├── LICENSE
├── README.md
├── audio
    ├── test_audio.mp4
    └── test_audio2.wav
├── featureExtraction.py
├── image
    ├── TS_model.pdf
    └── TS_models.png
├── melodyExtraction_NS.py
├── model.py
├── randAudioAugment.py
├── results
    ├── pitch_test_audio_file.mp4.txt
    └── pitch_test_audio_file2.wav.txt
├── weights
    └── ResNet_NS.hdf5
├── x_data_mean_total_31.npy
└── x_data_std_total_31.npy


/LICENSE:
--------------------------------------------------------------------------------
 1 | MIT License
 2 | 
 3 | Copyright (c) 2020 Sangeun Kum
 4 | 
 5 | Permission is hereby granted, free of charge, to any person obtaining a copy
 6 | of this software and associated documentation files (the "Software"), to deal
 7 | in the Software without restriction, including without limitation the rights
 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 | 
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 | 
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 | 


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/README.md:
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 1 | # Semi-supervised learning using teacher-student models for vocal melody extraction
 2 | The source code of *"Semi-supervised learning using teacher-student models for vocal melody extraction"*, ISMIR(2020) |<a href = "https://arxiv.org/abs/2008.06358" target="_blank">PDF</a>|
 3 | 
 4 | ## Abstract
 5 | 
 6 | The lack of labeled data is a major obstacle in many music information retrieval tasks such as melody extraction, where labeling is extremely laborious or costly. Semi-supervised learning (SSL) provides a solution to alleviate the issue by leveraging a large amount of unlabeled data.<br>
 7 | In this paper, we propose an SSL method using teacher-student models for vocal melody extraction. The teacher model is pre-trained with labeled data and guides the student model to make identical predictions given unlabeled input in a self-training setting.<br>
 8 | We examine three setups of teacher-student models with different data augmentation schemes and loss functions. Also, considering the scarcity of labeled data in the test phase, we artificially generate large-scale testing data with pitch labels from unlabeled data using an analysis-synthesis method.<br>
 9 | The results show that the SSL method significantly increases the performance against supervised learning only and the improvement depends on the teacher-student models, the size of unlabeled data, the number of self-training iterations, and other training details.
10 | We also find that it is essential to ensure that the unlabeled audio has vocal parts. Finally, we show that the proposed SSL method enables a baseline convolutional recurrent neural network model to achieve performance comparable to state-of-the-arts.
11 | 
12 | <img src="./image/TS_models.png" width="90%">
13 | 
14 | ## Dependencies
15 | 
16 | - OS: LINUX 
17 | 
18 | - Programming language: Python 3.7+
19 | 
20 | - Python Library 
21 |   - Keras 2.3.0 (Deep Learning library)
22 |   - Librosa 0.7.0 (for STFT)  
23 |   - madmom 0.16.1 (for loading audio and resampling)
24 |   - Numpy, SciPy
25 |   - pysndfx 0.3.6 (for randAudioAugment)
26 | 
27 | -  Hardware
28 |   -  1 GPU : GeForce GTX 1080ti
29 |   
30 |     
31 | ## Using melody_extraction_SSL from the command line
32 | 
33 | ``` 
34 | $ python melodyExtraction_NS.py -p ./audio.test_audio.mp4 
35 | $ python melodyExtraction_NS.py -p ./audio/test_audio_file.mp4 -o ./results/ -gpu 0 
36 | 
37 | [optional arguments]
38 |   -p filepath             Path to input audio(.wav) (default: ./test_audio_file.mp4)
39 |   -gpu gpu_index          Assign a gpu index for processing.
40 |                           It will run with cpu if None. (default: None)
41 |   -o output_dir           Path to output folder (default: ./results/)
42 | ```
43 | 
44 | 
45 | - The resulting 'pitch_(*file_name*).txt' contains 2 columns: 
46 |   - the first : timestamps (10 ms)
47 |   - the second : the predicted fundamental frequency in Hz
48 | 
49 | ```
50 | 0.08 0.0000
51 | 0.09 0.0000
52 | 0.10 0.0000
53 | 0.11 0.0000
54 | 0.12 182.3449
55 | 0.13 182.3449
56 | 0.14 183.6663
57 | 0.15 185.6663
58 | 0.16 187.0117
59 | 0.17 188.3669
60 | ```
61 | 
62 | ## Citation
63 | If you find our work useful, please consider citing our paper.
64 | ``` 
65 | @inproceedings{kum2020semi,
66 |   title={Semi-supervised learning using teacher-student models for vocal melody extraction},
67 |   author={Kum, Sangeun and Lin, Jing-Hua and Su, Li and Nam, Juhan},
68 |   booktitle={Proc. International Society of Music Information Retrieval Conference (ISMIR)},
69 |   year={2020}
70 | }
71 | ``` 
72 | 


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/audio/test_audio.mp4:
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https://raw.githubusercontent.com/keums/melodyExtraction_SSL/eb2525dd8ff466639755735eafe963572c0c8dbf/audio/test_audio.mp4


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/audio/test_audio2.wav:
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https://raw.githubusercontent.com/keums/melodyExtraction_SSL/eb2525dd8ff466639755735eafe963572c0c8dbf/audio/test_audio2.wav


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/featureExtraction.py:
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 1 | # -*- coding: utf-8 -*-
 2 | import librosa
 3 | from madmom.audio.signal import *
 4 | from pathlib import Path
 5 | import matplotlib.pyplot as plt
 6 | 
 7 | 
 8 | def spec_extraction(file_name, win_size):
 9 |     currentFilePath = str(Path(__file__).resolve().parent)
10 |     # print(currentFilePath)
11 | 
12 |     x_test = []
13 | 
14 |     # y, sr = librosa.load(file_name, sr=8000)
