pyzebra/ccl_findpeaks.py

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+import numpy as np
+import scipy as sc
+from scipy.interpolate import interp1d
+from scipy.signal import savgol_filter
+
+
+def ccl_findpeaks(
+    data, keys, int_threshold=0.8, prominence=50, smooth=False, window_size=7, poly_order=3
+):
+
+    """function iterates through the dictionary created by load_cclv2 and locates peaks for each measurement
+    args:   data (dictionary from load_cclv2),
+
+            int_threshold - fraction of threshold_intensity/max_intensity, must be positive num between 0 and 1
+                        i.e. will only detect peaks above 75% of max intensity
+
+            prominence - defines a drop of values that must be between two peaks, must be positive number
+                        i.e. if promimence is 20, it will detect two neigbouring peaks of 300 and 310 intesities,
+                        if none of the itermediate values are lower that 290
+
+            smooth  - if true, smooths data by savitzky golay filter, if false - no smoothing
+
+            window_size - window size for savgol filter, must be odd positive integer
+
+            poly_order =  order of the polynomial used in savgol filter, must be positive integer smaller than
+            window_size returns: dictionary with following structure:
+                                D{M34{  'num_of_peaks': 1,              #num of peaks
+                                        'peak_indexes': [20],           # index of peaks in omega array
+                                        'peak_heights': [90.],          # height of the peaks (if data vere smoothed
+                                                                        its the heigh of the peaks in smoothed data)
+    """
+
+    if type(data) is not dict and data["file_type"] != "ccl":
+        print("Data is not a dictionary or was not made from ccl file")
+
+    if not 0 <= int_threshold <= 1:
+        int_threshold = 0.8
+        print(
+            "Invalid value for int_threshold, select value between 0 and 1, new value set to:",
+            int_threshold,
+        )
+
+    if isinstance(window_size, int) is False or (window_size % 2) == 0 or window_size <= 1:
+        window_size = 7
+        print(
+            "Invalid value for window_size, select positive odd integer, new value set to!:",
+            window_size)
+
+    if isinstance(poly_order, int) is False or window_size < poly_order:
+        poly_order = 3
+        print(
+            "Invalid value for poly_order, select positive integer smaller than window_size, new value set to:",
+            poly_order,
+        )
+
+    if isinstance(prominence, (int, float)) is False and prominence < 0:
+        prominence = 50
+        print("Invalid value for prominence, select positive number, new value set to:", prominence)
+
+
+    omega = data["Measurements"][str(keys)]["omega"]
+    counts = np.array(data["Measurements"][str(keys)]["counts"])
+    if smooth is True:
+        itp = interp1d(omega, counts, kind="linear")
+        absintensity = [abs(number) for number in counts]
+        lowest_intensity = min(absintensity)
+        counts[counts < 0] = lowest_intensity
+        smooth_peaks = savgol_filter(itp(omega), window_size, poly_order)
+
+    else:
+        smooth_peaks = counts
+
+    indexes = sc.signal.find_peaks(
+        smooth_peaks, height=int_threshold * max(smooth_peaks), prominence=prominence
+    )
+    data["Measurements"][str(keys)]["num_of_peaks"] = len(indexes[0])
+    data["Measurements"][str(keys)]["peak_indexes"] = indexes[0]
+    data["Measurements"][str(keys)]["peak_heights"] = indexes[1]["peak_heights"]
+    data["Measurements"][str(keys)]["smooth_peaks"] = smooth_peaks  # smoothed curve
+
+    return data