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@Swarchal
Created June 24, 2016 10:05
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What's the $\\log_{10}$ probability of finding a sequence (`S`) of the same GC content for strings that have the GC content of strings in `A`."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[-61.95, -54.593, -50.707, -48.287, -47.464, -47.278, -47.332, -47.893, -48.717, -51.719, -52.426, -58.147, -59.889, -67.549, -82.844]\n"
]
}
],
"source": [
"from math import log10\n",
"\n",
"# get sequence and \n",
"path = \"/home/scott/Dropbox/rosalind/rosalind_prob.txt\"\n",
"file = open(path)\n",
"S = file.readline().strip()\n",
"A = map(float, file.read().split())\n",
"\n",
"gc = sum(S.count(i) for i in \"GC\")\n",
"at = len(S) - gc\n",
"\n",
"B = [gc * log10(0.5 * i) + at * log10(0.5 * (1 - i)) for i in A]\n",
"\n",
"print[round(i, 3) for i in B]"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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P0Gahoaqn+jqJyCPAK+5qKTA05Okh7rb9lJSUcM0111BQUABAdnY2kydP3vdrouVuiZ6w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\ns0LDGGOMb1ZoGGOM8e3/A8VdUajFBglqAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xb6577d2c>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"plt.style.use(\"bmh\")\n",
"\n",
"plt.plot(A, B)\n",
"plt.title(\"Bonus graph\")\n",
"plt.xlabel(\"Prop. GC content\")\n",
"plt.ylabel(\"log prob. of a string containing S\")\n",
"plt.show()"
]
}
],
"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": "2.7.11+"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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