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ZKFC_ACC/000补偿值计算/.ipynb_checkpoints/Die2数据计算-合格率预测-checkpoint.ipynb

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初始化
2025-11-23 20:43:12 +08:00
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "ad8424f1-4fd8-4f68-9557-f560d5a28e4b",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import sys\n",
"import os\n",
"sys.path.append('..')\n",
"from QX8800SP_DA import *\n",
"plt.rcParams['font.family'] = ['SimHei'] # 用来正常显示中文标签\n",
"plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号\n",
"pd.set_option('display.max_columns', None) #显示所有列，把行显示设置成最大\n",
"pd.set_option('display.max_rows', None) #显示所有行，把列显示设置成最大\n",
"#交互式绘图\n",
"%matplotlib widget"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "ccb60f92-e657-4732-a679-6ca67bfcf201",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MCX (96, 99)\n",
"MCY (96, 99)\n",
"Angle14 (96, 14)\n",
"M1X (96, 99)\n",
"M1Y (96, 99)\n",
"M4X (96, 14)\n",
"M4Y (96, 14)\n",
"Angle13 (96, 86)\n",
"M3X (96, 86)\n",
"M3Y (96, 86)\n",
"Note (4, 2)\n"
]
}
],
"source": [
"#写入TotalData\n",
"DieType = \"Die2\"\n",
"TotalData = pd.read_excel('../Die2AllData.xlsx',sheet_name=None,header=0,index_col = 0)\n",
"DieBC = pd.read_excel('SP-Die补偿模版.xlsx',index_col=0,header=0)\n",
"die_nums = -2\n",
"for i in TotalData:\n",
" print(i,TotalData[i].shape)"
]
},
{
"cell_type": "markdown",
"id": "8f9078d7",
"metadata": {},
"source": [
"## 对位Mark"
]
},
{
"cell_type": "markdown",
"id": "31b36a67",
"metadata": {},
"source": [
"### 对位MarkX"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "6de0e187",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>01.21.1-Die2</th>\n",
" <th>01.21.2-Die2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>69.000000</td>\n",
" <td>69.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>1.117094</td>\n",
" <td>0.886355</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.372240</td>\n",
" <td>0.306389</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>-0.273500</td>\n",
" <td>-0.089500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>0.965500</td>\n",
" <td>0.679500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>1.137500</td>\n",
" <td>0.927000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>1.425000</td>\n",
" <td>1.114500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1.722000</td>\n",
" <td>1.424500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>range</th>\n",
" <td>1.995500</td>\n",
" <td>1.514000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3sigma</th>\n",
" <td>1.116720</td>\n",
" <td>0.919166</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 01.21.1-Die2 01.21.2-Die2\n",
"count 69.000000 69.000000\n",
"mean 1.117094 0.886355\n",
"std 0.372240 0.306389\n",
"min -0.273500 -0.089500\n",
"25% 0.965500 0.679500\n",
"50% 1.137500 0.927000\n",
"75% 1.425000 1.114500\n",
"max 1.722000 1.424500\n",
"range 1.995500 1.514000\n",
"3sigma 1.116720 0.919166"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# AlignMarkX = TotalData['M3X'].dropna(subset='QX8800SP_Index').set_index('QX8800SP_Index').iloc[:,die_nums:]\n",
"AlignMarkX = TotalData['MCX'].dropna(subset='QX8800SP_Index').set_index('QX8800SP_Index').iloc[:,die_nums:].sort_index(axis=1)\n",
"AXdescibe = describe_3s(AlignMarkX)\n",
"AXdescibe"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "7101c157-d160-4c17-b781-5624766a7ef0",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.8863550724637681"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"AXdescibe.iloc[1,1]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "0b26cc5e-14db-4332-a67e-93f71cdfa7f8",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Die1对位MarkX局部补偿（um）')"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b1c20f8d5f724209bec9746f87012941",
"version_major": 2,
"version_minor": 0
},
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"\n",
" <div style=\"display: inline-block;\">\n",
" <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
" Figure\n",
" </div>\n",
" <img src='data:image/png;base64,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
" </div>\n",
" "
],
"text/plain": [
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"BC_X = pd.concat([AlignMarkX[i]-AlignMarkX[i].mean() for i in AlignMarkX.columns[-3:]],axis=1)\n",
"# BC_X = pd.concat([AlignMarkX[i] for i in AlignMarkX.columns[-4:]],axis=1)\n",
"BC_X.plot(marker='o')\n",
"plt.title('Die1对位MarkX局部补偿（um）')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "9e59e417-c32e-425a-b9f7-b8e819ed5ec4",
"metadata": {},
"outputs": [],
"source": [
"BC_X = BC_X.mean(axis=1)\n",
"DieBC['对位MarkX'] = BC_X.fillna(BC_X.interpolate()).values"
]
},
{
"cell_type": "markdown",
"id": "64f88c9a",
"metadata": {},
"source": [
"### 对位MarkY"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "9e294f7b-3ea3-4a33-99b5-92e22bd1a827",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>01.21.1-Die2</th>\n",
" <th>01.21.2-Die2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>69.000000</td>\n",
" <td>69.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.544116</td>\n",
" <td>1.284971</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.479535</td>\n",
" <td>0.316244</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>-0.154500</td>\n",
" <td>0.675000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>0.173000</td>\n",
" <td>1.046000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>0.448000</td>\n",
" <td>1.220000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>0.896500</td>\n",
" <td>1.570500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1.824000</td>\n",
" <td>2.126500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>range</th>\n",
" <td>1.978500</td>\n",
" <td>1.451500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3sigma</th>\n",
" <td>1.438605</td>\n",
" <td>0.948731</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 01.21.1-Die2 01.21.2-Die2\n",
"count 69.000000 69.000000\n",
"mean 0.544116 1.284971\n",
"std 0.479535 0.316244\n",
"min -0.154500 0.675000\n",
"25% 0.173000 1.046000\n",
"50% 0.448000 1.220000\n",
"75% 0.896500 1.570500\n",
"max 1.824000 2.126500\n",
"range 1.978500 1.451500\n",
"3sigma 1.438605 0.948731"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# AlignMarkY = TotalData['M3Y'].dropna(subset='QX8800SP_Index').set_index('QX8800SP_Index').iloc[:,die_nums:]\n",
"AlignMarkY = TotalData['MCY'].dropna(subset='QX8800SP_Index').set_index('QX8800SP_Index').iloc[:,die_nums:].sort_index(axis=1)\n",
"AYdescibe = describe_3s(AlignMarkY)\n",
"AYdescibe"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "3121e2c1-bb57-437c-b4eb-4b2e904f1221",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Die1对位MarkY局部补偿（um）')"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f01a2292cce4472cb076f0f3c9039ade",
"version_major": 2,
"version_minor": 0
},
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"text/html": [
"\n",
" <div style=\"display: inline-block;\">\n",
" <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
" Figure\n",
" </div>\n",
" <img src='data:image/png;base64,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
" </div>\n",
" "
],
"text/plain": [
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"BC_Y = pd.concat([AlignMarkY[i]-AlignMarkY[i].mean() for i in AlignMarkY.columns[-3:]],axis=1)\n",
"# BC_Y = pd.concat([AlignMarkY[i] for i in AlignMarkY.columns[-4:]],axis=1)\n",
"BC_Y.plot(marker='o')\n",
"plt.title('Die1对位MarkY局部补偿（um）')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "ca7990d9-cdd0-48c9-a33d-21708336113c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index\n",
"1 0.659707\n",
"2 0.508957\n",
"3 0.385832\n",
"4 0.262707\n",
"5 0.318957\n",
"6 0.216457\n",
"7 0.420707\n",
"8 0.187207\n",
"9 -0.467793\n",
"10 -0.293918\n",
"11 -0.120043\n",
"12 -0.150793\n",
"13 0.250707\n",
"14 0.650957\n",
"15 0.394957\n",
"16 -0.114543\n",
"17 -0.381293\n",
"18 -0.481543\n",
"19 -0.310257\n",
"20 -0.138971\n",
"21 -0.169543\n",
"22 -0.038293\n",
"23 0.551707\n",
"24 0.714707\n",
"25 0.096207\n",
"26 0.409957\n",
"27 -0.544293\n",
"28 -0.366293\n",
"29 -0.203043\n",
"30 -0.039793\n",
"31 -0.059293\n",
"32 -0.078793\n",
"33 -0.156793\n",
"34 -0.046293\n",
"35 0.760957\n",
"36 0.642207\n",
"37 0.142707\n",
"38 -0.478793\n",
"39 -0.363043\n",
"40 -0.119293\n",
"41 -0.408793\n",
"42 -0.538043\n",
"43 -0.264543\n",
"44 -0.193043\n",
"45 0.085957\n",
"46 0.381957\n",
"47 0.587207\n",
"48 0.156707\n",
"49 -0.282043\n",
"50 -0.361293\n",
"51 -0.392793\n",
"52 -0.424293\n",
"53 -0.378543\n",
"54 -0.332793\n",
"55 -0.287043\n",
"56 -0.241293\n",
"57 0.668207\n",
"58 -0.082293\n",
"59 -0.206918\n",
"60 -0.331543\n",
"61 -0.448793\n",
"62 -0.383043\n",
"63 -0.220543\n",
"64 -0.262293\n",
"65 -0.239543\n",
"66 -0.242593\n",
"67 -0.245643\n",
"68 -0.248693\n",
"69 -0.251743\n",
"70 -0.254793\n",
"71 -0.413043\n",
"72 -0.392293\n",
"73 -0.131543\n",
"74 -0.445793\n",
"75 -0.281793\n",
"76 0.261207\n",
"77 0.062707\n",
"78 0.285457\n",
"79 0.195207\n",
"80 0.066207\n",
"81 0.166457\n",
"82 0.142957\n",
"83 0.353207\n",
"84 0.368384\n",
"85 0.368384\n",
"86 0.368384\n",
"87 0.368384\n",
"Name: 对位MarkY, dtype: float64"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"BC_Y = BC_Y.mean(axis=1)\n",
"DieBC['对位MarkY'] = BC_Y.fillna(BC_Y.interpolate()).values\n",
"DieBC['对位MarkY']"
]
},
{
"cell_type": "markdown",
"id": "6ace8d23",
"metadata": {},
"source": [
"## Mark1"
]
},
{
"cell_type": "markdown",
"id": "c70c8ca9",
"metadata": {},
"source": [
"### MarkX1"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "87ad2953",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>01.21.1-Die2</th>\n",
" <th>01.21.2-Die2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>69.000000</td>\n",
" <td>69.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>1.058725</td>\n",
" <td>0.695971</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.379935</td>\n",
" <td>0.275594</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>-0.028000</td>\n",
" <td>-0.012000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>0.822000</td>\n",
" <td>0.489000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>1.164000</td>\n",
" <td>0.738000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>1.297000</td>\n",
" <td>0.903000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1.912000</td>\n",
" <td>1.268000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>range</th>\n",
" <td>1.940000</td>\n",
" <td>1.280000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3sigma</th>\n",
" <td>1.139804</td>\n",
" <td>0.826783</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 01.21.1-Die2 01.21.2-Die2\n",
"count 69.000000 69.000000\n",
"mean 1.058725 0.695971\n",
"std 0.379935 0.275594\n",
"min -0.028000 -0.012000\n",
"25% 0.822000 0.489000\n",
"50% 1.164000 0.738000\n",
"75% 1.297000 0.903000\n",
"max 1.912000 1.268000\n",
"range 1.940000 1.280000\n",
"3sigma 1.139804 0.826783"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Mark1X = TotalData['M1X'].dropna(subset='QX8800SP_Index').set_index('QX8800SP_Index').iloc[:,die_nums:].sort_index(axis=1)\n",
"Mark1X_3s = describe_3s(Mark1X)\n",
"Mark1X_3s"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "22fff56b-8fe3-455f-9e5b-ddae78b92985",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"QX8800SP_Index\n",
"1.0 0.21137\n",
"2.0 0.25337\n",
"3.0 NaN\n",
"4.0 -0.29713\n",
"5.0 -0.22563\n",
"6.0 0.07537\n",
"7.0 -0.10088\n",
"8.0 -0.56913\n",
"9.0 -0.25988\n",
"10.0 NaN\n",
"11.0 -0.41763\n",
"12.0 -0.13088\n",
"13.0 -0.26638\n",
"14.0 -0.21638\n",
"15.0 -0.14538\n",
"16.0 -0.56088\n",
"17.0 -0.35463\n",
"18.0 -0.16838\n",
"19.0 NaN\n",
"20.0 -0.21800\n",
"21.0 -0.21513\n",
"22.0 -0.06663\n",
"23.0 -0.37838\n",
"24.0 -0.20488\n",
"25.0 -0.36138\n",
"26.0 0.08387\n",
"27.0 -0.27138\n",
"28.0 -0.17413\n",
"29.0 NaN\n",
"30.0 -0.01988\n",
"31.0 NaN\n",
"32.0 -0.33238\n",
"33.0 -0.30713\n",
"34.0 -0.39213\n",
"35.0 -0.23213\n",
"36.0 0.04687\n",
"37.0 0.05437\n",
"38.0 -0.41338\n",
"39.0 -0.29688\n",
"40.0 -0.24213\n",
"41.0 -0.11663\n",
"42.0 -0.15538\n",
"43.0 -0.10663\n",
"44.0 -0.39613\n",
"45.0 0.06962\n",
"46.0 -0.51563\n",
"47.0 -0.24213\n",
"48.0 -0.13813\n",
"49.0 -0.43563\n",
"50.0 -0.27838\n",
"51.0 NaN\n",
"52.0 -0.00763\n",
"53.0 NaN\n",
"54.0 NaN\n",
"55.0 NaN\n",
"56.0 -0.17188\n",
"57.0 -0.08838\n",
"58.0 0.08712\n",
"59.0 NaN\n",
"60.0 -0.16213\n",
"61.0 -0.26713\n",
"62.0 -0.33938\n",
"63.0 -0.20063\n",
"64.0 -0.12113\n",
"65.0 -0.29563\n",
"66.0 NaN\n",
"67.0 NaN\n",
"68.0 NaN\n",
"69.0 NaN\n",
"70.0 -0.34938\n",
"71.0 -0.34613\n",
"72.0 -0.22663\n",
"73.0 -0.18138\n",
"74.0 -0.21188\n",
"75.0 0.04337\n",
"76.0 -0.10688\n",
"77.0 -0.32088\n",
"78.0 -0.31838\n",
"79.0 -0.12088\n",
"80.0 0.09612\n",
"81.0 -0.15363\n",
"82.0 -0.10188\n",
"83.0 0.05587\n",
"84.0 NaN\n",
"85.0 NaN\n",
"86.0 NaN\n",
"87.0 NaN\n",
"dtype: float64"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Mark1X_1 = Mark1X.iloc[:,1] - DieBC['对位MarkX'] - AXdescibe.iloc[1,1]\n",
"Mark1X_1"
]
},
{
"cell_type": "markdown",
"id": "fca6defb",
"metadata": {},
"source": [
"### Mark1Y"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "389557c5",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>01.21.1-Die2</th>\n",
" <th>01.21.2-Die2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>69.000000</td>\n",
" <td>69.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.179739</td>\n",
" <td>0.937768</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.526306</td>\n",
" <td>0.357511</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>-0.578000</td>\n",
" <td>0.224000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>-0.232000</td>\n",
" <td>0.694000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>0.019000</td>\n",
" <td>0.880000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>0.549000</td>\n",
" <td>1.108000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1.608000</td>\n",
" <td>2.057000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>range</th>\n",
" <td>2.186000</td>\n",
" <td>1.833000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3sigma</th>\n",
" <td>1.578918</td>\n",
" <td>1.072532</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 01.21.1-Die2 01.21.2-Die2\n",
"count 69.000000 69.000000\n",
"mean 0.179739 0.937768\n",
"std 0.526306 0.357511\n",
"min -0.578000 0.224000\n",
"25% -0.232000 0.694000\n",
"50% 0.019000 0.880000\n",
"75% 0.549000 1.108000\n",
"max 1.608000 2.057000\n",
"range 2.186000 1.833000\n",
"3sigma 1.578918 1.072532"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Mark1Y = TotalData['M1Y'].dropna(subset='QX8800SP_Index').set_index('QX8800SP_Index').iloc[:,die_nums:]\n",
"Mark1Y_3s = describe_3s(Mark1Y)\n",
"Mark1Y_3s"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "934a4d40-4b61-4665-a64b-636e438773b9",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"QX8800SP_Index\n",
"1.0 -1.296678\n",
"2.0 -0.504928\n",
"3.0 NaN\n",
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"8.0 0.158822\n",
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"51.0 NaN\n",
"52.0 -0.346678\n",
"53.0 NaN\n",
"54.0 NaN\n",
"55.0 NaN\n",
"56.0 -0.155678\n",
"57.0 0.103822\n",
"58.0 -0.978678\n",
"59.0 NaN\n",
"60.0 -0.280428\n",
"61.0 -0.262178\n",
"62.0 -0.135928\n",
"63.0 -0.265428\n",
"64.0 -0.116678\n",
"65.0 -0.163428\n",
"66.0 NaN\n",
"67.0 NaN\n",
"68.0 NaN\n",
"69.0 NaN\n",
"70.0 -0.241178\n",
"71.0 -0.303928\n",
