159 lines
37 KiB
Plaintext
159 lines
37 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"from pathlib import Path\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import scipy\n",
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"from glob import glob\n",
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"import os\n",
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"from lib.lib import read_sp, read_xyz_alt, read_xy_alt, aggregate_sp_fd"
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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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"outputs": [],
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"source": [
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"d2_raw = read_sp(\"../data/rust-sticking-probability\", read_xy_alt)\n",
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"d2 = aggregate_sp_fd(d2_raw)"
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],
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"metadata": {
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"collapsed": false
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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": 59,
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"outputs": [],
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"source": [
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"d3_raw = read_sp(\"../data/rust-3d-offaxis-sp\", read_xyz_alt)\n",
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"d3 = aggregate_sp_fd(d3_raw)"
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],
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"metadata": {
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"collapsed": false
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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": 60,
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"outputs": [
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{
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"data": {
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"text/plain": "<Figure size 640x480 with 1 Axes>",
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"image/png": 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"
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"plt.plot(\n",
|
|
" d2.index,\n",
|
|
" d2.fd,\n",
|
|
" color='tab:blue',\n",
|
|
" label='D2'\n",
|
|
")\n",
|
|
"\n",
|
|
"plt.plot(\n",
|
|
" d3.index,\n",
|
|
" d3.fd,\n",
|
|
" color='tab:orange',\n",
|
|
" label='D3'\n",
|
|
")\n",
|
|
"\n",
|
|
"plt.xlabel(\"$p_{stick}$\")\n",
|
|
"plt.ylabel(\"$fd$\")\n",
|
|
"plt.legend()\n",
|
|
"\n",
|
|
"plt.savefig('../figures/sp-fd-rust-vs-c.svg')\n",
|
|
"plt.savefig('../figures/sp-fd-rust-vs-c.png')\n",
|
|
"plt.show()"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false
|
|
}
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 24,
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": " fd fd_std\nprobability \n0.1 NaN 0.112454\n0.2 NaN 0.116460\n0.3 NaN 0.097406\n0.4 NaN 0.107908\n0.5 NaN 0.103990\n0.6 NaN 0.098772\n0.7 NaN 0.096520\n0.8 NaN 0.093455\n0.9 NaN 0.104862\n1.0 NaN 0.091404",
|
|
"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>fd</th>\n <th>fd_std</th>\n </tr>\n <tr>\n <th>probability</th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0.1</th>\n <td>NaN</td>\n <td>0.112454</td>\n </tr>\n <tr>\n <th>0.2</th>\n <td>NaN</td>\n <td>0.116460</td>\n </tr>\n <tr>\n <th>0.3</th>\n <td>NaN</td>\n <td>0.097406</td>\n </tr>\n <tr>\n <th>0.4</th>\n <td>NaN</td>\n <td>0.107908</td>\n </tr>\n <tr>\n <th>0.5</th>\n <td>NaN</td>\n <td>0.103990</td>\n </tr>\n <tr>\n <th>0.6</th>\n <td>NaN</td>\n <td>0.098772</td>\n </tr>\n <tr>\n <th>0.7</th>\n <td>NaN</td>\n <td>0.096520</td>\n </tr>\n <tr>\n <th>0.8</th>\n <td>NaN</td>\n <td>0.093455</td>\n </tr>\n <tr>\n <th>0.9</th>\n <td>NaN</td>\n <td>0.104862</td>\n </tr>\n <tr>\n <th>1.0</th>\n <td>NaN</td>\n <td>0.091404</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
|
},
|
|
"execution_count": 24,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"d3"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false
|
|
}
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 28,
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": " fd fd_std\nprobability \n0.1 2.481805 0.101417\n0.2 2.372849 0.091412\n0.3 2.318823 0.090325\n0.4 2.242464 0.088371\n0.5 2.215201 0.093227\n0.6 2.174664 0.086606\n0.7 2.161655 0.080741\n0.8 2.135019 0.080976\n0.9 2.112670 0.086004\n1.0 2.103077 0.083651",
|
|
"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>fd</th>\n <th>fd_std</th>\n </tr>\n <tr>\n <th>probability</th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0.1</th>\n <td>2.481805</td>\n <td>0.101417</td>\n </tr>\n <tr>\n <th>0.2</th>\n <td>2.372849</td>\n <td>0.091412</td>\n </tr>\n <tr>\n <th>0.3</th>\n <td>2.318823</td>\n <td>0.090325</td>\n </tr>\n <tr>\n <th>0.4</th>\n <td>2.242464</td>\n <td>0.088371</td>\n </tr>\n <tr>\n <th>0.5</th>\n <td>2.215201</td>\n <td>0.093227</td>\n </tr>\n <tr>\n <th>0.6</th>\n <td>2.174664</td>\n <td>0.086606</td>\n </tr>\n <tr>\n <th>0.7</th>\n <td>2.161655</td>\n <td>0.080741</td>\n </tr>\n <tr>\n <th>0.8</th>\n <td>2.135019</td>\n <td>0.080976</td>\n </tr>\n <tr>\n <th>0.9</th>\n <td>2.112670</td>\n <td>0.086004</td>\n </tr>\n <tr>\n <th>1.0</th>\n <td>2.103077</td>\n <td>0.083651</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
|
},
|
|
"execution_count": 28,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"d3"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false
|
|
}
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"outputs": [],
|
|
"source": [],
|
|
"metadata": {
|
|
"collapsed": false
|
|
}
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 2
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython2",
|
|
"version": "2.7.6"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 0
|
|
}
|