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SaveP2D v1

../_images/SaveP2D-v1_dlg.png

SaveP2D dialog.

Summary

The algorithm used to create a multidimensional ‘.p2d’ file from a 2D workspace.

Properties

Name Direction Type Default Description
Workspace Input Workspace Mandatory Workspace that should be used.
OutputFile Input string Mandatory Output File for “.p2d” Data.
RemoveNaN Input boolean True Remove DataPoints with NaN as intensity value
RemoveNegatives Input boolean True Remove data points with negative intensity values
CutData Input boolean False Use the following inputs to limit data in Theta, lambda, d and dp
TthMin Input number 50 Minimum for tth values
TthMax Input number 120 Maximum for tth values
LambdaMin Input number 0.3 Minimum for lambda values
LambdaMax Input number 1.1 Maximum for lambda values
DMin Input number 0.11 Minimum for d values
DMax Input number 1.37 Maximum for d values
DpMin Input number 0.48 Minimum for dp values
DpMax Input number 1.76 Maximum for dp values

Description

Input

This algorithm can be used to create a powder pattern 2d (“.p2d”) output file as useable for multidimensional Rietveld refinements. The input for this algorithm needs to be a 2D workspace containing information about dSpacing and dSpacingPerpendicular. A 2D workspace can be created using the Bin2DPowderDiffraction algorithm. The input values removeNaN and removeNegatives control whether intensity values that are negative or NaN, respectively, are automatically removed from the dataset. RemoveNegatives also removes intensities equal to zero. Turning cutDdata on, allows to cut the measuring data to the specified ranges of theta, lambda, dSpacing and dSpacingPerpendicular.

Output

The output file contains a short comment header giving the title, the instrument parameter file, the binning parameters of the workspace and the used instrument/detector bank. Thereafter the measuring data is written into 5 columns, namely, 2theta, lambda, dSpacing, dSpacingPerpendicular and intensity.

Usage

Example: Create a “.p2d” file from a 2D Workspace. Remember to change the Filepath for the OutputFile!

    # create a 2D Workspace
# repeat this block for each spectrum
xData = [1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0,9.0]                    # d values for one spectrum (one dPerpendicular value)
yData = ['1','2','3','4']                                                # dPerpendicular binedges
zData = [1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0]                    # intensity values
eData = [1,1,1,1,1,1,1,1,1]                              # error values

# used to join all spectra
xDataTotal = []                                                              # d Values for all spectra
zDataTotal = []                                                              # intensity values for all spectra
eDataTotal = []                                          # error values for all spectra
nSpec = len(yData)-1                                     # number of spectra

# Create d and intensity lists for workspace
for i in range(0,nSpec):
    xDataTotal.extend(xData)           # extends the list of x values in accordance to the number of spectra used
    zDataTotal.extend(zData)           # extends the list of intensity values in accordance to the number of spectra used
    eDataTotal.extend(eData)       # extends the list of error values in accordance to the number of spectra used

# Create a 2D Workspace containing d and dPerpendicular values with intensities
CreateWorkspace(OutputWorkspace = 'Usage_Example', DataX = xDataTotal, DataY = zDataTotal, DataE = eDataTotal, WorkspaceTitle = 'test', NSpec = nSpec, UnitX = 'dSpacing', VerticalAxisUnit = 'dSpacingPerpendicular', VerticalAxisValues = yData)

# Save to the users home directory
file_name = "Usage_Example"
path = os.path.join(os.path.expanduser("~"), file_name)

# Create a .p2d file containing the testdata
SaveP2D(Workspace = "Usage_Example", OutputFile = path, RemoveNaN = False, RemoveNegatives = False, CutData = False)

# Does the file exist? If it exists, print it!
path = os.path.join(os.path.expanduser("~"), file_name + '.p2d')
if os.path.isfile(path):
    with open(path, 'r') as of:
        data = of.readlines()
    for entry in data:
        print(entry[:-1]) # leave out the last character to remove unnecessary newlines

Output: The resulting output file (Usage_Example.p2d) looks like this(2theta and lambda get calculated in the algorithm):

 Exporting: ...

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 Exported: ...
 #Title: test
 #Inst: .prm
 #Binning: ddperp   0.8888889    1.0000000
 #Bank: 1
 #2theta   lambda   d-value   dp-value   counts
   81.3046911      1.3029352      1.0000000      1.5000000      1.0000000
   42.5730378      1.4521280      2.0000000      1.5000000      1.0000000
   28.5401669      1.4789581      3.0000000      1.5000000      1.0000000
   21.4420009      1.4882141      4.0000000      1.5000000      1.0000000
   17.1666094      1.4924723      5.0000000      1.5000000      1.0000000
   14.3112545      1.4947782      6.0000000      1.5000000      1.0000000
   12.2697184      1.4961662      7.0000000      1.5000000      1.0000000
   10.7376523      1.4970660      8.0000000      1.5000000      1.0000000
    9.5455787      1.4976825      9.0000000      1.5000000      1.0000000
  147.7039064      1.9210925      1.0000000      2.5000000      1.0000000
   74.0366222      2.4082809      2.0000000      2.5000000      1.0000000
   48.4687709      2.4628222      3.0000000      2.5000000      1.0000000
   36.1141714      2.4797153      4.0000000      2.5000000      1.0000000
   28.8035116      2.4871957      5.0000000      2.5000000      1.0000000
   23.9632304      2.4911738      6.0000000      2.5000000      1.0000000
   20.5194188      2.4935442      7.0000000      2.5000000      1.0000000
   17.9428625      2.4950714      8.0000000      2.5000000      1.0000000
   15.9421282      2.4961135      9.0000000      2.5000000      1.0000000
  178.1486860      1.9997390      1.0000000      3.5000000      1.0000000
  112.5838945      3.3275045      2.0000000      3.5000000      1.0000000
   70.0240404      3.4424897      3.0000000      3.5000000      1.0000000
   51.4130764      3.4700953      4.0000000      3.5000000      1.0000000
   40.7483187      3.4814929      5.0000000      3.5000000      1.0000000
   33.7894761      3.4873718      6.0000000      3.5000000      1.0000000
   28.8774112      3.4908181      7.0000000      3.5000000      1.0000000
   25.2199975      3.4930167      8.0000000      3.5000000      1.0000000
   22.3888960      3.4945073      9.0000000      3.5000000      1.0000000

Categories: AlgorithmIndex | Diffraction\DataHandling