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Some linting and plotting changes (in progress) #3

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merged 8 commits into from
Oct 12, 2015
Merged

Some linting and plotting changes (in progress) #3

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b30bf71
some linting
NathanEpstein Oct 4, 2015
84a2f67
some more linting
NathanEpstein Oct 4, 2015
a9da61d
a bit more linting
NathanEpstein Oct 4, 2015
7f9477b
initial chagnes to plotting
NathanEpstein Oct 12, 2015
08190cc
fix styling and multi-dataset issues
NathanEpstein Oct 12, 2015
b4d0113
add regression endpoints
NathanEpstein Oct 12, 2015
cfd8e9a
update testing
NathanEpstein Oct 12, 2015
11c6cbc
docs
NathanEpstein Oct 12, 2015
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98 changes: 60 additions & 38 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -21,57 +21,76 @@ var dk = require('datakitjs');
//READ A CSV FILE

//file.csv
// COL1,COL2
// val11,val12
// val21,val22
// COL1, COL2
// val11, val12
// val21, val22

dk.csv('file.csv',function(data){
dk.csv('file.csv', function(data) {
console.log(data);
});

//Output:
//[{COL1:val11,COL2:val12},{COL1:val21,COL2:val22}]
//[{ COL1: val11, COL2: val12 }, { COL1: val21, COL2: val22 }]


//GET A COLUMN FROM AN ARRAY OF ROW OBJECTS
dk.csv('file.csv',function(data){
var c2 = dk.col(data,'COL2');
dk.csv('file.csv', function(data) {
var c2 = dk.col(data, 'COL2');
console.log(c2);
});

//Output:
//[val12,val22]
//[val12, val22]

// By default, dk.csv will convert all values to strings. You can convert select
// columns to numbers by passing an array of column names to 'dk.numeric'.
// columns to numbers by passing an array of column names to 'dk.numeric'.

//file2.csv
// COL1, COL2
// val11, 1
// val21, 2

dk.csv('file2.csv', function(data){
dk.csv('file2.csv', function(data) {
var d = dk.numeric(data, ['COL2'], 0) // The third parameter value will be filled
// in to blank cells. Its default value is 0.
var c2 = dk.col(d, 'COL2');
console.log(c2);
});

//Output:
//[1,2]
//[1, 2]


//PLOT ARRAY(S) OF DATA

var x1 = dk.norm(25);
var plot = dk.plot(x1); // x1 will be plotted on the y-axis, index on the x-axis
//dk.plot returns a string of the html used to create the plot.

var chart = new dk.Chart({
//optional config
height: 500,
width: 500,
xLab: 'x-Axis Label',
yLab: 'y-Axis Label'
});

var labels = dk.seq(1,25);
chart.addDataSet({
x: [1, 2, 3],
y: [4, 5, 6],
z: [2, 3, 5],
colors: ['blue', 'green', 'red']
}).addDataSet({
x: [1, 10],
y: [2, -1],
type: 'line'
}).addDataSet({
x: [10, 5, 1],
y: [4, 5, 2],
labels: ["first", "second", "third"]
}).plot();


var labels = dk.seq(1, 25);
var x2 = dk.norm(25);
var x3 = dk.norm(25);
dk.plot(labels,x1,x2,x3); //labels on the x-axis, the next three arrays will each be plotted on the y-axis
dk.plot(labels, x1, x2, x3); //labels on the x-axis, the next three arrays will each be plotted on the y-axis
```

###Statistical Methods
Expand All @@ -80,29 +99,32 @@ dk.plot(labels,x1,x2,x3); //labels on the x-axis, the next three arrays will eac
var dk = require('datakitjs');

//MEAN OF AN ARRAY
dk.mean([1,2,3]); //returns 2
dk.mean([1, 2, 3]); //returns 2

//STANDARD DEVIATION AND VARIANCE OF AN ARRAY
dk.sd([1,2,3]); //returns 1
dk.vari([1,2,3]); //returns 1
dk.sd([1, 2, 3]); //returns 1
dk.vari([1, 2, 3]); //returns 1

//COVARIANCE OF TWO ARRAYS
dk.cov([1,2,3],[3,2,1]); //returns -1
dk.cov([1, 2, 3], [3, 2, 1]); //returns -1

//SIMPLE LINEAR REGRESSION

var x = [1,2,3];
var y = [2,1,3];
var x = [1, 2, 3];
var y = [2, 1, 3];

var model = dk.reg(x,y);
var model = dk.reg(x, y);

// model.f is a function that returns the estimated y for an input x (estimated via standard OLS regression)
// model.f = function(x){
// model.f = function(x) {
// return (a + b * x);
// }
// };

// model.pts is an array of the estimated y for each element of x
// model.pts = [1.5,2,2.5];
// model.pts = [1.5, 2, 2.5];

// model.endPoints is an object with the coordinates of the boundary points
// model.endPoints = { x1: 1, x2: 3, y1: 1.5, y2: 2.5 };

```

Expand All @@ -112,31 +134,31 @@ var dk = require('datakitjs');

//GENERATE AN ARRAY WITH A SEQUENCE OF NUMBERS

dk.seq(1,5); //returns [1,2,3,4,5]
dk.seq(1, 5); //returns [1, 2, 3, 4, 5]

dk.seq(0,1,0.25); //returns [0,0.25,0.5,0.75,1]
dk.seq(0, 1, 0.25); //returns [0, 0.25, 0.5, 0.75, 1]

//GENERATE AN ARRAY WITH REPEATED VALUE

dk.rep(1,5); //returns [1,1,1,1,1]
dk.rep(1, 5); //returns [1, 1, 1, 1, 1]

//CHECK IF NUMBERS ARE CLOSE
dk.isclose(0,Math.pow(10,-15)); //returns true
dk.isclose(0, Math.pow(10, -15)); //returns true

dk.isclose(0,Math.pow(10,-5)); //returns false
dk.isclose(0, Math.pow(10, -5)); //returns false

//SUM AN ARRAY OF NUMBERS
//uses Kahan summation

dk.sum([1,2,3]); //returns 6
dk.sum([1, 2, 3]); //returns 6

//PRODUCT OF AN ARRAY OF NUMBERS
//implementation from 'Accurate Floating Point Product' - Stef Graillat

dk.prod([1,2,3]); //returns 6
dk.prod([1, 2, 3]); //returns 6

//MAX AND MIN OF AN ARRAY
var x = [1,2,3];
var x = [1, 2, 3];
dk.min(x); //returns 1
dk.max(x); //returns 3

Expand All @@ -148,16 +170,16 @@ var dk = require('datakitjs');

//GET AN ARRAY OF EXPONENTIALLY DISTRIBUTED VALUES

dk.exp(3,1); //returns [ 0.3584189321510761, 1.0466439500242446, 0.08887770301056963 ]
dk.exp(3, 1); //returns [0.3584189321510761, 1.0466439500242446, 0.08887770301056963]


//GET AN ARRAY OF NORMALLY DISTRIBUTED VALUES

dk.norm(3,0,1); //returns [ -1.709768103193772, 0.23530041388459744, 0.4431320382580479 ]
dk.norm(3, 0, 1); //returns [-1.709768103193772, 0.23530041388459744, 0.4431320382580479]

