diff --git a/pyzebra/ccl_findpeaks.py b/pyzebra/ccl_findpeaks.py
new file mode 100644
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+++ b/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=None, prominence=None, smooth=True, window_size=None, poly_order=None):
+
+    """ 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 dict and data["file_type"] == 'ccl':
+        int_threshold = 0.75 if int_threshold is None else int_threshold
+        prominence = 50 if prominence is None else prominence
+        smooth = False if smooth is None else smooth
+        window_size = 7 if window_size is None else window_size
+        poly_order = 3 if poly_order is None else poly_order
+
+        if 0 <= int_threshold <= 1:
+            pass
+        else:
+            int_threshold = 0.75
+            print('Invalid value for int_threshold, select value between 0 and 1, new value set to:', int_threshold)
+        if isinstance(window_size, int) is True and (window_size % 2) != 0 and window_size >= 1:
+            pass
+        else:
+            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 True and window_size > poly_order >= 1:
+            pass
+        else:
+            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 True and prominence > 0:
+            pass
+        else:
+            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
+
+    else:
+        return print('Data is not a dictionary or was not made from ccl file')
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