I found an interesting demo of how to find the largest rectangle in an irregular shaped polygon here using D3plus.
I'm trying to recreate this for a polygon I'm working on but currently the code is not working. It seems to runs endlessly. The code I'm using is as follows:
d3.csv("data/polyPoints.csv", function(error, polyPoints) {
if (error) return console.error(error);
// coerce string values to numbers
polyPoints.forEach(function(d) {
d3.keys(d).forEach(function(k) {
d[k] = +d[k]
})
});
// settings for geom.largestRect
var rectOptions = {
angle: 0,
maxAspectRatio: 5,
nTries: 1
};
console.log(rectOptions);
console.log(polyPoints);
var lRect = d3plus.geom.largestRect(polyPoints, rectOptions);
console.log(lRect);
});
I suspect my polygon is not in the correct format.
Update
I'm making progress. My original polygon object was taken from a csv and created an array of arrays of key value pairs (e.g. {"x": 0 "y": 1},{"x": 2, "y": 1}....)
I converted this to an array of arrays (e.g. [[1,0],[2,0]....])
Now the code is running but the output is defining rectangles that cross the boundary of the original polygon.
For anyone working with this. The largestRect docs are https://d3plus.org/docs/#largestRect and can be run with the following code.
const d3p = require('d3plus');
const polygon = [[x,y],[x,y],[x,y]...]
const rectOptions = {
maxAspectRatio: 5,
nTries: 20
};
let lRect = d3p.largestRect(rdp, rectOptions);
The algorithm used is an approximation and random points inside the polygon are chosen to do calculations from. Because of this the edges of the box won't always be touching the edge but should be "close enough".
The options.tolerance value might affect this as well but I haven't played around with it much. This is a pretty old question but hopefully it helps someone.
Related
I am new to TensorflowJS and I try to code something but I am stuck...
I have two input layers like that:
const input1 = tf.input({ shape: [64, 64, 3] });
const input2 = tf.input({ shape: [1536] });
The first one is for an image of 64 by 64 and the 3 is for RGB.
The second one is for an array that contains 1536 numbers (floats).
I tried to concatenate them with .concatenate().apply(input1, input2) but got the following error:
ValueError: A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: [[null,64,64,3],[null,1536]]
I also tried to add { axis: -1 } or { axis: 1 } (found that on stack overflow but that doesnt work too).
I also tried that (answer by chat gpt) :
const flatten1 = tf.layers.flatten().apply(input1);
const flatten2 = tf.layers.flatten().apply(input2);
const concat = tf.layers.concatenate({ axis: -1 }).apply([flatten1, flatten2]);
but same error...
Can someone help me? I just want to add this to my tf.sequential() as an input...
PS: This is the module I use:
const tf = require('#tensorflow/tfjs-node');
This just shows you what concatenation is, and how can be done for those specific inputs.
This is a textual description of the code at the bottom of the post:
create an image-like shape (width, height, colorChannels)
create a random one dimensional array (aka vector) of 1536 values
1536 elements can be reshaped into a little "image" of 8 x 64 and 3 channels
Result is 72 x 64 x 8 so this should hint you what the code did (extend the axis with different number of values, that is it.
const original = tf.ones([64, 64, 3]);
const random = tf.randomNormal([1536]);
const reshaped = tf.reshape(random, [8, 64, 3]);
const axis = 0;
const concat = tf.concat([original, reshaped], axis);
console.log(concat);
<script src="https://cdn.jsdelivr.net/npm/#tensorflow/tfjs#2.0.0/dist/tf.min.js"></script>
Another possibility is flat, extend and then back re-shape but I find this simple enough.
Given a point, say [-75.343, 39.984], how do I go about finding all features/markers within a 5km radius of it? I'm utilizing turf.js so I'll be using their circle() function to generate the circle about the point.
Would this work?
const center = [-75.343, 39.984];
const radius = 5;
const options = {steps: 10, units: 'kilometers', properties: {foo: 'bar'}};
const circle = turf.circle(center, radius, options);
}
const features = map.queryRenderedFeatures(
circle,
{ filter: {["within", circle] : true} }
);
I'm hoping to find all features within the circle and be able to store them in an array or in a database for further processing like accessing the feature's lat/lng, etc.
Thank you for the help!
