def measure(self, segment, threshold=-20, location='center'):
if segment.peaks_values is None:
segment.compute_peaks()
if location is None:
return self.__measure_spectrum(segment, threshold)
j = get_location(segment, location)
i = segment.peaks_indexes[j]
value = apply_threshold(segment.spec[i, j], threshold)
if self.total:
min_freq = np.argwhere(segment.spec[:, j] >= value).min()
max_freq = np.argwhere(segment.spec[:, j] >= value).max()
else:
below = segment.spec[:, j] < value
below[i:] = False
min_freq = np.argwhere(below).max()
below = segment.spec[:, j] < value
below[:i] = False
max_freq = np.argwhere(below).min()
segment.measures_dict[self.name + '(' + location + ')'] = np.round(
segment.freqs[max_freq] - segment.freqs[min_freq],
DECIMAL_PLACES)
return True
def measure(self, segment, location='center'):
if location is None:
return self.__measure_spectrum(segment)
j = get_location(segment, location)
i = segment.peaks_indexes[j]
segment.measures_dict[self.name + '(' + location + ')'] = np.round(
segment.freqs[i], DECIMAL_PLACES)
return True
def measure(self, segment, location='center'):
if location is None:
return self.__measure_spectrum(segment)
j = get_location(segment, location)
data = np.array(segment.spec[:, j], np.float64)
value = np.sqrt(energy(data) / len(segment.data))
segment.measures_dict[self.name + '(' + location + ')'] = value
return True
def measure(self, segment, location='center'):
j = get_location(segment, location)
flux = 0.
for i in range(segment.spec.shape[0]):
flux += (segment.spec[i, j] - segment.spec[i, j - 1]) ** 2
value = np.sqrt(flux)
segment.measures_dict[self.name + '(' + location + ')'] = value
return True
def measure(self, segment, location='center'):
if location is None:
return self.__measure_spectrum(segment)
j = get_location(segment, location)
data = np.array(segment.spec[:, j], np.float64)
g_mean = geometric_mean(data)
a_mean = np.mean(data)
value = g_mean / a_mean
segment.measures_dict[self.name + '(' + location + ')'] = value
return True
def measure(self, segment, location='center'):
if segment.peaks_values is None:
segment.compute_peaks()
if location is None:
return self.__measure_spectrum(segment)
index_frame = get_location(segment, location)
j = segment.peaks_values[index_frame]
db_reference = segment.signal.db_reference
segment.measures_dict[self.name + '(' + location + ')'] = to_db(
j, db_reference)
return True
def measure(self, segment, location='center'):
if location is None:
return self.__measure_spectrum(segment)
j = get_location(segment, location)
data = np.array(segment.spec[:, j], np.float64)
entropy = 0.0
for i in range(len(data)):
if data[i] == 0.0:
entropy -= np.log2(1)
else:
entropy -= np.log2(data[i]) * data[i]
segment.measures_dict[self.name + '(' + location + ')'] = entropy
return True
def measure(self, segment, location='center'):
if location is None:
return self.__measure_spectrum(segment)
j = get_location(segment, location)
data = segment.spec[:, j]
indexes = np.array(range(1, len(data) + 1))
weights = np.sum(data)
if weights != 0.0:
value = ((np.sum(indexes * data) + segment.IndexFrom) / weights)
else:
value = 0.0
value *= segment.freqs[-1] / len(segment.freqs)
segment.measures_dict[self.name + '(' + location + ')'] = np.round(
value, DECIMAL_PLACES)
return True
def measure(self, segment, location='center', cutoff=0.95):
if location is None:
return self.__measure_spectrum(segment, cutoff)
j = get_location(segment, location)
data = segment.spec[:, j]
energy = np.sum(np.square(data))
cutoff *= energy
energy_sum = 0.
roll_off = None
for i in range(len(data)):
energy_sum += data[i]**2
if energy_sum >= cutoff:
roll_off = i
break
segment.measures_dict[self.name + '(' + location + ')'] = np.round(
segment.freqs[roll_off], DECIMAL_PLACES)
return True
