threeML.config package

Submodules

threeML.config.catalog_structure module

class threeML.config.catalog_structure.CatalogServer(url: str = '???')

Bases: object

url: str = '???'

class threeML.config.catalog_structure.Catalogs(Fermi: threeML.config.catalog_structure.InstrumentCatalog = <factory>, Swift: threeML.config.catalog_structure.InstrumentCatalog = <factory>)

Bases: object

Fermi: InstrumentCatalog

Swift: InstrumentCatalog

class threeML.config.catalog_structure.InstrumentCatalog(catalogs: Dict[str, threeML.config.catalog_structure.CatalogServer] = '???')

Bases: object

catalogs: Dict[str, CatalogServer] = '???'

class threeML.config.catalog_structure.PublicDataServer(public_ftp_location: Optional[str] = None, public_http_location: str = '???', query_form: Optional[str] = None)

Bases: object

public_ftp_location: Optional[str] = None

public_http_location: str = '???'

query_form: Optional[str] = None

threeML.config.config module

threeML.config.config_structure module

class threeML.config.config_structure.Config(logging: threeML.config.config_structure.Logging = <factory>, parallel: threeML.config.config_structure.Parallel = <factory>, interface: threeML.config.config_structure.Interface = <factory>, plugins: threeML.config.plugin_structure.Plugins = <factory>, time_series: threeML.config.plugin_structure.TimeSeries = <factory>, mle: threeML.config.fitting_structure.MLEDefault = <factory>, bayesian: threeML.config.fitting_structure.BayesianDefault = <factory>, plotting: threeML.config.plotting_structure.GenericPlotting = <factory>, model_plot: threeML.config.plotting_structure.ModelPlotting = <factory>, point_source: threeML.config.point_source_structure.PointSourceDefaults = <factory>, LAT: threeML.config.catalog_structure.PublicDataServer = <factory>, GBM: threeML.config.catalog_structure.PublicDataServer = <factory>, catalogs: threeML.config.catalog_structure.Catalogs = <factory>)

Bases: object

GBM: PublicDataServer

LAT: PublicDataServer

bayesian: BayesianDefault

catalogs: Catalogs

interface: Interface

logging: Logging

mle: MLEDefault

model_plot: ModelPlotting

parallel: Parallel

plotting: GenericPlotting

plugins: Plugins

point_source: PointSourceDefaults

time_series: TimeSeries

class threeML.config.config_structure.Interface(progress_bars: bool = 'on', multi_progress_color: bool = 'on', multi_progress_cmap: str = 'viridis', progress_bar_color: str = '#9C04FF')

Bases: object

multi_progress_cmap: str = 'viridis'

multi_progress_color: bool = 'on'

progress_bar_color: str = '#9C04FF'

progress_bars: bool = 'on'

class threeML.config.config_structure.Logging(path: str = '~/.threeml/log', developer: bool = 'off', usr: bool = 'on', console: bool = 'on', level: threeML.config.config_structure.LoggingLevel = <LoggingLevel.INFO: 20>, startup_warnings: bool = 'on')

Bases: object

console: bool = 'on'

developer: bool = 'off'

level: LoggingLevel = 20

path: str = '~/.threeml/log'

startup_warnings: bool = 'on'

usr: bool = 'on'

class threeML.config.config_structure.LoggingLevel(value)

Bases: IntEnum

An enumeration.

CRITICAL = 50

DEBUG = 10

ERROR = 40

INFO = 20

WARNING = 30

class threeML.config.config_structure.Parallel(profile_name: str = 'default', use_parallel: bool = False, use_joblib: bool = False)

Bases: object

profile_name: str = 'default'

use_joblib: bool = False

use_parallel: bool = False

threeML.config.config_utils module

threeML.config.config_utils.get_current_configuration_copy(file_name: str = 'threeML_config.yml', overwrite: bool = False)

write a copy of the CURRENT configuration to the config directory

threeML.config.config_utils.get_value(name, user_value, par_type, config_value)