"72.0 -0.323678\n",
"73.0 -0.375428\n",
"74.0 -0.245178\n",
"75.0 -0.397178\n",
"76.0 -0.110178\n",
"77.0 -0.276678\n",
"78.0 -0.603428\n",
"79.0 -0.465178\n",
"80.0 -0.802178\n",
"81.0 -0.639428\n",
"82.0 -0.436928\n",
"83.0 -0.530178\n",
"84.0 NaN\n",
"85.0 NaN\n",
"86.0 NaN\n",
"87.0 NaN\n",
"dtype: float64"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Mark1Y_1 = Mark1Y.iloc[:,1] - DieBC['对位MarkY'] - AYdescibe.iloc[1,1]\n",
"Mark1Y_1"
]
},
{
"cell_type": "markdown",
"id": "29ccb9f3-8aff-4d61-8805-2b445234f7de",
"metadata": {},
"source": [
"## Mark4"
]
},
{
"cell_type": "markdown",
"id": "aef3365d-8e5b-49cc-9b17-5e51b9bfa88b",
"metadata": {},
"source": [
"### Mark4X"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "42a5ab44-f1f8-43ad-b93d-70699d393bc5",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
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" <th></th>\n",
" <th>01.21.1-Die2</th>\n",
" <th>01.21.2-Die2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>69.000000</td>\n",
" <td>69.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>1.175464</td>\n",
" <td>1.076739</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.468110</td>\n",
" <td>0.404190</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>-0.519000</td>\n",
" <td>-0.167000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>0.859000</td>\n",
" <td>0.863000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>1.193000</td>\n",
" <td>1.147000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>1.550000</td>\n",
" <td>1.344000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>2.052000</td>\n",
" <td>1.903000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>range</th>\n",
" <td>2.571000</td>\n",
" <td>2.070000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3sigma</th>\n",
" <td>1.404331</td>\n",
" <td>1.212571</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 01.21.1-Die2 01.21.2-Die2\n",
"count 69.000000 69.000000\n",
"mean 1.175464 1.076739\n",
"std 0.468110 0.404190\n",
"min -0.519000 -0.167000\n",
"25% 0.859000 0.863000\n",
"50% 1.193000 1.147000\n",
"75% 1.550000 1.344000\n",
"max 2.052000 1.903000\n",
"range 2.571000 2.070000\n",
"3sigma 1.404331 1.212571"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Mark4X = TotalData['M4X'].dropna(subset='QX8800SP_Index').set_index('QX8800SP_Index').iloc[:,die_nums:].sort_index(axis=1)\n",
"Mark4X_3s = describe_3s(Mark4X)\n",
"Mark4X_3s"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "64a98f12-8adf-4a63-8a5d-08082bf46860",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"QX8800SP_Index\n",
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"10.0 NaN\n",
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"29.0 NaN\n",
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"31.0 NaN\n",
"32.0 0.28062\n",
"33.0 0.09887\n",
"34.0 0.56487\n",
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"36.0 0.18187\n",
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"38.0 0.07962\n",
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"40.0 0.39287\n",
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"42.0 0.28762\n",
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"47.0 0.25787\n",
"48.0 -0.00213\n",
"49.0 0.23437\n",
"50.0 0.05762\n",
"51.0 NaN\n",
"52.0 0.19837\n",
"53.0 NaN\n",
"54.0 NaN\n",
"55.0 NaN\n",
"56.0 0.24512\n",
"57.0 0.34962\n",
"58.0 0.43212\n",
"59.0 NaN\n",
"60.0 0.21887\n",
"61.0 0.02487\n",
"62.0 0.31962\n",
"63.0 -0.06463\n",
"64.0 0.16487\n",
"65.0 0.19037\n",
"66.0 NaN\n",
"67.0 NaN\n",
"68.0 NaN\n",
"69.0 NaN\n",
"70.0 -0.11538\n",
"71.0 0.03087\n",
"72.0 0.19137\n",
"73.0 0.19562\n",
"74.0 0.19912\n",
"75.0 0.03037\n",
"76.0 0.29412\n",
"77.0 0.34012\n",
"78.0 0.19162\n",
"79.0 0.07512\n",
"80.0 -0.38188\n",
"81.0 -0.64363\n",
"82.0 0.01312\n",
"83.0 0.12587\n",
"84.0 NaN\n",
"85.0 NaN\n",
"86.0 NaN\n",
"87.0 NaN\n",
"dtype: float64"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Mark4X_1 = Mark4X.iloc[:,1] - DieBC['对位MarkX'] - AXdescibe.iloc[1,1]\n",
"Mark4X_1"
]
},
{
"cell_type": "markdown",
"id": "8039c33e-01fd-456a-8921-b4dcb5d24826",
"metadata": {},
"source": [
"### Mark4Y"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "7e7351ef-d7f4-4011-8514-f3d9afdad01e",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>01.21.1-Die2</th>\n",
" <th>01.21.2-Die2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>69.000000</td>\n",
" <td>69.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.908493</td>\n",
" <td>1.632174</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.528764</td>\n",
" <td>0.391825</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.010000</td>\n",
" <td>1.019000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>0.481000</td>\n",
" <td>1.328000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>0.798000</td>\n",
" <td>1.558000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>1.312000</td>\n",
" <td>1.974000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>2.676000</td>\n",
" <td>2.430000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>range</th>\n",
" <td>2.666000</td>\n",
" <td>1.411000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3sigma</th>\n",
" <td>1.586292</td>\n",
" <td>1.175474</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 01.21.1-Die2 01.21.2-Die2\n",
"count 69.000000 69.000000\n",
"mean 0.908493 1.632174\n",
"std 0.528764 0.391825\n",
"min 0.010000 1.019000\n",
"25% 0.481000 1.328000\n",
"50% 0.798000 1.558000\n",
"75% 1.312000 1.974000\n",
"max 2.676000 2.430000\n",
"range 2.666000 1.411000\n",
"3sigma 1.586292 1.175474"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Mark4Y = TotalData['M4Y'].dropna(subset='QX8800SP_Index').set_index('QX8800SP_Index').iloc[:,die_nums:].sort_index(axis=1)\n",
"Mark4Y_3s = describe_3s(Mark4Y)\n",
"Mark4Y_3s"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "958cbc54-e717-4800-a242-9488dbd96c0b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
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"51.0 NaN\n",
"52.0 0.221322\n",
"53.0 NaN\n",
"54.0 NaN\n",
"55.0 NaN\n",
"56.0 0.390322\n",
"57.0 0.242822\n",
"58.0 -0.076678\n",
"59.0 NaN\n",
"60.0 0.333572\n",
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"62.0 0.424072\n",
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"64.0 0.592322\n",
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"66.0 NaN\n",
"67.0 NaN\n",
"68.0 NaN\n",
"69.0 NaN\n",
"70.0 0.659822\n",
"71.0 0.505072\n",
"72.0 0.435322\n",
"73.0 0.317572\n",
"74.0 0.518822\n",
"75.0 0.697822\n",
"76.0 0.341822\n",
"77.0 -0.039678\n",
"78.0 0.152572\n",
"79.0 0.693822\n",
"80.0 0.981822\n",
"81.0 0.978572\n",
"82.0 0.671072\n",
"83.0 0.529822\n",
"84.0 NaN\n",
"85.0 NaN\n",
"86.0 NaN\n",
"87.0 NaN\n",
"dtype: float64"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Mark4Y_1 = Mark4Y.iloc[:,1] - DieBC['对位MarkY'] - AYdescibe.iloc[1,1]\n",
"Mark4Y_1"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "ad5c6154-797e-4a30-a5c4-c85207d70a37",
"metadata": {},
"outputs": [
{
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" <td>-0.392928</td>\n",
" <td>0.38737</td>\n",
" <td>0.326072</td>\n",
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" <td>0.07537</td>\n",
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" <td>-0.37363</td>\n",
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" <th>7.0</th>\n",
" <td>-0.10088</td>\n",
" <td>-0.825678</td>\n",
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" <td>0.674322</td>\n",
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" <td>-0.56913</td>\n",
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" <td>0.61987</td>\n",
" <td>0.026822</td>\n",
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" <th>9.0</th>\n",
" <td>-0.25988</td>\n",
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" <td>0.478822</td>\n",
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" <th>10.0</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11.0</th>\n",
" <td>-0.41763</td>\n",
" <td>-0.123928</td>\n",
" <td>0.34237</td>\n",
" <td>0.430072</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12.0</th>\n",
" <td>-0.13088</td>\n",
" <td>-0.300178</td>\n",
" <td>0.38012</td>\n",
" <td>0.280822</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13.0</th>\n",
" <td>-0.26638</td>\n",
" <td>0.078322</td>\n",
" <td>0.28662</td>\n",
" <td>0.149322</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14.0</th>\n",
" <td>-0.21638</td>\n",
" <td>-0.570928</td>\n",
" <td>0.53362</td>\n",
" <td>0.166072</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15.0</th>\n",
" <td>-0.14538</td>\n",
" <td>-0.514928</td>\n",
" <td>0.30062</td>\n",
" <td>0.359072</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16.0</th>\n",
" <td>-0.56088</td>\n",
" <td>0.212572</td>\n",
" <td>0.42712</td>\n",
" <td>0.075572</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17.0</th>\n",
" <td>-0.35463</td>\n",
" <td>0.052322</td>\n",
" <td>0.21637</td>\n",
" <td>0.254322</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18.0</th>\n",
" <td>-0.16838</td>\n",
" <td>-0.338428</td>\n",
" <td>-0.10838</td>\n",
" <td>0.565572</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19.0</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20.0</th>\n",
" <td>-0.21800</td>\n",
" <td>-0.276000</td>\n",
" <td>0.21800</td>\n",
" <td>0.276000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21.0</th>\n",
" <td>-0.21513</td>\n",
" <td>-0.361428</td>\n",
" <td>-0.04313</td>\n",
" <td>0.470572</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22.0</th>\n",
" <td>-0.06663</td>\n",
" <td>-0.354678</td>\n",
" <td>0.05337</td>\n",
" <td>0.445322</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23.0</th>\n",
" <td>-0.37838</td>\n",
" <td>-0.179678</td>\n",
" <td>0.33262</td>\n",
" <td>0.170322</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24.0</th>\n",
" <td>-0.20488</td>\n",
" <td>-0.508678</td>\n",
" <td>0.19612</td>\n",
" <td>-0.014678</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25.0</th>\n",
" <td>-0.36138</td>\n",
" <td>-0.605178</td>\n",
" <td>0.17262</td>\n",
" <td>0.101822</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26.0</th>\n",
" <td>0.08387</td>\n",
" <td>-0.921928</td>\n",
" <td>-0.00113</td>\n",
" <td>0.279072</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27.0</th>\n",
" <td>-0.27138</td>\n",
" <td>-0.164678</td>\n",
" <td>0.46762</td>\n",
" <td>0.278322</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28.0</th>\n",
" <td>-0.17413</td>\n",
" <td>-0.360678</td>\n",
" <td>0.19687</td>\n",
" <td>0.349322</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29.0</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30.0</th>\n",
" <td>-0.01988</td>\n",
" <td>-0.494178</td>\n",
" <td>0.17312</td>\n",
" <td>0.352822</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31.0</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32.0</th>\n",
" <td>-0.33238</td>\n",
" <td>-0.115178</td>\n",
" <td>0.28062</td>\n",
" <td>0.111822</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33.0</th>\n",
" <td>-0.30713</td>\n",
" <td>-0.293178</td>\n",
" <td>0.09887</td>\n",
" <td>0.344822</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34.0</th>\n",
" <td>-0.39213</td>\n",
" <td>0.007322</td>\n",
" <td>0.56487</td>\n",
" <td>0.092322</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35.0</th>\n",
" <td>-0.23213</td>\n",
" <td>-0.614928</td>\n",
" <td>0.03087</td>\n",
" <td>0.342072</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36.0</th>\n",
" <td>0.04687</td>\n",
" <td>-1.082178</td>\n",
" <td>0.18187</td>\n",
" <td>-0.012178</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37.0</th>\n",
" <td>0.05437</td>\n",
" <td>-0.476678</td>\n",
" <td>-0.01263</td>\n",
" <td>0.671322</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38.0</th>\n",
" <td>-0.41338</td>\n",
" <td>-0.084178</td>\n",
" <td>0.07962</td>\n",
" <td>0.523822</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39.0</th>\n",
" <td>-0.29688</td>\n",
" <td>-0.226928</td>\n",
" <td>-0.07988</td>\n",
" <td>0.467072</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40.0</th>\n",
" <td>-0.24213</td>\n",
" <td>-0.202678</td>\n",
" <td>0.39287</td>\n",
" <td>0.216322</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41.0</th>\n",
" <td>-0.11663</td>\n",
" <td>-0.344178</td>\n",
" <td>0.31537</td>\n",
" <td>0.183822</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42.0</th>\n",
" <td>-0.15538</td>\n",
" <td>-0.179928</td>\n",
" <td>0.28762</td>\n",
" <td>0.289072</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43.0</th>\n",
" <td>-0.10663</td>\n",
" <td>-0.410428</td>\n",
" <td>0.20437</td>\n",
" <td>0.123572</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44.0</th>\n",
" <td>-0.39613</td>\n",
" <td>-0.167928</td>\n",
" <td>0.37387</td>\n",
" <td>0.143072</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45.0</th>\n",
" <td>0.06962</td>\n",
" <td>0.262072</td>\n",
" <td>0.25762</td>\n",
" <td>0.459072</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46.0</th>\n",
" <td>-0.51563</td>\n",
" <td>-0.066928</td>\n",
" <td>0.44637</td>\n",
" <td>-0.066928</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47.0</th>\n",
" <td>-0.24213</td>\n",
" <td>-0.598178</td>\n",
" <td>0.25787</td>\n",
" <td>0.129822</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48.0</th>\n",
" <td>-0.13813</td>\n",
" <td>-0.559678</td>\n",
" <td>-0.00213</td>\n",
" <td>0.361322</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49.0</th>\n",
" <td>-0.43563</td>\n",
" <td>-0.044928</td>\n",
" <td>0.23437</td>\n",
" <td>0.223072</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50.0</th>\n",
" <td>-0.27838</td>\n",
" <td>-0.170678</td>\n",
" <td>0.05762</td>\n",
" <td>0.254322</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51.0</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52.0</th>\n",
" <td>-0.00763</td>\n",
" <td>-0.346678</td>\n",
" <td>0.19837</td>\n",
" <td>0.221322</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53.0</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54.0</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55.0</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>56.0</th>\n",
" <td>-0.17188</td>\n",
" <td>-0.155678</td>\n",
" <td>0.24512</td>\n",
" <td>0.390322</td>\n",
" </tr>\n",
" <tr>\n",
" <th>57.0</th>\n",
" <td>-0.08838</td>\n",
" <td>0.103822</td>\n",
" <td>0.34962</td>\n",
" <td>0.242822</td>\n",
" </tr>\n",
" <tr>\n",
" <th>58.0</th>\n",
" <td>0.08712</td>\n",
" <td>-0.978678</td>\n",
" <td>0.43212</td>\n",
" <td>-0.076678</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59.0</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>60.0</th>\n",
" <td>-0.16213</td>\n",
" <td>-0.280428</td>\n",
" <td>0.21887</td>\n",
" <td>0.333572</td>\n",
" </tr>\n",
" <tr>\n",
" <th>61.0</th>\n",
" <td>-0.26713</td>\n",
" <td>-0.262178</td>\n",
" <td>0.02487</td>\n",
" <td>0.529822</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62.0</th>\n",
" <td>-0.33938</td>\n",
" <td>-0.135928</td>\n",
" <td>0.31962</td>\n",
" <td>0.424072</td>\n",
" </tr>\n",
" <tr>\n",
" <th>63.0</th>\n",
" <td>-0.20063</td>\n",
" <td>-0.265428</td>\n",
" <td>-0.06463</td>\n",
" <td>0.425572</td>\n",
" </tr>\n",
" <tr>\n",
" <th>64.0</th>\n",
" <td>-0.12113</td>\n",
" <td>-0.116678</td>\n",
" <td>0.16487</td>\n",
