Source code for gammapy.spectrum.flux_point

# Licensed under a 3-clause BSD style license - see LICENSE.rst
import logging
import numpy as np
from astropy import units as u
from import IORegistryError
from astropy.table import Table, vstack
from gammapy.modeling import Dataset, Datasets, Fit, Parameters
from gammapy.modeling.models import (
from gammapy.utils.interpolation import interpolate_profile
from gammapy.utils.scripts import make_path
from gammapy.utils.table import table_from_row_data, table_standardise_units_copy
from .dataset import SpectrumDatasetOnOff

__all__ = ["FluxPoints", "FluxPointsEstimator", "FluxPointsDataset"]

log = logging.getLogger(__name__)

    "dnde": ["e_ref", "dnde"],
    "e2dnde": ["e_ref", "e2dnde"],
    "flux": ["e_min", "e_max", "flux"],
    "eflux": ["e_min", "e_max", "eflux"],
    # TODO: extend required columns
    "likelihood": [

    "dnde": ["dnde_err", "dnde_errp", "dnde_errn", "dnde_ul", "is_ul"],
    "e2dnde": ["e2dnde_err", "e2dnde_errp", "e2dnde_errn", "e2dnde_ul", "is_ul"],
    "flux": ["flux_err", "flux_errp", "flux_errn", "flux_ul", "is_ul"],
    "eflux": ["eflux_err", "eflux_errp", "eflux_errn", "eflux_ul", "is_ul"],

    "dnde": u.Unit("cm-2 s-1 TeV-1"),
    "e2dnde": u.Unit("erg cm-2 s-1"),
    "flux": u.Unit("cm-2 s-1"),
    "eflux": u.Unit("erg cm-2 s-1"),

[docs]class FluxPoints: """Flux points container. The supported formats are described here: :ref:`gadf:flux-points` In summary, the following formats and minimum required columns are: * Format ``dnde``: columns ``e_ref`` and ``dnde`` * Format ``e2dnde``: columns ``e_ref``, ``e2dnde`` * Format ``flux``: columns ``e_min``, ``e_max``, ``flux`` * Format ``eflux``: columns ``e_min``, ``e_max``, ``eflux`` Parameters ---------- table : `~astropy.table.Table` Table with flux point data Attributes ---------- table : `~astropy.table.Table` Table with flux point data Examples -------- The `FluxPoints` object is most easily created by reading a file with flux points given in one of the formats documented above:: from gammapy.spectrum import FluxPoints filename = '$GAMMAPY_DATA/tests/spectrum/flux_points/flux_points.fits' flux_points = flux_points.plot() An instance of `FluxPoints` can also be created by passing an instance of `astropy.table.Table`, which contains the required columns, such as `'e_ref'` and `'dnde'`. The corresponding `sed_type` has to be defined in the meta data of the table:: from astropy import units as u from astropy.table import Table from gammapy.spectrum import FluxPoints from gammapy.modeling.models import PowerLawSpectralModel table = Table() pwl = PowerLawSpectralModel() e_ref = np.logspace(0, 2, 7) * u.TeV table['e_ref'] = e_ref table['dnde'] = pwl(e_ref) table.meta['SED_TYPE'] = 'dnde' flux_points = FluxPoints(table) flux_points.plot() If you have flux points in a different data format, the format can be changed by renaming the table columns and adding meta data:: from astropy import units as u from astropy.table import Table from gammapy.spectrum import FluxPoints table ='$GAMMAPY_DATA/tests/spectrum/flux_points/flux_points_ctb_37b.txt', format='ascii.csv', delimiter=' ', comment='#') table.meta['SED_TYPE'] = 'dnde' table.rename_column('Differential_Flux', 'dnde') table['dnde'].unit = 'cm-2 s-1 TeV-1' table.rename_column('lower_error', 'dnde_errn') table['dnde_errn'].unit = 'cm-2 s-1 TeV-1' table.rename_column('upper_error', 'dnde_errp') table['dnde_errp'].unit = 'cm-2 s-1 TeV-1' table.rename_column('E', 'e_ref') table['e_ref'].unit = 'TeV' flux_points = FluxPoints(table) flux_points.plot() """ def __init__(self, table): self.table = table_standardise_units_copy(table) # validate that the table is a valid representation # of the given flux point sed type self._validate_table(self.table, table.meta["SED_TYPE"]) def __repr__(self): return f"{self.__class__.__name__}(sed_type={self.sed_type!r}, n_points={len(self.table)})" @property def table_formatted(self): """Return formatted version of the flux points table. Used for pretty printing""" table = self.table.copy() for column in table.colnames: if column.startswith(("dnde", "eflux", "flux", "e2dnde", "ref")): table[column].format = ".3e" elif column.startswith( ("e_min", "e_max", "e_ref", "sqrt_ts", "norm", "ts", "stat") ): table[column].format = ".3f" return table