Using queryRenderedFeatures you will be able to get the features that are actually in the viewport (visible). In case your source data is GeoJSON, you can use querySourceFeatures, so it will look to all your source features:
const filteredFeatures = map.querySourceFeatures('routes', {
filter: ['within', circle]
});
Three.js r.71
I'm just getting into Three.js (awesome btw) but am having an issue. I am trying to stream geometry and position/scale/rotation changes between clients using Socket.io and NodeJS. On the server I store the JSON representation of the scene and stream object changes between clients.
When the object's matrix changes (position, scale, rotation), I stream the new matrix to the server and forward it to the other clients. On the other clients I call applyMatrix() with the streamed object (the source object's matrix).
The problem I ran into is that when calling applyMatrix(sourceMatrix), it seems to multiple the existing scale by the scale found in sourceMatrix. For example, when the current object has a scale of x: 2, y:1, z:1, and I apply a matrix with the same scale, after calling applyMatrix, the destination object's scale is x:4, y:1, z:1.
This seems like a bug to me, but wanted to double check.
// Client JS:
client.changeMatrix = function (object) {
// Set the object's scale to x:2 y:1 z:1 then call this twice.
var data = {uuid: object.uuid, matrix: object.matrix};
socket.emit('object:changeMatrix', data);
};
socket.on('object:matrixChanged', function (data) {
var cIdx = getChildIndex(data.uuid);
if (cIdx >= 0) {
scene.children[cIdx].applyMatrix(data.matrix);
// At this point, the object's scale is incorrect
ng3.viewport.updateSelectionHelper();
ng3.viewport.refresh();
}
});
// Server JS:
socket.on('object:changeMatrix', function (data) {
socket.broadcast.emit('object:matrixChanged', data);
});
#WestLangley is correct, I really did not understand what apply matrix is doing (and still don't quite know what it is used for).
I solved my problem by manually setting each element in the source matrix and calling decompose:
// Client JS:
socket.on('object:matrixChanged', function (data) {
var cIdx = getChildIndex(data.uuid);
var child = null;
var key;
if (cIdx >= 0) {
child = scene.children[cIdx];
for (key in child.matrix.elements) {
if (child.matrix.elements.hasOwnProperty(key)) {
child.matrix.elements[key] = data.matrix.elements[key];
}
}
child.matrix.decompose(child.position, child.quaternion, child.scale);
}
}
Unfortunately, once the server picks up the Matrix object, calling:
child.matrix.copy(data.matrix);
does no work. That's why I ended up setting each element manually.
I have a group of graphs visualizing a bunch of data for me (here), based off a csv with approximately 25,000 lines of data, each having 12 parameters. However, doing any interaction (such as selecting a range with the brush on any of the graphs) is slow and unwieldy, completely unlike the dc.js demo found here, which deals with thousands of records as well but maintains smooth animations, or crossfilter's demo here which has 10 times as many records (flights) as I do.
I know the main resource hogs are the two line charts, since they have data points every 15 minutes for about 8 solid months. Removing either of them makes the charts responsive again, but they're the main feature of the visualizations, so is there any way I can make them show less fine-grained data?
The code for the two line graphs specifically is below:
var lineZoomGraph = dc.lineChart("#chart-line-zoom")
.width(1100)
.height(60)
.margins({top: 0, right: 50, bottom: 20, left: 40})
.dimension(dateDim)
.group(tempGroup)
.x(d3.time.scale().domain([minDate,maxDate]));
var tempLineGraph = dc.lineChart("#chart-line-tempPer15Min")
.width(1100).height(240)
.dimension(dateDim)
.group(tempGroup)
.mouseZoomable(true)
.rangeChart(lineZoomGraph)
.brushOn(false)
.x(d3.time.scale().domain([minDate,maxDate]));
Separate but relevant question; how do I modify the y-axis on the line charts? By default they don't encompass the highest and lowest values found in the dataset, which seems odd.
Edit: some code I wrote to try to solve the problem:
var graphWidth = 1100;
var dataPerPixel = data.length / graphWidth;
var tempGroup = dateDim.group().reduceSum(function(d) {
if (d.pointNumber % Math.ceil(dataPerPixel) === 0) {
return d.warmth;
}
});
d.pointNumber is a unique point ID for each data point, cumulative from 0 to 22 thousand ish. Now however the line graph shows up blank. I checked the group's data using tempGroup.all() and now every 21st data point has a temperature value, but all the others have NaN. I haven't succeeded in reducing the group size at all; it's still at 22 thousand or so. I wonder if this is the right approach...
Edit 2: found a different approach. I create the tempGroup normally but then create another group which filters the existing tempGroup even more.
var tempGroup = dateDim.group().reduceSum(function(d) { return d.warmth; });
var filteredTempGroup = {
all: function () {
return tempGroup.top(Infinity).filter( function (d) {
if (d.pointNumber % Math.ceil(dataPerPixel) === 0) return d.value;
} );
}
};
The problem I have here is that d.pointNumber isn't accessible so I can't tell if it's the Nth data point (or a multiple of that). If I assign it to a var it'll just be a fixed value anyway, so I'm not sure how to get around that...
When dealing with performance problems with d3-based charts, the usual culprit is the number of DOM elements, not the size of the data. Notice the crossfilter demo has lots of rows of data, but only a couple hundred bars.
It looks like you might be attempting to plot all the points instead of aggregating them. I guess since you are doing a time series it may be unintuitive to aggregate the points, but consider that your plot can only display 1100 points (the width), so it is pointless to overwork the SVG engine plotting 25,000.
I'd suggest bringing it down to somewhere between 100-1000 bins, e.g. by averaging each day:
var daysDim = data.dimension(function(d) { return d3.time.day(d.time); });
function reduceAddAvg(attr) {
return function(p,v) {
if (_.isLegitNumber(v[attr])) {
++p.count
p.sums += v[attr];
p.averages = (p.count === 0) ? 0 : p.sums/p.count; // gaurd against dividing by zero
}
return p;
};
}
function reduceRemoveAvg(attr) {
return function(p,v) {
if (_.isLegitNumber(v[attr])) {
--p.count
p.sums -= v[attr];
p.averages = (p.count === 0) ? 0 : p.sums/p.count;
}
return p;
};
}
function reduceInitAvg() {
return {count:0, sums:0, averages:0};
}
...
// average a parameter (column) named "param"
var daysGroup = dim.group().reduce(reduceAddAvg('param'), reduceRemoveAvg('param'), reduceInitAvg);
(reusable average reduce functions from the FAQ)
Then specify your xUnits to match, and use elasticY to auto-calculate the y axis:
chart.xUnits(d3.time.days)
.elasticY(true)
I have been racking my brain on how to make this work. I can find no examples of this and actually no previous questions. Basically I have a 121 thumbnail images (with the exact same dimensions), arrange them in a grid with gutters and I want to take the first image and place it in the center. (this allows for an 11x11 image grid) Then I would like to take each next image and begin to arrange them around the center image using the next closest available vacant location to the center image until all used up. It is assumed the list of images will be gotten from an array object. What is the most efficient way of doing this?
Most likely not the most efficient way of solving this, but I wanted to play with it:
You could iterate over all the points in your grid, calculate their distances to the center point and then sort the points by this distance. The advantage over the algorithmic solutions is that you can use all sorts of distance functions:
// Setup constants
var arraySize = 11;
var centerPoint = {x:5, y:5};
// Calculate the Euclidean Distance between two points
function distance(point1, point2) {
return Math.sqrt(Math.pow(point1.x - point2.x, 2) + Math.pow(point1.y - point2.y, 2));
}
// Create array containing points with distance values
var pointsWithDistances = [];
for (var i=0; i<arraySize; i++) {
for (var j=0; j<arraySize; j++) {
var point = {x:i, y:j};
point.distance = distance(centerPoint, point);
pointsWithDistances.push(point);
}
}
// Sort points by distance value
pointsWithDistances.sort(function(point1, point2) {
return point1.distance == point2.distance ? 0 : point1.distance < point2.distance ? -1 : 1;
});
The resulting pointsWithDistances array will look like this:
[
{x:5, y:5, distance:0},
{x:4, y:5, distance:1},
{x:5, y:4, distance:1},
...
{x:4, y:4, distance:1.4142135623730951},
{x:4, y:6, distance:1.4142135623730951},
...
{x:3, y:5, distance:2},
...
]
By iterating over the array in this order you are effectively filling the grid from the center outwards.
(Thanks for Andreas Carlbom's idea how to display this structure.)
Check out the difference to using Rectilinear Distances:
// Rectilinear Distance between two points
function distance(point1, point2) {
return Math.abs(point1.x - point2.x) + Math.abs(point1.y - point2.y);
}
For the shell-like structure of the algorithmic approaches you can use the Maximum Metric:
// 'Maximum Metric' Distance between two points
function distance(point1, point2) {
return Math.max(Math.abs(point1.x - point2.x), Math.abs(point1.y - point2.y));
}
You can play with the code here: http://jsfiddle.net/green/B3cF8/