dddm.plotting package

Submodules

dddm.plotting.confidence_figures module

Utility for opening and displaying results from multinest optimization

class dddm.plotting.confidence_figures.DDDMResult(path, sampler='multinest')

Bases: object

Parse results from fitting from nested sampling

config_summary(get_props=('detector', 'mass', 'sigma', 'nlive', 'halo_model', 'notes', 'n_parameters')) → DataFrame

property detector

get_from_config(to_get: str, if_not_available=None)

get_samples()

property halo_model

property mass

property n_parameters

property nlive

property notes

result: dict = None

result_summary() → DataFrame

setup()

property sigma

summary() → DataFrame

class dddm.plotting.confidence_figures.ResultsManager(pattern=None, sampler='multinest')

Bases: object

add_result(path: str)

apply_mask(mask)

build_df()

property df

Lazy alias

register_pattern(pattern, show_tqdm=True)

result_cache: list = None

result_df: DataFrame = None

class dddm.plotting.confidence_figures.SeabornPlot(result: DDDMResult)

Bases: object

best_fit() → tuple

plot_bench(c='cyan', **kwargs)

plot_best_fit(**kwargs) → None

plot_kde(**kwargs)

plot_samples(**kwargs) → None

plot_sigma_contours(nsigma=2, **kwargs)

property samples: ndarray

samples_to_df() → DataFrame

dddm.plotting.plot_basics module

Some basic functions for plotting et cetera. Used to for instance to check that the likelihood function is well behaved

dddm.plotting.plot_basics.error_bar_hist(ax, data, data_range=None, nbins=50, **kwargs)

dddm.plotting.plot_basics.get_color_from_range(val, _range=(0, 1), it=0)

dddm.plotting.plot_basics.hist_data(data, data_range=None, nbins=50)

dddm.plotting.plot_basics.ll_element_wise(x, y, clip_val=-10000.0)

dddm.plotting.plot_basics.open_pickle_figure(name)

dddm.plotting.plot_basics.pickle_dump_figure(name)

dddm.plotting.plot_basics.plot_spectrum(data, color='blue', label='label', linestyle='none', plot_error=True)

dddm.plotting.plot_basics.plt_ll_mass_det(det_class=<class 'dddm.detectors.examples.XenonSimple'>, bins=10, m=50, sig=1e-45)

dddm.plotting.plot_basics.plt_ll_mass_spec(det_class=<class 'dddm.detectors.examples.XenonSimple'>, bins=10, m=50, sig=1e-45)

dddm.plotting.plot_basics.plt_ll_sigma_det(det_class=<class 'dddm.detectors.examples.XenonSimple'>, bins=10, m=50, sig=1e-45)

dddm.plotting.plot_basics.plt_ll_sigma_mass(spec_clas, vary, det_class=<class 'dddm.detectors.examples.XenonSimple'>, bins=10, m=50, sig=1e-45)

dddm.plotting.plot_basics.plt_ll_sigma_spec(det_class=<class 'dddm.detectors.examples.XenonSimple'>, bins=10, m=50, sig=1e-45)

dddm.plotting.plot_basics.plt_priors(itot=100)

dddm.plotting.plot_basics.save_canvas(name, save_dir='./figures', dpi=200, tight_layout=False, pickle_dump=True)

Wrapper for saving current figure

dddm.plotting.plot_basics.show_ll_function(npoints=10000.0, clip_val=-10000.0, min_val=0.1)

dddm.plotting.plot_basics.simple_hist(y: ndarray)

dddm.plotting.seaborn_utils module

” Small script to extract the results from seaborn to calculate confidence intervals

I’m sorry for this script, I wanted to have something robust but I couldn’t find it anyware. Seaborn is doing a great job, so let’s use it’s functionality.

This work is mostly based on: https://github.com/mwaskom/seaborn/blob/ff0fc76b4b65c7bcc1d2be2244e4ca1a92e4e740/seaborn/distributions.py

dddm.plotting.seaborn_utils.one_sigma_area(x, y, clf=True, **kwargs)

Module contents