" <td>0.592322</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65.0</th>\n",
" <td>-0.29563</td>\n",
" <td>-0.163428</td>\n",
" <td>0.19037</td>\n",
" <td>0.512572</td>\n",
" </tr>\n",
" <tr>\n",
" <th>66.0</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>67.0</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68.0</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>69.0</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>70.0</th>\n",
" <td>-0.34938</td>\n",
" <td>-0.241178</td>\n",
" <td>-0.11538</td>\n",
" <td>0.659822</td>\n",
" </tr>\n",
" <tr>\n",
" <th>71.0</th>\n",
" <td>-0.34613</td>\n",
" <td>-0.303928</td>\n",
" <td>0.03087</td>\n",
" <td>0.505072</td>\n",
" </tr>\n",
" <tr>\n",
" <th>72.0</th>\n",
" <td>-0.22663</td>\n",
" <td>-0.323678</td>\n",
" <td>0.19137</td>\n",
" <td>0.435322</td>\n",
" </tr>\n",
" <tr>\n",
" <th>73.0</th>\n",
" <td>-0.18138</td>\n",
" <td>-0.375428</td>\n",
" <td>0.19562</td>\n",
" <td>0.317572</td>\n",
" </tr>\n",
" <tr>\n",
" <th>74.0</th>\n",
" <td>-0.21188</td>\n",
" <td>-0.245178</td>\n",
" <td>0.19912</td>\n",
" <td>0.518822</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75.0</th>\n",
" <td>0.04337</td>\n",
" <td>-0.397178</td>\n",
" <td>0.03037</td>\n",
" <td>0.697822</td>\n",
" </tr>\n",
" <tr>\n",
" <th>76.0</th>\n",
" <td>-0.10688</td>\n",
" <td>-0.110178</td>\n",
" <td>0.29412</td>\n",
" <td>0.341822</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77.0</th>\n",
" <td>-0.32088</td>\n",
" <td>-0.276678</td>\n",
" <td>0.34012</td>\n",
" <td>-0.039678</td>\n",
" </tr>\n",
" <tr>\n",
" <th>78.0</th>\n",
" <td>-0.31838</td>\n",
" <td>-0.603428</td>\n",
" <td>0.19162</td>\n",
" <td>0.152572</td>\n",
" </tr>\n",
" <tr>\n",
" <th>79.0</th>\n",
" <td>-0.12088</td>\n",
" <td>-0.465178</td>\n",
" <td>0.07512</td>\n",
" <td>0.693822</td>\n",
" </tr>\n",
" <tr>\n",
" <th>80.0</th>\n",
" <td>0.09612</td>\n",
" <td>-0.802178</td>\n",
" <td>-0.38188</td>\n",
" <td>0.981822</td>\n",
" </tr>\n",
" <tr>\n",
" <th>81.0</th>\n",
" <td>-0.15363</td>\n",
" <td>-0.639428</td>\n",
" <td>-0.64363</td>\n",
" <td>0.978572</td>\n",
" </tr>\n",
" <tr>\n",
" <th>82.0</th>\n",
" <td>-0.10188</td>\n",
" <td>-0.436928</td>\n",
" <td>0.01312</td>\n",
" <td>0.671072</td>\n",
" </tr>\n",
" <tr>\n",
" <th>83.0</th>\n",
" <td>0.05587</td>\n",
" <td>-0.530178</td>\n",
" <td>0.12587</td>\n",
" <td>0.529822</td>\n",
" </tr>\n",
" <tr>\n",
" <th>84.0</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>85.0</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>86.0</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87.0</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0 1 2 3\n",
"QX8800SP_Index \n",
"1.0 0.21137 -1.296678 0.45437 0.056322\n",
"2.0 0.25337 -0.504928 0.61237 0.620072\n",
"3.0 NaN NaN NaN NaN\n",
"4.0 -0.29713 -0.261678 0.39787 0.335322\n",
"5.0 -0.22563 -0.392928 0.38737 0.326072\n",
"6.0 0.07537 -1.003428 -0.37363 0.805572\n",
"7.0 -0.10088 -0.825678 -0.25588 0.674322\n",
"8.0 -0.56913 0.158822 0.61987 0.026822\n",
"9.0 -0.25988 -0.123178 0.45212 0.478822\n",
"10.0 NaN NaN NaN NaN\n",
"11.0 -0.41763 -0.123928 0.34237 0.430072\n",
"12.0 -0.13088 -0.300178 0.38012 0.280822\n",
"13.0 -0.26638 0.078322 0.28662 0.149322\n",
"14.0 -0.21638 -0.570928 0.53362 0.166072\n",
"15.0 -0.14538 -0.514928 0.30062 0.359072\n",
"16.0 -0.56088 0.212572 0.42712 0.075572\n",
"17.0 -0.35463 0.052322 0.21637 0.254322\n",
"18.0 -0.16838 -0.338428 -0.10838 0.565572\n",
"19.0 NaN NaN NaN NaN\n",
"20.0 -0.21800 -0.276000 0.21800 0.276000\n",
"21.0 -0.21513 -0.361428 -0.04313 0.470572\n",
"22.0 -0.06663 -0.354678 0.05337 0.445322\n",
"23.0 -0.37838 -0.179678 0.33262 0.170322\n",
"24.0 -0.20488 -0.508678 0.19612 -0.014678\n",
"25.0 -0.36138 -0.605178 0.17262 0.101822\n",
"26.0 0.08387 -0.921928 -0.00113 0.279072\n",
"27.0 -0.27138 -0.164678 0.46762 0.278322\n",
"28.0 -0.17413 -0.360678 0.19687 0.349322\n",
"29.0 NaN NaN NaN NaN\n",
"30.0 -0.01988 -0.494178 0.17312 0.352822\n",
"31.0 NaN NaN NaN NaN\n",
"32.0 -0.33238 -0.115178 0.28062 0.111822\n",
"33.0 -0.30713 -0.293178 0.09887 0.344822\n",
"34.0 -0.39213 0.007322 0.56487 0.092322\n",
"35.0 -0.23213 -0.614928 0.03087 0.342072\n",
"36.0 0.04687 -1.082178 0.18187 -0.012178\n",
"37.0 0.05437 -0.476678 -0.01263 0.671322\n",
"38.0 -0.41338 -0.084178 0.07962 0.523822\n",
"39.0 -0.29688 -0.226928 -0.07988 0.467072\n",
"40.0 -0.24213 -0.202678 0.39287 0.216322\n",
"41.0 -0.11663 -0.344178 0.31537 0.183822\n",
"42.0 -0.15538 -0.179928 0.28762 0.289072\n",
"43.0 -0.10663 -0.410428 0.20437 0.123572\n",
"44.0 -0.39613 -0.167928 0.37387 0.143072\n",
"45.0 0.06962 0.262072 0.25762 0.459072\n",
"46.0 -0.51563 -0.066928 0.44637 -0.066928\n",
"47.0 -0.24213 -0.598178 0.25787 0.129822\n",
"48.0 -0.13813 -0.559678 -0.00213 0.361322\n",
"49.0 -0.43563 -0.044928 0.23437 0.223072\n",
"50.0 -0.27838 -0.170678 0.05762 0.254322\n",
"51.0 NaN NaN NaN NaN\n",
"52.0 -0.00763 -0.346678 0.19837 0.221322\n",
"53.0 NaN NaN NaN NaN\n",
"54.0 NaN NaN NaN NaN\n",
"55.0 NaN NaN NaN NaN\n",
"56.0 -0.17188 -0.155678 0.24512 0.390322\n",
"57.0 -0.08838 0.103822 0.34962 0.242822\n",
"58.0 0.08712 -0.978678 0.43212 -0.076678\n",
"59.0 NaN NaN NaN NaN\n",
"60.0 -0.16213 -0.280428 0.21887 0.333572\n",
"61.0 -0.26713 -0.262178 0.02487 0.529822\n",
"62.0 -0.33938 -0.135928 0.31962 0.424072\n",
"63.0 -0.20063 -0.265428 -0.06463 0.425572\n",
"64.0 -0.12113 -0.116678 0.16487 0.592322\n",
"65.0 -0.29563 -0.163428 0.19037 0.512572\n",
"66.0 NaN NaN NaN NaN\n",
"67.0 NaN NaN NaN NaN\n",
"68.0 NaN NaN NaN NaN\n",
"69.0 NaN NaN NaN NaN\n",
"70.0 -0.34938 -0.241178 -0.11538 0.659822\n",
"71.0 -0.34613 -0.303928 0.03087 0.505072\n",
"72.0 -0.22663 -0.323678 0.19137 0.435322\n",
"73.0 -0.18138 -0.375428 0.19562 0.317572\n",
"74.0 -0.21188 -0.245178 0.19912 0.518822\n",
"75.0 0.04337 -0.397178 0.03037 0.697822\n",
"76.0 -0.10688 -0.110178 0.29412 0.341822\n",
"77.0 -0.32088 -0.276678 0.34012 -0.039678\n",
"78.0 -0.31838 -0.603428 0.19162 0.152572\n",
"79.0 -0.12088 -0.465178 0.07512 0.693822\n",
"80.0 0.09612 -0.802178 -0.38188 0.981822\n",
"81.0 -0.15363 -0.639428 -0.64363 0.978572\n",
"82.0 -0.10188 -0.436928 0.01312 0.671072\n",
"83.0 0.05587 -0.530178 0.12587 0.529822\n",
"84.0 NaN NaN NaN NaN\n",
"85.0 NaN NaN NaN NaN\n",
"86.0 NaN NaN NaN NaN\n",
"87.0 NaN NaN NaN NaN"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"PreWafer1 = pd.concat([Mark1X_1,Mark1Y_1,Mark4X_1,Mark4Y_1],axis=1)\n",
"PreWafer1"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "215ced5d-732e-4a8e-99ac-748a6a3996e5",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>0</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>69.000000</td>\n",
" <td>69.000000</td>\n",
" <td>69.000000</td>\n",
" <td>69.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>-0.191860</td>\n",
" <td>-0.343526</td>\n",
" <td>0.188908</td>\n",
" <td>0.350879</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.173492</td>\n",
" <td>0.300672</td>\n",
" <td>0.231354</td>\n",
" <td>0.234457</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>-0.569130</td>\n",
" <td>-1.296678</td>\n",
" <td>-0.643630</td>\n",
" <td>-0.076678</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>-0.307130</td>\n",
" <td>-0.504928</td>\n",
" <td>0.053370</td>\n",
" <td>0.170322</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>-0.211880</td>\n",
" <td>-0.300178</td>\n",
" <td>0.199120</td>\n",
" <td>0.341822</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>-0.106630</td>\n",
" <td>-0.163428</td>\n",
" <td>0.340120</td>\n",
" <td>0.505072</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>0.253370</td>\n",
" <td>0.262072</td>\n",
" <td>0.619870</td>\n",
" <td>0.981822</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0 1 2 3\n",
"count 69.000000 69.000000 69.000000 69.000000\n",
"mean -0.191860 -0.343526 0.188908 0.350879\n",
"std 0.173492 0.300672 0.231354 0.234457\n",
"min -0.569130 -1.296678 -0.643630 -0.076678\n",
"25% -0.307130 -0.504928 0.053370 0.170322\n",
"50% -0.211880 -0.300178 0.199120 0.341822\n",
"75% -0.106630 -0.163428 0.340120 0.505072\n",
"max 0.253370 0.262072 0.619870 0.981822"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"PreWafer1.describe()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "0fa2096d-a032-487b-ad1c-5a31378464b8",
"metadata": {},
"outputs": [],
"source": [
"data_pass = PreWafer1.iloc[:,:4].copy()\n",
"data_pass[\"L1\"] = (data_pass[0]**2 + data_pass[1]**2)**0.5\n",
"# data_pass[\"L2\"] = (data_pass[\"M3X\"]**2 + data_pass[\"M3Y\"]**2)**0.5\n",
"data_pass[\"L2\"] = (data_pass[2]**2 + data_pass[3]**2)**0.5\n",
"data_pass_1000 = data_pass[data_pass < 1].dropna()\n",
"data_pass_800 = data_pass[data_pass < 0.8].dropna()\n",
"data_pass_500 = data_pass[data_pass < 0.5].dropna()\n",
"data_pass_200 = data_pass[data_pass < 0.2].dropna()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "08a6bea5-6451-43e4-9bf7-c8b8e0396c13",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<1000nm 合格率： 86.49%\n",
"< 800nm 合格率： 81.08%\n",
"< 500nm 合格率： 37.84%\n",
"< 200nm 合格率： 0.0%\n"
]
}
],
"source": [
"pass_list =[74,\n",
" len(data_pass_1000),\n",
" len(data_pass_800),\n",
" len(data_pass_500),\n",
" len(data_pass_200)]\n",
"pass_rate = [str(round(i,2))+\"%\" for i in np.array(pass_list[1:])/pass_list[0] * 100]\n",
"pass_rate_name = [\"<1000nm\",\"< 800nm\",\"< 500nm\",\"< 200nm\"]\n",
"for i,j in zip(pass_rate,pass_rate_name):\n",
" print(j,\"合格率：\",i)"
]
},
{
"cell_type": "markdown",
"id": "57aab54c-ca77-46e9-bdfa-becc3323ab8f",
"metadata": {},
"source": [
"## 角度"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "3b9aba3d-417d-4292-ac07-8c9d25d260b8",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>01.21.1-Die1</th>\n",
" <th>01.21.2-Die1</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>73.000000</td>\n",
" <td>74.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>-0.002403</td>\n",
" <td>0.000378</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.001820</td>\n",
" <td>0.001571</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>-0.007254</td>\n",
" <td>-0.004145</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>-0.003526</td>\n",
" <td>-0.000334</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>-0.002290</td>\n",
" <td>0.000375</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>-0.001205</td>\n",
" <td>0.001515</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>0.002092</td>\n",
" <td>0.003469</td>\n",
" </tr>\n",
" <tr>\n",
" <th>range</th>\n",
" <td>0.009345</td>\n",
" <td>0.007615</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3sigma</th>\n",
" <td>0.005459</td>\n",
" <td>0.004712</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 01.21.1-Die1 01.21.2-Die1\n",
"count 73.000000 74.000000\n",
"mean -0.002403 0.000378\n",
"std 0.001820 0.001571\n",
"min -0.007254 -0.004145\n",
"25% -0.003526 -0.000334\n",
"50% -0.002290 0.000375\n",
"75% -0.001205 0.001515\n",
"max 0.002092 0.003469\n",
"range 0.009345 0.007615\n",
"3sigma 0.005459 0.004712"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Angle = TotalData['Angle14'].dropna(subset='QX8800SP_Index').set_index('QX8800SP_Index').iloc[:,die_nums:].sort_index(axis=1)\n",
"Angdescibe = describe_3s(Angle)\n",
"Angdescibe"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "e4805a99-5123-41cf-ba9c-f7c0bbbec0fa",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"-0.0010124878547705769"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Angdescibe.loc['mean'].mean()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "5ce2eec7-a959-4716-92a0-4aaad88b96b3",
"metadata": {},
"outputs": [
{
"data": {
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"text/html": [
"\n",
" <div style=\"display: inline-block;\">\n",
" <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
" Figure\n",
" </div>\n",
" <img src='data:image/png;base64,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
" </div>\n",
" "
],
"text/plain": [
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(2,1)\n",
"ax[0].plot([i+1 for i in range(len(Angle.columns))],Angdescibe.loc['mean'],linestyle = '-.',marker = 'o',color='w')\n",
"Angle.boxplot(ax=ax[0])\n",
"ax[0].axhline(0,c='orange',ls='-.',label=r'Mean_Angle:$0°\\pm0.0005°$')\n",
"for i in range(len(Angle.columns)):\n",
" ax[0].annotate(round(Angdescibe.loc['mean'][i],5), \n",
" xy=(i+1,Angdescibe.loc['mean'][i]),\n",
" xytext=(i+0.95,Angdescibe.loc['mean'][i]),\n",
" fontsize=12,\n",
" color=\"r\")\n",
"ax[0].legend()\n",
"ax[0].set_title('mean_Angle/Day')\n",
"labels = ax[0].get_xticklabels()\n",
"plt.setp(labels, rotation=90)\n",
"ax[1].plot([i for i in Angle.columns],Angdescibe.loc['3sigma'],marker = 'o')\n",
"ax[1].axhline(0.001,c='orange',ls='-.',label=r'3sigma_Angle:$<0.001°$')\n",
"for i in range(len(Angle.columns)):\n",
" ax[1].annotate(round(Angdescibe.loc['3sigma'][i],5), \n",
" xy=(i,Angdescibe.loc['3sigma'][i]),\n",
" xytext=(i,Angdescibe.loc['3sigma'][i]),\n",
" fontsize=12,\n",
" color=\"r\")\n",
"ax[1].legend() \n",
"ax[1].set_title('3sigam_Angle/Day')\n",
"labels = ax[1].get_xticklabels()\n",
"plt.setp(labels, rotation=90)\n",
"# ax[2].plot([i for i in Angle.columns],Angdescibe.loc['range'],marker = 'o')\n",
"# for i in range(len(Angle.columns)):\n",
"# ax[2].annotate(round(Angdescibe.loc['range'][i],3), \n",
"# xy=(i,Angdescibe.loc['range'][i]),\n",
"# xytext=(i,Angdescibe.loc['range'][i]),\n",
"# color=\"r\")\n",
"# ax[2].set_title('Range_Angle/Day')\n",
"plt.suptitle('Die1 角度（°）')\n",
"fig.tight_layout()\n",
"plt.savefig('Die1/Die1角度.jpg',dpi=200)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "639173af",
"metadata": {},
"source": [
"### 补偿值计算"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "6e7764ef-386a-4936-84cc-72021ca594d2",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Die1对位MarkX局部补偿（um）')"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f9fa088cab9b49e392703618f408a9d4",
"version_major": 2,
"version_minor": 0
},
"image/png": "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
"text/html": [
"\n",
" <div style=\"display: inline-block;\">\n",
" <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
" Figure\n",
" </div>\n",
" <img src='data:image/png;base64,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
" </div>\n",
" "
],
"text/plain": [
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"BC_X = pd.concat([AlignMarkX[i]-AlignMarkX[i].mean() for i in AlignMarkX.columns[-3:]],axis=1)\n",
"# BC_X = pd.concat([AlignMarkX[i] for i in AlignMarkX.columns[-4:]],axis=1)\n",
"BC_X.plot(marker='o')\n",
"plt.title('Die1对位MarkX局部补偿（um）')"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "7b6651ce-0935-4386-b6a4-634fd78aaee3",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Die1对位MarkX局部补偿（um）')"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0dd020e1f948435ab1ea167261bb9b10",
"version_major": 2,
"version_minor": 0
},
"image/png": "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
"text/html": [
"\n",
" <div style=\"display: inline-block;\">\n",
" <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
" Figure\n",
" </div>\n",
" <img src='data:image/png;base64,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
" </div>\n",
" "
],
"text/plain": [
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# BC_X = pd.concat([AlignMarkX[i]-AlignMarkX[i].mean() for i in AlignMarkX.columns[-3:]],axis=1)\n",
"BC_X = pd.concat([AlignMarkX[i] for i in AlignMarkX.columns[-4:]],axis=1)\n",
"BC_X.plot(marker='o')\n",
