2630 lines
1.1 MiB
Plaintext
2630 lines
1.1 MiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "ad8424f1-4fd8-4f68-9557-f560d5a28e4b",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"import sys\n",
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"import os\n",
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"sys.path.append('..')\n",
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"from QX8800SP_DA import *\n",
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"plt.rcParams['font.family'] = ['SimHei'] # 用来正常显示中文标签\n",
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"plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号\n",
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"pd.set_option('display.max_columns', None) #显示所有列，把行显示设置成最大\n",
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"pd.set_option('display.max_rows', None) #显示所有行，把列显示设置成最大\n",
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"#交互式绘图\n",
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"%matplotlib widget"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "ccb60f92-e657-4732-a679-6ca67bfcf201",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"MCX (96, 8)\n",
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"MCY (96, 9)\n",
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"Angle14 (96, 9)\n",
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"RunOut (96, 8)\n",
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"M1X (96, 8)\n",
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"M1Y (96, 8)\n",
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"M2X (96, 8)\n",
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"M2Y (96, 8)\n",
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"M3X (96, 8)\n",
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"M3Y (96, 8)\n",
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"M4X (96, 8)\n",
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"M4Y (96, 8)\n",
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"Angle13 (96, 86)\n",
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"Note (4, 2)\n"
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]
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}
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],
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"source": [
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"#写入TotalData\n",
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"DieType = \"Die2\"\n",
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"TotalData = pd.read_excel('../Die2AllData.xlsx',sheet_name=None,header=0,index_col = 0)\n",
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"die_nums = -1\n",
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"for i in TotalData:\n",
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" print(i,TotalData[i].shape)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8f9078d7",
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"metadata": {},
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"source": [
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"## 对位Mark"
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]
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},
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{
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"cell_type": "markdown",
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"id": "31b36a67",
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"metadata": {},
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"source": [
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"### 对位MarkX"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "6de0e187",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>11.22.1-Die2</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>count</th>\n",
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" <td>87.000000</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>mean</th>\n",
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" <td>-0.306572</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>std</th>\n",
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" <td>0.942614</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>min</th>\n",
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" <td>-5.560580</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>25%</th>\n",
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" <td>-0.282852</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>50%</th>\n",
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" <td>-0.081254</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>75%</th>\n",
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" <td>0.086942</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>max</th>\n",
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" <td>0.355402</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>range</th>\n",
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" <td>5.915982</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3sigma</th>\n",
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" <td>2.827843</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" 11.22.1-Die2\n",
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"count 87.000000\n",
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"mean -0.306572\n",
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"std 0.942614\n",
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"min -5.560580\n",
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"25% -0.282852\n",
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"50% -0.081254\n",
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"75% 0.086942\n",
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"max 0.355402\n",
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"range 5.915982\n",
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"3sigma 2.827843"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# AlignMarkX = TotalData['M3X'].dropna(subset='QX8800SP_Index').set_index('QX8800SP_Index').iloc[:,die_nums:]\n",
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"AlignMarkX = TotalData['MCX'].reset_index().dropna(subset='QX8800SP_Index').set_index('QX8800SP_Index').iloc[:,die_nums:].sort_index(axis=1)\n",
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"AXdescibe = describe_3s(AlignMarkX)\n",
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"AXdescibe"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "5355743f",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"C:\\Users\\yangdongdong\\AppData\\Local\\Temp\\ipykernel_21964\\2188852286.py:6: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
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" ax[0].annotate(round(AXdescibe.loc['mean'][i],3),\n",
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"C:\\Users\\yangdongdong\\AppData\\Local\\Temp\\ipykernel_21964\\2188852286.py:7: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
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" xy=(i+1,AXdescibe.loc['mean'][i]),\n",
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"C:\\Users\\yangdongdong\\AppData\\Local\\Temp\\ipykernel_21964\\2188852286.py:8: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
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" xytext=(i+0.95,AXdescibe.loc['mean'][i]),\n",
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"C:\\Users\\yangdongdong\\AppData\\Local\\Temp\\ipykernel_21964\\2188852286.py:19: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
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" ax[1].annotate(round(AXdescibe.loc['3sigma'][i],3),\n",
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"C:\\Users\\yangdongdong\\AppData\\Local\\Temp\\ipykernel_21964\\2188852286.py:20: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
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" xy=(i+1,AXdescibe.loc['3sigma'][i]),\n",
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"C:\\Users\\yangdongdong\\AppData\\Local\\Temp\\ipykernel_21964\\2188852286.py:21: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
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" xytext=(i+0.95,AXdescibe.loc['3sigma'][i]),\n"
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]
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "91bbae4f5b78467e9ee5e9fcbf1adee4",
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"version_major": 2,
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"version_minor": 0
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},
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"image/png": "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
|
|||
|
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"text/html": [
|
|||
|
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"\n",
|
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|
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" <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,sharex=True)\n",
