MikuAI/train_unsloth.ipynb

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{
"cells": [
{
"cell_type": "markdown",
"id": "0ff91594",
"metadata": {
"id": "IqM-T1RTzY6C",
"papermill": {
"duration": 0.022416,
"end_time": "2024-11-19T19:01:59.936783",
"exception": false,
"start_time": "2024-11-19T19:01:59.914367",
"status": "completed"
},
"tags": []
},
"source": [
"To run this, press \"*Runtime*\" and press \"*Run all*\" on a **free** Tesla T4 Google Colab instance!\n",
"<div class=\"align-center\">\n",
" <a href=\"https://github.com/unslothai/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n",
" <a href=\"https://discord.gg/u54VK8m8tk\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord button.png\" width=\"145\"></a>\n",
" <a href=\"https://ko-fi.com/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Kofi button.png\" width=\"145\"></a></a> Join Discord if you need help + support us if you can!\n",
"</div>\n",
"\n",
"To install Unsloth on your own computer, follow the installation instructions on our Github page [here](https://github.com/unslothai/unsloth#installation-instructions---conda).\n",
"\n",
"You will learn how to do [data prep](#Data), how to [train](#Train), how to [run the model](#Inference), & [how to save it](#Save) (eg for Llama.cpp)."
]
},
{
"cell_type": "markdown",
"id": "9f31fd0e",
"metadata": {
"papermill": {
"duration": 0.01882,
"end_time": "2024-11-19T19:01:59.975791",
"exception": false,
"start_time": "2024-11-19T19:01:59.956971",
"status": "completed"
},
"tags": []
},
"source": [
"## Kaggle is slow - you'll have to wait **5 minutes** for it to install.\n",
"\n",
"I suggest you to use our free Colab notebooks instead. I linked our Mistral Colab notebook here: [notebook](https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?usp=sharing)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "5da70b6b",
"metadata": {
"execution": {
"iopub.execute_input": "2024-11-19T19:02:00.014824Z",
"iopub.status.busy": "2024-11-19T19:02:00.014491Z",
"iopub.status.idle": "2024-11-19T19:06:21.486688Z",
"shell.execute_reply": "2024-11-19T19:06:21.485746Z"
},
"papermill": {
"duration": 261.495285,
"end_time": "2024-11-19T19:06:21.489744",
"exception": false,
"start_time": "2024-11-19T19:01:59.994459",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting pip3-autoremove\r\n",
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"torch 2.4.0 (/opt/conda/lib/python3.10/site-packages)\r\n",
" sympy 1.13.3 (/opt/conda/lib/python3.10/site-packages)\r\n",
" mpmath 1.3.0 (/opt/conda/lib/python3.10/site-packages)\r\n",
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" torch 2.4.0 (/opt/conda/lib/python3.10/site-packages)\r\n",
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" mpmath 1.3.0 (/opt/conda/lib/python3.10/site-packages)\r\n",
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" torch 2.4.0 (/opt/conda/lib/python3.10/site-packages)\r\n",
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"Found existing installation: sympy 1.13.3\r\n",
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"Installing collected packages: mpmath, triton, sympy, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, torch, xformers, torchvision, torchaudio\r\n",
"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\r\n",
"fastai 2.7.17 requires torch<2.5,>=1.10, but you have torch 2.5.1+cu121 which is incompatible.\u001b[0m\u001b[31m\r\n",
"\u001b[0mSuccessfully installed mpmath-1.3.0 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.21.5 nvidia-nvjitlink-cu12-12.1.105 nvidia-nvtx-cu12-12.1.105 sympy-1.13.1 torch-2.5.1+cu121 torchaudio-2.5.1+cu121 torchvision-0.20.1+cu121 triton-3.1.0 xformers-0.0.28.post3\r\n",
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"\u001b[?25hCollecting unsloth-zoo>=2024.11.1 (from unsloth[kaggle-new])\r\n",
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" Downloading trl-0.12.1-py3-none-any.whl.metadata (10 kB)\r\n",
"Collecting peft!=0.11.0,>=0.7.1 (from unsloth[kaggle-new])\r\n",
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"\u001b[?25hDownloading unsloth_zoo-2024.11.5-py3-none-any.whl (31 kB)\r\n",
"Downloading hf_transfer-0.1.8-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.6 MB)\r\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.6/3.6 MB\u001b[0m \u001b[31m82.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
"\u001b[?25hDownloading tyro-0.9.1-py3-none-any.whl (111 kB)\r\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m111.9/111.9 kB\u001b[0m \u001b[31m6.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
"\u001b[?25hDownloading unsloth-2024.11.7-py3-none-any.whl (163 kB)\r\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m163.9/163.9 kB\u001b[0m \u001b[31m9.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
"\u001b[?25hDownloading shtab-1.7.1-py3-none-any.whl (14 kB)\r\n",
"Installing collected packages: shtab, hf-transfer, tyro, transformers, bitsandbytes, trl, peft, unsloth-zoo, unsloth\r\n",
" Attempting uninstall: transformers\r\n",
" Found existing installation: transformers 4.45.1\r\n",
" Uninstalling transformers-4.45.1:\r\n",
" Successfully uninstalled transformers-4.45.1\r\n",
"Successfully installed bitsandbytes-0.44.1 hf-transfer-0.1.8 peft-0.13.2 shtab-1.7.1 transformers-4.46.3 trl-0.12.1 tyro-0.9.1 unsloth-2024.11.7 unsloth-zoo-2024.11.5\r\n",
"Found existing installation: unsloth 2024.11.7\r\n",
"Uninstalling unsloth-2024.11.7:\r\n",
" Successfully uninstalled unsloth-2024.11.7\r\n",
"Collecting git+https://github.com/unslothai/unsloth.git@a2f8db3e7341f983af5814a2c56f54fa29ee548d\r\n",
" Cloning https://github.com/unslothai/unsloth.git (to revision a2f8db3e7341f983af5814a2c56f54fa29ee548d) to /tmp/pip-req-build-7w3hakz0\r\n",
" Running command git clone --filter=blob:none --quiet https://github.com/unslothai/unsloth.git /tmp/pip-req-build-7w3hakz0\r\n",
" Running command git rev-parse -q --verify 'sha^a2f8db3e7341f983af5814a2c56f54fa29ee548d'\r\n",
" Running command git fetch -q https://github.com/unslothai/unsloth.git a2f8db3e7341f983af5814a2c56f54fa29ee548d\r\n",
" Running command git checkout -q a2f8db3e7341f983af5814a2c56f54fa29ee548d\r\n",
" Resolved https://github.com/unslothai/unsloth.git to commit a2f8db3e7341f983af5814a2c56f54fa29ee548d\r\n",
" Installing build dependencies ... \u001b[?25l-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \bdone\r\n",
"\u001b[?25h Getting requirements to build wheel ... \u001b[?25l-\b \bdone\r\n",
"\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25l-\b \bdone\r\n",
"\u001b[?25hBuilding wheels for collected packages: unsloth\r\n",
" Building wheel for unsloth (pyproject.toml) ... \u001b[?25l-\b \b\\\b \bdone\r\n",
"\u001b[?25h Created wheel for unsloth: filename=unsloth-2024.10.7-py3-none-any.whl size=164376 sha256=318d24041afad463f487f3927388d766e913ffa5b694f3e2e3b1a7851fa67a1c\r\n",
" Stored in directory: /root/.cache/pip/wheels/d5/c3/0d/98b9068092121456c620edb0a24e05fda5934229b776b63a7b\r\n",
"Successfully built unsloth\r\n",
"Installing collected packages: unsloth\r\n",
"Successfully installed unsloth-2024.10.7\r\n"
]
}
],
"source": [
"#%%capture\n",
"!pip install pip3-autoremove\n",
"!pip-autoremove torch torchvision torchaudio -y\n",
"!pip install torch torchvision torchaudio xformers --index-url https://download.pytorch.org/whl/cu121\n",
"# https://github.com/unslothai/unsloth/issues/1284\n",
"!pip install unsloth[kaggle-new]\n",
"# Also get the latest nightly Unsloth!\n",
"!pip uninstall unsloth -y && pip install git+https://github.com/unslothai/unsloth.git@a2f8db3e7341f983af5814a2c56f54fa29ee548d"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "6018b225",
"metadata": {
"execution": {
"iopub.execute_input": "2024-11-19T19:06:21.619747Z",
"iopub.status.busy": "2024-11-19T19:06:21.618961Z",
"iopub.status.idle": "2024-11-19T19:06:41.479598Z",
"shell.execute_reply": "2024-11-19T19:06:41.478738Z"
},
"papermill": {
"duration": 19.925903,
"end_time": "2024-11-19T19:06:41.482153",
"exception": false,
"start_time": "2024-11-19T19:06:21.556250",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting git+https://github.com/unslothai/unsloth-zoo.git\r\n",
" Cloning https://github.com/unslothai/unsloth-zoo.git to /tmp/pip-req-build-0xpxksif\r\n",
" Running command git clone --filter=blob:none --quiet https://github.com/unslothai/unsloth-zoo.git /tmp/pip-req-build-0xpxksif\r\n",
" Resolved https://github.com/unslothai/unsloth-zoo.git to commit f5421838ef8278cf96d0092d8271ecd6d433588c\r\n",
" Installing build dependencies ... \u001b[?25l-\b \b\\\b \b|\b \b/\b \bdone\r\n",
"\u001b[?25h Getting requirements to build wheel ... \u001b[?25l-\b \bdone\r\n",
"\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25l-\b \bdone\r\n",
"\u001b[?25hRequirement already satisfied: torch in /opt/conda/lib/python3.10/site-packages (from unsloth_zoo==2024.11.5) (2.5.1+cu121)\r\n",
"Requirement already satisfied: triton in /opt/conda/lib/python3.10/site-packages (from unsloth_zoo==2024.11.5) (3.1.0)\r\n",
"Requirement already satisfied: packaging in /opt/conda/lib/python3.10/site-packages (from unsloth_zoo==2024.11.5) (21.3)\r\n",
"Requirement already satisfied: tyro in /opt/conda/lib/python3.10/site-packages (from unsloth_zoo==2024.11.5) (0.9.1)\r\n",
"Requirement already satisfied: transformers>=4.46.1 in /opt/conda/lib/python3.10/site-packages (from unsloth_zoo==2024.11.5) (4.46.3)\r\n",
"Requirement already satisfied: datasets>=2.16.0 in /opt/conda/lib/python3.10/site-packages (from unsloth_zoo==2024.11.5) (3.0.1)\r\n",
"Requirement already satisfied: sentencepiece>=0.2.0 in /opt/conda/lib/python3.10/site-packages (from unsloth_zoo==2024.11.5) (0.2.0)\r\n",
"Requirement already satisfied: tqdm in /opt/conda/lib/python3.10/site-packages (from unsloth_zoo==2024.11.5) (4.66.4)\r\n",
"Requirement already satisfied: psutil in /opt/conda/lib/python3.10/site-packages (from unsloth_zoo==2024.11.5) (5.9.3)\r\n",
"Requirement already satisfied: wheel>=0.42.0 in /opt/conda/lib/python3.10/site-packages (from unsloth_zoo==2024.11.5) (0.43.0)\r\n",
"Requirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from unsloth_zoo==2024.11.5) (1.26.4)\r\n",
"Requirement already satisfied: accelerate>=0.34.1 in /opt/conda/lib/python3.10/site-packages (from unsloth_zoo==2024.11.5) (0.34.2)\r\n",
"Requirement already satisfied: trl!=0.9.0,!=0.9.1,!=0.9.2,!=0.9.3,>=0.7.9 in /opt/conda/lib/python3.10/site-packages (from unsloth_zoo==2024.11.5) (0.12.1)\r\n",
"Requirement already satisfied: peft!=0.11.0,>=0.7.1 in /opt/conda/lib/python3.10/site-packages (from unsloth_zoo==2024.11.5) (0.13.2)\r\n",
"Requirement already satisfied: protobuf<4.0.0 in /opt/conda/lib/python3.10/site-packages (from unsloth_zoo==2024.11.5) (3.20.3)\r\n",
"Requirement already satisfied: huggingface-hub in /opt/conda/lib/python3.10/site-packages (from unsloth_zoo==2024.11.5) (0.25.1)\r\n",
"Requirement already satisfied: hf-transfer in /opt/conda/lib/python3.10/site-packages (from unsloth_zoo==2024.11.5) (0.1.8)\r\n",
"Requirement already satisfied: pyyaml in /opt/conda/lib/python3.10/site-packages (from accelerate>=0.34.1->unsloth_zoo==2024.11.5) (6.0.2)\r\n",
"Requirement already satisfied: safetensors>=0.4.3 in /opt/conda/lib/python3.10/site-packages (from accelerate>=0.34.1->unsloth_zoo==2024.11.5) (0.4.5)\r\n",
"Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from datasets>=2.16.0->unsloth_zoo==2024.11.5) (3.15.1)\r\n",
"Requirement already satisfied: pyarrow>=15.0.0 in /opt/conda/lib/python3.10/site-packages (from datasets>=2.16.0->unsloth_zoo==2024.11.5) (16.1.0)\r\n",
"Requirement already satisfied: dill<0.3.9,>=0.3.0 in /opt/conda/lib/python3.10/site-packages (from datasets>=2.16.0->unsloth_zoo==2024.11.5) (0.3.8)\r\n",
"Requirement already satisfied: pandas in /opt/conda/lib/python3.10/site-packages (from datasets>=2.16.0->unsloth_zoo==2024.11.5) (2.2.2)\r\n",
"Requirement already satisfied: requests>=2.32.2 in /opt/conda/lib/python3.10/site-packages (from datasets>=2.16.0->unsloth_zoo==2024.11.5) (2.32.3)\r\n",
"Requirement already satisfied: xxhash in /opt/conda/lib/python3.10/site-packages (from datasets>=2.16.0->unsloth_zoo==2024.11.5) (3.4.1)\r\n",
"Requirement already satisfied: multiprocess in /opt/conda/lib/python3.10/site-packages (from datasets>=2.16.0->unsloth_zoo==2024.11.5) (0.70.16)\r\n",
"Requirement already satisfied: fsspec<=2024.6.1,>=2023.1.0 in /opt/conda/lib/python3.10/site-packages (from fsspec[http]<=2024.6.1,>=2023.1.0->datasets>=2.16.0->unsloth_zoo==2024.11.5) (2024.6.1)\r\n",
"Requirement already satisfied: aiohttp in /opt/conda/lib/python3.10/site-packages (from datasets>=2.16.0->unsloth_zoo==2024.11.5) (3.9.5)\r\n",
"Requirement already satisfied: typing-extensions>=3.7.4.3 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub->unsloth_zoo==2024.11.5) (4.12.2)\r\n",
"Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.10/site-packages (from packaging->unsloth_zoo==2024.11.5) (3.1.2)\r\n",
"Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch->unsloth_zoo==2024.11.5) (3.3)\r\n",
"Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch->unsloth_zoo==2024.11.5) (3.1.4)\r\n",
"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch->unsloth_zoo==2024.11.5) (12.1.105)\r\n",
"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch->unsloth_zoo==2024.11.5) (12.1.105)\r\n",
"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch->unsloth_zoo==2024.11.5) (12.1.105)\r\n",
"Requirement already satisfied: nvidia-cudnn-cu12==9.1.0.70 in /opt/conda/lib/python3.10/site-packages (from torch->unsloth_zoo==2024.11.5) (9.1.0.70)\r\n",
"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.10/site-packages (from torch->unsloth_zoo==2024.11.5) (12.1.3.1)\r\n",
"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /opt/conda/lib/python3.10/site-packages (from torch->unsloth_zoo==2024.11.5) (11.0.2.54)\r\n",
"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.10/site-packages (from torch->unsloth_zoo==2024.11.5) (10.3.2.106)\r\n",
"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /opt/conda/lib/python3.10/site-packages (from torch->unsloth_zoo==2024.11.5) (11.4.5.107)\r\n",
"Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.10/site-packages (from torch->unsloth_zoo==2024.11.5) (12.1.0.106)\r\n",
"Requirement already satisfied: nvidia-nccl-cu12==2.21.5 in /opt/conda/lib/python3.10/site-packages (from torch->unsloth_zoo==2024.11.5) (2.21.5)\r\n",
"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.10/site-packages (from torch->unsloth_zoo==2024.11.5) (12.1.105)\r\n",
"Requirement already satisfied: sympy==1.13.1 in /opt/conda/lib/python3.10/site-packages (from torch->unsloth_zoo==2024.11.5) (1.13.1)\r\n",
"Requirement already satisfied: nvidia-nvjitlink-cu12 in /opt/conda/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch->unsloth_zoo==2024.11.5) (12.1.105)\r\n",
"Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/conda/lib/python3.10/site-packages (from sympy==1.13.1->torch->unsloth_zoo==2024.11.5) (1.3.0)\r\n",
"Requirement already satisfied: regex!=2019.12.17 in /opt/conda/lib/python3.10/site-packages (from transformers>=4.46.1->unsloth_zoo==2024.11.5) (2024.5.15)\r\n",
"Requirement already satisfied: tokenizers<0.21,>=0.20 in /opt/conda/lib/python3.10/site-packages (from transformers>=4.46.1->unsloth_zoo==2024.11.5) (0.20.0)\r\n",
"Requirement already satisfied: rich in /opt/conda/lib/python3.10/site-packages (from trl!=0.9.0,!=0.9.1,!=0.9.2,!=0.9.3,>=0.7.9->unsloth_zoo==2024.11.5) (13.7.1)\r\n",
"Requirement already satisfied: docstring-parser>=0.16 in /opt/conda/lib/python3.10/site-packages (from tyro->unsloth_zoo==2024.11.5) (0.16)\r\n",
"Requirement already satisfied: shtab>=1.5.6 in /opt/conda/lib/python3.10/site-packages (from tyro->unsloth_zoo==2024.11.5) (1.7.1)\r\n",
"Requirement already satisfied: aiosignal>=1.1.2 in /opt/conda/lib/python3.10/site-packages (from aiohttp->datasets>=2.16.0->unsloth_zoo==2024.11.5) (1.3.1)\r\n",
"Requirement already satisfied: attrs>=17.3.0 in /opt/conda/lib/python3.10/site-packages (from aiohttp->datasets>=2.16.0->unsloth_zoo==2024.11.5) (23.2.0)\r\n",
"Requirement already satisfied: frozenlist>=1.1.1 in /opt/conda/lib/python3.10/site-packages (from aiohttp->datasets>=2.16.0->unsloth_zoo==2024.11.5) (1.4.1)\r\n",
"Requirement already satisfied: multidict<7.0,>=4.5 in /opt/conda/lib/python3.10/site-packages (from aiohttp->datasets>=2.16.0->unsloth_zoo==2024.11.5) (6.0.5)\r\n",
"Requirement already satisfied: yarl<2.0,>=1.0 in /opt/conda/lib/python3.10/site-packages (from aiohttp->datasets>=2.16.0->unsloth_zoo==2024.11.5) (1.9.4)\r\n",
"Requirement already satisfied: async-timeout<5.0,>=4.0 in /opt/conda/lib/python3.10/site-packages (from aiohttp->datasets>=2.16.0->unsloth_zoo==2024.11.5) (4.0.3)\r\n",
"Requirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.10/site-packages (from requests>=2.32.2->datasets>=2.16.0->unsloth_zoo==2024.11.5) (3.3.2)\r\n",
"Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests>=2.32.2->datasets>=2.16.0->unsloth_zoo==2024.11.5) (3.7)\r\n",
"Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests>=2.32.2->datasets>=2.16.0->unsloth_zoo==2024.11.5) (1.26.18)\r\n",
"Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests>=2.32.2->datasets>=2.16.0->unsloth_zoo==2024.11.5) (2024.8.30)\r\n",
"Requirement already satisfied: markdown-it-py>=2.2.0 in /opt/conda/lib/python3.10/site-packages (from rich->trl!=0.9.0,!=0.9.1,!=0.9.2,!=0.9.3,>=0.7.9->unsloth_zoo==2024.11.5) (3.0.0)\r\n",
"Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /opt/conda/lib/python3.10/site-packages (from rich->trl!=0.9.0,!=0.9.1,!=0.9.2,!=0.9.3,>=0.7.9->unsloth_zoo==2024.11.5) (2.18.0)\r\n",
"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch->unsloth_zoo==2024.11.5) (2.1.5)\r\n",
"Requirement already satisfied: python-dateutil>=2.8.2 in /opt/conda/lib/python3.10/site-packages (from pandas->datasets>=2.16.0->unsloth_zoo==2024.11.5) (2.9.0.post0)\r\n",
"Requirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.10/site-packages (from pandas->datasets>=2.16.0->unsloth_zoo==2024.11.5) (2024.1)\r\n",
"Requirement already satisfied: tzdata>=2022.7 in /opt/conda/lib/python3.10/site-packages (from pandas->datasets>=2.16.0->unsloth_zoo==2024.11.5) (2024.1)\r\n",
"Requirement already satisfied: mdurl~=0.1 in /opt/conda/lib/python3.10/site-packages (from markdown-it-py>=2.2.0->rich->trl!=0.9.0,!=0.9.1,!=0.9.2,!=0.9.3,>=0.7.9->unsloth_zoo==2024.11.5) (0.1.2)\r\n",
"Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.10/site-packages (from python-dateutil>=2.8.2->pandas->datasets>=2.16.0->unsloth_zoo==2024.11.5) (1.16.0)\r\n"
]
}
],
"source": [
"!pip install git+https://github.com/unslothai/unsloth-zoo.git\n",
"import os\n",
"os.environ[\"UNSLOTH_IS_PRESENT\"] = \"1\""
]
},
{
"cell_type": "markdown",
"id": "6c8091fe",
"metadata": {
"id": "r2v_X2fA0Df5",
"papermill": {
"duration": 0.064606,
"end_time": "2024-11-19T19:06:41.612002",
"exception": false,
"start_time": "2024-11-19T19:06:41.547396",
"status": "completed"
},
"tags": []
},
"source": [
"* We support Llama, Mistral, CodeLlama, TinyLlama, Vicuna, Open Hermes etc\n",
"* And Yi, Qwen ([llamafied](https://huggingface.co/models?sort=trending&search=qwen+llama)), Deepseek, all Llama, Mistral derived archs.\n",
"* We support 16bit LoRA or 4bit QLoRA. Both 2x faster.\n",
"* `max_seq_length` can be set to anything, since we do automatic RoPE Scaling via [kaiokendev's](https://kaiokendev.github.io/til) method.\n",
"* [**NEW**] With [PR 26037](https://github.com/huggingface/transformers/pull/26037), we support downloading 4bit models **4x faster**! [Our repo](https://huggingface.co/unsloth) has Llama, Mistral 4bit models."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "c7d55dc3",
"metadata": {
"execution": {
"iopub.execute_input": "2024-11-19T19:06:41.737888Z",
"iopub.status.busy": "2024-11-19T19:06:41.737538Z",
"iopub.status.idle": "2024-11-19T19:08:58.672000Z",
"shell.execute_reply": "2024-11-19T19:08:58.671103Z"
},
"id": "QmUBVEnvCDJv",
"outputId": "5eff0d61-05b4-471c-eea2-c2e84a915109",
"papermill": {
"duration": 136.999725,
"end_time": "2024-11-19T19:08:58.674026",
"exception": false,
"start_time": "2024-11-19T19:06:41.674301",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n",
"🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n",
"==((====))== Unsloth 2024.10.7: Fast Llama patching. Transformers = 4.46.3.\n",
" \\\\ /| GPU: Tesla T4. Max memory: 14.741 GB. Platform = Linux.\n",
"O^O/ \\_/ \\ Pytorch: 2.5.1+cu121. CUDA = 7.5. CUDA Toolkit = 12.1.\n",
"\\ / Bfloat16 = FALSE. FA [Xformers = 0.0.28.post3. FA2 = False]\n",
" \"-____-\" Free Apache license: http://github.com/unslothai/unsloth\n",
"Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n"
]
},
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]
},
"metadata": {},
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},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Unsloth: We successfully patched the tokenizer to add a {% if add_generation_prompt %} to the chat_template.\n",
"This is not a bug, but please notify the Unsloth maintainers - thanks!\n",
"mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated does not have a padding token! Will use pad_token = <|finetune_right_pad_id|>.\n"
]
}
],
"source": [
"from unsloth import FastLanguageModel\n",
"import torch\n",
"max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!\n",
"dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+\n",
"load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.\n",
"\n",
"# 4bit pre quantized models we support for 4x faster downloading + no OOMs.\n",
"fourbit_models = [\n",
" \"unsloth/mistral-7b-bnb-4bit\",\n",
" \"unsloth/mistral-7b-instruct-v0.2-bnb-4bit\",\n",
" \"unsloth/llama-2-7b-bnb-4bit\",\n",
" \"unsloth/llama-2-13b-bnb-4bit\",\n",
" \"unsloth/codellama-34b-bnb-4bit\",\n",
" \"unsloth/tinyllama-bnb-4bit\",\n",
" \"unsloth/llama-3-8b-bnb-4bit\",\n",
" \"unsloth/llama-3-70b-bnb-4bit\",\n",
"] # More models at https://huggingface.co/unsloth\n",
"\n",
"model, tokenizer = FastLanguageModel.from_pretrained(\n",
" model_name = \"mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated\", # Choose ANY! eg teknium/OpenHermes-2.5-Mistral-7B\n",
" max_seq_length = max_seq_length,\n",
" dtype = dtype,\n",
" load_in_4bit = load_in_4bit,\n",
" # token = \"hf_...\", # use one if using gated models like meta-llama/Llama-2-7b-hf\n",
")"
]
},
{
"cell_type": "markdown",
"id": "2775c72b",
"metadata": {
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"exception": false,
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"status": "completed"
},
"tags": []
},
"source": [
"We now add LoRA adapters so we only need to update 1 to 10% of all parameters!"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "d4d1a72a",
"metadata": {
"execution": {
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"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Unsloth 2024.10.7 patched 32 layers with 32 QKV layers, 32 O layers and 32 MLP layers.\n"
]
}
],
"source": [
"model = FastLanguageModel.get_peft_model(\n",
" model,\n",
" r = 32, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128\n",
" target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n",
" \"gate_proj\", \"up_proj\", \"down_proj\",],\n",
" lora_alpha = 16,\n",
" lora_dropout = 0, # Supports any, but = 0 is optimized\n",
" bias = \"none\", # Supports any, but = \"none\" is optimized\n",
" use_gradient_checkpointing = \"unsloth\", # 4x longer contexts auto supported!\n",
" random_state = 3407,\n",
" use_rslora = False, # We support rank stabilized LoRA\n",
" loftq_config = None, # And LoftQ\n",
")"
]
},
{
"cell_type": "markdown",
"id": "cca764a5",
"metadata": {
"id": "vITh0KVJ10qX",
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"end_time": "2024-11-19T19:09:04.622692",
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"status": "completed"
},
"tags": []
},
"source": [
"<a name=\"Data\"></a>\n",
"### Data Prep\n",
"We now use the Alpaca dataset from [yahma](https://huggingface.co/datasets/yahma/alpaca-cleaned), which is a filtered version of 52K of the original [Alpaca dataset](https://crfm.stanford.edu/2023/03/13/alpaca.html). You can replace this code section with your own data prep.\n",
"\n",
"**[NOTE]** To train only on completions (ignoring the user's input) read TRL's docs [here](https://huggingface.co/docs/trl/sft_trainer#train-on-completions-only).\n",
"\n",
"**[NOTE]** Remember to add the **EOS_TOKEN** to the tokenized output!! Otherwise you'll get infinite generations!\n",
"\n",
"If you want to use the `ChatML` template for ShareGPT datasets, try our conversational [notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing).\n",
"\n",
"For text completions like novel writing, try this [notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing)."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "69a832a3",
"metadata": {
"execution": {
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"status": "completed"
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"tags": []
},
"outputs": [],
"source": [
"from datasets import load_dataset\n",
"import json\n",
"from unsloth.chat_templates import get_chat_template\n",
"\n",
"tokenizer = get_chat_template(\n",
" tokenizer,\n",
" chat_template = \"llama-3\", # Supports zephyr, chatml, mistral, llama, alpaca, vicuna, vicuna_old, unsloth\n",
" #mapping = {\"role\" : \"from\", \"content\" : \"value\", \"user\" : \"human\", \"assistant\" : \"gpt\"}, # ShareGPT style\n",
" map_eos_token = True, # Maps <|im_end|> to </s> instead\n",
")\n",
"\n",
"def formatting_prompts_func(convos):\n",
" texts = [tokenizer.apply_chat_template(convo, tokenize = False, add_generation_prompt = False) for convo in convos]\n",
" return { \"text\" : texts, }\n",
"\n",
"with open(\"/kaggle/input/the-group-chat/output-10k-c-dropout-nonames-replies.json\") as chatfile:\n",
" convos = [json.loads(j) for j in chatfile.readlines()]\n",
"\n",
"with open(\"/kaggle/input/toxicqa/toxicQAfinal.json\") as chatfile:\n",
" convos += [json.loads(j) for j in chatfile.readlines()]\n",
" \n",
"dataset = formatting_prompts_func(convos)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "6b4a347d",
"metadata": {
"execution": {
"iopub.execute_input": "2024-11-19T19:09:06.314334Z",
"iopub.status.busy": "2024-11-19T19:09:06.313377Z",
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"start_time": "2024-11-19T19:09:06.248194",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"from datasets import Dataset\n",
"dataset = Dataset.from_dict(dataset)"
]
},
{
"cell_type": "markdown",
"id": "4c45849c",
"metadata": {
"id": "idAEIeSQ3xdS",
"papermill": {
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"status": "completed"
},
"tags": []
},
"source": [
"<a name=\"Train\"></a>\n",
"### Train the model\n",
"Now let's use Huggingface TRL's `SFTTrainer`! More docs here: [TRL SFT docs](https://huggingface.co/docs/trl/sft_trainer). We do 60 steps to speed things up, but you can set `num_train_epochs=1` for a full run, and turn off `max_steps=None`. We also support TRL's `DPOTrainer`!"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "7bbc400a",
"metadata": {
"execution": {
"iopub.execute_input": "2024-11-19T19:09:07.001740Z",
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},
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"papermill": {
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"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0f0c38ccb6c0402f84a66639ce3b0a2c",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Map (num_proc=2): 0%| | 0/17983 [00:00<?, ? examples/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from trl import SFTTrainer\n",
"from transformers import TrainingArguments\n",
"\n",
"trainer = SFTTrainer(\n",
" model = model,\n",
" tokenizer = tokenizer,\n",
" train_dataset = dataset,\n",
" dataset_text_field = \"text\",\n",
" max_seq_length = max_seq_length,\n",
" dataset_num_proc = 2,\n",
" packing = False, # Can make training 5x faster for short sequences.\n",
" args = TrainingArguments(\n",
" per_device_train_batch_size = 2,\n",
" gradient_accumulation_steps = 4,\n",
" warmup_steps = 5,\n",
" num_train_epochs=1,\n",
" learning_rate = 2e-4,\n",
" fp16 = not torch.cuda.is_bf16_supported(),\n",
" bf16 = torch.cuda.is_bf16_supported(),\n",
" logging_steps = 1,\n",
" optim = \"adamw_8bit\",\n",
" weight_decay = 0.01,\n",
" lr_scheduler_type = \"linear\",\n",
" seed = 3407,\n",
" output_dir = \"outputs\",\n",
" report_to = \"none\",\n",
" ),\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "5f90acfb",
"metadata": {
"cellView": "form",
"execution": {
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},
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"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"GPU = Tesla T4. Max memory = 14.741 GB.\n",
"6.172 GB of memory reserved.\n"
]
}
],
"source": [
"#@title Show current memory stats\n",
"gpu_stats = torch.cuda.get_device_properties(0)\n",
"start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n",
"max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)\n",
"print(f\"GPU = {gpu_stats.name}. Max memory = {max_memory} GB.\")\n",
"print(f\"{start_gpu_memory} GB of memory reserved.\")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "1a3a38b4",
"metadata": {
"execution": {
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},
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"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"==((====))== Unsloth - 2x faster free finetuning | Num GPUs = 1\n",
" \\\\ /| Num examples = 17,983 | Num Epochs = 1\n",
"O^O/ \\_/ \\ Batch size per device = 2 | Gradient Accumulation steps = 4\n",
"\\ / Total batch size = 8 | Total steps = 2,248\n",
" \"-____-\" Number of trainable parameters = 83,886,080\n"
]
},
{
"data": {
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='2248' max='2248' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [2248/2248 8:44:22, Epoch 1/1]\n",
" </div>\n",
" <table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>Step</th>\n",
" <th>Training Loss</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>2.630800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>3.890600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>2.046200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>2.309300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>5</td>\n",
" <td>2.590900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>6</td>\n",
" <td>2.039400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>7</td>\n",
" <td>1.953500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>8</td>\n",
" <td>1.769300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>9</td>\n",
" <td>2.016900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>10</td>\n",
" <td>1.801700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>11</td>\n",
" <td>1.576400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>12</td>\n",
" <td>1.695400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>13</td>\n",
" <td>2.032200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>14</td>\n",
" <td>1.696800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>15</td>\n",
" <td>2.109500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>16</td>\n",
" <td>2.254800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>17</td>\n",
" <td>1.357900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>18</td>\n",
" <td>1.598300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>19</td>\n",
" <td>1.539700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>20</td>\n",
" <td>1.648300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>21</td>\n",
" <td>1.754000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>22</td>\n",
" <td>1.735000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>23</td>\n",
" <td>2.434300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>24</td>\n",
" <td>1.987900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>25</td>\n",
" <td>1.295100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>26</td>\n",
" <td>2.180100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>27</td>\n",
" <td>2.082700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>28</td>\n",
" <td>1.410300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>29</td>\n",
" <td>1.446500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>30</td>\n",
" <td>1.435300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>31</td>\n",
" <td>1.730600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>32</td>\n",
" <td>1.551800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>33</td>\n",
" <td>1.482700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>34</td>\n",
" <td>1.575600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>35</td>\n",
" <td>2.223500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>36</td>\n",
" <td>2.106000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>37</td>\n",
" <td>1.657500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>38</td>\n",
" <td>1.472100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>39</td>\n",
" <td>1.612800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>40</td>\n",
" <td>1.556300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>41</td>\n",
" <td>1.471300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>42</td>\n",
" <td>1.350800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>43</td>\n",
" <td>1.383000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>44</td>\n",
" <td>1.837300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>45</td>\n",
" <td>1.466900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>46</td>\n",
" <td>1.402600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>47</td>\n",
" <td>1.303800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>48</td>\n",
" <td>1.289400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>49</td>\n",
" <td>2.615500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>50</td>\n",
" <td>1.423800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>51</td>\n",
" <td>1.415600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>52</td>\n",
" <td>1.592000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>53</td>\n",
" <td>1.259700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>54</td>\n",
" <td>1.572500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>55</td>\n",
" <td>1.458800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>56</td>\n",
" <td>1.322500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>57</td>\n",
" <td>1.411800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>58</td>\n",
" <td>1.847200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>59</td>\n",
" <td>1.725800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>60</td>\n",
" <td>1.620000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>61</td>\n",
" <td>1.664900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>62</td>\n",
" <td>1.662400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>63</td>\n",
" <td>2.695700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>64</td>\n",
" <td>1.526500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>65</td>\n",
" <td>1.645500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>66</td>\n",
" <td>1.431200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>67</td>\n",
" <td>2.222500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>68</td>\n",
" <td>1.723900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>69</td>\n",
" <td>1.636600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>70</td>\n",
" <td>1.557700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>71</td>\n",
" <td>1.690900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>72</td>\n",
" <td>2.912400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>73</td>\n",
" <td>1.290300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>74</td>\n",
" <td>1.954400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>75</td>\n",
" <td>1.888500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>76</td>\n",
" <td>1.399600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>77</td>\n",
" <td>1.522700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>78</td>\n",
" <td>1.376900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>79</td>\n",
" <td>1.562900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>80</td>\n",
" <td>1.479700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>81</td>\n",
" <td>1.277100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>82</td>\n",
" <td>1.612200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>83</td>\n",
" <td>1.596400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>84</td>\n",
" <td>1.767900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>85</td>\n",
" <td>1.235800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>86</td>\n",
" <td>1.574400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>87</td>\n",
" <td>1.754300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>88</td>\n",
" <td>2.280800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>89</td>\n",
" <td>1.484400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>90</td>\n",
" <td>1.970000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>91</td>\n",
" <td>2.784900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>92</td>\n",
" <td>1.193300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>93</td>\n",
" <td>1.194500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>94</td>\n",
" <td>1.298700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>95</td>\n",
" <td>1.497400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>96</td>\n",
" <td>1.489800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>97</td>\n",
" <td>1.329000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>98</td>\n",
" <td>1.558400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>99</td>\n",
" <td>1.790600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>100</td>\n",
" <td>1.515000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>101</td>\n",
" <td>1.375700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>102</td>\n",
" <td>1.339900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>103</td>\n",
" <td>1.910300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>104</td>\n",
" <td>1.336000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>105</td>\n",
" <td>1.809300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>106</td>\n",
" <td>1.585100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>107</td>\n",
" <td>1.396300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>108</td>\n",
" <td>1.708700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>109</td>\n",
" <td>1.253400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>110</td>\n",
" <td>1.390500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>111</td>\n",
" <td>1.351900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>112</td>\n",
" <td>1.464300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>113</td>\n",
" <td>1.383400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>114</td>\n",
" <td>1.834200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>115</td>\n",
" <td>1.374400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>116</td>\n",
" <td>1.805500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>117</td>\n",
" <td>1.491200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>118</td>\n",
" <td>1.725400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>119</td>\n",
" <td>1.205700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>120</td>\n",
" <td>1.665300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>121</td>\n",
" <td>1.563900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>122</td>\n",
" <td>1.415700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>123</td>\n",
" <td>1.667000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>124</td>\n",
" <td>1.442300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>125</td>\n",
" <td>1.666900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>126</td>\n",
" <td>1.890500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>127</td>\n",
" <td>2.156600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>128</td>\n",
" <td>1.403700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>129</td>\n",
" <td>1.523600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>130</td>\n",
" <td>1.526900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>131</td>\n",
" <td>1.265600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>132</td>\n",
" <td>1.518900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>133</td>\n",
" <td>1.244900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>134</td>\n",
" <td>1.950600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>135</td>\n",
" <td>1.854500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>136</td>\n",
" <td>1.391500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>137</td>\n",
" <td>1.904300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>138</td>\n",
" <td>1.427200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>139</td>\n",
" <td>1.240400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>140</td>\n",
" <td>1.249600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>141</td>\n",
" <td>1.292200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>142</td>\n",
" <td>2.007600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>143</td>\n",
" <td>1.521400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>144</td>\n",
" <td>2.039100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>145</td>\n",
" <td>1.960900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>146</td>\n",
" <td>1.939400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>147</td>\n",
" <td>1.669800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>148</td>\n",
" <td>1.558000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>149</td>\n",
" <td>1.829700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>150</td>\n",
" <td>1.790300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>151</td>\n",
" <td>2.170600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>152</td>\n",
" <td>1.371500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>153</td>\n",
" <td>2.082900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>154</td>\n",
" <td>1.852700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>155</td>\n",
" <td>1.285800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>156</td>\n",
" <td>1.336700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>157</td>\n",
" <td>1.375500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>158</td>\n",
" <td>1.764100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>159</td>\n",
" <td>1.345000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>160</td>\n",
" <td>1.140800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>161</td>\n",
" <td>1.412700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>162</td>\n",
" <td>1.752300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>163</td>\n",
" <td>1.368300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>164</td>\n",
" <td>1.309200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>165</td>\n",
" <td>1.354100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>166</td>\n",
" <td>1.696000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>167</td>\n",
" <td>1.723500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>168</td>\n",
" <td>1.511200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>169</td>\n",
" <td>1.483300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>170</td>\n",
" <td>1.415700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>171</td>\n",
" <td>1.287400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>172</td>\n",
" <td>1.483200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>173</td>\n",
" <td>1.562700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>174</td>\n",
" <td>1.361400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>175</td>\n",
" <td>2.597900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>176</td>\n",
" <td>1.436000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>177</td>\n",
" <td>1.306000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>178</td>\n",
" <td>1.416300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>179</td>\n",
" <td>1.876800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>180</td>\n",
" <td>1.555100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>181</td>\n",
" <td>1.620600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>182</td>\n",
" <td>2.247000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>183</td>\n",
" <td>1.629000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>184</td>\n",
" <td>2.113300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>185</td>\n",
" <td>1.246700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>186</td>\n",
" <td>1.565700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>187</td>\n",
" <td>1.504300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>188</td>\n",
" <td>1.827100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>189</td>\n",
" <td>1.559800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>190</td>\n",
" <td>1.321600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>191</td>\n",
" <td>1.795400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>192</td>\n",
" <td>1.367100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>193</td>\n",
" <td>1.439100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>194</td>\n",
" <td>1.281500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>195</td>\n",
" <td>1.306900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>196</td>\n",
" <td>1.875500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>197</td>\n",
" <td>1.467800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>198</td>\n",
" <td>2.002600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>199</td>\n",
" <td>1.374800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>200</td>\n",
" <td>1.397600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>201</td>\n",
" <td>1.292000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>202</td>\n",
" <td>1.574200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>203</td>\n",
" <td>1.563400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>204</td>\n",
" <td>1.942800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>205</td>\n",
" <td>1.298600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>206</td>\n",
" <td>1.613900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>207</td>\n",
" <td>1.309400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>208</td>\n",
" <td>1.714200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>209</td>\n",
" <td>1.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>210</td>\n",
" <td>1.453800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>211</td>\n",
" <td>1.489000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>212</td>\n",
" <td>1.647800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>213</td>\n",
" <td>1.430400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>214</td>\n",
" <td>1.426000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>215</td>\n",
" <td>1.686200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>216</td>\n",
" <td>1.649600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>217</td>\n",
" <td>1.787100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>218</td>\n",
" <td>1.456800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>219</td>\n",
" <td>1.419800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>220</td>\n",
" <td>1.432400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>221</td>\n",
" <td>1.380800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>222</td>\n",
" <td>1.598300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>223</td>\n",
" <td>1.831000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>224</td>\n",
" <td>1.740900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>225</td>\n",
" <td>1.429500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>226</td>\n",
" <td>1.643800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>227</td>\n",
" <td>2.096100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>228</td>\n",
" <td>1.973500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>229</td>\n",
" <td>1.752800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>230</td>\n",
" <td>1.656700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>231</td>\n",
" <td>1.238000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>232</td>\n",
" <td>1.988300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>233</td>\n",
" <td>1.654400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>234</td>\n",
" <td>1.746100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>235</td>\n",
" <td>1.384200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>236</td>\n",
" <td>1.585900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>237</td>\n",
" <td>1.876900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>238</td>\n",
" <td>1.353400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>239</td>\n",
" <td>1.578700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>240</td>\n",
" <td>1.437200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>241</td>\n",
" <td>1.253500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>242</td>\n",
" <td>1.366800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>243</td>\n",
" <td>1.374000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>244</td>\n",
" <td>1.716100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>245</td>\n",
" <td>1.207600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>246</td>\n",
" <td>1.333400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>247</td>\n",
" <td>1.428700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>248</td>\n",
" <td>1.522900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>249</td>\n",
" <td>1.558700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>250</td>\n",
" <td>1.341900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>251</td>\n",
" <td>1.483800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>252</td>\n",
" <td>1.863500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>253</td>\n",
" <td>1.339100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>254</td>\n",
" <td>1.506000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>255</td>\n",
" <td>1.699300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>256</td>\n",
" <td>1.450600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>257</td>\n",
" <td>1.396900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>258</td>\n",
" <td>1.934000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>259</td>\n",
" <td>1.916600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>260</td>\n",
" <td>1.467200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>261</td>\n",
" <td>1.481400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>262</td>\n",
" <td>1.489800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>263</td>\n",
" <td>1.483800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>264</td>\n",
" <td>1.201600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>265</td>\n",
" <td>1.425700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>266</td>\n",
" <td>1.960000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>267</td>\n",
" <td>1.179500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>268</td>\n",
" <td>1.530600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>269</td>\n",
" <td>1.489700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>270</td>\n",
" <td>1.752900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>271</td>\n",
" <td>1.117600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>272</td>\n",
" <td>1.522700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>273</td>\n",
" <td>1.337000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>274</td>\n",
" <td>1.435400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>275</td>\n",
" <td>1.799900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>276</td>\n",
" <td>1.513800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>277</td>\n",
" <td>1.675900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>278</td>\n",
" <td>1.576400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>279</td>\n",
" <td>1.201000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>280</td>\n",
" <td>1.514300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>281</td>\n",
" <td>1.182400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>282</td>\n",
" <td>1.476700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>283</td>\n",
" <td>1.749500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>284</td>\n",
" <td>1.393500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>285</td>\n",
" <td>1.219900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>286</td>\n",
" <td>2.029000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>287</td>\n",
" <td>1.613700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>288</td>\n",
" <td>1.534200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>289</td>\n",
" <td>1.598400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>290</td>\n",
" <td>1.638300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>291</td>\n",
" <td>1.297900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>292</td>\n",
" <td>1.190500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>293</td>\n",
" <td>1.308400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>294</td>\n",
" <td>1.684000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>295</td>\n",
" <td>1.529900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>296</td>\n",
" <td>1.313900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>297</td>\n",
" <td>1.689900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>298</td>\n",
" <td>1.836100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>299</td>\n",
" <td>0.988100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>300</td>\n",
" <td>2.004800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>301</td>\n",
" <td>1.471100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>302</td>\n",
" <td>1.772600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>303</td>\n",
" <td>1.634900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>304</td>\n",
" <td>1.552100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>305</td>\n",
" <td>1.773300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>306</td>\n",
" <td>1.281600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>307</td>\n",
" <td>1.880300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>308</td>\n",
" <td>1.302500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>309</td>\n",
" <td>1.628900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>310</td>\n",
" <td>1.379500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>311</td>\n",
" <td>1.751200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>312</td>\n",
" <td>1.635100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>313</td>\n",
" <td>1.433400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>314</td>\n",
" <td>1.383600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>315</td>\n",
" <td>1.943200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>316</td>\n",
" <td>1.407600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>317</td>\n",
" <td>1.611600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>318</td>\n",
" <td>1.418900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>319</td>\n",
" <td>1.279200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>320</td>\n",
" <td>1.244300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>321</td>\n",
" <td>1.520300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>322</td>\n",
" <td>1.269600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>323</td>\n",
" <td>1.691100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>324</td>\n",
" <td>1.492600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>325</td>\n",
" <td>1.520900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>326</td>\n",
" <td>1.526200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>327</td>\n",
" <td>1.318200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>328</td>\n",
" <td>1.447700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>329</td>\n",
" <td>1.462800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>330</td>\n",
" <td>1.310700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>331</td>\n",
" <td>1.142200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>332</td>\n",
" <td>1.602700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>333</td>\n",
" <td>1.547900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>334</td>\n",
" <td>1.257900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>335</td>\n",
" <td>1.455500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>336</td>\n",
" <td>1.856100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>337</td>\n",
" <td>1.951500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>338</td>\n",
" <td>1.285300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>339</td>\n",
" <td>1.459400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>340</td>\n",
" <td>1.330600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>341</td>\n",
" <td>1.553900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>342</td>\n",
" <td>1.273900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>343</td>\n",
" <td>1.747800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>344</td>\n",
" <td>1.244400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>345</td>\n",
" <td>1.430000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>346</td>\n",
" <td>1.529500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>347</td>\n",
" <td>1.239300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>348</td>\n",
" <td>1.446900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>349</td>\n",
" <td>1.354200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>350</td>\n",
" <td>1.366100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>351</td>\n",
" <td>1.577100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>352</td>\n",
" <td>1.198800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>353</td>\n",
" <td>1.002100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>354</td>\n",
" <td>1.733200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>355</td>\n",
" <td>1.396900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>356</td>\n",
" <td>1.196100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>357</td>\n",
" <td>2.214000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>358</td>\n",
" <td>1.258000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>359</td>\n",
" <td>1.507500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>360</td>\n",
" <td>1.523100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>361</td>\n",
" <td>1.775900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>362</td>\n",
" <td>1.635000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>363</td>\n",
" <td>1.403300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>364</td>\n",
" <td>1.290600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>365</td>\n",
" <td>1.910600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>366</td>\n",
" <td>1.062600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>367</td>\n",
" <td>1.305800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>368</td>\n",
" <td>1.496100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>369</td>\n",
" <td>1.966700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>370</td>\n",
" <td>1.938000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>371</td>\n",
" <td>1.379900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>372</td>\n",
" <td>1.668600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>373</td>\n",
" <td>1.817900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>374</td>\n",
" <td>1.280400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>375</td>\n",
" <td>1.392400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>376</td>\n",
" <td>1.321900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>377</td>\n",
" <td>1.183100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>378</td>\n",
" <td>1.154900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>379</td>\n",
" <td>1.798800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>380</td>\n",
" <td>1.418800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>381</td>\n",
" <td>1.549300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>382</td>\n",
" <td>1.545200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>383</td>\n",
" <td>1.501500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>384</td>\n",
" <td>1.887700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>385</td>\n",
" <td>1.446700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>386</td>\n",
" <td>1.279900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>387</td>\n",
" <td>1.308700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>388</td>\n",
" <td>1.602800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>389</td>\n",
" <td>1.582900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>390</td>\n",
" <td>1.423400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>391</td>\n",
" <td>1.529300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>392</td>\n",
" <td>1.696300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>393</td>\n",
" <td>1.673200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>394</td>\n",
" <td>1.109700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>395</td>\n",
" <td>1.248800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>396</td>\n",
" <td>1.089700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>397</td>\n",
" <td>1.326600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>398</td>\n",
" <td>1.688600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>399</td>\n",
" <td>1.681000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>400</td>\n",
" <td>1.423900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>401</td>\n",
" <td>1.131800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>402</td>\n",
" <td>1.154600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>403</td>\n",
" <td>1.463200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>404</td>\n",
" <td>1.229600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>405</td>\n",
" <td>2.188300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>406</td>\n",
" <td>1.538900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>407</td>\n",
" <td>1.662500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>408</td>\n",
" <td>1.718800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>409</td>\n",
" <td>1.526500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>410</td>\n",
" <td>1.792600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>411</td>\n",
" <td>1.354700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>412</td>\n",
" <td>1.364100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>413</td>\n",
" <td>1.441500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>414</td>\n",
" <td>1.432600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>415</td>\n",
" <td>1.684900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>416</td>\n",
" <td>1.885400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>417</td>\n",
" <td>2.052100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>418</td>\n",
" <td>1.424000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>419</td>\n",
" <td>1.474100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>420</td>\n",
" <td>1.130200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>421</td>\n",
" <td>2.011000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>422</td>\n",
" <td>1.323600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>423</td>\n",
" <td>1.810000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>424</td>\n",
" <td>1.666700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>425</td>\n",
" <td>1.281500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>426</td>\n",
" <td>1.930800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>427</td>\n",
" <td>1.210800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>428</td>\n",
" <td>2.097600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>429</td>\n",
" <td>1.300800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>430</td>\n",
" <td>1.525600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>431</td>\n",
" <td>2.123900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>432</td>\n",
" <td>1.948600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>433</td>\n",
" <td>1.202800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>434</td>\n",
" <td>1.412100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>435</td>\n",
" <td>1.424500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>436</td>\n",
" <td>1.254200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>437</td>\n",
" <td>1.594300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>438</td>\n",
" <td>1.343600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>439</td>\n",
" <td>2.224800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>440</td>\n",
" <td>1.648500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>441</td>\n",
" <td>1.470300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>442</td>\n",
" <td>1.676900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>443</td>\n",
" <td>1.660600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>444</td>\n",
" <td>1.278800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>445</td>\n",
" <td>1.455500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>446</td>\n",
" <td>1.843400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>447</td>\n",
" <td>1.452500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>448</td>\n",
" <td>1.401100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>449</td>\n",
" <td>1.349800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>450</td>\n",
" <td>1.570700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>451</td>\n",
" <td>1.419100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>452</td>\n",
" <td>1.579500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>453</td>\n",
" <td>1.726000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>454</td>\n",
" <td>1.226900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>455</td>\n",
" <td>1.650000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>456</td>\n",
" <td>2.521900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>457</td>\n",
" <td>1.394800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>458</td>\n",
" <td>1.665600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>459</td>\n",
" <td>1.412600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>460</td>\n",
" <td>1.723900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>461</td>\n",
" <td>1.355500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>462</td>\n",
" <td>1.423500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>463</td>\n",
" <td>1.738900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>464</td>\n",
" <td>1.365700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>465</td>\n",
" <td>1.528600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>466</td>\n",
" <td>1.501800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>467</td>\n",
" <td>1.463700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>468</td>\n",
" <td>1.329600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>469</td>\n",
" <td>1.329900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>470</td>\n",
" <td>2.145800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>471</td>\n",
" <td>1.581700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>472</td>\n",
" <td>1.282900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>473</td>\n",
" <td>1.661500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>474</td>\n",
" <td>1.645100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>475</td>\n",
" <td>1.325900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>476</td>\n",
" <td>1.704000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>477</td>\n",
" <td>1.312400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>478</td>\n",
" <td>1.279000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>479</td>\n",
" <td>1.162900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>480</td>\n",
" <td>1.459400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>481</td>\n",
" <td>1.444600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>482</td>\n",
" <td>1.411800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>483</td>\n",
" <td>1.143400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>484</td>\n",
" <td>1.720400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>485</td>\n",
" <td>1.269200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>486</td>\n",
" <td>1.291000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>487</td>\n",
" <td>1.524500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>488</td>\n",
" <td>1.729100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>489</td>\n",
" <td>1.271900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>490</td>\n",
" <td>1.582800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>491</td>\n",
" <td>1.221200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>492</td>\n",
" <td>1.439500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>493</td>\n",
" <td>1.528500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>494</td>\n",
" <td>1.775500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>495</td>\n",
" <td>1.594600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>496</td>\n",
" <td>1.560900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>497</td>\n",
" <td>1.791400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>498</td>\n",
" <td>1.397800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>499</td>\n",
" <td>1.740400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>500</td>\n",
" <td>1.209500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>501</td>\n",
" <td>1.385600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>502</td>\n",
" <td>1.062200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>503</td>\n",
" <td>1.355400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>504</td>\n",
" <td>1.768400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>505</td>\n",
" <td>1.225800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>506</td>\n",
" <td>1.263000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>507</td>\n",
" <td>1.456800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>508</td>\n",
" <td>1.314900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>509</td>\n",
" <td>1.377100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>510</td>\n",
" <td>1.589900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>511</td>\n",
" <td>1.439100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>512</td>\n",
" <td>1.394000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>513</td>\n",
" <td>1.307200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>514</td>\n",
" <td>1.466100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>515</td>\n",
" <td>1.367400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>516</td>\n",
" <td>1.782700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>517</td>\n",
" <td>1.335600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>518</td>\n",
" <td>1.384600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>519</td>\n",
" <td>1.289300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>520</td>\n",
" <td>1.386800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>521</td>\n",
" <td>1.249500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>522</td>\n",
" <td>1.728500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>523</td>\n",
" <td>1.524500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>524</td>\n",
" <td>1.428600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>525</td>\n",
" <td>1.170900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>526</td>\n",
" <td>1.681900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>527</td>\n",
" <td>1.852700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>528</td>\n",
" <td>1.664800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>529</td>\n",
" <td>1.413400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>530</td>\n",
" <td>1.575700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>531</td>\n",
" <td>1.728200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>532</td>\n",
" <td>1.336700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>533</td>\n",
" <td>1.720700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>534</td>\n",
" <td>1.595600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>535</td>\n",
" <td>1.270200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>536</td>\n",
" <td>1.291800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>537</td>\n",
" <td>1.491500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>538</td>\n",
" <td>1.836400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>539</td>\n",
" <td>1.031600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>540</td>\n",
" <td>1.526600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>541</td>\n",
" <td>1.660300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>542</td>\n",
" <td>1.350900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>543</td>\n",
" <td>1.407000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>544</td>\n",
" <td>1.463300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>545</td>\n",
" <td>2.063900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>546</td>\n",
" <td>1.244900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>547</td>\n",
" <td>1.768600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>548</td>\n",
" <td>1.484100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>549</td>\n",
" <td>1.710700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>550</td>\n",
" <td>1.859800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>551</td>\n",
" <td>1.277100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>552</td>\n",
" <td>1.769000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>553</td>\n",
" <td>1.948400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>554</td>\n",
" <td>1.805500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>555</td>\n",
" <td>2.075900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>556</td>\n",
" <td>1.129500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>557</td>\n",
" <td>1.152000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>558</td>\n",
" <td>1.914600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>559</td>\n",
" <td>1.250400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>560</td>\n",
" <td>1.497500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>561</td>\n",
" <td>1.593700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>562</td>\n",
" <td>1.610600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>563</td>\n",
" <td>1.300700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>564</td>\n",
" <td>1.357500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>565</td>\n",
" <td>1.227800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>566</td>\n",
" <td>1.638200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>567</td>\n",
" <td>1.665600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>568</td>\n",
" <td>1.277100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>569</td>\n",
" <td>1.279300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>570</td>\n",
" <td>1.357200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>571</td>\n",
" <td>1.219100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>572</td>\n",
" <td>1.378900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>573</td>\n",
" <td>1.306300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>574</td>\n",
" <td>1.673900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>575</td>\n",
" <td>1.395800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>576</td>\n",
" <td>1.278400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>577</td>\n",
" <td>1.794600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>578</td>\n",
" <td>1.351700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>579</td>\n",
" <td>1.798200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>580</td>\n",
" <td>1.687900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>581</td>\n",
" <td>1.532900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>582</td>\n",
" <td>1.552200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>583</td>\n",
" <td>1.116400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>584</td>\n",
" <td>1.615600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>585</td>\n",
" <td>1.610900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>586</td>\n",
" <td>1.367900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>587</td>\n",
" <td>1.230200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>588</td>\n",
" <td>1.145400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>589</td>\n",
" <td>1.199000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>590</td>\n",
" <td>1.694700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>591</td>\n",
" <td>1.578000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>592</td>\n",
" <td>1.340700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>593</td>\n",
" <td>1.530900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>594</td>\n",
" <td>1.495900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>595</td>\n",
" <td>1.540700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>596</td>\n",
" <td>1.519800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>597</td>\n",
" <td>1.513500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>598</td>\n",
" <td>1.302300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>599</td>\n",
" <td>1.565300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>600</td>\n",
" <td>1.628200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>601</td>\n",
" <td>1.244700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>602</td>\n",
" <td>1.480900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>603</td>\n",
" <td>1.217700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>604</td>\n",
" <td>1.417000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>605</td>\n",
" <td>1.412500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>606</td>\n",
" <td>1.271400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>607</td>\n",
" <td>1.465700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>608</td>\n",
" <td>1.351600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>609</td>\n",
" <td>2.184100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>610</td>\n",
" <td>1.668600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>611</td>\n",
" <td>1.573500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>612</td>\n",
" <td>1.146500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>613</td>\n",
" <td>1.460900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>614</td>\n",
" <td>2.015100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>615</td>\n",
" <td>1.843800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>616</td>\n",
" <td>1.539300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>617</td>\n",
" <td>1.631000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>618</td>\n",
" <td>1.281700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>619</td>\n",
" <td>1.026100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>620</td>\n",
" <td>1.626800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>621</td>\n",
" <td>1.691800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>622</td>\n",
" <td>1.194600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>623</td>\n",
" <td>1.689400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>624</td>\n",
" <td>1.213000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>625</td>\n",
" <td>1.371200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>626</td>\n",
" <td>1.780900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>627</td>\n",
" <td>1.491700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>628</td>\n",
" <td>1.341300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>629</td>\n",
" <td>1.266200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>630</td>\n",
" <td>1.129100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>631</td>\n",
" <td>1.422300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>632</td>\n",
" <td>1.339000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>633</td>\n",
" <td>1.364800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>634</td>\n",
" <td>1.196100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>635</td>\n",
" <td>1.522000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>636</td>\n",
" <td>1.601500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>637</td>\n",
" <td>1.464600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>638</td>\n",
" <td>1.826800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>639</td>\n",
" <td>1.684900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>640</td>\n",
" <td>1.434100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>641</td>\n",
" <td>1.318800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>642</td>\n",
" <td>1.990200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>643</td>\n",
" <td>1.540200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>644</td>\n",
" <td>1.243100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>645</td>\n",
" <td>1.208400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>646</td>\n",
" <td>1.669200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>647</td>\n",
" <td>1.667100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>648</td>\n",
" <td>1.764100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>649</td>\n",
" <td>2.274200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>650</td>\n",
" <td>1.929500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>651</td>\n",
" <td>1.532800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>652</td>\n",
" <td>1.949200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>653</td>\n",
" <td>1.194300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>654</td>\n",
" <td>1.081300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>655</td>\n",
" <td>1.703300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>656</td>\n",
" <td>1.786600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>657</td>\n",
" <td>1.638000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>658</td>\n",
" <td>1.452200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>659</td>\n",
" <td>1.484700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>660</td>\n",
" <td>1.704700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>661</td>\n",
" <td>1.414400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>662</td>\n",
" <td>1.265200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>663</td>\n",
" <td>1.442600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>664</td>\n",
" <td>1.787400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>665</td>\n",
" <td>1.491300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>666</td>\n",
" <td>1.252600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>667</td>\n",
" <td>1.486600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>668</td>\n",
" <td>1.734800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>669</td>\n",
" <td>1.486900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>670</td>\n",
" <td>1.394600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>671</td>\n",
" <td>1.541100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>672</td>\n",
" <td>1.947600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>673</td>\n",
" <td>1.548600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>674</td>\n",
" <td>1.357000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>675</td>\n",
" <td>1.737100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>676</td>\n",
" <td>1.911500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>677</td>\n",
" <td>1.460300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>678</td>\n",
" <td>1.295200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>679</td>\n",
" <td>1.480900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>680</td>\n",
" <td>1.638200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>681</td>\n",
" <td>1.077400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>682</td>\n",
" <td>1.665800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>683</td>\n",
" <td>1.515100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>684</td>\n",
" <td>1.075100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>685</td>\n",
" <td>1.205900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>686</td>\n",
