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Getting Started With Plotapi Line Fight

Published

on

Preamble

In [1]:
from plotapi import LineFight

LineFight.set_license(“your username”, “your license key”)

Introduction

Our first Plotapi Line Fight Diagram!

Plotapi Line Fight is a beautiful and feature rich take on the popular Line Chart Race. As we can see, we have set our license details in the preamble with LineFight.set_license().

Dataset

Plotapi Line Fight expects at minimum a list of dictionary items, these will define the value of our lines over time.

In [38]:
samples = [
{“order”: 0, “name”: “Sankey”, “value”: 10},
{“order”: 0, “name”: “Terminus”, “value”: 10},
{“order”: 0, “name”: “Chord”, “value”: 40},
{“order”: 0, “name”: “Bar Fight”, “value”: 90},
{“order”: 0, “name”: “Pie Fight”, “value”: 70},

{“order”: 1, “name”: “Sankey”, “value”: 30},
{“order”: 1, “name”: “Terminus”, “value”: 20},
{“order”: 1, “name”: “Chord”, “value”: 40},
{“order”: 1, “name”: “Bar Fight”, “value”: 120},
{“order”: 1, “name”: “Pie Fight”, “value”: 55},

{“order”: 2, “name”: “Sankey”, “value”: 35},
{“order”: 2, “name”: “Terminus”, “value”: 45},
{“order”: 2, “name”: “Chord”, “value”: 60},
{“order”: 2, “name”: “Bar Fight”, “value”: 85},
{“order”: 2, “name”: “Pie Fight”, “value”: 100},

{“order”: 3, “name”: “Sankey”, “value”: 25},
{“order”: 3, “name”: “Terminus”, “value”: 60},
{“order”: 3, “name”: “Chord”, “value”: 90},
{“order”: 3, “name”: “Bar Fight”, “value”: 50},
{“order”: 3, “name”: “Pie Fight”, “value”: 105},

{“order”: 4, “name”: “Sankey”, “value”: 60},
{“order”: 4, “name”: “Terminus”, “value”: 80},
{“order”: 4, “name”: “Chord”, “value”: 120},
{“order”: 4, “name”: “Bar Fight”, “value”: 30},
{“order”: 4, “name”: “Pie Fight”, “value”: 95},
]

We can see that each dictionary item has three properties:

order which determines with time period this item belongs to. This should be numerical, but can be formatted e.g. as dates.
name the name of the item, and the text that appears on the lines.
value the value of the lines at the associated point in time.

Visualisation

Creating our first Line Fight Diagram is as easy as calling Plotapi with our one input.

Here we’re using .show() which outputs to a Jupyter Notebook cell, however, we may want to output to an HTML file with .to_html() instead. More on the different output methods later!

Be sure to interact with the visualisation to see what the default settings can do!

In [39]:
LineFight(samples).show()

Plotapi – Line Fight Diagram


);
});

d3.select(“#plotapi-chart-0216fe66_svg .event_group”)
.interrupt()
.transition()
.duration(250)
.style(“opacity”, 1)
;
}

function update_current(current) {
var event_fired = false;

var event_element = events.find(item => {
return item.order === current
})

if(event_element != undefined){
event_fired = true;
show_event(event_element)
}

x_travel_scale = d3
.scaleLinear()
.domain([0, 2000])
.range([sequence[sequence_index-1], sequence[sequence_index]]);

if (sequence_index > 0) {
update_minimap();
}

current_data = data.filter((d) => d.order == current);

for (var index = 0; index d.id == element.id);
if (contestant.length != 0) {
if(sequence_index > 1 && contestant[0].line_data.length == 1){
contestant[0].icon.transition()
.duration(200)
.style(“opacity”, 1)
contestant[0].line_path.transition()
.duration(200)
.style(“opacity”, 1)
contestant[0].line_path_bg.transition()
.duration(200)
.style(“opacity”, 0.25)
}

if (!isNaN(element.value)) {
contestant[0].current_value = contestant[0].target_value;
contestant[0].target_value = element.value;
contestant[0].line_data.push({“x”:sequence[sequence_index],”y”: contestant[0].current_value})
contestant[0].travel_scale = d3
.scaleLinear()
.domain([0, 2000])
.range([contestant[0].current_value, element.value]);
}
} else {
var target_value = !isNaN(element.value) ? element.value : 0;

var x_pos = 0;

contestant_rect = d3
.select(“#plotapi-chart-0216fe66_svg .bar_group”)
.append(“rect”)
.attr(“x”, 0)
.attr(“y”, y_scale(index))
.attr(“width”, x_pos)
.attr(“height”, bar_height)
.style(“fill”, color(element.id))

.style(“display”, “none”)
;

contestant_icon_image = icon(element.id);

if(contestant_icon_image == undefined){

contestant_icon = d3
.select(“#plotapi-chart-0216fe66_svg .bar_group”)
.append(‘circle’)
.attr(“cx”, 0)
.attr(“cy”, y_scale(index) + icon_padding)
.attr(‘r’, icon_size/2)
.style(“opacity”, 1)
.style(“stroke-opacity”, 0.25)
.attr(“stroke-width”, 8)
.attr(“stroke”, darken_color(color(element.id),-0.5))
.attr(‘fill’, color(element.id));
}
else{
contestant_icon = d3
.select(“#plotapi-chart-0216fe66_svg .bar_group”)
.append(“image”)
.attr(“x”, 0)
.attr(“y”, y_scale(index) + icon_padding)
.attr(“width”, icon_size)
.attr(“height”, icon_size)
.style(“opacity”, 1)
.attr(“xlink:href”, contestant_icon_image)

}
contestant_icon
.on(“mouseover”, function (d, i) {
d3.selectAll(“#plotapi-chart-0216fe66_svg .bartext_group .grp”+element.id)
.transition()
.duration(200)
.style(“opacity”, 1);
}
)
.on(“mouseout”, function (d, i) {
d3.selectAll(“#plotapi-chart-0216fe66_svg .bartext_group .grp”+element.id)
.transition()
.transition()
.duration(200)
.style(“opacity”, 0);
}
);

contestant_line_path_bg = line_group.append(“path”)
.attr(“stroke-width”, 12)
.style(“stroke”, color(element.id))
.style(“opacity”, 0.25);

contestant_line_path = line_group.append(“path”)
.style(“stroke”, color(element.id))
.attr(“stroke-width”, 4)

if(sequence_index > 0){
contestant_icon.style(“opacity”, 0);
contestant_line_path.style(“opacity”, 0);
contestant_line_path_bg.style(“opacity”, 0);
}

contestant_text_value = d3
.select(“#plotapi-chart-0216fe66_svg .bartext_group”)
.append(“text”)
.attr(“x”, x_pos – 5)
.attr(“y”, bar_text_upper_y(index))
.text(element.value)
.style(“text-anchor”, “end”)
.style(“dominant-baseline”, “central”)
.style(“fill”, “black”)
.attr(“opacity”,”0″)
.style(“font-size”, bartext_font_size + “px”)
.classed(“grp”+element.id,true);

contestant_text_name = d3
.select(“#plotapi-chart-0216fe66_svg .bartext_group”)
.append(“text”)
.attr(“x”, 0)
.attr(“y”, bar_text_lower_y(index))
.text(unique_names[element.id])
.style(“text-anchor”, “end”)
.style(“dominant-baseline”, “central”)
.style(“fill”, “black”)
.attr(“opacity”,”0″)
.style(“font-weight”, “900”)
.style(“font-size”, bartext_font_size + “px”)
.classed(“grp”+element.id,true);

contestant = {
id: element.id,
rect: contestant_rect,
line_data: [{“x”:sequence[sequence_index],”y”: target_value}],
line_path: contestant_line_path,
line_path_bg: contestant_line_path_bg,
icon_image: !(contestant_icon_image == undefined),
icon: contestant_icon,
text_value: contestant_text_value,
text_name: contestant_text_name,
current_value: target_value,
target_value: target_value,
travel_scale: d3
.scaleLinear()
.domain([0, 2000])
.range([target_value, target_value]),
};

contestants.push(contestant);
}
}
}

function force_order(easeFn) {
return;
bar_height = d3.max([420 / top_n – bar_padding, 0]);
bartext_font_size = 14;
y_scale = d3.scaleLinear().domain([0, top_n]).range([0, 420]);

for (var index = 0; index top_n) {
easing = d3.easeCubic;
duration = 0;
}

const element = contestants[index];

var x_scale_current_x = x_scale(element.current_value);

element.text_name.attr(
“x”,
x_scale_current_x –
text_padding –
(element.icon_image ? bar_height – icon_padding : 0)
);

element.text_value
.attr(
“x”,
x_scale_current_x –
text_padding –
(element.icon_image ? bar_height – icon_padding : 0)
)
.text(format_value(element.current_value));

element.rect.attr(“width”, x_scale_current_x);

