Module functions for retrieving a grouping or quality array from source and background files.
Integer_Type get_groups([Integer_Type])
Integer_Type get_bgroups([Integer_Type])
Integer_Type get_quality([Integer_Type])
Integer_Type get_bquality([Integer_Type])
Success/Error Return Values: 1/0
Arguments:
(1) Dataset number (default 1).
The get_(b)groups
and get_(b)quality functions allow the user
to retrieve grouping and quality information for source and background datasets.
If the file is not grouped or quality is not set, NULL is returned.
If the files is grouped or quality defined, an array is returned.
The retrieved array is a Integer_Type array of the same
length as the input (grouped) dataset. A grouping array
element set to -1 marks the beginning of a group, while array element
set to 1 marks members of that group, so the corresponding bins are
treated as one during fitting.
A quality array contains the quality flags for each group:
0 for good (grouped) data; 5 for data labeled as
bad by the user (within a tab), and 2 for data labeled as
questionable by dmgroup (incomplete groups, etc.).
The grouping and quality definitions are based on OGIP standard.
See the related Sherpa commands
GROUP and QUALITY for more
information.
Retrive and apply a grouping scheme from the other data set
to an ungrouped data set.
sherpa> DATA spec.pha
sherpa> show
....
-----------------
Input data files:
-----------------
Data 1: spec.pha pha.
Total Size: 1024 bins (or pixels)
Dimensions: 1
Total counts (or values): 2231
.......
sherpa> DATA 2 spec_grp.pha
sherpa> show
....
-----------------
Input data files:
-----------------
Data 1: spec.pha pha.
Total Size: 1024 bins (or pixels)
Dimensions: 1
Total counts (or values): 2231
......
Data 2: spec_grp.pha pha.
Total Size: 131 bins (or pixels)
Dimensions: 1
Total counts (or values): 2231
......
sherpa> g=get_groups(2)
sherpa> print(g)
1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
1
-1
.....
sherpa> set_groups(1,g)
WARNING: any applied filters are being deleted!
1
sherpa> show
Data 1: spec.pi pha.
Total Size: 131 bins (or pixels)
Dimensions: 1
Total counts (or values): 2231
.....
In this example, ungrouped and group data are read into
Sherpa, and then group information is retrieved using
get_groups from the grouped dataset. A new array
g is defined whose elements are defined by the grouping
of the dataset 2. This grouping scheme is then apply to the ungrouped
data set with set_groups.
Retrieve and apply a grouping scheme from the source data set
to ungrouped background data.
sherpa> DATA spec.pha
sherpa> show
....
-----------------
Input data files:
-----------------
Data 1: spec.pha pha.
Total Size: 1024 bins (or pixels)
Dimensions: 1
Total counts (or values): 2231
Background 1: bg.pha pha.
Total Size: 1024 bins (or pixels)
Dimensions: 1
Total counts (or values): 662
.......
sherpa> g=get_groups(1)
sherpa> print(g)
1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
1
-1
.....
sherpa> set_bgroups(1,g)
WARNING: any applied filters are being deleted!
1
sherpa> show
Data 1: spec.pi pha.
Total Size: 131 bins (or pixels)
Dimensions: 1
Total counts (or values): 2231
Background 1: bg.pha pha.
Total Size: 131 bins (or pixels)
Dimensions: 1
Total counts (or values): 662
......
In this example, group data and ungrouped background data are read into
Sherpa, and then group information is retrieved using
get_groups from the grouped dataset. A new array
g is defined whose elements are defined by the grouping
of the source data set. This grouping scheme is then apply to the ungrouped
background data set with set_bgroups.
- sherpa
-
analysis,
ignore,
notice,
set_filter,
set_groups,
set_ignore,
set_ignore2d,
set_ignore_all,
set_ignore_bad,
set_notice,
set_notice2d,
set_notice_all
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