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ZKFC_ACC/000补偿值计算/.ipynb_checkpoints/Die4数据计算-Copy4-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 (94, 118)\n",
"MCY (94, 118)\n",
"Angle14 (94, 18)\n",
"M1X (94, 118)\n",
"M1Y (94, 118)\n",
"M4X (94, 18)\n",
"M4Y (94, 18)\n",
"Angle13 (94, 101)\n",
"M3X (94, 101)\n",
"M3Y (94, 101)\n",
"Note (7, 2)\n"
]
}
],
"source": [
"#写入TotalData\n",
"DieType = 'Die4'\n",
"TotalData = pd.read_excel(f'../{DieType}AllData.xlsx',sheet_name=None,header=0,index_col = 0)\n",
"die_nums = -4\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>01.22.1-Die4</th>\n",
" <th>01.24.1-Die4</th>\n",
" <th>01.27.1-Die4</th>\n",
" <th>02.01.1-Die4</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>73.000000</td>\n",
" <td>73.000000</td>\n",
" <td>75.000000</td>\n",
" <td>73.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.768096</td>\n",
" <td>0.753986</td>\n",
" <td>0.023787</td>\n",
" <td>-0.058281</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.212475</td>\n",
" <td>0.252671</td>\n",
" <td>0.241951</td>\n",
" <td>0.301363</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.110000</td>\n",
" <td>-0.177000</td>\n",
" <td>-0.426000</td>\n",
" <td>-0.803000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>0.641000</td>\n",
" <td>0.589000</td>\n",
" <td>-0.129750</td>\n",
" <td>-0.248000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>0.773500</td>\n",
" <td>0.730000</td>\n",
" <td>-0.024500</td>\n",
" <td>-0.036500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>0.939500</td>\n",
" <td>0.883500</td>\n",
" <td>0.134000</td>\n",
" <td>0.169500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1.195500</td>\n",
" <td>1.657000</td>\n",
" <td>0.676500</td>\n",
" <td>0.728500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>range</th>\n",
" <td>1.085500</td>\n",
" <td>1.834000</td>\n",
" <td>1.102500</td>\n",
" <td>1.531500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3sigma</th>\n",
" <td>0.637425</td>\n",
" <td>0.758012</td>\n",
" <td>0.725854</td>\n",
" <td>0.904090</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 01.22.1-Die4 01.24.1-Die4 01.27.1-Die4 02.01.1-Die4\n",
"count 73.000000 73.000000 75.000000 73.000000\n",
"mean 0.768096 0.753986 0.023787 -0.058281\n",
"std 0.212475 0.252671 0.241951 0.301363\n",
"min 0.110000 -0.177000 -0.426000 -0.803000\n",
"25% 0.641000 0.589000 -0.129750 -0.248000\n",
"50% 0.773500 0.730000 -0.024500 -0.036500\n",
"75% 0.939500 0.883500 0.134000 0.169500\n",
"max 1.195500 1.657000 0.676500 0.728500\n",
"range 1.085500 1.834000 1.102500 1.531500\n",
"3sigma 0.637425 0.758012 0.725854 0.904090"
]
},
"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": "5355743f",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fd8522100daf475ebf21650ea29065fd",
"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": [
"fig, ax = plt.subplots(2,1)\n",
"ax[0].plot([i+1 for i in range(len(AlignMarkX.columns))],AXdescibe.loc['mean'],linestyle = '-.',marker = 'o',color='w')\n",
"AlignMarkX.boxplot(ax=ax[0])\n",
"ax[0].axhline(0,c='orange',ls='-.',label=r'Mean_X:$0um\\pm0.10um$')\n",
"for i in range(len(AlignMarkX.columns)):\n",
" ax[0].annotate(round(AXdescibe.loc['mean'][i],3), \n",
" xy=(i+1,AXdescibe.loc['mean'][i]),\n",
" xytext=(i+0.95,AXdescibe.loc['mean'][i]),\n",
" fontsize=15,\n",
" color=\"r\")\n",
"ax[0].legend()\n",
"ax[0].set_title('mean_X/Day')\n",
"labels = ax[0].get_xticklabels()\n",
"plt.setp(labels, rotation=90)\n",
"ax[1].plot([i for i in AlignMarkX.columns],AXdescibe.loc['3sigma'],marker = 'o')\n",
"ax[1].axhline(0.8,c='orange',ls='-.',label=r'3sigma_X:$<0.800um$')\n",
"ax[1].axhline(0.57,c='green',ls='-.',label=r'3sigma_X:$<0.570um$')\n",
"for i in range(len(AlignMarkX.columns)):\n",
" ax[1].annotate(round(AXdescibe.loc['3sigma'][i],3), \n",
" xy=(i,AXdescibe.loc['3sigma'][i]),\n",
" xytext=(i,AXdescibe.loc['3sigma'][i]),\n",
" fontsize=15,\n",
" color=\"r\")\n",
"ax[1].legend() \n",
"ax[1].set_title('3sigam_X/Day')\n",
"labels = ax[1].get_xticklabels()\n",
"plt.setp(labels, rotation=90)\n",
"# ax[2].plot([i for i in AlignMarkX.columns],AXdescibe.loc['range'],marker = 'o')\n",
"# for i in range(len(AlignMarkX.columns)):\n",
"# ax[2].annotate(round(AXdescibe.loc['range'][i],3), \n",
"# xy=(i,AXdescibe.loc['range'][i]),\n",
"# xytext=(i,AXdescibe.loc['range'][i]),\n",
"# color=\"r\")\n",
"# ax[2].set_title('Range_X/Day')\n",
"plt.suptitle(f'{DieType} 对位Mark：X')\n",
"fig.tight_layout()\n",
"plt.savefig(f'{DieType}/{DieType}对位MarkX.jpg',dpi=200)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "64f88c9a",
"metadata": {},
"source": [
"### 对位MarkY"
]
},
{
"cell_type": "code",
"execution_count": 5,
"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>01.22.1-Die4</th>\n",
" <th>01.24.1-Die4</th>\n",
" <th>01.27.1-Die4</th>\n",
" <th>02.01.1-Die4</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>73.000000</td>\n",
" <td>73.000000</td>\n",
" <td>75.000000</td>\n",
" <td>73.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>-0.206288</td>\n",
" <td>-0.112151</td>\n",
" <td>0.425573</td>\n",
" <td>-0.030158</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.266299</td>\n",
" <td>0.228874</td>\n",
" <td>0.267416</td>\n",
" <td>0.232521</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>-0.849500</td>\n",
" <td>-0.722000</td>\n",
" <td>-0.402500</td>\n",
" <td>-0.640500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>-0.403500</td>\n",
" <td>-0.278000</td>\n",
" <td>0.280000</td>\n",
" <td>-0.193000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>-0.193500</td>\n",
" <td>-0.082000</td>\n",
" <td>0.432500</td>\n",
" <td>-0.029000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>-0.025000</td>\n",
" <td>0.035000</td>\n",
" <td>0.583250</td>\n",
" <td>0.156500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>0.340000</td>\n",
" <td>0.288000</td>\n",
" <td>0.976000</td>\n",
" <td>0.413500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>range</th>\n",
" <td>1.189500</td>\n",
" <td>1.010000</td>\n",
" <td>1.378500</td>\n",
" <td>1.054000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3sigma</th>\n",
" <td>0.798897</td>\n",
" <td>0.686621</td>\n",
" <td>0.802248</td>\n",
" <td>0.697562</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 01.22.1-Die4 01.24.1-Die4 01.27.1-Die4 02.01.1-Die4\n",
"count 73.000000 73.000000 75.000000 73.000000\n",
"mean -0.206288 -0.112151 0.425573 -0.030158\n",
"std 0.266299 0.228874 0.267416 0.232521\n",
"min -0.849500 -0.722000 -0.402500 -0.640500\n",
"25% -0.403500 -0.278000 0.280000 -0.193000\n",
"50% -0.193500 -0.082000 0.432500 -0.029000\n",
"75% -0.025000 0.035000 0.583250 0.156500\n",
"max 0.340000 0.288000 0.976000 0.413500\n",
"range 1.189500 1.010000 1.378500 1.054000\n",
"3sigma 0.798897 0.686621 0.802248 0.697562"
]
},
"execution_count": 5,
"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": 6,
"id": "81162e4f-1ed2-4365-9e55-2a0177174f18",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a05b947a655c4795892a66bc1fe01e22",
"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": [
"fig, ax = plt.subplots(2,1)\n",
"ax[0].plot([i+1 for i in range(len(AlignMarkY.columns))],AYdescibe.loc['mean'],linestyle = '-.',marker = 'o',color='w')\n",
"AlignMarkY.boxplot(ax=ax[0])\n",
"ax[0].axhline(0,c='orange',ls='-.',label=r'Mean_Y:$0um\\pm0.10um$')\n",
"for i in range(len(AlignMarkY.columns)):\n",
" ax[0].annotate(round(AYdescibe.loc['mean'][i],2), \n",
" xy=(i+1,AYdescibe.loc['mean'][i]),\n",
" xytext=(i+0.95,AYdescibe.loc['mean'][i]),\n",
" fontsize=15,\n",
" color=\"r\")\n",
"ax[0].legend()\n",
"ax[0].set_title('mean_Y/Day')\n",
"labels = ax[0].get_xticklabels()\n",
"plt.setp(labels, rotation=90)\n",
"ax[1].plot([i for i in AlignMarkY.columns],AYdescibe.loc['3sigma'],marker = 'o')\n",
"ax[1].axhline(0.8,c='orange',ls='-.',label=r'3sigma_Y:$<0.800um$')\n",
"ax[1].axhline(0.57,c='green',ls='-.',label=r'3sigma_Y:$<0.570um$')\n",
"for i in range(len(AlignMarkY.columns)):\n",
" ax[1].annotate(round(AYdescibe.loc['3sigma'][i],3), \n",
" xy=(i,AYdescibe.loc['3sigma'][i]),\n",
" xytext=(i,AYdescibe.loc['3sigma'][i]),\n",
" fontsize=15,\n",
" color=\"r\")\n",
"ax[1].legend() \n",
"ax[1].set_title('3sigam_Y/Day')\n",
"labels = ax[1].get_xticklabels()\n",
"plt.setp(labels, rotation=90)\n",
"# ax[2].plot([i for i in AlignMarkY.columns],AYdescibe.loc['range'],marker = 'o')\n",
"# for i in range(len(AlignMarkY.columns)):\n",
"# ax[2].annotate(round(AYdescibe.loc['range'][i],3), \n",
"# xy=(i,AYdescibe.loc['range'][i]),\n",
"# xytext=(i,AYdescibe.loc['range'][i]),\n",
"# color=\"r\")\n",
"# ax[2].set_title('Range_Y/Day')\n",
"plt.suptitle(f'{DieType} 对位Mark：Y')\n",
"fig.tight_layout()\n",
"plt.savefig(f'{DieType}/{DieType}对位MarkY.jpg',dpi=200)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "6ace8d23",
"metadata": {},
"source": [
"## 角度Mark"
]
},
{
"cell_type": "markdown",
"id": "c70c8ca9",
"metadata": {},
"source": [
"### 角度MarkX"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "87ad2953",
"metadata": {},
"outputs": [],
"source": [
"# AngleMarkX = TotalData['M1X'].dropna(subset='QX8800SP_Index').set_index('QX8800SP_Index').iloc[:,die_nums:].sort_index(axis=1)\n",
"# RXdescibe = describe_3s(AngleMarkX)\n",
"# RXdescibe"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "2dcd5e1f-bcd3-4100-8aa0-f2125301c1e3",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# fig, ax = plt.subplots(2,1)\n",
"# ax[0].plot([i+1 for i in range(len(AngleMarkX.columns))],RXdescibe.loc['mean'],linestyle = '-.',marker = 'o',color='w')\n",
"# AngleMarkX.boxplot(ax=ax[0])\n",
"# ax[0].axhline(0,c='orange',ls='-.',label=r'Mean_X:$0um\\pm0.10um$')\n",
"# for i in range(len(AngleMarkX.columns)):\n",
"# ax[0].annotate(round(RXdescibe.loc['mean'][i],2), \n",
"# xy=(i+1,RXdescibe.loc['mean'][i]),\n",
"# xytext=(i+0.95,RXdescibe.loc['mean'][i]+2),\n",
"# fontsize=15,\n",
"# color=\"r\")\n",
"# ax[0].legend()\n",
"# ax[0].set_title('mean_X/Day')\n",
"# labels = ax[0].get_xticklabels()\n",
"# plt.setp(labels, rotation=90)\n",
"# ax[1].plot([i for i in AngleMarkX.columns],RXdescibe.loc['3sigma'],marker = 'o')\n",
"# ax[1].axhline(0.8,c='orange',ls='-.',label=r'3sigma_X:$<0.800um$')\n",
"# for i in range(len(AngleMarkX.columns)):\n",
"# ax[1].annotate(round(RXdescibe.loc['3sigma'][i],3), \n",
"# xy=(i,RXdescibe.loc['3sigma'][i]),\n",
"# xytext=(i,RXdescibe.loc['3sigma'][i]),\n",
"# fontsize=15,\n",
