diff --git a/tools/interferenceanalysis/.ipynb_checkpoints/analysis-checkpoint.ipynb b/tools/interferenceanalysis/.ipynb_checkpoints/analysis-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..3156cad94f6a4732753985ea9f538e501011a07a
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+++ b/tools/interferenceanalysis/.ipynb_checkpoints/analysis-checkpoint.ipynb
@@ -0,0 +1,951 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a1a5e1dd-4466-41e3-bae1-c2c33ce44bf7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn import ensemble, neighbors, svm, linear_model, model_selection, tree, metrics, preprocessing \n",
+    "from sklearn.cluster import KMeans\n",
+    "from itertools import product\n",
+    "import streamlit as st\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import altair as alt\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f7777ac6-b6df-4023-8cc1-8410963fae70",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_chart(data):\n",
+    "    hover = alt.selection_single(\n",
+    "        fields=[\"timestamp\"],\n",
+    "        nearest=True,\n",
+    "        on=\"mouseover\",\n",
+    "        empty=\"none\",\n",
+    "    )\n",
+    "\n",
+    "    lines = (\n",
+    "        alt.Chart(data.reset_index(drop=True).melt(\"timestamp\"),\n",
+    "                  title=\"Metrics variation\")\n",
+    "        .mark_line()\n",
+    "        .encode(\n",
+    "            x=\"timestamp:T\",\n",
+    "            y=\"value:Q\",\n",
+    "            color=\"variable\",\n",
+    "            # strokeDash=\"variable\",\n",
+    "        ).properties(width=800, height=400)\n",
+    "    )\n",
+    "\n",
+    "    # Draw points on the line, and highlight based on selection\n",
+    "    points = lines.transform_filter(hover).mark_circle(size=65)\n",
+    "\n",
+    "    # Draw a rule at the location of the selection\n",
+    "    tooltips = (\n",
+    "        alt.Chart(data.reset_index(drop=True).melt(\"timestamp\"),\n",
+    "                  title=\"Metrics variation\")\n",
+    "        .mark_rule()\n",
+    "        .encode(\n",
+    "            x=\"timestamp:T\",\n",
+    "            y=\"value:Q\",\n",
+    "            opacity=alt.condition(hover, alt.value(0.3), alt.value(0)),\n",
+    "            tooltip=[\n",
+    "                alt.Tooltip(\"timestamp\", title=\"Time\"),\n",
+    "                alt.Tooltip(\"value\", title=\"Metrics\"),\n",
+    "            ],\n",
+    "        )\n",
+    "        .add_selection(hover)\n",
+    "    )\n",
+    "\n",
+    "    return (lines + points + tooltips).interactive()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "e030a95d-6865-47b2-9e34-120ed27dbb25",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_stress_chart(data, symbol):\n",
+    "    hover = alt.selection_single(\n",
+    "        fields=[\"stress\"],\n",
+    "        nearest=True,\n",
+    "        on=\"mouseover\",\n",
+    "        empty=\"none\",\n",
+    "    )\n",
+    "\n",
+    "    lines = (\n",
+    "        alt.Chart(data, title=\"Qos variation with stress \" + symbol)\n",
+    "        .mark_line()\n",
+    "        .encode(\n",
+    "            x=alt.X(\"stress:Q\", axis=alt.Axis(orient=\"top\")),\n",
+    "            y=alt.Y(\"degradation-percent:Q\", sort='descending',\n",
+    "                    title=\"degradation percent(%)\"),\n",
+    "            color=\"type\",\n",
+    "            strokeDash=\"type\",\n",
+    "        )\n",
+    "    )\n",
+    "\n",
+    "    # Draw points on the line, and highlight based on selection\n",
+    "    points = lines.transform_filter(hover).mark_circle(size=65)\n",
+    "\n",
+    "    # Draw a rule at the location of the selection\n",
+    "    tooltips = (\n",
+    "        alt.Chart(data)\n",
+    "        .mark_rule()\n",
+    "        .encode(\n",
+    "            x=\"stress:Q\",\n",
+    "            y=\"degradation-percent:Q\",\n",
+    "            opacity=alt.condition(hover, alt.value(0.3), alt.value(0)),\n",
+    "            tooltip=[\n",
+    "                alt.Tooltip(\"stress\", title=\"Stress Info\"),\n",
+    "                alt.Tooltip(\"avg-qos\", title=\"Qos\"),\n",
+    "                alt.Tooltip(\"degradation-percent\", title=\"Degradation Percent\")\n",
+    "            ],\n",
+    "        )\n",
+    "        .add_selection(hover)\n",
+    "    )\n",
+    "\n",
+    "    return (lines + points + tooltips).interactive()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "9c76ddde-ec6d-4cfa-ab2e-72b08bb3117a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def stress_sensitivity(stress_degrade):\n",
+    "    if stress_degrade['degradation-percent'] <= 5:\n",
+    "        return \"no\"\n",
+    "\n",
+    "    if stress_degrade['degradation-percent'] <= 10:\n",
+    "        return \"low\"\n",
+    "\n",
+    "    if stress_degrade['degradation-percent'] <= 20:\n",
+    "        return \"medium\"\n",
+    "\n",
+    "    return \"high\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "db3cb0b0-b2ab-4252-85b3-de435535612b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_usage_chart(data, symbol):\n",
+    "    hover = alt.selection_single(\n",
+    "        fields=[\"stress\"],\n",
+    "        nearest=True,\n",
+    "        on=\"mouseover\",\n",
+    "        empty=\"none\",\n",
+    "    )\n",
+    "\n",
+    "    lines = (\n",
+    "        alt.Chart(data, title=\"CPU usage variation with stress \" + symbol)\n",
+    "        .mark_line()\n",
+    "        .encode(\n",
+    "            x=alt.X(\"stress:Q\", axis=alt.Axis(orient=\"top\")),\n",
+    "            y=alt.Y(\"avg-cpu-usage:Q\", sort='descending',\n",
+    "                    title=\"cpu usage percent(%)\"),\n",
+    "            color=\"type\",\n",
+    "            strokeDash=\"type\",\n",
+    "        )\n",
+    "    )\n",
+    "\n",
+    "    # Draw points on the line, and highlight based on selection\n",
+    "    points = lines.transform_filter(hover).mark_circle(size=65)\n",
+    "\n",
+    "    # Draw a rule at the location of the selection\n",
+    "    tooltips = (\n",
+    "        alt.Chart(data)\n",
+    "        .mark_rule()\n",
+    "        .encode(\n",
+    "            x=\"stress:Q\",\n",
+    "            y=\"avg-cpu-usage:Q\",\n",
+    "            opacity=alt.condition(hover, alt.value(0.3), alt.value(0)),\n",
+    "            tooltip=[\n",
+    "                alt.Tooltip(\"stress\", title=\"Stress Info\"),\n",
+    "                alt.Tooltip(\"avg-cpu-usage\", title=\"CPU Usage Percent\")\n",
+    "            ],\n",
+    "        )\n",
+    "        .add_selection(hover)\n",
+    "    )\n",
+    "\n",
+    "    return (lines + points + tooltips).interactive()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "83e7f01c-b6f9-4fc8-88c4-46fd1927a462",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def normalize_table(df):\n",
+    "    cols = list(df)\n",
+    "    for item in cols:\n",
+    "        if df[item].dtype == 'int64' or df[item].dtype == 'float64':\n",
+    "            max_tmp = np.max(np.array(df[item]))\n",
+    "            min_tmp = np.min(np.array(df[item]))\n",
+    "            if (max_tmp != min_tmp):\n",
+    "                df[item] = df[item].apply(\n",
+    "                    lambda x: (x - min_tmp) * 100 / (max_tmp - min_tmp))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ea310115-3e99-496c-86e9-4957d6bc3910",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "def standardize_table(df):\n",
+    "    cols = list(df)\n",
+    "    for item in cols:\n",
+    "        if df[item].dtype == 'int64' or df[item].dtype == 'float64':\n",
+    "            mean_tmp = np.mean(np.array(df[item]))\n",
+    "            std_tmp = np.std(np.array(df[item]))\n",
+    "            if(std_tmp):\n",
+    "                df[item] = df[item].apply(lambda x: (x - mean_tmp) / std_tmp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "18974afa-aff8-4620-b62c-a64944bdeb9a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2022-09-25 16:59:18.854 \n",
+      "  \u001b[33m\u001b[1mWarning:\u001b[0m to view this Streamlit app on a browser, run it with the following\n",
+      "  command:\n",
+      "\n",
+      "    streamlit run /home/bupt/anaconda3/envs/rubik/lib/python3.6/site-packages/ipykernel_launcher.py [ARGUMENTS]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "DeltaGenerator(_root_container=0, _provided_cursor=None, _parent=None, _block_type=None, _form_data=None)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "st.set_page_config(layout=\"centered\", page_icon=\"⎈\",\n",
+    "                   page_title=\"rubik analysis\")\n",
+    "\n",
+    "st.markdown(\"# ⎈ 混部干扰建模分析工具\")\n",
+    "st.markdown(\"## 业务介绍 TODO\")\n",
+    "st.markdown(\"## 环境说明\")\n",
+    "\n",
+    "node_info = pd.read_csv(\"../tests/data/default/node.csv\", index_col=\"Item\")\n",
+    "uploaded_node_file = st.file_uploader(\"上传测试环境配置\", type=\"csv\")\n",
+    "if uploaded_node_file is not None:\n",
+    "    node_info = pd.read_csv(uploaded_node_file, index_col=\"Item\")\n",
+    "\n",
+    "st.table(node_info)\n",
+    "\n",
+    "st.markdown(\"## 指标数据\")\n",
+    "# data = pd.read_csv(\"../tests/data/default/nginx.csv\")\n",
+    "data = pd.read_csv(\"../tests/data/clickhouse/l3cache_stress.csv\")\n",
+    "uploaded_metrics_file = st.file_uploader(\"上传指标数据-QoS数据\", type=\"csv\")\n",
+    "if uploaded_metrics_file is not None:\n",
+    "    data = pd.read_csv(uploaded_metrics_file)\n",
+    "\n",
+    "mode = st.radio(\n",
+    "    \"Please select a mode to visualize the data:\",\n",
+    "    ('origin', 'normalization', 'standardization'))\n",
+    "\n",
+    "if mode == 'normalization':\n",
+    "    normalize_table(data)\n",
+    "elif mode == 'standardization':\n",
+    "    standardize_table(data)\n",
+    "\n",
+    "all_symbols = list(data.columns[1:])\n",
+    "symbols = st.multiselect(\"Choose metrics to visualize\",\n",
+    "                         all_symbols, all_symbols[:3])\n",
+    "symbols.insert(0, data.columns[0])\n",
+    "\n",
+    "source = data[symbols]\n",
+    "\n",
+    "chart = get_chart(source)\n",
+    "st.altair_chart(chart, use_container_width=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "c8990835-8086-46bb-ac5a-4daccdd87ef5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "st.markdown(\"## 资源敏感度分析\")\n",
+    "\n",
+    "# type stress avg-qos degradation-percent\n",
+    "stress = pd.read_csv(\"../tests/data/default/stress.csv\", keep_default_na=False)\n",
+    "uploaded_stress_file = st.file_uploader(\"上传压力测试指标数据\", type=\"csv\")\n",
+    "if uploaded_stress_file is not None:\n",
+    "    stress = pd.read_csv(uploaded_stress_file)\n",
+    "\n",
+    "symbol_transform = {\n",
+    "    \"icache\": \"cache\",\n",
+    "    \"L1 cache\": \"cache\",\n",
+    "    \"L2 cache\": \"cache\",\n",
+    "    \"L3 cache\": \"cache\",\n",
+    "\n",
+    "    \"network in\": \"network\",\n",
+    "    \"network out\": \"network\",\n",
+    "}\n",
+    "def get_key(dict, value):\n",
+    "    return [k for k,v in dict.items() if v == value]\n",
+    "\n",
+    "stress_unique_symbols = stress.type.unique()\n",
+    "stress_symbols = []\n",
+    "\n",
+    "for symbol in stress_unique_symbols:\n",
+    "    if symbol in symbol_transform:\n",
+    "        symbol = symbol_transform[symbol]\n",
+    "\n",
+    "    if symbol == 'none':\n",
+    "        continue\n",
+    "\n",
+    "    if symbol not in stress_symbols:\n",
+    "        stress_symbols.append(symbol)\n",
+    "\n",
+    "stress_symbols = st.multiselect(\"Choose resource symbols\",\n",
+    "                                stress_symbols, stress_symbols)\n",
+    "\n",
+    "for stress_symbol in stress_symbols:\n",
+    "    # 插入无压力数据\n",
+    "    nstress = stress[stress.type == \"none\"]\n",
+    "\n",
+    "    if stress_symbol in symbol_transform.values():\n",
+    "        keys = get_key(symbol_transform, stress_symbol)\n",
+    "        stress_source = stress[stress['type'].isin(keys)]\n",
+    "        for key in keys:\n",
+    "            stress_source = pd.concat([stress_source, nstress.replace(\n",
+    "                \"none\", key)], axis=0, ignore_index=True)\n",
+    "    else:\n",
+    "        stress_source = stress[stress['type'] == stress_symbol]\n",
+    "        stress_source = pd.concat([stress_source, nstress.replace(\n",
+    "                \"none\", stress_symbol)], axis=0, ignore_index=True)\n",
+    "\n",
+    "    stress_chart = get_stress_chart(stress_source, stress_symbol)\n",
+    "    st.altair_chart(stress_chart, use_container_width=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "aa761760-9939-4d40-9c58-27d3d130be78",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DeltaGenerator(_root_container=0, _provided_cursor=None, _parent=None, _block_type=None, _form_data=None)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "st.markdown(\"#### 资源敏感度排序\")\n",
+    "\n",
+    "stress_degrade = (\n",
+    "    stress.drop(stress[stress['type'] == \"none\"].index)[\n",
+    "        ['type', 'degradation-percent']]\n",
+    "    .groupby(by='type')\n",
+    "    .max()\n",
+    "    .sort_values(by='degradation-percent', ascending=False)\n",
+    ")\n",
+    "\n",
+    "stress_degrade.loc[:, 'sensitivity'] = stress_degrade.apply(\n",
+    "    stress_sensitivity, axis=1)\n",
+    "st.table(stress_degrade)\n",
+    "\n",
+    "st.info(\n",
+    "    \"degradation-percent in (, 5]:no ; (5, 10]:low ; (10, 20]:medinum ; (20,):high\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "93c71736-f5fd-44c9-9633-81f5653fbe33",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "st.markdown(\"## 资源利用率\")\n",
+    "\n",
+    "# type stress avg-qos degradation-percent\n",
+    "usage = pd.read_csv(\"../tests/data/default/machine.csv\", keep_default_na=False)\n",
+    "uploaded_usage_file = st.file_uploader(\"上传利用率数据\", type=\"csv\")\n",
+    "if uploaded_usage_file is not None:\n",
+    "    usage = pd.read_csv(uploaded_usage_file)\n",
+    "\n",
+    "usage_unique_symbols = usage.type.unique()\n",
+    "usage_symbols = []\n",
+    "\n",
+    "for symbol in usage_unique_symbols:\n",
+    "    if symbol in symbol_transform:\n",
+    "        symbol = symbol_transform[symbol]\n",
+    "\n",
+    "    if symbol == 'none':\n",
+    "        continue\n",
+    "\n",
+    "    if symbol not in usage_symbols:\n",
+    "        usage_symbols.append(symbol)\n",
+    "\n",
+    "usage_symbols = st.multiselect(\"Choose resource symbols to display\",\n",
+    "                                usage_symbols, usage_symbols)\n",
+    "\n",
+    "for usage_symbol in usage_symbols:\n",
+    "    # 插入无压力数据\n",
+    "    nusage = usage[usage['type'] == \"none\"]\n",
+    "\n",
+    "    if usage_symbol in symbol_transform.values():\n",
+    "        keys = get_key(symbol_transform, usage_symbol)\n",
+    "        usage_source = usage[usage['type'].isin(keys)]\n",
+    "        for key in keys:\n",
+    "            usage_source = pd.concat([usage_source, nusage.replace(\n",
+    "                \"none\", key)], axis=0, ignore_index=True)\n",
+    "    else:\n",
+    "        usage_source = usage[usage['type'] == usage_symbol]\n",
+    "        usage_source = pd.concat([usage_source, nusage.replace(\n",
+    "                \"none\", usage_symbol)], axis=0, ignore_index=True)\n",
+    "\n",
+    "    usage_chart = get_usage_chart(usage_source, usage_symbol)\n",
+    "    st.altair_chart(usage_chart, use_container_width=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "f52b05f1-e9aa-40a1-9813-9a1c94f63d1e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "st.markdown(\"## 相关性分析\")\n",
+    "st.markdown(\"### 热力图\")\n",
+    "# pearson相关系数:  连续、正态分布、线性数据\n",
+    "# spearman相关系数: 非线性的、非正态数据\n",
+    "# Kendall相关系数:  分类变量、无序数据\n",
+    "fig, ax = plt.subplots()\n",
+    "corr_mode = st.radio(\n",
+    "    \"\"\"选择相关性分析方法:\n",
+    "    pearson: 连续、正态分布、线性数据;\n",
+    "    spearman: 非线性的、非正态数据;\n",
+    "    kendall: 分类变量、无序数据\"\"\",\n",
+    "    ('pearson', 'spearman', 'Kendall'))\n",
+    "\n",
+    "if corr_mode == 'spearman':\n",
+    "    metrics_correlation = data.corr(method=\"spearman\")\n",
+    "elif corr_mode == 'Kendall':\n",
+    "    metrics_correlation = data.corr(method=\"kendall\")\n",
+    "else:\n",
+    "    metrics_correlation = data.corr(method=\"pearson\")\n",
+    "\n",
+    "sns.heatmap(metrics_correlation, ax=ax)\n",
+    "st.write(fig)\n",
+    "\n",
+    "# fig = sns.pairplot(data)\n",
+    "# st.pyplot(fig)\n",
+    "\n",
+    "st.info(\"|r|>0.95存在显著性相关;|r|≥0.8高度相关;0.5≤|r|<0.8 中度相关;0.3≤|r|<0.5低度相关;|r|<0.3关系极弱\")\n",
+    "st.markdown(\"### 相关性指标排序\")\n",
+    "sorted_metrics_correlation = abs(metrics_correlation.iloc[-1]).sort_values(\n",
+    "    ascending=False).dropna(axis=0, how='any')\n",
+    "st.table(sorted_metrics_correlation)\n",
+    "\n",
+    "vaild_metrics = sorted_metrics_correlation[abs(\n",
+    "    metrics_correlation[\"qos\"]) > 0.3].index.tolist()\n",
+    "vaild_metrics.remove(\"qos\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "707e49f0-8c41-4b97-9083-e3c8466795e1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x432 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x432 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x432 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x432 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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239FZ0Ec4YA+50XSk02LGGPa0RVm/t4OX9nbylx0tbMm0pJpeFeKai+bwV2dO5+zGap0OU8MyKRLUyQ3ldK/7DZb1P8UORakc2xLCAZswI+tS8sw//hOfzyRBxzXEkg7RZJpo0qE3maY36dAbT9ObTNOTSNPamyCacDD97kcEygJHkldZXgIL+ixCc8/huZ2thPxeIkukHZJpl0TapTeZpqU7QVN3ggMdMXa29LKzuTc3zRn0eQUon3v7It6woI7TZ1Tpi0M1YpMiQSk11p3Zx4ptCeUhb71qKK4xRJMOPYk0vYl0v/cO7dEU+9pjJPJGZFM/8BU+sGL1kPfrs4SplSHm15fxt+fOZNG0Ss5qrGLh1Ar8WuGoTpAmKDU5FLEzeymwRHJTfENJOS49iTTJtMv/fuoKnnx6FfGUg2sMIb9NwGcR9FmEAzb15UFqIgEdGamC0QSllMrx2xY1Ea+6NbF/c67kXqli0DG4UkqpkqQJSimlVEnSBKWUUqokaYJSSilVkrRIQik1sAlamq/GD01QSqmBjUFpfqmW5avSIMb0319e2kTkceBYta91QMsYhHOixkOcGuPoGQ9xaoyjZyRxthhjLitkMOPRuEtQwyEia40xS4odx7GMhzg1xtEzHuLUGEfPeImzlGmRhFJKqZKkCUoppY6TiNwrIk0isnEY135DRNZn3raKSMcYhDiuTdQiiRXFDmCYxkOcGuPoGQ9xaowjcz/wbeCBAb7XJ05jzGeyH4vIJ4BzChrZBDAh16CUUmqsiMhc4DfGmNMzn58E3AnUA1HgY8aYLf1u8xfgy8aYJ8Y43HFloo6glFKqWFYANxpjtonIhcB3gDdnvykic4B5wFNFim/c0ASllFKjRETKgdcBP83b5Bzsd9kVwCPGGGcsYxuPNEEppdTosYAOY8zZQ1xzBXDT2IQzvmkVn1JKjRJjTBfwmoi8H0A8Z2W/LyKLgRrg2SKFOK6MuxHUZZddZh5//PFih6GUUlx55ZVMmzaNlpYWZs2aZf7t3/6NnTt38vd///cXnXXWWZxyyilcccUVueu//OUvE4/H+epXv+r2u6thNT2cwM9/A/78466Kb8mSJWbt2rXFDkMppUbTsBLUBH7+G/Dn1yk+pZRSJUkTlFJKqZKkCUoppVRJ0gSllFKqJGmCUkopVZI0QSmllCpJmqCUUkqVJE1QSimlSpImKKWUUiVJE5RSSqmSNO568Y3Uyi1N3LVqJ3vbozTWRFh+yXyWLW4odlhKKaWOYUKPoFZuaeKWRzfR1B2nOuynqTvOLY9uYuWWpmKHppRSJyyWdIglJ+6xUhM6Qd21aid+W4gEfIh47/22cNeqncUOTSmlTsjetihn3/p7fv3SgWKHUjATOkHtbY8S9tt9vhb22+xrjxYpIqWUOn4vv7wBEUFEeN3ZpzClLMBTE3hGaEInqMaaCLGUN/zNHisSSznMqokUMyyllDouqVSS23//Krf//lX27d3DmxY38MdtzSTSE3Oab0InqOWXzCflGHriKdqjSXoTKVKOYfkl84sdmlJKnbC3LG6gN+nwlx2txQ6lICZ0glq2uIF/vmwxzT1J9nfESbvwpXedolV8SqkJ4eIFdVSEfPzmpYPFDqUgJnyZ+cUL61gwtZwX93Swty3KzpZeEmmHoM8+9o3HKS2tV2pyCPps3n7aNH638RCJ9OkT7nltQo+gACpCfh74yAWcPrMSA3z1sS3c+6fXJuycrZbWKzW5vPusGXQn0jzzanOxQxl1Ez5BgZek7r72fE6b4SWp/378Ve6ZoElKS+uVmlxed1IttWUBfr5uf7FDGXWTIkEBTKsKce+Hz8+NpP778Ve5+487SabdYoeWs3JLE1euWM3Ftz3FlStWH9eoR0vrlZpc/LbF3543iyc3H6apO17scEbVpElQAFMrQ9zzofM5Y2YVAF/73VbuWrWjJJLUaE3N5ZfWZ2lpvVIT2xXnN5J2DT9du6/YoYyqSZWgIJuklnDmLC9J/c/vt3LXM8VPUqM1NZctrY8m0xjjvdfSeqUmtvn15Vw0v5aH1uzBdU2xwxk1ky5BATRUhrj72rwk9cRWvvvM9qImqdGamlu2uIFb33MaDRUhOmMpGipC3Pqe07SKT6kJ7uqls9nXHptQnSUKVmYuIiFgFRDMPM4jxpgv97smCDwAnAe0Ah8wxuwqVEz5GjIjqY898ALr93Zw+xPbMEb4+2UnEfCNfd5urInQ1B0nEjjyT3K8U3PLFjdoQlJqknn7adOYWR1mxaqdvPXUqcUOZ1QU8pk4AbzZGHMWcDZwmYgs7XfNR4F2Y8zJwDeA2woYz1HqK0Lc/aHzOKexGoBvPLmV7zxdnJGUTs0ppU6E37b46MXzWLOrjXV72osdzqgoWIIynp7Mp/7MW//J0fcC3898/AjwFhGRQsU0kLryEHd/aAnnzK4G4H//sI07xyBJ9a/YA0Z1am40KgKVUuPLB85vpCrsZ8UzE2NbSUE7SYiIDbwAnAzcaYx5rt8lM4G9AMaYtIh0ArVAy2jHMlR3hdryIPdcez7XP/A86/Z08M0/bMMY+PibTy7IdF+2Ys9vS5+KvVvfcxoP3dB/kDmK9w869afUBFYW9HHN0jncuXI7rx7qZtG0imKHdEIKuthijHGMMWcDs4ALROT047kfEblBRNaKyNrm5pHvlh5OCfeU8gD3fOh8zptTA8AdT23jjj9sK8hIqtCbaXWzrlITR/7z33Cu/+jF8ygP+PjGE1sLHVrBjUk1gDGmA3gauKzft/YDjQAi4gOq8Iol+t9+hTFmiTFmSX19/Ygff7hP2DVlAe750BLOn+slqW8/vZ07/rCNlDO6SarQm2l1s65SE0f+899wrq8pC/DRN8zj8U2H2LCvs9DhFVTBEpSI1ItIdebjMPA2YEu/yx4FPpT5+HLgKZM9uGkUDfaEve1w11HrNNWRAN+7tm+S+t8nto5aklq5pYmuWIoth7rZ2dxDVywFjO5mWt2sq9Tk9tGL51ET8fP1379a7FBOSCFHUNOBp0XkZeB54AljzG9E5FYReU/mmnuAWhHZDtwM/FMhAhnoCbulJ0F3whlw2q86EuDDF82lIuQt0d25cgeffvjFE05S2anGsqCNAEnH5UBnjObu+KhW7GlFoFKTW0XIz41vPIlntjbz/K62Yodz3KQAA5aCWrJkiVm7dlhTsTn5RQNhv00s5bCvPcaUMj915aHcddFkmoaKEMsvmc8tj25CxNDcnSSa9JLbO0+fxjevPAe/bY34SIuVW5r45MMv0ptME/LZlAd99CTSxNMOZQEfd1xxzqgWMGTj29ceZZYeuaFUqRtW9bKImNszo6KbL13EUM/fsaTDJV97mrm1EX6y/CLGuEB6pAYMblIkKDj6CXtbUzfTKkN9/tGMMXTGUszK2zSbdl32tEbpzSSp5ZfM5/y5Ndz6m819El7KMYOWhWcT5IGOGLYFIBgDM6pDlAd9HOqKs6ChQs9vUmryGvUEBfCj5/bwhV9s4NtXncNfnTnjxKMsnMmdoPq7csXqozo3ZEdQe9ujVIf9ueSVdl12t0ZzI6npVSHCfouyoP+o2w5UJp59rEOdcdKOwbIE1xh8llAZ9tHWm2JWTXhYyU4pNSEVJEE5ruHd3/oTnbEUT978RsKBkj3QcMCff9L14stuYN16uIt97TFaeuJHrdP0X7PyWRZTKwJUZtakDnbG6Yyl+/xyDFUlly3SqCsP4mIyzRwN8bRDW2+Kmoi/T4VhynH45MMv6iZbpdQJsS3hy+8+lf0dMe5ataPY4YzYpEpQ+fuhpleFqYn4aetNcagr3qdzw0BFBo4RbvubM3jdSbUAtPYm2Xywiw37O9m4v5Oth7sp6/fqJJsMm7sTbG/qQQRmVIXx2YLjQlnAR3nQS1xZ3fEULZl1L1vgxb3tfPSBtbzjf1dpolJKjdiF82t515nT+e4zO9jfESt2OCMyqab4hprW6z81N1iRQU88zTvvWMWetqP/oavCPr75Aa/YIb8wI+247O/wDhKbWR3CZ1u5aby7Vu3sE9PO5h6SjosFuICFYDBYIjRUhrj83Jk8u7NN16uUmliGOcVnmVzHOLHADK+y2K6sZ8b13yW2bTUtv/5an+/NapzN3j27Rxbt6Bvw5y9oq6NSk11bytd/aq5/dd5X3nt6nwRQHvIxtTLE3rZYn8aClkBXLM3yH7zAubNraO9N5DYHe4TD3XEOdSU4d3ZNn8Ryy6ObiCbThP028bSDJQIiWAYsSzB4c8kpx+HOlTuYVRPWFkZKTUqG249zb9Pqna08538jH/rg1cysCee+fvOli0YruFE3qRLUsY60yB/15E+vLWwo5x2nT8uNXJq7E4CX8rNJKntGmGsMTd1xdrX2Mqv6yC9BZdhPRchHZyzVZ7S2bHEDtwK3Pb6FbU09uAYsDGkDfst7UWEMBGyLzmgKxzW5+CMBH9FkmrtW7dQEpZQa0nlzath0oItntjVzxfmN3gvhEjep1qCOtYE12xIp7RgOZCruHNew+VA3tz+5jc0HOzPVfd79Cd7IKZ9PvMThtywOZxJZ1lDdHHqTDrNqwsyuCWMQXNeQdl1cYzAG6iuCJByXYL/mtQMVZ2gnc6VUf37b4uKT62juTvDKga5ihzMskypBHeu02Wy1XUtPAmO8abX8/NMZS9MdTzO1IoSQXSPqyyBsP9xFyjXEUy7N3UdXCfaX3yuwMhxgZnUYnwVp10uC06uC2Jbgs6xcd4us/klvOI1xlVKT08Kp5cyoCvGXHa0k+nXXKUWTYopvuF0fslOAScfFcY8uHjF4LZLm15fTOCXM/o4YtmWRTru56b542itwsAQCttAeTZF2XBZMrRz0cfe2R7HlSIFEwLaYUR2mN5FmwdRK9rVHaagI8d6zZvDIuv259arsnqn8pJef7ECnAZVSR4gIb1xYz0PP7+W519q4ZOHIm2+PpQmfoEZyNlK2xZFtCSnnyOgpm3ws8frnAfhsi/Pn1vLQDUtZuaWJL/5yAwc647jGG1lhYEZlEL/PHnQDbzZxHu6Mk3YNtnj3m3YM+zvinFxfdtTtzpxVPWQLo+EUgiilJq+GyhCnzajkpX0dnDGzqtjhDGnCJ6iRjCiyBQtffWwzWw73YPAKFQyQzoyoArZ11HTdssUN/Pv7zuBjDzyPyRSBukBPwmVa0DdgcshPnJZ4t0kbENfFsixcx7CnPcbFtz3VZ9SXfRvMsQpBlFLqovm1bDvcwx+3j/rZsKNqwieokY4osgngjie3cufKHTiuIeizCGHoTbpEkw7N3Qmuv3hen0SxbHEDS+bWcqgzyqGuBLGUS1s0SUcsyfTKIFeuWN1nijE/cbp404Epx5B2ISAGsSCRzmzW3dPOR77/PEGfRSRgs3CI6cLsKHCwacCRNrlVSk08ZUEf58+r4c/bWwnNPbvY4QxqwhdJHO/ZSJ9860Lu+uB5nD93CuGATcqFqZVBTptRSX1FkEfW7T+q8GD5JfPpSTgkUm5uetA1sL8zwY6mrj5TjNuaunNnVAVsCxEh6LPw2YLPtrBE8FsWBzsTOMbgGoinXLriaV5r6Rm08GGoQhAtoFBKZZ3dWE1V2E/Nm68nPcqHso6WCZ+gRuNspO54GhEI+uwhT+RdtriB2rIAAb+FJWDnlQC2RVMYY3K3TabdXOKsrwh6VYPGELAtEmn3yGZxoU/BhoXQHU8PeYT7ssUNPHTDUv74+Tfz0A1LcyMkPQpeKZXlsyxef3Itgfq5PPz83mKHM6AJP8WXXVca6dlI+WtEjutiiXCg02tvVBn2DzpN2JN0OLm+HBHBdV02HewGvJLx7MfZ/VOBzJ6m8qCP2nKvL2DYb2FbQlnQpqUniW15R3OAl6wkU6hxPIUPA1UL1pUHtIBCqUnq5Ppy4ns2cPsTAd579gwqQv5j32gMTfgEBQxZWDDYmkz+aCPos0m7BjFemXll2E8s5VAe9B21tpRfpJA9Q6o/AzgGUo6L3xI6Yynm1pbzX389v89UnC3eSCu/W0Uy7RIO2MdV+FAR9LGtqQfbEmxLSLteteCChvIR3Y9SamIQEdqfvofQ7DO478+7+ORbFhQ7pD4m/BTfUIZak8lu2gVvhJNMuyQcr0iipSdOVyxFc0/iqNteNH9KbkqxqSueOaDwaBaQSLnUlAX5yntPB+CLv9rIlStW8/K+DiJ+i6Tj0n87lot3sOLxHOGebQzsuIZEyiWRdkm7hu5YckT3o5SaOJKHtnPpqVP53qqddERL67lgUieoodZkssUV3fEU7dEUtiW5woe23hRBW6gK+4+67bM723JFCgnHEPLZA7bpdfE29a55rZWPPbiWNbtaaetJsOVQJ998ajud8RSDNZpPHOeBhj1Jh5qID8c1uX1dtsDB7oQWSig1id186UJ6kmm+98fSWo+e1Akqf5SUlV3byRZXHOqMAwZbBJ8tzJ4SYVZNmObe1KC3zRYpXDB3Cn5bGCTPANmpPu/+42mX9mgaxzUc7Exg6NuDXvDWoFzXDCs59e/JVx6w6U44BGyLsN8m6LPx2RZ+y9JCCaUmscXTKvmrM2dw35930dZbOqOoSZ2ghipBz5Zre2ffgs8WZlSFcwUS2WuzumIptjf30NSdyDVonVYZoCOWHlYsScccNZ0H9EluBnKjqmONeAaavmztTWbWtLz/so1op1YGtVBCqUnuE28+mWjS4aE1e4odSs6kTlDHKkFftriBc2fXMHtKhPn15VRmNvzGUg7z68pyt+2KJdnfESPtGKZVBnPrUb97pQnbOrrj+fHK3k1NxN9nxDNQ9/KBpi8rw35CfhtLBMc1+CyhJuLncFeiT2JVSk0+C6dW8IYFdXz/L7tIpktjX9SkqOIbzLFK0FduaaK9N8Gu1l78lsXUymDuNNwvvWsxZG67bk87PkuYVhXKlWlGk2miSWfQYzLzz5IaLhFoKA9SXxFkW1M3V65YzbambrrjaWoifurKveT4D4+8RGc0hcEQ9NnUVwSpCHkjv4jfIhL0D3jS767WHj724FqMMdiWxfy6Mj5/2WLtNKHUJPGRi+dx3X3P89sNB3nfOTOLHc7kTlBwpAQ9O+r47E/Xk3K81JFIu9RE/MyqDnO4O8G+jjgLG8r50rsW524DXp++oN03FWV3Zhu8xNI/G40kOXmd0b3B7uHuRO6cqZae1tw1LT1Jgj5v6rEjmsI1XrPbaMphV2uUkM+iOuLPdVXPJVZbvONDBJq7k145PWCJYVtTD5975CW+dvlZmqSUmgTeuKCek+rLuO/Pr5VEgprUU3xZ2fWa11p66IqniaUcuuJp0o5La2+SaNLBFq+Kb39HrM9tmrrjhHxe94fdrVE2H+xiZ3MPBzvjuf+5/avxRvo/3TVexV98iGF32jUc7orT0pPAEu82jjny2PG0S3NPkmmVgdy+L4CpFUEA9rRFcw1xDWBb3obh7nhaCyiUmiQsS7j6wjm8tK+TbYe7ix2OJig4Um7eHU9j4R0MCJmiBNfQ3OMlqbRr6Iqn+diDa7n+gec51BljX1uU3qTjJQO8PUZJxyXpDDFGGsGa1EiWrxJpN3cciCXgyyuNJ/MT/WbDoVzhhAjsbYuxvyN2VIFG2nERgbTragGFUpPIu8+agW0Jv1y/v9ihaIKCI+XmycyTMtBnFNJftut40jEk+l2QTVKQOReKTHk4YGeqJQaq1htM9tLhFlrYluQqAtOZ/U7Z2ztuJnbHIOJN7WWPEul//06mws9nWXpUh1KTSH1FkItPruPRlw7kNvcXy6ROUNnqt+buBNuberDlSN+77Mbcof55BssZ/RNQtlTdzaxVjfR/us8aXlKzLSjzD3zv2dsLXrsm8HoKWpnMZEnf0ZabOfK+IuQbcccKpdT49rZTp7K3LcaO5t6ixjEpiyRWbmnitse3sLWpB78tVIZs2qNpXNc7h8lyvRMEy4I2PQlvr1M2PxxP9V2WbQlpYwgHvNFaaqhpwDzD7YS/oKGCnS1D/0Jlj6XviqVo6Ul4PzMwqyZMLOnQ1JPIJenpVSG+8t7TtUBCqZIh3HzpolG+S++4n3x2ZT2z/v4+zn3XB+le+6vRfbyMWY2z2btn95DXFCxBiUgj8AAwFe95cYUx5pv9rqkCfgDMzsTydWPMfYWKCfKKG7ri2ALGhY6YV6bdnfAKI7IbcRNpF1v6TvPlpxRf5pDB4cpWyIEZ0e1Eji606K8yaNEeTXpHdRyD4xr2tkdzp/8C7G6NYsmRUaEINHUneHlfx6glKD0sUakTZbj996+OySN9/9ldzPvAp3nPf/53Qe5/OIm2kFN8aeCzxphTgaXATSJyar9rbgJeMcacBSwD/kdEAgWMKVcQ4RiDZYn3hhDNHJMxvSrMy//6dk6bUcWsmjBTygYOpyrkwxjvCd1nDVSQMDABepMj2wR3rOk9AebUVdA+ghYlrjl6VJit+rMtIWBbuMZw58odo7J5Vw9LVGp8mVYZoqkrXtQYCpagjDEHjTHrMh93A5uB/oX1BqgQb3xZDrThJbaCyRZEBGyrzzlLScftc4RF9rpo0sEnfYsUArbXN296VQif5Z315LiGytCxB6TWaLWVyOOzYPOBzqMKNoYr/1YB28KfOeHXO5LDHXGZ+XA7W+hhiUqVrqmVIXqTDj3xgj4lD2lMiiREZC5wDvBcv299GzgFOABsAD5ljCloj41s/73sKbau8XrS2Zb0aXOUvS7puPhsi6DPJuizKAvYLJxaQUXIe6KdXVvGnClh/LbQOYx/yIEq5k5UyoX0KBXb5E9FGwNB2xpRmflgI6Wth7sGba6rlCo92dmjjiIex1PwBCUi5cDPgE8bY7r6ffvtwHpgBnA28G0RqRzgPm4QkbUisra5ufmE4sn237MtYXpVEMErqZ47JdLnCIv867JJzBivBDOWckim3dyIoDIcwG9bg579dJTiVm4OyTF9G8lWRfzMqokMOCoayGAjpZRjBm3Mq5QaXP7z31g+bnZGqGuYDa8LoaBVfCLix0tOPzTG/HyAS64Dvmq8YvvtIvIasBhYk3+RMWYFsAJgyZIlJ/T03r//3jmzawZcrM9e99XHNrOtuQe/CNOqgrmRlt+WPiOCeNoddrVdabRhHFjaMV45vM+iqsyP37a5aP4UPvfIS3TH06Rdl5buxKAtkPa2R6kO9z02Ouy3Mcawu7U39//IbwuVYT9felf/ZUmlVL785z8RGbOXt9m+ot2J1Fg95FEKWcUnwD3AZmPM7YNctgd4C/BHEZkKLAIKvigx1BHwA12XXUPZ1x6loSKU62WXPdq9K5bCHcnu2zy2Jbh5G2qLySdQVxGkPZoiEvQxt7ac5ZfM57bHt+QObfRl1u7aoylue3zLUf8f84+8z2rpSRBLObk1P4O3yTk13IyulBpztiW5F+TFUsgR1OuBa4ANIrI+87Uv4JWUY4z5LvAV4H4R2YBXTPZ5Y0xLAWM6LoMltFse3UQ0maalJzGsUvCBOMeZ2ArBti1CfhshRXc8TXtvgi/9aiN7273+g45rMlOZghEz4J6r5ZfMz/1/CfttYimH9mjK2wgskisScY137Pxdq3ZqqblSJcpvSa7xdTEULEEZY/7EMVrJGWMOAJcWKobR1n8fz+XnzuTZnW25buF+2xpWoUQpsvAKOA50eCcIOwa2NfUctQcs6biIk+nSjuEd/7uK5p5Ebk2uvjyIMYZ97V556rzaCBUhH52xVJ/iEBGv358WSShVukS8LTnFMik7SRyPbHWa35Zcddoj6/Zz+bkz2XSgk95kGhHJnLNUGlN2I+ECuAafT8AIxriDjghN3vutmY7HliX0Jg1tvSmvG7qA37Zo7U1SVxagJ5HGuEeqBLXPn1KlL+W4uaN+imFS9+IbiYGq01KOw50rdxAJeKfUJh0XN6+M3NvA631sDzGW9M5fGr2Td09EMu2ScNw+G3mH4hgvufksC8fNjqyO9B9sj6boTTqUB304xuC4buZN+/wpVcqMMaRdg08TVOnLbtzN1xlN4biG+ooQM6rCBDIbXIN+m7DfwgBBn01DeYBQv9tmk1G2y3nYb+O3raL/gxjAb43smI+jMllmPc4Swcq0TPr65Wdxcn0ZklmLWtBQrgchKlXCoklvW0gkYB/jysLRKb5hGqg6LeG4hDKn2FaG/VSG/Rhj6IyleP95s7hz5Y5cb7yKkM9bvyFzIGB2xJU5FoO0g+sOXIJeFbSJpoffXPZECGBZFjYmd4DhQLLHkWQl0kf2OGWnBhNpB0u8n3W4lZNKqdLQGfPKyytD/mNcWTjFfsE+bmQ37kaTaYzx3vssi4p+7Y1iKYeygM0j6/YzpcyfaYvk0NqbxM48qRtjiKfdPk/w6Uxy8tZuhJDfwhKYMyXM7LpyzBhV+xm8eeehkhN4vQfzbzPQ5V7yhfKA/popNd4cSVDFG8foM8cwLVvcwK3vOY2GihCdsRQNFSFuWnYSAZ/dJ2mlMocB+m2hrjzESQ0VnDq9ClsERJhZHT7qyTzst3OHGob8NounVbKgoYKAz+JwV4KuWGrAgxMLZTi5MP/E4GOtn3UlnFznieF2pFBKFVdzTwLbEqojBe3fPSSd4huBgaapzpxVndvEOytzhMQXf7XxqG4KaddFxOueYHcKrmP6rPNkn+6TeXsOplYE2dcR43B3fKBlHoTidaWwMnOVbiaOxikR9rbHjtrX5be8QyCzTWH7V0Le8ugmbgWd/lOqxDR1JagvD+ZOAi8GTVAnaKCk1bjq6PUqn3Wk8iBgW6Qdb81G8oolst/L3ca2WFBfzmut0dz3cxtlMcRTbu422a4MhR5oZROT1/HcOzPLMbC/IzZgNw1jDMFMU9j8SkiASMBHNJnWzbpKlRjHNTR1xzl1+lGtUceUTvEVwEDrVRUhH+VB7wm5rjyAJUdKsh3XzU2RVYZ9dMWSbGvqZldrFBFhXm2EqZVBfHbmGHrTt01Q2nW9I+oL/ELHZwmu8Q5q9I6IF3yZB/Ua6x59G8d4BSKzaiIDVkJqR3OlSs+hzjgpx9A4pbj7FDVBFcBA61Vfu/wsvn75WQRsi0NdXmukoM/KtQBa0FDOZ966gKqQn30dMTAwqzpE0nFp7U2Sdg21ZYHMKb59G9Ma473i8VmF/eccaC+Xz7ZyX8+uvWUJ3ggx4LNZfsn83BEm+Y7V0VzXrJQae3vavFmbWdXhosahU3wFMtDU38otTfQmHWbVhHN96lKOybVM+skL++iKpWioCFJXHupz24BtUR0JsK89SmcshW1BTzxNyh14/aoQEo63CTntGAST6z8oIkR8FtOrQ0QCPrrjKZq7E8TTDmUBX59jTPr36cs/g6u/gbp36JqVUoW3o6WH6VUhgv7i7YECTVCjIr9HX3nARkToTqRpzBRNZJ9MB1qDae6Oc+fKHcyqCVMd9nOoM04s6RD02bl2917PuhjlQW/Y3Z3p91foKb2BRPwW0ZSLa/qO4mrLA7my1PKgL9cFOT859T/qZFa//z/96ZqVUmOvrTdJa0+SNy6sL3YomqBOVP6rfFtge7PX4XtmdeioV/wDnZWUPWMp+yQc9FkkHZdDnfHMKMTNTN8JXZnElOuFl3dkff++ef2/JoxOAUXCMVSFffQmnFxRRND2joYXvKq9zlhq0OQzkg27g50tpWtWShXOtiavv+bJ9eVFjkTXoE5Y/qv8lp4ktnhnqLT0JHOnyWZLrAdag0mkXYJ51QV15UHczEbeVKa3H3jdJrINVvsbqKlr/6+NRnIK2BYWQjTpkswc2hjyWyBCa08Kny3UlAX54+ffzEM3LD3hUc7xrFkppY6fMYZth3uYURWivIgbdLOKH8E4l/8qP+m4mQ25R9aD8l/xD3RWkm0JVZEjo4TKsB+rA4wcSSp+C1Ju9jymo9sMZT/PHyXlX3M8o6cpET/t0VSu0jBX3p5JnuC1MBK86kEXQ2c0xT5r9EY3A/3/GmrNSqmJT7j50kUFu/fAjMVMv+brtP7uTp79wmMFexyAWY2zj3mNJqgTlN+jz9vf5KWC7H6m/Ff8A63BvPesGTyybn+fJ2EXaKwJUxkOsLO5h7RjsORIayTbEoxjsCxwXe9zMcY7zgLw+ywc12Aw3ohOBDfTmXg4icq2oDzkZ2pliI5YktaelDdliMmdDRP0WZkCCe82Il5vwvKgjytXrM6dmTXUGtOxjHTNSqmJz3D7718d1pU3X7oIM8KznD798Iv8YXMTm1Y/Slmw+Omh+BGMc/mv8uvKA+zviIOBaZXBXOuj/Ff8w+lG4beEVCYb1ZUHOdDpnWgreHueXNdLTo7rjZR8llAW8FFfEcIYk6kUjHDR/Cl886ntXqKyvdNsHddgi7eWNJhs5R14VXe15V7n9oTjemc4VQVIG2jtTULmjCfHGCwR7/BCxx21qjttMqvU2GjpSfDbDYe46sLZJZGcQBPUCev/Kj97pERPIk1DRWhYr/j7PwlnCy+yG3xr0wHaoykCtuC4EHcdArbFrOogPts6qlou32MbD/FaSy+OawjYFtOrQtiWELAtmnsStPYk+4yqaiM+/ufvzsndV+5ns46MYCCTuMoCdMfTJNLeRuH6Mj/BgE+r7pQah378/F6SjssHl84pdig5mqBGwWi/yu+f9KrDfmoiAboTabpiKaZG+u6TGioJfP6yxbkqw/x1nC+9azEAX31sc5+j3asiQc6bU3PMn22gqbcv/mqjdopQahxKOy4/XL2b159cy8kNxa/ey9IEVaKyiaH/ZtWB9kkNlQSOtY6Tff/As7u45Veb2NnSywfveY4fXH9h7v6Hii/fQD0ItepOqdL3hy1NHOiMc8u7Tyt2KH1ogipx/TerZvdJNXcncgnkWElgOCO8ay+aiwjc8stNvLSvk6u+9xw/vP4CKsNeq/38zciDFT9o1Z1S49ODz+5melWIt55SWlPxug+qxPVvsFpXHgQgnnb6nEE1GkngmqVz+ff3nY4AG/Z3ctXdz9EVS+ZGcU3d8T7FD/374g3Ug3CwtTGlVGnY0dzDn7a3cNUFs/EN1PG5iHQEVeL6HzVfGfaTSDtEk86QHRuO19VL5yAC//KLjWzc38VV33uOkN8adsshrbpTanx58Nnd+G3higuOvS9prGmCKnEDTZsFfDZf/ZszC5YIrrpwDoLwhV9sYOOBLgBCNkyrjgxr3Wsww5kmVEqNnd5Emp+9sI93nD6d+opgscM5iiaoEleszarTq0JUZ7pJAMQd2NcWZdYUL0mNtPhhtDqTa5JTavT8cv1+uhNprr2odErL82mCGgeKMW1216qdTCkLEAnY3uZjIG3gUEcUe0rZsNe9sgll3Z52RGBqRQgJyHHtkdLjN5QaPcYYHnx2N6dMr+yztaSUaIKa5AYbkWR7DHrrTsL+Dq+bRdyBmrCfj795wYBJIf/+KoI+mnsSVIX9mVODJdcVozLsH/E0oR6/odToWbu7nS2HuvmvvzkDKcbZPcOgCWqCG+qsqovmT+GRdftzR4W8uKedjz7wPAvqy6kI+oilHCIBH1PKvFLzbJLqSTqc1Vg94GPlj3C2N/WQdg1lAR8+yyLhuBjjVSY2EsFny4imCfX4DaVGzwPP7qYi5OO9Z88odiiDKq2aQjWq8svDs2dVbWvqwRZoyhyUmHIcHNdwsDOBwetQvqstSnNPgq5YimgyjTEG17i5o923HOrm3d/6E209iT6Plz/CEZFMfz443BXPHRcCXpf1/R0xOmOp3DThcI521+M3lBodzd0JHt94kPef19hnY32p0QQ1gR3rrCrH9Y7IaO5OIAKWCJZ4DWWrwn5qywI0VIQ41BWnpSeJJV5zWoB9HTHe/e2+Sar/nq1sR/dsrz47bxbBNYaQz+rTLSObSLMjucu+8UyfRLX8kvmkHJNLmqO5B0ypyeSRF/aRcgxXXVh6peX5CpagRKRRRJ4WkVdEZJOIfGqQ65aJyPrMNc8UKp5SNJxRw4nY2x4l7bjsbO6hN+mQdFyMMbmzqoI+b9ot6bi5YzOM8RJL2G/Tm3R46IalhGzv+PaU630/+0uzvyPOB1aspjWTpPqPcOorgrjGO4vKcd1cvz87c9rvvo44l33jGW57fAt+WwYcyeVvCNaNwEqdONc1PPz8Hi6YN6Wk+u4NpJBjuzTwWWPMOhGpAF4QkSeMMa9kLxCRauA7wGXGmD0iMmmeaQpVkZa/5tTWmySedPD7rNwBhknHG7kAVIR8pKMGwfulFbyDB+vKQ7mps5VbmtjX6SWg7ADI4CUpF9jW1MMVK1bz0MeWHrVny7aEmoifnkSaeMo7Et7OHPmR7aC+5XAPAA3lAXqTTm4k5yU1kzuROL93oCYkpY7fsztb2d0a5TNvXVjsUI6pYCMoY8xBY8y6zMfdwGZgZr/LrgJ+bozZk7ludIcQJaz/ek3/4+GPR/+WRGnHxQUcx+0zvZadHgv4bG5adhLz6spwDIgFM6pC+DIjpuWXzO8bj5DLUi7gs7yEk01Sp8+sOmqE87XLz+L/rj4PyxL8tndwYv+TqARo6kkSTw88ktMiCKVGz4/W7KEq7Oey06cVO5RjGpPVMRGZC5wDPNfvWwsBv4isBCqAbxpjHhjg9jcANwDMnl3ac6bDVYiKtP5l2AYvibjGmzIL2oAIadf0Oavqk29dmBt57WuP9vneF3+10ZsKTLvkzn/PWNBQwU1vPplPPbye7c09fGDFs/z4hqU8dMPSo2Jb2FDOay29uP1O+LTEm/JLud4ozhUGHMkpNVnlP/+dqNaeBL/fdIhrls4l1O9onFJU8AQlIuXAz4BPG2O6Bnj884C3AGHgWRFZbYzZmn+RMWYFsAJgyZIlIzvDuET177EHJ16R1j/pBWyLVGZ9afG0SsA7O6qhInRUEhls6qyxJkJPPEUi7Xojn8z/fb8tfP6yxSxb3IAgfPLhF9nR3MvfrVjNj29Yyqb9XX32V73j9Gk8sm4/TV1x4mk3d/+uIXeUvWUJjgG/DTMq+o7k+tOOEmqyyH/+E5ETev77/zYcJOUY/u78WaMSW6EVtIpPRPx4yemHxpifD3DJPuB3xpheY0wLsAo4q5AxlYpCVKQNVqRgW4IxhubuOPvaY2xr6h52UcZF86fQnXCwBPL38v3VGdNyCeFdZ07nW1eeg88Sdjb38u5v/Zkv/GIDr7X00N6b5Pldbdy5cgfnza4ast+X4xpmVIUo89vsaY+xo7mXlu4EL+/r6HPdHU9uZfkPXmDNrlbaehLsau0ZsLu6UqqvR9cfYOHU8twL1lJXyCo+Ae4BNhtjbh/ksl8BF4uIT0QiwIV4a1UTXiEq0vonvWyRwtwpEQ51xmiPpphS5mdaZWjQIzP6e3ZnGw0VAcJ+G58llAVsplcFOdSV7HPdO8+Yzrev8pLUoa44h7sTNHcnSDsGn+WtPf1mwyFiKYfq8MADd1vgQEeM1mgKMd5UXzTlcPuT2/jMw+sAb+R058oduMbgty0cA609KVKOc0Lrd0pNdPs7Yqzd3c57zirdjbn9FXKK7/XANcAGEVmf+doXgNkAxpjvGmM2i8jjwMt46+53G2M2FjCmklLoo+Jn1UT40rtOZdniBq5csbrPlOJw2wTtbY9SW9b3iHljzIBrZZedPp07rxKW/+AFnMy8nQ+DiIUtEE+59CTSLGiooOtAJ3Bkes9vCbYtxFPe9F92ElDwZhV/sf4gWw6tYmdLL8m0i88CwULEW6/qjKbYZ2kxhVKD+fVLBwB4tyYoMMb8iT5L6oNe9zXga4WKY7IZLOkdb1HGSNfK3n76NBZOLWdrpnw86RgCHNkDFU95+7L8tgUGEpk9WT7bIu26R91f/oT7ay29OJlrvDzm4rO9JJVw3FxMuj6l1NEeXX+AsxqrmVNbVuxQhq10e1yoUXW8RRnHc4z7F95xCtc/8DzZwr+E03ddN5FycAERyb2CcY0h7Qy9/ptIuwRsAQvSriHtGmzL4BiDz7JYfsl87XiuJjjh5ksXDevKWY1HKp53t/byysEuvviuUwoVWEFogpokjifRwPGdR7VscQOffPMC7nhqG+mjB0WkjbfeZGGQTBn8cHopGyCV2VBsZzYep12DCNSXB/jirzbSFUsRCdhUhb0pSe14riYWw+2/f3XQ79586SKMOfqF3tOZtea3njK1YJEVgiaoSeJEDj48nrWyM2dVM70qzN722IDfd403rXfXB88D4LbHt7D5UHduzWkwriHTfd3rNjGvNkJrb5Kg3ybstznYGSOWcgj6bCrDx3/6r1ITyVOvNjO/roy5deNneg80QU0qY9EmaOWWJr762Ga2NfcwwJJSjm0J586uycVz16qd1ER8dETTAH0SVfbj7PuUY6gu95rZvtYaRcQbKUlACPlsko5LS08il6B0s6+azKLJNKt3tnLN0tI8NXco2s1cjZrs+s+utmhuhDMYoe/04tbDXfQkHGxLjhpFhfw2gUwxhHfbI9N9juviuoYDnTG6Yilvn5Xx1qu047lS8JftrSTTLm8eh1PcOoJSoybbaslxDbYIlphcGXl/aRdu+tELhPw2C6dWEk16G4z9tkW2A0t207HreudK+S0Lx3gNZKsyp/0Gfba3DmWgpSfB/Ppy6iocehMOnbHUiKYylZqIVm5toixgc/7cKcUOZcQ0QalRky1lD9hWZoOu12opf3ouywC9SZd4yuW1lp5crz9LvKKH7LEeLl4TW8f1Nh5XB/0YY3LnTtVXBDnQEQcMycwmZb9tc8cVZ2pSUgpYvbONC+ZNIeAbfxNm4y9iVbKyrZbqyoO4ZKrtLC85WRaE/RbTq4IE81qrOwY6okkCtoXPFnyZ4zh8ljC1KsjiqeWc01jDtMoQ5zTW8PXLz2Lh1Mrc6Koi5GdGdSh32KKeEaXUEa09CbY39XD+vPE3egIdQalRlC1l99vCjKoQh7sTGEdYPK2cz1+2mC/+aiPVYT8tPUn8VnazLcTThhmVAQ73JGmsCvUpg/+nd3j7Nu5atZOth7v45MMvZg5dNEwp81NbFsS2hIbK0UlMuslXTSTP72oH4EJNUGqy61/Kfk5jTZ8n+MZV3mbhgG2Rdg1+DKnMItXhniRzpoRpqAj1KYMHuOXRTSTTDu3RFCZzQq8t0NKTJOUYFjRUjEoi0U2+aqJZ81obQZ/FGTOrix3KcdEEpUbVUKXs2RFWZdhHS3cSMmdBOcZbY2qPprj/uguYndeK5coVq/HbwuHOZJ+CC9eAGKgrCwx4/tTx6H+elm7yVePdml2tnDu7ZlyuP4GuQakxlO3gPre2nKqIn7Dfpibi59RpFQRsi/Zoivff9Sy7W3pzt9nbHiXst0lm2iDlHzvvuIYdedeeqOxj5dNNvmq8iqccNh/s5rw5NcUO5bjpCEqNqcFGWM/tbOXD9z3P4a4Ef7fiWX58w0XMrSvL9RDM6l8JmHIMK7c0jcoIpxCHSCpVLK8c7MJxDWfMqip2KMdNR1CqJFw4v5b7rzufcMDmcFeCD6x4lteae3JnXPntgbv1BWwZtXOgCnGIpFLFsnG/d6TNGTM1QSl1wo5OUquZUxvh1vecxvx+PcS8EnZhelVo1KbgCnGIpFLF8vK+TmrLAkyvCh374hKlU3yqpFw4r5YHPnIBH7p3DU3dCf5uxWp+fMNSHv/MG7nsG8+wqy2K4xoCtkVdeRCf7e19Gi1j0a9QqbGwcX8np8+sQmQ4ZwWUJh1BqZJz/twpPPiRCygL2jR3eyOpHU09/NM7TqGhIkRdecA71bcjyr72GBfNH597PJQqlHjKYVtTD2eO4/Un0ASlStR5c6fw4EcuzEtSzzJrSpjLz51JW2+KpGMI+bwqwAdW7+Yd/7uKi297iitXrGZl5uwbpSarbIHE6eN4/Ql0ik+VsHPn1PCDj1zINfeuoaUnyRUrVjOzKkxNxE93PE3ScWnvTZJ2DT3xNCc3lLOrtYflP3iB8qDXhFY7QajJaCIUSIAmKFXizplTw4MfvYBr7vGSVGtPMve9PiXnjsv2ph7ieUf49u8EoW2M1GSxaX8XU8Z5gQRoglLjwDmza/hBJkl1J9K5r2e7noPXWSLhHElOibTLzuZe/LZw2+NbAPq0MdKRlprINh/q4pTpFeO6QAJ0DUqNE2fPruGHH7uwz9dMv7OmjvocSDqGVw93c9vjW3JtjHoSaVp7UrjGEE+5uZGWrl2piSDtuLx6qJtTplUWO5QTpglKjRtnzqrmeFuK7WzpzbUxau5OIAK2CEnHJRLw4R/FDb9KFdOu1l4SaZdTpmuCUmpMnVxfPvJf2szIKpZy6I6niCYdkmmXRNrFykyBaM89NVG8crAbYEIkKF2DUuPKP73jFG54cG2ueexwBHwW5UGbHc1HN5ZNOS5dsRQ+W7TnnhoHhJsvXTTEty0+9MkvUHnB+zitsRbcNLMaZ7N3z+6xC3EUaYJSJWO4VXYjSU4AliW09qaO+roAPks43B2noSKkPffUOGC4/fevDnnFL9fvpzeR5tOPbwIYOqGVOE1QqiRkDwtMph2642kOdcZZu7uNqeUBsKxcwhrpOpEtYIzBZwtpx2A4Uv1nANcYxIj23FMTRktPgsYJMhugCUqVhLtW7SSZdmjtTWIhCN5RGvs6E8yZEs6VhSfz9jkNh2Mgljr6NoKXoOorgnTG0iz/wQsA1JcHKA/66Ek6uldKjTuxpENvwqG+PFjsUEaFFkmokrC3PUp3PI2FYFmCk1czvqctxqHOBGnHRQSsE9zakR09ARzuShBPOhhjcFyXfR1xth7uwRa0/FyNO809CQDqKjRBDUlEGkXkaRF5RUQ2icinhrj2fBFJi8jlhYpHlbbGmgiJtJeAgD7Hu2c/HOHS07C4BiwLbMvKPaYBWnqSWn6uxp2WbIIqDxQ5ktFRyBFUGvisMeZUYClwk4ic2v8iEbGB24DfFzAWVeKWXzIfOzNyMgyQiQq4IT7teiXo+QkqmelKoeXnajxp6U4QCdh9ToUezwqWoIwxB40x6zIfdwObgZkDXPoJ4GeAzqNMYssWN3DTspOwREg5R68ZZWf8jOk7uioUOzOU0yPf1XjS3JOYMOtPMEZrUCIyFzgHeK7f12cCfw383zFuf4OIrBWRtc3NzQWLUxXXJ9+6kLs+eB4XzK0l6LMI2ELQFmzLK5oYy65iruvqke+qJOQ//w11neMa2nqTE2b9Ccagik9EyvFGSJ82xnT1+/b/Ap83xrhDNTU0xqwAVgAsWbJkDF4/q2LJnmibLTv320LYb9Pam+BQZ8Lbu2QLqUIsSAE+yxs9pQ25vVFaxaeKKf/5T0QG/cVv603imomz/gQFTlAi4sdLTj80xvx8gEuWAA9nklMd8E4RSRtjflnIuFTpW7a4gVvxys/3tUeZW1tOMu0yvSqMiHCoM0ZLT3Kg1arj4hMI+m3qyoMc7o7j05dBapzJFkhMpCm+giUo8bLOPcBmY8ztA11jjJmXd/39wG80Oams7Ggq68oVq2nqjhMJ+JhWFWZaVZhXDnTmqvsErwT9uAZXIkQCFvvaozjGu58X97TzuUde4muXn6WjKFXyWnoS2JZQE5k4I6hCrkG9HrgGeLOIrM+8vVNEbhSRGwv4uGqCWn7JfFKOIZpMY4z3HhHqy/2UBWx8thDy29SX+4G+a1b2Me57WkWAjlgax3jtjwI+CwO0R1O586SUKmXNPQlqywJYJ7pRsIQUbARljPkTI1jXNsZ8uFCxqImh/7TfrJoIAdsi6bhMqzryqxxNpkmkDfUVQSIBH12xFC09CXqTTp/7C9iCiFeA0VhbTnNvOwHb2xMFmZZIYtjZcnSTWaVKiTGGlu4k8+rKih3KqJoYxfJq0ug/7Zctpogm04T9NrGUQ8oxXH/xPB5Zt59oMk1FyIfPFjpjKVrz1q2SjsEWw6yasO51UuNaNOkQSzkTqkACjmOKT0QsERn/B42oCWHZ4gZufc9pNFSE6IylaKgIcet7TuOTb1141Ne/fvlZLJ5WQcA+MrB3jXfkxqyaCPNqI7gGXNdgjMF1Da6BebW6D0qVtmyLo/oJVGIOwxxBiciPgBsBB3geqBSRbxpjvlbI4JQajv6jqmN9/ZZHNwGGAx1x0q7hYGeCay+ay+JplfzDIy/Rk0jjuAbbEqqDfv7pHaeMwU+h1PFr6c62OJpYCWq4I6hTM3uY3gc8BszDK4BQalzJjrhmVkeoLw8QsL1iiG/+YTshv83XLz+LcxprmFYZ4pzGGr6uFXxqHGjuSVAe9BHyH6scaHwZ7hqUP7On6X3At40xqaE2jClVyvJHVnvbovzdXc9ysDPOdfc/z30fPp+Hblha5AiVGpmWnuSEm96D4Y+g7gJ2AWXAKhGZA/TvCqHUuNM4JcJPll/EjKoQsZTDh+9fw7PbW4odllLDlnZc2qPJCVcgAcNMUMaYO4wxM40x7zSe3cCbChybUmOicUqEn9x4ETOqQ8RTLtd9/3n+oklKjROtvUmMmVgdJLKGlaBEpEpEbs82LBSR/8EbTSk1IcyqifDTG1/HzOqwl6Tuf54/b9MkpUrfkTOgJmmCAu4FuoG/y7x1AfcVKiilimFmdZif3HgRs2rCJNIuH/n+8/xxq3bPV6WtrTeJbQlVEX+xQxl1w01QJxljvmyM2Zl5+zdAzyBQE87M6jA/WX4RjZkk9dEH1rJKk5QqYa29SWoifqwhToQYr8SYYxfjicizwOcy7YsQkdcDXzfGXFTg+I6yZMkSs3btkMeiKHXCDnbG+LvvPsve9hgBn8X3rjmPNy7ScnNVMMPKLiKWoV8P/5k33kNi/2Zafv31AW8zq3E2e/fsPvEIC2vAn3+4I6gbgTtFZJeI7AK+DSwfpcCUKjnTq8L89MbXMXtKhGTa5WMPvsAzr+qhz6rYvORkjNftpCeewlc1lVs+dUPua/3fxkFyGtRwE9RbgO/jrUXdCzwIvFVEbhaRmwsVnFLFNK0qxE+WX3QkST3wAiu3aJJSpWNHcw8AC6aWFzmSwhhugloC/D1QCVThjajOBSoyb0pNSNOqQvz0xouYUxsh6bjc8OALPK1JSpWIbYe9BHVyw8R8Gh5ugpoFnGuM+QdjzGeB84DZxph/yxRMKDVhTa0M8dPlFzGvriyTpNbyh82Hix2WUuxo7sFnCXMmaEPj4SaoqUAy7/Nk5mtKTQoNlSF+vHwp8+rKSDmGG3/wAk++oklKFdfe9hgza8L47UKePVs8w/2pHgDWiMi/isi/As8B9xcqKKVKUUOFl6Tm5yWpJ145VOyw1CS2ty3KrJpwscMomOG2OvoP4DqgPfN2nTHmvwoZmFKlyEtSFzG/voy0a/j7H6zj95s0Sani2Nceo7FmYk7vwQgOLDTGrDPGfDPz9mIhg1KqlNVXBPnxDRdxUiZJ/b8fruPxjZqk1NiKJR1aehI6glJK9VVfEeTHyy/i5IZy0q7h4z/SJKXG1v6OKOD1kZyoNEEpdZzqyoP8+IalLMgkqZt+tI7fbjhY7LDUJNHU5TWJnVoZKnIkhaMJSqkTUFse5OEblrJwajmOa/jEQy/y25c1SanCa+31CqtrJ+A5UFmaoJQ6QV6SuohFUytySeo3Lx0odlhqgmvLJKgpZZqglFJDmFIW4OEblrJ4WgWOMXzq4fX8WpOUKqDWngQiUBPRBKWUOoaasgAPfexIkvr0w+t5dL0mKVUYrb1JqsN+bGviHbORpQlKqVFUkxlJnTLdS1Kf+fF6frV+f7HDUhNQVzxNVXjiHVKYTxOUUqOsOuKNpPKT1C9e1CSlRlci5RDy28UOo6A0QSnVz8otTVy5YjUX3/YUV65YfVxHbFRHAjz8sYs4dXolroHP/mQ9v1i3rwDRqskqkXYJaoJSavJYuaWJWx7dRFN3nOqwn6buOLc8uum4klRVxM9DNyzltBmZJPXTl/i5Jik1ShJph6BvYj+FF+ynE5FGEXlaRF4RkU0i8qkBrrlaRF4WkQ0i8hcROatQ8Sg1HHet2onfFiIBHyLee78t3LVq53HdX1XYz48+tpTTM0nqH376Eo+8oElKnbhE2tUEdQLSwGeNMacCS4GbROTUfte8BrzRGHMG8BVgRQHjUeqY9rZHCfebNgn7bfa1R4/7PqvCfn50w1LOmFmFa+BzP32Jn67de6KhqkkumXYJTNBjNrIK9tMZYw4aY9ZlPu4GNgMz+13zF2NMe+bT1XgHIypVNI01EWIpp8/XYinnhPudVYb8/PBjF3LmrCoM8I+PvMxPnt9zQvepJjfbEhxjih1GQY1J+hWRucA5eOdIDeajwGOD3P4GEVkrImubm5sLEKFSnuWXzCflGKLJNMZ471OOYfkl80/4vitDfn54/YWclUlSn//ZBh5eo0lKDS3/+S//67YlOK4mqBMiIuXAz4BPG2O6BrnmTXgJ6vMDfd8Ys8IYs8QYs6S+vr5wwapJb9niBm59z2k0VITojKVoqAhx63tOY9nihlG5/4qQnx9cfyFnN1ZjgH/++QYeem73qNy3mpjyn//yv+63LFKOW6ywxoSvkHcuIn685PRDY8zPB7nmTOBu4B3GmNZCxqPUcCxb3DBqCWkg2SR1zd3P8eLeDv75FxtxDVy9dE7BHlNNFILt8562fbaQTE/sBFXIKj4B7gE2G2NuH+Sa2cDPgWuMMVsLFYtSpaY86OPB6y/k3NnVAPzLLzfyg9U6klLHYnDSKQB8tkVKp/iO2+uBa4A3i8j6zNs7ReRGEbkxc80tQC3wncz31w56b0pNMOVBHw9+9EiS+uIvN/Lgs7uKGpMaPyJ+m2giXewwCqpgU3zGmD8BQ3YxNMZcD1xfqBiUKnVlmSR17b1reGF3O1/61SZcAx963dxih6ZKXFXYT1c8VewwCmpiF9ErNQ54SeoClsytAeDLj27i/r+8VuSoVKmrDPvojGmCUkoVWCTg44GPXMAF86YA8K+PvsL9f9YkpQZXFfYTT7kk0s6xLx6nNEEpVSIiAR/fv+4CLswmqV+/wn2apNQgKjNHbXTHJ+46lCYopUpIOGBz/3UXsHS+l6T+7devcM+fjq8PoJrYqjMn6WaPfp+INEEpVWKySeqi+bUAfOU3m/necTarVRPX1IogAIe74kWOpHA0QSlVgkJ+m/uuO5/XneQlqf/47WZWrNpR5KhUKZleFQbgYKcmKKXUGAv5be798Pm8/mQvSf3nb7dw1zOapJSnoTIzgtIEpZQqhpDf5p4Pnc/FJ9cB8F+PbeH/VmqSUt7vRk3EzyGd4lNKFUvIb3P3h5bwhgVekrrt8S3838rtRY5KlYJpVWEO6QhKKVVMIb/N965dwiW5JPUqdz6tSWqym1YZ1BGUUqr4Qn6b731oCW9c6B0587Xfvcq3/rCtyFGpYtIRlFKqZAR9NiuuPY83LfKS1P88sZVvPqkHAUxW0ypDtPYmJ2w3CU1QSo0zQZ/Nd685jzct9pLUN57cxv9qkpqUplV5lXxNXYkiR1IYmqCUGoeCPpu7PriEN2cOVvzfJ7fxjSc0SU020zJ7oSbqOpQmKKXGqYDP4rsfPI+3nuIlqW/+YRv/8/tXixyVGkvTKkMAE3YdShOUUiVs5ZYmrlyxmotve4orV6xm5ZamPt8P+Cz+Ly9Jfeup7Xz9d1uKEaoqgmyCmqjtjjRBKVWiVm5p4pZHN9HUHac67KepO84tj246Kkn5bS9JXXrqVAC+/fQO/vvxLRgzsY8DV96ZUGG/PWHbHWmCUqpE3bVqJ35biAR8iHjv/bZw1wCNY/22xZ1Xn8ulp3lJ6jsrd/C1372qSWqCExGmVYUm7BpUwY58V0qN3MotTdy1aid726M0dyeYlum3lhX22+xrjw54W79tcedV5/LxH63jd5sO852VOzAG/vGyRYjIWISvimBqZZCmCZqgdASlVInoP6UnwP6OOF15x3rHUg6zaiKD3v7ae9awcX8nUzJnBf3fMzv46mM63TeRTa0McXiClpnrCEqpEvHVxzbT1B3HcQ0B26Iy5KMtmuJwd5yKkI9YyiHlGJZfMv+o22aTm98WaiIBgr40sZRDLOVw16qdGOCf37FYR1LjnoBIn3/H6mXXUXHuXx3z33ZW42z27tld6ABHlSYopUrAyi1NbGvuwRbBFiHtGNpjKWoiPrriDp2xFLNqIiy/ZD7LMnuf8uWvVwGUBf1Mr4KOWJq23iQrVu1kT2svHdEU+zpiNA5xX6qUGW7vV6W5bk87f9zWwn/+f68Q8tuD3vLmSxcVOrhRpwlKqRJw16qd+C0Lg7fwLQK40J1wOHd2DQ/dsHTI2+9tj1Id9vf5WiTgI5l2ed2Z0/nNywd5fNNhqsI+ZlWHcxWBt4ImqXGuPOg9jfcm0kMmqPFIE5RSBZRf9DDUqGVve5SplUEOdiZwMRhjSDoGHOiIJrnjya08u7Nt0PtprInQ1B0nEvDRFUvR0pMgkXaJBGz+5uyZPLujldbeJJ2xND4rwfSqEOBN/2mCGt/KMqPmnkSa2vLgMa4eXzRBKVUg+etC2X1Mn3vkJWrLAvQknT6JprEmwq7WHiyBeNrtcz9bDnWz+VA31WEfszKJqP/oZ/kl87nl0U00d8dp7U0CIEBZ0OZff/MKxrhUhXx0xtO570+rDA5aEajGj7KgN2rqTU68hrFaxadUgfTfx9QTT9Pck2TL4R7aehLsau3hlkc3cceTW9nb1svBzsRRyQkgW3/XGUvTHU8PuB9q2eIGbn3PaUSTDq7xiixm1oSpKw/ht4W0C7XlAaoy04CtvUn2tseYWR0ei/8VqoDKgkdGUBONjqCUKpD8daHueIqm7iOlwI6B1p4UZUGHO1fuYDi1dQbY3XZkxLOjuZcl//4E9eVBumNJmntTJNIuQVuorwhSEfIeO+y3Cfgs0i6E/UJP3Hv8rrhX6WeM0eq+ccxvW/htITYBR1CaoJQqkPx1oebuRG4kZAkYAynXpT3q5r52PFp6krT0JPt8LeEYdrX2nbqzBDCG1t4U+TuiXtrXyUfuf557P3y+JqlxLOS3iacmXoLSKT6lCmT5JfNJOYZoMt3nQDnXQNJxyd876xZ4H61roCWTnPwWBCzJjdqefrWZL/5yo27mHcc0QY2QiDSKyNMi8oqIbBKRTw1wjYjIHSKyXUReFpFzCxWPUmMtuy7UUBFChjWJNzYcA7Zt4bcEOxPWD5/bw7/8QpPUeBXyW8RTR69fjneFHEGlgc8aY04FlgI3icip/a55B7Ag83YD8H8FjEepMbdscQMP3bCUkxvK8VmlkaayozUrM69YnylN/tGaPfzzzzfgFno4p0Zd2GcTn4DHvhdsDcoYcxA4mPm4