|
|||
|
|
"ax[0].plot([i+1 for i in range(len(AlignMarkX.columns))],AXdescibe.loc['mean'],linestyle = '-.',marker = 'o',color='b')\n",
|
|||
|
|
"AlignMarkX.boxplot(ax=ax[0])\n",
|
|||
|
|
"ax[0].axhline(0,c='orange',ls='-.',label=r'Mean_X:$0um\\pm0.05um$')\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+1 for i in range(len(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(0.5,c='green',ls='-.',label=r'3sigma_X:$<0.500um$')\n",
|
|||
|
|
"for i in range(len(AlignMarkX.columns)):\n",
|
|||
|
|
" ax[1].annotate(round(AXdescibe.loc['3sigma'][i],3), \n",
|
|||
|
|
" xy=(i+1,AXdescibe.loc['3sigma'][i]),\n",
|
|||
|
|
" xytext=(i+0.95,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": 6,
|
|||
|
|
"id": "9e294f7b-3ea3-4a33-99b5-92e22bd1a827",
|
|||
|
|
"metadata": {
|
|||
|
|
"tags": []
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
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"\n",
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" .dataframe tbody tr th {\n",
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"\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
|
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" <tr style=\"text-align: right;\">\n",
|
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" <th></th>\n",
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" <th>11.22.1-Die2</th>\n",
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" </tr>\n",
|
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" </thead>\n",
|
|||
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" <tbody>\n",
|
|||
|
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" <tr>\n",
|
|||
|
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" <th>count</th>\n",
|
|||
|
|
" <td>87.000000</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>mean</th>\n",
|
|||
|
|
" <td>-0.417391</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>std</th>\n",
|
|||
|
|
" <td>0.740471</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>min</th>\n",
|
|||
|
|
" <td>-2.998350</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>25%</th>\n",
|
|||
|
|
" <td>-0.341091</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>50%</th>\n",
|
|||
|
|
" <td>-0.162250</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>75%</th>\n",
|
|||
|
|
" <td>-0.036255</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>max</th>\n",
|
|||
|
|
" <td>0.607952</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>range</th>\n",
|
|||
|
|
" <td>3.606302</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>3sigma</th>\n",
|
|||
|
|
" <td>2.221412</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
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|
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"</div>"
|
|||
|
|
],
|
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"text/plain": [
|
|||
|
|
" 11.22.1-Die2\n",
|
|||
|
|
"count 87.000000\n",
|
|||
|
|
"mean -0.417391\n",
|
|||
|
|
"std 0.740471\n",
|
|||
|
|
"min -2.998350\n",
|
|||
|
|
"25% -0.341091\n",
|
|||
|
|
"50% -0.162250\n",
|
|||
|
|
"75% -0.036255\n",
|
|||
|
|
"max 0.607952\n",
|
|||
|
|
"range 3.606302\n",
|
|||
|
|
"3sigma 2.221412"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 6,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"# AlignMarkY = TotalData['M3Y'].dropna(subset='QX8800SP_Index').set_index('QX8800SP_Index').iloc[:,die_nums:]\n",
|
|||
|
|
"AlignMarkY = TotalData['MCY'].reset_index().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": 7,
|
|||
|
|
"id": "81162e4f-1ed2-4365-9e55-2a0177174f18",
|
|||
|
|
"metadata": {
|
|||
|
|
"tags": []
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"C:\\Users\\yangdongdong\\AppData\\Local\\Temp\\ipykernel_21964\\3640321897.py:6: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
|
|||
|
|
" ax[0].annotate(round(AYdescibe.loc['mean'][i],3),\n",
|
|||
|
|
"C:\\Users\\yangdongdong\\AppData\\Local\\Temp\\ipykernel_21964\\3640321897.py:7: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
|
|||
|
|
" xy=(i+1,AYdescibe.loc['mean'][i]),\n",
|
|||
|
|
"C:\\Users\\yangdongdong\\AppData\\Local\\Temp\\ipykernel_21964\\3640321897.py:8: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
|
|||
|
|
" xytext=(i+0.95,AYdescibe.loc['mean'][i]),\n",
|
|||
|
|
"C:\\Users\\yangdongdong\\AppData\\Local\\Temp\\ipykernel_21964\\3640321897.py:19: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
|
|||
|
|
" ax[1].annotate(round(AYdescibe.loc['3sigma'][i],3),\n",
|
|||
|
|
"C:\\Users\\yangdongdong\\AppData\\Local\\Temp\\ipykernel_21964\\3640321897.py:20: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
|
|||
|
|
" xy=(i+1,AYdescibe.loc['3sigma'][i]),\n",
|
|||
|
|
"C:\\Users\\yangdongdong\\AppData\\Local\\Temp\\ipykernel_21964\\3640321897.py:21: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
|
|||
|
|
" xytext=(i+0.95,AYdescibe.loc['3sigma'][i]),\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
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|
|||
|
|
"model_id": "c79178ae78384c1aa609159762f5e308",
|
|||
|
|
"version_major": 2,
|
|||
|
|
"version_minor": 0
|
|||
|
|
},
|
|||
|
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|||
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" <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": [
|
|||
|
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"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,sharex=True)\n",
|
|||
|
|
"ax[0].plot([i+1 for i in range(len(AlignMarkY.columns))],AYdescibe.loc['mean'],linestyle = '-.',marker = 'o',color='b')\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],3), \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+1 for i in range(len(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(0.5,c='green',ls='-.',label=r'3sigma_Y:$<0.500um$')\n",
|
|||
|
|
"for i in range(len(AlignMarkY.columns)):\n",
|
|||
|
|
" ax[1].annotate(round(AYdescibe.loc['3sigma'][i],3), \n",
|
|||
|
|
" xy=(i+1,AYdescibe.loc['3sigma'][i]),\n",
|
|||
|
|
" xytext=(i+0.95,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": "57aab54c-ca77-46e9-bdfa-becc3323ab8f",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"## 角度"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 8,
|
|||
|
|
"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",
|
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|
|
" }\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>11.22.1-Die2</th>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </thead>\n",
|
|||
|
|
" <tbody>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>count</th>\n",
|
|||
|
|
" <td>87.000000</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>mean</th>\n",
|
|||
|
|
" <td>-0.003187</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>std</th>\n",
|
|||
|
|
" <td>0.007621</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>min</th>\n",
|
|||
|
|
" <td>-0.034374</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>25%</th>\n",
|
|||
|
|
" <td>-0.002316</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>50%</th>\n",
|
|||
|
|
" <td>0.000006</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>75%</th>\n",
|
|||
|
|
" <td>0.000614</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>max</th>\n",
|
|||
|
|
" <td>0.002762</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>range</th>\n",
|
|||
|
|
" <td>0.037136</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>3sigma</th>\n",
|
|||
|
|
" <td>0.022863</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"</div>"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
" 11.22.1-Die2\n",
|
|||
|
|
"count 87.000000\n",
|
|||
|
|
"mean -0.003187\n",
|
|||
|
|
"std 0.007621\n",
|
|||
|
|
"min -0.034374\n",
|
|||
|
|
"25% -0.002316\n",
|
|||
|
|
"50% 0.000006\n",
|
|||
|
|
"75% 0.000614\n",
|
|||
|
|
"max 0.002762\n",
|
|||
|
|
"range 0.037136\n",
|
|||
|
|
"3sigma 0.022863"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 8,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"Angle = TotalData['Angle14'].reset_index().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": 9,
|
|||
|
|
"id": "5ce2eec7-a959-4716-92a0-4aaad88b96b3",
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stderr",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"C:\\Users\\yangdongdong\\AppData\\Local\\Temp\\ipykernel_21964\\3274916886.py:6: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
|
|||
|
|
" ax[0].annotate(round(Angdescibe.loc['mean'][i],5),\n",
|
|||
|
|
"C:\\Users\\yangdongdong\\AppData\\Local\\Temp\\ipykernel_21964\\3274916886.py:7: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
|
|||
|
|
" xy=(i+1,Angdescibe.loc['mean'][i]),\n",
|
|||
|
|
"C:\\Users\\yangdongdong\\AppData\\Local\\Temp\\ipykernel_21964\\3274916886.py:8: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
|
|||
|
|
" xytext=(i+0.95,Angdescibe.loc['mean'][i]),\n",
|
|||
|
|
"C:\\Users\\yangdongdong\\AppData\\Local\\Temp\\ipykernel_21964\\3274916886.py:18: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
|
|||
|
|
