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commit
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node_modules
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pypy3.8-v7.3.7-win64
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cli
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.vscode
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const { Chart } = require('echarts-ssr');
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|
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const option = {
|
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title: {
|
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text: 'ECharts entry example'
|
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},
|
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backgroundColor: 'white',
|
||||
tooltip: {},
|
||||
legend: {
|
||||
data:['Sales']
|
||||
},
|
||||
xAxis: {
|
||||
data: ["shirt","cardign","chiffon shirt","pants","heels","socks"]
|
||||
},
|
||||
yAxis: {},
|
||||
series: [{
|
||||
name: 'Sales',
|
||||
type: 'bar',
|
||||
data: [5, 20, 36, 10, 10, 20]
|
||||
}]
|
||||
};
|
||||
|
||||
const chart = new Chart(800, 600);
|
||||
chart.renderToFileSync(option, 'render-to-file.png');
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@ -0,0 +1,68 @@
|
|||
{
|
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"index": [
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1,
|
||||
2,
|
||||
3,
|
||||
9999,
|
||||
"*"
|
||||
],
|
||||
"mean Jak og\u00f3lnie ocenia P. jako\u015b\u0107 kszta\u0142cenia na swoim kierunku studi\u00f3w?": [
|
||||
4.026791277258567,
|
||||
3.9738903394255876,
|
||||
4.008928571428571,
|
||||
3.4444444444444446,
|
||||
4.0111016225448335
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||||
],
|
||||
"median Jak og\u00f3lnie ocenia P. jako\u015b\u0107 kszta\u0142cenia na swoim kierunku studi\u00f3w?": [
|
||||
4.0,
|
||||
4.0,
|
||||
4.0,
|
||||
3.5,
|
||||
4.0
|
||||
],
|
||||
"rows Jak og\u00f3lnie ocenia P. jako\u015b\u0107 kszta\u0142cenia na swoim kierunku studi\u00f3w?": [
|
||||
1605,
|
||||
383,
|
||||
336,
|
||||
18,
|
||||
2342
|
||||
],
|
||||
"share Jak og\u00f3lnie ocenia P. jako\u015b\u0107 kszta\u0142cenia na swoim kierunku studi\u00f3w?": [
|
||||
{
|
||||
"2": 34,
|
||||
"3": 290,
|
||||
"4": 880,
|
||||
"5": 401
|
||||
},
|
||||
{
|
||||
"2": 18,
|
||||
"3": 74,
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||||
"4": 191,
|
||||
"5": 100
|
||||
},
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||||
{
|
||||
"2": 12,
|
||||
"3": 52,
|
||||
"4": 193,
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||||
"5": 79
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},
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{
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||||
"2": 1,
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"3": 8,
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"4": 9
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},
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{
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||||
"2": 65,
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"3": 424,
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||||
"4": 1273,
|
||||
"5": 580
|
||||
}
|
||||
],
|
||||
"std Jak og\u00f3lnie ocenia P. jako\u015b\u0107 kszta\u0142cenia na swoim kierunku studi\u00f3w?": [
|
||||
0.717542901077322,
|
||||
0.8020570794050175,
|
||||
0.7309229787418631,
|
||||
0.6156987634551994,
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||||
0.7347223270238585
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||||
]
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||||
}
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<text dominant-baseline="central" text-anchor="end" style="font-size:12px;font-family:sans-serif;" transform="translate(32 213)" fill="#6E7079">Tom</text>
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<text dominant-baseline="central" text-anchor="end" style="font-size:12px;font-family:sans-serif;" transform="translate(32 179)" fill="#6E7079">Dick</text>
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<text dominant-baseline="central" text-anchor="end" style="font-size:12px;font-family:sans-serif;" transform="translate(32 145)" fill="#6E7079">Harry</text>
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<path d="M40 65.3l320 0l0 23.5l-320 0Z" fill="#5470c6" class="zr0-cls-4"></path>
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<style ><![CDATA[
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.zr0-cls-0 {
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animation:zr0-ani-0 1s cubic-bezier(0.65,0,0.35,1) both;
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}
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.zr0-cls-1 {
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animation:zr0-ani-1 1s cubic-bezier(0.65,0,0.35,1) both;
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}
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.zr0-cls-2 {
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animation:zr0-ani-2 1s cubic-bezier(0.65,0,0.35,1) both;
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}
