6.4

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LoadDNSLegacy v1

../_images/LoadDNSLegacy-v1_dlg.png

LoadDNSLegacy dialog.

Summary

Load the DNS Legacy data file to the mantid workspace.

Properties

Name Direction Type Default Description
Filename Input string Mandatory Name of DNS experimental data file. Allowed extensions: [‘.d_dat’]
CoilCurrentsTable Input string   Name of file containing table of coil currents and polarisations. Allowed extensions: [‘.txt’]
OutputWorkspace Output Workspace Mandatory Name of the workspace to store the experimental data.
Normalization Input string duration Kind of data normalization. Allowed values: [‘duration’, ‘monitor’, ‘no’]
ElasticChannel Input number 0 Time channel number where elastic peak is observed. Only for TOF data.
Wavelength Input number 0 Wavelength in nm. If 0 will be read from data file.

Description

Warning

This algorithm is being developed for a specific instrument. It might get changed or even removed without a notification, should instrument scientists decide to do so.

This algorithm loads a DNS legacy data file into a Workspace2D. The loader rotates the detector bank in the position given in the data file.

Note

If the data file contains wrong neutron wavelength, the correct wavelength (in Angstrom) can be specified in the Wavelength field. Leave it 0 if you are not sure. In this case wavelength will be read from the data file.

Output

  • For diffraction mode data (only one time channel) output is the Workspace2D with the X-axis in the wavelength units.
  • For TOF data (more than one time channel) output is the Workspace2D with the X-axis in TOF units. The lower bin boundary for the channel \(i\), \(t_i\) is calculated as \(t_i = t_1 + t_{delay} + i*\Delta t\), where \(\Delta t\) is the channel width and \(t_1\) is the time-of-flight from the source (chopper) to sample. Given in the data file channel width is scaled by the channel_width_factor which can be set in the parameter file.

Note

Since zero time channel is not specified, the algorithm can roll the TOF data to get elastic peak at the right position. For this the ElasticChannel - channel number where the elastic peak is observed without correction - should be specified. For commissioning period, the algorithm ignores the elastic channel number given in the data file.

Normalization

The Normalization option offers the following choices:

  • duration: data in the output workspace will be divided by experiment duration. The normalized sample log will be set to duration.
  • monitor: data in the output workspace will be divided by monitor counts. The normalized sample log will be set to monitor.
  • no: no normalization will be performed, data will be loaded as is. The normalized sample log will be set to no.

Polarisation

Since polarisation is not specified in the DNS legacy files, coil currents table is required to lookup for the polarisation and set the polarisation sample log. The default coil currents are given as x_currents, y_currents and z_currents parameters in the parameter file for x, y, and z polarisations, respectively.

Alternatively, the text file with the coil currents table may be provided (optionally). The coil currents table is a text file containing the following table.

polarisation comment C_a C_b C_c C_z
x 7 0 -2 -0.77 2.21
y 7 0 1.6 -2.77 2.21
z 7 0 0.11 -0.50 0
x 7 0 -2.1 -0.97 2.21

First row must contain the listed column headers, other rows contain coil currents for each polarisation. Rows with different currents for one polarisation are allowed. Columns are separated by tab symbols.

This algorithm only supports DNS instrument in its configuration with one detector bank (polarisation analysis).

Usage

Example 1 - Load DNS diffraction mode .d_dat file:

# data file
datafile = 'dn134011vana.d_dat'

# Load dataset
ws = LoadDNSLegacy(datafile, Normalization='monitor')

print("This workspace has {} dimensions and has {} histograms.".format(ws.getNumDims(), ws.getNumberHistograms()))

Output:

This workspace has 2 dimensions and has 24 histograms.

Example 2 - Load DNS TOF mode .d_dat file and find the elastic channel:

# data file
datafile = 'dnstof.d_dat'

# Load dataset
ws = LoadDNSLegacy(datafile, Normalization='no')
print("This workspace has {} dimensions and has {} histograms.".format(ws.getNumDims(), ws.getNumberHistograms()))

# sum spectra over all detectors
ws_sum = SumSpectra(ws)
# perform fit
# Warning: this will work only if elastic peak is stronger than the other peaks!
peak_center, sigma = FitGaussian(ws_sum, 0)
print("Elastic peak center is at {:.0f} microseconds and has sigma={:.0f}.".format(round(peak_center), round(sigma)))

# calculate the elastic channel number
channel_width = ws.getRun().getProperty("channel_width").value
tof1 = ws.getRun().getProperty("TOF1").value
t_delay = ws.getRun().getProperty("delay_time").value
epp = round((peak_center - tof1 - t_delay)/channel_width)

print("The channel width is {} microseconds.".format(channel_width))
print("The elastic channel number is: {:.0f}.".format(epp))

Output:

This workspace has 2 dimensions and has 24 histograms.
Elastic peak center is at 3023 microseconds and has sigma=62.
The channel width is 40.1 microseconds.
The elastic channel number is: 65.

Example 3 - Load DNS TOF mode .d_dat file and specify the elastic channel and wavelength:

# data file
datafile = 'dnstof.d_dat'

# Load dataset
ws = LoadDNSLegacy(datafile, ElasticChannel=65, Normalization='no', Wavelength=4.2)

# let's check that the elastic peak is at the right position
from scipy.constants import m_n, h

l1 = 0.4     # distance from chopper to sample, m
l2 = 0.85   # distance from sample to detector, m
wavelength = ws.getRun().getProperty("wavelength").value   # neutron wavelength, Angstrom

# neutron velocity
velocity = h/(m_n*wavelength*1e-10)

# calculate elastic TOF (total)
tof2_elastic = 1e+06*l2/velocity
tof1 = ws.getRun().getProperty("TOF1").value
t_delay = ws.getRun().getProperty("delay_time").value
tof_elastic = t_delay + tof1 + tof2_elastic
print ("Calculated elastic TOF: {:.0f} microseconds".format(round(tof_elastic)))

# get elastic TOF from file
ws_sum = SumSpectra(ws)
peak_center, sigma = FitGaussian(ws_sum, 0)
print ("Elastic TOF in the workspace: {:.0f} microseconds".format(round(peak_center)))

# compare difference to the channel width
channel_width = ws.getRun().getProperty("channel_width").value
print("Difference = {:.0f} microseconds < channel width = {} microseconds."
      .format(round(tof_elastic - peak_center), channel_width, round(sigma)))
channel_width = ws.getRun().getProperty("channel_width").value

Output:

Calculated elastic TOF: 1327 microseconds
Elastic TOF in the workspace: 1299 microseconds
Difference = 28 microseconds < channel width = 40.1 microseconds.

Categories: AlgorithmIndex | Workflow\MLZ\DNS | DataHandling\Text