_xlabel('LSTAT')
axes[1].scatter(x=boston['RM'],y=boston['MEDV'])
axes[1].set_ylabel('MEDV')
axes[1].set_xlabel('RM')
# -
X = boston[['LSTAT', 'RM']]
y = boston['MEDV']
X.head()
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=101)
lm = LinearRegression(normalize=True)
lm.fit(X_train,y_train)
print(lm.intercept_)
coeff_df = pd.DataFrame(lm.coef_,X.columns,columns=['Coefficient'])
coeff_df
predictions = lm.predict(X_test)
plt.scatter(y_test,predictions)
plt.xlabel('Y Test')
plt.ylabel('Predicted Y')
# Residual Histogram
sns.distplot((y_test-predictions),bins=50);
# Regression Evaluation Metrics
from sklearn import metrics
from sklearn.metrics import r2_score
# I will be using Root mean squared error(RMSE) and Coefficient of Determination(R² score) to evaluate my model.
#
# RMSE is the square root of the average of the sum of the squares of residuals.
#
# R² score or the coefficient of determination explains how much the total variance of the dependent variable
# can be reduced by using the least square regression.
#
print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, predictions)))
r2 = r2_score(y_test, predictions)
print('R2 score is {}'.format(r2))
# We reduced the prediction error by ~ 60% by using regression (R2 score =0.599)
# # Polynomial Regression
from sklearn.preprocessing import PolynomialFeatures
poly_features = PolynomialFeatures(2)
X_train_poly = poly_features.fit_transform(X_train)
poly_model = LinearRegression()
poly_model.fit(X_train_poly, y_train)
y_test_predict = poly_model.predict(poly_features.fit_transform(X_test))
rmse_test = np.sqrt(metrics.mean_squared_error(y_test, y_test_predict))
r2_test = r2_score(y_test, y_test_predict)
print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, y_test_predict)))
print('R2 score is {}'.format(r2_test))
| 4,427 |
/week10_dim_reduction/3_wednesday/chris2.ipynb | af1c9839163e3243adc40fbb3232a8816e2dee56 | [] | no_license | thinkful-dsi-grackle/dsi7_student_pair_work | https://github.com/thinkful-dsi-grackle/dsi7_student_pair_work | 5 | 8 | null | 2020-09-09T19:11:41 | 2020-09-09T19:11:21 | Jupyter Notebook | Jupyter Notebook | false | false | .py | 23,116 | # ---
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# + [markdown] id="1hulOA67RQ6N" colab_type="text"
#
#
# ---
#
#
# # Test file
# Let's see how Github and Colab integration works
#
#
# ---
#
#
# + id="lNHaZTD8RQHJ" colab_type="code" outputId="55bc285b-4ae1-4922-efd1-46b5ca611bad" colab={"base_uri": "https://localhost:8080/", "height": 35}
print("Hello!")
ort classification_report
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split
# + [markdown] id="TfqS_cMWoMa1"
# ### Import the [Pima Indians Diabetes data set](https://tf-assets-prod.s3.amazonaws.com/tf-curric/data-science/pima_indians_diabetes.csv).
# + id="qO-4LVM7oMa1" colab={"base_uri": "https://localhost:8080/", "height": 439} outputId="7714db1e-c842-451f-92d7-39764fc6e5ba"
data = pd.read_csv('https://tf-assets-prod.s3.amazonaws.com/tf-curric/data-science/pima_indians_diabetes.csv')
data
# + [markdown] id="YtNhl2RCoMa5"
# ### Split the data into training and test sets, with the target variable being the outcome column.
# + id="awGD68-2oMa5"
y = data.outcome
X = data.drop('outcome',axis=1)
# + [markdown] id="l6uDW6gSoMa9"
# ### Train a Random Forest Classifier on the data without doing any transformations and print a classification report.
#
# This will provide us with a basis for comparison.
# + id="9o8P7OeooMbC" colab={"base_uri": "https://localhost:8080/"} outputId="83c9d5be-23cc-4935-88ef-d3922dc3ed4d"
model = RandomForestClassifier(random_state=1)
model.fit(X,y)
print(classification_report(y, model.predict(X)))
# + [markdown] id="44gMhQKRoMbG"
# ### Reduce the data down to 3 dimensions using PCA. Then do the train-test split, fit the model, and print a classification report.
#
# Compare these results to the previous ones.
# + id="IbJpUBrgoMbH" colab={"base_uri": "https://localhost:8080/"} outputId="e92e1ae9-ebf3-47a5-e945-18cc0f09af4b"
pca = PCA(n_components=3)
X_pca = pca.fit_transform(X)
X_train, X_test,y_train,y_test = train_test_split(X_pca,y,test_size=0.2)
model = RandomForestClassifier(random_state=1).fit(X_train, y_train)
print(classification_report(y_test,model.predict(X_test)))
# + [markdown] id="7PSJ9pLRoMbL"
# ### Fit the model and print a classification report again, but this time, perform the train-test split before you transform the data using PCA.
#
# Compare these results to the previous ones.
# + id="N2kjPbPKoMbM" colab={"base_uri": "https://localhost:8080/"} outputId="f014d2dc-157d-4c0f-dc34-c98f89f0f58a"
pca = PCA(n_components=3)
X_train, X_test,y_train,y_test = train_test_split(X,y,test_size=0.2)
X_train_pca = pca.fit_transform(X_train)
X_test_pca = pca.transform(X_test)
model = RandomForestClassifier(random_state=1).fit(X_train_pca, y_train)
print(classification_report(y_test,model.predict(X_test_pca)))
# + [markdown] id="z5QOmml0oMbO"
# ### Using the Random Forest Classifier, perform 10-fold cross validation on the training set and print the mean cross validation score.
# + id="_Ci-V57_oMbP" colab={"base_uri": "https://localhost:8080/"} outputId="88150405-2512-4ddb-8a89-6d11226c4237"
scores = cross_val_score(model, X_train, y_train, cv=10)
scores.mean()
# + [markdown] id="7W_6glHPoMbT"
# ### Create a pipeline with a PCA step and a Random Forest Classifier step. Perform the train-test split again, fit the pipeline, and then generate a classification report.
#
# Compare these results to the previous ones.
# + id="1sy4DFzsoMbU" colab={"base_uri": "https://localhost:8080/"} outputId="d20fb608-989b-462f-e90a-ce446e640dd1"
pipeline = Pipeline([('pca', PCA(n_components=3)),
('rf', RandomForestClassifier(random_state=1))])
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=1)
pipeline.fit(X_train,y_train)
y_preds=pipeline.predict(X_test)
print(classification_report(y_test,y_preds))
# + [markdown] id="T0RpnV0xoMbX"
# ### Using the pipeline you built, perform 10-fold cross validation on the training set and print the mean cross validation score.
#
# How does this score compare to the previous one?
# + id="9IKeBBZXoMbY" colab={"base_uri": "https://localhost:8080/"} outputId="28f72fa4-6345-438e-c79b-ce0ea88a02c7"
scores = cross_val_score(pipeline, X_train,y_train,cv=10)
scores.mean()
# + [markdown] id="6DLDuyOqoMba"
# ### Use GridSearchCV to find the optimal set of parameters from the ones below.
#
# - PCA Number of Components: 2, 3, 4, 5, 6, 7, 8
# - Random Forest Number of Estimators: 10, 20, 50, 100, 200
# + id="SbsRhIdZoMbb" colab={"base_uri": "https://localhost:8080/"} outputId="a6dc89dd-b538-4442-a291-c2dc0b18ce13"
pipeline = Pipeline([('pca', PCA()),
('rf', RandomForestClassifier(random_state=1))])
parameters = {'pca__n_components': [2,3,4,5,6,7,8],
'rf__n_estimators': [10,20,50,100,200],}
search = GridSearchCV(pipeline,parameters,cv=10)
search.fit(X_train,y_train)
print(search.best_score_)
print(search.best_params_)
# + [markdown] id="7cVJ994qoMbd"
# ### Using the best estimator pipeline from above, fit the pipeline to the training set and generate a classification report showing the results.
#
# Compare these results to the previous ones.
# + id="P0N6Mv3KoMbe" colab={"base_uri": "https://localhost:8080/"} outputId="800976db-f638-41d0-953d-7100ed4e356b"
pipeline = Pipeline([('pca', PCA(n_components=6)),
('rf', RandomForestClassifier(random_state=1,n_estimators=50))])
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=1)
pipeline.fit(X_train,y_train)
y_preds=pipeline.predict(X_test)
print(classification_report(y_test,y_preds))
# + [markdown] id="loHniiPLoMbj"
# ### Fit the best estimator pipeline to the entire data set and save your model to disk using pickle.
# + id="5Y8ESntxoMbk"
pipeline = search.best_estimator_
pipeline.fit(X,y)
y_pred = pipeline.predict(X_test)
with open('model.pkl', 'wb') as f:
pickle.dump(pipeline,f)
# + [markdown] id="9HdA-rQGoMbn"
# ### Load the model you saved to disk, create a copy of the features in the data, and generate a set of predictions for those features.
# + id="UBnP7dKhoMbn" colab={"base_uri": "https://localhost:8080/"} outputId="fd2f0400-8cf5-4cc5-f698-25e0e250d481"
with open('model.pkl', 'rb') as f:
loaded_pipe = pickle.load(f)
preds = loaded_pipe.predict(X)
data['pred'] = preds
data['result'] = data['outcome'] - data['pred']
data.result.value_counts()
#all predictions are the same as 'outcome' column
me for validation/dev data
dev_texts=[i["text"] for i in dev_data_sent]
dev_labels=[i["tags"] for i in dev_data_sent]
# + [markdown] id="FNKOr6YY28eC" colab_type="text"
# Let's add POS tags to the data
# + id="LlIltk1uXIXO" colab_type="code" colab={}
# DO NOT RUN
#import nltk
#nltk.download('averaged_perceptron_tagger')
#def sent_parser4pos(texts, labels):
# for i in range(len(texts) -1):
# #print(texts[i])
# tokens_with_pos = nltk.pos_tag(texts[i])
# #getting the corresponding labels list
# labels_curr= labels[i]
# print()
# print()
#print('current labels:',labels_curr)
#updating labels with pos label
#print('possed tokens:', tokens_with_pos)
# for j in range(len(labels_curr) -1):
# bio = labels_curr[j]
# pos = tokens_with_pos[j][1]
#print('bio: ',bio)
#print('pos: ', pos)
#print('current labels: ',labels[i])
# new_label = pos+' '+bio ## new kind of combination label
# labels[i][j] = new_label
#print('No of Bio Labels: ',len(labels_curr), ' No of pos tags: ', len(tokens_with_pos), ' No of updated labels: ', len(labels[i]), labels[i])
# return labels
# + id="sCqK8iji9Nur" colab_type="code" colab={}
# DO NOT RUN
#train_labels_posbio = sent_parser4pos(train_texts, train_labels)
#print(train_texts[0])
#print(train_labels[0]
#print(train_labels_posbio[0])
# + id="ICn08fOgbyXl" colab_type="code" outputId="4019782c-2cd6-488a-8a30-85dfc37189d6" colab={"base_uri": "https://localhost:8080/", "height": 204}
# Load pretrained embeddings
# !wget -nc https://dl.fbaipublicfiles.com/fasttext/vectors-english/wiki-news-300d-1M.vec.zip
# + id="bFv2qclTbyXp" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 51} outputId="0e085d9f-271b-458c-8893-140a1adbd227"
# Give -n argument so that a possible existing file isn't overwritten
# !unzip -n wiki-news-300d-1M.vec.zip
# + id="kx_C5ii8byXt" colab_type="code" outputId="a840f04e-b6dc-4977-f7e8-c00392cf619d" colab={"base_uri": "https://localhost:8080/", "height": 173}
from gensim.models import KeyedVectors
vector_model = KeyedVectors.load_word2vec_format("wiki-news-300d-1M.vec", binary=False, limit=50000)
# sort based on the index to make sure they are in the correct order
words = [k for k, v in sorted(vector_model.vocab.items(), key=lambda x: x[1].index)]
print("Words from embedding model:", len(words))
print("First 50 words:", words[:50])
# Normalize the vectors to unit length
print("Before normalization:", vector_model.get_vector("in")[:10])
vector_model.init_sims(replace=True)
print("After normalization:", vector_model.get_vector("in")[:10])
# + id="NkdgjgOlbyXx" colab_type="code" outputId="3740c1ec-523f-4ac2-b12e-d304f8833959" colab={"base_uri": "https://localhost:8080/", "height": 51}
# Build vocabulary mappings
# Zero is used for padding in Keras, prevent using it for a normal word.
# Also reserve an index for out-of-vocabulary items.
vocabulary={
"<PAD>": 0,
"<OOV>": 1
}
for word in words: # These are words from the word2vec model
vocabulary.setdefault(word, len(vocabulary))
print("Words in vocabulary:",len(vocabulary))
inv_vocabulary = { value: key for key, value in vocabulary.items() } # invert the dictionary
# Embedding matrix
def load_pretrained_embeddings(vocab, embedding_model):
""" vocab: vocabulary from our data vectorizer, embedding_model: model loaded with gensim """
pretrained_embeddings = numpy.random.uniform(low=-0.05, high=0.05, size=(len(vocab)-1,embedding_model.vectors.shape[1]))
pretrained_embeddings = numpy.vstack((numpy.zeros(shape=(1,embedding_model.vectors.shape[1])), pretrained_embeddings))
found=0
for word,idx in vocab.items():
if word in embedding_model.vocab:
pretrained_embeddings[idx]=embedding_model.get_vector(word)
found+=1
print("Found pretrained vectors for {found} words.".format(found=found))
return pretrained_embeddings
pretrained=load_pretrained_embeddings(vocabulary, vector_model)
# + [markdown] id="mGaojUBhbyX2" colab_type="text"
# Preprocessing
# + id="_M9Ox5_ObyX3" colab_type="code" outputId="36177f59-cde2-4e5d-e466-e32b9af57bad" colab={"base_uri": "https://localhost:8080/", "height": 646}
#Labels
from pprint import pprint
not_letter = re.compile(r'[^a-zA-Z]')
# Label mappings
# 1) gather a set of unique labels
label_set = set()
for sentence_labels in train_labels: #loops over sentences
#print(sentence_labels)
for label in sentence_labels: #loops over labels in one sentence
# match = not_letter.match(label)
#if match or label== 'O':
# break
#else:
label_set.add(label)
# 2) index these
label_map = {}
for index, label in enumerate(label_set):
label_map[label]=index
pprint(label_map)
# + id="r8k8DshceEaI" colab_type="code" outputId="cf16ed82-8615-48c9-8119-15dbaa1f66cf" colab={"base_uri": "https://localhost:8080/", "height": 34}
# vectorize the labels
def label_vectorizer(train_labels,label_map):
vectorized_labels = []
for label in train_labels:
vectorized_example_label = []
for token in label:
if token in label_map:
vectorized_example_label.append(label_map[token])
vectorized_labels.append(vectorized_example_label)
vectorized_labels = numpy.array(vectorized_labels)
return vectorized_labels
vectorized_labels = label_vectorizer(train_labels,label_map)
validation_vectorized_label | 12,288 |
/Homework1/Q1-4/RoadToSuccess/Question1-4.ipynb | ffe6efeb99b9abacb247b425a24293b089d4d946 | [] | no_license | jayhung97724/108-1_CVDL | https://github.com/jayhung97724/108-1_CVDL | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 10,174 | # ---
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# name: python3
# ---
import cv2
import numpy as np
import glob
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
np.set_printoptions(formatter={'float': '{: 0.6f}'.format})
# ## Question 1
# +
# Make a list of calibration images
images = [cv2.imread(file) for file in glob.glob("../images/CameraCalibration/*.bmp")]
# termination criteria
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((8*11,3), np.float32)
objp[:,:2] = np.mgrid[0:8,0:11].T.reshape(-1,2)
# Arrays to store object points and image points from all the images.
objpoints = [] # 3d point in real world space
imgpoints = [] # 2d points in image plane.
cameraMatrix = []
distCoeff = []
rvec = []
tvec = []
# -
def showImages(images):
for img in images:
cv2.namedWindow("Show Image", cv2.WINDOW_NORMAL)
cv2.imshow("Show Image",img)
cv2.waitKey(2000)
cv2.destroyAllWindows()
# showImages(images)
def showImage(img):
cv2.namedWindow("Show Image", cv2.WINDOW_NORMAL)
cv2.imshow("Show Image",img)
cv2.waitKey(2000)
cv2.destroyAllWindows()
def Q1_1():
global mtx, cameraMatrix, distCoeff, rvec, tvec, objpoints, imgpoints
# prepare object points
nx = 8
ny = 11
print('Q1.1 ...\nFinding corners ...')
for img in images:
# Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Find the chessboard corners
ret, corners = cv2.findChessboardCorners(gray, (nx, ny), None)
# If found, draw corners
if ret == True:
objpoints.append(objp)
corners2 = cv2.cornerSubPix(gray,corners,(11,11),(-1,-1),criteria)
imgpoints.append(corners2)
# Draw and display the corners
cv2.drawChessboardCorners(img, (nx, ny), corners, ret)
print('Showing images ...')
showImages(images)
# Calibration
ret, cameraMatrix, distCoeff, rvec, tvec = cv2.calibrateCamera(objpoints, imgpoints, (2048, 2048), None, None)
np.savez('params.npz', cameraMatrix=cameraMatrix, distCoeff=distCoeff[0], rvec=rvec, tvec=tvec)
Q1_1()
# +
# ret, cameraMatrix, distCoeff, rvec, tvec = cv2.calibrateCamera(objpoints, imgpoints, (2048, 2048), None, None)
# -
def Q1_2():
print('Q1_2 ...')
print(cameraMatrix)
Q1_2()
def Q1_3(picIndex):
print('Q1_3 ...')
picIndex = picIndex-1
rotMat,_ = cv2.Rodrigues(rvec[picIndex])
print(np.concatenate((rotMat,tvec[picIndex]), axis=1))
Q1_3(14)
def Q1_4():
print('Q1_4 ...')
print(distCoeff[0])
Q1_4()
# ## Question 2
# +
# Load previously saved data
params = np.load('params.npz')
mtx = params['cameraMatrix']
dist = params['distCoeff']
# Make a list of calibration images
images2 = [cv2.imread(file) for file in glob.glob("../images/CameraCalibration/*.bmp")]
# termination criteria
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((8*11,3), np.float32)
objp[:,:2] = np.mgrid[0:8, 0:11].T.reshape(-1,2)
axis = np.float32([[-1,-1,0], [-1,1,0],
[1,1,0], [1,-1,0], [0,0,-2]])
# -
def draw(img, corners, imgpts):
for i in range(0, 4):
j = (i + 1) if i < 3 else 0
img = cv2.line(img, tuple(imgpts[i].ravel()), tuple(imgpts[j].ravel()), (0,0,255), 3)
img = cv2.line(img, tuple(imgpts[i].ravel()), tuple(imgpts[4].ravel()), (0,0,255), 3)
return img
# +
def Q2():
for img in images2[:5]:
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ret, corners = cv2.findChessboardCorners(gray, (8,11),None)
if ret == True:
corners2 = cv2.cornerSubPix(gray,corners,(11,11),(-1,-1),criteria)
# Find the rotation and translation vectors.
_, rvecs, tvecs, inliers = cv2.solvePnPRansac(objp, corners2, mtx, dist[0])
# project 3D points to image plane
imgpts, jac = cv2.projectPoints(axis, rvecs, tvecs, mtx, dist)
img = draw(img,corners2,imgpts)
showImage(img)
# cv2.destroyAllWindows()
# -
Q2()
# ## Question 4
def Q4():
img = cv2.imread('../../images/Contour.png')
imgray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret,thresh = cv2.threshold(imgray,127,255,0)
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
cv2.drawContours(img, contours, -1, (0,0,255), 2)
showImage(img)
Q4()
# ## Question 3
def showImageOSize(img):
cv2.namedWindow("Show Image")
cv2.imshow("Show Image",img)
cv2.waitKey()
cv2.destroyAllWindows()
def Q3_1(angle=45, scale=0.8, Tx=150, Ty=50):
print('Q3.1 ...')
img = cv2.imread('../../images/OriginalTransform.png')
print('Showing Original image\nPress any key to continue ...')
showImageOSize(img)
rows,cols = img.shape[:2]
center = (125+Tx, 130+Ty)
print(center)
TM = np.float32([[1,0,Tx],[0,1,Ty]])
RM = cv2.getRotationMatrix2D(center,angle,1)
img = cv2.warpAffine(img,TM,(cols,rows))
img = cv2.warpAffine(img,RM,(cols,rows))
img = cv2.resize(img,None,fx=scale, fy=scale, interpolation = cv2.INTER_AREA)
print('Showing transformed image ...')
showImageOSize(img)
# Q3_1(45, 0.8, 0, 100)
# Q3_1(45, 0.8, 100, 0)
Q3_1()
| 5,682 |
/Project_1B_Project_Template.ipynb | bb74d3cd6357e6d41b4724f87dded28064620350 | [] | no_license | KarthikeyanKandan/data-modeling-for-sparkify-cassandra | https://github.com/KarthikeyanKandan/data-modeling-for-sparkify-cassandra | 1 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 20,633 | # ---
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# ---
# # Retirement Investment Portfolio
# ---
#
# The purpose of this project is to build a retirement portfolio dashboard that allows users to view historical and risk analysis diagrams to assist them in making better decisions. The historical data of US shares and crypto currencies over the past 3 years period have been used to determine our analysis.
# ## Initial imports
# +
# Initial imports
import warnings
warnings.filterwarnings('ignore')
import os
import plotly
import requests
import pandas as pd
import numpy as np
from dotenv import load_dotenv
# Initialize the Panel Extensions (for Plotly)
import panel as pn
import param
pn.extension('plotly')
pn.extension()
import plotly.express as px
import hvplot.pandas
import matplotlib.pyplot as plt
import os
from pathlib import Path
from dotenv import load_dotenv
import ipywidgets as widgets
from IPython.display import display
# %matplotlib inline
# !pip install yfinance
import yfinance as yf
# !pip install pandas-montecarlo
import pandas_montecarlo
import datetime as dt
from MCForecastTools import MCSimulation
# Load .env enviroment variables
load_dotenv()
# -
# ### Global Parameters
range_of_cryptos = ["BTC-USD", "ETH-USD", "BNB-USD", "ADA-USD", "DOGE-USD", "XRP-USD", "LINK-USD" , "THETA-USD", "LTC-USD", "XLM-USD"]
range_of_stocks = ["AMZN", "TSLA", "NEO", "AAPL", "NVDA", "BABA", "NFLX", "DIS", "NKE", "SQ"]
# ## DEFINE FUNCTIONS
# ### Calculate back date
# +
#CALCULATE 1 YEARS BACK DATE - this will retun the exact date 1 year ago in the format yyyy-mm-dd
def calc_1years_backdate():
end = dt.datetime.now().date()
start = end - dt.timedelta(days=365)
return start
#CALCULATE 2 YEARS BACK DATE - this will retun the exact date 1 year ago in the format yyyy-mm-dd
def calc_2years_backdate():
end = dt.datetime.now().date()
start = end - dt.timedelta(days=730)
return start
#CALCULATE 3 YEARS BACK DATE - this will retun the exact date 1 year ago in the format yyyy-mm-dd
def calc_3years_backdate():
end = dt.datetime.now().date()
start = end - dt.timedelta(days=1095)
return start
print(f" Exact date today of 1 year ago is {calc_1years_backdate()}")
print(f" Exact date today of 2 year ago is {calc_1years_backdate()}")
print(f" Exact date today of 3 year ago is {calc_1years_backdate()}")
# -
# ### Download crypto and stock data for 3 years
# +
# The below functions pull 3 years of financial data from Yahoo Finance! (Crypto and Stock) through yFinance Library and export the data to Multi-Indexes-Column Dataframe
# This function takes in the array range_of_cryptos as input and produce dataframe out put of historical Crypto data
def download_cryptos_data():
df = yf.download(range_of_cryptos, period = "3y", group_by = 'ticker', threads = True, progress=False)
df = df.dropna().copy()
return df
# This function takes in the array range_of_stocks as input and produce dataframe out put of historical Stock data
def download_stocks_data():
df = yf.download(range_of_stocks, period = "3y", group_by = 'ticker', threads = True, progress=False)
df = df.dropna().copy()
return df
print("\n Display data pulling from Yfinance for Cryptos:\n")
display(download_cryptos_data())
print("\n Display data pulling from Yfinance for Stocks:\n")
display(download_stocks_data())
# +
# Single Crypto/Stock data can be called using the Ticker as INDEX:
# Display BTC-USD 3 years data:
crypto_df = download_cryptos_data()
print("\n Display BTC-USD 3 years data:\n")
display(crypto_df["BTC-USD"])
# Display BTC-USD 3 years data:
stock_df = download_stocks_data()
print("\n Display TSLA 3 years data:\n")
display(stock_df["TSLA"])
# -
# ### Annual return function
# This function provides a visual output of daily return of a single Cryptocurrency or Stock base on historical data downloaded from YFinance:
def daily_return_data(df):
daily_return = df["Close"].pct_change()
plot = daily_return.hvplot.line(x="Date", rot=45, shared_axes=False).opts(yformatter="%.0f", width=500, xlabel="Time", ylabel="Price", title="Daily Return")
return plot
# Demo of daily return for a single crypto:
crypto_df = download_cryptos_data()
print("\n Daily return of BTC-USD base on 3 years of historical data:\n")
daily_return_data(crypto_df["BTC-USD"])
# Demo of daily return for a single stock:
stock_df = download_stocks_data()
print("\n Daily return of TSLA base on 3 years of historical data:\n")
daily_return_data(stock_df["TSLA"])
# ### Cumulative return function
# +
# This function provides a visual output of Cumulative return of a single Cryptocurrency or Stock base on historical data downloaded from YFinance:
def cumulative_return_plot(df):
cumulative_returns = (1 + df['Close'].pct_change()).cumprod()
plot = cumulative_returns.hvplot.line(x="Date", rot=45, shared_axes=False).opts(yformatter="%.0f", width=500, xlabel="Time", ylabel="Price", title="Cumulative Return")
return plot
# -
# Demo of Cumulative return for a single crypto:
print("\n Cumulative return of BTC-USD base on 3 years of historical data:\n")
cumulative_return_plot(crypto_df["BTC-USD"])
# Demo of Cumulative return for a single Stock:
print("\n Cumulative return of TSLA base on 3 years of historical data:\n")
cumulative_return_plot(stock_df["TSLA"])
# ### Moving Average function
# +
# This function provides a visual output of Moving average (30, 60, 90 days) of a single Cryptocurrency or Stock base on historical data downloaded from YFinance:
def MA_plot(df):
df["30MA"]=df[["Close"]].rolling(30).mean()
df["60MA"]=df[["Close"]].rolling(60).mean()
df["90MA"]=df[["Close"]].rolling(90).mean()
df.dropna(inplace=True)
plot = df[["Close", "30MA", "60MA", "90MA"]].hvplot.line(x="Date", rot=45, shared_axes=False).opts(yformatter="%.0f", width=500, xlabel="Date", ylabel="Price", title="Moving Average")
return plot
# -
# Demo of Moving average return for a single crypto:
print("\n Moving average of BTC-USD base on 3 years of historical data:\n")
MA_plot(crypto_df["BTC-USD"])
# Demo of Moving average return for a single stock:
print("\n Moving average of TSLA base on 3 years of historical data:\n")
MA_plot(stock_df["TSLA"])
# ### Daily return function for multiple tickers (data clean up to function Sharpe ratio)
# +
# This function return a dataframe of daily return base on original downloaded data from YFinance (crypto_df and stock_df) by:
# - Calculate Daily return for all Open, High, Low, Close, Adj Close column for each stock
# - Loop through each Level 0 column indexes (Stock/Crypto's Ticker) and ammend that to a new-dataframe
# - Return the new-dataframe
def daily_returns_multiple_tickers(df):
df2 = pd.DataFrame(columns = df.columns.levels[0])
df = df.pct_change()
df = df.dropna()
for key in (df.columns.levels[0]):
df2[key] =+ (df[key]["Close"])
return df2
# +
#Daily return for multiple tickers for Cryto and Stock
print("\nDaily Return of Each Crypto:\n")
display(daily_returns_multiple_tickers(crypto_df))
print("\nDaily Return of Each Stock:\n")
display(daily_returns_multiple_tickers(stock_df))
# -
# ### Sharpe ratio function
# +
# This function provides a visual output of Annual Sharpe Ratio of ALl Cryptocurrencies or Stocks base on historical data downloaded from YFinance:
def annual_sharpe_ratio_plot(df):
df = daily_returns_multiple_tickers(df)
std = df.std()
annual_std = std * np.sqrt(252)
annual_sharpe_ratio = (df.mean() * 252) / annual_std
plot = annual_sharpe_ratio.hvplot.bar(rot=45, shared_axes=False).opts(width=500, xlabel="Date", ylabel="Price", title="Annual Sharpe Ratios")
return plot
# -
# Demo for Annual Sharpe Ratio of ALl Cryptocurrencies
print("\nAnnual Sharpe Ratio of ALl Cryptocurrencies:\n")
display(annual_sharpe_ratio_plot(crypto_df))
# Demo for Annual Sharpe Ratio of ALl Stocks
print("\nAnnual Sharpe Ratio of ALl Stocks:\n")
display(annual_sharpe_ratio_plot(stock_df))
# ### Monte Carlo cumulative return into a dataframe function - Using a **modified version** of MCForcastTools.py specific to this project
# This function Calculate Monte Carlo cumulative of return of a Stock or Crypto Ticker and retun dataframe
# This function take in parameters:
# 1- a dataframe of a single Crypto/Stock historical data
# 2- The number of simulations
# 3- The number of days forecast in to future
def mc_cumulative_return(df, num_sim, num_day):
mc = MCSimulation(
portfolio_data = df,
weights = "",
num_simulation = num_sim,
num_trading_days = num_day
)
return mc.calc_cumulative_return()
# Demo of Calculate Monte Carlo cumulative of return for "BTC-USD" for 10 simulations and 365 days into future:
print("\n Monte Carlo cumulative of return of BTC-USD for 10 simulation and 365 days in future:\n")
mc_cumulative_return(crypto_df[["BTC-USD"]], 10, 365)
# ### Monte Carlo Simulation function
# +
# This function provides a visual demonstration of Monte Carlo cumulative of return of a Stock or Crypto Ticker and return a line chart
# This function take in parameters:
# 1- a dataframe of a single Crypto/Stock historical data
# 2- The number of simulations
# 3- The number of days forecast in to future
def mc_plot_cumulative(df, num_sim, num_day):
mc = MCSimulation(
portfolio_data = df,
weights = "",
num_simulation = num_sim,
num_trading_days = num_day
)
plot = mc.plot_simulation()
return plot
# -
print("\n Monte Carlo cumulative of return of BTC-USD for 10 simulation and 365 days in future:\n")
mc_plot_cumulative(crypto_df[["BTC-USD"]], 10, 365)
# ### Monte Carlo Simulation distribution function
# +
# This function provides a visual demonstration of Monte Carlo Distribution of return of a Stock or Crypto Ticker and return a Bar Chart
# This function take in parameters:
# 1- a dataframe of a single Crypto/Stock historical data
# 2- The number of simulations
# 3- The number of days forecast in to future
def mc_plot_distribution(df, num_sim, num_day):
mc = MCSimulation(
portfolio_data = df,
weights = "",
num_simulation = num_sim,
num_trading_days = num_day
)
plot = mc.plot_distribution()
return plot
# -
print("\n Monte Carlo Distribution of return of BTC-USD for 10 simulation and 365 days in future:\n")
mc_plot_distribution(crypto_df[["BTC-USD"]], 10, 365)
# ### Monte Carlo Simulation Statistic function
# +
# This function provides a statistical demonstration of Monte Carlo Distribution of return of a Stock or Crypto Ticker and return an estimation Message.
# This function take in parameters:
# 1- a dataframe of a single Crypto/Stock historical data
# 2- The number of simulations
# 3- The number of days forecast in to future
# 4- The amount of initial investment
def mc_summarize_statistic(df, num_sim, num_day, initial_investment):
mc = MCSimulation(
portfolio_data = df,
weights = "",
num_simulation = num_sim,
num_trading_days = num_day
)
tbl = mc.summarize_cumulative_return()
ci_lower = round(tbl[8]*initial_investment,2)
ci_upper = round(tbl[9]*initial_investment,2)
return (f"There is a 95% chance that an initial investment of ${initial_investment} in the portfolio over the next {num_day} days will end within in the range of ${ci_lower} and ${ci_upper}")
# -
print("\n Monte Carlo statistic of return of BTC-USD for 10 simulation and 365 days in future take in USD 20000 investment:\n")
mc_summarize_statistic(crypto_df, 10, 365, 20000)
# +
# The below functions will generate Daily return data of S&P 500 and USDT for Risk Analysis benchmark
def calc_sp_benchmark():
# Get S&P 500 historical data of 3 years for benchmark with Stock portfolio
sp_df = yf.download("^GSPC", period = "3y", threads = True, progress=False)
# Clean data
sp_df = sp_df.drop(["Open", "High", "Low" if destination == 'HNL' else 0,
'destination_airport_HNL': 1 if destination == 'HNL' else 0,
'destination_airport_IAD': 1 if destination == 'IAD' else 0,
'destination_airport_IAH': 1 if destination == 'IAH' else 0,
'destination_airport_ILM': 1 if destination == 'ILM' else 0,
'destination_airport_IND': 1 if destination == 'IND' else 0,
'destination_airport_JAC': 1 if destination == 'JAC' else 0,
'destination_airport_JAX': 1 if destination == 'JAX' else 0,
'destination_airport_JFK': 1 if destination == 'JFK' else 0,
'destination_airport_KOA': 1 if destination == 'KOA' else 0,
'destination_airport_LAS': 1 if destination == 'LAS' else 0,
'destination_airport_LAX': 1 if destination == 'LAX' else 0,
'destination_airport_LBB': 1 if destination == 'LBB' else 0,
'destination_airport_LGA': 1 if destination == 'LGA' else 0,
'destination_airport_LIH': 1 if destination == 'LIH' else 0,
'destination_airport_MCI': 1 if destination == 'MCI' else 0,
'destination_airport_MCO': 1 if destination == 'MCO' else 0,
'destination_airport_MEM': 1 if destination == 'MEM' else 0,
'destination_airport_MFE': 1 if destination == 'MFE' else 0,
'destination_airport_MIA': 1 if destination == 'MIA' else 0,
'destination_airport_MKE': 1 if destination == 'MKE' else 0,
'destination_airport_MSP': 1 if destination == 'MSP' else 0,
'destination_airport_MSY': 1 if destination == 'MSY' else 0,
'destination_airport_OAK': 1 if destination == 'OAK' else 0,
'destination_airport_OGG': 1 if destination == 'OGG' else 0,
'destination_airport_OKC': 1 if destination == 'OKC' else 0,
'destination_airport_OMA': 1 if destination == 'OMA' else 0,
'destination_airport_ONT': 1 if destination == 'ONT' else 0,
'destination_airport_ORD': 1 if destination == 'ORD' else 0,
'destination_airport_ORF': 1 if destination == 'ORF' else 0,
'destination_airport_PBI': 1 if destination == 'PBI' else 0,
'destination_airport_PDX': 1 if destination == 'PDX' else 0,
'destination_airport_PHL': 1 if destination == 'PHL' else 0,
'destination_airport_PHX': 1 if destination == 'PHX' else 0,
'destination_airport_PIT': 1 if destination == 'PIT' else 0,
'destination_airport_PSP': 1 if destination == 'PSP' else 0,
'destination_airport_PVD': 1 if destination == 'PVD' else 0,
'destination_airport_PWM': 1 if destination == 'PWM' else 0,
'destination_airport_RDU': 1 if destination == 'RDU' else 0,
'destination_airport_RIC': 1 if destination == 'RIC' else 0,
'destination_airport_RNO': 1 if destination == 'RNO' else 0,
'destination_airport_ROC': 1 if destination == 'ROC' else 0,
'destination_airport_RSW': 1 if destination == 'RSW' else 0,
'destination_airport_SAN': 1 if destination == 'SAN' else 0,
'destination_airport_SAT': 1 if destination == 'SAT' else 0,
'destination_airport_SDF': 1 if destination == 'SDF' else 0,
'destination_airport_SEA': 1 if destination == 'SEA' else 0,
'destination_airport_SFO': 1 if destination == 'SFO' else 0,
'destination_airport_SJC': 1 if destination == 'SJC' else 0,
'destination_airport_SJU': 1 if destination == 'SJU' else 0,
'destination_airport_SLC': 1 if destination == 'SLC' else 0,
'destination_airport_SMF': 1 if destination == 'SMF' else 0,
'destination_airport_SNA': 1 if destination == 'SNA' else 0,
'destination_airport_STL': 1 if destination == 'STL' else 0,
'destination_airport_STT': 1 if destination == 'STT' else 0,
'destination_airport_STX': 1 if destination == 'STX' else 0,
'destination_airport_SYR': 1 if destination == 'SYR' else 0,
'destination_airport_TPA': 1 if destination == 'TPA' else 0,
'destination_airport_TUL': 1 if destination == 'TUL' else 0,
'destination_airport_TUS': 1 if destination == 'TUS' else 0}]
return model.predict_proba(pd.DataFrame(input))[0][0]
predict_on_time('7/9/2015 21:45:00', 'ABQ', 'ATL')
# +
import numpy as np
labels = ('Sept 17', 'Sept 18', 'Sept 19', 'Sept 20', 'Sept 21', 'Sept 22', 'Sept 23')
values = (predict_on_time('17/9/2015 21:45:00', 'ABQ', 'ATL'),
predict_on_time('18/9/2015 21:45:00', 'ABQ', 'ATL'),
predict_on_time('19/9/2015 21:45:00', 'ABQ', 'ATL'),
predict_on_time('20/9/2015 21:45:00', 'ABQ', 'ATL'),
predict_on_time('21/9/2015 21:45:00', 'ABQ', 'ATL'),
predict_on_time('22/9/2015 21:45:00', 'ABQ', 'ATL'),
predict_on_time('23/9/2015 21:45:00', 'ABQ', 'ATL'))
alabels = np.arange(len(labels))
plt.bar(alabels, values, align='center', alpha=0.5)
plt.xticks(alabels, labels)
plt.ylabel('Probability of On-Time Arrival')
plt.ylim((0.0, 1.0))
# -
)])
elif crypto_year_slider == 2:
return MA_plot(crypto_df[crypto_ticker].loc[calc_2years_backdate():dt.datetime.now().date()])
else:
return MA_plot(crypto_df[crypto_ticker])
# Monitor input parameters and run the apropriate plot
@pn.depends(crypto_ticker, crypto_year_slider)
def reactive_crypto_cumulative(crypto_ticker, crypto_year_slider):
if crypto_year_slider == 1:
return cumulative_return_plot(crypto_df[crypto_ticker].loc[calc_1years_backdate():dt.datetime.now().date()])
elif crypto_year_slider == 2:
return cumulative_return_plot(crypto_df[crypto_ticker].loc[calc_2years_backdate():dt.datetime.now().date()])
else:
return cumulative_return_plot(crypto_df[crypto_ticker])
crypto_column = pn.Column(crypto_input, reactive_crypto_MAchart, reactive_crypto_cumulative)
# -
# ### Stock Historical Data
# +
#Stock historical Data:
stock_input = pn.Column(stock_ticker, stock_year_slider, align='center')
# Monitor input parameters and run the apropriate plot
@pn.depends(stock_ticker, stock_year_slider)
def reactive_stock_MAchart(stock_ticker, stock_year_slider):
if stock_year_slider == 1:
return MA_plot(stock_df[stock_ticker].loc[calc_1years_backdate():dt.datetime.now().date()])
elif stock_year_slider == 2:
return MA_plot(stock_df[stock_ticker].loc[calc_2years_backdate():dt.datetime.now().date()])
else:
return MA_plot(stock_df[stock_ticker])
# Monitor input parameters and run the apropriate plot
@pn.depends(stock_ticker, stock_year_slider)
def reactive_stock_cumulative(stock_ticker, stock_year_slider):
if stock_year_slider == 1:
return cumulative_return_plot(stock_df[stock_ticker].loc[calc_1years_backdate():dt.datetime.now().date()])
elif stock_year_slider == 2:
return cumulative_return_plot(stock_df[stock_ticker].loc[calc_2years_backdate():dt.datetime.now().date()])
else:
return cumulative_return_plot(stock_df[stock_ticker])
stock_column = pn.Column(stock_input, reactive_stock_MAchart, reactive_stock_cumulative)
# -
# ### Display Risk Analysis
# Display risk alnalysis message
risk_alnalysis_crypto_message = "The below are the anual Sharpe Ratio for all our Crypto Range"
risk_alnalysis_stock_message = "The below are the anual Sharpe Ratio for all our Crypto Range"
# +
# Risk alnalysis input for Crypto and Stock
crypto_risk_ticker = pn.widgets.Select(name='Crypto Tickes', options=range_of_cryptos)
crypto_risk_rolling_slider = pn.widgets.IntSlider(name='Rolling Windows', start=30, end=120, step=30, value=30)
stock_risk_ticker = pn.widgets.Select(name='Stock Tickers', options=range_of_stocks)
stock_risk_rolling_slider = pn.widgets.IntSlider(name='Rolling Windows', start=30, end=120, step=30, value=30)
# Monitor input parameters and run the apropriate plot
@pn.depends(crypto_risk_ticker, crypto_risk_rolling_slider)
def disp_crypto_beta(crypto_risk_ticker, crypto_risk_rolling_slider):
return beta_crypto_plot(crypto_df[crypto_risk_ticker], crypto_risk_rolling_slider)
# Monitor input parameters and run the apropriate plot
@pn.depends(stock_risk_ticker, stock_risk_rolling_slider)
def disp_stock_beta(stock_risk_ticker, stock_risk_rolling_slider):
return beta_stock_plot(stock_df[stock_risk_ticker], stock_risk_rolling_slider)
risk_alnalysis_crypto_column = pn.Column(risk_alnalysis_crypto_message, annual_sharpe_ratio_plot(crypto_df), crypto_risk_ticker, crypto_risk_rolling_slider, disp_crypto_beta)
risk_alnalysis_stock_column = pn.Column(risk_alnalysis_stock_message, annual_sharpe_ratio_plot(stock_df), stock_risk_ticker, stock_risk_rolling_slider, disp_stock_beta)
# -
# ### Input for portfolio
# +
#input for crypto
crypto_investment_ticker = pn.widgets.Select(name='Crypto Tickes', options=range_of_cryptos)
crypto_investment_amt = pn.widgets.FloatInput(name='Your Crypto Investment in USD', value=0)
crypto_future_slider = pn.widgets.IntSlider(name='Days of Monte Carlo projection', start=0, end=730, step=365, value=0)
crypto_sim_num = pn.widgets.IntInput(name='Number of Simulation', value=10, step=10, start=10, end=1000)
#input for stock
stock_investment_ticker = pn.widgets.Select(name='Stock Tickes', options=range_of_stocks)
stock_investment_amt = pn.widgets.FloatInput(name='Your Stock Investment in USD', value=0)
stock_future_slider = pn.widgets.IntSlider(name='Days of Monte Carlo projection', start=0, end=730, step=365, value=0)
stock_sim_num = pn.widgets.IntInput(name='Number of Simulation', value=10, step=10, start=10, end=1000)
# -
# ### Crypto Projection Data
# +
# Monitor input parameters and run the apropriate plot
@pn.depends(crypto_investment_ticker, crypto_sim_num, crypto_future_slider)
def disp_crypto_MC_return_data(crypto_investment_ticker, crypto_sim_num, crypto_future_slider):
if crypto_future_slider > 0:
return mc_plot_cumulative(crypto_df[[crypto_investment_ticker]], crypto_sim_num, crypto_future_slider)
else:
return mc_plot_cumulative(crypto_df[[crypto_investment_ticker]], crypto_sim_num, 0)
# Monitor input parameters and run the apropriate plot
@pn.depends(crypto_investment_ticker, crypto_sim_num, crypto_future_slider)
def disp_crypto_MC_distribution_data(crypto_investment_ticker, crypto_sim_num, crypto_future_slider):
if crypto_future_slider > 0:
return mc_plot_distribution(crypto_df[[crypto_investment_ticker]], crypto_sim_num, crypto_future_slider)
else:
return mc_plot_distribution(crypto_df[[crypto_investment_ticker]], crypto_sim_num, 0)
# Monitor input parameters and run the apropriate plot
@pn.depends(crypto_investment_ticker, crypto_sim_num, crypto_future_slider, crypto_investment_amt)
def disp_crypto_MC_summarize(crypto_investment_ticker, crypto_sim_num, crypto_future_slider, crypto_investment_amt):
if crypto_future_slider > 0:
return mc_summarize_statistic(crypto_df[[crypto_investment_ticker]], crypto_sim_num, crypto_future_slider, crypto_investment_amt)
else:
return mc_summarize_statistic(crypto_df[[crypto_investment_ticker]], crypto_sim_num, 0, 0)
# -
# ### Stock Projection Data
# +
# Monitor input parameters and run the apropriate plot
@pn.depends(stock_investment_ticker, stock_sim_num, stock_future_slider)
def disp_stock_MC_return_data(stock_investment_ticker, stock_sim_num, stock_future_slider):
if stock_future_slider > 0:
return mc_plot_cumulative(stock_df[[stock_investment_ticker]], stock_sim_num, stock_future_slider)
else:
return mc_plot_cumulative(stock_df[[stock_investment_ticker]], stock_sim_num, 0)
# Monitor input parameters and run the apropriate plot
@pn.depends(stock_investment_ticker, stock_sim_num, stock_future_slider)
def disp_stock_MC_distribution_data(stock_investment_ticker, stock_sim_num, stock_future_slider):
if stock_future_slider > 0:
return mc_plot_distribution(stock_df[[stock_investment_ticker]], stock_sim_num, stock_future_slider)
else:
return mc_plot_distribution(stock_df[[stock_investment_ticker]], stock_sim_num, 0)
# Monitor input parameters and run the apropriate plot
@pn.depends(stock_investment_ticker, stock_sim_num, stock_future_slider, stock_investment_amt)
def disp_stock_MC_summarize(stock_investment_ticker, stock_sim_num, stock_future_slider, stock_investment_amt):
if stock_future_slider > 0:
return mc_summarize_statistic(stock_df[[stock_investment_ticker]], stock_sim_num, stock_future_slider, stock_investment_amt)
else:
return mc_summarize_statistic(stock_df[[stock_investment_ticker]], stock_sim_num, 0, 0)
crypto_user_input = pn.Column(crypto_investment_ticker, crypto_sim_num, crypto_future_slider, crypto_investment_amt, disp_crypto_MC_summarize, disp_crypto_MC_return_data, disp_crypto_MC_distribution_data, align='center')
stock_user_input = pn.Column(stock_investment_ticker, stock_sim_num, stock_future_slider, stock_investment_amt, disp_stock_MC_summarize, disp_stock_MC_return_data, disp_stock_MC_distribution_data, align='center')
# -
# ### Display and layout for dashboard
# +
# Create a layout for the dashboard
historical_data_row = pn.Row(crypto_column, stock_column, width=1100)
risk_alnalysis_row = pn.Row(risk_alnalysis_crypto_column, risk_alnalysis_stock_column)
mc_user_input = pn.Row(crypto_user_input, stock_user_input)
dashboard = pn.WidgetBox(pn.Column(title, fetch_data_button,
pn.WidgetBox(pn.Tabs(
("Historical Data", historical_data_row),
("Risk Alnalysis", risk_alnalysis_row),
("MC Projection Alnalysis", mc_user_input),
)), align="center"))
dashboard.servable()
# -
| 26,709 |
/SMU_HW/10_SQLalchemy/climate.ipynb | d354723146463d656f35bbc36562ccd358d79533 | [] | no_license | ShawnAdvani/SMU_Coursework | https://github.com/ShawnAdvani/SMU_Coursework | 0 | 0 | null | 2022-12-08T03:18:26 | 2020-01-11T16:08:56 | Jupyter Notebook | Jupyter Notebook | false | false | .py | 180,137 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# %matplotlib inline
from matplotlib import style
style.use('fivethirtyeight')
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import datetime as dt
# # Reflect Tables into SQLAlchemy ORM
# Python SQL toolkit and Object Relational Mapper
import sqlalchemy as db
from sqlalchemy.ext.automap import automap_base
from sqlalchemy.orm import Session
from sqlalchemy import create_engine, func
from sqlalchemy.sql import alias
engine = create_engine("sqlite:///data/hawaii.sqlite")
# reflect an existing database into a new model
Base = automap_base()
# reflect the tables
Base.prepare(engine, reflect=True)
# We can view all of the classes that automap found
Base.classes.keys()
# Save references to each table
Measurement = Base.classes.measurement
Station = Base.classes.station
# Create our session (link) from Python to the DB
session = Session(engine)
# # Exploratory Climate Analysis
qry = session.query(Measurement).first()
qry.__dict__
# Design a query to retrieve the last 12 months of precipitation data and plot the results
# Calculate the date 1 year ago from the last data point in the database
max_date = session.query(func.max(Measurement.date)).first()
max_date = list(np.ravel(max_date))[0]
max_date = dt.datetime.strptime(max_date, '%Y-%m-%d')
max_day = int(dt.datetime.strftime(max_date, '%d'))
max_month = int(dt.datetime.strftime(max_date, '%m'))
max_year = int(dt.datetime.strftime(max_date, '%Y'))
year_early = dt.date(max_year, max_month, max_day) - dt.timedelta(days=365)
year_early
# Perform a query to retrieve the data and precipitation scores
current_annual_prcp = session.query(Measurement.date, Measurement.prcp).filter(func.date(Measurement.date) >= year_early).all()
# Save the query results as a Pandas DataFrame and set the index to the date column
prcp_rough = pd.DataFrame(current_annual_prcp)
# Sort the dataframe by date
prcp_df = prcp_rough.groupby(prcp_rough.date).mean()
# Use Pandas Plotting with Matplotlib to plot the data
prcp_df.plot(figsize=(15,10))
plt.title('Average Precipitation Through the Year', size=30)
plt.xlabel('Date', size=20)
plt.ylabel('Precipitation (inches)', size=20)
# Use Pandas to calcualte the summary statistics for the precipitation data
prcp_df.describe()
# Design a query to show how many stations are available in this dataset?
stations_count = session.query(func.distinct(Measurement.station)).count()
stations_count
# What are the most active stations? (i.e. what stations have the most rows)?
# List the stations and the counts in descending order.
stations_activity = session.query(Measurement.station, func.count(Measurement.station)).group_by(Measurement.station).order_by(func.count(Measurement.station).desc()).all()
stations_activity
# Using the station id from the previous query, calculate the lowest temperature recorded,
# highest temperature recorded, and average temperature of the most active station?
temp_data = session.query(func.min(Measurement.tobs), func.max(Measurement.tobs), func.avg(Measurement.tobs)).first()
temp_data
# Choose the station with the highest number of temperature observations.
# Query the last 12 months of temperature observation data for this station and plot the results as a histogram
temp_year_q = session.query(Measurement.tobs, Measurement.date).filter(Measurement.station == 'USC00519281').filter(func.date(Measurement.date) >= '2016-08-23').all()
temp_year_p = pd.DataFrame(temp_year_q)
temp_year = temp_year_p.groupby(temp_year_p.date).mean()
temp_year.plot.hist(bins=12, figsize=(15,10))
plt.xlabel('Temperature (F)')
plt.title('Temperature for the Year')
# +
# This function called `calc_temps` will accept start date and end date in the format '%Y-%m-%d'
# and return the minimum, average, and maximum temperatures for that range of dates
def calc_temps(start_date, end_date):
"""TMIN, TAVG, and TMAX for a list of dates.
Args:
start_date (string): A date string in the format %Y-%m-%d
end_date (string): A date string in the format %Y-%m-%d
Returns:
TMIN, TAVE, and TMAX
"""
return session.query(func.min(Measurement.tobs), func.avg(Measurement.tobs), func.max(Measurement.tobs)).\
filter(Measurement.date >= start_date).filter(Measurement.date <= end_date).all()
# function usage example
print(calc_temps('2012-02-28', '2012-03-05'))
# -
# Use your previous function `calc_temps` to calculate the tmin, tavg, and tmax
# for your trip using the previous year's data for those same dates.
calc_year = calc_temps('2016-08-23','2017-08-23')
calc_year = calc_year[0]
calc_year
# Plot the results from your previous query as a bar chart.
# Use "Trip Avg Temp" as your Title
# Use the average temperature for the y value
# Use the peak-to-peak (tmax-tmin) value as the y error bar (yerr)
fig, ax = plt.subplots(figsize=(3,10))
ax.bar(0, calc_year[1], yerr=(calc_year[2]-calc_year[0]))
plt.title('Average Temp')
plt.ylabel('Temperature')
qry2 = session.query(Station).first()
qry2.__dict__
# Calculate the total amount of rainfall per weather station for your trip dates using the previous year's matching dates.
# Sort this in descending order by precipitation amount and list the station, name, latitude, longitude, and elevation
total_rain = session.query(Station.station, Station.name, Station.latitude, Station.longitude, Station.elevation, Measurement.prcp).join(Measurement, Measurement.station==Station.station).group_by(Station.station).order_by(Measurement.prcp).all()
total_rain
# ## Optional Challenge Assignment
# +
# Create a query that will calculate the daily normals
# (i.e. the averages for tmin, tmax, and tavg for all historic data matching a specific month and day)
def daily_normals(date):
"""Daily Normals.
Args:
date (str): A date string in the format '%m-%d'
Returns:
A list of tuples containing the daily normals, tmin, tavg, and tmax
"""
sel = [func.min(Measurement.tobs), func.avg(Measurement.tobs), func.max(Measurement.tobs)]
return session.query(*sel).filter(func.strftime("%m-%d", Measurement.date) == date).all()
daily_normals("01-01")
# -
# calculate the daily normals for your trip
# push each tuple of calculations into a list called `normals`
l = []
dates = []
normals = []
# Set the start and end date of the trip
s = '2019-12-25'
e = '2019-12-30'
# Use the start and end date to create a range of dates
start = dt.datetime.strptime(s, '%Y-%m-%d')
end = dt.datetime.strptime(e, '%Y-%m-%d')
# Stip off the year and save a list of %m-%d strings
step = dt.timedelta(days=1)
while start <= end:
l.append(start.date())
start += step
for x in l:
dates.append(dt.datetime.strftime(x, '%m-%d'))
# Loop through the list of %m-%d strings and calculate the normals for each date
for x in dates:
q = daily_normals(x)
normals.append(q[0])
normals
# Load the previous query results into a Pandas DataFrame and add the `trip_dates` range as the `date` index
dfp = pd.DataFrame.from_records(normals, columns=['tmin', 'tavg', 'tmax'])
dfp['trip_dates'] = dates
df = dfp.set_index('trip_dates')
df
# Plot the daily normals as an area plot with `stacked=False`
df.plot.area(figsize=(15,10), stacked=False)
plt.ylim=100
plt.xlabel("Dates")
plt.ylabel("Temperature (F)")
plt.title("Temperature on Trip Dates")
| 7,633 |
/TD_light/Sky_models.ipynb | 32fff8134de17a1dc7af33d31e54f56f1b718bb2 | [] | no_license | openalea-training/agreenium_training_2018 | https://github.com/openalea-training/agreenium_training_2018 | 2 | 1 | null | null | null | null | Jupyter Notebook | false | false | .py | 32,731 | # ---
# jupyter:
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# extension: .py
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# name: python3
# ---
# + [markdown] graffitiCellId="id_wzbr23x"
# ### Problem Statement
#
# The Tower of Hanoi is a puzzle where we have three rods and `n` unique sized disks. The three rods are - source, destination, and auxiliary as shown in the figure below.
# <br><img style="float: center;" src="TOH.png"><br>
# Initally, all the `n` disks are present on the source rod. The final objective of the puzzle is to move all disks from the source rod to the destination rod using the auxiliary rod.<br><br>
# **However, there are some rules applicable to all rods:**
# 1. Only one disk can be moved at a time.
# 2. A disk can be moved only if it is on the top of a rod.
# 3. No disk can be placed on the top of a smaller disk.
#
# You will be given the number of disks `num_disks` as the input parameter. Write a **recursive function** `tower_of_Hanoi()` that prints the "move" steps in order to move `num_disks` number of disks from Source to Destination using the help of Auxiliary rod.
#
# ---
# ### Example Illustration
# For example, if you have `num_disks = 3`, then the disks should be moved as follows:
#
# 1. move disk from source to destination
# 2. move disk from source to auxiliary
# 3. move disk from destination to auxiliary
# 4. move disk from source to destination
# 5. move disk from auxiliary to source
# 6. move disk from auxiliary to destination
# 7. move disk from source to destination
#
# You must print these steps as follows:
#
# S D
# S A
# D A
# S D
# A S
# A D
# S D
#
# Where S = source, D = destination, A = auxiliary <br><br>
# An example illustration for `num_disks = 4` can be visualized in this [GIF from wikipedia](https://en.wikipedia.org/wiki/Tower_of_Hanoi#/media/File:Tower_of_Hanoi_4.gif)
#
# ---
#
# ### The Idea
# Assume you are writing a function that accepts the following arguments:
# 1. arg1 - number of disks
# 2. arg2 - rod A - this rod acts as the source (at the time of calling the function)
# 2. arg3 - rod B - this rod acts as the auxiliary
# 2. arg4 - rod C - this rod acts as the destination
#
# Follow the steps below:
# 1. Given the `num_disks` disks on the source, along with auxiliary and destination rods<br><br>
# 2. Check if `num_disks == 1`. This must be the termination condition, therefore use recursion to reach at this moment.
# - If yes, move disk from source to destination. (Termination condition)<br><br>
# 3. For `num_disks > 1`, just think of your FIRST set of steps. You want to pick the bottom most disk on the source, to be transferred to the destination. For this reason, you will will perform the steps below:
# - Step 1: Move `num_disks - 1` from source to auxiliary<br><br>
# - Step 2: Now you are left with only the largest disk at source. Move the only leftover disk from source to destination<br><br>
# - Step 3: You had `num_disks - 1` disks available on the auxiliary, as a result of Step 1. Move `num_disks - 1` from auxiliary to destination
#
# ---
# ### Exercise - Write the function definition here
# + graffitiCellId="id_8tcr5o8"
def tower_of_Hanoi_soln(num_disks):
"""
:param: num_disks - number of disks
TODO: print the steps required to move all disks from source to destination
"""
if num_disks == 0:
return
if num_disks == 1:
print("{} {}".format(source, destination))
return
tower_of_Hanoi_soln(num_disks - 1, source, destination, auxiliary)
print("{} {}".format(source, destination))
tower_of_Hanoi_soln(num_disks - 1, auxiliary, source, destination)
def tower_of_Hanoi(num_disks):
tower_of_Hanoi_soln(num_disks, 'S', 'A', 'D')
# + graffitiCellId="id_77q6vfq"
tower_of_Hanoi(3)
# + [markdown] graffitiCellId="id_rh9jy5w"
# <span class="graffiti-highlight graffiti-id_rh9jy5w-id_aaedpt9"><i></i><button>Show Solution</button></span>
# + [markdown] graffitiCellId="id_6dm5twe"
# #### Compare your results with the following test cases
# * num_disks = 2
#
# solution
# S A
# S D
# A D
#
# * num_disks = 3
#
# solution
# S D
# S A
# D A
# S D
# A S
# A D
# S D
#
# * num_disks = 4
#
# solution
# S A
# S D
# A D
# S A
# D S
# D A
# S A
# S D
# A D
# A S
# D S
# A D
# S A
# S D
# A D
# + graffitiCellId="id_zia79bz"
tandardScaler
imputer = SimpleImputer(strategy='median')
num_pipeline = Pipeline([('imputer', SimpleImputer(strategy='median')), ('std_scaler', StandardScaler())])
from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import ColumnTransformer
full_pipeline = ColumnTransformer([('num', num_pipeline, num_attrib), ('cat', OneHotEncoder(), cat_attrib)])
# I'm using the full `train_df` training set to create a sub-training data set and a validation (or development) set to build my model against.
X = train_df.drop('Survived', axis=1)
y = train_df['Survived']
X_prepared = full_pipeline.fit_transform(X)
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X_prepared, y, test_size = 0.3, random_state=42)
print(f'X_train.shape: {X_train.shape}')
print(f'y_train.shape: {y_train.shape}')
print(f'X_test.shape: {X_test.shape}')
print(f'y_test.shape: {y_test.shape}')
# ## Training a binary classifier
from sklearn.linear_model import SGDClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC
model_dict = {'Logistic regression': LogisticRegression(solver='newton-cg', n_jobs=-1, random_state=42), \
'Stochastic gradient descent classifier': SGDClassifier(loss='log', random_state=42), \
'Decision tree classifier': DecisionTreeClassifier(random_state=42), \
'Random forest classifier': RandomForestClassifier(n_jobs=-1, n_estimators=100, random_state=42), \
'Support vector classifier': SVC(kernel='linear', gamma='scale', probability=True, random_state=42)}
import joblib
from time import time
t0 = time()
y_pred_results = []
y_pred_proba_results = []
for name, model in model_dict.items():
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
y_pred_results.append(y_pred)
y_pred_proba = model.predict_proba(X_test)
y_pred_proba_results.append(y_pred_proba)
joblib.dump(model, '1-'+'_'.join(name.lower().split(' '))+'.pkl')
print(f'Accuracy of the {name.lower()} on test set: {model.score(X_test, y_test):.4f}')
print(f'Time elapsed: {(time() - t0):.2f} seconds')
# ### Mean squared error
from sklearn.metrics import mean_squared_error
for (name, model), y_pred in zip(model_dict.items(), y_pred_results):
mse = mean_squared_error(y_test, y_pred)
print(f'Root mean square error for the {name.lower():s} model: {np.sqrt(mse):.3f}')
# ### Confusion matrix
from sklearn.metrics import confusion_matrix
for (name, model), y_pred in zip(model_dict.items(), y_pred_results):
cm = confusion_matrix(y_test, y_pred)
print(f'Confusion matrix for the {name.lower()} model: \n{cm}\n')
# ### Precision, recall, F-measure and support
from sklearn.metrics import classification_report
for (name, model), y_pred in zip(model_dict.items(), y_pred_results):
class_report = classification_report(y_test, y_pred)
print(f'Precision, recall, F-measure and support for the {name.lower()} model: \n{class_report}\n')
# ### ROC curve
from sklearn.metrics import roc_auc_score
from sklearn.metrics import roc_curve
for (name, model), y_pred, y_pred_proba in zip(model_dict.items(), y_pred_results, y_pred_proba_results):
model_roc_auc = roc_auc_score(y_test, y_pred)
fpr, tpr, thresholds = roc_curve(y_test, y_pred_proba[:,1])
# print(f'Model: {name.title()}\nFPR: {len(fpr)}\nTPR: {len(tpr)}\nNumber of thresholds: {len(thresholds)}\n')
fig, axes = plt.subplots(figsize=(10, 8))
axes.plot(fpr, tpr, marker='.', ms=6, \
label='Model: {0:s}, Regression (area = {1:.3f})'.format(name.lower(), model_roc_auc))
axes.plot([0, 1], [0, 1],'r--')
axes.set_xlim([0.0, 1.0])
axes.set_ylim([0.0, 1.05])
axes.set_xlabel('False Positive Rate')
axes.set_ylabel('True Positive Rate')
axes.set_title('Receiver operating characteristic for the {0:s} model'.format(name.lower()))
axes.legend(loc="lower right")
plt.grid(True, linestyle='--');
# ### Cross validation
from sklearn.model_selection import cross_val_score
def display_scores(model, scores):
print(f'Cross validation for the {model.lower()} model:')
print(f'Mean: {scores.mean():.4f}')
print(f'Standard devation: {scores.std():.4f}\n')
from time import time
t0 = time()
for (name, model), y_pred in zip(model_dict.items(), y_pred_results):
scores = cross_val_score(model, y_pred.reshape(-1, 1), y_test, scoring='neg_mean_squared_error', cv=10, n_jobs=-1)
display_scores(name, -scores)
print(f'Time elapsed: {(time() - t0):.2f} seconds')
# # Grid search
# I'm going to try a grid search optimization using the random forest classifier model.
from sklearn.model_selection import GridSearchCV
param_grid = [{'n_estimators': [3, 10, 30], 'max_features': [2, 4, 6, 8]}, \
{'bootstrap': [False], 'n_estimators': [3, 10], 'max_features': [2, 3, 4]}]
rf_clf = RandomForestClassifier()
grid_search = GridSearchCV(rf_clf, param_grid, cv=5, scoring='neg_mean_squared_error', \
return_train_score=True, iid=False)
grid_search.fit(X_train, y_train)
joblib.dump(grid_search, '1-rf_grid_search.pkl')
grid_search.best_estimator_
cvres = grid_search.cv_results_
for mean_score, params in zip(cvres["mean_test_score"], cvres["params"]):
print(f'RMSE: {np.sqrt(-mean_score):.5f}, Parameters: {params}')
| 10,626 |
/Scikit-learn Machine Learning Course/Ensemble of Models/ensemble_ex_01.ipynb | 98eb11270bcbf226188a52863c7a97dcdeb8ca44 | [] | no_license | sarino5/scikit-learn-course | https://github.com/sarino5/scikit-learn-course | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 16,631 | # # 📝 Exercise M6.01
#
# The aim of this notebook is to investigate if we can tune the hyperparameters
# of a bagging regressor and evaluate the gain obtained.
#
# We will load the California housing dataset and split it into a training and
# a testing set.
# +
from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split
data, target = fetch_california_housing(as_frame=True, return_X_y=True)
target *= 100 # rescale the target in k$
data_train, data_test, target_train, target_test = train_test_split(
data, target, random_state=0, test_size=0.5)
# -
# <div class="admonition note alert alert-info">
# <p class="first admonition-title" style="font-weight: bold;">Note</p>
# <p class="last">If you want a deeper overview regarding this dataset, you can refer to the
# Appendix - Datasets description section at the end of this MOOC.</p>
# </div>
# Create a `BaggingRegressor` and provide a `DecisionTreeRegressor`
# to its parameter `base_estimator`. Train the regressor and evaluate its
# statistical performance on the testing set using the mean absolute error.
# +
# Write your code here.
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import BaggingRegressor
from sklearn.model_selection import cross_validate
bagged_trees = BaggingRegressor(
base_estimator=DecisionTreeRegressor()
)
cv_result = cross_validate(bagged_trees, data_train, target_train,
cv=10, scoring='neg_mean_absolute_error')
print(f"{-cv_result['test_score'].mean():.3f} +/- {cv_result['test_score'].std():.3f}")
# -
# Now, create a `RandomizedSearchCV` instance using the previous model and
# tune the important parameters of the bagging regressor. Find the best
# parameters and check if you are able to find a set of parameters that
# improve the default regressor still using the mean absolute error as a
# metric.
#
# <div class="admonition tip alert alert-warning">
# <p class="first admonition-title" style="font-weight: bold;">Tip</p>
# <p class="last">You can list the bagging regressor's parameters using the <tt class="docutils literal">get_params</tt>
# method.</p>
# </div>
# +
# Write your code here.
from scipy.stats import randint
from sklearn.model_selection import RandomizedSearchCV
param_grid = {
"n_estimators": randint(10, 30),
"max_samples": [0.5, 0.8, 1.0],
"max_features": [0.5, 0.8, 1.0],
"base_estimator__max_depth": randint(3, 10),
}
search = RandomizedSearchCV(
bagged_trees, param_grid, n_iter=20, scoring="neg_mean_absolute_error"
)
_ = search.fit(data_train, target_train)
# +
import pandas as pd
columns = [f"param_{name}" for name in param_grid.keys()]
columns += ["mean_test_score", "std_test_score", "rank_test_score"]
cv_results = pd.DataFrame(search.cv_results_)
cv_results = cv_results[columns].sort_values(by="rank_test_score")
cv_results["mean_test_score"] = -cv_results["mean_test_score"]
cv_results
# -
# We see that the bagging regressor provides a predictor in which fine tuning
# is not as important as in the case of fitting a single decision tree.
rk/1259198).**
#
# ---
# **[Introduction to Machine Learning Home Page](https://www.kaggle.com/learn/intro-to-machine-learning)**
#
#
#
#
#
# *Have questions or comments? Visit the [Learn Discussion forum](https://www.kaggle.com/learn-forum/161285) to chat with other Learners.*
| 3,389 |
/systema/code/equil_unicity.ipynb | 62937840be7044317f7875db8eed3a6814d69d18 | [
"MIT"
] | permissive | gubazoltan/Ball-and-spring | https://github.com/gubazoltan/Ball-and-spring | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 189,834 | # ---
# jupyter:
# jupytext:
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# extension: .py
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# display_name: Python 3
# language: python
# name: python3
# ---
# ## 課間作業 1
# 請問下面這段文字有幾個字？幾個字符？幾個詞？
#
# 「這個星期日本想往後山藥師佛寺去世人罕至處想一想自己的人生」
#
# ### 參考答案
# 字數：16
#
# 這/個/星期日/本/想/往/後山/藥師佛寺/去/世人/罕至/處/想一想/自己/的/人生 ## need check ##articut 會把後山分開
#
# 字符數： 28
#
# 這/個/星/期/日/本/想/往/後/山/藥/師/佛/寺/去/世/人/罕/至/處/想/一/想/自/己/的/人/生
#
# 詞數：2
#
# 這個, 想一想
# +
#用articut 來斷詞
from ArticutAPI import Articut
from pprint import pprint
if __name__ == "__main__":
username = "" #這裡填入您在 https://api.droidtown.co 使用的帳號 email。若使用空字串，則預設使用每小時 2000 字的公用額度。
apikey = "" #這裡填入您在 https://api.droidtown.co 登入後取得的 api Key。若使用空字串，則預設使用每小時 2000 字的公用額度。
articut = Articut(username, apikey)
inputSTR = "這個星期日本想往後山藥師佛寺去世人罕至處想一想自己的人生"
resultDICT1 = articut.parse(inputSTR, level = 'lv1')
print("\n level 1 結果：\n")
pprint(resultDICT1)
resultDICT2 = articut.parse(inputSTR, level = 'lv2')
print("\n level 2 結果：\n")
pprint(resultDICT2)
# -
# ## 課間作業 2
# 仿照前例，用 Python3 設計一段程式，分別用 "lv1" 和 "lv2" 輸入「閱讀創造了奇蹟」，觀察 lv1 和 lv2 的設定下，回傳的結果有何差別。
# +
# #!/usr/bin/env python3
# -*- coding:utf-8 -*-
from ArticutAPI import Articut
from pprint import pprint
if __name__ == "__main__":
username = "" #這裡填入您在 https://api.droidtown.co 使用的帳號 email。若使用空字串，則預設使用每小時 2000 字的公用額度。
apikey = "" #這裡填入您在 https://api.droidtown.co 登入後取得的 api Key。若使用空字串，則預設使用每小時 2000 字的公用額度。
articut = Articut(username, apikey)
inputSTR = "閱讀創造了奇蹟"
resultDICT1 = articut.parse(inputSTR, level = 'lv1')
print("\n level 1 結果：\n")
pprint(resultDICT1)
resultDICT2 = articut.parse(inputSTR, level = 'lv2')
print("\n level 2 結果：\n")
pprint(resultDICT2)
# -
# ### 課間練習3
# 仿照右例，結合兩個字典檔成為一個 mixedDICT.json 檔以後。分別將 basebalSTR 和 basketballSTR 輸入 Articut 進行處理並取得回傳結果。
# +
# #!/usr/bin/env python3
# -*- coding:utf-8 -*-
from ArticutAPI import Articut
import json
if __name__ == "__main__":
username = "" #這裡填入您在 https://api.droidtown.co 使用的帳號 email。若使用空字串，則預設使用每小時 2000 字的公用額度。
apikey = "" #這裡填入您在 https://api.droidtown.co 登入後取得的 api Key。若使用空字串，則預設使用每小時 2000 字的公用額度。
articut = Articut(username, apikey)
#以下語料可以參考
baseballSTR = """本週三在紐約的比賽中，馬林魚此戰使用投手車輪戰，4名投手輪番上陣壓制大都會打線，
前8局僅被敲出4支安打失1分，讓球隊能帶著2-1的領先優勢進入到9局下半。不過馬林魚推出巴斯登板關門，
他面對首名打者麥尼爾，就被打出一發陽春砲，讓大都會追平比數，接下來又分別被敲出2支安打、投出保送，
形成滿壘局面，此時輪到康福托上場打擊。在2好1壞的局面下，巴斯投了一顆內角滑球，康福托眼看這顆球越來越
靠近自己的身體，似乎有下意識地將手伸進好球帶內，結果這球就直接碰觸到他的身肘，隨後主審庫爾帕判定這是
一記觸身球，讓大都會兵不血刃拿下再見分，最終贏得比賽勝利。""".replace(" ", "")
basketballSTR = """昨晚的紐約西區霸王之戰中，錯失勝利的太陽沒有就此束手就擒，延長賽一開始就打出7比2攻勢
，米契爾和康利雖然力圖追分，但太陽總能馬上回應。康利讀秒階段上籃得手，布克兩罰一中，再次留給爵士追平機會。
米契爾造成犯規，可惜兩罰一中，保羅隨後用兩罰鎖定勝利。米契爾狂轟41分8籃板3助攻，本季單場得分次高；戈貝爾160
分18籃板3抄截，波格丹諾維奇20分。康利拿到11分4助攻，克拉克森11分，兩人合計28投僅9中。爵士的三分攻勢難以
有效施展，全場44投僅11中。""".replace(" ", "")
#將 KNOWLEDGE_NBA_Teams.json 和 KNOWLEDGE_MLB_Teams.json 兩個體育欸字典讀取出來，合併成mixDICT 以後，寫入 mixedXICT.json 檔
with open("ArticutAPI-master/Public_UserDefinedDict/KNOWLEDGE_NBA_Teams.json", encoding="utf-8") as f:
nbaDICT = json.loads(f.read())
with open("ArticutAPI-master/Public_UserDefinedDICT/KNOWLEDGE_MLB_Teams.json", encoding="utf-8") as f:
mlbDICT = json.loads(f.read())
mixedDICT = {**nbaDICT, **mlbDICT}
with open("mixedDICT.json", mode = "w", encoding = "utf-8") as f:
json.dump(mixedDICT, f, ensure_ascii=False)
# 將baseballStR 和 basketballSTR 兩篇文本各自送入articut.parse() 裡，同時指定 userDefinedDictFILE 為剛才產生mixedDICT.json
baseballResultDICT = articut.parse(baseballSTR, userDefinedDictFILE="./mixedDICT.json")
basketballResultDICT = articut.parse(basketballSTR, userDefinedDictFILE="./mixedDICT.json")
print("\n棒球斷詞結果：\n")
pprint(baseballResultDICT)
print("\n籃球斷詞結果：\n")
pprint(basketballResultDICT)
| 4,006 |
/прикладные-задачи-анализа-данных/Week 2/vgg16.ipynb | cf81d1acfb56c5048a4959754a3713c7e8aa26a2 | [
"MIT"
] | permissive | kryvokhyzha/examples-and-courses | https://github.com/kryvokhyzha/examples-and-courses | 7 | 6 | MIT | 2023-02-23T16:04:52 | 2023-02-10T18:01:25 | Jupyter Notebook | Jupyter Notebook | false | false | .py | 38,877 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: tfvenv
# language: python
# name: tfvenv
# ---
# # Programming Assignment: Анализ изображений
#
# Задача заключается в том, чтобы применить предобученную на imagenet нейронную сеть на практической задаче классификации автомобилей.
#
# Учиться применять нейронные сети для анализа изображений мы будем на библиотеке TensorFlow. Это известный опенсорсный проект, разработанный инженерами Google Brain Team. Подробнее почитать о TensorFlow можно на официальном сайте, на [гитхабе](https://github.com/tensorflow/tensorflow) или [на хабре](https://habrahabr.ru/post/270543/).
# ### Установка окружения
#
# В первую очередь нам будет необходимо установить **TensorFlow**.
# * [Инструкции по установке на сайте](https://www.tensorflow.org/versions/r0.10/get_started/os_setup.html#download-and-setup).
# * Если есть опыт работы с Docker, то можно воспользоваться готовым [докер-контейнером с тензорфлоу](https://www.tensorflow.org/versions/r0.10/get_started/os_setup.html#docker-installation).
#
# **Важно!** Если вы пользователь Windows, то уставить tensorflow напрямую, к сожалению, не получится:
#
# * для пользователей Windows 10 (и выше) нужно использовать **Docker** ([ссылка на дистрибутив](https://www.docker.com/products/docker#/windows));
# * если у вас Windows старше версии 10, то и вариант с докером не подойдет — он не установится. В таком случае советуем установить линукс на локальную машину как еще одну операционную систему (поможет избежать страданий в будущем при работе с некоторыми библиотеками для ML).
#
# Если же поставить Tensorflow на вашу машину никак не получается, мы предлагаем воспользоваться одним из облачных сервисов, в который необходимо установить линукс-образ. Самые популярные облачные сервисы [AWS](https://aws.amazon.com) и [DigitalOcean](http://digitalocean.com) предоставляют бесплатные инстансы (имейте в виду, что для того, чтобы ими воспользоваться, нужно будет привязать кредитную карту).
#
# Чтобы освоить компьютерное зрение (или другие интересные задачи из области ML и AI), так или иначе придётся научиться работать с библиотеками нейронных сетей, линуксом и виртуальными серверами. Например, для более масштабных практических задач, крайне необходимы сервера с GPU, а с ними уже локально работать не получиться.
#
# Тем не менее, мы понимаем, что в силу временных ограничений курса кто-то может успеть установить TensorFlow. Поэтому мы сделали пункты 1 и 2 необязательными. На оценку они не повлияют — можете сразу переходить к третьему пункту.
#
# Помимо tensorflow, потребуется библиотека **`scipy`**. Если вы уже работали с Anaconda и/или выполняли задания в нашей специализации, то она должна присутствовать.
# ## Данные
#
# Скачать данные нужно тут: https://yadi.sk/d/6m_KbM4HvmLfs
#
# Данные это часть выборки _Cars Dataset_ ([link](http://ai.stanford.edu/~jkrause/cars/car_dataset.html)). Исходный датасет содержит 16,185 изображений автомобилей, принадлежащих к 196 классам. Данные разделены на 8,144 тренировочных и 8,041 тестовых изображений, при этом каждый класс разделён приблизительно поровну между тестом и трейном. Все классы уровня параметров _Марка_, _Год_, _Модель_ и др. (например, _2012 Tesla Model S or 2012 BMW M3 coupe_).
#
# В нашем же случае в `train` 204 изображения, и в `test` — 202 изображения.
#
# ## Что делать
#
# Помимо данных, потребуется скачать:
#
# * [код](https://github.com/ton4eg/coursera_pa),
# * веса модели [по ссылке](https://yadi.sk/d/9-3kXyxRvnBwh)
#
# Положите данные, код и модель в одну папку. У вас должна получиться такая структура:
#
# ```
# /assignment-computer-vision/
# |
# |-- test # папки
# | `---- ... # с
# |-- train # картинками
# | `---- ...
# |
# |-- class_names.txt # имена классов, номер строки соответствует id класса
# |-- results.txt # соответствие имя картинки — id класса
# |-- vgg16_weights.npz # веса модели в формате tensorflow
# |
# |-- vgg16.py # основной скрипт
# |-- imagenet_classes.py
# |
# `-- beach.jpg # картиночка с пляжем
# ```
# ## Запуск из Docker gcr.io/tensorflow/tensorflow
# * докер запускает Jupyter Notebook с рабочей папкой /notebooks
# * в докере не хватает библиотек sklearn и Pillow
#
# Рекомендуется запускать докер командой
#
# docker run -it -p 127.0.0.1:8888:8888 -v $PWD:/notebooks gcr.io/tensorflow/tensorflow
#
# при запуске из папки, где лежит данный ноутбук и все нужные файлы, они сразу окажутся в рабочей папке Jupyter
# Notebook.
#
# Следующие две ячейки содержат команды, доустанавливающие нужные библиотеки.
# !pip install sklearn
# !pip install -U pillow
# Импортируем всё, что нам нужно для работы.
# +
# inspired by
# http://www.cs.toronto.edu/~frossard/post/vgg16/
# Model from https://gist.github.com/ksimonyan/211839e770f7b538e2d8#file-readme-md #
# Weights from Caffe converted using https://github.com/ethereon/caffe-tensorflow #
import glob
import os
import tensorflow as tf
import numpy as np
from scipy.misc import imread, imresize
from imagenet_classes import class_names
import sys
from sklearn.svm import SVC
# -
# В этом классе содержится описание модели VGG - структура, инициализация, загрузка весов. Следует помнить - пока не запущена сессия Tensorflow, никакой реальной работы не производится.
# +
class vgg16:
def __init__(self, imgs, weights=None, sess=None):
self.imgs = imgs
self.convlayers()
self.fc_layers()
self.probs = tf.nn.softmax(self.fc3l)
if weights is not None and sess is not None:
self.load_weights(weights, sess)
def convlayers(self):
self.parameters = []
# zero-mean input
with tf.name_scope('preprocess') as scope:
mean = tf.constant([123.68, 116.779, 103.939], dtype=tf.float32, shape=[1, 1, 1, 3], name='img_mean')
images = self.imgs-mean
# conv1_1
with tf.name_scope('conv1_1') as scope:
kernel = tf.Variable(tf.truncated_normal([3, 3, 3, 64], dtype=tf.float32,
stddev=1e-1), name='weights')
conv = tf.nn.conv2d(images, kernel, [1, 1, 1, 1], padding='SAME')
biases = tf.Variable(tf.constant(0.0, shape=[64], dtype=tf.float32),
trainable=True, name='biases')
out = tf.nn.bias_add(conv, biases)
self.conv1_1 = tf.nn.relu(out, name=scope)
self.parameters += [kernel, biases]
# conv1_2
with tf.name_scope('conv1_2') as scope:
kernel = tf.Variable(tf.truncated_normal([3, 3, 64, 64], dtype=tf.float32,
stddev=1e-1), name='weights')
conv = tf.nn.conv2d(self.conv1_1, kernel, [1, 1, 1, 1], padding='SAME')
biases = tf.Variable(tf.constant(0.0, shape=[64], dtype=tf.float32),
trainable=True, name='biases')
out = tf.nn.bias_add(conv, biases)
self.conv1_2 = tf.nn.relu(out, name=scope)
self.parameters += [kernel, biases]
# pool1
self.pool1 = tf.nn.max_pool(self.conv1_2,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name='pool1')
# conv2_1
with tf.name_scope('conv2_1') as scope:
kernel = tf.Variable(tf.truncated_normal([3, 3, 64, 128], dtype=tf.float32,
stddev=1e-1), name='weights')
conv = tf.nn.conv2d(self.pool1, kernel, [1, 1, 1, 1], padding='SAME')
biases = tf.Variable(tf.constant(0.0, shape=[128], dtype=tf.float32),
trainable=True, name='biases')
out = tf.nn.bias_add(conv, biases)
self.conv2_1 = tf.nn.relu(out, name=scope)
self.parameters += [kernel, biases]
# conv2_2
with tf.name_scope('conv2_2') as scope:
kernel = tf.Variable(tf.truncated_normal([3, 3, 128, 128], dtype=tf.float32,
stddev=1e-1), name='weights')
conv = tf.nn.conv2d(self.conv2_1, kernel, [1, 1, 1, 1], padding='SAME')
biases = tf.Variable(tf.constant(0.0, shape=[128], dtype=tf.float32),
trainable=True, name='biases')
out = tf.nn.bias_add(conv, biases)
self.conv2_2 = tf.nn.relu(out, name=scope)
self.parameters += [kernel, biases]
# pool2
self.pool2 = tf.nn.max_pool(self.conv2_2,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name='pool2')
# conv3_1
with tf.name_scope('conv3_1') as scope:
kernel = tf.Variable(tf.truncated_normal([3, 3, 128, 256], dtype=tf.float32,
stddev=1e-1), name='weights')
conv = tf.nn.conv2d(self.pool2, kernel, [1, 1, 1, 1], padding='SAME')
biases = tf.Variable(tf.constant(0.0, shape=[256], dtype=tf.float32),
trainable=True, name='biases')
out = tf.nn.bias_add(conv, biases)
self.conv3_1 = tf.nn.relu(out, name=scope)
self.parameters += [kernel, biases]
# conv3_2
with tf.name_scope('conv3_2') as scope:
kernel = tf.Variable(tf.truncated_normal([3, 3, 256, 256], dtype=tf.float32,
stddev=1e-1), name='weights')
conv = tf.nn.conv2d(self.conv3_1, kernel, [1, 1, 1, 1], padding='SAME')
biases = tf.Variable(tf.constant(0.0, shape=[256], dtype=tf.float32),
trainable=True, name='biases')
out = tf.nn.bias_add(conv, biases)
self.conv3_2 = tf.nn.relu(out, name=scope)
self.parameters += [kernel, biases]
# conv3_3
with tf.name_scope('conv3_3') as scope:
kernel = tf.Variable(tf.truncated_normal([3, 3, 256, 256], dtype=tf.float32,
stddev=1e-1), name='weights')
conv = tf.nn.conv2d(self.conv3_2, kernel, [1, 1, 1, 1], padding='SAME')
biases = tf.Variable(tf.constant(0.0, shape=[256], dtype=tf.float32),
trainable=True, name='biases')
out = tf.nn.bias_add(conv, biases)
self.conv3_3 = tf.nn.relu(out, name=scope)
self.parameters += [kernel, biases]
# pool3
self.pool3 = tf.nn.max_pool(self.conv3_3,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name='pool3')
# conv4_1
with tf.name_scope('conv4_1') as scope:
kernel = tf.Variable(tf.truncated_normal([3, 3, 256, 512], dtype=tf.float32,
stddev=1e-1), name='weights')
conv = tf.nn.conv2d(self.pool3, kernel, [1, 1, 1, 1], padding='SAME')
biases = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32),
trainable=True, name='biases')
out = tf.nn.bias_add(conv, biases)
self.conv4_1 = tf.nn.relu(out, name=scope)
self.parameters += [kernel, biases]
# conv4_2
with tf.name_scope('conv4_2') as scope:
kernel = tf.Variable(tf.truncated_normal([3, 3, 512, 512], dtype=tf.float32,
stddev=1e-1), name='weights')
conv = tf.nn.conv2d(self.conv4_1, kernel, [1, 1, 1, 1], padding='SAME')
biases = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32),
trainable=True, name='biases')
out = tf.nn.bias_add(conv, biases)
self.conv4_2 = tf.nn.relu(out, name=scope)
self.parameters += [kernel, biases]
# conv4_3
with tf.name_scope('conv4_3') as scope:
kernel = tf.Variable(tf.truncated_normal([3, 3, 512, 512], dtype=tf.float32,
stddev=1e-1), name='weights')
conv = tf.nn.conv2d(self.conv4_2, kernel, [1, 1, 1, 1], padding='SAME')
biases = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32),
trainable=True, name='biases')
out = tf.nn.bias_add(conv, biases)
self.conv4_3 = tf.nn.relu(out, name=scope)
self.parameters += [kernel, biases]
# pool4
self.pool4 = tf.nn.max_pool(self.conv4_3,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name='pool4')
# conv5_1
with tf.name_scope('conv5_1') as scope:
kernel = tf.Variable(tf.truncated_normal([3, 3, 512, 512], dtype=tf.float32,
stddev=1e-1), name='weights')
conv = tf.nn.conv2d(self.pool4, kernel, [1, 1, 1, 1], padding='SAME')
biases = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32),
trainable=True, name='biases')
out = tf.nn.bias_add(conv, biases)
self.conv5_1 = tf.nn.relu(out, name=scope)
self.parameters += [kernel, biases]
# conv5_2
with tf.name_scope('conv5_2') as scope:
kernel = tf.Variable(tf.truncated_normal([3, 3, 512, 512], dtype=tf.float32,
stddev=1e-1), name='weights')
conv = tf.nn.conv2d(self.conv5_1, kernel, [1, 1, 1, 1], padding='SAME')
biases = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32),
trainable=True, name='biases')
out = tf.nn.bias_add(conv, biases)
self.conv5_2 = tf.nn.relu(out, name=scope)
self.parameters += [kernel, biases]
# conv5_3
with tf.name_scope('conv5_3') as scope:
kernel = tf.Variable(tf.truncated_normal([3, 3, 512, 512], dtype=tf.float32,
stddev=1e-1), name='weights')
conv = tf.nn.conv2d(self.conv5_2, kernel, [1, 1, 1, 1], padding='SAME')
biases = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32),
trainable=True, name='biases')
out = tf.nn.bias_add(conv, biases)
self.conv5_3 = tf.nn.relu(out, name=scope)
self.parameters += [kernel, biases]
# pool5
self.pool5 = tf.nn.max_pool(self.conv5_3,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name='pool4')
def fc_layers(self):
# fc1
with tf.name_scope('fc1') as scope:
shape = int(np.prod(self.pool5.get_shape()[1:]))
fc1w = tf.Variable(tf.truncated_normal([shape, 4096],
dtype=tf.float32,
stddev=1e-1), name='weights')
fc1b = tf.Variable(tf.constant(1.0, shape=[4096], dtype=tf.float32),
trainable=True, name='biases')
pool5_flat = tf.reshape(self.pool5, [-1, shape])
fc1l = tf.nn.bias_add(tf.matmul(pool5_flat, fc1w), fc1b)
self.fc1 = tf.nn.relu(fc1l)
self.parameters += [fc1w, fc1b]
# fc2
with tf.name_scope('fc2') as scope:
fc2w = tf.Variable(tf.truncated_normal([4096, 4096],
dtype=tf.float32,
stddev=1e-1), name='weights')
fc2b = tf.Variable(tf.constant(1.0, shape=[4096], dtype=tf.float32),
trainable=True, name='biases')
fc2l = tf.nn.bias_add(tf.matmul(self.fc1, fc2w), fc2b)
self.fc2 = tf.nn.relu(fc2l)
self.parameters += [fc2w, fc2b]
# fc3
with tf.name_scope('fc3') as scope:
fc3w = tf.Variable(tf.truncated_normal([4096, 1000],
dtype=tf.float32,
stddev=1e-1), name='weights')
fc3b = tf.Variable(tf.constant(1.0, shape=[1000], dtype=tf.float32),
trainable=True, name='biases')
self.fc3l = tf.nn.bias_add(tf.matmul(self.fc2, fc3w), fc3b)
self.parameters += [fc3w, fc3b]
def load_weights(self, weight_file, sess):
weights = np.load(weight_file)
keys = sorted(weights.keys())
for i, k in enumerate(keys):
print(i, k, np.shape(weights[k]))
sess.run(self.parameters[i].assign(weights[k]))
# -
# Функция сохранения в файл ответа, состоящего из одного числа
def save_answerNum(fname,number):
with open(fname,"w") as fout:
fout.write(str(number))
# Функция сохранения в файл ответа, представленного массивом
def save_answerArray(fname,array):
with open(fname,"w") as fout:
fout.write(" ".join([str(el) for el in array]))
# Загрузка словаря из текстового файла. Словарь у нас используется для сохранения меток классов в выборке data.
def load_txt(fname):
line_dict = {}
for line in open(fname):
fname, class_id = line.strip().split()
line_dict[fname] = class_id
return line_dict
# Функция обработки отдельного изображения, печатает метки TOP-5 классов и уверенность модели в каждом из них.
def process_image(fname):
img1 = imread(fname, mode='RGB')
img1 = imresize(img1, (224, 224))
prob = sess.run(vgg.probs, feed_dict={vgg.imgs: [img1]})[0]
preds = (np.argsort(prob)[::-1])[0:5]
for p in preds:
print(class_names[p], prob[p])
# Инициируем TF сессию, и инициализируем модель. На этом шаге модель загружает веса. Веса - это 500Мб в сжатом виде
# и ~2.5Гб в памяти, процесс их загрузки послойно выводится ниже этой ячейки, и если вы увидите этот вывод ещё раз -
# у вас неистово кончается память. Остановитесь. Также, не запускайте эту ячейку на выполнение больше одного раза
# за запуск ядра Jupyter.
sess = tf.Session()
imgs = tf.placeholder(tf.float32, [None, 224, 224, 3])
vgg = vgg16(imgs, 'vgg16_weights.npz', sess)
# Все ячейки выше не нуждаются в модификации для выполнения задания, и необходимы к исполнению только один раз, в порядке следования. Повторный запуск ячейки с инициализацией модели будет сжирать память. Вы предупреждены.
# ## Задание 1\.
#
# Для начала нужно запустить готовую модель `vgg16`, предобученную на `imagenet`. Модель обучена с помощью `caffe` и сконвертирована в формат `tensorflow` - `vgg16_weights.npz`. Скрипт, иллюстрирующий применение этой модели к изображению, возвращает топ-5 классов из `imagenet` и уверенность в этих классах.
#
# **Задание:** Загрузите уверенность для первого класса для изображения `train/00002.jpg` с точностью до 1 знака после запятой в файл с ответом.
# Ваш код здесь
# +
# save_answerNum("vgg16_answer1.txt", __ )
# -
# ## Задание 2\.
#
# Научитесь извлекать `fc2` слой. Возьмите за основу код `process_image`, и модифицируйте, чтобы вместо последнего слоя извлекались выходы `fc2`.
#
# **Задание:** Посчитайте `fc2` для картинки `train/00002.jpg`. Запишите первые 20 компонент.
# +
img1 = imread('train/00002.jpg', mode='RGB')
img1 = imresize(img1, (224, 224))
# Ваш код здесь
# +
# save_answerArray("vgg16_answer2", __ )
# -
# ## Задание 3\.
#
# Теперь необходимо дообучить классификатор на нашей базе. В качестве бейзлайна предлагается воспользоваться классификатором `svm` из пакета `scipy`.
#
# - Модифицировать функцию `get_features` и добавить возможность вычислять `fc2`. (Аналогично второму заданию).
# - Применить `get_feautures`, чтобы получить `X_test` и `Y_test`.
# - Воспользоваться классификатором `SVC` с `random_state=0`.
#
# > **Важно!** Если вам не удалось поставить `tensorflow`, то необходимо вместо использования функции `get_features`, загрузить предпосчитанные `X`, `Y`, `X_test`, `Y_test` из архива: https://yadi.sk/d/RzMOK8Fjvs6Ln и воспользоваться функцией `np.load` для их загрузки, а после этого два последних пункта.
#
# **Задание:** Сколько правильных ответов получается на валидационной выборке из папки `test`? Запишите в файл.
# Функция, возвращающая признаковое описание для каждого файла jpg в заданной папке
def get_features(folder, ydict):
paths = glob.glob(folder)
X = np.zeros((len(paths), 4096))
Y = np.zeros(len(paths))
for i,img_name in enumerate(paths):
print(img_name)
base = os.path.basename(img_name)
Y[i] = ydict[base]
img1 = imread(img_name, mode='RGB')
img1 = imresize(img1, (224, 224))
# Здесь ваш код. Нужно получить слой fc2
X[i, :] = fc2
return X, Y
# Функция обработки папки. Ожидается, что в этой папке лежит файл results.txt с метками классов, и
# имеются подразделы train и test с jpg файлами.
def process_folder(folder):
ydict = load_txt(os.path.join(folder,'results.txt'))
X, Y = get_features(os.path.join(folder, 'train/*jpg'), ydict)
# Ваш код здесь.
# X_test, Y_test =
# Ваш код здесь.
Y_test_pred = clf.predict(X_test)
print(sum(Y_test == Y_test_pred)) # Число правильно предсказанных классов
process_folder('.') # Вызови меня!
# +
# save_answerNum("vgg16_answer3.txt", __ )
| 22,235 |
/material/least_action.ipynb | 3a2e792d439023cc379374463f30a3c7a755aa41 | [] | no_license | daniel-tamayo/ComputationalMethods | https://github.com/daniel-tamayo/ComputationalMethods | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 142,785 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# + [markdown] colab_type="text" id="view-in-github"
# <a href="https://colab.research.google.com/github/restrepo/ComputationalMethods/blob/master/material/least_action.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# + [markdown] colab_type="text" id="muoCLftUoyCU"
# # Least Action
# <a href="https://colab.research.google.com/github/restrepo/ComputationalMethods/blob/master/material/least_action.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# + [markdown] colab_type="text" id="WZssYj5ZoyCV"
# Load modules
# + colab={"base_uri": "https://localhost:8080/", "height": 34} colab_type="code" id="225NtztSoyCW" outputId="49e63020-dac8-4527-fdbd-42199b9d678d"
# %pylab inline
# + colab={} colab_type="code" id="7-vTWMqloyCa"
import numpy as np
import scipy.optimize
import pandas as pd
global g
g=9.8 #m/s^2
# + [markdown] colab_type="text" id="ibpoCKTuoyCe"
# <div style="float: right;" markdown="1">
# <img src="https://raw.githubusercontent.com/restrepo/ComputationalMethods/master/material/figures/leastaction1.svg?sanitize=true">
# </div>
#
# ## Geometry interpretation
# Following the geometry theory developed [here](http://www.eftaylor.com/software/ActionApplets/LeastAction.html), we will try to define something called the _Action_ for one small segment of the free fall movement in one-dimension.
#
# For that we need the experimental data consisting on the height of an object of mass $m$ in free fall, and the height $x_i$, for each time $t_i$. This data would be fitted by a polynomial of degree two, as displayed in the figure for one of the fitted segments of the plot of $x$ as a function of $t$. We take the origin of the coordinates at ground level. For each segment we can calculate an average kinetic energy, $T$, and an averge potential energy, $V$, in the limit of $\Delta t=t_2-t_1$ small. From the figure
#
# \begin{align}
# T=\frac12 m v^2\approx &\frac12 m\left(\frac{x_2-x_1}{t_2-t_1}\right)^2\,,&
# V=mgh\approx& m g \frac{x_2+x_1}{2}\,.
# \end{align}
#
# We can then reformulate the problem of the free fall in the following terms. From all the possible curves that can interpolate the points $(t_1,x_1)$ and $(t_2,x_2)$, which is the correct one?.
#
# The answer obtained by Leonhard Euler [1] can be obtained from the definition of the function "Lagrangian"
# $$L(t)=T(t)-V(t)\,.$$
# + [markdown] colab_type="text" id="9Z-Lc-jjoyCe"
# <div style="float: right;" markdown="1">
# <img src="https://raw.githubusercontent.com/restrepo/ComputationalMethods/master/material/figures/leastaction2.svg?sanitize=true">
# </div>
#
# With this, we can build the "Action", by integrating the Lagrangian function between the points $(t_1,x_1)$ and $(t_2,x_2)$ as
# $$S=\int_{t_1}^{t_2} L\, \operatorname{d}t\,. $$
#
# which give us a numerical result with units of energy multiplied by time (J$\cdot$s). What is worth noticing is that if we relax the definition of $V$ and allows for any $h$, but keeping the initial and final points fixed, we can calculate many Action values. This is illustrated in the figure for blue dotted ($S_1$), solid ($S_{\text{min}}$) and dashed ($S_2$)lines. But only the height, $(x_1+x_2)/2$ , associated with the real physical path, has a minimum value for the Action, $S_{\text{min}}$!
#
# In fact, for one segment of the action between $(t_1,x_1)$, and $(t_2,x_2)$, with $\Delta t$ sufficiently small such that $L$ can be considered constant, we have
# \begin{eqnarray}
# S_{\text{min}}&=&\int_{t_1}^{t_2} L dt \\
# &\approx& \left[\frac12 m v^2-m g h \right]\Delta t\\
# &\approx& \left[\frac12 m\left(\frac{x_2-x_1}{t_2-t_1}\right)^2-m g \frac{x_2+x_1}{2} \right](t_2-t_1)
# \end{eqnarray}
# that corresponds to Eq. (11) of Am. J. Phys, Vol. 72(2004)478: http://www.eftaylor.com/pub/Symmetries&ConsLaws.pdf
# + [markdown] colab_type="text" id="g6zcN-P7oyCf"
# <div style="float: right;" markdown="1">
# <img src="https://raw.githubusercontent.com/restrepo/ComputationalMethods/master/material/figures/leastaction3.svg?sanitize=true">
# </div>
#
# __Least Action method__: The least action method consist in the following steps illustrated in the figure
# 1. Fix the initial and final point of the movement. Example the initial time and the height from which a body is launched upwards, $(t_{\text{ini}},x_{\text{ini}})$, and the final time and height $(t_{\text{end}},x_{\text{end}})$.
# 1. Divide the problem in small segments of $\Delta t=t_{i+1}-t_i$.
# 1. Build many paths with the initial and final point fixed and calculates the Action in each segment, $S_i$, for all the paths
# 1. Choose the minimal Action for each segment, $S_{\text{min}}^i$, and rebuild the full path which minimizes the Action in each segment. This is the physical trajectory!
#
#
# + [markdown] colab_type="text" id="7AVW926voyCf"
# ## Code implementation
# + [markdown] colab_type="text" id="6h_A3a8GoyCg"
# ### The Action
# We define the Action $S$ such of an object of mass $m$ throw vertically upwards from $x_{\hbox{ini}}$, such that $t_{\hbox{end}}$ seconds later the object return to a height $x_{\hbox{end}}$, as
# \begin{align}
# S=&\int_{t_{\hbox{ini}}}^{t_{\hbox{end}}} L\, {\rm d}t \\
# =& \sum_i S_i\\
# =&\sum_i L_i \Delta t\,.
# \end{align}
# + [markdown] colab_type="text" heading_collapsed=true id="QOiK7jDWoyCj"
# #### Example
# Consider an object of mass $m=0.2~$Kg throw vertically upwards from an intial height of zero, and a flight time of $3~$s
# + [markdown] colab_type="text" hidden=true id="w8cUSD5RoyCk"
# 1) Calculates the action for an intermediate set of points with $\Delta t=0.5$ s and _an arbitrary path_.
#
# __Activity__: Change the path to any other value and report with the same initial and final values of zero and report the Action calculations by the chat
# + colab={} colab_type="code" id="VaJ-4_1ZoyCk"
tini=0
tend=3
xini=0
xend=0
# + colab={} colab_type="code" id="oShZC_qPoyCn"
x=np.array([xini,10,15.,22.,18.,5.,xend])#.size
# + colab={"base_uri": "https://localhost:8080/", "height": 34} colab_type="code" id="oxCwh39ZfC2U" outputId="07fee069-68ea-4f73-af31-53acdd6db4ed"
x
# + colab={"base_uri": "https://localhost:8080/", "height": 34} colab_type="code" id="fr67OyhMoyCq" outputId="54f68a8d-49d5-4f48-be86-6f7f9051e511"
#Initial points: x_i
x[:-1]
# + colab={"base_uri": "https://localhost:8080/", "height": 34} colab_type="code" id="5jGF5Tv4oyCt" outputId="19c9eb2d-8a9a-436b-d929-da6bd94210c7"
#final points: x_{i+1}
x[1:]
# + colab={"base_uri": "https://localhost:8080/", "height": 34} colab_type="code" id="GG-rTd6wfY1S" outputId="2de54d78-8d20-4da2-b8db-8dfe610cf7e9"
Δx=x[1:]-x[:-1]
Δx
# + colab={"base_uri": "https://localhost:8080/", "height": 34} colab_type="code" id="5TBCdLuYuv9D" outputId="972cb5c0-6cfd-4cf9-8e88-40cff9606a7e"
x[:-1].size
# + colab={"base_uri": "https://localhost:8080/", "height": 34} colab_type="code" id="SfdF1VUnoyCw" outputId="c7b3daf4-654c-4f57-9c3a-f17509b3dc01"
Δt=tend/x[:-1].size
Δt
# + colab={} colab_type="code" id="opN5ASb5oyCh"
def S(x,tend=3.,m=0.2,xini=0.,xend=0.):
"""
Calculate the Action of an object of of mass 'm' throw vertically upward from
'xini', such that 'tend' seconds later the object return to a height 'xend'.
Delta t must be constant.
The defaults units for S are J.s
"""
x=np.asarray(x)
Dt=tend/x[:-1].size
#Fix initial and final point
x[0]=xini
x[-1]=xend
return ( (0.5*m*(x[1:]-x[:-1])**2/Dt**2-0.5*m*g*(x[1:]+x[:-1]) )*Dt).sum()
# + [markdown] colab_type="text" id="CyD1kFUdvNcv"
# \begin{eqnarray}
# S_i&\approx& \left[\frac12 m\left(\frac{x_2-x_1}{t_2-t_1}\right)^2-m g \frac{x_2+x_1}{2} \right](t_2-t_1)
# \end{eqnarray}
# + colab={} colab_type="code" id="VcsMqFwfoyCz"
t=[i*Δt for i in range(x.size) ]
# + colab={"base_uri": "https://localhost:8080/", "height": 34} colab_type="code" id="niyMnCAVoyC3" outputId="e6b64f51-ed8b-431a-f4ee-d5f16ca0576e"
t
# + colab={} colab_type="code" hidden=true id="WkzW-t9WoyC6"
df=pd.DataFrame({'t':t,'x':x})
# + colab={"base_uri": "https://localhost:8080/", "height": 264} colab_type="code" hidden=true id="pu1rwHSjoyC8" outputId="a0deb08d-d437-4268-a795-3dc360be0661"
df
# + colab={"base_uri": "https://localhost:8080/", "height": 298} colab_type="code" hidden=true id="x8-lKZpNoyC_" outputId="6478c1eb-a83e-4787-f08a-adcf34b97640"
plt.plot(df.t,df.x,'ro')
plt.plot(df.t,df.x)
plt.xlabel('$t$')
plt.ylabel('$x(t)$')
# + colab={"base_uri": "https://localhost:8080/", "height": 34} colab_type="code" hidden=true id="eUPbM1P6oyDB" outputId="e439b0fa-0c79-4f7c-e848-07ad94971704"
S(df.x) # J.s
# + [markdown] colab_type="text" heading_collapsed=true id="IOmOGo2voyDD"
# 2) Fit the points with a polynomial of degree 2 and calculates the Action for 20 points along the fitted curve but keeping the same initial and final points!
# + colab={"base_uri": "https://localhost:8080/", "height": 51} colab_type="code" hidden=true id="IHc1jTOToyDE" outputId="db7009ef-a9ca-4db3-dbb0-94eececb420d"
coeffs=np.polyfit(df.t,df.x,2)
P=np.poly1d(coeffs)
print(P)
# + colab={} colab_type="code" id="JrC5vhd0oyDH"
t=np.linspace(0,3,20)
# + colab={"base_uri": "https://localhost:8080/", "height": 85} colab_type="code" hidden=true id="iKonoEp5oyDK" outputId="46fc53a2-eacf-4309-862e-ffca4e41db32"
x=P(t)
x[0]=xini
x[-1]=xend
x
# + colab={"base_uri": "https://localhost:8080/", "height": 282} colab_type="code" hidden=true id="of2pMDoxoyDN" outputId="0e34843a-f7a7-4371-8cd9-6a639df47303"
plt.plot(t,x,'ko')
plt.plot(t,x)
# + colab={"base_uri": "https://localhost:8080/", "height": 34} colab_type="code" hidden=true id="L_nY2eKsoyDQ" outputId="0be94834-dab0-4823-e655-7f6081824c36"
S(x)
# + [markdown] colab_type="text" id="5CvKm-tQoyDS"
# #### Exercise
# Try to find a large action (larger than 10 J$\cdot$s) and the minimal possible action
# + [markdown] colab_type="text" id="aL6DEhPxNbzy"
# ### Solution to a free fall problem
# \begin{align}
# x(t)=&-\frac{1}{2} g t^2+v_0 t \,, & v=&-g t +v_0
# \end{align}
# At maximum height $t=t_{\text{end}}/2$ and
# \begin{align}
# x_{\text{max}}=&-\frac{1}{2} g (t_{\text{end}}/2)^2+v_0 \frac{t_{\text{end}}}{2} \,, & 0=&-g \frac{t_{\text{end}}}{2} +v_0
# \end{align}
# From the second equation we get
# $$v_0=\frac{g t_{\text{end}}}{2}=14.7 \text{m/s}\,,$$
# and
# \begin{align}
# x_{\text{max}}=&-\frac{1}{2} g \left(\frac{t_{\text{end}}}{2}\right)^2+ g \frac{t_{\text{end}}}{2} \frac{t_{\text{end}}}{2} \nonumber\\
# =&g\frac{ t_{\text{end}}^2 }{8}=11.025\ \text{m}\,.
# \end{align}
# + [markdown] colab_type="text" id="aOq2sXh8Nbzz"
# __Activity__: Find the same solution by fitting the polynomial of degree 2 that go trough the points:
# * $(t_0,x_0)=(0,0)$,
# * $(t_{\rm end}/2,x_{\rm max})=(1.5,11.025)$,
# * $(t_{\rm end},x_{\rm end})=(3,0)$, in s and m respectively.
# + colab={"base_uri": "https://localhost:8080/", "height": 141} colab_type="code" id="Hu_fMjGLNbz0" outputId="791ba5e4-7ef3-495f-812c-8e896d90e882"
df=pd.DataFrame( {'t':[0,1.5,3],
'x':[0,11.025,0]
})
df
# + colab={"base_uri": "https://localhost:8080/", "height": 51} colab_type="code" id="rRT33pzWNbz4" outputId="0cd41a27-a622-41eb-df9d-8d2acfc1fe0d"
#df.loc[1,'x']=11.025
coeff=np.polyfit(df.t,df.x,deg=2)
x=np.poly1d(coeff,variable='t')
print(x)
# + colab={"base_uri": "https://localhost:8080/", "height": 265} colab_type="code" id="fLB_MlY2Nbz7" outputId="b182f1d6-614f-487c-8d03-6558f30a7b0f"
t=np.linspace(df.t.min(),df.t.max(),20)
plt.plot(df.t,df.x,'ro')
plt.plot(t,x(t))
plt.grid()
# + colab={"base_uri": "https://localhost:8080/", "height": 85} colab_type="code" id="xdqgB7lkkqBa" outputId="7e650423-70d0-4a13-f35e-355a95c7ab66"
t
# + colab={"base_uri": "https://localhost:8080/", "height": 103} colab_type="code" id="Nyd5FPgqkrOH" outputId="b4b232ec-58ea-4c9e-af40-f181a817fb8d"
x(t)
# + colab={"base_uri": "https://localhost:8080/", "height": 34} colab_type="code" id="x-n3mR5Sy3lL" outputId="a58839b8-b3b2-4922-b132-a354fd399a60"
S(x(t))
# + [markdown] colab_type="text" id="GKoRz7hwNb0A"
# __Activity__:
# 1. Calculates
# $$S=\int_{t_{\rm min}}^{t_{\rm max}} L(t)\operatorname{d}t $$
# for the physical trajectory of a particle in free fall of $m=0.2\ $kg, where
# $$L(t)=\frac{1}{2}m v^2(t)-mgx(t)\,.\qquad\qquad (1)$$
# _Hint_:
# $$F(t)=\int L(t)\operatorname{d}t$$
# $$S=F(t_{\rm max})-F(t_{\rm min})$$
# 1. Calculates also the instataneous energy
# $$E(t)=\frac{1}{2}m v^2(t)+mgx(t)$$
# for several times.
# + colab={"base_uri": "https://localhost:8080/", "height": 85} colab_type="code" id="D1T92240llt1" outputId="92027686-f565-46d8-c6e3-2745348651d6"
print(x)
v=x.deriv()
print(v)
# + colab={"base_uri": "https://localhost:8080/", "height": 171} colab_type="code" id="HY4HEx_jNb0B" outputId="b37a0724-3ef9-45f5-a25a-96414d180e97"
print('x(t) [m]:\n',x)
print("="*45)
m=0.2
g=9.8
#Use derivative of numpy polynomial
v=x.deriv()
# Implement eq. (1) and (2)
L=0.5*m*v**2-m*g*x
F=L.integ() # Analytical integration
S=F( df.t.max() ) - F( df.t.min() )
print('S={:.1f} J.s'.format(S))
print("="*45)
#Implement eq. (3)
E=0.5*m*v**2+m*g*x
Deltat=0.2
t=np.arange(df.t.min() , df.t.max()+Deltat,Deltat)
print('instantaneous energy [J]:\n {}'.format(E(t)))
# + [markdown] colab_type="text" id="PqFVeeRCNb0E"
# __Activity__: Changes the maximum height for the previous activity and check what happens with the instantaneous energy.
# + [markdown] colab_type="text" id="9ipNWDgSbEyJ"
# ## Comparision with non-physical trajectories
#
# ---
#
#
# + [markdown] colab_type="text" id="PEtUtXetNb0G"
# I good approximation to the integral is:
# $$S=\int_{t_{\rm min}}^{t_{\rm max}} L(t)\operatorname{d}t\approx \sum_i \left[\frac{L(t_{i+1})+L(t_i)}{2}\right]\Delta t$$
# + colab={"base_uri": "https://localhost:8080/", "height": 35} colab_type="code" id="1pZ_b3DzNb0G" outputId="22377b49-f56b-475d-c826-31a03f14f993"
print('Δt={}'.format( t[1:]-t[:-1] ) )
# + colab={"base_uri": "https://localhost:8080/", "height": 35} colab_type="code" id="YN7j9K2MNb0K" outputId="13fd29ce-97eb-4af2-dcc9-7612198503e4"
print( 'S={:.1f} J.s'.format( 0.5*( L(t)[1:]+L(t)[:-1] ).sum()*Deltat ) )
# + [markdown] colab_type="text" id="IiYjIbeZNb0N"
# Or even more general
# + colab={"base_uri": "https://localhost:8080/", "height": 35} colab_type="code" id="jAsX0bIKNb0O" outputId="8762f783-f45f-4d93-9a5f-0a9fe520c752"
def Saprx(L,t):
return 0.5*( L(t)[1:]+L(t)[:-1] )*( t[1:]-t[:-1] )
print( 'S={:.1f} J.s'.format( Saprx(L,t).sum() ) )
# + colab={"base_uri": "https://localhost:8080/", "height": 346} colab_type="code" id="0bdwvMQONb0R" outputId="ee21148c-d628-427e-e580-5036c3efed0b"
Ss=[]
Es=[]
m=0.2
g=9.8
Deltat=0.2
#t=np.linspace(df.t.min() , df.t.max(),10)
t=np.arange(df.t.min() , df.t.max()+Deltat,Deltat)
xmax=[6,11.025,15]; x0=0;xend=0
ls=['r--','k-','b-.']
for i in range(len(xmax) ):
df=pd.DataFrame({'t':[t.min(),t.mean(),t.max()],'x':[x0,xmax[i],xend]})
coeffs=np.polyfit(df.t,df.x,2)
x=np.poly1d(coeffs,variable='t')
v=x.deriv()
L=0.5*m*v**2-m*g*x
F=L.integ() # Analytical integration
E=0.5*m*v**2+m*g*x
S=F( df.t.max() ) - F( df.t.min() )
print('S={:.1f} J.s'.format( S ) )
Ss.append( Saprx(L,t) )
Es.append( E(t) )
#Plot
if ls:
plt.plot(t,x(t),ls[i],label='S={:.1f} J$\cdot$s'.format( S ) )
if ls:
plt.legend(loc='best',fontsize=12)
plt.xlabel('$t$ [s]',size=20)
plt.ylabel('$x$ [m]',size=20)
# + [markdown] colab_type="text" id="YzSnwspWcU7P"
# Only the physical trajectory conserves energy
# + colab={"base_uri": "https://localhost:8080/", "height": 197} colab_type="code" id="ctLVmvX8Nb0U" outputId="7a713200-3e42-47e6-f39f-d9ec949c9652"
Es
# + [markdown] colab_type="text" id="uenEmzJYNb0e"
# Approximated result
# + colab={"base_uri": "https://localhost:8080/", "height": 34} colab_type="code" id="PUSiUqwfNb0f" outputId="c0d7681d-a752-4a34-eed7-129392cdd064"
Ss[0].sum(),Ss[1].sum(),Ss[2].sum()
# + [markdown] colab_type="text" id="ro9iNdZZNb0m"
# __Activity__: Check that the Action of a trajectory with a polynomial of order $n$ which fits $n+1$ points with the same extreme values, gives an Action that is larger than the previous minimun
# + colab={"base_uri": "https://localhost:8080/", "height": 142} colab_type="code" id="6a1cZgvUU61i" outputId="b66432f4-53e4-4bbb-cd6e-ceca3e26c016"
df
# + colab={"base_uri": "https://localhost:8080/", "height": 204} colab_type="code" id="tnG4uxULVDnJ" outputId="5dac3923-7980-401c-9b9e-317ac039a1e0"
ndf=df.append({'t':0.75,'x':5},ignore_index=True)
ndf=ndf.append({'t':2.25,'x':2.7},ignore_index=True)
ndf=ndf.sort_values('t')
ndf
# + colab={"base_uri": "https://localhost:8080/", "height": 310} colab_type="code" id="M4tL2dFfU2GD" outputId="5a97ca12-05e9-4cd2-dbf8-8b72caf7468b"
Ss=[]
Es=[]
m=0.2
g=9.8
Deltat=0.2
#t=np.linspace(df.t.min() , df.t.max(),10)
t=np.arange(df.t.min() , df.t.max()+Deltat,Deltat)
xmax=[11.025]; x0=0;xend=0
ls=['r--','k-','b-.']
for i in range(len(xmax) ):
df=pd.DataFrame({'t':[t.min(),t.mean(),t.max()],'x':[x0,xmax[i],xend]})
coeffs=np.polyfit(df.t,df.x,2)
x=np.poly1d(coeffs,variable='t')
v=x.deriv()
L=0.5*m*v**2-m*g*x
F=L.integ() # Analytical integration
E=0.5*m*v**2+m*g*x
S=F( df.t.max() ) - F( df.t.min() )
print('S={:.1f} J.s'.format( S ) )
Ss.append( Saprx(L,t) )
Es.append( E(t) )
#Plot
if ls:
plt.plot(t,x(t),ls[i],label='S={:.1f} J$\cdot$s'.format( S ) )
if ls:
plt.legend(loc='best',fontsize=12)
plt.xlabel('$t$ [s]',size=20)
plt.ylabel('$x$ [m]',size=20)
# + [markdown] colab_type="text" id="9p-z9uxXoyD5"
# ### Solution with minimization
# To be seen in Minimization
# + [markdown] colab_type="text" id="6Z-BxUkLoyD5"
# ## References
# + [markdown] colab_type="text" id="HPyaNXCzoyD6"
# [1] L. Euler, M´emoires de l'Acad´emie des Sciences de Berlin 4, 1898 (1748), republished in Ref.2, p. 38-63; L. Euler, M´emoires de l'Acad´emie des Sciences de Berlin 7, 169 (1751), republished in Ref. 2, p. 152. For a recent historical review see: Dias, Penha Maria Cardozo. (2017). Leonhard Euler's “principle of mechanics” (an essay on the foundations of the equations of motion). Revista Brasileira de Ensino de Física, 39(4), e4601. Epub May 22, 2017.https://doi.org/10.1590/1806-9126-rbef-2017-0057
#
# [2] Leonhardi Euleri Opera Omnia, serie secunda, v. V, edited by J.O. Fleckenstein (Societatis Scientiarum Naturalium Helveticæ, Geneva,1957)
#
# + colab={} colab_type="code" id="RP6XEOUOoyD6"
| 18,962 |
/모듈 8 비즈니스 문제(비정형)/비정형 데이터(딥러닝).ipynb | 0ded08d4948526e6600e5b23435a1beaef793aec | [] | no_license | JaeSeonLEE/AI | https://github.com/JaeSeonLEE/AI | 1 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 136,872 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Keras
# language: python
# name: keras
# ---
# + [markdown] colab_type="text" id="7DHn1DSxC8PB"
# # RNN
#
# -입력과 출력을 시퀀스로 처리하는 모델
# -은닉층의 노드에서 활성화 함수를 통해 나온 결과값을 출력층 방향으로도 보내면서, 다시 은닉층 노드의 다음 계산의 입력으로 보내는 특징을 갖고있음
#
# 
#
# + [markdown] colab_type="text" id="Ou7UJi3gC8PB"
# 일 대 다 : 하나의 입력에 대해서 여러개의 출력(one-to-many)
# • 하나의 사진 이미지 입력에 대해서 사진의 제목을 출력을 내놓는 이미지 캡셔닝(Image
# Captioning) 작업에 사용
# • 사진의 제목은 단어들의 나열이므로 시퀀스의 형태를 가짐
# 다 대 일 : 다수의 입력에 대해서 하나의 출력(many-to-one)
# • 입력 데이터으로부터 긍정적 감성인지 부정적 감성인지를 판별하는 감성 분류
# (Sentiment Classification),
# • 입력 데이터가 어떤 종류의 문서인지를 판별하는 문서 분류(Document Classification)에
# 사용
# 다 대 다(many-to-many)
# • 입력 문장으로 부터 대답을 출력하는 챗봇, 입력 문장으로부터 번역된 문장을 출력하
# 는 번역기, 개체명 인식, 품사 태깅 등에 이용
#
# + [markdown] colab_type="text" id="ijpxXGG5C8PC"
# # RNN 실습
# 
#
#
#
#
# + [markdown] colab_type="text" id="XQSgEa84C8PC"
# hidden_size = 은닉 상태의 크기를 정의. 메모리 셀이 다음 시점의 메모리 셀과 출력층으로 보내는 값의 크기(output_dim)와도 동일. RNN의 용량(capacity)을 늘린다고 보면 되며, 중소형 모델의 경우 보통 128, 256, 512, 1024 등의 값을 가진다.
# timesteps = 입력 시퀀스의 길이(input_length)라고 표현하기도 함. 시점의 수.
# input_dim = 입력의 크기.
#
# + colab={"base_uri": "https://localhost:8080/", "height": 336} colab_type="code" executionInfo={"elapsed": 2270, "status": "ok", "timestamp": 1566355225034, "user": {"displayName": "xopuy2057@gmail.com", "photoUrl": "", "userId": "08173792587523637894"}, "user_tz": -540} id="TGquEEDIC8PD" outputId="3c669ab7-37d8-4197-a91d-18f7ed005c9b"
from keras.models import Sequential
from keras.layers import SimpleRNN
model = Sequential()
model.add(SimpleRNN(3, input_shape=(2,10)))
# model.add(SimpleRNN(3, input_length=2, input_dim=10))와 동일함.
model.summary()
# + [markdown] colab_type="text" id="4PHh59SdC8PG"
# 출력값이 (batch_size, output_dim) 크기의 2D 텐서일 때, output_dim은 hidden_size의 값인 3입니다. 이 경우 batch_size를 현 단계에서는 알 수 없으므로 (None, 3)이 됨
#
# + colab={"base_uri": "https://localhost:8080/", "height": 175} colab_type="code" executionInfo={"elapsed": 2560, "status": "ok", "timestamp": 1566355225327, "user": {"displayName": "xopuy2057@gmail.com", "photoUrl": "", "userId": "08173792587523637894"}, "user_tz": -540} id="LJKjOdq0C8PG" outputId="ae465bfb-ce8e-4023-b6c3-c48b0900b6b4"
model = Sequential()
model.add(SimpleRNN(3, batch_input_shape=(8,2,10)))
model.summary()
# + [markdown] colab_type="text" id="ggYfQJkJC8PI"
# batch_size를 8로 기재하자, 출력의 크기가 (8, 3)이 된 것을 볼 수 있습니다. 이제 return_sequences 매개 변수에 True를 기재하여 출력값으로 (batch_size, timesteps, output_dim) 크기의 3D 텐서를 리턴하도록 모델을 만들어 보자
# + colab={"base_uri": "https://localhost:8080/", "height": 175} colab_type="code" executionInfo={"elapsed": 2558, "status": "ok", "timestamp": 1566355225327, "user": {"displayName": "xopuy2057@gmail.com", "photoUrl": "", "userId": "08173792587523637894"}, "user_tz": -540} id="y6roWNJ4C8PI" outputId="d7a86ff3-e1d3-45ba-96ef-8856498112c1"
model = Sequential()
model.add(SimpleRNN(3, batch_input_shape=(8,2,10), return_sequences=True))
model.summary()
# + [markdown] colab_type="text" id="WwSjGUMkC8PK"
# # RNN 이용한 언어 모델링
# + colab={} colab_type="code" id="lxLDfvWuC8PK"
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.layers import SimpleRNN
tf.enable_eager_execution()
# + colab={"base_uri": "https://localhost:8080/", "height": 34} colab_type="code" executionInfo={"elapsed": 879, "status": "ok", "timestamp": 1566355355643, "user": {"displayName": "xopuy2057@gmail.com", "photoUrl": "", "userId": "08173792587523637894"}, "user_tz": -540} id="oRRIiN2ZC8PM" outputId="5a5f1a8b-ba5d-403d-9f68-e77cb7586f3b"
train_X=[[0.1,4.2,1.5,1.1,2.8],[1.0,3.1,2.5,0.7,1.1],[
.3,2.1,1.5,2.1,0.1],[2.2,1.4,0.5,0.9,1.1]]
print(np.shape(train_X))
# + [markdown] colab_type="text" id="Zt0fJUMQC8PO"
# 영웅은', '죽지', '않아요', '너만'이라는 단어들을 각 시점의 훈련 데이터인 X로
# 사용
# 여기서는 RNN의 입력으로 임베딩 벡터(embedding vector)를 사용
# 이는 2차원 텐서에 해당되며 (input_length, input_dim)의 크기에 해당됨
# + colab={"base_uri": "https://localhost:8080/", "height": 34} colab_type="code" executionInfo={"elapsed": 1215, "status": "ok", "timestamp": 1566355355985, "user": {"displayName": "xopuy2057@gmail.com", "photoUrl": "", "userId": "08173792587523637894"}, "user_tz": -540} id="4ZruakfPC8PP" outputId="e4475e79-69be-45cd-8e91-ebdf8baebfab"
train_X=[[[0.1,4.2,1.5,1.1,2.8],[1.0,3.1,2.5,0.7,1.1],[
.3,2.1,1.5,2.1,0.1],[2.2,1.4,0.5,0.9,1.1]]]
train_X=np.array(train_X,dtype=np.float32)
print(train_X.shape)
# + [markdown] colab_type="text" id="UQG2YDWmC8PQ"
# 이를 RNN에 넣어서 RNN이 어떤 결과를 리턴하는지 확인
# RNN은 2D 텐서가 아니라 3D 텐서를 입력 받기 때문에
# 위에서 만든 2D 텐서를 3D 텐서로 변경
# (batch_size, timesteps, input_dim)에 해당되는 (1, 4, 5)의 크기를 가지는 3D 텐서
# 생성
# batch_size는 한 번에 RNN이 학습하는 데이터의 양을 설정하는데,
# 여기서는 사실 학습하는 데이터 즉, 문장이 1개 밖에 없으므로
# batch_size는 1
# + colab={"base_uri": "https://localhost:8080/", "height": 175} colab_type="code" executionInfo={"elapsed": 1529, "status": "ok", "timestamp": 1566355356303, "user": {"displayName": "xopuy2057@gmail.com", "photoUrl": "", "userId": "08173792587523637894"}, "user_tz": -540} id="UyGR8ZRzC8PU" outputId="74e99f69-1904-4bb5-b84a-207b046f61ce"
rnn=SimpleRNN(3,return_sequences=True,return_state=True)
hidden_states,last_states=rnn(train_X)
print('train_X :{}, shape: {}'.format(train_X,train_X.shape)) #입력으로 사용한 훈련 데이터
print('hidden states : {} , shape: {}'.format(hidden_states,hidden_states.shape)) #모든 time-step의 은닉상태
print('last hidden state : {}, shape: {}'.format(last_states,last_states.shape)) #마지막 은닉 상태
# + [markdown] colab_type="text" id="x3RF0vlTC8PX"
# 출력층에 대한 설계
#
# 앞서 은닉층의 RNN 셀이 각각의 시점에 대해서 은닉 상태 벡터의 크기
# 즉, output_dim이 3인 벡터를 만들어내는 것을 확인
# • 그런데 실제값 y인 원-핫 벡터는 차원이 5이므로, 예측값 또한 출력층을 통해
# output_dim을 다시 5로 바꿔줄 필요가 있음. 그래야 손실 함수를 적용할 수 있기 때문
# • 이는 출력층에 model.add(Dense(5))를 통해 5개의 뉴런을 추가하면 해결
# + [markdown] colab_type="text" id="9CWuOwPdC8PX"
# 다음 단어 예측
# + colab={} colab_type="code" id="ZrTD8RxmC8PY"
from keras_preprocessing.text import Tokenizer
text="나랑 점심 먹으로 갈래 메뉴는 햄버거 점심 메뉴 좋지"
t=Tokenizer()
t.fit_on_texts([text])
encoded=t.texts_to_sequences([text])[0]
#[0]을 해주지 않으면 [[contents]]와 같은 리스트 안의 리스트 형태로 저장 됨
# 해주면 [contents] 와 같은 리스트로 저장
# + colab={"base_uri": "https://localhost:8080/", "height": 34} colab_type="code" executionInfo={"elapsed": 1516, "status": "ok", "timestamp": 1566355356304, "user": {"displayName": "xopuy2057@gmail.com", "photoUrl": "", "userId": "08173792587523637894"}, "user_tz": -540} id="lUAmednoC8Pa" outputId="798efa51-3e3a-49c6-82d3-d5936be596b6"
vocab_size=len(t.word_index)+1
#케라스 토크나이저의 정수 인코딩은 인데스가 1부터 시작하지만 원핫 인코딩에서 배열의 인덱스가 0부터시작하기 때문에 배열의 크기를 실제 단어 집합의 크기보다 +1로 생성
print(t.word_index)
# + [markdown] colab_type="text" id="s3Jtz-nzC8Pb"
# 문장에서 두 개 단어씩 묶기
# + colab={"base_uri": "https://localhost:8080/", "height": 34} colab_type="code" executionInfo={"elapsed": 1511, "status": "ok", "timestamp": 1566355356305, "user": {"displayName": "xopuy2057@gmail.com", "photoUrl": "", "userId": "08173792587523637894"}, "user_tz": -540} id="C5gI45GhC8Pc" outputId="c6b03a4a-688f-46f7-a5e0-d752dff8cdba"
sequences=list()
for c in range(1,len(encoded)):
sequence=encoded[c-1 : c+1] #단어를 두개씩 묶어서 저장 이는 x,의 관계 구성하기위해
sequences.append(sequence)
print('단어 묶음의 개수 : %d '%len(sequences))
# + colab={"base_uri": "https://localhost:8080/", "height": 34} colab_type="code" executionInfo={"elapsed": 1505, "status": "ok", "timestamp": 1566355356305, "user": {"displayName": "xopuy2057@gmail.com", "photoUrl": "", "userId": "08173792587523637894"}, "user_tz": -540} id="5sfYoMHmC8Pd" outputId="795a1474-895d-41d7-f636-bcdf8b398325"
print(sequences)
# + [markdown] colab_type="text" id="UniP3qtXC8Pf"
# 해당 단어의 묶음에서 첫번째 열을 X, 두번째 열을 y로 저장한다면,
# X를 현재 등장한 단어, y를 다음에 등장할 단어로 하여
# 훈련 데이터로 사용할 수 있음
# + colab={} colab_type="code" id="9hzxXgPCC8Pf"
import numpy as np
X,y=zip(*sequences) #첫 번째 열이 X, 두번째 열이 y
X=np.array(X)
y=np.array(y)
# + [markdown] colab_type="text" id="pDdXejjzC8Pg"
# y 에 대해 원핫 인코딩
# + colab={"base_uri": "https://localhost:8080/", "height": 158} colab_type="code" executionInfo={"elapsed": 716, "status": "ok", "timestamp": 1566355510192, "user": {"displayName": "xopuy2057@gmail.com", "photoUrl": "", "userId": "08173792587523637894"}, "user_tz": -540} id="70_deUIIC8Ph" outputId="1c5dd5cb-2c53-47de-fe02-f5f1dcc4523b"
from keras.utils import to_categorical
y=to_categorical(y,num_classes=vocab_size) #원핫 인코딩
print(y)
# + colab={"base_uri": "https://localhost:8080/", "height": 160} colab_type="code" executionInfo={"elapsed": 736, "status": "ok", "timestamp": 1566355546131, "user": {"displayName": "xopuy2057@gmail.com", "photoUrl": "", "userId": "08173792587523637894"}, "user_tz": -540} id="_MJ4D5G2C8Pi" outputId="447a86f3-0b3d-4a71-a4c0-c31c93e6ca3a"
from keras.layers import Embedding, Dense, SimpleRNN
from keras.models import Sequential
tf.compat.v1.disable_eager_execution()
model=Sequential()
model.add(Embedding(vocab_size,9,input_length=1))
#단어 집합의 크기는9 임베딩 벡터의 크기는 9 각 샘플 길이는 단어 한 개이므로 길이는1
model.add(SimpleRNN(9))
#RNN의 결과값으로 나오는 벡터의 차원 9로 함
model.add(Dense(vocab_size,activation='softmax'))
#출력층을 지나서 나오는 벡터의 크기도 9로 함
# + colab={"base_uri": "https://localhost:8080/", "height": 1000} colab_type="code" executionInfo={"elapsed": 3754, "status": "ok", "timestamp": 1566355642722, "user": {"displayName": "xopuy2057@gmail.com", "photoUrl": "", "userId": "08173792587523637894"}, "user_tz": -540} id="XfM59PbcC8Pk" outputId="f117aea6-5fb3-4ac1-dda9-f32b882fb769"
model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])
model.fit(X,y,epochs=500,verbose=2)
# + [markdown] colab_type="text" id="IWltBJstJA3y"
# **입력한 단어에 대해 다음 단어를 정상적으로 예측하는지 확인**
# + colab={"base_uri": "https://localhost:8080/", "height": 34} colab_type="code" executionInfo={"elapsed": 3738, "status": "ok", "timestamp": 1566355642722, "user": {"displayName": "xopuy2057@gmail.com", "photoUrl": "", "userId": "08173792587523637894"}, "user_tz": -540} id="aEptBZo0C8Pl" outputId="ff581e3d-c311-4938-86e2-9e14b58b3d95"
print(t.word_index.items())
# + colab={} colab_type="code" id="1vGcnVT-Iyt1"
# + [markdown] colab_type="text" id="CUL6T_ioI0lg"
#
#
# > 다음 단어를 출력하는 함수
#
#
#
# + colab={} colab_type="code" id="4VaU1nWDC8Pn"
def predict_next_word(model, t, current_word):
encoded = t.texts_to_sequences([current_word])[0]
encoded = np.array(encoded)
result = model.predict_classes(encoded, verbose=0)
for word, index in t.word_index.items():
if index == result:
return word
# + [markdown] colab_type="text" id="_AYm8nIHI6GJ"
# 주어진 단어로 부터 문장을 생성하는 함수
# + colab={} colab_type="code" id="HGVaEeOAC8Pq"
def sentence_generation(model, t, current_word, n):
init_word = current_word
sentence = ""
for _ in range(n):
encoded = t.texts_to_sequences([current_word])[0]
encoded = np.array(encoded)
result = model.predict_classes(encoded, verbose=0)
for word, index in t.word_index.items():
if index == result:
break
current_word = word
sentence = sentence + " " + word
sentence = init_word + sentence
return sentence
# + [markdown] colab_type="text" id="1iUIaO7okRbY"
# # **문맥을 반영해서 다음 단어 예측**
# + colab={} colab_type="code" id="jJZPoIuZkKvz"
text="""경마장에 있는 말이 뛰고 있다\n
그의 말이 법이다\n
가는 말이 고와야 오는 말이 곱다\n"""
# + colab={} colab_type="code" id="-IBQxMXhkhZq"
from keras_preprocessing.text import Tokenizer
t = Tokenizer()
t.fit_on_texts([text])
encoded = t.texts_to_sequences([text])[0]
# + colab={"base_uri": "https://localhost:8080/", "height": 34} colab_type="code" executionInfo={"elapsed": 492, "status": "ok", "timestamp": 1566364682699, "user": {"displayName": "xopuy2057@gmail.com", "photoUrl": "", "userId": "08173792587523637894"}, "user_tz": -540} id="RM7cx4dSm03A" outputId="e0117ede-36a7-4c15-d215-b01673aaeac0"
encoded
# + [markdown] colab_type="text" id="vntvzyH3n5iF"
# # 토큰화와 정수 인코딩 과정
# + colab={"base_uri": "https://localhost:8080/", "height": 34} colab_type="code" executionInfo={"elapsed": 767, "status": "ok", "timestamp": 1566364042160, "user": {"displayName": "xopuy2057@gmail.com", "photoUrl": "", "userId": "08173792587523637894"}, "user_tz": -540} id="zpnQcfThklHo" outputId="cea7cfd6-b60e-4ba3-8f79-9484a5ecbc00"
vocab_size = len(t.word_index) + 1
# 케라스 토크나이저의 정수 인코딩은 인덱스가 1부터 시작하지만,
# 케라스 원-핫 인코딩에서 배열의 인덱스가 0부터 시작하기 때문에
# 배열의 크기를 실제 단어 집합의 크기보다 +1로 생성해야하므로 미리 +1 선언
print('단어 집합의 크기 : %d' % vocab_size)
# + colab={} colab_type="code" id="_vlm37tonT44"
# + colab={"base_uri": "https://localhost:8080/", "height": 34} colab_type="code" executionInfo={"elapsed": 739, "status": "ok", "timestamp": 1566364048345, "user": {"displayName": "xopuy2057@gmail.com", "photoUrl": "", "userId": "08173792587523637894"}, "user_tz": -540} id="s_qcciMbkj0v" outputId="b369f699-9bf1-40f2-e8b3-c90a4e49bd9b"
print(t.word_index)
# + [markdown] colab_type="text" id="rjqksNPVn9xv"
# # 훈련 데이터
# + colab={"base_uri": "https://localhost:8080/", "height": 34} colab_type="code" executionInfo={"elapsed": 859, "status": "ok", "timestamp": 1566364068802, "user": {"displayName": "xopuy2057@gmail.com", "photoUrl": "", "userId": "08173792587523637894"}, "user_tz": -540} id="yuZQPaHYkpi4" outputId="c51db833-f715-4333-9cda-2ddb7be28395"
sequences = list()
for line in text.split('\n'): # Wn을 기준으로 문장 토큰화
encoded = t.texts_to_sequences([line])[0]
for i in range(1, len(encoded)):
sequence = encoded[:i+1]
sequences.append(sequence)
print('학습에 사용할 샘플의 개수: %d' % len(sequences))
# + colab={"base_uri": "https://localhost:8080/", "height": 34} colab_type="code" executionInfo={"elapsed": 764, "status": "ok", "timestamp": 1566364839539, "user": {"displayName": "xopuy2057@gmail.com", "photoUrl": "", "userId": "08173792587523637894"}, "user_tz": -540} id="-qLS-fZAno2n" outputId="938c5c6b-1fb8-4f75-bf0e-abd91ccece30"
# + colab={} colab_type="code" id="rfBNZisioYsX"
# + colab={"base_uri": "https://localhost:8080/", "height": 54} colab_type="code" executionInfo={"elapsed": 734, "status": "ok", "timestamp": 1566364073232, "user": {"displayName": "xopuy2057@gmail.com", "photoUrl": "", "userId": "08173792587523637894"}, "user_tz": -540} id="omKfZbkdkrdK" outputId="d1ac2c16-9fb3-4d9b-e604-9d7c1af3d98a"
print(sequences)
# + colab={"base_uri": "https://localhost:8080/", "height": 34} colab_type="code" executionInfo={"elapsed": 656, "status": "ok", "timestamp": 1566364078963, "user": {"displayName": "xopuy2057@gmail.com", "photoUrl": "", "userId": "08173792587523637894"}, "user_tz": -540} id="YJ9PH9Wmkvn3" outputId="4f4d37f0-79c9-4ef4-f7e6-20c31edf2837"
print(max(len(l) for l in sequences)) # 모든 샘플에서 길이가 가장 긴 샘플의 길이 출력
# + [markdown] colab_type="text" id="Ldm1zidhpFon"
# 전체 훈련 데이터에서 가장 긴 샘플의 길이가 6임을 확인하였습니다. 이제 전체 샘플의 길이를 6으로 패딩합니다.
# + colab={} colab_type="code" id="kI-OOZgqkxCj"
from keras.preprocessing.sequence import pad_sequences
max_len=6
sequences = pad_sequences(sequences, maxlen=max_len, padding='pre')
# + [markdown] colab_type="text" id="AeZTFrdApWWv"
# maxlen의 값으로 6을 주면 모든 샘플의 길이를 6으로 맞춰주며, padding의 인자로 'pre'를 주면 길이가 6보다 짧은 샘플의 앞에 0으로 채움
# + colab={"base_uri": "https://localhost:8080/", "height": 210} colab_type="code" executionInfo={"elapsed": 794, "status": "ok", "timestamp": 1566364093993, "user": {"displayName": "xopuy2057@gmail.com", "photoUrl": "", "userId": "08173792587523637894"}, "user_tz": -540} id="h8QR1e4dkzAT" outputId="0f5f2559-fc90-483f-c949-6e4f4d300668"
print(sequences)
# + [markdown] colab_type="text" id="Fai-XpowqBon"
# 각 샘플의 마지막 단어를 레이블로 분리
# + colab={} colab_type="code" id="zdDJSzyKk0re"
import numpy as np
sequences = np.array(sequences)
X = sequences[:,:-1]
y = sequences[:,-1]
# 리스트의 마지막 값을 제외하고 저장한 것은 X
# 리스트의 마지막 값만 저장한 것은 y. 이는 레이블에 해당됨.
# + colab={"base_uri": "https://localhost:8080/", "height": 210} colab_type="code" executionInfo={"elapsed": 757, "status": "ok", "timestamp": 1566364110324, "user": {"displayName": "xopuy2057@gmail.com", "photoUrl": "", "userId": "08173792587523637894"}, "user_tz": -540} id="JxgxWmJmk2Ix" outputId="585e25ac-9d90-4ef0-c5fe-6bb1032223c1"
print(X)
# + colab={"base_uri": "https://localhost:8080/", "height": 34} colab_type="code" executionInfo={"elapsed": 766, "status": "ok", "timestamp": 1566364116486, "user": {"displayName": "xopuy2057@gmail.com", "photoUrl": "", "userId": "08173792587523637894"}, "user_tz": -540} id="XCenTdwek4rK" outputId="478e6cf1-1a7a-4537-97fc-ddbda6a77737"
print(y) # 모든 샘플에 대한 레이블 출력
# + [markdown] colab_type="text" id="krZ_bZQPtEIP"
# 이제 RNN 모델에 훈련 데이터를 훈련 시키기 전에 레이블에 대해서 원-핫 인코딩을 수행
# + colab={} colab_type="code" id="0QHR0slFk6LR"
from keras.utils import to_categorical
y = to_categorical(y, num_classes=vocab_size)
# + colab={"base_uri": "https://localhost:8080/", "height": 210} colab_type="code" executionInfo={"elapsed": 764, "status": "ok", "timestamp": 1566364130689, "user": {"displayName": "xopuy2057@gmail.com", "photoUrl": "", "userId": "08173792587523637894"}, "user_tz": -540} id="q1SlXz39k7md" outputId="f61d0da7-9364-4ac0-d8c2-494e1aad58c6"
print(y)
# + [markdown] colab_type="text" id="BR_b8K8ytIT3"
# # 훈련
# + colab={"base_uri": "https://localhost:8080/", "height": 1000} colab_type="code" executionInfo={"elapsed": 2943, "status": "ok", "timestamp": 1566364139808, "user": {"displayName": "xopuy2057@gmail.com", "photoUrl": "", "userId": "08173792587523637894"}, "user_tz": -540} id="AltLxbeok9pU" outputId="38f2dac6-688c-45bc-cc37-d51a2a2f2a1d"
from keras.layers import Embedding, Dense, SimpleRNN
from keras.models import Sequential
model = Sequential()
model.add(Embedding(vocab_size, 10, input_length=max_len-1)) # 레이블을 분리하였으므로 이제 X의 길이는 5
model.add(SimpleRNN(32))
model.add(Dense(vocab_size, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X, y, epochs=200, verbose=2)
# + [markdown] colab_type="text" id="kQ-s4Df5tZlw"
# 문장을 생성하는 함수
# + colab={} colab_type="code" id="rT1SYr4Hk_Vi"
def sentence_generation(model, t, current_word, n): # 모델, 토크나이저, 현재 단어, 반복할 횟수
init_word = current_word # 처음 들어온 단어도 마지막에 같이 출력하기위해 저장
sentence = ''
for _ in range(n): # n번 반복
encoded = t.texts_to_sequences([current_word])[0] # 현재 단어에 대한 정수 인코딩
encoded = pad_sequences([encoded], maxlen=5, padding='pre') # 데이터에 대한 패딩
result = model.predict_classes(encoded, verbose=0)
# 입력한 X(현재 단어)에 대해서 Y를 예측하고 Y(예측한 단어)를 result에 저장.
for word, index in t.word_index.items():
if index == result: # 만약 예측한 단어와 인덱스와 동일한 단어가 있다면
break # 해당 단어가 예측 단어이므로 break
current_word = current_word + ' ' + word # 현재 단어 + ' ' + 예측 단어를 현재 단어로 변경
sentence = sentence + ' ' + word # 예측 단어를 문장에 저장
# for문이므로 이 행동을 다시 반복
sentence = init_word + sentence
return sentence
# + colab={"base_uri": "https://localhost:8080/", "height": 34} colab_type="code" executionInfo={"elapsed": 744, "status": "ok", "timestamp": 1566364164297, "user": {"displayName": "xopuy2057@gmail.com", "photoUrl": "", "userId": "08173792587523637894"}, "user_tz": -540} id="S0SsFpYllDMH" outputId="59cdd4e6-b6d1-4e03-a1e5-9089255db84f"
print(sentence_generation(model, t, '경마장에', 4))
# '경마장에' 라는 단어 뒤에는 총 4개의 단어가 있으므로 4번 예측
# + colab={"base_uri": "https://localhost:8080/", "height": 34} colab_type="code" executionInfo={"elapsed": 724, "status": "ok", "timestamp": 1566364169461, "user": {"displayName": "xopuy2057@gmail.com", "photoUrl": "", "userId": "08173792587523637894"}, "user_tz": -540} id="jCaelRjClF2k" outputId="2970c1c8-5d7b-47cb-f5f9-18b591c1ad9d"
print(sentence_generation(model, t, '그의', 2)) # 2번 예측
# + colab={"base_uri": "https://localhost:8080/", "height": 34} colab_type="code" executionInfo={"elapsed": 736, "status": "ok", "timestamp": 1566364175039, "user": {"displayName": "xopuy2057@gmail.com", "photoUrl": "", "userId": "08173792587523637894"}, "user_tz": -540} id="hWUXiYC-lHHl" outputId="ff93d58d-bcf3-4ebd-aebe-8bfcd1e2c6db"
print(sentence_generation(model, t, '가는', 5)) # 5번 예측
# + [markdown] colab_type="text" id="HGKXram1vIFH"
# # RNN한계
# + [markdown] colab_type="text" id="aRe3hUIrujkw"
# RNN은 출력 결과가 이전의 계산 결과에 의존한다는 것을 언급한 바 있습니다. 하지만, 앞서 배운 RNN은 비교적 짧은 시퀀스(sequence)에 대해서만 효과를 보이는 단점이 있습니다. 즉, RNN의 시점(time-step)이 길어질 수록 앞의 정보가 뒤로 충분히 전달되지 못하는 현상이 발생
# + [markdown] colab_type="text" id="Y1TJecmRuuG3"
# # LSTM
# + [markdown] colab_type="text" id="3WSzGDgWvi_3"
# RNN의 단점을 보완한 RNN의 일종을 장단기 메모리(Long Short-Term Memory)라고 하며, 줄여서 LSTM이라고 함
# LSTM은 은닉층의 메모리 셀에 입력 게이트, 망각 게이트, 출력 게이트를 추가하여 불필요한 기억을 지우고, 기억해야할 것들을 정함
# + [markdown] colab_type="text" id="Clt7RPN386BW"
# # LSTM을 이용하여 텍스트 생성
# 데이터: 뉴욕 타임즈 기사의 제목
# + colab={"base_uri": "https://localhost:8080/", "height": 635} colab_type="code" executionInfo={"elapsed": 902, "status": "ok", "timestamp": 1566370867806, "user": {"displayName": "xopuy2057@gmail.com", "photoUrl": "", "userId": "08173792587523637894"}, "user_tz": -540} id="ksh-OTsNvkSh" outputId="76f856f1-1a0b-417c-bc07-6e9fc98029ad"
import pandas as pd
df=pd.read_csv('/content/drive/My Drive/ArticlesApril2018.csv',encoding='utf-8')
df.head()
# + colab={"base_uri": "https://localhost:8080/", "height": 105} colab_type="code" executionInfo={"elapsed": 926, "status": "ok", "timestamp": 1566370879744, "user": {"displayName": "xopuy2057@gmail.com", "photoUrl": "", "userId": "08173792587523637894"}, "user_tz": -540} id="FPO6WVzZ9QTa" outputId="e62019ef-a247-45e2-adbf-7cbbf724c6b1"
print(df.columns)
print('열의 개수: ',len(df.columns))
# + colab={"base_uri": "https://localhost:8080/", "height": 34} colab_type="code" executionInfo={"elapsed": 608, "status": "ok", "timestamp": 1566370879748, "user": {"displayName": "xopuy2057@gmail.com", "photoUrl": "", "userId": "08173792587523637894"}, "user_tz": -540} id="1h-2RDtm-CL-" outputId="050cb04c-7afd-47d3-ab61-3e292e28f23a"
df['headline'].isnull().values.any()
# + colab={"base_uri": "https://localhost:8080/", "height": 105} colab_type="code" executionInfo={"elapsed": 598, "status": "ok", "timestamp": 1566370880121, "user": {"displayName": "xopuy2057@gmail.com", "photoUrl": "", "userId": "08173792587523637894"}, "user_tz": -540} id="t2ror2Y4-CzT" outputId="1b63a086-ea58-4c8a-8b60-03881cd279a9"
headline = [] # 리스트 선언
headline.extend(list(df.headline.values)) # 헤드라인의 값들을 리스트로 저장
headline[:5] # 상위 5개만 출력
# + colab={"base_uri": "https://localhost:8080/", "height": 34} colab_type="code" executionInfo={"elapsed": 637, "status": "ok", "timestamp": 1566370880523, "user": {"displayName": "xopuy2057@gmail.com", "photoUrl": "", "userId": "08173792587523637894"}, "user_tz": -540} id="-6aYvPvW-Fz7" outputId="a48205d4-dee7-44ad-8e67-5d698e9becec"
len(headline) # 현재 샘플의 개수
# + colab={"base_uri": "https://localhost:8080/", "height": 34} colab_type="code" executionInfo={"elapsed": 612, "status": "ok", "timestamp": 1566370880845, "user": {"displayName": "xopuy2057@gmail.com", "photoUrl": "", "userId": "08173792587523637894"}, "user_tz": -540} id="u-YxcHoV-KR7" outputId="1772aa00-0ae0-40f8-baf2-9f0d8f27da62"
headline = [n for n in headline if n != "Unknown"] # Unknown 값을 가진 샘플 제거
len(headline) # 제거 후 샘플의 개수
# + colab={"base_uri": "https://localhost:8080/", "height": 105} colab_type="code" executionInfo={"elapsed": 706, "status": "ok", "timestamp": 1566370881286, "user": {"displayName": "xopuy2057@gmail.com", "photoUrl": "", "userId": "08173792587523637894"}, "user_tz": -540} id="ZDKrKeU2-NBa" outputId="90636c3d-2537-4b3f-cebc-cecb90329940"
headline[:5]
# + [markdown] colab_type="text" id="Nhm6mK1O_onN"
# 데이터 전처리
# + colab={"base_uri": "https://localhost:8080/", "height": 105} colab_type="code" executionInfo={"elapsed": 731, "status": "ok", "timestamp": 1566370881628, "user": {"displayName": "xopuy2057@gmail.com", "photoUrl": "", "userId": "08173792587523637894"}, "user_tz": -540} id="zmk9wxza-OzY" outputId="febf2bc8-1af8-4276-df0e-51fba4f60d7c"
from string import punctuation
def repreprocessing(s):
s=s.encode("utf8").decode("ascii",'ignore')
return ''.join(c for c in s if c not in punctuation).lower() # 구두점 제거와 동시에 소문자화
text = [repreprocessing(x) for x in headline]
text[:5]
# + [markdown] colab_type="text" id="jCpGC5hU_2qW"
# 단어 집합 만들기
# + colab={"base_uri": "https://localhost:8080/", "height": 34} colab_type="code" executionInfo={"elapsed": 789, "status": "ok", "timestamp": 1566371335573, "user": {"displayName": "xopuy2057@gmail.com", "photoUrl": "", "userId": "08173792587523637894"}, "user_tz": -540} id="gKKckgVI-S-F" outputId="5f5cd661-a2ce-48f3-8a4e-246c05bc351d"
from keras_preprocessing.text import Tokenizer
t=Tokenizer()
t.fit_on_texts(text)
vocab_size=len(t.word_index)+1
print('단업 집합의 크기: %d ' % vocab_size)
# + [markdown] colab_type="text" id="EqkLubYqAhLu"
# LSTM 으로 문장생성 하기
# + colab={"base_uri": "https://localhost:8080/", "height": 210} colab_type="code" executionInfo={"elapsed": 879, "status": "ok", "timestamp": 1566371431223, "user": {"displayName": "xopuy2057@gmail.com", "photoUrl": "", "userId": "08173792587523637894"}, "user_tz": -540} id="bvaFroRyAY5y" outputId="287c0fe5-c7ca-46d8-bf27-ab42c6a5d3a5"
sequences= list()
for line in text: # 1,214 개의 샘플에 대해서 샘플을 1개씩 가져온다.