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.zr0-cls-3 {
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animation:zr0-ani-3 1s cubic-bezier(0.65,0,0.35,1) both;
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.zr0-cls-4 {
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animation:zr0-ani-4 1s cubic-bezier(0.65,0,0.35,1) both;
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0% {
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0% {
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d:path("M40 133.3l0 0l0 23.5l0 0Z");
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d:path("M40 133.3l192 0l0 23.5l-192 0Z");
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135.7143 146.3333 L 192.8571 153.7333 L 250 161.1333 L 307.1429 263.5 L 364.2857 248.7 L 421.4286 109.3333" fill="none" stroke="#5470c6" stroke-width="2" paint-order="fill" stroke-opacity="1" stroke-dasharray="none" stroke-linecap="butt" stroke-linejoin="bevel" stroke-miterlimit="10"></path></g><path d="M 1 0 A 1 1 0 1 1 1 -0.0001" fill="#fff" fill-opacity="1" stroke="#5470c6" stroke-width="0" paint-order="fill" stroke-opacity="1" stroke-dasharray="none" stroke-linecap="butt" stroke-miterlimit="10" transform="matrix(0,0,0,0,78.5714,245)"></path><path d="M 1 0 A 1 1 0 1 1 1 -0.0001" fill="#fff" fill-opacity="1" stroke="#5470c6" stroke-width="0" paint-order="fill" stroke-opacity="1" stroke-dasharray="none" stroke-linecap="butt" stroke-miterlimit="10" transform="matrix(0,0,0,0,135.7143,146.3333)"></path><path d="M 1 0 A 1 1 0 1 1 1 -0.0001" fill="#fff" fill-opacity="1" stroke="#5470c6" stroke-width="0" paint-order="fill" stroke-opacity="1" stroke-dasharray="none" stroke-linecap="butt" stroke-miterlimit="10" transform="matrix(0,0,0,0,192.8571,153.7333)"></path><path d="M 1 0 A 1 1 0 1 1 1 -0.0001" fill="#fff" fill-opacity="1" stroke="#5470c6" stroke-width="0" paint-order="fill" stroke-opacity="1" stroke-dasharray="none" stroke-linecap="butt" stroke-miterlimit="10" transform="matrix(0,0,0,0,250,161.1333)"></path><path d="M 1 0 A 1 1 0 1 1 1 -0.0001" fill="#fff" fill-opacity="1" stroke="#5470c6" stroke-width="0" paint-order="fill" stroke-opacity="1" stroke-dasharray="none" stroke-linecap="butt" stroke-miterlimit="10" transform="matrix(0,0,0,0,307.1429,263.5)"></path><path d="M 1 0 A 1 1 0 1 1 1 -0.0001" fill="#fff" fill-opacity="1" stroke="#5470c6" stroke-width="0" paint-order="fill" stroke-opacity="1" stroke-dasharray="none" stroke-linecap="butt" stroke-miterlimit="10" transform="matrix(0,0,0,0,364.2857,248.7)"></path><path d="M 1 0 A 1 1 0 1 1 1 -0.0001" fill="#fff" fill-opacity="1" stroke="#5470c6" stroke-width="0" paint-order="fill" stroke-opacity="1" stroke-dasharray="none" stroke-linecap="butt" stroke-miterlimit="10" transform="matrix(0,0,0,0,421.4286,109.3333)"></path></g></svg>
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const print = (x)=>console.log(x)
|
||||
const fs = require('fs');
|
||||
const mockData = require("./data.js");
|
||||
var { performance } = require('perf_hooks');
|
||||
const {init, createSingleChart} = require("./nativeSsr.js")
|
||||
const { fig2img } = require("./ssr.js");
|
||||
const renderer = require("./ssr.js")
|
||||
const {getMultipleBarsChartDataFromAnkieterDataFormat, rawData,prepareOptionsForMultipleBarsChartData} = require("./optionsGenerator.js")
|
||||
|
||||
const benchmark_to_csv = (results)=>{
|
||||
let csv = "";
|
||||
for (let [k,v] of Object.entries(results))
|
||||
csv += `${k}, ${v}\n`
|
||||
return csv
|
||||
}
|
||||
const timeit = (fun, number=1,prepare=()=>null)=>{
|
||||
let time=0
|
||||
for(let _=0; _<number;_++){
|
||||
// console.log(_)
|
||||
prepare()
|
||||
const startTime = performance.now();
|
||||
fun()
|
||||
const endTime = performance.now();
|
||||
// console.log(endTime-startTime)
|
||||
time+=endTime-startTime;
|
||||
}
|
||||
|
||||
return time
|
||||
}
|
||||
const benchmark_linear=(start,end,func,prepare=()=>null)=>{
|
||||
const out ={};
|
||||
for (let i =start; i<end; i++)
|
||||
out[i] = timeit(func, i,prepare);
|
||||
return out;
|
||||
}
|
||||
const benchmark_series = (number, func, prepare=()=>null)=>{
|
||||
const out ={};
|
||||
for (let i =0; i<number; i++)
|
||||
out[i] = timeit(func, 1,prepare);
|
||||
return out;
|
||||
}
|
||||
|
||||
|
||||
function sleep(ms) {
|
||||
return new Promise((resolve) => {
|
||||
setTimeout(resolve, ms);
|
||||
});
|
||||
}
|
||||
// let root = renderer.createNewRoot()
|
||||
// console.log(root)
|
||||
const measureUnits = async ()=>{
|
||||
const c = performance.now()
|
||||
await sleep(1000)
|
||||
console.log(performance.now()-c)
|
||||
}
|
||||
let option = prepareOptionsForMultipleBarsChartData(getMultipleBarsChartDataFromAnkieterDataFormat(mockData.response))
|
||||
fs.writeFileSync("option.txt",JSON.stringify(option))
|
||||
option = {
|
||||
legend: {},
|
||||
tooltip: {},
|
||||
dataset: {
|
||||
// Define the dimension of array. In cartesian coordinate system,
|
||||
// if the type of x-axis is category, map the first dimension to
|
||||
// x-axis by default, the second dimension to y-axis.
|
||||
// You can also specify 'series.encode' to complete the map
|
||||
// without specify dimensions. Please see below.
