validphys.plotoptions package

Submodules

validphys.plotoptions.core module

class validphys.plotoptions.core.KinLabel(*values)

Bases: Enum

k1 = 1

k2 = 2

k3 = 3

class validphys.plotoptions.core.PlotInfo(kinlabels, dataset_label, *, experiment=None, x=None, extra_labels=None, func_labels=None, figure_by=None, line_by=None, result_transform=None, y_label=None, x_label=None, x_scale=None, y_scale=None, ds_metadata=None, process_description='-', nnpdf31_process, **kwargs)

Bases: object

property experiment

classmethod from_commondata(commondata, cuts=None, normalize=False)

get_xcol(table)

Return a numpy array with the x column or the index as appropriate

group_label(same_vals, groupby)

name_to_label(name)

property process_type

property xlabel

class validphys.plotoptions.core.PlottingFile(func_labels: dict = <factory>, dataset_label: Optional[str] = None, experiment: Optional[str] = None, nnpdf31_process: Optional[str] = None, data_reference: Optional[str] = None, theory_reference: Optional[str] = None, process_description: Optional[str] = None, y_label: Optional[str] = None, x_label: Optional[str] = None, result_transform: Optional[validphys.plotoptions.plottingoptions.ResultTransformations] = None, x: Optional[str] = None, plot_x: Optional[str] = None, x_scale: Optional[validphys.plotoptions.plottingoptions.Scale] = None, y_scale: Optional[validphys.plotoptions.plottingoptions.Scale] = None, line_by: Optional[list] = None, figure_by: Optional[list] = None, extra_labels: Optional[Mapping[str, List]] = None, normalize: Optional[validphys.plotoptions.plottingoptions.PlottingOptions] = None, already_digested: Optional[bool] = False)

Bases: PlottingOptions

normalize: PlottingOptions | None = None

validphys.plotoptions.core.dict_factory(key_value_pairs)

A dictionary factory to be used in conjunction with dataclasses.asdict to remove nested None values and convert enums to their name.

https://stackoverflow.com/questions/59481989/dict-from-nested-dataclasses

validphys.plotoptions.core.get_info(data, *, normalize=False, cuts=None, use_plotfiles=True)

Retrieve and process the plotting information for the input data (which could be a DatasetSpec or a CommonDataSpec).

If use_plotfiles is True (the default), the PLOTTING files will be used to retrieve the infromation. Otherwise the default configuration (which depends of the process type) will be used.

If cuts is None, the cuts of the dataset will be used, but no cuts for commondata.

If cuts is False, no cuts will be used.

If cuts is an instance of Cuts, it will be used.

If normalize is True, the specialization for ratio plots will be used to generate the PlotInfo objects.

validphys.plotoptions.core.kitable(data, info, *, cuts=None)

Obtain a DataFrame with the kinematics for each data point

Parameters:

• data ((DataSetSpec, CommonDataSpec, Dataset, CommonData)) – A data object to extract the kinematics from.

• info (PlotInfo) – The description of the transformations to apply to the kinematics. See get_info()

• cuts (Cuts or None, default=None) – An object to load the cuts from. It is an error to set this if data is a dataset. If data is a CommonData, these must be the same as those passed to get_info().

Returns:

table – A DataFrame containing the kinematics for all points after cuts.

Return type:

pd.DataFrame

validphys.plotoptions.core.transform_result(cv, error, kintable, info)

validphys.plotoptions.labelers module

Created on Fri Mar 11 20:44:21 2016

@author: Zahari Kassabov

validphys.plotoptions.labelers.high_xq(k1, k2, k3, **kwargs)

validphys.plotoptions.labelers.jet_eta(k1, k2, k3, **kwargs)

validphys.plotoptions.labelers.k2bins10(k1, k2, k3, **kwargs)

validphys.plotoptions.labelers.k2bins5(k1, k2, k3, **kwargs)

validphys.plotoptions.labelers.k2bins6(k1, k2, k3, **kwargs)

validphys.plotoptions.labelers.pt_ratio(k1, k2, k3, **kwargs)

validphys.plotoptions.labelers.two_Q2_bins(k1, k2, k3, **kwargs)

validphys.plotoptions.plottingoptions module

This module is separated from other plotoptions modules to avoid circular dependencies

