From 43a205d0363dc4ea9384593777e8623f3675d2ea Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?F=C3=A9lix=20Boisselier?= Date: Wed, 29 Nov 2023 11:43:21 +0100 Subject: [PATCH 1/7] now using a venv to run the scripts --- .gitignore | 160 +++++++++++++++++++++ K-ShakeTune/IS_shaper_calibrate.cfg | 6 +- K-ShakeTune/IS_vibrations_measurements.cfg | 2 +- K-ShakeTune/IS_workflow_cmd.cfg | 4 +- K-ShakeTune/scripts/is_workflow.py | 5 +- K-ShakeTune/scripts/shaketune.sh | 5 + README.md | 8 +- install.sh | 39 +++-- requirements.txt | 11 ++ 9 files changed, 220 insertions(+), 20 deletions(-) create mode 100644 .gitignore create mode 100755 K-ShakeTune/scripts/shaketune.sh create mode 100644 requirements.txt diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..68bc17f --- /dev/null +++ b/.gitignore @@ -0,0 +1,160 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/#use-with-ide +.pdm.toml + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +#.idea/ diff --git a/K-ShakeTune/IS_shaper_calibrate.cfg b/K-ShakeTune/IS_shaper_calibrate.cfg index a5ee719..baf2bae 100644 --- a/K-ShakeTune/IS_shaper_calibrate.cfg +++ b/K-ShakeTune/IS_shaper_calibrate.cfg @@ -55,7 +55,7 @@ gcode: RESPOND MSG="X axis frequency profile generation..." RESPOND MSG="This may take some time (1-3min)" {% endif %} - RUN_SHELL_COMMAND CMD=plot_graph PARAMS=SHAPER + RUN_SHELL_COMMAND CMD=shaketune PARAMS=SHAPER {% endif %} {% if Y %} @@ -66,7 +66,7 @@ gcode: RESPOND MSG="Y axis frequency profile generation..." RESPOND MSG="This may take some time (1-3min)" {% endif %} - RUN_SHELL_COMMAND CMD=plot_graph PARAMS=SHAPER + RUN_SHELL_COMMAND CMD=shaketune PARAMS=SHAPER {% endif %} @@ -88,7 +88,7 @@ gcode: RESPOND MSG="Belts comparative frequency profile generation..." RESPOND MSG="This may take some time (3-5min)" {% endif %} - RUN_SHELL_COMMAND CMD=plot_graph PARAMS=BELTS + RUN_SHELL_COMMAND CMD=shaketune PARAMS=BELTS [gcode_macro EXCITATE_AXIS_AT_FREQ] diff --git a/K-ShakeTune/IS_vibrations_measurements.cfg b/K-ShakeTune/IS_vibrations_measurements.cfg index 7ebe8ad..186c4d7 100644 --- a/K-ShakeTune/IS_vibrations_measurements.cfg +++ b/K-ShakeTune/IS_vibrations_measurements.cfg @@ -168,7 +168,7 @@ gcode: {% if verbose %} RESPOND MSG="Graphs generation... Please wait a minute or two and look in the configured folder." {% endif %} - RUN_SHELL_COMMAND CMD=plot_graph PARAMS="VIBRATIONS {direction}" + RUN_SHELL_COMMAND CMD=shaketune PARAMS="VIBRATIONS {direction}" # Restore the previous acceleration values SET_VELOCITY_LIMIT ACCEL={old_accel} ACCEL_TO_DECEL={old_accel_to_decel} SQUARE_CORNER_VELOCITY={old_sqv} diff --git a/K-ShakeTune/IS_workflow_cmd.cfg b/K-ShakeTune/IS_workflow_cmd.cfg index 4664227..bb48844 100644 --- a/K-ShakeTune/IS_workflow_cmd.cfg +++ b/K-ShakeTune/IS_workflow_cmd.cfg @@ -1,4 +1,4 @@ -[gcode_shell_command plot_graph] -command: ~/printer_data/config/K-ShakeTune/scripts/is_workflow.py +[gcode_shell_command shaketune] +command: ~/printer_data/config/K-ShakeTune/scripts/shaketune.sh timeout: 600.0 verbose: True diff --git a/K-ShakeTune/scripts/is_workflow.py b/K-ShakeTune/scripts/is_workflow.py index 4f88ba5..1eec782 100755 --- a/K-ShakeTune/scripts/is_workflow.py +++ b/K-ShakeTune/scripts/is_workflow.py @@ -1,4 +1,5 @@ #!/usr/bin/env python3 + ############################################ ###### INPUT SHAPER KLIPPAIN WORKFLOW ###### ############################################ @@ -223,7 +224,7 @@ def main(): os.makedirs(folder) if len(sys.argv) < 2: - print("Usage: plot_graphs.py [SHAPER|BELTS|VIBRATIONS]") + print("Usage: is_workflow.py [SHAPER|BELTS|VIBRATIONS]") sys.exit(1) if sys.argv[1].lower() == 'belts': @@ -233,7 +234,7 @@ def main(): elif sys.argv[1].lower() == 'vibrations': fig, png_filename = get_vibrations_graph(axis_name=sys.argv[2]) else: - print("Usage: plot_graphs.py [SHAPER|BELTS|VIBRATIONS]") + print("Usage: is_workflow.py [SHAPER|BELTS|VIBRATIONS]") sys.exit(1) fig.savefig(png_filename) diff --git a/K-ShakeTune/scripts/shaketune.sh b/K-ShakeTune/scripts/shaketune.sh new file mode 100755 index 0000000..952a161 --- /dev/null +++ b/K-ShakeTune/scripts/shaketune.sh @@ -0,0 +1,5 @@ +#!/usr/bin/env bash + +source ~/klippain_shaketune-env/bin/activate +python ~/klippain_shaketune/K-ShakeTune/scripts/is_workflow.py "$@" +deactivate diff --git a/README.md b/README.md index 26113bf..8951412 100644 --- a/README.md +++ b/README.md @@ -19,16 +19,16 @@ Check out the **[detailed documentation of the Shake&Tune module here](./docs/RE ## Installation -For those not using the full [Klippain](https://github.com/Frix-x/klippain), follow these steps to integrate this Shake&Tune module in your setup: +Follow these steps to install the Shake&Tune module in your printer: 1. Run the install script over SSH on your printer: ```bash wget -O - https://raw.githubusercontent.com/Frix-x/klippain-shaketune/main/install.sh | bash ``` - 2. Append the following to your `printer.cfg` file: + 1. Append the following to your `printer.cfg` file: ``` [include K-ShakeTune/*.cfg] ``` - 3. Optionally, if you want to get automatic updates, add the following to your `moonraker.cfg` file: + 1. Optionally, if you want to get automatic updates, add the following to your `moonraker.cfg` file: ``` [update_manager Klippain-ShakeTune] type: git_repo @@ -42,7 +42,7 @@ For those not using the full [Klippain](https://github.com/Frix-x/klippain), fol > **Note**: > - > If already using my old IS workflow scripts, please remove everything before installing this new module. This include the macros, the Python scripts, the `plot_graph.sh` and the `[gcode_shell_command plot_graph]` section. + > If already using my old IS workflow scripts, please remove everything before installing this new module. This include the macros, the Python scripts, the `plot_graph.sh` and the `[gcode_shell_command plot_graph]` section that are not needed anymore. ## Usage diff --git a/install.sh b/install.sh index 45d962a..d6aa777 100755 --- a/install.sh +++ b/install.sh @@ -2,7 +2,9 @@ USER_CONFIG_PATH="${HOME}/printer_data/config" KLIPPER_PATH="${HOME}/klipper" + K_SHAKETUNE_PATH="${HOME}/klippain_shaketune" +K_SHAKETUNE_VENV_PATH="${HOME}/klippain_shaketune-env" set -eu export LC_ALL=C @@ -14,19 +16,17 @@ function preflight_checks { exit -1 fi + if ! command -v python3 &> /dev/null; then + echo "[ERROR] Python 3 is not installed. Please install Python 3 to use the Shake&Tune module!" + exit -1 + fi + if [ "$(sudo systemctl list-units --full -all -t service --no-legend | grep -F 'klipper.service')" ]; then printf "[PRE-CHECK] Klipper service found! Continuing...\n\n" else echo "[ERROR] Klipper service not found, please install Klipper first!" exit -1 fi - - if [ -d "${HOME}/klippain_config" ]; then - if [ -f "${USER_CONFIG_PATH}/.VERSION" ]; then - echo "[ERROR] Klippain full installation found! Nothing is needed in order to use the K-Shake&Tune module!" - exit -1 - fi - fi } function check_download { @@ -48,9 +48,31 @@ function check_download { fi } +function setup_venv { + if [ ! -d "${K_SHAKETUNE_VENV_PATH}" ]; then + echo "[SETUP] Creating Python virtual environment..." + python3 -m venv "${K_SHAKETUNE_VENV_PATH}" + else + echo "[SETUP] Virtual environment already exists. Continuing..." + fi + + source "${K_SHAKETUNE_VENV_PATH}/bin/activate" + echo "[SETUP] Installing/Updating K-Shake&Tune dependencies..." + pip install --upgrade pip + pip install -r "${K_SHAKETUNE_PATH}/requirements.txt" + deactivate + printf "\n" +} + function link_extension { echo "[INSTALL] Linking scripts to your config directory..." - ln -frsn ${K_SHAKETUNE_PATH}/K-ShakeTune ${USER_CONFIG_PATH}/K-ShakeTune + + if [ -d "${HOME}/klippain_config" ] && [ -f "${USER_CONFIG_PATH}/.VERSION" ]; then + echo "[INSTALL] Klippain full installation found! Linking module to the script folder of Klippain" + ln -frsn ${K_SHAKETUNE_PATH}/K-ShakeTune ${USER_CONFIG_PATH}/scripts/K-ShakeTune + else + ln -frsn ${K_SHAKETUNE_PATH}/K-ShakeTune ${USER_CONFIG_PATH}/K-ShakeTune + fi } function link_gcodeshellcommandpy { @@ -76,6 +98,7 @@ printf "=============================================\n\n" # Run steps preflight_checks check_download +setup_venv link_extension link_gcodeshellcommandpy restart_klipper diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..be8c8d0 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,11 @@ +contourpy==1.2.0 +cycler==0.12.1 +fonttools==4.45.1 +kiwisolver==1.4.5 +matplotlib==3.8.2 +numpy==1.26.2 +packaging==23.2 +Pillow==10.1.0 +pyparsing==3.1.1 +python-dateutil==2.8.2 +six==1.16.0 From 3a9cb57f310b2978d78ece4cfaf5c94a089e9c84 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?F=C3=A9lix=20Boisselier?= Date: Wed, 29 Nov 2023 12:04:59 +0100 Subject: [PATCH 2/7] splitted cfg files to allow a selection of only some of the macros --- K-ShakeTune/IS_shaper_calibrate.cfg | 112 ------------------ K-ShakeTune/K-SH_axis.cfg | 47 ++++++++ K-ShakeTune/K-SH_belts.cfg | 24 ++++ K-ShakeTune/K-SH_static_freq.cfg | 24 ++++ ...s_measurements.cfg => K-SH_vibrations.cfg} | 8 -- ...