" ax[1].annotate(round(Angdescibe.loc['3sigma'][i],5),\n",
|
|||
|
|
"C:\\Users\\yangdongdong\\AppData\\Local\\Temp\\ipykernel_21964\\3274916886.py:19: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
|
|||
|
|
" xy=(i+1,Angdescibe.loc['3sigma'][i]),\n",
|
|||
|
|
"C:\\Users\\yangdongdong\\AppData\\Local\\Temp\\ipykernel_21964\\3274916886.py:20: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
|
|||
|
|
" xytext=(i+0.95,Angdescibe.loc['3sigma'][i]),\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
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" <div style=\"display: inline-block;\">\n",
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" <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
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" Figure\n",
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" </div>\n",
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" <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,sharex=True)\n",
|
|||
|
|
"ax[0].plot([i+1 for i in range(len(Angdescibe.columns))],Angdescibe.loc['mean'],linestyle = '-.',marker = 'o',color='b')\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+1 for i in range(len(Angdescibe.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(Angdescibe.columns)):\n",
|
|||
|
|
" ax[1].annotate(round(Angdescibe.loc['3sigma'][i],5), \n",
|
|||
|
|
" xy=(i+1,Angdescibe.loc['3sigma'][i]),\n",
|
|||
|
|
" xytext=(i+0.95,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": "639173af",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"### 补偿值计算"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 17,
|
|||
|
|
"id": "9d6ddd82-d7f5-4b21-8227-2c0ea3de2051",
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"application/vnd.jupyter.widget-view+json": {
|
|||
|
|
"model_id": "107c99ce330d46b0931b6980f8542e1a",
|
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" </div>\n",
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" "
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|||
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],
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|||
|
|
"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[-2:]],axis=1)\n",
|
|||
|
|
"fig, ax = plt.subplots(3,1,sharex=True)\n",
|
|||
|
|
"\n",
|
|||
|
|
"BC_X = pd.concat([AlignMarkX[i] for i in AlignMarkX.columns[-3:]],axis=1)\n",
|
|||
|
|
"BC_X.plot(ax=ax[0],marker='o')\n",
|
|||
|
|
"ax[0].set_title(f'{DieType}')\n",
|
|||
|
|
"ax[0].set_ylabel('TX/um')\n",
|
|||
|
|
"ax[0].xaxis.set_major_locator(MultipleLocator(5))\n",
|
|||
|
|
"ax[0].yaxis.set_major_locator(MultipleLocator(0.2))\n",
|
|||
|
|
"\n",
|
|||
|
|
"TX_Mean_per_col = BC_X.mean()\n",
|
|||
|
|
"for col, mean_val in TX_Mean_per_col.items():\n",
|
|||
|
|
" ax[0].axhline(mean_val, c='orange', ls='-.', alpha=0.7,\n",
|
|||
|
|
" label=r'Mean_TX: ${:.4f}±0.3um$'.format(mean_val))\n",
|
|||
|
|
"\n",
|
|||
|
|
"TX_Mean_per_col_down = BC_X.mean() - 0.3\n",
|
|||
|
|
"for col, mean_val in TX_Mean_per_col_down.items():\n",
|
|||
|
|
" ax[0].axhline(mean_val, c='green', ls='-.', alpha=0.7) \n",
|
|||
|
|
" \n",
|
|||
|
|
"TX_Mean_per_col_up = BC_X.mean() + 0.3\n",
|
|||
|
|
"for col, mean_val in TX_Mean_per_col_up.items():\n",
|
|||
|
|
" ax[0].axhline(mean_val, c='green', ls='-.', alpha=0.7) \n",
|
|||
|
|
"ax[0].legend()\n",
|
|||
|
|
"ax[0].grid()\n",
|
|||
|
|
"\n",
|
|||
|
|
"plt.subplot(312)\n",
|
|||
|
|
"BC_Y = pd.concat([AlignMarkY[i] for i in AlignMarkY.columns[-3:]],axis=1)\n",
|
|||
|
|
"BC_Y.plot(ax=ax[1],marker='o')\n",
|
|||
|
|
"ax[1].set_ylabel('TY/um')\n",
|
|||
|
|
"ax[1].xaxis.set_major_locator(MultipleLocator(5))\n",
|
|||
|
|
"ax[1].yaxis.set_major_locator(MultipleLocator(0.2))\n",
|
|||
|
|
"\n",
|
|||
|
|
"TY_Mean_per_col = BC_Y.mean() # 结果是 Series，索引为列名，值为对应列的均值\n",
|
|||
|
|
"for col, mean_val in TY_Mean_per_col.items():\n",
|
|||
|
|
" ax[1].axhline(mean_val, c='orange', ls='-.', alpha=0.7,\n",
|
|||
|
|
" label=r'Mean_TY: ${:.4f}±0.3um$'.format(mean_val)) # 标注列名和均值\n",
|
|||
|
|
"TY_Mean_per_col_down = BC_Y.mean() - 0.3\n",
|
|||
|
|
"for col, mean_val in TY_Mean_per_col_down.items():\n",
|
|||
|
|
" ax[1].axhline(mean_val, c='green', ls='-.', alpha=0.7) \n",
|
|||
|
|
" \n",
|
|||
|
|
"TY_Mean_per_col_up = BC_Y.mean() + 0.3\n",
|
|||
|
|
"for col, mean_val in TY_Mean_per_col_up.items():\n",
|
|||
|
|
" ax[1].axhline(mean_val, c='green', ls='-.', alpha=0.7) \n",
|
|||
|
|
"ax[1].legend()\n",
|
|||
|
|
"ax[1].grid()\n",
|
|||
|
|
"\n",
|
|||
|
|
"plt.subplot(313)\n",
|
|||
|
|
"BC_A = pd.concat([Angle[i] for i in Angle.columns[-3:]],axis=1)\n",
|
|||
|
|
"BC_A.plot(ax=ax[2],marker='o')\n",
|
|||
|
|
"ax[2].set_ylabel('Angle/°')\n",
|
|||
|
|
"ax[2].xaxis.set_major_locator(MultipleLocator(5))\n",
|
|||
|
|
"ax[2].yaxis.set_major_locator(MultipleLocator(0.001))\n",
|
|||
|
|
"\n",
|
|||
|
|
"ax[2].grid()\n",
|
|||
|
|
"\n",
|
|||
|
|
"plt.tight_layout()\n",
|
|||
|
|
"plt.show()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 10,
|
|||
|
|
"id": "dc26aac4-aed0-49cf-98f0-16dd0aba33f1",
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"# # BC_Y = pd.concat([AlignMarkY[i]-AlignMarkY[i].mean() for i in AlignMarkY.columns[-2:]],axis=1)\n",
|
|||
|
|
"# BC_Y = pd.concat([AlignMarkY[i] for i in AlignMarkY.columns[-4:]],axis=1)\n",
|
|||
|
|
"# BC_Y.plot(marker='o')\n",
|
|||
|
|
"# plt.title(f'{DieType}-TY（um）')\n",
|
|||
|
|
"# plt.grid()\n",
|
|||
|
|
"# plt.show()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 11,
|
|||
|
|
"id": "a94845fc-49f0-41a1-99a0-b5f5cdff1a69",
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"# # BC_A = pd.concat([Angle[i]-Angle[i].mean() for i in Angle.columns[-2:]],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（°）')\n",
|
|||
|
|
"# plt.grid()\n",
|
|||
|
|
"# plt.show()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 12,
|
|||
|
|
"id": "01b6766e",
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"# (((6210 + 6210)/2)**2 + ((6215-6025.8)/2)**2)**0.5\n",
|
|||
|
|
"# 6210.720502486003"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 18,
|
|||
|
|
"id": "b62b7df1",
|
|||
|
|
"metadata": {
|
|||
|
|
"scrolled": true
|
|||
|
|
},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/html": [
|
|||
|
|
"<div>\n",
|
|||
|
|
"<style scoped>\n",
|
|||
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|||
|
|
" vertical-align: middle;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe tbody tr th {\n",
|
|||
|
|
" vertical-align: top;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe thead th {\n",
|
|||
|
|
" text-align: right;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"</style>\n",
|
|||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|||
|
|
" <thead>\n",
|
|||
|
|
" <tr style=\"text-align: right;\">\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
|
" <th>TX</th>\n",
|
|||
|
|
" <th>TY</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>-0.114749</td>\n",
|
|||
|
|
" <td>-0.239131</td>\n",
|
|||
|
|
" <td>2.762160e-03</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2</th>\n",
|
|||
|
|
" <td>-0.406722</td>\n",
|
|||
|
|
" <td>-0.161811</td>\n",
|
|||
|
|
" <td>2.018869e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>3</th>\n",
|
|||
|
|
" <td>-0.313172</td>\n",
|
|||
|
|
" <td>-0.014656</td>\n",
|
|||
|
|
" <td>3.015735e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>4</th>\n",
|
|||
|
|
" <td>-0.222745</td>\n",
|
|||
|
|
" <td>-0.265825</td>\n",
|
|||
|
|
" <td>6.313797e-06</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>5</th>\n",
|
|||
|
|
" <td>-0.231231</td>\n",
|
|||
|
|
" <td>-0.294186</td>\n",
|
|||
|
|
" <td>4.740228e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>6</th>\n",
|
|||
|
|
" <td>0.261930</td>\n",
|
|||
|
|
" <td>-0.004719</td>\n",
|
|||
|
|
" <td>3.477923e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>7</th>\n",
|
|||
|
|
" <td>0.037204</td>\n",
|
|||
|
|
" <td>-0.096786</td>\n",
|
|||
|
|
" <td>1.626487e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>8</th>\n",
|
|||
|
|
" <td>0.754761</td>\n",
|
|||
|
|
" <td>-0.883850</td>\n",
|
|||
|
|
" <td>-6.749060e-03</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>9</th>\n",
|
|||
|
|
" <td>0.098801</td>\n",
|
|||
|
|
" <td>-0.666815</td>\n",
|
|||
|
|
" <td>-2.020698e-03</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>10</th>\n",
|
|||
|
|
" <td>-0.066598</td>\n",
|
|||
|
|
" <td>-0.209071</td>\n",
|
|||
|
|
" <td>6.313458e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>11</th>\n",
|
|||
|
|
" <td>-0.133028</td>\n",
|
|||
|
|
" <td>-0.195213</td>\n",
|
|||
|
|
" <td>5.060460e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>12</th>\n",
|
|||
|
|
" <td>-0.469327</td>\n",
|
|||
|
|
" <td>-0.244079</td>\n",
|
|||
|
|
" <td>2.518664e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>13</th>\n",
|
|||
|
|
" <td>0.007747</td>\n",
|
|||
|
|
" <td>-0.058811</td>\n",
|
|||
|
|
" <td>-2.032572e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>14</th>\n",
|
|||
|
|
" <td>-0.395248</td>\n",
|
|||
|
|
" <td>-0.050926</td>\n",
