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PeakMatching v1¶
Summary¶
Matches peaks from table to a database to find probable transitions
See Also¶
Properties¶
Name |
Direction |
Type |
Default |
Description |
|---|---|---|---|---|
PeakTable |
Input |
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 |
all_matches |
Name of the table containing all of the peak matches |
|
PrimaryPeaks |
Output |
primary_matches |
Name of the table containing the primary peak matches |
|
SecondaryPeaks |
Output |
secondary_matches |
Name of the table containing the secondary peak matches |
|
SortedByEnergy |
Output |
all_matches_sorted_by_energy |
Name of the table containing all of the peak matches sorted by energy |
|
ElementLikelihood |
Output |
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