def _downsample(a, by, fnc=nanmean):
"""
visible: False
desc:
Downsamples a single array.
"""
# Resize the array so that its length is a multiple of by
a = a[:by * (a.shape[0] // by)]
return fnc(a.reshape(-1, by), axis=1)
def threshold(series, fnc, min_length=1):
"""
desc: |
Finds samples that satisfy some threshold criterion for a given period.
__Example:__
%--
python: |
import numpy as np
from matplotlib import pyplot as plt
from datamatrix import DataMatrix, SeriesColumn, series
LENGTH = 1 # Number of rows
DEPTH = 100 # Depth (or length) of SeriesColumns
sinewave = np.sin(np.linspace(0, 2*np.pi, DEPTH))
dm = DataMatrix(length=LENGTH)
# First create five identical rows with a sinewave
dm.y = SeriesColumn(depth=DEPTH)
dm.y.setallrows(sinewave)
# And also a bit of random jitter
dm.y += np.random.random( (LENGTH, DEPTH) )
# Threshold the signal by > 0 for at least 10 samples
dm.t = series.threshold(dm.y, fnc=lambda y: y > 0, min_length=10)
plt.clf()
# Mark the thresholded signal
plt.fill_between(np.arange(DEPTH), dm.t[0], color='black', alpha=.25)
plt.plot(dm.y.plottable)
plt.savefig('content/pages/img/series/threshold.png')
print(dm)
--%
%--
figure:
source: threshold.png
id: FigThreshold
--%
arguments:
series:
desc: A signal to threshold.
type: SeriesColumn
fnc:
desc: A function that takes a single value and returns True if
this value exceeds a threshold, and False otherwise.
type: FunctionType
keywords:
min_length:
desc: The minimum number of samples for which `fnc` must return
True.
type: int
returns:
desc: A series where 0 indicates below threshold, and 1 indicates
above threshold.
type: SeriesColumn
"""
threshold_series = _SeriesColumn(series._datamatrix, series.depth)
threshold_series[:] = 0
# First walk through all rows
for i, trace in enumerate(series):
# Then walk through all samples within a row
nhit = 0
for j, val in enumerate(trace):
hit = fnc(val)
if hit:
nhit += 1
continue
if nhit >= min_length:
threshold_series[i, j - nhit:j] = 1
nhit = 0
if nhit >= min_length:
threshold_series[i, j - nhit + 1:j + 1] = 1
return threshold_series