element.icon.attr(
“x”,
x_scale_current_x – (bar_height – icon_padding)
);

element.rect
.transition()
.ease(easing)
.duration(duration)
.attr(“height”, bar_height)
.attr(“y”, y_scale(index));

icon_size = 40

element.icon
.attr(“width”, icon_size)
.attr(“height”, icon_size)
.transition()
.ease(easing)
.duration(duration)
.attr(“y”, y_scale(index) + icon_padding);

element.text_name
.style(“font-size”, bartext_font_size + “px”)
.transition()
.ease(easing)
.duration(duration)
.attr(“y”, bar_text_lower_y(index));

element.text_value
.style(“font-size”, bartext_font_size + “px”)

.transition()
.ease(easing)
.duration(duration)
.attr(“y”, bar_text_upper_y(index));
}
}

function draw(delta) {
x_axis_current_x = x_travel_scale(delta);
x_scale = d3
.scaleLinear()
.domain([sequence[0], sequence[sequence.length-1]])
.range([0, 760.0]);

x_axis.scale(x_scale);
x_scale_current_x = x_scale(x_axis_current_x);

d3.select(“#plotapi-chart-0216fe66_svg”)
.select(“.x_axis”)
.transition()
.duration(250)
.ease(d3.easeLinear)
.call(x_axis);

for (var index = 0; index d.current_value);

if (new_max != current_max) {
current_max = new_max;

y_scale = d3
.scaleLinear()
.domain([10.0, 120.0])
.range([420, 0]);

y_axis.scale(y_scale);

d3.select(“#plotapi-chart-0216fe66_svg”)
.select(“.y_axis”)
.transition()
.duration(250)
.ease(d3.easeLinear)
.call(y_axis);
}

var y_scale_current_y = y_scale(element.current_value);

element.line_data[element.line_data.length-1].y = element.current_value
element.line_data[element.line_data.length-1].x = x_axis_current_x

var line = d3.line()
.x(function(d,i) { return x_scale(d.x);})
.y(function(d) { return y_scale(d.y);})

var path = element.line_path
.attr(“d”, line(element.line_data))

var path_bg = element.line_path_bg
.attr(“d”, line(element.line_data))

element.text_name
.attr(
“x”,
x_scale_current_x –
text_padding –
(icon_size/2)
)
.attr(“y”, bar_text_lower_y(y_scale_current_y));

element.text_value
.attr(
“x”,
x_scale_current_x –
text_padding –
(icon_size/2)
)
.text(format_value(element.current_value))
.attr(“y”, bar_text_upper_y(y_scale_current_y));

element.rect.attr(“width”, x_scale_current_x);

element.icon
.attr(
“x”,
x_scale_current_x – (icon_size/2)
)
.attr(“y”, y_scale_current_y – ((icon_size)/2))
.attr(
“cx”,
x_scale_current_x
)
.attr(“cy”, y_scale_current_y);

if (
false

) {
element.rect
.interrupt()
.transition()
.ease(d3.easeSinOut)
.duration(500)
.attr(“y”, y_scale(index));

element.icon
.interrupt()
.transition()
.ease(d3.easeSinOut)
.duration(500)
.attr(“y”, y_scale(index) + icon_padding);

element.text_name
.interrupt()
.transition()
.ease(d3.easeSinOut)
.duration(500)
.attr(“y”, bar_text_lower_y(index));

element.text_value
.interrupt()
.transition()
.ease(d3.easeSinOut)
.duration(500)
.attr(“y”, bar_text_upper_y(index));
}
}
}
}

function initialise() {
contestants = [];
d3.select(“#plotapi-chart-0216fe66_svg .line_group”)
.selectAll(“*”)
.remove();
d3.select(“#plotapi-chart-0216fe66_svg .bar_group”)
.selectAll(“*”)
.remove();
d3.select(“#plotapi-chart-0216fe66_svg .bartext_group”)
.selectAll(“*”)
.remove();

d3.select(“#plotapi-chart-0216fe66_svg .minimap_group”)
.selectAll(“*”)
.remove();
last_proc = 0;
top_n = 5;

bar_padding = 0;
bar_height = d3.max([420 / 5 – bar_padding, 0]);
icon_padding =0;
text_padding = 5;
current_order = [];
last_order = [];
elapsed_time = 0;
sequence_index = 0;
current_max = null;
bartext_font_size = 14;
icon_size = 40
y_scale = d3
.scaleLinear()
.domain([10.0, 120.0])
.range([420, 0]);

update_current(sequence_index);

current_order_text.text(format_current_order(sequence[sequence_index]))

new_max = d3.max(contestants, (d) => d.current_value);

if (new_max != current_max) {
current_max = new_max;

