Module thunderfish.checkpulse
Check for pulse-type weakly electric fish
Check whether a pulse-type or a wave-type weakly electric fish is present in a recording.
Main functions
check_pulse(): checks for pulse-type fish.
Configuration parameter
add_check_pulse_config(): add parameters forcheck_pulse()to configuration.check_pulse_args(): retrieve parameters forcheck_pulse()from configuration.
Expand source code
"""
Check for pulse-type weakly electric fish
Check whether a pulse-type or a wave-type weakly electric fish is present in a recording.
## Main functions
- `check_pulse()`: checks for pulse-type fish.
## Configuration parameter
- `add_check_pulse_config()`: add parameters for `check_pulse()` to configuration.
- `check_pulse_args()`: retrieve parameters for `check_pulse()` from configuration.
"""
import numpy as np
from thunderlab.eventdetection import percentile_threshold, detect_peaks, trim
def check_pulse(data, sem, samplerate, thresh_fac=0.8, percentile=0.0,
sem_fac=0.05, pulse_thresh=0.15, verbose=0):
"""Detects if a fish is pulse- or wave-type based on the proportion of the time distance
between a peak and its following trough, relative to the time between consecutive peaks.
Parameters
----------
data: 1-D array
The data to be analyzed.
sem: 1-D array or None
Standard error of the mean corresponding to data.
samplerate: float
Sampling rate of the data in Hertz.
percentile: float
The interpercentile range is computed at percentile and 100.0-percentile.
If zero take maxmimum minus minimum.
thresh_fac: float
The threshold for peak detection is the inter-percentile-range
multiplied by this factor.
sem_fac: float
Base analysis on peaks and troughs with the corresponding standard error of the mean
of the data below `sem_fac` times the interpercentile range.
pulse_thresh: float
A positive number setting the minimum distance between peaks and troughs.
verbose: int
If > 1, print information in the command line.
Returns
-------
pulse_fish: bool
True if algorithm suggests a pulse-type fish.
peak_ratio: float
Returns a float between 0. and 1. which gives the proportion of peak-2-trough,
from peak-2-peak time distance, i.e. pulse width relative to pulse interval.
"""
def ratio(peak_idx, trough_idx):
if len(peak_idx) < 2:
return 1.0 if len(peak_idx)+len(trough_idx) < 1 else 0.0
# ratio of peak-to-trough to peak-to-peak time distances:
ratios = np.abs((trough_idx - peak_idx))[:-1].astype(float) / np.diff(peak_idx)
# fix for cases where trough of eod comes before peak:
ratios[ratios > 0.5] = 1.0 - ratios[ratios > 0.5]
return np.median(ratios)
# threshold for peak detection:
pp_ampl = percentile_threshold(data, thresh_fac=1.0, percentile=percentile)
threshold = thresh_fac*pp_ampl
if verbose > 1:
print(' check_pulse(): amplitude threshold for peak detection is %g' % threshold)
# detect large peaks and troughs:
peak_idx, trough_idx = detect_peaks(data, threshold)
# XXX check here for less than 2 peaks + troughs XXX
if verbose > 1:
print(' check_pulse(): detected %d peaks %d troughs' %
(len(peak_idx), len(trough_idx)))
peak_idx, trough_idx = trim(peak_idx, trough_idx)
if verbose > 1:
print(' check_pulse(): trim %d peaks %d troughs' %
(len(peak_idx), len(trough_idx)))
# take only peaks and troughs of reliable sem:
if sem is not None:
athresh = sem_fac*pp_ampl
sel = (sem[peak_idx]<athresh) & (sem[trough_idx]<athresh)
peak_idx = peak_idx[sel]
trough_idx = trough_idx[sel]
if verbose > 1:
print(' check_pulse(): sem left %d peaks %d troughs' %
(len(peak_idx), len(trough_idx)))
# compute ratios:
peak_ratio = np.mean([ratio(peak_idx, trough_idx), ratio(trough_idx, peak_idx)])
pulse_fish = peak_ratio < pulse_thresh
if verbose > 0:
print(' check_pulse(): classified as %s fish: pulse-width-ratio is %.3f' %
('pulse' if pulse_fish else 'wave', peak_ratio))
return pulse_fish, peak_ratio
def add_check_pulse_config(cfg, thresh_fac=0.4, percentile=0.0,
sem_fac=0.05, pulse_thresh=0.1):
""" Add parameter needed for `check_pulse()` as a new section to a configuration.
Parameters
----------
cfg: ConfigFile
The configuration.
See check_pulse() for details on the remaining arguments.
"""
cfg.add_section('Classify waveforms:')
cfg.add('pulseWidthPercentile', percentile, '%', 'Threshold for detecing peaks is based on interpercentile range. If zero use maximum minus minimum amplitude.')
cfg.add('pulseWidthThresholdFactor', thresh_fac, '', 'The threshold for peak detection is this factor multiplied with the interpercentile range. Should be smaller than 0.5 if only a single pulse is contained in the data.')
cfg.add('pulseWidthSEMFactor', sem_fac, '', 'Peaks and troughs where the standarad error of the mean of the data is larger than the interpercentile range times this factor are discarded from the analysis.')
cfg.add('pulseWidthThresholdRatio', pulse_thresh, '', 'A pulsefish is detected if the width of the pulses relative to the intervals is smaller than this threshold.')
def check_pulse_args(cfg):
""" Translates a configuration to the
respective parameter names of the function `check_pulse()`.
The return value can then be passed as key-word arguments to this function.
Parameters
----------
cfg: ConfigFile
The configuration.
Returns
-------
a: dict
Dictionary with names of arguments of the check_pulse() function
and their values as supplied by `cfg`.
"""
a = cfg.map({'thresh_fac': 'pulseWidthThresholdFactor',
'percentile': 'pulseWidthPercentile',
'sem_fac': 'pulseWidthSEMFactor',
'pulse_thresh': 'pulseWidthThresholdRatio'})
return a
if __name__ == "__main__":
import sys
import matplotlib.pyplot as plt
from .bestwindow import best_window
from .fakefish import wavefish_eods, pulsefish_eods
print("\nChecking checkpulse module ...\n")
# generate data:
rate = 44100.0
if len(sys.argv) < 2:
data = wavefish_eods('Eigenmannia', 80.0, rate, 8.0)
elif sys.argv[1] == '-w':
data = wavefish_eods('Alepto', 600.0, rate, 8.0)
elif sys.argv[1] == '-m':
data = pulsefish_eods('monophasic', 80.0, rate, 8.0)
elif sys.argv[1] == '-b':
data = pulsefish_eods('biphasic', 80.0, rate, 8.0)
elif sys.argv[1] == '-t':
data = pulsefish_eods('triphasic', 80.0, rate, 8.0)
else: # load data given by the user
from thunderlab.dataloader import load_data
file_path = sys.argv[1]
print("loading %s ...\n" % file_path)
rawdata, rate, unit, amax = load_data(sys.argv[1])
data, _ = best_window(rawdata[:,0], rate)
# run pulse-width-based detector:
pulse_fish, ratio = check_pulse(data, None, rate)Functions
def check_pulse(data, sem, samplerate, thresh_fac=0.8, percentile=0.0, sem_fac=0.05, pulse_thresh=0.15, verbose=0)-
Detects if a fish is pulse- or wave-type based on the proportion of the time distance between a peak and its following trough, relative to the time between consecutive peaks.
Parameters
data:1-D array- The data to be analyzed.
sem:1-D arrayorNone- Standard error of the mean corresponding to data.
samplerate:float- Sampling rate of the data in Hertz.
percentile:float- The interpercentile range is computed at percentile and 100.0-percentile. If zero take maxmimum minus minimum.
thresh_fac:float- The threshold for peak detection is the inter-percentile-range multiplied by this factor.
sem_fac:float- Base analysis on peaks and troughs with the corresponding standard error of the mean
of the data below
sem_factimes the interpercentile range. pulse_thresh:float- A positive number setting the minimum distance between peaks and troughs.
verbose:int- If > 1, print information in the command line.
Returns
pulse_fish:bool- True if algorithm suggests a pulse-type fish.
peak_ratio:float- Returns a float between 0. and 1. which gives the proportion of peak-2-trough, from peak-2-peak time distance, i.e. pulse width relative to pulse interval.