15 |     # *********** madmom.Signal() is faster than librosa.load() ***********
16 |     y = Signal(file_name, sample_rate=8000, dtype=np.float32, num_channels=1)
17 |     S = librosa.core.stft(y, n_fft=1024, hop_length=80*1, win_length=1024)
18 |     x_spec = np.abs(S)
19 |     x_spec = librosa.core.power_to_db(x_spec, ref=np.max)
20 |     x_spec = x_spec.astype(np.float32)
21 |     num_frames = x_spec.shape[1]
22 | 
23 |     # for padding
24 |     padNum = num_frames % win_size
25 |     if padNum != 0:
26 |         len_pad = win_size - padNum
27 |         padding_feature = np.zeros(shape=(513, len_pad))
28 |         x_spec = np.concatenate((x_spec, padding_feature), axis=1)
29 |         num_frames = num_frames + len_pad
30 | 
31 |     for j in range(0, num_frames, win_size):
32 |         x_test_tmp = x_spec[:, range(j, j + win_size)].T
33 |         x_test.append(x_test_tmp)
34 |     x_test = np.array(x_test)
35 | 
36 |     # for normalization
37 | 
38 |     x_train_mean = np.load(currentFilePath+'/x_data_mean_total_31.npy')
39 |     x_train_std = np.load(currentFilePath+'/x_data_std_total_31.npy')
40 |     x_test = (x_test-x_train_mean)/(x_train_std+0.0001)
41 |     x_test = x_test[:, :, :, np.newaxis]
42 | 
43 |     return x_test, x_spec
44 | 


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/image/TS_model.pdf:
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https://raw.githubusercontent.com/keums/melodyExtraction_SSL/eb2525dd8ff466639755735eafe963572c0c8dbf/image/TS_model.pdf


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/image/TS_models.png:
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https://raw.githubusercontent.com/keums/melodyExtraction_SSL/eb2525dd8ff466639755735eafe963572c0c8dbf/image/TS_models.png


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/melodyExtraction_NS.py:
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 1 | 
 2 | # -*- coding: utf-8 -*-
 3 | 
 4 | import numpy as np
 5 | import argparse
 6 | import os
 7 | import sys
 8 | import matplotlib.pyplot as plt
 9 | import glob
10 | 
11 | from model import *
12 | from featureExtraction import *
13 | 
14 | 
15 | def melodyExtraction_NS(file_name, output_path, gpu_index):
16 |     os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
17 |     if gpu_index is None:
18 |         os.environ['CUDA_VISIBLE_DEVICES'] = ''
19 |     else:
20 |         os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu_index)
21 | 
22 |     note_res = 8
23 |     pitch_range = np.arange(40, 95 + 1.0/note_res, 1.0/note_res)
24 |     pitch_range = np.concatenate([np.zeros(1), pitch_range])
25 | 
26 |     '''  Features extraction'''
27 |     X_test, X_spec = spec_extraction(
28 |         file_name=file_name, win_size=31)
29 | 
30 |     '''  melody predict'''
31 |     model = melody_ResNet()
32 |     model.load_weights('./weights/ResNet_NS.hdf5')
33 |     y_predict = model.predict(X_test, batch_size=64, verbose=1)
34 | 
35 |     y_shape = y_predict.shape
36 |     num_total_frame = y_shape[0]*y_shape[1]
37 |     est_pitch = np.zeros(num_total_frame)
38 |     index_predict = np.zeros(num_total_frame)
39 | 
40 |     y_predict = np.reshape(y_predict, (num_total_frame, y_shape[2]))
41 | 
42 |     for i in range(num_total_frame):
43 |         index_predict[i] = np.argmax(y_predict[i, :])
44 |         pitch_MIDI = pitch_range[np.int32(index_predict[i])]
45 |         if pitch_MIDI >= 45 and pitch_MIDI <= 95:
46 |             est_pitch[i] = 2 ** ((pitch_MIDI - 69) / 12.) * 440
47 | 
48 |     ''' save results '''
49 |     PATH_est_pitch = output_path+ 'pitch_'+file_name.split('/')[-1]+'.txt'
50 |     if not os.path.exists(os.path.dirname(PATH_est_pitch)):
51 |         os.makedirs(os.path.dirname(PATH_est_pitch))
52 |     f = open(PATH_est_pitch, 'w')
53 |     for j in range(len(est_pitch)):
54 |         est = "%.2f %.4f\n" % (0.01 * j, est_pitch[j])
55 |         f.write(est)
56 |     f.close()
57 | 
58 |     est_arr = np.loadtxt(PATH_est_pitch)
59 |     return est_arr
60 | 
61 | 
62 | def parser():
63 |     p = argparse.ArgumentParser()
64 |     p.add_argument('-p', '--filepath',
65 |                    help='Path to input audio (default: %(default)s',
66 |                    type=str, default='test_audio_file.mp4')
67 |     p.add_argument('-o', '--output_dir',
68 |                    help='Path to output folder (default: %(default)s',
69 |                    type=str, default='./results/')
70 |     p.add_argument('-gpu', '--gpu_index',
71 |                    help='Assign a gpu index for processing. It will run with cpu if None.  (default: %(default)s',
72 |                    type=int, default=None)
73 |     return p.parse_args()
74 | 
75 | 
76 | if __name__ == '__main__':
77 |     args = parser()
78 |     melodyExtraction_NS(file_name=args.filepath,
79 |                         output_path=args.output_dir, gpu_index=args.gpu_index)
80 | 
81 | 


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/model.py:
--------------------------------------------------------------------------------
 1 | import keras.backend as KK
 2 | import math
 3 | from keras import backend as K
 4 | from keras.regularizers import l2
 5 | from keras.layers.advanced_activations import LeakyReLU, ReLU
 6 | from keras.models import model_from_json, Model, Sequential
 7 | from keras.layers.core import Dense, Dropout, Activation, Flatten
 8 | from keras.layers import Conv2D, MaxPooling2D, BatchNormalization, Dropout,\
 9 |     LSTM, Reshape, Bidirectional, TimeDistributed, Input, add
10 | 
11 | 
12 | 
13 | def ResNet_Block(input, block_id, filterNum):
14 |     ''' Create a ResNet block
15 |     Args:
16 |         input: input tensor