W0Q2AzOBV/Iuey/wgPFetq0WkWoRmZ65rVITRnNPAlu8V20jIUAh0oXB4BiDz7a47W/P4HebDvPjtXt5+Pm9GOC//voMVm1tHtYeLlV8wQk6xTcmRRIiMhc4B3iu37dmAnvzPt+X+VqfBCUiN+CNsJg9e3bB4lSqEFZuaaI7nsbFK1YYbIBii+DkTbH5LMFvWyTTDs4oZ6mU4+KzLG5adhJvPmUqyxY1gMCPn9/Lj5/fy8H2GK+19hLwWX3OotLOE2Mv//lvMGG/TSLlTriKzIIXSYhIOfAz4NPGmK7juQ9jzApjzBJjzJL6+vrRDVCpArtr1U5qIl7J92B/cBZeQsoK+ix8tuBmnnAaa8L4LHJThMfzHOS3j9yoLODjpmUn8cm3LvQe3xK++jdncMX5jQCs2t5CRzRJ2G8f8ywqVVj5z3+DXRP0e22yEgPsoxvPCpqgRMSPl5x+aIz5+QCX7Aca8z6flfmaUhPG3vYodeVBZlSFCfptLPFGUoKXNPwW2LYgFlQEbarCPgRIOy4C1ET8fOW9p3P+3Frm15dxxswqZtdEhr2e5beFoC0Y4yXBOVPC1FcEeWTd/j6n84oI//U3Z3DVBd4sRXfCYV97LFc4MdRZVKq4wpkefBNtmq+QVXwC3ANsNsbcPshljwLXZqr5lgKduv6kJprGmgixlENl2M/8+nJOm1HFvLoyls6v5XvXLGHJ3FqmVYY4p7GGb115Lt/8wDnMqyvDzjSPrS3zznbKL1tv6UnkRlH2IJnKb3l/4JYIrgGfLcysDlMZDgw6IhIR/uOvT2dqhVc40RFL5ZLUUGdRqeIK+r2n8olWyVfINajXA9cAG0RkfeZrXwBmAxhjvgv8FngnsB2IAtcVMB6liiLbJy+aTBP2233OdVq2uOGoNZ2VW5roTTrMqgnnrv+HR16ivjxIbyJFyvGSRchnUR700dLbd6NuwLZIuS4pFypDPu644hy++KuN3iGIeXODg42IRLzpvk88/CI9CYeOWIq061ITCQx4FpUqvtwIaoJV8hWyiu9PMPQsRKZ676ZCxaBUKVi2uIFb8dai9rVHhzzXCY4+2yntGDqiKXriaU5uKCeWckikXaoifurKQ7THUvjA636OtwlY8KYR77jiHK8Z7aojXS2yhhoRvemUqXzrinP4p19s4HBXgp6Ewxkzw7xhoa4Bl6KQb2JO8WmrI6XGwEAjpcH0P9uppSeBJeBkCiYiAR81ET9tvSlvqs4SEs7RUzvZmouVW5po702wq7UXv2UxtTKIz7YGPZ03602nTGX14gb+7devcP9fdvHszjY+8+P1fOMDZ2Mfb28mVRCh3BrUxJri01ZHSpWY7JpVVjKTfAL2kT/XuvIgFUGbhooQkaAP13gJKeSzCNpeBWBdeZCvPraZWx7dRMo1zKoOg8C+jjgB2+LW95x2zKQpInz53afytlO86x596QAX/MeTPPXK4QL85Op4ZdegYhNsBKUJSqkxtnJLE1euWM3Ftz3FlStW96mkg77FEMYYbMsrcqivOHIYXSzlsGBqJQ/dsJS1X3wbUyJ+wn47s/lWmFEVpq48yGut0dx0YWU4wIKGCubWRqiOBIY9onvm1Wa2HOqmKuxNuLT2Jvn4wy9qkiohlghBnzXhpvg0QSk1hrKHGDZ1x/tsgM1PUvk9/DpjKeZOiVAT8WNbgjFe4uo/PbdwaiXTqkIsnlbJ/PpyKsP+3KvpcL9jwEdaLn7Xqp0EfBaNNZFcRWE06fAPP3uZ9ABTi6o4JmLDWE1QSo2h/ocYDlbune3h98fPv5nHP/NGvnb5WbmE1VAROmp6rv+oK5vE5teVHTXtM9Jy8b3t0dyG3elVIeoySaqtN8nHH3pRk1SJCPvtCbcGpUUSSo2h/gUQMLwRzbGKLAarFAQGLXEfrvxzrUSEaVUhUq5LZyzN4xsP8fGHXuTbV56Dz9bXu8UU8ltEJ9ihhTLe2usvWbLErF27tthhKHVcrlyx+qhy72gyTUNFiIduWFqQx1y5pWnYJe6D3f6WRzfhtyWX5JJpl9NnVvHYxkMAXHraVL5z1bmapI7fsMoiRSwzWPvg2nfdTKjxdPZ/9yMDfn9W42z27tl9/BEW1oA/vyYopcbQQE/2KccMq6KumAZKcm9cVM9tj7/Kd5/ZAcClp07lzqvPxa9J6ngMM0GJAQY8t+vWX7/Cw8/v4ZVbLxvl0MbEgD+/TvEpNYZGumn3eGUTymgdlTHYFOPnL1uEJfCdlTv4/SuH+X8/XMd3NEkVRV1FgGjSIZpM9xmhj2cT46dQahwZyabd45E/Siv0URkiwj9ethgB7ly5gydeOcyND77Ad685T5PUGJtWGQLgUGec+fXlRY5mdOhvkFITzHArBUfT5y5bzMffdDIAf9jSxPIHXyA5wY5+KHXTqo4kqIlCE5RSE0y2LDzfWByV8Q9vX5RLUk9taWL5g2s1SY2h6VVhAA5qglJKlar+rZJg5Hufjtc/vH0Rn3izl6SefrWZGx7QJDVWclN8XZqglFIlarBNu2N1VMZnL13Ep96yAICVW5u5/vvPk5hgx0CUonDApjriZ39HrNihjBpNUEpNMP1bJQ3UeaLQPvO2hXzmrV6SWrWtheu/v1aT1BiYW1vGrpbeYocxarSKT6kJqNCVgsPxqbcuRBBuf3Irf9zWwkfvX8s9H15C0Gcf+8bquMyvK+PZna3FDmPU6AhKKVUwn3zrAj77toUA/Gm7l6QmWkPTUjK/voyDnXGiyXSxQxkVmqCUUgX1ibcs4HOXLgK8JPWR+5/XJFUg8+q8/U+7WgpbsTlWNEEppQrupjefzD++3UtSf9nRynX3aZIqhHl1ZQDsaO4pciSjQxOUUmpM/L83ncznM0nq2Z2tfPi+NZqkRtlJDWX4bWHTga5ihzIqNEEppcbM37/pZP7pssUArN7ZxrX3riE2wY6IKKagz2bxtEpe3tdR7FBGhSYopdSYunHZSfzzO7wktea1Nj6kSWpUnTmrig37O3Hd8XVSxUA0QSmlxtzyN57Ev7zzFADW7Grjmnuf0yQ1Ss6aVU13PM2u1vG/H0oTlFKqKD52yXy++C4vSa3d1c419zw3Ycqji+mMWVUAvLyvs8iRnDhNUEqporn+DXlJanc719yzRpPUCVrQUE7Ib2mCUkqpE3X9G+Zzy1+dCsALu9v54N06kjoRPtvi9BlVE6JQQhOUUqroPnLxPP713V6SWreng6vvfo7ehCap43XGrCo2Hugk7YzvTvKaoJRSo27lliauXLGai297iitXrGbllqZj3ubDr5/Hv73nNABe1CR1Qs6aVU085bJ9nG/Y1QSllBpV2SPnm7rjfY6cH06S+tDr5nLre70ktX5vB1d9bzU9mqRG7MxsocTe8b0OVbAEJSL3ikiTiGwc5PtVIvJrEXlJRDaJyHWFikUpNXZO9Mj5ay+ay1fedxoCvLSvk6u/t5rueKqwQU8wc2vLqAj6eGmcr0MVcgR1P3DZEN+/CXjFGHMWsAz4HxEJFDAepdQYGI0j569ZOpevvO/0I0nq7ufoiiVHOdKJy7KEMzIbdsezgiUoY8wqoG2oS4AKERGgPHOtjuWVGudG68j5Dy6dw7//tZekXt7XydV3r9EkhYBYiMgx3x774Xd5aXcL4vMP6/qRvjXOnlPwn7aYBxZ+G3gUOABUAB8wxgxYciIiNwA3AMyePXvMAlRKjdzyS+Zzy6ObiCbThP02sZRz3EfOX33hHAT4l19sZMP+Tq66+zl+eP2FVIUnz2RL/vMfwO2/2zKs22073M1vNx7iH3/8AtMqQ6Me182ZI1QKqZhFEm8H1gMzgLOBb4tI5UAXGmNWGGOWGGOW1NfXj12ESqkRG+0j56+6cA7/9TdnIMDG/V1cueI5/r+X9o+4SnC8yn/+G8ntpmaSUlNXvCBxjYVijqCuA75qjDHAdhF5DVgMrCliTEqpUTDaR85fccFsEPjCzzfwysEuPv2Tl5hRFepTJXhr5nGVpyLkI+izaO5OFDuU41bMEdQe4C0AIjIVWAQMr8xHKTXpXHH+bP7rb84AIOUYDnclcFwz4irByUJEqK8I0tyjCeooIvIQ8CywSET2ichHReRGEbkxc8lXgNeJyAbgD8DnjTEthYpHKTX+feD82VRH/ADE095G1O2Hu9jTFmXdnvYJPdV3PBoqgrT0JMft0RsFm+Izxlx5jO8fAC4t1OMrpSamU6ZVsr25m+buJCnHkAZsAbFFp/r6qS8P4riGtmiSuvJgscMZsWKuQSml1IhlqwR9VpK06+1XSRuYVh7oM9V316qd7G2P0lgTYfkl80claa3c0lSQ+y2U+govKbV0J8ZlgtJWR0qpcSVbJWhbFpL39X0dcfa39bLpQOdxt1oayom0cCqW6kgAAdqj47MThyYopVTRjbS57LLFDZw7u4ZpVUHsvCwVd6ArniaZdo671dJgTrSFUzHYllAR8tExTjc4a4JSShXV8Y5Mll8yn7beFJYl+Po9k3VG+z4hh/022w53ndDeqdFo4VQMVRE/nbHxOYLSNSilVFHlj0wAIgEf0WSau1btHHJ9Z9niBsqDNvGUS9JxCdiQdLxqtVjakEo7+H02XbEUBztjJB1D1+42BDjUGWfdnnZuWnYSZ86qHta6UmNNhKbueC5OOL4WTmOtKuxne9P4PHZDE5RSqqj2tkepDvv7fC1/xDNU4lg4tbJP0miPJtnXHgNgR0svDeUBDncnSbsGS44kML8Facdw+5PbsAQCtsXUyuCQm35Hs4XTWCoL+IinXFzXYFly7BuUEJ3iU0oV1UDNZVt6EnQnnCGn/VZuaaIjmmRXa5RtTd10xZI4rpub7ks5hv2dCSwMPksweVuBUi5kchXGeJWA+zvi7G+Psb8jyicffvGoKcDRbuE0VrLTkvG0c4wrS4+OoJRSRTXQyKQ9mmJKmb/PtF9LT5xPPvwilWE/5QGb1t4klWE/s6pDHO5KsLc9hiVQVx7EZ1ns6/BGUi6ChRn0qASDl6TSrsFxDQGfEE06A46kRruF01gI+r2MHU+5RMZZj11NUEqpolq2uIFb8dai9rVHmVUTobk7QWc0RUtPkoBtUR700dabxACzp0R49VAXaRfaoilCPouKkI/WXm9fVHc8TSRgE7CFpGNID6OLQtLxDlIweFN/PtuiqTvO8h+8wPy6MowxtPQmSaZd/LawcGplye+BygplR1ApHUEppdSI5Y9MVm5pYvkPXsA1BtsS0q6hqTuBJd6TbXc8TSpzMI/jGnqTDr3JI0++/T8fKceAk/amCo2BbU09GGMwBmxbiKXgtZaecdO1whZv3ck146/dka5BKaVKyl2rdlKT6bdnXBC8kY1rvOm7ljFqfprOrFOlXYNjwMVb10o5hkNdCQ51xvjqY5vHJJYTMf7S0hGaoJRSJWVve5S68iAzqsL4bMExXgWeAJVhf246rtiSjmHL4R7ueHJrsUMZFmF8VfCBJiilVInJVvVVhv3Mry9n8bRKplYG8fssosk0Abu0nrbuXLmjpNsdmXE4tZdVWv/SSqlJb/kl80k5hmgyjTHee79tc9Oyk2ioCBH2l9bTluMavvrY5pI94Tc74gz0b7cxDmiRhFKqpAxU1ZetmPtk5prPPLyOX6w/WMwwAW/a0RbY1tzDXNeU5Am/sUzBSCRgH+PK0qMJSilVco613+gbV5wLFD9JWZZgAL9ljbhV01iJZhJU/z6C48H4G/MppRRekrr/w+ezeGp5UZb/gz6L+vIAjoGplX3PWiqlJrKxpEPIb427NkegIyil1DiWHWllDxJcv7edWGr0q/wqgzb1lSEaKkIsv2R+n+nHmkjyqMrCQjWR9fsD3HzpohHdpuz0t+Cvm83N/37fqMYyq3H2qN7fQDRBKaXGvfwpwTue3Mr/PbMjl6h8lren6XjVl/upDAdyjWH7Tz9mjwsZiyayZ555BmvXrj3OW987qrGMBU1QSqkJ5ZNvXcgn37rwqK9nR1n72qOUB3259kXd8TRhv0VnLJ3b1GoDM2rClAVsepNObuQ00JrSUEUd6sRoglJKTQqDFV5kE9e2pm7aepO4BuqrQtz/kfM5qb7ihO5bnRhNUEqpSS0/uTy95TB//8N1HOqMc/X3nuMH11/IyQ3DS1Jq9GkVn1JKZbxp8VS++8HzCPktDnUluPru59je1F3ssCYtTVBKKZVn2aIG7vrgeYT9Noe7Elz1vefYdliTVDFoglJKqX7euKiBFdecRyRg09Sd4Kq7n2OrJqkxpwlKKaUG8IaF9dyVSVLN3d5036uHuood1qSiCUoppQbxhgX1rLjmPMryktQWTVJjRhOUUkoN4eIF9ay41ktSLT1JPqhJasxoglJKqWN4/cn1fO/aJZQFvSR19fee45UDncUOa8IrWIISkXtFpElENg5xzTIRWS8im0TkmULFopRSJ+p1J9dxdyZJtfYmueaeNZqkCqyQI6j7gcsG+6aIVAPfAd5jjDkNeH8BY1FKqRN20Ul13POh8ykP+mjtTfLBe9awab8mqUIpWIIyxqwC2oa45Crg58aYPZnrS+cISqWUGsTS+bXc/aEllAd9tPUm+eA9z7Fhf0exw5qQirkGtRCoEZGVIvKCiFw72IUicoOIrBWRtc3NzWMYolJKHS2bpCpCPtqjKa69Zw0v7+soyGNN5ue/YiYoH3Ae8C7g7cCXROToFsSAMWaFMWaJMWZJfX39WMaolFIDWjq/lruvPZKkPnzfmoJs5p3Mz3/FTFD7gN8ZY3qNMS3AKuCsIsajlFIjcuH8Wu750BIqQz7aelNcffdz9CbSxQ5rwihmgvoVcLGI+EQkAlwIbC5iPEopNWIXzPOm+2oifv75HYspC+ohEaOlYP8nReQhYBlQJyL7gC8DfgBjzHeNMZtF5HHgZcAF7jbGDFqSrpRSpeqCebX84bNvZEpZsNihTCgFS1DGmCuHcc3XgK8VKgallBormpxGn3aSUEopVZI0QSmllCpJmqCUUkqVJE1QSimlSpImKKWUUiVJE5RSSqmSpAlKKaVUSdIEpZRSqiRpglJKKVWSNEEppZQqSZqglFJKlSRNUEoppUqSJiillFIlSYwxxY5hRESkGdh9jMvqgJYxCOdEjYc4NcbRMx7i1BhHz0jibDHGXHasi0Tk8eFcN1GMuwQ1HCKy1hizpNhxHMt4iFNjHD3jIU6NcfSMlzhLmU7xKaWUKkmaoJRSSpWkiZqgVhQ7gGEaD3FqjKNnPMSpMY6e8RJnyZqQa1BKKaXGv4k6glJKKTXOaYJSSilVksZ1ghKRe0WkSUQ2DnHNMhFZLyKbROSZsYwv8/hDxigiVSLyaxF5KRPjdUWIsVFEnhaRVzIxfGqAa0RE7hCR7SLysoicW4IxXp2JbYOI/EVEziq1GPOuPV9E0iJy+VjGmHnsYcVZzL+dYf57F/VvR0RCIrIm7/H/bYBrgiLy48zfzXMiMncsYxz3jDHj9g24BDgX2DjI96uBV4DZmc8bSjDGLwC3ZT6uB9qAwBjHOB04N/NxBbAVOLXfNe8EHgMEWAo8V4Ixvg6oyXz8jlKMMfM9G3gK+C1w+VjGOIL/l0X92xlmjEX928n8LZRnPvYDzwFL+13z/4DvZj6+AvjxWP97j+e3cT2CMsaswvulHMxVwM+NMXsy1zeNSWB5hhGjASpERIDyzLXpsYgtF4AxB40x6zIfdwObgZn9Lnsv8IDxrAaqRWR6KcVojPmLMaY98+lqYNZYxTfcGDM+AfwMGPPfRxh2nEX92xlmjEX928n8LfRkPvVn3vpXnb0X+H7m40eAt2TiVcMwrhPUMCwEakRkpYi8ICLXFjugAXwbOAU4AGwAPmWMcYsVTGYK4hy8V4P5ZgJ78z7fx8BPvgU3RIz5Poo34iuKwWIUkZnAXwP/V4SwjjLE/8uS+dsZIsai/+2IiC0i6/FebDxhjBn078YYkwY6gdqxjHE88xU7gALzAecBbwHCwLMistoYs7W4YfXxdmA98GbgJOAJEfmjMaZrrAMRkXK8V/afLsbjD8dwYhSRN+ElqIvHMra8xx8qxv8FPm+McYv9QvoYcZbE384xYiz6344xxgHOFpFq4BcicroxZtA1cTUyE30EtQ/4nTGm1xjTAqwCxnThfBiuw5tKMcaY7cBrwOKxDkJE/HhPBD80xvx8gEv2A415n8/KfG3MDCNGRORM4G7gvcaY1rGML/P4x4pxCfCwiOwCLge+IyLvG7sIPcOIs+h/O8OIsST+dgCMMR3A00D/Rq65vxsR8QFVwJj/Xo5XEz1B/Qq4WER8IhIBLsSbyy4le/BepSIiU4FFwM6xDCAzJ34PsNkYc/sglz0KXJup5lsKdBpjDpZSjCIyG/g5cE0xRsnDidEYM88YM9cYMxdvTeL/GWN+OXZRDvvfu6h/O8OMsah/OyJSnxk5ISJh4G3Aln6XPQp8KPPx5cBTxhjtjjBM43qKT0QeApYBdSKyD/gy3kIlxpjvGmM2i8jjwMuAC9w91sPvY8UIfAW4X0Q24FUFfT7zinUsvR64BtiQmU8Hr0Jqdl6cv8Wr5NsORPFevZZajLfgze9/JzN9ljZj2016ODGWgmPGWQJ/O8P5f1nsv53pwPdFxMZ7sf8TY8xvRORWYK0x5lG8JPugiGzHK+K4YgzjG/e01ZFSSqmSNNGn+JRSSo1TmqCUUkqVJE1QSimlSpImKKWUUiVJE5RSSh0nGUbD6rxrv5FpvrteRLaKSMcYhDiuaYJSBSMic4fzhzuKj7dSRI5ZVi4i98sodBEfjZ9PRP5yonGoorqfozfnDsgY8xljzNnGmLOBb+Ht2VND0ASliiqzh2TSMsa8rtgxqOM3UDNoETlJRB7P9DD8o4gM1N3iSuChMQlyHNMEpQrNJyI/FJHNIvKIiEREZJeI3CYi64D3i8jHROR58c7V+Vmmc0F2pHOHeGc77cwf9YjI58U79+klEflq3uO9X7wzeraKyBuOFZyIvEVEXszc170iEsx8/ZZMTBtFZEWmswEicl7mMV8CbhrifldmpnTWZn7280Xk5yKyTUT+Pe+6nsz76SKyKjP9s1FE3iBeI9L7M59vEJHPZK4d8AlQRN6fufYlEVk1gn8jNbpWAJ8wxpwH/APwnfxvisgcYB7ekStqKKN5doe+6Vv+GzAX7/iB12c+vxfvD3YX8I9519XmffzveH/c4E2f/BTvhdSpwPbM198B/AWIZD6fknm/EvifzMfvBJ4cJK778drOhPA6TS/MfP0BvKakufvMfPwg8O7Mxy8Dl2Q+/hqDn/O1kiNnFX0Kr+P2dCCI1+euNvO9nsz7zwL/kvnYxjsD6Ty8DtnZ+6zOvP8DsCDz8YV47XPA6+g9M/9afRuz3/ONmY/LgRheE9vs2+Z+138e+Fax4x4PbzqCUoW21xjz58zHP+BIh/Ef511zemYksAG4Gjgt73u/NMa4xphXgKmZr70VuM8YEwUwxuRPsWTn9V/Ae+IYyiLgNXOkb9/38Q6YBHiTeCegbsDrln1apu9atfGmdcBLXEN5NPN+A7DJeGccJfD6xTX2u/Z54DoR+VfgDOOdgbQTmC8i3xKRy4Au8bp7vw74aaYF0F14iQ/gz3itfz6Gl+TU2LOADpNZa8q8ndLvmivQ6b1h0QSlCq1/L63s5715X7sf+Lgx5gzg3/BGNlmJvI+Hcz5F9nqHTK9JEbkvM3X22+EELCIhvGmZyzMxfa9fTAPdZqDHyMbi0vfncOnXBzOT9C7B6359v4hca7zDF8/CG43diNelfdAnQGPMjcAX8ZLfCyKi5w6NMeMd9fGaiLwfvKa3IpLrAp+Zjq0Bni1SiOOKJihVaLNF5KLMx1cBfxrgmgrgoHjHK1w9jPt8Am+0kV2rmjLUxcaY6zJP5O/s961XgbkicnLm82uAZziSjFoyI5bLM/fTAXSISHYUmIt1iMcYlsy6xGFjzPfwEtG5IlIHWMaYn+ElnnOHegIUkZOMMc8ZY24Bmjl6lKZGmXjNoJ8FFonIPhH5KN7vxUcz65Sb8E7VzboCeNhk5vrU0MZ1N3M1LrwK3CQi9wKv4J0k+4l+13wJ77TU5sz7iqHu0BjzuIicDawVkSRep/UvjDQwY0xcRK7Dmy7z4U2zfdcYkxCR7wEbgUOZr2ddB9wrIgb4/UgfcwjLgM+JSAroAa7FO431PhHJvpD858z7q4H/E5Ev4nXGfxh4CfiaiCzAG2n+IfM1VUDGmCsH+daApefGmH8tXDQTj3YzV0opVZJ0ik8ppVRJ0gSllFKqJGmCUkopVZI0QSmllCpJmqCUUkqVJE1QSimlSpImKKWUUiXp/wdQSItyv7ubwAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x432 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "st.markdown(\"#### 筛选有效指标\")\n",
+    "if len(vaild_metrics) == 0:\n",
+    "    st.markdown(\"        ** 指标相关性过低，无法分析** \")\n",
+    "\n",
+    "vaild_metrics = vaild_metrics[:5]\n",
+    "vaild_metrics_cols = st.columns(len(vaild_metrics))\n",
+    "\n",
+    "for col in range(len(vaild_metrics)):\n",
+    "    corr_value = \"{:.5}\".format(\n",
+    "        metrics_correlation.iloc[-1][vaild_metrics[col]])\n",
+    "    vaild_metrics_cols[col].metric(vaild_metrics[col], corr_value, col + 1)\n",
+    "\n",
+    "for i in range(len(vaild_metrics)):\n",
+    "    fig = sns.jointplot(x=vaild_metrics[i], y='qos', data=data, kind='reg')\n",
+    "    st.pyplot(fig)\n",
+    "\n",
+    "st.markdown(\"### 回归拟合分析\")\n",
+    "# 数据预处理: 去除无效值; 特性缩放:标准化; 模型训练\n",
+    "x = data[vaild_metrics]\n",
+    "y = data[[\"qos\"]]\n",
+    "x_train, x_test, y_train, y_test = model_selection.train_test_split(x, y, random_state=1)\n",
+    "\n",
+    "def draw_comparison_altair_chart(y_test, y_pred):\n",
+    "    y_test_list = y_test[\"qos\"]\n",
+    "    y_pred_list = y_pred if type(y_pred[0]) is not np.ndarray else [\n",
+    "        i[0] for i in y_pred]\n",
+    "    list_of_tuples = list(zip(y_test_list, y_pred_list))\n",
+    "    source = pd.DataFrame(list_of_tuples, columns=['Measured', 'Predicted'],\n",
+    "                          index=pd.RangeIndex(len(y_pred), name='index'))\n",
+    "    source = source.reset_index().melt('index', var_name='category', value_name='qos')\n",
+    "    nearest = alt.selection(type='single', nearest=True, on='mouseover',\n",
+    "                            fields=['index'], empty='none')\n",
+    "    line = alt.Chart(source).mark_line(interpolate='basis').encode(\n",
+    "        x='index:Q',\n",
+    "        y='qos:Q',\n",
+    "        color='category:N'\n",
+    "    )\n",
+    "\n",
+    "    selectors = alt.Chart(source).mark_point().encode(\n",
+    "        x='index:Q',\n",
+    "        opacity=alt.value(0),\n",
+    "    ).add_selection(\n",
+    "        nearest\n",
+    "    )\n",
+    "\n",
+    "    points = line.mark_point().encode(\n",
+    "        opacity=alt.condition(nearest, alt.value(1), alt.value(0))\n",
+    "    )\n",
+    "\n",
+    "    # Draw text labels near the points, and highlight based on selection\n",
+    "    text = line.mark_text(align='left', dx=5, dy=-5).encode(\n",
+    "        text=alt.condition(nearest, 'qos:Q', alt.value(' '))\n",
+    "    )\n",
+    "\n",
+    "    # Draw a rule at the location of the selection\n",
+    "    rules = alt.Chart(source).mark_rule(color='gray').encode(\n",
+    "        x='index:Q',\n",
+    "    ).transform_filter(\n",
+    "        nearest\n",
+    "    )\n",
+    "\n",
+    "    charts = alt.layer(\n",
+    "        line, selectors, points, rules, text\n",
+    "    ).interactive()\n",
+    "    st.altair_chart(charts, use_container_width=True)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "a8ffd883-a837-471e-a2c9-a4225bd5fbad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def draw_comparison_matplotlib_chart(y_test, y_pred):\n",
+    "    fig = plt.figure()\n",
+    "    plt.plot(np.arange(len(y_pred)),\n",
+    "             y_test[[\"qos\"]].values, 'go-', label='Measured')\n",
+    "    plt.plot(np.arange(len(y_pred)), y_pred, 'ro-', label='Predicted')\n",
+    "    plt.title(\"Interference Model Analysis\")\n",
+    "    plt.xlabel(\"Index\")\n",
+    "    plt.ylabel(\"QoS\")\n",
+    "    plt.legend()\n",
+    "    st.pyplot(fig)\n",
+    "\n",
+    "\n",
+    "class RegressionModel():\n",
+    "    \"\"\"regression model for metrics\"\"\"\n",
+    "\n",
+    "    def __init__(self, model) -> None:\n",
+    "        self.model = model\n",
+    "\n",
+    "        self.mse = 0\n",
+    "        self.rmse = 0\n",
+    "\n",
+    "    def train_and_test_model(self):\n",
+    "        self.model.fit(x_train, np.ravel(y_train))\n",
+    "\n",
+    "        y_pred = self.model.predict(x_test)\n",
+    "        # score = model.score(x_test, y_test)\n",
+    "        draw_comparison_altair_chart(y_test, y_pred)\n",
+    "\n",
+    "        self.mse = metrics.mean_squared_error(y_test, y_pred)\n",
+    "        self.rmse = np.sqrt(metrics.mean_squared_error(y_test, y_pred))\n",
+    "        model_evaluation = \"MSE: {}, RMSE: {}\".format(self.mse, self.rmse)\n",
+    "        st.write(model_evaluation)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "23d8fb12-3bea-44e2-8110-2f5082df9ec7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class PolynomialRegressionModel():\n",
+    "    \"\"\"polynomial regression model for metrics\"\"\"\n",
+    "\n",
+    "    def __init__(self, degree) -> None:\n",
+    "        self.model = linear_model.LinearRegression()\n",
+    "\n",
+    "        self.mse = 0\n",
+    "        self.rmse = 0\n",
+    "        self.degree = degree\n",
+    "\n",
+    "    def train_and_test_model(self):\n",
+    "        self.poly = preprocessing.PolynomialFeatures(degree = self.degree) \n",
+    "        X_poly = self.poly.fit_transform(x_train)\n",
+    "\n",
+    "        self.model.fit(X_poly, y_train)\n",
+    "\n",
+    "        y_pred = self.model.predict(self.poly.transform(x_test))\n",
+    "        # score = model.score(x_test, y_test)\n",
+    "        draw_comparison_altair_chart(y_test, y_pred)\n",
+    "\n",
+    "        self.mse = metrics.mean_squared_error(y_test, y_pred)\n",
+    "        self.rmse = np.sqrt(metrics.mean_squared_error(y_test, y_pred))\n",
+    "        model_evaluation = \"MSE: {}, RMSE: {}\".format(self.mse, self.rmse)\n",
+    "        st.write(model_evaluation)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "a8565c5f-8e08-4962-9ba3-6f583544c47f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "polynomial_regression_list = [\n",
+    "    {'name': '1st Degree Polynomial Regression', 'degree': 1},\n",
+    "    {'name': '2nd Degree Polynomial Regression', 'degree': 2},\n",
+    "    {'name': '3rd Degree Polynomial Regression', 'degree': 3},\n",
+    "    {'name': '4th Degree Polynomial Regression', 'degree': 4},\n",
+    "    {'name': '5th Degree Polynomial Regression', 'degree': 5},\n",
+    "]\n",
+    "\n",
+    "other_regression_list = [\n",
+    "    {'name': 'Decision Tree Regression', 'regressor': tree.DecisionTreeRegressor()},\n",
+    "    {'name': 'SVM Regression', 'regressor': svm.SVR()},\n",
+    "    {'name': 'KNN Regression', 'regressor': neighbors.KNeighborsRegressor()},\n",
+    "    {'name': 'Random Forest Regression', 'regressor': ensemble.RandomForestRegressor(n_estimators=20)},\n",
+    "    {'name': 'Adaboost Regression', 'regressor': ensemble.AdaBoostRegressor(n_estimators=50)},\n",
+    "    {'name': 'Gradient Boosting Regression', 'regressor': ensemble.GradientBoostingRegressor(n_estimators=100)},\n",
+    "    {'name': 'Bagging Regression', 'regressor': ensemble.BaggingRegressor()},\n",
+    "    {'name': 'ExtraTree Regression', 'regressor': tree.ExtraTreeRegressor()},\n",
+    "]\n",
+    "\n",
+    "mse_list = []\n",
+    "polynomial_list = []"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "90b9db7b-e3ab-4b86-a337-339e3ce56c06",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "for polynomial_regression in polynomial_regression_list:\n",
+    "    st.markdown(\"#### \" + polynomial_regression['name'])\n",
+    "    degree = polynomial_regression['degree']\n",
+    "    \n",
+    "    regression_model = PolynomialRegressionModel(degree)\n",
+    "    regression_model.train_and_test_model()\n",
+    "\n",
+    "    polynomial_list.append({'name': polynomial_regression['name'], 'rmse': regression_model.rmse, 'regression_model': regression_model})\n",
+    "    mse_list.append({'name': polynomial_regression['name'], 'mse': regression_model.mse, 'rmse': regression_model.rmse})\n",
+    "\n",
+    "for other_regression in other_regression_list:\n",
+    "    st.markdown(\"#### \" + other_regression['name'])\n",
+    "    regressor = other_regression['regressor']\n",
+    "    \n",
+    "    regression_model = RegressionModel(regressor)\n",
+    "    regression_model.train_and_test_model()\n",
+    "\n",
+    "    mse_list.append({'name': other_regression['name'], 'mse': regression_model.mse, 'rmse': regression_model.rmse})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "af3bc43d-e597-48d9-8363-e9f03c41738f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DeltaGenerator(_root_container=0, _provided_cursor=None, _parent=None, _block_type=None, _form_data=None)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def takeMse(elem):\n",
+    "    return elem['rmse']\n",
+    "\n",
+    "mse_list.sort(key=takeMse)\n",
+    "st.table(pd.DataFrame(mse_list).set_index('name'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "e439d2f2-896e-4079-8427-242cfb057ba7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "               timestamp  context-switches  branch-misses  branch-load-misses  \\\n",
+      "0    2022-03-09 08:29:01               384       29086876            29067390   \n",
+      "1    2022-03-09 08:29:11               384       28395073            28400706   \n",
+      "2    2022-03-09 08:29:21               412       29354478            29364502   \n",
+      "3    2022-03-09 08:29:31               395       28489483            28484761   \n",
+      "4    2022-03-09 08:29:41               427       28812242            28823944   \n",
+      "..                   ...               ...            ...                 ...   \n",
+      "613  2022-03-09 10:11:12               359       28861030            28865360   \n",
+      "614  2022-03-09 10:11:22               464       29090083            29091901   \n",
+      "615  2022-03-09 10:11:32               371       29057983            29060496   \n",
+      "616  2022-03-09 10:11:42               371       29223022            29231777   \n",
+      "617  2022-03-09 10:11:52               379       28365251            28363547   \n",
+      "\n",
+      "      ipc  cpu-migrations  page-faults  cache-references  cache-misses  \\\n",
+      "0    2.68             103         3295         156126454      58757176   \n",
+      "1    2.61             145         6309         155585113      58593036   \n",
+      "2    2.67             115         2608         157103650      59814473   \n",
+      "3    2.65             103         4421         154754165      58395113   \n",
+      "4    2.66             121         8171         154372987      57059560   \n",
+      "..    ...             ...          ...               ...           ...   \n",
+      "613  2.66             130         5136         155183593      57915231   \n",
+      "614  2.60             155         5085         156190418      58616594   \n",
+      "615  2.67             133         4739         156514632      58504259   \n",
+      "616  2.68             138         3723         155683474      58555856   \n",
