def test_integrate(self):
d=Data(path.join(self.datadir,"SLD_200919.dat"))
d.setas="x..y"
d.integrate(result=True,header="Total_M")
result=d["Total_M"]
self.assertAlmostEqual(result,4.19687459365,7,"Integrate returned the wrong result!")
d.setas[-1]="y"
d.plot(multiple="y2")
self.assertEqual(len(d.axes),2,"Failed to produce plot with double y-axis")
d.close("all")
d.setas="x..y"
fx=d.interpolate(None)
self.assertEqual(fx(np.linspace(1,1500,101)).shape,(101,7),"Failed to get the interpolated shape right")
d=Data(filename,setas="xy") #Load the low angle scan
#Now get the section of the data file that has the peak positions
# This is really doing the hard work
# We differentiate the data using a Savitsky-Golay filter with a 5 point window fitting quartics.
# This has proved most succesful for me looking at some MdV data.
# We then threshold for zero crossing of the derivative
# And check the second derivative to see whether we like the peak as signficant. This is the significance parameter
# and seems to be largely empirical
# Finally we interpolate back to the complete data set to make sure we get the angle as well as the counts.
d.lmfit(ExponentialModel,result=True,replace=False,header="Envelope")
d.subtract("Counts","Envelope",replace=False,header="peaks")
d.setas="xy"
sys.exit()
t=Data(d.interpolate(d.peaks(significance=sensitivity,width=8,poly=4)))
t.column_headers=copy(d.column_headers)
d%='peaks'
t%='peaks'
d.setas="xy"
d.labels[d.find_col('Angle')]=r"Reflection Angle $\theta$"
t.del_rows(0, lambda x,y: x<critical_edge)
t.setas="xy"
t.template.fig_width=7.0
t.template.fig_height=5.0
t.plot(fmt='go', plotter=pyplot.semilogy)
main_fig=d.plot(figure=t.fig, plotter=pyplot.semilogy)
d.show()
#Now convert the angle to sin^2
t.apply(lambda x: np.sin(np.radians(x[0]/2.0))**2, 0,header=r"$sin^2\theta$")
d = Data(filename, setas="xy") # Load the low angle scan
# Now get the section of the data file that has the peak positions
# This is really doing the hard work
# We differentiate the data using a Savitsky-Golay filter with a 5 point window fitting quartics.
# This has proved most succesful for me looking at some MdV data.
# We then threshold for zero crossing of the derivative
# And check the second derivative to see whether we like the peak as signficant. This is the significance parameter
# and seems to be largely empirical
# Finally we interpolate back to the complete data set to make sure we get the angle as well as the counts.
d.lmfit(ExponentialModel, result=True, replace=False, header="Envelope")
d.subtract("Counts", "Envelope", replace=False, header="peaks")
d.setas = "xy"
sys.exit()
t = Data(d.interpolate(d.peaks(significance=sensitivity, width=8, poly=4)))
t.column_headers = copy(d.column_headers)
d %= "peaks"
t %= "peaks"
d.setas = "xy"
d.labels[d.find_col("Angle")] = r"Reflection Angle $\theta$"
t.del_rows(0, lambda x, y: x < critical_edge)
t.setas = "xy"
t.template.fig_width = 7.0
t.template.fig_height = 5.0
t.plot(fmt="go", plotter=pyplot.semilogy)
main_fig = d.plot(figure=t.fig, plotter=pyplot.semilogy)
d.show()
# Now convert the angle to sin^2
t.apply(lambda x: np.sin(np.radians(x[0] / 2.0)) ** 2, 0, header=r"$sin^2\theta$")