|
||||
|
||||
dimensions: ['1','2015','2016'],
|
||||
source: [
|
||||
{ "1": 5, '2015': 43.3, '2016': 85.8, '2017': 93.7 },
|
||||
{ "1": 2, '2015': 83.1, '2016': 73.4, '2017': 55.1 },
|
||||
{ "1": 1, '2015': 86.4, '2016': 65.2, '2017': 82.5 },
|
||||
{ "1": 8, '2015': 72.4, '2016': 53.9, '2017': 39.1 }
|
||||
]
|
||||
},
|
||||
xAxis: { type: 'value' },
|
||||
yAxis: {type:"category"},
|
||||
series: [{ type: 'bar', 'stack':'total' }, { type: 'bar','stack':'total' }, { type: 'bar','stack':'total' }]
|
||||
};
|
||||
|
||||
option = {
|
||||
xAxis: {
|
||||
type: 'category',
|
||||
data: ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']
|
||||
},
|
||||
yAxis: {
|
||||
type: 'value'
|
||||
},
|
||||
series: [
|
||||
{
|
||||
data: [150, 230, 224, 218, 135, 147, 260],
|
||||
type: 'line'
|
||||
}
|
||||
]
|
||||
};
|
||||
|
||||
option = {
|
||||
xAxis: {
|
||||
type: 'value',
|
||||
|
||||
},
|
||||
yAxis: {
|
||||
type: 'category',
|
||||
data: ['Tom', 'Dick', 'Harry', 'Slim', 'Jim']
|
||||
},
|
||||
series: [
|
||||
{
|
||||
data: [1,2,3,4,5],
|
||||
type: 'bar'
|
||||
}
|
||||
]
|
||||
};
|
||||
|
||||
const benchmarkDataTransformationFromAnkieterToEchartsFormat = ()=>{
|
||||
console.log(benchmark_linear(1,100, ()=>prepareOptionsForMultipleBarsChartData(getMultipleBarsChartDataFromAnkieterDataFormat(mockData.response))))
|
||||
console.log(benchmark_to_csv(benchmark_linear(1,100, ()=>prepareOptionsForMultipleBarsChartData(getMultipleBarsChartDataFromAnkieterDataFormat(mockData.response)))))
|
||||
}
|
||||
|
||||
const warmup = ()=>{
|
||||
benchmark_to_csv(benchmark_linear(1,20,
|
||||
()=>{
|
||||
return createSingleChart(option)
|
||||
},
|
||||
()=>{
|
||||
option.series[0].data=[Math.random(),Math.random(),Math.random(),Math.random(),Math.random(),Math.random(),Math.random()]
|
||||
}
|
||||
)
|
||||
)
|
||||
}
|
||||
// warmup()
|
||||
const number=100
|
||||
|
||||
benchmarkDataTransformationFromAnkieterToEchartsFormat()
|
||||
// fs.writeFileSync("barPlotNode.svg", createSingleChart(option))
|
||||
|
||||
// const res1 = benchmark_to_csv(benchmark_linear(1,number,
|
||||
// ()=>{
|
||||
// return createSingleChart(option)
|
||||
// },
|
||||
// ()=>{
|
||||
// // option = JSON.parse(JSON.stringify(option))
|
||||
// // option.series[0].data=[Math.random(),Math.random(),Math.random(),Math.random(),Math.random(),Math.random(),Math.random()]
|
||||
|
||||
// }
|
||||
// )
|
||||
// )
|
||||
// print("Liniowy benchmark z generowanien nowej instancji wykresu za każdym razem")
|
||||
// print(res1)
|
||||
// console.log(createSingleChart(option))
|
||||
// const chartInstance = generateChartObject(root)
|
||||
// const linearBenchmarkOnExistingInstance = benchmark_to_csv(benchmark_linear(1,number, ()=>{
|
||||
// return generateChart(root,chartInstance,option)
|
||||
// }))
|
||||
// print("Liniowy benchmark na wczesniej przygotowanej instancji wykresu")
|
||||
// print(linearBenchmarkOnExistingInstance)
|
||||
|
||||
// const res2 = benchmark_to_csv(benchmark_linear(1,number,()=>fig2img(root,generateChart(root, chartInstance,option), false) ))
|
||||
// print("Liniowy benchmark create from scratch + save")
|
||||
// print(res2)
|
||||
|
||||
// let linearBufforSaveInstance = generateChartObject(root)
|
||||
// let figure = generateChart(root,linearBufforSaveInstance, option)
|
||||
|
||||
// const res3= benchmark_to_csv(benchmark_linear(1,number,()=>fig2img(root, figure, false) ))
|
||||
// print("Liniowy benchmark zapisywania do bufora")
|
||||
// print(res3)
|
||||
|
|
@ -0,0 +1 @@
|
|||
{"ocena ogólna":{"1":0.21,"2":0.2,"3":0.213,"4":0.183,"5":0.194},"ocena jedzenia":{"1":0.18,"2":0.189,"3":0.202,"4":0.223,"5":0.206},"ocena obsługi":{"1":0.215,"2":0.196,"3":0.196,"4":0.194,"5":0.199},"Czy przyjdziesz ponownie?":{"0":0.468,"1":0.532}}
|
||||
|
|
@ -0,0 +1,39 @@
|
|||
<script src="https://cdn.jsdelivr.net/npm/chart.js@3.7.0/dist/chart.min.js"></script>
|
||||
<canvas id="myChart" width="400" height="400"></canvas>
|
||||
<script>
|
||||
var ctx = document.getElementById('myChart').getContext('2d');
|
||||
const data = {
|
||||
labels: ['January', 'February', 'April', 'March', 'June', 'July'],
|
||||
datasets: [
|
||||
{
|
||||
label: 'Dataset 1',
|
||||
data: [1,2,3,4,5,6],
|
||||
},
|
||||
|
||||
]
|
||||
};
|
||||
|
||||
const config = {
|
||||
type: 'bar',
|
||||
data: data,
|
||||
options: {
|
||||
indexAxis: 'y',
|
||||
elements: {
|
||||
bar: {
|
||||
borderWidth: 2,
|
||||
}
|
||||
},
|
||||
|
||||
plugins: {
|
||||
legend: {
|
||||
position: 'right',
|
||||
},
|
||||
title: {
|
||||
display: true,
|
||||
text: 'Chart.js Horizontal Bar Chart'
|
||||
}
|
||||
}
|
||||
},
|
||||
};
|
||||
var myChart = new Chart(ctx, config);
|
||||
</script>
|
||||
|
|
@ -0,0 +1 @@
|
|||
<script src="csv_generator.js"></script>
|
||||
|
|
@ -0,0 +1,66 @@
|
|||
const fs = require('fs');
|
||||
const { Obj } = require('prelude-ls');
|
||||
|
||||
const timeit = (fun, number)=>{
|
||||
const startTime = performance.now();
|
||||
for(let _=0; _<number;_++)fun();
|
||||
const endTime = performance.now();
|
||||
return endTime-startTime;
|
||||
}
|
||||
|
||||
const randint = (min,max)=>Math.floor(Math.random() * (max - min + 1)) + min;
|
||||
const zeroToFiveGrade = ()=>randint(1,5)
|
||||
const yesOrNo = ()=>randint(0,1)
|
||||
const columns = {
|
||||
"ocena ogólna":zeroToFiveGrade,
|
||||
"ocena jedzenia":zeroToFiveGrade,
|
||||
"ocena obsługi":zeroToFiveGrade,
|
||||
"Czy przyjdziesz ponownie?":yesOrNo
|
||||
}
|
||||
const rows_count=1000
|
||||
const raw_data = new Array(rows_count).fill(undefined).map(x=>Object.values(columns).map(v=>v()))
|
||||
|
||||
const transform_csv = (columns,raw_data)=>{
|
||||
const count_data = {}
|
||||
for (let [i, column] of Object.keys(columns).entries()){
|
||||
const values_count = {}
|
||||
const values_percent = {}
|
||||
let total = 0
|
||||
for (let row of raw_data){
|
||||
if (!values_count[row[i]]) values_count[row[i]]=0
|
||||
values_count[row[i]]+=1
|
||||
total+=1
|
||||
}
|
||||
for (let value of Object.keys(values_count))
|
||||
values_percent[value] = values_count[value]/total
|
||||
count_data[column]=values_percent
|
||||
}
|
||||
|
||||
return count_data
|
||||
}
|
||||
|
||||
if (!performance) //if running in node.js
|
||||
var { performance } = require('perf_hooks');
|
||||
|
||||
|
||||
console.log(timeit(()=>transform_csv(columns,raw_data), 1000))
|
||||
console.log(timeit(()=>JSON.parse(JSON.stringify(raw_data)), 1000))
|
||||
|
||||
fs.writeFileSync('chartData.json', JSON.stringify(transform_csv(columns, raw_data)), 'utf-8');
|
||||
let generateDataset = (transformed_csv)=>{
|
||||
console.log(transformed_csv)
|
||||
const dataset = {dimensions: ["question", "1","2","3","4","5"]}
|
||||
const source = Object.entries(transformed_csv).map(([key,value])=>{
|
||||
return {question:key, ...value}
|
||||
})
|
||||
dataset.source=source;
|
||||
return dataset
|
||||
}
|
||||
let generateSeries = (dataset)=>{
|
||||
return dataset.map(x=>({ type: 'bar', stack:'total'}))
|
||||
}
|
||||
console.log(generateDataset(transform_csv(columns,raw_data)))
|
||||
const renderer = require("./ssr.js")
|
||||
console.log("imported..")
|
||||
|
||||
renderer.generateChart(renderer.root, {})
|
||||
|
|
@ -0,0 +1,29 @@
|
|||
from collections import defaultdict
|
||||
import copy
|
||||
import random
|
||||
|
||||
from numpy.core.fromnumeric import sort
|
||||
oneToFiveGrade = lambda: random.randint(1,5)
|
||||
yesOrNo = lambda: random.randint(0,1)
|
||||
columns = {
|
||||
"ocena ogolna":oneToFiveGrade,
|
||||
"ocena jedzenia":oneToFiveGrade,
|
||||
"ocena obslugi":oneToFiveGrade,
|
||||
"ocena wystroju":oneToFiveGrade,
|
||||
"ocena karty dan":oneToFiveGrade,
|
||||
"ocena cen":oneToFiveGrade,
|
||||
"ocena lokalizacji":oneToFiveGrade,
|
||||
"ocena przystawek":oneToFiveGrade,
|
||||
"ocena karty win":oneToFiveGrade,
|
||||
"Co ci sie podobalo - obsluga":yesOrNo,
|
||||
"Co ci sie podobalo - ceny":yesOrNo,
|
||||
"Co ci sie podobalo - wystroj":yesOrNo,
|
||||
"Czy przyjdziesz ponownie - prosze zaznaczyc wszystkie powody - obsluga":yesOrNo,
|
||||
"Czy przyjdziesz ponownie - prosze zaznaczyc wszystkie powody - ceny":yesOrNo,
|
||||
"Czy przyjdziesz ponownie - prosze zaznaczyc wszystkie powody - jakosc dan":yesOrNo,
|
||||
}
|
||||
rows_count = 1000
|
||||
raw_data = [[func() for func in columns.values()] for _ in range(rows_count)]
|
||||
string_data = "\n".join([",".join([str(entry) for entry in row]) for row in [columns.keys()] + raw_data])
|
||||
with open("data.csv", "w+", encoding="utf-8") as csv_file:
|
||||
csv_file.write(string_data)
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 77 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 41 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 41 KiB |
|
|
@ -0,0 +1,125 @@
|
|||
const query = {
|
||||
"as":[
|
||||
"share",
|
||||
"median",
|
||||
"std",
|
||||
"mean",
|
||||
"rows"
|
||||
],
|
||||
"by":[
|
||||
"Na którym stopniu studiów P. studiuje?",
|
||||
"*"
|
||||
],
|
||||
"get":[
|
||||
[
|
||||
"Jak ogólnie ocenia P. jakość kształcenia na swoim kierunku studiów?"