Get the value for a parameter. If value is None returns the config value. :param name: Name of parameter :param user_value: user value (can be None if no value given) :param par_type: Type of the paramter :param config_value: value in config :returns: parameter value

threeML.config.config_utils.get_value_kwargs(name, par_type, config_value, **kwargs)

Read the value of a parameter from the kwargs or the config if it does not exist in the kwargs. :param name: Name of parameter in kwargs :param par_type: Type of the parameter :param config_value: Value in the config :param kwargs: :returns: value of parameter, rest of kwargs

threeML.config.config_utils.recurse_dict(d, tree)

threeML.config.config_utils.show_configuration(sub_menu: Optional[str] = None)

display the current configuration or a sub menu if provided

threeML.config.fitting_structure module

class threeML.config.fitting_structure.BayesianDefault(use_median_fit: bool = False, default_sampler: threeML.config.fitting_structure.Sampler = <Sampler.emcee: 'emcee'>, emcee_setup: Optional[Dict[str, Any]] = <factory>, multinest_setup: Optional[Dict[str, Any]] = <factory>, ultranest_setup: Optional[Dict[str, Any]] = <factory>, zeus_setup: Optional[Dict[str, Any]] = <factory>, dynesty_nested_setup: Optional[Dict[str, Any]] = <factory>, dynesty_dynmaic_setup: Optional[Dict[str, Any]] = <factory>, corner_style: threeML.config.plotting_structure.CornerStyle = <factory>)

Bases: object

corner_style: CornerStyle

default_sampler: Sampler = 'emcee'

dynesty_dynmaic_setup: Optional[Dict[str, Any]]

dynesty_nested_setup: Optional[Dict[str, Any]]

emcee_setup: Optional[Dict[str, Any]]

multinest_setup: Optional[Dict[str, Any]]

ultranest_setup: Optional[Dict[str, Any]]

use_median_fit: bool = False

zeus_setup: Optional[Dict[str, Any]]

class threeML.config.fitting_structure.MLEDefault(default_minimizer: threeML.config.fitting_structure.Optimizer = <Optimizer.minuit: 'minuit'>, default_minimizer_algorithm: Optional[str] = None, default_minimizer_callback: Optional[str] = None, contour_cmap: threeML.config.plotting_structure.MPLCmap = <MPLCmap.Pastel1: 'Pastel1'>, contour_background: str = 'white', contour_level_1: str = '#ffa372', contour_level_2: str = '#ed6663', contour_level_3: str = '#0f4c81', profile_color: str = 'k', profile_level_1: str = '#ffa372', profile_level_2: str = '#ed6663', profile_level_3: str = '#0f4c81')

Bases: object

contour_background: str = 'white'

contour_cmap: MPLCmap = 'Pastel1'

contour_level_1: str = '#ffa372'

contour_level_2: str = '#ed6663'

contour_level_3: str = '#0f4c81'

default_minimizer: Optimizer = 'minuit'

default_minimizer_algorithm: Optional[str] = None

default_minimizer_callback: Optional[str] = None

profile_color: str = 'k'

profile_level_1: str = '#ffa372'

profile_level_2: str = '#ed6663'

profile_level_3: str = '#0f4c81'

class threeML.config.fitting_structure.Optimizer(value)

Bases: Enum

An enumeration.

ROOT = 'ROOT'

minuit = 'minuit'

scipy = 'scipy'

class threeML.config.fitting_structure.Sampler(value)

Bases: Enum

An enumeration.