|
|||
|
|
" <td>-3.027080e-07</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>15</th>\n",
|
|||
|
|
" <td>0.051409</td>\n",
|
|||
|
|
" <td>-0.096212</td>\n",
|
|||
|
|
" <td>1.150579e-03</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>16</th>\n",
|
|||
|
|
" <td>0.152462</td>\n",
|
|||
|
|
" <td>0.031098</td>\n",
|
|||
|
|
" <td>9.681923e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>17</th>\n",
|
|||
|
|
" <td>-0.370008</td>\n",
|
|||
|
|
" <td>-0.327332</td>\n",
|
|||
|
|
" <td>1.844886e-03</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>18</th>\n",
|
|||
|
|
" <td>-0.216884</td>\n",
|
|||
|
|
" <td>-0.099730</td>\n",
|
|||
|
|
" <td>8.683741e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>19</th>\n",
|
|||
|
|
" <td>-0.352239</td>\n",
|
|||
|
|
" <td>-0.275509</td>\n",
|
|||
|
|
" <td>1.255561e-03</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>20</th>\n",
|
|||
|
|
" <td>-0.058490</td>\n",
|
|||
|
|
" <td>0.174214</td>\n",
|
|||
|
|
" <td>-1.005134e-03</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>21</th>\n",
|
|||
|
|
" <td>0.011801</td>\n",
|
|||
|
|
" <td>0.090914</td>\n",
|
|||
|
|
" <td>6.764195e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>22</th>\n",
|
|||
|
|
" <td>0.021550</td>\n",
|
|||
|
|
" <td>0.035880</td>\n",
|
|||
|
|
" <td>-1.403649e-06</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>23</th>\n",
|
|||
|
|
" <td>1.457966</td>\n",
|
|||
|
|
" <td>-2.998350</td>\n",
|
|||
|
|
" <td>-3.437422e-02</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>24</th>\n",
|
|||
|
|
" <td>0.239157</td>\n",
|
|||
|
|
" <td>0.260998</td>\n",
|
|||
|
|
" <td>-5.768552e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>25</th>\n",
|
|||
|
|
" <td>0.194222</td>\n",
|
|||
|
|
" <td>0.208055</td>\n",
|
|||
|
|
" <td>-2.399758e-03</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>26</th>\n",
|
|||
|
|
" <td>-0.098915</td>\n",
|
|||
|
|
" <td>-0.132729</td>\n",
|
|||
|
|
" <td>1.341740e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>27</th>\n",
|
|||
|
|
" <td>-0.026623</td>\n",
|
|||
|
|
" <td>-0.239152</td>\n",
|
|||
|
|
" <td>3.415376e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>28</th>\n",
|
|||
|
|
" <td>-0.419360</td>\n",
|
|||
|
|
" <td>-0.264902</td>\n",
|
|||
|
|
" <td>2.445601e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>29</th>\n",
|
|||
|
|
" <td>-0.215015</td>\n",
|
|||
|
|
" <td>-0.135662</td>\n",
|
|||
|
|
" <td>1.081101e-03</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>30</th>\n",
|
|||
|
|
" <td>0.742880</td>\n",
|
|||
|
|
" <td>-1.814711</td>\n",
|
|||
|
|
" <td>-1.353388e-02</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>31</th>\n",
|
|||
|
|
" <td>0.523318</td>\n",
|
|||
|
|
" <td>-1.430335</td>\n",
|
|||
|
|
" <td>-1.129055e-02</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>32</th>\n",
|
|||
|
|
" <td>-0.194297</td>\n",
|
|||
|
|
" <td>-0.062513</td>\n",
|
|||
|
|
" <td>9.364155e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>33</th>\n",
|
|||
|
|
" <td>-0.995301</td>\n",
|
|||
|
|
" <td>-0.202951</td>\n",
|
|||
|
|
" <td>1.075866e-03</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>34</th>\n",
|
|||
|
|
" <td>0.008950</td>\n",
|
|||
|
|
" <td>-0.067359</td>\n",
|
|||
|
|
" <td>-4.124640e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>35</th>\n",
|
|||
|
|
" <td>-0.205027</td>\n",
|
|||
|
|
" <td>-0.162327</td>\n",
|
|||
|
|
" <td>-1.374335e-03</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>36</th>\n",
|
|||
|
|
" <td>-0.091320</td>\n",
|
|||
|
|
" <td>-0.205404</td>\n",
|
|||
|
|
" <td>5.102801e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>37</th>\n",
|
|||
|
|
" <td>-0.238676</td>\n",
|
|||
|
|
" <td>-0.298704</td>\n",
|
|||
|
|
" <td>8.463431e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>38</th>\n",
|
|||
|
|
" <td>-0.332189</td>\n",
|
|||
|
|
" <td>-0.188340</td>\n",
|
|||
|
|
" <td>5.046316e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>39</th>\n",
|
|||
|
|
" <td>0.057358</td>\n",
|
|||
|
|
" <td>-0.001675</td>\n",
|
|||
|
|
" <td>-3.160405e-03</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>40</th>\n",
|
|||
|
|
" <td>0.073560</td>\n",
|
|||
|
|
" <td>-0.005965</td>\n",
|
|||
|
|
" <td>-1.509830e-03</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>41</th>\n",
|
|||
|
|
" <td>0.141621</td>\n",
|
|||
|
|
" <td>-0.021585</td>\n",
|
|||
|
|
" <td>1.686072e-03</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>42</th>\n",
|
|||
|
|
" <td>0.248755</td>\n",
|
|||
|
|
" <td>-0.098528</td>\n",
|
|||
|
|
" <td>5.958213e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>43</th>\n",
|
|||
|
|
" <td>0.064610</td>\n",
|
|||
|
|
" <td>-0.089520</td>\n",
|
|||
|
|
" <td>-1.267509e-03</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>44</th>\n",
|
|||
|
|
" <td>0.078177</td>\n",
|
|||
|
|
" <td>-0.293342</td>\n",
|
|||
|
|
" <td>1.244631e-03</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>45</th>\n",
|
|||
|
|
" <td>0.570312</td>\n",
|
|||
|
|
" <td>-1.085044</td>\n",
|
|||
|
|
" <td>-9.777408e-03</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>46</th>\n",
|
|||
|
|
" <td>0.251440</td>\n",
|
|||
|
|
" <td>0.446081</td>\n",
|
|||
|
|
" <td>-4.819596e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>47</th>\n",
|
|||
|
|
" <td>0.094565</td>\n",
|
|||
|
|
" <td>0.229372</td>\n",
|
|||
|
|
" <td>-9.553477e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>48</th>\n",
|
|||
|
|
" <td>-0.001961</td>\n",
|
|||
|
|
" <td>-0.165008</td>\n",
|
|||
|
|
" <td>1.450343e-03</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>49</th>\n",
|
|||
|
|
" <td>0.094196</td>\n",
|
|||
|
|
" <td>-0.697044</td>\n",
|
|||
|
|
" <td>-3.353926e-03</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>50</th>\n",
|
|||
|
|
" <td>-0.330864</td>\n",
|
|||
|
|
" <td>-0.141907</td>\n",
|
|||
|
|
" <td>2.302730e-03</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>51</th>\n",
|
|||
|
|
" <td>0.193089</td>\n",
|
|||
|
|
" <td>-1.141168</td>\n",
|
|||
|
|
" <td>-9.603330e-03</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>52</th>\n",
|
|||
|
|
" <td>-0.158175</td>\n",
|
|||
|
|
" <td>-0.096959</td>\n",
|
|||
|
|
" <td>1.866471e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>53</th>\n",
|
|||
|
|
" <td>-0.023629</td>\n",
|
|||
|
|
" <td>-0.148910</td>\n",
|
|||
|
|
" <td>5.451310e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>54</th>\n",
|
|||
|
|
" <td>-0.231563</td>\n",
|
|||
|
|
" <td>-0.162250</td>\n",
|
|||
|
|
" <td>5.686287e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>55</th>\n",
|
|||
|
|
" <td>-3.381194</td>\n",
|
|||
|
|
" <td>-2.254454</td>\n",
|
|||
|
|
" <td>-2.010549e-02</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>56</th>\n",
|
|||
|
|
" <td>1.542894</td>\n",
|
|||
|
|
" <td>-2.212277</td>\n",
|
|||
|
|
" <td>-1.582713e-02</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>57</th>\n",
|
|||
|
|
" <td>0.217780</td>\n",
|
|||
|
|
" <td>-0.054810</td>\n",
|
|||
|
|
" <td>1.269606e-03</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>58</th>\n",
|
|||
|
|
" <td>-1.766127</td>\n",
|
|||
|
|
" <td>-1.219275</td>\n",
|
|||
|
|
" <td>-8.567332e-03</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>59</th>\n",
|
|||
|
|
" <td>0.388737</td>\n",
|
|||
|
|
" <td>0.607952</td>\n",
|
|||
|
|
" <td>-6.702906e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>60</th>\n",
|
|||
|
|
" <td>-0.165562</td>\n",
|
|||
|
|
" <td>-0.106051</td>\n",
|
|||
|
|
" <td>1.887424e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>61</th>\n",
|
|||
|
|
" <td>0.021792</td>\n",
|
|||
|
|
" <td>-0.113425</td>\n",
|
|||
|
|
" <td>1.042199e-03</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>62</th>\n",
|
|||
|
|
" <td>-3.379116</td>\n",
|
|||
|
|
" <td>-2.156740</td>\n",
|
|||
|
|
" <td>-1.887207e-02</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>63</th>\n",
|
|||
|
|
" <td>0.080315</td>\n",
|
|||
|
|
" <td>-0.354850</td>\n",
|
|||
|
|
" <td>4.032307e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>64</th>\n",
|
|||
|
|
" <td>-0.052211</td>\n",
|
|||
|
|
" <td>-0.828428</td>\n",
|
|||
|
|
" <td>-5.572701e-03</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>65</th>\n",
|
|||
|
|
" <td>0.012754</td>\n",
|
|||
|
|
" <td>-0.155862</td>\n",
|
|||
|
|
" <td>3.374983e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>66</th>\n",
|
|||
|
|
" <td>-0.207965</td>\n",
|
|||
|
|
" <td>-0.203637</td>\n",
|
|||
|
|
" <td>-2.232226e-03</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>67</th>\n",
|
|||
|
|
" <td>0.029527</td>\n",
|
|||
|
|
" <td>0.082579</td>\n",
|
|||
|
|
" <td>8.624772e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>68</th>\n",
|
|||
|
|
" <td>-0.044517</td>\n",
|
|||
|
|
" <td>-0.192720</td>\n",
|
|||
|
|
" <td>2.061893e-03</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>69</th>\n",
|
|||
|
|
" <td>0.599895</td>\n",
|
|||
|
|
" <td>-0.854489</td>\n",
|
|||
|
|