+      "617  2.64             174         4305         154287133      57530772   \n",
+      "\n",
+      "     LLC-loads  ...  L1-dcache-load-misses  L1-icache-load-misses  \\\n",
+      "0      7789949  ...              243692903               33757849   \n",
+      "1      8153357  ...              248034851               34849021   \n",
+      "2      7803900  ...              251846933               33950272   \n",
+      "3      7701376  ...              253549460               34322874   \n",
+      "4      7729852  ...              251193277               34617487   \n",
+      "..         ...  ...                    ...                    ...   \n",
+      "613    7946499  ...              252001592               34714451   \n",
+      "614    7988599  ...              247979319               34507837   \n",
+      "615    8207487  ...              243555095               34689432   \n",
+      "616    7735026  ...              251859896               34829052   \n",
+      "617    7808932  ...              250356746               34101148   \n",
+      "\n",
+      "     dTLB-load-misses   dTLB-loads  dTLB-store-misses  dTLB-stores  \\\n",
+      "0              359147  13660089049             563717   4755805513   \n",
+      "1              399561  13591777510             611925   4758125952   \n",
+      "2              364048  13601421822             550633   4796035193   \n",
+      "3              378664  13634078821             539258   4785515079   \n",
+      "4              379189  13534862954             625461   4776076428   \n",
+      "..                ...          ...                ...          ...   \n",
+      "613            415236  13667827741             585778   4680454646   \n",
+      "614            396124  13581536032             556685   4691274898   \n",
+      "615            392974  13598209386             547165   4534175673   \n",
+      "616            417168  13642033841             644607   4741262835   \n",
+      "617            397473  13421719385             632825   4777111177   \n",
+      "\n",
+      "     iTLB-load-misses  node-load-misses  node-loads      qos  \n",
+      "0              551421                 0     4354843  1.69833  \n",
+      "1              537069                 0     4322310  1.70167  \n",
+      "2              639177                 0     4471451  1.68700  \n",
+      "3              569687                 0     4393632  1.68833  \n",
+      "4              554582                 0     4382613  1.69933  \n",
+      "..                ...               ...         ...      ...  \n",
+      "613            562063                 0     4437182  1.69333  \n",
+      "614            557561                 0     4280858  1.70467  \n",
+      "615            552906                 0     4225687  1.73233  \n",
+      "616            550115                 0     4335937  1.68867  \n",
+      "617            558718                 0     4409793  1.69933  \n",
+      "\n",
+      "[618 rows x 24 columns]\n"
+     ]
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "could not convert string to float: '2022-03-09 08:29:01'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-24-ed587aa4bfc7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mst\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmarkdown\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"聚类分析\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mkms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mKMeans\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_clusters\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/rubik/lib/python3.6/site-packages/sklearn/cluster/_kmeans.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    980\u001b[0m                                 \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat64\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    981\u001b[0m                                 \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'C'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy_x\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 982\u001b[0;31m                                 accept_large_sparse=False)\n\u001b[0m\u001b[1;32m    983\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    984\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/rubik/lib/python3.6/site-packages/sklearn/base.py\u001b[0m in \u001b[0;36m_validate_data\u001b[0;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[1;32m    419\u001b[0m             \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    420\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'no_validation'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 421\u001b[0;31m             \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mcheck_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    422\u001b[0m             \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    423\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/rubik/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     61\u001b[0m             \u001b[0mextra_args\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mextra_args\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     64\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m             \u001b[0;31m# extra_args > 0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/rubik/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001b[0m\n\u001b[1;32m    671\u001b[0m                     \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcasting\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"unsafe\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    672\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 673\u001b[0;31m                     \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    674\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mComplexWarning\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mcomplex_warning\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    675\u001b[0m                 raise ValueError(\"Complex data not supported\\n\"\n",
+      "\u001b[0;32m~/anaconda3/envs/rubik/lib/python3.6/site-packages/numpy/core/_asarray.py\u001b[0m in \u001b[0;36masarray\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m     81\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     82\u001b[0m     \"\"\"\n\u001b[0;32m---> 83\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     84\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     85\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/rubik/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__array__\u001b[0;34m(self, dtype)\u001b[0m\n\u001b[1;32m   1779\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1780\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__array__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1781\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1782\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1783\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__array_wrap__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/envs/rubik/lib/python3.6/site-packages/numpy/core/_asarray.py\u001b[0m in \u001b[0;36masarray\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m     81\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     82\u001b[0m     \"\"\"\n\u001b[0;32m---> 83\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     84\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     85\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: could not convert string to float: '2022-03-09 08:29:01'"
+     ]
+    }
+   ],
+   "source": [
+    "st.markdown(\"聚类分析\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "03df3ec0-8661-47c5-b030-2bcc450d4a7b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "st.markdown(\"### 多项式回归拟合筛选\")\n",
+    "\n",
+    "polynomial = min(polynomial_list, key=takeMse)\n",
+    "\n",
+    "if polynomial['rmse'] > 15:\n",
+    "    st.write(\"指标相关性较弱，不适合进行多项式回归拟合\")\n",
+    "else:\n",
+    "    st.markdown(\"**\" + polynomial['name'] + \"**\")\n",
+    "    model_evaluation = \"MSE: {}, RMSE: {}\".format(polynomial['regression_model'].mse, polynomial['regression_model'].rmse)\n",
+    "    st.write(model_evaluation)\n",
+    "\n",
+    "    coef = polynomial['regression_model'].model.coef_\n",
+    "    feature_names = polynomial['regression_model'].poly.get_feature_names_out(vaild_metrics)\n",
+    "\n",
+    "    polynomial_df = pd.DataFrame(feature_names, columns = ['features'])\n",
+    "    polynomial_df['coef'] = np.ravel(coef)\n",
+    "    st.table(polynomial_df.style.format({'coef': \"{:.10f}\"}))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6c775243-a13e-428e-b5ff-60f66f05e424",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "rubik_kernel",
+   "language": "python",
+   "name": "rubik_kernel"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/tools/interferenceanalysis/.ipynb_checkpoints/ayin-checkpoint.ipynb b/tools/interferenceanalysis/.ipynb_checkpoints/ayin-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..7453e494cdaf824c380c37298f927fd5d0a9b99f
--- /dev/null
+++ b/tools/interferenceanalysis/.ipynb_checkpoints/ayin-checkpoint.ipynb
@@ -0,0 +1,156 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "efa6013d-ed9f-4ee9-a43e-f279f5b67589",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn import ensemble, neighbors, svm, linear_model, model_selection, tree, metrics, preprocessing \n",
+    "from sklearn.cluster import KMeans\n",
+    "from sklearn.decomposition import PCA\n",
+    "from itertools import product\n",
+    "import streamlit as st\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import altair as alt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "8f0c398f-e42d-4f03-838b-9c05139f5ec6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(618, 24)\n",
+      "[3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n",
+      " 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
+      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
+      " 1 1 1 1 1 1 1 1 1 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 2 2 2 2\n",
+      " 2 2 0 2 2 2 0 2 0 2 2 2 0 2 0 2 2 2 2 2 2 2 0 2 2 0 2 2 0 0 0 2 2 0 2 0 2\n",
+      " 2 2 2 0 0 0 2 0 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
+      " 2 2 2 2 0 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
+      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
+      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
+      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
+      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
+      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
+      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
+      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n",
+      " 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n",
+      " 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3]\n"
+     ]
+    }
+   ],
+   "source": [
+    "data = pd.read_csv(\"../tests/data/clickhouse/l3cache_stress.csv\")\n",
+    "print(data.shape)\n",
+    "kms=KMeans(n_clusters=4)\n",
+    "kms.fit(data.drop(columns='timestamp'))\n",
+    "data_predict=kms.predict(data.drop(columns='timestamp'))\n",
+    "print(data_predict)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "0520d481-5ddd-4d3d-a0fa-15d4ba150e19",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 5.12111767e+09 -1.86155328e+08 -8.92831759e+07 -8.50470357e+05]\n",
+      " [ 5.09249405e+09 -1.12654911e+08 -9.87844304e+07 -1.15543945e+06]\n",
+      " [ 5.26150549e+09  8.77606967e+07 -1.02596338e+08 -3.60872253e+05]\n",
+      " ...\n",
+      " [ 5.03307925e+09 -1.35980516e+08  1.17710558e+08 -1.18700782e+06]\n",
+      " [ 5.31706596e+09  1.12066654e+08 -3.45362780e+07 -2.58064478e+06]\n",
+      " [ 5.09212041e+09  1.61878354e+08 -1.21372126e+08 -3.56561032e+06]]\n",
+      "<class 'numpy.ndarray'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_drop=data.drop(columns='timestamp')\n",
+    "pca=PCA(n_components=3)\n",
+    "pca.fit(data_drop)\n",
+    "pca_predict=pca.transform(data_drop)\n",
+    "print(pca_predict)\n",
+    "print(type(pca_predict))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "92ffd114-5b6a-407b-b16b-dac6d63e66ee",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plt_data(data):\n",
+    "    plt.xlabel('x label')\n",
+    "    plt.title('pca decomposition data')\n",
+    "    rev_data=pca_predict.T\n",
+    "    plt.plot(rev_data[0])\n",
+    "    plt.plot(rev_data[1])\n",
+    "    plt.plot(rev_data[2])\n",
+    "    # plt.plot(rev_data[3])\n",
+    "    # plt.plot(rev_data[4])\n",
+    "    # plt.plot(rev_data[5])\n",
+    "    plt.show()\n",
+    "plt_data(pca_predict)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f8261baf-7918-49fb-92a9-55334c1b397c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "rubik",
+   "language": "python",
+   "name": "rubik"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/tools/interferenceanalysis/VERSION b/tools/interferenceanalysis/VERSION
new file mode 100644
index 0000000000000000000000000000000000000000..6e8bf73aa550d4c57f6f35830f1bcdc7a4a62f38
--- /dev/null
+++ b/tools/interferenceanalysis/VERSION
@@ -0,0 +1 @@
+0.1.0
diff --git a/tools/interferenceanalysis/__init__.py b/tools/interferenceanalysis/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..dfe59032a9fa77712a868418d6793ac326659163
--- /dev/null
+++ b/tools/interferenceanalysis/__init__.py
@@ -0,0 +1,4 @@
+from .base import BaseClass, base_function
+from .preprocess import Preprocess, StressProcess, MachineProcess
+
+__all__ = ["BaseClass", "base_function", "Preprocess", "StressProcess", "MachineProcess"]
diff --git a/tools/interferenceanalysis/__main__.py b/tools/interferenceanalysis/__main__.py
new file mode 100644
index 0000000000000000000000000000000000000000..909fe218d3f4b5cdb60c1e6d699171f21df87fe5
--- /dev/null
+++ b/tools/interferenceanalysis/__main__.py
@@ -0,0 +1,72 @@
+import argparse
+
+from . import Preprocess, StressProcess, MachineProcess
+
+
+def preprocess_main(args):
+    preprocess = Preprocess(args.metrics, args.qos, args.output)
+    preprocess.execute()
+
+
+def stress_process_main(args):
+    stress_process = StressProcess(args.stress, args.qos, args.output)
+    stress_process.execute()
+
+def machine_process_main(args):
+    machine_process = MachineProcess(args.stress, args.machine, args.output)
+    machine_process.execute()
+
+def main() -> None:
+    """
+    The main function executes on commands:
+    `python -m rubikanalysis` and `$ rubikanalysis `.
+
+    This is your program's entry point.
+    """
+    parser = argparse.ArgumentParser(
+        description="A tool for metrics analysis of co-Location container workloads",
+        epilog=("Through the software-hardware collaborative analysis method, the application"
+                " characteristics and system-level characteristics of load execution under the"
+                " conditions of differnet configurations of clusters and different co-location"
+                " modes are studied to guide the resource planning and scheduling management of"
+                " cloud clusters"),
+    )
+
+    subparsers = parser.add_subparsers()
+    # create the parser for the "preprocess" command
+    parser_preprocess = subparsers.add_parser(
+        'preprocess', help='Indicator data preprocessing')
+    parser_preprocess.add_argument(
+        '-m', "--metrics", type=str, help='Low level metrics', required=True)
+    parser_preprocess.add_argument(
+        '-q', "--qos", type=str, help='High level SLAs', required=True)
+    parser_preprocess.add_argument(
+        '-o', "--output", type=str, help='Preprocessed merged file', required=True)
+    parser_preprocess.set_defaults(func=preprocess_main)
+
+    parser_stress_process = subparsers.add_parser(
+        'stress-process', help='Stress data processing')
+    parser_stress_process.add_argument(
+        '-s', "--stress", type=str, help='Stress info file', required=True)
+    parser_stress_process.add_argument(
+        '-q', "--qos", type=str, help='High level SLAs', required=True)
+    parser_stress_process.add_argument(
+        '-o', "--output", type=str, help='Stress merged file', required=True)
+    parser_stress_process.set_defaults(func=stress_process_main)
+
+    parser_machine_process = subparsers.add_parser(
+        'machine-process', help='Machine data processing')
+    parser_machine_process.add_argument(
+        '-s', "--stress", type=str, help='Stress info file', required=True)
+    parser_machine_process.add_argument(
+        '-m', "--machine", type=str, help='Machine data file', required=True)
+    parser_machine_process.add_argument(
+        '-o', "--output", type=str, help='Merged file', required=True)
+    parser_machine_process.set_defaults(func=machine_process_main)
+
+    args = parser.parse_args()
+    args.func(args)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/tools/interferenceanalysis/analysis.ipynb b/tools/interferenceanalysis/analysis.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..e724edee784c3f6c0f0a1841a228dde87ab5891f
--- /dev/null
+++ b/tools/interferenceanalysis/analysis.ipynb
@@ -0,0 +1,935 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "a1a5e1dd-4466-41e3-bae1-c2c33ce44bf7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn import ensemble, neighbors, svm, linear_model, model_selection, tree, metrics, preprocessing\n",
+    "from sklearn.cluster import KMeans\n",
+    "from itertools import product\n",
+    "import streamlit as st\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import altair as alt\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "f7777ac6-b6df-4023-8cc1-8410963fae70",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_chart(data):\n",
+    "    hover = alt.selection_single(\n",
+    "        fields=[\"timestamp\"],\n",
+    "        nearest=True,\n",
+    "        on=\"mouseover\",\n",
+    "        empty=\"none\",\n",
+    "    )\n",
+    "\n",
+    "    lines = (\n",
+    "        alt.Chart(data.reset_index(drop=True).melt(\"timestamp\"),\n",
+    "                  title=\"Metrics variation\")\n",
+    "        .mark_line()\n",
+    "        .encode(\n",
+    "            x=\"timestamp:T\",\n",
+    "            y=\"value:Q\",\n",
+    "            color=\"variable\",\n",
+    "            # strokeDash=\"variable\",\n",
+    "        ).properties(width=800, height=400)\n",
+    "    )\n",
+    "\n",
+    "    # Draw points on the line, and highlight based on selection\n",
+    "    points = lines.transform_filter(hover).mark_circle(size=65)\n",
+    "\n",
+    "    # Draw a rule at the location of the selection\n",
+    "    tooltips = (\n",
+    "        alt.Chart(data.reset_index(drop=True).melt(\"timestamp\"),\n",
+    "                  title=\"Metrics variation\")\n",
+    "        .mark_rule()\n",
+    "        .encode(\n",
+    "            x=\"timestamp:T\",\n",
+    "            y=\"value:Q\",\n",
+    "            opacity=alt.condition(hover, alt.value(0.3), alt.value(0)),\n",
+    "            tooltip=[\n",
+    "                alt.Tooltip(\"timestamp\", title=\"Time\"),\n",
+    "                alt.Tooltip(\"value\", title=\"Metrics\"),\n",
+    "            ],\n",
+    "        )\n",
+    "        .add_selection(hover)\n",
+    "    )\n",
+    "\n",
+    "    return (lines + points + tooltips).interactive()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "e030a95d-6865-47b2-9e34-120ed27dbb25",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_stress_chart(data, symbol):\n",
+    "    hover = alt.selection_single(\n",
+    "        fields=[\"stress\"],\n",
+    "        nearest=True,\n",
+    "        on=\"mouseover\",\n",
+    "        empty=\"none\",\n",
+    "    )\n",
+    "\n",
+    "    lines = (\n",
+    "        alt.Chart(data, title=\"Qos variation with stress \" + symbol)\n",
+    "        .mark_line()\n",
+    "        .encode(\n",
+    "            x=alt.X(\"stress:Q\", axis=alt.Axis(orient=\"top\")),\n",
+    "            y=alt.Y(\"degradation-percent:Q\", sort='descending',\n",
+    "                    title=\"degradation percent(%)\"),\n",
+    "            color=\"type\",\n",
+    "            strokeDash=\"type\",\n",
+    "        )\n",
+    "    )\n",
+    "\n",
+    "    # Draw points on the line, and highlight based on selection\n",
+    "    points = lines.transform_filter(hover).mark_circle(size=65)\n",
+    "\n",
+    "    # Draw a rule at the location of the selection\n",
+    "    tooltips = (\n",
+    "        alt.Chart(data)\n",
+    "        .mark_rule()\n",
+    "        .encode(\n",
+    "            x=\"stress:Q\",\n",
+    "            y=\"degradation-percent:Q\",\n",
+    "            opacity=alt.condition(hover, alt.value(0.3), alt.value(0)),\n",
+    "            tooltip=[\n",
+    "                alt.Tooltip(\"stress\", title=\"Stress Info\"),\n",
+    "                alt.Tooltip(\"avg-qos\", title=\"Qos\"),\n",
+    "                alt.Tooltip(\"degradation-percent\", title=\"Degradation Percent\")\n",
+    "            ],\n",
+    "        )\n",
+    "        .add_selection(hover)\n",
+    "    )\n",
+    "\n",
+    "    return (lines + points + tooltips).interactive()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "9c76ddde-ec6d-4cfa-ab2e-72b08bb3117a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def stress_sensitivity(stress_degrade):\n",
+    "    if stress_degrade['degradation-percent'] <= 5:\n",
+    "        return \"no\"\n",
+    "\n",
+    "    if stress_degrade['degradation-percent'] <= 10:\n",
+    "        return \"low\"\n",
+    "\n",
+    "    if stress_degrade['degradation-percent'] <= 20:\n",
+    "        return \"medium\"\n",
+    "\n",
+    "    return \"high\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "db3cb0b0-b2ab-4252-85b3-de435535612b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_usage_chart(data, symbol):\n",
+    "    hover = alt.selection_single(\n",
+    "        fields=[\"stress\"],\n",
+    "        nearest=True,\n",
+    "        on=\"mouseover\",\n",
+    "        empty=\"none\",\n",
+    "    )\n",
+    "\n",
+    "    lines = (\n",
+    "        alt.Chart(data, title=\"CPU usage variation with stress \" + symbol)\n",
+    "        .mark_line()\n",
+    "        .encode(\n",
+    "            x=alt.X(\"stress:Q\", axis=alt.Axis(orient=\"top\")),\n",
+    "            y=alt.Y(\"avg-cpu-usage:Q\", sort='descending',\n",
+    "                    title=\"cpu usage percent(%)\"),\n",
+    "            color=\"type\",\n",
+    "            strokeDash=\"type\",\n",
+    "        )\n",
+    "    )\n",
+    "\n",
+    "    # Draw points on the line, and highlight based on selection\n",
+    "    points = lines.transform_filter(hover).mark_circle(size=65)\n",
+    "\n",
+    "    # Draw a rule at the location of the selection\n",
+    "    tooltips = (\n",
+    "        alt.Chart(data)\n",
+    "        .mark_rule()\n",
+    "        .encode(\n",
+    "            x=\"stress:Q\",\n",
+    "            y=\"avg-cpu-usage:Q\",\n",
+    "            opacity=alt.condition(hover, alt.value(0.3), alt.value(0)),\n",
+    "            tooltip=[\n",
+    "                alt.Tooltip(\"stress\", title=\"Stress Info\"),\n",
+    "                alt.Tooltip(\"avg-cpu-usage\", title=\"CPU Usage Percent\")\n",
+    "            ],\n",
+    "        )\n",
+    "        .add_selection(hover)\n",
+    "    )\n",
+    "\n",
+    "    return (lines + points + tooltips).interactive()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "83e7f01c-b6f9-4fc8-88c4-46fd1927a462",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def normalize_table(df):\n",
+    "    cols = list(df)\n",
+    "    for item in cols:\n",
+    "        if df[item].dtype == 'int64' or df[item].dtype == 'float64':\n",
+    "            max_tmp = np.max(np.array(df[item]))\n",
+    "            min_tmp = np.min(np.array(df[item]))\n",
+    "            if (max_tmp != min_tmp):\n",
+    "                df[item] = df[item].apply(\n",
+    "                    lambda x: (x - min_tmp) * 100 / (max_tmp - min_tmp))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "ea310115-3e99-496c-86e9-4957d6bc3910",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "def standardize_table(df):\n",
+    "    cols = list(df)\n",
+    "    for item in cols:\n",
+    "        if df[item].dtype == 'int64' or df[item].dtype == 'float64':\n",
+    "            mean_tmp = np.mean(np.array(df[item]))\n",
+    "            std_tmp = np.std(np.array(df[item]))\n",
+    "            if(std_tmp):\n",
+    "                df[item] = df[item].apply(lambda x: (x - mean_tmp) / std_tmp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2b54310b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "18974afa-aff8-4620-b62c-a64944bdeb9a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DeltaGenerator(_root_container=0, _provided_cursor=None, _parent=None, _block_type=None, _form_data=None)"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "st.set_page_config(layout=\"centered\", page_icon=\"⎈\",\n",
+    "                   page_title=\"rubik analysis\")\n",
+    "\n",
+    "st.markdown(\"# ⎈ 混部干扰建模分析工具\")\n",
+    "st.markdown(\"## 业务介绍 TODO\")\n",
+    "st.markdown(\"## 环境说明\")\n",
+    "\n",
+    "node_info = pd.read_csv(\"../tests/data/default/node.csv\", index_col=\"Item\")\n",
+    "uploaded_node_file = st.file_uploader(\"上传测试环境配置\", type=\"csv\")\n",
+    "if uploaded_node_file is not None:\n",
+    "    node_info = pd.read_csv(uploaded_node_file, index_col=\"Item\")\n",
+    "\n",
+    "st.table(node_info)\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "fefbc2a5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "st.markdown(\"## 指标数据\")\n",
+    "data = pd.read_csv(\"../tests/data/default/nginx.csv\")\n",
+    "data_select = st.radio(\n",
+    "    \"\"\"选择分析的数据集\n",
+    "    \"\"\",\n",
+    "    ('l3cache_stress.csv', 'nginx.csv'))\n",
+    "if data_select == 'l3cache_stress.csv':\n",
+    "    data = pd.read_csv(\"../tests/data/clickhouse/l3cache_stress.csv\")\n",
+    "else:\n",
+    "    data=pd.read_csv(\"../tests/data/default/nginx.csv\")\n",
+    "mode = st.radio(\n",
+    "    \"Please select a mode to visualize the data:\",\n",
+    "    ('origin', 'normalization', 'standardization'))\n",
+    "\n",
+    "if mode == 'normalization':\n",
+    "    normalize_table(data)\n",
+    "elif mode == 'standardization':\n",
+    "    standardize_table(data)\n",
+    "# data = pd.read_csv(\"../tests/data/default/nginx.csv\")\n",
+    "# data = pd.read_csv(\"../tests/data/clickhouse/l3cache_stress.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "4753aa4d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DeltaGenerator(_root_container=0, _provided_cursor=None, _parent=None, _block_type=None, _form_data=None)"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "uploaded_metrics_file = st.file_uploader(\"上传指标数据-QoS数据\", type=\"csv\")\n",
+    "if uploaded_metrics_file is not None:\n",
+    "    data = pd.read_csv(uploaded_metrics_file)\n",
+    "\n",
+    "\n",
+    "\n",
+    "all_symbols = list(data.columns[1:])\n",
+    "symbols = st.multiselect(\"Choose metrics to visualize\",\n",
+    "                         all_symbols, all_symbols[:3])\n",
+    "symbols.insert(0, data.columns[0])\n",
+    "\n",
+    "source = data[symbols]\n",
+    "\n",
+    "chart = get_chart(source)\n",
+    "st.altair_chart(chart, use_container_width=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "c8990835-8086-46bb-ac5a-4daccdd87ef5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "st.markdown(\"## 资源敏感度分析\")\n",
+    "\n",
+    "# type stress avg-qos degradation-percent\n",
+    "stress = pd.read_csv(\"../tests/data/default/stress.csv\", keep_default_na=False)\n",
+    "uploaded_stress_file = st.file_uploader(\"上传压力测试指标数据\", type=\"csv\")\n",
+    "if uploaded_stress_file is not None:\n",
+    "    stress = pd.read_csv(uploaded_stress_file)\n",
+    "\n",
+    "symbol_transform = {\n",
+    "    \"icache\": \"cache\",\n",
+    "    \"L1 cache\": \"cache\",\n",
+    "    \"L2 cache\": \"cache\",\n",
+    "    \"L3 cache\": \"cache\",\n",
+    "\n",
+    "    \"network in\": \"network\",\n",
+    "    \"network out\": \"network\",\n",
+    "}\n",
+    "def get_key(dict, value):\n",
+    "    return [k for k,v in dict.items() if v == value]\n",
+    "\n",
+    "stress_unique_symbols = stress.type.unique()\n",
+    "stress_symbols = []\n",
+    "\n",
+    "for symbol in stress_unique_symbols:\n",
+    "    if symbol in symbol_transform:\n",
+    "        symbol = symbol_transform[symbol]\n",
+    "\n",
+    "    if symbol == 'none':\n",
+    "        continue\n",
+    "\n",
+    "    if symbol not in stress_symbols:\n",
+    "        stress_symbols.append(symbol)\n",
+    "\n",
+    "stress_symbols = st.multiselect(\"Choose resource symbols\",\n",
+    "                                stress_symbols, stress_symbols)\n",
+    "\n",
+    "for stress_symbol in stress_symbols:\n",
+    "    # 插入无压力数据\n",
+    "    nstress = stress[stress.type == \"none\"]\n",
+    "\n",
+    "    if stress_symbol in symbol_transform.values():\n",
+    "        keys = get_key(symbol_transform, stress_symbol)\n",
+    "        stress_source = stress[stress['type'].isin(keys)]\n",
+    "        for key in keys:\n",
+    "            stress_source = pd.concat([stress_source, nstress.replace(\n",
+    "                \"none\", key)], axis=0, ignore_index=True)\n",
+    "    else:\n",
+    "        stress_source = stress[stress['type'] == stress_symbol]\n",
+    "        stress_source = pd.concat([stress_source, nstress.replace(\n",
+    "                \"none\", stress_symbol)], axis=0, ignore_index=True)\n",
+    "\n",
+    "    stress_chart = get_stress_chart(stress_source, stress_symbol)\n",
+    "    st.altair_chart(stress_chart, use_container_width=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "aa761760-9939-4d40-9c58-27d3d130be78",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DeltaGenerator(_root_container=0, _provided_cursor=None, _parent=None, _block_type=None, _form_data=None)"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "st.markdown(\"#### 资源敏感度排序\")\n",
+    "\n",
+    "stress_degrade = (\n",
+    "    stress.drop(stress[stress['type'] == \"none\"].index)[\n",
+    "        ['type', 'degradation-percent']]\n",
+    "    .groupby(by='type')\n",
+    "    .max()\n",
+    "    .sort_values(by='degradation-percent', ascending=False)\n",
+    ")\n",
+    "\n",
+    "stress_degrade.loc[:, 'sensitivity'] = stress_degrade.apply(\n",
+    "    stress_sensitivity, axis=1)\n",
+    "st.table(stress_degrade)\n",
+    "\n",
+    "st.info(\n",
+    "    \"degradation-percent in (, 5]:no ; (5, 10]:low ; (10, 20]:medinum ; (20,):high\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "93c71736-f5fd-44c9-9633-81f5653fbe33",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "st.markdown(\"## 资源利用率\")\n",
+    "\n",
+    "# type stress avg-qos degradation-percent\n",
+    "usage = pd.read_csv(\"../tests/data/default/machine.csv\", keep_default_na=False)\n",
+    "uploaded_usage_file = st.file_uploader(\"上传利用率数据\", type=\"csv\")\n",
+    "if uploaded_usage_file is not None:\n",
+    "    usage = pd.read_csv(uploaded_usage_file)\n",
+    "\n",
+    "usage_unique_symbols = usage.type.unique()\n",
+    "usage_symbols = []\n",
+    "\n",
+    "for symbol in usage_unique_symbols:\n",
+    "    if symbol in symbol_transform:\n",
+    "        symbol = symbol_transform[symbol]\n",
+    "\n",
+    "    if symbol == 'none':\n",
+    "        continue\n",
+    "\n",
+    "    if symbol not in usage_symbols:\n",
+    "        usage_symbols.append(symbol)\n",
+    "\n",
+    "usage_symbols = st.multiselect(\"Choose resource symbols to display\",\n",
+    "                                usage_symbols, usage_symbols)\n",
+    "\n",
+    "for usage_symbol in usage_symbols:\n",
+    "    # 插入无压力数据\n",
+    "    nusage = usage[usage['type'] == \"none\"]\n",
+    "\n",
+    "    if usage_symbol in symbol_transform.values():\n",
+    "        keys = get_key(symbol_transform, usage_symbol)\n",
+    "        usage_source = usage[usage['type'].isin(keys)]\n",
+    "        for key in keys:\n",
+    "            usage_source = pd.concat([usage_source, nusage.replace(\n",
+    "                \"none\", key)], axis=0, ignore_index=True)\n",
+    "    else:\n",
+    "        usage_source = usage[usage['type'] == usage_symbol]\n",
+    "        usage_source = pd.concat([usage_source, nusage.replace(\n",
+    "                \"none\", usage_symbol)], axis=0, ignore_index=True)\n",
+    "\n",
+    "    usage_chart = get_usage_chart(usage_source, usage_symbol)\n",
+    "    st.altair_chart(usage_chart, use_container_width=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "f52b05f1-e9aa-40a1-9813-9a1c94f63d1e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "st.markdown(\"## 相关性分析\")\n",
+    "st.markdown(\"### 热力图\")\n",
+    "# pearson相关系数:  连续、正态分布、线性数据\n",
+    "# spearman相关系数: 非线性的、非正态数据\n",
+    "# Kendall相关系数:  分类变量、无序数据\n",
+    "fig, ax = plt.subplots()\n",
+    "corr_mode = st.radio(\n",
+    "    \"\"\"选择相关性分析方法:\n",
+    "    pearson: 连续、正态分布、线性数据;\n",
+    "    spearman: 非线性的、非正态数据;\n",
+    "    kendall: 分类变量、无序数据\"\"\",\n",
+    "    ('pearson', 'spearman', 'Kendall'))\n",
+    "\n",
+    "if corr_mode == 'spearman':\n",
+    "    metrics_correlation = data.corr(method=\"spearman\")\n",
+    "elif corr_mode == 'Kendall':\n",
+    "    metrics_correlation = data.corr(method=\"kendall\")\n",
+    "else:\n",
+    "    metrics_correlation = data.corr(method=\"pearson\")\n",
+    "\n",
+    "sns.heatmap(metrics_correlation, ax=ax,cmap=\"GnBu\")\n",
+    "st.write(fig)\n",
+    "\n",
+    "# fig = sns.pairplot(data)\n",
+    "# st.pyplot(fig)\n",
+    "\n",
+    "st.info(\"|r|>0.95存在显著性相关;|r|≥0.8高度相关;0.5≤|r|<0.8 中度相关;0.3≤|r|<0.5低度相关;|r|<0.3关系极弱\")\n",
+    "st.markdown(\"### 相关性指标排序\")\n",
+    "sorted_metrics_correlation = abs(metrics_correlation.iloc[-1]).sort_values(\n",
+    "    ascending=False).dropna(axis=0, how='any')\n",
+    "st.table(sorted_metrics_correlation)\n",
+    "\n",
+    "vaild_metrics = sorted_metrics_correlation[abs(\n",
+    "    metrics_correlation[\"qos\"]) > 0.3].index.tolist()\n",
+    "vaild_metrics.remove(\"qos\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "707e49f0-8c41-4b97-9083-e3c8466795e1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x432 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x432 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x432 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x432 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x432 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "st.markdown(\"#### 筛选有效指标\")\n",
+    "if len(vaild_metrics) == 0:\n",
+    "    st.markdown(\"        ** 指标相关性过低，无法分析** \")\n",
+    "\n",
+    "vaild_metrics = vaild_metrics[:5]\n",
+    "vaild_metrics_cols = st.columns(len(vaild_metrics))\n",
+    "\n",
+    "for col in range(len(vaild_metrics)):\n",
+    "    corr_value = \"{:.5}\".format(\n",
+    "        metrics_correlation.iloc[-1][vaild_metrics[col]])\n",
+    "    vaild_metrics_cols[col].metric(vaild_metrics[col], corr_value, col + 1)\n",
+    "\n",
+    "for i in range(len(vaild_metrics)):\n",
+    "    fig = sns.jointplot(x=vaild_metrics[i], y='qos', data=data, kind='reg')\n",
+    "    st.pyplot(fig)\n",
+    "\n",
+    "st.markdown(\"### 回归拟合分析\")\n",
+    "# 数据预处理: 去除无效值; 特性缩放:标准化; 模型训练\n",
+    "x = data[vaild_metrics]\n",
+    "y = data[[\"qos\"]]\n",
+    "x_train, x_test, y_train, y_test = model_selection.train_test_split(x, y, random_state=1)\n",
+    "\n",
+    "def draw_comparison_altair_chart(y_test, y_pred):\n",
+    "    y_test_list = y_test[\"qos\"]\n",
+    "    y_pred_list = y_pred if type(y_pred[0]) is not np.ndarray else [\n",
+    "        i[0] for i in y_pred]\n",
+    "    list_of_tuples = list(zip(y_test_list, y_pred_list))\n",
+    "    source = pd.DataFrame(list_of_tuples, columns=['Measured', 'Predicted'],\n",
+    "                          index=pd.RangeIndex(len(y_pred), name='index'))\n",
+    "    source = source.reset_index().melt('index', var_name='category', value_name='qos')\n",
+    "    nearest = alt.selection(type='single', nearest=True, on='mouseover',\n",
+    "                            fields=['index'], empty='none')\n",
+    "    line = alt.Chart(source).mark_line(interpolate='basis').encode(\n",
+    "        x='index:Q',\n",
+    "        y='qos:Q',\n",
+    "        color='category:N'\n",
+    "    )\n",
+    "\n",
+    "    selectors = alt.Chart(source).mark_point().encode(\n",
+    "        x='index:Q',\n",
+    "        opacity=alt.value(0),\n",
+    "    ).add_selection(\n",
+    "        nearest\n",
+    "    )\n",
+    "\n",
+    "    points = line.mark_point().encode(\n",
+    "        opacity=alt.condition(nearest, alt.value(1), alt.value(0))\n",
+    "    )\n",
+    "\n",
+    "    # Draw text labels near the points, and highlight based on selection\n",
+    "    text = line.mark_text(align='left', dx=5, dy=-5).encode(\n",
+    "        text=alt.condition(nearest, 'qos:Q', alt.value(' '))\n",
+    "    )\n",
+    "\n",
+    "    # Draw a rule at the location of the selection\n",
+    "    rules = alt.Chart(source).mark_rule(color='gray').encode(\n",
+    "        x='index:Q',\n",
+    "    ).transform_filter(\n",
+    "        nearest\n",
+    "    )\n",
+    "\n",
+    "    charts = alt.layer(\n",
+    "        line, selectors, points, rules, text\n",
+    "    ).interactive()\n",
+    "    st.altair_chart(charts, use_container_width=True)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "a8ffd883-a837-471e-a2c9-a4225bd5fbad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def draw_comparison_matplotlib_chart(y_test, y_pred):\n",
+    "    fig = plt.figure()\n",
+    "    plt.plot(np.arange(len(y_pred)),\n",
+    "             y_test[[\"qos\"]].values, 'go-', label='Measured')\n",
+    "    plt.plot(np.arange(len(y_pred)), y_pred, 'ro-', label='Predicted')\n",
+    "    plt.title(\"Interference Model Analysis\")\n",
+    "    plt.xlabel(\"Index\")\n",
+    "    plt.ylabel(\"QoS\")\n",
+    "    plt.legend()\n",
+    "    st.pyplot(fig)\n",
+    "\n",
+    "\n",
+    "class RegressionModel():\n",
+    "    \"\"\"regression model for metrics\"\"\"\n",
+    "\n",
+    "    def __init__(self, model) -> None:\n",
+    "        self.model = model\n",
+    "\n",
+    "        self.mse = 0\n",
+    "        self.rmse = 0\n",
+    "\n",
+    "    def train_and_test_model(self):\n",
+    "        self.model.fit(x_train, np.ravel(y_train))\n",
+    "\n",
+    "        y_pred = self.model.predict(x_test)\n",
+    "        # score = model.score(x_test, y_test)\n",
+    "        draw_comparison_altair_chart(y_test, y_pred)\n",
+    "\n",
+    "        self.mse = metrics.mean_squared_error(y_test, y_pred)\n",
+    "        self.rmse = np.sqrt(metrics.mean_squared_error(y_test, y_pred))\n",
+    "        model_evaluation = \"MSE: {}, RMSE: {}\".format(self.mse, self.rmse)\n",
+    "        st.write(model_evaluation)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "23d8fb12-3bea-44e2-8110-2f5082df9ec7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class PolynomialRegressionModel():\n",
+    "    \"\"\"polynomial regression model for metrics\"\"\"\n",
+    "\n",
+    "    def __init__(self, degree) -> None:\n",
+    "        self.model = linear_model.LinearRegression()\n",
+    "\n",
+    "        self.mse = 0\n",
+    "        self.rmse = 0\n",
+    "        self.degree = degree\n",
+    "\n",
+    "    def train_and_test_model(self):\n",
+    "        self.poly = preprocessing.PolynomialFeatures(degree = self.degree) \n",
+    "        X_poly = self.poly.fit_transform(x_train)\n",
+    "\n",
+    "        self.model.fit(X_poly, y_train)\n",
+    "\n",
+    "        y_pred = self.model.predict(self.poly.transform(x_test))\n",
+    "        # score = model.score(x_test, y_test)\n",
+    "        draw_comparison_altair_chart(y_test, y_pred)\n",
+    "\n",
+    "        self.mse = metrics.mean_squared_error(y_test, y_pred)\n",
+    "        self.rmse = np.sqrt(metrics.mean_squared_error(y_test, y_pred))\n",
+    "        model_evaluation = \"MSE: {}, RMSE: {}\".format(self.mse, self.rmse)\n",
+    "        st.write(model_evaluation)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "a8565c5f-8e08-4962-9ba3-6f583544c47f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "polynomial_regression_list = [\n",
+    "    {'name': '1st Degree Polynomial Regression', 'degree': 1},\n",
+    "    {'name': '2nd Degree Polynomial Regression', 'degree': 2},\n",
+    "    {'name': '3rd Degree Polynomial Regression', 'degree': 3},\n",
+    "    {'name': '4th Degree Polynomial Regression', 'degree': 4},\n",
+    "    {'name': '5th Degree Polynomial Regression', 'degree': 5},\n",
+    "]\n",
+    "\n",
+    "other_regression_list = [\n",
+    "    {'name': 'Decision Tree Regression', 'regressor': tree.DecisionTreeRegressor()},\n",
+    "    {'name': 'SVM Regression', 'regressor': svm.SVR()},\n",
+    "    {'name': 'KNN Regression', 'regressor': neighbors.KNeighborsRegressor()},\n",
+    "    {'name': 'Random Forest Regression', 'regressor': ensemble.RandomForestRegressor(n_estimators=20)},\n",
+    "    {'name': 'Adaboost Regression', 'regressor': ensemble.AdaBoostRegressor(n_estimators=50)},\n",
+    "    {'name': 'Gradient Boosting Regression', 'regressor': ensemble.GradientBoostingRegressor(n_estimators=100)},\n",
+    "    {'name': 'Bagging Regression', 'regressor': ensemble.BaggingRegressor()},\n",
+    "    {'name': 'ExtraTree Regression', 'regressor': tree.ExtraTreeRegressor()},\n",
+    "    {'name': 'ElasticNet','regressor':linear_model.ElasticNet()},\n",
+    "]\n",
+    "\n",
+    "mse_list = []\n",
+    "polynomial_list = []"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "90b9db7b-e3ab-4b86-a337-339e3ce56c06",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "f:\\anaconda3\\envs\\rubik\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:532: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.4740358851651643, tolerance: 0.009145689418493823\n",
+      "  positive)\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "for polynomial_regression in polynomial_regression_list:\n",
+    "    st.markdown(\"#### \" + polynomial_regression['name'])\n",
+    "    degree = polynomial_regression['degree']\n",
+    "    \n",
+    "    regression_model = PolynomialRegressionModel(degree)\n",
+    "    regression_model.train_and_test_model()\n",
+    "\n",
+    "    polynomial_list.append({'name': polynomial_regression['name'], 'rmse': regression_model.rmse, 'regression_model': regression_model})\n",
+    "    mse_list.append({'name': polynomial_regression['name'], 'mse': regression_model.mse, 'rmse': regression_model.rmse})\n",
+    "\n",
+    "for other_regression in other_regression_list:\n",
+    "    st.markdown(\"#### \" + other_regression['name'])\n",
+    "    regressor = other_regression['regressor']\n",
+    "    \n",
+    "    regression_model = RegressionModel(regressor)\n",
+    "    regression_model.train_and_test_model()\n",
+    "\n",
+    "    mse_list.append({'name': other_regression['name'], 'mse': regression_model.mse, 'rmse': regression_model.rmse})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "af3bc43d-e597-48d9-8363-e9f03c41738f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DeltaGenerator(_root_container=0, _provided_cursor=None, _parent=None, _block_type=None, _form_data=None)"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def takeMse(elem):\n",
+    "    return elem['rmse']\n",
+    "\n",
+    "mse_list.sort(key=takeMse)\n",
+    "st.table(pd.DataFrame(mse_list).set_index('name'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "e439d2f2-896e-4079-8427-242cfb057ba7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DeltaGenerator(_root_container=0, _provided_cursor=None, _parent=None, _block_type=None, _form_data=None)"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "st.markdown(\"聚类分析\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "03df3ec0-8661-47c5-b030-2bcc450d4a7b",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'PolynomialFeatures' object has no attribute 'get_feature_names_out'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-65-f5946633049a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m     \u001b[0mcoef\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpolynomial\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'regression_model'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcoef_\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 13\u001b[1;33m     \u001b[0mfeature_names\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpolynomial\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'regression_model'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpoly\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_feature_names_out\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvaild_metrics\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     14\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     15\u001b[0m     \u001b[0mpolynomial_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfeature_names\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'features'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mAttributeError\u001b[0m: 'PolynomialFeatures' object has no attribute 'get_feature_names_out'"
+     ]
+    }
+   ],
+   "source": [
+    "st.markdown(\"### 多项式回归拟合筛选\")\n",
+    "\n",
+    "polynomial = min(polynomial_list, key=takeMse)\n",
+    "\n",
+    "if polynomial['rmse'] > 15:\n",
+    "    st.write(\"指标相关性较弱，不适合进行多项式回归拟合\")\n",
+    "else:\n",
+    "    st.markdown(\"**\" + polynomial['name'] + \"**\")\n",
+    "    model_evaluation = \"MSE: {}, RMSE: {}\".format(polynomial['regression_model'].mse, polynomial['regression_model'].rmse)\n",
+    "    st.write(model_evaluation)\n",
+    "\n",
+    "    coef = polynomial['regression_model'].model.coef_\n",
+    "    feature_names = polynomial['regression_model'].poly.get_feature_names_out(vaild_metrics)\n",
+    "\n",
+    "    polynomial_df = pd.DataFrame(feature_names, columns = ['features'])\n",
+    "    polynomial_df['coef'] = np.ravel(coef)\n",
+    "    st.table(polynomial_df.style.format({'coef': \"{:.10f}\"}))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6c775243-a13e-428e-b5ff-60f66f05e424",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.6.13 ('rubik')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.13"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "6ca2f00ff8e4a90af02cda2236bfe1137f5db7cf8e475bba2dc7289ac4d86388"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/tools/interferenceanalysis/analysis.py b/tools/interferenceanalysis/analysis.py
new file mode 100644
index 0000000000000000000000000000000000000000..3174bc36e885d1e9c69f5520307f34832c1a8303
--- /dev/null
+++ b/tools/interferenceanalysis/analysis.py
@@ -0,0 +1,599 @@
+# %%
+from sklearn import ensemble, neighbors, svm, linear_model, model_selection, tree, metrics, preprocessing
+from sklearn.cluster import KMeans
+from itertools import product
+import streamlit as st
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+import altair as alt
+
+
+# %%
+def get_chart(data):
+    hover = alt.selection_single(
+        fields=["timestamp"],
+        nearest=True,
+        on="mouseover",
+        empty="none",
+    )
+
+    lines = (
+        alt.Chart(data.reset_index(drop=True).melt("timestamp"),
+                  title="Metrics variation")
+        .mark_line()
+        .encode(
+            x="timestamp:T",
+            y="value:Q",
+            color="variable",
+            # strokeDash="variable",
+        ).properties(width=800, height=400)
+    )
+
+    # Draw points on the line, and highlight based on selection
+    points = lines.transform_filter(hover).mark_circle(size=65)
+
+    # Draw a rule at the location of the selection
+    tooltips = (
+        alt.Chart(data.reset_index(drop=True).melt("timestamp"),
+                  title="Metrics variation")
+        .mark_rule()
+        .encode(
+            x="timestamp:T",
+            y="value:Q",
+            opacity=alt.condition(hover, alt.value(0.3), alt.value(0)),
+            tooltip=[
+                alt.Tooltip("timestamp", title="Time"),
+                alt.Tooltip("value", title="Metrics"),
+            ],
+        )
+        .add_selection(hover)
+    )
+
+    return (lines + points + tooltips).interactive()
+
+# %%
+def get_stress_chart(data, symbol):
+    hover = alt.selection_single(
+        fields=["stress"],
+        nearest=True,
+        on="mouseover",
+        empty="none",
+    )
+
+    lines = (
+        alt.Chart(data, title="Qos variation with stress " + symbol)
+        .mark_line()
+        .encode(
+            x=alt.X("stress:Q", axis=alt.Axis(orient="top")),
+            y=alt.Y("degradation-percent:Q", sort='descending',
+                    title="degradation percent(%)"),
+            color="type",
+            strokeDash="type",
+        )
+    )
+
+    # Draw points on the line, and highlight based on selection
+    points = lines.transform_filter(hover).mark_circle(size=65)
+
+    # Draw a rule at the location of the selection
+    tooltips = (
+        alt.Chart(data)
+        .mark_rule()
+        .encode(
+            x="stress:Q",
+            y="degradation-percent:Q",
+            opacity=alt.condition(hover, alt.value(0.3), alt.value(0)),
+            tooltip=[
+                alt.Tooltip("stress", title="Stress Info"),
+                alt.Tooltip("avg-qos", title="Qos"),
+                alt.Tooltip("degradation-percent", title="Degradation Percent")
+            ],
+        )
+        .add_selection(hover)
+    )
+
+    return (lines + points + tooltips).interactive()
+
+# %%
+def stress_sensitivity(stress_degrade):
+    if stress_degrade['degradation-percent'] <= 5:
+        return "no"
+
+    if stress_degrade['degradation-percent'] <= 10:
+        return "low"
+
+    if stress_degrade['degradation-percent'] <= 20:
+        return "medium"
+
+    return "high"
+
+# %%
+def get_usage_chart(data, symbol):
+    hover = alt.selection_single(
+        fields=["stress"],
+        nearest=True,
+        on="mouseover",
+        empty="none",
+    )
+
+    lines = (
+        alt.Chart(data, title="CPU usage variation with stress " + symbol)
+        .mark_line()
+        .encode(
+            x=alt.X("stress:Q", axis=alt.Axis(orient="top")),
+            y=alt.Y("avg-cpu-usage:Q", sort='descending',
+                    title="cpu usage percent(%)"),
+            color="type",
+            strokeDash="type",
+        )
+    )
+
+    # Draw points on the line, and highlight based on selection
+    points = lines.transform_filter(hover).mark_circle(size=65)
+
+    # Draw a rule at the location of the selection
+    tooltips = (
+        alt.Chart(data)
+        .mark_rule()
+        .encode(
+            x="stress:Q",
+            y="avg-cpu-usage:Q",
+            opacity=alt.condition(hover, alt.value(0.3), alt.value(0)),
+            tooltip=[
+                alt.Tooltip("stress", title="Stress Info"),
+                alt.Tooltip("avg-cpu-usage", title="CPU Usage Percent")
+            ],
+        )
+        .add_selection(hover)
+    )
+
+    return (lines + points + tooltips).interactive()
+
+# %%
+def normalize_table(df):
+    cols = list(df)
+    for item in cols:
+        if df[item].dtype == 'int64' or df[item].dtype == 'float64':
+            max_tmp = np.max(np.array(df[item]))
+            min_tmp = np.min(np.array(df[item]))
+            if (max_tmp != min_tmp):
+                df[item] = df[item].apply(
+                    lambda x: (x - min_tmp) * 100 / (max_tmp - min_tmp))
+
+# %%
+
+def standardize_table(df):
+    cols = list(df)
+    for item in cols:
+        if df[item].dtype == 'int64' or df[item].dtype == 'float64':
+            mean_tmp = np.mean(np.array(df[item]))
+            std_tmp = np.std(np.array(df[item]))
+            if(std_tmp):
+                df[item] = df[item].apply(lambda x: (x - mean_tmp) / std_tmp)
+
+# %%
+
+
+# %%
+st.set_page_config(layout="centered", page_icon="⎈",
+                   page_title="rubik analysis")
+
+st.markdown("# ⎈ 混部干扰建模分析工具")
+st.markdown("## 业务介绍 TODO")
+st.markdown("## 环境说明")
+
+node_info = pd.read_csv("../tests/data/default/node.csv", index_col="Item")
+uploaded_node_file = st.file_uploader("上传测试环境配置", type="csv")
+if uploaded_node_file is not None:
+    node_info = pd.read_csv(uploaded_node_file, index_col="Item")
+
+st.table(node_info)
+
+
+
+
+# %%
+st.markdown("## 指标数据")
+data = pd.read_csv("../tests/data/default/nginx.csv")
+data_select = st.radio(
+    """选择分析的数据集
+    """,
+    ('l3cache_stress.csv', 'nginx.csv'))
+if data_select == 'l3cache_stress.csv':
+    data = pd.read_csv("../tests/data/clickhouse/l3cache_stress.csv")
+else:
+    data=pd.read_csv("../tests/data/default/nginx.csv")
+mode = st.radio(
+    "Please select a mode to visualize the data:",
+    ('origin', 'normalization', 'standardization'))
+
+if mode == 'normalization':
+    normalize_table(data)
+elif mode == 'standardization':
+    standardize_table(data)
+# data = pd.read_csv("../tests/data/default/nginx.csv")
+# data = pd.read_csv("../tests/data/clickhouse/l3cache_stress.csv")
+
+# %%
+uploaded_metrics_file = st.file_uploader("上传指标数据-QoS数据", type="csv")
+if uploaded_metrics_file is not None:
+    data = pd.read_csv(uploaded_metrics_file)
+
+
+
+all_symbols = list(data.columns[1:])
+symbols = st.multiselect("Choose metrics to visualize",
+                         all_symbols, all_symbols[:3])
+symbols.insert(0, data.columns[0])
+
+source = data[symbols]
+
+chart = get_chart(source)
+st.altair_chart(chart, use_container_width=True)
+
+# %%
+st.markdown("## 资源敏感度分析")
+
+# type stress avg-qos degradation-percent
+stress = pd.read_csv("../tests/data/default/stress.csv", keep_default_na=False)
+uploaded_stress_file = st.file_uploader("上传压力测试指标数据", type="csv")
+if uploaded_stress_file is not None:
+    stress = pd.read_csv(uploaded_stress_file)
+
+symbol_transform = {
+    "icache": "cache",
+    "L1 cache": "cache",
+    "L2 cache": "cache",
+    "L3 cache": "cache",
+
+    "network in": "network",
+    "network out": "network",
+}
+def get_key(dict, value):
+    return [k for k,v in dict.items() if v == value]
+
+stress_unique_symbols = stress.type.unique()
+stress_symbols = []
+
+for symbol in stress_unique_symbols:
+    if symbol in symbol_transform:
+        symbol = symbol_transform[symbol]
+
+    if symbol == 'none':
+        continue
+
+    if symbol not in stress_symbols:
+        stress_symbols.append(symbol)
+
+stress_symbols = st.multiselect("Choose resource symbols",
+                                stress_symbols, stress_symbols)
+
+for stress_symbol in stress_symbols:
+    # 插入无压力数据
+    nstress = stress[stress.type == "none"]
+
+    if stress_symbol in symbol_transform.values():
+        keys = get_key(symbol_transform, stress_symbol)
+        stress_source = stress[stress['type'].isin(keys)]
+        for key in keys:
+            stress_source = pd.concat([stress_source, nstress.replace(
+                "none", key)], axis=0, ignore_index=True)
+    else:
+        stress_source = stress[stress['type'] == stress_symbol]
+        stress_source = pd.concat([stress_source, nstress.replace(
+                "none", stress_symbol)], axis=0, ignore_index=True)
+
+    stress_chart = get_stress_chart(stress_source, stress_symbol)
+    st.altair_chart(stress_chart, use_container_width=True)
+
+# %%
+st.markdown("#### 资源敏感度排序")
+
+stress_degrade = (
+    stress.drop(stress[stress['type'] == "none"].index)[
+        ['type', 'degradation-percent']]
+    .groupby(by='type')
+    .max()
+    .sort_values(by='degradation-percent', ascending=False)
+)
+
+stress_degrade.loc[:, 'sensitivity'] = stress_degrade.apply(
+    stress_sensitivity, axis=1)
+st.table(stress_degrade)
+
+st.info(
+    "degradation-percent in (, 5]:no ; (5, 10]:low ; (10, 20]:medinum ; (20,):high")
+
+# %%
+st.markdown("## 资源利用率")
+
+# type stress avg-qos degradation-percent
+usage = pd.read_csv("../tests/data/default/machine.csv", keep_default_na=False)
+uploaded_usage_file = st.file_uploader("上传利用率数据", type="csv")
+if uploaded_usage_file is not None:
+    usage = pd.read_csv(uploaded_usage_file)
+
+usage_unique_symbols = usage.type.unique()
+usage_symbols = []
+
+for symbol in usage_unique_symbols:
+    if symbol in symbol_transform:
+        symbol = symbol_transform[symbol]
+
+    if symbol == 'none':
+        continue
+
+    if symbol not in usage_symbols:
+        usage_symbols.append(symbol)
+
+usage_symbols = st.multiselect("Choose resource symbols to display",
+                                usage_symbols, usage_symbols)
+
+for usage_symbol in usage_symbols:
+    # 插入无压力数据
+    nusage = usage[usage['type'] == "none"]
+
+    if usage_symbol in symbol_transform.values():
+        keys = get_key(symbol_transform, usage_symbol)
+        usage_source = usage[usage['type'].isin(keys)]
+        for key in keys:
+            usage_source = pd.concat([usage_source, nusage.replace(
+                "none", key)], axis=0, ignore_index=True)
+    else:
+        usage_source = usage[usage['type'] == usage_symbol]
+        usage_source = pd.concat([usage_source, nusage.replace(
+                "none", usage_symbol)], axis=0, ignore_index=True)
+
+    usage_chart = get_usage_chart(usage_source, usage_symbol)
+    st.altair_chart(usage_chart, use_container_width=True)
+
+# %%
+st.markdown("## 相关性分析")
+st.markdown("### 热力图")
+# pearson相关系数:  连续、正态分布、线性数据
+# spearman相关系数: 非线性的、非正态数据
+# Kendall相关系数:  分类变量、无序数据
+fig, ax = plt.subplots()
+corr_mode = st.radio(
+    """选择相关性分析方法:
+    pearson: 连续、正态分布、线性数据;
+    spearman: 非线性的、非正态数据;
+    kendall: 分类变量、无序数据""",
+    ('pearson', 'spearman', 'Kendall'))
+
+if corr_mode == 'spearman':
+    metrics_correlation = data.corr(method="spearman")
+elif corr_mode == 'Kendall':
+    metrics_correlation = data.corr(method="kendall")
+else:
+    metrics_correlation = data.corr(method="pearson")
+
+sns.heatmap(metrics_correlation, ax=ax,cmap="GnBu")
+st.write(fig)
+
+# fig = sns.pairplot(data)
+# st.pyplot(fig)
+
+st.info("|r|>0.95存在显著性相关;|r|≥0.8高度相关;0.5≤|r|<0.8 中度相关;0.3≤|r|<0.5低度相关;|r|<0.3关系极弱")
+st.markdown("### 相关性指标排序")
+sorted_metrics_correlation = abs(metrics_correlation.iloc[-1]).sort_values(
+    ascending=False).dropna(axis=0, how='any')
+st.table(sorted_metrics_correlation)
+
+vaild_metrics = sorted_metrics_correlation[abs(
+    metrics_correlation["qos"]) > 0.3].index.tolist()
+vaild_metrics.remove("qos")
+
+
+# %%
+st.markdown("#### 筛选有效指标")
+if len(vaild_metrics) == 0:
+    st.markdown("        ** 指标相关性过低，无法分析** ")
+
+vaild_metrics = vaild_metrics[:5]
+vaild_metrics_cols = st.columns(len(vaild_metrics))
+
+for col in range(len(vaild_metrics)):
+    corr_value = "{:.5}".format(
+        metrics_correlation.iloc[-1][vaild_metrics[col]])
+    vaild_metrics_cols[col].metric(vaild_metrics[col], corr_value, col + 1)
+
+for i in range(len(vaild_metrics)):
+    fig = sns.jointplot(x=vaild_metrics[i], y='qos', data=data, kind='reg')
+    st.pyplot(fig)
+
+st.markdown("### 回归拟合分析")
+# 数据预处理: 去除无效值; 特性缩放:标准化; 模型训练
+x = data[vaild_metrics]
+y = data[["qos"]]
+x_train, x_test, y_train, y_test = model_selection.train_test_split(x, y, random_state=1)
+
+def draw_comparison_altair_chart(y_test, y_pred):
+    y_test_list = y_test["qos"]
+    y_pred_list = y_pred if type(y_pred[0]) is not np.ndarray else [
+        i[0] for i in y_pred]
+    list_of_tuples = list(zip(y_test_list, y_pred_list))
+    source = pd.DataFrame(list_of_tuples, columns=['Measured', 'Predicted'],
+                          index=pd.RangeIndex(len(y_pred), name='index'))
+    source = source.reset_index().melt('index', var_name='category', value_name='qos')
+    nearest = alt.selection(type='single', nearest=True, on='mouseover',
+                            fields=['index'], empty='none')
+    line = alt.Chart(source).mark_line(interpolate='basis').encode(
+        x='index:Q',
+        y='qos:Q',
+        color='category:N'
+    )
+
+    selectors = alt.Chart(source).mark_point().encode(
+        x='index:Q',
+        opacity=alt.value(0),
+    ).add_selection(
+        nearest
+    )
+
+    points = line.mark_point().encode(
+        opacity=alt.condition(nearest, alt.value(1), alt.value(0))
+    )
+
+    # Draw text labels near the points, and highlight based on selection
+    text = line.mark_text(align='left', dx=5, dy=-5).encode(
+        text=alt.condition(nearest, 'qos:Q', alt.value(' '))
+    )
+
+    # Draw a rule at the location of the selection
+    rules = alt.Chart(source).mark_rule(color='gray').encode(
+        x='index:Q',
+    ).transform_filter(
+        nearest
+    )
+
+    charts = alt.layer(
+        line, selectors, points, rules, text
+    ).interactive()
+    st.altair_chart(charts, use_container_width=True)
+
+
+
+# %%
+def draw_comparison_matplotlib_chart(y_test, y_pred):
+    fig = plt.figure()
+    plt.plot(np.arange(len(y_pred)),
+             y_test[["qos"]].values, 'go-', label='Measured')
+    plt.plot(np.arange(len(y_pred)), y_pred, 'ro-', label='Predicted')
+    plt.title("Interference Model Analysis")
+    plt.xlabel("Index")
+    plt.ylabel("QoS")
+    plt.legend()
+    st.pyplot(fig)
+
+
+class RegressionModel():
+    """regression model for metrics"""
+
+    def __init__(self, model) -> None:
+        self.model = model
+
+        self.mse = 0
+        self.rmse = 0
+
+    def train_and_test_model(self):
+        self.model.fit(x_train, np.ravel(y_train))
+
+        y_pred = self.model.predict(x_test)
+        # score = model.score(x_test, y_test)
+        draw_comparison_altair_chart(y_test, y_pred)
+
+        self.mse = metrics.mean_squared_error(y_test, y_pred)
+        self.rmse = np.sqrt(metrics.mean_squared_error(y_test, y_pred))
+        model_evaluation = "MSE: {}, RMSE: {}".format(self.mse, self.rmse)
+        st.write(model_evaluation)
+
+
+# %%
+class PolynomialRegressionModel():
+    """polynomial regression model for metrics"""
+
+    def __init__(self, degree) -> None:
+        self.model = linear_model.LinearRegression()
+
+        self.mse = 0
+        self.rmse = 0
+        self.degree = degree
+
+    def train_and_test_model(self):
+        self.poly = preprocessing.PolynomialFeatures(degree = self.degree) 
+        X_poly = self.poly.fit_transform(x_train)
+
+        self.model.fit(X_poly, y_train)
+
+        y_pred = self.model.predict(self.poly.transform(x_test))
+        # score = model.score(x_test, y_test)
+        draw_comparison_altair_chart(y_test, y_pred)
+
+        self.mse = metrics.mean_squared_error(y_test, y_pred)
+        self.rmse = np.sqrt(metrics.mean_squared_error(y_test, y_pred))
+        model_evaluation = "MSE: {}, RMSE: {}".format(self.mse, self.rmse)
+        st.write(model_evaluation)
+
+
+# %%
+polynomial_regression_list = [
+    {'name': '1st Degree Polynomial Regression', 'degree': 1},
+    {'name': '2nd Degree Polynomial Regression', 'degree': 2},
+    {'name': '3rd Degree Polynomial Regression', 'degree': 3},
+    {'name': '4th Degree Polynomial Regression', 'degree': 4},
+    {'name': '5th Degree Polynomial Regression', 'degree': 5},
+]
+
+other_regression_list = [
+    {'name': 'Decision Tree Regression', 'regressor': tree.DecisionTreeRegressor()},
+    {'name': 'SVM Regression', 'regressor': svm.SVR()},
+    {'name': 'KNN Regression', 'regressor': neighbors.KNeighborsRegressor()},
+    {'name': 'Random Forest Regression', 'regressor': ensemble.RandomForestRegressor(n_estimators=20)},