[docs] @classmethod def read(cls, filename, **kwargs): """Read flux points. Parameters ---------- filename : str Filename kwargs : dict Keyword arguments passed to ``. """ filename = make_path(filename) try: table =, **kwargs) except IORegistryError: kwargs.setdefault("format", "ascii.ecsv") table =, **kwargs) if "SED_TYPE" not in table.meta.keys(): sed_type = cls._guess_sed_type(table) table.meta["SED_TYPE"] = sed_type # TODO: check sign and factor 2 here # # The idea below is to support the format here: # # but internally to go to the uniform "stat" if "loglike" in table.colnames and "stat" not in table.colnames: table["stat"] = 2 * table["loglike"] if "loglike_null" in table.colnames and "stat_null" not in table.colnames: table["stat_null"] = 2 * table["loglike_null"] if "dloglike_scan" in table.colnames and "stat_scan" not in table.colnames: table["stat_scan"] = 2 * table["dloglike_scan"] return cls(table=table)
[docs] def write(self, filename, **kwargs): """Write flux points. Parameters ---------- filename : str Filename kwargs : dict Keyword arguments passed to `astropy.table.Table.write`. """ filename = make_path(filename) try: self.table.write(filename, **kwargs) except IORegistryError: kwargs.setdefault("format", "ascii.ecsv") self.table.write(filename, **kwargs)
[docs] @classmethod def stack(cls, flux_points): """Create flux points by stacking list of flux points. The first `FluxPoints` object in the list is taken as a reference to infer column names and units for the stacked object. Parameters ---------- flux_points : list of `FluxPoints` List of flux points to stack. Returns ------- flux_points : `FluxPoints` Flux points without upper limit points. """ reference = flux_points[0].table tables = [] for _ in flux_points: table = _.table for colname in reference.colnames: column = reference[colname] if column.unit: table[colname] = table[colname] tables.append(table[reference.colnames]) table_stacked = vstack(tables) table_stacked.meta["SED_TYPE"] = reference.meta["SED_TYPE"] return cls(table_stacked)
[docs] def drop_ul(self): """Drop upper limit flux points. Returns ------- flux_points : `FluxPoints` Flux points with upper limit points removed. Examples -------- >>> from gammapy.spectrum import FluxPoints >>> filename = '$GAMMAPY_DATA/tests/spectrum/flux_points/flux_points.fits' >>> flux_points = >>> print(flux_points) FluxPoints(sed_type="flux", n_points=24) >>> print(flux_points.drop_ul()) FluxPoints(sed_type="flux", n_points=19) """ table_drop_ul = self.table[~self.is_ul] return self.__class__(table_drop_ul)
def _flux_to_dnde(self, e_ref, table, model, pwl_approx): if model is None: model = PowerLawSpectralModel() e_min, e_max = self.e_min, self.e_max flux = table["flux"].quantity dnde = self._dnde_from_flux(flux, model, e_ref, e_min, e_max, pwl_approx) # Add to result table table["e_ref"] = e_ref table["dnde"] = dnde if "flux_err" in table.colnames: table["dnde_err"] = dnde * table["flux_err"].quantity / flux if "flux_errn" in table.colnames: table["dnde_errn"] = dnde * table["flux_errn"].quantity / flux table["dnde_errp"] = dnde * table["flux_errp"].quantity / flux if "flux_ul" in table.colnames: flux_ul = table["flux_ul"].quantity dnde_ul = self._dnde_from_flux( flux_ul, model, e_ref, e_min, e_max, pwl_approx ) table["dnde_ul"] = dnde_ul return table @staticmethod def _dnde_to_e2dnde(e_ref, table): for suffix in ["", "_ul", "_err", "_errp", "_errn"]: try: data = table["dnde" + suffix].quantity table["e2dnde" + suffix] = (e_ref ** 2 * data).to( DEFAULT_UNIT["e2dnde"] ) except KeyError: continue return table @staticmethod def _e2dnde_to_dnde(e_ref, table): for suffix in ["", "_ul", "_err", "_errp", "_errn"]: try: data = table["e2dnde" + suffix].quantity table["dnde" + suffix] = (data / e_ref ** 2).to(DEFAULT_UNIT["dnde"]) except KeyError: continue return table
[docs] def to_sed_type(self, sed_type, method="log_center", model=None, pwl_approx=False): """Convert to a different SED type (return new `FluxPoints`). See: for details on the `'lafferty'` method. Parameters ---------- sed_type : {'dnde'} SED type to convert to. model : `~gammapy.modeling.models.SpectralModel` Spectral model assumption. Note that the value of the amplitude parameter does not matter. Still it is recommended to use something with the right scale and units. E.g. `amplitude = 1e-12 * u.Unit('cm-2 s-1 TeV-1')` method : {'lafferty', 'log_center', 'table'} Flux points `e_ref` estimation method: * `'laferty'` Lafferty & Wyatt model-based e_ref * `'log_center'` log bin center e_ref * `'table'` using column 'e_ref' from input flux_points pwl_approx : bool Use local power law appoximation at e_ref to compute differential flux from the integral flux. This method is used by the Fermi-LAT catalogs. Returns ------- flux_points : `FluxPoints` Flux points including differential quantity columns `dnde` and `dnde_err` (optional), `dnde_ul` (optional). Examples -------- >>> from gammapy.spectrum import FluxPoints >>> from gammapy.modeling.models import PowerLawSpectralModel >>> filename = '$GAMMAPY_DATA/tests/spectrum/flux_points/flux_points.fits' >>> flux_points = >>> model = PowerLawSpectralModel(index=2.2) >>> flux_points_dnde = flux_points.to_sed_type('dnde', model=model) """ # TODO: implement other directions. table = self.table.copy() if self.sed_type == "flux" and sed_type == "dnde": # Compute e_ref if method == "table": e_ref = table["e_ref"].quantity elif method == "log_center": e_ref = np.sqrt(self.e_min * self.e_max) elif method == "lafferty": # set e_ref that it represents the mean dnde in the given energy bin e_ref = self._e_ref_lafferty(model, self.e_min, self.e_max) else: raise ValueError(f"Invalid method: {method}") table = self._flux_to_dnde(e_ref, table, model, pwl_approx) elif self.sed_type == "dnde" and sed_type == "e2dnde": table = self._dnde_to_e2dnde(self.e_ref, table) elif self.sed_type == "e2dnde" and sed_type == "dnde": table = self._e2dnde_to_dnde(self.e_ref, table) elif self.sed_type == "likelihood" and sed_type in ["dnde", "flux", "eflux"]: for suffix in ["", "_ul", "_err", "_errp", "_errn"]: try: table[sed_type + suffix] = ( table["ref_" + sed_type] * table["norm" + suffix] ) except KeyError: continue else: raise NotImplementedError table.meta["SED_TYPE"] = sed_type return FluxPoints(table)
@staticmethod def _e_ref_lafferty(model, e_min, e_max): """Helper for `to_sed_type`. Compute e_ref that the value at e_ref corresponds to the mean value between e_min and e_max. """ flux = model.integral(e_min, e_max) dnde_mean = flux / (e_max - e_min) return model.inverse(dnde_mean) @staticmethod def _dnde_from_flux(flux, model, e_ref, e_min, e_max, pwl_approx): """Helper for `to_sed_type`. Compute dnde under the assumption that flux equals expected flux from model. """ dnde_model = model(e_ref) if pwl_approx: index = model.spectral_index(e_ref) flux_model = PowerLawSpectralModel.evaluate_integral( emin=e_min, emax=e_max, index=index, reference=e_ref, amplitude=dnde_model, ) else: flux_model = model.integral(e_min, e_max, intervals=True) return dnde_model * (flux / flux_model) @property def sed_type(self): """SED type (str). One of: {'dnde', 'e2dnde', 'flux', 'eflux'} """ return self.table.meta["SED_TYPE"] @staticmethod def _guess_sed_type(table): """Guess SED type from table content.""" valid_sed_types = list(REQUIRED_COLUMNS.keys()) for sed_type in valid_sed_types: required = set(REQUIRED_COLUMNS[sed_type]) if required.issubset(table.colnames): return sed_type @staticmethod def _guess_sed_type_from_unit(unit): """Guess SED type from unit.""" for sed_type, default_unit in DEFAULT_UNIT.items(): if unit.is_equivalent(default_unit): return sed_type @staticmethod def _validate_table(table, sed_type): """Validate input table.""" required = set(REQUIRED_COLUMNS[sed_type]) if not required.issubset(table.colnames): missing = required.difference(table.colnames) raise ValueError( "Missing columns for sed type '{}':" " {}".format(sed_type, missing) ) @staticmethod def _get_y_energy_unit(y_unit): """Get energy part of the given y unit.""" try: return [_ for _ in y_unit.bases if _.physical_type == "energy"][0] except IndexError: return u.Unit("TeV") def _plot_get_energy_err(self): """Compute energy error for given sed type""" try: e_min = self.table["e_min"].quantity e_max = self.table["e_max"].quantity e_ref = self.e_ref x_err = ((e_ref - e_min), (e_max - e_ref)) except KeyError: x_err = None return x_err def _plot_get_flux_err(self, sed_type=None): """Compute flux error for given sed type""" try: # asymmetric error y_errn = self.table[sed_type + "_errn"].quantity y_errp = self.table[sed_type + "_errp"].quantity y_err = (y_errn, y_errp) except KeyError: try: # symmetric error y_err = self.table[sed_type + "_err"].quantity y_err = (y_err, y_err) except KeyError: # no error at all y_err = None return y_err @property def is_ul(self): try: return self.table["is_ul"].data.astype("bool") except KeyError: return np.isnan(self.table[self.sed_type]) @property def e_ref(self): """Reference energy. Defined by `e_ref` column in `FluxPoints.table` or computed as log center, if `e_min` and `e_max` columns are present in `FluxPoints.table`. Returns ------- e_ref : `~astropy.units.Quantity` Reference energy. """ try: return self.table["e_ref"].quantity except KeyError: return np.sqrt(self.e_min * self.e_max) @property def e_edges(self): """Edges of the energy bin. Returns ------- e_edges : `~astropy.units.Quantity` Energy edges. """ e_edges = list(self.e_min) e_edges += [self.e_max[-1]] return u.Quantity(e_edges, self.e_min.unit, copy=False) @property def e_min(self): """Lower bound of energy bin. Defined by `e_min` column in `FluxPoints.table`. Returns ------- e_min : `~astropy.units.Quantity` Lower bound of energy bin. """ return self.table["e_min"].quantity @property def e_max(self): """Upper bound of energy bin. Defined by ``e_max`` column in ``table``. Returns ------- e_max : `~astropy.units.Quantity` Upper bound of energy bin. """ return self.table["e_max"].quantity
[docs] def plot( self, ax=None, energy_unit="TeV", flux_unit=None, energy_power=0, **kwargs ): """Plot flux points. Parameters ---------- ax : `~matplotlib.axes.Axes` Axis object to plot on. energy_unit : str, `~astropy.units.Unit`, optional Unit of the energy axis flux_unit : str, `~astropy.units.Unit`, optional Unit of the flux axis energy_power : int Power of energy to multiply y axis with kwargs : dict Keyword arguments passed to :func:`matplotlib.pyplot.errorbar` Returns ------- ax : `~matplotlib.axes.Axes` Axis object """ import matplotlib.pyplot as plt if ax is None: ax = plt.gca() sed_type = self.sed_type y_unit = u.Unit(flux_unit or DEFAULT_UNIT[sed_type]) y = self.table[sed_type] x = # get errors and ul is_ul = self.is_ul x_err_all = self._plot_get_energy_err() y_err_all = self._plot_get_flux_err(sed_type) # handle energy power e_unit = self._get_y_energy_unit(y_unit) y_unit = y.unit * e_unit ** energy_power y = (y * np.power(x, energy_power)).to(y_unit) y_err, x_err = None, None if y_err_all: y_errn = (y_err_all[0] * np.power(x, energy_power)).to(y_unit) y_errp = (y_err_all[1] * np.power(x, energy_power)).to(y_unit) y_err = (y_errn[~is_ul].to_value(y_unit), y_errp[~is_ul].to_value(y_unit)) if x_err_all: x_errn, x_errp = x_err_all x_err = ( x_errn[~is_ul].to_value(energy_unit), x_errp[~is_ul].to_value(energy_unit), ) # set flux points plotting defaults kwargs.setdefault("marker", "+") kwargs.setdefault("ls", "None") ebar = ax.errorbar( x[~is_ul].value, y[~is_ul].value, yerr=y_err, xerr=x_err, **kwargs ) if is_ul.any(): if x_err_all: x_errn, x_errp = x_err_all x_err = ( x_errn[is_ul].to_value(energy_unit), x_errp[is_ul].to_value(energy_unit), ) y_ul = self.table[sed_type + "_ul"].quantity y_ul = (y_ul * np.power(x, energy_power)).to(y_unit) y_err = (0.5 * y_ul[is_ul].value, np.zeros_like(y_ul[is_ul].value)) kwargs.setdefault("color", ebar[0].get_color()) # pop label keyword to avoid that it appears twice in the legend kwargs.pop("label", None) ax.errorbar( x[is_ul].value, y_ul[is_ul].value, xerr=x_err, yerr=y_err, uplims=True, **kwargs, ) ax.set_xscale("log", nonposx="clip") ax.set_yscale("log", nonposy="clip") ax.set_xlabel(f"Energy ({energy_unit})") ax.set_ylabel(f"{self.sed_type} ({y_unit})") return ax
[docs] def plot_ts_profiles( self, ax=None, energy_unit="TeV", add_cbar=True, y_values=None, y_unit=None, **kwargs, ): """Plot fit statistic SED profiles as a density plot. Parameters ---------- ax : `~matplotlib.axes.Axes` Axis object to plot on. energy_unit : str, `~astropy.units.Unit`, optional Unit of the energy axis y_values : `astropy.units.Quantity` Array of y-values to use for the fit statistic profile evaluation. y_unit : str or `astropy.units.Unit` Unit to use for the y-axis. add_cbar : bool Whether to add a colorbar to the plot. kwargs : dict Keyword arguments passed to :func:`matplotlib.pyplot.pcolormesh` Returns ------- ax : `~matplotlib.axes.Axes` Axis object """ import matplotlib.pyplot as plt if ax is None: ax = plt.gca() self._validate_table(self.table, "likelihood") y_unit = u.Unit(y_unit or DEFAULT_UNIT[self.sed_type]) if y_values is None: ref_values = self.table["ref_" + self.sed_type].quantity y_values = np.logspace( np.log10(0.2 * ref_values.value.min()), np.log10(5 * ref_values.value.max()), 500, ) y_values = u.Quantity(y_values, y_unit, copy=False) x = # Compute fit statistic "image" one energy bin at a time # by interpolating e2dnde at the log bin centers z = np.empty((len(self.table), len(y_values))) for idx, row in enumerate(self.table): y_ref = self.table["ref_" + self.sed_type].quantity[idx] norm = (y_values / y_ref).to_value("") norm_scan = row["norm_scan"] ts_scan = row["stat_scan"] - row["stat"] interp = interpolate_profile(norm_scan, ts_scan) z[idx] = interp((norm,)) kwargs.setdefault("vmax", 0) kwargs.setdefault("vmin", -4) kwargs.setdefault("zorder", 0) kwargs.setdefault("cmap", "Blues") kwargs.setdefault("linewidths", 0) # clipped values are set to NaN so that they appear white on the plot z[-z < kwargs["vmin"]] = np.nan caxes = ax.pcolormesh(x.value, y_values.value, -z.T, **kwargs) ax.set_xscale("log", nonposx="clip") ax.set_yscale("log", nonposy="clip") ax.set_xlabel(f"Energy ({energy_unit})") ax.set_ylabel(f"{self.sed_type} ({y_values.unit})") if add_cbar: label = "fit statistic difference" ax.figure.colorbar(caxes, ax=ax, label=label) return ax
[docs]class FluxPointsEstimator: """Flux points estimator. Estimates flux points for a given list of spectral datasets, energies and spectral model. To estimate the flux point the amplitude of the reference spectral model is fitted within the energy range defined by the energy group. This is done for each group independently. The amplitude is re-normalized using the "norm" parameter, which specifies the deviation of the flux from the reference model in this energy group. See for details. The method is also described in the Fermi-LAT catalog paper or the HESS Galactic Plane Survey paper Parameters ---------- datasets : list of `~gammapy.spectrum.SpectrumDataset` Spectrum datasets. e_edges : `~astropy.units.Quantity` Energy edges of the flux point bins. source : str For which source in the model to compute the flux points. norm_min : float Minimum value for the norm used for the fit statistic profile evaluation. norm_max : float Maximum value for the norm used for the fit statistic profile evaluation. norm_n_values : int Number of norm values used for the fit statistic profile. norm_values : `numpy.ndarray` Array of norm values to be used for the fit statistic profile. sigma : int Sigma to use for asymmetric error computation. sigma_ul : int Sigma to use for upper limit computation. reoptimize : bool Re-optimize other free model parameters. """ def __init__( self, datasets, e_edges, source="", norm_min=0.2, norm_max=5, norm_n_values=11, norm_values=None, sigma=1, sigma_ul=2, reoptimize=False, ): # make a copy to not modify the input datasets if not isinstance(datasets, Datasets): datasets = Datasets(datasets) if not datasets.is_all_same_type and datasets.is_all_same_shape: raise ValueError( "Flux point estimation requires a list of datasets" " of the same type and data shape." ) self.datasets = datasets.copy() self.e_edges = e_edges dataset = self.datasets[0] # TODO: this is complex and non-obvious behaviour. Simlify! if len(dataset.models) > 1: model = dataset.models[source].spectral_model else: model = dataset.models[0].spectral_model self.model = ScaleSpectralModel(model) self.model.norm.min = 0 self.model.norm.max = 1e3 if norm_values is None: norm_values = np.logspace( np.log10(norm_min), np.log10(norm_max), norm_n_values ) self.norm_values = norm_values self.sigma = sigma self.sigma_ul = sigma_ul self.reoptimize = reoptimize self.source = source = Fit(self.datasets) self._set_scale_model() self._contribute_to_stat = False def _freeze_parameters(self): # freeze other parameters for par in self.datasets.parameters: if par is not self.model.norm: par.frozen = True def _freeze_empty_background(self): from gammapy.cube import MapDataset counts_all = self.estimate_counts()["counts"] for counts, dataset in zip(counts_all, self.datasets): if isinstance(dataset, MapDataset) and counts == 0: if dataset.background_model is not None: dataset.background_model.parameters.freeze_all() def _set_scale_model(self): # set the model on all datasets for dataset in self.datasets: if len(dataset.models) > 1: dataset.models[self.source].spectral_model = self.model else: dataset.models[0].spectral_model = self.model @property def ref_model(self): return self.model.model @property def e_groups(self): """Energy grouping table `~astropy.table.Table`""" dataset = self.datasets[0] if isinstance(dataset, SpectrumDatasetOnOff): energy_axis = else: energy_axis = dataset.counts.geom.get_axis_by_name("energy") return energy_axis.group_table(self.e_edges) def __str__(self): s = f"{self.__class__.__name__}:\n" s += str(self.datasets) + "\n" s += str(self.e_edges) + "\n" s += str(self.model) + "\n" return s
[docs] def run(self, steps="all"): """Run the flux point estimator for all energy groups. Returns ------- flux_points : `FluxPoints` Estimated flux points. steps : list of str Which steps to execute. See `estimate_flux_point` for details and available options. """ rows = [] for e_group in self.e_groups: if e_group["bin_type"].strip() != "normal": log.debug("Skipping under-/ overflow bin in flux point estimation.") continue row = self.estimate_flux_point(e_group, steps=steps) rows.append(row) table = table_from_row_data(rows=rows, meta={"SED_TYPE": "likelihood"}) return FluxPoints(table).to_sed_type("dnde")
def _energy_mask(self, e_group, dataset): energy_mask = np.zeros(dataset.data_shape) energy_mask[e_group["idx_min"] : e_group["idx_max"] + 1] = 1 return energy_mask.astype(bool)
[docs] def estimate_flux_point(self, e_group, steps="all"): """Estimate flux point for a single energy group. Parameters ---------- e_group : `~astropy.table.Row` Energy group to compute the flux point for. steps : list of str Which steps to execute. Available options are: * "err": estimate symmetric error. * "errn-errp": estimate asymmetric errors. * "ul": estimate upper limits. * "ts": estimate ts and sqrt(ts) values. * "norm-scan": estimate fit statistic profiles. By default all steps are executed. Returns ------- result : dict Dict with results for the flux point. """ e_min, e_max = e_group["energy_min"], e_group["energy_max"] # Put at log center of the bin e_ref = np.sqrt(e_min * e_max) result = { "e_ref": e_ref, "e_min": e_min, "e_max": e_max, "ref_dnde": self.ref_model(e_ref), "ref_flux": self.ref_model.integral(e_min, e_max), "ref_eflux": self.ref_model.energy_flux(e_min, e_max), "ref_e2dnde": self.ref_model(e_ref) * e_ref ** 2, } for dataset in self.datasets: dataset.mask_fit = self._energy_mask(e_group=e_group, dataset=dataset) mask = dataset.mask_fit if dataset.mask_safe is not None: mask &= dataset.mask_safe self._contribute_to_stat |= mask.any() with self.datasets.parameters.restore_values: self._freeze_empty_background() if not self.reoptimize: self._freeze_parameters() result.update(self.estimate_norm()) if not result.pop("success"): log.warning( "Fit failed for flux point between {e_min:.3f} and {e_max:.3f}," " setting NaN.".format(e_min=e_min, e_max=e_max) ) if steps == "all": steps = ["err", "counts", "errp-errn", "ul", "ts", "norm-scan"] if "err" in steps: result.update(self.estimate_norm_err()) if "counts" in steps: result.update(self.estimate_counts()) if "errp-errn" in steps: result.update(self.estimate_norm_errn_errp()) if "ul" in steps: result.update(self.estimate_norm_ul()) if "ts" in steps: result.update(self.estimate_norm_ts()) if "norm-scan" in steps: result.update(self.estimate_norm_scan()) return result
[docs] def estimate_norm_errn_errp(self): """Estimate asymmetric errors for a flux point. Returns ------- result : dict Dict with asymmetric errors for the flux point norm. """ if not self._contribute_to_stat: return {"norm_errp": np.nan, "norm_errn": np.nan} result =, sigma=self.sigma) return {"norm_errp": result["errp"], "norm_errn": result["errn"]}
[docs] def estimate_norm_err(self): """Estimate covariance errors for a flux point. Returns ------- result : dict Dict with symmetric error for the flux point norm. """ if not self._contribute_to_stat: return {"norm_err": np.nan} result = norm_err = result.parameters.error(self.model.norm) return {"norm_err": norm_err}
[docs] def estimate_counts(self): """Estimate counts for the flux point. Returns ------- result : dict Dict with an array with one entry per dataset with counts for the flux point. """ if not self._contribute_to_stat: return {"counts": np.zeros(len(self.datasets))} counts = [] for dataset in self.datasets: mask = dataset.mask_fit if dataset.mask_safe is not None: mask &= dataset.mask_safe counts.append([mask].sum()) return {"counts": np.array(counts, dtype=int)}
[docs] def estimate_norm_ul(self): """Estimate upper limit for a flux point. Returns ------- result : dict Dict with upper limit for the flux point norm. """ if not self._contribute_to_stat: return {"norm_ul": np.nan} norm = self.model.norm # TODO: the minuit backend has convergence problems when the fit statistic is not # of parabolic shape, which is the case, when there are zero counts in the # energy bin. For this case we change to the scipy backend. counts = self.estimate_counts()["counts"] if np.all(counts == 0): result = parameter=norm, sigma=self.sigma_ul, backend="scipy", reoptimize=self.reoptimize, ) else: result =, sigma=self.sigma_ul) return {"norm_ul": result["errp"] + norm.value}
[docs] def estimate_norm_ts(self): """Estimate ts and sqrt(ts) for the flux point. Returns ------- result : dict Dict with ts and sqrt(ts) for the flux point. """ if not self._contribute_to_stat: return {"sqrt_ts": np.nan, "ts": np.nan} stat = self.datasets.stat_sum() # store best fit amplitude, set amplitude of fit model to zero self.model.norm.value = 0 self.model.norm.frozen = True if self.reoptimize: _ = stat_null = self.datasets.stat_sum() # compute sqrt TS ts = np.abs(stat_null - stat) sqrt_ts = np.sqrt(ts) return {"sqrt_ts": sqrt_ts, "ts": ts}
[docs] def estimate_norm_scan(self): """Estimate fit statistic profile for the norm parameter. Returns ------- result : dict Keys: "norm_scan", "stat_scan" """ if not self._contribute_to_stat: nans = np.nan * np.empty_like(self.norm_values) return {"norm_scan": nans, "stat_scan": nans} result = self.model.norm, values=self.norm_values, reoptimize=self.reoptimize ) return {"norm_scan": result["values"], "stat_scan": result["stat"]}
[docs] def estimate_norm(self): """Fit norm of the flux point. Returns ------- result : dict Dict with "norm" and "stat" for the flux point. """ if not self._contribute_to_stat: return {"norm": np.nan, "stat": np.nan, "success": False} # start optimization with norm=1 self.model.norm.value = 1.0 self.model.norm.frozen = False result = if result.success: norm = self.model.norm.value else: norm = np.nan return {"norm": norm, "stat": result.total_stat, "success": result.success}
[docs]class FluxPointsDataset(Dataset): """ Fit a set of flux points with a parametric model. Parameters ---------- models : `~gammapy.modeling.models.SkyModels` Models (only spectral part needs to be set) data : `~gammapy.spectrum.FluxPoints` Flux points. mask_fit : `numpy.ndarray` Mask to apply for fitting mask_safe : `numpy.ndarray` Mask defining the safe data range. 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.spectrum import FluxPoints, FluxPointsDataset filename = "$GAMMAPY_DATA/tests/spectrum/flux_points/diff_flux_points.fits" flux_points = model = SkyModel(spectral_model=PowerLawSpectralModel()) dataset = FluxPointsDataset(model, flux_points) fit = Fit([dataset]) result = print(result) print(result.parameters.to_table()) """ likelihood_type = "chi2" tag = "FluxPointsDataset" def __init__(self, models, data, mask_fit=None, mask_safe=None, name=""): = data self.mask_fit = mask_fit = name self.models = models if data.sed_type != "dnde": raise ValueError("Currently only flux points of type 'dnde' are supported.") if mask_safe is None: mask_safe = np.isfinite(data.table["dnde"]) self.mask_safe = mask_safe @property def models(self): return self._models @models.setter def models(self, value): if isinstance(value, SkyModels): models = value elif isinstance(value, list): models = SkyModels(value) elif isinstance(value, SkyModel): models = SkyModels([value]) else: raise TypeError(f"Invalid: {value!r}") self._models = models @property def parameters(self): """List of parameters (`~gammapy.modeling.Parameters`)""" parameters = [] for component in self.models: parameters.append(component.spectral_model.parameters) return Parameters.from_stack(parameters)
[docs] def write(self, filename, overwrite=True, **kwargs): """Write flux point dataset to file. Parameters ---------- filename : str Filename to write to. overwrite : bool Overwrite existing file. **kwargs : dict Keyword arguments passed to `~astropy.table.Table.write`. """ table = if self.mask_fit is None: mask_fit = self.mask_safe else: mask_fit = self.mask_fit table["mask_fit"] = mask_fit table["mask_safe"] = self.mask_safe table.write(filename, overwrite=overwrite, **kwargs)
[docs] @classmethod def from_dict(cls, data, components, models): """Create flux point dataset from dict. Parameters ---------- data : dict Dict containing data to create dataset from. components : list of dict Not used. models : list of `SkyModel` List of model components. Returns ------- dataset : `FluxPointsDataset` Flux point datasets. """ models = [model for model in models if in data["models"]] # TODO: assumes that the model is a skymodel # so this will work only when this change will be effective table =["filename"]) mask_fit = table["mask_fit"].data.astype("bool") mask_safe = table["mask_safe"].data.astype("bool") table.remove_columns(["mask_fit", "mask_safe"]) return cls( models=models, name=data["name"], data=FluxPoints(table), mask_fit=mask_fit, mask_safe=mask_safe, )
[docs] def to_dict(self, filename=""): """Convert to dict for YAML serialization.""" if self.models is not None: models = [ for _ in self.models] else: models = [] return { "name":, "type": self.tag, "models": models, "filename": str(filename), }
def __str__(self): str_ = f"{self.__class__.__name__}: \n" str_ += "\n" if self.models is None: str_ += "\t{:32}: {} \n".format("Model Name", "No Model") else: str_ += "\t{:32}: {} \n".format("Total flux points", len( str_ += "\t{:32}: {} \n".format( "Points used for the fit", self.mask.sum() ) str_ += "\t{:32}: {} \n".format( "Excluded for safe energy range", (~self.mask_safe).sum() ) if self.mask_fit is None: str_ += "\t{:32}: {} \n".format("Excluded by user", "0") else: str_ += "\t{:32}: {} \n".format( "Excluded by user", (~self.mask_fit).sum() ) str_ += "\t{:32}: {}\n".format( "Model Name", self.models.__class__.__name__ ) str_ += "\t{:32}: {}\n".format("N parameters", len(self.parameters)) str_ += "\t{:32}: {}\n".format( "N free parameters", len(self.parameters.free_parameters) ) str_ += "\tList of parameters\n" for par in self.parameters: if par.frozen: if == "amplitude": str_ += "\t \t {:14} (Frozen): {:.2e} {} \n".format(, par.value, par.unit ) else: str_ += "\t \t {:14} (Frozen): {:.2f} {} \n".format(, par.value, par.unit ) else: if == "amplitude": str_ += "\t \t {:23}: {:.2e} {} \n".format(, par.value, par.unit ) else: str_ += "\t \t {:23}: {:.2f} {} \n".format(, par.value, par.unit ) str_ += "\t{:32}: {:.2f}\n".format("Likelihood value", self.stat_sum()) return str_
