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PeakMatching v1

../_images/PeakMatching-v1_dlg.png

PeakMatching dialog.

Summary

Matches peaks from table to a database to find probable transitions

See Also

FitGaussianPeaks, FindPeaks, FindPeaksAutomatic

Properties

Name

Direction

Type

Default

Description

PeakTable

Input

TableWorkspace

Mandatory

Table containing peaks to match to database

PeakDatabase

Input

string

json file with peak database, if none is given default database will be used. Allowed values: [‘json’]

PeakCentreColumn

Input

string

centre

Name of column containing centre of peaks

SigmaColumn

Input

string

sigma

Name of column containing standard deviation of peaks

AllPeaks

Output

TableWorkspace

all_matches

Name of the table containing all of the peak matches

PrimaryPeaks

Output

TableWorkspace

primary_matches

Name of the table containing the primary peak matches

SecondaryPeaks

Output

TableWorkspace

secondary_matches

Name of the table containing the secondary peak matches

SortedByEnergy

Output

TableWorkspace

all_matches_sorted_by_energy

Name of the table containing all of the peak matches sorted by energy

ElementLikelihood

Output

TableWorkspace

element_likelihood

Name of the table containing the weighted count of elements in all matches

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:

{
  "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*

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:

--------------------------------------------------
{'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*

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:

--------------------------------------------------
{'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*

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:

--------------------------------------------------
{'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: AlgorithmIndex | Muon

Source

Python: PeakMatching.py