autoemcee = 'autoemcee'

dynesty_dynamic = 'dynesty_dynamic'

dynesty_nested = 'dynesty_nested'

emcee = 'emcee'

multinest = 'multinest'

ultranest = 'ultranest'

zeus = 'zeus'

threeML.config.plotting_structure module

class threeML.config.plotting_structure.BinnedSpectrumPlot(data_cmap: threeML.config.plotting_structure.MPLCmap = <MPLCmap.Set1: 'Set1'>, model_cmap: threeML.config.plotting_structure.MPLCmap = <MPLCmap.Set1: 'Set1'>, background_cmap: threeML.config.plotting_structure.MPLCmap = <MPLCmap.Set1: 'Set1'>, n_colors: int = 5, step: bool = False, show_legend: bool = True, show_residuals: bool = True, data_color: Optional[str] = None, model_color: Optional[str] = None, background_color: Optional[str] = None, show_background: bool = False, data_mpl_kwargs: Optional[Dict[str, Any]] = None, model_mpl_kwargs: Optional[Dict[str, Any]] = None, background_mpl_kwargs: Optional[Dict[str, Any]] = None)

Bases: object

background_cmap: MPLCmap = 'Set1'

background_color: Optional[str] = None

background_mpl_kwargs: Optional[Dict[str, Any]] = None

data_cmap: MPLCmap = 'Set1'

data_color: Optional[str] = None

data_mpl_kwargs: Optional[Dict[str, Any]] = None

model_cmap: MPLCmap = 'Set1'

model_color: Optional[str] = None

model_mpl_kwargs: Optional[Dict[str, Any]] = None

n_colors: int = 5

show_background: bool = False

show_legend: bool = True

show_residuals: bool = True

step: bool = False

class threeML.config.plotting_structure.ContourStyle(alpha: float = 0.4)

Bases: object

alpha: float = 0.4

class threeML.config.plotting_structure.CornerStyle(show_titles: bool = True, smooth: float = 0.9, title_fmt: str = '.2g', bins: int = 25, quantiles: List[float] = <factory>, fill_contours: bool = True, cmap: threeML.config.plotting_structure.MPLCmap = <MPLCmap.viridis: 'viridis'>, extremes: str = 'white', contourf_kwargs: Dict[str, Any] = <factory>, levels: List[float] = <factory>)

Bases: object

bins: int = 25

cmap: MPLCmap = 'viridis'

contourf_kwargs: Dict[str, Any]

extremes: str = 'white'

fill_contours: bool = True

levels: List[float]

quantiles: List[float]

show_titles: bool = True

smooth: float = 0.9

title_fmt: str = '.2g'

class threeML.config.plotting_structure.DataHistPlot(counts_color: str = '#500472', background_color: str = '#79cbb8', warn_channels_color: str = '#C79BFE', bad_channels_color: str = '#FE3131', masked_channels_color: str = '#566573')

Bases: object

background_color: str = '#79cbb8'

bad_channels_color: str = '#FE3131'

counts_color: str = '#500472'

masked_channels_color: str = '#566573'

warn_channels_color: str = '#C79BFE'

class threeML.config.plotting_structure.FermiSpectrumPlot(total_model_color: str = 'k', show_background_sources: bool = True, shade_fixed_sources: bool = True, shade_secondary_sources: bool = False, fixed_sources_color: str = 'grey', secondary_sources_color: str = 'k', data_cmap: threeML.config.plotting_structure.MPLCmap = <MPLCmap.Set1: 'Set1'>, model_cmap: threeML.config.plotting_structure.MPLCmap = <MPLCmap.Set1: 'Set1'>, background_cmap: threeML.config.plotting_structure.MPLCmap = <MPLCmap.Set1: 'Set1'>, step: bool = False, show_legend: bool = True, show_residuals: bool = True, data_color: Optional[str] = None, model_color: Optional[str] = None, background_color: Optional[str] = None, data_mpl_kwargs: Optional[Dict[str, Any]] = None, model_mpl_kwargs: Optional[Dict[str, Any]] = None, background_mpl_kwargs: Optional[Dict[str, Any]] = None)