//GET AN ARRAY OF UNIFORMLY DISTRIBUTED VALUES

dk.uni(3); //returns [ 0.30658303829841316, 0.1601463456172496, 0.8538850131444633 ]
dk.uni(3); //returns [0.30658303829841316, 0.1601463456172496, 0.8538850131444633]

```

Expand Down
181 changes: 181 additions & 0 deletions lib/chart.js
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Original file line number Diff line number Diff line change
@@ -0,0 +1,181 @@
var Chart = function(config) {
var self = this;

// initial configuration of size and datasets
var datasets = [];

self.config = config || {};
self.config.height = self.config.height || 500;
self.config.width = self.config.width || 500;
self.config.bufferHeight = self.config.height * 0.8;
self.config.bufferWidth = self.config.width * 0.6;

// public method to plot by calling all private methods
this.render = function() {
buildCanvas();
buildMapping();
buildAxes();
plotDataSets();
return self;
};

// public methods to get and set datasets
this.datasets = function() {
return datasets.slice();
};

this.addDataSet = function(dataset) {
datasets.push(dataset);
return self;
};

// private methods
var buildCanvas = function() {
self.canvas = d3.select(self.config.selector || 'body')
.append('svg')
.attr('height', self.config.height)
.attr('width', self.config.width);

self.buffer = self.canvas.append('g');

var xTranslate = (self.config.width - self.config.bufferWidth) / 2;
var yTranslate = (self.config.height - self.config.bufferHeight) / 2;

self.buffer.attr(
'transform',
'translate(' + xTranslate + ', ' + yTranslate + ')'
);
};

var buildMapping = function() {
var xMin = mini(datasets[0].x),
yMin = mini(datasets[0].y),
xMax = maxi(datasets[0].x),
yMax = maxi(datasets[0].y);

datasets.forEach(function(dataset, index) {
if (index > 0) {
xMin = Math.min(mini(dataset.x), xMin);
yMin = Math.min(mini(dataset.y), yMin);
xMax = Math.max(maxi(dataset.x), xMax);
yMax = Math.max(maxi(dataset.y), yMax);
}
});

self.xMap = d3.scale.linear()
.domain([xMin, xMax])
.range([0, self.config.bufferWidth]);

self.yMap = d3.scale.linear()
.domain([yMax, yMin])
.range([0, self.config.bufferHeight]);

};

var buildAxes = function() {
var xAxis = d3.svg.axis()
.scale(self.xMap);

var yAxis = d3.svg.axis()
.scale(self.yMap)
.orient('left');

self.buffer.append('g')
.attr('transform','translate(0,' + self.config.bufferHeight + ')')
.call(xAxis);

self.buffer.append('g')
.call(yAxis);

var xLabel = self.buffer.append('text')
.attr('x', self.config.bufferWidth * 0.5)
.attr('y', self.config.bufferHeight + 50)
.text(self.config.xLab)
.attr('text-anchor','middle');

var yLabel = self.buffer.append('text')
.attr('x', -self.config.bufferHeight * 0.5)
.attr('y', -50)
.attr('transform','rotate(-90)')
.text(self.config.yLab)
.attr('text-anchor','middle');

};

var plotDataSets = function() {
datasets.forEach(function(dataset) {
if (dataset.type == 'line') {
for (var i = 1; i < dataset.x.length; i++) {
self.buffer.append('line')
.attr('stroke-width', 1)
.attr('stroke', 'black')
.attr('x1', self.xMap(dataset.x[i-1]))
.attr('x2', self.xMap(dataset.x[i]))
.attr('y1', self.yMap(dataset.y[i-1]))
.attr('y2', self.yMap(dataset.y[i]));
};
}
else if (typeof dataset.labels !== 'undefined') {
for (var i = 0; i < dataset.x.length; i++) {
self.buffer.append('text')
.attr('x', self.xMap(dataset.x[i]))
.attr('y', self.yMap(dataset.y[i]))
.text(dataset.labels[i])
.attr('text-anchor','middle')
.attr('stroke', dataset.color || 'black');
};
}
else {
// make a scatter plot if not a line
for (var i = 0; i < dataset.x.length; i++) {
var zMin = typeof dataset.z === 'undefined' ? null : mini(dataset.z);
var zMax = typeof dataset.z === 'undefined' ? null : maxi(dataset.z);

self.buffer.append('circle')
.attr('r', function() {
if (typeof dataset.z === 'undefined') {
return self.config.height * self.config.width * (0.00002);
}
else {
var minSize = self.config.height * self.config.width * 0.000025;
var sizeMultiplier = self.config.height * self.config.width * (0.0001);
var proportionOfMaxValue = (dataset.z[i] - zMin) / (zMax - zMin);

return (minSize + sizeMultiplier * proportionOfMaxValue);
}
})
.attr('cx', self.xMap(dataset.x[i]))
.attr('cy', self.yMap(dataset.y[i]))
.attr('opacity',function() {
if (typeof z === 'undefined') {
return 1;
}
else{
return 0.3;
}
})
.attr('fill',function() {
if (typeof dataset.colors === 'undefined') {
return 'none';
}
else{
return dataset.colors[i];
}
})
.attr('stroke', 'black');
};
};
});
};

function mini(arr) {
return Math.min.apply(null, arr);
};

function maxi(arr) {
return Math.max.apply(null, arr);
};
};

module.exports = Chart;

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