|
||||
]
|
||||
],
|
||||
"if":[
|
||||
[
|
||||
"Jak ogólnie ocenia P. jakość kształcenia na swoim kierunku studiów?",
|
||||
"in",
|
||||
"3",
|
||||
"2",
|
||||
"5",
|
||||
"4"
|
||||
],
|
||||
[
|
||||
0,
|
||||
"notin",
|
||||
"9999",
|
||||
"999"
|
||||
],
|
||||
[
|
||||
3,
|
||||
"notin",
|
||||
"9999"
|
||||
],
|
||||
[
|
||||
1,
|
||||
"notin",
|
||||
"9999"
|
||||
],
|
||||
[
|
||||
2,
|
||||
"notin",
|
||||
"9999"
|
||||
]
|
||||
],
|
||||
"except":[
|
||||
|
||||
]
|
||||
}
|
||||
const response = {
|
||||
"index": [
|
||||
1,
|
||||
2,
|
||||
3,
|
||||
9999,
|
||||
"*"
|
||||
],
|
||||
"mean Jak og\u00f3lnie ocenia P. jako\u015b\u0107 kszta\u0142cenia na swoim kierunku studi\u00f3w?": [
|
||||
4.026791277258567,
|
||||
3.9738903394255876,
|
||||
4.008928571428571,
|
||||
3.4444444444444446,
|
||||
4.0111016225448335
|
||||
],
|
||||
"median Jak og\u00f3lnie ocenia P. jako\u015b\u0107 kszta\u0142cenia na swoim kierunku studi\u00f3w?": [
|
||||
4.0,
|
||||
4.0,
|
||||
4.0,
|
||||
3.5,
|
||||
4.0
|
||||
],
|
||||
"rows Jak og\u00f3lnie ocenia P. jako\u015b\u0107 kszta\u0142cenia na swoim kierunku studi\u00f3w?": [
|
||||
1605,
|
||||
383,
|
||||
336,
|
||||
18,
|
||||
2342
|
||||
],
|
||||
"share Jak og\u00f3lnie ocenia P. jako\u015b\u0107 kszta\u0142cenia na swoim kierunku studi\u00f3w?": [
|
||||
{
|
||||
"2": 34,
|
||||
"3": 290,
|
||||
"4": 880,
|
||||
"5": 401
|
||||
},
|
||||
{
|
||||
"2": 18,
|
||||
"3": 74,
|
||||
"4": 191,
|
||||
"5": 100
|
||||
},
|
||||
{
|
||||
"2": 12,
|
||||
"3": 52,
|
||||
"4": 193,
|
||||
"5": 79
|
||||
},
|
||||
{
|
||||
"2": 1,
|
||||
"3": 8,
|
||||
"4": 9
|
||||
},
|
||||
{
|
||||
"2": 65,
|
||||
"3": 424,
|
||||
"4": 1273,
|
||||
"5": 580
|
||||
}
|
||||
],
|
||||
"std Jak og\u00f3lnie ocenia P. jako\u015b\u0107 kszta\u0142cenia na swoim kierunku studi\u00f3w?": [
|
||||
0.717542901077322,
|
||||
0.8020570794050175,
|
||||
0.7309229787418631,
|
||||
0.6156987634551994,
|
||||
0.7347223270238585
|
||||
]
|
||||
}
|
||||
const mockData = {query: query, response:response}
|
||||
module.exports = {
|
||||
mockData:mockData,
|
||||
response:response,
|
||||
}
|
||||
|
|
@ -0,0 +1,131 @@
|
|||
from collections import defaultdict
|
||||
import copy
|
||||
import random
|
||||
from PIL import Image
|
||||
from numpy.core.fromnumeric import sort
|
||||
|
||||
with open("data.csv") as f:
|
||||
raw_data = [line.replace("\n","").split(",") for line in f.readlines()]
|
||||
columns=copy.deepcopy(raw_data[0])
|
||||
raw_data=raw_data[1:]
|
||||
|
||||
def transform_csv(columns, raw_data):
|
||||
count_data={}
|
||||
for i,column in enumerate(columns):
|
||||
values_count=defaultdict(lambda: 0)
|
||||
values_percent={}
|
||||
total=0
|
||||
for row in raw_data:
|
||||
values_count[row[i]]+=1
|
||||
total+=1
|
||||
for value in values_count.keys():
|
||||
values_percent[value] = values_count[value]/total
|
||||
count_data[column] = values_percent
|
||||
return count_data
|
||||
import timeit
|
||||
time_result = timeit.timeit(lambda: transform_csv(columns,raw_data), number=1)
|
||||
print(time_result*1000)
|
||||
def generate_dataset(transformed_csv):
|
||||
# print(transformed_csv)
|
||||
legend = list(transformed_csv.keys())
|
||||
series = [[entry[1] for entry in sorted(list(values.items()), key= lambda x: x[0])] for values in transformed_csv.values()]
|
||||
return (legend,series)
|
||||
|
||||
def fig2img(fig):
|
||||
import io
|
||||
buf = io.BytesIO()
|
||||
fig.savefig(buf, format="svg")
|
||||
buf.seek(0)
|
||||
plt.close(fig)
|
||||
return buf
|
||||
# importing package
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
def benchmark_linear_to_csv(results):
|
||||
csv = ""
|
||||
for k, v in results.items():
|
||||
csv += f"{k}, {v}\n"
|
||||
return csv
|
||||
|
||||
def benchmark_linear(start,end,function):
|
||||
out = {}
|
||||
for i in range(start,end):
|
||||
out[i] = timeit.timeit(function, number=i)*1000
|
||||
return out
|
||||
# create data
|
||||
def createLinePlot():
|
||||
x = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']
|
||||
y = [150, 230, 224, 218, 135, 147, 260]
|
||||
fig,ax1 = plt.subplots()
|
||||
ax1.plot(x,y)
|
||||
return fig
|
||||
|
||||
def createBarPlot():
|
||||
np.random.seed(19680801)
|
||||
plt.rcdefaults()
|
||||
fig, ax = plt.subplots()
|
||||
# Example data
|
||||
people = ('Tom', 'Dick', 'Harry', 'Slim', 'Jim')
|
||||
y_pos = np.arange(len(people))
|
||||
performance = [1,2,3,4,5]
|
||||
|
||||
ax.barh(y_pos, performance, align='center')
|
||||
ax.set_yticks(y_pos, labels=people)
|
||||
ax.invert_yaxis()
|
||||
ax.set_xlabel('Performance')
|
||||
ax.set_title('How fast do you want to go today?')
