FluxPointsDataset#

class gammapy.datasets.FluxPointsDataset(models=None, data=None, mask_fit=None, mask_safe=None, name=None, meta_table=None)[source]#

Bases: gammapy.datasets.core.Dataset

Bundle a set of flux points with a parametric model, to compute fit statistic function using chi2 statistics.

For more information see Datasets (DL4).

Parameters

modelsModels: Models (only spectral part needs to be set).
dataFluxPoints: Flux points. Must be sorted along the energy axis.
mask_fitnumpy.ndarray: Mask to apply for fitting.
mask_safenumpy.ndarray: Mask defining the safe data range. By default, upper limit values are excluded.
meta_tableTable: Table listing information on observations used to create the dataset. One line per observation for stacked datasets.

Examples

Load flux points from file and fit with a power-law model:

>>> from gammapy.modeling import Fit
>>> from gammapy.modeling.models import PowerLawSpectralModel, SkyModel
>>> from gammapy.estimators import FluxPoints
>>> from gammapy.datasets import FluxPointsDataset
>>> filename = "$GAMMAPY_DATA/tests/spectrum/flux_points/diff_flux_points.fits"
>>> dataset = FluxPointsDataset.read(filename)
>>> model = SkyModel(spectral_model=PowerLawSpectralModel())
>>> dataset.models = model

Make the fit:

>>> fit = Fit()
>>> result = fit.run([dataset])
>>> print(result)
OptimizeResult

    backend    : minuit
    method     : migrad
    success    : True
    message    : Optimization terminated successfully.
    nfev       : 135
    total stat : 25.21

CovarianceResult

    backend    : minuit
    method     : hesse
    success    : True
    message    : Hesse terminated successfully.

>>> print(result.parameters.to_table())
type    name     value         unit      ... frozen is_norm link prior
---- --------- ---------- -------------- ... ------ ------- ---- -----
         index 2.2159e+00                ...  False   False
     amplitude 2.1619e-13 TeV-1 s-1 cm-2 ...  False    True
     reference 1.0000e+00            TeV ...   True   False

Note: In order to reproduce the example, you need the tests datasets folder. You may download it with the command: gammapy download datasets --tests --out $GAMMAPY_DATA

Attributes Summary

`gti`	Good time interval info (`GTI`).
`mask`	Combined fit and safe mask.
`models`
`name`
`stat_type`
`tag`

Methods Summary

`copy`([name])	A deep copy.
`data_shape`()	Shape of the flux points data (tuple).
`flux_pred`()	Compute predicted flux.
`from_dict`(data, **kwargs)	Create flux point dataset from dict.
`plot_fit`([ax_spectrum, ax_residuals, ...])	Plot flux points, best fit model and residuals in two panels.
`plot_residuals`([ax, method])	Plot flux point residuals.
`plot_spectrum`([ax, kwargs_fp, kwargs_model, ...])	Plot flux points and model.
`read`(filename[, name])	Read pre-computed flux points and create a dataset.
`residuals`([method])	Compute flux point residuals.
`stat_array`()	Fit statistic array.
`stat_sum`()	Total statistic given the current model parameters and priors.
`to_dict`()	Convert to dict for YAML serialization.
`write`(filename[, overwrite, checksum])	Write flux point dataset to file.

Attributes Documentation

gti#: Good time interval info (GTI).

mask#: Combined fit and safe mask.

models#

name#

stat_type = 'chi2'#

tag = 'FluxPointsDataset'#

Methods Documentation

copy(name=None)#

A deep copy.

Parameters

namestr, optional: Name of the copied dataset. Default is None.

Returns

datasetDataset: Copied datasets.

data_shape()[source]#: Shape of the flux points data (tuple).

flux_pred()[source]#: Compute predicted flux.

classmethod from_dict(data, **kwargs)[source]#

Create flux point dataset from dict.

Parameters

datadict: Dictionary containing data to create dataset from.
Returns
——-
datasetFluxPointsDataset: Flux point datasets.

plot_fit(ax_spectrum=None, ax_residuals=None, kwargs_spectrum=None, kwargs_residuals=None, axis_name='energy')[source]#

Plot flux points, best fit model and residuals in two panels.

Calls plot_spectrum and plot_residuals.