{IS_workflow_cmd.cfg => shaketune_cmd.cfg} | 0 6 files changed, 95 insertions(+), 120 deletions(-) delete mode 100644 K-ShakeTune/IS_shaper_calibrate.cfg create mode 100644 K-ShakeTune/K-SH_axis.cfg create mode 100644 K-ShakeTune/K-SH_belts.cfg create mode 100644 K-ShakeTune/K-SH_static_freq.cfg rename K-ShakeTune/{IS_vibrations_measurements.cfg => K-SH_vibrations.cfg} (95%) rename K-ShakeTune/{IS_workflow_cmd.cfg => shaketune_cmd.cfg} (100%) diff --git a/K-ShakeTune/IS_shaper_calibrate.cfg b/K-ShakeTune/IS_shaper_calibrate.cfg deleted file mode 100644 index baf2bae..0000000 --- a/K-ShakeTune/IS_shaper_calibrate.cfg +++ /dev/null @@ -1,112 +0,0 @@ -################################################ -###### STANDARD INPUT_SHAPER CALIBRATIONS ###### -################################################ -# Written by Frix_x#0161 # -# @version: 1.5 - -# CHANGELOG: -# v1.5: modified EXCITATE_AXIS_AT_FREQ to allow A or B belt testing -# v1.4: added possibility to only run one axis at a time for the axes shaper calibration -# v1.3: added possibility to override the default parameters -# v1.2: added EXCITATE_AXIS_AT_FREQ to hold a specific excitating frequency on an axis and diagnose mechanical problems -# v1.1: added M400 to validate that the files are correctly saved to disk -# v1.0: first version of the automatic input shaper workflow - - -### What is it ? ### -# This macro helps you to configure the input shaper algorithm of Klipper by running the tests sequencially and calling an automatic script -# that generate the graphs, manage the files and so on. It's basically a fully automatic input shaper calibration workflow. -# Results can be found in your config folder using FLuidd/Maisail file manager. - -# The goal is to make it easy to set, share and use it. - -# Usage: -# 1. Call the AXES_SHAPER_CALIBRATION macro, wait for it to end and compute the graphs. Then look for the results in the results folder. -# 2. Call the BELTS_SHAPER_CALIBRATION macro, wait for it to end and compute the graphs. Then look for the results in the results folder. -# 3. If you find out some strange noise, you can use the EXCITATE_AXIS_AT_FREQ macro to diagnose the origin - - -[gcode_macro AXES_SHAPER_CALIBRATION] -description: Perform standard axis input shaper tests on one or both XY axes to select the best input shaper filter -gcode: - {% set verbose = params.VERBOSE|default(true) %} - {% set min_freq = params.FREQ_START|default(5)|float %} - {% set max_freq = params.FREQ_END|default(133.3)|float %} - {% set hz_per_sec = params.HZ_PER_SEC|default(1)|float %} - {% set axis = params.AXIS|default("all")|string|lower %} - - {% set X, Y = False, False %} - - {% if axis == "all" %} - {% set X, Y = True, True %} - {% elif axis == "x" %} - {% set X = True %} - {% elif axis == "y" %} - {% set Y = True %} - {% else %} - { action_raise_error("AXIS selection invalid. Should be either all, x or y!") } - {% endif %} - - {% if X %} - TEST_RESONANCES AXIS=X OUTPUT=raw_data NAME=x FREQ_START={min_freq} FREQ_END={max_freq} HZ_PER_SEC={hz_per_sec} - M400 - - {% if verbose %} - RESPOND MSG="X axis frequency profile generation..." - RESPOND MSG="This may take some time (1-3min)" - {% endif %} - RUN_SHELL_COMMAND CMD=shaketune PARAMS=SHAPER - {% endif %} - - {% if Y %} - TEST_RESONANCES AXIS=Y OUTPUT=raw_data NAME=y FREQ_START={min_freq} FREQ_END={max_freq} HZ_PER_SEC={hz_per_sec} - M400 - - {% if verbose %} - RESPOND MSG="Y axis frequency profile generation..." - RESPOND MSG="This may take some time (1-3min)" - {% endif %} - RUN_SHELL_COMMAND CMD=shaketune PARAMS=SHAPER - {% endif %} - - -[gcode_macro BELTS_SHAPER_CALIBRATION] -description: Perform a custom half-axis test to analyze and compare the frequency profiles of individual belts on CoreXY printers -gcode: - {% set verbose = params.VERBOSE|default(true) %} - {% set min_freq = params.FREQ_START|default(5)|float %} - {% set max_freq = params.FREQ_END|default(133.33)|float %} - {% set hz_per_sec = params.HZ_PER_SEC|default(1)|float %} - - TEST_RESONANCES AXIS=1,1 OUTPUT=raw_data NAME=b FREQ_START={min_freq} FREQ_END={max_freq} HZ_PER_SEC={hz_per_sec} - M400 - - TEST_RESONANCES AXIS=1,-1 OUTPUT=raw_data NAME=a FREQ_START={min_freq} FREQ_END={max_freq} HZ_PER_SEC={hz_per_sec} - M400 - - {% if verbose %} - RESPOND MSG="Belts comparative frequency profile generation..." - RESPOND MSG="This may take some time (3-5min)" - {% endif %} - RUN_SHELL_COMMAND CMD=shaketune PARAMS=BELTS - - -[gcode_macro EXCITATE_AXIS_AT_FREQ] -description: Maintain a specified input shaper excitation frequency for a period of time to diagnose and locate a source of vibration -gcode: - {% set frequency = params.FREQUENCY|default(25)|int %} - {% set time = params.TIME|default(10)|int %} - {% set axis = params.AXIS|default("x")|string|lower %} - - {% if axis not in ["x", "y", "a", "b"] %} - { action_raise_error("AXIS selection invalid. Should be either x, y, a or b!") } - {% endif %} - - {% if axis == "a" %} - {% set axis = "1,-1" %} - {% elif axis == "b" %} - {% set axis = "1,1" %} - {% endif %} - - TEST_RESONANCES OUTPUT=raw_data AXIS={axis} FREQ_START={frequency-1} FREQ_END={frequency+1} HZ_PER_SEC={1/(time/3)} - M400 diff --git a/K-ShakeTune/K-SH_axis.cfg b/K-ShakeTune/K-SH_axis.cfg new file mode 100644 index 0000000..69da343 --- /dev/null +++ b/K-ShakeTune/K-SH_axis.cfg @@ -0,0 +1,47 @@ +################################################ +###### STANDARD INPUT_SHAPER CALIBRATIONS ###### +################################################ +# Written by Frix_x#0161 # + +[gcode_macro AXES_SHAPER_CALIBRATION] +description: Perform standard axis input shaper tests on one or both XY axes to select the best input shaper filter +gcode: + {% set verbose = params.VERBOSE|default(true) %} + {% set min_freq = params.FREQ_START|default(5)|float %} + {% set max_freq = params.FREQ_END|default(133.3)|float %} + {% set hz_per_sec = params.HZ_PER_SEC|default(1)|float %} + {% set axis = params.AXIS|default("all")|string|lower %} + + {% set X, Y = False, False %} + + {% if axis == "all" %} + {% set X, Y = True, True %} + {% elif axis == "x" %} + {% set X = True %} + {% elif axis == "y" %} + {% set Y = True %} + {% else %} + { action_raise_error("AXIS selection invalid. Should be either all, x or y!") } + {% endif %} + + {% if X %} + TEST_RESONANCES AXIS=X OUTPUT=raw_data NAME=x FREQ_START={min_freq} FREQ_END={max_freq} HZ_PER_SEC={hz_per_sec} + M400 + + {% if verbose %} + RESPOND MSG="X axis frequency profile generation..." + RESPOND MSG="This may take some time (1-3min)" + {% endif %} + RUN_SHELL_COMMAND CMD=shaketune PARAMS=SHAPER + {% endif %} + + {% if Y %} + TEST_RESONANCES AXIS=Y OUTPUT=raw_data NAME=y FREQ_START={min_freq} FREQ_END={max_freq} HZ_PER_SEC={hz_per_sec} + M400 + + {% if verbose %} + RESPOND MSG="Y axis frequency profile generation..." + RESPOND MSG="This may take some time (1-3min)" + {% endif %} + RUN_SHELL_COMMAND CMD=shaketune PARAMS=SHAPER + {% endif %} diff --git a/K-ShakeTune/K-SH_belts.cfg b/K-ShakeTune/K-SH_belts.cfg new file mode 100644 index 0000000..4ee8e88 --- /dev/null +++ b/K-ShakeTune/K-SH_belts.cfg @@ -0,0 +1,24 @@ +################################################ +###### STANDARD INPUT_SHAPER CALIBRATIONS ###### +################################################ +# Written by Frix_x#0161 # + +[gcode_macro BELTS_SHAPER_CALIBRATION] +description: Perform a custom half-axis test to analyze and compare the frequency profiles of individual belts on CoreXY printers +gcode: + {% set verbose = params.VERBOSE|default(true) %} + {% set min_freq = params.FREQ_START|default(5)|float %} + {% set max_freq = params.FREQ_END|default(133.33)|float %} + {% set hz_per_sec = params.HZ_PER_SEC|default(1)|float %} + + TEST_RESONANCES AXIS=1,1 OUTPUT=raw_data NAME=b FREQ_START={min_freq} FREQ_END={max_freq} HZ_PER_SEC={hz_per_sec} + M400 + + TEST_RESONANCES AXIS=1,-1 OUTPUT=raw_data NAME=a FREQ_START={min_freq} FREQ_END={max_freq} HZ_PER_SEC={hz_per_sec} + M400 + + {% if verbose %} + RESPOND MSG="Belts comparative frequency profile generation..." + RESPOND MSG="This may take some time (3-5min)" + {% endif %} + RUN_SHELL_COMMAND CMD=shaketune PARAMS=BELTS diff --git a/K-ShakeTune/K-SH_static_freq.cfg b/K-ShakeTune/K-SH_static_freq.cfg new file mode 100644 index 0000000..fb410d8 --- /dev/null +++ b/K-ShakeTune/K-SH_static_freq.cfg @@ -0,0 +1,24 @@ +################################################ +###### STANDARD INPUT_SHAPER CALIBRATIONS ###### +################################################ +# Written by Frix_x#0161 # + +[gcode_macro EXCITATE_AXIS_AT_FREQ] +description: Maintain a specified excitation frequency for a period of time to diagnose and locate a source of vibration +gcode: + {% set frequency = params.FREQUENCY|default(25)|int %} + {% set time = params.TIME|default(10)|int %} + {% set axis = params.AXIS|default("x")|string|lower %} + + {% if axis not in ["x", "y", "a", "b"] %} + { action_raise_error("AXIS selection invalid. Should be either x, y, a or b!") } + {% endif %} + + {% if axis == "a" %} + {% set axis = "1,-1" %} + {% elif axis == "b" %} + {% set axis = "1,1" %} + {% endif %} + + TEST_RESONANCES OUTPUT=raw_data AXIS={axis} FREQ_START={frequency-1} FREQ_END={frequency+1} HZ_PER_SEC={1/(time/3)} + M400 diff --git a/K-ShakeTune/IS_vibrations_measurements.cfg b/K-ShakeTune/K-SH_vibrations.cfg similarity index 95% rename from K-ShakeTune/IS_vibrations_measurements.cfg rename to K-ShakeTune/K-SH_vibrations.cfg index 186c4d7..76e3452 100644 --- a/K-ShakeTune/IS_vibrations_measurements.cfg +++ b/K-ShakeTune/K-SH_vibrations.cfg @@ -2,14 +2,6 @@ ###### VIBRATIONS AND SPEED OPTIMIZATIONS ###### ################################################ # Written by Frix_x#0161 # -# @version: 2.2 - -# CHANGELOG: -# v2.2: allow custom accel values and set sane defaults (3k) to avoid using very high accels that produce bad results -# v2.1: allow decimal entries for speed and increment and added the E axis as an option to be neasured -# v2.0: added the possibility to measure mutliple axis -# v1.0: first speed and vibrations optimization macro - [gcode_macro VIBRATIONS_CALIBRATION] gcode: diff --git a/K-ShakeTune/IS_workflow_cmd.cfg b/K-ShakeTune/shaketune_cmd.cfg similarity index 100% rename from K-ShakeTune/IS_workflow_cmd.cfg rename to K-ShakeTune/shaketune_cmd.cfg From 7ba692954fcea9f96acf509fb3387902cbf86816 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?F=C3=A9lix=20Boisselier?= Date: Wed, 29 Nov 2023 12:09:30 +0100 Subject: [PATCH 3/7] some cleaning --- K-ShakeTune/{K-SH_axis.cfg => K-SnT_axis.cfg} | 0 K-ShakeTune/{K-SH_belts.cfg => K-SnT_belts.cfg} | 0 ...