17 |         filterNum: number of output filters
18 |     Returns: a keras tensor
19 |     '''
20 | 
21 |     x = Conv2D(filterNum, (1, 1), name='conv_s'+str(block_id)+'_1x1', padding='same',
22 |                kernel_initializer='he_normal', use_bias=False, kernel_regularizer=l2(1e-5))(input)
23 |     shortcut = BatchNormalization()(x)
24 |     x = LeakyReLU(0.01)(shortcut)
25 | 
26 |     x = Conv2D(filterNum, (3, 3), name='conv'+str(block_id)+'_1', padding='same',
27 |                kernel_initializer='he_normal', use_bias=False, kernel_regularizer=l2(1e-5))(x)
28 |     x = BatchNormalization()(x)
29 |     x = LeakyReLU(0.01)(x)
30 | 
31 |     x = Conv2D(filterNum, (3, 3), name='conv'+str(block_id)+'_2', padding='same',
32 |                kernel_initializer='he_normal', use_bias=False, kernel_regularizer=l2(1e-5))(x)
33 |     x = BatchNormalization()(x)
34 |     x = LeakyReLU(0.01)(x)
35 | 
36 |     x = Conv2D(filterNum, (1, 1), name='conv_f'+str(block_id)+'_1x1', padding='same',
37 |                kernel_initializer='he_normal', use_bias=False, kernel_regularizer=l2(1e-5))(x)
38 |     x = BatchNormalization()(x)
39 | 
40 |     x = add([x, shortcut])
41 |     x = LeakyReLU(0.01)(x)
42 |     x = MaxPooling2D((1, 4))(x)
43 |     return x
44 | 
45 | 
46 | 
47 | 
48 | def melody_ResNet():
49 |     num_output = int(55 * 2 ** (math.log(8, 2)) + 2)
50 |     input = Input(shape=(31, 513, 1))
51 | 
52 |     block_1 = ResNet_Block(input=input, block_id=1,
53 |                              filterNum=64)
54 |     block_2 = ResNet_Block(input=block_1, block_id=2,
55 |                              filterNum=128)
56 |     block_3 = ResNet_Block(input=block_2, block_id=3,
57 |                              filterNum=192)
58 |     block_4 = ResNet_Block(input=block_3, block_id=4,
59 |                              filterNum=256)
60 |     block_4 = Dropout(0.5)(block_4)
61 | 
62 |     numOutput_P = block_4._keras_shape[2] * block_4._keras_shape[3]
63 |     output = Reshape((31, numOutput_P))(block_4)
64 |     output = Bidirectional(
65 |         LSTM(256, return_sequences=True, recurrent_dropout=0.3, dropout=0.3))(output)
66 |     output = TimeDistributed(Dense(num_output))(output)
67 |     output = TimeDistributed(Activation("softmax", name='softmax'))(output)
68 | 
69 | 
70 |     model = Model(inputs=input, outputs=output)
71 |     return model
72 | 


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/randAudioAugment.py:
--------------------------------------------------------------------------------
 1 | 
 2 | # -*- coding: utf-8 -*-
 3 | import os
 4 | import sys
 5 | import random
 6 | import argparse
 7 | import numpy as np
 8 | import matplotlib.pyplot as plt
 9 | import glob
10 | import librosa
11 | 
12 | 
13 | from madmom.audio.signal import *
14 | from pysndfx import AudioEffectsChain
15 | 
16 | def randAudioAugment():
17 |     fx = (AudioEffectsChain())
18 |     effect = [random.randint(0, 1) for i in range(6)]
19 |     
20 |     if effect[0] == 1:  # lowshelf
21 |         randGain = random.randint(0, 12) * random.choice([-1, 1])
22 |         randFreq = random.randint(20, 300)
23 |         randSlop = random.uniform(1, 7)/10  # 0.1~0.7
24 |         fx.lowshelf(gain=randGain, frequency=randFreq, slope=randSlop)
25 |     if effect[1] == 1:  # highshelf
26 |         randGain = random.randint(0, 12) * random.choice([-1, 1])
27 |         randFreq = random.randint(1000, 3000)
28 |         randSlop = random.uniform(1, 7)/10  # 0.1~0.7
29 |         fx.highshelf(gain=randGain, frequency=randFreq, slope=randSlop)
30 |     if effect[2] == 1:  # equalizer
31 |         randFreq = random.randint(100, 3000)
32 |         randQ = random.uniform(5, 15)/10  # 0.5~1.5
33 |         randDB = random.randint(0, 6) * random.choice([-1, 1])
34 |         fx.equalizer(frequency=randFreq, q=randQ, db=randDB)
35 |     if effect[3] == 1:  # overdrive
36 |         randGain = random.randint(3, 7)
37 |         fx.overdrive(gain=randGain, colour=40)
38 |     if effect[4] == 1:  # phaser
39 |         fx.phaser(gain_in=0.9, gain_out=0.8, delay=1,
40 |                   decay=0.25, speed=2, triangular=False)
41 |     if effect[5] == 1:  # reverb
42 |         randReverb = random.randint(30, 70)
43 |         randDamp = random.randint(30, 70)
44 |         randRoom = random.randint(30, 70)
45 |         randWet = random.randint(1, 6)
46 |         fx.reverb(reverberance=randReverb, hf_damping=randDamp, room_scale=randRoom,
47 |                   stereo_depth=100, pre_delay=20, wet_gain=randWet, wet_only=False)    
48 |     return fx
49 | 
50 | 
51 | def save_STFT(file_name, file_path):
52 |     # y, sr = librosa.load(file_name, sr=8000)
53 |     y = Signal(file_path, sample_rate=8000, dtype=np.float32, num_channels=1)
54 |     S = librosa.core.stft(y, n_fft=1024, hop_length=80*1, win_length=1024)
55 |     x_spec = np.abs(S)
56 |     x_spec = librosa.core.power_to_db(x_spec, ref=np.max)
57 |     x_spec = x_spec.astype(np.float32)
58 |     PATH_spec = '/data/FMA_l/original/features/'
59 |     if not os.path.exists(os.path.dirname(PATH_spec)):
60 |         os.makedirs(os.path.dirname(PATH_spec))
61 |     np.save(PATH_spec + file_name, x_spec)
62 | 
63 | 
64 | def save_STFT_RAA(file_name, file_path):
65 |     fx = randAudioAugment()
66 |     y = Signal(file_path, sample_rate=8000, dtype=np.float32, num_channels=1)
67 |     y_raa = fx(y)
68 |     S_raa = librosa.core.stft(
69 |         y_raa, n_fft=1024, hop_length=80*1, win_length=1024)
70 |     x_spec_r = np.abs(S_raa)
71 |     x_spec_r = librosa.core.power_to_db(x_spec_r, ref=np.max)
72 |     x_spec_r = x_spec_r.astype(np.float32)
73 |     
74 |     PATH_spec_r = '/data/FMA_l/randAugment/features/'