" <td>1.628200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>687</td>\n",
" <td>1.186100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>688</td>\n",
" <td>1.170100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>689</td>\n",
" <td>1.561000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>690</td>\n",
" <td>1.678000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>691</td>\n",
" <td>1.413300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>692</td>\n",
" <td>1.252300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>693</td>\n",
" <td>1.607600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>694</td>\n",
" <td>1.710500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>695</td>\n",
" <td>1.323000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>696</td>\n",
" <td>1.507300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>697</td>\n",
" <td>1.679300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>698</td>\n",
" <td>1.545000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>699</td>\n",
" <td>1.221800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>700</td>\n",
" <td>1.493300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>701</td>\n",
" <td>1.181700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>702</td>\n",
" <td>1.171100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>703</td>\n",
" <td>1.686500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>704</td>\n",
" <td>1.423300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>705</td>\n",
" <td>1.660100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>706</td>\n",
" <td>1.263900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>707</td>\n",
" <td>1.125800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>708</td>\n",
" <td>1.991300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>709</td>\n",
" <td>1.165800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>710</td>\n",
" <td>1.751300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>711</td>\n",
" <td>1.185500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>712</td>\n",
" <td>1.643000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>713</td>\n",
" <td>1.357500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>714</td>\n",
" <td>2.634900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>715</td>\n",
" <td>1.853700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>716</td>\n",
" <td>1.403100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>717</td>\n",
" <td>1.271200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>718</td>\n",
" <td>1.794200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>719</td>\n",
" <td>1.340800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>720</td>\n",
" <td>1.588000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>721</td>\n",
" <td>1.327900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>722</td>\n",
" <td>1.568700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>723</td>\n",
" <td>1.304600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>724</td>\n",
" <td>1.843200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>725</td>\n",
" <td>1.635400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>726</td>\n",
" <td>1.483500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>727</td>\n",
" <td>1.584500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>728</td>\n",
" <td>1.464600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>729</td>\n",
" <td>1.496400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>730</td>\n",
" <td>1.813200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>731</td>\n",
" <td>1.410100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>732</td>\n",
" <td>1.810800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>733</td>\n",
" <td>1.155400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>734</td>\n",
" <td>1.378800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>735</td>\n",
" <td>1.349900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>736</td>\n",
" <td>1.917600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>737</td>\n",
" <td>1.043300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>738</td>\n",
" <td>1.605200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>739</td>\n",
" <td>1.224700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>740</td>\n",
" <td>1.507200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>741</td>\n",
" <td>1.447900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>742</td>\n",
" <td>1.591600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>743</td>\n",
" <td>2.113700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>744</td>\n",
" <td>1.404600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>745</td>\n",
" <td>1.592900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>746</td>\n",
" <td>1.695800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>747</td>\n",
" <td>1.412600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>748</td>\n",
" <td>1.391600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>749</td>\n",
" <td>1.363000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>750</td>\n",
" <td>1.516600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>751</td>\n",
" <td>1.384200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>752</td>\n",
" <td>1.572800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>753</td>\n",
" <td>1.747500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>754</td>\n",
" <td>1.254300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>755</td>\n",
" <td>1.083300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>756</td>\n",
" <td>1.563300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>757</td>\n",
" <td>1.572800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>758</td>\n",
" <td>1.516600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>759</td>\n",
" <td>1.577300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>760</td>\n",
" <td>1.028100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>761</td>\n",
" <td>1.663800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>762</td>\n",
" <td>1.441500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>763</td>\n",
" <td>2.225800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>764</td>\n",
" <td>2.147200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>765</td>\n",
" <td>1.190000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>766</td>\n",
" <td>1.563400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>767</td>\n",
" <td>1.739800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>768</td>\n",
" <td>1.711500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>769</td>\n",
" <td>1.489900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>770</td>\n",
" <td>1.457400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>771</td>\n",
" <td>1.406400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>772</td>\n",
" <td>1.829300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>773</td>\n",
" <td>1.324700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>774</td>\n",
" <td>1.587400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>775</td>\n",
" <td>1.433100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>776</td>\n",
" <td>1.378400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>777</td>\n",
" <td>1.606600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>778</td>\n",
" <td>1.879500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>779</td>\n",
" <td>1.306400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>780</td>\n",
" <td>1.581200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>781</td>\n",
" <td>1.278500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>782</td>\n",
" <td>1.535900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>783</td>\n",
" <td>1.225800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>784</td>\n",
" <td>2.003700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>785</td>\n",
" <td>1.088100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>786</td>\n",
" <td>1.354800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>787</td>\n",
" <td>1.332300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>788</td>\n",
" <td>1.819000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>789</td>\n",
" <td>1.608200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>790</td>\n",
" <td>1.327000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>791</td>\n",
" <td>1.225600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>792</td>\n",
" <td>1.277900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>793</td>\n",
" <td>1.327900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>794</td>\n",
" <td>1.108800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>795</td>\n",
" <td>1.391200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>796</td>\n",
" <td>1.240200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>797</td>\n",
" <td>1.476700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>798</td>\n",
" <td>1.279800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>799</td>\n",
" <td>1.272400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>800</td>\n",
" <td>1.265800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>801</td>\n",
" <td>1.765700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>802</td>\n",
" <td>1.198300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>803</td>\n",
" <td>1.513900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>804</td>\n",
" <td>1.733100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>805</td>\n",
" <td>1.162400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>806</td>\n",
" <td>1.225300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>807</td>\n",
" <td>1.477200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>808</td>\n",
" <td>1.258100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>809</td>\n",
" <td>1.463000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>810</td>\n",
" <td>1.287500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>811</td>\n",
" <td>1.286400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>812</td>\n",
" <td>2.015000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>813</td>\n",
" <td>1.541200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>814</td>\n",
" <td>1.361500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>815</td>\n",
" <td>1.675500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>816</td>\n",
" <td>2.102900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>817</td>\n",
" <td>1.497300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>818</td>\n",
" <td>1.325800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>819</td>\n",
" <td>1.106700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>820</td>\n",
" <td>1.995500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>821</td>\n",
" <td>1.417900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>822</td>\n",
" <td>1.346900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>823</td>\n",
" <td>1.236300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>824</td>\n",
" <td>1.389600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>825</td>\n",
" <td>1.390000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>826</td>\n",
" <td>1.554000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>827</td>\n",
" <td>1.300400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>828</td>\n",
" <td>1.295400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>829</td>\n",
" <td>0.997900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>830</td>\n",
" <td>1.612000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>831</td>\n",
" <td>1.074200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>832</td>\n",
" <td>1.533200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>833</td>\n",
" <td>1.859500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>834</td>\n",
" <td>1.348300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>835</td>\n",
" <td>1.154200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>836</td>\n",
" <td>1.120100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>837</td>\n",
" <td>1.237400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>838</td>\n",
" <td>1.442200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>839</td>\n",
" <td>1.551300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>840</td>\n",
" <td>1.410900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>841</td>\n",
" <td>1.100900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>842</td>\n",
" <td>1.564200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>843</td>\n",
" <td>1.406200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>844</td>\n",
" <td>1.343700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>845</td>\n",
" <td>1.035800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>846</td>\n",
" <td>1.610900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>847</td>\n",
" <td>1.361900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>848</td>\n",
" <td>1.297900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>849</td>\n",
" <td>1.282300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>850</td>\n",
" <td>1.441000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>851</td>\n",
" <td>1.709000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>852</td>\n",
" <td>1.403900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>853</td>\n",
" <td>1.521900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>854</td>\n",
" <td>1.834800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>855</td>\n",
" <td>1.336400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>856</td>\n",
" <td>1.626300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>857</td>\n",
" <td>1.509100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>858</td>\n",
" <td>1.253900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>859</td>\n",
" <td>1.510500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>860</td>\n",
" <td>1.065700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>861</td>\n",
" <td>1.415800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>862</td>\n",
" <td>1.461300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>863</td>\n",
" <td>1.270700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>864</td>\n",
" <td>1.240900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>865</td>\n",
" <td>1.191800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>866</td>\n",
" <td>1.753400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>867</td>\n",
" <td>1.428500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>868</td>\n",
" <td>1.065300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>869</td>\n",
" <td>1.848800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>870</td>\n",
" <td>1.081000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>871</td>\n",
" <td>1.730700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>872</td>\n",
" <td>1.389900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>873</td>\n",
" <td>1.115400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>874</td>\n",
" <td>1.822700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>875</td>\n",
" <td>1.337400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>876</td>\n",
" <td>1.350700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>877</td>\n",
" <td>1.734600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>878</td>\n",
" <td>1.393500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>879</td>\n",
" <td>2.038900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>880</td>\n",
" <td>1.410700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>881</td>\n",
" <td>1.389000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>882</td>\n",
" <td>1.274000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>883</td>\n",
" <td>1.177900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>884</td>\n",
" <td>1.888800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>885</td>\n",
" <td>1.646000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>886</td>\n",
" <td>1.487500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>887</td>\n",
" <td>1.067000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>888</td>\n",
" <td>1.575100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>889</td>\n",
" <td>1.559200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>890</td>\n",
" <td>1.549200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>891</td>\n",
" <td>1.540300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>892</td>\n",
" <td>1.419300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>893</td>\n",
" <td>1.712500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>894</td>\n",
" <td>1.350700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>895</td>\n",
" <td>1.752100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>896</td>\n",
" <td>1.261200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>897</td>\n",
" <td>1.434600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>898</td>\n",
" <td>1.274000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>899</td>\n",
" <td>1.536000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>900</td>\n",
" <td>1.542900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>901</td>\n",
" <td>1.209600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>902</td>\n",
" <td>1.548400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>903</td>\n",
" <td>2.120500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>904</td>\n",
" <td>1.336600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>905</td>\n",
" <td>1.544500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>906</td>\n",
" <td>1.206500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>907</td>\n",
" <td>1.657200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>908</td>\n",
" <td>1.786100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>909</td>\n",
" <td>1.586900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>910</td>\n",
" <td>1.827000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>911</td>\n",
" <td>1.245700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>912</td>\n",
" <td>1.145600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>913</td>\n",
" <td>2.626100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>914</td>\n",
" <td>1.461700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>915</td>\n",
" <td>1.441800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>916</td>\n",
" <td>1.404300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>917</td>\n",
" <td>1.342300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>918</td>\n",
" <td>1.377500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>919</td>\n",
" <td>1.206200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>920</td>\n",
" <td>2.012700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>921</td>\n",
" <td>1.423500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>922</td>\n",
" <td>1.192800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>923</td>\n",
" <td>1.137000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>924</td>\n",
" <td>1.858500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>925</td>\n",
" <td>1.419500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>926</td>\n",
" <td>1.384400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>927</td>\n",
" <td>1.302900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>928</td>\n",
" <td>1.399100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>929</td>\n",
" <td>1.561600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>930</td>\n",
" <td>1.058800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>931</td>\n",
" <td>1.486500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>932</td>\n",
" <td>1.497200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>933</td>\n",
" <td>1.427400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>934</td>\n",
" <td>1.555000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>935</td>\n",
" <td>1.311100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>936</td>\n",
" <td>1.726100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>937</td>\n",
" <td>1.289000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>938</td>\n",
" <td>1.301300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>939</td>\n",
" <td>1.256300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>940</td>\n",
" <td>1.718900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>941</td>\n",
" <td>1.212500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>942</td>\n",
" <td>1.311300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>943</td>\n",
" <td>2.020900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>944</td>\n",
" <td>1.301500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>945</td>\n",
" <td>1.505000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>946</td>\n",
" <td>1.237800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>947</td>\n",
" <td>1.695500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>948</td>\n",
" <td>1.220300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>949</td>\n",
" <td>1.371200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>950</td>\n",
" <td>1.465800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>951</td>\n",
" <td>1.393900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>952</td>\n",
" <td>1.552600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>953</td>\n",
" <td>1.494400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>954</td>\n",
" <td>1.475600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>955</td>\n",
" <td>1.151900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>956</td>\n",
" <td>1.538300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>957</td>\n",
" <td>1.274300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>958</td>\n",
" <td>1.254600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>959</td>\n",
" <td>1.485200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>960</td>\n",
" <td>1.351000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>961</td>\n",
" <td>1.379900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>962</td>\n",
" <td>1.929800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>963</td>\n",
" <td>1.618700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>964</td>\n",
" <td>2.524200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>965</td>\n",
" <td>1.339300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>966</td>\n",
" <td>1.133800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>967</td>\n",
" <td>1.306300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>968</td>\n",
" <td>1.940100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>969</td>\n",
" <td>1.781500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>970</td>\n",
" <td>1.331300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>971</td>\n",
" <td>1.667500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>972</td>\n",
" <td>1.111500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>973</td>\n",
" <td>1.619100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>974</td>\n",
" <td>1.439200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>975</td>\n",
" <td>1.011600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>976</td>\n",
" <td>1.163300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>977</td>\n",
" <td>1.780100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>978</td>\n",
" <td>1.316300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>979</td>\n",
" <td>1.294600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>980</td>\n",
" <td>1.178600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>981</td>\n",
" <td>1.461700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>982</td>\n",
" <td>1.427500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>983</td>\n",
" <td>1.259800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>984</td>\n",
" <td>1.858700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>985</td>\n",
" <td>1.791300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>986</td>\n",
" <td>1.220500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>987</td>\n",
" <td>1.316500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>988</td>\n",
" <td>1.131000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>989</td>\n",
" <td>1.311100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>990</td>\n",
" <td>1.336700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>991</td>\n",
" <td>1.160000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>992</td>\n",
" <td>1.800800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>993</td>\n",
" <td>1.271700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>994</td>\n",
" <td>1.853600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>995</td>\n",
" <td>1.378400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>996</td>\n",
" <td>1.437100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>997</td>\n",
" <td>1.333300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>998</td>\n",
" <td>1.166500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>999</td>\n",
" <td>1.269800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1000</td>\n",
" <td>1.610900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1001</td>\n",
" <td>1.289500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1002</td>\n",
" <td>1.112500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1003</td>\n",
" <td>1.724400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1004</td>\n",
" <td>1.691700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1005</td>\n",
" <td>1.222600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1006</td>\n",
" <td>1.334900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1007</td>\n",
" <td>1.215500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1008</td>\n",
" <td>1.903400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1009</td>\n",
" <td>1.353200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1010</td>\n",
" <td>1.596800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1011</td>\n",
" <td>1.202200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1012</td>\n",
" <td>1.346700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1013</td>\n",
" <td>1.326600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1014</td>\n",
" <td>1.306600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1015</td>\n",
" <td>2.119000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1016</td>\n",
" <td>1.609300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1017</td>\n",
" <td>1.680300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1018</td>\n",
" <td>1.040800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1019</td>\n",
" <td>2.032100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1020</td>\n",
" <td>1.320300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1021</td>\n",
" <td>1.080100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1022</td>\n",
" <td>1.722700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1023</td>\n",
" <td>1.397200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1024</td>\n",
" <td>1.408400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1025</td>\n",
" <td>1.321100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1026</td>\n",
" <td>1.503500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1027</td>\n",
" <td>1.384200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1028</td>\n",
" <td>1.466300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1029</td>\n",
" <td>1.999200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1030</td>\n",
" <td>1.522700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1031</td>\n",
" <td>1.206000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1032</td>\n",
" <td>1.448000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1033</td>\n",
" <td>1.549400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1034</td>\n",
" <td>1.835900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1035</td>\n",
" <td>1.354500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1036</td>\n",
" <td>1.361400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1037</td>\n",
" <td>1.382400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1038</td>\n",
