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Completed basic local project tutorial
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bicarlsen committed Apr 15, 2020
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189 changes: 189 additions & 0 deletions ...ireworks/project_steps/03-moving_up/.ipynb_checkpoints/recipe_comparison-checkpoint.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Recipe Comparison"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from thot.thot import LocalProject"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"root_path = (\n",
" 'project/data'\n",
" if LocalProject.dev_mode() else\n",
" None\n",
")\n",
"\n",
"thot = LocalProject( root_path )"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"# get noise statistice data\n",
"recipe_stats = thot.find_assets( { 'type': 'recipe-stats' } )\n",
"\n",
"df = []\n",
"for stat in recipe_stats:\n",
" # read data for each batch\n",
" tdf = pd.read_pickle( stat.file )\n",
" tdf.rename( { 0: stat.metadata[ 'recipe' ] }, axis = 1, inplace = True )\n",
" \n",
" df.append( tdf )\n",
"\n",
"# combine into one dataframe\n",
"df = pd.concat( df, axis = 1 )"
]
},
{
"cell_type": "code",
"execution_count": 13,
"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>b</th>\n",
" <th>a</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>94.400000</td>\n",
" <td>125.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>3.405556</td>\n",
" <td>3.244444</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" b a\n",
"mean 94.400000 125.500000\n",
"std 3.405556 3.244444"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"means = df.loc[ 'mean' ]\n",
"errs = df.loc[ 'std' ]\n",
"\n",
"ax = means.plot( kind = 'bar', yerr = errs )"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ax.get_figure()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
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