Public
Edited
May 28, 2024
1 star
Object.assign(
Plot.plot({
height: 15000,
width: 500,
caption: "",
marginTop: 40,
marginRight: 20,
marginLeft: 100,
marginBottom: 30,
padding: 0,
color: { scheme: "YlGnBu" },
style: { "font-size": 10, fontstroke: "#91bfdb", maxWidth: "20000px" },
x: {
type: "band",
label: "Imaging at timepoint available",
paddingInner: 0.1,
domain: [
"ses-clinical",
"ses-study-MR",
"18-mon-follow-up",
"36-mon-follow-up"
]
},
y: {
type: "band",
padding: 0.15,
label: "All NorCOAST subjects"
},
marks: [
Plot.cell(timepoinst_data, {
x: "timepoint",
y: "subject-id",
fill: "count"
}),
Plot.text(timepoinst_data, {
x: "timepoint",
y: "subject-id",
text: "timepoint"
}),
Plot.gridX({ stroke: "#0868a3" }),
Plot.gridY({ stroke: "#0868a3" })
]
}),
{
style: `max-width: ${width}px; overflow: scroll; max-height: 700px; overflow-y: scroll`
}
)
Object.assign(
Plot.plot({
height: 15000,
caption: "",
width: 2000,
marginTop: 40,
marginRight: 20,
marginLeft: 100,
marginBottom: 30,
padding: 0,
color: { scheme: "YlGnBu" },
style: { "font-size": 10, fontstroke: "#91bfdb", maxWidth: "20000px" },
x: {
type: "band",
label: "Available Imaging Data",
paddingInner: 0.1,
domain: [
"CT-1",
"CT-2",
"CTA",
"DWI",
"ADC",
"3D-T1",
"3D-FLAIR",
"T1",
"T2",
"T2-FLAIR"
]
},
fx: {
domain: [
"ses-clinical",
"ses-study-MR",
"18-mon-follow-up",
"36-mon-follow-up"
]
},
facet: { data: summary, x: "timepoint" },
y: {
type: "band",
padding: 0.15,
label: "All NorCOAST subjects"
},
marks: [
Plot.cell(summary, {
x: "modality",
y: "subject-id",
fill: "count"
}),
Plot.text(summary, { x: "modality", y: "subject-id", text: "modality" }),
Plot.gridX({ stroke: "#0868a3" }),
Plot.gridY({ stroke: "#0868a3" })
]
}),
{
style: `max-width: ${width}px; overflow: scroll; max-height: 700px; overflow-y: scroll`
}
)
timepoinst_data = aq_bas.groupby("subject-id", "timepoint").rollup({
count: op.count("timepoint")
})
timepoinst_data1 = aq_bas
.groupby("subject-id", "timepoint")
.rollup({
count: op.count("timepoint")
})
.view()
Object.assign(
Plot.plot({
height: 15000,
width: 200,
caption: "",
marginTop: 40,
marginRight: 20,
marginLeft: 100,
marginBottom: 30,
padding: 0,
color: { scheme: "YlGnBu" },
style: { "font-size": 10, fontstroke: "#91bfdb", maxWidth: "20000px" },
marks: [
Plot.dot(unique_timepoints_per_subject, {
x: "timepoint",
y: "subject-id"
})
]
}),
{
style: `max-width: ${width}px; overflow: scroll; max-height: 700px; overflow-y: scroll`
}
)
unique_timepoints_per_subject = lodash.uniqWith(unique_input, lodash.isEqual)
unique_input = data.map((d) => ({
"subject-id": d["subject-id"],
timepoint: d.timepoint
}))
lodash = require("lodash")
sub_time = data.map((d) => ({
subject_id: d["subject-id"],
timepoint: d.timepoint
}))
aq_bas = aq.from(data)
import { aq, op } from "@uwdata/arquero"
bianca_filtered = aq_bianca.filter((d) =>
op.startswith(d["subject-id"], "sub-")
)
aq_bianca = aq.from(bianca)
bianca = d3.csv(
"https://raw.githubusercontent.com/landge/NorCOAST_Imaging_Dataset/master/code/freesurfer_and_bianca_results.csv?token=GHSAT0AAAAAACS4BQ5SZX6M5GMFUGIA45DKZSVUZHQ"
)
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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.
