fixed #7 and code optimizations

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)
 
-    d = {'x': data[:,1], 'y': data[:,2], 'z': data[:,3]}
+    def _specgram(x):
-    pdata, bins, t = _specgram(d['x'])
+        x_detrended = x - np.mean(x)  # Detrending by subtracting the mean value
-    for ax in 'yz':
+        return scipy.signal.spectrogram(
-        pdata += _specgram(d[ax])[0]
+            x_detrended, fs=Fs, window=window, nperseg=M, noverlap=M//2,
-    return pdata, bins, t
+            detrend='constant', scaling='density', mode='psd')
+
+    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
+# Interpolate source_data (2D) to match target_x and target_y in order to
-# 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
 # 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)))
+    # 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])
-    for i, y in enumerate(target_y):
+    target_points = np.array([(x, y) for y in target_y for x in target_x])
-        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:
+    # Flatten the source data to match the flattened source points
-                x_indices -= 1
+    source_values = source_data.flatten()
-            if y_indices == len(source_y) - 2:
+
-                y_indices -= 1
+    # 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')
-            x1, x2 = source_x[x_indices], source_x[x_indices + 1]
+    interpolated_data = interpolated_data.reshape((len(target_y), len(target_x)))
-            y1, y2 = source_y[y_indices], source_y[y_indices + 1]
+    interpolated_data = np.nan_to_num(interpolated_data)
-            
-            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)))
 
     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
+        start = max(0, path[-1]-n_average)
-        vicinity = magnitude[max(0, path[-1]-n_average):min(pdata.shape[0], path[-1]+n_average+1), j]
+        end = min(pdata.shape[0], path[-1]+n_average+1)
-        # Take an average of top few points
+        vicinity = magnitude[start:end, j]
-        sorted_indices = np.argsort(vicinity)
+        next_idx = start + np.mean(np.argsort(vicinity)[-n_average:])
-        top_indices = sorted_indices[-n_average:]
+        path.append(int(next_idx))
-        next_idx = int(np.mean(top_indices) + max(0, path[-1]-n_average))
+
-        path.append(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
+    max_len = max(len(ridge1), len(ridge2))
-    if len(ridge1) < len(ridge2):
+    ridge1 = np.pad(ridge1, (0, max_len - len(ridge1)), 'constant')
-        ridge1 = np.pad(ridge1, (0, len(ridge2) - len(ridge1)))
+    ridge2 = np.pad(ridge2, (0, max_len - len(ridge2)), 'constant')
-    elif len(ridge1) > len(ridge2):
-        ridge2 = np.pad(ridge2, (0, len(ridge1) - len(ridge2)))
 
-    cross_corr = np.fft.fftshift(np.fft.ifft(np.fft.fft(ridge1) * np.conj(np.fft.fft(ridge2))))
+    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)) - len(ridge1) // 2
+    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:]
+        return np.concatenate([data[:, -shift_amount:], np.zeros((data.shape[0], -shift_amount))], axis=1)
-    else:
+    return data
-        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')

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)
 
-    d = {'x': data[:,1], 'y': data[:,2], 'z': data[:,3]}
+    def _specgram(x):
-    pdata, bins, t = _specgram(d['x'])
+        x_detrended = x - np.mean(x)  # Detrending by subtracting the mean value
-    for ax in 'yz':
+        return scipy.signal.spectrogram(
-        pdata += _specgram(d[ax])[0]
+            x_detrended, fs=Fs, window=window, nperseg=M, noverlap=M//2,
-    return pdata, bins, t
+            detrend='constant', scaling='density', mode='psd')
+
+    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