SimpleDataFlow (not ready yet)

A simple tool used to analyze data flow.

Minimal Example - Data Sets - Tools - Modifiers - Inspectors

Minimal Example

Although not very useful, below is the minimal code required to start:

import simple_data_flow as sdf

app = sdf.App()
app.start()

Basics

A Simple Data Flow application consists of a few components, namely:

1. Data Graph - the center region that contains nodes
2. Data sets - data of any form that can be dropped onto the data graph
3. Modifiers - components that take in data and performs operation on it, outputting another data set.
4. Inspectors - components that take in data and takes measurements but does not affect the data set.
5. Tools - similar to Inspectors but these components do not have an output.

Data Sets

Data sets are the roots in node graph. The data itself can be any form. Below is a simple example of adding data sets:

import simple_data_flow as sdf

app = sdf.App()

app.add_data_set("New Data", [-5.0, -5.0, -3.0, -3.0, 0.0, 0.0, 3.0, 3.0, 5.0, 5.0])

app.start()

Custom Tools

Creating a new tool requires the developer to create a new class derived from Node. It is the developers responsibility to create a factory static method and override the execute method. The constructor will add however many input attributes are required to perform its operation. Most tools will also need to add a static attribute. The typical steps involved within the execute method are as follows:

1. Retrieve data from input attributes.
2. Retrieve local data.
3. Perform operations.
4. Set some values in the static attributes.
5. Call finish.

Below is a simple tool that displays the length of the incoming data:

import simple_data_flow as sdf
from simple_data_flow import dpg


class NewNode(sdf.Node):

    @staticmethod
    def factory(name, data):
        node = NewNode(name, data)
        return node

    def __init__(self, label: str, data):
        super().__init__(label, data)

        # add input attributes
        self.add_input_attribute(sdf.InputNodeAttribute("input"))
        
        self.custom_id = dpg.generate_uuid()

    def custom(self):

        dpg.add_input_int(label="Length", width=150, id=self.custom_id, readonly=True, step=0)
        
    def execute(self):

        # input data
        input_data_1 = self._input_attributes[0].get_data()

        # perform operations
        result = len(input_data_1)

        # set values
        dpg.set_value(self.custom_id, result)

        # call finish
        self.finish()


app = sdf.App()

app.add_tool("New Tool", NewNode.factory)

app.start()

Custom Modifiers

Creating a new modifier requires the developer to create a new class derived from Node. It is the developers responsibility to create a factory static method and override the execute method. The constructor will add however many input attributes are required to perform its operation. The constructor will also add the required number of output attributes. Most tools will also need to add a static attribute. The typical steps involved within the execute method are as follows:

1. Retrieve data from input attributes.
2. Retrieve local data.
3. Perform operations.
4. Call execute method of each output attribute, passing in output data.
5. Call finish.

Below is a simple example of a modifier that subtracts some value from each value in the data set:

import simple_data_flow as sdf
from simple_data_flow import dpg


class NewNode(sdf.Node):

    @staticmethod
    def factory(name, data):
        node = NewNode(name, data)
        return node

    def __init__(self, label: str, data):
        super().__init__(label, data)

        # add input attributes
        self.add_input_attribute(sdf.InputNodeAttribute("input"))

        self.custom_id = dpg.generate_uuid()

        # add output attributes
        self.add_output_attribute(sdf.OutputNodeAttribute("output"))

    def custom(self):

        dpg.add_input_int(label="Subtract Value", width=150, id=self.custom_id, step=0)
        
    def execute(self):

        # input data
        input_data_1 = self._input_attributes[0].get_data()

        # static attribute value
        subtract_value = dpg.get_value(self.custom_id)

        # perform operations
        new_data = []
        for value in input_data_1:
            new_data.append(value - subtract_value)

        # call execute on output attributes with data
        self._output_attributes[0].execute(new_data)

        # call finish
        self.finish()


app = sdf.App()

app.add_modifier("New Modifier", NewNode.factory)

app.start()

Custom Inspectors

Creating a new inspector requires the developer to create a new class derived from Node. It is the developers responsibility to create a factory static method and override the execute method. The constructor will add however many input attributes are required to perform its operation. The constructor will also add the required number of output attributes. Most tools will also need to add a static attribute. The typical steps involved within the execute method are as follows:

1. Retrieve data from input attributes.
2. Retrieve local data.
3. Perform operations.
4. Call execute method of each output attribute, passing in output data.
5. Call finish.

Below is a simple example of an inspector that finds the maximum value of a data set:

import simple_data_flow as sdf
from simple_data_flow import dpg

class NewNode(sdf.Node):

    @staticmethod
    def factory(name, data):
        node = NewNode(name, data)
        return node

    def __init__(self, label: str, data):
        super().__init__(label, data)

        # add input attributes
        self.add_input_attribute(sdf.InputNodeAttribute("input"))

        # add output attributes
        self.add_output_attribute(sdf.OutputNodeAttribute("output"))

    def execute(self):

        # input data
        input_data_1 = self._input_attributes[0].get_data()

        # perform operations
        max_value = input_data_1[0]
        for value in input_data_1:
            if value > max_value:
                max_value = value

        # call execute on output attributes with data
        self._output_attributes[0].execute(max_value)

        # call finish
        self.finish()


app = sdf.App()

app.add_inspector("New Inspector", NewNode.factory)

app.start()