|
||||
return fig
|
||||
|
||||
|
||||
|
||||
def createPlotFromStratch():
|
||||
|
||||
x = ['1', '2', '3', '4', '5']
|
||||
dataset = generate_dataset(transform_csv(columns,raw_data))
|
||||
|
||||
set_already = []
|
||||
|
||||
dataset = (dataset[0][0], dataset[1][0])
|
||||
|
||||
# print("dataset")
|
||||
# print(dataset)
|
||||
labels = dataset[0]
|
||||
fig,ax = plt.subplots()
|
||||
already = [0]
|
||||
for bar_series in zip(x,dataset[1]):
|
||||
# print(already)
|
||||
# print(bar_series)
|
||||
ax.barh(labels, bar_series[1], 0.1, label=bar_series[0], left=sum(already))
|
||||
already=already+[bar_series[1]]
|
||||
ax.set_ylabel("Wyniki")
|
||||
ax.legend()
|
||||
return fig
|
||||
# print("Czas tworzenia i zapisywania 100 wykresów:")
|
||||
# time_result = timeit.timeit(lambda: fig2img(createPlotFromStratch()), number=100)
|
||||
# print(time_result*1000)
|
||||
|
||||
# print("Czas zapisywania 100 wykresów:")
|
||||
# figure = createPlotFromStratch()
|
||||
# time_result = timeit.timeit(lambda: fig2img(figure), number=100)
|
||||
# print(time_result*1000)
|
||||
with open("barPlotPython.svg", 'wb+') as f:
|
||||
f.write(fig2img(createBarPlot()).read())
|
||||
input()
|
||||
print("Liniowy benchmark create from scratch")
|
||||
def generate_and_close():
|
||||
fig = createPlotFromStratch()
|
||||
plt.close('all')
|
||||
plt.close()
|
||||
plt.cla()
|
||||
plt.clf()
|
||||
|
||||
# print(benchmark_linear_to_csv(benchmark_linear(1,5,lambda:plt.close(createPlotFromStratch()) )))
|
||||
|
||||
print(benchmark_linear_to_csv(benchmark_linear(1,20,lambda:plt.close(createBarPlot()) )))
|
||||
|
||||
# print("Liniowy benchmark create from scratch + save")
|
||||
# print(benchmark_linear_to_csv(benchmark_linear(1,20,lambda: fig2img(createPlotFromStratch()) )))
|
||||
|
||||
# print("Liniowy benchmark zapisywania do bufora")
|
||||
# print(benchmark_linear_to_csv(benchmark_linear(1,25,lambda: fig2img(figure) )))
|
||||
|
||||
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|
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|
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|
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|
|
|
@ -0,0 +1,41 @@
|
|||
|
||||
Przykładowe dane zostały wygenerowane następującym skryptem w języku Python 3:
|
||||
\begin{lstlisting}
|
||||
import random
|
||||
oneToFiveGrade = lambda: random.randint(1,5)
|
||||
yesOrNo = lambda: random.randint(0,1)
|
||||
columns = {
|
||||
"ocena ogolna":oneToFiveGrade,
|
||||
"ocena jedzenia":oneToFiveGrade,
|
||||
"ocena obslugi":oneToFiveGrade,
|
||||
"ocena wystroju":oneToFiveGrade,
|
||||
"ocena karty dan":oneToFiveGrade,
|
||||
"ocena cen":oneToFiveGrade,
|
||||
"ocena lokalizacji":oneToFiveGrade,
|
||||
"ocena przystawek":oneToFiveGrade,
|
||||
"ocena karty win":oneToFiveGrade,
|
||||
"Co ci sie podobalo - obsluga":yesOrNo,
|
||||
"Co ci sie podobalo - ceny":yesOrNo,
|
||||
"Co ci sie podobalo - wystroj":yesOrNo,
|
||||
"Czy przyjdziesz ponownie - prosze zaznaczyc wszystkie powody
|
||||
- obsluga":yesOrNo,
|
||||
"Czy przyjdziesz ponownie - prosze zaznaczyc wszystkie powody
|
||||
- ceny":yesOrNo,
|
||||
"Czy przyjdziesz ponownie - prosze zaznaczyc wszystkie powody
|
||||
- jakosc dan":yesOrNo,
|
||||
}
|
||||
rows_count = 1000
|
||||
raw_data = [
|
||||
[func() for func in columns.values()]
|
||||
for _ in range(rows_count)
|
||||
]
|
||||
string_data = "\n".join(
|
||||
[",".join(
|
||||
[str(entry) for entry in row]
|
||||
) for row in [columns.keys()] + raw_data]
|
||||
)
|
||||
with open("data.csv", "w+", encoding="utf-8") as csv_file:
|
||||
csv_file.write(string_data)
|
||||
|
||||
\end{lstlisting}
|
||||
Skrypt generuje plik CSV z losowymi danymi używanymi w dalszej części.
|
||||
|
|
@ -0,0 +1,16 @@
|
|||
import json
|
||||
import sys
|
||||
sys.stdout.reconfigure(encoding='utf-8')
|
||||
f = json.loads(open("har.har", 'r', encoding='utf-8').read())
|
||||
csv = "rozmiar, czas, czas blokady, czas polaczenia, czas procesowania\n"
|
||||
for entry in f['log']['entries']:
|
||||
if 'data' in entry['request']['url']:
|
||||
size = entry['request']['bodySize'] + entry['request']['headersSize'] + entry['response']['bodySize'] + entry['response']['headersSize']
|
||||
size=size*0.001
|
||||
time = entry['time']
|
||||
blocking = entry['timings']['blocked']
|
||||
if blocking<0: blocking=0
|
||||
connection = entry['timings']['dns'] + entry['timings']['connect'] + entry['timings']['ssl']
|
||||
processingTime = entry['timings']['wait']
|
||||
csv += f'{size}, {time}, {blocking}, {connection}, {processingTime}\n'
|
||||
print(csv)
|
||||
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|
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|
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|
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|
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|
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|
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|
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|
|
@ -0,0 +1,20 @@
|
|||
const echarts = require('echarts');
|
||||
|
||||
const init = (w=400,h=300)=> echarts.init(null, null, {
|
||||
renderer: 'svg',
|
||||
ssr: true,
|
||||
width: w,
|
||||
height: h
|
||||
});
|
||||
const createSingleChart = (option,w=400,h=300)=>{
|
||||
const chart = init(w,h)
|
||||
chart.setOption(option);
|
||||
const str = chart.renderToSVGString()
|
||||
chart.dispose()
|
||||
return str;
|
||||
}
|
||||
|
||||
module.exports = {
|
||||
init:init,
|
||||
createSingleChart:createSingleChart
|
||||
}
|
||||
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|
Can't render this file because it is too large.