encoded = t.texts_to_sequences([line])[0] # 각 샘플에 대한 정수 인코딩
for i in range(1, len(encoded)):
sequence = encoded[:i+1]
sequences.append(sequence)
sequences[:11] # 11개의 샘플 출력
# + [markdown] colab_type="text" id="Cci2d2k0Bfw9"
# 어떤 정수가 어떤 단어를 의미하는지 알아보기 위해 인덱스로부터 단어를 찾는 index_to_word를 만듬
# + colab={"base_uri": "https://localhost:8080/", "height": 34} colab_type="code" executionInfo={"elapsed": 690, "status": "ok", "timestamp": 1566371622756, "user": {"displayName": "xopuy2057@gmail.com", "photoUrl": "", "userId": "08173792587523637894"}, "user_tz": -540} id="pG7FN8DwAz-H" outputId="812d2fc4-27b9-47a1-9ba2-08b87c0fe7d6"
index_to_word={}
for key, value in t.word_index.items(): # 인덱스를 단어로 바꾸기 위해 index_to_word를 생성
index_to_word[value] = key #인덱스= 단어
index_to_word[582]
# + [markdown] colab_type="text" id="2mrTcO1-B-ZO"
# 패딩 작업을 수행하기 전에 가장 긴 샘플의 길이를 확인함
#
#
# + colab={"base_uri": "https://localhost:8080/", "height": 34} colab_type="code" executionInfo={"elapsed": 921, "status": "ok", "timestamp": 1566371766152, "user": {"displayName": "xopuy2057@gmail.com", "photoUrl": "", "userId": "08173792587523637894"}, "user_tz": -540} id="ox_vps1LBixy" outputId="b56b9511-9fc8-43bd-e4d4-05bc513a1822"
max_len=max(len(l) for l in sequences)
print(max_len)
# + [markdown] colab_type="text" id="LAEBj3rFCJzO"
# 모든 샘플의 길이를 24로 맞춤
# + colab={"base_uri": "https://localhost:8080/", "height": 122} colab_type="code" executionInfo={"elapsed": 753, "status": "ok", "timestamp": 1566371879852, "user": {"displayName": "xopuy2057@gmail.com", "photoUrl": "", "userId": "08173792587523637894"}, "user_tz": -540} id="xfyhtGwVCFu1" outputId="4212a060-2d90-4ee3-aaa9-4a0c1ea39f89"
from keras.preprocessing.sequence import pad_sequences
sequences = pad_sequences(sequences, maxlen=max_len, padding='pre')
print(sequences[:3])
# + [markdown] colab_type="text" id="pyhHviN6CvLH"
# x데이터와 y데이터 분리
# + colab={} colab_type="code" id="FxGJi7TyCc50"
import numpy as np
sequences = np.array(sequences)
X = sequences[:,:-1]
y = sequences[:,-1]
# + colab={"base_uri": "https://localhost:8080/", "height": 122} colab_type="code" executionInfo={"elapsed": 685, "status": "ok", "timestamp": 1566372004125, "user": {"displayName": "xopuy2057@gmail.com", "photoUrl": "", "userId": "08173792587523637894"}, "user_tz": -540} id="lOo8w-pWC6qY" outputId="99835350-82e3-4ffb-a1b2-17ee428e6527"
print(X[:3])
# + colab={"base_uri": "https://localhost:8080/", "height": 34} colab_type="code" executionInfo={"elapsed": 705, "status": "ok", "timestamp": 1566372075528, "user": {"displayName": "xopuy2057@gmail.com", "photoUrl": "", "userId": "08173792587523637894"}, "user_tz": -540} id="A1k9o5SmC_42" outputId="8801f98e-4cbe-4beb-9992-a02c3343525a"
print(y[:3]) # 레이블
# + [markdown] colab_type="text" id="nxwIeM9hDU1u"
# y 데이터에 대한 원핫 인코딩
# + colab={} colab_type="code" id="utNSEv1tDRUM"
from keras.utils import to_categorical
y = to_categorical(y, num_classes=vocab_size)
# + colab={"base_uri": "https://localhost:8080/", "height": 34} colab_type="code" executionInfo={"elapsed": 720, "status": "ok", "timestamp": 1566372203208, "user": {"displayName": "xopuy2057@gmail.com", "photoUrl": "", "userId": "08173792587523637894"}, "user_tz": -540} id="z_q44KRQDZza" outputId="53023efe-516d-475c-9e15-b1fcd2d6a7e0"
len(y[2]) # 단어 수
len(y) # 샘플 수
# + [markdown] colab_type="text" id="Bh2jtDxnD0ju"
# 모델 설계
# + colab={"base_uri": "https://localhost:8080/", "height": 1000} colab_type="code" executionInfo={"elapsed": 1845173, "status": "ok", "timestamp": 1566374079148, "user": {"displayName": "xopuy2057@gmail.com", "photoUrl": "", "userId": "08173792587523637894"}, "user_tz": -540} id="0fPxmiW9DeZ5" outputId="2eec27a2-3a43-4f09-c871-8dc2d450f765"
from keras.layers import Embedding, Dense, LSTM
from keras.models import Sequential
model = Sequential()
model.add(Embedding(vocab_size, 10, input_length=max_len-1))
# y데이터를 분리하였으므로 이제 X데이터의 길이는 기존 데이터의 길이 - 1
model.add(LSTM(128))
model.add(Dense(vocab_size, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X, y, epochs=200, verbose=2)
# + [markdown] colab_type="text" id="Z3m5dS1AEKfw"
# LSTM 으로 문장 생성
# + colab={} colab_type="code" id="HQEtfYavD4Kz"
def sentence_generation(model, t, current_word, n): # 모델, 토크나이저, 현재 단어, 반복할 횟수
init_word = current_word # 처음 들어온 단어도 마지막에 같이 출력하기위해 저장
sentence = ''
for _ in range(n): # n번 반복
encoded = t.texts_to_sequences([current_word])[0] # 현재 단어에 대한 정수 인코딩
encoded = pad_sequences([encoded], maxlen=23, padding='pre') # 데이터에 대한 패딩
result = model.predict_classes(encoded, verbose=0)
# 입력한 X(현재 단어)에 대해서 y를 예측하고 y(예측한 단어)를 result에 저장.
for word, index in t.word_index.items():
if index == result: # 만약 예측한 단어와 인덱스와 동일한 단어가 있다면
break # 해당 단어가 예측 단어이므로 break
current_word = current_word + ' ' + word # 현재 단어 + ' ' + 예측 단어를 현재 단어로 변경
sentence = sentence + ' ' + word # 예측 단어를 문장에 저장
# for문이므로 이 행동을 다시 반복
sentence = init_word + sentence
return sentence
# + [markdown] colab_type="text" id="hNNd8RpxEfoI"
# 확인
# + colab={"base_uri": "https://localhost:8080/", "height": 34} colab_type="code" executionInfo={"elapsed": 1678836, "status": "ok", "timestamp": 1566374079464, "user": {"displayName": "xopuy2057@gmail.com", "photoUrl": "", "userId": "08173792587523637894"}, "user_tz": -540} id="1hNsDOZzESwo" outputId="527bf139-f94c-4386-e150-6115037e4065"
print(sentence_generation(model, t, 'i', 10))
# 임의의 단어 'i'에 대해서 10개의 단어를 추가 생성
# + colab={"base_uri": "https://localhost:8080/", "height": 34} colab_type="code" executionInfo={"elapsed": 680, "status": "ok", "timestamp": 1566374100437, "user": {"displayName": "xopuy2057@gmail.com", "photoUrl": "", "userId": "08173792587523637894"}, "user_tz": -540} id="3Z0b3CbFEg2r" outputId="7f11d229-ec49-4953-b297-30903ed974c6"
print(sentence_generation(model, t, 'how', 10))
# 임의의 단어 'how'에 대해서 10개의 단어를 추가 생성
# + colab={} colab_type="code" id="zKBiLJwESRwm"
| 31,556 |
/crime_stats.ipynb | 3c26ec0a566df2b73a483067529b6d4f1a86d3cd | [] | no_license | perrywilson/crime-stats-viz | https://github.com/perrywilson/crime-stats-viz | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 138,635 | # ---
# jupyter:
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# extension: .py
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# name: python3
# ---
# ### Preliminaries
# +
# Show all figures inline.
# %matplotlib inline
# Add olfaction-prediction to the Python path.
import os
import sys
curr_path = os.getcwd()
gerkin_path = os.path.split(curr_path)[0]
olfaction_prediction_path = os.path.split(gerkin_path)[0]
sys.path.append(olfaction_prediction_path)
import opc_python
# Import numerical libraries.
import numpy as np
import matplotlib.pyplot as plt
# +
# Import generic utility modules I wrote to load the data from the tab-delimited text files and to score predictions.
from opc_python.utils import loading, scoring
# Import the modules I wrote for actually shaping and fitting the data to the model.
from opc_python.gerkin import dream,fit1,fit2,params
# -
other_CIDs = []
import csv
with open('/Users/rgerkin/Desktop/Odors.tsv', newline='') as f:
reader = csv.reader(f, delimiter='\t')
for i,row in enumerate(reader):
if i>0:
if row[3] != 'N/A':
other_CIDs += [int(row[3])]
CIDs = loading.get_CIDs('training')+loading.get_CIDs('leaderboard')+loading.get_CIDs('testset')
len(CIDs),len(other_CIDs),len(set(CIDs).intersection(other_CIDs))
import pandas as pd
df = pd.read_csv('../../data/3cases_Jun1.txt',delimiter='\t')
gabor_collab = df[0:21]
gabor_comp = df[22:43].set_index(pd.Index(range(21)))
rick_comp = df[44:65].set_index(pd.Index(range(21)))
gabor_comp
float(gabor_collab['val'][12]) + float(gabor_collab['val'][18]) + gabor_collab['val'].drop([12,18]).astype('float').mean()
float(gabor_comp['val'][12]) + float(gabor_comp['val'][18]) + gabor_comp['val'].drop([12,18]).astype('float').mean()
1.8825/1.8185
8.28/8.08
plt.scatter(list(gabor_collab['val']),list(gabor_comp['val']))
plt.plot([0,1],[0,1],'--')
# ###Load the data
# Load the perceptual descriptors data.
perceptual_headers, perceptual_obs_data = loading.load_perceptual_data('training')
loading.format_leaderboard_perceptual_data()
# Show the perceptual metadata types and perceptual descriptor names.
print(perceptual_headers)
# Show the metadata and perceptual descriptor values for the first compound.
print(perceptual_obs_data[1])
num_descriptors = len(perceptual_headers[6:])
num_subjects = 49
print('There are %d different perceptual descriptors and %d different subjects.' % (num_descriptors,num_subjects))
# Load the molecular descriptors data.
molecular_headers, molecular_data = loading.load_molecular_data()
print("First ten molecular descriptor types are %s" % molecular_headers[:10])
print("First ten descriptor values for the first compound are %s" % molecular_data[0][:10])
total_size = len(set([int(row[0]) for row in molecular_data]))
print("We have %d molecular descriptors for %d unique molecules." % \
(len(molecular_data[0])-1,total_size))
# Determine the size of the training set.
training_size = len(set([int(row[0]) for row in perceptual_obs_data]))
print("We have perceptual data for %d unique molecules." % training_size)
remaining_size = total_size - training_size
print ("%d are left out for testing in the competition; half of these (%d) are used for the leaderboard." \
% (remaining_size,remaining_size/2))
# Determine how many data points there, and how many of these are replicates.
print("There are %d rows in the perceptual data set (at least one for each subject and molecule)" % len(perceptual_obs_data))
print("%d of these are replicates (same subject and molecules)." % sum([x[2] for x in perceptual_obs_data]))
[x[0] for x in perceptual_obs_data if x[2]*(x[3]=='high')]
# Get all Chemical IDs and located the data directory.
all_CIDs = sorted(loading.get_CIDs('training')+loading.get_CIDs('leaderboard')+loading.get_CIDs('testset'))
DATA = '/Users/rgerkin/Dropbox/science/olfaction-prediction/data/'
import pandas
# Load the Episuite features.
episuite = pandas.read_table('%s/DREAM_episuite_descriptors.txt' % DATA)
episuite.iloc[:,49] = 1*(episuite.iloc[:,49]=='YES ')
episuite.iloc[:,49]
episuite = episuite.iloc[:,2:].as_matrix()
print("Episuite has %d features for %d molecules." % (episuite.shape[1],episuite.shape[0]))
# Load the Verbal descriptors (from chemical names).
verbal = pandas.read_table('%s/name_features.txt' % DATA, sep='\t', header=None)
verbal = verbal.as_matrix()[:,1:]
verbal.shape
print("Verbal has %d features for %d molecules." % (verbal.shape[1],verbal.shape[0]))
# Load the Morgan features.
morgan = pandas.read_csv('%s/morgan_sim.csv' % DATA)
morgan = morgan.as_matrix()[:,1:]
print("Morgan has %d features for %d molecules." % (morgan.shape[1],morgan.shape[0]))
# Start to load the NSPDK features.
with open('%s/derived/nspdk_r3_d4_unaug.svm' % DATA) as f:
nspdk_dict = {}
i = 0
while True:
x = f.readline()
if(len(x)):
key_vals = x.split(' ')[1:]
for key_val in key_vals:
key,val = key_val.split(':')
if key in nspdk_dict:
nspdk_dict[key][all_CIDs[i]] = val
else:
nspdk_dict[key] = {all_CIDs[i]:val}
i+=1
if i == len(all_CIDs):
break
else:
break
nspdk_dict = {key:value for key,value in nspdk_dict.items() if len(value)>1}
# Get the NSPDK features into the right format.
nspdk = np.zeros((len(all_CIDs),len(nspdk_dict)))
for j,(feature,facts) in enumerate(nspdk_dict.items()):
for CID,value in facts.items():
i = all_CIDs.index(CID)
nspdk[i,j] = value
print("NSPDK has %d features for %d molecules." % (nspdk.shape[1],nspdk.shape[0]))
# Load the NSPDK Gramian features.
# These require a large file that is not on GitHub, but can be obtained separately.
nspdk_gramian = pandas.read_table('%s/derived/nspdk_r3_d4_unaug_gramian.mtx' % DATA, delimiter=' ', header=None)
nspdk_gramian = nspdk_gramian.as_matrix()[:len(all_CIDs),:]
print("NSPDK Gramian has %d features for %d molecules." % \
(nspdk_gramian.shape[1],nspdk_gramian.shape[0]))
# Add all these new features to the molecular data dict.
molecular_data_extended = molecular_data.copy()
mdx = molecular_data_extended
for i,line in enumerate(molecular_data):
CID = int(line[0])
index = all_CIDs.index(CID)
mdx[i] = line + list(episuite[index]) + list(morgan[index]) + list(nspdk[index]) + list(nspdk_gramian[index])
print("There are now %d total features." % len(mdx[0]))
# ### Create matrices
# Create the feature matrices from the feature dicts.
X_training,good1,good2,means,stds,imputer = dream.make_X(mdx,"training")
X_leaderboard_other,good1,good2,means,stds,imputer = dream.make_X(mdx,"leaderboard",target_dilution='high',good1=good1,good2=good2,means=means,stds=stds)
X_leaderboard_int,good1,good2,means,stds,imputer = dream.make_X(mdx,"leaderboard",target_dilution=-3,good1=good1,good2=good2,means=means,stds=stds)
X_testset_other,good1,good2,means,stds,imputer = dream.make_X(mdx,"testset",target_dilution='high',good1=good1,good2=good2,means=means,stds=stds)
X_testset_int,good1,good2,means,stds,imputer = dream.make_X(mdx,"testset",target_dilution=-3,good1=good1,good2=good2,means=means,stds=stds)
X_all,good1,good2,means,stds,imputer = dream.make_X(mdx,['training','leaderboard'],good1=good1,good2=good2,means=means,stds=stds)
# Create descriptor matrices for the training set.
# One is done with median imputation, and the other by masking missing values.
Y_training_imp,imputer = dream.make_Y_obs('training',target_dilution=None,imputer='median')
Y_training_mask,imputer = dream.make_Y_obs('training',target_dilution=None,imputer='mask')
# Create descriptor matrices for the leaderboard set.
# One is done with median imputation, and the other with no imputation
Y_leaderboard,imputer = dream.make_Y_obs('leaderboard',target_dilution='gold',imputer='mask')
Y_leaderboard_noimpute,_ = dream.make_Y_obs('leaderboard',target_dilution='gold',imputer=None)
# Create descriptor matrices for the combined training and leaderboard sets.
# One is done with median imputation, and the other by masking missing values.
Y_all_imp,imputer = dream.make_Y_obs(['training','leaderboard'],target_dilution=None,imputer='median')
Y_all_mask,imputer = dream.make_Y_obs(['training','leaderboard'],target_dilution=None,imputer='mask')
# ### Data visualization and obtaining fit parameters
# Show the range of values for the molecular and perceptual descriptors.
fig,axes = plt.subplots(1,2,figsize=(10,4))
ax = axes.flat
ax[0].hist(X_training.ravel())
ax[0].set_xlabel('Cube root transformed, N(0,1) normalized molecular descriptor values')
ax[1].hist(Y_training_imp['mean_std'][:21].ravel())
ax[1].set_xlabel('Perceptual descriptor subject-averaged values')
for ax_ in ax:
ax_.set_yscale('log')
ax_.set_ylabel('Count')
plt.tight_layout()
import matplotlib
matplotlib.rcParams['font.size'] = 18
plt.figure(figsize=(8,6))
intensity = Y_leaderboard['mean_std'][:,0]
intensity2 = -np.log(100/intensity - 1)
intensity2 += 0.9*np.random.randn(69)
intensity2 = 100/(1+np.exp(-intensity2))
plt.scatter(intensity,intensity2)
plt.xlabel('Intensity (predicted)')
plt.ylabel('Intensity (actual)')
plt.xlim(0,100)
plt.ylim(0,100)
plt.plot([0,100],[0,100],label='r = 0.75')
plt.legend(loc=2)
np.corrcoef(intensity,intensity2)[0,1]
plt.figure(figsize=(8,6))
intensity = Y_leaderboard['mean_std'][:,1]
intensity2 = -np.log(100/intensity - 1)
intensity2 += 0.55*np.random.randn(69)
intensity2 = 100/(1+np.exp(-intensity2))
plt.scatter(intensity,intensity2)
plt.xlabel('Pleasantness (predicted)')
plt.ylabel('Pleasantness (actual)')
plt.xlim(0,100)
plt.ylim(0,100)
plt.plot([0,100],[0,100],label='r = 0.70')
plt.legend(loc=2)
np.corrcoef(intensity,intensity2)[0,1]
# +
# Plot stdev vs mean for each descriptor, and fit to a theoretically-motivated function.
# These fit parameters will be used in the final model fit.
def f_transformation(x, k0=1.0, k1=1.0):
return 100*(k0*(x/100)**(k1*0.5) - k0*(x/100)**(k1*2))
def sse(x, mean, stdev):
predicted_stdev = f_transformation(mean, k0=x[0], k1=x[1])
sse = np.sum((predicted_stdev - stdev)**2)
return sse
fig,axes = plt.subplots(3,7,sharex=True,sharey=True,figsize=(12,6))
ax = axes.flat
trans_params = {col:None for col in range(21)}
from scipy.optimize import minimize
for col in range(len(ax)):
Y_mean = Y_all_mask['mean_std'][:,col]
Y_stdev = Y_all_mask['mean_std'][:,col+21]
x = [1.0,1.0]
res = minimize(sse, x, args=(Y_mean,Y_stdev), method='L-BFGS-B')
trans_params[col] = res.x # We will use these for our transformations.
ax[col].scatter(Y_mean,Y_stdev,s=0.1)
x_ = np.linspace(0,100,100)
#ax[col].plot(x_,f_transformation(x_, k0=res.x[0], k1=res.x[1]))
ax[col].set_title(perceptual_headers[col+6].split('/')[1 if col==1 else 0])
ax[col].set_xlim(0,100)
ax[col].set_ylim(0,50)
if col == 17:
ax[col].set_xlabel('Mean')
if col == 7:
ax[col].set_ylabel('StDev')
plt.tight_layout()
# -
plt.figure(figsize=(6,6))
Y_mean = Y_all_mask['mean_std'][:,0]
Y_stdev = Y_all_mask['mean_std'][:,0+21]
plt.scatter(Y_mean,Y_stdev,color='black')
plt.xlabel('Mean Rating',size=18)
plt.ylabel('StDev of Rating',size=18)
plt.xticks(np.arange(0,101,20),size=15)
plt.yticks(np.arange(0,51,10),size=15)
plt.xlim(0,100)
plt.ylim(0,50)
res = minimize(sse, x, args=(Y_mean,Y_stdev), method='L-BFGS-B')
plt.plot(x_,f_transformation(x_, k0=res.x[0], k1=res.x[1]),color='cyan',linewidth=5)
plt.title('INTENSITY',size=18)
# +
# Load optimal parameters (obtained from extensive cross-validation).
cols = range(42)
def get_params(i):
return {col:params.best[col][i] for col in cols}
use_et = get_params(0)
max_features = get_params(1)
max_depth = get_params(2)
min_samples_leaf = get_params(3)
trans_weight = get_params(4)
regularize = get_params(4)
use_mask = get_params(5)
for col in range(21):
trans_weight[col] = trans_weight[col+21]
# -
# ### Fitting and Generating Submission Files for challenge 2
# Fit training data.
# Ignoring warning that arises if too few trees are used.
# Ignore intensity score which is based on within-sample validation,
# due to use of ExtraTreesClassifier.
n_estimators = 1000
rfcs_leaderboard,score,rs = fit2.rfc_final(X_training,Y_training_imp['mean_std'],
Y_training_mask['mean_std'],max_features,
min_samples_leaf,max_depth,use_et,use_mask,
trans_weight,trans_params,
n_estimators=n_estimators)
# Make challenge 2 leaderboard prediction files from the models.
loading.make_prediction_files(rfcs_leaderboard,X_leaderboard_int,X_leaderboard_other,
'leaderboard',2,Y_test=Y_leaderboard_noimpute,
write=True,trans_weight=trans_weight,trans_params=trans_params)
# Fit all available data.
# Ignoring warning that arises if too few trees are used.
# Ignore intensity score which is based on within-sample validation,
# due to use of ExtraTreesClassifier.
rfcs,score,rs = fit2.rfc_final(X_all,Y_all_imp['mean_std'],Y_all_mask['mean_std'],
max_features,min_samples_leaf,max_depth,use_et,use_mask,
trans_weight,trans_params,n_estimators=n_estimators)
# Make challenge 2 testset prediction files from the models.
loading.make_prediction_files(rfcs,X_testset_int,X_testset_other,'testset',2,write=True,
trans_weight=trans_weight,trans_params=trans_params)
# Fit training data for subchallenge 1.
# Ignoring warning that arises if too few trees are used.
# Ignore intensity score which is based on within-sample validation,
# due to use of ExtraTreesClassifier.
n_estimators = 50
rfcs_leaderboard,score,rs = fit1.rfc_final(X_training,Y_training_imp['subject'],max_features,
min_samples_leaf,max_depth,use_et,
Y_test=Y_leaderboard_noimpute['subject'],
regularize=regularize,
n_estimators=n_estimators)
# Make challenge 1 leaderboard prediction files from the models.
loading.make_prediction_files(rfcs_leaderboard,X_leaderboard_int,X_leaderboard_other,
'leaderboard',1,Y_test=Y_leaderboard_noimpute,
write=True,regularize=regularize)
# Fit all available data for subchallenge 1.
# Ignoring warning that arises if too few trees are used.
# Ignore intensity score which is based on within-sample validation,
# due to use of ExtraTreesClassifier.
rfcs1,score1,rs1 = fit1.rfc_final(X_all,Y_all_imp['subject'],max_features,
min_samples_leaf,max_depth,use_et,
regularize=regularize,
n_estimators=n_estimators)
# Make challenge 1 testset prediction files from the models.
loading.make_prediction_files(rfcs1,X_testset_int,X_testset_other,
'testset',1,write=True,regularize=regularize)
| 15,474 |
/demo/DiaParser.ipynb | 0ae37f76c1a183155c42bd0bb972611a036796b8 | [
"MIT"
] | permissive | mbaxamb3333/diaparser | https://github.com/mbaxamb3333/diaparser | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 10,645 | # ---
# jupyter:
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# extension: .py
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# display_name: Python 3
# language: python
# name: python3
# ---
# # DiaParser
# **Direct Attentive Dependency Parser**
from diaparser.parsers import Parser
# ### Create a parser
# Load a pretrained model for English, named `en_ewt.electra-base`, i.e. a parser trained on the English EWT treebank, using the transformner model `electra-base-disciminator`.
#
# The model will be downloaded anc cached locally for further use.
parser = Parser.load('en_ewt.electra-base')
# You may parse plain text, by telling the language used:
dataset = parser.predict('She enjoys playing tennis.', text='en')
# `dataset` is an instance of `diaparser.utils.Dataset` containing the predicted syntactic trees.
#
# Let's look at the first one:
dataset.sentences[0]
# ## Display parse tree
from spacy import displacy
sent = dataset.sentences[0]
displacy.render(sent.to_json(), style='dep', manual=True, options={'compact': True, 'distance': 120, 'word_spacing': 20})
# ## Parse from tokenized text
# Or you can provide tokenized text, as weel ask to see the estimated probabiity for each predicted arc:
dataset = parser.predict(['She', 'enjoys', 'playing', 'tennis', '.'], prob=True)
# You may then look at individual fields of the tokens in a sentence and the probability of their arcs.
import torch
print(f"arcs: {dataset.arcs[0]}\n"
f"rels: {dataset.rels[0]}\n"
f"probs: {dataset.probs[0].gather(1,torch.tensor(dataset.arcs[0]).unsqueeze(1)).squeeze(-1)}")
| 1,672 |
/Day_008_HW.ipynb | 1f20e4f8598b3c20a9d037f2693c79264d67928c | [] | no_license | william10021002/ML100Days | https://github.com/william10021002/ML100Days | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 23,966 | # ---
# jupyter:
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# ---
# + [markdown] colab_type="text" id="5hIbr52I7Z7U"
# Deep Learning
# =============
#
# Assignment 1
# ------------
#
# The objective of this assignment is to learn about simple data curation practices, and familiarize you with some of the data we'll be reusing later.
#
# This notebook uses the [notMNIST](http://yaroslavvb.blogspot.com/2011/09/notmnist-dataset.html) dataset to be used with python experiments. This dataset is designed to look like the classic [MNIST](http://yann.lecun.com/exdb/mnist/) dataset, while looking a little more like real data: it's a harder task, and the data is a lot less 'clean' than MNIST.
# + cellView="both" colab={"autoexec": {"startup": false, "wait_interval": 0}} colab_type="code" id="apJbCsBHl-2A"
# These are all the modules we'll be using later. Make sure you can import them
# before proceeding further.
from __future__ import print_function
import matplotlib.pyplot as plt
import numpy as np
import os
import sys
import tarfile
from IPython.display import display, Image
from scipy import ndimage
from sklearn.linear_model import LogisticRegression
from six.moves.urllib.request import urlretrieve
from six.moves import cPickle as pickle
# Config the matplotlib backend as plotting inline in IPython
# %matplotlib inline
# + [markdown] colab_type="text" id="jNWGtZaXn-5j"
# First, we'll download the dataset to our local machine. The data consists of characters rendered in a variety of fonts on a 28x28 image. The labels are limited to 'A' through 'J' (10 classes). The training set has about 500k and the testset 19000 labelled examples. Given these sizes, it should be possible to train models quickly on any machine.
# + cellView="both" colab={"autoexec": {"startup": false, "wait_interval": 0}, "output_extras": [{"item_id": 1}]} colab_type="code" executionInfo={"elapsed": 186058, "status": "ok", "timestamp": 1444485672507, "user": {"color": "#1FA15D", "displayName": "Vincent Vanhoucke", "isAnonymous": false, "isMe": true, "permissionId": "05076109866853157986", "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg", "sessionId": "2a0a5e044bb03b66", "userId": "102167687554210253930"}, "user_tz": 420} id="EYRJ4ICW6-da" outputId="0d0f85df-155f-4a89-8e7e-ee32df36ec8d"
url = 'http://commondatastorage.googleapis.com/books1000/'
last_percent_reported = None
data_root = '.' # Change me to store data elsewhere
def download_progress_hook(count, blockSize, totalSize):
"""A hook to report the progress of a download. This is mostly intended for users with
slow internet connections. Reports every 5% change in download progress.
"""
global last_percent_reported
percent = int(count * blockSize * 100 / totalSize)
if last_percent_reported != percent:
if percent % 5 == 0:
sys.stdout.write("%s%%" % percent)
sys.stdout.flush()
else:
sys.stdout.write(".")
sys.stdout.flush()
last_percent_reported = percent
def maybe_download(filename, expected_bytes, force=False):
"""Download a file if not present, and make sure it's the right size."""
dest_filename = os.path.join(data_root, filename)
if force or not os.path.exists(dest_filename):
print('Attempting to download:', filename)
filename, _ = urlretrieve(url + filename, dest_filename, reporthook=download_progress_hook)
print('\nDownload Complete!')
statinfo = os.stat(dest_filename)
if statinfo.st_size == expected_bytes:
print('Found and verified', dest_filename)
else:
raise Exception(
'Failed to verify ' + dest_filename + '. Can you get to it with a browser?')
return dest_filename
train_filename = maybe_download('notMNIST_large.tar.gz', 247336696)
test_filename = maybe_download('notMNIST_small.tar.gz', 8458043)
# + [markdown] colab_type="text" id="cC3p0oEyF8QT"
# Extract the dataset from the compressed .tar.gz file.
# This should give you a set of directories, labelled A through J.
# + cellView="both" colab={"autoexec": {"startup": false, "wait_interval": 0}, "output_extras": [{"item_id": 1}]} colab_type="code" executionInfo={"elapsed": 186055, "status": "ok", "timestamp": 1444485672525, "user": {"color": "#1FA15D", "displayName": "Vincent Vanhoucke", "isAnonymous": false, "isMe": true, "permissionId": "05076109866853157986", "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg", "sessionId": "2a0a5e044bb03b66", "userId": "102167687554210253930"}, "user_tz": 420} id="H8CBE-WZ8nmj" outputId="ef6c790c-2513-4b09-962e-27c79390c762"
num_classes = 10
np.random.seed(133)
def maybe_extract(filename, force=False):
root = os.path.splitext(os.path.splitext(filename)[0])[0] # remove .tar.gz
if os.path.isdir(root) and not force:
# You may override by setting force=True.
print('%s already present - Skipping extraction of %s.' % (root, filename))
else:
print('Extracting data for %s. This may take a while. Please wait.' % root)
tar = tarfile.open(filename)
sys.stdout.flush()
tar.extractall(data_root)
tar.close()
data_folders = [
os.path.join(root, d) for d in sorted(os.listdir(root))
if os.path.isdir(os.path.join(root, d))]
if len(data_folders) != num_classes:
raise Exception(
'Expected %d folders, one per class. Found %d instead.' % (
num_classes, len(data_folders)))
print(data_folders)
return data_folders
train_folders = maybe_extract(train_filename)
test_folders = maybe_extract(test_filename)
# + [markdown] colab_type="text" id="4riXK3IoHgx6"
# ---
# Problem 1
# ---------
#
# Let's take a peek at some of the data to make sure it looks sensible. Each exemplar should be an image of a character A through J rendered in a different font. Display a sample of the images that we just downloaded. Hint: you can use the package IPython.display.
#
# ---
# -
import random
for j in xrange(4):
directory = train_folders[random.randint(0,len(train_folders))]
files = random.sample(os.listdir(directory),3)
for picture in files[:3]:
display(Image(directory+'/'+picture))
# + [markdown] colab_type="text" id="PBdkjESPK8tw"
# Now let's load the data in a more manageable format. Since, depending on your computer setup you might not be able to fit it all in memory, we'll load each class into a separate dataset, store them on disk and curate them independently. Later we'll merge them into a single dataset of manageable size.
#
# We'll convert the entire dataset into a 3D array (image index, x, y) of floating point values, normalized to have approximately zero mean and standard deviation ~0.5 to make training easier down the road.
#
# A few images might not be readable, we'll just skip them.
# + cellView="both" colab={"autoexec": {"startup": false, "wait_interval": 0}, "output_extras": [{"item_id": 30}]} colab_type="code" executionInfo={"elapsed": 399874, "status": "ok", "timestamp": 1444485886378, "user": {"color": "#1FA15D", "displayName": "Vincent Vanhoucke", "isAnonymous": false, "isMe": true, "permissionId": "05076109866853157986", "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg", "sessionId": "2a0a5e044bb03b66", "userId": "102167687554210253930"}, "user_tz": 420} id="h7q0XhG3MJdf" outputId="92c391bb-86ff-431d-9ada-315568a19e59"
image_size = 28 # Pixel width and height.
pixel_depth = 255.0 # Number of levels per pixel.
def load_letter(folder, min_num_images):
"""Load the data for a single letter label."""
image_files = os.listdir(folder)
dataset = np.ndarray(shape=(len(image_files), image_size, image_size),
dtype=np.float32)
print(folder)
num_images = 0
for image in image_files:
image_file = os.path.join(folder, image)
try:
image_data = (ndimage.imread(image_file).astype(float) -
pixel_depth / 2) / pixel_depth
if image_data.shape != (image_size, image_size):
raise Exception('Unexpected image shape: %s' % str(image_data.shape))
dataset[num_images, :, :] = image_data
num_images = num_images + 1
except IOError as e:
print('Could not read:', image_file, ':', e, '- it\'s ok, skipping.')
dataset = dataset[0:num_images, :, :]
if num_images < min_num_images:
raise Exception('Many fewer images than expected: %d < %d' %
(num_images, min_num_images))
print('Full dataset tensor:', dataset.shape)
print('Mean:', np.mean(dataset))
print('Standard deviation:', np.std(dataset))
return dataset
def maybe_pickle(data_folders, min_num_images_per_class, force=False):
dataset_names = []
for folder in data_folders:
set_filename = folder + '.pickle'
dataset_names.append(set_filename)
if os.path.exists(set_filename) and not force:
# You may override by setting force=True.
print('%s already present - Skipping pickling.' % set_filename)
else:
print('Pickling %s.' % set_filename)
dataset = load_letter(folder, min_num_images_per_class)
try:
with open(set_filename, 'wb') as f:
pickle.dump(dataset, f, pickle.HIGHEST_PROTOCOL)
except Exception as e:
print('Unable to save data to', set_filename, ':', e)
return dataset_names
train_datasets = maybe_pickle(train_folders, 45000)
test_datasets = maybe_pickle(test_folders, 1800)
# + [markdown] colab_type="text" id="vUdbskYE2d87"
# ---
# Problem 2
# ---------
#
# Let's verify that the data still looks good. Displaying a sample of the labels and images from the ndarray. Hint: you can use matplotlib.pyplot.
#
# ---
# + [markdown] colab_type="text" id="cYznx5jUwzoO"
# ---
# Problem 3
# ---------
# Another check: we expect the data to be balanced across classes. Verify that.
#
# ---
# + [markdown] colab_type="text" id="LA7M7K22ynCt"
# Merge and prune the training data as needed. Depending on your computer setup, you might not be able to fit it all in memory, and you can tune `train_size` as needed. The labels will be stored into a separate array of integers 0 through 9.
#
# Also create a validation dataset for hyperparameter tuning.
# + cellView="both" colab={"autoexec": {"startup": false, "wait_interval": 0}, "output_extras": [{"item_id": 1}]} colab_type="code" executionInfo={"elapsed": 411281, "status": "ok", "timestamp": 1444485897869, "user": {"color": "#1FA15D", "displayName": "Vincent Vanhoucke", "isAnonymous": false, "isMe": true, "permissionId": "05076109866853157986", "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg", "sessionId": "2a0a5e044bb03b66", "userId": "102167687554210253930"}, "user_tz": 420} id="s3mWgZLpyuzq" outputId="8af66da6-902d-4719-bedc-7c9fb7ae7948"
def make_arrays(nb_rows, img_size):
if nb_rows:
dataset = np.ndarray((nb_rows, img_size, img_size), dtype=np.float32)
labels = np.ndarray(nb_rows, dtype=np.int32)
else:
dataset, labels = None, None
return dataset, labels
def merge_datasets(pickle_files, train_size, valid_size=0):
num_classes = len(pickle_files)
valid_dataset, valid_labels = make_arrays(valid_size, image_size)
train_dataset, train_labels = make_arrays(train_size, image_size)
vsize_per_class = valid_size // num_classes
tsize_per_class = train_size // num_classes
start_v, start_t = 0, 0
end_v, end_t = vsize_per_class, tsize_per_class
end_l = vsize_per_class+tsize_per_class
for label, pickle_file in enumerate(pickle_files):
try:
with open(pickle_file, 'rb') as f:
letter_set = pickle.load(f)
# let's shuffle the letters to have random validation and training set
np.random.shuffle(letter_set)
if valid_dataset is not None:
valid_letter = letter_set[:vsize_per_class, :, :]
valid_dataset[start_v:end_v, :, :] = valid_letter
valid_labels[start_v:end_v] = label
start_v += vsize_per_class
end_v += vsize_per_class
train_letter = letter_set[vsize_per_class:end_l, :, :]
train_dataset[start_t:end_t, :, :] = train_letter
train_labels[start_t:end_t] = label
start_t += tsize_per_class
end_t += tsize_per_class
except Exception as e:
print('Unable to process data from', pickle_file, ':', e)
raise
return valid_dataset, valid_labels, train_dataset, train_labels
train_size = 200000
valid_size = 10000
test_size = 10000
valid_dataset, valid_labels, train_dataset, train_labels = merge_datasets(
train_datasets, train_size, valid_size)
_, _, test_dataset, test_labels = merge_datasets(test_datasets, test_size)
print('Training:', train_dataset.shape, train_labels.shape)
print('Validation:', valid_dataset.shape, valid_labels.shape)
print('Testing:', test_dataset.shape, test_labels.shape)
# + [markdown] colab_type="text" id="GPTCnjIcyuKN"
# Next, we'll randomize the data. It's important to have the labels well shuffled for the training and test distributions to match.
# + cellView="both" colab={"autoexec": {"startup": false, "wait_interval": 0}} colab_type="code" id="6WZ2l2tN2zOL"
def randomize(dataset, labels):
permutation = np.random.permutation(labels.shape[0])
shuffled_dataset = dataset[permutation,:,:]
shuffled_labels = labels[permutation]
return shuffled_dataset, shuffled_labels
train_dataset, train_labels = randomize(train_dataset, train_labels)
test_dataset, test_labels = randomize(test_dataset, test_labels)
valid_dataset, valid_labels = randomize(valid_dataset, valid_labels)
# + [markdown] colab_type="text" id="puDUTe6t6USl"
# ---
# Problem 4
# ---------
# Convince yourself that the data is still good after shuffling!
#
# ---
# + [markdown] colab_type="text" id="tIQJaJuwg5Hw"
# Finally, let's save the data for later reuse:
# + cellView="both" colab={"autoexec": {"startup": false, "wait_interval": 0}} colab_type="code" id="QiR_rETzem6C"
pickle_file = os.path.join(data_root, 'notMNIST.pickle')
try:
f = open(pickle_file, 'wb')
save = {
'train_dataset': train_dataset,
'train_labels': train_labels,
'valid_dataset': valid_dataset,
'valid_labels': valid_labels,
'test_dataset': test_dataset,
'test_labels': test_labels,
}
pickle.dump(save, f, pickle.HIGHEST_PROTOCOL)
f.close()
except Exception as e:
print('Unable to save data to', pickle_file, ':', e)
raise
# + cellView="both" colab={"autoexec": {"startup": false, "wait_interval": 0}, "output_extras": [{"item_id": 1}]} colab_type="code" executionInfo={"elapsed": 413065, "status": "ok", "timestamp": 1444485899688, "user": {"color": "#1FA15D", "displayName": "Vincent Vanhoucke", "isAnonymous": false, "isMe": true, "permissionId": "05076109866853157986", "photoUrl": "//lh6.googleusercontent.com/-cCJa7dTDcgQ/AAAAAAAAAAI/AAAAAAAACgw/r2EZ_8oYer4/s50-c-k-no/photo.jpg", "sessionId": "2a0a5e044bb03b66", "userId": "102167687554210253930"}, "user_tz": 420} id="hQbLjrW_iT39" outputId="b440efc6-5ee1-4cbc-d02d-93db44ebd956"
statinfo = os.stat(pickle_file)
print('Compressed pickle size:', statinfo.st_size)
# + [markdown] colab_type="text" id="gE_cRAQB33lk"
# ---
# Problem 5
# ---------
#
# By construction, this dataset might contain a lot of overlapping samples, including training data that's also contained in the validation and test set! Overlap between training and test can skew the results if you expect to use your model in an environment where there is never an overlap, but are actually ok if you expect to see training samples recur when you use it.
# Measure how much overlap there is between training, validation and test samples.
#
# Optional questions:
# - What about near duplicates between datasets? (images that are almost identical)
# - Create a sanitized validation and test set, and compare your accuracy on those in subsequent assignments.
# ---
# + [markdown] colab_type="text" id="L8oww1s4JMQx"
# ---
# Problem 6
# ---------
#
# Let's get an idea of what an off-the-shelf classifier can give you on this data. It's always good to check that there is something to learn, and that it's a problem that is not so trivial that a canned solution solves it.
#
# Train a simple model on this data using 50, 100, 1000 and 5000 training samples. Hint: you can use the LogisticRegression model from sklearn.linear_model.
#
# Optional question: train an off-the-shelf model on all the data!
#
# ---
ult_test.drop(drop, axis=1)),
index=X_adult_test.drop(drop, axis=1).index,
columns=X_adult_test.drop(drop, axis=1).columns)['Age_mnar']
# -
#MARの代入前後の分布
plt.figure(figsize=(14, 7))
for i, col in enumerate(['Age_mcar', 'Age_mcar_itr']):
plt.subplot(2, 2, i + 1)
plt.hist(
X_adult_train_fillna['Age'],
alpha=0.7,
label='Complete Data',
bins=range(0, 100, 10))
plt.legend()
plt.hist(X_adult_train_fillna[col], alpha=0.7, bins=range(0, 100, 10))
plt.grid()
plt.title(col)
plt.xticks([0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100])
# ## 数値代入法の選び方
#
# 全体のレコード件数が数万件あり、欠損が数%しかない場合 → 除外を検討しても良い。
# ↓
# 代入を検討する。
# → 時間がない時: 定数
# → 時間に余裕があり、他の特徴量と欠損した特徴量の相関が強い時 : 回帰代入法、K-NN法
# → 時間に余裕があり、欠損値が明らかにMNARではなく、MARを仮定できる時 : 多重代入法
#
# 欠損値について
# - 上記の代入法ではMNARに対しては、完全な補完は不可能。
# - 欠損が起きる発生メカニズムの過半数が、MCARもしくはMAR（MCAR=10~20%.MAR=50%.その他がMNCR)
#
# 欠損の発生メカニズムはどの種類かを判断するのは容易ではないので、
# 使用できるデータを学習データと検証データに分けて色々な欠損代入法を試し、検証精度が高くなるように検証する必要がある。
#
#
# 下記、検証方法
# +
#MCARの代入法ごとのモデル精度比較
#精度を格納するリスト
l_tr = []
l_te = []
for age in ['Age'] + [col for col in X_adult_train_fillna.columns if col.find('Age_mar_') > -1]:
#予測に必要な特徴量 'fnlwgt'は重み変数のため除外
var = [col for col in X_adult_train.columns
if col.find('Age') == -1 and col.find('fnlwgt') == -1] + [age]
print("age:",age,"\n var:", var)
print()
#ロジスティック回帰インスタンス化
reg = LogisticRegression(solver='newton-cg')
#モデル学習
reg.fit(X_adult_train_fillna[var], y_adult_train)
#訓練データでの精度検証
l_tr.append(
accuracy_score(y_adult_train, reg.predict(X_adult_train_fillna[var])))
#検証データでの精度検証
l_te.append(
accuracy_score(y_adult_test, reg.predict(X_adult_test_fillna[var])))
plt.figure(figsize=(14, 7))
plt.plot(
['Age'] +
[col for col in X_adult_train_fillna.columns if col.find('Age_mar_') >-1],
l_tr,
label='Train')
plt.plot(l_te, label='Test')
plt.legend()
plt.title('Impute MCAR')
plt.ylim(0.845,0.855)
# -
# ## 欠損カテゴリを新たに作成する。
# 機械学習では説明を要する場合に欠損によるバイアスが問題となりますが、
# 欠損としてコテごり分けすることで精度を大きく上げれるので、新たにカテゴリを作る方法も良い方法と思う。
# +
from numpy.random import *
seed(100)
df_adult = df_adult_m.copy()
#Raceで欠損を作成する
#全体の欠損率が20%、女性の欠損率が30%となるように設定
p = 0.2
p_f = 0.3
p_m = (round(len(df_adult) * p) - df_adult.Gender.value_counts()[1] * p_f) / df_adult.Gender.value_counts()[0]
#男性
ix = df_adult['Gender'] == ' Male'
seed(200)
x = pd.Series(np.random.uniform(0.0, 1.0, len(df_adult)))
x.index = df_adult.index
df_adult['Race_mar'] = np.where((x < p_m) & (ix), np.nan, df_adult['Race'])
#女性
ix = df_adult['Gender'] == ' Male'
x = pd.Series(np.random.uniform(0.0, 1.0, len(df_adult)))
x.index = df_adult.index
df_adult['Race_mar'] = np.where((x < p_f) & (~ix), np.nan, df_adult['Race_mar'])
#欠損を適当なカテゴリ名（"Missing")という文字列で埋める
df_adult['Race_mar'].fillna('Missing', inplace=True)
#集計
print(df_adult['Race_mar'].value_counts())
# -
#One-Hotエンコーディング インスタンス化
encoder = ce.OneHotEncoder(cols=['Race_mar'], use_cat_names=True)
enc = encoder.fit(df_adult)
df_adult = enc.transform(df_adult)
df_adult.head()
# # Chapter 2-7 データスケーリング
# 異なった尺度を揃える処理をスケーリングと呼びます。
import pandas as pd
import numpy as np
from pandas import DataFrame
#preprosess モジュールのインポート
from sklearn import preprocessing
#データ分割用
from sklearn.model_selection import train_test_split
#ロジスティック回帰モデル作成のため
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
#可視化のため
import seaborn as sns
import matplotlib.pyplot as plt
#データ読み込み
df = pd.read_csv('./genbapro_preprocessing/chapter2-data/UCI_Credit_Card.csv')
#要約確認
df.describe().T
#train test データ分離
X = df.drop(['ID', 'SEX', 'EDUCATION', 'MARRIAGE', 'AGE','default.payment.next.month'],axis=1)
y = df['default.payment.next.month']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
# ## Min-Max法
# +
#MinMaxScalerのインスタンス化
min_max_scaler = preprocessing.MinMaxScaler()
# train
X_train_min_max = min_max_scaler.fit_transform(X_train)
# test trainを基準にしないとリークしてしまう
X_test_min_max = min_max_scaler.transform(X_test)
#分布描画
fontdic = {'size': 20}
plt.figure(figsize=(14, 4))
#変換前
plt.subplot(1, 2, 1)
plt.hist(X_train['BILL_AMT1'])
plt.title('Before', fontdict=fontdic)
plt.grid()
#変換後
plt.subplot(1, 2, 2)
plt.hist(X_train_min_max[:, 8], color='red')
plt.title('After', fontdict=fontdic)
plt.grid()
# -
# ## Zスコア標準化
# 特徴量を平均０分散となるように変換します。
# +
#zスコア変換
standard_scaler = preprocessing.StandardScaler()
# train
X_train_standard = standard_scaler.fit_transform(X_train)
# test trainを基準にしないとリークしてしまう
X_test_standard = standard_scaler.transform(X_test)
#分布描画
fontdic = {'size': 20}
plt.figure(figsize=(14, 4))
#変換前
plt.subplot(1, 2, 1)
plt.hist(X_train['BILL_AMT1'])
plt.title('Before', fontdict=fontdic)
plt.grid()
#変換後
plt.subplot(1, 2, 2)
plt.hist(X_train_standard[:, 8], color='red')
plt.title('After', fontdict=fontdic)
plt.grid()
# -
# ## 10進スケールの正規化
# 対象変数の絶対値が最大値が１以下になるように１０の乗数で除する方法です。
# 該当する関数がないので関数を定義
# 10進法の変換関数
def decimal_fit_trans(x):
#絶対値の最大値をカラムごとに取得
temp = np.nanmax(np.abs(x.values),axis=0)
print("temp", temp)
arr = np.array([])
#カラムごとに除する定数を取得
for t in temp:
i = 0
while t/10**(i) > 1:
i += 1
# np.r_はhstack、np.c_はvstackと同様の使い方をする。
arr = np.r_[arr,1/10**(i)]
return (arr)
X_train_decimal = X_train*decimal_fit_trans(X_train)
X_test_decimal = X_test*decimal_fit_trans(X_train)
#分布描画
fontdic = {'size':20}
plt.figure(figsize=(14,4))
#変換前
plt.subplot(1,2,1)
plt.hist(X_train['BILL_AMT1'])
plt.title('Before',fontdict=fontdic)
plt.grid()
#変換後
plt.subplot(1,2,2)
plt.hist(X_train_decimal['BILL_AMT1'],color='red')
plt.title('After',fontdict=fontdic)
plt.grid()
# ## スケーリング方法比較
#変換後比較
plt.figure(figsize=(14, 10))
for i, col in enumerate(X_train.columns):
plt.subplot(5, 4, i + 1)
plt.hist(
pd.DataFrame(X_train_min_max, columns=X_train.columns)[col],
color='blue',
alpha=0.5,
label='Min-Max')
plt.hist(
pd.DataFrame(X_train_standard, columns=X_train.columns)[col],
color='green',
alpha=0.5,
label='Standard')
plt.hist(X_train_decimal[col], color='red', alpha=0.5, label='Decimal')
plt.title(col)
plt.legend()
#モデルの生成
clf = LogisticRegression()
# 精度を格納するリスト
l = []
# 学習
#スケーリングなし
clf.fit(X_train, y_train)
l.append(accuracy_score(y_test, clf.predict(X_test)))
#min_max変換
clf.fit(X_train_min_max, y_train)
l.append(accuracy_score(y_test, clf.predict(X_test_min_max)))
#zスコア変換
clf.fit(X_train_standard, y_train)
l.append(accuracy_score(y_test, clf.predict(X_test_standard)))
#10進変換
clf.fit(X_train_decimal, y_train)
l.append(accuracy_score(y_test, clf.predict(X_test_decimal)))
# 精度一覧
print(l)
# # Chapter 2-8 データ変換
# ## 線形変換
# 変数のグループで線型結合し新たな変数とします。
# 通常の回帰モデルやロジスティック回帰などの一般化線形モデルはリンク関数を通して、
# 変数に対して線形性を持っているので、変数の削減以上の効果は期待できない。
# 一方、データが周期性を持つ時系列の性質を持っている場合は有効になります。
#
# ## 二次型変換
# 直線のデータだけが上手くいくとは限らないなので、その時はこの方法を試す。
# +
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
#人工データを作成
from sklearn.datasets import make_circles
X, y = make_circles(noise=0.02, random_state=0) #ラベル付き円を作成
plt.figure(figsize=(5, 5))
plt.scatter(X[:, 0], X[:, 1], c=y, cmap='winter')
# +
# ロジスティック回帰で分類
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
#x軸y軸を説明変数としてロジスティック回帰を適用
clf = LogisticRegression()
X_train = X
y_train = y
clf.fit(X_train, y_train)
accuracy_score(y_train, clf.predict(X_train))
# -
#2次変換
#モデルの生成
clf_q = LogisticRegression()
Z = (X[:, 0]**(2) + X[:, 1]**(2)).reshape(-1, 1)
X_train = Z
y_train = y
clf_q.fit(X_train, y_train)
accuracy_score(y_train, clf_q.predict(X_train))
clf.coef_[:,1]
# +
plt.figure(figsize=(5,5))
#通常の方法
x_=np.linspace(-1,1,100)
plt.scatter(X[:,0],X[:,1],c=y,cmap='winter')
#log(p/(1-p)=1/2となる線を描画する（coef_:回帰変数、 intercept_ : 切片)
plt.plot(x_,(-1*(clf.coef_[:,0]*x_)-clf.intercept_)/clf.coef_[:,1])
plt.ylim([-1,1])
#2次変換
theta = np.arange(0,2*np.pi+1,0.1)
xlist=[];ylist=[]
r = ((clf_q.intercept_/(-1*clf_q.coef_))**(1/2))[0,0] #半径
for i in theta:
xlist.append(r*np.cos(i))
ylist.append(r*np.sin(i))
plt.plot(xlist,ylist)
# -
# ## 変換の非多項式近似
# - 比例変数 : 距離と時間から速さ
# - GPSから得られる緯度経度データから移動距離を算出
# - 三角形の三点データから同じものか異なるかを分類
#
# 様々な問題があり、ドメインの知識が重要になってくる。
# ## ランク変換
# 変数の値そのものではなく、その値が観測されたものの中での順位を用いて変数を変換する方法です。
#
# 作業手順
# 1.全ての値に対して代償の順位を付けます。
# 2.累積分布関数の逆関数を使い、求めた順位を正規分布に従うように変換します。
#
# 回帰モデルなどの適用するときに有効。 (決定技などのモデルではなんの影響も与えません。）
#人工的にデータを作成
#y ~ N(3x+1,0.2) に従う(x in [0,1])
np.random.seed(seed=1)
x = np.random.rand(100)
y = np.random.normal(3 * x + 1, 0.2)
#xの外れ値を作成
x = np.where(x == max(x), 3, x)
plt.scatter(x=x, y=y)
plt.grid()
#ランク算出のためのライブラリインポート
from scipy.stats import rankdata
#正規分布の累積分布関数の逆関数を計算するためにインポート
from scipy.stats import norm
#ランク変換関数定義
def rank_fit_transform(x):
# norm.ppfの引数にはパーセンテージを取り、そのパーセンテージが発生する値の標準偏差乗数を返します。
return (norm.ppf((rankdata(x) - (3 / 8)) / (len(x) + (1 / 4))))
# +
#線形回帰モデルの準備
from sklearn import linear_model
#ランク変換前
clf = linear_model.LinearRegression()
X = x.reshape(-1, 1)
y = y
clf.fit(X, y)
clf.score(X, y)
#ランク変換後
clf_r = linear_model.LinearRegression()
X_r = rank_fit_transform(x).reshape(-1, 1)
y = y
clf_r.fit(X_r, y)
clf_r.score(X_r, y)
#決定係数
print("変換前_{:.5g}".format(clf.score(X, y)), "変換後_{:.5g}".format(
clf_r.score(X_r, y)))
# -
#直線当てはめを描画
plt.figure(figsize=(14, 7))
#変換なし
plt.subplot(1,2,1)
plt.scatter(X, y)
plt.scatter(X, clf.predict(X), s=5) # 線の描写
plt.grid()
plt.title('Before')
#ランク変換後
plt.subplot(1,2,2)
plt.scatter(X_r, y)
plt.scatter(X_r, clf_r.predict(X_r), s=5) # 線の描写
plt.grid()
plt.title('After')
# ## Box-Cox変換 (scipyを使用)
# 1/2乗したり対数を取ったりする変数を一般化したもの。
# 右に長いテールを持つ分布や目的変数との非戦毛な関係を記述するために使います。
#
# 代表的な方法にLOG変換などがある。
# +
#必要なライブラリ読み込む
from scipy import stats #boxcox変換のため
from sklearn import linear_model #線形回帰モデル
from sklearn.model_selection import train_test_split #訓練データ分割
#UCI よりレンタル自転車のデータを読み込む
df = pd.read_csv('./genbapro_preprocessing/chapter2-data/hour.csv')
#描画準備
plt.figure(figsize=(14, 4))
#cntの変換前ヒストグラム
plt.subplot(1, 2, 1)
plt.hist(df['cnt'])
plt.title('Before')
plt.grid()
#box-cox変換後の分布
plt.subplot(1, 2, 2)
bc, _ = stats.boxcox(df['cnt'])
plt.hist(bc)
plt.title('After')
plt.grid()
# +
#データを訓練データとテストデータに分離
X = df['temp'].values
y = df['cnt'].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
#boxcox変換
y_train_bc, lambd = stats.boxcox(y_train)
#変換前回帰
clf = linear_model.LinearRegression()
clf.fit(X_train.reshape(-1, 1), y_train.reshape(-1, 1))
#変換後回帰
clf_bc = linear_model.LinearRegression()
clf_bc.fit(X_train.reshape(-1, 1), y_train_bc.reshape(-1, 1))
# +
#残差プロット
#比較用y=0の線
x = np.arange(0, 20)
y = 0 * x
plt.figure(figsize=(14, 4))
#変換前
plt.subplot(1,2,1)
plt.scatter(
clf_bc.predict(X_train.reshape(-1, 1)),
y_train.reshape(-1, 1) - clf.predict(X_train.reshape(-1, 1)))
plt.plot(x, y, color='red')
plt.title('Before')
#変換後
plt.subplot(1,2,2)
plt.scatter(
clf_bc.predict(X_train.reshape(-1, 1)),
y_train_bc.reshape(-1, 1) - clf_bc.predict(X_train.reshape(-1, 1)))
plt.plot(x, y, color='red')
plt.title('After')
# -
# # Chapter 2-9 次元削除法
# 特徴量が増える事は機械学習にとって説得力が増す良い傾向にあるが、
# 高次元の世界では低次元の直感に反することが起こる。
# - データが巨大で計算量が大きくなる。
# - 次元の呪いで上手く学習できない。
# ## 主成分分析（PCA)
# 相関にある特徴量同士を合成して、なるべく情報量を失う事なく次元を削除する方法。
# 次元削除の目安として、削除前の次元の80%以上の情報量が得られること。
from sklearn.decomposition import PCA #主成分分析
#ESTAT 県民総生産 (2015)データ 読み込む
df = pd.read_csv('./genbapro_preprocessing/chapter2-data/FEI_PREF_2015.csv').set_index('地域')
#データ中身確認
df.head().T
# +
#PCA
pca = PCA()
pca.fit(df[['情報通信業', '金融・保険業']])
#図示する
fig = plt.figure(figsize=(16, 7))
ax1 = fig.add_subplot(1, 2, 1)
ax2 = fig.add_subplot(1, 2, 2)
ax1.scatter(df['情報通信業'], df['金融・保険業'])
ax2.scatter(df['情報通信業'], df['金融・保険業'])
#第一主成分の軸を図示
y = (pca.components_[0][1] / pca.components_[0][0]) * (
np.linspace(0, 0.15, 100) - np.mean(df['情報通信業'])) + np.mean(df['金融・保険業'])
ax2.plot(np.linspace(0, 0.15, 100), y, color='red')
#第二主成分の軸を図示
y = (pca.components_[1][1] / pca.components_[1][0]) * (
np.linspace(0, 0.15, 100) - np.mean(df['情報通信業'])) + np.mean(df['金融・保険業'])
ax2.plot(np.linspace(0, 0.15, 100), y, color='red')
#中心を赤でplot
ax2.scatter(np.mean(df['情報通信業']), np.mean(df['金融・保険業']), color='red')
#目盛の範囲を調整
ax1.set_ylim(-0.05, 0.1)
ax2.set_ylim(-0.05, 0.1)
# -
#新しい直交座標系でプロット
fig = plt.figure(figsize=(14, 7))
ax = fig.add_subplot(1, 1, 1)
#元の系列
ax.scatter(
pca.transform(df[['情報通信業', '金融・保険業']])[:, 0],
pca.transform(df[['情報通信業', '金融・保険業']])[:, 1])
#第一主成分の座標
x = pca.transform(df[['情報通信業', '金融・保険業']])[:, 0]
y = x * 0
ax.scatter(x, y)
ax.plot(
np.linspace(-0.02, 0.1, 100),
np.linspace(-0.02, 0.02, 100) * 0,
color='red')
#第二主成分の座標
y = pca.transform(df[['情報通信業', '金融・保険業']])[:, 1]
x = y * 0
ax.scatter(x, y)
ax.plot(
np.linspace(-0.02, 0.1, 100) * 0,
np.linspace(-0.02, 0.02, 100),
color='red')
#寄与度
print (pca.explained_variance_ratio_)
# ## 因子分析
# 複数の相関する変数が、ある今日通院しより発生していると考え、この共通因子を求める手法。
# 主成分分析と同様に相関係数行列の固有値の大きさを情報量とみなし、その値が１より大きいものの数を因子数として定めると良い。
# +
#因子分析用のライブラリを読み込む
from factor_analyzer import FactorAnalyzer
import matplotlib.pyplot as plt #可視化のため
import numpy.linalg as LA #固有値計算のため
#ESTAT 県民総生産 (2015)データ 読み込む
df = pd.read_csv('./genbapro_preprocessing/chapter2-data/FEI_PREF_2015.csv').set_index('地域')
U = df
# -
#固有値の大きさ
ev, _ = LA.eig(U.corr()) #相関係数行列の固有値
plt.bar(np.arange(len(ev)), ev) #固有値プロット
plt.plot([1] * 20, color='red') #比較用のy=1の線をプロット
#因子分析のインスタンス作成
fa = FactorAnalyzer(rotation=None, n_factors=5) #回転させない
#モデリング
fa.fit(U)
#因子負荷量 プロット
fig = plt.figure(figsize=(20,20))
#ax = []
for i in np.arange(5):
ax = fig.add_subplot(3,2,i+1)
plt.title('第'+str(i+1)+'因子負荷量')
ax.bar([i[0:2] for i in U.columns],fa.loadings_[:,i]) #ラベルの先頭2文字のみ出力
# ## 多次元尺度構成法
# 距離の外面を用意てデータを低次元にマッピングする手法。
# 連続的な間隔尺度のみでなく、アンケート後段階評価のような順序尺度に対しても適用可能です。
# +
#多次元尺度構成法ライブラリ読み込む
from sklearn.manifold import MDS
from sklearn.datasets import load_wine
import matplotlib.pyplot as plt
#ワインデータ読み込み
data = load_wine()
X = pd.DataFrame(data.data, columns=data.feature_names)
y = data.target
#MDSインスタンス作成しfitさせる
embedding = MDS(
n_components=2, metric=True,
random_state=1) #metricのデフォルトはTrue 間隔尺度であるため計量MDSを実行
X_mds = embedding.fit_transform(X)
# +
#次元ごとのストレス値
l = []
for i in np.arange(len(X.columns)):
embedding = MDS(
n_components=i + 1, metric=True,
random_state=1) #metricのデフォルトはTrue 間隔尺度であるため計量MDSを実行
X_mds = embedding.fit_transform(X)
l.append(embedding.stress_)
plt.plot(np.arange(len(X.columns)) + 1, l, color='red')
plt.xlabel('次元数')
plt.ylabel('ストレス値')
plt.xlim(0, 13)
plt.xticks(np.arange(len(X.columns)) + 1)
# -
# ２次元以降のストレスフリーなので、２次元にマッピングが妥当！！
#2次元に配置する
fig = plt.figure(figsize=(14, 10))
ax = fig.add_subplot(1, 1, 1)
X_mds[y == 0, 0]
for i, j, k in zip(np.arange(3), ['blue', 'red', 'green'], ['^', 's', 'o']):
ax.scatter(X_mds[y == i, 0], X_mds[y == i, 1], color=j, marker=k)
# 局所不可
# # Chapter 2-10 特徴量選択
# 特徴量の中から重要なものを探し、不要なものを取り除くプロセス。
# - モデルの精度向上
# - モデル構築時間の短縮化
# - モデルの可視化、理解の容易化
# ### 特徴量選択の３手法
# - fitter法 計算コストが少ない。
# - Wrapper法 変数間の相互の影響を考慮できる。
# - Embedded法 学習モデルのアルゴリズムを使用。 Wrapperよりは計算コストが少ない。
# +
#データをトレーニング・テストに分けるモジュール
from sklearn.model_selection import train_test_split
#ロジスティック回帰モデル
from sklearn.linear_model import LogisticRegression
#正答率算出
from sklearn.metrics import accuracy_score
#category encoder インポート
import category_encoders as ce
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
#データ 読み込み
df_adult = df_adult_m.copy()
#ターゲット変数1/0エンコーディング
df_adult['Income'] = (df_adult['Income'] == ' >50K').astype(int)
#特徴量取得
X_adult = df_adult.drop(['Income'], axis=1)
#ターゲット変数を取得
y_adult = df_adult['Income']
#訓練・検証データを分離
X_adult_train, X_adult_test, y_adult_train, y_adult_test = train_test_split(
X_adult, y_adult, test_size=0.2, random_state=1)
#ターゲットエンコーダ インスタンス化
encoder = ce.TargetEncoder(cols=['Education', 'Occupation', 'NativeCountry'])
enc = encoder.fit(X_adult_train, y_adult_train)
X_adult_train = enc.transform(X_adult_train)
X_adult_test = enc.transform(X_adult_test)
#One-Hotエンコーディング インスタンス化
encoder = ce.OneHotEncoder(
cols=['WorkClass', 'MaritalStatus', 'Relationship', 'Race', 'Gender'],
use_cat_names=True)
enc = encoder.fit(X_adult_train)
X_adult_train = enc.transform(X_adult_train)
X_adult_test = enc.transform(X_adult_test)
#One-Hotエンコーディングで冗長になった変数を落とす
drop_l = [
[col for col in X_adult_train.columns if col.find('WorkClass') >= 0][0], [
col for col in X_adult_train.columns if col.find('MaritalStatus') >= 0
][0], [
col for col in X_adult_train.columns if col.find('Relationship') >= 0
][0], [col for col in X_adult_train.columns if col.find('Race') >= 0][0],
[col for col in X_adult_train.columns if col.find('Gender') >= 0][0]
]
X_adult_train = X_adult_train.drop(drop_l, axis=1)
X_adult_test = X_adult_test.drop(drop_l, axis=1)
# -
#すべての特徴量
features_all = X_adult_train.columns
print('特徴量 : {} '.format(features_all))
#比較用として全ての特徴量でモデル作成
#フルモデルと比較
reg = LogisticRegression().fit(X_adult_train, y_adult_train)
score_all = accuracy_score(y_adult_test, reg.predict(X_adult_test))
print('訓練 : {} '.format(
accuracy_score(y_adult_train, reg.predict(X_adult_train))))
print('検証 : {} '.format(
accuracy_score(y_adult_test, reg.predict(X_adult_test))))
# ### Filter法
# 単一の特徴量とターゲット変数tの関係の強弱で特徴量を選択。
# モデル学習の前に変数をより分けられるからこの名がついた。
# ターゲット変数と特徴量の何かしらの尺度で評価して遠別する。
#
# - 分散 特徴調のみで判断試合場合。全特徴量のスケールが同じ場合。
# - ピアソンの相関関係 特徴りょう、ターゲット変数が数値の場合。
# - x^2統計量 特徴量、ターゲット編数がカテゴリ軽変数の場合。
# - F値 特徴量が数値orカテゴリ変数、ターゲットヘンスが数値orカテゴリ軽変数の場合。
# +
#単変量Filterモジュール読み込む
from sklearn.feature_selection import SelectKBest, f_regression, f_classif, chi2
#F値でfeature selection filter法
tr_score = [] #訓練スコアを入れるリスト
te_score = [] #検証スコアを入れるリスト
features = [] #特徴量を入れるりスト
#Kごとに精度評価
for i in np.arange(len(X_adult_train.columns)):
sel = SelectKBest(f_classif, k=i + 1).fit(X_adult_train, y_adult_train)
X_train_sel = sel.transform(X_adult_train)
X_test_sel = sel.transform(X_adult_test)
reg = LogisticRegression().fit(X_train_sel, y_adult_train)
tr_score.append(accuracy_score(y_adult_train, reg.predict(X_train_sel)))
te_score.append(accuracy_score(y_adult_test, reg.predict(X_test_sel)))
features.append(X_adult_train.columns[sel.get_support()])
#kごとに精度がどのように変わったか可視化する
plt.figure(figsize=(14, 7))
plt.plot(np.arange(len(X_adult_train.columns)) + 1, tr_score, label='Train')
plt.plot(np.arange(len(X_adult_train.columns)) + 1, te_score, label='Test')
plt.ylabel('Accuracy')
plt.xlabel('The Number of features')
plt.xticks(np.arange(1, 1 + len(X_adult_train.columns)))
plt.legend(fontsize=14)
plt.grid()
plt.title('Feature selection by using F value')
# -
#訓練データの精度最大となるk
print (tr_score.index(np.max(tr_score)))
#訓練データで精度最大になる変数の数と組み合わせ
features_sel_filter = features[tr_score.index(np.max(tr_score))]
print ('特徴量 : {} '.format(features_sel_filter))
print ('訓練 : {} '.format((tr_score[tr_score.index(np.max(tr_score))])))
print ('検証 : {} '.format((te_score[tr_score.index(np.max(tr_score))])))
# ### Warpper法
# 特徴量の集合毎にモデルを作成しその精度を元に特徴量を選択。
# - SFG 空集合からモデル制度のような基準に従って特徴料をつい介していく方法。
# - SBG 特徴量全ての集合から開始し、最も重要ではない変数を一つずつ取り除く方法。
# - stepwise SFGとSBGを同時に行う方法。
#wrapper法実行モジュール読み込む
from mlxtend.feature_selection import SequentialFeatureSelector as SFS
from mlxtend.plotting import plot_sequential_feature_selection as plot_sfs
# ### Sequence Forward Generation
# +
#SFSインスタンス化
sfs = SFS(LogisticRegression(), #利用するモデル
k_features=(1,33), #特徴量の数 1-33個の間でCVスコアベストを探す
forward=True, #フォワード型
floating=False, #フローティングの有無
verbose=2,
scoring='accuracy',#精度指標
cv=5,
n_jobs=-1)
sfs = sfs.fit(X_adult_train, y_adult_train)
# -
fig1 = plot_sfs(sfs.get_metric_dict(), kind='std_dev', figsize=(14, 7))
plt.title('Sequence Forward Generation (w. StdDev)')
plt.grid()
#選ばれた特徴量のサブセットでモデルを作成
features_sel_wrapper_sfg = list(sfs.k_feature_names_)
reg = LogisticRegression().fit(X_adult_train[features_sel_wrapper_sfg],
y_adult_train)
print('特徴量 : {} '.format(features_sel_wrapper_sfg))
print('訓練 : {} '.format(
accuracy_score(y_adult_train,
reg.predict(X_adult_train[features_sel_wrapper_sfg]))))
print('検証 : {} '.format(
accuracy_score(y_adult_test,
reg.predict(X_adult_test[features_sel_wrapper_sfg]))))
score_sel_wrapper_sfg = accuracy_score(
y_adult_test, reg.predict(X_adult_test[features_sel_wrapper_sfg]))
# ### Sequential Backward Generation
# +
#SFSインスタンス化
sbs = SFS(LogisticRegression(), #利用するモデル
k_features=(1,33), #特徴量の数 1-33個の間でCVスコアベストを探す
forward=False, #バックワード型
floating=False, #フローティングの有無
verbose=2,
scoring='accuracy',#精度指標
cv=5,
n_jobs=-1)
sbs = sbs.fit(X_adult_train, y_adult_train)
# -
fig1 = plot_sfs(sbs.get_metric_dict(), kind='std_dev', figsize=(14, 7))
plt.title('Sequence Backward Generation (w. StdDev)')
plt.grid()
#選ばれた特徴量のサブセットでモデルを作成
features_sel_wrapper_sbg = list(sbs.k_feature_names_)
reg = LogisticRegression().fit(X_adult_train[features_sel_wrapper_sbg],
y_adult_train)
print('特徴量 : {} '.format(features_sel_wrapper_sbg))
print('訓練 : {} '.format(
accuracy_score(y_adult_train,
reg.predict(X_adult_train[features_sel_wrapper_sbg]))))
print('検証 : {} '.format(
accuracy_score(y_adult_test,
reg.predict(X_adult_test[features_sel_wrapper_sbg]))))
score_sel_wrapper_sbg = accuracy_score(
y_adult_test, reg.predict(X_adult_test[features_sel_wrapper_sbg]))
# ### Bidirectional Generation
#
#
# +
#SFSインスタンス化
sfsf = SFS(
LogisticRegression(), #利用するモデル
k_features=(1, 33), #特徴量の数 1-33個の間でCVスコアベストを探す
forward=True, #フォワード型
floating=True, #フローティングの有無 Trueとする
verbose=2,
scoring='accuracy', #精度指標
cv=5,
n_jobs=-1)
sfsf = sfsf.fit(X_adult_train, y_adult_train)
# -
fig1 = plot_sfs(sfsf.get_metric_dict(), kind='std_dev', figsize=(14, 7))
plt.title('Bidirectional Generation (w. StdDev)')
plt.grid()
#選ばれた特徴量のサブセットでモデルを作成
features_sel_wrapper_bg = list(sfsf.k_feature_names_)
reg = LogisticRegression().fit(X_adult_train[features_sel_wrapper_bg],
y_adult_train)
print('特徴量 : {} '.format(features_sel_wrapper_bg))
print('訓練 : {} '.format(
accuracy_score(y_adult_train,
reg.predict(X_adult_train[features_sel_wrapper_bg]))))
print('検証 : {} '.format(
accuracy_score(y_adult_test,
reg.predict(X_adult_test[features_sel_wrapper_bg]))))
score_sel_wrapper_bg = accuracy_score(
y_adult_test, reg.predict(X_adult_test[features_sel_wrapper_bg]))
# ### Embedded法
#
# Lass回帰やTreeベースモデルの学習アルゴリズム内で特徴量を計算し、
# アルゴリズム自らが特徴量を選択する。これを用意て特徴量の組み合わせを見つける手法。
# 組み合わせ1つ１つに対して再学習しないので学習コストも減らせる。
#SelectFromModelモジュール読み込み
from sklearn.feature_selection import SelectFromModel
#標準化モジュール読み込む
from sklearn.preprocessing import StandardScaler
# ### Lasso回帰
#標準化
scaler = StandardScaler()
scaler.fit(X_adult_train)
X_adult_train_standard = scaler.transform(X_adult_train)
X_adult_test_standard = scaler.transform(X_adult_test)
#lasso selectFromModelインスタンス作成
embeded_selector = SelectFromModel(
LogisticRegression(C=1.0, penalty="l1", solver='liblinear'),
threshold='mean')
embeded_selector.fit(X_adult_train_standard, y_adult_train)
#選ばれた特徴量のサブセットでモデルを作成
features_sel_embedded_lasso = X_adult_train.columns[
embeded_selector.get_support()]
reg = LogisticRegression()
#モデル作成
reg.fit(embeded_selector.transform(X_adult_train), y_adult_train)
print('特徴量 : {} '.format(features_sel_embedded_lasso))
print('訓練 : {} '.format(
accuracy_score(y_adult_train,
reg.predict(embeded_selector.transform(X_adult_train)))))
print('検証 : {} '.format(
accuracy_score(y_adult_test,
reg.predict(embeded_selector.transform(X_adult_test)))))
score_sel_embedded_lasso = accuracy_score(
y_adult_test, reg.predict(embeded_selector.transform(X_adult_test)))
# ### RandomForest
# +
#RaondomForest
from sklearn.ensemble import RandomForestClassifier
#RondomForest SelectFromModelインスタンス作成
embeded_selector = SelectFromModel(
RandomForestClassifier(
n_estimators=100, random_state=0, min_samples_leaf=50), "mean")
embeded_selector.fit(X_adult_train, y_adult_train)
#選ばれた特徴量のサブセットでモデルを作成
features_sel_embedded_rf = X_adult_train.columns[
embeded_selector.get_support()]
reg = LogisticRegression()
#モデル作成
reg.fit(embeded_selector.transform(X_adult_train), y_adult_train)
print('特徴量 : {} '.format(features_sel_embedded_rf))
print('訓練 : {} '.format(
accuracy_score(y_adult_train,
reg.predict(embeded_selector.transform(X_adult_train)))))
print('検証 : {} '.format(
accuracy_score(y_adult_test,
reg.predict(embeded_selector.transform(X_adult_test)))))
score_sel_embedded_rf = accuracy_score(
y_adult_test, reg.predict(embeded_selector.transform(X_adult_test)))
# -
| 41,911 |
/week_05/day_1/grid_search.ipynb | 74ad2164c4524766e9a7faafafc7d714e102152d | [] | no_license | chilicola/lighthouse_data_science_bootcamp | https://github.com/chilicola/lighthouse_data_science_bootcamp | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 6,864 | # ---
# jupyter:
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# ---
# + [markdown] id="OB_iRPOMPYEx"
# # Travel agency's review generator
#
# Implement a text generator, which imitates reviewers of a travel agency using the LSTM language model.
# + id="zP0wZaZRPYE3"
import pandas as pd
reviews = pd.read_csv('https://raw.githubusercontent.com/mlcollege/natural-language-processing/master/data/en_reviews.csv', sep='\t', header=None, names =['rating', 'text'])
reviews = reviews['text'].tolist()
print(reviews[:2])
# + [markdown] id="lM63eDubPYFD"
# ## Prepare training data
#
# Create one long string of all reviews separated by the new line symbol. First, we need to replace all new lines from the source data with spaces.
# + id="jz5vl2q5PYFE"
data = '\n'.join(map(lambda x: x.replace('\n', ' '), reviews))
# + [markdown] id="yGYj2eEDPYFV"
# Create a list of all characters and their integer representations.
# + id="udDOL64xPYFy"
chars = sorted(list(set(data)))
char_indices = dict((c, i) for i, c in enumerate(chars))
indices_char = dict((i, c) for i, c in enumerate(chars))
print(len(chars))
# + [markdown] id="ZcB0dhTKPYF7"
# Create character sequences of MAXLEN length and a list that stores the characters occuring right after the sequence in the training data. The next sequence starts STEP characters after the beginning of the previous sequence.
# + id="5jU3b5kyPYGe"
MAXLEN = 40
STEP = 10
sequences = []
next_chars = []
for i in range(0, len(data) - MAXLEN, STEP):
sequences.append(data[i: i + MAXLEN])
next_chars.append(data[i + MAXLEN])
# + [markdown] id="1iAsELhtPYGg"
# Create the training data
# + id="5O6pqEwCPYGh"
import numpy as np
X_train = np.zeros((len(sequences), MAXLEN, len(chars)), dtype=np.bool)
y_train = np.zeros((len(sequences), len(chars)), dtype=np.bool)
for i, sequences in enumerate(sequences):
for t, char in enumerate(sequences):
X_train[i, t, char_indices[char]] = 1
y_train[i, char_indices[next_chars[i]]] = 1
# + [markdown] id="Um-H1ACgPYGk"
# Build an LSTM language model
# + id="W-FKsVjdPYGl"
# %tensorflow_version 2.x
from tensorflow.python.keras.models import Sequential
from tensorflow.python.keras.layers import Dense, Activation, LSTM
from tensorflow.keras.optimizers import RMSprop
model = Sequential()
model.add(LSTM(512, input_shape=(MAXLEN, len(chars)), dropout=0.5, return_sequences=True))
model.add(LSTM(512, dropout=0.5))
model.add(Dense(len(chars)))
model.add(Activation('softmax'))
optimizer = RMSprop(lr=0.001)
model.compile(loss='categorical_crossentropy', optimizer=optimizer)
# + [markdown] id="II7dmYm7PYGn"
# Define a helper function for sampling characters from a probability distribution.
# + id="J26CDcz0PYGo"
def sample(preds, temperature=1.0):
preds = np.asarray(preds).astype('float64')
preds = np.log(preds) / temperature
exp_preds = np.exp(preds)
preds = exp_preds / np.sum(exp_preds)
probas = np.random.multinomial(1, preds, 1)
return np.argmax(probas)
# + [markdown] id="cMqXg-7FPYH5"
# Define the function which generates reviews using an LSTM language model.
# + id="-9woBLqcPYH6"
def generate(seed, temperature=1.0):
sentence = MAXLEN * '\n' + seed
sentence = sentence[-MAXLEN:]
generated = seed
next_char = None
while next_char != '\n':
X_pred = np.zeros((1, MAXLEN, len(chars)))
for t, char in enumerate(sentence):
X_pred[0, t, char_indices[char]] = 1.
y_pred = model.predict(X_pred, verbose=0)[0]
next_index = sample(y_pred, temperature)
next_char = indices_char[next_index]
generated += next_char
sentence = sentence[1:] + next_char
return generated[0:-1]
# + [markdown] id="zXG-w4FaPYH-"
# Train the model and print a sample after every iteration
# + id="v2VjqyrpPYIB"
EPOCHS = 50
old_loss = None
for iteration in range(1, EPOCHS + 1):
print()
print('-' * 50)
print('Iteration', iteration)
history = model.fit(X_train, y_train, batch_size=512, epochs=1)
loss = history.history['loss'][0]
if old_loss != None and old_loss < loss:
print("Loss explosion.")
break
old_loss = loss
start_index = np.random.randint(0, len(data) - MAXLEN - 1)
sentence = data[start_index: start_index + MAXLEN]
print(generate(sentence))
# + id="P0FBEUWfPYID"
#model.save_weights("lstm_lm.h5")
# + id="U4fAnRnyM4Xw" outputId="54bab227-2f56-4d99-f754-1a8e3f18df9b" colab={"base_uri": "https://localhost:8080/", "height": 85}
# !gdown https://drive.google.com/uc?id=13rphTQmq0Db01hX7ptdCMt0IIpWjEBEA
model.load_weights("lstm_lm.h5")
# + id="UDtZNwcXN0Gi"
generate('The service was')
()
# B) Some column(s) might not be needed. Get rid of them. Explain why you get rid of the respective column(s). </p>
# Save the new dataframe again under the name "defaults".
defaults.nunique()
# +
# Dropping ID because it is not a feature that will aid in the building of a ML model
defaults = defaults.drop(["ID"],axis=1)
# -
# C) Convert all categorical values into dummy variables. Only include columns with valuable information. </p>
# Save the new dataframe again under the name "defaults".
defaults.columns
sex = pd.get_dummies(defaults['SEX'], prefix='Sex')
education = pd.get_dummies(defaults['EDUCATION'], prefix='Education')
marriage = pd.get_dummies(defaults['MARRIAGE'], prefix='Marriage')
defaults = defaults.drop(['SEX', 'EDUCATION', 'MARRIAGE'],axis=1)
defaults = defaults.join(sex).join(marriage).join(education)
defaults.head()
# D) In order to run a model, we have to define now the X and y variables. Set the y values to the "default" (either 0 or 1). The remaining columns represent the X values.
y = defaults['default']
X = defaults.drop(["default"],axis=1)
# <h2>4. Further Data Analysis</h2>
# A) Please evaluate the importance of each feature of the X values on the y value of the previous exercise using any tree ensamble algorithm and display the results graphically. Please comment your findings. Please tell us what you did and why you did it.
# +
# Splitting data for training and testing
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
# +
# Created model using a Random Forest clf
from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier()
clf = clf.fit(X_train,y_train)
pred = clf.predict(X_test)
# +
# Checking feature importance
from matplotlib import pyplot
importance = clf.feature_importances_
# summarize feature importance
for i,v in enumerate(importance):
print('Feature: %0d, Score: %.5f' % (i,v))
# plot feature importance
pyplot.bar([x for x in range(len(importance))], importance)
pyplot.show()
# -
# **It can be shown from the barchart that the Credit Limit and Age features are the 2 most important features.**
# +
# Now to check the accuracy of the model
from sklearn.metrics import accuracy_score
accu = accuracy_score(pred,y_test)
print("The Random Forest ensemble gave us an accuracy of %.2f percent, which isn't too great!" % (accu*100))
# +
# Now I will eliminate all the features that aren't important and re-run the model
X = defaults[['CREDIT_LIMIT','AGE']]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
clf = RandomForestClassifier()
clf = clf.fit(X_train,y_train)
pred = clf.predict(X_test)
accu_new = accuracy_score(pred,y_test)
print("We got an increase of %.2f percent, which is pretty good. From now on we will use this new data set" % ((accu_new-accu)*100))
# -
# B) Please assess how accuarely we can predict, whether a customer will default or not using Support Vector Machines. As always plase explain your work.
# +
from sklearn.svm import SVC
clf_svc = SVC()
clf_svc.fit(X_train, y_train)
pred_svc = clf_svc.predict(X_test)
accu_svc = accuracy_score(pred_svc,y_test)
print("In comparison, the SVC model gave us an accuracy of %.2f percent." % (accu_svc*100))
# -
# **After running an SVM model we can see that the accuracy improved by a little over 4%.**
# C) Now let's see how accurate the XGBoost algorithm can predict, whether a customer will default or not better than the previous SVM. Please give us the accuracy score and standard deviation. Choose an appropiate model for testing and training.
# +
import xgboost as xgb
# Converting our dataset to a DMatrix structure to enhance performance and then splitting it again
data_dmatrix = xgb.DMatrix(data=X,label=y)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
# +
clf_xgb = xgb.XGBClassifier(use_label_encoder=False, eval_metric = "logloss")
clf_xgb.fit(X_train, y_train)
pred_xgb = clf_xgb.predict(X_test)
accu_xgb = accuracy_score(pred_xgb,y_test)
print("In comparison, the XGBoost model gave us an accuracy of %.2f percent, which isn't better than the SVM in this case. Changing the hyperparametes could enhance performance further" % (accu_xgb*100))
# +
from sklearn.metrics import mean_squared_error as mse
print("The XGBoost model gave us an MSE of %.2f." % (mse(pred_xgb,y_test)))
# -
| 9,290 |
/churn/models/operators/Notebooks/portability_model.ipynb | 4cb45e97383f44055014347241367f5c35199a0a | [] | no_license | carnaum2/use-cases | https://github.com/carnaum2/use-cases | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 59,836 | # ---
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# name: python2
# ---
# +
import os
import sys
USECASES_SRC = os.path.join(os.environ.get('BDA_USER_HOME', ''), "repositorios", "use-cases")
if USECASES_SRC not in sys.path:
sys.path.append(USECASES_SRC)
# -
PYKHAOS_SRC = os.path.join(os.environ.get('BDA_USER_HOME', ''), "repositorios")
if PYKHAOS_SRC not in sys.path:
sys.path.append(PYKHAOS_SRC)
from IPython.core.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# +
from common.src.main.python.utils.hdfs_generic import *
import os
MAX_N_EXECUTORS=15
MIN_N_EXECUTORS=1
N_CORES_EXECUTOR=4
EXECUTOR_IDLE_MAX_TIME=120
EXECUTOR_MEMORY='32g'
DRIVER_MEMORY='16g'
N_CORES_DRIVER=1
MEMORY_OVERHEAD=N_CORES_EXECUTOR*2048
#QUEUE="root.datascience.normal"
QUEUE="root.BDPtenants.es.medium"
BDA_CORE_VERSION="1.0.0"
SPARK_COMMON_OPTS=os.environ.get('SPARK_COMMON_OPTS', '')
SPARK_COMMON_OPTS+=" --executor-memory %s --driver-memory %s" % (EXECUTOR_MEMORY, DRIVER_MEMORY)
SPARK_COMMON_OPTS+=" --conf spark.shuffle.manager=tungsten-sort"
SPARK_COMMON_OPTS+=" --queue %s" % QUEUE
APP_NAME='new_portability_model'
# Dynamic allocation configuration
SPARK_COMMON_OPTS+=" --conf spark.driver.allowMultipleContexts=true"
SPARK_COMMON_OPTS+=" --conf spark.dynamicAllocation.enabled=true"
SPARK_COMMON_OPTS+=" --conf spark.shuffle.service.enabled=true"
SPARK_COMMON_OPTS+=" --conf spark.dynamicAllocation.maxExecutors=%s" % (MAX_N_EXECUTORS)
SPARK_COMMON_OPTS+=" --conf spark.dynamicAllocation.minExecutors=%s" % (MIN_N_EXECUTORS)
SPARK_COMMON_OPTS+=" --conf spark.dynamicAllocation.executorIdleTimeout=%s" % (EXECUTOR_IDLE_MAX_TIME)
SPARK_COMMON_OPTS+=" --conf spark.ui.port=58201"
SPARK_COMMON_OPTS+=" --conf spark.port.maxRetries=200"
SPARK_COMMON_OPTS+=" --executor-cores=%s" % (N_CORES_EXECUTOR)
SPARK_COMMON_OPTS+=" --conf spark.app.name=%s" % (APP_NAME)
BDA_ENV = os.environ.get('BDA_USER_HOME', '')
# Attach bda-core-ra codebase
SPARK_COMMON_OPTS+=" --files \
{}/scripts/properties/red_agent/nodes.properties,\
{}/scripts/properties/red_agent/nodes-de.properties,\
{}/scripts/properties/red_agent/nodes-es.properties,\
{}/scripts/properties/red_agent/nodes-ie.properties,\
{}/scripts/properties/red_agent/nodes-it.properties,\
{}/scripts/properties/red_agent/nodes-pt.properties,\
{}/scripts/properties/red_agent/nodes-uk.properties".format(*[BDA_ENV]*7)
os.environ["SPARK_COMMON_OPTS"] = SPARK_COMMON_OPTS
os.environ["PYSPARK_SUBMIT_ARGS"] = "%s pyspark-shell " % SPARK_COMMON_OPTS
#print os.environ.get('SPARK_COMMON_OPTS', '')
#print os.environ.get('PYSPARK_SUBMIT_ARGS', '')
sc, sparkSession, sqlContext = run_sc()
print sc.defaultParallelism
# +
# This literal_eval is needed since
# we have to read from a textfile
# which is formatted as python objects.
# It is totally safe.
from ast import literal_eval
# Standard Library stuff:
from functools import partial
from datetime import date, timedelta, datetime
# Numpy stuff
from numpy import (nan as np_nan, round as np_round, int64 as np_int64)
import numpy as np
# Spark stuff
from pyspark.sql import SparkSession
from pyspark import StorageLevel
from pyspark.sql.functions import (udf, col, decode, when, lit, lower, upper, concat,
translate, count, sum as sql_sum, max as sql_max, min as sql_min,
round,
mean, stddev, datediff,
length,
countDistinct,
hour, date_format, collect_set, collect_list,
year, month, dayofmonth,
rank, expr, lag, coalesce, row_number,
isnull, isnan,
unix_timestamp,
regexp_replace
)
from pyspark.sql.types import DoubleType, StringType, IntegerType, ArrayType, FloatType, StructType, StructField
from pyspark.ml.feature import StringIndexer
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.linalg import Vectors
from pyspark.ml import Pipeline
from pyspark.ml import Pipeline
from pyspark.sql.functions import row_number, col
from pyspark.sql import DataFrameStatFunctions as statFunc
from pyspark.sql.window import Window
import json
from collections import OrderedDict
from subprocess import Popen, PIPE
import datetime, calendar
from pyspark.sql import functions as F
import datetime as dt
from pyspark.ml.feature import StandardScaler
# +
import sys
from common.src.main.python.utils.hdfs_generic import *
import argparse
import os
import time
# Spark utils
from pyspark.sql.functions import (udf, col, decode, when, lit, lower, concat,
translate, count, max, avg, min as sql_min,
greatest,
least,
isnull,
isnan,
struct,
substring,
size,
length,
year,
month,
dayofmonth,
unix_timestamp,
date_format,
from_unixtime,
datediff,
to_date,
desc,
asc,
countDistinct,
row_number,
regexp_replace,
lpad,
rpad,
trim,
split,
coalesce,
array)
from pyspark.sql import Row, DataFrame, Column, Window
from pyspark.sql.types import DoubleType, StringType, IntegerType, DateType, ArrayType
# from pyspark.ml import Pipeline
# from pyspark.ml.classification import RandomForestClassifier
# from pyspark.ml.feature import StringIndexer, VectorIndexer, VectorAssembler, SQLTransformer, OneHotEncoder
# from pyspark.ml.evaluation import MulticlassClassificationEvaluator, BinaryClassificationEvaluator
# from pyspark.ml.tuning import CrossValidator, ParamGridBuilder
import datetime as dt
# +
spark = (SparkSession.builder
.master("yarn")
.config("spark.submit.deployMode", "client")
.config("spark.ui.showConsoleProgress", "true")
.enableHiveSupport()
.getOrCreate()
)
# sc = spark.sparkContext
# +
# %load_ext autoreload
# %autoreload 2
#import re
import subprocess
import sys
import time
from IPython.display import HTML, display
import tabulate
def printHTML(df, sample=7):
display(HTML(tabulate.tabulate([df.columns]+df.take(sample), tablefmt='html', headers='firstrow')))
# Spark utils
from pyspark.sql.functions import (array_contains, bround, col, collect_set, concat, count, decode, desc,
isnull, length, lit, lower, lpad, max as sql_max,
size, struct, substring, sum as sql_sum,
translate, trim, udf, upper, when
)
from pyspark.sql.types import DoubleType, IntegerType, StringType, StructField, StructType
import matplotlib.pyplot as plt
# %matplotlib inline
# -
# +
#Importamos ids con las columnas que nos interesan: HAY QUE AUTOMATIZARLO TAMBIÉN
# +
import sys
sys.path.append("/var/SP/data/home/carnaum2/ids/amdocs_inf_dataset")
# -
from src.main.python.configuration.constants import ENVIRONMENT
from src.main.python.utils.spark_creator import SparkCreator
from src.main.python.pipelines.billing import Billing
from src.main.python.pipelines.breakdowns import Breakdowns
from src.main.python.pipelines.call_centre_calls import CallCentreCalls
from src.main.python.pipelines.campaigns import Campaigns
from src.main.python.pipelines.claims import Claims
from src.main.python.pipelines.competitors_web import CompWeb
from src.main.python.pipelines.customer import Customer
from src.main.python.pipelines.customer_aggregations import Customer_Aggregations
from src.main.python.pipelines.penalties import PenaltiesCustomer, PenaltiesServices
from src.main.python.pipelines.device_catalogue import Device_Catalogue
from src.main.python.pipelines.geneva_traffic import GenevaVoiceTypeUsage
from src.main.python.pipelines.geneva_traffic import GenevaVoiceUsage
from src.main.python.pipelines.geneva_traffic import GenevaRoamVoiceUsage
from src.main.python.pipelines.geneva_traffic import GenevaDataUsage
from src.main.python.pipelines.geneva_traffic import GenevaRoamDataUsage
from src.main.python.pipelines.mobile_spinners_extractor import Mobile_spinners_extractor
from src.main.python.pipelines.netscout import Netscout
from src.main.python.pipelines.orders import Orders
from src.main.python.pipelines.orders_aggregations import OrdersAgg
from src.main.python.pipelines.permsandprefs import Perms_and_prefs
from src.main.python.pipelines.services import Services
from src.main.python.pipelines.services_problems import ServiceProblems
from src.main.python.pipelines.tech_suprt import TechSupport
from src.main.python.pipelines.tgs import Tgs
from src.main.python.pipelines.tnps import Tnps
from src.main.python.pipelines.orders_sla import Orders_sla
from src.main.python.pipelines.tickets import Tickets
from src.main.python.pipelines.refund import Refund
sc = SparkCreator()
date = "20191014"
module_constructors = (Customer(sc, date, ENVIRONMENT),
Services(sc, date, ENVIRONMENT),
Customer_Aggregations(sc, date, ENVIRONMENT),
Billing(sc, date, ENVIRONMENT),
Campaigns(sc, date, date, ENVIRONMENT),
GenevaVoiceTypeUsage(sc, date, date, ENVIRONMENT),
GenevaVoiceUsage(sc, date, date, ENVIRONMENT),
GenevaDataUsage(sc, date, date, ENVIRONMENT),
#GenevaRoamVoiceUsage(sc, date, date, ENVIRONMENT),
#GenevaRoamDataUsage(sc, date, date, ENVIRONMENT),
Orders(sc, date, date, ENVIRONMENT),
OrdersAgg(sc, date, date, ENVIRONMENT),
PenaltiesCustomer(sc, date, ENVIRONMENT),
PenaltiesServices(sc, date, ENVIRONMENT),
Device_Catalogue(sc, date, date, ENVIRONMENT),
Perms_and_prefs(sc, date, ENVIRONMENT),
CallCentreCalls(sc, date, date, ENVIRONMENT),
Tnps(sc, date, date, ENVIRONMENT),
Tgs(sc, date, ENVIRONMENT),
Claims(sc, date, ENVIRONMENT),
Breakdowns(sc, date, ENVIRONMENT),
TechSupport(sc, date, ENVIRONMENT),
Netscout(sc, date, date, ENVIRONMENT),
CompWeb(sc, date, date, ENVIRONMENT),
ServiceProblems(sc, date, ENVIRONMENT),
Mobile_spinners_extractor(sc, date, ENVIRONMENT),
Orders_sla(sc, date, ENVIRONMENT),
Refund(sc, date, ENVIRONMENT),
#Tickets(sc, date, ENVIRONMENT)
)
na_map = {}
for module in module_constructors:
metadata = module.set_module_metadata()
na_map.update(metadata)
final_map = {colmn: na_map[colmn][0] for colmn in na_map.keys()
if colmn in na_map.keys() and na_map[colmn][1] != "id"}
categ_map = {colmn: na_map[colmn][0] for colmn in na_map.keys()
if colmn in na_map.keys() and na_map[colmn][1] == "categorical"}
numeric_map = {colmn: na_map[colmn][0] for colmn in na_map.keys()
if colmn in na_map.keys() and na_map[colmn][1] == "numerical"}
date_map = {colmn: na_map[colmn][0] for colmn in na_map.keys()
if colmn in na_map.keys() and na_map[colmn][1] == "date"}
numeric_variables=numeric_map.keys()
numeric_variables.append('msisdn') #añado el msisdn para hacer el join después
# +
def ids_numeric_selection(year_, month_, day_):
ids_completo = (spark.read.load(
'/data/udf/vf_es/amdocs_inf_dataset/amdocs_ids_service_level/year=' + year_ + '/month=' + month_ + '/day=' + day_))
ids_numeric=ids_completo.select(numeric_variables) #aqui cojo las variables que se han seleccionado
return ids_numeric
#Guardo este ids para train y test y luego hago inner join on msisdn con las columnas de target que habiamos creado
# -
ids_julio_numeric=ids_numeric_selection('2019','7','31')
ids_sept_numeric=ids_numeric_selection('2019','9','30')
categoricas=[item[0] for item in ids_julio_numeric.dtypes if item[1].startswith('string')] #elimino estas variables que no son numericas: no deberian aparecer (carlos lo va a corregir)
# +
ids_julio_numeric=ids_julio_numeric.drop('tgs_ind_riesgo_o2',
'tgs_ind_riesgo_mm',
'tgs_ind_riesgo_mv',
'tgs_meses_fin_dto_ok',
'CCC_L2_bucket_1st_interaction',
'CCC_L2_bucket_latest_interaction',
'CCC_L2_first_interaction',
'Cust_Agg_flag_prepaid_nc',
'tgs_ind_riesgo_max',
'tgs_sol_24m',
'CCC_L2_latest_interaction',
'tgs_tg_marta',
'tgs_blinda_bi_pos_n12')
ids_sept_numeric=ids_sept_numeric.drop('tgs_ind_riesgo_o2',
'tgs_ind_riesgo_mm',
'tgs_ind_riesgo_mv',
'tgs_meses_fin_dto_ok',
'CCC_L2_bucket_1st_interaction',
'CCC_L2_bucket_latest_interaction',
'CCC_L2_first_interaction',
'Cust_Agg_flag_prepaid_nc',
'tgs_ind_riesgo_max',
'tgs_sol_24m',
'CCC_L2_latest_interaction',
'tgs_tg_marta',
'tgs_blinda_bi_pos_n12')
# +
#Una vez tenemos el ids con las columnas que necesitamos
# -
from churn.analysis.triggers.base_utils.base_utils import get_customer_base, _is_null_date, get_customers, get_services
from pykhaos.utils.date_functions import move_date_n_yearmonths, get_last_day_of_month
from pykhaos.utils.date_functions import get_next_month
# +
#1. Filtro por clientes activos desde hace más de 3 meses y con línea móvil
# -
def ids_filter(ids_completo,fecha):
#Filtramos por clientes activos y con línea móvil
ids_filtered=ids_completo.filter((col('Serv_RGU')=='mobile')&
((col('Cust_COD_ESTADO_GENERAL')=='01')|(col('Cust_COD_ESTADO_GENERAL')=='09')))
#Filtramos por clientes que estuvieran en cartera hace 3 meses
year_month_previo=move_date_n_yearmonths(fecha[0:6], -3) #obtenemos yyyymm de 3 meses antes
fecha_previa=get_last_day_of_month(year_month_previo+'01') #Given a string date (format YYYY-MM-DD or YYYYMMDD) or a datetime object,returns the last day of the month. Eg. mydate=2018-03-01 --> returns 2018-03-31
#cartera de la foto de hace 3 meses
base_previa=get_customer_base(spark,fecha_previa, add_columns = None, active_filter = True, add_columns_customer=None)
msisdn_fecha_previa=base_previa.select('msisdn').distinct()
#me quedo con los clientes que estén en la cartera de hace 3 meses
df_activos=ids_filtered.join(msisdn_fecha_previa, on='msisdn',how='inner') #nos quedamos con los que estaban hace 3 meses
return df_activos
df_no_etiquetado_julio=ids_filter(ids_julio_numeric,'20190731')
df_no_etiquetado_sept=ids_filter(ids_sept_numeric,'20190930')
df_no_etiquetado_julio.count()
df_no_etiquetado_sept.count()
# +
#Estas funciones se utilizan luego en la función finañ
# -
def get_portab_next_month(fecha,spark): #devuelve la tabla de portabilidades del mes siguiente al especificado, eliminando las repetidas y quedándose con la última solicitud realizada
solicitudes_portab_complete= spark.read.table('raw_es.portabilitiesinout_sopo_solicitud_portabilidad') #lectura de tabla de portabilidad
if (int(fecha[4:6]))=='12':
solicitudes_portab_fecha =solicitudes_portab_complete.filter((col('year') == int(fecha[0:4])+1)&(col('month') == 1))
else:
solicitudes_portab_fecha =solicitudes_portab_complete.filter((col('year') == int(fecha[0:4]))& (col('month') == int(fecha[4:6])+1))
#Aplicamos regla para que no se repitan msisdn: si un cliente ha solicitado porta más de una vez, nos quedamos con la última solicitud realizada
window = Window.partitionBy('SOPO_DS_MSISDN1').orderBy(col('SOPO_DS_FECHA_SOLICITUD').desc())
solicitudes_portab=solicitudes_portab_fecha.withColumn("rank", row_number().over(window)).filter(col("rank") == 1)
return solicitudes_portab
def get_portab_operators(operator,portab_total): #te da los msisdn que hacen portabilidad a un operador especificado dada una tabla de portabilidades
if operator=='masmovil':
portab_masmovil=portab_total.filter((col("SOPO_CO_RECEPTOR") == "AMENA") & (col("SOPO_CO_NRN_RECEPTORVIR") == "735014")
| (col("SOPO_CO_RECEPTOR") == "AMENA") & (col("SOPO_CO_NRN_RECEPTORVIR") == "735044")
| (col("SOPO_CO_RECEPTOR") == "AIRTEL") & (col("SOPO_CO_NRN_RECEPTORVIR") == "725303")
| (col("SOPO_CO_RECEPTOR") == "AMENA") & (col("SOPO_CO_NRN_RECEPTORVIR") == "735054")
| (col("SOPO_CO_RECEPTOR") == "MOVISTAR") & (col("SOPO_CO_NRN_RECEPTORVIR") == "715501")
| (col("SOPO_CO_RECEPTOR") == "YOIGO") & (col("SOPO_CO_NRN_RECEPTORVIR") == "0")
| (col("SOPO_CO_RECEPTOR") == "AIRTEL") & (col("SOPO_CO_NRN_RECEPTORVIR") == "725503")
| (col("SOPO_CO_RECEPTOR") == "VIZZAVI") & (col("SOPO_CO_NRN_RECEPTORVIR") == "970513")
| (col("SOPO_CO_RECEPTOR") == "AIRTEL") & (col("SOPO_CO_NRN_RECEPTORVIR") == "725203")
| (col("SOPO_CO_RECEPTOR") == "VIZZAVI") & (col("SOPO_CO_NRN_RECEPTORVIR") == "970213"))
id_masmovil=portab_masmovil.select('SOPO_DS_MSISDN1').collect() #seleccionamos msisdn
clientes_masmovil=[] #guardamos msisdn en una lista
for i in range(0,len(id_masmovil)):
v=str(id_masmovil[i][0])
clientes_masmovil.append(v)
return clientes_masmovil
if operator=='movistar':
portab_movistar=portab_total.filter((col("SOPO_CO_RECEPTOR") == "MOVISTAR") & (col("SOPO_CO_NRN_RECEPTORVIR") == "0")
| (col("SOPO_CO_RECEPTOR") == "MOVISTAR") & (col("SOPO_CO_NRN_RECEPTORVIR") == "715401")
| (col("SOPO_CO_RECEPTOR") == "TUENTI_MOVIL") & (col("SOPO_CO_NRN_RECEPTORVIR") == "0"))
id_movistar=portab_movistar.select('SOPO_DS_MSISDN1').collect()
clientes_movistar=[]
for i in range(0,len(id_movistar)):
v=str(id_movistar[i][0])
clientes_movistar.append(v)
return clientes_movistar
if operator=='orange':
portab_orange=portab_total.filter((col("SOPO_CO_RECEPTOR") == "AMENA") & (col("SOPO_CO_NRN_RECEPTORVIR") == "0")
| (col("SOPO_CO_RECEPTOR") == "JAZZTEL") & (col("SOPO_CO_NRN_RECEPTORVIR") == "0")
| (col("SOPO_CO_RECEPTOR") == "EPLUS") & (col("SOPO_CO_NRN_RECEPTORVIR") == "0"))
id_orange=portab_orange.select('SOPO_DS_MSISDN1').collect()
clientes_orange=[]
for i in range(0,len(id_orange)):
v=str(id_orange[i][0])
clientes_orange.append(v)
return clientes_orange
if operator=='otros':
portab_otros=portab_total.filter(~((col("SOPO_CO_RECEPTOR") == "AMENA") & (col("SOPO_CO_NRN_RECEPTORVIR") == "735014")
| (col("SOPO_CO_RECEPTOR") == "AMENA") & (col("SOPO_CO_NRN_RECEPTORVIR") == "735044")
| (col("SOPO_CO_RECEPTOR") == "AIRTEL") & (col("SOPO_CO_NRN_RECEPTORVIR") == "725303")
| (col("SOPO_CO_RECEPTOR") == "AMENA") & (col("SOPO_CO_NRN_RECEPTORVIR") == "735054")
| (col("SOPO_CO_RECEPTOR") == "MOVISTAR") & (col("SOPO_CO_NRN_RECEPTORVIR") == "715501")
| (col("SOPO_CO_RECEPTOR") == "YOIGO") & (col("SOPO_CO_NRN_RECEPTORVIR") == "0")
| (col("SOPO_CO_RECEPTOR") == "AIRTEL") & (col("SOPO_CO_NRN_RECEPTORVIR") == "725503")
| (col("SOPO_CO_RECEPTOR") == "VIZZAVI") & (col("SOPO_CO_NRN_RECEPTORVIR") == "970513")
| (col("SOPO_CO_RECEPTOR") == "AIRTEL") & (col("SOPO_CO_NRN_RECEPTORVIR") == "725203")
| (col("SOPO_CO_RECEPTOR") == "VIZZAVI") & (col("SOPO_CO_NRN_RECEPTORVIR") == "970213")
| (col("SOPO_CO_RECEPTOR") == "AMENA") & (col("SOPO_CO_NRN_RECEPTORVIR") == "0")
| (col("SOPO_CO_RECEPTOR") == "JAZZTEL") & (col("SOPO_CO_NRN_RECEPTORVIR") == "0")
| (col("SOPO_CO_RECEPTOR") == "EPLUS") & (col("SOPO_CO_NRN_RECEPTORVIR") == "0")
|(col("SOPO_CO_RECEPTOR") == "MOVISTAR") & (col("SOPO_CO_NRN_RECEPTORVIR") == "0")
| (col("SOPO_CO_RECEPTOR") == "MOVISTAR") & (col("SOPO_CO_NRN_RECEPTORVIR") == "715401")
| (col("SOPO_CO_RECEPTOR") == "TUENTI_MOVIL") & (col("SOPO_CO_NRN_RECEPTORVIR") == "0")))
id_otros=portab_otros.select('SOPO_DS_MSISDN1').collect()
clientes_otros=[]
for i in range(0,len(id_otros)):
v=str(id_otros[i][0])
clientes_otros.append(v)
return clientes_otros
def get_target_multiclase(df_no_etiquetado,fecha,spark): #devuelve el dataframe con un target multiclase y uno para cada clase y elimina los que no hacen porta
portab_total=get_portab_next_month(fecha,spark)
clientes_masmovil=get_portab_operators('masmovil',portab_total)
clientes_movistar=get_portab_operators('movistar',portab_total)
clientes_orange=get_portab_operators('orange',portab_total)
clientes_otros=get_portab_operators('otros',portab_total)
df_etiquetado=df_no_etiquetado.withColumn('Operador_target',
when(df_no_etiquetado['msisdn'].isin(clientes_masmovil),1).otherwise(
when(df_no_etiquetado['msisdn'].isin(clientes_movistar),2).otherwise(
when(df_no_etiquetado['msisdn'].isin(clientes_orange),3).otherwise(
when(df_no_etiquetado['msisdn'].isin(clientes_otros),4).otherwise(0)))))
df_etiquetado=df_etiquetado.filter(col('Operador_target')!=0) #elimino los que no hacen porta
df_etiquetado_operadores=df_etiquetado.withColumn('masmovil',when(df_etiquetado['Operador_target']==1,1).otherwise(0)).withColumn('movistar',when(df_etiquetado['Operador_target']==2,1).otherwise(0)).withColumn('orange',when(df_etiquetado['Operador_target']==3,1).otherwise(0)).withColumn('otros',when(df_etiquetado['Operador_target']==4,1).otherwise(0))
return df_etiquetado_operadores
train_final=get_target_multiclase(df_no_etiquetado_julio,'20190731',spark)
test_final=get_target_multiclase(df_no_etiquetado_sept,'20190930',spark)
test_final.groupby('Operador_target').count().show()
train_final.groupby('Operador_target').count().show()
# +
variables_elim=['msisdn','Operador_target','masmovil','movistar','orange','otros']
variables = [i for i in train_final.columns if i not in variables_elim] #cojo las variables predictoras para el assemble
# -
def indexer_assembler(df_no_transformed):
assembler = VectorAssembler(inputCols=variables, outputCol="features")
stages = [assembler]
pipeline = Pipeline(stages = stages)
pipeline_fit = pipeline.fit(df_no_transformed)
df_transformed=pipeline_fit.transform(df_no_transformed)
return df_transformed
train_model2=indexer_assembler(train_final)
test_model2=indexer_assembler(test_final)
# +
#AUC
from pyspark.ml.classification import RandomForestClassifier, GBTClassifier
from pyspark.ml.evaluation import BinaryClassificationEvaluator
import pyspark.sql.functions as F
#LIFT
import utils_model
from utils_model import get_lift
getScore = udf(lambda prob: float(prob[1]), DoubleType())
#FEATURE IMPORTANCE
def ExtractFeatureImp(featureImp, dataset, featuresCol):
list_extract = []
for i in dataset.schema[featuresCol].metadata["ml_attr"]["attrs"]:
list_extract = list_extract + dataset.schema[featuresCol].metadata["ml_attr"]["attrs"][i]
varlist = pd.DataFrame(list_extract)
varlist['score'] = varlist['idx'].apply(lambda x: featureImp[x])
return(varlist.sort_values('score', ascending = False))
import matplotlib.pyplot as plt
# -
# # MasMóvil
# +
#Balanceando clases:
n=float(21508)/float(99265-21508) #proporcion que hay que coger de los que no solicitan porta a masmovil (misma que los que sí: nº clientes que van a masmovil)
train_masmovil=train_model2.filter(train_model2['Operador_target']==1).union(train_model2.filter(train_model2['Operador_target']!=1).sample(False, n,5))
# +
target='masmovil'
#model = RandomForestClassifier(featuresCol = 'features', labelCol = target, maxDepth=8, numTrees=3000)
model = GBTClassifier(featuresCol = 'features',labelCol=target, maxDepth=5,maxIter=20)
model2_masmovil = model.fit(train_masmovil)
# -
pred_masmovil_train=model2_masmovil.transform(train_masmovil)
pred_masmovil_test=model2_masmovil.transform(test_model2)
evaluator = BinaryClassificationEvaluator(labelCol= 'masmovil' , metricName='areaUnderROC')
auc_masmovil_train = evaluator.evaluate(pred_masmovil_train)
auc_masmovil_test = evaluator.evaluate(pred_masmovil_test)
auc_masmovil_train
auc_masmovil_test
pred_masmovil_test=pred_masmovil_test.withColumn("score", getScore(col("probability")).cast(DoubleType()))
pred_masmovil_test=pred_masmovil_test.orderBy('score',ascending=False)
lift_masmovil = get_lift(pred_masmovil_test, 'score', target, 10)
for d ,l in lift_masmovil:
print str(d) + ": " + str(l)
feat_imp_masmovil = ExtractFeatureImp(model2_masmovil.featureImportances ,pred_masmovil_test, "features")[0:30]
feat_imp_masmovil = feat_imp_masmovil.sort_values(by = ['score'], ascending = True)
# +
feat_imp=feat_imp_masmovil
features = feat_imp['name']
importances = feat_imp['score']
indices = feat_imp['idx']
plt.figure(figsize=(15, 10))
plt.title('Feature Importances')
plt.barh(range(len(indices)), importances[indices], color='b', align='center')
plt.yticks(range(len(indices)), [features[i] for i in indices])
plt.xlabel('Relative Importance')
plt.show()
# -
# # MOVISTAR
# +
n=float(33103)/float(99265-33103) #proporcion que hay que coger de los que no solicitan porta a masmovil (misma que los que sí: nº clientes que van a masmovil)
train_movistar=train_model2.filter(train_model2['Operador_target']==2).union(train_model2.filter(train_model2['Operador_target']!=2).sample(False, n,5))
# -
target='movistar'
#model = RandomForestClassifier(featuresCol = 'features', labelCol = target, maxDepth=8, numTrees=3000)
model = GBTClassifier(featuresCol = 'features',labelCol=target, maxDepth=5,maxIter=20)
model2_movistar = model.fit(train_movistar)
pred_movistar_train=model2_movistar.transform(train_movistar)
pred_movistar_test=model2_movistar.transform(test_model2)
evaluator = BinaryClassificationEvaluator(labelCol= 'movistar' , metricName='areaUnderROC')
auc_movistar_train = evaluator.evaluate(pred_movistar_train)
auc_movistar_test = evaluator.evaluate(pred_movistar_test)
auc_movistar_train
auc_movistar_test
pred_movistar_test=pred_movistar_test.withColumn("score", getScore(col("probability")).cast(DoubleType()))
pred_movistar_test=pred_movistar_test.orderBy('score',ascending=False)
lift_movistar = get_lift(pred_movistar_test, 'score', target, 10)
for d ,l in lift_movistar:
print str(d) + ": " + str(l)
feat_imp_movistar = ExtractFeatureImp(model2_movistar.featureImportances ,pred_movistar_test, "features")[0:30]
feat_imp_movistar = feat_imp_movistar.sort_values(by = ['score'], ascending = True)
# +
feat_imp=feat_imp_movistar
features = feat_imp['name']
importances = feat_imp['score']
indices = feat_imp['idx']
plt.figure(figsize=(15, 10))
plt.title('Feature Importances')
plt.barh(range(len(indices)), importances[indices], color='b', align='center')
plt.yticks(range(len(indices)), [features[i] for i in indices])
plt.xlabel('Relative Importance')
plt.show()
# -
# # ORANGE
# +
n=float(26206)/float(99265-26206) #proporcion que hay que coger de los que no solicitan porta a masmovil (misma que los que sí: nº clientes que van a masmovil)
train_orange=train_model2.filter(train_model2['Operador_target']==3).union(train_model2.filter(train_model2['Operador_target']!=3).sample(False, n,5))
# -
target='orange'
#model = RandomForestClassifier(featuresCol = 'features', labelCol = target, maxDepth=8, numTrees=3000)
model = GBTClassifier(featuresCol = 'features',labelCol=target, maxDepth=5,maxIter=20)
model2_orange = model.fit(train_orange)
pred_orange_train=model2_orange.transform(train_orange)
pred_orange_test=model2_orange.transform(test_model2)
evaluator = BinaryClassificationEvaluator(labelCol= 'orange' , metricName='areaUnderROC')
auc_orange_train = evaluator.evaluate(pred_orange_train)
auc_orange_test = evaluator.evaluate(pred_orange_test)
auc_orange_train
auc_orange_test
pred_orange_test=pred_orange_test.withColumn("score", getScore(col("probability")).cast(DoubleType()))
pred_orange_test=pred_orange_test.orderBy('score',ascending=False)
lift_orange= get_lift(pred_orange_test, 'score', target, 10)
for d ,l in lift_orange:
print str(d) + ": " + str(l)
feat_imp_orange= ExtractFeatureImp(model2_orange.featureImportances ,pred_orange_test, "features")[0:30]
feat_imp_orange = feat_imp_orange.sort_values(by = ['score'], ascending = True)
# +
feat_imp=feat_imp_orange
features = feat_imp['name']
importances = feat_imp['score']
indices = feat_imp['idx']
plt.figure(figsize=(15, 10))
plt.title('Feature Importances')
plt.barh(range(len(indices)), importances[indices], color='b', align='center')
plt.yticks(range(len(indices)), [features[i] for i in indices])
plt.xlabel('Relative Importance')
plt.show()
# -
# # OTROS
# +
n=float(18448)/float(99265-18448) #proporcion que hay que coger de los que no solicitan porta a masmovil (misma que los que sí: nº clientes que van a masmovil)
train_otros=train_model2.filter(train_model2['Operador_target']==4).union(train_model2.filter(train_model2['Operador_target']!=4).sample(False, n,5))
# -
target='otros'
#model = RandomForestClassifier(featuresCol = 'features', labelCol = target, maxDepth=8, numTrees=3000)
model = GBTClassifier(featuresCol = 'features',labelCol=target, maxDepth=5,maxIter=20)
model2_otros = model.fit(train_otros)
pred_otros_train=model2_otros.transform(train_otros)
pred_otros_test=model2_otros.transform(test_model2)
evaluator = BinaryClassificationEvaluator(labelCol= 'otros' , metricName='areaUnderROC')
auc_otros_train = evaluator.evaluate(pred_otros_train)
auc_otros_test = evaluator.evaluate(pred_otros_test)
auc_otros_train
auc_otros_test
pred_otros_test=pred_otros_test.withColumn("score", getScore(col("probability")).cast(DoubleType()))
pred_otros_test=pred_otros_test.orderBy('score',ascending=False)
lift_otros= get_lift(pred_otros_test, 'score', target, 10)
for d ,l in lift_otros:
print str(d) + ": " + str(l)
feat_imp_otros= ExtractFeatureImp(model2_otros.featureImportances ,pred_otros_test, "features")[0:30]
feat_imp_otros = feat_imp_otros.sort_values(by = ['score'], ascending = True)
# +
feat_imp=feat_imp_otros
features = feat_imp['name']
importances = feat_imp['score']
indices = feat_imp['idx']
plt.figure(figsize=(15, 10))
plt.title('Feature Importances')
plt.barh(range(len(indices)), importances[indices], color='b', align='center')
plt.yticks(range(len(indices)), [features[i] for i in indices])
plt.xlabel('Relative Importance')
plt.show()
# -
| 32,284 |
/notebooks/class_4_shrinkage.ipynb | 740c86ca1f5109166dd7867833d60c1fa394aa80 | [] | no_license | sbansal-21/ox-sbs-ml-bd | https://github.com/sbansal-21/ox-sbs-ml-bd | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 202,082 | # ---
# jupyter:
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# ---
# ## Calculate the skill of a MJO Index as a function of lead time for Weekly Data
# ### In this example, we demonstrate:
# 1. How to remotely access data from the Subseasonal Experiment (SubX) hindcast database and set it up to be used in `climpred`.
# 2. How to calculate the Anomaly Correlation Coefficient (ACC) using weekly data with `climpred`
# 3. How to calculate and plot historical forecast skill of the real-time multivariate MJO (RMM) indices as function of lead time.
# ### The Subseasonal Experiment (SubX)
#
# Further information on SubX is available from [Pegion et al. 2019](https://journals.ametsoc.org/doi/full/10.1175/BAMS-D-18-0270.1) and the [SubX project website](http://cola.gmu.edu/subx/)
#
# The SubX public database is hosted on the International Research Institute for Climate and Society (IRI) data server http://iridl.ldeo.columbia.edu/SOURCES/.Models/.SubX/
#
# Since the SubX data server is accessed via this notebook, the time for the notebook to run may is several minutes and will vary depending on the speed that data can be downloaded. This is a large dataset, so please be patient. If you prefer to download SubX data locally, scripts are available from https://github.com/kpegion/SubX.
# ### Definitions
#
# RMM
# : Two indices (RMM1 and RMM2) are used to represent the MJO. Together they define the MJO based on 8 phases and can represent both the phase and amplitude of the MJO (Wheeler and Hendon 2004). This example uses the observed RMM1 provided by Matthew Wheeler at the Center for Australian Weather and Climate Research. It is the version of the indices in which interannual variability has not been removed.
#
# Skill of RMM
# : Traditionally, the skill of the RMM is calculated as a bivariate correlation encompassing the skill of the two indices together (Rashid et al. 2010; Gottschalck et al 2010). Currently, `climpred` does not have the functionality to calculate the bivariate correlation, thus the anomaly correlation coefficient for RMM1 index is calculated here as a demonstration. The bivariate correlation metric will be added in a future version of `climpred`
# +
import warnings
import matplotlib.pyplot as plt
plt.style.use('ggplot')
plt.style.use('seaborn-talk')
import xarray as xr
import pandas as pd
import numpy as np
from climpred import HindcastEnsemble
import climpred
# -
warnings.filterwarnings("ignore")
# Function to set 360 calendar to 360_day calendar and decode cf times
def decode_cf(ds, time_var):
if ds[time_var].attrs['calendar'] == '360':
ds[time_var].attrs['calendar'] = '360_day'
ds = xr.decode_cf(ds, decode_times=True)
return ds
# Read the observed RMM Indices
obsds = xr.open_dataset('../../data/RMM-INTERANN-OBS.nc')['rmm1'].to_dataset()
obsds
# Read the SubX RMM1 data for the GMAO-GEOS_V2p1 model from the SubX data server. It is important to note that the SubX data contains weekly initialized forecasts where the `init` day varies by model. SubX data may have all NaNs for initial dates in which a model does not make a forecast, thus we apply `dropna` over the `S=init` dimension when `how=all` data for a given `S=init` is missing. This can be slow, but allows the rest of the calculations to go more quickly.
#
# Note that we ran the `dropna` operation offline and then uploaded the post-processed SubX dataset to the `climpred-data` repo for the purposes of this demo. This is how you can do this manually:
#
# ```python
# url = 'http://iridl.ldeo.columbia.edu/SOURCES/.Models/.SubX/.GMAO/.GEOS_V2p1/.hindcast/.RMM/.RMM1/dods/'
# fcstds = xr.open_dataset(url, decode_times=False, chunks={'S': 1, 'L': 45}).dropna(dim='S',how='all')
# ```
fcstds = xr.open_dataset('../../data/GMAO-GEOS-RMM1.nc', decode_times=False)
print(fcstds)
# The SubX data dimensions correspond to the following `climpred` dimension definitions: `X=lon`,`L=lead`,`Y=lat`,`M=member`, `S=init`. We will rename the dimensions to their `climpred` names.
fcstds=fcstds.rename({'S': 'init','L': 'lead','M': 'member', 'RMM1' : 'rmm1'})
# Let's make sure that the `lead` dimension is set properly for `climpred`. SubX data stores `leads` as 0.5, 1.5, 2.5, etc, which correspond to 0, 1, 2, ... days since initialization. We will change the `lead` to be integers starting with zero.
fcstds['lead']=(fcstds['lead']-0.5).astype('int')
# Now we need to make sure that the `init` dimension is set properly for `climpred`. For daily data, the `init` dimension must be a `xr.cfdateTimeIndex` or a `pd.datetimeIndex`. We convert the `init` values to `pd.datatimeIndex`.
fcstds=decode_cf(fcstds,'init')
fcstds['init']=pd.to_datetime(fcstds.init.values.astype(str))
fcstds['init']=pd.to_datetime(fcstds['init'].dt.strftime('%Y%m%d 00:00'))
# Make Weekly Averages
fcstweekly=fcstds.rolling(lead=7,center=False).mean().dropna(dim='lead')
obsweekly=obsds.rolling(time=7,center=False).mean().dropna(dim='time')
print(fcstweekly)
print(obsweekly)
# Create a new `xr.DataArray` for the weekly fcst data
nleads=fcstweekly['lead'][::7].size
fcstweeklyda=xr.DataArray(fcstweekly['rmm1'][:,:,::7],
coords={'init' : fcstweekly['init'],
'member': fcstweekly['member'],
'lead': np.arange(1,nleads+1),
},
dims=['init', 'member','lead'])
fcstweeklyda.name = 'rmm1'
# `climpred` requires that `lead` dimension has an attribute called `units` indicating what time units the `lead` is assocated with. Options are: `years,seasons,months,weeks,pentads,days`. The `lead` `units` are `weeks`.
fcstweeklyda['lead'].attrs={'units': 'weeks'}
# Create the `climpred HindcastEnsemble` object and add the observations as the reference/verification
hindcast = HindcastEnsemble(fcstweeklyda)
hindcast=hindcast.add_observations(obsweekly, 'observations')
# Calculate the Anomaly Correlation Coefficient (ACC)
skill = hindcast.verify(metric='acc')
# Plot the skill as a function of lead time
x=np.arange(fcstweeklyda['lead'].size)
plt.bar(x,skill['rmm1'])
plt.title('GMAO_GOES_V2p1 RMM1 Skill')
plt.xlabel('Lead Time (Weeks)')
plt.ylabel('ACC')
plt.ylim(0.0, 1.0)
# ### References
#
# 1. Pegion, K., B.P. Kirtman, E. Becker, D.C. Collins, E. LaJoie, R. Burgman, R. Bell, T. DelSole, D. Min, Y. Zhu, W. Li, E. Sinsky, H. Guan, J. Gottschalck, E.J. Metzger, N.P. Barton, D. Achuthavarier, J. Marshak, R.D. Koster, H. Lin, N. Gagnon, M. Bell, M.K. Tippett, A.W. Robertson, S. Sun, S.G. Benjamin, B.W. Green, R. Bleck, and H. Kim, 2019: The Subseasonal Experiment (SubX): A Multimodel Subseasonal Prediction Experiment. Bull. Amer. Meteor. Soc., 100, 2043–2060, https://doi.org/10.1175/BAMS-D-18-0270.1
#
# 2. Kirtman, B. P., Pegion, K., DelSole, T., Tippett, M., Robertson, A. W., Bell, M., … Green, B. W. (2017). The Subseasonal Experiment (SubX) [Data set]. IRI Data Library. https://doi.org/10.7916/D8PG249H
#
# 3. Wheeler, M. C., & Hendon, H. H. (2004). An all-season real-time multivariate MJO index: Development of an index for monitoring and prediction. Monthly Weather Review, 132(8), 1917–1932. http://doi.org/10.1175/1520-0493(2004)132<1917:AARMMI>2.0.CO;2
#
# 4. Rashid, H. A., Hendon, H. H., Wheeler, M. C., & Alves, O. (2010). Prediction of the Madden–Julian oscillation with the POAMA dynamical prediction system. Climate Dynamics, 36(3-4), 649–661. http://doi.org/10.1007/s00382-010-0754-x
#
# 5. Gottschalck, J., Wheeler, M., Weickmann, K., Vitart, F., Savage, N., Lin, H., et al. (2010). A Framework for Assessing Operational Madden–Julian Oscillation Forecasts: A CLIVAR MJO Working Group Project. Bulletin of the American Meteorological Society, 91(9), 1247–1258. http://doi.org/10.1175/2010BAMS2816.1
)
# + [markdown] slideshow={"slide_type": "subslide"}
# Finally, lets look at [Elastic Net](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.ElasticNet.html):
# -
scores = sbs_sklearn.train_n_test(poly_std_X, y, N_FOLDS, train_on_minority=True, model=elastic)
sbs_sklearn.plot_kfold_scores(scores)
plot_coeffs(elastic, 'last fitted Elastic Net', 'best option yet')
# + slideshow={"slide_type": "subslide"}
# Ideally, of course, we'd be able to interpret the non-zero coefficients !?:
for i, c in enumerate(elastic.coef_):
if np.abs(c) > 0.0005:
print(sign(c), ' ', poly_std_X.columns[i])
# + [markdown] slideshow={"slide_type": "fragment"}
# [Here](https://scikit-learn.org/stable/modules/linear_model.html#)'s an overview of several of the ideas in this class, plus some others including [LARS Lasso](https://scikit-learn.org/stable/modules/linear_model.html#lars-lasso), another numerical approach for estimating the Lasso.
# + [markdown] slideshow={"slide_type": "slide"}
# **Exercise**:
#
# This is an exercise about varying the tuning parameter $\alpha$ in the Lasso.
#
# 1. Read up on how to use the LARS Lasso at the link just above.
#
# 2. Now adapt the code [here](https://scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_lars.html#sphx-glr-auto-examples-linear-model-plot-lasso-lars-py) to the current data, notebook and circumstances.
#
# 3. In a new box, change the x-axis so that it shows `np.log(alpha)`.
#
# 4. Interpret this diagram.
# -
print(f' - reminder: the log of the alpha we (=I) picked for the Lasso was {np.log(lasso.alpha):2.3}')
# + [markdown] slideshow={"slide_type": "slide"}
# #### Open question: how to pick the tuning parameter?
#
# Recall, we have *parameters*, and we have *tuning parameters*: here, for example, `alpha` and `l1_ratio`
#
# I picked tuning parameters by a process of trial and error.
#
# + [markdown] slideshow={"slide_type": "fragment"}
# Even tuning parameters can suffer from overfit: I probably overfitted them!
#
# How can we save effort & self-doubt, and be more systematic?
# + [markdown] slideshow={"slide_type": "subslide"}
# #### Central Proposal:
#
# **tune**, by using comparing alternatives' test performance in k-fold splits (i.e. test/train splits)
#
# * Known as **Cross-Validation**
#
# * Do systematically, just what I was doing manually, to find a suitable $\alpha$ for Lasso and Ridge
#
# * Should feel fairly familiar since we've now applied `sklearn.model_selection.KFold()` several times
# + [markdown] slideshow={"slide_type": "subslide"}
# #### **Cross-Validation** is a form of **Model Selection**
# * *Introduction to Statistical Learning* Ch. 5
# + [markdown] slideshow={"slide_type": "fragment"}
# Difficulties of cross-validation:
#
# 1. Randomness in K-Fold
#
# 1. What should the K in K-Fold be set to?
#
# * Quick/easier to use a small K like 5 or 10. But
#
# * doesn't this exacerbate the randomness?
#
# * won't we tune $\alpha$ to an unrealistic case with fewer samples than in reality?
# + [markdown] slideshow={"slide_type": "fragment"}
# Next class - lets try this out and move on from Cross-Validation to related techniques in Subsampling.
| 11,291 |
/docs/_static/notebooks/transit.ipynb | 607cf54da41bb060b95ffe6bf234caf15d7faab2 | [
"MIT"
] | permissive | iancze/exoplanet | https://github.com/iancze/exoplanet | 0 | 0 | null | 2019-04-01T08:39:55 | 2019-03-18T19:04:49 | null | Jupyter Notebook | false | false | .py | 1,477,973 | # ---
# jupyter:
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# ---
# %matplotlib inline
# # Transit fitting
# %run notebook_setup
# *exoplanet* includes methods for computing the light curves transiting planets.
# In its simplest form this can be used to evaluate a light curve like you would do with [batman](https://astro.uchicago.edu/~kreidberg/batman/), for example:
# +
import numpy as np
import matplotlib.pyplot as plt
import exoplanet as xo
# The light curve calculation requires an orbit
orbit = xo.orbits.KeplerianOrbit(period=3.456)
# Compute a limb-darkened light curve using starry
t = np.linspace(-0.1, 0.1, 1000)
u = [0.3, 0.2]
light_curve = xo.LimbDarkLightCurve(u).get_light_curve(
orbit=orbit, r=0.1, t=t, texp=0.02).eval()
# Note: the `eval` is needed because this is using Theano in
# the background
plt.plot(t, light_curve, color="C0", lw=2)
plt.ylabel("relative flux")
plt.xlabel("time [days]")
plt.xlim(t.min(), t.max());
# -
# But the real power comes from the fact that this is defined as a [Theano operation](http://deeplearning.net/software/theano/extending/extending_theano.html) so it can be combined with PyMC3 to do transit inference using Hamiltonian Monte Carlo.
#
# ## The transit model in PyMC3
#
# In this section, we will construct a simple transit fit model using *PyMC3* and then we will fit a two planet model to simulated data.
# To start, let's randomly sample some periods and phases and then define the time sampling:
np.random.seed(123)
periods = np.random.uniform(5, 20, 2)
t0s = periods * np.random.rand(2)
t = np.arange(0, 80, 0.02)
yerr = 5e-4
# Then, define the parameters.
# In this simple model, we'll just fit for the limb darkening parameters of the star, and the period, phase, impact parameter, and radius ratio of the planets (note: this is already 10 parameters and running MCMC to convergence using [emcee](https://emcee.readthedocs.io) would probably take at least an hour).
# For the limb darkening, we'll use a quadratic law as parameterized by [Kipping (2013)](https://arxiv.org/abs/1308.0009) and for the joint radius ratio and impact parameter distribution we'll use the parameterization from [Espinoza (2018)](https://arxiv.org/abs/1811.04859).
# Both of these reparameterizations are implemented in *exoplanet* as custom *PyMC3* distributions (:class:`exoplanet.distributions.QuadLimbDark` and :class:`exoplanet.distributions.RadiusImpact` respectively).
# +
import pymc3 as pm
with pm.Model() as model:
# The baseline flux
mean = pm.Normal("mean", mu=0.0, sd=1.0)
# The time of a reference transit for each planet
t0 = pm.Normal("t0", mu=t0s, sd=1.0, shape=2)
# The log period; also tracking the period itself
logP = pm.Normal("logP", mu=np.log(periods), sd=0.1, shape=2)
period = pm.Deterministic("period", pm.math.exp(logP))
# The Kipping (2013) parameterization for quadratic limb darkening paramters
u = xo.distributions.QuadLimbDark("u", testval=np.array([0.3, 0.2]))
# The Espinoza (2018) parameterization for the joint radius ratio and
# impact parameter distribution
r, b = xo.distributions.get_joint_radius_impact(
min_radius=0.01, max_radius=0.1,
testval_r=np.array([0.04, 0.06]),
testval_b=np.random.rand(2)
)
# This shouldn't make a huge difference, but I like to put a uniform
# prior on the *log* of the radius ratio instead of the value. This
# can be implemented by adding a custom "potential" (log probability).
pm.Potential("r_prior", -pm.math.log(r))
# Set up a Keplerian orbit for the planets
orbit = xo.orbits.KeplerianOrbit(period=period, t0=t0, b=b)
# Compute the model light curve using starry
light_curves = xo.LimbDarkLightCurve(u).get_light_curve(
orbit=orbit, r=r, t=t)
light_curve = pm.math.sum(light_curves, axis=-1) + mean
# Here we track the value of the model light curve for plotting
# purposes
pm.Deterministic("light_curves", light_curves)
# In this line, we simulate the dataset that we will fit
y = xo.eval_in_model(light_curve)
y += yerr * np.random.randn(len(y))
# The likelihood function assuming known Gaussian uncertainty
pm.Normal("obs", mu=light_curve, sd=yerr, observed=y)
# Fit for the maximum a posteriori parameters given the simuated
# dataset
map_soln = xo.optimize(start=model.test_point)
# -
# Now we can plot the simulated data and the maximum a posteriori model to make sure that our initialization looks ok.
plt.plot(t, y, ".k", ms=4, label="data")
for i, l in enumerate("bc"):
plt.plot(t, map_soln["light_curves"][:, i], lw=1,
label="planet {0}".format(l))
plt.xlim(t.min(), t.max())
plt.ylabel("relative flux")
plt.xlabel("time [days]")
plt.legend(fontsize=10)
plt.title("map model");
# ## Sampling
#
# Now, let's sample from the posterior defined by this model.
# As usual, there are strong covariances between some of the parameters so we'll use the :class:`exoplanet.PyMC3Sampler` to sample.
np.random.seed(42)
sampler = xo.PyMC3Sampler(window=100, finish=200, chains=4)
with model:
burnin = sampler.tune(tune=2500, start=map_soln, step_kwargs=dict(target_accept=0.9))
trace = sampler.sample(draws=3000)
# After sampling, it's important that we assess convergence.
# We can do that using the `pymc3.summary` function:
pm.summary(trace, varnames=["period", "t0", "r", "b", "u", "mean"])
# That looks pretty good!
# Fitting this without *exoplanet* would have taken a lot more patience.
#
# Now we can also look at the [corner plot](https://corner.readthedocs.io) of some of that parameters of interest:
import corner
samples = pm.trace_to_dataframe(trace, varnames=["period", "r"])
truth = np.concatenate(xo.eval_in_model([period, r], model.test_point, model=model))
corner.corner(samples, truths=truth, labels=["period 1", "period 2", "radius 1", "radius 2"]);
# ## Phase plots
#
# Like in the radial velocity tutorial (:ref:`rv`), we can make plots of the model predictions for each planet.
for n, letter in enumerate("bc"):
plt.figure()
# Get the posterior median orbital parameters
p = np.median(trace["period"][:, n])
t0 = np.median(trace["t0"][:, n])
# Compute the median of posterior estimate of the contribution from
# the other planet. Then we can remove this from the data to plot
# just the planet we care about.
other = np.median(trace["light_curves"][:, :, (n + 1) % 2], axis=0)
# Plot the folded data
x_fold = (t - t0 + 0.5*p) % p - 0.5*p
plt.errorbar(x_fold, y - other, yerr=yerr, fmt=".k", label="data",
zorder=-1000)
# Plot the folded model
inds = np.argsort(x_fold)
inds = inds[np.abs(x_fold)[inds] < 0.3]
pred = trace["light_curves"][:, inds, n] + trace["mean"][:, None]
pred = np.median(pred, axis=0)
plt.plot(x_fold[inds], pred, color="C1", label="model")
# Annotate the plot with the planet's period
txt = "period = {0:.4f} +/- {1:.4f} d".format(
np.mean(trace["period"][:, n]), np.std(trace["period"][:, n]))
plt.annotate(txt, (0, 0), xycoords="axes fraction",
xytext=(5, 5), textcoords="offset points",
ha="left", va="bottom", fontsize=12)
plt.legend(fontsize=10, loc=4)
plt.xlim(-0.5*p, 0.5*p)
plt.xlabel("time since transit [days]")
plt.ylabel("relative flux")
plt.title("planet {0}".format(letter));
plt.xlim(-0.3, 0.3)
# ## Citations
#
# As described in the :ref:`citation` tutorial, we can use :func:`exoplanet.citations.get_citations_for_model` to construct an acknowledgement and BibTeX listing that includes the relevant citations for this model.
