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ImJoy RPC

Symmetrical Transparent Remote Procedure Calls

The core library that powers ImJoy.

Python library

pip install imjoy-rpc

Javascript library

NPM

npm install imjoy-rpc

For RPC communication within the browser (typically connect from an iframe to the ImJoy core in the parent window):

import { imjoyRPC } from 'imjoy-rpc';

imjoyRPC.setupRPC({name: 'My Awesome App'}).then((api)=>{
 // call api.export to expose your plugin api
})

Browser

To connect to the ImJoy core ( typically from an iframe):

<script src="https://cdn.jsdelivr.net/npm/imjoy-rpc@latest/dist/imjoy-rpc.min.js"></script>
<script>
imjoyRPC.setupRPC({name: 'My Awesome App'}).then((api)=>{
 // call api.export to expose your plugin api
})
</script>

Jupyter notebook extension

imjoy-rpc handshaking protocol

To start a connection between the core and an rpc peer (a plugin), a handshaking process is required to exchange the api. For the rpc peer, it works like a service provider that takes a set of api from the core and produce a set of service functions. The handshaking defines the exchange of interface functions, as well as authentication and configuration.

Here is a schema illustrate the process: imjoy-rpc handshaking protocol

Data type representation

ImJoy RPC is built on top of two-way transport layer. Currently, we support 4 types of transport layer: Jupyter comm messages, Pyodide, Google Colab, and SocketIO. Data with different types are encoded into a unified representation and sent over the transport layer. It will then be decoded into the same or corresponding data type on the other side.

The data representation is a JSON object (but can contain binary data, e.g. ArrayBuffer in JS or bytes in Python). The goal is to represent more complex data types with primitive types that are commonly supported by many programming language, including strings, numbers, boolean, bytes, list/array and dictionary/object.

Javascript	Python	imjoy-rpc representation
String	str	v
Number	int/float	v
Boolean	bool	v
null/undefined	None	v
ArrayBuffer	bytes	v
Array([])	list/tuple	[_encode(v)]
Object({})	dict	{_encode(v)}
Set	Set	{_rtype: "set", _rvalue: _encode(Array.from(v))}
Map	OrderedDict	{_rtype: "orderedmap", _rvalue: _encode(Array.from(v))}
Error	Exception	{ _rtype: "error", _rvalue: v.toString() }
Blob	BytesIO/StringIO	{ _rtype: "blob", _rvalue: v, _rmime: v.type }
DataView	memoryview	{ _rtype: "memoryview", _rvalue: v.buffer }
TypedArray	1-D numpy array*	{_rtype: "typedarray", _rvalue: v.buffer, _rdtype: dtype}
tf.Tensor/nj.array	numpy array	{_rtype: "ndarray", _rvalue: v.buffer, _rshape: shape, _rdtype: _dtype}
Function*	function/callable*	{_rtype: "interface", _rid: _rid, _rvalue: name} <br> {_rtype: "callback", _rvalue: id}
Class	class/dotdict()*	{...}
custom	custom	encoder(v) (default `_rtype` = encoder name)

Notes:

_encode(...) in the imjoy-rpc representation means the type will be recursively encoded (decoded).
For any JS object or Python dictonary, if it has a key named _rintf and the value is set to true, then this object will be treated as an interface. All functions contained in the object/dictionary, including all the children dictionary at any level, will be treated as interface function. Otherwise, it will be encoded as callback. The difference is interface function can be called many times, but the callback function can only be called once (Also see "Remote Objects" below).
For n-D numpy array, there is no established n-D array library in javascript, the current behavior is, if there is tf(Tensorflow.js) detected, then it will be decoded into tf.Tensor. If nj(numjs) is detected, then it will be decoded into nj.array.

