Published
Edited
Aug 16, 2020
most_interesting_gene = {
var maxval = 1;
var maxobj = {};
for (var obj of data) {
if (obj.Fold > maxval) {
maxobj = obj;
maxval = obj.Fold;
}
}
return maxobj;
}
compounds_table = {
var tbl = [];
var i = 0;
for (var compound of metadata.Compounds) {
tbl.push({
'Compound': compound.Compound,
'Code': compound['Compound code'],
'Mol': svgs[i],
})
i = i + 1
}
return tbl;
}
svgs = Promise.all(promises)
promises = {
var promises = [];
for (var compound of metadata.Compounds) {
const smiles = compound.SMILES;
const safeSmiles = encodeURIComponent(smiles);
const svg_url = "https://chemidconvert.cloud.douglasconnect.com/v1/asSvg?width=200&height=200&smiles=" + safeSmiles;
const svg = fetch(svg_url).then(response => response.text());
promises.push(svg);
}
return promises;
}
metadata_to_table = function(section) {
// initiate empty array
var temp = [];
// loop over key, value pairs of metadata's section
for (const [key, value] of Object.entries(metadata[section])) {
// append object with Field-Value properties to the array
temp.push({
'Field': key,
'Value': value});
}
return temp
}
md`**Get the column names (i.e. keys of the object in "data" array)**`
columns = Object.keys(data[0])
filtered_data = data.filter(function(obj) {
if (obj.Gene == gene) {return obj}
})
// Select only unique gene names among the all gene names
genes = Array.from(new Set(valid_genes))
valid_genes = all_genes.filter(function (el) {
return el != null;
});
// First get the gene names in all of the rows of data. This is done by extracting the "Gene" property of each row in the data
all_genes = data.map(obj => obj.Gene)
// Extract data from the server's response. This is one by taking the the "data" property of each object (row) inside the raw_data.results array
data = raw_data.results.map(x => x.data)
// Access the data published under url_data and store it into a raw_data variable
raw_data = fetch(url_data)
.then(response => {
if (!response.ok) throw new Error(response.status);
return response.json();})
// Access the metadata published under the url_metadata and store it into a metadata variable
metadata = fetch(url_metadata)
.then(response => {
if (!response.ok) throw new Error(response.status);
return response.json();})
// URL to data is automatically generated from dataset ID
url_data = "https://api.develop.edelweiss.douglasconnect.com/datasets/" + dataset_id + "/versions/1/data"
// URL to metadata is automatically generated from dataset ID
url_metadata = "https://api.develop.edelweiss.douglasconnect.com/datasets/" + dataset_id + "/versions/1/metadata"
dataset_id = get_selected_dataset(datasets, dataset_name)
get_selected_dataset = function(datasets, dataset_name) {
var selected_datasets = datasets.filter(function(obj) {
if (obj['Dataset name'] == dataset_name) {return obj}
})
if (selected_datasets.length == 1) {
return selected_datasets[0]['Dataset ID']
} else {
return null
}
}
response = fetch("https://api.develop.edelweiss.douglasconnect.com/datasets/e62fa517-ee8f-41d5-8953-6dfba8120718/versions/1/data")
.then(response => {
if (!response.ok) throw new Error(response.status);
return response.json();})
// Import D3 library for graphics
d3 = require("d3@5")
// Import the select library
import {text} from "@jashkenas/inputs"
// Import the select library
import {select} from "@jashkenas/inputs"
// Import the vegalite library
import {vl} from "@vega/vega-lite-api"
// Import libraries for data table rendering
import {render_data_table, table_styles, displayImage, displayCaution} from '@info474/utilities'
Purpose-built for displays of data
Observable is your go-to platform for exploring data and creating expressive data visualizations. Use reactive JavaScript notebooks for prototyping and a collaborative canvas for visual data exploration and dashboard creation.