+    {'name': 'Adaboost Regression', 'regressor': ensemble.AdaBoostRegressor(n_estimators=50)},
+    {'name': 'Gradient Boosting Regression', 'regressor': ensemble.GradientBoostingRegressor(n_estimators=100)},
+    {'name': 'Bagging Regression', 'regressor': ensemble.BaggingRegressor()},
+    {'name': 'ExtraTree Regression', 'regressor': tree.ExtraTreeRegressor()},
+    {'name': 'ElasticNet','regressor':linear_model.ElasticNet()},
+]
+
+mse_list = []
+polynomial_list = []
+
+# %%
+
+for polynomial_regression in polynomial_regression_list:
+    st.markdown("#### " + polynomial_regression['name'])
+    degree = polynomial_regression['degree']
+    
+    regression_model = PolynomialRegressionModel(degree)
+    regression_model.train_and_test_model()
+
+    polynomial_list.append({'name': polynomial_regression['name'], 'rmse': regression_model.rmse, 'regression_model': regression_model})
+    mse_list.append({'name': polynomial_regression['name'], 'mse': regression_model.mse, 'rmse': regression_model.rmse})
+
+for other_regression in other_regression_list:
+    st.markdown("#### " + other_regression['name'])
+    regressor = other_regression['regressor']
+    
+    regression_model = RegressionModel(regressor)
+    regression_model.train_and_test_model()
+
+    mse_list.append({'name': other_regression['name'], 'mse': regression_model.mse, 'rmse': regression_model.rmse})
+
+# %%
+def takeMse(elem):
+    return elem['rmse']
+
+mse_list.sort(key=takeMse)
+st.table(pd.DataFrame(mse_list).set_index('name'))
+
+# %%
+st.markdown("聚类分析")
+
+
+# %%
+st.markdown("### 多项式回归拟合筛选")
+
+polynomial = min(polynomial_list, key=takeMse)
+
+if polynomial['rmse'] > 15:
+    st.write("指标相关性较弱，不适合进行多项式回归拟合")
+else:
+    st.markdown("**" + polynomial['name'] + "**")
+    model_evaluation = "MSE: {}, RMSE: {}".format(polynomial['regression_model'].mse, polynomial['regression_model'].rmse)
+    st.write(model_evaluation)
+
+    coef = polynomial['regression_model'].model.coef_
+    feature_names = polynomial['regression_model'].poly.get_feature_names_out(vaild_metrics)
+
+    polynomial_df = pd.DataFrame(feature_names, columns = ['features'])
+    polynomial_df['coef'] = np.ravel(coef)
+    st.table(polynomial_df.style.format({'coef': "{:.10f}"}))
+
+# %%
+
+
+
diff --git a/tools/interferenceanalysis/ayin.ipynb b/tools/interferenceanalysis/ayin.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..b24132f4f70209e32dada0f335ea23c099cc5249
--- /dev/null
+++ b/tools/interferenceanalysis/ayin.ipynb
@@ -0,0 +1,5007 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "id": "efa6013d-ed9f-4ee9-a43e-f279f5b67589",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn import ensemble, neighbors, svm, linear_model, model_selection, tree, metrics, preprocessing \n",
+    "from sklearn.cluster import KMeans\n",
+    "from sklearn.decomposition import PCA\n",
+    "from itertools import product\n",
+    "import streamlit as st\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import altair as alt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "id": "91954f6e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def normalize_table(df):\n",
+    "    cols = list(df)\n",
+    "    for item in cols:\n",
+    "        if df[item].dtype == 'int64' or df[item].dtype == 'float64':\n",
+    "            max_tmp = np.max(np.array(df[item]))\n",
+    "            min_tmp = np.min(np.array(df[item]))\n",
+    "            if (max_tmp != min_tmp):\n",
+    "                df[item] = df[item].apply(\n",
+    "                    lambda x: (x - min_tmp) * 100 / (max_tmp - min_tmp))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "id": "5454ce5b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def standardize_table(df):\n",
+    "    cols = list(df)\n",
+    "    for item in cols:\n",
+    "        if df[item].dtype == 'int64' or df[item].dtype == 'float64':\n",
+    "            mean_tmp = np.mean(np.array(df[item]))\n",
+    "            std_tmp = np.std(np.array(df[item]))\n",
+    "            if(std_tmp):\n",
+    "                df[item] = df[item].apply(lambda x: (x - mean_tmp) / std_tmp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "id": "721c0391",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv(\"../tests/data/default/nginx.csv\")\n",
+    "data_select = st.radio(\n",
+    "    \"\"\"选择分析的数据集\n",
+    "    \"\"\",\n",
+    "    ('l3cache_stress.csv', 'nginx.csv'))\n",
+    "if data_select == 'l3cache_stress.csv':\n",
+    "    data = pd.read_csv(\"../tests/data/clickhouse/l3cache_stress.csv\")\n",
+    "else:\n",
+    "    data=pd.read_csv(\"../tests/data/default/nginx.csv\")\n",
+    "    data=data.drop(columns=['net','disk_io'])\n",
+    "mode = st.radio(\n",
+    "    \"Please select a mode to visualize the data:\",\n",
+    "    ('origin', 'normalization', 'standardization'))\n",
+    "standardize_table(data)\n",
+    "if mode == 'normalization':\n",
+    "    normalize_table(data)\n",
+    "elif mode == 'standardization':\n",
+    "    standardize_table(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "id": "8f0c398f-e42d-4f03-838b-9c05139f5ec6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "               timestamp  context-switches  branch-misses  branch-load-misses  \\\n",
+      "0    2022-03-09 08:29:01         -0.958754       1.678169            1.672848   \n",
+      "1    2022-03-09 08:29:11         -0.958754       1.511395            1.512181   \n",
+      "2    2022-03-09 08:29:21         -0.690142       1.742679            1.744449   \n",
+      "3    2022-03-09 08:29:31         -0.853227       1.534155            1.532438   \n",
+      "4    2022-03-09 08:29:41         -0.546243       1.611962            1.614179   \n",
+      "..                   ...               ...            ...                 ...   \n",
+      "613  2022-03-09 10:11:12         -1.198585       1.623724            1.624160   \n",
+      "614  2022-03-09 10:11:22         -0.191291       1.678942            1.678754   \n",
+      "615  2022-03-09 10:11:32         -1.083466       1.671203            1.671186   \n",
+      "616  2022-03-09 10:11:42         -1.083466       1.710989            1.712464   \n",
+      "617  2022-03-09 10:11:52         -1.006720       1.504206            1.503226   \n",
+      "\n",
+      "          ipc  cpu-migrations  page-faults  cache-references  cache-misses  \\\n",
+      "0    1.776784       -2.195219    -0.395324          1.533275     -0.894338   \n",
+      "1    1.539704       -1.430173     0.194767          1.500314     -0.943180   \n",
+      "2    1.742916       -1.976635    -0.529827          1.592775     -0.579728   \n",
+      "3    1.675178       -2.195219    -0.174872          1.449719     -1.002074   \n",
+      "4    1.709047       -1.867342     0.559316          1.426509     -1.399482   \n",
+      "..        ...             ...          ...               ...           ...   \n",
+      "613  1.709047       -1.703404    -0.034887          1.475866     -1.144868   \n",
+      "614  1.505836       -1.248019    -0.044871          1.537170     -0.936170   \n",
+      "615  1.742916       -1.648758    -0.112613          1.556911     -0.969596   \n",
+      "616  1.776784       -1.557681    -0.311528          1.506303     -0.954243   \n",
+      "617  1.641310       -0.901927    -0.197583          1.421282     -1.259268   \n",
+      "\n",
+      "     LLC-loads  ...  L1-dcache-load-misses  L1-icache-load-misses  \\\n",
+      "0     1.036473  ...               1.477641               1.280942   \n",
+      "1     1.645689  ...               1.599412               1.484699   \n",
+      "2     1.059861  ...               1.706323               1.316874   \n",
+      "3     0.887990  ...               1.754071               1.386451   \n",
+      "4     0.935727  ...               1.687991               1.441464   \n",
+      "..         ...  ...                    ...                    ...   \n",
+      "613   1.298913  ...               1.710660               1.459570   \n",
+      "614   1.369489  ...               1.597855               1.420989   \n",
+      "615   1.736433  ...               1.473776               1.454899   \n",
+      "616   0.944401  ...               1.706686               1.480970   \n",
+      "617   1.068296  ...               1.664530               1.345047   \n",
+      "\n",
+      "     dTLB-load-misses  dTLB-loads  dTLB-store-misses  dTLB-stores  \\\n",
+      "0           -2.354099    1.651359          -0.994910     1.721240   \n",
+      "1           -1.773260    1.623681          -0.267481     1.724594   \n",
+      "2           -2.283661    1.627589          -1.192339     1.779384   \n",
+      "3           -2.073597    1.640820          -1.363981     1.764179   \n",
+      "4           -2.066051    1.600622          -0.063231     1.750537   \n",
+      "..                ...         ...                ...          ...   \n",
+      "613         -1.547976    1.654494          -0.662023     1.612335   \n",
+      "614         -1.822658    1.619532          -1.101018     1.627973   \n",
+      "615         -1.867930    1.626287          -1.244669     1.400917   \n",
+      "616         -1.520209    1.644043           0.225670     1.700221   \n",
+      "617         -1.803269    1.554781           0.047887     1.752033   \n",
+      "\n",
+      "     iTLB-load-misses  node-load-misses  node-loads       qos  \n",
+      "0           -1.296187                 0    0.444005 -1.586636  \n",
+      "1           -1.615409                 0    0.315715 -1.579298  \n",
+      "2            0.655714                 0    0.903832 -1.611526  \n",
+      "3           -0.889908                 0    0.596964 -1.608604  \n",
+      "4           -1.225879                 0    0.553512 -1.584439  \n",
+      "..                ...               ...         ...       ...  \n",
+      "613         -1.059484                 0    0.768697 -1.597620  \n",
+      "614         -1.159619                 0    0.152255 -1.572708  \n",
+      "615         -1.263157                 0   -0.065304 -1.511944  \n",
+      "616         -1.325235                 0    0.369452 -1.607857  \n",
+      "617         -1.133884                 0    0.660692 -1.584439  \n",
+      "\n",
+      "[618 rows x 24 columns]\n",
+      "[array([[ 6.37994048],\n",
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+      "        -3.05999542e-01,  3.50978137e-02,  1.20211817e-02],\n",
+      "       [ 6.26194760e+00, -8.43913327e-01,  1.16510069e-01, ...,\n",
+      "         5.96673549e-01, -5.21460536e-01, -1.37907821e-01],\n",
+      "       ...,\n",
+      "       [ 6.21005700e+00, -1.59510575e+00,  3.71981410e-01, ...,\n",
+      "        -7.80415307e-01, -5.05234887e-01, -4.80566575e-02],\n",
+      "       [ 6.11622032e+00, -1.28121302e+00, -4.78746683e-01, ...,\n",
+      "        -3.87196628e-03, -1.49347073e-02,  9.14436273e-02],\n",
+      "       [ 6.01045594e+00, -1.41061897e+00, -2.02544599e-01, ...,\n",
+      "         2.53147742e-01,  2.05231971e-01, -6.73414415e-01]])]\n",
+      "<class 'list'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(data)\n",
+    "cluster_max=8\n",
+    "pca_predict=[]\n",
+    "for i in range(1,cluster_max):\n",
+    "    # kms=KMeans(n_clusters=i)\n",
+    "    # kms.fit(data.drop(columns='timestamp'))\n",
+    "    # data_predict.append(kms.predict(data.drop(columns='timestamp')))\n",
+    "    data_drop=data.drop(columns=['timestamp','qos'])\n",
+    "    pca=PCA(n_components=i)\n",
+    "    pca.fit(data_drop)\n",
+    "    pca_predict.append(pca.transform(data_drop))\n",
+    "    print(pca_predict)\n",
+    "    print(type(pca_predict))\n",
+    "    # print(data_predict)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0520d481-5ddd-4d3d-a0fa-15d4ba150e19",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "id": "92ffd114-5b6a-407b-b16b-dac6d63e66ee",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plt_data(i):\n",
+    "    plt.xlabel('x label')\n",
+    "    plt.title('pca decomposition data')\n",
+    "    rev_data=pca_predict[i].T\n",
+    "    for i in range(len(rev_data)):\n",
+    "        plt.plot(rev_data[i])\n",
+    "    plt.show()\n",
+    "for i in range(1,cluster_max):\n",
+    "    plt_data(i-1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "id": "14fadd67",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class PolynomialRegressionModel():\n",
+    "    \"\"\"polynomial regression model for metrics\"\"\"\n",
+    "\n",
+    "    def __init__(self, degree,i) -> None:\n",
+    "        self.model = linear_model.LinearRegression()\n",
+    "        self.x = pca_predict[i-1]\n",
+    "        self.y = data[[\"qos\"]]\n",
+    "        self.x_train, self.x_test, self.y_train, self.y_test = model_selection.train_test_split(self.x, self.y, random_state=1)\n",
+    "        self.mse = 0\n",
+    "        self.rmse = 0\n",
+    "        self.degree = degree\n",
+    "\n",
+    "    def train_and_test_model(self):\n",
+    "        self.poly = preprocessing.PolynomialFeatures(degree = self.degree) \n",
+    "        X_poly = self.poly.fit_transform(self.x_train)\n",
+    "\n",
+    "        self.model.fit(X_poly, self.y_train)\n",
+    "\n",
+    "        y_pred = self.model.predict(self.poly.transform(self.x_test))\n",
+    "        # score = model.score(x_test, y_test)\n",
+    "        # draw_comparison_altair_chart(y_test, y_pred)\n",
+    "\n",
+    "        self.mse = metrics.mean_squared_error(self.y_test, y_pred)\n",
+    "        self.rmse = np.sqrt(metrics.mean_squared_error(self.y_test, y_pred))\n",
+    "        # model_evaluation = \"MSE: {}, RMSE: {}\".format(self.mse, self.rmse)\n",
+    "        # st.write(model_evaluation)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "id": "2c8a3dc4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "st.markdown(\"### 回归拟合分析\")\n",
+    "# 数据预处理: 去除无效值; 特性缩放:标准化; 模型训练\n",
+    "\n",
+    "def draw_comparison_altair_chart(y_test, y_pred):\n",
+    "    y_test_list = y_test[\"qos\"]\n",
+    "    y_pred_list = y_pred if type(y_pred[0]) is not np.ndarray else [\n",
+    "        i[0] for i in y_pred]\n",
+    "    list_of_tuples = list(zip(y_test_list, y_pred_list))\n",
+    "    source = pd.DataFrame(list_of_tuples, columns=['Measured', 'Predicted'],\n",
+    "                          index=pd.RangeIndex(len(y_pred), name='index'))\n",
+    "    source = source.reset_index().melt('index', var_name='category', value_name='qos')\n",
+    "    nearest = alt.selection(type='single', nearest=True, on='mouseover',\n",
+    "                            fields=['index'], empty='none')\n",
+    "    line = alt.Chart(source).mark_line(interpolate='basis').encode(\n",
+    "        x='index:Q',\n",
+    "        y='qos:Q',\n",
+    "        color='category:N'\n",
+    "    )\n",
+    "\n",
+    "    selectors = alt.Chart(source).mark_point().encode(\n",
+    "        x='index:Q',\n",
+    "        opacity=alt.value(0),\n",
+    "    ).add_selection(\n",
+    "        nearest\n",
+    "    )\n",
+    "\n",
+    "    points = line.mark_point().encode(\n",
+    "        opacity=alt.condition(nearest, alt.value(1), alt.value(0))\n",
+    "    )\n",
+    "\n",
+    "    # Draw text labels near the points, and highlight based on selection\n",
+    "    text = line.mark_text(align='left', dx=5, dy=-5).encode(\n",
+    "        text=alt.condition(nearest, 'qos:Q', alt.value(' '))\n",
+    "    )\n",
+    "\n",
+    "    # Draw a rule at the location of the selection\n",
+    "    rules = alt.Chart(source).mark_rule(color='gray').encode(\n",
+    "        x='index:Q',\n",
+    "    ).transform_filter(\n",
+    "        nearest\n",
+    "    )\n",
+    "\n",
+    "    charts = alt.layer(\n",
+    "        line, selectors, points, rules, text\n",
+    "    ).interactive()\n",
+    "    st.altair_chart(charts, use_container_width=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "id": "d44ecaac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "class RegressionModel():\n",
+    "    \"\"\"regression model for metrics\"\"\"\n",
+    "\n",
+    "    def __init__(self, model,i) -> None:\n",
+    "        self.model = model\n",
+    "        self.mse = 0\n",
+    "        self.rmse = 0\n",
+    "        self.x = pca_predict[i-1]\n",
+    "        self.y = data[[\"qos\"]]\n",
+    "        self.x_train, self.x_test, self.y_train, self.y_test = model_selection.train_test_split(self.x, self.y, random_state=1)\n",
+    "    def train_and_test_model(self):\n",
+    "        self.model.fit(self.x_train, np.ravel(self.y_train))\n",
+    "\n",
+    "        y_pred = self.model.predict(self.x_test)\n",
+    "        # score = model.score(x_test, y_test)\n",
+    "        # draw_comparison_altair_chart(y_test, y_pred)\n",
+    "\n",
+    "        self.mse = metrics.mean_squared_error(self.y_test, y_pred)\n",
+    "        self.rmse = np.sqrt(metrics.mean_squared_error(self.y_test, y_pred))\n",
+    "        # model_evaluation = \"MSE: {}, RMSE: {}\".format(self.mse, self.rmse)\n",
+    "        # st.write(model_evaluation)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "id": "f8261baf-7918-49fb-92a9-55334c1b397c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "polynomial_regression_list = [\n",
+    "    {'name': '1st Degree Polynomial Regression', 'degree': 1},\n",
+    "    {'name': '2nd Degree Polynomial Regression', 'degree': 2},\n",
+    "    {'name': '3rd Degree Polynomial Regression', 'degree': 3},\n",
+    "    {'name': '4th Degree Polynomial Regression', 'degree': 4},\n",
+    "    {'name': '5th Degree Polynomial Regression', 'degree': 5},\n",
+    "]\n",
+    "\n",
+    "other_regression_list = [\n",
+    "    {'name': 'Decision Tree Regression', 'regressor': tree.DecisionTreeRegressor()},\n",
+    "    {'name': 'SVM Regression', 'regressor': svm.SVR()},\n",
+    "    {'name': 'KNN Regression', 'regressor': neighbors.KNeighborsRegressor()},\n",
+    "    {'name': 'Random Forest Regression', 'regressor': ensemble.RandomForestRegressor(n_estimators=20)},\n",
+    "    {'name': 'Adaboost Regression', 'regressor': ensemble.AdaBoostRegressor(n_estimators=50)},\n",
+    "    {'name': 'Gradient Boosting Regression', 'regressor': ensemble.GradientBoostingRegressor(n_estimators=100)},\n",
+    "    {'name': 'Bagging Regression', 'regressor': ensemble.BaggingRegressor()},\n",
+    "    {'name': 'ExtraTree Regression', 'regressor': tree.ExtraTreeRegressor()},\n",
+    "    {'name': 'ElasticNet','regressor':linear_model.ElasticNet()},\n",
+    "]\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "id": "395a65a1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i in range(1,cluster_max):\n",
+    "    mse_list = []\n",
+    "    polynomial_list = []\n",
+    "    st.write(\"### cluseter number :{}\".format(i))\n",
+    "    for polynomial_regression in polynomial_regression_list:\n",
+    "        # st.markdown(\"#### \" + polynomial_regression['name'])\n",
+    "        degree = polynomial_regression['degree']\n",
+    "        \n",
+    "        regression_model = PolynomialRegressionModel(degree,i)\n",
+    "        regression_model.train_and_test_model()\n",
+    "\n",
+    "        polynomial_list.append({'name': polynomial_regression['name'], 'rmse': regression_model.rmse, 'regression_model': regression_model})\n",
+    "        mse_list.append({'name': polynomial_regression['name'], 'mse': regression_model.mse, 'rmse': regression_model.rmse})\n",
+    "\n",
+    "    for other_regression in other_regression_list:\n",
+    "        # st.markdown(\"#### \" + other_regression['name'])\n",
+    "        regressor = other_regression['regressor']\n",
+    "        \n",
+    "        regression_model = RegressionModel(regressor,i)\n",
+    "        regression_model.train_and_test_model()\n",
+    "\n",
+    "        mse_list.append({'name': other_regression['name'], 'mse': regression_model.mse, 'rmse': regression_model.rmse})\n",
+    "    def takeMse(elem):\n",
+    "        return elem['rmse']\n",
+    "    mse_list.sort(key=takeMse)\n",
+    "    st.table(pd.DataFrame(mse_list).set_index('name'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "73ad3040",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.6.13 ('rubik')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.13"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "6ca2f00ff8e4a90af02cda2236bfe1137f5db7cf8e475bba2dc7289ac4d86388"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/tools/interferenceanalysis/base.py b/tools/interferenceanalysis/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..5c76e69aa8e5a09ae594e236fbec92b620c6974f
--- /dev/null
+++ b/tools/interferenceanalysis/base.py
@@ -0,0 +1,32 @@
+"""
+rubikanalysis base module.
+
+This is the principal module of the rubikanalysis project.
+here you put your main classes and objects.
+
+Be creative! do whatever you want!
+
+If you want to replace this with a Flask application run:
+
+    $ make init
+
+and then choose `flask` as template.
+"""
+
+
+class BaseClass:
+    def base_method(self) -> str:
+        """
+        Base method.
+        """
+        return "hello from BaseClass"
+
+    def __call__(self) -> str:
+        return self.base_method()
+
+
+def base_function() -> str:
+    """
+    Base function.
+    """
+    return "hello from base function"
diff --git a/tools/interferenceanalysis/pca.py b/tools/interferenceanalysis/pca.py
new file mode 100644
index 0000000000000000000000000000000000000000..12cc5c20aed7e4b87354373317de9b6d0d6d864a
--- /dev/null
+++ b/tools/interferenceanalysis/pca.py
@@ -0,0 +1,220 @@
+# %%
+from sklearn import ensemble, neighbors, svm, linear_model, model_selection, tree, metrics, preprocessing 
+from sklearn.cluster import KMeans
+from sklearn.decomposition import PCA
+from itertools import product
+import streamlit as st
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+import altair as alt
+
+def standardize_table(df):
+    cols = list(df)
+    for item in cols:
+        if df[item].dtype == 'int64' or df[item].dtype == 'float64':
+            mean_tmp = np.mean(np.array(df[item]))
+            std_tmp = np.std(np.array(df[item]))
+            if(std_tmp):
+                df[item] = df[item].apply(lambda x: (x - mean_tmp) / std_tmp)
+def normalize_table(df):
+    cols = list(df)
+    for item in cols:
+        if df[item].dtype == 'int64' or df[item].dtype == 'float64':
+            max_tmp = np.max(np.array(df[item]))
+            min_tmp = np.min(np.array(df[item]))
+            if (max_tmp != min_tmp):
+                df[item] = df[item].apply(
+                    lambda x: (x - min_tmp) * 100 / (max_tmp - min_tmp))
+# %%
+data = pd.read_csv("../tests/data/default/nginx.csv")
+data_select = st.radio(
+    """选择分析的数据集
+    """,
+    ('l3cache_stress.csv', 'nginx.csv'))
+if data_select == 'l3cache_stress.csv':
+    data = pd.read_csv("../tests/data/clickhouse/l3cache_stress.csv")
+else:
+    data=pd.read_csv("../tests/data/default/nginx.csv")
+    data=data.drop(columns=['net','disk_io'])
+mode = st.radio(
+    "Please select a mode to visualize the data:",
+    ('origin', 'normalization', 'standardization'))
+
+if mode == 'normalization':
+    normalize_table(data)
+elif mode == 'standardization':
+    standardize_table(data)
+
+# %%
+
+# %%
+data_drop=data.drop(columns=['timestamp','qos'])
+pca=PCA(n_components=6)
+pca.fit(data_drop)
+pca_predict=pca.transform(data_drop)
+
+# %%
+def plt_data(data):
+    plt.xlabel('x label')
+    plt.title('pca decomposition data')
+    rev_data=pca_predict.T
+    for i in range(len(rev_data)):
+        plt.plot(rev_data[i])
+    plt.show()
+plt_data(pca_predict)
+
+# %%
+class PolynomialRegressionModel():
+    """polynomial regression model for metrics"""
+
+    def __init__(self, degree) -> None:
+        self.model = linear_model.LinearRegression()
+
+        self.mse = 0
+        self.rmse = 0
+        self.degree = degree
+
+    def train_and_test_model(self):
+        self.poly = preprocessing.PolynomialFeatures(degree = self.degree) 
+        X_poly = self.poly.fit_transform(x_train)
+
+        self.model.fit(X_poly, y_train)
+
+        y_pred = self.model.predict(self.poly.transform(x_test))
+        draw_comparison_altair_chart(y_test, y_pred)
+
+        self.mse = metrics.mean_squared_error(y_test, y_pred)
+        self.rmse = np.sqrt(metrics.mean_squared_error(y_test, y_pred))
+        model_evaluation = "MSE: {}, RMSE: {}".format(self.mse, self.rmse)
+        st.write(model_evaluation)
+
+
+# %%
+st.markdown("### 回归拟合分析")
+# 数据预处理: 去除无效值; 特性缩放:标准化; 模型训练
+x = pca_predict
+print(x)
+y = data[["qos"]]
+x_train, x_test, y_train, y_test = model_selection.train_test_split(x, y, random_state=1)
+def draw_comparison_altair_chart(y_test, y_pred):
+    y_test_list = y_test["qos"]
+    y_pred_list = y_pred if type(y_pred[0]) is not np.ndarray else [
+        i[0] for i in y_pred]
+    list_of_tuples = list(zip(y_test_list, y_pred_list))
+    source = pd.DataFrame(list_of_tuples, columns=['Measured', 'Predicted'],
+                          index=pd.RangeIndex(len(y_pred), name='index'))
+    source = source.reset_index().melt('index', var_name='category', value_name='qos')
+    nearest = alt.selection(type='single', nearest=True, on='mouseover',
+                            fields=['index'], empty='none')
+    line = alt.Chart(source).mark_line(interpolate='basis').encode(
+        x='index:Q',
+        y='qos:Q',
+        color='category:N'
+    )
+
+    selectors = alt.Chart(source).mark_point().encode(
+        x='index:Q',
+        opacity=alt.value(0),
+    ).add_selection(
+        nearest
+    )
+
+    points = line.mark_point().encode(
+        opacity=alt.condition(nearest, alt.value(1), alt.value(0))
+    )
+
+    # Draw text labels near the points, and highlight based on selection
+    text = line.mark_text(align='left', dx=5, dy=-5).encode(
+        text=alt.condition(nearest, 'qos:Q', alt.value(' '))
+    )
+
+    # Draw a rule at the location of the selection
+    rules = alt.Chart(source).mark_rule(color='gray').encode(
+        x='index:Q',
+    ).transform_filter(
+        nearest
+    )
+
+    charts = alt.layer(
+        line, selectors, points, rules, text
+    ).interactive()
+    st.altair_chart(charts, use_container_width=True)
+
+
+# %%
+
+class RegressionModel():
+    """regression model for metrics"""
+
+    def __init__(self, model) -> None:
+        self.model = model
+
+        self.mse = 0
+        self.rmse = 0
+
+    def train_and_test_model(self):
+        self.model.fit(x_train, np.ravel(y_train))
+
+        y_pred = self.model.predict(x_test)
+        draw_comparison_altair_chart(y_test, y_pred)
+
+        self.mse = metrics.mean_squared_error(y_test, y_pred)
+        self.rmse = np.sqrt(metrics.mean_squared_error(y_test, y_pred))
+        model_evaluation = "MSE: {}, RMSE: {}".format(self.mse, self.rmse)
+        st.write(model_evaluation)
+
+
+# %%
+polynomial_regression_list = [
+    {'name': '1st Degree Polynomial Regression', 'degree': 1},
+    {'name': '2nd Degree Polynomial Regression', 'degree': 2},
+    {'name': '3rd Degree Polynomial Regression', 'degree': 3},
+    {'name': '4th Degree Polynomial Regression', 'degree': 4},
+    {'name': '5th Degree Polynomial Regression', 'degree': 5},
+]
+
+other_regression_list = [
+    {'name': 'Decision Tree Regression', 'regressor': tree.DecisionTreeRegressor()},
+    {'name': 'SVM Regression', 'regressor': svm.SVR()},
+    {'name': 'KNN Regression', 'regressor': neighbors.KNeighborsRegressor()},
+    {'name': 'Random Forest Regression', 'regressor': ensemble.RandomForestRegressor(n_estimators=20)},
+    {'name': 'Adaboost Regression', 'regressor': ensemble.AdaBoostRegressor(n_estimators=50)},
+    {'name': 'Gradient Boosting Regression', 'regressor': ensemble.GradientBoostingRegressor(n_estimators=100)},
+    {'name': 'Bagging Regression', 'regressor': ensemble.BaggingRegressor()},
+    {'name': 'ExtraTree Regression', 'regressor': tree.ExtraTreeRegressor()},
+    {'name': 'ElasticNet','regressor':linear_model.ElasticNet()},
+]
+
+mse_list = []
+polynomial_list = []
+
+
+# %%
+for polynomial_regression in polynomial_regression_list:
+    st.markdown("#### " + polynomial_regression['name'])
+    degree = polynomial_regression['degree']
+    
+    regression_model = PolynomialRegressionModel(degree)
+    regression_model.train_and_test_model()
+
+    polynomial_list.append({'name': polynomial_regression['name'], 'rmse': regression_model.rmse, 'regression_model': regression_model})
+    mse_list.append({'name': polynomial_regression['name'], 'mse': regression_model.mse, 'rmse': regression_model.rmse})
+
+for other_regression in other_regression_list:
+    st.markdown("#### " + other_regression['name'])
+    regressor = other_regression['regressor']
+    
+    regression_model = RegressionModel(regressor)
+    regression_model.train_and_test_model()
+
+    mse_list.append({'name': other_regression['name'], 'mse': regression_model.mse, 'rmse': regression_model.rmse})
+
+# %%
+def takeMse(elem):
+    return elem['rmse']
+mse_list.sort(key=takeMse)
+st.table(pd.DataFrame(mse_list).set_index('name'))
+
+
diff --git a/tools/interferenceanalysis/pca_predict.py b/tools/interferenceanalysis/pca_predict.py
new file mode 100644
index 0000000000000000000000000000000000000000..2299dfa46217e73dfebb996402599ea4228ccb31
--- /dev/null
+++ b/tools/interferenceanalysis/pca_predict.py
@@ -0,0 +1,242 @@
+# %%
+from sklearn import ensemble, neighbors, svm, linear_model, model_selection, tree, metrics, preprocessing 
+from sklearn.cluster import KMeans
+from sklearn.decomposition import PCA
+from itertools import product
+import streamlit as st
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+import altair as alt
+
+# %%
+def normalize_table(df):
+    cols = list(df)
+    for item in cols:
+        if df[item].dtype == 'int64' or df[item].dtype == 'float64':
+            max_tmp = np.max(np.array(df[item]))
+            min_tmp = np.min(np.array(df[item]))
+            if (max_tmp != min_tmp):
+                df[item] = df[item].apply(
+                    lambda x: (x - min_tmp) * 100 / (max_tmp - min_tmp))
+
+# %%
+def standardize_table(df):
+    cols = list(df)
+    for item in cols:
+        if df[item].dtype == 'int64' or df[item].dtype == 'float64':
+            mean_tmp = np.mean(np.array(df[item]))
+            std_tmp = np.std(np.array(df[item]))
+            if(std_tmp):
+                df[item] = df[item].apply(lambda x: (x - mean_tmp) / std_tmp)
+
+# %%
+data = pd.read_csv("../tests/data/default/nginx.csv")
+data_select = st.radio(
+    """选择分析的数据集
+    """,
+    ('l3cache_stress.csv', 'nginx.csv'))
+if data_select == 'l3cache_stress.csv':
+    data = pd.read_csv("../tests/data/clickhouse/l3cache_stress.csv")
+else:
+    data=pd.read_csv("../tests/data/default/nginx.csv")
+    data=data.drop(columns=['net','disk_io'])
+mode = st.radio(
+    "Please select a mode to visualize the data:",
+    ('origin', 'normalization', 'standardization'))
+
+if mode == 'normalization':
+    normalize_table(data)
+elif mode == 'standardization':
+    standardize_table(data)
+
+# %%
+print(data)
+cluster_max=8
+pca_predict=[]
+for i in range(1,cluster_max):
+    # kms=KMeans(n_clusters=i)
+    # kms.fit(data.drop(columns='timestamp'))
+    # data_predict.append(kms.predict(data.drop(columns='timestamp')))
+    data_drop=data.drop(columns=['timestamp','qos'])
+    pca=PCA(n_components=i)
+    pca.fit(data_drop)
+    pca_predict.append(pca.transform(data_drop))
+    print(pca_predict)
+    print(type(pca_predict))
+    # print(data_predict)
+
+# %%
+
+
+# %%
+def plt_data(i):
+    plt.xlabel('x label')
+    plt.title('pca decomposition data')
+    rev_data=pca_predict[i].T
+    for i in range(len(rev_data)):
+        plt.plot(rev_data[i])
+    plt.show()
+for i in range(1,cluster_max):
+    plt_data(i-1)
+
+# %%
+class PolynomialRegressionModel():
+    """polynomial regression model for metrics"""
+
+    def __init__(self, degree,i) -> None:
+        self.model = linear_model.LinearRegression()
+        self.x = pca_predict[i-1]
+        self.y = data[["qos"]]
+        self.x_train, self.x_test, self.y_train, self.y_test = model_selection.train_test_split(self.x, self.y, random_state=1)
+        self.mse = 0
+        self.rmse = 0
+        self.degree = degree
+
+    def train_and_test_model(self):
+        self.poly = preprocessing.PolynomialFeatures(degree = self.degree) 
+        X_poly = self.poly.fit_transform(self.x_train)
+
+        self.model.fit(X_poly, self.y_train)
+
+        y_pred = self.model.predict(self.poly.transform(self.x_test))
+        # score = model.score(x_test, y_test)
+        # draw_comparison_altair_chart(y_test, y_pred)
+
+        self.mse = metrics.mean_squared_error(self.y_test, y_pred)
+        self.rmse = np.sqrt(metrics.mean_squared_error(self.y_test, y_pred))
+        # model_evaluation = "MSE: {}, RMSE: {}".format(self.mse, self.rmse)
+        # st.write(model_evaluation)
+
+
+# %%
+st.markdown("### 回归拟合分析")
+# 数据预处理: 去除无效值; 特性缩放:标准化; 模型训练
+
+def draw_comparison_altair_chart(y_test, y_pred):
+    y_test_list = y_test["qos"]
+    y_pred_list = y_pred if type(y_pred[0]) is not np.ndarray else [
+        i[0] for i in y_pred]
+    list_of_tuples = list(zip(y_test_list, y_pred_list))
+    source = pd.DataFrame(list_of_tuples, columns=['Measured', 'Predicted'],
+                          index=pd.RangeIndex(len(y_pred), name='index'))
+    source = source.reset_index().melt('index', var_name='category', value_name='qos')
+    nearest = alt.selection(type='single', nearest=True, on='mouseover',
+                            fields=['index'], empty='none')
+    line = alt.Chart(source).mark_line(interpolate='basis').encode(
+        x='index:Q',
+        y='qos:Q',
+        color='category:N'
+    )
+
+    selectors = alt.Chart(source).mark_point().encode(
+        x='index:Q',
+        opacity=alt.value(0),
+    ).add_selection(
+        nearest
+    )
+
+    points = line.mark_point().encode(
+        opacity=alt.condition(nearest, alt.value(1), alt.value(0))
+    )
+
+    # Draw text labels near the points, and highlight based on selection
+    text = line.mark_text(align='left', dx=5, dy=-5).encode(
+        text=alt.condition(nearest, 'qos:Q', alt.value(' '))
+    )
+
+    # Draw a rule at the location of the selection
+    rules = alt.Chart(source).mark_rule(color='gray').encode(
+        x='index:Q',
+    ).transform_filter(
+        nearest
+    )
+
+    charts = alt.layer(
+        line, selectors, points, rules, text
+    ).interactive()
+    st.altair_chart(charts, use_container_width=True)
+
+
+# %%
+
+class RegressionModel():
+    """regression model for metrics"""
+
+    def __init__(self, model,i) -> None:
+        self.model = model
+        self.mse = 0
+        self.rmse = 0
+        self.x = pca_predict[i-1]
+        self.y = data[["qos"]]
+        self.x_train, self.x_test, self.y_train, self.y_test = model_selection.train_test_split(self.x, self.y, random_state=1)
+    def train_and_test_model(self):
+        self.model.fit(self.x_train, np.ravel(self.y_train))
+
+        y_pred = self.model.predict(self.x_test)
+        # score = model.score(x_test, y_test)
+        # draw_comparison_altair_chart(y_test, y_pred)
+
+        self.mse = metrics.mean_squared_error(self.y_test, y_pred)
+        self.rmse = np.sqrt(metrics.mean_squared_error(self.y_test, y_pred))
+        # model_evaluation = "MSE: {}, RMSE: {}".format(self.mse, self.rmse)
+        # st.write(model_evaluation)
+
+
+# %%
+polynomial_regression_list = [
+    {'name': '1st Degree Polynomial Regression', 'degree': 1},
+    {'name': '2nd Degree Polynomial Regression', 'degree': 2},
+    {'name': '3rd Degree Polynomial Regression', 'degree': 3},
+    {'name': '4th Degree Polynomial Regression', 'degree': 4},
+    {'name': '5th Degree Polynomial Regression', 'degree': 5},
+]
+
+other_regression_list = [
+    {'name': 'Decision Tree Regression', 'regressor': tree.DecisionTreeRegressor()},
+    {'name': 'SVM Regression', 'regressor': svm.SVR()},
+    {'name': 'KNN Regression', 'regressor': neighbors.KNeighborsRegressor()},
+    {'name': 'Random Forest Regression', 'regressor': ensemble.RandomForestRegressor(n_estimators=20)},
+    {'name': 'Adaboost Regression', 'regressor': ensemble.AdaBoostRegressor(n_estimators=50)},
+    {'name': 'Gradient Boosting Regression', 'regressor': ensemble.GradientBoostingRegressor(n_estimators=100)},
+    {'name': 'Bagging Regression', 'regressor': ensemble.BaggingRegressor()},
+    {'name': 'ExtraTree Regression', 'regressor': tree.ExtraTreeRegressor()},
+    {'name': 'ElasticNet','regressor':linear_model.ElasticNet()},
+]
+
+
+
+
+# %%
+for i in range(1,cluster_max):
+    mse_list = []
+    polynomial_list = []
+    st.write("### cluseter number :{}".format(i))
+    for polynomial_regression in polynomial_regression_list:
+        # st.markdown("#### " + polynomial_regression['name'])
+        degree = polynomial_regression['degree']
+        
+        regression_model = PolynomialRegressionModel(degree,i)
+        regression_model.train_and_test_model()
+
+        polynomial_list.append({'name': polynomial_regression['name'], 'rmse': regression_model.rmse, 'regression_model': regression_model})
+        mse_list.append({'name': polynomial_regression['name'], 'mse': regression_model.mse, 'rmse': regression_model.rmse})
+
+    for other_regression in other_regression_list:
+        # st.markdown("#### " + other_regression['name'])
+        regressor = other_regression['regressor']
+        
+        regression_model = RegressionModel(regressor,i)
+        regression_model.train_and_test_model()
+
+        mse_list.append({'name': other_regression['name'], 'mse': regression_model.mse, 'rmse': regression_model.rmse})
+    def takeMse(elem):
+        return elem['rmse']
+    mse_list.sort(key=takeMse)
+    st.table(pd.DataFrame(mse_list).set_index('name'))
+
+# %%
+
+
+
diff --git a/tools/interferenceanalysis/preprocess.py b/tools/interferenceanalysis/preprocess.py
new file mode 100644
index 0000000000000000000000000000000000000000..565b6e5c120ea45683abe2e206beeb949524860a
--- /dev/null
+++ b/tools/interferenceanalysis/preprocess.py
@@ -0,0 +1,254 @@
+"""
+Indicator data preprocessing module.
+
+The low-level metrics do not correspond to the high-level SLAs,
+and the indicators are corresponding and merged according to
+the method of time stamp proximity search..
+"""
+
+import time
+import sys
+import pandas as pd
+import numpy as np
+
+class Preprocess(object):
+    def __init__(self, metrics, qos, output):
+        self.metrics = metrics
+        self.qos = qos
+        self.output = output
+
+    def execute(self) -> None:
+        """
+        Execute preprocessing
+        """
+        self.__load_and_generate_output()
+
+    def load_table(self, path):
+        """some preprocessing on table
+
+        1) load table, set timestamp as first column instead of index column
+        2) drop duplicate rows based on column timestamp, keep first row
+        3) reset index
+        """
+        table = pd.read_table(path, header=0, sep=",")
+        first_column = table.columns.tolist()[0]
+        table.drop_duplicates(subset=first_column, keep="first", inplace=True)
+        table = table.reset_index(drop=True)
+        return table, first_column
+
+    def __load_and_generate_output(self):
+        # metric table: timestamp,context-switches,branch-misses,......
+        metrics, metric_first_column = self.load_table(self.metrics)
+        # qos table:timestamp,qos
+        qos, qos_first_column = self.load_table(self.qos)
+
+        metrics_timestamps = metrics[metric_first_column].tolist()
+        qos_timestamps = qos[qos_first_column].tolist()
+
+        # get matched index list
+        qos_filted_index, metrics_filted_index = self.__match_and_filter(
+            qos_timestamps, metrics_timestamps
+        )
+        assert len(qos_filted_index) == len(metrics_filted_index)
+
+        output_table = metrics.loc[metrics_filted_index]
+
+        qos_filtered = qos.loc[qos_filted_index]
+        qos_filtered.to_csv("qos.csv")
+        col = qos_filtered.iloc[:, 1]
+
+        output_table.insert(output_table.shape[1], "qos", col.values)
+        output_table.to_csv(self.output, index=False)
+
+    def __match_and_filter(self, qos_timestamps, metrics_timestamps):
+        """get matched index
+        Args:
+           qos_timestamps: timestamp column of qos table
+           metrics_timestamps: timestamp column of metric table
+        """
+        # should we expand qos table to get more data samples?
+        qos_tss, metric_tss = [], []
+
+        get_metrix_ts = lambda idx: int(
+            time.mktime(time.strptime(metrics_timestamps[idx], "%Y-%m-%d %H:%M:%S"))
+        )
+        get_qos_ts = lambda idx: int(
+            time.mktime(time.strptime(qos_timestamps[idx], "%Y-%m-%d %H:%M:%S"))
+        )
+
+        qos_index, metric_index = 0, 0
+        # max time difference (in seconds) we can tolerate. AFAIAC 5 seconds is appropriate.
+        max_diff = 5
+
+        qos_ts = get_qos_ts(qos_index)
+        metric_ts = get_metrix_ts(metric_index)
+
+        while True:
+            while abs(qos_ts - metric_ts) > max_diff:
+                if qos_ts < metric_ts:
+                    qos_index += 1
+                else:
+                    metric_index += 1
+                if qos_index >= len(qos_timestamps) or metric_index >= len(
+                    metrics_timestamps
+                ):
+                    break
+                qos_ts = get_qos_ts(qos_index)
+                metric_ts = get_metrix_ts(metric_index)
+
+            if qos_index >= len(qos_timestamps) or metric_index >= len(
+                metrics_timestamps
+            ):
+                break
+            # append ideal index
+            qos_tss.append(qos_index)
+            metric_tss.append(metric_index)
+
+            # increase index after append
+            qos_index += 1
+            metric_index += 1
+
+        return qos_tss, metric_tss
+
+
+class StressProcess(object):
+    def __init__(self, stress, qos, output):
+        self.stress = stress
+        self.qos = qos
+        self.qos_index = 0
+        self.output = output
+
+    def execute(self) -> None:
+        """
+        Execute Stress Data Process
+        """
+        self.__load_and_generate_output()
+
+    def __load_and_generate_output(self):
+        # begin-timestamp end-timestamp type stress command
+        stress_col_name = ['begin-timestamp', 'end-timestamp', 'type', 'stress', 'command']
+        stress_table = pd.read_table(self.stress, names=stress_col_name, header=0)
+        # timestamp qos
+        qos_col_name = ['timestamp', 'qos']
+        qos_table = pd.read_table(self.qos, names=qos_col_name, header=0, seq=",")
+
+        qos_len = len(qos_table)
+        output_list = []
+
+        # no_stress_qos = self.__get_rangetime_qos(None, stress_table.at[0, 'begin-timestamp'], qos_table)
+        # output_list.append({"type": "none", "stress": "0", "avg-qos": no_stress_qos})
+
+        for _, row in stress_table.iterrows():
+            if self.qos_index >= qos_len:
+                break
+
+            begin_timestamp = row['begin-timestamp']
+            end_timestamp = row['end-timestamp']
+            average_qos = self.__get_rangetime_qos(begin_timestamp, end_timestamp, qos_table)
+            output_list.append({"type": row['type'], "stress": row['stress'], "avg-qos": average_qos})
+
+        # type stress avg-qos degradation-percent
+        output_table = pd.DataFrame.from_records(output_list, columns=['type', 'stress', 'avg-qos', 'degradation-percent'])
+        # output_table['degradation-percent'] = 100 * (output_table['avg-qos'] - no_stress_qos) / no_stress_qos
+
+        no_stress_qos = -1
+        for index, row in output_table.iterrows():
+            if abs(row['stress']) <= 1e-5:
+                no_stress_qos = row['avg-qos']
+            row['degradation-percent'] = 100 * (row['avg-qos'] - no_stress_qos) / no_stress_qos
+            output_table.iloc[index] = row
+
+        output_table.to_csv(self.output, index=False)
+                
+    def __get_rangetime_qos(self, begin_time, end_time, qos_table):
+        qos_len = len(qos_table)
+        if self.qos_index >= qos_len:
+            return 0
+
+        if begin_time is not None:
+            while self.__compare_stimestamp_gt(begin_time, qos_table.at[self.qos_index, 'timestamp']):
+                self.qos_index += 1
+                if self.qos_index >= qos_len:
+                    return 0
+        begin_index = self.qos_index
+
+        while self.__compare_stimestamp_gt(end_time, qos_table.at[self.qos_index, 'timestamp']):
+            self.qos_index += 1
+            if self.qos_index >= qos_len:
+                break
+        end_index = self.qos_index
+
+        return np.mean(qos_table[begin_index:end_index]["qos"])
+
+    def __compare_stimestamp_gt(self, time1, time2):
+        time1_st = int(time.mktime(time.strptime(
+            time1, "%Y-%m-%d %H:%M:%S")))
+        time2_st = int(time.mktime(time.strptime(
+            time2, "%Y-%m-%d %H:%M:%S")))
+
+        return time1_st > time2_st
+
+class MachineProcess(object):
+    def __init__(self, stress, machine, output):
+        self.stress = stress
+        self.machine = machine
+        self.machine_index = 0
+        self.output = output
+
+    def execute(self) -> None:
+        """
+        Execute Cpu Usage Data Process
+        """
+        self.__load_and_generate_output()
+
+    def __load_and_generate_output(self):
+        # begin-timestamp end-timestamp type stress command
+        stress_col_name = ['begin-timestamp', 'end-timestamp', 'type', 'stress', 'command']
+        stress_table = pd.read_table(self.stress, names=stress_col_name, header=0)
+        # timestamp cpu-usage memory cpi mkpi llc lmb rmb network-io disk-io
+        machine_col_name = ['timestamp', 'cpu-usage', 'memory', 'cpi', 'mkpi', 'llc', 'lmb', 'rmb', 'network-io', 'disk-io']
+        machine_table = pd.read_table(self.machine, names=machine_col_name, header=0)
+
+        machine_len = len(machine_table)
+        output_list = []
+
+        for _, row in stress_table.iterrows():
+            if self.machine_index >= machine_len:
+                break
+
+            begin_timestamp = row['begin-timestamp']
+            end_timestamp = row['end-timestamp']
+            average_cpu_usage = self.__get_rangetime_cpu_usage(begin_timestamp, end_timestamp, machine_table)
+            output_list.append({"type": row['type'], "stress": row['stress'], "avg-cpu-usage": average_cpu_usage})
+
+        # type stress avg-cpu-usage
+        output_table = pd.DataFrame.from_records(output_list, columns=['type', 'stress', 'avg-cpu-usage'])
+        output_table.to_csv(self.output, index=False)
+                
+    def __get_rangetime_cpu_usage(self, begin_time, end_time, machine_table):
+        machine_len = len(machine_table)
+        if self.machine_index >= machine_len:
+            return 0
+
+        if begin_time is not None:
+            while self.__compare_stimestamp_gt(begin_time, machine_table.at[self.machine_index, 'timestamp']):
+                self.machine_index += 1
+                if self.machine_index >= machine_len:
+                    return 0
+        begin_index = self.machine_index
+
+        while self.__compare_stimestamp_gt(end_time, machine_table.at[self.machine_index, 'timestamp']):
+            self.machine_index += 1
+            if self.machine_index >= machine_len:
+                break
+        end_index = self.machine_index
+
+        return np.mean(machine_table[begin_index:end_index]["cpu-usage"])
+
+    def __compare_stimestamp_gt(self, time1, time2):
+        time1_st = int(time.mktime(time.strptime(
+            time1, "%Y-%m-%d %H:%M:%S")))
+        time2_st = int(time.mktime(time.strptime(
+            time2, "%Y-%m-%d %H:%M:%S")))
+
+        return time1_st > time2_st
diff --git a/tools/interferenceanalysis/spc_chart.r b/tools/interferenceanalysis/spc_chart.r
new file mode 100644
index 0000000000000000000000000000000000000000..c7ed9287805b9b2fbcbe5853a7e9573eac65de93
--- /dev/null
+++ b/tools/interferenceanalysis/spc_chart.r
@@ -0,0 +1,65 @@
+library(qcc)
+library(readr)
+
+spc_ipc_normal <- read.csv("Workspace/rubikAnalysis/tests/data/clickhouse/spc_ipc_abnormal.csv", sep="")
+View(spc_ipc_normal)
+
+measures <- spc_ipc_normal[,1]
+# Specify the sample size
+sample_size <- 3
+
+# Get the total number of samples
+number_of_samples <- length(measures)/sample_size
+
+# Assign the measures to the corresponding sample
+sample <- rep(1:number_of_samples, each = sample_size)
+
+# Create a data frame with the measures and sample columns
+df <- data.frame(measures, sample)
+
+# Group the measures per sample
+measure <- with(df, qcc.groups(measures, sample))
+
+# View the grouped measures
+head(measure)
+
+# Specify the measures unit
+measure_unit <- "lbs"  
+
+# Create the x-bar chart
+xbar <- qcc(measure, type = "xbar", data.name = measure_unit)
+
+# Specify the warning limits (2 sigmas)
+(warn.limits.2 = limits.xbar(xbar$center, xbar$std.dev, xbar$sizes, 2))
+
+# Specify the warning limits (1 sigmas)
+(warn.limits.1 = limits.xbar(xbar$center, xbar$std.dev, xbar$sizes, 1))
+
+# Plot the x-bar chart
+plot(xbar, restore.par = FALSE)
+
+# Plot the warning limit lines
+abline(h = warn.limits.2, lty = 2, col = "blue")
+abline(h = warn.limits.1, lty = 2, col = "lightblue")
+
+# Get the summary for x-bar chart
+summary(xbar)
+
+# Create the R-chart
+r_chart <- qcc(measure, type = "R", data.name = measure_unit)
+
+# Get the summaries for R-chart
+summary(r_chart)
+
+
+# Specify the lower control limit
+LSL <- as.numeric(2.57704)
+ 
+# Specify the upper control limit
+USL <- as.numeric(2.7223)
+
+# Specify the target
+Target <- as.numeric(2.6497)
+
+# Plot the process capability chart
+process.capability(xbar, spec.limits = c(LSL, USL), target = Target)