"# color=\"r\")\n",
"# ax[1].legend() \n",
"# ax[1].set_title('3sigam_X/Day')\n",
"# labels = ax[1].get_xticklabels()\n",
"# plt.setp(labels, rotation=90)\n",
"# # ax[2].plot([i for i in AngleMarkX.columns],RXdescibe.loc['range'],marker = 'o')\n",
"# # for i in range(len(AngleMarkX.columns)):\n",
"# # ax[2].annotate(round(RXdescibe.loc['range'][i],3), \n",
"# # xy=(i,RXdescibe.loc['range'][i]),\n",
"# # xytext=(i,RXdescibe.loc['range'][i]),\n",
"# # color=\"r\")\n",
"# # ax[2].set_title('Range_X/Day')\n",
"# plt.suptitle('Die4 角度Mark：X')\n",
"# fig.tight_layout()\n",
"# plt.savefig('Die4/Die4角度MarkX.jpg',dpi=200)\n",
"# plt.show()"
]
},
{
"cell_type": "markdown",
"id": "fca6defb",
"metadata": {},
"source": [
"### 角度MarkY"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "389557c5",
"metadata": {},
"outputs": [],
"source": [
"# AngleMarkY = TotalData['M1Y'].dropna(subset='QX8800SP_Index').set_index('QX8800SP_Index').iloc[:,die_nums:].sort_index(axis=1)\n",
"# RYdescibe = describe_3s(AngleMarkY)\n",
"# RYdescibe"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "c7a1606f-4530-4fa6-89ce-892a57c06493",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# fig, ax = plt.subplots(2,1)\n",
"# ax[0].plot([i+1 for i in range(len(AngleMarkY.columns))],RYdescibe.loc['mean'],linestyle = '-.',marker = 'o',color='w')\n",
"# AngleMarkY.boxplot(ax=ax[0])\n",
"# ax[0].axhline(0,c='orange',ls='-.',label=r'Mean_Y:$0um\\pm0.10um$')\n",
"# for i in range(len(AngleMarkY.columns)):\n",
"# ax[0].annotate(round(RYdescibe.loc['mean'][i],2), \n",
"# xy=(i+1,RYdescibe.loc['mean'][i]),\n",
"# xytext=(i+0.95,RYdescibe.loc['mean'][i]+2),\n",
"# fontsize=15,\n",
"# color=\"r\")\n",
"# ax[0].legend()\n",
"# ax[0].set_title('mean_Y/Day')\n",
"# labels = ax[0].get_xticklabels()\n",
"# plt.setp(labels, rotation=90)\n",
"# ax[1].plot([i for i in AngleMarkY.columns],RYdescibe.loc['3sigma'],marker = 'o')\n",
"# ax[1].axhline(0.8,c='orange',ls='-.',label=r'3sigma_Y:$<0.800um$')\n",
"# for i in range(len(AngleMarkY.columns)):\n",
"# ax[1].annotate(round(RYdescibe.loc['3sigma'][i],3), \n",
"# xy=(i,RYdescibe.loc['3sigma'][i]),\n",
"# xytext=(i,RYdescibe.loc['3sigma'][i]),\n",
"# fontsize=15,\n",
"# color=\"r\")\n",
"# ax[1].legend() \n",
"# ax[1].set_title('3sigam_Y/Day')\n",
"# labels = ax[1].get_xticklabels()\n",
"# plt.setp(labels, rotation=90)\n",
"# # ax[2].plot([i for i in AngleMarkY.columns],RYdescibe.loc['range'],marker = 'o')\n",
"# # for i in range(len(AngleMarkY.columns)):\n",
"# # ax[2].annotate(round(RYdescibe.loc['range'][i],3), \n",
"# # xy=(i,RYdescibe.loc['range'][i]),\n",
"# # xytext=(i,RYdescibe.loc['range'][i]),\n",
"# # color=\"r\")\n",
"# # ax[2].set_title('Range_Y/Day')\n",
"# plt.suptitle('Die4 角度Mark：Y')\n",
"# fig.tight_layout()\n",
"# plt.savefig('Die4/Die4角度MarkY.jpg',dpi=200)\n",
"# plt.show()"
]
},
{
"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>01.22.1-Die4</th>\n",
" <th>01.24.1-Die4</th>\n",
" <th>01.27.1-Die4</th>\n",
" <th>02.01.1-Die4</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>73.000000</td>\n",
" <td>73.000000</td>\n",
" <td>75.000000</td>\n",
" <td>73.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.000337</td>\n",
" <td>-0.000391</td>\n",
" <td>0.000640</td>\n",
" <td>0.000401</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.001984</td>\n",
" <td>0.002175</td>\n",
" <td>0.001908</td>\n",
" <td>0.001580</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>-0.005290</td>\n",
" <td>-0.006015</td>\n",
" <td>-0.004943</td>\n",
" <td>-0.003543</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>-0.000751</td>\n",
" <td>-0.001849</td>\n",
" <td>-0.000219</td>\n",
" <td>-0.000651</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>0.000494</td>\n",
" <td>-0.000465</td>\n",
" <td>0.000668</td>\n",
" <td>0.000503</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>0.001827</td>\n",
" <td>0.001259</td>\n",
" <td>0.001955</td>\n",
" <td>0.001452</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>0.004086</td>\n",
" <td>0.006093</td>\n",
" <td>0.004053</td>\n",
" <td>0.003912</td>\n",
" </tr>\n",
" <tr>\n",
" <th>range</th>\n",
" <td>0.009376</td>\n",
" <td>0.012107</td>\n",
" <td>0.008995</td>\n",
" <td>0.007455</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3sigma</th>\n",
" <td>0.005951</td>\n",
" <td>0.006526</td>\n",
" <td>0.005724</td>\n",
" <td>0.004741</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 01.22.1-Die4 01.24.1-Die4 01.27.1-Die4 02.01.1-Die4\n",
"count 73.000000 73.000000 75.000000 73.000000\n",
"mean 0.000337 -0.000391 0.000640 0.000401\n",
"std 0.001984 0.002175 0.001908 0.001580\n",
"min -0.005290 -0.006015 -0.004943 -0.003543\n",
"25% -0.000751 -0.001849 -0.000219 -0.000651\n",
"50% 0.000494 -0.000465 0.000668 0.000503\n",
"75% 0.001827 0.001259 0.001955 0.001452\n",
"max 0.004086 0.006093 0.004053 0.003912\n",
"range 0.009376 0.012107 0.008995 0.007455\n",
"3sigma 0.005951 0.006526 0.005724 0.004741"
]
},
"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": "5ce2eec7-a959-4716-92a0-4aaad88b96b3",
"metadata": {},
"outputs": [
{
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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": [
"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(f'{DieType} 角度（°）')\n",
"fig.tight_layout()\n",
"plt.savefig(f'{DieType}/{DieType}角度.jpg',dpi=200)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "f6b3c183-c253-46fb-80aa-45bb98f0eaad",
"metadata": {},
"source": [
"### 按wafer数据存储"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "c25e8976-f7cc-418f-8b1f-04a06a5b3386",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Die4对位MarkX局部补偿（um）')"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "97c6508c4f3e4f589b35df7f34305bfa",
"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[-4:]],axis=1)\n",
"BC_X = pd.concat([AlignMarkX[i] for i in AlignMarkX.columns[-3:]],axis=1)\n",
"BC_X.plot(marker='o')\n",
"plt.title(f'{DieType}对位MarkX局部补偿（um）')"
]
},
{
"cell_type": "markdown",
"id": "639173af",
"metadata": {},
"source": [
"### 补偿值计算"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "7b6651ce-0935-4386-b6a4-634fd78aaee3",
"metadata": {},
"outputs": [],
"source": [
"# # BC_X = pd.concat([AlignMarkX[i]-AlignMarkX[i].mean() for i in AlignMarkX.columns[-4:]],axis=1)\n",
"# BC_X = pd.concat([AlignMarkX[i] for i in AlignMarkX.columns[-3:]],axis=1)\n",
"# BC_X.plot(marker='o')\n",
"# plt.title(f'{DieType}对位MarkX局部补偿（um）')"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "c8f85626-8763-49b3-a9ee-61d8d0fa4f9d",
"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": "c83740f99243409d87fa0569fefcafc7",
"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 = BC_X.where(abs(BC_X)<1.2, 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": 15,
"id": "61ce5e50-3b7b-4b70-8917-cc6bac2a1ced",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Die4对位MarkY（um）')"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "94e14bf41e1d404e955ed3e421638265",
"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[-4:]],axis=1)\n",
"BC_Y = pd.concat([AlignMarkY[i] for i in AlignMarkY.columns[-3:]],axis=1)\n",
"BC_Y.plot(marker='o')\n",
"plt.title(f'{DieType}对位MarkY（um）')"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "ac86dfdd-f506-4ac7-9590-c563900d70df",
"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": "f7c95134eb12482e963ea20e25c028fc",
"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 = BC_Y.where(abs(BC_Y)<1.5, 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": 15,
"id": "a94845fc-49f0-41a1-99a0-b5f5cdff1a69",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Die4角度（°）')"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b2ecee68390248d6aba0d47710ddf1dd",
"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[-4:]],axis=1)\n",
"BC_A = pd.concat([Angle[i] for i in Angle.columns[-3:]],axis=1)\n",
"BC_A.plot(marker='o')\n",
"plt.title(f'{DieType}角度（°）')"
]
},
{
"cell_type": "code",
"execution_count": 16,
"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": 16,
"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": 17,
"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",
" }\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>1.200500</td>\n",
" <td>-0.074000</td>\n",
" <td>-0.002286</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1.302000</td>\n",
" <td>-0.215500</td>\n",
" <td>-0.001469</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1.403500</td>\n",
" <td>-0.357000</td>\n",
" <td>-0.000653</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.659500</td>\n",
" <td>-0.155500</td>\n",
" <td>-0.003349</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0.851750</td>\n",
" <td>0.020000</td>\n",
" <td>-0.005550</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>1.074750</td>\n",
" <td>-0.117500</td>\n",
" <td>-0.001236</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>0.468000</td>\n",
" <td>0.175750</td>\n",
" <td>0.002528</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>0.660625</td>\n",
" <td>0.052250</td>\n",
" <td>0.002228</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>0.853250</td>\n",
" <td>-0.071250</td>\n",
" <td>0.001929</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>0.840500</td>\n",
" <td>-0.334250</td>\n",
" <td>0.000419</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>0.997000</td>\n",
" <td>-0.419500</td>\n",
" <td>-0.000068</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>0.697750</td>\n",
" <td>-0.116500</td>\n",
" <td>-0.004727</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>0.771000</td>\n",
" <td>-0.240500</td>\n",
" <td>0.001345</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>0.667750</td>\n",
" <td>0.001000</td>\n",
" <td>0.001440</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>1.001500</td>\n",
" <td>-0.540750</td>\n",
" <td>0.002879</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>0.656125</td>\n",
" <td>-0.273625</td>\n",
" <td>0.000073</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>0.310750</td>\n",
" <td>-0.006500</td>\n",
" <td>-0.002733</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>0.767500</td>\n",
" <td>-0.219250</td>\n",
" <td>-0.000727</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>0.513250</td>\n",
" <td>-0.143250</td>\n",
" <td>-0.000273</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>0.595500</td>\n",
" <td>-0.123750</td>\n",
" <td>-0.000297</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>0.743750</td>\n",
