smoothing vs accel plot added

shaketune/graph_creators/shaper_graph_creator.py

@@ -22,7 +22,7 @@
 import optparse
 import os
 from datetime import datetime
-from typing import List, Optional
+from typing import Dict, List, Optional
 
 import matplotlib
 import matplotlib.font_manager
@@ -47,7 +47,8 @@ PEAKS_DETECTION_THRESHOLD = 0.05
 PEAKS_EFFECT_THRESHOLD = 0.12
 SPECTROGRAM_LOW_PERCENTILE_FILTER = 5
 MAX_VIBRATIONS = 5.0
-
+SMOOTHING_LIST = [0.1]
+# SMOOTHING_LIST = np.arange(0.001, 0.80, 0.05)
 KLIPPAIN_COLORS = {
     'purple': '#70088C',
     'orange': '#FF8D32',
@@ -112,15 +113,13 @@ def calibrate_shaper(datas: List[np.ndarray], max_smoothing: Optional[float], sc
     calibration_data = helper.process_accelerometer_data(datas)
     calibration_data.normalize_to_frequencies()
 
+    # We compute the damping ratio using the Klipper's default value if it fails
     fr, zeta, _, _ = compute_mechanical_parameters(calibration_data.psd_sum, calibration_data.freq_bins)
-
+    zeta = zeta if zeta is not None else 0.1
-    # If the damping ratio computation fail, we use Klipper default value instead
-    if zeta is None:
-        zeta = 0.1
 
     compat = False
     try:
-        shaper, all_shapers = helper.find_best_shaper(
+        k_shaper_choice, all_shapers = helper.find_best_shaper(
             calibration_data,
             shapers=None,
             damping_ratio=zeta,
@@ -129,23 +128,79 @@ def calibrate_shaper(datas: List[np.ndarray], max_smoothing: Optional[float], sc
             max_smoothing=max_smoothing,
             test_damping_ratios=None,
             max_freq=max_freq,
-            logger=ConsoleOutput.print,
+            logger=None,
+        )
+        ConsoleOutput.print(
+            (
+                f'Detected a square corner velocity of {scv:.1f} and a damping ratio of {zeta:.3f}. '
+                'These values will be used to compute the input shaper filter recommendations'
+            )
         )
     except TypeError:
         ConsoleOutput.print(
-            '[WARNING] You seem to be using an older version of Klipper that is not compatible with all the latest Shake&Tune features!'
+            (
-        )
+                '[WARNING] You seem to be using an older version of Klipper that is not compatible with all the latest '
-        ConsoleOutput.print(
+                'Shake&Tune features!\nShake&Tune now runs in compatibility mode: be aware that the results may be '
-            'Shake&Tune now runs in compatibility mode: be aware that the results may be slightly off, since the real damping ratio cannot be used to create the filter recommendations'
+                'slightly off, since the real damping ratio cannot be used to craft accurate filter recommendations'
+            )
         )
         compat = True
-        shaper, all_shapers = helper.find_best_shaper(calibration_data, max_smoothing, ConsoleOutput.print)
+        k_shaper_choice, all_shapers = helper.find_best_shaper(calibration_data, max_smoothing, None)
 
