Merge pull request #20817 from mantidproject/20185_Python3DocTestPoldiToPredict

Python3 doctest compatibility: PoldiCreatePeaksFromFile to PredictFractionalPeaks

docs/source/algorithms/PoldiCreatePeaksFromFile-v1.rst

@@ -71,11 +71,11 @@ The following usage example takes up the file showed above and passes it to the
     compounds = PoldiCreatePeaksFromFile('PoldiCrystalFileExample.dat', LatticeSpacingMin=0.7)
 
     compound_count = compounds.getNumberOfEntries()
-    print 'Number of loaded compounds:', compound_count
+    print('Number of loaded compounds: {}'.format(compound_count))
 
     for i in range(compound_count):
         ws = compounds.getItem(i)
-        print 'Compound ' + str(i + 1) +':', ws.getName(), 'has', ws.rowCount(), 'reflections in the resolution range.'
+        print('Compound {}: {} has {} reflections in the resolution range.'.format(str(i + 1),  ws.getName(), ws.rowCount()))
 
 
 The script produces a WorkspaceGroup which contains a table with reflections for each compound in the file:

docs/source/algorithms/PoldiDataAnalysis-v1.rst

@@ -61,7 +61,7 @@ The algorithm requires relatively little input and can be run like this:
     cell_a = np.round(cell.cell(0, 1), 5)
     cell_a_error = np.round(cell.cell(0, 2), 5)
 
-    print "Refined lattice parameter a =", cell_a, "+/-", cell_a_error
+    print("Refined lattice parameter a = {:.5f} +/- {}".format(cell_a, cell_a_error))
 
 This will print the following output:
 

docs/source/algorithms/PoldiFitPeaks1D-v1.rst

@@ -47,7 +47,7 @@ The following small usage example performs a peak fit on the sample data already
                     FitPlotsWorkspace = "fit_plots_6904",
                     Version=1)
                     
-    print "There are", mtd['fit_plots_6904'].getNumberOfEntries(), "plots available for inspection."
+    print("There are {} plots available for inspection.".format(mtd['fit_plots_6904'].getNumberOfEntries()))
     
 Output:
 

docs/source/algorithms/PoldiFitPeaks2D-v1.rst

@@ -152,7 +152,7 @@ The following example shows an example for refinement of lattice parameters usin
     cell_a = np.round(lattice_parameters.cell(0, 1), 5)
     cell_a_error = np.round(lattice_parameters.cell(0, 2), 5)
 
-    print "Refined lattice parameter a =", cell_a, "+/-", cell_a_error
+    print("Refined lattice parameter a = {:.5f} +/- {}".format(cell_a, cell_a_error))
 
 The refined lattice parameter is printed at the end:
 

docs/source/algorithms/PoldiIndexKnownCompounds-v1.rst

@@ -57,8 +57,8 @@ The following example extracts peaks from the correlation spectrum of a Silicon
                                     ScatteringContributions="1.0",
                                     OutputWorkspace="Indexed")
 
-    print "Indexed_Si contains", mtd['peaks_refined_6904_indexed_Si'].rowCount(), "indexed peaks."
-    print "Number of unindexed peaks:", mtd['peaks_refined_6904_unindexed'].rowCount()
+    print("Indexed_Si contains {} indexed peaks.".format(mtd['peaks_refined_6904_indexed_Si'].rowCount()))
+    print("Number of unindexed peaks: {}".format(mtd['peaks_refined_6904_unindexed'].rowCount()))
 
 Output:
 

docs/source/algorithms/PoldiLoadRuns-v1.rst

@@ -31,8 +31,8 @@ To load only one POLDI data file (in this case a run from a calibration measurem
     # calibration is a WorkspaceGroup, so we can use getNames() to query what's inside.
     workspaceNames = calibration.getNames()
     
-    print "Number of data files loaded:", len(workspaceNames)
-    print "Name of data workspace:", workspaceNames[0]
+    print("Number of data files loaded: {}".format(len(workspaceNames)))
+    print("Name of data workspace: {}".format(workspaceNames[0]))
 
 Since only one run number was supplied, only one workspace is loaded. The name corresponds to the scheme described above:
 
@@ -50,8 +50,8 @@ Actually, the silicon calibration measurement consists of more than one run, so
     
     workspaceNames = calibration.getNames()
     
-    print "Number of data files loaded:", len(workspaceNames)
-    print "Names of data workspaces:", workspaceNames
+    print("Number of data files loaded: {}".format(len(workspaceNames)))
+    print("Names of data workspaces: {}".format(workspaceNames))
     
