// Generated by CoffeeScript 1.9.2
(function() {
var get_z_scores, redraw_prob_plot, root;
root = typeof exports !== "undefined" && exports !== null ? exports : this;
get_z_scores = function(n) {
var i, j, osm_uniform, ref, x;
osm_uniform = new Array(n);
osm_uniform[n - 1] = Math.pow(0.5, 1.0 / n);
osm_uniform[0] = 1 - osm_uniform[n - 1];
for (i = j = 1, ref = n - 2; 1 <= ref ? j <= ref : j >= ref; i = 1 <= ref ? ++j : --j) {
osm_uniform[i] = (i + 1 - 0.3175) / (n + 0.365);
}
return (function() {
var k, len, results;
results = [];
for (k = 0, len = osm_uniform.length; k < len; k++) {
x = osm_uniform[k];
results.push(jStat.normal.inv(x, 0, 1));
}
return results;
})();
};
redraw_prob_plot = function(samples, sample_group) {
var container, h, margin, totalh, totalw, w;
h = 550;
w = 600;
margin = {
left: 60,
top: 40,
right: 40,
bottom: 40,
inner: 5
};
totalh = h + margin.top + margin.bottom;
totalw = w + margin.left + margin.right;
container = $("#prob_plot_container");
container.width(totalw);
container.height(totalh);
var W, all_samples, chart, data, intercept, make_data, names, pvalue, pvalue_str, slope, sorted_names, sorted_values, sw_result, test_str, x, z_scores;
all_samples = samples[sample_group];
names = (function() {
var j, len, ref, results;
ref = _.keys(all_samples);
results = [];
for (j = 0, len = ref.length; j < len; j++) {
x = ref[j];
if (all_samples[x] !== null) {
results.push(x);
}
}
return results;
})();
sorted_names = names.sort(function(x, y) {
return all_samples[x].value - all_samples[y].value;
});
max_decimals = 0
sorted_values = (function() {
var j, len, results;
results = [];
for (j = 0, len = sorted_names.length; j < len; j++) {
x = sorted_names[j];
results.push(all_samples[x].value);
if (all_samples[x].value.countDecimals() > max_decimals) {
max_decimals = all_samples[x].value.countDecimals()-1
}
}
return results;
})();
//ZS: 0.1 indicates buffer, increase to increase buffer
y_domain = [sorted_values[0] - (sorted_values.slice(-1)[0] - sorted_values[0])*0.1, sorted_values.slice(-1)[0] + (sorted_values.slice(-1)[0] - sorted_values[0])*0.1]
//sw_result = ShapiroWilkW(sorted_values);
//W = sw_result.w.toFixed(3);
//pvalue = sw_result.p.toFixed(3);
//pvalue_str = pvalue > 0.05 ? pvalue.toString() : "<span style='color:red'>" + pvalue + "</span>";
//test_str = "Shapiro-Wilk test statistic is " + W + " (p = " + pvalue_str + ")";
z_scores = get_z_scores(sorted_values.length);
//ZS: 0.1 indicates buffer, increase to increase buffer
x_domain = [z_scores[0] - (z_scores.slice(-1)[0] - z_scores[0])*0.1, z_scores.slice(-1)[0] + (z_scores.slice(-1)[0] - z_scores[0])*0.1]
slope = jStat.stdev(sorted_values);
intercept = jStat.mean(sorted_values);
make_data = function(group_name) {
var sample, value, z_score;
return {
key: js_data.sample_group_types[group_name],
slope: slope,
intercept: intercept,
values: (function() {
var j, len, ref, ref1, results;
ref = _.zip(get_z_scores(sorted_values.length), sorted_values, sorted_names);
results = [];
for (j = 0, len = ref.length; j < len; j++) {
ref1 = ref[j], z_score = ref1[0], value = ref1[1], sample = ref1[2];
if (sample in samples[group_name]) {
results.push({
x: z_score,
y: value,
name: sample
});
}
}
return results;
})()
};
};
data = [make_data('samples_primary'), make_data('samples_other'), make_data('samples_all')];
x_values = {}
y_values = {}
point_names = {}
for (i = 0; i < 3; i++){
these_x_values = []
these_y_values = []
these_names = []
for (j = 0; j < data[i].values.length; j++){
these_x_values.push(data[i].values[j].x)
these_y_values.push(data[i].values[j].y)
these_names.push(data[i].values[j].name)
}
if (i == 0){
x_values['samples_primary'] = these_x_values
y_values['samples_primary'] = these_y_values
point_names['samples_primary'] = these_names
} else if (i == 1) {
x_values['samples_other'] = these_x_values
y_values['samples_other'] = these_y_values
point_names['samples_other'] = these_names
} else {