" <td>-0.336750</td>\n",
" <td>-0.001582</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>0.256500</td>\n",
" <td>0.095500</td>\n",
" <td>0.001194</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>0.668750</td>\n",
" <td>-0.076000</td>\n",
" <td>0.000078</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>0.621750</td>\n",
" <td>0.236750</td>\n",
" <td>0.001409</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>0.806125</td>\n",
" <td>0.136875</td>\n",
" <td>0.000757</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>0.990500</td>\n",
" <td>0.037000</td>\n",
" <td>0.000106</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>0.878000</td>\n",
" <td>-0.187750</td>\n",
" <td>0.002208</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>0.925250</td>\n",
" <td>-0.540750</td>\n",
" <td>-0.002771</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>0.810750</td>\n",
" <td>-0.461000</td>\n",
" <td>0.001027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>1.102750</td>\n",
" <td>-0.548750</td>\n",
" <td>0.001561</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>0.954250</td>\n",
" <td>-0.166750</td>\n",
" <td>-0.002419</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>0.758750</td>\n",
" <td>0.050750</td>\n",
" <td>-0.000367</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>0.775250</td>\n",
" <td>0.119250</td>\n",
" <td>-0.000076</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>0.512500</td>\n",
" <td>0.114250</td>\n",
" <td>0.000664</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>0.847250</td>\n",
" <td>-0.538000</td>\n",
" <td>0.001407</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>0.604500</td>\n",
" <td>-0.133250</td>\n",
" <td>0.002469</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>0.821250</td>\n",
" <td>-0.169250</td>\n",
" <td>0.001636</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>1.032750</td>\n",
" <td>-0.231750</td>\n",
" <td>0.001538</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>0.885500</td>\n",
" <td>-0.471750</td>\n",
" <td>-0.001336</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>0.638750</td>\n",
" <td>-0.418250</td>\n",
" <td>-0.000188</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>0.846500</td>\n",
" <td>-0.255500</td>\n",
" <td>-0.000339</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>1.078250</td>\n",
" <td>-0.299250</td>\n",
" <td>-0.001601</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>0.505000</td>\n",
" <td>0.268500</td>\n",
" <td>-0.000849</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>0.800750</td>\n",
" <td>-0.078000</td>\n",
" <td>0.001630</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>0.568500</td>\n",
" <td>-0.025750</td>\n",
" <td>-0.000834</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>0.700500</td>\n",
" <td>0.008625</td>\n",
" <td>0.000779</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>0.832500</td>\n",
" <td>0.043000</td>\n",
" <td>0.002392</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>0.642500</td>\n",
" <td>-0.118500</td>\n",
" <td>0.001642</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>0.647125</td>\n",
" <td>-0.182500</td>\n",
" <td>0.001357</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>0.651750</td>\n",
" <td>-0.246500</td>\n",
" <td>0.001072</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td>0.649250</td>\n",
" <td>-0.417250</td>\n",
" <td>0.000135</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52</th>\n",
" <td>0.683500</td>\n",
" <td>-0.214250</td>\n",
" <td>-0.000032</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53</th>\n",
" <td>0.593750</td>\n",
" <td>-0.535000</td>\n",
" <td>0.002811</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54</th>\n",
" <td>0.497500</td>\n",
" <td>0.160500</td>\n",
" <td>-0.000593</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55</th>\n",
" <td>0.645000</td>\n",
" <td>0.159250</td>\n",
" <td>0.000997</td>\n",
" </tr>\n",
" <tr>\n",
" <th>56</th>\n",
" <td>0.737833</td>\n",
" <td>0.015083</td>\n",
" <td>0.000605</td>\n",
" </tr>\n",
" <tr>\n",
" <th>57</th>\n",
" <td>0.830667</td>\n",
" <td>-0.129083</td>\n",
" <td>0.000213</td>\n",
" </tr>\n",
" <tr>\n",
" <th>58</th>\n",
" <td>0.923500</td>\n",
" <td>-0.273250</td>\n",
" <td>-0.000179</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59</th>\n",
" <td>0.925250</td>\n",
" <td>-0.505000</td>\n",
" <td>0.001113</td>\n",
" </tr>\n",
" <tr>\n",
" <th>60</th>\n",
" <td>0.804750</td>\n",
" <td>-0.124000</td>\n",
" <td>0.000243</td>\n",
" </tr>\n",
" <tr>\n",
" <th>61</th>\n",
" <td>0.835917</td>\n",
" <td>-0.137917</td>\n",
" <td>-0.000061</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62</th>\n",
" <td>0.867083</td>\n",
" <td>-0.151833</td>\n",
" <td>-0.000366</td>\n",
" </tr>\n",
" <tr>\n",
" <th>63</th>\n",
" <td>0.898250</td>\n",
" <td>-0.165750</td>\n",
" <td>-0.000670</td>\n",
" </tr>\n",
" <tr>\n",
" <th>64</th>\n",
" <td>0.729500</td>\n",
" <td>-0.025000</td>\n",
" <td>-0.000796</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65</th>\n",
" <td>0.725500</td>\n",
" <td>0.119750</td>\n",
" <td>-0.003126</td>\n",
" </tr>\n",
" <tr>\n",
" <th>66</th>\n",
" <td>0.665000</td>\n",
" <td>0.029125</td>\n",
" <td>-0.001388</td>\n",
" </tr>\n",
" <tr>\n",
" <th>67</th>\n",
" <td>0.604500</td>\n",
" <td>-0.061500</td>\n",
" <td>0.000350</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68</th>\n",
" <td>0.529500</td>\n",
" <td>-0.057250</td>\n",
" <td>0.001281</td>\n",
" </tr>\n",
" <tr>\n",
" <th>69</th>\n",
" <td>0.822000</td>\n",
" <td>-0.170250</td>\n",
" <td>0.001578</td>\n",
" </tr>\n",
" <tr>\n",
" <th>70</th>\n",
" <td>0.636500</td>\n",
" <td>-0.190500</td>\n",
" <td>0.000577</td>\n",
" </tr>\n",
" <tr>\n",
" <th>71</th>\n",
" <td>0.578750</td>\n",
" <td>-0.245750</td>\n",
" <td>-0.000927</td>\n",
" </tr>\n",
" <tr>\n",
" <th>72</th>\n",
" <td>0.847750</td>\n",
" <td>-0.249250</td>\n",
" <td>0.001042</td>\n",
" </tr>\n",
" <tr>\n",
" <th>73</th>\n",
" <td>0.752000</td>\n",
" <td>-0.119250</td>\n",
" <td>0.000797</td>\n",
" </tr>\n",
" <tr>\n",
" <th>74</th>\n",
" <td>0.767250</td>\n",
" <td>-0.114000</td>\n",
" <td>-0.001578</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75</th>\n",
" <td>0.649250</td>\n",
" <td>-0.044250</td>\n",
" <td>-0.004601</td>\n",
" </tr>\n",
" <tr>\n",
" <th>76</th>\n",
" <td>0.506500</td>\n",
" <td>-0.053500</td>\n",
" <td>-0.000300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77</th>\n",
" <td>0.827000</td>\n",
" <td>0.032250</td>\n",
" <td>0.001737</td>\n",
" </tr>\n",
" <tr>\n",
" <th>78</th>\n",
" <td>0.659000</td>\n",
" <td>-0.130500</td>\n",
" <td>-0.001005</td>\n",
" </tr>\n",
" <tr>\n",
" <th>79</th>\n",
" <td>0.550250</td>\n",
" <td>-0.220750</td>\n",
" <td>-0.001191</td>\n",
" </tr>\n",
" <tr>\n",
" <th>80</th>\n",
" <td>0.671500</td>\n",
" <td>-0.288250</td>\n",
" <td>-0.001393</td>\n",
" </tr>\n",
" <tr>\n",
" <th>81</th>\n",
" <td>0.935750</td>\n",
" <td>-0.270500</td>\n",
" <td>0.000643</td>\n",
" </tr>\n",
" <tr>\n",
" <th>82</th>\n",
" <td>0.723250</td>\n",
" <td>-0.079250</td>\n",
" <td>0.004854</td>\n",
" </tr>\n",
" <tr>\n",
" <th>83</th>\n",
" <td>1.066250</td>\n",
" <td>-0.336250</td>\n",
" <td>-0.000493</td>\n",
" </tr>\n",
" <tr>\n",
" <th>84</th>\n",
" <td>0.925000</td>\n",
" <td>-0.143000</td>\n",
" <td>-0.001576</td>\n",
" </tr>\n",
" <tr>\n",
" <th>85</th>\n",
" <td>0.925000</td>\n",
" <td>-0.143000</td>\n",
" <td>-0.001576</td>\n",
" </tr>\n",
" <tr>\n",
" <th>86</th>\n",
" <td>0.925000</td>\n",
" <td>-0.143000</td>\n",
" <td>-0.001576</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87</th>\n",
" <td>0.925000</td>\n",
" <td>-0.143000</td>\n",
" <td>-0.001576</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 对位MarkX 对位MarkY Angle\n",
"Index \n",
"1 1.200500 -0.074000 -0.002286\n",
"2 1.302000 -0.215500 -0.001469\n",
"3 1.403500 -0.357000 -0.000653\n",
"4 0.659500 -0.155500 -0.003349\n",
"5 0.851750 0.020000 -0.005550\n",
"6 1.074750 -0.117500 -0.001236\n",
"7 0.468000 0.175750 0.002528\n",
"8 0.660625 0.052250 0.002228\n",
"9 0.853250 -0.071250 0.001929\n",
"10 0.840500 -0.334250 0.000419\n",
"11 0.997000 -0.419500 -0.000068\n",
"12 0.697750 -0.116500 -0.004727\n",
"13 0.771000 -0.240500 0.001345\n",
"14 0.667750 0.001000 0.001440\n",
"15 1.001500 -0.540750 0.002879\n",
"16 0.656125 -0.273625 0.000073\n",
"17 0.310750 -0.006500 -0.002733\n",
"18 0.767500 -0.219250 -0.000727\n",
"19 0.513250 -0.143250 -0.000273\n",
"20 0.595500 -0.123750 -0.000297\n",
"21 0.743750 -0.336750 -0.001582\n",
"22 0.256500 0.095500 0.001194\n",
"23 0.668750 -0.076000 0.000078\n",
"24 0.621750 0.236750 0.001409\n",
"25 0.806125 0.136875 0.000757\n",
"26 0.990500 0.037000 0.000106\n",
"27 0.878000 -0.187750 0.002208\n",
"28 0.925250 -0.540750 -0.002771\n",
"29 0.810750 -0.461000 0.001027\n",
"30 1.102750 -0.548750 0.001561\n",
"31 0.954250 -0.166750 -0.002419\n",
"32 0.758750 0.050750 -0.000367\n",
"33 0.775250 0.119250 -0.000076\n",
"34 0.512500 0.114250 0.000664\n",
"35 0.847250 -0.538000 0.001407\n",
"36 0.604500 -0.133250 0.002469\n",
"37 0.821250 -0.169250 0.001636\n",
"38 1.032750 -0.231750 0.001538\n",
"39 0.885500 -0.471750 -0.001336\n",
"40 0.638750 -0.418250 -0.000188\n",
"41 0.846500 -0.255500 -0.000339\n",
"42 1.078250 -0.299250 -0.001601\n",
"43 0.505000 0.268500 -0.000849\n",
"44 0.800750 -0.078000 0.001630\n",
"45 0.568500 -0.025750 -0.000834\n",
"46 0.700500 0.008625 0.000779\n",
"47 0.832500 0.043000 0.002392\n",
"48 0.642500 -0.118500 0.001642\n",
"49 0.647125 -0.182500 0.001357\n",
"50 0.651750 -0.246500 0.001072\n",
"51 0.649250 -0.417250 0.000135\n",
"52 0.683500 -0.214250 -0.000032\n",
"53 0.593750 -0.535000 0.002811\n",
"54 0.497500 0.160500 -0.000593\n",
"55 0.645000 0.159250 0.000997\n",
"56 0.737833 0.015083 0.000605\n",
"57 0.830667 -0.129083 0.000213\n",
"58 0.923500 -0.273250 -0.000179\n",
"59 0.925250 -0.505000 0.001113\n",
"60 0.804750 -0.124000 0.000243\n",
"61 0.835917 -0.137917 -0.000061\n",
"62 0.867083 -0.151833 -0.000366\n",
"63 0.898250 -0.165750 -0.000670\n",
"64 0.729500 -0.025000 -0.000796\n",
"65 0.725500 0.119750 -0.003126\n",
"66 0.665000 0.029125 -0.001388\n",
"67 0.604500 -0.061500 0.000350\n",
"68 0.529500 -0.057250 0.001281\n",
"69 0.822000 -0.170250 0.001578\n",
"70 0.636500 -0.190500 0.000577\n",
"71 0.578750 -0.245750 -0.000927\n",
"72 0.847750 -0.249250 0.001042\n",
