YAML Configuration Sub-Keys#

Note

Keys marked with an asterisk are optional and can be omitted.

`input`#

`bk_query`#

Key	Type	Meaning
`bk_query`	string	The bookkeeping location of the desired input data.
`n_test_lfns`*	int	The number of files to use as input to test jobs. Only to be used for samples with very few output candidates.
`dq_flags`*	sequence of strings	What quality of data to use. This can be set to any of `BAD`, `OK`, `UNCHECKED` or `EXPRESS_OK` (only for Runs 1 & 2). Multiple can be used at once by writing them as a sequence of values.
`extended_dq_ok`*	sequence of strings	In addition to requiring data quality (DQ) `OK`, extended DQ flags can be required such that runs without the specified subsystem DQ OK flag being set are not included.
`runs`*	sequence of integers	A sequence of data taking runs to use as input. This can either be written as a typical sequence or as A:B where runs from A to B inclusive will be used.
`input_plugin`*	string	The input plugin setting, either `default` or `by-run`. `default==default`
`keep_running`*	bool	Whether to keep running on new data as it comes in. `default==False`
`smog2_state`*	string	Gas injected in SMOG2, possible choices are: [Hydrogen, Deuterium, Helium, Nitrogen, Oxygen, Neon, Argon, Krypton, Xenon]. 2 possible states: `<Name>`, `<Name>Unstable` or both. The word. Unstable is appended if the gas injected pressure is not stable.

Here is a full example showing a bk_query input using all optional keys:

job_name:
  input:
    bk_query: /some/MagUp/bookkeeping/path.DST
    n_test_lfns: 3
    dq_flags:
      - BAD
      - OK
    runs:
      - 269370
      - 269371
      - 269372
      # equivalent to 269370:269372
    input_plugin: by-run
    keep_running: True
    smog2_state:
      - Argon
      - ArgonUnstable

`job_name`#

Key	Type	Meaning
`job_name`	string	The name of the job whose output should be the input of this job.
`filetype`*	string	The file type of the input file, for when your input job has multiple output files.

Here is a full example showing a job_name input using all optional keys:

strip_job:
  bk_query: /some/MagUp/bookkeeping/path.DST
  options: strip.py

tuple_job:
  input:
    job_name: strip_job
    filetype: DST
  options: tuple.py

`transform_ids`#

Key	Type	Meaning
`transform_ids`	sequence of integers	A sequence of transformation IDs to use as input file sources.
`filetype`	string	The file type of the input file, for when your input job has multiple output files.
`n_test_lfns`*	int	The number of files to use as input to test jobs. Only to be used for samples with very few output candidates.
`dq_flags`*	sequence of strings	What quality of data to use. This can be set to any of `BAD`, `OK`, `UNCHECKED` or `EXPRESS_OK` (only for Runs 1 & 2). Multiple can be used at once by writing them as a sequence of values.
`runs`*	sequence of integers	A sequence of data taking runs to use as input. This can either be written as a typical sequence or as A:B where runs from A to B inclusive will be used.

Here is a full example showing a transform_ids input using all optional keys:

job_name:
  input:
    transform_ids:
      - 1234
      - 5678
    filetype: DST
    n_test_lfns: 3
    dq_flags:
      - BAD
      - OK
    runs:
      - 269370
      - 269371
      - 269372
      # equivalent to 269370:269372

`checks`#

`range`: create a 1D histogram#

Key	Type	Meaning
`tree_pattern`	string	The name of the TTree to use. This is a regex and can use wildcards. `default==r"(./DecayTree)\|(./MCDecayTree)"`
`expression`	string	The name of a branch (or a combination of branch names) to evaluate and plot as a histogram.
`limits`	dict	Takes two keys: `min` and `max`, the values of which must both be floats. These define the min and max values of the horizontal histogram axis.
`blind_ranges`*	list or dict	Defines ranges to blind, i.e., ranges for which entries will not be included in the histogram. This uses similar `min`/`max` key syntax as the `limits` option uses. You can either provide a single range, or a list of ranges.
`exp_mean`*	float	Expected mean value of the 1D distribution. This is an optional parameter. If provided, it is compared with the observed value and their agreement within the provided `mean_tolerance` is checked.
`exp_std`*	float	Expected value for the standard deviation of the 1D distribution. This is an optional parameter. If provided, it is compared with the observed value and their agreement within the provided `std_tolerance` is checked.
`mean_tolerance`*	float	Maximum shift tolerated between expected and observed mean values. This is an optional parameter and it has to be provided only if also `exp_mean` is provided.
`std_tolerance`*	float	Maximum shift tolerated between expected and observed values of standard deviation. This is an optional parameter and it has to be provided only if also `exp_std` is provided.
`n_bins`*	int	Number of bins to use in the histogram, `default==50`.

Here is a full example showing a range check using all optional keys:

checks:
  Bd_DTF_M:
    type: range
    tree_pattern: TupleDstToD0pi_D0ToKK/DecayTree
    expression: 'Bd_constB0PV_M'
    limits:
      min: 5000
      max: 6000
    blind_ranges:
      - min: 5200
        max: 5400
      - min: 5600
        max: 5800
    exp_mean: 5279.65
    exp_std:  30.0
    mean_tolerance: 0.12
    std_tolerance: 10
    n_bins: 100

`range_bkg_subtracted`: create a 1D histogram with background subtracted#

Key	Type	Meaning
`tree_pattern`	string	The name of the TTree to use. This is a regex and can use wildcards. `default==r"(./DecayTree)\|(./MCDecayTree)"`
`expression`	string	The name of a branch (or a combination of branch names) to evaluate and plot on the horizontal histogram axis.
`limits`	dict	Takes two keys: `min` and `max`, the values of which must both be floats. These define the min and max values of the horizontal histogram axis.
`expr_for_subtraction`	string	The name of a control variable (branch or a combination of branch names) to be used to perform the background subtraction.
`mean_sig`	float	Expected mean value of the `expr_for_subtraction` variable. The signal window will be centered around this value.
`background_shift`	float	Shift, w.r.t the `mean_sig` value, used to define the two background regions.
`background_window`	float	Size of the background windows.
`signal_window`	float	Size of the signal window.
`blind_ranges` *	list or dict	Defines ranges to blind, ie. ranges for which entries will not be included in the histogram. This uses similar `min`/`max` key syntax as the `limits` option uses. You can either provide a single range, or a list of ranges.
`n_bins`*	int	Number of bins to use in the histograms, `default==50`.

