Merge pull request #6 from Frix-x/develop

replaced TwoSlopNorm by a custom norm
to allow older version of matplotlib to be used

K-ShakeTune/scripts/graph_belts.py

@@ -4,9 +4,10 @@
 ######## CoreXY BELTS CALIBRATION SCRIPT ########
 #################################################
 # Written by Frix_x#0161 #
-# @version: 2.0
+# @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
 
@@ -28,10 +29,6 @@ import locale
 from datetime import datetime
 
 matplotlib.use('Agg')
-try:
-    locale.setlocale(locale.LC_TIME, locale.getdefaultlocale())
-except locale.Error:
-    locale.setlocale(locale.LC_TIME, 'C')
 
 
 ALPHABET = "ABCDEFGHIJKLMNOPQRSTUVWXYZ" # For paired peaks names
@@ -54,6 +51,22 @@ KLIPPAIN_COLORS = {
 }
 
 
+# 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 of the PSD graph
 ######################################################################
@@ -330,15 +343,15 @@ def compute_mhi(combined_data, similarity_coefficient, num_unpaired_peaks):
 def mhi_lut(mhi):
     if 0 <= mhi <= 30:
         return "Excellent mechanical health"
-    elif 31 <= mhi <= 45:
+    elif 30 < mhi <= 45:
         return "Good mechanical health"
-    elif 46 <= mhi <= 55:
+    elif 45 < mhi <= 55:
         return "Acceptable mechanical health"
-    elif 56 <= mhi <= 70:
+    elif 55 < mhi <= 70:
         return "Potential signs of a mechanical issue"
-    elif 71 <= mhi <= 85:
+    elif 70 < mhi <= 85:
         return "Likely a mechanical issue"
-    elif 86 <= mhi <= 100:
+    elif 85 < mhi <= 100:
         return "Mechanical issue detected"
 
 
@@ -461,9 +474,13 @@ def plot_difference_spectrogram(ax, data1, data2, signal1, signal2, similarity_f
     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 norm to get light grey zero values and red for max values (vmin to vcenter is not used)
-    norm = matplotlib.colors.TwoSlopeNorm(vcenter=np.min(combined_data), vmax=np.max(combined_data))
-    ax.pcolormesh(bins, t, combined_data.T, cmap='RdBu_r', norm=norm, shading='gouraud')
+    # 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(bins, t, 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)')

K-ShakeTune/scripts/graph_shaper.py

@@ -35,15 +35,12 @@ import locale
 from datetime import datetime
 
 matplotlib.use('Agg')
-try:
-    locale.setlocale(locale.LC_TIME, locale.getdefaultlocale())
-except locale.Error:
-    locale.setlocale(locale.LC_TIME, 'C')
 
 
 PEAKS_DETECTION_THRESHOLD = 0.05
 PEAKS_EFFECT_THRESHOLD = 0.12
 SPECTROGRAM_LOW_PERCENTILE_FILTER = 5
+MAX_SMOOTHING = 0.1
 
 KLIPPAIN_COLORS = {
     "purple": "#70088C",
@@ -52,6 +49,22 @@ KLIPPAIN_COLORS = {
 }
 
 
+# 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
 ######################################################################
@@ -73,7 +86,7 @@ def calibrate_shaper_with_damping(datas, max_smoothing):
     fr, zeta = compute_damping_ratio(psd, freqs)
 
     print("Recommended shaper is %s @ %.1f Hz" % (shaper.name, shaper.freq))
-    print("Axis has a resonant frequency ω0=%.1fHz with an estimated damping ratio ζ=%.3f" % (fr, zeta))
+    print("Axis has a main resonant frequency at %.1fHz with an estimated damping ratio of %.3f" % (fr, zeta))
 
     return shaper.name, all_shapers, calibration_data, fr, zeta
 
@@ -151,7 +164,7 @@ def detect_peaks(psd, freqs, window_size=5, vicinity=3):
 # Graphing
 ######################################################################
 