" <td>1.966800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1039</td>\n",
" <td>1.604800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1040</td>\n",
" <td>1.461500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1041</td>\n",
" <td>1.213500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1042</td>\n",
" <td>1.228800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1043</td>\n",
" <td>0.991400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1044</td>\n",
" <td>1.196600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1045</td>\n",
" <td>1.400300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1046</td>\n",
" <td>1.420000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1047</td>\n",
" <td>1.525200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1048</td>\n",
" <td>1.411400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1049</td>\n",
" <td>1.460500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1050</td>\n",
" <td>1.420600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1051</td>\n",
" <td>1.494700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1052</td>\n",
" <td>1.551000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1053</td>\n",
" <td>1.313700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1054</td>\n",
" <td>1.379600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1055</td>\n",
" <td>1.488500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1056</td>\n",
" <td>1.287200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1057</td>\n",
" <td>1.806800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1058</td>\n",
" <td>1.338600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1059</td>\n",
" <td>1.134000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1060</td>\n",
" <td>1.426300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1061</td>\n",
" <td>1.611300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1062</td>\n",
" <td>1.382200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1063</td>\n",
" <td>2.067200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1064</td>\n",
" <td>1.176700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1065</td>\n",
" <td>1.128700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1066</td>\n",
" <td>1.119900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1067</td>\n",
" <td>1.895900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1068</td>\n",
" <td>1.778500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1069</td>\n",
" <td>1.480700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1070</td>\n",
" <td>1.344300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1071</td>\n",
" <td>1.535200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1072</td>\n",
" <td>1.550700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1073</td>\n",
" <td>1.289900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1074</td>\n",
" <td>1.590300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1075</td>\n",
" <td>1.492500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1076</td>\n",
" <td>1.674200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1077</td>\n",
" <td>1.299800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1078</td>\n",
" <td>1.476000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1079</td>\n",
" <td>1.461400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1080</td>\n",
" <td>1.435700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1081</td>\n",
" <td>1.338900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1082</td>\n",
" <td>1.746200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1083</td>\n",
" <td>1.885400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1084</td>\n",
" <td>1.761700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1085</td>\n",
" <td>1.308700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1086</td>\n",
" <td>1.307000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1087</td>\n",
" <td>1.316900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1088</td>\n",
" <td>1.603100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1089</td>\n",
" <td>1.658300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1090</td>\n",
" <td>1.408300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1091</td>\n",
" <td>1.949200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1092</td>\n",
" <td>1.438600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1093</td>\n",
" <td>1.185700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1094</td>\n",
" <td>1.747400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1095</td>\n",
" <td>1.380200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1096</td>\n",
" <td>1.158500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1097</td>\n",
" <td>1.666300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1098</td>\n",
" <td>1.125300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1099</td>\n",
" <td>2.101900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1100</td>\n",
" <td>1.879300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1101</td>\n",
" <td>1.678000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1102</td>\n",
" <td>1.548500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1103</td>\n",
" <td>1.427300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1104</td>\n",
" <td>2.457600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1105</td>\n",
" <td>1.466800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1106</td>\n",
" <td>1.528700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1107</td>\n",
" <td>1.625600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1108</td>\n",
" <td>1.894700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1109</td>\n",
" <td>1.312800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1110</td>\n",
" <td>1.518700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1111</td>\n",
" <td>1.514100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1112</td>\n",
" <td>2.010600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1113</td>\n",
" <td>1.466800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1114</td>\n",
" <td>1.521000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1115</td>\n",
" <td>1.305200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1116</td>\n",
" <td>1.599000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1117</td>\n",
" <td>1.804800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1118</td>\n",
" <td>1.336100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1119</td>\n",
" <td>1.254600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1120</td>\n",
" <td>1.398800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1121</td>\n",
" <td>1.063300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1122</td>\n",
" <td>1.207000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1123</td>\n",
" <td>1.495300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1124</td>\n",
" <td>1.231300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1125</td>\n",
" <td>1.728200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1126</td>\n",
" <td>2.126300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1127</td>\n",
" <td>2.018500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1128</td>\n",
" <td>1.624200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1129</td>\n",
" <td>1.161500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1130</td>\n",
" <td>1.503800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1131</td>\n",
" <td>1.332400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1132</td>\n",
" <td>1.562900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1133</td>\n",
" <td>1.580200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1134</td>\n",
" <td>1.498400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1135</td>\n",
" <td>1.512900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1136</td>\n",
" <td>1.405900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1137</td>\n",
" <td>1.751200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1138</td>\n",
" <td>1.314200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1139</td>\n",
" <td>1.039400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1140</td>\n",
" <td>1.476400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1141</td>\n",
" <td>1.444100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1142</td>\n",
" <td>1.300000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1143</td>\n",
" <td>1.718400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1144</td>\n",
" <td>1.544500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1145</td>\n",
" <td>1.687100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1146</td>\n",
" <td>1.323000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1147</td>\n",
" <td>1.182300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1148</td>\n",
" <td>1.496600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1149</td>\n",
" <td>1.649600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1150</td>\n",
" <td>1.240100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1151</td>\n",
" <td>1.802500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1152</td>\n",
" <td>1.696200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1153</td>\n",
" <td>1.507300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1154</td>\n",
" <td>1.295000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1155</td>\n",
" <td>1.589200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1156</td>\n",
" <td>1.376600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1157</td>\n",
" <td>1.524900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1158</td>\n",
" <td>1.631700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1159</td>\n",
" <td>1.017000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1160</td>\n",
" <td>1.094400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1161</td>\n",
" <td>1.613600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1162</td>\n",
" <td>1.334200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1163</td>\n",
" <td>1.955000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1164</td>\n",
" <td>1.406800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1165</td>\n",
" <td>1.483400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1166</td>\n",
" <td>1.711400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1167</td>\n",
" <td>1.293600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1168</td>\n",
" <td>1.297100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1169</td>\n",
" <td>1.654000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1170</td>\n",
" <td>1.539000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1171</td>\n",
" <td>1.529700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1172</td>\n",
" <td>1.385300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1173</td>\n",
" <td>1.089500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1174</td>\n",
" <td>1.307900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1175</td>\n",
" <td>1.504800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1176</td>\n",
" <td>1.451600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1177</td>\n",
" <td>1.484700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1178</td>\n",
" <td>1.412200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1179</td>\n",
" <td>1.428500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1180</td>\n",
" <td>1.376700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1181</td>\n",
" <td>1.706000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1182</td>\n",
" <td>1.187800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1183</td>\n",
" <td>1.530900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1184</td>\n",
" <td>1.286400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1185</td>\n",
" <td>1.724400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1186</td>\n",
" <td>1.609100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1187</td>\n",
" <td>1.617900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1188</td>\n",
" <td>1.065000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1189</td>\n",
" <td>1.117100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1190</td>\n",
" <td>1.956700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1191</td>\n",
" <td>1.354700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1192</td>\n",
" <td>1.865100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1193</td>\n",
" <td>2.131100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1194</td>\n",
" <td>1.591400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1195</td>\n",
" <td>1.849500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1196</td>\n",
" <td>1.525500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1197</td>\n",
" <td>1.450900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1198</td>\n",
" <td>1.307400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1199</td>\n",
" <td>1.872700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1200</td>\n",
" <td>1.588200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1201</td>\n",
" <td>1.449100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1202</td>\n",
" <td>1.411400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1203</td>\n",
" <td>1.585400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1204</td>\n",
" <td>1.290300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1205</td>\n",
" <td>1.147200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1206</td>\n",
" <td>1.840600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1207</td>\n",
" <td>1.325800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1208</td>\n",
" <td>1.216900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1209</td>\n",
" <td>1.902600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1210</td>\n",
" <td>1.520800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1211</td>\n",
" <td>1.263300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1212</td>\n",
" <td>1.249300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1213</td>\n",
" <td>1.093500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1214</td>\n",
" <td>1.435600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1215</td>\n",
" <td>1.266300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1216</td>\n",
" <td>1.614300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1217</td>\n",
" <td>1.778400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1218</td>\n",
" <td>1.526800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1219</td>\n",
" <td>1.430300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1220</td>\n",
" <td>1.375500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1221</td>\n",
" <td>1.417200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1222</td>\n",
" <td>1.565500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1223</td>\n",
" <td>1.168900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1224</td>\n",
" <td>1.239800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1225</td>\n",
" <td>1.166800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1226</td>\n",
" <td>1.398100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1227</td>\n",
" <td>1.797500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1228</td>\n",
" <td>1.994600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1229</td>\n",
" <td>1.690400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1230</td>\n",
" <td>1.449900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1231</td>\n",
" <td>1.287500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1232</td>\n",
" <td>1.498600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1233</td>\n",
" <td>1.461200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1234</td>\n",
" <td>1.885600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1235</td>\n",
" <td>1.407800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1236</td>\n",
" <td>1.654600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1237</td>\n",
" <td>1.026400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1238</td>\n",
" <td>1.328800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1239</td>\n",
" <td>1.286100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1240</td>\n",
" <td>1.599900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1241</td>\n",
" <td>1.119900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1242</td>\n",
" <td>1.882000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1243</td>\n",
" <td>1.423000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1244</td>\n",
" <td>1.220800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1245</td>\n",
" <td>1.370100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1246</td>\n",
" <td>1.252100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1247</td>\n",
" <td>1.357900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1248</td>\n",
" <td>1.383800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1249</td>\n",
" <td>1.654400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1250</td>\n",
" <td>1.593600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1251</td>\n",
" <td>1.137000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1252</td>\n",
" <td>1.604400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1253</td>\n",
" <td>1.332700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1254</td>\n",
" <td>1.173700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1255</td>\n",
" <td>1.276600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1256</td>\n",
" <td>1.261000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1257</td>\n",
" <td>1.435400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1258</td>\n",
" <td>1.003500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1259</td>\n",
" <td>1.403300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1260</td>\n",
" <td>1.775300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1261</td>\n",
" <td>1.873000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1262</td>\n",
" <td>2.009900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1263</td>\n",
" <td>1.677300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1264</td>\n",
" <td>1.659600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1265</td>\n",
" <td>1.565400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1266</td>\n",
" <td>1.737200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1267</td>\n",
" <td>1.250500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1268</td>\n",
" <td>1.790900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1269</td>\n",
" <td>1.344100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1270</td>\n",
" <td>1.609300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1271</td>\n",
" <td>1.532600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1272</td>\n",
" <td>1.511800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1273</td>\n",
" <td>1.218100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1274</td>\n",
" <td>1.897000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1275</td>\n",
" <td>1.576700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1276</td>\n",
" <td>1.715200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1277</td>\n",
" <td>1.483700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1278</td>\n",
" <td>1.669100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1279</td>\n",
" <td>1.831100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1280</td>\n",
" <td>1.341500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1281</td>\n",
" <td>1.286600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1282</td>\n",
" <td>2.172900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1283</td>\n",
" <td>1.279800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1284</td>\n",
" <td>1.541100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1285</td>\n",
" <td>1.510900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1286</td>\n",
" <td>1.738900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1287</td>\n",
" <td>2.022900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1288</td>\n",
" <td>1.392300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1289</td>\n",
" <td>1.726400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1290</td>\n",
" <td>1.726200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1291</td>\n",
" <td>1.194800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1292</td>\n",
" <td>1.868600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1293</td>\n",
" <td>1.385900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1294</td>\n",
" <td>1.286000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1295</td>\n",
" <td>1.194300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1296</td>\n",
" <td>1.382000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1297</td>\n",
" <td>1.404000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1298</td>\n",
" <td>1.408100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1299</td>\n",
" <td>1.501500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1300</td>\n",
" <td>1.490700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1301</td>\n",
" <td>1.724600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1302</td>\n",
" <td>1.490200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1303</td>\n",
" <td>1.325500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1304</td>\n",
" <td>1.328100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1305</td>\n",
" <td>1.446800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1306</td>\n",
" <td>1.585600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1307</td>\n",
" <td>1.568600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1308</td>\n",
" <td>1.239700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1309</td>\n",
" <td>1.486200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1310</td>\n",
" <td>1.259000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1311</td>\n",
" <td>1.582600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1312</td>\n",
" <td>1.492900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1313</td>\n",
" <td>1.945200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1314</td>\n",
" <td>1.244300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1315</td>\n",
" <td>1.230100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1316</td>\n",
" <td>1.198100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1317</td>\n",
" <td>1.960200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1318</td>\n",
" <td>1.218300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1319</td>\n",
" <td>1.480200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1320</td>\n",
" <td>2.038200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1321</td>\n",
" <td>1.254900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1322</td>\n",
" <td>1.398200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1323</td>\n",
" <td>2.160600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1324</td>\n",
" <td>1.808800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1325</td>\n",
" <td>1.161100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1326</td>\n",
" <td>1.524300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1327</td>\n",
" <td>1.753600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1328</td>\n",
" <td>1.516600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1329</td>\n",
" <td>1.550100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1330</td>\n",
" <td>1.396700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1331</td>\n",
" <td>1.267800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1332</td>\n",
" <td>1.887800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1333</td>\n",
" <td>1.668900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1334</td>\n",
" <td>1.200200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1335</td>\n",
" <td>1.406400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1336</td>\n",
" <td>1.766200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1337</td>\n",
" <td>1.383500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1338</td>\n",
" <td>1.061600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1339</td>\n",
" <td>1.454800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1340</td>\n",
" <td>1.675400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1341</td>\n",
" <td>1.154200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1342</td>\n",
" <td>1.308500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1343</td>\n",
" <td>1.265900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1344</td>\n",
" <td>1.240400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1345</td>\n",
" <td>1.778700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1346</td>\n",
" <td>1.686400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1347</td>\n",
" <td>1.446200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1348</td>\n",
" <td>1.640200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1349</td>\n",
" <td>1.769500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1350</td>\n",
" <td>1.058200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1351</td>\n",
" <td>1.490600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1352</td>\n",
" <td>1.314700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1353</td>\n",
" <td>1.228400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1354</td>\n",
" <td>2.681100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1355</td>\n",
" <td>1.487300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1356</td>\n",
" <td>1.359300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1357</td>\n",
" <td>1.479600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1358</td>\n",
" <td>1.591000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1359</td>\n",
" <td>1.147800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1360</td>\n",
" <td>1.390600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1361</td>\n",
" <td>1.457000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1362</td>\n",
" <td>1.591500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1363</td>\n",
" <td>1.562300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1364</td>\n",
" <td>1.697800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1365</td>\n",
" <td>1.536500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1366</td>\n",
" <td>1.566500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1367</td>\n",
" <td>2.623000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1368</td>\n",
" <td>1.648400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1369</td>\n",
" <td>1.464800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1370</td>\n",
" <td>1.142600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1371</td>\n",
" <td>2.087000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1372</td>\n",
" <td>1.228200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1373</td>\n",
" <td>1.964000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1374</td>\n",
" <td>1.372000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1375</td>\n",
" <td>1.094300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1376</td>\n",
" <td>1.238100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1377</td>\n",
" <td>1.355000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1378</td>\n",
" <td>1.280700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1379</td>\n",
" <td>1.578500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1380</td>\n",
" <td>1.246600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1381</td>\n",
" <td>1.291800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1382</td>\n",
" <td>1.567100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1383</td>\n",
" <td>1.458800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1384</td>\n",
" <td>1.404600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1385</td>\n",
" <td>1.314400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1386</td>\n",
" <td>1.485200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1387</td>\n",
" <td>1.367100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1388</td>\n",
" <td>1.231800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1389</td>\n",
" <td>1.532800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1390</td>\n",