Typed array will be represented as numpy array if available, otherwise it will be converted to raw bytes.
Type Conversion

Javascript	Numpy	_rdtype
Int8Array	int8	int8
Int16Array	int16	int16
Int32Array	int32	int32
Uint8Array	uint8	uint8
Uint16Array	uint16	uint16
Uint32Array	uint32	uint32
Float32Array	float32	float32
Float64Array	float64	float64
Array	array	array
note: 64-bit integers (signed or unsigned) are not supported

dotdict in Python is a simple wrapper over dict that support using the dot notation to get item, similar to what you can do with Javascript object.
In Python, file instances (inherit from io.IOBase) will be automatically encoded.

Encoding and decoding custom objects

For the data or object types that are not in the table above, for example, a custom class, you can support them by register your own codec(i.e. encoder and decoder) with api.registerCodec().

You need to provide a name, a type, encoder and decoder function. For example: in javascript, you can call api.registerCodec({"name": "my_custom_codec", "type": MyClass, "encoder": (obj)=>{ ... return encoded;}, "decoder": (obj)=>{... return decoded;}), or in Python you can do api.registerCodec(name="my_custom_codec", type=MyClass, encoder=my_encoder_func, decoder=my_decoder_func).

The basic idea of using a custom codec is to use the encoder to represent your custom data type into array/dictionary of primitive types (string, number etc.) such that they can be send via the transport layer of imjoy-rpc. Then use the decoder to reconstruct the object remotely based on the representation.

For the name, it will be assigned as _rtype for the data representation, therefore please be aware that you should not use a name that already used internally (see the table above), unless you want to overried the default encoding. Also note that you cannot overried the encoding of primitive types and functions.

The encoder function take an object as input and you need to return the represented object/dictionary. You can only use primitive types plus array/list and object/dict in the represented object. If you want to include function in the encoding, please also include a key _rintf and set the value to true. By default, if your returned object does not contain a key _rtype, the codec name will be used as _rtype. You can also assign a different _rtype name, that allows the conversion between different types.

The decoder function converts the encoded object into the actual object. It will only be called when the _rtype of an object matches the name of the codec.

Remote function calls and arguments

Remote function call is almost the same as calling a local function. The arguments are mapped directly, for example, you can call a Python function foo(a, b, c) from javascript or vise versa. However, since Javascript does not support named arguments as Python does, ImJoy does the following conversion:

For functions defined in Javascript, there is no difference when calling from Python
For functions defined in Python, when calling from Javascript, if the last argument is an object and its _rkwargs is set to true, then it will be converted into keyword arguments when calling the Python function. For example, if you have a Python function defined as def foo(a, b, c=None):, in Javascript, you should call it as foo(9, 10, {c: 33, _rkwargs: true}).

Support Zarr Array encoding

Zarr is a more scalable n-dimensional array format that has a numpy-like api and can be used with multiple backends. It is ideally suited for sending large n-dimensional array between imjoy-rpc peers in a lazy fashion. We have an [internal implementation] of codec that can support sending.

To use it, you can call import and call register_default_codecs.

from imjoy_rpc import register_default_codecs
register_default_codecs()

If you don't want to register all the default codecs, you can also pass an list of default codec names:

from imjoy_rpc import register_default_codecs
register_default_codecs(['zarr.Array', 'zarr.Group'])

Example 1: encoding and decoding custom classes with imjoy-rpc

In this example, we first define a Cat class, then we register a codec to do encoding and decoding of the Cat instances.

class Cat{
  constructor(name, color, age, clean){
    this.name = name
    this.color = color
    this.age = age
    this.clean = clean
  }
}

api.registerCodec({
    'name': 'cat', 
    'type': Cat, 
    'encoder': (obj)=>{
        // convert the Cat instance as a dictionary with all the properties
        return {name: obj.name, color: obj.color, age: obj.age, clean: obj.clean}
    },
    'decoder': (encoded_obj)=>{
        // recover the Cat instance
        return new Cat(encoded_obj.name, encoded_obj.color, encoded_obj.age, encoded_obj.clean)
    }
})

class Plugin {
    async setup(){
    }
    async run(){
        const dirtyCat = new Cat('boboshu', 'mixed', 0.67, false)
        // assuming we have a shower plugin
        const showerPlugin = await api.getPlugin('catShower')
        // now pass a cat into the shower plugin, and we should get a clean cat, the name should be the same
        // note that the other plugin is running in another sandboxed iframe or in Python
        // because we have the cat codec registered, we can send the Cat object to the other plugin
        // Also notice that the other plugin should also define custom encoding decoding following the same representation
        const cleanCat = await showerPlugin.wash(dirtyCat)
        if(cleanCat.clean) api.alert(cleanCat.name + ' is clean.')
    }
};
api.export(new Plugin())