}

x_scale = d3
.scaleLinear()
.domain([sequence[0], sequence[sequence.length-1]])
.range([0, 760.0]);

x_axis = d3
.axisBottom()
.scale(x_scale)
.ticks(sequence.length-1, undefined)
.tickSize(-420)
.tickFormat((d) => d3.format(“,”)(d));

y_axis = d3
.axisLeft()
.scale(y_scale)
.ticks(7.6, undefined)
.tickSize(-760.0)
.tickFormat((d) => d3.format(“,”)(d));

d3.select(“#plotapi-chart-0216fe66_svg .axis_group”)
.selectAll(“*”)
.remove();

var x_axis_line = d3
.select(“#plotapi-chart-0216fe66_svg .axis_group”)
.append(“g”)
.attr(“class”, “axis x_axis”)
.attr(“transform”, `translate(0, 420)`)
.call(x_axis)
.selectAll(“.tick line”)
.classed(“origin”, (d) => d == 0);

var y_axis_line = d3
.select(“#plotapi-chart-0216fe66_svg .axis_group”)
.append(“g”)
.attr(“class”, “axis y_axis”)
.attr(“transform”, `translate(0, 0))`)
.call(y_axis)

d3.select(“#plotapi-chart-0216fe66_svg”)
.select(“.y_axis”)
.style(“text-anchor”, “start”)

force_order(d3.easeCubic);
}

function darken_color(color, factor) {
return d3.color(color).darker(factor)
}

function color(index) {
node = nodes[index];
if (node.color) {
return node.color;
}

var ratio = index / (5);
return d3.interpolateRainbow(ratio);
}

function icon(index) {
node = nodes[index];
if (node.icon) {
return node.icon;
}

return undefined;
}

function bar_text_upper_y(index) {
return (index-(icon_size/2) ) + icon_size * 0.75;
}

function bar_text_lower_y(index) {
return (index-(icon_size/2) ) + icon_size * 0.25;
}

function update_minimap() {
var minimap_order = contestants.slice();
minimap_order.sort((a, b) => b.current_value – a.current_value);
minimap_order = minimap_order.slice(0,6);

min_value = d3.min([minimap_order[minimap_order.length-1].current_value,0]);

minimap_order = minimap_order.slice(0,5);

if(min_value Math.abs(min_value) + item.current_value)
.reduce((prev, next) => prev + next);
}
else{
current_sum = minimap_order
.map((item) => item.current_value)
.reduce((prev, next) => prev + next);
}

if (current_sum != 0) {
var mm_bar_height = 42.0;
var mm_bar_drop_height = 126.0;

mm_y_scale = d3
.scaleLinear()
.domain([0, current_sum])
.range([0, mm_bar_height]);
mm_x_scale = d3
.scaleLinear()
.domain([0, sequence.length])
.range([0, 253.33333333333334]);

var mm_pos_x = mm_x_scale(sequence_index – 1);
var mm_width = mm_x_scale(1);

var mm_running_total = 0;
for (var mm_index = 0; mm_index d.current_value > 0 && !isNaN(d.current_value)
);
if (top_n d.current_value > 0 && !isNaN(d.current_value)
);
if (top_n > 1) {
top_n–;
force_order(d3.easeElastic);
}
}
);

d3.select(“#plotapi-chart-0216fe66_minus”).on(
“mouseover”,
function (d, i) {
d3.select(“#plotapi-chart-0216fe66_minus”).style(“opacity”, 1);
}
);

d3.select(“#plotapi-chart-0216fe66_minus”).on(
“mouseout”,
function (d, i) {
d3.select(“#plotapi-chart-0216fe66_minus”).style(“opacity”, 0.6);
}
);

d3.select(“#plotapi-chart-0216fe66 svg”).on(
“mouseenter”,
function () {
d3.select(“#plotapi-chart-0216fe66_icon”).style(“opacity”, 0.6);
d3.select(“#plotapi-chart-0216fe66_plus”).style(“opacity”, 0.6);
d3.select(“#plotapi-chart-0216fe66_minus”).style(“opacity”, 0.6);
if (!finished) {
d3.select(“#plotapi-chart-0216fe66_restart”).style(
“opacity”,
0.6
);
}
}
);

d3.select(“#plotapi-chart-0216fe66 svg”).on(
“mouseleave”,
function () {
d3.select(“#plotapi-chart-0216fe66_icon”).style(“opacity”, 0);
d3.select(“#plotapi-chart-0216fe66_plus”).style(“opacity”, 0);
d3.select(“#plotapi-chart-0216fe66_minus”).style(“opacity”, 0);
if (!finished) {
d3.select(“#plotapi-chart-0216fe66_restart”).style(
“opacity”,
0
);
}
}
);

}

}());

Here we can see the default behaviour of Plotapi Line Fight.