-    ConsoleOutput.print(
+    # If max_smoothing is not None, we run the same computation but without a smoothing value
-        f'\n-> Recommended shaper is {shaper.name.upper()} @ {shaper.freq:.1f} Hz (when using a square corner velocity of {scv:.1f} and a damping ratio of {zeta:.3f})'
+    # to get the max smoothing values from the filters and create the testing list
+    all_shapers_nosmoothing = None
+    if max_smoothing is not None:
+        if compat:
+            _, all_shapers_nosmoothing = helper.find_best_shaper(calibration_data, None, None)
+        else:
+            _, all_shapers_nosmoothing = helper.find_best_shaper(
+                calibration_data,
+                shapers=None,
+                damping_ratio=zeta,
+                scv=scv,
+                shaper_freqs=None,
+                max_smoothing=None,
+                test_damping_ratios=None,
+                max_freq=max_freq,
+                logger=None,
+            )
+
+    # Then we iterate over the all_shaperts_nosmoothing list to get the max of the smoothing values
+    max_smoothing = 0.0
+    if all_shapers_nosmoothing is not None:
+        for shaper in all_shapers_nosmoothing:
+            if shaper.smoothing > max_smoothing:
+                max_smoothing = shaper.smoothing
+    else:
+        for shaper in all_shapers:
+            if shaper.smoothing > max_smoothing:
+                max_smoothing = shaper.smoothing
+
+    # Then we create a list of smoothing values to test (no need to test the max smoothing value as it was already tested)
+    smoothing_test_list = np.linspace(0.001, max_smoothing, 10)[:-1]
+    additional_all_shapers = {}
+    for smoothing in smoothing_test_list:
+        if compat:
+            _, all_shapers_bis = helper.find_best_shaper(calibration_data, smoothing, None)
+        else:
+            _, all_shapers_bis = helper.find_best_shaper(
+                calibration_data,
+                shapers=None,
+                damping_ratio=zeta,
+                scv=scv,
+                shaper_freqs=None,
+                max_smoothing=smoothing,
+                test_damping_ratios=None,
+                max_freq=max_freq,
+                logger=None,
+            )
+        additional_all_shapers[f'sm_{smoothing}'] = all_shapers_bis
+    additional_all_shapers['max_smoothing'] = (
+        all_shapers_nosmoothing if all_shapers_nosmoothing is not None else all_shapers
     )
 
-    return shaper.name, all_shapers, calibration_data, fr, zeta, compat
+    return k_shaper_choice.name, all_shapers, additional_all_shapers, calibration_data, fr, zeta, max_smoothing, compat
 
 
 ######################################################################
@@ -164,7 +219,7 @@ def plot_freq_response(
     fr: float,
     zeta: float,
     max_freq: float,
-) -> None:
+) -> Dict[str, List[Dict[str, str]]]:
     freqs = calibration_data.freqs
     psd = calibration_data.psd_sum
     px = calibration_data.psd_x
@@ -193,27 +248,40 @@ def plot_freq_response(
     ax2 = ax.twinx()
     ax2.yaxis.set_visible(False)
 
+    shaper_table_data = {
+        'shapers': [],
+        'recommendations': [],
+        'damping_ratio': zeta,
+    }
+
     # Draw the shappers curves and add their specific parameters in the legend
     perf_shaper_choice = None
     perf_shaper_vals = None
     perf_shaper_freq = None
     perf_shaper_accel = 0
     for shaper in shapers:
-        shaper_max_accel = round(shaper.max_accel / 100.0) * 100.0
+        ax2.plot(freqs, shaper.vals, label=shaper.name.upper(), linestyle='dotted')
-        label = f'{shaper.name.upper()} ({shaper.freq:.1f} Hz, vibr={shaper.vibrs * 100.0:.1f}%, sm~={shaper.smoothing:.2f}, accel<={shaper_max_accel:.0f})'
+
-        ax2.plot(freqs, shaper.vals, label=label, linestyle='dotted')
+        shaper_info = {
+            'type': shaper.name.upper(),
+            'frequency': shaper.freq,
+            'vibrations': shaper.vibrs,
+            'smoothing': shaper.smoothing,
+            'max_accel': shaper.max_accel,
+        }
+        shaper_table_data['shapers'].append(shaper_info)
 
         # Get the Klipper recommended shaper (usually it's a good low vibration compromise)
         if shaper.name == klipper_shaper_choice:
             klipper_shaper_freq = shaper.freq
             klipper_shaper_vals = shaper.vals
-            klipper_shaper_accel = shaper_max_accel
+            klipper_shaper_accel = shaper.max_accel
 
         # Find the shaper with the highest accel but with vibrs under MAX_VIBRATIONS as it's
         # a good performance compromise when injecting the SCV and damping ratio in the computation
-        if perf_shaper_accel < shaper_max_accel and shaper.vibrs * 100 < MAX_VIBRATIONS:
+        if perf_shaper_accel < shaper.max_accel and shaper.vibrs * 100 < MAX_VIBRATIONS:
             perf_shaper_choice = shaper.name
-            perf_shaper_accel = shaper_max_accel
+            perf_shaper_accel = shaper.max_accel
             perf_shaper_freq = shaper.freq
             perf_shaper_vals = shaper.vals
 