Bases: object

background_cmap: MPLCmap = 'Set1'

background_color: Optional[str] = None

background_mpl_kwargs: Optional[Dict[str, Any]] = None

data_cmap: MPLCmap = 'Set1'

data_color: Optional[str] = None

data_mpl_kwargs: Optional[Dict[str, Any]] = None

fixed_sources_color: str = 'grey'

model_cmap: MPLCmap = 'Set1'

model_color: Optional[str] = None

model_mpl_kwargs: Optional[Dict[str, Any]] = None

secondary_sources_color: str = 'k'

shade_fixed_sources: bool = True

shade_secondary_sources: bool = False

show_background_sources: bool = True

show_legend: bool = True

show_residuals: bool = True

step: bool = False

total_model_color: str = 'k'

class threeML.config.plotting_structure.GenericPlotting(use_threeml_style: bool = True, mplstyle: str = 'threeml.mplstyle', residual_plot: threeML.config.plotting_structure.ResidualPlot = <factory>)

Bases: object

mplstyle: str = 'threeml.mplstyle'

residual_plot: ResidualPlot

use_threeml_style: bool = True

class threeML.config.plotting_structure.LegendStyle(loc: str = 'best', fancybox: bool = True, shadow: bool = True)

Bases: object

fancybox: bool = True

loc: str = 'best'

shadow: bool = True

class threeML.config.plotting_structure.MPLCmap(value)

Bases: Enum

An enumeration.

Accent = 'Accent'

Accent_r = 'Accent_r'

Blues = 'Blues'

Blues_r = 'Blues_r'

BrBG = 'BrBG'

BrBG_r = 'BrBG_r'

BuGn = 'BuGn'

BuGn_r = 'BuGn_r'

BuPu = 'BuPu'

BuPu_r = 'BuPu_r'

CMRmap = 'CMRmap'

CMRmap_r = 'CMRmap_r'

Dark2 = 'Dark2'

Dark2_r = 'Dark2_r'

GnBu = 'GnBu'

GnBu_r = 'GnBu_r'

Grays = 'Grays'

Greens = 'Greens'

Greens_r = 'Greens_r'

Greys = 'Greys'

Greys_r = 'Greys_r'

OrRd = 'OrRd'

OrRd_r = 'OrRd_r'

Oranges = 'Oranges'

Oranges_r = 'Oranges_r'

PRGn = 'PRGn'

PRGn_r = 'PRGn_r'

Paired = 'Paired'

Paired_r = 'Paired_r'

Pastel1 = 'Pastel1'

Pastel1_r = 'Pastel1_r'

Pastel2 = 'Pastel2'

Pastel2_r = 'Pastel2_r'

PiYG = 'PiYG'

PiYG_r = 'PiYG_r'

PuBu = 'PuBu'

PuBuGn = 'PuBuGn'

PuBuGn_r = 'PuBuGn_r'

PuBu_r = 'PuBu_r'

PuOr = 'PuOr'

PuOr_r = 'PuOr_r'

PuRd = 'PuRd'

PuRd_r = 'PuRd_r'

Purples = 'Purples'

Purples_r = 'Purples_r'

RdBu = 'RdBu'

RdBu_r = 'RdBu_r'

RdGy = 'RdGy'

RdGy_r = 'RdGy_r'

RdPu = 'RdPu'

RdPu_r = 'RdPu_r'

RdYlBu = 'RdYlBu'

RdYlBu_r = 'RdYlBu_r'

RdYlGn = 'RdYlGn'

RdYlGn_r = 'RdYlGn_r'

Reds = 'Reds'

Reds_r = 'Reds_r'

Set1 = 'Set1'

Set1_r = 'Set1_r'

Set2 = 'Set2'

Set2_r = 'Set2_r'

Set3 = 'Set3'

Set3_r = 'Set3_r'

Spectral = 'Spectral'

Spectral_r = 'Spectral_r'

Wistia = 'Wistia'

Wistia_r = 'Wistia_r'

YlGn = 'YlGn'

YlGnBu = 'YlGnBu'

YlGnBu_r = 'YlGnBu_r'

YlGn_r = 'YlGn_r'

YlOrBr = 'YlOrBr'

YlOrBr_r = 'YlOrBr_r'

YlOrRd = 'YlOrRd'

