Python scipy.signal.find_peaks_cwt() Examples
The following are 6
code examples of scipy.signal.find_peaks_cwt().
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Example #1
| Source File: spectools.py From specidentify with BSD 3-Clause "New" or "Revised" License | 5 votes |
|
def detect_lines(x, y, kernal_size=3, centroid_kernal=default_kernal,
center=False):
"""Detect lines goes through a 1-D spectra and detect peaks
Parameters
----------
x: ~numpy.ndarray
Array describing the x-position
y: ~numpy.ndarray
Array describing the counts in each x-position
kernal_size: int
Size for the detection kernal
centroid_kernal:
Kernal to be used for centroiding
center: boolean
If True, centroid for detected peaks will be calculated
Returns
-------
xp: ~numpy.ndarray
Array of x-positions of peaks in the spectrum
"""
# find all peaks
xp = signal.find_peaks_cwt(y, np.array([kernal_size]))
xp = np.array(xp)
# set the output values
if center:
xdiff = int(0.5 * len(centroid_kernal) + 1)
x_arr = np.arange(len(x))
xp = xp * 1.0
for i in range(len(xp)):
xp[i] = mcentroid(x, y, kern=centroid_kernal, xdiff=xdiff, xc=x[xp[i]])
return xp
Example #2
| Source File: peaksearch.py From DynaPhoPy with MIT License | 5 votes |
|
def get_frequencies_from_correlation(correlation_vector,test_frequencies_range):
frequencies = []
for branch in range(correlation_vector.shape[1]):
peakind = signal.find_peaks_cwt(correlation_vector[:,branch].real, np.arange(1,200) )
# plt.plot(test_frequencies_range,correlation_vector[:,branch].real)
# plt.plot([test_frequencies_range[i] for i in peakind],[correlation_vector[i,branch].real for i in peakind],'ro')
# plt.show()
heights = [correlation_vector[i,branch] for i in peakind]
max_height_index = heights.index(max(heights))
frequencies.append(test_frequencies_range[peakind[max_height_index]])
return np.array(frequencies)
Example #3
| Source File: stats.py From enlopy with BSD 3-Clause "New" or "Revised" License | 5 votes |
|
def get_peaks(x, n):
return find_peaks_cwt(x, widths=np.arange(1, n + 1), wavelet=ricker)
Example #4
| Source File: elementaldistribution.py From dscribe with Apache License 2.0 | 5 votes |
|
def test_single_continuous_property(self):
# Tested on a water molecule
system = ase.build.molecule("H2O")
# Descriptor setup
std = 0.1
elements = ["H", "O"]
peaks = [0.3, 2.0]
values = dict(zip(elements, peaks))
elemdist = ElementalDistribution(
properties={
"first_property": {
"type": "continuous",
"min": 0,
"max": 2.5,
"std": std,
"n": 50,
"values": values
}
}
)
# Features
y = elemdist.create(system)
y = y.todense().A1
x = elemdist.get_axis("first_property")
# Test that the peak positions match
from scipy.signal import find_peaks_cwt
peak_indices = find_peaks_cwt(y, [std])
peak_loc = x[peak_indices]
# Test that the peak locations match within some tolerance
self.assertTrue(np.allclose(peaks, peak_loc, rtol=0, atol=0.05))
# Plot for visual inspection
# mpl.plot(x, y)
# mpl.show()
Example #5
| Source File: encode_task_fraglen_stat_pe.py From atac-seq-pipeline with MIT License | 4 votes |
|
def fragment_length_qc(data, prefix):
results = []
NFR_UPPER_LIMIT = 150
MONO_NUC_LOWER_LIMIT = 150
MONO_NUC_UPPER_LIMIT = 300
# % of NFR vs res
nfr_reads = data[data[:, 0] < NFR_UPPER_LIMIT][:, 1]
percent_nfr = nfr_reads.sum() / data[:, 1].sum()
results.append(
QCGreaterThanEqualCheck('Fraction of reads in NFR', 0.4)(percent_nfr))
# % of NFR vs mononucleosome
mono_nuc_reads = data[
(data[:, 0] > MONO_NUC_LOWER_LIMIT) &
(data[:, 0] <= MONO_NUC_UPPER_LIMIT)][:, 1]
percent_nfr_vs_mono_nuc = (
nfr_reads.sum() /
mono_nuc_reads.sum())
results.append(
QCGreaterThanEqualCheck('NFR / mono-nuc reads', 2.5)(
percent_nfr_vs_mono_nuc))
# peak locations
pos_start_val = data[0, 0] # this may be greater than 0
peaks = find_peaks_cwt(data[:, 1], np.array([25]))
nuc_range_metrics = [
('Presence of NFR peak', 20 - pos_start_val, 90 - pos_start_val),
('Presence of Mono-Nuc peak',
120 - pos_start_val, 250 - pos_start_val),
('Presence of Di-Nuc peak',
300 - pos_start_val, 500 - pos_start_val)]
for range_metric in nuc_range_metrics:
results.append(QCHasElementInRange(*range_metric)(peaks))
out = prefix + '.nucleosomal.qc'
with open(out, 'w') as fp:
for elem in results:
fp.write(
'\t'.join(
[elem.metric, str(elem.qc_pass), elem.message]) + '\n')
return out
Example #6
| Source File: encode_task_fraglen_stat_pe.py From chip-seq-pipeline2 with MIT License | 4 votes |
|
def fragment_length_qc(data, prefix):
results = []
NFR_UPPER_LIMIT = 150
MONO_NUC_LOWER_LIMIT = 150
MONO_NUC_UPPER_LIMIT = 300
# % of NFR vs res
nfr_reads = data[data[:, 0] < NFR_UPPER_LIMIT][:, 1]
percent_nfr = nfr_reads.sum() / data[:, 1].sum()
results.append(
QCGreaterThanEqualCheck('Fraction of reads in NFR', 0.4)(percent_nfr))
# % of NFR vs mononucleosome
mono_nuc_reads = data[
(data[:, 0] > MONO_NUC_LOWER_LIMIT) &
(data[:, 0] <= MONO_NUC_UPPER_LIMIT)][:, 1]
percent_nfr_vs_mono_nuc = (
nfr_reads.sum() /
mono_nuc_reads.sum())
results.append(
QCGreaterThanEqualCheck('NFR / mono-nuc reads', 2.5)(
percent_nfr_vs_mono_nuc))
# peak locations
pos_start_val = data[0, 0] # this may be greater than 0
peaks = find_peaks_cwt(data[:, 1], np.array([25]))
nuc_range_metrics = [
('Presence of NFR peak', 20 - pos_start_val, 90 - pos_start_val),
('Presence of Mono-Nuc peak',
120 - pos_start_val, 250 - pos_start_val),
('Presence of Di-Nuc peak',
300 - pos_start_val, 500 - pos_start_val)]
for range_metric in nuc_range_metrics:
results.append(QCHasElementInRange(*range_metric)(peaks))
out = prefix + '.nucleosomal.qc'
with open(out, 'w') as fp:
for elem in results:
fp.write(
'\t'.join(
[elem.metric, str(elem.qc_pass), elem.message]) + '\n')
return out