75 |     if not os.path.exists(os.path.dirname(PATH_spec_r)):
76 |         os.makedirs(os.path.dirname(PATH_spec_r))
77 |     np.save(PATH_spec_r + file_name, x_spec_r)
78 | 
79 | 


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/results/pitch_test_audio_file.mp4.txt:
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   7 | 0.06 0.0000
   8 | 0.07 238.1857
   9 | 0.08 246.9417
  10 | 0.09 248.7311
  11 | 0.10 250.5335
  12 | 0.11 250.5335
  13 | 0.12 250.5335
  14 | 0.13 250.5335
  15 | 0.14 252.3490
  16 | 0.15 252.3490
  17 | 0.16 252.3490
  18 | 0.17 252.3490
  19 | 0.18 254.1776
  20 | 0.19 256.0195
  21 | 0.20 256.0195
  22 | 0.21 256.0195
  23 | 0.22 256.0195
  24 | 0.23 257.8747
  25 | 0.24 257.8747
  26 | 0.25 257.8747
  27 | 0.26 257.8747
  28 | 0.27 257.8747
  29 | 0.28 256.0195
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  35 | 0.34 0.0000
  36 | 0.35 0.0000
  37 | 0.36 236.4721
  38 | 0.37 236.4721
  39 | 0.38 248.7311
  40 | 0.39 256.0195
  41 | 0.40 257.8747
  42 | 0.41 257.8747
  43 | 0.42 257.8747
  44 | 0.43 256.0195
  45 | 0.44 248.7311
  46 | 0.45 248.7311
  47 | 0.46 236.4721
  48 | 0.47 236.4721
  49 | 0.48 236.4721
  50 | 0.49 236.4721
  51 | 0.50 257.8747
  52 | 0.51 263.5214
  53 | 0.52 265.4310
  54 | 0.53 265.4310
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  56 | 0.55 265.4310
  57 | 0.56 263.5214
  58 | 0.57 236.4721
  59 | 0.58 236.4721
  60 | 0.59 229.7402
  61 | 0.60 0.0000
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  64 | 0.63 0.0000
  65 | 0.64 0.0000
  66 | 0.65 263.5214
  67 | 0.66 263.5214
  68 | 0.67 263.5214
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  70 | 0.69 0.0000
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  76 | 0.75 0.0000
  77 | 0.76 0.0000
  78 | 0.77 0.0000
  79 | 0.78 263.5214
  80 | 0.79 265.4310
  81 | 0.80 281.2143
  82 | 0.81 283.2521
  83 | 0.82 291.5521
  84 | 0.83 297.9362
  85 | 0.84 304.4602
  86 | 0.85 304.4602
  87 | 0.86 304.4602
  88 | 0.87 304.4602
  89 | 0.88 304.4602
  90 | 0.89 304.4602
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  92 | 0.91 302.2698
  93 | 0.92 302.2698
  94 | 0.93 300.0952
  95 | 0.94 300.0952
  96 | 0.95 300.0952
  97 | 0.96 300.0952
  98 | 0.97 0.0000
  99 | 0.98 0.0000
 100 | 0.99 0.0000
 101 | 1.00 0.0000
 102 | 1.01 0.0000
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 151 | 1.50 0.0000
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1038 | 10.37 369.9944
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1043 | 10.42 391.9954
1044 | 10.43 391.9954
1045 | 10.44 394.8360
1046 | 10.45 394.8360
1047 | 10.46 394.8360
1048 | 10.47 394.8360
1049 | 10.48 394.8360
1050 | 10.49 394.8360
1051 | 10.50 391.9954
1052 | 10.51 391.9954
1053 | 10.52 391.9954
1054 | 10.53 391.9954
1055 | 10.54 391.9954
1056 | 10.55 391.9954
1057 | 10.56 391.9954
1058 | 10.57 391.9954
1059 | 10.58 391.9954
1060 | 10.59 391.9954
1061 | 10.60 391.9954
1062 | 10.61 391.9954
1063 | 10.62 391.9954
1064 | 10.63 391.9954
1065 | 10.64 391.9954
1066 | 10.65 391.9954
1067 | 10.66 391.9954
1068 | 10.67 391.9954
1069 | 10.68 391.9954
1070 | 10.69 389.1753
1071 | 10.70 389.1753
1072 | 10.71 389.1753
1073 | 10.72 386.3755
1074 | 10.73 386.3755
1075 | 10.74 386.3755
1076 | 10.75 386.3755
1077 | 10.76 386.3755
1078 | 10.77 0.0000
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1080 | 10.79 0.0000
1081 | 10.80 0.0000
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1100 | 10.99 0.0000
1101 | 11.00 0.0000
1102 | 11.01 0.0000
1103 | 11.02 0.0000
1104 | 11.03 0.0000
1105 | 11.04 0.0000
1106 | 11.05 0.0000
1107 | 11.06 0.0000
1108 | 11.07 0.0000
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1111 | 11.10 0.0000
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1120 | 11.19 0.0000
1121 | 11.20 0.0000
1122 | 11.21 0.0000
1123 | 11.22 0.0000
1124 | 11.23 0.0000
1125 | 11.24 0.0000
1126 | 11.25 0.0000
1127 | 11.26 0.0000
1128 | 11.27 0.0000
1129 | 11.28 0.0000
1130 | 11.29 0.0000
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1177 | 11.76 0.0000
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1190 | 11.89 0.0000
1191 | 11.90 0.0000
1192 | 11.91 0.0000
1193 | 11.92 0.0000
1194 | 11.93 0.0000
1195 | 11.94 0.0000
1196 | 11.95 0.0000
1197 | 11.96 0.0000
1198 | 11.97 0.0000
1199 | 11.98 0.0000
1200 | 11.99 0.0000
1201 | 12.00 0.0000
1202 | 12.01 0.0000
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1207 | 12.06 0.0000
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1231 | 12.30 0.0000
1232 | 12.31 0.0000
1233 | 12.32 0.0000
1234 | 12.33 0.0000
1235 | 12.34 0.0000
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1286 | 12.85 0.0000
1287 | 12.86 375.3761
1288 | 12.87 375.3761
1289 | 12.88 375.3761
1290 | 12.89 375.3761
1291 | 12.90 375.3761
1292 | 12.91 375.3761
1293 | 12.92 375.3761
1294 | 12.93 375.3761
1295 | 12.94 375.3761
1296 | 12.95 375.3761
1297 | 12.96 375.3761
1298 | 12.97 375.3761
1299 | 12.98 375.3761
1300 | 12.99 375.3761
1301 | 13.00 375.3761
1302 | 13.01 383.5958
1303 | 13.02 391.9954
1304 | 13.03 391.9954
1305 | 13.04 394.8360
1306 | 13.05 397.6971
1307 | 13.06 397.6971
1308 | 13.07 397.6971
1309 | 13.08 397.6971
1310 | 13.09 400.5790
1311 | 13.10 400.5790
1312 | 13.11 400.5790
1313 | 13.12 400.5790
1314 | 13.13 400.5790
1315 | 13.14 400.5790
1316 | 13.15 403.4818
1317 | 13.16 403.4818
1318 | 13.17 403.4818
1319 | 13.18 403.4818
1320 | 13.19 403.4818
1321 | 13.20 400.5790
1322 | 13.21 400.5790
1323 | 13.22 400.5790
1324 | 13.23 400.5790
1325 | 13.24 406.4056
1326 | 13.25 406.4056
1327 | 13.26 421.3454
1328 | 13.27 440.0000
1329 | 13.28 459.4805
1330 | 13.29 462.8100
1331 | 13.30 466.1638
1332 | 13.31 466.1638
1333 | 13.32 466.1638
1334 | 13.33 466.1638
1335 | 13.34 462.8100
1336 | 13.35 459.4805
1337 | 13.36 456.1748
1338 | 13.37 456.1748
1339 | 13.38 456.1748
1340 | 13.39 456.1748
1341 | 13.40 456.1748
1342 | 13.41 452.8930
1343 | 13.42 452.8930
1344 | 13.43 452.8930
1345 | 13.44 440.0000
1346 | 13.45 440.0000
1347 | 13.46 440.0000
1348 | 13.47 440.0000
1349 | 13.48 440.0000
1350 | 13.49 440.0000
1351 | 13.50 397.6971
1352 | 13.51 397.6971
1353 | 13.52 397.6971
1354 | 13.53 397.6971
1355 | 13.54 397.6971
1356 | 13.55 397.6971
1357 | 13.56 397.6971
1358 | 13.57 397.6971
1359 | 13.58 397.6971
1360 | 13.59 397.6971
1361 | 13.60 394.8360
1362 | 13.61 394.8360
1363 | 13.62 391.9954
1364 | 13.63 391.9954
1365 | 13.64 391.9954
1366 | 13.65 391.9954
1367 | 13.66 394.8360
1368 | 13.67 397.6971
1369 | 13.68 406.4056
1370 | 13.69 418.3142
1371 | 13.70 421.3454
1372 | 13.71 436.8345
1373 | 13.72 452.8930
1374 | 13.73 456.1748
1375 | 13.74 456.1748
1376 | 13.75 456.1748
1377 | 13.76 456.1748
1378 | 13.77 452.8930
1379 | 13.78 452.8930
1380 | 13.79 449.6347
1381 | 13.80 449.6347
1382 | 13.81 449.6347
1383 | 13.82 449.6347
1384 | 13.83 452.8930
1385 | 13.84 452.8930
1386 | 13.85 452.8930
1387 | 13.86 446.3999
1388 | 13.87 443.1884
1389 | 13.88 443.1884
1390 | 13.89 440.0000
1391 | 13.90 440.0000
1392 | 13.91 440.0000
1393 | 13.92 440.0000
1394 | 13.93 440.0000
1395 | 13.94 440.0000
1396 | 13.95 0.0000
1397 | 13.96 0.0000
1398 | 13.97 0.0000
1399 | 13.98 0.0000
1400 | 13.99 0.0000
1401 | 14.00 0.0000
1402 | 14.01 0.0000
1403 | 14.02 0.0000
1404 | 14.03 0.0000
1405 | 14.04 0.0000
1406 | 14.05 0.0000
1407 | 14.06 493.8833
1408 | 14.07 493.8833
1409 | 14.08 504.6979
1410 | 14.09 504.6979
1411 | 14.10 504.6979
1412 | 14.11 504.6979
1413 | 14.12 504.6979
1414 | 14.13 501.0670
1415 | 14.14 501.0670
1416 | 14.15 501.0670
1417 | 14.16 501.0670
1418 | 14.17 501.0670
1419 | 14.18 501.0670
1420 | 14.19 501.0670
1421 | 14.20 501.0670
1422 | 14.21 501.0670
1423 | 14.22 497.4622
1424 | 14.23 497.4622
1425 | 14.24 497.4622
1426 | 14.25 497.4622
1427 | 14.26 497.4622
1428 | 14.27 497.4622
1429 | 14.28 497.4622
1430 | 14.29 497.4622
1431 | 14.30 497.4622
1432 | 14.31 497.4622
1433 | 14.32 497.4622
1434 | 14.33 497.4622
1435 | 14.34 497.4622
1436 | 14.35 497.4622
1437 | 14.36 497.4622
1438 | 14.37 497.4622
1439 | 14.38 497.4622
1440 | 14.39 497.4622
1441 | 14.40 497.4622
1442 | 14.41 497.4622
1443 | 14.42 497.4622
1444 | 14.43 497.4622
1445 | 14.44 497.4622
1446 | 14.45 493.8833
1447 | 14.46 493.8833
1448 | 14.47 493.8833
1449 | 14.48 493.8833
1450 | 14.49 493.8833
1451 | 14.50 493.8833
1452 | 14.51 493.8833
1453 | 14.52 493.8833
1454 | 14.53 493.8833
1455 | 14.54 497.4622
1456 | 14.55 497.4622
1457 | 14.56 501.0670
1458 | 14.57 504.6979
1459 | 14.58 508.3552
1460 | 14.59 508.3552
1461 | 14.60 508.3552
1462 | 14.61 508.3552
1463 | 14.62 508.3552
1464 | 14.63 504.6979
1465 | 14.64 501.0670
1466 | 14.65 497.4622
1467 | 14.66 497.4622
1468 | 14.67 497.4622
1469 | 14.68 497.4622
1470 | 14.69 501.0670
1471 | 14.70 508.3552
1472 | 14.71 508.3552
1473 | 14.72 508.3552
1474 | 14.73 508.3552
1475 | 14.74 508.3552
1476 | 14.75 508.3552
1477 | 14.76 504.6979
1478 | 14.77 501.0670
1479 | 14.78 501.0670
1480 | 14.79 497.4622
1481 | 14.80 493.8833
1482 | 14.81 483.3004
1483 | 14.82 476.3714
1484 | 14.83 472.9443
1485 | 14.84 440.0000
1486 | 14.85 436.8345
1487 | 14.86 427.4741
1488 | 14.87 424.3987
1489 | 14.88 421.3454
1490 | 14.89 418.3142
1491 | 14.90 418.3142
1492 | 14.91 418.3142
1493 | 14.92 418.3142
1494 | 14.93 418.3142
1495 | 14.94 415.3047
1496 | 14.95 415.3047
1497 | 14.96 415.3047
1498 | 14.97 412.3169
1499 | 14.98 412.3169
1500 | 14.99 412.3169
1501 | 15.00 412.3169
1502 | 15.01 412.3169
1503 | 15.02 412.3169
1504 | 15.03 412.3169
1505 | 15.04 412.3169
1506 | 15.05 412.3169
1507 | 15.06 412.3169
1508 | 15.07 412.3169
1509 | 15.08 412.3169
1510 | 15.09 412.3169
1511 | 15.10 412.3169
1512 | 15.11 412.3169
1513 | 15.12 412.3169
1514 | 15.13 412.3169
1515 | 15.14 412.3169
1516 | 15.15 332.0162
1517 | 15.16 329.6276
1518 | 15.17 327.2561
1519 | 15.18 327.2561
1520 | 15.19 327.2561
1521 | 15.20 324.9018
1522 | 15.21 324.9018
1523 | 15.22 324.9018
1524 | 15.23 324.9018
1525 | 15.24 327.2561
1526 | 15.25 327.2561
1527 | 15.26 329.6276
1528 | 15.27 329.6276
1529 | 15.28 332.0162
1530 | 15.29 332.0162
1531 | 15.30 332.0162
1532 | 15.31 332.0162
1533 | 15.32 332.0162
1534 | 15.33 332.0162
1535 | 15.34 332.0162
1536 | 15.35 334.4221
1537 | 15.36 334.4221
1538 | 15.37 334.4221
1539 | 15.38 334.4221
1540 | 15.39 334.4221
1541 | 15.40 336.8455
1542 | 15.41 336.8455
1543 | 15.42 336.8455
1544 | 15.43 336.8455
1545 | 15.44 336.8455
1546 | 15.45 336.8455
1547 | 15.46 336.8455
1548 | 15.47 336.8455
1549 | 15.48 334.4221
1550 | 15.49 334.4221
1551 | 15.50 0.0000
1552 | 15.51 0.0000
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1573 | 15.72 375.3761
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1575 | 15.74 378.0962
1576 | 15.75 378.0962
1577 | 15.76 378.0962
1578 | 15.77 378.0962
1579 | 15.78 375.3761
1580 | 15.79 375.3761
1581 | 15.80 375.3761
1582 | 15.81 375.3761
1583 | 15.82 375.3761
1584 | 15.83 375.3761
1585 | 15.84 375.3761
1586 | 15.85 375.3761
1587 | 15.86 375.3761
1588 | 15.87 375.3761
1589 | 15.88 375.3761
1590 | 15.89 375.3761
1591 | 15.90 375.3761
1592 | 15.91 375.3761
1593 | 15.92 375.3761
1594 | 15.93 375.3761
1595 | 15.94 375.3761
1596 | 15.95 375.3761
1597 | 15.96 372.6756
1598 | 15.97 372.6756
1599 | 15.98 372.6756
1600 | 15.99 372.6756
1601 | 16.00 372.6756
1602 | 16.01 372.6756
1603 | 16.02 375.3761
1604 | 16.03 375.3761
1605 | 16.04 375.3761
1606 | 16.05 0.0000
1607 | 16.06 0.0000
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1610 | 16.09 0.0000
1611 | 16.10 0.0000