-def plot_freq_response_with_damping(ax, calibration_data, shapers, selected_shaper, fr, zeta, max_freq):
+def plot_freq_response_with_damping(ax, calibration_data, shapers, performance_shaper, fr, zeta, max_freq):
     freqs = calibration_data.freq_bins
     psd = calibration_data.psd_sum[freqs <= max_freq]
     px = calibration_data.psd_x[freqs <= max_freq]
@@ -181,30 +194,50 @@ def plot_freq_response_with_damping(ax, calibration_data, shapers, selected_shap
     ax2 = ax.twinx()
     ax2.yaxis.set_visible(False)
     
-    best_shaper_vals = None
-    no_vibr_shaper = None
-    no_vibr_shaper_freq = None
-    no_vibr_shaper_accel = 0
+    lowvib_shaper_vibrs = float('inf')
+    lowvib_shaper = None
+    lowvib_shaper_freq = None
+    lowvib_shaper_accel = 0
     
     # Draw the shappers curves and add their specific parameters in the legend
-    # This adds also a way to find the best shaper with 0% of vibrations (to be printed in the legend later)
+    # This adds also a way to find the best shaper with a low level of vibrations (with a resonable level of smoothing)
     for shaper in shapers:
         shaper_max_accel = round(shaper.max_accel / 100.) * 100.
         label = "%s (%.1f Hz, vibr=%.1f%%, sm~=%.2f, accel<=%.f)" % (
                 shaper.name.upper(), shaper.freq,
                 shaper.vibrs * 100., shaper.smoothing,
                 shaper_max_accel)
-        linestyle = 'dotted'
-        if shaper.name == selected_shaper:
-            linestyle = 'dashdot'
-            selected_shaper_freq = shaper.freq
-            best_shaper_vals = shaper.vals
-        if (shaper.vibrs * 100 == 0.) and (shaper_max_accel > no_vibr_shaper_accel):
-            no_vibr_shaper_accel = shaper_max_accel
-            no_vibr_shaper = shaper.name
-            no_vibr_shaper_freq = shaper.freq
-        ax2.plot(freqs, shaper.vals, label=label, linestyle=linestyle)
-    ax.plot(freqs, psd * best_shaper_vals, label='With %s applied' % (selected_shaper.upper()), color='cyan')
+        ax2.plot(freqs, shaper.vals, label=label, linestyle='dotted')
+
+        # Get the performance shaper
+        if shaper.name == performance_shaper:
+            performance_shaper_freq = shaper.freq
+            performance_shaper_vibr = shaper.vibrs * 100.
+            performance_shaper_vals = shaper.vals
+
+        # Get the low vibration shaper
+        if (shaper.vibrs * 100 < lowvib_shaper_vibrs or (shaper.vibrs * 100 == lowvib_shaper_vibrs and shaper_max_accel > lowvib_shaper_accel)) and shaper.smoothing < MAX_SMOOTHING:
+            lowvib_shaper_accel = shaper_max_accel
+            lowvib_shaper = shaper.name
+            lowvib_shaper_freq = shaper.freq
+            lowvib_shaper_vibrs = shaper.vibrs * 100
+            lowvib_shaper_vals = shaper.vals
+
+    # User recommendations are added to the legend: one is Klipper's original suggestion that is usually good for performances
+    # and the other one is the custom "low vibration" recommendation that looks for a suitable shaper that doesn't have excessive
+    # smoothing and that have a lower vibration level. If both recommendation are the same shaper, or if no suitable "low
+    # vibration" shaper is found, then only a single line as the "best shaper" recommendation is added to the legend
+    if lowvib_shaper != None and lowvib_shaper != performance_shaper and lowvib_shaper_vibrs <= performance_shaper_vibr:
+        ax2.plot([], [], ' ', label="Recommended performance shaper: %s @ %.1f Hz" % (performance_shaper.upper(), performance_shaper_freq))
+        ax.plot(freqs, psd * performance_shaper_vals, label='With %s applied' % (performance_shaper.upper()), color='cyan')
+        ax2.plot([], [], ' ', label="Recommended low vibrations shaper: %s @ %.1f Hz" % (lowvib_shaper.upper(), lowvib_shaper_freq))
+        ax.plot(freqs, psd * lowvib_shaper_vals, label='With %s applied' % (lowvib_shaper.upper()), color='lime')
+    else:
+        ax2.plot([], [], ' ', label="Recommended best shaper: %s @ %.1f Hz" % (performance_shaper.upper(), performance_shaper_freq))
+        ax.plot(freqs, psd * performance_shaper_vals, label='With %s applied' % (performance_shaper.upper()), color='cyan')
+
+    # And the estimated damping ratio is finally added at the end of the legend
+    ax2.plot([], [], ' ', label="Estimated damping ratio (ζ): %.3f" % (zeta))
 
