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@agoose77
Created November 8, 2015 19:53
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Question 2 line
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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from numpy.linalg import lstsq\n",
"from numpy import array, vstack, log, ones, exp, sqrt\n",
"from math import pi\n",
"from matplotlib import pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Load roaw data\n",
"data = [(5012, 1), (3981, 2), (2512, 3), (1413, 4), (525, 5)]\n",
"counts, distances = zip(*data)\n",
"\n",
"counts = array(counts)\n",
"counts = counts / (1e6 * 60) # Convert from mins to seconds, mg to Kg\n",
"\n",
"distances = array(distances)\n",
"\n",
"t = 20 * 60 * 60"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# X and Y data\n",
"x = distances ** 2\n",
"y = log(counts)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Find linear regression\n",
"A = vstack([x, ones(len(x))]).T\n",
"m, c = lstsq(A, y)[0]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0xa714e06080>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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jNKf5aglMVNWzqroD58Zgdf2VldLtG5MW5ciag//W+S+bu2+mfpn63PXpXTz6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\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xa714cf2b38>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot ln(count) vs x^2 and y = mx + c\n",
"plt.plot(x, y)\n",
"plt.plot(x, m*x + c)\n",
"plt.ylabel(\"ln(count)\")\n",
"plt.xlabel(\"x^2 (m^2)\")\n",
"plt.title(\"Linear regression of Ln(c) against x^2\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Test model against raw data\n",
"t = 20 * 60 * 60\n",
"D = -1 / (4 * t * m)\n",
"N = exp(c) * sqrt(4 * pi * D * t)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Define count function\n",
"def count(x, N, D):\n",
" return N / sqrt(4* pi * D * t) * exp(-x**2 / (4 * D * t))"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"N=0.000556489825600159, D=3.71842757538519e-13(m^2s^-1)\n"
]
},
{
"data": {
"image/png": 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MjlPEux9d+mrUgOuvp/akv/PI601ZtizMyD1nTtyBOVd1ZPqXcmPg84T3S6Jt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\nrxyGkDKJ6BEAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xa715144208>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(distances, counts)\n",
"plt.plot(distances, count(distances, N, D), 'r--')\n",
"plt.ylabel(\"Count\")\n",
"plt.xlabel(\"x (m)\")\n",
"plt.title(\"Data vs model fit (linear regression)\")\n",
"\n",
"print(\"N={}, D={}(m^2s^-1)\".format(N, D * 1e-8))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.5.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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