mirror of https://github.com/Desuuuu/klipper.git
198 lines
6.3 KiB
Markdown
198 lines
6.3 KiB
Markdown
This document describes some of the Klipper debugging tools.
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Translating gcode files to micro-controller commands
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====================================================
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The Klippy host code can run in a batch mode to produce the low-level
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micro-controller commands associated with a gcode file. Inspecting
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these low-level commands is useful when trying to understand the
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actions of the low-level hardware. It can also be useful to compare
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the difference in micro-controller commands after a code change.
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To run Klippy in this batch mode, there is a one time step necessary
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to generate the micro-controller "data dictionary". This is done by
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compiling the micro-controller code to obtain the **out/klipper.dict**
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file:
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```
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make menuconfig
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make
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```
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Once the above is done it is possible to run Klipper in batch mode
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(see [installation](Installation.md) for the steps necessary to build
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the python virtual environment and a printer.cfg file):
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```
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~/klippy-env/bin/python ./klippy/klippy.py ~/printer.cfg -i test.gcode -o test.serial -v -d out/klipper.dict
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```
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The above will produce a file **test.serial** with the binary serial
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output. This output can be translated to readable text with:
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```
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~/klippy-env/bin/python ./klippy/parsedump.py out/klipper.dict test.serial > test.txt
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```
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The resulting file **test.txt** contains a human readable list of
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micro-controller commands.
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The batch mode disables certain response / request commands in order
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to function. As a result, there will be some differences between
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actual commands and the above output. The generated data is useful for
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testing and inspection; it is not useful for sending to a real
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micro-controller.
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Testing with simulavr
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=====================
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The [simulavr](http://www.nongnu.org/simulavr/) tool enables one to
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simulate an Atmel ATmega micro-controller. This section describes how
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one can run test gcode files through simulavr. It is recommended to
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run this on a desktop class machine (not a Raspberry Pi) as it does
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require significant cpu to run efficiently.
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To use simulavr, download the simulavr package and compile with python
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support:
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```
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git clone git://git.savannah.nongnu.org/simulavr.git
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cd simulavr
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./bootstrap
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./configure --enable-python
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make
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```
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Note that the build system may need to have some packages (such as
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swig) installed in order to build the python module. Make sure the
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file **src/python/_pysimulavr.so** is present after the above
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compilation.
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To compile Klipper for use in simulavr, run:
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```
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cd /patch/to/klipper
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make menuconfig
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```
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and compile the micro-controller software for an AVR atmega644p, set
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the MCU frequency to 20Mhz, and select SIMULAVR software emulation
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support. Then one can compile Klipper (run `make`) and then start the
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simulation with:
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```
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PYTHONPATH=/path/to/simulavr/src/python/ ./scripts/avrsim.py -m atmega644 -s 20000000 -b 250000 out/klipper.elf
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```
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Then, with simulavr running in another window, one can run the
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following to read gcode from a file (eg, "test.gcode"), process it
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with Klippy, and send it to Klipper running in simulavr (see
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[installation](Installation.md) for the steps necessary to build the
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python virtual environment):
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```
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~/klippy-env/bin/python ./klippy/klippy.py config/avrsim.cfg -i test.gcode -v
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```
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Using simulavr with gtkwave
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---------------------------
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One useful feature of simulavr is its ability to create signal wave
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generation files with the exact timing of events. To do this, follow
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the directions above, but run avrsim.py with a command-line like the
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following:
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```
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PYTHONPATH=/path/to/simulavr/src/python/ ./scripts/avrsim.py -m atmega644 -s 20000000 -b 250000 out/klipper.elf -t PORTA.PORT,PORTC.PORT
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```
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The above would create a file **avrsim.vcd** with information on each
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change to the GPIOs on PORTA and PORTB. This could then be viewed
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using gtkwave with:
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```
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gtkwave avrsim.vcd
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```
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Manually sending commands to the micro-controller
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=================================================
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Normally, the host klippy.py process would be used to translate gcode
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commands to Klipper micro-controller commands. However, it's also
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possible to manually send these MCU commands (functions marked with
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the DECL_COMMAND() macro in the Klipper source code). To do so, run:
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```
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~/klippy-env/bin/python ./klippy/console.py /tmp/pseudoserial 250000
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```
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See the "HELP" command within the tool for more information on its
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functionality.
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Generating load graphs
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======================
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The Klippy log file (/tmp/klippy.log) stores statistics on bandwidth,
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micro-controller load, and host buffer load. It can be useful to graph
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these statistics after a print.
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To generate a graph, a one time step is necessary to install the
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"matplotlib" package:
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```
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sudo apt-get update
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sudo apt-get install python-matplotlib
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```
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Then graphs can be produced with:
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```
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~/klipper/scripts/graphstats.py /tmp/klippy.log -o loadgraph.png
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```
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One can then view the resulting **loadgraph.png** file.
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Different graphs can be produced. For more information run:
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`~/klipper/scripts/graphstats.py --help`
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Extracting information from the klippy.log file
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===============================================
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The Klippy log file (/tmp/klippy.log) also contains debugging
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information. There is a logextract.py script that may be useful when
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analyzing a micro-controller shutdown or similar problem. It is
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typically run with something like:
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```
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mkdir work_directory
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cd work_directory
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cp /tmp/klippy.log .
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~/klipper/scripts/logextract.py ./klippy.log
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```
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The script will extract the printer config file and will extract MCU
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shutdown information. The information dumps from an MCU shutdown (if
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present) will be reordered by timestamp to assist in diagnosing cause
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and effect scenarios.
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Running the regression tests
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============================
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The main Klipper GitHub repository uses TravisCI to run a series of
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regression tests. It can be useful to run some of these tests locally.
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The source code "whitespace check" can be run with:
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```
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./scripts/check_whitespace.sh
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```
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The Klippy regression test suite requires "data dictionaries" from
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many platforms. The easiest way to obtain them is to
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[download them from github](https://github.com/KevinOConnor/klipper/issues/1438).
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Once the data dictionaries are downloaded, use the following to run
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the regression suite:
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```
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tar xfz klipper-dict-20??????.tar.gz
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~/klippy-env/bin/python ~/klipper/scripts/test_klippy.py -d dict/ ~/klipper/test/klippy/*.test
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```
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