"plt.title('Die1对位MarkX局部补偿（um）')"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "59afc9f3-aa42-42c9-b308-c1257aaa6ede",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Die1对位MarkY局部补偿（um）')"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5bd3e1f4d2624482ae70e9e4b2a7c6ac",
"version_major": 2,
"version_minor": 0
},
"image/png": "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
"text/html": [
"\n",
" <div style=\"display: inline-block;\">\n",
" <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
" Figure\n",
" </div>\n",
" <img src='data:image/png;base64,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
" </div>\n",
" "
],
"text/plain": [
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"BC_Y = pd.concat([AlignMarkY[i]-AlignMarkY[i].mean() for i in AlignMarkY.columns[-3:]],axis=1)\n",
"# BC_Y = pd.concat([AlignMarkY[i] for i in AlignMarkY.columns[-4:]],axis=1)\n",
"BC_Y.plot(marker='o')\n",
"plt.title('Die1对位MarkY局部补偿（um）')"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "61ce5e50-3b7b-4b70-8917-cc6bac2a1ced",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Die1对位MarkY局部补偿（um）')"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b172bfa51c7a430083d0767ee3b90034",
"version_major": 2,
"version_minor": 0
},
"image/png": "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
"text/html": [
"\n",
" <div style=\"display: inline-block;\">\n",
" <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
" Figure\n",
" </div>\n",
" <img src='data:image/png;base64,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
" </div>\n",
" "
],
"text/plain": [
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# BC_Y = pd.concat([AlignMarkY[i]-AlignMarkY[i].mean() for i in AlignMarkY.columns[-3:]],axis=1)\n",
"BC_Y = pd.concat([AlignMarkY[i] for i in AlignMarkY.columns[-4:]],axis=1)\n",
"BC_Y.plot(marker='o')\n",
"plt.title('Die1对位MarkY局部补偿（um）')"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "a94845fc-49f0-41a1-99a0-b5f5cdff1a69",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Die1角度（°）')"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c20caea7285a433a9c6c152b95429b1c",
"version_major": 2,
"version_minor": 0
},
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAyAAAAImCAYAAACrXu7BAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOy9eZwcdZ3//6qqvo/pnjv3QSaZhByEBEhEFMIhl2AQ47EuCv6Q5Vi/roggroKuosKKoq4oiKLCsotc4V5PEFRCICaQBDLJ5Jxcc3bP9H1V/f6o/lRXdVf1Nd090z3v5+PBg0zX2d3VVZ/35/V6v9+cJEkSCIIgCIIgCIIgagA/0SdAEARBEARBEMTUgQIQgiAIgiAIgiBqBgUgBEEQBEEQBEHUDApACIIgCIIgCIKoGRSAEARBEARBEARRMygAIQiCIAiCIAiiZlAAQhAEQRAEQRBEzaAAhCAIgiAIgiCImkEBCEEQBEEQBEEQNYMCEIIgCIIgCIIgagYFIARBEARBEARB1AwKQAiCIAiCIAiCqBkUgBAEQRAEQRAEUTMoACEIgiAIgiAIomZQAEIQBEEQBEEQRM2gAIQgCIIgCIIgiJpBAQhBEARBEARBEDWDAhCCIAiCIAiCIGoGBSAEQRAEQRAEQdQMCkAIgiAIgiAIgqgZFIAQBEEQBEEQBFEzKAAhCIIgCIIgCKJmUABCEARBEARBEETNoACEIAiCIAiCIIiaQQEIQRAEQRAEQRA1gwIQgiAIgiAIgiBqBgUgBEEQBEEQBEHUDApACIIgCIIgCIKoGRSAEARBEARBEARRMygAIQiCIAiCIAiiZlAAQhAEQRAEQRBEzaAAhCAIgiAIgiCImkEBCEEQBEEQBEEQNYMCEIIgCIIgCIIgagYFIARBEARBEARB1AwKQAiCIAiCIAiCqBkUgBAEQRAEQRAEUTMoACEIgiAIgiAIomZQAEIQBEEQBEEQRM2gAIQgCIIgCIIgiJpBAQhBEARBEARBEDWDAhCCIAiCIAiCIGoGBSAEQRAEQRAEQdQMCkAIgiAIDfF4HFdffTX+8Y9/jHtf+/btw5VXXomBgYEKnJkWn89X8X1Wc78EQRCEDAUgBEEQhIZ7770Xe/fuxbx588a9r+nTp+Ott97C448/rrwmSRJisRgikUjZ+/3b3/6GCy64ICewSSaT+O1vf4t77rkHR48eLXm/x48fxwUXXIDXXnut7HMjCIIg8sNJkiRN9EkQBEFMVV5//XV86lOfAgA4HA4sWLAAH/vYx7Bhwwbd9Xp6eso+1mOPPYZnnnkGDz30kOE6f//733H11VcjlUrpLv/yl7+Mq666SnfZ+eefjwMHDhR9PhdddBF+8IMfFL0+44033sC//uu/4s4778RZZ52lWXb33XfD7Xbj/e9/P374wx/ipz/9acn7/9Of/oSvfOUr+OlPf4pVq1aVvD1BEASRH9NEnwBBEAQBfO9734PX68XGjRvx1a9+FcePH8fnPvc5ZfnSpUs1KkKpvPnmm/jmN7+Jk046yXCdrVu34vOf/zxuueUWfPzjH9cs+9znPodgMIhPf/rThtubzWbcfPPN+Od//mcAwM9//nN0dHTgQx/6EABZnbjhhhtw3XXX4cQTTwTHcSW/D5/Ph5tuugnf+ta3coIPANizZw/uvfde8DwPt9td8v4B4JxzzsHtt9+OL37xi9i4cSM8Hk9Z+yEIgiD0IQsWQRDEJKCrqwvve9/7cPfdd+O8887Dfffdh+HhYWW5y+XC8uXLy9r3Sy+9hM9+9rNYsGCB4TqPP/44rrzySlx++eX4yEc+gmQyqfz36quv4rXXXsPXvvY1RCIRhEIhRKPRnH1wHAez2QxRFGEymbBjxw7s3bsXVqsVVqsVTqcTmzZtQjKZhN1uh9lsLvm93H///TjjjDNw3nnn6S5ft24dbrvtNjz44IMQBKHk/TMuuuginHbaaXjggQfK3gdBEAShDykgBEEQk4yPfexj+MMf/oBXX30V69evH/f+Nm/ejHvuuQdvv/02Nm/enLP8pZdewte+9jWsWrUKDz74IB588EHd/ajPZfny5bqKDM/zOP300xGLxSBJEjiOwyOPPKIslyQJ11xzDURRxOmnn45f/OIXRb+PeDyOJ554Av/93/9tuM7HPvYxLFu2DH6/H1dccUXR+9bjqquuwpVXXon/9//+X1nBEkEQBKEPBSAEQRCTjMWLFwMA9u7dq7yWLwfk3XffxXe+8x1s27YN7e3tuOKKK3DllVcqy7/0pS+B53m8/fbbusdbt24dnnnmGezatQtHjhzBM888oyz71re+hVQqhdtvv1157Re/+AXeeOONnP1IkgRBEPD666/DYrHg2muvxfz583HLLbco65x44on45S9/iTVr1iCRSBT/oQDYv38/XC4XFi5cmHe9pUuXlrRfIxYvXgyz2YxDhw7lVY8IgiCI0iALFkEQxCSD5RyMjo4WXNfn8+HKK6+E1+vFAw88gKuuugp33XUXHnvsMWUdni98q1+4cCFEUQTHcRAEQfmP53nd1/TsTdFoFGazGRaLBQAgiiISiQRCoZDyH3sdQMmqwrFjx9DR0VHSNuOls7MTx44dq+kxCYIgGh1SQAiCICYZpSRnP/zww+B5HnfffTfMZjNOO+00vP7663j66adzKmkVIhqN4ujRo7qVn5599lnN36eddpru9uqE7Ugkgoceeiin6lY8Hi/pvBhms1kJYmpFMBgk+xVBEESFoQCEIAhikjE2NgYARVVf2r17N0ZGRrBs2TLN6+3t7SUf1+/3Y+XKlXj00UeV1/793/8dqVQK3/3ud5XX7r33Xt0+GaFQCO3t7QgGg0gkEvjpT39qGEwlk0mYTKU9gubNm4dDhw4hFovBarWWtG05RCIRHD58uCL9UAiCIIgMFIAQBEFMMnbv3g1AroxVDCtWrMB//Md/aF4rpwLU/v374XQ6NSpDKpVCMpnUvKaXuzE6OopwOIz29nb8+Mc/xm9/+1tEo1HYbDbFkgXIQYrdbsf//d//obW1taTzmzlzJmbPno1nn30WH/nIR0p+f6Xy9NNPo6urC52dnVU/FkEQxFSCAhCCIIhJxm9/+1tYrVaceeaZBddduHAhtm7digULFigD/RdeeAFvvvkmbrvttpKO+/bbb2Pv3r1lWbAOHjwIQRDQ3t6OW2+9Fbfeeit+9KMfYdOmTfjNb34Dk8mEvXv3YsOGDfjGN75RcvDBuOqqq/CTn/wE5513XlX7c/h8Pvz0pz/FjTfeWLVjEARBTFUoCZ0gCGIS0Nvbi7/97W+4+eab8cILL+BLX/oSvF5vwe3++Z//GfF4HF/4whfw2muv4bnnnsM3vvGNohLP1Wzfvh179+7Fj3/8Y/T09Cj/feQjH8Fll12mee3zn/98zvabNm3C4sWLNWrHddddh0QigS9/+cvo6+vD9ddfj0984hO46KKLSjo3NR/+8IcxZ84cfOlLXyo7l6QQsVgMN910E7q6unDppZdW5RgEQRBTGQpACIIgJgE33XQTbrjhBvT19eEnP/lJ0T0sWlpa8Ktf/QqBQAD/8i//gjvvvBMf+chHcPPNNxd97EQigTvuuANz5szB2WefrVkWjUYhSZLytyRJGBkZyQlwfve732HNmjWa18xmM/7rv/4Lr776Ki688EKccMIJuOmmm4o+Lz04jsM999yDgYEBfOYzn0EwGBzX/rIJBoO48sor4fP58IMf/KCsbu0EQRBEfjhJ/WQhCIIgphxPPfUUvva1r+FXv/oVTjnlFM2ya665Bna7HT/84Q/x5ptv4oorrgDHcbj55puVXiObN2/GFVdcgaeffhoejwcDAwPYvXs33njjDfzlL3/B7NmzccIJJ+APf/gDmpubcdJJJ2H69OloaWnB6tWrcfLJJ5d8zsFgEA8//DA++9nPjqvjeTapVAo///nPccUVV8DpdFZsvwRBEEQGCkAIgiAIHDt2DNOnTy+43s6dOzFnzhy43W7lNb/fjxdffBGf+MQn8LOf/Qz/+7//i2XLluGkk07C+eefjzlz5gCQy+9u3rwZW7ZswTvvvIN9+/b
"text/html": [
"\n",
" <div style=\"display: inline-block;\">\n",
" <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
" Figure\n",
" </div>\n",
" <img src='data:image/png;base64,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
" </div>\n",
" "
],
"text/plain": [
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"BC_A = pd.concat([Angle[i]-Angle[i].mean() for i in Angle.columns[-3:]],axis=1)\n",
"# BC_A = pd.concat([Angle[i] for i in Angle.columns[-4:]],axis=1)\n",
"BC_A.plot(marker='o')\n",
"plt.title(f'{DieType}角度（°）')"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "0b74b5bd-965f-49be-9703-e8afcae83b3b",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Die1角度（°）')"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "10420d664d28404a99cdd983c45b0fd4",
"version_major": 2,
"version_minor": 0
},
"image/png": "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
"text/html": [
"\n",
" <div style=\"display: inline-block;\">\n",
" <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
" Figure\n",
" </div>\n",
" <img src='data:image/png;base64,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
" </div>\n",
" "
],
"text/plain": [
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# BC_A = pd.concat([Angle[i]-Angle[i].mean() for i in Angle.columns[-3:]],axis=1)\n",
"BC_A = pd.concat([Angle[i] for i in Angle.columns[-4:]],axis=1)\n",
"BC_A.plot(marker='o')\n",
"plt.title(f'{DieType}角度（°）')"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "c8f85626-8763-49b3-a9ee-61d8d0fa4f9d",
"metadata": {},
"outputs": [],
"source": [
"# BC_X = BC_X.where(abs(BC_X)<1, np.nan)\n",
"# BC_X.plot(marker='o')\n",
"# plt.title('Die1对位MarkX局部补偿（um）')"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "ac86dfdd-f506-4ac7-9590-c563900d70df",
"metadata": {},
"outputs": [],
"source": [
"# BC_Y = BC_Y.where(abs(BC_Y)<1, np.nan)\n",
"# BC_Y.plot(marker='o')\n",
"# plt.title('Die1对位MarkY局部补偿（um）')"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "8baa7aa8-4528-451c-bada-63635cd2989b",
"metadata": {},
"outputs": [],
"source": [
"# BC_X.corr()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "b62b7df1",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>对位MarkX</th>\n",
" <th>对位MarkY</th>\n",
" <th>Angle</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Index</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>56</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>57</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>58</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>60</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>61</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>63</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>64</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>66</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>67</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>69</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>70</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>71</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>72</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>73</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>74</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>76</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>78</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>79</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>80</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>81</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>82</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>83</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>84</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>85</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>86</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 对位MarkX 对位MarkY Angle\n",
"Index \n",
"1 NaN NaN NaN\n",
"2 NaN NaN NaN\n",
"3 NaN NaN NaN\n",
"4 NaN NaN NaN\n",
"5 NaN NaN NaN\n",
"6 NaN NaN NaN\n",
"7 NaN NaN NaN\n",
"8 NaN NaN NaN\n",
"9 NaN NaN NaN\n",
"10 NaN NaN NaN\n",
"11 NaN NaN NaN\n",
"12 NaN NaN NaN\n",
"13 NaN NaN NaN\n",
"14 NaN NaN NaN\n",
"15 NaN NaN NaN\n",
"16 NaN NaN NaN\n",
"17 NaN NaN NaN\n",
"18 NaN NaN NaN\n",
"19 NaN NaN NaN\n",
"20 NaN NaN NaN\n",
"21 NaN NaN NaN\n",
"22 NaN NaN NaN\n",
"23 NaN NaN NaN\n",
"24 NaN NaN NaN\n",
"25 NaN NaN NaN\n",
"26 NaN NaN NaN\n",
"27 NaN NaN NaN\n",
"28 NaN NaN NaN\n",
"29 NaN NaN NaN\n",
"30 NaN NaN NaN\n",
"31 NaN NaN NaN\n",
"32 NaN NaN NaN\n",
"33 NaN NaN NaN\n",
"34 NaN NaN NaN\n",
"35 NaN NaN NaN\n",
"36 NaN NaN NaN\n",
"37 NaN NaN NaN\n",
"38 NaN NaN NaN\n",
"39 NaN NaN NaN\n",
"40 NaN NaN NaN\n",
"41 NaN NaN NaN\n",
"42 NaN NaN NaN\n",
"43 NaN NaN NaN\n",
"44 NaN NaN NaN\n",
"45 NaN NaN NaN\n",
"46 NaN NaN NaN\n",
"47 NaN NaN NaN\n",
"48 NaN NaN NaN\n",
"49 NaN NaN NaN\n",
"50 NaN NaN NaN\n",
"51 NaN NaN NaN\n",
"52 NaN NaN NaN\n",
"53 NaN NaN NaN\n",
"54 NaN NaN NaN\n",
"55 NaN NaN NaN\n",
"56 NaN NaN NaN\n",
"57 NaN NaN NaN\n",
"58 NaN NaN NaN\n",
"59 NaN NaN NaN\n",
"60 NaN NaN NaN\n",
"61 NaN NaN NaN\n",
"62 NaN NaN NaN\n",
"63 NaN NaN NaN\n",
"64 NaN NaN NaN\n",
"65 NaN NaN NaN\n",
"66 NaN NaN NaN\n",
"67 NaN NaN NaN\n",
"68 NaN NaN NaN\n",
"69 NaN NaN NaN\n",
"70 NaN NaN NaN\n",
"71 NaN NaN NaN\n",
"72 NaN NaN NaN\n",
"73 NaN NaN NaN\n",
"74 NaN NaN NaN\n",
"75 NaN NaN NaN\n",
"76 NaN NaN NaN\n",
"77 NaN NaN NaN\n",
"78 NaN NaN NaN\n",
"79 NaN NaN NaN\n",
"80 NaN NaN NaN\n",
"81 NaN NaN NaN\n",
"82 NaN NaN NaN\n",
"83 NaN NaN NaN\n",
"84 NaN NaN NaN\n",
"85 NaN NaN NaN\n",
"86 NaN NaN NaN\n",
"87 NaN NaN NaN"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"DieBC = pd.read_excel('SP-Die补偿模版.xlsx',index_col=0,header=0)\n",
"DieBC"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "f2ae7ed6-c025-4390-8bd1-4e3b82783c36",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>对位MarkX</th>\n",
" <th>对位MarkY</th>\n",
" <th>Angle</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Index</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-3.074000</td>\n",
" <td>0.177500</td>\n",
" <td>-0.009325</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-2.720000</td>\n",
" <td>0.138750</td>\n",
" <td>-0.002766</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-2.986250</td>\n",
" <td>0.153375</td>\n",
" <td>-0.004000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-3.252500</td>\n",
" <td>0.168000</td>\n",
" <td>-0.005233</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>-2.867500</td>\n",
" <td>0.220500</td>\n",
" <td>-0.003531</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>-2.712500</td>\n",
" <td>0.000750</td>\n",
" <td>-0.002685</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>-3.222000</td>\n",
" <td>0.185500</td>\n",
" <td>-0.002354</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>-3.313000</td>\n",
" <td>0.150750</td>\n",
" <td>-0.005350</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>-3.226750</td>\n",
" <td>0.331000</td>\n",
" <td>-0.005559</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>-3.384000</td>\n",
" <td>0.365625</td>\n",
" <td>-0.005957</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>-3.541250</td>\n",
" <td>0.400250</td>\n",
" <td>-0.006355</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>-3.421500</td>\n",
" <td>0.178000</td>\n",
" <td>-0.005234</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>-3.237750</td>\n",
" <td>0.348000</td>\n",
" <td>-0.004982</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>-2.807500</td>\n",
" <td>0.377750</td>\n",
" <td>-0.003507</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>-3.107500</td>\n",
" <td>0.354250</td>\n",
" <td>-0.003964</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>-2.980000</td>\n",
" <td>0.601500</td>\n",
" <td>-0.005093</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>-3.288000</td>\n",
" <td>0.618500</td>\n",
" <td>-0.002862</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>-3.445500</td>\n",
" <td>0.528750</td>\n",
" <td>-0.005188</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>-3.135500</td>\n",
" <td>0.590000</td>\n",
" <td>-0.004002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>-3.396250</td>\n",
" <td>0.309000</td>\n",
" <td>-0.006996</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>-3.360333</td>\n",
" <td>0.240667</td>\n",
" <td>-0.006527</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>-3.357667</td>\n",
" <td>0.056333</td>\n",
" <td>-0.005720</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>-3.214750</td>\n",
" <td>0.182000</td>\n",
" <td>-0.003231</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>-2.972833</td>\n",
" <td>0.235667</td>\n",
" <td>-0.005118</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>-3.259000</td>\n",
" <td>0.253000</td>\n",
" <td>-0.003865</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>-3.250833</td>\n",
" <td>0.537667</td>\n",
" <td>-0.005293</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>-3.115000</td>\n",
" <td>0.669833</td>\n",
" <td>-0.003265</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>-3.601667</td>\n",
" <td>0.463333</td>\n",
" <td>-0.004712</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>-3.472000</td>\n",
" <td>0.397833</td>\n",