" <td>1.629000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1391</td>\n",
" <td>1.505700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1392</td>\n",
" <td>1.467900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1393</td>\n",
" <td>1.284300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1394</td>\n",
" <td>1.297300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1395</td>\n",
" <td>1.279500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1396</td>\n",
" <td>1.329700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1397</td>\n",
" <td>1.841900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1398</td>\n",
" <td>1.885200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1399</td>\n",
" <td>1.766500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1400</td>\n",
" <td>1.658700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1401</td>\n",
" <td>1.696900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1402</td>\n",
" <td>1.440200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1403</td>\n",
" <td>1.331800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1404</td>\n",
" <td>1.311300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1405</td>\n",
" <td>1.169700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1406</td>\n",
" <td>1.191900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1407</td>\n",
" <td>1.683000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1408</td>\n",
" <td>1.387800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1409</td>\n",
" <td>1.485200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1410</td>\n",
" <td>1.584100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1411</td>\n",
" <td>1.308300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1412</td>\n",
" <td>1.844900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1413</td>\n",
" <td>1.937500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1414</td>\n",
" <td>1.423400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1415</td>\n",
" <td>1.248800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1416</td>\n",
" <td>1.455400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1417</td>\n",
" <td>1.848600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1418</td>\n",
" <td>1.498000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1419</td>\n",
" <td>1.732700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1420</td>\n",
" <td>1.614900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1421</td>\n",
" <td>1.280700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1422</td>\n",
" <td>1.175900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1423</td>\n",
" <td>1.487100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1424</td>\n",
" <td>1.210500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1425</td>\n",
" <td>1.976600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1426</td>\n",
" <td>1.080200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1427</td>\n",
" <td>1.539300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1428</td>\n",
" <td>1.173200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1429</td>\n",
" <td>1.548800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1430</td>\n",
" <td>1.209700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1431</td>\n",
" <td>1.931000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1432</td>\n",
" <td>1.474700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1433</td>\n",
" <td>1.220800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1434</td>\n",
" <td>1.301300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1435</td>\n",
" <td>1.387300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1436</td>\n",
" <td>1.237200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1437</td>\n",
" <td>1.428600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1438</td>\n",
" <td>1.408800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1439</td>\n",
" <td>2.004600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1440</td>\n",
" <td>1.161100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1441</td>\n",
" <td>1.000800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1442</td>\n",
" <td>2.192800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1443</td>\n",
" <td>1.224800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1444</td>\n",
" <td>1.447600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1445</td>\n",
" <td>1.323800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1446</td>\n",
" <td>1.293800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1447</td>\n",
" <td>1.486600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1448</td>\n",
" <td>1.599800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1449</td>\n",
" <td>1.612000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1450</td>\n",
" <td>1.127600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1451</td>\n",
" <td>1.466000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1452</td>\n",
" <td>1.097500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1453</td>\n",
" <td>1.224200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1454</td>\n",
" <td>1.343300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1455</td>\n",
" <td>1.112000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1456</td>\n",
" <td>1.416500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1457</td>\n",
" <td>1.659900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1458</td>\n",
" <td>1.646200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1459</td>\n",
" <td>1.207200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1460</td>\n",
" <td>1.412400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1461</td>\n",
" <td>1.771300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1462</td>\n",
" <td>1.281900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1463</td>\n",
" <td>1.614400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1464</td>\n",
" <td>1.293200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1465</td>\n",
" <td>1.331500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1466</td>\n",
" <td>1.752700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1467</td>\n",
" <td>1.356000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1468</td>\n",
" <td>1.526300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1469</td>\n",
" <td>2.003600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1470</td>\n",
" <td>1.281600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1471</td>\n",
" <td>1.410900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1472</td>\n",
" <td>1.276200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1473</td>\n",
" <td>1.268100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1474</td>\n",
" <td>1.431900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1475</td>\n",
" <td>1.241500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1476</td>\n",
" <td>1.260600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1477</td>\n",
" <td>1.129800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1478</td>\n",
" <td>1.080700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1479</td>\n",
" <td>1.496200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1480</td>\n",
" <td>1.541800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1481</td>\n",
" <td>1.462100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1482</td>\n",
" <td>1.237400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1483</td>\n",
" <td>1.323200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1484</td>\n",
" <td>1.332900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1485</td>\n",
" <td>1.342000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1486</td>\n",
" <td>1.252700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1487</td>\n",
" <td>1.497700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1488</td>\n",
" <td>1.855800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1489</td>\n",
" <td>1.537900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1490</td>\n",
" <td>1.347500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1491</td>\n",
" <td>1.382100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1492</td>\n",
" <td>1.553000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1493</td>\n",
" <td>2.608600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1494</td>\n",
" <td>2.119100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1495</td>\n",
" <td>1.491000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1496</td>\n",
" <td>1.352300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1497</td>\n",
" <td>1.630800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1498</td>\n",
" <td>1.560000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1499</td>\n",
" <td>1.456100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1500</td>\n",
" <td>1.157400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1501</td>\n",
" <td>1.693000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1502</td>\n",
" <td>1.260400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1503</td>\n",
" <td>1.274100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1504</td>\n",
" <td>1.389800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1505</td>\n",
" <td>1.730500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1506</td>\n",
" <td>1.047200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1507</td>\n",
" <td>1.146200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1508</td>\n",
" <td>1.249000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1509</td>\n",
" <td>1.045600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1510</td>\n",
" <td>1.205500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1511</td>\n",
" <td>1.487500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1512</td>\n",
" <td>1.188200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1513</td>\n",
" <td>1.481400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1514</td>\n",
" <td>1.218600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1515</td>\n",
" <td>1.323700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1516</td>\n",
" <td>2.026800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1517</td>\n",
" <td>1.314900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1518</td>\n",
" <td>1.493400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1519</td>\n",
" <td>1.359100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1520</td>\n",
" <td>1.337100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1521</td>\n",
" <td>1.477900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1522</td>\n",
" <td>1.739700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1523</td>\n",
" <td>1.452900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1524</td>\n",
" <td>1.505000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1525</td>\n",
" <td>1.768000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1526</td>\n",
" <td>1.347100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1527</td>\n",
" <td>1.325500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1528</td>\n",
" <td>1.483200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1529</td>\n",
" <td>1.399800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1530</td>\n",
" <td>1.430400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1531</td>\n",
" <td>1.611100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1532</td>\n",
" <td>1.109700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1533</td>\n",
" <td>1.618700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1534</td>\n",
" <td>1.765500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1535</td>\n",
" <td>1.579700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1536</td>\n",
" <td>1.667300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1537</td>\n",
" <td>1.191600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1538</td>\n",
" <td>1.372400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1539</td>\n",
" <td>1.266700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1540</td>\n",
" <td>1.937600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1541</td>\n",
" <td>1.326100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1542</td>\n",
" <td>1.659100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1543</td>\n",
" <td>1.468500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1544</td>\n",
" <td>2.073200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1545</td>\n",
" <td>1.997600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1546</td>\n",
" <td>1.534800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1547</td>\n",
" <td>1.339500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1548</td>\n",
" <td>1.869700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1549</td>\n",
" <td>1.356400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1550</td>\n",
" <td>1.344300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1551</td>\n",
" <td>1.465400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1552</td>\n",
" <td>1.675600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1553</td>\n",
" <td>2.032900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1554</td>\n",
" <td>1.158700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1555</td>\n",
" <td>1.408200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1556</td>\n",
" <td>1.188300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1557</td>\n",
" <td>1.628000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1558</td>\n",
" <td>1.787000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1559</td>\n",
" <td>1.257100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1560</td>\n",
" <td>1.495700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1561</td>\n",
" <td>1.378000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1562</td>\n",
" <td>1.278900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1563</td>\n",
" <td>1.384600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1564</td>\n",
" <td>1.221200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1565</td>\n",
" <td>1.072200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1566</td>\n",
" <td>1.319900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1567</td>\n",
" <td>1.257300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1568</td>\n",
" <td>1.475100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1569</td>\n",
" <td>1.778200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1570</td>\n",
" <td>1.154000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1571</td>\n",
" <td>1.781600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1572</td>\n",
" <td>1.409800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1573</td>\n",
" <td>1.491800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1574</td>\n",
" <td>1.261600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1575</td>\n",
" <td>1.139500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1576</td>\n",
" <td>1.614000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1577</td>\n",
" <td>1.224200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1578</td>\n",
" <td>1.096800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1579</td>\n",
" <td>1.484000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1580</td>\n",
" <td>1.140000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1581</td>\n",
" <td>1.441500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1582</td>\n",
" <td>1.300100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1583</td>\n",
" <td>1.394300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1584</td>\n",
" <td>1.371300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1585</td>\n",
" <td>1.244600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1586</td>\n",
" <td>1.527500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1587</td>\n",
" <td>2.437100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1588</td>\n",
" <td>1.579000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1589</td>\n",
" <td>1.894700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1590</td>\n",
" <td>1.187700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1591</td>\n",
" <td>1.296600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1592</td>\n",
" <td>2.054600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1593</td>\n",
" <td>1.280000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1594</td>\n",
" <td>1.070100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1595</td>\n",
" <td>1.627400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1596</td>\n",
" <td>1.642800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1597</td>\n",
" <td>1.528000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1598</td>\n",
" <td>1.416800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1599</td>\n",
" <td>1.370400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1600</td>\n",
" <td>1.583100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1601</td>\n",
" <td>1.469200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1602</td>\n",
" <td>1.558900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1603</td>\n",
" <td>1.554000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1604</td>\n",
" <td>1.136600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1605</td>\n",
" <td>1.786800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1606</td>\n",
" <td>1.758200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1607</td>\n",
" <td>0.953700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1608</td>\n",
" <td>1.620400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1609</td>\n",
" <td>1.345700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1610</td>\n",
" <td>1.281400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1611</td>\n",
" <td>1.447800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1612</td>\n",
" <td>2.103000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1613</td>\n",
" <td>1.548000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1614</td>\n",
" <td>1.446800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1615</td>\n",
" <td>1.200200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1616</td>\n",
" <td>2.596100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1617</td>\n",
" <td>1.905400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1618</td>\n",
" <td>1.535200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1619</td>\n",
" <td>1.465600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1620</td>\n",
" <td>1.019500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1621</td>\n",
" <td>1.119800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1622</td>\n",
" <td>1.291300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1623</td>\n",
" <td>1.706000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1624</td>\n",
" <td>1.296200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1625</td>\n",
" <td>1.559600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1626</td>\n",
" <td>1.714100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1627</td>\n",
" <td>1.329800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1628</td>\n",
" <td>1.166700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1629</td>\n",
" <td>1.662600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1630</td>\n",
" <td>1.293900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1631</td>\n",
" <td>1.357800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1632</td>\n",
" <td>1.420500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1633</td>\n",
" <td>1.679700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1634</td>\n",
" <td>1.514300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1635</td>\n",
" <td>1.709600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1636</td>\n",
" <td>1.140300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1637</td>\n",
" <td>1.351100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1638</td>\n",
" <td>1.620900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1639</td>\n",
" <td>1.325700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1640</td>\n",
" <td>1.669100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1641</td>\n",
" <td>1.196700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1642</td>\n",
" <td>1.799600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1643</td>\n",
" <td>2.356400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1644</td>\n",
" <td>1.440900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1645</td>\n",
" <td>1.170000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1646</td>\n",
" <td>1.751900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1647</td>\n",
" <td>1.661000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1648</td>\n",
" <td>1.412100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1649</td>\n",
" <td>1.389200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1650</td>\n",
" <td>1.585800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1651</td>\n",
" <td>1.676900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1652</td>\n",
" <td>1.647500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1653</td>\n",
" <td>1.095800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1654</td>\n",
" <td>1.028700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1655</td>\n",
" <td>1.265500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1656</td>\n",
" <td>1.192700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1657</td>\n",
" <td>1.682300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1658</td>\n",
" <td>1.137500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1659</td>\n",
" <td>1.226300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1660</td>\n",
" <td>1.419300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1661</td>\n",
" <td>1.490500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1662</td>\n",
" <td>1.404000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1663</td>\n",
" <td>1.138800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1664</td>\n",
" <td>1.637600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1665</td>\n",
" <td>1.024700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1666</td>\n",
" <td>1.229500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1667</td>\n",
" <td>1.366200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1668</td>\n",
" <td>1.519400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1669</td>\n",
" <td>1.155800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1670</td>\n",
" <td>1.503000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1671</td>\n",
" <td>1.375900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1672</td>\n",
" <td>1.220400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1673</td>\n",
" <td>2.008600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1674</td>\n",
" <td>1.705800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1675</td>\n",
" <td>1.622200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1676</td>\n",
" <td>1.551000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1677</td>\n",
" <td>1.181000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1678</td>\n",
" <td>2.058300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1679</td>\n",
" <td>1.616300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1680</td>\n",
" <td>1.422900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1681</td>\n",
" <td>0.961000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1682</td>\n",
" <td>1.238500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1683</td>\n",
" <td>1.534600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1684</td>\n",
" <td>1.718300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1685</td>\n",
" <td>1.256400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1686</td>\n",
" <td>1.467500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1687</td>\n",
" <td>1.802200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1688</td>\n",
" <td>1.959200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1689</td>\n",
" <td>1.751000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1690</td>\n",
" <td>1.609300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1691</td>\n",
" <td>1.105800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1692</td>\n",
" <td>1.000300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1693</td>\n",
" <td>2.068200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1694</td>\n",
" <td>1.725000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1695</td>\n",
" <td>1.488500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1696</td>\n",
" <td>1.433400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1697</td>\n",
" <td>1.736800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1698</td>\n",
" <td>1.422700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1699</td>\n",
" <td>1.147900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1700</td>\n",
" <td>1.804000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1701</td>\n",
" <td>2.336700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1702</td>\n",
" <td>1.770800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1703</td>\n",
" <td>1.413700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1704</td>\n",
" <td>1.201600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1705</td>\n",
" <td>1.279500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1706</td>\n",
" <td>1.805600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1707</td>\n",
" <td>1.776300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1708</td>\n",
" <td>1.390500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1709</td>\n",
" <td>1.560100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1710</td>\n",
" <td>1.389400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1711</td>\n",
" <td>1.311000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1712</td>\n",
" <td>1.451800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1713</td>\n",
" <td>1.491600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1714</td>\n",
" <td>1.891500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1715</td>\n",
" <td>1.476800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1716</td>\n",
" <td>1.431300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1717</td>\n",
" <td>1.287700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1718</td>\n",
" <td>1.384600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1719</td>\n",
" <td>1.401400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1720</td>\n",
" <td>1.637300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1721</td>\n",
" <td>1.033600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1722</td>\n",
" <td>1.715000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1723</td>\n",
" <td>1.154200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1724</td>\n",
" <td>1.557200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1725</td>\n",
" <td>1.558400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1726</td>\n",
" <td>1.122800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1727</td>\n",
" <td>1.365000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1728</td>\n",
" <td>1.269300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1729</td>\n",
" <td>1.484500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1730</td>\n",
" <td>1.556000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1731</td>\n",
" <td>1.230000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1732</td>\n",
" <td>1.976800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1733</td>\n",
" <td>1.576700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1734</td>\n",
" <td>1.796700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1735</td>\n",
" <td>1.328300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1736</td>\n",
" <td>1.240400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1737</td>\n",
" <td>1.299600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1738</td>\n",
" <td>1.243100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1739</td>\n",
" <td>1.652900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1740</td>\n",
" <td>1.394200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1741</td>\n",
" <td>2.429400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1742</td>\n",
" <td>1.249000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1743</td>\n",
