def dataview_populate(json, index, params):
"""
Populates a data view.
Operates on a single data and populates the data in the json from it.
"""
results = index.query(start=params.start, end=params.end, label=params.chrom)
fitdata = []
data = map(list, (results.idx,results.val))
xscale = (params.start, params.end)
for row in json:
style = row['style']
if style.startswith('FIT'):
# fit the data
fitdata = fitlib.gaussian_smoothing(data[0], data[1], sigma=params.sigma, epsilon=0.01 )
fitdata = map(list, fitdata)
row['data'] = fitdata
elif style.startswith('SEGMENT'):
assert fitdata, 'smoothing must be applied first'
peakdata = fitlib.fixed_width_predictor( x=fitdata[0], y=fitdata[1], params=params )
row['data'] = peakdata
else:
row['data'] = data
row['xscale'] = xscale
row['w'] = params.image_width
return json
def predict(x, y):
fx, fy = fitlib.gaussian_smoothing(x=x,
y=y,
sigma=options.sigma,
epsilon=options.level)
peaks = fitlib.detect_peaks(x=fx, y=fy)
if options.mode != 'all':
peaks = fitlib.select_peaks(peaks=peaks,
exclusion=options.exclude,
threshold=options.level)
return peaks
def predict(x, y):
fx, fy = fitlib.gaussian_smoothing(x=x, y=y, sigma=options.sigma, epsilon=options.level )
peaks = fitlib.detect_peaks(x=fx, y=fy )
if options.mode != 'all':
peaks = fitlib.select_peaks(peaks=peaks, exclusion=options.exclude, threshold=options.level)
return peaks
def fit(x, y, params):
"Fits the x and y data"
fx, fy = fitlib.gaussian_smoothing(x, y, sigma=params.sigma, epsilon=0.01 )
return list(fx), list(fy)