This document describes some of the Klipper debugging tools.
Running the regression tests¶
The main Klipper GitHub repository uses "github actions" to run a series of regression tests. It can be useful to run some of these tests locally.
The source code "whitespace check" can be run with:
The Klippy regression test suite requires "data dictionaries" from many platforms. The easiest way to obtain them is to download them from github. Once the data dictionaries are downloaded, use the following to run the regression suite:
tar xfz klipper-dict-20??????.tar.gz ~/klippy-env/bin/python ~/klipper/scripts/test_klippy.py -d dict/ ~/klipper/test/klippy/*.test
Manually sending commands to the micro-controller¶
Normally, the host klippy.py process would be used to translate gcode commands to Klipper micro-controller commands. However, it's also possible to manually send these MCU 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
See the "HELP" command within the tool for more information on its functionality.
Some command-line options are available. For more information run:
~/klippy-env/bin/python ./klippy/console.py --help
Translating gcode files to micro-controller commands¶
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.
Motion analysis and data logging¶
Klipper supports logging its internal motion history, which can be later analyzed. To use this feature, Klipper must be started with the API Server enabled.
Data logging is enabled with the
data_logger.py tool. For example:
~/klipper/scripts/motan/data_logger.py /tmp/klippy_uds mylog
This command will connect to the Klipper API Server, subscribe to
status and motion information, and log the results. Two files are
generated - a compressed data file and an index file (eg,
mylog.index.gz). After starting the logging, it
is possible to complete prints and other actions - the logging will
continue in the background. When done logging, hit
ctrl-c to exit
The resulting files can be read and graphed using the
tool. To generate graphs on a Raspberry Pi, a one time step is
necessary to install the "matplotlib" package:
sudo apt-get update sudo apt-get install python-matplotlib
However, it may be more convenient to copy the data files to a desktop
class machine along with the Python code in the
directory. The motion analysis scripts should run on any machine with
a recent version of Python and
Graphs can be generated with a command like the following:
~/klipper/scripts/motan/motan_graph.py mylog -o mygraph.png
One can use the
-g option to specify the datasets to graph (it takes
a Python literal containing a list of lists). For example:
~/klipper/scripts/motan/motan_graph.py mylog -g '[["trapq(toolhead,velocity)"], ["trapq(toolhead,accel)"]]'
The list of available datasets can be found using the
-l option -
It is also possible to specify matplotlib plot options for each dataset:
~/klipper/scripts/motan/motan_graph.py mylog -g '[["trapq(toolhead,velocity)?color=red&alpha=0.4"]]'
Many matplotlib options are available; some examples are "color", "label", "alpha", and "linestyle".
motan_graph.py tool supports several other command-line
options - use the
--help option to see a list. It may also be
convenient to view/modify the
motan_graph.py script itself.
Generating load graphs¶
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 -o loadgraph.png
One can then view the resulting loadgraph.png file.
Different graphs can be produced. For more information run:
Extracting information from the klippy.log file¶
The Klippy log file (/tmp/klippy.log) also contains debugging information. There is a logextract.py script that may be useful when analyzing a micro-controller shutdown or similar problem. It is typically run with something like:
mkdir work_directory cd work_directory cp /tmp/klippy.log . ~/klipper/scripts/logextract.py ./klippy.log
The script will extract the printer config file and will extract MCU shutdown information. The information dumps from an MCU shutdown (if present) will be reordered by timestamp to assist in diagnosing cause and effect scenarios.
Testing with simulavr¶
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 /path/to/klipper make menuconfig
and compile the micro-controller software for an AVR atmega644p and
select SIMULAVR software emulation support. Then one can compile
make) and then start the simulation with:
PYTHONPATH=/path/to/simulavr/src/python/ ./scripts/avrsim.py 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/generic-simulavr.cfg -i test.gcode -v
Using simulavr with gtkwave¶
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 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: