def _findLine(self, ws):
"""Return a peak position workspace."""
# TODO There should be a better algorithm in Mantid to achieve this.
integratedWSName = self._names.withSuffix('integrated')
integratedWS = Integration(InputWorkspace=ws,
OutputWorkspace=integratedWSName,
EnableLogging=self._subalgLogging)
transposedWSName = self._names.withSuffix('transposed')
transposedWS = Transpose(InputWorkspace=integratedWS,
OutputWorkspace=transposedWSName,
EnableLogging=self._subalgLogging)
self._cleanup.cleanup(integratedWS)
# Convert spectrum numbers to WS indices.
wsIndices = numpy.arange(0, ws.getNumberHistograms())
xs = transposedWS.dataX(0)
ys = transposedWS.readY(0)
numpy.copyto(xs, wsIndices)
indexOfMax = ys.argmax()
heightGuess = ys[indexOfMax]
posGuess = xs[indexOfMax]
sigmaGuess = 3
f = 'name=Gaussian, PeakCentre={}, Height={}, Sigma={}'.format(
posGuess, heightGuess, sigmaGuess)
fitResult = Fit(Function=f,
InputWorkspace=transposedWS,
EnableLogging=self._subalgLogging)
self._cleanup.cleanup(transposedWS)
peakPos = fitResult.Function.PeakCentre
posTable = self._createFakePeakPositionTable(peakPos)
return posTable
def _findLine(self, ws):
"""Return a peak position workspace."""
# TODO There should be a better algorithm in Mantid to achieve this.
integratedWSName = self._names.withSuffix('integrated')
integratedWS = Integration(InputWorkspace=ws,
OutputWorkspace=integratedWSName,
EnableLogging=self._subalgLogging)
transposedWSName = self._names.withSuffix('transposed')
transposedWS = Transpose(InputWorkspace=integratedWS,
OutputWorkspace=transposedWSName,
EnableLogging=self._subalgLogging)
self._cleanup.cleanup(integratedWS)
# Convert spectrum numbers to WS indices.
wsIndices = numpy.arange(0, ws.getNumberHistograms())
xs = transposedWS.dataX(0)
ys = transposedWS.readY(0)
numpy.copyto(xs, wsIndices)
indexOfMax = ys.argmax()
heightGuess = ys[indexOfMax]
posGuess = xs[indexOfMax]
sigmaGuess = 3
f = 'name=Gaussian, PeakCentre={}, Height={}, Sigma={}'.format(posGuess, heightGuess, sigmaGuess)
fitResult = Fit(Function=f,
InputWorkspace=transposedWS,
EnableLogging=self._subalgLogging)
self._cleanup.cleanup(transposedWS)
peakPos = fitResult.Function.PeakCentre
posTable = self._createFakePeakPositionTable(peakPos)
return posTable
def plot_specular_pixel_check(input_workspace: EventWorkspace,
flood_workspace: EventWorkspace, ax):
flooded_ws = ApplyFloodWorkspace(input_workspace, flood_workspace)
integrated = Integration(flooded_ws,
RangeLower=9000,
RangeUpper=88000,
StartWorkspaceIndex=70,
EndWorkspaceIndex=95)
integrated_transposed = Transpose(integrated)
def _1gaussian(x, ampl, cent, sigma):
return ampl * (1 / sigma *
(np.sqrt(2 * np.pi))) * (np.exp(-((x - cent)**2) /
(2 * sigma)**2))
xval = integrated_transposed.readX(0)
yval = integrated_transposed.readY(0)
popt_gauss, pcov_gauss = optimize.curve_fit(_1gaussian,
xval,
yval,
p0=[56000, 86, 0.8])
perr_gauss = np.sqrt(np.diag(pcov_gauss))
fit_yvals = _1gaussian(xval, *popt_gauss)
ax.plot(xval, yval, "rx")
ax.plot(xval, fit_yvals, 'k--')
ax.axvline(x=86.0, color='b', linestyle='--')
ax.set_xlabel("Spectrum")
ax.set_ylabel("Counts")
max_pos = fit_yvals.argmax()
annot_y = fit_yvals[max_pos]
annot_x = xval[max_pos]
ax.annotate(f"X:{annot_x}, Y:{annot_y}",
xy=(annot_x, annot_y),
xytext=(annot_x * 1.02, annot_y))
ax.minorticks_on()
ax.grid(True, which="both")
ax.set_title("Specular pixel")
# make interactive plotly figure
fig = go.Figure()
fig.add_trace(
go.Scatter(x=xval,
y=yval,
name="Data",
mode="markers",
marker_symbol=4))
fig.add_trace(go.Scatter(x=xval, y=fit_yvals, mode="lines", name="Fit"))
fig.add_vline(x=86, line_dash="dash", line_color="blue")
fig.update_layout(xaxis_title="Spectrum",
yaxis_title="Counts",
width=600,
title_text="Specular pixel",
title_x=0.5)
return fig
def load_data_from_bin(bin_file_name):
"""
"""
ws_name = os.path.basename(bin_file_name).split('.')[0]
LoadSpiceXML2DDet(Filename=bin_file_name,
OutputWorkspace=ws_name,
LoadInstrument=False)
# get vector of counts
counts_ws = Transpose(InputWorkspace=ws_name, OutputWorkspace='temp')
count_vec = counts_ws.readY(0)
return ws_name, count_vec