Here is a full example showing a range_bkg_subtracted check using all optional keys:

checks:
  D_PT:
    type: range_bkg_subtracted
    tree_pattern: TupleDstToD0pi_D0ToKK/DecayTree
    expression: D0_PT
    limits:
        min: 0.0
        max: 500000.0
    expr_for_subtraction: D0_MM
    mean_sig: 1865.0
    background_shift: 30.0
    background_window: 10.0
    signal_window: 20.0
    blind_ranges:
        min: 10000.0
        max: 30000.0
    n_bins: 50

`range_nd`: create a 2D or 3D histogram#

Key	Type	Meaning
`tree_pattern`	string	The name of the TTree to use. This can use wildcards.
`expressions`	dict	Keys are the axes of the histograms, (in order) `x`, `y`, and `z`. Values for each are the expression for that axis, using the same syntax as `expression` for `range` checks. The `z` axis is optional.
`limits`	dict	Keys are the axes of the histograms, (in order) `x`, `y`, and `z`. Values for each are a dictionary for max/min values for that axis, using the same syntax as `limits` for `range` checks. The `z` axis is optional.
`blind_ranges`*	list	A list of dictionaries defining ranges that will be blinded. For each entry, the keys are the axes of the histograms, (in order) `x`, `y`, and `z`, and the values for each are a dictionary for max/min values for that axis, using the same syntax as `limits` for `range` checks. The `z` axis is optional.
`n_bins`*	dict	Keys are the axes of the histograms, (in order) `x`, `y`, and `z`. Values for each are an integer defining the number of bins to use in that axis of the histogram. The default number of bins for each axis is 50. The `z` axis is optional.

Here is a full example showing a range_nd check using all optional keys:

checks:
  momenta:
    type: range_nd
    expressions:
        x: H1_PX
        y: H2_PX
        z: H2_PY
    limits:
        x:
            min: 0.0
            max: 500000.0
        y:
            min: 0.0
            max: 500000.0
        z:
            min: 0.0
            max: 500000.0
    blind_ranges:
        x:
            min: 10000.0
            max: 30000.0
        y:
            min: 10000.0
            max: 30000.0
        z:
            min: 10000.0
            max: 30000.0
    n_bins:
        x: 50
        y: 50
        z: 50
    tree_pattern: TupleDstToD0pi_D0ToKK/DecayTree

`num_entries`: require a minimum number of entries in a TTree#

Key	Type	Meaning
`tree_pattern`	string	The name of the TTree to use. This can use wildcards.
`count`	int	The minimum number of entries required.

Here is a full example showing a num_entries check:

checks:
  num_entries:
    type: num_entries
    count: 1000
    tree_pattern: TupleDstToD0pi_D0ToKK/DecayTree

`num_entries_per_invpb`: require a minimum number of entries per pb^-1 in a TTree#

Key	Type	Meaning
`tree_pattern`	string	The name of the TTree to use. This can use wildcards.
`count_per_invpb`	int	The minimum number of entries per pb^-1 required.
`lumi_pattern`*	string	Regex used to identify the object inside the ntuple containing luminosity information. Default value is `(.*/LumiTuple)`.

Here is a full example showing a num_entries_per_invpb check using all optional keys:

checks:
  num_entries_per_invpb:
    type: num_entries_per_invpb
    count_per_invpb: 1000
    tree_pattern: TupleDstToD0pi_D0ToKK/DecayTree
    lumi_pattern: LumiTuple

`branches_exist`: require a set of branches to exist in a TTree#

Key	Type	Meaning
`tree_pattern`	string	The name of the TTree to use. This can use wildcards.
`branches`	sequence of strings	A list of branch names to check for.

Here is a full example showing a branches_exist check:

checks:
  branches_exist:
    type: branches_exist
    branches:
      - H1_PX
      - H1_PY
      - H1_PZ
      - H2_PX
      - H2_PY
      - H2_PZ
    tree_pattern: TupleDstToD0pi_D0ToKK/DecayTree

YAML Configuration Sub-Keys#

input#

bk_query#

job_name#

transform_ids#

checks#

range: create a 1D histogram#

range_bkg_subtracted: create a 1D histogram with background subtracted#

range_nd: create a 2D or 3D histogram#

num_entries: require a minimum number of entries in a TTree#

num_entries_per_invpb: require a minimum number of entries per pb^-1 in a TTree#

branches_exist: require a set of branches to exist in a TTree#

`input`#

`bk_query`#

`job_name`#

`transform_ids`#

`checks`#

`range`: create a 1D histogram#

`range_bkg_subtracted`: create a 1D histogram with background subtracted#

`range_nd`: create a 2D or 3D histogram#

`num_entries`: require a minimum number of entries in a TTree#

`num_entries_per_invpb`: require a minimum number of entries per pb^-1 in a TTree#

`branches_exist`: require a set of branches to exist in a TTree#