Note that, you need to implement the same encoding and decoding for the two connection peers. Otherwise the object will remain undecoded.

Example 2: sending `itk.Image` from Python to Javascript

Here is another example for supporting a new type itk.Image for displaying 2D/3D image in Python in the itk-vtk-viewer (Javascript).

We will first encode all the itk.Image instances with the itkimage_to_json function.

# Run `pip install itk itkwidgets` before trying this example
from imjoy_rpc import api
import numpy as np
import itk
from itkwidgets.trait_types import itkimage_to_json, itkimage_from_json
from itkwidgets._transform_types import to_itk_image

# register an encoder for encoding the itk.Image, the name `itkimage` will be used for decoding
# this example only use the encoder part
api.registerCodec({'name': 'itkimage', 'type': itk.Image, 'encoder': itkimage_to_json, 'decoder': itkimage_from_json})

class ImJoyPlugin():
    def setup(self):
        api.log('plugin initialized')

    async def run(self, ctx):
        image_array = np.random.randint(0, 255, [10,10,10], dtype='uint8')
        itk_image = to_itk_image(image_array)
        # here the itk_image will be encoded via the registered encoder function (i.e.: itkimage_to_json)
        api.createWindow(type="itk-vtk-viewer", src="https://oeway.github.io/itk-vtk-viewer/", data={"image_array": itk_image})

api.export(ImJoyPlugin())

For the Javascript part, we also need a codec to decode the itkImage, we will use an existing function called decompressImage. Here is the implementation we made for the viewer served on https://oeway.github.io/itk-vtk-viewer/:


// register a decoder to decode custom type `itkimage`
api.registerCodec({name: 'itkimage', decoder: itkVtkViewer.utils.decompressImage})

api.export({
    setup() {
        api.log("itk-vtk-viewer loaded successfully.")
    },
    async run(ctx) {
        await this.imshow(ctx.data.image_array)
    },
    async imshow(image_array) {
        // image_array is the decoded image
        const vtkImage = itkVtkViewer.utils.vtkITKHelper.convertItkToVtkImage(image_array)
        const dims = vtkImage.getDimensions()
        const is2D = dims.length === 2 || (dims.length === 3 && dims[2] === 1)
        itkVtkViewer.createViewer(container, {
            image: vtkImage,
            pointSets: null,
            geometries: null,
            use2D: is2D,
            rotate: false
        })
    }
})

Example 3: sending lazy object

Since an encoder can also contain function, this allow us to make a lazy object that can be used to fetch data gradually.

# Run `pip install dask[array]` before trying this example
from imjoy_rpc import api
import dask.array as da
import numpy as np

def lazy_encoder(obj):
    encoded = {}

    # slicing on the array with a list of slices, each slice list uses [start, stop, step] format
    # the default step is 1
    # for example get_slice([(0, 10, 2), (0, 1)])
    def get_slice(slices):
        indexes = tuple([slice(s[0], s[1], None if len(s)==2 else s[2]) for s in slices])
        return np.array(obj[indexes].compute())

    encoded["slice"] = get_slice
    encoded["_rintf"] = True
    return encoded


api.registerCodec({'name': 'daskimage', 'type': da.Array, 'encoder': lazy_encoder})

class ImJoyPlugin():
    def setup(self):
        api.log('plugin initialized')

    async def run(self, ctx):
        # make a dask array
        array1 = da.random.random((10000, 10000), chunks=(1000, 1000))
        # Send the array and get it back,
        # because of the decoding, we will get an slice interface to the array instead
        array2 = await api.echo(array1)
        
        # Here we use the echo function as an example
        # but you can also send this array to, for example a javascript plugin,
        # it can bet the same slice function
        # we perform slicing on two dimensions
        # start=0, stop=4, step=2 for the first dimension
        # start=5, stop=6 for the second dimension
        slices = await array2.slice([[0, 4, 2], [5, 6]])

        api.alert(str(slices))

        # dispose the array when we don't need it
        api.dispose_object(array2)

api.export(ImJoyPlugin())

Example 4: passing Zarr array from Python to Javascript

Zarr is an emerging standard for storing or serving chunked, compressed N-dimensional arrays. It is ideally suited for storing large amount of data remotely. For example, we can use store large images in zarr format on a blob storage (e.g. AWS S3) and visualize it in a web app. With imjoy-rpc, we can define a custom codec for zarr arrays/groups to support sending Zarr arrays, for example from Python to Javascript (use zarr.js).

This is the codec looks like in Python:

import zarr
from imjoy import api
from tifffile import imread

def encode_zarr_store(zobj):
    def getItem(key):
        return zobj.store[key]

    def setItem(key, value):
        zobj.store[key] = value

    def containsItem(key):
        return key in zobj.store

    return {
        "_rintf": True,
        "_rtype": 'zarr-array' if isinstance(zobj, zarr.Array) else 'zarr-group',
        "getItem": getItem,
        "setItem": setItem,
        "containsItem": containsItem,
    }

api.registerCodec({'name': 'zarr-array', 'type': zarr.Array, "encoder": encode_zarr_store})
api.registerCodec({'name': 'zarr-group', 'type': zarr.hierarchy.Group, "encoder": encode_zarr_store})

...
    def run(self, ctx):
        image = imread('cell_membranes.tif')
        image = (image*65536).astype('uint16')
        z_array = zarr.array(image, chunks=(50, 50))
        ...
        # let's assume viewer is a web app that supports imjoy-rpc
        # 
        viwer.imshow(z_array)

The encoded object can be directly used as a zarr array or group for zarr.js:

This is the relevant part in the viwer plugin in Javascript:


// assuming that we already loaded zarr.js

function imshow(z_array){
    // we don't need an decoder, the z_array object can be directly read by zarr.openArray
    zarrArr = await zarr.openArray({ store: z_array });
    ...
}

For an real example notebook, see here for the source code or try it on Binder:

Remote Objects

When sending an object (a JS Object, Class or Python dict, class) remotely via imjoy-rpc, a proxy object will be created on the other side. The object can contain supported data types which will be send directly to remote locations.

Specifically for the methods contained in the object, there are two types of encoding modes (interface and callback). The difference is that a remote method encoded as callback can only be called once and will be destroyed after calling, and interface mode can be used without this limitation.

The two types can be switched automatically or manually:

if the object contains a key or property: _rintf and the value is true, then all the member function will be treated as interface recursively.
if the object is not a basic Object/array or dict/list but other general class, then it will be treated as interface.

Note: if a method's name starts with _, it will not be sent and mapped remotely.

Importantly, When an object is sent to remote location, the object will be stored in an internal object store. Because the object store will always hold object, it will not be possible for the garbage collector to recycle the resources. To get rid of this issue, you need to dispose the remote object manually by calling api.disposeObject(obj) function (or api.dispose_object(obj) for Python). This will notify the remote peer to remove the object from the object store, such that the garbage collector can then collect the occupied resources.

Therefore, please always call api.disposeObject(obj) when you don't need a remote object anymore. This is necessary only for those object encoded in interface mode.

In Python, if you use async with syntax, api.dispose_object() will be automatically called when you exit the context. For example:

async def run():
    async with remote_open_file() as remote_file:
        content = await remote_file.read()
    # No need to call api.dispose_object(remote_file)