Expand source code
def check_pulse(data, sem, samplerate, thresh_fac=0.8, percentile=0.0, sem_fac=0.05, pulse_thresh=0.15, verbose=0): """Detects if a fish is pulse- or wave-type based on the proportion of the time distance between a peak and its following trough, relative to the time between consecutive peaks. Parameters ---------- data: 1-D array The data to be analyzed. sem: 1-D array or None Standard error of the mean corresponding to data. samplerate: float Sampling rate of the data in Hertz. percentile: float The interpercentile range is computed at percentile and 100.0-percentile. If zero take maxmimum minus minimum. thresh_fac: float The threshold for peak detection is the inter-percentile-range multiplied by this factor. sem_fac: float Base analysis on peaks and troughs with the corresponding standard error of the mean of the data below `sem_fac` times the interpercentile range. pulse_thresh: float A positive number setting the minimum distance between peaks and troughs. verbose: int If > 1, print information in the command line. Returns ------- pulse_fish: bool True if algorithm suggests a pulse-type fish. peak_ratio: float Returns a float between 0. and 1. which gives the proportion of peak-2-trough, from peak-2-peak time distance, i.e. pulse width relative to pulse interval. """ def ratio(peak_idx, trough_idx): if len(peak_idx) < 2: return 1.0 if len(peak_idx)+len(trough_idx) < 1 else 0.0 # ratio of peak-to-trough to peak-to-peak time distances: ratios = np.abs((trough_idx - peak_idx))[:-1].astype(float) / np.diff(peak_idx) # fix for cases where trough of eod comes before peak: ratios[ratios > 0.5] = 1.0 - ratios[ratios > 0.5] return np.median(ratios) # threshold for peak detection: pp_ampl = percentile_threshold(data, thresh_fac=1.0, percentile=percentile) threshold = thresh_fac*pp_ampl if verbose > 1: print(' check_pulse(): amplitude threshold for peak detection is %g' % threshold) # detect large peaks and troughs: peak_idx, trough_idx = detect_peaks(data, threshold) # XXX check here for less than 2 peaks + troughs XXX if verbose > 1: print(' check_pulse(): detected %d peaks %d troughs' % (len(peak_idx), len(trough_idx))) peak_idx, trough_idx = trim(peak_idx, trough_idx) if verbose > 1: print(' check_pulse(): trim %d peaks %d troughs' % (len(peak_idx), len(trough_idx))) # take only peaks and troughs of reliable sem: if sem is not None: athresh = sem_fac*pp_ampl sel = (sem[peak_idx]<athresh) & (sem[trough_idx]<athresh) peak_idx = peak_idx[sel] trough_idx = trough_idx[sel] if verbose > 1: print(' check_pulse(): sem left %d peaks %d troughs' % (len(peak_idx), len(trough_idx))) # compute ratios: peak_ratio = np.mean([ratio(peak_idx, trough_idx), ratio(trough_idx, peak_idx)]) pulse_fish = peak_ratio < pulse_thresh if verbose > 0: print(' check_pulse(): classified as %s fish: pulse-width-ratio is %.3f' % ('pulse' if pulse_fish else 'wave', peak_ratio)) return pulse_fish, peak_ratio def add_check_pulse_config(cfg, thresh_fac=0.4, percentile=0.0, sem_fac=0.05, pulse_thresh=0.1)-
Add parameter needed for
check_pulse()as a new section to a configuration.Parameters
cfg:ConfigFile- The configuration.
See check_pulse() for details on the remaining arguments.
Expand source code
def add_check_pulse_config(cfg, thresh_fac=0.4, percentile=0.0, sem_fac=0.05, pulse_thresh=0.1): """ Add parameter needed for `check_pulse()` as a new section to a configuration. Parameters ---------- cfg: ConfigFile The configuration. See check_pulse() for details on the remaining arguments. """ cfg.add_section('Classify waveforms:') cfg.add('pulseWidthPercentile', percentile, '%', 'Threshold for detecing peaks is based on interpercentile range. If zero use maximum minus minimum amplitude.') cfg.add('pulseWidthThresholdFactor', thresh_fac, '', 'The threshold for peak detection is this factor multiplied with the interpercentile range. Should be smaller than 0.5 if only a single pulse is contained in the data.') cfg.add('pulseWidthSEMFactor', sem_fac, '', 'Peaks and troughs where the standarad error of the mean of the data is larger than the interpercentile range times this factor are discarded from the analysis.') cfg.add('pulseWidthThresholdRatio', pulse_thresh, '', 'A pulsefish is detected if the width of the pulses relative to the intervals is smaller than this threshold.') def check_pulse_args(cfg)-
Translates a configuration to the respective parameter names of the function
check_pulse(). The return value can then be passed as key-word arguments to this function.Parameters
cfg:ConfigFile- The configuration.
Returns
a:dict- Dictionary with names of arguments of the check_pulse() function
and their values as supplied by
cfg.
Expand source code
def check_pulse_args(cfg): """ Translates a configuration to the respective parameter names of the function `check_pulse()`. The return value can then be passed as key-word arguments to this function. Parameters ---------- cfg: ConfigFile The configuration. Returns ------- a: dict Dictionary with names of arguments of the check_pulse() function and their values as supplied by `cfg`. """ a = cfg.map({'thresh_fac': 'pulseWidthThresholdFactor', 'percentile': 'pulseWidthPercentile', 'sem_fac': 'pulseWidthSEMFactor', 'pulse_thresh': 'pulseWidthThresholdRatio'}) return a