@@ -226,32 +294,30 @@ def plot_freq_response(
         and perf_shaper_choice != klipper_shaper_choice
         and perf_shaper_accel >= klipper_shaper_accel
     ):
-        ax2.plot(
+        perf_shaper_string = f'Recommended performance shaper: {perf_shaper_choice.upper()} @ {perf_shaper_freq:.1f} Hz'
-            [],
+        lowvibr_shaper_string = (
-            [],
+            f'Recommended low vibrations shaper: {klipper_shaper_choice.upper()} @ {klipper_shaper_freq:.1f} Hz'
-            ' ',
-            label=f'Recommended performance shaper: {perf_shaper_choice.upper()} @ {perf_shaper_freq:.1f} Hz',
         )
+        shaper_table_data['recommendations'].append(perf_shaper_string)
+        shaper_table_data['recommendations'].append(lowvibr_shaper_string)
+        ConsoleOutput.print(f'{perf_shaper_string} (with a damping ratio of {zeta:.3f})')
+        ConsoleOutput.print(f'{lowvibr_shaper_string} (with a damping ratio of {zeta:.3f})')
         ax.plot(
             freqs,
             psd * perf_shaper_vals,
             label=f'With {perf_shaper_choice.upper()} applied',
             color='cyan',
         )
-        ax2.plot(
+        ax.plot(
-            [],
+            freqs,
-            [],
+            psd * klipper_shaper_vals,
-            ' ',
+            label=f'With {klipper_shaper_choice.upper()} applied',
-            label=f'Recommended low vibrations shaper: {klipper_shaper_choice.upper()} @ {klipper_shaper_freq:.1f} Hz',
+            color='lime',
         )
-        ax.plot(freqs, psd * klipper_shaper_vals, label=f'With {klipper_shaper_choice.upper()} applied', color='lime')
     else:
-        ax2.plot(
+        shaper_string = f'Recommended best shaper: {klipper_shaper_choice.upper()} @ {klipper_shaper_freq:.1f} Hz'
-            [],
+        shaper_table_data['recommendations'].append(shaper_string)
-            [],
+        ConsoleOutput.print(f'{shaper_string} (with a damping ratio of {zeta:.3f})')
-            ' ',
-            label=f'Recommended performance shaper: {klipper_shaper_choice.upper()} @ {klipper_shaper_freq:.1f} Hz',
-        )
         ax.plot(
             freqs,
             psd * klipper_shaper_vals,
@@ -259,9 +325,6 @@ def plot_freq_response(
             color='cyan',
         )
 
-    # And the estimated damping ratio is finally added at the end of the legend
-    ax2.plot([], [], ' ', label=f'Estimated damping ratio (ζ): {zeta:.3f}')
-
     # Draw the detected peaks and name them
     # This also draw the detection threshold and warning threshold (aka "effect zone")
     ax.plot(peaks_freqs, psd[peaks], 'x', color='black', markersize=8)
@@ -297,7 +360,7 @@ def plot_freq_response(
     ax.legend(loc='upper left', prop=fontP)
     ax2.legend(loc='upper right', prop=fontP)
 
-    return
+    return shaper_table_data
 
 
 # Plot a time-frequency spectrogram to see how the system respond over time during the
@@ -350,6 +413,96 @@ def plot_spectrogram(
     return
 