" <td>1.087400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1744</td>\n",
" <td>1.984900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1745</td>\n",
" <td>1.716300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1746</td>\n",
" <td>1.388500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1747</td>\n",
" <td>1.552100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1748</td>\n",
" <td>1.265400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1749</td>\n",
" <td>1.290600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1750</td>\n",
" <td>1.256300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1751</td>\n",
" <td>1.636700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1752</td>\n",
" <td>1.518100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1753</td>\n",
" <td>1.470100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1754</td>\n",
" <td>1.171900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1755</td>\n",
" <td>1.188500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1756</td>\n",
" <td>1.068700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1757</td>\n",
" <td>1.221800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1758</td>\n",
" <td>1.329400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1759</td>\n",
" <td>1.368200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1760</td>\n",
" <td>1.488300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1761</td>\n",
" <td>1.155600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1762</td>\n",
" <td>1.554500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1763</td>\n",
" <td>1.608900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1764</td>\n",
" <td>1.308300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1765</td>\n",
" <td>1.215500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1766</td>\n",
" <td>1.417500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1767</td>\n",
" <td>1.134500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1768</td>\n",
" <td>1.357100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1769</td>\n",
" <td>1.532100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1770</td>\n",
" <td>1.204100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1771</td>\n",
" <td>1.691600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1772</td>\n",
" <td>1.774600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1773</td>\n",
" <td>0.943600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1774</td>\n",
" <td>1.458000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1775</td>\n",
" <td>1.329100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1776</td>\n",
" <td>1.531200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1777</td>\n",
" <td>1.644400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1778</td>\n",
" <td>1.598000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1779</td>\n",
" <td>1.380400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1780</td>\n",
" <td>1.974700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1781</td>\n",
" <td>1.094100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1782</td>\n",
" <td>1.476000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1783</td>\n",
" <td>1.434500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1784</td>\n",
" <td>1.174300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1785</td>\n",
" <td>1.293600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1786</td>\n",
" <td>1.651100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1787</td>\n",
" <td>1.706500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1788</td>\n",
" <td>1.309400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1789</td>\n",
" <td>1.055200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1790</td>\n",
" <td>1.560100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1791</td>\n",
" <td>1.621100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1792</td>\n",
" <td>1.362200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1793</td>\n",
" <td>1.581300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1794</td>\n",
" <td>1.439300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1795</td>\n",
" <td>1.299800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1796</td>\n",
" <td>1.108900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1797</td>\n",
" <td>1.234900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1798</td>\n",
" <td>1.420900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1799</td>\n",
" <td>1.247500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1800</td>\n",
" <td>1.209700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1801</td>\n",
" <td>1.833500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1802</td>\n",
" <td>1.369300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1803</td>\n",
" <td>1.236900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1804</td>\n",
" <td>1.576300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1805</td>\n",
" <td>1.491300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1806</td>\n",
" <td>1.096700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1807</td>\n",
" <td>1.299100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1808</td>\n",
" <td>1.450900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1809</td>\n",
" <td>1.293600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1810</td>\n",
" <td>1.529600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1811</td>\n",
" <td>1.606500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1812</td>\n",
" <td>1.229800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1813</td>\n",
" <td>1.729600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1814</td>\n",
" <td>2.069400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1815</td>\n",
" <td>1.329100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1816</td>\n",
" <td>1.600400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1817</td>\n",
" <td>1.749900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1818</td>\n",
" <td>1.199500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1819</td>\n",
" <td>1.189900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1820</td>\n",
" <td>1.206800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1821</td>\n",
" <td>2.264400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1822</td>\n",
" <td>1.283800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1823</td>\n",
" <td>1.405200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1824</td>\n",
" <td>1.227800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1825</td>\n",
" <td>1.621800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1826</td>\n",
" <td>1.393800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1827</td>\n",
" <td>1.234300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1828</td>\n",
" <td>1.360500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1829</td>\n",
" <td>1.422900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1830</td>\n",
" <td>1.388800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1831</td>\n",
" <td>1.206300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1832</td>\n",
" <td>1.281400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1833</td>\n",
" <td>1.219400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1834</td>\n",
" <td>1.233900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1835</td>\n",
" <td>1.692200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1836</td>\n",
" <td>1.649800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1837</td>\n",
" <td>1.328300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1838</td>\n",
" <td>1.920600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1839</td>\n",
" <td>1.649000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1840</td>\n",
" <td>1.306800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1841</td>\n",
" <td>1.040500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1842</td>\n",
" <td>1.506200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1843</td>\n",
" <td>1.162700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1844</td>\n",
" <td>1.144300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1845</td>\n",
" <td>1.752300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1846</td>\n",
" <td>1.480600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1847</td>\n",
" <td>1.344200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1848</td>\n",
" <td>1.239000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1849</td>\n",
" <td>1.035800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1850</td>\n",
" <td>1.217000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1851</td>\n",
" <td>1.141900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1852</td>\n",
" <td>1.149500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1853</td>\n",
" <td>1.251000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1854</td>\n",
" <td>1.430700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1855</td>\n",
" <td>1.378100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1856</td>\n",
" <td>1.654700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1857</td>\n",
" <td>1.147900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1858</td>\n",
" <td>1.401800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1859</td>\n",
" <td>1.811800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1860</td>\n",
" <td>1.690600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1861</td>\n",
" <td>1.007700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1862</td>\n",
" <td>1.311000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1863</td>\n",
" <td>1.186500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1864</td>\n",
" <td>1.114800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1865</td>\n",
" <td>1.577400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1866</td>\n",
" <td>1.390000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1867</td>\n",
" <td>1.382800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1868</td>\n",
" <td>1.575000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1869</td>\n",
" <td>1.406900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1870</td>\n",
" <td>1.411900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1871</td>\n",
" <td>1.071300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1872</td>\n",
" <td>1.575200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1873</td>\n",
" <td>1.449300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1874</td>\n",
" <td>1.752000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1875</td>\n",
" <td>1.119500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1876</td>\n",
" <td>1.629200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1877</td>\n",
" <td>1.250900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1878</td>\n",
" <td>1.278500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1879</td>\n",
" <td>1.146100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1880</td>\n",
" <td>1.473300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1881</td>\n",
" <td>1.767300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1882</td>\n",
" <td>2.117000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1883</td>\n",
" <td>1.203400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1884</td>\n",
" <td>1.110900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1885</td>\n",
" <td>1.209700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1886</td>\n",
" <td>1.846700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1887</td>\n",
" <td>1.157100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1888</td>\n",
" <td>1.283200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1889</td>\n",
" <td>1.315900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1890</td>\n",
" <td>1.324700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1891</td>\n",
" <td>1.127500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1892</td>\n",
" <td>1.395200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1893</td>\n",
" <td>1.597100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1894</td>\n",
" <td>1.311900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1895</td>\n",
" <td>1.535100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1896</td>\n",
" <td>1.238000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1897</td>\n",
" <td>1.085500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1898</td>\n",
" <td>2.029100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1899</td>\n",
" <td>1.333500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1900</td>\n",
" <td>2.012700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1901</td>\n",
" <td>1.641400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1902</td>\n",
" <td>1.488000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1903</td>\n",
" <td>1.340500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1904</td>\n",
" <td>1.455900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1905</td>\n",
" <td>1.677300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1906</td>\n",
" <td>1.308700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1907</td>\n",
" <td>1.223900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1908</td>\n",
" <td>1.346900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1909</td>\n",
" <td>1.164800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1910</td>\n",
" <td>1.174300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1911</td>\n",
" <td>1.026200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1912</td>\n",
" <td>1.380600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1913</td>\n",
" <td>1.522100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1914</td>\n",
" <td>1.313400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1915</td>\n",
" <td>1.511100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1916</td>\n",
" <td>1.089300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1917</td>\n",
" <td>1.535000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1918</td>\n",
" <td>1.491000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1919</td>\n",
" <td>2.140200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1920</td>\n",
" <td>1.641000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1921</td>\n",
" <td>1.373200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1922</td>\n",
" <td>1.744200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1923</td>\n",
" <td>1.527400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1924</td>\n",
" <td>1.944600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1925</td>\n",
" <td>1.717700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1926</td>\n",
" <td>1.371700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1927</td>\n",
" <td>1.276700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1928</td>\n",
" <td>1.350800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1929</td>\n",
" <td>1.415100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1930</td>\n",
" <td>1.429200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1931</td>\n",
" <td>1.726000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1932</td>\n",
" <td>1.432200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1933</td>\n",
" <td>1.130500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1934</td>\n",
" <td>1.152500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1935</td>\n",
" <td>1.406900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1936</td>\n",
" <td>0.945800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1937</td>\n",
" <td>2.123700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1938</td>\n",
" <td>1.462600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1939</td>\n",
" <td>1.302800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1940</td>\n",
" <td>1.542700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1941</td>\n",
" <td>1.646700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1942</td>\n",
" <td>1.091100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1943</td>\n",
" <td>1.525800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1944</td>\n",
" <td>1.805100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1945</td>\n",
" <td>1.385600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1946</td>\n",
" <td>1.384300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1947</td>\n",
" <td>1.424400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1948</td>\n",
" <td>1.356500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1949</td>\n",
" <td>1.430500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1950</td>\n",
" <td>1.129100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1951</td>\n",
" <td>1.396000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1952</td>\n",
" <td>1.267200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1953</td>\n",
" <td>1.109400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1954</td>\n",
" <td>1.476600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1955</td>\n",
" <td>1.661100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1956</td>\n",
" <td>1.362800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1957</td>\n",
" <td>1.185100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1958</td>\n",
" <td>1.316000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1959</td>\n",
" <td>1.235400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1960</td>\n",
" <td>1.674900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1961</td>\n",
" <td>1.447400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1962</td>\n",
" <td>1.646300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1963</td>\n",
" <td>1.040400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1964</td>\n",
" <td>1.741700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1965</td>\n",
" <td>1.412700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1966</td>\n",
" <td>1.575200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1967</td>\n",
" <td>1.043200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1968</td>\n",
" <td>1.716600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1969</td>\n",
" <td>1.285700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1970</td>\n",
" <td>1.453900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1971</td>\n",
" <td>1.383000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1972</td>\n",
" <td>1.758500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1973</td>\n",
" <td>1.173800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1974</td>\n",
" <td>1.188800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1975</td>\n",
" <td>1.487500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1976</td>\n",
" <td>1.367200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1977</td>\n",
" <td>1.105000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1978</td>\n",
" <td>1.591300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1979</td>\n",
" <td>1.161100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1980</td>\n",
" <td>1.501300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1981</td>\n",
" <td>1.301500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1982</td>\n",
" <td>1.481200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1983</td>\n",
" <td>1.153500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1984</td>\n",
" <td>1.289400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1985</td>\n",
" <td>1.539300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1986</td>\n",
" <td>1.703700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1987</td>\n",
" <td>1.267300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1988</td>\n",
" <td>1.294200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1989</td>\n",
" <td>1.357100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1990</td>\n",
" <td>1.253700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1991</td>\n",
" <td>1.334600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1992</td>\n",
" <td>1.718800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1993</td>\n",
" <td>1.563400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1994</td>\n",
" <td>1.647900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1995</td>\n",
" <td>1.547600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1996</td>\n",
" <td>1.389200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1997</td>\n",
" <td>1.322900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1998</td>\n",
" <td>1.340500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1999</td>\n",
" <td>1.504700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2000</td>\n",
" <td>1.334000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2001</td>\n",
" <td>1.203100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2002</td>\n",
" <td>1.322800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2003</td>\n",
" <td>1.123500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2004</td>\n",
" <td>1.375200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2005</td>\n",
" <td>1.306000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2006</td>\n",
" <td>1.186800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2007</td>\n",
" <td>1.512000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2008</td>\n",
" <td>1.284300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2009</td>\n",
" <td>1.442800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2010</td>\n",
" <td>1.155800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2011</td>\n",
" <td>1.905600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2012</td>\n",
" <td>1.182600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2013</td>\n",
" <td>1.731600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2014</td>\n",
" <td>1.117500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2015</td>\n",
" <td>1.741300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2016</td>\n",
" <td>1.252900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2017</td>\n",
" <td>1.029700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2018</td>\n",
" <td>1.505600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2019</td>\n",
" <td>1.401000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2020</td>\n",
" <td>1.187700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2021</td>\n",
" <td>1.833800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2022</td>\n",
" <td>1.286800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2023</td>\n",
" <td>1.372400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2024</td>\n",
" <td>1.391300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2025</td>\n",
" <td>1.304800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2026</td>\n",
" <td>1.163900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2027</td>\n",
" <td>1.471400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2028</td>\n",
" <td>1.281000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2029</td>\n",
" <td>1.183200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2030</td>\n",
" <td>1.678900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2031</td>\n",
" <td>1.595700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2032</td>\n",
" <td>1.195000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2033</td>\n",
" <td>1.263200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2034</td>\n",
" <td>1.158200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2035</td>\n",
" <td>1.103000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2036</td>\n",
" <td>1.349300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2037</td>\n",
" <td>1.183100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2038</td>\n",
" <td>1.350600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2039</td>\n",
" <td>1.523100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2040</td>\n",
" <td>1.237700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2041</td>\n",
" <td>1.607700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2042</td>\n",
" <td>1.245600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2043</td>\n",
" <td>1.104900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2044</td>\n",
" <td>1.557800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2045</td>\n",
" <td>1.367800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2046</td>\n",
" <td>1.236800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2047</td>\n",
" <td>1.188600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2048</td>\n",
" <td>1.180500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2049</td>\n",
" <td>1.279400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2050</td>\n",
" <td>1.853500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2051</td>\n",
" <td>1.236400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2052</td>\n",
" <td>1.266600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2053</td>\n",
" <td>1.298100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2054</td>\n",
" <td>1.339700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2055</td>\n",
" <td>1.247300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2056</td>\n",
" <td>1.892200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2057</td>\n",
" <td>1.289800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2058</td>\n",
" <td>1.443800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2059</td>\n",
" <td>1.269000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2060</td>\n",
" <td>1.321000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2061</td>\n",
" <td>1.594500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2062</td>\n",
" <td>1.992100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2063</td>\n",
" <td>1.409600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2064</td>\n",
" <td>1.185900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2065</td>\n",
" <td>1.257600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2066</td>\n",
" <td>1.630700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2067</td>\n",
" <td>1.443100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2068</td>\n",
" <td>1.848100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2069</td>\n",
" <td>1.965000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2070</td>\n",
" <td>1.972600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2071</td>\n",
" <td>1.723600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2072</td>\n",
" <td>1.100800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2073</td>\n",
" <td>1.829900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2074</td>\n",
" <td>1.374600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2075</td>\n",
" <td>1.558600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2076</td>\n",
" <td>1.320900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2077</td>\n",
" <td>1.538300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2078</td>\n",
" <td>1.125100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2079</td>\n",
" <td>1.539000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2080</td>\n",
" <td>1.351400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2081</td>\n",
" <td>1.666900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2082</td>\n",
" <td>1.358900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2083</td>\n",
" <td>1.170800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2084</td>\n",
" <td>1.263400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2085</td>\n",
" <td>1.038400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2086</td>\n",
" <td>1.350100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2087</td>\n",
" <td>1.527600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2088</td>\n",
" <td>1.416600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2089</td>\n",
" <td>1.632500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2090</td>\n",
" <td>1.022900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2091</td>\n",
" <td>1.270300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2092</td>\n",
" <td>1.265800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2093</td>\n",
" <td>1.895400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2094</td>\n",
" <td>1.294000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2095</td>\n",
