def test_HistogramSeries():
class Overlay(object):
def name(self):
return str(id(self))
hs = hseries.HistogramSeries(Overlay(), None, None, None)
data1 = np.random.randint(1, 1000, 1000)
data2 = np.random.randint(-1000, 1000, 1000)
hs.setHistogramData(data1, 'data1')
# HistogramSeries.dataRange upper bound is exclusive
d1min, d1max = data1.min(), data1.max()
drange = (d1min, d1max + (d1max - d1min) / 10000.0)
nbins = hseries.autoBin(data1, drange)
hx, hy, nvals = hseries.histogram(data1, nbins, drange, drange)
gotx, goty = hs.getData()
assert hs.nbins == nbins
assert hs.numHistogramValues == nvals
assert np.all(np.isclose(hx, gotx))
assert np.all(np.isclose(hy, goty))
hs.autoBin = False
hs.nbins = 124
hx, hy, nvals = hseries.histogram(data1, 124, drange, drange)
gotx, goty = hs.getData()
assert np.all(np.isclose(hx, gotx))
assert np.all(np.isclose(hy, goty))
drange = (10, 80.1)
hs.dataRange.x = 10, 80.1
hx, hy, nvals = hseries.histogram(data1, 124, drange, drange)
gotx, goty = hs.getData()
assert np.all(np.isclose(hx, gotx))
assert np.all(np.isclose(hy, goty))
hs.setHistogramData(data2, 'data2')
hs.autoBin = True
hs.ignoreZeros = False
d2min, d2max = data2.min(), data2.max()
drange = d2min, d2max + (d2max - d2min) / 10000.0
nbins = hseries.autoBin(data2, drange)
hx, hy, nvals = hseries.histogram(data2, nbins, drange, drange)
gotx, goty = hs.getData()
assert np.all(np.isclose(hx, gotx))
assert np.all(np.isclose(hy, goty))
def _test_ComplexHistogramSeries(panel, overlayList, displayCtx):
displayCtx = panel.displayCtx
data = np.random.randint(1, 255, (10, 10, 10)) + \
1j * np.random.randint(1, 255, (10, 10, 10))
data.flags.writeable = False
data = np.array(data, dtype=np.complex64)
img = Image(data, xform=np.eye(4))
overlayList.append(img)
realYield()
hs = panel.getDataSeries(img)
hs.plotReal = True
hs.plotImaginary = True
hs.plotMagnitude = True
hs.plotPhase = True
his, hms, hps = hs.extraSeries()
real = data.real
imag = data.imag
mag = (real**2 + imag**2)**0.5
phase = np.arctan2(imag, real)
for hdata, hhs in zip([real, imag, mag, phase], [hs, his, hms, hps]):
dmin, dmax = hdata.min(), hdata.max()
drange = dmin, dmax + (dmax - dmin) / 10000
nbins = hseries.autoBin(hdata, drange)
hx, hy, _ = hseries.histogram(hdata, nbins, drange, drange)
gotx, goty = hhs.getData()
assert np.all(gotx == hx)
assert np.all(goty == hy)
def check(gotx, goty, img, vol):
data = img[..., vol]
dmin = data.min()
dmax = data.max()
drange = dmin, dmax + (dmax - dmin) / 10000
nbins = hseries.autoBin(data, drange)
expx, expy, nvals = hseries.histogram(data, nbins, drange, drange)
assert np.all(np.isclose(gotx, expx))
assert np.all(np.isclose(goty, expy))
def __addROIHistogram(self):
"""Prompts the user to select an ROI mask, calculates the histogram
of that mask on the currently selected overlay, and adds the result
to the ``HistogramPanel``.
"""
overlay = self.displayCtx.getSelectedOverlay()
opts = self.displayCtx.getOpts(overlay)
roiOptions = self.__roiOptions
frame = wx.GetApp().GetTopWindow()
msg = strings.messages[self, 'selectMask'].format(overlay.name)
title = strings.titles[self, 'selectMask'].format(overlay.name)
dlg = addmaskdataseries.MaskDialog(frame, [o.name for o in roiOptions],
title=title,
message=msg,
checkbox=False)
if dlg.ShowModal() != wx.ID_OK:
return
maskimg = roiOptions[dlg.GetChoice()]
mask = maskimg[:] > 0
if overlay.ndim > 3: data = overlay[opts.index()][mask]
else: data = overlay[mask]
count = self.__plotPanel.histType == 'count'
drange = (np.nanmin(data), np.nanmax(data))
nbins = histogramseries.autoBin(data, drange)
xdata, ydata, _ = histogramseries.histogram(data,
nbins,
drange,
drange,
includeOutliers=False,
count=count)
ds = dataseries.DataSeries(overlay, self.overlayList, self.displayCtx,
self.__plotPanel)
ds.colour = self.__plotPanel.getOverlayPlotColour(overlay)
ds.lineStyle = self.__plotPanel.getOverlayPlotStyle(overlay)
ds.lineWidth = 2
ds.alpha = 1
ds.label = '{} [mask: {}]'.format(overlay.name, maskimg.name)
# We have to run the data through
# prepareDataSeries to preprocess
# (e.g. smooth) it
ds.setData(xdata, ydata)
ds.setData(*self.__plotPanel.prepareDataSeries(ds))
self.__plotPanel.dataSeries.append(ds)
def test_histogram():
def check(bins, counts):
for i, blo in enumerate(bins[:-1]):
count = counts[i]
bhi = bins[i + 1]
if i + 1 == len(hx) - 1:
exp = len([v for v in data if v >= blo and v <= bhi])
else:
exp = len([v for v in data if v >= blo and v < bhi])
assert count == exp
data = np.random.randint(1, 100, 1000)
dmin = data.min()
dmax = data.max()
hx, hy, nvals = hseries.histogram(data, 10, (dmin, dmax), (dmin, dmax))
assert np.all(hx == np.linspace(dmin, dmax, 11))
assert len(hy) == 10
assert nvals == 1000
check(hx, hy)
hx, hy, nvals = hseries.histogram(data,
10, (dmin, dmax), (dmin, dmax),
count=False)
check(hx, hy * nvals)
hx, hy, nvals = hseries.histogram(data, 8, (10, 90), (dmin, dmax))
assert np.all(hx == np.linspace(10, 90, 9))
assert len(hy) == 8
check(hx, hy)
hx, hy, nvals = hseries.histogram(data,
8, (10, 90), (dmin, dmax),
includeOutliers=True)
expbins = list(np.linspace(10, 90, 9))
expbins[0] = dmin
expbins[-1] = dmax
assert np.all(hx == expbins)
assert len(hy) == 8
check(hx, hy)
def calc(d):
dmin, dmax = d.min(), d.max()
drange = dmin, dmax + (dmax - dmin) / 10000
nbins = hseries.autoBin(d, drange)
dx, dy, _ = hseries.histogram(d, nbins, drange, drange)
return dx, dy