" <td>-5.500268e-03</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>70</th>\n",
|
|||
|
|
" <td>-0.044501</td>\n",
|
|||
|
|
" <td>-0.104632</td>\n",
|
|||
|
|
" <td>2.355586e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>71</th>\n",
|
|||
|
|
" <td>-0.002545</td>\n",
|
|||
|
|
" <td>-0.266839</td>\n",
|
|||
|
|
" <td>1.413923e-03</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>72</th>\n",
|
|||
|
|
" <td>-0.143387</td>\n",
|
|||
|
|
" <td>-0.107351</td>\n",
|
|||
|
|
" <td>-2.433931e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>73</th>\n",
|
|||
|
|
" <td>0.132513</td>\n",
|
|||
|
|
" <td>-0.163646</td>\n",
|
|||
|
|
" <td>-4.036292e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>74</th>\n",
|
|||
|
|
" <td>0.191467</td>\n",
|
|||
|
|
" <td>0.032348</td>\n",
|
|||
|
|
" <td>-1.143953e-03</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>75</th>\n",
|
|||
|
|
" <td>1.898314</td>\n",
|
|||
|
|
" <td>-2.056515</td>\n",
|
|||
|
|
" <td>-2.221197e-02</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>76</th>\n",
|
|||
|
|
" <td>0.099290</td>\n",
|
|||
|
|
" <td>0.192330</td>\n",
|
|||
|
|
" <td>-7.050176e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>77</th>\n",
|
|||
|
|
" <td>0.773973</td>\n",
|
|||
|
|
" <td>-2.154864</td>\n",
|
|||
|
|
" <td>-2.127032e-02</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>78</th>\n",
|
|||
|
|
" <td>0.676468</td>\n",
|
|||
|
|
" <td>-0.727293</td>\n",
|
|||
|
|
" <td>-7.159576e-03</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>79</th>\n",
|
|||
|
|
" <td>1.207605</td>\n",
|
|||
|
|
" <td>-2.156680</td>\n",
|
|||
|
|
" <td>-2.054013e-02</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>80</th>\n",
|
|||
|
|
" <td>0.242952</td>\n",
|
|||
|
|
" <td>0.082132</td>\n",
|
|||
|
|
" <td>2.912115e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>81</th>\n",
|
|||
|
|
" <td>0.325843</td>\n",
|
|||
|
|
" <td>-0.396186</td>\n",
|
|||
|
|
" <td>-3.203713e-03</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>82</th>\n",
|
|||
|
|
" <td>0.150434</td>\n",
|
|||
|
|
" <td>0.482240</td>\n",
|
|||
|
|
" <td>7.763590e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>83</th>\n",
|
|||
|
|
" <td>0.281124</td>\n",
|
|||
|
|
" <td>0.131128</td>\n",
|
|||
|
|
" <td>-4.730062e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>84</th>\n",
|
|||
|
|
" <td>2.191230</td>\n",
|
|||
|
|
" <td>-2.641316</td>\n",
|
|||
|
|
" <td>-3.320662e-02</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>85</th>\n",
|
|||
|
|
" <td>0.089528</td>\n",
|
|||
|
|
" <td>0.089461</td>\n",
|
|||
|
|
" <td>-1.002055e-03</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>86</th>\n",
|
|||
|
|
" <td>0.196797</td>\n",
|
|||
|
|
" <td>0.075971</td>\n",
|
|||
|
|
" <td>-3.150078e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>87</th>\n",
|
|||
|
|
" <td>1.700738</td>\n",
|
|||
|
|
" <td>-1.546396</td>\n",
|
|||
|
|
" <td>-1.953671e-02</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"</div>"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
" TX TY Angle\n",
|
|||
|
|
"Index \n",
|
|||
|
|
"1 -0.114749 -0.239131 2.762160e-03\n",
|
|||
|
|
"2 -0.406722 -0.161811 2.018869e-04\n",
|
|||
|
|
"3 -0.313172 -0.014656 3.015735e-04\n",
|
|||
|
|
"4 -0.222745 -0.265825 6.313797e-06\n",
|
|||
|
|
"5 -0.231231 -0.294186 4.740228e-04\n",
|
|||
|
|
"6 0.261930 -0.004719 3.477923e-04\n",
|
|||
|
|
"7 0.037204 -0.096786 1.626487e-04\n",
|
|||
|
|
"8 0.754761 -0.883850 -6.749060e-03\n",
|
|||
|
|
"9 0.098801 -0.666815 -2.020698e-03\n",
|
|||
|
|
"10 -0.066598 -0.209071 6.313458e-04\n",
|
|||
|
|
"11 -0.133028 -0.195213 5.060460e-04\n",
|
|||
|
|
"12 -0.469327 -0.244079 2.518664e-04\n",
|
|||
|
|
"13 0.007747 -0.058811 -2.032572e-04\n",
|
|||
|
|
"14 -0.395248 -0.050926 -3.027080e-07\n",
|
|||
|
|
"15 0.051409 -0.096212 1.150579e-03\n",
|
|||
|
|
"16 0.152462 0.031098 9.681923e-04\n",
|
|||
|
|
"17 -0.370008 -0.327332 1.844886e-03\n",
|
|||
|
|
"18 -0.216884 -0.099730 8.683741e-04\n",
|
|||
|
|
"19 -0.352239 -0.275509 1.255561e-03\n",
|
|||
|
|
"20 -0.058490 0.174214 -1.005134e-03\n",
|
|||
|
|
"21 0.011801 0.090914 6.764195e-04\n",
|
|||
|
|
"22 0.021550 0.035880 -1.403649e-06\n",
|
|||
|
|
"23 1.457966 -2.998350 -3.437422e-02\n",
|
|||
|
|
"24 0.239157 0.260998 -5.768552e-04\n",
|
|||
|
|
"25 0.194222 0.208055 -2.399758e-03\n",
|
|||
|
|
"26 -0.098915 -0.132729 1.341740e-04\n",
|
|||
|
|
"27 -0.026623 -0.239152 3.415376e-04\n",
|
|||
|
|
"28 -0.419360 -0.264902 2.445601e-04\n",
|
|||
|
|
"29 -0.215015 -0.135662 1.081101e-03\n",
|
|||
|
|
"30 0.742880 -1.814711 -1.353388e-02\n",
|
|||
|
|
"31 0.523318 -1.430335 -1.129055e-02\n",
|
|||
|
|
"32 -0.194297 -0.062513 9.364155e-04\n",
|
|||
|
|
"33 -0.995301 -0.202951 1.075866e-03\n",
|
|||
|
|
"34 0.008950 -0.067359 -4.124640e-04\n",
|
|||
|
|
"35 -0.205027 -0.162327 -1.374335e-03\n",
|
|||
|
|
"36 -0.091320 -0.205404 5.102801e-04\n",
|
|||
|
|
"37 -0.238676 -0.298704 8.463431e-04\n",
|
|||
|
|
"38 -0.332189 -0.188340 5.046316e-04\n",
|
|||
|
|
"39 0.057358 -0.001675 -3.160405e-03\n",
|
|||
|
|
"40 0.073560 -0.005965 -1.509830e-03\n",
|
|||
|
|
"41 0.141621 -0.021585 1.686072e-03\n",
|
|||
|
|
"42 0.248755 -0.098528 5.958213e-04\n",
|
|||
|
|
"43 0.064610 -0.089520 -1.267509e-03\n",
|
|||
|
|
"44 0.078177 -0.293342 1.244631e-03\n",
|
|||
|
|
"45 0.570312 -1.085044 -9.777408e-03\n",
|
|||
|
|
"46 0.251440 0.446081 -4.819596e-04\n",
|
|||
|
|
"47 0.094565 0.229372 -9.553477e-04\n",
|
|||
|
|
"48 -0.001961 -0.165008 1.450343e-03\n",
|
|||
|
|
"49 0.094196 -0.697044 -3.353926e-03\n",
|
|||
|
|
"50 -0.330864 -0.141907 2.302730e-03\n",
|
|||
|
|
"51 0.193089 -1.141168 -9.603330e-03\n",
|
|||
|
|
"52 -0.158175 -0.096959 1.866471e-04\n",
|
|||
|
|
"53 -0.023629 -0.148910 5.451310e-04\n",
|
|||
|
|
"54 -0.231563 -0.162250 5.686287e-04\n",
|
|||
|
|
"55 -3.381194 -2.254454 -2.010549e-02\n",
|
|||
|
|
"56 1.542894 -2.212277 -1.582713e-02\n",
|
|||
|
|
"57 0.217780 -0.054810 1.269606e-03\n",
|
|||
|
|
"58 -1.766127 -1.219275 -8.567332e-03\n",
|
|||
|
|
"59 0.388737 0.607952 -6.702906e-04\n",
|
|||
|
|
"60 -0.165562 -0.106051 1.887424e-04\n",
|
|||
|
|
"61 0.021792 -0.113425 1.042199e-03\n",
|
|||
|
|
"62 -3.379116 -2.156740 -1.887207e-02\n",
|
|||
|
|
"63 0.080315 -0.354850 4.032307e-04\n",
|
|||
|
|
"64 -0.052211 -0.828428 -5.572701e-03\n",
|
|||
|
|
"65 0.012754 -0.155862 3.374983e-04\n",
|
|||
|
|
"66 -0.207965 -0.203637 -2.232226e-03\n",
|
|||
|
|
"67 0.029527 0.082579 8.624772e-04\n",
|
|||
|
|
"68 -0.044517 -0.192720 2.061893e-03\n",
|
|||
|
|
"69 0.599895 -0.854489 -5.500268e-03\n",
|
|||
|
|
"70 -0.044501 -0.104632 2.355586e-04\n",
|
|||
|
|
"71 -0.002545 -0.266839 1.413923e-03\n",
|
|||
|
|
"72 -0.143387 -0.107351 -2.433931e-04\n",
|
|||
|
|
"73 0.132513 -0.163646 -4.036292e-04\n",
|
|||
|
|
"74 0.191467 0.032348 -1.143953e-03\n",
|
|||
|
|
"75 1.898314 -2.056515 -2.221197e-02\n",
|
|||
|
|
"76 0.099290 0.192330 -7.050176e-04\n",
|
|||
|
|
"77 0.773973 -2.154864 -2.127032e-02\n",
|
|||
|
|
"78 0.676468 -0.727293 -7.159576e-03\n",
|
|||
|
|
"79 1.207605 -2.156680 -2.054013e-02\n",
|
|||
|
|
"80 0.242952 0.082132 2.912115e-04\n",
|
|||
|
|
"81 0.325843 -0.396186 -3.203713e-03\n",
|
|||
|
|
"82 0.150434 0.482240 7.763590e-04\n",
|
|||
|
|
"83 0.281124 0.131128 -4.730062e-04\n",
|
|||
|
|
"84 2.191230 -2.641316 -3.320662e-02\n",
|
|||
|
|
"85 0.089528 0.089461 -1.002055e-03\n",
|
|||
|
|
"86 0.196797 0.075971 -3.150078e-04\n",
|
|||
|
|
"87 1.700738 -1.546396 -1.953671e-02"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 18,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"DieBC = pd.read_excel('SP-Die补偿模版.xlsx',index_col=0,header=0)\n",
|
|||
|
|
"\n",
|
|||
|
|
"BC_X = BC_X.mean(axis=1)\n",
|
|||
|
|
"# DieBC['TX'] = BC_X.fillna(BC_X.interpolate()).values\n",
|
|||
|
|
"DieBC['TX'] = BC_X\n",
|
|||
|
|
"\n",
|
|||
|
|
"BC_Y = BC_Y.mean(axis=1)\n",
|
|||
|
|
"# DieBC['TY'] = BC_Y.fillna(BC_Y.interpolate()).values\n",
|
|||
|
|
"DieBC['TY'] = BC_Y\n",
|
|||
|
|
"\n",
|
|||
|
|
"BC_A = BC_A.mean(axis=1)\n",
|
|||
|
|
"# DieBC['Angle'] = BC_A.fillna(BC_A.interpolate()).values\n",
|
|||
|
|
"DieBC['Angle'] = BC_A\n",
|
|||
|
|
"\n",
|
|||
|
|
"DieBC['TX'] = DieBC['TX'] - 6210.7205 * np.sin(DieBC['Angle']*np.pi/180)\n",
|
|||
|
|
"DieBC"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 19,
|
|||
|
|
"id": "56dee351",
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"[8, 23, 30, 31, 45, 51, 55, 56, 58, 62, 75, 77, 78, 79, 84, 87]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 19,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"DieBC[DieBC['Angle']<-0.006].index.to_list()\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"id": "a7183ab2",
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
"[23, 30, 31, 55, 56, 58, 59, 62, 75, 77, 79, 84, 87]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"id": "053db6a9",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"# 计算补偿值"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 20,
|
|||
|
|
"id": "e48c1723",
|
|||
|
|
