Creating Your First Block

From GNU Radio
Jump to navigation Jump to search
Beginner Tutorials

Introducing GNU Radio

  1. What is GNU Radio?
  2. Installing GNU Radio
  3. Your First Flowgraph

Flowgraph Fundamentals

  1. Python Variables in GRC
  2. Variables in Flowgraphs
  3. Runtime Updating Variables
  4. Signal Data Types
  5. Converting Data Types
  6. Packing Bits
  7. Streams and Vectors
  8. Hier Blocks and Parameters

Creating and Modifying Python Blocks

  1. Creating Your First Block
  2. Python Block With Vectors
  3. Python Block Message Passing
  4. Python Block Tags

DSP Blocks

  1. Low Pass Filter Example
  2. Designing Filter Taps
  3. Sample Rate Change

This tutorial shows how to create a signal processing block with the Embedded Python Block. The example block either adds or multiplys the two inputs based on a parameter.

This tutorial uses the Embedded Python Block' which can only be used in the flowgraph it was created in. The tutorial Creating Python OOT with gr-modtool demonstrates how to create a custom Python block as an out-of-tree (OOT) module which can be installed and used in any flowgraph.

The previous tutorial, Hier Blocks and Parameters, describes how to create a hierarchical block and how to use parameters. The next tutorial, Python Block with Vectors, demonstrates how to write an Embedded Python Block with vector inputs and outputs.

Opening Code Editor[edit]

The Embedded Python Block is a tool to quickly prototype a block within a flowgraph. Search for the Python Block and add it to the workspace:

AddPythonBlockToWorkspace.png


Double-click the block to edit the properties. The Embedded Python Block has two properties,

  1. Code, a click-box which contains a link to the Python code for the block and
  2. Example_Param, an input parameter to the block.


Click on Open in Editor to edit the Python code:

EmbeddedPythonBlockProperties.png


A prompt is displayed with a choice of which text editor to use to write the Python code. Click Use Default:

ClickUseDefault.png


An editor window displays the Python code for the Embedded Python Block:

PythonCodeGedit.png

Components of a Python Block[edit]

There are three important sections in the Python block code:

  1. import statements in green
  2. __init__ function in blue
  3. work function in red

PythonBlockCodeFunctions.png


The import statement includes the NumPy and GNU Radio libraries.

The __init__ statement:

  1. Accepts the example_param parameter with a default argument of 1.0
  2. Declares the block to have a np.complex64 input and output, which is the GNU Radio Complex Float 32 data type
  3. Stores the self.example_param variable from the input parameter

The work function:

  1. Has the input input_items and output output_items parameters
  2. Applies a mathematical operation to input_items and stores the result in output_items
  3. Returns the number of samples produced

Changing Parameter Name[edit]

The code is modified to add the custom behavior.

The first step is to rename example_param to additionFlag to be more descriptive. From the editor menu select Find and Replace:

SelectFindAndReplace.png

Enter:

  • Find > example_param
  • Replace with > additionFlag
  • Click Replace All

FindReplaceExampleParam.png


The parameter is changed. The Python code is updated:

PythonBlockAdditionFlag.png


Change the default value to be True:

AdditionFlagDefaultParam.png


Save the file:

SaveButtonGedit.png


Return back to the GRC window.

The Embedded Python Block displays the Additionflag parameter instead of example_param:

AdditionFlagUpdatedBlock.png

Editing Block Inputs[edit]

The default block has a single input and a single output, however we need two inputs for the block. To add an input, add a second np.complex64 to the in_sig list:

AddSecondInput.png


Change the block name to Add or Multiply Block:

EditBlockName.png


Save the file. GRC displays the block with a second input and the block name is updated:

SecondInputOnBlockGRC.png

Editing Work Function[edit]

The work function needs to be modified.

The pseudo-code for the Python block is:

if (additionFlag is True)
    then add the two inputs
else
    then multiply the two inputs

Modify the work function so it has the following code:

EditWorkFunction.png


Remember to indent with multiples of 4 spaces (4, 8, 12, etc.) when starting new lines in Python!

Save the the code.

Connecting the Flowgraph[edit]

Return to GRC. Double-click the Add or Multiply Block. Enter True for the Additionflag property:

SetAdditionFlagProperty.png

Click OK to save.

Drag and drop two Signal Source blocks, a Throttle block, a QT GUI Time Sink and a QT GUI Frequency Sink block into the GRC workspace and connect them according to the following flowgraph. Set the frequency of the second Signal Source to 3000:

ConnectAddMultiplyFlowgraph.png

Running the Flowgraph[edit]

Selecting True in the Add or Multiply Block performs the addition of the two Signal Sources. Running the flowgraph gives the following two plots:

SinusoidAddition.png


The plots show the summation of the two sinusoids, one at a frequency of 1,000 and another at 3,000. The y-axis in the QT GUI Time Sink plot is partially cutting off the amplitude of the sinusoids. Click the scroll-wheel button to bring up the display menu and select Auto Scale:

SelectAutoScaleTimeSink.png


The full amplitude of the two sinusoids can then be seen:

TimeSinkFullAmplitude.png

Stop the flowgraph by closing the QT GUI Time Sink or by pressing the square button in GRC:

StopFlowgraphButtons.png


Enter False for the Additionflag property:

SetFalseAdditionFlag.png

Click OK to save.

By definition, the multiplication of two complex sinusoids produces a sinusoid at the summation of the two frequencies. Therefore, the multiplication of the Signal Source of frequency 1,000 and frequency 3,000 is a complex sinusoid of frequency 4,000. This complex sinusoid is seen when running the flowgraph:

MultiplicationSinusoids.png


The next tutorial, Python Block With Vectors describes how the Python Embedded Block can be modified to accept vector inputs and outputs, and how the input_items vector indexing is different between vectors and streams.