"73 0.752000 -0.119250 0.000797\n",
"74 0.767250 -0.114000 -0.001578\n",
"75 0.649250 -0.044250 -0.004601\n",
"76 0.506500 -0.053500 -0.000300\n",
"77 0.827000 0.032250 0.001737\n",
"78 0.659000 -0.130500 -0.001005\n",
"79 0.550250 -0.220750 -0.001191\n",
"80 0.671500 -0.288250 -0.001393\n",
"81 0.935750 -0.270500 0.000643\n",
"82 0.723250 -0.079250 0.004854\n",
"83 1.066250 -0.336250 -0.000493\n",
"84 0.925000 -0.143000 -0.001576\n",
"85 0.925000 -0.143000 -0.001576\n",
"86 0.925000 -0.143000 -0.001576\n",
"87 0.925000 -0.143000 -0.001576"
]
},
"execution_count": 17,
"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['Angle'] = 0\n",
"DieBC"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "1f25b0bc-e515-4bc4-b74f-8dbb38c728af",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2025-01-26\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": 19,
"id": "2dddf61b-818e-4592-8b46-765bfa119856",
"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>5.011179</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>全局补偿Y</th>\n",
" <td>4.3037</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>全局补偿角度</th>\n",
" <td>0.021272</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>4.955996</td>\n",
" <td>0.036802</td>\n",
" <td>-0.002776</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4.845454</td>\n",
" <td>0.023719</td>\n",
" <td>-0.002758</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4.734913</td>\n",
" <td>0.010635</td>\n",
" <td>-0.00274</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4.669163</td>\n",
" <td>0.193469</td>\n",
" <td>-0.001076</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>4.738763</td>\n",
" <td>0.38358</td>\n",
" <td>0.003192</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>4.164096</td>\n",
" <td>0.274163</td>\n",
" <td>-0.002247</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>3.320013</td>\n",
" <td>0.41383</td>\n",
" <td>-0.007047</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>3.48043</td>\n",
" <td>0.358413</td>\n",
" <td>-0.006823</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>3.640846</td>\n",
" <td>0.302997</td>\n",
" <td>-0.0066</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>3.823513</td>\n",
" <td>0.322163</td>\n",
" <td>-0.005644</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>4.214013</td>\n",
" <td>0.147163</td>\n",
" <td>-0.003388</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>4.212846</td>\n",
" <td>0.409247</td>\n",
" <td>0.000336</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>3.49368</td>\n",
" <td>0.156413</td>\n",
" <td>-0.005122</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>3.50793</td>\n",
" <td>0.25258</td>\n",
" <td>-0.005712</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>3.71168</td>\n",
" <td>-0.252087</td>\n",
" <td>-0.006045</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>3.697055</td>\n",
" <td>-0.060545</td>\n",
" <td>-0.005397</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>3.68243</td>\n",
" <td>0.130997</td>\n",
" <td>-0.004749</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>3.91468</td>\n",
" <td>0.23333</td>\n",
" <td>-0.004538</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>3.73943</td>\n",
" <td>0.274163</td>\n",
" <td>-0.006261</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>3.767013</td>\n",
" <td>0.303413</td>\n",
" <td>-0.005471</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>3.770013</td>\n",
" <td>0.272997</td>\n",
" <td>-0.003697</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>3.295596</td>\n",
" <td>0.25383</td>\n",
" <td>-0.004555</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>3.671346</td>\n",
" <td>0.009997</td>\n",
" <td>-0.003558</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>3.612763</td>\n",
" <td>0.221747</td>\n",
" <td>-0.004734</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>3.79643</td>\n",
" <td>0.184413</td>\n",
" <td>-0.004589</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>3.980096</td>\n",
" <td>0.14708</td>\n",
" <td>-0.004444</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>3.678346</td>\n",
" <td>0.180163</td>\n",
" <td>-0.006355</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>3.98993</td>\n",
" <td>0.033913</td>\n",
" <td>-0.004611</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>3.708596</td>\n",
" <td>0.327997</td>\n",
" <td>-0.006599</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>3.96918</td>\n",
" <td>-0.09067</td>\n",
" <td>-0.00728</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>4.268607</td>\n",
" <td>0.611591</td>\n",
" <td>0.000686</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>3.766513</td>\n",
" <td>0.224247</td>\n",
" <td>-0.003924</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>3.695263</td>\n",
" <td>0.238663</td>\n",
" <td>-0.004108</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>3.53868</td>\n",
" <td>0.268497</td>\n",
" <td>-0.004894</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>3.779763</td>\n",
" <td>-0.273753</td>\n",
" <td>-0.00588</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>3.43768</td>\n",
" <td>0.11958</td>\n",
" <td>-0.007756</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>3.680096</td>\n",
" <td>0.162663</td>\n",
" <td>-0.007284</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>3.86693</td>\n",
" <td>-0.003337</td>\n",
" <td>-0.007504</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>3.790596</td>\n",
" <td>0.161247</td>\n",
" <td>-0.006409</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>3.696346</td>\n",
" <td>0.158247</td>\n",
" <td>-0.006057</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>3.863013</td>\n",
" <td>0.202247</td>\n",
" <td>-0.006893</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>4.05944</td>\n",
" <td>0.677174</td>\n",
" <td>-0.002701</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>3.711513</td>\n",
" <td>0.355997</td>\n",
" <td>-0.003252</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>3.56443</td>\n",
" <td>0.062413</td>\n",
" <td>-0.004971</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>3.733513</td>\n",
" <td>0.07758</td>\n",
" <td>-0.004119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>3.703763</td>\n",
" <td>0.194538</td>\n",
" <td>-0.005883</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>3.674013</td>\n",
" <td>0.311497</td>\n",
" <td>-0.007648</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>3.566096</td>\n",
" <td>0.140497</td>\n",
" <td>-0.00704</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>3.606221</td>\n",
" <td>0.14658</td>\n",
" <td>-0.006525</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>3.646346</td>\n",
" <td>0.152663</td>\n",
" <td>-0.006011</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td>3.61543</td>\n",
" <td>0.145163</td>\n",
" <td>-0.006475</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52</th>\n",
" <td>3.81568</td>\n",
" <td>0.077163</td>\n",
" <td>-0.006455</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53</th>\n",
" <td>3.393774</td>\n",
" <td>0.009497</td>\n",
" <td>-0.005527</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54</th>\n",
" <td>3.671846</td>\n",
" <td>0.353913</td>\n",
" <td>-0.003943</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55</th>\n",
" <td>3.748346</td>\n",
" <td>0.321497</td>\n",
" <td>-0.004599</td>\n",
" </tr>\n",
" <tr>\n",
" <th>56</th>\n",
" <td>3.821846</td>\n",
" <td>0.200941</td>\n",
" <td>-0.00481</td>\n",
" </tr>\n",
" <tr>\n",
" <th>57</th>\n",
" <td>3.895346</td>\n",
" <td>0.080386</td>\n",
" <td>-0.005021</td>\n",
" </tr>\n",
" <tr>\n",
" <th>58</th>\n",
" <td>3.968846</td>\n",
" <td>-0.04017</td>\n",
" <td>-0.005232</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59</th>\n",
" <td>3.923513</td>\n",
" <td>-0.006503</td>\n",
" <td>-0.006732</td>\n",
" </tr>\n",
" <tr>\n",
" <th>60</th>\n",
" <td>3.79443</td>\n",
" <td>0.227997</td>\n",
" <td>-0.005791</td>\n",
" </tr>\n",
" <tr>\n",
" <th>61</th>\n",
" <td>3.791957</td>\n",
" <td>0.167219</td>\n",
" <td>-0.005876</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62</th>\n",
" <td>3.789485</td>\n",
" <td>0.106441</td>\n",
" <td>-0.005961</td>\n",
" </tr>\n",
" <tr>\n",
" <th>63</th>\n",
" <td>3.787013</td>\n",
" <td>0.045663</td>\n",
" <td>-0.006046</td>\n",
" </tr>\n",
" <tr>\n",
" <th>64</th>\n",
" <td>3.674263</td>\n",
" <td>0.485497</td>\n",
" <td>-0.005151</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65</th>\n",
" <td>4.023846</td>\n",
" <td>0.227247</td>\n",
" <td>-0.000757</td>\n",
" </tr>\n",
" <tr>\n",
" <th>66</th>\n",
" <td>3.842346</td>\n",
" <td>0.155163</td>\n",
" <td>-0.002659</td>\n",
" </tr>\n",
" <tr>\n",
" <th>67</th>\n",
" <td>3.660846</td>\n",
" <td>0.08308</td>\n",
" <td>-0.004561</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68</th>\n",
" <td>3.518096</td>\n",
" <td>0.138163</td>\n",
" <td>-0.005465</td>\n",
" </tr>\n",
" <tr>\n",
" <th>69</th>\n",
" <td>3.711096</td>\n",
" <td>0.128163</td>\n",
" <td>-0.006331</td>\n",
" </tr>\n",
" <tr>\n",
" <th>70</th>\n",
" <td>3.626263</td>\n",
" <td>0.121413</td>\n",
" <td>-0.005901</td>\n",
" </tr>\n",
" <tr>\n",
" <th>71</th>\n",
" <td>3.68318</td>\n",
" <td>0.25133</td>\n",
" <td>-0.004428</td>\n",
" </tr>\n",
" <tr>\n",
" <th>72</th>\n",
" <td>3.75218</td>\n",
" <td>0.260997</td>\n",
" <td>-0.005047</td>\n",
" </tr>\n",
" <tr>\n",
" <th>73</th>\n",
" <td>3.56493</td>\n",
" <td>0.231663</td>\n",
" <td>-0.007636</td>\n",
" </tr>\n",
" <tr>\n",
" <th>74</th>\n",
" <td>3.939263</td>\n",
" <td>0.281997</td>\n",
" <td>-0.002727</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75</th>\n",
" <td>4.255346</td>\n",
" <td>0.155413</td>\n",
" <td>0.000239</td>\n",
" </tr>\n",
" <tr>\n",
" <th>76</th>\n",
" <td>3.69343</td>\n",
" <td>0.157663</td>\n",
" <td>-0.003123</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77</th>\n",
" <td>3.66618</td>\n",
" <td>0.191247</td>\n",
" <td>-0.005459</td>\n",
" </tr>\n",
" <tr>\n",
" <th>78</th>\n",
" <td>3.85643</td>\n",
" <td>0.15958</td>\n",
" <td>-0.004388</td>\n",
" </tr>\n",
" <tr>\n",
" <th>79</th>\n",
" <td>3.747846</td>\n",
" <td>0.057747</td>\n",
" <td>-0.004162</td>\n",
" </tr>\n",
" <tr>\n",
" <th>80</th>\n",
" <td>3.935346</td>\n",
" <td>0.14633</td>\n",
" <td>-0.004026</td>\n",
" </tr>\n",
" <tr>\n",
" <th>81</th>\n",
" <td>3.855596</td>\n",
" <td>0.177913</td>\n",
" <td>-0.006738</td>\n",
" </tr>\n",