You can do so much more than what’s presented in this example, and we’ll cover this in later sections. If you want to see the full list of growing features, check out the Plotapi Documentation.

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Original Article: datacrayon.com

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Technology

Changing the Colours and Icons

Published

on

Preamble?

In [1]:

from plotapi import BarFight

BarFight.set_license(“your username”, “your license key”)

Introduction?

Let’s take a look at how we can change the colours and icons for nodes in our Bar Fight diagram.
Plotapi Bar Fight is a beautiful and feature rich take on the popular Bar Chart Race. As we can see, we have set our license details in the preamble with BarFight.set_license().

Dataset?

Plotapi Bar Fight expects at minimum a list of dictionary items, these will define the value of our segments over time.

In [2]:

samples = [
{“order”: 0, “name”: “Sankey”, “value”: 10},
{“order”: 0, “name”: “Terminus”, “value”: 12},
{“order”: 0, “name”: “Chord”, “value”: 8},
{“order”: 0, “name”: “Bar Fight”, “value”: 9},
{“order”: 0, “name”: “Pie Fight”, “value”: 12},

{“order”: 1, “name”: “Sankey”, “value”: 18},
{“order”: 1, “name”: “Terminus”, “value”: 24},
{“order”: 1, “name”: “Chord”, “value”: 22},
{“order”: 1, “name”: “Bar Fight”, “value”: 14},
{“order”: 1, “name”: “Pie Fight”, “value”: 17},

{“order”: 2, “name”: “Sankey”, “value”: 24},
{“order”: 2, “name”: “Terminus”, “value”: 40},
{“order”: 2, “name”: “Chord”, “value”: 32},
{“order”: 2, “name”: “Bar Fight”, “value”: 19},
{“order”: 2, “name”: “Pie Fight”, “value”: 42},

{“order”: 3, “name”: “Sankey”, “value”: 32},
{“order”: 3, “name”: “Terminus”, “value”: 62},
{“order”: 3, “name”: “Chord”, “value”: 40},
{“order”: 3, “name”: “Bar Fight”, “value”: 25},
{“order”: 3, “name”: “Pie Fight”, “value”: 64},

{“order”: 4, “name”: “Sankey”, “value”: 38},
{“order”: 4, “name”: “Terminus”, “value”: 75},
{“order”: 4, “name”: “Chord”, “value”: 55},
{“order”: 4, “name”: “Bar Fight”, “value”: 45},
{“order”: 4, “name”: “Pie Fight”, “value”: 120},
]

We can see that each dictionary item has three properties:

order which determines with time period this item belongs to. This should be numerical, but can be formatted e.g. as dates.

name the name of the item, and the text that appears on the bar.

value the value of the bar at the associated point in time.

Next, we’ll start customising the bars, or nodes, that will represent our data over time. Plotapi Bar Fight expects a list of dictionary items to configure each node.

In [3]:

nodes = [
{
“name”: “Sankey”,
“color”: “#ffd166”,
“icon”: “https://datacrayon.com/datasets/pokemon_img/003.png”
},
{
“name”: “Terminus”,
“color”: “#06d6a0”,
“icon”: “https://datacrayon.com/datasets/pokemon_img/004.png”
},
{
“name”: “Chord”,
“color”: “#118ab2”,
“icon”: “https://datacrayon.com/datasets/pokemon_img/025.png”
},
{
“name”: “Bar Fight”,
“color”: “#073b4c”,
“icon”: “https://datacrayon.com/datasets/pokemon_img/151.png”
},
{
“name”: “Pie Fight”,
“color”: “#ef476f”,
“icon”: “https://datacrayon.com/datasets/pokemon_img/232.png”
}
]

We can see that each dictionary item has three properties:

name, the name of the item, which corresponds to names specified in the samples definitions above.

color, the desired colour of the bars (CSS colour e.g. “#073b4c” or “red”).

icon, the location of the image to use for the icon.

Visualisation?

Here we’re using .show() which outputs to a Jupyter Notebook cell, however, we may want to output to an HTML file with .to_html() instead. More on the different output methods later!
Be sure to interact with the visualisation to see what the default settings can do!