" <td>-0.003806</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>-3.610500</td>\n",
" <td>0.347417</td>\n",
" <td>-0.004990</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>-3.749000</td>\n",
" <td>0.297000</td>\n",
" <td>-0.006173</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>-3.624000</td>\n",
" <td>0.178000</td>\n",
" <td>-0.006722</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>-3.163833</td>\n",
" <td>0.298500</td>\n",
" <td>-0.005309</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>-3.019500</td>\n",
" <td>0.211250</td>\n",
" <td>-0.004588</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>-3.352500</td>\n",
" <td>-0.069500</td>\n",
" <td>-0.006906</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>-2.985750</td>\n",
" <td>0.520250</td>\n",
" <td>-0.005315</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>-3.087500</td>\n",
" <td>0.473833</td>\n",
" <td>-0.002120</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>-3.270167</td>\n",
" <td>0.401333</td>\n",
" <td>-0.003770</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>-3.632833</td>\n",
" <td>0.213333</td>\n",
" <td>-0.007240</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>-3.621333</td>\n",
" <td>0.470333</td>\n",
" <td>-0.005348</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>-3.751167</td>\n",
" <td>0.201167</td>\n",
" <td>-0.008750</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>-3.697000</td>\n",
" <td>0.236833</td>\n",
" <td>-0.007800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>-3.642833</td>\n",
" <td>0.272500</td>\n",
" <td>-0.006850</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>-3.565167</td>\n",
" <td>0.345833</td>\n",
" <td>-0.006051</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>-3.202000</td>\n",
" <td>0.506667</td>\n",
" <td>-0.005736</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>-3.405000</td>\n",
" <td>0.232500</td>\n",
" <td>-0.006315</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>-2.790167</td>\n",
" <td>0.292500</td>\n",
" <td>-0.003578</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>-3.049667</td>\n",
" <td>0.689500</td>\n",
" <td>-0.002700</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>-3.525167</td>\n",
" <td>0.480833</td>\n",
" <td>-0.005791</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>-3.393250</td>\n",
" <td>0.414500</td>\n",
" <td>-0.004618</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td>-3.261333</td>\n",
" <td>0.348167</td>\n",
" <td>-0.003444</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52</th>\n",
" <td>-3.365667</td>\n",
" <td>0.299000</td>\n",
" <td>-0.004197</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53</th>\n",
" <td>-3.507500</td>\n",
" <td>0.355333</td>\n",
" <td>-0.006194</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54</th>\n",
" <td>-3.375333</td>\n",
" <td>0.329667</td>\n",
" <td>-0.005615</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55</th>\n",
" <td>-3.243167</td>\n",
" <td>0.304000</td>\n",
" <td>-0.005035</td>\n",
" </tr>\n",
" <tr>\n",
" <th>56</th>\n",
" <td>-3.165500</td>\n",
" <td>0.375500</td>\n",
" <td>-0.004429</td>\n",
" </tr>\n",
" <tr>\n",
" <th>57</th>\n",
" <td>-2.736500</td>\n",
" <td>0.385333</td>\n",
" <td>-0.006230</td>\n",
" </tr>\n",
" <tr>\n",
" <th>58</th>\n",
" <td>-3.605000</td>\n",
" <td>0.393000</td>\n",
" <td>-0.006654</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59</th>\n",
" <td>-2.956333</td>\n",
" <td>0.592667</td>\n",
" <td>-0.004287</td>\n",
" </tr>\n",
" <tr>\n",
" <th>60</th>\n",
" <td>-3.159833</td>\n",
" <td>0.511722</td>\n",
" <td>-0.004702</td>\n",
" </tr>\n",
" <tr>\n",
" <th>61</th>\n",
" <td>-3.363333</td>\n",
" <td>0.430778</td>\n",
" <td>-0.005116</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62</th>\n",
" <td>-3.566833</td>\n",
" <td>0.349833</td>\n",
" <td>-0.005531</td>\n",
" </tr>\n",
" <tr>\n",
" <th>63</th>\n",
" <td>-3.422667</td>\n",
" <td>0.262167</td>\n",
" <td>-0.006041</td>\n",
" </tr>\n",
" <tr>\n",
" <th>64</th>\n",
" <td>-3.548000</td>\n",
" <td>0.348833</td>\n",
" <td>-0.005574</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65</th>\n",
" <td>-3.575667</td>\n",
" <td>0.227833</td>\n",
" <td>-0.005954</td>\n",
" </tr>\n",
" <tr>\n",
" <th>66</th>\n",
" <td>-3.121500</td>\n",
" <td>0.237333</td>\n",
" <td>-0.005561</td>\n",
" </tr>\n",
" <tr>\n",
" <th>67</th>\n",
" <td>-3.035667</td>\n",
" <td>0.262417</td>\n",
" <td>-0.005002</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68</th>\n",
" <td>-2.949833</td>\n",
" <td>0.287500</td>\n",
" <td>-0.004443</td>\n",
" </tr>\n",
" <tr>\n",
" <th>69</th>\n",
" <td>-3.507833</td>\n",
" <td>0.468667</td>\n",
" <td>-0.005000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>70</th>\n",
" <td>-3.631333</td>\n",
" <td>0.240833</td>\n",
" <td>-0.006539</td>\n",
" </tr>\n",
" <tr>\n",
" <th>71</th>\n",
" <td>-3.440500</td>\n",
" <td>0.227500</td>\n",
" <td>-0.006182</td>\n",
" </tr>\n",
" <tr>\n",
" <th>72</th>\n",
" <td>-3.476000</td>\n",
" <td>0.319500</td>\n",
" <td>-0.006765</td>\n",
" </tr>\n",
" <tr>\n",
" <th>73</th>\n",
" <td>-3.420000</td>\n",
" <td>0.421333</td>\n",
" <td>-0.005230</td>\n",
" </tr>\n",
" <tr>\n",
" <th>74</th>\n",
" <td>-3.538333</td>\n",
" <td>0.241000</td>\n",
" <td>-0.005673</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75</th>\n",
" <td>-3.201500</td>\n",
" <td>0.183167</td>\n",
" <td>-0.004941</td>\n",
" </tr>\n",
" <tr>\n",
" <th>76</th>\n",
" <td>-2.971000</td>\n",
" <td>0.267000</td>\n",
" <td>-0.003830</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77</th>\n",
" <td>-2.961000</td>\n",
" <td>0.078333</td>\n",
" <td>-0.004163</td>\n",
" </tr>\n",
" <tr>\n",
" <th>78</th>\n",
" <td>-2.754667</td>\n",
" <td>-0.007833</td>\n",
" <td>-0.003109</td>\n",
" </tr>\n",
" <tr>\n",
" <th>79</th>\n",
" <td>-3.366000</td>\n",
" <td>0.404167</td>\n",
" <td>-0.003920</td>\n",
" </tr>\n",
" <tr>\n",
" <th>80</th>\n",
" <td>-3.364167</td>\n",
" <td>0.284000</td>\n",
" <td>-0.005575</td>\n",
" </tr>\n",
" <tr>\n",
" <th>81</th>\n",
" <td>-3.263500</td>\n",
" <td>0.260833</td>\n",
" <td>-0.003760</td>\n",
" </tr>\n",
" <tr>\n",
" <th>82</th>\n",
" <td>-3.368833</td>\n",
" <td>0.307167</td>\n",
" <td>-0.005953</td>\n",
" </tr>\n",
" <tr>\n",
" <th>83</th>\n",
" <td>-3.120833</td>\n",
" <td>0.303333</td>\n",
" <td>-0.005129</td>\n",
" </tr>\n",
" <tr>\n",
" <th>84</th>\n",
" <td>-3.120833</td>\n",
" <td>0.303333</td>\n",
" <td>-0.005129</td>\n",
" </tr>\n",
" <tr>\n",
" <th>85</th>\n",
" <td>-3.120833</td>\n",
" <td>0.303333</td>\n",
" <td>-0.005129</td>\n",
" </tr>\n",
" <tr>\n",
" <th>86</th>\n",
" <td>-3.120833</td>\n",
" <td>0.303333</td>\n",
" <td>-0.005129</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87</th>\n",
" <td>-3.120833</td>\n",
" <td>0.303333</td>\n",
" <td>-0.005129</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 对位MarkX 对位MarkY Angle\n",
"Index \n",
"1 -3.074000 0.177500 -0.009325\n",
"2 -2.720000 0.138750 -0.002766\n",
"3 -2.986250 0.153375 -0.004000\n",
"4 -3.252500 0.168000 -0.005233\n",
"5 -2.867500 0.220500 -0.003531\n",
"6 -2.712500 0.000750 -0.002685\n",
"7 -3.222000 0.185500 -0.002354\n",
"8 -3.313000 0.150750 -0.005350\n",
"9 -3.226750 0.331000 -0.005559\n",
"10 -3.384000 0.365625 -0.005957\n",
"11 -3.541250 0.400250 -0.006355\n",
"12 -3.421500 0.178000 -0.005234\n",
"13 -3.237750 0.348000 -0.004982\n",
"14 -2.807500 0.377750 -0.003507\n",
"15 -3.107500 0.354250 -0.003964\n",
"16 -2.980000 0.601500 -0.005093\n",
"17 -3.288000 0.618500 -0.002862\n",
"18 -3.445500 0.528750 -0.005188\n",
"19 -3.135500 0.590000 -0.004002\n",
"20 -3.396250 0.309000 -0.006996\n",
"21 -3.360333 0.240667 -0.006527\n",
"22 -3.357667 0.056333 -0.005720\n",
"23 -3.214750 0.182000 -0.003231\n",
"24 -2.972833 0.235667 -0.005118\n",
"25 -3.259000 0.253000 -0.003865\n",
"26 -3.250833 0.537667 -0.005293\n",
"27 -3.115000 0.669833 -0.003265\n",
"28 -3.601667 0.463333 -0.004712\n",
"29 -3.472000 0.397833 -0.003806\n",
"30 -3.610500 0.347417 -0.004990\n",
"31 -3.749000 0.297000 -0.006173\n",
"32 -3.624000 0.178000 -0.006722\n",
"33 -3.163833 0.298500 -0.005309\n",
"34 -3.019500 0.211250 -0.004588\n",
"35 -3.352500 -0.069500 -0.006906\n",
"36 -2.985750 0.520250 -0.005315\n",
"37 -3.087500 0.473833 -0.002120\n",
"38 -3.270167 0.401333 -0.003770\n",
"39 -3.632833 0.213333 -0.007240\n",
"40 -3.621333 0.470333 -0.005348\n",
"41 -3.751167 0.201167 -0.008750\n",
"42 -3.697000 0.236833 -0.007800\n",
"43 -3.642833 0.272500 -0.006850\n",
"44 -3.565167 0.345833 -0.006051\n",
"45 -3.202000 0.506667 -0.005736\n",
"46 -3.405000 0.232500 -0.006315\n",
"47 -2.790167 0.292500 -0.003578\n",
"48 -3.049667 0.689500 -0.002700\n",
"49 -3.525167 0.480833 -0.005791\n",
"50 -3.393250 0.414500 -0.004618\n",
"51 -3.261333 0.348167 -0.003444\n",
"52 -3.365667 0.299000 -0.004197\n",
"53 -3.507500 0.355333 -0.006194\n",
"54 -3.375333 0.329667 -0.005615\n",
"55 -3.243167 0.304000 -0.005035\n",
"56 -3.165500 0.375500 -0.004429\n",
"57 -2.736500 0.385333 -0.006230\n",
"58 -3.605000 0.393000 -0.006654\n",
"59 -2.956333 0.592667 -0.004287\n",
"60 -3.159833 0.511722 -0.004702\n",
"61 -3.363333 0.430778 -0.005116\n",
"62 -3.566833 0.349833 -0.005531\n",
"63 -3.422667 0.262167 -0.006041\n",
"64 -3.548000 0.348833 -0.005574\n",
"65 -3.575667 0.227833 -0.005954\n",
"66 -3.121500 0.237333 -0.005561\n",
"67 -3.035667 0.262417 -0.005002\n",
"68 -2.949833 0.287500 -0.004443\n",
"69 -3.507833 0.468667 -0.005000\n",
"70 -3.631333 0.240833 -0.006539\n",
"71 -3.440500 0.227500 -0.006182\n",
"72 -3.476000 0.319500 -0.006765\n",
"73 -3.420000 0.421333 -0.005230\n",
"74 -3.538333 0.241000 -0.005673\n",
"75 -3.201500 0.183167 -0.004941\n",
"76 -2.971000 0.267000 -0.003830\n",
"77 -2.961000 0.078333 -0.004163\n",
"78 -2.754667 -0.007833 -0.003109\n",
"79 -3.366000 0.404167 -0.003920\n",
"80 -3.364167 0.284000 -0.005575\n",
"81 -3.263500 0.260833 -0.003760\n",
"82 -3.368833 0.307167 -0.005953\n",
"83 -3.120833 0.303333 -0.005129\n",
"84 -3.120833 0.303333 -0.005129\n",
"85 -3.120833 0.303333 -0.005129\n",
"86 -3.120833 0.303333 -0.005129\n",
"87 -3.120833 0.303333 -0.005129"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"BC_X = BC_X.mean(axis=1)\n",
"DieBC['对位MarkX'] = BC_X.fillna(BC_X.interpolate()).values\n",
"BC_Y = BC_Y.mean(axis=1)\n",
"DieBC['对位MarkY'] = BC_Y.fillna(BC_Y.interpolate()).values\n",
"BC_A = BC_A.mean(axis=1)\n",
"DieBC['Angle'] = BC_A.fillna(BC_A.interpolate()).values\n",
"DieBC"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "2dddf61b-818e-4592-8b46-765bfa119856",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2025-01-12\n"
]
}
],
"source": [
"import datetime\n",
"daytime = str(datetime.datetime.now())[:10]\n",
"print(daytime)\n",
"# DieBC.to_excel(f'{DieType}/{DieType}局部补偿{daytime}.xlsx')"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "7c602ca9-f73a-4584-a051-bd1e84f86d5e",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>全局补偿X</th>\n",
" <td>5.589459</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>全局补偿Y</th>\n",
" <td>5.057021</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>全局补偿角度</th>\n",
" <td>0.02032</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Index</th>\n",
" <td>对位MarkX</td>\n",
" <td>对位MarkY</td>\n",
" <td>Angle</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.47427</td>\n",
" <td>0.2982</td>\n",
" <td>-0.000996</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.09377</td>\n",
" <td>0.198534</td>\n",
" <td>-0.000556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-0.008563</td>\n",
" <td>0.185409</td>\n",
" <td>-0.000638</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-0.110896</td>\n",
" <td>0.172284</td>\n",
" <td>-0.00072</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>-0.038896</td>\n",
" <td>0.140867</td>\n",
" <td>-0.002075</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>0.371354</td>\n",
" <td>0.331034</td>\n",
" <td>-0.000228</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>-0.261396</td>\n",
" <td>0.068284</td>\n",
" <td>0.002302</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>-0.26173</td>\n",
" <td>0.076034</td>\n",
" <td>0.000238</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>-0.225563</td>\n",
" <td>0.055367</td>\n",
" <td>-0.000682</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>-0.26223</td>\n",
" <td>-0.0138</td>\n",
" <td>0.000175</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>-0.298896</td>\n",
" <td>-0.082966</td>\n",
" <td>0.001031</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>-0.181396</td>\n",
" <td>-0.09355</td>\n",
" <td>0.000011</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>-0.127813</td>\n",
" <td>0.127284</td>\n",
" <td>-0.000897</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>0.147437</td>\n",
" <td>0.355534</td>\n",
" <td>-0.001769</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>-0.07798</td>\n",
" <td>0.05095</td>\n",
" <td>-0.000638</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>-0.183646</td>\n",
" <td>0.3962</td>\n",
" <td>-0.000643</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>-0.397063</td>\n",
" <td>0.040284</td>\n",
" <td>-0.000673</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>-0.219563</td>\n",
" <td>-0.238966</td>\n",
" <td>0.002379</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>-0.22098</td>\n",
" <td>0.3437</td>\n",
" <td>0.001064</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>-0.12398</td>\n",
" <td>-0.232633</td>\n",
" <td>0.001511</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>-0.002646</td>\n",
" <td>-0.14405</td>\n",
" <td>-0.000204</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>-0.085896</td>\n",
" <td>-0.185383</td>\n",
" <td>-0.000299</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>-0.049563</td>\n",
" <td>0.000367</td>\n",
" <td>-0.000787</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>0.12676</td>\n",
" <td>0.186436</td>\n",
" <td>-0.001889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>-0.31798</td>\n",
" <td>0.255034</td>\n",
" <td>0.000808</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>-0.161063</td>\n",
" <td>0.23645</td>\n",
" <td>-0.000316</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>-0.11048</td>\n",
" <td>0.065367</td>\n",
" <td>-0.000716</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>-0.367563</td>\n",
" <td>-0.122216</td>\n",
" <td>0.003322</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>-0.39348</td>\n",
" <td>-0.079216</td>\n",
" <td>0.002546</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>-0.369646</td>\n",
" <td>-0.130133</td>\n",
" <td>0.00168</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>-0.345813</td>\n",
" <td>-0.18105</td>\n",
" <td>0.000815</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>-0.20373</td>\n",
" <td>-0.1758</td>\n",
" <td>0.000684</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>0.02277</td>\n",
" <td>0.032534</td>\n",
" <td>-0.001091</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>0.201687</td>\n",
" <td>0.50145</td>\n",
" <td>-0.001473</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>0.00352</td>\n",
" <td>-0.231633</td>\n",
" <td>0.001917</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>-0.110896</td>\n",
" <td>0.426867</td>\n",
" <td>-0.000707</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>-0.158396</td>\n",
" <td>-0.114466</td>\n",
" <td>-0.000707</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>-0.162813</td>\n",
