fit module

Defines the basic components of the reaction plane fit.

class reaction_plane_fit.fit.BackgroundFitComponent(rp_orientation: str, *args: Any, **kwargs: Any)[source]

Bases: reaction_plane_fit.fit.FitComponent

Fit component in the background region.

Parameters

  • rp_orientation (str) – Reaction plane orientation for the component.

  • *args (list) – Only use named args.

  • **kwargs (dict) – Named arguments to be passed on to the base component.

determine_fit_function(resolution_parameters: Dict[str, float], reaction_plane_parameter: reaction_plane_fit.base.ReactionPlaneParameter) → None[source]

Use the class parameters to determine the fit and background functions and store them.

set_data_limits(hist: pachyderm.histogram.Histogram1D) → pachyderm.histogram.Histogram1D[source]

Set the limits of the fit to only use near-side data (ie dPhi < pi/2)

Only the near-side will be used for the fit to avoid the signal at large dPhi on the away-side.

Parameters

hist (histogram.Histogram1D) – The input data.

Returns

The data limited to the near-side.

Return type

histogram.Histogram1D

class reaction_plane_fit.fit.FitComponent(fit_type: reaction_plane_fit.base.FitType, resolution_parameters: Dict[str, float], use_log_likelihood: bool = False)[source]

Bases: abc.ABC

A component of the fit.

Parameters

  • fit_type (FitType) – Type (region and orientation) of the fit component.

  • resolution_parameters (dict) – Maps resolution parameters of the form “R22” (for the R_{2,2} parameter) to the value. Expects “R22” - “R82”

  • use_log_likelihood (bool) – If true, use log likelihood cost function. Often used when statistics are limited. Default: False

fit_type

Overall type of the fit.

Type

FitType

use_log_likelihood

True if the fit should be performed using log likelihood.

Type

bool

fit_function

Function of the component.

Type

function

cost_func

Cost function associated with the fit component.

Type

pachyderm.fit.CostFunctionBase

calculate_background_function_errors(x: numpy.ndarray) → numpy.ndarray[source]

Calculate the background function errors based on values from the fit.

Parameters

x – x values where the errors will be evaluated.

Returns

The calculated error values.

calculate_fit_errors(x: numpy.ndarray) → numpy.ndarray[source]

Calculate the fit function errors based on values from the fit.

Parameters

x – x values where the errors will be evaluated.

Returns

The calculated error values.

abstract determine_fit_function(resolution_parameters: Dict[str, float], reaction_plane_parameter: reaction_plane_fit.base.ReactionPlaneParameter) → None[source]

Use the class parameters to determine the fit and background functions and store them.

determine_parameters_limits() → Dict[str, Union[bool, float, Tuple[float, float], Tuple[int, int]]][source]

Determine the parameter seed values, limits, step sizes for the component.

Note

These values should be specified with the proper names so that they will be recognized by Minuit. This is the user’s responsibility.

Parameters

None. –

Returns

Dictionary of the function arguments which specifies their

evaluate_background(x: numpy.ndarray) → numpy.ndarray[source]

Evaluates the background components of the fit function.

In the case of a background component, this is identical to evaluate_fit(...). However, in the case of a signal component, this describes the background contribution to the signal.

Parameters

x – x values where the fit component will be evaluated.

Returns

Background function values at the given x values.

evaluate_fit(x: numpy.ndarray) → numpy.ndarray[source]

Evaluate the fit component.

Parameters

x – x values where the fit component will be evaluated.

Returns

Function values at the given x values.

property region
property rp_orientation
set_data_limits(hist: pachyderm.histogram.Histogram1D) → pachyderm.histogram.Histogram1D[source]

Extract the data from the histogram.

set_fit_type(region: Optional[str] = None, orientation: Optional[str] = None) → None[source]

Update the fit type.

Will use the any of the existing parameters if they are not specified.

Parameters

  • region (str) – Region of the fit type.

  • orientation (str) – Orientation of the fit type.

Returns

The fit type is set in the component.

Return type

None

class reaction_plane_fit.fit.ReactionPlaneFit(resolution_parameters: Dict[str, float], use_log_likelihood: bool, signal_region: Optional[Tuple[float, float]] = None, background_region: Optional[Tuple[float, float]] = None, use_minos: bool = False)[source]

Bases: abc.ABC

Contains the reaction plane fit for one particular set of data and fit components.

Parameters

  • resolution_parameters (dict) – Maps resolution parameters of the form “R22” (for the R_{2,2} parameter) to the value. Expects “R22” - “R82” (even RP only).