" <tr>\n",
" <th>82</th>\n",
" <td>3.08668</td>\n",
" <td>0.447663</td>\n",
" <td>-0.010005</td>\n",
" </tr>\n",
" <tr>\n",
" <th>83</th>\n",
" <td>4.245263</td>\n",
" <td>0.095163</td>\n",
" <td>-0.003276</td>\n",
" </tr>\n",
" <tr>\n",
" <th>84</th>\n",
" <td>4.167502</td>\n",
" <td>0.103486</td>\n",
" <td>-0.002822</td>\n",
" </tr>\n",
" <tr>\n",
" <th>85</th>\n",
" <td>4.167502</td>\n",
" <td>0.103486</td>\n",
" <td>-0.002822</td>\n",
" </tr>\n",
" <tr>\n",
" <th>86</th>\n",
" <td>4.167502</td>\n",
" <td>0.103486</td>\n",
" <td>-0.002822</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87</th>\n",
" <td>4.167502</td>\n",
" <td>0.103486</td>\n",
" <td>-0.002822</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 1 2 3\n",
"0 \n",
"全局补偿X 5.011179 NaN NaN\n",
"全局补偿Y 4.3037 NaN NaN\n",
"全局补偿角度 0.021272 NaN NaN\n",
"Index 对位MarkX 对位MarkY Angle\n",
"1 4.955996 0.036802 -0.002776\n",
"2 4.845454 0.023719 -0.002758\n",
"3 4.734913 0.010635 -0.00274\n",
"4 4.669163 0.193469 -0.001076\n",
"5 4.738763 0.38358 0.003192\n",
"6 4.164096 0.274163 -0.002247\n",
"7 3.320013 0.41383 -0.007047\n",
"8 3.48043 0.358413 -0.006823\n",
"9 3.640846 0.302997 -0.0066\n",
"10 3.823513 0.322163 -0.005644\n",
"11 4.214013 0.147163 -0.003388\n",
"12 4.212846 0.409247 0.000336\n",
"13 3.49368 0.156413 -0.005122\n",
"14 3.50793 0.25258 -0.005712\n",
"15 3.71168 -0.252087 -0.006045\n",
"16 3.697055 -0.060545 -0.005397\n",
"17 3.68243 0.130997 -0.004749\n",
"18 3.91468 0.23333 -0.004538\n",
"19 3.73943 0.274163 -0.006261\n",
"20 3.767013 0.303413 -0.005471\n",
"21 3.770013 0.272997 -0.003697\n",
"22 3.295596 0.25383 -0.004555\n",
"23 3.671346 0.009997 -0.003558\n",
"24 3.612763 0.221747 -0.004734\n",
"25 3.79643 0.184413 -0.004589\n",
"26 3.980096 0.14708 -0.004444\n",
"27 3.678346 0.180163 -0.006355\n",
"28 3.98993 0.033913 -0.004611\n",
"29 3.708596 0.327997 -0.006599\n",
"30 3.96918 -0.09067 -0.00728\n",
"31 4.268607 0.611591 0.000686\n",
"32 3.766513 0.224247 -0.003924\n",
"33 3.695263 0.238663 -0.004108\n",
"34 3.53868 0.268497 -0.004894\n",
"35 3.779763 -0.273753 -0.00588\n",
"36 3.43768 0.11958 -0.007756\n",
"37 3.680096 0.162663 -0.007284\n",
"38 3.86693 -0.003337 -0.007504\n",
"39 3.790596 0.161247 -0.006409\n",
"40 3.696346 0.158247 -0.006057\n",
"41 3.863013 0.202247 -0.006893\n",
"42 4.05944 0.677174 -0.002701\n",
"43 3.711513 0.355997 -0.003252\n",
"44 3.56443 0.062413 -0.004971\n",
"45 3.733513 0.07758 -0.004119\n",
"46 3.703763 0.194538 -0.005883\n",
"47 3.674013 0.311497 -0.007648\n",
"48 3.566096 0.140497 -0.00704\n",
"49 3.606221 0.14658 -0.006525\n",
"50 3.646346 0.152663 -0.006011\n",
"51 3.61543 0.145163 -0.006475\n",
"52 3.81568 0.077163 -0.006455\n",
"53 3.393774 0.009497 -0.005527\n",
"54 3.671846 0.353913 -0.003943\n",
"55 3.748346 0.321497 -0.004599\n",
"56 3.821846 0.200941 -0.00481\n",
"57 3.895346 0.080386 -0.005021\n",
"58 3.968846 -0.04017 -0.005232\n",
"59 3.923513 -0.006503 -0.006732\n",
"60 3.79443 0.227997 -0.005791\n",
"61 3.791957 0.167219 -0.005876\n",
"62 3.789485 0.106441 -0.005961\n",
"63 3.787013 0.045663 -0.006046\n",
"64 3.674263 0.485497 -0.005151\n",
"65 4.023846 0.227247 -0.000757\n",
"66 3.842346 0.155163 -0.002659\n",
"67 3.660846 0.08308 -0.004561\n",
"68 3.518096 0.138163 -0.005465\n",
"69 3.711096 0.128163 -0.006331\n",
"70 3.626263 0.121413 -0.005901\n",
"71 3.68318 0.25133 -0.004428\n",
"72 3.75218 0.260997 -0.005047\n",
"73 3.56493 0.231663 -0.007636\n",
"74 3.939263 0.281997 -0.002727\n",
"75 4.255346 0.155413 0.000239\n",
"76 3.69343 0.157663 -0.003123\n",
"77 3.66618 0.191247 -0.005459\n",
"78 3.85643 0.15958 -0.004388\n",
"79 3.747846 0.057747 -0.004162\n",
"80 3.935346 0.14633 -0.004026\n",
"81 3.855596 0.177913 -0.006738\n",
"82 3.08668 0.447663 -0.010005\n",
"83 4.245263 0.095163 -0.003276\n",
"84 4.167502 0.103486 -0.002822\n",
"85 4.167502 0.103486 -0.002822\n",
"86 4.167502 0.103486 -0.002822\n",
"87 4.167502 0.103486 -0.002822"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"OldDieBCFileName = f'{DieType}局部补偿2025-01-22.xlsx'\n",
"OldDieBC = pd.read_excel(f'{DieType}/{OldDieBCFileName}',sheet_name=\"Result\",index_col=0,header=None)\n",
"OldDieBC"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "672f1656-d7bf-4c63-a708-56199a5d27ab",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
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"<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>4.955996</td>\n",
" <td>0.036802</td>\n",
" <td>-0.002776</td>\n",
" <td>1.200500</td>\n",
" <td>-0.074000</td>\n",
" <td>-0.002286</td>\n",
" <td>3.755496</td>\n",
" <td>0.110802</td>\n",
" <td>-0.005062</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4.845454</td>\n",
" <td>0.023719</td>\n",
" <td>-0.002758</td>\n",
" <td>1.302000</td>\n",
" <td>-0.215500</td>\n",
" <td>-0.001469</td>\n",
" <td>3.543454</td>\n",
" <td>0.239219</td>\n",
" <td>-0.004228</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4.734913</td>\n",
" <td>0.010635</td>\n",
" <td>-0.00274</td>\n",
" <td>1.403500</td>\n",
" <td>-0.357000</td>\n",
" <td>-0.000653</td>\n",
" <td>3.331413</td>\n",
" <td>0.367635</td>\n",
" <td>-0.003393</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4.669163</td>\n",
" <td>0.193469</td>\n",
" <td>-0.001076</td>\n",
" <td>0.659500</td>\n",
" <td>-0.155500</td>\n",
" <td>-0.003349</td>\n",
" <td>4.009663</td>\n",
" <td>0.348969</td>\n",
" <td>-0.004425</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>4.738763</td>\n",
" <td>0.38358</td>\n",
" <td>0.003192</td>\n",
" <td>0.851750</td>\n",
" <td>0.020000</td>\n",
" <td>-0.005550</td>\n",
" <td>3.887013</td>\n",
" <td>0.36358</td>\n",
" <td>-0.002357</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>4.164096</td>\n",
" <td>0.274163</td>\n",
" <td>-0.002247</td>\n",
" <td>1.074750</td>\n",
" <td>-0.117500</td>\n",
" <td>-0.001236</td>\n",
" <td>3.089346</td>\n",
" <td>0.391663</td>\n",
" <td>-0.003483</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>3.320013</td>\n",
" <td>0.41383</td>\n",
" <td>-0.007047</td>\n",
" <td>0.468000</td>\n",
" <td>0.175750</td>\n",
" <td>0.002528</td>\n",
" <td>2.852013</td>\n",
" <td>0.23808</td>\n",
" <td>-0.004519</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>3.48043</td>\n",
" <td>0.358413</td>\n",
" <td>-0.006823</td>\n",
" <td>0.660625</td>\n",
" <td>0.052250</td>\n",
" <td>0.002228</td>\n",
" <td>2.819805</td>\n",
" <td>0.306163</td>\n",
" <td>-0.004595</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>3.640846</td>\n",
" <td>0.302997</td>\n",
" <td>-0.0066</td>\n",
" <td>0.853250</td>\n",
" <td>-0.071250</td>\n",
" <td>0.001929</td>\n",
" <td>2.787596</td>\n",
" <td>0.374247</td>\n",
" <td>-0.004671</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>3.823513</td>\n",
" <td>0.322163</td>\n",
" <td>-0.005644</td>\n",
" <td>0.840500</td>\n",
" <td>-0.334250</td>\n",
" <td>0.000419</td>\n",
" <td>2.983013</td>\n",
" <td>0.656413</td>\n",
" <td>-0.005226</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>4.214013</td>\n",
" <td>0.147163</td>\n",
" <td>-0.003388</td>\n",
" <td>0.997000</td>\n",
" <td>-0.419500</td>\n",
" <td>-0.000068</td>\n",
" <td>3.217013</td>\n",
" <td>0.566663</td>\n",
" <td>-0.003456</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>4.212846</td>\n",
" <td>0.409247</td>\n",
" <td>0.000336</td>\n",
" <td>0.697750</td>\n",
" <td>-0.116500</td>\n",
" <td>-0.004727</td>\n",
" <td>3.515096</td>\n",
" <td>0.525747</td>\n",
" <td>-0.004392</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>3.49368</td>\n",
" <td>0.156413</td>\n",
" <td>-0.005122</td>\n",
" <td>0.771000</td>\n",
" <td>-0.240500</td>\n",
" <td>0.001345</td>\n",
" <td>2.72268</td>\n",
" <td>0.396913</td>\n",
" <td>-0.003776</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>3.50793</td>\n",
" <td>0.25258</td>\n",
" <td>-0.005712</td>\n",
" <td>0.667750</td>\n",
" <td>0.001000</td>\n",
" <td>0.001440</td>\n",
" <td>2.84018</td>\n",
" <td>0.25158</td>\n",
" <td>-0.004271</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>3.71168</td>\n",
" <td>-0.252087</td>\n",
" <td>-0.006045</td>\n",
" <td>1.001500</td>\n",
" <td>-0.540750</td>\n",
" <td>0.002879</td>\n",
" <td>2.71018</td>\n",
" <td>0.288663</td>\n",
" <td>-0.003166</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>3.697055</td>\n",
" <td>-0.060545</td>\n",
" <td>-0.005397</td>\n",
" <td>0.656125</td>\n",
" <td>-0.273625</td>\n",
" <td>0.000073</td>\n",
" <td>3.04093</td>\n",
" <td>0.21308</td>\n",
" <td>-0.005324</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>3.68243</td>\n",
" <td>0.130997</td>\n",
" <td>-0.004749</td>\n",
" <td>0.310750</td>\n",
" <td>-0.006500</td>\n",
" <td>-0.002733</td>\n",
" <td>3.37168</td>\n",
" <td>0.137497</td>\n",
" <td>-0.007482</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>3.91468</td>\n",
" <td>0.23333</td>\n",
" <td>-0.004538</td>\n",
" <td>0.767500</td>\n",
" <td>-0.219250</td>\n",
" <td>-0.000727</td>\n",
" <td>3.14718</td>\n",
" <td>0.45258</td>\n",
" <td>-0.005265</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>3.73943</td>\n",
" <td>0.274163</td>\n",
" <td>-0.006261</td>\n",
" <td>0.513250</td>\n",
" <td>-0.143250</td>\n",
" <td>-0.000273</td>\n",
" <td>3.22618</td>\n",
" <td>0.417413</td>\n",
" <td>-0.006534</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>3.767013</td>\n",
" <td>0.303413</td>\n",
" <td>-0.005471</td>\n",
" <td>0.595500</td>\n",
" <td>-0.123750</td>\n",
" <td>-0.000297</td>\n",
" <td>3.171513</td>\n",
" <td>0.427163</td>\n",
" <td>-0.005768</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>3.770013</td>\n",
" <td>0.272997</td>\n",
" <td>-0.003697</td>\n",
" <td>0.743750</td>\n",
" <td>-0.336750</td>\n",
" <td>-0.001582</td>\n",
" <td>3.026263</td>\n",
" <td>0.609747</td>\n",
" <td>-0.005279</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>3.295596</td>\n",
" <td>0.25383</td>\n",
" <td>-0.004555</td>\n",
" <td>0.256500</td>\n",
" <td>0.095500</td>\n",
" <td>0.001194</td>\n",
" <td>3.039096</td>\n",
" <td>0.15833</td>\n",
" <td>-0.003361</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>3.671346</td>\n",
" <td>0.009997</td>\n",
" <td>-0.003558</td>\n",
" <td>0.668750</td>\n",
" <td>-0.076000</td>\n",
" <td>0.000078</td>\n",
" <td>3.002596</td>\n",
" <td>0.085997</td>\n",
" <td>-0.00348</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>3.612763</td>\n",
" <td>0.221747</td>\n",
" <td>-0.004734</td>\n",
" <td>0.621750</td>\n",
