FluxPointsDataset¶
-
class
gammapy.datasets.
FluxPointsDataset
(models=None, data=None, mask_fit=None, mask_safe=None, name=None, meta_table=None)[source]¶ Bases:
gammapy.datasets.Dataset
Fit a set of flux points with a parametric model.
- Parameters
- models
Models
Models (only spectral part needs to be set)
- data
FluxPoints
Flux points.
- mask_fit
numpy.ndarray
Mask to apply for fitting
- mask_safe
numpy.ndarray
Mask defining the safe data range.
- meta_table
Table
Table listing informations on observations used to create the dataset. One line per observation for stacked datasets.
- models
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" flux_points = FluxPoints.read(filename) model = SkyModel(spectral_model=PowerLawSpectralModel()) dataset = FluxPointsDataset(model, flux_points) fit = Fit([dataset]) result = fit.run() print(result) print(result.parameters.to_table())
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
Combined fit and safe mask
Methods Summary
copy
([name])A deep copy.
Shape of the flux points data (tuple).
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 spectrum including flux points and model.
residuals
([method])Compute the flux point residuals ().
Fit statistic array.
stat_sum
()Total statistic given the current model parameters.
to_dict
([filename])Convert to dict for YAML serialization.
write
(filename[, overwrite])Write flux point dataset to file.
Attributes Documentation
-
mask
¶ Combined fit and safe mask
-
models
¶
-
name
¶
-
stat_type
= 'chi2'¶
-
tag
= 'FluxPointsDataset'¶
Methods Documentation
-
copy
(name=None)¶ A deep copy.
-
classmethod
from_dict
(data, **kwargs)[source]¶ Create flux point dataset from dict.
- Parameters
- datadict
Dict containing data to create dataset from.
- Returns
- dataset
FluxPointsDataset
Flux point datasets.
- dataset
-
plot_fit
(ax_spectrum=None, ax_residuals=None, kwargs_spectrum=None, kwargs_residuals=None)[source]¶ Plot flux points, best fit model and residuals in two panels.
Calls
plot_spectrum
andplot_residuals
.- Parameters
- ax_spectrum
Axes
Axes to plot flux points and best fit model on.
- ax_residuals
Axes
Axes to plot residuals on.
- kwargs_spectrumdict
Keyword arguments passed to
plot_spectrum
.- kwargs_residualsdict
Keyword arguments passed to
plot_residuals
.
- ax_spectrum
- Returns
- ax_spectrum, ax_residuals
Axes
Flux points, best fit model and residuals plots.
- ax_spectrum, ax_residuals
-
plot_residuals
(ax=None, method='diff', **kwargs)[source]¶ Plot flux point residuals.
- Parameters
- ax
Axes
Axes to plot on.
- method{“diff”, “diff/model”}
Normalization used to compute the residuals, see
FluxPointsDataset.residuals
.- **kwargsdict
Keyword arguments passed to
errorbar
.
- ax
- Returns
- ax
Axes
Axes object.
- ax
-
plot_spectrum
(ax=None, kwargs_fp=None, kwargs_model=None, **kwargs)[source]¶ Plot spectrum including flux points and model.
- Parameters
- ax
Axes
Axes to plot on.
- kwargs_fpdict
Keyword arguments passed to
gammapy.estimators.FluxPoints.plot
.- kwargs_modeldict
Keyword arguments passed to
gammapy.modeling.models.SpectralModel.plot
andgammapy.modeling.models.SpectralModel.plot_error
.- **kwargs: dict
Keyword arguments passed to all plot methods.
- ax
- Returns
- ax
Axes
Axes object.
- ax
-
residuals
(method='diff')[source]¶ Compute the flux point residuals ().
- Parameters
- method: {“diff”, “diff/model”, “diff/sqrt(model)”}
- Method used to compute the residuals. Available options are:
diff
(default): data - modeldiff/model
: (data - model) / modeldiff/sqrt(model)
: (data - model) / sqrt(model)norm='sqrt_model'
for: (flux points - model)/sqrt(model)
- Returns
- residuals
ndarray
Residuals array.
- residuals
-
stat_sum
()¶ Total statistic given the current model parameters.