Parameters

ax_spectrumAxes, optional: Axes to plot flux points and best fit model on. Default is None.
ax_residualsAxes, optional: Axes to plot residuals on. Default is None.
kwargs_spectrumdict, optional: Keyword arguments passed to plot_spectrum. Default is None.
kwargs_residualsdict, optional: Keyword arguments passed to plot_residuals. Default is None.
Returns
——-
ax_spectrum, ax_residualsAxes: Flux points, best fit model and residuals plots.

Examples

>>> from gammapy.modeling.models import PowerLawSpectralModel, SkyModel
>>> from gammapy.estimators import FluxPoints
>>> from gammapy.datasets import FluxPointsDataset

>>> #load precomputed flux points
>>> filename = "$GAMMAPY_DATA/tests/spectrum/flux_points/diff_flux_points.fits"
>>> flux_points = FluxPoints.read(filename)
>>> model = SkyModel(spectral_model=PowerLawSpectralModel())
>>> dataset = FluxPointsDataset(model, flux_points)
>>> #configuring optional parameters
>>> kwargs_spectrum = {"kwargs_model": {"color":"red", "ls":"--"}, "kwargs_fp":{"color":"green", "marker":"o"}}
>>> kwargs_residuals = {"color": "blue", "markersize":4, "marker":'s', }
>>> dataset.plot_fit(kwargs_residuals=kwargs_residuals, kwargs_spectrum=kwargs_spectrum) 

plot_residuals(ax=None, method='diff', **kwargs)[source]#

Plot flux point residuals.

Parameters

axAxes, optional: Axes to plot on. Default is None.
method{“diff”, “diff/model”}: Normalization used to compute the residuals, see FluxPointsDataset.residuals. Default is “diff”.
**kwargsdict: Keyword arguments passed to errorbar.

Returns

axAxes: Axes object.

plot_spectrum(ax=None, kwargs_fp=None, kwargs_model=None, axis_name='energy')[source]#

Plot flux points and model.

Parameters

axAxes, optional: Axes to plot on. Default is None.
kwargs_fpdict, optional: Keyword arguments passed to gammapy.estimators.FluxPoints.plot to configure the plot style. Default is None.
kwargs_modeldict, optional: Keyword arguments passed to gammapy.modeling.models.SpectralModel.plot and gammapy.modeling.models.SpectralModel.plot_error to configure the plot style. Default is None.
axis_namestr: Axis along which to plot the flux points for multiple axes. Default is energy.

Returns

axAxes: Axes object.

Examples

>>> from gammapy.modeling.models import PowerLawSpectralModel, SkyModel
>>> from gammapy.estimators import FluxPoints
>>> from gammapy.datasets import FluxPointsDataset

>>> #load precomputed flux points
>>> filename = "$GAMMAPY_DATA/tests/spectrum/flux_points/diff_flux_points.fits"
>>> flux_points = FluxPoints.read(filename)
>>> model = SkyModel(spectral_model=PowerLawSpectralModel())
>>> dataset = FluxPointsDataset(model, flux_points)
>>> #configuring optional parameters
>>> kwargs_model = {"color":"red", "ls":"--"}
>>> kwargs_fp = {"color":"green", "marker":"o"}
>>> dataset.plot_spectrum(kwargs_fp=kwargs_fp, kwargs_model=kwargs_model) 

classmethod read(filename, name=None)[source]#

Read pre-computed flux points and create a dataset.

Parameters

filenamestr: Filename to read from.
namestr, optional: Name of the new dataset. Default is None.
Returns
——-
datasetFluxPointsDataset: FluxPointsDataset.

residuals(method='diff')[source]#

Compute flux point residuals.

Parameters

method: {“diff”, “diff/model”}

Method used to compute the residuals. Available options are:

diff (default): data - model.
diff/model: (data - model) / model.

Default is “diff”.

Returns

residualsndarray: Residuals array.

stat_array()[source]#: Fit statistic array.

stat_sum()#: Total statistic given the current model parameters and priors.

to_dict()#: Convert to dict for YAML serialization.

write(filename, overwrite=False, checksum=False, **kwargs)[source]#

Write flux point dataset to file.

Parameters

filenamestr: Filename to write to.
overwritebool, optional: Overwrite existing file. Default is False.
checksumbool: When True adds both DATASUM and CHECKSUM cards to the headers written to the FITS file. Applies only if filename has .fits suffix. Default is False.
**kwargsdict, optional: Keyword arguments passed to write.