" <td>0.236750</td>\n",
" <td>0.001409</td>\n",
" <td>2.991013</td>\n",
" <td>-0.015003</td>\n",
" <td>-0.003325</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>3.79643</td>\n",
" <td>0.184413</td>\n",
" <td>-0.004589</td>\n",
" <td>0.806125</td>\n",
" <td>0.136875</td>\n",
" <td>0.000757</td>\n",
" <td>2.990305</td>\n",
" <td>0.047538</td>\n",
" <td>-0.003832</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>3.980096</td>\n",
" <td>0.14708</td>\n",
" <td>-0.004444</td>\n",
" <td>0.990500</td>\n",
" <td>0.037000</td>\n",
" <td>0.000106</td>\n",
" <td>2.989596</td>\n",
" <td>0.11008</td>\n",
" <td>-0.004338</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>3.678346</td>\n",
" <td>0.180163</td>\n",
" <td>-0.006355</td>\n",
" <td>0.878000</td>\n",
" <td>-0.187750</td>\n",
" <td>0.002208</td>\n",
" <td>2.800346</td>\n",
" <td>0.367913</td>\n",
" <td>-0.004147</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>3.98993</td>\n",
" <td>0.033913</td>\n",
" <td>-0.004611</td>\n",
" <td>0.925250</td>\n",
" <td>-0.540750</td>\n",
" <td>-0.002771</td>\n",
" <td>3.06468</td>\n",
" <td>0.574663</td>\n",
" <td>-0.007382</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>3.708596</td>\n",
" <td>0.327997</td>\n",
" <td>-0.006599</td>\n",
" <td>0.810750</td>\n",
" <td>-0.461000</td>\n",
" <td>0.001027</td>\n",
" <td>2.897846</td>\n",
" <td>0.788997</td>\n",
" <td>-0.005573</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>3.96918</td>\n",
" <td>-0.09067</td>\n",
" <td>-0.00728</td>\n",
" <td>1.102750</td>\n",
" <td>-0.548750</td>\n",
" <td>0.001561</td>\n",
" <td>2.86643</td>\n",
" <td>0.45808</td>\n",
" <td>-0.005719</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>4.268607</td>\n",
" <td>0.611591</td>\n",
" <td>0.000686</td>\n",
" <td>0.954250</td>\n",
" <td>-0.166750</td>\n",
" <td>-0.002419</td>\n",
" <td>3.314357</td>\n",
" <td>0.778341</td>\n",
" <td>-0.001733</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>3.766513</td>\n",
" <td>0.224247</td>\n",
" <td>-0.003924</td>\n",
" <td>0.758750</td>\n",
" <td>0.050750</td>\n",
" <td>-0.000367</td>\n",
" <td>3.007763</td>\n",
" <td>0.173497</td>\n",
" <td>-0.004291</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>3.695263</td>\n",
" <td>0.238663</td>\n",
" <td>-0.004108</td>\n",
" <td>0.775250</td>\n",
" <td>0.119250</td>\n",
" <td>-0.000076</td>\n",
" <td>2.920013</td>\n",
" <td>0.119413</td>\n",
" <td>-0.004184</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>3.53868</td>\n",
" <td>0.268497</td>\n",
" <td>-0.004894</td>\n",
" <td>0.512500</td>\n",
" <td>0.114250</td>\n",
" <td>0.000664</td>\n",
" <td>3.02618</td>\n",
" <td>0.154247</td>\n",
" <td>-0.00423</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>3.779763</td>\n",
" <td>-0.273753</td>\n",
" <td>-0.00588</td>\n",
" <td>0.847250</td>\n",
" <td>-0.538000</td>\n",
" <td>0.001407</td>\n",
" <td>2.932513</td>\n",
" <td>0.264247</td>\n",
" <td>-0.004473</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>3.43768</td>\n",
" <td>0.11958</td>\n",
" <td>-0.007756</td>\n",
" <td>0.604500</td>\n",
" <td>-0.133250</td>\n",
" <td>0.002469</td>\n",
" <td>2.83318</td>\n",
" <td>0.25283</td>\n",
" <td>-0.005287</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>3.680096</td>\n",
" <td>0.162663</td>\n",
" <td>-0.007284</td>\n",
" <td>0.821250</td>\n",
" <td>-0.169250</td>\n",
" <td>0.001636</td>\n",
" <td>2.858846</td>\n",
" <td>0.331913</td>\n",
" <td>-0.005647</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>3.86693</td>\n",
" <td>-0.003337</td>\n",
" <td>-0.007504</td>\n",
" <td>1.032750</td>\n",
" <td>-0.231750</td>\n",
" <td>0.001538</td>\n",
" <td>2.83418</td>\n",
" <td>0.228413</td>\n",
" <td>-0.005965</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>3.790596</td>\n",
" <td>0.161247</td>\n",
" <td>-0.006409</td>\n",
" <td>0.885500</td>\n",
" <td>-0.471750</td>\n",
" <td>-0.001336</td>\n",
" <td>2.905096</td>\n",
" <td>0.632997</td>\n",
" <td>-0.007745</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>3.696346</td>\n",
" <td>0.158247</td>\n",
" <td>-0.006057</td>\n",
" <td>0.638750</td>\n",
" <td>-0.418250</td>\n",
" <td>-0.000188</td>\n",
" <td>3.057596</td>\n",
" <td>0.576497</td>\n",
" <td>-0.006245</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>3.863013</td>\n",
" <td>0.202247</td>\n",
" <td>-0.006893</td>\n",
" <td>0.846500</td>\n",
" <td>-0.255500</td>\n",
" <td>-0.000339</td>\n",
" <td>3.016513</td>\n",
" <td>0.457747</td>\n",
" <td>-0.007232</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>4.05944</td>\n",
" <td>0.677174</td>\n",
" <td>-0.002701</td>\n",
" <td>1.078250</td>\n",
" <td>-0.299250</td>\n",
" <td>-0.001601</td>\n",
" <td>2.98119</td>\n",
" <td>0.976424</td>\n",
" <td>-0.004302</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>3.711513</td>\n",
" <td>0.355997</td>\n",
" <td>-0.003252</td>\n",
" <td>0.505000</td>\n",
" <td>0.268500</td>\n",
" <td>-0.000849</td>\n",
" <td>3.206513</td>\n",
" <td>0.087497</td>\n",
" <td>-0.004101</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>3.56443</td>\n",
" <td>0.062413</td>\n",
" <td>-0.004971</td>\n",
" <td>0.800750</td>\n",
" <td>-0.078000</td>\n",
" <td>0.001630</td>\n",
" <td>2.76368</td>\n",
" <td>0.140413</td>\n",
" <td>-0.003341</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>3.733513</td>\n",
" <td>0.07758</td>\n",
" <td>-0.004119</td>\n",
" <td>0.568500</td>\n",
" <td>-0.025750</td>\n",
" <td>-0.000834</td>\n",
" <td>3.165013</td>\n",
" <td>0.10333</td>\n",
" <td>-0.004953</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>3.703763</td>\n",
" <td>0.194538</td>\n",
" <td>-0.005883</td>\n",
" <td>0.700500</td>\n",
" <td>0.008625</td>\n",
" <td>0.000779</td>\n",
" <td>3.003263</td>\n",
" <td>0.185913</td>\n",
" <td>-0.005104</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>3.674013</td>\n",
" <td>0.311497</td>\n",
" <td>-0.007648</td>\n",
" <td>0.832500</td>\n",
" <td>0.043000</td>\n",
" <td>0.002392</td>\n",
" <td>2.841513</td>\n",
" <td>0.268497</td>\n",
" <td>-0.005256</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>3.566096</td>\n",
" <td>0.140497</td>\n",
" <td>-0.00704</td>\n",
" <td>0.642500</td>\n",
" <td>-0.118500</td>\n",
" <td>0.001642</td>\n",
" <td>2.923596</td>\n",
" <td>0.258997</td>\n",
" <td>-0.005398</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>3.606221</td>\n",
" <td>0.14658</td>\n",
" <td>-0.006525</td>\n",
" <td>0.647125</td>\n",
" <td>-0.182500</td>\n",
" <td>0.001357</td>\n",
" <td>2.959096</td>\n",
" <td>0.32908</td>\n",
" <td>-0.005168</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>3.646346</td>\n",
" <td>0.152663</td>\n",
" <td>-0.006011</td>\n",
" <td>0.651750</td>\n",
" <td>-0.246500</td>\n",
" <td>0.001072</td>\n",
" <td>2.994596</td>\n",
" <td>0.399163</td>\n",
" <td>-0.004939</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td>3.61543</td>\n",
" <td>0.145163</td>\n",
" <td>-0.006475</td>\n",
" <td>0.649250</td>\n",
" <td>-0.417250</td>\n",
" <td>0.000135</td>\n",
" <td>2.96618</td>\n",
" <td>0.562413</td>\n",
" <td>-0.006341</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52</th>\n",
" <td>3.81568</td>\n",
" <td>0.077163</td>\n",
" <td>-0.006455</td>\n",
" <td>0.683500</td>\n",
" <td>-0.214250</td>\n",
" <td>-0.000032</td>\n",
" <td>3.13218</td>\n",
" <td>0.291413</td>\n",
" <td>-0.006486</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53</th>\n",
" <td>3.393774</td>\n",
" <td>0.009497</td>\n",
" <td>-0.005527</td>\n",
" <td>0.593750</td>\n",
" <td>-0.535000</td>\n",
" <td>0.002811</td>\n",
" <td>2.800024</td>\n",
" <td>0.544497</td>\n",
" <td>-0.002716</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54</th>\n",
" <td>3.671846</td>\n",
" <td>0.353913</td>\n",
" <td>-0.003943</td>\n",
" <td>0.497500</td>\n",
" <td>0.160500</td>\n",
" <td>-0.000593</td>\n",
" <td>3.174346</td>\n",
" <td>0.193413</td>\n",
" <td>-0.004535</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55</th>\n",
" <td>3.748346</td>\n",
" <td>0.321497</td>\n",
" <td>-0.004599</td>\n",
" <td>0.645000</td>\n",
" <td>0.159250</td>\n",
" <td>0.000997</td>\n",
" <td>3.103346</td>\n",
" <td>0.162247</td>\n",
" <td>-0.003603</td>\n",
" </tr>\n",
" <tr>\n",
" <th>56</th>\n",
" <td>3.821846</td>\n",
" <td>0.200941</td>\n",
" <td>-0.00481</td>\n",
" <td>0.737833</td>\n",
" <td>0.015083</td>\n",
" <td>0.000605</td>\n",
" <td>3.084013</td>\n",
" <td>0.185858</td>\n",
" <td>-0.004205</td>\n",
" </tr>\n",
" <tr>\n",
" <th>57</th>\n",
" <td>3.895346</td>\n",
" <td>0.080386</td>\n",
" <td>-0.005021</td>\n",
" <td>0.830667</td>\n",
" <td>-0.129083</td>\n",
" <td>0.000213</td>\n",
" <td>3.06468</td>\n",
" <td>0.209469</td>\n",
" <td>-0.004808</td>\n",
" </tr>\n",
" <tr>\n",
" <th>58</th>\n",
" <td>3.968846</td>\n",
" <td>-0.04017</td>\n",
" <td>-0.005232</td>\n",
" <td>0.923500</td>\n",
" <td>-0.273250</td>\n",
" <td>-0.000179</td>\n",
" <td>3.045346</td>\n",
" <td>0.23308</td>\n",
" <td>-0.005411</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59</th>\n",
" <td>3.923513</td>\n",
" <td>-0.006503</td>\n",
" <td>-0.006732</td>\n",
" <td>0.925250</td>\n",
" <td>-0.505000</td>\n",
" <td>0.001113</td>\n",
" <td>2.998263</td>\n",
" <td>0.498497</td>\n",
" <td>-0.005619</td>\n",
" </tr>\n",
" <tr>\n",
" <th>60</th>\n",
" <td>3.79443</td>\n",
" <td>0.227997</td>\n",
" <td>-0.005791</td>\n",
" <td>0.804750</td>\n",
" <td>-0.124000</td>\n",
" <td>0.000243</td>\n",
" <td>2.98968</td>\n",
" <td>0.351997</td>\n",
" <td>-0.005548</td>\n",
" </tr>\n",
" <tr>\n",
" <th>61</th>\n",
" <td>3.791957</td>\n",
" <td>0.167219</td>\n",
" <td>-0.005876</td>\n",
" <td>0.835917</td>\n",
" <td>-0.137917</td>\n",
" <td>-0.000061</td>\n",
" <td>2.956041</td>\n",
" <td>0.305136</td>\n",
" <td>-0.005937</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62</th>\n",
" <td>3.789485</td>\n",
" <td>0.106441</td>\n",
" <td>-0.005961</td>\n",
" <td>0.867083</td>\n",
" <td>-0.151833</td>\n",
" <td>-0.000366</td>\n",
" <td>2.922402</td>\n",
" <td>0.258275</td>\n",
" <td>-0.006327</td>\n",
" </tr>\n",
" <tr>\n",
" <th>63</th>\n",
" <td>3.787013</td>\n",
" <td>0.045663</td>\n",
" <td>-0.006046</td>\n",
" <td>0.898250</td>\n",
" <td>-0.165750</td>\n",
" <td>-0.000670</td>\n",
" <td>2.888763</td>\n",
" <td>0.211413</td>\n",
" <td>-0.006716</td>\n",
" </tr>\n",
" <tr>\n",
" <th>64</th>\n",
" <td>3.674263</td>\n",
" <td>0.485497</td>\n",
" <td>-0.005151</td>\n",
" <td>0.729500</td>\n",
" <td>-0.025000</td>\n",
