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{
"cells": [
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"x = np.genfromtxt('results.txt', delimiter=\" \")\n",
"x.sort(0)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"# some smoothing\n",
"ex = np.exp(x[:, 1])\n",
"ex_smooth = ex.copy()\n",
"ex_smooth[1:-1] = (ex[1:-1] + ex[0:-2] + ex[2:]) / 3\n",
"ex_smooth[0] = (ex[0] + ex[1]) / 2\n",
"ex_smooth[-1] = (ex[-1] + ex[-2]) / 2\n",
"y_smooth = np.log(ex_smooth)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"# restrict bitrate range\n",
"bitrates = list(x[:, 0])\n",
"mos = list(x[:, 1])\n",
"stop_idx = bitrates.index(22000) + 1\n",
"\n",
"bitrates = bitrates[:stop_idx]\n",
"mos = mos[:stop_idx]\n",
"\n",
"\n",
"num_bitrates = 100\n",
"a, b = mos[0], mos[-1]\n",
"points = a + (b - a) * np.arange(num_bitrates) / (num_bitrates - 1)\n",
"\n",
"sample_bitrates = np.interp(points, mos, bitrates)\n",
"\n",
"print(f\"\")"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 6000, 6059, 6119, 6178, 6237, 6296, 6356, 6415, 6474,\n",
" 6520, 6556, 6592, 6628, 6664, 6700, 6736, 6772, 6808,\n",
" 6844, 6880, 6916, 6952, 6988, 7028, 7071, 7114, 7157,\n",
" 7199, 7242, 7285, 7327, 7370, 7413, 7455, 7498, 7527,\n",
" 7556, 7584, 7613, 7642, 7670, 7699, 7728, 7756, 7785,\n",
" 7813, 7842, 7871, 7899, 7928, 7956, 7985, 8039, 8121,\n",
" 8202, 8284, 8366, 8447, 8533, 8625, 8718, 8810, 8903,\n",
" 8995, 9119, 9244, 9369, 9495, 9569, 9640, 9712, 9783,\n",
" 9855, 9926, 9998, 10162, 10329, 10495, 10691, 10888, 11108,\n",
" 11359, 11562, 11704, 11846, 11988, 12299, 12678, 13126, 13519,\n",
" 13750, 13980, 14521, 15223, 15708, 16108, 17163, 17817, 19010,\n",
" 20000], dtype=int32)"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sample_bitrates.round().astype(np.int32)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"100"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(sample_bitrates)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "torch",
"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": "3.9.12"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
|
