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@flixr
Created October 7, 2014 16:29
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qnh.ipynb
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{
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"from __future__ import division\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 67
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"p0 = 101325\n",
"L = 0.0065\n",
"cp = 1007\n",
"T0 = 288.15\n",
"g = 9.80665\n",
"M = 0.0289644\n",
"R = 8.31447\n",
"\n",
"M_OF_P_CONST = (R * T0) / (g * M)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 68
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# pprz_isa_altitude_of_pressure_qnh (simplified)\n",
"def altitude_of_pressure_qnh(p, qnh):\n",
" return M_OF_P_CONST * np.log(qnh / p)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 64
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# pprz_isa_altitude_of_pressure_qnh_full\n",
"def altitude_of_pressure_qnh_full(p, qnh):\n",
" return (1 - np.power(p / qnh, R * L / g / M)) * T0 / L"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 73
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# input parameters: qnh and pressure in Pascal\n",
"qnh = 102325\n",
"pressures = np.arange(qnh, 80000, -10)\n",
"\n",
"amsl_simple = altitude_of_pressure_qnh(pressures, qnh)\n",
"amsl_full = altitude_of_pressure_qnh_full(pressures, qnh)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 78
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline\n",
"plt.subplot(211)\n",
"plt.plot(pressures, amsl_simple, label='simple')\n",
"plt.plot(pressures, amsl_full, label='full')\n",
"plt.legend()\n",
"plt.ylabel('altitude [m]')\n",
"plt.subplot(212)\n",
"plt.plot(pressures, amsl_simple - amsl_full, label='error')\n",
"plt.xlabel('pressure [Pa]')\n",
"plt.ylabel('error [m]')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 82,
"text": [
"<matplotlib.text.Text at 0x5f3c090>"
]
},
{
"metadata": {},
"output_type": "display_data",
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46ddYjhRv41p5FTaXA+inDWRi/wBG+DvQtKmhSymMgdEEzX/+8x8WLlxIZmYm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\ngEEuP4yAtgAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x4ac3e10>"
]
}
],
"prompt_number": 82
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.plot(amsl_full, amsl_simple - amsl_full)\n",
"plt.xlabel('altitude [m]')\n",
"plt.ylabel('error [m]')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 84,
"text": [
"<matplotlib.text.Text at 0x612ea10>"
]
},
{
"metadata": {},
"output_type": "display_data",
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kpMQIDw83du/ebRQXFxvR0dHGli1brI7lcWFhYcYvv/xy1nVPPvmkkZqaahiG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\nIiJSioqDiIiUouIgIiKlqDiIiEgp/w+UPvLqVF2k5AAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x6129d90>"
]
}
],
"prompt_number": 84
}
],
"metadata": {}
}
]
}
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