"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>TX</th>\n",
|
|||
|
|
" <th>TY</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>-0.114749</td>\n",
|
|||
|
|
" <td>-0.239131</td>\n",
|
|||
|
|
" <td>2.762160e-03</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2</th>\n",
|
|||
|
|
" <td>-0.406722</td>\n",
|
|||
|
|
" <td>-0.161811</td>\n",
|
|||
|
|
" <td>2.018869e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>3</th>\n",
|
|||
|
|
" <td>-0.313172</td>\n",
|
|||
|
|
" <td>-0.014656</td>\n",
|
|||
|
|
" <td>3.015735e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>4</th>\n",
|
|||
|
|
" <td>-0.222745</td>\n",
|
|||
|
|
" <td>-0.265825</td>\n",
|
|||
|
|
" <td>6.313797e-06</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>5</th>\n",
|
|||
|
|
" <td>-0.231231</td>\n",
|
|||
|
|
" <td>-0.294186</td>\n",
|
|||
|
|
" <td>4.740228e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>6</th>\n",
|
|||
|
|
" <td>0.261930</td>\n",
|
|||
|
|
" <td>-0.004719</td>\n",
|
|||
|
|
" <td>3.477923e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>7</th>\n",
|
|||
|
|
" <td>0.037204</td>\n",
|
|||
|
|
" <td>-0.096786</td>\n",
|
|||
|
|
" <td>1.626487e-04</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>0.098801</td>\n",
|
|||
|
|
" <td>-0.666815</td>\n",
|
|||
|
|
" <td>-2.020698e-03</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>10</th>\n",
|
|||
|
|
" <td>-0.066598</td>\n",
|
|||
|
|
" <td>-0.209071</td>\n",
|
|||
|
|
" <td>6.313458e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>11</th>\n",
|
|||
|
|
" <td>-0.133028</td>\n",
|
|||
|
|
" <td>-0.195213</td>\n",
|
|||
|
|
" <td>5.060460e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>12</th>\n",
|
|||
|
|
" <td>-0.469327</td>\n",
|
|||
|
|
" <td>-0.244079</td>\n",
|
|||
|
|
" <td>2.518664e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>13</th>\n",
|
|||
|
|
" <td>0.007747</td>\n",
|
|||
|
|
" <td>-0.058811</td>\n",
|
|||
|
|
" <td>-2.032572e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>14</th>\n",
|
|||
|
|
" <td>-0.395248</td>\n",
|
|||
|
|
" <td>-0.050926</td>\n",
|
|||
|
|
" <td>-3.027080e-07</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>15</th>\n",
|
|||
|
|
" <td>0.051409</td>\n",
|
|||
|
|
" <td>-0.096212</td>\n",
|
|||
|
|
" <td>1.150579e-03</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>16</th>\n",
|
|||
|
|
" <td>0.152462</td>\n",
|
|||
|
|
" <td>0.031098</td>\n",
|
|||
|
|
" <td>9.681923e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>17</th>\n",
|
|||
|
|
" <td>-0.370008</td>\n",
|
|||
|
|
" <td>-0.327332</td>\n",
|
|||
|
|
" <td>1.844886e-03</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>18</th>\n",
|
|||
|
|
" <td>-0.216884</td>\n",
|
|||
|
|
" <td>-0.099730</td>\n",
|
|||
|
|
" <td>8.683741e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>19</th>\n",
|
|||
|
|
" <td>-0.352239</td>\n",
|
|||
|
|
" <td>-0.275509</td>\n",
|
|||
|
|
" <td>1.255561e-03</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>20</th>\n",
|
|||
|
|
" <td>-0.058490</td>\n",
|
|||
|
|
" <td>0.174214</td>\n",
|
|||
|
|
" <td>-1.005134e-03</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>21</th>\n",
|
|||
|
|
" <td>0.011801</td>\n",
|
|||
|
|
" <td>0.090914</td>\n",
|
|||
|
|
" <td>6.764195e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>22</th>\n",
|
|||
|
|
" <td>0.021550</td>\n",
|
|||
|
|
" <td>0.035880</td>\n",
|
|||
|
|
" <td>-1.403649e-06</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>0.239157</td>\n",
|
|||
|
|
" <td>0.260998</td>\n",
|
|||
|
|
" <td>-5.768552e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>25</th>\n",
|
|||
|
|
" <td>0.194222</td>\n",
|
|||
|
|
" <td>0.208055</td>\n",
|
|||
|
|
" <td>-2.399758e-03</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>26</th>\n",
|
|||
|
|
" <td>-0.098915</td>\n",
|
|||
|
|
" <td>-0.132729</td>\n",
|
|||
|
|
" <td>1.341740e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>27</th>\n",
|
|||
|
|
" <td>-0.026623</td>\n",
|
|||
|
|
" <td>-0.239152</td>\n",
|
|||
|
|
" <td>3.415376e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>28</th>\n",
|
|||
|
|
" <td>-0.419360</td>\n",
|
|||
|
|
" <td>-0.264902</td>\n",
|
|||
|
|
" <td>2.445601e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>29</th>\n",
|
|||
|
|
" <td>-0.215015</td>\n",
|
|||
|
|
" <td>-0.135662</td>\n",
|
|||
|
|
" <td>1.081101e-03</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>-0.194297</td>\n",
|
|||
|
|
" <td>-0.062513</td>\n",
|
|||
|
|
" <td>9.364155e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>33</th>\n",
|
|||
|
|
" <td>-0.995301</td>\n",
|
|||
|
|
" <td>-0.202951</td>\n",
|
|||
|
|
" <td>1.075866e-03</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>34</th>\n",
|
|||
|
|
" <td>0.008950</td>\n",
|
|||
|
|
" <td>-0.067359</td>\n",
|
|||
|
|
" <td>-4.124640e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>35</th>\n",
|
|||
|
|
" <td>-0.205027</td>\n",
|
|||
|
|
" <td>-0.162327</td>\n",
|
|||
|
|
" <td>-1.374335e-03</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>36</th>\n",
|
|||
|
|
" <td>-0.091320</td>\n",
|
|||
|
|
" <td>-0.205404</td>\n",
|
|||
|
|
" <td>5.102801e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>37</th>\n",
|
|||
|
|
" <td>-0.238676</td>\n",
|
|||
|
|
" <td>-0.298704</td>\n",
|
|||
|
|
" <td>8.463431e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>38</th>\n",
|
|||
|
|
" <td>-0.332189</td>\n",
|
|||
|
|
" <td>-0.188340</td>\n",
|
|||
|
|
" <td>5.046316e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>39</th>\n",
|
|||
|
|
" <td>0.057358</td>\n",
|
|||
|
|
" <td>-0.001675</td>\n",
|
|||
|
|
" <td>-3.160405e-03</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>40</th>\n",
|
|||
|
|
" <td>0.073560</td>\n",
|
|||
|
|
" <td>-0.005965</td>\n",
|
|||
|
|
" <td>-1.509830e-03</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>41</th>\n",
|
|||
|
|
" <td>0.141621</td>\n",
|
|||
|
|
" <td>-0.021585</td>\n",
|
|||
|
|
" <td>1.686072e-03</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>42</th>\n",
|
|||
|
|
" <td>0.248755</td>\n",
|
|||
|
|
" <td>-0.098528</td>\n",
|
|||
|
|
" <td>5.958213e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>43</th>\n",
|
|||
|
|
" <td>0.064610</td>\n",
|
|||
|
|
" <td>-0.089520</td>\n",
|
|||
|
|
" <td>-1.267509e-03</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>44</th>\n",
|
|||
|
|
" <td>0.078177</td>\n",
|
|||
|
|
" <td>-0.293342</td>\n",
|
|||
|
|
" <td>1.244631e-03</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>0.251440</td>\n",
|
|||
|
|
" <td>0.446081</td>\n",
|
|||
|
|
" <td>-4.819596e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>47</th>\n",
|
|||
|
|
" <td>0.094565</td>\n",
|
|||
|
|
" <td>0.229372</td>\n",
|
|||
|
|
" <td>-9.553477e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>48</th>\n",
|
|||
|
|
" <td>-0.001961</td>\n",
|
|||
|
|
" <td>-0.165008</td>\n",
|
|||
|
|
" <td>1.450343e-03</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>49</th>\n",
|
|||
|
|
" <td>0.094196</td>\n",
|
|||
|
|
" <td>-0.697044</td>\n",
|
|||
|
|
" <td>-3.353926e-03</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>50</th>\n",
|
|||
|
|
" <td>-0.330864</td>\n",
|
|||
|
|
" <td>-0.141907</td>\n",
|
|||
|
|
" <td>2.302730e-03</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>-0.158175</td>\n",
|
|||
|
|
" <td>-0.096959</td>\n",
|
|||
|
|
" <td>1.866471e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>53</th>\n",
|
|||
|
|
" <td>-0.023629</td>\n",
|
|||
|
|
" <td>-0.148910</td>\n",
|
|||
|
|
" <td>5.451310e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>54</th>\n",
|
|||
|
|
" <td>-0.231563</td>\n",
|
|||
|
|
" <td>-0.162250</td>\n",
|
|||
|
|
" <td>5.686287e-04</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>0.217780</td>\n",
|
|||
|
|
" <td>-0.054810</td>\n",
|
|||
|
|
" <td>1.269606e-03</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>0.388737</td>\n",
|
|||
|
|
" <td>0.607952</td>\n",
|
|||
|
|
" <td>-6.702906e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>60</th>\n",
|
|||
|
|
" <td>-0.165562</td>\n",
|
|||
|
|
" <td>-0.106051</td>\n",
|
|||
|
|
" <td>1.887424e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>61</th>\n",
|
|||
|
|
" <td>0.021792</td>\n",
|
|||
|
|
" <td>-0.113425</td>\n",
|
|||
|
|
" <td>1.042199e-03</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>0.080315</td>\n",
|
|||
|
|
" <td>-0.354850</td>\n",
|
|||
|
|
" <td>4.032307e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>64</th>\n",
|
|||
|
|
" <td>-0.052211</td>\n",
|
|||
|
|
" <td>-0.828428</td>\n",
|
|||
|
|
" <td>-5.572701e-03</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>65</th>\n",
|
|||
|
|
" <td>0.012754</td>\n",
|
|||
|
|
" <td>-0.155862</td>\n",
|
|||
|
|
" <td>3.374983e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>66</th>\n",
|
|||
|
|
" <td>-0.207965</td>\n",
|
|||
|
|
" <td>-0.203637</td>\n",
|
|||
|
|
" <td>-2.232226e-03</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>67</th>\n",
|
|||
|
|
" <td>0.029527</td>\n",
|
|||
|
|
" <td>0.082579</td>\n",
|
|||
|
|
" <td>8.624772e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>68</th>\n",
|
|||
|
|
" <td>-0.044517</td>\n",
|
|||
|
|
" <td>-0.192720</td>\n",
|
|||
|
|
" <td>2.061893e-03</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>69</th>\n",
|
|||
|
|
" <td>0.599895</td>\n",
|
|||
|
|
" <td>-0.854489</td>\n",
|
|||
|
|
" <td>-5.500268e-03</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>70</th>\n",
|
|||
|
|
" <td>-0.044501</td>\n",
|
|||
|
|
" <td>-0.104632</td>\n",
|
|||
|
|
" <td>2.355586e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>71</th>\n",
|
|||
|
|
" <td>-0.002545</td>\n",
|
|||
|
|
" <td>-0.266839</td>\n",
|
|||
|
|
" <td>1.413923e-03</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>72</th>\n",
|
|||
|
|
" <td>-0.143387</td>\n",
|
|||
|
|
" <td>-0.107351</td>\n",
|
|||
|
|
" <td>-2.433931e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>73</th>\n",
|
|||
|
|
" <td>0.132513</td>\n",
|
|||
|
|
" <td>-0.163646</td>\n",
|
|||
|
|
" <td>-4.036292e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>74</th>\n",
|
|||
|
|
" <td>0.191467</td>\n",
|
|||
|
|
" <td>0.032348</td>\n",
|
|||
|
|
" <td>-1.143953e-03</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>0.099290</td>\n",
|
|||
|
|
" <td>0.192330</td>\n",
|
|||
|
|
" <td>-7.050176e-04</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>0.242952</td>\n",
|
|||
|
|
" <td>0.082132</td>\n",
|
|||
|
|
" <td>2.912115e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>81</th>\n",
|
|||
|
|
" <td>0.325843</td>\n",
|
|||
|
|
" <td>-0.396186</td>\n",
|
|||
|
|
" <td>-3.203713e-03</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>82</th>\n",
|
|||
|
|
" <td>0.150434</td>\n",
|
|||
|
|
" <td>0.482240</td>\n",
|
|||
|
|
" <td>7.763590e-04</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>83</th>\n",
|
|||
|
|
" <td>0.281124</td>\n",
|
|||
|
|
" <td>0.131128</td>\n",
|
|||
|
|
" <td>-4.730062e-04</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>0.089528</td>\n",
|
|||
|
|
" <td>0.089461</td>\n",
|
|||
|
|
" <td>-1.002055e-03</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>86</th>\n",
|
|||
|
|
" <td>0.196797</td>\n",
|
|||
|
|
" <td>0.075971</td>\n",
|
|||
|
|
" <td>-3.150078e-04</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": [
|
|||
|
|
" TX TY Angle\n",
|
|||
|
|
"Index \n",
|
|||
|
|
"1 -0.114749 -0.239131 2.762160e-03\n",
|
|||
|
|
"2 -0.406722 -0.161811 2.018869e-04\n",
|
|||
|
|
"3 -0.313172 -0.014656 3.015735e-04\n",
|
|||
|
|
"4 -0.222745 -0.265825 6.313797e-06\n",
|
|||
|
|
"5 -0.231231 -0.294186 4.740228e-04\n",
|
|||
|
|
"6 0.261930 -0.004719 3.477923e-04\n",
|
|||
|
|
"7 0.037204 -0.096786 1.626487e-04\n",
|
|||
|
|
"8 NaN NaN NaN\n",
|
|||
|
|
"9 0.098801 -0.666815 -2.020698e-03\n",
|
|||
|
|
"10 -0.066598 -0.209071 6.313458e-04\n",
|
|||
|
|
"11 -0.133028 -0.195213 5.060460e-04\n",
|
|||
|
|
"12 -0.469327 -0.244079 2.518664e-04\n",
|
|||
|
|
"13 0.007747 -0.058811 -2.032572e-04\n",
|
|||
|
|
"14 -0.395248 -0.050926 -3.027080e-07\n",
|
|||
|
|
"15 0.051409 -0.096212 1.150579e-03\n",
|
|||
|
|
"16 0.152462 0.031098 9.681923e-04\n",
|
|||
|
|
"17 -0.370008 -0.327332 1.844886e-03\n",
|
|||
|
|
"18 -0.216884 -0.099730 8.683741e-04\n",
|
|||
|
|
"19 -0.352239 -0.275509 1.255561e-03\n",
|
|||
|
|
"20 -0.058490 0.174214 -1.005134e-03\n",
|
|||
|
|
"21 0.011801 0.090914 6.764195e-04\n",
|
|||
|
|
"22 0.021550 0.035880 -1.403649e-06\n",
|
|||
|
|
"23 NaN NaN NaN\n",
|
|||
|
|
"24 0.239157 0.260998 -5.768552e-04\n",
|
|||
|
|
"25 0.194222 0.208055 -2.399758e-03\n",
|
|||
|
|
"26 -0.098915 -0.132729 1.341740e-04\n",
|
|||
|
|
"27 -0.026623 -0.239152 3.415376e-04\n",
|
|||
|
|
"28 -0.419360 -0.264902 2.445601e-04\n",
|
|||
|
|
"29 -0.215015 -0.135662 1.081101e-03\n",
|
|||
|
|
"30 NaN NaN NaN\n",
|
|||
|
|
"31 NaN NaN NaN\n",
|
|||
|
|
"32 -0.194297 -0.062513 9.364155e-04\n",
|
|||
|
|
"33 -0.995301 -0.202951 1.075866e-03\n",
|
|||
|
|
"34 0.008950 -0.067359 -4.124640e-04\n",
|
|||
|
|
"35 -0.205027 -0.162327 -1.374335e-03\n",
|
|||
|
|
"36 -0.091320 -0.205404 5.102801e-04\n",
|
|||
|
|
"37 -0.238676 -0.298704 8.463431e-04\n",
|
|||
|
|
"38 -0.332189 -0.188340 5.046316e-04\n",
|
|||
|
|
"39 0.057358 -0.001675 -3.160405e-03\n",
|
|||
|
|
"40 0.073560 -0.005965 -1.509830e-03\n",
|
|||
|
|
"41 0.141621 -0.021585 1.686072e-03\n",
|
|||
|
|
"42 0.248755 -0.098528 5.958213e-04\n",
|
|||
|
|
"43 0.064610 -0.089520 -1.267509e-03\n",
|
|||
|
|
"44 0.078177 -0.293342 1.244631e-03\n",
|
|||
|
|
"45 NaN NaN NaN\n",
|
|||
|
|
"46 0.251440 0.446081 -4.819596e-04\n",
|
|||
|
|
"47 0.094565 0.229372 -9.553477e-04\n",
|
|||
|
|
"48 -0.001961 -0.165008 1.450343e-03\n",
|
|||
|
|
"49 0.094196 -0.697044 -3.353926e-03\n",
|
|||
|
|
"50 -0.330864 -0.141907 2.302730e-03\n",
|
|||
|
|
"51 NaN NaN NaN\n",
|
|||
|
|
"52 -0.158175 -0.096959 1.866471e-04\n",
|
|||
|
|
"53 -0.023629 -0.148910 5.451310e-04\n",
|
|||
|
|
"54 -0.231563 -0.162250 5.686287e-04\n",
|
|||
|
|
"55 NaN NaN NaN\n",
|
|||
|
|
"56 NaN NaN NaN\n",
|
|||
|
|
"57 0.217780 -0.054810 1.269606e-03\n",
|
|||
|
|
"58 NaN NaN NaN\n",
|
|||
|
|
"59 0.388737 0.607952 -6.702906e-04\n",
|
|||
|
|
"60 -0.165562 -0.106051 1.887424e-04\n",
|
|||
|
|
"61 0.021792 -0.113425 1.042199e-03\n",
|
|||
|
|
"62 NaN NaN NaN\n",
|
|||
|
|
"63 0.080315 -0.354850 4.032307e-04\n",
|
|||
|
|
"64 -0.052211 -0.828428 -5.572701e-03\n",
|
|||
|
|
"65 0.012754 -0.155862 3.374983e-04\n",
|
|||
|
|
"66 -0.207965 -0.203637 -2.232226e-03\n",
|
|||
|
|
"67 0.029527 0.082579 8.624772e-04\n",
|
|||
|
|
"68 -0.044517 -0.192720 2.061893e-03\n",
|
|||
|
|
"69 0.599895 -0.854489 -5.500268e-03\n",
|
|||
|
|
"70 -0.044501 -0.104632 2.355586e-04\n",
|
|||
|
|
"71 -0.002545 -0.266839 1.413923e-03\n",
|
|||
|
|
"72 -0.143387 -0.107351 -2.433931e-04\n",
|
|||
|
|
"73 0.132513 -0.163646 -4.036292e-04\n",
|
|||
|
|
"74 0.191467 0.032348 -1.143953e-03\n",
|
|||
|
|
"75 NaN NaN NaN\n",
|
|||
|
|
"76 0.099290 0.192330 -7.050176e-04\n",
|
|||
|
|
"77 NaN NaN NaN\n",
|
|||
|
|
"78 NaN NaN NaN\n",
|
|||
|
|
"79 NaN NaN NaN\n",
|
|||
|
|
"80 0.242952 0.082132 2.912115e-04\n",
|
|||
|
|
"81 0.325843 -0.396186 -3.203713e-03\n",
|
|||
|
|
"82 0.150434 0.482240 7.763590e-04\n",
|
|||
|
|
"83 0.281124 0.131128 -4.730062e-04\n",
|
|||
|
|
"84 NaN NaN NaN\n",
|
|||
|
|
"85 0.089528 0.089461 -1.002055e-03\n",
|
|||
|
|
"86 0.196797 0.075971 -3.150078e-04\n",
|
|||
|
|
"87 NaN NaN NaN"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 20,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"StartIndex = 1\n",
|
|||
|
|
"EndIndex = None\n",
|
|||
|
|
"error_list = [8, 23, 30, 31, 45, 51, 55, 56, 58, 62, 75, 77, 78, 79, 84, 87]\n",
|
|||
|
|
"DieBC.loc[error_list,:] = np.nan\n",
|
|||
|
|
"DieBC.loc[StartIndex:EndIndex,:]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 21,
|
|||
|
|
"id": "3451a831",
|
|||
|
|
"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>TX</th>\n",
|
|||
|
|
" <th>TY</th>\n",
|
|||
|
|
" <th>Angle</th>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </thead>\n",
|
|||
|
|
" <tbody>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>count</th>\n",
|
|||
|
|
" <td>71.000000</td>\n",
|
|||
|
|
" <td>71.000000</td>\n",
|
|||
|
|
" <td>71.000000</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>mean</th>\n",
|
|||
|
|
" <td>-0.032729</td>\n",
|
|||
|
|
" <td>-0.110334</td>\n",
|
|||
|
|
" <td>-0.000065</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>std</th>\n",
|
|||
|
|
" <td>0.242202</td>\n",
|
|||
|
|
" <td>0.248432</td>\n",
|
|||
|
|
" <td>0.001536</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>min</th>\n",
|
|||
|
|
" <td>-0.995301</td>\n",
|
|||
|
|
" <td>-0.854489</td>\n",
|
|||
|
|
" <td>-0.005573</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>25%</th>\n",
|
|||
|
|
" <td>-0.199662</td>\n",
|
|||
|
|
" <td>-0.207238</td>\n",
|
|||
|
|
" <td>-0.000529</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>50%</th>\n",
|
|||
|
|
" <td>0.007747</td>\n",
|
|||
|
|
" <td>-0.107351</td>\n",
|
|||
|
|
" <td>0.000252</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>75%</th>\n",
|
|||
|
|
" <td>0.099045</td>\n",
|
|||
|
|
" <td>-0.003197</td>\n",
|
|||
|
|
" <td>0.000854</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>max</th>\n",
|
|||
|
|
" <td>0.599895</td>\n",
|
|||
|
|
" <td>0.607952</td>\n",
|
|||
|
|
" <td>0.002762</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>range</th>\n",
|
|||
|
|
" <td>1.595196</td>\n",
|
|||
|
|
" <td>1.462441</td>\n",
|
|||
|
|
" <td>0.008335</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>3sigma</th>\n",
|
|||
|
|
" <td>0.726605</td>\n",
|
|||
|
|
" <td>0.745295</td>\n",
|
|||
|
|
" <td>0.004607</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"</div>"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
" TX TY Angle\n",
|
|||
|
|
"count 71.000000 71.000000 71.000000\n",
|
|||
|
|
"mean -0.032729 -0.110334 -0.000065\n",
|
|||
|
|
"std 0.242202 0.248432 0.001536\n",
|
|||
|
|
"min -0.995301 -0.854489 -0.005573\n",
|
|||
|
|
"25% -0.199662 -0.207238 -0.000529\n",