{K-SH_static_freq.cfg => K-SnT_static_freq.cfg} | 0 .../{K-SH_vibrations.cfg => K-SnT_vibrations.cfg} | 0 K-ShakeTune/scripts/graph_belts.py | 7 ------- K-ShakeTune/scripts/graph_shaper.py | 12 ------------ K-ShakeTune/scripts/graph_vibrations.py | 9 --------- K-ShakeTune/scripts/is_workflow.py | 14 -------------- 8 files changed, 42 deletions(-) rename K-ShakeTune/{K-SH_axis.cfg => K-SnT_axis.cfg} (100%) rename K-ShakeTune/{K-SH_belts.cfg => K-SnT_belts.cfg} (100%) rename K-ShakeTune/{K-SH_static_freq.cfg => K-SnT_static_freq.cfg} (100%) rename K-ShakeTune/{K-SH_vibrations.cfg => K-SnT_vibrations.cfg} (100%) diff --git a/K-ShakeTune/K-SH_axis.cfg b/K-ShakeTune/K-SnT_axis.cfg similarity index 100% rename from K-ShakeTune/K-SH_axis.cfg rename to K-ShakeTune/K-SnT_axis.cfg diff --git a/K-ShakeTune/K-SH_belts.cfg b/K-ShakeTune/K-SnT_belts.cfg similarity index 100% rename from K-ShakeTune/K-SH_belts.cfg rename to K-ShakeTune/K-SnT_belts.cfg diff --git a/K-ShakeTune/K-SH_static_freq.cfg b/K-ShakeTune/K-SnT_static_freq.cfg similarity index 100% rename from K-ShakeTune/K-SH_static_freq.cfg rename to K-ShakeTune/K-SnT_static_freq.cfg diff --git a/K-ShakeTune/K-SH_vibrations.cfg b/K-ShakeTune/K-SnT_vibrations.cfg similarity index 100% rename from K-ShakeTune/K-SH_vibrations.cfg rename to K-ShakeTune/K-SnT_vibrations.cfg diff --git a/K-ShakeTune/scripts/graph_belts.py b/K-ShakeTune/scripts/graph_belts.py index 4c31f8f..e56a00d 100755 --- a/K-ShakeTune/scripts/graph_belts.py +++ b/K-ShakeTune/scripts/graph_belts.py @@ -4,13 +4,6 @@ ######## CoreXY BELTS CALIBRATION SCRIPT ######## ################################################# # Written by Frix_x#0161 # -# @version: 2.1 - -# CHANGELOG: -# v2.1: replaced the TwoSlopNorm by a custom made norm to allow the script to work on older versions of matplotlib -# v2.0: updated the script to align it to the new K-Shake&Tune module -# v1.0: first version of this tool for enhanced vizualisation of belt graphs - # Be sure to make this script executable using SSH: type 'chmod +x ./graph_belts.py' when in the folder! diff --git a/K-ShakeTune/scripts/graph_shaper.py b/K-ShakeTune/scripts/graph_shaper.py index fdada5f..95caebe 100755 --- a/K-ShakeTune/scripts/graph_shaper.py +++ b/K-ShakeTune/scripts/graph_shaper.py @@ -6,19 +6,7 @@ # Derived from the calibrate_shaper.py official Klipper script # Copyright (C) 2020 Dmitry Butyugin # Copyright (C) 2020 Kevin O'Connor -# # Written by Frix_x#0161 # -# @version: 2.0 - -# CHANGELOG: -# v2.0: updated the script to align it to the new K-Shake&Tune module -# v1.1: - improved the damping ratio computation with linear approximation for more precision -# - reworked the top graph to add more information to it with colored zones, -# automated peak detection, etc... -# - added a full spectrogram of the signal on the bottom to allow deeper analysis -# v1.0: first version of this script inspired from the official Klipper -# shaper calibration script to add an automatic damping ratio estimation to it - # Be sure to make this script executable using SSH: type 'chmod +x ./graph_shaper.py' when in the folder! diff --git a/K-ShakeTune/scripts/graph_vibrations.py b/K-ShakeTune/scripts/graph_vibrations.py index 0ba6a63..450a4cc 100755 --- a/K-ShakeTune/scripts/graph_vibrations.py +++ b/K-ShakeTune/scripts/graph_vibrations.py @@ -4,15 +4,6 @@ ###### SPEED AND VIBRATIONS PLOTTING SCRIPT ###### ################################################## # Written by Frix_x#0161 # -# @version: 2.0 - -# CHANGELOG: -# v2.0: - updated the script to align it to the new K-Shake&Tune module -# - new features for peaks detection and advised speed zones -# v1.2: fixed a bug that could happen when username is not "pi" (thanks @spikeygg) -# v1.1: better graph formatting -# v1.0: first version of the script - # Be sure to make this script executable using SSH: type 'chmod +x ./graph_vibrations.py' when in the folder ! diff --git a/K-ShakeTune/scripts/is_workflow.py b/K-ShakeTune/scripts/is_workflow.py index 1eec782..762cf42 100755 --- a/K-ShakeTune/scripts/is_workflow.py +++ b/K-ShakeTune/scripts/is_workflow.py @@ -4,20 +4,6 @@ ###### INPUT SHAPER KLIPPAIN WORKFLOW ###### ############################################ # Written by Frix_x#0161 # -# @version: 2.1 - -# CHANGELOG: -# v2.1: added more filesystem sync and file handler checks to avoid using corrupted CSV files by going to fast -# v2.0: new version of this as a Python script (to replace the old bash script) and implement the newer and improved shaper plotting scripts -# v1.7: updated the handling of shaper files to account for the new analysis scripts as we are now using raw data directly -# v1.6: - updated the handling of shaper graph files to be able to optionnaly account for added positions in the filenames and remove them -# - fixed a bug in the belt graph on slow SD card or Pi clones (Klipper was still writing in the file while we were already reading it) -# v1.5: fixed klipper unnexpected fail at the end of the execution, even if graphs were correctly generated (unicode decode error fixed) -# v1.4: added the ~/klipper dir parameter to the call of graph_vibrations.py for a better user handling (in case user is not "pi") -# v1.3: some documentation improvement regarding the line endings that needs to be LF for this file -# v1.2: added the movement name to be transfered to the Python script in vibration calibration (to print it on the result graphs) -# v1.1: multiple fixes and tweaks (mainly to avoid having empty files read by the python scripts after the mv command) -# v1.0: first version of the script based on a Zellneralex script # Usage: # This script was designed to be used with gcode_shell_commands directly from Klipper From 8721488d8c783f817f23c08ef26c43c731a79663 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?F=C3=A9lix=20Boisselier?= Date: Fri, 1 Dec 2023 00:08:32 +0100 Subject: [PATCH 4/7] added axes_map computation --- K-ShakeTune/K-SnT_axes_map.cfg | 60 +++++++++ K-ShakeTune/K-SnT_axis.cfg | 13 +- K-ShakeTune/K-SnT_belts.cfg | 7 +- K-ShakeTune/K-SnT_vibrations.cfg | 19 +-- K-ShakeTune/scripts/analyze_axesmap.py | 171 ++++++++++++++++++++++++ K-ShakeTune/scripts/graph_vibrations.py | 4 +- K-ShakeTune/scripts/is_workflow.py | 60 +++++++-- K-ShakeTune/shaketune_cmd.cfg | 2 + docs/README.md | 5 +- docs/macros/axis_tuning.md | 1 - docs/macros/belts_tuning.md | 1 - docs/macros/vibrations_tuning.md | 1 - requirements.txt | 1 + 13 files changed, 298 insertions(+), 47 deletions(-) create mode 100644 K-ShakeTune/K-SnT_axes_map.cfg create mode 100755 K-ShakeTune/scripts/analyze_axesmap.py diff --git a/K-ShakeTune/K-SnT_axes_map.cfg b/K-ShakeTune/K-SnT_axes_map.cfg new file mode 100644 index 0000000..81bcfeb --- /dev/null +++ b/K-ShakeTune/K-SnT_axes_map.cfg @@ -0,0 +1,60 @@ +############################################################ +###### AXE_MAP DETECTION AND ACCELEROMETER VALIDATION ###### +############################################################ +# Written by Frix_x#0161 # + +[gcode_macro AXES_MAP_CALIBRATION] +gcode: + {% set z_height = params.Z_HEIGHT|default(20)|int %} # z height to put the toolhead before starting the movements + {% set speed = params.SPEED|default(80)|float * 60 %} # feedrate for the movements + {% set accel = params.ACCEL|default(1500)|int %} # accel value used to move on the pattern + {% set feedrate_travel = params.TRAVEL_SPEED|default(120)|int * 60 %} # travel feedrate between moves + {% set accel_chip = params.ACCEL_CHIP|default("adxl345") %} # ADXL chip name in the config + + {% set mid_x = printer.toolhead.axis_maximum.x|float / 2 %} + {% set mid_y = printer.toolhead.axis_maximum.y|float / 2 %} + + {% set accel = [accel, printer.configfile.settings.printer.max_accel]|min %} + {% set old_accel = printer.toolhead.max_accel %} + {% set old_accel_to_decel = printer.toolhead.max_accel_to_decel %} + {% set old_sqv = printer.toolhead.square_corner_velocity %} + + + {% if not 'xyz' in printer.toolhead.homed_axes %} + { action_raise_error("Must Home printer first!") } + {% endif %} + + {action_respond_info("")} + {action_respond_info("Starting accelerometer axe_map calibration")} + {action_respond_info("This operation can not be interrupted by normal means. Hit the \"emergency stop\" button to stop it if needed")} + {action_respond_info("")} + + SAVE_GCODE_STATE NAME=STATE_AXESMAP_CALIBRATION + + G90 + + # Set the wanted acceleration values (not too high to avoid oscillation, not too low to be able to reach constant speed on each segments) + SET_VELOCITY_LIMIT ACCEL={accel} ACCEL_TO_DECEL={accel} SQUARE_CORNER_VELOCITY={[(accel / 1000), 5.0]|max} + + # Going to the start position + G1 Z{z_height} F{feedrate_travel / 8} + G1 X{mid_x - 15} Y{mid_y - 15} F{feedrate_travel} + G4 P500 + + ACCELEROMETER_MEASURE CHIP={accel_chip} + G4 P1000 # This first waiting time is to record the background accelerometer noise before moving + G1 X{mid_x + 15} F{speed} + G4 P1000 + G1 Y{mid_y + 15} F{speed} + G4 P1000 + G1 Z{z_height + 15} F{speed} + G4 P1000 + ACCELEROMETER_MEASURE CHIP={accel_chip} NAME=axemap + + RESPOND MSG="Analysis of the movements..." + RUN_SHELL_COMMAND CMD=shaketune PARAMS="AXESMAP {accel}" + + # Restore the previous acceleration values + SET_VELOCITY_LIMIT ACCEL={old_accel} ACCEL_TO_DECEL={old_accel_to_decel} SQUARE_CORNER_VELOCITY={old_sqv} + + RESTORE_GCODE_STATE NAME=STATE_AXESMAP_CALIBRATION diff --git a/K-ShakeTune/K-SnT_axis.cfg b/K-ShakeTune/K-SnT_axis.cfg index 69da343..5fbb589 100644 --- a/K-ShakeTune/K-SnT_axis.cfg +++ b/K-ShakeTune/K-SnT_axis.cfg @@ -6,7 +6,6 @@ [gcode_macro AXES_SHAPER_CALIBRATION] description: Perform standard axis input shaper tests on one or both XY axes to select the best input shaper filter gcode: - {% set verbose = params.VERBOSE|default(true) %} {% set min_freq = params.FREQ_START|default(5)|float %} {% set max_freq = params.FREQ_END|default(133.3)|float %} {% set hz_per_sec = params.HZ_PER_SEC|default(1)|float %} @@ -28,10 +27,8 @@ gcode: TEST_RESONANCES AXIS=X OUTPUT=raw_data NAME=x FREQ_START={min_freq} FREQ_END={max_freq} HZ_PER_SEC={hz_per_sec} M400 - {% if verbose %} - RESPOND MSG="X axis frequency profile generation..." - RESPOND MSG="This may take some time (1-3min)" - {% endif %} + RESPOND MSG="X axis frequency profile generation..." + RESPOND MSG="This may take some time (1-3min)" RUN_SHELL_COMMAND CMD=shaketune PARAMS=SHAPER {% endif %} @@ -39,9 +36,7 @@ gcode: TEST_RESONANCES AXIS=Y OUTPUT=raw_data NAME=y FREQ_START={min_freq} FREQ_END={max_freq} HZ_PER_SEC={hz_per_sec} M400 - {% if verbose %} - RESPOND MSG="Y axis frequency profile generation..." - RESPOND MSG="This may take some time (1-3min)" - {% endif %} + RESPOND MSG="Y axis frequency profile generation..." + RESPOND MSG="This may take some time (1-3min)" RUN_SHELL_COMMAND CMD=shaketune PARAMS=SHAPER {% endif %} diff --git a/K-ShakeTune/K-SnT_belts.cfg b/K-ShakeTune/K-SnT_belts.cfg index 4ee8e88..059efea 100644 --- a/K-ShakeTune/K-SnT_belts.cfg +++ b/K-ShakeTune/K-SnT_belts.cfg @@ -6,7 +6,6 @@ [gcode_macro BELTS_SHAPER_CALIBRATION] description: Perform a custom half-axis test to analyze and compare the frequency profiles of individual belts on CoreXY printers gcode: - {% set verbose = params.VERBOSE|default(true) %} {% set min_freq = params.FREQ_START|default(5)|float %} {% set max_freq = params.FREQ_END|default(133.33)|float %} {% set hz_per_sec = params.HZ_PER_SEC|default(1)|float %} @@ -17,8 +16,6 @@ gcode: TEST_RESONANCES AXIS=1,-1 OUTPUT=raw_data NAME=a FREQ_START={min_freq} FREQ_END={max_freq} HZ_PER_SEC={hz_per_sec} M400 - {% if verbose %} - RESPOND MSG="Belts comparative frequency profile generation..." - RESPOND MSG="This may take