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1623 | 16.22 0.0000
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1627 | 16.26 0.0000
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1631 | 16.30 0.0000
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1633 | 16.32 0.0000
1634 | 16.33 0.0000
1635 | 16.34 0.0000
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1686 | 16.85 0.0000
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1690 | 16.89 0.0000
1691 | 16.90 0.0000
1692 | 16.91 0.0000
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1695 | 16.94 0.0000
1696 | 16.95 0.0000
1697 | 16.96 0.0000
1698 | 16.97 0.0000
1699 | 16.98 0.0000
1700 | 16.99 0.0000
1701 | 17.00 0.0000
1702 | 17.01 0.0000
1703 | 17.02 0.0000
1704 | 17.03 0.0000
1705 | 17.04 0.0000
1706 | 17.05 0.0000
1707 | 17.06 0.0000
1708 | 17.07 0.0000
1709 | 17.08 0.0000
1710 | 17.09 0.0000
1711 | 17.10 0.0000
1712 | 17.11 0.0000
1713 | 17.12 0.0000
1714 | 17.13 0.0000
1715 | 17.14 0.0000
1716 | 17.15 0.0000
1717 | 17.16 0.0000
1718 | 17.17 0.0000
1719 | 17.18 0.0000
1720 | 17.19 0.0000
1721 | 17.20 0.0000
1722 | 17.21 0.0000
1723 | 17.22 0.0000
1724 | 17.23 0.0000
1725 | 17.24 0.0000
1726 | 17.25 0.0000
1727 | 17.26 0.0000
1728 | 17.27 0.0000
1729 | 17.28 0.0000
1730 | 17.29 0.0000
1731 | 17.30 0.0000
1732 | 17.31 0.0000
1733 | 17.32 0.0000
1734 | 17.33 0.0000
1735 | 17.34 0.0000
1736 | 17.35 0.0000
1737 | 17.36 0.0000
1738 | 17.37 0.0000
1739 | 17.38 0.0000
1740 | 17.39 0.0000
1741 | 17.40 0.0000
1742 | 17.41 0.0000
1743 | 17.42 0.0000
1744 | 17.43 0.0000
1745 | 17.44 0.0000
1746 | 17.45 0.0000
1747 | 17.46 0.0000
1748 | 17.47 0.0000
1749 | 17.48 356.8753
1750 | 17.49 359.4614
1751 | 17.50 362.0662
1752 | 17.51 367.3326
1753 | 17.52 369.9944
1754 | 17.53 372.6756
1755 | 17.54 375.3761
1756 | 17.55 375.3761
1757 | 17.56 375.3761
1758 | 17.57 378.0962
1759 | 17.58 378.0962
1760 | 17.59 378.0962
1761 | 17.60 378.0962
1762 | 17.61 378.0962
1763 | 17.62 378.0962
1764 | 17.63 378.0962
1765 | 17.64 378.0962
1766 | 17.65 380.8361
1767 | 17.66 380.8361
1768 | 17.67 383.5958
1769 | 17.68 383.5958
1770 | 17.69 383.5958
1771 | 17.70 383.5958
1772 | 17.71 383.5958
1773 | 17.72 386.3755
1774 | 17.73 389.1753
1775 | 17.74 406.4056
1776 | 17.75 406.4056
1777 | 17.76 406.4056
1778 | 17.77 406.4056
1779 | 17.78 406.4056
1780 | 17.79 400.5790
1781 | 17.80 400.5790
1782 | 17.81 400.5790
1783 | 17.82 400.5790
1784 | 17.83 400.5790
1785 | 17.84 400.5790
1786 | 17.85 403.4818
1787 | 17.86 406.4056
1788 | 17.87 406.4056
1789 | 17.88 406.4056
1790 | 17.89 406.4056
1791 | 17.90 400.5790
1792 | 17.91 400.5790
1793 | 17.92 400.5790
1794 | 17.93 400.5790
1795 | 17.94 400.5790
1796 | 17.95 397.6971
1797 | 17.96 0.0000
1798 | 17.97 0.0000
1799 | 17.98 0.0000
1800 | 17.99 0.0000
1801 | 18.00 383.5958
1802 | 18.01 386.3755
1803 | 18.02 391.9954
1804 | 18.03 400.5790
1805 | 18.04 400.5790
1806 | 18.05 400.5790
1807 | 18.06 400.5790
1808 | 18.07 400.5790
1809 | 18.08 403.4818
1810 | 18.09 403.4818
1811 | 18.10 406.4056
1812 | 18.11 409.3506
1813 | 18.12 415.3047
1814 | 18.13 421.3454
1815 | 18.14 427.4741
1816 | 18.15 433.6918
1817 | 18.16 436.8345
1818 | 18.17 440.0000
1819 | 18.18 440.0000
1820 | 18.19 440.0000
1821 | 18.20 440.0000
1822 | 18.21 440.0000
1823 | 18.22 440.0000
1824 | 18.23 436.8345
1825 | 18.24 436.8345
1826 | 18.25 436.8345
1827 | 18.26 436.8345
1828 | 18.27 436.8345
1829 | 18.28 436.8345
1830 | 18.29 0.0000
1831 | 18.30 0.0000
1832 | 18.31 0.0000
1833 | 18.32 0.0000
1834 | 18.33 0.0000
1835 | 18.34 0.0000
1836 | 18.35 0.0000
1837 | 18.36 0.0000
1838 | 18.37 339.2864
1839 | 18.38 339.2864
1840 | 18.39 339.2864
1841 | 18.40 339.2864
1842 | 18.41 339.2864
1843 | 18.42 339.2864
1844 | 18.43 339.2864
1845 | 18.44 341.7450
1846 | 18.45 346.7158
1847 | 18.46 349.2282
1848 | 18.47 354.3079
1849 | 18.48 356.8753
1850 | 18.49 359.4614
1851 | 18.50 364.6899
1852 | 18.51 369.9944
1853 | 18.52 369.9944
1854 | 18.53 372.6756
1855 | 18.54 372.6756
1856 | 18.55 372.6756
1857 | 18.56 372.6756
1858 | 18.57 369.9944
1859 | 18.58 367.3326
1860 | 18.59 367.3326
1861 | 18.60 334.4221
1862 | 18.61 0.0000
1863 | 18.62 0.0000
1864 | 18.63 297.9362
1865 | 18.64 297.9362
1866 | 18.65 293.6648
1867 | 18.66 293.6648
1868 | 18.67 291.5521
1869 | 18.68 289.4546
1870 | 18.69 289.4546
1871 | 18.70 289.4546
1872 | 18.71 289.4546
1873 | 18.72 289.4546
1874 | 18.73 289.4546
1875 | 18.74 289.4546
1876 | 18.75 289.4546
1877 | 18.76 291.5521
1878 | 18.77 291.5521
1879 | 18.78 291.5521
1880 | 18.79 291.5521
1881 | 18.80 291.5521
1882 | 18.81 291.5521
1883 | 18.82 291.5521
1884 | 18.83 293.6648
1885 | 18.84 293.6648
1886 | 18.85 293.6648
1887 | 18.86 293.6648
1888 | 18.87 293.6648
1889 | 18.88 0.0000
1890 | 18.89 0.0000
1891 | 18.90 0.0000
1892 | 18.91 289.4546
1893 | 18.92 289.4546
1894 | 18.93 289.4546
1895 | 18.94 289.4546
1896 | 18.95 289.4546
1897 | 18.96 291.5521
1898 | 18.97 291.5521
1899 | 18.98 291.5521
1900 | 18.99 297.9362
1901 | 19.00 306.6664
1902 | 19.01 313.3815
1903 | 19.02 317.9398
1904 | 19.03 329.6276
1905 | 19.04 332.0162
1906 | 19.05 332.0162
1907 | 19.06 332.0162
1908 | 19.07 332.0162
1909 | 19.08 332.0162
1910 | 19.09 332.0162
1911 | 19.10 332.0162
1912 | 19.11 332.0162
1913 | 19.12 329.6276
1914 | 19.13 329.6276
1915 | 19.14 329.6276
1916 | 19.15 329.6276
1917 | 19.16 329.6276
1918 | 19.17 329.6276
1919 | 19.18 329.6276
1920 | 19.19 329.6276
1921 | 19.20 329.6276
1922 | 19.21 329.6276
1923 | 19.22 329.6276
1924 | 19.23 329.6276
1925 | 19.24 329.6276
1926 | 19.25 329.6276
1927 | 19.26 329.6276
1928 | 19.27 329.6276
1929 | 19.28 332.0162
1930 | 19.29 332.0162
1931 | 19.30 334.4221
1932 | 19.31 334.4221
1933 | 19.32 334.4221
1934 | 19.33 334.4221
1935 | 19.34 334.4221
1936 | 19.35 334.4221