" <td>-0.000796</td>\n",
" <td>2.944763</td>\n",
" <td>0.510497</td>\n",
" <td>-0.005947</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65</th>\n",
" <td>4.023846</td>\n",
" <td>0.227247</td>\n",
" <td>-0.000757</td>\n",
" <td>0.725500</td>\n",
" <td>0.119750</td>\n",
" <td>-0.003126</td>\n",
" <td>3.298346</td>\n",
" <td>0.107497</td>\n",
" <td>-0.003883</td>\n",
" </tr>\n",
" <tr>\n",
" <th>66</th>\n",
" <td>3.842346</td>\n",
" <td>0.155163</td>\n",
" <td>-0.002659</td>\n",
" <td>0.665000</td>\n",
" <td>0.029125</td>\n",
" <td>-0.001388</td>\n",
" <td>3.177346</td>\n",
" <td>0.126038</td>\n",
" <td>-0.004047</td>\n",
" </tr>\n",
" <tr>\n",
" <th>67</th>\n",
" <td>3.660846</td>\n",
" <td>0.08308</td>\n",
" <td>-0.004561</td>\n",
" <td>0.604500</td>\n",
" <td>-0.061500</td>\n",
" <td>0.000350</td>\n",
" <td>3.056346</td>\n",
" <td>0.14458</td>\n",
" <td>-0.00421</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68</th>\n",
" <td>3.518096</td>\n",
" <td>0.138163</td>\n",
" <td>-0.005465</td>\n",
" <td>0.529500</td>\n",
" <td>-0.057250</td>\n",
" <td>0.001281</td>\n",
" <td>2.988596</td>\n",
" <td>0.195413</td>\n",
" <td>-0.004184</td>\n",
" </tr>\n",
" <tr>\n",
" <th>69</th>\n",
" <td>3.711096</td>\n",
" <td>0.128163</td>\n",
" <td>-0.006331</td>\n",
" <td>0.822000</td>\n",
" <td>-0.170250</td>\n",
" <td>0.001578</td>\n",
" <td>2.889096</td>\n",
" <td>0.298413</td>\n",
" <td>-0.004753</td>\n",
" </tr>\n",
" <tr>\n",
" <th>70</th>\n",
" <td>3.626263</td>\n",
" <td>0.121413</td>\n",
" <td>-0.005901</td>\n",
" <td>0.636500</td>\n",
" <td>-0.190500</td>\n",
" <td>0.000577</td>\n",
" <td>2.989763</td>\n",
" <td>0.311913</td>\n",
" <td>-0.005324</td>\n",
" </tr>\n",
" <tr>\n",
" <th>71</th>\n",
" <td>3.68318</td>\n",
" <td>0.25133</td>\n",
" <td>-0.004428</td>\n",
" <td>0.578750</td>\n",
" <td>-0.245750</td>\n",
" <td>-0.000927</td>\n",
" <td>3.10443</td>\n",
" <td>0.49708</td>\n",
" <td>-0.005355</td>\n",
" </tr>\n",
" <tr>\n",
" <th>72</th>\n",
" <td>3.75218</td>\n",
" <td>0.260997</td>\n",
" <td>-0.005047</td>\n",
" <td>0.847750</td>\n",
" <td>-0.249250</td>\n",
" <td>0.001042</td>\n",
" <td>2.90443</td>\n",
" <td>0.510247</td>\n",
" <td>-0.004005</td>\n",
" </tr>\n",
" <tr>\n",
" <th>73</th>\n",
" <td>3.56493</td>\n",
" <td>0.231663</td>\n",
" <td>-0.007636</td>\n",
" <td>0.752000</td>\n",
" <td>-0.119250</td>\n",
" <td>0.000797</td>\n",
" <td>2.81293</td>\n",
" <td>0.350913</td>\n",
" <td>-0.006839</td>\n",
" </tr>\n",
" <tr>\n",
" <th>74</th>\n",
" <td>3.939263</td>\n",
" <td>0.281997</td>\n",
" <td>-0.002727</td>\n",
" <td>0.767250</td>\n",
" <td>-0.114000</td>\n",
" <td>-0.001578</td>\n",
" <td>3.172013</td>\n",
" <td>0.395997</td>\n",
" <td>-0.004305</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75</th>\n",
" <td>4.255346</td>\n",
" <td>0.155413</td>\n",
" <td>0.000239</td>\n",
" <td>0.649250</td>\n",
" <td>-0.044250</td>\n",
" <td>-0.004601</td>\n",
" <td>3.606096</td>\n",
" <td>0.199663</td>\n",
" <td>-0.004362</td>\n",
" </tr>\n",
" <tr>\n",
" <th>76</th>\n",
" <td>3.69343</td>\n",
" <td>0.157663</td>\n",
" <td>-0.003123</td>\n",
" <td>0.506500</td>\n",
" <td>-0.053500</td>\n",
" <td>-0.000300</td>\n",
" <td>3.18693</td>\n",
" <td>0.211163</td>\n",
" <td>-0.003422</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77</th>\n",
" <td>3.66618</td>\n",
" <td>0.191247</td>\n",
" <td>-0.005459</td>\n",
" <td>0.827000</td>\n",
" <td>0.032250</td>\n",
" <td>0.001737</td>\n",
" <td>2.83918</td>\n",
" <td>0.158997</td>\n",
" <td>-0.003723</td>\n",
" </tr>\n",
" <tr>\n",
" <th>78</th>\n",
" <td>3.85643</td>\n",
" <td>0.15958</td>\n",
" <td>-0.004388</td>\n",
" <td>0.659000</td>\n",
" <td>-0.130500</td>\n",
" <td>-0.001005</td>\n",
" <td>3.19743</td>\n",
" <td>0.29008</td>\n",
" <td>-0.005393</td>\n",
" </tr>\n",
" <tr>\n",
" <th>79</th>\n",
" <td>3.747846</td>\n",
" <td>0.057747</td>\n",
" <td>-0.004162</td>\n",
" <td>0.550250</td>\n",
" <td>-0.220750</td>\n",
" <td>-0.001191</td>\n",
" <td>3.197596</td>\n",
" <td>0.278497</td>\n",
" <td>-0.005354</td>\n",
" </tr>\n",
" <tr>\n",
" <th>80</th>\n",
" <td>3.935346</td>\n",
" <td>0.14633</td>\n",
" <td>-0.004026</td>\n",
" <td>0.671500</td>\n",
" <td>-0.288250</td>\n",
" <td>-0.001393</td>\n",
" <td>3.263846</td>\n",
" <td>0.43458</td>\n",
" <td>-0.005419</td>\n",
" </tr>\n",
" <tr>\n",
" <th>81</th>\n",
" <td>3.855596</td>\n",
" <td>0.177913</td>\n",
" <td>-0.006738</td>\n",
" <td>0.935750</td>\n",
" <td>-0.270500</td>\n",
" <td>0.000643</td>\n",
" <td>2.919846</td>\n",
" <td>0.448413</td>\n",
" <td>-0.006095</td>\n",
" </tr>\n",
" <tr>\n",
" <th>82</th>\n",
" <td>3.08668</td>\n",
" <td>0.447663</td>\n",
" <td>-0.010005</td>\n",
" <td>0.723250</td>\n",
" <td>-0.079250</td>\n",
" <td>0.004854</td>\n",
" <td>2.36343</td>\n",
" <td>0.526913</td>\n",
" <td>-0.005151</td>\n",
" </tr>\n",
" <tr>\n",
" <th>83</th>\n",
" <td>4.245263</td>\n",
" <td>0.095163</td>\n",
" <td>-0.003276</td>\n",
" <td>1.066250</td>\n",
" <td>-0.336250</td>\n",
" <td>-0.000493</td>\n",
" <td>3.179013</td>\n",
" <td>0.431413</td>\n",
" <td>-0.003769</td>\n",
" </tr>\n",
" <tr>\n",
" <th>84</th>\n",
" <td>4.167502</td>\n",
" <td>0.103486</td>\n",
" <td>-0.002822</td>\n",
" <td>0.925000</td>\n",
" <td>-0.143000</td>\n",
" <td>-0.001576</td>\n",
" <td>3.242502</td>\n",
" <td>0.246486</td>\n",
" <td>-0.004398</td>\n",
" </tr>\n",
" <tr>\n",
" <th>85</th>\n",
" <td>4.167502</td>\n",
" <td>0.103486</td>\n",
" <td>-0.002822</td>\n",
" <td>0.925000</td>\n",
" <td>-0.143000</td>\n",
" <td>-0.001576</td>\n",
" <td>3.242502</td>\n",
" <td>0.246486</td>\n",
" <td>-0.004398</td>\n",
" </tr>\n",
" <tr>\n",
" <th>86</th>\n",
" <td>4.167502</td>\n",
" <td>0.103486</td>\n",
" <td>-0.002822</td>\n",
" <td>0.925000</td>\n",
" <td>-0.143000</td>\n",
" <td>-0.001576</td>\n",
" <td>3.242502</td>\n",
" <td>0.246486</td>\n",
" <td>-0.004398</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87</th>\n",
" <td>4.167502</td>\n",
" <td>0.103486</td>\n",
" <td>-0.002822</td>\n",
" <td>0.925000</td>\n",
" <td>-0.143000</td>\n",
" <td>-0.001576</td>\n",
" <td>3.242502</td>\n",
" <td>0.246486</td>\n",
" <td>-0.004398</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 4.955996 0.036802 -0.002776 1.200500 -0.074000 -0.002286 3.755496 \n",
"2 4.845454 0.023719 -0.002758 1.302000 -0.215500 -0.001469 3.543454 \n",
"3 4.734913 0.010635 -0.00274 1.403500 -0.357000 -0.000653 3.331413 \n",
"4 4.669163 0.193469 -0.001076 0.659500 -0.155500 -0.003349 4.009663 \n",
"5 4.738763 0.38358 0.003192 0.851750 0.020000 -0.005550 3.887013 \n",
"6 4.164096 0.274163 -0.002247 1.074750 -0.117500 -0.001236 3.089346 \n",
"7 3.320013 0.41383 -0.007047 0.468000 0.175750 0.002528 2.852013 \n",
"8 3.48043 0.358413 -0.006823 0.660625 0.052250 0.002228 2.819805 \n",
"9 3.640846 0.302997 -0.0066 0.853250 -0.071250 0.001929 2.787596 \n",
"10 3.823513 0.322163 -0.005644 0.840500 -0.334250 0.000419 2.983013 \n",
"11 4.214013 0.147163 -0.003388 0.997000 -0.419500 -0.000068 3.217013 \n",
"12 4.212846 0.409247 0.000336 0.697750 -0.116500 -0.004727 3.515096 \n",
"13 3.49368 0.156413 -0.005122 0.771000 -0.240500 0.001345 2.72268 \n",
"14 3.50793 0.25258 -0.005712 0.667750 0.001000 0.001440 2.84018 \n",
"15 3.71168 -0.252087 -0.006045 1.001500 -0.540750 0.002879 2.71018 \n",
"16 3.697055 -0.060545 -0.005397 0.656125 -0.273625 0.000073 3.04093 \n",
"17 3.68243 0.130997 -0.004749 0.310750 -0.006500 -0.002733 3.37168 \n",
"18 3.91468 0.23333 -0.004538 0.767500 -0.219250 -0.000727 3.14718 \n",
"19 3.73943 0.274163 -0.006261 0.513250 -0.143250 -0.000273 3.22618 \n",
"20 3.767013 0.303413 -0.005471 0.595500 -0.123750 -0.000297 3.171513 \n",
"21 3.770013 0.272997 -0.003697 0.743750 -0.336750 -0.001582 3.026263 \n",
"22 3.295596 0.25383 -0.004555 0.256500 0.095500 0.001194 3.039096 \n",
"23 3.671346 0.009997 -0.003558 0.668750 -0.076000 0.000078 3.002596 \n",
"24 3.612763 0.221747 -0.004734 0.621750 0.236750 0.001409 2.991013 \n",
"25 3.79643 0.184413 -0.004589 0.806125 0.136875 0.000757 2.990305 \n",
"26 3.980096 0.14708 -0.004444 0.990500 0.037000 0.000106 2.989596 \n",
"27 3.678346 0.180163 -0.006355 0.878000 -0.187750 0.002208 2.800346 \n",
"28 3.98993 0.033913 -0.004611 0.925250 -0.540750 -0.002771 3.06468 \n",
"29 3.708596 0.327997 -0.006599 0.810750 -0.461000 0.001027 2.897846 \n",
"30 3.96918 -0.09067 -0.00728 1.102750 -0.548750 0.001561 2.86643 \n",
"31 4.268607 0.611591 0.000686 0.954250 -0.166750 -0.002419 3.314357 \n",
"32 3.766513 0.224247 -0.003924 0.758750 0.050750 -0.000367 3.007763 \n",
"33 3.695263 0.238663 -0.004108 0.775250 0.119250 -0.000076 2.920013 \n",
"34 3.53868 0.268497 -0.004894 0.512500 0.114250 0.000664 3.02618 \n",
"35 3.779763 -0.273753 -0.00588 0.847250 -0.538000 0.001407 2.932513 \n",
"36 3.43768 0.11958 -0.007756 0.604500 -0.133250 0.002469 2.83318 \n",
"37 3.680096 0.162663 -0.007284 0.821250 -0.169250 0.001636 2.858846 \n",
"38 3.86693 -0.003337 -0.007504 1.032750 -0.231750 0.001538 2.83418 \n",
"39 3.790596 0.161247 -0.006409 0.885500 -0.471750 -0.001336 2.905096 \n",
"40 3.696346 0.158247 -0.006057 0.638750 -0.418250 -0.000188 3.057596 \n",
"41 3.863013 0.202247 -0.006893 0.846500 -0.255500 -0.000339 3.016513 \n",
"42 4.05944 0.677174 -0.002701 1.078250 -0.299250 -0.001601 2.98119 \n",
"43 3.711513 0.355997 -0.003252 0.505000 0.268500 -0.000849 3.206513 \n",
"44 3.56443 0.062413 -0.004971 0.800750 -0.078000 0.001630 2.76368 \n",
"45 3.733513 0.07758 -0.004119 0.568500 -0.025750 -0.000834 3.165013 \n",
"46 3.703763 0.194538 -0.005883 0.700500 0.008625 0.000779 3.003263 \n",
"47 3.674013 0.311497 -0.007648 0.832500 0.043000 0.002392 2.841513 \n",
"48 3.566096 0.140497 -0.00704 0.642500 -0.118500 0.001642 2.923596 \n",
"49 3.606221 0.14658 -0.006525 0.647125 -0.182500 0.001357 2.959096 \n",
"50 3.646346 0.152663 -0.006011 0.651750 -0.246500 0.001072 2.994596 \n",
"51 3.61543 0.145163 -0.006475 0.649250 -0.417250 0.000135 2.96618 \n",
"52 3.81568 0.077163 -0.006455 0.683500 -0.214250 -0.000032 3.13218 \n",
"53 3.393774 0.009497 -0.005527 0.593750 -0.535000 0.002811 2.800024 \n",
"54 3.671846 0.353913 -0.003943 0.497500 0.160500 -0.000593 3.174346 \n",
"55 3.748346 0.321497 -0.004599 0.645000 0.159250 0.000997 3.103346 \n",
"56 3.821846 0.200941 -0.00481 0.737833 0.015083 0.000605 3.084013 \n",
"57 3.895346 0.080386 -0.005021 0.830667 -0.129083 0.000213 3.06468 \n",
"58 3.968846 -0.04017 -0.005232 0.923500 -0.273250 -0.000179 3.045346 \n",
