.. algorithm::

.. summary::

.. relatedalgorithms::

.. properties::

Description
-----------
This algorithm takes a table of peak centres and standard deviations, then finds overlap with a database of known values
to find probable energy transitions for peaks.


Input Table and datafile requirement
####################################

- input table must have at least 2 column in which one column is the peak centre and the other is the standard deviation, the names of the columns must be given if they differ from the default values: centre and sigma respectively.
- json file can be loaded to override the default but must follow structure below:

.. code-block:: json

    {
      "Ag": {
        "Z": 47,
        "A": 107.87,
        "Primary": {
          "K(4->1)": 3177.7,
          "L(4->2)": 900.7,
          "M(4->3)": 304.7,
          "6->5": 141
        },
        "Secondary": {
          "K(2->1)": 3140.6,
          "L(8->2)": 1347.8,
          "M(10->3)": 567,
          "8->6": 122.2
        },
        "Gammas": {
          "72Ge(n,n')72Ge": 691,
          "73Ge(n,g)74Ge": null,
          "74Ge(n,n')74Ge": 595.7
        }
      }
    }

Usage
-----

*Example: Using all defaults**

.. testcode:: OnlyPeaktable

    from mantid.simpleapi import *
    import matplotlib.pyplot as plt
    import numpy

    def formatdict(row):
        row = dict([(column, "{:.2f}".format(value)) if type(value) == float else (column, value)  for column , value in row.items()])
        return row

    table = CreateEmptyTableWorkspace(OutputWorkspace="input")
    rows = [(900, 0.8), (306, 0.8), (567, 0.8), (3, 0.8)]

    table.addColumn("double","centre")
    table.addColumn("double","sigma")

    for row in rows:
        table.addRow(row)

    PeakMatching(table)

    primary_matches = mtd['primary_matches']
    secondary_matches = mtd['secondary_matches']
    all_matches = mtd['all_matches']
    sorted_by_energy = mtd['all_matches_sorted_by_energy']
    element_likelihood = mtd[ 'element_likelihood']

    print("--"*25)
    print(formatdict(primary_matches.row(0)))
    print("--"*25)
    print(formatdict(secondary_matches.row(0)))
    print("--"*25)
    print(formatdict(all_matches.row(0)))
    print("--"*25)
    print(formatdict(sorted_by_energy.row(0)))
    print("--"*25)
    print(formatdict(element_likelihood.row(0)))

Output:

.. testoutput:: OnlyPeaktable

    --------------------------------------------------
    {'Peak centre': '3.00', 'Database Energy': '3.40', 'Element': 'Li', 'Transition': 'L(3d->2p)', 'Error': '0.80', 'Difference': '0.40'}
    --------------------------------------------------
    {'Peak centre': '567.00', 'Database Energy': '567.00', 'Element': 'Ag', 'Transition': 'M(7f->3d)', 'Error': '0.00', 'Difference': '0.00'}
    --------------------------------------------------
    {'Peak centre': '567.00', 'Database Energy': '567.00', 'Element': 'Ag', 'Transition': 'M(7f->3d)', 'Error': '0.00', 'Difference': '0.00'}
    --------------------------------------------------
    {'Peak centre': '3.00', 'Database Energy': '3.40', 'Element': 'Li', 'Transition': 'L(3d->2p)', 'Error': '0.80', 'Difference': '0.40'}
    --------------------------------------------------
    {'Element': 'Ag', 'Likelihood(arbitrary units)': 10}

*Example: Renaming tables**

.. testcode:: renametables

    from mantid.simpleapi import *
    import matplotlib.pyplot as plt
    import numpy

    def formatdict(row):
        row = dict([(column, "{:.2f}".format(value)) if type(value) == float else (column, value)  for column , value in row.items()])
        return row

    table = CreateEmptyTableWorkspace(OutputWorkspace="input")
    rows = [(900, 0.8), (306, 0.8), (567, 0.8), (3, 0.8)]

    table.addColumn("double","centre")
    table.addColumn("double","sigma")

    for row in rows:
        table.addRow(row)