 Now all files from the specified range are in the `calibration` WorkspaceGroup:
     
@@ -69,8 +69,8 @@ But in fact, these data files should not be processed separately, they belong to
     
     workspaceNames = calibration.getNames()
     
-    print "Number of data files loaded:", len(workspaceNames)
-    print "Names of data workspaces:", workspaceNames
+    print("Number of data files loaded: {}".format(len(workspaceNames)))
+    print("Names of data workspaces: {}".format(workspaceNames))
     
 The merged files will receive the name of the last file in the merged range:
     
@@ -93,8 +93,8 @@ A situation that occurs often is that one sample consists of multiple ranges of
     
     workspaceNames = calibration.getNames()
     
-    print "Number of data files loaded:", len(workspaceNames)
-    print "Names of data workspaces:", workspaceNames
+    print("Number of data files loaded: {}".format(len(workspaceNames)))
+    print("Names of data workspaces: {}".format(workspaceNames))
     
 The result is the same as in the example above, two files are in the WorkspaceGroup:
     
@@ -115,8 +115,8 @@ On the other hand it is also possible to overwrite an existing WorkspaceGroup, f
     
     workspaceNames = calibration.getNames()
     
-    print "Number of data files loaded:", len(workspaceNames)
-    print "Names of data workspaces:", workspaceNames
+    print("Number of data files loaded: {}".format(len(workspaceNames)))
+    print("Names of data workspaces: {}".format(workspaceNames))
     
 The data loaded in the first call to the algorithm have been overwritten with the merged data set:
     

docs/source/algorithms/PoldiMerge-v1.rst

@@ -46,7 +46,7 @@ This small usage example merges two compatible POLDI-files which have been loade
     
     # The result has one spectrum with one bin, which contains the total counts.
     counts_6903 = int(total_6903.dataY(0)[0])
-    print "6903 contains a total of", counts_6903, "counts."
+    print("6903 contains a total of {} counts.".format(counts_6903))
     
     # The same with the second data file
     raw_6904 = LoadSINQFile(Filename = "poldi2013n006904.hdf", Instrument = "POLDI")
@@ -56,7 +56,7 @@ This small usage example merges two compatible POLDI-files which have been loade
     total_6904 = SumSpectra(spectra_6904)
     
     counts_6904 = int(total_6904.dataY(0)[0])
-    print "6904 contains a total of", counts_6904, "counts."
+    print("6904 contains a total of {} counts.".format(counts_6904))
 
     # Now PoldiMerge is used to merge the two raw spectra by supplying a list of workspace names.
     raw_summed = PoldiMerge("raw_6903,raw_6904")
@@ -66,8 +66,8 @@ This small usage example merges two compatible POLDI-files which have been loade
     spectra_summed = Integration(histo_summed)
     total_summed = SumSpectra(spectra_summed)
 
-    print "6903+6904 contains a total of", int(total_summed.dataY(0)[0]), "counts."
-    print "Summing the counts of the single data files leads to", counts_6903 + counts_6904, "counts."
+    print("6903+6904 contains a total of {} counts.".format(int(total_summed.dataY(0)[0])))
+    print("Summing the counts of the single data files leads to {} counts.".format(int(counts_6903 + counts_6904)))
 
 Output:
 

docs/source/algorithms/PoldiPeakSearch-v1.rst

@@ -70,7 +70,7 @@ A typical peak search procedure would be performed on correlation data, so this
     peaks_6904 = PoldiPeakSearch(correlated_6904)
     
     # The tableworkspace should contain 14 peaks.
-    print "The correlation spectrum of sample 6904 contains", peaks_6904.rowCount(), "peaks."
+    print("The correlation spectrum of sample 6904 contains {} peaks.".format(peaks_6904.rowCount()))
     
 Output:
 

docs/source/algorithms/PoldiPeakSummary-v1.rst

@@ -37,8 +37,8 @@ Usage
                     
     summary_6904 = PoldiPeakSummary(mtd["peaks_refined_6904"])
     
-    print "Number of refined peaks:", summary_6904.rowCount()
-    print "Number of columns that describe a peak:", summary_6904.columnCount()
+    print("Number of refined peaks: {}".format(summary_6904.rowCount()))
+    print("Number of columns that describe a peak: {}".format(summary_6904.columnCount()))
 
