def norm_group(pos, _, **kargs):
"""Takes the drain current for each file in group and builds an analysis file and works out the mean drain"""
if "signal" in kargs:
signal = kargs["signal"]
else:
signal = "fluo"
lfit = kargs["lfit"]
rfit = kargs["rfit"]
posfile = Data()
posfile.metadata = pos[0].metadata
posfile = posfile & pos[0].column(0)
posfile.column_headers = ["Energy"]
for f in pos:
print(str(f["run"]) + str(f.find_col(signal)))
posfile = posfile & f.column(signal)
posfile.add_column(lambda r: np.mean(r[1:]), "mean drain")
ec = posfile.find_col("Energy")
md = posfile.find_col("mean drain")
linearfit = scipy.poly1d(
posfile.polyfit(ec, md, 1, lambda x, y: lfit[0] <= x <= lfit[1]))
posfile.add_column(lambda r: r[md] - linearfit(r[ec]), "minus linear")
highend = posfile.mean("minus", lambda r: rfit[0] <= r[ec] <= rfit[1])
ml = posfile.find_col("minus linear")
posfile.add_column(lambda r: r[ml] / highend, "normalised")
if "group_key" in kargs:
posfile[kargs["group_key"]] = pos.key
return posfile
def norm_group(pos,_,**kargs):
"""Takes the drain current for each file in group and builds an analysis file and works out the mean drain"""
if "signal" in kargs:
signal=kargs["signal"]
else:
signal="fluo"
lfit=kargs["lfit"]
rfit=kargs["rfit"]
posfile=Data()
posfile.metadata=pos[0].metadata
posfile=posfile&pos[0].column(0)
posfile.column_headers=['Energy']
for f in pos:
print(str(f["run"])+str(f.find_col(signal)))
posfile=posfile&f.column(signal)
posfile.add_column(lambda r:np.mean(r[1:]),"mean drain")
ec=posfile.find_col('Energy')
md=posfile.find_col('mean drain')
linearfit=scipy.poly1d(posfile.polyfit(ec,md,1,lambda x,y:lfit[0]<=x<=lfit[1]))
posfile.add_column(lambda r:r[md]-linearfit(r[ec]),'minus linear')
highend=posfile.mean('minus',lambda r:rfit[0]<=r[ec]<=rfit[1])
ml=posfile.find_col('minus linear')
posfile.add_column(lambda r:r[ml]/highend,"normalised")
if "group_key" in kargs:
posfile[kargs["group_key"]]=pos.key
return posfile
def LoadData(self, data_item_number, filename):
"""LoadData(self, data_item_number, filename) --> none
Loads the data from filename into the data_item_number.
"""
try:
datafile = Data(str(filename),
debug=True) # does all the hard work here
except Exception as e:
ShowWarningDialog(
self.parent,
"Could not load the file: " + filename +
" \nPlease check the format.\n\n Stoner.Data" +
" gave the following error:\n" + str(e),
)
else:
# For the freak case of only one data point
try:
if datafile.setas.cols["axes"] == 0:
self.x_col = datafile.find_col(self.x_col)
self.y_col = datafile.find_col(self.y_col)
self.e_col = datafile.find_col(self.e_col)
datafile.etsas(x=self.x_col, y=self.y_col, e=self.e_col)
else:
self.x_col = datafile.setas.cols["xcol"]
self.y_col = datafile.setas.cols["ycol"][0]
if len(datafile.setas.cols["yerr"]) > 0:
self.e_col = datafile.setas.cols["yerr"][0]
else:
datafile.add_column(np.ones(len(datafile)))
datafile.setas[-1] = "e"
except Exception as e:
ShowWarningDialog(
self.parent,
"The data file does not contain" +
"all the columns specified in the opions\n" + e.message,
)
# Okay now we have showed a dialog lets bail out ...
return
# The data is set by the default Template.__init__ function, neat hu
# Know the loaded data goes into *_raw so that they are not
# changed by the transforms
datafile.y = np.where(datafile.y == 0.0, 1e-8, datafile.y)
self.data[data_item_number].x_raw = datafile.x
self.data[data_item_number].y_raw = datafile.y
self.data[data_item_number].error_raw = datafile.e
# Run the commands on the data - this also sets the x,y, error memebers
# of that data item.
self.data[data_item_number].run_command()
# Send an update that new data has been loaded
self.SendUpdateDataEvent()
def LoadData(self, data_item_number, filename):
"""LoadData(self, data_item_number, filename) --> none
Loads the data from filename into the data_item_number.
"""
try:
datafile=Data(str(filename),debug=True) # does all the hard work here
except Exception as e:
ShowWarningDialog(self.parent, 'Could not load the file: ' +\
filename + ' \nPlease check the format.\n\n Stoner.Data'\
+ ' gave the following error:\n' + str(e))
else:
# For the freak case of only one data point
try:
if datafile.setas.cols["axes"]==0:
self.x_col=datafile.find_col(self.x_col)
self.y_col=datafile.find_col(self.y_col)
self.e_col=datafile.find_col(self.e_col)
datafile.etsas(x=self.x_col,y=self.y_col,e=self.e_col)
else:
self.x_col=datafile.setas.cols["xcol"]
self.y_col=datafile.setas.cols["ycol"][0]
if len(datafile.setas.cols["yerr"])>0:
self.e_col=datafile.setas.cols["yerr"][0]
else:
datafile.add_column(np.ones(len(datafile)))
datafile.setas[-1]="e"
except Exception as e:
ShowWarningDialog(self.parent, 'The data file does not contain'\
+ 'all the columns specified in the opions\n'+e.message)
# Okay now we have showed a dialog lets bail out ...
return
# The data is set by the default Template.__init__ function, neat hu
# Know the loaded data goes into *_raw so that they are not
# changed by the transforms
datafile.y=np.where(datafile.y==0.0,1E-8,datafile.y)
self.data[data_item_number].x_raw = datafile.x
self.data[data_item_number].y_raw = datafile.y
self.data[data_item_number].error_raw = datafile.e
# Run the commands on the data - this also sets the x,y, error memebers
# of that data item.
self.data[data_item_number].run_command()
# Send an update that new data has been loaded
self.SendUpdateDataEvent()
# 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"$sin^2\theta$")
# Now create the m^2 order
m=np.arange(len(t))+fringe_offset
m=m**2
#And add it to t
t.add_column(m, column_header='$m^2$')
#Now we can it a straight line
# 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"$sin^2\theta$")
# Now create the m^2 order
m = np.arange(len(t)) + fringe_offset
m = m ** 2
# And add it to t
t.add_column(m, column_header="$m^2$")
# Now we can it a straight line
# 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 %= "Envelope"
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"$sin^2\theta$")
# Now create the m^2 order
m = np.arange(len(t)) + fringe_offset
m = m**2
#And add it to t
while True:
try:
return int(input(message))
except ValueError:
print("Please select a column number")
continue
# Load a datafile
d = Data(False)
t_pat = [re.compile(r"^[Tt][\s\(]"), re.compile(r"[Tt]emp")]
r_pat = [re.compile(r"[Rr]ho"), re.compile(r"[Rr]es")]
for pat in t_pat:
t_col = d.find_col(pat, force_list=True)
if len(t_col) == 1:
t_col = t_col[0]
break
else:
t_col = select_col(d, "Select column for temperature data :")
for pat in r_pat:
r_col = d.find_col(pat, force_list=True)
if len(r_col) == 1:
r_col = r_col[0]
break
else:
r_col = select_col(d, "Select column for resistance data :")
rho0 = d.min(r_col)[0]