" <td>-0.0973</td>\n",
" <td>0.000025</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>-0.079646</td>\n",
" <td>0.107367</td>\n",
" <td>0.003811</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>-0.23123</td>\n",
" <td>-0.25755</td>\n",
" <td>0.002151</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>-0.14073</td>\n",
" <td>-0.260966</td>\n",
" <td>0.00202</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>-0.139521</td>\n",
" <td>-0.21455</td>\n",
" <td>0.00176</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>-0.138313</td>\n",
" <td>-0.168133</td>\n",
" <td>0.0015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>-0.117646</td>\n",
" <td>0.12995</td>\n",
" <td>-0.000115</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>0.10752</td>\n",
" <td>0.518117</td>\n",
" <td>-0.000488</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>-0.048313</td>\n",
" <td>0.17845</td>\n",
" <td>-0.000727</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>0.04327</td>\n",
" <td>0.4112</td>\n",
" <td>-0.000644</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>-0.093313</td>\n",
" <td>0.24395</td>\n",
" <td>-0.000871</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>-0.235063</td>\n",
" <td>-0.108466</td>\n",
" <td>0.002393</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>-0.144021</td>\n",
" <td>-0.2158</td>\n",
" <td>0.002029</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td>-0.05298</td>\n",
" <td>-0.323133</td>\n",
" <td>0.001664</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52</th>\n",
" <td>-0.122896</td>\n",
" <td>-0.1213</td>\n",
" <td>0.000262</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53</th>\n",
" <td>-0.11173</td>\n",
" <td>-0.053383</td>\n",
" <td>0.001749</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54</th>\n",
" <td>-0.022355</td>\n",
" <td>-0.060675</td>\n",
" <td>0.000903</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55</th>\n",
" <td>0.06702</td>\n",
" <td>-0.067966</td>\n",
" <td>0.000056</td>\n",
" </tr>\n",
" <tr>\n",
" <th>56</th>\n",
" <td>0.02052</td>\n",
" <td>0.22945</td>\n",
" <td>-0.00229</td>\n",
" </tr>\n",
" <tr>\n",
" <th>57</th>\n",
" <td>0.224937</td>\n",
" <td>0.167117</td>\n",
" <td>-0.001635</td>\n",
" </tr>\n",
" <tr>\n",
" <th>58</th>\n",
" <td>-0.444896</td>\n",
" <td>0.127367</td>\n",
" <td>0.000382</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59</th>\n",
" <td>-0.011813</td>\n",
" <td>0.355284</td>\n",
" <td>-0.002298</td>\n",
" </tr>\n",
" <tr>\n",
" <th>60</th>\n",
" <td>-0.096757</td>\n",
" <td>0.16045</td>\n",
" <td>-0.000978</td>\n",
" </tr>\n",
" <tr>\n",
" <th>61</th>\n",
" <td>-0.181702</td>\n",
" <td>-0.034383</td>\n",
" <td>0.000343</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62</th>\n",
" <td>-0.266646</td>\n",
" <td>-0.229216</td>\n",
" <td>0.001663</td>\n",
" </tr>\n",
" <tr>\n",
" <th>63</th>\n",
" <td>-0.151396</td>\n",
" <td>-0.31205</td>\n",
" <td>0.002032</td>\n",
" </tr>\n",
" <tr>\n",
" <th>64</th>\n",
" <td>-0.05073</td>\n",
" <td>-0.14405</td>\n",
" <td>0.001137</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65</th>\n",
" <td>-0.07698</td>\n",
" <td>-0.0758</td>\n",
" <td>0.002136</td>\n",
" </tr>\n",
" <tr>\n",
" <th>66</th>\n",
" <td>0.019854</td>\n",
" <td>0.019367</td>\n",
" <td>-0.000318</td>\n",
" </tr>\n",
" <tr>\n",
" <th>67</th>\n",
" <td>-0.029313</td>\n",
" <td>0.147534</td>\n",
" <td>-0.000117</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68</th>\n",
" <td>-0.07848</td>\n",
" <td>0.2757</td>\n",
" <td>0.000084</td>\n",
" </tr>\n",
" <tr>\n",
" <th>69</th>\n",
" <td>-0.369396</td>\n",
" <td>0.012534</td>\n",
" <td>0.000919</td>\n",
" </tr>\n",
" <tr>\n",
" <th>70</th>\n",
" <td>-0.288813</td>\n",
" <td>-0.236883</td>\n",
" <td>0.00284</td>\n",
" </tr>\n",
" <tr>\n",
" <th>71</th>\n",
" <td>-0.061896</td>\n",
" <td>-0.22405</td>\n",
" <td>0.001407</td>\n",
" </tr>\n",
" <tr>\n",
" <th>72</th>\n",
" <td>-0.100146</td>\n",
" <td>-0.182383</td>\n",
" <td>0.002034</td>\n",
" </tr>\n",
" <tr>\n",
" <th>73</th>\n",
" <td>-0.035146</td>\n",
" <td>-0.022883</td>\n",
" <td>0.001108</td>\n",
" </tr>\n",
" <tr>\n",
" <th>74</th>\n",
" <td>-0.16673</td>\n",
" <td>-0.02355</td>\n",
" <td>0.002439</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75</th>\n",
" <td>-0.033813</td>\n",
" <td>0.003284</td>\n",
" <td>-0.000338</td>\n",
" </tr>\n",
" <tr>\n",
" <th>76</th>\n",
" <td>0.010354</td>\n",
" <td>0.19645</td>\n",
" <td>-0.002826</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77</th>\n",
" <td>0.02177</td>\n",
" <td>0.119867</td>\n",
" <td>-0.001393</td>\n",
" </tr>\n",
" <tr>\n",
" <th>78</th>\n",
" <td>0.199604</td>\n",
" <td>-0.091883</td>\n",
" <td>-0.002099</td>\n",
" </tr>\n",
" <tr>\n",
" <th>79</th>\n",
" <td>-0.189896</td>\n",
" <td>-0.0253</td>\n",
" <td>0.000202</td>\n",
" </tr>\n",
" <tr>\n",
" <th>80</th>\n",
" <td>-0.018896</td>\n",
" <td>-0.0323</td>\n",
" <td>0.000449</td>\n",
" </tr>\n",
" <tr>\n",
" <th>81</th>\n",
" <td>0.045187</td>\n",
" <td>0.093367</td>\n",
" <td>0.000598</td>\n",
" </tr>\n",
" <tr>\n",
" <th>82</th>\n",
" <td>-0.017146</td>\n",
" <td>0.223617</td>\n",
" <td>-0.000021</td>\n",
" </tr>\n",
" <tr>\n",
" <th>83</th>\n",
" <td>0.19227</td>\n",
" <td>0.2617</td>\n",
" <td>-0.000891</td>\n",
" </tr>\n",
" <tr>\n",
" <th>84</th>\n",
" <td>0.19227</td>\n",
" <td>0.2617</td>\n",
" <td>-0.000891</td>\n",
" </tr>\n",
" <tr>\n",
" <th>85</th>\n",
" <td>0.19227</td>\n",
" <td>0.2617</td>\n",
" <td>-0.000891</td>\n",
" </tr>\n",
" <tr>\n",
" <th>86</th>\n",
" <td>0.19227</td>\n",
" <td>0.2617</td>\n",
" <td>-0.000891</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87</th>\n",
" <td>0.19227</td>\n",
" <td>0.2617</td>\n",
" <td>-0.000891</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 1 2 3\n",
"0 \n",
"全局补偿X 5.589459 NaN NaN\n",
"全局补偿Y 5.057021 NaN NaN\n",
"全局补偿角度 0.02032 NaN NaN\n",
"Index 对位MarkX 对位MarkY Angle\n",
"1 0.47427 0.2982 -0.000996\n",
"2 0.09377 0.198534 -0.000556\n",
"3 -0.008563 0.185409 -0.000638\n",
"4 -0.110896 0.172284 -0.00072\n",
"5 -0.038896 0.140867 -0.002075\n",
"6 0.371354 0.331034 -0.000228\n",
"7 -0.261396 0.068284 0.002302\n",
"8 -0.26173 0.076034 0.000238\n",
"9 -0.225563 0.055367 -0.000682\n",
"10 -0.26223 -0.0138 0.000175\n",
"11 -0.298896 -0.082966 0.001031\n",
"12 -0.181396 -0.09355 0.000011\n",
"13 -0.127813 0.127284 -0.000897\n",
"14 0.147437 0.355534 -0.001769\n",
"15 -0.07798 0.05095 -0.000638\n",
"16 -0.183646 0.3962 -0.000643\n",
"17 -0.397063 0.040284 -0.000673\n",
"18 -0.219563 -0.238966 0.002379\n",
"19 -0.22098 0.3437 0.001064\n",
"20 -0.12398 -0.232633 0.001511\n",
"21 -0.002646 -0.14405 -0.000204\n",
"22 -0.085896 -0.185383 -0.000299\n",
"23 -0.049563 0.000367 -0.000787\n",
"24 0.12676 0.186436 -0.001889\n",
"25 -0.31798 0.255034 0.000808\n",
"26 -0.161063 0.23645 -0.000316\n",
"27 -0.11048 0.065367 -0.000716\n",
"28 -0.367563 -0.122216 0.003322\n",
"29 -0.39348 -0.079216 0.002546\n",
"30 -0.369646 -0.130133 0.00168\n",
"31 -0.345813 -0.18105 0.000815\n",
"32 -0.20373 -0.1758 0.000684\n",
"33 0.02277 0.032534 -0.001091\n",
"34 0.201687 0.50145 -0.001473\n",
"35 0.00352 -0.231633 0.001917\n",
"36 -0.110896 0.426867 -0.000707\n",
"37 -0.158396 -0.114466 -0.000707\n",
"38 -0.162813 -0.0973 0.000025\n",
"39 -0.079646 0.107367 0.003811\n",
"40 -0.23123 -0.25755 0.002151\n",
"41 -0.14073 -0.260966 0.00202\n",
"42 -0.139521 -0.21455 0.00176\n",
"43 -0.138313 -0.168133 0.0015\n",
"44 -0.117646 0.12995 -0.000115\n",
"45 0.10752 0.518117 -0.000488\n",
"46 -0.048313 0.17845 -0.000727\n",
"47 0.04327 0.4112 -0.000644\n",
"48 -0.093313 0.24395 -0.000871\n",
"49 -0.235063 -0.108466 0.002393\n",
"50 -0.144021 -0.2158 0.002029\n",
"51 -0.05298 -0.323133 0.001664\n",
"52 -0.122896 -0.1213 0.000262\n",
"53 -0.11173 -0.053383 0.001749\n",
"54 -0.022355 -0.060675 0.000903\n",
"55 0.06702 -0.067966 0.000056\n",
"56 0.02052 0.22945 -0.00229\n",
"57 0.224937 0.167117 -0.001635\n",
"58 -0.444896 0.127367 0.000382\n",
"59 -0.011813 0.355284 -0.002298\n",
"60 -0.096757 0.16045 -0.000978\n",
"61 -0.181702 -0.034383 0.000343\n",
"62 -0.266646 -0.229216 0.001663\n",
"63 -0.151396 -0.31205 0.002032\n",
"64 -0.05073 -0.14405 0.001137\n",
"65 -0.07698 -0.0758 0.002136\n",
"66 0.019854 0.019367 -0.000318\n",
"67 -0.029313 0.147534 -0.000117\n",
"68 -0.07848 0.2757 0.000084\n",
"69 -0.369396 0.012534 0.000919\n",
"70 -0.288813 -0.236883 0.00284\n",
"71 -0.061896 -0.22405 0.001407\n",
"72 -0.100146 -0.182383 0.002034\n",
"73 -0.035146 -0.022883 0.001108\n",
"74 -0.16673 -0.02355 0.002439\n",
"75 -0.033813 0.003284 -0.000338\n",
"76 0.010354 0.19645 -0.002826\n",
"77 0.02177 0.119867 -0.001393\n",
"78 0.199604 -0.091883 -0.002099\n",
"79 -0.189896 -0.0253 0.000202\n",
"80 -0.018896 -0.0323 0.000449\n",
"81 0.045187 0.093367 0.000598\n",
"82 -0.017146 0.223617 -0.000021\n",
"83 0.19227 0.2617 -0.000891\n",
"84 0.19227 0.2617 -0.000891\n",
"85 0.19227 0.2617 -0.000891\n",
"86 0.19227 0.2617 -0.000891\n",
"87 0.19227 0.2617 -0.000891"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"OldDieBCFileName = f'{DieType}局部补偿2025-0108.xlsx'\n",
"OldDieBC = pd.read_excel(f'{DieType}/{OldDieBCFileName}',sheet_name=\"Result\",index_col=0,header=None)\n",
"OldDieBC"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "b7bb39f8-9366-4698-9a4d-98a03f916ab7",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>对位MarkX</th>\n",
" <th>对位MarkY</th>\n",
" <th>Angle</th>\n",
" <th>Calc-X</th>\n",
" <th>Calc-Y</th>\n",
" <th>Calc-Angle</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.47427</td>\n",
" <td>0.2982</td>\n",
" <td>-0.000996</td>\n",
" <td>-3.074000</td>\n",
" <td>0.177500</td>\n",
" <td>-0.009325</td>\n",
" <td>3.54827</td>\n",
" <td>0.1207</td>\n",
" <td>-0.010321</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.09377</td>\n",
" <td>0.198534</td>\n",
" <td>-0.000556</td>\n",
" <td>-2.720000</td>\n",
" <td>0.138750</td>\n",
" <td>-0.002766</td>\n",
" <td>2.81377</td>\n",
" <td>0.059784</td>\n",
" <td>-0.003322</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-0.008563</td>\n",
" <td>0.185409</td>\n",
" <td>-0.000638</td>\n",
" <td>-2.986250</td>\n",
" <td>0.153375</td>\n",
" <td>-0.004000</td>\n",
" <td>2.977687</td>\n",
" <td>0.032034</td>\n",
" <td>-0.004638</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-0.110896</td>\n",
" <td>0.172284</td>\n",
" <td>-0.00072</td>\n",
" <td>-3.252500</td>\n",
" <td>0.168000</td>\n",
" <td>-0.005233</td>\n",
" <td>3.141604</td>\n",
" <td>0.004284</td>\n",
" <td>-0.005953</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>-0.038896</td>\n",
" <td>0.140867</td>\n",
" <td>-0.002075</td>\n",
" <td>-2.867500</td>\n",
" <td>0.220500</td>\n",
" <td>-0.003531</td>\n",
" <td>2.828604</td>\n",
" <td>-0.079633</td>\n",
" <td>-0.005606</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>0.371354</td>\n",
" <td>0.331034</td>\n",
" <td>-0.000228</td>\n",
" <td>-2.712500</td>\n",
" <td>0.000750</td>\n",
" <td>-0.002685</td>\n",
" <td>3.083854</td>\n",
" <td>0.330284</td>\n",
" <td>-0.002912</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>-0.261396</td>\n",
" <td>0.068284</td>\n",
" <td>0.002302</td>\n",
" <td>-3.222000</td>\n",
" <td>0.185500</td>\n",
" <td>-0.002354</td>\n",
" <td>2.960604</td>\n",
" <td>-0.117216</td>\n",
" <td>-0.000052</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>-0.26173</td>\n",
" <td>0.076034</td>\n",
" <td>0.000238</td>\n",
" <td>-3.313000</td>\n",
" <td>0.150750</td>\n",
" <td>-0.005350</td>\n",
" <td>3.05127</td>\n",
" <td>-0.074716</td>\n",
" <td>-0.005112</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>-0.225563</td>\n",
" <td>0.055367</td>\n",
" <td>-0.000682</td>\n",
" <td>-3.226750</td>\n",
" <td>0.331000</td>\n",
" <td>-0.005559</td>\n",
" <td>3.001187</td>\n",
" <td>-0.275633</td>\n",
" <td>-0.00624</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>-0.26223</td>\n",
" <td>-0.0138</td>\n",
" <td>0.000175</td>\n",
" <td>-3.384000</td>\n",
" <td>0.365625</td>\n",
" <td>-0.005957</td>\n",
" <td>3.12177</td>\n",
" <td>-0.379425</td>\n",
" <td>-0.005782</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>-0.298896</td>\n",
" <td>-0.082966</td>\n",
" <td>0.001031</td>\n",
" <td>-3.541250</td>\n",
" <td>0.400250</td>\n",
" <td>-0.006355</td>\n",
" <td>3.242354</td>\n",
" <td>-0.483216</td>\n",
" <td>-0.005325</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>-0.181396</td>\n",
" <td>-0.09355</td>\n",
" <td>0.000011</td>\n",
" <td>-3.421500</td>\n",
" <td>0.178000</td>\n",
" <td>-0.005234</td>\n",
" <td>3.240104</td>\n",
" <td>-0.27155</td>\n",
" <td>-0.005222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>-0.127813</td>\n",
" <td>0.127284</td>\n",
" <td>-0.000897</td>\n",
" <td>-3.237750</td>\n",
" <td>0.348000</td>\n",
" <td>-0.004982</td>\n",
" <td>3.109937</td>\n",
" <td>-0.220716</td>\n",
" <td>-0.005879</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>0.147437</td>\n",
" <td>0.355534</td>\n",
" <td>-0.001769</td>\n",
" <td>-2.807500</td>\n",
" <td>0.377750</td>\n",
" <td>-0.003507</td>\n",
" <td>2.954937</td>\n",
" <td>-0.022216</td>\n",
" <td>-0.005276</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>-0.07798</td>\n",
" <td>0.05095</td>\n",
" <td>-0.000638</td>\n",
" <td>-3.107500</td>\n",
" <td>0.354250</td>\n",
" <td>-0.003964</td>\n",
" <td>3.02952</td>\n",
" <td>-0.3033</td>\n",
" <td>-0.004603</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>-0.183646</td>\n",
" <td>0.3962</td>\n",
" <td>-0.000643</td>\n",
" <td>-2.980000</td>\n",
" <td>0.601500</td>\n",
" <td>-0.005093</td>\n",
" <td>2.796354</td>\n",
" <td>-0.2053</td>\n",
" <td>-0.005736</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>-0.397063</td>\n",
" <td>0.040284</td>\n",
" <td>-0.000673</td>\n",
" <td>-3.288000</td>\n",
" <td>0.618500</td>\n",
" <td>-0.002862</td>\n",
" <td>2.890937</td>\n",
" <td>-0.578216</td>\n",
" <td>-0.003535</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>-0.219563</td>\n",
" <td>-0.238966</td>\n",
" <td>0.002379</td>\n",
" <td>-3.445500</td>\n",
" <td>0.528750</td>\n",
" <td>-0.005188</td>\n",
" <td>3.225937</td>\n",
" <td>-0.767716</td>\n",
" <td>-0.002809</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>-0.22098</td>\n",
" <td>0.3437</td>\n",
" <td>0.001064</td>\n",
" <td>-3.135500</td>\n",
" <td>0.590000</td>\n",
" <td>-0.004002</td>\n",
" <td>2.91452</td>\n",
" <td>-0.2463</td>\n",
" <td>-0.002937</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>-0.12398</td>\n",
" <td>-0.232633</td>\n",
" <td>0.001511</td>\n",
" <td>-3.396250</td>\n",
" <td>0.309000</td>\n",
" <td>-0.006996</td>\n",
" <td>3.27227</td>\n",
" <td>-0.541633</td>\n",
" <td>-0.005485</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>-0.002646</td>\n",
" <td>-0.14405</td>\n",
" <td>-0.000204</td>\n",
" <td>-3.360333</td>\n",
" <td>0.240667</td>\n",
" <td>-0.006527</td>\n",
" <td>3.357687</td>\n",
" <td>-0.384716</td>\n",
" <td>-0.006731</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>-0.085896</td>\n",
" <td>-0.185383</td>\n",
" <td>-0.000299</td>\n",
" <td>-3.357667</td>\n",
" <td>0.056333</td>\n",
" <td>-0.005720</td>\n",
" <td>3.27177</td>\n",
" <td>-0.241716</td>\n",
" <td>-0.006019</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>-0.049563</td>\n",
" <td>0.000367</td>\n",
" <td>-0.000787</td>\n",
" <td>-3.214750</td>\n",
" <td>0.182000</td>\n",
" <td>-0.003231</td>\n",
" <td>3.165187</td>\n",
" <td>-0.181633</td>\n",
" <td>-0.004018</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>0.12676</td>\n",
" <td>0.186436</td>\n",
" <td>-0.001889</td>\n",
" <td>-2.972833</td>\n",
" <td>0.235667</td>\n",
" <td>-0.005118</td>\n",
" <td>3.099593</td>\n",
" <td>-0.049231</td>\n",
" <td>-0.007006</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>-0.31798</td>\n",
" <td>0.255034</td>\n",
" <td>0.000808</td>\n",
" <td>-3.259000</td>\n",
" <td>0.253000</td>\n",
" <td>-0.003865</td>\n",
" <td>2.94102</td>\n",
" <td>0.002034</td>\n",
" <td>-0.003057</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>-0.161063</td>\n",
" <td>0.23645</td>\n",
" <td>-0.000316</td>\n",
" <td>-3.250833</td>\n",
" <td>0.537667</td>\n",
" <td>-0.005293</td>\n",
" <td>3.08977</td>\n",
" <td>-0.301216</td>\n",
" <td>-0.005608</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>-0.11048</td>\n",
" <td>0.065367</td>\n",
" <td>-0.000716</td>\n",
" <td>-3.115000</td>\n",
" <td>0.669833</td>\n",
" <td>-0.003265</td>\n",
" <td>3.00452</td>\n",
" <td>-0.604466</td>\n",
" <td>-0.003981</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>-0.367563</td>\n",
" <td>-0.122216</td>\n",
" <td>0.003322</td>\n",
" <td>-3.601667</td>\n",
" <td>0.463333</td>\n",
" <td>-0.004712</td>\n",
" <td>3.234104</td>\n",
" <td>-0.58555</td>\n",
" <td>-0.00139</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>-0.39348</td>\n",
" <td>-0.079216</td>\n",
" <td>0.002546</td>\n",
" <td>-3.472000</td>\n",
" <td>0.397833</td>\n",
" <td>-0.003806</td>\n",
" <td>3.07852</td>\n",
" <td>-0.47705</td>\n",
" <td>-0.001261</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>-0.369646</td>\n",
" <td>-0.130133</td>\n",
" <td>0.00168</td>\n",
" <td>-3.610500</td>\n",
" <td>0.347417</td>\n",
" <td>-0.004990</td>\n",
" <td>3.240854</td>\n",
" <td>-0.47755</td>\n",
" <td>-0.003309</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>-0.345813</td>\n",
" <td>-0.18105</td>\n",
" <td>0.000815</td>\n",
" <td>-3.749000</td>\n",
" <td>0.297000</td>\n",
" <td>-0.006173</td>\n",
" <td>3.403187</td>\n",
" <td>-0.47805</td>\n",
" <td>-0.005358</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>-0.20373</td>\n",
" <td>-0.1758</td>\n",
" <td>0.000684</td>\n",
" <td>-3.624000</td>\n",
" <td>0.178000</td>\n",
" <td>-0.006722</td>\n",
" <td>3.42027</td>\n",
" <td>-0.3538</td>\n",
" <td>-0.006039</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>0.02277</td>\n",
" <td>0.032534</td>\n",
" <td>-0.001091</td>\n",
" <td>-3.163833</td>\n",
" <td>0.298500</td>\n",
" <td>-0.005309</td>\n",
" <td>3.186604</td>\n",
" <td>-0.265966</td>\n",
" <td>-0.0064</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>0.201687</td>\n",
" <td>0.50145</td>\n",
" <td>-0.001473</td>\n",
" <td>-3.019500</td>\n",
" <td>0.211250</td>\n",
" <td>-0.004588</td>\n",
" <td>3.221187</td>\n",
" <td>0.2902</td>\n",
" <td>-0.00606</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>0.00352</td>\n",
" <td>-0.231633</td>\n",
" <td>0.001917</td>\n",
" <td>-3.352500</td>\n",
" <td>-0.069500</td>\n",
" <td>-0.006906</td>\n",
" <td>3.35602</td>\n",
" <td>-0.162133</td>\n",
" <td>-0.004989</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>-0.110896</td>\n",
" <td>0.426867</td>\n",
" <td>-0.000707</td>\n",
" <td>-2.985750</td>\n",
" <td>0.520250</td>\n",
" <td>-0.005315</td>\n",
" <td>2.874854</td>\n",
" <td>-0.093383</td>\n",
" <td>-0.006022</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>-0.158396</td>\n",
" <td>-0.114466</td>\n",
" <td>-0.000707</td>\n",
" <td>-3.087500</td>\n",
" <td>0.473833</td>\n",
" <td>-0.002120</td>\n",
" <td>2.929104</td>\n",
" <td>-0.5883</td>\n",
" <td>-0.002827</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>-0.162813</td>\n",
" <td>-0.0973</td>\n",
" <td>0.000025</td>\n",
" <td>-3.270167</td>\n",
" <td>0.401333</td>\n",
" <td>-0.003770</td>\n",
" <td>3.107354</td>\n",
" <td>-0.498633</td>\n",
" <td>-0.003746</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>-0.079646</td>\n",
" <td>0.107367</td>\n",
" <td>0.003811</td>\n",
" <td>-3.632833</td>\n",
" <td>0.213333</td>\n",
" <td>-0.007240</td>\n",
" <td>3.553187</td>\n",
" <td>-0.105966</td>\n",
" <td>-0.003428</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>-0.23123</td>\n",
" <td>-0.25755</td>\n",
" <td>0.002151</td>\n",
" <td>-3.621333</td>\n",
" <td>0.470333</td>\n",
" <td>-0.005348</td>\n",
" <td>3.390104</td>\n",
" <td>-0.727883</td>\n",
" <td>-0.003197</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>-0.14073</td>\n",
" <td>-0.260966</td>\n",
" <td>0.00202</td>\n",
" <td>-3.751167</td>\n",
" <td>0.201167</td>\n",
" <td>-0.008750</td>\n",
" <td>3.610437</td>\n",
" <td>-0.462133</td>\n",
" <td>-0.00673</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>-0.139521</td>\n",
" <td>-0.21455</td>\n",
" <td>0.00176</td>\n",
" <td>-3.697000</td>\n",
" <td>0.236833</td>\n",
" <td>-0.007800</td>\n",
" <td>3.557479</td>\n",
" <td>-0.451383</td>\n",
" <td>-0.00604</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>-0.138313</td>\n",
" <td>-0.168133</td>\n",
" <td>0.0015</td>\n",
" <td>-3.642833</td>\n",
" <td>0.272500</td>\n",
" <td>-0.006850</td>\n",
" <td>3.50452</td>\n",
" <td>-0.440633</td>\n",
" <td>-0.005349</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>-0.117646</td>\n",
" <td>0.12995</td>\n",
" <td>-0.000115</td>\n",
" <td>-3.565167</td>\n",
" <td>0.345833</td>\n",
" <td>-0.006051</td>\n",
" <td>3.44752</td>\n",
" <td>-0.215883</td>\n",
" <td>-0.006166</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>0.10752</td>\n",
" <td>0.518117</td>\n",
" <td>-0.000488</td>\n",
" <td>-3.202000</td>\n",
" <td>0.506667</td>\n",
" <td>-0.005736</td>\n",
" <td>3.30952</td>\n",
" <td>0.01145</td>\n",
" <td>-0.006224</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>-0.048313</td>\n",
" <td>0.17845</td>\n",
" <td>-0.000727</td>\n",
" <td>-3.405000</td>\n",
" <td>0.232500</td>\n",
" <td>-0.006315</td>\n",
" <td>3.356687</td>\n",
" <td>-0.05405</td>\n",
" <td>-0.007042</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>0.04327</td>\n",
" <td>0.4112</td>\n",
" <td>-0.000644</td>\n",
" <td>-2.790167</td>\n",
" <td>0.292500</td>\n",
" <td>-0.003578</td>\n",
" <td>2.833437</td>\n",
" <td>0.1187</td>\n",
" <td>-0.004221</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>-0.093313</td>\n",
" <td>0.24395</td>\n",
" <td>-0.000871</td>\n",
" <td>-3.049667</td>\n",
" <td>0.689500</td>\n",
" <td>-0.002700</td>\n",
" <td>2.956354</td>\n",
" <td>-0.44555</td>\n",
" <td>-0.003571</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>-0.235063</td>\n",
" <td>-0.108466</td>\n",
" <td>0.002393</td>\n",
" <td>-3.525167</td>\n",
" <td>0.480833</td>\n",
" <td>-0.005791</td>\n",
" <td>3.290104</td>\n",
" <td>-0.5893</td>\n",
" <td>-0.003397</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>-0.144021</td>\n",
" <td>-0.2158</td>\n",
" <td>0.002029</td>\n",
" <td>-3.393250</td>\n",
" <td>0.414500</td>\n",
" <td>-0.004618</td>\n",
" <td>3.249229</td>\n",
" <td>-0.6303</td>\n",
" <td>-0.002589</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td>-0.05298</td>\n",
" <td>-0.323133</td>\n",
" <td>0.001664</td>\n",
" <td>-3.261333</td>\n",
" <td>0.348167</td>\n",
" <td>-0.003444</td>\n",
" <td>3.208354</td>\n",
" <td>-0.6713</td>\n",
" <td>-0.001781</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52</th>\n",
" <td>-0.122896</td>\n",
" <td>-0.1213</td>\n",
" <td>0.000262</td>\n",
" <td>-3.365667</td>\n",
" <td>0.299000</td>\n",
" <td>-0.004197</td>\n",
" <td>3.24277</td>\n",
" <td>-0.4203</td>\n",
" <td>-0.003934</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53</th>\n",
" <td>-0.11173</td>\n",
" <td>-0.053383</td>\n",
" <td>0.001749</td>\n",
" <td>-3.507500</td>\n",
" <td>0.355333</td>\n",
" <td>-0.006194</td>\n",
" <td>3.39577</td>\n",
" <td>-0.408716</td>\n",
" <td>-0.004445</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54</th>\n",
" <td>-0.022355</td>\n",
" <td>-0.060675</td>\n",
" <td>0.000903</td>\n",
" <td>-3.375333</td>\n",
" <td>0.329667</td>\n",
" <td>-0.005615</td>\n",
" <td>3.352979</td>\n",
" <td>-0.390341</td>\n",
" <td>-0.004712</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55</th>\n",
" <td>0.06702</td>\n",
" <td>-0.067966</td>\n",
" <td>0.000056</td>\n",
" <td>-3.243167</td>\n",
" <td>0.304000</td>\n",
" <td>-0.005035</td>\n",
" <td>3.310187</td>\n",
" <td>-0.371966</td>\n",
" <td>-0.004979</td>\n",
" </tr>\n",
" <tr>\n",
" <th>56</th>\n",
" <td>0.02052</td>\n",
" <td>0.22945</td>\n",
" <td>-0.00229</td>\n",
" <td>-3.165500</td>\n",
" <td>0.375500</td>\n",
" <td>-0.004429</td>\n",
" <td>3.18602</td>\n",
" <td>-0.14605</td>\n",
" <td>-0.006719</td>\n",
" </tr>\n",
" <tr>\n",
" <th>57</th>\n",
" <td>0.224937</td>\n",
" <td>0.167117</td>\n",
" <td>-0.001635</td>\n",
" <td>-2.736500</td>\n",
" <td>0.385333</td>\n",
" <td>-0.006230</td>\n",
" <td>2.961437</td>\n",
" <td>-0.218216</td>\n",
" <td>-0.007865</td>\n",
" </tr>\n",
" <tr>\n",
" <th>58</th>\n",
" <td>-0.444896</td>\n",
" <td>0.127367</td>\n",
" <td>0.000382</td>\n",
" <td>-3.605000</td>\n",
" <td>0.393000</td>\n",
" <td>-0.006654</td>\n",
" <td>3.160104</td>\n",
" <td>-0.265633</td>\n",
" <td>-0.006272</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59</th>\n",
" <td>-0.011813</td>\n",
" <td>0.355284</td>\n",
" <td>-0.002298</td>\n",
" <td>-2.956333</td>\n",
" <td>0.592667</td>\n",
" <td>-0.004287</td>\n",
" <td>2.94452</td>\n",
" <td>-0.237383</td>\n",
" <td>-0.006585</td>\n",
" </tr>\n",
" <tr>\n",
" <th>60</th>\n",
" <td>-0.096757</td>\n",
" <td>0.16045</td>\n",
" <td>-0.000978</td>\n",
" <td>-3.159833</td>\n",
" <td>0.511722</td>\n",
" <td>-0.004702</td>\n",
" <td>3.063076</td>\n",
" <td>-0.351272</td>\n",
" <td>-0.005679</td>\n",
" </tr>\n",
" <tr>\n",
" <th>61</th>\n",
" <td>-0.181702</td>\n",
" <td>-0.034383</td>\n",
" <td>0.000343</td>\n",
" <td>-3.363333</td>\n",
" <td>0.430778</td>\n",
" <td>-0.005116</td>\n",
" <td>3.181632</td>\n",
" <td>-0.465161</td>\n",
" <td>-0.004774</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62</th>\n",
" <td>-0.266646</td>\n",
" <td>-0.229216</td>\n",
" <td>0.001663</td>\n",
" <td>-3.566833</td>\n",
" <td>0.349833</td>\n",
" <td>-0.005531</td>\n",
" <td>3.300187</td>\n",
" <td>-0.57905</td>\n",
" <td>-0.003868</td>\n",
" </tr>\n",
" <tr>\n",
" <th>63</th>\n",
" <td>-0.151396</td>\n",
" <td>-0.31205</td>\n",
" <td>0.002032</td>\n",
" <td>-3.422667</td>\n",
" <td>0.262167</td>\n",
" <td>-0.006041</td>\n",
" <td>3.27127</td>\n",
" <td>-0.574216</td>\n",
" <td>-0.004009</td>\n",
" </tr>\n",
" <tr>\n",
" <th>64</th>\n",
" <td>-0.05073</td>\n",
" <td>-0.14405</td>\n",
" <td>0.001137</td>\n",
" <td>-3.548000</td>\n",
" <td>0.348833</td>\n",
" <td>-0.005574</td>\n",
" <td>3.49727</td>\n",
" <td>-0.492883</td>\n",
" <td>-0.004437</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65</th>\n",
" <td>-0.07698</td>\n",
" <td>-0.0758</td>\n",
" <td>0.002136</td>\n",
" <td>-3.575667</td>\n",
" <td>0.227833</td>\n",
" <td>-0.005954</td>\n",
" <td>3.498687</td>\n",
" <td>-0.303633</td>\n",
" <td>-0.003819</td>\n",
" </tr>\n",
" <tr>\n",
" <th>66</th>\n",
" <td>0.019854</td>\n",
" <td>0.019367</td>\n",
" <td>-0.000318</td>\n",
" <td>-3.121500</td>\n",
" <td>0.237333</td>\n",
" <td>-0.005561</td>\n",
" <td>3.141354</td>\n",
" <td>-0.217966</td>\n",
" <td>-0.005879</td>\n",
" </tr>\n",
" <tr>\n",
" <th>67</th>\n",
" <td>-0.029313</td>\n",
" <td>0.147534</td>\n",
" <td>-0.000117</td>\n",
" <td>-3.035667</td>\n",
" <td>0.262417</td>\n",
" <td>-0.005002</td>\n",
" <td>3.006354</td>\n",
" <td>-0.114883</td>\n",
" <td>-0.005119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68</th>\n",
" <td>-0.07848</td>\n",
" <td>0.2757</td>\n",
" <td>0.000084</td>\n",
" <td>-2.949833</td>\n",
" <td>0.287500</td>\n",
" <td>-0.004443</td>\n",
" <td>2.871354</td>\n",
" <td>-0.0118</td>\n",
" <td>-0.004359</td>\n",
" </tr>\n",
" <tr>\n",
" <th>69</th>\n",
" <td>-0.369396</td>\n",
" <td>0.012534</td>\n",
" <td>0.000919</td>\n",
" <td>-3.507833</td>\n",
" <td>0.468667</td>\n",
" <td>-0.005000</td>\n",
" <td>3.138437</td>\n",
" <td>-0.456133</td>\n",
" <td>-0.00408</td>\n",
" </tr>\n",
" <tr>\n",
" <th>70</th>\n",
" <td>-0.288813</td>\n",
" <td>-0.236883</td>\n",
" <td>0.00284</td>\n",
" <td>-3.631333</td>\n",
" <td>0.240833</td>\n",
" <td>-0.006539</td>\n",
" <td>3.34252</td>\n",
" <td>-0.477716</td>\n",
" <td>-0.003699</td>\n",
" </tr>\n",
" <tr>\n",
" <th>71</th>\n",
" <td>-0.061896</td>\n",
" <td>-0.22405</td>\n",
" <td>0.001407</td>\n",
" <td>-3.440500</td>\n",
" <td>0.227500</td>\n",
" <td>-0.006182</td>\n",
" <td>3.378604</td>\n",
" <td>-0.45155</td>\n",
" <td>-0.004775</td>\n",
" </tr>\n",
" <tr>\n",
" <th>72</th>\n",
" <td>-0.100146</td>\n",
" <td>-0.182383</td>\n",
" <td>0.002034</td>\n",
" <td>-3.476000</td>\n",
" <td>0.319500</td>\n",
" <td>-0.006765</td>\n",
" <td>3.375854</td>\n",
" <td>-0.501883</td>\n",
" <td>-0.004731</td>\n",
" </tr>\n",
" <tr>\n",
" <th>73</th>\n",
" <td>-0.035146</td>\n",
" <td>-0.022883</td>\n",
" <td>0.001108</td>\n",
" <td>-3.420000</td>\n",
" <td>0.421333</td>\n",
" <td>-0.005230</td>\n",
" <td>3.384854</td>\n",
" <td>-0.444216</td>\n",
" <td>-0.004122</td>\n",
" </tr>\n",
" <tr>\n",
" <th>74</th>\n",
" <td>-0.16673</td>\n",
" <td>-0.02355</td>\n",
" <td>0.002439</td>\n",
" <td>-3.538333</td>\n",
" <td>0.241000</td>\n",
" <td>-0.005673</td>\n",
" <td>3.371604</td>\n",
" <td>-0.26455</td>\n",
" <td>-0.003234</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75</th>\n",
" <td>-0.033813</td>\n",
" <td>0.003284</td>\n",
" <td>-0.000338</td>\n",
" <td>-3.201500</td>\n",
" <td>0.183167</td>\n",
" <td>-0.004941</td>\n",
" <td>3.167687</td>\n",
" <td>-0.179883</td>\n",
" <td>-0.00528</td>\n",
" </tr>\n",
" <tr>\n",
" <th>76</th>\n",
" <td>0.010354</td>\n",
" <td>0.19645</td>\n",
" <td>-0.002826</td>\n",
" <td>-2.971000</td>\n",
" <td>0.267000</td>\n",
" <td>-0.003830</td>\n",
" <td>2.981354</td>\n",
" <td>-0.07055</td>\n",
" <td>-0.006656</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77</th>\n",
" <td>0.02177</td>\n",
" <td>0.119867</td>\n",
" <td>-0.001393</td>\n",
" <td>-2.961000</td>\n",
" <td>0.078333</td>\n",
" <td>-0.004163</td>\n",
" <td>2.98277</td>\n",
" <td>0.041534</td>\n",
" <td>-0.005557</td>\n",
" </tr>\n",
" <tr>\n",
" <th>78</th>\n",
" <td>0.199604</td>\n",
" <td>-0.091883</td>\n",
" <td>-0.002099</td>\n",
" <td>-2.754667</td>\n",
" <td>-0.007833</td>\n",
" <td>-0.003109</td>\n",
" <td>2.95427</td>\n",
" <td>-0.08405</td>\n",
" <td>-0.005208</td>\n",
" </tr>\n",
" <tr>\n",
" <th>79</th>\n",
" <td>-0.189896</td>\n",
" <td>-0.0253</td>\n",
" <td>0.000202</td>\n",
" <td>-3.366000</td>\n",
" <td>0.404167</td>\n",
" <td>-0.003920</td>\n",
" <td>3.176104</td>\n",
" <td>-0.429466</td>\n",
" <td>-0.003718</td>\n",
" </tr>\n",
" <tr>\n",
" <th>80</th>\n",
" <td>-0.018896</td>\n",
" <td>-0.0323</td>\n",
" <td>0.000449</td>\n",
" <td>-3.364167</td>\n",
" <td>0.284000</td>\n",
" <td>-0.005575</td>\n",
" <td>3.34527</td>\n",
" <td>-0.3163</td>\n",
" <td>-0.005126</td>\n",
" </tr>\n",
" <tr>\n",
" <th>81</th>\n",
" <td>0.045187</td>\n",
" <td>0.093367</td>\n",
" <td>0.000598</td>\n",
" <td>-3.263500</td>\n",
" <td>0.260833</td>\n",
" <td>-0.003760</td>\n",
" <td>3.308687</td>\n",
" <td>-0.167466</td>\n",
" <td>-0.003162</td>\n",
" </tr>\n",
" <tr>\n",
" <th>82</th>\n",
" <td>-0.017146</td>\n",
" <td>0.223617</td>\n",
" <td>-0.000021</td>\n",
" <td>-3.368833</td>\n",
" <td>0.307167</td>\n",
" <td>-0.005953</td>\n",