|
|
|
@ -0,0 +1,26 @@
|
|||
var { performance } = require('perf_hooks');
|
||||
const timeit = (fun, number=1,prepare=()=>null)=>{
|
||||
let time=0
|
||||
for(let _=0; _<number;_++){
|
||||
// console.log(_)
|
||||
prepare()
|
||||
const startTime = performance.now();
|
||||
fun()
|
||||
const endTime = performance.now();
|
||||
// console.log(endTime-startTime)
|
||||
time+=endTime-startTime;
|
||||
}
|
||||
|
||||
return time
|
||||
}
|
||||
|
||||
|
||||
|
||||
//inline caches
|
||||
function load(a){
|
||||
return a.key;
|
||||
}
|
||||
const o = {key:Math.random()}
|
||||
for (let i=0; i<10000; i++){
|
||||
console.log(i+","+timeit(()=>load(o), 1, ()=>o.key = Math.random() ))
|
||||
}
|
||||
|
|
@ -0,0 +1,20 @@
|
|||
import timeit,random
|
||||
def precise_timeit(fun, number=1,prepare=lambda:None):
|
||||
time=0
|
||||
for _ in range(number):
|
||||
# prepare()
|
||||
time+=timeit.timeit(lambda: fun())
|
||||
return time
|
||||
|
||||
|
||||
class TestClass:
|
||||
def __init__(self):
|
||||
self.key=random.randint(0,99999)
|
||||
|
||||
def load(a):
|
||||
return a.key
|
||||
def set_value(a):
|
||||
a.key=random.randint(0,99999)
|
||||
o = TestClass()
|
||||
for i in range(10000):
|
||||
print(f"{i},{timeit.timeit(lambda:load(o), number=1)*1000}")
|
||||
|
|
@ -0,0 +1 @@
|
|||
{"animation":false,"color":"#3b3b3b","pxHeight":280,"legend":{"textStyle":{"fontSize":11}},"dataset":{"dimensions":["1","2","3","4","5"],"source":[{"1":0,"2":34,"3":290,"4":880,"5":401},{"1":0,"2":18,"3":74,"4":191,"5":100},{"1":0,"2":12,"3":52,"4":193,"5":79},{"1":0,"2":1,"3":8,"4":9,"5":0},{"1":0,"2":65,"3":424,"4":1273,"5":580}]},"grid":{"top":60,"left":"3%","right":"4%","bottom":"3%","containLabel":true},"xAxis":{"type":"value","show":false,"animation":false,"max":100,"axisLabel":{}},"yAxis":{"type":"category","show":true,"data":[1,2,3,9999,"*"]},"series":[{"type":"bar","stack":"total"},{"type":"bar","stack":"total"},{"type":"bar","stack":"total"}]}
|
||||
|
|
@ -0,0 +1,97 @@
|
|||
const fs = require('fs');
|
||||
const rawData = fs.readFileSync("chartData.json",{encoding:"utf-8"})
|
||||
const restaurantData = JSON.parse(rawData)
|
||||
const { mockData,response } = require("./data.js");
|
||||
|
||||
|
||||
|
||||
const R = require("ramda")
|
||||
let print = R.curry((label, x)=>{console.log("\n"+label);console.log("-----");console.dir(x, {depth:null}); return x});
|
||||
const transform = (data)=>{
|
||||
const entries = Object.entries(data)
|
||||
const groups = data.index
|
||||
const entriesExceptForIndices = entries.filter(x=>x[0]!='index')
|
||||
const tableHeader = Object.entries(entriesExceptForIndices.map(x=>x[0]))
|
||||
const tableRows = entriesExceptForIndices.map(x=>Object.entries(x[1]))
|
||||
return {headers:tableHeader, rows:tableRows, groups:groups}
|
||||
}
|
||||
|
||||
const transpose = (data)=>{
|
||||
return {groups:data.groups, headers:data.headers.map(z=>z[1]), rows: data.headers.map(x=>{
|
||||
const index = x[0]
|
||||
return data.rows.map(v=>v.find(inner=>inner[0]===index)[1])
|
||||
})}
|
||||
}
|
||||
const mergeIntoOneArray = (data)=>{
|
||||
return {groups:data.groups,data: [data.headers, ...data.rows]}
|
||||
}
|
||||
const getData = R.curry((data)=>data.data)
|
||||
const transformToDataset = R.compose(mergeIntoOneArray, transpose, transform)
|
||||
|
||||
const extractSeriesByName = R.curry((name,data)=>{
|
||||
const index = R.findIndex((x)=>x.includes(name))(getData(data)[0])
|
||||
|
||||
return {groups:data.groups, name:getData(data)[0][index], data:
|
||||
R.slice(1,Infinity)(
|
||||
R.map(
|
||||
R.curry((x)=>x[index]),
|
||||
)(getData(data)))}
|
||||
})
|
||||
const complementShareSeries = R.curry((allKeys, data)=>{
|
||||
const template = R.fromPairs(
|
||||
R.map(o=>[o,0])(allKeys)
|
||||
)
|
||||
|
||||
return {groups:data.groups, name:data.name, data: R.map(
|
||||
(x)=>{
|
||||
return R.fromPairs()(R.map(
|
||||
key=>{
|
||||
const surveyValue = x[key]
|
||||
if (surveyValue) return [key,surveyValue]
|
||||
return [key,0]
|
||||
}
|
||||
)(R.keys(template))
|
||||
)
|
||||
}
|
||||
)(data.data)}
|
||||
})
|
||||
|
||||
const extractShareSeries = extractSeriesByName("share")
|
||||
|
||||
const getChartData = R.compose(complementShareSeries(['1','2','3','4','5']), extractShareSeries,transformToDataset)
|
||||
const prepareOptions = (chartData)=>{
|
||||
return {
|
||||
animation:false,
|
||||
color: '#3b3b3b',
|
||||
pxHeight: chartData.groups.length * (120 / 3) + 80,
|
||||
legend: {
|
||||
|
||||
textStyle:{fontSize:11},
|
||||
},
|
||||
dataset:{
|
||||
dimensions:Object.keys(chartData.data[0]),
|
||||
source:chartData.data
|
||||
},
|
||||
grid: {
|
||||
top:60,
|
||||
left: '3%',
|
||||
right: '4%',
|
||||
bottom: '3%',
|
||||
containLabel: true
|
||||
},
|
||||