    PeakMatching(table,PrimaryPeaks="primary",SecondaryPeaks="secondary",AllPeaks="all",SortedByEnergy="sort",ElementLikelihood="count")

    primary_matches = mtd['primary']
    secondary_matches = mtd['secondary']
    all_matches = mtd['all']
    sorted_by_energy = mtd['sort']
    element_likelihood = mtd[ 'count']

    print("--"*25)
    print(formatdict(primary_matches.row(1)))
    print("--"*25)
    print(formatdict(secondary_matches.row(1)))
    print("--"*25)
    print(formatdict(all_matches.row(1)))
    print("--"*25)
    print(formatdict(sorted_by_energy.row(1)))
    print("--"*25)
    print(formatdict(element_likelihood.row(1)))

Output:

.. testoutput:: renametables

    --------------------------------------------------
    {'Peak centre': '900.00', 'Database Energy': '900.70', 'Element': 'Ag', 'Transition': 'L(3d3/2->2p3/2)', 'Error': '0.80', 'Difference': '0.70'}
    --------------------------------------------------
    {'Peak centre': '567.00', 'Database Energy': '567.00', 'Element': 'In', 'Transition': 'M(6f->3d)', 'Error': '0.00', 'Difference': '0.00'}
    --------------------------------------------------
    {'Peak centre': '567.00', 'Database Energy': '567.00', 'Element': 'In', 'Transition': 'M(6f->3d)', 'Error': '0.00', 'Difference': '0.00'}
    --------------------------------------------------
    {'Peak centre': '306.00', 'Database Energy': '304.10', 'Element': 'W', 'Transition': 'O(7i->5g)', 'Error': '2.40', 'Difference': '1.90'}
    --------------------------------------------------
    {'Element': 'Tm', 'Likelihood(arbitrary units)': 6}


*Example: Using non default column names**

.. testcode:: non-defaultcolumns

    from mantid.simpleapi import *
    import matplotlib.pyplot as plt
    import numpy

    def formatdict(row):
        row = dict([(column, "{:.2f}".format(value)) if type(value) == float else (column, value)  for column , value in row.items()])
        return row

    table = CreateEmptyTableWorkspace(OutputWorkspace="input")
    rows = [(900, 0.8), (306, 0.8), (567, 0.8), (3, 0.8)]

    table.addColumn("double","center")
    table.addColumn("double","standard deviation")

    for row in rows:
        table.addRow(row)

    PeakMatching(table, PeakCentreColumn = "center",SigmaColumn = "standard deviation")

    primary_matches = mtd['primary_matches']
    secondary_matches = mtd['secondary_matches']
    all_matches = mtd['all_matches']
    sorted_by_energy = mtd['all_matches_sorted_by_energy']
    element_likelihood = mtd[ 'element_likelihood']

    print("--"*25)
    print(formatdict(primary_matches.row(2)))
    print("--"*25)
    print(formatdict(secondary_matches.row(2)))
    print("--"*25)
    print(formatdict(all_matches.row(2)))
    print("--"*25)
    print(formatdict(sorted_by_energy.row(2)))
    print("--"*25)
    print(formatdict(element_likelihood.row(2)))

Output:

.. testoutput:: non-defaultcolumns

    --------------------------------------------------
    {'Peak centre': '900.00', 'Database Energy': '899.20', 'Element': 'Au', 'Transition': 'M(4f5/2->3d3/2)', 'Error': '0.80', 'Difference': '0.80'}
    --------------------------------------------------
    {'Peak centre': '567.00', 'Database Energy': '566.70', 'Element': 'I', 'Transition': 'M(5f->3d)', 'Error': '0.80', 'Difference': '0.30'}
    --------------------------------------------------
    {'Peak centre': '567.00', 'Database Energy': '566.70', 'Element': 'I', 'Transition': 'M(5f->3d)', 'Error': '0.80', 'Difference': '0.30'}
    --------------------------------------------------
    {'Peak centre': '306.00', 'Database Energy': '304.50', 'Element': 'Tm', 'Transition': 'N(5g->4f)', 'Error': '1.60', 'Difference': '1.50'}
    --------------------------------------------------
    {'Element': 'In', 'Likelihood(arbitrary units)': 4}


.. categories::

.. sourcelink::