YlOrRd_r = 'YlOrRd_r'

afmhot = 'afmhot'

afmhot_r = 'afmhot_r'

autumn = 'autumn'

autumn_r = 'autumn_r'

binary = 'binary'

binary_r = 'binary_r'

bone = 'bone'

bone_r = 'bone_r'

brg = 'brg'

brg_r = 'brg_r'

bwr = 'bwr'

bwr_r = 'bwr_r'

cividis = 'cividis'

cividis_r = 'cividis_r'

cool = 'cool'

cool_r = 'cool_r'

coolwarm = 'coolwarm'

coolwarm_r = 'coolwarm_r'

copper = 'copper'

copper_r = 'copper_r'

cubehelix = 'cubehelix'

cubehelix_r = 'cubehelix_r'

flag = 'flag'

flag_r = 'flag_r'

gist_earth = 'gist_earth'

gist_earth_r = 'gist_earth_r'

gist_gray = 'gist_gray'

gist_gray_r = 'gist_gray_r'

gist_grey = 'gist_grey'

gist_heat = 'gist_heat'

gist_heat_r = 'gist_heat_r'

gist_ncar = 'gist_ncar'

gist_ncar_r = 'gist_ncar_r'

gist_rainbow = 'gist_rainbow'

gist_rainbow_r = 'gist_rainbow_r'

gist_stern = 'gist_stern'

gist_stern_r = 'gist_stern_r'

gist_yarg = 'gist_yarg'

gist_yarg_r = 'gist_yarg_r'

gist_yerg = 'gist_yerg'

gnuplot = 'gnuplot'

gnuplot2 = 'gnuplot2'

gnuplot2_r = 'gnuplot2_r'

gnuplot_r = 'gnuplot_r'

gray = 'gray'

gray_r = 'gray_r'

grey = 'grey'

hot = 'hot'

hot_r = 'hot_r'

hsv = 'hsv'

hsv_r = 'hsv_r'

inferno = 'inferno'

inferno_r = 'inferno_r'

jet = 'jet'

jet_r = 'jet_r'

magma = 'magma'

magma_r = 'magma_r'

nipy_spectral = 'nipy_spectral'

nipy_spectral_r = 'nipy_spectral_r'

ocean = 'ocean'

ocean_r = 'ocean_r'

pink = 'pink'

pink_r = 'pink_r'

plasma = 'plasma'

plasma_r = 'plasma_r'

prism = 'prism'

prism_r = 'prism_r'

rainbow = 'rainbow'

rainbow_r = 'rainbow_r'

seismic = 'seismic'

seismic_r = 'seismic_r'

spring = 'spring'

spring_r = 'spring_r'

summer = 'summer'

summer_r = 'summer_r'

tab10 = 'tab10'

tab10_r = 'tab10_r'

tab20 = 'tab20'

tab20_r = 'tab20_r'

tab20b = 'tab20b'

tab20b_r = 'tab20b_r'

tab20c = 'tab20c'

tab20c_r = 'tab20c_r'

terrain = 'terrain'

terrain_r = 'terrain_r'

turbo = 'turbo'

turbo_r = 'turbo_r'

twilight = 'twilight'

twilight_r = 'twilight_r'

twilight_shifted = 'twilight_shifted'

twilight_shifted_r = 'twilight_shifted_r'

viridis = 'viridis'

viridis_r = 'viridis_r'

winter = 'winter'

winter_r = 'winter_r'

class threeML.config.plotting_structure.ModelPlotting(point_source_plot: threeML.config.plotting_structure.PointSourcePlot = <factory>)

Bases: object

point_source_plot: PointSourcePlot

class threeML.config.plotting_structure.PlotStyle(linestyle: Optional[str] = '-', linewidth: Optional[float] = 1.7)