In [4]:

BarFight(samples,
nodes=nodes).show()

Plotapi – Bar Fight Diagram


);
});

d3.select(“#plotapi-chart-c7486add_svg .event_group”)
.interrupt()
.transition()
.duration(250)
.style(“opacity”, 1)
;
}

function update_current(current) {
var event_fired = false;

var event_element = events.find(item => {
return item.order === current
})

if(event_element != undefined){
event_fired = true;
show_event(event_element)
}

current_data = data.filter((d) => d.order == current);

for (var index = 0; index d.id == element.id);
if (contestant.length != 0) {
if (!isNaN(element.value)) {
contestant[0].current_value = contestant[0].target_value;
contestant[0].target_value = element.value;
contestant[0].travel_scale = d3
.scaleLinear()
.domain([0, 2000])
.range([contestant[0].current_value, element.value]);
}
} else {
var target_value = !isNaN(element.value) ? element.value : 0;

var x_pos = 0;

contestant_rect = d3
.select(“#plotapi-chart-c7486add_svg .bar_group”)
.append(“rect”)
.attr(“x”, 0)
.attr(“y”, y_scale(index))
.attr(“width”, x_pos)
.attr(“height”, bar_height)
.style(“fill”, color(element.id))
.attr(“stroke-width”, 4)
.attr(“stroke-opacity”, 1)
.style(“stroke”, darken_color(color(element.id),0.3))
;

contestant_icon_image = icon(element.id);

contestant_icon = d3
.select(“#plotapi-chart-c7486add_svg .bar_group”)
.append(“image”)
.attr(“x”, 0)
.attr(“y”, y_scale(index) + icon_padding)
.attr(“width”, icon_size)
.attr(“height”, icon_size)
.style(“opacity”, 1)
.attr(“xlink:href”, contestant_icon_image);

contestant_text_value = d3
.select(“#plotapi-chart-c7486add_svg .bartext_group”)
.append(“text”)
.attr(“x”, x_pos – 5)
.attr(“y”, bar_text_upper_y(index))
.text(element.value)
.style(“text-anchor”, “end”)
.style(“dominant-baseline”, “central”)
.style(“fill”, “white”)
.style(“font-size”, bartext_font_size + “px”);

contestant_text_name = d3
.select(“#plotapi-chart-c7486add_svg .bartext_group”)
.append(“text”)
.attr(“x”, 0)
.attr(“y”, bar_text_lower_y(index))
.text(unique_names[element.id])
.style(“text-anchor”, “end”)
.style(“dominant-baseline”, “central”)
.style(“fill”, “white”)
.style(“font-weight”, “900”)
.style(“font-size”, bartext_font_size + “px”);

contestant = {
id: element.id,
rect: contestant_rect,
icon_image: !(contestant_icon_image == undefined),
icon: contestant_icon,
text_value: contestant_text_value,
text_name: contestant_text_name,
current_value: target_value,
target_value: target_value,
travel_scale: d3
.scaleLinear()
.domain([0, 2000])
.range([target_value, target_value]),
};

contestants.push(contestant);
}
}

if (sequence_index > 0) {
update_minimap();
}

}

function force_order(easeFn) {
bar_height = d3.max([420 / top_n – bar_padding, 0]);
bartext_font_size = d3.max([(bar_height – 10) / 2, 0]);
y_scale = d3.scaleLinear().domain([0, top_n]).range([0, 420]);

for (var index = 0; index top_n) {
easing = d3.easeCubic;
duration = 0;
}

const element = contestants[index];

var x_scale_current_x = x_scale(element.current_value);

element.text_name.attr(
“x”,
x_scale_current_x –
text_padding –
(element.icon_image ? bar_height – icon_padding : 0)
);

element.text_value
.attr(
“x”,
x_scale_current_x –
text_padding –
(element.icon_image ? bar_height – icon_padding : 0)
)
.text(format_value(element.current_value));

element.rect.attr(“width”, x_scale_current_x);

element.icon.attr(
“x”,
x_scale_current_x – (bar_height – icon_padding)
);

element.rect
.transition()
.ease(easing)
.duration(duration)
.attr(“height”, bar_height)
.attr(“y”, y_scale(index));

icon_size = d3.max([bar_height – (icon_padding * 2), 0]);

element.icon
.attr(“width”, icon_size)
.attr(“height”, icon_size)
.transition()
.ease(easing)
.duration(duration)
.attr(“y”, y_scale(index) + icon_padding);

element.text_name
.style(“font-size”, bartext_font_size + “px”)
.transition()
.ease(easing)
.duration(duration)
.attr(“y”, bar_text_lower_y(index));

element.text_value
.style(“font-size”, bartext_font_size + “px”)

