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bjackman/gfxinf_wat.ipynb

Created September 22, 2017 13:29
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gfxinf_wat.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Get all the unique frame data from the raw gfxinfo output into a CSV file\n",
"in_path = \"/home/brendan/sources/wa3-stuff/wa_output/wk1-exoplayer-1/frames.csv.raw\"\n",
"out_path = \"/home/brendan/sources/wa3-stuff/frames_really_raw.csv\"\n",
"!cat $in_path | grep \"[0-9],\" | sort | uniq > $out_path"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
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" <tr>\n",
" <th>33</th>\n",
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" <th>34</th>\n",
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" <tr>\n",
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" <tr>\n",
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" <tr>\n",
" <th>52</th>\n",
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]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv('/home/brendan/sources/wa3-stuff/frames_really_raw.csv', header=None)\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fe8ceee1110>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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StkhCNXQ4jhoFkyZB585xRyT5ymaCjcojIgk0dSqMHasOx3KTTUtbSVskQerrYfx4eOgh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\nkiBK2iIiCaKkLSKSIEraIiIJ8v8BSDwc6LRMFD0AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe8ba7ab5d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# column 1 is Intended Vsync in nanoseconds\n",
"df['time'] = (df[1] - df[1].iloc[0]) / 1e9\n",
"\n",
"df['time'].plot()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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