Editing Handling Flowgraphs

[[Category:Usage Manual]]
== Operating a Flowgraph ==

The basic data structure in GNU Radio is the flowgraph, which
represents the connections of the blocks through which a continuous
stream of samples flows. The concept of a flowgraph is an acyclic
directional graph with one or more source blocks (to insert samples
into the flowgraph), one or more sink blocks (to terminate or export
samples from the flowgraph), and any signal processing blocks in
between.

A program must at least create a GNU Radio 'top_block', which
represents the top-most structure of the flowgraph. The top blocks
provide the overall control and hold methods such as 'start,' 'stop,'
and 'wait'.

The general construction of a GNU Radio application is to create a
gr_top_block, instantiate the blocks, connect the blocks together, and
then start the gr_top_block. The following program shows how this is
done. A single source and sink are used with a FIR filter between
them.
<syntaxhighlight lang="python">
    from gnuradio import gr, blocks, filter, analog
 
    class my_topblock(gr.top_block):
        def __init__(self):
            gr.top_block.__init__(self)
 
            amp = 1
            taps = filter.firdes.low_pass(1, 1, 0.1, 0.01)
 
            self.src = analog.noise_source_c(analog.GR_GAUSSIAN, amp)
            self.flt = filter.fir_filter_ccf(1, taps)
            self.snk = blocks.null_sink(gr.sizeof_gr_complex)
 
            self.connect(self.src, self.flt, self.snk)
 
    if __name__ == "__main__":
        tb = my_topblock()
        tb.start()
        tb.wait()
</syntaxhighlight>
The 'tb.start()' starts the data flowing through the flowgraph while
the 'tb.wait()' is the equivalent of a thread's 'join' operation and
blocks until the gr_top_block is done.

An alternative to using the 'start' and 'wait' methods, a 'run' method is
also provided for convenience that is a blocking start call;
equivalent to the above 'start' followed by a 'wait.'

=== Latency and Throughput ===

By default, GNU Radio runs a scheduler that attempts to optimize
throughput. Using a dynamic scheduler, blocks in a flowgraph pass
chunks of items from sources to sinks. The sizes of these chunks will
vary depending on the speed of processing. For each block, the number
of items it can process is dependent on how much space it has in its
output buffer(s) and how many items are available on the input
buffer(s).

The consequence of this is that often a block may be called with a very
large number of items to process (several thousand). In terms of
speed, this is efficient since now the majority of the processing time
is taken up with processing samples. Smaller chunks mean more calls
into the scheduler to retrieve more data. The downside to this is that
it can lead to large latency while a block is processing a large chunk
of data.

To combat this problem, the gr_top_block can be passed a limit on the
number of output items a block will ever receive. A block may get less
than this number, but never more, and so it serves as an upper limit
to the latency any block will exhibit. By limiting the number of items
per call to a block, though, we increase the overhead of the
scheduler, and so reduce the overall efficiency of the application.

To set the maximum number of output items, we pass a value into the
'start' or 'run' method of the gr_top_block:

     tb.start(1000)
     tb.wait()
or
     tb.run(1000)

Using this method, we place a global restriction on the size of items
to all blocks. Each block, though, has the ability to overwrite this
with its own limit. Using the 'set_max_noutput_items(m)' method for an
individual block will overwrite the global setting. For example, in
the following code, the global setting is 1000 items max, except for
the FIR filter, which can receive up to 2000 items.

     tb.flt.set_max_noutput_items(2000)
     tb.run(1000)

In some situations, you might actually want to restrict the size of
the buffer itself. This can help to prevent a buffer who is blocked
for data from just increasing the amount of items in its buffer, which
will then cause an increased latency for new samples. You can set the
size of an output buffer for each output port for every block.

WARNING: This is an advanced feature in GNU Radio and should not be
used without a full understanding of this concept as explained below.

To set the output buffer size of a block, you simply call:

     tb.blk0.set_max_output_buffer(2000)
     tb.blk1.set_max_output_buffer(1, 2000)
     tb.start()
     print tb.blk1.max_output_buffer(0)
     print tb.blk1.max_output_buffer(1)

In the above example, all ports of blk0 are set to a buffer size of
2000 in ''items'' (not bytes), and blk1 only sets the size for output
port 1, any and all other ports use the default. The third and fourth
lines just print out the buffer sizes for ports 0 and 1 of blk1. This
is done after start() is called because the values are updated based
on what is actually allocated to the block's buffers.

NOTES:

1. Buffer length assignment is done once at runtime (i.e., when run()
or start() is called). So to set the max buffer lengths, the
set_max_output_buffer calls must be done before this.

2. Once the flowgraph is started, the buffer lengths for a block are
set and cannot be dynamically changed, even during a
lock()/unlock(). If you need to change the buffer size, you will have
to delete the block and rebuild it, and therefore must disconnect and
reconnect the blocks.

3. This can affect throughput. Large buffers are designed to improve
the efficiency and speed of the program at the expense of
latency. Limiting the size of the buffer may decrease performance.

4. The real buffer size is actually based on a minimum granularity of
the system. Typically, this is a page size, which is typically 4096
bytes. This means that any buffer size that is specified with this
command will get rounded up to the nearest granularity (e.g., page size). 
When calling max_output_buffer(port) after the flowgraph is
started, you will get how many items were actually allocated in the
buffer, which may be different than what was initially specified.

== Reconfiguring Flowgraphs ==

It is possible to reconfigure the flowgraph at runtime. The
reconfiguration is meant for changes in the flowgraph structure, not
individual parameter settings of the blocks. For example, changing the
constant in a gr::blocks::add_const_cc block can be done while the flowgraph is
running using the 'set_k(k)' method.

Reconfiguration is done by locking the flowgraph, which stops it from
running and processing data, performing the reconfiguration, and then
restarting the graph by unlocking it.

The following example code shows a graph that first adds two
gr::analog::noise_source_c blocks and then replaces the
gr::blocks::add_cc block with a gr::blocks::sub_cc block to then
subtract the sources.

 from gnuradio import gr, analog, blocks
 import time
 
 class mytb(gr.top_block):
     def __init__(self):
         gr.top_block.__init__(self)
 
         self.src0 = analog.noise_source_c(analog.GR_GAUSSIAN, 1)
         self.src1 = analog.noise_source_c(analog.GR_GAUSSIAN, 1)
         self.add  = blocks.add_cc()
         self.sub  = blocks.sub_cc()
         self.head = blocks.head(gr.sizeof_gr_complex, 1000000)
         self.snk  = blocks.file_sink(gr.sizeof_gr_complex, "output.32fc")
 
         self.connect(self.src0, (self.add,0))
         self.connect(self.src1, (self.add,1))
         self.connect(self.add, self.head)
         self.connect(self.head, self.snk) 
 
 def main():
     tb = mytb()
     tb.start()
     time.sleep(0.01) 
 
     # Stop flowgraph and disconnect the add block
     tb.lock()
     tb.disconnect(tb.add, tb.head)
     tb.disconnect(tb.src0, (tb.add,0))
     tb.disconnect(tb.src1, (tb.add,1))
 
     # Connect the sub block and restart
     tb.connect(tb.sub, tb.head)
     tb.connect(tb.src0, (tb.sub,0))
     tb.connect(tb.src1, (tb.sub,1))
     tb.unlock()
 
     tb.wait()
 
 if __name__ == "__main__":
     main()

During reconfiguration, the maximum noutput_items value can be changed
either globally using the 'set_max_noutput_items(m)' on the gr_top_block
object or locally using the 'set_max_noutput_items(m)' on any given
block object.

A block also has a 'unset_max_noutput_items()' method that unsets the
local max noutput_items value so that block reverts back to using the
global value.

The following example expands the previous example but sets and resets
the max noutput_items both locally and globally.
<syntaxhighlight lang="python">
 from gnuradio import gr, analog, blocks
 import time
 
 class mytb(gr.top_block):
     def __init__(self):
         gr.top_block.__init__(self)
 
         self.src0 = analog.noise_source_c(analog.GR_GAUSSIAN, 1)
         self.src1 = analog.noise_source_c(analog.GR_GAUSSIAN, 1)
         self.add  = blocks.add_cc()
         self.sub  = blocks.sub_cc()
         self.head = blocks.head(gr.sizeof_gr_complex, 1000000)
         self.snk  = blocks.file_sink(gr.sizeof_gr_complex, "output.32fc")
 
         self.connect(self.src0, (self.add,0))
         self.connect(self.src1, (self.add,1))
         self.connect(self.add, self.head)
         self.connect(self.head, self.snk)
 
 def main():
     # Start the gr_top_block after setting some max noutput_items.
     tb = mytb()
     tb.src1.set_max_noutput_items(2000)
     tb.start(100)
     time.sleep(0.01)
 
     # Stop flowgraph and disconnect the add block
     tb.lock()
 
     tb.disconnect(tb.add, tb.head)
     tb.disconnect(tb.src0, (tb.add,0))
     tb.disconnect(tb.src1, (tb.add,1))
 
     # Connect the sub block
     tb.connect(tb.sub, tb.head)
     tb.connect(tb.src0, (tb.sub,0))
     tb.connect(tb.src1, (tb.sub,1))
 
     # Set new max_noutput_items for the gr_top_block
     # and unset the local value for src1
     tb.set_max_noutput_items(1000)
     tb.src1.unset_max_noutput_items()
     tb.unlock()
 
     tb.wait()
 
 if __name__ == "__main__":
     main()
</syntaxhighlight>