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JakHolzer 2020-09-11 15:30:30 +02:00 committed by Ivan Usov
parent e5609a8cd5
commit 8b603c5924
1 changed files with 66 additions and 55 deletions
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47
pyzebra/ccl_findpeaks.py
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@ -4,7 +4,9 @@ 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
args: data (dictionary from load_cclv2),
@ -13,22 +15,24 @@ def ccl_findpeaks(data, keys, int_threshold=None, prominence=None, smooth=True,
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 dict and data["file_type"] == 'ccl':
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
@ -39,28 +43,36 @@ def ccl_findpeaks(data, keys, int_threshold=None, prominence=None, smooth=True,
pass
else:
int_threshold = 0.75
print('Invalid value for int_threshold, select value between 0 and 1, new value set to:', int_threshold)
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)
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)
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)
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')
itp = interp1d(omega, counts, kind="linear")
absintensity = [abs(number) for number in counts]
lowest_intensity = min(absintensity)
counts[counts < 0] = lowest_intensity
@ -69,13 +81,12 @@ def ccl_findpeaks(data, keys, int_threshold=None, prominence=None, smooth=True,
else:
smooth_peaks = counts
indexes = sc.signal.find_peaks(smooth_peaks, height=int_threshold*max(smooth_peaks), prominence=prominence)
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')