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v.2.5.0
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,121 @@ | ||
| #!/usr/bin/env python3 | ||
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| # Common functions for the Shake&Tune package | ||
| # Written by Frix_x#0161 # | ||
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| import math | ||
| import os, sys | ||
| from importlib import import_module | ||
| from pathlib import Path | ||
| import numpy as np | ||
| from scipy.signal import spectrogram | ||
| from git import GitCommandError, Repo | ||
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| 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 Shake&Tune. Please use the official Klipper " | ||
| "script to process it instead." % (logname,)) | ||
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| def setup_klipper_import(kdir): | ||
| kdir = os.path.expanduser(kdir) | ||
| sys.path.append(os.path.join(kdir, 'klippy')) | ||
| return import_module('.shaper_calibrate', 'extras') | ||
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| # This is used to print the current S&T version on top of the png graph file | ||
| def get_git_version(): | ||
| try: | ||
| # Get the absolute path of the script, resolving any symlinks | ||
| # Then get 2 times to parent dir to be at the git root folder | ||
| script_path = Path(__file__).resolve() | ||
| repo_path = script_path.parents[2] | ||
| repo = Repo(repo_path) | ||
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| try: | ||
| version = repo.git.describe('--tags') | ||
| except GitCommandError: | ||
| # If no tag is found, use the simplified commit SHA instead | ||
| version = repo.head.commit.hexsha[:7] | ||
| return version | ||
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| except Exception as e: | ||
| return None | ||
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| # This is Klipper's spectrogram generation function adapted to use Scipy | ||
| def compute_spectrogram(data): | ||
| N = data.shape[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.) | ||
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| def _specgram(x): | ||
| return spectrogram(x, fs=Fs, window=window, nperseg=M, noverlap=M//2, | ||
| detrend='constant', scaling='density', mode='psd') | ||
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| 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 | ||
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| # Compute natural resonant frequency and damping ratio by using the half power bandwidth method with interpolated frequencies | ||
| def compute_mechanical_parameters(psd, freqs): | ||
| max_power_index = np.argmax(psd) | ||
| fr = freqs[max_power_index] | ||
| max_power = psd[max_power_index] | ||
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| half_power = max_power / math.sqrt(2) | ||
| idx_below = np.where(psd[:max_power_index] <= half_power)[0][-1] | ||
| idx_above = np.where(psd[max_power_index:] <= half_power)[0][0] + max_power_index | ||
| freq_below_half_power = freqs[idx_below] + (half_power - psd[idx_below]) * (freqs[idx_below + 1] - freqs[idx_below]) / (psd[idx_below + 1] - psd[idx_below]) | ||
| freq_above_half_power = freqs[idx_above - 1] + (half_power - psd[idx_above - 1]) * (freqs[idx_above] - freqs[idx_above - 1]) / (psd[idx_above] - psd[idx_above - 1]) | ||
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| bandwidth = freq_above_half_power - freq_below_half_power | ||
| zeta = bandwidth / (2 * fr) | ||
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| return fr, zeta, max_power_index | ||
|
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| # This find all the peaks in a curve by looking at when the derivative term goes from positive to negative | ||
| # Then only the peaks found above a threshold are kept to avoid capturing peaks in the low amplitude noise of a signal | ||
| def detect_peaks(data, indices, detection_threshold, relative_height_threshold=None, window_size=5, vicinity=3): | ||
| # Smooth the curve using a moving average to avoid catching peaks everywhere in noisy signals | ||
| kernel = np.ones(window_size) / window_size | ||
| smoothed_data = np.convolve(data, kernel, mode='valid') | ||
| mean_pad = [np.mean(data[:window_size])] * (window_size // 2) | ||
| smoothed_data = np.concatenate((mean_pad, smoothed_data)) | ||
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| # Find peaks on the smoothed curve | ||
| smoothed_peaks = np.where((smoothed_data[:-2] < smoothed_data[1:-1]) & (smoothed_data[1:-1] > smoothed_data[2:]))[0] + 1 | ||
| smoothed_peaks = smoothed_peaks[smoothed_data[smoothed_peaks] > detection_threshold] | ||
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| # Additional validation for peaks based on relative height | ||
| valid_peaks = smoothed_peaks | ||
| if relative_height_threshold is not None: | ||
| valid_peaks = [] | ||
| for peak in smoothed_peaks: | ||
| peak_height = smoothed_data[peak] - np.min(smoothed_data[max(0, peak-vicinity):min(len(smoothed_data), peak+vicinity+1)]) | ||
| if peak_height > relative_height_threshold * smoothed_data[peak]: | ||
| valid_peaks.append(peak) | ||
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| # Refine peak positions on the original curve | ||
| refined_peaks = [] | ||
| for peak in valid_peaks: | ||
| local_max = peak + np.argmax(data[max(0, peak-vicinity):min(len(data), peak+vicinity+1)]) - vicinity | ||
| refined_peaks.append(local_max) | ||
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| num_peaks = len(refined_peaks) | ||
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| return num_peaks, np.array(refined_peaks), indices[refined_peaks] |
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