some time (3-5min)" - {% endif %} + RESPOND MSG="Belts comparative frequency profile generation..." + RESPOND MSG="This may take some time (3-5min)" RUN_SHELL_COMMAND CMD=shaketune PARAMS=BELTS diff --git a/K-ShakeTune/K-SnT_vibrations.cfg b/K-ShakeTune/K-SnT_vibrations.cfg index 76e3452..1598a2b 100644 --- a/K-ShakeTune/K-SnT_vibrations.cfg +++ b/K-ShakeTune/K-SnT_vibrations.cfg @@ -8,7 +8,6 @@ gcode: {% set size = params.SIZE|default(60)|int %} # size of the area where the movements are done {% set direction = params.DIRECTION|default('XY') %} # can be set to either XY, AB, ABXY, A, B, X, Y, Z {% set z_height = params.Z_HEIGHT|default(20)|int %} # z height to put the toolhead before starting the movements - {% set verbose = params.VERBOSE|default(true) %} # Wether to log the current speed in the console {% set min_speed = params.MIN_SPEED|default(20)|float * 60 %} # minimum feedrate for the movements {% set max_speed = params.MAX_SPEED|default(200)|float * 60 %} # maximum feedrate for the movements @@ -100,9 +99,7 @@ gcode: } %} - # - # STARTING... - # + {% if not 'xyz' in printer.toolhead.homed_axes %} { action_raise_error("Must Home printer first!") } {% endif %} @@ -126,22 +123,19 @@ gcode: SAVE_GCODE_STATE NAME=STATE_VIBRATIONS_CALIBRATION - M83 G90 # Set the wanted acceleration values (not too high to avoid oscillation, not too low to be able to reach constant speed on each segments) SET_VELOCITY_LIMIT ACCEL={accel} ACCEL_TO_DECEL={accel} SQUARE_CORNER_VELOCITY={[(accel / 1000), 5.0]|max} # Going to the start position - G1 Z{z_height} + G1 Z{z_height} F{feedrate_travel / 10} G1 X{mid_x + (size * direction_factor[direction].start.x) } Y{mid_y + (size * direction_factor[direction].start.y)} F{feedrate_travel} # vibration pattern for each frequency {% for curr_sample in range(0, nb_samples) %} {% set curr_speed = min_speed + curr_sample * speed_increment %} - {% if verbose %} - RESPOND MSG="{"Current speed: %.2f mm/s" % (curr_speed / 60)|float}" - {% endif %} + RESPOND MSG="{"Current speed: %.2f mm/s" % (curr_speed / 60)|float}" ACCELEROMETER_MEASURE CHIP={accel_chip} {% if direction == 'E' %} @@ -152,14 +146,13 @@ gcode: {% endfor %} {% endif %} ACCELEROMETER_MEASURE CHIP={accel_chip} NAME=sp{("%.2f" % (curr_speed / 60)|float)|replace('.','_')}n1 - G4 P300 + M400 {% endfor %} - {% if verbose %} - RESPOND MSG="Graphs generation... Please wait a minute or two and look in the configured folder." - {% endif %} + RESPOND MSG="Machine and motors vibration graph generation..." + RESPOND MSG="This may take some time (3-5min)" RUN_SHELL_COMMAND CMD=shaketune PARAMS="VIBRATIONS {direction}" # Restore the previous acceleration values diff --git a/K-ShakeTune/scripts/analyze_axesmap.py b/K-ShakeTune/scripts/analyze_axesmap.py new file mode 100755 index 0000000..370225d --- /dev/null +++ b/K-ShakeTune/scripts/analyze_axesmap.py @@ -0,0 +1,171 @@ +#!/usr/bin/env python3 + +###################################### +###### AXE_MAP DETECTION SCRIPT ###### +###################################### +# Written by Frix_x#0161 # + +# Be sure to make this script executable using SSH: type 'chmod +x ./analyze_axesmap.py' when in the folder ! + +##################################################################### +################ !!! DO NOT EDIT BELOW THIS LINE !!! ################ +##################################################################### + +import optparse +import numpy as np +import locale +from scipy.signal import butter, filtfilt + + +NUM_POINTS = 500 + + +# Set the best locale for time and date formating (generation of the titles) +try: + locale.setlocale(locale.LC_TIME, locale.getdefaultlocale()) +except locale.Error: + locale.setlocale(locale.LC_TIME, 'C') + +# Override the built-in print function to avoid problem in Klipper due to locale settings +original_print = print +def print_with_c_locale(*args, **kwargs): + original_locale = locale.setlocale(locale.LC_ALL, None) + locale.setlocale(locale.LC_ALL, 'C') + original_print(*args, **kwargs) + locale.setlocale(locale.LC_ALL, original_locale) +print = print_with_c_locale + + +###################################################################### +# Computation +###################################################################### + +def accel_signal_filter(data, cutoff=2, fs=100, order=5): + nyq = 0.5 * fs + normal_cutoff = cutoff / nyq + b, a = butter(order, normal_cutoff, btype='low', analog=False) + filtered_data = filtfilt(b, a, data) + filtered_data -= np.mean(filtered_data) + return filtered_data + +def find_first_spike(data): + min_index, max_index = np.argmin(data), np.argmax(data) + return ('-', min_index) if min_index < max_index else ('', max_index) + +def get_movement_vector(data, start_idx, end_idx): + if start_idx < end_idx: + vector = [] + for i in range(3): + vector.append(np.mean(data[i][start_idx:end_idx], axis=0)) + return vector + else: + return np.zeros(3) + +def angle_between(v1, v2): + v1_u = v1 / np.linalg.norm(v1) + v2_u = v2 / np.linalg.norm(v2) + return np.arccos(np.clip(np.dot(v1_u, v2_u), -1.0, 1.0)) + +def compute_errors(filtered_data, spikes_sorted, accel_value, num_points): + # Get the movement start points in the correct order from the sorted bag of spikes + movement_starts = [spike[0][1] for spike in spikes_sorted] + + # Theoretical unit vectors for X, Y, Z printer axes + printer_axes = { + 'x': np.array([1, 0, 0]), + 'y': np.array([0, 1, 0]), + 'z': np.array([0, 0, 1]) + } + + alignment_errors = {} + sensitivity_errors = {} + for i, axis in enumerate(['x', 'y', 'z']): + movement_start = movement_starts[i] + movement_end = movement_start + num_points + movement_vector = get_movement_vector(filtered_data, movement_start, movement_end) + alignment_errors[axis] = angle_between(movement_vector, printer_axes[axis]) + + measured_accel_magnitude = np.linalg.norm(movement_vector) + if accel_value != 0: + sensitivity_errors[axis] = abs(measured_accel_magnitude - accel_value) / accel_value * 100 + else: + sensitivity_errors[axis] = None + + return alignment_errors, sensitivity_errors + + +###################################################################### +# Startup and main routines +###################################################################### + +def parse_log(logname): + with open(logname) as f: + for header in f: + if not header.startswith('#'): + break + if not header.startswith('freq,psd_x,psd_y,psd_z,psd_xyz'): + # Raw accelerometer data + return np.loadtxt(logname, comments='#', delimiter=',') + # Power spectral density data or shaper calibration data + raise ValueError("File %s does not contain raw accelerometer data and therefore " + "is not supported by this script. Please use the official Klipper " + "calibrate_shaper.py script to process it instead." % (logname,)) + + +def axesmap_calibration(lognames, accel=None): + # Parse the raw data and get them ready for analysis + raw_datas = [parse_log(filename) for filename in lognames] + if len(raw_datas) > 1: + raise ValueError("Analysis of multiple CSV files at once is not possible with this script") + + filtered_data = [accel_signal_filter(raw_datas[0][:, i+1]) for i in range(3)] + spikes = [find_first_spike(filtered_data[i]) for i in range(3)] + spikes_sorted = sorted([(spikes[0], 'x'), (spikes[1], 'y'), (spikes[2], 'z')], key=lambda x: x[0][1]) + + # Using the previous variables to get the axes_map and errors + axes_map = ','.join([f"{spike[0][0]}{spike[1]}" for spike in spikes_sorted]) + # alignment_error, sensitivity_error = compute_errors(filtered_data, spikes_sorted, accel, NUM_POINTS) + + results = f"Detected axes_map:\n {axes_map}\n" + + # TODO: work on this function that is currently not giving good results... + # results += "Accelerometer angle deviation:\n" + # for axis, angle in alignment_error.items(): + # angle_degrees = np.degrees(angle) # Convert radians to degrees + # results += f" {axis.upper()} axis: {angle_degrees:.2f} degrees\n" + + # results += "Accelerometer sensitivity error:\n" + # for axis, error in sensitivity_error.items(): + # results += f" {axis.upper()} axis: {error:.2f}%\n" + + return results + + +def main(): + # Parse command-line arguments + usage = "%prog [options] " + opts = optparse.OptionParser(usage) + opts.add_option("-o", "--output", type="string", dest="output", + default=None, help="filename of output graph") + opts.add_option("-a", "--accel", type="string", dest="accel", + default=None, help="acceleration value used to do the movements") + options, args = opts.parse_args() + if len(args) < 1: + opts.error("No CSV file(s) to analyse") + if options.accel is None: + opts.error("You must specify the acceleration value used when generating the CSV file (option -a)") + try: + accel_value = float(options.accel) + except ValueError: + opts.error("Invalid acceleration value. It should be a numeric value.") + + results = axesmap_calibration(args, accel_value) + print(results) + + if options.output is not None: + with open(options.output, 'w') as f: + f.write(results) + + +if __name__ == '__main__': + main() diff --git a/K-ShakeTune/scripts/graph_vibrations.py b/K-ShakeTune/scripts/graph_vibrations.py index 450a4cc..4ca466d 100755 --- a/K-ShakeTune/scripts/graph_vibrations.py +++ b/K-ShakeTune/scripts/graph_vibrations.py @@ -317,7 +317,7 @@ def parse_log(logname): return np.loadtxt(logname, comments='#', delimiter=',') # Power spectral density data or shaper calibration data raise ValueError("File %s does not contain raw accelerometer data and therefore " - "is not supported by graph_vibrations.py script. Please use " + "is not supported by this script. Please use the official Klipper" "calibrate_shaper.py script to process it instead." % (logname,)) @@ -326,7 +326,7 @@ def extract_speed(logname): speed = re.search('sp(.+?)n', os.path.basename(logname)).group(1).replace('_','.') except AttributeError: raise ValueError("File %s does not contain speed in its name and therefore " - "is not supported by graph_vibrations.py script." % (logname,)) + "is not supported by this script." % (logname,)) return float(speed) diff --git a/K-ShakeTune/scripts/is_workflow.py b/K-ShakeTune/scripts/is_workflow.py index 762cf42..998b7d6 100755 --- a/K-ShakeTune/scripts/is_workflow.py +++ b/K-ShakeTune/scripts/is_workflow.py @@ -30,6 +30,7 @@ from graph_belts import belts_calibration from graph_shaper import shaper_calibration from graph_vibrations import vibrations_calibration +from analyze_axesmap import axesmap_calibration RESULTS_SUBFOLDERS = ['belts', 'inputshaper', 'vibrations'] @@ -50,7 +51,7 @@ def is_file_open(filepath): return False -def get_belts_graph(): +def create_belts_graph(): current_date = datetime.now().strftime('%Y%m%d_%H%M%S') lognames = [] @@ -61,6 +62,7 @@ def get_belts_graph(): if len(globbed_files) < 2: print("Not enough CSV files found in the /tmp folder. Two files are required for the belt graphs!") sys.exit(1) + sorted_files = sorted(globbed_files, key=os.path.getmtime, reverse=True) for filename in sorted_files[:2]: @@ -84,11 +86,12 @@ def get_belts_graph(): # Generate the belts graph and its name fig = belts_calibration(lognames, KLIPPER_FOLDER) png_filename = os.path.join(RESULTS_FOLDER, RESULTS_SUBFOLDERS[0], f'belts_{current_date}.png') - - return fig, png_filename + + fig.savefig(png_filename) + return -def get_shaper_graph(): +def create_shaper_graph(): current_date = datetime.now().strftime('%Y%m%d_%H%M%S') # Get all the files and sort them based on last modified time to select the most recent one @@ -96,6 +99,7 @@ def get_shaper_graph(): if not globbed_files: print("No CSV files found in the /tmp folder to create the input shaper graphs!") sys.exit(1) + sorted_files = sorted(globbed_files, key=os.path.getmtime, reverse=True) filename = sorted_files[0] @@ -117,10 +121,11 @@ def get_shaper_graph(): fig = shaper_calibration([new_file], KLIPPER_FOLDER) png_filename = os.path.join(RESULTS_FOLDER, RESULTS_SUBFOLDERS[1], f'resonances_{current_date}_{axis}.png') - return fig, png_filename + fig.savefig(png_filename) + return -def get_vibrations_graph(axis_name): +def create_vibrations_graph(axis_name): current_date = datetime.now().strftime('%Y%m%d_%H%M%S') lognames = [] @@ -159,7 +164,35 @@ def get_vibrations_graph(axis_name): tar.add(csv_file, recursive=False) os.remove(csv_file) - return fig, png_filename + fig.savefig(png_filename) + return + + +def find_axesmap(accel): + current_date = datetime.now().strftime('%Y%m%d_%H%M%S') + result_filename = os.path.join(RESULTS_FOLDER, f'axes_map_{current_date}.txt') + lognames = [] + + globbed_files = glob.glob('/tmp/adxl345-*.csv') + if not globbed_files: + print("No CSV files found in the /tmp folder to analyze and find the axes_map!") + sys.exit(1) + + sorted_files = sorted(globbed_files, key=os.path.getmtime, reverse=True) + filename = sorted_files[0] + + # Wait for the file handler to be released by Klipper + while is_file_open(filename): + time.sleep(2) + + # Analyze the CSV to find the axes_map parameter + lognames.append(filename) + results = axesmap_calibration(lognames, accel) + + with open(result_filename, 'w') as f: + f.write(results) + + return # Utility function to get old files based on their modification time @@ -210,20 +243,21 @@ def main(): os.makedirs(folder) if len(sys.argv) < 2: - print("Usage: is_workflow.py [SHAPER|BELTS|VIBRATIONS]") + print("Usage: is_workflow.py [BELTS|SHAPER|VIBRATIONS|AXESMAP]") sys.exit(1) if sys.argv[1].lower() == 'belts': - fig, png_filename = get_belts_graph() + create_belts_graph() elif sys.argv[1].lower() == 'shaper': - fig, png_filename = get_shaper_graph() + create_shaper_graph() elif sys.argv[1].lower() == 'vibrations': - fig, png_filename = get_vibrations_graph(axis_name=sys.argv[2]) + create_vibrations_graph(axis_name=sys.argv[2]) + elif sys.argv[1].lower() == 'axesmap': + find_axesmap(accel=sys.argv[2]) else: - print("Usage: is_workflow.py [SHAPER|BELTS|VIBRATIONS]") + print("Usage: is_workflow.py [BELTS|SHAPER|VIBRATIONS|AXESMAP]") sys.exit(1) - fig.savefig(png_filename) clean_files() print(f"Graphs created. You will find the results in {RESULTS_FOLDER}") diff --git a/K-ShakeTune/shaketune_cmd.cfg b/K-ShakeTune/shaketune_cmd.cfg index bb48844..e4d667c 100644 --- a/K-ShakeTune/shaketune_cmd.cfg +++ b/K-ShakeTune/shaketune_cmd.cfg @@ -2,3 +2,5 @@ command: ~/printer_data/config/K-ShakeTune/scripts/shaketune.sh timeout: 600.0 verbose: True + +[respond] diff --git a/docs/README.md b/docs/README.md index 28e66dc..c3488c7 100644 --- a/docs/README.md +++ b/docs/README.md @@ -4,8 +4,9 @@ ### Detailed documentation -First, check out **[input shaping and tuning generalities](./is_tuning_generalities.md)** to understand how it all works and what to look for when taking these measurements. -Then look at the documentation for each type of graph by clicking on them below to better understand your results and tune your machine! +Before running any tests, you should first run the `AXES_MAP_CALIBRATION` macro to detect the and set Klipper's accelerometer axes_map parameter and validate that your accelerometer is working correctly and properly mounted. Then, check out **[input shaping and tuning generalities](./is_tuning_generalities.md)** to understand how it all works and what to look for when taking these measurements. + +Finally look at the documentation for each type of graph by clicking on them below tu run the tests and better understand your results to tune your machine! | [Belts graph](./macros/belts_tuning.md) | [Axis input shaper graphs](./macros/axis_tuning.md) | [Vibrations graph](./macros/vibrations_tuning.md) | |:----------------:|:------------:|:---------------------:| diff --git a/docs/macros/axis_tuning.md b/docs/macros/axis_tuning.md index 842cd68..b09a14c 100644 --- a/docs/macros/axis_tuning.md +++ b/docs/macros/axis_tuning.md @@ -11,7 +11,6 @@ Then, call the `AXES_SHAPER_CALIBRATION` macro and look for the graphs in the re | parameters | default value | description | |-----------:|---------------|-------------| -|VERBOSE|1|Wether to log things in the console| |FREQ_START|5|Starting excitation frequency| |FREQ_END|133|Maximum excitation frequency| |HZ_PER_SEC|1|Number of Hz per seconds for the test| diff --git a/docs/macros/belts_tuning.md b/docs/macros/belts_tuning.md index 2e60cc2..fc6551a 100644 --- a/docs/macros/belts_tuning.md +++ b/docs/macros/belts_tuning.md @@ -11,7 +11,6 @@ Then, call the `BELTS_SHAPER_CALIBRATION` macro and look for the graphs in the r | parameters | default value | description | |-----------:|---------------|-------------| -|VERBOSE|1|Wether to log things in the console| |FREQ_START|5|Starting excitation frequency| |FREQ_END|133|Maximum excitation frequency| |HZ_PER_SEC|1|Number of Hz per seconds for the test| diff --git a/docs/macros/vibrations_tuning.md b/docs/macros/vibrations_tuning.md index 309ad27..84d1f10 100644 --- a/docs/macros/vibrations_tuning.md +++ b/docs/macros/vibrations_tuning.md @@ -16,7 +16,6 @@ Call the `VIBRATIONS_CALIBRATION` macro with the direction and speed range you w |SIZE|60|size in mm of the area where the movements are done| |DIRECTION|"XY"|direction vector where you want to do the measurements. Can be set to either "XY", "AB", "ABXY", "A", "B", "X", "Y", "Z", "E"| |Z_HEIGHT|20|z height to put the toolhead before starting the movements. Be careful, if your ADXL is under the nozzle, increase it to avoid a crash of the ADXL on the bed of the machine| -|VERBOSE|1|Wether to log the current speed in the console| |ACCEL|3000 (or max printer accel)|accel in mm/s^2 used for all the moves. Try to keep it relatively low to avoid bad oscillations that affect the measurements, but but high enough to reach constant speed for >~70% of the segments| |MIN_SPEED|20|minimum speed of the toolhead in mm/s for the movements| |MAX_SPEED|200|maximum speed of the toolhead in mm/s for the movements| diff --git a/requirements.txt b/requirements.txt index be8c8d0..ce083b9 100644 --- a/requirements.txt +++ b/requirements.txt @@ -8,4 +8,5 @@ packaging==23.2 Pillow==10.1.0 pyparsing==3.1.1 python-dateutil==2.8.2 +scipy==1.11.4 six==1.16.0 From 70500182741be147df7941a761f1f8c39e8136a8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?F=C3=A9lix=20Boisselier?= Date: Sun, 10 Dec 2023 06:40:08 +0100 Subject: [PATCH 5/7] fixed #7 and code optimizations --- K-ShakeTune/scripts/graph_belts.py | 126 ++++++++++------------------ K-ShakeTune/scripts/graph_shaper.py | 23 ++--- 2 files changed, 58 insertions(+), 91 deletions(-) diff --git a/K-ShakeTune/scripts/graph_belts.py b/K-ShakeTune/scripts/graph_belts.py index e56a00d..27369b7 100755 --- a/K-ShakeTune/scripts/graph_belts.py +++ b/K-ShakeTune/scripts/graph_belts.py @@ -12,9 +12,9 @@ ##################################################################### import optparse, matplotlib, sys, importlib, os -from textwrap import wrap from collections import namedtuple import numpy as np +import scipy import matplotlib.pyplot, matplotlib.dates, matplotlib.font_manager import matplotlib.ticker, matplotlib.gridspec, matplotlib.colors import matplotlib.patches @@ -165,73 +165,42 @@ def pair_peaks(peaks1, freqs1, psd1, peaks2, freqs2, psd2): def compute_spectrogram(data): N = data.shape[0] - Fs = N / (data[-1,0] - data[0,0]) + Fs = N / (data[-1, 0] - data[0, 0]) # Round up to a power of 2 for faster FFT M = 1 << int(.5 * Fs - 1).bit_length() window = np.kaiser(M, 6.) + def _specgram(x): - return matplotlib.mlab.specgram( - x, Fs=Fs, NFFT=M, noverlap=M//2, window=window, - mode='psd', detrend='mean', scale_by_freq=False) + x_detrended = x - np.mean(x) # Detrending by subtracting the mean value + return scipy.signal.spectrogram( + x_detrended, fs=Fs, window=window, nperseg=M, noverlap=M//2, + detrend='constant', scaling='density', mode='psd') - d = {'x': data[:,1], 'y': data[:,2], 'z': data[:,3]} - pdata, bins, t = _specgram(d['x']) - for ax in 'yz': - pdata += _specgram(d[ax])[0] - return pdata, bins, t + d = {'x': data[:, 1], 'y': data[:, 2], 'z': data[:, 3]} + f, t, pdata = _specgram(d['x']) + for axis in 'yz': + pdata += _specgram(d[axis])[2] + return pdata, t, f ###################################################################### # Computation of the differential spectrogram ###################################################################### -# Performs a standard bilinear interpolation for a given x, y point based on surrounding input grid values. This function -# is part of the logic to re-align both belts spectrogram in order to combine them in the differential spectrogram. -def bilinear_interpolate(x, y, points, values): - x1, x2 = points[0] - y1, y2 = points[1] - - f11, f12 = values[0] - f21, f22 = values[1] - - interpolated_value = ( - (f11 * (x2 - x) * (y2 - y) + - f21 * (x - x1) * (y2 - y) + - f12 * (x2 - x) * (y - y1) + - f22 * (x - x1) * (y - y1)) / ((x2 - x1) * (y2 - y1)) - ) - - return interpolated_value - - -# Interpolate source_data (2D) to match target_x and target_y in order to interpolate and +# Interpolate source_data (2D) to match target_x and target_y in order to # get similar time and frequency dimensions for the differential spectrogram def interpolate_2d(target_x, target_y, source_x, source_y, source_data): - interpolated_data = np.zeros((len(target_y), len(target_x))) - - for i, y in enumerate(target_y): - for j, x in enumerate(target_x): - # Find indices of surrounding points in source data - # and ensure we don't exceed array bounds - x_indices = np.searchsorted(source_x, x) - 1 - y_indices = np.searchsorted(source_y, y) - 1 - x_indices = max(0, min(len(source_x) - 1, x_indices)) - y_indices = max(0, min(len(source_y) - 1, y_indices)) - - if x_indices == len(source_x) - 2: - x_indices -= 1 - if y_indices == len(source_y) - 2: - y_indices -= 1 - - x1, x2 = source_x[x_indices], source_x[x_indices + 1] - y1, y2 = source_y[y_indices], source_y[y_indices + 1] - - f11 = source_data[y_indices, x_indices] - f12 = source_data[y_indices, x_indices + 1] - f21 = source_data[y_indices + 1, x_indices] - f22 = source_data[y_indices + 1, x_indices + 1] - - interpolated_data[i, j] = bilinear_interpolate(x, y, ((x1, x2), (y1, y2)), ((f11, f12), (f21, f22))) + # Create a grid of points in the source and target space + source_points = np.array([(x, y) for y in source_y for x in source_x]) + target_points = np.array([(x, y) for y in target_y for x in target_x]) + + # Flatten the source data to match the flattened source points + source_values = source_data.flatten() + + # Interpolate and reshape the interpolated data to match the target grid shape and replace NaN with zeros + interpolated_data = scipy.interpolate.griddata(source_points, source_values, target_points, method='nearest') + interpolated_data = interpolated_data.reshape((len(target_y), len(target_x))) + interpolated_data = np.nan_to_num(interpolated_data) return interpolated_data @@ -242,21 +211,18 @@ def interpolate_2d(target_x, target_y, source_x, source_y, source_data): def detect_ridge(pdata, n_average=3): grad_y, grad_x = np.gradient(pdata) magnitude = np.sqrt(grad_x**2 + grad_y**2) - + # Start at the maximum value in the first column start_idx = np.argmax(pdata[:, 0]) path = [start_idx] - - # Walk through the spectrogram following the path of the ridge + for j in range(1, pdata.shape[1]): - # Look in the vicinity of the previous point - vicinity = magnitude[max(0, path[-1]-n_average):min(pdata.shape[0], path[-1]+n_average+1), j] - # Take an average of top few points - sorted_indices = np.argsort(vicinity) - top_indices = sorted_indices[-n_average:] - next_idx = int(np.mean(top_indices) + max(0, path[-1]-n_average)) - path.append(next_idx) - + start = max(0, path[-1]-n_average) + end = min(pdata.shape[0], path[-1]+n_average+1) + vicinity = magnitude[start:end, j] + next_idx = start + np.mean(np.argsort(vicinity)[-n_average:]) + path.append(int(next_idx)) + return np.array(path) @@ -264,24 +230,21 @@ def detect_ridge(pdata, n_average=3): # the frequency domain (using FFT). The result provides the lag (or offset) at which the two sequences are most similar. # This is used to re-align both belts spectrograms on their resonances lines in order to create the combined spectrogram. def compute_cross_correlation_offset(ridge1, ridge2): - # Ensure that the two arrays have the same shape - if len(ridge1) < len(ridge2): - ridge1 = np.pad(ridge1, (0, len(ridge2) - len(ridge1))) - elif len(ridge1) > len(ridge2): - ridge2 = np.pad(ridge2, (0, len(ridge1) - len(ridge2))) + max_len = max(len(ridge1), len(ridge2)) + ridge1 = np.pad(ridge1, (0, max_len - len(ridge1)), 'constant') + ridge2 = np.pad(ridge2, (0, max_len - len(ridge2)), 'constant') - cross_corr = np.fft.fftshift(np.fft.ifft(np.fft.fft(ridge1) * np.conj(np.fft.fft(ridge2)))) - return np.argmax(np.abs(cross_corr)) - len(ridge1) // 2 + cross_corr = np.fft.fftshift(np.fft.irfft(np.fft.rfft(ridge1) * np.conj(np.fft.rfft(ridge2)))) + return np.argmax(np.abs(cross_corr)) - max_len // 2 # This function shifts data along its second dimension - ie. time here - by a specified shift_amount def shift_data_in_time(data, shift_amount): if shift_amount > 0: - return np.pad(data, ((0, 0), (shift_amount, 0)), mode='constant')[:, :-shift_amount] + return np.concatenate([np.zeros((data.shape[0], shift_amount)), data[:, :-shift_amount]], axis=1) elif shift_amount < 0: - return np.pad(data, ((0, 0), (0, -shift_amount)), mode='constant')[:, -shift_amount:] - else: - return data + return np.concatenate([data[:, -shift_amount:], np.zeros((data.shape[0], -shift_amount))], axis=1) + return data # Main logic function to combine two similar spectrogram - ie. from both belts paths - by detecting similarities (ridges), computing @@ -289,7 +252,7 @@ def shift_data_in_time(data, shift_amount): # This result of a mostly zero-ed new spectrogram with some colored zones highlighting differences in the belts paths. def combined_spectrogram(data1, data2): pdata1, bins1, t1 = compute_spectrogram(data1) - pdata2, _, _ = compute_spectrogram(data2) + pdata2, bins2, t2 = compute_spectrogram(data2) # Detect ridges ridge1 = detect_ridge(pdata1) @@ -298,7 +261,7 @@ def combined_spectrogram(data1, data2): # Compute offset using cross-correlation and shit/align and interpolate the spectrograms offset = compute_cross_correlation_offset(ridge1, ridge2) pdata2_aligned = shift_data_in_time(pdata2, offset) - pdata2_interpolated = interpolate_2d(t1, bins1, t1, bins1, pdata2_aligned) + pdata2_interpolated = interpolate_2d(bins1, t1, bins2, t2, pdata2_aligned) # Combine the spectrograms combined_data = np.abs(pdata1 - pdata2_interpolated) @@ -458,6 +421,7 @@ def plot_compare_frequency(ax, lognames, signal1, signal2, max_freq): def plot_difference_spectrogram(ax, data1, data2, signal1, signal2, similarity_factor, max_freq): combined_data, bins, t = combined_spectrogram(data1, data2) + # combined_data, bins, t = compute_spectrogram(data1) # Compute the MHI value from the differential spectrogram sum of gradient, salted with # the similarity factor and the number or unpaired peaks from the belts frequency profile @@ -472,12 +436,12 @@ def plot_difference_spectrogram(ax, data1, data2, signal1, signal2, similarity_f n_bins = [0, 0.12, 0.9, 1] # These values where found experimentaly to get a good higlhlighting of the differences only cm = matplotlib.colors.LinearSegmentedColormap.from_list('WhiteToRed', list(zip(n_bins, colors))) norm = matplotlib.colors.Normalize(vmin=np.min(combined_data), vmax=np.max(combined_data)) - ax.pcolormesh(bins, t, combined_data.T, cmap=cm, norm=norm, shading='gouraud') + ax.pcolormesh(t, bins, combined_data.T, cmap=cm, norm=norm, shading='gouraud') ax.set_xlabel('Frequency (hz)') ax.set_xlim([0., max_freq]) ax.set_ylabel('Time (s)') - ax.set_ylim([0, t[-1]]) + ax.set_ylim([0, bins[-1]]) fontP = matplotlib.font_manager.FontProperties() fontP.set_size('medium') diff --git a/K-ShakeTune/scripts/graph_shaper.py b/K-ShakeTune/scripts/graph_shaper.py index 95caebe..3850ca4 100755 --- a/K-ShakeTune/scripts/graph_shaper.py +++ b/K-ShakeTune/scripts/graph_shaper.py @@ -17,6 +17,7 @@ import optparse, matplotlib, sys, importlib, os, math from textwrap import wrap import numpy as np +import scipy import matplotlib.pyplot, matplotlib.dates, matplotlib.font_manager import matplotlib.ticker, matplotlib.gridspec import locale @@ -99,20 +100,22 @@ def compute_damping_ratio(psd, freqs): def compute_spectrogram(data): N = data.shape[0] - Fs = N / (data[-1,0] - data[0,0]) + Fs = N / (data[-1, 0] - data[0, 0]) # Round up to a power of 2 for faster FFT M = 1 << int(.5 * Fs - 1).bit_length() window = np.kaiser(M, 6.) + def _specgram(x): - return matplotlib.mlab.specgram( - x, Fs=Fs, NFFT=M, noverlap=M//2, window=window, - mode='psd', detrend='mean', scale_by_freq=False) + x_detrended = x - np.mean(x) # Detrending by subtracting the mean value + return scipy.signal.spectrogram( + x_detrended, fs=Fs, window=window, nperseg=M, noverlap=M//2, + detrend='constant', scaling='density', mode='psd') - d = {'x': data[:,1], 'y': data[:,2], 'z': data[:,3]} - pdata, bins, t = _specgram(d['x']) - for ax in 'yz': - pdata += _specgram(d[ax])[0] - return pdata, bins, t + d = {'x': data[:, 1], 'y': data[:, 2], 'z': data[:, 3]} + f, t, pdata = _specgram(d['x']) + for axis in 'yz': + pdata += _specgram(d[axis])[2] + return pdata, t, f # This find all the peaks in a curve by looking at when the derivative term goes from positive to negative @@ -270,7 +273,7 @@ def plot_spectrogram(ax, data, peaks, max_freq): vmin_value = np.percentile(pdata, SPECTROGRAM_LOW_PERCENTILE_FILTER) ax.set_title("Time-Frequency Spectrogram", fontsize=14, color=KLIPPAIN_COLORS['dark_orange'], weight='bold') - ax.pcolormesh(bins, t, pdata.T, norm=matplotlib.colors.LogNorm(vmin=vmin_value), + ax.pcolormesh(t, bins, pdata.T, norm=matplotlib.colors.LogNorm(vmin=vmin_value), cmap='inferno', shading='gouraud') # Add peaks lines in the spectrogram to get hint from peaks found in the first graph From b32abe2ecaf95d2401b6d30d1777249af1c67ad6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?F=C3=A9lix=20Boisselier?= Date: Sun, 10 Dec 2023 12:47:59 +0100 Subject: [PATCH 6/7] belt differential spectrogram now show the impact of each belt with its own color --- K-ShakeTune/scripts/graph_belts.py | 103 +++++++++-------------------- 1 file changed, 30 insertions(+), 73 deletions(-) diff --git a/K-ShakeTune/scripts/graph_belts.py b/K-ShakeTune/scripts/graph_belts.py index 27369b7..2dfaf9d 100755 --- a/K-ShakeTune/scripts/graph_belts.py +++ b/K-ShakeTune/scripts/graph_belts.py @@ -205,67 +205,23 @@ def interpolate_2d(target_x, target_y, source_x, source_y, source_data): return interpolated_data -# This function identifies a "ridge" of high gradient magnitude in a spectrogram (pdata) - ie. a resonance diagonal line. Starting from -# the maximum value in the first column, it iteratively follows the direction of the highest gradient in the vicinity (window configured using -# the n_average parameter). The result is a sequence of indices that traces the resonance line across the original spectrogram. -def detect_ridge(pdata, n_average=3): - grad_y, grad_x = np.gradient(pdata) - magnitude = np.sqrt(grad_x**2 + grad_y**2) - - # Start at the maximum value in the first column - start_idx = np.argmax(pdata[:, 0]) - path = [start_idx] - - for j in range(1, pdata.shape[1]): - start = max(0, path[-1]-n_average) - end = min(pdata.shape[0], path[-1]+n_average+1) - vicinity = magnitude[start:end, j] - next_idx = start + np.mean(np.argsort(vicinity)[-n_average:]) - path.append(int(next_idx)) - - return np.array(path) - - -# This function calculates the time offset between two resonances lines (ridge1 and ridge2) using cross-correlation in -# the frequency domain (using FFT). The result provides the lag (or offset) at which the two sequences are most similar. -# This is used to re-align both belts spectrograms on their resonances lines in order to create the combined spectrogram. -def compute_cross_correlation_offset(ridge1, ridge2): - max_len = max(len(ridge1), len(ridge2)) - ridge1 = np.pad(ridge1, (0, max_len - len(ridge1)), 'constant') - ridge2 = np.pad(ridge2, (0, max_len - len(ridge2)), 'constant') - - cross_corr = np.fft.fftshift(np.fft.irfft(np.fft.rfft(ridge1) * np.conj(np.fft.rfft(ridge2)))) - return np.argmax(np.abs(cross_corr)) - max_len // 2 - - -# This function shifts data along its second dimension - ie. time here - by a specified shift_amount -def shift_data_in_time(data, shift_amount): - if shift_amount > 0: - return np.concatenate([np.zeros((data.shape[0], shift_amount)), data[:, :-shift_amount]], axis=1) - elif shift_amount < 0: - return np.concatenate([data[:, -shift_amount:], np.zeros((data.shape[0], -shift_amount))], axis=1) - return data - - -# Main logic function to combine two similar spectrogram - ie. from both belts paths - by detecting similarities (ridges), computing -# the time lag and realigning them. Finally this function combine (by substracting signals) the aligned spectrograms in a new one. -# This result of a mostly zero-ed new spectrogram with some colored zones highlighting differences in the belts paths. +# Main logic function to combine two similar spectrogram - ie. from both belts paths - by substracting signals in order to create +# a new composite spectrogram. This result of a divergent but mostly centered new spectrogram (center will be white) with some colored zones +# highlighting differences in the belts paths. The summative spectrogram is used for the MHI calculation. def combined_spectrogram(data1, data2): pdata1, bins1, t1 = compute_spectrogram(data1) pdata2, bins2, t2 = compute_spectrogram(data2) - # Detect ridges - ridge1 = detect_ridge(pdata1) - ridge2 = detect_ridge(pdata2) + # Interpolate the spectrograms and apply logarithmic scaling + pdata2_interpolated = interpolate_2d(bins1, t1, bins2, t2, pdata2) + pdata1_log = np.log1p(pdata1 / np.max(pdata1)) + pdata2_log = np.log1p(pdata2_interpolated / np.max(pdata2_interpolated)) - # Compute offset using cross-correlation and shit/align and interpolate the spectrograms - offset = compute_cross_correlation_offset(ridge1, ridge2) - pdata2_aligned = shift_data_in_time(pdata2, offset) - pdata2_interpolated = interpolate_2d(bins1, t1, bins2, t2, pdata2_aligned) + # Combined spectrograms + combined_sum = np.abs(pdata1 - pdata2_interpolated) + combined_divergent = pdata1_log - pdata2_log - # Combine the spectrograms - combined_data = np.abs(pdata1 - pdata2_interpolated) - return combined_data, bins1, t1 + return combined_sum, combined_divergent, bins1, t1 # Compute a composite and highly subjective value indicating the "mechanical health of the printer (0 to 100%)" that represent the @@ -274,7 +230,8 @@ def combined_spectrogram(data1, data2): # This result in a percentage value quantifying the machine behavior around the main resonances that give an hint if only touching belt tension # will give good graphs or if there is a chance of mechanical issues in the background (above 50% should be considered as probably problematic) def compute_mhi(combined_data, similarity_coefficient, num_unpaired_peaks): - filtered_data = combined_data[combined_data > 100] + # filtered_data = combined_data[combined_data > 100] + filtered_data = np.abs(combined_data) # First compute a "total variability metric" based on the sum of the gradient that sum the magnitude of will emphasize regions of the # spectrogram where there are rapid changes in magnitude (like the edges of resonance peaks). @@ -420,23 +377,23 @@ def plot_compare_frequency(ax, lognames, signal1, signal2, max_freq): def plot_difference_spectrogram(ax, data1, data2, signal1, signal2, similarity_factor, max_freq): - combined_data, bins, t = combined_spectrogram(data1, data2) - # combined_data, bins, t = compute_spectrogram(data1) + combined_sum, combined_divergent, bins, t = combined_spectrogram(data1, data2) # Compute the MHI value from the differential spectrogram sum of gradient, salted with # the similarity factor and the number or unpaired peaks from the belts frequency profile # Be careful, this value is highly opinionated and is pretty experimental! - mhi, textual_mhi = compute_mhi(combined_data, similarity_factor, len(signal1.unpaired_peaks) + len(signal2.unpaired_peaks)) + mhi, textual_mhi = compute_mhi(combined_sum, similarity_factor, len(signal1.unpaired_peaks) + len(signal2.unpaired_peaks)) print(f"[experimental] Mechanical Health Indicator: {textual_mhi.lower()} ({mhi:.1f}%)") ax.set_title(f"Differential Spectrogram", fontsize=14, color=KLIPPAIN_COLORS['dark_orange'], weight='bold') ax.plot([], [], ' ', label=f'{textual_mhi} (experimental)') - # Draw the differential spectrogram with a specific custom norm to get white or light orange zero values and red for max values - colors = ['white', 'bisque', 'red', 'black'] - n_bins = [0, 0.12, 0.9, 1] # These values where found experimentaly to get a good higlhlighting of the differences only - cm = matplotlib.colors.LinearSegmentedColormap.from_list('WhiteToRed', list(zip(n_bins, colors))) - norm = matplotlib.colors.Normalize(vmin=np.min(combined_data), vmax=np.max(combined_data)) - ax.pcolormesh(t, bins, combined_data.T, cmap=cm, norm=norm, shading='gouraud') + # Draw the differential spectrogram with a specific custom norm to get orange or purple values where there is signal or white near zeros + colors = [KLIPPAIN_COLORS['orange'], 'white', 'white', KLIPPAIN_COLORS['purple']] + n_bins = [0, 0.3, 0.7, 1] # These values where found experimentaly to get a good higlhlighting of the differences only + cm = matplotlib.colors.LinearSegmentedColormap.from_list('klippain_divergent', list(zip(n_bins, colors))) + # norm = matplotlib.colors.Normalize(vmin=np.min(combined_divergent), vmax=np.max(combined_divergent)) + norm = matplotlib.colors.SymLogNorm(linthresh=0.01, vmin=np.min(combined_divergent), vmax=np.max(combined_divergent), base=10, clip=False) + ax.pcolormesh(t, bins, combined_divergent.T, cmap=cm, norm=norm, shading='gouraud') ax.set_xlabel('Frequency (hz)') ax.set_xlim([0., max_freq]) @@ -450,16 +407,16 @@ def plot_difference_spectrogram(ax, data1, data2, signal1, signal2, similarity_f # Plot vertical lines for unpaired peaks unpaired_peak_count = 0 for _, peak in enumerate(signal1.unpaired_peaks): - ax.axvline(signal1.freqs[peak], color='red', linestyle='dotted', linewidth=1.5) + ax.axvline(signal1.freqs[peak], color=KLIPPAIN_COLORS['red_pink'], linestyle='dotted', linewidth=1.5) ax.annotate(f"Peak {unpaired_peak_count + 1}", (signal1.freqs[peak], t[-1]*0.05), - textcoords="data", color='red', rotation=90, fontsize=10, + textcoords="data", color=KLIPPAIN_COLORS['red_pink'], rotation=90, fontsize=10, verticalalignment='bottom', horizontalalignment='right') unpaired_peak_count +=1 for _, peak in enumerate(signal2.unpaired_peaks): - ax.axvline(signal2.freqs[peak], color='red', linestyle='dotted', linewidth=1.5) + ax.axvline(signal2.freqs[peak], color=KLIPPAIN_COLORS['red_pink'], linestyle='dotted', linewidth=1.5) ax.annotate(f"Peak {unpaired_peak_count + 1}", (signal2.freqs[peak], t[-1]*0.05), - textcoords="data", color='red', rotation=90, fontsize=10, + textcoords="data", color=KLIPPAIN_COLORS['red_pink'], rotation=90, fontsize=10, verticalalignment='bottom', horizontalalignment='right') unpaired_peak_count +=1 @@ -468,11 +425,11 @@ def plot_difference_spectrogram(ax, data1, data2, signal1, signal2, similarity_f label = ALPHABET[idx] x_min = min(peak1[1], peak2[1]) x_max = max(peak1[1], peak2[1]) - ax.axvline(x_min, color=KLIPPAIN_COLORS['purple'], linestyle='dotted', linewidth=1.5) - ax.axvline(x_max, color=KLIPPAIN_COLORS['purple'], linestyle='dotted', linewidth=1.5) - ax.fill_between([x_min, x_max], 0, np.max(combined_data), color=KLIPPAIN_COLORS['purple'], alpha=0.3) + ax.axvline(x_min, color=KLIPPAIN_COLORS['dark_purple'], linestyle='dotted', linewidth=1.5) + ax.axvline(x_max, color=KLIPPAIN_COLORS['dark_purple'], linestyle='dotted', linewidth=1.5) + ax.fill_between([x_min, x_max], 0, np.max(combined_divergent), color=KLIPPAIN_COLORS['dark_purple'], alpha=0.3) ax.annotate(f"Peaks {label}", (x_min, t[-1]*0.05), - textcoords="data", color=KLIPPAIN_COLORS['purple'], rotation=90, fontsize=10, + textcoords="data", color=KLIPPAIN_COLORS['dark_purple'], rotation=90, fontsize=10, verticalalignment='bottom', horizontalalignment='right') return From 87cb9015fa59b3daeb46ea70c4f440fef4519cff Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?F=C3=A9lix=20Boisselier?= Date: Mon, 11 Dec 2023 13:23:59 +0000 Subject: [PATCH 7/7] updated documentation --- K-ShakeTune/scripts/graph_belts.py | 18 +++---- README.md | 10 ++-- docs/README.md | 49 ++++++++++++++++-- .../belt_graphs/belt_graph_explanation.png | Bin 209149 -> 252947 bytes docs/is_tuning_generalities.md | 8 +-- docs/macros/axis_tuning.md | 2 +- docs/macros/vibrations_tuning.md | 4 +- 7 files changed, 65 insertions(+), 26 deletions(-) diff --git a/K-ShakeTune/scripts/graph_belts.py b/K-ShakeTune/scripts/graph_belts.py index 2dfaf9d..dc32c1b 100755 --- a/K-ShakeTune/scripts/graph_belts.py +++ b/K-ShakeTune/scripts/graph_belts.py @@ -212,14 +212,12 @@ def combined_spectrogram(data1, data2): pdata1, bins1, t1 = compute_spectrogram(data1) pdata2, bins2, t2 = compute_spectrogram(data2) - # Interpolate the spectrograms and apply logarithmic scaling + # Interpolate the spectrograms pdata2_interpolated = interpolate_2d(bins1, t1, bins2, t2, pdata2) - pdata1_log = np.log1p(pdata1 / np.max(pdata1)) - pdata2_log = np.log1p(pdata2_interpolated / np.max(pdata2_interpolated)) - - # Combined spectrograms + + # Cobine them in two form: a summed diff for the MHI computation and a diverging diff for the spectrogram colors combined_sum = np.abs(pdata1 - pdata2_interpolated) - combined_divergent = pdata1_log - pdata2_log + combined_divergent = pdata1 - pdata2_interpolated return combined_sum, combined_divergent, bins1, t1 @@ -388,11 +386,9 @@ def plot_difference_spectrogram(ax, data1, data2, signal1, signal2, similarity_f ax.plot([], [], ' ', label=f'{textual_mhi} (experimental)') # Draw the differential spectrogram with a specific custom norm to get orange or purple values where there is signal or white near zeros - colors = [KLIPPAIN_COLORS['orange'], 'white', 'white', KLIPPAIN_COLORS['purple']] - n_bins = [0, 0.3, 0.7, 1] # These values where found experimentaly to get a good higlhlighting of the differences only - cm = matplotlib.colors.LinearSegmentedColormap.from_list('klippain_divergent', list(zip(n_bins, colors))) - # norm = matplotlib.colors.Normalize(vmin=np.min(combined_divergent), vmax=np.max(combined_divergent)) - norm = matplotlib.colors.SymLogNorm(linthresh=0.01, vmin=np.min(combined_divergent), vmax=np.max(combined_divergent), base=10, clip=False) + colors = [KLIPPAIN_COLORS['dark_orange'], KLIPPAIN_COLORS['orange'], 'white', KLIPPAIN_COLORS['purple'], KLIPPAIN_COLORS['dark_purple']] + cm = matplotlib.colors.LinearSegmentedColormap.from_list('klippain_divergent', list(zip([0, 0.25, 0.5, 0.75, 1], colors))) + norm = matplotlib.colors.TwoSlopeNorm(vmin=np.min(combined_divergent), vcenter=0, vmax=np.max(combined_divergent)) ax.pcolormesh(t, bins, combined_divergent.T, cmap=cm, norm=norm, shading='gouraud') ax.set_xlabel('Frequency (hz)') diff --git a/README.md b/README.md index 8951412..d9a68e4 100644 --- a/README.md +++ b/README.md @@ -20,11 +20,12 @@ Check out the **[detailed documentation of the Shake&Tune module here](./docs/RE ## Installation Follow these steps to install the Shake&Tune module in your printer: - 1. Run the install script over SSH on your printer: + 1. Be sure to have a working accelerometer on your machine. You can follow the official [Measuring Resonances Klipper documentation](https://www.klipper3d.org/Measuring_Resonances.html) to configure one. Validate with an `ACCELEROMETER_QUERY` command that everything works correctly. + 1. Then, you can install the Shake&Tune package by running over SSH on your printer: ```bash wget -O - https://raw.githubusercontent.com/Frix-x/klippain-shaketune/main/install.sh | bash ``` - 1. Append the following to your `printer.cfg` file: + 1. Finally, append the following to your `printer.cfg` file and restart Klipper (if prefered, you can include only the needed macros: using `*.cfg` is a convenient way to include them all at once): ``` [include K-ShakeTune/*.cfg] ``` @@ -47,9 +48,10 @@ Follow these steps to install the Shake&Tune module in your printer: ## Usage Ensure your machine is homed, then invoke one of the following macros as needed: + - `AXES_MAP_CALIBRATION` to automatically find Klipper's `axes_map` parameter for your accelerometer orientation (be careful, this is experimental for now). - `BELTS_SHAPER_CALIBRATION` for belt resonance graphs, useful for verifying belt tension and differential belt paths behavior. - `AXES_SHAPER_CALIBRATION` for input shaper graphs to mitigate ringing/ghosting by tuning Klipper's input shaper system. - - `VIBRATIONS_CALIBRATION` for machine vibration graphs to optimize your slicer speed profiles. + - `VIBRATIONS_CALIBRATION` for machine and motors vibration graphs, used to optimize your slicer speed profiles and TMC drivers parameters. - `EXCITATE_AXIS_AT_FREQ` to sustain a specific excitation frequency, useful to let you inspect and find out what is resonating. -For further insights on the usage of the macros and the generated graphs, refer to the [K-Shake&Tune module documentation](./docs/README.md). +For further insights on the usage of these macros and the generated graphs, refer to the [K-Shake&Tune module documentation](./docs/README.md). diff --git a/docs/README.md b/docs/README.md index c3488c7..cb55236 100644 --- a/docs/README.md +++ b/docs/README.md @@ -2,17 +2,58 @@ ![](./banner_long.png) -### Detailed documentation +## Resonance testing -Before running any tests, you should first run the `AXES_MAP_CALIBRATION` macro to detect the and set Klipper's accelerometer axes_map parameter and validate that your accelerometer is working correctly and properly mounted. Then, check out **[input shaping and tuning generalities](./is_tuning_generalities.md)** to understand how it all works and what to look for when taking these measurements. +First, check out the **[input shaping and tuning generalities](./is_tuning_generalities.md)** documentation to understand how it all works and what to look for when taking these measurements. -Finally look at the documentation for each type of graph by clicking on them below tu run the tests and better understand your results to tune your machine! +Then look at the documentation for each type of graph by clicking on them below tu run the tests and better understand your results to tune your machine! | [Belts graph](./macros/belts_tuning.md) | [Axis input shaper graphs](./macros/axis_tuning.md) | [Vibrations graph](./macros/vibrations_tuning.md) | |:----------------:|:------------:|:---------------------:| | [](./macros/belts_tuning.md) | [](./macros/axis_tuning.md) | [](./macros/vibrations_tuning.md) | -### Complementary ressources +## Additional macros + +### AXES_MAP_CALIBRATION + +All graphs generated by this package show plots based on accelerometer measurements, typically labeled with the X, Y, and Z axes. It's important to note that if the accelerometer is rotated, its axes may not align correctly with the machine axes, making the plots more difficult to interpret, analyze, and understand. The `AXES_MAP_CALIBRATION` is designed to automatically measure the alignement of the accelerometer in order to set it correctly. + + > **Note**: + > + > This misalignment doesn't affect the measurements because the total sum across all axes is used to set the input shaper filters. It's just an optional but convenient way to configure Klipper's `[adxl345]` (or whichever accelerometer you have) "axes_map" parameter. + +Here are the parameters available when calling this macro: + +| parameters | default value | description | +|-----------:|---------------|-------------| +|Z_HEIGHT|20|z height to put the toolhead before starting the movements. Be careful, if your accelerometer is mounted under the nozzle, increase it to avoid crashing it on the bed of the machine| +|SPEED|80|speed of the toolhead in mm/s for the movements| +|ACCEL|1500 (or max printer accel)|accel in mm/s^2 used for all the moves| +|TRAVEL_SPEED|120|speed in mm/s used for all the travels moves| +|ACCEL_CHIP|"adxl345"|accelerometer chip name in the config| + +The machine will move slightly in +X, +Y, and +Z, and output in the console: `Detected axes_map: -z,y,x`. + +Use this value in your `printer.cfg` config file: +``` +[adxl345] # replace "adxl345" by your correct accelerometer name +axes_map: -z,y,x +``` + +### EXCITATE_AXIS_AT_FREQ + +The `EXCITATE_AXIS_AT_FREQ` macro is particularly useful for troubleshooting mechanical vibrations or resonance issues. This macro allows you to maintain a specific excitation frequency for a set duration, enabling hands-on diagnostics. By touching different components during the excitation, you can identify the source of the vibration, as contact usually stops it. + +Here are the parameters available when calling this macro: + +| parameters | default value | description | +|-----------:|---------------|-------------| +|FREQUENCY|25|excitation frequency (in Hz) that you want to maintain. Usually, it's the frequency of a peak on one of the graphs| +|TIME|10|time in second to maintain this excitation| +|AXIS|x|axis you want to excitate. Can be set to either "x", "y", "a", "b"| + + +## Complementary ressources - [Sineos post](https://klipper.discourse.group/t/interpreting-the-input-shaper-graphs/9879) in the Klipper knowledge base diff --git a/docs/images/belt_graphs/belt_graph_explanation.png b/docs/images/belt_graphs/belt_graph_explanation.png index 9d706b9e87fd9ffa673a85f7bd0ea134540a4ce1..bb640c497bd89729682902458141ce37bf56eb9f 100644 GIT binary patch literal 252947 zcmZU)b6BPS+BTkT8z~9jT-siHtyi00stzEG_k21q=)t^eeYNQ#2hOcR}g z{(v$Ukrx32tB*%~|MAa5;T@#3ox#9R2LJg1AGI(42?q9ABK=)N&BNd#2UZJ9l5q66 zFDW#-KMEs@(y zwjGX+VsiH*gmZ)@%r-IIZFD$qxLhMUv*LEqbM`B@_V=afvRTFoHZg)dw|gJ4Wo~x- zM9sI)Td_1xHr(wdzh8+nXMB z?DvkvBpxWheWe3%;cpIF=T`a0Xl<^3{v>5<;KC2BPV;l7qR?;mhpcn&JK zjyY3+=tx#(`bpVh_wzd!TNNz+kxZET7eAC@MdbMs2+I$FiWlVi*Th9&;_ADcja6c* zpCJ;7Xi)cWEqe^^fEY{JE?BMj?b4?Z`UH^r;X=vccQ~LI(lMm{h zA8xi6OcM2FB@+IIgB>BJko^MM41`E-sfl-1oRu|ipTVJq;ZkEJ;K6?ANYhv~iTk74 zO7AV%%|Hl+=*jsxf|>di2j%JW=X;&vmscDTTQRSgrmXSK-~a!|hcvR@4{xN%h9HXD 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While there isn't much room for easy adjustments to enhance them, most of you should only utilize them to configure your slicer profile to avoid problematic speeds. -However, if you want to go the rabbit hole, as the data in these graphs largely hinges on the type of motors and their physical characteristic and their control by the TMC black magic, there are opportunities for optimization. Tweaking TMC parameters allow to adjust the peaks, enhance machine performance, or diminish overall machine noise. For this process, I recommend to directly use the [Klipper TMC Autotune](https://github.com/andrewmcgr/klipper_tmc_autotune) plugin, which should simplify everything considerably. But keep in mind that it's still an experimental plugin and it's not perfect. +However, if you want to go the rabbit hole, as the data in these graphs largely hinges on the type of motors, their physical characteristic and the way they are controlled by the TMC drivers black magic, there are opportunities for optimization. Tweaking TMC parameters allow to adjust the peaks, enhance machine performance, or diminish overall machine noise. For this process, I recommend to directly use the [Klipper TMC Autotune](https://github.com/andrewmcgr/klipper_tmc_autotune) plugin, which should simplify everything considerably. But keep in mind that it's still an experimental plugin and it's not perfect. For individuals inclined to reach the bottom of the rabbit hole and that want to handle this manually, the use of an oscilloscope is mandatory. Majority of the necessary resources are available directly on the Trinamics TMC website: 1. You should first consult the datasheet specific to your TMC model for guidance on parameter names and their respective uses.