def test_logistic_and_linear_function(self):
x_data = np.arange(-10, 10, 0.1)
_ = logistic(x_data, x0=0, alpha=1)
self.assertTrue(logistic(0, x0=0, alpha=1) == 0.5)
_ = linear_function(x_data, 1, 2)
self.assertTrue(linear_function(0, 1, 2) == 2)
self.assertTrue(linear_function(3, 1, 2) == 5)
def peakScores(peaksx, x, y, hwtypical=10, verbose=1, fig=None):
""" Calculate scores for list of peaks
Args:
x (array): independent variable data
y (array): dependent variable data
peaksx (list): list with detected peaks
"""
lowvalue = np.percentile(y, 5)
highvalue = np.percentile(y, 99)
kk = np.ones(3) / 3.
ys = y
for ki in range(8):
ys = scipy.ndimage.filters.correlate1d(ys, kk, mode='nearest')
noise = np.std(ys - y)
stn2 = np.log2((highvalue - lowvalue) / noise)
if not fig is None:
plotPeaks(x, y, peaksx, plotLabels=True, fig=fig)
plt.plot(x, ys, '-g')
noisefac = logistic(stn2, x0=3.5, alpha=1)
if verbose:
print('peakScores: noise factor %.2f' % noisefac)
for ii, peak in enumerate(peaksx):
if not peak['valid']:
peak['score'] = 0
continue
h = peak['height'] # original height
h = peak['y'] - peak['ybottoml'] # diff between top and bottom left
h2 = 2 * (peak['y'] - peak['yhalfl'])
if (h2 / h) < .3:
# special case
h = (h2 + h) / 2
hw = peak['p'] - peak['pbottom']
hw = np.abs(x[peak['p']] - peak['xhalfl'])
vv = peak['phalf0']
slope1 = (y[vv + 1] - y[vv]) / (x[vv + 1] - x[vv])
slope2 = (y[vv] - y[vv - 1]) / (x[vv] - x[vv - 1])
slope1 = (ys[vv + 1] - ys[vv]) / (x[vv + 1] - x[vv])
slope2 = (ys[vv] - ys[vv - 1]) / (x[vv] - x[vv - 1])
peak['slope'] = (slope1 + slope2) / 2
peak['heightscore'] = h / (highvalue - lowvalue)
peak['score'] = (h) * (2 / (1 + hw / hwtypical))
peak['scorerelative'] = (h / (highvalue - lowvalue)) * (
2 / (1 + hw / hwtypical))
peak['noisefactor'] = noisefac
if verbose:
print('peakScores: %d: height %.1f halfwidth %.1f, score %.2f' %
(ii, h, hw, peak['score']))
if verbose >= 2:
print(' slope: %.1f, heightscore %.2f, score %.2f' %
(peak['slope'], peak['heightscore'], peak['score']))
def test_analyseGateSweep(self, fig=None, verbose=0):
x = np.arange(-800, 0, 1) # mV
y = logistic(x, x0=-400, alpha=.05)
data_set = makeDataSet1Dplain('plunger', x, 'current', y)
result = analyseGateSweep(data_set, fig=fig, verbose=verbose)
self.assertAlmostEqual(result['_pinchvalueX'], -450.0)
self.assertAlmostEqual(result['lowvalue'], 9.445888759986548e-18, 6)
self.assertAlmostEqual(result['highvalue'], 0.9999999999999998, 6)
self.assertAlmostEqual(result['midvalue'], 0.29999999999999993, 6)
self.assertEqual(result['type'], 'gatesweep')
if fig:
plot_pinchoff(result, ds=data_set, fig=fig)
plt.close('all')
def test_logistic():
y = logistic(0.0, 1.0, alpha=1.0)
np.testing.assert_almost_equal(y, 0.11920292202211755, decimal=6)