     # Draw the detected peaks and name them
     # This also draw the detection threshold and warning threshold (aka "effect zone")
@@ -228,10 +261,6 @@ def plot_freq_response_with_damping(ax, calibration_data, shapers, selected_shap
     ax.fill_between(freqs, 0, peaks_warning_threshold, color='green', alpha=0.15, label='Relax Region')
     ax.fill_between(freqs, peaks_warning_threshold, peaks_effect_threshold, color='orange', alpha=0.2, label='Warning Region')
 
-    # Final user recommendations added to the legend with an added 0% vibration shaper and the estimated damping ratio over stock Klipper's algorithms
-    ax2.plot([], [], ' ', label="Recommended shaper: %s @ %.1f Hz" % (selected_shaper.upper(), selected_shaper_freq))
-    ax2.plot([], [], ' ', label="Recommended low vibrations shaper: %s @ %.1f Hz" % (no_vibr_shaper.upper(), no_vibr_shaper_freq))
-    ax2.plot([], [], ' ', label="Estimated damping ratio (ζ): %.3f" % (zeta))
 
     # Add the main resonant frequency and damping ratio of the axis to the graph title
     ax.set_title("Axis Frequency Profile (ω0=%.1fHz, ζ=%.3f)" % (fr, zeta), fontsize=14, color=KLIPPAIN_COLORS['dark_orange'], weight='bold')
@@ -303,7 +332,7 @@ def shaper_calibration(lognames, klipperdir="~/klipper", max_smoothing=None, max
     datas = [parse_log(fn) for fn in lognames]
 
     # Calibrate shaper and generate outputs
-    selected_shaper, shapers, calibration_data, fr, zeta = calibrate_shaper_with_damping(datas, max_smoothing)
+    performance_shaper, shapers, calibration_data, fr, zeta = calibrate_shaper_with_damping(datas, max_smoothing)
 
     fig = matplotlib.pyplot.figure()
     gs = matplotlib.gridspec.GridSpec(2, 1, height_ratios=[4, 3])
@@ -323,7 +352,7 @@ def shaper_calibration(lognames, klipperdir="~/klipper", max_smoothing=None, max
     fig.text(0.12, 0.957, title_line2, ha='left', va='top', fontsize=16, color=KLIPPAIN_COLORS['dark_purple'])
 
     # Plot the graphs
-    peaks = plot_freq_response_with_damping(ax1, calibration_data, shapers, selected_shaper, fr, zeta, max_freq)
+    peaks = plot_freq_response_with_damping(ax1, calibration_data, shapers, performance_shaper, fr, zeta, max_freq)
     plot_spectrogram(ax2, datas[0], peaks, max_freq)
 
     fig.set_size_inches(8.3, 11.6)

K-ShakeTune/scripts/graph_vibrations.py

@@ -29,10 +29,6 @@ import locale
 from datetime import datetime
 
 matplotlib.use('Agg')
-try:
-    locale.setlocale(locale.LC_TIME, locale.getdefaultlocale())
-except locale.Error:
-    locale.setlocale(locale.LC_TIME, 'C')
 
 
 PEAKS_DETECTION_THRESHOLD = 0.05
@@ -46,6 +42,22 @@ KLIPPAIN_COLORS = {
 }
 
 
+# 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
 ######################################################################

K-ShakeTune/scripts/is_workflow.py

@@ -63,7 +63,16 @@ def get_belts_graph():
     current_date = datetime.now().strftime('%Y%m%d_%H%M%S')
     lognames = []
 
-    for filename in glob.glob('/tmp/raw_data_axis*.csv'):
+    globbed_files = glob.glob('/tmp/raw_data_axis*.csv')
+    if not globbed_files:
+        print("No CSV files found in the /tmp folder to create the belt graphs!")
+        sys.exit(1)
+    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]:
         # Wait for the file handler to be released by Klipper
         while is_file_open(filename):
             time.sleep(3)
@@ -86,13 +95,13 @@ def get_belts_graph():
 def get_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
     globbed_files = glob.glob('/tmp/raw_data*.csv')
-    if len(globbed_files) > 1:
-        print("There is more than 1 measurement.csv found in the /tmp folder. Unable to plot the shaper graphs!")
-        print("Please clean the files in the /tmp folder and start again.")
+    if not globbed_files:
+        print("No CSV files found in the /tmp folder to create the input shaper graphs!")
         sys.exit(1)
-
-    filename = globbed_files[0]
+    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):
@@ -114,7 +123,15 @@ def get_vibrations_graph(axis_name):
     current_date = datetime.now().strftime('%Y%m%d_%H%M%S')
     lognames = []
 
-    for filename in glob.glob('/tmp/adxl345-*.csv'):
+    globbed_files = glob.glob('/tmp/adxl345-*.csv')
+    if not globbed_files:
+        print("No CSV files found in the /tmp folder to create the vibration graphs!")
+        sys.exit(1)
+    if len(globbed_files) < 3:
+        print("Not enough CSV files found in the /tmp folder. At least 3 files are required for the vibration graphs!")
+        sys.exit(1)
+
+    for filename in globbed_files:
         # Wait for the file handler to be released by Klipper
         while is_file_open(filename):
             time.sleep(3)

docs/macros/axis_tuning.md

@@ -40,10 +40,10 @@ For setting your Input Shaping filters, rely on the auto-computed values display
     * `2HUMP_EI` and `3HUMP_EI` are last-resort choices. Usually, they lead to a high level of smoothing in order to suppress the ringing while also using relatively low acceleration values. If they pop up as suggestions, it's likely your machine has underlying mechanical issues (that lead to pretty bad or "wide" graphs).
   - **Recommended Acceleration** (`accel<=...`): This isn't a standalone figure. It's essential to also consider the `vibr` and `sm` values as it's a compromise between the three. They will give you the percentage of remaining vibrations and the smoothing after Input Shaping, when using the recommended acceleration. Nothing will prevent you from using higher acceleration values; they are not a limit. However, when doing so, Input Shaping may not be able to suppress all the ringing on your parts. Finally, keep in mind that high acceleration values are not useful at all if there is still a high level of remaining vibrations: you should address any mechanical issues first.
   - **The remaining vibrations** (`vibr`): This directly correlates with ringing. It correspond to the total value of the blue "after shaper" signal. Ideally, you want a filter with minimal or zero vibrations.
-  - **Shaper recommendations**: This script will give you two recommandation. Pick the one that suit your needs:
-    * The first is Klipper's original suggestion, for best performance and acceleration on your machine while also allowing a little bit of remaining vibrations.
-    * The second aims for no remaining vibration to ensure the best print quality.
-  - The final line provides the estimated damping ratio for the axis. This value is generated automatically and is only accurate if the graph displays a clear and well detached single peak.
+  - **Shaper recommendations**: This script will give you some tailored recommendations based on your graphs. Pick the one that suit your needs:
+    * The "performance" shaper is Klipper's original suggestion that is good for high acceleration while also sometimes allowing a little bit of remaining vibrations. Use it if your goal is speed printing and you don't care much about some remaining ringing.
+    * The "low vibration" shaper aims for the lowest level of remaining vibration to ensure the best print quality with minimal ringing. This should be the best bet for most users.
+    * Sometimes, only a single recommendation called "best" shaper is presented. This means that either no suitable "low vibration" shaper was found (due to a high level of vibration or with too much smoothing) or because the "performance" shaper is also the one with the lowest vibration level.
   - **Damping Ratio**: Displayed at the end, this estimatation is only reliable when the graph shows a distinct, standalone and clean peak. On a well tuned machine, setting the damping ratio (instead of Klipper's 0.1 default value) can further reduce the ringing at high accelerations and with higher square corner velocities.
 
 Then, add to your configuration: