MetaPlot

An experimental playground for what might become a plotting library, which targets JSON based visualization libraries. At the moment that means plotly.js (via nim-plotly) and vega-lite (via monocle).

The idea for now is just to provide a JSON macro that avoids the need for explicit object notation {} (inferred from indentation) and have some sort of compile time checking of the written JSON based on the JSON Schema for vega-lite (via JsonSchemaValidator) as well as the schema defined by plotly.js (which unfortunately is not JSON Schema). A small binary checkSchema is compiled, which from then on is called from this library at CT. This binary receives the generated JSON (in case of vega-lite) or used keywords (in case of plotly.js) and checks whether each makes sense. If not a compile time error is thrown.

All this is in a sort of works kind of state. Obviously it’s not the most user friendly at the moment, because one has to write explicit vega-lite JSON or plotly.js (for plotly.js not exactly the JSON, but rather the JavaScript wrapper code, which deviates somewhat from their JSON schema).

Examples

First call

nimble buildCheck

to build the checkSchema binary.

Plotly.js:

import metaplot
let someData = [3, 4, 7]

build("plotly"):
  scatter: # e.g. replace by `scatterTypo` and it'll throw a CT error
    x = someData
    y = [4, 5, 6]
    marker:
      size = 5
    name = "Oh a name!"
  scatter:
    x = [20, 30, 40]
    y = [50, 60, 70]
    xaxis = "x2"
    yaxis = "y2"
  layout:
    title = "simple subplot!"
    grid:
      rows = 1
      columns = 2
      pattern = "independent"
# creates a `traces` and `layout` JsonNode, which can be added to a `PlotJson` object
let plt = PlotJson(traces: traces,
                   layout: layout)
plt.show()

Vega-lite:

import metaplot, parsecsv

# read some CSV data 
var p: CsvParser
p.open("seattle-weather.csv")
p.readHeaderRow()
var vegaData = newJArray()
while p.readRow():
  var row = newJObject()
  for col in items(p.headers):
    if col != "weather" and col != "date":
      row[col] = (% (p.rowEntry(col).parseFloat))
    else:
      row[col] = (% p.rowEntry(col))
  vegaData.add row

p.close()


build("vega"):
  data:
    values = vegaData # bad for CT checking
    #url = "seattle-weather.csv" # if this line is used, CT checking works
    format = "csv"
  mark = "bar"
  encoding:
    x:
      timeUnit = "month"
      field = "date"
      `type` = "ordinal"
      axis:
        title = "Month of the year"
    y:
      aggregate = "count"
      `type` = "quantitative"
    color:
      field = "weather"
      `type` = "nominal"
      scale:
        domain = ["sun","fog","drizzle","rain","snow"]
        range = ["#e7ba52","#c7c7c7","#aec7e8","#1f77b4","#9467bd"]
      legend:
        title = "Weather type"

# if building vega currently doesn't call monocle, but rather creates a `vega` variable
# so only a single call possible :P
monocle.show(vega)

NOTE: for vega-lite CT checking at the moment doesn’t work if Nim data is being handed to the macro, because the resulting code is not valid JSON by itself (only the result of the called %* macro is).