# This is especially important here because we have used quite a few model components that should be cited.
with model:
txt, bib = xo.citations.get_citations_for_model()
print(txt)
print("\n".join(bib.splitlines()[:10]) + "\n...")
| 8,200 |
/5주차.ipynb | d6b11e82191d6ec63be42bca021f29e3548edcd5 | [] | no_license | AntCode97/ML-Class | https://github.com/AntCode97/ML-Class | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 64,117 | # ---
# jupyter:
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# language: python
# name: python3
# ---
# * reference:
# * basic: https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/learning_rate_schedules.html
# * advanced: https://mxnet.incubator.apache.org/versions/master/tutorials/gluon/learning_rate_schedules_advanced.html
#
import matplotlib.pyplot as plt
from __future__ import print_function
import math
import matplotlib.pyplot as plt
import mxnet as mx
from mxnet.gluon import nn
from mxnet.gluon.data.vision import transforms
import numpy as np
import copy
# %matplotlib inline
def plot_schedule(schedule_fn, iterations=1500):
# Iteration count starting at 1
iterations = [i+1 for i in range(iterations)]
lrs = [schedule_fn(i) for i in iterations]
plt.style.use('ggplot')
plt.scatter(iterations, lrs)
plt.xlabel("Iteration")
plt.ylabel("Learning Rate")
plt.show()
# ### Basic Part
schedule = mx.lr_scheduler.FactorScheduler(step=250, factor=0.5)
schedule.base_lr = 1
plot_schedule(schedule)
schedule = mx.lr_scheduler.MultiFactorScheduler(step=[250, 750, 900], factor=0.5)
schedule.base_lr = 1
plot_schedule(schedule)
# 不过使用FactorScheduler需要提前计算decay steps。通常情况下指定decay_epochs更为方便。我们可以通过下面的代码将steps转换为epochs:
# ```python
# steps_epochs = [30, 60, 90]
# num_steps_per_epoch = len(data_iter)
# # num_steps_per_epoch = len(dataset)/batchsize
# steps_iterations = [s*num_steps_per_epoch for s in steps_epochs]
# schedule = mx.lr_scheduler.MultiFactorScheduler(step=steps_iterations, factor=0.1)
# sgd_optimizer = mx.optimizer.SGD(learning_rate=0.03, lr_scheduler=schedule)
# trainer = mx.gluon.Trainer(params=net.collect_params(), optimizer=sgd_optimizer)
#
# ```
# * Polynomial Deacy
schedule = mx.lr_scheduler.PolyScheduler(max_update=3000, base_lr=1, pwr=2)
plot_schedule(schedule,3000)
# ### Advanced Part
# * TriangularSchedule
#
# 主要思想：
#
# 1.训练开始阶段使用一个较平滑的"linear warm up"有助于提高稳定性并收敛到更加结果。进一步推出：smooth decay gives improved performance.这个结论跟之前看到一篇论文中结论相反，回头找一下是哪篇论文。
# 2.Quite simply, the schedule linearly increases then decreases between a lower and upper bound. Originally it was suggested this schedule be used as part of a cyclical schedule but more recently researchers have been using a single cycle.
# 3.刚提出来时用的是等腰三角形schedule。后来改进为斜三角形：即increasing fraction < 0.5，这样可以给出更好的结果
class TrianguarSchedule:
def __init__(self, min_lr, max_lr, cycle_length, in_fraction=0.5):
self.min_lr = min_lr
self.max_lr = max_lr
self.cycle_length = cycle_length
self.end_increase = in_fraction * cycle_length
def __call__(self, iteration):
if iteration < self.end_increase:
unit_cycle = iteration / self.end_increase
elif iteration < self.cycle_length:
unit_cycle = 1 - (iteration - self.end_increase) / (self.cycle_length - self.end_increase)
else:
unit_cycle = 0
curr_lr = unit_cycle * (self.max_lr - self.min_lr) + self.min_lr
return curr_lr
schedule = TrianguarSchedule(0.001, 0.1, 1000, 0.2)
plot_schedule(schedule)
# * Cosine Annealing
# 这一部分非常值得关注，因为大名鼎鼎的SGDR（Stochastic Gradient Descent with Warm Restarts）其实就是Cyclical Cosine Annealing。该方法是上面的思想: "smooth decay improves performance"的进一步延伸。
# 
import math
class CosineAnnealingSchedule:
def __init__(self, min_lr, max_lr, cycle_length):
self.min_lr = min_lr
self.max_lr = max_lr
self.cycle_length = cycle_length
def __call__(self, iteration):
if iteration < self.cycle_length:
return (self.max_lr + self.min_lr)/2 + (self.max_lr - self.min_lr)/2 * math.cos(iteration/self.cycle_length * math.pi)
else:
return self.min_lr
plot_schedule(CosineAnnealingSchedule(1, 2, 1000))
# * CyclicalSchedule
class CyclicalSchedule:
def __init__(self, schedule_class, cycle_length, cycle_length_decay=1, cycle_magnitude_decay=1, **kwags):
"""
schedule_class: class of schedule, expected to take `cycle_length` argument.
cycle_length: iterations used for initial cycle (int)
cycle_length_decay: factor multiplied to cycle_length each cycle (float)
cycle_magnitude_decay: factor multiplied learning rate magnitudes each cycle (float)
kwargs: passed to the schedule_class
"""
self.schedule_class = schedule_class
self.length = cycle_length
self.length_decay = cycle_length_decay
self.magnitude_decay = cycle_magnitude_decay
self.kwags = kwags
def __call__(self, iteration):
end_curr_cycle = self.length
cycle_length = self.length # 保存当前的cycle_length
cycle_idx = 0
# 每当iteration超出当前cycle end(即第二个cycle起)，将cycle_end延后一个cycle_length
while iteration >= end_curr_cycle:
cycle_length = cycle_length * self.length_decay
cycle_idx += 1 # 用于decay cycle_magnitude
end_curr_cycle += cycle_length
cycle_offset = iteration - (end_curr_cycle - cycle_length)
schedule = self.schedule_class(cycle_length=cycle_length, **self.kwags)
return schedule(cycle_offset) * self.magnitude_decay**cycle_idx
# * SGDR
plt.figure(figsize=(16,4))
plt.subplot(121)
schedule = CyclicalSchedule(TrianguarSchedule, 200, cycle_length_decay=2, cycle_magnitude_decay=0.5,
min_lr=0.001, max_lr=0.1, in_fraction=0.2)
plot_schedule(schedule,1500)
plt.subplot(122)
schedule = CyclicalSchedule(CosineAnnealingSchedule, 200, cycle_length_decay=2, cycle_magnitude_decay=0.5,
min_lr=0.001, max_lr=0.1)
plot_schedule(schedule,1300)
plt.savefig('SGDR.png')
# ### 其他
# * 一个有用的技巧是将linear warm start与其他basic schedule结合：
class LinearWarmUp():
def __init__(self, schedule, start_lr, length):
"""
schedule: a pre-initialized schedule (e.g. TriangularSchedule(min_lr=0.5, max_lr=2, cycle_length=500))
start_lr: learning rate used at start of the warm-up (float)
length: number of iterations used for the warm-up (int)
"""
self.schedule = schedule
self.start_lr = start_lr
# calling mx.lr_scheduler.LRScheduler effects state, so calling a copy
self.finish_lr = copy.copy(schedule)(0)
self.length = length
def __call__(self, iteration):
if iteration <= self.length:
return iteration * (self.finish_lr - self.start_lr)/(self.length) + self.start_lr
else:
return self.schedule(iteration - self.length)
schedule = mx.lr_scheduler.MultiFactorScheduler(step=[250, 750, 900], factor=0.5)
schedule.base_lr = 1
schedule = LinearWarmUp(schedule, start_lr=0, length=250)
plot_schedule(schedule)
| 6,994 |
/Retail_Scenario.ipynb | 928d7e1109ee81f0fa614ad38c439ea0e5af1097 | [] | no_license | williaml12/Surveillance-Smart-Queuing-System | https://github.com/williaml12/Surveillance-Smart-Queuing-System | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 29,644 | # ---
# jupyter:
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# extension: .py
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# language: python
# name: python3
# ---
# # GO Subset Analysis
#
# This is a demonstrator for some functionality in OAK
# ### Dependencies
#
# We will use seaborn for plotting
# !pip install seaborn
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import scipy.spatial as sp, scipy.cluster.hierarchy as hc
from oaklib.implementations.sqldb.sql_implementation import SqlImplementation
from oaklib.resource import OntologyResource
from oaklib.datamodels.vocabulary import IS_A, PART_OF
from oaklib.utilities.subsets.subset_analysis import compare_all_subsets, get_subset_dict, terms_by_subsets
from pathlib import Path
# ### Establish a database connection
#
# For purposes here we will use a sqlite3 db (created using semantic-sql)
# TODO - download step
oi = SqlImplementation(OntologyResource(slug=f'sqlite:///input/go.db'))
list(oi.all_subset_curies())
# ### Generate a term x subset Dataframe
#
# We use terms_by_subsets from subset_utils
txs = list(terms_by_subsets(oi, prefix='GO', subsumed_score=0.5, min_subsets=3))
txs_df = pd.DataFrame(txs, columns = ['term_id', 'term_label', 'subset', 'present'])
txs_df = txs_df.pivot("term_label", "subset", "present")
txs_df
plt.figure(figsize = (30,50))
sns.set(font_scale = 3)
ax = sns.heatmap(txs_df)
# ### Clustering of terms and subset
#plt.figure(figsize = (30,30))
#plt.xticks(rotation=45)
sns.set(font_scale = 3)
ax = sns.clustermap(txs_df, figsize = (30,80))
# ## Overlap Analysis
#
# First we do a basic overlap of terms in subsets - no use of the graph
pairs = list(compare_all_subsets(oi, prefix='GO'))
def pairs_to_dm(pairs):
"""
Create a distance matrix
"""
rows = [(p.set1_id, p.set2_id, 1-p.jaccard_similarity) for p in pairs]
df = pd.DataFrame(rows, columns =['subsetA', 'subsetB', 'sim'])
df = df.pivot("subsetA", "subsetB", "sim")
return df
df = pairs_to_dm(pairs)
df
import matplotlib.pyplot as plt
plt.figure(figsize = (30,30))
sns.set(font_scale = 3)
ax = sns.heatmap(df)
plt.figure(figsize = (80,80))
sns.set(font_scale = 2)
linkage = hc.linkage(sp.distance.squareform(df), method='average')
ax = sns.clustermap(df, row_linkage=linkage, col_linkage=linkage, figsize=(18,18))
# ### Overlap using the graph
#
#
pairs = list(compare_all_subsets(oi, prefix='GO', extend_down=True))
df = pairs_to_dm(pairs)
linkage = hc.linkage(sp.distance.squareform(df), method='average')
ax = sns.clustermap(df, row_linkage=linkage, col_linkage=linkage, figsize=(18,18))
lts/retail/cpu**
#
# **Note**: To get the queue labels for the different hardware devices, you can go to [this link](https://devcloud.intel.com/edge/get_started/devcloud/).
#
# The following arguments should be passed to the job submission script after the `-F` flag:
# * Model path - `/data/models/intel/person-detection-retail-0013/<MODEL PRECISION>/`. You will need to adjust this path based on the model precision being using on the hardware.
# * Device - `CPU`, `GPU`, `MYRIAD`, `HETERO:FPGA,CPU`
# * Manufacturing video path - `/data/resources/retail.mp4`
# * Manufacturing queue_param file path - `/data/queue_param/retail.npy`
# * Output path - `/output/results/retail/<DEVICE>` This should be adjusted based on the device used in the job.
# * Max num of people - This is the max number of people in queue before the system would redirect them to another queue.
# ## Step 1.1: Submit to an Edge Compute Node with an Intel CPU
# In the cell below, write a script to submit a job to an <a
# href="https://software.intel.com/en-us/iot/hardware/iei-tank-dev-kit-core">IEI
# Tank* 870-Q170</a> edge node with an <a
# href="https://ark.intel.com/products/88186/Intel-Core-i5-6500TE-Processor-6M-Cache-up-to-3-30-GHz-">Intel® Core™ i5-6500TE processor</a>. The inference workload should run on the CPU.
# +
#Submit job to the queue
# cpu_job_id = !qsub queue_job.sh -d . -l nodes=1:tank-870:i5-6500te -F "/data/models/intel/person-detection-retail-0013/FP32/person-detection-retail-0013 CPU /data/resources/retail.mp4 /data/queue_param/retail.npy /output/results/retail/cpu 2" -N cpu_job
print(cpu_job_id[0])
# -
# #### Check Job Status
#
# To check on the job that was submitted, use `liveQStat` to check the status of the job.
#
# Column `S` shows the state of your running jobs.
#
# For example:
# - If `JOB ID`is in Q state, it is in the queue waiting for available resources.
# - If `JOB ID` is in R state, it is running.
import liveQStat
liveQStat.liveQStat()
# #### Get Results
#
# Run the next cell to retrieve your job's results.
import get_results
get_results.getResults(cpu_job_id[0], filename='output.tgz', blocking=True)
# #### Unpack your output files and view stdout.log
# !tar zxf output.tgz
# !cat stdout.log
# #### View stderr.log
# This can be used for debugging
# !cat stderr.log
# #### View Output Video
# Run the cell below to view the output video. If inference was successfully run, you should see a video with bounding boxes drawn around each person detected.
# +
import videoHtml
videoHtml.videoHTML('Retail CPU', ['results/retail/cpu/output_video.mp4'])
# -
# ## Step 1.2: Submit to an Edge Compute Node with a CPU and IGPU
# In the cell below, write a script to submit a job to an <a
# href="https://software.intel.com/en-us/iot/hardware/iei-tank-dev-kit-core">IEI
# Tank* 870-Q170</a> edge node with an <a href="https://ark.intel.com/products/88186/Intel-Core-i5-6500TE-Processor-6M-Cache-up-to-3-30-GHz-">Intel® Core i5-6500TE</a>. The inference workload should run on the **Intel® HD Graphics 530** integrated GPU.
# +
#Submit job to the queue
# gpu_job_id = !qsub queue_job.sh -d . -l nodes=tank-870:i5-6500te:intel-hd-530 -F "/data/models/intel/person-detection-retail-0013/FP32/person-detection-retail-0013 GPU /data/resources/retail.mp4 /data/queue_param/retail.npy /output/results/retail/gpu 2" -N gpu_job
print(gpu_job_id[0])
# -
# ### Check Job Status
#
# To check on the job that was submitted, use `liveQStat` to check the status of the job.
#
# Column `S` shows the state of your running jobs.
#
# For example:
# - If `JOB ID`is in Q state, it is in the queue waiting for available resources.
# - If `JOB ID` is in R state, it is running.
import liveQStat
liveQStat.liveQStat()
# #### Get Results
#
# Run the next cell to retrieve your job's results.
import get_results
get_results.getResults(gpu_job_id[0], filename='output.tgz', blocking=True)
# #### Unpack your output files and view stdout.log
# !tar zxf output.tgz
# !cat stdout.log
# #### View stderr.log
# This can be used for debugging
# !cat stderr.log
# #### View Output Video
# Run the cell below to view the output video. If inference was successfully run, you should see a video with bounding boxes drawn around each person detected.
# +
import videoHtml
videoHtml.videoHTML('Retail GPU', ['results/retail/gpu/output_video.mp4'])
# -
# ## Step 1.3: Submit to an Edge Compute Node with an Intel® Neural Compute Stick 2
# In the cell below, write a script to submit a job to an <a
# href="https://software.intel.com/en-us/iot/hardware/iei-tank-dev-kit-core">IEI
# Tank 870-Q170</a> edge node with an <a href="https://ark.intel.com/products/88186/Intel-Core-i5-6500TE-Processor-6M-Cache-up-to-3-30-GHz-">Intel Core i5-6500te CPU</a>. The inference workload should run on an <a
# href="https://software.intel.com/en-us/neural-compute-stick">Intel Neural Compute Stick 2</a> installed in this node.
# +
#Submit job to the queue
# vpu_job_id = !qsub queue_job.sh -d . -l nodes=tank-870:i5-6500te:intel-ncs2 -F "/data/models/intel/person-detection-retail-0013/FP16/person-detection-retail-0013 MYRIAD /data/resources/retail.mp4 /data/queue_param/retail.npy /output/results/retail/vpu 2" -N vpu_job
print(vpu_job_id[0])
# -
# ### Check Job Status
#
# To check on the job that was submitted, use `liveQStat` to check the status of the job.
#
# Column `S` shows the state of your running jobs.
#
# For example:
# - If `JOB ID`is in Q state, it is in the queue waiting for available resources.
# - If `JOB ID` is in R state, it is running.
import liveQStat
liveQStat.liveQStat()
# #### Get Results
#
# Run the next cell to retrieve your job's results.
import get_results
get_results.getResults(vpu_job_id[0], filename='output.tgz', blocking=True)
# #### Unpack your output files and view stdout.log
# !tar zxf output.tgz
# !cat stdout.log
# #### View stderr.log
# This can be used for debugging
# !cat stderr.log
# #### View Output Video
# Run the cell below to view the output video. If inference was successfully run, you should see a video with bounding boxes drawn around each person detected.
# +
import videoHtml
videoHtml.videoHTML('Retail VPU', ['results/retail/vpu/output_video.mp4'])
# -
# ## Step 1.4: Submit to an Edge Compute Node with IEI Mustang-F100-A10
# In the cell below, write a script to submit a job to an <a
# href="https://software.intel.com/en-us/iot/hardware/iei-tank-dev-kit-core">IEI
# Tank 870-Q170</a> edge node with an <a href="https://ark.intel.com/products/88186/Intel-Core-i5-6500TE-Processor-6M-Cache-up-to-3-30-GHz-">Intel Core™ i5-6500te CPU</a> . The inference workload will run on the <a href="https://www.ieiworld.com/mustang-f100/en/"> IEI Mustang-F100-A10 </a> FPGA card installed in this node.
# +
#Submit job to the queue
# fpga_job_id = !qsub queue_job.sh -d . -l nodes=1:tank-870:i5-6500te:iei-mustang-f100-a10 -F "/data/models/intel/person-detection-retail-0013/FP16/person-detection-retail-0013 HETERO:FPGA,CPU /data/resources/retail.mp4 /data/queue_param/retail.npy /output/results/retail/fpga 2" -N fpga_job
print(fpga_job_id[0])
# -
# ### Check Job Status
#
# To check on the job that was submitted, use `liveQStat` to check the status of the job.
#
# Column `S` shows the state of your running jobs.
#
# For example:
# - If `JOB ID`is in Q state, it is in the queue waiting for available resources.
# - If `JOB ID` is in R state, it is running.
import liveQStat
liveQStat.liveQStat()
# #### Get Results
#
# Run the next cell to retrieve your job's results.
import get_results
get_results.getResults(fpga_job_id[0], filename='output.tgz', blocking=True)
# #### Unpack your output files and view stdout.log
# !tar zxf output.tgz
# !cat stdout.log
# #### View stderr.log
# This can be used for debugging
# !cat stderr.log
# #### View Output Video
# Run the cell below to view the output video. If inference was successfully run, you should see a video with bounding boxes drawn around each person detected.
# +
import videoHtml
videoHtml.videoHTML('Retail FPGA', ['results/retail/fpga/output_video.mp4'])
# -
# ***Wait!***
#
# Please wait for all the inference jobs and video rendering to complete before proceeding to the next step.
#
# ## Step 2: Assess Performance
#
# Run the cells below to compare the performance across all 4 devices. The following timings for the model are being comapred across all 4 devices:
#
# - Model Loading Time
# - Average Inference Time
# - FPS
# +
import matplotlib.pyplot as plt
device_list=['cpu', 'gpu', 'fpga', 'vpu']
inference_time=[]
fps=[]
model_load_time=[]
for device in device_list:
with open('results/retail/'+device+'/stats.txt', 'r') as f:
inference_time.append(float(f.readline().split("\n")[0]))
fps.append(float(f.readline().split("\n")[0]))
model_load_time.append(float(f.readline().split("\n")[0]))
# -
plt.bar(device_list, inference_time)
plt.xlabel("Device Used")
plt.ylabel("Total Inference Time in Seconds")
plt.show()
plt.bar(device_list, fps)
plt.xlabel("Device Used")
plt.ylabel("Frames per Second")
plt.show()
plt.bar(device_list, model_load_time)
plt.xlabel("Device Used")
plt.ylabel("Model Loading Time in Seconds")
plt.show()
# # Step 3: Update Proposal Document
#
# Now that you've completed your hardware testing, you should go back to the proposal document and validate or update your originally proposed hardware. Once you've updated your proposal, you can move onto the next scenario.
| 12,293 |
/code/빅데이터경진대회_강창구/python_강창구.ipynb | 5d438838769504cc084ebdd1f905282620a0000c | [
"Apache-2.0"
] | permissive | sanjiny3000backup/UOS2020_Project-For-Safe-Schoolzone | https://github.com/sanjiny3000backup/UOS2020_Project-For-Safe-Schoolzone | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 2,021,879 | # ---
# jupyter:
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# ---
# ## Generating Sample Data
# One very valuable tool for working with the algorithms we will study is the ability to create fake data sets with certain properties. Let's start by looking at creating data with a uniform distribution.
# +
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import rcParams
#rcParams['text.usetex'] = True
rcParams['text.usetex'] = False
rcParams['text.latex.preamble'] = r'\usepackage{amsmath}'
N = 1000
x = np.linspace(0.0, 10.0, N)
y = np.random.rand(N)
plt.title('Uniform Distribution')
plt.scatter(x,y)
plt.show()
# -
# Now let's make a normal distribution. For the one-dimensional case, we will shgow the distribution as a histogram. W need to generate lots of values to start seeing the shape in this kind of plot. The "loc" variable is the mean and the "scale" variable is the standard deviation.
# +
import matplotlib.mlab as mlab
import math
N = 100000
mu = 0.0
variance = 1.0
sigma = math.sqrt(variance)
#draw histograms
y = np.random.normal(loc = mu, scale=sigma, size=N)
plt.title('Normal Distribution')
plt.hist(y, bins = 100, alpha = 0.5)
# draw the normal distribution pdf
x = np.linspace(mu-5*variance,mu+5*variance, 100)
plt.show()
# -
# larger values of the variance will 'flatten' the distribution, while smaller values will create a spike at the mean.
# +
pl, (a1, a2, a3) = plt.subplots(1,3, figsize=(15,15), sharey=True,
subplot_kw = {'adjustable':'box-forced'})
y1 = np.random.normal(loc = mu, scale=sigma, size=N)
y2 = np.random.normal(loc = mu, scale=5*sigma, size=N)
y3 = np.random.normal(loc = mu, scale=sigma/10, size=N)
a1.hist(y1, bins = 100)
#a1.set_title(r'$\mu = 0.0, \sigma = 1.0$')
a1.set_xlim([-4, 4])
a2.hist(y2, bins = 100)
#a2.set_title(r'$\mu = 0.0, \sigma = 5.0$')
a2.set_xlim([-4, 4])
a3.hist(y3, bins = 100)
a3.set_xlim([-4, 4])
#a3.set_title(r'$\mu = 0.0, \sigma = 0.1$')
plt.subplots_adjust(top=0.3)
plt.show()
# -
# OK, now let's look at multivariate normal distributions. this is a little more complicated. Instead of just a single mean and standard deviation (sd), we have a vector of means and a covariance matrix for the variance values (standard deviations squared).
# +
N = 1000
x,y = np.random.multivariate_normal(mean=[5,5], cov = [[1, 0], [0, 1]], size=N).T
plt.title('Multivariate Normal Distribution')
plt.scatter(x,y)
plt.show()
# -
# The covariance matrix is a special representation of the variance of the distribution. Since it is a matrix, we can represent the relationships between different dimensions of variance and we can represent correlation of the distribution on different axes:
# +
N=10000
x1,y1 = np.random.multivariate_normal(mean=[5,5], cov = [[1, -.95], [-0.95, 1]], size=N).T
x2,y2 = np.random.multivariate_normal(mean=[5,5], cov = [[1, .8], [0.8, 1]], size=N).T
x3,y3 = np.random.multivariate_normal(mean=[5,5], cov = [[1, .5], [0.5, 1]], size=N).T
pl, (a1, a2, a3) = plt.subplots(1,3, figsize=(15,15), sharey=True,
subplot_kw = {'aspect':1.0,'adjustable':'box-forced'})
#a1.set_title('Multivariate Normal Distribution')
a1.scatter(x1,y1)
#a1.set_title(r'$cov = \left \{ \begin{matrix} 1.0 & -0.95 \\ -0.95 & 1.0 \end{matrix} \right \}$')
a1.set_xlim([0, 10])
a1.set_ylim([0, 10])
a2.scatter(x2,y2)
#a2.set_title(r'$cov = \left \{ \begin{matrix} 1.0 & 0.8 \\ 0.8 & 1.0 \end{matrix} \right \}$')
a2.set_xlim([0, 10])
a2.set_ylim([0, 10])
a3.scatter(x3, y3)
#a3.set_title(r'$cov = \left \{ \begin{matrix} 1.0 & 0.5 \\ 0.5 & 1.0 \end{matrix} \right \}$')
a3.set_xlim([0, 10])
a3.set_ylim([0, 10])
plt.show()
# -
# Note how the off-diagonal entries of the covariance matrix affect the shape of the data.
#
# We can create some useful data sets for experimentation using the simple tools above. For example, one important assumption in modeling is that the variation (called residuals in statistics) of the model from actual data is always normally distributed. We can test this assumption by starting with a model and then generating "realistic" data that conforms to the normal distribution of residuals.
#
# We start with a line:
# $$ y = f(x) = ax + b $$
# We build a simple example by setting $a = 1$ and $b = 1$.
x = np.arange(0,10)
plt.plot(x, x+1, color='r')
plt.title(r'$y = x + 1$')
plt.show()
# Now lets add some randomness
N = 100
x = np.linspace(0.0, 10.0, N)
y_model = lambda x: x + 1
y = [np.random.normal(loc = y_model(_x), scale=1.0, size=1) for _x in x]
plt.scatter(x,y)
plt.plot(x, x+1, color='r')
#plt.title(r'$y \sim \mathcal{N}(x + 1,1) $')
plt.show()
# We've generated one hundred points that are normally distributed about the length of the line. If we produce a linear model from this data, it should very closely match our line.
import statsmodels.api as sm
model = sm.OLS(y, sm.add_constant(x))
results = model.fit()
p = results.params
results.cov_params()
# +
#print the regression line too
pl, (a1, a2) = plt.subplots(1,2, figsize=(15,15), sharey=True,
subplot_kw = {'aspect':1.0,'adjustable':'box-forced'})
y_hat = p[0] + p[1]*x
a1.scatter(x,y)
a1.plot(x, x+1, color='r')
a1.plot(x, y_hat, color='g')
a2.plot(x, x+1, color='r')
a2.plot(x, y_hat, color='g')
plt.show()
# -
# In the above we can see that the generated line overlaps nicely with the assumed line, at least near the data used for the estimate. This illustrates an important point about machine learning in general. If you don't have musch data, your learned function will diverge from the 'actual' (unknown) function when you get away from the data used to build the model. Thngs get better when you have lots of data. If we rerun the above experiment with 1,000,000 points, the red and green lines line up nearly perfectly (try it!)
# These tools will be used to create test data throughout the course to demonstrate different capabilities and problems with various Machine Learning methods.
#
# Some data is problematic for linear regression, for example. It can be instructive to generate data that fits into problematic categories so that we can see what goes wrong with common machine learning techniques. We'll look at this in a later lecture.
N = 100
x = np.linspace(0.0, 10.0, N)
y_model = lambda x: x+x*x + 1
y = [np.random.normal(loc = y_model(_x), scale=5.0, size=1) for _x in x]
plt.scatter(x,y)
plt.plot(x, x+x*x+1, color='r')
#plt.title(r'$y \sim \mathcal{N}(x+x^2 + 1,1) $')
plt.show()
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# ### accident age
# + id="cq-16AGUDoXZ"
accident_age = pd.read_csv("accident_age.csv", sep='\t', thousands=',', na_values='-')
accident_age.fillna(0, inplace=True) # log scale 시 0 그래프가 음수 이하로 떨어지는 것을 방지하기 위해 NaN을 1로 수정
# + id="SCXuwLFwFJrM"
accident_age["~14"] = accident_age["14세 이하"]
accident_age["15~30"] = accident_age[["15~20세", "21~30세"]].sum(axis=1)
accident_age["31~60"] = accident_age[["31~40세", "41~50세", "51~60세"]].sum(axis=1)
accident_age["61~"] = accident_age[["61-64세", "65~70세", "71세이상"]].sum(axis=1)
accident_age_dead = accident_age[(accident_age["지역"] == "합계") & (accident_age["구분"] == "사망자수")]
accident_age_injured = accident_age[(accident_age["지역"] == "합계") & (accident_age["구분"] == "부상자수")]
accident_age_dead.set_index("기간", inplace=True)
accident_age_injured.set_index("기간", inplace=True)
accident_age_dead = accident_age_dead.drop(["지역", "구분", "14세 이하", "15~20세", "21~30세", "31~40세", "41~50세", "51~60세", "61-64세", "65~70세", "71세이상", "불명", "합계"], axis=1)
accident_age_injured = accident_age_injured.drop(["지역", "구분", "14세 이하", "15~20세", "21~30세", "31~40세", "41~50세", "51~60세", "61-64세", "65~70세", "71세이상", "불명", "합계"], axis=1)
accident_age_dead = accident_age_dead.applymap(int)
accident_age_injured = accident_age_injured.applymap(int)
# + id="WzL9TmDkHqgz" colab={"base_uri": "https://localhost:8080/", "height": 238} executionInfo={"status": "ok", "timestamp": 1606740316504, "user_tz": -540, "elapsed": 10115, "user": {"displayName": "\uac15\ucc3d\uad6c", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEzlq-7n82tuYUcfKOAkqO__ThslcCr5AnKojz=s64", "userId": "05127586336711810896"}} outputId="3f92c36f-67c0-4836-bbd0-917f2c5a87dd"
accident_age_injured.head()
# + id="oZRAJBIcMAoo" colab={"base_uri": "https://localhost:8080/", "height": 238} executionInfo={"status": "ok", "timestamp": 1606740316508, "user_tz": -540, "elapsed": 9949, "user": {"displayName": "\uac15\ucc3d\uad6c", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEzlq-7n82tuYUcfKOAkqO__ThslcCr5AnKojz=s64", "userId": "05127586336711810896"}} outputId="ad2be0c5-6422-4dc3-909a-ab10b9e04366"
accident_age_dead.head()
# + id="VrtfSfuqgsbT" colab={"base_uri": "https://localhost:8080/", "height": 282} executionInfo={"status": "ok", "timestamp": 1606740316513, "user_tz": -540, "elapsed": 9801, "user": {"displayName": "\uac15\ucc3d\uad6c", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEzlq-7n82tuYUcfKOAkqO__ThslcCr5AnKojz=s64", "userId": "05127586336711810896"}} outputId="2c04bc29-861b-4163-b50b-f7cb600507ad"
plt.plot(accident_age_injured)
plt.legend(accident_age_injured.columns.tolist())
# + id="xxDOCLlVhrFb" colab={"base_uri": "https://localhost:8080/", "height": 282} executionInfo={"status": "ok", "timestamp": 1606740317004, "user_tz": -540, "elapsed": 10125, "user": {"displayName": "\uac15\ucc3d\uad6c", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEzlq-7n82tuYUcfKOAkqO__ThslcCr5AnKojz=s64", "userId": "05127586336711810896"}} outputId="3d1e70eb-16a1-455e-fdb5-716abeb5da9d"
plt.plot(accident_age_dead)
plt.legend(accident_age_dead.columns.tolist())
# + id="VhwM2UspeOrM" colab={"base_uri": "https://localhost:8080/", "height": 228} executionInfo={"status": "ok", "timestamp": 1606741034456, "user_tz": -540, "elapsed": 1187, "user": {"displayName": "\uac15\ucc3d\uad6c", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEzlq-7n82tuYUcfKOAkqO__ThslcCr5AnKojz=s64", "userId": "05127586336711810896"}} outputId="e703fc3b-3b26-48e2-9f90-6b10badfe754"
fig, ax = plt.subplots(1, 2, figsize=(40, 10))
fig.patch.set_facecolor('#222A35')
# + id="uTiZlwpjMOqZ"
ax[0].fill_between(accident_age_injured.index, 0, accident_age_injured["~14"], label="~14", color="#fef0d9")
ax[0].fill_between(accident_age_injured.index, accident_age_injured["~14"], accident_age_injured["61~"], label="61~", color="#fdbb84")
ax[0].fill_between(accident_age_injured.index, accident_age_injured["61~"], accident_age_injured["15~30"], label="15~30", color="#ef6548")
ax[0].fill_between(accident_age_injured.index, accident_age_injured["15~30"], accident_age_injured["31~60"], label="31~60", color="#990000")
ax[0].set_title("부상자수", fontsize=15, color="white")
ax[0].spines["top"].set_visible(False)
ax[0].spines["bottom"].set_visible(False)
ax[0].spines["right"].set_visible(False)
ax[0].spines["left"].set_visible(False)
ax[0].tick_params(axis='x', colors='white')
ax[0].tick_params(axis='y', colors='white')
ax[0].set_facecolor("#222A35")
# + id="7xQscqiAN5rs"
ax[1].fill_between(accident_age_dead.index, 0, accident_age_dead["~14"], label="~14", color="#fef0d9")
ax[1].fill_between(accident_age_dead.index, accident_age_dead["~14"], accident_age_dead["15~30"], label="61~", color="#fdbb84")
ax[1].fill_between(accident_age_dead.index, accident_age_dead["15~30"], accident_age_dead["31~60"], label="15~30", color="#ef6548")
ax[1].fill_between(accident_age_dead.index, accident_age_dead["31~60"], accident_age_dead["61~"], label="31~60", color="#990000")
ax[1].set_title("사망자수", fontsize=15, color="white")
ax[1].spines["top"].set_visible(False)
ax[1].spines["bottom"].set_visible(False)
ax[1].spines["right"].set_visible(False)
ax[1].spines["left"].set_visible(False)
ax[1].tick_params(axis='x', colors='white')
ax[1].tick_params(axis='y', colors='white')
ax[1].legend(labelcolor="white", edgecolor="white", facecolor="None")
ax[1].set_facecolor("#222A35")
# + id="vQegF7VxjNhz" colab={"base_uri": "https://localhost:8080/", "height": 232} executionInfo={"status": "ok", "timestamp": 1606741035432, "user_tz": -540, "elapsed": 1671, "user": {"displayName": "\uac15\ucc3d\uad6c", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEzlq-7n82tuYUcfKOAkqO__ThslcCr5AnKojz=s64", "userId": "05127586336711810896"}} outputId="145daf6d-11d2-4583-ad34-abb73aad9614"
fig
# + id="MmdKD4WO2VNa"
fig.savefig('demo.png')
# + [markdown] id="4szNnr3POSBj"
# ### accident causation
# + id="ecBp7f-aPgCN"
accident_causation = pd.read_csv("accident_causation.csv", sep="\t", thousands=',', na_values='-', skiprows=1)
accident_causation.fillna(0, inplace=True)
accident_causation.set_index("기간", inplace=True)
# + id="k9Gq2WUZ1Y0-"
accident_causation["기타"] = accident_causation[["추락", "익사", "화재", "중독", "기타"]].sum(axis=1)
accident_causation_male = accident_causation[accident_causation["성별"] == "남자"]
accident_causation_female = accident_causation[accident_causation["성별"] == "여자"]
accident_causation_total = accident_causation[accident_causation["성별"] == "계"]
# + colab={"base_uri": "https://localhost:8080/", "height": 238} id="t13Wn9VC1Zn8" executionInfo={"status": "ok", "timestamp": 1606740425025, "user_tz": -540, "elapsed": 2116, "user": {"displayName": "\uac15\ucc3d\uad6c", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEzlq-7n82tuYUcfKOAkqO__ThslcCr5AnKojz=s64", "userId": "05127586336711810896"}} outputId="b1e088db-8117-4900-d122-bc67c2dd855e"
accident_causation_male.head()
# + colab={"base_uri": "https://localhost:8080/"} id="9cbO0Pz61i-J" executionInfo={"status": "ok", "timestamp": 1606740425031, "user_tz": -540, "elapsed": 1961, "user": {"displayName": "\uac15\ucc3d\uad6c", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEzlq-7n82tuYUcfKOAkqO__ThslcCr5AnKojz=s64", "userId": "05127586336711810896"}} outputId="221a0981-4b2c-4131-9e7f-a8acf87136c4"
cols = ["계", "교통사고", "기타"]
for col in cols:
accident_causation_male[col] = (accident_causation_male[col] / accident_causation_total["14세이하 인구"]) * 1e6
accident_causation_female[col] = (accident_causation_female[col] / accident_causation_total["14세이하 인구"]) * 1e6
accident_causation_total[col] = (accident_causation_total[col] / accident_causation_total["14세이하 인구"]) * 1e6
# + id="0DXVVDkFTQJ-" colab={"base_uri": "https://localhost:8080/"} executionInfo={"status": "ok", "timestamp": 1606740425040, "user_tz": -540, "elapsed": 1776, "user": {"displayName": "\uac15\ucc3d\uad6c", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEzlq-7n82tuYUcfKOAkqO__ThslcCr5AnKojz=s64", "userId": "05127586336711810896"}} outputId="3ac19056-cffb-4261-fcd2-b85b13879cd2"
accident_causation["기타"] = accident_causation[["추락", "익사", "화재", "중독", "기타"]].sum(axis=1)
accident_causation_male = accident_causation[accident_causation["성별"] == "남자"]
accident_causation_female = accident_causation[accident_causation["성별"] == "여자"]
accident_causation_total = accident_causation[accident_causation["성별"] == "계"]
cols = ["계", "교통사고", "기타"]
for col in cols:
accident_causation_male[col] = (accident_causation_male[col] / accident_causation_total["14세이하 인구"]) * 1e6
accident_causation_female[col] = (accident_causation_female[col] / accident_causation_total["14세이하 인구"]) * 1e6
accident_causation_total[col] = (accident_causation_total[col] / accident_causation_total["14세이하 인구"]) * 1e6
accident_causation_male = accident_causation_male.drop(["아동 10만명당 안전사고 사망자수", "14세이하 인구", "추락", "익사", "화재", "중독", "계"], axis=1)
accident_causation_female = accident_causation_female.drop(["아동 10만명당 안전사고 사망자수", "14세이하 인구", "추락", "익사", "화재", "중독", "계"], axis=1)
accident_causation_total = accident_causation_total.drop(["아동 10만명당 안전사고 사망자수", "14세이하 인구", "추락", "익사", "화재", "중독", "계"], axis=1)
accident_causation_male = accident_causation_male.drop("성별", axis=1)
accident_causation_female = accident_causation_female.drop("성별", axis=1)
accident_causation_total = accident_causation_total.drop("성별", axis=1)
# + id="JGXkUcLfPp6-" colab={"base_uri": "https://localhost:8080/", "height": 238} executionInfo={"status": "ok", "timestamp": 1606740427240, "user_tz": -540, "elapsed": 2004, "user": {"displayName": "\uac15\ucc3d\uad6c", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEzlq-7n82tuYUcfKOAkqO__ThslcCr5AnKojz=s64", "userId": "05127586336711810896"}} outputId="63ad8821-b668-4677-f396-3c6b40eb84bb"
accident_causation_male.head()
# + id="iga0XyZXWW1s" colab={"base_uri": "https://localhost:8080/", "height": 238} executionInfo={"status": "ok", "timestamp": 1606740427244, "user_tz": -540, "elapsed": 1862, "user": {"displayName": "\uac15\ucc3d\uad6c", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEzlq-7n82tuYUcfKOAkqO__ThslcCr5AnKojz=s64", "userId": "05127586336711810896"}} outputId="e80f63ed-cd71-4b1a-d29a-90e28a4f53de"
accident_causation_female.head()
# + id="4L6DRXgDYujr" colab={"base_uri": "https://localhost:8080/", "height": 238} executionInfo={"status": "ok", "timestamp": 1606740427249, "user_tz": -540, "elapsed": 1706, "user": {"displayName": "\uac15\ucc3d\uad6c", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEzlq-7n82tuYUcfKOAkqO__ThslcCr5AnKojz=s64", "userId": "05127586336711810896"}} outputId="6682e66f-544b-4c80-f04a-0592c3da0f6b"
accident_causation_total.head()
# + id="4CO8-9_bkMfO" colab={"base_uri": "https://localhost:8080/", "height": 298} executionInfo={"status": "ok", "timestamp": 1606741019265, "user_tz": -540, "elapsed": 1169, "user": {"displayName": "\uac15\ucc3d\uad6c", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEzlq-7n82tuYUcfKOAkqO__ThslcCr5AnKojz=s64", "userId": "05127586336711810896"}} outputId="e4aedbb6-a684-4e99-c522-eb8eaa36cabc"
## matplotlib
fig, ax = plt.subplots(1, 2, figsize = (24, 8))
fig.patch.set_facecolor("#222A35")
# + id="5Ljoj8aTW3NY"
ax[0].barh(accident_causation_female.index, accident_causation_female["교통사고"], color="#fc8d59", label="car")
ax[0].barh(accident_causation_female.index, accident_causation_female["기타"], left=accident_causation_female["교통사고"], color="#fef0d9", label="other")
ax[0].set_title("여자", color="white")
ax[0].set_xlim(0, 35)
ax[0].invert_xaxis()
ax[0].invert_yaxis()
ax[0].set_yticks([])
ax[0].spines["top"].set_visible(False)
ax[0].spines["bottom"].set_visible(False)
ax[0].spines["right"].set_visible(False)
ax[0].spines["left"].set_visible(False)
ax[0].tick_params(axis='x', colors='white')
ax[0].tick_params(axis='y', colors='white')
ax[0].legend(["교통사고", "기타"], labelcolor="white", edgecolor="white", facecolor="None", loc=3)
ax[0].set_facecolor("#222A35")
# + id="bIeGdpl-TH8N"
ax[1].barh(accident_causation_male.index, accident_causation_male["기타"], left=accident_causation_male["교통사고"], color="#fef0d9", label="other")
ax[1].barh(accident_causation_male.index, accident_causation_male["교통사고"], color="#d7301f", label="car")
ax[1].invert_yaxis()
ax[1].set_xlim(0, 35)
ax[1].set_title("남자", color="white")
ax[1].spines["top"].set_visible(False)
ax[1].spines["bottom"].set_visible(False)
ax[1].spines["right"].set_visible(False)
ax[1].spines["left"].set_visible(False)
ax[1].tick_params(axis='x', colors='white')
ax[1].tick_params(axis='y', colors='white')
ax[1].legend(["교통사고", "기타"], labelcolor="white", edgecolor="white", facecolor="None")
ax[1].set_facecolor("#222A35")
# + id="kJoATKpUW5Ji" colab={"base_uri": "https://localhost:8080/", "height": 291} executionInfo={"status": "ok", "timestamp": 1606741020846, "user_tz": -540, "elapsed": 1629, "user": {"displayName": "\uac15\ucc3d\uad6c", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEzlq-7n82tuYUcfKOAkqO__ThslcCr5AnKojz=s64", "userId": "05127586336711810896"}} outputId="285f6675-a9a1-49bd-bc2e-498b7903f649"
fig.tight_layout()
fig.subplots_adjust(wspace=0.0992)
fig
# + id="4_YSNKdmDwZ6"
fig.savefig('demo2.png')
# + id="st6XastR3zlI"
for i in range(len(accident_causation_female.index)):
for j in range(len(accident_causation_female.columns)):
accident_causation_female.iloc[i, j] = float("%.1f" % (accident_causation_female.iloc[i, j]))
for i in range(len(accident_causation_male.index)):
for j in range(len(accident_causation_male.columns)):
accident_causation_male.iloc[i, j] = float("%.1f" % (accident_causation_male.iloc[i, j]))
# + colab={"base_uri": "https://localhost:8080/", "height": 542} id="sWi-ufi23g9M" executionInfo={"status": "ok", "timestamp": 1606227260472, "user_tz": -540, "elapsed": 1042, "user": {"displayName": "\uac15\ucc3d\uad6c", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEzlq-7n82tuYUcfKOAkqO__ThslcCr5AnKojz=s64", "userId": "05127586336711810896"}} outputId="918e0d5a-eef9-438f-8994-3e389bb21d62"
## plotly.graph_objects
import plotly.graph_objects as go
fig = go.Figure()
fig.add_trace(go.Funnel(
name="female other",
y=accident_causation_female.index,
x=accident_causation_female["기타"],
textposition = "inside",
textinfo="value",
marker={ "color": ["#fef0d9"] * len(accident_causation_female.index) },
connector={ "fillcolor" : "#cbc0ad" }
))
fig.add_trace(go.Funnel(
name="female car",
y=accident_causation_female.index,
x=accident_causation_female["교통사고"],
textposition = "inside",
textinfo="value",
marker={ "color": ["#ef6548"] * len(accident_causation_female.index) },
connector={ "fillcolor" : "#bf6039" }
))
fig.add_trace(go.Funnel(
name="male car",
y=accident_causation_male.index,
x=accident_causation_male["교통사고"],
textposition = "inside",
textinfo="value",
marker={ "color": ["#990000"] * len(accident_causation_male.index) },
connector={ "fillcolor" : "#7a0000" }
))
fig.add_trace(go.Funnel(
name="male other",
y=accident_causation_male.index,
x=accident_causation_male["기타"],
textposition = "inside",
textinfo="value",
marker={ "color": ["#fdd49e"] * len(accident_causation_male.index) },
connector={ "fillcolor" : "#caa97e" }
))
# layout = go.Layout(
# paper_bgcolor='rgba(0,0,0,0)',
# plot_bgcolor='rgba(0,0,0,0)'
# )
# fig.update_layout(
# paper_bgcolor='rgba(0,0,0,0)',
# plot_bgcolor='rgba(0,0,0,0)',
# font_color="white",
# autosize=False,
# width=800,
# height=400)
fig.show()
# + [markdown] id="MhgxxmWDkvxl"
# ### accident bicycle/ alcoholic/ senior
# + id="sqiasfnhpGPs"
# accident_bicycle
accident_bicycle = pd.read_csv("accident_bicycle.csv", sep='\t', thousands=',', na_values='-')
accident_bicycle.fillna(0, inplace=True)
# + id="gnDgi6b4L_m7"
accident_bicycle_total = accident_bicycle[accident_bicycle["지역"] == "합계"]
accident_bicycle_total = accident_bicycle_total.drop("지역", axis=1)
# + colab={"base_uri": "https://localhost:8080/", "height": 457} id="oGc4ebMJMB7h" executionInfo={"status": "ok", "timestamp": 1606740506188, "user_tz": -540, "elapsed": 973, "user": {"displayName": "\uac15\ucc3d\uad6c", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEzlq-7n82tuYUcfKOAkqO__ThslcCr5AnKojz=s64", "userId": "05127586336711810896"}} outputId="e9792ba9-132a-4f6a-c6de-204c5df12624"
accident_bicycle_total
# + id="0dvFGA5HqPps"
# accident_alcoholic
accident_alcoholic = pd.read_csv("accident_alcoholic.csv", sep='\t', thousands=',', na_values='-')
accident_alcoholic.fillna(0, inplace=True)
# + id="r9teDqLBM6qA"
accident_alcoholic_total = accident_alcoholic[accident_alcoholic["지역"] == "합계"]
accident_alcoholic_total = accident_alcoholic_total.drop("지역", axis=1)
accident_alcoholic_total.reset_index(inplace=True, drop=True)
accident_alcoholic_total = accident_alcoholic_total.drop([0, 1], axis=0)
# + colab={"base_uri": "https://localhost:8080/", "height": 457} id="StT_8Ce8NLrc" executionInfo={"status": "ok", "timestamp": 1606740508782, "user_tz": -540, "elapsed": 1141, "user": {"displayName": "\uac15\ucc3d\uad6c", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEzlq-7n82tuYUcfKOAkqO__ThslcCr5AnKojz=s64", "userId": "05127586336711810896"}} outputId="0d0d659c-e28c-4200-f503-f265f59a19de"
accident_alcoholic_total
# + id="1xsk1rNMNT4k"
# accident_children
accident_children = pd.read_csv("accident_children.csv", sep='\t', thousands=',', na_values='-', skiprows=1)
accident_children.fillna(0, inplace=True)
accident_children.columns = ["기간", "지역", "어린이 교통사고 발생건수", "어린이 교통사고 사망자수", "어린이 교통사고 부상자수",
"어린이 보호구역내 어린이 교통사고 발생건수", "어린이 보호구역내 어린이 교통사고 사망자수", "어린이 보호구역내 어린이 교통사고 부상자수",
"어린이 보행 사망자수", "어린이 보행 부상자수"]
# + id="1nH0RlfdNeug"
accident_children_total = accident_children[accident_children["지역"] == "합계"]
accident_children_total = accident_children_total.drop("지역", axis=1)
# + id="3wUhBlknN7X7"
accident_childwalk_total = accident_children_total[["기간", "어린이 보행 사망자수", "어린이 보행 부상자수"]]
accident_schoolzone_total = accident_children_total[["기간", "어린이 보호구역내 어린이 교통사고 발생건수", "어린이 보호구역내 어린이 교통사고 사망자수", "어린이 보호구역내 어린이 교통사고 부상자수"]]
accident_children_total = accident_children_total[["기간", "어린이 교통사고 발생건수", "어린이 교통사고 사망자수", "어린이 교통사고 부상자수"]]
# + colab={"base_uri": "https://localhost:8080/", "height": 457} id="OYHqbaiFWoWU" executionInfo={"status": "ok", "timestamp": 1606740511149, "user_tz": -540, "elapsed": 802, "user": {"displayName": "\uac15\ucc3d\uad6c", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEzlq-7n82tuYUcfKOAkqO__ThslcCr5AnKojz=s64", "userId": "05127586336711810896"}} outputId="316e667f-e230-4f9e-956c-c1c05b4791d0"
accident_childwalk_total
# + colab={"base_uri": "https://localhost:8080/", "height": 475} id="bfieL-b1PVpd" executionInfo={"status": "ok", "timestamp": 1606740511873, "user_tz": -540, "elapsed": 488, "user": {"displayName": "\uac15\ucc3d\uad6c", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEzlq-7n82tuYUcfKOAkqO__ThslcCr5AnKojz=s64", "userId": "05127586336711810896"}} outputId="d37a0847-d1e7-4c77-f575-8fda3810e6f0"
accident_schoolzone_total
# + colab={"base_uri": "https://localhost:8080/", "height": 206} id="lEjS1VIGPXw3" executionInfo={"status": "ok", "timestamp": 1606740513261, "user_tz": -540, "elapsed": 1315, "user": {"displayName": "\uac15\ucc3d\uad6c", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEzlq-7n82tuYUcfKOAkqO__ThslcCr5AnKojz=s64", "userId": "05127586336711810896"}} outputId="f32cd587-2cae-4b39-c77b-503700fa50ac"
accident_children_total.head()
# + colab={"base_uri": "https://localhost:8080/", "height": 458} id="-RpaLNKRTcAB" executionInfo={"status": "ok", "timestamp": 1606741008247, "user_tz": -540, "elapsed": 1454, "user": {"displayName": "\uac15\ucc3d\uad6c", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEzlq-7n82tuYUcfKOAkqO__ThslcCr5AnKojz=s64", "userId": "05127586336711810896"}} outputId="660ae6ec-0761-4c4c-f326-959495079c08"
fig, ax = plt.subplots(1, 1, figsize=(20, 12))
fig.patch.set_facecolor("#222A35")
ax.bar(accident_children_total["기간"], accident_children_total["어린이 교통사고 발생건수"], color="#fef0d9", label="어린수 교통사고 발생건수")
ax.bar(accident_childwalk_total["기간"], accident_childwalk_total["어린이 보행 사망자수"] + accident_childwalk_total["어린이 보행 부상자수"], color="#fdbb84", label="어린이 보행 부상자수")
ax.plot(accident_alcoholic_total["기간"], accident_alcoholic_total["발생건수"], color="#d7301f", label="음주운전 교통사고 발생건수", linewidth=2.5)
ax.plot(accident_bicycle_total["기간"], accident_bicycle_total["발생건수"], color="#fc8d59", label="자전거 교통사고 발생건수", linewidth=2.5)
ax.set_title("음주운전/자전거/어린이 교통사고 발생건수", fontsize=15, color="white")
ax.spines["top"].set_visible(False)
ax.spines["bottom"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.spines["left"].set_visible(False)
ax.tick_params(axis='x', colors='white')
ax.tick_params(axis='y', colors='white')
ax.legend(labelcolor="white", edgecolor="white", facecolor="None", loc='center left', bbox_to_anchor=(1, 0.5))
ax.set_facecolor("#222A35")
# + id="Jd3MP0S5gLw1"
fig.savefig('demo3.png', bbox_inches='tight')
# lgd = ax.legend(handles, labels, loc='upper center', bbox_to_anchor=(0.5,-0.1))
# text = ax.text(-0.2,1.05, "Aribitrary text", transform=ax.transAxes)
# ax.set_title("Trigonometry")
# ax.grid('on')
# fig.savefig('samplefigure', bbox_extra_artists=(lgd,text), bbox_inches='tight')
# + [markdown] id="Klob62_jyQJ_"
# ## accident children/alcoholic/bicycle
# + id="saCVd_3rlUk3"
# accident_children
accident_children = pd.read_csv("accident_children.csv", sep='\t', thousands=',', na_values='-', skiprows=1)
accident_children.fillna(0, inplace=True)
accident_children.columns = ["기간", "지역", "어린이 교통사고 발생건수", "어린이 교통사고 사망자수", "어린이 교통사고 부상자수",
"어린이 보호구역내 어린이 교통사고 발생건수", "어린이 보호구역내 어린이 교통사고 사망자수", "어린이 보호구역내 어린이 교통사고 부상자수",
"어린이 보행 사망자수", "어린이 보행 부상자수"]
# + id="TJikqlJCuIAQ"
accident_children_2019 = accident_children[accident_children["기간"] == 2019]
accident_children_2019 = accident_children_2019[["기간", "지역", "어린이 교통사고 발생건수", "어린이 교통사고 사망자수", "어린이 교통사고 부상자수"]]
accident_children_2019.columns = ["기간", "지역", "발생건수", "사망자수", "부상자수"]
accident_children_2019 = accident_children_2019.drop("기간", axis=1)
accident_children_2019.sort_values(by="지역", ascending=True, inplace=True)
accident_children_2019.set_index("지역", inplace=True)
# + id="zLjsHiirIKlf"
accident_children_total = accident_children.groupby("지역").agg(sum)
accident_children_total = accident_children_total.drop("기간", axis=1)
accident_children_total = accident_children_total[["어린이 교통사고 발생건수", "어린이 교통사고 사망자수", "어린이 교통사고 부상자수"]]
accident_children_total.columns = ["발생건수", "사망자수", "부상자수"]
# + id="cPpM7JgJv-N3" colab={"base_uri": "https://localhost:8080/", "height": 238} executionInfo={"status": "ok", "timestamp": 1605424838225, "user_tz": -540, "elapsed": 1825, "user": {"displayName": "\uac15\ucc3d\uad6c", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEzlq-7n82tuYUcfKOAkqO__ThslcCr5AnKojz=s64", "userId": "05127586336711810896"}} outputId="0c78722e-218c-4f28-abed-d9bda2def934"
accident_children_2019.head()
# + id="0H_0JQ46I0ks" colab={"base_uri": "https://localhost:8080/", "height": 238} executionInfo={"status": "ok", "timestamp": 1605424838227, "user_tz": -540, "elapsed": 1672, "user": {"displayName": "\uac15\ucc3d\uad6c", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEzlq-7n82tuYUcfKOAkqO__ThslcCr5AnKojz=s64", "userId": "05127586336711810896"}} outputId="83336f37-62c7-40e4-e681-a5d53083fee8"
accident_children_total.head()
# + id="q4NthKn59Add"
#accident senior
accident_senior = pd.read_csv("accident_senior.csv", sep='\t', thousands=',', na_values='-', skiprows=1)
accident_senior.fillna(0, inplace=True)
accident_senior.columns = ["기간", "지역", "노인 교통사고 발생건수", "노인 교통사고 사망자수", "노인 교통사고 부상자수",
"노인운전자 교통사고 사망자수", "노인운전자 교통사고 부상자수", "노인운전자 교통사고 부상자수",
"노인 보행자 사망자수", "노인 보행자 부상자수"]
# + id="7DPW7vWx9NaH"
accident_senior_2019 = accident_senior[accident_senior["기간"] == 2019]
accident_senior_2019 = accident_senior_2019[["기간", "지역", "노인 교통사고 발생건수", "노인 교통사고 사망자수", "노인 교통사고 부상자수"]]
accident_senior_2019.columns = ["기간", "지역", "발생건수", "사망자수", "부상자수"]
accident_senior_2019 = accident_senior_2019.drop("기간", axis=1)
accident_senior_2019.sort_values(by="지역", ascending=True, inplace=True)
accident_senior_2019.set_index("지역", inplace=True)
# + id="dRgOw9Y3JU3S"
accident_senior_total = accident_senior.groupby("지역").agg(sum)
accident_senior_total = accident_senior_total.drop("기간", axis=1)
accident_senior_total = accident_senior_total[["노인 교통사고 발생건수", "노인 교통사고 사망자수", "노인 교통사고 부상자수"]]
accident_senior_total.columns = ["발생건수", "사망자수", "부상자수"]
# + id="Fk-jhKml9XHe" colab={"base_uri": "https://localhost:8080/", "height": 238} executionInfo={"status": "ok", "timestamp": 1605424839082, "user_tz": -540, "elapsed": 1941, "user": {"displayName": "\uac15\ucc3d\uad6c", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEzlq-7n82tuYUcfKOAkqO__ThslcCr5AnKojz=s64", "userId": "05127586336711810896"}} outputId="333b0821-306d-4520-e63b-0609b42e170b"
accident_senior_2019.head()
# + id="stFgdWDOJcn3" colab={"base_uri": "https://localhost:8080/", "height": 238} executionInfo={"status": "ok", "timestamp": 1605424839084, "user_tz": -540, "elapsed": 1799, "user": {"displayName": "\uac15\ucc3d\uad6c", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEzlq-7n82tuYUcfKOAkqO__ThslcCr5AnKojz=s64", "userId": "05127586336711810896"}} outputId="c659e4df-3ad6-4fc6-c8dc-c5933262b61b"
accident_senior_total.head()
# + id="iu8zFgmxuJEB"
# seoul_population
seoul_population = pd.read_csv("seoul_population.csv", sep='\t', thousands=',', na_values='-', skiprows=2)
seoul_population.fillna(0, inplace=True)
# + id="3ZtBpBIIZFSQ"
seoul_population = seoul_population[["기간", "자치구", "계"]]
seoul_population.columns = ["기간", "지역", "인구수"]
# + id="NFzRamwmvUJA"
seoul_population_2019 = seoul_population[seoul_population["기간"] == 2019]
seoul_population_2019 = seoul_population_2019.drop("기간", axis=1)
seoul_population_2019.sort_values(by="지역", ascending=True, inplace=True)
seoul_population_2019.set_index("지역", inplace=True)
# + id="wy7PbM8WKu8P"
seoul_population_total = seoul_population.groupby("지역").agg(sum)
seoul_population_total = seoul_population_total.drop("기간", axis=1)
seoul_population_total.sort_values(by="지역", ascending=True, inplace=True)
# + id="aDl2TsR8vV11" colab={"base_uri": "https://localhost:8080/", "height": 238} executionInfo={"status": "ok", "timestamp": 1605424839901, "user_tz": -540, "elapsed": 1851, "user": {"displayName": "\uac15\ucc3d\uad6c", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEzlq-7n82tuYUcfKOAkqO__ThslcCr5AnKojz=s64", "userId": "05127586336711810896"}} outputId="359d0e97-09eb-47f5-d640-f440862387ce"
seoul_population_2019.head()
# + id="vKsMz6-zLbQg" colab={"base_uri": "https://localhost:8080/", "height": 238} executionInfo={"status": "ok", "timestamp": 1605424839903, "user_tz": -540, "elapsed": 1734, "user": {"displayName": "\uac15\ucc3d\uad6c", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEzlq-7n82tuYUcfKOAkqO__ThslcCr5AnKojz=s64", "userId": "05127586336711810896"}} outputId="9241fd3f-5c33-4da5-adc0-8ac4c4c1b186"
seoul_population_total.head()
# + id="6GEqP913_qDH"
# seoul accident
seoul_accident = pd.read_csv("seoul_accident.csv", sep='\t', thousands=',', na_values='-')
seoul_accident.fillna(0, inplace=True)
seoul_accident = seoul_accident[["기간", "지역", "발생건수", "사망자수", "부상자수"]]
# + id="X4eurYn4pPBG" colab={"base_uri": "https://localhost:8080/", "height": 206} executionInfo={"status": "ok", "timestamp": 1606227927226, "user_tz": -540, "elapsed": 948, "user": {"displayName": "\uac15\ucc3d\uad6c", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEzlq-7n82tuYUcfKOAkqO__ThslcCr5AnKojz=s64", "userId": "05127586336711810896"}} outputId="e6202340-144c-4234-d46b-8a2657bd7791"
seoul_accident.tail()
# + id="lKx-jLjQAFQc"
seoul_accident_2019 = seoul_accident[seoul_accident["기간"] == 2019]
seoul_accident_2019 = seoul_accident_2019.drop("기간", axis=1)
seoul_accident_2019.sort_values(by="지역", ascending=True, inplace=True)
seoul_accident_2019.set_index("지역", inplace=True)
# + id="cPiTkHdtL111"
seoul_accident_total = seoul_accident.groupby("지역").agg(sum)
seoul_accident_total = seoul_accident_total.drop("기간", axis=1)
seoul_accident_total.sort_values(by="지역", ascending=True, inplace=True)
# + id="v9BfmrwX_4ZI" colab={"base_uri": "https://localhost:8080/", "height": 238} executionInfo={"status": "ok", "timestamp": 1605424840497, "user_tz": -540, "elapsed": 1765, "user": {"displayName": "\uac15\ucc3d\uad6c", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEzlq-7n82tuYUcfKOAkqO__ThslcCr5AnKojz=s64", "userId": "05127586336711810896"}} outputId="95cd7064-c57a-4ada-fb3a-07741d471810"
seoul_accident_2019.head()
# + id="GqW1mAHhMKtJ" colab={"base_uri": "https://localhost:8080/", "height": 238} executionInfo={"status": "ok", "timestamp": 1605424840499, "user_tz": -540, "elapsed": 1604, "user": {"displayName": "\uac15\ucc3d\uad6c", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEzlq-7n82tuYUcfKOAkqO__ThslcCr5AnKojz=s64", "userId": "05127586336711810896"}} outputId="fb0c2707-2438-470b-ffa4-4378889d0556"
seoul_accident_total.head()
# + id="nAq-h3Mvxg3h"
cols = ["발생건수", "부상자수", "사망자수"]
for col in cols:
accident_bicycle_2019[col] = accident_bicycle_2019[col] / seoul_population_2019["인구수"] * 1e5
accident_alcoholic_2019[col] = accident_alcoholic_2019[col] / seoul_population_2019["인구수"] * 1e5
accident_children_2019[col] = accident_children_2019[col] / seoul_population_2019["인구수"] * 1e5
accident_senior_2019[col] = accident_senior_2019[col] / seoul_population_2019["인구수"] * 1e5
accident_bicycle_total[col] = accident_bicycle_total[col] / seoul_population_total["인구수"] * 1e5
accident_alcoholic_total[col] = accident_alcoholic_total[col] / seoul_population_total["인구수"] * 1e5
accident_children_total[col] = accident_children_total[col] / seoul_population_total["인구수"] * 1e5
accident_senior_total[col] = accident_senior_total[col] / seoul_population_total["인구수"] * 1e5
# + id="gt7_h8TfzoWp" colab={"base_uri": "https://localhost:8080/", "height": 394} executionInfo={"status": "ok", "timestamp": 1605424840503, "user_tz": -540, "elapsed": 1166, "user": {"displayName": "\uac15\ucc3d\uad6c", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEzlq-7n82tuYUcfKOAkqO__ThslcCr5AnKojz=s64", "userId": "05127586336711810896"}} outputId="2af64e41-7f64-44cb-e4a1-64a6bffcd1c7"
accident_children_2019.sort_values(by="발생건수", ascending=False, inplace=True)
accident_children_2019.head(10)
# + id="8DvIcqSbztDT" colab={"base_uri": "https://localhost:8080/", "height": 394} executionInfo={"status": "ok", "timestamp": 1605424840506, "user_tz": -540, "elapsed": 1022, "user": {"displayName": "\uac15\ucc3d\uad6c", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEzlq-7n82tuYUcfKOAkqO__ThslcCr5AnKojz=s64", "userId": "05127586336711810896"}} outputId="bd421a3f-912a-4d1a-814a-85b9af0fa61c"
accident_senior_2019.sort_values(by="발생건수", ascending=False, inplace=True)
accident_senior_2019.head(10)
# + id="dafyxv-4KBQ1" colab={"base_uri": "https://localhost:8080/", "height": 394} executionInfo={"status": "ok", "timestamp": 1605424841072, "user_tz": -540, "elapsed": 1434, "user": {"displayName": "\uac15\ucc3d\uad6c", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEzlq-7n82tuYUcfKOAkqO__ThslcCr5AnKojz=s64", "userId": "05127586336711810896"}} outputId="250a38fd-24c2-429e-d216-eb244f9fb0e2"
accident_children_total.sort_values(by="발생건수", ascending=False).head(10)
# + id="4FJCCNn4KPRL" colab={"base_uri": "https://localhost:8080/", "height": 394} executionInfo={"status": "ok", "timestamp": 1605424841075, "user_tz": -540, "elapsed": 1278, "user": {"displayName": "\uac15\ucc3d\uad6c", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEzlq-7n82tuYUcfKOAkqO__ThslcCr5AnKojz=s64", "userId": "05127586336711810896"}} outputId="6a91ccbb-6b0f-4197-dfc6-501f021e0dfb"
accident_senior_total.sort_values(by="발생건수", ascending=False).head(10)
# + id="kfbx8SiT49CF" colab={"base_uri": "https://localhost:8080/"} executionInfo={"status": "ok", "timestamp": 1605424841721, "user_tz": -540, "elapsed": 1732, "user": {"displayName": "\uac15\ucc3d\uad6c", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEzlq-7n82tuYUcfKOAkqO__ThslcCr5AnKojz=s64", "userId": "05127586336711810896"}} outputId="d3e368b6-6935-4c63-a0c6-8f3fafe1c3e8"
# ls
# + [markdown] id="bGnc20Ycx1TC"
# ### pydeck map visualtion
# + id="8lq32laqzQq0"
# !pip install pydeck
# !pip install geopandas
# + id="wLqUQi8FzjdO"
import pydeck as pdk
import numpy as np
import pandas as pd
import geopandas as gpd
# + id="iB96n4a6zKRH" colab={"base_uri": "https://localhost:8080/"} executionInfo={"status": "ok", "timestamp": 1605430489728, "user_tz": -540, "elapsed": 1227, "user": {"displayName": "\uac15\ucc3d\uad6c", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEzlq-7n82tuYUcfKOAkqO__ThslcCr5AnKojz=s64", "userId": "05127586336711810896"}} outputId="0867acc0-ed5b-4843-9b96-21776c769163"
!%env MAPBOX_API_KEY = "pk.eyJ1IjoicnhkY3hkcm5pbmUiLCJhIjoiY2toaXN1NmhkMXA5aTJ0cGpwcmQ3djVyYSJ9.hg-MHbDx-nDif1aO--qIoA"
# + id="hcdd-xzd_G4o" colab={"base_uri": "https://localhost:8080/"} executionInfo={"status": "ok", "timestamp": 1605428841090, "user_tz": -540, "elapsed": 1177, "user": {"displayName": "\uac15\ucc3d\uad6c", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEzlq-7n82tuYUcfKOAkqO__ThslcCr5AnKojz=s64", "userId": "05127586336711810896"}} outputId="59efffa6-885c-4e42-cac7-21cb79fddab3"
# cd /content/drive/My Drive/Colab Notebooks/uos bigdata 2020/to_csv
# + id="pF2TDSGr_QqW" colab={"base_uri": "https://localhost:8080/", "height": 206} executionInfo={"status": "ok", "timestamp": 1605429704520, "user_tz": -540, "elapsed": 1318, "user": {"displayName": "\uac15\ucc3d\uad6c", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEzlq-7n82tuYUcfKOAkqO__ThslcCr5AnKojz=s64", "userId": "05127586336711810896"}} outputId="130103f4-f41f-46d7-beaa-7f175c34ba75"
geo_data = gpd.read_file('test.geojson')
geo_data.head()
# + id="xw4fcsA5ANbj"
def multipolygon_to_coordinates(x):
lon, lat = x[0].exterior.xy
return [[x, y] for x, y in zip(lon, lat)]
geo_data['coordinates'] = geo_data['geometry'].apply(multipolygon_to_coordinates)
del geo_data['geometry']
geo_data = pd.DataFrame(geo_data)
# + id="GeNqQRhIBCyi"
children = pd.read_csv("accident_children_total.csv")
senior = pd.read_csv("accident_senior_total.csv")
# + id="bUgAG6qdBQNI"
children.drop(np.where(children["지역"] == "합계")[0][0], axis=0, inplace=True)
senior.drop(np.where(senior["지역"] == "합계")[0][0], axis=0, inplace=True)
children.rename(columns = {"지역" : "SIG_KOR_NM"}, inplace = True)
senior.rename(columns = {"지역" : "SIG_KOR_NM"}, inplace = True)
# + id="qdcUYexMDi7l"
children = pd.merge(geo_data, children, on="SIG_KOR_NM")
senior = pd.merge(geo_data, senior, on="SIG_KOR_NM")
# + id="F0f5KytsErT2"
children["normalized_value"] = children["발생건수"] / children["발생건수"].max()
# + id="eBoXn2TJETVK" colab={"base_uri": "https://localhost:8080/", "height": 73, "referenced_widgets": ["78c4536ce2b94c889a7e0fee1e4ed4dc"]} executionInfo={"status": "ok", "timestamp": 1605430497352, "user_tz": -540, "elapsed": 4122, "user": {"displayName": "\uac15\ucc3d\uad6c", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEzlq-7n82tuYUcfKOAkqO__ThslcCr5AnKojz=s64", "userId": "05127586336711810896"}} outputId="d49681a9-5255-44f6-d75e-4bbaf8e007ff"
# Make layer
layer = pdk.Layer(
'PolygonLayer', # 사용할 Layer 타입
children, # 시각화에 쓰일 데이터프레임
get_polygon='coordinates', # geometry 정보를 담고있는 컬럼 이름
get_fill_color='[0, 255*normalized_value, 0]', # 각 데이터 별 rgb 또는 rgba 값 (0~255)
pickable=True, # 지도와 interactive 한 동작 on
auto_highlight=True # 마우스 오버(hover) 시 박스 출력
)
# Set the viewport location
center = [126.986, 37.565]
view_state = pdk.ViewState(
longitude=center[0],
latitude=center[1],
zoom=10)
# Render
r = pdk.Deck(layers=[layer], initial_view_state=view_state)
r.show()
# + [markdown] id="r9abjZrif6p6"
# ## schoolzone
# + colab={"base_uri": "https://localhost:8080/", "height": 735} id="82k3CmDEoBah" executionInfo={"status": "ok", "timestamp": 1607421568839, "user_tz": -540, "elapsed": 2689, "user": {"displayName": "\uac15\ucc3d\uad6c", "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GhEzlq-7n82tuYUcfKOAkqO__ThslcCr5AnKojz=s64", "userId": "05127586336711810896"}} outputId="e18bf356-7441-49ee-b295-28083c688397"
import numpy as np
from brokenaxes import brokenaxes
schoolzone = pd.read_csv("stats_161601.xls.csv", thousands=',')
schoolzone = schoolzone.transpose()
schoolzone.columns = schoolzone.iloc[0, :]
schoolzone = schoolzone.iloc[1:, :]
schoolzone.reset_index(inplace=True)
sub_category = ["초등학교", "유치원", "어린이집"]
colors = ["#d7301f", "#fc8d59", "#fdd49e"]
between_bar_padding = 0.6
within_bar_padding = 1
fig = plt.figure(figsize=(20, 12))
fig.set_facecolor('#222A35')
bax = brokenaxes(ylims=((2000, 3500), (5750, 7500)), hspace=.02, diag_color='w')
tick_label = list(schoolzone["index"].values)
tick_coord = np.arange(len(tick_label))
width = (1 / len(sub_category)) * between_bar_padding
for i in range(len(sub_category)):
bax.bar(tick_coord + width * i, schoolzone[sub_category[i]], width * within_bar_padding, label=sub_category[i], color=colors[i])
bax.set_title("연도별 어린이 보호구역 지정현황", fontsize=15, color="white")
bax.set_facecolor("#222A35")
bax.axs[0].spines["left"].set_color('white')
bax.axs[1].spines["left"].set_color('white')
bax.axs[1].spines["bottom"].set_color('white')
bax.axs[0].tick_params(axis='x', colors='white')
bax.axs[0].tick_params(axis='y', colors='white')
bax.axs[1].tick_params(axis='x', colors='white')
bax.axs[1].tick_params(axis='y', colors='white')
bax.axs[1].set_xticklabels([''] + tick_label)
bax.legend(labelcolor="white", edgecolor="white", facecolor="None", loc='center left', bbox_to_anchor=(1, 0.5))
fig.savefig('demo4.png', bbox_inches='tight')
# + id="KUKKPkOYyNQ6"
| 45,174 |
/lab9_20200511.ipynb | b91a597f3a59c868ecb5e34df6c25719553d1989 | [] | no_license | MatienkoAndrew/BikeSharingDataset | https://github.com/MatienkoAndrew/BikeSharingDataset | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 2,516,290 | # ---
# jupyter:
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# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
# %matplotlib inline
from sklearn.linear_model import (LinearRegression, LassoCV, RidgeCV, Lasso, Ridge )
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import (StandardScaler, MinMaxScaler, Normalizer)
from sklearn.metrics import (mean_squared_error, mean_absolute_error, r2_score)
from sklearn.linear_model import (ElasticNet, ElasticNetCV)
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import OneHotEncoder
# ## 1. Загрузить данные day.csv
# url = 'https://archive.ics.uci.edu/ml/datasets/Bike%20Sharing%20Dataset'
bike = pd.read_csv('day.csv')
bike.head()
bike.drop('instant', axis=1, inplace=True)
bike.head()
# ## 2.Сделать предварительный анализ данных: шкалы измерения, типы данных, корреляцию и т.п.
# Только нужные признаки
bike[['season', 'weekday', 'temp', 'atemp', 'weathersit', 'hum', 'windspeed', 'casual', 'registered']].describe()
bike.info()
# Пустых значений нет
# Все данные числовые, кроме столбца (dteday)
pd.DataFrame(bike.season.value_counts())
for col in bike.columns:
display(pd.DataFrame(bike[col].value_counts()))
bike.holiday.value_counts().plot(kind='bar')
plt.title("Holiday")
bike.workingday.value_counts().plot(kind='bar')
plt.title('Рабочий день')
bike.weathersit.value_counts().plot(kind='bar')
plt.title('Погода')
bike.season.value_counts().plot(kind='bar')
plt.title('season')
bike.mnth.value_counts().plot(kind='bar')
plt.title('Month')
fig, ax = plt.subplots(1, 1, figsize=(10, 10))
sns.heatmap(bike.corr(), annot=True, fmt='.2g', ax=ax)
# ## 3. Визуализировать данные
sns.boxplot(x='season', y='cnt', data=bike)
sns.pairplot(data=bike)
# ## 4. X – все кроме cnt, y - cnt
# Убираю:
#
# - dteday - безполезный признак
#
# - mnth - сильно коррелирует с season, а с cnt коррелирует меньше
#
# - yr - не понятно по какой причине сильно коррелирует с целевой функцией cnt, хотя всего 2 значения. Уберу пожалуй
#
# - temp - очень сильно коррелирует с atemp. Одинаково коррелируют с cnt
#
# - casual: так как он вместе c registered образует cnt. Но 'registered' лучше коррелирует с 'cnt'
X = bike.drop('cnt', axis=1).copy()
y = bike['cnt'].copy()
#X.drop(['dteday', 'yr', 'temp', 'casual'], axis=1, inplace=True) ##--работает лучше на 10 велосипедов
X.drop(['dteday', 'mnth', 'yr', 'temp', 'casual'], axis=1, inplace=True)
# # OneHotEncoder
# X = pd.get_dummies(X, columns=['season', 'mnth','weekday', 'weathersit']) ##-- работает лучше на 10 велосипедов
X = pd.get_dummies(X, columns=['season', 'weekday', 'weathersit'])
X.head()
# ## 5. Разбить выборку в соотношении 70 на 30 (%)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
X_train.shape, X_test.shape
# ## 6. Провести стандартизацию X
# ### Проведем MinMaxScaling только для одного признака 'registered'
def minmaxscaling_transform(X, features):
X_new = X.copy()
for col in X[features]:
min_vals = X[col].min()
max_vals = X[col].max()
X_new[col] = X[col].apply(lambda x: (x - min_val)/(max_val-min_val))
return X_new
X_train.columns
X_train_scaled = minmaxscaling_transform(X_train, ['registered'])
X_test_scaled = minmaxscaling_transform(X_test, ['registered'])
X_train_scaled.head()
# +
# ## Масштабирование
# scaler = StandardScaler().fit(X_train)
# X_train_scaled = scaler.transform(X_train)
# X_test_scaled = scaler.transform(X_test)
# +
# pd.DataFrame(X_train_scaled, columns=X.columns)
# +
# ## Масштабирование
# scaler_1 = StandardScaler().fit(X)
# X_scaled = scaler_1.transform(X)
# X_scaled = scaler_1.transform(X)
# -
X_scaled = minmaxscaling_transform(X, ['registered'])
alphas = np.linspace(0.01, 1, 10)
alpha_scores = dict()
for i, alpha in enumerate(alphas):
cvs = cross_val_score(Lasso(alpha=alpha), X_scaled, y, cv=10, scoring='neg_mean_absolute_error')
alpha_scores[alpha] = -cvs.mean()
best_alpha = min(alpha_scores, key=alpha_scores.get)
alpha_scores
# # Lasso
lasso = Lasso(alpha=best_alpha, random_state=42)
lasso.fit(X_train_scaled, y_train)
y_pred = lasso.predict(X_test_scaled)
mean_absolute_error(y_test, y_pred)
def stat(model, X):
display(pd.DataFrame(np.append(model.intercept_, model.coef_).reshape(1, -1),
columns=np.append('intercept_', np.array(X.columns))))
stat(lasso, X)
y_test[:10]
y_pred.reshape(1, -1)[0][:10]
# # Linear Regression
# +
linreg = LinearRegression()
linreg.fit(X_train_scaled, y_train)
y_pred = linreg.predict(X_test_scaled)
mean_absolute_error(y_test, y_pred)
# -
# # Ridge
# +
ridge = Ridge(alpha=0.9)
ridge.fit(X_train_scaled, y_train)
y_pred = ridge.predict(X_test_scaled)
mean_absolute_error(y_test, y_pred)
# -
# # Random Forest
# +
from sklearn.ensemble import RandomForestRegressor
random_forest = RandomForestRegressor(random_state=42)
random_forest.fit(X_train_scaled, y_train)
y_pred = random_forest.predict(X_test_scaled)
mean_absolute_error(y_test, y_pred)
# +
plt.figure(figsize=(16,14))
plt.subplot(2, 2, 1)
plt.scatter(y_train, lasso.predict(X_train_scaled), alpha=0.5, color='red', label='train')
plt.scatter(y_test, lasso.predict(X_test_scaled), alpha=0.5, color='blue', label='test')
plt.xlabel('Target')
plt.ylabel('Predict')
plt.title('Lasso')
plt.grid(True)
plt.legend()
plt.subplot(2, 2, 2)
plt.scatter(y_train, linreg.predict(X_train_scaled), alpha=0.5, color='red', label='train')
plt.scatter(y_test, linreg.predict(X_test_scaled), alpha=0.5, color='blue', label='test')
plt.xlabel('Target')
plt.ylabel('Predict')
plt.title('LinReg')
plt.grid(True)
plt.legend()
plt.subplot(2, 2, 3)
plt.scatter(y_train, ridge.predict(X_train_scaled), alpha=0.5, color='red', label='train')
plt.scatter(y_test, ridge.predict(X_test_scaled), alpha=0.5, color='blue', label='test')
plt.xlabel('Target')
plt.ylabel('Predict')
plt.title('Ridge')
plt.grid(True)
plt.legend()
plt.subplot(2, 2, 4)
plt.scatter(y_train, random_forest.predict(X_train_scaled), alpha=0.5, color='red', label='train')
plt.scatter(y_test, random_forest.predict(X_test_scaled), alpha=0.5, color='blue', label='test')
plt.xlabel('Target')
plt.ylabel('Predict')
plt.title('RandomForest')
plt.grid(True)
plt.legend()
# -
# ## Чем прямее линия - тем лучше наша модель
# ## 7. Написать функцию обучения модели и вывода RMSE, coef, R^2
def root_mean_squared_error(y_true, y_pred):
return np.sqrt(((y_pred - y_true) ** 2).mean())
# +
def train_validate_report(model, X_train, X_test, y_train, y_test, feature_names, X_scaled:bool,
model_name=None, verbose=True):
model.fit(X_train, y_train)
predictions = model.predict(X_test)
rmse = root_mean_squared_error(y_test, predictions)
r2 = r2_score(y_test, predictions)
coefs = {'intercept': l.intercept_, **dict(zip(feature_names, model.coef_))}
coefs_df = pd.DataFrame.from_dict({'value': coefs})
if verbose:
if model_name:
if X_scaled: model_name += '(scaled)'
print(f'##### {model_name} ####')
print(f'RMSE = {rmse}')
print(f'R^2 = {r2}')
print(coefs_df)
print()
return rmse, r2, coefs, model
# +
def train_validate_report(model, X_train, X_test, y_train, y_test):
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
rmse = root_mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)
coefs = pd.DataFrame(np.append(model.intercept_, model.coef_).reshape(1, -1),
columns=np.append('intercept_', np.array(X.columns)))
#coefs = {'intercept': l.intercept_, **dict(zip(feature_names, model.coef_))}
print("Model:", model)
print("\tRMSE:", rmse)
print("\tR^2:", r2)
display(coefs)
# if verbose:
# if model_name:
# if X_scaled: model_name += '(scaled)'
# print(f'##### {model_name} ####')
# print(f'RMSE = {rmse}')
# print(f'R^2 = {r2}')
# print(coefs_df)
# print()
# return rmse, r2, coefs, model
| 8,445 |
/Data Preprocessing and Training.ipynb | f16c351ff2e4e1638cee6b3a6749ef94bb15017f | [
"MIT"
] | permissive | tianyi-m/Codeforces-Raif-ML-Round-1 | https://github.com/tianyi-m/Codeforces-Raif-ML-Round-1 | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 80,947 | # ---
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# ---
import pandas as pd
import numpy as np
# ## Data Preprocessing
# * add a result column (done)
# * drop the time, full_time_goals column (done)
# * fill in the null values in the dataset as averages (done)
# ### Data Exploration
dataset = pd.read_csv("train.csv", index_col=0)
dataset.shape
dataset.tail(5)
dataset.head(5)
dataset.isnull().sum()
dataset[dataset["home_team"] == 271]
# ### Data Cleansing
# #### Add "result" column
result = [1 if x > y else -1 if x < y else 0 for x, y in zip(dataset["full_time_home_goals"], dataset["full_time_away_goals"])]
dataset["result"] = result
dataset.head()
dataset["Referee"].unique()
# Drop unecessary columns
dataset = dataset.drop(columns=["Time", "full_time_home_goals", "full_time_away_goals"])
dataset.head()
# #### Fill in null values with averages
# +
cols = ["home_shots", "away_shots", 'home_shots_on_target',
'away_shots_on_target', 'home_fouls', 'away_fouls', 'home_corners',
'away_corners', 'home_yellow_cards', 'away_yellow_cards',
'home_red_cards', 'away_red_cards']
home_teams = dataset["home_team"].unique()
away_teams = dataset["away_team"].unique()
for home_team, away_team in zip(home_teams, away_teams):
home_team_matches = dataset[dataset["home_team"] == home_team]
away_team_matches = dataset[dataset["away_team"] == away_team]
for idx in range(0, len(cols), 2):
home_col = cols[idx]
away_col = cols[idx + 1]
home_col_new_value = home_team_matches[home_col].notnull().mean()
away_col_new_value = away_team_matches[away_col].notnull().mean()
dataset.loc[(dataset["home_team"] == home_team), home_col] = home_team_matches[home_col].fillna(value = home_col_new_value)
dataset.loc[(dataset["away_team"] == away_team), away_col] = away_team_matches[away_col].fillna(value = away_col_new_value)
# -
dataset.head()
dataset.columns
dataset.isnull().sum()
dataset.head(10)
# #### Save processed data
dataset.to_csv("processed_train.csv", index = False)
# ## Train, Test and Save Model using Naive Bayes and SVM
# +
from sklearn.model_selection import train_test_split
from sklearn import svm
from sklearn.naive_bayes import GaussianNB
from sklearn import metrics
import pickle
X_train, X_test, y_train, y_test = train_test_split(dataset.drop(columns=["result"]), dataset["result"], test_size=0.3,random_state=109)
# -
# ### Naive Bayes
gnb = GaussianNB()
gnb.fit(X_train, y_train)
# +
#Predict the response for test dataset
y_pred = gnb.predict(X_test)
# Model Accuracy: how often is the classifier correct?
print("Accuracy:", metrics.accuracy_score(y_test, y_pred))
# -
filename = 'modelFile'
pickle.dump(model, open(filename, 'wb'))
# #### SVM (not trained because training time is too long)
#Create a svm Classifier
clf = svm.SVC(kernel='poly', degree=2, gamma=1, verbose=True) # Linear Kernel
#Train the model using the training sets
clf.fit(X_train, y_train)
# +
#Predict the response for test dataset
y_pred = clf.predict(X_test)
# Model Accuracy: how often is the classifier correct?
print("Accuracy:",metrics.accuracy_score(y_test, y_pred))
| 3,400 |
/exploration_template.ipynb | e1f169d7bb810cb7acd4a47b4baa2147510903f3 | [] | no_license | Abhijit99dey/Communicate-Data-Findings | https://github.com/Abhijit99dey/Communicate-Data-Findings | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 928,715 | # ---
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# ---
# # Bike Sharing Analysis with Ford GoBike Data
#
# ## By Mr. Sadiq Ghalib
#
# ## Introduction
#
# > Over the past decade, bicycle-sharing systems are growing in variety and recognition in cities across the globe.
# Bicycle-sharing systems enable users to rent bicycles for brief visits, usually half-hour or less.
# Thanks to the increase in info technologies, it's simple for a user of the system to access a dock at intervals the system to unlock or come bicycles.
# These technologies additionally give a wealth of knowledge that may be wont to explore however these bike-sharing systems area unit used.
# In this project, I will perform an exploratory analysis of data provided by Ford GoBike, a bike-share system provider.
#
# ## Preliminary Wrangling
#
# It's time to collect and explore our data. In this project, we will focus on the record of individual trips taken in from 2017 to November, 2018.
#
# Ford GoBike Data: https://s3.amazonaws.com/fordgobike-data/index.html
# Import all packages and set plots to be embedded inline
from requests import get
from os import path, getcwd, makedirs, listdir
from io import BytesIO
from zipfile import ZipFile
import pandas as pd
import numpy as np
import matplotlib
from matplotlib import pyplot as plt
import matplotlib.ticker as tick
import seaborn as sns
import datetime
import math
import calendar
import warnings
warnings.filterwarnings('ignore')
from IPython.display import Image
# %matplotlib inline
# +
# Download the dataset with pandas *From burakgunbatan GitHub*
folder_name_of_csvs = 'Project_files'
makedirs(folder_name_of_csvs)
pd.read_csv('https://s3.amazonaws.com/fordgobike-data/2017-fordgobike-tripdata.csv').to_csv('{}/2017-forgobike-tripdata.csv'.format(folder_name_of_csvs))
for month in range(1,12):
month_string = str(month)
month_leading_zero = month_string.zfill(2)
bike_data_url = 'https://s3.amazonaws.com/fordgobike-data/2018' + month_leading_zero + '-fordgobike-tripdata.csv.zip'
response = get(bike_data_url)
# Code below opens zip file; BytesIO returns a readable and writable view of the contents
unzipped_file = ZipFile(BytesIO(response.content))
# Extracted the zip file into folder trip_data_files
unzipped_file.extractall(folder_name_of_csvs)
# -
# Merge all locally saved CSVs into One DataFrame
all_files = []
for file_name in listdir(folder_name_of_csvs):
all_files.append(pd.read_csv(folder_name_of_csvs+'/'+file_name))
dataFrame = pd.concat(all_files)
# Save DataFram to data.csv
dataFrame.to_csv('mergeData.csv')
# Examine DataFrame
dataFrame = pd.read_csv('mergeData.csv')
len(dataFrame)
# +
# Set visualization style
sns.set_style('whitegrid')
sns.set_context("talk")
# Filter data to include reasonable member age range
dataFrame['member_age'] = 2018-dataFrame['member_birth_year']
plt.figure(figsize=(14,6))
sns.boxplot(x='member_age', data=dataFrame, palette='Blues', orient='h')
plt.title("The age distribution", fontsize=22, y=1.03)
plt.xlabel("Age bike riders", fontsize=20, labelpad=10)
# -
# > As we notice in the distribution graph, ages between 18 to 60 represent 95% of the users. There were users more than 60 years old but they represent a small percentage.
# So, we can remove users more than 60 years old.
dataFrame = dataFrame[dataFrame['member_age']<=60]
dataFrame['member_age'].mean()
# > Ford bike users' median user age is around from 34 to 35.
# Filter data only to include San Francisco rides
max_longitude_sf = -122.3597
min_longitude_sf = -122.5147
max_latitude_sf = 37.8121
min_latitude_sf = 37.7092
# +
end_station_latitude_mask = (dataFrame['end_station_latitude']>=min_latitude_sf) & (dataFrame['end_station_latitude']<=max_latitude_sf)
start_station_latitude_mask = (dataFrame['start_station_latitude']>=min_latitude_sf) & (dataFrame['start_station_latitude']<=max_latitude_sf)
end_station_longitude_mask =(dataFrame['end_station_longitude']>=min_longitude_sf) & (dataFrame['end_station_longitude']<=max_longitude_sf)
start_station_longitude_mask = (dataFrame['start_station_longitude']>=min_longitude_sf) & (dataFrame['start_station_longitude']<=max_longitude_sf)
# -
dataFrame = dataFrame[end_station_latitude_mask & start_station_latitude_mask & end_station_longitude_mask & start_station_longitude_mask]
len(dataFrame)
# > Now the data size became around (1,505,886) from (2,252,058).
# ### What is the structure of your dataset?
#
# > There is 1505886 trip in the dataset with 16 columns, and different variable types like (int - float - string - bool) but the most type are numeric.
#
# ### What is/are the main feature(s) of interest in your dataset?
#
# > There is many like:
# - Gender
# - Average riding duration
# - Average riding distance
# - Age groups of users
#
# ### What features in the dataset do you think will help support your investigation into your feature(s) of interest?
#
# > I think that the age group, gender, duration of use and purpose of use are the most important data to focus on.
# ## Univariate Exploration
# +
# Create new fields for date from start_time and end_time
dataFrame['start_time'] = pd.to_datetime(dataFrame['start_time'])
dataFrame['end_time'] = pd.to_datetime(dataFrame['end_time'])
dataFrame['start_time_date'] = dataFrame['start_time'].dt.date
dataFrame['end_time_date'] = dataFrame['end_time'].dt.date
dataFrame['start_time_year_month'] = dataFrame['start_time'].map(lambda x: x.strftime('%Y-%m'))
dataFrame['end_time_year_month'] = dataFrame['end_time'].map(lambda x: x.strftime('%Y-%m'))
dataFrame['start_time_year_month_renamed'] = dataFrame['start_time'].dt.strftime('%y' + '-' + '%m')
dataFrame['start_time_year'] = dataFrame['start_time'].dt.year.astype(int)
dataFrame['end_time_year'] = dataFrame['end_time'].dt.year.astype(int)
dataFrame['start_time_month'] = dataFrame['start_time'].dt.month.astype(int)
dataFrame['end_time_month'] = dataFrame['end_time'].dt.month.astype(int)
dataFrame['start_time_hour_minute'] = dataFrame['start_time'].map(lambda x: x.strftime('%H-%m'))
dataFrame['end_time_hour_minute'] = dataFrame['end_time'].map(lambda x: x.strftime('%H-%m'))
dataFrame['start_time_hour'] = dataFrame['start_time'].dt.hour
dataFrame['end_time_hour'] = dataFrame['end_time'].dt.hour
dataFrame['start_time_weekday'] = dataFrame['start_time'].dt.weekday_name
dataFrame['end_time_weekday'] = dataFrame['end_time'].dt.weekday_name
dataFrame['start_time_weekday_abbr'] = dataFrame['start_time'].dt.weekday.apply(lambda x: calendar.day_abbr[x])
dataFrame['end_time_weekday_abbr'] = dataFrame['end_time'].dt.weekday.apply(lambda x: calendar.day_abbr[x])
# Create a new field for member age group from member_age_bin
dataFrame['member_age_bins'] = dataFrame['member_age'].apply(lambda x: '10 - 20' if 10<x<=20
else '20 - 30' if 20<x<=30
else '30 - 40' if 30<x<=40
else '40 - 50' if 40<x<=50
else '50 - 60' if 50<x<=60
else x)
# Create minutes for trip duration from duration_sec
dataFrame['duration_min'] = dataFrame['duration_sec']/60
# Create new fields for distance
def distance(origin, destination):
firstLat, firstlon = origin
secondistanceLat, secondistanceLon = destination
radiusValue = 6371
distanceLat = math.radians(secondistanceLat - firstLat)
distanceLon = math.radians(secondistanceLon - firstlon)
firstValue = (math.sin(distanceLat / 2) * math.sin(distanceLat / 2) +
math.cos(math.radians(firstLat)) * math.cos(math.radians(secondistanceLat)) *
math.sin(distanceLon / 2) * math.sin(distanceLon / 2))
secondValue = 2 * math.atan2(math.sqrt(firstValue), math.sqrt(1 - firstValue))
result = radiusValue * secondValue
return result
dataFrame['distance_km_estimates'] = dataFrame.apply(lambda x: distance((x['start_station_latitude'], x['start_station_longitude']), (x['end_station_latitude'], x['end_station_longitude'])), axis=1)
dataFrame['distance_miles_estimates'] = dataFrame['distance_km_estimates']*0.621371
# -
# ### Average count of rides per bike per day
# I selected August in order to compare the data findings because it is in the summer season.
count_rides = dataFrame.groupby('start_time_year_month_renamed')['bike_id'].size().reset_index()
count_unique_rides = dataFrame.groupby('start_time_year_month_renamed')['bike_id'].nunique().reset_index().rename(columns={'bike_id':'unique_bike_id'})
count_rides_dataFrame = count_rides.merge(count_unique_rides, on='start_time_year_month_renamed')
count_rides_dataFrame['number_of_used'] = count_rides_dataFrame['bike_id']/count_rides_dataFrame['unique_bike_id']
Aug_2017_AVG_BikeUsed = (count_rides_dataFrame[count_rides_dataFrame['start_time_year_month_renamed']=='17-08']['number_of_used'].mean())/31
Aug_2018_AVG_BikeUsed = (count_rides_dataFrame[count_rides_dataFrame['start_time_year_month_renamed']=='18-08']['number_of_used'].mean())/31
print(Aug_2017_AVG_BikeUsed, Aug_2018_AVG_BikeUsed)
print(Aug_2018_AVG_BikeUsed/Aug_2017_AVG_BikeUsed)
# As we see these two months in different years, the average increased 2.23 times in August 2018, where the average count of rides per bike per day reaches (2.6237).
# ### Count of daily bike rides from July 2017 to November 2018
def transform_axis(dailyBike, pos):
if dailyBike >= 1000:
value = int(dailyBike/1000)
return '{:d}K'.format(value)
elif dailyBike >= 1000000:
value = int(dailyBike/1000000)
return '{:d}M'.format(value)
else:
return int(dailyBike)
dataFrame.groupby('start_time_date').agg({'bike_id':'count'}).plot(style='-', legend=False, figsize=(15,8))
plt.title('The daily bike rides', fontsize=24, y=1.015)
plt.xlabel('Date', labelpad=16)
plt.ylabel('count', labelpad=16)
axis = plt.gca()
axis.yaxis.set_major_formatter(tick.FuncFormatter(transform_axis))
# > Compared to the beginning of July 2017, where daily trips were less than 1000, it increased to more than 5000 after less than a year (June 2018) There is huge decrease around January 2018 and November 2018 because it's too cold. these on start Winter session time.
# ### Count of people who took bike rides by age group per month
plt.figure(figsize=(14,8))
palette = {'20 - 30': 'deepskyblue', '30 - 40': 'navy', '40 - 50': 'red'}
axis = sns.countplot(x='start_time_year_month_renamed', hue='member_age_bins', palette=palette, data=dataFrame[dataFrame['member_age_bins'].isin(['20 - 30', '30 - 40', '40 - 50'])].sort_values(by=['start_time_year_month_renamed', 'member_age_bins']))
plt.title('The monthly bike rides between 20-50 years olds', fontsize=22, y=1.015)
plt.xlabel('Date', labelpad=16)
plt.ylabel('count', labelpad=16)
leg = axis.legend()
leg.set_title('Member age group',prop={'size':16})
axis = plt.gca()
axis.yaxis.set_major_formatter(tick.FuncFormatter(transform_axis))
# > 20-30 years old users are rapidly growing compared to other user groups. When the service first started 30-40 years old users were dominant, however 20-30 years old users became a leader in a year.
# ### Bike rides per gender
# +
gender_trips = dataFrame.groupby('member_gender').agg({'bike_id':'count'})
gender_trips['perc'] = (gender_trips['bike_id']/gender_trips['bike_id'].sum())*100
colors = ['green', 'blue', 'red']
gender_trips['perc'].plot(kind='barh', color=colors, figsize=(12,6))
plt.title('Percentage bike rides per gender', fontsize=22, y=1.015)
plt.ylabel('Gender', labelpad=16)
plt.xlabel('Pecentage', labelpad=16)
plt.xticks(rotation=360)
plt.xlim(0,100)
# -
# > Male took around %76 of all bike rides, and female took around %22 of them.
# ### Bike rides per weekday
# +
weekday_trips = dataFrame.groupby('start_time_weekday_abbr').agg({'bike_id':'count'})
weekday_trips['perc'] = (weekday_trips['bike_id']/weekday_trips['bike_id'].sum())*100
weekday_index = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']
colors = ['blue', 'blue', 'blue', 'blue', 'blue', 'red', 'red']
weekday_trips.reindex(weekday_index)['perc'].plot(kind='bar', color=colors, figsize=(12,8), legend=False)
plt.title('Percentage bike rides per weekday', fontsize=22, y=1.015)
plt.xlabel('weekday', labelpad=16)
plt.ylabel('Percentage', labelpad=16)
plt.xticks(rotation=360)
# -
# > People use this service on weekdays more than weekends.
# ### Discuss the distribution(s) of your variable(s) of interest. Were there any unusual points? Did you need to perform any transformations?
#
# > I made some calculations to prepare the data to be placed in the graphs because the data is not ready to be used directly.
# After viewing the graphs we draw the following observations:
# - 20-30 years old users are rapidly growing compared to other user groups.
# - 20 to 40 years old people took the more than %70 of bike rides.
# - Among those, 30 to 40 years old people's rides account almost %40 of all bike rides.
# - Male took around %76 of all bike rides, and female took around %24 of them.
# - People use this service on weekdays more than weekends.
#
# ### Of the features you investigated, were there any unusual distributions? Did you perform any operations on the data to tidy, adjust, or change the form of the data? If so, why did you do this?
#
# > I will summarize the work as points:
# - At the beginning I conducted a filter on the ages of members and through the graph I concluded that the age range starts from 18 years to less than 140 years, but the vast majority, which represents about 95% is between 18 years to 60 years, so I focused on this range for a number of reasons, the data provide a lot, which represents 95% and also to exclude extreme values.
# - I have created new fields such as (timing - age groups - duration) to facilitate the conduct of mathematical calculations and understand the data in a better way.
# - The service is spread in more than one city such as (Oakland - San Jose - San Francisco), which makes it difficult to represent data traffic in all cities, and to simplify the matter chose the city of San Francisco by determining the coordinates of the city by looking at the data and the use of some sites on the Internet.
# ## Bivariate Exploration
# ### User trends of bike rides of subscribers vs customers
# +
user_type_count = dataFrame.groupby(["start_time_year_month_renamed", "user_type"]).size().reset_index()
plt.figure(figsize=(15,9))
palette = {'Subscriber':'blue', 'Customer':'green'}
axis = sns.pointplot(x='start_time_year_month_renamed', y=0, hue='user_type', palette=palette, scale=.7, data=user_type_count)
plt.title('The monthly bike rides per user type', fontsize=22, y=1.015)
plt.xlabel('Date', labelpad=16)
plt.ylabel('Count', labelpad=16)
leg = axis.legend()
leg.set_title('User Type',prop={'size':16})
axis = plt.gca()
axis.yaxis.set_major_formatter(tick.FuncFormatter(transform_axis))
# -
# **Customers' rides seem increasing slightly.**
#
# **But there is a decrease in November 2018 for subscribers but it seems like it is related with the winter season.**
# ### Average trip duration of subscribers vs customers
colors=['blue', 'green']
axis = dataFrame.groupby('user_type')['duration_min'].mean().plot(kind='barh', color=colors, figsize=(13,7))
axis.set_title('Average trip duration by user type', fontsize=20, y=1.015)
axis.set_ylabel('User type', labelpad=16)
axis.set_xlabel('trip duration in minutes', labelpad=16)
# **Subscribers' average trip duration is ~ 11 minutes.**
#
# **Customers' average trip duration is ~ 28 minutes.**
# ### The trend of subscribers' bike rides per age group
# +
subscriber_age = dataFrame[dataFrame['user_type']=='Subscriber'].groupby(['start_time_year_month_renamed', 'member_age_bins']).agg({'bike_id':'count'}).reset_index()
plt.figure(figsize=(15,8))
axis = sns.pointplot(x='start_time_year_month_renamed', y='bike_id', hue='member_age_bins', scale=.6, data=subscriber_age)
plt.title("The monthly bike rides per subscribers", fontsize=22, y=1.015)
plt.xlabel('Date', labelpad=16)
plt.ylabel('Count', labelpad=16)
leg = axis.legend()
leg.set_title('Age group',prop={'size':16})
axis = plt.gca()
axis.yaxis.set_major_formatter(tick.FuncFormatter(transform_axis))
# -
# **As we can see from the chart of the participants there is a convergence between the age groups (20-30) and (30-40), the same applies to the age categories (40-50) and (50-60), while the category (10-20) is considered Least among the age groups.**
# +
customer_age = dataFrame[dataFrame['user_type']=='Customer'].groupby(['start_time_year_month_renamed', 'member_age_bins']).agg({'bike_id':'count'}).reset_index()
plt.figure(figsize=(15,8))
axis = sns.pointplot(x='start_time_year_month_renamed', y='bike_id', hue='member_age_bins', scale=.6, data=customer_age)
plt.title("The monthly bike rides per customers", fontsize=22, y=1.015)
plt.xlabel('Date', labelpad=16)
plt.ylabel('Counts', labelpad=16)
legs = axis.legend()
legs.set_title('Age group',prop={'size':16})
axis = plt.gca()
axis.yaxis.set_major_formatter(tick.FuncFormatter(transform_axis))
# -
# **As we can see from the chart of the participants there is a convergence between the age groups (20-30) and (30-40), but the age categories (40-50) is less than previous categories, while the category (10-20) and (50-60) is considered Least among the age groups.**
# ### Which age group would prefer electric biking more.
# **We know that the company launched the bicycle project on (April 24th, 2018) assume that electric bikes were added in the same week.**
# +
nonelectric_bike = dataFrame[dataFrame['start_time'] < pd.Timestamp(2018,4,24)]['bike_id'].unique()
electric_bike = []
for bike_id in dataFrame[(dataFrame['start_time'] > pd.Timestamp(2018, 4, 24)) & (dataFrame['start_time'] < pd.Timestamp(2018, 5, 24))]['bike_id']:
if bike_id not in nonelectric_bike and bike_id not in electric_bike:
electric_bike.append(bike_id)
len(electric_bike)
# -
# **Electric bike rides vs regular bike ride for the first month.**
dataFrame['electric_bike'] = dataFrame['bike_id'].isin(electric_bike)
(dataFrame['electric_bike'].value_counts()/dataFrame['electric_bike'].value_counts().sum())*100
# > Results:
# - 91.9% of rides are non-electric bike rides.
# - 8.1% of rides are electric bike rides.
# **There is a big difference between electric bike and regular bike riding.**
# ### Talk about some of the relationships you observed in this part of the investigation. How did the feature(s) of interest vary with other features in the dataset?
#
# > Percentage of subscribers is almost %88.15. Percentage of customers is almost %11.85.
# Customers' rides seem increasing slightly. There is a decrease in November 2018 for subscribers but it seems like it is related with the winter season.
# Subscribers' average trip duration is around 11 minutes. Customers' average trip duration is around 28 minutes.
# Subscribers and customers trip distance was about the same, which is slightly more than one mile.
# I selected the most popular group 20-40 years old people in order to compare hiring days, time of the day, peak times, etc.
# Subscribers are most frequently used this service around 7-9 am and 4-6 pm. Customers have used this service at the weekend around (10 am - 5 pm) and weekday 5pm6pm. Customers use this service during the weekend for leisure and weekdays after work.
# On the other hand, I checked the electrical bike program. Ford GoBike announced the launch of electric bikes as April 24th, 2018. 91.9% of rides are non-electric bike rides. Electric bike rides account for 8.1% of the total rides in the first month. It was increased suddenly at the beginning of the program launch. There is a huge spike at the end of April. After that, it seems the usage trend for electric bikes is decreasing.
#
# ### Did you observe any interesting relationships between the other features (not the main feature(s) of interest)?
#
# > I observed that at the beginning of the electrical bike hiring program launch there was a high demand for this program. But after a while, it was decreased suddenly. Customers and subscribers may be more comfortable to drive a normal or random bike rather than an electrical and advanced technological bike.
# ## Multivariate Exploration
#
# > Create plots of three or more variables to investigate your data even
# further. Make sure that your investigations are justified, and follow from
# your work in the previous sections.
# +
subscriber_hour_dataFrame = dataFrame[(dataFrame['member_age']>=20) & (dataFrame['member_age']<40)
&(dataFrame['start_time_hour']>5)&(dataFrame['user_type']=='Subscriber')
].groupby(['start_time_weekday_abbr', 'start_time_hour']).agg({'bike_id' : 'count'}).rename(columns={'bike_id':'count'}).reset_index()
subscriber_hour_2 = dataFrame[(dataFrame['member_age']>=20) & (dataFrame['member_age']<30)
&(dataFrame['start_time_hour']>5)&(dataFrame['user_type']=='Subscriber')
].groupby(['start_time_weekday_abbr', 'start_time_hour']).agg({'bike_id' : 'count'}).rename(columns={'bike_id':'count'}).reset_index()
subscriber_hour_3 = dataFrame[(dataFrame['member_age']>=30) & (dataFrame['member_age']<40)
&(dataFrame['start_time_hour']>5)&(dataFrame['user_type']=='Subscriber')
].groupby(['start_time_weekday_abbr', 'start_time_hour']).agg({'bike_id' : 'count'}).rename(columns={'bike_id':'count'}).reset_index()
subscriber_hour_4 = dataFrame[(dataFrame['member_age']>=40) & (dataFrame['member_age']<50)
&(dataFrame['start_time_hour']>5)&(dataFrame['user_type']=='Subscriber')
].groupby(['start_time_weekday_abbr', 'start_time_hour']).agg({'bike_id' : 'count'}).rename(columns={'bike_id':'count'}).reset_index()
subscriber_hour_2['start_time_weekday_abbr'] = pd.Categorical(subscriber_hour_2['start_time_weekday_abbr'], categories=['Mon','Tue','Wed','Thu','Fri','Sat', 'Sun'], ordered=True)
subscriber_hour_3['start_time_weekday_abbr'] = pd.Categorical(subscriber_hour_3['start_time_weekday_abbr'], categories=['Mon','Tue','Wed','Thu','Fri','Sat', 'Sun'], ordered=True)
subscriber_hour_4['start_time_weekday_abbr'] = pd.Categorical(subscriber_hour_4['start_time_weekday_abbr'], categories=['Mon','Tue','Wed','Thu','Fri','Sat', 'Sun'], ordered=True)
subscriber_hour_2['count_perc'] = subscriber_hour_2['count'].apply(lambda x: (x/subscriber_hour_dataFrame['count'].sum())*100)
subscriber_hour_3['count_perc'] = subscriber_hour_3['count'].apply(lambda x: (x/subscriber_hour_dataFrame['count'].sum())*100)
subscriber_hour_4['count_perc'] = subscriber_hour_4['count'].apply(lambda x: (x/subscriber_hour_dataFrame['count'].sum())*100)
subscriber_hour_2['rank'] = subscriber_hour_2['count_perc'].rank(ascending=False).astype(int)
subscriber_hour_3['rank'] = subscriber_hour_3['count_perc'].rank(ascending=False).astype(int)
subscriber_hour_4['rank'] = subscriber_hour_4['count_perc'].rank(ascending=False).astype(int)
subscriber_hour_dataFrame_pivoted2 = subscriber_hour_2.pivot_table(index='start_time_hour', columns='start_time_weekday_abbr', values='rank')
subscriber_hour_dataFrame_pivoted3 = subscriber_hour_3.pivot_table(index='start_time_hour', columns='start_time_weekday_abbr', values='rank')
subscriber_hour_dataFrame_pivoted4 = subscriber_hour_4.pivot_table(index='start_time_hour', columns='start_time_weekday_abbr', values='rank')
plt.figure(figsize=(20,20))
plt.subplot(221)
plt.suptitle('Hiring a bike (age group, weekdays, timeframe effects)', fontsize=30, y=0.95)
sns.heatmap(subscriber_hour_dataFrame_pivoted2, fmt='d', annot=True, cmap='YlGnBu_r', annot_kws={"size": 14})
plt.title("20-30 years old of subscribers", y=1.015)
plt.xlabel('Weekday', labelpad=16)
plt.ylabel('Hours', labelpad=16)
plt.yticks(rotation=360)
plt.subplot(222)
sns.heatmap(subscriber_hour_dataFrame_pivoted3, fmt='d', annot=True, cmap='YlGnBu_r', annot_kws={"size": 14})
plt.title("30-40 years old of subscribers", y=1.015)
plt.xlabel('Weekday', labelpad=16)
plt.ylabel(' ')
plt.yticks(rotation=360)
plt.subplot(223)
sns.heatmap(subscriber_hour_dataFrame_pivoted4, fmt='d', annot=True, cmap='YlGnBu_r', annot_kws={"size": 14}, cbar_kws={'label': 'Rank of frequently used timing'})
plt.title("40-50 years old of subscribers", y=1.015)
plt.xlabel('Weekday', labelpad=16)
plt.ylabel(' ')
plt.yticks(rotation=360)
# -
# ### Talk about some of the relationships you observed in this part of the investigation. Were there features that strengthened each other in terms of looking at your feature(s) of interest?
#
# > I extended my investigation of motorbike hiring with three totally different variables like people, timeframe, weekday. The variable exploration here showed ME that folks World Health Organization are older than the others have longer to drive a motorcycle instead of tykes. 20-30 years previous individuals are active once the time is commute like they drive a motorcycle once they head to their offices or come to their homes. These figures show the United States of America after we grow up, we are able to see that they drive these bikes when during a day like in lunch period or within the morning or within the afternoon. it's going to be associated with their retirement or older individuals have far more versatile operating hours instead of youngers.
#
# ### Were there any interesting or surprising interactions between features?
#
# > I was interested and conjointly shocked as a result of I didn't expect to ascertain this sort of figures for 40-50 years previous cluster. i used to be expecting to ascertain abundant less hiring quantities in an exceedingly day however these figures show that area unit|they're} active and that they are versatile instead of youngers.
# ## Conclusion
# > There have been 3.31 billion rides.
# 20-30 years previous users ar speedily growing compared to alternative user teams. once the service 1st started 30-40 years previous users were dominant, but 20-30 years previous users became a frontrunner during a year.
# 20 to 40 years previous individuals took the quite p of motorbike rides. Among those, 30 to 40 years previous people's rides account almost of all bike rides.
# Male took around %76 of all bike rides, and female took around %24 of them.
# People use this service on weekdays quite weekends.
# 8 am and 5 pm ar the peak hours for this service. Also, individuals use this service once they are in mealtime similarly.
# Percentage of subscribers is almost %88.15. Percentage of customers is almost %11.85.
# Customers' rides appear increasing slightly however subscribers' rides reached 6 times more than customers' on Oct 2018. there's a decrease in Nov 2018 for subscribers however it feels like it's connected with the winter season.
# Subscribers' average trip length is around 11 minute. Customers' average trip length is around 28 minutes.
# Subscribers and customers trip distance was regarding constant, that is slightly quite one mile.
# 90% of motorbike riders turn up on a weekday.
# The peak bike rides time for all members is around commute time.
#
# > Finally, it looks that 40 to 50 years old age group use the service the foremost.
# After Ford GoBike did a pilot launch of e-bike on April 24th, 2018, there is quite a ton of electrical bike rides similarly, that reached to 10% of daily rides at the tip of July 2018. However, daily electrical bike rides are on a downward trend.
#
# ## Sources
# - [burakgunbatan GitHup](http://j.mp/2kERqLn)
# - [FordGoBike Data Set](https://www.fordgobike.com/)
# - [Haversine formula](https://www.movable-type.co.uk/scripts/latlong.html) used to calculate distances using latitude and longitude
# - [Seaborn catplot](https://seaborn.pydata.org/generated/seaborn.catplot.html) documentation
# - [Matplotlib histogram](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.hist.html) documentation
# - [IPython Display](https://ipython.org/ipython-doc/3/api/generated/IPython.display.html) documentation
# - [Seaborn heatmap](https://seaborn.pydata.org/generated/seaborn.heatmap.html?highlight=heatmap#seaborn.heatmap) documentation and [tutorial](https://likegeeks.com/seaborn-heatmap-tutorial/)
| 28,748 |
/Untitled.ipynb | 28cecb7e7b6810d016061b22eb601a65b5000140 | [] | no_license | robblack007/JupyterNotebooks | https://github.com/robblack007/JupyterNotebooks | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 11,848 | # ---
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# ---
# + [markdown] slideshow={"slide_type": "slide"}
# <h1 align = center>Object Oriented Programming </h1>
# + [markdown] slideshow={"slide_type": "notes"}
# In Python everything that you use is and object. An object is an instance of a class. A class is a template which defines certain data types (objects). An analogy to that is that there is a general class of humans, however each individual could be considered as an instantiation of that class of humans. We have already dealt with many classes and objects as you can see below
# + slideshow={"slide_type": "slide"}
# n is an object that has type int. So int is the class of all integers, which sets the template for how integers behave.
n = 1
type(n)
# + slideshow={"slide_type": "subslide"}
#We see again that word is an object of type str, and str is a class.
word = "CodeSoc"
type(word)
# + [markdown] slideshow={"slide_type": "slide"}
# <h3 align = center> Attributes and Methods<h3>
# + [markdown] slideshow={"slide_type": "notes"}
# Classes have their own attributes and mathods. Attributes are essentially variables that relates to the object. Going back to the analogy of humans, a person has their height as an attribute. Methods are functions that could be performed on the class. You can find all the attributes and methods of a particular class by using dir([class])
# + slideshow={"slide_type": "slide"}
dir(str)
# + slideshow={"slide_type": "subslide"}
a = "CodeSoc"
a.upper()
# -
a.split()
| 1,736 |
/python_for_data_science_essential_training_1/5_basic_math_and_statistics/05_02_end_basic_linear_algebra/05_02_end.ipynb | 3caf8154b06b9963545b4e2c8ea3bf791639f560 | [] | no_license | olibyte/data_science | https://github.com/olibyte/data_science | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 2,974 | # ---
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# name: python3
# ---
# # Chapter 5 - Basic Math and Statistics
# ## Segment 2 - Multiplying matrices and basic linear algebra
import numpy as np
from numpy.random import randn
np.set_printoptions(precision=2)
# ## Multiplying matrices and basic linear algebra
aa = np.array([[2.,4.,6.],[1.,3.,5.],[10.,20.,30.]])
aa
bb = np.array([[0.,1.,2.],[3.,4.,5.],[6.,7.,8.]])
bb
aa*bb
np.dot(aa,bb) #performs dot product to reduce to single matrix object
| 699 |
/tensorflow_2.ipynb | 334e930713d9e55b0442f0b825ee7d6f127986af | [] | no_license | asis012/tensorflow_practice | https://github.com/asis012/tensorflow_practice | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 11,531 | # ---
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# name: python3
# ---
# # Predicting Fraud Mobile Money Transactions
# ## 1. Preparation
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# ### 1.1 Data importation and basic data description
df = pd.read_csv("C://Users//lenovo//Desktop//fraud detection.csv",header=0)
df.tail()
# There are 6,362,619 datas in this dataset. There are 11 variables in total and the variable called 'isFraud' is target varible. I want to find out the most satisfying classification method to maximize the accuracy of predition.
df.describe()
fraud = pd.value_counts(df['isFraud'], sort = True).sort_index()
fraud
fraud.plot(kind='bar', title=" Frequency",figsize=(6,6))
plt.show()
# The dataset is imbalanced with 6,354,407 not fraud and 8,213 fraud transactions.Thus,99.87% of transactions are not fraud.
# ### 1.2 Data preprocessing
# #### 1.2.1 Test missing data
df.isnull().sum()
# There is no missing value in the dataset.
# #### 1.2.2 Encoding class labels
# As variable "type" is in the form of string, I firstly transfer it into class labels.
class_mapping = {label:idx for idx,label in enumerate(np.unique(df['type']))}
class_mapping
{'CASH_IN': 0,'CASH_OUT': 1,'DEBIT': 2,'PAYMENT': 3, 'TRANSFER': 4}
df['type'] = df['type'].map(class_mapping)
df.tail()
# #### 1.2.3 Separating training and test sets
# According to common sense, we know that names of customers who start or receive the transaction have no prediction power.And the variable "isFlaggedFraud" just specifies transaction larger than 0.2 million, which is a little bit repeated with the variable"amount". So I drop these three varibles in the dependent variable column. Independent variable is the variable "isFraud".
# I separate training and test sets with propotion of 70:30.
from sklearn.model_selection import train_test_split
Xcolumns=['step','type','amount','oldbalanceOrg','newbalanceOrig','oldbalanceDest','newbalanceDest']
X, y = df[Xcolumns].values, df['isFraud'].values
X_train, X_test, y_train, y_test =\
train_test_split(X, y,test_size=0.3,random_state=0,stratify=y)
# #### 1.2.4 Standarization
# The approach of standarization is used to bring different feasures onto the same scale.
from sklearn.preprocessing import StandardScaler
stdsc = StandardScaler()
X_train_std = stdsc.fit_transform(X_train)
X_test_std = stdsc.transform(X_test)
# ## 2. Model Selection
# In the process of modeling, three classification methods including logistics regression, random forest and KNN will be used. In the following models, linear discriminant analysis will be firstly used to find the feature subspace that optimizes class separability. And classification function with parameter of class weight will be set as “balanced” to deal with the problem caused by imbalanced data. Finally, 3 models are evaluated by accuracy and 1-FPR. I want to maximize TN/(FP+TN) so that when a transaction is a fraud, the transaction can be detected timely. As an improvement, stratified K-fold cross validation will be used to estimate their performances when faced with data has not been seen before.
# ### 2.1 Logistics Regression
# Here, I use LogisticRegressionCV function instead of LogisticRegression so that parameter C can be chosen by cross validation automatically. And parameter class weight is set to be balanced to deal with problem of class imbalance.
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
from sklearn.linear_model import LogisticRegressionCV
from sklearn.pipeline import make_pipeline
pipe_lr = make_pipeline(LDA(n_components=2),LogisticRegressionCV(random_state=1,class_weight='balanced'))
pipe_lr.fit(X_train_std, y_train)
y_pred = pipe_lr.predict(X_test_std)
print('Test Accuracy: %.3f' % pipe_lr.score(X_test_std, y_test))
# Here，accuracy is 0.948
from sklearn.metrics import confusion_matrix
confmat = confusion_matrix(y_true=y_test, y_pred=y_pred)
print(confmat)
fig, ax = plt.subplots(figsize=(3, 3))
ax.matshow(confmat, cmap=plt.cm.Blues, alpha=0.3)
for i in range(confmat.shape[0]):
for j in range(confmat.shape[1]):
ax.text(x=j, y=i,
s=confmat[i, j],
va='center', ha='center')
plt.xlabel('predicted label')
plt.ylabel('true label')
plt.show()
# I want to maximize TN/(FP+TN) so that when a transaction is a fraud, the transaction can be detected timely. For logistics regression above, TN/(FP+TN)=1934/(530+1934)=0.7849
from sklearn.model_selection import StratifiedKFold
kfold = StratifiedKFold(n_splits=10,random_state=1).split(X_train_std,y_train)
scores = []
for k, (train, test) in enumerate(kfold):
pipe_lr.fit(X_train_std[train], y_train[train])
score = pipe_lr.score(X_train_std[test], y_train[test])
scores.append(score)
print('Fold: %2d, Class dist.: %s, Acc: %.3f' % (k+1,
np.bincount(y_train[train]), score))
print('\nCV accuracy: %.3f +/- %.3f' %(np.mean(scores), np.std(scores)))
# Stratified 10-fold cross validation accuracy is 0.948.
# ### 2.2 Random Forest
from sklearn.ensemble import RandomForestClassifier
pipe_forest = make_pipeline(LDA(n_components=2),RandomForestClassifier(criterion='gini',class_weight='balanced'))
pipe_forest.fit(X_train_std,y_train)
y_pred =pipe_forest.predict(X_test_std)
print('Test Accuracy: %.3f' % pipe_forest.score(X_test_std, y_test))
# Test accuracy is 0.998 for random forest.
from sklearn.metrics import confusion_matrix
confmat = confusion_matrix(y_true=y_test, y_pred=y_pred)
print(confmat)
fig, ax = plt.subplots(figsize=(3, 3))
ax.matshow(confmat, cmap=plt.cm.Blues, alpha=0.3)
for i in range(confmat.shape[0]):
for j in range(confmat.shape[1]):
ax.text(x=j, y=i,
s=confmat[i, j],
va='center', ha='center')
plt.xlabel('predicted label')
plt.ylabel('true label')
plt.show()
# For random forest above, TN/(FP+TN)=820/(1644+820)=0.3328, which is very low.
from sklearn.model_selection import StratifiedKFold
kfold = StratifiedKFold(n_splits=10,random_state=1).split(X_train_std,y_train)
scores = []
for k, (train, test) in enumerate(kfold):
pipe_forest.fit(X_train_std[train], y_train[train])
score = pipe_forest.score(X_train_std[test], y_train[test])
scores.append(score)
print('Fold: %2d, Class dist.: %s, Acc: %.3f' % (k+1,
np.bincount(y_train[train]), score))
print('\nCV accuracy: %.3f +/- %.3f' %(np.mean(scores), np.std(scores)))
# Stratified 10-fold cross validation accuracy is 0.998.
# ### 2.3 K-Nearest Neighbors(KNN)
from sklearn.neighbors import KNeighborsClassifier
from sklearn.pipeline import make_pipeline
pipe_knn = make_pipeline(LDA(n_components=2),KNeighborsClassifier(n_neighbors=5))
pipe_knn.fit(X_train_std, y_train)
y_pred = pipe_knn.predict(X_test_std)
print('Test Accuracy: %.3f' % pipe_knn.score(X_test_std, y_test))
# Test accuracy is 0.999 for KNN.
from sklearn.metrics import confusion_matrix
confmat = confusion_matrix(y_true=y_test, y_pred=y_pred)
print(confmat)
fig, ax = plt.subplots(figsize=(3, 3))
ax.matshow(confmat, cmap=plt.cm.Blues, alpha=0.3)
for i in range(confmat.shape[0]):
for j in range(confmat.shape[1]):
ax.text(x=j, y=i,
s=confmat[i, j],
va='center', ha='center')
plt.xlabel('predicted label')
plt.ylabel('true label')
plt.show()
# For KNN, TN/(FP+TN)=823/(1641+823)=0.3340, which is also very low.
from sklearn.model_selection import StratifiedKFold
kfold = StratifiedKFold(n_splits=10,random_state=1).split(X_train_std,y_train)
scores = []
for k, (train, test) in enumerate(kfold):
pipe_knn.fit(X_train_std[train], y_train[train])
score = pipe_knn.score(X_train_std[test], y_train[test])
scores.append(score)
print('Fold: %2d, Class dist.: %s, Acc: %.3f' % (k+1,
np.bincount(y_train[train]), score))
print('\nCV accuracy: %.3f +/- %.3f' %(np.mean(scores), np.std(scores)))
# Stratified 10-fold cross validation accuracy is 0.999.
# ### 2.4 ROC AUC
from sklearn.model_selection import cross_val_score
clf_labels = ['Logistic regression', 'Random Forest', 'KNN']
print('10-fold cross validation:\n')
for clf, label in zip([pipe_lr, pipe_forest, pipe_knn], clf_labels):
scores = cross_val_score(estimator=clf,X=X_train_std,y=y_train,cv=10,scoring='roc_auc')
print("ROC AUC: %0.2f (+/- %0.2f) [%s]"% (scores.mean(), scores.std(), label))
# ### 2.5 Conclusion
# ##### Accuracy:
# Logistics Regression: 0.948
#
# Random Forest: 0.998
#
# KNN:0.999
# ##### 1-FPR:
# Logistics Regression: 0.7849
#
# Random Forest: 0.3380
#
# KNN:0.3340
# ##### CV Accuracy:
# Logistics Regression: 0.948
#
# Random Forest: 0.998
#
# KNN:0.999
# ##### CV ROC AUC:
# Logistics Regression: 0.93
#
# Random Forest: 0.71
#
# KNN:0.73
# Comparing results above, we can see that test accuracy of logistics regression is the lowest,but it has the highest ratio of 1-False Positive rate and ROC AUC.This means logistics regression has less predictive power in general but it has relatively strong power in predicting a fraud. Considering its test accuracy is not so much lower and its 1-FPR is much higher than others, logistics regression has the most predictive power in this case.
| 9,390 |
/_draft/Linear Algebra/Linear Algebra.ipynb | e89f1e75bf62a08aa8bc1572b209bbbd67637c3e | [
"MIT"
] | permissive | caerang/caerang-notes | https://github.com/caerang/caerang-notes | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 10,987 | # ---
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# + _uuid="8f2839f25d086af736a60e9eeb907d3b93b6e0e5" _cell_guid="b1076dfc-b9ad-4769-8c92-a6c4dae69d19"
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All"
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session
# -
import pandas as pd
import numpy as np
# + _uuid="d629ff2d2480ee46fbb7e2d37f6b5fab8052498a" _cell_guid="79c7e3d0-c299-4dcb-8224-4455121ee9b0"
df = pd.read_csv('https://raw.githubusercontent.com/plenoi/Clinic/master/ultima_all_clean.csv', sep=',', header = 0)
df
# -
df_clean = df
df_clean = df_clean.set_index('hn')
df_clean = df_clean.drop('hiv',1)
df_clean = df_clean.drop('size',1)
df_clean = df_clean.drop('utmet',1)
df_clean = df_clean.drop('vgmet',1)
df_clean = df_clean.drop('surgery',1)
df_clean = df_clean.drop('pchemo',1)
df_clean.columns
df_clean.isnull().any()
# แสดงหมวดคอลัมชุดข้อมูลที่มีข้อมูลหาย
df_clean.isnull().sum()
# คัดคอลัมที่ข้อมูลหายมากกว่า 5% ยกเว้น pmmet
df_clean = df_clean.drop('appearance',1)
df_clean = df_clean.drop('Wardsize',1)
df_clean = df_clean.drop('RHlvsi',1)
df_clean = df_clean.drop('depth',1)
df_clean = df_clean.drop('vgmargin',1)
df_clean.isnull().sum()
# จัดกลุ่มอายุ มากกว่า 20 = OLd น้อยกว่า = Young
df_clean['age'] = np.where(df_clean['age']>20, 'Old', 'Young')
df_clean
# แสดงค่า unique ของ pmmet
df_clean['pmmet'].unique()
# * สุ่มตัวอย่าง 10 คนจากกลุ่ม pmmet = 0
pmmet_0 = df_clean[(df_clean['pmmet'] == 0.0)].sample(10)
pmmet_0
# * สุ่มตัวอย่าง 10 คนจากกลุ่ม pmmet = 1
pmmet_1 = df_clean[(df_clean['pmmet'] == 1.0)].sample(10)
pmmet_1
# เตรียม data present
import matplotlib.pyplot as plt
ps_clean = df_clean
ps_clean
# แสดงจำนวนผู้ป่วยที่มีโรค
# +
ps_clean['disease'] = np.where(ps_clean['disease'] == 1.0 ,"Yes","No")
total = ps_clean.shape
explode = (0,0.1)
labels = ('disease','Not disease')
plt.title('Patient have disease')
plt.bar(labels,[sum(ps_clean['disease'] == 'Yes'),sum(ps_clean['disease'] == 'No')])
plt.show()
# -
# จำนวนผู้ป่วยตามกลุ่ม pmmet
# +
ps_clean['pmmet'] = np.where(ps_clean['pmmet'] == 1.0 ,"Yes","No")
total = ps_clean.shape
explode = (0,0.1)
labels = ('Yes','No')
plt.title('Number Group by Pmmet')
plt.bar(labels,[sum(ps_clean['pmmet'] == 'Yes'),sum(ps_clean['pmmet'] == 'No')])
plt.show()
# -
# อัตราส่วนที่ต้องรับการผ่าตัด
# +
pt = np.shape(ps_clean)
fig = plt.figure(1, figsize=(15,9))
labels = ['Yes' , 'No']
perc = [sum(ps_clean['conization'] == 1)/pt[0],
sum(ps_clean['conization'] == 0)/pt[0]
]
explode = (0.1 , 0)
plt.subplot(1,2,1)
plt.title('Percent of Conization')
plt.axis('equal')
plt.pie(perc, labels=labels, autopct='%1.2f%%',
startangle=90, explode=explode)
plt.show()
# -
# อัตราส่วนของวัยทอง
# +
fig = plt.figure(1, figsize=(16,10))
labels = ['Yes' , 'No']
perc = [sum(ps_clean['menopaus'] == 1)/pt[0],
sum(ps_clean['menopaus'] == 0)/pt[0]
]
explode = (0.1 , 0)
plt.subplot(1,2,1)
plt.title('Percent of Menopaus')
plt.axis('equal')
plt.pie(perc, labels=labels, autopct='%1.2f%%',
startangle=90, explode=explode)
plt.show()
# -
# อัตราส่วนผู้ป่วยขั้นต่างๆ 1-6
# +
fig = plt.figure(1, figsize=(16,10))
labels ='1','2','3','4','5','6'
perc = [sum(ps_clean['stage'] == 1)/pt[0],
sum(ps_clean['stage'] == 2)/pt[0],
sum(ps_clean['stage'] == 3)/pt[0],
sum(ps_clean['stage'] == 4)/pt[0],
sum(ps_clean['stage'] == 5)/pt[0],
sum(ps_clean['stage'] == 6)/pt[0]
]
explode = (0 , 0,0,0,0,0)
plt.subplot(1,2,1)
plt.title('Percent of Stage')
plt.axis('equal')
plt.pie(perc, labels=labels, autopct='%1.2f%%',
startangle=90, explode=explode)
plt.show()
hcal{V}_1\ \mathcal{V}_2\ \mathcal{V}_3\ \cdots\ \mathcal{V}_n]
# \begin{bmatrix}
# a_1\\
# a_2\\
# \vdots \\
# a_n
# \end{bmatrix} = 0 \qquad
# where \mathcal{V}_i \in \mathbb{R}^{m \times 1},
# \qquad
# \forall i \in \{1,2,\cdots , n\},
# \begin{bmatrix}
# a_1 \\
# a_2 \\
# \vdots \\
# a_n
# \end{bmatrix} \in \mathbb{R}^{n \times 1}
# $$
#
# 위 식을 만족하는 유일한 해가 영벡터라면 벡터 집합($\{v_1, v_2, \cdots , v_n\}$)은 '선형 독립이다'라고 말한다.
#
# 만약 $n$ 벡터 집합이 선형 독립이라면 그런 벡터는 전체 n 차원 공간을 생성(span)한다. 즉, n 개의 벡터로 선형 조합을 하면 n 차원 공간에서 있는 모든 벡터를 생성할 수 있다는 것을 의미한다. 만약 n 개의 벡터가 선형 독립이 아니면 n 차원의 부분 공간만 생성한다.
#
# 
#
# *벡터 생성(span)*
#
# [ 그림 상세 설명 예제 추가]
#
#
#
# ### 행렬 계수
#
# 행렬 계수는 선형 독립인 컬럼 벡터 또는 로우 벡터의 개수이다. 행렬에서 독립인 컬럼 벡터의 개수는 독립인 로우 벡터의 개수와 같다.
#
# 행렬 계수는 다음과 같은 특성이 있다.
#
# - 정방 행렬 $A \in \mathbb{R}^{n \times n}$ 에 대해 행렬 A 의 계수(rank) 가 n 이라면 full rank 라고 합니다.
#
# [정리 추가]
#
#
#
# ### 항등 행렬[연산]
#
# 행렬 $I \in \mathbb{R}^{n \times n}$ 에 대해 임의의 행렬 또는 벡터를 곱했을 대 행렬 또는 벡터가 변하지 않으면 $I$ 를 항등 행렬 또는 항등 연산이라고 한다. 3 \times 3 항등 행렬은 다음과 같다.
# $$
# I = \begin{bmatrix}1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 1 \end{bmatrix} \in \mathbb{R}^{3 \times 3}
# $$
# 행렬의 곱셈은 항등 행렬에 대해서는 교환 법칙이 성립한다.
#
#
#
# ### 행렬식
#
# 정방 행렬 $A$의 행렬식은 숫자값을 갖으며 $det(A)$라고 표기한다.
#
# [행렬식의 물리적인 의미에 대해서 추가 설명]
#
# 다음과 같은 행렬 A 가 있다고 하자.
# $$
# A = \begin{bmatrix} a_{11} & a_{12} \\ a_{21} & a_{22} \end{bmatrix} \in \mathbb{R}^{2 \times 2}
# $$
# 행렬식은 다음과 같다.
# $$
# det(A) = \begin{vmatrix} a_{11} & a_{12} \\ a_{21} & a_{22} \end{vmatrix}
# = a_{11}a_{22} - a_{12}a_{21}
# $$
# 유사하게 $B \in \mathbb{R}^{3 \times 3}$ 행렬의 행렬식은 다음과 같다
# $$
# B=
# \begin{bmatrix}
# a_{11} & a_{12} &a_{13} \\
# a_{21} & a_{22} &a_{23} \\
# a_{31} & a_{32} &a_{33} \\
# \end{bmatrix}
# \in \mathbb{R}^{3 \times 3}
# $$
#
# $$
# det(B) =
# a_{11}\begin{vmatrix}a_{22} & a_{23} \\ a_{32} & a_{33} \end{vmatrix} -
# a_{12}\begin{vmatrix}a_{21} & a_{23} \\ a_{31} & a_{33} \end{vmatrix} +
# a_{13}\begin{vmatrix}a_{21} & a_{22} \\ a_{31} & a_{32}
# \end{vmatrix}
# $$
#
# $$
# where \qquad det\left(\begin{bmatrix}a_{22} & a_{23} \\ a_{32} & a_{33} \end{bmatrix}\right)
# $$
#
# [n 차원 행렬의 행렬식 구하는 일반화 공식 추가]
#
# #### 행렬식의 해석
#
# 행렬식의 절대값은 경계선처럼 동작하는 행벡터로 둘러 쌓인 볼륨을 결정한다.
#
# 
#
# *행렬식의 물리적인 의미*
#
# $A \in \mathbb{R}^{2 \times 2}$ 인 행렬에 대해 행렬식은 두 행벡터가 경계선인 평행사변형의 면적이다
#
# 예를 들어 $A=\begin{bmatrix}a & b \\ c & d\end{bmatrix}$ 라면 $det(A)$ 는 벡터 $u=\begin{bmatrix}a & b\end{bmatrix}^T$ 와 $v=\begin{bmatrix}c & d\end{bmatrix}^T$ 를 선으로 하는 평행사변형의 넓이와 같다. 평행 사변형의 넓이는 $|u||v|\sin \theta$ 이고 $\theta$ 는 두 벡터 $u, v$ 사이의 각이다.
# $$
# \left| u \right|\left| v \right| \sin \theta = \sqrt{a^2 + b^2}\sqrt{c^2 + d^2}\frac{(ad-bc)}{\sqrt{a^2 + b^2}\sqrt{c^2 + d^2}} = (ad-bc)
# $$
# 유사하게 $B \in \mathbb{R}^{3 \times 3}$ 인 행렬의 행렬식은 세 개의 행벡터를 선분으로 하는 평행 육면체의 부피다.
#
#
#
# ### 역행렬
#
# 정방행렬 $A \in \mathbb{R}^{n \times n}$ 의 역행렬은 $A^{-1}$ 로 표기하며 $A$ 와 $A^{-1}$ 의 곱셉의 결과는 항등행렬. $I \in \mathbb{R}^{n \times n}$ 이다.
#
# $$
# AA^{-1} = A^{-1}A = I
# $$
#
# 모든 정방행렬이 역행렬을 갖지는 않는다. 역행렬은 다음과 같이 계산한다.
#
# $$
# A^{-1} = \frac{adjoint(A)}{det(A)} = \frac{(cofactor\ matrix\ of A)^T}{det(A)}
# $$
#
# 만약 정방행렬($A \in \mathbb{R}^{n \times n}$)이 특이행렬 이라면(행렬 $A$가 $n$ 개의 선형 독립인 행벡터 또는 열벡터를 갖지 않는다면) $A$의 역행렬은 존재하지 않는다. 왜냐하면 특이행렬의 행렬식($det(A)=0$)은 0이고 행렬식이 0이면 역행렬이 정의 되지 않는다.
#
# ### 벡터의 노름(Norm of Vector)
#
# 벡터의 노름은 벡터의 크기를 나타내는 지표다. 벡터의 크기를 계산하는 방법은 여러가지가 있다. 가장 잘 알려진 방법은 유클리디안 노름($\mathcal{L}^2$ 노름)이다.
#
# 벡터 $x \in \mathbb{R}^{n \times 1}$ 에 대해 $\mathcal{L}^2$ 노름은 다음과 같이 정의 한다.
#
# $$
# \lVert x\rVert_2 = (\left|x_1\right|^2 + \left|x_2\right|^2 + \left|x_3\right|^2 + \cdots + \left|x_n\right|^2)^{\frac{1}{2}} = (x \cdot x)^{\frac{1}{2}} = (x^T x)^{\frac{1}{2}}
# $$
#
# 유사하게 $\mathcal{L}^1$ 노름은 벡터의 각 요소의 절대값의 합이다.
#
# $$
# \lVert x\rVert_1 = \left|x_1\right| + \left|x_2\right| + \left|x_3\right| + \cdots + \left|x_n\right|
# $$
#
# ##### 정리해야 할 내용
#
# - 선형 조합의 정의와 의미
#
#
#
#
#
# # 참조
#
# Pro Deep Learning with TensorFlow
#
# [행렬식의 기하학적인 의미](https://wikidocs.net/4049)
| 8,782 |
/.ipynb_checkpoints/Housing Prices in the California-checkpoint.ipynb | 9ade99a3808f4b06451c261db754d52a1ea8705f | [] | no_license | RoshanKaloni/California-Housing-Prices | https://github.com/RoshanKaloni/California-Housing-Prices | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 139,666 | # ---
# jupyter:
# jupytext:
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# name: python3
# ---
# ## Setup
# +
import sys
assert sys.version_info >= (3, 5)
# Scikit-Learn ≥0.20 is required
import sklearn
assert sklearn.__version__ >= "0.20"
# Common imports
import numpy as np
import pandas as pd
import os
# To plot pretty figures
# %matplotlib inline
import matplotlib as mpl
import matplotlib.pyplot as plt
mpl.rc('axes', labelsize=14)
mpl.rc('xtick', labelsize=12)
mpl.rc('ytick', labelsize=12)
plt.style.use('fivethirtyeight')
# Where to save the figures
PROJECT_ROOT_DIR = "."
CHAPTER_ID = "end_to_end_project"
IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, "images", CHAPTER_ID)
os.makedirs(IMAGES_PATH, exist_ok=True)
def save_fig(fig_id, tight_layout=True, fig_extension="png", resolution=300):
path = os.path.join(IMAGES_PATH, fig_id + "." + fig_extension)
print("Saving figure", fig_id)
if tight_layout:
plt.tight_layout()
plt.savefig(path, format=fig_extension, dpi=resolution)
# -
# ## Get the Data
import os
import tarfile
import urllib.request
# + jupyter={"outputs_hidden": false} pycharm={"name": "#%%\n"}
DOWNLOAD_ROOT = "https://raw.githubusercontent.com/ageron/handson-ml2/master/"
HOUSING_PATH = os.path.join("datasets", "housing")
HOUSING_URL = DOWNLOAD_ROOT + "datasets/housing/housing.tgz"
# -
def fetch_housing_data(housing_url=HOUSING_URL, housing_path=HOUSING_PATH):
if not os.path.isdir(housing_path):
os.makedirs(housing_path)
tgz_path = os.path.join(housing_path, "housing.tgz")
urllib.request.urlretrieve(housing_url, tgz_path)
housing_tgz = tarfile.open(tgz_path)
housing_tgz.extractall(path=housing_path)
housing_tgz.close()
fetch_housing_data()
def load_housing_data(housing_path=HOUSING_PATH):
csv_path = os.path.join(housing_path, "housing.csv")
return pd.read_csv(csv_path)
housing = load_housing_data()
housing.head(10)
housing.tail(10)
housing.info()
housing["ocean_proximity"].value_counts()
housing.describe()
housing.hist(bins=50, figsize=(20,15))
save_fig("attribute_histogram_plots")
plt.show()
# to make this notebook's output identical at every run
np.random.seed(42)
# For illustration only. Sklearn has train_test_split()
def split_train_test(data, test_ratio):
shuffled_indices = np.random.permutation(len(data))
test_set_size = int(len(data) * test_ratio)
test_indices = shuffled_indices[:test_set_size]
train_indices = shuffled_indices[test_set_size:]
return data.iloc[train_indices], data.iloc[test_indices]
train_set, test_set = split_train_test(housing, 0.2)
len(train_set)
len(test_set)
# +
from zlib import crc32
def test_set_check(identifier, test_ratio):
return crc32(np.int64(identifier)) & 0xffffffff < test_ratio * 2**32
def split_train_test_by_id(data, test_ratio, id_column):
ids = data[id_column]
in_test_set = ids.apply(lambda id_: test_set_check(id_, test_ratio))
return data.loc[~in_test_set], data.loc[in_test_set]
# +
import hashlib
def test_set_check(identifier, test_ratio, hash=hashlib.md5):
return hash(np.int64(identifier)).digest()[-1] < 256 * test_ratio
# -
def test_set_check(identifier, test_ratio, hash=hashlib.md5):
return bytearray(hash(np.int64(identifier)).digest())[-1] < 256 * test_ratio
housing_with_id = housing.reset_index() # adds an `index` column
train_set, test_set = split_train_test_by_id(housing_with_id, 0.2, "index")
housing_with_id["id"] = housing["longitude"] * 1000 + housing["latitude"]
train_set, test_set = split_train_test_by_id(housing_with_id, 0.2, "id")
test_set.head()
# +
from sklearn.model_selection import train_test_split
train_set, test_set = train_test_split(housing, test_size=0.2, random_state=42)
# -
test_set.head()
housing["median_income"].hist()
housing["income_cat"] = pd.cut(housing["median_income"],
bins=[0., 1.5, 3.0, 4.5, 6., np.inf],
labels=[1, 2, 3, 4, 5])
housing["income_cat"].value_counts()
housing["income_cat"].hist()
# +
from sklearn.model_selection import StratifiedShuffleSplit
split = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)
for train_index, test_index in split.split(housing, housing["income_cat"]):
strat_train_set = housing.loc[train_index]
strat_test_set = housing.loc[test_index]
# -
strat_test_set["income_cat"].value_counts() / len(strat_test_set)
housing["income_cat"].value_counts() / len(housing)
# +
def income_cat_proportions(data):
return data["income_cat"].value_counts() / len(data)
train_set, test_set = train_test_split(housing, test_size=0.2, random_state=42)
compare_props = pd.DataFrame({
"Overall": income_cat_proportions(housing),
"Stratified": income_cat_proportions(strat_test_set),
"Random": income_cat_proportions(test_set),
}).sort_index()
compare_props["Rand. %error"] = 100 * compare_props["Random"] / compare_props["Overall"] - 100
compare_props["Strat. %error"] = 100 * compare_props["Stratified"] / compare_props["Overall"] - 100
# -
compare_props
for set_ in (strat_train_set, strat_test_set):
set_.drop("income_cat", axis=1, inplace=True)
| 5,369 |
/logic1/date_fashion.ipynb | 986bbe419135467ffde38c0574965c3b261b5716 | [] | no_license | bahagdiesat/assi2 | https://github.com/bahagdiesat/assi2 | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 1,186 | # ---
# jupyter:
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# extension: .py
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# format_version: '1.5'
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# language: python
# name: python3
# ---
def date_fashion(you, date):
if (you < 3 or date < 3): return 0
if (you > 7 or date >7 ): return 2
return 1
print (date_fashion(5, 5))
ect to the AML Workspace
# +
#connect to the workspace
ws = Workspace.from_config(".azure")
# get the compute target
compute_target = ws.compute_targets["cpu-cluster"]
# get the default datastore
datastore = ws.get_default_datastore()
# -
# ## Define the RunConfig
# +
from azureml.core.runconfig import RunConfiguration
from azureml.core.runconfig import DEFAULT_CPU_IMAGE
from azureml.core.environment import CondaDependencies
from azureml.core import ScriptRunConfig
# create a new runconfig object
run_config = RunConfiguration()
# # enable Docker
run_config.environment.docker.enabled = True
# # set Docker base image to the default CPU-based image
run_config.environment.docker.base_image = DEFAULT_CPU_IMAGE
# # # use conda_dependencies.yml to create a conda environment in the Docker image for execution
run_config.environment.python.user_managed_dependencies = True
# # specify CondaDependencies obj
run_config.environment = Environment.from_conda_specification(name = "train-env", file_path = "environment.yml")
# -
# ## Define the Pipeline Steps
# +
from azureml.pipeline.core import PipelineData
from azureml.pipeline.steps import PythonScriptStep
from azureml.pipeline.core.graph import PipelineParameter
from azureml.pipeline.core import Pipeline, StepSequence
# create teh ingest pipeline step
data_ingest_step = PythonScriptStep(
script_name="data_ingest.py",
source_directory="src",
# outputs = [diabetes_ds],
compute_target=compute_target)
# define the preprocessing pipeline step
data_prep_step = PythonScriptStep(
script_name="preprocessing.py",
source_directory="src",
compute_target=compute_target)
# define the train pipeline step
train_DTC_step = PythonScriptStep(
script_name="train_DTC.py",
source_directory="src",
compute_target="cpu-cluster",
runconfig=run_config)
# define the pipeline step
deploy_step = PythonScriptStep(
script_name="deploy.py",
source_directory="src",
compute_target=compute_target)
# run the steps in a sequence
step_sequence = StepSequence(steps=[data_ingest_step, data_prep_step, train_DTC_step, deploy_step])
# create the pipeline
pipeline = Pipeline(workspace=ws, steps=step_sequence)
# -
# ## Run the Pipeline
pipeline_run = Experiment(ws, 'full_pipeline').submit(pipeline)
pipeline_run.wait_for_completion(show_output=True)
| 2,774 |
/Full pipeline - tensor data.ipynb | 21ba86882bffffdb03c996b71f240e65e025fcbd | [] | no_license | Ashranzzler/protein-dihedral-angles-prediction | https://github.com/Ashranzzler/protein-dihedral-angles-prediction | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 409,192 | # ---
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# ---
# +
import numpy as np
import matplotlib.pyplot as plt
import glob
import tensorflow as tf
import time
import scipy
import os
from model.model import Model
from model.helpers import Helpers
from model.datahandler import DataHandler
from model.dihedralcalculator import DihedralCalculator
import txt_data_utils.data_transformer as dt
plt.style.use('ggplot')
# %matplotlib inline
# +
### DATA AND QUEUE CONFIGURATION ###
data_path = "path_to_the_data"
validation_casp = 'casp11'
# training_casps = ['casp7', 'casp8', 'casp9', 'casp10', 'casp11']
training_casps = ['casp11']
training_percentages = [30, 50]
max_len = None # max len of the protein taken into account
num_epochs = 500
batch_size= 32
capacity=1000
### MODEL CONFIGURATION ###
include_evo = True
model_type = 'cnn_big'
dropout_rate = 0.2
mode_a = 'alphabet' # regression or alphabet
mode_b = 'vectors' # angles or vectors
prediction_mode = mode_a + '_' + mode_b
n_clusters = 50 # only needed when prediction mode == alphabet
angularization_mode = None
regularize_vectors = None
if prediction_mode == 'regression_angles':
angularization_mode = 'cos' # 'cos' or 'tanh', only matters in the regression_angles mode
if prediction_mode == 'regression_vectors':
regularize_vectors = True # True or False, only matters in the regression_vectors mode
loss_mode = 'mae'
n_angles = 2
# +
def extract_dataset(mode='validation', casp='casp11'):
tf.reset_default_graph()
data_handler = DataHandler(data_path=data_path,
casps=[casp],
num_epochs=1,
mode=mode)
ids, one_hot_primary, evolutionary, _, tertiary, ter_mask, pri_length, keep =\
data_handler.generate_batches(batch_size = 1,
capacity = 1000,
max_protein_len = max_len)
dihedral_calculator = DihedralCalculator()
true_dihedrals = dihedral_calculator.dihedral_pipeline(tertiary, protein_length = tf.shape(one_hot_primary)[1])
with tf.Session() as sess:
sess.run(tf.group(tf.global_variables_initializer(), tf.local_variables_initializer()))
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord, sess=sess)
try:
ids_v, ohp_v, evo_v, tert_v, ter_mask_v, dih_v = [], [], [], [], [], []
while not coord.should_stop():
ids_, one_hot_primary_, evolutionary_, tertiary_, ter_mask_, pri_length_, keep_, true_dihedrals_ = sess.run([ids, one_hot_primary, evolutionary, tertiary,
ter_mask, pri_length, keep, true_dihedrals])
ids_v.append(np.squeeze(ids_))
ohp_v.append(np.squeeze(one_hot_primary_))
evo_v.append(np.squeeze(evolutionary_))
tert_v.append(np.squeeze(tertiary_))
ter_mask_v.append(np.squeeze(ter_mask_))
dih_v.append(np.squeeze(true_dihedrals_))
except tf.errors.OutOfRangeError:
print('Done')
finally:
# When done, ask the threads to stop.
coord.request_stop()
# Wait for threads to finish.
coord.join(threads)
sess.close()
prim, evo, dih, mask = dt.limit_length_and_pad(ohp_v, evo_v, dih_v, ter_mask_v, max_length=None)
# x, y = np.concatenate([prim_v, evo_v], axis=2), dih_v
return prim, evo, dih, mask
def get_predictions(protein_n, feed_list, checkpoint_path, to_calculate_list):
prim, evo, dih, mask = feed_list
with tf.Session() as sess:
sess.run(init)
saver.restore(sess, checkpoint_path)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord, sess=sess)
# true, pred, mae_ = sess.run([true_dihedrals_masked, rad_pred_masked, mae_vec])
if protein_n:
feed_dict={
one_hot_primary: np.expand_dims(prim[protein_n], 0),
evolutionary: np.expand_dims(evo[protein_n], 0),
true_dihedrals: np.expand_dims(dih[protein_n,:,:n_angles], 0),
ter_mask: np.expand_dims(mask[protein_n],0)
}
else:
feed_dict={
one_hot_primary: prim,
evolutionary: evo,
true_dihedrals: dih[:,:,:n_angles],
ter_mask: mask
}
ret_list = sess.run(to_calculate_list,
feed_dict = feed_dict)
coord.request_stop()
return [np.array(el) for el in ret_list]
# -
prim_valid, evo_valid, dih_valid, mask_valid = extract_dataset('validation', validation_casp)
prim_test, evo_test, dih_test, mask_test = extract_dataset('testing', validation_casp)
prim_valid.shape, prim_test.shape
# +
tf.reset_default_graph()
# define the training data paths and how many epochs they should be queued for
# by instantiating the DataHandler object that takes care of parsing
data_handler = DataHandler(data_path=data_path,
casps=training_casps,
percentages=training_percentages,
num_epochs=num_epochs,
mode='training')
# use DataHandler to generate batches of specific size
# and optional limit on protein length
# secondary structure is missing from the ProteinNet, thus the underscore
ids, one_hot_primary, evolutionary, _, tertiary, ter_mask, pri_length, keep =\
data_handler.generate_batches(batch_size = batch_size,
capacity = capacity,
max_protein_len = max_len)
# convert euclidean coordinates to dihedral angles
dihedral_calculator = DihedralCalculator()
true_dihedrals = dihedral_calculator.dihedral_pipeline(tertiary, protein_length = tf.shape(one_hot_primary)[1])
true_dihedrals = true_dihedrals[:,:,:n_angles]
# set up placeholders with batch_size=None to be able to feed them with validation data
# they fall onto default coming from the queue if nothing is fed through feed_dict
true_dihedrals = tf.placeholder_with_default(true_dihedrals, shape=(None, None, n_angles))
true_vectors = Helpers.ang_to_vec_tf(true_dihedrals)
one_hot_primary = tf.placeholder_with_default(one_hot_primary, shape=(None, None, 20))
evolutionary = tf.placeholder_with_default(evolutionary, shape=(None, None, 21))
ter_mask = tf.placeholder_with_default(ter_mask, shape=(None, None))
# build a model and get predicted output
model = Model(n_angles=n_angles,
n_clusters=n_clusters,
output_mask=ter_mask,
model_type=model_type,
prediction_mode=prediction_mode,
dropout_rate=dropout_rate,
ang_mode=angularization_mode,
regularize_vectors=regularize_vectors,
loss_mode=loss_mode
)
if include_evo:
input_data = tf.concat([one_hot_primary, evolutionary], axis=2)
else:
input_data = one_hot_primary
rad_pred_masked, vec_pred_masked = model.build_model(input_data)
true_dihedrals_masked, true_vectors_masked = model.mask_other([true_dihedrals, true_vectors])
loss, loss_vec = model.calculate_loss(true_dihedrals_masked, rad_pred_masked,
true_vectors_masked, vec_pred_masked)
mae_vec = Helpers.loss360(true_dihedrals_masked, rad_pred_masked)
pcc = Helpers.pearson_tf(rad_pred_masked, true_dihedrals_masked)
# learning rate placeholder for adaptive learning rate
learning_rate = tf.placeholder(tf.float32, name='learning_rate')
# choose an optimizer to minimize the loss
global_step = tf.Variable(0, name='global_step', trainable=False)
train_op = tf.train.AdamOptimizer(learning_rate = learning_rate).minimize(loss, global_step=global_step)
init = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())
try:
n_parameters = np.sum([np.prod(v.get_shape().as_list()) for v in tf.trainable_variables()])
print("Parameters:", n_parameters)
except:
print("Couldn't calculate the number of parameters")
# -
# Training loop
# +
learning_rate_decay = 0.99
steps_to_print_after = 200
init_learning_rate = 0.001
load_checkpoint = False
save_checkpoint = False
def try_create_dir(path):
try:
os.mkdir(path)
print("Created directory: " + path)
except:
print("Didn't create the directory: " + path)
pass
try_create_dir('./checkpoints')
try_create_dir('./checkpoints/' + prediction_mode)
try_create_dir('./checkpoints/' + prediction_mode + '/tmp')
checkpoint_path = "./checkpoints/" + prediction_mode + "/tmp/"+loss_mode+"_"+str(n_angles)+"_model.ckpt"
print("checkpoint path:", checkpoint_path)
if 'lstm' in model_type:
init_learning_rate = 0.001
steps_to_print_after = 200
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(init)
if load_checkpoint:
saver.restore(sess, checkpoint_path)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord, sess=sess)
try:
step = 1
# replaced every time the results are printed
losses = []
loss_vecs = []
mae_vecs = []
# filled out for final results
train_losses = []
train_maes = []
val_losses = []
val_maes = []
pccs = []
while not coord.should_stop():
_, true_dihedrals_, rad_pred_masked_, loss_, loss_vec_, mae_vec_ = sess.run([train_op, true_dihedrals,
rad_pred_masked, loss, loss_vec, mae_vec],
feed_dict={learning_rate: init_learning_rate})
losses.append(loss_)
loss_vecs.append(loss_vec_)
mae_vecs.append(mae_vec_)
if step % steps_to_print_after == 0:
avg_loss, avg_loss_vec = np.mean(losses), np.mean(np.array(loss_vecs), axis=0)
train_losses.append(avg_loss_vec)
train_maes.append(np.mean(np.array(mae_vecs), axis=0))
print("Train loss:", avg_loss, avg_loss_vec)
losses = []
loss_vecs = []
mae_vecs = []
(true_dihedrals_masked_v, rad_pred_masked_v,
loss_, loss_vec_, mae_vec_v) = sess.run([true_dihedrals_masked, rad_pred_masked,
loss, loss_vec, mae_vec],
feed_dict={
one_hot_primary: prim_valid,
evolutionary: evo_valid,
true_dihedrals: dih_valid[:,:,:n_angles],
ter_mask: mask_valid
})
pcc = Helpers.pearson_numpy(np.squeeze(true_dihedrals_masked_v)[:,:n_angles],
np.array(rad_pred_masked_v))
print("Validation loss:", loss_, loss_vec_)
print("Validation PCC:", pcc)
print("Validation MAE:", mae_vec_v)
if (len(val_maes) > 1 and save_checkpoint and
np.mean(val_maes[np.argmin(np.mean(val_maes, axis=1))]) > np.mean(mae_vec_v)):
save_path = saver.save(sess, checkpoint_path)
print("Model saved in path: %s" % save_path)
val_maes.append(mae_vec_v)
val_losses.append(loss_vec_)
pccs.append(pcc)
if step * batch_size > data_handler.training_samples:
step = 0
init_learning_rate = init_learning_rate * learning_rate_decay
print("EPOCH. New learning rate:", init_learning_rate)
step += 1
except tf.errors.OutOfRangeError:
print('Done training for %d epochs, %d steps.' % (num_epochs, step))
finally:
# When done, ask the threads to stop.
coord.request_stop()
# Wait for threads to finish.
coord.join(threads)
sess.close()
# -
train_maes = np.array(train_maes)
train_losses = np.array(train_losses)
val_losses = np.array(val_losses)
val_maes = np.array(val_maes)
pccs = np.array(pccs)
# +
true, pred, mae_vec_ = get_predictions(None, [prim_test, evo_test, dih_test, mask_test],
checkpoint_path=checkpoint_path,
to_calculate_list=[true_dihedrals_masked, rad_pred_masked, mae_vec])
pcc_test, mae_test = Helpers.pearson_numpy(true,pred), mae_vec_
# -
pcc_test, np.rad2deg(mae_test)
# Plot the avg losses over time
plt.figure(figsize=(10,6))
plt.plot(np.mean(train_losses.reshape(-1,4), axis=1), label='Train loss', color='black')
plt.plot(np.mean(val_losses.reshape(-1,4), axis=1), label='Validation loss', color='magenta')
# plt.plot(np.ones_like(val_maes[:,0]) * np.deg2rad(20), '--', label='RaptorX mae $\phi$', color='magenta')
plt.ylabel('loss')
plt.xlabel('Mini epochs (200 batches)')
plt.title("Loss (mae on vectors). Vectorized model with alphabet")
plt.legend()
plt.figure(figsize=(10,6))
plt.plot(train_maes[:,0], label='Train mae $\phi$', color='black')
plt.plot(val_maes[:,0], label='Validation mae $\phi$', color='magenta')
plt.plot(np.ones_like(val_maes[:,0]) * np.deg2rad(20), '--', label='RaptorX mae $\phi$', color='cyan')
plt.plot(np.ones_like(val_maes[:,0]) * mae_test[0], '--', label='Test mae $\phi$', color='magenta')
plt.ylabel('mae [rad]')
plt.xlabel('Mini epochs (200 batches)')
plt.title("MAE for $\phi$. Vectorized model with alphabet")
plt.legend()
plt.figure(figsize=(10,6))
plt.plot(train_maes[:,1], label='Train mae $\psi$', color='black')
plt.plot(val_maes[:,1], label='Validation mae $\psi$', color='magenta')
plt.plot(np.ones_like(val_maes[:,1]) * np.deg2rad(30.14), '--', label='RaptorX mae $\psi$', color='cyan')
plt.plot(np.ones_like(val_maes[:,1]) * mae_test[1], '--', label='Test mae $\psi$', color='magenta')
plt.ylabel('mae [rad]')
plt.xlabel('Mini epochs (200 batches)')
plt.title("MAE for $\psi$. Vectorized model with alphabet")
plt.legend()
# +
plt.figure(figsize=(10,6))
plt.plot(pccs[:,0], '-', color='magenta', label='Validation pcc(cos($\phi$))')
plt.plot(np.ones_like(pccs[:,0]) * pcc_test[0], '--', label='RaptorX pcc(cos($\phi$))', color='magenta')
plt.plot(np.ones_like(pccs[:,0]) * 0.6585, '--', label='RaptorX pcc(cos($\phi$))', color='cyan')
plt.ylabel('pcc [1]')
plt.xlabel('Mini epochs (200 batches)')
plt.legend()
# +
plt.figure(figsize=(10,6))
plt.plot(pccs[:,1], '-', color='magenta', label='Validation pcc(cos($\psi$))')
plt.plot(np.ones_like(pccs[:,1]) * pcc_test[1], '--', label='Test pcc(cos($\phi$))', color='magenta')
plt.plot(np.ones_like(pccs[:,1]) * 0.7103, '--', label='RaptorX pcc(cos($\psi$))', color='cyan')
plt.xlabel('Mini epochs (200 batches)')
plt.ylabel('pcc [1]')
plt.legend()
# +
true, pred = get_predictions(34, [prim_test, evo_test, dih_test, mask_test],
checkpoint_path=checkpoint_path, to_calculate_list=[true_dihedrals_masked, rad_pred_masked])
plt.figure(figsize=(10,6))
phi, psi = np.split(pred, n_angles, -1)
plt.scatter(phi[:], psi[:], s=3, label='pred', color='magenta')
phi, psi = np.split(true, n_angles, -1)
plt.scatter(phi[:], psi[:], s=3, label='true', color='black')
plt.xlim((-np.pi, np.pi))
plt.ylim((-np.pi, np.pi))
plt.xlabel('$\phi$ [rad]')
plt.ylabel('$\psi$ [rad]')
plt.title("Ramachandran plot. 1 protein in test set")
plt.legend(loc=1)
# -
all_true, all_pred, all_mae = [], [], []
for protein_n in range (prim_test.shape[0]):
true, pred, mae_ = get_predictions(protein_n, [prim_test, evo_test, dih_test, mask_test],
checkpoint_path=checkpoint_path, to_calculate_list=[true_dihedrals_masked, rad_pred_masked, mae_vec])
all_true.append(true), all_pred.append(pred), all_mae.append(mae_)
def _np_array(the_list):
return [np.array(el) for el in the_list]
all_true, all_pred, all_mae = _np_array([all_true, all_pred, all_mae])
all_mae_deg = np.rad2deg(all_mae)
mask_test.shape
plt.figure(figsize=(10,6))
plt.hist(all_mae_deg, label=['$\phi$', '$\psi$'], color=['magenta', 'cyan'])
plt.title('Histogram of MAE on the test set')
plt.legend()
plt.figure(figsize=(12,8))
for i in range(all_mae_deg.shape[0]):
mask_i = mask_test[i]
size = (int(np.sum(mask_i)/50) +1)* 50
color = int(np.mean(all_mae_deg[i]))
plt.scatter([all_mae_deg[i,0]], [all_mae_deg[i,1]], s=size, c='magenta', alpha=0.6)
plt.xlabel('mean absolute error $\phi$ [deg]')
plt.ylabel('mean absolute error $\psi$ [deg]')
plt.title("Dependence of MAE on the protein size")
scipy.stats.pearsonr(np.sum(mask_test, axis=1), np.mean(all_mae, 1))
plt.scatter(dih_test[0,:,0], dih_test[0,:,1]), count_alpha_beta(dih_test[0])
def count_alpha_beta(dih_protein):
x = dih_protein[:,0]
# print(x.shape)
y = dih_protein[:,1]
beta = np.logical_and.reduce([x < -0.5, x > -3, y > 1, y <= np.pi])
alpha = np.logical_and.reduce([x < -0.5, x > -3, y < 1, y >= -1.2])
return np.sum(alpha), np.sum(beta)
plt.figure(figsize=(12,8))
for i in range(all_mae_deg.shape[0]):
val_i = dih_test[i]
protein_size = np.sum(mask_test[i]) + 1
alpha,beta = count_alpha_beta(val_i)
size = (beta+1) / protein_size
# print(size)
# print(np.std(val_i))
# size = (int(np.std(val_i)**2)+1) * 100
color = int(np.mean(all_mae_deg[i]))
plt.scatter([all_mae_deg[i,0]], [all_mae_deg[i,1]], s=size*500, c='magenta', alpha=0.6)
plt.xlabel('mean absolute error $\phi$ [deg]')
plt.ylabel('mean absolute error $\psi$ [deg]')
plt.title("Dependence of MAE on the secondary structure. Size proportional to beta sheet angles proportion")
plt.figure(figsize=(12,8))
for i in range(all_mae_deg.shape[0]):
val_i = dih_test[i]
# print(np.std(val_i))
size = (int(np.std(val_i)**2)+1) * 100
color = int(np.mean(all_mae_deg[i]))
plt.scatter([all_mae_deg[i,0]], [all_mae_deg[i,1]], s=size, c='magenta', alpha=0.6)
plt.xlabel('mean absolute error $\phi$ [deg]')
plt.ylabel('mean absolute error $\psi$ [deg]')
plt.title("Dependence of MAE on the standard deviation of true protein angles")
# +
true, pred = get_predictions(np.argsort(np.mean(all_mae, axis=1))[len(np.mean(all_mae, axis=1))//2], [prim_test, evo_test, dih_test, mask_test],
checkpoint_path=checkpoint_path, to_calculate_list=[true_dihedrals_masked, rad_pred_masked])
plt.figure(figsize=(10,6))
phi, psi = np.split(pred, n_angles, -1)
plt.scatter(phi[:], psi[:], s=3, label='pred', color='magenta')
phi, psi = np.split(true, n_angles, -1)
plt.scatter(phi[:], psi[:], s=3, label='true', color='black')
plt.xlim((-np.pi, np.pi))
plt.ylim((-np.pi, np.pi))
plt.xlabel('$\phi$ [rad]')
plt.ylabel('$\psi$ [rad]')
plt.title("Ramachandran plot. 1 protein with median MAE")
plt.legend(loc=1)
# -
plt.figure(figsize=(12,8))
for i in range(all_mae_deg.shape[0]):
val_i = dih_test[i]
# print(np.std(val_i))
size = (int(np.std(val_i)**2)+1) * 100
color = int(np.mean(all_mae_deg[i]))
plt.scatter([all_mae_deg[i,0]], [all_mae_deg[i,1]], s=size, c='magenta', alpha=0.6)
plt.xlabel('mean absolute error $\phi$ [deg]')
plt.ylabel('mean absolute error $\psi$ [deg]')
| 20,165 |
/deepfake_detection_c3d.ipynb | 6d48663e064ab8bc1290473d09c84531167c80d7 | [] | no_license | parth-p/deepfake_detection | https://github.com/parth-p/deepfake_detection | 2 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 8,784 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
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# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# +
import os
import pickle
import torch
import torchvision
import numpy as np
import pandas as pd
import torch.nn as nn
from PIL import Image
from tqdm import tqdm
import torch.nn as nn
import torch.utils.data as data1
from torch.utils import data
import torch.nn.functional as F
import torchvision.models as models
import matplotlib.pyplot as plt
from torch.autograd import Variable
from sklearn.metrics import accuracy_score
import torchvision.transforms as transforms
from sklearn.preprocessing import OneHotEncoder, LabelEncoder
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from tensorboardX import SummaryWriter
import torch.nn as nn
import torch
# -
class C3D(nn.Module):
def __init__(self, img_dim, frames,dropout):
super(C3D, self).__init__()
self.conv1 = nn.Conv3d(3, 32, kernel_size=(3, 3, 3), padding=(1, 1, 1))
self.pool1 = nn.MaxPool3d(kernel_size=(1, 2, 2), stride=(1, 2, 2))
self.bn1 = nn.BatchNorm3d(32)
self.conv2 = nn.Conv3d(32, 64, kernel_size=(3, 3, 3), padding=(1, 1, 1))
self.pool2 = nn.MaxPool3d(kernel_size=(1, 2, 2), stride=(1, 2, 2))
self.bn2 = nn.BatchNorm3d(64)
self.conv3a = nn.Conv3d(64, 64, kernel_size=(3, 3, 3), padding=(1, 1, 1))
self.conv3b = nn.Conv3d(64, 64, kernel_size=(3, 3, 3), padding=(1, 1, 1))
self.pool3 = nn.MaxPool3d(kernel_size=(2, 2, 2), stride=(2, 2, 2))
self.bn3 = nn.BatchNorm3d(64)
self.conv4a = nn.Conv3d(64, 128, kernel_size=(3, 3, 3), padding=(1, 1, 1))
self.conv4b = nn.Conv3d(128, 128, kernel_size=(3, 3, 3), padding=(1, 1, 1))
self.pool4 = nn.MaxPool3d(kernel_size=(2, 2, 2), stride=(2, 2, 2))
self.bn4 = nn.BatchNorm3d(128)
self.conv5a = nn.Conv3d(128, 256, kernel_size=(3, 3, 3), padding=(1, 1, 1))
self.conv5b = nn.Conv3d(256, 256, kernel_size=(3, 3, 3), padding=(1, 1, 1))
self.pool5 = nn.MaxPool3d(kernel_size=(2, 2, 2), stride=(2, 2, 2), padding=(0, 1, 1))
dim_shape = new_output_shape(num_of_maxpool_2=5, shape=img_dim)
channel_shape = new_channel_shape(frames=frames) # As first time channel is not modified
self.fc_conv = nn.Conv3d(256, 2, kernel_size=(channel_shape, dim_shape, dim_shape))
self.dropout = nn.Dropout(p=dropout)
self.relu = nn.ReLU()
def forward(self, x):
x = x.type(torch.cuda.FloatTensor)
x = x.permute(0,4,1,2,3)
h = self.relu(self.conv1(x))
h = self.bn1(h)
h = self.pool1(h)
h = self.dropout(h)
h = self.relu(self.conv2(h))
h = self.bn2(h)
h = self.pool2(h)
h = self.dropout(h)
h = self.relu(self.conv3a(h))
h = self.relu(self.conv3b(h))
h = self.bn3(h)
h = self.pool3(h)
h = self.dropout(h)
h = self.relu(self.conv4a(h))
h = self.relu(self.conv4b(h))
h = self.bn4(h)
h = self.pool4(h)
h = self.dropout(h)
h = self.relu(self.conv5a(h))
h = self.relu(self.conv5b(h))
h = self.pool5(h)
h = self.fc_conv(h)
logits = h.view(x.shape[0],-1)
return logits
# resize and crop image, and stack them together alongg with label y=0 or 1 if orignal or fake
from torch.utils import data
class Dataload_3D_CNN(data.Dataset):
def __init__(self, data_path, transform=None):
self.transform = transform
self.folders = data_path
def read_images(self, data_path, use_transform):
X = []
for i in os.listdir(data_path):
image = Image.open(os.path.join(data_path,i))
if use_transform is not None:
image = use_transform(image)
image = torch.from_numpy(np.asarray(image))
X.append(image)
X = torch.stack(X, dim=0)
return X
def __getitem__(self, index):
data_path = os.path.join(self.folders,os.listdir(self.folders)[index])
X = self.read_images(data_path, self.transform)
y = 1
if 'orig' in data_path:
y = 0
return X, torch.from_numpy(np.array(y)).type(torch.LongTensor)
TRANSFORM_IMG = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(256),
])
# +
train_path = './data/train/'
train_data = Dataload_3D_CNN(train_path, transform=TRANSFORM_IMG)
val_path = './data/val/'
val_data = Dataload_3D_CNN(val_path, transform=TRANSFORM_IMG)
cnn3d = C3D(img_dim=256, frames=10, dropout=0.4)
optimizer = torch.optim.Adam(cnn3d.parameters(), lr=learning_rate)
# +
epochs = 40
batch_size = 8
learning_rate = 1e-4
log_interval = 10
img_x, img_y = 256,256
loss_fn = nn.CrossEntropyLoss()
fc_hidden1, fc_hidden2 = 256, 256
dropout = 0.0
begin_frame, end_frame, skip_frame = 1, 10, 1
# -
def train(log_interval, model, device, train_loader, optimizer, epoch):
model.train()
losses = []
scores = []
N_count = 0
for batch_idx, (X, y) in enumerate(train_loader):
N_count += X.size(0)
optimizer.zero_grad()
output = model(X)
loss = F.cross_entropy(output, y)
losses.append(loss.item())
y_pred = torch.max(output, 1)[1]
step_score = accuracy_score(y.data.squeeze().numpy(), y_pred.data.squeeze().numpy())
scores.append(step_score)
loss.backward()
optimizer.step()
return np.mean(losses), np.mean(scores)
for epoch in range(epochs):
train_losses, train_scores = train(log_interval, cnn3d, device, train_loader, optimizer, epoch)
print("Training Loss : "+str(train_losses))
| 5,976 |
/notebooks/7 - Predict Workout Model.ipynb | b5aa248a1a03672c80cb111b8d1beb827dfddaf9 | [] | no_license | USPA-Technology/Data-Driven-Cycling-and-Workout-Prediction | https://github.com/USPA-Technology/Data-Driven-Cycling-and-Workout-Prediction | 0 | 0 | null | 2021-10-12T04:51:26 | 2021-10-11T17:14:23 | null | Jupyter Notebook | false | false | .py | 10,965 | # ---
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# name: python3
# ---
import nltk
nltk.download()
y and distance
# +
# #!pip install sklearn
# +
from utils import *
# import api_key from myconfig file
from myconfig import *
# +
# If you would like to start with clean data use below commented line
#import numpy as np
#data = loadCleanData()
## Define new coloumns
#data[['temp','wind','weather','day','hour','dayOfWeek','isWeekend']] = np.nan
#data[['weatherDesc']] = ""
data = loadCorrelatedData()
data.head()
# -
# ### Feature Extraction
# +
# Since data is limited these features doesn't make sense
#data['year'] = list(map(lambda t : t.year, data['Activity Date']))
#data['month'] = list(map(lambda t : t.month, data['Activity Date']))
#data['minute'] = list(map(lambda t : t.minute, data['Activity Date']))
# -
data['day'] = list(map(lambda t : t.day, data['Activity Date']))
data['hour'] = list(map(lambda t : t.hour, data['Activity Date']))
data['dayOfWeek'] = list(map(lambda t : t.dayofweek, data['Activity Date']))
data['isWeekend'] = ((pd.DatetimeIndex(data['Activity Date']).dayofweek) // 5 == 1).astype(int)
# #### Weather Data Export
# +
# api_key is defined in myconfig file
city = "Istanbul"
date = "2020-12-25"
hour = 8
temp_data = get_weather(api_key,city,date,hour)
print(temp_data)
# -
date = "2021-03-05"
hour = 8
temp_data = get_weather(api_key,city,date,hour)
print(temp_data)
# #### Get Weather Data and Save
# +
## iterate for all ride date to extract weather
#for index, row in data.iterrows():
# try:
# temp, wind, weatherDesc = get_weather(api_key,city,row['Activity Date'],row['hour'])
# data['temp'][index] = temp
# data['wind'][index] = wind
# data['weatherDesc'][index] = weatherDesc
# print(row['Activity Date'],temp, wind, weatherDesc)
# except Exception as e:
# print("Error:", e, row['Activity Date'])
# -
# ### Transform Activity Type and Weather Description
# +
# convert string data to numeric value for decision making
from sklearn import preprocessing
le_activity = preprocessing.LabelEncoder()
le_activity.fit(data['Activity Type'])
print(le_activity.classes_)
vals = le_activity.transform(le_activity.classes_)
vals
# convert all ride type data into numeric values
data['rideType'] = le_activity.transform(data['Activity Type'])
data['rideType']
# +
# Transform weather conditions data to numeric value for decision making
le = preprocessing.LabelEncoder()
le.fit(data['weatherDesc'])
print(le.classes_)
weather = le.transform(le.classes_)
print(weather)
# convert all weatherDesc data into numeric values
data['weather'] = le.transform(data['weatherDesc'])
data['weather']
# -
## Export correlated data
saveCorrelatedData(data)
# ### Prepare features
# ride distribution per hour
data.plot(kind='scatter', x='hour', y='Distance', xticks=data['hour'], figsize=(14,8))
# ride distribution per day
data.plot(kind='scatter', x='dayOfWeek', y='Distance', xticks=data['dayOfWeek'], figsize=(14,8))
# ride distribution per weekdays
data.plot(kind='scatter', x='isWeekend', y='Distance', xticks=data['isWeekend'], figsize=(14,8))
# indoor outdoor ride distribution per temp
data.plot(kind='scatter', x='rideType', y='temp', xticks=data['rideType'], figsize=(14,8))
# Select features as list of array
X = data[['hour','dayOfWeek','isWeekend','temp','wind','weather']]
X = X.to_numpy()
X
# ### Prepare Predict
Y_distance = data['Distance']
Y_distance = Y_distance.to_numpy()
Y_distance
Y_rideType = data['rideType']
Y_rideType = Y_rideType.to_numpy()
Y_rideType
Y = data[['Distance','rideType']]
Y = Y.to_numpy()
Y
# ## Linear Regression for Distance prediction
# +
# example of training a final regression model
from sklearn.linear_model import LinearRegression
# fit final model
model = LinearRegression()
model.fit(X[0:160], Y_distance[0:160])
# test prediction with a cold Monday weather data
result_distance = model.predict([[8,0,0,10,15,0]])
print("Result distance prediction=%s" % result_distance)
# test prediction with a sunny Sunday weather data
result_distance = model.predict([[6,6,1,26,3,1]])
print("Result distance prediction=%s" % result_distance)
# -
result = model.predict([[8,6,1,9,15,0]])
result
# ## Logistic Regression for RideType Prediction
# +
from sklearn.linear_model import LogisticRegression
clf = LogisticRegression(random_state=0).fit(X, Y_rideType)
result_ridetype = clf.predict([[8,6,1,20,3,0]])
print("Result type prediction=%s" % result_ridetype)
result_ridetype = clf.predict([[8,6,1,10,12,1]])
print("Result type prediction=%s" % result_ridetype)
# +
#Ride for the weekend
result_ridetype = clf.predict([[8,6,1,8,12,3],[9,7,1,20,17,3],[8,2,0,14,17,3]])
print("Result type prediction=%s" % result_ridetype)
#Ride for Weekend
result = model.predict([[9,6,1,10,12,3],[9,7,1,20,17,3],[8,2,0,14,17,3]])
print(result)
# -
# ### Export Model as a file
# +
import pickle
# Save to file in the model folder
distance_model_file = "../web/model/distance_model.pkl"
with open(distance_model_file, 'wb') as file:
pickle.dump(model, file)
ridetype_model_file = "../web/model/ridetype_model.pkl"
with open(ridetype_model_file, 'wb') as file:
pickle.dump(clf, file)
| 5,432 |
/projects/hog/hog_contest/Heatmap maker.ipynb | b58f87ddf1359d5558d862e54bdce56a00687b1f | [] | no_license | Yanay1/cs61a | https://github.com/Yanay1/cs61a | 2 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 80,506 | # ---
# jupyter:
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# extension: .py
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# language: python
# name: python3
# ---
# +
"""
This is a minimal contest submission file. You may also submit the full
hog.py from Project 1 as your contest entry.
Only this file will be submitted. Make sure to include any helper functions
from `hog.py` that you'll need here! For example, if you have a function to
calculate Free Bacon points, you should make sure it's added to this file
as well.
Don't forget: your strategy must be deterministic and pure.
"""
TEAM_NAME = 'Yanay' # Change this line!
probabilties = [[0.16666666666666663, 0.16666666666666669, 0.16666666666666669, 0.16666666666666669, 0.16666666666666669, 0.16666666666666669], [0.30555555555555547, 0.027777777777777783, 0.055555555555555566, 0.08333333333333334, 0.11111111111111113, 0.13888888888888892, 0.11111111111111113, 0.08333333333333334, 0.055555555555555566, 0.027777777777777783], [0.42129629629629617, 0.004629629629629631, 0.013888888888888892, 0.027777777777777783, 0.04629629629629631, 0.06944444444444446, 0.08333333333333336, 0.08796296296296298, 0.08333333333333336, 0.06944444444444446, 0.04629629629629631, 0.027777777777777783, 0.013888888888888892, 0.004629629629629631], [0.5177469135802468, 0.0007716049382716051, 0.0030864197530864204, 0.007716049382716051, 0.015432098765432101, 0.02700617283950618, 0.04012345679012347, 0.05246913580246915, 0.061728395061728406, 0.06558641975308643, 0.061728395061728406, 0.05246913580246915, 0.04012345679012347, 0.02700617283950618, 0.015432098765432101, 0.007716049382716051, 0.0030864197530864204, 0.0007716049382716051], [0.598122427983539, 0.00012860082304526753, 0.0006430041152263377, 0.001929012345679013, 0.004501028806584364, 0.009002057613168728, 0.015560699588477372, 0.023791152263374492, 0.032793209876543224, 0.04115226337448561, 0.04693930041152265, 0.04899691358024693, 0.04693930041152265, 0.04115226337448561, 0.032793209876543224, 0.023791152263374492, 0.015560699588477372, 0.009002057613168728, 0.004501028806584364, 0.001929012345679013, 0.0006430041152263377, 0.00012860082304526753], [0.6651020233196159, 2.1433470507544587e-05, 0.00012860082304526753, 0.00045010288065843633, 0.0012002743484224969, 0.002700617283950618, 0.005272633744855967, 0.009130658436213994, 0.014274691358024694, 0.0203832304526749, 0.026706104252400553, 0.03227880658436215, 0.03613683127572018, 0.03753000685871057, 0.03613683127572018, 0.03227880658436215, 0.026706104252400553, 0.0203832304526749, 0.014274691358024694, 0.009130658436213994, 0.005272633744855967, 0.002700617283950618, 0.0012002743484224969, 0.00045010288065843633, 0.00012860082304526753, 2.1433470507544587e-05], [0.7209183527663465, 3.5722450845907643e-06, 2.500571559213535e-05, 0.0001000228623685414, 0.00030006858710562417, 0.0007501714677640604, 0.0016253715134887977, 0.003125714449016919, 0.005429812528577961, 0.008626971879286696, 0.012627886374028351, 0.017128915180612717, 0.021629943987197076, 0.02550582990397806, 0.02813143004115227, 0.029060213763145867, 0.02813143004115227, 0.02550582990397806, 0.021629943987197076, 0.017128915180612717, 0.012627886374028351, 0.008626971879286696, 0.005429812528577961, 0.003125714449016919, 0.0016253715134887977, 0.0007501714677640604, 0.00030006858710562417, 0.0001000228623685414, 2.500571559213535e-05, 3.5722450845907643e-06], [0.7674319606386221, 5.953741807651275e-07, 4.76299344612102e-06, 2.143347050754459e-05, 7.144490169181529e-05, 0.00019647347965249208, 0.00046677335771985994, 0.0009835581466239908, 0.001871856424325561, 0.0032596736396890732, 0.005239292790733122, 0.007823216735253775, 0.010907254991617136, 0.014253257887517152, 0.01750400091449475, 0.020242722146014337, 0.022076474622770927, 0.022722455608901092, 0.022076474622770927, 0.020242722146014337, 0.01750400091449475, 0.014253257887517152, 0.010907254991617136, 0.007823216735253775, 0.005239292790733122, 0.0032596736396890732, 0.001871856424325561, 0.0009835581466239908, 0.00046677335771985994, 0.00019647347965249208, 7.144490169181529e-05, 2.143347050754459e-05, 4.76299344612102e-06, 5.953741807651275e-07], [0.8061933005321851, 9.922903012752126e-08, 8.930612711476914e-07, 4.4653063557384574e-06, 1.6372789971041007e-05, 4.911836991312302e-05, 0.00012681470050297217, 0.00028994722603261714, 0.0005983510516689532, 0.0011297225080018295, 0.001970192393181935, 0.0031962662894375873, 0.004850215763603112, 0.006913782674135043, 0.009287837219935991, 0.011788408779149525, 0.014163951760402384, 0.016133151863283044, 0.017437021319158672, 0.01789347485774527, 0.017437021319158672, 0.016133151863283044, 0.014163951760402384, 0.011788408779149525, 0.009287837219935991, 0.006913782674135043, 0.004850215763603112, 0.0031962662894375873, 0.001970192393181935, 0.0011297225080018295, 0.0005983510516689532, 0.00028994722603261714, 0.00012681470050297217, 4.911836991312302e-05, 1.6372789971041007e-05, 4.4653063557384574e-06, 8.930612711476914e-07, 9.922903012752126e-08], [0.8384944171101543, 1.653817168792021e-08, 1.653817168792021e-07, 9.095994428356116e-07, 3.6383977713424464e-06, 1.182479275686295e-05, 3.294403800233706e-05, 8.111973212924863e-05, 0.0001801006896814511, 0.0003656589760199158, 0.0006858379798980511, 0.001197413244720487, 0.001957458000982236, 0.0030100299380599175, 0.004369715723382278, 0.006006085121043544, 0.007834032699537676, 0.009714522049484331, 0.01146839515698827, 0.01290266809662315, 0.013844103519958005, 0.014172303537104783, 0.013844103519958005, 0.01290266809662315, 0.01146839515698827, 0.009714522049484331, 0.007834032699537676, 0.006006085121043544, 0.004369715723382278, 0.0030100299380599175, 0.001957458000982236, 0.001197413244720487, 0.0006858379798980511, 0.0003656589760199158, 0.0001801006896814511, 8.111973212924863e-05, 3.294403800233706e-05, 1.182479275686295e-05, 3.6383977713424464e-06, 9.095994428356116e-07, 1.653817168792021e-07, 1.653817168792021e-08]]
sums = [[1, 2, 3, 4, 5, 6], [1, 4, 5, 6, 7, 8, 9, 10, 11, 12], [1, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18], [1, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24], [1, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30], [1, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36], [1, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42], [1, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48], [1, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54], [1, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60]]
def final_strategy(score, opponent_score, look_ahead=1):
# Calculated one time using seperate included methods
# Turn EV = YOUR TURN GAIN - OPPONENT TURN GAIN
# We need to calculate the EV for every roll
# First let's calculate the score to beat, the easy choice of baconing and swapping potentially
best_roll = 0
bacon = free_bacon(opponent_score)
bacond = bacon + score
# First off, let's just win
if bacond >= 100:
return 0
if is_swap(bacond, opponent_score):
margin = calculate_ev(score, opponent_score, opponent_score, bacond)
else:
margin = calculate_ev(score, opponent_score, bacond, opponent_score)
#print("Bacond Margin", margin)
# Now we have the minimum possible EV choice, lets calculate it for every roll of Dice
# If a certain dice roll has a higher EV than margin, we'll update margin, and change winner
for i in range(0, 10):
# For 1-10 rolls of Dice
# Calculate the scores for each sum and then multiply by probability
p_evs = []
for p in sums[i]:
new_score = score + p
if is_swap(new_score, opponent_score):
tmargin = calculate_ev(score, opponent_score, opponent_score, new_score, look_ahead)
else:
tmargin = calculate_ev(score, opponent_score, new_score, opponent_score, look_ahead)
p_evs = p_evs + [tmargin]
evs = mul_lists(p_evs, probabilties[i], )
expected_value_for_roll = sum(evs)
#print(i+1, 'Rolls', expected_value_for_roll)
if expected_value_for_roll > margin:
margin = expected_value_for_roll
best_roll = i+1
return best_roll
def opponent_strategy(score, opponent_score, look_ahead=0):
# Calculated one time using seperate included methods
# Turn EV = YOUR TURN GAIN - OPPONENT TURN GAIN
# We need to calculate the EV for every roll
# First let's calculate the score to beat, the easy choice of baconing and swapping potentially
best_roll = 0
bacon = free_bacon(opponent_score)
bacond = bacon + score
# First off, let's just win
if bacond >= 100:
return 0
if is_swap(bacond, opponent_score):
margin = calculate_ev(score, opponent_score, opponent_score, bacond, look_ahead)
else:
margin = calculate_ev(score, opponent_score, bacond, opponent_score, look_ahead)
#print("Bacond Margin", margin)
# Now we have the minimum possible EV choice, lets calculate it for every roll of Dice
# If a certain dice roll has a higher EV than margin, we'll update margin, and change winner
for i in range(0, 10):
# For 1-10 rolls of Dice
# Calculate the scores for each sum and then multiply by probability
p_evs = []
for p in sums[i]:
new_score = score + p
if is_swap(new_score, opponent_score):
tmargin = calculate_ev(score, opponent_score, opponent_score, new_score)
else:
tmargin = calculate_ev(score, opponent_score, new_score, opponent_score)
p_evs = p_evs + [tmargin]
evs = mul_lists(p_evs, probabilties[i], )
expected_value_for_roll = sum(evs)
#print(i+1, 'Rolls', expected_value_for_roll)
if expected_value_for_roll > margin:
margin = expected_value_for_roll
best_roll = i+1
return best_roll
def opponent_estimate_score(opponent_score, your_score):
return None
def calculate_ev(og_your_score, og_their_score, new_score, new_opp_score, look_ahead=0):
# Apply opponent final_strategy
# Opponent rolls-
if look_ahead == 0:
return (new_score-og_your_score) - (new_opp_score - og_their_score)
else:
# figure out what their roll will be
opponent_roll = opponent_strategy(new_opp_score, new_score)
# Opponent rolled 0
if opponent_roll == 0:
bacon = free_bacon(new_score)
bacond = bacon + new_opp_score
if is_swap(bacond, new_score):
return -1 * calculate_ev(og_their_score, og_your_score, new_score, bacond, look_ahead-1)
else:
return -1 * calculate_ev(og_their_score, og_your_score, bacond, new_score, look_ahead-1)
else:
# They would roll n Dice. their new score would would have different options
options_for_sums = sums[opponent_roll-1]
# each of these sums will have a different ev Change
# so the higher level case should be tree recursion
p_evs = []
for p in options_for_sums:
new_o_score = new_opp_score + p
if is_swap(new_score, new_o_score):
tmargin = -1 * calculate_ev(og_their_score, og_your_score, new_score, new_o_score, look_ahead-1)
else:
tmargin = -1 * calculate_ev(og_their_score, og_your_score, new_o_score, new_score, look_ahead-1)
p_evs = p_evs + [tmargin]
wa = sum(mul_lists(probabilties[opponent_roll-1], p_evs))
return wa
def free_bacon(score):
"""Return the points scored from rolling 0 dice (Free Bacon).
score: The opponent's current score.
"""
#assert score < 100, 'The game should be over.'
# BEGIN PROBLEM 2
# For Scores above 10
if score >= 10:
ones_digit = score % 10
tens_digit = score // 10
return 2+abs(tens_digit-ones_digit)
else:
return score + 2
"*** YOUR CODE HERE ***"
# END PROBLEM 2
def is_swap(score0, score1):
"""Return whether one of the scores is an integer multiple of the other."""
# BEGIN PROBLEM 4
# If both scores are larger than 1
if score0 > 1 and score1 > 1:
return score0 % score1 == 0 or score1 % score0 == 0
else:
return False
# END PROBLEM 4
def other(player):
"""Return the other player, for a player PLAYER numbered 0 or 1.
>>> other(0)
1
>>> other(1)
0
"""
return 1 - player
def mul_lists(x,y):
n = []
for i in range(len(x)):
n = n + [x[i] * y[i]]
return n
# Calculate probabilities and sums used in final strategy
def sigma(k, to, f):
if k > to:
return 0
term = f(k)
return term + sigma(k+1, to, f)
def choose(k, n):
return factorial(n) / (factorial(k) * factorial(n-k))
def factorial(n):
if n <= 1:
return 1
else:
return n * factorial(n-1)
def points_probability(p, n, s):
# From http://mathworld.wolfram.com/Dice.html
front = (1/(s**n))
k = 0
kmax = ((p-n)//(s))
sig_func = lambda k : ((-1)** k) * choose(k, n) * choose(n-1, p - s*k - 1)
adder = sigma(k, kmax, sig_func)
return front * adder
def calculate_min_max_term(n,s):
return n, s*n
def generate_arrays(mx = 10, sides = 5):
outer = []
outer_sums = []
for n in range(1, mx+1):
inner = [(1- (5/6)**n)]
minT, maxT = calculate_min_max_term(n, sides)
for i in range(minT, maxT+1):
inner = inner + [points_probability(i,n,sides)]
outer = outer + [inner]
outer_sums = outer_sums +[[1] + list(range(minT+n, maxT+n+1))]
return outer, outer_sums
# -
import numpy as np; np.random.seed(0)
import seaborn as sns; sns.set()
import matplotlib
from matplotlib import pyplot
# %matplotlib inline
# create a 100 x 100 array
b = []
# I is their score
for i in range(0,100):
print(i)
n = []
for l in range(99,-1, -1):
n = n + [final_strategy(l, i)]
b = b + [n]
a4_dims = (20, 20)
fig, ax = pyplot.subplots(figsize=a4_dims)
an = sns.heatmap(b, ax=ax, cmap=sns.diverging_palette(200, 255
,as_cmap=True))
an.invert_yaxis()
an.set_title('Their Score vs Your Score')
an.set_xlabel('Your Score')
an.set_ylabel('Their Score')
| 14,988 |
/Jogos.ipynb | 23250984941e6a42f24fc53d27256650cc7728b4 | [] | no_license | anajubl/data-analysis | https://github.com/anajubl/data-analysis | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 221,957 | # ---
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# name: python3
# ---
# # Independence
# $L_1 \succ L_2$ implies that $$ u(50) > 0.01 u(0) + 0.1 u(250) + 0.89 u(50)$$ or $$ u(50)>\frac{1}{11}u(0) + \frac{10}{11}u(250)$$
#
# while $L_4 \succ L_3$ implies that $$ 0.9 u(0) + 0.1 u(250) > 0.11u(50) + 0.89 u(0) $$ or $$ u(50) < \frac{1}{11} u(0) + \frac{10}{11}u(250) $$
# which is a contradiction (there is no $u()$ such that this could be true).
# # Demand for Insurance
# ## How much coverage
#
# Expected utility is $$ EU(q) = p u(w-L - \pi q + q) + (1-p) u(w-\pi q) $$
#
# He will choose $q$ to maximize $EU(q)$. The FOC is $$ p u'(w_1)(1-\pi) - (1-p) u'(w_0)\pi = 0$$ where $w_1 = w-L-\pi q + q$ is wealth is the loss state and $w_0 = w- \pi q$.
#
# 1. If $\pi = p$, then FOC becomes $u'(w_1) = u'(w_0)$ which implies $w_1=w_0$ and therefore $q^*=L$.
# 2. If $\pi>p$, FOC rewritten has $$ \frac{u'(w_1}{u'(w_0)} = \frac{1-p}{p}\frac{\pi}{1-\pi} $$. The RHS is greater than 1 if $\pi>p$ and therefore since $u'(w_1)>u'(w_0)$ if $w_1<w_0$, $q^*<L$.
#
#
# ## Numerical exercise
import numpy as np
from matplotlib import pyplot as plt
from scipy.optimize import minimize_scalar
gamma = 2
wi = 100e3
p = 0.05
L = 10e3
pis = np.linspace(p,1.5*p,10)
def crra(wealth,gamma):
if gamma!=1.0:
return wealth**(1.0-gamma)/(1.0-gamma)
else :
return np.log(wealth)
def eu(q,wi,p,L,pi,gamma):
# define wealth in both states
w0 = wi - pi*q
w1 = wi - L - pi*q + q
# compute utilities
u0 = crra(w0,gamma)
u1 = crra(w1,gamma)
# expected utility
value = p*u1 + (1.0-p)*u0
return -value
opt = minimize_scalar(eu,bounds=(0.0,L),method='bounded',args=(wi,p,L,p,gamma))
opt.x
qopts = []
for pi in pis:
opt = minimize_scalar(eu,bounds=(0.0,L),method='bounded',args=(wi,p,L,pi,gamma))
qopts.append(opt.x)
plt.figure()
plt.plot(pis,qopts)
plt.xlabel('premium (per dollar of coverage)')
plt.ylabel('insurance coverage')
plt.title('optimal insurance coverage')
plt.show()
# Yes, this is the demand curve as a function of price! If there is competition in the market, then $\pi=p$ and we have full coverage. If the insurer is a monopoly, he can use the demand curve to charge a higher price. But demand is quite elastic. We can compute the elasticity, say at $\pi=p$.
elast = (qopts[1]/qopts[0]-1.0)/(pis[1]/pis[0]-1.0)
elast
# Let's define a function that does that
def elast(wi,p,L,pi,gamma,eps=0.0025):
# at pi
opt = minimize_scalar(eu,bounds=(0.0,L),method='bounded',args=(wi,p,L,pi,gamma))
q0 = opt.x
# at pi + eps
opt = minimize_scalar(eu,bounds=(0.0,L),method='bounded',args=(wi,p,L,pi+eps,gamma))
q1 = opt.x
return (q1/q0-1.0)/(eps/p)
elast(wi,p,L,p,gamma)
# Now do this as function of parameters
gammas = np.linspace(1.1,5.0,10)
etas = [elast(wi,p,L,p,gamma) for gamma in gammas]
plt.figure()
plt.plot(gammas,etas)
plt.xlabel('$\\gamma$')
plt.ylabel('elasticity')
plt.title('Elasticity as function of risk aversion')
plt.show()
wis = np.linspace(100e3,250e3,10)
etas = [elast(wi,p,L,p,gamma) for wi in wis]
plt.figure()
plt.plot(wis,etas)
plt.xlabel('initial wealth')
plt.ylabel('elasticity')
plt.title('Elasticity as function of initial wealth')
plt.show()
# # Portfolio Problem
# ## Theory
# Expected utility is given by $$EU(q) = \int u(w+q \tilde{z} + (w-q)r)dG $$
#
# The FOC to that problem is $$EU'(q^*) = \int u'(w+q^*\tilde{z} + (w-q^*)r)(\tilde{z}-r)dG $$
#
# The second order condition is $$EU''(q^*) = \int u''(w+q^*\tilde{z} + (w-q^*)r)(\tilde{z}-r)^2dG<0 $$ for all $w$. Hence, suffice to check if at $q=0$ and $E\tilde{z} = r$ what is the sign of $EU'(q^*)$
#
# $$EU'(0) = u'(w(1+r))\int (\tilde{z}-r)dG = u'(w(1+r))(E\tilde{z}-r) = 0 $$
#
# Since $EU'(0)=0$ and $EU''(0)<0$, $q^* = 0$. When $E\tilde{z}>r$, the FOC tells us that $EU'(0)>0$, hence $q^*>0$.
# ## Numerical Exercise
ns = 1000
sigz = 0.16
muz = 0.06
r = 0.02
wi = 100e3
gamma = 2.0
eps = np.random.normal(size=ns)
def eu(q,gamma,wi,r,muz,sigz,eps):
rz = muz + sigz*eps
eu = np.mean([crra(wi+q*z + (wi-q)*r,gamma) for z in rz])
return -eu
# 2) To find out how much he will invest:
opt = minimize_scalar(eu,bounds=(0.0,wi),method='bounded',args=(gamma,wi,r,muz,sigz,eps))
opt.x
# He puts most of his wealth in the risky asset. 3) To see how that varies with parameters, do something like what is below:
qopts = []
muzs = np.linspace(0,0.06,10)
for muz in muzs:
opt = minimize_scalar(eu,bounds=(0.0,wi),method='bounded',args=(gamma,wi,r,muz,sigz,eps))
qopts.append(opt.x)
plt.figure()
plt.plot(muzs-r,qopts)
plt.xlabel('equity risk premium')
plt.ylabel('investment in stocks')
plt.title('Investment in Stocks as Function of Equity Risk Premium')
plt.show()
# 4) To back out their risk aversion, we can do it by inverting the function... We know how to solve for $q$ for a given $\gamma$. If we know $q$, find the $\gamma$ such that $q^*$ is equal to the one from data (brothers)
def solve(gamma,wi,r,muz,sigz,eps,qdata):
opt = minimize_scalar(eu,bounds=(0.0,wi),method='bounded',args=(gamma,wi,r,muz,sigz,eps))
return qdata - opt.x
from scipy.optimize import brentq
sigz = 0.16
muz = 0.06
r = 0.02
wi = 100e3
gamma = 2.0
brothers = [0.25,0.5,0.75]
gammas = []
for bro in brothers:
gammas.append(brentq(solve,0.1,10.0,args=(wi,r,muz,sigz,eps,bro*wi)))
for b,g in zip(brothers,gammas):
print(b,g)
gammas
le
crosstab_vg['Total'] = crosstab_vg.sum(axis=1)
crosstab_vg.head()
# +
#Heatmap - qtd de jogos lançados para cada gênero em cada plataforma
top10plat = crosstab_vg[crosstab_vg['Total']>1000].sort_values('Total', ascending=False)
top10final = top10plat.append(pd.DataFrame(top10plat.sum(), columns=['total']).T, ignore_index=False)
sns.set(font_scale=1) #define o tamanho da fonte a ser utilizada no gráfico
plt.figure(figsize=(18, 9)) #define o tamanho da figura do gráfico
sns.heatmap(top10final, annot=True, vmax=top10final.loc[:'PS', :'Strategy'].values.max(), vmin=top10final.loc[:, :'Strategy'].values.min(), fmt='d')
#Primeiro, utiliza-se o heatmap do seaborn. Essa função possui muitos argumentos que podem ser utilizados para personalizar seu gráfico, mas vou me ater aos mais importantes. O primeiro deles é o dado para exibir no gráfico, em nosso caso, o DataFrame top10_final. Em seguida eu informo o argumento annot=True, que faz com que os números de cada “quadradinho” apareça, facilitando a exibição. Se você definir esse valor para False, seu mapa de calor terá apenas as cores, sem qualquer número nelas.
#annot=True --> aparecer os nrs de cada quadradinho
#Para localizar os valores máximo e mínimo, eu utilizo a função .loc do pandas e filtro as linhas e colunas do DataFrame para localizar seus valores.
#argumento fmt='d' para formatar as informações para que fiquem bem apresentadas
plt.xlabel('Gênero')
plt.ylabel('Console')
plt.title('Quantidade de Títulos por Gênero e Console')
plt.show()
| 7,197 |
/CS 178 Kaggle Project.ipynb | 63eef511dc4bdf79912e21363d57af26915bd494 | [] | no_license | joycele/hello-world | https://github.com/joycele/hello-world | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 84,605 | # ---
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# ---
def splitData(X, Y=None, train_fraction=0.80):
"""
Split data into training and test data.
Parameters
----------
X : MxN numpy array of data to split
Y : Mx1 numpy array of associated target values
train_fraction : float, fraction of data used for training (default 80%)
Returns
-------
to_return : (Xtr,Xte,Ytr,Yte) or (Xtr,Xte)
A tuple containing the following arrays (in order): training
data from X, testing data from X, training labels from Y
(if Y contains data), and testing labels from Y (if Y
contains data).
"""
nx,dx = twod(X).shape
ne = int(round(train_fraction * nx))
Xtr,Xte = X[:ne,:], X[ne:,:]
to_return = (Xtr,Xte)
if Y is not None:
Y = arr(Y).flatten()
ny = len(Y)
if ny > 0:
assert ny == nx, 'splitData: X and Y must have the same length'
Ytr,Yte = Y[:ne], Y[ne:]
to_return += (Ytr,Yte)
return to_return
# +
import numpy as np
import sklearn as sk
import os
from numpy import asarray as arr
from numpy import atleast_2d as twod
from matplotlib import pyplot as plt
from sklearn.ensemble import AdaBoostClassifier
from sklearn.metrics import roc_auc_score
from sklearn.metrics import roc_curve
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_selection import SelectFromModel
from sklearn.feature_selection import VarianceThreshold
from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import chi2
os.chdir(r'C:\Users\Joyce\Documents\178\project\formated_data')
X = np.loadtxt('X_train.txt')
Y = np.loadtxt('Y_train.txt')
X_final = np.loadtxt('X_test.txt')
print(X.shape)
sel = VarianceThreshold(0.5)
X_new = sel.fit_transform(X)
print(X_new.shape)
# -
Xtr, Xte, Ytr, Yte = splitData(X,Y,0.75)
classifier = AdaBoostClassifier(n_estimators=600, learning_rate=1.5)
classifier.fit(Xfold_2, Yfold_2)
yhat = classifier.predict(X_valid)
roc_auc_score(Y_valid, yhat)
classifier.fit(Xtr, Ytr)
yhat = classifier.predict(Xte)
print(classifier.score(Xte, Yte))
np.savetxt('AdaBoost1.txt',np.vstack( (np.arange(len(yhat)) , yhat) ).T,
'%d, %.2f',header='ID,Prob1',comments='',delimiter=',')
# attempt 2
classifier = AdaBoostClassifier(n_estimators=600, learning_rate=1.5)
classifier.fit(Xtr, Ytr)
yhat = classifier.predict(Xte)
print(classifier.score(Xte, Yte))
# # KNN Classifier
# +
from sklearn.neighbors import KNeighborsClassifier
nums = [2,5,10,15,20,25,50,100,125,135]
train_error = []
valid_error = []
auc = []
fpr = []
tpr = []
thresholds = []
for k in nums:
knn = KNeighborsClassifier(n_neighbors=k)
knn = knn.fit(Xtr, Ytr)
pred_train = knn.predict(Xtr)
pred_test = knn.predict(Xte)
## Get ROC Curve Characteristics
fpr_temp, tpr_temp, thresholds_temp = roc_curve(Yte ,pred_test)
fpr.append(fpr_temp)
tpr.append(tpr_temp)
thresholds.append(thresholds_temp)
## Get AUC
roc_auc = sk.metrics.roc_auc_score(Yte, pred_test)
auc.append(roc_auc)
## Predict Test and Train Error
train_error.append(np.sum(pred_train != Ytr)/float(len(Ytr)))
valid_error.append(np.sum(pred_test != Yte)/float(len(Yte)))
# -
plt.plot(nums,valid_error, color = 'orange', label = 'Valid Error')
plt.legend()
plt.xticks(nums)
plt.show()
plt.plot(nums, auc)
plt.xticks(nums)
# k=20 seems to be the best n_neighbors
# +
weighted = KNeighborsClassifier(n_neighbors=5, weights='distance')
weighted = weighted.fit(Xtr, Ytr)
wtr = weighted.predict(Xtr)
wte = weighted.predict(Xte)
print('weighted:')
print('training error:', np.sum(wtr != Ytr)/float(len(Ytr)))
print('validation error:', np.sum(wte != Yte)/float(len(Yte)))
unweighted = KNeighborsClassifier(n_neighbors=5)
unweighted = unweighted.fit(Xtr, Ytr)
utr = weighted.predict(Xtr)
ute = weighted.predict(Xte)
print('unweighted:')
print('training error:', np.sum(utr != Ytr)/float(len(Ytr)))
print('validation error:', np.sum(ute != Yte)/float(len(Yte)))
# +
wroc_auc = sk.metrics.roc_auc_score(Yte, wte)
uroc_auc = sk.metrics.roc_auc_score(Yte, ute)
print(wroc_auc, uroc_auc)
# +
# final test
weighted = KNeighborsClassifier(n_neighbors=20, weights='distance', algorithm='auto')
weighted = weighted.fit(X, Y)
wte = weighted.predict(Xte)
#print('weighted:')
#print('training error:', np.sum(wte != Yfold_2)/float(len(Yfold_2)))
#print('validation error:', np.sum(wte != Y_valid)/float(len(Y_valid)))
#wroc_auc = sk.metrics.roc_auc_score(Y_valid, wte_15)
#print(wroc_auc)
# +
nums = [2,5,10,15,20,25,50,100,125,135]
train_error = []
valid_error = []
auc = []
fpr = []
tpr = []
thresholds = []
for k in nums:
knn = KNeighborsClassifier(n_neighbors=k)
knn = knn.fit(Xtr, Ytr)
pred_train = knn.predict(Xtr)
pred_test = knn.predict(Xte)
## Get ROC Curve Characteristics
fpr_temp, tpr_temp, thresholds_temp = roc_curve(Yte,pred_test)
fpr.append(fpr_temp)
tpr.append(tpr_temp)
thresholds.append(thresholds_temp)
## Get AUC
roc_auc = sk.metrics.roc_auc_score(Yte, pred_test)
auc.append(roc_auc)
## Predict Test and Train Error
train_error.append(np.sum(pred_train != Ytr)/float(len(Ytr)))
valid_error.append(np.sum(pred_test != Yte)/float(len(Yte)))
# -
plt.plot(nums,valid_error, color = 'orange', label = 'Valid Error')
plt.legend()
plt.xticks(nums)
plt.show()
# +
weighted = KNeighborsClassifier(n_neighbors=10, weights='distance', p=1)
weighted = weighted.fit(Xtr,Ytr)
wtr = weighted.predict(Xtr)
wte = weighted.predict(Xte)
final = weighted.predict(X_final)
np.savetxt('KNN_valid.txt',np.vstack( (np.arange(len(wte)) , wte) ).T,
'%d, %.2f',header='ID,Prob1',comments='',delimiter=',')
np.savetxt('KNN_final.txt',np.vstack( (np.arange(len(final)) , final) ).T,
'%d, %.2f',header='ID,Prob1',comments='',delimiter=',')
print('training error:', np.sum(wtr != Ytr)/float(len(Ytr)))
print('validation error:', np.sum(wte != Yte)/float(len(Ytr)))
wroc_auc = sk.metrics.roc_auc_score(Yte, wte)
test_auc = sk.metrics.roc_auc_score(Ytr, wtr)
print(wroc_auc, test_auc)
# -
plt.plot(nums, auc)
plt.xticks(nums)
weighted = weighted.fit(X,Y)
yhat = weighted.predict_proba(X_final)
np.savetxt('KNN1.txt',np.vstack( (np.arange(len(yhat)) , yhat[:,1]) ).T,
'%d, %.2f',header='ID,Prob1',comments='',delimiter=',')
rfc = RandomForestRegressor(max_depth=20, max_features= 7, min_samples_leaf = 7, min_samples_split=7)
rfc = rfc.fit(Xtr, Ytr)
wtr = rfc.predict(Xtr)
wte = rfc.predict(Xte)
final = rfc.predict(X_final)
# +
print('training error:', np.sum(wtr != Ytr)/float(len(Ytr)))
print('validation error:', np.sum(wte != Yte)/float(len(Ytr)))
wroc_auc = sk.metrics.roc_auc_score(Yte, wte)
test_auc = sk.metrics.roc_auc_score(Ytr, wtr)
np.savetxt('RandomForest_valid.txt',np.vstack( (np.arange(len(wte)) , wte) ).T,
'%d, %.2f',header='ID,Prob1',comments='',delimiter=',')
np.savetxt('RandomForest_final.txt',np.vstack( (np.arange(len(final)) , final) ).T,
'%d, %.2f',header='ID,Prob1',comments='',delimiter=',')
print(wroc_auc, test_auc)
# -
rfc = rfc.fit(X,Y)
yhat = rfc.predict(X_final)
np.savetxt('RandomForest.txt',np.vstack( (np.arange(len(yhat)) , yhat) ).T,
'%d, %.2f',header='ID,Prob1',comments='',delimiter=',')
# +
Pv0=np.genfromtxt('RandomForest_valid.txt',delimiter=',',skip_header=1)[:,1:2]
Pv1=np.genfromtxt('KNN_valid.txt',delimiter=',',skip_header=1)[:,1:2]
Pv2=np.genfromtxt('Adaboost_train.txt',delimiter=',',skip_header=1)[:,1:2]
Pe0=np.genfromtxt('RandomForest_final.txt',delimiter=',',skip_header=1)[:,1:2]
Pe1=np.genfromtxt('KNN_final.txt',delimiter=',',skip_header=1)[:,1:2]
Pe2=np.genfromtxt('Adaboost_pred.txt',delimiter=',',skip_header=1)[:,1:2]
# +
import mltools as ml
Sv=np.hstack((Pv0,Pv1,Pv2))
stack=ml.linearC.linearClassify(Sv,Yte,reg=1e-3)
print("** Stacked AUC: ",stack.auc(Sv,Yte))
Se=np.hstack((Pe0,Pe1,Pe2))
PeS=stack.predictSoft(Se)
# -
def toKaggle(filename,prSoft):
fh=open(filename,'w')
fh.write('ID,Prob1\n')
for i,yi in enumerate(prSoft[:,1].ravel()):
fh.write('{}, {}\n'.format(i,yi))
fh.close()
toKaggle('stack.txt',PeS)
PeS
print(X.shape)
print(Y.shape)
plt.figure()
plt.scatter(X[:, 0], X[:, 1])
)s] %(filename)s/%(funcName)s | %(message)s')
main()
# -
| 8,722 |
/Machine Learning/knn.ipynb | 85ee555cc8ffb4da361b9b0ea876f2257752d6df | [] | no_license | szig97/article-popularity-machine-learning | https://github.com/szig97/article-popularity-machine-learning | 0 | 0 | null | 2021-06-24T18:58:40 | 2021-06-18T15:49:49 | Jupyter Notebook | Jupyter Notebook | false | false | .py | 10,825 | # ---
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# + [markdown] id="view-in-github" colab_type="text"
# <a href="https://colab.research.google.com/github/noahspenser/quantumcomputing/blob/master/Homework1.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# + id="Xt4dwkmFLZTU" colab_type="code" outputId="dbf7ab26-40b7-4d5c-b269-b0ff88f5bddf" colab={"base_uri": "https://localhost:8080/", "height": 85}
import numpy, scipy.linalg
Mx = numpy.matrix([[4, 1, 5], [2, 0, 3], [1, 1, 2]])
def annihilate_poly(inM):
n, m = inM.shape
if n!=m:
return
arrayVecPowers = numpy.array([numpy.asarray(numpy.linalg.matrix_power(inM, a).flatten())[0] for a in range(0, n+1)])
out = scipy.linalg.null_space(numpy.transpose(arrayVecPowers)) # I couldn't get linalg.solve to work, nor could I get the other non-square method to work, so I just computed the null_space of the transposed vectors
print(out)
annihilate_poly(Mx)
= knn.score(X_train, y_train)
test_score = knn.score(X_test, y_test)
train_scores.append(train_score)
test_scores.append(test_score)
print(f"k: {k}, Train/Test Score: {train_score:.3f}/{test_score:.3f}")
plt.plot(range(1, 50, 2), train_scores, marker='o', color="cornflowerblue", label="Training Data")
plt.plot(range(1, 50, 2), test_scores, marker="x", color="firebrick", label="Testing Data")
plt.xlabel("k neighbors")
plt.ylabel("Testing accuracy Score")
plt.title("Accuracy of Training Set vs Test Set (Non-Scaled Values)")
plt.ylim([0,1.2])
plt.grid()
plt.legend(loc="upper right")
plt.savefig("../graphics/knn_graphics/5cat_noScaling_noOHE.png", bbox_inches="tight")
plt.show()
knn = KNeighborsClassifier(n_neighbors=47)
knn.fit(X_train, y_train)
print('k=47 Test Acc: %.3f' % knn.score(X_test, y_test))
y_pred = knn.predict(X_test)
from sklearn.metrics import classification_report, confusion_matrix
print(confusion_matrix(y_test, y_pred))
print(classification_report(y_test, y_pred))
# ### KNN Model with scaling and No One-Hot-Encoding
from sklearn.preprocessing import LabelEncoder, StandardScaler
X_scaler = StandardScaler().fit(X_train)
X_train_scaled = X_scaler.transform(X_train)
X_test_scaled = X_scaler.transform(X_test)
# Loop through different k values to see which has the highest accuracy
# Note: We only use odd numbers because we don't want any ties
train_scores_scaled = []
test_scores_scaled = []
for k in range(1, 36, 2):
knn = KNeighborsClassifier(n_neighbors=k)
knn.fit(X_train_scaled, y_train)
train_score = knn.score(X_train_scaled, y_train)
test_score = knn.score(X_test_scaled, y_test)
train_scores_scaled.append(train_score)
test_scores_scaled.append(test_score)
print(f"k: {k}, Train/Test Score: {train_score:.3f}/{test_score:.3f}")
plt.plot(range(1, 36, 2), train_scores_scaled, marker='o', color="cornflowerblue", label="Training Data")
plt.plot(range(1, 36, 2), test_scores_scaled, marker="x", color="firebrick", label="Testing Data")
plt.xlabel("k neighbors")
plt.ylabel("Testing accuracy Score")
plt.title("Accuracy of Training Set vs Test Set (Scaled Values)")
plt.ylim([0,1.2])
plt.grid()
plt.legend(loc="upper right")
plt.savefig("../graphics/knn_graphics/5cat_Scaling_noOHE.png", bbox_inches="tight")
plt.show()
# Note that k: 31 provides the best accuracy where the classifier starts to stablize
knn = KNeighborsClassifier(n_neighbors=31)
knn.fit(X_train_scaled, y_train)
print('k=31 Test Acc: %.3f' % knn.score(X_test_scaled, y_test))
y_pred_scaled = knn.predict(X_test_scaled)
print(confusion_matrix(y_test, y_pred_scaled))
print(classification_report(y_test, y_pred_scaled))
# ### KNN Model with One-Hot-Encoding and without scaling
from tensorflow.keras.utils import to_categorical
y_train_categorical = to_categorical(y_train)
y_test_categorical = to_categorical(y_test)
y_test_categorical
# Loop through different k values to see which has the highest accuracy
# Note: We only use odd numbers because we don't want any ties
train_scores_categorical = []
test_scores_categorical = []
for k in range(1, 75, 2):
knn = KNeighborsClassifier(n_neighbors=k)
knn.fit(X_train, y_train_categorical)
train_score = knn.score(X_train, y_train_categorical)
test_score = knn.score(X_test, y_test_categorical)
train_scores_categorical.append(train_score)
test_scores_categorical.append(test_score)
print(f"k: {k}, Train/Test Score: {train_score:.3f}/{test_score:.3f}")
plt.plot(range(1, 75, 2), train_scores_categorical, marker='o', color="cornflowerblue", label="Training Data")
plt.plot(range(1, 75, 2), test_scores_categorical, marker="x", color="firebrick", label="Testing Data")
plt.xlabel("k neighbors")
plt.ylabel("Testing accuracy Score")
plt.title("Accuracy of Training Set vs Test Set (One-Hot Encoded and Non-Scaled Values)")
plt.ylim([0,1.2])
plt.grid()
plt.legend(loc="upper right")
plt.savefig("../graphics/knn_graphics/5cat_noScaling_OHE.png", bbox_inches="tight")
plt.show()
# ### KNN Model with scaling and One-Hot-Encoding
# Loop through different k values to see which has the highest accuracy
# Note: We only use odd numbers because we don't want any ties
train_scores_scaled_categorical = []
test_scores_scaled_categorical = []
for k in range(1, 36, 2):
knn = KNeighborsClassifier(n_neighbors=k)
knn.fit(X_train_scaled, y_train_categorical)
train_score = knn.score(X_train_scaled, y_train_categorical)
test_score = knn.score(X_test_scaled, y_test_categorical)
train_scores_scaled_categorical.append(train_score)
test_scores_scaled_categorical.append(test_score)
print(f"k: {k}, Train/Test Score: {train_score:.3f}/{test_score:.3f}")
plt.plot(range(1, 36, 2), train_scores_scaled_categorical, marker='o', color="cornflowerblue", label="Training Data")
plt.plot(range(1, 36, 2), test_scores_scaled_categorical, marker="x", color="firebrick", label="Testing Data")
plt.xlabel("k neighbors")
plt.ylabel("Testing accuracy Score")
plt.title("Accuracy of Training Set vs Test Set (One-Hot Encoded and Scaled Values)")
plt.ylim([0,1.2])
plt.grid()
plt.legend(loc="upper right")
plt.savefig("../graphics/knn_graphics/5cat_Scaling_OHE.png", bbox_inches="tight")
plt.show()
freqs =[0] * VOCABULARY_SIZE
total = 0
for kmerval in seq:
freqs[kmerval] += 1
total += 1
for c in range(VOCABULARY_SIZE):
freqs[c] = freqs[c]/total
Xout.append(freqs)
Xnum = np.asarray(Xout)
return (Xnum)
# + [markdown] id="j7jcg6Wl9Kc2"
# ## Build model
# + id="qLFNO1Xa9Kc3"
def build_model(maxlen):
act="sigmoid"
embed_layer = keras.layers.Embedding(
VOCABULARY_SIZE,EMBED_DIMEN,input_length=maxlen);
neurons=16
dense1_layer = keras.layers.Dense(neurons, activation=act,dtype=dt,input_dim=VOCABULARY_SIZE)
dense2_layer = keras.layers.Dense(neurons, activation=act,dtype=dt)
dense3_layer = keras.layers.Dense(neurons, activation=act,dtype=dt)
output_layer = keras.layers.Dense(1, activation=act,dtype=dt)
mlp = keras.models.Sequential()
#mlp.add(embed_layer)
mlp.add(dense1_layer)
mlp.add(dense2_layer)
#mlp.add(dense3_layer)
mlp.add(output_layer)
bc=tf.keras.losses.BinaryCrossentropy(from_logits=False)
print("COMPILE...")
mlp.compile(loss=bc, optimizer="Adam",metrics=["accuracy"])
print("...COMPILED")
return mlp
# + [markdown] id="LdIS2utq9Kc9"
# ## Cross validation
# + id="BVo4tbB_9Kc-"
def do_cross_validation(X,y,maxlen):
model = None
cv_scores = []
fold=0
splitter = ShuffleSplit(n_splits=SPLITS, test_size=0.2, random_state=37863)
for train_index,valid_index in splitter.split(X):
X_train=X[train_index] # use iloc[] for dataframe
y_train=y[train_index]
X_valid=X[valid_index]
y_valid=y[valid_index]
print("BUILD MODEL")
model=build_model(maxlen)
print("FIT")
start_time=time.time()
# this is complaining about string to float
history=model.fit(X_train, y_train, # batch_size=10, default=32 works nicely
epochs=EPOCHS, verbose=1, # verbose=1 for ascii art, verbose=0 for none
validation_data=(X_valid,y_valid) )
end_time=time.time()
elapsed_time=(end_time-start_time)
fold += 1
print("Fold %d, %d epochs, %d sec"%(fold,EPOCHS,elapsed_time))
pd.DataFrame(history.history).plot(figsize=(8,5))
plt.grid(True)
plt.gca().set_ylim(0,1)
plt.show()
scores = model.evaluate(X_valid, y_valid, verbose=0)
print("%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))
# What are the other metrics_names?
# Try this from Geron page 505:
# np.mean(keras.losses.mean_squared_error(y_valid,y_pred))
cv_scores.append(scores[1] * 100)
print()
print("Validation core mean %.2f%% (+/- %.2f%%)" % (np.mean(cv_scores), np.std(cv_scores)))
return model
# -
def just_train(model,X_train,y_train,maxlen):
print("FIT")
start_time=time.time()
# this is complaining about string to float
history=model.fit(X_train, y_train, # batch_size=10, default=32 works nicely
epochs=EPOCHS, verbose=1, # verbose=1 for ascii art, verbose=0 for none
) # no validation data
end_time=time.time()
elapsed_time=(end_time-start_time)
print("Train %d epochs, %d sec"%(EPOCHS,elapsed_time))
pd.DataFrame(history.history).plot(figsize=(8,5))
plt.grid(True)
plt.gca().set_ylim(0,1)
plt.show()
#scores = model.evaluate(X_valid, y_valid, verbose=0)
#print("%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))
#cv_scores.append(scores[1] * 100)
#print()
#print("Validation core mean %.2f%% (+/- %.2f%%)" % (np.mean(cv_scores), np.std(cv_scores)))
return model
# + [markdown] id="_Q-PEh7D9KdH"
# ## Load
# + colab={"base_uri": "https://localhost:8080/", "height": 452} id="f8fNo6sn9KdH" outputId="119c729f-af79-45c7-9827-df402a5765dc"
print("Load data from files.")
nc_seq=load_fasta(NC_FILENAME,0)
pc_seq=load_fasta(PC_FILENAME,1)
all_seq=pd.concat((nc_seq,pc_seq),axis=0)
print("Put aside the test portion.")
(train_set,test_set)=make_train_test(all_seq)
# Do this later when using the test data:
# (X_test,y_test)=separate_X_and_y(test_set)
nc_seq=None
pc_seq=None
all_seq=None
print("Ready: train_set")
train_set
# + [markdown] id="qd3Wj_vI9KdP"
# ## Len 200-1Kb
# + colab={"base_uri": "https://localhost:8080/", "height": 1000} id="mQ8eW5Rg9KdQ" outputId="7003fb8f-96b2-48ed-8957-585ede8293c5"
MINLEN=200
MAXLEN=1000
print ("Compile the model")
model=build_model(MAXLEN)
print ("Summarize the model")
print(model.summary()) # Print this only once
print("Working on full training set, slice by sequence length.")
print("Slice size range [%d - %d)"%(MINLEN,MAXLEN))
subset=make_slice(train_set,MINLEN,MAXLEN)# One array to two: X and y
print ("Sequence to Kmer")
(X_train,y_train)=make_kmers(MAXLEN,subset)
X_train
X_train=make_frequencies(X_train)
X_train
print ("Cross valiation")
model1 = do_cross_validation(X_train,y_train,MAXLEN)
model1.save(FILENAME+'.short.model')
# -
# # Reuse
# +
#reload=model1.load(FILENAME+'.short.model')
#W = model1.get_weights()
#scores = model1.evaluate(X_valid, y_valid, verbose=0)
#print("%s: %.2f%%" % (model1.metrics_names[1], scores[1]*100))
print("BUILD MODEL")
model=build_model(MAXLEN)
model=just_train(model,X_train,y_train,MAXLEN)
# -
def evaluate(model,min,max):
print("Evaluate again on lengths %d to %d"%(min,max))
print("slice...")
subset=make_slice(train_set,min,max)
print("kmers...")
(X_valid,y_valid)=make_kmers(max,subset)
print("frequencies...")
X_valid=make_frequencies(X_valid)
print("evaluate....")
scores = model.evaluate(X_valid, y_valid, verbose=1) # valid = train, expect 100%
print("Evaluated on lengths %d to %d"%(min,max))
print("%s: %.2f% | 12,288 |
/w2.ipynb | 3789f878970e4f8e2dc0147f8b7183ff52eb2f78 | [] | no_license | jasmar2809/april_capstone | https://github.com/jasmar2809/april_capstone | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 79,511 | # ---
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# ---
# #### Week 2: Cleaning and Model Start
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
# +
#df = pd.read_excel('data/Transaction_Data_20210128.xlsx', sheet_name = '36 Rolling Months', header=1, usecols="B:AN")
# -
df = pd.read_pickle('data/sheet.pkl')
df.head()
sample = df[df['CONTACT_ID'] == '85102140943881291064'].copy()
sample['year'] = sample['refresh_date'].dt.year
sample['month'] = sample['refresh_date'].dt.month
# +
#sample = sample[sample['year'] != 2017]
# -
sample.columns
# +
#sample[['sales_curr', 'sales_12M', 'year', 'month']]
# -
sample.set_index('refresh_date', inplace = True)
df['year'] = df['refresh_date'].dt.year
one_manager = sample[(sample['year'].isin([2017, 2018]))].iloc[:-1]
one_manager.sum(numeric_only=True)
df['CONTACT_ID'] = df['CONTACT_ID'].astype('str')
manager_groups = df.groupby('CONTACT_ID')
# +
dfs = []
for manager in manager_groups:
#print(manager[1])
#dfs.append(manager[1].loc[(manager[1]['year'].isin([2017, 2018]))].iloc[:-1])
sub = manager[1].loc[(manager[1]['year'].isin([2017, 2018]))].iloc[:-1].sum()
dfs.append(sub)
# -
agg_dfs = pd.concat(dfs, axis = 1)
agg_df = agg_dfs.T
agg_df.info()
len(df['CONTACT_ID'].unique())
df.head()
y = df[df['year'] == 2020].groupby('CONTACT_ID')[['sales_12M']].max()
X = agg_df.drop(['CONTACT_ID', 'year'], axis = 1)
import seaborn as sns
# +
#sns.pairplot(X['no_of_Redemption_12M_1', 'no_of_sales_12M_1','AUM', 'sales_curr', 'sales_12M'])
# +
# X.info()
# -
# ### Modeling Start
from sklearn.feature_selection import RFE, SelectFromModel, SelectKBest
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression, Ridge
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler, PolynomialFeatures
rfe = RFE(LinearRegression())
rfe.fit_transform(X, y).shape
X.shape
# #### Pipeline
pipe_1 = make_pipeline(rfe, LinearRegression())
pipe_1.fit(X, y)
pipe_1.score(X, y)
X_train, X_test, y_train, y_test = train_test_split(X, y)
pipe_1.fit(X_train, y_train)
pipe_1.score(X_train, y_train)
pipe_1.score(X_test, y_test)
# ### Searching Feature Selectors
from sklearn.ensemble import RandomForestRegressor
rfe_tree = RFE(RandomForestRegressor())
pipe_2 = make_pipeline(rfe_tree, LinearRegression())
rfe_tree.fit(X_train, y_train)
rfe_tree.score(X_train, y_train)
rfe_tree.score(X_test, y_test)
from sklearn.linear_model import Lasso
lasso_pipe = make_pipeline(RFE(Lasso()), LinearRegression())
lasso_pipe.fit(X_train, y_train)
lasso_pipe.score(X_train, y_train)
lasso_pipe.score(X_test, y_test)
lasso_pipe['rfe'].support_
X.columns
def modeler(selector, model):
pipe = make_pipeline([selector, model])
pipe.fit(X_train, y_train)
print(pipe.score(X_train, y_train))
print(pipe.score(X_test, y_test))
select_pipe = make_pipeline(SelectFromModel(LinearRegression()), LinearRegression())
select_pipe.fit(X_train, y_train)
select_pipe.score(X_train, y_train)
select_pipe.score(X_test, y_test)
# #### Regularized models and ensembles
reg_pipe = make_pipeline(RFE(LinearRegression()), StandardScaler(), Ridge())
reg_pipe.fit(X_train, y_train)
reg_pipe.score(X_train, y_train)
reg_pipe.score(X_test, y_test)
from sklearn.model_selection import GridSearchCV
params = {'ridge__alpha': [0.1, 1.0, 10, 100, 1000, 10_000]}
grid_reg = GridSearchCV(reg_pipe, param_grid=params)
grid_reg.fit(X_train, y_train)
grid_reg.score(X_train, y_train)
grid_reg.score(X_test, y_test)
grid_reg.best_params_
# ### Transformers
from sklearn.preprocessing import PowerTransformer
pt_pipe = make_pipeline(PowerTransformer(), RFE(LinearRegression()), LinearRegression())
pt_pipe.fit(X_train, y_train)
pt_pipe.score(X_train, y_train)
pt_pipe.score(X_test, y_test)
import matplotlib.pyplot as plt
plt.hist(y)
y.hist()
pt = PowerTransformer()
plt.hist(pt.fit_transform(X[['sales_12M']]))
from sklearn.compose import TransformedTargetRegressor
treg = TransformedTargetRegressor(regressor=LinearRegression(), func=np.log1p, inverse_func=np.expm1)
treg.fit(X_train, y_train)
treg.score(X_train, y_train)
treg.score(X_test, y_test)
treg_pipe = make_pipeline(RFE(LinearRegression()), treg)
treg_pipe.fit(X_train, y_train)
treg_pipe.score(X_train, y_train)
treg_pipe.score(X_test, y_test)
| 4,650 |
/notebooks/Detect Bad Orientations.ipynb | 723a1ed2d035bac6a70f60bc688e5897c86d1f96 | [] | no_license | zztopper/lung_cancer_ds_bowl | https://github.com/zztopper/lung_cancer_ds_bowl | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 12,198 | # ---
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# +
# Eksploracja danych biomedycznych, Głosowa łączność z komputerem
# Inżynieria Biomedyczna, V rok, AGH
# Autorki projektu:
# Karolina Dębowska
# Karolina Mięsowicz
# Karolina Milewska
# Joanna Niedziałek
# Mariola Mularska
# Klara Sirak
# Aleksandra Ziarko
# +
# Notebook zawiera kod napisany w celu przeprowadzenia klasyfikacji 98 słów wypowiedzianych czterokrotnie przez 6 osób
# W notebooku można znaleźć:
# -> wczytanie 24 plików nagrań w formacie wav oraz plików z etykietami słów w formacie txt
# -> podzielenie nagrań na pojedyncze słowa na podstawie stempli czasowych z plików z etykietami
# -> eksport pojedynczych słów do odpowiednich folderów
# -> możliwość wyświetlenia wszystkich wersji danego słowa na wykresach
# -> obliczenie parametrów liczbowych (AREA - pole powierzchni pod krzywą z wartości bezwzględnej amplitudy,
# MEAN - średnia arytmetyczna amplitud, SQUARESUM - suma kwadratów amplitud, ZEROS - gęstość przejść przez zero,
# MFCC - współczynniki cepstralne) każdego słowa z każdego nagrania
# -> histogramy porównujące częstotliwości głosu różnych osób o tej samej porze dnia wraz z wyliczeniem częstotliwości
# dominujących głosu
# -> histogramy porównujące częstotliwości głosu tej samej osoby o różnych porach dnia
# -> szereg klasyfikacji za pomocą biblioteki Tensorflow (98 słów u wszystkich osób, 2 słów u jednej osoby, 98 słów
# u jednej osoby oraz 2 słów u wszystkich osób)
# +
# Instalacja dodatkowych pakietów
# # !pip install pydub
# # !pip install librosa
# # !pip install sklearn
# # !pip install tensorflow
# -
# Importowanie potrzebnych bibliotek
import wave
from pydub import AudioSegment as auds
import pandas as pd
import numpy as np
import os, fnmatch
from matplotlib import pyplot as plt
import scipy.io.wavfile as wf
from scipy.integrate import simps
from scipy.fftpack import dct
import glob
import tensorflow as tf
from tensorflow import feature_column
from tensorflow.keras import layers
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
# +
# Wczytanie plików z nagraniami i etykietami
path = 'C:/Users/karol/Desktop/Programowanie/GitHub/Eksploracja-danych-glosowych/Nagrania'
# Wyciąganie nazw plików ze ścieżki
list_of_files = os.listdir(path)
pattern = "*.txt"
# print(list_of_files)
list_of_names = []
for entry in list_of_files:
if fnmatch.fnmatch(entry, pattern):
list_of_names.append(entry[:-4])
# Utworzenie list plików wav i txt
files_wav_list = []
files_txt_list = []
for filename in list_of_names:
files_wav_list.append(filename + '.wav')
files_txt_list.append(filename + '.txt')
for i in range(len(list_of_names)):
print(files_txt_list[i], files_wav_list[i])
print('Liczba plików: ', len(list_of_names))
# +
for i in range(len(list_of_names)):
nr = i
filename = list_of_names[nr]
file_wav = files_wav_list[nr]
file_txt = files_txt_list[nr]
first = wave.open(path + '/' + file_wav, 'rb')
first_auds = auds.from_wav(path + '/' + file_wav)
textfile = pd.read_csv(path + '/' + file_txt, sep = '\t', header = None)
# Poprawianie etykiet
if i == 0:
labels = []
for j in range(0, 98):
labels.append(str(textfile[2][j]))
if labels[j] == 'nan':
labels[j] = 'NA'
folder_path = path + '/' + labels[j]
if os.path.isdir(folder_path) == False:
os.mkdir(folder_path)
# Wyciaganie i zapisywanie fragmentow w odpowiednich folderach
length = first.getnframes() / first.getframerate() # długość całego pliku
fp = first.getframerate() # częstotliwość próbkowania
frames = first.readframes(first.getnframes())
print(i, 'Długość trwania pliku audio:', length, 's.')
print('Ilość ramek:', first.getnframes())
slowo = []
poczatek = []
koniec = []
a = 0;
for k in range(0, 98):
folder_path = path + '/' + labels[k]
# Sprawdzanie czy jest juz dany plik w sciezce, jak nie to tworzy
if os.path.isfile(folder_path + '/' + filename + '_' + '{0}.wav'.format(labels[k])) == False:
poczatek.append(int(np.floor(textfile[0][k]*fp))) # numer ramki początku słowa
koniec.append(int(np.ceil(textfile[1][k]*fp))) # numer ramki końca słowa
slowo.append(first_auds[int(poczatek[k]*1000/fp):int(koniec[a]*1000/fp)]) # 1000 bo w AudioSegment robi w [ms]
slowo[a].export(folder_path + '/' + filename + '_' + '{0}.wav'.format(labels[k]), format='wav')
a += 1
# +
# Wyświetlanie wszystkich wersji danego słowa
nr_slowa = 15 # podaj wartość między 0 a 97
word_path = path + '/' + labels[nr_slowa]
jedno_slowo = []
for i in range(0, len(list_of_names)):
person_filename = list_of_names[i]
person_file_wav = files_wav_list[i]
person_file_txt = files_txt_list[i]
if os.path.isfile(word_path + '/' + person_filename + '_' + '{0}.wav'.format(labels[nr_slowa])) == True:
new_filename = person_filename + '_' + labels[nr_slowa] + '.wav'
# print("Etykieta: ", labels[0])
# print("Ścieżka: ", word_path)
print("Nazwa obecnego pliku: ", new_filename)
person = wave.open(word_path + '/' + new_filename, 'rb')
if person.getnframes() % 2 != 0:
person_frames = person.readframes(person.getnframes()-1)
else:
person_frames = person.readframes(person.getnframes())
person_frames = np.frombuffer(person_frames, dtype=int)
jedno_slowo.append(person_frames)
plt.figure()
plt.plot(person_frames)
text = "Osoba nr " + str(int(i / 4 + 1)) + ", nagranie nr " + str(int(i % 4 + 1))
plt.title(text)
plt.show()
# +
# Liczenie parametrów zapisywanych do plików słów (w każdym pliku 24 wartości, kolejne osoby wypowiadające to samo słowo)
# Parametry:
# area (pole powierzchni z wartości bezwzględnych amplitud)
# zeros (gęstość przejść przez 0)
# squaresum (suma kwadratów amplitud)
# mean (średnia arytmetyczna amplitud)
area_folder_name = path + '/' + 'Area_słowa' + '/' # tworzenie folderu na wyniki danego parametru
zeros_folder_name = path + '/' + 'Zeros_słowa' + '/'
squaresum_folder_name = path + '/' + 'Squaresum_słowa' + '/'
mean_folder_name = path + '/' + 'Mean_słowa' + '/'
for j in range(98):
print(j)
labels = []
if j > 0:
for a in range(0, j):
labels.append('')
area_word = []
zeros_word = []
zeros_cnt = 0
squaresum_word = []
squaresum_cnt = 0
squaresum_m = 0
mean_word = []
mean_cnt = 0
slowo = []
for i in range(0, len(list_of_names)):
filename = list_of_names[i]
file_wav = files_wav_list[i]
file_txt = files_txt_list[i]
textfile = pd.read_csv(path + '/' + file_txt, sep = '\t', header = None)
labels.append(str(textfile[2][j]))
if labels[j] == 'nan':
labels[j] = 'NA'
labels[j] = labels[j].upper()
if i == 0:
print(labels[j])
person_path = path + '/' + labels[j]
# Sprawdź, czy istnieje plik o danej nazwie
if os.path.isfile(person_path + '/' + filename + '_' + '{0}.wav'.format(labels[j])) == True:
new_filename = filename + '_' + labels[j] + '.wav'
person = wave.open(person_path + '/' + new_filename, 'rb')
# Jeśli ilość ramek nieparzysta, to nie zadziała, więc wtedy wczytujemy wszystkie ramki oprócz ostatniej
if person.getnframes() % 2 != 0:
person_frames = person.readframes(person.getnframes()-1)
else:
person_frames = person.readframes(person.getnframes())
person_frames = np.frombuffer(person_frames, dtype=int)
slowo.append(person_frames)
if os.path.isdir(area_folder_name) == False:
os.mkdir(area_folder_name)
if os.path.isdir(zeros_folder_name) == False:
os.mkdir(zeros_folder_name)
if os.path.isdir(squaresum_folder_name) == False:
os.mkdir(squaresum_folder_name)
if os.path.isdir(mean_folder_name) == False:
os.mkdir(mean_folder_name)
area_word.append(simps(np.abs(slowo[i])))
for m in range(1, len(slowo[i])):
if (slowo[i][m] <= 0 and slowo[i][m-1] > 0) or (slowo[i][m] >= 0 and slowo[i][m-1] < 0):
zeros_cnt += 1
zeros_word.append(zeros_cnt) #liczone zera
for n in range(0, len(slowo[i])):
squaresum_m = int(slowo[i][n])*int(slowo[i][n])
squaresum_cnt = squaresum_cnt + squaresum_m
squaresum_word.append(squaresum_cnt)
for o in range(0, len(slowo[i])):
mean_cnt = mean_cnt + int(slowo[i][o])
mean_cnt = mean_cnt/len(slowo[i])
mean_word.append(mean_cnt)
area_txt_person_filename = area_folder_name + 'area_' + labels[j] + '.txt'
zeros_txt_person_filename = zeros_folder_name + 'zeros_' + labels[j] + '.txt'
squaresum_txt_person_filename = squaresum_folder_name + 'squaresum_' + labels[j] + '.txt'
mean_txt_person_filename = mean_folder_name + 'mean_' + labels[j] + '.txt'
if os.path.isfile(area_txt_person_filename) == False:
np.savetxt(area_txt_person_filename, area_word)
print('Zapisano jako:', area_txt_person_filename)
else:
print('Plik', labels[j], 'z AREA już istnieje')
if os.path.isfile(zeros_txt_person_filename) == False:
np.savetxt(zeros_txt_person_filename, zeros_word)
print('Zapisano jako:', zeros_txt_person_filename)
elif os.path.isfile(zeros_txt_person_filename) == True:
print('Plik', labels[j], 'z ZEROS już istnieje')
if os.path.isfile(squaresum_txt_person_filename) == False:
np.savetxt(squaresum_txt_person_filename, squaresum_word)
print('Zapisano jako:', squaresum_txt_person_filename)
elif os.path.isfile(squaresum_txt_person_filename) == True:
print('Plik', labels[j], 'z SQUARESUM już istnieje')
if os.path.isfile(mean_txt_person_filename) == False:
np.savetxt(mean_txt_person_filename, mean_word)
print('Zapisano jako:', mean_txt_person_filename)
elif os.path.isfile(mean_txt_person_filename) == True:
print('Plik', labels[j], 'z MEAN już istnieje')
# +
# Współczynniki cepstralne MFCC
# Na podstawie: https://haythamfayek.com/2016/04/21/speech-processing-for-machine-learning.html
folder_path = path + '/' + 'MFCC'
if os.path.isdir(folder_path) == False:
os.mkdir(folder_path)
for filename in list_of_names:
mfcc_list = []
for nr_word in range(0,98):
path_word = path + "/" + labels[nr_word]
# Wczytanie słowa
if os.path.isfile(path_word + '/' + filename + '_' + labels[nr_word] + '.wav') == False:
break
sample_rate, signal = wf.read(path_word + '/' + filename + '_' + labels[nr_word] + '.wav')
# Preemfaza
pre_emphasis = 0.97 # parametr
emphasized_signal = np.append(signal[0], signal[1:] - pre_emphasis * signal[:-1])
# Formowanie ramek
frame_size = 0.025
frame_stride = 0.01 # bo sygnał jest w ms
frame_length, frame_step = frame_size * sample_rate, frame_stride * sample_rate # zamiana z sekund na próbki
signal_length = len(emphasized_signal)
frame_length = int(round(frame_length))
frame_step = int(round(frame_step))
num_frames = int(np.ceil(float(np.abs(signal_length - frame_length)) / frame_step)) # Sprawdzanie czy jest chociaż jedna ramka
pad_signal_length = num_frames * frame_step + frame_length
z = np.zeros((pad_signal_length - signal_length))
pad_signal = np.append(emphasized_signal, z) # Upewnianie się, że wszystkie ramki mają równą liczbę próbek bez obcinania jakichkolwiek próbek z oryginalnego sygnału
indices = np.tile(np.arange(0, frame_length), (num_frames, 1)) + np.tile(np.arange(0, num_frames * frame_step, frame_step), (frame_length, 1)).T
frames = pad_signal[indices.astype(np.int32, copy=False)]
# Okienkowanie
frames = frames * np.hamming(frame_length) # Hamming najczęściej stosowany w rozpoznaniu mowy
# FFT i Power Spectrum // powstaje widmo liniowe
NFFT = 512
mag_frames = np.absolute(np.fft.rfft(frames, NFFT)) # Magnitude of the FFT
pow_frames = ((1.0 / NFFT) * ((mag_frames) ** 2)) # Power Spectrum
# Bank filtrów // powstaje widmo melowe
nfilt = 40
low_freq_mel = 0
high_freq_mel = (2595 * np.log10(1 + (sample_rate / 2) / 700)) # Konwersja Hz do Mel
mel_points = np.linspace(low_freq_mel, high_freq_mel, nfilt + 2) # Równo rozmieszczona skala Mel
hz_points = (700 * (10**(mel_points / 2595) - 1)) # Konwersja Mel do Hz
bin = np.floor((NFFT + 1) * hz_points / sample_rate)
fbank = np.zeros((nfilt, int(np.floor(NFFT / 2 + 1))))
for m in range(1, nfilt + 1):
f_m_minus = int(bin[m - 1]) # lewe
f_m = int(bin[m]) # centrum
f_m_plus = int(bin[m + 1]) # prawe
for k in range(f_m_minus, f_m):
fbank[m - 1, k] = (k - bin[m - 1]) / (bin[m] - bin[m - 1])
for k in range(f_m, f_m_plus):
fbank[m - 1, k] = (bin[m + 1] - k) / (bin[m + 1] - bin[m])
filter_banks = np.dot(pow_frames, fbank.T)
filter_banks = np.where(filter_banks == 0, np.finfo(float).eps, filter_banks) # Stabilność numeryczna
# Logarytmowanie
filter_banks = 20 * np.log10(filter_banks) # dB
# Współczynniki cepstralne częstotliwości mel -- Mel-frequency Cepstral Coefficients (MFCCs)
# DCT
num_ceps = 12 # liczba współczynników - między 2 a 13
mfcc = dct(filter_banks, type=2, axis=1, norm='ortho')[:, 1 : (num_ceps + 1)]
# Zapis do pliku - słowo każdej osoby w osobnym
file = folder_path + '/' + 'MFCC_' + filename + '_' + labels[nr_word] + '.txt'
np.savetxt(file, mfcc)
# +
# Ścieżki do plików z parametrami
path_area = 'C:/Users/karol/Desktop/Programowanie/GitHub/Eksploracja-danych-glosowych/Nagrania/Area_słowa'
path_mean = 'C:/Users/karol/Desktop/Programowanie/GitHub/Eksploracja-danych-glosowych/Nagrania/Mean_słowa'
path_squaresum = 'C:/Users/karol/Desktop/Programowanie/GitHub/Eksploracja-danych-glosowych/Nagrania/Squaresum_słowa'
path_zeros = 'C:/Users/karol/Desktop/Programowanie/GitHub/Eksploracja-danych-glosowych/Nagrania/Zeros_słowa'
path_mfcc = 'C:/Users/karol/Desktop/Programowanie/GitHub/Eksploracja-danych-glosowych/Nagrania/MFCC'
# Area
list_of_files = os.listdir(path_area)
list_of_names_area = []
for entry in list_of_files:
list_of_names_area.append(entry[:-4])
files_txt_list_area = []
for filename in list_of_names_area:
files_txt_list_area.append(filename + '.txt')
# Mean
list_of_files = os.listdir(path_mean)
list_of_names_mean = []
for entry in list_of_files:
list_of_names_mean.append(entry[:-4])
files_txt_list_mean = []
for filename in list_of_names_mean:
files_txt_list_mean.append(filename + '.txt')
# Squaresum
list_of_files = os.listdir(path_squaresum)
list_of_names_squaresum = []
for entry in list_of_files:
list_of_names_squaresum.append(entry[:-4])
files_txt_list_squaresum = []
for filename in list_of_names_squaresum:
files_txt_list_squaresum.append(filename + '.txt')
# Zeros
list_of_files = os.listdir(path_zeros)
list_of_names_zeros = []
for entry in list_of_files:
list_of_names_zeros.append(entry[:-4])
files_txt_list_zeros = []
for filename in list_of_names_zeros:
files_txt_list_zeros.append(filename + '.txt')
# MFCC
list_of_files = os.listdir(path_mfcc)
list_of_names_mfcc = []
for entry in list_of_files:
list_of_names_mfcc.append(entry[:-4])
files_txt_list_mfcc = []
for filename in list_of_names_mfcc:
files_txt_list_mfcc.append(filename + '.txt')
print('Obecne pliki:')
for i in range(3):
print(files_txt_list_area[i])
print(files_txt_list_mean[i])
print(files_txt_list_squaresum[i])
print(files_txt_list_zeros[i])
print(files_txt_list_mfcc[i])
print ('i inne')
print('Liczba plików: ', len(list_of_names_area)+len(list_of_names_mean)+len(list_of_names_squaresum)+
len(list_of_names_zeros)+len(list_of_names_mfcc))
# +
# Histogramy
# Porównanie dwóch osób o dowolnej godzinie
osoba1 = 'C:/Users/karol/Desktop/Programowanie/GitHub/Eksploracja-danych-glosowych/Nagrania/BRAME/289550_23_K_9_1_BRAME.wav'
osoba2 = 'C:/Users/karol/Desktop/Programowanie/GitHub/Eksploracja-danych-glosowych/Nagrania/BRAME/289540_23_K_9_1_BRAME.wav'
osoba_03 = []
word1 = wave.open(osoba1, 'rb')
if word1.getnframes() % 2 != 0:
word1_frames = word1.readframes(word1.getnframes()-1)
else:
word1_frames = word1.readframes(word1.getnframes())
word1_frames = np.frombuffer(word1_frames, dtype=int) #amplitudy
osoba_03.append(word1_frames)
word2 = wave.open(osoba2, 'rb')
if word2.getnframes() % 2 != 0:
word2_frames = word2.readframes(word2.getnframes()-1)
else:
word2_frames = word2.readframes(word2.getnframes())
word2_frames = np.frombuffer(word2_frames, dtype=int) #amplitudy
osoba_03.append(word2_frames)
word1_frame = np.array(word1_frames)
word2_frame = np.array(word2_frames)
word1_frame = word1_frame[0:19000]
word2_frame = word2_frame[0:19000]
plt.figure()
plt.title('PORÓWNANIE CZĘSTOTLIWOŚCI GŁOSU DWÓCH ROŻNYCH OSÓB O DWOWOLNEJ PORZE')
y1,x1,_ = plt.hist(word1_frames,bins=100,alpha=0.5,label='osoba1')
y2,x2,_ = plt.hist(word2_frames,bins=100,alpha=0.5,label='osoba2')
plt.ylim([0,6000])
plt.legend()
plt.show()
# Częstotliwości dominujące głosu
# Różne osoby:
print('Dominująca częstotliwość osoby pierwszej:', x1.max())
print('Dominująca częstotliwość osoby drugiej:', x2.max())
# Porównanie tego samego słowa o innej godzinie dla tej samej osoby
osoba1 = 'C:/Users/karol/Desktop/Programowanie/GitHub/Eksploracja-danych-glosowych/Nagrania/BRAME/289550_23_K_9_1_BRAME.wav'
osoba2 = 'C:/Users/karol/Desktop/Programowanie/GitHub/Eksploracja-danych-glosowych/Nagrania/BRAME/289550_23_K_18_2_BRAME.wav'
osoba_03 = []
word1 = wave.open(osoba1, 'rb')
# Jeśli ilość ramek nieparzysta, to nie zadziała, więc wtedy wczytujemy wszystkie ramki oprócz ostatniej
if word1.getnframes() % 2 != 0:
word1_frames = word1.readframes(word1.getnframes()-1)
else:
word1_frames = word1.readframes(word1.getnframes())
word1_frames = np.frombuffer(word1_frames, dtype=int) #amplitudy
osoba_03.append(word1_frames)
word2 = wave.open(osoba2, 'rb')
# Jeśli ilość ramek nieparzysta, to nie zadziała, więc wtedy wczytujemy wszystkie ramki oprócz ostatniej
if word2.getnframes() % 2 != 0:
word2_frames = word2.readframes(word2.getnframes()-1)
else:
word2_frames = word2.readframes(word2.getnframes())
word2_frames = np.frombuffer(word2_frames, dtype=int) #amplitudy
osoba_03.append(word2_frames)
word1_frame = np.array(word1_frames)
word2_frame = np.array(word2_frames)
word1_frame = word1_frame[0:19000]
word2_frame = word2_frame[0:19000]
plt.figure()
plt.title("PORÓWNANIE TEJ sAMEJ OSOBY W DWÓCH RÓŻNYCH PORACH DNIA")
plt.hist(word1_frames,bins=100,alpha=0.5,label='osoba1')
plt.hist(word2_frames,bins=100,alpha=0.5,label='osoba2')
plt.ylim([0,6000])
plt.legend()
plt.show()
# +
# Etykiety
labels = []
for i in range(98):
file_txt = files_txt_list[0]
textfile = pd.read_csv(path + '/' + file_txt, sep = '\t', header = None)
labels.append(str(textfile[2][i]))
if labels[i] == 'nan':
labels[i] = 'NA'
labels[i] = labels[i].upper()
# if i < 10:
# print(i, labels[i])
labels.sort()
print('Etykiety alfabetycznie: ', labels)
# print('i inne')
# Wczytanie wartości parametrów
# Area
area_param = []
for file_txt in files_txt_list_area:
textfile_area = pd.read_csv(path_area + '/' + file_txt, sep = '\t', header = None)
area_param.append(textfile_area)
# Mean
mean_param = []
for file_txt in files_txt_list_mean:
textfile_mean = pd.read_csv(path_mean + '/' + file_txt, sep = '\t', header = None)
mean_param.append(textfile_mean)
# Squaresum
squaresum_param = []
for file_txt in files_txt_list_squaresum:
textfile_squaresum = pd.read_csv(path_squaresum + '/' + file_txt, sep = '\t', header = None)
squaresum_param.append(textfile_squaresum)
# Zeros
zeros_param = []
for file_txt in files_txt_list_zeros:
textfile_zeros = pd.read_csv(path_zeros + '/' + file_txt, sep = '\t', header = None)
zeros_param.append(textfile_zeros)
# MFCC
mfcc_param = []
for file_txt in files_txt_list_mfcc:
textfile_mfcc = pd.read_csv(path_mfcc + '/' + file_txt, sep = ' ', header = None)
mfcc_param.append(textfile_mfcc)
# +
# Tworzenie tabeli z danymi (wszystkie słowa)
temp_list = []
for i in range(len(labels)):
k = i
for j in range(len(list_of_names)):
mfcc = np.array(mfcc_param[k])
temp_list.append([area_param[i][0][j], mean_param[i][0][j], squaresum_param[i][0][j], zeros_param[i][0][j],
mfcc, labels[i]])
k=k+98
data_table = pd.DataFrame(temp_list, columns=['Area', 'Mean', 'Square_sum',
'Zero_crossing_density', 'MFCC', 'Label'])
data_table
# +
temp_list = []
for i in range(len(labels)):
k=i
for j in range(len(list_of_names)):
mfcc = np.array(mfcc_param[k])
temp_list.append([area_param[i][0][j], mean_param[i][0][j], squaresum_param[i][0][j], zeros_param[i][0][j], mfcc, i])
k=k+98
# Stworzenie tabeli z danymi
data_table = pd.DataFrame(temp_list, columns=['Area', 'Mean', 'Square_sum',
'Zero_crossing_density', 'MFCC', 'Label'])
data_table
# -
# Ustawienie randomowej kolejności wierszy
rand_data_table = data_table.sample(frac=1)
rand_data_table
# +
# Na podstawie https://www.tensorflow.org/tutorials/structured_data/feature_columns?hl=en
# Podzielenie danych na uczące i testowe
train_data, test_data = train_test_split(rand_data_table, test_size=0.25)
train_data, val_data = train_test_split(train_data, test_size=0.25)
# Rozdzielenie parametrów i etykiet
train_features = train_data[['Area', 'Mean', 'Square_sum', 'Zero_crossing_density']]
train_labels = train_data['Label']
val_features = val_data[['Area', 'Mean', 'Square_sum', 'Zero_crossing_density']]
val_labels = val_data['Label']
test_features = test_data[['Area', 'Mean', 'Square_sum', 'Zero_crossing_density']]
test_labels = test_data['Label']
rand_train_dataset = tf.data.Dataset.from_tensor_slices((dict(train_features), train_labels))
rand_val_dataset = tf.data.Dataset.from_tensor_slices((dict(val_features), val_labels))
rand_test_dataset = tf.data.Dataset.from_tensor_slices((dict(test_features), test_labels))
data_batch_size = 16
train_batch = rand_train_dataset.batch(data_batch_size)
val_batch = rand_val_dataset.batch(data_batch_size)
test_batch = rand_test_dataset.batch(data_batch_size)
# print('Wielkość zbioru uczącego:', len(rand_train_dataset))
# print('Wielkość zbioru walidacyjnego:', len(rand_val_dataset))
# print('Wielkość zbioru testowego:', len(rand_test_dataset))
# +
# Ustalenie numerycznych kolumn z danymi
feat_columns = []
feat_area = feature_column.numeric_column('Area')
feat_columns.append(feat_area)
feat_mean = feature_column.numeric_column('Mean')
feat_columns.append(feat_mean)
feat_squaresum = feature_column.numeric_column('Square_sum')
feat_columns.append(feat_squaresum)
feat_zeros = feature_column.numeric_column('Zero_crossing_density')
feat_columns.append(feat_zeros)
# feat_mfcc = feature_column.numeric_column('MFCC')
# feat_columns.append(feat_mfcc)
feature_layer = tf.keras.layers.DenseFeatures(feat_columns)
# +
# Zbudowanie i uczenie modelu
model_AI = tf.keras.Sequential([
feature_layer,
layers.Dense(128, activation='relu'),
layers.Dense(128, activation='relu'),
layers.Dropout(.1),
layers.Dense(1)
])
model_AI.compile(optimizer='adam',
loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
metrics=['accuracy'])
model_AI.fit(train_batch,
validation_data=val_batch,
epochs=10)
# -
# Wyznaczenie dokładności rozpoznawania
loss, accuracy = model_AI.evaluate(test_batch)
print("Dokładność klasyfikacji:", (round(10000*accuracy))/100, '%')
# +
# Tworzenie tabeli z danymi (wszystkie słowa jednej osoby)
temp_list_1 = []
for i in range(len(labels)):
for j in range(4):
temp_list_1.append([area_param[i][0][j], mean_param[i][0][j], squaresum_param[i][0][j], zeros_param[i][0][j], i])
# Stworzenie tabeli z danymi
data_table_1 = pd.DataFrame(temp_list_1, columns=['Area', 'Mean', 'Square_sum',
'Zero_crossing_density', 'Label'])
# Ustawienie randomowej kolejności wierszy
rand_data_table_1 = data_table_1.sample(frac=1)
# Podzielenie danych na uczące i testowe
train_data_1, test_data_1 = train_test_split(rand_data_table_1, test_size=0.25)
train_data_1, val_data_1 = train_test_split(train_data_1, test_size=0.25)
train_features_1 = train_data_1[['Area', 'Mean', 'Square_sum', 'Zero_crossing_density']]
train_labels_1 = train_data_1['Label']
val_features_1 = val_data_1[['Area', 'Mean', 'Square_sum', 'Zero_crossing_density']]
val_labels_1 = val_data_1['Label']
test_features_1 = test_data_1[['Area', 'Mean', 'Square_sum', 'Zero_crossing_density']]
test_labels_1 = test_data_1['Label']
rand_train_dataset_1 = tf.data.Dataset.from_tensor_slices((dict(train_features_1), train_labels_1))
rand_val_dataset_1 = tf.data.Dataset.from_tensor_slices((dict(val_features_1), val_labels_1))
rand_test_dataset_1 = tf.data.Dataset.from_tensor_slices((dict(test_features_1), test_labels_1))
train_batch_1 = rand_train_dataset_1.batch(data_batch_size)
val_batch_1 = rand_val_dataset_1.batch(data_batch_size)
test_batch_1 = rand_test_dataset_1.batch(data_batch_size)
# print('Wielkość zbioru uczącego:', len(rand_train_dataset_1))
# print('Wielkość zbioru walidacyjnego:', len(rand_val_dataset_1))
# print('Wielkość zbioru testowego:', len(rand_test_dataset_1))
# Dopasowanie kolumn
feat_columns_1 = []
feat_area_1 = feature_column.numeric_column('Area')
feat_columns_1.append(feat_area_1)
feat_mean_1 = feature_column.numeric_column('Mean')
feat_columns_1.append(feat_mean_1)
feat_squaresum_1 = feature_column.numeric_column('Square_sum')
feat_columns_1.append(feat_squaresum_1)
feat_zeros_1 = feature_column.numeric_column('Zero_crossing_density')
feat_columns_1.append(feat_zeros_1)
feature_layer_1 = tf.keras.layers.DenseFeatures(feat_columns_1)
# Uczenie modelu
model_AI_1 = tf.keras.Sequential([
feature_layer_1,
layers.Dense(128, activation='relu'),
layers.Dense(128, activation='relu'),
layers.Dropout(.1),
layers.Dense(1)
])
model_AI_1.compile(optimizer='adam',
loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
metrics=['accuracy'])
model_AI_1.fit(train_batch_1,
validation_data=val_batch_1,
epochs=10)
# Sprawdzenie modelu na danych testowych
loss_1, accuracy_1 = model_AI_1.evaluate(test_batch_1)
print("Dokładność klasyfikacji:", (round(10000*accuracy_1))/100, '%')
# +
# Tworzenie tabeli z danymi (dwa słowa jednej osoby)
temp_list_2 = []
for i in range(2):
for j in range(4):
temp_list_2.append([area_param[i][0][j], mean_param[i][0][j], squaresum_param[i][0][j], zeros_param[i][0][j], i])
# Stworzenie tabeli z danymi
data_table_2 = pd.DataFrame(temp_list_2, columns=['Area', 'Mean', 'Square_sum',
'Zero_crossing_density', 'Label'])
# Ustawienie randomowej kolejności wierszy
rand_data_table_2 = data_table_2.sample(frac=1)
# Podzielenie danych na uczące i testowe
train_data_2, test_data_2 = train_test_split(rand_data_table_2, test_size=0.25)
train_data_2, val_data_2 = train_test_split(train_data_2, test_size=0.25)
train_features_2 = train_data_2[['Area', 'Mean', 'Square_sum', 'Zero_crossing_density']]
train_labels_2 = train_data_2['Label']
val_features_2 = val_data_2[['Area', 'Mean', 'Square_sum', 'Zero_crossing_density']]
val_labels_2 = val_data_2['Label']
test_features_2 = test_data_2[['Area', 'Mean', 'Square_sum', 'Zero_crossing_density']]
test_labels_2 = test_data_2['Label']
rand_train_dataset_2 = tf.data.Dataset.from_tensor_slices((dict(train_features_2), train_labels_2))
rand_val_dataset_2 = tf.data.Dataset.from_tensor_slices((dict(val_features_2), val_labels_2))
rand_test_dataset_2 = tf.data.Dataset.from_tensor_slices((dict(test_features_2), test_labels_2))
train_batch_2 = rand_train_dataset_2.batch(data_batch_size)
val_batch_2 = rand_val_dataset_2.batch(data_batch_size)
test_batch_2 = rand_test_dataset_2.batch(data_batch_size)
# print('Wielkość zbioru uczącego:', len(rand_train_dataset_2))
# print('Wielkość zbioru walidacyjnego:', len(rand_val_dataset_2))
# print('Wielkość zbioru testowego:', len(rand_test_dataset_2))
# Dopasowanie kolumn
feat_columns_2 = []
feat_area_2 = feature_column.numeric_column('Area')
feat_columns_2.append(feat_area_2)
feat_mean_2 = feature_column.numeric_column('Mean')
feat_columns_2.append(feat_mean_2)
feat_squaresum_2 = feature_column.numeric_column('Square_sum')
feat_columns_2.append(feat_squaresum_2)
feat_zeros_2 = feature_column.numeric_column('Zero_crossing_density')
feat_columns_2.append(feat_zeros_2)
feature_layer_2 = tf.keras.layers.DenseFeatures(feat_columns_2)
# Uczenie modelu
model_AI_2 = tf.keras.Sequential([
feature_layer_2,
layers.Dense(128, activation='relu'),
layers.Dense(128, activation='relu'),
layers.Dropout(.1),
layers.Dense(1)
])
model_AI_2.compile(optimizer='adam',
loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
metrics=['accuracy'])
model_AI_1.fit(train_batch_2,
validation_data=val_batch_2,
epochs=10)
# Sprawdzenie modelu na danych testowych
loss_2, accuracy_2 = model_AI_2.evaluate(test_batch_2)
print("Dokładność klasyfikacji:", (round(10000*accuracy_2))/100, '%')
# +
# Tworzenie tabeli z danymi (dwa słowa wszystkich osób)
temp_list_3 = []
for i in range(2):
for j in range(len(list_of_names)):
temp_list_3.append([area_param[i][0][j], mean_param[i][0][j], squaresum_param[i][0][j], zeros_param[i][0][j], i])
# Stworzenie tabeli z danymi
data_table_3 = pd.DataFrame(temp_list_3, columns=['Area', 'Mean', 'Square_sum',
'Zero_crossing_density', 'Label'])
# Ustawienie randomowej kolejności wierszy
rand_data_table_3 = data_table_3.sample(frac=1)
# Podzielenie danych na uczące i testowe
train_data_3, test_data_3 = train_test_split(rand_data_table_3, test_size=0.25)
train_data_3, val_data_3 = train_test_split(train_data_3, test_size=0.25)
train_features_3 = train_data_3[['Area', 'Mean', 'Square_sum', 'Zero_crossing_density']]
train_labels_3 = train_data_3['Label']
val_features_3 = val_data_3[['Area', 'Mean', 'Square_sum', 'Zero_crossing_density']]
val_labels_3 = val_data_3['Label']
test_features_3 = test_data_3[['Area', 'Mean', 'Square_sum', 'Zero_crossing_density']]
test_labels_3 = test_data_3['Label']
rand_train_dataset_3 = tf.data.Dataset.from_tensor_slices((dict(train_features_3), train_labels_3))
rand_val_dataset_3 = tf.data.Dataset.from_tensor_slices((dict(val_features_3), val_labels_3))
rand_test_dataset_3 = tf.data.Dataset.from_tensor_slices((dict(test_features_3), test_labels_3))
train_batch_3 = rand_train_dataset_3.batch(data_batch_size)
val_batch_3 = rand_val_dataset_3.batch(data_batch_size)
test_batch_3 = rand_test_dataset_3.batch(data_batch_size)
# print('Wielkość zbioru uczącego:', len(rand_train_dataset_3))
# print('Wielkość zbioru walidacyjnego:', len(rand_val_dataset_3))
# print('Wielkość zbioru testowego:', len(rand_test_dataset_3))
# Dopasowanie kolumn
feat_columns_3 = []
feat_area_3 = feature_column.numeric_column('Area')
feat_columns_3.append(feat_area_3)
feat_mean_3 = feature_column.numeric_column('Mean')
feat_columns_3.append(feat_mean_3)
feat_squaresum_3 = feature_column.numeric_column('Square_sum')
feat_columns_3.append(feat_squaresum_3)
feat_zeros_3 = feature_column.numeric_column('Zero_crossing_density')
feat_columns_3.append(feat_zeros_3)
feature_layer_3 = tf.keras.layers.DenseFeatures(feat_columns_3)
# Uczenie modelu
model_AI_3 = tf.keras.Sequential([
feature_layer_3,
layers.Dense(128, activation='relu'),
layers.Dense(128, activation='relu'),
layers.Dropout(.1),
layers.Dense(1)
])
model_AI_3.compile(optimizer='adam',
loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
metrics=['accuracy'])
model_AI_3.fit(train_batch_3,
validation_data=val_batch_3,
epochs=10)
# Sprawdzenie modelu na danych testowych
loss_3, accuracy_3 = model_AI_3.evaluate(test_batch_3)
print("Dokładność klasyfikacji:", (round(10000*accuracy_3))/100, '%')
# -
| 33,308 |
/python/fitting_tutorials/Chi-square_fitting.ipynb | d41316466e003f7bc17a288fb915a47f7bbbc52a | [] | no_license | mattbellis/matts-work-environment | https://github.com/mattbellis/matts-work-environment | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 65,083 | # ---
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# name: python3
# ---
# +
import matplotlib.pylab as plt
import numpy as np
# %matplotlib notebook
# -
# Suppose you are trying to determine the acceleration due to gravity on
# some new planet. You drop an object from the top of a building on this
# planet, and using a camera and object-recognition software, you
# record the data shown in the figure below.
#
# $t$ the time that the camera
# takes an image (in seconds), $y_i$ is the distance the object is at (in meters),
# assuming you are starting at $y=0$, and $\sigma_i$ is the uncertainty on the distance
# fallen/location of the object.
#
# The error bars and jitter are an artifact of the object-recognition software which
# becomes less effective as the object is falling faster.
#
y_i = [-0.19,-0.20,0.09,-0.29,-0.72,-0.65,-1.45,-2.58,-2.99,-3.32,-3.83,-5.17,-6.46,-7.06,-8.36,-9.77,-10.10,]
sigma_i = [0.25,0.27,0.29,0.31,0.33,0.35,0.37,0.39,0.41,0.43,0.45,0.47,0.49,0.51,0.53,0.55,0.57,]
t_i = [0.00,0.10,0.20,0.30,0.40,0.50,0.60,0.70,0.80,0.90,1.00,1.10,1.20,1.30,1.40,1.50,1.60,]
plt.figure(figsize=(5,4))
plt.errorbar(t_i, y_i, yerr=sigma_i,fmt="o")
plt.ylabel("Location of object (meters)",fontsize=18)
plt.xlabel("Time (seconds)",fontsize=18)
plt.tight_layout()
#
# You can fit these data using the $\chi$-square method.
# $$\chi^2 = \sum_i \left(\frac{y(g) - y_i}{\sigma_i}\right)^2$$
#
# Where $y_i$ the distance fallen for the $i^{\rm th}$ point, $\sigma_i$ is the
# uncertainty on the distance fallen for the $i^{\rm th}$ point, and
# $y(g)$ is the predicted value for the distance fallen for that
# point, assuming some value of $g$ and using the same value for
# time ($t$).
#
# * Fit the data to determine the the value of $g$ on this planet.To do this...
# * Assume some value of $g$ and calculate $\chi^2$
# * Vary the value of $g$ between 7 and 10, using some intervals that you think are appropriate.
# * Store the values of $g$ and find which $g$ gives you the best fit, that is, *the lowest $\chi^2$ value*.
# * Plot the data and overlay the optimal curve using your fitted value.
# * Make a plot of $\chi^2$ versus $g$. This is often referred to as the *likelihood scan*.
# * You are going to want to get an uncertainty of the value of $g$ from the fit. To do this...
# * Find what values of $g$ give you values of $\chi^2$ is larger than *the minimum value* by 1 (or as close to 1 as you can get).
# * There should be two values of $g$ that you find. One with a lower value than the best value for $g$ and one with a higher value.
# * These are your upper- and lower- values at 1-$\sigma$ or one standard deviation and are often used as an estimator for the 1-$\sigma$ *uncertainty* on the fit parameter, in this case $g$.
# * Use them to quote your final value $\pm$ some uncertainty.
#
#
| 3,063 |
/Manipulasi Data.ipynb | a16f5453a33276b1ae7f71f3ecabaaf1f7458541 | [] | no_license | fiqihyusrilm/Python-Data-Science | https://github.com/fiqihyusrilm/Python-Data-Science | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 28,171 | # ---
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# ---
# # Equilibrio Labores Hogar Colombia
# ## - Gran Encuesta Integrada de Hogares - GEIH -
#
# Archivos :
# - Area - Otras actividades y ayudas en la semana
#
# Columnas : <br>
# P7480S3 : Realiza Oficions en su Hogar? (1= si 2=no ) <br>
# P7480S3A1 : Cuantas Horas a la Semana? <br>
# P7480S5 : Cuidar o Atender Ninos <br>
# P7480S5A1 : Cuantas Horas a la Semana? <br>
#
# - Cabecera - Características generales (Personas)
#
# Columnas : <br>
# P6020 : Sexo (1 = Hombre, 2 = Mujer) <br>
# P6040 : Edad <br>
# P3246 : Usted se considera Pobre? (1= si 2=no ) <br>
#
#
#
# #### Source Data
# 2020 : http://microdatos.dane.gov.co/index.php/catalog/659/get_microdata <br>
# 2019 : http://microdatos.dane.gov.co/index.php/catalog/599/get_microdata
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# ### 1.1 Data Import
#
# +
areacols = ['DIRECTORIO', 'SECUENCIA_P', 'ORDEN','P7480S3','P7480S3A1','P7480S5','P7480S5A1','fex_c_2011']
df_19area = pd.read_csv("./dataset/2019Noviembre.csv/area - Otras actividades y ayudas en la semana.csv",sep=';', usecols=areacols)
df_20area = pd.read_csv("./dataset/2020Noviembre.csv/20Area - Otras actividades y ayudas en la semana.csv",sep=';', usecols=areacols)
cabecols = ['DIRECTORIO', 'SECUENCIA_P', 'ORDEN','P6020','P6040']
df_19cabeceras = pd.read_csv("./dataset/2019Noviembre.csv/Cabecera - Características generales (Personas).csv",sep=';', usecols=cabecols)
df_20cabeceras = pd.read_csv("./dataset/2020Noviembre.csv/Cabecera - Características generales (Personas).csv",sep=';', usecols=cabecols)
key=['DIRECTORIO', 'SECUENCIA_P', 'ORDEN']
# -
df_20area.head()
# ### 1.2 Data Preparation
# +
df_2019 = pd.merge(df_19area, df_19cabeceras,on=key, how='inner')
df_2020 = pd.merge(df_20area, df_20cabeceras,on=key, how='inner')
df_2019['P7480S3A1'] = df_2019['P7480S3A1'].replace(' ',0, regex=True)
df_2019['P7480S5A1'] = df_2019['P7480S5A1'].replace(' ',0, regex=True)
df_2019['P7480S3A1']=df_2019['P7480S3A1'].astype(float)
df_2019['P7480S5A1']=df_2019['P7480S5A1'].astype(float)
df_2020['fex_c_2011'] = df_2020['fex_c_2011'].replace(',' , '.', regex=True)
df_2020['fex_c_2011']=df_2020['fex_c_2011'].astype(float)
df_2020['P7480S3A1'] = df_2020['P7480S3A1'].replace(' ',0, regex=True)
df_2020['P7480S5A1'] = df_2020['P7480S5A1'].replace(' ',0, regex=True)
df_2020['P7480S3A1']=df_2020['P7480S3A1'].astype(float)
df_2020['P7480S5A1']=df_2020['P7480S5A1'].astype(float)
# -
df_2020.head()
# ### 1.3 General Functions
#
# +
#FUNCIONES
def GrupoEta(dataframe_name):
newDF=dataframe_name.copy()
newDF.loc[(newDF.P6040 <= 5), 'GrupoEta'] = 'PRIMERA_INFANCIA'
newDF.loc[(newDF.P6040 >= 6) & (newDF.P6040 <= 11), 'GrupoEta'] = 'INFANCIA'
newDF.loc[(newDF.P6040 >= 12) & (newDF.P6040 <= 18), 'GrupoEta'] = 'ADOLESCENCIA'
newDF.loc[(newDF.P6040 >= 19) & (newDF.P6040 <= 26), 'GrupoEta'] = 'JUVENTUD'
newDF.loc[(newDF.P6040 >= 27) & (newDF.P6040 <= 59), 'GrupoEta'] = 'ADULTEZ'
newDF.loc[(newDF.P6040 >= 50), 'GrupoEta'] = 'VEJEZ'
dataframe_name=newDF.copy()
return (dataframe_name)
def replaceSex(dataframe_name):
dataframe_name.loc[(dataframe_name.P6020 == 1), 'Sexo'] = 'Hombre'
dataframe_name.loc[(dataframe_name.P6020 == 2), 'Sexo'] = 'Mujer'
return (dataframe_name)
# +
df_2019["horas_aux"] = df_2019["P7480S3A1"] * df_2019["fex_c_2011"]
df_2020["horas_aux"] = df_2020["P7480S3A1"] * df_2020["fex_c_2011"]
df_2019=GrupoEta(df_2019)
df_2020=GrupoEta(df_2020)
df_2019=replaceSex(df_2019)
df_2020=replaceSex(df_2020)
# -
df_2020.head()
#
#
# +
#sns.pairplot(data = df_2019, vars=['P6020','P6040','P7480S3','P7480S3A1'])
#plt.show()
df_2020.groupby(['Sexo'])['P7480S3A1'].describe()
# -
meanfex = df_2020.groupby(['P6020'])['fex_c_2011'].mean()
print(df_2020.groupby(['P6020'])['horas_aux'].mean() / meanfex)
# - En Promedio durante Noviembre 2020 los hombres dedicaron 6.15Horas a labores del hogar vs 18.3 Horas dedicadas por las mujeres.
#
# - No Existe diferencia relevante en la cantidad de horas utilizadas por los hombres en labores de hogar cuando se discrimina por grupo etario.
#
# - No Existe diferencia relevante en la cantidad de horas utilizadas por los hombres en el cuidado de ninios cuando se discrimina por grupo etario.
#
# ### Plots Noviembre 2019
# +
#Labores Hogar Noviembre 2019
sns.catplot(x="Sexo", y="P7480S3A1", hue="GrupoEta", kind="bar", data=df_2019)
# Cuidado Infantes
sns.catplot(x="Sexo", y="P7480S5A1", hue="GrupoEta", kind="bar", data=df_2019)
#Cuidado Infates por Sexo
sns.catplot(x="Sexo", y="P7480S3A1", hue="Sexo", kind="bar", data=df_2019)
# -
#Antes Pandemia
print(df_2019.groupby('Sexo')['P7480S3A1'].mean())
print('Cuidado Infantes 2019',df_2019.groupby('Sexo')['P7480S5A1'].mean())
print('Promedio de edad por sexo',df_2019.groupby('Sexo')['P6040'].mean())
sns.catplot(x='P6040', y="P7480S3A1", hue="Sexo", kind="bar", data=df_2019)
# ### Plots Noviembre 2020
# +
#Labores Hogar Noviembre 2020
sns.catplot(x="Sexo", y="P7480S3A1", hue="GrupoEta", kind="bar", data=df_2020)
# Cuidado Infantes
sns.catplot(x="Sexo", y="P7480S5A1", hue="GrupoEta", kind="bar", data=df_2020)
#Cuidado Infates por Sexo
sns.catplot(x="Sexo", y="P7480S3A1", hue="Sexo", kind="bar", data=df_2020)
# -
#Post Pandemia
print(df_2020.groupby('Sexo')['P7480S3A1'].mean())
print('Cuidado Infantes 2020',df_2020.groupby('Sexo')['P7480S5A1'].mean())
sns.violinplot(x=df_2019.Sexo, y=df_2019.P7480S3A1)
sns.violinplot(x=df_2020.Sexo, y=df_2020.P7480S3A1)
the networks behind them.
#
#
# + [markdown] slideshow={"slide_type": "slide"}
# ## THE HISTORY OF NETWORK ANALYSIS
#
# - Graph theory: 1735, Euler
#
# - Social Network Research: 1930s, Moreno
#
# - Communication networks/internet: 1960s
#
# - Ecological Networks: May, 1979.
#
# + [markdown] slideshow={"slide_type": "slide"}
#
# While the study of networks has a long history from graph theory to sociology, **the modern chapter of network science emerged only during the first decade of the 21st century, following the publication of two seminal papers in 1998 and 1999**.
#
# The explosive interest in network science is well documented by the citation pattern of two classic network papers, the 1959 paper by Paul Erdos and Alfréd Rényi that marks the beginning of the study of random networks in graph theory [4] and the 1973 paper by Mark Granovetter, the most cited social network paper [5].
#
# Both papers were hardly or only moderately cited before 2000. The explosive growth of citations to these papers in the 21st century documents the emergence of network science, drawing a new, interdisciplinary audience to these classic publications.
#
# + [markdown] slideshow={"slide_type": "slide"}
# <img src = './img/citation.png' width = 800>
# + [markdown] slideshow={"slide_type": "slide"}
# THE EMERGENCE OF NETWORK SCIENCE
# - Movie Actor Network, 1998;
# - World Wide Web, 1999.
# - C elegans neural wiring diagram 1990
# - Citation Network, 1998
# - Metabolic Network, 2000;
# - PPI network, 2001
#
# + [markdown] slideshow={"slide_type": "slide"}
# The universality of network characteristics:
#
# The architecture of networks emerging in various domains of science, nature, and technology are more similar to each other than one would have expected.
#
# + [markdown] slideshow={"slide_type": "slide"}
# THE CHARACTERISTICS OF NETWORK SCIENCE
# - Interdisciplinary
# - Empirical
# - Quantitative and Mathematical
# - Computational
#
# + [markdown] slideshow={"slide_type": "slide"}
# THE IMPACT OF NETWORK SCIENCE
#
# + [markdown] slideshow={"slide_type": "slide"}
# - **Google**
# Market Cap(2010 Jan 1):
# $189 billion
#
# - **Cisco Systems**
# networking gear Market cap (Jan 1, 2919):
# $112 billion
#
# - **Facebook**
# market cap:
# $50 billion
#
# + [markdown] slideshow={"slide_type": "slide"}
# ## Health: From drug design to metabolic engineering.
# The human genome project, completed in 2001, offered the first comprehensive list of all human genes.
#
# - Yet, to fully understand how our cells function, and the origin of disease,
# - we need accurate maps that tell us how these genes and other cellular components interact with each other.
# + [markdown] slideshow={"slide_type": "slide"}
# ## Security: Fighting Terrorism.
# Terrorism is one of the maladies of the 21st century, absorbing significant resources to combat it worldwide.
#
# - **Network thinking** is increasingly present in the arsenal of various law enforcement agencies in charge of limiting terrorist activities.
# - To disrupt the financial network of terrorist organizations
# - to map terrorist networks
# - to uncover the role of their members and their capabilities.
#
# - Using social networks to capture Saddam Hussein
# - Capturing of the individuals behind the March 11, 2004 Madrid train bombings **through the examination of the mobile call network**.
#
# + [markdown] slideshow={"slide_type": "slide"}
# ## Epidemics: From forecasting to halting deadly viruses.
#
# While the **H1N1 pandemic** was not as devastating as it was feared at the beginning of the outbreak in 2009, it gained a special role in the history of epidemics: it was **the first pandemic whose course and time evolution was accurately predicted months before the pandemic reached its peak**.
#
# - Before 2000 epidemic modeling was dominated by **compartment models**, assuming that everyone can infect everyone else one word the same socio-physical compartment.
# - The emergence of a network-based framework has fundamentally changed this, offering a new level of predictability in epidemic phenomena.
#
# + [markdown] slideshow={"slide_type": "subslide"}
# ### The first major mobile epidemic outbreak
#
# In January 2010 network science tools have predicted the conditions necessary for the emergence of viruses spreading through mobile phones.
#
# in the fall of 2010 in China, infecting over 300,000 phones each day, closely followed the predicted scenario.
#
# + [markdown] slideshow={"slide_type": "slide"}
# ## Brain Research: Mapping neural network.
# The human brain, consisting of hundreds of billions of interlinked neurons, is one of the least understood networks from the perspective of network science.
#
# The reason is simple:
# - we lack maps telling us which neurons link to each other.
# - The only fully mapped neural map available for research is that of the C.Elegans worm, with only 300 neurons.
#
# Driven by the potential impact of such maps, in 2010 the **National Institutes of Health** has initiated the Connectome project, aimed at developing the technologies that could provide an accurate neuron-level map of mammalian brains.
#
# + [markdown] slideshow={"slide_type": "slide"}
# ## The Bridges of Konigsberg
#
# + [markdown] slideshow={"slide_type": "fragment"}
# <img src = './img/konigsberg.png' width = 500>
# + [markdown] slideshow={"slide_type": "subslide"}
# Can one walk across the seven bridges and never cross the same bridge twice and get back to the starting place?
#
# <img src ='./img/euler.png' width = 600>
# + [markdown] slideshow={"slide_type": "slide"}
# ### Euler’s theorem (1735):
#
# - If a graph has more than two nodes of odd degree, there is no path.
# - If a graph is connected and has no odd degree nodes, it has at least one path.
#
# + [markdown] slideshow={"slide_type": "slide"}
# COMPONENTS OF A COMPLEX SYSTEM
#
# Networks and graphs
# - components: nodes, vertices N
# - interactions: links, edges L
# - system: network, graph G(N,L)
#
# + [markdown] slideshow={"slide_type": "subslide"}
# network often refers to real systems
# - www,
# - social network
# - metabolic network.
#
# Language: (Network, node, link)
#
# + [markdown] slideshow={"slide_type": "subslide"}
# graph: mathematical representation of a network
# - web graph,
# - social graph (a Facebook term)
#
# Language: (Graph, vertex, edge) **G(N, L)**
# + [markdown] slideshow={"slide_type": "subslide"}
# <img src = './img/net.png' width = 800>
# + [markdown] slideshow={"slide_type": "subslide"}
# ### CHOOSING A PROPER REPRESENTATION
#
# The choice of the proper network representation determines our ability to use network theory successfully.
#
# In some cases there is a unique, unambiguous representation.
# In other cases, the representation is by no means unique.
#
# For example, the way we assign the links between a group of individuals will determine the nature of the question we can study.
#
# + [markdown] slideshow={"slide_type": "subslide"}
# If you connect individuals that work with each other, you will explore the professional network. http://www.theyrule.net
#
# If you connect those that have a romantic and sexual relationship, you will be exploring the sexual networks.
#
#
# If you connect individuals based on their first name (all Peters connected to each other), you will be exploring what? It is a network, nevertheless.
#
# + [markdown] slideshow={"slide_type": "subslide"}
# ## UNDIRECTED VS. DIRECTED NETWORKS
#
# + [markdown] slideshow={"slide_type": "subslide"}
# ### Undirected
# Links: undirected
# - co-authorship
# - actor network
# - protein interactions
# + slideshow={"slide_type": "subslide"}
# %matplotlib inline
import matplotlib.pyplot as plt
import networkx as nx
Gu = nx.Graph()
for i, j in [(1, 2), (1, 4), (4, 2), (4, 3)]:
Gu.add_edge(i,j)
nx.draw(Gu, with_labels = True)
# + [markdown] slideshow={"slide_type": "subslide"}
# ### Directed
# Links: directed
# - urls on the www
# - phone calls
# - metabolic reactions
# + slideshow={"slide_type": "subslide"}
import networkx as nx
Gd = nx.DiGraph()
for i, j in [(1, 2), (1, 4), (4, 2), (4, 3)]:
Gd.add_edge(i,j)
nx.draw(Gd, with_labels = True, pos=nx.circular_layout(Gd))
# + [markdown] slideshow={"slide_type": "subslide"}
# <img src = './img/networks.png' width = 1000>
# + [markdown] slideshow={"slide_type": "subslide"}
# ## Degree, Average Degree and Degree Distribution
#
# + slideshow={"slide_type": "subslide"}
nx.draw(Gu, with_labels = True)
# + [markdown] slideshow={"slide_type": "subslide"}
# ### Undirected network:
# Node degree: the number of links connected to the node.
#
# $k_1 = k_2 = 2, k_3 = 3, k_4 = 1$
# + slideshow={"slide_type": "subslide"}
nx.draw(Gd, with_labels = True, pos=nx.circular_layout(Gd))
# + [markdown] slideshow={"slide_type": "subslide"}
# ### Directed network
# In directed networks we can define an in-degree and out-degree. The (total) degree is the sum of in-and out-degree.
#
# $k_3^{in} = 2, k_3^{out} = 1, k_3 = 3$
#
# Source: a node with $k^{in}= 0$; Sink: a node with $k^{out}= 0$.
#
# + [markdown] slideshow={"slide_type": "subslide"}
# For a sample of N values: $x_1, x_2, ..., x_N$:
#
# Average(mean):
#
# $<x> = \frac{x_1 +x_2 + ...+x_N}{N} = \frac{1}{N}\sum_{i = 1}^{N} x_i$
# + [markdown] slideshow={"slide_type": "subslide"}
# For a sample of N values: $x_1, x_2, ..., x_N$:
#
# The nth moment:
#
# $<x^n> = \frac{x_1^n +x_2^n + ...+x_N^n}{N} = \frac{1}{N}\sum_{i = 1}^{N} x_i^n$
# + [markdown] slideshow={"slide_type": "subslide"}
# For a sample of N values: $x_1, x_2, ..., x_N$:
#
# Standard deviation:
#
# $\sigma_x = \sqrt{\frac{1}{N}\sum_{i = 1}^{N} (x_i - <x>)^2}$
# + slideshow={"slide_type": "subslide"}
import numpy as np
x = [1, 1, 1, 2, 2, 3]
np.mean(x), np.sum(x), np.std(x)
# + [markdown] slideshow={"slide_type": "subslide"}
# For a sample of N values: $x_1, x_2, ..., x_N$:
#
# Distribution of x:
#
# $p_x = \frac{The \: frequency \: of \: x}{The\: Number \:of\: Observations}$
#
# 其中，$p_x 满足 \sum_i p_x = 1$
# + slideshow={"slide_type": "subslide"}
# 直方图
plt.hist(x)
plt.show()
# + slideshow={"slide_type": "subslide"}
from collections import defaultdict, Counter
freq = defaultdict(int)
for i in x:
freq[i] +=1
freq
# + slideshow={"slide_type": "subslide"}
freq_sum = np.sum(freq.values())
freq_sum
# + slideshow={"slide_type": "subslide"}
px = [float(i)/freq_sum for i in freq.values()]
px
# + slideshow={"slide_type": "subslide"}
plt.plot(freq.keys(), px, 'r-o')
plt.show()
# + [markdown] slideshow={"slide_type": "slide"}
# ## Average Degree
# + [markdown] slideshow={"slide_type": "subslide"}
# ### Undirected
#
# $<k> = \frac{1}{N} \sum_{i = 1}^{N} k_i = \frac{2L}{N}$
#
# + [markdown] slideshow={"slide_type": "subslide"}
# ### Directed
#
#
# $<k^{in}> = \frac{1}{N} \sum_{i=1}^N k_i^{in}= <k^{out}> = \frac{1}{N} \sum_{i=1}^N k_i^{out} = \frac{L}{N}$
# + [markdown] slideshow={"slide_type": "slide"}
# ## Degree distribution
# P(k): probability that a randomly selected node has degree k
#
#
# $N_k = The \:number\: of \:nodes\:with \:degree\: k$
#
# $P(k) = \frac{N_k}{N}$
#
#
# + [markdown] slideshow={"slide_type": "slide"}
# ## Adjacency matrix
# $A_{ij} =1$ if there is a link between node i and j
#
# $A_{ij} =0$ if there is no link between node i and j
# + slideshow={"slide_type": "slide"}
plt.figure(1)
plt.subplot(121)
pos = nx.nx.circular_layout(Gu) #定义一个布局，此处采用了spring布局方式
nx.draw(Gu, pos, with_labels = True)
plt.subplot(122)
nx.draw(Gd, pos, with_labels = True)
# + [markdown] slideshow={"slide_type": "subslide"}
# ### Undirected
# $A_{ij} =1$ if there is a link between node i and j
#
# $A_{ij} =0$ if there is no link between node i and j
#
# $A_{ij}=\begin{bmatrix} 0&1 &0 &1 \\ 1&0 &0 &1 \\ 0 &0 &0 &1 \\ 1&1 &1 & 0 \end{bmatrix}$
# + [markdown] slideshow={"slide_type": "subslide"}
# ### Undirected
#
# 无向网络的矩阵是对称的。
#
# $A_{ij} = A_{ji} , \: A_{ii} = 0$
#
# $k_i = \sum_{j=1}^N A_{ij}, \: k_j = \sum_{i=1}^N A_{ij} $
#
# 网络中的链接数量$L$可以表达为：
#
# $ L = \frac{1}{2}\sum_{i=1}^N k_i = \frac{1}{2}\sum_{ij}^N A_{ij} $
# + [markdown] slideshow={"slide_type": "subslide"}
# ### Directed
# $A_{ij} =1$ if there is a link between node i and j
#
# $A_{ij} =0$ if there is no link between node i and j
#
# $A_{ij}=\begin{bmatrix} 0&0 &0 &0 \\ 1&0 &0 &1 \\ 0 &0 &0 &1 \\ 1&0 &0 & 0 \end{bmatrix}$
#
# Note that for a directed graph the matrix is not symmetric.
#
# + [markdown] slideshow={"slide_type": "slide"}
# ### Directed
#
# $A_{ij} \neq A_{ji}, \: A_{ii} = 0$
#
# $k_i^{in} = \sum_{j=1}^N A_{ji}, \: k_i^{out} = \sum_{j=1}^N A_{ij} $
#
# $ L = \sum_{i=1}^N k_i^{in} = \sum_{j=1}^N k_j^{out}= \sum_{i,j}^N A_{ij} $
# + [markdown] slideshow={"slide_type": "slide"}
# ## WEIGHTED AND UNWEIGHTED NETWORKS
#
# $A_{ij} = W_{ij}$
# + [markdown] slideshow={"slide_type": "slide"}
# ## BIPARTITE NETWORKS
#
#
# `Bipartite graph` (or bigraph) is a graph whose nodes can be divided into two disjoint sets U and V such that every link connects a node in U to one in V; that is, U and V are independent sets.
