108 lines
1.8 KiB
Plaintext
108 lines
1.8 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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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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"[Plots.jl] Default backend: immerse"
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]
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}
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],
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"source": [
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"using Plots\n",
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"n = 1000\n",
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"a = rand(n)\n",
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"y = Float64[i*a[i]+j*a[j] for i in 1:n, j in 1:n]\n",
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"y = float(y .> mean(y));"
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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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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"[Plots.jl] Initializing backend: immerse"
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]
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}
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],
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"source": [
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"spy(y, nbins=(20,100))"
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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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"# get the indices (I,J) and the values (V) of non-zero values in y\n",
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"I,J,V = findnz(y);\n",
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"# plot the J's vs the I's in a heatmap to recreate the spy call\n",
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"heatmap(J,I)"
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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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"pyplot()"
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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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"histogram(randn(1000), "
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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": "Julia 0.4.0-rc2",
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"language": "julia",
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"name": "julia-0.4"
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},
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"language_info": {
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"file_extension": ".jl",
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"mimetype": "application/julia",
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"name": "julia",
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"version": "0.4.0"
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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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}
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