Project architecture includes two main components:
Those two components are connected through OpenCPU as a bridge, and inside the JavaScript implementation the RPC-call to server is used.
Consequently, in order to add new tool one needs:
All of these steps will be described in details in this vignette.
Each analysis method in this package takes three compulsory arguments:
es – ExpressionSet object;rows – same thing with rows;columns – specified indices of columns that are taken into consideration.Also, some methods require to replace NA values in series matrix in order to be used, so usually also replacena argument is needed.
Other arguments depend on the method’s specifics.
Before calculating the method, the considered data from the whole series matrix needs to be extracted. For that reason, the package has non-exported method prepareData, that takes as arguments es, columns, rows,replacena`.
After the method is used, the result can be sent back in two ways:
jsonlite::toJSON for that), if the result is adequately small;protolite::serialize_pb), if it is large.The approximate code structure is demonstrated here:
# instead of ellipsis would be specific arguments
method <- function(es, rows = c(), columns = c(), replacea = "mean", ...) {
# Here may be some assertions
# Data preparation
data <- prepareData(es, rows, columns, replacena)
# Using method
res <- ...
# Sending back the result as JSON:
return(jsonlite::toJSON(res))
# Or as ProtoBuf
f <- tempfile(pattern = "pat", tmpdir = getwd(), fileext = ".bin")
writeBin(protolite::serialize_pb(res), f)
jsonlite::toJSON(f)
}Remember, that your tool must be exported, fully documented and tested.
The structure of the tool description:
toString – method that returns tool’s name;gui – field’s description, represented as an array of JSON-objects, which have following values:
name – field’s name;value – default value;type – field’s type (most used types are: select, checkbox-list, bootstrap-list, text);multiple – if multiple values may be chosen (relevant for checkbox-list);init – initialization of tool’s input fields;execute – main method of the tool, which includes:
options.input.fieldName);Here is the approximate JavaScript description of the tool:
phantasus.NewTool = function () {
};
phantasus.NewTool.prototype = {
// Tool name:
toString: function () {
return 'new';
},
// Initialization of tool's input fields
init: function (project, form) {
// Here is your initialization code
},
// Description of tool' GUI
gui: function (project) {
return [{
name : 'fieldName',
type : 'type',
options : [],
value : 'default_value'
}];
},
// Main function of the Tool
execute: function (options) {
var project = options.project;
// Getting the input
var field = options.input.fieldName;
// Reading actual dataset
var dataset = project.getSortedFilteredDataset();
// Each dataset has es session as a field
var es = dataset.getESSession();
// Get indices of selected rows and columns if they are selected
var trueIndices = phantasus.Util.getTrueIndices(dataset);
// Further calculation may proceed only when esSession is ready
es.then(function (essession) {
// Function arguments, there also should be method-specific arguments
var args = {
es: essession
};
if (trueIndices.rows.length > 0) {
args.rows = trueIndices.rows;
}
if (trueIndices.columns.length > 0) {
args.columns = trueIndices.columns;
}
// RPC-call to OpenCPU-server
var req = ocpu.call("methodName", args, function (session) {
session.getObject(function (success) {
// success -- returned result, needs to be processed
// Result getting depends on its type
// JSON:
var result = JSON.parse(success);
// after that you can proceed with result
// ProtoBuf:
var r = new FileReader();
var filePath = phantasus.Util.getFilePath(session,
JSON.parse(success)[0]);
r.onload = function (e) {
var contents = e.target.result;
var ProtoBuf = dcodeIO.ProtoBuf;
// message.proto is file with specified protocol
ProtoBuf.protoFromFile("./message.proto",
function (error, success) {
if (error) {
throw new Error(error);
}
var builder = success,
rexp = builder.build("rexp"),
REXP = rexp.REXP,
rclass = REXP.RClass;
var res = REXP.decode(contents);
var data = phantasus.Util.getRexpData(res, rclass);
var names = phantasus.Util.getFieldNames(res, rclass);
// here you can proceed with result
})
};
phantasus.BlobFromPath.getFileObject(filePath, function (file) {
r.readAsArrayBuffer(file);
});
})
}, false, '::' + dataset.getESVariable());
req.fail(function () {
// failed request procession
throw new Error("Method call failed" + req.responseText);
});
});
}
};