The class PlottingOptions is used by the commondata reader to check that the plotting options set in plotting are acceptable.

class validphys.plotoptions.plottingoptions.PlottingOptions(func_labels: dict = <factory>, dataset_label: Optional[str] = None, experiment: Optional[str] = None, nnpdf31_process: Optional[str] = None, data_reference: Optional[str] = None, theory_reference: Optional[str] = None, process_description: Optional[str] = None, y_label: Optional[str] = None, x_label: Optional[str] = None, result_transform: Optional[validphys.plotoptions.plottingoptions.ResultTransformations] = None, x: Optional[str] = None, plot_x: Optional[str] = None, x_scale: Optional[validphys.plotoptions.plottingoptions.Scale] = None, y_scale: Optional[validphys.plotoptions.plottingoptions.Scale] = None, line_by: Optional[list] = None, figure_by: Optional[list] = None, extra_labels: Optional[Mapping[str, List]] = None, normalize: Optional[dict] = None, already_digested: Optional[bool] = False)

Bases: object

property all_labels

already_digested: bool | None = False

data_reference: str | None = None

dataset_label: str | None = None

experiment: str | None = None

extra_labels: Mapping[str, List] | None = None

figure_by: list | None = None

func_labels: dict

line_by: list | None = None

nnpdf31_process: str | None = None

normalize: dict | None = None

parse_figure_by()

parse_line_by()

parse_x()

plot_x: str | None = None

process_description: str | None = None

result_transform: ResultTransformations | None = None

theory_reference: str | None = None

x: str | None = None

x_label: str | None = None

x_scale: Scale | None = None

y_label: str | None = None

y_scale: Scale | None = None

class validphys.plotoptions.plottingoptions.ResultTransformations(*values)

Bases: Enum

bins = 1

half = 2

qbinEMC = 3

qbindis = 4

qbindyp = 5

qbinexp = 6

qbinjets = 7

class validphys.plotoptions.plottingoptions.Scale(*values)

Bases: Enum

linear = 1

log = 2

symlog = 3

validphys.plotoptions.resulttransforms module

Created on Tue Apr 19 09:52:43 2016

@author: Zahari Kassabov

Transform the result (central value and error) for plotting.

The functions here receive as arguments the original cental value and error (from e.g. the convolution or commondata) as well as all the labels defined in the plotting file, as keyword arguments. They are expected to return a new central value and a new error. Therefore the signature is:

def xbinexp(cv, error, **labels):
    ...
    return newcv, newerror

exception validphys.plotoptions.resulttransforms.MissingLabelError(key_error)

Bases: KeyError

validphys.plotoptions.resulttransforms.half(cv, error, **labels)

validphys.plotoptions.resulttransforms.qbinEMC(cv, error, **labels)

validphys.plotoptions.resulttransforms.qbindis(cv, error, **labels)

validphys.plotoptions.resulttransforms.qbindyp(cv, error, **labels)

validphys.plotoptions.resulttransforms.qbinexp(cv, error, **labels)

validphys.plotoptions.resulttransforms.qbinjets(cv, error, **labels)

validphys.plotoptions.utils module

Created on Tue Mar 15 12:49:39 2016

@author: Zahari Kassabov

validphys.plotoptions.utils.apply_to_all_columns(df, func)

Apply a function to all columns of a dataframe at the saem time. The parameter names are the names of the column and the values are arrays containing the each column’s values.

validphys.plotoptions.utils.bins(arr)

Return bins corresponding to unique values of arr sorted by value.

bins([-3, 5, -3, -3, 0, 1,1,0])

array([0, 3, 0, 0, 1, 2, 2, 1])

validphys.plotoptions.utils.get_subclasses(obj, base)

Return the classes in obj that are subclasses of base

validphys.plotoptions.utils.label(label)

validphys.plotoptions.utils.new_labels(k1label, k2lebel, k3label)

Module contents