rename folders in measurement and post-processing

shaketune/post_processing/analyze_axesmap.py

@@ -0,0 +1,154 @@
+#!/usr/bin/env python3
+
+######################################
+###### AXE_MAP DETECTION SCRIPT ######
+######################################
+# Written by Frix_x#0161 #
+
+import optparse
+
+import numpy as np
+from scipy.signal import butter, filtfilt
+
+from ..helpers.console_output import ConsoleOutput
+
+NUM_POINTS = 500
+
+
+######################################################################
+# 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] <raw logs>'
+    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)
+    ConsoleOutput.print(results)
+
+    if options.output is not None:
+        with open(options.output, 'w') as f:
+            f.write(results)
+
+
+if __name__ == '__main__':
+    main()