Bases: object

linestyle: Optional[str] = '-'

linewidth: Optional[float] = 1.7

class threeML.config.plotting_structure.PointSourcePlot(fit_cmap: threeML.config.plotting_structure.MPLCmap = <MPLCmap.Set1: 'Set1'>, contour_cmap: threeML.config.plotting_structure.MPLCmap = <MPLCmap.Set1: 'Set1'>, bayes_cmap: threeML.config.plotting_structure.MPLCmap = <MPLCmap.Set1: 'Set1'>, plot_style: threeML.config.plotting_structure.PlotStyle = <factory>, contour_style: threeML.config.plotting_structure.ContourStyle = <factory>, show_legend: bool = True, legend_style: threeML.config.plotting_structure.LegendStyle = <factory>, flux_unit: str = '1/(keV s cm2)', emin: float = 10.0, emax: float = 10000.0, num_ene: int = 100, ene_unit: str = 'keV')

Bases: object

bayes_cmap: MPLCmap = 'Set1'

contour_cmap: MPLCmap = 'Set1'

contour_style: ContourStyle

emax: float = 10000.0

emin: float = 10.0

ene_unit: str = 'keV'

fit_cmap: MPLCmap = 'Set1'

flux_unit: str = '1/(keV s cm2)'

legend_style: LegendStyle

num_ene: int = 100

plot_style: PlotStyle

show_legend: bool = True

class threeML.config.plotting_structure.ResidualPlot(linewidth: float = 1, marker: str = '.', size: float = 3, legend_font_size: float = 6.94)

Bases: object

legend_font_size: float = 6.94

linewidth: float = 1

marker: str = '.'

size: float = 3

threeML.config.plugin_structure module

class threeML.config.plugin_structure.Fermipy(fit_plot: threeML.config.plotting_structure.FermiSpectrumPlot = <factory>)

Bases: object

fit_plot: FermiSpectrumPlot

class threeML.config.plugin_structure.OGIP(fit_plot: threeML.config.plotting_structure.BinnedSpectrumPlot = <factory>, data_plot: threeML.config.plotting_structure.DataHistPlot = <factory>, response_cmap: threeML.config.plotting_structure.MPLCmap = <MPLCmap.viridis: 'viridis'>, response_zero_color: str = 'k')

Bases: object

data_plot: DataHistPlot

fit_plot: BinnedSpectrumPlot

response_cmap: MPLCmap = 'viridis'

response_zero_color: str = 'k'

class threeML.config.plugin_structure.Photo(fit_plot: threeML.config.plotting_structure.BinnedSpectrumPlot = <factory>)

Bases: object

fit_plot: BinnedSpectrumPlot

class threeML.config.plugin_structure.Plugins(ogip: threeML.config.plugin_structure.OGIP = <factory>, photo: threeML.config.plugin_structure.Photo = <factory>, fermipy: threeML.config.plugin_structure.Fermipy = <factory>)

Bases: object

fermipy: Fermipy

ogip: OGIP

photo: Photo

class threeML.config.plugin_structure.TimeSeries(light_curve_color: str = '#05716c', selection_color: str = '#1fbfb8', background_color: str = '#C0392B', background_selection_color: str = '#E74C3C', fit: threeML.config.plugin_structure.TimeSeriesFit = <factory>)

Bases: object

background_color: str = '#C0392B'

background_selection_color: str = '#E74C3C'

fit: TimeSeriesFit

light_curve_color: str = '#05716c'

selection_color: str = '#1fbfb8'

class threeML.config.plugin_structure.TimeSeriesFit(fit_poly: bool = True, unbinned: bool = False, bayes: bool = False)

Bases: object

bayes: bool = False

fit_poly: bool = True

unbinned: bool = False

threeML.config.point_source_structure module

class threeML.config.point_source_structure.IntegrateMethod(value)

Bases: IntEnum

An enumeration.

quad = 1

trapz = 0

class threeML.config.point_source_structure.PointSourceDefaults(integrate_flux_method: threeML.config.point_source_structure.IntegrateMethod = <IntegrateMethod.trapz: 0>, max_number_samples: int = 5000)

Bases: object

integrate_flux_method: IntegrateMethod = 0

max_number_samples: int = 5000

Module contents