"59 3.923513 -0.006503 -0.006732 0.925250 -0.505000 0.001113 2.998263 \n",
"60 3.79443 0.227997 -0.005791 0.804750 -0.124000 0.000243 2.98968 \n",
"61 3.791957 0.167219 -0.005876 0.835917 -0.137917 -0.000061 2.956041 \n",
"62 3.789485 0.106441 -0.005961 0.867083 -0.151833 -0.000366 2.922402 \n",
"63 3.787013 0.045663 -0.006046 0.898250 -0.165750 -0.000670 2.888763 \n",
"64 3.674263 0.485497 -0.005151 0.729500 -0.025000 -0.000796 2.944763 \n",
"65 4.023846 0.227247 -0.000757 0.725500 0.119750 -0.003126 3.298346 \n",
"66 3.842346 0.155163 -0.002659 0.665000 0.029125 -0.001388 3.177346 \n",
"67 3.660846 0.08308 -0.004561 0.604500 -0.061500 0.000350 3.056346 \n",
"68 3.518096 0.138163 -0.005465 0.529500 -0.057250 0.001281 2.988596 \n",
"69 3.711096 0.128163 -0.006331 0.822000 -0.170250 0.001578 2.889096 \n",
"70 3.626263 0.121413 -0.005901 0.636500 -0.190500 0.000577 2.989763 \n",
"71 3.68318 0.25133 -0.004428 0.578750 -0.245750 -0.000927 3.10443 \n",
"72 3.75218 0.260997 -0.005047 0.847750 -0.249250 0.001042 2.90443 \n",
"73 3.56493 0.231663 -0.007636 0.752000 -0.119250 0.000797 2.81293 \n",
"74 3.939263 0.281997 -0.002727 0.767250 -0.114000 -0.001578 3.172013 \n",
"75 4.255346 0.155413 0.000239 0.649250 -0.044250 -0.004601 3.606096 \n",
"76 3.69343 0.157663 -0.003123 0.506500 -0.053500 -0.000300 3.18693 \n",
"77 3.66618 0.191247 -0.005459 0.827000 0.032250 0.001737 2.83918 \n",
"78 3.85643 0.15958 -0.004388 0.659000 -0.130500 -0.001005 3.19743 \n",
"79 3.747846 0.057747 -0.004162 0.550250 -0.220750 -0.001191 3.197596 \n",
"80 3.935346 0.14633 -0.004026 0.671500 -0.288250 -0.001393 3.263846 \n",
"81 3.855596 0.177913 -0.006738 0.935750 -0.270500 0.000643 2.919846 \n",
"82 3.08668 0.447663 -0.010005 0.723250 -0.079250 0.004854 2.36343 \n",
"83 4.245263 0.095163 -0.003276 1.066250 -0.336250 -0.000493 3.179013 \n",
"84 4.167502 0.103486 -0.002822 0.925000 -0.143000 -0.001576 3.242502 \n",
"85 4.167502 0.103486 -0.002822 0.925000 -0.143000 -0.001576 3.242502 \n",
"86 4.167502 0.103486 -0.002822 0.925000 -0.143000 -0.001576 3.242502 \n",
"87 4.167502 0.103486 -0.002822 0.925000 -0.143000 -0.001576 3.242502 \n",
"\n",
" Calc-Y Calc-Angle \n",
"1 0.110802 -0.005062 \n",
"2 0.239219 -0.004228 \n",
"3 0.367635 -0.003393 \n",
"4 0.348969 -0.004425 \n",
"5 0.36358 -0.002357 \n",
"6 0.391663 -0.003483 \n",
"7 0.23808 -0.004519 \n",
"8 0.306163 -0.004595 \n",
"9 0.374247 -0.004671 \n",
"10 0.656413 -0.005226 \n",
"11 0.566663 -0.003456 \n",
"12 0.525747 -0.004392 \n",
"13 0.396913 -0.003776 \n",
"14 0.25158 -0.004271 \n",
"15 0.288663 -0.003166 \n",
"16 0.21308 -0.005324 \n",
"17 0.137497 -0.007482 \n",
"18 0.45258 -0.005265 \n",
"19 0.417413 -0.006534 \n",
"20 0.427163 -0.005768 \n",
"21 0.609747 -0.005279 \n",
"22 0.15833 -0.003361 \n",
"23 0.085997 -0.00348 \n",
"24 -0.015003 -0.003325 \n",
"25 0.047538 -0.003832 \n",
"26 0.11008 -0.004338 \n",
"27 0.367913 -0.004147 \n",
"28 0.574663 -0.007382 \n",
"29 0.788997 -0.005573 \n",
"30 0.45808 -0.005719 \n",
"31 0.778341 -0.001733 \n",
"32 0.173497 -0.004291 \n",
"33 0.119413 -0.004184 \n",
"34 0.154247 -0.00423 \n",
"35 0.264247 -0.004473 \n",
"36 0.25283 -0.005287 \n",
"37 0.331913 -0.005647 \n",
"38 0.228413 -0.005965 \n",
"39 0.632997 -0.007745 \n",
"40 0.576497 -0.006245 \n",
"41 0.457747 -0.007232 \n",
"42 0.976424 -0.004302 \n",
"43 0.087497 -0.004101 \n",
"44 0.140413 -0.003341 \n",
"45 0.10333 -0.004953 \n",
"46 0.185913 -0.005104 \n",
"47 0.268497 -0.005256 \n",
"48 0.258997 -0.005398 \n",
"49 0.32908 -0.005168 \n",
"50 0.399163 -0.004939 \n",
"51 0.562413 -0.006341 \n",
"52 0.291413 -0.006486 \n",
"53 0.544497 -0.002716 \n",
"54 0.193413 -0.004535 \n",
"55 0.162247 -0.003603 \n",
"56 0.185858 -0.004205 \n",
"57 0.209469 -0.004808 \n",
"58 0.23308 -0.005411 \n",
"59 0.498497 -0.005619 \n",
"60 0.351997 -0.005548 \n",
"61 0.305136 -0.005937 \n",
"62 0.258275 -0.006327 \n",
"63 0.211413 -0.006716 \n",
"64 0.510497 -0.005947 \n",
"65 0.107497 -0.003883 \n",
"66 0.126038 -0.004047 \n",
"67 0.14458 -0.00421 \n",
"68 0.195413 -0.004184 \n",
"69 0.298413 -0.004753 \n",
"70 0.311913 -0.005324 \n",
"71 0.49708 -0.005355 \n",
"72 0.510247 -0.004005 \n",
"73 0.350913 -0.006839 \n",
"74 0.395997 -0.004305 \n",
"75 0.199663 -0.004362 \n",
"76 0.211163 -0.003422 \n",
"77 0.158997 -0.003723 \n",
"78 0.29008 -0.005393 \n",
"79 0.278497 -0.005354 \n",
"80 0.43458 -0.005419 \n",
"81 0.448413 -0.006095 \n",
"82 0.526913 -0.005151 \n",
"83 0.431413 -0.003769 \n",
"84 0.246486 -0.004398 \n",
"85 0.246486 -0.004398 \n",
"86 0.246486 -0.004398 \n",
"87 0.246486 -0.004398 "
]
},
"execution_count": 20,
"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": 21,
"id": "6020a1a1-d22b-4af2-9531-c3db5b8b0042",
"metadata": {},
"outputs": [],
"source": [
"Old_New_DieBC.to_excel(f'{DieType}/{DieType}局部补偿{daytime}.xlsx')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e721ea93-7fe2-4d1c-b29a-225e2e81a2dc",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "552af3cc-4d07-4a5b-b88a-1c414f88ce84",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 51,
"id": "4ed8868d-4098-4959-9fcc-b631a5b81cca",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 0.642186\n",
"dtype: float64"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# pd.DataFrame(AlignMarkY['10.14.3-Die1'].values - BC_Y.mean(axis=1).values).std()*3"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "20e83f4f-eed9-409e-9720-027bdfbd96a5",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10.14.1-Die1:X 0 0.631753\n",
"dtype: float64\n",
"10.14.2-Die1:X 0 0.830351\n",
"dtype: float64\n",
"10.14.3-Die1:X 0 0.544247\n",
"dtype: float64\n"
]
}
],
"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": 27,
"id": "d4f89c43-d128-489b-89f0-e35dab1c6c87",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10.14.1-Die1:Y 0 0.813346\n",
"dtype: float64\n",
"10.14.2-Die1:Y 0 0.852093\n",
"dtype: float64\n",
"10.14.3-Die1:Y 0 0.669707\n",
"dtype: float64\n"
]
}
],
"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": 17,
"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": 18,
"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": 19,
"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": 10,
"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": 11,
"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": null,
"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,iVBORw0KGgoAAAANSUhEUgAAA+gAAAKvCAYAAAD5ieWEAAAAAXNSR0IArs4c6QAAIABJREFUeF7snQeYU0XXx//J9l5YwE71BQTFLoLSRKSLgKhYQEW6gIIICqgUQRBBsFBUqvopoggoIk2KiO8LqBQrIqJYgO29JPmeM3dvNjebbG6SyW7u7pnnQTB37pm5v5lb/jNnzphsNpsNnJgAE2ACTIAJMAEmwASYABNgAkyACTCBKiVgYoFepfy5cCbABJgAE2ACTIAJMAEmwASYABNgAoIAC3TuCEyACTABJsAEmAATYAJMgAkwASbABIKAAAv0IGgErgITYAJMgAkwASbABJgAE2ACTIAJMAEW6NwHmAATYAJMgAkwASbABJgAE2ACTIAJBAEBFuhB0AhcBSbABJgAE2ACTIAJMAEmwASYABNgAizQuQ8wASbABJgAE2ACTIAJMAEmwASYABMIAgIs0IOgEbgKTIAJMAEmwASYABNgAkyACTABJsAEWKBzH2ACTIAJMAEmwASYABNgAkyACTABJhAEBFigB0EjcBWYABNgAkyACTABJsAEmAATYAJMgAmwQOc+wASYABNgAkyACTABJsAEmAATYAJMIAgIsEAPgkbgKjABJsAEmAATYAJMgAkwASbABJgAE2CBzn2ACTABJsAEmAATYAJMgAkwASbABJhAEBBggR4EjcBVYAJMgAkwgZpNYNiwYWjRogWGDBmC8PDwmg2Dr54JMAEmwASYQA0mwAK9Bjc+XzoTYAJMgAlUPYHTp0+jY8eOaN68OT788MOqrxDXgAkwASbABJgAE6gyAizQqww9F8wEmAATYAKuCPz555+45ZZbxCGTyYSEhAQ0atQI3bt3R//+/REWFuYSHInbSZMmYdWqVbjhhhv8gpuWloa5c+di9+7dyMjIQGJiIvr164fRo0cjJCTEL9vOJ69fvx5PPvkkRo4cKewHMp09exZ9+vTBY489Jv6WlaZNm4a3334by5cvR+vWrTVmU1NTMWvWLBw4cADp6ek477zz0K1bN5DXQEREhKwqlLPzf//3f3jmmWeEV8K4cePsx3/44Qf07t0brVq1wsqVKwNWPhtmAkyACTABJuALARbovlDjc5gAE2ACTCBgBFSBfsEFF6BXr144c+YMdu7cKcQdCe+lS5ciMjKyXPmyBHpubq4QrydPnsQll1yCBg0a4ODBg8jJycF9992HKVOmeH3teXl5MJvNYnDBWeCPHz8eGzduFAL32muvdWm7pKQEhYWF4nxfXeCLioowaNAgcS0kmGUJ9CNHjoiBk06dOmHRokWa+lOd+/bti7///htdu3ZFVFQUvvnmG9A5HTp0wOLFi71mqfcEul6qE9Vh165d9j5DgzjUV1asWIEbb7xRrznOxwSYABNgAkygUgiwQK8UzFwIE2ACTIAJ6CWgCvTrr78eq1evFqdlZWWJmVASdzTzSjPAzik7O1uIeRL2JAR9TfPnzxfCkYTz4MGDxSz+P//8I4Qmzax/+eWXSE5O9sr8iBEjsH37dq/OcZXZV2FNbEaNGoX9+/cLs77aca6TxWLBnXfeiV9//RWffvopLrzwQk2W9957D1OnTsUHH3yAyy+/3H7siSeewIYNG/DRRx/hsssu85uLOwMkwulap0+fLgYRqP3atWsnyqS6cWICTIAJMAEmEGwEWKAHW4twfZgAE2ACNZyAK4FOSH777TcxC0su7ySSQ0NDpZOyWq1i1rVp06Z47bXXNPZVN25fZl5JiFL91RlwEv2USNiSizvN5Dq7hquF22w2kBCmmeBbb71VBJPzNtFs+alTp/DQQw/h5ZdflibQyUX8+eefx6OPPioGAJwTifOtW7fiq6++0hwicU4inQZDyN09UCk/P1+s709JSRFeCq+++ioWLlwoBmBoBp8TE2ACTIAJMIFgI8ACPdhahOvDBJgAE6jhBNwJdMJCa4dpDfHatWtxxRVXBIQUiXRyj3Z2oyexSbOuNKtPs/syEq2N3rRpE9atW+eT8NZbh4EDB4r12LQG/YEHHpAi0P/9918xYELr82n23NWyA5q9Jl579uxBrVq17NWdN2+eWKpA68SvuuoqvZfhUz4S4zQQQOU9/fTToh4ff/yxT7b4JCbABJgAE2ACgSbAAj3QhNk+E2ACTIAJeEWgIoE+YcIEIa5mz56NO+64Q2NXzxr0Y8eOiRlUWoddXFwsxCG5snualaZZbApcRwKXZoNjY2O9uiZXmWldNs3WN2vWTLiABzLRDDytff/666+lCfSxY8di8+bNuO2224RrO62NJ0aOAycUGI7W7ZN3wLPPPiuWBtB6cBLKtBSBZrVlB91z5kixA2i2nDwuyMV9wYIFYmCBExNgAkyACTCBYCTAAj0YW4XrxASYABOowQQqEugvvPAC3nrrLUycOBEPPvigVwKdxOkjjzyCuLg4dO7cGRR4jWZ+SbySQG7cuLFb6p988gkef/xxsQ6dXLplpBkzZojZ5WXLlqFt27YyTHq0UZFAp1lmT4ki2V988cUiFsDdd99dLju57lM0enJ5VxN5O0yePFmTlwZGqLzzzz/fU5FSjpMof/3110XAP2pzCtjHiQkwASbABJhAMBJggR6MrcJ1YgJMgAnUYAIVCXRaP01rw8eMGQMKvOaYKppBJ7f1Ll26gCK005rv2rVri1MPHTqEe+65B7fffjvmzJnjkjrNwNIWb5mZmULc0cyvv4nc9Ens00z80KFDPZqjddQkLv1NFQn0Jk2aeDSvbmFHdf7iiy/EoAaJ7/r164O8E2hrOop+T2u9yTuAotdT5PjvvvtObKkWHx+Pc+fOiSB+w4cPF4H/nJPajvS7asdjxTxkoAj5FEPguuuuw5o1a/w1x+czASbABJgAEwgYARboAUPLhpkAE2ACTMAXAhUJdBLRb775ptcz6EePHhWCmKJ3O89W04w8CfYdO3a4rC7tUU6i/qmnngKt5fY3keCnmWgKGqc3yRKqMlzcaX3+1VdfbfdAaNiwof0yfvrpJ7E1nrqFGgn2N954Q0Tep5l1coM/fvy42Jf8xx9/FAHraODEMckW6OQpQR4TtKSABmoq2s5Ob3twPibABJgAE2ACgSLAAj1QZNkuE2ACTIAJ+ESgIoGu7mFNru4UMM6VsFNneR2P0cy3q63Z1DwkHGlvbuekRhu/6aabhNBUo6/7dGGACD5HM8d79+4VJlzV1dE2DUbQoASVffPNN/tarP08GQKdgsPRIAetNSf3defUqlUrJCUlifXpNPNPQeRIdDum33//XYhmmmWnwQfHRFHfKYgcJQps5+9e5RSAjwZXKGo87dNOe80TV05MgAkwASbABIKRAAv0YGwVrhMTYAJMoAYTqEig08wzCWkSfM2bN9dQqsjFXRXortauu0P9yy+/iL2zac06zaB7u/e5s10KSkfr2D///HMhWjMyMnQLdE9CXm93kSHQyQPgmmuuEdujuVq3Tm7ktLacBjdo73PaGu6ll14qV0UKzEcB8lyJfL3X4ykfxRegetLSBvKQmDlzpogcH8hdADzViY8zASbABJgAE6iIAAt07h9MgAkwASYQVATcCXT6ncQe7WlN65+do39XJNBVF/e77rpLrEV2TFu2bBHromktuho8LDU1FZT3n3/+ESKaXLr9SSRqSZxTBHOaeabZf6qHJ+GtzqB7yqe3bjIEOpVFM+N169bFu+++qymaeLVr1w49evQQs+D0b1o+4BylntzcaV0/zcRTkLxAJYoST1H6KeI8eS7QsgIS7O3btxdB4zgxASbABJgAEwg2AizQg61FuD5MgAkwgRpOwJVAJ4FLQeFIYJKbOwUec06egsTRdmDp6elCLFJQM0q0Lpm23KL/p1lySjTbeu+994r91snFesCAAX61CG3tRTZIHF566aVClNMAA12HJ+EdrAKd3O5p7T653pP7PyWaraZt8Ghfd3XN/ODBg4U7P+2HToMStETgjz/+EO7mFAmetlujfdkdkywXd9oar2fPnqD+RLzJa4ES9SOaTaf2btq0qV9tyyczASbABJgAE5BN
"_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