x_values['samples_all'] = these_x_values
y_values['samples_all'] = these_y_values
point_names['samples_all'] = these_names
}
}
intercept_line = {}
if (sample_group == "samples_primary"){
first_x = Math.floor(x_values['samples_primary'][0])
first_x = first_x - first_x*0.1
last_x = Math.ceil(x_values['samples_primary'][x_values['samples_primary'].length - 1])
last_x = last_x + last_x*0.1
first_value = data[0].intercept + data[0].slope * first_x
last_value = data[0].intercept + data[0].slope * last_x
intercept_line['samples_primary'] = [[first_x, last_x], [first_value, last_value]]
} else if (sample_group == "samples_other") {
first_x = Math.floor(x_values['samples_other'][0])
first_x = first_x - first_x*0.1
last_x = Math.ceil(x_values['samples_other'][x_values['samples_other'].length - 1])
last_x = last_x + last_x*0.1
first_value = data[1].intercept + data[1].slope * first_x
last_value = data[1].intercept + data[1].slope * last_x
intercept_line['samples_other'] = [[first_x, last_x], [first_value, last_value]]
} else {
first_x = Math.floor(x_values['samples_all'][0])
first_x = first_x - first_x*0.1
last_x = Math.ceil(x_values['samples_all'][x_values['samples_all'].length - 1])
first_value = data[2].intercept + data[2].slope * first_x
last_x = last_x + last_x*0.1
last_value = data[2].intercept + data[2].slope * last_x
intercept_line['samples_all'] = [[first_x, last_x], [first_value, last_value]]
}
val_range = Math.max(...y_values['samples_all']) - Math.min(...y_values['samples_all'])
if (val_range < 4){
tick_digits = '.1f'
} else if (val_range < 0.4) {
tick_digits = '.2f'
} else {
tick_digits = 'f'
}
var layout = {
title: {
x: 0,
y: 10,
xanchor: 'left',
text: "<b>Trait " + js_data.trait_id + ": " + js_data.short_description + "</b>",
},
margin: {
l: 100,
r: 30,
t: 100,
b: 60
},
legend: {
x: 0.05,
y: 0.9,
xanchor: 'left'
},
xaxis: {
title: "<b>normal quantiles</b>",
range: [first_x, last_x],
zeroline: false,
visible: true,
linecolor: 'black',
linewidth: 1,
titlefont: {
family: "arial",
size: 16
},
ticklen: 4,
tickfont: {
size: 16
}
},
yaxis: {
zeroline: false,
visible: true,
linecolor: 'black',
linewidth: 1,
title: "<b>" + js_data.unit_type + "</b>",
titlefont: {
family: "arial",
size: 16
},
ticklen: 4,
tickfont: {
size: 16
},
tickformat: tick_digits,
automargin: true
},
width: 600,
height: 600,
hovermode: "closest",
dragmode: false
}
var primary_trace = {
x: x_values['samples_primary'],
y: y_values['samples_primary'],
mode: 'markers',
type: 'scatter',
name: 'Samples',
text: point_names['samples_primary'],
marker: {
color: 'blue',
width: 6
}
}
if ("samples_other" in js_data.sample_group_types) {
var other_trace = {
x: x_values['samples_other'],
y: y_values['samples_other'],
mode: 'markers',
type: 'scatter',
name: js_data.sample_group_types['samples_other'],
text: point_names['samples_other'],
marker: {
color: 'blue',
width: 6
}
}
}
if (sample_group == "samples_primary"){
var primary_intercept_trace = {
x: intercept_line['samples_primary'][0],
y: intercept_line['samples_primary'][1],
mode: 'lines',
type: 'scatter',
name: 'Normal Function',
line: {
color: 'black',
width: 1
}
}
} else if (sample_group == "samples_other"){
var other_intercept_trace = {
x: intercept_line['samples_other'][0],
y: intercept_line['samples_other'][1],
mode: 'lines',
type: 'scatter',
name: 'Normal Function',
line: {
color: 'black',
width: 1
}
}
} else {
var all_intercept_trace = {
x: intercept_line['samples_all'][0],
y: intercept_line['samples_all'][1],
mode: 'lines',
type: 'scatter',
name: 'Normal Function',
line: {
color: 'black',
width: 1
}
}
}
if (sample_group == "samples_primary"){
var data = [primary_intercept_trace, primary_trace]
} else if (sample_group == "samples_other"){
var data = [other_intercept_trace, other_trace]
} else {
var data = [all_intercept_trace, primary_trace, other_trace]
}
Plotly.newPlot('prob_plot_div', data, layout, root.modebar_options)
};
root.redraw_prob_plot_impl = redraw_prob_plot;
}).call(this);