 Output:
 

docs/source/algorithms/PoldiTruncateData-v1.rst

@@ -33,12 +33,12 @@ In the first example, POLDI data is cropped to the correct workspace size:
     raw_6903 = LoadSINQFile(Filename = "poldi2013n006903.hdf", Instrument = "POLDI")
     LoadInstrument(raw_6903, InstrumentName = "POLDI", RewriteSpectraMap=True)
 
-    print "The raw data workspace contains", len(raw_6903.readX(0)), "time bins."
+    print("The raw data workspace contains {} time bins.".format(len(raw_6903.readX(0))))
     
     # Truncate the data
     truncated_6903 = PoldiTruncateData(raw_6903)
     
-    print "The truncated data workspace contains", len(truncated_6903.readX(0)), "time bins."
+    print("The truncated data workspace contains {} time bins.".format(len(truncated_6903.readX(0))))
 
 Output:
 
@@ -62,8 +62,8 @@ The second example also examines the extra time bins:
     extra_6903 = mtd['extra_6903']
     
     # Examine the workspace a bit
-    print "The extra data workspace contains", extra_6903.getNumberHistograms(), "spectrum."
-    print "The bins contain the following data:", [int(x) for x in extra_6903.readY(0)]
+    print("The extra data workspace contains {} spectrum.".format(extra_6903.getNumberHistograms()))
+    print("The bins contain the following data: {}".format([int(x) for x in extra_6903.readY(0)]))
     
 Output:
 

docs/source/algorithms/PolynomialCorrection-v1.rst

@@ -37,8 +37,8 @@ Usage
    coefficients = [1., 3., 5.] #  1 + 3x + 5x^2
    data_ws = PolynomialCorrection(data_ws, coefficients, Operation="Divide")
 
-   print "First 5 y values:", data_ws.readY(0)[0:5]
-   print "First 5 error values:", data_ws.readE(0)[0:5]
+   print("First 5 y values: {}".format(data_ws.readY(0)[0:5]))
+   print("First 5 error values: {}".format(data_ws.readE(0)[0:5]))
 
 .. testoutput::
 
@@ -58,8 +58,8 @@ Usage
    coefficients = [2., 4.] #  2 + 4x
    data_ws = PolynomialCorrection(data_ws, coefficients, Operation="Multiply")
 
-   print "First 5 y values:", data_ws.readY(0)[0:5]
-   print "First 5 error values:", data_ws.readE(0)[0:5]
+   print("First 5 y values: {}".format(data_ws.readY(0)[0:5]))
+   print("First 5 error values: {}".format(data_ws.readE(0)[0:5]))
 
 .. testoutput::
 

docs/source/algorithms/Power-v1.rst

@@ -37,8 +37,8 @@ Usage
    data_ws = CreateWorkspace(dataX, dataY, NSpec=2)
    result_ws = Power(data_ws, 2)
 
-   print "Squared values of first spectrum:", result_ws.readY(0)
-   print "Squared values of second spectrum:", result_ws.readY(1)
+   print("Squared values of first spectrum: {}".format(result_ws.readY(0)))
+   print("Squared values of second spectrum: {}".format(result_ws.readY(1)))
 
 Output:
 

docs/source/algorithms/PowerLawCorrection-v1.rst

@@ -28,9 +28,9 @@ Usage
   #Now we are ready to run the correction
   wsCorrected = PowerLawCorrection(ws,C0=3,C1=2)
 
-  print ("The correction counts and errors are multiplied by function 3*x^2")
+  print("The correction counts and errors are multiplied by function 3*x^2")
   for i in range(0,wsCorrected.blocksize(),10):
-    print ("The correct value in bin %i is %.2f compared to %.2f" % (i,wsCorrected.readY(0)[i],ws.readY(0)[i]))
+    print ("The correct value in bin {} is {:.2f} compared to {:.2f}".format(i, wsCorrected.readY(0)[i], ws.readY(0)[i]))
 
 Output:
 

docs/source/algorithms/PredictFractionalPeaks-v1.rst

@@ -34,7 +34,7 @@ Usage
    IndexPeaks(peaks)
 
    fractional_peaks = PredictFractionalPeaks(peaks, HOffset=[-0.5,0,0.5],KOffset=0,LOffset=0.2)
-   print "Number of fractional peaks:",fractional_peaks.getNumberPeaks()
+   print("Number of fractional peaks: {}".format(fractional_peaks.getNumberPeaks()))
 
 .. testoutput:: TopazExample