75 lines
13 KiB
Plaintext
75 lines
13 KiB
Plaintext
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{
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"metadata": {
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"name": "",
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"signature": "sha256:cf9769ed030dba90588e4e94007d226dd2897d23c8792fd8974c73bb04381b2c"
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},
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"nbformat": 3,
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"nbformat_minor": 0,
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"worksheets": [
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{
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"cells": [
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"from tmtubes import gas\n",
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"air=gas('air')\n",
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"L=1.\n",
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"c0=air.cm(293.15)\n",
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"fres=c0/(4*L)\n",
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"print(\"fres:\",fres)"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"stream": "stdout",
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"text": [
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"fres: 85.7847876535731\n"
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]
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}
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],
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"prompt_number": 6
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"plot(x,u)"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"metadata": {},
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"output_type": "pyout",
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"prompt_number": 3,
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"text": [
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"[<matplotlib.lines.Line2D at 0x7f6c683d7278>]"
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]
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},
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{
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"metadata": {},
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"output_type": "display_data",
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"png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEACAYAAABCl1qQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nN9Q2RKqTmqFEssjq1XD++SFZ
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"text": [
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"<matplotlib.figure.Figure at 0x7f6c68d6a080>"
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]
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}
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],
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"prompt_number": 3
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [],
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"language": "python",
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"metadata": {},
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"outputs": []
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}
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],
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"metadata": {}
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}
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]
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}
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