The Klippy host code has some tools to help in debugging.
The Klippy host code can run in a batch mode to produce the low-level micro-controller commands associated with a gcode file. Inspecting these low-level commands is useful when trying to understand the actions of the low-level hardware. It can also be useful to compare the difference in micro-controller commands after a code change.
To run Klippy in this batch mode, there is a one time step necessary to generate the micro-controller "data dictionary". This is done by compiling the micro-controller code to obtain the out/klipper.dict file:
make menuconfig
make
Once the above is done it is possible to run Klipper in batch mode (see installation for the steps necessary to build the python virtual environment and a printer.cfg file):
~/klippy-env/bin/python ./klippy/klippy.py ~/printer.cfg -i test.gcode -o test.serial -v -d out/klipper.dict
The above will produce a file test.serial with the binary serial output. This output can be translated to readable text with:
~/klippy-env/bin/python ./klippy/parsedump.py out/klipper.dict test.serial > test.txt
The resulting file test.txt contains a human readable list of micro-controller commands.
The batch mode disables certain response / request commands in order to function. As a result, there will be some differences between actual commands and the above output. The generated data is useful for testing and inspection; it is not useful for sending to a real micro-controller.
The simulavr tool enables one to simulate an Atmel ATmega micro-controller. This section describes how one can run test gcode files through simulavr. It is recommended to run this on a desktop class machine (not a Raspberry Pi) as it does require significant cpu to run efficiently.
To use simulavr, download the simulavr package and compile with python support:
git clone git://git.savannah.nongnu.org/simulavr.git
cd simulavr
./bootstrap
./configure --enable-python
make
Note that the build system may need to have some packages (such as swig) installed in order to build the python module. Make sure the file src/python/_pysimulavr.so is present after the above compilation.
To compile Klipper for use in simulavr, run:
cd /patch/to/klipper
make menuconfig
and compile the micro-controller software for an AVR atmega644p,
disable the AVR watchdog timer, and set the MCU frequency
to 20000000. Then one can compile Klipper (run make) and then start
the simulation with:
PYTHONPATH=/path/to/simulavr/src/python/ ./scripts/avrsim.py -m atmega644 -s 20000000 -b 250000 out/klipper.elf
Then, with simulavr running in another window, one can run the following to read gcode from a file (eg, "test.gcode"), process it with Klippy, and send it to Klipper running in simulavr (see installation for the steps necessary to build the python virtual environment):
~/klippy-env/bin/python ./klippy/klippy.py config/avrsim.cfg -i test.gcode -v
One useful feature of simulavr is its ability to create signal wave generation files with the exact timing of events. To do this, follow the directions above, but run avrsim.py with a command-line like the following:
PYTHONPATH=/path/to/simulavr/src/python/ ./scripts/avrsim.py -m atmega644 -s 20000000 -b 250000 out/klipper.elf -t PORTA.PORT,PORTC.PORT
The above would create a file avrsim.vcd with information on each change to the GPIOs on PORTA and PORTB. This could then be viewed using gtkwave with:
gtkwave avrsim.vcd
Normally, Klippy would be used to translate gcode commands to Klipper commands. However, it's also possible to manually send Klipper commands (functions marked with the DECL_COMMAND() macro in the Klipper source code). To do so, run:
~/klippy-env/bin/python ./klippy/console.py /tmp/pseudoserial 250000
The Klippy log file (/tmp/klippy.log) stores statistics on bandwidth, micro-controller load, and host buffer load. It can be useful to graph these statistics after a print.
To generate a graph, a one time step is necessary to install the "matplotlib" package:
sudo apt-get update
sudo apt-get install python-matplotlib
Then graphs can be produced with:
~/klipper/scripts/graphstats.py /tmp/klippy.log loadgraph.png
One can then view the resulting loadgraph.png file.