|
|||
|
|
"50% 0.007747 -0.107351 0.000252\n",
|
|||
|
|
"75% 0.099045 -0.003197 0.000854\n",
|
|||
|
|
"max 0.599895 0.607952 0.002762\n",
|
|||
|
|
"range 1.595196 1.462441 0.008335\n",
|
|||
|
|
"3sigma 0.726605 0.745295 0.004607"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 21,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"describe_3s(DieBC.loc[StartIndex:EndIndex,:])"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 22,
|
|||
|
|
"id": "9714555d",
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"全局补偿X: 0.0327\n",
|
|||
|
|
"全局补偿Y: 0.1103\n",
|
|||
|
|
"全局补偿角度: 0.000065\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"X_overall,y_overall,theta_overall = DieBC.loc[StartIndex:EndIndex,:].dropna().describe().loc['mean',:]\n",
|
|||
|
|
"print(f'全局补偿X: {-X_overall:.4f}')\n",
|
|||
|
|
"print(f'全局补偿Y: {-y_overall:.4f}')\n",
|
|||
|
|
"print(f'全局补偿角度: {-theta_overall:.6f}')"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"id": "845cd488",
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": []
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"kernelspec": {
|
|||
|
|
"display_name": "base",
|
|||
|
|
"language": "python",
|
|||
|
|
"name": "python3"
|
|||
|
|
},
|
|||
|
|
"language_info": {
|
|||
|
|
"codemirror_mode": {
|
|||
|
|
"name": "ipython",
|
|||
|
|
"version": 3
|
|||
|
|
},
|
|||
|
|
"file_extension": ".py",
|
|||
|
|
"mimetype": "text/x-python",
|
|||
|
|
"name": "python",
|
|||
|
|
"nbconvert_exporter": "python",
|
|||
|
|
"pygments_lexer": "ipython3",
|
|||
|
|
"version": "3.12.3"
|
|||
|
|
},
|
|||
|
|
"widgets": {
|
|||
|
|
"application/vnd.jupyter.widget-state+json": {
|
|||
|
|
"state": {
|
|||
|
|
"21d8b0ba77b54fbfabcdf5f52b80df1a": {
|
|||
|
|
"model_module": "@jupyter-widgets/base",
|
|||
|
|
"model_module_version": "2.0.0",
|
|||
|
|
"model_name": "LayoutModel",
|
|||
|
|
"state": {}
|
|||
|
|
},
|
|||
|
|
"2a0670114f11457b80a6ef3ef22a34fc": {
|
|||
|
|
"model_module": "jupyter-matplotlib",
|
|||
|
|
"model_module_version": "^0.11",
|
|||
|
|
"model_name": "ToolbarModel",
|
|||
|
|
"state": {
|
|||
|
|
"_model_module_version": "^0.11",
|
|||
|
|
"_view_module_version": "^0.11",
|
|||
|
|
"collapsed": true,
|
|||
|
|
"layout": "IPY_MODEL_f7658ea254754f89ae1eaaca3822afd1",
|
|||
|
|
"orientation": "vertical",
|
|||
|
|
"toolitems": [
|
|||
|
|
[
|
|||
|
|
"Home",
|
|||
|
|
"Reset original view",
|
|||
|
|
"home",
|
|||
|
|
"home"
|
|||
|
|
],
|
|||
|
|
[
|
|||
|
|
"Back",
|
|||
|
|
"Back to previous view",
|
|||
|
|
"arrow-left",
|
|||
|
|
"back"
|
|||
|
|
],
|
|||
|
|
[
|
|||
|
|
"Forward",
|
|||
|
|
"Forward to next view",
|
|||
|
|
"arrow-right",
|
|||
|
|
"forward"
|
|||
|
|
],
|
|||
|
|
[
|
|||
|
|
"Pan",
|
|||
|
|
"Left button pans, Right button zooms\nx/y fixes axis, CTRL fixes aspect",
|
|||
|
|
"arrows",
|
|||
|
|
"pan"
|
|||
|
|
],
|
|||
|
|
[
|
|||
|
|
"Zoom",
|
|||
|
|
"Zoom to rectangle\nx/y fixes axis",
|
|||
|
|
"square-o",
|
|||
|
|
"zoom"
|
|||
|
|
],
|
|||
|
|
[
|
|||
|
|
"Download",
|
|||
|
|
"Download plot",
|
|||
|
|
"floppy-o",
|
|||
|
|
"save_figure"
|
|||
|
|
]
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"2d1533973ff749659dd783abe0db740f": {
|
|||
|
|
"model_module": "jupyter-matplotlib",
|
|||
|
|
"model_module_version": "^0.11",
|
|||
|
|
"model_name": "ToolbarModel",
|
|||
|
|
"state": {
|
|||
|
|
"_model_module_version": "^0.11",
|
|||
|
|
"_view_module_version": "^0.11",
|
|||
|
|
"collapsed": true,
|
|||
|
|
"layout": "IPY_MODEL_b8d4c7781ff5497da21ab97b922bac2f",
|
|||
|
|
"orientation": "vertical",
|
|||
|
|
"toolitems": [
|
|||
|
|
[
|
|||
|
|
"Home",
|
|||
|
|
"Reset original view",
|
|||
|
|
"home",
|
|||
|
|
"home"
|
|||
|
|
],
|
|||
|
|
[
|
|||
|
|
"Back",
|
|||
|
|
"Back to previous view",
|
|||
|
|
"arrow-left",
|
|||
|
|
"back"
|
|||
|
|
],
|
|||
|
|
[
|
|||
|
|
"Forward",
|
|||
|
|
"Forward to next view",
|
|||
|
|
"arrow-right",
|
|||
|
|
"forward"
|
|||
|
|
],
|
|||
|
|
[
|
|||
|
|
"Pan",
|
|||
|
|
"Left button pans, Right button zooms\nx/y fixes axis, CTRL fixes aspect",
|
|||
|
|
"arrows",
|
|||
|
|
"pan"
|
|||
|
|
],
|
|||
|
|
[
|
|||
|
|
"Zoom",
|
|||
|
|
"Zoom to rectangle\nx/y fixes axis",
|
|||
|
|
"square-o",
|
|||
|
|
"zoom"
|
|||
|
|
],
|
|||
|
|
[
|
|||
|
|
"Download",
|
|||
|
|
"Download plot",
|
|||
|
|
"floppy-o",
|
|||
|
|
"save_figure"
|
|||
|
|
]
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"30e39652821f45d4b6a059be76bf8b9b": {
|
|||
|
|
"model_module": "@jupyter-widgets/base",
|
|||
|
|
"model_module_version": "2.0.0",
|
|||
|
|
"model_name": "LayoutModel",
|
|||
|
|
"state": {}
|
|||
|
|
},
|
|||
|
|
"403a3e58c30643c69e94e76f12bc0587": {
|
|||
|
|
"model_module": "jupyter-matplotlib",
|
|||
|
|
"model_module_version": "^0.11",
|
|||
|
|
"model_name": "MPLCanvasModel",
|
|||
|
|
"state": {
|
|||
|
|
"_data_url": "data:image/png;base64,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
|
|||
|
|
"_figure_label": "Figure 2",
|
|||
|
|
"_model_module_version": "^0.11",
|
|||
|
|
"_size": [
|
|||
|
|
800,
|
|||
|
|
550
|
|||
|
|
],
|
|||
|
|
"_view_module_version": "^0.11",
|
|||
|
|
"layout": "IPY_MODEL_6bd4aa27ce3d4da78e5e37533a8df70a",
|
|||
|
|
"toolbar": "IPY_MODEL_8a5b8401a4764aa19048fef48718488b",
|
|||
|
|
"toolbar_position": "left"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"6bd4aa27ce3d4da78e5e37533a8df70a": {
|
|||
|
|
"model_module": "@jupyter-widgets/base",
|
|||
|
|
"model_module_version": "2.0.0",
|
|||
|
|
"model_name": "LayoutModel",
|
|||
|
|
"state": {}
|
|||
|
|
},
|
|||
|
|
"8a5b8401a4764aa19048fef48718488b": {
|
|||
|
|
"model_module": "jupyter-matplotlib",
|
|||
|
|
"model_module_version": "^0.11",
|
|||
|
|
"model_name": "ToolbarModel",
|
|||
|
|
"state": {
|
|||
|
|
"_model_module_version": "^0.11",
|
|||
|
|
"_view_module_version": "^0.11",
|
|||
|
|
"collapsed": true,
|
|||
|
|
"layout": "IPY_MODEL_30e39652821f45d4b6a059be76bf8b9b",
|
|||
|
|
"orientation": "vertical",
|
|||
|
|
"toolitems": [
|
|||
|
|
[
|
|||
|
|
"Home",
|
|||
|
|
"Reset original view",
|
|||
|
|
"home",
|
|||
|
|
"home"
|
|||
|
|
],
|
|||
|
|
[
|
|||
|
|
"Back",
|
|||
|
|
"Back to previous view",
|
|||
|
|
"arrow-left",
|
|||
|
|
"back"
|
|||
|
|
],
|
|||
|
|
[
|
|||
|
|
"Forward",
|
|||
|
|
"Forward to next view",
|
|||
|
|
"arrow-right",
|
|||
|
|
"forward"
|
|||
|
|
],
|
|||
|
|
[
|
|||
|
|
"Pan",
|
|||
|
|
"Left button pans, Right button zooms\nx/y fixes axis, CTRL fixes aspect",
|
|||
|
|
"arrows",
|
|||
|
|
"pan"
|
|||
|
|
],
|
|||
|
|
[
|
|||
|
|
"Zoom",
|
|||
|
|
"Zoom to rectangle\nx/y fixes axis",
|
|||
|
|
"square-o",
|
|||
|
|
"zoom"
|
|||
|
|
],
|
|||
|
|
[
|
|||
|
|
"Download",
|
|||
|
|
"Download plot",
|
|||
|
|
"floppy-o",
|
|||
|
|
"save_figure"
|
|||
|
|
]
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"8de2ee2a201f4e78b83bb73202597800": {
|
|||
|
|
"model_module": "@jupyter-widgets/base",
|
|||
|
|
"model_module_version": "2.0.0",
|
|||
|
|
"model_name": "LayoutModel",
|
|||
|
|
"state": {}
|
|||
|
|
},
|
|||
|
|
"b8d4c7781ff5497da21ab97b922bac2f": {
|
|||
|
|
"model_module": "@jupyter-widgets/base",
|
|||
|
|
"model_module_version": "2.0.0",
|
|||
|
|
"model_name": "LayoutModel",
|
|||
|
|
"state": {}
|
|||
|
|
},
|
|||
|
|
"cc2cbd4941e54dc4ab6e09927cb3f90b": {
|
|||
|
|
"model_module": "jupyter-matplotlib",
|
|||
|
|
"model_module_version": "^0.11",
|
|||
|
|
"model_name": "MPLCanvasModel",
|
|||
|
|
"state": {
|
|||
|
|
"_data_url": "data:image/png;base64,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
|
|||
|
|
"_figure_label": "Figure 3",
|
|||
|
|
"_model_module_version": "^0.11",
|
|||
|
|
"_size": [
|
|||
|
|
800,
|
|||
|
|
550
|
|||
|
|
],
|
|||
|
|
"_view_module_version": "^0.11",
|
|||
|
|
"layout": "IPY_MODEL_8de2ee2a201f4e78b83bb73202597800",
|
|||
|
|
"toolbar": "IPY_MODEL_2d1533973ff749659dd783abe0db740f",
|
|||
|
|
"toolbar_position": "left"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"ee97a7eebb9a425eb0ade6e31817aa89": {
|
|||
|
|
"model_module": "jupyter-matplotlib",
|
|||
|
|
"model_module_version": "^0.11",
|
|||
|
|
"model_name": "MPLCanvasModel",
|
|||
|
|
"state": {
|
|||
|
|
"_data_url": "data:image/png;base64,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
|
|||
|
|
"_figure_label": "Figure 1",
|
|||
|
|
"_model_module_version": "^0.11",
|
|||
|
|
"_size": [
|
|||
|
|
800,
|
|||
|
|
550
|
|||
|
|
],
|
|||
|
|
"_view_module_version": "^0.11",
|
|||
|
|
"layout": "IPY_MODEL_21d8b0ba77b54fbfabcdf5f52b80df1a",
|
|||
|
|
"toolbar": "IPY_MODEL_2a0670114f11457b80a6ef3ef22a34fc",
|
|||
|
|
"toolbar_position": "left"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"f7658ea254754f89ae1eaaca3822afd1": {
|
|||
|
|
"model_module": "@jupyter-widgets/base",
|
|||
|
|
"model_module_version": "2.0.0",
|
|||
|
|
"model_name": "LayoutModel",
|
|||
|
|
"state": {}
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"version_major": 2,
|
|||
|
|
"version_minor": 0
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"nbformat": 4,
|
|||
|
|
"nbformat_minor": 5
|
|||
|
|
}
|