xAxis: {type: 'value', show: false, animation: false, max: 100, axisLabel: {formatter: (value, index) => `${value}%`}},
|
||||
yAxis: {
|
||||
type: 'category', show: true, data:chartData.groups
|
||||
},
|
||||
series: [
|
||||
{type: 'bar', stack:'total'},
|
||||
{type: 'bar',stack:'total'},
|
||||
{type: 'bar',stack:'total'}
|
||||
]
|
||||
}
|
||||
}
|
||||
module.exports = {
|
||||
getMultipleBarsChartDataFromAnkieterDataFormat:getChartData,
|
||||
prepareOptionsForMultipleBarsChartData:prepareOptions,
|
||||
rawData:rawData,
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -0,0 +1,19 @@
|
|||
{
|
||||
"name": "em_praca",
|
||||
"version": "1.0.0",
|
||||
"description": "",
|
||||
"main": "main.js",
|
||||
"scripts": {
|
||||
"test": "echo \"Error: no test specified\" && exit 1"
|
||||
},
|
||||
"author": "",
|
||||
"license": "ISC",
|
||||
"dependencies": {
|
||||
"canvas": "^2.9.0",
|
||||
"echarts": "^5.3.0",
|
||||
"echarts-ssr": "0.0.1",
|
||||
"jsdom": "^18.1.0",
|
||||
"ramda": "^0.28.0",
|
||||
"svg-png-converter": "^0.0.9"
|
||||
}
|
||||
}
|
||||
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|
After Width: | Height: | Size: 42 KiB |
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|
After Width: | Height: | Size: 80 KiB |
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|
Can't render this file because it is too large.
|
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|
After Width: | Height: | Size: 12 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 12 KiB |
|
|
@ -0,0 +1 @@
|
|||
<img src="barChart.png">
|
||||
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|
After Width: | Height: | Size: 7.2 KiB |
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|
After Width: | Height: | Size: 7.2 KiB |
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|
After Width: | Height: | Size: 21 KiB |
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|
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|
After Width: | Height: | Size: 115 KiB |
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|
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|
After Width: | Height: | Size: 41 KiB |
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|
After Width: | Height: | Size: 76 KiB |
|
|
@ -0,0 +1,70 @@
|
|||
|
||||
var { performance } = require('perf_hooks');
|
||||
const timeit = (fun, number)=>{
|
||||
|
||||
const startTime = performance.now();
|
||||
for(let _=0; _<number;_++)fun();
|
||||
const endTime = performance.now();
|
||||
return endTime-startTime;
|
||||
}
|
||||
const echarts = require("echarts");
|
||||
const {createCanvas} = require('canvas');
|
||||
const {svg2png} = require('svg-png-converter')
|
||||
|
||||
const fs = require('fs');
|
||||
const { number } = require('echarts');
|
||||
// echarts.setCanvasCreator(() => {
|
||||
// return createCanvas(100, 100);
|
||||
// });
|
||||
|
||||
|
||||
const {JSDOM} = require('jsdom');
|
||||
const {window} = new JSDOM();
|
||||
global.window = window;
|
||||
global.navigator = window.navigator;
|
||||
global.document = window.document;
|
||||
Object.defineProperty(window, "clientWidth", {value: 500});
|
||||
Object.defineProperty(window, "clientHeight", {value: 500});
|
||||
console.log("finished canvas setup.")
|
||||
const createNewRoot = (window)=>{
|
||||
let root = window.document.createElement('div');
|
||||
root.style.cssText = 'width: 500px; height: 500px;';
|
||||
window.document.body.appendChild(root)
|
||||
return root
|
||||
}
|
||||
const generateChartObject = (root)=>{
|
||||
const chart = echarts.init(root, null, {
|
||||
renderer: 'svg'
|
||||
});
|
||||
return chart;
|
||||
}
|
||||
const generateChart = (root,chart, options)=>{
|
||||
chart.setOption(options);
|
||||
return chart
|
||||
}
|
||||
const fig2img = (root,chart,dispose=true)=>{
|
||||
const svgFile = root.querySelector('svg').outerHTML;
|
||||
if (dispose) chart.dispose();
|
||||
return svgFile;
|
||||
}
|
||||
|
||||
const convertToPng = async (src)=>{
|
||||
let s = await svg2png({
|
||||
input:src,
|
||||
encoding: 'dataURL',
|
||||
format: 'png',
|
||||
width: 100,
|
||||
height: 100,
|
||||
multiplier: .7,
|
||||
quality: .5
|
||||
})
|
||||
}
|
||||
module.exports = {
|
||||
echarts:echarts,
|
||||
g:global,
|
||||
createNewRoot:createNewRoot,
|
||||
convert:convertToPng,
|
||||
fig2img:fig2img,
|
||||
generateChart:generateChart,
|
||||
generateChartObject:generateChartObject,
|
||||
}
|
||||
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|
After Width: | Height: | Size: 48 KiB |
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
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|
After Width: | Height: | Size: 253 KiB |
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|
After Width: | Height: | Size: 2.5 KiB |
Loading…
Reference in New Issue