" <td>1.276000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2096</td>\n",
" <td>1.436200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2097</td>\n",
" <td>1.248000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2098</td>\n",
" <td>1.505700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2099</td>\n",
" <td>1.201300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2100</td>\n",
" <td>1.612800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2101</td>\n",
" <td>1.577500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2102</td>\n",
" <td>2.045800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2103</td>\n",
" <td>1.448800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2104</td>\n",
" <td>1.463300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2105</td>\n",
" <td>1.385300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2106</td>\n",
" <td>1.318200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2107</td>\n",
" <td>1.241900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2108</td>\n",
" <td>2.427100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2109</td>\n",
" <td>1.897000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2110</td>\n",
" <td>2.441200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2111</td>\n",
" <td>1.286000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2112</td>\n",
" <td>1.421300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2113</td>\n",
" <td>1.428900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2114</td>\n",
" <td>1.471300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2115</td>\n",
" <td>1.356700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2116</td>\n",
" <td>1.223000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2117</td>\n",
" <td>1.253100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2118</td>\n",
" <td>1.542300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2119</td>\n",
" <td>1.530200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2120</td>\n",
" <td>1.381900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2121</td>\n",
" <td>1.474300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2122</td>\n",
" <td>1.542500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2123</td>\n",
" <td>1.249200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2124</td>\n",
" <td>1.272600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2125</td>\n",
" <td>1.536700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2126</td>\n",
" <td>1.666900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2127</td>\n",
" <td>1.646300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2128</td>\n",
" <td>1.243100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2129</td>\n",
" <td>1.347400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2130</td>\n",
" <td>1.240400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2131</td>\n",
" <td>1.707300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2132</td>\n",
" <td>1.480700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2133</td>\n",
" <td>1.199700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2134</td>\n",
" <td>1.202100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2135</td>\n",
" <td>1.802800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2136</td>\n",
" <td>1.467500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2137</td>\n",
" <td>1.199000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2138</td>\n",
" <td>1.374700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2139</td>\n",
" <td>1.688600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2140</td>\n",
" <td>1.698300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2141</td>\n",
" <td>1.324000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2142</td>\n",
" <td>1.414500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2143</td>\n",
" <td>1.875900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2144</td>\n",
" <td>1.325200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2145</td>\n",
" <td>1.566500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2146</td>\n",
" <td>1.250600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2147</td>\n",
" <td>1.428000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2148</td>\n",
" <td>1.498400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2149</td>\n",
" <td>1.564300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2150</td>\n",
" <td>1.161100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2151</td>\n",
" <td>1.302200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2152</td>\n",
" <td>2.096400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2153</td>\n",
" <td>2.035500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2154</td>\n",
" <td>1.613100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2155</td>\n",
" <td>1.231100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2156</td>\n",
" <td>1.586100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2157</td>\n",
" <td>1.632300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2158</td>\n",
" <td>1.241100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2159</td>\n",
" <td>1.634800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2160</td>\n",
" <td>1.406300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2161</td>\n",
" <td>1.202800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2162</td>\n",
" <td>1.786200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2163</td>\n",
" <td>1.317200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2164</td>\n",
" <td>1.662700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2165</td>\n",
" <td>1.107200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2166</td>\n",
" <td>1.316000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2167</td>\n",
" <td>1.307700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2168</td>\n",
" <td>1.530900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2169</td>\n",
" <td>1.149300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2170</td>\n",
" <td>1.932500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2171</td>\n",
" <td>1.565200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2172</td>\n",
" <td>1.171800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2173</td>\n",
" <td>1.433600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2174</td>\n",
" <td>1.202100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2175</td>\n",
" <td>1.938400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2176</td>\n",
" <td>1.752000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2177</td>\n",
" <td>1.347400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2178</td>\n",
" <td>1.149800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2179</td>\n",
" <td>1.058000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2180</td>\n",
" <td>1.166900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2181</td>\n",
" <td>1.536500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2182</td>\n",
" <td>1.125400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2183</td>\n",
" <td>1.385100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2184</td>\n",
" <td>1.353000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2185</td>\n",
" <td>1.516800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2186</td>\n",
" <td>1.530400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2187</td>\n",
" <td>1.435800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2188</td>\n",
" <td>1.716300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2189</td>\n",
" <td>1.272100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2190</td>\n",
" <td>2.123100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2191</td>\n",
" <td>1.586500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2192</td>\n",
" <td>1.136500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2193</td>\n",
" <td>1.392300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2194</td>\n",
" <td>1.025900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2195</td>\n",
" <td>1.360300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2196</td>\n",
" <td>1.496100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2197</td>\n",
" <td>2.067000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2198</td>\n",
" <td>1.226700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2199</td>\n",
" <td>1.702900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2200</td>\n",
" <td>1.249700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2201</td>\n",
" <td>1.100700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2202</td>\n",
" <td>0.975700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2203</td>\n",
" <td>1.589000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2204</td>\n",
" <td>1.240000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2205</td>\n",
" <td>1.398200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2206</td>\n",
" <td>1.490700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2207</td>\n",
" <td>1.447900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2208</td>\n",
" <td>1.478700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2209</td>\n",
" <td>1.427600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2210</td>\n",
" <td>1.725500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2211</td>\n",
" <td>1.476800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2212</td>\n",
" <td>1.958500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2213</td>\n",
" <td>1.426400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2214</td>\n",
" <td>1.639300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2215</td>\n",
" <td>1.646200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2216</td>\n",
" <td>1.823300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2217</td>\n",
" <td>1.333400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2218</td>\n",
" <td>1.142500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2219</td>\n",
" <td>1.508600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2220</td>\n",
" <td>2.200100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2221</td>\n",
" <td>1.579700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2222</td>\n",
" <td>1.151400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2223</td>\n",
" <td>1.449600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2224</td>\n",
" <td>1.169100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2225</td>\n",
" <td>1.495000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2226</td>\n",
" <td>1.555500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2227</td>\n",
" <td>1.301300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2228</td>\n",
" <td>1.158000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2229</td>\n",
" <td>1.273100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2230</td>\n",
" <td>1.725400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2231</td>\n",
" <td>1.451500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2232</td>\n",
" <td>1.227900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2233</td>\n",
" <td>1.666000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2234</td>\n",
" <td>1.284600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2235</td>\n",
" <td>1.223300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2236</td>\n",
" <td>1.857500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2237</td>\n",
" <td>1.610700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2238</td>\n",
" <td>1.853600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2239</td>\n",
" <td>1.503600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2240</td>\n",
" <td>1.569900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2241</td>\n",
" <td>1.335400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2242</td>\n",
" <td>1.489300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2243</td>\n",
" <td>1.528300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2244</td>\n",
" <td>1.360300</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2245</td>\n",
" <td>1.085500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2246</td>\n",
" <td>1.272100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2247</td>\n",
" <td>1.243700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2248</td>\n",
" <td>1.471000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"trainer_stats = trainer.train()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "c4c8f35c",
"metadata": {
"cellView": "form",
"execution": {
"iopub.execute_input": "2024-11-20T03:54:09.565224Z",
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},
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"outputId": "cf63d152-e152-468c-ba0d-938e0d2f71a0",
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"duration": 0.079663,
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"start_time": "2024-11-20T03:54:09.493024",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"31481.3839 seconds used for training.\n",
"524.69 minutes used for training.\n",
"Peak reserved memory = 9.363 GB.\n",
"Peak reserved memory for training = 3.191 GB.\n",
"Peak reserved memory % of max memory = 63.517 %.\n",
"Peak reserved memory for training % of max memory = 21.647 %.\n"
]
}
],
"source": [
"#@title Show final memory and time stats\n",
"used_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n",
"used_memory_for_lora = round(used_memory - start_gpu_memory, 3)\n",
"used_percentage = round(used_memory /max_memory*100, 3)\n",
"lora_percentage = round(used_memory_for_lora/max_memory*100, 3)\n",
"print(f\"{trainer_stats.metrics['train_runtime']} seconds used for training.\")\n",
"print(f\"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.\")\n",
"print(f\"Peak reserved memory = {used_memory} GB.\")\n",
"print(f\"Peak reserved memory for training = {used_memory_for_lora} GB.\")\n",
"print(f\"Peak reserved memory % of max memory = {used_percentage} %.\")\n",
"print(f\"Peak reserved memory for training % of max memory = {lora_percentage} %.\")"
]
},
{
"cell_type": "markdown",
"id": "6176fe1e",
"metadata": {
"id": "ekOmTR1hSNcr",
"papermill": {
"duration": 0.070529,
"end_time": "2024-11-20T03:54:09.714353",
"exception": false,
"start_time": "2024-11-20T03:54:09.643824",
"status": "completed"
},
"tags": []
},
"source": [
"<a name=\"Inference\"></a>\n",
"### Inference\n",
"Let's run the model! You can change the instruction and input - leave the output blank!"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "04171c34",
"metadata": {
"execution": {
"iopub.execute_input": "2024-11-20T03:54:09.856688Z",
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"id": "kR3gIAX-SM2q",
"outputId": "5b71f982-38c0-44c8-a4e5-58cd20b5a585",
"papermill": {
"duration": 0.077355,
"end_time": "2024-11-20T03:54:09.862368",
"exception": false,
"start_time": "2024-11-20T03:54:09.785013",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"if False:\n",
" # alpaca_prompt = Copied from above\n",
" FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
" inputs = tokenizer(\n",
" [\n",
" alpaca_prompt.format(\n",
" \"Continue the fibonnaci sequence.\", # instruction\n",
" \"1, 1, 2, 3, 5, 8\", # input\n",
" \"\", # output - leave this blank for generation!\n",
" )\n",
" ], return_tensors = \"pt\").to(\"cuda\")\n",
"\n",
" outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n",
" tokenizer.batch_decode(outputs)"
]
},
{
"cell_type": "markdown",
"id": "ec51acf7",
"metadata": {
"id": "CrSvZObor0lY",
"papermill": {
"duration": 0.070507,
"end_time": "2024-11-20T03:54:10.004648",
"exception": false,
"start_time": "2024-11-20T03:54:09.934141",
"status": "completed"
},
"tags": []
},
"source": [
" You can also use a `TextStreamer` for continuous inference - so you can see the generation token by token, instead of waiting the whole time!"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "97035490",
"metadata": {
"execution": {
"iopub.execute_input": "2024-11-20T03:54:10.145654Z",
"iopub.status.busy": "2024-11-20T03:54:10.145402Z",
"iopub.status.idle": "2024-11-20T03:54:10.150359Z",
"shell.execute_reply": "2024-11-20T03:54:10.149572Z"
},
"id": "e2pEuRb1r2Vg",
"outputId": "084aab62-2122-436a-c0cb-8871986640eb",
"papermill": {
"duration": 0.077256,
"end_time": "2024-11-20T03:54:10.151976",
"exception": false,
"start_time": "2024-11-20T03:54:10.074720",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"if False:\n",
" # alpaca_prompt = Copied from above\n",
" FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
" inputs = tokenizer(\n",
" [\n",
" alpaca_prompt.format(\n",
" \"Continue the fibonnaci sequence.\", # instruction\n",
" \"1, 1, 2, 3, 5, 8\", # input\n",
" \"\", # output - leave this blank for generation!\n",
" )\n",
" ], return_tensors = \"pt\").to(\"cuda\")\n",
"\n",
" from transformers import TextStreamer\n",
" text_streamer = TextStreamer(tokenizer)\n",
" _ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)"
]
},
{
"cell_type": "markdown",
"id": "a4179ebd",
"metadata": {
"id": "uMuVrWbjAzhc",
"papermill": {
"duration": 0.070519,
"end_time": "2024-11-20T03:54:10.292849",
"exception": false,
"start_time": "2024-11-20T03:54:10.222330",
"status": "completed"
},
"tags": []
},
"source": [
"<a name=\"Save\"></a>\n",
"### Saving, loading finetuned models\n",
"To save the final model as LoRA adapters, either use Huggingface's `push_to_hub` for an online save or `save_pretrained` for a local save.\n",
"\n",
"**[NOTE]** This ONLY saves the LoRA adapters, and not the full model. To save to 16bit or GGUF, scroll down!"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "3974619b",
"metadata": {
"execution": {
"iopub.execute_input": "2024-11-20T03:54:10.434709Z",
"iopub.status.busy": "2024-11-20T03:54:10.434458Z",
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"id": "upcOlWe7A1vc",
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"duration": 4.681486,
"end_time": "2024-11-20T03:54:15.044402",
"exception": false,
"start_time": "2024-11-20T03:54:10.362916",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "20ebd44d861249a9bbba6b4d7cf7a1af",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"README.md: 0%| | 0.00/615 [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5699440a265c4a44a6f21a6551c0a49e",
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" 0%| | 0/1 [00:00<?, ?it/s]"
]
},
"metadata": {},
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},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "208d530d5bc9489c84b120adee331875",
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"text/plain": [
"adapter_model.safetensors: 0%| | 0.00/336M [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Saved model to https://huggingface.co/scoliono/groupchat_lora_abliterated_instruct-3.1-8b\n"
]
}
],
"source": [
"#model.save_pretrained(\"lora_model\") # Local saving\n",
"from kaggle_secrets import UserSecretsClient\n",
"user_secrets = UserSecretsClient()\n",
"hf_token = user_secrets.get_secret(\"hf_token\")\n",
"\n",
"model.push_to_hub(\"scoliono/groupchat_lora_abliterated_instruct-3.1-8b\", token = hf_token)"
]
},
{
"cell_type": "markdown",
"id": "28be027e",
"metadata": {
"id": "AEEcJ4qfC7Lp",
"papermill": {
"duration": 0.070774,
"end_time": "2024-11-20T03:54:15.188324",
"exception": false,
"start_time": "2024-11-20T03:54:15.117550",
"status": "completed"
},
"tags": []
},
"source": [
"Now if you want to load the LoRA adapters we just saved for inference, set `False` to `True`:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "f190efeb",
"metadata": {
"execution": {
"iopub.execute_input": "2024-11-20T03:54:15.329954Z",
"iopub.status.busy": "2024-11-20T03:54:15.329369Z",
"iopub.status.idle": "2024-11-20T03:54:15.334813Z",
"shell.execute_reply": "2024-11-20T03:54:15.333988Z"
},
"id": "MKX_XKs_BNZR",
"outputId": "05e5a193-dab0-41db-e07c-4b3afbdd7932",
"papermill": {
"duration": 0.077834,
"end_time": "2024-11-20T03:54:15.336380",
"exception": false,
"start_time": "2024-11-20T03:54:15.258546",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"if False:\n",
" from unsloth import FastLanguageModel\n",
" model, tokenizer = FastLanguageModel.from_pretrained(\n",
" model_name = \"scoliono/groupchat_lora_abliterated_instruct-3.1-8b\", # YOUR MODEL YOU USED FOR TRAINING\n",
" max_seq_length = max_seq_length,\n",
" dtype = dtype,\n",
" load_in_4bit = load_in_4bit,\n",
" )\n",
" FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
"\n",
" # alpaca_prompt = You MUST copy from above!\n",
"\n",
" inputs = tokenizer(\n",
" [\n",
" alpaca_prompt.format(\n",
" \"What is a famous tall tower in Paris?\", # instruction\n",
" \"\", # input\n",
" \"\", # output - leave this blank for generation!\n",
" )\n",
" ], return_tensors = \"pt\").to(\"cuda\")\n",
"\n",
" outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n",
" tokenizer.batch_decode(outputs)"
]
},
{
"cell_type": "markdown",
"id": "0dbf42b6",
"metadata": {
"id": "QQMjaNrjsU5_",
"papermill": {
"duration": 0.070237,
"end_time": "2024-11-20T03:54:15.514274",
"exception": false,
"start_time": "2024-11-20T03:54:15.444037",
"status": "completed"
},
"tags": []
},
"source": [
"You can also use Hugging Face's `AutoModelForPeftCausalLM`. Only use this if you do not have `unsloth` installed. It can be hopelessly slow, since `4bit` model downloading is not supported, and Unsloth's **inference is 2x faster**."
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "ec3b9df7",
"metadata": {
"execution": {
"iopub.execute_input": "2024-11-20T03:54:15.656780Z",
"iopub.status.busy": "2024-11-20T03:54:15.656173Z",
"iopub.status.idle": "2024-11-20T03:54:15.660705Z",
"shell.execute_reply": "2024-11-20T03:54:15.659857Z"
},
"id": "yFfaXG0WsQuE",
"papermill": {
"duration": 0.077345,
"end_time": "2024-11-20T03:54:15.662229",
"exception": false,
"start_time": "2024-11-20T03:54:15.584884",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"if False:\n",
" # I highly do NOT suggest - use Unsloth if possible\n",
" from peft import AutoPeftModelForCausalLM\n",
" from transformers import AutoTokenizer\n",
" model = AutoPeftModelForCausalLM.from_pretrained(\n",
" \"groupchat_lora_abliterated_instruct-3.1-8b\", # YOUR MODEL YOU USED FOR TRAINING\n",
" load_in_4bit = load_in_4bit,\n",
" )\n",
" tokenizer = AutoTokenizer.from_pretrained(\"groupchat_lora_abliterated_instruct-3.1-8b\")"
]
},
{
"cell_type": "markdown",
"id": "b599046e",
"metadata": {
"id": "f422JgM9sdVT",
"papermill": {
"duration": 0.07051,
"end_time": "2024-11-20T03:54:15.803541",
"exception": false,
"start_time": "2024-11-20T03:54:15.733031",
"status": "completed"
},
"tags": []
},
"source": [
"### Saving to float16 for VLLM\n",
"\n",
"We also support saving to `float16` directly. Select `merged_16bit` for float16 or `merged_4bit` for int4. We also allow `lora` adapters as a fallback. Use `push_to_hub_merged` to upload to your Hugging Face account! You can go to https://huggingface.co/settings/tokens for your personal tokens."
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "56eec57b",
"metadata": {
"execution": {
"iopub.execute_input": "2024-11-20T03:54:15.946500Z",
"iopub.status.busy": "2024-11-20T03:54:15.945712Z",
"iopub.status.idle": "2024-11-20T03:54:15.951204Z",
"shell.execute_reply": "2024-11-20T03:54:15.950368Z"
},
"id": "iHjt_SMYsd3P",
"papermill": {
"duration": 0.079821,
"end_time": "2024-11-20T03:54:15.952812",
"exception": false,
"start_time": "2024-11-20T03:54:15.872991",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"# Merge to 16bit\n",
"if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_16bit\",)\n",
"if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"merged_16bit\", token = \"\")\n",
"\n",
"# Merge to 4bit\n",
"if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_4bit\",)\n",
"if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"merged_4bit\", token = \"\")\n",
"\n",
"# Just LoRA adapters\n",
"if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"lora\",)\n",
"if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"lora\", token = \"\")"
]
},
{
"cell_type": "markdown",
"id": "d87b3974",
"metadata": {
"id": "TCv4vXHd61i7",
"papermill": {
"duration": 0.0692,
"end_time": "2024-11-20T03:54:16.091909",
"exception": false,
"start_time": "2024-11-20T03:54:16.022709",
"status": "completed"
},
"tags": []
},
"source": [
"### GGUF / llama.cpp Conversion\n",
"To save to `GGUF` / `llama.cpp`, we support it natively now! We clone `llama.cpp` and we default save it to `q8_0`. We allow all methods like `q4_k_m`. Use `save_pretrained_gguf` for local saving and `push_to_hub_gguf` for uploading to HF.\n",
"\n",
"Some supported quant methods (full list on our [Wiki page](https://github.com/unslothai/unsloth/wiki#gguf-quantization-options)):\n",
"* `q8_0` - Fast conversion. High resource use, but generally acceptable.\n",
"* `q4_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K.\n",
"* `q5_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K."
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "ec62bb3e",
"metadata": {
"execution": {
"iopub.execute_input": "2024-11-20T03:54:16.232549Z",
"iopub.status.busy": "2024-11-20T03:54:16.232264Z",
"iopub.status.idle": "2024-11-20T03:54:16.237502Z",
"shell.execute_reply": "2024-11-20T03:54:16.236662Z"
},
"id": "FqfebeAdT073",
"papermill": {
"duration": 0.07695,
"end_time": "2024-11-20T03:54:16.239011",
"exception": false,
"start_time": "2024-11-20T03:54:16.162061",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"# Save to 8bit Q8_0\n",
"if False: model.save_pretrained_gguf(\"model\", tokenizer,)\n",
"if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, token = \"\")\n",
"\n",
"# Save to 16bit GGUF\n",
"if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"f16\")\n",
"if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"f16\", token = \"\")\n",
"\n",
"# Save to q4_k_m GGUF\n",
"if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"q4_k_m\")\n",
"if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"q4_k_m\", token = \"\")"
]
},
{
"cell_type": "markdown",
"id": "8bb60882",
"metadata": {
"id": "bDp0zNpwe6U_",
"papermill": {
"duration": 0.070344,
"end_time": "2024-11-20T03:54:16.380192",
"exception": false,
"start_time": "2024-11-20T03:54:16.309848",
"status": "completed"
},
"tags": []
},
"source": [
"Now, use the `model-unsloth.gguf` file or `model-unsloth-Q4_K_M.gguf` file in `llama.cpp` or a UI based system like `GPT4All`. You can install GPT4All by going [here](https://gpt4all.io/index.html)."
]
},
{
"cell_type": "markdown",
"id": "e36cffc3",
"metadata": {
"id": "Zt9CHJqO6p30",
"papermill": {
"duration": 0.070243,
"end_time": "2024-11-20T03:54:16.520198",
"exception": false,
"start_time": "2024-11-20T03:54:16.449955",
"status": "completed"
},
"tags": []
},
"source": [
"And we're done! If you have any questions on Unsloth, we have a [Discord](https://discord.gg/u54VK8m8tk) channel! If you find any bugs or want to keep updated with the latest LLM stuff, or need help, join projects etc, feel free to join our Discord!\n",
"\n",
"Some other links:\n",
"1. Zephyr DPO 2x faster [free Colab](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing)\n",
"2. Llama 7b 2x faster [free Colab](https://colab.research.google.com/drive/1lBzz5KeZJKXjvivbYvmGarix9Ao6Wxe5?usp=sharing)\n",
"3. TinyLlama 4x faster full Alpaca 52K in 1 hour [free Colab](https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing)\n",
"4. CodeLlama 34b 2x faster [A100 on Colab](https://colab.research.google.com/drive/1y7A0AxE3y8gdj4AVkl2aZX47Xu3P1wJT?usp=sharing)\n",
"5. Mistral 7b [free Kaggle version](https://www.kaggle.com/code/danielhanchen/kaggle-mistral-7b-unsloth-notebook)\n",
"6. We also did a [blog](https://huggingface.co/blog/unsloth-trl) with 🤗 HuggingFace, and we're in the TRL [docs](https://huggingface.co/docs/trl/main/en/sft_trainer#accelerate-fine-tuning-2x-using-unsloth)!\n",
"7. `ChatML` for ShareGPT datasets, [conversational notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing)\n",
"8. Text completions like novel writing [notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing)\n",
"\n",
"<div class=\"align-center\">\n",
" <a href=\"https://github.com/unslothai/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n",
" <a href=\"https://discord.gg/u54VK8m8tk\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord.png\" width=\"145\"></a>\n",
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