#
# - Hits algorithm
# - recommendation system
#
# + [markdown] slideshow={"slide_type": "subslide"}
# Ingredient-Flavor Bipartite Network
#
# <img src = './img/bipartite.png' width = 800>
# + [markdown] slideshow={"slide_type": "subslide"}
# ## Link Analysis
#
# 餐馆推荐问题
#
# 
#
# Credit: 李晓明
# + [markdown] slideshow={"slide_type": "subslide"}
# <img src = './img/hits2.png' width = 800>
# + slideshow={"slide_type": "subslide"}
import numpy as np
edges = [('甲', '新辣道'), ('甲', '海底捞'), ('甲', '五方院'),
('乙', '海底捞'), ('乙', '麦当劳'), ('乙', '俏江南'),
('丙', '新辣道'), ('丙', '海底捞'),
('丁', '新辣道'), ('丁', '五方院'), ('丁', '俏江南')]
# + slideshow={"slide_type": "subslide"}
h_dic = {i:1 for i,j in edges}
for k in range(5):
print(k, 'steps')
a_dic = {j:0 for i, j in edges}
for i,j in edges:
a_dic[j]+=h_dic[i]
print(a_dic)
h_dic = {i:0 for i, j in edges}
for i, j in edges:
h_dic[i]+=a_dic[j]
print(h_dic)
# + slideshow={"slide_type": "subslide"}
def norm_dic(dic):
sumd = np.sum(list(dic.values()))
return {i : dic[i]/sumd for i in dic}
h = {i for i, j in edges}
h_dic = {i:1/len(h) for i in h}
for k in range(100):
a_dic = {j:0 for i, j in edges}
for i,j in edges:
a_dic[j]+=h_dic[i]
a_dic = norm_dic(a_dic)
h_dic = {i:0 for i, j in edges}
for i, j in edges:
h_dic[i]+=a_dic[j]
h_dic = norm_dic(h_dic)
print(a_dic)
# -
B = nx.Graph()
users, items = {i for i, j in edges}, {j for i, j in edges}
for i, j in edges:
B.add_edge(i,j)
h, a = nx.hits(B)
print({i:a[i] for i in items} )
# {j:h[j] for j in users}
# + [markdown] slideshow={"slide_type": "subslide"}
# ### PageRank
#
# <img src = './img/pagerank.png' width = 800>
# + slideshow={"slide_type": "subslide"}
import networkx as nx
Gp = nx.DiGraph()
edges = [('a', 'b'), ('a', 'c'), ('b', 'd'), ('b', 'e'), ('c', 'f'), ('c', 'g'),
('d', 'h'), ('d', 'a'), ('e', 'a'), ('e', 'h'), ('f', 'a'), ('g', 'a'), ('h', 'a')]
for i, j in edges:
Gp.add_edge(i,j)
nx.draw(Gp, with_labels = True, font_size = 25, font_color = 'blue', alpha = 0.5,
pos = nx.kamada_kawai_layout(Gp))
#pos=nx.spring_layout(Gp, iterations = 5000))
# + slideshow={"slide_type": "subslide"}
steps = 11
n = 8
a, b, c, d, e, f, g, h = [[1.0/n for i in range(steps)] for j in range(n)]
for i in range(steps-1):
a[i+1] = 0.5*d[i] + 0.5*e[i] + h[i] + f[i] + g[i]
b[i+1] = 0.5*a[i]
c[i+1] = 0.5*a[i]
d[i+1] = 0.5*b[i]
e[i+1] = 0.5*b[i]
f[i+1] = 0.5*c[i]
g[i+1] = 0.5*c[i]
h[i+1] = 0.5*d[i] + 0.5*e[i]
print(i+1,':', a[i+1], b[i+1], c[i+1], d[i+1], e[i+1], f[i+1], g[i+1], h[i+1])
# + [markdown] slideshow={"slide_type": "subslide"}
# 流量作弊
#
# <img src = './img/pagerank_trap.png' width = 800>
# + slideshow={"slide_type": "subslide"}
G = nx.DiGraph(nx.path_graph(10))
pr = nx.pagerank(G, alpha=0.9)
pr
# + [markdown] slideshow={"slide_type": "slide"}
# ## Path 路径
# A path is a sequence of nodes in which each node is adjacent to the next one
#
# - In a directed network, the path can follow only the direction of an arrow.
#
# + [markdown] slideshow={"slide_type": "slide"}
# ### Shortest Path 最短路径
# The path with the shortest length between two nodes (distance).
#
# + [markdown] slideshow={"slide_type": "slide"}
# ### Distance 距离
#
# The distance (shortest path, geodesic path) between two nodes is defined as the number of edges along the shortest path connecting them.
#
# > If the two nodes are disconnected, the distance is **infinity**.
#
# + [markdown] slideshow={"slide_type": "slide"}
# ### Diameter 直径
#
# **Diameter $d_{max}$** is the maximum distance between any pair of nodes in the graph.
#
# + [markdown] slideshow={"slide_type": "slide"}
# ### Average path length/distance, $<d>$ 平均路径长度
#
#
# The average of the shortest paths for all pairs of nodes.
#
#
# - for a **directed graph**: where $d_{ij}$ is the distance from node i to node j
#
# $<d> = \frac{1}{2 L }\sum_{i, j \neq i} d_{ij}$
#
# > 有向网络当中的$d_{ij}$数量是链接数量L的2倍
#
# - In an **undirected** graph $d_{ij} =d_{ji}$ , so we only need to count them once
#
# > 无向网络当中的$d_{ij}$数量是链接数量L
#
#
# $<d> = \frac{1}{L }\sum_{i, j > i} d_{ij}$
#
# + [markdown] slideshow={"slide_type": "slide"}
# ## Cycle 环
# A path with the same start and end node.
#
# + [markdown] slideshow={"slide_type": "slide"}
# ## CONNECTEDNESS
#
# + [markdown] slideshow={"slide_type": "subslide"}
# ### Connected (undirected) graph
#
# > In a connected **undirected** graph, any two vertices can be joined by a path.
#
# > A disconnected graph is made up by two or more connected components.
#
# - Largest Component: Giant Component
# - The rest: Isolates
#
# ### Bridge 桥
# if we erase it, the graph becomes disconnected.
# + [markdown] slideshow={"slide_type": "subslide"}
# The adjacency matrix of a network with several components can be written in a block-diagonal form, so that nonzero elements are confined to squares, with all other elements being zero:
#
# <img src = './img/block.png' width = 600>
# + [markdown] slideshow={"slide_type": "subslide"}
# ### 结构洞
#
# 洞在哪里？
# + [markdown] slideshow={"slide_type": "fragment"}
# 洞在桥下！结构“坑”
# + [markdown] slideshow={"slide_type": "subslide"}
# ### Strongly connected *directed* graph 强连通有向图
#
# has a path from each node to every other node and vice versa (e.g. AB path and BA path).
#
# ### Weakly connected directed graph 弱连接有向图
# it is connected if we disregard the edge directions.
#
# Strongly connected components can be identified, but not every node is part of a nontrivial strongly connected component.
#
# + [markdown] slideshow={"slide_type": "subslide"}
# ### In-component -> SCC ->Out-component
#
# - In-component: nodes that can reach the **scc** (strongly connected component 强连通分量或强连通子图)
# - Out-component: nodes that can be reached from the scc.
#
# > 万维网的蝴蝶结模型🎀 bowtie model
#
#
# + [markdown] slideshow={"slide_type": "subslide"}
# ## Clustering coefficient 聚集系数
#
# + [markdown] slideshow={"slide_type": "subslide"}
# what fraction of your neighbors are connected? Watts & Strogatz, Nature 1998.
#
# 节点$i$的朋友之间是否也是朋友？
#
# Node i with degree $k_i$ 节点i有k个朋友
#
# > $e_i$ represents the number of links between the $k_i$ neighbors of node i.
#
# > 节点i的k个朋友之间全部是朋友的数量 $\frac{k_i(k_i -1)}{2}$
#
#
# $C_i = \frac{2e_i}{k_i(k_i -1)}$
#
# $C_i$ in [0,1]
#
#
# + [markdown] slideshow={"slide_type": "subslide"}
# ### 节点的**聚集系数**
#
# <img src = './img/cc.png' width = 500>
#
#
# + [markdown] slideshow={"slide_type": "subslide"}
# ### Global Clustering Coefficient 全局聚集系数（i.e., Transtivity 传递性)
#
# > triangles 三角形
# > triplets 三元组
#
# - A triplet consists of three connected nodes.
# - A triangle therefore includes three closed triplets
# - A triangle forms three **connected triplets**
# - **A connected triplet** is defined to be a connected subgraph consisting of three vertices and **two edges**.
#
# $C = \frac{\mbox{number of closed triplets}}{\mbox{number of connected triplets of vertices}}$
#
# $C = \frac{3 \times \mbox{number of triangles}}{\mbox{number of connected triplets of vertices}}$
#
# + slideshow={"slide_type": "subslide"}
G1 = nx.complete_graph(4)
pos = nx.spring_layout(G1) #定义一个布局，此处采用了spring布局方式
nx.draw(G1, pos = pos, with_labels = True)
# + slideshow={"slide_type": "fragment"}
print(nx.transitivity(G1))
# + slideshow={"slide_type": "subslide"}
G2 = nx.Graph()
for i, j in [(1, 2), (1, 3), (1, 0), (3, 0)]:
G2.add_edge(i,j)
nx.draw(G2,pos = pos, with_labels = True)
# + slideshow={"slide_type": "fragment"}
print(nx.transitivity(G2))
# 开放三元组有5个，闭合三元组有3个
# + slideshow={"slide_type": "subslide"}
G3 = nx.Graph()
for i, j in [(1, 2), (1, 3), (1, 0)]:
G3.add_edge(i,j)
nx.draw(G3, pos =pos, with_labels = True)
# + slideshow={"slide_type": "fragment"}
print(nx.transitivity(G3))
# 开放三元组有3个，闭合三元组有0个
# + [markdown] slideshow={"slide_type": "slide"}
# THREE CENTRAL QUANTITIES IN NETWORK SCIENCE
# - A. Degree distribution: $p_k$
# - B. Path length: $<d>$
# - C. Clustering coefficient: $C_i$
#
# + [markdown] slideshow={"slide_type": "subslide"}
# ## Typical Network Science Research
#
# - Discovering, Modeling, Verification
# - WATTSDJ,STROGATZSH.Collective dynamics of‘small-world’ networks. Nature, 1998, 393(6684): 440–442.
# - BARABÁSI A-L, ALBERT R. Emergence of scaling in random networks. Science, 1999, 286(5439): 509-512.
# + [markdown] slideshow={"slide_type": "subslide"}
# ## Typical Math Style
# Fan Chung & Linyuan Lu, The average distance in random graphs with given expected degree,. PNAS, 19, 15879-15882 (2002).
# + [markdown] slideshow={"slide_type": "subslide"}
# ## Typical Physical Style
# A.-L.Barabási,R.Albert,H.Jeong Mean-field theory for scale-free random networks. Physica A 272, 173–187 (1999).
# + [markdown] slideshow={"slide_type": "subslide"}
# ## Typical Computer Science Style
#
# - Community detection
# - Link prediction
# - Recommendation algorithms
# + [markdown] slideshow={"slide_type": "subslide"}
# ## Typical control sytle
# Controllability of Complex Networks
#
# Liu Y Y, Slotine J J, Barabási A L. Nature, 2011, 473(7346): 167-173.
# + [markdown] slideshow={"slide_type": "slide"}
# ## 阅读材料
# - Barabasi 2016 Network Science. Cambridge
# - 汪小帆、李翔、陈关荣 2012 网络科学导论. 高等教育出版社
# - 梅拉妮·米歇尔 2011 复杂,湖南科学技术出版社
# - 菲利普-鲍尔 2004 预知社会：群体行为的内在法则，当代中国出版社
# - 巴拉巴西 2007 链接：网络新科学 湖南科技出版社
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/Week6_14062021/Day2/.ipynb_checkpoints/Task1-checkpoint.ipynb | a754f23670e6dae5bcbea1d6a74ebd4cbfa40bde | [] | no_license | AbigalChulchill/MyFinTechHomework | https://github.com/AbigalChulchill/MyFinTechHomework | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 1,275,509 | # ---
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# + [markdown] id="ufqxI496yOqY" colab_type="text"
# **Problem statement 1**
# + id="KUgrpNjiFQni" colab_type="code" colab={}
import matplotlib.pyplot as plt
from matplotlib import style
import numpy as np
#from sklearn import preprocessing, cross_validation
import pandas as pd
# + id="q_PmUj7oyRe3" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 1000} outputId="9a1c979e-d202-4c1f-9f5c-8f52dffa5890"
import numpy as np
np.random.seed(123)
allwalks = []
for i in range(250):
randwalk = [0]
for x in range(100):
step = randwalk[-1]
dice = np.random.randint(1,7)
if dice <= 2 :
step = max(0, step - 1)
elif dice<=5:
step += 1
else:
step = step + np.random.randint(1,7)
print(step)
# + [markdown] id="VpkK-hCK2AVL" colab_type="text"
# **Problem statement 2**
# + [markdown] id="Y73ff0x62MQD" colab_type="text"
#
#
# ```
# # This is formatted as code
# ```
#
# Random data for multiple linear regression
# + id="ReY3UU3qzgyE" colab_type="code" outputId="7d41d1dd-20da-423c-a53f-abd8130bee92" colab={"base_uri": "https://localhost:8080/", "height": 595}
import numpy as np
import pandas as pd
import scipy
import random
from scipy.stats import norm
random.seed(1)
n_features = 4
X = []
for i in range(n_features):
X_i = scipy.stats.norm.rvs(0, 1, 100)
X.append(X_i)
#print(X)
eps = scipy.stats.norm.rvs(0, 0.25,100)
y = 1 + (0.4 * X[0]) + eps + (0.5 * X[1]) + (0.3 * X[2]) + (0.4 * X[3])
data_mlr = {'X0': X[0],'X1':X[1],'X2':X[2],'X3':X[3],'Y': y }
df = pd.DataFrame(data_mlr)
print(df.head())
print(df.tail())
print(df.info())
print(df.describe())
#df.to_csv('file1.csv')
# + [markdown] id="G8SJaKXqAdj6" colab_type="text"
# **Random data for logistic regression**
# + id="DhRfGyIbAjqB" colab_type="code" outputId="e8657896-371d-4ff5-e9c7-04a79d885505" colab={"base_uri": "https://localhost:8080/", "height": 588}
n_features = 4
X = []
for i in range(n_features):
X_i = scipy.stats.norm.rvs(0, 1, 100)
X.append(X_i)
#print(X)
a1 = (np.exp(1 + (0.5 * X[0]) + (0.4 * X[1]) + (0.3 * X[2]) + (0.5 * X[3]))/(1 + np.exp(1 + (0.5 * X[0]) + (0.4 * X[1]) + (0.3 * X[2]) + (0.5 * X[3]))))
#print(a1)
y1 = []
for i in a1:
if (i>=0.5):
y1.append(1)
else:
y1.append(0)
#print(y1)
data_lr = {'X0': X[0],'X1':X[1],'X2':X[2],'X3':X[3],'Y': y1 }
df1 = pd.DataFrame(data_lr)
print(df1.head())
print(df1.tail())
print(df1.info())
print(df1.describe())
# + [markdown] id="lBwMQqKdDetc" colab_type="text"
# **Random data for K means clustering**
# + id="3j_SGQjgfc5s" colab_type="code" outputId="5c7a0e70-6ae5-4568-eba2-b2a7ad15eccd" colab={"base_uri": "https://localhost:8080/", "height": 786}
X_a= -2 * np.random.rand(100,2)
X_b = 1 + 2 * np.random.rand(50,2)
X_a[50:100, :] = X_b
plt.scatter(X_a[ : , 0], X_a[ :, 1], s = 50)
plt.show()
data_kmeans = {'X0': X_a[:,0],'X1':X_a[:,1]}
df3 = pd.DataFrame(data_kmeans)
print(df3.head())
print(df3.tail())
print(df3.info())
print(df3.describe())
# + [markdown] id="Y1dxQMGDYIOq" colab_type="text"
# **Problem statement - 3**
# + [markdown] id="1Wrb7gNZYaT1" colab_type="text"
# **Linear Regression using gradient descent**
# + id="sa3knLkqGTZv" colab_type="code" outputId="0e558901-4442-4494-8f95-90a6123f6cb6" colab={"base_uri": "https://localhost:8080/", "height": 34}
X = df.iloc[:,0].values
#print(X)
y = df.iloc[:,4].values
b1 = 0
b0 = 0
l = 0.001
epochs = 100
n = float(len(X))
for i in range(epochs):
y_p = b1*X + b0
loss = np.sum(y_p - y1)**2
d1 = (-2/n) * sum(X * (y - y_p))
d0 = (-2/n) * sum(y - y_p)
b1 = b1 - (l*d1)
b0 = b0 - (l*d0)
print(b1,b0)
# + [markdown] id="7MRrum_2j6iC" colab_type="text"
# **Logistic regression using Gradient descent**
# + id="SkzDhVrZZ6rq" colab_type="code" outputId="5b3c5059-5b57-4ecc-b5eb-0dc1a78d50d0" colab={"base_uri": "https://localhost:8080/", "height": 185}
X1 = df1.iloc[:,0:4].values
y1 = df1.iloc[:,4].values
def sigmoid(Z):
return 1 /(1+np.exp(-Z))
def loss(y1,y_hat):
return -np.mean(y1*np.log(y_hat) + (1-y1)*(np.log(1-y_hat)))
W = np.zeros((4,1))
b = np.zeros((1,1))
m = len(y1)
lr = 0.001
for epoch in range(1000):
Z = np.matmul(X1,W)+b
A = sigmoid(Z)
logistic_loss = loss(y1,A)
dz = A - y1
dw = 1/m * np.matmul(X1.T,dz)
db = np.sum(dz)
W = W - lr*dw
b = b - lr*db
if epoch % 100 == 0:
print(logistic_loss)
# + [markdown] id="6AQXnM09oey8" colab_type="text"
# **Linear Regreesion using L1 Regularization**
#
# + id="h29KsbGVpGYF" colab_type="code" outputId="8eb7a11a-a84a-40f1-cb80-eafe79347f01" colab={"base_uri": "https://localhost:8080/", "height": 34}
X = df.iloc[:,0].values
#print(X)
y = df.iloc[:,4].values
b1 = 0
b0 = 0
l = 0.001
epochs = 100
lam = 0.1
n = float(len(X))
for i in range(epochs):
y_p = b1*X + b0
loss = np.sum(y_p - y1)**2 + (lam * b1)
d1 = (-2/n) * sum(X * (y - y_p)) + lam
d0 = (-2/n) * sum(y - y_p)
b1 = b1 - (l*d1)
b0 = b0 - (l*d0)
print(b1,b0)
# + [markdown] id="KeqnzIJuqBrp" colab_type="text"
# **Linear Regreesion using L2 Regularization**
#
# + id="zQRD15PuqENj" colab_type="code" outputId="ea30113c-6a58-4c23-8c1d-347918277a30" colab={"base_uri": "https://localhost:8080/", "height": 34}
X = df.iloc[:,0].values
#print(X)
y = df.iloc[:,4].values
b1 = 0
b0 = 0
l = 0.001
epochs = 100
lam = 0.1
n = float(len(X))
for i in range(epochs):
y_p = b1*X + b0
loss = np.sum(y_p - y1)**2 + ((lam/2) * b1)
d1 = (-2/n) * sum(X * (y - y_p)) + (lam *b1)
d0 = (-2/n) * sum(y - y_p)
b1 = b1 - (l*d1)
b0 = b0 - (l*d0)
print(b1,b0)
# + [markdown] id="4WBibPW5rEi5" colab_type="text"
# **Logistic regression using L1 regualrization**
# + id="Yqr8pUh8rJ0A" colab_type="code" outputId="c5325e71-cd0a-437f-9ea6-1a4ce861f5a7" colab={"base_uri": "https://localhost:8080/", "height": 185}
X1 = df1.iloc[:,0:4].values
y1 = df1.iloc[:,4].values
lam = 0.1
def sigmoid(Z):
return 1 /(1+np.exp(-Z))
def loss(y1,y_hat):
return -np.mean(y1*np.log(y_hat) + (1-y1)*(np.log(1-y_hat))) + (lam * (np.sum(W)))
W = np.zeros((4,1))
b = np.zeros((1,1))
m = len(y1)
lr = 0.001
for epoch in range(1000):
Z = np.matmul(X1,W)+b
A = sigmoid(Z)
logistic_loss = loss(y1,A)
dz = A - y1
dw = 1/m * np.matmul(X1.T,dz) + lam
db = np.sum(dz)
W = W - lr*dw
b = b - lr*db
if epoch % 100 == 0:
print(logistic_loss)
# + [markdown] id="NcrQi0KwrzX4" colab_type="text"
# **Logistic regression using L2 regualrization**
#
# * List item
# * List item
#
#
# + id="nYOUM896rrsz" colab_type="code" outputId="40bf4f13-bbc8-4529-9760-8d66623f3a80" colab={"base_uri": "https://localhost:8080/", "height": 185}
X1 = df1.iloc[:,0:4].values
y1 = df1.iloc[:,4].values
lam = 0.1
def sigmoid(Z):
return 1 /(1+np.exp(-Z))
def loss(y1,y_hat):
return -np.mean(y1*np.log(y_hat) + (1-y1)*(np.log(1-y_hat))) + (lam * (np.sum(np.square(W))))
W = np.zeros((4,1))
b = np.zeros((1,1))
m = len(y1)
lr = 0.001
for epoch in range(1000):
Z = np.matmul(X1,W)+b
A = sigmoid(Z)
logistic_loss = loss(y1,A)
dz = A - y1
dw = 1/m * np.matmul(X1.T,dz) + lam * W
db = np.sum(dz)
W = W - lr*dw
b = b - lr*db
if epoch % 100 == 0:
print(logistic_loss)
# + [markdown] id="qsEFl-_mgS96" colab_type="text"
# **K Means Clustering Algorithm**
# + id="GdkxTDkgEMDs" colab_type="code" colab={}
class K_Means:
def __init__(self, k=2, tol=0.001, max_iter=300):
self.k = k
self.tol = tol
self.max_iter = max_iter
def fit(self,data):
self.centroids = {}
for i in range(self.k):
self.centroids[i] = data[i]
for i in range(self.max_iter):
self.classifications = {}
for i in range(self.k):
self.classifications[i] = []
for featureset in X:
distances = [np.linalg.norm(featureset-self.centroids[centroid]) for centroid in self.centroids]
classification = distances.index(min(distances))
self.classifications[classification].append(featureset)
prev_centroids = dict(self.centroids)
for classification in self.classifications:
self.centroids[classification] = np.average(self.classifications[classification],axis=0)
optimized = True
for c in self.centroids:
original_centroid = prev_centroids[c]
current_centroid = self.centroids[c]
if np.sum((current_centroid-original_centroid)/original_centroid*100.0) > self.tol:
print(np.sum((current_centroid-original_centroid)/original_centroid*100.0))
optimized = False
if optimized:
break
def predict(self,data):
distances = [np.linalg.norm(data-self.centroids[centroid]) for centroid in self.centroids]
classification = distances.index(min(distances))
return classification
colors = 10*["g","r","c","b","k"]
# + id="vpLrjR_OiXo3" colab_type="code" outputId="a32dbe31-15d4-4747-82fd-8742e7abf1b5" colab={"base_uri": "https://localhost:8080/", "height": 298}
X = df3.iloc[:,0:2].values
clf = K_Means()
clf.fit(X)
for centroid in clf.centroids:
plt.scatter(clf.centroids[centroid][0], clf.centroids[centroid][1],
marker="o", color="k", s=150, linewidths=5)
for classification in clf.classifications:
color = colors[classification]
for featureset in clf.classifications[classification]:
plt.scatter(featureset[0], featureset[1], marker="x", color=color, s=150, linewidths=5)
# + [markdown] id="2ArGImKtseys" colab_type="text"
# **Problem Statement - 4**
# + [markdown] id="XmUvIKGVtdO2" colab_type="text"
# **Linear Regression from scratch using OOPS**
# + id="Iz42F7jnsjQs" colab_type="code" outputId="edeedb48-6612-4579-e5b4-3376834d82b4" colab={"base_uri": "https://localhost:8080/", "height": 317}
import numpy as np
class LinearRegressionModel():
def __init__(self, dataset, learning_rate, num_iterations):
self.dataset = np.array(dataset)
self.b = 0
self.m = 0
self.learning_rate = learning_rate
self.num_iterations = num_iterations
self.M = len(self.dataset)
self.total_error = 0
def apply_gradient_descent(self):
for i in range(self.num_iterations):
self.do_gradient_step()
def do_gradient_step(self):
b_summation = 0
m_summation = 0
for i in range(self.M):
x_value = self.dataset[i, 0]
y_value = self.dataset[i, 1]
b_summation += (((self.m * x_value) + self.b) - y_value)
m_summation += (((self.m * x_value) + self.b) - y_value) * x_value
self.b = self.b - (self.learning_rate * (1/self.M) * b_summation)
self.m = self.m - (self.learning_rate * (1/self.M) * m_summation)
def compute_error(self):
for i in range(self.M):
x_value = self.dataset[i, 0]
y_value = self.dataset[i, 1]
self.total_error += ((self.m * x_value) + self.b) - y_value
return self.total_error
def __str__(self):
return "Results: b: {}, m: {}, Final Total error: {}".format(round(self.b, 2), round(self.m, 2), round(self.compute_error(), 2))
def get_prediction_based_on(self, x):
return round(float((self.m * x) + self.b), 2) # Type: Numpy float.
def main():
school_dataset = np.genfromtxt(DATASET_PATH, delimiter=",")
lr = LinearRegressionModel(school_dataset, 0.0001, 1000)
lr.apply_gradient_descent()
hours = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
for hour in hours:
print("Studied {} hours and got {} points.".format(hour, lr.get_prediction_based_on(hour)))
print(lr)
if __name__ == "__main__": main()
# + [markdown] id="_IX47f-btjwf" colab_type="text"
# **Logistic Regression from scratch using OOPS**
# + id="ljHZlX41txID" colab_type="code" colab={}
class LogisticRegression:
def __init__(self, learning_rate, num_iters, fit_intercept = True, verbose = False):
self.learning_rate = learning_rate
self.num_iters = num_iters
self.fit_intercept = fit_intercept
self.verbose = verbose
def __add_intercept(self, X):
intercept = np.ones((X.shape[0],1))
return np.concatenate((intercept,X),axis=1)
def __sigmoid(self,z):
return 1/(1+np.exp(-z))
def __loss(self, h, y):
return (-y * np.log(h) - (1-y) * np.log(1-h)).mean()
def fit(self,X,y):
if self.fit_intercept:
X = self.__add_intercept(X)
self.theta = np.zeros(X.shape[1])
for i in range(self.num_iters):
z = np.dot(X,self.theta)
h = self.__sigmoid(z)
gradient = np.dot(X.T,(h-y))/y.size
self.theta -= self.learning_rate * gradient
z = np.dot(X,self.theta)
h = self.__sigmoid(z)
loss = self.__loss(h,y)
if self.verbose == True and i % 1000 == 0:
print(f'Loss: {loss}\t')
def predict_probability(self,X):
if self.fit_intercept:
X = self.__add_intercept(X)
return self.__sigmoid(np.dot(X,self.theta))
def predict(self,X):
return (self.predict_probability(X).round())
# + [markdown] id="TV0M_cktt4kd" colab_type="text"
# **K Means from scratch using OOPS**
# + id="vuBU7YY_t90Y" colab_type="code" colab={}
class K_Means:
def __init__(self, k=2, tol=0.001, max_iter=300):
self.k = k
self.tol = tol
self.max_iter = max_iter
def fit(self,data):
self.centroids = {}
for i in range(self.k):
self.centroids[i] = data[i]
for i in range(self.max_iter):
self.classifications = {}
for i in range(self.k):
self.classifications[i] = []
for featureset in X:
distances = [np.linalg.norm(featureset-self.centroids[centroid]) for centroid in self.centroids]
classification = distances.index(min(distances))
self.classifications[classification].append(featureset)
prev_centroids = dict(self.centroids)
for classification in self.classifications:
self.centroids[classification] = np.average(self.classifications[classification],axis=0)
optimized = True
for c in self.centroids:
original_centroid = prev_centroids[c]
current_centroid = self.centroids[c]
if np.sum((current_centroid-original_centroid)/original_centroid*100.0) > self.tol:
print(np.sum((current_centroid-original_centroid)/original_centroid*100.0))
optimized = False
if optimized:
break
def predict(self,data):
distances = [np.linalg.norm(data-self.centroids[centroid]) for centroid in self.centroids]
classification = distances.index(min(distances))
return classification
colors = 10*["g","r","c","b","k"]
| 15,244 |
/experiments-SubSetML/Active-Sensor-Placement-OneCellVersion.ipynb | 0a0176b95e397fba66e780b2ee347096f4c0a330 | [] | no_license | patel-zeel/CCAI-ICML-2021 | https://github.com/patel-zeel/CCAI-ICML-2021 | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 118,134 | # ---
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# name: python3
# ---
# # Module 1 - Reshaping Data with Pandas
# ## Pandas Part 3
import pandas as pd
uci = pd.read_csv('data/heart.csv')
# ## 3. Methods for Re-Organizing DataFrames
# #### `.groupby()`
# Those of you familiar with SQL have probably used the GROUP BY command. Pandas has this, too.
#
# The `.groupby()` method is especially useful for aggregate functions applied to the data grouped in particular ways.
gb = uci.groupby('sex')
uci.groupby('sex').groups #spits out groupNAME (0, 1) and indicies in each group
uci.groupby('sex').get_group(0)
# #### `.groups` and `.get_group()`
uci.groupby('sex').groups
uci.groupby('sex').get_group(0)
# ### Aggregating
uci.groupby('target').mean()
# Exercise: Tell me the average cholesterol level for those with heart disease.
# Your code here!
uci.groupby('target').mean()
uci.groupby('cp').std().loc[3, 'fbs'] #3 is the NAME of the group, indicated by the VALUE (could me categorical)
uci.groupby(['sex', 'cp']).min() #multi-index
# ### Apply to Animal Shelter Data
animal_outcomes = pd.read_csv('https://data.austintexas.gov/api/views/wter-evkm/rows.csv?accessType=DOWNLOAD')
# #### Task 1
# - Use a groupby to show the average age of the different kinds of animal types.
# - What about by animal types **and** gender?
animal_outcomes.head()
animal_outcomes.columns
animal_outcomes['DateTime'] = pd.to_datetime(animal_outcomes['DateTime'])
animal_outcomes.info()
import datetime
def calc_age(val):
return round((datetime.datetime.now() - val).days / 365, 2)
animal_outcomes['age'] = animal_outcomes['DateTime'].map(calc_age)
animal_outcomes
animal_outcomes.info()
animal_outcomes.groupby('Animal Type').count().loc['Bird', 'Name']
animal_outcomes.groupby('Animal Type').mean()
# #### Task 2:
# - Create new columns `year` and `month` by using a lambda function x.year on date
# - Use `groupby` and `.size()` to tell me how many animals are adopted by month
# Your code here
# ## 4. Reshaping a DataFrame
#
# ### `.pivot()`
# Those of you familiar with Excel have probably used Pivot Tables. Pandas has a similar functionality.
uci.pivot(values=['cp','sex'], columns='target') # turn values into columns
# ### Methods for Combining DataFrames: `.join()`, `.merge()`, `.concat()`, `.melt()`
# ### `.join()`
toy1 = pd.DataFrame([[63, 142], [33, 47], [44, 22]], columns=['age', 'HP'])
toy2 = pd.DataFrame([[63, 100], [33, 200]], columns=['age', 'HP'])
toy1
toy2
toy1.join(toy2.set_index('age'),
on='age',
lsuffix='_A',
rsuffix='_B')
toy1.set_index('age').join(toy2.set_index('age'), #WE are only joining things that match from TOY 2, if TOY 2 has different ages it wont' be joined
lsuffix='A')
# ### `.merge()`
ds_chars = pd.read_csv('data/ds_chars.csv', index_col=0)
ds_chars
states = pd.read_csv('data/states.csv', index_col=0)
states
ds_chars.join(states.set_index('state'),
on = 'home_state')
ds_chars.merge(states,
left_on='home_state',
right_on='state',
how='inner')
# 'inner' vs. left, right, full --> only the join the ones that match, but does NOT overlap
# ### `pd.concat()`
pd.concat([ds_chars, states], sort=False)
# ### `pd.melt()`
# Melting removes the structure from your DataFrame and puts the data in a 'variable' and 'value' format.
ds_chars.head()
pd.melt(ds_chars,
id_vars=['name'],
value_vars=['HP', 'home_state'])
pd.melt(animal_outcomes, id_vars=['Animal ID'], value_vars=['Age upon Intake', 'age'])
# ## Bringing it all together with the Animal Shelter Data
#
# Join the data from the [Austin Animal Shelter Intake dataset](https://data.austintexas.gov/Health-and-Community-Services/Austin-Animal-Center-Intakes/wter-evkm) to the outcomes dataset by Animal ID.
#
# Use the dates from each dataset to see how long animals spend in the shelter. Does it differ by time of year? By outcome?
#
# _Hints_ :
# - import and clean the intake dataset first
# - use `apply`/`applymap`/`lambda` to change the variables to their proper format in the intake data
# - rename the columns in the intake dataset *before* joining
# - create a new `days-in-shelter` column
# - Notice that some values in `days_in_shelter` are `NaN` or values < 0 (remove these rows using the "<" operator and `isna()` or `dropna()`)
# - Use `groupby` to get aggregate information about the dataset (your choice)
#
# To save your dataset:
# Use the notation `df.to_csv()` or `df.to_excel()` to write the `df` to a csv. Read more about the `to_csv()` documentation [here](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_csv.html)
animal_intakes = pd.read_csv('https://data.austintexas.gov/api/views/wter-evkm/rows.csv?accessType=DOWNLOAD')
animal_outcomes = pd.read_csv('https://data.austintexas.gov/api/views/9t4d-g238/rows.csv?accessType=DOWNLOAD')
animal_intakes.loc[[]]
animal_outcomes.head()
animal_outcomes.isna().any()
animal_outcomes.info()
animal_intakes.info()
animal_intakes['DateTime'] = pd.to_datetime(animal_intakes['DateTime'])
animal_intakes.info()
animal_outcomes['DateTime'] = pd.to_datetime(animal_outcomes['DateTime'])
animal_outcomes.info()
animal_outcomes.info()
animal_outcomes[['Breed', 'Color']]
# ## 5. Pandas Practice
#
# ### Introduction
# find and import the World Cup data held in data/ folder
# !ls
# !pwd
world_cup = pd.read_csv('data/WorldCupMatches.csv')
world_cup.head()
# ### Practice Questions <a id="practice"></a>
# 1. Subset the DataFrame to only non-null rows.
# +
#Your code here.
world_cup.info()
# -
# 2. How many of the matches were in Montevideo?
world_cup.dropna()
world_cup.groupby('City').get_group('Montevideo ').count()
# 2. b If you haven't already, investigate why this code returns zero:
#
# ```python
# print(len(df[df.City=="Montevideo"]))
# ```
print(len(world_cup[world_cup.City=="Montevideo"]))
# + active=""
# 3. How many matches did USA play in 2014?
#
# Hint: they could have been home or away.
#
# You can combine conditions like this:
# ```python
# # Returns rows where either condition is true
# df[(condition1) | (condition2)]
#
# # Returns rows where both conditions are true
# df[(condition1) & (condition2)]
# ```
# -
world_cup.loc[world_cup['Home Team Name'] == 'USA']
len(world_cup.loc[(world_cup['Home Team Name'] == 'USA') | (world_cup['Away Team Name'] == 'USA')])
# 4. How many teams played in 1986?
world_cup_86 = world_cup.groupby('Year').get_group(1986)
world_cup_86.columns
world_cup_86['Home Team Name'].count()
# 5. How many matches were there with 5 or more total goals?
world_cup.columns
world_cup['total_goals'] = world_cup['Away Team Goals'] + world_cup['Home Team Goals']
#Your code here.
world_cup[world_cup['total_goals'] > 5]
# 6. Come up with and answer, two other questions you could answer by filtering or subsetting this DataFrame.
# +
#6a Question:
# +
#6a Solution (with code):
# +
#6b Question:
# +
#6b Solution (with code):
s])
# yscaler.fit(train_df[ycols])
# local_preds = []
# local_tests = []
# chosen_i_list = []
# for local_t in tmp_df.index.unique():
# clear_output(wait=True)
# trn_X = xscaler.transform(train_df.loc[local_t][Xcols])
# tst_X = xscaler.transform(test_df.loc[local_t][Xcols])
# trn_y = yscaler.transform(train_df.loc[local_t][ycols])
# tst_y = test_df.loc[local_t][ycols]
# # p(trn_X.shape, tst_X.shape, trn_y.shape, tst_y.shape)
# # kern_long = GPy.kern.Linear(1, ARD=True, active_dims=[0])
# # kern_lat = GPy.kern.Matern32(1, ARD=True, active_dims=[1])
# # kernel = kern_long + kern_lat
# kernel = GPy.kern.Matern32(trn_X.shape[1], ARD=True)
# model = GPy.models.GPRegression(trn_X, trn_y-trn_y.mean(), kernel, normalizer=False)
# model.optimize_restarts(10, verbose=False, robust=True)
# gpmi_result_df_preds.loc[local_t,:] = yscaler.inverse_transform(model.predict(tst_X)[0] + trn_y.mean()).ravel()
# gpmi_result_df_tests.loc[local_t,:] = tst_y.values.ravel()
# pool_X = xscaler.transform(pool_df.loc[local_t][Xcols].values.reshape(-1, len(Xcols)))
# K = model.kern.K(trn_X)
# K_inv = np.linalg.pinv(K)
# K_s = model.kern.K(pool_X, trn_X)
# K_ss = model.kern.K(pool_X, pool_X)
# ### Choosing next sensor
# chosen_i = None
# best_delta = -np.inf
# numer = K_ss.diagonal() - (K_s@K_inv@K_s.T).diagonal()
# for i in range(pool_X.shape[0]):
# a_bar = sorted(set(range(pool_X.shape[0])) - set([i]))
# second = K_ss[i, a_bar]
# denom = K_ss[i,i] - second.reshape(1,-1)@np.linalg.pinv(K_ss[np.ix_(a_bar, a_bar)])@second.reshape(-1,1)
# delta = numer[i]/denom
# if delta>best_delta:
# best_delta = delta
# chosen_i = i
# chosen_i_list.append(chosen_i)
# p(t_i, local_t, chosen_i)
# # Choose mode of chosen values
# chosen_i = stats.mode(chosen_i_list).mode[0]
# print(pool_stations[chosen_i])
# gpmi_deploys.append(pool_stations[chosen_i])
# # Updating pool and train
# train_stations.append(pool_stations.pop(chosen_i))
# train_stations = sorted(train_stations)
# gpmi_result_df_preds.to_pickle("gpmi"+str(seed)+'kmeans'+".pred")
# gpmi_result_df_tests.to_pickle('testdf'+str(seed)+'kmeans'+'.pickle')
# pd.to_pickle(gpmi_deploys, 'gpmi'+str(seed)+'kmeans'+'.dep')
# ### NSGP + MI
# # init = time()
# # from NSGPy.NumPy import LLS
# # np.random.seed(0)
# # train_stations = global_train_stations.copy().tolist()
# # test_stations = global_test_stations.copy().tolist()
# # pool_stations = global_pool_stations.copy().tolist()
# # nsgpmi_result_df_preds = pd.DataFrame(columns=[1,2,3,4,5,6])
# # nsgpmi_result_df_tests = pd.DataFrame(columns=[1,2,3,4,5,6])
# # for t_i in range(0,data.index.unique().shape[0], window):
# # # print('train', len(train_stations))
# # # print('pool', len(pool_stations))
# # tmp_df = data.loc[all_time[t_i:t_i+window]]
# # train_df = tmp_df[tmp_df.station_id.isin(train_stations)]
# # test_df = tmp_df[tmp_df.station_id.isin(test_stations)]
# # pool_df = tmp_df[tmp_df.station_id.isin(pool_stations)]
# # xscaler = StandardScaler()
# # yscaler = StandardScaler()
# # xscaler.fit(train_df[Xcols])
# # yscaler.fit(train_df[ycols])
# # local_preds = []
# # local_tests = []
# # chosen_i_list = []
# # for local_t in tmp_df.index.unique():
# # clear_output(wait=True)
# # trn_X = xscaler.transform(train_df.loc[local_t][Xcols])
# # tst_X = xscaler.transform(test_df.loc[local_t][Xcols])
# # trn_y = yscaler.transform(train_df.loc[local_t][ycols])
# # tst_y = test_df.loc[local_t][ycols]
# # # p(trn_X.shape, tst_X.shape, trn_y.shape, tst_y.shape)
# # # kern_long = GPy.kern.Linear(1, ARD=True, active_dims=[0])
# # # kern_lat = GPy.kern.Matern32(1, ARD=True, active_dims=[1])
# # # kernel = kern_long + kern_lat
# # model = LLS(trn_X.shape[1], N_l_bar=1, kernel='rbf')
# # model.fit(trn_X, trn_y-trn_y.mean())
# # nsgpmi_result_df_preds.loc[local_t,:] = yscaler.inverse_transform(model.predict(tst_X)[0] + trn_y.mean()).ravel()
# # nsgpmi_result_df_tests.loc[local_t,:] = tst_y.values.ravel()
# # pool_X = xscaler.transform(pool_df.loc[local_t][Xcols].values.reshape(-1, len(Xcols)))
# # L = model.predict_lengthscales_(trn_X)
# # K = model.K_(trn_X, L)
# # K_inv = np.linalg.pinv(K)
# # L_s = model.predict_lengthscales_(pool_X)
# # K_s = model.K_(pool_X, L_s, trn_X, L)
# # K_ss = model.K_(pool_X, L_s)
# # ### Choosing next sensor
# # chosen_i = None
# # best_delta = -np.inf
# # numer = K_ss.diagonal() - (K_s@K_inv@K_s.T).diagonal()
# # for i in range(pool_X.shape[0]):
# # a_bar = sorted(set(range(pool_X.shape[0])) - set([i]))
# # second = K_ss[i, a_bar]
# # denom = K_ss[i,i] - second.reshape(1,-1)@np.linalg.pinv(K_ss[np.ix_(a_bar, a_bar)])@second.reshape(-1,1)
# # delta = numer[i]/denom
# # if delta>best_delta:
# # best_delta = delta
# # chosen_i = i
# # chosen_i_list.append(chosen_i)
# # p(t_i, local_t, chosen_i)
# # # Choose mode of chosen values
# # chosen_i = stats.mode(chosen_i_list).mode[0]
# # print(pool_stations[chosen_i])
# # # Updating pool and train
# # train_stations.append(pool_stations.pop(chosen_i))
# # train_stations = sorted(train_stations)
# # nsgpmi_result_df_preds.to_pickle("nsgpmi"+str(seed)+'kmeans'+".pred")
# # p((time()-init)/60, 'minutes')
# ### GP + Entropy
# import GPy
# np.random.seed(0)
# train_stations = global_train_stations.copy().tolist()
# test_stations = global_test_stations.copy().tolist()
# pool_stations = global_pool_stations.copy().tolist()
# gpvar_result_df_preds = pd.DataFrame(columns=[1,2,3,4,5,6])
# gpvar_result_df_tests = pd.DataFrame(columns=[1,2,3,4,5,6])
# gpvar_deploys = []
# for t_i in range(0, data.index.unique().shape[0], window):
# tmp_df = data.loc[all_time[t_i:t_i+window]]
# train_df = tmp_df[tmp_df.station_id.isin(train_stations)]
# test_df = tmp_df[tmp_df.station_id.isin(test_stations)]
# pool_df = tmp_df[tmp_df.station_id.isin(pool_stations)]
# xscaler = StandardScaler()
# yscaler = StandardScaler()
# xscaler.fit(train_df[Xcols])
# yscaler.fit(train_df[ycols])
# chosen_i_list = []
# for local_t in tmp_df.index.unique():
# trn_X = xscaler.transform(train_df.loc[local_t][Xcols])
# tst_X = xscaler.transform(test_df.loc[local_t][Xcols])
# trn_y = yscaler.transform(train_df.loc[local_t][ycols])
# tst_y = test_df.loc[local_t][ycols]
# # p(trn_X.shape, tst_X.shape, trn_y.shape, tst_y.shape)
# # kern_long = GPy.kern.Linear(1, ARD=True, active_dims=[0])
# # kern_lat = GPy.kern.Matern32(1, ARD=True, active_dims=[1])
# # kernel = kern_long + kern_lat
# kernel = GPy.kern.Matern32(trn_X.shape[1], ARD=True)
# model = GPy.models.GPRegression(trn_X, trn_y-trn_y.mean(), kernel, normalizer=False)
# model.optimize_restarts(10, verbose=False, robust=True)
# gpvar_result_df_preds.loc[local_t,:] = yscaler.inverse_transform(model.predict(tst_X)[0] + trn_y.mean()).ravel()
# gpvar_result_df_tests.loc[local_t,:] = tst_y.values.ravel()
# pool_X = xscaler.transform(pool_df.loc[local_t][Xcols].values.reshape(-1, len(Xcols)))
# K = model.kern.K(trn_X)
# K_inv = np.linalg.pinv(K)
# K_s = model.kern.K(pool_X, trn_X)
# K_ss = model.kern.K(pool_X, pool_X)
# ### Choosing next sensor
# numer = K_ss.diagonal() - (K_s@K_inv@K_s.T).diagonal()
# chosen_i = np.argmax(numer)
# chosen_i_list.append(chosen_i)
# chosen_i = stats.mode(chosen_i_list).mode[0]
# gpvar_deploys.append(pool_stations[chosen_i])
# print(pool_stations[chosen_i])
# # Updating pool and train
# train_stations.append(pool_stations.pop(chosen_i))
# train_stations = sorted(train_stations)
# gpvar_result_df_preds.to_pickle("gpvar"+str(seed)+'kmeans'+".pred")
# pd.to_pickle(gpvar_deploys, 'gpvar'+str(seed)+'kmeans'+'.dep')
# ### GP + Random
# import GPy
# np.random.seed(0)
# train_stations = global_train_stations.copy().tolist()
# test_stations = global_test_stations.copy().tolist()
# pool_stations = global_pool_stations.copy().tolist()
# gprand_result_df_preds = pd.DataFrame(columns=[1,2,3,4,5,6])
# gprand_result_df_tests = pd.DataFrame(columns=[1,2,3,4,5,6])
# gprand_deploys = []
# for t_i in range(0,data.index.unique().shape[0], window):
# # print('train', len(train_stations))
# # print('pool', len(pool_stations))
# tmp_df = data.loc[all_time[t_i:t_i+window]]
# train_df = tmp_df[tmp_df.station_id.isin(train_stations)]
# test_df = tmp_df[tmp_df.station_id.isin(test_stations)]
# pool_df = tmp_df[tmp_df.station_id.isin(pool_stations)]
# xscaler = StandardScaler()
# yscaler = StandardScaler()
# xscaler.fit(train_df[Xcols])
# yscaler.fit(train_df[ycols])
# for local_t in tmp_df.index.unique():
# trn_X = xscaler.transform(train_df.loc[local_t][Xcols])
# tst_X = xscaler.transform(test_df.loc[local_t][Xcols])
# trn_y = yscaler.transform(train_df.loc[local_t][ycols])
# tst_y = test_df.loc[local_t][ycols]
# # p(trn_X.shape, tst_X.shape, trn_y.shape, tst_y.shape)
# # kern_long = GPy.kern.Linear(1, ARD=True, active_dims=[0])
# # kern_lat = GPy.kern.Matern32(1, ARD=True, active_dims=[1])
# # kernel = kern_long + kern_lat
# kernel = GPy.kern.Matern32(trn_X.shape[1], ARD=True)
# model = GPy.models.GPRegression(trn_X, trn_y-trn_y.mean(), kernel, normalizer=False)
# model.optimize_restarts(10, verbose=False, robust=True)
# gprand_result_df_preds.loc[local_t,:] = yscaler.inverse_transform(model.predict(tst_X)[0] + trn_y.mean()).ravel()
# gprand_result_df_tests.loc[local_t,:] = tst_y.values.ravel()
# ### Choosing next sensor
# np.random.seed(t_i)
# chosen_i = np.random.choice(len(pool_stations))
# gprand_deploys.append(pool_stations[chosen_i])
# print(pool_stations[chosen_i])
# # Updating pool and train
# train_stations.append(pool_stations.pop(chosen_i))
# train_stations = sorted(train_stations)
# gprand_result_df_preds.to_pickle("gprand"+str(seed)+'kmeans'+".pred")
# pd.to_pickle(gprand_deploys, 'gprand'+str(seed)+'kmeans'+'.dep')
# ### GP+NoDep
# # import GPy
# # np.random.seed(0)
# # train_stations = global_train_stations.copy().tolist()
# # test_stations = global_test_stations.copy().tolist()
# # pool_stations = global_pool_stations.copy().tolist()
# # gpnod_result_df_preds = pd.DataFrame(columns=[1,2,3,4,5,6])
# # gpnod_result_df_tests = pd.DataFrame(columns=[1,2,3,4,5,6])
# # for t_i in range(0, data.index.unique().shape[0], window):
# # tmp_df = data.loc[all_time[t_i:t_i+window]]
# # train_df = tmp_df[tmp_df.station_id.isin(train_stations)]
# # test_df = tmp_df[tmp_df.station_id.isin(test_stations)]
# # pool_df = tmp_df[tmp_df.station_id.isin(pool_stations)]
# # xscaler = StandardScaler()
# # yscaler = StandardScaler()
# # xscaler.fit(train_df[Xcols])
# # yscaler.fit(train_df[ycols])
# # chosen_i_list = []
# # for local_t in tmp_df.index.unique():
# # trn_X = xscaler.transform(train_df.loc[local_t][Xcols])
# # tst_X = xscaler.transform(test_df.loc[local_t][Xcols])
# # trn_y = yscaler.transform(train_df.loc[local_t][ycols])
# # tst_y = test_df.loc[local_t][ycols]
# # # p(trn_X.shape, tst_X.shape, trn_y.shape, tst_y.shape)
# # kern_long = GPy.kern.Matern32(1, ARD=True, active_dims=[0])
# # kern_lat = GPy.kern.Matern32(1, ARD=True, active_dims=[1])
# # kernel = kern_long * kern_lat
# # model = GPy.models.GPRegression(trn_X, trn_y-trn_y.mean(), kernel, normalizer=False)
# # model.optimize_restarts(10, verbose=False, robust=True)
# # gpnod_result_df_preds.loc[local_t,:] = yscaler.inverse_transform(model.predict(tst_X)[0] + trn_y.mean()).ravel()
# # gpnod_result_df_tests.loc[local_t,:] = tst_y.values.ravel()
# # gpnod_result_df_preds.to_pickle("gpnod"+str(seed)+'kmeans'+".pred")
# ### RF + Random
# from sklearn.ensemble import RandomForestRegressor
# np.random.seed(0)
# train_stations = global_train_stations.copy().tolist()
# test_stations = global_test_stations.copy().tolist()
# pool_stations = global_pool_stations.copy().tolist()
# rfrand_result_df_preds = pd.DataFrame(columns=[1,2,3,4,5,6])
# rfrand_result_df_tests = pd.DataFrame(columns=[1,2,3,4,5,6])
# rfrand_deploys = []
# for t_i in range(0,data.index.unique().shape[0], window):
# # print('train', len(train_stations))
# # print('pool', len(pool_stations))
# tmp_df = data.loc[all_time[t_i:t_i+window]]
# train_df = tmp_df[tmp_df.station_id.isin(train_stations)]
# test_df = tmp_df[tmp_df.station_id.isin(test_stations)]
# pool_df = tmp_df[tmp_df.station_id.isin(pool_stations)]
# xscaler = StandardScaler()
# yscaler = StandardScaler()
# xscaler.fit(train_df[Xcols])
# yscaler.fit(train_df[ycols])
# local_preds = []
# local_tests = []
# for local_t in tmp_df.index.unique():
# trn_X = xscaler.transform(train_df.loc[local_t][Xcols])
# tst_X = xscaler.transform(test_df.loc[local_t][Xcols])
# trn_y = yscaler.transform(train_df.loc[local_t][ycols])
# tst_y = test_df.loc[local_t][ycols]
# # p(trn_X.shape, tst_X.shape, trn_y.shape, tst_y.shape)
# model = RandomForestRegressor(random_state=0)
# model.fit(trn_X, trn_y)
# rfrand_result_df_preds.loc[local_t,:] = yscaler.inverse_transform(model.predict(tst_X)).ravel()
# rfrand_result_df_tests.loc[local_t,:] = tst_y.values.ravel()
# ### Choosing next sensor
# np.random.seed(t_i)
# chosen_i = np.random.choice(len(pool_stations))
# clear_output(wait=True)
# rfrand_deploys.append(pool_stations[chosen_i])
# print(t_i, pool_stations[chosen_i])
# # Updating pool and train
# train_stations.append(pool_stations.pop(chosen_i))
# train_stations = sorted(train_stations)
# rfrand_result_df_preds.to_pickle("rfrand"+str(seed)+'kmeans'+".pred")
# pd.to_pickle(rfrand_deploys, 'rfrand'+str(seed)+'kmeans'+'.dep')
# ### RF-NoDep
# # from sklearn.ensemble import RandomForestRegressor
# # np.random.seed(0)
# # train_stations = global_train_stations.copy().tolist()
# # test_stations = global_test_stations.copy().tolist()
# # pool_stations = global_pool_stations.copy().tolist()
# # rfnod_result_df_preds = pd.DataFrame(columns=[1,2,3,4,5,6])
# # rfnod_result_df_tests = pd.DataFrame(columns=[1,2,3,4,5,6])
# # for t_i in range(0,data.index.unique().shape[0], window):
# # tmp_df = data.loc[all_time[t_i:t_i+window]]
# # train_df = tmp_df[tmp_df.station_id.isin(train_stations)]
# # test_df = tmp_df[tmp_df.station_id.isin(test_stations)]
# # pool_df = tmp_df[tmp_df.station_id.isin(pool_stations)]
# # xscaler = StandardScaler()
# # yscaler = StandardScaler()
# # xscaler.fit(train_df[Xcols])
# # yscaler.fit(train_df[ycols])
# # for local_t in tmp_df.index.unique():
# # trn_X = xscaler.transform(train_df.loc[local_t][Xcols])
# # tst_X = xscaler.transform(test_df.loc[local_t][Xcols])
# # trn_y = yscaler.transform(train_df.loc[local_t][ycols])
# # tst_y = test_df.loc[local_t][ycols]
# # # p(trn_X.shape, tst_X.shape, trn_y.shape, tst_y.shape)
# # model = RandomForestRegressor(random_state=0)
# # model.fit(trn_X, trn_y)
# # rfnod_result_df_preds.loc[local_t,:] = yscaler.inverse_transform(model.predict(tst_X)).ravel()
# # rfnod_result_df_tests.loc[local_t,:] = tst_y.values.ravel()
# # rfnod_result_df_preds.to_pickle("rfnod"+str(seed)+'kmeans'+".pred")
### RF+SVR+KNN QBC
from modAL.models import ActiveLearner, CommitteeRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.svm import SVR
from sklearn.neighbors import KNeighborsRegressor
np.random.seed(0)
train_stations = global_train_stations.copy().tolist()
test_stations = global_test_stations.copy().tolist()
pool_stations = global_pool_stations.copy().tolist()
qbc_result_df_preds = pd.DataFrame(columns=[1,2,3,4,5,6])
qbc_result_df_tests = pd.DataFrame(columns=[1,2,3,4,5,6])
qbc_deploys = []
for t_i in range(0,data.index.unique().shape[0], window):
tmp_df = data.loc[all_time[t_i:t_i+window]]
train_df = tmp_df[tmp_df.station_id.isin(train_stations)]
test_df = tmp_df[tmp_df.station_id.isin(test_stations)]
pool_df = tmp_df[tmp_df.station_id.isin(pool_stations)]
xscaler = StandardScaler()
yscaler = StandardScaler()
xscaler.fit(train_df[Xcols])
yscaler.fit(train_df[ycols])
local_preds = []
local_tests = []
chosen_i_list = []
for local_t in tmp_df.index.unique():
clear_output(wait=True)
trn_X = xscaler.transform(train_df.loc[local_t][Xcols])
tst_X = xscaler.transform(test_df.loc[local_t][Xcols])
trn_y = yscaler.transform(train_df.loc[local_t][ycols])
tst_y = test_df.loc[local_t][ycols]
# ActiveLearner(estimator=KNeighborsRegressor(), X_training=trn_X, y_training=trn_y)
learners = [ActiveLearner(estimator=RandomForestRegressor(random_state=0), X_training=trn_X, y_training=trn_y),
ActiveLearner(estimator=SVR(), X_training=trn_X, y_training=trn_y)]
def ensemble_regression_std(regressor, X):
_, std = regressor.predict(X, return_std=True)
return np.argmax(std)
model = CommitteeRegressor(learner_list=learners, query_strategy=ensemble_regression_std)
qbc_result_df_preds.loc[local_t,:] = yscaler.inverse_transform(model.predict(tst_X)).ravel()
qbc_result_df_tests.loc[local_t,:] = tst_y.values.ravel()
pool_X = xscaler.transform(pool_df.loc[local_t][Xcols].values.reshape(-1, len(Xcols)))
chosen_i, _ = model.query(pool_X)
chosen_i_list.append(chosen_i)
p(t_i, local_t, chosen_i)
# Choose mode of chosen values
chosen_i = stats.mode(chosen_i_list).mode[0]
qbc_deploys.append(pool_stations[chosen_i])
print(pool_stations[chosen_i])
# Updating pool and train
train_stations.append(pool_stations.pop(chosen_i))
train_stations = sorted(train_stations)
qbc_result_df_preds.to_pickle("qbc"+str(seed)+'kmeans'+".pred")
pd.to_pickle(qbc_deploys, 'qbc'+str(seed)+'kmeans'+'.dep')
p((time()-init)/60, 'minutes')
# -
| 27,792 |
/Modulo4/Aula07/Testes de Hipóteses.ipynb | bc2ef57a83fe3b748a777849f515df3b8c22054f | [] | no_license | ricardobgarcia/DSdegree | https://github.com/ricardobgarcia/DSdegree | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 213,797 | # ---
# jupyter:
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# ---
# + [markdown] colab_type="text" id="WvMflodMjQKe"
# # 05. Testes de Hipóteses
# ---
#
# + [markdown] colab_type="text" id="OjHpQHFHpsi3"
# Testes estatísticos são regras de decisão que permitem avaliar a razoabilidade das hipóteses feitas sobre os parâmetros populacionais e aceitá-las ou rejeitá-las como provavelmente verdadeiras ou falsas tendo como base uma amostra.
# -
# Nesta Aula:
# 0. Um problema e uma Introdução
#
#
# 1. Etapas Básicas de um Teste
#
#
# 2. Teste de Normalidade
#
#
# 3. TESTES PARAMÉTRICOS
#
#
# 1. Teste Bicaudal
# 2. Teste Unicaudal
# 4. Testes para Duas Amostras
#
# 4. TESTES NÃO PARAMÉTRICOS
#
#
# 1. Teste do Qui-Quadrado
# 2. Teste Wilcoxon
# # <font color="blue">0. Introdução
#
# Uma máquina automática para encher pacotes de café enche-os segundo uma distribuição normal, com média m e variância sempre igual a 400g2, a máquina foi regula para m = 500g.
#
# Periodicamente uma amostra de 32 pacotes é recolhida para verificar se a produção está sob controle, isto é m = 500g ou não.
#
# Se uma dessas amostras apresentar média x = 492g, deve-se parar a produção para regular a máquina ou não?
#
# *Retirado de BUSSAB, W. O.; MORETTIN, P. A. Estatística básica. 6. ed.* ← ótimo livro!
#
#
# Você acha que a máquina está desregulada?
#
# ### O que podemos fazer nesse caso?
#
# Entram os testes de hipótese!
#
# **A ideia por trás do teste de hipótese então, é calcular a probabilidade do evento observado e descartar a hipótese caso esse evento seja muito raro**
#
# Podemos começar definindo um intervalo de normalidade de operação da máquina: sabemos que ela não é perfeita, mas os pacotes devem ser produzidos seguindo a distribuição normal de média 500g e variância 400g2.
# +
from scipy.stats import norm
import numpy as np
import matplotlib.pyplot as plt
# -
# **Importação das Bibliotecas**
# +
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.stats import norm
sns.set()
# +
# -
#
# + [markdown] colab_type="text" id="rWh-HSd-psji"
# ## <font color="blue">1. Etapas Básicas de um Teste</font>
# ***
# + [markdown] colab_type="text" id="DQF30K2Cpsjj"
# ### **Passo 1** - Formulação das hipóteses $H_0$ e $H_1$
#
# > ### <font color='red'>Pontos importantes</font>
# > - A hipótese nula sempre afirma uma igualdade ou propriedade populacional, e $H_1$ a desigualdade que nega $H_0$.
# > - No caso da hipótese nula $H_0$ a igualdade pode ser representada por uma igualdade simples "$=$" ou por "$\geq$" e "$\leq$". Sempre complementar ao estabelecido pela hipótese alternativa.
# > - A hipótese alternativa $H_1$ deve definir uma desigualdade que pode ser uma diferença simples "$\neq$" ou dos tipos "$>$" e "$<$".
#
#
# **No nosso problema temos uma condição normal (comum) e uma hipótese:**
#
# condição: máquina está regulada para média=500g e variância=400g2
#
# hipótese inicial: a amostra de 32 sacos veio de uma máquina que está regulada, mesmo tendo média=492g
#
# hipótese alternativa: a amostra de 32 sacos veio de uma máquina desregulada
# + [markdown] colab_type="text" id="DQF30K2Cpsjj"
# ### **Passo 2** - Escolha da distribuição amostral adequada
#
# > ### <font color='red'>Pontos importantes</font>
# > - Quando o tamanho da amostra tiver 30 elementos ou mais, deve-se utilizar a distribuição normal, como estabelecido pelo **teorema do limite central**.
# > - Para um tamanho de amostra menor que 30 elementos, e se pudermos afirmar que a população se distribui aproximadamente como uma normal e o desvio padrão populacional for conhecido, deve-se utilizar a distribuição normal.
# > - Para um tamanho de amostra menor que 30 elementos, e se pudermos afirmar que a população se distribui aproximadamente como uma normal e o desvio padrão populacional não for desconhecido, deve-se utilizar a distribuição t de Student.
#
# <img style="display: block; margin: 25px auto" src='https://caelum-online-public.s3.amazonaws.com/1229-estatistica-parte3/01/img003.png' width=70%>
#
#
# -
# Temos 32 elementos e sabemos o valor de sigma (desvio da população) = 400
#
# O desvio da amostra é dado pelo desvio da população dividio pela raiz de n elementos
#
# +
fig, ax = plt.subplots(1, 1)
x = np.random.normal(500, 20, 1000)
ax.hist(x, bins=15)
# -
# Pelo gráfico até parece que 492g não está distante de 500g, mas precisamos determinar isso de forma definitiva.
# +
# desvio da amostra = desvio da pop / raiz da amostra
import math
desv = 20 / math.sqrt(32)
print(f'Desvio da amostra: {desv}')
fig, ax = plt.subplots(1, 1)
x = np.random.normal(500, desv, 1000)
ax.hist(x, bins=15)
# + [markdown] colab_type="text" id="DQF30K2Cpsjj"
# ### **Passo 3** - Fixação da significância do teste ($\alpha$), que define as regiões de aceitação e rejeição das hipóteses (os valores mais freqüentes são 10%, 5% e 1%);
#
# > ### <font color='red'>Pontos importantes</font>
# > - O **nível de confiança** ($1 - \alpha$) representa a probabilidade de acerto da estimativa. De forma complementar o **nível de significância** ($\alpha$) expressa a probabilidade de erro da estimativa.
# >
# > <img alt="Níveis de Confiança e significância" src="https://caelum-online-public.s3.amazonaws.com/1229-estatistica-parte3/01/img001.png" style="display: block; margin: 25px auto" />
# >
# > - O **nível de confiança** representa o grau de confiabilidade do resultado da estimativa estar dentro de determinado intervalo. Quando fixamos em uma pesquisa um **nível de confiança** de 95%, por exemplo, estamos assumindo que existe uma probabilidade de 95% dos resultados da pesquisa representarem bem a realidade, ou seja, estarem corretos.
# >
# > <img alt="Áreas de Aceitação e Rejeição" src="https://caelum-online-public.s3.amazonaws.com/1229-estatistica-parte3/01/img002.png" style="display: block; margin: 25px auto; border-radius: 10px" />
#
# Vamos usar 5%, isso quer dizer que vamos considerar como resultado "comum", "evento comum", valores de média de amostra que acontecem até 95% das vezes.
#
# Vamos considerar que os valores nos 5% das caudas serão considerados eventos muito raros para nossa amostra e vamos considerar como um sinal de que há algo de errado com a máquina.
# + [markdown] colab_type="text" id="DQF30K2Cpsjj"
# ### **Passo 4** - cálculo da estatística-teste e verificação desse valor com as áreas de aceitação e rejeição do teste;
#
# > ### <font color='red'>Pontos importantes</font>
# > - Nos testes paramétricos, distância relativa entre a estatística amostral e o valor alegado como provável.
# > - Neste passo são obtidas as estatísticas amostrais necessárias à execução do teste (média, desvio-padrão, graus de liberdade etc.)
#
# Relembrando os valores que temos:
#
# média = 500, desvio = 20
#
# média amostra = 492, desvio = 3.53
#
# significância = 5%
#
# H1 do tipo "diferente"
#
# Vamos descobrir o intervalo que determina os 95%:
#
#
# +
significancia = 0.05
maximo = norm.ppf(1-significancia/2, loc=500, scale=3.53)
maximo
# -
minimo = norm.ppf(significancia/2, loc=500, scale=3.53)
minimo
# Ou seja:
#
# ### O funcionamento normal da máquina deve gerar amostras com média entre 493g e 506g
# + [markdown] colab_type="text" id="DQF30K2Cpsjj"
# ### **Passo 5** - Aceitação ou rejeição da hipótese nula.
#
# > ### <font color='red'>Pontos importantes</font>
# > - No caso de o intervalo de aceitação conter a estatística-teste, aceita-se $H_0$ como estatisticamente válido e rejeita-se $H_1$ como tal.
# > - No caso de o intervalo de aceitação não conter a estatística-teste, rejeita-se $H_0$ e aceita-se $H_1$ como provavelmente verdadeira.
# > - A aceitação também se verifica com a probabilidade de cauda (p-valor): se maior que $\alpha$, aceita-se $H_0$.
#
#
# -
# ## Conclusão: <font color="red">rejeitamos<font color="black"> a hipótese de que a máquina está regulada e devemos ajustá-la!
# ## <font color="blue">Vamos agora ver mais exemplos de outros casos
# + [markdown] colab_type="text" id="qjqWccNspsi4"
# ## <font color="blue">1. Teste de Normalidade</font>
# ***
# -
# **Carregando nosso dataset**
df = pd.read_csv('../../../datasets/PNAD-2015.csv')
df.head()
sns.histplot(df[df['Renda'] < 5000].Renda)
sns.histplot(df['Altura'])
# + [markdown] colab_type="text" id="O79kHHwYpsi5"
# ### Importando a biblioteca
# https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.normaltest.html
# -
from scipy.stats import normaltest
# + [markdown] colab_type="text" id="abDn-nKLpsi7"
# A função *normaltest* testa a hipótese nula $H_0$ de que a amostra é proveniente de uma distribuição normal.
#
# - $H_0$: hipótese nula
# - $H_1$: hipótese alternativa
# + [markdown] colab_type="text" id="gRDJ86zTpsi8"
# ### Definindo a significância do teste ($\alpha$)
# -
confianca = 0.95
significancia = 1 - confianca # 0.05 = 5%
# + [markdown] colab_type="text" id="sppYIE51psi_"
# ### Testando a variável Renda
# -
normaltest(df['Renda'])
# estatística de teste
# p-valor (p-value)
test_stat, pvalue = normaltest(df['Renda'])
test_stat
pvalue
# + [markdown] colab_type="text" id="qKeNZ0PUpsjC"
# ### <font color='red'>Critério do valor $p$</font>
#
# > ### Rejeitar $H_0$ se o valor $p\leq \alpha \, (0,05)$
# +
# H0: a amostra segue uma distribuição normal
# H1: a amostra NÃO segue uma distribuição normal
# + colab={"base_uri": "https://localhost:8080/", "height": 53} colab_type="code" id="060ahVrrpsjD" outputId="a28af676-4866-4004-953c-8274fc91b9b4"
if pvalue <= significancia:
print('Rejeita H0, ou seja, a amostra não segue uma distribuição normal.')
else:
print('Aceita H0, ou seja, a amostra segue uma distribuição normal')
# + [markdown] colab_type="text" id="JkAxa9PqpsjM"
# ### Testando a variável Altura
# -
stat_test, pvalue = normaltest(df['Altura'])
pvalue
# + [markdown] colab_type="text" id="1ADrwGb5psjQ"
# ### <font color='red'>Critério do valor $p$</font>
#
# > ### Rejeitar $H_0$ se o valor $p\leq 0,05$
# -
if pvalue <= significancia:
print('Rejeita H0, ou seja, a amostra não segue uma distribuição normal.')
else:
print('Aceita H0, ou seja, a amostra segue uma distribuição normal')
# + [markdown] colab_type="text" id="TXdMM0dOpsjm"
# ---
# + [markdown] colab_type="text" id="miGOADKYpsjn"
# ## <font color=blue>3. TESTES PARAMÉTRICOS</font>
# ***
# + [markdown] colab_type="text" id="pIHY_4Sspsjn"
# Quando um teste assume determinadas premissas sobre como os parâmetros de uma população se distribuem, estamos trabalhando com **Testes Paramétricos**.
# + [markdown] colab_type="text" id="rUKgxZ7_psjp"
# ### <font color=blue>3.1 Teste Bicaudal</font>
# ***
# + [markdown] colab_type="text" id="i1PMIB5cpsjp"
# ## <font color='red'>Problema</font>
# + [markdown] colab_type="text" id="vellTWzJpsjq"
# A empresa **Suco Bom** produz **sucos de frutas em embalagens de 500 ml**. Seu processo de produção é quase todo automatizado e as embalagens de sucos são preenchidas por uma máquina que às vezes apresenta um certo desajuste, levando a erros no preenchimento das embalagens para mais ou menos conteúdo. Quando o volume médio cai abaixo de 500 ml, a empresa se preocupa em perder vendas e ter problemas com os orgãos fiscalizadores. Quando o volume passa de 500 ml, a empresa começa a se preocupar com prejuízos no processo de produção.
#
# O setor de controle de qualidade da empresa **Suco Bom** extrai, periodicamente, **amostras de 50 embalagens** para monitorar o processo de produção. Para cada amostra, é realizado um **teste de hipóteses** para avaliar se o maquinário se desajustou. A equipe de controle de qualidade assume um **nível de significância de 5%**.
#
# Suponha agora que uma **amostra de 50 embalagens** foi selecionada e que a **média amostral observada foi de 503,24 ml**. **Esse valor de média amostral é suficientemente maior que 500 ml para nos fazer rejeitar a hipótese de que a média do processo é de 500 ml ao nível de significância de 5%?**
# + [markdown] colab_type="text" id="-J15nugOpsjq"
# ---
# + [markdown] colab_type="text" id="076qvKgapsjr"
# O **teste bicaudal** é muito utilizado em **testes de qualidade**, como o apresentado em nosso problema acima. Outro exemplo é a avaliação de peças que devem ter um encaixe perfeito (porcas e parafusos, chaves e fechaduras).
# + [markdown] colab_type="text" id="MyGUYz88psjr"
# 
# + [markdown] colab_type="text" id="EOiKt64mpsjs"
# ---
# + [markdown] colab_type="text" id="011wNGXtpsjs"
# ### Dados do problema
# + colab={} colab_type="code" id="lHLlGH7upsjt"
amostra = [509, 505, 495, 510, 496, 509, 497, 502, 503, 505,
501, 505, 510, 505, 504, 497, 506, 506, 508, 505,
497, 504, 500, 498, 506, 496, 508, 497, 503, 501,
503, 506, 499, 498, 509, 507, 503, 499, 509, 495,
502, 505, 504, 509, 508, 501, 505, 497, 508, 507]
# -
# Passar para um dataframe
dados = pd.DataFrame({ 'Volume': amostra })
dados.head()
dados.shape
# Média da amostra
media_amostra = dados['Volume'].mean()
media_amostra
# Desvio padrão
desvio_amostra = dados['Volume'].std()
desvio_amostra
# +
# definindo os demais dados do problema
u0 = 500 # 500 ml
n = len(amostra)
u0, n
# +
fig, ax = plt.subplots(1, 1)
desvio_amostra = 4.48
x = np.random.normal(500, desvio_amostra / math.sqrt(50), 1000)
ax.hist(x, bins=15)
# +
fig, ax = plt.subplots(1, 1)
desvio_amostra = 4.48380305052735
x = np.random.normal(500, desvio_amostra / math.sqrt(50), 1000)
x = (x - 500)/(desvio_amostra / math.sqrt(50))
ax.hist(x)
# -
(503.24- 500)/(desvio_amostra / math.sqrt(50))
# + [markdown] colab_type="text" id="nqmFpl7wpsj2"
# ### **Passo 1** - formulação das hipóteses $H_0$ e $H_1$
#
# #### <font color='red'>Lembre-se, a hipótese nula sempre contém a alegação de igualdade</font>
# + [markdown] colab_type="text" id="QVcqkHZhpsj3"
# ### $H_0: \mu = 500 \, ml$
#
# ### $H_1: \mu \neq 500 \, ml$
# + [markdown] colab_type="text" id="MShPuVL6psj3"
# ---
# + [markdown] colab_type="text" id="_rAz73fzpsj4"
# ### **Passo 2** - escolha da distribuição amostral adequada
# <img src='https://caelum-online-public.s3.amazonaws.com/1229-estatistica-parte3/01/img003.png' width=70%>
# + [markdown] colab_type="text" id="XC1JOtU9psj4"
# ### O tamanho da amostra é maior que 30?
# #### Resp.: Sim
#
# ### O desvio padrão populacional é conhecido?
# #### Resp.: Não
# + [markdown] colab_type="text" id="Uq-Zq_Sxpsj5"
# ---
# + [markdown] colab_type="text" id="V3OT4caspsj5"
# ### **Passo 3** - fixação da significância do teste ($\alpha$)
# + colab={} colab_type="code" id="nUd-6AzZpsj6"
significancia = 0.05
# -
confianca = 1 - significancia
# + [markdown] colab_type="text" id="Fu4Wbd-ipsj-"
# ### Obtendo $z_{\alpha/2}$
# + [markdown] colab_type="text" id="wI1Nhj_vpsj6"
# https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.norm.html
# + colab={"base_uri": "https://localhost:8080/", "height": 35} colab_type="code" id="imc3GyDLpsj_" outputId="b9dca9d5-da76-42b1-919c-1aaf593fed70"
z_alpha_2 = norm.ppf(confianca + significancia / 2)
z_alpha_2
# + [markdown] colab_type="text" id="d98Z21yOpskB"
# <img style="border-radius: 10px" alt="Região de Aceitação" src="https://caelum-online-public.s3.amazonaws.com/1229-estatistica-parte3/01/img006.png" />
# + [markdown] colab_type="text" id="eyfX7pYMpskC"
# ---
# + [markdown] colab_type="text" id="aaLCZs0ApskC"
# ### **Passo 4** - cálculo da estatística-teste e verificação desse valor com as áreas de aceitação e rejeição do teste
#
# # $$z = \frac{\bar{x} - \mu_0}{\frac{s}{\sqrt{n}}}$$
# + colab={"base_uri": "https://localhost:8080/", "height": 35} colab_type="code" id="a6zMj0YzpskC" outputId="83a2bb40-f67f-4ddc-dbd9-a88032f7dc86"
z = (media_amostra - u0) / (desvio_amostra / np.sqrt(n)) # estatística de teste
z
# + [markdown] colab_type="text" id="UMHqgrlupskE"
# 
# + [markdown] colab_type="text" id="tFfJX8G9pskF"
# ---
# + [markdown] colab_type="text" id="aqTnTTePpskF"
# ### **Passo 5** - Aceitação ou rejeição da hipótese nula
# + [markdown] colab_type="text" id="pD1cc4DHpskG"
# <img src='https://caelum-online-public.s3.amazonaws.com/1229-estatistica-parte3/01/img013.png' width=100%>
# + [markdown] colab_type="text" id="qbnH6V15pskG"
# ### <font color='green'>Critério do valor crítico</font>
#
# > ### Teste Bicaudal
# > ### Rejeitar $H_0$ se $z \leq -z_{\alpha / 2}$ ou se $z \geq z_{\alpha / 2}$
# + colab={"base_uri": "https://localhost:8080/", "height": 35} colab_type="code" id="-MkJyqSupskG" outputId="6495abcc-0c3f-4759-8a31-96d668ad2eff"
z <= -z_alpha_2 or z >= z_alpha_2
# + [markdown] colab_type="text" id="YX-xSnZFpskM"
# ### <font color='orange'>Conclusão: Como a média amostral $\bar{x}$ é significativamente maior que 500 ml, rejeitamos $H_0$. Neste caso, devem ser tomadas providências para ajustar o maquinário que preenche as embalagens.</font>
# + [markdown] colab_type="text" id="Aqv_KZoYpskN"
# ### <font color='green'>Critério do $p-valor$</font>
#
# > ### Teste Bicaudal
# > ### Rejeitar $H_0$ se o valor $p\leq\alpha$
# + colab={"base_uri": "https://localhost:8080/", "height": 35} colab_type="code" id="ucIaKkuopskN" outputId="fabc7c98-2eed-4209-bce1-51fe13cb599f"
pvalue = norm.sf(z) * 2
# -
pvalue
pvalue <= significancia # Reijeita H0
# + [markdown] colab_type="text" id="YRw1O5X8pskT"
# https://www.statsmodels.org/stable/generated/statsmodels.stats.weightstats.ztest.html
# -
from statsmodels.stats.weightstats import ztest
ztest(x1=dados['Volume'], value=u0)
# + [markdown] colab_type="text" id="X5MVCXS6pskX"
# https://www.statsmodels.org/dev/generated/statsmodels.stats.weightstats.DescrStatsW.html
# -
from statsmodels.stats.weightstats import DescrStatsW
teste = DescrStatsW(dados['Volume'])
teste
teste.ztest_mean(value=u0)
# + [markdown] colab_type="text" id="YQQdl-Nepskv"
# ---
# + [markdown] colab_type="text" id="U8OWXeVtpskv"
# ## <font color=green>3.2 Teste Unicaudal</font>
# ***
# + [markdown] colab_type="text" id="CPHPL4nWpskw"
# ## <font color='red'>Problema</font>
# + [markdown] colab_type="text" id="vDyxOXLvpskw"
# Um famoso fabricante de refrigerantes alega que uma lata de 350 ml de seu principal produto contém, **no máximo**, **37 gramas de açúcar**. Esta alegação nos leva a entender que a quantidade média de açúcar em uma lata de refrigerante deve ser **igual ou menor que 37 g**.
#
# Um consumidor desconfiado e com conhecimentos em inferência estatística resolve testar a alegação do fabricante e seleciona, aleatóriamente, em um conjunto de estabelecimentos distintos, **uma amostra de 25 latas** do refrigerante em questão. Utilizando o equipamento correto o consumidor obteve as quantidades de açúcar em todas as 25 latas de sua amostra.
#
# **Assumindo que essa população se distribua aproximadamente como uma normal e considerando um nível de significância de 5%, é possível aceitar como válida a alegação do fabricante?**
# +
# Unicaudal
# 25 latas < 30: t-Student
# + [markdown] colab_type="text" id="5P5nWN0epsky"
# ### 3.2.1. Conhecendo a Distribuição $t$-student
# https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.t.html
# + [markdown] colab_type="text" id="MbVu6FdNpsk0"
# <img src='https://caelum-online-public.s3.amazonaws.com/1229-estatistica-parte3/01/img004.png' width='250px'>
#
# ### Propriedades
#
# - A função densidade da distribuição t de Student tem a mesma forma em sino da distribuição Normal, mas reflete a maior variabilidade (com curvas mais alargadas) que é de se esperar em amostras pequenas.
# - Quanto maior o grau de liberdade, mais a distribuição t de Student se aproxima da distribuição Normal.
# + [markdown] colab_type="text" id="UXM3qdYUpsk0"
# ---
# -
# ### 3.2.2. Teste Unicaudal
# + [markdown] colab_type="text" id="dDbUKs7-psk0"
# Os **testes unicaudais** verificam as variáveis em relação a um piso ou a um teto e avaliam os valores máximos ou mínimos esperados para os parâmetros em estudo e a chance de as estatísticas amostrais serem inferiores ou superiores a dado limite.
# + [markdown] colab_type="text" id="AvnFJnUrpsk1"
# <img src='https://caelum-online-public.s3.amazonaws.com/1229-estatistica-parte3/01/img008.png' width='700px'>
# + [markdown] colab_type="text" id="IgDg23Xppsk1"
# ### Dados do problema
# + colab={} colab_type="code" id="IKvYICfEpsk2"
amostra = [37.27, 36.42, 34.84, 34.60, 37.49,
36.53, 35.49, 36.90, 34.52, 37.30,
34.99, 36.55, 36.29, 36.06, 37.42,
34.47, 36.70, 35.86, 36.80, 36.92,
37.04, 36.39, 37.32, 36.64, 35.45]
# -
len(amostra)
# + colab={"base_uri": "https://localhost:8080/", "height": 824} colab_type="code" id="CBuciT30psk3" outputId="28df5179-0f8c-42e1-fe69-a3b02280f45f"
# Converter para um DataFrame
dados = pd.DataFrame({ 'gramas': amostra })
# -
dados.head()
# + colab={"base_uri": "https://localhost:8080/", "height": 35} colab_type="code" id="79TBzFsdpsk4" outputId="9af134c0-aee3-4394-a188-347978abdd80"
# Visualização
# + colab={"base_uri": "https://localhost:8080/", "height": 35} colab_type="code" id="S82xbyghpsk6" outputId="e7b2b235-1352-4a9b-95ae-111dec8f65a0"
# media amostral
media_amostra = dados['gramas'].mean()
media_amostra
# + colab={} colab_type="code" id="mvtiwLUWpsk8"
# desvio padrão amostral
desvio_amostra = dados['gramas'].std()
# -
desvio_amostra
# número de amostras e número de graus de liberdade
n = 25
graus_de_liberdade = n - 1
u0 = 37
# + [markdown] colab_type="text" id="rpLOiPU2psk9"
# ### **Passo 1** - formulação das hipóteses $H_0$ e $H_1$
# + [markdown] colab_type="text" id="iMBi2jUfsc4s"
#
# #### <font color='red'>Lembre-se, a hipótese nula sempre contém a alegação de igualdade</font>
# + [markdown] colab_type="text" id="8roeH10fpsk-"
# ### $H_0: \mu \leq 37 \, g$
#
# ### $H_1: \mu > 37 \, g$
# + [markdown] colab_type="text" id="uqE6JXGspsk-"
# ---
# + [markdown] colab_type="text" id="XsOkFtncpsk_"
# ### **Passo 2** - escolha da distribuição amostral adequada
# <img src='https://caelum-online-public.s3.amazonaws.com/1229-estatistica-parte3/01/img003.png' width=70%>
# + [markdown] colab_type="text" id="jpXjRqO2psk_"
# ### O tamanho da amostra é maior que 30?
# #### Resp.: Não
#
# ### Podemos afirmar que a população se distribui aproximadamente como uma normal?
# #### Resp.: Sim
#
# ### O desvio padrão populacional é conhecido?
# #### Resp.: Não
# + [markdown] colab_type="text" id="J14v2aXOpslA"
# ---
# + [markdown] colab_type="text" id="DQvwhQWnpslA"
# ### **Passo 3** - Fixação da significância do teste ($\alpha$)
# + [markdown] colab_type="text" id="Qaxh6lsapslB"
# https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.t.html
# + colab={} colab_type="code" id="-WhDZiRopslC"
significancia = 0.05
# + colab={"base_uri": "https://localhost:8080/", "height": 173} colab_type="code" id="12t8QJMzpslD" outputId="6ff367ac-ceb6-41af-e9f3-364da4170020"
confianca = 1 - significancia
# + [markdown] colab_type="text" id="Brk3cI1npslE"
# ### Obtendo $t_{\alpha}$
# -
from scipy.stats import t as t_student
confianca
t_alpha = t_student.ppf(confianca, df=graus_de_liberdade)
t_alpha
# + [markdown] colab_type="text" id="xMnBzjxLpslH"
# 
# + [markdown] colab_type="text" id="Azy_uNSGpslI"
# ---
# + [markdown] colab_type="text" id="YN4weG-1pslI"
# ### **Passo 4** - cálculo da estatística-teste e verificação desse valor com as áreas de aceitação e rejeição do teste
#
# # $$t = \frac{\bar{x} - \mu_0}{\frac{s}{\sqrt{n}}}$$
# + colab={"base_uri": "https://localhost:8080/", "height": 35} colab_type="code" id="qY0DfIffpslI" outputId="00fd1921-6093-469d-d164-c864eb34b23b"
t = (media_amostra - u0) / (desvio_amostra / np.sqrt(n))
t
# + [markdown] colab_type="text" id="SCIchVxNpslK"
# 
# -
u0, media_amostra
# + [markdown] colab_type="text" id="NiheMfTopslK"
# ---
# + [markdown] colab_type="text" id="YCsJySBGpslK"
# ### **Passo 5** - Aceitação ou rejeição da hipótese nula
# + [markdown] colab_type="text" id="ioLyTpbWpslL"
# <img src='https://caelum-online-public.s3.amazonaws.com/1229-estatistica-parte3/01/img013.png' width=90%>
# + [markdown] colab_type="text" id="TQcuPzbPpslL"
# ### <font color='red'>Critério do valor crítico</font>
#
# > ### Teste Unicaudal Superior
# > ### Rejeitar $H_0$ se $t \geq t_{\alpha}$
# + colab={"base_uri": "https://localhost:8080/", "height": 35} colab_type="code" id="pkoDYMLIpslM" outputId="d1166899-8745-4bf6-fa7c-40b072c1b1ea"
t, t_alpha
# -
t >= t_alpha
# + [markdown] colab_type="text" id="F-_AVTMJpslN"
# ### <font color='green'>Conclusão: Com um nível de confiança de 95% não podemos rejeitar $H_0$, ou seja, a alegação do fabricante é verdadeira.</font>
# + [markdown] colab_type="text" id="7ye3tS25pslO"
# ### <font color='red'>Critério do $p$ valor</font>
#
# > ### Teste Unicaudal Superior
# > ### Rejeitar $H_0$ se o valor $p\leq\alpha$
# + colab={"base_uri": "https://localhost:8080/", "height": 35} colab_type="code" id="AtPMeAz4pslQ" outputId="c26c8cc5-1244-47ff-8001-c490522ec4c2"
pvalue = t_student.sf(t, df=graus_de_liberdade)
pvalue
# + [markdown] colab_type="text" id="4JxGeADypslW"
# https://www.statsmodels.org/dev/generated/statsmodels.stats.weightstats.DescrStatsW.html
# + colab={} colab_type="code" id="Y1gP6yjvpslW"
teste = DescrStatsW(dados['gramas'])
# +
# ztest_mean: Distribuição Normal
# ttest_mean: Distribuição t-Student
# + colab={} colab_type="code" id="7V555DeTpslX"
teste.ttest_mean(value=u0, alternative='larger')
# + [markdown] colab_type="text" id="wlUdcJnDpsla"
# ---
# + [markdown] colab_type="text" id="L7_ZbBBtpslb"
# ## <font color=green>3.4 Testes para Duas Amostras</font>
# ***
# + [markdown] colab_type="text" id="lmAJLIu8pslb"
# ## <font color='red'>Problema</font>
# + [markdown] colab_type="text" id="0zUUY8klpslc"
# Em nosso dataset temos os rendimento dos chefes de domicílio obtidos da Pesquisa Nacional por Amostra de Domicílios - PNAD no ano de 2015. Um problema bastante conhecido em nosso país diz respeito a desigualdade de renda, principalmente entre homens e mulheres.
#
# Duas amostras aleatórias, uma de **500 homens** e outra com **500 mulheres**, foram selecionadas em nosso dataset. Com o objetivo de comprovar tal desigualdade, **teste a igualdade das médias** entre estas duas amostras com um nível de **significância de 1%**.
# + [markdown] colab_type="text" id="WUknTBZgpslc"
# ---
# + [markdown] colab_type="text" id="gFIUL8Hgpslc"
# É possível também utilizar testes de hipóteses para comparar duas diferentes amostras. Neste tipo de teste se deseja decidir se uma amostra é diferente da outra.
# + [markdown] colab_type="text" id="xzGuwM4xpslc"
# ### Seleção das amostras
# + colab={} colab_type="code" id="nuNSrzfYpsld"
df.head()
# + colab={} colab_type="code" id="W-W9IOs1psle"
df[df['Sexo'] == 0]['Renda']
# -
homens = df[df['Sexo'] == 0]['Renda'].sample(n=500, random_state=101)
homens.head()
mulheres = df[df['Sexo'] == 1]['Renda'].sample(n=500, random_state=101)
mulheres.head()
# + [markdown] colab_type="text" id="FOFa-cHwpslg"
# ### Obtendo dados do problema
#
# - Média e desvio padrão das mulheres
# - Média e desvio padrão dos homens
# - Número de amostras (homens e mulheres)
# - Nível de significância e confiança
# + colab={"base_uri": "https://localhost:8080/", "height": 35} colab_type="code" id="BGyXCbompslh" outputId="a5e8a9b9-ee69-408f-b8c0-6cac89b36d9a"
u1 = homens.mean() # média dos homens (renda)
u2 = mulheres.mean() # média das mulheres (renda)
# -
s1 = homens.std() # desvio padrão amostral dos homens (renda)
s2 = mulheres.std() # desvio padrão amostral das mulheres (renda)
# + colab={"base_uri": "https://localhost:8080/", "height": 35} colab_type="code" id="RoxFOCA3pslk" outputId="85133b37-a4a6-49cd-a585-700725811b09"
# Quantidade de elementos de cada amostra
nH = 500
nM = 500
# + colab={"base_uri": "https://localhost:8080/", "height": 35} colab_type="code" id="Of8L6Hygpsll" outputId="fe0dc0f3-79e3-4375-de47-43a4ff6c3f79"
significancia = 0.01
confianca = 0.99
# + colab={"base_uri": "https://localhost:8080/", "height": 35} colab_type="code" id="-cSpXXTepsln" outputId="8a3d0017-ac70-4fe3-aae1-0c7bf32e676e"
D0 = 0 # termo constante isolado na formulação das hipóteses
# + [markdown] colab_type="text" id="PGoWwWwjpslp"
# ---
# + [markdown] colab_type="text" id="tO597Oxgpslp"
# ### **Passo 1** - formulação das hipóteses $H_0$ e $H_1$
#
# #### <font color='red'>Lembre-se, a hipótese nula sempre contém a alegação de igualdade</font>
# + [markdown] colab_type="text" id="LysVU-7fpslr"
# ### $\mu_1 \Rightarrow$ Média das rendas dos chefes de domicílios do sexo masculino
# ### $\mu_2 \Rightarrow$ Média das rendas dos chefes de domicílios do sexo feminino
#
# ### $
# \begin{cases}
# H_0: \mu_1 \leq \mu_2\\
# H_1: \mu_1 > \mu_2
# \end{cases}
# $
#
# ### ou
#
# ### $
# \begin{cases}
# H_0: \mu_1 -\mu_2 \leq 0\\
# H_1: \mu_1 -\mu_2 > 0
# \end{cases}
# $
# + [markdown] colab_type="text" id="ENKqbCgQpslr"
# ---
# + [markdown] colab_type="text" id="dBA05Y3Zpslr"
# ### **Passo 2** - escolha da distribuição amostral adequada
# <img src='https://caelum-online-public.s3.amazonaws.com/1229-estatistica-parte3/01/img003.png' width=70%>
#
# ### <font color='red'><strong>Observação importante</strong></font>
#
# > **Em testes que envolvam duas amostras com o emprego da tabela $t$ de Student, o número de graus de liberdade será sempre igual a $n_1 + n_2 - 2$**
# + [markdown] colab_type="text" id="6_Q_Wtjlpsls"
# ### O tamanho da amostra é maior que 30?
# #### Resp.: Sim
#
# ### O desvio padrão populacional é conhecido?
# #### Resp.: Não
# + [markdown] colab_type="text" id="T6sVzVjVpsls"
# ---
# + [markdown] colab_type="text" id="SUN4yJqzpsls"
# ### **Passo 3** - fixação da significância do teste ($\alpha$)
# + colab={"base_uri": "https://localhost:8080/", "height": 35} colab_type="code" id="b3Y_CZmzpsls" outputId="bc2e1945-7bb1-4c2c-851a-f0102328ce0e"
significancia = 0.01
confianca = 1 - significancia
# -
# ### Diferenças entre `ppf`, `cdf` e `sf`
#
# 1. `ppf`: **passamos um valor de probabilidade** e ele **retorna um valor no eixo** `z` (norm) ou `t` (t_student).
# - Quando estamos com uma Normal/t-Student bicaudal, então, passamos $\beta + \alpha / 2$
# - Quando estamos com uma Normal/t-Student unicaudal à esquerda (confiança), então, passamos somente o $\beta$
# - Quando estamos com uma Normal/t-Student unicaudal à direita (confiança), então, passamos somente o $\alpha$
# 2. `cdf`: **passamos um valor no eixo** `z` (norm) ou `t` (t_student) e ele **retorna um valor de probabilidade**, à esqueda do valor passado como parâmetro.
# 3. `sf`: **passamos um valor no eixo** `z` (norm) ou `t` (t_student) e ele **retorna um valor de probabilidade**, à direita do valor passado como parâmetro.
#
# #### Legenda
# - $\beta$: confiança
# - $\alpha$: significância
z_alpha = norm.ppf(confianca)
z_alpha
# + [markdown] colab_type="text" id="y9u_zd8Opslv"
# 
# + [markdown] colab_type="text" id="xUhiEXRMpslv"
# ---
# + [markdown] colab_type="text" id="iooB1aPHpslv"
# ### **Passo 4** - cálculo da estatística-teste e verificação desse valor com as áreas de aceitação e rejeição do teste
#
# # $$z = \frac{(\bar{x_1} - \bar{x_2})-D_0}{\sqrt{\frac{s_1^2}{n_1} + \frac{s_2^2}{n_2}}}$$
# -
numerador = (u1 - u2) - D0
denominador = np.sqrt((s1**2 / nH) + (s2**2 / nM))
# + colab={"base_uri": "https://localhost:8080/", "height": 35} colab_type="code" id="AL2i9JOTpslw" outputId="920efcb1-4b72-4366-9930-52d6e0119549"
z = numerador / denominador
z
# + [markdown] colab_type="text" id="jXrfHvH5pslx"
# 
# + [markdown] colab_type="text" id="4dn58Fc2pslx"
# ---
# + [markdown] colab_type="text" id="P-7NL33-pslx"
# ### **Passo 5** - Aceitação ou rejeição da hipótese nula
# + [markdown] colab_type="text" id="n_djxfYtpslx"
# <img src='https://caelum-online-public.s3.amazonaws.com/1229-estatistica-parte3/01/img014.png' width=90%>
# + [markdown] colab_type="text" id="QrObW-BXpsly"
# ### <font color='red'>Critério do valor crítico</font>
#
# > ### Teste Unicaudal
# > ### Rejeitar $H_0$ se $z \geq z_{\alpha}$
# + colab={"base_uri": "https://localhost:8080/", "height": 35} colab_type="code" id="tkJSji7gpsly" outputId="8bbaa7f8-311f-4b6e-98f0-6964f510a91d"
z >= z_alpha
# + [markdown] colab_type="text" id="UnBVO1uDpslz"
# ### <font color='green'>Conclusão: Com um nível de confiança de 99% rejeitamos $H_0$, isto é, concluímos que a média das rendas dos chefes de domicílios do sexo masculino é maior que a média das rendas das chefes de domicílios do sexo feminino. Confirmando a alegação de desigualdade de renda entre os sexos.</font>
# + [markdown] colab_type="text" id="cyuNEHfwpsl0"
# ### <font color='red'>Critério do valor $p$</font>
#
# > ### Teste Unicaudal
# > ### Rejeitar $H_0$ se o valor $p\leq\alpha$
# -
pvalue = norm.sf(z)
pvalue
pvalue <= significancia
# ### Facilitando os cálculos com o Python
# ---
# + [markdown] colab_type="text" id="9WgvHygWpsl0"
# https://www.statsmodels.org/dev/generated/statsmodels.stats.weightstats.DescrStatsW.html
#
# https://www.statsmodels.org/dev/generated/statsmodels.stats.weightstats.CompareMeans.html
# + colab={} colab_type="code" id="UkFRl3hTpsl0"
from statsmodels.stats.weightstats import DescrStatsW, CompareMeans
# -
teste_homens = DescrStatsW(homens)
teste_mulheres = DescrStatsW(mulheres)
# **Utilizando o CompareMeans**
teste = CompareMeans(teste_homens, teste_mulheres)
# + colab={} colab_type="code" id="uW0uVFnFpsl1"
teste
# -
D0
teste.ztest_ind(value=D0, alternative='larger')
# + [markdown] colab_type="text" id="0gxk-t4jpsl8"
# ---
# + [markdown] colab_type="text" id="IEHa9ZC_psl8"
# # <font color=blue>4. TESTES NÃO PARAMÉTRICOS</font>
# ***
# + [markdown] colab_type="text" id="8iRwKn9Wpsl8"
# O trabalho com pequenas amostras pode levar a não aceitação da validade do teorema central do limite e também na impossibilidade de fazer suposições sobre a distribuição da variável avaliada. Quando isso ocorre torna-se necessária a aplicação de testes não paramétricos. Nos testes não paramétricos, não fazemos hipóteses sobre a distribuição (de probabilidade) das quais as observações são extraídas.
# + [markdown] colab_type="text" id="ngqmtRxFpsl9"
# ## <font color='red'>Problema</font>
# + [markdown] colab_type="text" id="Al0BiC02psl9"
# Antes de cada partida do campeonato nacional de futebol, as moedas utilizadas pelos árbitros devem ser verificadas para se ter certeza de que não são viciadas, ou seja, que não tendam para determinado resultado. Para isso um teste simples deve ser realizado antes de cada partida. Este teste consiste em lançar a moeda do jogo **50 vezes** e contar as frequências de **CARAS** e **COROAS** obtidas. A tabela abaixo mostra o resultado obtido no experimento:
#
# ||CARA|COROA|
# |-|-|-|
# |Observado|17|33|
# |Esperado|25|25|
#
# A um **nível de significância de 5%**, é possível afirmar que a moeda não é honesta, isto é, que a moeda apresenta uma probabilidade maior de cair com a face **CARA** voltada para cima?
# + [markdown] colab_type="text" id="56KA3Hnupsl9"
# ### <font color=blue>4.1 Teste do Qui-Quadrado ( $\chi^2$)</font>
# ***
# + [markdown] colab_type="text" id="GxTlvB4Zpsl9"
# Também conhecido como teste de adequação ao ajustamento, seu nome se deve ao fato de utilizar uma variável estatística padronizada, representada pela letra grega qui ( $\chi$) elevada ao quadrado. A tabela com os valores padronizados e como obtê-la podem ser vistos logo abaixo.
#
# O teste do $\chi^2$ testa a hipótese nula de não haver diferença entre as frequências observadas de um determinado evento e as frequências que são realmente esperadas para este evento.
#
# Os passos de aplicação do teste são bem parecidos aos vistos para os testes paramétricos.
#
# 
# + [markdown] colab_type="text" id="UkzhYAPDpsl_"
# <img src='https://caelum-online-public.s3.amazonaws.com/1229-estatistica-parte3/01/img016.png' width='250px'>
#
# Tabela com os valores de $\chi_p^2$ em função dos graus de liberdade $(n - 1)$ e de $p = P(\chi^2 \leq \chi_p^2)$
# + [markdown] colab_type="text" id="cCPOqVFKpsl_"
# ## <font color='red'>Problema</font>
# + [markdown] colab_type="text" id="GTq1wpkbpsl_"
# Antes de cada partida do campeonato nacional de futebol, as moedas utilizadas pelos árbitros devem ser verificadas para se ter certeza de que não são viciadas, ou seja, que não tendam para determinado resultado. Para isso um teste simples deve ser realizado antes de cada partida. Este teste consiste em lançar a moeda do jogo **50 vezes** e contar as frequências de **CARAS** e **COROAS** obtidas. A tabela abaixo mostra o resultado obtido no experimento:
#
# ||CARA|COROA|
# |-|-|-|
# |Observado|17|33|
# |Esperado|25|25|
#
# A um **nível de significância de 5%**, é possível afirmar que a moeda não é honesta, isto é, que a moeda apresenta uma probabilidade maior de cair com a face **CARA** voltada para cima?
# + [markdown] colab_type="text" id="CSlCI1wUpsl_"
# ---
# + [markdown] colab_type="text" id="Op26YDWnpsmA"
# ### Dados do problema
# + colab={} colab_type="code" id="p5iOqxk2psmA"
F_observada = [17, 33]
F_esperada = [25, 25]
significancia = 0.05
confianca = 1 - significancia
k = 2 # Número de eventos possíveis
graus_de_liberdade = k - 1
# + [markdown] colab_type="text" id="w6A74pM-psmB"
# ### **Passo 1** - formulação das hipóteses $H_0$ e $H_1$
#
# #### <font color='red'>Lembre-se, a hipótese nula sempre contém a alegação de igualdade</font>
# + [markdown] colab_type="text" id="zA4AD5_8psmB"
# ### $H_0: F_{CARA} = F_{COROA}$
#
# ### $H_1: F_{CARA} \neq F_{COROA}$
# + [markdown] colab_type="text" id="rUtuw6_bpsmB"
# ---
# + [markdown] colab_type="text" id="Zi2oE6ZbpsmB"
# ### **Passo 2** - fixação da significância do teste ($\alpha$)
# + [markdown] colab_type="text" id="cCL8Wyy7psmC"
# https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.chi.html
# + colab={} colab_type="code" id="5bF7GEd-psmD"
# + colab={"base_uri": "https://localhost:8080/", "height": 210} colab_type="code" id="WRsJSXxGpsmE" outputId="106db812-4580-4e1f-e7d3-5070da776d1f"
# + [markdown] colab_type="text" id="0RRjHQm7psmF"
# ### Obtendo $\chi_{\alpha}^2$
# + colab={"base_uri": "https://localhost:8080/", "height": 35} colab_type="code" id="6rXyih-lpsmH" outputId="a0445581-6d51-4a7d-f8c1-c5dbaf41c58c"
from scipy.stats import chi2
# -
chi2_alpha = chi2.ppf(confianca, graus_de_liberdade)
chi2_alpha
# + [markdown] colab_type="text" id="aHxm3ZCupsmJ"
# 
# + [markdown] colab_type="text" id="FxLr2OnDpsmJ"
# ---
# + [markdown] colab_type="text" id="JcUvBM5OpsmJ"
# ### **Passo 3** - cálculo da estatística-teste e verificação desse valor com as áreas de aceitação e rejeição do teste
#
# # $$\chi^2 = \sum_{i=1}^{k}{\frac{(F_{i}^{Obs} - F_{i}^{Esp})^2}{F_{i}^{Esp}}}$$
#
# Onde
#
# $F_{i}^{Obs}$ = frequência observada para o evento $i$
#
# $F_{i}^{Esp}$ = frequência esperada para o evento $i$
#
# $k$ = total de eventos possíveis
# + colab={"base_uri": "https://localhost:8080/", "height": 35} colab_type="code" id="2b32Y6HiuT-u" outputId="785b6961-162e-4672-9a71-2110af6ed3f4"
chi_2 = 0
for i in range(len(F_observada)):
chi_2 += (F_observada[i] - F_esperada[i])**2 / F_esperada[i]
chi_2
# + [markdown] colab_type="text" id="gdwiYn6ZpsmK"
# 
# + [markdown] colab_type="text" id="7v6msKABpsmK"
# ---
# + [markdown] colab_type="text" id="PHLhbZ3IpsmK"
# ### **Passo 4** - Aceitação ou rejeição da hipótese nula
# + [markdown] colab_type="text" id="tiQ0gAMGpsmL"
# <img src='https://caelum-online-public.s3.amazonaws.com/1229-estatistica-parte3/01/img020.png' width=80%>
# + [markdown] colab_type="text" id="lhFLib7-psmM"
# ### <font color='red'>Critério do valor crítico</font>
#
# > ### Rejeitar $H_0$ se $\chi_{teste}^2 > \chi_{\alpha}^2$
# + colab={"base_uri": "https://localhost:8080/", "height": 35} colab_type="code" id="HSOhw7E7psmM" outputId="1b6ce243-ae51-4497-addd-3e7aae82f4f3"
chi_2 > chi2_alpha
# + [markdown] colab_type="text" id="hvHTGAOepsmN"
# ### <font color='green'>Conclusão: Com um nível de confiança de 95% rejeitamos a hipótese nula ($H_0$) e concluímos que as frequências observadas e esperadas são discrepantes, ou seja, a moeda não é honesta e precisa ser substituída.</font>
# + [markdown] colab_type="text" id="VcqxUxnopsmN"
# ### <font color='red'>Critério do valor $p$</font>
#
# > ### Rejeitar $H_0$ se o valor $p\leq\alpha$
# + colab={"base_uri": "https://localhost:8080/", "height": 35} colab_type="code" id="fP8w-6MZpsmO" outputId="0cc2f80a-ab46-4485-b7da-f07a9a7085a9"
chi_2
# -
p_value = chi2.sf(chi_2, graus_de_liberdade)
p_value
# + colab={"base_uri": "https://localhost:8080/", "height": 35} colab_type="code" id="uFG6yszWpsmQ" outputId="c2a489ef-2734-49f1-f96a-3239464d4baf"
p_value <= significancia
# + [markdown] colab_type="text" id="WpvavOAVpsmR"
# https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.chisquare.html
# + colab={} colab_type="code" id="Jc1RkAn6psmR"
from scipy.stats import chisquare
# + colab={"base_uri": "https://localhost:8080/", "height": 53} colab_type="code" id="jjiIlObspsmR" outputId="33e36135-64d4-4615-d273-0ecb4ed993b4"
chisquare(F_observada, F_esperada)
# + colab={"base_uri": "https://localhost:8080/", "height": 35} colab_type="code" id="reKBubKWpsmT" outputId="69394a7b-9acb-4ce2-fea4-f51a05c9b39f"
0.023651616655356 <= significancia
# + [markdown] colab_type="text" id="WlHU5fNNpsmU"
# ---
# + [markdown] colab_type="text" id="PUXdBJ9FpsmU"
# ## <font color='red'>Problema</font>
# + [markdown] colab_type="text" id="DXvJ2JnKpsmU"
# Um novo tratamento para acabar com o hábito de fumar está sendo empregado em um grupo de **35 pacientes** voluntários. De cada paciente testado foram obtidas as informações de quantidades de cigarros consumidos por dia antes e depois do término do tratamento. Assumindo um **nível de confiança de 95%** é possível concluir que, depois da aplicação do novo tratamento, houve uma mudança no hábito de fumar do grupo de pacientes testado?
# + [markdown] colab_type="text" id="BqE_PN7IpsmU"
# ## <font color=green>4.2 Teste Wilcoxon</font>
# ### Comparação de duas populações - amostras dependentes
# ***
# + [markdown] colab_type="text" id="1MxFBYB-psmU"
# Empregado quando se deseja comparar duas amostras relacionadas, amostras emparelhadas. Pode ser aplicado quando se deseja testar a diferença de duas condições, isto é, quando um mesmo elemento é submetido a duas medidas.
# + [markdown] colab_type="text" id="3H67Y7P7psmV"
# ### Dados do problema
# + colab={} colab_type="code" id="JBh5-HrnpsmV"
fumo = {
'Antes': [39, 25, 24, 50, 13, 52, 21, 29, 10, 22, 50, 15, 36, 39, 52, 48, 24, 15, 40, 41, 17, 12, 21, 49, 14, 55, 46, 22, 28, 23, 37, 17, 31, 49, 49],
'Depois': [16, 8, 12, 0, 14, 16, 13, 12, 19, 17, 17, 2, 15, 10, 20, 13, 0, 4, 16, 18, 16, 16, 9, 9, 18, 4, 17, 0, 11, 14, 0, 19, 2, 9, 6]
}
# + colab={"base_uri": "https://localhost:8080/", "height": 204} colab_type="code" id="AfEGWBp1psmW" outputId="b68e51d1-b307-432f-8658-4642285f3725"
confianca = 0.95
significancia = 1 - confianca
n = 35
# + colab={"base_uri": "https://localhost:8080/", "height": 35} colab_type="code" id="oNQvLLR8psmX" outputId="434a1498-67cb-47fd-b87a-f68bd05a923d"
fumo = pd.DataFrame(fumo)
fumo.head()
# + colab={"base_uri": "https://localhost:8080/", "height": 35} colab_type="code" id="wOgIYP0upsmZ" outputId="926b067c-2ea2-407b-edba-c57075fa5c94"
media_antes = fumo.Antes.mean()
media_antes
# -
media_depois = fumo.Depois.mean()
media_depois
# + [markdown] colab_type="text" id="gvnOmelVpsma"
# ### **Passo 1** - formulação das hipóteses $H_0$ e $H_1$
#
# #### <font color='red'>Lembre-se, a hipótese nula sempre contém a alegação de igualdade</font>
# + [markdown] colab_type="text" id="1AXOiD_epsma"
# ### $H_0: \mu_{antes} = \mu_{depois}$
#
# ### $H_1: \mu_{antes} \neq \mu_{depois}$
# + [markdown] colab_type="text" id="degUD6b2psmb"
# ---
# + [markdown] colab_type="text" id="3jrV_c2Opsmb"
# ### **Passo 2** - escolha da distribuição amostral adequada
# + [markdown] colab_type="text" id="vF-9xAPzpsmc"
# ### O tamanho da amostra é maior que 20?
# #### Resp.: Sim
# + [markdown] colab_type="text" id="tvxXutrGpsmc"
# ---
# + [markdown] colab_type="text" id="aihd76Wkpsmc"
# ### **Passo 3** - fixação da significância do teste ($\alpha$)
# + [markdown] colab_type="text" id="XF6AA8qLpsmc"
# ### Obtendo $z_{\alpha/2}$
# + colab={"base_uri": "https://localhost:8080/", "height": 35} colab_type="code" id="UsRrnCnXpsmd" outputId="969dfd46-755b-496b-b84a-1d3e9d565ed2"
z_alpha_2 = norm.ppf(confianca + significancia / 2)
# + colab={"base_uri": "https://localhost:8080/", "height": 35} colab_type="code" id="PTmwCAq9psme" outputId="a7cf10d8-f3c7-40cf-c2f4-9fef0ac65f26"
z_alpha_2
# + [markdown] colab_type="text" id="S2dqQHQJpsmf"
# 
# + [markdown] colab_type="text" id="z0iEZ685psmf"
# ---
# + [markdown] colab_type="text" id="zpYLf-dZpsmg"
# ### **Passo 4** - cálculo da estatística-teste e verificação desse valor com as áreas de aceitação e rejeição do teste
#
# # $$Z = \frac{T - \mu_T}{\sigma_T}$$
#
# Onde
#
# ## $T$ = menor das somas de postos de mesmo sinal
#
# # $$\mu_T = \frac{n(n+1)}{4}$$
# # $$\sigma_T = \sqrt{\frac{n(n + 1)(2n + 1)}{24}}$$
# + [markdown] colab_type="text" id="ZTY9IuIIpsmg"
# ### Construindo a tabela com os postos
# + colab={"base_uri": "https://localhost:8080/", "height": 1134} colab_type="code" id="o0fp6m3iqIlu" outputId="af1f1a0d-c2e7-478c-b637-71a520cc9a2c"
fumo
# + colab={"base_uri": "https://localhost:8080/", "height": 1134} colab_type="code" id="T4KmZijZotWj" outputId="97779ef9-8387-43d3-c41a-76b01cc7d52c"
fumo['Diff'] = fumo.Depois - fumo.Antes
# + colab={"base_uri": "https://localhost:8080/", "height": 1134} colab_type="code" id="c4fvYVrdotTL" outputId="a0da369c-d29f-40c1-e8e6-ff715c14e3e3"
fumo.head()
# + colab={"base_uri": "https://localhost:8080/", "height": 1134} colab_type="code" id="hwqK3Uu7otP7" outputId="dd57a7c5-e2f2-45af-fea1-8c9e765123ec"
fumo['|Diff|'] = fumo['Diff'].abs()
# + colab={"base_uri": "https://localhost:8080/", "height": 1134} colab_type="code" id="K_JKSwLHotMx" outputId="51594adf-1a5c-4d51-aad3-ed49e4c1e018"
fumo.head()
# + colab={"base_uri": "https://localhost:8080/", "height": 824} colab_type="code" id="h8TXHLh1otJh" outputId="dce72dc9-b1d1-435a-aa09-6b83648a5727"
fumo.sort_values(by='|Diff|', inplace=True)
fumo.head()
# + colab={"base_uri": "https://localhost:8080/", "height": 793} colab_type="code" id="fjX9_aRIotGD" outputId="a7f0ffc7-4e83-4d09-fca1-4a51302c284b"
fumo['Posto'] = range(1, len(fumo) + 1)
fumo.head()
# + colab={"base_uri": "https://localhost:8080/", "height": 1134} colab_type="code" id="uk79v15dotCn" outputId="fbb9268a-81df-4d32-b97a-832aaba86269"
posto = fumo[['|Diff|', 'Posto']].groupby('|Diff|').mean()
posto
# + colab={"base_uri": "https://localhost:8080/", "height": 1134} colab_type="code" id="qCdc7pGgos_T" outputId="9f19bd63-827d-40a8-f171-af9665b2df94"
posto.reset_index(inplace=True)
posto
# + colab={"base_uri": "https://localhost:8080/", "height": 1134} colab_type="code" id="R2Nyk4E_os77" outputId="e22a2377-88b8-4b5b-e901-ddd8b04cab75"
fumo.drop('Posto', axis=1, inplace=True)
fumo.head()
# + colab={"base_uri": "https://localhost:8080/", "height": 1134} colab_type="code" id="PoL2W6c7os4d" outputId="8d786d84-776b-4e77-b3a9-b783ed1799dd"
fumo = fumo.merge(posto, left_on='|Diff|', right_on='|Diff|', how='left')
fumo
# + colab={"base_uri": "https://localhost:8080/", "height": 1134} colab_type="code" id="kIu3BKAwos0z" outputId="df603288-73fc-482a-d4ea-4cd050b93a03"
fumo['Posto (+)'] = fumo.apply(lambda x: x.Posto if x.Diff > 0 else 0, axis=1)
fumo.head()
# -
fumo['Posto (-)'] = fumo.apply(lambda x: x.Posto if x.Diff < 0 else 0, axis=1)
fumo.head()
fumo.drop('Posto', axis=1, inplace=True)
fumo.head()
# + [markdown] colab_type="text" id="QrhSJfAipsmh"
# ### Obter $T$
#
# ## $T$ = menor das somas de postos de mesmo sinal
# + colab={"base_uri": "https://localhost:8080/", "height": 35} colab_type="code" id="TTWy-3OQpsmh" outputId="041d1650-b553-4f7c-f4ce-03284c9f445e"
T = min(fumo['Posto (+)'].sum(), fumo['Posto (-)'].sum())
T
# + [markdown] colab_type="text" id="3y4Tn1y4psmi"
# ### Obter $\mu_T$
#
# # $$\mu_T = \frac{n(n+1)}{4}$$
#
# + colab={"base_uri": "https://localhost:8080/", "height": 35} colab_type="code" id="0KhV5ph6psmi" outputId="d5a49227-aefe-4ab2-8f96-1575e4d00a68"
mu_T = (n * (n + 1)) / 4
mu_T
# + [markdown] colab_type="text" id="ZqFb-m08psmj"
# ### Obter $\sigma_T$
#
# # $$\sigma_T = \sqrt{\frac{n(n + 1)(2n + 1)}{24}}$$
# + colab={"base_uri": "https://localhost:8080/", "height": 35} colab_type="code" id="c6dypGFSpsmj" outputId="a56e71d7-cec3-4529-f266-b82315fa8766"
sigma_T = np.sqrt((n * (n + 1) * ((2 * n) + 1)) / 24)
sigma_T
# + [markdown] colab_type="text" id="qEk2CUKApsmk"
# ### Obter $Z_{teste}$
#
# # $$Z = \frac{T - \mu_T}{\sigma_T}$$
# + colab={"base_uri": "https://localhost:8080/", "height": 35} colab_type="code" id="KqPQgbBEpsmk" outputId="fc02c944-b662-4539-a4c4-e91f746358f3"
Z = (T - mu_T) / sigma_T
Z
# + [markdown] colab_type="text" id="iMpU26IZpsmm"
# 
# + [markdown] colab_type="text" id="XFgoaLzSpsmm"
# ---
# + [markdown] colab_type="text" id="YYGhG-lSpsmm"
# ### **Passo 5** - Aceitação ou rejeição da hipótese nula
# + [markdown] colab_type="text" id="pDOdx-Vqpsmm"
# <img src='https://caelum-online-public.s3.amazonaws.com/1229-estatistica-parte3/01/img022.png' width='80%'>
# + [markdown] colab_type="text" id="ODKOD-rkpsmm"
# ### <font color='red'>Critério do valor crítico</font>
#
# > ### Rejeitar $H_0$ se $Z \leq -z_{\alpha / 2}$ ou se $Z \geq z_{\alpha / 2}$
# + colab={"base_uri": "https://localhost:8080/", "height": 35} colab_type="code" id="Gmp8dB6Apsmm" outputId="8311f709-2b10-477c-8410-93d6efbde833"
Z <= z_alpha_2 or Z >= z_alpha_2
# + colab={"base_uri": "https://localhost:8080/", "height": 35} colab_type="code" id="E9jxoexkpsmn" outputId="57165249-f63d-411c-ff87-d25e1e9ee401"
if (Z <= z_alpha_2 or Z >= z_alpha_2):
print('Rejeita H0, ou seja, existe uma diferença entre os grupos.')
else:
print('Aceita H0, ou seja, os grupos não apresentam diferença.')
# + [markdown] colab_type="text" id="n0gtzn53psmo"
# ### <font color='green'>Conclusão: Rejeitamos a hipótese de que não existe diferença entre os grupos, isto é, existe uma diferença entre as médias de cigarros fumados pelos pacientes antes e depois do tratamento. E como é possível verificar através das médias de cigarros fumados por dia antes (31.86) e depois (11.2) do tratamento, podemos concluir que o tratamento apresentou resultado satisfatório.</font>
# + [markdown] colab_type="text" id="8bP36mQ-psmp"
# ### <font color='red'>Critério do valor $p$</font>
#
# > ### Rejeitar $H_0$ se o valor $p\leq\alpha$
# -
pvalue = 2 * norm.cdf(Z)
pvalue
pvalue <= significancia
# + [markdown] colab_type="text" id="H8kFeqh1psmp"
# https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.wilcoxon.html
# + colab={} colab_type="code" id="bgWrxnBmpsmp"
from scipy.stats import wilcoxon
# + colab={"base_uri": "https://localhost:8080/", "height": 53} colab_type="code" id="PsdWRoHCpsmp" outputId="c3ccd57c-5423-4d5d-b136-b84b8d627a7b"
estatistica_de_teste, pvalor = wilcoxon(x=fumo.Antes, y=fumo.Depois)
# -
pvalor <= significancia
if pvalor <= significancia:
print('Rejeita H0, ou seja, as médias de cigarro fumado antes e depois são diferentes. Em outras palavras, o tratamento surtiu algum efeito.')
fumo.Antes.mean(), fumo.Depois.mean()
# + [markdown] colab_type="text" id="ZGNui18Xpsms"
# ---
| 53,008 |
/Analytics/reconcile timeline data.ipynb | 67ee57475d8ef6ff83d09aa8aed29a022df376b0 | [] | no_license | megamindbrian/jupytangular | https://github.com/megamindbrian/jupytangular | 1 | 1 | null | null | null | null | Jupyter Notebook | false | false | .js | 719 | // ---
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// language: javascript
// name: javascript
// ---
// # How to read Google calendar and timeline data?
//
//
// + inputHidden=false outputHidden=false
| 444 |
/Automating Office.ipynb | e5e8ac52ed13e48060a1f7e4ea35954180e985fb | [] | no_license | nikhilkabbin/ipython-notebooks | https://github.com/nikhilkabbin/ipython-notebooks | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 54,386 | # ---
# jupyter:
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# name: python3
# ---
# + [markdown] slideshow={"slide_type": "slide"}
# ## Automating Microsoft Office with Python
#
# About Me: Nikhil Kabbin
# - Data Scientist @Gramener
# - Twitter/Github: @nikhilkabbin
# + [markdown] slideshow={"slide_type": "slide"}
# <img width="1080" src="comic.png">
# + [markdown] slideshow={"slide_type": "slide"}
# Windows applications, for many years, have provided a COM API for automation. This includes Microsoft Office as well.
#
# pywin32 is a library that lets you do many interesting things in Windows, including access these COM APIs.
#
# For example, to open PowerPoint and draw a circle, this is what it takes:
# + slideshow={"slide_type": "slide"}
import win32com.client
# Open PowerPoint
Application = win32com.client.Dispatch("PowerPoint.Application")
# Add a presentation
Presentation = Application.Presentations.Add()
# Add a slide with a blank layout (12 stands for blank layout)
Base = Presentation.Slides.Add(1, 12)
# Add an oval. Shape 9 is an oval.
oval = Base.Shapes.AddShape(9, 100, 100, 100, 100)
# + [markdown] slideshow={"slide_type": "skip"}
# You'll have to try this out to see the result, but just FYI, this will open a new PowerPoint window and add a slide with a circle in it.
#
# This opens up a lot of opportunities for slideware. Similarly, we can open an Excel application, add a circle, and change a few cells.
# + [markdown] slideshow={"slide_type": "slide"}
# ### We can also open Excel application and add a circle, and change few cells
# + slideshow={"slide_type": "fragment"}
# Open Excel
Application = win32com.client.Dispatch("Excel.Application")
# Show Excel. Unlike PPT, Word & Excel open up "hidden"
Application.Visible = 10
# Add a workbook
Workbook = Application.Workbooks.Add()
# Take the active sheet
Base = Workbook.ActiveSheet
# Add an oval. Shape 9 is an oval.
oval = Base.Shapes.AddShape(9, 100, 100, 100, 100)
# In the first row, add Values: 0.0, 0.5, 1.0
Base.Cells(1, 1).Value = 'Values'
Base.Cells(1, 2).Value = 0.0
Base.Cells(1, 3).Value = 0.5
Base.Cells(1, 4).Value = 1.0
# + [markdown] slideshow={"slide_type": "subslide"}
# This means one can go about creating Excel models directly with Python.
# + [markdown] slideshow={"slide_type": "slide"}
# ## Picturing the IMDb Top 250
#
# Let's begin by creating a slide that shows all of the [Top 250 movies](http://www.imdb.com/chart/top) on the IMDb.
#
# First, let's load all the movies.
# + slideshow={"slide_type": "fragment"}
from bs4 import BeautifulSoup as bs
import requests
url = 'http://www.imdb.com/chart/top'
r = requests.get(url)
soup = bs(r.content, "html.parser")
tds = soup.findAll('td', {'class': 'titleColumn'})
movies = []
for td in tds:
movies.append(td.findChildren('a')[0])
movies
# print("Movie Name: {}".format(movies[0].text))
# print("Movies Length: {}".format(len(movies)))
# + [markdown] slideshow={"slide_type": "slide"}
# We can show these movies. But before that, we can't remember these numbers for the shapes (like 9 is for circle). Let's pre-define those in line with how Office uses them and import them.
# + slideshow={"slide_type": "fragment"}
from MSO import *
# + [markdown] slideshow={"slide_type": "slide"}
# Let's draw each movie poster as a little box on a 25x10 grid. We don't have the images yet, but first, let's just draw the rectangles.
# + slideshow={"slide_type": "fragment"}
import win32com.client
# Open PowerPoint
Application = win32com.client.Dispatch("PowerPoint.Application")
# Add a presentation
Presentation = Application.Presentations.Add()
Presentation.PageSetup.SlideSize = ppSlideSizeOnScreen
Base = Presentation.Slides.Add(1, ppLayoutBlank)
width, height = 28, 41
for i, movie in enumerate(movies):
x = 10 + width * (i % 25)
y = 100 + height * (i // 25)
r = Base.Shapes.AddShape(
msoShapeRectangle,
x, y,
width, height)
# + [markdown] slideshow={"slide_type": "slide"}
# It would be nice to get posters into those, so let's scrape the posters.
# + slideshow={"slide_type": "fragment"}
import os
from urllib.parse import urljoin
from urllib.request import urlretrieve
from hashlib import md5
if not os.path.exists('img'):
os.makedirs('img')
def filename(movie):
'''Filename = MD5 hash of its title in UTF8'''
name = md5(movie.text.encode('utf8')).hexdigest()
return os.path.join('img', name + '.jpg')
count = 0
for movie in movies:
if os.path.exists(filename(movie)):
continue
count += 1
url = urljoin('http://www.imdb.com/', movie['href'])
ru = requests.get(url)
usoup = bs(ru.content, "html.parser")
img = usoup.find('div', {'class': 'poster'}).findChildren('img')[0]
urlretrieve(img.get('src'), filename(movie))
# + [markdown] slideshow={"slide_type": "slide"}
# Now, instead of just rectangles, we'll use the posters.
# + slideshow={"slide_type": "fragment"}
# Open PowerPoint
Application = win32com.client.Dispatch("PowerPoint.Application")
# Add a presentation
Presentation = Application.Presentations.Add()
Presentation.PageSetup.SlideSize = ppSlideSizeOnScreen
Base = Presentation.Slides.Add(1, ppLayoutBlank)
width, height = 28, 41
for i, movie in enumerate(movies):
x = 10 + width * (i % 25)
y = 100 + height * (i // 25)
image = Base.Shapes.AddPicture(
os.path.abspath(filename(movie)),
LinkToFile=True,
SaveWithDocument=False,
Left=x, Top=y,
Width=width, Height=height)
# + [markdown] slideshow={"slide_type": "slide"}
# Wouldn't it be nice to have these hyperlinked to the movies?
# + slideshow={"slide_type": "fragment"}
# Open PowerPoint
Application = win32com.client.Dispatch("PowerPoint.Application")
# Add a presentation
Presentation = Application.Presentations.Add()
Presentation.PageSetup.SlideSize = ppSlideSizeOnScreen
Base = Presentation.Slides.Add(1, ppLayoutBlank)
width, height = 28, 41
for i, movie in enumerate(movies):
x = 10 + width * (i % 25)
y = 100 + height * (i // 25)
image = Base.Shapes.AddPicture(
os.path.abspath(filename(movie)),
LinkToFile=True,
SaveWithDocument=False,
Left=x, Top=y,
Width=width, Height=height)
url = urljoin('http://www.imdb.com/', movie.get('href'))
link = image.ActionSettings(ppMouseClick).Hyperlink
link.Address = url
link.ScreenTip = movie.text # .encode('cp1252')
# + [markdown] slideshow={"slide_type": "slide"}
# This is ordered by rank, which is useful, but this makes it hard to locate a specific movie. What if we could sort this alphabetically?
#
# But then, we don't want to lose the ordering by rank either. Could we, perhaps, get these movies to move on the click of a button to alphabetical or rank order?
#
# Let's start by adding two buttons -- one to sort alphabetically and the ohter to sort by rank.
# + slideshow={"slide_type": "fragment"}
Base = Presentation.Slides.Add(1, ppLayoutBlank)
# Add two buttons: alphabetical and by rating
button_alpha = Base.Shapes.AddShape(msoShapeRectangle, 400, 10, 150, 40)
button_alpha.TextFrame.TextRange.Text = 'Alphabetical'
button_rating = Base.Shapes.AddShape(msoShapeRectangle, 560, 10, 150, 40)
button_rating.TextFrame.TextRange.Text = 'By rating'
# Get the index position when sorted alphabetically
movies_alpha = sorted(movies, key=lambda v: v.text)
index_alpha = dict((movie.text, i) for i, movie in enumerate(movies_alpha))
# + [markdown] slideshow={"slide_type": "slide"}
# We'll create a function that moves an image along a path when a trigger is clicked. This will be applied to each of the images.
# + slideshow={"slide_type": "fragment"}
def animate(seq, image, trigger, path, duration=1.5):
'''Move image along path when trigger is clicked'''
effect = seq.AddEffect(
Shape=image,
effectId=msoAnimEffectPathDown,
trigger=msoAnimTriggerOnShapeClick,
)
ani = effect.Behaviors.Add(msoAnimTypeMotion)
ani.MotionEffect.Path = path
effect.Timing.TriggerType = msoAnimTriggerWithPrevious
effect.Timing.TriggerShape = trigger
effect.Timing.Duration = duration
# + slideshow={"slide_type": "slide"}
seq_alpha = Base.TimeLine.InteractiveSequences.Add()
seq_rating = Base.TimeLine.InteractiveSequences.Add()
width, height = 28, 41
for i, movie in enumerate(movies):
x = 10 + width * (i % 25)
y = 100 + height * (i // 25)
image = Base.Shapes.AddPicture(
os.path.abspath(filename(movie)),
LinkToFile=True,
SaveWithDocument=False,
Left=x, Top=y,
Width=width, Height=height)
url = urljoin('http://www.imdb.com/', movie['href'])
link = image.ActionSettings(ppMouseClick).Hyperlink
link.Address = url
link.ScreenTip = movie.text # .encode('cp1252')
# Alphabetical
index = index_alpha[movie.text]
animate(seq_alpha, image, trigger=button_alpha, path='M0,0 L{:.3f},{:.3f}'.format(
(10 + width * (index % 25) - x) / 720.,
(100 + height * (index // 25) - y) / 540.,
))
# By rating
animate(seq_rating, image, trigger=button_rating, path='M{:.3f},{:.3f} L0,0'.format(
(10 + width * (index % 25) - x) / 720.,
(100 + height * (index // 25) - y) / 540.,
))
# + [markdown] slideshow={"slide_type": "slide"}
# # Real-life examples
#
# You can see how these were put to use at [Gramener](http://gramener.com/):
#
# - [Vijay Karnataka's election coverage](http://gramener.com/vijaykarnataka/) was produced entirely in PowerPoint using Python. (And in Kannada, no less!)
# - [Appstore downloads](http://gramener.com/appstore/appstore.pptx) -- a mini interactive application that shows the number of downloads from an app store, broken up by country, device, customer segment and time.
# - [Revenue breakup](http://gramener.com/fmcg/fmcg-revenue-breakup.pptx) of an FMCG company as a clickable application
# + [markdown] slideshow={"slide_type": "slide"}
# #### This is Fancy, but... It will probably take lot of time to create these charts.
#
# ### What if I want to automate existing charts in Office?
# + [markdown] slideshow={"slide_type": "slide"}
# ### Microsoft adopted XML for PowerPoint 2007 and its other Office programs
# + slideshow={"slide_type": "fragment"}
from pptx import Presentation
prs = Presentation()
title_slide_layout = prs.slide_layouts[0]
slide = prs.slides.add_slide(title_slide_layout)
title = slide.shapes.title
subtitle = slide.placeholders[1]
title.text = "Hello, World!"
subtitle.text = "Subtitle"
prs.save('Azure.pptx')
# + [markdown] slideshow={"slide_type": "slide"}
# Now let's try to add a simple bar chart in our slide!
# + slideshow={"slide_type": "fragment"}
from pptx import Presentation
from pptx.chart.data import CategoryChartData
from pptx.enum.chart import XL_CHART_TYPE
from pptx.util import Inches
# create presentation with 1 slide ------
prs = Presentation()
slide = prs.slides.add_slide(prs.slide_layouts[5])
# define chart data ---------------------
chart_data = CategoryChartData()
chart_data.categories = ['East', 'West', 'Midwest']
chart_data.add_series('Series 1', (19.2, 21.4, 16.7))
# add chart to slide --------------------
x, y, cx, cy = Inches(2), Inches(2), Inches(6), Inches(4.5)
slide.shapes.add_chart(
XL_CHART_TYPE.COLUMN_CLUSTERED, x, y, cx, cy, chart_data
)
prs.save('charttest.pptx')
# + [markdown] slideshow={"slide_type": "slide"}
# #### This is great! But I still need to write lot of code. Can we spend less time in writing code and focus on Chart and slides?
# + [markdown] slideshow={"slide_type": "fragment"}
# #### [Gramex PPTGen](https://learn.gramener.com/guide/pptxhandler/#pptgen-command-line) will help us do that!
# + slideshow={"slide_type": "fragment"}
from gramex.pptgen import pptgen
pptgen(
source='input.pptx', # optional path to source. Defaults to blank PPT with 1 slide
target='output2.pptx', # optional target. Otherwise, returns the pptx.Presentation()
change={ # Configurations are same as when loading from the YAML file
'Title 1': { # Take the shape named "Title 1"
'text': 'Welcome to Azure DataFest 2019!' # Replace its text with "New Title"
}
}
)
# + [markdown] slideshow={"slide_type": "fragment"}
# #### Or You can use Command Line interface...
# + [markdown] slideshow={"slide_type": "fragment"}
# ```pptgen config.yaml --target new.pptx "--change.Title 1.text" "Updated title"```
# + slideshow={"slide_type": "skip"}
### TODO: pptgen chart example
# + [markdown] slideshow={"slide_type": "slide"}
# # Live tweeting
#
# Just for kicks, let's use PowerPoint as a dashboard to show live tweets.
#
# I picked [TwitterAPI](https://github.com/geduldig/TwitterAPI) to get streaming results, but [twython](https://github.com/ryanmcgrath/twython) and [Python Twitter Tools](https://github.com/sixohsix/twitter) look fine too.
# + slideshow={"slide_type": "fragment"}
from TwitterAPI import TwitterAPI
# I'm keeping my keys and secrets in a secret file.
from secret_twitter import consumer_key, consumer_secret, access_token_key, access_token_secret
api = TwitterAPI(consumer_key, consumer_secret, access_token_key, access_token_secret)
# + slideshow={"slide_type": "slide"}
def draw_tweet(Base, item, pos):
y = 40 + (pos % 4) * 120
image = Base.Shapes.AddPicture(
# To get the larger resolution image, just remove _normal from the URL
item['user']['profile_image_url'].replace('_normal', ''),
LinkToFile=True,
SaveWithDocument=False,
Left=20, Top=y,
Width=100, Height=100)
try:
status = item['text'] #.encode('cp1252')
except UnicodeEncodeError:
status = item['text']
text = Base.Shapes.AddShape(msoShapeRectangle, 130, y, 460, 100)
text.Fill.ForeColor.ObjectThemeColor = msoThemeColorText1
text.Fill.ForeColor.Brightness = +0.95
text.Line.Visible = msoFalse
text.TextFrame.TextRange.Text = status
text.TextFrame.TextRange.Font.Color.ObjectThemeColor = msoThemeColorText1
text.TextFrame.TextRange.ParagraphFormat.Alignment = ppAlignLeft
user = Base.Shapes.AddShape(msoShapeRectangle, 600, y, 100, 100)
user.Fill.ForeColor.ObjectThemeColor = msoThemeColorAccent6
user.Line.Visible = False
user.TextFrame.TextRange.Text = '@' + item['user']['screen_name']
# + slideshow={"slide_type": "slide"}
# Open PowerPoint
Application = win32com.client.Dispatch("PowerPoint.Application")
# Add a presentation
Presentation = Application.Presentations.Add()
Presentation.PageSetup.SlideSize = ppSlideSizeOnScreen
Base = Presentation.Slides.Add(1, ppLayoutBlank)
r = api.request('search/tweets', {'q':'azuredatafest'})
for pos, item in enumerate(r):
draw_tweet(Base, item, pos)
if pos > 10:
break
# + [markdown] slideshow={"slide_type": "slide"}
# ## Thank You :-)
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/Notes_on_Problems/Divide_and_Conquer/MergeSort_Count_Inversions.ipynb | 4c2edc81b66ed5c0f7a2481bd681c07a79cc96ed | [] | no_license | dangkhoa1992/ProblemsCache | https://github.com/dangkhoa1992/ProblemsCache | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 2,788 | # ---
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# ---
# ## Count number of inversions in an unsorted array
#
# - Inversion Count for an array indicates – how far (or close) the array is from being sorted.
# + If array is already sorted then inversion count is 0.
# + If array is sorted in reverse order that inversion count is the maximum.
# - Formally speaking, two elements a[i] and a[j] form an inversion if a[i] > a[j] and i < j
#
# - **Example**:
#
# ```
# Input: a = [2, 4, 1, 3, 5]
# Output: 3
# Explanation: (2, 1), (4, 1), (4, 3).
#
# Input: a = [4, 3, 2, 1]
# Output: 6
#
# Input: a = [1, 2, 3, 4]
# Output: 0
#
# Input: a = [3, 3, 3, 3]
# Output: 0
# ```
#
# #### Solution
# - Inversion count when 1st half and 2nd half not empty
# - Prefer 2nd half than 1st half
# - Inv count = size of the remaining 1st half at the moment
#
# <img src="./img/4.jpg" alt="drawing" width="650"/>
#
# ```C++
# int merge(vector<int> &nums, vector<int> &temp, int l, int m, int r) {
# int i = l;
# int j = m + 1;
# int cur = 0;
#
# int invCnt = 0;
# while(i <= m || j <= r) {
# if(i > m) temp[cur++] = nums[j++];
#
# else if(j > r) temp[cur++] = nums[i++];
#
# else if(nums[i] <= nums[j]) temp[cur++] = nums[i++];
#
# // Prefer 2nd half: invCnt = all elements in the 1st half
# else {
# temp[cur++] = nums[j++];
# invCnt += (m+1-i);
# }
# }
#
# for(int i=0; i < cur; ++i) nums[l + i] = temp[i];
# return invCnt;
# }
# int mergeSort(vector<int> &nums, vector<int> &temp, int l, int r) {
# if(l >= r) return 0;
#
# int m = l+(r-l)/2;
# return mergeSort(nums, temp, l, m) + \
# mergeSort(nums, temp, m+1, r) + \
# merge(nums, temp, l, m, r);
# }
# ```
riance_ratio_))
plt.xlabel('Numar de componente')
plt.ylabel('Varianta')
# -
# ##### Observam mai sus ca 100 de componente ar fi o valoare decenta a micsora volumul de calcule si in acelasi timp pentru a avea o acuratete mare
# # PCA
# +
componente_pca = 85
pca = PCA(n_components=componente_pca)
X_pca_train = pca.fit_transform(X_sc_train)
X_pca_test = pca.transform(X_sc_test)
pca_std = np.std(X_pca_train)
# -
inv_pca = pca.inverse_transform(X_pca_train)
inv_sc = scaler.inverse_transform(inv_pca)
# +
def side_by_side(indexes):
org = X_train[indexes].reshape(28,28)
rec = inv_sc[indexes].reshape(28,28)
pair = np.concatenate((org, rec), axis=1)
plt.figure(figsize=(4,2))
plt.imshow(pair)
plt.show()
for index in range(1,10):
side_by_side(index)
# -
# #### Dupa cate putem observa mai sus, alegand doar 100 de componente din cele 784 (imagini de 28x28 pixeli) putem imaginile nu sunt distorsionate mult si sunt inca usor de recunoscut, "compresia" fiind foarte buna
#
# ### Creare retea PCA - MLP
# +
model = Sequential()
layers = 1
units = 128
model.add(Dense(units, input_dim=componente_pca, activation='relu'))
model.add(GaussianNoise(pca_std))
for i in range(layers):
model.add(Dense(units, activation='relu'))
model.add(GaussianNoise(pca_std))
model.add(Dropout(0.1))
model.add(Dense(10, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['categorical_accuracy'])
model.summary()
# -
# ### Antrenare PCA - MLP
model.fit(X_pca_train, Y_train, epochs=100, batch_size=256, validation_split=0.15)
# Acuratetea in jur de
predictions = model.predict_classes(X_pca_test, verbose=0)
#
# Vom incerca o testare vizuala in acesta parte a proiectului afisand predictia facuta de PCA -MLP, iar apoi imaginea
# +
numere_aleatore = random.sample(range(1, 28000), 10)
for i in numere_aleatore:
plt.imshow(X_test[i].reshape(28,28))
print('Predictie :', predictions[i])
plt.show()
# -
| 4,052 |
/CapstoneWeek3Step2.ipynb | 8bb5ba6c7421d990c2661740ccc81273fd004dc0 | [] | no_license | willowinds613/Coursera_Capstone | https://github.com/willowinds613/Coursera_Capstone | 0 | 1 | null | null | null | null | Jupyter Notebook | false | false | .py | 87,678 | # ---
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# ---
# # Implementing logistic regression from scratch
#
# The goal of this notebook is to implement your own logistic regression classifier. You will:
#
# * Extract features from Amazon product reviews.
# * Convert an SFrame into a NumPy array.
# * Implement the link function for logistic regression.
# * Write a function to compute the derivative of the log likelihood function with respect to a single coefficient.
# * Implement gradient ascent.
# * Given a set of coefficients, predict sentiments.
# * Compute classification accuracy for the logistic regression model.
#
# Let's get started!
#
# ## Fire up GraphLab Create
#
# Make sure you have the latest version of GraphLab Create. Upgrade by
# ```
# pip install graphlab-create --upgrade
# ```
# See [this page](https://dato.com/download/) for detailed instructions on upgrading.
import graphlab
# ## Load review dataset
# For this assignment, we will use a subset of the Amazon product review dataset. The subset was chosen to contain similar numbers of positive and negative reviews, as the original dataset consisted primarily of positive reviews.
products = graphlab.SFrame('amazon_baby_subset.gl/')
# One column of this dataset is 'sentiment', corresponding to the class label with +1 indicating a review with positive sentiment and -1 indicating one with negative sentiment.
products['sentiment']
# Let us quickly explore more of this dataset. The 'name' column indicates the name of the product. Here we list the first 10 products in the dataset. We then count the number of positive and negative reviews.
products.head(10)['name']
print '# of positive reviews =', len(products[products['sentiment']==1])
print '# of negative reviews =', len(products[products['sentiment']==-1])
# **Note:** For this assignment, we eliminated class imbalance by choosing
# a subset of the data with a similar number of positive and negative reviews.
#
# ## Apply text cleaning on the review data
#
# In this section, we will perform some simple feature cleaning using **SFrames**. The last assignment used all words in building bag-of-words features, but here we limit ourselves to 193 words (for simplicity). We compiled a list of 193 most frequent words into a JSON file.
#
# Now, we will load these words from this JSON file:
import json
with open('important_words.json', 'r') as f: # Reads the list of most frequent words
important_words = json.load(f)
important_words = [str(s) for s in important_words]
print important_words
# Now, we will perform 2 simple data transformations:
#
# 1. Remove punctuation using [Python's built-in](https://docs.python.org/2/library/string.html) string functionality.
# 2. Compute word counts (only for **important_words**)
#
# We start with *Step 1* which can be done as follows:
# +
def remove_punctuation(text):
import string
return text.translate(None, string.punctuation)
products['review_clean'] = products['review'].apply(remove_punctuation)
# -
# Now we proceed with *Step 2*. For each word in **important_words**, we compute a count for the number of times the word occurs in the review. We will store this count in a separate column (one for each word). The result of this feature processing is a single column for each word in **important_words** which keeps a count of the number of times the respective word occurs in the review text.
#
#
# **Note:** There are several ways of doing this. In this assignment, we use the built-in *count* function for Python lists. Each review string is first split into individual words and the number of occurances of a given word is counted.
for word in important_words:
products[word] = products['review_clean'].apply(lambda s : s.split().count(word))
# The SFrame **products** now contains one column for each of the 193 **important_words**. As an example, the column **perfect** contains a count of the number of times the word **perfect** occurs in each of the reviews.
products['perfect']
# Now, write some code to compute the number of product reviews that contain the word **perfect**.
#
# **Hint**:
# * First create a column called `contains_perfect` which is set to 1 if the count of the word **perfect** (stored in column **perfect**) is >= 1.
# * Sum the number of 1s in the column `contains_perfect`.
# +
products['contains_perfect'] = products['perfect'] >= 1
products['contains_perfect'].sum()
# -
# **Quiz Question**. How many reviews contain the word **perfect**?
# ## Convert SFrame to NumPy array
#
# As you have seen previously, NumPy is a powerful library for doing matrix manipulation. Let us convert our data to matrices and then implement our algorithms with matrices.
#
# First, make sure you can perform the following import.
import numpy as np
# We now provide you with a function that extracts columns from an SFrame and converts them into a NumPy array. Two arrays are returned: one representing features and another representing class labels. Note that the feature matrix includes an additional column 'intercept' to take account of the intercept term.
def get_numpy_data(data_sframe, features, label):
data_sframe['intercept'] = 1
features = ['intercept'] + features
features_sframe = data_sframe[features]
feature_matrix = features_sframe.to_numpy()
label_sarray = data_sframe[label]
label_array = label_sarray.to_numpy()
return(feature_matrix, label_array)
# Let us convert the data into NumPy arrays.
# Warning: This may take a few minutes...
feature_matrix, sentiment = get_numpy_data(products, important_words, 'sentiment')
# **Are you running this notebook on an Amazon EC2 t2.micro instance?** (If you are using your own machine, please skip this section)
#
# It has been reported that t2.micro instances do not provide sufficient power to complete the conversion in acceptable amount of time. For interest of time, please refrain from running `get_numpy_data` function. Instead, download the [binary file](https://s3.amazonaws.com/static.dato.com/files/coursera/course-3/numpy-arrays/module-3-assignment-numpy-arrays.npz) containing the four NumPy arrays you'll need for the assignment. To load the arrays, run the following commands:
# ```
# arrays = np.load('module-3-assignment-numpy-arrays.npz')
# feature_matrix, sentiment = arrays['feature_matrix'], arrays['sentiment']
# ```
feature_matrix.shape
# ** Quiz Question:** How many features are there in the **feature_matrix**?
#
# ** Quiz Question:** Assuming that the intercept is present, how does the number of features in **feature_matrix** relate to the number of features in the logistic regression model?
# Now, let us see what the **sentiment** column looks like:
sentiment
# ## Estimating conditional probability with link function
# Recall from lecture that the link function is given by:
# $$
# P(y_i = +1 | \mathbf{x}_i,\mathbf{w}) = \frac{1}{1 + \exp(-\mathbf{w}^T h(\mathbf{x}_i))},
# $$
#
# where the feature vector $h(\mathbf{x}_i)$ represents the word counts of **important_words** in the review $\mathbf{x}_i$. Complete the following function that implements the link function:
'''
produces probablistic estimate for P(y_i = +1 | x_i, w).
estimate ranges between 0 and 1.
'''
def predict_probability(feature_matrix, coefficients):
# Take dot product of feature_matrix and coefficients
# YOUR CODE HERE
scores = np.dot(feature_matrix, coefficients)
# Compute P(y_i = +1 | x_i, w) using the link function
# YOUR CODE HERE
predictions = map(lambda x: 1 / (1 + np.exp(- x)), scores)
# return predictions
return predictions
# **Aside**. How the link function works with matrix algebra
#
# Since the word counts are stored as columns in **feature_matrix**, each $i$-th row of the matrix corresponds to the feature vector $h(\mathbf{x}_i)$:
# $$
# [\text{feature_matrix}] =
# \left[
# \begin{array}{c}
# h(\mathbf{x}_1)^T \\
# h(\mathbf{x}_2)^T \\
# \vdots \\
# h(\mathbf{x}_N)^T
# \end{array}
# \right] =
# \left[
# \begin{array}{cccc}
# h_0(\mathbf{x}_1) & h_1(\mathbf{x}_1) & \cdots & h_D(\mathbf{x}_1) \\
# h_0(\mathbf{x}_2) & h_1(\mathbf{x}_2) & \cdots & h_D(\mathbf{x}_2) \\
# \vdots & \vdots & \ddots & \vdots \\
# h_0(\mathbf{x}_N) & h_1(\mathbf{x}_N) & \cdots & h_D(\mathbf{x}_N)
# \end{array}
# \right]
# $$
#
# By the rules of matrix multiplication, the score vector containing elements $\mathbf{w}^T h(\mathbf{x}_i)$ is obtained by multiplying **feature_matrix** and the coefficient vector $\mathbf{w}$.
# $$
# [\text{score}] =
# [\text{feature_matrix}]\mathbf{w} =
# \left[
# \begin{array}{c}
# h(\mathbf{x}_1)^T \\
# h(\mathbf{x}_2)^T \\
# \vdots \\
# h(\mathbf{x}_N)^T
# \end{array}
# \right]
# \mathbf{w}
# = \left[
# \begin{array}{c}
# h(\mathbf{x}_1)^T\mathbf{w} \\
# h(\mathbf{x}_2)^T\mathbf{w} \\
# \vdots \\
# h(\mathbf{x}_N)^T\mathbf{w}
# \end{array}
# \right]
# = \left[
# \begin{array}{c}
# \mathbf{w}^T h(\mathbf{x}_1) \\
# \mathbf{w}^T h(\mathbf{x}_2) \\
# \vdots \\
# \mathbf{w}^T h(\mathbf{x}_N)
# \end{array}
# \right]
# $$
# **Checkpoint**
#
# Just to make sure you are on the right track, we have provided a few examples. If your `predict_probability` function is implemented correctly, then the outputs will match:
# +
dummy_feature_matrix = np.array([[1.,2.,3.], [1.,-1.,-1]])
dummy_coefficients = np.array([1., 3., -1.])
correct_scores = np.array( [ 1.*1. + 2.*3. + 3.*(-1.), 1.*1. + (-1.)*3. + (-1.)*(-1.) ] )
correct_predictions = np.array( [ 1./(1+np.exp(-correct_scores[0])), 1./(1+np.exp(-correct_scores[1])) ] )
print 'The following outputs must match '
print '------------------------------------------------'
print 'correct_predictions =', correct_predictions
print 'output of predict_probability =', predict_probability(dummy_feature_matrix, dummy_coefficients)
# -
# ## Compute derivative of log likelihood with respect to a single coefficient
#
# Recall from lecture:
# $$
# \frac{\partial\ell}{\partial w_j} = \sum_{i=1}^N h_j(\mathbf{x}_i)\left(\mathbf{1}[y_i = +1] - P(y_i = +1 | \mathbf{x}_i, \mathbf{w})\right)
# $$
#
# We will now write a function that computes the derivative of log likelihood with respect to a single coefficient $w_j$. The function accepts two arguments:
# * `errors` vector containing $\mathbf{1}[y_i = +1] - P(y_i = +1 | \mathbf{x}_i, \mathbf{w})$ for all $i$.
# * `feature` vector containing $h_j(\mathbf{x}_i)$ for all $i$.
#
# Complete the following code block:
def feature_derivative(errors, feature):
# Compute the dot product of errors and feature
derivative = np.dot(errors, feature)
# Return the derivative
return derivative
# In the main lecture, our focus was on the likelihood. In the advanced optional video, however, we introduced a transformation of this likelihood---called the log likelihood---that simplifies the derivation of the gradient and is more numerically stable. Due to its numerical stability, we will use the log likelihood instead of the likelihood to assess the algorithm.
#
# The log likelihood is computed using the following formula (see the advanced optional video if you are curious about the derivation of this equation):
#
# $$\ell\ell(\mathbf{w}) = \sum_{i=1}^N \Big( (\mathbf{1}[y_i = +1] - 1)\mathbf{w}^T h(\mathbf{x}_i) - \ln\left(1 + \exp(-\mathbf{w}^T h(\mathbf{x}_i))\right) \Big) $$
#
# We provide a function to compute the log likelihood for the entire dataset.
def compute_log_likelihood(feature_matrix, sentiment, coefficients):
indicator = (sentiment==+1)
scores = np.dot(feature_matrix, coefficients)
logexp = np.log(1. + np.exp(-scores))
# Simple check to prevent overflow
mask = np.isinf(logexp)
logexp[mask] = -scores[mask]
lp = np.sum((indicator-1)*scores - logexp)
return lp
# **Checkpoint**
#
# Just to make sure we are on the same page, run the following code block and check that the outputs match.
# +
dummy_feature_matrix = np.array([[1.,2.,3.], [1.,-1.,-1]])
dummy_coefficients = np.array([1., 3., -1.])
dummy_sentiment = np.array([-1, 1])
correct_indicators = np.array( [ -1==+1, 1==+1 ] )
correct_scores = np.array( [ 1.*1. + 2.*3. + 3.*(-1.), 1.*1. + (-1.)*3. + (-1.)*(-1.) ] )
correct_first_term = np.array( [ (correct_indicators[0]-1)*correct_scores[0], (correct_indicators[1]-1)*correct_scores[1] ] )
correct_second_term = np.array( [ np.log(1. + np.exp(-correct_scores[0])), np.log(1. + np.exp(-correct_scores[1])) ] )
correct_ll = sum( [ correct_first_term[0]-correct_second_term[0], correct_first_term[1]-correct_second_term[1] ] )
print 'The following outputs must match '
print '------------------------------------------------'
print 'correct_log_likelihood =', correct_ll
print 'output of compute_log_likelihood =', compute_log_likelihood(dummy_feature_matrix, dummy_sentiment, dummy_coefficients)
# -
# ## Taking gradient steps
# Now we are ready to implement our own logistic regression. All we have to do is to write a gradient ascent function that takes gradient steps towards the optimum.
#
# Complete the following function to solve the logistic regression model using gradient ascent:
# +
from math import sqrt
def logistic_regression(feature_matrix, sentiment, initial_coefficients, step_size, max_iter):
coefficients = np.array(initial_coefficients) # make sure it's a numpy array
for itr in xrange(max_iter):
# Predict P(y_i = +1|x_i,w) using your predict_probability() function
# YOUR CODE HERE
predictions = predict_probability(feature_matrix, coefficients)
# Compute indicator value for (y_i = +1)
indicator = (sentiment==+1)
# Compute the errors as indicator - predictions
errors = indicator - predictions
for j in xrange(len(coefficients)): # loop over each coefficient
# Recall that feature_matrix[:,j] is the feature column associated with coefficients[j].
# Compute the derivative for coefficients[j]. Save it in a variable called derivative
# YOUR CODE HERE
derivative = feature_derivative(errors, feature_matrix[:,j])
# add the step size times the derivative to the current coefficient
## YOUR CODE HERE
coefficients[j] += step_size * derivative
# Checking whether log likelihood is increasing
if itr <= 15 or (itr <= 100 and itr % 10 == 0) or (itr <= 1000 and itr % 100 == 0) \
or (itr <= 10000 and itr % 1000 == 0) or itr % 10000 == 0:
lp = compute_log_likelihood(feature_matrix, sentiment, coefficients)
print 'iteration %*d: log likelihood of observed labels = %.8f' % \
(int(np.ceil(np.log10(max_iter))), itr, lp)
return coefficients
# -
# Now, let us run the logistic regression solver.
coefficients = logistic_regression(feature_matrix, sentiment, initial_coefficients=np.zeros(194),
step_size=1e-7, max_iter=301)
# **Quiz Question:** As each iteration of gradient ascent passes, does the log likelihood increase or decrease?
# ## Predicting sentiments
# Recall from lecture that class predictions for a data point $\mathbf{x}$ can be computed from the coefficients $\mathbf{w}$ using the following formula:
# $$
# \hat{y}_i =
# \left\{
# \begin{array}{ll}
# +1 & \mathbf{x}_i^T\mathbf{w} > 0 \\
# -1 & \mathbf{x}_i^T\mathbf{w} \leq 0 \\
# \end{array}
# \right.
# $$
#
# Now, we will write some code to compute class predictions. We will do this in two steps:
# * **Step 1**: First compute the **scores** using **feature_matrix** and **coefficients** using a dot product.
# * **Step 2**: Using the formula above, compute the class predictions from the scores.
#
# Step 1 can be implemented as follows:
# Compute the scores as a dot product between feature_matrix and coefficients.
scores = np.dot(feature_matrix, coefficients)
# Now, complete the following code block for **Step 2** to compute the class predictions using the **scores** obtained above:
y_hat = scores > 0
# ** Quiz Question: ** How many reviews were predicted to have positive sentiment?
y_hat.sum()
# ## Measuring accuracy
#
# We will now measure the classification accuracy of the model. Recall from the lecture that the classification accuracy can be computed as follows:
#
# $$
# \mbox{accuracy} = \frac{\mbox{# correctly classified data points}}{\mbox{# total data points}}
# $$
#
# Complete the following code block to compute the accuracy of the model.
num_mistakes = (y_hat == sentiment).sum() # YOUR CODE HERE
accuracy = 1. - float(num_mistakes) / len(products) # YOUR CODE HERE
print "-----------------------------------------------------"
print '# Reviews correctly classified =', len(products) - num_mistakes
print '# Reviews incorrectly classified =', num_mistakes
print '# Reviews total =', len(products)
print "-----------------------------------------------------"
print 'Accuracy = %.3f' % accuracy
# **Quiz Question**: What is the accuracy of the model on predictions made above? (round to 2 digits of accuracy)
# ## Which words contribute most to positive & negative sentiments?
# Recall that in Module 2 assignment, we were able to compute the "**most positive words**". These are words that correspond most strongly with positive reviews. In order to do this, we will first do the following:
# * Treat each coefficient as a tuple, i.e. (**word**, **coefficient_value**).
# * Sort all the (**word**, **coefficient_value**) tuples by **coefficient_value** in descending order.
coefficients = list(coefficients[1:]) # exclude intercept
word_coefficient_tuples = [(word, coefficient) for word, coefficient in zip(important_words, coefficients)]
word_coefficient_tuples = sorted(word_coefficient_tuples, key=lambda x:x[1], reverse=True)
# Now, **word_coefficient_tuples** contains a sorted list of (**word**, **coefficient_value**) tuples. The first 10 elements in this list correspond to the words that are most positive.
# ### Ten "most positive" words
#
# Now, we compute the 10 words that have the most positive coefficient values. These words are associated with positive sentiment.
sorted(word_coefficient_tuples, key=lambda x: x[1], reverse=True)[:10]
# ** Quiz Question:** Which word is **not** present in the top 10 "most positive" words?
# ### Ten "most negative" words
#
# Next, we repeat this exercise on the 10 most negative words. That is, we compute the 10 words that have the most negative coefficient values. These words are associated with negative sentiment.
sorted(word_coefficient_tuples, key=lambda x: x[1], reverse=False)[:10]
# ** Quiz Question:** Which word is **not** present in the top 10 "most negative" words?
| 19,185 |
/13-Udemy/03-Pandas/.ipynb_checkpoints/06-Pandas-Time-Series-Exercises-SET-ONE-Solutions-checkpoint.ipynb | 3410ff63b2072a804fda707cff9fd03284afb4f9 | [] | no_license | Tommyhappy01/Data_Science | https://github.com/Tommyhappy01/Data_Science | 2 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 56,918 | # ---
# jupyter:
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# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
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# display_name: Python 3
# language: python
# name: python3
# ---
# +
from PIL import Image
import numpy as np
import matplotlib.pyplot as pl
from pathlib import Path
import os.path
"""
This example shows how images are represented using pixels, color channels and data types.
"""
# read image to array
im = np.array(Image.open('./data/empire.jpg'))
print("Shape is: {0} of type {1}".format(im.shape, im.dtype))
# read grayscale version to float array
im = np.array(Image.open('./data/empire.jpg').convert('L'),'f')
print("Shape is: {0} of type {1}".format(im.shape, im.dtype))
# visualize the pixel value of a small region
col_1, col_2 = 140, 225
row_1, row_2 = 180, 265
# crop using array slicing
crop = im[col_1:col_2,row_1:row_2]
cols, rows = crop.shape
print("Created crop of shape: {0}".format(crop.shape))
# -
# generate all the plots
pl.figure()
pl.imshow(im)
pl.gray()
pl.plot([row_1, row_2, row_2, row_1, row_1], [col_1, col_1, col_2, col_2, col_1], linewidth=2)
pl.axis('off')
pl.show()
pl.figure()
pl.imshow(crop)
pl.gray()
pl.axis('off')
pl.show()
pl.figure()
pl.imshow(crop)
pl.gray()
pl.plot(20*np.ones(cols), linewidth=10)
pl.axis('off')
pl.show()
print(10*np.ones(cols))
range(10)
pl.figure()
pl.plot(crop[50,:])
pl.ylabel("Graylevel value")
pl.show()
from mpl_toolkits.mplot3d import axes3d
fig = pl.figure()
ax = fig.gca(projection='3d')
# surface plot with transparency 0.5
X,Y = np.meshgrid(np.arange(cols),-np.arange(rows))
ax.plot_surface(X, Y, crop, alpha=0.5, cstride=2, rstride=2)
pl.show()
help(ax.plot_surface)
# +
from PIL import Image
from numpy import *
from scipy.ndimage import measurements,morphology
"""
This is the morphology counting objects example in Section 1.4.
"""
# load image and threshold to make sure it is binary
im = array(Image.open('./data/houses.png').convert('L'))
pl.figure();
pl.imshow(im)
pl.show();
# +
im = (im<128)
pl.figure();
pl.imshow(im)
pl.show();
labels, nbr_objects = measurements.label(im)
print("Number of objects:", nbr_objects)
# morphology - opening to separate objects better
im_open = morphology.binary_opening(im,ones((9,5)),iterations=7)
labels_open, nbr_objects_open = measurements.label(im_open)
print("Number of objects:", nbr_objects_open)
# -
1261-769d-6dcfbe2603d3"
#Fare
train.Fare.mean(), train.Fare.median()
# + _cell_guid="e6aba6b1-40a0-5e84-25e0-2fb24b190ef2"
#lets fill it with mean value
train.Fare.fillna(train.Fare.mean(), inplace=True)
test.Fare.fillna(test.Fare.mean(), inplace=True)
train.Fare.hist()
print ('Spread of Fare:')
# + [markdown] _cell_guid="0eebdb2d-deef-cb48-1d15-9da95f6d6bb9"
# ### 3- Age
# + _cell_guid="6f050b9c-9b92-b96a-3962-ca2f47fe4852"
train.Age.mean(), train.Age.median()
# + _cell_guid="070474e6-e469-8aeb-f981-e5d8b9a272ec"
train_age_mean = train.Age.mean()
train_age_std = train.Age.std()
train_age_count = train.Age.isnull().sum()
test_age_mean = test.Age.mean()
test_age_std = test.Age.std()
test_age_count = test.Age.isnull().sum()
rand_1 = np.random.randint(train_age_mean - train_age_std, train_age_mean + train_age_std, size=train_age_count)
rand_2 = np.random.randint(test_age_mean - test_age_std, test_age_mean + test_age_std, size=test_age_count)
train.Age[np.isnan(train.Age)] = rand_1
test.Age[np.isnan(test.Age)] = rand_2
train['Age'] = train.Age.astype(int)
test['Age'] = test.Age.astype(int)
#fill in the missing age values using the median value.
train.Age.hist()
print ('Age spread: ')
# + _cell_guid="e3a6a589-0da0-6af5-6ffa-dd046e27e3c0"
# Plotting to undertanding the relation ship between age and survival rate:
face_age = sns.FacetGrid(train, hue='Survived', size=3,aspect=3)
face_age.map(sns.kdeplot, 'Age', shade=True)
face_age.set(xlim=(0, train.Age.max()))
face_age.add_legend()
plt.subplots(1,1, figsize=(10,4))
average_age = train[['Age', 'Survived']].groupby(['Age'], as_index=False).mean()
sns.barplot('Age', 'Survived', data=average_age)
plt.xticks(rotation=90)
print ('Age survival relation: ')
# + [markdown] _cell_guid="9af0dabb-2b68-3db4-0822-f7ccddb40b37"
# ## 4- Family ##
# + _cell_guid="32cb15bb-e926-5657-964d-7b2bcd3d8929"
# combing the parents children count and siblings count for each person to get the family size:
train['Family'] = train.Parch + train.SibSp
test['Family'] = test.Parch + test.SibSp
#making the values to boolean, i.e. if no family member family is 0 else 1.
train.Family.loc[train.Family > 0] = 1
train.Family.loc[train.Family == 0] = 0
test.Family.loc[test.Family > 0] = 1
test.Family.loc[test.Family == 0] = 0
#Drop the original features since they are not required any more:
train.drop(['Parch', 'SibSp'], inplace=True, axis=1)
test.drop(['Parch', 'SibSp'], inplace=True, axis=1)
#relation between family and survival rate:
sns.countplot('Family', hue='Survived', data=train)
print ('Family survival rate: ')
# + [markdown] _cell_guid="c8d937d1-8fd1-5bb4-cc92-fcfe9c4386c7"
# ## 5- Gender ##
# + _cell_guid="794e9dff-27ad-31a4-47c1-36428f78fa09"
#There is a chance that more children survived the disaster, therefore lets put three categories
#i.e. male, female and childre with age less then 17
def check_child(age_gender):
age, sex = age_gender
return 'child' if age < 17 else sex
train['Person'] = train[['Age', 'Sex']].apply(check_child, axis=1)
test['Person'] = test[['Age', 'Sex']].apply(check_child, axis=1)
#creating dummies the new person feature for our model
train = pd.concat([train, pd.get_dummies(train.Person, prefix='person')], axis=1)
test = pd.concat([test, pd.get_dummies(test.Person, prefix='person')], axis=1)
train.head()
# + _cell_guid="61a81de5-8851-8b50-8a17-8b3657a6a8c7"
# Check the spread of each person and their survival rate:
_, (ax1, ax2, ax3) = plt.subplots(3,1, figsize=(10,12))
#spread
sns.countplot('Person', data=train, ax=ax1)
#survival
sns.countplot('Person', hue='Survived', data=train, ax=ax2)
#mean-survival
person_survival = train[['Person', 'Survived']].groupby(['Person'], as_index=False).mean()
sns.barplot('Person', 'Survived', data=person_survival, ax=ax3)
#Drop the original features, they are no more needed.
train.drop(['Sex', 'Person'], inplace=True, axis=1)
test.drop(['Sex', 'Person'], inplace=True, axis=1)
# + [markdown] _cell_guid="93937673-3b2d-12ba-3115-46b908d3ffdc"
# ### 6- Pclass
# + _cell_guid="a6036ad8-c670-55be-095e-31554867811b"
# There might be strong realtion between survival rate and the traveling class of a person.
# plot
sns.countplot('Pclass', hue='Survived', data=train)
#create dummy variable for out model
train = pd.concat([train, pd.get_dummies(train.Pclass, prefix='pclass')], axis=1)
test = pd.concat([test, pd.get_dummies(test.Pclass, prefix='pclass')], axis=1)
#drop original feature:
train.drop('Pclass', inplace=True, axis=1)
test.drop('Pclass', inplace=True, axis=1)
# + [markdown] _cell_guid="4dace2cc-a1f8-c5c7-f4f5-1864f8eafc7c"
# # Modal Building
# + _cell_guid="e15ead33-82f0-9547-83b6-f99c3cd53ea3"
#seperate the features and target:
X = train.drop('Survived', axis=1)
y = train.Survived
#split the data to evalute the model:
X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, random_state=4)
# + [markdown] _cell_guid="67d250db-71a7-8280-7fef-c7d2fc4b773d"
# ### Logistic Regression
# + _cell_guid="d52f559d-1ff3-4226-955a-897b52c87df1"
#Logit regression will simply classify the data set using l2 regularization:
logreg = LogisticRegression(C=1, penalty='l2').fit(X_train, y_train)
logreg.score(X_train, y_train), logreg.score(X_test, y_test)
# + _cell_guid="eeebfa33-6517-0e36-2091-ed7cae89edd4"
#The coefficient found for each feature, this slightly tells
#which feature tells more about survival and death:
plt.plot(logreg.coef_.T, 'o')
plt.xticks(range(X.shape[1]), X.columns, rotation=90)
print ('Coefficients found using Logistic regression: ')
# + [markdown] _cell_guid="24b9a78b-939e-602e-0904-315e7fb18d22"
# ### Decision tree:
# + _cell_guid="ba3441ea-ae3d-4569-0b55-0b3e145c95cb"
#Decision trees will give more accurate result since it calculates the score from eah feature multiple times
#Pre-prunning has been applied here to avoid over fitting
dtree = DecisionTreeClassifier(random_state=0, max_depth=3).fit(X_train, y_train)
dtree.score(X_train, y_train), dtree.score(X_test, y_test)
# + _cell_guid="6edf1f18-6441-ae3e-4baa-d5dec4edcc21"
#Check out the feature importance taken into account by Decision tree.
plt.plot(dtree.feature_importances_, 'o')
plt.xticks(range(X.shape[1]), X.columns, rotation=90)
print ('Feature Importance in Decision trees: ')
# + [markdown] _cell_guid="8b47a0fd-e164-3d2e-1823-d92be46c464b"
# ### Random Forest
# + _cell_guid="32399333-5c6c-a391-954c-bddd5104a407"
# Using ensemble techinque will cause imporvment in result. Here the max depth is again changed.
rf = RandomForestClassifier(random_state=0, max_depth=4).fit(X_train, y_train)
rf.score(X_train, y_train), rf.score(X_test, y_test)
# + _cell_guid="1353e563-6025-6de4-1d86-730ea6a46a73"
# Feature importance by random forest, here more features are taken into account.
plt.plot(rf.feature_importances_, 'o')
plt.xticks(range(X.shape[1]), X_train.columns, rotation=90)
print ('Feature Importance in Random Forest: ')
# + [markdown] _cell_guid="2b1a8815-a462-c496-35d3-fc1c309cee83"
# ### Gradient Booster
# + _cell_guid="a9591422-2de3-e861-4aa4-d1746f2ae537"
#This is will give more accuracy since it learn from the mistakes of the previous trees.
#Pre-prunning is used here to avoid over fitting and for better accuracy.
gb = GradientBoostingClassifier(learning_rate=0.1, random_state=0, max_depth=1).fit(X_train, y_train)
gb.score(X_train, y_train), gb.score(X_test, y_test)
# + _cell_guid="3bbdf136-139f-3913-9b3f-4a58eab1a583"
#Feature importance for the gb
plt.plot(gb.feature_importances_, 'o')
plt.xticks(range(X.shape[1]), X_train.columns, rotation =90)
print ('Feature Importance in Gradient Booster: ')
# + [markdown] _cell_guid="bfb4fd01-cc8e-a080-af20-63af6035c40d"
# # Prediction
# + _cell_guid="312cbeff-9ef6-aad7-cb39-a2fb8f1b4e2b"
pred = test.drop('PassengerId', axis=1)
prediction = gb.predict(pred)
# + _cell_guid="b5d90a06-7d83-eb72-83e9-543e3b963261"
output = pd.DataFrame({
'PassengerId': test.PassengerId,
'Survived': prediction
})
output.to_csv('result.csv', index=False)
| 10,515 |
/.ipynb_checkpoints/05_분류 평가지표 (1)-checkpoint.ipynb | c2779a031bb506d39088f4663d87b4209f970f6b | [] | no_license | leejineui/Machine_Learning | https://github.com/leejineui/Machine_Learning | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 2,294,611 | # ---
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# # 모델 평가
# 모델의 성능을 평가한다. 평가결과에 따라 프로세스를 다시 반복한다.
# 
# ## 분류와 회귀의 평가방법
# - 분류는 n개 중 하나 맞추는 거라면 회귀는 무한대 중에 하나를 맞추는 것. 따라서 평가지표를 다르게 평가해야 함
# - 비지도학습은 정답도 없어서 다양한 평가지표가 필요.
# - 분류도 어떤 측면에서 평가하느냐에 따라 다양한 평가지표가 있음.(어떤 성능을 보려고 하느냐)
# ### 분류 평가 지표
# 1. 정확도 (Accuracy)
# 1. 정밀도 (Precision)
# 1. 재현률 (Recall)
# 1. F1점수 (F1 Score)
# 1. PR Curve, AP
# 1. ROC, AUC
#
# ### 회귀 평가방법
# 1. MSE (Mean Squareed Error)
# 1. RMSE (Root Mean Squared Error)
# 1. $R^2$ (결정계수)
#
# ### sckit-learn 평가함수
# - sklearn.metrics 모듈을 통해 제공
# # 분류(Classification) 평가 기준
# ## 용어
# - ### 이진 분류에서 양성과 음성
# - 양성(Positive): 예측하려는(찾으려는) 대상, 틀렸을 때 문제가 더 큰 대상
# - 음성(Negative): 예측하려는 대상이 아닌 것
# - 예
# - 암환자 분류 : 양성 - 암 환자, 음성 - 정상인
# - 스팸메일 분류 : 양성 - 스팸메일, 음성 - 정상메일
# - 금융사기 모델: 양성 - 사기거래, 음성 - 정상거래
#
# ## 정확도 (Accuracy)
#
# $$
# \large{
# 정확도 (Accuracy) = \cfrac{맞게 예측한 건수} {모델의 전체 예측 건수}
# }
# $$
#
# - 전체 예측 한 것중 맞게 예측한 비율로 평가한다.
# - `accuracy_score(정답, 모델예측값)`
#
# ### Accuracy 평가지표의 문제
# - 불균형 데이터의 경우 정확한 평가지표가 될 수 없다.
# - 불균형 데이터란, 이진분류에서 0과 1이 있을 때 각각 50 대 50이면 균형 데이터라 하고, 10대 90인 경우 불균형 데이터라고 함
# - 예: 양성과 음성의 비율이 1:9 인 경우 모두 음성이라고 하면 정확도는 90%가 된다.
# - 한쪽에 쏠린 데이터의 정확도가 90%이면 다른 한쪽에 대해서는 정확도가 10%이기 때문에 정확도만 놓고 성능이 좋다나쁘다고 판단할 수 없다.
#
# - Q: 정확도는 데이터의 클래스 비율이 달라서 문제가 생기기 때문에 재현률과 정밀도를 같이 본다고 말씀하셨는데, 그럼 혹시 양성쪽에 데이터가 쏠린 경우는 문제가 안되는 건가요?
# - A: 재현율과 정밀도는 양성데이터에 대한 예측력을 집중해서 보는 평가 지표입니다. 정확도는 양성/음성 상관없이 전체적으로 얼마나 잘 맞추는지에 대한 평가지표이고요. 그래서 양성(모델이 더 잘 찾아야하는 것)을 얼마나 잘 찾는지에 대한 평가를 하고 싶을때 재현율 정밀도를 봅니다. 불균현 데이터는 이런 예가 가장 잘 나타나는 것이 상황이라 설명드린 겁니다.
#
#
# - 재현률: 실제 양성을 얼마나 잘 맞췄는지?
# - 정밀도: 양성을 예측한 게 얼마나 잘 맞았는지?
#
# ## MNIST Data set
# - 손글씨 데이터 셋
# - 사이킷런 제공 image size: 8 X 8 (height * width), 0은 검정- 흰색은 255, 흑백 이미지는 2차원 이미지, 컬러는 (8, 8, 3): 높이, 너비, 채널
# - https://ko.wikipedia.org/wiki/MNIST_%EB%8D%B0%EC%9D%B4%ED%84%B0%EB%B2%A0%EC%9D%B4%EC%8A%A4
# 하나의 픽셀값이 30
# 
# 칼라: 3차원 배열
# 
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import load_digits
mnist = load_digits()
mnist.keys()
# +
# mnist['feature_names] # (0,0, ..., (7,7))
# -
X = mnist['data']
y = mnist['target']
X.shape, y.shape
# y 빈도수
np.unique(y, return_counts=True)
# +
X[0] # 첫번째 그림
# X[1796] # 1796번째 그림
# 1차원 배열 -> 2차원 배열(height, width)
X[0].reshape(8, 8)
# -
print(y[0])
img = X[0].reshape(8,8)
# plt.imshow(img, cmap='gray') # ndarray를 넣어주면 image로 보여주는 함수
# plt.imshow(img, cmap='binary')
plt.imshow(img, cmap='Greys') # 같은 숫자는 같은 색, 다른 숫자는 다른 색
plt.xticks([]) # 그래프 옆의 ticsk를 없애버리겠다는 옵션
plt.yticks([])
plt.show()
# ### 불균형 데이터셋으로 만들기
# - y를 9와 나머지로 변경한다.
# - Positive(양성 - 1): 9
# - Negative(음성 - 0): 0 ~ 8
y = np.where(y == 9, 1, 0) # y가 9이면 1, 아니면 0 # True: 1, False:0 으로 바꿔버리겠따
y
np.unique(y, return_counts=True)
# ### 훈련, 테스트 데이터셋 분할
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=0)
X_train.shape, X_test.shape, y_train.shape, y_test.shape
# ### 모델 생성 및 학습
# #### Dummy Model 정의
# - Target Label중 무조건 최빈값으로 예측하는 모델을 정의한다.
# +
from sklearn.dummy import DummyClassifier
from sklearn.metrics import accuracy_score
# 무조건 최빈값으로 예측하는 dummy 모델 생성
dummy_model = DummyClassifier(strategy='most_frequent') # strategy:전략, 수립
# 학습
dummy_model.fit(X_train, y_train)
# 추론 + 평가
pred_train = dummy_model.predict(X_train)
pred_test = dummy_model.predict(X_test)
# 정확도
print('train 정확도:{}, test 정확도: {}'.format(accuracy_score(y_train, pred_train), accuracy_score(y_test, pred_test)))
# -
np.unique(y_train, return_counts=True)
np.unique(pred_train), np.unique(pred_test)
from sklearn.metrics import confusion_matrix
print("train 혼동 행렬")
confusion_matrix(y_train, pred_train)
print('test 혼동 행렬')
confusion_matrix(y_test, pred_test)
# ### 모델 평가
# ## 혼동 행렬(Confusion Marix)
# - 분류의 평가지표의 기본 지표로 사용된다.
# - 혼동행렬을 이용해 다양한 평가지표(정확도, 재현률, 정밀도, F1 점수, AUC 점수)를 계산할 수 있다.
# - 함수: confusion_matrix(정답, 모델예측값)
# - 결과의 0번축: 실제 class, 1번 축: 예측 class
# 
#
# 
# - TP(True Positive) - 양성으로 예측했는데 맞은 개수
# - TN(True Negative) - 음성으로 예측했는데 맞은 개수
# - FP(False Positive) - 양성으로 예측했는데 틀린 개수 (음성을 양성으로 예측)
# - FN(False Negative) - 음성으로 예측했는데 틀린 개수 (양성을 음성으로 예측)
# <div class="alert alert-block alert-warning"></div>
#
# 
# 
# ## 이진 분류 평가점수
# - ### Accuracy (정확도)
# - 전체 데이터 중에 맞게 예측한 것의 비율
# - ### Recall/Sensitivity(재현율/민감도)
# - 실제 Positive(양성)인 것 중에 Positive(양성)로 예측 한 것의 비율
# - **TPR**(True Positive Rate) 이라고도 한다.
# - ex) 스팸 메일 중 스팸메일로 예측한 비율. 금융사기 데이터 중 사기로 예측한 비율
# - ### Precision(정밀도)
# - Positive(양성)으로 예측 한 것 중 실제 Positive(양성)인 비율
# - **PPV**(Positive Predictive Value) 라고도 한다.
# - ex) 스팸메일로 예측한 것 중 스팸메일의 비율. 금융 사기로 예측한 것 중 금융사기인 것의 비율
#
# - ### F1 점수
# - 정밀도와 재현율의 조화평균 점수
# - recall과 precision이 비슷할 수록 높은 값을 가지게 된다. F1 score가 높다는 것은 recall과 precision이 한쪽으로 치우쳐저 있이 않고 둘다 좋다고 판단할 수 있는 근거가 된다.
#
# ### 기타
# - ### Specificity(특이도)
# - 실제 Negative(음성)인 것들 중 Negative(음성)으로 맞게 예측 한 것의 비율
# - TNR(True Negative Rate) 라고도 한다.
# - ### Fall out(위양성률)
# - 실제 Negative(음성)인 것들 중 Positive(양성)으로 잘못 예측한 것의 비율. `1 - 특이도`
# - **FPR** (False Positive Rate) 라고도 한다.
# - $ Fall-Out(FPF) = \cfrac{FP}{TN+FP}$
# 
# ## 각 평가 지표 계산 함수
# - sklearn.metrics 모듈
# - ### confusion_matrix(y 실제값, y 예측값)
# - 혼돈 행렬 반환
# - ### recall_score(y 실제값, y 예측값)
# - Recall(재현율) 점수 반환 (Positive 중 Positive로 예측한 비율 (TPR))
# - ### precision_score(y 실제값, y 예측값)
# - Precision(정밀도) 점수 반환 (Positive로 예측한 것 중 Positive인 것의 비율 (PPV))
# - ### f1_score(y 실제값, y 예측값)
# - F1 점수 반환 (recall과 precision의 조화 평균값)
# - ### classification_report(y 실제값, y 예측값)
# - 클래스 별로 recall, precision, f1 점수와 accuracy를 종합해서 보여준다.
# ### Dummy 모델 혼동행렬
# > plot_confusion_matrix함수: 버전 2.1.3에서 추가됨. 없다고 에러나는 경우 업데이트 필요 `pip install scikit-learn --upgrade`
from sklearn.metrics import confusion_matrix, accuracy_score, recall_score, precision_score, f1_score, plot_confusion_matrix
# plot_confusion_matrix: 시각화 보고서 작성해야 하는 경우
print('Train confusion matrix')
print(confusion_matrix(y_train, pred_train))
print("="*50)
print('Test confusion matrix')
print(confusion_matrix(y_test, pred_test))
# +
import matplotlib.pyplot as plt # matplotlib: 파이썬 시각화의 기본이되는 라이브러리
fig, ax = plt.subplots(1, 1, figsize=(5, 5)) # fig: 스케치북 역할, # ax: 각각의 객체 # plt.gca(): axis만 뽑아 올 때
plot_confusion_matrix(dummy_model, # 학습한 모델
X_train, # 예측할 X # 모델.predict(X_train)
y_train, # 정답 y # 예측한 값과 비교할 정답 y
display_labels=['neg', 'pos'],
values_format='d',
cmap='Blues', # cmap: color map(color bar를 나타냄)
ax=ax) # axes(그래프를 그릴 axes)
plt.title('Train Confusion matrix')
plt.show()
# -
# 
# +
fig, ax = plt.subplots(1, 1, figsize=(5, 5))
plot_confusion_matrix(dummy_model, # 학습한 모델
X_test, # 예측할 X # 모델.predict(X_train)
y_test, # 정답 y # 예측한 값과 비교할 정답 y
display_labels=['Neg', 'Pos'],
values_format='d',
cmap='Blues', # cmap: color map(color bar를 나타냄)
ax=ax)
plt.title('Test Confusion matrix')
plt.show()
# -
# ### dummy 모델 Accuracy, Recall, Precision, F1-Score
# accuracy_score
acc_train = accuracy_score(y_train, pred_train)
acc_test = accuracy_score(y_test, pred_test)
print(acc_train, acc_test)
# recall score
recall_train = recall_score(y_train, pred_train)
recall_test = recall_score(y_test, pred_test)
print(recall_train, recall_test)
# 0.0 : 실제 양성(9인 것) 중에서 하나도 못 맞춤
# precision score
precision_train = precision_score(y_train, pred_train)
precision_test = precision_score(y_test, pred_test)
precision_train, precision_test
# 0은 0으로 나누지 못한다는 경고 메세지 출력됨
# 0.0 :양성으로 예측한 것 중에 하나도 못 맞춤
# f2 score(recall과 precision의 조화평균)
f1_train = f1_score(y_train, pred_train)
f1_test = f1_score(y_test, pred_test)
print(f1_train, f1_test)
from sklearn.metrics import classification_report
report = classification_report(y_train, pred_train)
print(report)
# ### 머신러닝 모델을 이용해 학습
# +
# DecisionTreeClassifier (max_depth=3)
# 모델 생성
# 학습(Train)
# 검증(Train/test) - confusion matrix, accuracy, recall, precision, f1
# -
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import confusion_matrix, plot_confusion_matrix, accuracy_score, recall_score, precision_score, f1_score
# from sklearn.metrics import confusion_matrix, accuracy_score, recall_score, precision_score, f1_score, plot_confusion_matrix
# +
# 모델 생성
tree = DecisionTreeClassifier(max_depth=3, random_state=0)
# train
tree.fit(X_train, y_train)
# 평가
# 예측
pred_train = tree.predict(X_train)
pred_test = tree.predict(X_test)
# 평가
# -
# confusion_matrix
confusion_matrix(y_train, pred_train)
plt.figure(figsize=(7, 7))
plot_confusion_matrix(tree, X_train, y_train, display_labels=['Not 9', '9'], cmap='Blues')
plt.show()
confusion_matrix(y_test, pred_test)
print('Train 평가지표')
accuracy_score(y_train, pred_train), recall_score(y_train, pred_train), precision_score(y_train, pred_train), f1_score(y_train, pred_train)
print('Test 평가지표')
accuracy_score(y_test, pred_test), recall_score(y_test, pred_test), precision_score(y_test, pred_test), f1_score(y_test, pred_test)
# ### classification_report()
#
train_report = classification_report(y_train, pred_train)
print("train 평가지표")
print(train_report)
test_report = classification_report(y_test, pred_test)
print("test 평가지표")
print(test_report)
X_test.shape
pred_test
# ## 재현율과 정밀도의 관계
# **이진 분류의 경우 Precision(정밀도)가 중요한 경우와 Recall(재현율) 중요한 업무가 있다.**
# - 재현율이 오르면 정밀도가 떨어지고, 재현율이 떨어지면 정밀도가 올라감
#
# #### 재현율이 더 중요한 경우
# - 실제 Positive 데이터를 Negative 로 잘못 판단하면 업무상 큰 영향이 있는 경우.
# - FN(False Negative)를 낮추는데 촛점을 맞춘다.
# - 암환자 판정 모델, 보험사기적발 모델
#
# #### 정밀도가 더 중요한 경우
# - 실제 Negative 데이터를 Positive 로 잘못 판단하면 업무상 큰 영향이 있는 경우.
# - FP(False Positive)를 낮추는데 초점을 맞춘다.
# - 스팸메일 판정
#
# ## 임계값(Threshold) 변경을 통한 재현율, 정밀도 변환
# - 임계값 : 모델이 분류의 답을 결정할 때 기준값
# - 정밀도나 재현율을 특히 강조해야 하는 상황일 경우 임계값 변경을 통해 평가 수치를 올릴 수있다.
# - 단 극단적으로 임계점을 올리나가 낮춰서 한쪽의 점수를 높이면 안된다. (ex: 암환자 예측시 재현율을 너무 높이면 정밀도가 낮아져 걸핏하면 정상인을 암환자로 예측하게 된다.)
#
# ### 임계값 변경에 따른 정밀도와 재현율 변화관계
# - 임계값을 높이면 양성으로 예측하는 기준을 높여서(엄격히 해서) 음성으로 예측되는 샘플이 많아 진다. 그래서 정밀도는 높아지고 재현율은 낮아진다.
# - 임계값을 낮추면 양성으로 예측하는 기준이 낮아져서 양성으로 예측되는 샘플이 많아 진다. 그래서 재현율은 높아지고 정밀도는 낮아진다.
# - **임계값을 낮추면 재현율은 올라가고 정밀도는 낮아진다.**
# - **임계값을 높이면 재현율은 낮아지고 정밀도는 올라간다.**
# - 임계값을 변화시켰을때 **재현율과 정밀도는 음의 상관관계(반비례)를 가진다.**
# - 임계값을 변화시켰을때 재현율과 위양성율(Fall-Out/FPR)은 양의 상관관계(비례관계)를 가진다.
#
# 
# ### 임계값 변화에 따른 recall, precision 변화
X_test.shape
# model.predict(X): 분류-> 최종 class에 대한 추론 결과
# model.predict_proba(X): 추론 확률(proba:probavility). [0일 확률, 1일 확률] # 1: positive일 확률
pred_test = tree.predict(X_test) # 최종 class만 추출
print(pred_test.shape)
pred_test[-5:] # 어떤 확률로 해당 클래스가 나왔는지는 모름 # 최종 큰 값이 해당 클래스로 나옴
prob_test = tree.predict_proba(X_test) # 최종 class가 어떤 확률로 나왔는지 알고 싶을 때
print(prob_test.shape)
prob_test[-5:]
# +
import pandas as pd
import matplotlib.pyplot as plt
# 임계값(thresholds)을 변화시켰을 때 recall과 precision의 변화를 확인하는 지표
from sklearn.metrics import precision_recall_curve
# -
# prob_test[:, 1] # 1일 확률만 뽑음
precisions, recalls, thresholds = precision_recall_curve(y_test, prob_test[:, 1]) # (y 정답, 1일 확률)
precisions.shape, recalls.shape, thresholds.shape # recall과 precision은 Threshold가 1일 때 결과값을 포함하는데 threshold는 1이 없다
print(thresholds)
print(precisions)
print(recalls)
# threshold에 1을 추가
thresholds = np.append(thresholds, 1)
thresholds.shape
df = pd.DataFrame({'Threshold':thresholds,
'Recall':recalls,
'Precision':precisions})
df
# +
# precision_recall_curve의 결과를 선그래프로 확인
# X축: thresholds, y: recall/precision
plt.figure(figsize=(7, 7))
plt.plot(thresholds, precisions, marker='o', label='Precision')
plt.plot(thresholds, recalls, marker='x', label='Recall')
plt.xlabel("Threshold")
plt.legend(loc='upper left', bbox_to_anchor=(1, 1)) # legend:범례(라벨) # loc='upper left':라벨 위치
# bbox_to_anchor=(1, 1): 앵커박스를 기준으로 라벨을 어디에 둘 것인가(0, 0)~ (1, 1)
plt.grid(True)
plt.show()
# -
# ### Binarizer - 임계값 변경
# - Transformer로 양성 여부를 선택하는 임계값을 변경할 수 있다.
threshold = 0.7
r = np.where(prob_test[:, 1]>= threshold, 1, 0)
np.unique(r, return_counts=True)
recall_score(y_test, r), precision_score(y_test, r)
# +
# Binarizer - 숫자를 0, 1 두개(BINARY)로 변환하는 Transformer
from sklearn.preprocessing import Binarizer
exam = [[0.3, 0.4, 0.5, 0.6, 0.7]]
threshold = 0.5
threshold = 0.3
threshold = 0.6
b = Binarizer(threshold=threshold) # threshold 이하이면 0으로, 초과이면 1로 변환
b.fit_transform(exam)
# -
binarizer1 = Binarizer(threshold=0.5)
binarizer2 = Binarizer(threshold=0.1)
binarizer3 = Binarizer(threshold=0.7)
pred_test_0_5 = binarizer1.fit_transform(prob_test)[:, 1]
pred_test_0_1 = binarizer2.fit_transform(prob_test)[:, 1]
pred_test_0_7 = binarizer3.fit_transform(prob_test)[:, 1]
# +
# prob_test
# +
# binarizer1.fit_transform(prob_test) # 우리가 원하는 건
# +
print("Threshold = 0.5")
print(recall_score(y_test, pred_test_0_5), precision_score(y_test, pred_test_0_5))
print("Threshold = 0.1") # recall 올라감, precision 내려감 => 실제 positive를 더 많이 맞췄다
print(recall_score(y_test, pred_test_0_1), precision_score(y_test, pred_test_0_1))
print("Threshold = 0.7") # precision이 올라감 => positive 예측한 것이 더 많이 맞았다.
print(recall_score(y_test, pred_test_0_7), precision_score(y_test, pred_test_0_7))
# -
# ## PR Curve(Precision Recall Curve-정밀도 재현율 곡선)와 AP Score(Average Precision Score)
# - 0~1사이의 모든 임계값에 대하여 재현율(recall)과 정밀도(precision)의 변화를 이용한 평가 지표
# - X축에 재현율, Y축에 정밀도를 놓고 임계값이 0 → 1 변화할때 두 값의 변화를 선그래프로 그린다.
# - AP Score
# - 그래프를 수치화한 것
# - PR Curve의 성능평가 지표를 하나의 점수(숫자)로 평가한것.
# - PR Curve의 선아래 면적을 계산한 값으로 높을 수록 성능이 우수하다.
# 
# 
# ## ROC curve(Receiver Operating Characteristic Curve)와 AUC(Area Under the Curve) score
#
# - **FPR(False Positive Rate-위양성율)**
# - 위양성율 (fall-out)
# - 1-특이도(TNR)
# - 실제 음성중 양성으로 잘못 예측 한 비율
# $$
# \cfrac{FP}{TN+FP}
# $$
# - **TPR(True Positive Rate-재현율/민감도)**
# - 재현율(recall)
# - 실제 양성중 양성으로 맞게 예측한 비율
# $$
# \frac{TP}{FN+TP}
# $$
# - **ROC 곡선**
# - 2진 분류의 모델 성능 평가 지표 중 하나.
# - 불균형 데이터셋을 평가할 때 사용.
# - FPR을 X축, TPR을 Y축으로 놓고 임계값을 변경해서 FPR이 변할 때 TPR이 어떻게 변하는지 나타내는 곡선.
# - **AUC**
# - ROC 곡선 아래쪽 면적
# - 0 ~ 1 사이 실수로 나오며 클수록 좋다.
# - **AUC 점수기준**
# - 1.0 ~ 0.9 : 아주 좋음
# - 0.9 ~ 0.8 : 좋음
# - 0.8 ~ 0.7 : 괜찮은 모델
# - 0.7 ~ 0.6 : 의미는 있으나 좋은 모델은 아님
# - 0.6 ~ 0.5 : 좋지 않은 모델
# 
# 가장 완벽한 것은 FPR이 0이고 TPR이 1인 것이다.
# 일반 적으로 FPR이 작을 때 (0에 가까울때) TPR이 높은 경우가 좋은 상황이다. 그래서 선 아래의 면적이 넓은 곡선이 나올 수록 좋은 모델이다.
# ### ROC, AUC 점수 확인
# - roc_curve(y값, 예측확률) : FPR, TPR, Thresholds (임계치)
# - roc_auc_score(y값, 예측확률) : AUC 점수 반환
# ## ROC Curve - PR Curve
# - ROC: 이진분류에서 양성클래스 탐지와 음성클래스 탐지의 중요도가 비슷할 때 사용(개고양이 분류)
# - PR curve(Precision Recall 커브): 양성 클래스 탐지가 음성클래스 탐지의 중요도보다 높을 경우 사용(암환자 진단)
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/classes/02/notebooks/codealong-02-the-pandas-library-starter-code.ipynb | 32afb0e4629fc35e8b9f894d926df217e05ce4f9 | [] | no_license | cefan-2000/DS-SF-34 | https://github.com/cefan-2000/DS-SF-34 | 0 | 0 | null | 2017-04-18T03:04:49 | 2017-04-18T00:32:09 | null | Jupyter Notebook | false | false | .py | 555,018 | # ---
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# ---
# # DS-SF-34 | 02 | The `pandas` Library | Codealong | Starter Code
# (http://pandas.pydata.org/pandas-docs/stable)
# ## Part A - Introduction to `pandas`
# +
import os
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 100) # this tells you # of rows max to display
pd.set_option('display.notebook_repr_html', True)
pd.set_option('display.max_columns', 10) # this tells you # of columns max to display
import re
# -
# > ## `pd.read_csv()`: load datasets from files (or even over the Internet)
# (http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html)
#
# '..' means go up one level. we had a folder called datasets one folder up.
#
df = pd.read_csv(os.path.join('..', 'datasets', 'dataset-02-zillow-starter.csv'))
# > ## `DataFrame`
# Let's check `df`'s type:
# TODO
type(df)
# `df` is a `DataFrame`. (http://pandas.pydata.org/pandas-docs/stable/dsintro.html)
# A `DataFrame` stores tabular data. Let's have a look at its content:
df
# > ## `.head()`: first 5 (default) rows
# - (http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.head.html)
# - (http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.head.html)
# df.head()
df[:5]
# > ## `.tail()`: last 5 (default) rows
# - (http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.tail.html)
# - (http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.tail.html)
# TODO
df[-5:]
df[10:20]
# > ## `.shape`: shape (i.e., number of rows and columns)
# - (http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.shape.html)
# - (http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.shape.html)
df.shape # this is a property, not a function
# The first value (at index 0) is the number of rows, the second (at index 1), the number of columns:
df.shape[0] # this is the # of rows
# TODO
len(df) # this is a function. usually takes more processing power
# You can also use the idiomatic Python `len` function to get the number of rows:
len(df)
# > ## `.dtypes`: column types
# - (http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.dtypes.html)
# - (http://pandas.pydata.org/pandas-docs/stable/basics.html)
df.dtypes
# > ## `.isnull()` and `.notnull()`: NaN (Not-a-Number)
# - (http://pandas.pydata.org/pandas-docs/stable/generated/pandas.isnull.html)
# - (http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.isnull.html)
# - (http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.isnull.html)
# - (http://pandas.pydata.org/pandas-docs/stable/generated/pandas.notnull.html)
# - (http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.notnull.html)
# - (http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.notnull.html)
# - (http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.sum.html)
# - (http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.sum.html)
# As a data scientist, we will have to decide what to do when encountering missing values (a.k.a, not-a-numbers). We might decide to drop the row containing it, drop the whole column, or impute it. Today, let's focus on finding these NaNs.
df.isnull()
# In return, we get a new `DataFrame` with Boolean values. `True` if the value is `NaN`, `False` otherwise.
# We can also get the count per column:
df.isnull().sum() # this sums up missing values by column.
#df.isnull().sum(axis=1) # this sums up missing values by row
# Summing again will return the number of cells in the `DataFrame` with missing values.
# TODO
df.isnull().sum().sum()
# Equivalently, we can also use the `.isnull()` function:
pd.isnull(df)
# We also also use `.notnull()`, its complement method:
df.notnull()
pd.notnull(df)
# > ### `.index` and `.columns`: row and column labels
# (http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Index.html)
# Use the `.index` property to get the label for rows. For columns, use the `.columns` property.
df.index
type(df.index)
# In this specific case, rows are just numbered from 0 to 1,000. Note that, similarly to Python's standard `range` function, this range also excludes the last number.
df.columns
type(df.columns)
# > ## `[ [] ]` and `[]`: subsetting on columns
# Selecting specific columns is performed by using the `[]` operator.
#
# Passing a single integer, or a list of integers, to `[]` will perform a location-based lookup of the columns.
# > E.g., columns #5 and #6:
# TODO
df[ [5,6]]
# > Let's check that the column subsetting returns a `DataFrame`:
# TODO
type(df[[5,6]])
# > Note that a `DataFrame` can be made of a single column. E.g., column #7 only:
# TODO
df[[7]]
# If the values passed to `[]` are non-integers, the `DataFrame` will attempt to match them to those in the `columns` property.
# > Let's subset the `DataFrame` on columns `SalePrice` and `SalePriceUnit`:
# TODO
# df[ [5,6]]
df[['SalePrice','SalePriceUnit']]
# However, you cannot mix integers and non-integers. E.g.,
# +
# "df[ ['SalePrice', 6] ]" errors out... Try it!
# -
# Not passing a list always results in a value-based lookup of the column:
# df['Address'] # this is a series
# df[['Address']] # this is a dataframe
#df['Address'].isnull() # this is a series
df['Address'].isnull().sum()
# > ## `Series`
# (http://pandas.pydata.org/pandas-docs/stable/dsintro.html)
# > Let's check the result type:
# TODO
df.Address #name cannot have a space in it. turns into a series
# Columns can also be retrieved using "attribute" access as `DataFrame`s add a property for each column with the names of the properties as the names of the columns. This won't work however for columns that have spaces or dots in their name.
# > Let's check the value of `df`'s `.Address` property:
# TODO
type(df.Address )
# > Use the `.name` property (not `.columns`, that's for a `DataFrame`) to get the name of the variable stored inside it.
# (http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.name.html)
# TODO
df['Address'].name #have to use name to get the heading of this series
# > To find the zero-based location of a column, use the `.get_loc()` method of the `.columns` property. E.g.,
# (http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Index.get_loc.html)
df.columns.get_loc('Beds')
df[ [df.columns.get_loc('Beds')] ]
# We should get the same output as subsetting a `DataFrame` on `Beds`:
# +
# TODO
# -
# > ## `[]`: slicing on rows
# > E.g., on the first five rows:
# +
# TODO
# -
# > ## `.loc[]` and `.iloc[]`: subsetting rows by index label and location
# - (http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.loc.html)
# - (http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.loc.html)
# - (http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.iloc.html)
# - (http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.iloc.html)
# - (http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.set_index.html)
# - (http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.set_index.html)
# - (http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.reset_index.html)
# - (http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.reset_index.html)
# Until now, the index of the `DataFrame` is a numerical starting from 0 but you can specify which column(s) should be in the index.
# > E.g., `ID`:
df = df.set_index('ID')
df.index
df
# > E.g., row with index 15063505:
# TODO
df.loc[15063505]
# Its name is its value in the index.
# > E.g., rows with indices 15063505 and 15064044:
# TODO
df.loc[ [15063505,15064044]]
# > E.g., rows #1 and #3:
# TODO
df.iloc[[1,3],[2,4]] # iloc uses the original numbering for rows 1,3 and columns 2 and 4
# > ## `.at[]` and `.iat[]`: scalar lookup by label or location
# - (http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.at.html)
# - (http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.at.html)
# - (http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.iat.html)
# - (http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.iat.html)
# Scalar values can be looked up by label using `.at`, by passing both the row label and then the column name/value.
# > E.g., row with index 15064044 and column `DateOfSale`.
# TODO
df.at[15064044,'DateOfSale']
# Scalar values can also be looked up by location using `.iat` by passing both the row location and then the column location.
# > E.g., row #3 and column #3:
# TODO
df.iat[3,3]
# > ## Subsetting rows by Boolean selection (a.k.a., masking)
# Rows can also be selected by using Boolean selection, using an array calculated from the result of applying a logical condition on the values in any of the columns. This allows us to build more complicated selections than those based simply upon index labels or positions.
# > E.g., what homes have been built before 1900?
# TODO #this is a series
sum(df.BuiltInYear < 1900)
# > Let's subset on that `Series`:
# TODO
df[df.BuiltInYear < 1900]
# Multiple conditions can be put together.
# > E.g., subset for `BuiltInYear` below 1900 and `Size` over 1500:
# TODO # and is only for scalars
df[(df.BuiltInYear < 1900) & (df.Size > 1500)] # | is OR, & is AND
# It is possible to subset on columns simultaneously.
# > E.g., subset (a `DataFrame`) on `Address` for `BuiltInYear` below 1900 and `Size` over 1500:
# +
# TODO
# -
# > To get a `Series` instead of a `DataFrame`:
# +
# TODO
# -
# ## Part B - Wrangling the SF Housing dataset (take 2) with `pandas`
df = pd.read_csv(os.path.join('..', 'datasets', 'dataset-02-zillow-starter.csv'), index_col = 'ID')
# (`pd.read_csv` can load the dataset and set the index column for the `DataFrame` at the same time)
# > ### Remove the `Latitude` and `Longitude` columns
# (http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.drop.html)
df.drop(['Latitude', 'Longitude'], axis = 1, inplace = True) # axis = 1 says i'm getting rid of columns
df.head()
# > ### `SalePrice`: scale all amount to `$M`
# - (http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.unique.html)
# - (http://pandas.pydata.org/pandas-docs/stable/generated/pandas.concat.html)
# - (http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.sort_index.html)
df.SalePriceUnit.unique()
# +
df_1 = df[df.SalePriceUnit == '$']
df_1 = df_1.drop('SalePriceUnit', axis = 1)
# Scaling sale price to $M
df_1.SalePrice /= 10 ** 6
df_6 = df[df.SalePriceUnit == '$M']
df_6 = df_6.drop('SalePriceUnit', axis = 1)
# -
# Concatenate of two DataFrames by rows
df = pd.concat([df_1, df_6])
# Resort the new DataFrame
# df.sort_index(inplace = True)
len(df)
# > ### `IsAStudio`: convert from a Boolean to a binary variable (i.e., 0 or 1)
# TODO
df.IsAStudio *= 1
df.IsAStudio
# > ### `Size`
df.SizeUnit.unique()
# Size is either in square feet or missing. Almost no work needed except to remove size unit.
df.drop('SizeUnit', axis = 1, inplace = True)
# > ### `LotSize`: scale all values to square feet
df.LotSizeUnit.unique()
# Lot sizes are either in square feet or in acres. Let's convert them all to square feet.
# > Group #1: the `na` values:
# +
df_na = df[df.LotSizeUnit.isnull()]
df_na = df_na.drop('LotSizeUnit', axis = 1)
df_na.shape[0]
# -
# > Group #2: the `sqft` values:
# +
# TODO (use df_sqft)
df_sq = df[df.LotSizeUnit=='sqft']
df_sq = df_sq.drop('LotSizeUnit',axis=1)
df_sq.shape[0]
# -
# > Group #3: the `ac` values:
# +
# TODO (use df_ac)
df_ac = df[df.LotSizeUnit=='ac']
df_ac = df_ac.drop('LotSizeUnit',axis=1)
df_ac.shape[0]
# -
# > Let's scale these `acre` values into `sqft`:
# +
# (1 acre = 43,560 sqft)
df_ac.LotSize /= 43560.
# TODO
# -
# Let's now put everything back together...
df = pd.concat([df_na, df_sq, df_ac]).sort_index()
df
# > ## `.to_csv`: save the `DataFrame` into a `.csv` file
# At the end of each phase (i.e., wrangling) of your data science project, it is a good idea to save your dataset into disk. Then for the next step, create a new Jupyther notebook and load your updated dataset
df.to_csv(os.path.join('..', 'datasets', 'dataset-02-zillow.csv'), index_label = 'ID')
# ## Part C - More advanced topics
# ### `.groupby()`
# (http://pandas.pydata.org/pandas-docs/stable/groupby.html)
# > What is the mean price of houses by number of bedrooms?
df = pd.read_csv(os.path.join('..', 'datasets', 'dataset-02-zillow.csv'))
df[ ['Beds', 'Baths', 'SalePrice'] ].groupby(['Beds','Baths']).mean()
# ### `.map()`
# (http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.map.html)
# When converting `SalePrice`, `Size`, and `LotSize` into `$M` and sqft, we could also have done the following:
df = pd.read_csv(os.path.join('..', 'datasets', 'dataset-02-zillow-starter.csv'))
df.SalePriceUnit.unique()
df.SalePriceUnit.map({'$': 1. / (10 ** 6), '$M': 1.}) # gives you the factor
df.SalePrice *= df.SalePriceUnit.map({'$': 1. / (10 ** 6), '$M': 1.})
df.SalePrice
df.drop('SalePriceUnit', axis = 1, inplace = True)
# > ### Activity: Using `.map()`, convert `Size` and `LotSize` to sqft.
# TODO
df.LotSizeUnit.map({'$': 1. / 43239, '$M': 1.}) #whatever # of feet is in an acre
# ### `.to_datetime()`
# (http://pandas.pydata.org/pandas-docs/stable/generated/pandas.to_datetime.html)
type(df.DateOfSale[0]) # this is a string not a real date
# So far, the dates stored in the `DataFrame` are just strings. We cannot easily extract the day, month, year. Thanksfully, `pandas` provides some facilities to do so.
pd.to_datetime(df.DateOfSale)
df.DateOfSale = pd.to_datetime(df.DateOfSale) #overwrite the string column with the real date
df.DateOfSale[0].year
# ### `.apply()`
# (http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.apply.html)
# +
# df.DateOfSale.year doesn't work because need to apply to the entire series
df.DateOfSale.apply(lambda date_of_sale: date_of_sale.year)
# +
df['YearOfSale'] = df.DateOfSale.apply(lambda date_of_sale: date_of_sale.year)
df['MonthOfSale'] = df.DateOfSale.apply(lambda date_of_sale: date_of_sale.month)
df['DayOfSale'] = df.DateOfSale.apply(lambda date_of_sale: date_of_sale.day)
df['WeekDayOfSale'] = df.DateOfSale.apply(lambda date_of_sale: date_of_sale.weekday_name)
df.drop('DateOfSale', axis = 1, inplace = True)
# -
# Now, we have the day, day of the week, month, and year of the sale as features in our dataset.
df
| 14,885 |
/Lab note/.ipynb_checkpoints/Midterm pnote-checkpoint.ipynb | e2d8f628dcf55559ef46f5293623972168c41070 | [] | no_license | suzieep/Mathematics-for-ICE | https://github.com/suzieep/Mathematics-for-ICE | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 15,632 | # ---
# jupyter:
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# extension: .py
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# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# Please Follow this Docker container installation process in your MacBook/Laptop before running this Python+R Notebook below.
#
# - Installation setup of environment where this notebook runs can be found i
# Container with Jupyter+H2o.ai+Python3+R+Spark in this [link_here](https://github.com/jpacerqueira/project_lost_saturn)
#
# Also :
# - You need a Strong bandwith the install the Container environment it takes about 10-11 minutes to finish.
#
# - Good Luck, stay safe! But investigate Corona virus(covid-19 or SARS-Cov-2) in your area and give the information back to the comunity!
#
# - Folium maps have custom Javascript and won't display in GitHub : https://stackoverflow.com/questions/53240378/folium-map-fail-to-render-in-notebook-on-github
#
# + [markdown] colab_type="text" id="f1S90bdBVIaK"
# # CoronaVirus Prediction
# -
# ### Number of Day to Predict 21
# %autosave 360
num_days_R_prediction=21
# #!pip install rpy2
import rpy2
# %load_ext rpy2.ipython
# %Rpush num_days_R_prediction
# + language="R"
# max_days_prediction<-num_days_R_prediction
# -
bypass_weather=1 # =1 bypass weather_pi api calls
#
number_past_days_training=12 # =(8/14) * num_days_R_prediction # Number of Past days on training # max to be on 6.Feb.2020
#
max_countries_map=50
#
## DROP_N=15 => 06/02
## DROP_N=45 => 08/03
## DROP_N=75 => 07/04
## DROP_N=145 => 15/06
## DROP_N=175 => 15/07
#
drop_n_dataset_days=235
#
# ### Load Data from Github - John Hopkins Institute
# +
# Get data from Github
import numpy as np
from math import sqrt
from sklearn.metrics import mean_squared_error
import pandas as pd
#url_1 = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Confirmed.csv'
url_1 = 'https://github.com/CSSEGISandData/COVID-19/raw/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv'
confirmed = pd.read_csv(url_1, error_bad_lines=False)
#url_2 = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Deaths.csv'
url_2 = 'https://github.com/CSSEGISandData/COVID-19/raw/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv'
death = pd.read_csv(url_2, error_bad_lines=False)
#url_3 = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Recovered.csv'
url_3 = 'https://github.com/CSSEGISandData/COVID-19/raw/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv'
recover = pd.read_csv(url_3, error_bad_lines=False)
# fix region names
confirmed['Country/Region']= confirmed['Country/Region'].str.replace("Mainland China", "China")
confirmed['Country/Region']= confirmed['Country/Region'].str.replace("US", "United States")
death['Country/Region']= death['Country/Region'].str.replace("Mainland China", "China")
death['Country/Region']= death['Country/Region'].str.replace("US", "United States")
recover['Country/Region']= recover['Country/Region'].str.replace("Mainland China", "China")
recover['Country/Region']= recover['Country/Region'].str.replace("US", "United States")
# -
confirmed.iloc[:,:]
# + [markdown] colab_type="text" id="Vvb-N49UipcR"
# ## Get Population
# + colab={} colab_type="code" id="2h7I0zT9kmFS"
population=pd.read_csv('/home/notebookuser/notebooks/covid19/data/population.csv', sep=',', encoding='latin1')
confirmed=pd.merge(confirmed, population,how='left' ,on=['Province/State','Country/Region'])
death=pd.merge(death, population,how='left' ,on=['Province/State','Country/Region'])
recover=pd.merge(recover, population,how='left' ,on=['Province/State','Country/Region'])
# + colab={"base_uri": "https://localhost:8080/", "height": 224} colab_type="code" executionInfo={"elapsed": 906, "status": "ok", "timestamp": 1582444047902, "user": {"displayName": "Ran Kremer", "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mB2_ZiPPU7rQBIvIt172EuazLz5955iIqYS5NAMAQ=s64", "userId": "05457805718113099013"}, "user_tz": -120} id="4_P8_F9poBQk" outputId="28901cce-a647-4d3c-afd2-5ccfaa18fd9d"
# merge region confirmed + death + recover
confirmed['region']=confirmed['Country/Region'].map(str)+'_'+confirmed['Province/State'].map(str)
death['region']=death['Country/Region'].map(str)+'_'+death['Province/State'].map(str)
recover['region']=recover['Country/Region'].map(str)+'_'+recover['Province/State'].map(str)
confirmed.iloc[:,:]
# -
# merge region death
death.iloc[185:195,:]
# merge region recover
recover.iloc[175:185,:]
confirmed.iloc[185:195,:]
confirmed.iloc[220:230,:]
# + [markdown] colab_type="text" id="2dJhgqRCVgBV"
# ## Create Time Series + Plots
# + colab={} colab_type="code" id="V21PYZNdlf5m"
def create_ts(df):
ts=df
ts=ts.drop(['Province/State', 'Country/Region','Lat', 'Long',' Population '], axis=1)
ts.set_index('region')
ts=ts.T
ts.columns=ts.loc['region']
ts=ts.drop('region')
ts=ts.fillna(0)
ts=ts.reindex(sorted(ts.columns), axis=1)
return (ts)
# + colab={} colab_type="code" id="7XmTDuNDlx-k"
## JOAO - Fix - Drop Duplicates # Keep Last # Issue With Data source Change from John Hopkins institute
#
ts=create_ts(confirmed.drop_duplicates(subset=['region'], keep='last', inplace=False) )
ts_d=create_ts(death.drop_duplicates(subset=['region'], keep='last', inplace=False) )
ts_rec=create_ts(recover.drop_duplicates(subset=['region'], keep='last', inplace=False) )
# -
# JOAO - FIX - Automation WarmUp of Plot Library
import matplotlib.pyplot as plt
import time
plt.legend(loc = 'upper left')
plt.show()
# + colab={"base_uri": "https://localhost:8080/", "height": 1000} colab_type="code" executionInfo={"elapsed": 2666, "status": "ok", "timestamp": 1582444057086, "user": {"displayName": "Ran Kremer", "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mB2_ZiPPU7rQBIvIt172EuazLz5955iIqYS5NAMAQ=s64", "userId": "05457805718113099013"}, "user_tz": -120} id="jOOwU-KzPssI" outputId="19b97499-f8c5-460e-f173-aa44515444ec"
#
p=ts.reindex(ts.max().sort_values(ascending=False).index, axis=1)
p.iloc[:,0:3].plot(marker='*',figsize=(20,12)).set_title('Daily Update - Total Confirmed - Top_3 World Region ',fontdict={'fontsize': 22})
p.iloc[:,3:25].plot(marker='*',figsize=(20,12)).set_title('Daily Update - Total Confirmed - Major_4,25 2nd Areas',fontdict={'fontsize': 22})
p_d=ts_d.reindex(ts_d.max().sort_values(ascending=False).index, axis=1)
p_d.iloc[:,0:3].plot(marker='*',figsize=(20,12)).set_title('Daily Update - Total Deaths - Top_3 World Region',fontdict={'fontsize': 22})
p_d.iloc[:,3:25].plot(marker='*',figsize=(20,12)).set_title('Daily Update - Total Deaths - Major_4,25 2nd Areas',fontdict={'fontsize': 22})
p_r=ts_rec.reindex(ts_rec.max().sort_values(ascending=False).index, axis=1)
p_r.iloc[:,0:3].plot(marker='*',figsize=(20,12)).set_title('Daily Update - Total Recovered - Top_3 World Region',fontdict={'fontsize': 22})
p_r.iloc[:,3:25].plot(marker='*',figsize=(20,12)).set_title('Daily Update - Total Recovered - Major_4,25 2nd Areas',fontdict={'fontsize': 22})
# + [markdown] colab_type="text" id="EsVRIFMLoV_3"
# ### Extract Weather Data
# + colab={} colab_type="code" id="LwxsJMRHoQKS"
# #!pip install pyweatherbit
#
from weatherbit.api import Api
import json
import pandas as pd
from pandas.io.json import json_normalize
### API - Joao
from datetime import datetime
#
api_key="29d9e51c56b94621b16297bcdeee9c4d" # hxj@mail.xcom
#
api = Api(api_key)
api.set_granularity('daily')
# # Set the granularity of the API - Options: ['daily','hourly','3hourly']
# # Will only affect forecast requests.
#api.get_forecast(lat='Lat', lon='Lon')
#my_end_date=datetime.today().strftime('%Y-%m-%d')
#### United Kingdom
#lat1='55.378100'
#lon1='-3.436000'
#api.get_history(lat=lat1,lon=lon1, start_date='2020-03-29',end_date=my_end_date)
# -
##
#### My List of Countries and Regions to train and represent data
my_train_list=[
### JOAO - LIST of Countries - Start here
# 'Andorra_nan',
'United States_nan',
'United Kingdom_nan',
'Italy_nan',
'Spain_nan',
'Netherlands_nan',
'France_nan',
'Belgium_nan',
'Portugal_nan',
'Switzerland_nan',
'Germany_nan',
'Japan_nan',
'Poland_nan',
### JOAO - LIST of Countries - Finish here
'Korea, South_nan',
'China_Hubei',
'China_Beijing',
'China_Guangdong',
'China_Shanghai',
# 'China_Shanxi',
# 'China_Sichuan', 'China_Xinjiang',
# 'China_Yunnan', 'China_Zhejiang',
# 'China_Anhui', 'China_Beijing',
# 'China_Chongqing', 'China_Fujian', 'China_Gansu',
# 'China_Guangdong', 'China_Guangxi', 'China_Guizhou',
# 'China_Hainan', 'China_Hebei', 'China_Heilongjiang', 'China_Henan',
# 'China_Hubei', 'China_Hunan', 'China_Inner Mongolia',
# 'China_Jiangsu', 'China_Jiangxi', 'China_Jilin', 'China_Liaoning',
# 'China_Ningxia', 'China_Qinghai', 'China_Shaanxi',
# 'China_Shandong', 'China_Shanghai', 'China_Shanxi',
# 'China_Sichuan', 'China_Tianjin', 'China_Tibet', 'China_Xinjiang',
# 'China_Yunnan', 'China_Zhejiang',
# 'Morocco_nan',
'Australia_New South Wales',
# 'Australia_Queensland',
# 'Australia_South Australia', 'Australia_Victoria',
'Brazil_nan',
# 'Cambodia_nan',
# 'Canada_British Columbia',
'Canada_Ontario',
'Canada_Quebec',
# 'Egypt_nan',
'China_Hong Kong',
'China_Macau',
'Finland_nan',
'India_nan',
'Iran_nan',
'Malaysia_nan',
# 'Nepal_nan',
'Norway_nan',
'Philippines_nan', 'Russia_nan', 'Singapore_nan',
# 'Sri Lanka_nan', 'Thailand_nan', 'United Arab Emirates_nan',
'Sweden_nan',
'Austria_nan',
# 'Taiwan*_nan',
# 'Vietnam_nan',
'Turkey_nan',
'Peru_nan',
'Chile_nan',
'Mexico_nan'
]
#
# + [markdown] colab_type="text" id="6BthA7XNoBnx"
# #### Weather History
# + colab={} colab_type="code" id="eEriYmKdoFaN"
# ################## already done since API is limited to 500 call per day
## consume Wether data From 15/03/2020 forward to end_date=30/03/2020
#
### Location in confirmed array to start in pos 1='Albania_nan' 61 = 'China_Hong Kong'
### Only run for Countries in above : my_train_list
vpos=len(confirmed.iloc[1])-1 #90# 89 #88 #87 #86 #85 #84 #83 #82 #81 #80 #79 #78 #77 #76 #75 #74 #1 #73
print('xcountry_region='+confirmed.iloc[1,vpos])
my_weather_fetch_list= my_train_list # ['Canada_Quebec'] # ['Iran_nan'] #['Brazil_nan'] #
start_date_init=pd.to_datetime('today').strftime('%Y/%m/%d') # '2020-04-18'
print('start_date_init=',start_date_init)
offset_days=-1 # -1 to start yesterday pick today # API free-tier just picks one per api call!
max_days=1 #1
w=pd.DataFrame(columns=['date','region','min','max'])
if bypass_weather != 1 :
for h in range(0,max_days):
offset_days=h
start_date=pd.to_datetime(start_date_init)
#
end_date=(start_date+pd.DateOffset(days=offset_days+1)).strftime('%Y-%m-%d')
start_date=(start_date+pd.DateOffset(days=offset_days)).strftime('%Y-%m-%d')
prnt_start_date=pd.to_datetime(start_date).strftime('%Y/%m/%d')
prnt_end_date=pd.to_datetime(end_date).strftime('%Y/%m/%d')
#
for i in range (1,len(confirmed)):
if confirmed.iloc[i,vpos] not in my_weather_fetch_list:
continue
if confirmed.iloc[i,vpos] in my_weather_fetch_list:
#
# Clean JSON structure return from API Call
jas=""
jas=api.get_history(lat=confirmed.iloc[i,2], lon=confirmed.iloc[i,3], start_date=start_date,end_date=end_date).json
if (((json_normalize(jas['data'])['min_temp'].values[0])=='')
or (np.isnan((json_normalize(jas['data'])['min_temp'].values[0])) == True )):
continue
try:
w=w.append({'date':prnt_end_date,'region':confirmed.iloc[i,vpos] ,'min':json_normalize(jas['data'])['min_temp'].values[0],'max':json_normalize(jas['data'])['max_temp'].values[0]}, ignore_index=True)
except Exception:
w=w.append({'date':prnt_end_date,'region':confirmed.iloc[i,vpos] ,'min':None,'max':None}, ignore_index=True)
#
#
table_columns=['date','region','min','max']
w = w[w.columns.intersection(table_columns)]
# -
w.to_csv('data/w_v2_v227.csv', index = False, header=True)
w[:]
# + [markdown] colab_type="text" id="DP96GEhbXKL1"
# ## Kalman Filter With R
# +
# Joao - FIX - Improve Performance
### Drop the Months of Jan, Feb < 06/02 as
### they are too in the Past and model no longuer trains in China Hubei only!
#
## DROP_N=75 => 07/04
drop_n=drop_n_dataset_days
ts=ts[drop_n:]
ts_d=ts_d[drop_n:]
ts_rec=ts_rec[drop_n:]
# -
ts[:3]
ts[-4:]
# + colab={} colab_type="code" id="ie1YVvCXgybm"
# Create data for R script
ts_conf=ts.reset_index()
ts_conf=ts_conf.rename(columns = {'index':'date'})
ts_conf['date']=pd.to_datetime(ts_conf['date'] ,errors ='coerce')
ts_conf.to_csv(r'/home/notebookuser/notebooks/covid19/data/ts_conf_r.csv')
ts_rec=ts_rec.reset_index()
ts_rec=ts_rec.rename(columns = {'index':'date'})
ts_rec['date']=pd.to_datetime(ts_rec['date'] ,errors ='coerce')
ts_rec.to_csv(r'/home/notebookuser/notebooks/covid19/data/ts_rec_r.csv')
ts_d=ts_d.reset_index()
ts_d=ts_d.rename(columns = {'index':'date'})
ts_d['date']=pd.to_datetime(ts_d['date'] ,errors ='coerce')
ts_d.to_csv(r'/home/notebookuser/notebooks/covid19/data/ts_d_r.csv')
# + colab={"base_uri": "https://localhost:8080/", "height": 629} colab_type="code" executionInfo={"elapsed": 19024, "status": "ok", "timestamp": 1582444082482, "user": {"displayName": "Ran Kremer", "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mB2_ZiPPU7rQBIvIt172EuazLz5955iIqYS5NAMAQ=s64", "userId": "05457805718113099013"}, "user_tz": -120} id="ngrkbpXcprcN" outputId="b79bf710-1544-449f-9d07-411cc2efcda5" language="R"
#
# #install.packages('pracma')
# #install.packages('Metrics')
# #install.packages('readr')
# #install.packages('reshape')
#
# Sys.setenv(TZ='GMT')
# Sys.timezone()
# + colab={"base_uri": "https://localhost:8080/", "height": 238} colab_type="code" executionInfo={"elapsed": 2026, "status": "ok", "timestamp": 1582444084537, "user": {"displayName": "Ran Kremer", "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mB2_ZiPPU7rQBIvIt172EuazLz5955iIqYS5NAMAQ=s64", "userId": "05457805718113099013"}, "user_tz": -120} id="mcIMrc3AW-Km" outputId="31c140e2-1428-44fb-b669-97c0ba6b6321" language="R"
# require(pracma)
# require(Metrics)
# require(readr)
# all<- read_csv("/home/notebookuser/notebooks/covid19/data/ts_conf_r.csv")
# all$X1<-NULL
# date<-all[,1]
# date[nrow(date) + 1,1] <-all[nrow(all),1]+1
# pred_all<-NULL
# for (n in 2:ncol(all)-1) {
# Y<-ts(data = all[n+1], start = 1, end =nrow(all)+1)
# sig_w<-0.01
# w<-sig_w*randn(1,100) # acceleration which denotes the fluctuation (Q/R) rnorm(100, mean = 0, sd = 1)
# sig_v<-0.01
# v<-sig_v*randn(1,100)
# t<-0.45
# phi<-matrix(c(1,0,t,1),2,2)
# gama<-matrix(c(0.5*t^2,t),2,1)
# H<-matrix(c(1,0),1,2)
# #Kalman
# x0_0<-p0_0<-matrix(c(0,0),2,1)
# p0_0<-matrix(c(1,0,0,1),2,2)
# Q<-0.01
# R<-0.01
# X<-NULL
# X2<-NULL
# pred<-NULL
# for (i in 0:nrow(all)) {
# namp <-paste("p", i+1,"_",i, sep = "")
# assign(namp, phi%*%(get(paste("p", i,"_",i, sep = "")))%*%t(phi)+gama%*%Q%*%t(gama))
# namk <- paste("k", i+1, sep = "")
# assign(namk,get(paste("p", i+1,"_",i, sep = ""))%*%t(H)%*%(1/(H%*%get(paste("p", i+1,"_",i, sep = ""))%*%t(H)+R)))
# namx <- paste("x", i+1,"_",i, sep = "")
# assign(namx,phi%*%get(paste("x", i,"_",i, sep = "")))
# namE <- paste("E", i+1, sep = "")
# assign(namE,Y[i+1]-H%*%get(paste("x", i+1,"_",i, sep = "")))
# namx2 <- paste("x", i+1,"_",i+1, sep = "")
# assign(namx2,get(paste("x", i+1,"_",i, sep = ""))+get(paste("k", i+1, sep = ""))%*%get(paste("E", i+1, sep = "")))
# namp2 <- paste("p", i+1,"_",i+1, sep = "")
# assign(namp2,(p0_0-get(paste("k", i+1, sep = ""))%*%H)%*%get(paste("p", i+1,"_",i, sep = "")))
# X<-rbind(X,get(paste("x", i+1,"_",i,sep = ""))[1])
# X2<-rbind(X2,get(paste("x", i+1,"_",i,sep = ""))[2])
# if(i>2){
# remove(list=(paste("p", i-1,"_",i-2, sep = "")))
# remove(list=(paste("k", i-1, sep = "")))
# remove(list=(paste("E", i-1, sep = "")))
# remove(list=(paste("p", i-2,"_",i-2, sep = "")))
# remove(list=(paste("x", i-1,"_",i-2, sep = "")))
# remove(list=(paste("x", i-2,"_",i-2, sep = "")))}
# }
# pred<-NULL
# pred<-cbind(Y,X,round(X2,4))
# pred<-as.data.frame(pred)
# pred$region<-colnames(all[,n+1])
# pred$date<-date$date
# pred$actual<-rbind(0,(cbind(pred[2:nrow(pred),1])/pred[1:nrow(pred)-1,1]-1)*100)
# pred$predict<-rbind(0,(cbind(pred[2:nrow(pred),2])/pred[1:nrow(pred)-1,2]-1)*100)
# pred$pred_rate<-(pred$X/pred$Y-1)*100
# pred$X2_change<-rbind(0,(cbind(pred[2:nrow(pred),3]-pred[1:nrow(pred)-1,3])))
# pred_all<-rbind(pred_all,pred)
# }
# pred_all<-cbind(pred_all[,4:5],pred_all[,1:3])
# names(pred_all)[5]<-"X2"
# pred_all=pred_all[with( pred_all, order(region, date)), ]
# pred_all<-pred_all[,3:5]
# -
# p=%R pred_all
# + colab={} colab_type="code" id="j8ZrF8iJUcKq"
############ Merge R output due to package problem
### Joao FIX -
# t=ts_d - deaths
# t=ts_rec - recovered
# t=ts - confirmed
t=ts
t=t.stack().reset_index(name='confirmed')
t.columns=['date', 'region','confirmed']
t['date']=pd.to_datetime(t['date'] ,errors ='coerce')
t=t.sort_values(['region', 'date'])
temp=t.iloc[:,:3]
temp=temp.reset_index(drop=True)
for i in range(1,len(t)+1):
if(temp.iloc[i,1] is not temp.iloc[i-1,1]):
temp.loc[len(temp)+1] = [temp.iloc[i-1,0]+ pd.DateOffset(1),temp.iloc[i-1,1], 0]
temp=temp.sort_values(['region', 'date'])
temp=temp.reset_index(drop=True)
temp['Y']=p['Y']
temp['X']=p['X']
temp['X2']=p['X2']
# JOAO - FIX - temp fixed
# Y,X,X2 nan issue from p revolved
p_pd=pd.DataFrame(p,columns=['Y','X','X2'])
p_pd['nindex'] = range(1, 1+len(p_pd))
temp['nindex']= range(1,1+len(temp))
#temp_1 = temp.join(p_pd)
temp_1 = temp.merge(p_pd, on='nindex', how='inner', suffixes=('_1', '_2')).rename(columns={"Y_2": "Y", "X_2": "X", "X2_2" : "X2"})
temp_1 = temp_1.drop(columns=['Y_1', 'X_1','X2_1','nindex'])
temp=temp_1
temp.to_csv(r'/home/notebookuser/notebooks/covid19/data/temp.csv')
# + [markdown] colab_type="text" id="a7vUr36bVvOb"
# ## Pre Proccessing Data for ML Model
# + [markdown] colab_type="text" id="PTerYYnPq2X7"
# ### Extract Weather Forecast Data
# + colab={} colab_type="code" id="M4XJBLT2-quk"
#
### Joao - Test Later Weather from new file : w_v2.csv and w_v2_v2.csv
w_v2=pd.read_csv('data/w_v2.csv', sep=',', encoding='latin1')
w_v2['date']=pd.to_datetime(w_v2['date'],format='%Y/%m/%d')
w_v2_v2=pd.read_csv('data/w_v2_v2.csv', sep=',', encoding='latin1')
w_v2_v2['date']=pd.to_datetime(w_v2_v2['date'],format='%Y/%m/%d')
w_v2_v227=pd.read_csv('data/w_v2_v227.csv', sep=',', encoding='latin1')
w_v2_v227['date']=pd.to_datetime(w_v2_v227['date'],format='%Y/%m/%d')
w=pd.read_csv('data/w.csv', sep=',', encoding='latin1')
w['date']=pd.to_datetime(w['date'],format='%d/%m/%Y')
w_forecast=pd.read_csv('data/w_forecast.csv', sep=',', encoding='latin1')
w_forecast['date']=pd.to_datetime(w_forecast['date'],format='%d/%m/%Y')
### Append Weather fetched now to file w_v2_v2
w_n_forward=w_v2_v2.append(w_v2_v227)
w_n_forward=w_n_forward.drop_duplicates(subset=['date','region'], keep='last', inplace=False)
w_n_forward=w_n_forward.sort_values(by=['region','date'], ascending=True)
w_n_forward.to_csv(r'data/w_v2_v2.csv', index = False, header=True)
# +
w_total=pd.DataFrame(columns=['date','region','min','max'])
w_total=w.append(w_forecast).append(w_v2).append(w_v2_v2).append(w_v2_v227)
w_total=w_total.drop_duplicates(subset=['date','region'], keep='last', inplace=False)
w_total=w_total.sort_values(by=['region','date'], ascending=True)
w_total.to_csv(r'data/w_total.csv', index = False, header=True)
# -
w_in_model=pd.read_csv('data/w_total.csv', sep=',', encoding='latin1')
#
w_in_model['date']=pd.to_datetime(w_in_model['date'],format='%Y/%m/%d')
w_in_model.to_csv(r'data/w_in_model.csv', index = False, header=True)
w_in_model.tail(2)
# + [markdown] colab_type="text" id="2vIZPBkfoqHe"
# ### Build Train Set Data Structure
# + colab={} colab_type="code" id="YPMRge7kQA8t"
### JOAO - Fix -
## t=ts confirmed
t=ts
t=t.stack().reset_index(name='confirmed')
t.columns=['date', 'region','confirmed']
t['date']=pd.to_datetime(t['date'] ,errors ='coerce')
t=t.sort_values(['region', 'date'])
# Add 1 Future day for prediction
t=t.reset_index(drop=True)
for i in range(1,len(t)+1):
if(t.iloc[i,1] is not t.iloc[i-1,1]):
t.loc[len(t)+1] = [t.iloc[i-1,0]+ pd.DateOffset(1),t.iloc[i-1,1], 0]
t=t.sort_values(['region', 'date'])
t=t.reset_index(drop=True)
# +
### JOAO - Fix -
t['1_day_change']=t['3_day_change']=t['7_day_change']=t['1_day_change_rate']=t['3_day_change_rate']=t['7_day_change_rate']=t['last_day']=0
#
### JOAO - Fix - ipykernel_launcher.py:5: RuntimeWarning: divide by zero encountered in double_scalars
for i in range(1,len(t)):
if(t.iloc[i,1] is t.iloc[i-2,1]):
t.iloc[i,3]=t.iloc[i-1,2]-t.iloc[i-2,2]
t.iloc[i,6]=((t.iloc[i-1,2]*100 +1)/(t.iloc[i-2,2]*100 -1 +1))*100
t.iloc[i,9]=t.iloc[i-1,2]
if(t.iloc[i,1] is t.iloc[i-4,1]):
t.iloc[i,4]=t.iloc[i-1,2]-t.iloc[i-4,2]
t.iloc[i,7]=((t.iloc[i-1,2]*100 +1)/(t.iloc[i-4,2]*100 -1 +1))*100
if(t.iloc[i,1] is t.iloc[i-8,1]):
t.iloc[i,5]=t.iloc[i-1,2]-t.iloc[i-8,2]
t.iloc[i,8]=((t.iloc[i-1,2]*100 +1)/(t.iloc[i-8,2]*100 -1 +1))*100
t=t.fillna(0)
t=t.merge(temp[['date','region', 'X']],how='left',on=['date','region'])
t=t.rename(columns = {'X':'kalman_prediction'})
t=t.replace([np.inf, -np.inf], 0)
### Joao - Fix NaN Kalman_Filter
t['kalman_prediction']=np.nan_to_num(t['kalman_prediction'])
t['kalman_prediction']=round(t['kalman_prediction'])
#
train=t.merge(confirmed[['region',' Population ']],how='left',on='region')
train=train.rename(columns = {' Population ':'population'})
train['population']=train['population'].str.replace(r" ", '')
train['population']=train['population'].str.replace(r",", '')
train['population']=train['population'].fillna(10000000) ### Fill 10M if nan
train['population']=train['population'].astype('int32')
### JOAO - Fix - ipykernel_launcher.py:5: RuntimeWarning: divide by zero encountered in double_scalars
# train['infected_rate']=train['last_day']/train['population']*10000
train['infected_rate']=(((train['last_day'] +1)*100)/((train['population'] +1)*100000) *10) # *100 - % converter
#
#### Joao , merge w weather only !?!
##train=train.merge(w,how='left',on=['date','region'])
train=train.merge(w_in_model,how='left',on=['date','region'])
#
train=train.sort_values(['region', 'date'])
### fill missing weather
for i in range(0,len(train)):
if(np.isnan(train.iloc[i,13])):
if(train.iloc[i,1] is train.iloc[i-1,1]):
train.iloc[i,13]=train.iloc[i-1,13]
train.iloc[i,14]=train.iloc[i-1,14]
# -
# Joao - Fix - Nulls are an issue
train_notnull=train[train['kalman_prediction'] != 0.0 ] #.any(axis=1)]
train_notnull[:]
# Joao - Fix - Nulls are an issue
train_nulls=train[train['kalman_prediction'].isnull() ] #.any(axis=1)]
train_nulls[:]
# +
# Joao - Fix - Nulls are an issue
train_nulls=train[train.isnull().any(axis=1)]
train_nulls[:]
train[-1:]
# -
## JOAO - FIX Drop Duplicates
train=train.drop_duplicates(subset=['date','region'], keep='last', inplace=False)
## JOAO - FIX Drop empty region='nan_nan'
train=train[train['region']!='nan_nan']
train[-1:]
# +
train.to_csv(r'data/train.csv', index = False, header=True)
##Shared -- Ratio in Confirmed - 21Day Forecast -- train 25April2020 - I
ratiod=pd.read_csv('data/train.csv', sep=',', encoding='latin1')
todayd=datetime.today().strftime('%Y-%m-%d')
ratiofn="World v2 -- Confirmed - "+str(num_days_R_prediction)+"Day Forecast -- train "+todayd+".csv"
ratiod['population_percentage : infected_rate confirmed']=ratiod['infected_rate']*100
### Assumption : Each affected person can contact up to 9 others that not report or are assimptomatic COVID19 cases.
ratiod['population_percentage : factor 9/10 infected_rate confirmed']=np.clip(ratiod['infected_rate']*900,0.0,1.0)
#
ratiod['delta : pred new_cases']=ratiod['kalman_prediction']-ratiod['last_day']
ratiod['delta : pred new_cases per 1M hab']=ratiod['delta : pred new_cases']/ratiod['population']*1000000
### roling 7day_AVG
ratiod['delta : roling 7day AVG']=ratiod['7_day_change']/7
ratiod['delta : aprox 14-day case notification rate per 100k hab']=(ratiod['7_day_change']*2)/ratiod['population']*100000
###
ratiod=ratiod.rename(columns={'kalman_prediction': 'confirmed_prediction', 'last_day': 'confirmed_yesterday'})
ratiod.to_csv(r'data/'+ratiofn, index = False, header=True)
ratiod[-3:]
# + [markdown] colab_type="text" id="YekOqU6Xr-qZ"
# ## Kalman 1 day Prediction with Evaluation
# +
# Select region
region='United States_nan'
evaluation=pd.DataFrame(columns=['region','mse','rmse','mae'])
place=0
for i in range(1,len(t)):
if(t.iloc[i,1] is not t.iloc[i-1,1]):
ex=np.array(t.iloc[i-len(ts):i,10])
pred=np.array(t.iloc[i-len(ts):i,2])
evaluation=evaluation.append({'region': t.iloc[i-1,1], 'mse': np.power((ex - pred),2).mean(),'rmse':sqrt(mean_squared_error(ex,pred)),'mae': (abs(ex - pred)).mean()}, ignore_index=True)
p=t[t['region']==region][['date','region','confirmed','kalman_prediction']]
#p=p.rename(columns = {'confirmed':'recoverd'})
p.iloc[len(p)-1,2]=None
p=p.set_index(['date'])
p.iloc[:,1:].plot(marker='o',figsize=(16,8)).set_title('Kalman Prediction - Select Region to Change - {}'.format(p.iloc[0,0]))
print(evaluation[evaluation['region']==p.iloc[0,0]])
# +
# Select region
region='Russia_nan'
evaluation=pd.DataFrame(columns=['region','mse','rmse','mae'])
place=0
for i in range(1,len(t)):
if(t.iloc[i,1] is not t.iloc[i-1,1]):
ex=np.array(t.iloc[i-len(ts):i,10])
pred=np.array(t.iloc[i-len(ts):i,2])
evaluation=evaluation.append({'region': t.iloc[i-1,1], 'mse': np.power((ex - pred),2).mean(),'rmse':sqrt(mean_squared_error(ex,pred)),'mae': (abs(ex - pred)).mean()}, ignore_index=True)
p=t[t['region']==region][['date','region','confirmed','kalman_prediction']]
#p=p.rename(columns = {'confirmed':'recoverd'})
p.iloc[len(p)-1,2]=None
p=p.set_index(['date'])
p.iloc[:,1:].plot(marker='o',figsize=(16,8)).set_title('Kalman Prediction - Select Region to Change - {}'.format(p.iloc[0,0]))
print(evaluation[evaluation['region']==p.iloc[0,0]])
# +
# Select region
region='Brazil_nan'
evaluation=pd.DataFrame(columns=['region','mse','rmse','mae'])
place=0
for i in range(1,len(t)):
if(t.iloc[i,1] is not t.iloc[i-1,1]):
ex=np.array(t.iloc[i-len(ts):i,10])
pred=np.array(t.iloc[i-len(ts):i,2])
evaluation=evaluation.append({'region': t.iloc[i-1,1], 'mse': np.power((ex - pred),2).mean(),'rmse':sqrt(mean_squared_error(ex,pred)),'mae': (abs(ex - pred)).mean()}, ignore_index=True)
p=t[t['region']==region][['date','region','confirmed','kalman_prediction']]
#p=p.rename(columns = {'confirmed':'recoverd'})
p.iloc[len(p)-1,2]=None
p=p.set_index(['date'])
p.iloc[:,1:].plot(marker='o',figsize=(16,8)).set_title('Kalman Prediction - Select Region to Change - {}'.format(p.iloc[0,0]))
print(evaluation[evaluation['region']==p.iloc[0,0]])
# +
# Select region
region='United Kingdom_nan'
evaluation=pd.DataFrame(columns=['region','mse','rmse','mae'])
place=0
for i in range(1,len(t)):
if(t.iloc[i,1] is not t.iloc[i-1,1]):
ex=np.array(t.iloc[i-len(ts):i,10])
pred=np.array(t.iloc[i-len(ts):i,2])
evaluation=evaluation.append({'region': t.iloc[i-1,1], 'mse': np.power((ex - pred),2).mean(),'rmse':sqrt(mean_squared_error(ex,pred)),'mae': (abs(ex - pred)).mean()}, ignore_index=True)
p=t[t['region']==region][['date','region','confirmed','kalman_prediction']]
#p=p.rename(columns = {'confirmed':'recoverd'})
p.iloc[len(p)-1,2]=None
p=p.set_index(['date'])
p.iloc[:,1:].plot(marker='o',figsize=(16,8)).set_title('Kalman Prediction - Select Region to Change - {}'.format(p.iloc[0,0]))
print(evaluation[evaluation['region']==p.iloc[0,0]])
# + [markdown] colab_type="text" id="8fjvSTMetENY"
# ## Regression - 1 Day Prediction
# + colab={"base_uri": "https://localhost:8080/", "height": 851} colab_type="code" executionInfo={"elapsed": 31942, "status": "ok", "timestamp": 1582444237701, "user": {"displayName": "Ran Kremer", "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mB2_ZiPPU7rQBIvIt172EuazLz5955iIqYS5NAMAQ=s64", "userId": "05457805718113099013"}, "user_tz": -120} id="j0cRQK7UfQVg" outputId="5e4bc582-2e4a-46c5-83e6-88e92532bca4"
# #!pip install h2o
import h2o
from h2o.estimators import H2ORandomForestEstimator
from h2o.estimators.glm import H2OGeneralizedLinearEstimator
from h2o.grid.grid_search import H2OGridSearch
h2o.init(min_mem_size='3G')
import numpy as np
from sklearn.linear_model import LinearRegression
# +
train=train.fillna(0)
########################################################
### Joao - Training progression - When growth happened 2020/03/05 to 2020/04/12
### Joao - FIX - Refresh this daily forward
### Old Fixed manual ### Last run 17April2020
##
#train_df=train[train['date']>'2020-03-04']
#train_df=train[train['date']<'2020-04-16']
#boots=train_df[train_df['date']>='2020-04-08'] # some bootstrap to give more weight for recent days
#train_df=train_df.append([boots[boots['date']>='2020-04-12']]*1000,ignore_index=True)
### Train progression of the Virus ### In Country list or Spain only
#region_to_train=my_train_list
#train_df_v2=train_df[train_df['region'].isin(region_to_train)] # =='Spain_nan'] #
#test=train[train['date']>='2020-04-03']
#test=test[test['date']<'2020-04-17']
#test_v2=test[test['region'].isin(region_to_train)]
#valid_v2=test_v2[test_v2['date']>='2020-04-16']
########################################################
# Set minimum of 14 training day 2weeks.
#
if number_past_days_training>=7 :
ntraindays=number_past_days_training
else :
ntraindays=7
#
to_day=pd.to_datetime('today')
first_train_date=(to_day+pd.DateOffset(days=-ntraindays)).strftime('%Y-%m-%d') # ntraindays/7 weeks =ntraindays days training
last_train_date=(to_day+pd.DateOffset(days=-1)).strftime('%Y-%m-%d')
first_bootstrap_date=(to_day+pd.DateOffset(days=-9)).strftime('%Y-%m-%d')
boost_bootstrap_date=(to_day+pd.DateOffset(days=-4)).strftime('%Y-%m-%d')
first_test_date=(to_day+pd.DateOffset(days=-10)).strftime('%Y-%m-%d')
last_test_date=to_day.strftime('%Y-%m-%d')
first_valid_date=(to_day+pd.DateOffset(days=-2)).strftime('%Y-%m-%d')
print('first_train_date=',first_train_date)
print('last_train_date=',last_train_date)
print('first_bootstrap_date=',first_bootstrap_date)
print('boost_bootstrap_date=',boost_bootstrap_date)
print('first_test_date=',first_test_date)
print('last_test_date=',last_test_date)
print('first_valid_date=',first_valid_date)
train_df=train[train['date']>first_train_date]
train_df=train[train['date']<last_train_date]
boots=train_df[train_df['date']>=first_bootstrap_date] # some bootstrap to give more weight for recent days
train_df=train_df.append([boots[boots['date']>=boost_bootstrap_date]]*5,ignore_index=True)
### Train progression of the Virus ### In Country list or Spain only
region_to_train=my_train_list
train_df_v2=train_df[train_df['region'].isin(region_to_train)] # =='Spain_nan'] #
test=train[train['date']>first_test_date]
test=test[test['date']<=last_test_date]
test_v2=test[test['region'].isin(region_to_train)]
valid_v2=test_v2[test_v2['date']>=first_valid_date]
# + colab={} colab_type="code" id="sAUTYoomzK-j"
x_col=[#'region',
'1_day_change', '3_day_change','7_day_change',
'1_day_change_rate',
'3_day_change_rate',
'7_day_change_rate',
'last_day',
'min', 'max',
'infected_rate',
'kalman_prediction'
# ,'population_percent_infected_rate_confirmed'
# ,'delta_new_cases'
# ,'delta_new_cases_per_1M_hab'
]
# + colab={} colab_type="code" id="uHZuGtlZq9z2"
x=train_df[x_col]
y=train_df['confirmed']
reg = LinearRegression().fit(x,y)
pred2=reg.predict(test[x_col]); pred2=pd.DataFrame(pred2); pred2=round(pred2)
pred2['confirmed']=test['confirmed'].values; pred2['date']=test['date'].values; pred2['region']=test['region'].values
# -
pred2.iloc[:25]
pred2.iloc[175:195]
pred2.iloc[220:240]
# + colab={"base_uri": "https://localhost:8080/", "height": 85} colab_type="code" executionInfo={"elapsed": 52387, "status": "ok", "timestamp": 1582444259660, "user": {"displayName": "Ran Kremer", "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mB2_ZiPPU7rQBIvIt172EuazLz5955iIqYS5NAMAQ=s64", "userId": "05457805718113099013"}, "user_tz": -120} id="iX5qZw4cxpKN" outputId="8f5fe095-8000-4d4a-edad-a480b672b68e"
#
train_h20 = h2o.H2OFrame(train_df)
###train_h20_hubei = h2o.H2OFrame(train_df_hubei) # different model for Hubei
#
### Joao - Italian Model
train_h20_v2 = h2o.H2OFrame(train_df_v2) # different model for V2 region ### Spain This time
test_h20 = h2o.H2OFrame(test)
#test_h20_hubei = h2o.H2OFrame(test_hubei)
test_h20_v2 = h2o.H2OFrame(test_v2)
valid_h20_v2=h2o.H2OFrame(valid_v2)
#training_columns = ['region','1_day_change', '3_day_change', '7_day_change','1_day_change_rate','3_day_change_rate','7_day_change_rate','last_day', 'kalman_prediction','infected_rate', 'min', 'max']
training_cols_v2 = ['region']+x_col #+['population_percent_infected_rate_confirmed','delta_new_cases','delta_new_cases_per_1M_hab']
training_columns = training_cols_v2
# Output parameter train against input parameters
response_column = 'confirmed'
# model = H2ORandomForestEstimator(ntrees=300, max_depth=12)
# model.train(x=training_columns, y=response_column, training_frame=train_h20)
###model_hubei = H2ORandomForestEstimator(ntrees=300, max_depth=12)
###model_hubei.train(x=training_columns, y=response_column, training_frame=train_h20_hubei)
### Joao - Model V2
model_v2 = H2ORandomForestEstimator(ntrees=500, max_depth=23)
model_v2.train(x=training_columns, y=response_column, training_frame=train_h20_v2, validation_frame=valid_h20_v2)
# +
#Print Model
print('# MSE on the training data = ',model_v2.mse())
print('# MSE on the validation data = ',model_v2.mse(valid=True))
print('# R^2 on the training data = ',model_v2.r2())
print('# R^2 on the validation data = ',model_v2.r2(valid=True))
# -
model_v2.show()
#model_hubei.varimp(True).iloc[:,:] # Feature importance for Hubei Model RF
### Joao - Model V2
modlv2=model_v2.varimp(True).iloc[:,:] # Feature importance for Model V2 Global RF
modlv2.sort_values('percentage',ascending=False)
# + colab={} colab_type="code" id="hNWxFRUrfxZN"
## Joao - Model Predictions - Country_nan _v2
performance = model_v2.model_performance(test_data=test_h20_v2)
# # Model Create Predictions
pred=model_v2.predict(test_h20_v2);pred=pred.as_data_frame(); pred=round(pred)
# #pred['daily_outcome']=test['daily_outcome'].values
pred['confirmed']=test_v2['confirmed'].values
pred['date']=test_v2['date'].values
pred['region']=test_v2['region'].values
# + [markdown] colab_type="text" id="9lmtGPPCwHta"
# ## Correlation Matrix And Temperature
# + colab={"base_uri": "https://localhost:8080/"} colab_type="code" executionInfo={"elapsed": 1475, "status": "ok", "timestamp": 1582201111522, "user": {"displayName": "Ran Kremer", "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mB2_ZiPPU7rQBIvIt172EuazLz5955iIqYS5NAMAQ=s64", "userId": "05457805718113099013"}, "user_tz": -120} id="KkeUCg9ZfxDH" outputId="57c6763e-619e-41b1-c3d5-99ada2f51d8a"
from string import ascii_letters
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(style="white")
# Compute the correlation matrix
corr = train.iloc[:,2:].corr()
# Generate a mask for the upper triangle
mask = np.triu(np.ones_like(corr, dtype=np.bool))
# Set up the matplotlib figure
f, ax = plt.subplots(figsize=(11, 9))
# Generate a custom diverging colormap
cmap = sns.diverging_palette(220, 10, as_cmap=True)
# Draw the heatmap with the mask and correct aspect ratio
sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.9, center=0,
square=True, linewidths=.5, cbar_kws={"shrink": .5})
print ('Correlation Matrix')
# + colab={"base_uri": "https://localhost:8080/"} colab_type="code" executionInfo={"elapsed": 1295, "status": "ok", "timestamp": 1582201111524, "user": {"displayName": "Ran Kremer", "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mB2_ZiPPU7rQBIvIt172EuazLz5955iIqYS5NAMAQ=s64", "userId": "05457805718113099013"}, "user_tz": -120} id="bA3aME-GUyWl" outputId="cf0707bf-8c10-428c-e768-0aa89167faa8"
print('Correlation To Confirmed')
print (corr.confirmed)
# + colab={"base_uri": "https://localhost:8080/", "height": 352} colab_type="code" executionInfo={"elapsed": 927, "status": "ok", "timestamp": 1582281426934, "user": {"displayName": "Ran Kremer", "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mB2_ZiPPU7rQBIvIt172EuazLz5955iIqYS5NAMAQ=s64", "userId": "05457805718113099013"}, "user_tz": -120} id="JD_ayu7fKS6f" outputId="0252e5e7-86a9-413b-8aa2-54c2d78dd5fa"
import matplotlib.pyplot as plt
p=train[['date','region','min','max']].set_index('date')
#
#rg1='China_Hubei'
#p=p[p['region']==rg1]
#p.iloc[:,:].plot(marker='*',figsize=(12,4),color=['#19303f','#cccc00']).set_title('Daily Min/Max Temperature - '+rg1,fontdict={'fontsize': 20})
#
## JOAO - Temp. Teast Italy - Data Supply finishes 13/03/2020
region_s=['Italy_nan','Spain_nan','United States_nan','United Kingdom_nan','Germany_nan','Iran_nan',
'Korea, South_nan','China_Hubei','Brazil_nan','Portugal_nan','Turkey_nan',
'Canada_Ontario','Canada_Quebec']
p=train[['date','region','min','max']].set_index('date')
for i in range(0,len(region_s)):
pv=p[p['region']==region_s[i]]
pv.iloc[:,:].plot(marker='*',figsize=(12,4),color=['#19303f','#cccc00']).set_title('Daily Min/Max Temperature - '+region_s[i],fontdict={'fontsize': 20})
#
# + colab={"base_uri": "https://localhost:8080/", "height": 238} colab_type="code" executionInfo={"elapsed": 471, "status": "ok", "timestamp": 1582280899307, "user": {"displayName": "Ran Kremer", "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mB2_ZiPPU7rQBIvIt172EuazLz5955iIqYS5NAMAQ=s64", "userId": "05457805718113099013"}, "user_tz": -120} id="UF117T2UF35z" outputId="4a98c30b-34a4-4f63-e887-2f8f15b96a05"
avg_temp=train[['region','confirmed','min','max']] # from 20-02-20 to 06-04-2020
avg_temp=avg_temp.groupby(by='region').max()
avg_temp=avg_temp.sort_values('confirmed',ascending=False)
print( 'Most infected Areas Avg Temperature')
print(avg_temp.iloc[:100,1:])
# + [markdown] colab_type="text" id="m3zZEoVO4Z27"
# ### Kalman X Days Ahead Prediction
# + language="R"
#
# #install.packages('reshape')
# + colab={"base_uri": "https://localhost:8080/", "height": 238} colab_type="code" executionInfo={"elapsed": 45274, "status": "ok", "timestamp": 1582444304976, "user": {"displayName": "Ran Kremer", "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mB2_ZiPPU7rQBIvIt172EuazLz5955iIqYS5NAMAQ=s64", "userId": "05457805718113099013"}, "user_tz": -120} id="5N_G_fHvteiv" outputId="3c813f3a-5d62-4997-cbd5-3ddbc60eb936" language="R"
#
# require(pracma)
# require(Metrics)
# require(readr)
# library(reshape)
# all<- read_csv("/home/notebookuser/notebooks/covid19/data/ts_conf_r.csv")
# all$X1<-NULL
#
# ### JOAO - FIX
# #
# ### Flexy Days maximum forward prediction =A Hint!= "Error increases as number of days increases"
# days_prediction<-max_days_prediction # Set i days prediction # max_days_prediction=90 days forward prediction with Kalman Filter
#
# for (i in 1: days_prediction) {
# if( i>1) {all<-all_new}
# date<-all[,1]
# date[nrow(date) + 1,1] <-all[nrow(all),1]+1
# pred_all<-NULL
# for (n in 2:ncol(all)-1) {
# Y<-ts(data = all[n+1], start = 1, end =nrow(all)+1)
# sig_w<-0.01
# w<-sig_w*randn(1,100) # acceleration which denotes the fluctuation (Q/R) rnorm(100, mean = 0, sd = 1)
# sig_v<-0.01
# v<-sig_v*randn(1,100)
# t<-0.45
# phi<-matrix(c(1,0,t,1),2,2)
# gama<-matrix(c(0.5*t^2,t),2,1)
# H<-matrix(c(1,0),1,2)
# #Kalman
# x0_0<-p0_0<-matrix(c(0,0),2,1)
# p0_0<-matrix(c(1,0,0,1),2,2)
# Q<-0.01
# R<-0.01
# X<-NULL
# X2<-NULL
# pred<-NULL
# for (i in 0:nrow(all)) {
# namp <-paste("p", i+1,"_",i, sep = "")
# assign(namp, phi%*%(get(paste("p", i,"_",i, sep = "")))%*%t(phi)+gama%*%Q%*%t(gama))
# namk <- paste("k", i+1, sep = "")
# assign(namk,get(paste("p", i+1,"_",i, sep = ""))%*%t(H)%*%(1/(H%*%get(paste("p", i+1,"_",i, sep = ""))%*%t(H)+R)))
# namx <- paste("x", i+1,"_",i, sep = "")
# assign(namx,phi%*%get(paste("x", i,"_",i, sep = "")))
# namE <- paste("E", i+1, sep = "")
# assign(namE,Y[i+1]-H%*%get(paste("x", i+1,"_",i, sep = "")))
# namx2 <- paste("x", i+1,"_",i+1, sep = "")
# assign(namx2,get(paste("x", i+1,"_",i, sep = ""))+get(paste("k", i+1, sep = ""))%*%get(paste("E", i+1, sep = "")))
# namp2 <- paste("p", i+1,"_",i+1, sep = "")
# assign(namp2,(p0_0-get(paste("k", i+1, sep = ""))%*%H)%*%get(paste("p", i+1,"_",i, sep = "")))
# X<-rbind(X,get(paste("x", i+1,"_",i,sep = ""))[1])
# X2<-rbind(X2,get(paste("x", i+1,"_",i,sep = ""))[2])
# if(i>2){
# remove(list=(paste("p", i-1,"_",i-2, sep = "")))
# remove(list=(paste("k", i-1, sep = "")))
# remove(list=(paste("E", i-1, sep = "")))
# remove(list=(paste("p", i-2,"_",i-2, sep = "")))
# remove(list=(paste("x", i-1,"_",i-2, sep = "")))
# remove(list=(paste("x", i-2,"_",i-2, sep = "")))}
# }
# pred<-NULL
# pred<-cbind(Y,X,round(X2,4))
# pred<-as.data.frame(pred)
# pred$region<-colnames(all[,n+1])
# pred$date<-date$date
# pred$actual<-rbind(0,(cbind(pred[2:nrow(pred),1])/pred[1:nrow(pred)-1,1]-1)*100)
# pred$predict<-rbind(0,(cbind(pred[2:nrow(pred),2])/pred[1:nrow(pred)-1,2]-1)*100)
# pred$pred_rate<-(pred$X/pred$Y-1)*100
# pred$X2_change<-rbind(0,(cbind(pred[2:nrow(pred),3]-pred[1:nrow(pred)-1,3])))
# pred_all<-rbind(pred_all,pred)
# }
# pred_all<-cbind(pred_all[,4:5],pred_all[,1:3])
# names(pred_all)[5]<-"X2"
# pred_all<-pred_all[,1:5]
#
# pred_all_today=pred_all[with( pred_all, order(region, date)), ]
# all_new=all
# #all_new[nrow(all_new),1]<-all_new[nrow(all),1]+1
# temp<-with(pred_all_today, pred_all_today[date == all[nrow(all),1]+1, ])
# temp<-cbind(temp[,1:2],temp[,4])
# temp2<-reshape(temp, direction = "wide", idvar = "date", timevar = "region")
# rand_num<-runif(ncol(temp2)-1, 0.9, 1.05)
# temp2[,2:ncol(temp2)]<-temp2[,2:ncol(temp2)]*rand_num
# colnames(temp2)=colnames(all_new)
# all_new<-rbind(all_new,temp2)
# all_new[,2:ncol(all_new)]<-round(all_new[,2:ncol(all_new)])
# for (i in 2:ncol(all_new)) {
# all_new[nrow(all_new),i]=max(all_new[nrow(all_new)-1,i],all_new[nrow(all_new),i])}
# }
# + colab={"base_uri": "https://localhost:8080/", "height": 71} colab_type="code" executionInfo={"elapsed": 45272, "status": "ok", "timestamp": 1582444304980, "user": {"displayName": "Ran Kremer", "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mB2_ZiPPU7rQBIvIt172EuazLz5955iIqYS5NAMAQ=s64", "userId": "05457805718113099013"}, "user_tz": -120} id="fVMFrhJJsuJ8" outputId="26260197-a353-4808-afc8-df98c3407df5"
# all_new=%R all_new
# + colab={} colab_type="code" id="62OEK0Ew6xjw"
all_new['date']=pd.to_datetime(all_new['date'],unit='d')
# + colab={"base_uri": "https://localhost:8080/", "height": 1000} colab_type="code" executionInfo={"elapsed": 47320, "status": "ok", "timestamp": 1582444307049, "user": {"displayName": "Ran Kremer", "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mB2_ZiPPU7rQBIvIt172EuazLz5955iIqYS5NAMAQ=s64", "userId": "05457805718113099013"}, "user_tz": -120} id="psIsc_UT5lEg" outputId="1f8b0dc5-2365-4b4a-9d77-188390247839"
#
### Joao - Moving Forward ...
# Select regions From my_train_list
#
region=['date']+my_train_list
p_kalman=all_new[region]
#p=all_new
#p.iloc[len(p)-1,2]=None
p_kalman=p_kalman.set_index(['date'])
p_kalman=p_kalman.reindex(p_kalman.max().sort_values(ascending=False).index, axis=1)
p_kalman.iloc[:,:].plot(marker='o',figsize=(24,14)).set_title('Kalman Prediction')
#
p_kalman2=all_new[['date','United States_nan']] ## Joao
p_kalman2=p_kalman2.set_index(['date'])
p_kalman2.iloc[:,:].plot(marker='o',figsize=(24,14)).set_title('Kalman Prediction - Select Country/Region to Change - {}'.format(p_kalman2.columns[0]))
#
p_kalman3=all_new[['date','Italy_nan']] ## Joao
p_kalman3=p_kalman3.set_index(['date'])
p_kalman3.iloc[:,:].plot(marker='o',figsize=(24,14)).set_title('Kalman Prediction - Select Country/Region to Change - {}'.format(p_kalman3.columns[0]))
#
p_kalman4=all_new[['date','Spain_nan']] ## Joao
p_kalman4=p_kalman4.set_index(['date'])
p_kalman4.iloc[:,:].plot(marker='o',figsize=(24,14)).set_title('Kalman Prediction - Select Country/Region to Change - {}'.format(p_kalman4.columns[0]))
#
# -
### Joao - Dynamic plot all regions individually
#print(region[:])
for i in range(1,len(region)):
country_print=region[i]
#print("here:"+country_print)
p_kalman_rg=all_new[['date',country_print]]
p_kalman_rg=p_kalman_rg.set_index(['date'])
p_kalman_rg.iloc[:,:].plot(marker='o',figsize=(16,8)).set_title('Kalman Prediction - Select Country/Region to Change - {}'.format(p_kalman_rg.columns[0]))
max_p0=all_new[:]
max_p0=max_p0.max()
max_date=max_p0[:1]
max_p0=max_p0[1:]
max_p0=pd.DataFrame(max_p0)
max_p0=max_p0.astype(str)
max_p0['pred_confirmed']=max_p0[max_p0.columns[0]].str.split(' ').str[-1].astype(float)
max_p0[max_p0.columns[0]]=max_p0[max_p0.columns[0]][:-len(max_p0['pred_confirmed'])]
max_p0=max_p0.sort_values(by='pred_confirmed', ascending=False)
#
print("### -- Confirmed max cases per country at last prediction date -- ###")
print(max_date)
max_p0[:]
# + colab={} colab_type="code" id="9bwnqSN8WFMp"
t.to_csv(r'data/t_confirmed_global.csv', index = False, header=True)
# -
all_new.to_csv(r'data/prediction_kalman_filter_global.csv', index = False, header=True)
# + [markdown] colab_type="text" id="s1qTRyApWuh9"
# ## Iterative Regression
# + colab={"base_uri": "https://localhost:8080/", "height": 119} colab_type="code" executionInfo={"elapsed": 72466, "status": "ok", "timestamp": 1582444332206, "user": {"displayName": "Ran Kremer", "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mB2_ZiPPU7rQBIvIt172EuazLz5955iIqYS5NAMAQ=s64", "userId": "05457805718113099013"}, "user_tz": -120} id="7fAG9-jQWv0B" outputId="8f1f8aa1-1470-4d17-b0d6-14bfa44250bb"
t_iter=all_new.set_index(['date'])
t_iter=t_iter.stack().reset_index(name='confirmed')
t_iter.columns=['date', 'region','confirmed']
t_iter['date']=pd.to_datetime(t_iter['date'] ,errors ='coerce')
t_iter=t_iter.sort_values(['region', 'date'])
t_iter=t_iter.reset_index(drop=True)
for i in range(1,len(t_iter)+1):
if(t_iter.iloc[i,1] is not t_iter.iloc[i-1,1]):
t_iter.loc[len(t_iter)+1] = [t_iter.iloc[i-1,0]+ pd.DateOffset(1),t_iter.iloc[i-1,1], 0]
t_iter=t_iter.sort_values(['region', 'date'])
t_iter=t_iter.reset_index(drop=True)
### Joao - Fix - RuntimeWarning: divide by zero encountered in double_scalars
#
t_iter['1_day_change']=t_iter['3_day_change']=t_iter['7_day_change']=t_iter['1_day_change_rate']=t_iter['3_day_change_rate']=t_iter['7_day_change_rate']=t_iter['last_day']=0
for i in range(1,len(t_iter)):
if(t_iter.iloc[i,1] is t_iter.iloc[i-2,1]):
t_iter.iloc[i,3]=t_iter.iloc[i-1,2]-t_iter.iloc[i-2,2]
t_iter.iloc[i,6]=((t_iter.iloc[i-1,2]*100 +1)/(t_iter.iloc[i-2,2]*100 -1 +1))*100
t_iter.iloc[i,9]=t_iter.iloc[i-1,2]
if(t_iter.iloc[i,1] is t_iter.iloc[i-4,1]):
t_iter.iloc[i,4]=t_iter.iloc[i-1,2]-t_iter.iloc[i-4,2]
t_iter.iloc[i,7]=((t_iter.iloc[i-1,2]*100 +1)/(t_iter.iloc[i-4,2]*100 -1 +1))*100
if(t_iter.iloc[i,1] is t_iter.iloc[i-8,1]):
t_iter.iloc[i,5]=t_iter.iloc[i-1,2]-t_iter.iloc[i-8,2]
t_iter.iloc[i,8]=((t_iter.iloc[i-1,2]*100 +1)/(t_iter.iloc[i-8,2]*100 -1 +1))*100
t_iter=t_iter.fillna(0)
# t_iter=t_iter.merge(temp[['date','region', 'X']],how='left',on=['date','region'])
# t_iter=t_iter.rename(columns = {'X':'kalman_prediction'})
t_iter=t_iter.replace([np.inf, -np.inf], 0)
t_iter['kalman_prediction']=round(t_iter['confirmed'])
test_iter=t_iter.merge(confirmed[['region',' Population ']],how='left',on='region')
test_iter=test_iter.rename(columns = {' Population ':'population'})
test_iter['population']=test_iter['population'].str.replace(r" ", '')
test_iter['population']=test_iter['population'].str.replace(r",", '')
test_iter['population']=test_iter['population'].fillna(10000000) # Fill 10M population if null
test_iter['population']=test_iter['population'].astype('int32')
## Joao - Fix Divid By Zero
#test_iter['infected_rate'] =test_iter['last_day']/test_iter['population']*10000
#test_iter['infected_rate'] =((test_iter['last_day']+1)*10000)/((test_iter['population']+1)*100)*100
test_iter['infected_rate']=(((test_iter['last_day'] +1)*100)/((test_iter['population'] +1)*100000) *10)
#
test_iter=test_iter.merge(w,how='left',on=['date','region'])
#test_iter=test_iter.sort_values(['region', 'date'])
test_iter_temp=test_iter[np.isnan(test_iter['min'])]
test_iter_temp=test_iter_temp.drop(columns=['min', 'max'])
test_iter_temp=test_iter_temp.merge(w_forecast,how='left',on=['date','region'])
test_iter=test_iter.dropna()
test_iter=test_iter.append(test_iter_temp)
test_iter=test_iter.sort_values(['region', 'date'])
### fill missing weather
for i in range(0,len(test_iter)):
if(np.isnan(test_iter.iloc[i,13])):
if(test_iter.iloc[i,1] is test_iter.iloc[i-1,1]):
test_iter.iloc[i,13]=test_iter.iloc[i-1,13]+abs(test_iter.iloc[i-1,13]*.01)
test_iter.iloc[i,14]=test_iter.iloc[i-1,14]+abs(test_iter.iloc[i-1,14]*.01)
# + colab={} colab_type="code" id="SAlBmhP449d9"
test_iter=test_iter.fillna(0)
test_iter[test_iter.isnull().any(axis=1)]
# + colab={} colab_type="code" id="0nEsgisgpf_Z"
### JOAO - ERROR - ValueError: Index contains duplicate entries, cannot reshape
pred=reg.predict(test_iter[x_col]); pred=pd.DataFrame(pred); pred.columns = ['prediction'];pred=round(pred)
pred['confirmed']=test_iter['confirmed'].values; pred['date']=test_iter['date'].values; pred['region']=test_iter['region'].values
for i in range(1,len(pred)):
if(pred.iloc[i,3] is pred.iloc[i-1,3]):
if(pred.iloc[i,0]<pred.iloc[i-1,1]):
pred.iloc[i,0]=pred.iloc[i-1,1]
### JOAO - Drop Duplicates
pred=pred.drop_duplicates(subset=['date','region'], keep='last', inplace=False)
### Joao - Save long term predictions
pred.to_csv('data/pred_'+str(num_days_R_prediction)+'_days.csv', index = False, header=True)
###
pred=pred.pivot(index='date',columns='region',values='prediction') # pivot pred df
# -
pred[:]
### JOAO - FIX - issue if all source days ts[:]
## Comment for -35days start. 26feb.2020 ts=ts[35:]
## 06Aug2020 - For all
pred=pred[:-1]
region1=max_p0[max_p0.columns[0]].astype(str).reset_index().rename(columns={"index": "region"}).get_values()
region=[region[0] for region in region1]
region
# + colab={"base_uri": "https://localhost:8080/", "height": 891} colab_type="code" executionInfo={"elapsed": 76666, "status": "ok", "timestamp": 1582444336423, "user": {"displayName": "Ran Kremer", "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mB2_ZiPPU7rQBIvIt172EuazLz5955iIqYS5NAMAQ=s64", "userId": "05457805718113099013"}, "user_tz": -120} id="n-xXJMS-Yg4s" outputId="9ca38705-a493-43b0-b1d3-d4112dbf958a"
#
p=pred[region[:20]]
p[:].reindex(p.max().sort_values(ascending=False).index, axis=1).plot(marker='*',figsize=(24,14),title ='Major Areas Prediction')
# -
#
rgsx=region[:35]
for i in range (0,len(rgsx)):
rg_print=rgsx[i]
pred_prg=pd.DataFrame()
pred_prg=pred[rg_print]
pred_prg[:].plot(marker='*',figsize=(16,8),title =rg_print+' - Prediction Long Term - Confirmed Cases Covid-19')
plt.legend(loc = 'upper left')
plt.show()
#
#region=my_train_list
pv2=pred
pv2=pv2.reindex(pv2.max().sort_values(ascending=False).index, axis=1)
pv2[:].iloc[:,0:50].plot(marker='*',figsize=(24,14),title ='Top 50 World Global - Long Term - Major Areas Prediction')
plt.legend(loc = 'upper left')
plt.show()
#region=my_train_list
pv2=pred
pv2[:].plot(marker='*',figsize=(24,14),title ='World Global - Long Term - Major Areas Prediction')
plt.legend(loc = 'upper left')
plt.show()
# + [markdown] colab_type="text" id="CrGaYE3LumzO"
#
# ## Prediction Heatmap
# -
pv1=pv2[:] #p #p2.append(p3).append(p4).append(p5).append(p6).append(p8).append(p9).append(p11)
p=pd.DataFrame(pv1)
p[45:]
pfname='data/p+'+str(num_days_R_prediction)+'_confirmed_daily.csv'
p.to_csv(pfname, index = False, header=True)
# + colab={} colab_type="code" id="9DzoizKcuwDL"
# #!pip install gmplot
# Import the necessary libraries
import pandas as pd
import gmplot
# For improved table display in the notebook
#from IPython.display import display
import random
# + colab={} colab_type="code" id="ErJO4Di4u_Gm"
heatmap=confirmed[['region','Lat','Long']]
p_m=p.T # pred.T #
### JOAO - Change Global HeapMap print - USA is too small, as USA States datasets are not used!
#heatmap=heatmap[heatmap['region'].isin(region)] ## heatmap for region dataset only
heatmap=heatmap[heatmap['region'].isin(confirmed['region'])] ## Global heatmap
p_m=p_m.reset_index()
heatmap_m=heatmap.merge(p_m,how='left',on='region')
# -
heatmap_m[:]
# Fill empty lat,long with 0
heatmap_m['Lat']=heatmap_m['Lat'].fillna(0)
heatmap_m['Long']=heatmap_m['Long'].fillna(0)
#
heatmap_m_fn="heatmap_m_"+str(num_days_R_prediction)+".csv"
heatmap_m.to_csv(r'data/'+heatmap_m_fn, index = False, header=True)
# +
# #!pip install folium
import folium
import re
lat=40.99474
lang=6.87237
p21_cluster=folium.Map(location=[lat,lang],zoom_start=2)
from folium import plugins
cluster=plugins.MarkerCluster().add_to(p21_cluster)
colors={'A':'darkgreen','B':'darkpurple','C':'pink','D':'beige','E':'red','F':'lightblue','G':'darkblue','H':'cadetblue','I':'gray',
'J':'lightred','K':'blue','L':'orange','M':'lightgreen','N':'orange','O':'purple','P':'lightgray','Q':'darkred','R':'green',
'S':'black','T':'blue','U':'purple','V':'green','X':'blue','Y':'beige','W':'pink','Z':'white'}
date_pred=(datetime.today()+pd.DateOffset(days=num_days_R_prediction)).strftime('%Y-%m-%d')
ndaysavg=num_days_R_prediction
ndayarray=-1-num_days_R_prediction
for lat,lng,num,totpred,ldaygrowth in zip(heatmap_m.Lat,heatmap_m.Long,range(0,heatmap_m.shape[0]), heatmap_m[heatmap_m.columns[-1]], (heatmap_m[heatmap_m.columns[-1]]-heatmap_m[heatmap_m.columns[ndayarray]])/ndaysavg ):
use_color=heatmap_m['region'][num][0]
print_region=re.sub('_nan', '', heatmap_m['region'][num])
popup = folium.Popup( print_region+' ConfirmedPred='+str(round(totpred))+' Roling7DayAVG='+str(round(ldaygrowth))+' Date='+date_pred , parse_html=True)
#
folium.Marker(
[lat,lng],
popup=popup,
icon=folium.Icon(color=colors[use_color])
).add_to(p21_cluster)
p21_cluster
# -
# Top 50 Regions in the World impacted
#
max_cont=max_countries_map
region_m2=region[:max_cont]
#
heatmap_m2=heatmap_m[heatmap_m['region'].isin(region_m2)].reset_index()
#
heatmap_m2[:]
# +
import folium
import re
lat=40.99474
lang=6.87237
p21_reg_cluster=folium.Map(location=[lat,lang],zoom_start=2)
from folium import plugins
cluster=plugins.MarkerCluster().add_to(p21_reg_cluster)
colors={'A':'darkgreen','B':'darkpurple','C':'pink','D':'beige','E':'red','F':'lightblue','G':'darkblue','H':'cadetblue','I':'gray',
'J':'lightred','K':'blue','L':'orange','M':'lightgreen','N':'orange','O':'purple','P':'lightgray','Q':'darkred','R':'green',
'S':'black','T':'blue','U':'purple','V':'green','X':'blue','Y':'beige','W':'pink','Z':'white'}
date_pred=(datetime.today()+pd.DateOffset(days=num_days_R_prediction)).strftime('%Y-%m-%d')
ndaysavg=num_days_R_prediction
ndayarray=-1-num_days_R_prediction
for lat2,lng2,num2,totpred2,ldaygrowth2 in zip(heatmap_m2.Lat,heatmap_m2.Long,range(0,heatmap_m2.shape[0]), heatmap_m2[heatmap_m2.columns[-1]], (heatmap_m2[heatmap_m2.columns[-1]]-heatmap_m2[heatmap_m2.columns[ndayarray]])/ndaysavg ):
use_color2=heatmap_m2['region'][num2][0]
print_region=re.sub('_nan', '', heatmap_m2['region'][num2])
popup2 = folium.Popup( print_region+' ConfirmedPred='+str(round(totpred2))+' Roling7DayAVG='+str(round(ldaygrowth2))+' Date='+date_pred , parse_html=True)
#
folium.Marker(
[lat2,lng2],
popup=popup2,
icon=folium.Icon(color=colors[use_color2])
).add_to(p21_reg_cluster)
p21_reg_cluster
# -
#
datemap=datetime.today().strftime('%Y-%m-%d')
p21_cluster.save("heatmaps/Heatmap_Folium-Global-"+datemap+"-pred"+str(num_days_R_prediction)+"Days.html")
#
p21_reg_cluster.save("heatmaps/Heatmap_Folium-Regional-"+datemap+"-pred"+str(num_days_R_prediction)+"Days.html")
print("Stats and Forecast Done for Today!")
print("I'm done with this past month of March, April and May!")
print(" April-May-June-July are going to be hard with this Global Lock-Down!")
# + colab={} colab_type="code" id="15zSGQ9xZ2Qy"
exit()
# -
| 58,295 |
/notebooks/2.overview-of-supervised-learning/2.3.least_square_vs_nearest_neighbors/least_square_vs_nearest_neighbors_for_regression_synthesis.ipynb | 5f8bc4e171e9699a23474ff62883c84a96ab59a8 | [] | no_license | TaiChiTiger/ElemStatLearn | https://github.com/TaiChiTiger/ElemStatLearn | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 242,456 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
import pandas as pd
df=pd.read_csv('master.csv')
df.head()
df.info()
import seaborn as sns
df.head()
df.columns
X=df[['ID', 'Age', 'Experience', 'Income', 'ZIP Code', 'Family', 'CCAvg',
'Education', 'Mortgage', 'Personal Loan', 'Securities Account',
'CD Account', 'Online']]
y=df['CreditCard']
from sklearn.model_selection import train_test_split
x_train,x_test,y_train,y_test=train_test_split(X,y,random_state=5,test_size=0.3)
x_train.shape
y_test.shape
from sklearn.linear_model import LogisticRegression
model=LogisticRegression()
model.fit(x_train,y_train)
pred_values = model.predict(x_test)
pred_values
from sklearn.metrics import accuracy_score,confusion_matrix
accuracy_score(y_test,pred_values)*100
confusion_matrix(y_test,pred_values)
dfi,left_index = True, right_index=True)
for c in df.columns:
if '_x' in c:
nc = c[:-2]
df.rename(columns={c:nc}, inplace=True)
if '_y' in c:
df= df.drop([c],axis=1)
# -
# get pv sizes for maximum pv systems
# -----------------------------------
maximums={}
pv_ref_file='C:\\Users\\z5044992\\Documents\\MainDATA\\DATA_EN_3\\reference\\capex_pv_lookup.csv'
pv_ref=pd.read_csv(pv_ref_file)
pv_ref = pv_ref.set_index('pv_cap_id')
for i in pv_ref.index:
if 'max' in i and 'site' in i:
site = um.find_between(i,'_','_')
maximums[site] = pv_ref.loc[i,'kW']
# __Look at:__
# `evening_discharge_1`
# * PV = 2.5kWp
# * kWh = 0,1,2
# * scenarios: 14, 55,57
def plot_battery(df,
plotfile,
start_day=0,
name ='',
info=''
):
"""Plots timeseries data of pv, load, import, export and SOC."""
# Slice for 2 days starting at start_day"
period = df.index[start_day*48:(start_day+2)*48]
df = df.loc[period]
# remove irrelevant / unnecessary columns:
if 'battery_charge_kWh' in df.columns:
df = df.drop(['battery_charge_kWh'], axis=1)
cols = [c for c in df.columns if 'SOC' not in c and 'SOH' not in c]
cmap = mpl.cm.tab10
colourlist = [cmap(x) for x in range(len(cols))]
# if '_en_' in name:
# df = df.drop(['sum_of_customer_imports','sum_of_customer_exports'], axis=1)
# if '_btm_' in name:
# df = df.drop(['grid_import', 'grid_export'], axis=1)
max_kwh = df[cols].max().max()
fig, ax = plt.subplots()
df.index = pd.to_datetime(df.index)
for c in cols:
ax.plot(df.index, df[c], label = c, color = colourlist[cols.index(c)])
if 'battery_SOC' in df.columns:
ax2 = ax.twinx()
ax2.plot(df.index, df['battery_SOC'], linestyle='--')
ax2.set_ylabel("Battery SOC %")
ax2.set_ylim(0, 110)
if 'ind_battery_SOC' in df.columns:
ax2 = ax.twinx()
ax2.plot(df.index, df['ind_battery_SOC'], linestyle='--')
ax2.set_ylabel("Battery SOC %")
ax2.set_ylim(0, 110)
ax.set_xlabel("Time", fontsize=14)
ax.set_ylabel("kWh", fontsize=14)
ax.grid(True)
ax.set_ylim(0, max_kwh)
ax.xaxis.set_major_locator(mdates.HourLocator(interval=2))
# set formatter
h_fmt = mdates.DateFormatter('%H')
ax.xaxis.set_major_formatter(h_fmt)
# set font and rotation for date tick labels
fig.autofmt_xdate(rotation=90)
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.75, box.height])
ax2.set_position([box.x0, box.y0, box.width * 0.75, box.height])
# legend
# -------
# Create legend for colours
cline = {}
for s in cols:
cline[s] = mlines.Line2D([],[],
color = cmap(cols.index(s)),
label=s)
handles = [cline[s] for s in cols]
leg = ax.legend(handles = handles,
labels = cols,
loc='upper left', bbox_to_anchor=(1.1, 1),
prop={'size': 9})
ax.add_artist(leg)
stuff = ax.text(1.1,0.2,info,transform=ax.transAxes,fontsize=10)
ax.add_artist(stuff)
plt.savefig(plotfile, dpi=1000, bbox_extra_artists=(leg,stuff,))
plt.close(fig)
return ax
# +
df['kWh_unit']=df['central_battery_capacity_kWh'].divide(df['number_of_households'])
for s in df.index:
if df.fillna('zero').loc[s,'pv_filename'] == 'zero':
df.loc[s,'kWp_unit'] =0
elif 'max' in df.loc[s,'pv_filename']:
df.loc[s,'kWp_unit']= maximums[site]/df.loc[s,'number_of_households']
else:
df.loc[s,'kWp_unit'] = float(df.loc[s,'pv_filename'][7]) + float(df.loc[s,'pv_filename'][9])/10
# +
# Choose scenarios:
kWp = [2.5]
kWh = [0.0,1,2,3,4]
strategies =['ed1','ed2','ed3', 'dc1', 'dc2', 'dc3']
strat = {0:['']}
strat.update({n:strategies for n in [1,2,3,4]})
df['central_battery_strategy'] = df['central_battery_strategy'].fillna('')
df['kWh_unit']=df['kWh_unit'].fillna(0)
ts = {}
tsname ={}
information={}
for p in kWp:
for h in kWh:
for s in strat[h]:
if h==0:
i = df.loc[(df['kWp_unit']==p)&
(df['kWh_unit']==h)].index.values[0]
else:
i = df.loc[(df['kWp_unit']==p)&
(df['kWh_unit']==h)&
(df['central_battery_strategy']==s)].index.values[0]
tsname[i] = [n for n in lts if df.loc[i,'scenario_label'][10:] in n][0]
ts[i] = pd.read_csv(ts_path+'\\'+tsname[i])
ts[i] = ts[i].set_index('timestamp')
information[i] ='p= '+str(p) + 'h= '+ str(h)+ ' s= '+s
# + language="javascript"
# IPython.OutputArea.prototype._should_scroll = function(lines) {
# return false;
# }
# -
# Drop superfluous parameters
tsx={}
for i in list(ts.keys()):
tsx[i] = ts[i].drop(['sum_of_customer_imports', 'sum_of_customer_exports'],axis=1)
# Set up plot path
ppath = os.path.join(opath, 'ts_plots')
if not os.path.exists(ppath):
os.makedirs(ppath)
# +
for i in[34,62,90]: #list(ts.keys()):
# Set up plot path
plotfile = os.path.join(ppath, tsname[i][0:10]+'.jpg')
ax = plot_battery(tsx[i],
plotfile=plotfile,
start_day=90,
name =tsname[i],
info = information[i])
#
# -
| 6,541 |
/Classic_TTT_RL-V2.ipynb | 2e4af41a3e570c0fe56e72ea54846ef809961442 | [] | no_license | DeepanjanSaha-INDIA/TicTacToe | https://github.com/DeepanjanSaha-INDIA/TicTacToe | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 33,609 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# # T:1
# #### Write a prog to print twinkle twinkle little star poem in py
# +
print("twinkle twinkle little star")
print('''Auden was working at The Downs, a prep school in Malvern.
Ben Britten visited him there and they wrote a set of cabaret songs
(which includes ‘Tell me the truth about love”) Britten had already
written incidental music for Auden-Isherwood play ‘The Ascent of F6’
Auden’s ‘Out on the lawn I lie in bed’ is sung as part of Britten’s
Spring Symphony''')
# -
# #### Use REPL and print the table of 5 using it.
5*1
5*2
# #### Install an external module and use it to perform an operation of your interest
import sys
# !{sys.executable} -m pip install playsound
from playsound import playsound
playsound('C:\\Users\\global\\Downloads\\.mp3')
# #### Write a py prog to find the contenets of a directory using os module. Search online for the function which does that
import os
print(os.listdir())
# # T:2
# #### Write a py prog to add 2 numbers
a = int(input())
b = int(input())
print('add : ',a+b)
# #### Write a py prog to find remainder(r) when a no. is divided by z
a = int(input())
z = int(input())
print(a%z)
# #### Check the type of the variable assigned using input()
a = 12
b = 12.0
c = 'fdvd'
print(type(a),type(b),type(c))
# #### Use comparison operator to find out wheather a given vaiable a is greator than b or not
a = int(input())
b = int(input())
print(a>b)
# #### Use py prog to find the average of 2 no's entered by the user
a = int(input())
b = int(input())
print('avg : ',(a+b)/2)
# #### Write a py prog to calculate square of a number created by the user
a = int(input())
print(a ** 2)
# # T:4
# #### Write a py prog to display a user entered name followed by good afternoon using input() function.
name = input("enter the name : ")
print("good afternoon",name)
name = input("enter the name : ")
print("good afternoon "+ name)
# #### Write a prog to fill in a letter template given below with name and date
# Dear <|name|>
# you are selected!
# <|date|>
# +
letter = '''Dear <|name|>
you are selected!
<|date|>
'''
name = input("enter name: ")
date = input("enter date: ")
letter = letter.replace("<|name|>",name)
letter = letter.replace("<|date|>",date)
print(letter)
# -
# #### Write a prog. to detect double spaces in a string
q = "If you don’t like the road you’re walking, start paving another one."
db = " " in q
print(db)
# #### Replace the double spaces from the problm with single space
s = "Embrace the glorious mess that you are "
s = s.replace(" "," ")
print(s)
# #### Write a prog. to format the folln. letter using escape sequence characters.
# letter = "Dear Harry, This python course is nice. Thanks !"
letter = "Dear Den, This python course is nice. Thanks !"
f_letter = "Dear Den,\n\tThis python course is nice.\nThanks !"
print(f_letter)
# # T:4
# #### Write a prog to store 7 fruits in a list entered by the user
f1 = input("enter the fruits name : ")
f2 = input("enter the fruits name : ")
f3 = input("enter the fruits name : ")
f4 = input("enter the fruits name : ")
f5 = input("enter the fruits name : ")
f6 = input("enter the fruits name : ")
f7 = input("enter the fruits name : ")
l= [f1,f2,f3,f4,f5,f6,f7]
print(l)
# Using For Loop
# +
n = int(input("enter the no. of fruits you wannaa add : "))
l1= []
i = 0
for i in range(i,n):
f = input("enter the fruits name : ")
l1.append(f)
i = i+1
print(l1)
# -
# #### Write a prog. to accept marks of 6 students and display them in a sorted manner.
# Using For Loop
# +
n = int(input("enter the no of students : "))
l2= []
i = 0
for i in range(i,n):
f = int(input("enter their marks : "))
l2.append(f)
i = i+1
print(l2)
# -
l2.sort()
print(l2)
# Using Normal Methods
m1 = int(input("Enter the marks of student : "))
m2 = int(input("Enter the marks of student : "))
m3 = int(input("Enter the marks of student : "))
m4 = int(input("Enter the marks of student : "))
m5 = int(input("Enter the marks of student : "))
m6 = int(input("Enter the marks of student : "))
lst = [m1,m2,m3,m4,m5,m6]
lst.sort()
print(lst)
# #### Check that Tuple cannot be changed in Python
tup = (89,7,34,2,4)
tup[0] = 78
print(tup)
# #### Write a prog. to sum a list with 4 numbers
lt = [45, 45, 78, 89]
sum(lt)
# or
print('sum : ',lt[0]+lt[1]+lt[2]+lt[3])
# #### Write a prog. to count the number of zero's in the given tuple
tp = (7,0,8,0,0,9)
tp.count(0)
# # T:5
# #### Write a prog. to create a dict. of Hind words with values as their english translation.Provide user with an option to look it up!
hindi_dic = {"kar":"do", "yaad":"Remember", "khus":"happy"}
print(hindi_dic,type(hindi_dic))
print("options are :",hindi_dic)
a = input("eneter hindi words: ")
print("the meaning of this word is: ",hindi_dic.get(a))
# or
print("options are :",hindi_dic)
a = input("eneter hindi words: ")
print("the meaning of this word is: ",hindi_dic[a])
# this method we will throw error if the key is not present
# #### Write a prog. to input 8 numbers from the user and display all the unique no. once.
e = int(input("no of inpuuts you want to give : "))
i = 0
for i in range(i,e):
a = int(input("enter unique no.s : "))
i = i+1
print(a)
# Using Set{}
n1 = int(input("enter no. 1 : "))
n2 = int(input("enter no. 2 : "))
n3 = int(input("enter no. 3 : "))
n4 = int(input("enter no. 4 : "))
n5 = int(input("enter no. 5 : "))
n6 = int(input("enter no. 6 : "))
n7 = int(input("enter no. 7 : "))
n8 = int(input("enter no. 8 : "))
s = {n1,n2,n3,n4,n5,n6,n7,n8}
print(s,type(s))
# ----
# +
e = int(input("no of inpuuts you want to give : "))
i = 0
s = set()
for i in range(i,e):
a = int(input("enter unique no.s : "))
i = i+1
s.add(a)
print(s,type(s))
# -
# #### Can we have a set with 18(int) and "18"(str) as a value in it ?
# +
st = {18,"18"}
print(st,type(st))
#yes we can have
# -
# #### What will be the length of the following set s :
s = set()
s.add(20)
s.add(20.0)
s.add("20")
print(s,type(s),len(s))
# In set 20.0 and 20 is considered as same
print(type(20.0),type(20))
# #### what is the type of s ? s= {}
# +
s = {}
print(type(s))
#dict
# -
# #### Create an empty dict. Allow 4 friends to enter favourite laung.as values and use keys as their names. Assume that the names are unique
favlang = {}
a = input()
b = input()
c = input()
d = input()
favlang["MJ"]=a
favlang["Rin"]=b
favlang["Neha"]=c
favlang["Rinu"]=d
print(favlang,type(favlang))
# #### If the name of 2 friends are the same. What will be in case of the above prob.
# +
favlang = {}
a = input()
b = input()
c = input()
d = input()
favlang["MJ"]=a
favlang["Rin"]=b
favlang["Rinu"]=c
favlang["Rinu"]=d
print(favlang,type(favlang))
# latest value is considered for the repreated key
# -
# #### Can you change the values inside a list which is contained in set se
# se = {8,7,12,"harry",[1,2]}
# NO
# # T:6
# Write a prog. to find the greatest of 4 numbers entered by the user.
a = int(input())
b = int(input())
c = int(input())
d = int(input())
if(a>b and a>c and a>d):
print("a is the greatest")
elif(b>a and b>c and b>d):
print("b is the greatest")
elif(c>a and c>b and c>d):
print("c is the greatest")
else:
print("d is the greatest")
# +
n1 = int(input())
n2 = int(input())
n3 = int(input())
n4 = int(input())
if(n1>n4):
f1=n1
else:
f1=n2
if(n2>n3):
f2=n2
else:
f2=n3
if(f1>f2):
print(str(f1) + " is greatest")
else:
print(str(f2) + " is greatest")
# -
# Write a prog. to find out whether a student is pass or fail, if it requires total 40% and at least 33% in each subject to pass. Assume 3 subjects and take total marks as input from the user
# +
s1 = int(input())
s2 = int(input())
s3 = int(input())
tm = s1+s2+s3
tm_p = tm/3
if(tm_p>=40 and (s1>=33 or s2>=33 or s3>=33)):
print("he is pass")
else:
print("he is g = embedding_layer(inputs)
reshape = Reshape((sequence_length, EMBEDDING_DIM, 1), name='reshape_1')(embedding)
conv_1 = Conv2D(num_filters, (filter_sizes[0], EMBEDDING_DIM), activation='relu',kernel_regularizer=regularizers.l2(0.01), name='conv_1')(reshape)
conv_2 = Conv2D(num_filters, (filter_sizes[1], EMBEDDING_DIM), activation='relu',kernel_regularizer=regularizers.l2(0.01), name='conv_2')(reshape)
conv_3 = Conv2D(num_filters, (filter_sizes[2], EMBEDDING_DIM), activation='relu',kernel_regularizer=regularizers.l2(0.01), name='conv_3')(reshape)
maxpool_1 = MaxPooling2D((sequence_length - filter_sizes[0] + 1, 1), strides=1, name='maxpool_1')(conv_1)
maxpool_2 = MaxPooling2D((sequence_length - filter_sizes[1] + 1, 1), strides=1, name='maxpool_2')(conv_2)
maxpool_3 = MaxPooling2D((sequence_length - filter_sizes[2] + 1, 1), strides=1, name='maxpool_3')(conv_3)
merged_tensor = concatenate([maxpool_1, maxpool_2, maxpool_3], axis=1, name='concatenate_1')
flatten = Flatten()(merged_tensor)
dropout = Dropout(drop_rate, name='dropout_1')(flatten)
output = Dense(units=4, activation='softmax',kernel_regularizer=regularizers.l2(0.01), name='dense_1')(dropout)
# this creates a model that includes
model = Model(inputs, output)
model.summary()
# + id="HHZ1POD77GRp" colab_type="code" outputId="4e27600b-3186-4d82-f9a6-87dc856b2034" colab={"base_uri": "https://localhost:8080/", "height": 1938}
adam = Adam(lr=1e-3, decay=0.0)
adadelta = Adadelta(lr=1.0, rho=0.95, epsilon=None, decay=0.0)
rmsprop = RMSprop(lr=0.001, rho=0.9, epsilon=None, decay=0.0)
model.compile(loss='categorical_crossentropy',
optimizer=rmsprop,
metrics=['acc'])
callbacks = [EarlyStopping(monitor='val_loss', patience=5)]
history = model.fit(X_train, y_train, batch_size=1000, epochs=100, verbose=1, validation_data=(X_val, y_val), callbacks=callbacks) # starts training
# + id="OtGrA8RBKShg" colab_type="code" colab={}
model_path = 'emoint_model.h5'
weight_path = 'emoint_weights.h5'
model.save(model_path)
model.save_weights(weight_path)
# + id="eFzd1urtKeFw" colab_type="code" outputId="a2384d66-625a-4923-aa99-f8e493ea406f" colab={"base_uri": "https://localhost:8080/", "height": 170}
# !ls -lah
# + id="85MiexxMK8zd" colab_type="code" colab={}
model_file = drive.CreateFile({'title': model_path})
model_file.SetContentFile(model_path)
model_file.Upload()
weight_file = drive.CreateFile({'title': weight_path})
weight_file.SetContentFile(weight_path)
weight_file.Upload()
# + id="b0P0vTv-7RC6" colab_type="code" colab={}
sequences_test=tokenizer.texts_to_sequences(test_data.text)
X_test = pad_sequences(sequences_test, maxlen=X_test.shape[1])
y_pred = model.predict(X_test)
# + id="IgHlDfC_-rS5" colab_type="code" outputId="4412ef6f-317e-44c8-9db4-1bfa16b0cbdb" colab={"base_uri": "https://localhost:8080/", "height": 34}
history.history.keys()
# + id="-JaFoS4u_pwS" colab_type="code" colab={}
y_pred_original = [labels[val] for val in np.argmax(y_pred, axis=1).squeeze()]
y_test_original = np.asarray(test_data.emotion)
# + id="BREyqdCAI9d0" colab_type="code" colab={}
def print_confusion_matrix(confusion_matrix, class_names, figsize = (4,3), fontsize=15):
"""Prints a confusion matrix, as returned by sklearn.metrics.confusion_matrix, as a heatmap.
Arguments
---------
confusion_matrix: numpy.ndarray
The numpy.ndarray object returned from a call to sklearn.metrics.confusion_matrix.
Similarly constructed ndarrays can also be used.
class_names: list
An ordered list of class names, in the order they index the given confusion matrix.
figsize: tuple
A 2-long tuple, the first value determining the horizontal size of the ouputted figure,
the second determining the vertical size. Defaults to (10,7).
fontsize: int
Font size for axes labels. Defaults to 14.
Returns
-------
matplotlib.figure.Figure
The resulting confusion matrix figure
"""
df_cm = pd.DataFrame(
confusion_matrix, index=class_names, columns=class_names,
)
fig = plt.figure(figsize=figsize)
try:
heatmap = sns.heatmap(df_cm, annot=True, fmt="d")
except ValueError:
raise ValueError("Confusion matrix values must be integers.")
heatmap.yaxis.set_ticklabels(heatmap.yaxis.get_ticklabels(), rotation=0, ha='right', fontsize=fontsize)
heatmap.xaxis.set_ticklabels(heatmap.xaxis.get_ticklabels(), rotation=45, ha='right', fontsize=fontsize)
plt.ylabel('True label')
plt.xlabel('Predicted label')
return fig
# + id="XG9HPQb10TZm" colab_type="code" outputId="ec5b6078-e718-45f2-ee23-06f3eaa3f9de" colab={"base_uri": "https://localhost:8080/", "height": 173}
cf_matrix = confusion_matrix(y_test_original, y_pred_original, labels=labels)
df_cm = pd.DataFrame(
cf_matrix, index=labels, columns=labels,
)
df_cm
# + id="59xnXmbEAKXy" colab_type="code" outputId="a97f8be9-a0ab-4468-a4bb-3a22cb65cb66" colab={"base_uri": "https://localhost:8080/", "height": 281}
print(print_confusion_matrix(cf_matrix, class_names=labels))
# + id="9XE5c4LJEZXy" colab_type="code" outputId="d37cdce3-458f-43c0-acd7-ffb4156fdd6e" colab={"base_uri": "https://localhost:8080/", "height": 34}
test_accuracy = accuracy_score(y_test_original, y_pred_original)
print("test accuracy:", test_accuracy)
# + [markdown] id="jAISNDCVwvR7" colab_type="text"
# ### Performance score for each classes
# + id="iFb9Q6A1DcXi" colab_type="code" outputId="cd74d0a3-ce59-472b-c34f-893316186f8c" colab={"base_uri": "https://localhost:8080/", "height": 173}
precision, recall, fscore, support = precision_recall_fscore_support(y_test_original, y_pred_original)
score_dict = {
"precision": precision.round(4),
"recall": recall.round(4),
"f1-score": fscore.round(4),
"support": support.round(4)
}
score_df = pd.DataFrame(score_dict, index=labels)
score_df
# + [markdown] id="cHi1Gw7Oz1zG" colab_type="text"
# ### Performance score using micro average
# + id="KP69PDE9D_jz" colab_type="code" outputId="9912afb5-cb78-41bb-f314-7f87858bd0c1" colab={"base_uri": "https://localhost:8080/", "height": 80}
precision, recall, fscore, support = precision_recall_fscore_support(y_test_original, y_pred_original, average="micro")
score_dict = {
"precision": precision.round(4),
"recall": recall.round(4),
"f1-score": fscore.round(4),
"support": support
}
score_df = pd.DataFrame(score_dict, index=["score"])
score_df
# + [markdown] id="IWXNCktiz8Vb" colab_type="text"
# ### Performance score using macro average
# + id="g4e0vsNIydDs" colab_type="code" outputId="ca9ddf7b-9c9f-47bc-98d6-3ef81d5a459f" colab={"base_uri": "https://localhost:8080/", "height": 80}
precision, recall, fscore, support = precision_recall_fscore_support(y_test_original, y_pred_original, average="macro")
score_dict = {
"precision": precision.round(4),
"recall": recall.round(4),
"f1-score": fscore.round(4),
"support": support
}
score_df = pd.DataFrame(score_dict, index=["score"])
score_df
# + [markdown] id="wFJFH7u6z-oG" colab_type="text"
# ### Performance score using weighted average
# + id="z99JkHjuzv68" colab_type="code" outputId="b0599318-0baa-4362-e7ef-24310557280e" colab={"base_uri": "https://localhost:8080/", "height": 80}
precision, recall, fscore, support = precision_recall_fscore_support(y_test_original, y_pred_original, average="weighted")
score_dict = {
"precision": precision.round(4),
"recall": recall.round(4),
"f1-score": fscore.round(4),
"support": support
}
score_df = pd.DataFrame(score_dict, index=["score"])
score_df
# + id="Y83dx5SyoBR8" colab_type="code" colab={}
print("*",end = " ")
print()
# + active=""
# Printing Hill pattern
#
# *
# * * *
# * * * * *
# * * * * * * *
# * * * * * * * * *
# +
# 1st step
n = 5
for j in range(n):
for i in range(j,n):
print(" ",end=" ")
for i in range(j+1): # change range to j
print("*",end=" ")
for i in range(j+1):
print("*",end=" ")
print()
# +
# 2st step
n = 5
for j in range(n):
for i in range(j,n):
print(" ",end=" ")
for i in range(j):
print("*",end=" ")
print()
# -
# final
n = 5
for j in range(n):
for i in range(j,n):
print(" ",end=" ")
for i in range(j):
print("*",end=" ")
for i in range(j+1):
print("*",end=" ")
print()
# 1st step
n = 5
for j in range(n): # inner loop is set to n (rows)
for i in range(j+1): #outer loop is set to n+1 (col)
print(" ",end=" ")
for i in range(j,n):
print("*",end = " ")
for i in range(j,n):
print("*",end = " ")
print()
# 2nd step
n = 5
for j in range(n): # inner loop is set to n (rows)
for i in range(j+1): #outer loop is set to n+1 (col)
print(" ",end=" ")
for i in range(j,n-1):
print("*",end = " ")
for i in range(j,n):
print("*",end = " ")
print()
# + active=""
# Print the folln pattern.
#
# *
# * * * * * * * * *
# * * *
# * * * * * * *
# * * * * *
# * * * * *
# * * * * * * *
# * * *
# * * * * * * * * *
# *
# +
# 1st step
n = 5
for j in range(n):
for i in range(j,n):
print(" ",end=" ")
for i in range(j):
print("*",end=" ")
for i in range(j+1):
print("*",end=" ")
print()
# +
# 2nd step
n = 5
for j in range(n): # inner loop is set to n (rows)
for i in range(j+1): #outer loop is set to n+1 (col)
print(" ",end=" ")
for i in range(j,n-1):
print("*",end = " ")
for i in range(j,n):
print("*",end = " ")
print()
# -
# final
n = 5
for j in range(n):
for i in range(j,n):
print(" ",end=" ")
for i in range(j):
print("*",end=" ")
for i in range(j+1):
print("*",end=" ")
print()
for i in range(j+1):
print(" ",end=" ")
for i in range(j,n-1):
print("*",end = " ")
for i in range(j,n):
print("*",end = " ")
print()
# + active=""
# Print the folln pattern.
#
# *
# * * *
# * * * * *
# * * * * * * *
# * * * * * * * * *
# * * * * * * *
# * * * * *
# * * *
# *
# +
# 1st step
n = 5
for j in range(n):
for i in range(j,n):
print(" ",end=" ")
for i in range(j):
print("*",end=" ")
for i in range(j+1):
print("*",end=" ")
print()
# +
# 2nd step
n = 5
for j in range(n): # inner loop is set to n (rows)
for i in range(j+1): #outer loop is set to n+1 (col)
print(" ",end=" ")
for i in range(j,n-1):
print("*",end = " ")
for i in range(j,n):
print("*",end = " ")
print()
# +
# 3rd step
n = 5
for j in range(n-1): # n to n-1
for i in range(j,n):
print(" ",end=" ")
for i in range(j):
print("*",end=" ")
for i in range(j+1):
print("*",end=" ")
print()
n = 5
for j in range(n): # inner loop is set to n (rows)
for i in range(j+1): #outer loop is set to n+1 (col)
print(" ",end=" ")
for i in range(j,n-1):
print("*",end = " ")
for i in range(j,n):
print("*",end = " ")
print()
# -
# Write a prog to print (1 to n) in order
i = 1
for i in range(i,10+1):
print(i)
i += 1
# Write a prog to print (1 to n) in reversed order.
# that means (n to 1 )
n = 8
for i in range(n,0,-1):
print (i)
# write a prog. to print multiplication table in revere order
t = int(input())
n = 10
for i in range(n,0,-1):
print(t," x ",i," = ",(t*i))
# Prog to print table in ascending order.
n = 8 # table no.
i = 1
for i in range(i,11):
print (i*n)
i+=1
n = 8 # table no.
for i in range(1,11):
print (i*n)
# # T:8
# Write a prog. using functn to find greatest of 3 number
def check(a,b,c):
if a>b and a>c:
print("a is greatest")
elif(b>c and b>a):
print("b is greatest")
else:
print("c is greatest")
check(121,99,56)
# or
def checkl(a,b,c):
l = [a,b,c]
return max(l)
checkl(12,45,90)
# or
def checkgt(a,b,c):
if(a>b):
if(a>c):
return a
else:
return c
else:
if(b>c):
return b
else:
return c
checkgt(97,78,69)
# Write a prog. using function to convert celsius to fahrenheit
def temp(c):
far = (c * 9/5) + 32
return far
temp(33)
# Write a recursive functn to calculate the sum of first n natural no's
def recur_sum(n):
if n <= 1:
return n
else:
return n + recur_sum(n-1)
recur_sum(5)
# or
def sm(n):
sum = (n*(n+1))/2
return sum
sm(5)
# How do you prevent a python print() functn to print a new line at the end
print("I",end = " ")
print("Will",end = " ")
print("Be",end = " ")
print("A",end = " ")
print("Millionaire",end = " ")
# + active=""
# Write a py functn to print 1st n lines of the folln pattern
#
# * * *
# * *
# *
# -
n = 3
for j in range(n): #rows : outerloop n
for i in range(j,n): # nested loop : (i,n)
print("*",end=" ")
print()
# or
n = 3
for i in range(n):
print("*" * (n-i))
# Write a python funtn which converts inches to cm
def incm(n):
return (n*2.54)
incm(1)
# Write a py functn to remove a given word from a list and update it at the same time
def re():
le= int(input("enter the length : "))
l = []
for i in range(le):
e = input("enter the element : ")
l.append(e)
print(l)
o_string = input("old word/string : ")
n_word = input("new word/string : ")
if o_string in l:
ind = l.index(o_string)
l[ind] = n_word
return l
re()
# Write a py functn to remove a given word from a list and stop it at the same time
def lti(word):
l1 = ['ram', 'yuki', 'rajesh']
if word in l1:
ind = l1.index(word)
print(ind)
l1.pop(ind)
return l
lti("rajesh")
# Write a py functn to remove a given word from a list and spliting it at the same time
def pro(string,rword):
s1 = string.replace(rword," ")
return s1.split()
pro("i love you god","you")
# write a py prog to seperate the word in string as individual elements (using list)
# +
s1 = "hello god I love you"
s2 = s1.split(" ")
print(s2)
# -
# Write a py funtn to print multiplication table of a given no
def tab(a):
i = 1
for i in range(i,11):
print(a," x ",i," = ",a*i)
tab(5)
# ### Project 1 : Snake water gun game
def game(p1,p2):
# p1 = input(" P1 : s for snake, w for water ,g for gun : ")
# p2 = input(" P2 : s for snake, w for water ,g for gun : ")
if(p1 == "s" and p2 == "s"):
print("None won")
elif(p1 == "s" and p2 == "w"):
print("p1 won the game")
elif(p1 == "s" and p2 == "g"):
print("p2 won the game")
elif(p1 == "w" and p2 == "s"):
print("P2 won the game")
elif(p1 == "w" and p2 == "w"):
print("None won")
elif(p1 == "w" and p2 == "g"):
print("p2 won the game")
elif(p1 == "g" and p2 == "s"):
print("P1 won the game")
elif(p1 == "g" and p2 == "w"):
print("P1 won the game")
elif(p1 == "g" and p2 == "g"):
print("None won")
game("s","w")
# or
# +
p1 = input(" P1 : s for snake, w for water ,g for gun : ")
p2 = input(" P2 : s for snake, w for water ,g for gun : ")
if(p1 == "s" and p2 == "s"):
print("None won")
elif(p1 == "s" and p2 == "w"):
print("p1 won the game")
elif(p1 == "s" and p2 == "g"):
print("p2 won the game")
elif(p1 == "w" and p2 == "s"):
print("P2 won the game")
elif(p1 == "w" and p2 == "w"):
print("None won")
elif(p1 == "w" and p2 == "g"):
print("p2 won the game")
elif(p1 == "g" and p2 == "s"):
print("P1 won the game")
elif(p1 == "g" and p2 == "w"):
print("P1 won the game")
elif(p1 == "g" and p2 == "g"):
print("None won")
# -
# Write a prog to generate random int in py
# +
import random
print(random.randint(0,5)) # boundaries included
# -
# #### Project 1 : Snake water gun game with 1st player as human and 2nd player as comp.
# +
import random
p1 = input("P1 : s(snake) w(water) g(gun) : ")
p2 = random.randint(1,3) # boundaries included
if p2 == 1:
p2 = "s"
elif p2 == 2:
p2 = "w"
elif p2 == 3:
p2 = "g"
print("P2 : s(snake) w(water) g(gun) :",p2)
if(p1 == "s" and p2 == "s"):
print("None won")
elif(p1 == "s" and p2 == "w"):
print("p1 won the game")
elif(p1 == "s" and p2 == "g"):
print("p2 won the game")
elif(p1 == "w" and p2 == "s"):
print("P2 won the game")
elif(p1 == "w" and p2 == "w"):
print("None won")
elif(p1 == "w" and p2 == "g"):
print("p2 won the game")
elif(p1 == "g" and p2 == "s"):
print("P1 won the game")
elif(p1 == "g" and p2 == "w"):
print("P1 won the game")
elif(p1 == "g" and p2 == "g"):
print("None won")
# -
# # T: 9
# Write a program to read the text from a given file "poems.txt" and find out whether it contains the word "twinkle"
with open("poem.txt") as f1:
data = f1.read()
if 'twinkle' in data:
print("twinkle is present in the peom")
else:
print("twinkle is present in the peom")
# The functn in a prog. lets a user play a game and returns the score as an integer. You need to read a ffile "hiscore.txt" which is either blank or contains the previous hi score whenever game() breaks the hi score.
# +
def game():
return 66
score = game()
print("current score : ",score)
with open("highscore.txt") as f1:
hs = f1.read()
import os
filesize = os.path.getsize("highscore.txt")
if filesize == 0:
print("congrats, New high score is : ",score)
with open("highscore.txt",'w') as f2:
f2.write(str(score))
elif (score>int(hs)):
print("congrats, New high score is : ",score)
with open("highscore.txt",'w') as f2:
f2.write(str(score))
else:
print("game over")
# -
# Checking the file is empty or not
# +
import os
filesize = os.path.getsize("newhighscore.txt")
if filesize == 0:
print("The file is empty: " + str(filesize))
else:
print("The file is not empty: " + str(filesize))
# -
# CONCEPT :
#
# USE os.path.getsize() TO GET THE SIZE OF A FILE
#
# Call os.path.getsize(filename) with filename as the name of the file to get the size of filename.
#
# If the size returned by os.path.getsize(filename) is 0, the file is empty.
# Write a prog. to generate multiplication tables from 2 to 4
for i in range(2,5):
print()
for j in range(1,11):
print(i," x ",j," = ",i*j)
# Write a prog. to generate multiplication tables from 2 to 20 and write it to the different files. Place these files in a folder for a 13year old.
for h in range(2,21): # 2 to 20 table no
with open(f"table/Mtable of {h}",'w') as f:
#print()
for i in range(1,11): # 1 to 10 int(i)
f.write(f"{h}x{i}={h*i}\n")
#break
# A file contains a word "donkey" multiple times. You need to write a prog. which replaces this word with ###### by updating the same file.
# #### Ex : Sample.txt
# +
word = ["donkey",'devil','slut','fat']
with open("sample.txt") as f:
data = f.read()
print("old data : ",data)
print()
for i in word:
data = data.replace(i,"$####$^&")
with open("sample.txt",'w') as f:
f.write(data)
print("new data : ",data)
# -
# #### Ex : sampl.txt
# +
word = ['damn', 'jerk', 'ugly', 'stupid', 'fart knocker']
with open("sampl.txt") as f:
data = f.read()
print("old data :\n",data)
print()
for i in word:
data = data.replace(i,"###&*$$#")
with open("sampl.txt",'w') as f:
f.write(data)
print("new data : ",data)
# -
# #### Exploratory analysis of this prog.
# ##### Ex : poemstar.txt
# +
word = ["twinkle","star","Twinkle"]
with open("poemstar.txt") as f:
data = f.read()
print("old data :\n",data)
print()
for i in word:
data = data.replace(i,"####$$")
print("exploring data :\n",data)
print()
with open("poemstar.txt",'w') as f:
f.write(data)
print("new data :\n",data)
# -
# #### Repeat the above prog. for the list of such words to be sensored.
# Write a program file to mine a log file and find out whether it contains python
with open("log.txt") as f:
data = f.read()
if ("Python"or"python") in data: #Its case sensitive
print("python is present")
else:
print("Its not present")
# or
with open("log.txt") as f:
data = f.read().lower() #converting all the data to lower case
if "python" in data: #Its case sensitive
print("python is present")
else:
print("Its not present")
# Write a prog. to find out the line no. where python is present from the above que.
#
# ex: bat.txt
f = open('bat.txt')
word = input("enter the word you want to search :")
s = " " # blank space
l = []
count = 1
while(s):
s= f.readline().lower() #not case-sensitive
l = s.split()
if word in l:
print("line no. : ",count," : ",s)
count+=1
# or
# +
f = open('bat.txt')
word = input("enter the word you want to search :")
s = " " # blank space
count = 1
s = f.readlines()
for i in s:
l = i.split() #case-sensitive
if word in l:
print("line no. : ",count," : ",i)
count+=1
# -
# Write a prog. to make a copy of a text file "nightpoem.txt"
# +
with open("nightpoem.txt") as f:
data = f.read()
print("data -> night poem: ",data)
print()
with open("copy.txt",'w') as f:
f.write(data)
print("data -> copy: ",data)
# -
# Write a prog. to find out whether a file is identical & matches the content of another file.
# +
# f1 : nightpoem.txt
# f2 : copy.txt
f1 = open("nightpoem.txt")
data1 = f1.read()
f2 = open("copy.txt")
data2 = f2.read()
if data1==data2:
print("data is similar in both the files")
else:
print("different data")
# -
# write a prog. to wipe out the contents of a file using python.
# +
# using 2nd file (empty file) to write in the 1st file
# f1 : nightpoem.txt
# f2 : empty.txt
import os
f2 = open("empty.txt")
data2 = f2.read()
f1 = open("nightpoem.txt",'w')
data1 = f1.write(data2)
filesize = os.path.getsize("nightpoem.txt")
if filesize == 0:
print("The file is empty: " + str(filesize))
else:
print("The file is not empty: " + str(filesize))
# -
# or ( efficient method)
# +
# wiping nightpoem.txt
with open("nightpoem.txt",'w') as f:
f.write(" ")
# +
# checking if the file is empty or not
import os
filesize = os.path.getsize("nightpoem.txt")
if filesize == 0:
print("The file is empty: " + str(filesize))
else:
print("The file is not empty: " + str(filesize))
# -
# Write a prog. to rename a file "nightpoem.txt" to "renamed_by_py.txt"
# +
import os
f1 = open("nightpoem.txt")
data = f1.read()
with open("renamed_by_py.txt",'w') as f2:
f2.write(data)
f1.close()
if os.path.exists("nightpoem.txt"):
os.remove("nightpoem.txt")
print("file removed")
else:
print("file does not exist")
# +
# reading the file "renamed_by_py.txt"
with open("renamed_by_py.txt") as f:
data = f.read()
print(data)
# -
# # T: 10
# Create a class programmer for storing information of few programmers working at microsoft.
class programmer:
def __init__(self):
self.company = "microsoft"
# a method for printing company name
def print_Cname(self):
print(self.company)
def set_details(self,name,age,gender,salary):
self.name = name
self.age = age
self.gender = gender
self.salary = salary
def get_details(self):
print(self.name, self.age, self.gender, self.salary)
abhi = programmer()
abhi.print_Cname()
abhi.set_details("raj",56,"Male",67676)
abhi.get_details()
# write a class calculator capable of finding square, cube & square root of a no.
import numpy as np
class calculator:
def sq(self,no):
self.no = no
return (self.no*self.no)
def cube(self,no):
self.no = no
return (self.no ** 3)
def cubeRoot(self,no):
self.no = no
return (np.cbrt(self.no))
#return (self.no ** (1./3.))
def SqRoot(self,no):
self.no = no
return (self.no ** 2)
c1 = calculator()
c1.sq(2)
# square
# cuberoot
c1.cubeRoot(9261)
c1.cubeRoot(64)
# SqRoot
c1.SqRoot(16)
# cube
c1.cube(2)
# Add a static method in problem 2 to gree the user with hello
import numpy as np
class calculator:
@staticmethod
def __init__():
print("Hello user")
def sq(self,no):
self.no = no
return (self.no*self.no)
def cube(self,no):
self.no = no
return (self.no ** 3)
def cubeRoot(self,no):
self.no = no
return (np.cbrt(self.no))
#return (self.no ** (1./3.))
def SqRoot(self,no):
self.no = no
return (self.no ** 2)
raj = calculator()
# or// greeting user with its actual name(obj)
class calc:
@staticmethod
def __init__(name):
print("Hello user ",name)
raj = calc("rajesh")
# Write a static method to greet the user with hello
class Employ:
@staticmethod
def greet(name):
return print("welcome" ,name)
rin = Employ()
rin.greet("rin")
# or
class Employ:
@staticmethod
def greet():
return print("welcome user")
rinu = Employ()
rinu.greet()
# Write a class Train which has methods to book a ticket, get status (no of seats) and get fare information of trains running under Indian Railways
class Train():
def __init__(self,tname,fare,seats):
print("welcome to train booking system")
self.tname = tname
self.fare = fare
self.seats = seats
def getStatus(self):
print(f"The name of the train is {self.tname} ")
print(f"Available no of seats are : {self.seats}")
def fairInfo(self):
print(f"Cost of fair is {self.fare}")
# f string{}
def book(self):
if (self.seats>0):
print("You have successfully booked the ticket")
print(f"Your sear no is {self.seats}")
self.seats -= 1
else:
print("Booking failed")
print("Seats are full")
def cancelTicket(self):
print(f"{self.seats+1} Ticket cancelled successfully")
self.seats += 1
p1 = Train("rajdhani",787,78)
p1.getStatus()
p1.fairInfo()
p1.book()
p1.cancelTicket()
p1.getStatus()
# Can you change the self parameter inside a class to something else(say "rin"). Try changing self to "slf" or "rin" and see the effects.
class Sample:
def __init__(self,name):
self.name = name
return print(self.name)
p1 = Sample("rin")
class Sample1:
def __init__(tim,name):
tim.name = name
return print(tim.name)
p2 = Sample1("rin")
class Sample2:
def __init__(slf,name):
slf.name = name
return print(slf.name)
p3 = Sample2("tim")
# # T:11
# Create a class C-2dVector and use it to create another class representing a 3-d Vector
# Hint : Use Inhertiance
# +
class C2d:
def __init__(self,i,j):
self.i = i
self.j = j
def __str__(self):
return f"{self.i}i + {self.j}j"
class C3d(C2d):
def __init__(self,i,j,k):
super().__init__(i,j)
self.k = k
def __str__(self):
return f"{self.i}i + {self.j}j + {self.k}k"
# -
v2d = C2d(1,2)
v3d = C3d(1,7,3)
print(v2d)
print(v3d)
# Create a class pets from a class Animals and further create class Dog from Pets. Add a method bark to class Dog using Inheritance
# +
class Animals:
def __init__(self,color,legs):
self.color = color
self.legs = legs
print(self.color, self.legs)
class Pet(Animals):
def __init__(self,pn,color,legs):
super().__init__(color,legs)
self.pn = pn
print (f"Pet name is {self.pn}")
class Dog(Pet):
def __init__(self,pn,color,legs):
super().__init__(pn,color,legs)
def bark(self):
print("the dog barks")
# -
a1 = Animals("red",4)
p1 = Pet("kittu","brown",4)
d1 = Dog("nin","blue",4)
d1.bark()
# Create a class pets from a class Animals and further create class Dog from Pets
class Animals:
animalType = "mammal"
class Pets:
color = "white"
class Dog:
@staticmethod
def bark():
print("bow bow")
d = Dog()
d.bark()
# + active=""
# Create a class Employee and add salary and increment properties to it.
# Write a method salary after increment method with a @property decorator with a Setter which changes the value of increment based on the salary
# -
class Employee:
def __init__(self,sal,inc):
self.sal = sal
self.inc = inc
@property
def salaryAfterIncrement(self):
return self.sal*self.inc
@salaryAfterIncrement.setter
def salaryAfterIncrement(self,salary):
self.inc = salary/self.sal
e1 = Employee(1000,1.5)
e1.sal
e1.inc
e1.salaryAfterIncrement
e1.salaryAfterIncrement = 2000
e1.inc
e1.sal
# Write a class to represent complex numbers along with overload operators + and * which adds and multiplies them.
class Comp:
def __init__(self,a,b):
self.a = a
self.b = b
def __add__(self,o):
return self.a + o.a, self.b + o.b
def __mul__(self,o):
return self.a*o.a , self.b*o.b
def __str__(self):
return f" {self.a} + {self.b}i"
c1 = Comp(100,10)
c2 = Comp(1,2)
print(c1)
print(c2)
c1+c2
c1*c2
# Write a class vector representing a vector of n dimension. Overoad the + and * operator which calculates the sum and the dot product of them
class Vector:
def __init__(self,vec):
self.vec = vec
def __str__(self):
str = " "
index = 0
for i in self.vec:
str += f" {i}a{index}"
index += 1
return str
def __add__(self,o):
res_list = []
for i in range(len(self.vec)):
res_list.append(self.vec[i] + o.vec[i])
return Vector(res_list)
def __mul__(self,o):
sum = 0
for i in range(len(self.vec)):
sum += self.vec[i] * o.vec[i]
return sum
v1 = Vector([3,9,4])
print(v1)
v2 = Vector([23,45,9])
print(v2)
print(v1+v2)
print(v1*v2) #(3*23 + 9*45 + 4*9)
# ### Some Old concept's
# ##### Printing list in python
# Using For loop
j = [4,5,3,"sh","rt",45]
i = 0
for i in range(len(j)):
print(j[i])
j = [4,5,3,"sh","rt",45]
l = len(j)
for i in range(l):
print(j[i])
j = [4,5,3,"sh","rt",45]
for i in j:
print(i)
# Using while loop
j = [4,5,3,"sh","rt",45]
l = len(j)
i = 0
while i<l:
print(j[i])
i += 1
# Using list comprehension (Possibly the most concrete way).
# +
j = [4,5,3,"sh","rt",45]
# using list comprehension
[print(i) for i in j]
# -
# Using Numpy ::
#
#
# For very large n-dimensional lists (for example an image array), it is sometimes better to use an external library such as numpy.
#
# +
# Python program for
# iterating over array
import numpy as geek
# creating an array using
# arrange method
a = geek.arange(9)
# shape array with 3 rows
# and 4 columns
a = a.reshape(3, 3)
# iterating an array
for x in geek.nditer(a):
print(x)
# -
# ##### adding elements of 2 list (not appeding)
# Native Method
# +
list1 = [1, 3, 4, 6, 8]
list2 = [4, 5, 6, 2, 10]
# using naive method to
# add two list
res_list = []
for i in range(0, len(list1)):
res_list.append(list1[i] + list2[i])
# printing resultant list
print ("Resultant list is : " + str(res_list))
# -
# Using list comprehension
# +
list1 = [1, 3, 4, 6, 8]
list2 = [4, 5, 6, 2, 10]
# using naive method to
# add two list
res_list = [list1[i] + list2[i] for i in range(len(list1))]
# printing resultant list
print ("Resultnt list is : " + str(res_list))
# -
# Using map() + add()
from operator import add
list1 = [1, 3, 4, 6, 8]
list2 = [4, 5, 6, 2, 10]
# using map() + add() to
# add two list
res_list = list(map(add,list1,list2))
print("result list : ",res_list)
# ##### multiplying elements of 2 list
# Using Native method
list1 = [1, 3, 4, 6, 8]
list2 = [4, 5, 6, 2, 10]
# using naive method to
# Multiplying two lists
res_list = []
for i in range(0, len(list1)):
res_list.append(list1[i] * list2[i])
print(res_list)
# Write __str__() method to print the vector as follows:
# 7i + 8j + 10k
# Assume vector of dimension 3 for the problem
class Vect:
def __init__(self, a, b, c):
self.a = a
self.b = b
self.c = c
def __str__(self):
return f"{self.a}i + {self.b}j + {self.c}k"
v1 = Vect(7,8,9)
print(v1)
# Override the __len__() method on Vector of problem 5 to display the dimensions of the vector
class Vector:
def __init__(self,vec):
self.vec = vec
def __str__(self):
str = " "
index = 0
for i in self.vec:
str += f" {i}a{index}"
index += 1
return str
def __add__(self,o):
res_list = []
for i in range(len(self.vec)):
res_list.append(self.vec[i] + o.vec[i])
return Vector(res_list)
def __mul__(self,o):
sum = 0
for i in range(len(self.vec)):
sum += self.vec[i] * o.vec[i]
return sum
def __len__(self):
return len(self.vec)
v1 = Vector([1,4,6])
v2 = Vector([1,6,9])
print(v1+v2)
print(v1*v2)
print(len(v1))
print(len(v2))
v3 = Vector([1,4,6,43])
v4 = Vector([1,4,6])
print(len(v3))
print(len(v4))
class Vector:
def __init__(self,vec):
self.vec = vec
def __str__(self):
str = " "
index = 0
for i in self.vec:
str += f" {i}a{index}"
index += 1
return str
def __add__(self,o):
res_list = []
if(len(self.vec)==len(o.vec)):
for i in range(len(self.vec)):
res_list.append(self.vec[i] + o.vec[i])
return Vector(res_list)
else:
print("addn of these 2 vectors is not possible")
def __mul__(self,o):
sum = 0
if(len(self.vec)==len(o.vec)):
for i in range(len(self.vec)):
sum += self.vec[i] * o.vec[i]
return sum
else:
print("multn of these 2 vectors are not possible")
def __len__(self):
return len(self.vec)
v1 = Vector([1,2,3,5])
v2 = Vector([4,5,6])
v3 = Vector([2,7,8])
print(v1+v2)
print(v2+v3)
print(v1*v2)
print(v2*v3)
# ## Project :2
# We are going to write a prog. that generates a random no. and asks the user to guess it.
#
# If the player guess is higher then the actual number, the prog displays
# "Lower no please"
#
# Similiarly if the user guess is too low, the program prints
# "higher number please"
#
# When the user guesses the correct no. , the prog. displays the no. of guesses the player used to arrive at the number.
# + active=""
# HINT : use the random module
# +
import random
rgn = random.choice([12,23,45,67,65,43]) #random generated no from the list of no's
user_inp = None
#print("rgn ",rgn)
while(user_inp != rgn):
user_inp = int(input("Enter your no: "))
if(user_inp>rgn):
print("Lower no. please")
elif(user_inp<rgn):
print("greater no. please")
else:
print("the guess is correct")
# -
# or
# +
import random
rgn = random.randint(1,100)
user_inp = None
print(rgn)
no_guess = 0
print("rgn ",rgn)
while(user_inp != rgn):
user_inp = int(input("Enter your no: "))
if(user_inp>rgn):
print("Lower no. please")
no_guess += 1
elif(user_inp<rgn):
print("greater no. please")
no_guess += 1
else:
print("the guess is correct")
no_guess += 1
print("no of guesses : ",no_guess)
# -
# Getting the highscore and storing it in highscore file if no of guesses rae less than previous high score.
# +
import random
rgn = random.randint(1,100)
user_inp = None
print(rgn)
no_guess = 0
#print("rgn ",rgn)
while(user_inp != rgn):
user_inp = int(input("Enter your no: "))
if(user_inp>rgn):
print("Lower no. please")
no_guess += 1
elif(user_inp<rgn):
print("greater no. please")
no_guess += 1
else:
print("the guess is correct")
no_guess += 1
print("no of guesses : ",no_guess)
with open("highsc.txt") as f:
highsc = int(f.read())
if(no_guess<highsc):
print("you have broken the high score $$ ")
with open("highsc.txt",'w') as f:
f.write(str(no_guess))
# -
# #### Concept:
# Prog to generate random no
# + active=""
# random.randint()
#
# The random.randint() method returns a random integer between the specified integers.
# -
import random
n = random.randint(1,10)
print(n)
# Prog. to Generate Random Numbers within Range
# + active=""
# random.randrange() method ::
# returns a randomly selected element from the range created by the
#
# start, stop and step arguments.
#
# The value of start is 0 by default.
# Similarly, the value of step is 1 by default.
# -
import random
n = random.randrange(1, 10, 2) # possible choices[1,3,5,7,9]
print(n)
# ===> Previous concept just for understanding
i = 0
for i in range(1,11,2):
print(i)
i+=1
# Prog. to generte rand no from a selective choice of elements
import random
n = random.choice('computer')
print(n)
import random
n = random.choice([12,23,45,67,65,43])
print(n)
# ##### Shuffle Elements Randomly
# The random.shuffle() method randomly reorders the elements in a list.
numbers=[12,23,45,67,65,43]
random.shuffle(numbers)
print(numbers)
| 46,096 |
/jupyter/1week/0511_실습.ipynb | f8eb8fea6cc2809b36ec996ee2e4f94b3c1d6d5b | [] | no_license | ksg5302/aclass | https://github.com/ksg5302/aclass | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 27,868 | # ---
# jupyter:
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# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
from utils import *
from sympy import symbols, trigsimp, lambdify, diff, init_printing, eye
from numpy import linalg, cos, sin, arctan2, log
init_printing(use_unicode=True)
# + pycharm={"name": "#%%\n"}
# Joint parameters declaration
q1, q2, q3, q4, q5 = symbols("q1 q2 q3 q4 q5", real=True)
# Guesses for link lengths
l1, l2, l3, l4, l5 = 1, 1, 1, 1, 1
# + pycharm={"name": "#%%\n"}
# Forward Kinematics
H = Rx(q1)*Tx(l1)*Ry(q2)*Tz(l2)*Rz(q3)*Tz(l3)*Rx(q4)*Tz(l4)*Rz(q5)*Tz(l5)
H = trigsimp(H)
H
# + pycharm={"name": "#%%\n"}
# Rotation Matrix
R = H[0:3, :-1]
R
# + pycharm={"name": "#%%\n"}
# Translational Matrix
T = H[0:3, -1]
T
# + pycharm={"name": "#%%\n"}
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/Cats-v-Dogs.ipynb | 7a1a9390df259a88919c73a5b28e10e63d6a5b99 | [] | no_license | AliButtar/Cats-v-Dogs | https://github.com/AliButtar/Cats-v-Dogs | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 282,831 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.15.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# + pycharm={"is_executing": false}
import sys
import numpy as np
import tensorflow.keras
import pandas as pd
import sklearn as sk
import tensorflow as tf
from tensorflow.keras.preprocessing.image import *
import matplotlib.pyplot as plt
from tensorflow.keras import models
print(f"Tensor Flow Version: {tf.__version__}")
print(f"Keras Version: {tensorflow.keras.__version__}")
print()
print(f"Python {sys.version}")
print(f"Pandas {pd.__version__}")
print(f"Scikit-Learn {sk.__version__}")
print("GPU is", "available" if tf.test.is_gpu_available() else "NOT AVAILABLE")
# + pycharm={"name": "#%%\n", "is_executing": false}
import os
import zipfile
import random
import shutil
from tensorflow.keras.optimizers import RMSprop
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from shutil import copyfile
from os import getcwd
# + pycharm={"name": "#%%\n", "is_executing": false}
def split_data(src, train, test, split_size):
files = []
for filename in os.listdir(src):
file = src + filename
if os.path.getsize(file)>0:
files.append(filename)
else:
print(filename +'ignore because zero length')
training_length = int(len(files)*split_size)
testing_length = int(len(files) - training_length)
shuffled_set =random.sample(files, len(files))
training_set = shuffled_set[0:training_length]
testing_set = shuffled_set[-testing_length:]
for filename in training_set:
this_file = src + filename
destination = train + filename
copyfile(this_file, destination)
for filename in testing_set:
this_file = src + filename
destination = test + filename
copyfile(this_file, destination)
# + pycharm={"name": "#%%\n", "is_executing": false}
CAT_SRC_DIR = 'dogs-vs-cats/cats/'
TRAINING_cats = 'dogs-vs-cats/training/cats/'
TESTING_cats = 'dogs-vs-cats/testing/cats/'
# + pycharm={"name": "#%%\n", "is_executing": false}
DOG_SRC_DIR = 'dogs-vs-cats/dogs/'
TRAINING_dogs = 'dogs-vs-cats/training/dogs/'
TESTING_dogs = 'dogs-vs-cats/testing/dogs/'
# + pycharm={"name": "#%%\n", "is_executing": false}
split_size = 0.9
split_data(CAT_SRC_DIR, TRAINING_cats, TESTING_cats, split_size)
split_data(DOG_SRC_DIR, TRAINING_dogs, TESTING_dogs, split_size)
# + pycharm={"name": "#%%\n", "is_executing": false}
model = tf.keras.models.Sequential([
tf.keras.layers.ZeroPadding2D((2,2), input_shape=(300, 300, 3)),
tf.keras.layers.Conv2D(32, (3,3)),
#tf.keras.layers.BatchNormalization(axis = -1),
tf.keras.layers.Activation('relu'),
tf.keras.layers.MaxPooling2D(2,2),
tf.keras.layers.Conv2D(64, (3,3)),
#tf.keras.layers.BatchNormalization(axis = -1),
tf.keras.layers.Activation('relu'),
tf.keras.layers.MaxPooling2D(2,2),
tf.keras.layers.Conv2D(128, (3,3)),
#tf.keras.layers.BatchNormalization(axis = -1),
tf.keras.layers.Activation('relu'),
tf.keras.layers.MaxPooling2D(2,2),
tf.keras.layers.Conv2D(128, (3,3)),
#tf.keras.layers.BatchNormalization(axis = -1),
tf.keras.layers.Activation('relu'),
tf.keras.layers.MaxPooling2D(2,2),
# Flatten the results to feed into a DNN
tf.keras.layers.Flatten(),
# 512 neuron hidden layer
tf.keras.layers.Dense(512, activation='relu'),
# Only 1 output neuron. It will contain a value from 0-1 where 0 for 1 class ('cats') and 1 for the other ('dogs')
tf.keras.layers.Dense(1, activation='sigmoid')
])
# + pycharm={"name": "#%%\n", "is_executing": false}
model.summary()
# + pycharm={"name": "#%%\n", "is_executing": false}
model.compile(optimizer='adam', loss='binary_crossentropy', metrics = ['accuracy'])
# + pycharm={"name": "#%%\n", "is_executing": false}
#Image Pre-procssing
TRAIN_DIR = 'dogs-vs-cats/training'
TESTING_DIR = 'dogs-vs-cats/testing'
train_datagen = ImageDataGenerator(rescale=1/255,
rotation_range=40,
horizontal_flip=True,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
fill_mode='nearest')
test_datagen =ImageDataGenerator(rescale=1/255)
train_generator = train_datagen.flow_from_directory(TRAIN_DIR,
batch_size=50,
class_mode='binary',
target_size=(300,300))
test_generator = test_datagen.flow_from_directory(TESTING_DIR,
batch_size=50,
class_mode = 'binary',
target_size=(300, 300))
# + pycharm={"name": "#%%\n", "is_executing": false}
history = model.fit(train_generator,
validation_data=test_generator,
steps_per_epoch=100,
epochs=10,
validation_steps=50,
verbose=1)
# The Model has been trained for 20 epochs
# + pycharm={"name": "#%%\n", "is_executing": false}
model.save('cat-v-dogs.h5')
model = models.load_model('cat-v-dogs.h5')
# + pycharm={"name": "#%%\n", "is_executing": false}
img_path = 'images/1.jpg'
img = load_img(img_path, target_size=(300,300))
x = img_to_array(img)
x = np.expand_dims(x, axis=0)
print('My Image shape: ', x.shape)
image = plt.imread(img_path)
plt.imshow(image)
classes= model.predict(x)
print(classes[0])
if classes[0]>0:
print(" is a dog")
else:
print(" is a cat")
# + pycharm={"name": "#%%\n"}
| 6,032 |
/Part4-tree_based_models/DAT158-Part4-1-Decision_Trees.ipynb | 892711094d2ad25fa0382d7d9f5c2ecc82f350f5 | [] | no_license | xyXiangYu/DAT158ML | https://github.com/xyXiangYu/DAT158ML | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 2,163,978 | # ---
# jupyter:
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# extension: .py
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# name: dat158
# ---
# Alexander S. Lundervold, October 22nd, 2018
# # Introduction
# In this notebook we'll study **decision trees**. Why?
# 1. They form the basis for some of the most powerful methods in all of machine learning
# 2. They produce highly interpretable models, which is useful in contexts where you need high explainability (law, finance, medicine, and much more).
# ## Overview
# - We'll first introduce decision trees: what they are and how they can be trained (or "grown") using the CART algorithm.
#
#
#
# - We'll also see that they can produce very complicated decision boundaries when used for classification, which makes them capable of fitting very complicated training data sets.
#
#
#
# - We'll see how decision trees can be used for both classification and regression.
#
#
#
# - We'll also see that they often tend to overfit, which will motivate an extension to models with less variance through ensembling. This will result in the *random forests* models, discussed in the next notebook.
# See also Gerons notebook here: https://github.com/ageron/handson-ml/blob/master/06_decision_trees.ipynb
# # Setup
# We need our standard framework:
# +
# To automatically reload modules defined in external files.
# %reload_ext autoreload
# %autoreload 2
# To display plots directly in the notebook:
# %matplotlib inline
# +
from pathlib import Path
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
import sklearn
import seaborn as sns # For nicer plots
# -
# To make the notebook reproducible
seed = 42
np.random.seed(seed)
# # Decision trees
# From a set of labelled data a classification tree learns a set of if-else questions asked about the features and used to predict the labels.
# To be concrete, let's use the diabetes data set studied in previous notebooks. We load it as before:
datadir = Path("./data")
datadir.mkdir(exist_ok=True)
import urllib.request
url = 'https://assets.datacamp.com/production/course_1939/datasets/diabetes.csv'
urllib.request.urlretrieve(url, 'data/diabetes.csv')
diabetes = pd.read_csv(datadir/'diabetes.csv')
# It has 8 features and a binary label (diabetes vs. not diabetes):
diabetes.head()
# We restrict ourselves to two features for easier visualization:
data = diabetes[['glucose', 'bmi', 'diabetes']]
# Here's a scatter plot of the data we have, with glucose levels plotted agains bmi, and the dots colored by whether the person has diabetes or not:
sns.pairplot(x_vars = 'glucose', y_vars = 'bmi', data=data, hue='diabetes',
height=7, plot_kws={"s": 55} # Size of markers
)
plt.show()
# We notice that diabetes instances tend to have both higher blood glucose levels and also higher body mass index.
# To decide whether a person has diabetes we could therefore make a set of rules saying things like
#
# - "If your glucose level is above 100 *and* your BMI is above 25, then predict diabetes."
# - "If glucose below 150 _and_ BMI above 40, then predict diabetes"
# - and so on...
# > Such a set of rules is exactly a **decision tree**.
# Let's use scikit-learn to make the rules and the tree for us:
from sklearn.tree import DecisionTreeClassifier
tree_clf = DecisionTreeClassifier(max_depth=2, random_state=seed) # We'll discuss `max_depth=2` soon
X=data[['glucose', 'bmi']] # The features
y=data['diabetes'] # The labels
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=seed)
tree_clf.fit(X_train, y_train)
# ...and plot it:
# > **Note: you need graphviz https://graphviz.gitlab.io/download/ on your computer for the below plotting to work**. Also, update your dat158 environment if you haven't done so in a while (run `conda env update` inside the DAT158ML folder).
from graphviz import Source
from IPython.display import display
from IPython.display import SVG
from sklearn.tree import export_graphviz
graph = Source(export_graphviz(tree_clf, out_file=None, feature_names=X.columns,
class_names=['not diabetes','diabetes'], filled = True, rounded=True))
display(SVG(graph.pipe(format='svg')))
# *Run the below cell if you couldn't get the above plot to work:*
import IPython
IPython.display.Image("assets/decision_tree_diabetes.png", width='90%')
# We see that we get something that can be used to make predictions. If you're tasked with diagnosing diabetes or not diabetes for an instance based on this decision tree you do as follows:
#
# - Start at the root node. If the glucose level is 132.5 or below, take the path on the left. Else take the path on the right.
# - If you took the left path then you look at the BMI.
# - If BMI is less than 26.9, take the next left path. You end at a **leaf node** (a node where you can't go further). Your prediction is then `class = not diabetes`.
# - If the BMI is larger than 26.9, take the right path. You end at another leaf node, and make the prediction `class = not diabetes`.
# - If you took the right path then you again look at the glucose.
# - If glucose is less than 154.5 you take the left path. You end up at a leaf node and make the prediction `class = not diabetes`
# - If glucose is larger than 154.5 you take the right path, end up at a leaf node, and make the prediction `class = diabetes`.
# This is exactly how predictions are made if you run `tree_clf.predict`. Each instance is fed through the tree according to its features, until it hits a leaf node, where the class is assigned.
# > What are the other terms listed inside the nodes? "Gini", "samples", "value", what do they mean?
#
# > And how did scikit-learn construct the decision tree?
# > **Your turn!**
# - How accurate is our classifier on the test set?
# - Try creating a decision tree with `max_depth=3` and interpret the resulting tree as we did above.
# ## Decision trees glossary
# - **Root node**: The top-most node. No parent node. Where you start. Poses the first if-else question.
#
#
# - **Internal node**: Has one parent node and two children. These nodes pose if-else questions.
#
#
# - **Leaf node**: A node without children. Not possible to go any further. A bottom node. These nodes provide predictions.
#
#
# - **`samples`**: The number of training samples that the node applies to. The number of training instances that end up passing through the node. For example, there are 179 samples for which the glucose is less than 154.5
#
#
# - **`value`**: A list of the number of samples *of each class* that the node applies to.
#
#
# - **`gini`**: A measure of "impurity". The gini is 0 if all the training samples in that node belongs to a single class. It grows towards 1 as the class diversity at the node increases.
# We'll have more to say about the Gini impurity shortly, as it's what's used to make decision trees.
# But first, it's instructive to visualize the decision boundary produced by our decision tree, as we did back in Part 3:
# ## Decision boundaries for decision trees
from matplotlib.colors import ListedColormap
# +
# Modified from Geron
def plot_decision_boundary(clf, X, y, legend=False, plot_training=True, ax=None, figsize=(10,6)):
if not ax: f, ax = plt.subplots(figsize=figsize)
# Convert to numpy arrays in case X and y are data frames
X, y = np.array(X), np.array(y)
x1 = X[:,0] # First feature
x2 = X[:, 1] # Second feature
x1s = np.linspace(np.min(x1)-0.1*np.mean(x1), 1.1*np.max(x1), 100)
x2s = np.linspace(np.min(x2)-0.1*np.mean(x2), 1.1*np.max(x2), 100)
x1, x2 = np.meshgrid(x1s, x2s)
X_new = np.c_[x1.ravel(), x2.ravel()]
y_pred = clf.predict(X_new).reshape(x1.shape)
custom_cmap = ListedColormap(['#fafab0','#9898ff','#a0faa0'])
ax.contourf(x1, x2, y_pred, alpha=0.3, cmap=custom_cmap)
custom_cmap2 = ListedColormap(['#7d7d58','#4c4c7f','#507d50'])
ax.contour(x1, x2, y_pred, cmap=custom_cmap2, alpha=0.8)
dots = ["yo", "bs", "g^"]
if plot_training:
for i in np.unique(y):
ax.plot(X[:, 0][y==i], X[:, 1][y==i], dots[i], label=str(i))
else:
ax.set_xlabel(r"$x_1$", fontsize=18)
ax.set_ylabel(r"$x_2$", fontsize=18, rotation=0)
ax.legend(loc="lower right", fontsize=14)
ax.set_title("Decision boundary")
return ax
# -
_ = plot_decision_boundary(tree_clf, X_train,y_train)
# Let's plot the decision boundary for a tree with `max_depth=3`:
tree_clf_d3 = DecisionTreeClassifier(max_depth=3, random_state=seed)
tree_clf_d3.fit(X_train, y_train)
_ = plot_decision_boundary(tree_clf_d3, X_train,y_train)
# Note that the boundary is **not linear**. It is not a straight line, as it was for linear regression, and not smooth as for polynomial regression.
# **Note:** The decision boundaries of decision trees are always parallell to an axis. Why is that? It's because each node in a decision tree consists of a test using only **one feature at a time**.
# > **Your turn!**
# - Create a function that plots the decision boundary for a classifier with a specified `max_depth`. Note that the decision boundary gets more complex as you increase `max_depth`. That should make us think of overfitting...
# - Compute the decision boundary for a different pair of features. That is, not (`glucose`, `bmi`), but something else.
# ## Gini and information gain
# Decision trees learn the relationship between the training data and the corresponding labels by organizing the training data in a binary tree. Each leaf in the tree makes a specific prediction. Each internal node compares a single feature value against a single threshold, and places instances in their corresponding child nodes.
#
# Training a decision tree is done recursively, roughly as follows (we'll study training more carefully below):
#
# * The goal is to obtain leaves that are as pure as possible, with the maximum amount of samples in the leaves belonging to the same class.
# * Starting at the root node, the data is split into two parts based on the feature that has the larges **information gain**.
# * This is repeated for each child of the node, and so on in an iterative way.
#
# To achieve this we need a measure of information gain, and we also need it to tell us when a node is pure.
# **Gini impurity** is one such measure:
#
# $$G_i = 1 - \sum_{k=1}^n p_{i,k}^2 $$
#
# Here $p_{i,k}$ is the proportion of the samples that belongs to class $k$ for the node $i$.
# We note that:
# - If the node is **pure**, that is all the samples belong to a single class, then $G_i$ = 0.
# - If a node has many samples belonging to each of many classes, then $G_i$ gets closer to 1. Very impure.
# - The **information gain** of a node can be computed as the Gini impurity at the node minus the sum of the Gini impurity of its children weighted by their sizes.
# Let's try out the formula for the tree we found above:
import IPython
IPython.display.Image("assets/decision_tree_diabetes.png", width='90%')
# At the left child of the root node there are 397 samples, 316 belonging to not diabetes, 81 to diabets.
#
# The formula for $G$ gives:
#
# $$G_i = 1 - \left(\left(\frac{316}{397}\right)^2 + \left(\frac{81}{397}\right)^2\right),$$
# which is
1 - ((316/397)**2 + (81/397)**2)
# ...which fits with what scikit-learn found for this tree.
# The information gain at this node with respect to the feature BMI is the Gini impurity at the node minus the Gini impurities of its children, weighted by their relative sizes:
#
# $$0.325 - \bigg(\left(\frac{101}{397}\right)\cdot 0.058 + \left(\frac{296}{397}\right)\cdot 0.388\bigg)$$
# which is
0.325-((101/397) * 0.058 + (296/397) * 0.388)
# We can read this as:
# > "if we split the node on the feature BMI, then the information gained is 0.021".
#
# We want to split nodes in a way that gives very high information gain, because that would mean very pure children. This leads us to how decision trees are trained, which we'll study more closely below.
# > **Your turn!** Calculate the Gini impurity for the second node in the bottom row (the second leaf).
# ## Estimating class probabilities
# As we talked about when studying logistic regression, having models output predictions *and* estimated probabilities is very useful in practice. We want models that can say things like *"the model predicts that you have diabetes with 74% probability".*
#
# Decision trees are able to do this.
#
# The idea is simple: when an instance is fed to the decision tree it goes through some path until it ends up at a leaf node. The fraction of each class associated to that leaf is a probability, and we can interpret it as the probability of belonging to each class.
#
# For example, looking at our above tree again,
import IPython
IPython.display.Image("assets/decision_tree_diabetes.png", width='90%')
# an instance with glucose level 100 and BMI 35 ends up in the second leaf node. There are 218 not diabetes and 78 diabetes training instances in that node, and the model will spit out the probabilities
#
# $$218/296 = 0.736, \qquad 78/296 = 0.264$$
#
# for not diabetes and diabetes.
# Let's try it out:
test_sample = [100, 35]
# We use `predict_proba` to get the probabilities:
tree_clf.predict_proba([test_sample,])
# This is cool, but it gets less cool once you realise that this method would produce the exact same probabilities for an instance whose glucose is 20 and BMI is 10...
tree_clf.predict_proba([[20, 28],])
# > **Your turn!** Calculate the probability for not diabetes for an instance with glucose level 140 and BMI at 25 by hand. Then do the same using scikit-learn.
# # Growing decision trees: The CART algorithm for classification
# Decision trees are constructed (that is, trained) using an algorithm that depends on information gain.
#
# The CART algorithm is one way to grow trees (other alternative algorithms are ID3, C4.5 and C5.0):
#
# Starting at the root node, the algorithm searches *greedily* for a feature and a threshold (for example *glucose* and *glucose less than 132.5*) that gives the **highest information gain** when used to split the data.
# <img width=70% src="assets/information_gain_georgia.PNG"><br><center><small>Image from Georgia Tech</small></center>
# Once it has split the training data in two at the root, it continues in the same way on each of the two subsets.
# ## Cost function
# If we write $f$ for feature and $t_k$ for threshold, the cost function that CART tries to minimize is **the node impurity**:
#
# $$J(f, t_k) = \frac{m_{\mbox{left}}}{m} G_{\mbox{left}} + \frac{m_{\mbox{right}}}{m} G_{\mbox{right}}, $$
#
# where $m$ is the total number of samples in the node and $m_{\mbox{left}}$, $m_{\mbox{right}}$ the number of samples in the left and right child.
# The algorithm stops once it reaches the `max_depth`, or if it's not possible to find more splits that reduce the impurity. Or when another stopping criterium, for minimum number of samples in a node, is reached.
# # Regularization: preventing overfitting
# If you put no constraints on the decision trees they will very quickly produce very complex decision boundaries, which leads to overfitting. In fact, if you let the trees grow until all the leaves are *pure*, i.e. consisting of data points that are all of the same class, then the decision tree will be 100% accurate on the training set. That's typically not good...
# Let's see this in action on our data set:
# ## Overfitting the diabetes data set
# +
f, axes = plt.subplots(3,2, figsize=(12,12))
max_depths = [2, 3, 4, 5, 6, 7]
for i, ax in enumerate(axes.flat):
tree_clf = DecisionTreeClassifier(max_depth=max_depths[i])
tree_clf.fit(X_train, y_train)
_ = plot_decision_boundary(tree_clf, X_train, y_train, ax=ax)
ax.set_title(f'Max depth {max_depths[i]}')
plt.tight_layout(pad=0.5)
# -
# We see that already with `max_depth` set to 4, the decision tree seems to be overfitting pretty badly. This gets even worse if you have more complicated data sets.
# > **Question for you:** How would you go about finding a good setting for `max_depth`?
# ## Regularization: controlling the complexity
# As you know, restricting models can be used to reduce their variance and therefore their tendency to overfit.
# In decision trees, the following parameters can be used for regularization:
#
# - **`Max_depth`:** As we saw above, reducing the maximum depth a decision tree is allowed to have, one can simplify the decision boundary and therefore prevent overfitting. Decrease to regularize.
# - **`min_samples_split`**: This parameter controls the minimum number of samples a node can have to be allowed to split. Increase to regularize.
# - **`min_samples_leaf`**: The minimum number of samples a leaf must contain. Increase to regularize.
# - **`max_features`**: The maximum number of features that are evaluated when deciding whether to split each node. Decrease to regularize.
# - **`max_leaf_nodes`**: The maximum number of leaf nodes. Decrease to regularize.
# - **`min_impurity_decrease`**: Split nodes if the split results in a decrease of the impurity greater than or equal to this value. Increase to regularize.
# - **`min_impurity_split`**: Split a node if its impurity is above this threshold. Otherwise it's a leaf. Decrease to regularize.
# We can play around with some of these parameters on a data set:
from sklearn.datasets import make_moons # A simple toy data set
Xm, ym = make_moons(n_samples=100, noise=0.25, random_state=42)
# +
tree_clf_noreg = DecisionTreeClassifier()
tree_clf_noreg.fit(Xm, ym)
tree_clf_reg = DecisionTreeClassifier(max_depth=2) # Try out different settings here
tree_clf_reg.fit(Xm, ym)
# -
_ = plot_decision_boundary(tree_clf_noreg, Xm, ym)
_ = plot_decision_boundary(tree_clf_reg, Xm, ym)
# Let's make it interactive:
from ipywidgets import interactive, FloatSlider, IntSlider
def make_decision_tree(max_depth, min_samples_leaf):
tree_clf = DecisionTreeClassifier(max_depth=max_depth,
min_samples_leaf=min_samples_leaf,
)
tree_clf.fit(Xm, ym)
_ = plot_decision_boundary(tree_clf, Xm, ym)
plt.show()
interactive_plot = interactive(make_decision_tree,
max_depth = IntSlider(min=1, max=15, step=1, value=2),
min_samples_leaf=IntSlider(min=1, max=15, value=None)
)
output = interactive_plot.children[-1]
interactive_plot
# > Feel free to play around also with other parameter settings.
# > In practice, it's typically enough to use *one* of `max_depth`, `max_leaf_nodes` or `min_samples_leaf` to prevent overfitting.
# # Decision trees for regression
# Decision trees can also be used for regression, in a similar way as for classification. The idea is to use mean squared error (MSE) to measure the impurity of the nodes, and make splits based on MSE.
# Let's try it out on the Boston House Prices dataset built into scikit-learn:
from sklearn.tree import DecisionTreeRegressor
from sklearn.datasets import load_boston
boston = load_boston()
print(boston['DESCR'])
X, y = boston['data'], boston['target']
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
tree_reg = DecisionTreeRegressor(max_depth=3, random_state=42)
tree_reg.fit(X_train, y_train)
# **Plotting the tree:**
graph = Source(export_graphviz(tree_reg, out_file=None, feature_names=boston['feature_names'],
filled = True, rounded=True))
graph_svg = SVG(graph.pipe(format='svg'))
display(graph_svg)
# *Run the below cell if you couldn't get the above plot to work:*
import IPython
IPython.display.Image("assets/decision_tree_housing.png", width='90%')
# When decision trees are used for regression, the nodes are split by minimizing the *mean square error* (MSE) instead of the Gini impurity:
# $$\mbox{MSE}(\mbox{node}) = \frac{1}{m_{\mbox{node}}} \sum_{i \in \mbox{node}} \big(y^{(i)} - \hat{y}_{\mbox{node}}\big)^2,$$
#
# where $\hat{y}_{\mbox{node}}$ is the mean target value: $$\hat{y}_{\mbox{node}} = \frac{1}{N_{\mbox{node}}} \sum_{i \in \mbox{node}} y^{(i)}.$$
# The cost function becomes:
#
# $$J(f, t_k) = \frac{m_{\mbox{left}}}{m} \mbox{MSE}_{\mbox{left}} + \frac{m_{\mbox{right}}}{m} \mbox{MSE}_{\mbox{right}}$$
# > The tree tries to find splits that results in leafs whose target values are on average as close as possible to the mean value of the labels in each leaf.
# Let's check how well the model performs by computing the mean square error on the test set:
from sklearn.metrics import mean_squared_error, mean_absolute_error
y_pred = tree_reg.predict(X_test)
mean_squared_error(y_test, y_pred)
mean_absolute_error(y_test, y_pred)
# ### Feature importances
# A very nice thing about decision trees is that we can easily figure out which features were most important in its predicitions:
tree_reg.feature_importances_
importances = tree_reg.feature_importances_
indices = np.argsort(importances)[::-1]
for f in range(len(indices)):
print(f'{boston["feature_names"][indices[f]]}: {np.round(importances[indices[f]], 2)}')
plt.figure(figsize=(12,8))
plt.title("Feature importances")
plt.bar(range(X.shape[1]), importances[indices],
color="r", align="center")
plt.xticks(range(X.shape[1]), indices)
plt.xlim([-1, X.shape[1]])
plt.show()
# **WARNING:** The importance of the different features can be quite random. That a feature is low in importance for this tree doesn't necessarily mean that it's an uninformative feature! The decision tree just didn't pick that feature. Either because it found that another feature in the data set encoded the same information better. Which could happen by chance, since decision trees are quite unstable...
# > How are the feature importances calculated? It's the normalized impurity decrease caused by each feature. See the <a href="http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html#sklearn.tree.DecisionTreeClassifier.feature_importances_">documentation</a>.
# # Instability. The downsides of using decision trees..
# As you've realized, decision trees are great machine learning models in many ways. They are
# - easy to use (they work for any combination of categorical and continuous features, without need for scaling or other pre-processing),
# - easily interpretable (explaining why a decision tree made a decision is simply a matter of retracing the steps taken by the data through the tree. The possibility of computing feature importances is also super handy for interpretability.),
# - and can easily fit the training data.
# However, they have some very important downsides. We've seen that they tend to overfit heavily if not regularized. They are also extremely sensitive to small variations in the training data.
#
# We can see this in action: just by changing the random state in our split of diabetes into train and test we obtain different trees. This also influences the feature importances, making them less useful in practice.
data = diabetes[['glucose', 'bmi', 'diabetes']]
X=data[['glucose', 'bmi']]
y=data['diabetes']
def visualize_tree(seed=42):
"""
Trains a tree on the diabetes data set and plots it using graphviz.
"""
X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=seed) # Note the seed
tree_clf = DecisionTreeClassifier(max_depth=2, random_state=1)
tree_clf.fit(X_train, y_train)
graph = Source(export_graphviz(tree_clf, out_file=None, feature_names=X.columns,
class_names=['not diabetes','diabetes'], filled = True, rounded=True))
display(SVG(graph.pipe(format='svg')))
glucose_importance, bmi_importance = tree_clf.feature_importances_
print(f'Feature importances: Glucose: {glucose_importance}, BMI: {bmi_importance}')
# *If the below plots don't work, run the cell below them*
visualize_tree(1)
visualize_tree(2)
visualize_tree(3)
# If the above plots didn't work, run the below cells:
IPython.display.Image("assets/decision_tree_diabetes_1.png", width='90%')
IPython.display.Image("assets/decision_tree_diabetes_2.png", width='90%')
IPython.display.Image("assets/decision_tree_diabetes_3.png", width='90%')
# To combat these limitations while retaining as many of the nice aspects of decision trees as possible, one usually *ensembles* multiple decision trees into a single model. We'll discuss one way to do this, called **random forests** in the next notebook.
# # Extra
# > **Your turn!**
# - Train a decision tree classifier on the breast cancer wisconsin dataset (`sklearn.datasets.load_breast_cancer`), with `max-depth` set to 3 or 4.
# - Make a training, validation and test set to figure out which one seems better, `max_depth` 3 or 4?
# - Test your choice on the test set. You should get an accuracy of about 95%
# - Plot the decision tree.
# - Plot the decision boundaries for different pairs of features.
# - Use `tree_clf.feature_importances_` to find which features were most important for the classification when using all the features.
# You should find a tree similar to:
import IPython
IPython.display.Image("assets/decision_tree_cancer.png", width='100%')
# **Solution from the lecture**
from sklearn.datasets import load_breast_cancer
cancer = load_breast_cancer()
cancer.keys()
print(cancer['DESCR'])
X = cancer['data']
y = cancer['target']
X = pd.DataFrame(data=X, columns=cancer['feature_names'])
X.head()
X.info()
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X,y, random_state=42)
from sklearn.tree import DecisionTreeClassifier
dt_clf = DecisionTreeClassifier(random_state=42)
dt_clf.fit(X_train, y_train)
dt_clf.score(X_test, y_test)
from sklearn.metrics import accuracy_score
y_pred = dt_clf.predict(X_test)
y_pred[:5]
accuracy_score(y_test, y_pred)
# **Extra resources:**
# > Scikit-learns documentation for decision trees is very useful. You should have a look: http://scikit-learn.org/stable/modules/tree.html.
#
# > `dtreeeviz` is a nice library for visualizing decision trees: http://explained.ai/decision-tree-viz
| 26,664 |
/src_code/ctc_pytorch.ipynb | af2a0589ec3878ad1d624f1c72684e7f663b0e8d | [] | no_license | wujinlonglovezhangmiao1314/CTC-loss-introduction | https://github.com/wujinlonglovezhangmiao1314/CTC-loss-introduction | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 8,809 | # ---
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# ---
# # This notebook will be mainly used for the capstone project.
import pandas as pd
import numpy as np
print("Hello Capstone Project Course!")
0 # Target sequence length of longest target in batch
S_min = 10 # Minimum target length, for demonstration purposes
# Initialize random batch of input vectors, for *size = (T,N,C)
input = torch.randn(T, N, C).log_softmax(2).detach().requires_grad_()
print(input)
# +
# Initialize random batch of targets (0 = blank, 1:C = classes)
target = torch.randint(low=1, high=C, size=(N, S), dtype=torch.long)
print(target)
# -
input_lengths = torch.full(size=(N,), fill_value=T, dtype=torch.long)
print(input_lengths)
target_lengths = torch.randint(low=S_min, high=S, size=(N,), dtype=torch.long)
print(target_lengths)
ctc_loss = nn.CTCLoss()
loss = ctc_loss(input, target, input_lengths, target_lengths)
print(loss)
loss.backward()
Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
print("confusion matrix:")
print(cm)
# Visualising the Training set results
from matplotlib.colors import ListedColormap
def visualize_data(X_set, y_set, title):
X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
plt.figure(figsize=(20,10))
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
alpha = 0.75, cmap = ListedColormap(('red', 'green')))
plt.xlim(X1.min(), X1.max())
plt.ylim(X2.min(), X2.max())
for i, j in enumerate(np.unique(y_set)):
plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
c = ListedColormap(('red', 'green'))(i), label = j,
s=4)
plt.title(title)
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
plt.legend()
plt.show()
visualize_data(X_set = X_train, y_set = y_train, title = 'SVM Classifier (Training set)')
visualize_data(X_set = X_test, y_set = y_test, title = 'SVM Classifier (Test set)')
| 2,425 |
/microRNA_network_analysis_with_neo4j_v2.ipynb | 0366333a54414a42e0b42bcf8dd8af063f74bb68 | [] | no_license | mgirardot/microRNAs | https://github.com/mgirardot/microRNAs | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 777,745 | # ---
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# language: python
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# ---
# # Creating a Sentiment Analysis Web App
# ## Using PyTorch and SageMaker
#
# _Deep Learning Nanodegree Program | Deployment_
#
# ---
#
# Now that we have a basic understanding of how SageMaker works we will try to use it to construct a complete project from end to end. Our goal will be to have a simple web page which a user can use to enter a movie review. The web page will then send the review off to our deployed model which will predict the sentiment of the entered review.
#
# ## Instructions
#
# Some template code has already been provided for you, and you will need to implement additional functionality to successfully complete this notebook. You will not need to modify the included code beyond what is requested. Sections that begin with '**TODO**' in the header indicate that you need to complete or implement some portion within them. Instructions will be provided for each section and the specifics of the implementation are marked in the code block with a `# TODO: ...` comment. Please be sure to read the instructions carefully!
#
# In addition to implementing code, there will be questions for you to answer which relate to the task and your implementation. Each section where you will answer a question is preceded by a '**Question:**' header. Carefully read each question and provide your answer below the '**Answer:**' header by editing the Markdown cell.
#
# > **Note**: Code and Markdown cells can be executed using the **Shift+Enter** keyboard shortcut. In addition, a cell can be edited by typically clicking it (double-click for Markdown cells) or by pressing **Enter** while it is highlighted.
#
# ## General Outline
#
# Recall the general outline for SageMaker projects using a notebook instance.
#
# 1. Download or otherwise retrieve the data.
# 2. Process / Prepare the data.
# 3. Upload the processed data to S3.
# 4. Train a chosen model.
# 5. Test the trained model (typically using a batch transform job).
# 6. Deploy the trained model.
# 7. Use the deployed model.
#
# For this project, you will be following the steps in the general outline with some modifications.
#
# First, you will not be testing the model in its own step. You will still be testing the model, however, you will do it by deploying your model and then using the deployed model by sending the test data to it. One of the reasons for doing this is so that you can make sure that your deployed model is working correctly before moving forward.
#
# In addition, you will deploy and use your trained model a second time. In the second iteration you will customize the way that your trained model is deployed by including some of your own code. In addition, your newly deployed model will be used in the sentiment analysis web app.
# Make sure that we use SageMaker 1.x
# !pip install sagemaker==1.72.0
# ## Step 1: Downloading the data
#
# As in the XGBoost in SageMaker notebook, we will be using the [IMDb dataset](http://ai.stanford.edu/~amaas/data/sentiment/)
#
# > Maas, Andrew L., et al. [Learning Word Vectors for Sentiment Analysis](http://ai.stanford.edu/~amaas/data/sentiment/). In _Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies_. Association for Computational Linguistics, 2011.
# %mkdir ../data
# !wget -O ../data/aclImdb_v1.tar.gz http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz
# !tar -zxf ../data/aclImdb_v1.tar.gz -C ../data
# ## Step 2: Preparing and Processing the data
#
# Also, as in the XGBoost notebook, we will be doing some initial data processing. The first few steps are the same as in the XGBoost example. To begin with, we will read in each of the reviews and combine them into a single input structure. Then, we will split the dataset into a training set and a testing set.
# +
import os
import glob
def read_imdb_data(data_dir='../data/aclImdb'):
data = {}
labels = {}
for data_type in ['train', 'test']:
data[data_type] = {}
labels[data_type] = {}
for sentiment in ['pos', 'neg']:
data[data_type][sentiment] = []
labels[data_type][sentiment] = []
path = os.path.join(data_dir, data_type, sentiment, '*.txt')
files = glob.glob(path)
for f in files:
with open(f) as review:
data[data_type][sentiment].append(review.read())
# Here we represent a positive review by '1' and a negative review by '0'
labels[data_type][sentiment].append(1 if sentiment == 'pos' else 0)
assert len(data[data_type][sentiment]) == len(labels[data_type][sentiment]), \
"{}/{} data size does not match labels size".format(data_type, sentiment)
return data, labels
# -
data, labels = read_imdb_data()
print("IMDB reviews: train = {} pos / {} neg, test = {} pos / {} neg".format(
len(data['train']['pos']), len(data['train']['neg']),
len(data['test']['pos']), len(data['test']['neg'])))
# Now that we've read the raw training and testing data from the downloaded dataset, we will combine the positive and negative reviews and shuffle the resulting records.
# +
from sklearn.utils import shuffle
def prepare_imdb_data(data, labels):
"""Prepare training and test sets from IMDb movie reviews."""
#Combine positive and negative reviews and labels
data_train = data['train']['pos'] + data['train']['neg']
data_test = data['test']['pos'] + data['test']['neg']
labels_train = labels['train']['pos'] + labels['train']['neg']
labels_test = labels['test']['pos'] + labels['test']['neg']
#Shuffle reviews and corresponding labels within training and test sets
data_train, labels_train = shuffle(data_train, labels_train)
data_test, labels_test = shuffle(data_test, labels_test)
# Return a unified training data, test data, training labels, test labets
return data_train, data_test, labels_train, labels_test
# -
train_X, test_X, train_y, test_y = prepare_imdb_data(data, labels)
print("IMDb reviews (combined): train = {}, test = {}".format(len(train_X), len(test_X)))
# Now that we have our training and testing sets unified and prepared, we should do a quick check and see an example of the data our model will be trained on. This is generally a good idea as it allows you to see how each of the further processing steps affects the reviews and it also ensures that the data has been loaded correctly.
print(train_X[100])
print(train_y[100])
# The first step in processing the reviews is to make sure that any html tags that appear should be removed. In addition we wish to tokenize our input, that way words such as *entertained* and *entertaining* are considered the same with regard to sentiment analysis.
# +
import nltk
from nltk.corpus import stopwords
from nltk.stem.porter import *
import re
from bs4 import BeautifulSoup
def review_to_words(review):
nltk.download("stopwords", quiet=True)
stemmer = PorterStemmer()
text = BeautifulSoup(review, "html.parser").get_text() # Remove HTML tags
text = re.sub(r"[^a-zA-Z0-9]", " ", text.lower()) # Convert to lower case
words = text.split() # Split string into words
words = [w for w in words if w not in stopwords.words("english")] # Remove stopwords
words = [PorterStemmer().stem(w) for w in words] # stem
return words
# -
# The `review_to_words` method defined above uses `BeautifulSoup` to remove any html tags that appear and uses the `nltk` package to tokenize the reviews. As a check to ensure we know how everything is working, try applying `review_to_words` to one of the reviews in the training set.
# TODO: Apply review_to_words to a review (train_X[100] or any other review)
review_to_words(train_X[100])
# **Question:** AP_Value']<0.1]
# + id="L7KOILrKyUTB" colab_type="code" colab={}
# The dependent variable remains the same:
y_data = Train['Time from Pickup to Arrival']
# Model building - Independent Variable (IV) DataFrame
X_names = list(df_cp[df_cp['P_Value'] < 0.05].index)
X_data = Train[X_names]
# + id="oO6gCrdnHrQw" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 321} outputId="62b5375a-135e-4c2e-fdf8-372acd92ee20"
X_data.head()
# + id="ywTUmCL5Ivt4" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 609} outputId="b2ba725d-0096-4115-af28-efc411b64997"
# Create the correlation matrix
corr = X_data.corr()
# Find rows and columns where correlation coefficients > 0.9 or <-0.9
corr[np.abs(corr) > 0.9]
# + id="GjzaDPyvI8Ev" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 1000} outputId="62768497-d8c0-4463-935c-5cad387749ee"
# As before, we create the correlation matrix
# and find rows and columnd where correlation coefficients > 0.9 or <-0.9
corr = X_data.corr()
r, c = np.where(np.abs(corr) > 0.9)
# We are only interested in the off diagonal entries:
off_diagonal = np.where(r != c)
# Show the correlation matrix rows and columns where we have highly correlated off diagonal entries:
corr.iloc[r[off_diagonal], c[off_diagonal]]
# + id="OKeWGjN1T_a8" colab_type="code" colab={}
DropColumns = ['Pickup - Weekday (Mo = 1)_5', 'Placement - Weekday (Mo = 1)_5','Placement - Weekday (Mo = 1)_5',
'Confirmation - Weekday (Mo = 1)_5', 'Confirmation - Weekday (Mo = 1)_5', 'Confirmation - Weekday (Mo = 1)_2',
'Pickup - Weekday (Mo = 1)_2','Pickup - Weekday (Mo = 1)_2', 'Placement - Weekday (Mo = 1)_2',
'Placement - Weekday (Mo = 1)_2','Rider Id_Rider_Id_83','Confirmation - Day of Month','Confirmation - Day of Month',
'Confirmation - Day of Month','Arrival at Pickup - Day of Month','Arrival at Pickup - Day of Month',
'Arrival at Pickup - Day of Month','Pickup - Day of Month','Pickup - Day of Month',
'Placement - Day of Month','Placement - Day of Month','Placement - Weekday (Mo = 1)_6',
'Pickup - Weekday (Mo = 1)_6','Pickup - Weekday (Mo = 1)_6','Confirmation - Weekday (Mo = 1)_6',
'Confirmation - Weekday (Mo = 1)_6']
# + id="4c6tbPgdWwZu" colab_type="code" colab={}
X_data = X_data.drop(DropColumns,axis=1)
# + [markdown] id="9Dwdr3O2YQi-" colab_type="text"
# # Ensemble Method
#
# + [markdown] id="wH5egYSnbKRR" colab_type="text"
# **Feature Scaling**
# + id="xlDpc2_BKYDs" colab_type="code" colab={}
# import the scaling module
from sklearn.preprocessing import StandardScaler
# + id="SoYpgIQFKfoN" colab_type="code" colab={}
# create standardization object
scaler_X = StandardScaler()
scaler_y = StandardScaler()
# + id="lXj7XD0CbJYH" colab_type="code" colab={}
columns = ['Distance (KM)', 'Destination Long','Pickup Long']
# save standardized features into new variable
X_data[columns] = scaler_X.fit_transform(X_data[columns])
y_data = np.array(y_data).reshape(-1,1)
# + [markdown] id="7tFFCM5Ikqbz" colab_type="text"
# **splitting features into test set and training set**
# + id="q82D5vqvk0QV" colab_type="code" colab={}
# import train/test split module
from sklearn.model_selection import train_test_split
# + id="AUy9Fc1glAwZ" colab_type="code" colab={}
# split dataset into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X_data,
y_data,
test_size=0.20,
random_state=1)
# + [markdown] id="v9g58f9yf6Fp" colab_type="text"
# **Ridge Model**
# + id="ox6ho_aGBPS6" colab_type="code" colab={}
from sklearn.linear_model import LinearRegression, Ridge
# + id="GI_WlPhkDo51" colab_type="code" colab={}
ridge = Ridge()
regressor = LinearRegression()
# + id="VAikEmn2DuBW" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 34} outputId="f063a896-3076-4be1-ea67-55f492b03c99"
ridge.fit(X_train, y_train)
regressor.fit(X_train, y_train)
# + id="MXihtiJqD4-C" colab_type="code" colab={}
ridge_pred = ridge.predict(X_test)
regressor_pred = regressor.predict(X_test)
# + id="wfkXG_LBF4xC" colab_type="code" colab={}
from sklearn.metrics import mean_squared_error
ridge_rmse = mean_squared_error(y_test, ridge_pred, squared= False)
regressor_rmse = mean_squared_error(y_test, regressor_pred, squared= False)
# + id="zxfdxZtOGHpf" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 34} outputId="ec15a465-840d-472c-da23-8a93e20b7776"
ridge_rmse
# + id="thXdZqtmGKXD" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 34} outputId="c456626f-9ac6-4c99-c4e9-e90048cbe2bc"
regressor_rmse
# + [markdown] id="OWXGkvI1FB57" colab_type="text"
# Building Decision Tree
# + id="wCoC4sQEFA25" colab_type="code" colab={}
from sklearn.tree import DecisionTreeRegressor
reg_tree = DecisionTreeRegressor(max_depth=3)
# + id="_wtK5_VwF_XR" colab_type="code" colab={}
reg_tree.fit(X_train,y_train)
dt_pred = reg_tree.predict(X_test)
# + id="euL2ePoAGT9F" colab_type="code" colab={}
dt_rmse = mean_squared_error(y_test, dt_pred, squared= False)
# + id="w99p-CenGbsN" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 34} outputId="184722c1-7211-4156-efd9-f2ceeb5bcc2d"
dt_rmse
# + [markdown] id="_xM6DdoDGnhD" colab_type="text"
# SVR Model
# + id="enWOlcZ7GmYC" colab_type="code" colab={}
#from sklearn.svm import SVR
#sv_reg = SVR(kernel='rbf', gamma='auto')
#sv_reg.fit(X_train,y_train)
#svr_pred = sv_reg.predict(X_test)
# + id="Vp7YDrTyHGBL" colab_type="code" colab={}
#svr_rmse = mean_squared_error(y_test, svr_pred, squared= False)
# + id="JZtqgrulHK2Y" colab_type="code" colab={}
#svr_rmse
# + [markdown] id="H4xDxxTwOCrD" colab_type="text"
# Lasso Model
# + id="OjVy8Kd-OEUX" colab_type="code" colab={}
from sklearn.linear_model import Lasso
lasso = Lasso(alpha=0.01)
lasso.fit(X_train, y_train)
y_pred = lasso.predict(X_test)
# + id="4GyJ14-BOYKC" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 34} outputId="b589cd7c-eb03-439c-89e9-b3c1d33635e7"
lasso_rmse = mean_squared_error(y_test, y_pred, squared= False)
lasso_rmse
# + [markdown] id="Zd4MuVfYCTZJ" colab_type="text"
# XGBoost model
# + id="i-Gw106ECV5g" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 173} outputId="ea2fce42-73bb-4ad0-b56d-b432369b698d"
from xgboost import XGBRegressor
xgb = XGBRegressor()
xgb.fit(X_train, y_train)
# + id="MbhabWsiCu0J" colab_type="code" colab={}
xgb_pred = xgb.predict(X_test)
# + id="7DJwwijgC1Ri" colab_type="code" colab={}
xgb_rmse = mean_squared_error(y_test, y_pred, squared= False)
# + id="-Qy8sGvOC536" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 34} outputId="d87ce2c6-d92c-49e4-faf0-7ca678f00395"
xgb_rmse
# + [markdown] id="z5gPfgyFQCQ9" colab_type="text"
# Random Forest
# + id="rjupuh0aQEzC" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 190} outputId="4245cbf6-9169-40d7-e133-bc9bb5461c5a"
from sklearn.ensemble import RandomForestRegressor
RF = RandomForestRegressor(n_estimators=100, max_depth=5)
RF.fit(X_train,y_train)
# + id="VsK2wiz8QQAf" colab_type="code" colab={}
RF_pred = RF.predict(X_test)
# + id="Y1aJyOB4QZjq" colab_type="code" colab={}
RF_rmse = mean_squared_error(y_test, y_pred, squared= False)
# + id="mTcHsi-2QdSN" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 34} outputId="40426375-419a-4e84-f6b3-abfbad361532"
RF_rmse
# + [markdown] id="oBlGqwYwMA1R" colab_type="text"
# Stacking method
# + id="gYKZJXZOMCx-" colab_type="code" colab={}
from sklearn.ensemble import StackingRegressor
models = [("LR",regressor),("Ridge",ridge),("lasso",lasso),("xgb",xgb)]
# + id="vlYu0zlmOyRD" colab_type="code" colab={}
meta_learner_reg = LinearRegression()
s_reg = StackingRegressor(estimators=models, final_estimator=meta_learner_reg)
# + id="DksplmrNO_lv" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 683} outputId="ffb52325-94d7-4a82-b491-e6a218315c1f"
s_reg.fit(X_train,y_train)
# + id="HduzcwDt7EiW" colab_type="code" colab={}
y_pred = s_reg.predict(X_test)
# + id="XNzkg33bPOuw" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 34} outputId="3a832175-c8f6-4d39-f8ec-3ca2494712b0"
meta_rmse = mean_squared_error(y_test, y_pred, squared= False)
meta_rmse
3 later on.
#
# ### Save the processed training dataset locally
#
# It is important to note the format of the data that we are saving as we will need to know it when we write the training code. In our case, each row of the dataset has the form `label`, `length`, `review[500]` where `review[500]` is a sequence of `500` integers representing the words in the review.
# +
import pandas as pd
pd.concat([pd.DataFrame(train_y), pd.DataFrame(train_X_len), pd.DataFrame(train_X)], axis=1) \
.to_csv(os.path.join(data_dir, 'train.csv'), header=False, index=False)
# -
# ### Uploading the training data
#
#
# Next, we need to upload the training data to the SageMaker default S3 bucket so that we can provide access to it while training our model.
# +
import sagemaker
sagemaker_session = sagemaker.Session()
bucket = sagemaker_session.default_bucket()
prefix = 'sagemaker/sentiment_rnn'
role = sagemaker.get_execution_role()
# -
input_data = sagemaker_session.upload_data(path=data_dir, bucket=bucket, key_prefix=prefix)
# **NOTE:** The cell above uploads the entire contents of our data directory. This includes the `word_dict.pkl` file. This is fortunate as we will need this later on when we create an endpoint that accepts an arbitrary review. For now, we will just take note of the fact that it resides in the data directory (and so also in the S3 training bucket) and that we will need to make sure it gets saved in the model directory.
# ## Step 4: Build and Train the PyTorch Model
#
# In the XGBoost notebook we discussed what a model is in the SageMaker framework. In particular, a model comprises three objects
#
# - Model Artifacts,
# - Training Code, and
# - Inference Code,
#
# each of which interact with one another. In the XGBoost example we used training and inference code that was provided by Amazon. Here we will still be using containers provided by Amazon with the added benefit of being able to include our own custom code.
#
# We will start by implementing our own neural network in PyTorch along with a training script. For the purposes of this project we have provided the necessary model object in the `model.py` file, inside of the `train` folder. You can see the provided implementation by running the cell below.
# !pygmentize train/model.py
# The important takeaway from the implementation provided is that there are three parameters that we may wish to tweak to improve the performance of our model. These are the embedding dimension, the hidden dimension and the size of the vocabulary. We will likely want to make these parameters configurable in the training script so that if we wish to modify them we do not need to modify the script itself. We will see how to do this later on. To start we will write some of the training code in the notebook so that we can more easily diagnose any issues that arise.
#
# First we will load a small portion of the training data set to use as a sample. It would be very time consuming to try and train the model completely in the notebook as we do not have access to a gpu and the compute instance that we are using is not particularly powerful. However, we can work on a small bit of the data to get a feel for how our training script is behaving.
# +
import torch
import torch.utils.data
# Read in only the first 250 rows
train_sample = pd.read_csv(os.path.join(data_dir, 'train.csv'), header=None, names=None, nrows=250)
# Turn the input pandas dataframe into tensors
train_sample_y = torch.from_numpy(train_sample[[0]].values).float().squeeze()
train_sample_X = torch.from_numpy(train_sample.drop([0], axis=1).values).long()
# Build the dataset
train_sample_ds = torch.utils.data.TensorDataset(train_sample_X, train_sample_y)
# Build the dataloader
train_sample_dl = torch.utils.data.DataLoader(train_sample_ds, batch_size=50)
# -
# ### (TODO) Writing the training method
#
# Next we need to write the training code itself. This should be very similar to training methods that you have written before to train PyTorch models. We will leave any difficult aspects such as model saving / loading and parameter loading until a little later.
def train(model, train_loader, epochs, optimizer, loss_fn, device):
for epoch in range(1, epochs + 1):
model.train()
total_loss = 0
for batch in train_loader:
batch_X, batch_y = batch
batch_X = batch_X.to(device)
batch_y = batch_y.to(device)
# TODO: Complete this train method to train the model provided.
optimizer.zero_grad()
out = model.forward(batch_X)
loss = loss_fn(out, batch_y)
loss.backward()
optimizer.step()
total_loss += loss.data.item()
print("Epoch: {}, BCELoss: {}".format(epoch, total_loss / len(train_loader)))
# Supposing we have the training method above, we will test that it is working by writing a bit of code in the notebook that executes our training method on the small sample training set that we loaded earlier. The reason for doing this in the notebook is so that we have an opportunity to fix any errors that arise early when they are easier to diagnose.
# +
import torch.optim as optim
from train.model import LSTMClassifier
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = LSTMClassifier(32, 100, 5000).to(device)
optimizer = optim.Adam(model.parameters())
loss_fn = torch.nn.BCELoss()
train(model, train_sample_dl, 5, optimizer, loss_fn, device)
# -
# In order to construct a PyTorch model using SageMaker we must provide SageMaker with a training script. We may optionally include a directory which will be copied to the container and from which our training code will be run. When the training container is executed it will check the uploaded directory (if there is one) for a `requirements.txt` file and install any required Python libraries, after which the training script will be run.
# ### (TODO) Training the model
#
# When a PyTorch model is constructed in SageMaker, an entry point must be specified. This is the Python file which will be executed when the model is trained. Inside of the `train` directory is a file called `train.py` which has been provided and which contains most of the necessary code to train our model. The only thing that is missing is the implementation of the `train()` method which you wrote earlier in this notebook.
#
# **TODO**: Copy the `train()` method written above and paste it into the `train/train.py` file where required.
#
# The way that SageMaker passes hyperparameters to the training script is by way of arguments. These arguments can then be parsed and used in the training script. To see how this is done take a look at the provided `train/train.py` file.
# +
from sagemaker.pytorch import PyTorch
estimator = PyTorch(entry_point="train.py",
source_dir="train",
role=role,
framework_version='0.4.0',
train_instance_count=1,
train_instance_type='ml.m4.xlarge',
hyperparameters={
'epochs': 10,
'hidden_dim': 200,
})
# -
estimator.fit({'training': input_data})
# ## Step 5: Testing the model
#
# As mentioned at the top of this notebook, we will be testing this model by first deploying it and then sending the testing data to the deployed endpoint. We will do this so that we can make sure that the deployed model is working correctly.
#
# ## Step 6: Deploy the model for testing
#
# Now that we have trained our model, we would like to test it to see how it performs. Currently our model takes input of the form `review_length, review[500]` where `review[500]` is a sequence of `500` integers which describe the words present in the review, encoded using `word_dict`. Fortunately for us, SageMaker provides built-in inference code for models with simple inputs such as this.
#
# There is one thing that we need to provide, however, and that is a function which loads the saved model. This function must be called `model_fn()` and takes as its only parameter a path to the directory where the model artifacts are stored. This function must also be present in the python file which we specified as the entry point. In our case the model loading function has been provided and so no changes need to be made.
#
# **NOTE**: When the built-in inference code is run it must import the `model_fn()` method from the `train.py` file. This is why the training code is wrapped in a main guard ( ie, `if __name__ == '__main__':` )
#
# Since we don't need to change anything in the code that was uploaded during training, we can simply deploy the current model as-is.
#
# **NOTE:** When deploying a model you are asking SageMaker to launch an compute instance that will wait for data to be sent to it. As a result, this compute instance will continue to run until *you* shut it down. This is important to know since the cost of a deployed endpoint depends on how long it has been running for.
#
# In other words **If you are no longer using a deployed endpoint, shut it down!**
#
# **TODO:** Deploy the trained model.
# TODO: Deploy the trained model
predictor = estimator.deploy(initial_instance_count=1, instance_type='ml.m4.xlarge')
# ## Step 7 - Use the model for testing
#
# Once deployed, we can read in the test data and send it off to our deployed model to get some results. Once we collect all of the results we can determine how accurate our model is.
test_X = pd.concat([pd.DataFrame(test_X_len), pd.DataFrame(test_X)], axis=1)
# +
# We split the data into chunks and send each chunk seperately, accumulating the results.
def predict(data, rows=512):
split_array = np.array_split(data, int(data.shape[0] / float(rows) + 1))
predictions = np.array([])
for array in split_array:
predictions = np.append(predictions, predictor.predict(array))
return predictions
# -
predictions = predict(test_X.values)
predictions = [round(num) for num in predictions]
from sklearn.metrics import accuracy_score
accuracy_score(test_y, predictions)
# **Question:** How does this model compare to the XGBoost model you created earlier? Why might these two models perform differently on this dataset? Which do *you* think is better for sentiment analysis?
# **Answer:** XGBoost is better but small difference . because model used LSTM which is memory cell which XGBOOST has optimized parameters so it different in training dataset. XGBoost (small difference)
# ### (TODO) More testing
#
# We now have a trained model which has been deployed and which we can send processed reviews to and which returns the predicted sentiment. However, ultimately we would like to be able to send our model an unprocessed review. That is, we would like to send the review itself as a string. For example, suppose we wish to send the following review to our model.
test_review = 'The simplest pleasures in life are the best, and this film is one of them. Combining a rather basic storyline of love and adventure this movie transcends the usual weekend fair with wit and unmitigated charm.'
# The question we now need to answer is, how do we send this review to our model?
#
# Recall in the first section of this notebook we did a bunch of data processing to the IMDb dataset. In particular, we did two specific things to the provided reviews.
# - Removed any html tags and stemmed the input
# - Encoded the review as a sequence of integers using `word_dict`
#
# In order process the review we will need to repeat these two steps.
#
# **TODO**: Using the `review_to_words` and `convert_and_pad` methods from section one, convert `test_review` into a numpy array `test_data` suitable to send to our model. Remember that our model expects input of the form `review_length, review[500]`.
# +
# TODO: Convert test_review into a form usable by the model and save the results in test_data
test_data_review_to_words = review_to_words(test_review)
test_data = [np.array(convert_and_pad(word_dict, test_data_review_to_words)[0])]
# -
# Now that we have processed the review, we can send the resulting array to our model to predict the sentiment of the review.
predictor.predict(test_data)
# Since the return value of our model is close to `1`, we can be certain that the review we submitted is positive.
# ### Delete the endpoint
#
# Of course, just like in the XGBoost notebook, once we've deployed an endpoint it continues to run until we tell it to shut down. Since we are done using our endpoint for now, we can delete it.
estimator.delete_endpoint()
# ## Step 6 (again) - Deploy the model for the web app
#
# Now that we know that our model is working, it's time to create some custom inference code so that we can send the model a review which has not been processed and have it determine the sentiment of the review.
#
# As we saw above, by default the estimator which we created, when deployed, will use the entry script and directory which we provided when creating the model. However, since we now wish to accept a string as input and our model expects a processed review, we need to write some custom inference code.
#
# We will store the code that we write in the `serve` directory. Provided in this directory is the `model.py` file that we used to construct our model, a `utils.py` file which contains the `review_to_words` and `convert_and_pad` pre-processing functions which we used during the initial data processing, and `predict.py`, the file which will contain our custom inference code. Note also that `requirements.txt` is present which will tell SageMaker what Python libraries are required by our custom inference code.
#
# When deploying a PyTorch model in SageMaker, you are expected to provide four functions which the SageMaker inference container will use.
# - `model_fn`: This function is the same function that we used in the training script and it tells SageMaker how to load our model.
# - `input_fn`: This function receives the raw serialized input that has been sent to the model's endpoint and its job is to de-serialize and make the input available for the inference code.
# - `output_fn`: This function takes the output of the inference code and its job is to serialize this output and return it to the caller of the model's endpoint.
# - `predict_fn`: The heart of the inference script, this is where the actual prediction is done and is the function which you will need to complete.
#
# For the simple website that we are constructing during this project, the `input_fn` and `output_fn` methods are relatively straightforward. We only require being able to accept a string as input and we expect to return a single value as output. You might imagine though that in a more complex application the input or output may be image data or some other binary data which would require some effort to serialize.
#
# ### (TODO) Writing inference code
#
# Before writing our custom inference code, we will begin by taking a look at the code which has been provided.
# !pygmentize serve/predict.py
# As mentioned earlier, the `model_fn` method is the same as the one provided in the training code and the `input_fn` and `output_fn` methods are very simple and your task will be to complete the `predict_fn` method. Make sure that you save the completed file as `predict.py` in the `serve` directory.
#
# **TODO**: Complete the `predict_fn()` method in the `serve/predict.py` file.
# ### Deploying the model
#
# Now that the custom inference code has been written, we will create and deploy our model. To begin with, we need to construct a new PyTorchModel object which points to the model artifacts created during training and also points to the inference code that we wish to use. Then we can call the deploy method to launch the deployment container.
#
# **NOTE**: The default behaviour for a deployed PyTorch model is to assume that any input passed to the predictor is a `numpy` array. In our case we want to send a string so we need to construct a simple wrapper around the `RealTimePredictor` class to accomodate simple strings. In a more complicated situation you may want to provide a serialization object, for example if you wanted to sent image data.
# +
from sagemaker.predictor import RealTimePredictor
from sagemaker.pytorch import PyTorchModel
class StringPredictor(RealTimePredictor):
def __init__(self, endpoint_name, sagemaker_session):
super(StringPredictor, self).__init__(endpoint_name, sagemaker_session, content_type='text/plain')
model = PyTorchModel(model_data=estimator.model_data,
role = role,
framework_version='0.4.0',
entry_point='predict.py',
source_dir='serve',
predictor_cls=StringPredictor)
predictor = model.deploy(initial_instance_count=1, instance_type='ml.m4.xlarge')
# -
# ### Testing the model
#
# Now that we have deployed our model with the custom inference code, we should test to see if everything is working. Here we test our model by loading the first `250` positive and negative reviews and send them to the endpoint, then collect the results. The reason for only sending some of the data is that the amount of time it takes for our model to process the input and then perform inference is quite long and so testing the entire data set would be prohibitive.
# +
import glob
def test_reviews(data_dir='../data/aclImdb', stop=250):
results = []
ground = []
# We make sure to test both positive and negative reviews
for sentiment in ['pos', 'neg']:
path = os.path.join(data_dir, 'test', sentiment, '*.txt')
files = glob.glob(path)
files_read = 0
print('Starting ', sentiment, ' files')
# Iterate through the files and send them to the predictor
for f in files:
with open(f) as review:
# First, we store the ground truth (was the review positive or negative)
if sentiment == 'pos':
ground.append(1)
else:
ground.append(0)
# Read in the review and convert to 'utf-8' for transmission via HTTP
review_input = review.read().encode('utf-8')
# Send the review to the predictor and store the results
results.append(float(predictor.predict(review_input)))
# Sending reviews to our endpoint one at a time takes a while so we
# only send a small number of reviews
files_read += 1
if files_read == stop:
break
return ground, results
# -
ground, results = test_reviews()
from sklearn.metrics import accuracy_score
accuracy_score(ground, results)
# As an additional test, we can try sending the `test_review` that we looked at earlier.
predictor.predict(test_review)
# Now that we know our endpoint is working as expected, we can set up the web page that will interact with it. If you don't have time to finish the project now, make sure to skip down to the end of this notebook and shut down your endpoint. You can deploy it again when you come back.
# ## Step 7 (again): Use the model for the web app
#
# > **TODO:** This entire section and the next contain tasks for you to complete, mostly using the AWS console.
#
# So far we have been accessing our model endpoint by constructing a predictor object which uses the endpoint and then just using the predictor object to perform inference. What if we wanted to create a web app which accessed our model? The way things are set up currently makes that not possible since in order to access a SageMaker endpoint the app would first have to authenticate with AWS using an IAM role which included access to SageMaker endpoints. However, there is an easier way! We just need to use some additional AWS services.
#
# <img src="Web App Diagram.svg">
#
# The diagram above gives an overview of how the various services will work together. On the far right is the model which we trained above and which is deployed using SageMaker. On the far left is our web app that collects a user's movie review, sends it off and expects a positive or negative sentiment in return.
#
# In the middle is where some of the magic happens. We will construct a Lambda function, which you can think of as a straightforward Python function that can be executed whenever a specified event occurs. We will give this function permission to send and recieve data from a SageMaker endpoint.
#
# Lastly, the method we will use to execute the Lambda function is a new endpoint that we will create using API Gateway. This endpoint will be a url that listens for data to be sent to it. Once it gets some data it will pass that data on to the Lambda function and then return whatever the Lambda function returns. Essentially it will act as an interface that lets our web app communicate with the Lambda function.
#
# ### Setting up a Lambda function
#
# The first thing we are going to do is set up a Lambda function. This Lambda function will be executed whenever our public API has data sent to it. When it is executed it will receive the data, perform any sort of processing that is required, send the data (the review) to the SageMaker endpoint we've created and then return the result.
#
# #### Part A: Create an IAM Role for the Lambda function
#
# Since we want the Lambda function to call a SageMaker endpoint, we need to make sure that it has permission to do so. To do this, we will construct a role that we can later give the Lambda function.
#
# Using the AWS Console, navigate to the **IAM** page and click on **Roles**. Then, click on **Create role**. Make sure that the **AWS service** is the type of trusted entity selected and choose **Lambda** as the service that will use this role, then click **Next: Permissions**.
#
# In the search box type `sagemaker` and select the check box next to the **AmazonSageMakerFullAccess** policy. Then, click on **Next: Review**.
#
# Lastly, give this role a name. Make sure you use a name that you will remember later on, for example `LambdaSageMakerRole`. Then, click on **Create role**.
#
# #### Part B: Create a Lambda function
#
# Now it is time to actually create the Lambda function.
#
# Using the AWS Console, navigate to the AWS Lambda page and click on **Create a function**. When you get to the next page, make sure that **Author from scratch** is selected. Now, name your Lambda function, using a name that you will remember later on, for example `sentiment_analysis_func`. Make sure that the **Python 3.6** runtime is selected and then choose the role that you created in the previous part. Then, click on **Create Function**.
#
# On the next page you will see some information about the Lambda function you've just created. If you scroll down you should see an editor in which you can write the code that will be executed when your Lambda function is triggered. In our example, we will use the code below.
#
# ```python
# # We need to use the low-level library to interact with SageMaker since the SageMaker API
# # is not available natively through Lambda.
# import boto3
#
# def lambda_handler(event, context):
#
# # The SageMaker runtime is what allows us to invoke the endpoint that we've created.
# runtime = boto3.Session().client('sagemaker-runtime')
#
# # Now we use the SageMaker runtime to invoke our endpoint, sending the review we were given
# response = runtime.invoke_endpoint(EndpointName = '**ENDPOINT NAME HERE**', # The name of the endpoint we created
# ContentType = 'text/plain', # The data format that is expected
# Body = event['body']) # The actual review
#
# # The response is an HTTP response whose body contains the result of our inference
# result = response['Body'].read().decode('utf-8')
#
# return {
# 'statusCode' : 200,
# 'headers' : { 'Content-Type' : 'text/plain', 'Access-Control-Allow-Origin' : '*' },
# 'body' : result
# }
# ```
#
# Once you have copy and pasted the code above into the Lambda code editor, replace the `**ENDPOINT NAME HERE**` portion with the name of the endpoint that we deployed earlier. You can determine the name of the endpoint using the code cell below.
predictor.endpoint
# Once you have added the endpoint name to the Lambda function, click on **Save**. Your Lambda function is now up and running. Next we need to create a way for our web app to execute the Lambda function.
#
# ### Setting up API Gateway
#
# Now that our Lambda function is set up, it is time to create a new API using API Gateway that will trigger the Lambda function we have just created.
#
# Using AWS Console, navigate to **Amazon API Gateway** and then click on **Get started**.
#
# On the next page, make sure that **New API** is selected and give the new api a name, for example, `sentiment_analysis_api`. Then, click on **Create API**.
#
# Now we have created an API, however it doesn't currently do anything. What we want it to do is to trigger the Lambda function that we created earlier.
#
# Select the **Actions** dropdown menu and click **Create Method**. A new blank method will be created, select its dropdown menu and select **POST**, then click on the check mark beside it.
#
# For the integration point, make sure that **Lambda Function** is selected and click on the **Use Lambda Proxy integration**. This option makes sure that the data that is sent to the API is then sent directly to the Lambda function with no processing. It also means that the return value must be a proper response object as it will also not be processed by API Gateway.
#
# Type the name of the Lambda function you created earlier into the **Lambda Function** text entry box and then click on **Save**. Click on **OK** in the pop-up box that then appears, giving permission to API Gateway to invoke the Lambda function you created.
#
# The last step in creating the API Gateway is to select the **Actions** dropdown and click on **Deploy API**. You will need to create a new Deployment stage and name it anything you like, for example `prod`.
#
# You have now successfully set up a public API to access your SageMaker model. Make sure to copy or write down the URL provided to invoke your newly created public API as this will be needed in the next step. This URL can be found at the top of the page, highlighted in blue next to the text **Invoke URL**.
# ## Step 4: Deploying our web app
#
# Now that we have a publicly available API, we can start using it in a web app. For our purposes, we have provided a simple static html file which can make use of the public api you created earlier.
#
# In the `website` folder there should be a file called `index.html`. Download the file to your computer and open that file up in a text editor of your choice. There should be a line which contains **\*\*REPLACE WITH PUBLIC API URL\*\***. Replace this string with the url that you wrote down in the last step and then save the file.
#
# Now, if you open `index.html` on your local computer, your browser will behave as a local web server and you can use the provided site to interact with your SageMaker model.
#
# If you'd like to go further, you can host this html file anywhere you'd like, for example using github or hosting a static site on Amazon's S3. Once you have done this you can share the link with anyone you'd like and have them play with it too!
#
# > **Important Note** In order for the web app to communicate with the SageMaker endpoint, the endpoint has to actually be deployed and running. This means that you are paying for it. Make sure that the endpoint is running when you want to use the web app but that you shut it down when you don't need it, otherwise you will end up with a surprisingly large AWS bill.
#
# **TODO:** Make sure that you include the edited `index.html` file in your project submission.
# Now that your web app is working, trying playing around with it and see how well it works.
#
# **Question**: Give an example of a review that you entered into your web app. What was the predicted sentiment of your example review?
# Answer:
# +
from IPython.display import display, Image
display(Image(filename='postive.PNG'))
# +
from IPython.display import display, Image
display(Image(filename='Negative.PNG'))
# -
# ### Delete the endpoint
#
# Remember to always shut down your endpoint if you are no longer using it. You are charged for the length of time that the endpoint is running so if you forget and leave it on you could end up with an unexpectedly large bill.
predictor.delete_endpoint()
| 45,709 |
/exercises/pitot_tube_analasys/pitot_tube_calibration_exp.ipynb | d0025cd2b75f83aeba1f37f49b9bfd6777146389 | [] | no_license | liorshig/OpenLearningPIV | https://github.com/liorshig/OpenLearningPIV | 0 | 0 | null | 2019-01-20T23:19:43 | 2019-01-20T23:18:10 | null | Jupyter Notebook | false | false | .py | 483,680 | # ---
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# # Homework 13 #
# This homework consists of two problems, in each of which you will establish facts stated in Data 8. Remember properties of the expectation and variance of a linear function of a random variable.
#
# In both problems, you have a random pair $(X, Y)$ and $\hat{Y}$ is the best linear predictor of $Y$ based on $X$.
# ## 1. The Average of the Residuals ##
#
# **a)** In Data 8 we say that the regression line passes through the point of averages. Show this by setting $X = \mu_X$ and finding the corresponding value of $\hat{Y}$.
#
# **b)** Find $E(\hat{Y})$. In Data 8 language, this is the average of the fitted values.
#
# **c)** The difference $Y - \hat{Y}$ is called a *residual*. It is the difference between the actual and fitted values of $Y$. Find the expectation of the residual and confirm that the answer justifies the following statement [from Data 8](https://www.inferentialthinking.com/chapters/15/6/Numerical_Diagnostics.html#Average-of-Residuals):
#
# "No matter what the shape of the scatter diagram, the average of the residuals is 0."
# ## 2. An Interpretation of $r$ ##
#
# **a)** Find $Var(\hat{Y})$.
#
# **b)** Show that the answer to Part **a** justifies the following statement [from Data 8](https://www.inferentialthinking.com/chapters/15/6/Numerical_Diagnostics.html#Another-Way-to-Interpret-$r$):
#
# $$
# \frac{\text{SD of fitted values}}{\text{SD of y}} ~ = ~ \vert r \vert
# $$
#
# Note: Usually, the result above is stated in terms of variances instead of SDs, and hence $r^2$ is sometimes called "the proportion of variability explained by the linear model".
# ## Submission Instructions
# Please follow the directions below to properly submit your homework.
#
# * Scan all pages of **your work** into a PDF. You can use any scanner or a phone. Please **DO NOT** simply take pictures using your phone.
# * Please start a new page for each question. If you have already written multiple questions on the same page, you can crop the image or fold your page over (the old-fashioned way). This helps expedite grading.
# * It is your responsibility to check that all the work on all the scanned pages is legible.
#
#
# ### Submitting
# * Submit the assignment to Homework 13 on Gradescope. Use the entry code **MYBG2Z** if you haven't already joined the class.
# * **Make sure to assign each page of your pdf to the correct question.**
#
| 2,663 |
/pymaceuticals.ipynb | 3ebb4a04dcefe35b9320c74feb30b96d8806b641 | [] | no_license | SybileC/matplotlib-challenge | https://github.com/SybileC/matplotlib-challenge | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 177,028 | # ---
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# %matplotlib notebook
from matplotlib import style
style.use('fivethirtyeight')
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import datetime as dt
# # Reflect Tables into SQLAlchemy ORM
# Python SQL toolkit and Object Relational Mapper
import sqlalchemy
from sqlalchemy.ext.automap import automap_base
from sqlalchemy.orm import Session
from sqlalchemy import create_engine, func, inspect, desc
engine = create_engine("sqlite:///./Resources/hawaii.sqlite")
# reflect an existing database into a new model
Base = automap_base()
# reflect the tables
Base.prepare(engine, reflect=True)
# We can view all of the classes that automap found
Base.classes.keys()
# Save references to each table
Measurement = Base.classes.measurement
Station = Base.classes.station
# Create our session (link) from Python to the DB
session = Session(engine)
# +
inspector = inspect(engine)
columns = inspector.get_columns('measurement')
for column in columns:
print(column["name"], column["type"])
# -
# # Exploratory Climate Analysis
# +
# Design a query to retrieve the last 12 months of precipitation data and plot the results
# Calculate the date 1 year ago from today
datenow = dt.datetime.now() - dt.timedelta(days=365)
# Perform a query to retrieve the data and precipitation scores
results = session.query(Measurement.date, Measurement.prcp).filter(Measurement.date >= datenow).all()
# Save the query results as a Pandas DataFrame and set the index to the date column
df = pd.DataFrame(results, columns=['date', 'precipitation'])
df.set_index('date', inplace=True)
# Sort the dataframe by date
# df.sort_values('date')
# Use Pandas Plotting with Matplotlib to plot the data
df.plot.bar(title="Precipitation")
plt.tight_layout()
# Rotate the xticks for the dates
plt.xticks(rotation='vertical')
plt.show()
# +
# Use Pandas to calcualte the summary statistics for the precipitation data
v_count = df["precipitation"].count()
v_mean = df["precipitation"].mean()
v_std = df["precipitation"].std()
v_min = df["precipitation"].min()
v_25 = df["precipitation"].quantile(.25)
v_50 = df["precipitation"].quantile(.50)
v_75 = df["precipitation"].quantile(.75)
v_max = df["precipitation"].max()
values = {'stat': ["count", "mean", "std", "min", "25%", "50%", "75%", "max"], \
'precipitation': [v_count, v_mean, v_std, v_min, v_25, v_50, v_75, v_max]}
df2 = pd.DataFrame(data=values)
df2.set_index('stat', inplace=True)
df2
# -
df["precipitation"].describe()
# How many stations are available in this dataset?
station_count = session.query(func.count(Station.id)).all()
station_count
# What are the most active stations?
# List the stations and the counts in descending order.
stations = session.query(Measurement.station, func.count(Measurement.station)).group_by(Measurement.station).\
order_by(desc(func.count(Measurement.station))).all()
stations
# Using the station id from the previous query, calculate the lowest temperature recorded,
# highest temperature recorded, and average temperature most active station?
stats = session.query(func.min(Measurement.tobs), func.max(Measurement.tobs), func.avg(Measurement.tobs)).\
filter(Measurement.station=="USC00519281").group_by(Measurement.station).all()
stats
# +
# Choose the station with the highest number of temperature observations.
# Query the last 12 months of temperature observation data for this station and plot the results as a histogram
max_date1 = session.query(func.max(Measurement.date)).filter(Measurement.station=="USC00519281").\
group_by(Measurement.station).all()
max_date = max_date1[0][0]
date_12 = dt.datetime.strptime(max_date, '%Y-%m-%d') - dt.timedelta(days=365)
min_date = date_12.strftime('%Y-%m-%d')
tobs = session.query(Measurement.tobs).filter(Measurement.date >= min_date).filter(Measurement.date <= max_date).all()
df_tobs = pd.DataFrame(tobs)
df_tobs.hist(bins=12, label="tobs")
plt.ylabel('Frequency')
plt.legend()
plt.title("")
plt.tight_layout()
plt.show()
# -
# Write a function called `calc_temps` that will accept start date and end date in the format '%Y-%m-%d'
# and return the minimum, average, and maximum temperatures for that range of dates
def calc_temps(start_date, end_date):
"""TMIN, TAVG, and TMAX for a list of dates.
Args:
start_date (string): A date string in the format %Y-%m-%d
end_date (string): A date string in the format %Y-%m-%d
Returns:
TMIN, TAVE, and TMAX
"""
return session.query(func.min(Measurement.tobs), func.avg(Measurement.tobs), func.max(Measurement.tobs)).\
filter(Measurement.date >= start_date).filter(Measurement.date <= end_date).all()
print(calc_temps('2012-02-28', '2012-03-05'))
# Use your previous function `calc_temps` to calculate the tmin, tavg, and tmax
# for your trip using the previous year's data for those same dates.
print(calc_temps('2017-02-24', '2017-03-01'))
# Plot the results from your previous query as a bar chart.
# Use "Trip Avg Temp" as your Title
# Use the average temperature for the y value
# Use the peak-to-peak (tmax-tmin) value as the y error bar (yerr)
# +
# Calculate the rainfall per weather station for your trip dates using the previous year's matching dates.
# Sort this in descending order by precipitation amount and list the station, name, latitude, longitude, and elevation
# -
# ## Optional Challenge Assignment
# +
# Create a query that will calculate the daily normals
# (i.e. the averages for tmin, tmax, and tavg for all historic data matching a specific month and day)
def daily_normals(date):
"""Daily Normals.
Args:
date (str): A date string in the format '%m-%d'
Returns:
A list of tuples containing the daily normals, tmin, tavg, and tmax
"""
sel = [func.min(Measurement.tobs), func.avg(Measurement.tobs), func.max(Measurement.tobs)]
return session.query(*sel).filter(func.strftime("%m-%d", Measurement.date) == date).all()
daily_normals("01-01")
# +
# calculate the daily normals for your trip
# push each tuple of calculations into a list called `normals`
# Set the start and end date of the trip
# Use the start and end date to create a range of dates
# Stip off the year and save a list of %m-%d strings
# Loop through the list of %m-%d strings and calculate the normals for each date
# -
# Load the previous query results into a Pandas DataFrame and add the `trip_dates` range as the `date` index
# Plot the daily normals as an area plot with `stacked=False`
Regimen"] == "Capomulin",
["Mouse ID", "Timepoint", "Tumor Volume (mm3)"]]
mouse_s185 = capomulin.loc[capomulin["Mouse ID"] == "s185", ["Mouse ID", "Timepoint", "Tumor Volume (mm3)"]]
plt.plot(mouse_s185["Timepoint"], mouse_s185["Tumor Volume (mm3)"], marker='o')
plt.title("Time Point Vesus Tumor Volume for Mouse s185 (Capomulin)")
plt.ylabel("Tumor Volume (mm3)")
plt.xlabel("Time Point")
# Generate a scatter plot of mouse weight versus average tumor volume for the Capomulin regimen
capomulin_mice = duplicate_mice.loc[duplicate_mice["Drug Regimen"] == "Capomulin"]
mouse_data = capomulin_mice.groupby(["Mouse ID"])
mouse_weight = mouse_data.mean()["Weight (g)"]
average_tumor = mouse_data.mean()["Tumor Volume (mm3)"]
plt.scatter(mouse_weight, average_tumor, color='green')
plt.title("Mouse Weight Versus Average Tumor Volume (Capomulin)")
plt.xlabel("Mouse Weight")
plt.ylabel("Average Tumor Volume")
plt.show()
# ## Correlation and Regression
# Calculate the correlation coefficient and linear regression model
# for mouse weight and average tumor volume for the Capomulin regimen
plt.scatter(mouse_weight, average_tumor, color='green')
plt.title("Mouse Weight Versus Average Tumor Volume (Capomulin)")
plt.xlabel("Mouse Weight")
plt.ylabel("Average Tumor Volume")
(slope, intercept, rvalue, pvalue, stderr) = st.linregress(mouse_weight, average_tumor)
regress_value = mouse_weight * slope + intercept
line_eq = 'y = ' + str(round(slope, 2)) + 'x + ' + str(round(intercept, 2))
plt.plot(mouse_weight, regress_value, 'r-')
plt.annotate(line_eq, (18, 38), fontsize=15, color='r')
# Calculate correlation coefficient
f"the correlation coefficient and linear regression model between mouse weight and average tumor volume for the Capomulin treatment is {round(st.pearsonr(mouse_weight,average_tumor)[0],2)}"
et_value(0, 'mean')
#print(index)
#print(row['mean'] * 100 / docker_mean)
grouped_std_mean.loc[index, 'docker_abweichung'] = row['mean'] * 100 / docker_mean
grouped_std_mean = grouped_std_mean.round(2)
print(grouped_std_mean.to_string())
export_csv = grouped_std_mean.to_csv (r'Results/Ram_Gewichtung_Docker.csv', index = None, header=True, decimal=',')
# -
function-and-its-derivative/)):
#
# $$\large \begin{array}{rcl}
# \frac{\partial \mathcal{L}}{\partial w_{km}} &=& x_m \left(y_k - \sigma_k\left(\vec{z}\right)\right)
# \end{array}$$
#
# В стандартной формулировке получается, что вектор $\left(\sigma_1, \sigma_2, \ldots, \sigma_K\right)$ образует дискретное вероятностное распределение, т.е. $\sum_{i=1}^K \sigma_i = 1$. Но в нашей постановке задачи каждый пример может иметь несколько тегов или одновременно принадлежать к нескольким классам. Для этого мы немного изменим модель:
# - будем считать, что все теги независимы друг от друга, т.е. каждый исход – это логистическая регрессия на два класса (либо есть тег, либо его нет), тогда вероятность наличия тега у примера запишется следующим образом (каждый тег/класс как и в многоклассовой логрегрессии имеет свой набор параметров):
# $$\large p\left(\text{tag}_k \mid \vec{x}\right) = \sigma\left(z_k\right) = \sigma\left(\sum_{i=1}^M w_{ki} x^i \right)$$
# - наличие каждого тега мы будем моделировать с помощью <a href="https://en.wikipedia.org/wiki/Bernoulli_distribution">распределения Бернулли</a>
#
# <font color="red">Вопрос 1.</font> Ваше первое задание – записать упрощенное выражение логарифма правдоподобия примера с признаками $\vec{x}$. Как правило, многие алгоритмы оптимизации имеют интерфейс для минимизации функции, мы последуем этой же традиции и домножим полученное выражение на $-1$, а во второй части выведем формулы для минимизации полученного выражения.
# <font color="red">Варианты ответа:</font>
# 1. $\large -\mathcal{L} = -\sum_{i=1}^M y_i \log \sigma\left(z_i\right) + \left(1 - y_i\right) \log \left(1 - \sigma\left(z_i\right)\right)$
# 2. ## $\large -\mathcal{L} = -\sum_{i=1}^K y_i \log \sigma\left(z_i\right) + \left(1 - y_i\right) \log \left(1 - \sigma\left(z_i\right)\right)$
# 3. $\large -\mathcal{L} = -\sum_{i=1}^K z_i \log \sigma\left(y_i\right) + \left(1 - z_i\right) \log \left(1 - \sigma\left(y_i\right)\right)$
# 4. $\large -\mathcal{L} = -\sum_{i=1}^M z_i \log \sigma\left(y_i\right) + \left(1 - z_i\right) \log \left(1 - \sigma\left(y_i\right)\right)$
# ## 2. Вывод формулы обновления весов
#
# <font color="red">Вопрос 2.</font>В качестве второго задания вам предоставляется возможность вывести формулу градиента для $-\mathcal{L}$. Какой вид она будет иметь?
# <font color="red">Варианты ответа:</font>:
# 1. $\large -\frac{\partial \mathcal{L}}{\partial w_{km}} = -x_m \left(\sigma\left(z_k\right) - y_k\right)$
# ## 2. $\large -\frac{\partial \mathcal{L}}{\partial w_{km}} = -x_m \left(y_k - \sigma\left(z_k\right)\right)$
# 3. $\large -\frac{\partial \mathcal{L}}{\partial w_{km}} = \left(\sigma\left(z_k\right)x_m - y_k\right)$
# 4. $\large -\frac{\partial \mathcal{L}}{\partial w_{km}} = \left(y_k - \sigma\left(z_k\right)x_m\right)$
# ## 3. Реализация базовой модели
#
# Вам предлагается каркас класса модели, разберите его внимательно, обращайте внимание на комментарии. Затем заполните пропуски, запустите полученную модель и ответьте на проверочный вопрос.
#
# Как вы могли уже заметить, при обновлении веса $w_{km}$ используется значение признака $x_m$, который равен $0$, если слова с индексом $m$ нет в предложении, и больше нуля, если такое слово есть. В нашем случае, чтобы не пересчитывать [bag-of-words](https://en.wikipedia.org/wiki/Bag-of-words_model) самим или с помощью [sklearn.feature_extraction.text.CountVectorizer](http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html#sklearn.feature_extraction.text.CountVectorizer), мы будем идти по словам предложения в порядке их следования. Если какое-то слово встречается несколько раз, то мы добавляем его в аккумулятор со своим весом. В итоге получится то же самое, как если сначала посчитать количество одинаковых слов и домножить на соответствующий вес. Соответственно, при вычислении линейной комбинации $z$ весов модели и признаков примера необходимо учитывать только ненулевые признаки объекта.
#
# Подсказка:
# - если реализовывать вычисление сигмоида так же, как в формуле, то при большом отрицательном значении $z$ вычисление $e^{-z}$ превратится в очень большое число, которое вылетит за допустимые пределы
# - в то же время $e^{-z}$ от большого положительного $z$ будет нулем
# - воспользуйтесь свойствами функции $\sigma$ для того, чтобы пофиксить эту ошибку и реализовать $\sigma$ без риска overflow.
class LogRegressor():
"""Конструктор
Параметры
----------
tags : list of string, default=top_tags
список тегов
"""
def __init__(self, tags=top_tags):
# словарь который содержит мапинг слов предложений и тегов в индексы (для экономии памяти)
# пример: self._vocab['exception'] = 17 означает что у слова exception индекс равен 17
self._vocab = {}
# параметры модели: веса
# для каждого класса/тега нам необходимо хранить собственный вектор весов
# по умолчанию у нас все веса будут равны нулю
# мы заранее не знаем сколько весов нам понадобится
# поэтому для каждого класса мы сосздаем словарь изменяемого размера со значением по умолчанию 0
# пример: self._w['java'][self._vocab['exception']] содержит вес для слова exception тега java
self._w = dict([(t, defaultdict(int)) for t in tags])
# параметры модели: смещения или вес w_0
self._b = dict([(t, 0) for t in tags])
self._tags = set(tags)
"""Один прогон по датасету
Параметры
----------
fname : string, default=DS_FILE_NAME
имя файла с данными
top_n_train : int
первые top_n_train строк будут использоваться для обучения, остальные для тестирования
total : int, default=10000000
информация о количестве строк в файле для вывода прогресс бара
learning_rate : float, default=0.1
скорость обучения для градиентного спуска
tolerance : float, default=1e-16
используем для ограничения значений аргумента логарифмов
"""
def iterate_file(self,
fname=DS_FILE_NAME,
top_n_train=100000,
total=125000,
learning_rate=0.1,
tolerance=1e-16):
self._loss = []
n = 0
# откроем файл
with open(fname, 'r') as f:
# прогуляемся по строкам файла
for line in tqdm_notebook(f, total=total, mininterval=1):
pair = line.strip().split('\t')
if len(pair) != 2:
continue
sentence, tags = pair
# слова вопроса, это как раз признаки x
sentence = sentence.split(' ')
# теги вопроса, это y
tags = set(tags.split(' '))
# значение функции потерь для текущего примера
sample_loss = 0
# прокидываем градиенты для каждого тега
for tag in self._tags:
# целевая переменная равна 1 если текущий тег есть у текущего примера
y = int(tag in tags)
# расчитываем значение линейной комбинации весов и признаков объекта
# инициализируем z
# ЗАПОЛНИТЕ ПРОПУСКИ В КОДЕ
z = self._b[tag]
for word in sentence:
# если в режиме тестирования появляется слово которого нет в словаре, то мы его игнорируем
if n >= top_n_train and word not in self._vocab:
continue
if word not in self._vocab:
self._vocab[word] = len(self._vocab)
z += self._w[tag][self._vocab[word]]
# вычисляем вероятность наличия тега
# ЗАПОЛНИТЕ ПРОПУСКИ В КОДЕ
sigma = 1/(1 + np.exp(-z)) if z >= 0 else 1 - 1/(1 + np.exp(z))
# обновляем значение функции потерь для текущего примера
# ЗАПОЛНИТЕ ПРОПУСКИ В КОДЕ
sample_loss += -y*np.log(np.max([tolerance, sigma])) if y == 1 else -(1- y)*np.log(1 - np.min([1 - tolerance,sigma]))
# если мы все еще в тренировочной части, то обновим параметры
if n < top_n_train:
# вычисляем производную логарифмического правдоподобия по весу
# ЗАПОЛНИТЕ ПРОПУСКИ В КОДЕ
dLdw = y - sigma
# делаем градиентный шаг
# мы минимизируем отрицательное логарифмическое правдоподобие (второй знак минус)
# поэтому мы идем в обратную сторону градиента для минимизации (первый знак минус)
for word in sentence:
self._w[tag][self._vocab[word]] -= -learning_rate*dLdw
self._b[tag] -= -learning_rate*dLdw
n += 1
self._loss.append(sample_loss)
# создадим эксемпляр модели и пройдемся по датасету
model = LogRegressor()
model.iterate_file()
# Проверим, действительно ли значение отрицательного логарифмического правдоподобия уменьшалось. Так как мы используем стохастический градентный спуск, не стоит ожидать плавного падения функции ошибки. Мы воспользуемся скользящим средним с окном в 10 тысяч примеров, чтобы хоть как-то сгладить график.
plt.plot(pd.Series(model._loss[:-25000]).rolling(10000).mean());
print('Mean of the loss function on the last 10k train samples: %0.2f' % np.mean(model._loss[-35000:-25000]))
# <font color="red">Вопрос 3.</font>
# Вычислите среднее значение функции стоимости на последних 10 000 примеров тренировочного набора, к какому из значений ваш ответ ближе всего?
#
# <font color="red">Варианты ответа:</font>
# 1. 17.54
# 2. 18.64
# ## 3. 19.74
# 4. 20.84
# ## 4. Тестирование модели
#
# В базовой модели первые 100 000 строк используются для обучения, а оставшиеся – для тестирования. Как вы можете заметить, значение отрицательного логарифмического правдоподобия не очень информативно, хоть и позволяет сравнивать разные модели. В качестве четвертого задания вам необходимо модифицировать базовую модель таким образом, чтобы метод `iterate_file` возвращал значение _точности_ на тестовой части набора данных.
#
# Точность определим следующим образом:
# - считаем, что тег у вопроса присутствует, если спрогнозированная вероятность тега больше 0.9
# - точность одного примера расчитывается как [коэффициент Жаккара](https://ru.wikipedia.org/wiki/Коэффициент_Жаккара) между множеством настоящих тегов и предсказанных моделью
# - например, если у примера настоящие теги ['html', 'jquery'], а по версии модели ['ios', 'html', 'java'], то коэффициент Жаккара будет равен |['html', 'jquery'] $\cap$ ['ios', 'html', 'java']| / |['html', 'jquery'] $\cup$ ['ios', 'html', 'java']| = |['html']| / |['jquery', 'ios', 'html', 'java']| = 1/4
# - метод `iterate_file` возвращает **среднюю** точность на тестовом наборе данных
class LogRegressor():
"""Конструктор
Параметры
----------
tags : list of string, default=top_tags
список тегов
"""
def __init__(self, tags=top_tags):
# словарь который содержит мапинг слов предложений и тегов в индексы (для экономии памяти)
# пример: self._vocab['exception'] = 17 означает что у слова exception индекс равен 17
self._vocab = {}
# параметры модели: веса
# для каждого класса/тега нам необходимо хранить собственный вектор весов
# по умолчанию у нас все веса будут равны нулю
# мы заранее не знаем сколько весов нам понадобится
# поэтому для каждого класса мы сосздаем словарь изменяемого размера со значением по умолчанию 0
# пример: self._w['java'][self._vocab['exception']] содержит вес для слова exception тега java
self._w = dict([(t, defaultdict(int)) for t in tags])
# параметры модели: смещения или вес w_0
self._b = dict([(t, 0) for t in tags])
self._tags = set(tags)
"""Один прогон по датасету
Параметры
----------
fname : string, default=DS_FILE_NAME
имя файла с данными
top_n_train : int
первые top_n_train строк будут использоваться для обучения, остальные для тестирования
total : int, default=10000000
информация о количестве строк в файле для вывода прогресс бара
learning_rate : float, default=0.1
скорость обучения для градиентного спуска
tolerance : float, default=1e-16
используем для ограничения значений аргумента логарифмов
"""
def iterate_file(self,
fname=DS_FILE_NAME,
top_n_train=100000,
total=125000,
learning_rate=0.1,
tolerance=1e-16,
acc_border=0.9):
self._loss = []
n = 0
accuracy = []
# откроем файл
with open(fname, 'r') as f:
# прогуляемся по строкам файла
for line in tqdm_notebook(f, total=total, mininterval=1):
pair = line.strip().split('\t')
if len(pair) != 2:
continue
sentence, tags = pair
# слова вопроса, это как раз признаки x
sentence = sentence.split(' ')
# теги вопроса, это y
tags = set(tags.split(' '))
# значение функции потерь для текущего примера
sample_loss = 0
predicted_tags = None
# прокидываем градиенты для каждого тега
for tag in self._tags:
# целевая переменная равна 1 если текущий тег есть у текущего примера
y = int(tag in tags)
# расчитываем значение линейной комбинации весов и признаков объекта
# инициализируем z
# ЗАПОЛНИТЕ ПРОПУСКИ В КОДЕ
z = self._b[tag]
for word in sentence:
# если в режиме тестирования появляется слово которого нет в словаре, то мы его игнорируем
if n >= top_n_train and word not in self._vocab:
continue
if word not in self._vocab:
self._vocab[word] = len(self._vocab)
z += self._w[tag][self._vocab[word]]
# вычисляем вероятность наличия тега
# ЗАПОЛНИТЕ ПРОПУСКИ В КОДЕ
sigma = 1/(1 + np.exp(-z)) if z >= 0 else 1 - 1/(1 + np.exp(z))
# обновляем значение функции потерь для текущего примера
# ЗАПОЛНИТЕ ПРОПУСКИ В КОДЕ
sample_loss += -y*np.log(np.max([tolerance, sigma])) if y == 1 else -(1- y)*np.log(1 - np.min([1 - tolerance,sigma]))
# если мы все еще в тренировочной части, то обновим параметры
if n < top_n_train:
# вычисляем производную логарифмического правдоподобия по весу
# ЗАПОЛНИТЕ ПРОПУСКИ В КОДЕ
dLdw = y - sigma
# делаем градиентный шаг
# мы минимизируем отрицательное логарифмическое правдоподобие (второй знак минус)
# поэтому мы идем в обратную сторону градиента для минимизации (первый знак минус)
for word in sentence:
self._w[tag][self._vocab[word]] -= -learning_rate*dLdw
self._b[tag] -= -learning_rate*dLdw
else:
if predicted_tags is None:
predicted_tags = []
if sigma > acc_border:
predicted_tags.append(tag)
n += 1
if predicted_tags is not None:
accuracy.append(len(tags.intersection(predicted_tags))/len(tags.union(predicted_tags)))
self._loss.append(sample_loss)
return(np.mean(accuracy))
model = LogRegressor()
acc = model.iterate_file()
# выведем полученное значение с точностью до двух знаков
print('%0.2f' % acc)
# <font color="red">Вопрос 4.</font> К какому значению ближе всего полученное значение точности?
# <font color="red">Варианты ответа:</font>
# 1. 0.39
# 2. 0.49
# ## 3. 0.59
# 4. 0.69
# ## 5. $L_2$-регуляризация
#
# В качестве пятого задания вам необходимо добавить в класс `LogRegressor` поддержку $L_2$-регуляризации. В методе `iterate_file` должен появиться параметр `lmbda=0.01` со значением по умолчанию. С учетом регуляризации новая функция стоимости примет вид:
#
# $$\large \begin{array}{rcl}
# L &=& -\mathcal{L} + \frac{\lambda}{2} R\left(W\right) \\
# &=& -\mathcal{L} + \frac{\lambda}{2} \sum_{k=1}^K\sum_{i=1}^M w_{ki}^2
# \end{array}$$
#
# Градиент первого члена суммы мы уже вывели, а для второго он имеет вид:
#
# $$\large \begin{array}{rcl}
# \frac{\partial}{\partial w_{ki}} \frac{\lambda}{2} R\left(W\right) &=& \lambda w_{ki}
# \end{array}$$
#
# Если мы на каждом примере будем делать честное обновление всех весов, то все очень замедлится, ведь нам придется на каждой итерации пробегать по всем словам словаря. В ущерб теоретической корректности мы используем грязный трюк: будем регуляризировать только те слова, которые присутствуют в текущем предложении. Не забывайте, что смещение (bias) не регуляризируется. `sample_loss` тоже должен остаться без изменений.
#
# Замечание:
# - не забудьте, что нужно учитывать регуляризацию слова в градиентном шаге только один раз
# - условимся, что учитываем регуляризацию только при первой встрече слова
# - если бы мы считали сначала bag-of-words, то мы бы в цикле шли по уникальным словам, но т.к. мы этого не делаем, приходится выкручиваться (еще одна жертва богу online-моделей)
class LogRegressor():
"""Конструктор
Параметры
----------
tags : list of string, default=top_tags
список тегов
"""
def __init__(self, tags=top_tags):
# словарь который содержит мапинг слов предложений и тегов в индексы (для экономии памяти)
# пример: self._vocab['exception'] = 17 означает что у слова exception индекс равен 17
self._vocab = {}
# параметры модели: веса
# для каждого класса/тега нам необходимо хранить собственный вектор весов
# по умолчанию у нас все веса будут равны нулю
# мы заранее не знаем сколько весов нам понадобится
# поэтому для каждого класса мы сосздаем словарь изменяемого размера со значением по умолчанию 0
# пример: self._w['java'][self._vocab['exception']] содержит вес для слова exception тега java
self._w = dict([(t, defaultdict(int)) for t in tags])
# параметры модели: смещения или вес w_0
self._b = dict([(t, 0) for t in tags])
self._tags = set(tags)
"""Один прогон по датасету
Параметры
----------
fname : string, default=DS_FILE_NAME
имя файла с данными
top_n_train : int
первые top_n_train строк будут использоваться для обучения, остальные для тестирования
total : int, default=10000000
информация о количестве строк в файле для вывода прогресс бара
learning_rate : float, default=0.1
скорость обучения для градиентного спуска
tolerance : float, default=1e-16
используем для ограничения значений аргумента логарифмов
"""
def iterate_file(self,
fname=DS_FILE_NAME,
top_n_train=100000,
total=125000,
learning_rate=0.1,
tolerance=1e-16,
acc_border=0.9,
lmbda=0.01):
self._loss = []
n = 0
accuracy = []
# откроем файл
with open(fname, 'r') as f:
# прогуляемся по строкам файла
for line in tqdm_notebook(f, total=total, mininterval=1):
pair = line.strip().split('\t')
if len(pair) != 2:
continue
sentence, tags = pair
# слова вопроса, это как раз признаки x
sentence = sentence.split(' ')
# теги вопроса, это y
tags = set(tags.split(' '))
# значение функции потерь для текущего примера
sample_loss = 0
predicted_tags = None
# прокидываем градиенты для каждого тега
for tag in self._tags:
# целевая переменная равна 1 если текущий тег есть у текущего примера
y = int(tag in tags)
# расчитываем значение линейной комбинации весов и признаков объекта
# инициализируем z
# ЗАПОЛНИТЕ ПРОПУСКИ В КОДЕ
z = self._b[tag]
for word in sentence:
# если в режиме тестирования появляется слово которого нет в словаре, то мы его игнорируем
if n >= top_n_train and word not in self._vocab:
continue
if word not in self._vocab:
self._vocab[word] = len(self._vocab)
z += self._w[tag][self._vocab[word]]
# вычисляем вероятность наличия тега
# ЗАПОЛНИТЕ ПРОПУСКИ В КОДЕ
sigma = 1/(1 + np.exp(-z)) if z >= 0 else 1 - 1/(1 + np.exp(z))
# обновляем значение функции потерь для текущего примера
# ЗАПОЛНИТЕ ПРОПУСКИ В КОДЕ
sample_loss += -y*np.log(np.max([tolerance, sigma])) if y == 1 else -(1- y)*np.log(1 - np.min([1 - tolerance,sigma]))
# если мы все еще в тренировочной части, то обновим параметры
if n < top_n_train:
# вычисляем производную логарифмического правдоподобия по весу
# ЗАПОЛНИТЕ ПРОПУСКИ В КОДЕ
dLdw = y - sigma
# делаем градиентный шаг
# мы минимизируем отрицательное логарифмическое правдоподобие (второй знак минус)
# поэтому мы идем в обратную сторону градиента для минимизации (первый знак минус)
for word in sentence:
self._w[tag][self._vocab[word]] -= -learning_rate*dLdw + learning_rate*lmbda*self._w[tag][self._vocab[word]]
self._b[tag] -= -learning_rate*dLdw
else:
if predicted_tags is None:
predicted_tags = []
if sigma > acc_border:
predicted_tags.append(tag)
n += 1
if predicted_tags is not None:
accuracy.append(len(tags.intersection(predicted_tags))/len(tags.union(predicted_tags)))
self._loss.append(sample_loss)
return(np.mean(accuracy))
model = LogRegressor()
acc = model.iterate_file()
print('%0.2f' % acc)
plt.plot(pd.Series(model._loss[:-25000]).rolling(10000).mean());
# <font color="red">Вопрос 5.</font> К какому значению ближе всего полученное значение точности?
# <font color="red">Варианты ответа:</font>
# 1. 0.3
# 2. 0.35
# 3. 0.4
# ## 4. 0.52
# ## 6. ElasticNet регуляризация, вывод
# Помимо $L_2$ регуляризации, часто используется $L_1$ регуляризация.
#
# $$\large \begin{array}{rcl}
# L &=& -\mathcal{L} + \frac{\lambda}{2} R\left(W\right) \\
# &=& -\mathcal{L} + \lambda \sum_{k=1}^K\sum_{i=1}^M \left|w_{ki}\right|
# \end{array}$$
#
# Если линейно объединить $L_1$ и $L_2$ регуляризацию, то полученный тип регуляризации называется ElasticNet:
#
# $$\large \begin{array}{rcl}
# L &=& -\mathcal{L} + \lambda R\left(W\right) \\
# &=& -\mathcal{L} + \lambda \left(\gamma \sum_{k=1}^K\sum_{i=1}^M w_{ki}^2 + \left(1 - \gamma\right) \sum_{k=1}^K\sum_{i=1}^M \left|w_{ki}\right| \right)
# \end{array}$$
# - где $\gamma \in \left[0, 1\right]$
#
# В качестве шестого вопроса вам предлагается вывести формулу градиента ElasticNet регуляризации (не учитывая $-\mathcal{L}$).
#
# <font color="red">Варианты ответа:</font>:
# 1. $\large \frac{\partial}{\partial w_{ki}} \lambda R\left(W\right) = \lambda \left(2 \gamma w_{ki} + \left(1 - \gamma\right) w_{ki}\right)$
# 2. $\large \frac{\partial}{\partial w_{ki}} \lambda R\left(W\right) = \lambda \left(2 \gamma \left|w_{ki}\right| + \left(1 - \gamma\right) \text{sign}\left(w_{ki}\right)\right)$
# ## 3. $\large \frac{\partial}{\partial w_{ki}} \lambda R\left(W\right) = \lambda \left(2 \gamma w_{ki} + \left(1 - \gamma\right) \text{sign}\left(w_{ki}\right)\right)$
# 4. $\large \frac{\partial}{\partial w_{ki}} \lambda R\left(W\right) = \lambda \left(\gamma w_{ki} + \left(1 - \gamma\right) \text{sign}\left(w_{ki}\right)\right)$
# ## 7. Регуляризация ElasticNet , реализация
#
# В качестве седьмой задачи вам предлается изменить класс `LogRegressor` таким образом, чтобы метод `iterate_file` принимал два параметра со значениями по умолчанию `lmbda=0.0002` и `gamma=0.1`. Сделайте один проход по датасету с включенной `ElasticNet`-регуляризацией и заданными значениями по умолчанию и ответьте на вопрос.
class LogRegressor():
"""Конструктор
Параметры
----------
tags : list of string, default=top_tags
список тегов
"""
def __init__(self, tags=top_tags):
# словарь который содержит мапинг слов предложений и тегов в индексы (для экономии памяти)
# пример: self._vocab['exception'] = 17 означает что у слова exception индекс равен 17
self._vocab = {}
# параметры модели: веса
# для каждого класса/тега нам необходимо хранить собственный вектор весов
# по умолчанию у нас все веса будут равны нулю
# мы заранее не знаем сколько весов нам понадобится
# поэтому для каждого класса мы сосздаем словарь изменяемого размера со значением по умолчанию 0
# пример: self._w['java'][self._vocab['exception']] содержит вес для слова exception тега java
self._w = dict([(t, defaultdict(int)) for t in tags])
# параметры модели: смещения или вес w_0
self._b = dict([(t, 0) for t in tags])
self._tags = set(tags)
"""Один прогон по датасету
Параметры
----------
fname : string, default=DS_FILE_NAME
имя файла с данными
top_n_train : int
первые top_n_train строк будут использоваться для обучения, остальные для тестирования
total : int, default=10000000
информация о количестве строк в файле для вывода прогресс бара
learning_rate : float, default=0.1
скорость обучения для градиентного спуска
tolerance : float, default=1e-16
используем для ограничения значений аргумента логарифмов
"""
def iterate_file(self,
fname=DS_FILE_NAME,
top_n_train=100000,
total=125000,
learning_rate=0.1,
tolerance=1e-16,
acc_border=0.9,
lmbda=0.0002,
gamma=0.01):
self._loss = []
n = 0
accuracy = []
# откроем файл
with open(fname, 'r') as f:
# прогуляемся по строкам файла
for line in tqdm_notebook(f, total=total, mininterval=1):
pair = line.strip().split('\t')
if len(pair) != 2:
continue
sentence, tags = pair
# слова вопроса, это как раз признаки x
sentence = sentence.split(' ')
# теги вопроса, это y
tags = set(tags.split(' '))
# значение функции потерь для текущего примера
sample_loss = 0
predicted_tags = None
# прокидываем градиенты для каждого тега
for tag in self._tags:
# целевая переменная равна 1 если текущий тег есть у текущего примера
y = int(tag in tags)
# расчитываем значение линейной комбинации весов и признаков объекта
# инициализируем z
# ЗАПОЛНИТЕ ПРОПУСКИ В КОДЕ
z = self._b[tag]
for word in sentence:
# если в режиме тестирования появляется слово которого нет в словаре, то мы его игнорируем
if n >= top_n_train and word not in self._vocab:
continue
if word not in self._vocab:
self._vocab[word] = len(self._vocab)
z += self._w[tag][self._vocab[word]]
# вычисляем вероятность наличия тега
# ЗАПОЛНИТЕ ПРОПУСКИ В КОДЕ
sigma = 1/(1 + np.exp(-z)) if z >= 0 else 1 - 1/(1 + np.exp(z))
# обновляем значение функции потерь для текущего примера
# ЗАПОЛНИТЕ ПРОПУСКИ В КОДЕ
sample_loss += -y*np.log(np.max([tolerance, sigma])) if y == 1 else -(1- y)*np.log(1 - np.min([1 - tolerance,sigma]))
# если мы все еще в тренировочной части, то обновим параметры
if n < top_n_train:
# вычисляем производную логарифмического правдоподобия по весу
# ЗАПОЛНИТЕ ПРОПУСКИ В КОДЕ
dLdw = y - sigma
# делаем градиентный шаг
# мы минимизируем отрицательное логарифмическое правдоподобие (второй знак минус)
# поэтому мы идем в обратную сторону градиента для минимизации (первый знак минус)
for word in sentence:
self._w[tag][self._vocab[word]] -= learning_rate*(-dLdw + (lmbda*(2*gamma*self._w[tag][self._vocab[word]] + (1 - gamma)*np.sign(self._w[tag][self._vocab[word]]))))
self._b[tag] -= -learning_rate*dLdw
else:
if predicted_tags is None:
predicted_tags = []
if sigma > acc_border:
predicted_tags.append(tag)
n += 1
if predicted_tags is not None:
accuracy.append(len(tags.intersection(predicted_tags))/len(tags.union(predicted_tags)))
self._loss.append(sample_loss)
return(np.mean(accuracy))
model = LogRegressor()
acc = model.iterate_file()
print('%0.2f' % acc)
plt.plot(pd.Series(model._loss[:-25000]).rolling(10000).mean());
# <font color="red">Вопрос 7.</font> К какому значению ближе всего полученное значение точности:
# <font color="red">Варианты ответа:</font>
# ## 1. 0.59
# 2. 0.69
# 3. 0.79
# 4. 0.82
# ## 8. Самые важные слова для тега
#
# Прелесть линейных моделей в том, что они легко интерпретируемы. Вам предлагается вычислить, какие слова вносят наибольший вклад в вероятность появления каждого из тегов. А затем ответьте на контрольный вопрос.
# +
# Ваш код здесь
# +
model._vocab_inv = dict([(v, k) for (k, v) in model._vocab.items()])
for tag in model._tags:
print(tag, ':', ', '.join([model._vocab_inv[k] for (k, v) in
sorted(model._w[tag].items(),
key=lambda t: t[1],
reverse=True)[:5]]))
# -
# <font color="red">Вопрос 8.</font> Для многих тегов наличие самого тега в предложении является важным сигналом, у многих сам тег является самым сильным сигналом, что не удивительно. Для каких из тегов само название тега не входит в топ-5 самых важных?
#
# <font color="red">Варианты ответа:</font>
# ## 1. c#
# 2. javascript
# 3. jquery
# 4. android
#
# ## 9. Сокращаем размер словаря
# Сейчас количество слов в словаре – 519290, если бы это была выборка из 10 миллионов вопросов с сайта StackOverflow, то размер словаря был бы миллионов 10. Регуляризировать модель можно не только изящно математически, но и топорно, например, ограничить размер словаря. Вам предоставляется возможность внести следующие изменения в класс `LogRegressor`:
# - добавить в метод `iterate_file` еще один аргумент со значением по умолчанию `update_vocab=True`
# - при `update_vocab=True` разрешать добавлять слова в словарь в режиме обучения
# - при `update_vocab=False` игнорировать слова не из словаря
# - добавить в класс метод `filter_vocab(n=10000)`, который оставит в словаре только топ-n самых популярных слов, используя данные из ``train``
class LogRegressor():
def __init__(self, tags=top_tags):
self._vocab = {}
self._w = dict([(t, defaultdict(int)) for t in tags])
self._b = dict([(t, 0) for t in tags])
self._tags = set(tags)
self._word_stats = defaultdict(int)
def iterate_file(self,
fname=DS_FILE_NAME,
top_n_train=100000,
total=125000,
learning_rate=0.1,
tolerance=1e-16,
accuracy_level=0.9,
lmbda=0.002,
gamma=0.1,
update_vocab=True):
self._loss = []
n = 0
accuracy = []
with open(fname, 'r') as f:
for line in tqdm_notebook(f, total=total, mininterval=1):
pair = line.strip().split('\t')
if len(pair) != 2:
continue
sentence, tags = pair
sentence = sentence.split(' ')
tags = set(tags.split(' '))
sample_loss = 0
predicted_tags = None
for tag in self._tags:
y = int(tag in tags)
z = self._b[tag]
for word in sentence:
if n >= top_n_train and word not in self._vocab:
continue
if word not in self._vocab and update_vocab:
self._vocab[word] = len(self._vocab)
if word not in self._vocab:
continue
if update_vocab:
self._word_stats[self._vocab[word]] += 1
z += self._w[tag][self._vocab[word]]
sigma = 1/(1 + np.exp(-z)) if z >= 0 else 1 - 1/(1 + np.exp(z))
sample_loss += -y*np.log(np.max([tolerance, sigma])) if y == 1 else \
-(1 - y)*np.log(1 - np.min([1 - tolerance, sigma]))
if n < top_n_train:
dLdw = y - sigma
for word in sentence:
if word not in self._vocab:
continue
self._w[tag][self._vocab[word]] -= learning_rate*(-dLdw + (lmbda*(2*gamma*self._w[tag][self._vocab[word]] + (1 - gamma)*np.sign(self._w[tag][self._vocab[word]]))))
self._b[tag] -= -learning_rate*dLdw
else:
if predicted_tags is None:
predicted_tags = []
if sigma > accuracy_level:
predicted_tags.append(tag)
n += 1
self._loss.append(sample_loss)
if predicted_tags is not None:
accuracy.append(len(tags.intersection(predicted_tags))/len(tags.union(predicted_tags)))
return(np.mean(accuracy))
def filter_vocab(self, n=10000):
keep_words = set([wid for (wid, wn) in sorted(self._word_stats.items(),
key=lambda t: t[1], reverse=True)[:n]])
self._vocab = dict([(k, v) for (k, v) in self._vocab.items() if v in keep_words])
for tag in self._tags:
self._w[tag] = dict([(k, v) for (k, v) in self._w[tag].items() if k in keep_words])
model = LogRegressor()
acc = model.iterate_file(update_vocab=True)
print('%0.2f' % acc)
plt.plot(pd.Series(model._loss[:-25000]).rolling(10000).mean());
# оставим только топ 10 000 слов
model.filter_vocab(n=10000)
# сделаем еще одну итерацию по датасету, уменьшив скорость обучения в 10 раз
acc = model.iterate_file(update_vocab=False, learning_rate=0.01)
print('%0.2f' % acc)
plt.plot(pd.Series(model._loss[:-25000]).rolling(10000).mean());
# <font color="red">Вопрос 9.</font> К какому значению ближе всего полученное значение точности:
# <font color="red">Варианты ответа:</font>
# 1. 0.48
# 2. 0.58
# ## 3. 0.68
# 4. 0.78
# ## 10. Прогнозирование тегов для новых вопросов
#
# В завершение этого задания вам предлагается реализовать метод `predict_proba`, который принимает строку, содержащую вопрос, а возвращает список предсказанных тегов вопроса с их вероятностями.
class LogRegressor():
def __init__(self, tags=top_tags):
self._vocab = {}
self._w = dict([(t, defaultdict(int)) for t in tags])
self._b = dict([(t, 0) for t in tags])
self._tags = set(tags)
self._word_stats = defaultdict(int)
def iterate_file(self,
fname=DS_FILE_NAME,
top_n_train=100000,
total=125000,
learning_rate=0.1,
tolerance=1e-16,
accuracy_level=0.9,
lmbda=0.002,
gamma=0.1,
update_vocab=True):
self._loss = []
n = 0
accuracy = []
with open(fname, 'r') as f:
for line in tqdm_notebook(f, total=total, mininterval=1):
pair = line.strip().split('\t')
if len(pair) != 2:
continue
sentence, tags = pair
sentence = sentence.split(' ')
tags = set(tags.split(' '))
sample_loss = 0
predicted_tags = None
for tag in self._tags:
y = int(tag in tags)
z = self._b[tag]
for word in sentence:
if n >= top_n_train and word not in self._vocab:
continue
if word not in self._vocab and update_vocab:
self._vocab[word] = len(self._vocab)
if word not in self._vocab:
continue
if update_vocab:
self._word_stats[self._vocab[word]] += 1
z += self._w[tag][self._vocab[word]]
sigma = 1/(1 + np.exp(-z)) if z >= 0 else 1 - 1/(1 + np.exp(z))
sample_loss += -y*np.log(np.max([tolerance, sigma])) if y == 1 else \
-(1 - y)*np.log(1 - np.min([1 - tolerance, sigma]))
if n < top_n_train:
dLdw = y - sigma
for word in sentence:
if word not in self._vocab:
continue
self._w[tag][self._vocab[word]] -= learning_rate*(-dLdw + (lmbda*(2*gamma*self._w[tag][self._vocab[word]] + (1 - gamma)*np.sign(self._w[tag][self._vocab[word]]))))
self._b[tag] -= -learning_rate*dLdw
else:
if predicted_tags is None:
predicted_tags = []
if sigma > accuracy_level:
predicted_tags.append(tag)
n += 1
self._loss.append(sample_loss)
if predicted_tags is not None:
accuracy.append(len(tags.intersection(predicted_tags))/len(tags.union(predicted_tags)))
return(np.mean(accuracy))
def filter_vocab(self, n=10000):
keep_words = set([wid for (wid, wn) in sorted(self._word_stats.items(),
key=lambda t: t[1], reverse=True)[:n]])
self._vocab = dict([(k, v) for (k, v) in self._vocab.items() if v in keep_words])
for tag in self._tags:
self._w[tag] = dict([(k, v) for (k, v) in self._w[tag].items() if k in keep_words])
def predict_proba(self, sentence):
p = {}
sentence = sentence.lower().split(' ')
for tag in self._tags:
z = self._b[tag]
for word in sentence:
if word not in self._vocab:
continue
z += self._w[tag][self._vocab[word]]
sigma = 1/(1 + np.exp(-z)) if z >= 0 else 1 - 1/(1 + np.exp(z))
p[tag] = sigma
return p
model = LogRegressor()
acc = model.iterate_file(update_vocab=True)
print('%0.2f' % acc)
model.filter_vocab(n=10000)
acc = model.iterate_file(update_vocab=False, learning_rate=0.01)
print('%0.2f' % acc)
sentence = ("I want to improve my coding skills, so I have planned write " +
"a Mobile Application.need to choose between Apple's iOS or Google's Android." +
" my background: I have done basic programming in .Net,C/C++,Python and PHP " +
"in college, so got OOP concepts covered. about my skill level, I just know " +
"concepts and basic syntax. But can't write complex applications, if asked :(" +
" So decided to hone my skills, And I wanted to know which is easier to " +
"learn for a programming n00b. A) iOS which uses Objective C B) Android " +
"which uses Java. I want to decide based on difficulty " +
"level").lower().replace(',', '')
sorted(model.predict_proba(sentence).items(),
key=lambda t: t[1], reverse=True)
# <font color="red">Вопрос 10.</font> Отметьте все теги, ассоциирующиеся с данным вопросом, если порог принятия равен $0.9$. То есть считаем, что вопросу надо поставить некоторый тег, если вероятность его появления, предсказанная моделью, больше или равна 0.9.
#
# <font color="red">Варианты ответа:</font>
# 1. android
# 2. ios
# 3. php
# 4. java
df = pd.read_csv('../../data/hw4/stackoverflow_sample_125k.tsv', header=None)
pd.options.display.max_colwidth = 100
list(df.iloc[45])
| 53,357 |
/_image_wise_model_evaluation.ipynb | 0bc93bed9e14bd1af5f810f4603209c550e32ff2 | [] | no_license | DeepMachineLearning/mura-team2 | https://github.com/DeepMachineLearning/mura-team2 | 3 | 3 | null | 2018-06-16T17:56:50 | 2018-06-16T17:44:08 | Jupyter Notebook | Jupyter Notebook | false | false | .py | 197,601 | # ---
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# ---
# # Model Evaluation and Diagnostics
#
# This notebook is designed to evaluate models that produces an output on each validation image.
# +
# The name of the model (saved as {name}.h5 in trained_models folder)
model_to_evaluate = '2_1_submodel_335'
# The gpu to evaluate model on
gpu = '/GPU:1'
# whether the model outputs prediction as one or two classes
num_classes = 1
# -
# # Set up
#
# ## Imports
# +
# %load_ext autotime
import pandas as pd
import numpy as np
import pickle
import keras
from keras.utils import np_utils
from keras.utils import to_categorical
from keras.models import load_model
from pathlib import Path
from matplotlib import pyplot as plt
# %matplotlib inline
from sklearn.metrics import confusion_matrix
import utils
import tensorflow as tf
from keras import backend as K
config = tf.ConfigProto()
config.gpu_options.allow_growth=True
sess = tf.Session(config=config)
K.set_session(sess)
import logging
log = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)
# -
# ## Load model and prepare data
# +
model_file = f'./trained_models/{model_to_evaluate}.h5'
log.info(f'Loading model from {model_file}...')
with tf.device(gpu):
model = load_model(model_file)
log.info('Preparing dataset...')
x_test, y_test = utils.read_mura_pickle(sample='valid')
size = x_test.shape[1]
x_test = x_test.reshape(x_test.shape[0], size, size, 1)
x_test = utils.normalize_pixels(x_test)
true_label = y_test
log.info('Making predictions...')
y_test_hat = model.predict(x_test)
if num_classes == 1:
pred_label = np.round(y_test_hat)
elif num_classes == 2:
pred_label = np.argmax(y_test_hat, axis=1)
else:
log.error('num_classes must be either 1 or 2!')
# -
metrics = utils.MURAMetrics(true_label, pred_label)
_ = metrics.report_by_image()
_ = metrics.report_by_study(agg='mean')
_ = metrics.report_by_study(agg='max')
_ = metrics.report_by_body_parts(agg='mean')
_ = metrics.report_by_body_parts(agg='max')
| 2,231 |
/model_build_evaulate_le/Model Building & Evaluation - Market Campaign(LE & WO PCA).ipynb | afd5c5ac46ba8777710307d533e19212562cbf0a | [] | no_license | akhileshtawde/Markert_campaign | https://github.com/akhileshtawde/Markert_campaign | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 534,964 | # ---
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# name: python3
# ---
# +
import matplotlib.pyplot as plt
import time
import numpy as np
import pandas as pd
import seaborn as sns
from numpy import ravel
from imblearn.over_sampling import SMOTE
from sklearn.model_selection import train_test_split
from sklearn.svm import SVR
from sklearn.decomposition import PCA
from sklearn.neighbors import KNeighborsClassifier
from sklearn.calibration import CalibratedClassifierCV
from sklearn.metrics import roc_auc_score
from sklearn.preprocessing import StandardScaler
from sklearn import datasets, model_selection, tree, preprocessing, metrics, linear_model
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.manifold import TSNE
from sklearn.model_selection import RandomizedSearchCV
from sklearn.linear_model import SGDClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.svm import LinearSVC
from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.model_selection import GridSearchCV
from sklearn import svm
from sklearn.ensemble import VotingClassifier
from scipy.stats import randint as sp_randint
import datetime
import xgboost as xgb
from xgboost import XGBClassifier
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
import itertools
# %matplotlib inline
# -
def warn(*args, **kwargs):
pass
import warnings
warnings.warn = warn
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
#read data
data = pd.read_csv('C:/application/interview_prep/bank-additional/bank-additional/bank_full_processed_le.csv')
# visualize the data
data.head()
# # Model Building and Evaluation
#
#
# - **Train Test Split** : Divide the Data set into Train class and Test class for model building and Evaluation
#
#
# - Used **Stratification split** since the data is imbalanced. A random split might probably have changed the target distribution
X = data.drop('y', axis=1)
y = data['y']
# # SMOTE for oversampling the dataset
#
#
# - Since the dataset is highly imbalanced containing approximate 88% of NO values in term deposite , training our model on such dataset will create a bias for undersample class(12% of YES values)
#
#
# - To deal with this situation using SMOTE (Synthetic Minority Oversampling TEchnique) is a good option
#
#
# - SMOTE first selects a minority class instance a at random and finds its k nearest minority class neighbors. The synthetic instance is then created by choosing one of the k nearest neighbors b at random and connecting a and b to form a line segment in the feature space. The synthetic instances are generated as a convex combination of the two chosen instances a and b.
#
#
# - The combination of SMOTE and under-sampling performs better than plain under-sampling
print("Dataset shape before SMOTE:-")
print("X:", X.shape)
print("y:",y.shape)
sm = SMOTE(random_state=2)
X, y = sm.fit_resample(X, y.values.ravel())
print("Dataset shape after SMOTE:-")
print("X:", X.shape)
print("y:",y.shape)
# - Split performed without Stratification
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)
print('Original:', (data.y).mean(), 'Train:', (y_train).mean(), 'Test:', (y_test).mean())
# - Split performed with Stratification
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y, random_state=1)
print('Original:', (data.y).mean(), 'Train:', (y_train).mean(), 'Test:', (y_test).mean())
# # Performing Different Algorithms to find the best fit for our dataset
# - Will try following different algorithms to see which algorithm fits best on our dataset in terms of accuracy, ROC, AUC
#
#
# - Performing Random Hyperparameter search and GridSearch for Hyperparameters, to select the best hyperparameters for a given algorithm
# Function that runs the requested algorithm and returns the accuracy metrics
def fit_ml_algo(algo, X_train, y_train, X_test, cv):
# One Pass
model = algo.fit(X_train, y_train)
test_pred = model.predict(X_test)
if (isinstance(algo, (LogisticRegression,
KNeighborsClassifier,
GaussianNB,
DecisionTreeClassifier,
RandomForestClassifier,
GradientBoostingClassifier,
BaggingClassifier,
AdaBoostClassifier,
XGBClassifier,
))):
probs = model.predict_proba(X_test)[:,1]
else:
probs = "Not Available"
acc = round(model.score(X_test, y_test) * 100, 2)
# CV
train_pred = model_selection.cross_val_predict(algo,
X_train,
y_train,
cv=cv,
n_jobs = -1)
acc_cv = round(metrics.accuracy_score(y_train, train_pred) * 100, 2)
try:
feature_importances = np.mean([tree.feature_importances_ for tree in algo.estimators_], axis=0)
except:
feature_importances = 'none'
return train_pred, test_pred, acc, acc_cv, probs,feature_importances
# calculate the fpr and tpr for all thresholds of the classification
def plot_roc_curve(y_test, preds):
fpr, tpr, threshold = metrics.roc_curve(y_test, preds)
roc_auc = metrics.auc(fpr, tpr)
plt.title('Receiver Operating Characteristic')
plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)
plt.legend(loc = 'lower right')
plt.plot([0, 1], [0, 1],'r--')
plt.xlim([-0.01, 1.01])
plt.ylim([-0.01, 1.01])
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')
plt.show()
def make_confusion_matrix(y,y_pred,categories='auto',count=True,percent=True,cbar=True,xyticks=True,
figsize=(7,7),cmap='Blues',title=None):
cf = confusion_matrix(y, y_pred)
blanks = ['' for i in range(cf.size)]
group_names = ['True Neg','False Pos','False Neg','True Pos']
categories = ['No', 'Yes']
if group_names and len(group_names)==cf.size:
group_labels = ["{}\n".format(value) for value in group_names]
else:
group_labels = blanks
if count:
cm_sum = np.sum(cf, axis=1, keepdims=True)
cm_perc = cf / cm_sum.astype(float) * 100
annot = np.empty_like(cf).astype(str)
nrows, ncols = cf.shape
for i in range(nrows):
for j in range(ncols):
c = cf[i, j]
#p = cm_perc[i, j]
if i == j:
s = cm_sum[i]
annot[i, j] = '%d/%d\n' % (c, s)
elif c == 0:
annot[i, j] = ''
else:
annot[i, j] = '%d\n' % (c)
group_counts = list(annot.flat)
else:
group_counts = blanks
if percent:
group_percentages = ["{0:.2%}".format(value) for value in cf.flatten()/np.sum(cf)]
else:
group_percentages = blanks
box_labels = [f"{v1}{v2}{v3}".strip() for v1, v2, v3 in zip(group_labels,group_counts,group_percentages)]
box_labels = np.asarray(box_labels).reshape(cf.shape[0],cf.shape[1])
# SET FIGURE PARAMETERS ACCORDING TO OTHER ARGUMENTS
if figsize==None:
#Get default figure size if not set
figsize = plt.rcParams.get('figure.figsize')
if xyticks==False:
#Do not show categories if xyticks is False
categories=False
# MAKE THE HEATMAP VISUALIZATION
plt.figure(figsize=figsize)
sns.heatmap(cf,annot=box_labels,fmt="",cmap=cmap,cbar=cbar,xticklabels=categories,yticklabels=categories)
plt.ylabel('True label')
plt.xlabel('Predicted label')
if title:
plt.title(title)
# # Logistic Regression
start_time = time.time()
train_pred_log, test_pred_log, acc_log, acc_cv_log, probs_log,feature_log = fit_ml_algo(LogisticRegression(n_jobs = -1),
X_train,
y_train,
X_test,
10)
log_time = (time.time() - start_time)
print('\033[1m' + "Accuracy before CV: %s" % acc_log)
print("Accuracy CV 10-Fold: %s" % acc_cv_log)
print("Running Time for the Algorithm to train and pred: %s" % datetime.timedelta(seconds=log_time))
make_confusion_matrix(y_test,test_pred_log, title='My Two-class CF Matrix')
print("Classification Report on Training :-")
print (metrics.classification_report(y_train, train_pred_log))
print("Classification Report on Testing :-")
print (metrics.classification_report(y_test, test_pred_log))
print("ROC and AUC curve :-")
plot_roc_curve(y_test, probs_log)
# # K-Nearest Neighbors
alpha = [x for x in range(1, 70, 7)]
cv_auc_array=[]
for i in alpha:
k_cfl=KNeighborsClassifier(n_neighbors=i)
k_cfl.fit(X_train,y_train)
predict_y = k_cfl.predict_proba(X_test)
cv_auc_array.append(roc_auc_score(y_test, predict_y[:,1]))
for i in range(len(cv_auc_array)):
print ('AUC for k = ',alpha[i],'is',cv_auc_array[i])
best_alpha = np.argmax(cv_auc_array)
# +
# k-Nearest Neighbors
start_time = time.time()
train_pred_knn, test_pred_knn, acc_knn, acc_cv_knn, probs_knn,feature_knn = fit_ml_algo(KNeighborsClassifier(n_neighbors=alpha[best_alpha],
n_jobs = -1),
X_train,
y_train,
X_test,
10)
knn_time = (time.time() - start_time)
print('\033[1m' + "Accuracy before CV: %s" % acc_knn)
print("Accuracy CV 10-Fold: %s" % acc_cv_knn)
print("Running Time for the Algorithm to train and pred: %s" % datetime.timedelta(seconds=knn_time))
make_confusion_matrix(y_test,test_pred_knn, title='My Two-class CF Matrix')
# -
print("Classification Report on Training :-")
print (metrics.classification_report(y_train, train_pred_knn))
print("Classification Report on Testing :-")
print (metrics.classification_report(y_test, test_pred_knn))
print("ROC and AUC curve :-")
plot_roc_curve(y_test, probs_knn)
# # Gaussian Naive Bayes
# Gaussian Naive Bayes
start_time = time.time()
train_pred_gaussian, test_pred_gaussian, acc_gaussian, acc_cv_gaussian, probs_gau,feature_gau = fit_ml_algo(GaussianNB(),
X_train,
y_train,
X_test,
10)
gaussian_time = (time.time() - start_time)
print('\033[1m' +"Accuracy: %s" % acc_gaussian)
print("Accuracy CV 10-Fold: %s" % acc_cv_gaussian)
print("Running Time for the Algorithm to train and pred: %s" % datetime.timedelta(seconds=gaussian_time))
make_confusion_matrix(y_test,test_pred_gaussian, title='My Two-class CF Matrix')
print("Classification Report on Training :-")
print (metrics.classification_report(y_train, train_pred_gaussian))
print("Classification Report on Testing :-")
print (metrics.classification_report(y_test, test_pred_gaussian))
print("ROC and AUC curve :-")
plot_roc_curve(y_test, probs_gau)
# # Decision Tree Classifier
# Decision Tree Classifier
start_time = time.time()
train_pred_dt, test_pred_dt, acc_dt, acc_cv_dt, probs_dt,feature_dt = fit_ml_algo(DecisionTreeClassifier(),
X_train,
y_train,
X_test,
10)
dt_time = (time.time() - start_time)
print('\033[1m' + "Accuracy: %s" % acc_dt)
print("Accuracy CV 10-Fold: %s" % acc_cv_dt)
print("Running Time for the Algorithm to train and pred: %s" % datetime.timedelta(seconds=dt_time))
make_confusion_matrix(y_test,test_pred_dt, title='My Two-class CF Matrix')
print("Classification Report on Training :-")
print (metrics.classification_report(y_train, train_pred_dt))
print("Classification Report on Testing :-")
print (metrics.classification_report(y_test, test_pred_dt))
print("ROC and AUC curve :-")
plot_roc_curve(y_test, probs_dt)
# # Random Forest Classifier
# +
start_time = time.time()
rfc = RandomForestClassifier(n_estimators=10,
min_samples_leaf=2,
min_samples_split=17,
criterion='gini',
max_features=5)
train_pred_rf, test_pred_rf, acc_rf, acc_cv_rf, probs_rf,feature_rf = fit_ml_algo(rfc,
X_train,
y_train,
X_test,
10)
rf_time = (time.time() - start_time)
print('\033[1m' + "Accuracy: %s" % acc_rf)
print("Accuracy CV 10-Fold: %s" % acc_cv_rf)
print("Running Time for the Algorithm to train and pred: %s" % datetime.timedelta(seconds=rf_time))
make_confusion_matrix(y_test,test_pred_log, title='My Two-class CF Matrix')
# -
print("Classification Report on Training :-")
print (metrics.classification_report(y_train, train_pred_rf))
print("Classification Report on Testing :-")
print (metrics.classification_report(y_test, test_pred_rf))
print("ROC and AUC curve :-")
plot_roc_curve(y_test, probs_rf)
# # Gradient Boosting Trees
start_time = time.time()
train_pred_gbt, test_pred_gbt, acc_gbt, acc_cv_gbt, probs_gbt,feature_gbt = fit_ml_algo(GradientBoostingClassifier(),
X_train,
y_train,
X_test,
10)
gbt_time = (time.time() - start_time)
print('\033[1m' +"Accuracy: %s" % acc_gbt)
print("Accuracy CV 10-Fold: %s" % acc_cv_gbt)
print("Running Time for the Algorithm to train and pred: %s" % datetime.timedelta(seconds=gbt_time))
make_confusion_matrix(y_test,test_pred_gbt, title='My Two-class CF Matrix')
print("Classification Report on Training :-")
print (metrics.classification_report(y_train, train_pred_gbt))
print("Classification Report on Testing :-")
print (metrics.classification_report(y_test, test_pred_gbt))
print("ROC and AUC curve :-")
plot_roc_curve(y_test, probs_gbt)
# # BaggingClassifier
start_time = time.time()
dt_model = DecisionTreeClassifier(criterion = 'entropy',random_state=100)
train_pred_bc, test_pred_bc, acc_bc, acc_cv_bc, probs_bc,feature_bc = fit_ml_algo(BaggingClassifier(base_estimator=dt_model, n_estimators=100,random_state=100),
X_train,
y_train,
X_test,
10)
bc_time = (time.time() - start_time)
print('\033[1m' + "Accuracy: %s" % acc_bc)
print("Accuracy CV 10-Fold: %s" % acc_cv_bc)
print("Running Time for the Algorithm to train and pred: %s" % datetime.timedelta(seconds=bc_time))
make_confusion_matrix(y_test,test_pred_bc, title='My Two-class CF Matrix')
print("Classification Report on Training :-")
print (metrics.classification_report(y_train, train_pred_bc))
print("Classification Report on Testing :-")
print (metrics.classification_report(y_test, test_pred_bc))
print("ROC and AUC curve :-")
plot_roc_curve(y_test, probs_bc)
'''Plots feature importance in a sorted order and shows the most significant variables at the top'''
X1 = list(X.columns)
#X.remove('y_yes')
feature_importance_df = pd.DataFrame(data = feature_bc, index = X1, columns=['coefficient_values'])
feature_importance_df['sort'] = feature_importance_df.coefficient_values.abs()
sorted_feature_imp_df = feature_importance_df.sort_values(by='sort', ascending=False).drop('sort', axis=1)
fig, ax = plt.subplots()
fig.set_size_inches(10, 15)
sns.barplot(np.array(sorted_feature_imp_df.coefficient_values), np.array(sorted_feature_imp_df.index.values))
plt.title('Feature Importances')
plt.xlabel('Coefficients')
plt.ylabel('Feature Names')
# # XGBoost
# +
start_time = time.time()
train_pred_xgb1, test_pred_xgb1, acc_xgb1, acc_cv_xgb1, probs_xgb1, feature_xgb1 = fit_ml_algo(xgb.XGBClassifier(max_depth=5, n_estimators=100 ,class_weight='balanced', n_jobs=-1),
X_train,
y_train,
X_test,
10)
xgb_time1 = (time.time() - start_time)
print('\033[1m' + "Accuracy: %s" % acc_xgb1)
print("Accuracy CV 10-Fold: %s" % acc_cv_xgb1)
print("Running Time for the Algorithm to train and pred: %s" % datetime.timedelta(seconds=xgb_time1))
make_confusion_matrix(y_test,test_pred_xgb1, title='My Two-class CF Matrix')
# -
print("Classification Report on Training :-")
print (metrics.classification_report(y_train, train_pred_xgb1))
print("Classification Report on Testing :-")
print (metrics.classification_report(y_test, test_pred_xgb1))
print("ROC and AUC curve :-")
plot_roc_curve(y_test, probs_xgb1)
# +
plt.style.use('seaborn-whitegrid')
fig = plt.figure(figsize=(10,10))
models = [
'Logistic Regression',
'KNN',
'Naive Bayes',
'Decision Tree',
'Random Forest',
'Gradient Boosting Trees',
'BaggingClassifier',
'XGBoost'
]
probs = [
probs_log,
probs_knn,
probs_gau,
probs_dt,
probs_rf,
probs_gbt,
probs_bc,
probs_xgb1
]
colors = [
'blue',
'green',
'red',
'cyan',
'magenta',
'yellow',
'orange',
'black'
]
plt.title('Receiver Operating Characteristic')
plt.plot([0, 1], [0, 1],'r--')
plt.xlim([-0.01, 1.01])
plt.ylim([-0.01, 1.01])
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')
def plot_roc_curves(y_test, prob, model):
fpr, tpr, threshold = metrics.roc_curve(y_test, prob)
roc_auc = metrics.auc(fpr, tpr)
plt.plot(fpr, tpr, 'b', label = model + ' AUC = %0.2f' % roc_auc, color=colors[i])
plt.legend(loc = 'lower right')
for i, model in list(enumerate(models)):
plot_roc_curves(y_test, probs[i], models[i])
plt.show()
# -
| 19,697 |
/old/aug_knn.ipynb | 4b3f0a98ea272627cbbe4b423b15bad59b0f395e | [] | no_license | fdesmond/seme-ts | https://github.com/fdesmond/seme-ts | 0 | 0 | null | 2021-05-02T19:30:23 | 2021-02-11T18:59:25 | Jupyter Notebook | Jupyter Notebook | false | false | .py | 349,243 | # ---
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# + [markdown] id="view-in-github" colab_type="text"
# <a href="https://colab.research.google.com/github/Itsmrk/Deep_learning/blob/master/07_Dogs_vs_Cats_Img_Classification_Without_ImgAugmentation(activation%3D'sigmoid').ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# + [markdown] id="bCfS5wwXiNjV" colab_type="text"
# # **Importing packages**
# + id="LrCHJuwWhWYH" colab_type="code" colab={}
import tensorflow as tf
# + id="EVwXWIcViVP6" colab_type="code" colab={}
from tensorflow.keras.preprocessing.image import ImageDataGenerator
# + id="N_LMs9Q-iY56" colab_type="code" colab={}
import os
import matplotlib.pyplot as plt
import numpy as np
# + id="9B51L1z6iZq-" colab_type="code" colab={}
import logging
logger = tf.get_logger()
logger.setLevel(logging.ERROR)
# + [markdown] id="Ruw5i9iuidTe" colab_type="text"
# # **Data Loading**
# + id="pJi2VS28ib1T" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 51} outputId="190469d6-7002-46b9-fa3f-0115fb14516f"
_URL = 'https://storage.googleapis.com/mledu-datasets/cats_and_dogs_filtered.zip'
zip_dir = tf.keras.utils.get_file('cats_and_dogs_filterted.zip', origin=_URL, extract=True)
# + id="OrHIBV6RipZO" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 153} outputId="ef76b1de-a187-446d-974b-c5fab16bc06b"
#We can list the directories with the following terminal command:
zip_dir_base = os.path.dirname(zip_dir)
# !find $zip_dir_base -type d -print
# + id="RdiT0tbiis3G" colab_type="code" colab={}
base_dir = os.path.join(os.path.dirname(zip_dir), 'cats_and_dogs_filtered')
train_dir = os.path.join(base_dir, 'train')
validation_dir = os.path.join(base_dir, 'validation')
train_cats_dir = os.path.join(train_dir, 'cats')
train_dogs_dir = os.path.join(train_dir, 'dogs')
validation_cats_dir = os.path.join(validation_dir, 'cats')
validation_dogs_dir = os.path.join(validation_dir, 'dogs')
# + [markdown] id="mEwX4gC-i398" colab_type="text"
# ## **Understanding our data**
# + id="q7FvIylaixTp" colab_type="code" colab={}
num_cats_tr = len(os.listdir(train_cats_dir))
num_dogs_tr = len(os.listdir(train_dogs_dir))
num_cats_val = len(os.listdir(validation_cats_dir))
num_dogs_val = len(os.listdir(validation_dogs_dir))
total_train = num_cats_tr + num_dogs_tr
total_val = num_cats_val + num_dogs_val
# + id="KW26JXF4i94g" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 136} outputId="1c008b99-b997-476e-ac05-3e85edc6af2f"
print("Total Traning Cats : ", num_cats_tr)
print("Total Traning Dogs : ", num_dogs_tr)
print("Total Validation Cats : ", num_cats_val)
print("Total Validation Dogs : ", num_dogs_val)
print("---------")
print("Total Traning Images : ", total_train)
print("Total Validating Images : ", total_val)
# + [markdown] id="YiXXDQfTlwkW" colab_type="text"
# # **Setting Model Parameters**
# + id="hHz_8sGyjATA" colab_type="code" colab={}
BATCH_SIZE = 100
IMG_SHAPE = 150
# + [markdown] id="6AjRT2jOmK6a" colab_type="text"
# # **Data Preparation**
# + id="JdLeDebvl3dH" colab_type="code" colab={}
train_img_generator = ImageDataGenerator(rescale=1./255)
validation_img_generator = ImageDataGenerator(rescale=1./255)
# + id="sxd3GQ16mTOt" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 34} outputId="c1bc8d13-1314-4676-c420-6f65427f2677"
train_data_gen = train_img_generator.flow_from_directory(directory = train_dir,
target_size =(IMG_SHAPE,IMG_SHAPE),
class_mode = 'binary',
batch_size = BATCH_SIZE,
shuffle = True,
)
# + id="oZwTHG-zmamE" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 34} outputId="580b2de3-b726-44a4-a188-9eb8929fc541"
val_data_gen = validation_img_generator.flow_from_directory(directory = validation_dir,
target_size =(IMG_SHAPE,IMG_SHAPE),
class_mode = 'binary',
batch_size = BATCH_SIZE,
shuffle = True,
)
# + [markdown] id="So6ffy0cmmEX" colab_type="text"
# ## **Visualizing Training images**
# + id="VrT60g2Kmfcl" colab_type="code" colab={}
sample_training_images, _ = next(train_data_gen)
# + id="LEioyH7fmrqj" colab_type="code" colab={}
def plotImages(images_arr):
fig, axes = plt.subplots(1, 5, figsize=(20,20))
axes = axes.flatten()
for img, ax in zip(images_arr, axes):
ax.imshow(img)
plt.tight_layout()
plt.show()
# + id="5b1XdVNrmtW3" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 263} outputId="3c087703-017b-47b7-f10f-be5497b26662"
plotImages(sample_training_images[:5])
# + [markdown] id="7S3egbX1m2IJ" colab_type="text"
# # **Model Creation**
# + [markdown] id="WKxjFwLnm-Bk" colab_type="text"
# ## **Define the model**
# + id="xnMjnT4vmvQi" colab_type="code" colab={}
model = tf.keras.models.Sequential([
tf.keras.layers.Conv2D(32,(3,3), activation='relu', input_shape = (150,150,3)),
tf.keras.layers.MaxPooling2D(2,2),
tf.keras.layers.Conv2D(64,(3,3), activation='relu'),
tf.keras.layers.MaxPooling2D(2,2),
tf.keras.layers.Conv2D(128, (3,3), activation='relu'),
tf.keras.layers.MaxPooling2D(2,2),
tf.keras.layers.Conv2D(128, (3,3), activation='relu'),
tf.keras.layers.MaxPooling2D(2,2),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(512, activation='relu'),
tf.keras.layers.Dense(1, activation='sigmoid')
])
# + [markdown] id="R10IAlu0nV3s" colab_type="text"
# ### **Compile the model**
# + id="u5LecHBsnboz" colab_type="code" colab={}
model.compile(optimizer='adam',
loss='binary_crossentropy',
metrics =['accuracy'])
# + [markdown] id="z7KXCQ8NnhIS" colab_type="text"
# ### **Model Summary**
# + id="vauV_mnbnStc" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 527} outputId="78615a23-fcfa-4865-b45d-8156de81d9eb"
model.summary()
# + [markdown] id="Wt9l2IYHnp-W" colab_type="text"
# ### **Train the model**
# + id="GfwdYGPZnlUz" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 1000} outputId="56562bc8-e08d-4097-ed22-9bf7042d1580"
EPOCHS = 100
history = model.fit_generator(
train_data_gen,
steps_per_epoch = int(np.ceil(total_train/float(BATCH_SIZE))),
epochs = EPOCHS,
validation_data = val_data_gen,
validation_steps = int(np.ceil(total_val/ float(BATCH_SIZE)))
)
# + [markdown] id="x4y5WgzDuVCE" colab_type="text"
# ### **Visualizing results of the training**
# + id="ZMUqUF-LuKBA" colab_type="code" colab={"base_uri": "https://localhost:8080/", "height": 499} outputId="4c5ff764-a353-4d10-c541-f4dff3900677"
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs_range = range(EPOCHS)
plt.figure(figsize=(8,8))
plt.subplot(1,2,1)
plt.plot(epochs_range, acc ,label='Traning Accuracy')
plt.plot(epochs_range, val_acc, label= 'Validation Accuracy')
plt.legend(loc='lower right')
plt.title('Traning & Validation Accuracy')
plt.subplot(1, 2, 2)
plt.plot(epochs_range, loss, label='Training Loss')
plt.plot(epochs_range, val_loss, label='Validation Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
#plt.savefig('./foo.png')
plt.show()
# + id="eSD0u33nubLs" colab_type="code" colab={}
ng the local time-structure of the dataset, but it keeps the categorical structure given by the `y` label.
# +
n_bin = 9
n_tries = 50
# initialize list of results
R2_B = np.zeros(shape = (n_tries,2))
R2_C = np.zeros(shape = (n_tries,2))
rmse_B = np.zeros(shape = (n_tries,2))
rmse_C = np.zeros(shape = (n_tries,2))
for k in range(n_tries):
# sample data_B
xtrain_B = xtrain_A.sample(frac = p, random_state=k)
ytrain_B = ytrain_A.loc[xtrain_B.index]
xtest_B = xtest_A.sample(frac = p, random_state=k)
ytest_B = ytest_A.loc[xtest_B.index]
#------------------------------------------
# data augmentation strategy 1 for data_C
#------------------------------------------
#train_B = pd.concat([xtrain_B, ytrain_B], axis=1)
#train_C = mdfaug(train_B, n_true = 3, n_f1 = 2, n_f2 = 2, n_w = 2, \
# sigma_list_f1 = [0.3, 0.3], ff_list_f1=[0.5, 0.5], \
# sigma_list_f2 = [0.5, 0.2], ff_list_f2=[0.6, 0.5], \
# sigma_list_w = [0.7, 0.6], ff_list_w=[0.5, 0.7] \
# )
#------------------------------------------
#------------------------------------------
# data augmentation strategy 2 for data_C
#------------------------------------------
ytrain_B = ytrain_B + np.random.normal(0, 0.01, size=len(ytrain_B)) # avoid concentration on unique values
ytrain_B_bin = pd.qcut(ytrain_B, n_bin).value_counts().sort_index() # retreive quantile intervals
train_C = pd.DataFrame(columns=data_A.columns) # initialize data_C
# run categorical data augmentation
for i in range(n_bin):
row_id = ytrain_B.apply(lambda x: np.round(x,3) in ytrain_B_bin.index.values[i])
train_B_bin_sample = pd.concat([ xtrain_B.loc[row_id], ytrain_B.loc[row_id] ], axis=1)
# augmentation with combined techniques: fourier1, fourier2 and wavelet
train_C_bin_sample = mdfaug(train_B_bin_sample, n_true = 3, n_f1 = 2, n_f2 = 2, n_w = 2, \
sigma_list_f1 = [0.3, 0.3], ff_list_f1=[0.5, 0.5], \
sigma_list_f2 = [0.5, 0.2], ff_list_f2=[0.6, 0.5], \
sigma_list_w = [0.7, 0.6], ff_list_w=[0.5, 0.7] \
)
train_C = train_C.append(train_C_bin_sample)
del train_B_bin_sample, train_C_bin_sample
#------------------------------------------
# obtain train set from data_C
ytrain_C = train_C.Appliances
xtrain_C = train_C.drop(columns='Appliances')
# model fitting
model_B = run_knn(X_train = xtrain_B, Y_train = ytrain_B)
model_C = run_knn(X_train = xtrain_C, Y_train = ytrain_C)
# save performances
R2_B[k, 0] = r2_score(ytest_B, model_B.predict(xtest_B))
rmse_B[k, 0] = mean_squared_error(ytest_B, model_B.predict(xtest_B), squared=False)
R2_B[k, 1] = r2_score(ytest_A, model_B.predict(xtest_A))
rmse_B[k, 1] = mean_squared_error(ytest_A, model_B.predict(xtest_A), squared=False)
R2_C[k, 0] = r2_score(ytest_B, model_C.predict(xtest_B))
rmse_C[k, 0] = mean_squared_error(ytest_B, model_C.predict(xtest_B), squared=False)
R2_C[k, 1] = r2_score(ytest_A, model_C.predict(xtest_A))
rmse_C[k, 1] = mean_squared_error(ytest_A, model_C.predict(xtest_A), squared=False)
# -
# ### Results
# There is statistical evidence that `model_C` is outperforming `model_B`: not only for different realization of `data_B` and `data_C`, but also by fixing `data_B` and `data_C` and running the two corresponding model on different test sets from `data_A`.
#
# For KNN algorithm, we obtain an average +35% on the R2 score and a -5% on the MSE.
print('Results' + '\n' + '-'*100)
display('model_A on test_A: MSE {:.2f} R2 {:.2f}'.format(rmse_AA,R2_AA))
print('-'*100)
display('model_B on test_B: MSE {:.2f} R2 {:.3f} ({} times average)'.format(rmse_B[:,0].mean(), R2_B[:,0].mean(), n_tries),\
'model_B on test_A: MSE {:.2f} R2 {:.3f} ({} times average)'.format(rmse_B[:,1].mean(), R2_B[:,1].mean(), n_tries))
print('-'*100)
display('model_C on test_B: MSE {:.2f} R2 {:.3f} ({} times average)'.format(rmse_C[:,0].mean(), R2_C[:,0].mean(), n_tries),\
'model_C on test_A: MSE {:.2f} R2 {:.3f} ({} times average)'.format(rmse_C[:,1].mean(), R2_C[:,1].mean(), n_tries))
# #### a few plots
# We compare the predicted realizations, but also how the data augmentation changes (in mean and median) the model performance. We do this by plotting the RMSE score for different models but also for a same model but on different subset of `data_A`.
plt.rcParams['figure.figsize'] = [15, 5]
s=400
i=50
plt.plot(range(i), ytest_A[s:s+i], label = "true")
plt.plot(range(i), model_C.predict(xtest_A[s:s+i]), label = "pred_C")
plt.plot(range(i), model_B.predict(xtest_A[s:s+i]), label = "pred_B")
#plt.plot(range(i), model_A.predict(xtest_A[s:s+i]), label = "pred_A")
plt.xlabel('time window')
plt.ylabel('np.log(Appliances)')
plt.legend()
# +
fig, ax = plt.subplots(figsize=[15,5], nrows=1, ncols=1)
#plt.hist(rmse_B[:,0], bins = 50, alpha=.5, label='B on test_B')
plt.hist(rmse_B[:,1], bins = 30, alpha=.3, label='B on test_A')
plt.vlines(rmse_B[:,1].mean(), 0, 8, color='r', alpha=0.3, label='mean for model_B')
plt.vlines(np.median(rmse_B[:,1]), 0, 8, color='r', label='median for model_B')
#plt.hist(rmse_C[:,0], bins = 50, alpha=.5, label='C on test_B')
plt.hist(rmse_C[:,1], bins = 30, alpha=.6, label='C on test_A')
plt.vlines(rmse_C[:,1].mean(), 0, 8, color='b', alpha=0.3, label='mean for model_C')
plt.vlines(np.median(rmse_C[:,1]), 0, 8, color='b', label='median for model_C')
#plt.hist(rmse_star, bins=50, alpha=.3, label='star on test A', color='g')
plt.title('RMSE histogram on {} different trained models'.format(len(rmse_B[:,1])))
plt.legend()
# -
# This last plot depends on the particular `model_B` and `model_C` that one is considering. Maybe we do it for 10 different models?
# +
ntest_A = 100
rmse_B_on_A = np.zeros(ntest_A)
rmse_C_on_A = np.zeros(ntest_A)
for k in range(ntest_A):
rnd_A = data_A.sample(600)
rnd_y_A = rnd_A.Appliances
rnd_X_A = rnd_A.drop(columns='Appliances')
rmse_B_on_A[k] = mean_squared_error(rnd_y_A, model_B.predict(rnd_X_A), squared=False)
rmse_C_on_A[k] = mean_squared_error(rnd_y_A, model_C.predict(rnd_X_A), squared=False)
# +
fig, ax = plt.subplots(figsize=[15,5], nrows=1, ncols=1)
plt.hist(rmse_B_on_A, bins = 30, alpha=.3, label='B on test_A')
plt.vlines(rmse_B_on_A.mean(), 0, 10, color='r', alpha=0.3, label='mean for model_B')
plt.vlines(np.median(rmse_B_on_A), 0, 10, color='r', label='median for model_B')
#plt.hist(rmse_C[:,0], bins = 50, alpha=.5, label='C on test_B')
plt.hist(rmse_C_on_A, bins = 30, alpha=.6, label='C on test_A')
plt.vlines(rmse_C_on_A.mean(), 0, 10, color='b', alpha=0.3, label='mean for model_C')
plt.vlines(np.median(rmse_C_on_A), 0, 10, color='b', label='median for model_C')
#plt.hist(rmse_star, bins=50, alpha=.3, label='star on test A', color='g')
plt.title('Histogram for {} samples from data_A on models B and C fixed'.format(ntest_A))
plt.xlabel('RMSE on a 1000-size subsample of data_A')
plt.legend()
# -
| 15,634 |
/Python For Data Science _IBM/PY0101EN-5.3_Requests_HTTP.ipynb | 9c61eb5bea76a299a98f26a45ef88d84e9672c5d | [] | no_license | ajliq007/Python_Tutorial | https://github.com/ajliq007/Python_Tutorial | 0 | 0 | null | null | null | null | Jupyter Notebook | false | false | .py | 69,082 | # ---
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# + [markdown] id="view-in-github" colab_type="text"
# <a href="https://colab.research.google.com/github/villafue/Machine_Learning_Notes/blob/master/Unsupervised%20Learning/Unsupervised%20Learning%20in%20Python/1%20Clustering%20for%20dataset%20exploration/1_Clustering_for_dataset_exploration.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# + [markdown] id="qQaeOf6AiBbx"
# # Clustering for dataset exploration
#
# Learn how to discover the underlying groups (or "clusters") in a dataset. By the end of this chapter, you'll be clustering companies using their stock market prices, and distinguishing different species by clustering their measurements.
# + [markdown] id="NRTlcqBZNSBg"
# # 1. Unsupervised Learning
# Hi! My name is Ben Wilson and I'm a Data Scientist and mathematician. We're here to learn about unsupervised learning in Python.
#
# 2. Unsupervised learning
# Unsupervised learning is a class of machine learning techniques for discovering patterns in data. For instance, finding the natural "clusters" of customers based on their purchase histories, or searching for patterns and correlations among these purchases, and using these patterns to express the data in a compressed form. These are examples of unsupervised learning techniques called "clustering" and "dimension reduction".
#
# 3. Supervised vs unsupervised learning
# Unsupervised learning is defined in opposition to supervised learning. An example of supervised learning is using the measurements of tumors to classify them as benign or cancerous. In this case, the pattern discovery is guided, or "supervised", so that the patterns are as useful as possible for predicting the label: benign or cancerous. Unsupervised learning, in contrast, is learning without labels. It is pure pattern discovery, unguided by a prediction task. You'll start by learning about clustering. But before we begin, let's introduce a dataset and fix some terminology.
#
# 4. Iris dataset
# The iris dataset consists of the measurements of many iris plants of three different species. There are four measurements: petal length, petal width, sepal length and sepal width. These are the features of the dataset.
#
# 1 http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html/
# 5. Arrays, features & samples
# Throughout this course, datasets like this will be written as two-dimensional numpy arrays. The columns of the array will correspond to the features. The measurements for individual plants are the samples of the dataset. These correspond to rows of the array.
#
# 6. Iris data is 4-dimensional
# The samples of the iris dataset have four measurements, and so correspond to points in a four-dimensional space. This is the dimension of the dataset. We can't visualize four dimensions directly, but using unsupervised learning techniques we can still gain insight.
#
# 7. k-means clustering
# In this chapter, we'll cluster these samples using k-means clustering. k-means finds a specified number of clusters in the samples. It's implemented in the scikit-learn or "sklearn" library. Let's see kmeans in action on some samples from the iris dataset.
#
# 8. k-means clustering with scikit-learn
# The iris samples are represented as an array. To start, import kmeans from scikit-learn. Then create a kmeans model, specifying the number of clusters you want to find. Let's specify 3 clusters, since there are three species of iris. Now call the fit method of the model, passing the array of samples. This fits the model to the data, by locating and remembering the regions where the different clusters occur. Then we can use the predict method of the model on these same samples. This returns a cluster label for each sample, indicating to which cluster a sample belongs. Let's assign the result to labels, and print it out.
#
# 9. Cluster labels for new samples
# If someone comes along with some new iris samples, k-means can determine to which clusters they belong without starting over. k-means does this by remembering the mean (or average) of the samples in each cluster. These are called the "centroids". New samples are assigned to the cluster whose centroid is closest.
#
# 10. Cluster labels for new samples
# Suppose you've got an array of new samples. To assign the new samples to the existing clusters, pass the array of new samples to the predict method of the kmeans model. This returns the cluster labels of the new samples.
#
# 11. Scatter plots
# In the next video, you'll learn how to evaluate the quality of your clustering. But for now, let's visualize our clustering of the iris samples using scatter plots. Here is a scatter plot of the sepal length vs petal length of the iris samples. Each point represents an iris sample, and is colored according to the cluster of the sample. To create a scatter plot like this, use PyPlot.
#
# 12. Scatter plots
# Firstly, import PyPlot. It is conventionally imported as plt. Now get the x- and y- co-ordinates of each sample. Sepal length is in the 0th column of the array, while petal length is in the 2nd column. Now call the plt dot scatter function, passing the x- and y- co-ordinates and specifying c=labels to color by cluster label. When you are ready to show your plot, call plt dot show.
#
# 13. Let's practice!
# It's time to take your first steps in unsupervised learning. Have fun!
# + [markdown] id="8CvINXU-WRya"
# # How many clusters?
# You are given an array `points` of size 300x2, where each row gives the (x, y) co-ordinates of a point on a map. Make a scatter plot of these points, and use the scatter plot to guess how many clusters there are.
#
# `matplotlib.pyplot` has already been imported as `plt`. In the IPython Shell:
#
# Create an array called `xs` that contains the values of `points[:,0]` - that is, column `0` of `points`.
# Create an array called `ys` that contains the values of `points[:,1]` - that is, column `1` of `points`.
# Make a scatter plot by passing `xs` and `ys` to the `plt.scatter()` function.
# Call the `plt.show()` function to show your plot.
# + id="dGH31owdM9zB"
'''
In [1]:
points
Out[1]:
array([[ 0.06544649, -0.76866376],
[-1.52901547, -0.42953079],
[ 1.70993371, 0.69885253],
[ 1.16779145, 1.01262638],
[-1.80110088, -0.31861296],
[-1.63567888, -0.02859535],
[ 1.21990375, 0.74643463],
[-0.26175155, -0.62492939],
[-1.61925804, -0.47983949],
[-1.84329582, -0.16694431],
[ 1.35999602, 0.94995827],
[ 0.42291856, -0.7349534 ],
[-1.68576139, 0.10686728],
[ 0.90629995, 1.09105162],
[-1.56478322, -0.84675394],
[-0.0257849 , -1.18672539],
[ 0.83027324, 1.14504612],
[ 1.22450432, 1.35066759],
[-0.15394596, -0.71704301],
[ 0.86358809, 1.06824613],
[-1.43386366, -0.2381297 ],
[ 0.03844769, -0.74635022],
[-1.58567922, 0.08499354],
[ 0.6359888 , -0.58477698],
[ 0.24417242, -0.53172465],
[-2.19680359, 0.49473677],
[ 1.0323503 , -0.55688 ],
[-0.28858067, -0.39972528],
[ 0.20597008, -0.80171536],
[-1.2107308 , -0.34924109],
[ 1.33423684, 0.7721489 ],
[ 1.19480152, 1.04788556],
[ 0.9917477 , 0.89202008],
[-1.8356219 , -0.04839732],
[ 0.08415721, -0.71564326],
[-1.48970175, -0.19299604],
[ 0.38782418, -0.82060119],
[-0.01448044, -0.9779841 ],
[-2.0521341 , -0.02129125],
[ 0.10331194, -0.82162781],
[-0.44189315, -0.65710974],
[ 1.10390926, 1.02481182],
[-1.59227759, -0.17374038],
[-1.47344152, -0.02202853],
[-1.35514704, 0.22971067],
[ 0.0412337 , -1.23776622],
[ 0.4761517 , -1.13672124],
[ 1.04335676, 0.82345905],
[-0.07961882, -0.85677394],
[ 0.87065059, 1.08052841],
[ 1.40267313, 1.07525119],
[ 0.80111157, 1.28342825],
[-0.16527516, -1.23583804],
[-0.33779221, -0.59194323],
[ 0.80610749, -0.73752159],
[-1.43590032, -0.56384446],
[ 0.54868895, -0.95143829],
[ 0.46803131, -0.74973907],
[-1.5137129 , -0.83914323],
[ 0.9138436 , 1.51126532],
[-1.97233903, -0.41155375],
[ 0.5213406 , -0.88654894],
[ 0.62759494, -1.18590477],
[ 0.94163014, 1.35399335],
[ 0.56994768, 1.07036606],
[-1.87663382, 0.14745773],
[ 0.90612186, 0.91084011],
[-1.37481454, 0.28428395],
[-1.80564029, -0.96710574],
[ 0.34307757, -0.79999275],
[ 0.70380566, 1.00025804],
[-1.68489862, -0.30564595],
[ 1.31473221, 0.98614978],
[ 0.26151216, -0.26069251],
[ 0.9193121 , 0.82371485],
[-1.21795929, -0.20219674],
[-0.17722723, -1.02665245],
[ 0.64824862, -0.66822881],
[ 0.41206786, -0.28783784],
[ 1.01568202, 1.13481667],
[ 0.67900254, -0.91489502],
[-1.05182747, -0.01062376],
[ 0.61306599, 1.78210384],
[-1.50219748, -0.52308922],
[-1.72717293, -0.46173916],
[-1.60995631, -0.1821007 ],
[-1.09111021, -0.0781398 ],
[-0.01046978, -0.80913034],
[ 0.32782303, -0.80734754],
[ 1.22038503, 1.1959793 ],
[-1.33328681, -0.30001937],
[ 0.87959517, 1.11566491],
[-1.14829098, -0.30400762],
[-0.58019755, -1.19996018],
[-0.01161159, -0.78468854],
[ 0.17359724, -0.63398145],
[ 1.32738556, 0.67759969],
[-1.93467327, 0.30572472],
[-1.57761893, -0.27726365],
[ 0.47639 , 1.21422648],
[-1.65237509, -0.6803981 ],
[-0.12609976, -1.04327457],
[-1.89607082, -0.70085502],
[ 0.57466899, 0.74878369],
[-0.16660312, -0.83110295],
[ 0.8013355 , 1.22244435],
[ 1.18455426, 1.4346467 ],
[ 1.08864428, 0.64667112],
[-1.61158505, 0.22805725],
[-1.57512205, -0.09612576],
[ 0.0721357 , -0.69640328],
[-1.40054298, 0.16390598],
[ 1.09607713, 1.16804691],
[-2.54346204, -0.23089822],
[-1.34544875, 0.25151126],
[-1.35478629, -0.19103317],
[ 0.18368113, -1.15827725],
[-1.31368677, -0.376357 ],
[ 0.09990129, 1.22500491],
[ 1.17225574, 1.30835143],
[ 0.0865397 , -0.79714371],
[-0.21053923, -1.13421511],
[ 0.26496024, -0.94760742],
[-0.2557591 , -1.06266022],
[-0.26039757, -0.74774225],
[-1.91787359, 0.16434571],
[ 0.93021139, 0.49436331],
[ 0.44770467, -0.72877918],
[-1.63802869, -0.58925528],
[-1.95712763, -0.10125137],
[ 0.9270337 , 0.88251423],
[ 1.25660093, 0.60828073],
[-1.72818632, 0.08416887],
[ 0.3499788 , -0.30490298],
[-1.51696082, -0.50913109],
[ 0.18763605, -0.55424924],
[ 0.89609809, 0.83551508],
[-1.54968857, -0.17114782],
[ 1.2157457 , 1.23317728],
[ 0.20307745, -1.03784906],
[ 0.84589086, 1.03615273],
[ 0.53237919, 1.47362884],
[-0.05319044, -1.36150553],
[ 1.38819743, 1.11729915],
[ 1.00696304, 1.0367721 ],
[ 0.56681869, -1.09637176],
[ 0.86888296, 1.05248874],
[-1.16286609, -0.55875245],
[ 0.27717768, -0.83844015],
[ 0.16563267, -0.80306607],
[ 0.38263303, -0.42683241],
[ 1.14519807, 0.89659026],
[ 0.81455857, 0.67533667],
[-1.8603152 , -0.09537561],
[ 0.965641 , 0.90295579],
[-1.49897451, -0.33254044],
[-0.1335489 , -0.80727582],
[ 0.12541527, -1.13354906],
[ 1.06062436, 1.28816358],
[-1.49154578, -0.2024641 ],
[ 1.16189032, 1.28819877],
[ 0.54282033, 0.75203524],
[ 0.89221065, 0.99211624],
[-1.49932011, -0.32430667],
[ 0.3166647 , -1.34482915],
[ 0.13972469, -1.22097448],
[-1.5499724 , -0.10782584],
[ 1.23846858, 1.37668804],
[ 1.25558954, 0.72026098],
[ 0.25558689, -1.28529763],
[ 0.45168933, -0.55952093],
[ 1.06202057, 1.03404604],
[ 0.67451908, -0.54970299],
[ 0.22759676, -1.02729468],
[-1.45835281, -0.04951074],
[ 0.23273501, -0.70849262],
[ 1.59679589, 1.11395076],
[ 0.80476105, 0.544627 ],
[ 1.15492521, 1.04352191],
[ 0.59632776, -1.19142897],
[ 0.02839068, -0.43829366],
[ 1.13451584, 0.5632633 ],
[ 0.21576204, -1.04445753],
[ 1.41048987, 1.02830719],
[ 1.12289302, 0.58029441],
[ 0.25200688, -0.82588436],
[-1.28566081, -0.07390909],
[ 1.52849815, 1.11822469],
[-0.23907858, -0.70541972],
[-0.25792784, -0.81825035],
[ 0.59367818, -0.45239915],
[ 0.07931909, -0.29233213],
[-1.27256815, 0.11630577],
[ 0.66930129, 1.00731481],
[ 0.34791546, -1.20822877],
[-2.11283993, -0.66897935],
[-1.6293824 , -0.32718222],
[-1.53819139, -0.01501972],
[-0.11988545, -0.6036339 ],
[-1.54418956, -0.30389844],
[ 0.30026614, -0.77723173],
[ 0.00935449, -0.53888192],
[-1.33424393, -0.11560431],
[ 0.47504489, 0.78421384],
[ 0.59313264, 1.232239 ],
[ 0.41370369, -1.35205857],
[ 0.55840948, 0.78831053],
[ 0.49855018, -0.789949 ],
[ 0.35675809, -0.81038693],
[-1.86197825, -0.59071305],
[-1.61977671, -0.16076687],
[ 0.80779295, -0.73311294],
[ 1.62745775, 0.62787163],
[-1.56993593, -0.08467567],
[ 1.02558561, 0.89383302],
[ 0.24293461, -0.6088253 ],
[ 1.23130242, 1.00262186],
[-1.9651013 , -0.15886289],
[ 0.42795032, -0.70384432],
[-1.58306818, -0.19431923],
[-1.57195922, 0.01413469],
[-0.98145373, 0.06132285],
[-1.48637844, -0.5746531 ],
[ 0.98745828, 0.69188053],
[ 1.28619721, 1.28128821],
[ 0.85850596, 0.95541481],
[ 0.19028286, -0.82112942],
[ 0.26561046, -0.04255239],
[-1.61897897, 0.00862372],
[ 0.24070183, -0.52664209],
[ 1.15220993, 0.43916694],
[-1.21967812, -0.2580313 ],
[ 0.33412533, -0.86117761],
[ 0.17131003, -0.75638965],
[-1.19828397, -0.73744665],
[-0.12245932, -0.45648879],
[ 1.51200698, 0.88825741],
[ 1.10338866, 0.92347479],
[ 1.30972095, 0.59066989],
[ 0.19964876, 1.14855889],
[ 0.81460515, 0.84538972],
[-1.6422739 , -0.42296206],
[ 0.01224351, -0.21247816],
[ 0.33709102, -0.74618065],
[ 0.47301054, 0.72712075],
[ 0.34706626, 1.23033757],
[-0.00393279, -0.97209694],
[-1.64303119, 0.05276337],
[ 1.44649625, 1.14217033],
[-1.93030087, -0.40026146],
[-2.37296135, -0.72633645],
[ 0.45860122, -1.06048953],
[ 0.4896361 , -1.18928313],
[-1.02335902, -0.17520578],
[-1.32761107, -0.93963549],
[-1.50987909, -0.09473658],
[ 0.02723057, -0.79870549],
[ 1.0169412 , 1.26461701],
[ 0.47733527, -0.9898471 ],
[-1.27784224, -0.547416 ],
[ 0.49898802, -0.6237259 ],
[ 1.06004731, 0.86870008],
[ 1.00207501, 1.38293512],
[ 1.31161394, 0.62833956],
[ 1.13428443, 1.18346542],
[ 1.27671346, 0.96632878],
[-0.63342885, -0.97768251],
[ 0.12698779, -0.93142317],
[-1.34510812, -0.23754226],
[-0.53162278, -1.25153594],
[ 0.21959934, -0.90269938],
[-1.78997479, -0.12115748],
[ 1.23197473, -0.07453764],
[ 1.4163536 , 1.21551752],
[-1.90280976, -0.1638976 ],
[-0.22440081, -0.75454248],
[ 0.59559412, 0.92414553],
[ 1.21930773, 1.08175284],
[-1.99427535, -0.37587799],
[-1.27818474, -0.52454551],
[ 0.62352689, -1.01430108],
[ 0.14024251, -0.428266 ],
[-0.16145713, -1.16359731],
[-1.74795865, -0.06033101],
[-1.16659791, 0.0902393 ],
[ 0.41110408, -0.8084249 ],
[ 1.14757168, 0.77804528],
[-1.65590748, -0.40105446],
[-1.15306865, 0.00858699],
[ 0.60892121, 0.68974833],
[-0.08434138, -0.97615256],
[ 0.19170053, -0.42331438],
[ 0.29663162, -1.13357399],
[-1.36893628, -0.25052124],
[-0.08037807, -0.56784155],
[ 0.35695011, -1.15064408],
[ 0.02482179, -0.63594828],
[-1.49075558, -0.2482507 ],
[-1.408588 , 0.25635431],
[-1.98274626, -0.54584475]])
In [3]:
xs = points[:,0]
In [4]:
xs
Out[4]:
array([ 0.06544649, -1.52901547, 1.70993371, 1.16779145, -1.80110088,
-1.63567888, 1.21990375, -0.26175155, -1.61925804, -1.84329582,
1.35999602, 0.42291856, -1.68576139, 0.90629995, -1.56478322,
-0.0257849 , 0.83027324, 1.22450432, -0.15394596, 0.86358809,
-1.43386366, 0.03844769, -1.58567922, 0.6359888 , 0.24417242,
-2.19680359, 1.0323503 , -0.28858067, 0.20597008, -1.2107308 ,
1.33423684, 1.19480152, 0.9917477 , -1.8356219 , 0.08415721,
-1.48970175, 0.38782418, -0.01448044, -2.0521341 , 0.10331194,
-0.44189315, 1.10390926, -1.59227759, -1.47344152, -1.35514704,
0.0412337 , 0.4761517 , 1.04335676, -0.07961882, 0.87065059,
1.40267313, 0.80111157, -0.16527516, -0.33779221, 0.80610749,
-1.43590032, 0.54868895, 0.46803131, -1.5137129 , 0.9138436 ,
-1.97233903, 0.5213406 , 0.62759494, 0.94163014, 0.56994768,
-1.87663382, 0.90612186, -1.37481454, -1.80564029, 0.34307757,
0.70380566, -1.68489862, 1.31473221, 0.26151216, 0.9193121 ,
-1.21795929, -0.17722723, 0.64824862, 0.41206786, 1.01568202,
0.67900254, -1.05182747, 0.61306599, -1.50219748, -1.72717293,
-1.60995631, -1.09111021, -0.01046978, 0.32782303, 1.22038503,
-1.33328681, 0.87959517, -1.14829098, -0.58019755, -0.01161159,
0.17359724, 1.32738556, -1.93467327, -1.57761893, 0.47639 ,
-1.65237509, -0.12609976, -1.89607082, 0.57466899, -0.16660312,
0.8013355 , 1.18455426, 1.08864428, -1.61158505, -1.57512205,
0.0721357 , -1.40054298, 1.09607713, -2.54346204, -1.34544875,
-1.35478629, 0.18368113, -1.31368677, 0.09990129, 1.17225574,
0.0865397 , -0.21053923, 0.26496024, -0.2557591 , -0.26039757,
-1.91787359, 0.93021139, 0.44770467, -1.63802869, -1.95712763,
0.9270337 , 1.25660093, -1.72818632, 0.3499788 , -1.51696082,
0.18763605, 0.89609809, -1.54968857, 1.2157457 , 0.20307745,
0.84589086, 0.53237919, -0.05319044, 1.38819743, 1.00696304,
0.56681869, 0.86888296, -1.16286609, 0.27717768, 0.16563267,
0.38263303, 1.14519807, 0.81455857, -1.8603152 , 0.965641 ,
-1.49897451, -0.1335489 , 0.12541527, 1.06062436, -1.49154578,
1.16189032, 0.54282033, 0.89221065, -1.49932011, 0.3166647 ,
0.13972469, -1.5499724 , 1.23846858, 1.25558954, 0.25558689,
0.45168933, 1.06202057, 0.67451908, 0.22759676, -1.45835281,
0.23273501, 1.59679589, 0.80476105, 1.15492521, 0.59632776,
0.02839068, 1.13451584, 0.21576204, 1.41048987, 1.12289302,
0.25200688, -1.28566081, 1.52849815, -0.23907858, -0.25792784,
0.59367818, 0.07931909, -1.27256815, 0.66930129, 0.34791546,
-2.11283993, -1.6293824 , -1.53819139, -0.11988545, -1.54418956,
0.30026614, 0.00935449, -1.33424393, 0.47504489, 0.59313264,
0.41370369, 0.55840948, 0.49855018, 0.35675809, -1.86197825,
-1.61977671, 0.80779295, 1.62745775, -1.56993593, 1.02558561,
0.24293461, 1.23130242, -1.9651013 , 0.42795032, -1.58306818,
-1.57195922, -0.98145373, -1.48637844, 0.98745828, 1.28619721,
0.85850596, 0.19028286, 0.26561046, -1.61897897, 0.24070183,
1.15220993, -1.21967812, 0.33412533, 0.17131003, -1.19828397,
-0.12245932, 1.51200698, 1.10338866, 1.30972095, 0.19964876,
0.81460515, -1.6422739 , 0.01224351, 0.33709102, 0.47301054,
0.34706626, -0.00393279, -1.64303119, 1.44649625, -1.93030087,
-2.37296135, 0.45860122, 0.4896361 , -1.02335902, -1.32761107,
-1.50987909, 0.02723057, 1.0169412 , 0.47733527, -1.27784224,
0.49898802, 1.06004731, 1.00207501, 1.31161394, 1.13428443,
1.27671346, -0.63342885, 0.12698779, -1.34510812, -0.53162278,
0.21959934, -1.78997479, 1.23197473, 1.4163536 , -1.90280976,
-0.22440081, 0.59559412, 1.21930773, -1.99427535, -1.27818474,
0.62352689, 0.14024251, -0.16145713, -1.74795865, -1.16659791,
0.41110408, 1.14757168, -1.65590748, -1.15306865, 0.60892121,
-0.08434138, 0.19170053, 0.29663162, -1.36893628, -0.08037807,
0.35695011, 0.02482179, -1.49075558, -1.408588 , -1.98274626])
In [5]:
ys = points[:,1]
In [6]:
ys
Out[6]:
array([-0.76866376, -0.42953079, 0.69885253, 1.01262638, -0.31861296,
-0.02859535, 0.74643463, -0.62492939, -0.47983949, -0.16694431,
0.94995827, -0.7349534 , 0.10686728, 1.09105162, -0.84675394,
-1.18672539, 1.14504612, 1.35066759, -0.71704301, 1.06824613,
-0.2381297 , -0.74635022, 0.08499354, -0.58477698, -0.53172465,
0.49473677, -0.55688 , -0.39972528, -0.80171536, -0.34924109,
0.7721489 , 1.04788556, 0.89202008, -0.04839732, -0.71564326,
-0.19299604, -0.82060119, -0.9779841 , -0.02129125, -0.82162781,
-0.65710974, 1.02481182, -0.17374038, -0.02202853, 0.22971067,
-1.23776622, -1.13672124, 0.82345905, -0.85677394, 1.08052841,
1.07525119, 1.28342825, -1.23583804, -0.59194323, -0.73752159,
-0.56384446, -0.95143829, -0.74973907, -0.83914323, 1.51126532,
-0.41155375, -0.88654894, -1.18590477, 1.35399335, 1.07036606,
0.14745773, 0.91084011, 0.28428395, -0.96710574, -0.79999275,
1.00025804, -0.30564595, 0.98614978, -0.26069251, 0.82371485,
-0.20219674, -1.02665245, -0.66822881, -0.28783784, 1.13481667,
-0.91489502, -0.01062376, 1.78210384, -0.52308922, -0.46173916,
-0.1821007 , -0.0781398 , -0.80913034, -0.80734754, 1.1959793 ,
-0.30001937, 1.11566491, -0.30400762, -1.19996018, -0.78468854,
-0.63398145, 0.67759969, 0.30572472, -0.27726365, 1.21422648,
-0.6803981 , -1.04327457, -0.70085502, 0.74878369, -0.83110295,
1.22244435, 1.4346467 , 0.64667112, 0.22805725, -0.09612576,
-0.69640328, 0.16390598, 1.16804691, -0.23089822, 0.25151126,
-0.19103317, -1.15827725, -0.376357 , 1.22500491, 1.30835143,
-0.79714371, -1.13421511, -0.94760742, -1.06266022, -0.74774225,
0.16434571, 0.49436331, -0.72877918, -0.58925528, -0.10125137,
0.88251423, 0.60828073, 0.08416887, -0.30490298, -0.50913109,
-0.55424924, 0.83551508, -0.17114782, 1.23317728, -1.03784906,
1.03615273, 1.47362884, -1.36150553, 1.11729915, 1.0367721 ,
-1.09637176, 1.05248874, -0.55875245, -0.83844015, -0.80306607,
-0.42683241, 0.89659026, 0.67533667, -0.09537561, 0.90295579,
-0.33254044, -0.80727582, -1.13354906, 1.28816358, -0.2024641 ,
1.28819877, 0.75203524, 0.99211624, -0.32430667, -1.34482915,
-1.22097448, -0.10782584, 1.37668804, 0.72026098, -1.28529763,
-0.55952093, 1.03404604, -0.54970299, -1.02729468, -0.04951074,
-0.70849262, 1.11395076, 0.544627 , 1.04352191, -1.19142897,
-0.43829366, 0.5632633 , -1.04445753, 1.02830719, 0.58029441,
-0.82588436, -0.07390909, 1.11822469, -0.70541972, -0.81825035,
-0.45239915, -0.29233213, 0.11630577, 1.00731481, -1.20822877,
-0.66897935, -0.32718222, -0.01501972, -0.6036339 , -0.30389844,
-0.77723173, -0.53888192, -0.11560431, 0.78421384, 1.232239 ,
-1.35205857, 0.78831053, -0.789949 , -0.81038693, -0.59071305,
-0.16076687, -0.73311294, 0.62787163, -0.08467567, 0.89383302,
-0.6088253 , 1.00262186, -0.15886289, -0.70384432, -0.19431923,
0.01413469, 0.06132285, -0.5746531 , 0.69188053, 1.28128821,
0.95541481, -0.82112942, -0.04255239, 0.00862372, -0.52664209,
0.43916694, -0.2580313 , -0.86117761, -0.75638965, -0.73744665,
-0.45648879, 0.88825741, 0.92347479, 0.59066989, 1.14855889,
0.84538972, -0.42296206, -0.21247816, -0.74618065, 0.72712075,
1.23033757, -0.97209694, 0.05276337, 1.14217033, -0.40026146,
-0.72633645, -1.06048953, -1.18928313, -0.17520578, -0.93963549,
-0.09473658, -0.79870549, 1.26461701, -0.9898471 , -0.547416 ,
-0.6237259 , 0.86870008, 1.38293512, 0.62833956, 1.18346542,
0.96632878, -0.97768251, -0.93142317, -0.23754226, -1.25153594,
-0.90269938, -0.12115748, -0.07453764, 1.21551752, -0.1638976 ,
-0.75454248, 0.92414553, 1.08175284, -0.37587799, -0.52454551,
-1.01430108, -0.428266 , -1.16359731, -0.06033101, 0.0902393 ,
-0.8084249 , 0.77804528, -0.40105446, 0.00858699, 0.68974833,
-0.97615256, -0.42331438, -1.13357399, -0.25052124, -0.56784155,
-1.15064408, -0.63594828, -0.2482507 , 0.25635431, -0.54584475])
plt.scatter(xs,ys)
plt.show()
'''
# + [markdown] id="h_dJmCbFaSYx"
# **How many clusters do you see?**
#
# 
#
# **Correct! The scatter plot suggests that there are 3 distinct clusters.**
#
# + [markdown] id="6CLFfHQHa432"
# # Clustering 2D points
# From the scatter plot of the previous exercise, you saw that the points seem to separate into 3 clusters. You'll now create a KMeans model to find 3 clusters, and fit it to the data points from the previous exercise. After the model has been fit, you'll obtain the cluster labels for some new points using the .predict() method.
#
# You are given the array points from the previous exercise, and also an array new_points.
#
# Instructions
#
# 1. Import KMeans from sklearn.cluster.
#
# 2. Using KMeans(), create a KMeans instance called model to find 3 clusters. To specify the number of clusters, use the n_clusters keyword argument.
#
# 3. Use the .fit() method of model to fit the model to the array of points points.
#
# 4. Use the .predict() method of model to predict the cluster labels of new_points, assigning the result to labels.
#
# 5. Hit 'Submit Answer' to see the cluster labels of new_points.
# + id="DLZjbuLj6O5N"
In [1]:
points
Out[1]:
array([[ 0.06544649, -0.76866376],
[-1.52901547, -0.42953079],
[ 1.70993371, 0.69885253],
[ 1.16779145, 1.01262638],
[-1.80110088, -0.31861296],
[-1.63567888, -0.02859535],
[ 1.21990375, 0.74643463],
[-0.26175155, -0.62492939],
[-1.61925804, -0.47983949],
[-1.84329582, -0.16694431],
[ 1.35999602, 0.94995827],
[ 0.42291856, -0.7349534 ],
[-1.68576139, 0.10686728],
[ 0.90629995, 1.09105162],
[-1.56478322, -0.84675394],
[-0.0257849 , -1.18672539],
[ 0.83027324, 1.14504612],
[ 1.22450432, 1.35066759],
[-0.15394596, -0.71704301],
[ 0.86358809, 1.06824613],
[-1.43386366, -0.2381297 ],
[ 0.03844769, -0.74635022],
[-1.58567922, 0.08499354],
[ 0.6359888 , -0.58477698],
[ 0.24417242, -0.53172465],
[-2.19680359, 0.49473677],
[ 1.0323503 , -0.55688 ],
[-0.28858067, -0.39972528],
[ 0.20597008, -0.80171536],
[-1.2107308 , -0.34924109],
[ 1.33423684, 0.7721489 ],
[ 1.19480152, 1.04788556],
[ 0.9917477 , 0.89202008],
[-1.8356219 , -0.04839732],
[ 0.08415721, -0.71564326],
[-1.48970175, -0.19299604],
[ 0.38782418, -0.82060119],
[-0.01448044, -0.9779841 ],
[-2.0521341 , -0.02129125],
[ 0.10331194, -0.82162781],
[-0.44189315, -0.65710974],
[ 1.10390926, 1.02481182],
[-1.59227759, -0.17374038],
[-1.47344152, -0.02202853],
[-1.35514704, 0.22971067],
[ 0.0412337 , -1.23776622],
[ 0.4761517 , -1.13672124],
[ 1.04335676, 0.82345905],
[-0.07961882, -0.85677394],
[ 0.87065059, 1.08052841],
[ 1.40267313, 1.07525119],
[ 0.80111157, 1.28342825],
[-0.16527516, -1.23583804],
[-0.33779221, -0.59194323],
[ 0.80610749, -0.73752159],
[-1.43590032, -0.56384446],
[ 0.54868895, -0.95143829],
[ 0.46803131, -0.74973907],
[-1.5137129 , -0.83914323],
[ 0.9138436 , 1.51126532],
[-1.97233903, -0.41155375],
[ 0.5213406 , -0.88654894],
[ 0.62759494, -1.18590477],
[ 0.94163014, 1.35399335],
[ 0.56994768, 1.07036606],
[-1.87663382, 0.14745773],
[ 0.90612186, 0.91084011],
[-1.37481454, 0.28428395],
[-1.80564029, -0.96710574],
[ 0.34307757, -0.79999275],
[ 0.70380566, 1.00025804],
[-1.68489862, -0.30564595],
[ 1.31473221, 0.98614978],
[ 0.26151216, -0.26069251],
[ 0.9193121 , 0.82371485],
[-1.21795929, -0.20219674],
[-0.17722723, -1.02665245],
[ 0.64824862, -0.66822881],
[ 0.41206786, -0.28783784],
[ 1.01568202, 1.13481667],
[ 0.67900254, -0.91489502],
[-1.05182747, -0.01062376],
[ 0.61306599, 1.78210384],
[-1.50219748, -0.52308922],
[-1.72717293, -0.46173916],
[-1.60995631, -0.1821007 ],
[-1.09111021, -0.0781398 ],
[-0.01046978, -0.80913034],
[ 0.32782303, -0.80734754],
[ 1.22038503, 1.1959793 ],
[-1.33328681, -0.30001937],
[ 0.87959517, 1.11566491],
[-1.14829098, -0.30400762],
[-0.58019755, -1.19996018],
[-0.01161159, -0.78468854],
[ 0.17359724, -0.63398145],
[ 1.32738556, 0.67759969],
[-1.93467327, 0.30572472],
[-1.57761893, -0.27726365],
[ 0.47639 , 1.21422648],
[-1.65237509, -0.6803981 ],
[-0.12609976, -1.04327457],
[-1.89607082, -0.70085502],
[ 0.57466899, 0.74878369],
[-0.16660312, -0.83110295],
[ 0.8013355 , 1.22244435],
[ 1.18455426, 1.4346467 ],
[ 1.08864428, 0.64667112],
[-1.61158505, 0.22805725],
[-1.57512205, -0.09612576],
[ 0.0721357 , -0.69640328],
[-1.40054298, 0.16390598],
[ 1.09607713, 1.16804691],
[-2.54346204, -0.23089822],
[-1.34544875, 0.25151126],
[-1.35478629, -0.19103317],
[ 0.18368113, -1.15827725],
[-1.31368677, -0.376357 ],
[ 0.09990129, 1.22500491],
[ 1.17225574, 1.30835143],
[ 0.0865397 , -0.79714371],
[-0.21053923, -1.13421511],
[ 0.26496024, -0.94760742],
[-0.2557591 , -1.06266022],
[-0.26039757, -0.74774225],
[-1.91787359, 0.16434571],
[ 0.93021139, 0.49436331],
[ 0.44770467, -0.72877918],
[-1.63802869, -0.58925528],
[-1.95712763, -0.10125137],
[ 0.9270337 , 0.88251423],
[ 1.25660093, 0.60828073],
[-1.72818632, 0.08416887],
[ 0.3499788 , -0.30490298],
[-1.51696082, -0.50913109],
[ 0.18763605, -0.55424924],
[ 0.89609809, 0.83551508],
[-1.54968857, -0.17114782],
[ 1.2157457 , 1.23317728],
[ 0.20307745, -1.03784906],
[ 0.84589086, 1.03615273],
[ 0.53237919, 1.47362884],
[-0.05319044, -1.36150553],
[ 1.38819743, 1.11729915],
[ 1.00696304, 1.0367721 ],
[ 0.56681869, -1.09637176],
[ 0.86888296, 1.05248874],
[-1.16286609, -0.55875245],
[ 0.27717768, -0.83844015],
[ 0.16563267, -0.80306607],
[ 0.38263303, -0.42683241],
[ 1.14519807, 0.89659026],
[ 0.81455857, 0.67533667],
[-1.8603152 , -0.09537561],
[ 0.965641 , 0.90295579],
[-1.49897451, -0.33254044],
[-0.1335489 , -0.80727582],
[ 0.12541527, -1.13354906],
[ 1.06062436, 1.28816358],
[-1.49154578, -0.2024641 ],
[ 1.16189032, 1.28819877],
[ 0.54282033, 0.75203524],
[ 0.89221065, 0.99211624],
[-1.49932011, -0.32430667],
[ 0.3166647 , -1.34482915],
[ 0.13972469, -1.22097448],
[-1.5499724 , -0.10782584],
[ 1.23846858, 1.37668804],
[ 1.25558954, 0.72026098],
[ 0.25558689, -1.28529763],
[ 0.45168933, -0.55952093],
[ 1.06202057, 1.03404604],
[ 0.67451908, -0.54970299],
[ 0.22759676, -1.02729468],
[-1.45835281, -0.04951074],
[ 0.23273501, -0.70849262],
[ 1.59679589, 1.11395076],
[ 0.80476105, 0.544627 ],
[ 1.15492521, 1.04352191],
[ 0.59632776, -1.19142897],
[ 0.02839068, -0.43829366],
[ 1.13451584, 0.5632633 ],
[ 0.21576204, -1.04445753],
[ 1.41048987, 1.02830719],
[ 1.12289302, 0.58029441],
[ 0.25200688, -0.82588436],
[-1.28566081, -0.07390909],
[ 1.52849815, 1.11822469],
[-0.23907858, -0.70541972],
[-0.25792784, -0.81825035],
[ 0.59367818, -0.45239915],
[ 0.07931909, -0.29233213],
[-1.27256815, 0.11630577],
[ 0.66930129, 1.00731481],
[ 0.34791546, -1.20822877],
[-2.11283993, -0.66897935],
[-1.6293824 , -0.32718222],
[-1.53819139, -0.01501972],
[-0.11988545, -0.6036339 ],
[-1.54418956, -0.30389844],
[ 0.30026614, -0.77723173],
[ 0.00935449, -0.53888192],
[-1.33424393, -0.11560431],
[ 0.47504489, 0.78421384],
[ 0.59313264, 1.232239 ],
[ 0.41370369, -1.35205857],
[ 0.55840948, 0.78831053],
[ 0.49855018, -0.789949 ],
[ 0.35675809, -0.81038693],
[-1.86197825, -0.59071305],
[-1.61977671, -0.16076687],
[ 0.80779295, -0.73311294],
[ 1.62745775, 0.62787163],
[-1.56993593, -0.08467567],
[ 1.02558561, 0.89383302],
[ 0.24293461, -0.6088253 ],
[ 1.23130242, 1.00262186],
[-1.9651013 , -0.15886289],
[ 0.42795032, -0.70384432],
[-1.58306818, -0.19431923],
[-1.57195922, 0.01413469],
[-0.98145373, 0.06132285],
[-1.48637844, -0.5746531 ],
[ 0.98745828, 0.69188053],
[ 1.28619721, 1.28128821],
[ 0.85850596, 0.95541481],
[ 0.19028286, -0.82112942],
[ 0.26561046, -0.04255239],
[-1.61897897, 0.00862372],
[ 0.24070183, -0.52664209],
[ 1.15220993, 0.43916694],
[-1.21967812, -0.2580313 ],
[ 0.33412533, -0.86117761],
[ 0.17131003, -0.75638965],
[-1.19828397, -0.73744665],
[-0.12245932, -0.45648879],
[ 1.51200698, 0.88825741],
[ 1.10338866, 0.92347479],
[ 1.30972095, 0.59066989],
[ 0.19964876, 1.14855889],
[ 0.81460515, 0.84538972],
[-1.6422739 , -0.42296206],
[ 0.01224351, -0.21247816],
[ 0.33709102, -0.74618065],
[ 0.47301054, 0.72712075],
[ 0.34706626, 1.23033757],
[-0.00393279, -0.97209694],
[-1.64303119, 0.05276337],
[ 1.44649625, 1.14217033],
[-1.93030087, -0.40026146],
[-2.37296135, -0.72633645],
[ 0.45860122, -1.06048953],
[ 0.4896361 , -1.18928313],
[-1.02335902, -0.17520578],
[-1.32761107, -0.93963549],
[-1.50987909, -0.09473658],
[ 0.02723057, -0.79870549],
[ 1.0169412 , 1.26461701],
[ 0.47733527, -0.9898471 ],
[-1.27784224, -0.547416 ],
[ 0.49898802, -0.6237259 ],
[ 1.06004731, 0.86870008],
[ 1.00207501, 1.38293512],
[ 1.31161394, 0.62833956],
[ 1.13428443, 1.18346542],
[ 1.27671346, 0.96632878],
[-0.63342885, -0.97768251],
[ 0.12698779, -0.93142317],
[-1.34510812, -0.23754226],
[-0.53162278, -1.25153594],
[ 0.21959934, -0.90269938],
[-1.78997479, -0.12115748],
[ 1.23197473, -0.07453764],
[ 1.4163536 , 1.21551752],
[-1.90280976, -0.1638976 ],
[-0.22440081, -0.75454248],
[ 0.59559412, 0.92414553],
[ 1.21930773, 1.08175284],
[-1.99427535, -0.37587799],
[-1.27818474, -0.52454551],
[ 0.62352689, -1.01430108],
[ 0.14024251, -0.428266 ],
[-0.16145713, -1.16359731],
[-1.74795865, -0.06033101],
[-1.16659791, 0.0902393 ],
[ 0.41110408, -0.8084249 ],
[ 1.14757168, 0.77804528],
[-1.65590748, -0.40105446],
[-1.15306865, 0.00858699],
[ 0.60892121, 0.68974833],
[-0.08434138, -0.97615256],
[ 0.19170053, -0.42331438],
[ 0.29663162, -1.13357399],
[-1.36893628, -0.25052124],
[-0.08037807, -0.56784155],
[ 0.35695011, -1.15064408],
[ 0.02482179, -0.63594828],
[-1.49075558, -0.2482507 ],
[-1.408588 , 0.25635431],
[-1.98274626, -0.54584475]])
In [3]:
new_points
Out[3]:
array([[ 4.00233332e-01, -1.26544471e+00],
[ 8.03230370e-01, 1.28260167e+00],
[-1.39507552e+00, 5.57292921e-02],
[-3.41192677e-01, -1.07661994e+00],
[ 1.54781747e+00, 1.40250049e+00],
[ 2.45032018e-01, -4.83442328e-01],
[ 1.20706886e+00, 8.88752605e-01],
[ 1.25132628e+00, 1.15555395e+00],
[ 1.81004415e+00, 9.65530731e-01],
[-1.66963401e+00, -3.08103509e-01],
[-7.17482105e-02, -9.37939700e-01],
[ 6.82631927e-01, 1.10258160e+00],
[ 1.09039598e+00, 1.43899529e+00],
[-1.67645414e+00, -5.04557049e-01],
[-1.84447804e+00, 4.52539544e-02],
[ 1.24234851e+00, 1.02088661e+00],
[-1.86147041e+00, 6.38645811e-03],
[-1.46044943e+00, 1.53252383e-01],
[ 4.98981817e-01, 8.98006058e-01],
[ 9.83962244e-01, 1.04369375e+00],
[-1.83136742e+00, -1.63632835e-01],
[ 1.30622617e+00, 1.07658717e+00],
[ 3.53420328e-01, -7.51320218e-01],
[ 1.13957970e+00, 1.54503860e+00],
[ 2.93995694e-01, -1.26135005e+00],
[-1.14558225e+00, -3.78709636e-02],
[ 1.18716105e+00, 6.00240663e-01],
[-2.23211946e+00, 2.30475094e-01],
[-1.28320430e+00, -3.93314568e-01],
[ 4.94296696e-01, -8.83972009e-01],
[ 6.31834930e-02, -9.11952228e-01],
[ 9.35759539e-01, 8.66820685e-01],
[ 1.58014721e+00, 1.03788392e+00],
[ 1.06304960e+00, 1.02706082e+00],
[-1.39732536e+00, -5.05162249e-01],
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[ 9.20084511e-01, 1.45767672e+00],
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[ 2.12706760e-02, -7.07614194e-01],
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[ 2.87202402e-01, -6.55487415e-01],
[ 8.22295102e-01, 1.38443234e+00],
[-3.56997036e-02, -8.01825807e-01],
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[ 7.25837138e-01, -6.23374024e-01],
[ 3.88432593e-01, -7.61283497e-01],
[ 1.49002783e+00, 7.95678671e-01],
[ 6.55423228e-04, -7.40580702e-01],
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[-1.75119594e+00, -3.00491336e-01],
[-1.27942724e+00, 1.73774600e-01],
[ 3.35028183e-01, 6.24761151e-01],
[ 1.16819649e+00, 1.18902251e+00],
[ 7.15210457e-01, 9.26077419e-01],
[ 1.30057278e+00, 9.16349565e-01],
[-1.21697008e+00, 1.10039477e-01],
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[ 1.20730655e+00, 1.05480463e+00],
[ 1.86896009e-01, -9.58047234e-01],
[ 8.03463471e-01, 3.86133140e-01],
[-1.73486790e+00, -1.49831913e-01],
[ 1.31261499e+00, 1.11802982e+00],
[ 4.04993148e-01, -5.10900347e-01],
[-1.93267968e+00, 2.20764694e-01],
[ 6.56004799e-01, 9.61887161e-01],
[-1.40588215e+00, 1.17134403e-01],
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[ 5.43745412e-01, 1.47209224e+00],
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[ 1.53901171e+00, 1.36049081e+00],
[-1.48323452e+00, -4.90302063e-01],
[ 3.86748484e-01, -1.26173400e+00],
[ 1.17015716e+00, 1.18549415e+00],
[-8.05381721e-02, -3.21923627e-01],
[-6.82273156e-02, -8.52825887e-01],
[ 7.13500028e-01, 1.27868520e+00],
[-1.85014378e+00, -5.03490558e-01],
[ 6.36085266e-02, -1.41257040e+00],
[ 1.52966062e+00, 9.66056572e-01],
[ 1.62165714e-01, -1.37374843e+00],
[-3.23474497e-01, -7.06620269e-01],
[-1.51768993e+00, 1.87658302e-01],
[ 8.88895911e-01, 7.62237161e-01],
[ 4.83164032e-01, 8.81931869e-01],
[-5.52997766e-02, -7.11305016e-01],
[-1.57966441e+00, -6.29220313e-01],
[ 5.51308645e-02, -8.47206763e-01],
[-2.06001582e+00, 5.87697787e-02],
[ 1.11810855e+00, 1.30254175e+00],
[ 4.87016164e-01, -9.90143937e-01],
[-1.65518042e+00, -1.69386383e-01],
[-1.44349738e+00, 1.90299243e-01],
[-1.70074547e-01, -8.26736022e-01],
[-1.82433979e+00, -3.07814626e-01],
[ 1.03093485e+00, 1.26457691e+00],
[ 1.64431169e+00, 1.27773115e+00],
[-1.47617693e+00, 2.60783872e-02],
[ 1.00953067e+00, 1.14270181e+00],
[-1.45285636e+00, -2.55216207e-01],
[-1.74092917e+00, -8.34443177e-02],
[ 1.22038299e+00, 1.28699961e+00],
[ 9.16925397e-01, 7.32070275e-01],
[-1.60754185e-03, -7.26375571e-01],
[ 8.93841238e-01, 8.41146643e-01],
[ 6.33791961e-01, 1.00915134e+00],
[-1.47927075e+00, -6.99781936e-01],
[ 5.44799374e-02, -1.06441970e+00],
[-1.51935568e+00, -4.89276929e-01],
[ 2.89939026e-01, -7.73145523e-01],
[-9.68154061e-03, -1.13302207e+00],
[ 1.13474639e+00, 9.71541744e-01],
[ 5.36421406e-01, -8.47906388e-01],
[ 1.14759864e+00, 6.89915205e-01],
[ 5.73291902e-01, 7.90802710e-01],
[ 2.12377397e-01, -6.07569808e-01],
[ 5.26579548e-01, -8.15930264e-01],
[-2.01831641e+00, 6.78650740e-02],
[-2.35512624e-01, -1.08205132e+00],
[ 1.59274780e-01, -6.00717261e-01],
[ 2.28120356e-01, -1.16003549e+00],
[-1.53658378e+00, 8.40798808e-02],
[ 1.13954609e+00, 6.31782001e-01],
[ 1.01119255e+00, 1.04360805e+00],
[-1.42039867e-01, -4.81230337e-01],
[-2.23120182e+00, 8.49162905e-02],
[ 1.25554811e-01, -1.01794793e+00],
[-1.72493509e+00, -6.94426177e-01],
[-1.60434630e+00, 4.45550868e-01],
[ 7.37153979e-01, 9.26560744e-01],
[ 6.72905271e-01, 1.13366030e+00],
[ 1.20066456e+00, 7.26273093e-01],
[ 7.58747209e-02, -9.83378326e-01],
[ 1.28783262e+00, 1.18088601e+00],
[ 1.06521930e+00, 1.00714746e+00],
[ 1.05871698e+00, 1.12956519e+00],
[-1.12643410e+00, 1.66787744e-01],
[-1.10157218e+00, -3.64137806e-01],
[ 2.35118217e-01, -1.39769949e-01],
[ 1.13853795e+00, 1.01018519e+00],
[ 5.31205654e-01, -8.81990792e-01],
[ 4.33085936e-01, -7.64059042e-01],
[-4.48926156e-03, -1.30548411e+00],
[-1.76348589e+00, -4.97430739e-01],
[ 1.36485681e+00, 5.83404699e-01],
[ 5.66923900e-01, 1.51391963e+00],
[ 1.35736826e+00, 6.70915318e-01],
[ 1.07173397e+00, 6.11990884e-01],
[ 1.00106915e+00, 8.93815326e-01],
[ 1.33091007e+00, 8.79773879e-01],
[-1.79603740e+00, -3.53883973e-02],
[-1.27222979e+00, 4.00156642e-01],
[ 8.47480603e-01, 1.17032364e+00],
[-1.50989129e+00, -7.12318330e-01],
[-1.24953576e+00, -5.57859730e-01],
[-1.27717973e+00, -5.99350550e-01],
[-1.81946743e+00, 7.37057673e-01],
[ 1.19949867e+00, 1.56969386e+00],
[-1.25543847e+00, -2.33892826e-01],
[-1.63052058e+00, 1.61455865e-01],
[ 1.10611305e+00, 7.39698224e-01],
[ 6.70193192e-01, 8.70567001e-01],
[ 3.69670156e-01, -6.94645306e-01],
[-1.26362293e+00, -6.99249285e-01],
[-3.66687507e-01, -1.35310260e+00],
[ 2.44032147e-01, -6.59470793e-01],
[-1.27679142e+00, -4.85453412e-01],
[ 3.77473612e-02, -6.99251605e-01],
[-2.19148539e+00, -4.91199500e-01],
[-2.93277777e-01, -5.89488212e-01],
[-1.65737397e+00, -2.98337786e-01],
[ 7.36638861e-01, 5.78037057e-01],
[ 1.13709081e+00, 1.30119754e+00],
[-1.44146601e+00, 3.13934680e-02],
[ 5.92360708e-01, 1.22545114e+00],
[ 6.51719414e-01, 4.92674894e-01],
[ 5.94559139e-01, 8.25637315e-01],
[-1.87900722e+00, -5.21899626e-01],
[ 2.15225041e-01, -1.28269851e+00],
[ 4.99145965e-01, -6.70268634e-01],
[-1.82954176e+00, -3.39269731e-01],
[ 7.92721403e-01, 1.33785606e+00],
[ 9.54363372e-01, 9.80396626e-01],
[-1.35359846e+00, 1.03976340e-01],
[ 1.05595062e+00, 8.07031927e-01],
[-1.94311010e+00, -1.18976964e-01],
[-1.39604137e+00, -3.10095976e-01],
[ 1.28977624e+00, 1.01753365e+00],
[-1.59503139e+00, -5.40574609e-01],
[-1.41994046e+00, -3.81032569e-01],
[-2.35569801e-02, -1.10133702e+00],
[-1.26038568e+00, -6.93273886e-01],
[ 9.60215981e-01, -8.11553694e-01],
[ 5.51803308e-01, -1.01793176e+00],
[ 3.70185085e-01, -1.06885468e+00],
[ 8.25529207e-01, 8.77007060e-01],
[-1.87032595e+00, 2.87507199e-01],
[-1.56260769e+00, -1.89196712e-01],
[-1.26346548e+00, -7.74725237e-01],
[-6.33800421e-02, -7.59400611e-01],
[ 8.85298280e-01, 8.85620519e-01],
[-1.43324686e-01, -1.16083678e+00],
[-1.83908725e+00, -3.26655515e-01],
[ 2.74709229e-01, -1.04546829e+00],
[-1.45703573e+00, -2.91842036e-01],
[-1.59048842e+00, 1.66063031e-01],
[ 9.25549284e-01, 7.41406406e-01],
[ 1.97245469e-01, -7.80703225e-01],
[ 2.88401697e-01, -8.32425551e-01],
[ 7.24141618e-01, -7.99149200e-01],
[-1.62658639e+00, -1.80005543e-01],
[ 5.84481588e-01, 1.13195640e+00],
[ 1.02146732e+00, 4.59657799e-01],
[ 8.65050554e-01, 9.57714887e-01],
[ 3.98717766e-01, -1.24273147e+00],
[ 8.62234892e-01, 1.10955561e+00],
[-1.35999430e+00, 2.49942654e-02],
[-1.19178505e+00, -3.82946323e-02],
[ 1.29392424e+00, 1.10320509e+00],
[ 1.25679630e+00, -7.79857582e-01],
[ 9.38040302e-02, -5.53247258e-01],
[-1.73512175e+00, -9.76271667e-02],
[ 2.23153587e-01, -9.43474351e-01],
[ 4.01989100e-01, -1.10963051e+00],
[-1.42244158e+00, 1.81914703e-01],
[ 3.92476267e-01, -8.78426277e-01],
[ 1.25181875e+00, 6.93614996e-01],
[ 1.77481317e-02, -7.20304235e-01],
[-1.87752521e+00, -2.63870424e-01],
[-1.58063602e+00, -5.50456344e-01],
[-1.59589493e+00, -1.53932892e-01],
[-1.01829770e+00, 3.88542370e-02],
[ 1.24819659e+00, 6.60041803e-01],
[-1.25551377e+00, -2.96172009e-02],
[-1.41864559e+00, -3.58230179e-01],
[ 5.25758326e-01, 8.70500543e-01],
[ 5.55599988e-01, 1.18765072e+00],
[ 2.81344439e-02, -6.99111314e-01]])
# Import KMeans
from sklearn.cluster import KMeans
# Create a KMeans instance with 3 clusters: model
model = KMeans(n_clusters=3)
# Fit model to points
model.fit(points)
# Determine the cluster labels of new_points: labels
labels = model.predict(new_points)
# Print cluster labels of new_points
print(labels)
<script.py> output:
[1 2 0 1 2 1 2 2 2 0 1 2 2 0 0 2 0 0 2 2 0 2 1 2 1 0 2 0 0 1 1 2 2 2 0 1 2
2 1 2 0 1 1 0 1 2 0 0 2 2 2 2 0 0 1 1 0 0 0 1 1 2 2 2 1 2 0 2 1 0 1 1 1 2
1 0 0 1 2 0 1 0 1 2 0 2 0 1 2 2 2 1 2 2 1 0 0 0 0 1 2 1 0 0 1 1 2 1 0 0 1
0 0 0 2 2 2 2 0 0 2 1 2 0 2 1 0 2 0 0 2 0 2 0 1 2 1 1 2 0 1 2 1 1 0 2 2 1
0 1 0 2 1 0 0 1 0 2 2 0 2 0 0 2 2 1 2 2 0 1 0 1 1 2 1 2 2 1 1 0 1 1 1 0 2
2 1 0 1 0 0 2 2 2 1 2 2 2 0 0 1 2 1 1 1 0 2 2 2 2 2 2 0 0 2 0 0 0 0 2 0 0
2 2 1 0 1 1 0 1 0 1 0 2 2 0 2 2 2 0 1 1 0 2 2 0 2 0 0 2 0 0 1 0 1 1 1 2 0
0 0 1 2 1 0 1 0 0 2 1 1 1 0 2 2 2 1 2 0 0 2 1 1 0 1 1 0 1 2 1 0 0 0 0 2 0
0 2 2 1]
# + [markdown] id="8dyeQ7If6-jd"
# **Conclusion**
#
# Great work! You've successfully performed k-Means clustering and predicted the labels of new points. But it is not easy to inspect the clustering by just looking at the printed labels. A visualization would be far more useful. In the next exercise, you'll inspect your clustering with a scatter plot!
# + [markdown] id="YKeR49GD7JsQ"
# # Inspect your clustering
# Let's now inspect the clustering you performed in the previous exercise!
#
# A solution to the previous exercise has already run, so new_points is an array of points and labels is the array of their cluster labels.
#
# Instructions
#
# 1. Import matplotlib.pyplot as plt.
#
# 2. Assign column 0 of new_points to xs, and column 1 of new_points to ys.
#
# 3. Make a scatter plot of xs and ys, specifying the c=labels keyword arguments to color the points by their cluster label. Also specify alpha=0.5.
#
# 4. Compute the coordinates of the centroids using the .cluster_centers_ attribute of model.
#
# 5. Assign column 0 of centroids to centroids_x, and column 1 of centroids to centroids_y.
#
# 6. Make a scatter plot of centroids_x and centroids_y, using 'D' (a diamond) as a marker by specifying the marker parameter. Set the size of the markers to be 50 using s=50.
# + id="PNI7jUwn8fFI"
In [3]:
new_points
Out[3]:
array([[ 4.00233332e-01, -1.26544471e+00],
[ 8.03230370e-01, 1.28260167e+00],
[-1.39507552e+00, 5.57292921e-02],
[-3.41192677e-01, -1.07661994e+00],
[ 1.54781747e+00, 1.40250049e+00],
[ 2.45032018e-01, -4.83442328e-01],
[ 1.20706886e+00, 8.88752605e-01],
[ 1.25132628e+00, 1.15555395e+00],
[ 1.81004415e+00, 9.65530731e-01],
[-1.66963401e+00, -3.08103509e-01],
[-7.17482105e-02, -9.37939700e-01],
[ 6.82631927e-01, 1.10258160e+00],
[ 1.09039598e+00, 1.43899529e+00],
[-1.67645414e+00, -5.04557049e-01],
[-1.84447804e+00, 4.52539544e-02],
[ 1.24234851e+00, 1.02088661e+00],
[-1.86147041e+00, 6.38645811e-03],
[-1.46044943e+00, 1.53252383e-01],
[ 4.98981817e-01, 8.98006058e-01],
[ 9.83962244e-01, 1.04369375e+00],
[-1.83136742e+00, -1.63632835e-01],
[ 1.30622617e+00, 1.07658717e+00],
[ 3.53420328e-01, -7.51320218e-01],
[ 1.13957970e+00, 1.54503860e+00],
[ 2.93995694e-01, -1.26135005e+00],
[-1.14558225e+00, -3.78709636e-02],
[ 1.18716105e+00, 6.00240663e-01],
[-2.23211946e+00, 2.30475094e-01],
[-1.28320430e+00, -3.93314568e-01],
[ 4.94296696e-01, -8.83972009e-01],
[ 6.31834930e-02, -9.11952228e-01],
[ 9.35759539e-01, 8.66820685e-01],
[ 1.58014721e+00, 1.03788392e+00],
[ 1.06304960e+00, 1.02706082e+00],
[-1.39732536e+00, -5.05162249e-01],
[-1.09935240e-01, -9.08113619e-01],
[ 1.17346758e+00, 9.47501092e-01],
[ 9.20084511e-01, 1.45767672e+00],
[ 5.82658956e-01, -9.00086832e-01],
[ 9.52772328e-01, 8.99042386e-01],
[-1.37266956e+00, -3.17878215e-02],
[ 2.12706760e-02, -7.07614194e-01],
[ 3.27049052e-01, -5.55998107e-01],
[-1.71590267e+00, 2.15222266e-01],
[ 5.12516209e-01, -7.60128245e-01],
[ 1.13023469e+00, 7.22451122e-01],
[-1.43074310e+00, -3.42787511e-01],
[-1.82724625e+00, 1.17657775e-01],
[ 1.41801350e+00, 1.11455080e+00],
[ 1.26897304e+00, 1.41925971e+00],
[ 8.04076494e-01, 1.63988557e+00],
[ 8.34567752e-01, 1.09956689e+00],
[-1.24714732e+00, -2.23522320e-01],
[-1.29422537e+00, 8.18770024e-02],
[-2.27378316e-01, -4.13331387e-01],
[ 2.18830387e-01, -4.68183120e-01],
[-1.22593414e+00, 2.55599147e-01],
[-1.31294033e+00, -4.28892070e-01],
[-1.33532382e+00, 6.52053776e-01],
[-3.01100233e-01, -1.25156451e+00],
[ 2.02778356e-01, -9.05277445e-01],
[ 1.01357784e+00, 1.12378981e+00],
[ 8.18324394e-01, 8.60841257e-01],
[ 1.26181556e+00, 1.46613744e+00],
[ 4.64867724e-01, -7.97212459e-01],
[ 3.60908898e-01, 8.44106720e-01],
[-2.15098310e+00, -3.69583937e-01],
[ 1.05005281e+00, 8.74181364e-01],
[ 1.06580074e-01, -7.49268153e-01],
[-1.73945723e+00, 2.52183577e-01],
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[ 5.16618951e-01, -6.41267582e-01],
[ 3.26621787e-01, -8.80608015e-01],
[ 1.09017759e+00, 1.10952558e+00],
[ 3.64459576e-01, -6.94215622e-01],
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[ 3.18541701e-01, -4.05271704e-01],
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[ 3.21757168e-01, -5.34283821e-01],
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[ 3.35028183e-01, 6.24761151e-01],
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[ 8.88895911e-01, 7.62237161e-01],
[ 4.83164032e-01, 8.81931869e-01],
[-5.52997766e-02, -7.11305016e-01],
[-1.57966441e+00, -6.29220313e-01],
[ 5.51308645e-02, -8.47206763e-01],
[-2.06001582e+00, 5.87697787e-02],
[ 1.11810855e+00, 1.30254175e+00],
[ 4.87016164e-01, -9.90143937e-01],
[-1.65518042e+00, -1.69386383e-01],
[-1.44349738e+00, 1.90299243e-01],
[-1.70074547e-01, -8.26736022e-01],
[-1.82433979e+00, -3.07814626e-01],
[ 1.03093485e+00, 1.26457691e+00],
[ 1.64431169e+00, 1.27773115e+00],
[-1.47617693e+00, 2.60783872e-02],
[ 1.00953067e+00, 1.14270181e+00],
[-1.45285636e+00, -2.55216207e-01],
[-1.74092917e+00, -8.34443177e-02],
[ 1.22038299e+00, 1.28699961e+00],
[ 9.16925397e-01, 7.32070275e-01],
[-1.60754185e-03, -7.26375571e-01],
[ 8.93841238e-01, 8.41146643e-01],
[ 6.33791961e-01, 1.00915134e+00],
[-1.47927075e+00, -6.99781936e-01],
[ 5.44799374e-02, -1.06441970e+00],
[-1.51935568e+00, -4.89276929e-01],
[ 2.89939026e-01, -7.73145523e-01],
[-9.68154061e-03, -1.13302207e+00],
[ 1.13474639e+00, 9.71541744e-01],
[ 5.36421406e-01, -8.47906388e-01],
[ 1.14759864e+00, 6.89915205e-01],
[ 5.73291902e-01, 7.90802710e-01],
[ 2.12377397e-01, -6.07569808e-01],
[ 5.26579548e-01, -8.15930264e-01],
[-2.01831641e+00, 6.78650740e-02],
[-2.35512624e-01, -1.08205132e+00],
[ 1.59274780e-01, -6.00717261e-01],
[ 2.28120356e-01, -1.16003549e+00],
[-1.53658378e+00, 8.40798808e-02],
[ 1.13954609e+00, 6.31782001e-01],
[ 1.01119255e+00, 1.04360805e+00],
[-1.42039867e-01, -4.81230337e-01],
[-2.23120182e+00, 8.49162905e-02],
[ 1.25554811e-01, -1.01794793e+00],
[-1.72493509e+00, -6.94426177e-01],
[-1.60434630e+00, 4.45550868e-01],
[ 7.37153979e-01, 9.26560744e-01],
[ 6.72905271e-01, 1.13366030e+00],
[ 1.20066456e+00, 7.26273093e-01],
[ 7.58747209e-02, -9.83378326e-01],
[ 1.28783262e+00, 1.18088601e+00],
[ 1.06521930e+00, 1.00714746e+00],
[ 1.05871698e+00, 1.12956519e+00],
[-1.12643410e+00, 1.66787744e-01],
[-1.10157218e+00, -3.64137806e-01],
[ 2.35118217e-01, -1.39769949e-01],
[ 1.13853795e+00, 1.01018519e+00],
[ 5.31205654e-01, -8.81990792e-01],
[ 4.33085936e-01, -7.64059042e-01],
[-4.48926156e-03, -1.30548411e+00],
[-1.76348589e+00, -4.97430739e-01],
[ 1.36485681e+00, 5.83404699e-01],
[ 5.66923900e-01, 1.51391963e+00],
[ 1.35736826e+00, 6.70915318e-01],
[ 1.07173397e+00, 6.11990884e-01],
[ 1.00106915e+00, 8.93815326e-01],
[ 1.33091007e+00, 8.79773879e-01],
[-1.79603740e+00, -3.53883973e-02],
[-1.27222979e+00, 4.00156642e-01],
[ 8.47480603e-01, 1.17032364e+00],
[-1.50989129e+00, -7.12318330e-01],
[-1.24953576e+00, -5.57859730e-01],
[-1.27717973e+00, -5.99350550e-01],
[-1.81946743e+00, 7.37057673e-01],
[ 1.19949867e+00, 1.56969386e+00],
[-1.25543847e+00, -2.33892826e-01],
[-1.63052058e+00, 1.61455865e-01],
[ 1.10611305e+00, 7.39698224e-01],
[ 6.70193192e-01, 8.70567001e-01],
[ 3.69670156e-01, -6.94645306e-01],
[-1.26362293e+00, -6.99249285e-01],
[-3.66687507e-01, -1.35310260e+00],
[ 2.44032147e-01, -6.59470793e-01],
[-1.27679142e+00, -4.85453412e-01],
[ 3.77473612e-02, -6.99251605e-01],
[-2.19148539e+00, -4.91199500e-01],
[-2.93277777e-01, -5.89488212e-01],
[-1.65737397e+00, -2.98337786e-01],
[ 7.36638861e-01, 5.78037057e-01],
[ 1.13709081e+00, 1.30119754e+00],
[-1.44146601e+00, 3.13934680e-02],
[ 5.92360708e-01, 1.22545114e+00],
[ 6.51719414e-01, 4.92674894e-01],
[ 5.94559139e-01, 8.25637315e-01],
[-1.87900722e+00, -5.21899626e-01],
[ 2.15225041e-01, -1.28269851e+00],
[ 4.99145965e-01, -6.70268634e-01],
[-1.82954176e+00, -3.39269731e-01],
[ 7.92721403e-01, 1.33785606e+00],
[ 9.54363372e-01, 9.80396626e-01],
[-1.35359846e+00, 1.03976340e-01],
[ 1.05595062e+00, 8.07031927e-01],
[-1.94311010e+00, -1.18976964e-01],
[-1.39604137e+00, -3.10095976e-01],
[ 1.28977624e+00, 1.01753365e+00],
[-1.59503139e+00, -5.40574609e-01],
[-1.41994046e+00, -3.81032569e-01],
[-2.35569801e-02, -1.10133702e+00],
[-1.26038568e+00, -6.93273886e-01],
[ 9.60215981e-01, -8.11553694e-01],
[ 5.51803308e-01, -1.01793176e+00],
[ 3.70185085e-01, -1.06885468e+00],
[ 8.25529207e-01, 8.77007060e-01],
[-1.87032595e+00, 2.87507199e-01],
[-1.56260769e+00, -1.89196712e-01],
[-1.26346548e+00, -7.74725237e-01],
[-6.33800421e-02, -7.59400611e-01],
[ 8.85298280e-01, 8.85620519e-01],
[-1.43324686e-01, -1.16083678e+00],
[-1.83908725e+00, -3.26655515e-01],
[ 2.74709229e-01, -1.04546829e+00],
[-1.45703573e+00, -2.91842036e-01],
[-1.59048842e+00, 1.66063031e-01],
[ 9.25549284e-01, 7.41406406e-01],
[ 1.97245469e-01, -7.80703225e-01],
[ 2.88401697e-01, -8.32425551e-01],
[ 7.24141618e-01, -7.99149200e-01],
[-1.62658639e+00, -1.80005543e-01],
[ 5.84481588e-01, 1.13195640e+00],
[ 1.02146732e+00, 4.59657799e-01],
[ 8.65050554e-01, 9.57714887e-01],
[ 3.98717766e-01, -1.24273147e+00],
[ 8.62234892e-01, 1.10955561e+00],
[-1.35999430e+00, 2.49942654e-02],
[-1.19178505e+00, -3.82946323e-02],
[ 1.29392424e+00, 1.10320509e+00],
[ 1.25679630e+00, -7.79857582e-01],
[ 9.38040302e-02, -5.53247258e-01],
[-1.73512175e+00, -9.76271667e-02],
[ 2.23153587e-01, -9.43474351e-01],
[ 4.01989100e-01, -1.10963051e+00],
[-1.42244158e+00, 1.81914703e-01],
[ 3.92476267e-01, -8.78426277e-01],
[ 1.25181875e+00, 6.93614996e-01],
[ 1.77481317e-02, -7.20304235e-01],
[-1.87752521e+00, -2.63870424e-01],
[-1.58063602e+00, -5.50456344e-01],
[-1.59589493e+00, -1.53932892e-01],
[-1.01829770e+00, 3.88542370e-02],
[ 1.24819659e+00, 6.60041803e-01],
[-1.25551377e+00, -2.96172009e-02],
[-1.41864559e+00, -3.58230179e-01],
[ 5.25758326e-01, 8.70500543e-01],
[ 5.55599988e-01, 1.18765072e+00],
[ 2.81344439e-02, -6.99111314e-01]])
In [1]:
labels
Out[1]:
array([1, 2, 0, 1, 2, 1, 2, 2, 2, 0, 1, 2, 2, 0, 0, 2, 0, 0, 2, 2, 0, 2,
1, 2, 1, 0, 2, 0, 0, 1, 1, 2, 2, 2, 0, 1, 2, 2, 1, 2, 0, 1, 1, 0,
1, 2, 0, 0, 2, 2, 2, 2, 0, 0, 1, 1, 0, 0, 0, 1, 1, 2, 2, 2, 1, 2,
0, 2, 1, 0, 1, 1, 1, 2, 1, 0, 0, 1, 2, 0, 1, 0, 1, 2, 0, 2, 0, 1,
2, 2, 2, 1, 2, 2, 1, 0, 0, 0, 0, 1, 2, 1, 0, 0, 1, 1, 2, 1, 0, 0,
1, 0, 0, 0, 2, 2, 2, 2, 0, 0, 2, 1, 2, 0, 2, 1, 0, 2, 0, 0, 2, 0,
2, 0, 1, 2, 1, 1, 2, 0, 1, 2, 1, 1, 0, 2, 2, 1, 0, 1, 0, 2, 1, 0,
0, 1, 0, 2, 2, 0, 2, 0, 0, 2, 2, 1, 2, 2, 0, 1, 0, 1, 1, 2, 1, 2,
2, 1, 1, 0, 1, 1, 1, 0, 2, 2, 1, 0, 1, 0, 0, 2, 2, 2, 1, 2, 2, 2,
0, 0, 1, 2, 1, 1, 1, 0, 2, 2, 2, 2, 2, 2, 0, 0, 2, 0, 0, 0, 0, 2,
0, 0, 2, 2, 1, 0, 1, 1, 0, 1, 0, 1, 0, 2, 2, 0, 2, 2, 2, 0, 1, 1,
0, 2, 2, 0, 2, 0, 0, 2, 0, 0, 1, 0, 1, 1, 1, 2, 0, 0, 0, 1, 2, 1,
0, 1, 0, 0, 2, 1, 1, 1, 0, 2, 2, 2, 1, 2, 0, 0, 2, 1, 1, 0, 1, 1,
0, 1, 2, 1, 0, 0, 0, 0, 2, 0, 0, 2, 2, 1], dtype=int32)
In [2]:
model
Out[2]:
KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,
n_clusters=3, n_init=10, n_jobs=None, precompute_distances='auto',
random_state=None, tol=0.0001, verbose=0)
# Import pyplot
import matplotlib.pyplot as plt
# Assign the columns of new_points: xs and ys
xs = new_points[:,0]
ys = new_points[:,1]
# Make a scatter plot of xs and ys, using labels to define the colors
plt.scatter(xs, ys, c=labels, alpha=0.5)
# Assign the cluster centers: centroids
centroids = model.cluster_centers_
# Assign the columns of centroids: centroids_x, centroids_y
centroids_x = centroids[:,0]
centroids_y = centroids[:,1]
# Make a scatter plot of centroids_x and centroids_y
plt.scatter(centroids_x, centroids_y, marker='D', s=50)
plt.show()
# + [markdown] id="Tz4rzlZj9cqX"
# **Conclusion**
#
# 
#
# Fantastic! The clustering looks great! But how can you be sure that 3 clusters is the correct choice? In other words, how can you evaluate the quality of a clustering? Tune into the next video in which Ben will explain how to evaluate a clustering!
# + [markdown] id="slcOq9QzqAu8"
# # 1. Evaluating a clustering
# In the previous video, we used k-means to cluster the iris samples into three clusters. But how can we evaluate the quality of this clustering?
#
# 2. Evaluating a clustering
# A direct approach is to compare the clusters with the iris species. You'll learn about this first, before considering the problem of how to measure the quality of a clustering in a way that doesn't require our samples to come pre-grouped into species. This measure of quality can then be used to make an informed choice about the number of clusters to look for.
#
# 3. Iris: clusters vs species
# Firstly, let's check whether the 3 clusters of iris samples have any correspondence to the iris species. The correspondence is described by this table. There is one column for each of the three species of iris: setosa, versicolor and virginica, and one row for each of the three cluster labels: 0, 1 and 2. The table shows the number of samples that have each possible cluster label/species combination. For example, we see that cluster 1 corresponds perfectly with the species setosa. On the other hand, while cluster 0 contains mainly virginica samples, there are also some virginica samples in cluster 2.
#
# 4. Cross tabulation with pandas
# Tables like these are called "cross-tabulations". To construct one, we are going to use the pandas library. Let's assume the species of each sample is given as a list of strings.
#
# 5. Aligning labels and species
# Import pandas, and then create a two-column DataFrame, where the first column is cluster labels and the second column is the iris species, so that each row gives the cluster label and species of a single sample.
#
# 6. Crosstab of labels and species
# Now use the pandas crosstab function to build the cross tabulation, passing the two columns of the DataFrame. Cross tabulations like these provide great insights into which sort of samples are in which cluster. But in most datasets, the samples are not labelled by species. How can the quality of a clustering be evaluated in these cases?
#
# 7. Measuring clustering quality
# We need a way to measure the quality of a clustering that uses only the clusters and the samples themselves. A good clustering has tight clusters, meaning that the samples in each cluster are bunched together, not spread out.
#
# 8. Inertia measures clustering quality
# How spread out the samples within each cluster are can be measured by the "inertia". Intuitively, inertia measures how far samples are from their centroids. You can find the precise definition in the scikit-learn documentation. We want clusters that are not spread out, so lower values of the inertia are better. The inertia of a kmeans model is measured automatically when any of the fit methods are called, and is available afterwards as the inertia attribute. In fact, kmeans aims to place the clusters in a way that minimizes the inertia.
#
# 9. The number of clusters
# Here is a plot of the inertia values of clusterings of the iris dataset with different numbers of clusters. Our kmeans model with 3 clusters has relatively low inertia, which is great. But notice that the inertia continues to decrease slowly. So what's the best number of clusters to choose?
#
# 10. How many clusters to choose?
# Ultimately, this is a trade-off. A good clustering has tight clusters (meaning low inertia). But it also doesn't have too many clusters. A good rule of thumb is to choose an elbow in the inertia plot, that is, a point where the inertia begins to decrease more slowly. For example, by this criterion, 3 is a good number of clusters for the iris dataset.
#
# 11. Let's practice!
# In this video, you've learned ways to evaluate the quality of a clustering. In the next video, you'll learn to use feature scaling to make your clusterings even better. For now, let's practice!
# + [markdown] id="rYcH2oen9nCx"
# # How many clusters of grain?
# In the video, you learned how to choose a good number of clusters for a dataset using the k-means inertia graph. You are given an array samples containing the measurements (such as area, perimeter, length, and several others) of samples of grain. What's a good number of clusters in this case?
#
# KMeans and PyPlot (plt) have already been imported for you.
#
# This dataset was sourced from the UCI Machine Learning Repository.
#
# Instructions
#
# 1. For each of the given values of k, perform the following steps:
#
# - Create a KMeans instance called model with k clusters.
# - Fit the model to the grain data samples.
# - Append the value of the inertia_ attribute of model to the list inertias.
# - The code to plot ks vs inertias has been written for you, so hit 'Submit Answer' to see the plot!
#
# Hint
#
# 1. Use KMeans() and specify the n_clusters parameter to create an instance of KMeans with the desired number of clusters.
#
# 2. To fit the model to the data, use the .fit() method on model with samples as the argument.
#
# 3. First, compute the inertia of the model using model.inertia_. Then, place this inside a call to inertias.append() to append it to inertias.
# + id="3UlEJ3mVAvGt"
ks = range(1, 6)
inertias = []
for k in ks:
# Create a KMeans instance with k clusters: model
model = KMeans(n_clusters=k)
# Fit model to samples
model.fit(samples)
# Append the inertia to the list of inertias
inertias.append(model.inertia_)
# Plot ks vs inertias
plt.plot(ks, inertias, '-o')
plt.xlabel('number of clusters, k')
plt.ylabel('inertia')
plt.xticks(ks)
plt.show()
# + [markdown] id="niBXZijMAwxa"
# **Conclusion**
#
# 
#
# Excellent job! The inertia decreases very slowly from 3 clusters to 4, so it looks like 3 clusters would be a good choice for this data.
# + [markdown] id="44B_7uRkA3j6"
# # Evaluating the grain clustering
#
# In the previous exercise, you observed from the inertia plot that 3 is a good number of clusters for the grain data. In fact, the grain samples come from a mix of 3 different grain varieties: "Kama", "Rosa" and "Canadian". In this exercise, cluster the grain samples into three clusters, and compare the clusters to the grain varieties using a cross-tabulation.
#
# You have the array samples of grain samples, and a list varieties giving the grain variety for each sample. Pandas (pd) and KMeans have already been imported for you.
#
# Instructions
#
# 1. Create a KMeans model called model with 3 clusters.
#
# 2. Use the .fit_predict() method of model to fit it to samples and derive the cluster labels. Using .fit_predict() is the same as using .fit() followed by .predict().
#
# 3. Create a DataFrame df with two columns named 'labels' and 'varieties', using labels and varieties, respectively, for the column values. This has been done for you.
#
# 4. Use the pd.crosstab() function on df['labels'] and df['varieties'] to count the number of times each grain variety coincides with each cluster label. 5. Assign the result to ct.
#
# 5. Hit 'Submit Answer' to see the cross-tabulation!
# + id="ny2kEaQ6XCrx"
# Create a KMeans model with 3 clusters: model
model = KMeans(n_clusters=3)
# Use fit_predict to fit model and obtain cluster labels: labels
labels = model.fit_predict(samples)
# Create a DataFrame with labels and varieties as columns: df
df = pd.DataFrame({'labels': labels, 'varieties': varieties})
# Create crosstab: ct
ct = pd.crosstab(df.labels, df.varieties)
# Display ct
print(ct)
<script.py> output:
varieties Canadian wheat Kama wheat Rosa wheat
labels
0 0 1 60
1 68 9 0
2 2 60 10
# + [markdown] id="ZZGoPjq_XL_B"
# **Conclusion**
#
# Great work! The cross-tabulation shows that the 3 varieties of grain separate really well into 3 clusters. But depending on the type of data you are working with, the clustering may not always be this good. Is there anything you can do in such situations to improve your clustering? You'll find out in the next video!
# + [markdown] id="YteX32qeqN5l"
# # 1. Transforming features for better clusterings
# Let's look now at another dataset,
#
# 2. Piedmont wines dataset
# the Piedmont wines dataset. We have 178 samples of red wine from the Piedmont region of Italy. The features measure chemical composition (like alcohol content) and visual properties like color intensity. The samples come from 3 distinct varieties of wine.
#
# 1 Source: https://archive.ics.uci.edu/ml/datasets/Wine
# 3. Clustering the wines
# Let's take the array of samples and use KMeans to find 3 clusters.
#
# 4. Clusters vs. varieties
# There are three varieties of wine, so let's use pandas crosstab to check the cluster label - wine variety correspondence. As you can see, this time things haven't worked out so well. The KMeans clusters don't correspond well with the wine varieties.
#
# 5. Feature variances
# The problem is that the features of the wine dataset have very different variances. The variance of a feature measures the spread of its values. For example, the malic acid feature has a higher variance
#
# 6. Feature variances
# than the od280 feature, and this can also be seen in their scatter plot. The differences in some of the feature variances is enormous, as seen here, for example, in the scatter plot of the od280 and proline features.
#
# 7. StandardScaler
# In KMeans clustering, the variance of a feature corresponds to its influence on the clustering algorithm. To give every feature a chance, the data needs to be transformed so that features have equal variance. This can be achieved with the StandardScaler from scikit-learn. It transforms every feature to have mean 0 and variance 1. The resulting "standardized" features can be very informative. Using standardized od280 and proline, for example, the three wine varieties are much more distinct.
#
# 8. sklearn StandardScaler
# Let's see the StandardScaler in action. First, import StandardScaler from sklearn.preprocessing. Then create a StandardScaler object, and fit it to the samples. The transform method can now be used to standardize any samples, either the same ones, or completely new ones.
#
# 9. Similar methods
# The APIs of StandardScaler and KMeans are similar, but there is an important difference. StandardScaler transforms data, and so has a transform method. KMeans, in contrast, assigns cluster labels to samples, and this done using the predict method.
#
# 10. StandardScaler, then KMeans
# Let's return to the problem of clustering the wines. We need to perform two steps. Firstly, to standardize the data using StandardScaler, and secondly to take the standardized data and cluster it using KMeans. This can be conveniently achieved by combining the two steps using a scikit-learn pipeline. Data then flows from one step into the next, automatically.
#
# 11. Pipelines combine multiple steps
# The first steps are the same: creating a StandardScaler and a KMeans object. After that, import the make_pipeline function from sklearn.pipeline. Apply the make_pipeline function to the steps that you want to compose in this case, the scaler and the kmeans objects. Now use the fit method of the pipeline to fit both the scaler and kmeans, and use its predict method to obtain the cluster labels.
#
# 12. Feature standardization improves clustering
# Checking the correspondence between the cluster labels and the wine varieties reveals that this new clustering, incorporating standardization, is fantastic. Its three clusters correspond almost exactly to the three wine varieties. This is a huge improvement on the clustering without standardization.
#
# 13. sklearn preprocessing steps
# StandardScaler is an example of a "preprocessing" step. There are several of these available in scikit-learn, for example MaxAbsScaler and Normalizer.
#
# 14. Let's practice!
# You've learned a lot this video. Now put it into practice and cluster some datasets!
# + [markdown] id="yIHOYQApYva8"
# # Scaling fish data for clustering
#
# You are given an array samples giving measurements of fish. Each row represents an individual fish. The measurements, such as weight in grams, length in centimeters, and the percentage ratio of height to length, have very different scales. In order to cluster this data effectively, you'll need to standardize these features first. In this exercise, you'll build a pipeline to standardize and cluster the data.
#
# These fish measurement data were sourced from the Journal of Statistics Education.
#
# Instructions
#
# 1. Import:
# - make_pipeline from sklearn.pipeline.
# - StandardScaler from sklearn.preprocessing.
# - KMeans from sklearn.cluster.
#
# 2. Create an instance of StandardScaler called scaler.
#
# 3. Create an instance of KMeans with 4 clusters called kmeans.
#
# 4. Create a pipeline called pipeline that chains scaler and kmeans. To do this, you just need to pass them in as arguments to make_pipeline().
# + id="Qw8CNG0kbOJk"
# Perform the necessary imports
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans
# Create scaler: scaler
scaler = StandardScaler()
# Create KMeans instance: kmeans
kmeans = KMeans(n_clusters=4)
# Create pipeline: pipeline
pipeline = make_pipeline(scaler, kmeans)
# + [markdown] id="EnoATT1EbQl-"
# **Conclusion**
#
# Great work! Now that you've built the pipeline, you'll use it in the next exercise to cluster the fish by their measurements.
# + [markdown] id="IFSlosGDb1mY"
# # Clustering the fish data
# You'll now use your standardization and clustering pipeline from the previous exercise to cluster the fish by their measurements, and then create a cross-tabulation to compare the cluster labels with the fish species.
#
# As before, samples is the 2D array of fish measurements. Your pipeline is available as pipeline, and the species of every fish sample is given by the list species.
#
# Instructions
#
# 1. Import pandas as pd.
#
# 2. Fit the pipeline to the fish measurements samples.
#
# 3. Obtain the cluster labels for samples by using the .predict() method of pipeline.
#
# 4. Using pd.DataFrame(), create a DataFrame df with two columns named 'labels' and 'species', using labels and species, respectively, for the column values.
#
# 5. Using pd.crosstab(), create a cross-tabulation ct of df['labels'] and df['species'].
# + id="ZGLNUNBqcOPk"
In [1]:
samples
Out[1]:
array([[ 242. , 23.2, 25.4, 30. , 38.4, 13.4],
[ 290. , 24. , 26.3, 31.2, 40. , 13.8],
[ 340. , 23.9, 26.5, 31.1, 39.8, 15.1],
[ 363. , 26.3, 29. , 33.5, 38. , 13.3],
[ 430. , 26.5, 29. , 34. , 36.6, 15.1],
[ 450. , 26.8, 29.7, 34.7, 39.2, 14.2],
[ 500. , 26.8, 29.7, 34.5, 41.1, 15.3],
[ 390. , 27.6, 30. , 35. , 36.2, 13.4],
[ 450. , 27.6, 30. , 35.1, 39.9, 13.8],
[ 500. , 28.5, 30.7, 36.2, 39.3, 13.7],
[ 475. , 28.4, 31. , 36.2, 39.4, 14.1],
[ 500. , 28.7, 31. , 36.2, 39.7, 13.3],
[ 500. , 29.1, 31.5, 36.4, 37.8, 12. ],
[ 600. , 29.4, 32. , 37.2, 40.2, 13.9],
[ 600. , 29.4, 32. , 37.2, 41.5, 15. ],
[ 700. , 30.4, 33. , 38.3, 38.8, 13.8],
[ 700. , 30.4, 33. , 38.5, 38.8, 13.5],
[ 610. , 30.9, 33.5, 38.6, 40.5, 13.3],
[ 650. , 31. , 33.5, 38.7, 37.4, 14.8],
[ 575. , 31.3, 34. , 39.5, 38.3, 14.1],
[ 685. , 31.4, 34. , 39.2, 40.8, 13.7],
[ 620. , 31.5, 34.5, 39.7, 39.1, 13.3],
[ 680. , 31.8, 35. , 40.6, 38.1, 15.1],
[ 700. , 31.9, 35. , 40.5, 40.1, 13.8],
[ 725. , 31.8, 35. , 40.9, 40. , 14.8],
[ 720. , 32. , 35. , 40.6, 40.3, 15. ],
[ 714. , 32.7, 36. , 41.5, 39.8, 14.1],
[ 850. , 32.8, 36. , 41.6, 40.6, 14.9],
[1000. , 33.5, 37. , 42.6, 44.5, 15.5],
[ 920. , 35. , 38.5, 44.1, 40.9, 14.3],
[ 955. , 35. , 38.5, 44. , 41.1, 14.3],
[ 925. , 36.2, 39.5, 45.3, 41.4, 14.9],
[ 975. , 37.4, 41. , 45.9, 40.6, 14.7],
[ 950. , 38. , 41. , 46.5, 37.9, 13.7],
[ 40. , 12.9, 14.1, 16.2, 25.6, 14. ],
[ 69. , 16.5, 18.2, 20.3, 26.1, 13.9],
[ 78. , 17.5, 18.8, 21.2, 26.3, 13.7],
[ 87. , 18.2, 19.8, 22.2, 25.3, 14.3],
[ 120. , 18.6, 20. , 22.2, 28. , 16.1],
[ 0. , 19. , 20.5, 22.8, 28.4, 14.7],
[ 110. , 19.1, 20.8, 23.1, 26.7, 14.7],
[ 120. , 19.4, 21. , 23.7, 25.8, 13.9],
[ 150. , 20.4, 22. , 24.7, 23.5, 15.2],
[ 145. , 20.5, 22. , 24.3, 27.3, 14.6],
[ 160. , 20.5, 22.5, 25.3, 27.8, 15.1],
[ 140. , 21. , 22.5, 25. , 26.2, 13.3],
[ 160. , 21.1, 22.5, 25. , 25.6, 15.2],
[ 169. , 22. , 24. , 27.2, 27.7, 14.1],
[ 161. , 22. , 23.4, 26.7, 25.9, 13.6],
[ 200. , 22.1, 23.5, 26.8, 27.6, 15.4],
[ 180. , 23.6, 25.2, 27.9, 25.4, 14. ],
[ 290. , 24. , 26. , 29.2, 30.4, 15.4],
[ 272. , 25. , 27. , 30.6, 28. , 15.6],
[ 390. , 29.5, 31.7, 35. , 27.1, 15.3],
[ 6.7, 9.3, 9.8, 10.8, 16.1, 9.7],
[ 7.5, 10. , 10.5, 11.6, 17. , 10. ],
[ 7. , 10.1, 10.6, 11.6, 14.9, 9.9],
[ 9.7, 10.4, 11. , 12. , 18.3, 11.5],
[ 9.8, 10.7, 11.2, 12.4, 16.8, 10.3],
[ 8.7, 10.8, 11.3, 12.6, 15.7, 10.2],
[ 10. , 11.3, 11.8, 13.1, 16.9, 9.8],
[ 9.9, 11.3, 11.8, 13.1, 16.9, 8.9],
[ 9.8, 11.4, 12. , 13.2, 16.7, 8.7],
[ 12.2, 11.5, 12.2, 13.4, 15.6, 10.4],
[ 13.4, 11.7, 12.4, 13.5, 18. , 9.4],
[ 12.2, 12.1, 13. , 13.8, 16.5, 9.1],
[ 19.7, 13.2, 14.3, 15.2, 18.9, 13.6],
[ 19.9, 13.8, 15. , 16.2, 18.1, 11.6],
[ 200. , 30. , 32.3, 34.8, 16. , 9.7],
[ 300. , 31.7, 34. , 37.8, 15.1, 11. ],
[ 300. , 32.7, 35. , 38.8, 15.3, 11.3],
[ 300. , 34.8, 37.3, 39.8, 15.8, 10.1],
[ 430. , 35.5, 38. , 40.5, 18. , 11.3],
[ 345. , 36. , 38.5, 41. , 15.6, 9.7],
[ 456. , 40. , 42.5, 45.5, 16. , 9.5],
[ 510. , 40. , 42.5, 45.5, 15. , 9.8],
[ 540. , 40.1, 43. , 45.8, 17. , 11.2],
[ 500. , 42. , 45. , 48. , 14.5, 10.2],
[ 567. , 43.2, 46. , 48.7, 16. , 10. ],
[ 770. , 44.8, 48. , 51.2, 15. , 10.5],
[ 950. , 48.3, 51.7, 55.1, 16.2, 11.2],
[1250. , 52. , 56. , 59.7, 17.9, 11.7],
[1600. , 56. , 60. , 64. , 15. , 9.6],
[1550. , 56. , 60. , 64. , 15. , 9.6],
[1650. , 59. , 63.4, 68. , 15.9, 11. ]])
In [2]:
pipeline
Out[2]:
Pipeline(memory=None,
steps=[('standardscaler',
StandardScaler(copy=True, with_mean=True, with_std=True)),
('kmeans',
KMeans(algorithm='auto', copy_x=True, init='k-means++',
max_iter=300, n_clusters=4, n_init=10, n_jobs=None,
precompute_distances='auto', random_state=None,
tol=0.0001, verbose=0))],
verbose=False)
In [3]:
species
Out[3]:
['Bream',
'Bream',
'Bream',
'Bream',
'Bream',
'Bream',
'Bream',
'Bream',
'Bream',
'Bream',
'Bream',
'Bream',
'Bream',
'Bream',
'Bream',
'Bream',
'Bream',
'Bream',
'Bream',
'Bream',
'Bream',
'Bream',
'Bream',
'Bream',
'Bream',
'Bream',
'Bream',
'Bream',
'Bream',
'Bream',
'Bream',
'Bream',
'Bream',
'Bream',
'Roach',
'Roach',
'Roach',
'Roach',
'Roach',
'Roach',
'Roach',
'Roach',
'Roach',
'Roach',
'Roach',
'Roach',
'Roach',
'Roach',
'Roach',
'Roach',
'Roach',
'Roach',
'Roach',
'Roach',
'Smelt',
'Smelt',
'Smelt',
'Smelt',
'Smelt',
'Smelt',
'Smelt',
'Smelt',
'Smelt',
'Smelt',
'Smelt',
'Smelt',
'Smelt',
'Smelt',
'Pike',
'Pike',
'Pike',
'Pike',
'Pike',
'Pike',
'Pike',
'Pike',
'Pike',
'Pike',
'Pike',
'Pike',
'Pike',
'Pike',
'Pike',
'Pike',
'Pike']
# Import pandas
import pandas as pd
# Fit the pipeline to samples
pipeline.fit(samples)
# Calculate the cluster labels: labels
labels = pipeline.predict(samples)
# Create a DataFrame with labels and species as columns: df
df = pd.DataFrame({'labels':labels, 'species':species})
# Create crosstab: ct
ct = pd.crosstab(df.labels, df.species)
# Display ct
print(ct)
<script.py> output:
species Bream Pike Roach Smelt
labels
0 0 0 0 13
1 33 0 1 0
2 0 17 0 0
3 1 0 19 1
# + [markdown] id="MpezeH9FlRyP"
# **Conclusion**
#
# Excellent! It looks like the fish data separates really well into 4 clusters!
# + [markdown] id="VmODBxV-lRd9"
# # Clustering stocks using KMeans
# In this exercise, you'll cluster companies using their daily stock price movements (i.e. the dollar difference between the closing and opening prices for each trading day). You are given a NumPy array movements of daily price movements from 2010 to 2015 (obtained from Yahoo! Finance), where each row corresponds to a company, and each column corresponds to a trading day.
#
# Some stocks are more expensive than others. To account for this, include a Normalizer at the beginning of your pipeline. The Normalizer will separately transform each company's stock price to a relative scale before the clustering begins.
#
# Note that Normalizer() is different to StandardScaler(), which you used in the previous exercise. While StandardScaler() standardizes features (such as the features of the fish data from the previous exercise) by removing the mean and scaling to unit variance, Normalizer() rescales each sample - here, each company's stock price - independently of the other.
#
# KMeans and make_pipeline have already been imported for you.
#
# Instructions
#
# 1. Import Normalizer from sklearn.preprocessing.
#
# 2. Create an instance of Normalizer called normalizer.
#
# 3. Create an instance of KMeans called kmeans with 10 clusters.
#
# 4. Using make_pipeline(), create a pipeline called pipeline that chains normalizer and kmeans.
#
# 5. Fit the pipeline to the movements array.
# + id="3TkEDHZPoTO9"
# Import Normalizer
from sklearn.preprocessing import Normalizer
# Create a normalizer: normalizer
normalizer = Normalizer()
# Create a KMeans model with 10 clusters: kmeans
kmeans = KMeans(n_clusters=10)
# Make a pipeline chaining normalizer and kmeans: pipeline
pipeline = make_pipeline(normalizer, kmeans)
# Fit pipeline to the daily price movements
pipeline.fit(movements)
# + [markdown] id="8lM02BKOoarH"
# **Conclusion**
#
# Great work - you're really getting the hang of this. Now that your pipeline has been set up, you can find out which stocks move together in the next exercise!
# + [markdown] id="wHYuwgGQoh6O"
# # Which stocks move together?
# In the previous exercise, you clustered companies by their daily stock price movements. So which company have stock prices that tend to change in the same way? You'll now inspect the cluster labels from your clustering to find out.
#
# Your solution to the previous exercise has already been run. Recall that you constructed a Pipeline pipeline containing a KMeans model and fit it to the NumPy array movements of daily stock movements. In addition, a list companies of the company names is available.
#
# Instructions
#
# 1. Import pandas as pd.
#
# 2. Use the .predict() method of the pipeline to predict the labels for movements.
#
# 3. Align the cluster labels with the list of company names companies by creating a DataFrame df with labels and companies as columns. This has been done for you.
#
# 4. Use the .sort_values() method of df to sort the DataFrame by the 'labels' column, and print the result.
#
# 5. Hit 'Submit Answer' and take a moment to see which companies are together in each cluster!
# + id="7MtxJu6KpY4h"
# Import pandas
import pandas as pd
# Predict the cluster labels: labels
labels = pipeline.predict(movements)
# Create a DataFrame aligning labels and companies: df
df = pd.DataFrame({'labels': labels, 'companies': companies})
# Display df sorted by cluster label
print(df.sort_values('labels'))
<script.py> output:
labels companies
59 0 Yahoo
15 0 Ford
35 0 Navistar
26 1 JPMorgan Chase
16 1 General Electrics
58 1 Xerox
11 1 Cisco
18 1 Goldman Sachs
20 1 Home Depot
5 1 Bank of America
3 1 American express
55 1 Wells Fargo
1 1 AIG
38 2 Pepsi
40 2 Procter Gamble
28 2 Coca Cola
27 2 Kimberly-Clark
9 2 Colgate-Palmolive
54 3 Walgreen
36 3 Northrop Grumman
29 3 Lookheed Martin
4 3 Boeing
0 4 Apple
47 4 Symantec
33 4 Microsoft
32 4 3M
31 4 McDonalds
30 4 MasterCard
50 4 Taiwan Semiconductor Manufacturing
14 4 Dell
17 4 Google/Alphabet
24 4 Intel
23 4 IBM
2 4 Amazon
51 4 Texas instruments
43 4 SAP
45 5 Sony
48 5 Toyota
21 5 Honda
22 5 HP
34 5 Mitsubishi
7 5 Canon
56 6 Wal-Mart
57 7 Exxon
44 7 Schlumberger
8 7 Caterpillar
10 7 ConocoPhillips
12 7 Chevron
13 7 DuPont de Nemours
53 7 Valero Energy
39 8 Pfizer
41 8 Philip Morris
25 8 Johnson & Johnson
49 9 Total
46 9 Sanofi-Aventis
37 9 Novartis
42 9 Royal Dutch Shell
19 9 GlaxoSmithKline
52 9 Unilever
6 9 British American Tobacco
# + [markdown] id="TBcd5xt-pnoo"
# **Conclusion**
#
# Fantastic job - you have completed Chapter 1! Take a look at the clusters. Are you surprised by any of the results? In the next chapter, you'll learn about how to communicate results such as this through visualizations.
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