145 lines
10 KiB
Plaintext
145 lines
10 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Populating the interactive namespace from numpy and matplotlib\n"
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]
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}
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],
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"source": [
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"%pylab inline\n",
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"from network.network import is_valid_network, get_number_of_activated_antenna, pp_network\n",
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"from numpy import mean\n",
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"from random import Random"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"random = Random(666)\n",
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"def get_some_possible_network(n, box_size):\n",
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" number_of_square = box_size * box_size\n",
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" for i in range(n):\n",
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" yield random.randint(0, 2 ** number_of_square - 1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"box_size = 5\n",
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"n = 1000000\n",
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"\n",
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"valid_networks = []\n",
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"for network in get_some_possible_network(n, box_size):\n",
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" if is_valid_network(network, box_size):\n",
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" valid_networks.append(network)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"image/png": 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32z0CbKyqbbPss5yBfm6DUxxHfk5hEn5mZprbsXnATFoqSaiqeZ37HHXoJs3XEVuAG5vl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\nAABJRU5ErkJggg==\n",
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"text/plain": [
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"<matplotlib.figure.Figure at 0x7fe8f4a177b8>"
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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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}
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],
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"source": [
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"hist(list(map(get_number_of_activated_antenna, valid_networks)), bins=100)\n",
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"None"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
|
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"text/plain": [
|
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"6.2328431372549016"
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]
|
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},
|
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
|
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],
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"source": [
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"mean(list(map(get_number_of_activated_antenna, valid_networks)))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"valid_networks[0]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
|
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"language_info": {
|
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"codemirror_mode": {
|
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"name": "ipython",
|
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"version": 3
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},
|
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"file_extension": ".py",
|
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"mimetype": "text/x-python",
|
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"name": "python",
|
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"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
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"version": "3.4.3+"
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}
|
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},
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|
"nbformat": 4,
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"nbformat_minor": 0
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}
|