fast_carpenter.define package

class fast_carpenter.define.Define(name, out_dir, variables)[source]

Bases: object

Creates new variables using a string-based expression.

There are two types of expressions:

  • Simple formulae, and
  • Reducing formulae.

The essential difference, unfortunately, is an internal one: simple expressions are nearly directly handled by numexpr, whereas reducing expressions add a layer on top.

From a users perspective, however, simple expressions are those that preserve the dimensionality of the input. If one of the input variables represents a list of values for each event (whose length might vary), then the output will contain an equal-length list of values for each event.

If, however, a reducing expression is used, then there will be one less dimension on the resulting variable. In this case, if an input variable has a list of values for each event, the result of the expression will only contain a single value per event.

Parameters:

variables (list[dictionary]) – A list of single-length dictionaries whose key is the name of the resulting variable, and whose value is the expression to create it.
Other Parameters:
 
  • name (str) – The name of this stage (handled automatically by fast-flow)
  • out_dir (str) – Where to put the summary table (handled automatically by fast-flow)

Example

variables:
  - Muon_pt: "sqrt(Muon_px**2 + Muon_py**2)"
  - Muon_is_good: (Muon_iso > 0.3) & (Muon_pt > 10)
  - NGoodMuons: {reduce: count_nonzero, formula: Muon_is_good}
  - First_Muon_pt: {reduce: 0, formula: Muon_pt}

See also

event(chunk)[source]
class fast_carpenter.define.SystematicWeights(name, out_dir, weights, out_format='weight_{}', extra_variations=[])[source]

Bases: object

Combines multiple weights and variations to produce a single event weight

To study the impact of systematic uncertainties it is common to re-weight events using a variation of the weights representing, for example, a 1-sigma increase or decrease in the weights. Once there are multiple weight schemes involved writing out each possible combination of these weights becomes tedious and potentially error-prone; this stage makes it easier.

It forms the nominal weight for each event by multiplying all nominal weights together, then the specific variation by replacing a given nominal weight with its corresponding “up” or “down” variation.

Each variation of a weight should just be a string giving an expression to use for that variation. This stage then combines these into a single expression by joining each set of variations with “*”, i.e. multiplying them together. The final results then use an internal Define stage to do the calculation.

Parameters:
  • weights (dictionary[str, dictionary]) – A Dictionary of weight variations to combine. The keys in this dictionary will determine how this variation is called in the output variable. The values of this dictionary should either be a single string – the name of the input variable to use for the “nominal” variation, or a dictionary containing any of the keys, nominal, up, or down. Each of these should then have a value providing the expression to use for that variation/
  • out_format (str) – The format string to use to build the name of the output variations. Defaults to “weight_{}”. Should contain a pair of empty braces which will be replaced with the name for the current variation, e.g. “nominal” or “PileUp_up”.
  • extra_variations (list[str]) – A list of additional variations to allow
Other Parameters:
 
  • name (str) – The name of this stage (handled automatically by fast-flow)
  • out_dir (str) – Where to put the summary table (handled automatically by fast-flow)

Example

syst_weights:
  energy_scale: {nominal: WeightEnergyScale, up: WeightEnergyScaleUp, down: WeightEnergyScaleDown}
  trigger: TriggerEfficiency
  recon: {nominal: ReconEfficiency, up: ReconEfficiency_up}

which will create 4 new variables:

weight_nominal =  WeightEnergyScale * TriggerEfficiency * ReconEfficiency
weight_energy_scale_up =  WeightEnergyScaleUp * TriggerEfficiency * ReconEfficiency
weight_energy_scale_down =  WeightEnergyScaleDown * TriggerEfficiency * ReconEfficiency
weight_recon_up =  WeightEnergyScale * TriggerEfficiency * ReconEfficiency_up
event(chunk)[source]