" <td>3.351687</td>\n",
" <td>-0.08355</td>\n",
" <td>-0.005974</td>\n",
" </tr>\n",
" <tr>\n",
" <th>83</th>\n",
" <td>0.19227</td>\n",
" <td>0.2617</td>\n",
" <td>-0.000891</td>\n",
" <td>-3.120833</td>\n",
" <td>0.303333</td>\n",
" <td>-0.005129</td>\n",
" <td>3.313104</td>\n",
" <td>-0.041633</td>\n",
" <td>-0.00602</td>\n",
" </tr>\n",
" <tr>\n",
" <th>84</th>\n",
" <td>0.19227</td>\n",
" <td>0.2617</td>\n",
" <td>-0.000891</td>\n",
" <td>-3.120833</td>\n",
" <td>0.303333</td>\n",
" <td>-0.005129</td>\n",
" <td>3.313104</td>\n",
" <td>-0.041633</td>\n",
" <td>-0.00602</td>\n",
" </tr>\n",
" <tr>\n",
" <th>85</th>\n",
" <td>0.19227</td>\n",
" <td>0.2617</td>\n",
" <td>-0.000891</td>\n",
" <td>-3.120833</td>\n",
" <td>0.303333</td>\n",
" <td>-0.005129</td>\n",
" <td>3.313104</td>\n",
" <td>-0.041633</td>\n",
" <td>-0.00602</td>\n",
" </tr>\n",
" <tr>\n",
" <th>86</th>\n",
" <td>0.19227</td>\n",
" <td>0.2617</td>\n",
" <td>-0.000891</td>\n",
" <td>-3.120833</td>\n",
" <td>0.303333</td>\n",
" <td>-0.005129</td>\n",
" <td>3.313104</td>\n",
" <td>-0.041633</td>\n",
" <td>-0.00602</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87</th>\n",
" <td>0.19227</td>\n",
" <td>0.2617</td>\n",
" <td>-0.000891</td>\n",
" <td>-3.120833</td>\n",
" <td>0.303333</td>\n",
" <td>-0.005129</td>\n",
" <td>3.313104</td>\n",
" <td>-0.041633</td>\n",
" <td>-0.00602</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 1 2 3 对位MarkX 对位MarkY Angle Calc-X \\\n",
"1 0.47427 0.2982 -0.000996 -3.074000 0.177500 -0.009325 3.54827 \n",
"2 0.09377 0.198534 -0.000556 -2.720000 0.138750 -0.002766 2.81377 \n",
"3 -0.008563 0.185409 -0.000638 -2.986250 0.153375 -0.004000 2.977687 \n",
"4 -0.110896 0.172284 -0.00072 -3.252500 0.168000 -0.005233 3.141604 \n",
"5 -0.038896 0.140867 -0.002075 -2.867500 0.220500 -0.003531 2.828604 \n",
"6 0.371354 0.331034 -0.000228 -2.712500 0.000750 -0.002685 3.083854 \n",
"7 -0.261396 0.068284 0.002302 -3.222000 0.185500 -0.002354 2.960604 \n",
"8 -0.26173 0.076034 0.000238 -3.313000 0.150750 -0.005350 3.05127 \n",
"9 -0.225563 0.055367 -0.000682 -3.226750 0.331000 -0.005559 3.001187 \n",
"10 -0.26223 -0.0138 0.000175 -3.384000 0.365625 -0.005957 3.12177 \n",
"11 -0.298896 -0.082966 0.001031 -3.541250 0.400250 -0.006355 3.242354 \n",
"12 -0.181396 -0.09355 0.000011 -3.421500 0.178000 -0.005234 3.240104 \n",
"13 -0.127813 0.127284 -0.000897 -3.237750 0.348000 -0.004982 3.109937 \n",
"14 0.147437 0.355534 -0.001769 -2.807500 0.377750 -0.003507 2.954937 \n",
"15 -0.07798 0.05095 -0.000638 -3.107500 0.354250 -0.003964 3.02952 \n",
"16 -0.183646 0.3962 -0.000643 -2.980000 0.601500 -0.005093 2.796354 \n",
"17 -0.397063 0.040284 -0.000673 -3.288000 0.618500 -0.002862 2.890937 \n",
"18 -0.219563 -0.238966 0.002379 -3.445500 0.528750 -0.005188 3.225937 \n",
"19 -0.22098 0.3437 0.001064 -3.135500 0.590000 -0.004002 2.91452 \n",
"20 -0.12398 -0.232633 0.001511 -3.396250 0.309000 -0.006996 3.27227 \n",
"21 -0.002646 -0.14405 -0.000204 -3.360333 0.240667 -0.006527 3.357687 \n",
"22 -0.085896 -0.185383 -0.000299 -3.357667 0.056333 -0.005720 3.27177 \n",
"23 -0.049563 0.000367 -0.000787 -3.214750 0.182000 -0.003231 3.165187 \n",
"24 0.12676 0.186436 -0.001889 -2.972833 0.235667 -0.005118 3.099593 \n",
"25 -0.31798 0.255034 0.000808 -3.259000 0.253000 -0.003865 2.94102 \n",
"26 -0.161063 0.23645 -0.000316 -3.250833 0.537667 -0.005293 3.08977 \n",
"27 -0.11048 0.065367 -0.000716 -3.115000 0.669833 -0.003265 3.00452 \n",
"28 -0.367563 -0.122216 0.003322 -3.601667 0.463333 -0.004712 3.234104 \n",
"29 -0.39348 -0.079216 0.002546 -3.472000 0.397833 -0.003806 3.07852 \n",
"30 -0.369646 -0.130133 0.00168 -3.610500 0.347417 -0.004990 3.240854 \n",
"31 -0.345813 -0.18105 0.000815 -3.749000 0.297000 -0.006173 3.403187 \n",
"32 -0.20373 -0.1758 0.000684 -3.624000 0.178000 -0.006722 3.42027 \n",
"33 0.02277 0.032534 -0.001091 -3.163833 0.298500 -0.005309 3.186604 \n",
"34 0.201687 0.50145 -0.001473 -3.019500 0.211250 -0.004588 3.221187 \n",
"35 0.00352 -0.231633 0.001917 -3.352500 -0.069500 -0.006906 3.35602 \n",
"36 -0.110896 0.426867 -0.000707 -2.985750 0.520250 -0.005315 2.874854 \n",
"37 -0.158396 -0.114466 -0.000707 -3.087500 0.473833 -0.002120 2.929104 \n",
"38 -0.162813 -0.0973 0.000025 -3.270167 0.401333 -0.003770 3.107354 \n",
"39 -0.079646 0.107367 0.003811 -3.632833 0.213333 -0.007240 3.553187 \n",
"40 -0.23123 -0.25755 0.002151 -3.621333 0.470333 -0.005348 3.390104 \n",
"41 -0.14073 -0.260966 0.00202 -3.751167 0.201167 -0.008750 3.610437 \n",
"42 -0.139521 -0.21455 0.00176 -3.697000 0.236833 -0.007800 3.557479 \n",
"43 -0.138313 -0.168133 0.0015 -3.642833 0.272500 -0.006850 3.50452 \n",
"44 -0.117646 0.12995 -0.000115 -3.565167 0.345833 -0.006051 3.44752 \n",
"45 0.10752 0.518117 -0.000488 -3.202000 0.506667 -0.005736 3.30952 \n",
"46 -0.048313 0.17845 -0.000727 -3.405000 0.232500 -0.006315 3.356687 \n",
"47 0.04327 0.4112 -0.000644 -2.790167 0.292500 -0.003578 2.833437 \n",
"48 -0.093313 0.24395 -0.000871 -3.049667 0.689500 -0.002700 2.956354 \n",
"49 -0.235063 -0.108466 0.002393 -3.525167 0.480833 -0.005791 3.290104 \n",
"50 -0.144021 -0.2158 0.002029 -3.393250 0.414500 -0.004618 3.249229 \n",
"51 -0.05298 -0.323133 0.001664 -3.261333 0.348167 -0.003444 3.208354 \n",
"52 -0.122896 -0.1213 0.000262 -3.365667 0.299000 -0.004197 3.24277 \n",
"53 -0.11173 -0.053383 0.001749 -3.507500 0.355333 -0.006194 3.39577 \n",
"54 -0.022355 -0.060675 0.000903 -3.375333 0.329667 -0.005615 3.352979 \n",
"55 0.06702 -0.067966 0.000056 -3.243167 0.304000 -0.005035 3.310187 \n",
"56 0.02052 0.22945 -0.00229 -3.165500 0.375500 -0.004429 3.18602 \n",
"57 0.224937 0.167117 -0.001635 -2.736500 0.385333 -0.006230 2.961437 \n",
"58 -0.444896 0.127367 0.000382 -3.605000 0.393000 -0.006654 3.160104 \n",
"59 -0.011813 0.355284 -0.002298 -2.956333 0.592667 -0.004287 2.94452 \n",
"60 -0.096757 0.16045 -0.000978 -3.159833 0.511722 -0.004702 3.063076 \n",
"61 -0.181702 -0.034383 0.000343 -3.363333 0.430778 -0.005116 3.181632 \n",
"62 -0.266646 -0.229216 0.001663 -3.566833 0.349833 -0.005531 3.300187 \n",
"63 -0.151396 -0.31205 0.002032 -3.422667 0.262167 -0.006041 3.27127 \n",
"64 -0.05073 -0.14405 0.001137 -3.548000 0.348833 -0.005574 3.49727 \n",
"65 -0.07698 -0.0758 0.002136 -3.575667 0.227833 -0.005954 3.498687 \n",
"66 0.019854 0.019367 -0.000318 -3.121500 0.237333 -0.005561 3.141354 \n",
"67 -0.029313 0.147534 -0.000117 -3.035667 0.262417 -0.005002 3.006354 \n",
"68 -0.07848 0.2757 0.000084 -2.949833 0.287500 -0.004443 2.871354 \n",
"69 -0.369396 0.012534 0.000919 -3.507833 0.468667 -0.005000 3.138437 \n",
"70 -0.288813 -0.236883 0.00284 -3.631333 0.240833 -0.006539 3.34252 \n",
"71 -0.061896 -0.22405 0.001407 -3.440500 0.227500 -0.006182 3.378604 \n",
"72 -0.100146 -0.182383 0.002034 -3.476000 0.319500 -0.006765 3.375854 \n",
"73 -0.035146 -0.022883 0.001108 -3.420000 0.421333 -0.005230 3.384854 \n",
"74 -0.16673 -0.02355 0.002439 -3.538333 0.241000 -0.005673 3.371604 \n",
"75 -0.033813 0.003284 -0.000338 -3.201500 0.183167 -0.004941 3.167687 \n",
"76 0.010354 0.19645 -0.002826 -2.971000 0.267000 -0.003830 2.981354 \n",
"77 0.02177 0.119867 -0.001393 -2.961000 0.078333 -0.004163 2.98277 \n",
"78 0.199604 -0.091883 -0.002099 -2.754667 -0.007833 -0.003109 2.95427 \n",
"79 -0.189896 -0.0253 0.000202 -3.366000 0.404167 -0.003920 3.176104 \n",
"80 -0.018896 -0.0323 0.000449 -3.364167 0.284000 -0.005575 3.34527 \n",
"81 0.045187 0.093367 0.000598 -3.263500 0.260833 -0.003760 3.308687 \n",
"82 -0.017146 0.223617 -0.000021 -3.368833 0.307167 -0.005953 3.351687 \n",
"83 0.19227 0.2617 -0.000891 -3.120833 0.303333 -0.005129 3.313104 \n",
"84 0.19227 0.2617 -0.000891 -3.120833 0.303333 -0.005129 3.313104 \n",
"85 0.19227 0.2617 -0.000891 -3.120833 0.303333 -0.005129 3.313104 \n",
"86 0.19227 0.2617 -0.000891 -3.120833 0.303333 -0.005129 3.313104 \n",
"87 0.19227 0.2617 -0.000891 -3.120833 0.303333 -0.005129 3.313104 \n",
"\n",
" Calc-Y Calc-Angle \n",
"1 0.1207 -0.010321 \n",
"2 0.059784 -0.003322 \n",
"3 0.032034 -0.004638 \n",
"4 0.004284 -0.005953 \n",
"5 -0.079633 -0.005606 \n",
"6 0.330284 -0.002912 \n",
"7 -0.117216 -0.000052 \n",
"8 -0.074716 -0.005112 \n",
"9 -0.275633 -0.00624 \n",
"10 -0.379425 -0.005782 \n",
"11 -0.483216 -0.005325 \n",
"12 -0.27155 -0.005222 \n",
"13 -0.220716 -0.005879 \n",
"14 -0.022216 -0.005276 \n",
"15 -0.3033 -0.004603 \n",
"16 -0.2053 -0.005736 \n",
"17 -0.578216 -0.003535 \n",
"18 -0.767716 -0.002809 \n",
"19 -0.2463 -0.002937 \n",
"20 -0.541633 -0.005485 \n",
"21 -0.384716 -0.006731 \n",
"22 -0.241716 -0.006019 \n",
"23 -0.181633 -0.004018 \n",
"24 -0.049231 -0.007006 \n",
"25 0.002034 -0.003057 \n",
"26 -0.301216 -0.005608 \n",
"27 -0.604466 -0.003981 \n",
"28 -0.58555 -0.00139 \n",
"29 -0.47705 -0.001261 \n",
"30 -0.47755 -0.003309 \n",
"31 -0.47805 -0.005358 \n",
"32 -0.3538 -0.006039 \n",
"33 -0.265966 -0.0064 \n",
"34 0.2902 -0.00606 \n",
"35 -0.162133 -0.004989 \n",
"36 -0.093383 -0.006022 \n",
"37 -0.5883 -0.002827 \n",
"38 -0.498633 -0.003746 \n",
"39 -0.105966 -0.003428 \n",
"40 -0.727883 -0.003197 \n",
"41 -0.462133 -0.00673 \n",
"42 -0.451383 -0.00604 \n",
"43 -0.440633 -0.005349 \n",
"44 -0.215883 -0.006166 \n",
"45 0.01145 -0.006224 \n",
"46 -0.05405 -0.007042 \n",
"47 0.1187 -0.004221 \n",
"48 -0.44555 -0.003571 \n",
"49 -0.5893 -0.003397 \n",
"50 -0.6303 -0.002589 \n",
"51 -0.6713 -0.001781 \n",
"52 -0.4203 -0.003934 \n",
"53 -0.408716 -0.004445 \n",
"54 -0.390341 -0.004712 \n",
"55 -0.371966 -0.004979 \n",
"56 -0.14605 -0.006719 \n",
"57 -0.218216 -0.007865 \n",
"58 -0.265633 -0.006272 \n",
"59 -0.237383 -0.006585 \n",
"60 -0.351272 -0.005679 \n",
"61 -0.465161 -0.004774 \n",
"62 -0.57905 -0.003868 \n",
"63 -0.574216 -0.004009 \n",
"64 -0.492883 -0.004437 \n",
"65 -0.303633 -0.003819 \n",
"66 -0.217966 -0.005879 \n",
"67 -0.114883 -0.005119 \n",
"68 -0.0118 -0.004359 \n",
"69 -0.456133 -0.00408 \n",
"70 -0.477716 -0.003699 \n",
"71 -0.45155 -0.004775 \n",
"72 -0.501883 -0.004731 \n",
"73 -0.444216 -0.004122 \n",
"74 -0.26455 -0.003234 \n",
"75 -0.179883 -0.00528 \n",
"76 -0.07055 -0.006656 \n",
"77 0.041534 -0.005557 \n",
"78 -0.08405 -0.005208 \n",
"79 -0.429466 -0.003718 \n",
"80 -0.3163 -0.005126 \n",
"81 -0.167466 -0.003162 \n",
"82 -0.08355 -0.005974 \n",
"83 -0.041633 -0.00602 \n",
"84 -0.041633 -0.00602 \n",
"85 -0.041633 -0.00602 \n",
"86 -0.041633 -0.00602 \n",
"87 -0.041633 -0.00602 "
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Old_New_DieBC = pd.concat([OldDieBC.iloc[4:,:],DieBC],axis=1)\n",
"# X、Y 是减，角度是加。\n",
"Old_New_DieBC['Calc-X'] = Old_New_DieBC[1] - Old_New_DieBC['对位MarkX']\n",
"Old_New_DieBC['Calc-Y'] = Old_New_DieBC[2] - Old_New_DieBC['对位MarkY']\n",
"Old_New_DieBC['Calc-Angle'] = Old_New_DieBC[3] + Old_New_DieBC['Angle']\n",
"Old_New_DieBC"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "43f1c8ec-6d94-4935-acd3-156dbeec3747",
"metadata": {},
"outputs": [],
"source": [
"Old_New_DieBC.to_excel(f'{DieType}/{DieType}局部补偿{daytime}.xlsx')"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "4ed8868d-4098-4959-9fcc-b631a5b81cca",
"metadata": {},
"outputs": [],
"source": [
"# pd.DataFrame(AlignMarkY['10.14.3-Die1'].values - BC_Y.mean(axis=1).values).std()*3"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "20e83f4f-eed9-409e-9720-027bdfbd96a5",
"metadata": {},
"outputs": [],
"source": [
"# for i in ['10.14.1-Die1','10.14.2-Die1','10.14.3-Die1']:\n",
" # print(i+\":X\",pd.DataFrame(AlignMarkX[i].values - BC_X.mean(axis=1).values).std()*3)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "d4f89c43-d128-489b-89f0-e35dab1c6c87",
"metadata": {},
"outputs": [],
"source": [
"# for i in ['10.14.1-Die1','10.14.2-Die1','10.14.3-Die1']:\n",
" # print(i+\":Y\",pd.DataFrame(AlignMarkY[i].values - BC_Y.mean(axis=1).values).std()*3)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "fb698448-6d4d-4477-8afd-35430692c8df",
"metadata": {},
"outputs": [],
"source": [
"# DieBC['Top Mark1 X'] = AlX981\n",
"# DieBC['Top Mark1 Y'] = AlY981\n",
"# DieBC['Top Mark2 X'] = AnX981\n",
"# DieBC['Top Mark2 Y'] = AnY981\n",
"# DieBC.to_excel(f'Die1/Die1设备方向偏差9-8-2.xlsx')"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "94ecaf78-98bb-45a4-b66f-d36519a42cd5",
"metadata": {},
"outputs": [],
"source": [
"# num = -1\n",
"# AlX982 = AlignMarkX.iloc[:,num]-AlignMarkX.iloc[:,num].mean()\n",
"# AlX982 = AlX982.fillna(AlX982.interpolate()).values\n",
"\n",
"# AlY982 = AlignMarkY.iloc[:,num]-AlignMarkY.iloc[:,num].mean()\n",
"# AlY982 = AlY982.fillna(AlY982.interpolate()).values\n",
"\n",
"# AnX982 = AngleMarkX.iloc[:,num]-AngleMarkX.iloc[:,num].mean()\n",
"# AnX982 = AnX982.fillna(AnX982.interpolate()).values\n",
"\n",
"# AnY982 = AngleMarkY.iloc[:,num]-AngleMarkY.iloc[:,num].mean()\n",
"# AnY982 = AnY982.fillna(AnY982.interpolate()).values"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "21da49b6-3576-4468-8c86-070e51ac0bce",
"metadata": {},
"outputs": [],
"source": [
"# DieBC['Top Mark1 X'] = AlX982\n",
"# DieBC['Top Mark1 Y'] = AlY982\n",
"# DieBC['Top Mark2 X'] = AnX982\n",
"# DieBC['Top Mark2 Y'] = AnY982\n",
"# DieBC.to_excel(f'Die1/Die1设备方向偏差9-9-1.xlsx')"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "1858da30",
"metadata": {},
"outputs": [],
"source": [
"# num = -1\n",
"# Die3BC['Top Mark1 X'] = AlignMarkX.iloc[:,num].fillna(AlignMarkX.iloc[:,num].interpolate()).values\n",
"# Die3BC['Top Mark1 Y'] = AlignMarkY.iloc[:,num].fillna(AlignMarkY.iloc[:,num].interpolate()).values\n",
"# Die3BC['Top Mark2 X'] = AngleMarkX.iloc[:,num].fillna(AngleMarkX.iloc[:,num].interpolate()).values\n",
"# Die3BC['Top Mark2 Y'] = AngleMarkY.iloc[:,num].fillna(AngleMarkY.iloc[:,num].interpolate()).values"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "30ccc3df-e5ad-430c-8ac8-0dd074d7f682",
"metadata": {},
"outputs": [],
"source": [
"# Die3BC.to_excel(f'Die1/Die1补偿值9-8.xlsx')"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "6d74cea0",
"metadata": {},
"outputs": [],
"source": [
"# num = -2\n",
"# AlX915 = AlignMarkX.iloc[:,num]-AlignMarkX.iloc[:,num].mean()\n",
"# AlX915 = AlX915.fillna(AlX981.interpolate()).values\n",
"\n",
"# AlY915 = AlignMarkY.iloc[:,num]-AlignMarkY.iloc[:,num].mean()\n",
"# AlY915 = AlY915.fillna(AlY981.interpolate()).values\n",
"\n",
"# Ang915 = Angle.iloc[:,num] - Angle.iloc[:,num],mean()"
]
}
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"_data_url": "data:image/png;base64,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
"_figure_label": "Figure 3",
"_model_module_version": "^0.11",
"_size": [
800,
550
],
"_view_module_version": "^0.11",
"layout": "IPY_MODEL_8de2ee2a201f4e78b83bb73202597800",
"toolbar": "IPY_MODEL_2d1533973ff749659dd783abe0db740f",
"toolbar_position": "left"
}
},
"ee97a7eebb9a425eb0ade6e31817aa89": {
"model_module": "jupyter-matplotlib",
"model_module_version": "^0.11",
"model_name": "MPLCanvasModel",
"state": {
"_data_url": "data:image/png;base64,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
"_figure_label": "Figure 1",
"_model_module_version": "^0.11",
"_size": [
800,
550
],
"_view_module_version": "^0.11",
"layout": "IPY_MODEL_21d8b0ba77b54fbfabcdf5f52b80df1a",
"toolbar": "IPY_MODEL_2a0670114f11457b80a6ef3ef22a34fc",
"toolbar_position": "left"
}
},
"f7658ea254754f89ae1eaaca3822afd1": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "2.0.0",
"model_name": "LayoutModel",
"state": {}
}
},
"version_major": 2,
"version_minor": 0
}
}
},
"nbformat": 4,
"nbformat_minor": 5
}
Reference in New Issue Copy Permalink