  • use_log_likelihood (bool) – If true, use log likelihood cost function. Often used when statistics are limited. Default: False

  • signal_region – Min and max extraction range for the signal dominated region. Should be provided as the absolute value (ex: (0., 0.6)).

  • background_region – Min and max extraction range for the background dominated region. Should be provided as the absolute value (ex: (0.8, 1.2)).

  • use_minos – Calculate the errors using Minos in addition to Hesse.

resolution_parameters

Maps resolution parameters of the form “R22” (for the R_{2,2} parameter) to the value. Expects “R22” - “R82” (even RP only).

Type

dict

use_log_likelihood

If true, use log likelihood cost function. Often used when statistics are limited. Default: False

Type

bool

signal_region

Min and max extraction range for the signal dominated region. Should be provided as the absolute value (ex: (0., 0.6)).

background_region

Min and max extraction range for the background dominated region. Should be provided as the absolute value (ex: (0.8, 1.2)).

use_minos

Calculate the errors using Minos in addition to Hesse. It will give a better error estimate, but it will be much slower. Errors will be printed but not stored because we don’t store the errors directly (rather, we store the covariance matrix, which one cannot extract from Minos). By printing out the values, the use can check that the Hesse and Minos errors are similar, which will indicate that the function is well approximated by a hyperparabola at the minima (and thus Hesse errors are okay to be used).

fit_result

Result of the RP fit.

_fit

Fit function for the RP fit.

Type

Callable

abstract create_full_set_of_components(input_data: Dict[FitType, pachyderm.histogram.Histogram1D]) → Dict[str, reaction_plane_fit.fit.FitComponent][source]

Create the full set of fit components.

fit(data: Union[Dict[str, Dict[str, Union[Any, pachyderm.histogram.Histogram1D]]], Dict[FitType, pachyderm.histogram.Histogram1D]], user_arguments: Optional[Dict[str, Union[bool, float, Tuple[float, float], Tuple[int, int]]]] = None) → Tuple[bool, Dict[reaction_plane_fit.base.FitType, pachyderm.histogram.Histogram1D], iminuit._libiminuit.Minuit][source]

Perform the actual fit.

Parameters

  • data – Input data to be used for the fit. The keys should either be of the form [region][orientation] or [FitType]. The values can be uproot or ROOT 1D histograms.

  • user_arguments – User arguments to override the arguments to the fit. Default: None.

Returns

(fit_success, formatted_data, minuit) where fit_success (bool) is True if the fitting

procedure was successful, formatted_data (dict) is the data reformatted in the preferred format for the fit, and minuit (iminuit.Minuit) is the minuit object, which is provided it is provided for specialized use cases. Note that the fit results (which contains most of the information in the minuit object) are stored in the class.

Return type

tuple

reaction_plane_parameters = {}
read_fit_results(filename: str, y: ruamel.yaml.main.YAML = None) → bool[source]

Read all fit results from the specified filename using YAML.

We don’t read the entire object from YAML because they we would have to deal with serializing many classes. Instead, we read the fit results, with the expectation that the fit object will be created independently, and then the results will be loaded.

Parameters

  • filename – Name of the file under which the fit results are saved.

  • y – YAML object to be used for reading the result. If none is specified, one will be created automatically.

Returns

True if the reading was successful. The results will be read into the fit object.

property rp_orientations

Get the RP orientations (excluding the inclusive, which is the last entry).

write_fit_results(filename: str, y: ruamel.yaml.main.YAML = None) → bool[source]

Write all fit results to the specified filename using YAML.

We don’t write the entire object to YAML because they we would have to deal with serializing many classes. Instead, we write the results, with the expectation that the fit object will be created independently, and then the results will be loaded.

Parameters

  • filename – Name of the file under which the fit results will be saved.

  • y – YAML object to be used for writing the result. If none is specified, one will be created automatically.

Returns

True if the writing was successful.

class reaction_plane_fit.fit.SignalFitComponent(rp_orientation: str, *args: Any, **kwargs: Any)[source]

Bases: reaction_plane_fit.fit.FitComponent

Fit component in the signal region.

Parameters

  • rp_orientation (str) – Reaction plane orientation for the component.

  • *args (list) – Only use named args.

  • **kwargs (dict) – Named arguments to be passed on to the base component.

determine_fit_function(resolution_parameters: Dict[str, float], reaction_plane_parameter: reaction_plane_fit.base.ReactionPlaneParameter) → None[source]

Use the class parameters to determine the fit and background functions and store them.