[docs] def data_shape(self): """Shape of the flux points data (tuple).""" return
[docs] def flux_pred(self): """Compute predicted flux.""" flux = 0.0 for component in self.models: flux += component.spectral_model( return flux
[docs] def stat_array(self): """Fit statistic array.""" model = self.flux_pred() data =["dnde"].quantity sigma =["dnde_err"].quantity return ((data - model) / sigma).to_value("") ** 2
[docs] def residuals(self, method="diff"): """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 - model - `diff/model`: (data - model) / model - `diff/sqrt(model)`: (data - model) / sqrt(model) - `norm='sqrt_model'` for: (flux points - model)/sqrt(model) Returns ------- residuals : `~numpy.ndarray` Residuals array. """ fp = data = fp.table[fp.sed_type] model = self.flux_pred() residuals = self._compute_residuals(data, model, method) # Remove residuals for upper_limits residuals[fp.is_ul] = np.nan return residuals
[docs] def peek(self, method="diff/model", **kwargs): """Plot flux points, best fit model and residuals. Parameters ---------- method : {"diff", "diff/model", "diff/sqrt(model)"} Method used to compute the residuals, see `MapDataset.residuals()` """ from matplotlib.gridspec import GridSpec import matplotlib.pyplot as plt gs = GridSpec(7, 1) ax_spectrum = plt.subplot(gs[:5, :]) self.plot_spectrum(ax=ax_spectrum, **kwargs) ax_spectrum.set_xticks([]) ax_residuals = plt.subplot(gs[5:, :]) self.plot_residuals(ax=ax_residuals, method=method) return ax_spectrum, ax_residuals
@property def _e_range(self): try: return u.Quantity([,]) except KeyError: return u.Quantity([,]) @property def _e_unit(self): return
[docs] def plot_residuals(self, ax=None, method="diff", **kwargs): """Plot flux point residuals. Parameters ---------- ax : `~matplotlib.pyplot.Axes` Axes object. method : {"diff", "diff/model", "diff/sqrt(model)"} Method used to compute the residuals, see `MapDataset.residuals()` **kwargs : dict Keyword arguments passed to `~matplotlib.pyplot.errorbar`. Returns ------- ax : `~matplotlib.pyplot.Axes` Axes object. """ import matplotlib.pyplot as plt ax = plt.gca() if ax is None else ax residuals = self.residuals(method=method) fp = xerr = fp._plot_get_energy_err() if xerr is not None: xerr = xerr[0].to_value(self._e_unit), xerr[1].to_value(self._e_unit) model = self.flux_pred() yerr = fp._plot_get_flux_err(fp.sed_type) if method == "diff": unit = yerr[0].unit yerr = yerr[0].to_value(unit), yerr[1].to_value(unit) elif method == "diff/model": unit = "" yerr = (yerr[0] / model).to_value(""), (yerr[1] / model).to_value(unit) else: raise ValueError("Invalid method, choose between 'diff' and 'diff/model'") kwargs.setdefault("marker", "+") kwargs.setdefault("ls", "None") kwargs.setdefault("color", "black") ax.errorbar(, residuals.value, xerr=xerr, yerr=yerr, **kwargs ) # format axes ax.axhline(0, color="black", lw=0.5) ax.set_ylabel("Residuals {}".format(unit.__str__())) ax.set_xlabel(f"Energy ({self._e_unit})") ax.set_xscale("log") ax.set_xlim(self._e_range.to_value(self._e_unit)) y_max = 2 * np.nanmax(residuals).value ax.set_ylim(-y_max, y_max) return ax
[docs] def plot_spectrum(self, ax=None, fp_kwargs=None, model_kwargs=None): """ Plot spectrum including flux points and model. Parameters ---------- ax : `~matplotlib.pyplot.Axes` Axes object. fp_kwargs : dict Keyword arguments passed to `FluxPoints.plot`. model_kwargs : dict Keywords passed to `SpectralModel.plot` and `SpectralModel.plot_error` Returns ------- ax : `~matplotlib.pyplot.Axes` Axes object. """ import matplotlib.pyplot as plt ax = plt.gca() if ax is None else ax fp_kwargs = {} if fp_kwargs is None else fp_kwargs model_kwargs = {} if model_kwargs is None else model_kwargs kwargs = { "flux_unit": "erg-1 cm-2 s-1", "energy_unit": "TeV", "energy_power": 2, } # plot flux points plot_kwargs = kwargs.copy() plot_kwargs.update(fp_kwargs) plot_kwargs.setdefault("label", "Flux points") ax =, **plot_kwargs) plot_kwargs = kwargs.copy() plot_kwargs.setdefault("energy_range", self._e_range) plot_kwargs.setdefault("zorder", 10) plot_kwargs.update(model_kwargs) plot_kwargs.setdefault("label", "Best fit model") for _ in self.models: _.spectral_model.plot(ax=ax, **plot_kwargs) plot_kwargs.setdefault("color", ax.lines[-1].get_color()) del plot_kwargs["label"] if self.models.parameters.covariance is not None: try: self.models.plot_error(ax=ax, **plot_kwargs) except AttributeError: log.debug("Model does not support evaluation of errors") # format axes ax.set_xlim(self._e_range.to_value(self._e_unit)) return ax