1937 | 19.36 0.0000
1938 | 19.37 0.0000
1939 | 19.38 0.0000
1940 | 19.39 0.0000
1941 | 19.40 0.0000
1942 | 19.41 0.0000
1943 | 19.42 0.0000
1944 | 19.43 0.0000
1945 | 19.44 0.0000
1946 | 19.45 0.0000
1947 | 19.46 0.0000
1948 | 19.47 0.0000
1949 | 19.48 0.0000
1950 | 19.49 0.0000
1951 | 19.50 0.0000
1952 | 19.51 0.0000
1953 | 19.52 0.0000
1954 | 19.53 0.0000
1955 | 19.54 0.0000
1956 | 19.55 0.0000
1957 | 19.56 0.0000
1958 | 19.57 0.0000
1959 | 19.58 0.0000
1960 | 19.59 0.0000
1961 | 19.60 0.0000
1962 | 19.61 0.0000
1963 | 19.62 0.0000
1964 | 19.63 0.0000
1965 | 19.64 0.0000
1966 | 19.65 0.0000
1967 | 19.66 0.0000
1968 | 19.67 0.0000
1969 | 19.68 0.0000
1970 | 19.69 0.0000
1971 | 19.70 0.0000
1972 | 19.71 0.0000
1973 | 19.72 0.0000
1974 | 19.73 0.0000
1975 | 19.74 0.0000
1976 | 19.75 0.0000
1977 | 19.76 0.0000
1978 | 19.77 0.0000
1979 | 19.78 0.0000
1980 | 19.79 0.0000
1981 | 19.80 0.0000
1982 | 19.81 0.0000
1983 | 19.82 0.0000
1984 | 19.83 0.0000
1985 | 19.84 0.0000
1986 | 19.85 0.0000
1987 | 19.86 0.0000
1988 | 19.87 0.0000
1989 | 19.88 0.0000
1990 | 19.89 0.0000
1991 | 19.90 0.0000
1992 | 19.91 0.0000
1993 | 19.92 0.0000
1994 | 19.93 0.0000
1995 | 19.94 0.0000
1996 | 19.95 0.0000
1997 | 19.96 0.0000
1998 | 19.97 0.0000
1999 | 19.98 0.0000
2000 | 19.99 0.0000
2001 | 20.00 0.0000
2002 | 20.01 0.0000
2003 | 20.02 0.0000
2004 | 20.03 0.0000
2005 | 20.04 0.0000
2006 | 20.05 0.0000
2007 | 20.06 0.0000
2008 | 20.07 0.0000
2009 | 20.08 0.0000
2010 | 20.09 0.0000
2011 | 20.10 0.0000
2012 | 20.11 0.0000
2013 | 20.12 0.0000
2014 | 20.13 0.0000
2015 | 20.14 0.0000
2016 | 20.15 0.0000
2017 | 20.16 0.0000
2018 | 20.17 0.0000
2019 | 20.18 0.0000
2020 | 20.19 0.0000
2021 | 20.20 0.0000
2022 | 20.21 0.0000
2023 | 20.22 0.0000
2024 | 20.23 0.0000
2025 | 20.24 0.0000
2026 | 20.25 0.0000
2027 | 20.26 0.0000
2028 | 20.27 0.0000
2029 | 20.28 0.0000
2030 | 20.29 0.0000
2031 | 20.30 0.0000
2032 | 20.31 0.0000
2033 | 20.32 0.0000
2034 | 20.33 0.0000
2035 | 20.34 0.0000
2036 | 20.35 0.0000
2037 | 20.36 0.0000
2038 | 20.37 0.0000
2039 | 20.38 0.0000
2040 | 20.39 0.0000
2041 | 20.40 0.0000
2042 | 20.41 0.0000
2043 | 20.42 0.0000
2044 | 20.43 0.0000
2045 | 20.44 0.0000
2046 | 20.45 0.0000
2047 | 20.46 0.0000
2048 | 20.47 0.0000
2049 | 20.48 0.0000
2050 | 20.49 0.0000
2051 | 20.50 0.0000
2052 | 20.51 0.0000
2053 | 20.52 0.0000
2054 | 20.53 0.0000
2055 | 20.54 0.0000
2056 | 20.55 0.0000
2057 | 20.56 0.0000
2058 | 20.57 0.0000
2059 | 20.58 0.0000
2060 | 20.59 0.0000
2061 | 20.60 0.0000
2062 | 20.61 329.6276
2063 | 20.62 329.6276
2064 | 20.63 329.6276
2065 | 20.64 329.6276
2066 | 20.65 329.6276
2067 | 20.66 327.2561
2068 | 20.67 327.2561
2069 | 20.68 327.2561
2070 | 20.69 327.2561
2071 | 20.70 327.2561
2072 | 20.71 329.6276
2073 | 20.72 329.6276
2074 | 20.73 329.6276
2075 | 20.74 332.0162
2076 | 20.75 332.0162
2077 | 20.76 332.0162
2078 | 20.77 332.0162
2079 | 20.78 332.0162
2080 | 20.79 332.0162
2081 | 20.80 332.0162
2082 | 20.81 329.6276
2083 | 20.82 329.6276
2084 | 20.83 329.6276
2085 | 20.84 329.6276
2086 | 20.85 329.6276
2087 | 20.86 329.6276
2088 | 20.87 329.6276
2089 | 20.88 329.6276
2090 | 20.89 329.6276
2091 | 20.90 329.6276
2092 | 20.91 329.6276
2093 | 20.92 329.6276
2094 | 20.93 329.6276
2095 | 20.94 329.6276
2096 | 20.95 329.6276
2097 | 20.96 329.6276
2098 | 20.97 329.6276
2099 | 20.98 334.4221
2100 | 20.99 336.8455
2101 | 21.00 344.2214
2102 | 21.01 344.2214
2103 | 21.02 344.2214
2104 | 21.03 344.2214
2105 | 21.04 336.8455
2106 | 21.05 334.4221
2107 | 21.06 334.4221
2108 | 21.07 334.4221
2109 | 21.08 0.0000
2110 | 21.09 0.0000
2111 | 21.10 0.0000
2112 | 21.11 0.0000
2113 | 21.12 0.0000
2114 | 21.13 0.0000
2115 | 21.14 0.0000
2116 | 21.15 0.0000
2117 | 21.16 0.0000
2118 | 21.17 0.0000
2119 | 21.18 0.0000
2120 | 21.19 0.0000
2121 | 21.20 0.0000
2122 | 21.21 0.0000
2123 | 21.22 0.0000
2124 | 21.23 0.0000
2125 | 21.24 0.0000
2126 | 21.25 0.0000
2127 | 21.26 0.0000
2128 | 21.27 0.0000
2129 | 21.28 0.0000
2130 | 21.29 0.0000
2131 | 21.30 0.0000
2132 | 21.31 0.0000
2133 | 21.32 0.0000
2134 | 21.33 0.0000
2135 | 21.34 0.0000
2136 | 21.35 0.0000
2137 | 21.36 0.0000
2138 | 21.37 0.0000
2139 | 21.38 0.0000
2140 | 21.39 364.6899
2141 | 21.40 364.6899
2142 | 21.41 364.6899
2143 | 21.42 364.6899
2144 | 21.43 362.0662
2145 | 21.44 362.0662
2146 | 21.45 362.0662
2147 | 21.46 362.0662
2148 | 21.47 362.0662
2149 | 21.48 362.0662
2150 | 21.49 362.0662
2151 | 21.50 359.4614
2152 | 21.51 0.0000
2153 | 21.52 0.0000
2154 | 21.53 0.0000
2155 | 21.54 0.0000
2156 | 21.55 0.0000
2157 | 21.56 0.0000
2158 | 21.57 0.0000
2159 | 21.58 0.0000
2160 | 21.59 0.0000
2161 | 21.60 0.0000
2162 | 21.61 0.0000
2163 | 21.62 0.0000
2164 | 21.63 0.0000
2165 | 21.64 0.0000
2166 | 21.65 0.0000
2167 | 21.66 0.0000
2168 | 21.67 0.0000
2169 | 21.68 0.0000
2170 | 21.69 0.0000
2171 | 21.70 0.0000
2172 | 21.71 0.0000
2173 | 21.72 0.0000
2174 | 21.73 0.0000
2175 | 21.74 0.0000
2176 | 21.75 0.0000
2177 | 21.76 0.0000
2178 | 21.77 0.0000
2179 | 21.78 0.0000
2180 | 21.79 0.0000
2181 | 21.80 0.0000
2182 | 21.81 0.0000
2183 | 21.82 0.0000
2184 | 21.83 0.0000
2185 | 21.84 0.0000
2186 | 21.85 0.0000
2187 | 21.86 0.0000
2188 | 21.87 0.0000
2189 | 21.88 0.0000
2190 | 21.89 0.0000
2191 | 21.90 0.0000
2192 | 21.91 0.0000
2193 | 21.92 0.0000
2194 | 21.93 0.0000
2195 | 21.94 0.0000
2196 | 21.95 0.0000
2197 | 21.96 0.0000
2198 | 21.97 0.0000
2199 | 21.98 0.0000
2200 | 21.99 0.0000
2201 | 22.00 0.0000
2202 | 22.01 391.9954
2203 | 22.02 391.9954
2204 | 22.03 391.9954
2205 | 22.04 391.9954
2206 | 22.05 391.9954
2207 | 22.06 391.9954
2208 | 22.07 391.9954
2209 | 22.08 391.9954
2210 | 22.09 391.9954
2211 | 22.10 391.9954
2212 | 22.11 391.9954
2213 | 22.12 391.9954
2214 | 22.13 391.9954
2215 | 22.14 391.9954