 
+def plot_smoothing_vs_accel(
+    ax: plt.Axes,
+    shaper_table_data: Dict[str, List[Dict[str, str]]],
+    additional_shapers: Dict[str, List[Dict[str, str]]],
+) -> None:
+    fontP = matplotlib.font_manager.FontProperties()
+    fontP.set_size('x-small')
+
+    ax.xaxis.set_minor_locator(matplotlib.ticker.MultipleLocator(1000))
+    ax.yaxis.set_minor_locator(matplotlib.ticker.AutoMinorLocator())
+    ax.grid(which='major', color='grey')
+    ax.grid(which='minor', color='lightgrey')
+
+    shaper_data = {}
+
+    # Extract data from additional_shapers first
+    for _, shapers in additional_shapers.items():
+        for shaper in shapers:
+            shaper_type = shaper.name.upper()
+            if shaper_type not in shaper_data:
+                shaper_data[shaper_type] = []
+            shaper_data[shaper_type].append(
+                {
+                    'smoothing': shaper.smoothing,
+                    'max_accel': shaper.max_accel,
+                    'vibrs': shaper.vibrs * 100.0,
+                }
+            )
+
+    # Extract data from shaper_table_data and insert into shaper_data
+    for shaper in shaper_table_data['shapers']:
+        shaper_type = shaper['type']
+        if shaper_type not in shaper_data:
+            shaper_data[shaper_type] = []
+        shaper_data[shaper_type].append(
+            {
+                'smoothing': float(shaper['smoothing']),
+                'max_accel': float(shaper['max_accel']),
+                'vibrs': float(shaper['vibrations']) * 100.0,
+            }
+        )
+
+    # Plot each shaper type and add colorbar for vibrations
+    for _, (shaper_type, data) in enumerate(shaper_data.items()):
+        smoothing_values = [d['smoothing'] for d in data]
+        max_accel_values = [d['max_accel'] for d in data]
+        vibrs_values = [d['vibrs'] for d in data]
+        ax.plot(max_accel_values, smoothing_values, linestyle=':', label=f'{shaper_type}', zorder=10)
+        scatter = ax.scatter(
+            max_accel_values, smoothing_values, c=vibrs_values, cmap='plasma', s=100, edgecolors='w', zorder=15
+        )
+
+    # Add colorbar for vibrations
+    cbar = plt.colorbar(scatter, ax=ax)
+    cbar.set_label('Remaining Vibrations (%)')
+    ax.set_xlabel('Max Acceleration')
+    ax.set_ylabel('Smoothing')
+    ax.set_title(
+        'Smoothing vs Max Acceleration',
+        fontsize=14,
+        color=KLIPPAIN_COLORS['dark_orange'],
+        weight='bold',
+    )
+    ax.legend(loc='upper right', prop=fontP)
+
+
+def print_shaper_table(fig: plt.Figure, shaper_table_data: Dict[str, List[Dict[str, str]]]) -> None:
+    columns = ['Type', 'Frequency', 'Vibrations', 'Smoothing', 'Max Accel']
+    table_data = []
+
+    for shaper_info in shaper_table_data['shapers']:
+        row = [
+            f'{shaper_info["type"].upper()}',
+            f'{shaper_info["frequency"]:.1f} Hz',
+            f'{shaper_info["vibrations"] * 100:.1f} %',
+            f'{shaper_info["smoothing"]:.3f}',
+            f'{round(shaper_info["max_accel"] / 10) * 10:.0f}',
+        ]
+        table_data.append(row)
+    table = plt.table(cellText=table_data, colLabels=columns, bbox=[1.12, -0.4, 0.75, 0.25], cellLoc='center')
+    table.auto_set_font_size(False)
+    table.set_fontsize(10)
+    table.auto_set_column_width([0, 1, 2, 3, 4])
+    table.set_zorder(100)
+
+    # Add the recommendations and damping ratio using fig.text
+    fig.text(0.58, 0.235, f'Estimated damping ratio (ζ): {shaper_table_data["damping_ratio"]:.3f}', fontsize=14)
+    fig.text(0.58, 0.210, '\n'.join(shaper_table_data['recommendations']), fontsize=14)
+
+
 ######################################################################
 # Startup and main routines
 ######################################################################
@@ -375,8 +528,8 @@ def shaper_calibration(
         ConsoleOutput.print('Warning: incorrect number of .csv files detected. Only the first one will be used!')
 
     # Compute shapers, PSD outputs and spectrogram
-    klipper_shaper_choice, shapers, calibration_data, fr, zeta, compat = calibrate_shaper(
+    klipper_shaper_choice, shapers, additional_shapers, calibration_data, fr, zeta, max_smoothing_computed, compat = (
-        datas[0], max_smoothing, scv, max_freq
+        calibrate_shaper(datas[0], max_smoothing, scv, max_freq)
     )
     pdata, bins, t = compute_spectrogram(datas[0])
     del datas
@@ -400,29 +553,31 @@ def shaper_calibration(
     peak_freqs_formated = ['{:.1f}'.format(f) for f in peaks_freqs]
     num_peaks_above_effect_threshold = np.sum(calibration_data.psd_sum[peaks] > peaks_threshold[1])
     ConsoleOutput.print(
-        f"\nPeaks detected on the graph: {num_peaks} @ {', '.join(map(str, peak_freqs_formated))} Hz ({num_peaks_above_effect_threshold} above effect threshold)"
+        f"Peaks detected on the graph: {num_peaks} @ {', '.join(map(str, peak_freqs_formated))} Hz ({num_peaks_above_effect_threshold} above effect threshold)"
     )
 