.transition()
.ease(easing)
.duration(duration)
.attr(“y”, bar_text_upper_y(index));
}
}

function draw(delta) {
for (var index = 0; index d.current_value);

if (new_max != current_max) {
current_max = new_max;

x_scale = d3
.scaleLinear()
.domain([0, current_max])
.range([0, 780]);

x_axis.scale(x_scale);

d3.select(“#plotapi-chart-c7486add_svg”)
.select(“.x_axis”)
.transition()
.duration(250)
.ease(d3.easeLinear)
.call(x_axis);
}

var x_scale_current_x = x_scale(element.current_value);

element.text_name.attr(
“x”,
x_scale_current_x –
text_padding –
(element.icon_image ? bar_height – icon_padding : 0)
);

element.text_value
.attr(
“x”,
x_scale_current_x –
text_padding –
(element.icon_image ? bar_height – icon_padding : 0)
)
.text(format_value(element.current_value));

element.rect.attr(“width”, x_scale_current_x);

element.icon.attr(
“x”,
x_scale_current_x – (bar_height – icon_padding)
);

if (
last_order.indexOf(element.id) !=
current_order.indexOf(element.id)
) {
element.rect
.interrupt()
.transition()
.ease(d3.easeSinOut)
.duration(500)
.attr(“y”, y_scale(index));

element.icon
.interrupt()
.transition()
.ease(d3.easeSinOut)
.duration(500)
.attr(“y”, y_scale(index) + icon_padding);

element.text_name
.interrupt()
.transition()
.ease(d3.easeSinOut)
.duration(500)
.attr(“y”, bar_text_lower_y(index));

element.text_value
.interrupt()
.transition()
.ease(d3.easeSinOut)
.duration(500)
.attr(“y”, bar_text_upper_y(index));
}
}
}
}

function initialise() {
contestants = [];
d3.select(“#plotapi-chart-c7486add_svg .bar_group”)
.selectAll(“*”)
.remove();
d3.select(“#plotapi-chart-c7486add_svg .bartext_group”)
.selectAll(“*”)
.remove();

d3.select(“#plotapi-chart-c7486add_svg .minimap_group”)
.selectAll(“*”)
.remove();
last_proc = 0;
top_n = 5;

bar_padding = 8;
bar_height = d3.max([420 / 5 – bar_padding, 0]);
icon_padding =2;
text_padding = 5;
current_order = [];
last_order = [];
elapsed_time = 0;
sequence_index = 0;
current_max = null;
y_scale = d3.scaleLinear().domain([0, top_n]).range([0, 420]);
bartext_font_size = d3.max([(bar_height – 10) / 2, 0]);
icon_size = d3.max([bar_height – (icon_padding * 2), 0]);

update_current(sequence_index);

current_order_text.text(format_current_order(sequence[sequence_index]))

new_max = d3.max(contestants, (d) => d.current_value);

if (new_max != current_max) {
current_max = new_max;

x_scale = d3
.scaleLinear()
.domain([0, current_max])
.range([0, 780]);

}

x_axis = d3
.axisTop()
.scale(x_scale)
.ticks(7.8, undefined)
.tickSize((-420))
.tickFormat((d) => d3.format(“,”)(d));

d3.select(“#plotapi-chart-c7486add_svg .axis_group”)
.selectAll(“*”)
.remove();

var x_axis_line = d3
.select(“#plotapi-chart-c7486add_svg .axis_group”)
.append(“g”)
.attr(“class”, “axis x_axis”)
.attr(“transform”, `translate(0, -2.0)`)
.call(x_axis)
.selectAll(“.tick line”)
.classed(“origin”, (d) => d == 0);

force_order(d3.easeCubic);
}

function darken_color(color, factor) {
return d3.color(color).darker(factor)
}

function color(index) {
node = nodes[index];
if (node.color) {
return node.color;
}

var ratio = index / (5);
return d3.interpolateRainbow(ratio);
}

function icon(index) {
node = nodes[index];
if (node.icon) {
return node.icon;
}

return undefined;
}

function bar_text_upper_y(index) {
return y_scale(index) + bar_height * 0.75;
}

function bar_text_lower_y(index) {
return y_scale(index) + bar_height * 0.25;
}

function update_minimap() {
var minimap_order = contestants.slice();
minimap_order.sort((a, b) => b.current_value – a.current_value);
minimap_order = minimap_order.slice(0,5);

current_sum = minimap_order
.map((item) => item.current_value)
.reduce((prev, next) => prev + next);

if (current_sum != 0) {
var mm_bar_height = 42.0;
var mm_bar_drop_height = 126.0;

mm_y_scale = d3
.scaleLinear()
.domain([0, current_sum])
.range([0, mm_bar_height]);
mm_x_scale = d3
.scaleLinear()
.domain([0, sequence.length])
.range([0, 260.0]);

var mm_pos_x = mm_x_scale(sequence_index – 1);
var mm_width = mm_x_scale(1);

var mm_running_total = 0;
for (
var mm_index = 0; mm_index d.current_value > 0 && !isNaN(d.current_value)
);
if (top_n d.current_value > 0 && !isNaN(d.current_value)
);
if (top_n > 1) {
top_n–;
force_order(d3.easeElastic);
}
}
);

d3.select(“#plotapi-chart-c7486add_minus”).on(
“mouseover”,
function (d, i) {
d3.select(“#plotapi-chart-c7486add_minus”).style(“opacity”, 1);
}
);

d3.select(“#plotapi-chart-c7486add_minus”).on(
“mouseout”,
function (d, i) {
d3.select(“#plotapi-chart-c7486add_minus”).style(“opacity”, 0.6);
}
);

d3.select(“#plotapi-chart-c7486add svg”).on(
“mouseenter”,
function () {
d3.select(“#plotapi-chart-c7486add_icon”).style(“opacity”, 0.6);
d3.select(“#plotapi-chart-c7486add_plus”).style(“opacity”, 0.6);
d3.select(“#plotapi-chart-c7486add_minus”).style(“opacity”, 0.6);
if (!finished) {
d3.select(“#plotapi-chart-c7486add_restart”).style(
“opacity”,
0.6
);
}
}
);

d3.select(“#plotapi-chart-c7486add svg”).on(
“mouseleave”,
function () {
d3.select(“#plotapi-chart-c7486add_icon”).style(“opacity”, 0);
d3.select(“#plotapi-chart-c7486add_plus”).style(“opacity”, 0);
d3.select(“#plotapi-chart-c7486add_minus”).style(“opacity”, 0);
if (!finished) {
d3.select(“#plotapi-chart-c7486add_restart”).style(
“opacity”,
0
);
}
}
);

}

}());

You can do so much more than what’s presented in this example, and we’ll cover this in later sections. If you want to see the full list of growing features, check out the Plotapi Documentation.Read More

Source: datacrayon.com

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Technology

Animal Crossing Villager Species and Personality

Published

on

Preamble?

In [1]:

from plotapi import Chord
import json

Chord.set_license(“your username”, “your license key”)

Introduction?

In this notebook we’re going to use Plotapi Chord to visualise the co-occurrences between the species and personality of Aniaml Crossing villagers. We”ll use Python, but Plotapi can be used from any programming language.
In a chord diagram (or radial network), entities are arranged radially as segments with their relationships visualised by ribbons that connect them. The size of the segments illustrates the numerical proportions, whilst the size of the arc illustrates the significance of the relationships. Chord diagrams are useful when trying to convey relationships between different entities, and they can be beautiful and eye-catching.

Dataset?

We’re going to use data from Animal Crossing New Horizons data. Previously, we made use of the data available in this repository. However, since the 2.0 update, we’ve created our own! Let’s get loading the data.

In [2]:

with open(“ac_species_personality.json”, “r”) as f:
data = json.load(f)

Visualisation?

Let’s use Plotapi Chord for this visualisation, you can see more examples in the Gallery.
We’re going to adjust some layout and template parameters, and flip the intro animation on too.
Because we’re using a data-table, we can also click on any part of the diagram to “lock” the selection.

In [3]:

Chord(
data[“matrix”],
data[“names”],
colors=data[“colors”],
details=data[“details”],
details_thumbs=data[“details_thumbs”],
noun=”villagers!”,
thumbs_width=50,
curved_labels=True,
popup_width=600,
bipartite=True,
bipartite_idx=data[“bipartite_idx”],
bipartite_size=0.4,
padding=0.0,
width=800,
data_table_column_width=100,
font_size_large=”16px”,
data_table=data[“data_table”],
data_table_show_indices=False
).show()

Plotapi – Chord DiagramRead More

Source Here: datacrayon.com

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