From 8b603c5924a07792cf4725f64adb20b02c13d4d4 Mon Sep 17 00:00:00 2001 From: JakHolzer <53743814+JakHolzer@users.noreply.github.com> Date: Fri, 11 Sep 2020 15:30:30 +0200 Subject: [PATCH] guard close changed --- pyzebra/ccl_findpeaks.py | 121 +++++++++++++++++++++------------------ 1 file changed, 66 insertions(+), 55 deletions(-) diff --git a/pyzebra/ccl_findpeaks.py b/pyzebra/ccl_findpeaks.py index a0d2fd6..a8df1d4 100644 --- a/pyzebra/ccl_findpeaks.py +++ b/pyzebra/ccl_findpeaks.py @@ -4,78 +4,89 @@ 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): +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 + """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 + 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: + 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) + '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") - """ + 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 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) - 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 + 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: - return print('Data is not a dictionary or was not made from ccl file') \ No newline at end of file + 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