     # Create graph layout
-    fig, (ax1, ax2) = plt.subplots(
+    fig, ((ax1, ax3), (ax2, ax4)) = plt.subplots(
+        2,
         2,
-        1,
         gridspec_kw={
             'height_ratios': [4, 3],
+            'width_ratios': [5, 4],
             'bottom': 0.050,
             'top': 0.890,
-            'left': 0.085,
+            'left': 0.048,
             'right': 0.966,
             'hspace': 0.169,
-            'wspace': 0.200,
+            'wspace': 0.150,
         },
     )
-    fig.set_size_inches(8.3, 11.6)
+    ax4.remove()
+    fig.set_size_inches(15, 11.6)
 
     # Add a title with some test info
     title_line1 = 'INPUT SHAPER CALIBRATION TOOL'
     fig.text(
-        0.12, 0.965, title_line1, ha='left', va='bottom', fontsize=20, color=KLIPPAIN_COLORS['purple'], weight='bold'
+        0.065, 0.965, title_line1, ha='left', va='bottom', fontsize=20, color=KLIPPAIN_COLORS['purple'], weight='bold'
     )
     try:
         filename_parts = (lognames[0].split('/')[-1]).split('_')
@@ -433,8 +588,11 @@ def shaper_calibration(
             title_line4 = '| and SCV are not used for filter recommendations!'
             title_line5 = f'| Accel per Hz used: {accel_per_hz} mm/s²/Hz' if accel_per_hz is not None else ''
         else:
+            max_smoothing_string = (
+                f'maximum ({max_smoothing_computed:0.3f})' if max_smoothing is None else f'{max_smoothing:0.3f}'
+            )
             title_line3 = f'| Square corner velocity: {scv} mm/s'
-            title_line4 = f'| Max allowed smoothing: {max_smoothing}'
+            title_line4 = f'| Allowed smoothing: {max_smoothing_string}'
             title_line5 = f'| Accel per Hz used: {accel_per_hz} mm/s²/Hz' if accel_per_hz is not None else ''
     except Exception:
         ConsoleOutput.print(f'Warning: CSV filename look to be different than expected ({lognames[0]})')
@@ -442,19 +600,22 @@ def shaper_calibration(
         title_line3 = ''
         title_line4 = ''
         title_line5 = ''
-    fig.text(0.12, 0.957, title_line2, ha='left', va='top', fontsize=16, color=KLIPPAIN_COLORS['dark_purple'])
+    fig.text(0.065, 0.957, title_line2, ha='left', va='top', fontsize=16, color=KLIPPAIN_COLORS['dark_purple'])
-    fig.text(0.58, 0.963, title_line3, ha='left', va='top', fontsize=10, color=KLIPPAIN_COLORS['dark_purple'])
+    fig.text(0.50, 0.990, title_line3, ha='left', va='top', fontsize=14, color=KLIPPAIN_COLORS['dark_purple'])
-    fig.text(0.58, 0.948, title_line4, ha='left', va='top', fontsize=10, color=KLIPPAIN_COLORS['dark_purple'])
+    fig.text(0.50, 0.968, title_line4, ha='left', va='top', fontsize=14, color=KLIPPAIN_COLORS['dark_purple'])
-    fig.text(0.58, 0.933, title_line5, ha='left', va='top', fontsize=10, color=KLIPPAIN_COLORS['dark_purple'])
+    fig.text(0.501, 0.945, title_line5, ha='left', va='top', fontsize=10, color=KLIPPAIN_COLORS['dark_purple'])
 
     # Plot the graphs
-    plot_freq_response(
+    shaper_table_data = plot_freq_response(
         ax1, calibration_data, shapers, klipper_shaper_choice, peaks, peaks_freqs, peaks_threshold, fr, zeta, max_freq
     )
     plot_spectrogram(ax2, t, bins, pdata, peaks_freqs, max_freq)
+    plot_smoothing_vs_accel(ax3, shaper_table_data, additional_shapers)
+
+    print_shaper_table(fig, shaper_table_data)
 
     # Adding a small Klippain logo to the top left corner of the figure
-    ax_logo = fig.add_axes([0.001, 0.8995, 0.1, 0.1], anchor='NW')
+    ax_logo = fig.add_axes([0.001, 0.924, 0.075, 0.075], anchor='NW')
     ax_logo.imshow(plt.imread(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'klippain.png')))
     ax_logo.axis('off')