2216 | 22.15 391.9954
2217 | 22.16 391.9954
2218 | 22.17 391.9954
2219 | 22.18 389.1753
2220 | 22.19 389.1753
2221 | 22.20 386.3755
2222 | 22.21 386.3755
2223 | 22.22 386.3755
2224 | 22.23 356.8753
2225 | 22.24 356.8753
2226 | 22.25 334.4221
2227 | 22.26 332.0162
2228 | 22.27 332.0162
2229 | 22.28 332.0162
2230 | 22.29 332.0162
2231 | 22.30 332.0162
2232 | 22.31 332.0162
2233 | 22.32 332.0162
2234 | 22.33 329.6276
2235 | 22.34 329.6276
2236 | 22.35 329.6276
2237 | 22.36 329.6276
2238 | 22.37 329.6276
2239 | 22.38 329.6276
2240 | 22.39 329.6276
2241 | 22.40 332.0162
2242 | 22.41 332.0162
2243 | 22.42 332.0162
2244 | 22.43 332.0162
2245 | 22.44 332.0162
2246 | 22.45 332.0162
2247 | 22.46 332.0162
2248 | 22.47 332.0162
2249 | 22.48 332.0162
2250 | 22.49 0.0000
2251 | 22.50 0.0000
2252 | 22.51 0.0000
2253 | 22.52 0.0000
2254 | 22.53 0.0000
2255 | 22.54 0.0000
2256 | 22.55 0.0000
2257 | 22.56 0.0000
2258 | 22.57 0.0000
2259 | 22.58 0.0000
2260 | 22.59 0.0000
2261 | 22.60 0.0000
2262 | 22.61 0.0000
2263 | 22.62 372.6756
2264 | 22.63 372.6756
2265 | 22.64 372.6756
2266 | 22.65 369.9944
2267 | 22.66 369.9944
2268 | 22.67 367.3326
2269 | 22.68 367.3326
2270 | 22.69 367.3326
2271 | 22.70 367.3326
2272 | 22.71 367.3326
2273 | 22.72 367.3326
2274 | 22.73 367.3326
2275 | 22.74 367.3326
2276 | 22.75 367.3326
2277 | 22.76 367.3326
2278 | 22.77 367.3326
2279 | 22.78 367.3326
2280 | 22.79 367.3326
2281 | 22.80 367.3326
2282 | 22.81 367.3326
2283 | 22.82 367.3326
2284 | 22.83 0.0000
2285 | 22.84 0.0000
2286 | 22.85 0.0000
2287 | 22.86 0.0000
2288 | 22.87 389.1753
2289 | 22.88 397.6971
2290 | 22.89 397.6971
2291 | 22.90 397.6971
2292 | 22.91 397.6971
2293 | 22.92 397.6971
2294 | 22.93 397.6971
2295 | 22.94 397.6971
2296 | 22.95 397.6971
2297 | 22.96 400.5790
2298 | 22.97 400.5790
2299 | 22.98 400.5790
2300 | 22.99 397.6971
2301 | 23.00 397.6971
2302 | 23.01 397.6971
2303 | 23.02 397.6971
2304 | 23.03 397.6971
2305 | 23.04 397.6971
2306 | 23.05 397.6971
2307 | 23.06 397.6971
2308 | 23.07 397.6971
2309 | 23.08 394.8360
2310 | 23.09 394.8360
2311 | 23.10 389.1753
2312 | 23.11 389.1753
2313 | 23.12 354.3079
2314 | 23.13 341.7450
2315 | 23.14 339.2864
2316 | 23.15 339.2864
2317 | 23.16 339.2864
2318 | 23.17 339.2864
2319 | 23.18 339.2864
2320 | 23.19 339.2864
2321 | 23.20 336.8455
2322 | 23.21 336.8455
2323 | 23.22 336.8455
2324 | 23.23 336.8455
2325 | 23.24 334.4221
2326 | 23.25 334.4221
2327 | 23.26 334.4221
2328 | 23.27 332.0162
2329 | 23.28 332.0162
2330 | 23.29 332.0162
2331 | 23.30 332.0162
2332 | 23.31 332.0162
2333 | 23.32 332.0162
2334 | 23.33 332.0162
2335 | 23.34 332.0162
2336 | 23.35 334.4221
2337 | 23.36 334.4221
2338 | 23.37 334.4221
2339 | 23.38 336.8455
2340 | 23.39 336.8455
2341 | 23.40 341.7450
2342 | 23.41 354.3079
2343 | 23.42 364.6899
2344 | 23.43 367.3326
2345 | 23.44 367.3326
2346 | 23.45 367.3326
2347 | 23.46 367.3326
2348 | 23.47 364.6899
2349 | 23.48 362.0662
2350 | 23.49 359.4614
2351 | 23.50 359.4614
2352 | 23.51 359.4614
2353 | 23.52 359.4614
2354 | 23.53 359.4614
2355 | 23.54 359.4614
2356 | 23.55 359.4614
2357 | 23.56 364.6899
2358 | 23.57 367.3326
2359 | 23.58 372.6756
2360 | 23.59 372.6756
2361 | 23.60 372.6756
2362 | 23.61 372.6756
2363 | 23.62 372.6756
2364 | 23.63 369.9944
2365 | 23.64 367.3326
2366 | 23.65 367.3326
2367 | 23.66 364.6899
2368 | 23.67 364.6899
2369 | 23.68 364.6899
2370 | 23.69 367.3326
2371 | 23.70 372.6756
2372 | 23.71 372.6756
2373 | 23.72 380.8361
2374 | 23.73 383.5958
2375 | 23.74 386.3755
2376 | 23.75 389.1753
2377 | 23.76 389.1753
2378 | 23.77 389.1753
2379 | 23.78 389.1753
2380 | 23.79 389.1753
2381 | 23.80 386.3755
2382 | 23.81 386.3755
2383 | 23.82 386.3755
2384 | 23.83 386.3755
2385 | 23.84 386.3755
2386 | 23.85 389.1753
2387 | 23.86 389.1753
2388 | 23.87 389.1753
2389 | 23.88 389.1753
2390 | 23.89 389.1753
2391 | 23.90 389.1753
2392 | 23.91 389.1753
2393 | 23.92 389.1753
2394 | 23.93 389.1753
2395 | 23.94 389.1753
2396 | 23.95 389.1753
2397 | 23.96 389.1753
2398 | 23.97 389.1753
2399 | 23.98 389.1753
2400 | 23.99 389.1753
2401 | 24.00 389.1753
2402 | 24.01 389.1753
2403 | 24.02 389.1753
2404 | 24.03 386.3755
2405 | 24.04 386.3755
2406 | 24.05 386.3755
2407 | 24.06 386.3755
2408 | 24.07 386.3755
2409 | 24.08 386.3755
2410 | 24.09 0.0000
2411 | 24.10 0.0000
2412 | 24.11 0.0000
2413 | 24.12 0.0000
2414 | 24.13 0.0000
2415 | 24.14 0.0000
2416 | 24.15 0.0000
2417 | 24.16 0.0000
2418 | 24.17 0.0000
2419 | 24.18 0.0000
2420 | 24.19 0.0000
2421 | 24.20 0.0000
2422 | 24.21 0.0000
2423 | 24.22 0.0000
2424 | 24.23 0.0000
2425 | 24.24 0.0000
2426 | 24.25 0.0000
2427 | 24.26 0.0000
2428 | 24.27 0.0000
2429 | 24.28 0.0000
2430 | 24.29 0.0000
2431 | 24.30 0.0000
2432 | 24.31 0.0000
2433 | 24.32 0.0000
2434 | 24.33 0.0000
2435 | 24.34 0.0000
2436 | 24.35 0.0000
2437 | 24.36 0.0000
2438 | 24.37 0.0000
2439 | 24.38 0.0000
2440 | 24.39 0.0000
2441 | 24.40 0.0000
2442 | 24.41 0.0000
2443 | 24.42 0.0000
2444 | 24.43 0.0000
2445 | 24.44 0.0000
2446 | 24.45 0.0000
2447 | 24.46 0.0000
2448 | 24.47 0.0000
2449 | 24.48 0.0000
2450 | 24.49 0.0000
2451 | 24.50 0.0000
2452 | 24.51 0.0000
2453 | 24.52 0.0000
2454 | 24.53 0.0000
2455 | 24.54 0.0000
2456 | 24.55 0.0000
2457 | 24.56 0.0000
2458 | 24.57 0.0000
2459 | 24.58 0.0000
2460 | 24.59 0.0000
2461 | 24.60 0.0000
2462 | 24.61 0.0000
2463 | 24.62 0.0000
2464 | 24.63 0.0000
2465 | 24.64 0.0000
2466 | 24.65 0.0000
2467 | 24.66 0.0000
2468 | 24.67 0.0000
2469 | 24.68 0.0000
2470 | 24.69 0.0000
2471 | 24.70 0.0000
2472 | 24.71 0.0000
2473 | 24.72 0.0000
2474 | 24.73 0.0000
2475 | 24.74 0.0000
2476 | 24.75 0.0000
2477 | 24.76 0.0000
2478 | 24.77 0.0000
2479 | 24.78 0.0000
2480 | 24.79 0.0000
2481 | 


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